Assessing Benefits and Limitations of Multiple Data Sources for Environmental Monitoring

This study investigates the impact of meteorological variables on air pollutant concentrations, focusing on Carbon Monoxide (CO), Nitrogen Dioxide (NO2), and Ozone (O3). By integrating data from static stations and other services, alongside weather data from online public repositories, we aim to enhance the understanding of air quality dynamics. The research highlights how temperature and solar radiation significantly influence air quality, with wind speed and precipitation aiding in pollutant dispersion. Utilizing the SHAP method, we offer a detailed and interpretable analysis of the factors affecting air quality, emphasizing the crucial role of integrating diverse data sources. Our findings demonstrate that merging various datasets fills critical gaps in environmental monitoring, leading to improved interpretability and reliability in air quality assessments. These insights support more effective environmental management strategies. Future directions include leveraging citizen-generated data to refine pollution modeling and enhance forecast transparency, ultimately contributing to more comprehensive environmental monitoring practices.