We proudly present the third proceeding of civil engineering research work by our final year students from the School of Civil Engineering, University Teknologi Malaysia Session 2017/2018. These students had undergone two semesters of final year project where literature reviews were carried out and proposals were prepared during the first semester while the research projects were executed and final year project reports were written up during the second semester. Each of the completed research project was presented by the student before a panel consisted of academic staffs that are well versed in the particular research area, together with a representative from the industry. The final year project presentation that was held on the 3rd and 4th of June 2018 allowed the dissemination of knowledge and results in theory, methodology and application on the different fields of civil engineering among the audience and served as a platform where any vague knowledge was clarified and any misunderstood theories, procedures and interpretation of the research works were corrected. All accepted technical papers here have been submitted to a peer-review process by a panel of expert referees, and selected based on the author’s passion in contributing to the proceeding. We hope that the proceeding provides a broad overview of the latest research results on related fields. The articles of the proceeding are published in three volumes and are organized in broad categories as follows: Volume 1- Structure and Materials Volume 2- Construction Management, Geotechnics and Transportation Volume 3- Water and Environmental Engineering We would like to express our sincere gratitude to all the Technical Proceeding Committee members for their hard work, precious time and endeavor preparing for the proceeding. Last but not least, we would like to thank each and every contributing final year project students for their efforts and especially the academic staff who served as supervisors for their support and extra editing of the technical paper to ensure a good quality proceeding.
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TABLE OF CONTENT Title Page Editorial Boards Preface Table of Content
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Application of Plant Based Natural Coagulant for Water Treatment...…………………………….... 1
Effect of Natural Coagulant on the Treatment of Polluted River Water ……………………………. 8
Integrating GIS Data with Meshfree-EFG Formulation for Channel Flow …………………………. 14
Rainwater Quality Assessment Collected from UTM Johor Bahru ………………………………… 19
Integrating GIS Data with Meshfree - PIM and RPIM for Channel Flow ………………..………… 25
Solid Waste Management among Rural Residents in Ulu Choh, Johor …………………………….. 30 Numerical Modeling of 3-Dimension Advection-Diffusion Equation for Pollutant Transport in River ……………………………………............................................................................................ 37 Issues and Problems in Aquaculture Industry at the Tebrau Strait, Johor…………………....……… 43 Performance Assessment of Upflow Anaerobic Sludge Blanket (UASB) Reactor in Treating Sewage ……………………................................................................................................................. 49
Tebrau Straits Development and Its Impact on Fishing Activity……………………………………. 56
Study of Trace Element Concentration in Johor River Basin ……………………………………...... 61 Future Climate Effects on Lake Volume of Sembrong Dam using MRI-AGCM3.2s……………..... 68 Water Quality Assessment at UTM Streams………...……………………………………………..... 76
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Application of Plant Based Natural Coagulant for Water Treatment Ahmad Bazli Sahir1, Khalida Muda1*, Nur Syamimi Zaidi1
1Faculty of Civil Engineering, Universiti Teknologi Malaysia, Malaysia
ABSTRACT. Lately, the interest on domestic water treatment has increased due to the rise of water consumption resulted from the world population growth. The excessive use of inorganic coagulants such as aluminum sulphate in domestic water treatment processes contributes to generate high negative environmental impacts and risks to living organisms. Meanwhile, coagulation –flocculation process is a commond method used in water treatment. Hence, this study has been focused on natural coagulants for water treatment. The present research work which was carried using grass plant based i.e. Pandanus (Pandan Leaves), Centella Asiatica (Pennywort) and Cymbopogon Citratus (Lemon Grass) for the treatment of river water which acted as natural coagulants. It was observed out of the above natural coagulant, Cymbopogon Citratus showed the best results. The active coagulating agent was extracted from the leaves of Cymbopogon Citratus using NaOH solutions with concentration of 2M. The turbidity removal was up to 92.8% with the initial turbidity of 207NTU. The final turbidity of the treated water was reduced up to 15NTU. The maximum turbidity removal obtained at the optimum dosage of 90mg/L with pH 12. The results obtained by adding Cymbopogon Citratus proved that the leaves can be used for water treatment.
Keywords:Natural coagulants, River Water, Jar Test, Water Treatment, Coagulation, Turbidity
INTRODUCTION
Water is a very important component not only for leaving things, but it also plays an essential role in many aspects including health, economy, food production and environment. In developing countries, large sections of the population may be dependent on raw water for drinking purposes without any treatment [1].
Thus, the purpose of water treatment is to treat the raw water to become clean and safe to be used. Safe drinking water is essential to the health and welfare of a community. Hence, water from all sources must have some form of purification before consumption [2].
Coagulation is a safe and effective approach for treating water which it could improve the water quality through coagulation-flocculation process. The coagulation-flocculation process will be able to remove high amount of suspended particles. The use of coagulants such as alum is one of the most common method employed in water treatment. The use of coagulants such as alum is one of the commonest methods in water treatment process by encouraging particle collision and floc formation [3].
The types of coagulants play an important role in water treatment that can be used to reduce turbidity and microorganisms in water [4]. With proper dosage applied in water treatment, the application of natural coagulant generally give no harmful effect. Other benefits on using natural coagulant includes cheap and save material and can be considered as eco-friendly substances.
Problem Statement A major problem in many developing countries is to obtain clean drinking water at low price. The use of chemical coagulants
may cause negative impact to the consumer and environment.. In addition, the use of alum salts is inappropriate because of the high costs of imported chemicals and low availability of chemical coagulants [5]. For example, the sludge obtained from water treatment using aluminum salts may lead to disposal problem. Aluminum is the common chemical substances used as the coagulation agent in the water treatment process. The using of chemical coagulant such as alum may leads to disposal problem such as aluminum accumulation in the environment and give negative impact to human body that will cause Alzheimer’s disease [6]. One of the possible approach that can be considered to eliminate the production of toxic sludge due to the usage of aluminium in water treatment, is through using natural substance as the coagulation agent.
Objectives The main objectives of the research are:
1. To select and prepare material as source of natural coagulant; 2. To investigate the potential removal of selected plants as the source of natural coagulant using jar test; 3. To optimize the removal of suspended particles using natural coagulant; and 4. To obtain the best performance between natural coagulant compared to chemical coagulant.
Scope of Study The study focused on the investigation of the physical and chemical properties of water treatment. The water samples were tested using jar test based on their physical and chemical characteristics which include the optimum dosage of natural coagulant, turbidity removal, optimum pH, effect of sodium hyroxide (NaOH) and sodium chloride (NaCl) as extractant. The removal performance using natural coagulants of the water treatment will be determined by comparing the final turbidity of water samples that were added with variation dosage of natural coagulant.
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LITERATURE REVIEW
In developing countries, large sections of the population dependent on raw water for drinking purposes without any appropriate treatment. Source of drinking water can be obtained from rivers and lakes. However, due to the increasing number of population globally, the source of fresh water become limited. Due to the increasing river water pollution, not all surface water can be used for drinking purposes without proper treatment process. Thus, the raw water needs to undergo several stages of treatment process before the water can be supplied for public uses.
The raw water needs to be properly treated in order to produce clean water. Water treatment plant contains several processes with the purpose to remove pollutants that can cause adverse impact to the public health. The water treatment plant consisted of five main stage of process which include the screening, coagulation-flocculation, sedimentation, filtration, and disinfection.
The first stage of water treatment process is screening. The purpose of this stage is to remove large objects so that it will not damage other compartment of the treatment process. Coagulation-flocculation is the second stage of water treatment process. The third stage of water treatment plant is sedimentation/clarification process.This process is to remove suspended solids particles. Densed particles will settled down and water become cleaner.The fourth stage of water treatment plant is filtration. The purpose of filtration is the removal of particulate impurities and floc.The fifth stage of water treatment plant is disinfection process. After the water has been filtered, a disinfectant such as chlorine was added in order to kill any remaining parasites, bacteria or viruses. Chlorination is one of the most important method of disinfection in Malaysia which the aims to obtain an acceptable and safe drinking water quality [7]. METHODOLOGY To achieve the objectives of this study, the following activities has been done: Collection of Water Sample For this study water was taken from Syarikat Air Johor (SAJ) inlet, which located at Sultan Ismail Water Treatment Plant, Skudai Johor. Samples were kept and stored in containers inside a refrigator at 4oC. The initial reading of turbidity and pH value was recorded during the water sampling. Collection and Identification of Natural Coagulant The natural coagulant used for this study is grass plant based such as Pandanus (Pandan Leave), Cymbopogon Citratus (Lemon Grass), and Centella Asiatica (Pennywort/ Pegaga).Fresh leaves of Pandanus, Centella Asiatica and Cymbopogon Citratus were obtained from the local market (Taman Universiti) in Skudai, Johor. The leaves were chopped using a clean stainless steel knife. The seeds were washed severally with water and sun-dried for an hour. Then, the natural coagulants were dried in an oven at temperatue of 103-110oC for a duration of one day before crushing stage. After that, the samples of natural coagulant were crush using small pestle and mortar available in the laboratory. Then, the samples were packed in an air tight container. Preparation of Sodium Hydroxide and Sodium Chloride Solutions The sodium hydroxide solution was prepared by following recipes. To get the 0.1, 0.2, 0.5, 1 and 2M of sodium hydroxide solution, the pallets were weighted at 4, 8, 20, 40 and 80 g. Besides, to prepare the 0.1, 0.2, 0.5, 1 and 2M of sodium chloride solutions, the pallets were weighted at 5.84, 11.69, 29.22, 58.44 and 116.88g. Then, start with one liter of distilled water and slowly stir in the solid sodium hydroxide and sodium chloride. After that, the solutions were poured into a 200mL of conical flask and each samples were added with 10mg of natural coagulants. Preparation of Aluminum Sulphate Solution For making a stock solution, 18mg of aluminum sulfate was weighted and diluted into 200mL of distilled water. Then, it made a solution with concentration at 90mg/L. The dosage of aluminum sulfate was compared with the optimum dosage of natural coagulant by measuring its turbidity removal and coagulation activity. Water Quality Test The experiment was investigated for chemical properties of the water sample by analyzing the turbidity characteristics based on the cloudiness or haziness of a water sample caused by suspended solids. Besides that, the performance of the water treatment is also tested by analyzing the optimum pH value. All tests were conducted based on the Standard Methods for the Examination of Water and Wastewater (APHA, 2005). Turbidity Removal Test The turbidity of water sample was measured before and after the treatment in accordance with the international method of water quality measurement. The water sample of each beaker was measured using a turbidity meter (2020we Portable Turbidity Meter).
pH Value Test
The pH value of the water sample with 2, 6, 10, 14, 18, 22 and 24mg/L of natural coagulant dosage solutions was adjusted to 2, 4, 7.5, 10 and 12 using titration. The pH was adjusted by adding drops of 0.02M of HCl and 1.0M of NaOH. The pH reading of the samples was taken using an electronic pH meter (Benchtop pH Meter 5500).
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Jar Test Procedure
The jar test apparatus was used to carry out coagulation and flocculation on the water samples. The first objective involved with the preparation of natural coagulant. The water samples were added into three beakers with different natural coagulant solution. To make the natural coagulant solutions, 10mg of Pandanus, Centella Asiatica and Cymbopogon Citratus were added into 200mL of ionized water. During this stage, jar tests were performed without pH control. Then, 500 mL of water sample in each beaker was added by 5mL of natural coagulant solutions. Then, the speed of stirrer was maintained at 200rpm for one minute and adjusted to 60rpm/min for 15 minutes. Then, the water sample in the beakers was allowed to settle down for 30 minutes. From the results obtained, the natural coagulants with the best results of turbidity removal were used for further investigation.
The above procedure was repeated by adding 10 mg of each natural coagulant into 0.1, 0.2, 0.5, 1 and 2M of sodium hydroxide and sodium chloride solutions which acted as the extractant. The concentration of coagulants with the best turbidity removal performance was selected and used in the following objectives of the study. Further investigation was conducted using different natural coagulant dosage. Each natural coagulant dosage of 2, 6, 10, 14, 18, 22 and 26mg were dilute into 2M of sodium hydroxide solutions. From the results obtained, the optimum coagulant dosage was selected from best results based on turbidity removal.
After obtaining the best coagulant dosage, the turbidity removal was investigated at different pH values. During this stage, each beaker was performed with pH controlled at 2, 4, 7.5, 10 and 12. The pH was adjusted by adding drops of 0.02mol of HCl or 1.0mol of NaOH. Then, 500mL of water samples in each beaker was added with 5 mL of Cymbopogon Citratus at 2M. From the results obtained, the natural coagulants with the best results of turbidity removal and optimum pH were recorded.
Lastly, the selected optimum dosage and pH of natural coagulant with highest turbidity removal was compared with aluminum sulfate (alum) solution. The pH of the water samples was set at 12 by the addition a few drops of 1M of NaOH. After that, the stirrer of jar test was maintained at 200rpm for one minute and then adjusted to 60rpm/min for 15 minutes. The water sample in the beakers were allowed to settle down for 30 minutes. From the results obtained, the optimum pH with effective dosage of selected natural coagulant from highest turbidity removal was compared with alum. RESULTS AND DISCUSSION Preparation of Natural coagulant
From Figure 1, the initial weight of Pandanus, Centella Asiatica and Cymbopogon Citratus were 77, 86 and 74 g. After drying process, the yield weight of the the samples were decreased to 13, 9 and 14g respectively. Figure 2 shows the moisture content of natural coagulant. The highest moisture content of natural coagulant was Centella Asiatica at 89.5%. Based on previous study, the leaves of the Centella Asiaticain Malaysia has a moisture content of about 88% [8]. Then, CymbopogonCitratusshowed the second higher moisture content of 83.1% and followed by Pandanuswith 81.0% moisture content.
Figure 1: Weight initial and yield of natural coagulants
Figure 2: Moisture content of natural coagulant
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Ionized Water as Extractant
From Figure 3, the initial pH of water sample is around 7.2. After coagulation process, the pH of water sample of Centella
Asiatica as coagulant increase to 7.6 and it showed alkaline properties. Then, the pH of Pandanus and Cymbopogon Citratus was decreases to 7.1 and 7 respectively. Moreover, Figure 4 shows the percentage turbidity removal of water sample by coagulation of Cympobogon Citratus, Cantella Asiatica and Pandanus. The initial turbidity of blank water sample was in the range of 40.6NTU. After coagulation-flocculation process, the turbidity removal of Pandanus was 11.6%, followed by 9.9% of Centella Asiatica and 9.4% of Cymbopogon Citratus. Sodium Hydroxide as Extractant
Figure 5 shows turbidity removal of water samples. During this study, the final turbidity and turbidity removal of blank water samples was 28.3 and 28.3%. After coagulation-flocculation process, the highest turbidity removal was obtained from 89.6% of Cantella Asiatica, 81.8% of Cymbopogon Citratus and 81.1% of Pandanus.Besides, Figure 6 shows the coagulation activity of water samples.The highest coagulation activity was obtained from 89.6% of Centella Asiatica, 81.8% of Cymbopogon Citratus and 81.1% of Pandanus. Furthermore, Figure 7 shows the initial and final pH values of water samples.The pH value is very important since the coagulation occurs within a specific pH range for the coagulant. In this study, the final pH of water sample was increase gradually due to increasing molarity number of natural coagulant. Cymbopogon Citratus was gave the highest reading of final pH at 11.7. Then, followed by 11.6 of Cantella Asiatica and 11.6 of Pandanus. Hence, from the figures above, it can be significantly observed that extractant of natural coagulant with 2M of NaOH solutions gave highest turbidity removal, coagulation activitiy and optimum pH at range 11-12.
Figure 3: Final pH of water samples Figure 4: Turbidity removal of water samples
Figure 5: Turbidity removal of water samples
Figure 7: Final pH of water samples
Figure 6: Coagulation activity of water samples
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Sodium Chloride as Extractant
Figure 8 shows turbidity removal of water samples. During this study, the final turbidity and turbidity removal of blank
samples was 28.3 and 28.3% respectively.After coagulation-flocculation process, the highest turbidity removal was obtained from 59.2% of Pandanus, 59.1% of Cymbopogon Citratus and 55.9% of Centella Asiatica.Then, Figure 9 shows the coagulation activity of water samples. The highest coagulation activity was obtained from 43.1% of Pandanus, 42.9% of Cymbopogon Citratus and 38.6% of Centella Asiatica.Besides, Figure 10 shows final pH values of water samples.In this study, the final pH of water sample was fluctuated due to increasing molarity of natural coagulant solutions.Hence, the lowest pH value of Pandanus was at 6.8 when extracted with 2M of NaCl solution. Then,Cymbopogon Citratus was gave the final pH value at 6.4 when extracted with 0.2M of NaCl and followed by Centella Asiatica at pH 6.5 when extracted with 0.5M of NaCl. From the figures above, it can be significantly observed that extractant of natural coagulant with NaOH solutions gave highest turbidity removal, coagulation activitiy and optimum pH ratherthan NaCl solution. The Optimum Dosage
Figure 11 shows the turbidity removal of water samples with different natural coagulation dosage. Cymbopogon Citratus,
Centella Asiatica and Pandanuswas extracted by 2.0M NaOH solution with dosage 10, 30, 50, 70, 90, 110 and 130mg/L. The dosage required depends on turbidity ranges mean that as initial turbidity of water sample increased, the required optimum dosage of coagulant also increased [9].The initial and final turbidity of blank water sample was 33 and 28NTU respectively.During this study, the data pattern was fluctuated and not consistent. Hence, from Figure 11 shows the highest turbidity removal of natural coagulant was 95.8% of Cymbopogon Citratus at dosage 90mg/L. Besides that, Figure 12 shows the coagulation activity of water samples. The highest coagulation activity of natural coagulants was 95.1% of Cympobogon Citratus. Furthermore, from the Figure 13 shows the final pH of water samplesat range 11-12. . The reading of final pH values shown the water samples slightly alkaline. Meanwhile, it can be significantly observed that Cymbopogon Citratusextract with 2M of NaOH solutions gave highest turbidity removal and coagulation activityat dosage 90mg/L.
Figure 8: Turbidity of water samples
Figure 10: Final pH of water samples
Figure 9: Coagulation activity of water samples
Figure 11: Turbidity removal of water samples
Figure 12: Coagulation activity of water samples
Figure 13: Final pH of water sample
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Effect of pH
Figure 14 shows the turbidity removal of Cympobogon Citratus powder as selected natural coagulant. During this stage, the
water samples were performed with pH control at 2, 4, 7.5, 10 and 12. Then, Cymbopogon Citratus as selected natural coagulant withturbidity removal of water samples was increase to 84.1, 85.3, 87.1, 89.1 and 92.8% due to increasing of pH number. During this study, the initial and final turbidity of blank water samples was 207 and 165NTU respectively. Besides, from Figure 15, the highest coagulation activity of water sample was pH 12. This analysis shown that increasing in pH, the turbidity removal and coagulation activity got increased.
Performance of Water Sample between Natural Coagulant (Cymbopogon Citratus) and Chemical Coagulant (Alum)
From Figure 16, the optimum dosage of 90mg/L also obtained the best results of Cymbopogon Citratus. During this stage, the
water samples were controlled with optimum pH at 12. In Figure 16 shows the Cymbopogon Citratuswith dosage of 90mg/L presented the highest turbidity removal at 92.8% NTU thanalum at 91.2%. Furthermore, Figure 17 had shown the coagulation activity of water samples. Cymbopogon Citratus was showed the highest coagulation activity at 80.8% than alum at 76.7%. Thus, according to previous study was proved the best turbidity removal of alum was at range 82.9-99% [10].
CONCLUSION
The conclusion that can be derived from this study are listed as below. 1. Cymbopogon Citratus (Lemon Grass) showed the highest performance of turbidity removal (92.8%) compared to others
natural coagulant investigated in this study. 2. The turbidity of the water sample was reduced up to 15NTU with lemon grass dosage of 90mg/L. 3. Turbidity removal by Cymbopogon Citratus (Lemon Grass) coagulant was found tob e the highest at pH 12 4. The result showed that 2.0M of NaOH solutions was the best solvents to extract the active coagulant components from
Cymbopogon Citratus(Lemon Grass). The removal performance was better in NaOH compared to the removal using NaCl.
Figure 14: Turbidity removal of water samples Figure 15: Coagulation activity of water samples
Figure 16: Turbidity removal water samples Figure 17: Coagulation activity of water samples
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REFERENCE
[1] Enderlin. (1997). Water quality requirements. In: Helmer R, Hespanhol I, eds. Water pollution control - A guide to the use of water quality management principles. UNEP, 23-54.
[2] Arnoldsson, E., Bergman, M., Matsinhe, N. and Persson, K. (2008). Assessment of drinking water treatment using Moringa oleifera natural coagulant, VATTEN, pp. 137–150.
[3] Moramudaii, M. A. and Fernando, P. (2001). Use of seeds of Moringa oleifera to clarify turbid waters and wastewaters, Vidyodaya J. of Sci., 10, 167-182.
[4] Mirjana, A. G., Marina, S. and Nada, P. (2010). Proteins form common bean seed as a natural coagulant for potential application in water turbidity removal, Bioresource Technology, 10(1), 2167-2172.
[5] Adejumo, M., Oloruntoba, E. O. and Sridhar, M. K. (2013). Use of Moringa oleifera (lam.) Seed powder as a coagulant for purification of water from unprotected sources in Nigeria, European Scientific Journal, IX(24).
[6] Othman, M. N., Pauzi, M. A. and Farina A. A. (2009). Removal of Aluminium from Drinking Water (Penyingkiran Aluminium daripada Air Minum), Sains Malaysiana 39(1) (2010): 51–55.
[7] Yee, L. F., Abdullah, M. P., Ata, A., Abdullah, A., Ishak, B., and Nidzham, K. (2008). Chlorination and Chloramines Formation,The Malaysian Journal of Analytical Sciences, 12(3), 528 – 535.
[8] Tee, E. S., Mohd Idris, N., Mohd Nasir, A. and Khatijah, I. (1997). Nutrient composition of Malaysian foods. 4th edn. Malaysian Food Composition Database Programme, P. 16. Kuala Lumpur, Malaysia: Institute for Medical Research.
[9] Katayon, S., Megat, M. J., Mohd, N., Asma, M., Ghani, L. A., Thamer, A. M., Azni, I., Ahmad, J., and Khor, B. C. (2006). Effects of storage conditions of Moringa Oleifera seeds on its performance in coagulation . Bioresource Technology, 97, 1455–1460.
[10] Zand, A. D. and Hoveidi, H. (2015). Comparing Aluminium Sulfate and Poly-Aluminium Chloride (PAC) Performance in Turbidity Removal from Synthetic Water. Journal of Applied Biotechnology Reports, 2(3).
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Effect of Natural Coagulant on the Treatment of Polluted River Water Maria Adibah Abdul Rahman1, Nur Syamimi Zaidi1*
1School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia *[email protected]
ABSTRACT. The effectiveness of chemicals as coagulants is well-recognized. However, the disadvantages associated with their usage such as high procurement costs, detrimental effects on human health, production of large sludge volumes and the fact that they significantly affect pH of treated water. It is therefore desirable to replace these chemical coagulants with plant-based coagulants to counteract the aforementioned drawbacks. The objectives of the study are (i) to determine the characteristics of selected natural coagulants to be used in treating polluted river water, (ii) to investigate the effect of solvent on the selected natural coagulants towards performance of polluted river water treatment, (iii) to investigate the effect of pH and dosage of selected natural coagulants towards performance of polluted river water treatment, and (iv) to determine the effectiveness of using natural coagulant compared to chemical coagulant (alum) in treating polluted river water. The ability of three plant materials, which are Chestnut, Maize cob and Bagasse, to act as natural coagulants were tested using jar test. The paramater that been analysed are yield, moisture contents, coagulation activity and turbidity removal. The materials were extracted using different solvents of sodium chloride (NaCl), sodium hydroxide (NaOH) and deionized water in order to to obtain the active coagulant agent from natural coagulants. Based on the findings, in terms of yield and moisture content, bagasse is more applicable to be used compared to with maize cob and chestnut peel. In terms of extraction, sodium hydroxide (NaOH) at 2.0 M was found to provide a high cogulation activity and turbidity removal of more than 90.0% compared to NaCl and deionized water extract. The study also found that Bagasse resulted in greater coagulation activity and turbidity removal at best condition pH and coagulant dosage of 7.5 and 90 mg/L, respectively. In comparison to the alum, the natural coagulant resulted in better removal whereby the alum only resulted in 64.1% turbidity removal, lesser than obtained by bagasse. Therefore, it can be concluded that natural coagulant in specific Bagasse poses a great chance in replacing chemical coagulant for the treatment of water.
The production of the potable water from most raw water sources usually entails the use of a coagulation/flocculation stage to remove turbidity in form of suspended and colloidal material. Aluminum and iron salts, as well as synthetic organic polymers are the common chemical coagulants that been used so far. The use of these chemical coagulants, however, drawn various concern in terms of chemicals’ residuals remain in the treated water, which consequently pose threat to humans health. Thus, in recent years, there has been considerable interest in development of natural coagulants to replace the use of chemical coagulants.
Problem Statement
Among the important stage in conventional polluted river water treatment process involving coagulation activity that commonly use chemical coagulants such as Alum (aluminium sulphate) Al2(SO4)3, ferric chloride FeCl3, ferric sulphate Fe2(SO4)3, cationic polymers and many more. Chemical coagulant has drawbacks towards coagulation activity of polluted river water treatment. It tends to produce more end sludge product and mostly, these sludge are toxic and hazard. Due to such matter, proper treatment of the sludge before disposal was very important. This sludge normally will be disposed to Kualiti Alam, which operates as integrated hazardous waste management center in Malaysia. Overall, the sludge treatment and disposal requires high operational cost. Another drawbacks due to the usage of chemical coagulant is residual which will remain presence in the treated water and causes hazard that can affect human’s health.
Moreover, chemical coagulant is not sustainable for coagulation activity of polluted river water treatment due to the adverse effect towards health and environment. Apparently, the study on coagulation activity has now shifting towards natural coagulant (plant-based) instead of using chemical coagulant. Natural coagulant is easy to be obtained. It is very cheap and pose less health problem or toxic issues. Therefore, this study is conducted to investigate the feasibility of selected plant-based natural coagulant in treating polluted river water.
Objectives The objectives of this study are:
1. To determine the characteristics of selected natural coagulants to be used in treating polluted river water. 2. To investigate the effect of solvent on the selected natural coagulants towards performance of polluted river water
treatment. 3. To investigate the effect of pH and dosage of selected natural coagulants towards performance of polluted river water
treatment. 4. To determine the effectiveness of using natural coagulant compared to chemical coagulant (alum) in treating polluted river
water. Scope of Study
This study was conducted at Environmental Engineering Laboratory, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM). Throughout this study, the selected plant materials will be prepared to be a natural coagulant. The preparation will be in form of drying, grinding, sieving and diluting into a diluted water, which at the end the solution can be used as natural
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coagulant agent. The polluted river water as a water sample will be collected from Water Treatment Plant at Taman Impian Emas, Skudai. As to investigate the performance of this natural coagulant towards coagulation activity, few sets of jar test will be set up. The effects of various types of solvent, pH and coagulant’s dosage towards the polluted river water treatment will also be investigated. LITERATURE REVIEW
Chemical coagulants such as alum, ferric chloride and ferric sulfate are the most common practiced coagulants in water and wastewater treatment processes [1, 2]. Although the use of chemical coagulants are very common and popular due to its cost-saving factor but its usage exhibit various drawbacks. Among the drawbacks is that the use of chemical coagulant requires pH adjustment not just before the treatment but also after the treatment. This is quite a tedious routine to be managed throughout the treatment process.
Nowadays, the utilization of natural coagulant for polluted river water treatment is not a fresh idea anymore. Natural coagulants that are available in abundance can certainly be considered in addressing the drawbacks associated with the use of chemical coagulants [3]. Characteristics of coagulant are commonly observed in terms of protein content, surface charge, and size morphology. According to Ramavandi [4], the extracted natural coagulant comprises majorly protein contents and less content of phospholipids acids. High content of protein could ensure the effectiveness of coagulation mechanism by the action of natural coagulant.
Natural coagulant is extracted from natural and renewable sources, such as microorganism, animal or plants. The raw plant extracts are often available locally hence a low cost natural coagulant can be retrieved as an alternative to the chemical coagulants. Natural coagulants have also been found to generate not only a much smaller sludge volume of up to five times lower but also with a higher nutritional sludge value [5]. As such, sludge treatment and handling costs are lowered making it a more sustainable option. Since natural coagulants do not consume alkalinity unlike alum, pH adjustments can be omitted and this provides extra cost savings. Natural coagulants are also non-corrosive thus it can eliminate the concerns of pipe corrosions [6]. Using the natural coagulant in treating water and wastewater can be considered as an effective way to protect the environment and sustaining the development industries as it can decrease the harmful effects. Nevertheless, towards pursuing the effort of replacing the chemical coagulant to the natural coagulant, there are various factors that hinder the commercialization of natural coagulant.
Preparation of natural coagulant covers various steps or processes. Most of the plant-based materials need to undergoes de-shell, drying, grinding, sieving and many other processes in order to be ready to be used. According to Lucas and Kanade [7], the seed cover need to be shelled by hand just before extraction. The extraction of the active ingredients was carried out by removing the shell to collect the kernel inside the shell. In order to ensure the efficiency of seeds extraction, the kernels need to be crushed and grinded to medium fine powder using the domestic blender every time when the preparation of seeds extraction was needed.
