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Environ Sci Technol. 2018 Nov 6;52(21):12563-12572. doi: 10.1021/acs.est.8b03395. Epub 2018 Oct 24.

Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression.

Author information

1
Department of Civil, Architectural and Environmental Engineering , University of Texas at Austin , Austin , Texas 78712 , United States.
2
Environmental Defense Fund, New York , New York 10010 , United States.
3
School of Population and Public Health , University of British Columbia , Vancouver , British Columbia V6T 1Z3 , Canada.
4
Institute for Risk Assessment Science , Utrecht University , Utrecht 3584 CM , Netherlands.
5
Aclima, Inc., 10 Lombard Street , San Francisco , California 94111 , United States.
6
Department of Civil and Environmental Engineering , University of Washington , Seattle , Washington 98195 , United States.
7
Department of Biostatistics , University of Washington , Seattle , Washington 98195 , United States.

Abstract

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.

PMID:
30354135
DOI:
10.1021/acs.est.8b03395
[Indexed for MEDLINE]
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