Groundwater in the Sudda Vagu basin, located in the Bhainsa region of Nirmal District, Telangana, serves as a critical source of water for both drinking and irrigation. To evaluate its quality and suitability, 25 groundwater samples were systematically collected during the pre-monsoon (May 2022) and post-monsoon (November 2022) periods and analyzed for major cations and anions. The concentrations of sodium (Na⁺), potassium (K⁺), carbonate (CO₃²⁻), bicarbonate (HCO₃⁻), and sulfate (SO₄²⁻) were found to remain within the permissible limits recommended by the Bureau of Indian Standards (BIS), whereas elevated levels of calcium (Ca²⁺), magnesium (Mg²⁺), chloride (Cl⁻), nitrate (NO₃⁻), and fluoride (F⁻) were detected in several samples, exceeding the prescribed thresholds. The pH of the groundwater ranged from 6.5 to 8.5, indicating alkaline conditions, and was deemed generally acceptable for drinking based on BIS guidelines. Hydrochemical facies classification using the Piper trilinear diagram revealed the predominance of Ca²⁺-HCO₃⁻, Na⁺-Cl⁻, and mixed water types. Irrigation suitability was further assessed through indicators including the Sodium Adsorption Ratio (SAR), Kelly Ratio (KR), and Residual Sodium Carbonate (RSC), along with the Wilcox diagram. Pre-monsoon evaluation indicated that 12 samples were categorized under the S1C2 class (low sodium hazard–medium salinity hazard), while 13 samples were assigned to the S1C3 class (low sodium hazard–high salinity hazard). Post-monsoon analysis revealed that four samples remained in S1C2, whereas 21 shifted into S1C3. The findings indicate that the majority of samples are suitable for drinking and irrigation. Continuous monitoring and the implementation of sustainable groundwater management strategies are therefore essential to ensure water security in this region.

The spatiotemporal dynamics of urban expansion and its impact on forest fragmentation within Dhaka City Corporation (DCC), a rapidly urbanizing megacity in South Asia, were critically investigated in this study. While prior research has predominantly focused on broad land-use changes and general vegetation loss, detailed analysis of forest fragmentation and its direct correlation with urban expansion intensity remains limited. This gap was addressed by integrating high-resolution Landsat satellite imagery from 2016, 2020, and 2024 with advanced landscape metrics and urban expansion indices, enabling a comprehensive and replicable assessment of urban-driven ecological disruption. Land use and land cover (LULC) classifications were generated through supervised classification in Google Earth Engine. Urban growth was quantified using the Urban Expansion Intensity Index (UEII) and the Annual Urban Expansion Rate (AUER), while forest fragmentation was evaluated via patch density, edge density, and a comprehensive fragmentation index derived from FRAGSTATS. Results indicated a marked intensification of urban expansion, with the urban area increasing from 133 km² in 2016 to 139 km² in 2024. This growth was accompanied by a rise in UEII from 0.67% to 1.35% and in AUER from 0.37% to 0.73%. Concurrently, forest ecosystems experienced significant fragmentation, as evidenced by an increase in the fragmentation index from 33 to 80 and edge density from 4 to 9 per km², indicating a progressive decline in forest continuity and heightened ecological vulnerability. Pearson correlation analysis revealed strong positive relationships between urban expansion and both edge density (r = 0.953) and the fragmentation index (r = 0.922), confirming the direct influence of urban sprawl on forest disintegration. These findings underscore the urgent need for ecologically informed urban planning. By providing a replicable methodological framework for quantifying urbanization-driven ecological disruption, this study contributes to the broader discourse on sustainable urban development and forest conservation in rapidly transforming urban landscapes.