Natural coagulants are known for their efficiency in reducing turbidity but little is known about the characteristics and properties of the resultant flocs formed. The knowledge on these parameters such as floc strength, structure and compactness could be the key in establishing the probable coagulation theory [8]. Although natural coagulants are green in nature, the use of crude extracts has been related to the leaching of organic matters into treated water, quantifiable in terms of total organic carbon (TOC), dissolved organic carbon (DOC) and others. Increased levels of organic matters in treated water would have serious implications on the subsequent disinfection process using chlorine. The use of purified active agents could tackle use of natural coagulants may be inadequate to achieve the desired turbidity removal. However, they can certainly be added as an aid to the latter [6, 9-11] . The blending of both coagulant types can decrease the reliance on chemical coagulants while addressing the lower turbidity removal and unavailability of certain plant-based coagulants, which may be seasonal. METHODOLOGY
Sampling and Characterization of Polluted River Water
In this study, polluted river water is taken as the samples to be used throughout the experiment. The selected Water Treatment Plant for sampling purposes is Water Treatment Plant, Taman Impian Emas, Skudai. The collected raw water samples were analyzed for water quality parameters including turbidity removal and coagulation activity.
Experimental Procedures
In this study, water sample was collected from Water Treatment Plant, Taman Impian Emas, Skudai. This water sample was treated using prepared natural coagulant and its effectiveness in terms of turbidity removal and coagulation activity is identified.
Initially, the chosen fruit seeds or fruit peels undergoes several preparation stages involving drying, grinding, and sieving. These processes are conducted until final product in form of powder is obtained. The powder is a natural coagulant that derived from the chosen plant-based materials. The selected plant-based materials were Bagasse, Whole chestnut, Maize cob and Chestnut peel. The natural coagulant is then characterized in terms of several properties such as weight including dry weight, yield and moisture content.
Later, the study is continued to investigate the effects of solvents on the efficiency of natural coagulants in reducing the turbidity. Jar Test with methodology of One-Factor-At-A-Time (OFAT) was used to conduct these following experiments. In this objective, two types of solvents were used. The solvents are sodium hydroxide (NaOH) and sodium chloride (NaCl). These solvents were investigated using various molar concentrations, which are 0.1M, 0.2M, 0.5M, 1.0M and 2.0M. These solvent concentrations were compared with deionized water. After that, the study is continued to determine the effect of pH and natural coagulant dosage on the efficiency of natural coagulants in reducing the turbidity. When the pH is constant at 7.5, the dosages were varied at 10 mg/L, 30 mg/L, 50 mg/L, 70 mg/L, 90 mg/L and 110 mg/L. This experiment resulted in an optimum dosage to be used at later experiment. Consecutively, the pH was varied at pH 2, 4, 7.5, 10 and 12 under the optimized dosage that obtained
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previously. Such optimization of best solvent, coagulant dosage and pH for each of the prepared natural coagulants were determined based on the maximum results of coagulation activity and turbidity removal. Lastly, the study is continued to determine the efficiency of selected natural coagulants in comparison to the used of Alum as representative of chemical coagulant. In this study, the best coagulant among four types coagulants that been tested previously, which resulted in maximum percentage of coagulation activity and turbidity removal was selected. This coagulant was experimented using best solvents with optimized molar concentration, pH and coagulant dosage that obtained in previous experiments. The alum that been compared are also experimented with similar pH and dosage as natural coagulant. The efficiency between these two coagulants - natural and chemical can then be determined based on maximum results of coagulation activity and turbidity removal of the treated water.
RESULTS AND DISCUSSION
Physical Characterization of Natural Coagulant Based on Figure 1, bagasse contributed to the highest yield with initial weight recorded as only 88 g with its resulted yield was 70 g. This contributes to about 79.5% of the whole prepared material in becoming a readiable natural coagulant. The second highest yield was contributed by chestnut peels. This particular material has its initial weight of 74 g and yields (powder) of about 58 g. This contribute to about 78.4% of the whole prepared material in becoming the readiable natural coagulant. Among all tested materials, maize cob contributed to the least percentage of yield which is 23.2% only. The major difference was observed between initial weight (345 g) and final yield (80 g), thus shows that maize cob is a least preferable material to be chosen. This is because the preparation of the initial material needs to be abundant with the final production of the natural coagulant is very limited.
Figure 1: Experimental results of yield ( Initial materials, Yield)
Based on Table 1, the measured values of moisture content reveals variability with the range between 13 to 75%. From the table, it shows that moisture content for maize cob is higher with 74.5% compared to the other natural coagulants. This could be because the water content in the maize cob is very slow to evaporate even the drying process took for 24 hours in oven at temperature 103°C. Such high moisture contents resulted in a difficulty to obtain yield at the end of preparation process. This results supported the observation of yield for maize cob that is lesser than the other tested materials.
Table 1: Data of initial weight, dry weight, yield and moisture content
Effect of Solvent on Coagulation Efficiency using Various Natural Coagulants The effect of solvent on the coagulation efficiency using different natural coagulant is illustrated in Figure 2 and Figure 3 by using deionized water. Hence, for Figure 4 and 5 shows the trend of coagulation activity and turbidity removal by using sodium chloride (NaCl). Meanwhile, Figure 6 and Figure 7 indicates pattern of coagulation activity and turbidity removal by using sodium hydroxide (NaOH) as a solvent. Based on Figure 2, it can be seen that whole chestnut resulted in higher coagulation activity of about 16.1% followed by bagasse, chestnut peel and maize cob with coagulation activity of 14.8%, 11.3%, and 9.1% respectively. Whole chestnut is good in terms of coagulation activity compared with the others natural coagulants because percentage of turbidity removal also give higher value for the whole chestnut. Figure 3 shows that the percentage of turbidity removal for the whole chestnut is higher of about 30.4% followed by bagasse (29.2%), chestnut peel (26.3%) and maize cob (24.6%). From the percentage that have been obtained, it shows that 30.4% is higher turbidity removal. Whole chestnut is good in terms of coagulation activity compared with the others natural coagulants because percentage of turbidity removal also give higher value for the whole chestnut.
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Figure 2: Coagulation activity of natural coagulant by using Figure 3: Turbidity removal of natural coagulant by using deionized water ( Whole chestnut, Chestnut peel, deionized water ( Whole chestnut, Chestnut peel, Maize cob, Bagasse) Maize cob, Bagasse) For the effect of NaCl solvent, the molarity used are 0.1M, 0.2M, 0.5M, 1.0M and 2.0M. Figure 4 shows the pattern of coagulation activity while Figure 5 shows the turbidity removal when natural coagulants were extracted using NaCl. Based on Figure 4, it can be seen that bagasse resulted in greater coagulation activity compared to the other natural coagulants. The highest coagulation activity was 15% obtained at 2.0M of NaCl. Consequently, Bagasse also resulted in greater turbidity removal of approximately 37% followed by maize cob, chestnut peel and whole chestnut with the removal of about 33%, 29% and 23%, respectively. These coagulation activity and turbidity removal were quite similar with the trends showed when natural coagulants were extracted using deionized water. This suggesting that NaCl is a solvent that contribute less effect in extracting the coagulant agent in each of the plant-based natural coagulants.
Figure 4 Coagulation activity when using NaCl Figure 5 Turbidity removal when using NaCl
Figure 6 shows the pattern of coagulation activity while Figure 7 shows the pattern of turbidity removals and both were extracted using NaOH. Based on both Figures, the coagulation activity and removal turbidity used molarity 0.1M, 0.2M, 0.5M, 1.0M and 2.0M. Based on Figure 6, it shows that whole chestnut resulted in higher coagulation activity compared to the other natural coagulants. The highest percentage of coagulation activity was 91.7% obtained at 2.0M of NaOH. In addition, whole chestnut also resulted in greater turbidity removal of about 92.5% followed by bagasse, chestnut peel and maize cob with the percentage of removal approximately 92.3%, 91.6% and 86.6%, respectively . In this experiment, NaOH is more efficient as a solvent compared to NaCl because the difference between the coagulation activity and turbidity removal for NaOH is much higher compared to NaCl, although using same molarity.
Figure 6 Coagulation activity when using NaOH Figure 7 Turbidity removal when using NaOH ( Whole Chesnut, Chesnut peel, Maize cob, ( Whole Chesnut, Chesnut peel, Maize cob, Bagasse) Bagasse)
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Effect of Coagulant Dosage and pH on Coagulation Efficiency using Various Natural Coagulants In order to obtain the best coagulant dosage of the natural coagulants to treat the water, various dosages are tested at 10, 30,
50, 70, 90 and 110 mg/L. Figure 8 and Figure 9 below represents the coagulation activity and turbidity removal when various dosages are experimented. Based on the Figures, it can be seen that the optimum dosage is 90 mg/L. Bagasse is recorded as the natural coagulant that resulted in higher percentage in terms of coagulation activity and turbidity removal followed by chestnut peel, maize cob and whole chestnut. At 90 mg/L, Bagasse resulted in coagulation activity of 96%, consequently resulted in higher turbidity removal of approximate 96%, as well. Although, all natural coagulants resulted in almost similar range of coagulation activity and turbidity removal at 90 mg/L, the starting percentage is rather high for bagasse compared the others coagulants. Hence, makes Bagasse as a feasible natural coagulants to be applied in water treatment.
Figure 8 Coagulation activity when varies the dosage Figure 9 Turbidity removal when varies the dosage
( Whole Chesnut, Chesnut peel, Maize cob, ( Whole Chesnut, Chesnut peel, Maize cob, Bagasse) Bagasse) In this study, varies pH also been tested at 2, 4, 7.5, 10 and 12 to obtained which pH will have greater percentage of coagulation activity and turbidity removal. Figure 8 and Figure 9 below represents the coagulation activity and turbidity removal when various pH are experimented. Based on both figures, it can be seen that the optimum pH is 7.5. Bagasse is recorded as the natural coagulant that resulted in higher percentage in terms of coagulation activity and turbidity removal followed by maize cob, whole chestnut and chestnut peel. At pH 7.5, bagasse resulted in coagulation activity of about 97.2%, consequently resulted in higher turbidity removal of 97.3%, as well. Although, all natural coagulants resulted in almost similar range of coagulation activity and turbidity removal at pH 7.5, the starting percentage is rather high for bagasse compared the others coagulants. Hence, makes bagasse as a sensible natural coagulants to be applied in treating polluted river water.
Figure 10 Coagulation activity when varies the pH Figure 11 Turbidity removal when varies the pH
( Whole Chesnut, Chesnut peel, Maize cob, ( Whole Chesnut, Chesnut peel, Maize cob, Bagasse) Bagasse) Comparison Effect of Natural Coagulant and Alum on Coagulation Efficiency Based on the previous results, bagasse is a chosen natural coagulant as it showed highest coaulation activity and turbidity removal at best pH and dosage of 7.5 and 90 mg/L, respectively. Figure 12 and 13 shows the coagulation activity and turbidity removal when natural coagulant (bagasse) is compared to chemical coagulant (alum). Based on Figure 12, it is illustrated that coagulation activity for bagasse is much higher of about 87.3% compared to alum which is only 54.6%. This consequently resulted in high turbidity removal by bagasse which is almost 90.0% compared to turbidity removal of alum which is only 64.1%. These results suggesting that bagasse is potential to substitute alum as a source of coagulant to efficiently treat the polluted water.
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Figure 12 Coagulation activity when natural Figure 13 Turbidity removal when natural coagulant (bagasse) compared to chemical coagulant (alum) (bagasse) compared to chemical coagulant (alum) ( Bagasse, Alum) ( Bagasse, Alum) CONCLUSIONS
Plant-based natural coagulants have assembled growing interests from researchers over the years due to their biodegradability and environmental friendly nature. The diverse types of plant-based coagulants which are chestnut, maize cob and bagasse as discussed in this study have demonstrated promising coagulation activity and turbidity removal in treating polluted river at Taman Impian Emas, Skudai. The following are the conclusions that are derived from the results of this study.
i. Bagasse shows great yield of 79.5%, the highest among all tested materials. Contrary, maize cob with the lowest yield of 23.2% indicated the least preferable material to be chosen because the preparation of the initial material needs to be abundant with the final production of the natural coagulant is very limited.
ii. NaOH is the best solvent used to extract the coagulant agent from the natural coagulant. At 2.0M, highest coagulation activity and turbidity removal was obtained, which is 91.7% and 92.5%, respectively.
iii. At optimum pH and coagulant dosage of 7.5 and 90 mg/L, highest coagulation activity and turbidity removal of 97.2% and 97.3%, respectively was recorder by Bagasse compared to the other natural coagulants.
iv. In comparison to the chemical coagulant, natural coagulant represented by bagasse showed greater coagulation activity and turbidity removal of 87.3% and 90.0%, respectively compared to alum which was only resulted in 54.6% coagulation activity and 64.1% turbdity removal in treating polluted river water.
REFERENCES
[1] Imen K, Benoît M, Philippe M, Raja BA (2011) Decolourization of the reconstituted textile effluent by different process treatments: enzymatic catalysis, coagulation/flocculation and nanofiltration processes. Desalination 68:27–37
[2] Huang X, Gao B, Wang Y, Yue Q, Li Q, Zhang Y (2014b) Coagulation performance and flocs properties of a new composite coagulant: Polytitanium–silicate–sulfate. Chem Eng J 245:173–179
[3] Choy, S.Y., Prasad, K.M.N., Wu, T.Y., Raghunandan, M.E., Ramanan, R.N., 2014.Utilization of plant-based natural coagulants as future alternatives towardssustainable water clarification. J. Environ. Sci. 26, 2178–2189.
[4] Ramavandi. B. (2014). Treatment of water turbidity and bacteria by using a coagulant extracted from Plantago ovate. Water Resources and Industry. 6, 36-50.
[5] Ndabigengesere, A., Subba Narasiah, K., Talbot, B.G., 1995. Active agents and mechanism of coagulation of turbid waters using Moringa oleifera. Water Res. 29 (2), 703–710.
[6] A H Birima, H A Hammad, M N M Desa, Z C Muda, Extraction of natural coagulant from peanut seeds for treatment of turbid water. 4th International Conference on Energy and Environment 2013 (ICEE 2013). IOP Conf. Series: Earth and Environmental Science 16 (2013).
[7] Lucas, B. D. and Kanade, T. (1981). An Iterative Image Registeration Technique with an Application to Stereo Vision. In Proceedings of the 7th International Joint Conference on Artificial Intelligence, 674-679.
[8] Li, T., Zhu, Z., Wang, D.S., Yao, C.H., Tang, H.X., 2006. Characterization of floc size, strength and structure under various coagulation mechanisms. Powder Technol. 168 (2), 104–110.
[9] Thakre, V.B., Bhole, A.G., 1985. Relative evaluation of a few natural coagulants. J. Inst. Eng. (India) Environ. Eng. Div. 65, 89–92.
[10] Bhole, A.G., 1995. Relative evaluation of a few natural coagulants. J. Water Supply Res Technol. 44 (6), 284–290. [11] Raghuwanshi N, Singh R, Wallender W and Pruitt W 2002. Estimating evapotranspiration using artificial neural network J. of Irrigation and Drainage Engineering 128-224.
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Integrating GIS Data with Meshfree-EFG Formulation for Channel Flow Mohamad Affihuraizi Mohamad1, M. Z. Abd Jamil1, H. Hirol1*
1School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia *[email protected]
ABSTRACT. This paper applies the kinematic wave equation known as Saint Venant Equation for solving the problem of shallow water flow conditions. A meshfree method known as the Element Free Galerkin (EFG) method is used to discretize the equation. Employed with the Piccard iterative scheme, the nonlinear EFG is verified with the simplified nonlinear modelling based on data from Litrico et.al al (2010). Once verified, the EFG formulation is used in analyzing the GIS data for channel flow in Sungai Skudai as a case study. The result from analysis of GIS data and EFG then verified with the result obtain with the HEC-RAS. As a conclusion, the Meshfree-EFG formulation has a great potential as an analysis method in the field of civil engineering, particularly in hydrology management.
Keywords: Saint Venant equation; Meshfree method; Element Free Galerkin; GIS; HEC-RAS.
INTRODUCTION
Hydrological numerical modeling is one of the methods that have been used quite extensively nowadays as it can help understand the hydrological processes, predicting and managing the hydrologic systems better with the help of computers. This is very important since rapid urbanization plays an important role in changing the land use and cover in which will cause significant effect to the ecological processes on a local and global scale, such as increase in flood risks. Numerically, a set of one-dimensional nonlinear partial differential equations known as Saint Venant equations, derived from Navier-Stokes Equations for shallow water flow conditions is required to solve the problem. However, due to the nonlinearity as well as the unsteady state of the equation, kinematic wave equations are commonly solved numerically with the help of computer programming. Instead of using Finite Difference Method (FDM) and Finite Element Method (FEM), Meshfree methods are considered as the latest output in the research development of numerical techniques especially using Element Free Galerkin (EFG) method. Furthermore, Geographical Information Systems (GIS) technique have particularly been handy in flood management, and can facilitate hydrological models in data collection. It is necessary to develop a Model that integrates GIS data with Meshfree-EFG formulation in conducting the analysis for channel flow. Problem Statement The hydrologic phenomenon of surface runoff and channel flow can be studied by solving kinematic wave equation. However, due to the nonlinearity and the unsteady state of the equation, no closed form solution is available except for the simplest case of no lateral flow and constant wave celerity. Despite the various works and formulations of FDM and FEM on kinematic wave equation, there are yet EFG formulations for the equation. Since the integration of GIS data with Meshfree formulation is relatively new, it is important to develop the potential of this future analysis method in the field of civil engineering, particularly in hydrology and river engineering.
Objectives The main purpose of the research is to study the feasibility of integrating GIS data with Meshfree-EFG formulation for analysing the channel flow by using MATLAB software. The objectives of this study are:
1. To derive and formulate EFG formulation for kinematic wave equation and write the corresponding MATLAB source code;
2. To validate the formulation against previous works; 3. To modeling and verify the HEC-RAS data with the formulation; 4. To conduct the performance study in assessing the potential of integrating the GIS data with the formulation.
Scope of Study In order to accomplish the objectives of this study, the scope are as follow;
1. EFG meshfree method is applied; 2. Due to time constraint, the study only involves with mathematical derivations and computer programming and no direct
experimental works are conducted. However, the absence of direct experimental work is compensated by the validation and verification which are carried out against the actual gauged data from the previous work;
3. All assumptions in Saint Venant equations and kinematic wave equation holds.
LITERATURE REVIEW
The one dimensional equations of motion for unsteady non uniform flow describing flood wave propagation for overland and open channel flow were first proposed by Saint-Venant in 1871, which later known as St. Venant equations (Saint Venant, 1871). These equations consist of continuity equation coupled with momentum equation. These nonlinear hyperbolic partial differential equations cannot be solved analytically. In 1957, Stoker provided the first numerical solution for St. Venant equations using an
15
explicit finite difference method (Stoker, 1957). However, later researchers sought to simplify the St. Venant equations whenever the physical and the boundary conditions can be justified (Tayfur et al., 1993, Vieira, 1983). Such simplifications mostly involved modification to the momentum equation. Dynamic, diffusion and kinematic wave equations are the common models resulting from such simplifications. Lightwill and Whitham (1955) showed that for Froude number less than one, the kinematic wave become dominant and preferable. Due to its simplicity, the kinematic wave equations is frequently applied to various type of flow modeling, including watershed runoff, flood routing in rivers, channels flow, erosion and sediment transport (Singh, 2003). Since the development of kinematic wave theory by Lighthill and Whitham (1955), the theory has been applied as the basis for the mathematical modelling of various hydrologic processes (Singh, 2003).
Earliest numerical works on kinematic wave equations was carried out by Henderson (1989). The work used method of characteristic to transform the conservation of momentum equation into characteristic equation. The solution of kinematic wave equations by using method of characteristic was shown as prone to shock development due to lack of diffusion (Ponce, 1991). However, recent studies on the numerical solution of kinematic wave focused on establishing new numerical technique to solve the problem. Litrico et. al. (2010) employed kinematic wave equations which were solved alternatively using Hayami Transfer Function to a real river that is Jacui river in Brazil. By using real inlet data taken from the river, the mathematical solution was shown as able to mimic the discharge gauged at the downstream. Such an employment to real data which was more complex than those hypothetically proposed is the evidence to the applicability of the kinematic wave equations in simulating channel flow. Due to the complex but clearly reported input and output data, the work is suitable for verification and benchmarking purposes hence considered in this study.
On the other hand, Element Free Galerkin (EFG) is a Meshfree method that employs moving least square (MLS) shape functions as the interpolation functions. The method was first proposed by Belytschko et. al. (1994). The work found that the method was not affected by the irregularity in the node distribution. Since then, application EFG has been extended to various problems both linear and nonlinear as well two dimensional and three dimensional (Belytschko et.al., 1997, Krsyl and Belytschko, 1999, Chen et al., 2003).
METHODOLOGY
This research consisted of the derivation of Saint Venant Kinematic Wave Equation, followed by the discretization of the equations using Galerkin weighted residual method which are general for finite element and Meshfree formulations. Finite Difference Method (FDM) is formulated for the problem followed by the derivations of the shape functions for Element Free Galerkin (EFG) method. The nonlinear iterative scheme; Piccard are detailed and employed. Despite that, all the derivation of the equation have been conducted in the previous study by H.Hirol et. al.(2017). Saint Venant Kinematic Wave Equation Saint Venant equations are time dependent partial differential equations which describe the distribution of flow as a function of distance x along the channel and time t. The equations were derived by considering the two conservation laws which are the conservation of mass and the conservation of momentum. The final forms of the partial differential equations are given as follows. Equation of mass
!"
#$+!&
!'= )(') (Equation 1)
where A is the cross-sectional area of the flow, Q is the flow rate and q (x) is the forcing term (i.e. precipitation, lateral flow). Equation of Momentum 1
"
!&
!$+1
"
!
!'
&.
"+ /
!0
!'− / 23 − 24 = 0
(Equation 2)
where 26 is the bed slope and 24 is the frictional slope, whilst 0 and / are the depth of water and gravitational pull, respectively. However, Equation 2 can be further approximated when it is assumed that 23 = 24. This is known as the kinematics wave assumption (Vieux, 1988; Singh, 2003). This condition can be equivalently treated in Manning form as
" = α&8 (Equation 3)
where 9 is :;./=/ 1.49 236.A
, : is Manning roughness coefficient, ; is wetted perimeter, 23 is bed slope and B is 0.6. Equation 1 and Equation 3 are the Saint Venant Kinematic Wave equations. By combining Equation 1 and Equation3, the following equation can be obtained. !&
!$+
1
9B& CD8
!&
!'= )
(Equation 4)
By discretizing Equation 4 in time by forward-difference, we obtain
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&EFC − &E
∆$+
1
9B& CD8 ,EFC
!&EFC
!'= )EFC
(Equation 5)
where t+1 and t refers to present and previous time-step, respectively. Rearranging gives
&EFC +∆$
9B& CD8 ,EFC
!&EFC
!'− &E = )EFC
(Equation 6)
Galerkin Weighted Residual Method of Kinematic Wave The kinematic wave equations can be solved numerically by converting the equations into weak form by employing Galerkin weighted residual method. By weighting Equation 6 by shape functions, IJ and expressing the flow rate, & = IK&Kwhere &K are the degree of freedoms or the nodal values of & (description of IK and &Kwill be detailed in next sections), the following is obtained.
IJ IK&K +∆$
9BIL&L
CD8!IK&K
!'− IK&K
E #'M
= IJ)#'M
(Equation 7)
In Equation 7 superscript $ + 1 is omitted for ease of notation. By collecting the degree of freedoms,&K and shifting known terms to the right hand side of the equation, Equation 7 can be given as
IJIK#' +∆$
9BIJ IL&L
CD8!IK
!'MM
#' &K = IJIK&KE
M
#' + IJ)#'M
(Equation 8)
In indicial notation, Equation 8 can be represented as NJK + OJK &K = −PJ (Equation 9)
or in matrix form as N + O & & = − P (Equation 10)
where OJK or O is the stiffness matrix, NJK or N is the mass matrix whilst &Kor & and PJ or P are the vector of degree of freedom and load, respectively. Each can be given as
N = NJK = IJIK#'M
(Equation 11)
O & = OJK =∆$
9BIJ IL&L
CD8!IK
!'M
#' (Equation 12)
P = PJ = IJIK&KE
M
#' + IJ)#'M
(Equation 13)
Iterative scheme As mentioned, Kinematic Wave Equations as given by Equation 1 and Equation 3.8 are nonlinear partial differential equations.
Thus, to solve the problem, iterative schemes are needed. Therefore, Picard schemes are detailed. It is known as the direct substitution iteration method due to the scheme involves direct substitution of initial (or previous solved degree of freedoms into the stiffness matrix in solving the current ones and this process is iterated until the solution is converged. But, such simplicity usually works only for mild nonlinear problems but diverges for severe nonlinearity. HEC-RAS Modeling
HEC-RAS software is basically used to perform an analysis and a steady flow simulation. Thus, in this study, the software is used to modeling and analyze the channel flow of the study area which is at Jacui river, Brazil. The important of the modeling is to run an analysis of the study area which the data obtained then is convert into a corresponding MatLab source-codes before performing verification with result obtained from the formulation. The data of the Jacui River is as follow
Parameter Value
Channel Length (L) 29600 m
Width (W) 55.6 m
Manning coefficient (n) 0.07
Slope (Sb) 0.00089
Table 1: Parameter of the study of Jacui River
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Application in GIS As mentioned before, GIS is one of many information technologies that have transformed the ways researches conduct
research and contribute to society especially in hydrology management. As GIS software is used for creating and using maps, compiling geographic data, analyzing mapped information, sharing and discovering geographic information, in this further study, the software is used to discovering, analyzing the data and information of the channel flow. By using HEC-GeoRAS, one of the tools for processing geopastial data in ArcGIS using a graphical user interface (GUI), the interface allow the preparation of geometric data to import into HEC-RAS. However, in this study the GIS data obtained is converted into a corresponding MatLab source-codes and perform the analysis by using the Meshfree-EFG formulation in MatLab. This is one of the purpose of this study which is to conduct the performance study in assessing the potential of integrating the GIS data with the formulation.
RESULTS AND DISCUSSION
The results firstly concerns the validation of the newly derived Meshfree formulations of the nonlinear kinematic wave equation. Therefore, the formulation of EFG is validated against one of the benchmark problem which is Litrico’s work. Litrico’s work is chosen because it is one of the latest works on FEM formulation concerning the solution of kinematic wave equation. Litrico et. al. (2010) Litrico et.al (2010) worked with actual data thus more realistic in nature. The data was taken from Baptista and Michel studies. The data consists of propagation of dam release on the Jacui River in Brazil between Itauba and Volta Grande, recorded at a time step of 30 minutes. However, Litrico et al (2010) consider the river as a single reach with the fix parameters as shown in the Table 1 with the average values in width and slope.
In this work, the flow was driven by a complex time-varying inflow as shown in Figure 1. To note, due to the absence of numerical data which were not reported in the work, herein the inflow data is manually digitized. Outflow data, both gauged and predicted by the work, which required for validation purposes are also manually digitized.
Figure 1: Upstream flow (Litrico et. al, 2010)
Validation of Element Free Galerkin (EFG) Formulation Figures 2 show the plot of discharge (flow rate) calculated using Piccard scheme. Close approximations are evident between the plots hence the validation of the formulation and the corresponding source code.
Figure 2: Piccard Nonlinear EFG versus Simplified Nonlinear Modelling based on data from Litrico et.al (2010)
Based on the result shown in Figure 2, formulations derived in this study have been validated against the benchmark problem. The problems are chosen since they represent various input and complexities. The validations are carried out by comparing the results obtained with the ones reported by the benchmark problems. All formulations and their corresponding source code have been validated after producing results which are close to the reported ones.
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Piccard Nonlinear EFG vs HEC-RAS Data
Figure 3: Piccard Nonlinear EFG versus HEC-RAS Modelling based on data from Jacui River, Brazil
As shown in Figure 3, we can conclude that the result obtained by using Meshfree-EFG formulation is more accurate than using HEC-RAS software. This is because in HEC-RAS software, it can only analyze for a simple channel flow, differ while using Meshfree method, the more detail and accurate of the formulation give the better result in the end. Integrating GIS Data with Meshfree Formulation
Figure 4: Integration of GIS data with Meshfree-EFG formulation
As shown in Figure 4, by using Piccard Nonlinear EFG formulation, the data integrate from GIS can be analyze accurately with this method. This will give an opportunity to the researcher and scientist to conduct the study as this is the great potential and achievement in hydrology field.
CONCLUSION
This study shows that the development of Meshfree formulations, EFG in solving numerically, kinematic wave equations for the modeling of channel flow can be achieves. This study also shows that EFG meshfree method analised the data from GIS modeling gives a good result as good as HEC-RAS software for hydrologic modeling. It is shows that the EFG methods serves as a potential alternative to the other methods for hydrologic analysis. It is also concludes that, Meshfree-EFG formulation is one of the method that can be considered in conducting an analysis of channel flow in the future. While GIS technique was frequently used nowadays in data collection, it is the great potential to used with the formulation for further development.
REFERENCE
[1] Saint-Venant, B. D. (1871). Theory of unsteady water flow, with application to river floods and to propagation of tides in river channels. French Academy of Science, 73(1871), 237-240.
[2] Litrico, X., Pomet, J. B., & Guinot, V. (2010). Simplified nonlinear modeling of river flow routing. Advances in Water Resources, 33(9), 1015-1023.
