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Research Paper

Geo spatial application in impact assessment of oil spill on sensitive coastal resources: A case study of oil spill accident in Chennai (India)

Accepted 30th October, 2018

ABSTRACT

An accidental discharge of oil in the near shore regions requires a comprehensive post- spill assessment of environmental impact and biological effects for planning the response and post mitigation efforts. This study discusses Remote Sensing and GIS based impact assessment on the coastal resources coupled with model simulation. The oil spill impact was estimated through spill simulation and incorporated environmental sensitivity index as level of concern to assess the impact. The best guess of the trajectory simulation was used to assess the spatial distribution and concentration of oil on the coastal region to notify the area and resources to analyze the impact of the spilled oil. Under the simulation of weathering process, it is estimated that 94% oil is stranded on the shoreline. S. Arockiaraj1*, Mary Angelin1, M. C. There has been attempt to document the oil on the water surface using remote John Milton1, G. Bhaskaran2 sensing data collected by Sentinel 1A, 2A and Landsat/OLI and trajectory of the released oil. Near real time detection of oil trajectory and quantification using 1PG & Research Department of remote sensing data help with possible oil landing information. The potential Advanced Zoology and Biotechnology, Loyola College, effect of oil on species was assessed through Total Petroleum Hydrocarbon Chennai 600034. Concentration on the soft tissues of Pernaviridiscollected from the most impact 2University of Madras, Department of region and the concentrations were found to be ~10 times higher than the Geography, Chepauk Campus, previously reported values. Chennai-600005.

*Corresponding author. E-mail: Key words: Oil spill modelling, impact assessment, coastal sensitivity, [email protected]. hydrocarbon concentration, GIS modeller.

INTRODUCTION

The oil pollution in coastal waters is mainly through variety bioconcentrate organic pollutants and serve as of sources such as shipping, offshore oil production, bioindicators for environmental conditions in marine industrial effluent sewage and accidental oil spills of crude environment, sessile organisms like mussels have been oil and it’s product. Most often oil pollutants entering the considered widely as pollution sentinels by many sea are considered to be very hazardous to the marine researchers (Ansari et al., 2012; Etuk et al., 2000; environment (Sun et al., 2015; Fan et al., 2015). Therefore, Veerasingam et al., 2011). Though there were numerous the persistence of this heavy concentration of oil in the efforts to assess the impact of oil spills in the coastal sand causes serious threat as they are actively carcinogenic, invertebrates, the TPH concentrations and its persistence teratogenic and mutagenic, threatening the biota (Sarma et status in the tissues of organisms are rarely reported al., 2016). These hydrocarbons are being bio-accumulative, (Bejarano and Michel, 2016). capable of causing serious toxic effect on aquatic flora and An oil spill accident took place on 28th January 2017, a fauna which eventually extends to humans. Petroleum liquefied petroleum gas tanker, the BW Maple, while hydrocarbons are incorporated in sediments and are also coming out of the Kamarajar port, Ennore, collided with bio-persistent (Nsikak et al., 2007). Several studies proven, another tanker, the MT DawnKanchipuram, laden with that the marine organisms can bioaccumulate and 32,813 tonnes of petroleum lubricant. As per the real- Academia Journal of Scientific Research; Arockiaraj et al. 383

Figure 1: Oil Spill incident location and sampling locations along Chennai coast.

time data of Port’s Vessel Traffic Management System with ports, fishing harbors, oil refineries, thermal power (VTMS), the Maple crashed on the side of the Dawn plants, tourist beaches, and monuments. Kanchipuram at about 3.45 AM, started leaking dark waxy Oil Spill Modeling is useful for predicting movement of oil bunker oil of the latter into the sea at about 2 nautical miles and thereby helps for vulnerability, risk and post impact from the coast. The oil spill spread across a stretch of 60 km assessment of habitats specific open coasts. Collected long from Ennore to Akkarai along three coastal districts primary data on wind, tides, currents and bathymetry are known as Thiruvallur, Chennai and Kancheepuram, which used to improve the reliability of generic wind based oil are located on the southeast coast of India, between the spill model for any coast (Kankara et al., 2016). The longitudes of 80° 10′51″E and 80 12′26″E and the latitudes hypothetical spill models have discrete droplets which of and 12° 33′ 00″N. 13° 33′27″N (Figure 1). Various coastal contain the results with mass, density, size and rate of activities were impaired due to the spill in the coastal zone, evaporation etc (Wei et al., 2015). Environmental modelling Academia Journal of Scientific Research; Arockiaraj et al. 384