Urban expansion, driven by rapid population growth, has increasingly encroached upon agricultural land and contributed to the degradation of ecological systems. In this study, the spatiotemporal dynamics of urban growth in Narayanganj District, Bangladesh, were assessed over a 20-year period (2003–2023) using integrated Geographic Information System (GIS) and remote sensing techniques. Land Use and Land Cover (LULC) changes were quantified, and their ecological consequences were evaluated through vegetation indices including the Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI), alongside the Normalized Difference Built-up Index (NDBI). An LULC classification revealed a net increase of 5.61% in built-up areas, accompanied by reductions of 7.61% and 1.61% in barren land and agricultural land, respectively. The spatial pattern of urban expansion was found to be uneven, with pronounced growth observed from the northern to north-northwestern sectors of the district. A two-phase conversion analysis indicated that 15.68% of agricultural land was transformed into urban areas between 2003 and 2013, followed by a slightly lower conversion rate of 14.74% from 2013 to 2023. Notably, a statistically significant inverse correlation was detected between NDBI and both NDVI and SAVI, suggesting a measurable decline in vegetation health associated with urban intensification. These findings provide empirical and geographically grounded evidence of the adverse ecological impacts of urbanization in a peri-urban context. The integration of multi-temporal satellite images with vegetation and built-up indices enabled a comprehensive evaluation of land transformation processes and their environmental implications. The insights gained from this research may inform sustainable land use planning, urban policy formulation, and conservation strategies aimed at mitigating the loss of agricultural land and safeguarding vegetation health in rapidly urbanizing regions.

Côte d’Ivoire is recognized as one of the principal gold-producing countries in West Africa, where artisanal and small-scale gold mining (ASGM) constitutes the second most prevalent livelihood activity after agriculture, particularly within rural communities. As a result, high concentrations of ASGM activity have been recorded in 78% of the country’s regions. In this context, the ecological impacts of ASGM on biodiversity in the Daoukro region were examined. A diachronic geospatial analysis was conducted using satellite imagery from 2010 to 2020, in conjunction with field-based spatial data collection and semi-structured interviews. The findings reveal that extensive environmental degradation has been driven by the unregulated techniques and substances employed in gold extraction processes, including the widespread use of mercury and cyanide. These practices have resulted in severe soil contamination, structural weakening due to erosion, and inhibited vegetative regeneration. Over the decade-long period, the proportion of bare soil increased at an annual growth rate of +7.90%, while forested areas declined markedly from 31,258 hectares to 24,750 hectares—representing a cumulative reduction of 20.34%. This deforestation has contributed to the disruption and loss of native biodiversity that relies on forest ecosystems for survival. Additionally, land fragmentation and habitat degradation have reduced ecological resilience, further intensifying species vulnerability in the region. These findings underscore the urgent need for sustainable land management policies and biodiversity conservation strategies tailored to mitigate the ecological footprint of ASGM in Côte d’Ivoire.

This study investigates the landscape dynamics and management practices affecting the Association for Forestry and Environmental Education (AFEE) forest massif, located in the monomodal agroecological zone of Cameroon. Using remote sensing data, including Landsat 8 imagery from 2014, 2019, and 2024, in conjunction with field observations, the spatio-temporal changes in land use over the past decade were mapped. Additionally, interviews were conducted with 30 local residents selected through snowball sampling to assess their perceptions of the forest's degradation and the impact of their livelihood activities on the surrounding environment. The results reveal a significant decline in the forest's ecological integrity, with the AFEE massif, originally covered entirely by mature secondary forest in 2014 (200 ha), experiencing a 77.7% reduction in forest cover by 2024. This loss has been primarily replaced by anthropogenic land uses, including young secondary forests (22.9%, 45.95 ha), swamps (17.6%, 35.35 ha), fallow lands (16.8%, 33.7 ha), rubber and oil palm plantations (1.46%, 2.91 ha), and agricultural plots (18.7%, 37.48 ha). Activities such as agriculture, hunting, artisanal sawmilling, and fishing, although central to the livelihoods of local people, have contributed significantly to the degradation of the natural landscape. These practices, while essential for local economic well-being, have negatively impacted the forest ecosystem. Given the critical role of the AFEE massif in environmental education, these findings are essential for the development of strategies that can balance the conservation of natural ecosystems with the socio-economic needs of local populations. The results underscore the need for integrated management approaches that promote both environmental preservation and sustainable livelihoods to ensure the continued provision of ecosystem services for future generations.

