High-Resolution Spatiotemporal Characterization of PM_2.5 across On-Road Microenvironments Using Large-Scale...

Mobile monitoring of air pollution has been extensively employed in environmental and epidemiological studies. However, previous monitoring campaigns, which typically involved only one or two vehicles operating during temporally incomplete and imbalanced daytime, were limited in spatiotemporal coverage, resolution, and representativeness. This leaves important gaps in understanding how air pollution varies spatiotemporally across urban on-road microenvironments, as well as in assessing the impact of fleet size and temporal sampling incompleteness (daytime/weekday/weekend-skewed vs 24 h continuous and full week) on monitoring coverage and PM_2.5 estimates, respectively. Here, we fill these gaps by employing a citywide taxi-based mobile monitoring fleet of over 200 taxis operating 24 h a day over a two-week period and equipped with fine particulate matter (PM_2.5) sensors to characterize PM_2.5 spatiotemporal variability and quantify traffic-related increment contributions across diverse urban on-road microenvironments. Leveraging this large-scale, spatiotemporally dense, and representative data set, we further investigated how fleet size influences monitoring coverage and repeated visits and how temporal sampling incompleteness and imbalance affect monitoring outcomes and bias. Our results show spatial and temporal heterogeneity in on-road PM_2.5 levels and traffic-related increment contributions across microenvironments. Increasing fleet size improves spatial coverage but exhibits diminishing returns, and temporally incomplete and imbalanced sampling can introduce biased outcomes, particularly at finer temporal resolutions. These findings have important implications for refined urban air quality management and epidemiological research, thereby offering guidance and insights for future studies.