1 23

Arabian Journal of Geosciences ISSN 1866-7511 Arab J GeosciDOI 10.1007/s12517-014-1539-z

Geochemical modeling to evaluate themangrove forest water

Ram Pravesh Kumar, Rajesh KumarRanjan, Ramanathan AL, Sudhir KumarSingh & Prashant K. Srivastava

1 23

Your article is protected by copyright and

all rights are held exclusively by Saudi

Society for Geosciences. This e-offprint is

for personal use only and shall not be self-

archived in electronic repositories. If you wish

to self-archive your article, please use the

accepted manuscript version for posting on

your own website. You may further deposit

the accepted manuscript version in any

repository, provided it is only made publicly

available 12 months after official publication

or later and provided acknowledgement is

given to the original source of publication

and a link is inserted to the published article

on Springer's website. The link must be

accompanied by the following text: "The final

publication is available at link.springer.com”.

ORIGINAL PAPER

Geochemical modeling to evaluate the mangrove forest water

Ram Pravesh Kumar & Rajesh Kumar Ranjan &

Ramanathan AL & Sudhir Kumar Singh &

Prashant K. Srivastava

Received: 5 August 2013 /Accepted: 4 July 2014# Saudi Society for Geosciences 2014

Abstract The knowledge about genetic origin of the chemi-cal elements is important for the evaluation of hydro-geochemistry of aquatic ecosystem. In the present study, pre-and post-monsoon samples were collected to identify the roleof rain and seawater in the hydro-geochemical processes.Geochemical model and multivariate statistical methods ofdata analysis were jointly used to define the variations andthe genetic origin of chemical parameters of water in man-grove ecosystem. The geochemical model, WATEQ4F, wasexecuted to compute the saturation indices of the mineralswith respect to surface water. The interpretation of the satura-tion indices for minerals shows that the majority of samplesfall in the category of under saturation state except for fluorite.An increase in the concentration of various nutrients, namely,nitrate and phosphate, was observed. Suitability of water waschecked on the basis of chemical categorisation by Aquachemsoftware. Grouping of waters on the Piper diagram suggesteda common composition and origins. Further results showedthat pre- and post-monsoon samples mainly consist of Na–Cland Ca–Cl water type indicating a significant contribution of

cations and anions from terrestrial and marine inputs in themangrove ecosystem.

Keywords Mangrove .Geochemicalmodeling .Multivariatestatistical techniques . Cluster analysis . Land use and landcover . Piper diagram

Introduction

Good quality of water is life-sustaining compound, which isrequired for all forms of lives. Many researchers in the pasthad studied and reported the impact of change in land use atthe terrestrial environment that leads to affect the environmentin general and coastal environment, too (Van Noordwijk et al.2004; Gorman et al. 2009; Deepika et al. 2014). The naturalprocesses and anthropogenic activities that influence the waterquality are now current areas of research in hydro-geochemistry (Simeonov et al. 2003; Panda et al. 2006;Zhang et al. 2007; Ibrahimi et al. 2013). Pichavaram man-grove has been studied in detail by various researchers fordifferent reasons like its plant communities, bacterial abun-dance (Blasco et al. 1975; Govindasamy and Kannan 1991;Jagtap et al. 1993; Dious and Kasinathan 1994; Kathiresanet al. 1994), and apprisal of land use/land cover (Singh et al.2014). The adjoining Coleroon and Vellar estuarine systemshave received much attention with regard to their geochemis-try (Seralathan and Seetharamasamy 1987; Ramanathan et al.1988). Mangroves are the woody community of tropical andsubtropical regions. They are the second highest source ofprimary production next to rainforests (Subramanian andVannucci 2004). These important intertidal estuarine wetlandsare exposed to anthropogenic contamination from tidal water,river water, and land-based sources (Klekowski et al. 1994;Kathiresan 2000; Prasad and Ramanathan 2010; Adamu et al.2013; Mujabar and Chandrasekar 2013; Deepika et al. 2014),

R. P. Kumar (*) : R. K. Ranjan :R. ALSchool of Environmental Science, Jawaharlal Nehru University,New Delhi 110067, Indiae-mail: praveshjnu@gmail.com

R. K. RanjanSchool of Earth, Biological and Environmental Sciences,Central University of Bihar, Patna 800014, India

S. K. SinghK. Banerjee Centre of Atmospheric and Ocean Studies, IIDS,Nehru Science Centre, University of Allahabad, Allahabad 211002,India

P. K. SrivastavaWater and Environment Management Research Centre,Department of Civil Engineering University of Bristol,Bristol BS8 1TR, UK

Arab J GeosciDOI 10.1007/s12517-014-1539-z

Author's personal copy

climate change impact (Srivastava et al. 2014), as well as thenatural calamities (Prasad et al. 2006; Seralathan et al. 2006;Ranjan et al. 2008c, d) very frequently.

The mangrove forests have been shown to play an impor-tant role in the hydro-geochemistry in tropical coastal areas,either as sources or as sinks for contaminants (Prasad andRamanathan 2009). Behavior of hydro-geochemistry in man-grove ecosystems is highly dependent on the physico-chemical conditions of mangrove sediments and pore waters.Hydro-geochemical cycle of mangrove environments vary inboth time and space due to variations in hydrodynamics,freshwater input, light, and phytoplankton productivity(Alongi 1996; Alongi et al. 2005; Deepika et al. 2014). Theextent of such variations will also depend upon tidal ampli-tude, although mangrove waters are characterized by lownutrient concentrations due to a high capacity for retainingand recycling of nutrients within the ecosystem. Generally,mangrove water is alkaline in nature without any systematicspatial variation irrespective of seasons. Considering the im-portance of physico-chemical parameters on the productivitypotential of coastal waters, numerous studies have been madein coastal waters of east coast of India to evaluate theirseasonal and spatial behavior (Bava and Seralathan 1999;Bouillon et al. 2003; Kumar et al. 2010; Ranjan et al. 2011).

Satellite remote sensing has the potential to provide accu-rate and timely geospatial information describing changes inland cover and land use (LULC) (Herold et al. 2002; Yuanet al. 2005; Singh et al. 2011; Misra et al. 2013). LULC studyis important for the near real-time decision making and poli-cies formulation to optimizing the use of natural resourcessuch as water and soil (Srivastava et al. 2010;Srivastava et al. 2011). Therefore, it can be used tounderstand the utilization pattern and assessment of thearea. Change detection generally employs post-classification comparison (Dennis and Colfer 2006;Dewidar 2004), it compares two or more separatelyclassified images of different dates (Shalaby andTateishi 2007). It is considered to be one of the mostappropriate and commonly used methods for changedetection (Lillesand et al. 2004). Hence, the aim of thispaper is to focus on an integrated approach of hydro-geochemical modeling by studying the changes in waterquality and its status in terms of saturation index com-puted through WATEQ4F and to develop a long-termpragmatic management strategy for monitoring and iden-tifying the variability of ions and types of water byPiper diagram.

Study area

Pichavaram mangrove (latitude, 11°23′–11°30′ N, and longi-tude, 79°45′–79°50′ E) is located between the Vellar and

Coleroon estuaries in Cuddalore district, Tamil Nadu, south-eastern India, and has direct opening to the Bay of Bengal atChinnavaikkal (Fig. 1). The total area is around 21 km2. Thismangrove forest has 13 mangrove species. The dominantspecies are Rhizophora spp., Avicennia marina, Excoecariaagallocha, Bruguiera cylindrica, Lumnitzera racemosa,Ceriops decandra, and Aegiceras corniculatum. In this area,22 species of seaweeds and three species of sea grasses havebeen identified in the mangroves (Kathiresan 2000). Unfortu-nately, Pichavaram is one of the best examples of a degradingmangrove ecosystem, and the forests lost over 75 % of theirgreen cover within the last century (Kathiresan 2002). Thearea of the mangrove forest is 11 km2, 50 % covered byforest, 40 % by waterways and rest filled by mud andsand flats. It has 51 islets ranging in size from 10 m2 to20 km2 separated by intricate waterways that connectsthe Vellar and Coleroon estuaries. The southern part ofthe mangrove forest is dominated by mangrove vegeta-tion, while northern part near the Vellar estuary isdominated by mud flats (Krishnamurthy and Jeyaseelan1983; Kathiresan 2000). Tides are semi-diurnal and varyin amplitude from 0.15 to 1 m (Kathiresan 2000). Tidalwater enters the Pichavaram mangrove through a smalld i rec t connect ion with the Bay of Bengal a tChinnavaikal and estuarine water finds its way throughthe two adjacent river systems. Alluvium soil is domi-nant in the western part, and fluvial sediment and beachsand cover eastern part of the mangrove. Major area iscovered by floodplain, sedimentary plain, and beachsand. Climate is tropical in nature with temperatureranging from 29 to 36 °C in summer and 18.2–25 °Cin winter, and the ratio of precipitation to evapo-transpiration(P/Etp) ranges from 0.5 to 0.75 (Selvam 2003) with maximumprecipitation all through the northeast monsoons. Annualrainfall is around 1,463.9 mm. The biogeochemical processesin this ecosystem are governed by a heavy input of sedimentsand anthropogenic discharges from the Vellar andColeroon rivers. Uppanar River and Khan Saheb Canalalso contribute discharge during monsoon season(Fig. 1; Ramanathan et al. 1999).