Drilling and blasting are essential operations within the mining industry, playing a critical role in material fragmentation. Despite advancements in various blasting technologies, the process remains a dominant contributor to overall mining costs. Achieving cost efficiency requires the precise configuration of blast design parameters, including explosive charge quantity, to attain desired outcomes in fragmentation, ground vibrations, fly rock, and air over-pressure. This study introduces a novel artificial intelligence (AI)-driven model, XGBoost-PSO-T, which combines eXtreme Gradient Boosting (XGBoost) with Particle Swarm Optimization (PSO) through the integration of the Tri-Weight technique. The PSO-Tri-Weight method optimizes the hyperparameters of the XGBoost model, enhancing its predictive capabilities. The model's performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²), with the results demonstrating that the XGBoost-PSO-T system outperforms the standard XGBoost approach, achieving an RMSE of 0.657 and an R² of 0.922. These findings suggest that the XGBoost-PSO-T model is a valuable tool for predicting fragmentation outcomes and optimizing blast designs in surface mining operations. The implementation of this system is recommended to improve blasting efficiency and reduce operational costs.

Rainfall is crucial for agricultural practices, and climate change has significantly altered rainfall patterns. Understanding the dynamic nature of rainfall in the context of climate change through Machine Learning (ML) and Deep Learning (DL) algorithms is essential for ensuring food security. ML techniques provide tools for processing large-scale data to extract meaningful insights. This study aims to compare the performance of a ML algorithm, Random Forest (RF), with a DL algorithm, Long Short-Term Memory (LSTM), in predicting rainfall in six state capitals in Southwest Nigeria: Osogbo, Ikeja, Ibadan, Akure, Ado-Ekiti, and Abeokuta. The dataset for this study was sourced from the HelioClim website archive, which offers high-quality solar radiation and meteorological data derived from satellite measurements. This archive is known for its accuracy and reliability, providing extensive and consistent historical datasets for various applications. The monthly rainfall data spanning from January 1, 1980, to December 31, 2022, were used, with 80% allocated for training and 20% for validation. As the data are time series, each model was constructed using a look-back period of five months, meaning the past five months' rainfall data served as input features. The performance of these algorithms was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results indicated that the RF algorithm yielded the lowest MSE, RMSE, and MAE across all selected cities in Southwest Nigeria. This study demonstrated the superiority of RF regression over LSTM in predicting rainfall in these regions, providing a valuable tool for agricultural planning and climate adaptation strategies.

The Jaintiapur-Jaflong region, strategically positioned between the subsiding Surma Basin to the south and the uplifting Shillong Massif to the north, presents a unique geological setting. This study employed geological clinometers and other field methods to ascertain the geological characteristics of the area. The regional strike was determined to be N66˚W, with a dip direction of S24˚W and a dip angle of 42.25˚. Through extensive field investigations, including geological mapping, stratigraphic logging, rock sampling, fossil analysis, and structural analysis, complemented by Global Positioning System (GPS), photography, remote sensing, and Geographic Information System (GIS) technologies, seven lithostratigraphic units were identified. These include the variegated color sandstone, mottled clay, yellowish to reddish-grey sandstone, sandy shale with intercalated silty shale, pinkish sandstone, bluish to blackish-grey shale, and limestone units, corresponding sequentially to Dupi Tila, Girujan Clay, Tipam Sandstone, Surma Group, Jenum Shale Fm, Kopili Shale, and Sylhet Limestone Fm, respectively. Five critical geological contact boundaries were delineated, with notable boundaries identified at the Dupigaon-Sari River Section, the Lalakhal-Tetulghat Section, the Nayagang-Gourishankar Section, and between the Barail and Jaintia groups at the Tamabil-Jaflong Highway Road Cut Section. These findings elucidate the geological contacts and stratigraphic units, providing significant implications for paleoenvironmental reconstruction, resource potential assessment, and stratigraphic correlation, thus enhancing the understanding of regional geological history and laying a foundation for future research endeavors.