[3] Ross, B. B., Contractor, D. N., & Shanholtz, V. O. (1979). A finite-element model of overland and channel flow for assessing the hydrologic impact of land-use change. Journal of Hydrology, 41(1), 11-30.
[4] Greco, F., & Panattoni, L. (1975). An implicit method to solve Saint Venant equations. Journal of Hydrology, 24(1), 171-185.
[5] Singh, V. P. (2001). Kinematic wave modelling in water resources: a historical perspective. Hydrological processes, 15(4), 671-706.
[6] Liu, G. R. (2002). Meshfree methods: moving beyond the finite element method. Taylor & Francis. [7] Litrico X, Pomet J-B. Nonlinear modeling and control of a long river stretch. European Control Conference, Cambridge,
UK; 2003. [8] Singh, V. P. (2003). Kinematic wave modeling in hydrology. Bridges,10(40685), 165.
19
Rainwater Quality Assessment Collected from UTM Johor Bahru Muhammad Ridzuan bin Harun1, Muzaffar Zainal Abideen1*
1School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia
ABSTRACT. This paper describes the quality of rainwater taken at Universiti Teknologi Malaysia, Johor Bahru, Johor. A study was conducted to evaluate the quality level of rainwater, to compare between rainwater collected from rooftop and open space as well as to determine the suitability of rainwater as raw water source for domestic consumption based on water quality standards stated by the Health Ministry of Malaysia (MoH). Rainwater was selected as an alternative source to reduce the dependency towards water resource for non-potable water usage such as gardening, agriculture and general cleaning. Rainwater samples were collected six times from March to April of 2018 at six different sampling stations. Samples of rainwater are taken at different locations from rooftop of building and from open space in UTM. Seven parameters were selected namely pH, total dissolved solids (TDS), chemical oxygen demand (COD), turbidity, zink (II) (Zn²+), magnesium (Mg²+) and manganese (II) (Mn²+) are tested to determine the quality of rainwater. The results for parameters obtained from rooftop and open space were the pH values were 6.5 ± 0.1 and 7.2 ± 0.9, TDS were 9 ± 3.6 and 22 ± 10mg/L, COD were 34 ± 12.5 to 45 ± 6.1mg/L and turbidity were 4 ± 1.1 to 8.6 ± 0.3 NTU respectively. Also, the result for Mn²+ readings were -0.014 ± 0.002 and 0.007 ± 0.007mg/L, Mg²+ were 0.027 ± 0.0001 and 0.09 ± 0.005mg/L and the Zn²+ were 0.036 ± 0.006 and 0.245 ± 0.004mg/L. The quality of rainwater collected from open space is better than from rooftops. Furthermore, the COD values had exceeded the recommended limit stated in the Raw Water Criteria by MoH. It is suggested that the rainwater collected at UTM has to be treated before it is used for general purposes.
Keywords: Rainwater, Water quality, Raw water
INTRODUCTION
Water is a vital necessity for both humans and nature. Increasing population growth year by year has caused demand for clean water supply to increase. Of all the issues related to water management, water tariff is considered to be one the most important issues. The Malaysian water tariffs are among the lowest in the world and most of the residents get their water supply. It is important to look into alternative sources that can replace the water supply is the rainwater.
The results show that the annual rainfall in the northwestern region of Peninsular Malaysia is the highest with an average of about 2400 mm. As an alternative to solve the water crisis in the future, the rainwater harvesting system has been proposed as part of the solution by the government [1]. Rainwater is chosed as an alternative to reduce the dependency on existing water resources.
Problem Statement Due to the limited clean water source, the rainwater that flows over the land must not be wasted. This is because rainwater can be utilized for agriculture, gardening, car wash and toilet. Using rainwater as an alternative, treated water can be spared and used for important purposes such as in food or drink and simultaneously decrease the clean water demand. Hence, this will reduce the water bill as well as the operational cost of the water treatment plants. This is anticipated to reduce our dependency on treated water for non potable usage. Objectives The main purpose of the research is to study the rainwater quality as an alternatives source for domestic consumption. The objectives of this study are:
1. To determine the rainwater quality in UTM; 2. To compare the rainwater quality collected from rooftop and open space; 3. To determine the suitability of rainwater as raw water source for domestic consumption based on raw water quality
standards stated by theHealth Ministry of Malaysia (MoH). Scope of Study The study covers the determination of the quality of rainwater collected from rooftop and open space. Rainwater harvesting was carried out at elected areas in UTM. There are six locations selected for this study; three were from rooftops at Kolej Tun Dr Ismail (KTDI), Kolej Tun Hussein Onn (KTHO) and Masjid Sultan Ismail (MSI) and another three locations for open space which were at Padang Ragbi (PR), Padang Kawad (PK) and Balai Cerap (BC). The water quality parameters involved in this study were pH, turbidity, total dissolved solid (TDS), chemical oxygen demand (COD), zink (II) (Zn²+), magnesium (Mg²+) and manganese (II) (Mn²+).
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LITERATURE REVIEW
When applying the rainwater as the alternative, the two important aspects need to be consider are the water quality requirements and potential uses. The water quality standards are usually determined according to the water usage, particularly with regard to the analysis of potential health risks. This is because a specific water source will require a specific level of treatment, depending on the potential use. Therefore, it is really important to know the initial quality of each water source in order to evaluate the potential usage, and determine the required treatment and recommend the appropriate storage. Rainwater water is very important especially towards sustainable future, rainwater harvesting and its quality are the focal point of on-going research. For example, a study conducted by Jiries had determined the metallic content and inorganic constituent during the low rainfall and long dry periods [2]. High level of copper and lead were recorded which might be caused by traffic pollution. An investigation of rainwater quality found that there is a relationship between rainwater quality and the intensity of rainfall [2]. Amount of pollutants such as chemical oxygen demand, biochemical oxygen demand, nitrate and phosphate were found to be high during a moderate rainfall. Samples taken during heavy rain were shown to contain less of these pollutants, as the rain will flushaway the contaminants [3].
Other studies showed that the location of sampling point, industrial, urban or agricultural activities have significant effect on the chemical composition of harvested rainwater. Zunckel found that there is a strong relationship between the presence of contaminants in the catchment area and rainwater quality [4]. A correlation between nitrate and ammonium is mainly contributed from trees, livestock and fertiliser used in that area.
METHODOLOGY
Study Areas Samples for the study were taken from the rooftops and open space in Universiti Teknologi Malaysia (UTM) and the locations were as follows:
Table 1: Sampling area description
Sample Research Area Coordinate Description Sample 1 Kolej Tun Dr Ismail
(KTDI) 1°33’46” N
103°38’2” E
College area, nearby parking lot and road area
Sample 2 Kolej Tun Hussein Onn
(KTHO) 1°33’51” N
103°37’46” E
College area, nearby trees and road area
Sample 3 Masjid Sultan Ismail
(MSI) 1°33’34” N
103°38’16” E
Pedestrian area and nearby tress
Sample 4 Padang Ragbi (PR) 1°34’11” N
103°38’40” E
Field area and nearby the road area
Sample 5 Padang Kawad (PK) 1°33’9” N
103°38’36” E
Yard field and nearby tress
Sample 6 Balai Cerap (BC) 1°33’29” N
103°38’28” E
Nearby building area and huts
Data Collection and Analysis A total of 18 samples were collected at six locations on different days. Samples were taken and analyzed for their water quality parameters. The parameters involved were pH, TDS, turbidity, COD, Zn²+, Mg2+ and Mn2+. In addition, these samples were preserved before tested at the environment engineering laboratory at School of Civil Engineering UTM according to United States Environmental Protection Agency [5]. The determination of pH and TDS were carried out using the YSI 55 Multimeter. For Zn²+, Mg2+ and Mn2+analyses, atomic absorption spectrometry (AAS) Aacle 900t Pin Model was used. The turbidity and COD were analysed using HACH turbidimeter and HACH DR6000 Spectrometer, respectively. The results of data analysis were further compared with recommended raw water quality criteria by MoH. In addition to the assessment of water quality, this study had compared the rainwater qualities from rooftops and open space. This comparison will determine either water quality from rooftops or open space are better.
RESULTS AND DISCUSSION
Table 2 shows the average value of the measured parameters taken three times at each location on different days. With regard to pH, TDS, Turbidity, Zn²+, Mg2+ and Mn2+ values all sample complied with the recommended raw water criteria set by MoH. Nonetheless, the value of COD from rooftop and open space exceeded the limits set by the MoH standard.
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Table 2: Rainwater quality analysis of parameter involved
Parameter Unit KTDI KTHO MSI PR PK BC pH - 6.7±0.7 6.8±0.5 6.5±0.1 7.2±0.9 6.9±0.7 7±1
Comparison between rainwater quality collected from rooftop and open space pH Figure 1 shows the average value of pH reading between from the rooftops and open space. From the figure, the minimum average pH value was 6.5 recorded at MSI while the maximum pH value was 7.2 recorded at PR. All the average reading of this pH is compared to the value of raw water quality standards by MoH.The pH values taken were within the prescribed limit between 5.5 and 9.0.
The average value of pH in the rooftops area and the open space did not give any significant differences as the values recorded were in the range of 6 to 7. Changes in the pH value, especially in rooftops areas, are caused by the roof conditions of the leaves, trees and surrounding pollutants which resulted the pH in the rooftop area to be more acidic compared to the open space.
Figure 1: pH reading during rainy day with location.
Turbidity Figure 2 shows the average reading of the lowest turbidity value recorded at PR which is 3.8 NTU while the highest average turbidity value was recorded at MSI which is 8.6 NTU. Referring to the raw water quality standards, all samples value for turbidity readings below 1000 NTU as specified required by the MoH.
In Figure 2, the turbidity values for rooftops area samples show high values especially at the MSI area compared to the open space. Turbidity values increase when rainwater comes in contact with rooftops by particle admission such as clay, mud, organic matter and biological materials that may be found on rooftops [6]. The turbidity values for open space are influenced by environmental pollution factors such as dust and smoke.
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Figure 2: Turbidityreading during rainy day with location.
Total dissolved solid The average reading of total suspended solids for each study location is shown in figure 3. The results of laboratory studies found that the lowest average value of TDS was recorded at PR is 9mg/L. The highest average value of TDS was recorded at KTDI is 22mg/L. The recommended value of TDS water quality is 1500mg/L. Hence, all the average values of the samples taken meet the requirements of the raw water quality set by the MoH.
TDS in rainwater are caused by natural environmental features such carbonate deposits, salt deposits that happen on the roof. TDS in rainwater, originating from particulate matter suspended in the atmosphere usually range from 2 to 20mg/L [7]. Pollutant such as leaves, animal dung and dust also sources of salt deposited on the roof. This contaminant shows that rainwater collected from rooftops probably high TDS then open space.
Figure 3: TDS reading during rainy day with location.
Chemical oxygen demand Figure 4 show the average reading of concentration COD for every location taken during rainy days. The lowest average COD value was recorded at PK is 34mg/L while the highest average COD value was recorded at KTDI is 45mg/L. According to the raw water quality standards set by the MoH, all the values of this COD sample exceed the recommended limit which is 10mg/L. This shows that COD concentration is relatively high and is not suitable for raw water.
Comparison between study location shows the value of COD in the rooftops area is higher than the open space. High COD concentrations representing the quality of rainwater in the area have inadequate oxygen and microbial activity on the roof of the catchment site and various parameters concentration results can depend on the weather, location and geographical activity around the study area [8]. Therefore, treatment needs to be done on rainwater samples to increase the use of rain water as raw water. Zinc. Figure 5 shows the difference average concentration reading of zinc of rainwater collected from rooftop area and open space. The highest average value of zinc readings was found at PK is 0.245mg/L. While the lowest average value of zinc was found atBC which is 0.036mg/L. Referring to the raw water quality standards, all samples values taken are within the prescribed limit for zinc readings which below 3 mg /L as determined by the MoH.
Based on the figure 5, most average reading of metal zinc that are taken from rooftops is higher than average reading from open space except average reading from PK.The impacts of roofing materials on the concentrations of metals such as zinc and other elements in stored rainwater have been subject to limited analysis [9]. As it is exposed to the atmosphere, with changes of weather, it will leadtowards the corrosion of the material. Rainwater runoff will flush away the metal deposited ontheroof. For PK, the effect contributes from the particulate lead concentrationin the air via motor vehicle exhaust emissions.
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Figure 4: COD reading during rainy day with location.
Figure 5: Zn reading during rainy day with location.
Magnesium Figure 6 shows the average value of the magnesium concentration reading of all the samples taken according to the location of the study between the rooftops area and the open space. The lowest average value of magnesium readings was recorded at PR is0.027mg/L while the highest average value magnesium reading was recorded at KTDI is 0.09mg /L. Referring to the raw water quality standards, all samples values taken are within the prescribed limit for magnesium readings which below 150mg/L as determined by the MoH.
From result analysis below, the average reading of metal magnesium is too small. So, it does not make a significant to make a comparison between rooftops area and open space. It wasconcluded that roof materials used in UTM did not have a significant impact on waterquality for metal magnesium.
Figure 6: Mg reading during rainy day with location.
Manganese Figure 7 shows the difference average concentration reading of the manganese of rainwater between the rooftops area and open space. The average value of manganese readings was highest at KTDIwhich is 0.007mg/L while the average reading value of
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manganese was lowest at BC which is -0.014mg/L or may be considered as non detectable. Referring to the raw water quality standards, all samples values taken are within the prescribed limitfor manganese readings below 0.2mg/L as determined by the MoH.
From the result below, the comparison of average concentration reading metal manganese from rooftops and open space does not make any significant because the reading is too small and negative. Otherwise, the effect of rooftops the does not make any significant to metal manganese. .
Figure 7: Mn reading during rainy day with location.
CONCLUSION From the data analysis, all the parameters such as pH, TDS, turbidity, Zn²+, Mg2+ and Mn2+are in compliance with the recommended raw water criteria set by MoH. For COD values, however, the readings exceeded the recommended limit which is 10 mg /L. This shows that further treatment is needed. The water quality of rainwater collected from open space is much better from rooftops. If properly collected, rainwater is relatively safe for domestic use but problems may arise if the roofs become heavily contaminated due to settlement of pollutants from the atmosphere and also from animal droppings.Water from rooftop and open space can be used as non-potable usage such as car washing, gardening and many more.
To conclude, from this study, the rainwater may be used as the raw water for non-potable usage provided that necessary treatment, especially for COD, is carried out. Rainwater harvesting should be carried out by all members of the public in effort to maximize the sustainable development. The effectiveness of this method and information about the water quality need to be updated from time to time. Here is a proposal that should be taken into account for the purposes of further research and the effectiveness of using these methods:
i) Conduct an analysis sampling test at residential area, commercial area and industrial to determine another factor that influence the rainwater quality
ii) Increases the sampling period to improve the findings of the rainwater quality. iii) Investigate the rainwater quality during dry and rainy season to determine the relationship between season and rainwater quality iv) Study the effect of different type of roof material towards the rainwater quality
REFERENCE
[1] Che-Ani, A. I., Shaari, N., Sairi, A., Zain, M. F. M., & Tahir, M. M. (2009). Rainwater harvesting as an alternative water supply in the future. European Journal of Scientific Research, 34(1), 132-140.
[2] Jiries, A., Hussein, H., & Halaseh, Z. (2001). The quality of water and sediments of street runoff in Amman, Jordan. Hydrological processes, 15(5), 815-824.
[3] Teemusk, A., & Mander, Ü. (2007). Rainwater runoff quantity and quality performance from a greenroof: The effects of short-term events. Ecological engineering, 30(3), 271-277.
[4] Zunckel, M., Saizar, C., & Zarauz, J. (2003). Rainwater composition in northeast Uruguay. Atmospheric Environment, 37(12), 1601-1611.
[5] Environmental Monitoring, Support Laboratory (Cincinnati, & Ohio). (1982). Handbook for sampling and sample preservation of water and wastewater (Vol. 83, No. 124503). US Environmental Protection Agency, Office of Research and Development, Environmental Monitoring and Support Laboratory.
[6] Owusu-Boateng, G., & Gadogbe, M. K. (2015). Domestic Rainwater Harvesting in a Water-Stressed Community and Variation in Rainwater Quality from Source to Storage. Consilience, (14), 225-243.
[7] Amponsah, N., Bakobie, N., Cobbina, S. J., & Duwiejuah, A. B. (2015). Assessment of Rainwater Quality in Ayanfuri, Ghana. American Chemical Science Journal 6 (3): 172, 182.
[8] Kasmin, H., Bakar, N. H., & Zubir, M. M. (2016, July). Monitoring on The Quality and Quantity of DIY Rainwater Harvesting System. In IOP Conference Series: Materials Science and Engineering (Vol. 136, No. 1, p. 012067). IOP Publishing.
[9] Morrow, A., Coombes, P., Dunstan, H., Evans, C., & Martin, A. (2007). Elements in tank water-comparisons with mains water and effects of locality and roofing materials. Rainwater and Urban Design 2007, 830.
25
Integrating GIS Data with Meshfree – PIM and RPIM for Channel Flow
Nur Nabilah Zainal Abidin1 M. A. Mohd Noor1, Mohd Zaki Nurillah1, H. Hirol1* 1School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia
*[email protected] ABSTRACT. This study concerns the integrating GIS data with Meshfree formulation namely Point Interpolation Method (PIM) and Radial Point Interpolation Method (RPIM) in solving numerically, St Venant’s kinematic wave equation for the hydrologic modelling of channel flow. It involves the verification of derivation of the formulation, provision of the corresponding solutions and Matlab sources code, verifications of results against established data, parametric study and assessment of performances of the newly derived Meshfree formulations against established numerical method, namely Finite Element Method (FEM) and Finite Difference Method (FDM). The originality and the main contribution of the study are the Meshfree formulation which are obtain by discretizing the kinematic wave equations into matrix forms. The formulation are verified when it is found that the result produced by the source codes are in general in close agreement with the benchmark data. A result obtain from HEC-RAS modelling will be compared with the Meshfree Method to see the difference between using the formulation and the HEC-RAS software by using the same data from Jacui River by Litrico et.al. (2010). The Meshfree formulation also verified by using the real data of Sungai Johor that been developed using the HEC-GEORAS and GIS software for the channel flow and integrating it into the source code of Meshfree formulation. Meshfree method perform in a comparable manner with the established methods in regards to the rate of convergences. This shows the potential of Meshfree as numerical method for its future development.
Keywords: Meshfree Method, Point Interpolation Method (PIM), Radial Point Interpolation Method (RPIM), Saint Venant kinematic wave, HEC-RAS, GEO-RAS, GIS, Finite Element Method, Finite Different Method
INTRODUCTION Hydrologic modeling issues the study of hydrologic processes like evapotranspiration, belowground flow, surface runoff and channel flow. Ways of study may be either settled or combination of the two. While random methodology employs probabilistic (statistical) approach, deterministic method basically involves attempt to solve a set of partial differential equation which describes the behavior of the flow. However, complex problem usually will include numerical method for equation rather than analytical. FEM is one amongst numerical technique that has its own potency in modelling the irregular body shapes and drawback domain. Meshfree method can be considered as the latest output in the research development of numerical techniques. The inventions were motivated by the attempt to remove the need for predefined meshes which are required in FEM. Therefore, since there could be various ways in doing this, meshfree method which are not refer to specific method but to the whole method such as Point Interpolation Method (PIM) and Radial Point Interpolation Method (RPIM). The purpose of this paper is to integrating GIS data with Meshfree – PIM formulation and RPIM formulation for channel flow. Problem Statement In this study area, the hydrologic phenomenon likes channel flow can be studied by kinematic wave equation. However, a closed form solution is not available due to the nonlinearity and the unsteady state of equation. Therefore, to obtain the solution, FDM or FEM are usually used in the kinematic wave equation [5]. The formulation also can be solved using Meshfree method. For the purpose of this study PIM and RPIM are used in formulation for kinematic wave equation to limit the study area.
Based on these, it is therefore the main and purpose of this study to verified the integrating GIS data with Meshfree – PIM and RPIM for channel flow. Objectives The main purpose of the research is to verify the integrating GIS data with Meshfree – PIM and RPIM for channel flow. The objectives of this study are:
1. To analyse the data from the GIS into the Meshfree – PIM and RPIM formulation and verified the result. 2. To modelling the GIS system and validate the GIS modelling. 3. To compare the PIM and RPIM formulation with HEC-RAS result.
Scope of Study All assumptions is in St Venant equation and kinematic wave equation holds. The study strictly involve in mathematical derivation and computer programming thus no direct experimental works are conducted due to time constraint. To limit the scope of study, only PIM and RPIM Meshfree methods are considered. Despite the availability of various nonlinear scheme and time-integration method are available, thus this study only follows Piccard as iterative schemes.
The study utilises the available image processing and computer vision system toolbox in MATLAB and GIS software to acquire, collect, and analyse data from the captured images. This also involves the development of programming codes in order to achieve the objectives of the study.
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LITERATURE REVIEW
The combination of physical process like earthquake, volcanoes, and flow of river would form a shape of Earth. By increasing from populations, agricultural expansions, changing flow the river also one factor formation of shape of Earth. Land use are about any ways in where human made use and manage the land and its resources. Growth of population is one of land use which they would change the land use in that area and automatically would affect the quality of river and flash flood would be form.
Since ancient, people use a map to make a point in their colony place. Usually map is formed into hard copy and its preparation process is too complicated and take a time. Nowadays, skill and techniques of mapping are exposed where it would increase the accurate and give a great performance to the physical features. GIS including a works that related to the development, management, environment and planning. GIS is a short form of Geographic Information System which is a system that including an effective software, data, user, organisation, and management for collect, manage, analyze, display spatially and reference data [8]. In GIS application, a management of data such as updating, collecting and acquisition can be easier due to relation of map and data [11]. ArcGIS device a professional-strength relational database, a feature critical of GIS software. Relational data manipulation is integrated with powerful geo processing for spatial queries, polygon overlay, and other location-based analyzes. This is supported so that data are moved easily to and from relational tables and geographic databases. ArcGIS supports datasets in its native geo database as well as multiple GIS file formats. ArcGIS works with geographic datasets that are managed in geo databases as well as in numerous GIS file formats. Geo database represented the native data structure of ArcGIS and are the primary data format used for editing and data management. As the result, a modelling with ArcGIS software will produce hydrographic network map [1].
Numerical Method are applied to solve the unsteady flow equation in hydrology. Kinematic wave equation are explore through finite element method and finite difference method [4]. Mathematically, kinematic wave equations are indicates by the law of conversation of mass through the continuity equation. The kinematic wave equation is a hyperbolic equations. Its mean that the equation [9]. In refer only the situation where the disturbance generate along the behavior of the equations [3].[5]also occupied kinematic wave equations which solved alternatively using Hayami Transfer Function to a real river that is Jacui River at Brazil. From here, it can be proved that the mathematical solution can be solved the discharged gauged at the downstream.
Meshfree Method is a main research in this study area in numerical analysis exclusively in the area of computational mechanics and fluid dynamics. Past few years, assorted Meshfree formulation and methods applied in many engineering areas to solve the problem. Meshfree is contribute as the alternative numerical method where able to present better numerical performances for certain engineering problems compared to regular techniques such as finite element method and finite difference method [6]. Meshfree also generates nodes either in form of uniformly distributed or scattered over the domain. Meshfree develop the shape functions based on the number of nodes that remain in the support domains. Anyhow, once the shape function have been set, Meshfree will have the same step as FEM.
METHODOLOGY
This research consisted of three key activities; understanding the derivation of Point Interpolation Method (PIM) and Radial Point Interpolation Method (RPIM), Comparison the result obtain from the formulation with result obtain from HEC-RAS software, and verified the result with data that have been contruct using HEC-GEORAS in GIS software. The derivation of the PIM and RPIM formulation have been done by [2]. The source coding for the formulation is develop using Matlab. Then the result from source coding of PIM and RPIM are compared with HEC-RAS software using the same data which is a secondary data from [5]. The result also compared with the real data of Sungai Johor would be compute into the GEO-RAS in GIS to integrate the data with PIM and RPIM and HEC-RAS to see the difference.
RESULTS AND DISCUSSION
The results on the verification of formulation of Meshfree Method - PIM against gauged (real) data [5] are discussed herein. Also showed here are the verification in integrating of GIS data with Meshfree – PIM for channel flow.
Verification of formulation Case 1: Verification against gauged (real) data Litrico et al. (2010). [5] handle with real data, gauged from the Jacui River in Brazil. The flow was consumed by time-varying upstream boundary positions (varying inflow) as shown in Table 1. The data be contained the producing of dam discharge release on the Jacui River in Brazil between Itauba and Volta Grande, recorded at a time step of 30 min. Table 1 show the data of the river.
Table 1: Parameter of the study by Litrico et al. (2010)
Parameters Values Channel Length (L) 29600 m
Width (W) 55.6 m Manning’s coefficient (n)
Slope (Sb) 800 – 1850
0.00089
Figure 1 shows the plot of discharge (flow rate) calculated using Picard Iteration scheme, respectively. The nearest approximations are proved between the plots hence the validation of the formulation and the correlative source code.
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Figure 1: PIM picard versus gauged and predicted data by Litrico et al. (2010)
Case 2: Verification against gauged (real) data Litrico et al. (2010) with HEC-RAS software. Firstly, data of [5] used in the HEC-RAS software to analyse the result and compared the result with the Meshfree method – PIM in the source code. Figure 2(a) shown the result of flow hydrograph from the HEC-RAS software to be a comparison with Meshfree method.
Figure 2: (a) Flow Hydrograph from HEC-RAS software (b) Relationship between Meshfree–PIM and HEC-RAS
Figure 2(b) shown the relationship between Meshfree–PIM and HEC-RAS using the same data from [5] to made the
comparison through their result. From the result, it show that PIM and HEC-RAS have a small different through their time and discharge. But, the shape are still maintain and can be accepted. The result can be supported and proved by [6] which their study is the comparison using numerical method FLDWAV and HEC-RAS. The result show that two models has a differences and still on the range of a few centimeters. From the study area, the result show in Figure 2(b) can be accepted. Case 3: Verification against data from GEO-RAS. In process of verification the formulation of PIM and RPIM, the real data from Sungai Johor was taken from the Earth Map. The data then would model in the ArcMap version 10.2.2 and GEO-RAS. Figure 3 show the modelling of the Sungai Johor.
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Figure 3: Sungai Johor in GEO-RAS ArcMAp version 10.2.2
Figure 3 show the modelling of Sungai Johor that convert from the Earth Map into TIN in ArcMap version 10.2.2. Then, a model would be turn into the GEO-RAS by created a RAS layer by assigned the river, bank and flow path of the river. All the data would be export and run to get the coordinates of the model. So that the result would be integrate into the source code of Meshfree method - PIM.
Figure 4: Integration of GEO-RAS into Meshfree-PIM
Figure 4 show the result of integrating GIS data with Meshfree method-PIM. From the result, can see the shape of the graph
are quietly same with the application of data from [5]. Close approximation are the evident between the plotting so, the formulation of Meshfree-PIM are valid and verified corresponding source code.
(a)
29
(b) (c)
Figure 5: (a) Graph from the Litrico et. al. Study. (b) Comparison between HEC-RAS vs RPIM (c) Result analysis Sg. Johor
Figure 3 show the result of integrating GIS data with RPIM. From the result, can see the shape of the graph are quietly same
with the application of data from) [5]. Close approximation are the evident between the plotting so, the formulation of RPIM are valid and verified corresponding source code.
CONCLUSION
This study has shows that PIM and RPIM formulation for kinematic wave equation for channel flow are verified by gauged data obtained from an actual river, Jacui River in Brazil and also verified by applying the integration of GIS with PIM and RPIM. Close agreements are obtained between the PIM and RPIM formulation thus highlight the potential of PIM and RPIM as an alternative way in the field of Hydrologic modelling.
REFERENCE
[1] Aleksandr, N., Vladimir, B., Vitaly, T., Issa, T., Yulia, V., Olga, S., Olga, N., Sergey, P. & Wilfried, M. (2016). Use of GIS-environment under the analysis of the managerial solutions for flood events protection measures. Procedia Engineering, 165, 1731-1740. Elsevier Ltd
[2] Halinawati Hirol (2016), Meshfree Formulations of Kinematic Wave for Surface Runoff and Channel Flow, Universiti Teknologi Malaysia
[3] Hirol, H., Noor, M, A.Kasiman, E. H, A. K. B. Hong a, Z. Yusop a, & A. Y. Mohd Yassin (2017). Meshless Point interpolation of Kinematic Wave Equation for Flood Routing. Journal of Engineering and Applied Sciences 12 (20), 5286-5293
[4] Hossain, M, M. and Ferdous, E, J (2013). Solution of Kinematic Wave Equation using finite Different Method and Finite Element Method. University of Dhaka, Bangladesh
[5] Litrico, X., J.B Pomet & V.Guinot (2010). Simplified nonlinear modelling of river flow routing. Adv. Water Resource., 33: 1015-1023
[6] Liu, G.R. & Gu, Y.T. (2004). Boundary mesh free method based on the boundary point interpolation methods. Eng. Anal. Boundary Elem., 28:475-487
[7] Ross, D, Z.,David, G, J. & Richard, C, D (2005). Comparison of HEC-RAS with FLDWAV and DAMBRK models for dam break analysis. CDA Annual Conference
[8] Sevim, P. O. & Cigdem, T. (2016). Detection of Flood Hazard in Urban Areas Using GIS-Izmir Case. Procedia Technology, 22, 373-381. University of Tirgu Mures : Elsevier Ltd
[9] Singh, V. P. (2003). Kinematic wave modelling in hydrology. Proceedings of the ASCE Conference on World Water and Environmental Resources Congress, June 23-26, 2003, ASCE, Philadelphia, Pennsylvania, pp: 1-38.
