Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Assessment of Coastal Water Quality Along South West Coast of India using Multile Regression Analysis on Satellite Data


Affiliations
1 Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Mangalore, Karnataka, India
2 Centre for Geoinformatics Applications in Rural Development (C-GARD), School of Science, Technology and Knowledge Systems, National Institute of Rural Development and Panchayati Raj (NIRD&PR), India
3 Data and information Management Group, INCOIS, Hyderabad, India
4 School of Engineering Sciences, Mahindra Ecole Centrale, Bahadurpally, Hyderabad, Telangana, India
5 Kerala State Pollution Control Board, Trivandrum, Kerala, India
     

   Subscribe/Renew Journal


The coastal waters being the ultimate receiver of all the wastes, shows a declining trend in its quality. It is of immense importance to know the extent of pollution for its monitoring and management. Measurement of dissolved oxygen (DO), biological oxygen demand (BOD), pH and fecal coliform (FC) are vital in water quality monitoring and assessment studies. Usually these parameters are determined by analysing water samples collected from various locations. Since this is tedious and expensive, it is limited to small scales. In this paper, an effort has been made to quickly assess the quality of coastal waters of Kerala directly from the satellite imagery by estimating National Sanitation Federation Water Quality Index (NSFWQI) along with DO, BOD, pH and FC. Multiple linear regression is used to develop statistically significant models using Sea Surface Temperature (SST) and Remote Sensing Reflectance (Rrs ) from Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ data available on DO, BOD, pH and FC. The models when validated showed good correlation between in situ values and predicted values with r values ranging from 0.73 (p = 0.001) for DO to 0.89 for NSFWQI (p = 0.018).Spatial maps are generated showing the distribution of these parameters along the coast. The parameters in the study are checked to see if they are in compliance with the standards. The study gives models to estimate the daily distribution of these parameters along the coast using MODIS data. Thus, appropriate control measures could be adopted to limit the effect on susceptible rural population.

Keywords

Water Quality, Moderate Resolution Imaging Spectroradiometer, Remote Sensing Reflectance, National Sanitation Federation Water Quality Index.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Bhuyan S, Islam S (2016), "Present status of socio-economic conditions of the fishing Community of the Meghna river adjacent to Narsingdi district, Bangladesh", Journal of Fisheries Livestock Production, 4 (192): 1-5.
  • Caroline, W (1996),"Combatting marine pollution from land-based activities: Australian initiatives", Ocean & Coastal Management, 33 (1-3): 87-112.
  • Chattopadhyay, S. and Franke, R. W (2006), "Striving for sustainability: environmental stress and democratic initiatives in Kerala", Concept Publishing Company, New Delhi.
  • CPCB (2001)," Environmental Atlas of India", Central Pollution Control Board.
  • CPCB (1986), "Manual for statistical analysis and interpretation of water quality data", Published technical report of Central Pollution Control Board, Delhi.
  • Islam, M. S. and Tanaka, M, (2004), "Impacts of pollution on coastal and marine ecosystems including coastal and marine fisheries and approach for management: a review and synthesis", Marine Pollution Bulletin, 48: 624–649.
  • Jamshidi, S., and Baker, N. B. A (2011), "Variability of dissaolved oxygen and active reaction in deep water of the Southern Caspitan Sea, Near Iranian Coast", Polish Journal of Environmental Studies, 20(5): 1167-1180.
  • Sheela, A.M., Letha, J., Sabu, J., Ramachandran, K.K. and Justus, J (2013), "Assessment of pollution status of a coastal lake system using satellite Imagery", Geophysics & Remote Sensing, 2(1): 1-11.
  • Vikas, M. and Dwarakish, G. S. (2015), "Coastal pollution: a review", Aquatic Procedia, 4: 381-388.
  • Walsh, P.J., Smith, S., Fleming, L., Gabriele, H.S. and Gerwck, W.H (2008), "Ocean and human health: risks and remedies from the seas", Academic Press.

Abstract Views: 195

PDF Views: 1




  • Assessment of Coastal Water Quality Along South West Coast of India using Multile Regression Analysis on Satellite Data

Abstract Views: 195  |  PDF Views: 1

Authors

Dinu Maria Jose
Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Mangalore, Karnataka, India
Venkata Ravibabu Mandla
Centre for Geoinformatics Applications in Rural Development (C-GARD), School of Science, Technology and Knowledge Systems, National Institute of Rural Development and Panchayati Raj (NIRD&PR), India
Saladi S. V. Subbarao
Data and information Management Group, INCOIS, Hyderabad, India
N. Srinivasa Rao
School of Engineering Sciences, Mahindra Ecole Centrale, Bahadurpally, Hyderabad, Telangana, India
Sheela A. Moses
Kerala State Pollution Control Board, Trivandrum, Kerala, India

Abstract


The coastal waters being the ultimate receiver of all the wastes, shows a declining trend in its quality. It is of immense importance to know the extent of pollution for its monitoring and management. Measurement of dissolved oxygen (DO), biological oxygen demand (BOD), pH and fecal coliform (FC) are vital in water quality monitoring and assessment studies. Usually these parameters are determined by analysing water samples collected from various locations. Since this is tedious and expensive, it is limited to small scales. In this paper, an effort has been made to quickly assess the quality of coastal waters of Kerala directly from the satellite imagery by estimating National Sanitation Federation Water Quality Index (NSFWQI) along with DO, BOD, pH and FC. Multiple linear regression is used to develop statistically significant models using Sea Surface Temperature (SST) and Remote Sensing Reflectance (Rrs ) from Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ data available on DO, BOD, pH and FC. The models when validated showed good correlation between in situ values and predicted values with r values ranging from 0.73 (p = 0.001) for DO to 0.89 for NSFWQI (p = 0.018).Spatial maps are generated showing the distribution of these parameters along the coast. The parameters in the study are checked to see if they are in compliance with the standards. The study gives models to estimate the daily distribution of these parameters along the coast using MODIS data. Thus, appropriate control measures could be adopted to limit the effect on susceptible rural population.

Keywords


Water Quality, Moderate Resolution Imaging Spectroradiometer, Remote Sensing Reflectance, National Sanitation Federation Water Quality Index.

References





DOI: https://doi.org/10.25175/jrd%2F2018%2Fv37%2Fi2%2F129673