In this study, we assessed the changes in the physical and chemical characteristics of the soil samples collected from the artisanal and small-scale limestone mining site in Sohra (Cherrapunjee), Meghalaya, by comparing them with the non-mining site. Eleven distinct soil parameters, namely pH, electrical conductivity (EC), texture (ST), moisture content (MC), bulk density (BD), total porosity (TP), water holding capacity (WHC), organic carbon (OC), total nitrogen (TN), available phosphorus (AP), and exchangeable potassium (EK), were evaluated seasonally (winter, pre-monsoon, and post-monsoon) for 2 years. The results showed that limestone mining has significantly affected the soil quality. The effect is evident by the substantial increases in EC values, sand content, and alkaline soils coupled with noticeably low concentrations of OC and TN. In addition, prominent changes were perceived in the soil MC and EK content, as well as in WHC, BD, and TP percent. Results from ANOVA revealed significant differences (p < 0.05) in mean values at different sampling seasons and sites. The multivariate statistical analysis results showed that the computed correlation coefficient (r) matrix data ranged from − 1.00 to 0.974. A strong positive correlation was highest between OC and TN (0.974), followed by OC with EK (0.828). Principal component (PC) analysis revealed two major components, PC 1 and PC 2, having eigenvalues of 6.276 and 1.747, respectively. Cumulatively, these two components explained 80.23% of the total variance. The loading factor in PC 1 is high and is attributed to OC (.974), TN (.970), and EK (.903). However, in PC 2, the loading factor is positively pooled by MC (0.894) and TP (0.765). The present study concludes that artisanal and small-scale limestone mining altered the soil’s physical and chemical properties, and these changes are likely to have a subsequent deteriorating impact on the area’s biodiversity, landscape, and natural ecosystem. Therefore, to minimize the impact and ensure sustainable soil management in the area, approaches for effective mitigation and remediation measures, including formulating steps for the conservation and enhancement of the soil’s environmental quality, are recommended.
PM(_{2.5}) is the most hazardous air pollutant due to its smaller size, which allows deeper bodily penetration. Three diverse regions from Gujarat, India, namely Sector 10, Maninagar, and Vatva, which have green space, high population concentration, and industries, respectively, were chosen to forecast PM(_{2.5}) concentration for the next day. Four statistical models, including Multiple Linear Regression (MLR), Principal Component Regression (PCR), Simple Exponential Smoothing (SES), and Autoregressive Integrated Moving Average (ARIMA), were chosen to forecast PM(_{2.5}) levels. For this study, data of various pollutants and meteorological parameters were collected from February 2019 to September 2023. Analysis of the seasonal patterns of PM(_{2.5}) revealed elevated concentrations during post-monsoon and winter, in contrast to reduced levels during summer and monsoon. Statistical analysis revealed that the concentration of PM(_{2.5}) in Sector 10 is much lower than in the other two regions. The analysis of the test results, utilising various accuracy measures like RMSE, MAE, MAPE, IA, and others, indicated that Sector 10 achieved the highest precision in its results. While assessing the models’ accuracy on the test data, the ARIMA model demonstrated the highest level of precision. The average RMSE, MAE, and MAPE values for the ARIMA model were 12.63, 8.59, and 0.24, respectively. In the comparison of the performance between these statistical models and the neural network-based Multilayer Perceptron (MLP) model, it was observed that the statistical model demonstrated superior performance over the MLP model.
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