Scalable Multipollutant Exposure Assessment Using Routine Mobile Monitoring Platforms.

Abstract

Introduction: The absence of spatially resolved air pollution measurements remains a major gap in health studies of air pollution, especially in disadvantaged communities in the United States and lower-income countries. Many urban air pollutants vary over short spatial scales, owing to unevenly distributed emissions sources, rapid dilution away from sources, and physicochemical transformations. Primary air pollutants from traffic have especially sharp spatial gradients, which lead to disparate effects on human health for populations who live near air pollution sources, with important consequences for environmental justice. Conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize these heterogeneous human exposures and localized pollution hotspots. In this study, we assessed the potential for repeated mobile air quality measurements to provide a scalable approach to developing high-resolution pollution exposure estimates. We assessed the utility and validity of mobile monitoring as an exposure assessment technique, compared the insights from this measurement approach against other widely accepted methods, and investigated the potential for mobile monitoring to be scaled up in the United States and low- and middle-income countries.

Methods: Our study had five key analysis modules (M1- M5). The core approach of the study revolved around repeated mobile monitoring to develop time-stable estimates of central-tendency air pollution exposures at high spatial resolution. All mobile monitoring campaigns in California were completed prior to beginning this study. In analysis M1, we conducted an intensive summerlong sampling campaign in West Oakland, California. In M2, we explored the dynamics of ultrafine particles (UFPs) in the San Francisco Bay Area. In analysis M3, we scaled up our multipollutant mobile monitoring approach to 13 different neighborhoods with ~450,000 inhabitants to evaluate within- and between-neighborhood heterogeneity. In M4, we evaluated the coupling of mobile monitoring with land use regression models to estimate intraurban variation. Finally, in M5, we reproduced our mobile monitoring approach in a pilot study in Bangalore, India.

Results: For M1, we found a moderate-to-high concordance in the time-averaged spatial patterns between mobile and fixed-site observations of black carbon (BC) in West Oakland. The dense fixed-site monitor network added substantial insight about spatial patterns and local hotspots. For M2, a seasonal divergence in the relationship between UFPs and other traffic-related air pollutants was evident from both approaches. In M3, we found distinct spatial distribution of exposures across the Bay Area for primary and secondary air pollutants. We found substantially unequal exposures by race and ethnicity, mostly driven by between-neighborhood concentration differences. In M4, we demonstrated that empirical modeling via land use regression could dramatically reduce the data requirements for building high-resolution air quality maps. In M5, we developed exposure maps of BC and UFPs in a Bangalore neighborhood and demonstrated that the measurement technique worked successfully.

Conclusions: We demonstrated that mobile monitoring can produce insights about air pollution exposure that are externally validated against multiple other analysis approaches, while adding complementary information about spatial patterns and exposure heterogeneity and inequity that is not readily obtained with other methods.