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 PM_2.5, 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 PM_2.5, 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 PM_2.5 (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 PM_2.5 by RF, SVM, bagged MARS, and BRNN models at Talcher in comparison to PM_2.5 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.

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: PM_2.5 (7–10%), CO (29–35%), NO₂ and NO_x (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 PM_2.5 (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 PM_2.5 (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.

Geospatial mapping and monitoring are crucial for designing slopes in opencast mining, as failure can result in significant economic and life loss. To overcome these issues, the present investigation evaluated the Digital Elevation Model (DEM) generated using high resolution Cartosat-1 satellite imagery. Simultaneously, mapping of shaded relief map, aspect map and slope maps was done for the monitoring of Shatabdi opencast coal mine of Jharia region, an eastern coalfield region of India. Shaded relief map was categorized in to low, moderate and high categories and maximum area was covered by moderate to high shades. The constructed aspect map encompasses the range between 0 and 360 degrees of direction, with categorization and representation through distinct colors. The slope map was classified into various categories based on degrees of slope, including very gentle, gentle, moderate, moderately steep, steep, and very steep slopes. The maps were ground validated using Differential Global Positioning System (DGPS) collected data points by field visit in the region. For opencast mines to operate safely and profitably, slope stability in different stages of mining must be mapped and monitored. The study synthesizes data from diverse sources, highlighting the role of geospatial technology in addressing multiple research gaps within the mining industries prior to leveraging Cartosat-1 satellite data. Slope mapping and monitoring of coalfield regions are crucial to reducing construction costs, mitigation of natural hazard risks like flooding and landslides, and effective conservation of natural resources such as soils, vegetation, and water systems. The present study benefits policymakers, environmental planners and government in formulating policies to ensure safety, reliability, and enhance the economic growth of workers and the country.

Study needs major revision. Study included too much data. I would suggest plotting the comparative graph of different variables for all locations in one plot. For example, plot all points (male hostel, female hostel, dumpsite, university campus,...

Assessment of air pollutants and quality is an intricate task because of dynamic nature, unpredictability and high inconsistency in space and time. In this study, a time series moving average (MA) model is employed to estimate air pollutants (PM_2.5, PM₁₀, NO₂, NO_X, O₃, SO₂ and CO) over the coalfield site of India. The estimated O₃ with Adj. R² = 0.958 was identified as the most accurate estimation followed by other estimated pollutants. Though, results for the estimated PM_2.5 (Adj. R² = 0.950) and NO₂ (Adj. R² = 0.949) were found almost similar to the results of O₃ (Adj. R² = 0.958). The estimated CO with Adj. R² = 0.887 was identified lower among all the estimated pollutants was also found very well. The existing results of the study demonstrate that MA model permits us to precisely estimate daily basis pollutant concentrations, for the different sites of India.