[10] Vieux, B, E., v. f. Bralts, L. J. Segerlind & R. B. Wallace, (1990). Finite element watershed modelling: One dimensional elements. J. Water Resource. Plan Manage., 116: 803-819
[11] Wallis, J. R. (1988). The GIS/hydrology interface: the present and the future. Environment Software, Vol 3, No.4, 171-173. Thomas J. Watson Research Center, USA
30
Solid Waste Management among Rural Residents in Ulu Choh, Johor Nurul Afifah Md Shukri1, Mohd Nor Othman1*
1School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia *[email protected]
ABSTRACT. A poor and massive unmanageable solid waste can lead to pollution which will affect the air, water and environment. This study highlights the observation of river pollution along Ulu Choh River, Ulu Choh in Johor due to the ineffective solid waste management system. This is caused by the irresponsible littering and poor management of solid waste. Therefore, this study focuses on finding the cause of river pollution and how the rural residents in Kampung Baru Cina, Kampung Melayu and Kampung Bahagia in Ulu Choh manage their solid waste. This research also investigates the recycling practices by rural residents and examines the elements of the recycling practice. The importance of the study is to improve the condition and increase the awareness towards solid waste management. This information was gathered through a survey questionnaire with its structure focuses on the research objectives. This survey was carried out on 60 respondents who were randomly selected around Ulu Choh area between February to March 2018. From the findings of this study, it can be observed that the awareness and behaviour of residents towards solid waste management is low. Moreover, it can be also concluded that the awareness of residents towards solid waste management practices in Ulu Choh is unsatisfactory.
Solid waste is categorized as the disposal of unwanted material, either raw or manufactured. Most of the waste comes in the form of plastics, food waste, corps, and domestic waste. To fulfill the urbanization and monitor the development cities, a system of solid waste management has been established. This paper presents an overview of the current solid waste management practices in a rural area called Kampung Ulu Choh, Johor and provides a brief discussion on the problems and factors relating to solid waste management in this selected area.
Problem Statement With only 76% of generated waste collected, the government ordered to privatize the solid waste management system and domestic disposal. A selected river named Ulu Choh River has been badly polluted with littering and unmanageable disposal. Due to these activities, the river had been exposed to pollutants and improper disposal of waste directly into the river. One of the reasons contributing to these problems was that there were no systematic solid waste disposal management being applied at Kampung Melayu compared to Kampung Bahagia and Kampung Baru Cina. Furthermore, the satisfaction levels of the residents’ awareness towards alternative solution need to be studied.
Objectives The main purpose of the research is to study the rural residential behavior in managing their solid waste at Kampung Ulu Choh, Johor. The objectives of this study are:
1. To study the social background behavior of residents and to analyse the relationship between demographic factor with perspective of residents towards littering issue in Ulu Choh.
2. To compare the effectiveness of solid waste management with different systems between Kampung Bahagia, Kampung Melayu and Kampung Baru Cina in Ulu Choh.
3. To compare the level of awareness of residents towards recycling and their alternatives in managing solid waste between Kampung Bahagia, Kampung Melayu and Kampung Baru Cina.
Scope of Study The general scope of this study is the solid waste management and the awareness of preserving the environment among residents of Kampung Ulu Choh. The specific scope that will be discussed are the behavior, awareness and attitude towards solid waste management. The study focuses on the issue of the polluted Ulu Choh River such as littering problem by residents, the efficiency of solid waste management itself and the behavioral of the residents towards the environment.
LITERATURE REVIEW
Municipal solid waste (MSW) management is an important environmental concern due to rapid population growth, increment in MSW generation rate, environmental protection, human health risk, and shrinking of disposal site options because of urbanization [1]. In line with the rapid population growth, the generation of MSW also increases. Based on MSW generation study in Malaysia, the total daily MSW generation was 29,711 tons/day in 2012 and an overall of 36,165 tons/day of MSW will be projected by 2020 [2].
Impacts on human health, ensuring environmental protection and shrinking of disposal site options or land availability for disposal sites have ruled out all the possibilities of having small landfills and dumping grounds in the vicinity of human settlements. Consequently, large sanitary landfilling has been found to be the only viable option for disposal of MSW [3]. Human
31
population and rural-urban migration has increased as well as industrialization without a complimentary increase in waste management systems to cater for the resultant high rate of waste generation [4].
The awareness of public on 3Rs (Reduce, Reuse and Recycle) is low, in spite of the Malaysian government’s funding for public information campaigns. There is a lack of policy to promote 3Rs and encourage public participation. In 1988, the Action Plan for a Beautiful and Clean (ABC) Malaysia was introduced but had only minimal responses from the general public [5]. However, in the case of sorting recyclables and other materials, for example, hazardous or bulky waste, there always appears the issue of a well-organized system of collecting this waste and efficient methods of further utilization. As was mentioned earlier waste collection has implications both humans and the environment. Additionally, some types of recycling processes consume more energy/water/other resources and emit more pollutants than production from raw materials [6].
A hazardous waste (HW) is defined as any waste that possesses hazard properties such as toxicity, flammability, carcinogenicity, reactivity, and corrosivity that make it as a substantial present or potential hazard to human and the environment and thus requires strict controls in the course of handling, transportation, processing and disposal. Hazardous waste management systems (HWMS) transport to facilities with proper processing technologies or final disposal [7].
Open burning is widely used in many developing countries while in developed countries it may either be strictly regulated, or otherwise occur more frequently in rural areas than in urban areas. Nevertheless, open burning of MSW including plastics, tyres, painted wood, used oils or paints are forbidden because they pose a serious threat to the environment [8]. Most of them do not meet environmentally safe MSW disposal levels because of a lack of sanitary landfills. In Kenya, for example, MSW is disposed in open dumps which lack of proper environmental pollution control and monitoring. None of the dumpsites in Nairobi or the other four local authorities meet the basic requirements in protecting groundwater from pollution by leachate as they have no liners [9].
METHODOLOGY
This study involved a face-to-face engagement with the local residents of Kampung Ulu Choh. A questionnaire survey form that includes the demography and various aspects of solid waste management was used. The total number of respondents was 60 comprising of various races and background. Visual observations of the study area were also carried out. Demography Demography can be used for the purpose of business, research, and policy development. In this method, it was observed that the changes in population and their characteristics depend on factors such as death, birth and immigration. Since the main data collection of this study is through survey method, a demographic analysis had validated the data obtained from survey method. Location of Study. The observation made in the research areas were Kampung Melayu, Kampung Baru Cina and Kampung Bahagia. The size of the population was collected based on their race, age, and gender. The results were compared with the results from the survey. The demographic analysis results were the factors of the resulted survey analysis. Figure 1 shows the map of Kampung Ulu Choh.
Figure 1: Location map of Kampung Ulu Choh
Demography Analysis. The data obtained from interviewing and survey done in the demographic study was compared to the living background, population, and other parameters. The conclusion was made and supported the results of the awareness of residents toward solid waste management. There were several steps needed to be followed to analyse the data that were the background of residents and population representative.
Survey A survey consisting of a series of question which also known as questionnaire, were given to selected person which are known as respondent, for answering the questions and recording the data. The selected group of 60 respondents consisted of 29 Malays, 18 Chinese, 3 Indians and 10 as other races or foreigners. Moreover, the data collected through this questionnaire enquired the respondent’s personal background, respondent’s perspective on Ulu Choh River, solid waste management and recycling and respondents‘ alternative disposal method.
Questionnaire. Generally, within a questionnaire, the survey questions listed were listed into two categories which are closed-ended questions and open-ended questions. Furthermore, the questionaires were divided into three sections whereas Section A represented the respondent’s personal information, Section B is the close-ended questions and Section C is the respondent’s perspective.
32
Survey Analysis. After the data were obtained, the results were then being presented in the form of percentage frequency, measure of central tendency, cross tabulation, normal distribution and correlation. All of the analysis was done by using SPSS (Statistical Package for the Social Science). Below are the formulas to analyze the survey in Section B of the questionnaire. Therefore, the response from the respondents obtained was analyzed, the score was calculated based on the formulas below and the level of awareness of the respondents towards environmental aspect was concluded based on Table 1.
i. Calculation of level of respondent’s awareness towards Ulu Choh River
Level of awareness (Equation 1)
ii. Calculation of level of respondent’s satisfaction towards SWM system provided.
Level of satisfaction (Equation 2)
iii. Calculation of level of respondent’s awareness towards recycling and alternative in SWM.
Level of awareness (Equation 3)
Table 1: Percentage of respondent’s level of awareness and satisfaction
Percentage (%) Level 0 – 20
21 – 40 41 – 60 61 – 80
81 – 100
Very Low Low
Normal High
Very High
RESULTS AND DISCUSSION
Data were collected and analyzed using SPSS and Microsoft Excel. In conjunction, the analysis was divided into three parts, which are preliminary analysis, survey analysis and statistical analysis.
Preliminary Analysis
The analysis involved in projecting results in term of demographic value of the survey. A total of 60 survey forms were distributed and collected among 60 respondents. The preliminary analysis focused on the statistics of residents and later supported the results obtained in survey analysis.
As the analysis of percentage frequency in age category was conducted, it was observed that the distribution of respondents were high from the old folks group and lowest from the teenager group with the percentage frequency of male respondents was 63% while female respondents was 37%. Based on the gender based profile of the villages, it can be observed that the male residents are higher than female residents. In addition, Malay respondents scored the highest among the three races with 46%. Then, the percentage frequency followed by Chinese respondents that contributed 30% among the 60 respondents. While the Indian respondents participation was only 7% and the other races comprised of foreigners, such as the worker or citizen from Bangladesh, Nepal and Indonesia. They were higher than the Indian with percentage frequency of 17% due to the occupation or industry needs in Kampung Ulu Choh.
Most of the residents in Ulu Choh choose to work on their own as they run business or agricultural activity, and can easily participate in the survey due to leisure time. Randomly picked, the respondents that give huge participation were the residents of Kampung Baru Cina with 42%. The residents of Kampung Bahagia exceeded the population of Kampung Melayu with 30% and 28% respectively. Survey Analysis Survey Analysis of Behavioral Factor Ulu Choh River was reported and claimed by Majlis Perbandaran Kulai (MPKU) as polluted with littering. Therefore, the analysis was conducted to observe the behavioral and perspective of residents towards cleanliness and pollution of Ulu Choh River. From the calculation obtained using Equation 1, the level of awareness of respondents towards the cleaning activity is low, whereas only scored 37.5%. It can be concluded that the awareness of residential in rural area towards pollution of Ulu Choh River is high but the behavior of residents towards cleaning and keeping the environment of Ulu Choh River is low. The
33
respondents themselves agreed with the statement that causing their river pollution is because of the attitude among them. Figure 2 (a) shows the large amount of respondents take no action towards the massive pollution of Ulu Choh River proving that the rural residential were still lacking of awareness towards environment and they did not have the effort to take control of their river cleanliness with the huge littering activity. Based on Figure 2 (b), the respondents themselves agreed with the statement that the cause to their river pollution is because of the attitude among them.
(a) (b)
Figure 2: (a) Respondent’s behavior towards pollution and (b) respondent’s perspective towards condition of Ulu Choh River Survey Analysis of Satisfaction Factor Satisfaction can become one of the factors that may lead to the residents’ behavior towards environment. As Kampung Bahagia has different SWM system than Kampung Baru Cina and Kampung Melayu, the effectiveness of each method was analyzed. From the calculation obtained using Equation 2, the respondents’ level of satisfaction towards SWM system is low, where only scored 35.3%. From the survey it was observed that the respondents’ satisfaction towards SWM system relating to the system effectiveness showed that most of the rural residential were satisfied with several systems but still many of them agreed with the irrelevance of the SWM system such as the service tax. However, due to the ineffective privatized SWM system, most of the residents also took the initiative to practice littering, open burning and decomposition as shown in Figure 3.
Figure 3: Method of waste disposal by respondents
Survey Analysis of Awareness Factor Awareness must be a priority for a community to preserve the environment and have an effective solid waste management in their daily life. Recycling is one of the most important preservation methods that can improve the environment. From the calculation using Equation 3, the respondents’ level of awareness toward recycling was low, whereas only scored 31.8%. From the analysis, it was concluded that most of the rural residential were unaware of the recycling or 3R campaign and other alternatives that can help to reduce environmental pollution. According to the problem statement, the recycling practice in Ulu Choh was low and less exposure to it. However, the results obtained were impressive where most of the respondents were aware of the recycling campaign and implemented it in their daily life. Figure 4 shows the level of the awareness towards recycling that were analyzed and most of them did not participate in the recycling activity but most of the respondents from each village choosed to recycle.
0 20 40
TooBadBad
GoodVerygood
729
231
FrequencyCond
ition
ofSgUluCho
h
ConditionofSg.UluChoh
34
Figure 4: Alternative disposal method
Statistical Analysis Normality Distribution of Gender with Behavior of Residents towards Environment. The Skewness and Kurtosis are known as z-values. Therefore, to determine the normality of z-values, the values must be satisfied between -1.96 and 1.96. The observation made through the statistical analysis shows that Normality Distribution (N) calculated for both male and female in terms of Skewness and Kurtosis were normal. This was because of the four z-values calculated are laid between -1.96 and 1.96. Based on the hypothesis, the gender factor is strongly related which effected the residents’ action on solid waste disposal in the river. It can be concluded that the data collected from the survey were acceptable accordingly to these two variables.
Shapiro-Wilk, which is the Normality Test, is used to measure the normality of the variables. For this parameter, hypothesis can be accepted if p-value is more than 0.05. Furthermore, the p-values obtained were 0 for both male and female respectively. Hence, the data obtained from the normality test was not normally distributed as their p-values are 0 and the hypothesis is rejected. Besides, a Normal Q-Q plot which also known as quantile-quantile plot is a graphical tool to assist and examine the inconsistency within data survey as well as to provide the distribution of the normality. As in Figure 5, it shows that the plotted data was normally distributed as it was aligned with the best fit line for male and female category. The expected normal indicated that the normality analyzed for each action taken matter showing that there are some data inconsistency that leads to p-values = 0.
Figure 5: Normal Q-Q plot of action taken by male and female respondents towards environment Correlation between Respondent’s Perspective and Behaviour on SWM system To survey and compare the respondents’ behavior and perspective towards SWM system in their villages, the analysis had presented the bivariate correlation and scatter plot. This correlation on respondent’s perspective and behavior used Pearson correlation where it classifies a strong relationship between the two variables when it is valued as 1 and it is true for their own variable correlation. Sig (2-Tailed) value indicates statically significant relation. The interpretation made was the correlation between respondent's perspectives on improving SWM system and how residents manage their solid waste was weak. This was because of their Pearson correlation value is 0.275 and it is approaching 0 which is the weakest. Next, the Sig. (2-tailed) indicated the value was 0.034 which is lower than 0.05. This means that the two variables had statically significant correlation where any movement from resident’s behavior is related with the movement of resident’s perspective. It can be concluded that the Pearson correlation shows differ strength of correlation with Sig. (2-Tailed). Therefore, a scatter plot was done for further analysis.
Table 2 shows the cross tabulation of relationship between respondent's perspective on improving the SWM system and residents solid waste management, it displayed that 88.2% of respondents that used bin as their main disposal method agreed to increase the frequency of waste collection. It is believed that resident’s perception and the way they manage their solid waste related to each other but some parts were still weak such as less respondents using decomposition and other methods.
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Table 2: Perspective and action taken by respondents to SWM
Properties How residents manage their solid waste
Total Using dustbin
Open burning Decomposition Other
Perspective by respondents on improving SWM system
Increase frequency of waste collection 88.2% 11.8% - - 100.0%
Punctual schedule of waste collection 68.8% 25.0% - 6.3% 100.0%
Increase volume of dustbin 56.3% 31.3% 12.5% - 100.0% Other 54.5% 27.3% 18.2% - 100.0%
Total 68.3% 23.3% 6.7% 1.7% 100.0%
Comparison of Means of Respondent’s Perspective in Recycling based on Age Means comparison indicate precisely that the two variables are not significantly related and same. Therefore, the Independent Sample T-test was conducted and the means comparison was discussed as well as the relationship between them. The analysis was presented using independent sample test. Levene’s Test in Table 3 was used in this study to check the variance equality when it was from a non-uniform distribution data. Therefore, data shows the significant value was 0.233 which is greater than 0.05. This value means that the variability in the two variables was about the same. It also demonstrates that the resident’s perspective to recycling campaign did not vary too much more than the age category. This puts the two variables as not significantly different.
Since the variability was small, this can be concluded that the means difference is small. In addition, this condition shall be further discussed with t-test analysis for equality of means. By considering of the equal variances assumed, the sig. (2-Tailed) or p-value obtained was 0.805 which is greater than 0.05. The differences between condition means were likely due to opened chance and inconsistency within data. In conclusion, there is no significantly difference between the two means and factor of age which did not give huge effect to the resident’s perspective to recycling.
This study presents the rural residential behavior of Kampung Ulu Choh towards solid waste management. The findings can be summarised as below.
1. The perspective of residents towards SWM is good. However, greater public participation among the residents can further improve the waste management in the future.
2. The effectiveness of SWM system between the three villages can be differentiated whereas Kampung Bahagia is the worst due to the high probability of littering in Ulu Choh River. While the intermediate position is the system from Kampung Melayu and lastly, the best system practice is from Kampung Baru Cina.
3. None of the respondents from Kampung Baru Cina disagreed with the recycling campaign which shows that the level of residential awareness of Kampung Baru Cina is the highest. The residents from Kampung Melayu are also highly agreed with recycling. The comparison between the residents of Kampung Melayu and Kampung Baru Cina had shown high level of recycling campaign awareness being achieved compared to Kampung Bahagia residents which was vice versa.
4. The results of the statistical analysis show that most of the variables are significantly related and some are not. This shows that the survey conducted represents the real population and their perspective and behavior on pollution and environmental awareness.
36
REFERENCE
[1] Huang, G. H. (2003). The Perspectives of Environmental Informatics and Systems Analysis. Journal of Environmental Informatics, 1(1), 1–7.
[2] Tarmudi Z. & Abdullah ML, T. A. (2009). An Overview of Municipal Solid Wastes Generation in Malaysia. Teknol, 51, 1–15.
[3] Stevens, E.S. (2002). Green Plastics: An Introduction to the New Science of Biodegradable Plastics. Princeton University Press.
[4] Afangideh, A. I., Joseph, K. U., & Atu, J. E. (2012). Attitude of Urban Dwellers to Waste Disposal and Management in Calabar, Nigeria. European Journal of Sustainable Development, 1(1), 22–34.
[5] Hassan, M. N. & Rahman, R. A. (1990). Recycling in Malaysia : Problems and Prospects. Waste Management & Research.
[6] Ajaykumar Soni & Deepak Patil, K. A. (2016). Municipal Solid Waste Management, In Procedia Environmental Sciences, 35, 119–126.
[7] Ozge Yilmaz, Bahar Y. & Kara, U. Y. (2017). Hazardous Waste Management System Design under Population and Environmental Impact Considerations. Journal of Environmental Management, 203, 720–731.
[8] Jouhara, H., Czajczyńska, D., Ghazal, H., Krzyżyńska, R., Anguilano, L., Reynolds, A. J. & Spencer, N. (2017). Municipal Waste Management Systems for Domestic Use, In Energy, 139, 485-506.
[9] Rotich K. Henry, Zhao Yongsheng & Dong Jun. (2006). Municipal Solid Waste Management Challenges In Developing Countries – Kenyan Case Study. Waste Management, 26, 92–100.
37
Numerical Modeling of 3-Dimension Advection-Diffusion Equation for Pollutant Transport in River
Tan Yu Chin1, Ponselvi Jeevaragagam
1, Mohd Ridza bin Mohd Haniffah
1
1School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia
ABSTRACT. Water is a vital resource that is essential for all human and ecosystem survival and health. It is important to make sure the river is clean and not being polluted at any time. Once polluted, mitigation actions have to be taken immediately so that the pollution does not spread into a wider area. This study presents a numerical modelling and analysis of the advection-diffusion equation in 3-D direction to quantify the movement of the pollution in a river. The advection-diffusion equation can be readily derived from the Conservation of Mass principle. The equation is discretized using Finite Difference Method in which the backward difference is applied for the advection term while Alternative Directional Implicit with central spacing is applied for the diffusion term. Once the partial differential equation has been transformed into algebraic equation, MATLAB is applied for the simulation. The model is validated with a 2-D advection process. The validated model is then used to simulate advection and diffusion processes of a pollutant in three dimensions. The results show that for a pollutant with diffusion coefficient of 0.617 m2/s, concentration of 100 kg/m3 and volume of about 0.03 m3 contaminating a river, the pollutant will spread to a volume of about 1 m3 with maximum concentration of 5 kg/m3 when subjected to a river velocity of 2 m/s over 0.4 s.
Pollutant transport model is an environmental modelling with key components and process involving environmental fate, transport, exposure and impact. Pollutants are transported across the air, water, soil and living things. To understand the workings of the environment and the impact of human activity better, scientists and engineers have responsibility to develop a conceptual framework to model the condition. Application of the model provide a basis for relating human activities with environment impact and thus for predicting the change and effect. Generally, environmental models are developed both for scientific purpose and as applied tools for policy development, implementation and management.
Partial differential equations are the basis of many mathematical models of physical, chemical and biological phenomena, and its model is useful for any engineering field. Because of exact analytical solutions are not generally able to be found, it is often necessary to resort to numerical methods to find approximate solution of these partial differential equation, in order to investigate the prediction of the mathematical models. The Navier-Stokes Equations (NSE) is an equation that describes the behavior of fluids, to include fluid transport phenomena of friction, thermal conduction and mass diffusion. The advection-diffusion equation (ADE) is a very simplified form of the Navier-Stokes Equation above in which can be derived using the Principle Conservation of Mass only.
In this study, contaminant transport in river is analysed numerically through the 3-D ADE. It is important to understand the movement of a pollutant once it entered the water body such as rivers. The spread of the pollution over time will be modelled and this information will help authorities in predicting the size and concentration of the polluted area based on the river velocity and also the diffusivity of the contaminant. ADE is discretized and solved by using Finite Difference method. The equation is solve using forward difference in time, first upwind difference for the advection term and Alternative Direction Implicit (ADI) for the diffusion term. Concentration of pollutant dispersal pattern is calculated, and the graph is plotted for several cases.
This study will determine the condition of pollutant at a distance and time. The result of the model stimulated is useful in guiding engineering and management decision concerned with the pollution problem in river. The study also can help to estimate and examine the rehabilitation process and management of polluted water after the elimination of pollutant. Problem Statement
Water is a vital resource that is essential for all human and ecosystem survival and health. In addition, water is used in production, industrial and manufacturing processes, such like hydroelectric power generation, waste assimilation, recreation, navigation, enhancement of fish and wildlife. There is a variety of purposes for water usage. Water pollution can be defined as changes in the level of constituents or parameters to an extent that it degrades the quality of water for an intended use.
New Straits Times published an article on October 2008, Unloved River on Malaysians with their disappointing attitude toward rivers. The article reported that even with the waterway improved, the spending on the rehabilitation of the polluted rivers is still rising and yet there is still a lack in the public awareness. This problem has been recognized and getting worst time by time. Dead river is not unusual nowadays. Experimental works will be costly. The problem can be solved computationally to retrieve important information such as the extend of damage due to the pollutant spillage, as an example. This will help relevant authorities in making decisions related to protecting the river from further damage.
38
Objectives The aim is to develop and validate a 3-D ADE taking into account the diffusivity of the pollutant and the river’s velocity. A
transient spatial river pollution problem is constructed and solve numerically using MATLAB. Therefore, the specific objectives of study are:
1. To study the transport of pollutant in a straight river using numerical model. 2. To discretize the 3-D ADE using finite difference for pollutant transport by using MATLAB. 3. To validate and analyze the dispersion pattern and change of concentration of pollutant according to the river velocity over
the time. Scope of Study
This study will use Finite Difference Method (FDM) to solve partial differential equation of pollutant transport in straight river and develop a MATLAB programming to obtain the pollutant transport pattern and behaviour. Only the effect of river velocity and diffusivity is taken into account. Other processes such as pollutant degradation/dying or even breeding or any temperature effect is not taken into account.
LITERATURE REVIEW
The range of water pollutant is vast, from sediments and suspended solids, nutrients mainly nitrogen and phosphorus which can promote accelerated eutrophication, heat by heated industrial effluents, municipal wastewaters and others [1]. Environmental models can play a role to explain the natural phenomenon. Results and information obtained by reliable model can be used for regulation implementations, environment controls and management and sustainability for the future. Pollutant transport can occur by considering two main mechanisms: advection and diffusion, which are common to surface water, ground water and air. Advection is the transport of contaminants along with the mean or bulk flow of the air or water. Diffusion is the mixing of contaminants that is driven by gradients in contaminant concentration [2].
The simplest model represent spatial variation in pollutant concentrations is the plug flow reactor, which represent the case of advection transport in one dimension. To develop a more realistic model, diffusion should be considered and the model will become two dimensions or three dimensions.
Pollutant transportation means a process of advection and diffusion of the pollutant in a fluid. According to the Navier-Stokes Equation, the pollutant transportation can be derived by combination of advection equation and diffusion equation in Partial Differential Equations form [3]. The 1-D, 2-D and 3-D ADE are as follow:
!"!#= −&'
!"!'+ *'
!+"!'+
(Equation 1)
!"!#= −&'
!"!'− &,
!"!,+ *'
!+"!'+
+ *,!+"!,+
(Equation 2) !"!#= −&'
!"!'− &,
!"!,− &-
!"!-+ *'
!+"!'+
+ *,!+"!,+
+ *-!+"!-+
(Equation 3)
in which c is the concentration of the pollution (kg/m3), u is the velocity and D is the diffusivity coefficient with subscripts x, y and z for the direction.
ADE is considered under Computational Fluid Dynamics or CFD. To discretize the equations, the classification of the equations (parabolic, hyperbolic or elliptic) needs to be considered in order to understand whether the finite difference scheme applied is stable and gives accurate result [4]. For stability, the advection equation must be applied using forward or backward difference depending on the direction of the river while central spacing will give a more accurate result for the diffusion term [5].
METHODOLOGY
This research consisted of two key steps; application of Finite Difference Method to solve the equation and development of the MATLAB program. Finite Difference Method
Finite Difference Method can be considered as the earliest numerical approach in solving partial differential equations. It is applied directly to differential form of the governing equations to yield the discretized form of equation. The basic theory of Finite Difference Method (FDM) is guest solution plus with the truncation error.
. / + ∆/ = . / + !1
!'∆/ (Equation 4)
To increase the accuracy of the solution, higher order terms can be added on to the equation. FDM can be derived from the Taylor’s series expansion.
39
& = & + !2!'∆/ + !+2
!'+'+
3+ !42
!'4'4
5+.. (Equation 5)
Explicit and implicit methods are used to dicretize the advection and diffusion equation. First, upwind differencing method is one of the explicit method to solving hyperbolic equation in which the advection term is one type of hyperbolic equation. Backward differencing of spatial derivative for Equation (3) produces a finite difference equation of the form as follow:
−&/ !7!'− &8 !7
!,− &9 !7
!-= −&/ 7:
;<7:=>;
∆'− &8
7?;<7?=>
;
∆,− &9 7@
;<7@=>;
∆- (Equation 6)
The diffusion term is discretized using Alternative Directional Implicit (ADI) method, which is accomplished by considering time intervals of n, n+1/3, n+2/3 and n+1. The resulting equation for the model equation for Equation (3) includes x-sweep, y-sweep and z-sweep:
x-sweep:
7:,?,@;B>4<7:,?,@
;
∆C4
= */7:B>,?,@;B>4 <37:,?,@
;B>4D7:=>,?,@;B>4
∆'++ *8
7:,?B>,@; <37:,?,@
; D7:,?=>,@;
∆,++ *9
7:,?,@B>; <37:,?,@
; D7:,?,@=>;
∆-+ (Equation 7)
y-sweep:
7:,?,@;B+4<7:,?,@
;B>4
∆C4
= */7:B>,?,@;B>4 <37:,?,@
;B>4D7:=>,?,@;B>4
∆'++ *8
7:,?B>,@;B+4 <37:,?,@
;B+4D7:,?=>,@;B+4
∆,++ *9
7:,?,@B>;B>4 <37:,?,@
;B>4D7:,?,@=>;B>4
∆-+ (Equation 8)
z-sweep:
7:,?,@;B><7:,?,@
;B+4
∆C4
= */7:B>,?,@;B+4 <37:,?,@
;B+4D7:=>,?,@;B+4
∆'++ *8
7:,?B>,@;B+4 <37:,?,@
;B+4D7:,?=>,@;B+4
∆,++ *9
7:,?,@B>;B> <37:,?,@
;B>D7:,?,@=>;B>
∆-+ (Equation 9)
Since each equation have been discretize, the discretize equation combination of advection- diffusion equation can determine by summation of advection and diffusion equation and it is used to determine the concentration of water pollutant. Development of MATLAB Program
There are several phases in the development of a MATLAB programming, which are model parameter inputs and calculations, stability of solution checking, numerical calculation for advection equation, numerical calculation for diffusion equation, summation of advection and diffusion calculation result and finally model presentation stage (results).
