From residence-based to mobility-based exposure assessment: a comparison of urban air pollution exposure modelling approaches for environmental health equity

Air pollution is a significant environmental risk for premature mortality and disease. Previous research has documented inequitable air pollution exposure and health outcomes among marginalized and biologically susceptible populations. Characterizing air pollution exposure levels is a key component of assessing public health risks to inform urban policies and planning decisions; however, traditional, residence-based approaches to exposure assessment can fail to capture real-world variability in exposure across space and time, and between households. In this work, we investigate the impact of considering mobility patterns and microenvironments on exposure variability across households.

In urban environments, air pollutant concentrations show strong spatial patterns with fine-scale heterogeneity across diverse microenvironments. For example, traffic-related air pollution (TRAP) often decays by approximately 50% within 150 meters of emission sources (e.g., major roads) and returns to the local background within 500 meters. Still, the decay is context-dependent, shaped by local meteorology and urban-modified flow. Indoor exposure differs further from outdoor concentrations, which are determined by factors such as indoor emissions, building characteristics, and air exchange rates. Individual routines also influence their exposure levels, as reflected in daily activity locations (origins/destinations), the travel trajectories between them, and mobility modes.

To better capture air pollution exposure and assess health risks by accounting for these sources of variability and uncertainty, our study utilizes hourly, 1-km resolution air pollution estimates (NO₂, PM₂.₅, and O₃) for Metro Vancouver, Canada, generated by the coupled WRF-CMAQ modelling system.  The high temporal resolution allows us to assess health risks associated with both short- and long-term exposure to air pollution. Beyond assigning exposure based on residential location, the fine resolution enables us to characterize concentration variability across microenvironments and to link exposures with individuals’ daily time-activity patterns and commuting trajectories. There is a wide range of microenvironment types relevant to air pollution exposure. For example, the U.S. EPA’s Hazardous Air Pollutant Exposure Model defines 18 microenvironments within broader categories such as indoor, outdoor, and in-vehicle, including residences, offices, public transit, and transit-waiting areas. Air pollution exposure within these microenvironments can be estimated using inputs such as the penetration of outdoor pollutants indoors and proximity to emission sources.

As a next step to explore exposure disparities, we will estimate exposures using developed, narrative household archetypes that are grounded in residents' lived experiences. These archetypes are designed to capture intersecting marginalization and disadvantage, reflecting mobility inequities (e.g., barriers to travel, mode choice constraints, and differences in commuting routes).

Together, we will compare and discuss: (1) differences in exposure estimates and their associated uncertainties across modelling methods; and (2) exposure disparities across groups that may contribute to health inequities. We hypothesize that mobility-based approaches offer higher granularity and therefore better capture exposure variability across households and populations than residence-based approaches. We further expect larger exposure differences between households after explicitly accounting for time-activity patterns and travel modes. By evaluating exposures under alternative policy and planning scenarios, our findings can inform sustainable travel mode shift as well as land-use and transportation planning to reduce exposures and enhance environmental health benefits.