Evaluation of SO2 emissions from large scale power plants using OMI SO2 retrievals for Turkey
E. Firatli & B. Kaynak-Tezel Department of Environmental Engineering, School of Civil Engineering, Istanbul Technical University, Turkey
Abstract
In this study, the spatiotemporal variation of sulfur dioxide (SO2) pollution over Turkey is observed via an Ozone Monitoring Instrument (OMI). The power plants having a working capacity higher than 48 MWe are selected as large scale power plants and their SO2 emissions are evaluated using OMI Planetary Boundary Layer SO2 columns. OMI SO2 columns are processed for 2005–2014. An SO2 emission inventory available from EMEP for the selected power plants is also obtained for comparison. The SO2 columns within a selected distance to the power plant locations are used to calculate the annual SO2 column averages and temporal change over the years is also investigated. The SO2 columns for EMEP grids are also calculated and compared with EMEP SO2 emissions. The results from this study indicate the commonalities and discrepancies in SO2 emissions and SO2 columns for selected power plants. The SO2 columns are also compared with available ground-based SO2 measurements around the selected power plants. Keywords: OMI, remote sensing, SO2, EMEP, power plants.
1 Introduction
SO2 is one of the major air pollutants in the atmosphere. It is produced mainly by volcanoes, power plants, refineries, metal smelting, burning of fossil fuels and biofuels [1]. Anthropogenic and natural SO2 emissions are oxidized fast in the atmosphere, which leads to aerosol formation and acid deposition by the formation of sulfuric acid which then causes atmospheric pollution and acid
rain [2]. Sulfate aerosol negatively affects human health, reduces visibility and also has effects on climate [3]. SO2 is conventionally monitored by ground measurement stations which yield accurate measurements at surface level. However, those measurements are mainly influenced by weather conditions and represent only a limited spatial area. In addition, contiguous spatial information cannot be obtained by a limited number of stations. Hence, understanding the spatial variation of the pollutants will not be possible on a macro-scale only with these observations. On the other hand, remote sensing measurements have several advantages on measuring atmospheric pollution such as broad coverage, fine resolution and continuous measurements. Satellite data can contribute to analysis of the trend of atmospheric SO2 and other trace gases. Recently, instruments such as OMI and GOME-2 were being used to retrieve some trace gases such as SO2 from space [4]. Using remote sensing for retrieving atmospheric pollution is rather a new process. Previously, volcanic SO2 emissions from volcanic eruptions were calculated using satellite SO2 observations [5, 6]. It was also shown that satellite measurements can identify signals from anthropogenic SO2 sources and observe changes in the emission from these sources [2, 3, 7, 8]. The Total Ozone Mapping Spectrometer (TOMS) was the first space-borne sensor able to detect SO2 column concentration caused by volcanic eruptions around the world [4]. Monitoring of tropospheric pollution by satellites were also performed with Global Ozone Monitoring Experiment (GOME, 1996–2003, European Remote Sensing Satellite-2) [9]. GOME was able to detect volcanic and anthropogenic SO2 in full-spectrum UV data. 3 days is necessary for GOME to cover the globe and this relatively long period resulted in the missing of some short term events [9–12]. A more recent instrument, the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY, 2002, ENVISAT-1) was able to detect volcanic and anthropogenic SO2 as well with a better spatial resolution, but its global coverage is within 6 days. The Ozone Monitoring Instrument (OMI) which was launched in 2004 on NASA’s satellite AURA, is an imaging spectrometer [13]. OMI’s global coverage is obtained in one day so that its detection for short term events is better than other instruments. Its highest spatial resolution is 13 km × 24 km at nadir which is currently the highest resolution for daily measurements of SO2. OMI measurements were used for evaluation of emission inventories for major sources for several countries. Major sources are generally coal-burning power plants and around these point sources, OMI is able to detect SO2 pollution. In some countries such as the U.S., India and China SO2 emission inventories were compared with OMI retrievals. A study for US power plants indicated a high correlation of 0.93 between OMI retrievals and emission inventory [14]. Beside its advantages, there are also some disadvantages for satellite measurements. Those are mainly noise, cloudy scenes, interference from ozone noise and row anomaly. Processing of OMI measurements are explained in the materials and methods section in detail.
In this study, our aim is to examine the large scale power plants in Turkey which have an installed power capacity higher than 48 MWe. The SO2 emissions from EMEP (European Monitoring and Evaluation Programme) emission inventory for the selected power plants are obtained for comparison with SO2 retrievals as well. The study is performed for the years between 2005 and 2014, so that the temporal SO2 trends for the power plants can be evaluated.
