Spatio-temporal variability and possible source identification of criteria pollutants from Ahmedabad-a megacity of Western India

This study addresses the spatio-temporal variability and plausible sources of criteria air pollutants in the Western Indian city-Ahmedabad. The air pollutants PM10, PM2.5, O3, NO2, SO2, and CO have been analyzed at ten locations in Ahmedabad from 2017 to 2019. The seasonal variability indicates that the air pollutant concentration is highest during winter, followed by pre-monsoon, post-monsoon, and monsoon seasons. The concentration of PM2.5 (59.52 ± 16.68–89.72 ± 20.68) and PM10 (107.25 ± 30.43–176.04 ± 38.34) crosses the National Ambient Air Quality Standards (NAAQS) in all seasons. However, the seasonal difference from winter to pre-monsoon is not highly significant (p > 0.05), indicating that the pollution remains fairly similar during these two seasons. The spatial variability of air pollutants over Ahmedabad indicates that the concentration is highest in the south and central region of Ahmedabad and lowest at the east location. The Ventilation Coefficient (VC) has been used to understand the dispersion of air pollutants. The K-means clustering was performed to assess the locations within Ahmedabad with similar air pollutants sources followed by source identification using Principal Component Analysis-Multiple Linear Regression method (PCA-MLR) of 5 clusters. The different locations identified were industrial, residential, and traffic which mainly contribute to the air pollutants in Ahmedabad city. The health risk assessment indicates PMs are the leading pollutant and causing excess risk (ER > 1) at all the locations. With the help of the different statistical techniques, it helps in ascertaining the hotspots of air pollution in a region which will be beneficial in studying health exposure and for policymakers to adopt mitigation strategies

Machine learning based quantification of VOC contribution in surface ozone prediction

The prediction of surface ozone is essential attributing to its impact on human and environmental health. Volatile organic compounds (VOCs) are crucial in driving ozone concentration; particularly in urban areas where VOC limited regimes are prominent. The limited measurements of VOCs, however, hinder assessing the VOC-ozone relationship. This work applies machine learning (ML) algorithms for temporal forecasting of surface ozone over a metropolitan city in India. The availability of continuous VOCs measurement data along with meteorology and other pollutants during 2014–2016 makes it possible to deduce the influence of various input parameters on surface ozone prediction. After evaluating the best ML model for ozone prediction, simulations were carried out using varied input combinations. The combination with isoprene, meteorology, NOx, and CO (Isop + MNC) was the best with RMSE 4.41 ppbv and MAPE 6.77%. A season-wise comparison of simulations having all data, only meteorological data and Isop + MNC as input showed that Isop + MNC simulation gives the best results during the summer season (RMSE: 5.86 ppbv, MAPE: 7.05%). This shows the increased ability of the model to capture ozone peaks (high ozone during summer) relatively better when isoprene data is used. The overall results highlight that using all available data doesn't necessarily give best prediction results; also critical thinking is essential when evaluating the model results