Integrated, Geospatial Risk Assessment of Air, Water, and Soil Pollution Impacts on Agricultural Sustainability using Advanced Digital Technologies - Dr. Kazi Kutubuddin Sayyad Liyakat (International Journal of Environmental Noise and Pollution Control, 2025)
The systemic threat posed by the convergence of air, water, and soil contaminants represents a critical challenge to global agricultural resilience and food security. Traditional, site-specific pollutant monitoring methods are insufficient for capturing the dynamic, diffuse, and often nonlinear nature of environmental risk pathways that permeate agrarian landscapes. This study presents a robust framework for comprehensive risk assessment utilizing a synergistic suite of modern tools designed for spatial, temporal, and molecular precision. The assessment framework integrates real-time data from Internet of Things (IoT) field sensors (measuring groundwater quality and volatile organic compounds), high-resolution satellite remote sensing (monitoring vegetation health indices and aerosol optical depth), and geospatial analysis (GIS) for hazard mapping. Crucially, the captured datasets were processed using advanced Machine Learning (ML) algorithms—specifically, Random Forest classifiers and predictive Neural Networks—to model contaminant fate, transport, and bioavailability within crops and livestock. Furthermore, next-generation sequencing and bioinformatics were employed to assess microbial ecotoxicity indicators in polluted soils, providing a molecular layer of risk characterization. The combined approach produced dynamic, high-fidelity risk maps that accurately identified spatially heterogeneous pollutant hotspots, correlating atmospheric deposition (air) with hydrological runoff (water) and soil accumulation. The ML models demonstrated a predictive accuracy exceeding 92% in forecasting areas susceptible to heavy metal accumulation and nutrient leaching before functional ecosystem collapse. This research confirms that the strategic deployment of modern digital tools moves risk assessment beyond reactive detection toward proactive, predictive environmental stewardship, offering a data-driven foundation for precision pollution mitigation and resilient agricultural policy formulation.
Keywords: Air; Modern Tools; Risk assessment; soil; water