You are here

Scalable multi-pollution exposure assessment using routine mobile monitoring platforms

Principal Investigator: 
,

University of California–Berkeley

This New Investigator Award study will measure air pollutants in intensive campaigns with Google Street View cars in Oakland and Delhi and compare exposure estimates to conventional methods.

Funded under
Status: 
In review
Abstract

Study abstract 2022

Scalable Multi-Pollutant Exposure Assessment Using Mobile Monitoring Platforms

Joshua S. Apte

University of California Berkeley, USA

Background. Urban air pollution concentrations can vary sharply over short distances. High spatial resolution surfaces of urban air quality data are needed for a variety of purposes, including exposure assessment for health studies, identification of emissions sources, and characterization of exposure. However, conventional techniques are generally unable to routinely provide data on intraurban exposure gradients.

Objectives. This project investigated the potential for routine, regular mobile monitoring using fleets of instrumented vehicles to fill current gaps in fine-scale air pollution exposure assessment. The emphasis here is on developing, validating, and challenging this method with a view to scalability: to addressing persistent air quality data gaps at large scale. Key research objectives include (i) assessing the validity of mobile monitoring estimates, (ii) comparing a variety of policy-relevant insights from mobile measurements against other techniques (fixed monitoring, statistical exposure models) and (iii) exploring approaches for scaling up mobile monitoring.

Study Design. Our study of mobile monitoring leverages two mobile monitoring datasets. First, we collected one of the world’s most extensive and intensive block-by-block air quality datasets for black carbon (BC), NO, NO2, and ultrafine particles (UFP) using a fleet of specially equipped Google Street View (GSV) cars in Oakland and the surrounding Bay Area region of California. In addition, we made detailed fixed-site observations of BC at 100 locations within one Oakland neighborhood to validate and challenge the mobile measurements. Second, we experimented with this transferability of this measurement technique to Bangalore, India, focusing on repeated mobile measurements BC, UFP, and fine particulate matter (PM2.5).

Key Results. We found that repeated mobile measurements resolved similar spatial patterns of time-averaged BC – a primary combustion related pollutant – when validated against 100 fixed sites within a neighborhood. In addition, mobile measurements revealed numerous localized pollution hotspots that were not evident from the coarser fixed-site measurements. Through multiple analysis approaches, we find that mobile measurements offered good temporal concordance with fixed site measurements of BC, NO, NO2 and PN over longer time scales – average patterns by time of day, weekday/weekend, and season. However, we find that mobile measurements are not well-suited to capturing evolving spatial patterns in real-time (e.g., over minutes and hours): a small fleet of vehicles cannot be everywhere at once. Our comparisons with models show that mobile measurements can resolve time-stable within-neighborhood spatial gradients and hotspots that are often missing from statistical exposure models. For the Bay Area, we demonstrated that within-neighborhood exposure heterogeneity often is as large or larger than between-neighborhood exposure differences, especially for primary combustion pollutants like NO and BC. Our investigations in Bangalore showed that mobile monitoring can resolve statistically robust concentration gradients within and between neighborhoods, but that quantitative interpretation of the results requires careful interpretation in locations prone to persistent traffic jams. Overall, we conclude that routine mobile measurements can offer a valid and valuable measurement technique for describing intraurban concentration gradients that usefully complements other traditional exposure assessment techniques.