The 3-D ADE is developed and validated for 2-D ADE so that the results can be clearly shown (3-D result has been proven difficult to be shown clearly through any graphical means). This means that the velocity and diffusion coefficient in the z-direction is set to 0 for the purpose of validation only. The different cases of study is applied to obtain advection and diffusion patterns of concentration under advection-diffusion process. In order to determine the concentration of pollutant in a specific width and length of the river, detailed information of the problem setup with its initial and boundary conditions are given as follow.
The dimensions of the river is 3.6 m by 3.6 m. The diffusion coefficient in all direction, Dx, Dy, and Dz is set as 0.617 m2/s and velocity in all direction, ux, uy, uz is assumed constant of 2 m/s. The small change in x-direction and y-direction, Δx and Δy is equal to 0.1 m. Change in timestep, Δt is equal to 0.01 s. The model is run until time, T = 0.4 s. The dispersion pattern and behavior of the pollutant concentration is observed. The initial condition is the concentration of the pollutant source when it enters the river while the boundary condition is the concentration of pollutant in the bounded region. The initial and boundary conditions of the river model and pollutant source are given as follow:
The validation results for the 2-D ADE (by setting the velocity and diffusion coefficient in the z-direction to 0) and simulation of the 3-D ADE is shown here through 4 different case studies. Case Study 1: Pollutant Transport in 2-D Advection. This case is determining the pollutant concentration without diffusion coefficient and velocity in z-direction. So, the diffusion coefficient in all direction, Dx, Dy, and Dz and velocity in z-direction, uz is assumed 0. Figure 1 shows that the pollutant source moving along x and y direction with a typical advection process as expected, but with a slight diffusion going on. 1-D advection does not exhibit this leakage process but when it comes to higher order dimensions, an additional term is needed to be inserted in the discretization to rectify the problem. This has not been done in the current study.
(a) (b) (c) Figure 1: Pollutant transport in 2-D advection at (a) t =0 s (b) t =0.1 s and (c) t =0.4 s
Case Study 2: Pollutant Transport in 2-D Diffusion. Velocity in all directions is set to 0 so advection is excluded and only diffusion is considered. As shown in Figure 2, the pollutant spreads outward and its maximum concentration decreased with time. In the meantime, the center of the pollutant source is not displaced from its original location. It is an obvious and typical diffusion process.
(a) (b) (c) Figure 2: Pollutant transport in 2-D diffusion at (a) t =0 s (b) t =0.1 s and (c) t =0.4 s
Case Study 3: Pollutant Transport in 2-D Advection-Diffusion. In this case, diffusion coefficient and velocity in z-directions is set to 0, only advection-diffusion in xy-direction is considered. Figure 3 shows that the pollutant spreads outward with the pollutant center source moving along the x and y direction.
(a) (b) (c) Figure 3: Pollutant transport in 2-D advection-diffusion at (a) t =0 s (b) t =0.1 s and (c) t =0.4 s
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Case Study 4: Pollutant transport in 3-D advection-diffusion. In this case, all direction of the velocity and diffusion coefficient is considered. Velocity in all direction is set as 2 m/s and diffusion coefficient is equal to 0.617 m2/s. The transport of the pollutant is as shown in Figure 4, 5 and 6. The pollutant source moves in all 3-directions, and spreads outward time by time with decreasing maximum concentration. It is because the pollutant is diffusing in all direction from the center of the source. The results show that for a pollutant with diffusion coefficient of 0.617 m2/s, concentration of 100 kg/m3 and volume of about 0.03 m3 contaminating a river, the pollutant will spread to a volume of about 1 m3 with maximum concentration of 5 kg/m3 when subjected to a river velocity of 2 m/s over 0.4 s.
Figure 4: Pollutant transport in 3-D advection-diffusion when t =0 s
Figure 5: Pollutant transport in 3-D advection-diffusion when t =0.1 s
Figure 6: Pollutant transport in 3-D advection-diffusion when t =0.4 s
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CONCLUSION
Numerical solution for 3-D ADE has been developed, discretized, validated and simulated for pollutant transport in river. To solve this equation, finite difference method is applied. The 3-D ADE has two main terms in it, which is the advection and diffusion term. For the advection, an upwind explicit scheme is applied to ensure stability while a central spacing implicit is applied for diffusion term in increase the accuracy. In particular, the Alternative Directional Implicit (ADI) is applied for the diffusion term which split the solution into 3 directions of calculation sweeping. This is to ensure a good and robust algorithm for the simulation. The numerical model has been validated by considering the advection and diffusion process separately for the 2-D ADE. Once validated, the pollutant transport for the full 3-D ADE is simulated. The results show that for a pollutant with diffusion coefficient of 0.617 m2/s, concentration of 100 kg/m3 and volume of about 0.03 m3 contaminating a river, the pollutant will spread to a volume of about 1 m3 with maximum concentration of 5 kg/m3 when subjected to a river velocity of 2 m/s over 0.4 s.
The model is useful in guiding engineering and management decision concerned with the pollution problem in river. The study also can help to estimate and examine the rehabilitation process and management of polluted water after the elimination of pollutant. To improve the accuracy and complexity of the model so that it is more general, it needs to consider more parameters apart from just taking into account the diffusivity and velocity of the river. If the pollutant is able to degenerate/decay or breed, then this process have to be taken into account in the ADE and is commonly known as the Advection-Diffusion-Reaction (ADR) equation. ADR in which the reaction term depends on the concentration of the pollutant itself is a non-linear equation and thus higher order discretization is needed such as Crank-Nicholson and Runge-Kutta [6]. Next, the results can be compared with experimental results for better validation.
REFERENCE
[1] Ruth F. Weiner, Robin Matthews (2003). Environmental Engineering, Fourth Edition. Butterworth Heinemann [2] Andrew S. Bero & Ronald J. Gibbs (1990). Mechanisms of pollutant transport in the Hudson estuary. Science of the Total
Environment, Vol 97-98. [3] Bird R.B., Stewart W.E., Lightfoot E.N. (1960). Transport Phenomena. John Wiley and Sons. [4] John D. Anderson Jr. (1995). Computational Fluid Dynamics, The Basics With Application. McGraw Hill. [5] K A. Hoffmann, Steve T Chiang (2000). Fourth Edition Computational Fluid Dynamics Volume I. EESbooks. [6] Tsegaye Simon, Purnachandra Rao Koya (2015). Modeling and Numerical Simulation of River Pollution
Using Diffusion-Reaction Equation. American Journal of Applied Mathematics. Science Publishing Group.
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Issues and Problems in Aquaculture Industry at the Tebrau Strait, Johor Nur Syazana Harun1, Mohd Badruddin Mohd Yusof1*
1School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia *[email protected]
ABSTRACT. Developments along coastal areas in Tebrau Strait has afftected marine water quality, thus created a less conducive environment to aqua culturists. A study was conducted to analyse impacts of the coastal developments on aquaculture industry along the strait and the communities’ livelihood. It covered socio-economic aspects, current aqua-culture management and the impact of the surrounding developments on the marine environment. The selected study areas involved 43 respondents of 6 villages along the strait where aquaculture activities were located. The data was analysed using descriptive and inferential statistics utilizing the Statistical Packages for Social Sciences (SPSS) software. The study found that majority of the respondents (42%) conducted aquaculture activities near the developments and reclaimed areas, and were indifferent about the development. However, 28% were against such activities as they believed that there had been a reduction in their monthly income as well as in the quality of the marine water in the area as a result of the recent developments. Majority (70%) were involved in the activity full time and were found to be unwilling to change their current profession. Most of them also were also involved in other activities than aquaculture to help provide an additional source of income. In general, the study was successful in meeting the objectives Based on result, it is recommended that the government and local authorities to implement transformation program for the aqua culturists.
Keywords: Aquaculture; fishing community; Tebrau Straits; development impacts; fish cages; Aquaculture Industrial Zone(ZIA).
INTRODUCTION
Currently, Malaysia is developing and moving towards a strong and competitive economy based on many sectors. One of the sectors is aquaculture industry. Fish and fish-based products are important to the daily diet of Malaysia as the major of protein sources. The present of fish catch in Malaysia is insufficient to meet rising demand due to increases in population growth, consumers’ cash flow, and changes in life style. The government of Johor is rectifying this situation by expanding aquaculture industry and improving the existing especially at Tebrau Straits. This is the one of the alternatives to improve socioeconomic in state of Johor.
Problem Statement The establishment of Iskandar Malaysia has become catalyst that contributes to the improvement of development phase in Johor. The phase included a coastal area which indirectly affects aquaculture and fishing activities. The continuous development and sea reclamation project at coastal area was said to have contributed to negative impacts to fishermen and aqua culturists alike, in terms of total amount of catches, limitation of fishing areas, and increasing number of fish deaths incidents associated with water pollution.. Thus, fishermen and aqua culturists along the Straits of Johor, especially the Tebrau Strait is slowly facing a loss of their earnings.
Objectives The aim of this study was to analyse impact of coastal development on aquaculture industry along the Strait of Tebrau, Johor. The detail objectives were described as follows:
1. To analyse background and current socio-economic profiles of aqua culturist at six villages. 2. To study the issues related to aquaculture industry. 3. To analyse respondents’ opinions on environment and economic impacts of development to the aquaculture activities. 4. To suggest recommendation to the government on how to help the aquaculture industry problems.
Scope of Study The scope of study covered socio-economic backgrounds of aqua culturists living at six villages located at the west of Tebrau Strait (Gelang Patah) and east (near Johor Bahru). The respondents were among the aqua culturists and their crew or workers. The questionnaires were distributed in March of 2018. Potential environmental impacts in this study were described based on opinions from the respondents on impact of surrounding coastal development to their aquaculture activities.
LITERATURE REVIEW
Johor is currently build a strong and competitive economy towards the increasing of the population and level of socio-economic. Sultan Ibrahim wanted a balance development in the south of Johor that will benefit his subjects and put Johor on the world’s map, as it will not only to the nation, but to Johoreans residing in any part of the state [1]. One of the ongoing projects in the south of Johor is Forest City. The ultra-mega Forest City project will create four man-made islands with 1,623 ha. constructed by reclamation [1].
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Fishing Industry in Malaysia plays a significant role in the national economy because Malaysians are amongst the highest consumers of fish in the world. Malaysia was top 15 world major marine capture production [2]. The State of Johor contributed to 8.6% of the national fishing landings catches and 15% aquaculture production [3]. Since fish and fish-based products are important to the daily diet of Malaysians, majority still depends on this type of food as the main source of animal protein [4]. To meet the expected increase in future global ocean-based food, the government is rectifying this situation by expanding aquaculture industry.
The goal of Department of Fisheries is to increase the number of capture and aquaculture production in 2020 by 8.4% and 103%, respectively compared to that in 2016 [5]. However, operators had to deal with high feed costs which represented 75%-90% of the total operating costs of the fish farms [6]. When the production of fish increased, the cost of treated water also increased to prevent water pollution and other environmental issues. Aquaculture was considered as inefficient activity in term of the use of fisheries resources. Food Conversion Ratio (F.C.R.) for major aquaculture species are 8-15:1 depending to the quality of the trash fish. That means between 8 to 15 kg feed would be needed to produce only 1 kg aquaculture fish [7].
While recognizing aquaculture as one of the thrust areas for development, the government of Malaysia is fully aware of growing concerns over sustainability and environmental impact [4]. The DOF have been mandated to manage fishing capacity with aims of bringing reasonable balance between fishing capacity and available resources for sustainable development and management of fisheries resources [8]. Aquaculture Industrial Zone (ZIA) is the programmed for zoning land and coastal areas which identified as suitable for development of aquaculture projects approved by the State Government through the State Executive Council (MMK) [9].
As businessmen and holiday-makers head to coastlines around the world, reclamation and coastal development project tended to have rapidly developed leading to an enormous impact on marine ecosystems. Marine life faces threats in many ways, such as over exploitation and harvesting, deposit of waste, contamination, exotic species, soil recovery, dredging and global climate changes [10]. The major impacts of coastal developments on environment are reduction in water quality, aquatic life and their habitat, and global warming. The production of fish in Johor State has been decreasing due to the contamination and continuous sea reclamation [3]. A smaller spill at the wrong time/wrong season and in a sensitive environment may prove much more harmful than a larger spill at another time of the year in another or even the same environment [11].
In 2014, the country’s supply of fish meal was 1,988,533 tonnes and that value was less 1.5% compared to 2013 [5]. To meet global demand, aquaculture is being promoted but in fact a large number of marine fish should be captured to meet the food needs of the aquaculture industry. The requests of fish stocks should be balanced with an adequate supply. The government should focus more on the conservation and protection of natural resources to improve and restore the nation’s fishery resources
METHODOLOGY
The study was conducted at Tebrau Strait as it is polluted due to nearby coastal developments, while it supports amongst the highest population of aqua culturist communities in the State of Johor. Thus, the study looked at the impacts of such coastal developments on the aquaculture industry. The selected study areas involved six villages located west of the Tebrau Strait, namely Gelang Patah areas as well as those in the eastern part of Johor Bahru. The respondents were 43 aqua culturists from six villages included Kg. Pendas, Kg. Teluk Jawa, Kg. Pasir Puteh, Kg. Sg. Melayu, Kg. Tg. Langsat and Kg. Kukup Laut.
Data of the study were divided into two categories, i.e. primary and secondary to allow a better and more comprehensive results. Questionnaire surveys were conducted to provide the primary data concerning the respondents’ socioeconomic background and their perceived opinions of coastal developments and their impacts on water qualities as well as their income and livelihood.. While, secondary data were obtained using literature reviews and published articles and news on the related issues. The study involved data collection using the standardized questionnaire developed, while results were analysed using SPSS Software.
Figure 1: Location of villages
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Questionnaire Development Questionnaire was used as a technique for collecting primary data. A set of questionnaires was developed using Likert scale (1-5 rank). It is the most universal method for survey collection and, the data will be easy to analyzed. Structured questionnaires were formed based on objectives of the study and were adopted from other similar studies conducted by previous researchers.
The structured questionnaires were divided into four sections. Section A looked at respondents’ socio-economic profiles and background. Section B was on aquaculture activities included harvesting period, operation cost and monthly income of aquaculturists. Furthermore, Section C covered quantitative data where respondents were required to fill the types of their productions, estimated harvest weights and its cash values. Meanwhile, the emphasis of effects of coastal developments near the aquaculture areas to the groups’ socio-economic well-beings and their willingness to accept the impacts were determined in Section D.
Data Collection This study used articles, journals, newspapers and thesis as secondary sources to develop literature reviews and familiarize with the ongoing issues. The interviews were done by interviewing the aqua culturists at the selected areas. Face-to-face communication gave a better response and honest opinions from the respondents. The latest secondary data was was collected from the Johor Department of Fisheries.. The analysis included name of farmers, reports, statistical and annual data in aquaculture industry along the Tebrau Straits. Summaries about the sample and the data were developed through descriptive statistics used. While, the analysis of primary data were represented in statistical outputs in the form of statistical reperesentation namely, mode, mean, standard deviation and percentage.
RESULTS AND DISCUSSION
The results were analysed using descriptive analyses and inferential analyses. The discussions on the findings and proposed of aquaculture activities and opinions from the aqua culturists about the development are described below.
Respondents by Villages The distribution of questionnaire was sorted into 6 villages along Tebrau Straits where aquaculture activities were located with 43 respondents involved. Table 1 describes the number of respondents involved according to villages. The highest number of respondents was from Kg. Pendas and Kg. Sungai Melayu which were 15 respondents (35%). There were two villages which shared the least number of respondents, i.e., from Kg. Tanjung Langsat and Kg. Kukup Laut (only 2 respondents or 5%). The distribution of the respondents in each village were not balanced due to lack of transportation (boat) and cooperation from them. From personal observations, a few aquaculturists did not give a full cooperation as their activities were not registered with the Department of Fisheries (DOF).
On the other hand, the percentages of those registered and unregistered with DOF were analysed in Table 1. Nine respondents from Kg. Pendas were registered operators, while 6 unregistered. Meanwhile, 5 out of 10 aquaculturists from Kg. Sg. Melayu were registered. However, total number of the respondents registered with DOF were higher (51%) than unregistered respondents (49%).
Table 1: Distribution of respondents according to villages
Name of Villages No. of Respondents Registered Respondents Unregistered Respondents
(with DOF) Pendas 15 (35%) 9 (60%) 6 (40%) Teluk Jawa 6 (14%) 4 (67%) 2 (33%) Pasir Puteh 3 (7%) 2 (67%) 1 (33%) Sungai Melayu 15 (35%) 5 (33%) 10 (67%) Tanjung Langsat 2 (5%) 1 (50%) 1 (50%) Kukup Laut 2 (5%) 1 (50%) 1 (50%) Total 43 22 (51%) 21 (49%)
Background of Respondents Table 2 describes the summary of background of respondents. The survey was conducted more on the employees (63%) than employers (37%). It shows the respondents comprised of 93% male and 7% female. Then,the largest group of respondents was between 21 and 30 years of age (28%). Meanwhile, percentage of Chinese and Malay almost the same. Chinese formed 51%, while Malays, 49%. The highest percentage of them finished their primary school education (48.8%). Majority (70%) were involved this activity full time. However, 67% of respondents did not focus only on the aquaculture industry and held another job at the same time.
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Table 2: Summary of background of respondents
No. Variables Result Highest (%) Lowest (%)
1 Position Employee (63%) Employer/Owner (37%) 2 Gender Male (93%) Female (7%) 3 Age 21 to 30 years old (28%) 17 to 20 years old (7%) 4 Racial background Chinese (51%) Malay (49%) 5 Religious background Muslim (44%) Other religion (2%) 6 Marital status Married (58%) Single (37%) 7 Number of dependents 1 to 3 persons (44%) 4 to 6 persons (14%) 8 Educational background Primary school (49%) Others (5%) 9 Category of aqua culturist Full time aqua culturist (70%) Part time aqua culturist (30%)
10 Working experience as aqua culturist 1 to 5 years (30%) Less than one year (12%) 11 Involvement in other activities Yes (67%) No (33%) 12 Type of other profession Fish catcher (23%) Grocery store worker (2%)
Background of Aquaculture Area As shown in Table 3, most of the respondents conducted aquaculture activities in brackish water (44%). Meanwhile, 49% of total respondents were using cages as their aquaculture system and majority (35%) had an aquaculture area bigger than 4000 m2. All of the respondents’ activity areas were located within 10 km or less from the nearest development (100%).
Table 3: Summary of background of aquaculture area
No. Variable Result Highest of Percentage Lowest of Percentage
1 Type of water body Brackish water (44%) Freshwater (16%) 2 Type of aquaculture system Cage (49%) Pond (16%) 3 Size of aquaculture area Bigger than 400!" (35%) 3001!" to 4000!" (5%) 4 Distance of the nearest development Below 10km (100%) -
5 Name of the nearest development Forest City and Danga Bay (34.9%)
Tanjung Langsat Port and Pontian (5%)
Aquaculture Activities of Respondents Table 4 summarizes the aquaculture activities of the respondents. Majority (81%) needed 5 to 8 months to harvest, while only a few needed 1 to 5 months (2%). Most of the respondents employed local workers (72%), while 28% foreigners. Furthermore, majority of them had between 1 to 5 workers (91%). The highest percentage of total respondents needed under RM500 per month (60%) for their operation cost. It was due to low maintenance cost as they did not have electrical supply. About 35% were earning between RM3000 to RM6000 per month and only 14% between RM6000 and RM9000.
Table 4: Summary of aquculture activities amongst respondents
No. Variable Result Highest of Percentage Lowest of Percentage
1 Harvesting period 5 to 8 months (81%) 1 to 5 months (2%) 2 Type of workers Local people (72%) Foreigner (28%) 3 Number of workers 1 to 5 workers (91%) 6 to 10 workers (9%)
4 Operation cost Under RM500 per month (60%) RM1001 to RM3000 per month (2%)
5 Monthly income RM3000 to RM6000 (35%) RM6000 to RM9000 per month (14%)
Table 5 describes the type of main equipment used by respondents for aquaculture activities. Majority of total respondents had boat (95%) as their main equipment. It followed by net (72%), cage (47%) and rope (42%). The highest average number of equipment per respondents is rope (17389 units) and followed by net (1069 units).
Table 5: Type of equipment according to respondents
Type of Equipment
Percentage of Respondents (%)
Average Number of Equipment
per Respondent Boat 95 3 Net 72 1069 Cage 47 71 Rope 42 17389 Pond 21 22 Float 12 3640
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As for breakdown details, Table 6 describes the production of aquaculture activities per harvesting period according to the type of fish/mussel. It was based on a 6-month harvesting period. The highest percentage of them bread Grouper (42%) as their main produce. The highest average estimate of harvest per respondent was Milk-fish (11,7667kg) while, anchovies had the lowest yield (10 kg per respondent per harvest). The most expensive harvest came from Greasy Grouper (RM100) while the cheapest cash value of harvest was Catfish (RM3).
Table 6: Production of aquaculture activities per harvesting period
*Based on 6 month harvesting period. Opinions of Respondents Meeting of the objectives of the study were derived from aquaculturists’opinions concerning their support or objection of the coastal developments nearby. The negative impacts would be supported by their comments on the changes in catches after the devlopments had taken place. Table below shows the results on the acceptance of respondents towards the nearest development and sea reclamation projects. Even though most of the respondents (42%) were neutral about the developments and reclamation projects, majority (89%) agreed the nearest development had negatively affected their aquaculture activities. Furthermore, all respondents (100%) agreed that overall water quality had reduced. These results clearly showed that the coastal developments had in fact disturbed their activities and thus reduced their income.
Table 7: Opinions of respondents regarding the nearest development
Support for the Development 7 16 6 14 18 42 11 26 1 2 Project’s benefits 11 26 8 19 16 37 8 19 Acceptance of negative impacts 5 12 12 28 26 61 Reduction in Water quality 12 28 31 72
*N - Number of Respondents *% - Percentage of Respondents
In the aspect of monthly income, Table 8 describes the opinions of respondents regarding this issue. Most of the respondents
were neutral (49%) about their current monthly income. However, 79% of them agreed that surrounding development and reclamation project had reduced their income.
Table 8: Opinions of respondents regarding monthly income
Income satisfaction 10 23 8 19 21 49 4 9 Income reduction 2 5 7 16 12 28 22 51
*N - Number of Respondents *% - Percentage of Respondents
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CONCLUSION
Based on the study, the outcomes are summarized below, along with recommended government policies on how to reduce the plight of the players in aquaculture industry:
1. Majority (70%) were involved the activity full time and unwilling to change their profession. 2. Most of them involved in other activities than aquaculture to support their source of income. 3. Number of surviving fish and mussels had decreased due to polluted water. 4. Majority interviewed (42%) conducted aquaculture activities near coastal development and reclamed areas and were
neutral about the development. However, 28% were against it due to their reduction in monthly income and negative changes in water quality.
5. Government can help respondents with additional loans and financial assistance needed to improve their current livelihood.
6. Government needs to ensure the coastal developments projects are sustainable and eco-friendly in their construction process.
7. Local authority should enforce more effort on transformation programs and strategies for development planning in aquaculture industry.
In general, the study was successful in meeting the objectives despite having a few limitations due to insufficient time to compile more information and reviews.
REFERENCE
[1] Jassmine (2015). News Straits Times: Forest City to Create 250,000 Jobs. Retrieved from http://www.nst.com.my/news/2015/09/forest-city-create-250000-jobs
[2] Food and Agriculture Organization of United Nation (2001). Information on Fisheries Management in Malaysia. Official Portal of FAO. Retrieved from http:// http://www.fao.org/fi/oldsite/FCP/en/mys/body.htm
[3] Department of Fisheries Malaysia (2016). Transformation Framework Program for Fisherman and Aqua Culturist around the Tebrau Strait. Unpublished report.
[4] Mohd Fariduddin (2008). Challenges Ahead in Meeting Aquaculture Production in Malaysia under the Third National Agricultural Policy, Nap3 (1998-2010).
[5] Department of Fisheries Malaysia (2016). Malaysia Fishing Industry Scenario: Fishery Sector Performance and Achievements. Official Portal of DOF. Retrieved from http://www.dof.gov.my/index.php/pages/view/
[6] M. El-Sayed, W. Dickson and El-Naggar (2014). Value Chain Analysis of The Aquaculture Feed Sector in Eqypt. Alexandria University, Egypt.
[7] SM Mohamed Idris, President of Consumer Association of Penang (2011). Ban Trawl Fishing. Malaysia Kini, 19 October 2011. Retrieved from http://www.malaysiakini
[8] Department of Fisheries Malaysia (2015). National Plan of Action for the Management of Fishing Capacity in Malaysia. (Plan 2). 2, 13.
[9] Department of Fisheries Malaysia (2014). Aquaculture Industrial Zone. Official Portal of DOF. Retrieved from https://www.dof.gov.my/index.php/pages/view/78
[10] Vikas M. and Dwarakish G.S. (2015). Coastal Pollution: A Review. International conference on Water Resources, Coastal and Ocean Engineering. National Institute of Technology Karnata, India.
[11] Bautista Hugo and Rahman K.M. Mijanur (2016). Effect of Crude Oil Pollution in the Tropical Rainforest Biodiversity of Ecuadorian Amazon Region. Journal of Biodiversity and Environmental Science. 8 (2): 249-254.
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Performance Assessment of Upflow Anaerobic Sludge Blanket (UASB) Reactor in Treating Sewage
Felicia Lim Pei Shih1, Azmi Aris1* 1School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia
ABSTRACT. This study analyses the performance of the Upflow Anaerobic Sludge Blanket (UASB) reactor by comparing the percentage removal of the UASB reactor with the reactors of the other stages in the system. This reactor was used for treating high strength wastewater and its efficiency in treating low strength wastewater is doubt to be as effective. In this study, the percentage removal of each reactor in the sewage treatment plant system were determined through laboratory tests and compared with each other to observe their trend and their contribution in removal of the contaminants. The laboratory tests results consists of filtered and unfiltered samples, where the filtered samples were tested to determine percentage removal without the interference of sludge especially samples from the EA tank and its effluent. The values of the parameters in the effluent were also compared with the Standard B sewage discharge criteria. The UASB reactor removed an average of 34.4% of chemical oxygen demand (COD), 35.6% of biochemical oxygen demand (BOD5), 3.0% of ammoniacal-nitrogen (NH3-N) and 83.2% of nitrate-nitrogen (NO3-N). The analysis shows the percentage removal of the UASB tank being lesser than that of the extended aeration (EA) tank. Some of the parameters were unable to be removed at the UASB reactor which could mean that this reactor acts as the support to the system. The highest percentage removal by the UASB reactor was for NO3-N. The UASB reactor was the only stage where NO3-N content was removed.
Keywords: Sewage treatment; UASB reactor; Process performance.
INTRODUCTION
The UASB reactor is a wastewater treatment reactor which uses anaerobic processes to digest wastewater. It uses methanogenic bacteria in its anaerobic digesting process for breakdown of organic compound. This process is where methane gas is generated for the reactor’s resource recovery. Methane gas collected from the digestion process can be used asfuel to cover up for its running power. In addition, aerobic treatment plants requires cost for aeration tank to make sure oxygen is available for its treatment processes. Due to this fact, the UASB reactor becomes a cheaper way for wastewater treatment. Based on previous researches, the use of UASB reactor is seen to be more advantageous for high strength wastewater treatment [1]. However, the UASB reactor is also used for treating low strength wastewater such as sewage. The issues regarding UASB reactor in sewage treatment includes effluent quality and its effect to the environment upon discharge, energy recovery and sludge generation. Besides, there are the factors affecting the performance of UASB reactor which include variables such as hydraulic retention time (HRT), temperature and management. This study assesses on the performance of UASB reactor in treating sewage.
Problem Statement The concept of using UASB reactor for sewage treatment was developed in India nearly three decades ago [2]. The reason it was used for most of India’s sewage treatment plant is due to its cost effectiveness, and environment and climate suitability. In terms of cost, it is simpler to build and has potential of resource recovery. The UASB reactor uses methane producing bacteria in which the methane gas produced can be used for generation of electricity.
However, the benefits of UASB reactor is doubtful when applied to low strength wastewater treatment [1]. This happens especially on the effluent quality where it is highly anoxic and contains high level of sulphate. When discharged into natural water bodies, it will exhibit high demand in oxygen causing the water to deplete in oxygen. Furthermore, sulphate causes corrosion in parts of the effluent channel. Power generation from methane gas is not as efficient compare to high .strength wastewater treatment, thus reducing its ability to generate cost recovery.
Many studies have been done on this type of reactor in treating sewage to determine new modifications that can be done. Throughout the years, the use of UASB reactor for sewage treatment has been improved. With performance assessment at different plants, the UASB reactor can be further evolved to contribute in sewage treatment.
Objectives The objectives of the study is to:
1. To study the performance of UASB reactor in treating sewage, and; 2. To evaluate the effectiveness of each unit process in the sewage treatment plant.
Scope of Study This study focuses on the performance of UASB reactor at the sewage treatment plant operated by Indah Water Konsortium (IWK). The study determined the effectiveness of UASB as part of the treatment process at IWK. Furthermore, the problems associated with UASB reactor in treating sewage was discussed based on the results of the water quality analysis. The study determines the performance of the reactor in BOD, COD, TSS, NH3N and NO3- removal.
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LITERATURE REVIEW
The UASB reactor is wastewater treatment system usually used in treating high organic concentration of wastewater. The reactor is methanogenic due to the fact that it uses methane producing anaerobic bacteria. The UASB reactor is simple to construct and operate as well as being able to tolerate extremely high organic and hydraulic loading rates [3]. However, it would require up to several months for sufficient granulation of sludge.