Materials and methods

Total 17 water samples were collected in pre-monsoonand 17 water samples were collected in post-monsoonduring the full moon and new moon day at the time ofhigh tide period from March 2006 to June 2007 atdifferent locations covering the entire Pichavaram man-grove and adjoining estuarine complex. The samplinglocations were carefully decided and fixed by GPS(Garmin Model 780). The pre- and post-monsoon sam-ples were collected from the same locations and during

Arab J Geosci

Author's personal copy

same terrestrial and climatic conditions. The physicalproperties like electrical conductivity (EC), temperature,pH, and Eh were measured in the field using thermo-Orion electrodes. The samples collected from each lo-cation were filtered using 0.45-μm Millipore membranefilters. The samples collected for major ions analysiswere first acidified using 1 % HNO3 to stabilize tracemetals, while samples collected for nitrate were acidi-fied with HBO3 acid. Samples were brought to labora-tory and stored at below 4 °C. The bicarbonate contentwas determined following the potentiometric titration,whereas chloride content was determined by titrationmethods as prescribed by APHA (1985). Nitrate in themangrove water was measured by Brucine–MBTH

method (Nagaraja et al. 2003). Ammonium was deter-mined calorimetrically (JENWAY UV/VIS spectropho-tometer) adopting the APHA (1985) prescribed bySolorzano (1969) and Presley(1971) and flame photom-eter was adopted for identification sodium, potassium,and calcium. Magnesium and iron as heavy metal weremeasured using atomic absorption spectrometer(SHIMADZU AA-6300) and lower detection limit were0.03 mg/L, while anions like phosphate, bromine, andfluoride were analyzed using ion chromatography andlower detection limit was 0.01 mg/L. All concentrationsexcept pH and EC are expressed in mg/L. Analyticalprecision for measurements of cations and anions, indi-cated by the ionic balance error (IBE) was computed on

Fig. 1 Location of study area,showing sampling sites locationsof Pichavarm Mangrove, TamilNadu, India

Arab J Geosci

Author's personal copy

the basis of ions expressed in mel−1. The value of IBEwas observed to be within a limit of ±5 % (Mandel andShiftan 1980; Domenico and Schwartz 1990). Integratedmethods of hydro-geochemical modeling, statisticalmodeling, remote sensing, and GIS as shown in Fig. 2have been used to evaluate hydro-geochemical status ofthe mangrove forest water in the study area.

WATEQ4F A geochemical model was run to computethe major and trace element speciation and mineralsaturation for natural water. WATEQ4F is a programfor the calculation of chemical equilibrium in naturalwaters. It models the thermodynamic speciation of ma-jor and important minor inorganic ions and complexspecies in solution for a given water analysis and insitu measurements of temperature, pH, and redox poten-tial. WATEQ4F was used to calculate the saturationindex (SI) of the minerals based on the availabledataset. The saturation index of a mineral is obtainedfrom Eq. (1) (Appelo 1996)

SI ¼ logIAP

Kt

� �ð1Þ

where IAP is the ion activity product and Kt the solubilityproduct (tabulated in the WATEQ4F database for a widevariety of mineral phases; Ball et al. 1991). When SI is belowzero, the water is under saturated with respect to the mineral inquestion. An SI of zero means waster is in equilibrium withthe mineral, whereas an SI greater than zero means a super-saturated solution with respect to the mineral in question(Singh et al. 2013a).

Cluster analysis Based on similarities within a class anddissimilarities between different classes, cluster analysis(CA) groups the objects into the classes or clusters(Johnson and Wichern 2002; Singh et al. 2009). CAhelps in data interpretation and reveals the patterns.Hierarchical agglomerative CA was performed on thenormalized data set (mean observations over the wholeperiod) by means of Ward’s method using squared Eu-clidean distances as a measure of similarity. CA wasapplied to the water quality data set with a view togroup the similar sampling sites, which resulted agglom-erative hierarchical cluster (dendrogram). It is applied todetect the similarity among different sampling sites,separately for two different seasons. The clustering re-veals the groups of similar site in a quite convincingway. These clusters of sampling sites with similar char-acteristic features and natural background were affectedby similar type sources. In this study, cluster analysiswas applied to the water quality data sets with a viewto group the similar pollution sites.

Results and discussion

Hydro-geochemistry of mangrove water

Monitoring of physical parameters

The toxicity of many ions are well known to human health,animals, and plants (Foy et al. 1978; Thakur et al. 2011). ThepH value of the pre-monsoon samples ranges from 8.5 to 8.1with mean value of 8.3, while pH value in the post-monsoon

Fig. 2 Flow chart depicting themethodology used in the study

Arab J Geosci

Author's personal copy

season ranges from 8.4 to 7.8 with an average of 8.1. Thehigher pH values observed may be attributed by the mixing ofseawater with estuarine waters and by the mangrove photo-synthetic activity utilizing CO2, thereby shifting the equilibri-um towards highly alkaline state of water (Ramanathan et al.1999). The mean value of EC observed in pre-monsoon was57,710 (ranges from 76,500 to 38,500 μs/cm), while its meanvalue observed during post-monsoon was 39,395 μs/cm(ranges from 44,500 to 34,500 μs/cm). Thus, low value ofEC in the post-monsoon in comparison to pre-monsoon couldbe resulted from the influence of atmospheric precipitation.The mean value of total dissolved solid (TDS) in the pre-monsoon season was 53,470 mg/L (ranging from 63,500 to28,500 mg/L), while its mean value during post-monsoon was38,264 mg/L (ranging from 44,000 to 32,500 mg/L). Thus,low value of TDS in the post-monsoon in comparison to pre-monsoon may be attributed to the influence of atmosphericprecipitation. The increase in the ionic concentration of man-grove water during the pre-monsoon periods may be due toless river water input via estuaries due to impediment of waterflow by dams, aided by evaporation due to an elevated tem-perature and friction due to high speed wind velocity in theregion. The mean value of oxidation and reduction potential(measured through the digital ORP Sensor) observed in thePichavaram mangrove was 133 mV (ranges from 149 to105 mV) during pre-monsoon, while its mean value duringpost monsoon was 113 mV (ranges from 151 to 93 mV).Salinity plays a critical role in the availability of nutrientsand their transformation in the saline aquatic environments.The mean value of salinity was 38 parts per thousands (rangesfrom 40 to 25 parts per thousands) in the pre-monsoon, whileits mean value during post-monsoon was 39 parts per thou-sands (ranges from 45 to 30 parts per thousands). This can bevery well attributed to not inflow of much freshwater fromVellar and Coleroon rivers during post-monsoon.

Monitoring of anions and cations

The variability of anions during the study period has shown atrend: Cl−>SO4

2−>HCO3−>Br−>NO3

−>PO43−. The vari-

ability of cations during shows the following trend: Na+>Mg2+>K+>Ca2+>NH4

+. The concentration of chlorideranges from 24,638 to 15,872 mg/L with its mean value inthe pre-monsoon was 21,362 mg/L, while its mean valueduring post-monsoon was 20,498 mg/L (ranges from 23,923to 16,223 mg/L). It can be attributed to involvement of freshwater system in mangrove water. In post-monsoon season, thefresh water received through runoff is more than pre-monsoon, so the chloride concentration was observed a littleless in post-monsoon season. The second most abundantanion was SO4

2− with a mean value of 3,677 mg/L (rangefrom 4,488 to 3,142 mg/L) in the pre-monsoon and a meanvalue of 3,182 mg/L (range from 4,676 to 2,078 mg/L) during

post-monsoon. The sulfate chemistry also indicates a similarfactor as chloride, which suggests involvement of fresh waterchemistry inside the mangrove water. The overall slight de-crease in the concentration of anions in the region has beenlargely due to the dilution by fresh water, which directlycomes from the rainfall and river water in the region by Vellarand Coleroon rivers. The third most abundant anion wasHCO3

− with mean value of 44.5 mg/L (61.1–30.5 mg/L) inpre-monsoon, and in the post-monsoon, we have a mean valueof 60 mg/L (82–42 mg/L). This increase during the post-monsoon is due to inflow of fresh water from Vellar andColeroon rivers and atmospheric precipitation. The fourthmost abundant anion was Br− with a mean value of 40.6 mg/L (49–27 mg/L) during pre-monsoon and a mean value of54.9 mg/L (62 to 29.9 mg/L) during post-monsoon; this maybe attributed to contribution from seaweeds. Nitrate meanvalue was observed about 28 mg/L (54–4 mg/L) in pre-monsoon and a mean value of 32 mg/L (69–14.6 mg/L)during post-monsoon. The increase in concentration of NO3

−

during post-monsoon can be attributed to increased contribu-tion from agricultural field during the monsoon because ofincrease in precipitation. The land use land cover (LULC)analysis also suggests that there is an increase in agriculturalarea in last 7 years. It is evident that the human pressuresmainly from the effluents continuously influence the man-grove ecosystems, which comes from the aquaculture fieldsand agricultural runoff. Phosphate mean value was observedaround 6.6 mg/L (12.84–0.0 mg/L) in pre-monsoon and amean value of 6.75 mg/L (11.34 to 2.56 mg/L) during post-monsoon, increased values during post-monsoon may be theresultant of increased agricultural runoff from adjoining fields(Ramanathan et al. 1999; Ranjan et al. 2008b). The analysisland cover change indicates that there is a drastic increase infallow land and aquaculture activities in the area. An increasein fallow land causes a high runoff of fresh water carryingfertilizers and other nutrients to the mangrove water.

