The Influence of Commuting on Population Exposure to Air Pollution: Toward Global Application With a Proxy on the Degree of Urbanization

Abstract

Urban populations are significantly affected by air pollution, which poses a major threat to public health. However, standardized and public mobility data, essential for an exposure assessment, are frequently unavailable. Earth observation-derived and model datasets can support large-scale health studies, especially in remote areas with limited data availability. This study investigates the use of a globally derivable variable from remote sensing data to estimate the static versus dynamic population exposure difference. A health risk assessment using a higher and a lower resolution air pollution data was performed. This was achieved by examining air pollution concentrations in two European regions, Lombardy, in Italy, and Germany, incorporating commuting datasets. Accordingly, a retrospective long-term exposure assessment to particulate matter less than 2.5 microns (PM2.5), nitrogen dioxide (NO2), and ozone (O3) was performed from 2013 to 2022. The study evaluates the difference between the resident and the dynamic population exposed to concentrations exceeding the new limits set by the World Health Organization (WHO). The relation between pollutant concentration and the Fraction of Settlement Area (FSA), a proxy of urbanization levels, derived from the global World Settlement Footprint dataset was explored. Two pollution datasets were used: with European, and global coverage. The analysis decouples daytime and nighttime hours. For each region and pollutant specific FSA thresholds were identified, that maximize the population exposure gap. Our findings highlight the impact of air pollution on population health, revealing widespread exposure exceeding WHO limits, particularly for PM2.5, and emphasizing the importance of considering diurnal exposure variations in health risk assessments.

Keywords

• Air pollution
• remote sensing
• risk analysis
• urban areas