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

Area of Science:

• Environmental Science
• Epidemiology
• Urban Planning

Background:

• Previous mobile air pollution monitoring campaigns had limited spatiotemporal coverage and representativeness due to small fleets and incomplete sampling.
• Gaps exist in understanding urban air pollution variability and the impact of monitoring design on data quality.

Purpose of the Study:

• To characterize fine particulate matter (PM2.5) spatiotemporal variability using a large, citywide taxi fleet.
• To quantify traffic-related PM2.5 contributions across urban microenvironments.
• To assess the influence of fleet size and temporal sampling on monitoring coverage and accuracy.

Main Methods:

• Deployed over 200 taxis equipped with PM2.5 sensors for 24-hour monitoring over two weeks.
• Collected large-scale, spatiotemporally dense data across diverse urban on-road microenvironments.
• Analyzed the impact of fleet size and temporal sampling strategies on monitoring outcomes.

Main Results:

• Observed significant spatial and temporal heterogeneity in on-road PM2.5 levels and traffic contributions.
• Increasing fleet size enhanced spatial coverage but with diminishing returns.
• Temporally incomplete and imbalanced sampling led to biased PM2.5 estimates, especially at finer resolutions.

Conclusions:

• A large, continuously operating mobile monitoring fleet provides a more representative assessment of urban air pollution.
• Optimizing fleet size and ensuring comprehensive temporal sampling are crucial for accurate air quality assessment.
• Findings inform urban air quality management and epidemiological studies, guiding future mobile monitoring designs.