Machine learning for high-resolution mapping of air pollutants over Italy (2021–2023)

Air pollution poses significant risks to human health and the environment. Nitrogen dioxide (NO₂) is a key air pollutant with well-documented adverse health effects and a precursor to ozone (O₃). Generally, particulate matter (PM) is a major global cause of mortality, with both short-term and long-term exposure linked to severe health outcomes (WHO, 2021). High-resolution maps of near-surface air pollutant concentrations are essential to assess these impacts effectively.

We will present daily gridded maps of near-surface NO₂ and O₃, as well as PM_2.5 and PM₁₀ (particles with an aerodynamic diameter equal to or less 2.5 and 10 µm respectively) concentrations across the Italian territory, generated for 2021–2023 at a spatial resolution of 0.01° × 0.01° (~1 km). Machine learning (ML) models are trained using a combination of spatial and spatiotemporal predictors, informed by in-situ ground measurements from over 300 monitoring stations sourced from the European Air Quality Portal (AQP), managed by the European Environmental Agency (EEA) (EEA, 2024). Key spatial predictors include CORINE Land Cover, which provides 44 thematic classes at 100 m resolution; the Global Human Settlement Layer, reflecting human presence; NASA’s Digital Elevation Model, offering topographical information; and the ESA Plant Functional Type dataset (Harper et al., 2023). Spatiotemporal predictors integrate meteorological fields from ERA5-Land and aerosol optical depth (AOD) data from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2).

We will evaluate various supervised ML models (Mansour et al., 2024b), including neural networks, regression ensembles, and regression trees across various landscapes (urban, suburban, and rural) to identify the optimal approach for each context. Additionally, explainable AI techniques (e.g., partial dependence analysis and Shapley additive exPlanations) and statistical analysis (e.g., clustering and empirical orthogonal functions) will be employed to elucidate the relationships between predictors and aerosol spatiotemporal distributions (Mansour et al., 2023; Mansour et al., 2024a), providing novel insights into the dynamics of air quality across regions. These advancements contribute to a more refined understanding of air pollution patterns and their underlying drivers.

Funding:

Project funded under the National Recovery and Resilience Plan (NRRP), Mission 04 Component 2 Investment 1.5 – NextGenerationEU, Call for tender n. 3277 dated 30/12/2021. Award Number: 0001052 dated 23/06/2022 (ECS_00000033_ECOSISTER).

References:

EEA: Europe’s air quality status (2024), https://www.eea.europa.eu//publications/europes-air-quality-status-2024.

Harper, et al. (2023), Earth System Science Data, 15, 1465-1499, 10.5194/essd-15-1465-2023.

Mansour, et al. (2023), Science of The Total Environment, 871, 10.1016/j.scitotenv.2023.162123.

Mansour, et al. (2024a), npj Climate and Atmospheric Science, 7, 10.1038/s41612-024-00830-y.

Mansour, et al. (2024b), Earth System Science Data, 16, 2717–2740, 10.5194/essd-16-2717-2024.

WHO (2021), World Health Organization, https://iris.who.int/handle/10665/345329.