plays vital role in impact assessment in ecological sciences resolutions. Sentinel-1A, an active remote sensor image can and helps in making a best professional judgments. be processed and be made available in Near Real Time Quantification of the impacts or the weighing factors are (NRT) and therefore, these data were used in this study to used as part of the modeling (simulation process) for detect surface oil slicks. deriving the impact index methods (Cartwright, 1993). Though there are several approaches, the filters and Coastal impact assessment integrated with species classification scheme is specific algorithm to analyze radar sensitivity distribution usually supports the professional images for the oil spill detection and determination (Fingas judgment approach to characterize potential impacts and and Brown, 2014). The manual inspection of oil spill evaluate the spill response actions in the aftermath of the requires contextual information as an important factor accident (Bejarano and Mearns, 2015). Most damage is while classifying the oil spill. That expert knowledge has done by spilled oil when it gets to shallow water or comes been incorporated in the classifier for the accurate ashore. The aim of oil spill response action is to prevent oil classification results. In oil spill detection setting, the from reaching the shore especially sensitive resources and amplitude VV polarized sigma0 data was converted to dB to prevent the long term effect by cleaning the shore using values and the threshold was set to 4.0 with the window various technological measures (Kirby and Law, 2010). size of 61. The dark patches of oil slicks were identified in As part of the coastal management perspective, model this process but challenge was to discriminate the oil slick prediction of damage assessment for oil spill modeling is a and lookalikes. Then the unsupervised classification dire need. This study was conducted in order to assess the technique was applied with K means cluster analysis to impact of an oil spill along Chennai coastal region through separate oil and lookalikes. Maximum number of (200) an integrated approach of modeling and species sensitivity classes were set for the classification with maximum (50) distribution. Field survey was conducted to measure the oil iterations which helped to achieve more accurate affected region continuously for five days to compare with classification result. the trajectory model output coupled impact assessment. In the multispectral and Landsat, Sentinel-2A/MSS The time required by the oil slick to reach the coast was images oil slick is seen to be distinguished from the water determined through a numerical model simulation of the surface. This is because of strong absorption in the blue and trajectory model and field observation as well; the UV wavelengths and enhanced backscattering in the NIR, weathering process of the pollutant in the specified SWIR wavelengths (Sun et al., 2015). Sentinel 2A carries hydrodynamic and meteorological environment was the wide-swath, high-resolution, multispectral imager calculated. This study also reports the concentration of oil (MSI) with 13 spectral bands with 10, 20 or 60 m in the species thrived in most impact locations during the resolution. In the case of oil classification in the water body, spill accident. The total petroleum hydrocarbon in the four spectral bands were selected with 10 m resolution muscles of species such as Pernaviridiswas assessed to namely B2 (blue), B3 (green), B4 (red) and B8 (near- show the impact of oil on species which are used for human infrared). k-means clustering is a classification method consumption. The GIS based approach developed here can used in this study where maximum number of classes were aid estimation of impact and thereby help to devise a plan assigned to classifier. to recovery of the coastal resources from the impact of oil A coastal resource information database which was persistence (Figure 1). prepared as part of study, was taken to analyze the impact on the resources at the oil spill event. A GIS modeler tool was used to integrate with the spill simulations to identify DATA AND METHODS the resources at risk in the GIS environment (Figure 2). The authors discussed with experts in the relevant areas to Satellite data and processing arrive at the qualitative scaling of impact assessment and sensitivity analysis for the resources of the Chennai coast. Satellite remote sensing technique aids oil spill response, An integrated oil trajectory modeling and coastal either in passive or active mode, and serve effectively for resource information is a handy tool to identify the detecting the surface oil spill. During the cloud cover, the protected areas which are likely to be affected during the optical sensors can complement microwave remote sensing oil spill event. The impact assessment was performed in the for more synoptic and repeated measurements (Leifer et al., GIS environment which involved trajectory model output 2012). The surface oil slick was complemented throughout and coastal resource information in the spill affected area. the event, with an analysis using satellite images and radar The oil mass balance calculation showed that about 94% of from 29th, 31st Jan, 05Feb and 10Feb of 2017 until the slick total volume of oil out of the 196 mt tons reached the coast was completely removed from the water surface. Only the and affected some 60 km stretch of the coastline in the past Sentinel-1A, 2A and Landsat 8 cloud free images were 5 days. This northeast (NE) monsoons play a havoc role available during the whole period of study. with regard to oil spill in Chennai region, because of the Landsat8 (OLI) and Sentinel-2A provide relatively short environmental condition of the near shore that it pushes revisit time, as well as relatively high spatial and spectral the major oil towards the coast in shorter time (Kankara et Academia Journal of Scientific Research; Arockiaraj et al. 385

