Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression (2024)

Messier, K. P., Chambliss, S. E., Gani, S., Alvarez, R., Brauer, M., Choi, J. J., Hamburg, S. P., Kerckhoffs, J., Lafranchi, B., Lunden, M. M., Marshall, J. D., Portier, C. J., Roy, A., Szpiro, A. A., Vermeulen, R. C. H., & Apte, J. S. (2018). Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression. Environmental Science and Technology, 52(21), 12563-12572. https://doi.org/10.1021/acs.est.8b03395

Messier, Kyle P. ; Chambliss, Sarah E. ; Gani, Shahzad et al. / Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression. In: Environmental Science and Technology. 2018 ; Vol. 52, No. 21. pp. 12563-12572.

@article{89ddc0376cdc4a2b8a2a1ee9e7d2cfe2,

title = "Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression",

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.",

author = "Messier, {Kyle P.} and Chambliss, {Sarah E.} and Shahzad Gani and Ramon Alvarez and Michael Brauer and Choi, {Jonathan J.} and Hamburg, {Steven P.} and Jules Kerckhoffs and Brian Lafranchi and Lunden, {Melissa M.} and Marshall, {Julian D.} and Portier, {Christopher J.} and Ananya Roy and Szpiro, {Adam A.} and Vermeulen, {Roel C.H.} and Apte, {Joshua S.}",

year = "2018",

month = nov,

day = "6",

doi = "10.1021/acs.est.8b03395",

language = "English",

volume = "52",

pages = "12563--12572",

journal = "Environmental Science and Technology",

issn = "0013-936X",

publisher = "American Chemical Society",

number = "21",

}

Messier, KP, Chambliss, SE, Gani, S, Alvarez, R, Brauer, M, Choi, JJ, Hamburg, SP, Kerckhoffs, J, Lafranchi, B, Lunden, MM, Marshall, JD, Portier, CJ, Roy, A, Szpiro, AA, Vermeulen, RCH & Apte, JS 2018, 'Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression', Environmental Science and Technology, vol. 52, no. 21, pp. 12563-12572. https://doi.org/10.1021/acs.est.8b03395

Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression. / Messier, Kyle P.; Chambliss, Sarah E.; Gani, Shahzad et al.
In: Environmental Science and Technology, Vol. 52, No. 21, 06.11.2018, p. 12563-12572.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression

AU - Messier, Kyle P.

AU - Chambliss, Sarah E.

AU - Gani, Shahzad

AU - Alvarez, Ramon

AU - Brauer, Michael

AU - Choi, Jonathan J.

AU - Hamburg, Steven P.

AU - Kerckhoffs, Jules

AU - Lafranchi, Brian

AU - Lunden, Melissa M.

AU - Marshall, Julian D.

AU - Portier, Christopher J.

AU - Roy, Ananya

AU - Szpiro, Adam A.

AU - Vermeulen, Roel C.H.

AU - Apte, Joshua S.

PY - 2018/11/6

Y1 - 2018/11/6

N2 - 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.

AB - 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.

U2 - 10.1021/acs.est.8b03395

DO - 10.1021/acs.est.8b03395

M3 - Article

C2 - 30354135

SN - 0013-936X

VL - 52

SP - 12563

EP - 12572

JO - Environmental Science and Technology

JF - Environmental Science and Technology

IS - 21

ER -

Messier KP, Chambliss SE, Gani S, Alvarez R, Brauer M, Choi JJ et al. Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression. Environmental Science and Technology. 2018 Nov 6;52(21):12563-12572. doi: 10.1021/acs.est.8b03395