Szczegóły publikacji
Opis bibliograficzny
A novel methodology for Explainable Artificial Intelligence integrated with geostatistics for air pollution control and environmental management / Mateusz ZARĘBA, Tomasz DANEK // Ecological Informatics ; ISSN 1574-9541 . — 2025 — vol. 92 art. no. 103450, s. 1–18. — Bibliogr. s. 16–18, Abstr. — Publikacja dostępna online od: 2025-10-03
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 163615 |
|---|---|
| Data dodania do BaDAP | 2025-10-21 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.ecoinf.2025.103450 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Ecological Informatics |
Abstract
This study focuses on understanding air pollution dynamics through the integration of Explainable Artificial Intelligence (XAI) and spatio-temporal geostatistics for air pollution control. It combines the classic approach with the simultaneous evaluation of multiple machine learning (ML) models to enhance the interpretability of model decisions. Data from a dense network of 52 IoT (internet of things) sensors deployed across an urban municipality and surrounding areas were analyzed to identify key predictors of air pollution trends. XAI is employed to interpret complex relationships between pollution levels, meteorological conditions, human activity patterns, and geographic factors. Spatio-temporal geostatistics serve as the foundation for interpreting XAI in both spatial and temporal contexts. July exhibited the least spatial variability in PM2.5 concentrations, with the hour of the day being the key predictor (13.04% of predictive importance), while December showed the highest spatial variability, with atmospheric pressure as the dominant factor (13.84% of predictive importance). Precipitation was the least influential predictor in both months (3.00 %). Four and nine distinct clusters with significant spatial variability in predictor importance were identified. Analysis of transition matrices revealed both stable and dynamic clusters, highlighting the complex nature of PM2.5 emissions as they vary between warm and cold seasons, characteristic of moderate climate zones. This methodology enhances the understanding of air pollution dynamics and provides a transparent framework for urban pollution control and management decision-making. The findings contribute to the development of smart urban management strategies, supporting sustainable city planning and pollution mitigation efforts, while advocating for the right of citizens to a cleaner environment.