The probable reason of very high phosphate and nitratevalues in both periods is due to the runoff from agriculturalfields, which are the main source of contamination of phos-phate and nitrate and high nitrate levels are also a sign ofpoorly maintained aquariums and fish culture. Therefore,many researchers observed that the distribution of nutrientsmainly based on the season, tidal conditions, and freshwaterflow from land sources. High concentration of phosphateobserved during monsoon season might possibly be due tointrusion of upwelling seawater into the creek that increasedthe level of phosphate (Nair et al. 1984). The recorded highestphosphate values during post-monsoon season could be at-tributed to the heavy rainfall, land runoff, nutrients-rich dis-charges from shrimp farms, its autochthonous origin, andalkali metal phosphates input fromweathering rocks liberation(Gowda et al. 2001). In addition, application of fertilizers inagricultural fields and use of detergents in households are the

Arab J Geosci

Author's personal copy

Tab

le1

Hydrochem

istryof

pre-monsoon

water

2006

Sam

pleID

Cond

TDS

Salinity

pHEh

Ca

Mg

Na

KCl

SO4

HCO3

Fe(total)

NH4

PO4

FNO3

Br

S/m

mg/L

mV

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

XA1

56,500

54,000

408.3

105

276

1,493

8,456

280

23,433

3,752

61.0

0.02

5.4

8.9

0.0

11.8

35.2

XA2

59,500

54,000

408.5

149

276

1,397

8,779

290

22,883

3,389

36.6

0.03

7.3

7.1

0.0

41.8

32.8

XA3

62,500

54,000

408.4

143

273

1,383

7,199

210

24,638

3,443

36.6

0.03

8.5

5.0

0.0

39.2

33.0

XA4

57,000

52,500

408.3

140

282

1,393

7,373

270

21,845

3,288

42.7

0.08

4.7

9.4

0.0

42.7

27.4

XA5

64,500

62,500

408.5

136

274

1,392

7,235

299

22,450

3,163

30.5

0.12

7.3

4.1

0.0

41.8

47.0

XA6

64,000

61,500

408.5

131

279

1,397

7,212

237

20,633

3,250

36.6

0.10

9.5

2.3

0.0

34.0

42.0

XA7

65,000

63,500

408.5

135

278

1,399

7,936

211

24,530

4,376

48.8

0.16

9.9

2.4

0.0

15.0

39.8

XA8

58,000

57,000

408.5

136

276

1,396

7,590

233

22,840

3,800

36.6

0.09

10.5

0.0

0.0

4.0

47.2

XA9

62,500

61,000

408.5

138

275

1,399

7,999

213

21,330

4,452

42.7

0.06

5.9

12.3

0.0

50.1

49.3

XA10

60,500

59,000

408.5

141

280

1,438

7,985

210

21,091

3,812

48.8

1.69

12.4

12.8

0.0

53.9

47.0

XA11

56,500

54,000

408.5

146

277

1,393

6,999

245

18,950

4,044

36.6

0.06

7.7

8.3

0.0

42.9

37.0

XA12

62,500

62,000

408.4

146

274

1,409

6,887

209

19,262

4,032

42.7

1.05

8.4

0.0

0.0

8.08

43.6

XA13

76,500

53,000

358.4

135

275

1,401

7,709

220

20,885

4,488

48.8

0.02

9.5

2.3

0.9

12.2

41.6

XA14

38,500

39,000

258.3

113

281

1,385

7,300

276

18,266

3,526

54.9

0.25

12.8

4.2

0.0

20.0

45.0

XV1

50,500

44,500

388.1

115

274

1,396

7,087

290

23,250

3,248

48.8

0.50

14.9

5.5

1.0

10.8

27.4

XC2

48,500

49,000

398.1

127

274

1,401

7,000

285

20,990

3,142

42.7

0.49

9.6

12.2

1.0

25.4

48.4

XC1

38,500

28,500

298.2

125

267

1,401

6,135

271

15,872

3,302

61.1

0.09

12.9

5.7

0.7

20.9

47.0

Avg

57,735

53,470

388.3

133

276

1,404

7,463

250

21,362

3,677

44.5

0.20

9.2

6.0

0.2

27.9

40.6

Min

38,500

28,500

258.1

105

267

1,383

6,135

209

15,872

3,142

30.5

0.01

4.7

0.0

0.0

4.0

27.4

Max

76,500

63,500

408.5

149

282

1,493

8,779

299

24,638

4,488

61.1

1.60

14.9

12.84

1.0

53.9

49.3

SD9519.90

9201.6

4.38

0.142

12.42

3.52

25.728

635.60

33.90

2311.11

462.06

8.84

0.44

2.80

4.12

0.39

16.21

7.26

Arab J Geosci

Author's personal copy

Tab

le2

Hydrochem

istryof

post-m

onsoon

ofsurfacewater

2007

SampleID

Cond.

TDS

Salinity

pHEh

Ca

Mg

Na

KCl

SO4

HCO3

Fe(total)

NH4

PO4

FNO3

Br

S/m

mg/L

mV

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

mg/L

YP1

44,500

44,000

408.1

144

241.78

1,384

7,122

364.34

21,851

3,463

72.7

0.0

12.3

11.32

0.0

43.6

41.6

YP2

42,000

41,500

418.4

121

280.12

1,392

7,824

356.98

23,512

3,427

82.1

0.1

9.2

11.34

0.0

23.6

62.0

YP3

42,000

40,500

398.0

96256.89

1,395

7,834

324.23

19,320

2,263

67.7

0.1

18.7

8.34

0.1

54.1

38.1

YP4

41,500

40,000

387.9

94288.45

1,401

6,689

299.56

20,621

2,856

62.8

0.1

14.6

4.56

0.1

33.5

57.3

YP5

39,500

38,500

448.2

137

290.2

1,380

7,012

324.89

18,353

2,078

810.0

11.3

7.42

0.4

19.4

46.2

YP6

38,500

38,000

418.0

111

276.71

1,371

7,224

304.11

21,632

2,199

46.8

0.0

20.4

5.11

0.0

23.5

29.9

YP7

42,000

40,500

458.2

104

273.45

1,374

7,892

289.67

23,923

3,465

46.7

0.0

13.3

7.02

0.2

23.6

37.8

YP8

41,000

39,500

408.1

115

269.2

1,378

6,989

353.43

18,211

3,954

48.8

0.1

9.8

9.45

0.0

19.7

48.8

YP9

38,500

38,000

408.1

95278.43

1,374

7,634

372.21

18,622

3,624

80.4

0.1

10.4

4.56

0.5

22.7

62.5

YP1

034,500

33,000

398.0

95254.89

1,388

7,983

299.23

20,234

3,798

76.8

0.0

11.6

9.73

0.0

24.7

42.2

YP11

39,500

38,500

398.2

132

245.89

1,398

7,653

286.85

23,116

2,543

48.8

0.0

15.4

7.35

0.1

45.6

40.2

YP1

238,500

38,000

428.3

151

288.55

1,392

6,713

298.13

19,432

3,478

56.2

0.0

12.5

2.76

0.2

46.7

47.2

YP1

337,000

36,500

388.0

105

267.91

1,372

6,313

364.89

16,223

3,895

67.1

0.0

13.6

8.34

0.0

33.7

53.3

YP1

442,500

40,000

398.1

107

276.31

1,368

6,982

268.53

18,462

4,676

46.6

0.0

14.6

5.22

0.2

68.8

15.1

YV1

35,000

32,500

408.4

115

268.98

1,297

6,321

278.94

21,148

2,241

42.3

0.0

7.8

5.89

0.1

23.9

61.7

YC2

38,500

38,000

328.2

115

279.33

1,288

6,933

290.46

23,853

2,499

48.8

0.0

9.6

2.56

0.0

14.6

70.1

YC1

34,500

33,500

307.8

93269.87

1,343

6,832

287.45

19,969

3,632

42.2

0.0

6.7

3.91

0.0

18.8

43.6

Average

39,382

38,264

398.1

113

271

1,370

7,173

315

20,498

3,182

59.9

0.07

12.5

6.75

0.1

31.9

54.9

Min

34,500

32,500

307.8

93241.78

1,288

6,313

268.53

16,223

2,078

42.2

0.0

6.7

2.56

0.0

14.6

29.9

Max

44,500

44,000

458.4

151

290.2

1,401

7,983

372.21

23,923

4,676

82.1

0.15

20.42

11.34

0.5

68.8

62.0

Std.Dev.

