Data fusion for enhancing urban air quality modeling using large-scale citizen science data

Abstract

Rapid urbanization has led to many environmental issues, including poor air quality. With urbanization set to continue, there is an urgent need to mitigate air pollution and minimize its adverse health impacts. This study aims to advance urban air quality modelling by integrating a dispersion model output with large-scale citizen science data, collected over a 4-week period by 642 participants in Cork City, Ireland. The dispersion model enabled the identification of major sources of NO₂ air pollution while also addressing gaps in regulatory monitoring efforts. Integrating the diffusion tube data with the dispersion model output, we developed a data fusion model that captured localized fluctuations in air quality, with increases of up to 22μg/m³ observed at major road intersections. The data fusion model provided a more accurate representation of NO₂ concentrations, with estimates within 1.3μg/m³ of the regulatory monitoring measurement at an urban traffic location, an improvement of 11.7μg/m³ from the baseline dispersion model. This enhanced accuracy enabled a more precise assessment of the population exposure to air pollution. The data fusion model showed a higher population exposure to NO₂ compared to the dispersion model, providing valuable insights that can inform environmental health policies aimed at safeguarding public health.