Exploring Spatial-Temporal Patterns of Air Pollution Concentration and Their Relationship with Land Use

Understanding the spatial-temporal patterns of air pollution is crucial for mitigation strategies, a task fostered nowadays by the generation of continuous concentration maps by remote sensing technologies. We applied spatial modelling to analyze such spatial-temporal patterns in Lombardy, Italy, one of the most polluted regions in Europe. We conducted monthly spatial autocorrelation (global and local) of the daily average concentrations of PM2.5, PM10, O-3, NO2, SO2, and CO from 2016 to 2020, using 10 x 10 km satellite data from the Copernicus Atmosphere Monitoring Service (CAMS), aggregated on districts of approximately 100,000 population. Land-use classes were computed on identified clusters, and the significance of the differences was evaluated through the Wilcoxon rank-sum test with Bonferroni correction. The global Moran's I autocorrelation was overall high (>0.6), indicating a strong clustering. The local autocorrelation revealed high-high clusters of PM2.5 and PM10 in the central urbanized zones in winter (January-December), and in the agrarian southern districts in summer and autumn (May-October). The temporal decomposition showed that values of PMs are particularly high in winter. Low-low clusters emerged in the northern districts for all the pollutants except O-3. Seasonal peaks for O-3 occurred in the summer months, with high-high clusters mostly in the hilly and mildly urban districts in the northwest. These findings elaborate the spatial patterns of air pollution concentration, providing insights for effective land-use-based pollution management strategies.