Academia Journal of Scientific Research 6(10): 382-393, October 2018 DOI: 10.15413/ajsr.2018.0152 ISSN 2315-7712 ©2018 Academia Publishing 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).