The reactor is a digester where its treatment process happens in a single tank. A sludge blanket filters the incoming wastewater from below as it passes through it. The granulated sludge blanket retains microorganisms, which are crucial for the digestion of organic compound, to prevent them from being washed out with the effluent [4]. The degradation of organic compound occurs mostly at the lower part of the reactor due to high concentration of active anaerobic sludge at the lower part and the partially purified effluent at the upper part [3].
There are many factors which contribute to the difference in performance at the UASB reactor at multiple locations. The factors includes the reactor’s operation, wastewater composition, granules and other adjustable variables. The effects of HRT are still arguable whether what is the suitable range of HRT for this reactor. In research by Rizvi et al. [5], the performance recorded also shows an increase with increase of HRT. Another possible factor that was seen in past research was the effect of temperature. The optimum performance of the reactor which was recorded was ranging from 32.67 – 40.32 ºC with the highest at 39.15 ºC [6]. The optimum temperature of the reactor is seen to be around 38 ºC.
In the UASB reactor, sludge exists in granular form which is better for settling due to higher density. The good settling ability of sludge allows higher biomass retention especially when the upflow velocity is high [7]. As stated by Schmidt and Ahring [8], the granular sludge development is the key factor for successful operation of UASB reactors. The UASB reactor consists of methane producing microorganism such as the Methanosaeta and Methanosarcina spp. Methane gas produced from the reactor can be used as fuel to generate electricity for the use of operating the reactor. The study on a two-phase digestion managed to produce biogas which can displace a large part of the industry’s fuel [9].
The UASB reactor is originally designed for treating high strength wastewater. To apply this system on sewage treatment, there some difference in performance is expected as compared to that of high strength wastewater. The treatment of municipal wastewater is widely used in India where this concept first started and is one of the most suitable and cost effective treatment plant for treating sewage according to environmental requirements in India [2].
The average percentage removal of BOD5 and COD is around 69% and TSS removal around 61.9%. The removal of oil and grease is 22% based on study by Barbosa and Sant’Anna [10]. However, with seeding of sludge, the removal becomes higher, with more than 60% removal [5].
The problems faced in UASB reactor in treating sewage or low strength wastewater relates to the effluent, the need for secondary treatment, power generation and resource recovery [1]. Sludge generated from UASB reactors has low sale value. The effluent of sewage treatment with UASB is not suitable to be release straight into rivers. Sewage treatment cause much outflow of sludge and require skilled labour to operate this reactor. The less biogas generation causes it to have extra energy cost. Another problem faced by most UASB reactor is the maintenance of the reactor which adds to the cost of the process.
METHODOLOGY
The process of this study includes site visits, collection of influent and effluent samples and laboratory analysis. This study focuses on analysing sewage influent and effluent to determine the performance of treatment. Location of Site and Sample Collection The wastewater treatment plant where the performance analysis was conducted is located at Jalan Denai 6, Ulu Tiram, Johor. After going to the wastewater treatment plant for site investigation, it was decided for samples to be taken from the influent (water from grit chamber) (P1), surface of UASB tank (P2), between EA tank and final clarifier (P3) and, lastly, the influent of the system (P4). Samples were collected with clean airtight 800 mL bottles that were labelled. Samples for oil and grease (O&G) testing were collected using 300 mL amber bottles to keep safe from UV light. The samples were taken back to the laboratory and stored in the cold room of 4°C prior to testing. Figure 1 depicts a schematic diagram of the processes at the plant.
Figure 1: Schematic diagram of the wastewater treatment processes
Materials and Equipment There are several equipment that were used for analysis of the parameter for the wastewater influent and effluent samples. Table 1 shows the laboratory equipment and apparatus that will be used for this study. Experimental Procedure The samples were obtained for laboratory tests which were conducted in UTM environment laboratory. It was conducted on 24th January, 29th January, 1st March and 15th April 2018. An additional sample was obtained at the extended aeration tank to test for the mixed liquor suspended solids (MLSS) and mixed liquor volatile suspended solids (MLVSS) during the 3rd and 4th sampling.
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Wastewater samples were stored under a temperature of 4°C to reduce bacterial activity. This also affects the chemical reactions and loss of DO in the wastewater. Wastewater sample to tests for MLSS, MLVSS and O&G are not stored by cooling. For oil and grease test, samples were stored in amber bottles to keep samples safe from UV lights. For phosphate analysis, commercial detergents containing phosphate were not used for cleaning the test apparatus.
Table 1: Laboratory equipment and apparatus
Items Function Filter paper Filter samples of wastewater to obtain its suspended solids
Analytical balance – GR200 AND Weigh filter paper and samples accurately when analysing TSS DO meter – ORION Measure DO content and temperature readings in the wastewater
pH meter – ORION 2 STAR To obtain pH of wastewater samples Digital Reactor – DRB 200 Used to heat up reagent and wastewater mix when testing for COD
Spectrophotometer – DR 6000 Take the value for COD, NH3-N, NO3-N, PO43- and P in wastewater
InfraCal TOG/TPH Analyzer – Model HATR-T2
Measure oil and grease present in the wastewater sample
Incubator To keep BOD samples at a designated temperature during testing period Oven To dry filter paper with suspended solid for TSS analysis
Analytical Method Analysis of the parameters was done according to Standard Method [11] and Hach methods from the DR 6000 Spectrophotometer Procedures Manual. The summary of the procedure applied on this study is shown in Table 2.
Table 2: Analysis of parameters Parameter Method of Analysis
PO43- and P HACH – Method 8048 PhosVer 3 (Ascorbic Acid) Method
Oil and grease APHA – 5520 B
RESULTS AND DISCUSSION
The IWK sewage treatment plant follows DOE Standard B requirements for its effluent quality. By analysing the influent and effluent of the plant (P1 and P4), the overview of the sewage treatment plant can be observed.
Overview of the sewage treatment plant performance Table 3 shows the analysis results of the influent and effluent compared to Standard B sewage discharge conditions.
Table 3: The overall performance of the STP percentage removal Parameter Influent Effluent % removal Standard B
O&G 8 ± 0.7 6 ± 0.7 26.8 10.0 Characterization of the raw sewage (after grit chamber) and performance of the system The raw sewage at this sewage treatment plant generally have a stable pH of between 6 and 7. The average of the four sampling times has a TSS value of 109 ± 18.4 mg/L which shows that it requires treatment before being discharged. According to analysis results of this study, the influent of this wastewater treatment plant have COD value and BOD5 value of about 300 mg/L and 100 mg/L respectively.
As for NH3-N, the values for the influent are mostly above 30 mg/L as compared to the typical ammonia composition of low strength untreated domestic wastewater which is 12 mg/L [12]. It is different for NO3-N and P values where the influent NO3-N and P content did not surpass the accepted condition. The average NO3-N content of the influent is 6.1 ± 3.51 mg/L. P content of the analysed influent are all below 5.0 mg/L. Since the influent was taken at the point after grit chamber, it can be seen that a low
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value for O&G was obtained. This content of O&G is accepted according Standard B discharge which shows that the grit chamber for this wastewater treatment plant is functioning well.
The removal of contaminant by the whole system are higher than 50% for TSS, COD, BOD5 and NH3-N. For the NO3-N, PO43-
and P, the removal can be analysed by observing the unfiltered sample. The initial average COD of the sewage is 343 ± 67.9 mg/L before going through treatment. The system removed 80.0% of the COD content from the influent. 84.9% of removal was analysed if the sampled is filtered. Based on the results on raw sewage for BOD5, 93.1% was removed from the sewage influent. Although there are differences in absolute value between filtered and unfiltered samples, it has still shown 92.8% removal of BOD5 by the system. An average of 81 mg/L TSS is removed from the sewage. This indicated TSS removal of 75.0% by the system whereas the removal of VSS is 68.3%. The removal of NH3-N for normal sample is 51.7% which is 15.4 mg/L. For filtered samples, 60.6% of NH3-N was removed. The average NO3-N content fluctuated steadily through the system ending with a 53.1% removal. There was a removal of 17.2% of PO4
3- and P tested from the sample of influent and effluent. For NO3-N, the percentage removal shows negative value which is -48.6% since its content increases after the sample is
filtered. The same goes for PO43- and P where the percentage removals are -30.3% and -30.2% respectively. The negative removal
obtained after filtration may be due to various reasons during filtration which caused the value to increase. The pH is seen to be stable around pH of 6.0 during every sampling at any point of the treatment plant and the O&G content drops from 8 mg/L to 6 mg/L.
Effluent quality compared with Standard B The pH, NO3-N, P and O&G values were consistent between the influent and the effluent. The average pH of the effluent is 6.20 which is between 5.5 and 9.0 as required by Standard B. Compared to Standard B discharge requirement, the NO3-N content in the sewage is suitable for discharge. The same goes for the P and O&G content as they were below 10.0 mg/L as required.
The COD and BOD5 of the effluent are less than the specified content in Standard B, which are 64 mg/L and 7.1 mg/L respectively. A high amount of TSS was removed from the influent from an average of 109 ± 18.4 mg/L to 28 ± 25.2 mg/L in the effluent. The acceptable TSS content for Standard B is 100 mg/L, hence the effluent TSS value met the requirement for discharge. An average of 15.4 mg/L of NH3-N was removed from the raw sewage which is about half of the NH3-N content. The final effluent had an average of 13.8 mg/L of NH3-N, also meeting the requirement of below 20.0 mg/L according to standard.
Based on the results of the four sampling activities conducted, the plant efficiently treated the sewage to meet the requirement for discharge condition of Standard B.
Performance of unit processes in the sewage treatment plant Figure 2 and 3 were referred on the average percentage, whereas Figure 4 to 6 were referred for the average absolute values.
(a) (b)
Figure 2: (a) Percentage removal of UASB tank; (b) Percentage removal of EA tank
Figure 3: Percentage removal of final clarifier
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(a) (b)
Figure 4: (a) Level of COD and BOD5 (filtered and unfiltered); (b) TSS and VSS of P1, P2, P3 and P4 plotted with MLSS and MLVSS inside EA tank (unfiltered sample)
(a) (b)
Figure 5: (a) Level of NH3-N, NO3-N, PO43- and P (unfiltered); (b) Level of NH3-N, NO3-N, PO4
3- and P (filtered)
Figure 6: pH and O&G (unfiltered sample)
UASB tank The UASB reactor was able to remove 34.4% of COD from the sewage. The BOD5 removal at the UASB tank is 35.6% according to the unfiltered sample results. The TSS value raised from 109 ± 18.4 mg/L to 149 ± 10.0 mg/L, whereas for VSS, the value raised from 102 mg/L ± 14.1 to 106 ± 36.8 mg/L. 0.9 mg/L of NH3-N was removed which is only 3.0%. The percentage removal for PO4
3- was calculated to be -18.4% for the original samples and -29.0% for the filtered sample. The UASB reactor contributes around 30% of the overall removal of the system. It may be serving as a support to the main
reactor of the system to reduce the work load in removing pollutants. The percentage removal by the UASB reactors are mostly around 60% and above for BOD5 and COD. The removal of COD and BOD5 of the UASB reactor in this study is only around 30%. This may be due to the type of granules and bacteria used in this tank.
Besides that, the TSS removal observed in previous studies are in a range of 40 – 80% compared to the UASB tank in this study which shows negative removal in TSS. The results obtained in previous studies also shows removal for O&G, however, there is no removal by the UASB reactor in this study. The average pH at this stage decreased a little from the pH of the influent but still remains stable in this stage of the system.
In this study, the UASB reactor shows the highest removal rate for NO3-N with removal of 83.2%. Comparing this with previous study, the removal is 79.3% which is quite close to the percentage removal obtained in this study. The removal of NH3-N is very low as the percentage removal obtained during previous studies is higher than 75%, whereas in this study, it is only 3.0%.
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For P removal, the percentage obtained was 55.4% but in this study, the result obtained is a negative percentage removal. This shows that the UASB reactor at this sewage treatment plant is mainly used for removal of NO3-N. Extended aeration tank The percentage removal of -2121.2% for TSS and -1897.5% for VSS was caused by the peak of value from P2 to P3. The TSS content went from 149 ± 10.0 mg/L to 3261 ± 711.1 mg/L, whereas for VSS content, the value raised from 106 ± 36.8 mg/L to 1990 ± 0.0 mg/L. The increase in suspended solid represents the sludge present in the EA tank.
The percentage removal COD also depicts negative value which is -1475.2%. After filtering the samples, the removal of COD was calculated to be 83.0%. Next, the change in BOD5 is observed where the unfiltered and filtered samples shows -423.8% and 87.0% removal respectively. This amount of BOD5 removal was also the highest removal rate among the three individual systems at the sewage treatment plant. The NH3-N content in the normal sample increased from 28.3 ± 2.85 mg/L to 39.1 ± 7.52 mg/L which is a negative removal of 40.5%. Hence, by taking the values for filtered samples, we can see a removal rate of 50.9%.
NO3-N was the only parameter that does not show any removal by the EA tank. Both percentage removal calculated for filtered and unfiltered samples were negative values which are -326.8% and -162.4% respectively.
The percentage removal of PO43- and P was -502.6% and -497.7% respectively. By looking at the filtered samples again, the
removal percentage are 18.9% and 17.5% for PO43- and P respectively.
The pH at the EA tank stays around 6.0 for every sampling but is overall lower than in the UASB tank. The O&G content is generally a drop from the previous stage. Average O&G removal is 5.0% but the average of the absolute value is not significant which is from 8 mg/L to 7 mg/L.
Final clarifier The function of the final clarifier is to let particles settle, hence, removal of TSS and VSS at this stage is the highest. The percentage removal can be seen to be 99.1% for TSS and 98.3% for VSS.
The removal of sludge can remove 97.6% of the BOD5 content in the sewage. By considering the filtered sample, the difference in BOD value is also not drastic. The removal of NH3-N due to removal of sludge is 65.5% which is 25.3 mg/L. For the filtered sample results, the NH3-N percentage removal is 4.1% which is 0.04 mg/L. For the results of unfiltered samples, 26.9% of NO3-N was removed from the sewage. As for PO4
3- and P, 86.3% removal was analysed for both. The percentage removal for NO3-N, PO4
3- and P showed negative value after the sample was filtered. The NO3-N, PO43- and P
percentage removal were -55.0%, -29.9% and -27.6% respectively. The pH of the effluent is generally a bit higher than that of P3 but still in the range of the required standard. The O&G content decreased from 7 mg/L to 6 mg/L, just 1 mg/L difference.
CONCLUSION
In this study, the performance of the system and each of the individual stages are analyzed. The performance of the UASB tank was observed and compared with the other reactors in the system as well as the removal performance from previous studies. The conclusion drawn as guided by the objectives in this study can be summarized as below:
1. The UASB reactor was able to remove an average of 34.4% of COD, 35.6% of BOD5, 3.0% of NH3-N and 83.2% of NO3-N.The final effluent of the system complied to the accepted condition of Standard B.
2. The removal of COD and BOD were mainly happening at the EA tank which is above 80% as compared to around 30% of removal at the UASB. NO3-N content shows negative removal at the EA tank, but there are removals for NH3-N, PO4
3- and P. The final clarifier was only able to remove 4.1% of NH3-N and the nutrients does not show removal.
The UASB reactor studied in this paper is satisfactory in its performance and is enough to aid the system is removal of
contaminants to meet the standard. Suitable modifications can be done to improve the system to funcation better in treating sewage.
REFERENCE
[1] Tare, V., Nema, A. (2010). UASB Technology—Expectations and Reality. United Nations Asian and Pacific Centre for Agricultural Engineering and Machinery.
[2] Khalil, N., Sinha, R., Raghav, A.K., Mittal, A.K. (2008). UASB technology for sewage treatment in India: experience, economic evaluation and its potential in other developing countries. Twelfth International Water Technology Conference, Alexandria, Egypt (2008).
[3] Lettinga, G., van Velsen, A. F. M., Hobma, S. W., de Zeeuw, W., Klapwijk, A. (1980). Use of the upflow sludge blanket (USB) reactor concept for biological wastewater treatment, especially for anaerobic treatment. Biotechnology and Bioengineeering, 22, 699–734.
[4] Haugen, F., Bakke, R., Lie, B., Hovland, J., Vasdal, K. (2015). Optimal design and operation of a UASB reactor for dairy cattle manure. Computers and Electronics in Agriculture, 111 (2015), 203-213.
[5] Rizvi, H., Ahmad, N., Abbas, F., Bukhari, I.H., Yasar, A., Ali, S., Yasmeen, T., Raiz, M. (2015). Start-up of UASB reactors treating municipal wastewater and effect of temperature/sludge age and hydraulic retention time (HRT) on its performance. Arabian Journal of Chemistry, 8(6) (2015), 780-786.
[6] Artsupho, L., Jutakridsada, P., Laungphairojana, A., Rodriguez, J.F., Kamwilaisak, K. (2016). Effect of temperature on increasing biogas production from sugar industrial wastewater treatment by UASB process in pilot scale. Energy Procedia, 100 (2016), 30-33.
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[7] Bhunia, P., Ghangrekar, M.M. (2007). Required minimum granule size in UASB reactor and characteristics variation with size. Bioresource Technology, 98(5) (2007), 994-999.
[8] Schmidt, J. E., Ahring, B. K. (1996). Granular sludge formation in upflow anaerobic sludge blanket (UASB) reactors. Biotechnology and Bioengineeering, 49, 229–246.
[9] Ghosh, S., Ombregt, J.P., Pipyn, P. (1985). Methane production from industrial wastes by two-phase anaerobic digestion. Water Research, 19(9) (1985), 1083-1088.
[10] Barbosa, R.A., Sant'Anna, G.L. (1989). Treatment of raw domestic sewage in an UASB reactor. Water Research, 23(12) (1989), 1483-1490.
[11] American Public Health Association (APHA) (2005). Standard methods for examination of water and wastewater, 21st edn. American Public Health Association, Washinton, DC.
[12] Metcalf and Eddy, Inc. (2003). Wastewater Engineering: Treatment and Reuse, 4th edn. Boston: McGraw-Hill.
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Tebrau Straits Development and Its Impact On Fishing Activity Mohammad Amirul Rosli1, Mohd Badruddin Mohd Yusof1*
School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia *[email protected]
ABSTRACT. Coastal developments at Tebrau Straits appear to have affected the fishermen’s activity around the area.A study involving 56 registered and unregistered fishermen within the 7 villages had been conducted between the March and April 2018 to analyze this issue. The study covered an area located west of the Johor State. The objectives of the study were to analyze the socio-economic and background of the fishermen communities along with the effects of water quality from nearby developments to the volume of fish catches through personal interviews .Respondents’ opinions on environmental and economic impacts of the developments to the fishing activities were considered in suggesting measures to improve the communities’ socio-economic wellbeing.. The data was analyzed using descriptive and inferential statistics using the Statistical Packages for Social Sciences (SPSS) software and Microsoft Excel. The study found that majority of respondents (73.2%) were registered with the Department of Fisheries (DOF) and most (80.4%) were involved the fishing activity full time and were nwilling to change their profession. A few of them involved in other activities than fishing to supplement an additional source of income. Most of respondents (79.2%) agreed to and supported the development projects, however, quite a large number of them (68%) strongly believed that they had caused a reduction in their fish catches and negatively affected the straits’ water quality. Based on the results, it is recommended that the government and local authorities plan and implement some form of transformation program to help improve the livelihood of the fishing communities in the areas involved.
Keywords: Fishing activity; Coastal development ; Tebrau Straits; Development impacts ; Questionnaire and interview ;
INTRODUCTION
The fisheries industry is among the most important industries in Malaysia. It is one of the main sources of protein for Malaysians. It is also a contributor to the country's income as a result of exporting fishery products abroad such as Singapore, Japan, and Taiwan. At the same time, humans also put pressure on the sources of protein in the earth. This can be seen when oceans, rivers and irrigation experience decline in quality ,as a result of human activities.Along with the rapid development of the world, Malaysia is also not left behind. The rapid development in Malaysia is one of the missions to achieve the nation's vision of being a developed country in 2020 [1]. Development projects had been carried out primarily in Iskandar Malaysia, a development corridor implemented in southern Johor, formerly known as the Iskandar Development Region. Another name for the corridor is also the Southern Region Economic Development of Johor. Many investors from overseas companies are keen to invest in Iskandar Malaysia including Country Garden and Greenland from China. However, every development inevitably will have negative impacts on water pollution in the surrounding area incouding surrounding fishermen communities.
Problem Statement Johor Strait is very important to the people in that area especially to those who earn their living as fishermen. Therefore, they rely heavily on marine produce to sustain their lives. However, since the rapid development of the area, residents found that marine capture had been decreasing. In fact, water pollution can be clearly seen at the nearby sea areas. Residents in the area believe that the sea was polluted as a result of development activities. According to past studies, the development at coastal area in Johor did not get majority support from most of local community due to negative changes in the marine environment as well as reduced catches in that area. However, there is no significant and comprehensive study on the seriousness of the pollution and its causes. Therefore, a comprehensive study should be undertaken to look into the effects of development in the Johor Straits. This study analyzed the socio-economic backgroiund of the fishermen and is an important step to ensure the continuity of fish catches by the fishermen. It analyzed the differences between current and prior status of the fishing activities in order to see how serious the effects were of such developments.
Objectives The objective of this research is:
1. To identify the socio-economic background of fishermen communities at west of the Tebrau Strait. 2. To analyze the effects of water quality to the volume of catches through the questionnaire surveys. 3. To obtain opinions on the environmental and economic impacts of developments to the fishing activities 4. To suggest measures to improve the socio-economic well-being of the fishermen.
Scope of Study The study was conducted using questionnaire survey, which covered the backgraound as well as the opinions regarding impacts of developments to the surrounding environment. It involved 56 fishermen living west of the Tebrau Straits indirectly affected by the major construction activities. The respondents were from 7 selected fishing communities, while primary data obtained from questionaires and interviews. Significance of environmental impacts on fishing activities in study area were derived from the opinions of the effects of the developments of surrounding projects on their income and daily activities as well as the volume of landed catches.
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LITERATURE REVIEW
Developments at the Tebrau Strait was part of Comprehensive Development Plan (CDP) for Iskandar Malaysia. An establishment of Iskandar Malaysia has initiated and contributed to the active development in the Strait. Figure 1 shows five flagship zones as developmental focal points in Iskandar Malaysia (221,634.1 hectares or 2,216.3 km² of land covered) [1]. The region’s Comprehensive Development Plan (CDP) requires the cooperation, support, active participation, commitment and sustained involvement of the many agencies, stakeholders and players at all levels. These actually include the federal, state and local governments, agencies, business communities, local leaders and communities, as well as global industry players. These are among the more employment-opportunity providers in the region. The total number of employed persons in Iskandar Malaysia is expected to reach 1.31 million [2].
All land developments inevitably have some degree of impact on the natural environment especially those that involve a large area and require a long time to complete. Amongst the effects include decreasing number of aquatic life, a reduction in water quality as well as overall environmental degradation. Aquatic life is a major source of protein for Malaysians in particular. It is also a source of income to fishermen . The development’s impact on marine life is an important aspect that needs to be emphasized because of its importance. Water quality is an aspect that the developers need to consider to ensure that the water quality before and after development is not drastically changed. Effective urban resource planning and management entails the mitigation of the impacts of urbanisation on the water environment[1]. According to Ng, water quality test conducted in July has shown a Water Quality Index (WQI) value of 89.4, a 10% improvement in quality compared to those tested in May (79.17) during commencement of reclamation works [1] . Based on the research by Syafawati [3], Skudai river experienced a decline in water quality with a WQI reading of 62.47 and was in Class III. The area was contaminated due to the reclamation activities carried out in the area.
Malaysian fisheries is one of the main commercial sectors that generate income to the nation along with its importance in
providing protein source to its people. The fisheries sector in Malaysia is divided into two categories, marine fishery (Marine) and aquaculture fishery sector. The sea fishery sector is an activity involving fishing in the Coastal Area (ZEE Economic Zone) and the deep sea area. Fishing in the sea employs various techniques and methods from traditional to modern methods using GPS, and ultrasonic sound,. The Department of Fisheries (DOF) Malaysia strives to achieve development and modernization in the fisheries sector. Theire mission is to manage and develop the fisheries sustainability dynamically and competitively based on the scientific information and to provide best service. Hence, the Department focused more on efforts to improve their service quality, efficiency and effective delivery system as well to enhance the visibility of the Department by empowering new and existing programs or projects through smart partnerships between the Department and its stakeholders [4].
METHODOLOGY
The location of study area involved Tebrau Strait as it appears to be polluted caused by coastal developments. It was chosen as it has high population of fishing communities in Johor. Thus, the areas appeared to be appropriate to analyze the impact of coastal developments on fishing activities. The selected study areas involved seven villages located west of Tebrau Strait. The respondents were 56 fisherman from seven villages which are Kg. Tanjung Piai, Kg. Sungai Durian, Kg. Kukup Laut, Kg. Sg. Melayu, Kg. Nelayan Air Masin ,Kg Sungai Danga and Kg. Tanjung Kupang.
Data were divided into two types namely primary and secondary data. Most of the researchers use both data to obtain a reliable result. Questionnaire survey were conducted to produce the primary data which included respondents’ socioeconomic background and their opinions towards coastal developments While, the secondary data were based on literature reviews and interviews, while the results was obtained and analyzed using the SPSS Software.
Figure 1 Locaton of the villages
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Questionnaire Development Questionnaire was used whereby a set of questionnaires was developed and ranked using Likert Scale (1-5 rank). It is the most universal method for survey collection allowing an ease in data analyses. The structural of questionnaire was formed based on objectives in this study and adopted from other studies from researchers. The structure questionnaires were divided in four parts for easier the process of analyzing the data. Part A is including respondents’ background profile and their current socio-economic. Then Part B is about background of fishing activities. From this part, the fishermen‘s background and monthly income of fishing activities were determined. Part C covered the quantitative data, where respondents were required to fill information about their catches, estimated current and previous weights (last 3 years) of catches. Meanwhile, the opinions of effects of the coastal developments to their socio-economic wellbeing were covered in Part D, along with their willingness to accept the impact of the development also determine in this part. Data Collection Secondary data were obtained from articles, journals, newspapers and thesis as well as information, reports, statistical data and realted to fishing industry along the Tebrau Straits collected from the Department of Fisheries. Personal interviews were conducted to get first hand information at the selected areas covering background and experiences of the respondents as well as income and opinions of surrounding projects. Descriptive statistics were used to determine outcomes of the study presented in statistical outputs such as mode, mean values, standard deviations and percentages,
RESULTS AND DISCUSSION
The results were analysed using both descriptive and inferential analyses. The discussions on the findings regarding perceived values and opinions from the fishermen about the developments was described below. Respondents by Villages The distribution of questionnaire was sorted into 7 villages along Tebrau Straits where fishing activities were located with 56 respondents involved. Table 1 describes number of the respondents involved according to villages. Most of the respondents were from Kg. Sungai Melayu (14 respondents or 25%), while the least from Kg. Nelayan Air Masin (3.6%). The distribution of the respondents in each village varied as some did not agrre to participate as they were not registered with Department of Fisheries (DOF). The number of registered and unregistered with the Department of Fisheries (DOF) is shown in Table 1, where the former group represented 78.6% compared to latter (21.4%).
Table 1: Distribution of the respondents according to village
Village Name
No. of Respondents Registered Respondents Unregistered Respondents Kg. Sungai Melayu 14(25%) 13(92%) 1(8%) Kg. Sungai Danga 9(16.1%) 3(33%) 6(67%) Kg. Tanjung Kupang 10(17.9%) 9(90%) 1(10%) Kg. Taanjung Piai 9(16.1%) 9 (100%) 0(0%) Kg. Kukup Laut 4(7.1%) 2(50%) 2(50%) Kg. Nelayan Air Masin Kg. Sungai Durian
2(3.6%) 8(14.3%)
1 (50%) 7(87.5 %)
1 (50%) 112.5%)
Total 56 44(78.6%) 12(21.4%)
Background of Respondents Table 2 describes the summary of background of respondents. It shows the respondents comprised of 98.2% male and 1.8% female and aged between 41 and 50 years old (41.1%). Meanwhile, percentage of Malay and Chinese respondents shows a big difference as the former represented 83.1% compared to the latter, 1.8%. Most of had some form of primary level of education (60.7%), with majority(80.4%) were involved this activity full time. About 21.4% of those interviewed were having another job at the same time.
Table 2 : Summary background of respondents
No.
Variable Result Highest Percentage Lowest Percentage
1 Gender Male (98.2%) Female (1.8%) 2 Age 41 to 50 years old (41.1%) 21 to 30 years old (3.6%) 3 Racial background Malay(83.1%) Chinese (1.8%) 4 Religious background Muslim (92.1%) Buddha (1.8%) 5 Marital status Married (85.7%) Single (14.3%) 6 Family size 4 to 6 persons (62.5%) 7 to 9 persons (10.7%) 7 Educational background Primary school (60.7%) Diploma (1.8%) 8 Category of fishermen Full time fisherman(80.4%) Part time fisherman(19.7%)
9 Boat ownership Owned (94.4%) Borrow/Rent (3.6%) 10 Involvement in other activities No (82.1%) Yes (21.4%)
11 Involvement with other professions Farmer (10.7%) Taxi Driver(5.4%)
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Background of Fishing Area
All of the respondents (100%) fished in Zone A located within 0 to 5 nautical miles from the shore fishing areas as well as located between 0 to 10km from the nearest development (100%). This has made the situations more critical, as far as the significance of the impacts of such developments were concerned.
