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Urban air pollution is quite complex and exhibits significant spatial variability within communities. Traditional centralized monitoring captures temporal variability as well as the long-term trends of air pollution very well, but mapping spatial variability of air pollution in communities at high resolution would require large number of air quality monitors distributed across the community. Mobile monitoring complements stationary monitoring approaches by measuring pollution levels on accessible road networks in urban communities. This paper presents the application of an integrated mobile measurement and data analysis approach to study community-level air pollution patterns, separate the regional background and local contributions, and identify high pollution zones for black carbon (BC), ultrafine particles (UFP), and fine particulate matter (PM2.5). This study was conducted during different periods from 2017 to 2019 in three California cities (Richmond, Stockton, and Commerce) with diverse community and source characteristics. The study found that traffic was the dominant primary source of air pollution in both urban and suburban settings. Urban areas adjacent to large area sources, such as ports and railyards had increased pollutant enhancements due to either direct or indirect emissions, while suburban areas containing unpaved roads were observed to have large PM2.5 enhancement due to the resuspended dust. The analysis disaggregated the contribution of regional and local sources to local air pollution and found that regional background sources contributed up to 75% of the PM2.5 concentrations, while local sources contributed more than half of BC and UFP. The study suggests that 15–30 repeated measurements may be sufficient to map the general air pollution patterns within the community, while some extreme high pollution zones can be identified with fewer repeats (5–10). These techniques can be used for initial screening of air pollution variability within the community, and help identify priority areas for conducting follow-up long-term air quality measurements. The techniques also informs the relative importance of regional and local actions to reduce community pollutant levels.
Chen et al. (Fri,) studied this question.