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.

Multiple crop yield estimation and forecasting have become essential because climate change and land degradation are deteriorating production of crops. The present study presents multiple crop yield estimation and forecasting efficiently and precisely using MERRA-2 model, satellite-gauge and MODIS-Terra satellite data by regression and time series models. Rice, wheat, maize, rapeseed & mustard, and millet crops yield data of India was used for crop yield estimation and forecasting using Multiple Linear Regression (MLR), Decomposition, Holt, Winters and Autoregressive Integrated Moving Average (ARIMA) models. Models were developed using crops yield historical data, weather variables (temperature, precipitation, surface radiation) and pollutants (O₃, CO) data of 1982–83 to 2014–15. Overall best correlation (R = 0.96) and poor correlation (R = 0.56) was found between observed and estimated rice and rapeseed & mustard crops using Decomposition and MLR models, respectively. After rigorous skill assessment and out of more than 20 combinations of ARIMA models, the ARIMA (0, 1, 1) time series model performed the best with Root Mean Square Error (RMSE = 4.59%) for wheat crop estimation. Forecasted crop yields were successfully cross-validated using four years’ data (2015–16, 2016–17, 2017–18 and 2018–19) for regression and time series models by comparing Relative Error (RE%) between observed and forecasted crops yields. Results were statistically analyzed using correlation coefficient (R), Bayesian Information Criteria (BIC), RMSE%, Mean Absolute Percentage Error (MAPE%), t-value and p-value. The crop yield estimation and forecasting results showed reasonably quantifiable information with the actual datasets. The outcomes of the present study have policy level implications for compensation to farming communities for the loss of crops.

The objective of this paper is to control the speed of Permanent Magnet Synchronous Motor (PMSM) over wide range of speed by consuming minimum time and low cost. Therefore, comparative performance analysis of PMSM on basis of speed regulation has been done in this study. Comparison of two control strategies i.e. Field oriented control (FOC) without sensor less Model Reference Adaptive System (MRAS) and FOC with sensor less MRAS has been carried out. Sensor less speed control of PMSM is achieved by using estimated speed deviation as feedback signal for the PI controller. Performance of the both control strategies has been evaluated in in MATLAB Simulink software. Simulation studies show the response of PMSM speed during various conditions of load and speed variations. Obtained results reveal that the proposed MRAS technique can effectively estimate the speed of rotor with high exactness and torque response is significantly quick as compared to the system without MRAS control system.

Recent satellite observations indicate sustained deterioration in ambient air quality across Indian urban and rural regions. In India, nearly 30 people die