Browsing by Author "Chinmay Pradhan"

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Evaluating air quality and criteria pollutants prediction disparities by data mining along a stretch of urban-rural agglomeration includes coal-mine belts and thermal power plants
(Frontiers Media SA, 2023) Arti Choudhary; Pradeep Kumar; Chinmay Pradhan; Saroj K. Sahu; Sumit K. Chaudhary; Pawan K. Joshi; Deep N. Pandey; Divya Prakash; Ashutosh Mohanty
Air pollution has become a threat to human life around the world since researchers have demonstrated several effects of air pollution to the environment, climate, and society. The proposed research was organized in terms of National Air Quality Index (NAQI) and air pollutants prediction using data mining algorithms for particular timeframe dataset (01 January 2019, to 01 June 2021) in the industrial eastern coastal state of India. Over half of the study period, concentrations of PM2.5, PM10 and CO were several times higher than the NAQI standard limit. NAQI, in terms of consistency and frequency analysis, revealed that moderate level (ranges 101–200) has the maximum frequency of occurrence (26–158 days), and consistency was 36%–73% throughout the study period. The satisfactory level NAQI (ranges 51–100) frequency occurrence was 4–43 days with a consistency of 13%–67%. Poor to very poor level of air quality was found 13–50 days of the year, with a consistency of 9%–25%. Random Forest (RF), Support Vector Machine (SVM), Bagged Multivariate Adaptive Regression Splines (MARS) and Bayesian Regularized Neural Networks (BRNN) are the data mining algorithms, that showed higher efficiency for the prediction of PM2.5, PM10, NO2 and SO2 except for CO and O3 at Talcher and CO at Brajrajnagar. The Root Mean Square Error (RMSE) between observed and predicted values of PM2.5 (ranges 12.40–17.90) and correlation coefficient (r) (ranges 0.83–0.92) for training and testing data indicate about slightly better prediction of PM2.5 by RF, SVM, bagged MARS, and BRNN models at Talcher in comparison to PM2.5 RMSE (ranges 13.06–21.66) and r (ranges 0.64–0.91) at Brajrajnagar. However, PM10 (RMSE: 25.80–43.41; r: 0.57–0.90), NO2 (RMSE: 3.00–4.95; r: 0.42–0.88) and SO2 (RMSE: 2.78–5.46; r: 0.31–0.88) at Brajrajnagar are better than PM10 (RMSE: 35.40–55.33; r: 0.68–0.91), NO2 (RMSE: 4.99–9.11; r: 0.48–0.92), and SO2 (RMSE: 4.91–9.47; r: 0.20–0.93) between observed and predicted values of training and testing data at Talcher using RF, SVM, bagged MARS and BRNN models, respectively. Taylor plots demonstrated that these algorithms showed promising accuracy for predicting air quality. The findings will help scientific community and policymakers to understand the distribution of air pollutants to strategize reduction in air pollution and enhance air quality in the study region. Copyright © 2023 Choudhary, Kumar, Pradhan, Sahu, Chaudhary, Joshi, Pandey, Prakash and Mohanty.
Health Risk Appraisal Associated with Air Quality over Coal-Fired Thermal Power Plants and Coalmine Complex Belts of Urban–Rural Agglomeration in the Eastern Coastal State of Odisha, India
(MDPI, 2022) Arti Choudhary; Pradeep Kumar; Saroj Kumar Sahu; Chinmay Pradhan; Pawan Kumar Joshi; Sudhir Kumar Singh; Pankaj Kumar; Cyrille A. Mezoue; Abhay Kumar Singh; Bhishma Tyagi
Manufacturing and mining sectors are serious pollution sources and risk factors that threaten air quality and human health. We analyzed pollutants at two study sites (Talcher and Brajrajnagar) in Odisha, an area exposed to industrial emissions, in the pre-COVID-19 year (2019) and consecutive pandemic years, including lockdowns (2020 and 2021). We observed that the annual data for pollutant concentration increased at Talcher: PM2.5 (7–10%), CO (29–35%), NO2 and NOx (8–57% at Talcher and 14–19% at Brajrajnagar); while there was slight to substantial increase in PM10 (up to 11%) and a significant increase in O3 (41–88%) at both sites. At Brajrajnagar, there was a decrease in PM2.5 (up to 15%) and CO (around half of pre-lockdown), and a decrease in SO2 concentration was observed (30–86%) at both sites. Substantial premature mortality was recorded, which can be attributed to PM2.5 (16–26%), PM10 (31–43%), NO2 (15–21%), SO2 (4–7%), and O3 (3–6%). This premature mortality caused an economic loss between 86–36 million USD to society. We found that although lockdown periods mitigated the losses, the balance of rest of the year was worse than in 2019. These findings are benchmarks to manage air quality over Asia’s largest coalmine fields and similar landscapes. © 2022 by the authors.