2022Nature Environment and Pollution TechnologyOpen access

A GIS-Based Study of Air and Water Quality Trends in Madurai City, India

L. Balaji, M. Muthukannan, R. Kanniga Devi

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Abstract

The objective of this study is to assess the air quality and water quality at Madurai city using the spatial interpolation technique of the Geographic Information System (GIS). Inverse Distance Weighting (IDW), Kriging, Gaussian- Kriging methods of interpolation are used in the study. The study area is divided into 8 zones and primary data is collected through direct sampling for water. The air pollution concentration data on Suspended Particulate Matter (SPM), Respirable Particulate Matter (RPM), and Lead are collected from an air quality monitoring network system of Tamilnadu Pollution Control Board (TNPCB). The results indicate that the Kriging interpolation method performs well with an error value of 0.02. The data collected from the years 2006 and 2020 are compared using GIS and observed data. The comparison of results indicates that there is a good accord with the predicted value using Inverse Distance Weighting (IDW) and Kriging interpolation with the observed data. The analysis report indicates that the pollution level at all 8 blocks was severely affected in 2020 and it is above the prescribed standard of TNPCB. Particularly, Kalavasal, Arapalyam, and Periyar blocks are severely affected. This work also presents suitable mitigation measures to reduce pollution.

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What this paper is about

The objective of this study is to assess the air quality and water quality at Madurai city using the spatial interpolation technique of the Geographic Information System (GIS). Inverse Distance Weighting (IDW), Kriging, Gaussian- Kriging methods of interpolation are used in the study. The study area is divided into 8 zones and primary data is collected through direct sampling for water. The air pollution concentration data on Suspended Particulate Matter (SPM), Respirable Particulate Matter (RPM), and Lead are collected from an air quality monitoring network system of Tamilnadu Pollution Control Board (TNPCB). The results indicate that the Kriging interpolation method performs well with an error value of 0.02. The data collected from the years 2006 and 2020 are compared using GIS and observed data. The comparison of results indicates that there is a good accord with the predicted value using Inverse Distance Weighting (IDW) and Kriging interpolation with the observed data. The analysis report indicates that the pollution level at all 8 blocks was severely affected in 2020 and it is above the prescribed standard of TNPCB. Particularly, Kalavasal, Arapalyam, and Periyar blocks are severely affected. This work also presents suitable mitigation measures to reduce pollution.

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Available abstract

The objective of this study is to assess the air quality and water quality at Madurai city using the spatial interpolation technique of the Geographic Information System (GIS). Inverse Distance Weighting (IDW), Kriging, Gaussian- Kriging methods of interpolation are used in the study. The study area is divided into 8 zones and primary data is collected through direct sampling for water. The air pollution concentration data on Suspended Particulate Matter (SPM), Respirable Particulate Matter (RPM), and Lead are collected from an air quality monitoring network system of Tamilnadu Pollution Control Board (TNPCB). The results indicate that the Kriging interpolation method performs well with an error value of 0.02. The data collected from the years 2006 and 2020 are compared using GIS and observed data. The comparison of results indicates that there is a good accord with the predicted value using Inverse Distance Weighting (IDW) and Kriging interpolation with the observed data. The analysis report indicates that the pollution level at all 8 blocks was severely affected in 2020 and it is above the prescribed standard of TNPCB. Particularly, Kalavasal, Arapalyam, and Periyar blocks are severely affected. This work also presents suitable mitigation measures to reduce pollution.

Key concepts: Inverse distance weighting, Kriging, Multivariate interpolation, Environmental science, Interpolation (computer graphics), Particulates, Pollution, Air pollution

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