Developing AI-Based Pollution Source Identification Models: A Deep Learning Approach Using Satellite Data in Industrial Regions

Abstract

The impact of industrial regions located in the most important contributors of environmental pollution is through air, water, and soil quality corresponding to both local and regional levels. Convicting varities of sources of pollution can be problematic because traditional monitoring systems, accurate as it is, have a reduced coverage in space and frequencies with time. This work presents a new research concept of an AI-based deep learning system to detect the source of pollution using satellite images of high quality. The framework uses spectral indices including the Normalized Difference Vegetation Index (NDVI), Aerosol Optical Depth (AOD) and Land Surface Temperatures (LST) to project and categorize pollution hotspots on industrial belts using both convolutional neural networks (CNN) and long short-term memory (LSTM). Indian chosen industrial clusters were used to apply the methodology in which ground-truth monitoring data were used to compose with Sentinel-2 and MODIS data to train and validate the model. Findings say that the AI model was 91.3% accurate at detecting areas where pollution occurs, compared to the conventional classification methods in remote sensing. Spatial analysis indicated that the industrial density, the concentration of pollutants, and the indices obtained by satellites were strongly correlated and made it possible to attribute the sources to a particular industrial activity with particularity. This paper shows that deep learning combined with satellite data is scalable, precise, and cost-efficient to conduct real-time pollution monitoring, source location and the application has important outcomes to policymakers, environmental controls, and sustainable cities.