Figure 2: Schematic representation of integrated oil spill model and GIS based impact assessment.

al., 2016). The climatic condition of the sea near chennai for many oil spill events worldwide (NOAA, 2004). The region during the spill incident shows mean highest trajectory output of GNOME model consists of path of temperature of 33.5C and the mean lowest temperature of spilled oil with estimated uncertainty. A well calibrated 20.2C. The winds were measured continuously close to the hydrodynamic (HD) model was simulated for the region in region of oil spill with the help of AWS. It was shown, an a present scenario using predicted tide at open boundaries average wind speed of 4 m/s and the wind direction of 115 as discussed by Mohan et al. (2014). The time series as average. The current during this time was predominantly synoptic wind data were obtained from Indian southerly and ranging from 6-10 cm/s along the chennai Meteorological Department information (IMD), the AWS coast. observatory location at Ennore. The spill location was set at The coastal impact assessment using ESI (Environmental 2 nautical miles from Ennore port where the accident took Sensitivity Index) is prevalent worldwide. Such ESI indices place as reported by Indian Coast Guard. The ship which when combined, either through model or field observation, had an accident was M T Dawn, Kanchipuram, loaded with is possible to bring out an impact index (Nelson and Bunker fuel Oil (Grade 6) and was on its way to Ennore. So Grubesic, 2017). A GIS based modeler tool was constructed the oil type for the model set up was Bunker oil and it was for the oil spill impact assessment from which an effective the 196 tonnes of oil that presumably spilt over the site. coastal impact mapping could be carried out. The impact Having set the real time scenario parameters, the model indexing of the coastal resources indicates a impact which was run in GNOME for five consecutive days. The fate may require immediate attention. analysis was carried out to calculate the mass balance of oil on the water column and beached. To calculate the oil budget using ADIOS2, the environmental conditions for NE Oil spill model setup monsoon were taken into consideration. The water temperature at the time of spill was 26C, wind speed: 3.5 In this study, we used a generic oil spill trajectory model m/s, wind direction: 45°, current speed and direction: 0.10 General NOAA Operational Modeling Environment m/s and 220°, wave height: 0.6 m, and salinity: 32 ppt (GNOME). This is a validated model against observations (Figure 3). A volume of 196tonnes of Bunker oil was Academia Journal of Scientific Research; Arockiaraj et al. 386

Figure 3: Satellite image analysis of oil slick area extraction and the real time wind speed and direction.

selected from the model. After setting the meteorological splots reaching the shore in a day and Risk2 is for the parameters, the model was run for a duration of 5 days. The contained oil splots within range of 1 km distance. Risk (b) Oil spill simulations results were exported to the GIS is the cumulative value of risk1 and risk2. modeler to calculate the impact. The modeler derived impact assessment helps to identify the most impact region and to faster clean-up remedy along the coast. The modeler Field survey tool generates table containing sensitivity and the Total Relative Response of sensitivity of the coastal resources. A field survey was conducted during the spill period in The risk values were assigned as 1,2 and 3 for low, medium order to assess the model based impact assessment. and high risk. Risk1 (Table 1) is for the total density of Continuous field observations (from 28/01/2017 to Academia Journal of Scientific Research; Arockiaraj et al. 387

Table 1: GIS modeler based impact assessment.

10)

weight (30%) weight

10) weight(30%) 10) 10)