2 Materials and methods
2.1 SO2 satellite retrievals
The Ozone Monitoring Instrument (OMI) on NASA’s EOS Aura satellite was launched on July 2004. OMI has a finer spatial resolution than other satellite instruments with its 13 × 24 km2 resolution than GOME (320 × 40 km2) and SCIAMACHY (60 × 30 km2) [15]. The values for total column SO2 were given in Dobson units (DU) where 1 DU is equal to 2.69·1026 molecules·km−2 [14]. OMI Level 2 (L2) data were downloaded from National Aeronautics and Space Administration (NASA) Goddard Earth Sciences Data and Information Services Center (GES DISC) [16]. In the L2 data, SO2 vertical columns are given for 4 layers; planetary boundary layer (PBL), lower tropospheric layer (TRL), middle tropospheric (TRM), and upper tropospheric and stratospheric layer (STL). In this study, the SO2 PBL data were used. PBL data were processed by Principal Component Analysis (PCA) after 2013, and all the data were reprocessed according to this algorithm. This algorithm has some advantages over the previous method, Band Residual Algorithm (BRD), by means of spatial smoothing and local bias correction [17]. OMI L2 data inside EMEP domain were selected spatially. In addition, other data quality criteria such as radiative cloud fraction, solar zenith angle, removal of missing data and O3 column were applied according to the advisory [18]. In several studies, it is advised that the data with solar zenith angle less than 60o, radiative solar fraction less than 0.3, O3 column amount less than 1500 DU should be used [3, 4, 19–22]. After filtration of the advised parameters, data was transferred into ArcGIS software. First, the data was transformed into shape files and then spatially joined with the power plants. Scans within 50 km around of selected power plants are chosen and processed.
2.2 SO2 emissions
In this study, an EMEP inventory for the year 2012 was used for SO2 emissions of Turkey. The EMEP grid system is based on a polar-stereographic projection and has 132 × 159 grids with the size of 50 × 50 km2 [23]. SO2 emissions for the most recent year available at the CEIP database were 2012 and the data were downloaded from EMEP, the CEIP database for SNAP (Selected Nomenclature of Air Pollutant) sectors [23, 24]. National total SO2 emissions from the EMEP emission inventory indicated energy and industrial combustion (Sector 1) being the major contributor followed by small combustions processes and industrial processes (Table 1).
Table 1: National total SO2 emission distribution according to SNAP sectors for 2012 in Turkey [23].
SOx SNAP SECTORS (SO2, Gg) (%) S1 – Combustion in energy and transformation industries (stationary sources) 1364.8 49.83 S2 – Non-industrial combustion plants (stationary sources) 657.1 23.99 S3 – Combustion in manufacturing industry (stationary sources) 697.8 25.48 S4 – Production processes (stationary sources) 3.7 0.13 S5 – Extraction and distribution of fossil fuels and geothermal energy 0.0 0.00 S6 – Solvent use and other product use 0.0 0.00 S7 – Road transport 0.0 0.00 S8 – Other mobile sources and machinery 15.1 0.55 S9 – Waste treatment and disposal 0.6 0.02 S10 – Agriculture 0.0 0.00 S11 – Other sources and sinks 0.0 0.00 Total 2739.1 100
2.3 Power plants
Large scale power plants having more than 48 MWe installed power and burning coal as fuel are chosen for evaluation and were downloaded from Energy Market Regulatory Authority (EPDK) database [25]. According to the list from EPDK, there are 17 power plants that met the selection criteria. EMEP grids were determined for the selected power plants. OMI SO2 retrievals within 50 km of selected power plants were determined and also compared with SO2 concentrations obtained from ground monitoring and EMEP emissions.
2.4 SO2 ground monitoring stations
There was a limited number of SO2 ground monitoring stations in Turkey with available measurements between 2005 and 2014. There were only 35 ground monitoring stations in Turkey in 2005. The number was gradually increased to over 100 in 2014 (National Air Quality Monitoring Network). Hourly SO2 data were downloaded from National Air Quality Monitoring Network website for the period between 2005 and 2014. The stations which are close to selected power plants were selected. There are 12 different ground monitoring stations which are at closest point to 17 power plants. Mean SO2 concentrations and variations of those ground monitoring stations are calculated for the years between 2005 and 2014.
3 Results and discussion
In this study, large scale coal power plants in Turkey were evaluated using SO2 OMI retrievals, ground observations and EMEP emissions. There are 17 coal
using large scale power plants in Turkey. According to OMI retrievals, it was observed that Mugla Yenikoy, Kemerkoy, Yatagan Power Plants, Silopi Power Plant, Afsin Elbistan A-B Power Plant, Kutahya Polatli, Tuncbilek and Seyitomer Power Plants have high SO2 column values. Afsin Elbistan Power Plant A-B combined is the largest power plant in Turkey with its 2435 MWe operating power (Table 2). Yearly average SO2 retrievals are increasing until 2013 with high values observed for the years between 2008 and 2012 (Figure 1). It has the highest 10 years average SO2 PBL column retrievals among all the selected power plants. On the other hand, SO2 measurements from the ground station located within 30 kms of the power plant does not indicate similar results with OMI retrievals. Ground measurements indicate low SO2 concentrations where OMI indicates an increasing trend (Figure 1). The meteorological factors such as wind speed and direction can have an effect on ground measurements; however, these effects should be minimized when the yearly average values are investigated. This result indicates representability issues of the selected station for this power plant.