Income and Fishing Activity
Table 3 shows the summary of respondents‘ income and fishing activities. Majority(92.9%) did not employ any worker and worked alone or with family members. Meanwhile most of respondents spent an average of RM30 per trip including deisel ,food and other expenses. There had been some reductions in their total income compared to previously recorded. Majority of the respondents needed under RM500 per month (60%) for their operation cost which was quite low. About 35% of them were earning between RM3000 to RM6000 per month, with only 14% between RM6000 and RM9000.
Table 3: Summary of income and fishing activities
No.
Variable Result Highest Percentage Lowest Percentage
1 No. of workers No employee (92.9 %) 1 worker (7.1%) 2 Average cost per trip RM 30 (37.5%) RM 10(1.8%) 3 4
Monthly income (Current ) Monthly income (Previous)
Less than RM 500 (46.3%) More than RM 2000 (19.64%)
RM1000 to RM1500(16.97%) Less than RM500 (8.93%)
Opinion on current water condition
Figure 2 below shows the opinions of the respondents towards the the current water condition and its problems due to the reclamation projects. Most of t h e respondents(46.4%) stated that the water was cloudy and oily (23.2%). Meanwhile, 26.8 % claimed that solid wastes were found floating on water surface.
Figure 2 Physical forms of pollution observed by respondents (percentage).
Opinions on Surrounding Developments Table 4 below shows the results on acceptance of respondents towards the nearest developments and sea reclamation projects. Most of respondents supported the projects (66%). However, majority (89%) agreed the nearest developments had negatively affected their fishing activities with 73.2% believed that they had caused a reduction in the overall water quality along with their total catches.
Table 4 : Respondent’s opinions toward to nearest development
Opinions Strongly Disagree
Disagree
Neutral
Agree Strongly Agree
N % N % N % N % N % Support for the development 0 0 2 3.67 10 18 7 12.5 37 66 Project benefits 1 1.8 0 0 17 30.4 24 42.6 12 21.4 Acceptance of negative impacts -
-
1 1.8 6 8.9 7 12.6 42 75 Reduction in water quality 6
11111fcjn10.7
1 2ww
10.7 2 3.6 3 5.36 4 7.14 41 73.2 *N - Number of respondents *% - Percentage of respondents
In the aspect of monthly income, Table 5 describes the respondent’s opinions regarding this issue. Most of the respondents were very unsatisfied (83.9%) with their current monthly income. Meanwhile, most of the respondents (73.2 %) also were not satisfied with the compensation given by the government as well as the developers.
46.4%
23.2%
26.8%
3.6%
C LOUDY WATER O I L Y WATER SO L ID WASTE O TH ER S
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Table 5: Opinions on monthly income and compensation Opinions Very
Unsatisfied Unsatisfied Neutral Satisfied Very satisfied
N % N % N % N % N % Income satisfaction 47 83.9 2 3.6 2 3.6 5 8.9 n/a n/a Satisfaction over compesnsation n/a n/a 41 73.2 10 17.7 5 8.9 n/a n/a
CONCLUSION
The findings of this study and recommendations on government policies are summarized below:
i. Majority of them was a full time fishermen but, and some also were doing other jobs to provide an additional income. ii. Negative effects from the developments included a reduction in their monthly income due to lesser volume of catches. iii. Number fish was said to have decreased due to pollution in the area. iv. Majority of the respondents supported and were aware about the Tebrau Straits developments and their consequences. v. Government can help respondents by increasing the amount of subsidy and compensation needed to improve their
livelihood. vi. Government needs to ensure future coastal developments project are sustainable and eco-friendly. vii. Local authority should enforce more efforts in implementing transformation programs and strategies for the
development in fishing industry as a whole. In general, the study was successful in meeting the objectives despite having a few limitations due to insufficient time to compile more information and reviews.
REFERENCES
[1] Iskandar Malaysia (2016). Our Development Plan. Iskandar Malaysia Official Website. Retrieved from http://iskandarmalaysia.com.my/
[2] Department of Statistic (2016). Official Portal of DOS. Retrieved from https://www.statistics.gov.my/ [3] Syafawati Binti Ab Rashid (2016). Kajian Kesan Tebus Guna Terhadap Kualiti Air Di Muara Sungai Skudai. Degree Thesis.
UniversitTeknologi Malaysia. [4] Department of Fisheries (2016). Malaysian Fishing Industry Scenario: Fishery Sector Performance and Achievements.
Retrieved from http://www.dof.gov.my/ [5] Department of Fisheries (2016). Aquaculture Industrial Zone: Aquaculture Industry Zone Areas for High Impact Project
(HIP). Retrieved from http://www.dof.gov.my/ [4] Department of Fisheries Johor (2016). Transformation Framework Programme For Fishermen And Aqua Culturist Around
Tebrau Strait.Unpublished report
61
Study of Trace Element Concentration in Johor River Basin Mohd Rizal Bin Roslie1,Lelavathy Samikan Mazilamani1
Alwievie Flories Alip1, Kogila Vani Annammala1*
1School of Civil Engineering, Universiti Teknologi Malaysia, Malaysia *[email protected]
ABSTRACT. This paper presents a systematic analysis of trace metal pollutions of water samples along the Johor river. Thus, an introductory study on trace elements in water samples accomplishes at 11 sampling stations along the Johor river. Trace element concentrations in water samples are detected using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). The results show that, the average concentrations of Cd, Cu, Mn, Ni and Zn (0.00035 mg/L, 0.719 mg/L, 0.064 mg/l, 0.019mg/L, 0.716 mg/L, and respectively) were satisfied the drinking water quality standards set by Malaysia’ Drinking water Quality Standard, USEPA and WHO. However, the mean concentrations of As and Fe (0.196 mg/L, 4.159 mg/L, and respectively) present in Johor river are exceeded the drinking water quality standards which indicates that the quality Johor river water deteriorating and not safe to drink. The maximum concentration of As, Cu, Fe and Zn recorded along the Johor river are higher compared to Tsurumi river in Japan, Axioms river in Greece Nhue River in Vietnam due to increasing anthropogenic activities along the Johor river.
A river is a natural flowing watercourse, usually freshwater, flowing towards an ocean, sea, lake or another river. River has lot of contribution to human life since the earliest generation. For example, platform for transportation to another place, agriculture, provides foods, recreation and for drinking. Before used for drinking, the water should be examined and evaluated and some quality control measures must be introduced to ensure the quality of the water. One of the major concern to river is water pollution.
Trace elements exist widely in specific concentrations in the natural environment. With the development of the economy and society human activities, such as mining, smelting, and processing, have allowed more trace elements to enter the atmosphere, water, and soil, thus resulting in serious environmental pollution. Pollution from trace elements has become the main source of global environmental pollution. Their emiSWion into the environment is harmful not only to ecosystems, but also poses a threat to human health because of refractory characteristics of bioaccumulation.
Problem Statement There are some precaution during sampling need to be taken in order to achieve the objective of this study. First of all, the
bottle for water sample can’t be light in colour so that can prevent from direct penetration of light to the water. For sediment sample, the sediment must be taken from the surface of soil where the water runoff present. Rain influence is not neceSWary for this study as the sampling days only took two days.
The concentration of trace element is highly affected by the surrounding activities. Along the Sungai Johor there are lot of different activities happening for example sand mining, agriculture and factories industry. Therefore for each station is expected to be different concentration of heavy metal present. There are some researcher prefer using Atomic Absorption Spectrometry (AAS), X-Rayfluorescene and inductively coupled plasma maSW spectrometry (ICP-MS) to conduct heavy metal concentration test. For this research, trace element concentration is done by using ICP-MS technic. There will be further discuSWion why this research use ICP-MS.
Some researcher suggested to not undergo digestion for water sample before running the ICP-MS analysis. For this research, digestion is needed since there is visible impurities can be seen from the water sample taken.
Objectives
The main purpose of this research is to study elements which exist in water of Sungai Johor. The objective of this study are: 1. To detect trace element concentration in water of Sungai Johor by using ICP-MS. 2. To report and analyse the trend of increasing or declining of the detested element. 3. To compare average concentrations of trace elements in research area with drinking water quality standards 4. To compare the maximum concentration tace elements present in Johor river with other rivers around the world.
Scope of Study
This study was mainly focused on detection of trace element concentration in Johor water river. The study began with reviewing and searching literature on the issues regarding heavy metals concentration in Johor river. Site visit to Johor river was done to identify the existing road to the study site. Water samples were collected from 11 stations along the Johor river. ICP-MS analytical tool was used to measure the concentration of trace elements that exist along Johor river. The concentration of trace elements found in Johor river were compared with concentrations limit established drinking water quality standards such as Malaysia’ Drinking Water Quality Standard, World Health Organization and U.S Enviromental Protection Agency. The maximum
62
concentration od trace elements present in Johor river were compared with Tsurumi river in Japan, Axioms river in Greece AND Nhue River in Vietnam.
LITERATURE REVIEW
Heavy metals are generally defined as metals with relatively high densities, atomic weights, or atomic numbers. Rajeev Bhat (2014) states that Heavy metals are natural components of the Earth's crust and cannot be degraded nor destroyed. Heavy metals constitute an ill-defined group of inorganic chemical hazards, and those most commonly found at contaminated sites are lead (Pb), chromium (Cr), arsenic (As), zinc (Zn), cadmium (Cd), copper (Cu), mercury (Hg), and nickel (Ni) [1]. Some of heavy metals are eSWential for human health, but an exceSW amount of these metals can give negative effect. It has been reported that metals such as Cobalt (Co), Copper (Cu), Chromium (Cr), Iron (Fe), Magnesium (Mg), Manganese (Mn), Molybdenum (Mo), Nickel (Ni), Selenium (Se) and Zinc (Zn) are eSWential nutrients that are required for various biochemical and physiological functions . Inadequate supply of these micro-nutrients results in a variety of deficiency diseases or syndromes [2]. The non-eSWential metals such as Cd, Pb, Ni, and Cr are toxic even at relatively low concentration and not eSWential for metabolic activities. The abundance of heavy metal may risk human health due to the consumption of contaminated bivalves. For examples, Cd may cause human carcinogen; Pb can damage blood circulation and exceSWive intake of Zn may cause electrolyte imbalance and lethargy [3].
Soil play a central role in food safety as it determines the poSWible composition of food and feed at the root of the food chain. However quality of soil resources as defined by their potential impact on human health by propagation of harmful elements through the food chain has been poorly studied in Europe due to the lack of data of adequate and reliabilit [4]. Soil contamination is one of the greatest worry among the threats to soil resources in Europe and globally. Despite of its importance there was only very course scale (1/5000 km2) data available on soil heavy metal concentrations prior to the LUCAS topsoil survey, which had a sampling density of 200 km2. Heavy metals (As, Cd, Cr, Cu, Hg, Pb, Zn, Sb, Co and Ni) were produced in the topsoil of the European Union based on the results of the LUCAS sampling and auxiliary information detailed and up-to-date maps. Most of the examined elements remain under the corresponding threshold values in the majority of the land of the EU. However, one or more of the elements exceed the applied threshold concentration on 1.2 M km2 , which is 28.3% of the total surface area of the EU. While natural backgrounds might be the reason for high concentrations on large proportion of the affected soils, historical and recent industrial and mining areas show elevated concentrations (predominantly of As, Cd, Pb and Hg) too, indicating the magnitude of anthropogenic effect on soil quality in Europe [4]
. Soils are sinks for trace elements because many species of trace ions are fixed, a characteristic which determines how they are cycled in the soil. The behaviour of trace elements in soil is fundamentally defined by their aSWociation with different soil components and phases. Uptake by plants depends on the rate and amount of trace elements applied as fertilizer, as well as soil and plant characteristics. There are permanent physical, chemical and biological proceSWes occurring in soils that cause the evolution of parent materials, determine sorption, speciation, redistribution, mobility and bioavailability of trace elements. Weathering relies on diSWolving of primary minerals and hydrolysis of released elements [5]. Soils may become contaminated by the accumulation of heavy metals and metalloids through emiSWions from the rapidly expanding industrial areas, mine tailings, disposal of high metal wastes, leaded gasoline and paints, land application of fertilizers, animal fertilizers, sewage sludge, pesticides, wastewater irrigation, coal combustion residues, spillage of petrochemicals, and atmospheric deposition. Heavy metals constitute an ill-defined group of inorganic chemical hazards, and those most commonly found at contaminated sites are Lead (Pb), Chromium (Cr), Arsenic (As), Zinc (Zn), Cadmium (Cd), Copper (Cu), Mercury (Hg), and Nickel (Ni) [1].
It is well known that safety of drinking water is of great concerns all over the world, especially for the developing countries like China. Heavy metal pollution is one of the most serious environmental problems in China and a large number of people are threatened by heavy metal pollution.China's water resources are distributed quite unevenly. The northern river basins containing 44% of the national population have no more than 13% of the water supply. On the one hand, leSW than half of China's major rivers, lakes and reservoirs are suitable for use as drinking water after treatment, on the other hand, water shortage compel population to use contaminated sources [7].Extensive damage to human organs, such as liver, kidney, digestion system, and nervous system can be caused by uptake of exceSW heavy metals. Heavy metals in the environment can originate from both natural and anthropogenic sources. Although contamination of heavy metals has been known to be a severe environmental problem for decades, it is still getting worse in recent years and there are few feasible remediation to resolve this problem. Due to their high toxicity, prevalent existence and persistence in the environment, lead (Pb), mercury (Hg), cadmium (Cd), chromium (Cr) and arsenic (As) are commonly considered as the priority heavy metals which should be concerned and their emiSWion should be controlled in China. Nationwide, lots of households do not have acceSW to a centralized public water supply and rely on untreated hand pump, well, or surface-water sources. These resulted in considerable aSWociation between water scarcity and adverse health outcomes, such as oesophageal cancer, liver cancer[7]. METHODOLOGY Study site Johor River is located in the central part of south Johor, which is in the southern portion of Penisular Malaysia. The river lies within the latitude1°27′00″N to 1°49’00″N and longitude 103°42′00″E to). The river has length of approximately 122.7 km and the total catchment area of this basin is approximately 2636km2. The river flows in north-south direction,originating from Mount Gemuruh(109m) and empties into Straits of Johor. Sayong, Linggiu, Tiram and Lebam rivers are the main tributaries of Johor river. The temperature of the basin ranges from 21ºC to 32ºC with average annual precipitation of 2,470mm(basin average). About 60% of the catchment is characterized by undulating highland(height of 366m) while remaining is lowland and swampy. The major land uses along the Johor River are urbanization(5.5%), oil palm cultivation(18.5%0, mangrove
63
areas(11.5%) [8].
Figure 1: Sampling points of Johor River (Google Earth Ima
Table 1: Description of sampling points of Johor River Station Latitude Longitude Site Description SW01 1° 41'56.7234"N 103° 55' 32.0874"E Sand mining, palm trees SW02 1° 41' 23.1354"N 103° 56' 55.968"E Palm trees SW03 1° 39' 55.4394"N 103° 55' 53.8674"E Pulau dendang, palm trees,mangrove and nipah trees SW04 1° 38' 4.4874"N 103° 58' 19.308"E Sand mining, industrial factory SW05 1° 43' 35.832"N 103° 53' 58.5954"E Kota tinggi, upstream, industrial SW06 1° 35' 3.9114"N 103° 59' 14.1714"E Palm trees SW07 1° 35' 34.4034"N 103° 57' 17.5314"E Downstream, village SW08 1° 36' 58.32"N 103° 57' 27.108"E Sand mining, small restaurant SW09 1° 37' 6.24"N 103° 59' 1.032"E Village, school, palm trees SW10 1° 37' 15.132"N 103° 58' 17.4"E Palm trees SW11 1° 37' 37.1994"N 103° 58' 2.1"E Village, palm trees
Sample collection Eleven sampling stations were selected randomly in order to collect water samples for metal analysis. The locations of sampling stations were recorded using Global Positioning System (GPS). The physicochemical properties of water (ph, temperature, turbidity, diSWolved oxyen and total diSWoved solid) were measured using water quality monitoring device. Total of 11 samples of water were collected in pre-cleaned bottles along the Johor river. The samples of water were transported to laboratory for further analysis. Open Digestion The water samples were digested using open digen method before the instrumental analaysis. Digestion is a process that dissolves all impurities present in sample to get an accurate. Firstly, the glSW is rinsed with 5% nitric acid (HNO3) and then washed with deionized water and dried.�100 mL water sample was preserved with 5mL 69% HNO3 acid. The conical flask solution mixture was swirled to homogenize the sample. Then, the solution is heated using low temperature for 24 hours. Inductively Coupled Plasma MaSW Spectrometry (ICP-MS) The concentration of trace elements in water samples were determined using ICP-MS. 5 ml of digested sample was diluted 10 times dilution using ultra-pure water. Multi-element Standard XSTC-13 was used to prepare calibration curve. The ICP-MS working standards that have been used in this research were 2,3,4 and 5. For each standard, calibration curves with R2 > 0.999 were accepted for heavy metal concentration calculation.
RESULTS AND DISCUSSION
There were 31 trace elements being detected by using ICP-MS in our research studies which are As, Cd, Co, Cr, Cu, Fe, Ni, Zn, Ba, P, Au, Ag, Ir, Mo, Pd, Rh, Ru, Sb, Sc, Sn, Te, Y, Zr, Be, Mn, Ga, Se, Rb, Sr, V and Al. However, this study was only focused on frequently existing heavy metal in the study area such as Mn, Cr, Fe, Zn, Co, Ni, Cu, Cd, Ag and As as shown in Table 2.
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Table 2: Concentration levels of trace elements in study area
The highest concentration of iron (Fe), Copper (Cu) and Arsenic (As) were measured at SW9 (6904.6 ppb, 1338.9 ppb and 294.1 ppb, respectively) as shown in Figure 2(a), 2(b), 2(c), respectively. Both natural and anthropogenic sources had contributed to highest concentration of these trace metals as SW9. Fe exist naturally in river through weathering action of rocks and decomposition of organic matter. The agricultural ruoff from the oil palm plantation at SW9 resulted in high concentration of Cu and As. The high contamination of As originated from use of fertilizer and pesticide at oil palmplantation.The prolonged exposure to As may cause damage to digestive tract, liver, lungs, skin pigmentation, hair fall and stopped nail growth. The increase in Fe and As concentrations entering the Johor river could create an significant changes to the geochemistry of the sediments and could harm the river ecosystem. High level of contaminations of Magnesium (Mg) and Silver (Ag) had occured at SW5 (147.3 ppb, and 47.1 ppb, respectively) as shown in Figure 2(d) and 2(e) This area was located near Kota Tinggi city. There were 2 villages located at SW5 (Kg. Tembioh and Kg. Makam Pasir Raja). The agricultural runoff from these two vilages resulted in high contamination Ag and Mg at SW5. The high input of these trace elements might come from organic effluents discharged from urban and industries at Kota Tinggi area and also from the agricultural runoff from the villages. High level of Mn in drinking water may lead to increased hyperactive behaviour, impaired cognitive development and a decrease in IQ point. The highest concentration Zinc (Zn) and Cadmium (Cd) in sediments were measured at SW3 (821.2 ppb and 0.8 ppb, respectively) as Shown in Figure 2(h) and 2(i) respectively.. The urban and industrial activities in coastal areas cause a significant increase in metal concentration in water , which converge to the estuary mangrove wetlands.The higher levels Cd found in this area due to anthropogenic inputs such agricultural discharges from the nearest water station. High consumption of Cd can cause kidney disease, “itai-itai“ disease and increased risk of cancer.The use of phosphate fertilizer in agricultural areas resulted in high contamination of Cd in rver water. Domestic contruction, car related sources and untreated waste water are considered as main inputs of Zn. Although Zn is an important dietary mineral for humans, the high intake of Zn can be toxic.
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Table 3: Maximum permitted heavy metal (mg/L) for drinking water quality Heavy metals Current research
(average concentration) Malaysia’s drinking water quality standarda
WHO (2004)b USEPA (2009)C
As 0.196 0.01 0.01 0.01 Cr 0.035 0.05 0.03 0.05 Cd 0.00035 0.003 0.05 0.1 Cu 0.719 1 2 1.3 Fe 4.159 0.3 - 0.3 Mn 0.064 150 0.4 0.05 Ni 0.019 0.02 0.07 - Zn 0.716 3 - 5
a[9], b[10], c[11], (WHO: World Health Organization, USEPA: United State Environmental Protection Agency)
Three standard have been used to compare quality of drinking water in Johor river ( Malaysia’ Drinking water Quality Standard, WHO and USEPA) as shown in Table 3. The mean concentration of As was exceeded the maximum permisibbleconcentration(0.01mg/L)setbyMalaysia’ Drinking water Quality Standard, WHO and USEPA. The mean concentration of Cr was exceeded the WHO standard (0.03 mg/L) but the value was below than standard set by USEPA (0.05mg/L) and Malaysia’ Drinking water Quality Standard (0.05 mg/L). The mean concentration of Fe was exceeded the standard set by both Malaysia’ Drinking water Quality Standard (0.3 mg/L) and USEPA (0.3 mg/L). However, the average concentrations of Cd, Cu, Mn, Ni and Zn were satisfied the drinking water quality standards set by Malaysia’ Drinking water Quality Standard, USEPA and WHO.
Table 5: Comparison of maximum heavy metal concentrations (ppb) in water samples with other rivers in world Locations As Cd Cr Cu Fe Mn Ni Zn Johor river 294.1 0.8 88.0 1338.9 6904.6 147.2 25.7 821.2 Tsurumi River, Japand - - 217 654 362 264 223 - Nhue River, Vietname 7.13 - 18.8 14.6 - 207 21.5 61.9 Axios River, Greecef 10.9 1.1 4.2 13.7 - - 8.2 56.2
d[12], e[13], f[14], Table above shows the comparison of maximum concentrations of heavy metals found in along the Johor river with three rivers in world (Tsurumi River, Japan; Nhue River, Vietnam; Axios River, Greece). The maximum concentration of AS along the Johor river was greater than Nhue River, Vietnam; Axios River, Greece. The sampling areas along The Johor river were mainly surrounded by industrial and agricultural area. The high input of As in water samples originated from use of pesticide in agricultural areas and organic effluents discharge from the urban and industry. The maximum Cu and Zn concentrations were found to be high along Johor river due to high input of organic waste which might come from both industrial and urban runoff. However, highest maximum concentrations of Cr, Mn and Ni were recorded at Tsurumi river, Japan. The leacheates from defused Ni-Cd batteries and wast water discharged from industrized and urbanized areas are the main sources of high concentration of Cr and Ni in Tsurumi river [12]. Among the rivers, the least contamination of heavy metals (Cr, Cu, Ni, Zn) in river water was found to be at Axios river, Greece followed by Nhue river, Vietnam. This may due to less development and anthroponic activities along the river compared to Johor and Tsurumi river. CONCLUSION The accumulation of trace metals in river water the have greater impacts on the health of ecosystem.Therefore, in this study, the contamination levels of trace metals along the Johor river were examined by analysing the water samples collected from 11 stations. There were 31 elements detected by ICP-MS. However, this study on focused on 10 frequently occuring elements such as Mn, Cr, Fe, Zn, Co, Ni, Cu, Cd, Ag and As. Based on the results, the mean concentrations of Cd, Cu, Mn, Ni and Zn (0.00035 mg/L, 0.719 mg/L, 0.064 mg/l, 0.019mg/L, 0.716 mg/L were comparable to the drinking water quality standards set by Malaysia’ Drinking water Quality Standard, USEPA and WHO. However, the mean concentrations of As and Fe (0.196 mg/L, 4.159 mg/L, and respectively) present in Johor river were exceeded the drinking water quality standards which showed that the Johor river water was at high risk if was consumed directly. Arsenic (As) might be introduced into the river water from industrial effluents and from pesticide which have been widely used in agricultural areas along the Johor river. On the other hand, the maximum concentration of As, Cu, Fe and Zn recorded along the Johor river were higher compared to Tsurumi river in Japan, Axioms river in Greece Nhue River in Vietnam. This proved that impact of developments such as industrial, urban , construction, agriculture along the both sider of Johor river were greater compared to Tsurumi river in Japan, Axioms river in Greece Nhue River in Vietnam. From the result, it can be concluded that anthropogenic activities along the river have brought significant physical and chemical changes to the river ecosystem. Therefore, an effective trace metal risk management strategies should be developed to save Johor river from continuous deterioration in quality.
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REFERENCE [1] Wuana A.R. & Okieimen.E (2011). Heavy Metals in Contaminated Soils: A Review of Sources, Chemistry, Risks and
Best Available Strategies for Remediation. [2] Tchounwou P. B., et al. (2014), Heavy Metals Toxicity and the Environment, 2. [3] Hossen M. F., Hamdan S, & Rahman M. R. (2016). Review on the Risk Asessment of Heavy Metals in Malaysian
Clams [4] Toth G., Herman T., Szatmari G., & Pasztor L. (2016). Maps of heavy metals in the soils of the European Union and
Proposed priority for detailed asessment [5] Chojnacka K. and Saied A. (2018) . Recent Advances in Trace Elements (1st Edition). Oxford, UK: Wiley and Sons Ltd [6] Khalid S., Shahid M., Niazi N.K., Murtaza B., Bibi I, and Dumat C. (2016) A Comparison Of Technologies For
Remediation of Heavy Metal Contaminated Soils. [7] Cao, Duan, Ma, Zhao, Qin, Liu & Li (2017). Health benefit from decreasing exposure to heavy metals and metalloid after
strict pollution control measures near a typical river basin area in China. [8] Hydrology & Water Resources Research Laboratory, Kyoto University,(2018). http://hywr.kuciv.kyoto- u.ac.jp/ihp/riverCatalogue/Vol_02/08_Malaysia-2.pdf [9] Drinking Water and Surveillance Programme, (2010). Engineering Services Division, Ministry of Health Malaysia http://kmam.moh.gov.my/public-user/drinking-water-quality-standard.html [10] USEPA. (2009). National Primary Drinking Water Regulations. United States Environmental Protection Agency. EPA 816-F-09-004. [11] WHO.(2004).Guidelines for Drinking Water Quality, 3rd edition. World Health Organization,Geneva. [12] Mohiuddin, K.M., Zakir, H.M., Otomo, K., Sharmin, S., Shikazono, N. (2010). Geochemical distribution of trace metal pollutants in water and sediments of downstream of an urban river. International Journal of Environmental Science and Technology 7, 17–28. [13] Kikuchi, T., Furuichi, T., Hai, H.T., Tanaka, S. (2009). Assessment of heavy metal pollution in river water of Hanoi, Vietnam using multivariate analyses. Bulletin of Environmental Contamination and Toxicology 83, 575–582. [14] Karageorgis, A.P., Nikolaidis, N.P., Karamanos, H., Skoulikidis, N. (2003). Water and sediment quality assessment of the Axios River and its coastal environment.Continental Shelf Research 23, 1929–1944.
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Future Climate Effects on Lake Volume of Sembrong Dam using MRI-AGCM3.2s
Farah Amirah Kamis1, Nor Eliza Alias2, M. Asyraf Haiqal Baharum3 Faculty of Civil Engineering, Universiti Teknologi Malaysia, Malaysia
ABSTRACT: Sembrong lake acts as a dam and provide water supply to the people around Batu Pahat and Kluang. In the future, the population may increase as the area around Sembrong Dam is still developing. Sembrong Dam has been observed to be having water quality problems such as an increasing of algae bloom. The problems of water quality somehow effect the storage of the dam. Besides water quality, water quantity is another factor contributing to the problems. Future climate implications on the hydrological processess may effect the water quantity of the dam . The study of the changes of lake volume in the future is also limited. In order to assess the climate effect on lake volume, water balance equation is used. Water balance model is develop to analyse the changes in water storage of the dam. The data used for the water balance analysis are rainfall, evaporation, runoff, water intake and environmental flow. Runoff is calculated based on a conceptual model called WATBAL. Rainfall, evaporation and water intake data were obtained from Department of Irrigation and Drainage (DID). Analysis were conducted for present and future conditions for comparison. Present data is represented by the year 2005 to 2013 while future data is represented by the year 2079 to 2099. Future rainfall and evaporation data were obtained from a Japanese Atmospherical General Circulation Model, MRI-AGCM3.2s. The future data have been calibrated and validated using bias-correction method and error analysis. Comparison between observed and calculated lake volume and changes in lake volume shows an RMSE of 0.10 and 0.33 and R2 of 0.88 and 0.21, respectively. Future changes in the lake volume shows a decreasing trend.
Keywords: Sembrong Dam, Water balance model, Storage Estimation; Japanese MRI-AGCM.
INTRODUCTION
Sembrong Dam is located about 10 km from Air Hitam town in the state of Johor. It was completed in 1984 initially as a flood-control dam. Later, it was tapped with Syarikat Air Johor (SAJ) for water supply since then. The dam is surrounded by oil palm plantation. The dam’s catchment area is about 130 km2 with capacity of 18 million m3 and dam height of 11 m as it drained from Sg. Sembrong. Along the Sembrong River, the land activities are inclusive of industrial areas, residential areas and agricultural activities such as palm oil mill and paddy fields. After around 10 years, SAJ Holdings Sdn. Bhd. were licensed to extract 2.6 million m3 per month. However, the maximum water intake recorded in 2015 is 2.1 million m3 [1]. Hydrologic changes related to climate change may affect the water-supply demand balance[2].
