From slag to green: Aided-phytoremediation as a sustainable tool to rehabilitate land contaminated by steel slag and assessment of CO2 sequestration.

Steel slag (SS) has many applications, but its immediate reuse is not possible due to its inherent swelling potential and presence of toxic metals. Therefore, it can only be used after the aging process, which can be either natural or artificial. While few large-scale steel plants afford artificial aging, many small-scale ones opt for natural aging through stockpiling of SS. This results in an increase in soil pH to over 12, thus damaging the ecosystem and making it unviable for plant growth. This research focuses on the reclamation of land affected by SS through the formation of a Phyto-barrier using 22 native plant species aided by the application of a 2 % (v/v) solution of the organic amendment. Furthermore, the superior performance of plants belonging to the Fabaceae family was ascertained, while establishing Sesbania grandiflora as an able species for aided-phytoremediation due to its remarkable growth (≈ 10 ft tall and 33 cm in circumference) during the study period. The CO2 sequestered by the plantation showed that maximum sequestration has been done by Sesbania grandiflora (49.96 kg CO2 / tree/ year), and least by Azadirachta indica (0.35 kg CO2/tree/year). The overall CO2 sequestered by the plantation stood at 3.85 tons/year. A cost-benefit analysis of using aided-phytoremediation indicates an expense of 90 $ per year as the recurring expense, while carbon credits if monetized, would yield 154 $ to 308 $ as returns. The investigations of this study established a new approach to vegetation over SS-affected land, through native species and the application of organic amendment.

Vegetation health conditions assessment and mapping using AVIRIS-NG hyperspectral and field spectroscopy data for -environmental impact assessment in coal mining sites.

This paper focuses on vegetation health conditions (VHC) assessment and mapping using high resolution airborne hyperspectral AVIRIS-NG imagery and validated with field spectroscopy-based vegetation spectral data. It also quantified the effect of mining on vegetation health for geo-environmental impact assessment at a fine level scale. In this study, we have developed and modified vegetation indices (VIs) based model for VHC assessment and mapping in coal mining sites. We have used thirty narrow banded VIs based on the statistical measurement for suitable VIs identification. The highest Pearson's r, R2, lowest RMSE, and P values indices have been used for VIs combined pixels analysis. The highest different (Healthy vs. unhealthy) vegetation combination index (VCI) has been selected for VHC assessment and mapping. We have also compared VIs model-based VHC results to ENVI (software) forest health tool and Spectral-based SAM classification results. The 1st VCI result showed the highest difference (72.07%) from other VCI. The AUC values of the ROC curve have shown a better fit for the VIs model (0.79) than Spectral classification (0.74), and ENVI FHT (0.68) based on VHC results. The VHC results showed that unhealthy vegetation classes are located at low distances from mine sites, and healthy vegetation classes are situated at high distances. It is also seen that there is a highly significant positive relationship (R2 =0.70) between VHC classes and distance from mines. These results will provide a guideline for geo-environmental impact assessment in coal mining sites.

Assessment and management of soil erosion in the hilltop mining dominated catchment using GIS integrated RUSLE model.