2950.26

3041.98

3.64

0.16

18.19

14.18

32.44

539.87

34.24

2251.72

763.08

14.70

0.046

3.61

2.74

0.14

14.96

13.53

Arab J Geosci

Author's personal copy

other sources for higher amount of inorganic nitrate andphosphate during the post-monsoon season (Das et al. 1997;Senthilkumar et al. 2002).

The concentration of Na+ is maximum among all the cat-ions. It means value in the pre-monsoon mangrove forests was7,463 mg/L (6,135–8,779 mg/L), while it has a mean value of7,172 mg/L (6,313–7,983 mg/L) during post-monsoon. ThisNa+ is followed by the secondmost abundant cationMg2+ thathas a mean value of 1,404 mg/L (1,383–1,493 mg/L) duringpre-monsoon and a mean value of 1,370 mg/L (1,288–1,401 mg/L) during post-monsoon. The third most abundantcation was K+ with a mean value of 250 mg/L (209–299 mg/L) during pre-monsoon and a mean value of 315 mg/L (269–372 mg/L) during post-monsoon. Phosphate values are higherand are contributed mainly from weathering of phosphaticnodules present in the drainage area (Coleroon) and fertilizer[di-ammonium phosphate (DAP) and N/P.K] application on

the adjoining agricultural fields (Tandon 1987; Vaidhyanathanet al. 1989; Ramanathan et al. 1994), and therefore, the K+

showed similar behaviour like NO3− and PO4

3−. The fourthmost abundant cation was Ca2+ with a mean value of 276 mg/L (267–282 mg/L) during pre-monsoon and a mean value of271 mg/L (242–290 mg/L) during post-monsoon. The fifthmost abundant cation was NH4

+ with a mean value of 9.2 mg/L (4.7–15 mg/L) during pre-monsoon and a mean value of12.5 mg/L (6.7–20 mg/L) during post-monsoon. The concen-tration of Na+ is higher than K+, which indicates that Na+ ismore mobile than K+ and dominates the natural solutions(Tables 1 and 2).

Generally, environmental problems are related to changesin LULC. However, available data on LULC change can helpin better decision making and planning in future especially inmatters related to coastal water (Singh et al. 2010; Misra et al.2013). Land use change can impact on the service functions

Fig. 3 Different land use in studyarea Pichavaram mangrovesduring study (Source: Ranjan2006)

Arab J Geosci

Author's personal copy

(Constanza et al. 1997) such as the supply of clean air andwater, which everybody expects the environment to providebut few want to take effort to maintain. Land use systems canbe classified by their influence on on-site as well as off-siteenvironmental service functions (Noordwijk et al. 2004). Thesampling location has large bearing on the surrounding area.The nearby activities will alter the aquatic system largely. Thesurface water bodies are more prone to the change in land useactivities as compare to groundwater, but once the groundwa-ter get impurities in the system, then it is very difficult toremove the contaminates and pollutant from the groundwatersystems. Our sampling falls in the same land use type catego-ries to avoid the direct impact of sampling locations on thewater quality. The tides have potential role in influencing boththe surface and groundwater, but the seawater intrusion playsa major role on surface water hydro-geochemistry. LULC(Fig. 3) of areas was divided into 11 classes, indicating theLULC pattern in the year 2006. Land use map of the studyarea during the study was adopted for better understanding therole of land use change on hydro-geochemistry. In Pichavaramover the last decade, the mangrove forest cover has beendecreasing by human activity. The forest cover has decreasedfrom 4.9 km2 (1970) to 3.7 km2 in 1996 (Ranjan et al. 2008a,b). The change in LULC in Pichavaram during 2000 and 2007is depicted in Fig. 4. There was an increase of 61.96 km2 areasin agriculture cover and subsequent decrease in mangrove,forest cover/forest plantation, and mud flat. This decrease inagricultural area can be related to the variation in

hydrochemistry, which shows that the changes are more an-thropogenic than natural (Ranjan et al. 2008b). During post-monsoon season, the amount of nutrient added by runoffinfluenced the hydrochemistry of water sampled duringpost-monsoon season. An increase of 1.2 km2 in aquaculturepond was may be at the cost of mangrove area. It has beenfound that there was an increase in fallow land of 16 km2 thatmight be ascribed to encroachment of forest cover/plantation,mangrove vegetation, and sand/beach. These increases furthersupplement to the alteration in hydrochemistry predominantlyby anthropogenic factors. The decrease of about 4.9 km2 areasin forest cover/forest plantation occurred due to land utiliza-tion for agriculture cover and aquaculture pond. The resultsare further supported by land conversion into fallow land,swamp, and waterlogged area. A decrease of 1.5 km2 inmangrove vegetation indicated that the mangrove forest deg-radation occurred in the Pichavaram belt is ascribed to exten-sive utilization of mangrove forest for agricultural practices,aquaculture, and conversion into fallow land, swamp, andwaterlogged area (Ranjan et al. 2008b) (Fig. 5).

Geochemical modeling: WATEQ4F estimations

Water for utilitarian purposes requires the qualitative classifi-cation. The majority of minerals during pre- and post-monsoon at different locations show much similar trends inthe values of SI. Minerals, namely, anhydrite (CaSO4), arago-nite (CaCO3), brucite (Mg(OH)2), calcite (CaCO3), dolomite(d) (MgCa(CO3)2), dolomite (c) (MgCa (CO3)2), epsomite(MgSO4⋅7H2O), fluorapatite (FCO3), fluorite (CaF2), gypsum(CaSO4⋅2H2O), halite (NaCl), and magnesite (MgCO3) werereported in our study. During pre-monsoon stage, mineralslike anhydrite, aragonite, brucite, epsomite, fluorite, gypsum,and halite are undersaturated almost everywhere and remain-ing minerals are in the state of super saturation (Table 3).Similarly Moreno et al. (2007) had studied the calcite carbon-ate equilibrium condition in the estuarine water sample. Dur-ing post-monsoon period, similar trends were observed and

Fig. 4 Change detection map of Pichavaram between the period 2000and 2007

Fig. 5 LULC distribution of Pichavaram and its scenario in last 7 years(2000–2007)

Arab J Geosci

Author's personal copy

minerals like anhydrite, aragonite, brucite, epsomite, fluorite,gypsum, and halite are under saturated almost at similarsampling locations and remaining minerals are in the state ofsuper saturation (Table 4). Nevertheless, changes in saturationindices are clearly observed in terms of their values, whichreflect the evaporation (pre-monsoon) and dilution (post-monsoon) process controlling the water quality. Fluoride isan important element in trace amount, but it is harmfulin large amount by creating dental fluorosis and skeletal

flouorsis (Singh et al. 2013b). At relatively high F-

concentration and pH close to neutral or slightly alka-line (7.4), the groundwater approaches saturation withrespect to fluorite. This condition could not always befulfilled since ion activities of other aqueous speciesvary in relation to changes in pCO2 and pH along flow(Amadi 1981). The fluorite mineral saturation trend isstrongly controlled by the ion activities of fluoride,calcium, and the pH (Amadi and Shaffer 1985).

