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Identification and Apportionment of Pollution Sources to Groundwater Quality

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Abstract

Characterizing groundwater quality and apportionment of pollution sources to groundwater pollution is important for managing water resources effectively. Owing to rapid industrialization and population growth in Bengaluru city, the groundwater quality is getting deteriorated. Receptor modeling by Multi-Linear Regression of the Absolute Principal Component Scores (APCS-MLR) has been used to evaluate the source apportionment of groundwater pollution in order to recognize and quantify the pollution sources. Groundwater quality data measured for pre-monsoon and post-monsoon in the year 2014, comprising 14 physico-chemical parameters from 68 sites distributed across the study area, have been used. Principal component analysis identified four factors explaining 79.2 % of the total variance. Receptor modeling using APCS-MLR provided apportionment of different sources responsible for the groundwater quality along with percentage contribution of the recognized sources to each parameter. Results revealed that most of the variables were primarily affected by rock water interactions, seepage of sewage and industrial effluent. It was also found that few parameters gained significant contribution from the unidentified sources. Finally, the model performance was evaluated based on the ratio of estimated mean to measured mean (E/M). It was found that except for Fe with (E/M) ratio as high as 7.1, the model showed moderate strength with (E/M) values ranging from 0.51 to 2.83 of all the other parameters.

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Authors and Affiliations

  1. Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, Srinivasanagara, P.O, Mangaluru, Karnataka, 575 025, India

    Mohammad Shahid Gulgundi & Amba Shetty

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  1. Mohammad Shahid Gulgundi
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  2. Amba Shetty
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Correspondence to Mohammad Shahid Gulgundi.

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Gulgundi, M.S., Shetty, A. Identification and Apportionment of Pollution Sources to Groundwater Quality. Environ. Process. 3, 451–461 (2016). https://doi.org/10.1007/s40710-016-0160-4

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  • DOI: https://doi.org/10.1007/s40710-016-0160-4

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