Within-urban variability in ambient air pollution: Comparison of estimation methods

Julian D. Marshall; Elizabeth Nethery; Michael Brauer

doi:10.1016/j.atmosenv.2007.08.012

Within-urban variability in ambient air pollution: Comparison of estimation methods

Julian D. Marshall, Elizabeth Nethery, Michael Brauer

Civil, Environmental, and Geo- Engineering

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

212 Scopus citations

Abstract

An important component of air quality management and health risk assessment is improved understanding of spatial and temporal variability in pollutant concentrations. We compare, for Vancouver, Canada, three approaches for estimating within-urban spatiotemporal variability in ambient concentrations: spatial interpolation of monitoring data; an empirical/statistical model based on geographic analyses ("land-use regression"; LUR); and an Eulerian grid model (community multiscale air quality model, CMAQ). Four pollutants are considered-nitrogen oxide (NO), nitrogen dioxide (NO₂), carbon monoxide, and ozone-represent varying levels of spatiotemporal heterogeneity. Among the methods, differences in central tendencies (mean, median) and variability (standard deviation) are modest. LUR and CMAQ perform well in predicting concentrations at monitoring sites (average absolute bias: <50% for NO; <20% for NO₂). Monitors (LUR) offer the greatest (least) temporal resolution; LUR (monitors) offers the greatest (least) spatial resolution. Of note, the length scale of spatial variability is shorter for LUR (units: km; 0.3 for NO, 1 for NO₂) than for the other approaches (3-6 for NO, 4-6 for NO₂), indicating that the approaches offer different information about spatial attributes of air pollution. Results presented here suggest that for investigations incorporating spatiotemporal variability in ambient concentrations, the findings may depend on which estimation method is employed.

Original language	English (US)
Pages (from-to)	1359-1369
Number of pages	11
Journal	Atmospheric Environment
Volume	42
Issue number	6
DOIs	https://doi.org/10.1016/j.atmosenv.2007.08.012
State	Published - Feb 2008

Bibliographical note

Funding Information:
We gratefully acknowledge the following funding sources: the Bridge Program (supported by the Michael Smith Foundation for Health Research (MSFHR) and the Canadian Institutes for Health Research) at The University of British Columbia (UBC); the Centre for Health and Environmental Research (funded by MSFHR) at UBC; the Border Air Quality Study (funded by Health Canada and the British Columbia Center for Disease Control); and, faculty start-up funds from University of Minnesota. The following UBC researchers generated, or provided important modifications to, datasets employed here: Sarah Henderson (land-use regression); Thomas Nipen and Roland Stull (CMAQ); and, Lillian Tamburic and Cornel Lencar (ambient monitoring data). Monitoring data were obtained from the British Columbia Ministry of Environment.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

Exposure analysis
Geographic information system (GIS)
Land-use regression (LUR)
Models-3/community multiscale air quality (CMAQ) model
Traffic emissions

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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title = "Within-urban variability in ambient air pollution: Comparison of estimation methods",

abstract = "An important component of air quality management and health risk assessment is improved understanding of spatial and temporal variability in pollutant concentrations. We compare, for Vancouver, Canada, three approaches for estimating within-urban spatiotemporal variability in ambient concentrations: spatial interpolation of monitoring data; an empirical/statistical model based on geographic analyses ({"}land-use regression{"}; LUR); and an Eulerian grid model (community multiscale air quality model, CMAQ). Four pollutants are considered-nitrogen oxide (NO), nitrogen dioxide (NO2), carbon monoxide, and ozone-represent varying levels of spatiotemporal heterogeneity. Among the methods, differences in central tendencies (mean, median) and variability (standard deviation) are modest. LUR and CMAQ perform well in predicting concentrations at monitoring sites (average absolute bias: <50% for NO; <20% for NO2). Monitors (LUR) offer the greatest (least) temporal resolution; LUR (monitors) offers the greatest (least) spatial resolution. Of note, the length scale of spatial variability is shorter for LUR (units: km; 0.3 for NO, 1 for NO2) than for the other approaches (3-6 for NO, 4-6 for NO2), indicating that the approaches offer different information about spatial attributes of air pollution. Results presented here suggest that for investigations incorporating spatiotemporal variability in ambient concentrations, the findings may depend on which estimation method is employed.",

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author = "Marshall, {Julian D.} and Elizabeth Nethery and Michael Brauer",

note = "Funding Information: We gratefully acknowledge the following funding sources: the Bridge Program (supported by the Michael Smith Foundation for Health Research (MSFHR) and the Canadian Institutes for Health Research) at The University of British Columbia (UBC); the Centre for Health and Environmental Research (funded by MSFHR) at UBC; the Border Air Quality Study (funded by Health Canada and the British Columbia Center for Disease Control); and, faculty start-up funds from University of Minnesota. The following UBC researchers generated, or provided important modifications to, datasets employed here: Sarah Henderson (land-use regression); Thomas Nipen and Roland Stull (CMAQ); and, Lillian Tamburic and Cornel Lencar (ambient monitoring data). Monitoring data were obtained from the British Columbia Ministry of Environment. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.",

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AB - An important component of air quality management and health risk assessment is improved understanding of spatial and temporal variability in pollutant concentrations. We compare, for Vancouver, Canada, three approaches for estimating within-urban spatiotemporal variability in ambient concentrations: spatial interpolation of monitoring data; an empirical/statistical model based on geographic analyses ("land-use regression"; LUR); and an Eulerian grid model (community multiscale air quality model, CMAQ). Four pollutants are considered-nitrogen oxide (NO), nitrogen dioxide (NO2), carbon monoxide, and ozone-represent varying levels of spatiotemporal heterogeneity. Among the methods, differences in central tendencies (mean, median) and variability (standard deviation) are modest. LUR and CMAQ perform well in predicting concentrations at monitoring sites (average absolute bias: <50% for NO; <20% for NO2). Monitors (LUR) offer the greatest (least) temporal resolution; LUR (monitors) offers the greatest (least) spatial resolution. Of note, the length scale of spatial variability is shorter for LUR (units: km; 0.3 for NO, 1 for NO2) than for the other approaches (3-6 for NO, 4-6 for NO2), indicating that the approaches offer different information about spatial attributes of air pollution. Results presented here suggest that for investigations incorporating spatiotemporal variability in ambient concentrations, the findings may depend on which estimation method is employed.

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KW - Models-3/community multiscale air quality (CMAQ) model

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Within-urban variability in ambient air pollution: Comparison of estimation methods

Abstract

Bibliographical note

Keywords

UN SDGs

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