Figure 1: Yearly OMI and ground monitoring SO2 observations between 2005 and 2014 for Afsin-Elbistan A-B power plants.
Another example is the Silopi Power Plant located in the eastern part of Turkey, with an operating capacity of 135 MWe (Table 2). It is not among the largest power plants, but it has significantly high OMI SO2 columns. Its yearly average is found especially high at 2011, which requires further investigation. Overall, the trend of increase over the years is similar in ground observations from the monitoring station that is 37 kms away from the power plant (Figure 2). In Mugla, there are three power plants which are close to each other; Yenikoy, Yatagan and Kemerkoy. They have the similar trends for OMI SO2 columns. In addition, OMI and ground monitoring SO2 values show very similar trends until 2011, but the ground observations increase after 2012, whereas OMI SO2 columns are decreasing (Figure 3).
Figure 2: Yearly OMI and ground monitoring SO2 observations between 2005 and 2014 for Silopi Power Plant.
Figure 3: Yearly OMI and ground monitoring SO2 observations between 2005 and 2014 for Mugla Yenikoy, Yatagan and Kemerkoy Power Plants.
In Kutahya, there are three power plants; Polatli, Seyitomer and Tuncbilek. OMI SO2 yearly averages of these power plants show almost same trend for the ten year period between 2005 and 2014. However, OMI and ground SO2 measurements indicate differences especially the early years of 2006 and 2007, indicating there might be issues for the measurements in ground monitoring station (Figure 4). EMEP national emissions for Turkey for the year 2012 are indicating high emissions for some of the power plants selected (Figure 5). Sector 1 emissions from stationary sources of combustion in energy and transformation industries (Figure 6) indicate that some of the significant coal power plants such as Afsin Elbistan and Sivas Kangal are not reported in EMEP emission inventory. According to Sector 1 emissions, highest emissions are in the cities of Kutahya,
Figure 4: Yearly OMI and ground monitoring SO2 observations between 2005 and 2014 for Kutahya Seyitomer, Tuncbilek and Polatli Power Plants.
Figure 5: Total SO2 emissions of Turkey for the year 2012 [23].
Mugla and Kocaeli. In Kutahya, there are three coal power plants with total operating capacity of 916 MWe. In Mugla, there are three coal power plants with total operating capacity of 1680 MWe. In Kocaeli, there is only one coal burning power plant with operating capacity of 190 MWe. But the significant industrial activities in Kocaeli can result in additional pollution of SO2. The OMI SO2 retrievals correlate well with operating power and gross production of the power plants except the following outliners; Icdas Enerji and Catalagzi and Soma power plants (Figure 7). Even though their operating power and gross production are high, lower SO2 columns are observed which can indicate cleaner combustion technologies or recent control measures. When the OMI SO2 retrievals compared with ground measurements close to the power plants, there is a positive correlation except Afsin Elbistan, where OMI indicates very high values and Catalagzi, where ground observations indicate very high
Figure 6: Sector 1 (combustion in energy and transformation industries) SO2 emissions of Turkey for the year 2012 [23].
Figure 7: Correlation of average OMI SO2 retrievals with operating power and gross production of the selected power plants (upper) and ground observation averages, EMEP emissions (lower).
values. The EMEP emissions for the available power plants indicate a high correlation (R2 =0.76) with OMI SO2 retrievals; however there are six out of 17 power plants which are not available in the 2012 inventory (Figure 7).
4 Conclusions
In this study, large scale coal power plants are selected in Turkey and investigated using SO2 OMI retrievals, ground measurements, and reported EMEP emissions. It was found out that the OMI and ground observations do not always match well together. The reason for these differences can be meteorological factors such as winds which ground observations were strongly affected by. In addition, the location and distance of the ground station from the power plants are also important for representativeness of the SO2 pollution around there large sources. SO2 is also emitted from residential heating when coal is used as fuel, which is a common practice in Turkey, especially in regions where natural gas distribution infrastructure is not complete. The selection criteria for satellite retrievals (50 km radius) can also have an effect on the comparison. A preliminary study was done to determine the effect of distance in the selection of OMI satellite retrievals around the power plants. The satellite scans for Afsin Elbistan power plant is selected for a year for different distances between 10 and 100 kms. It is found that between the distances 10–50 km, OMI SO2 averages are similar in magnitudes and the average SO2 retrievals start to decrease after 50 kms. In order to increase the number of satellite retrievals for the averaging, and reduce the uncertainty in the yearly averages 50 km is selected for this study. Lastly, EMEP emission inventory currently does not include some of the large-scale coal power plants that can be observed by satellite retrievals in Turkey. This study indicates issues with emission inventories and ground observation stations. However, a more detailed methodology should be applied for future investigation to SO2 retrievals, so that the exact reasons for the given discrepancies can be explained.
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