- -

Resources (Km) Shoreline Pollution for Oil Sensitivity (1 values(1 and Social Cultural weight(10%) 10) (1 value Scientific (20%) weight importance Environmental (1 (1 consideration Economic (10%) weight 10) Relative of Respose Total (%) (wi*Si) (a) = Sensitivity Density Splot patch Oil Risk1 Dist (m) Spill Risk2 Ennore Rip Rap 11.5 3 1 4 2 1 2 370 1 0 3 Chennai Port 7.4 2 7 2 4 8 4 4294 3 0 3 Marina Beach 3.1 10 9 5 8 6 8 1233 3 0 3 Thivanmiyur Beach 4.1 10 9 5 8 6 8 80 1 16 3 Akkarai Beach 2.7 10 9 5 8 6 8 12 1 120 1 Neelankarai Beach 3.0 10 9 5 8 6 8 53 1 64 2 Eliot Beach 3.0 10 9 5 8 6 8 594 2 1 3 Santhome Sandy Shore 2.6 8 4 5 5 2 6 313 1 0 3 Ennore Sandy Shore 2.2 8 4 5 5 2 6 3 1 45 3 Ennore Port 8.0 2 8 2 5 8 4 0 1 76 2 Karungali Sandy Shore 12.1 8 4 5 5 2 6 0 1 240 1 Ennore Creek 1.0 10 2 1 5 2 5 4 1 33 3 Adyar River Mouth 1.0 10 2 1 5 2 5 385 1 0 3

Impact Index (including sensitivity criteria [Si] [1-10], where 1 is least sensitive and 10is most sensitive; weighting factor(wi):1-low, 2-medium, 3-high; Risk: 1-low, 2-medium, 3-high.

01/02/2017) for five days were carried out using a GPS and after ashore (3rd February 2017). To access the mapped the areas of spill patch. The field points were rehabilitation of the mussel bed in the region, samples were attributed as heavy, medium and light deposition. The field collected again on 8th July 2017 (about 6 months later). observations were carried out for five times in five days as Similar sized mussels were collected using zip-lock bags there was continuous deposition of oil patches and tar balls and brought to the laboratory and preserved in a deep along the coast. The position of the oil patch and length of freezer at -20C till further processes. On the day of the coastline affected were recorded to use in oil spill trajectory analysis, the thawed samples were measured for standard model to identify the coastal resources at risk. length (cm) and total weight (g) before dissection. 1 g of A random set of points were generated for the region soft tissue from each specimen were weighed in triplicate covered by the area in the thematic surface data, and the and TPH were extracted with n-hexane as suggested by value for each point was identified. Then, these same Veerasingam et al. (2011) and determined using random points were used to identify each point’s known Fluorescence Spectrophotometer (Hitachi, Japan). The value in the field collected GPS samples (Figure 4). fluorescence of the samples was measured at an emission The results were then evaluated using the derived and excitation wavelengths of 364 and 310 nm, respectively thematic surface data and unbiased ground truth against Crude oil as the standards. The results were information as discussed by Jensen et al. (1998). The expressed as µg g-1 wet tissue. measurement of agreement based on Kappa statistics is useful for comparing two data sets. RESULTS AND DISCUSSION

Analysis of TPH in mussel tissue Oil slick assessment with remote sensing images

To access the impact of the oil spill on the regional marine Oil spill detection with active remote sensing is effective −1 lives, sites of maximum oil concentration derived through when there is a wind speeds from 2–3 to10 ms with an GNOME outputs were considered (Figure5). The selected incident angle of 20° to 45° (Leifer et al., 2012). The sites were found to be rich in the distribution of green multispectral images of Landsat8, Sentinel2A, provided mussel P. viridis, hence specimens were collected a week better results of oil slick classification. The spill detections Academia Journal of Scientific Research; Arockiaraj et al. 388

Figure 4: Field observation and GPS data collection to correlate with the model output. Oil deposited spots categorized as Heavy, Medium and Low as per the visual observation.

Figure 5: (a) Oil budget of (Bunker oil) the spill incident showing hourly evaporation, tonnes of beached oil and remaining on water column. (b) and (c) shows viscosity, thickness of oil on water column after spill. Academia Journal of Scientific Research; Arockiaraj et al. 389

Table 2: Satellite image acquired floating oil slick during the spill scenario.

Resolution Date of Time of Area of spill Wind Satellite data used (m) Acquisition Acquisition(IST) identified Direction(Deg) Sentinel 1A 23 29/01/2017 8.35 24.8 40 Sentinel 2A 10 31/01/2017 10.35 01.4 55 Sentinel 2A 10 05/02/2017 10.25 01.5 60 Landsat 8 OLI 30 10/02/2017 11.35 0.4 70

