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It is important to keep soil organic carbon (SOC) in balance to ensure soil health and quality. In this manner, mining activities have crucial impacts on SOC stocks, especially in semi-arid and arid regions such as Iran. For this purpose, SOC was measured at 180 randomly selected points in both natural and agricultural soils in the central part of Iran. Machine learning methods, such as GEP (Genetic Expression Programming), SVR (Support Vector Regression), and ANNs (Artificial Neural Networks), were developed and employed to estimate SOC for all sampled points, including both natural and agricultural soils. Following that, topography and remotely sensed data were employed as input variables to improve SOC prediction influenced by mining. The remotely sensed data and topography factors were extracted from Landsat 9 images and Digital Elevation Models (DEMs), respectively. Input variables were considered in three scenarios, including the use of topography factors (scenario I), the use of remote sensing data (scenario II), and the use of both topography factors and remote sensing data (scenario III). The results of this study showed that the most effective model for predicting SOC across all sampled data was SVR (ME = −0.1539%, R2 = 0.642 and RMSE = 0.620%) when employing scenario III. Furthermore, the results indicated that the optimal method for both natural and agricultural soils was the SVR method when employing scenario III. Further analysis through mapping SOC contents showed that mining activities influenced the distribution of SOC in the studied region. Overall, the predicted maps of SOC contents indicated that lower SOC contents were predominantly distributed in the vicinity of salt and sand mines, particularly in salt-rich areas, for both natural and agricultural soils. • Prediction of soil Organic carbon stocks influenced by mine. • Topography and remotely sensed data were employed. • GEP, SVR and ANNs were used to estimate SOC. • SVR was the best method for both of natural and agriculture soils. [ABSTRACT FROM AUTHOR]

A comparison between two digestion methods of hot plate Hossner (total-total) and USEPA method 3051 (total-recoverable) was carried out to suggest a proper method for determining nine heavy metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn) content of three urban soils affected by mining (Mahd AD'Dahab) or industrial activities (Riyadh and Jubail) at Saudi Arabia. The results showed no significant differences between two digestion methods for Cd, Cu, Pb and Zn in soils affected by mining and for Cr, Cu, Pb and Zn in soils affected by industrial activities. Additionally, lower biases were obtained between two methods for metals Cd, Cu, Zn and Pb in the urban soil samples from mining area with the percent biases of −16.5%, +6.24%, −12.4% and +24.1%, respectively. The results also revealed that only Cu and Zn in the soil samples from Riyadh were extracted satisfactorily using USEPA 3051 with low biases of +5.69% and −9.61%, respectively. Meanwhile, only Pb in soil samples from Jubail showed lower baise between two methods with satisfactory biase of −8.07%. The correlation coefficients were significant between total-recoverable and total-total concentrations for Cu (r = 0.66), Pb (r = 0.72) and Cd (r = 0.65) in soil samples from mining area. Overall, concentrations of Co, Cr, Fe, Mn, and Ni that may show soil background concentrations were found higher by Hossner method than by USEPA 3051; thus, this suggests the addition of hydrofluoric acid (HF) is necessary for the determination of lithogenic metal concentrations. It could be concluded that the USEPA 3051 may be recommended and applied for total Cd, Cu, Pb and Zn originated from anthropogenic source in mining and industrial areas. [ABSTRACT FROM AUTHOR]

Worldwide mining activities are one of the major anthropogenic activities that have caused high forest cover loss (FCL). In this study, we have quantified FCL in Odisha State due to mining activities analyzing Hansen Global Forest Change (HGFC) time series data for the period of 2001–2019 in Google Earth Engine platform. Our analysis suggests that Nabarangpur, Puri, Kendrapara, and Kalahandi districts lost more than 20% of their forest cover during this period. Rayagada and Koraput were the top two districts that recorded the highest FCL with mean change rates of 13.81 km2/year and 7.17 km2/year, respectively. The results point out that mining operations have grown in recent years in Odisha State, and the increase in these activities has contributed to the increase in FCL. This study offers a cost-effective methodology to monitor FCL in mining areas which will eventually contribute to the protection of forest biodiversity and forest dwelling tribal population. [Display omitted] • A cost-effective methodology to assess the deforestation across Odisha State. • The impact of key minerals was analyzed in Odisha State, India. • High-resolution global FCL datasets and Google Earth Engine platform were employed. • A spatialization of total accumulated FCL within Odisha State since 2000 was performed. • The highest FCL were recorded in Kandhamal, Rayagada and Kalahandi during 2001–2019. [ABSTRACT FROM AUTHOR]

This work mainly focused on deforestation susceptibility (DS) assessment and its prediction based on statistical models (FR, LR & AHP) in the Saranda forest, India. Also, efforts had been made to quantify the effect of mining on deforestation. We had considered twenty-five (twenty present and five predicted) causative variables of deforestation, including climate, natural or geomorphological, forestry, topographical, environmental, and anthropogenic. The predicted variables have been generated from different simulation models. Also, very high-resolution, Google Earth imagery have been used in time series analysis for deforestation from 1987 to 2020 data and generated dependent variable. On deforestation analysis, it was observed that a total of 4197.84 ha forest areas were lost in the study region due to illegal mining, agricultural and tribal people allied activities. The DS results have shown that of total existing forest area, 11.22% area were under very high, 16.08% under high, 16.18% under moderate, 24.25% under low, and 32.27% falls very low categories. According to the DS assessment and predicted results, the very high susceptibility classes were found at and close to mines, agricultural, roads and settlement's surrounding sites. The sensitivity analysis results also shown that some causative variables (maximum temperature (2.95%), minimum temperature (0.51%), rainfall (2.69%), LST (4.56%), hot spot (7.36%), aspect (1.14%), NDVI (2.64%), forest density (3.78%), lithology (3.26%), geomorphology (3.00%), distance from agricultural (19.40%), soil type (2.05%), solar radiation (5.97%), LULC (3.26%), drought (3.16%), altitude (2.85%), slope (5.97%), distance from mines (18.05%), roads (2.17%), and settlements (5.18%)) were more sensitive to deforestation. Most of the sensitive parameters showed a positive correlation with DS. The AUC values of the ROC curve had shown a better fit for AHP (0.72) than (0.69) FR and LR (0.68) models for present DS results. The correlation results had shown a good inverse relationship between DS and distance from mines and foliar dust concentration. This work will espouse the future work in the effective planning and management of the mining-affected forest region and predicted deforestation susceptibility would be helpful for forest ecosystem study and policymaking. • The study focuses on assessment and prediction of deforestation susceptibility (DS). • DS performed by FR, LR, and AHP models in GIS platform. • Total 25 causative parameters have been used in this study. • AHP model has better accuracy (0.72) than FR (0.69) and LR (0.68) models. • The study emphasizes on mining-affected forest region for effective forest planning and management. [ABSTRACT FROM AUTHOR]