Table 3 Saturation indices, SI, of the major minerals responsible for the hydrochemistry of the aquifers in the Pichavarm,Mangrove, Tamil Nadu duringthe pre-monsoon year 2006

Sample ID Anhydrite Aragonite Brucite Calcite Dolomite (d) Dolomite (c) Epsomite FCO3 apatite Fluorapatite Fluorite Gypsum Halite Magnesite

XA1 −0.936 −0.003 −2.512 0.141 0.876 1.426 −2.198 19.462 5.833 −4.709 −0.731 −2.631 0.704

XA2 −0.889 0.356 −1.906 0.499 1.535 2.085 −2.213 20.439 6.373 −5.443 −0.686 −2.559 1.004

XA3 −1.086 −0.08 −2.679 0.064 0.703 1.253 −2.363 19.882 5.855 −3.653 −0.881 −2.641 0.609

XA4 −0.931 −0.154 −2.892 −0.011 0.503 1.053 −2.26 17.513 4.654 −4.392 −0.726 −2.682 0.483

XA5 −1.056 0.231 −2.28 0.375 1.268 1.818 −2.388 22.674 7.036 −2.392 −0.85 −2.71 0.861

XA6 −1.06 −0.204 −2.685 −0.06 0.417 0.967 −2.378 7.432 4.767 −4.886 −0.856 −2.627 0.447

XA7 −0.893 −0.06 −2.312 0.084 0.708 1.258 −2.214 21.519 6.634 −2.81 −0.69 −2.548 0.593

XA8 −0.829 −0.137 −2.526 0.007 0.553 1.103 −2.14 18.178 5.366 −5.536 −0.623 −2.718 0.515

XA9 −0.857 0.103 −2.518 0.247 1.02 1.57 −2.183 21.206 6.156 −2.208 −0.652 −2.67 0.742

XA10 −0.884 −0.051 −2.717 0.092 0.756 1.306 −2.168 18.875 5.489 −4.692 −0.679 −2.616 0.632

XA11 −1.061 −0.074 −2.284 0.07 0.737 1.287 −2.322 19.477 5.767 −4.267 −0.857 −2.574 0.636

XA12 −0.85 0.135 −2.11 0.279 1.075 1.625 −2.184 20.151 5.667 −2.869 −0.644 −2.707 0.765

XA13 −0.824 −0.08 −2.728 0.064 0.665 1.215 −2.133 19.799 5.853 −3.74 −0.616 −2.811 0.57

XA14 −0.753 −0.152 −2.546 −0.008 0.506 1.056 −2.082 20.406 6.011 −2.771 −0.547 2.714 0.483

XV1 −1.045 0.081 −1.912 0.224 0.972 1.522 −2.374 21.215 6.534 −3.438 −0.839 −2.694 0.717

XC2 −0.858 −0.458 −3.13 −0.314 −0.098 0.452 −2.181 15.469 3.77 −5.004 −0.653 −2.688 0.185

XC1 −0.996 −0.005 −2.32 0.138 0.783 1.333 −2.349 18.318 4.903 −3.884 −0.793 −2.603 0.613

Table 4 Saturation indices, SI, of the major minerals responsible for the hydrochemistry of the aquifers in the Pichavarm,Mangrove, Tamil Nadu duringthe post-monsoon year 2007

Sample ID Anhydrite Aragonite Brucite Calcite Dolomite (d) Dolomite (c) Epsomite Gypsum Halite Magnesite FCO3 apatite Fluorapatite Fluorite

YP1 −0.873 0.13 −2.079 0.274 1.12 1.67 −2.162 −0.67 −2.528 0.815 – – –

YP2 −0.797 0.192 −1.723 0.336 1.209 1.759 −2.122 −0.595 −2.536 0.842 – – –

YP3 −0.816 0.065 −1.917 0.209 0.96 1.51 −2.125 −0.61 −2.701 0.72 – – –

YP4 −0.785 0.113 −1.928 0.256 1.052 1.602 −2.102 −0.581 −2.619 0.764 21.351 6.098 −1.702YP5 −0.92 −0.004 −1.701 0.14 0.824 0.374 −2.233 −0.716 −2.612 0.653 – – –

YP6 −0.888 0.063 −2.304 0.206 0.962 1.512 −2.183 −0.681 −2.831 0.725 22.275 6.774 −1.847YP7 −0.89 −0.003 −1.909 0.141 0.826 1.376 −2.207 −0.687 −2.575 0.654 – – –

YP8 −0.849 0.074 −1.713 0.217 0.976 1.526 −2.167 −0.646 −2.585 0.727 – – –

YP9 −1.028 0.082 −1.681 0.226 1.003 1.553 −2.329 −0.823 −2.644 0.746 – – –

YP10 −0.847 0.19 −1.7 0.334 1.214 1.764 −2.158 −0.643 −2.598 0.849 – – –

YP11 −0.81 0.067 −1.723 0.211 0.954 1.504 −2.129 −0.604 −2.701 0.712 – – –

YP12 −0.785 0.058 −1.929 0.202 0.942 1.492 −2.102 −0.58 −2.643 0.709 – – –

YP13 −0.859 0.11 −2.113 0.253 1.033 1.583 −2.184 −0.654 −2.697 0.748 – – –

YP14 −0.825 −0.014 −1.917 0.13 0.805 1.355 −2.133 −0.62 −2.661 0.644 – – –

YV1 −0.779 −0.021 −2.132 0.122 0.775 1.325 −2.1 −0.573 −2.694 0.621 – – –

YC1 −0.804 0.438 −1.929 0.582 1.701 2.251 −2.114 −0.597 −2.753 1.088 – – –

YC2 −0.798 0.053 −1.925 0.197 0.932 1.482 −2.111 −0.593 −2.687 0.704 – – –

Arab J Geosci

Author's personal copy

Piper analysis

For deeper and better interpretability, the analytical data arerequired to present in better forms, i.e., Piper–Trilinear plots.The Piper diagram is extensively being used by many re-searchers to understand problems concerning the geochemicalevolution of water, and it consists of three distinct fields—adiamond-shaped field and two triangular fields (Piper 1997;Prasanna et al. 2010; Hamzaoui-Azaza et al. 2011). Thepercentage equivalents per mole (epm) values of ions are usedfor the plot.

The overall characteristic of the water is represented in thediamond-shaped field by projecting the position of the plots inthe triangular fields. Different types of water can be distin-guished by their plotted position, occupying certain subareasof the diamond-shaped field. Piper–Trilinear plots were pre-pared using Aquachem software for the samples collectedduring field visits, and it revealed that sodium, calcium, andmagnesium ions are the dominant cations. Most of the pointsfall in the field in which strong acids exceed weak acids. Thefinal Piper diagram shows the overall water quality in thestudy area (Figs. 6 and 7). This can be explained in terms ofthe concentration and process, which is dominant during thesummer period when evaporation exceeds precipitation. Themajority of water samples belong to the category of Na-Cltype during pre and Ca–Cl type during post-monsoon indicat-ing sufficient recharge from freshwater during monsoon andsea water intrusion during pre-monsoon. Among the cationfacies, more than 50 % of the samples belong to the calciumtype and a little <50 % fall in the class of sodium andpotassium type.

Statistical analysis

The data for the pre- and post-monsoon samples were statis-tically analyzed to examine inter nutrient correlation ofPichavaram mangrove water. Salinity shows good correlationwith TDS, EC, and Cl− in the pre-monsoon (Table 5), whileduring post-monsoon, salinity is in good correlation withMg2+ and pH, which represents that the oceanic water hasan influence on surface water; this may be due to ions carriedby the seawater during the inundation process into thePichavaram mangrove water. Good correlation exists in pre-monsoon between PO4

3− and NO3−, which can be attributed to

leaching of these ions from sources such as aquaculture pondsand agricultural fields in the region. HCO3

− and PO43− also

showed good correlation in post-monsoon period, which maybe due to dissolution and leaching processes. During post-monsoon, significant correlation exists (Table 6) betweenNH4

+ and NO3−, which is contributed by increased nitrifica-

tion and agricultural processes, and disturbance of sedimentcolumn by natural and human process (Alongi et al. 2005).Sodium is in good correlation with pH during pre-monsoon.The good correlation between Mg2+ and HCO3

− and Na+ andHCO3

− exists; this correlation may be attributed to weatheringas a result of precipitation and seawater intrusion. This canpose serious problems to freshwater aquifers along the coastalareas having marine aquifer hydraulic interaction. The pH,salinity, EC, and total dissolve solute (TDS) and trace elementbromine have also served as excellent criteria for delineatingthe fresh water and salt water interface (Park et al. 2005; Sherifet al. 2006; Mondal et al. 2010; Naidu et al. 2013). These aremajor indicators of sea water intrusion and subsequent

Fig. 6 Pre-monsoon Piperdiagram major water type is Na–Cl

Arab J Geosci

Author's personal copy

withdrawal. The decrease in the bicarbonate concentra-tion is attributed to dilution by seawater (Ramanathanet al. 1993). These groups represent the identical behav-ior of genetic origin and pollution. Both in case of pre-and post-monsoon EC and TDS formed cluster indicat-ing the interdependence nature. In both cases, chloridewas the dominant anion and was significantly correlatedwith sodium and sulfate. Magnesium, potassium, andcalcium were in the same group. A noted difference

that was observed in case of pre- and post-monsoonsamples was linkage distance, which may be attributedto the quantity of fresh water input.