and slick area calculations are realistic in accordance with the fate analysis is important to understand the impact of the reported seashore pollution. The Sentinel image on oil spill and decide the response strategy (Figure 5). 29th Jan2017 showed maximum area of 24.8 sqkm slick The simulation was run for 5 days and thereafter the near shore in Ennore rip rap and groynes. There was a beached oil patches were taken in to consideration for continuous leakage from the accidental ship which was comparison with field collected information. The simulation documented through Sentinel 2A image on 31Jan2017 that used the 196 tonnes of oil that spilled in the accident showed the area of 01.4 sqkm slick near to Chennai port. On location. The simulation showed that the amount of 5th of February 2017, being the ninth day after the spill, the beached oil after weathering was 181 tonnes which is about floating oil on the water surface was 01.5 sqkm area found 94.6% of the released oil (Figure 5). Under the same from the Sentinel 2A image (Figure 3). The satellite image meteorological condition in the spill location, the viscosity captured on 10Feb2017 showed sparse patches of oil slick of oil dropped in 15h from the peak of 14591.5 centistokes which accounted 0.4 sqkm area near Ennore region (Table (cSt). Similarly, the oil thickness was more than 0.016 m at 2). The areas of oil slick at the time satellite image the initial stage and lost its thickness due to weathering acquisition was compared with the model output. process at that atmosphere. The trajectory model output was integrated with Coastal Resource for the impact analysis. A GIS based modeler was Field observation prepared to assess the comprehensive movement of oil slick and quantify the impact on coastal resources. The On field observations, which can be used to compare with impact assessment of the spill event showed in tablewith the spatial or temporal trends in relevant parameters, are details of priority index for the coastal resources. important for the preparation of an coastal impact Considering the spill event and amount of oil released, the assessment (Kirby and Law, 2010). Therefore, during the result showed that 8 km of oiling along the shore in 24 h of spill incident, the oil deposition along the coast was time. The coastal resource possibly at risk in the 1st day of mapped as point location and area where there was patch spill was Marina beach, Adyar river mouth and the area was of oil deposition. On the first day of the field survey, there approximately 2 km. The index was high for Marina Beach was one location close to the Ennore region which was and slightly lesser than Coovum river mouth and other less affected and on the consecutive four days the there was affected resources. The coastal impact assessment indicated heavy deposition along the coast from Ennore to Akkarai higher index for Marina Beach due to its significant cultural beach. The GPS positions of oil depositions spots were and socioeconomic value (Figure 6). From the second day attributed to Heavy, Medium and Low based on the visual to the fifth day (that is, 29/01/2017 to 01/02/2017) there observation (Figure 4). There were 18 locations identified was about 16.8 km stretch of coast oiled as per the field with oil deposition along Ennore region. On the second day, observation and the model showed that 15.2 km stretch has the number of locations increased to 56, among which 28 been affected from Marina beach to Thiruvanmiyur beach. locations were found to be heavily deposited (Table 3). The number of samples increased to 144 locations on the fifth day of the spill which implies that there was a continuous Oil spill impact assessment leakage of oil from the spill source. Spill mitigations did not take place until the fifth day of the accident. So it was The oil spill hazard quantification is a function of the considered important to collect the deposition information beached oil concentration and the number of hits which are to correlate with any model results. highly correlated (Al Shami et al., 2017). The oil spill simulation results helps to assess the hits concentrations which is based on the proximity of splots and the density of Mass balance of spilled oil the splots along the shoreline in a particular duration. To create an estimation of distribution of oil along the coast, a Petroleum products consist of hydrocarbons which GNOME analyst was used. The point splots that generated weather easily due to the atmospheric influence. Therefore, from GNOME was subjectedto compatibleprogramme Academia Journal of Scientific Research; Arockiaraj et al. 390

Figure 6: Model based impact assessment depicting the maximum deposition of oil on the coastal resources. Figuresa,b,c,d and e representing five days of oil concentration. Coastal impact classified as heavy, medium and light on the coastal resources. Figures a(1), b(1), c(1), d(1) and e(1) representing each day analysis showing the Slick distance from the coastal resource.

called GNOME Analyst (previously known as TAT) format for further analysis such as area calculation and light. The impact quantification is based on the that helps us to extract the oil concentration images and map representations etc. Thematic surface number of splots that reach within 24 h in the oil and contours using a Thiessen analysis method. The images were generated with this output and the oil spill simulation model. On the first day output at this stage is exported to GIS- compatible concentration was categorized as Heavy, Medium (28/01/2017) of the oil spill, the slick reached the Academia Journal of Scientific Research; Arockiaraj et al. 391

Figure 7: cumulative results of GIS modeler derived impact assessment.

