Designing AI-Driven Pollution Source Attribution Models: A Case Study in Industrial Clusters Using Satellite Imagery and Deep Learning

Abstract

Huge contributors of pollution of air and water around industrial areas are industrial clusters, but the difficulty of determining the contamination origin in the area is not an easy task since the nature of emissions is relatively complicated and industrial operations are overlapping. Traditional monitoring networks can be dependable, but they are typically limited in spatial resolution and can be expensive to operate, which is not sufficient to accomplish source-level attribution within highly industrialized areas. This research suggests a source attribution AI-based pollution framework, which combines satellite images with deep learning models to detect, classify, and track industrial emissions of pollution. A convolutional neural network (CNN) was trained using Sentinel-2 and Landsat-8/9 multispectral data, as well as using derived indexes like aerosol optical depth (AOD), land surface temperature (LST) and vegetation stress indicators to identify patterns of pollution, and match them to areas of industrial activity. Such methodology was proven to be effective on the example of several industrial clusters, such as petrochemical and steel, as well as textile hubs where the hotspots of emissions were observed with great precision. Findings indicate that remote sensing with AI can be used to identify the source of pollution and map its location up to 87 percent. The paper notes the opportunities of using geospatial intelligence with machine learning to facilitate real-time environmental monitoring, policy compliance and sustainable industrial management.