To investigate the results further, cluster analysis ofthe water quality data was performed. The cluster ren-dered dendrogram map was drawn using single linkageWard’s method. In dendrogram (Fig. 8), all the samplesfrom both the seasons are clustered into four groupsaccording to their similarity in pollution sources. Cluster

Table 5 Correlation matrix of physico-chemical parameters of Pichavaram surface water of pre-monsoon (n=17)

pH EC TDS ORP SAL F Cl Br SO4 PO4 NO3 HCO3 NH4 Na K Ca Mg

pH 1.00

EC 0.63 1.00

TDS 0.63 0.82 1.00

ORP 0.62 0.52 0.48 1.00

SAL 0.40 0.64 0.83 0.48 1.00

F −0.76 −0.24 −0.46 −0.36 −0.30 1.00

Cl 0.20 0.51 0.61 0.07 0.65 −0.20 1.00

Br 0.21 −0.07 0.00 0.02 −0.24 0.01 −0.44 1.00

NO3 0.37 0.45 0.30 0.00 0.15 −0.22 0.13 0.13 1.00

PO4 −0.20 −0.22 −0.07 −0.01 0.19 0.06 −0.03 0.02 0.00 1.00

SO4 0.38 0.13 0.20 0.49 0.27 −0.37 0.01 0.00 −0.22 0.62 1.00

HCO3 −0.52 −0.47 −0.64 −0.69 −0.59 0.33 −0.33 0.02 0.31 0.69 −0.41 1.00

NH4 −0.37 −0.45 −0.51 −0.34 −0.53 0.50 −0.28 0.18 −0.29 −0.24 −0.35 0.35 1.00

Na 0.45 0.44 0.46 0.07 0.38 −0.40 0.60 −0.18 0.46 0.22 0.17 −0.03 −0.40 1.00

K −0.52 −0.53 −0.42 −0.47 −0.24 0.30 −0.07 −0.24 −0.56 0.15 −0.04 0.09 0.02 −0.07 1.00

Ca 0.31 0.15 0.37 0.03 0.11 −0.51 0.18 −0.19 0.10 0.12 0.25 −0.11 −0.20 0.42 −0.11 1.00

Mg −0.07 0.05 0.08 −0.43 0.20 −0.10 0.14 −0.02 0.47 0.33 −0.15 0.52 −0.21 0.44 0.04 0.07 1.00

Fig. 7 Post-monsoon Piperdiagram major water type is Ca–Cl

Arab J Geosci

Author's personal copy

1 indicates that seven post-monsoon (series YP1, YP2,YP3, YP4, YP7, YP8, and YP14) and two pre-monsoonstations (XV1 and XC2) clustered under the same groupshowed similarity in their nutrient sources regulated bysimilar type of fluid chemistry. Cluster 2 from thedatasets (YC1, YP13, YV1, YP10, YP6, YP11, YC2,YP5, YP12, XA14, YP9, and XC1) shows similar typeof anions chemistry (phosphate, nitrate, and sulfate)

involved in this area mainly influenced by aquacultureactivities. In cluster 3, all stations (XA1, XA2, XA3,XA4, XA8, XA10, and XA11) belongs to pre-monsoonseason, and hence, they show a strong effect of dilutioncaused after the monsoon season. Cluster 4 (XA13,XA9, XA12, XA5, XA6, and XA7) also shows a sim-ilar behavior; however, it also shows a strong relation-ship with physical properties of the water such as TDS

Fig. 8 Cluster rendered dendrogram of pre- and post-monsoon season

Table 6 Correlation matrix of physiochemical parameters of Pichvaram water of post-monsoon (n=17)

pH EC TDS ORP SAL SO4 PO4 NO3 F Cl Br HCO3 NH4 Na K Ca Mg

pH 1.00

EC 0.14 1.00

TDS 0.13 0.98 1.00

ORP 0.60 0.28 0.34 1.00

SAL 0.49 0.40 0.37 0.38 1.00

SO4 −0.19 0.10 0.07 −0.18 −0.08 1.00

PO4 0.12 0.35 0.34 0.13 0.36 0.16 1.00

NO3 −0.04 0.45 0.37 0.17 0.13 0.26 0.04 1.00

F 0.22 0.05 0.04 0.06 0.42 0.05 −0.32 0.06 1.00

Cl 0.38 0.20 0.22 0.16 −0.01 −0.35 0.02 −0.22 −0.32 1.00

Br 0.12 0.20 0.09 −0.12 −0.14 0.49 −0.22 0.47 0.27 −0.20 1.00

HCO3 0.05 0.18 0.27 0.07 0.31 0.03 0.50 −0.06 0.28 −0.22 −0.15 1.00

NH4 −0.27 0.37 0.37 −0.04 0.32 −0.25 0.02 0.52 −0.06 −0.03 −0.10 −0.03 1.00

Na 0.04 0.27 0.28 −0.18 0.29 0.01 0.40 0.02 0.07 0.41 −0.22 0.36 0.25 1.00

K −0.01 0.28 0.39 0.10 0.20 0.18 0.55 −0.18 0.09 −0.32 −0.26 0.68 −0.09 0.10 1.00

Ca 0.16 −0.06 −0.05 −0.03 0.13 −0.06 −0.55 −0.28 0.43 −0.16 0.25 0.02 −0.15 −0.32 −0.11 1.00

Mg −0.21 0.45 0.48 0.11 0.49 0.21 0.44 0.41 0.03 −0.21 −0.22 0.49 0.49 0.44 0.34 −0.13 1.00

Arab J Geosci

Author's personal copy

and density, which is less prominent in cluster 3, influ-enced by mainly anions.

Conclusions

The important hydro-geochemical processes/activities thatmangroves carry out include the entrapment of sedimentsand pollutants, filtering of nutrients, remineralization of or-ganic and inorganic matter, and export of organic matter. Thesurface water collected from Pichavaram was alkaline innature and is generally not saturated. The pH had shown theincreasing trends in comparison to previously reported values,which indirectly suggest that it may be either due to change inhistorical land use pattern or change in the pattern of evapo-ration and precipitation. Increasing trend for salinity was alsoobserved. Elevated concentrations of phosphate and nitratewere observed in this particular year as compared to theprevious studies. However, there has been noted increase inthe concentration of chloride and sulfate. In both season(pre- and post-monsoon), it was observed that thehydro-geochemistry of Pichavaram mangrove is mainlygoverned by fresh water input rather than seawaterintrusion. Multivariate statistical method like CA wasapplied on the data sets to understand the complexnature of water quality issues and determine prioritiesto improve water quality depending on the geochemicalmodeling. This study suggests that point and nonpointsource pollution need to be controlled for their negativeeffects on ecologically sensitive ecosystem. This studyalso suggests the present state of land use change di-rectly affects the hydro-geochemistry of mangrove forestwater. Hence, there is a need for better management ofnatural land for the healthy growth of mangrove foreststhat protect coastal erosion and as a shield to protect thehuman settlements form the killer tsunami waves. Fur-ther study will be helpful to the managers and govern-ment agencies working on issues related to water qual-ity management, for making new policy regulation orpolicy shift, which is needed for the sustainable man-agement of the mangrove ecosystem. The future work inthis direction would be the validation of mineral phasespresent in the study area with the help of X-ray diffrac-tion or wave dispersive X-ray fluorescence spectropho-tometer (WD-XRF).

Acknowledgments The authors acknowledge the Ministry of For-est, Government of Tamil Nadu, for providing permission forsampling and School of Environmental Sciences, Jawaharlal Neh-ru University, New Delhi, India, for providing necessary facilitiesto carry out this work.