rocky rip rap shore along Ennore, contained The oil slick distance maps are shown in Figure reached the shore affecting from Marina Beach to between the groynes, spread about 500 sqm area 6.The figure shows the heavy, medium and light Adyar river mouth. There were oil patches and then moved along the current and the wind concentration of oil on the shoreline for each day deposition for about 4 km stretch. The entire influence. The second day of the spill impact from the day of spill. The oil concentration is Marina beach along the foreshore was fully covered assessment showed that some part of Ennore rip calculated in terms of splots shoring on each day in with oil patches for about 2.5 km stretch. The fourth rap structure and fishing harbour and the chennai the beach segment. day (31/01/2017) at 10AM, about 2.1 tonnes of oil port region were affected due to the slick movement On the first day (28/01/2017) of model result at reached the shore from Adyar to Neelankarai. The in southward direction. There was a continuous 10.30 AM, it was found that patch of oil found along lumps of tar balls have been found along the increase in the accumulation of oil along the coast a stretch of 1.2km beached in Bharathinagar. The beaches from Adyar to Neelankarai during the field on the third day (30/01/2017) and onwards as the model results showed that about 0.7 tonnes of oil observation. The location was affected for a stretch floating concentration was more and the reached the shore. The second day (29/01/2017) in of 2 km from Thiruvanmyur to Neelankarai. The weathering nature of the pollutant was very Ennore region where there was rip rap sea wall and fifth day (01/02/2017) model observation showed minimal. The oil impact was found up to Marina groynes constructed for 11 km stretch, faced a that the sandy shores from Neelankarai to Akkarai beach hampering recreational activities of the heavy deposition of oil which affected about 15.7 beach were covered with lumps of tar balls for locals. On the fourth and fifth days of the spill, it was km up to fishing harbour. About 182 tonnes of oil about 1.8 km stretch. found sever oil deposition up to Neelankarai beach reached the shore, causing a heavy impact. On The spilt oil is shown in Figure 7, indicating its (Figure 6). 30/01/2017 at 10 AM, about 2.4 tonnes of oil impact on the various coastal resources and its level Academia Journal of Scientific Research; Arockiaraj et al. 392

Table 3: Accuracy assessment from model and observations.

No of field GPS samples Surface layer Date RMSE R2 Heavy Medium Light samples 29-01-2017 13 0 5 18 14.2 0.331 31-01-2017 28 13 15 56 12.1 0.571 05-02-2017 49 0 47 96 8.6 0.742 10-02-2017 79 22 43 144 6.3 0.787

Figure 8: TPH concentration in P.viridis with comparison of concentration during spill and concentration after 6 months of the spill. Model results of Oil concentration on the shoreline shows maximum from site-1 to site-4 where TPH concentration comparatively more.

of importance for protection. The impact was more on the times higher than the earlier studies (Veerasingam et al., fifth day of the spill accident where heavy deposition of oil 2011) than reported values (3.20 µg/g) from the Tamil was found along Ennore rip rap, Chennai port and Marina Nadu coast and it further supports the hypothesis of Law et beach as well. The weathering model showed that about al. (1997) that the organisms which live in the polluted 94% oil was beached on the fifth day of the spill. The regions can have PHC concentrations that are 10-1000 comparison of the modeled slick area with the satellite times higher than the background levels. The higher derived slick area gave high confidence for the model concentrations were recorded during the oil spill from the output. Convexhull is a ratio of the area occupied by the oil site-3. Similarly, either side of site-3, site-2 and site-4 had splots in the model, basically it is the analysis of the higher concentration because these sites contained the oil concavity or convexity of objects. Also, it is the Area of for about 120 h during the event until the mechanical clean splots divided by Area of convex hull enclosing. up was initiated. The site-1 is creek and so it had the TPH concentration more than the normal level. Site-4 is a open coast and as such, showed very minimal TPH concentration Impact of oil spill on the mussel in the sample. The TPH concentrations in the same sites decreased after six months, ranged from 2.4 - 7.9 µg/g The locations of maximum oil concentration as per GNOME which is normal as per the previous studies (Figure 8). results were used for the assessment of potential risk to the water column organisms. The TPH assessment on the beds clearly indicates the extent of impact of oil on the water CONCLUSION column organisms. The concentrations of TPH in the P. viridiswere ranging from 6.1 – 110.1 µg/g with the mean An integration of simulation model and geographic value of 30.7±4.5 µg/g in the month of February 2017. The information system results in more rapid generation of present study found the TPH values in P. viridis was ~10 valuable coastal impact assessment of post oil spill incident. Academia Journal of Scientific Research; Arockiaraj et al. 393

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