The Saranda forest region, which is well known for its biodiversity in India, is now confronted by rapid socio-economic development, particularly the hilltop mining activities. Hilltop mining areas of this region have always been responsible for producing excessive soil erosion in the associated river basin. This erosion phenomenon becomes hazardous during the rainy season, thereby contributing to various environmental problems, and consequently, necessitating soil erosion control planning in the Saranda forest. Hence, this study aimed to estimate average annual soil erosion in the Saranda region in terms of the spatial distribution using the Geographic Information System (GIS) integrated Revised Universal Soil Loss Equation (RUSLE) model. The erosion was quantified at a spatial resolution of 10 m (pixel by pixel) using the GIS-based RUSLE inputs. This study also applies GIS integrated Analytic Hierarchy Process (AHP) model to identify the favorable zones for sediment deposition in the study area. On the basis of erosion severity, the entire study area is classified into six categories (very low to extreme). The study reveals that the Saranda forest's average annual soil erosion is 76 tons per hectare per year (t/ha/yr). Approximately 63% of the total area is categorized under very low to low erosion category, and the relevant area is mainly covered by forest land, whereas the mining region comprises less than 1% of the total study area with extremely high soil erosion (156 t/ha/yr) potential. As envisaged from the present study, the erosion-prone mining areas are located within a 1-5 km range of the adjacent Karo and Koina rivers, thereby, necessitating the erosion control strategy to avoid the possible threats. From this perspective, the study also investigates the favorable zones for sediment deposition using the GIS integrated AHP model to suggest the appropriate erosion control measures. Finally, the RUSLE and AHP models are combined on the GIS platform to identify the distressed catchment area. Moreover, 42% (41,060 ha) of the total area is disturbed due to the present mining activities, which involves 11 sub-watersheds, and their associated 50 micro-watersheds. From the context of watershed conservation, erosion control measures are also recommended. The methodology adopted in this study can be easily extended to any global mining-dominated catchment for sustainable conservation planning.

Deforestation susceptibility assessment and prediction in hilltop mining-affected forest region.

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.

Assessment of mining activities on tree species and diversity in hilltop mining areas using Hyperion and Landsat data.

The tree species and its diversity are two critical components to be monitored for sustainable management of forest as well as biodiversity conservation. In the present study, we have classified the tree species and estimated its diversity based on hyperspectral remote sensing data at a fine scale level in the Saranda forest. This area is situated near the mining fields and has a dense forest cover around it. The forest surrounding the study area is exhibiting high-stress condition as evidenced by the dying and dry plant material, consequently affecting tree species and its diversity. The preprocessing of 242 Hyperion (hyperspectral) spectral wavebands resulted in 145 corrected spectral wavebands. The 21 spectral wavebands were selected through discrimination analysis (Walk's Lambda test) for tree species analysis. The SVM (support vector machine), SAM (spectral angle mapper), and MD (minimum distance) algorithms were applied for tree species classification based on ground spectral data obtained from the spectroradiometer. We have identified six local tree species in the study area at the spatial level. The result shows that Sal and Teak tree species are located in the upper and lower hilly sides of two mines (Meghahatuburu and Kiriburu). We have also used hyperspectral narrow banded vegetation indices (VIs) for species diversity estimation based on the field-measured Shannon diversity index. The statistical result shows that NDVI705 (red edge normalized difference vegetation index) is having the best R2 (0.76) and lowest RMSE (0.04) for species diversity estimation. That is why we have used NDVI705 for species diversity estimation. The result shows that higher species diversity values are located in the upper and lower hilly sides of two mines. The linear regression between Hyperion and field measured Shannon index shows the R2 (0.72) and RMSE (0.15). This study will aid in effective geoenvironmental planning and management of forest in the hilltop mining areas.

Effective oil removal from water by magnetically driven superhydrophobic and oleophilic magnetic titania nanotubes.

Development of efficient techniques to combat the harmful effects of oil spill is an emerging field, where fabrication of new sorbents for selective removal of oil has become a hot topic for environmental scientists. The present study reports the preparation of superhydrophobic/oleophilic magnetic titania nanotubes via a facile hydrothermal method, followed by the treatment with octadecylamine, as potential magnetically driven sorbent for selective removal of oil from water surface. The magnetic nature (superparamagnetism at 300 K) of the nanotubes enabled magnetic removal of the oil-sorbed material from water surface. Wettability test of the material depicted a static water contact angle of 166 ± 1°, indicating its superhydrophobic character. Oil uptake experiments and contact angle measurements revealed its superoleophilicity with maximum oil sorption capacity >1.5 g/g for a variety of oils. In addition to the ease of magnetic removal, the nanotubes possess sufficient buoyancy, high selectivity, and quick rate of oil uptake and is more than five times reusable.