References

Adamu M, Ahmad ZA, Hafizan J, Mohammad FR (2013) Surface waterquality contamination source apportionment and physicochemicalcharacterization at the upper section of the Jakara Basin, Nigeria.Arab J Geosci 6:4903–4915. doi:10.1007/s12517-012-0731-2

Alongi DM (1996) The dynamics of benthic nutrient pools and fluxes intropical mangrove forests. J Mar Res 54(1):123–148

Alongi DM, Ramanathan AL, Kannan L, Tirendi F, Trott LA, PrasadMBK (2005) Influence of human-induced disturbance on benthicmicrobial metabolism in the Pichavaram mangroves, Vellar–Coleroon estuarine complex, India. Mar Biol 147(4):1033–1044.doi:10.1007/s00227-005-1634-5

Amadi UMP (1981) Ground-water chemistry and hydrochemical facièsdistribution as related to flow, in the Mississippian Carbonates,Harrison County, Indiana. Ph.D. dissertation, Indiana University,Bloomington

Amadi UMP, Shaffer NR (1985) Low sulfate ground water and itsrelationship to the Gypsum—fluorite replacement in the karst ter-rains of southern Indiana, U.S.A. Karst Water Resources. PAnkara–Antalya Symp IAHS Publ. no. 1Ç1, 449-466

APHAA (1985) Standard methods for the examination of water andwastewater. WPCF American Public Health Association,American Water Workers Association, Water Pollution ControlFederation

Appelo CAJ (1996) Geochemistry, groundwater and pollution. Taylor &Francis, The Netherlands

Ball JW, Nordstrom DK, Survey G (1991) User’s manual for WATEQ4F,with revised thermodynamic data base and test cases for calculatingspeciation of major, trace, and redox elements in natural waters.USGS Open-File Report 91-183

Bava K, Seralathan P (1999) Interstitial water and hydrochemistry of amangrove forest and adjoining water system, south west coast ofIndia. Environ Geol 38(1):47–52

Blasco F, Caratini C, Chanda S, Thanikaimani A (1975) Main character-istics of Indian mangroves. Proc Int Symp Biol Manag MangroveHawai 1:71–87

Bouillon SM, Frankignoulle F, Dehairs B, Velimirov A, Eiler G, Abril H,EtcheberBorges AV (2003) Inorganic and organic carbon biogeo-chemistry in the Gautami Godavari estuary (Andhra Pradesh, India)during pre-monsoon: the local impact of extensive mangrove for-ests. Glob Biogeochem Cycles 17(4):1114. doi:10.1029/2002GB002026

Constanza R, Arge RD, Groot RD, Farber S, Grasso M, Hannon B,Limburg K, Naeem S, Neill RVO, Paruelo J, Raskin RG, Sutton P,Belt VDM (1997) The value of the world’s ecosystem services andnatural capital. Nature 387:253–260

Das J, Das SN, Sahoo RK (1997) Semidiurnal variation of some physi-ochemical parameter inMahanadi estuary, east coast of India. IndianJ Mar Sci 26:323–326

Deepika B, Avinash K, Jayappa KS (2014) Impact of estuarine processesand hydro-meteorological forcingon landform changes: a remotesensing, GIS and statistical approach. Arab J Geosci. doi:10.1007/s12517-014-1264-7

Dennis RA, Colfer CP (2006) Impacts of land use and fire on the loss anddegradation of lowland forest in 1983–2000 in East Kutai District,East Kalimantan, Indonesia. Singap J Trop Geogr 27(1):30–48

Dewidar KHM (2004) Detection of land use/land cover changes for thenorthern part of the Nile delta (Burullus region), Egypt. Int J RemoteSens 25(20):4079–4089

Dious SRJ, Kasinathan R (1994) Tolerance limits of two pulmonate snailsCassidula nucleus andMelampus ceylonicus from Pichavaramman-groves. Environ Ecol 12(4):845–849

Domenico PA, Schwartz FW (1990) Physical and chemical hydrogeolo-gy. Wiley, New York, p 824

Arab J Geosci

Author's personal copy

Foy C, Chaney R, White M (1978) The physiology of metal toxicity inplants. Annu Rev Plant Physiol 29(1):511–566

Gorman D, Russell BD, Connell SD (2009) Land-to-sea connectivity:linking human-derived terrestrial subsidies to subtidal habitatchange on open rocky coasts. Ecological applications. Ecol SocAm 19(5):1114–1126

GovindasamyC, Kannan L (1991) Rotifers of the Pichavarammangroveshydrobiological approach.Mahasagar Bull Nat Inst Oceanogr 24(1):39–45

GowdaG,Gupta TRC, RajeshKM, GowdaH, Lingadhal C, RameshAM(2001) Seasonal distribution of phytoplankton in Nethravathi estu-ary, Mangalore. J Mar Biol Ass India 43:31–40

Hamzaoui-Azaza F, KetataM, Bouhlila R, Gueddari M, Riberio L (2011)Hydrogeochemical characteristics and assessment of drinking waterquality in Zeuss–Koutine aquifer, southeastern Tunisia. EnvironMonit Assess1-16

Herold M, Scepan J, Clarke KC (2002) The use of remote sensing andlandscape metrics to describe structures and changes in urban landuses. Environ Plann A 34(8):1443–1458

Ibrahimi MK, Miyazaki T, Nishimura T, Imoto H (2013) Contribution ofshallow groundwater rapid fluctuation to soil salinizationunder arid and semiarid climate. Arab J Geosci. doi:10.1007/s12517-013-1084-1

Jagtap TG, Chavan VS, Untawale AG (1993) Mangrove ecosystem ofIndia: a need for protection(synopsis). AMBIO 22(4):252–254

Johnson R, Wichern D (2002) Applied multivariate statistical analysis.Prentice-Hall, London

Kathiresan K (2000) A review of studies on Pichavaram mangrove,southeast India. Hydrobiologia 430(1):185–205

Kathiresan K (2002) Why are mangroves degrading? Curr Sci 83:1246–1249

Kathiresan K, Ramesh M, Venkatesan V (1994) Forest structure andprawn seeds in Pichavarammangrove. Environ Ecol 12(2):465–468

Klekowski EJ, Lowenfeld RL, Hepler PK (1994) Mangrove genetics II.Outcrossing and lower spontaneousmutation rates in Puerto RicanRhizophora. Int J Plant Sci 155:373–381

Krishnamurthy K, Jeyaseelan MJP (1983) The Pichavaram (India) man-grove ecosystem. Int J Ecol Environ Sci 9:79–85

Kumar G, Ramanathan AL, Rajkumar K (2010) Textural characteristicsof the surface sediments of a tropical mangrove ecosystem gulf ofkachchh, Gujarat, India. Indian J Mar Sci 39:415–422

Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing andimage interpretation. Wiley, New York

Mandel S, Shiftan ZL (1980) Groundwater resources investigation anddevelopment. Academic, New York, p 269

Misra A, Murali MR, Vethamony P (2013) Assessment of the land use/land cover (LU/LC) and mangrove changes along the Mandovi–Zuari estuarine complex of Goa. India Arab J Geosci. doi:10.1007/s12517-013-1220-y

Mondal NC, Singh VP, Singh VS, Saxena VK (2010) Determining theinteraction between groundwater and saline water through ground-water major ions chemistry. J Hydrol 388(1-2):100–111

Moreno J, Valente T, Moreno F, Fatela F, Guise L, Patinha C (2007)Occurrence of calcareous foraminifera and calcite–carbonate equi-librium conditions—a case study in Minho/Coura estuary (NorthernPortugal). Hydrobiologia 587(1):177–184

Mujabar SP, Chandrasekar N (2013) Shoreline change analysis along thecoast between Kanyakumari and Tuticorin of India using remotesensing and GIS. Arab J Geosci 6:647–664. doi:10.1007/s12517-011-0394-4

Nagaraja P, Kumar MH, Yathirajan H, Prakash J (2003) Highly selectivereaction of nitrate with brucine and 3-methyl-2- benzothiazolinonehydrazone hydrochloride for determination of nitrate in environ-mental samples. Anal Sci 19:961–963

Naidu LS, Rao GVVS, Rao GT, Mahesh J, Padalu G, Sarma VS, PrasadPR, Rao SM, Rao RBM (2013) An integrated approach to

investigate saline water intrusion and to identify the salinity sourcesin the Central Godavari delta, Andhra Pradesh, India. Arab J Geosci6:3709–3724. doi:10.1007/s12517-012-0634-2

Nair PVR, Gopinathan CP, Balachandran VK, Mathew KJ, RegunathanA, Rao DS, Murty AVS (1984) Ecology of mud banks: phytoplank-ton productivity I alleppey mudbank. Bull Cent Mar Fish Res Inst31:28–34

Noordwijk MV, Poulsen JG, Ericksen JP (2004) Quantifying off-siteeffects of land use change: filters, flows and fallacies agriculture.Ecos Environ 104:19–34

Panda UC, Sundaray SK, Rath P, Nayak BB, Bhatta D (2006)Application of factor and cluster analysis for characterization ofriver and estuarine water systems—a case study: Mahanadi River(India). J Hydrol 331(3-4):434–445

Park SC, Yun ST, Chae GT, Yoo IS, Shin KS, Heo CH et al(2005) Regional hydrochemical study on salinization ofcoastal aquifers, western coastal area of South Korea. JHydrol 313:182–194

Piper J (1997) Turner P, TurnerA (eds) (1995) Paleomagnetic applicationsin hydrocarbon exploration and production. Geol Soc SpecPubl London, pp 98–301. ISBN 1 897799 42 X. Geol Mag134(01):121–142

Prasad MBK, Ramanathan AL (2009) Organic matter characterization ina tropical estuarine-mangrove ecosystem of India: preliminary as-sessment by using stable isotopes and lignin phenols. Estuar CoastShelf Sci 84(4):617–624

Prasad MBK, Ramanathan AL (2010) Characterization of phosphorusfractions in the sediments of a tropical intertidal mangroveecosystem. Wet Ecol Manag 18(2):165–175. doi:10.1007/s11273-009-9157-3

