Abstract

The increasing complexity of environmental systems and the imperative for transparent decision-making have necessitated the incorporation of Explainable Artificial Intelligence (XAI) into environmental monitoring and pollutant detection. This study undertakes a Systematic Literature Review (SLR), adhering to the PRISMA methodology, to examine scientific advancements in the application of XAI models for the detection and localization of pollution sources. A total of 26 studies published between 2020 and 2025 were included in the final review corpus. The studies were identified through systematic searches conducted in Web of Science, Scopus, IEEE Xplore, ScienceDirect, and ACM Digital Library, complemented by a backward and forward snowballing process to ensure comprehensive coverage of the literature. The most commonly employed techniques were SHAP, LIME, Grad-CAM, and Layer-wise Relevance Propagation (LRP), which were applied to the evaluation of water, air, and soil quality. The findings indicate that XAI facilitates the identification of the most influential environmental factors, such as pH , dissolved oxygen, SO2, PM2.5, and temperature, and enables the generation of visual and quantitative explanations that are consistent with physical-chemical processes. Despite these advancements, there remains a lack of methodological or regulatory consensus to guide its practical implementation, thereby limiting its adoption in environmental governance. This study underscores opportunities to develop integrated and auditable frameworks that enhance transparency, scientific traceability, and sustainable and ethically responsible environmental decision-making.