A study to access and estimation of air pollution using a multivariate statistical model in Chennai, India

Abstract

Introduction: Rapid urbanization and industrial growth are the primary causes of deteriorating urban air quality in developing countries, including India. Vehicular emission is a significant cause of the degradation of air quality. It includes Air Pollution like carbon monoxide, hydrocarbons, oxides of nitrogen, oxides of sulfur, Suspended Particulate Matter (SPM), respiratory Particulate Matters (PM2.5 and PM10), and lead. Materials and methods: The study has considered land use,land cover, land surface temperature, vegetation, literacy rate, vehicle population, population density, and households, finding the responsible causes of air pollutionfor the analysis. Supervised classification using ArcGIS for extracting land use and land cover, Least Slack Time (LST) algorithms have used to extract land surface temperature, spatial interpolation methods like Inverse Distance Weighting (IDW) using ArcGIS for identifying the spatial distribution of Literacy rate, vehicle population, population density and households and finally the multivariate statistical model applied to identify the major responsible factor for air pollution using SPSS. Results: The study reveals that the particulate matter is below the standard value prescribed by the central pollution control board. The Highest air pollution is primarily responsible for vehicle population and industries. Wednesday and Thursday are the maximum pollution in Chennai, whereas it was recorded as very low on Sunday as compared to other days. Conclusion: Regression shows that the vehicle population is responsible for air pollution, followed by the population.

Keywords

• Air pollution; Particulate matter; Particulate matters less than 2.5 µm (PM2.5); Particulate matters less than 10 µm (PM10); Multivariate statistical model