Prasad MBK, Ramanathan AL, Alongi DM, Kannan L (2006) Seasonalvariations and decadal trends in concentrations of dissolved inor-ganic nutrients in Pichavaram mangrove waters, southeast India.Bull Mar Sci 79(2):287–300

Prasanna M, Chidambaram S, Shahul AH, Srinivasamoorthy K (2010)Study of evaluation of groundwater in Gadilam basin using hydro-geochemical and isotope data. Environ Monit Assess 168(1):63–90.doi:10.1007/s10661-009-1092-5

Presley BJ (1971) Appendix: techniques for analyzing interstitial watersamples. Part I: determination of selected minor andmajor inorganicconstituents. In Winterer EL et al (eds) Initial reports of the deep seadrilling project. US Govt Printing Office, Washington. vol VII, pp1749–1755

Ramanathan AL, Subramanian V, Vaidhyanathan P (1988) Chemical andsediment characteristics of the upper reaches of the Cauvery estuary,east coast of India. Indian J Mar Sci 17:114–120

Ramanathan AL, Vaidhyanathan P, Subramanian V, Das BK (1993)Geochemistry of the Cauvery estuary, east coast of India. Estuar16:459–474

Ramanathan AL, Vaidhyanathan P, Subramanian V, Das BK (1994)Nature and transport of solute load in the Cauvery River basin.India Wat Res 28(7):1585–1593

Ramanathan AL, Subramanian V, Ramesh R, Chidambaram S, James A(1999) Environmental geochemistry of the Pichavaram mangroveecosystem (tropical), southeast coast of India. Environ Geol 37(3):223–233

Ranjan RK (2006) Impact of tsunami on the biogeochemical changes insediments of Pichavaram mangroves, South east coast of India—post tsunami scenario. M.Phil, Jawaharlal Nehru University, NewDelhi, in English

Ranjan R, Ramanathan AL, Singh G, Chidambaram S (2008a)Assessment of metal enrichments in tsunamigenic sediments ofPichavaram mangroves, Southeast coast of India. Environ MonitAssess 147(1):389–411. doi:10.1007/s10661-007-0128-y

Ranjan R, Ramanathan AL, Singh G (2008b) Evaluation of geochemicalimpact of tsunami on Pichavaram mangrove ecosystem, Southeast

Arab J Geosci

Author's personal copy

coast of India. Environ Geol 55(3):687–697. doi:10.1007/s00254-007-1019-9

Ranjan RK, Ramanathan AL, Singh G (2008c) Evaluation of geochem-ical impact of tsunami on Pichavaram mangrove ecosystem,Southeast coast of India. Environ Geol 55(3):687–697

Ranjan RK, Ramanathan AL, Singh G, Chidambaram S (2008d)Assessment of metal enrichments in tsunamigenic sediments ofPichavaram mangroves, Southeast coast of India. Environ MonitAssess 147(1):389–411

Ranjan R, Ramanathan AL, Chauhan R, Singh G (2011) Phosphorusfractionation in sediments of the Pichavaram mangrove ecosystem,south-Eastern coast of India. Environ Earth Sci 62(8):1779–1787.doi:10.1007/s12665-010-0659-3

Selvam V (2003) Environmental classification of mangrove Etlands ofmangrove Etlands of India. Curr Sci 84(6):757–765

Senthilkumar S, Santhanam P, Perumal P (2002) Diversity of phytoplank-ton in Vellar estuary, south-east coast of India. The 5th Indian FishForm Proc.publ AFSIB,Mangalore and AeA, Bhubanewar, India pp245-248

Seralathan P, Seetharamasamy A (1987) Geochemistry of modern deltaicsediments of the Cauvery river, east coast of India. Indian J Mar Sci16:31–38

Seralathan P, Srinivasalu S, Ramanathan AL, Rajamanickam GV,Nagendra R, Singarasubramanian SR, Mukesh MV, Manoharan K(2006) Post tsunami sediments characteristics of Tamilnadu coast.In: Rajamanickam GV (ed) 26th December 2004 Tsunami causes,effects remedial measures, pre and post tsunami disaster manage-ment, a geoscientific perspective. Department of Science andTechnology report, New Delhi, pp 196–209

Shalaby A, Tateishi R (2007) Remote sensing and GIS for mapping andmonitoring land cover and land-use changes in the Northwesterncoastal zone of Egypt. Appl Geogr 27(1):28–41

Sherif M,Mahmoudi EA, GaramoonH,KacimovA,AkramS, EbraheemA (2006) Geoeletrical and hydrogeochemical studies for delineatingawater intrusion in the outlet of Wadi Ham. UA E nviron Geol 49:536–551

Simeonov V, Stratis J, Samara C, Zachariadis G, Voutsa D, AnthemidisA, Sofoniou M, Kouimtzis T (2003) Assessment of the surfacewater quality in Northern Greece. Water Res 37(17):4119–4124

Singh SK, Singh CK, Kumar KS, Gupta R, Mukherjee S (2009) Spatial-temporal monitoring of groundwater using multivariate statisticaltechniques in Bareilly district of Uttar Pradesh, India. J HydrolHydrmcs 57(1):45–54

Singh SK, Singh CK, Mukherjee S (2010) Impact of land-use and land-cover change on groundwater quality in the Lower Shiwalik hills: aremote sensing and GIS based approach. Centl Europn J Geosci2(2):124–131. doi:10.2478/v10085-010-0003-x

Singh P, Thakur JK, Kumar S, Singh UC (2011) Assessment of land use/land cover using geospatial techniques in a semi arid region ofMadhya Pradesh, India. In Thakur JK, Singh SK, Ramanathan A,

Prasad MBK, Gossel W, (ed) Springer and Capital publ, HeidelbergGermany. Geos Tech Manag Environ Res 152-163

Singh SK, Srivastava PK, Pandey AC, Gautam SK (2013a) Integratedassessment of groundwater influenced by a confluence river system:concurrence with remote sensing and geochemical modelling.WaterResour Manag 27(12):4291–313

Singh SK, Srivastva PK, Pandey AC (2013b) Flouride contaminationmapping of groundwater in northern India integrated with geochem-ical indicators and GIS. Water Sci Technol Water Supply. doi:10.2166/ws.2013.160

Singh S, Srivastava P, Gupta M, Thakur J, Mukherjee S (2014) Appraisalof land use/land cover of mangrove forest ecosystem using supportvector machine. Environ Earth Sci 71(5):2245–55. doi:10.1007/s12665-013-2628-0

Solorzano L (1969) Determination of ammonia in natural waters by thephenolhypochlorite method. Limnol Oceanogr 14:799–801

Srivastava P, Mukherjee S, Gupta M (2010) Impact of urbanization onland use/land cover change using remote sensing and GIS: a casestudy. Int J Ecol Econ Stat 18(S10):106–17

Srivastava P, Mukherjee S, Gupta M, Singh S (2011) Characterizingmonsoonal variation on water quality index of river Mahi in Indiausing geographical information system. Water Qual Expo Health2(3):193–203. doi:10.1007/s12403-011-0038-7

Srivastava PK, Mehta A, Gupta M, Singh SK, Islam T (2014) Assessingimpact of climate change on Mundra mangrove forest ecosystem,Gulf of Kutch, Western coast of India: a synergistic evalua-tion using remote sensing. Theor Appl Climatol. doi:10.1007/s00704-014-1206-z

Subramanian A, Vannucci M (2004) Status of Indian mangroves: pollu-tion status of the Pichavaram mangrove area, south-east coast ofIndia. UNU Press, pp. 59–75

Tandon HLS (1987) Phosphorous research and agricultural production inIndia. FDCO GOI, New Delhi

Thakur JK, Thakur RK, Ramanathan A, Kumar M, Singh SK (2011)Arsenic contamination of groundwater in Nepal—an overview.Water 3(1):1–20

Vaidhyanathan P, Subramanian V, Ramanathan AL (1989) Transport anddistribution of phosphorous by Indian rivers. Geol Soc India(Memoirs) 13:127–137

Van Noordwijk M, Poulsen JG, Ericksen PJ (2004) Quantifying off-siteeffects of land use change: filters, flows and fallacies. Agric EcosystEnviron 104(1):19–34

Yuan F, Sawaya KE, Loeffelholz BC, Bauer ME (2005) Land coverclassification and change analysis of the twin cities (Minnesota)Metropolitan area by multitemporal Landsat remote sensing.Remote Sens Environ 98(2-3):317–328

Zhang SR, Lu XX, Higgitt DL, Chen CTA, Sun HG, Han JT (2007)Water chemistry of the Zhujiang (Pearl River): natural processes andanthropogenic influences. J Geophys Res 112(F1), F01011. doi:10.1029/2006JF000493

Arab J Geosci

Author's personal copy