Spatiotemporal assessment of urban PM₁₀ and population exposure using integrated satellite, meteorological, and socio-spatial data

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Introduction The pollution with particulate matter is a major risk factor for population health, especially in urban environments, where the interaction between anthropogenic sources, meteorological conditions and built environment characteristics generates complex spatial patterns of exposure. In this context, the present study aims to estimate the spatial distribution of PM₁₀ concentrations and assess population exposure in a medium-sized city in Romania by integrating satellite data, in situ measurements and socio-spatial indicators. Methods The methodology is based on MODIS-MAIAC satellite products to retrieve Aerosol Optical Depth (AOD) values, which are correlated with PM₁₀ data and meteorological variables from four ground-based air quality monitoring stations. Based on these data, a multiple regression model was developed to estimate PM₁₀ concentrations, and subsequently, a composite exposure index was constructed by integrating relevant socio-spatial indicators, such as population density, built environment characteristics, the Normalized Difference Vegetation Index (NDVI), and terrain elevation. Results and discussion The results highlight pronounced seasonal variability in PM₁₀ concentrations, with maximum values in the cold season (up to 41.8 μg/m 3 ) and an annual average of approximately 20.5 μg/m 3 , exceeding the thresholds recommended by the World Health Organisation but falling within the limits set by the European Union. The direct relationship between AOD and PM₁₀ is relatively weak ( R = 0.28), but integrating meteorological variables significantly improves the model’s performance. The analysis of the composite index indicates that approximately 20.1% of the population of the Oradea Metropolitan Area lives in areas with a high predisposition to exposure, particularly near industrial areas and in densely built urban environments. The results confirm that population exposure to PM₁₀ is determined not exclusively by concentration levels but by the complex interactions among pollution, population distribution, and the characteristics of the built environment. The proposed integrated approach provides a robust tool for identifying vulnerable areas and supporting urban planning and air quality management, contributing to the development of strategies to reduce risks to human health.

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