Problem Statement
Climate change may impact the water storage due to the changes in precipitation, rising surface temperature and also the increasing of the evaporation. In order to ensure the water supplies continuously be available in the future, it is important to analyze the quantity of future water storage of the dam. However, using water balance model, only the changes of the lake volume may be assesed due to the limitation in estimating the initial dam volume in the future.
Objectives
The aim of this study is to assess the changes in water storage of Sembrong Dam in the future. The specific objectives that are going to be achieved by this study are:
1. To develop and validate water balance model for Sembrong Dam 2. To analyse the climate change effect towards the changes of lake volumes
Scope of Study
This study is focus on Sembrong Dam located at Johor. Investigating and determining the changes of lake volume of the dam. The global scale of MRI-AGCM used in this study is 20 km also known as MRI-AGCM3.2s. MRI-AGCM3.2s only was used to analyze the future water balance model to identify the changes of lake volume. WATBAL model (rainfall-runoff conceptual model) was used to calculate the value of runoff. Future climate data of the scenario RCP8.5 from the MRI-AGCM where considered as the worst case scenario regarding the situation today.
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LITERATURE REVIEW Sembrong Dam
Sembrong Dam is located around Batu Pahat and Kluang,Johor. This dam consists of lake area of 8.2km2. The average water depth of the dam is 9m. The size of the cathcment approximately around 130 km2. The previous study of Sembrong Dam that focus on water quality have been done by National Hydraulic Research Institute of Malaysia (NAHRIM). The condition of the lake also have been stated in The Star’s Newspaper on 17th March 2015 with the headline “Sembrong Dam ‘Slowly Dying’” where the condition of the dam may affect the future water storage of the dam. Besides water quality,water quantity of the dam might as well be affected in the future. Therefore, water balance equation is used to estimate the storage of the dam by analysing the changes of the lake volume. MRI-AGCM3.2s
Global Climate Model (GCM) is the primary source of information for constructing climate scenarios and it also provides the basis for climate change impact assessments at all scales. Basically, the models will give the output based on present, near future and future data. MRI-AGCM3.2s take the time series of present from 1979-2003 due to the release of carbon dioxide is high during that time from the industrial expansion. Future projections range from 2079-2099 to represent the end of the century. The present and future climates data were simulated by using the observed sea surface temperature (SST). The SST projected by Atmosphere–Ocean coupled models is used as the lower boundary condition for the MRI-AGCM3.2s. The Atmosphere-Ocean coupled models which are known as Couple Global Climate Models (CGCM) are under Climate Model Intercomparison Project Phase 5 (CMIP5) where this project is conducted by Intergovernmental Panel on Climate Change (IPCC). The Meteorological Research Institute (MRI) of Japan has developed one GCM model called the MRI-AGCM with a global scale of 60 km (MRI-AGCM3.2H) and 20 km (MRI-AGCM3.2S) resolution. A downscale of the 20 km MRI-AGCM is developed under the SOUSEI Program which downscale the GCM to Regional Climate Model (RCM) up to 5km and only in Japan.
(a) (b)
Figure 1: (a) Regional Models (b) Single Model Ensemble
Figure 1 shows the Single Model Ensemble from the AGCM. The scenario used for the future climate data is RCP8.5. From the scenario, the condition of the world today is in the worst case scenario (4 degree changes around the world). [6] Water Balance Model
The study of the water balance structure of lakes forms a basis for the hydrological substantiation of projects for the rational use, control and redistribution of water resources in time and space. Knowledge of the water balance assists the prediction of the consequences of artificial changes in the regime of streams, lakes, and ground-water basins [5] METHODOLOGY
This research consisted of two key activities; development of Water Balance Models and prediction of future water storage changes of the dam. Table 1 explains the sources of data used in the Water Balance Model. The description of the landuse data used in the study is shown in Table 2.
Table 1: Data and Lake Description
Type of data Present (1983-2013) Future (2079-2099) Precipitation JPS MRI-AGCM Evaporation JPS MRI-AGCM Landuse Palm oil , kc = 1
Lake area 8.2 km2 8.2 km2 Catchment Area 130 km2 130 km2
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Table 2: Landuse description
Landuse Types Present (1983-2013) Future (2079-2099) Palm oil 96.4% of total landuse
area 100% of total landuse area
Rubbber Tree 3.4% of total landuse area 0% of total landuse area Grass 0.2% of total landuse area 0% of total landuse area
Development of Water Balance Models Water balance model The water balance model estimates the changes in lake volume. Equation (1) is used to develop the model. The changes in storage is calculated using components of, Rainfall, Evaporation, Water Intake from SAJ, as well as the Environmental flow. Comparison between calculated changes in lake volume against observed data was conducted. The relationship between lake volume and lake levels is obtained from Equation (2). Relationship between lake level in meter against lake volume in meter cube uses the Polynomial Regression as sown in the Sembrong Dam Stage-storage curve (Figure 4).
(Equation 2) Where, MCM = Lake volume ,(million cubic metre) WL = Water lake level, (m)
Figure 1: Polynomial Regression for Sembrong Dam Stage-Storage Curve
Data of rainfall from unit mm need to convert to m before multiplying with area of lake which is 8.2 km2. The Evaporation components also need to change the unit and multiply with 0.9 which is the pan coefficient of open water before multiplying with area of lake. This is because all of the components in the water balance model should be in the unit of volume. Present Climate Data
Precipitation
Precipitation is a major contribution to the runoff so the value of precipitation is important to be included inside the Water Balance Model. Only one station is used in this study.the details and location of the station is shown in Table 3 and Figure 3.
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Table 3: Details of Precipitation data
No.
River Name Name of Station Station Number
1 Sg. Simpng Kiri Empangan Sg. Sembrong di Air Hitam 1931003
Figure 3: location of Precipitation station
Evaporation and Evapotranspiration
To estimates the rate of evaporation from a lake surface, the recorded pan evaporation (Epan) is multiplied by a pan coefficient of 0.9. The estimation of the evapotransipration of the Sembrong catchment is based on the landuse. The landuse area of Sembrong Dam’s catchment is 98.2km2. The catchment consists of 3 types of landuse which are palm oil plantation, rubber tress plantation as well as grass around the catchment. In order to calculate the value of evapotranspiration, the value of the coefficients of crop, kc(all) need to be obtained. This study combining the calculation of the 3 types of plantation consists in the cathcment. The formula to calculate kc(all) as stated in Equation (3).
+4KLL = [NO+4O + NG+4G + NP+4P]
NO + NG + NP
(Equation 3) Where, A1 , A2 , A3 = Area of the 3 different landuse,kc1, kc2, kc3 = coefficient of the 3 plantation. For palm oil and rubber trees, the value of kc is 1, while the grass kc value is 0.75. The value of kc is considering the crop in the mid season condition which is the average before and after cutting known as kcmid. Then, the value of evapotranspiration is calculated using Equation (4).[10]
8RS = +4KLL×85#$ (Equation 4)
Figure 2: Evaporation station details and location
Water Intake and Environmental Flow The water intake by SAJ with the value of 2.1 million m3 is obtained from DID where the maximum number recorded on March, 2015. As for the environmental flow, it is been recorded that the value of 1.2m3/s is continuously release from the dam.[1] Inflow or excess using WATBAL model The calculation of the excess is defined as the amount of water in excess of the maximum soil water storage. Due to the unknown value of the water seepage into the soil due to lack information on the soil profile, the WATBAL model is used by placing the
Station 1931003 location
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maximum value of soil water storage as 300 mm. The excess value is known as runoff from the catchment and inflow to the lake. WATBAL model is a rainfall-runoff conceptul model which uses the water balance algorithm that can be written as:
Where, STi+1 is the soil water storage with the daily time step, Pi is the daily precipitation, AETi is the actual evapotranspiration, STmax is the maximum estimation of soil water storage, and EXCESSi is the excess amount of the soil water storage. All this components are in mm unit. The model calculates the water required during the time step and the excess water at the end of each time step. The excess cumulated is defined as the amount of water in excess of the maximum soil water storage. The model may be overestimate the actual runoff into lake due to the unknown amount of the deep drainage area of the Sembrong Catchment.The WATBAL model requires weekly values of precipitation and potential evaporation (PET) as input data.[7]. However, for the study, daily data was used. Result on validation of future data The future data of precipitation and evaporation was calibrated and validated using a bias-correction method. Table 4 shows the error analysis of the precipitation and evaporation data that have been validated and corrected.The callibration of the data uses data in year 1983-1992 while the validation of the data uses data in year 1993-2003. [11] Table 4: Validation of Bias Corrected precipitation, evaporation and temperature using RMSE and R2 at Sembrong Dam, Kluang.
Method RMSE R2 Precipitation
(m) Evaporation
(m) Precipitation
(m) Evaporation
(m) 1 2 3 4
0.40 0.61 0.39 0.52
0.06 0.09 0.07 0.08
0.87 0.73 0.87 0.80
1.00 0.95 1.00 0.98
Optimization method Results of calculated and observed lake volume is plotted against the time. Comparison of the lines shows wether the calculated volume fits the observed. In order to improve the calculated values Optimization was used. suitable coefficients for each components in the Water Balance equation were identified so that both line achieved the best correlation and least root mean square error (RMSE). A good correlation and RMSE is when the value of correlation is near to 1 while the RMSE is near to zero. Water Intake and Environmental Flow components’ coefficients were set to 1 while try and error of the coefficients for the other 3 components were conducted for the optimization. The equation for Optimisation method is as follows:
U6-3#%&])^^&3&$4& = #2 + _` − 48 − ]<= − &8> (Equation 7) Where, a = coefficient for Precipitation (0 < a < 1), b = coefficient for Runoff (0 < b < 1), c = coefficient for Evaporation (0 < c < 1), d = coefficient for Water Intake (d = 1), e = coefficient for Environmental Flow (e = 1).
RESULT AND DISCUSSION Calibration data Present Water Balance Models
The water balance model is use to estimate the changes in lake volume. Figure 5 shows the lake volume of observed and calculated against the time. The results of the calculated lake volume have been optimized using the optimizsation method. Figure 6 shows the correlation between the calculated lake volume against the observed. The correlation between calculated and observed lake volume is 0.88. This positive correlation signifies that boths variables are correlated. The nearest value to 1 means the stronger the linear relationship. RMSE between observe and calculated lake volume is 0.10. While RMSE of changes in lake volume is 0.33. The components have been optimised the coefficient value for precipitation, excess and evaporation are a=1, b=0.3 and c=0.4 respectively.
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Figure 5: Lake volume of observe and calculated time series
Figure 6: Correlation of Observed against Calculated Lake Volume
Water Balance Analysis based on Climate Data Analysis without Environmental Flow and Water Intake
The analysis considers the water balance without including the value of environmental flow and water intake inside the models. This analysis is being carried out to observe the pattern of the water balance model with only considering the climate components, which are precipitation, excess from the catchment into the lake and evaporation. The results of the equation has been indicated in Figure 7 (a) present with year of 2005-2013 and (b) future with the year 2079-2099. From Figure 7, value of present rainfall is lower than the future while the future rainfall value shows flactuated pattern. For the evaporation value, the present and future were almost in a similar trend. For the excess value, present and future shows a significant difference because in the future there is a high value of excess around the year 2097 before it decrease to near zero.
(a)
(b)
Figure 3: Components of Water Balance Model (a) Present (year 2003 to 2015), (b) Future (year 2079 to 2099)
The analysis of the lake volume is indicated in Figure 8, (a) changes in lake volume considering all the components and (b) changes of lake volume consider only climate components. In order to balance the graph of present and future data, time range of the future data was taken from 2079-2096 only. In the condition (a), the value of future changes in lake volume is lower that the present value. While for condition (b), the pattern of future changes in lake volume almost the same with the present and above the zeroes value. For both condition, it shows that the future changes in lake volume in condition (b) is higher than in condition (a). The changes of trend between future and present in both condition is because the consideration of water intake and environmental flow in the water balance model. Where both of these components are the output value for the water balance model. When both of the components are taking out, is seems that the trend of the model is increasing.
(a)
(b)
Figure 8: Changes in lake volume (a) considering all components (b) considering only climate components
CONCLUSION
Based on the Water Balance Model, the trend of the lake volume for present and future water balance both shows a decreasing trend. This may be due to the decreasing value of precipitation and the increasing evaporation. Impact assessment of climate change around Asia has shown that in the future, approximately 15% of annual evaporation is increase.[12] Changes in lake volume between observed and calculated shows similar trend, however, the scale of the changes could not be modelled perfectly. This might be due to the limitation in estimating the inflow from Sembrong catchment to the lake. Better estimation might be obtained using physical Rainfall-Runoff model. The effect of climate changes does not effect the lake volume. The future changes in lake volume is lower value compared to present changes.When value of water intake and environmental flow is not considered, the changes in lake volume is higher. Since the variability of the changes in lake volume small, the changes in lake volume for present and future considered to not having any significance difference. Here can be concluded that in the future, climate change did not really effect the lake volume and the condition of the dam in the future might not be critical.
REFERENCE
[1] Institut Penyelidikan Hidraulik Kebangsaan Malaysia (NAHRIM). (2016). Integrated Lake Basin Management Plan (ILBMP): Sembrong Lakw, Johor.Vol.1
[2] Nima Ehsani et al. (2017). Reservoir Operations Under Climate Change: Storage Capacity Options to Mitigate Risk. [3] Benjamin, N. (2015, March 17). Sembrong Dam ‘slowly dying’. The Star. [4] Marina M. et al.(2014). Hydrodynamic Modelling Flood Mapping of Sembrong Catchment Area.13th International
Conference on Urban Drainage, Sarawak, Malaysia. [5] Sokolov & Chapman (1974). UNESCO : Methods for water balance computations
-2000.00
-1000.00 0.00
1000.00
2000.00
3000.00
4000.00
Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan JulChangesinLakeVolum
[6] Taichu Y. T. (2011). Technical Reports of the Meteorological Research Institute No.64. [7] Crapper P.F. et al.(1996). Prediction of lake levels. Environmental Software, Vol. 11. No.4, pp.251-258 [8] Scarf F. (1976). Evaporation in Peninsular Malaysia. Water Resources Publication No.5 [9] James Nelson E. (2009). Journal of Environmental Hydrology. Vol.17 [10] Allen R.G. et al. (1998). Crop Evapotranspiration. Guidelines for computing crop water requirements FAO Irrigation
and Drainage Paper 56. [11] Baharum et al. (2018). Analysis of Future Precipitation, Evaporation and Temperature at Sembrong Dam using MRI-
AGCM 3.2s Climate Model. [12] Helfer et. al. (2012). Impacts of climate change on temperature and evaporation from a large reservoir in Australia.
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Water Quality Assessment at UTM Streams Nur Ashikin Yahya1, Muzaffar Zainal Abideen1*
1School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia *[email protected]
ABSTRACT. A water quality study was carried out at Universiti Teknologi Malaysia Johor Bahru (UTMJB) streams. The procedure was carried out over eight sampling stations. The objective of the study was to analyze the water quality for all stations. Water quality parameters such as pH, temperature and dissolved oxygen (DO) were measured using YSI Proplus Water Checker. Biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal nitrogen and total suspended solids (TSS) were analyzed at the environmental engineering laboratory, UTMJB. Results of the analyses were utilized to generate the Water Quality Index (WQI). Activities occurred at the surroundings of the river are the main factor of stream pollution in UTMJB. From this study, the results showed that most of the streams were classified as Class II and III. The water quality status at one sampling station was identified as clean while water at other stations was considered as polluted. As a conclusion, the water quality of the selected streams in UTMJB can be classified as moderately polluted and may harm the aquatic life. In addition, the effects of uncontrolled anthropogenic activities can deteriorate the future of water quality
Keywords: Water Quality Index (WQI), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Suspended Solids (TSS), Ammoniacal Nitrogen (AN), pH.
INTRODUCTION
Rivers are the main water resources for the Malaysian community. In modern era, people are still relying on river as their water resource for their water supply, agricultural activities, fisheries and others. Nonetheless, rivers are usually contaminated either directly or indirectly with human, animal, industrial and other wastes. Sometimes, heavy metal waste is dumped into the river and river is used as a dumping area legally or illegally. Waste such as surface runoff from urban area, rainfall precipitation and factory waste may be taken by aquatic life such as fish, plankton, microorganisms and others. Although, some organisms such as bacteria consume waste as their nutrients but a large number of aquatic life can eventually perish due to increase generation of waste. Therefore, it is important to investigate the effects and cause of pollution by conducting a study to evaluate the water quality in the study area.
This study was conducted to determine the water quality of streams in UTMJB in which is located at suburban setting of medium-sized city of Johor Bahru [1]. Problem Statement
The number of population at UTMJB increases year by year and this may adversely affect the water quality of river. The increasing number of population led to a new development area that causes waste disposal increases every year. UTMJB streams receives direct and indirect effluent discharged continuously due to human activity such as sewage from residential college, food waste from cafeteria, fertilizers and pesticide, oil spills from vehicles. Therefore, it is crucial to investigate the conditions of UTMJB streams based on the measurement of water quality parameters and classification of the streams.
Objectives The main purpose of the study is to identify the water quality level at UTMJB streams. The following were the objectives of this study:
1. To determine the water quality parameters of UTMJB streams; 2. To determine the water quality index (WQI) of UTMJB streams.
Scope of Study
The study was carried out along the streams in UTMJB campus. Samples were taken by using a grab sampling method as this method is more suitable to be conducted at UTMJB streams, as the streams are not too wide and deep. In addition, the in-situ water quality parameters analyzed were the temperature, pH, and DO. While at the environmental engineering laboratory UTMJB, the parameters analyzed were BOD, COD, ammoniacal nitrogen and TSS. These parameters were then be used to compute the Water Quality Index (WQI).
LITERATURE REVIEW
River water quality can be determined by taking samples at the studied river. The water quality parameter at some points of the river has to be investigated in order to determine the river quality.. Grab sampling method was carried out at a not too deep and large river [2].
Generally, river pollution is caused by hazardous material produced by men and discharged into water. The presence of pollutants in water is unavoidable. Nonetheless, it should not exceed the permissible level. Through water analysis, amount and source of contaminants that present in the river will be obtained. In Malaysia, river pollution can occur as a result of disposal of sewage from urban areas [3].
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The cause of water pollution occurred in a river is attributed to two main sources which is point source and non-point source [4]. The cause of point source refers to pollution sources that are simple and can be detected with the naked eyes [5]. Waste dumped is an example of a point source pollution and can cause bad smell water and unpleasant view. Meanwhile, the cause of non-point source is from an agent or pollutants originally unknown and undetectable that flows into the river [6]. Contaminated materials can also be carried by surface runoff into rivers or through groundwater. In addition, river pollution will damage the ecosystem of the river and it requires enormous effort to rectify the river pollution problem. Parametric Study The Water Quality Index (WQI) is a standard used to measure water quality, especially river used as a domestic supply and aquaculture. It facilitates the process of analyzing water quality especially when involving large scale. In the process of calculating the WQI, there are several important parameters measured as elements of water quality. Six parameters of WQI which is the DO, BOD, COD, TSS, AN and pH which is considered the overall status assessment of water. Each of the parameter has certain value ranges for each class of water quality. Formulas for WQI are shown in Equation 1.
Where, WQI : Water Quality Index SIDO : Sub-index for Dissolved Oxygen SIBOD : Sub-index for Biochemical Oxygen Demand SICOD : Sub-index for Chemical Oxygen Demand SIAN : Sub-index for Ammoniacal Nitrogen SISS : Sub-index for Total Suspended Solids SIpH : Sub-index for pH
SI is a sub-index for those parameter based on the results obtained used to calculate the WQI as shown in Table 1. This index
serves to identify the type of main pollution of the river by referring to the percentage content of impurities that are analyzed as in Table 2. Basically, the WQI is not sufficient to determine that the river water is clean or not. Thus, the river is classified according to the standards of National Water Quality Standards of Malaysia (NWQS) which has 25 identifiable parameters include parameters studied, namely DO, BOD, COD, TSS, AN, and pH.
Table 3 and Table 4 shows the classification of water used in Malaysia based on and use either water used in Malaysia based on class and whether river water quality suitable for use or otherwise for the purpose of a day.
Table 1: Sub-index for calculation of WQI [7]
*Value of X is a concentration of parameter in mg/L except for pH and DO.
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Table 2: National Water Quality Standards of Malaysia (NWQS) [7]
Parameter Unit Class I Class II Class III Class IV Class V
Chemical Oxygen Demand (COD) mg/L <10 10-25 25-50 50-100 >100
Dissolved Oxygen (DO) mg/L <7 5-7 3-5 1-3 <1
pH - >7 6-7 5-6 <5 >5
Total Suspended Solids (TSS) mg/L <25 25-50 50-150 150-300 >300
Water Quality Index (WQI) - <92.5 76.5-92.7 51.9-76.5 31.0-51.9 >31.0
Table 3: Classification of Water Quality based on WQI [7]
Sub-index and WQI Average of Index
Clean Less Polluted Polluted
Biochemical Oxygen Demand (BOD) 91-100 80-90 0-79
Ammoniacal Nitrogen (AN) 92-100 71-91 0-70
Total Suspended Solids (TSS) 76-100 70-75 0-69
Water Quality Index (WQI) 81-100 60-80 0-59
Table 4: Table of WQI [7]
Water Quality Index (WQI) Classification of River
More than 92.7 I
80.0 - 92.7 II
51.9 – 80.0 III
23.0 - 51.9 IV
Less than 31 V
Biochemical Oxygen Demand (BOD). BOD measures the quantity of dissolved oxygen used by the microorganisms during the oxidation process of organic material in water. It is a basic measurement to determine the water quality and identify the level of water pollution by organic pollutant. The allowed amount of BOD in river is 20 mg/L and 50mg/L [7]. Lack of dissolved oxygen is leading to a death of aquatic life and this is because of dangerous organic material was released into the water. The value of BOD is influenced by three factors that are temperature, time, and quantity of light [8]. This measurement of 5 days BOD test (BOD5) to determine the content of organic matter in the water and wastewater [9]. From the BOD5 test, the value of dissolved oxygen in the water or wastewater samples are obtained after five days. The higher amount of oxygen used by microorganisms to decompose organic matter in the water, the higher the value of BOD. Chemical Oxygen Demand (COD). It is defined as the measurement of oxygen needed for the chemical process of oxidation and organic material in the water. It is an important water quality parameter because it can provide an index to know the level of pollution that has been found into the water. A high amount of COD will cause amount of DO to reduce which because there is an excessive oxidation of organic material in the water sample. Reduction amount of DO in water could change to anaerobic process, which can destroy an aquatic life [10]. Dissolved Oxygen (DO). DO is a non-compound oxygen that exists in the water and it is one of the important parameters that affecting aquatic life in water. Every species on Earth need oxygen and water and amount of DO can affect to aquatic life and water quality. In this context, the river is a place of aquatic life obtained sufficient amount of dissolved oxygen.
There are several factors that may affect the DO in water such as flow of river, temperature and also organic pollution in the river [10]. The value of the temperature must not exceed 40qC because it can increase the metabolic levels of microorganisms and demand of dissolved oxygen in water [11]. Total Suspended Solids (TSS). The TSS is very important to determine the value of water quality by measuring the total suspended solids in the water sample. The excess amount of suspended solids in river will produce unpleasant smell, thus categorized it as a polluted river.
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In addition, TSS contain a higher amount of bacteria and microorganisms, which will hinder the sunlight to penetrate the river. Thus, it will reduce the ability of organisms to seek food and demonstrate its provisions pesticide as well as increase the water turbidity. Furthermore, the sources of TSS are water surface run-off, soil erosion, algae growth and the discharged of wastewater. pH. This parameter measured a solution of alkalinity and acidity. The increasing amount of pH (alkaline) is due to high concentration of ammonia, which makes the water to become more toxic and poisonous [10]. Pure water has a pH of 7 that is neutral while pH less than 7 is acid. Organisms can live well on a pH level between 6.0 – 8.5 and it is very sensitive to changes of pH because these changes will affect the process of breeding [12]. In this context, pH value that less than 4, may cause acidity and may kill aquatic life and erosion of rocks in water [10].
Meanwhile, the pH value that is greater than 9 or less than 5, it is not suitable for aquatic life to live in the river and thus affect aquatic organisms.
Ammoniacal Nitrogen (AN). Nitrogen is a nutrient that is essential which plants and animals required it to form an amino acid [13]. Most nitrates come from the decomposition of die plants and animals carried out by bacteria. Ammonia is a colourless gas but has a strong odour and it will interact with water to form a weak basis [13]. The excessive amount of ammonia in water would lead to water quality problems and can harm aquatic life [14].
METHODOLOGY
Water Sampling Water samples were taken from eight (8) stations that have been identified. The information such as the date, weather, number of the station and the coordinates of the station was taken on date of sample. Figure 1 shows the locations of sampling was carried out. This study use grab sampling method in order to facilitate the procedure of taking water samples. In-situ test was carried out at the station to determine the parameters of the study that is water temperature, DO and pH by using YSI Proplus Water Checker. While the parameters that analyzed in the laboratory are the COD, AN, BOD and TSS.
Figure 1: Location of sampling Laboratory Test Biochemical Oxygen Demand (BOD). A 100 mL of water sample and 200 mL BOD reagent are mixed together and the pH value must be ensured to be in range of 6.5 to 7.5. The reading of initial DO [DOi] was taken for each samples and placed in the incubator at temperature of 20qC for 5 days. After 5 days of incubation, the final reading (DO5) was measured to further calculate the value of BOD5. Chemical Oxygen Demand (COD). This experiment was carried out by mixing 3mL of COD reagent and 2mL of water sample. Silver sulphate (Ag2SO4), mercury (II) sulphate (HgSO4) was added into each samples. The Ag2SO4 act as a catalyst and HgSO4
1
2
3 4
5
6
7
8
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react with chloride, bromide, and iodide to produce precipitates that are oxidized only partially [15]. Samples were heated simultaneously at 150qC for 2 hours and the results was obtained after cooling by using HACH DR6000 Spectrometer Total Suspended Solids (TSS). A glass microfiber filter paper (0.05Pm) and aluminium plate was weighed and 200mL of water sample was vacuumed in order to filter the suspended solids. The filtration paper was placed in oven at temperature 103 to 105qC for one day and then weighed again to get the weight of total suspended solids each station. Ammoniacal Nitrogen (AN). This test was carried out by using the same apparatus to analyze COD. A 25mL water sample and distilled water acted as control samples were mixed with three drops of mineral stabilizers into each measuring cylinder. Then, three drops of polyvinyl alcohol and 1mL Nessler reagent were mixed well before the concentration of AN is determined by using HACH DR6000 Spectrophotometer.
RESULTS AND DISCUSSION
Table 5 shows the results of each parameter while Table 6 shows the results of WQI for each station of sampling. Among all stations, Station 4 had the lowest temperature (27.2qC). This is not surprising as Station 4 is surrounded by trees. Station 2 had the highest temperature (29.5qC) and this may due to the position of the station, located at an open space. From Table 5, the pH values at every station were around 7.0, indicated that the river was in neutral condition. The lowest DO concentration recorded was 2.62mg/L at Station 6, located at Cengal café. Lowest DO at Station 6 might due to discharge of pollutants from café, creating an oxygen demand that depresses the DO concentration in the river. The effluent discharged from cafe often contains organic compounds that are decomposed by microorganisms which utilizes oxygen in the process. On the other hand, the highest DO concentration at Station 8 (3.51mg/L) might be due to less discharge of pollutants from surroundings. For the record, Station 8 is quite far from residential college, faculties, cafés and others. BOD level at Station 4 was the lowest at 1.0mg/L while the highest BOD level observed at Station 1 (3.7mg/L).
Meanwhile the highest and lowest COD values were recorded to be 177mg/L and 6mg/L at Station 7 and Station 8 respectively. Station 7 is located nearby the recreational place and running track and it is perceived that these activities contributed non-point as well as point pollution sources to Station 7 hence contributed to high COD values. AN concentration recorded at Station 8 was the lowest at 0.2mg/L and Station 6 had the highest AN concentration of 2.1mg/L. The TSS are particles larger than 2 microns found in the water body. Anything smaller than 2 microns are considered as dissolved solids. From Table 5, it is noted that Station 1 had the highest value of TSS of 4.3mg/L while Station 7 has the lowest value of TSS of 0.4mg/L. High value of TSS at Station 1 might be due to soil erosion observed at the station.
Table 5: Values of each parameter at each station of sampling
PARAMETER STATION 1 2 3 4 5 6 7 8
Temperature (qC) 28.3 29.5 29.3 27.2 27.9 28.4 29.1 28.7
The WQI is a compilation of a number of parameters that used to determine the overall quality of river. WQI was calculated for
each sampling station and the result was given in Table 6. The highest value of WQI was 81.69 and the lowest value was 58.70 and they were obtained at Station 8 and Station 6, respectively. The water quality at Station 8 was classified as Class II, which is clean while Station 6 fell under Class 3 indicating that the water is polluted. From Table 6, it can be seen that except for Station 8, all stations falls under Class III, which indicated that the UTMJB streams were polluted and must be treated.
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CONCLUSION AND RECOMMENDATION
As a conclusion, the water quality of the streams in UTMJB can be classified as moderately polluted and may harm the aquatic life. The WQIs of these streams were in the range of 58.70 to 81.69. In the future, it is recommended that a thorough study on the point and non-point source pollution of UTMJB streams must be carried out. A simulation study to determine the sources of pollution of UTMJB streams may also implemented.
Table 6: Classification of WQI for each station of sampling
Station 1 2 3 4 5 6 7 8
Water Quality Index 65.84 65.25 77.11 64.28 62.14 58.70 67.27 81.69
Classification of River III III III III III III III II
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