Development and evaluation of spatio-temporal air pollution exposure models and their combinations in the Greater London Area, UK
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Land use regression (LUR) and dispersion/chemical transport models (D/CTMs) are frequently applied to predict exposure to air pollution concentrations at a fine scale for use in epidemiological studies. Moreover, the use of satellite aerosol optical depth data has been a key predictor especially for particulate matter pollution and when studying large populations. Within the STEAM project we present a hybrid spatio-temporal modeling framework by (a) incorporating predictions from dispersion modeling of nitrogen dioxide (NO2), ozone (O3) and particulate matter with an aerodynamic diameter equal or less than 10 μm (PM10) and less than 2.5 μm (PM2.5) into a spatio-temporal LUR model; and (b) combining the predictions LUR and dispersion modeling and additionally, only for PM2.5, from an ensemble machine learning approach using a generalized additive model (GAM). We used air pollution measurements from 2009 to 2013 from 62 fixed monitoring sites for O3, 115 for particles and up to 130 for NO2, obtained from the dense network in the Greater London Area, UK. We assessed all models following a 10-fold cross validation (10-fold CV) procedure. The hybrid models performed better compared to separate LUR models. Incorporation of the dispersion estimates in the LUR models as a predictor, improved the LUR model fit: CV-R2 increased to 0.76 from 0.71 for NO2, to 0.79 from 0.57 for PM10, to 0.81 to 0.66 for PM2.5 and to 0.75 from 0.62 for O3. The CV-R2 obtained from the hybrid GAM framework was also increased compared to separate LUR models (CV-R2 = 0.80 for NO2, 0.76 for PM10, 0.79 for PM2.5 and 0.75 for O3). Our study supports the combined use of different air pollution exposure assessment methods in a single modeling framework to improve the accuracy of spatio-temporal predictions for subsequent use in epidemiological studies.
Date Issued
2022-05
Date Acceptance
2022-04-27
Citation
International Journal of Environmental Research and Public Health, 2022, 19 (9)
ISSN
1660-4601
Publisher
MDPI AG
Journal / Book Title
International Journal of Environmental Research and Public Health
Volume
19
Issue
9
Copyright Statement
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000794767900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
air pollution
chemical transport models
COHORT
Environmental Sciences
Environmental Sciences & Ecology
exposure modeling
land use regression
LAND-USE REGRESSION
Life Sciences & Biomedicine
LONG-TERM EXPOSURE
machine learning
MORTALITY
NO2
OZONE
particulate matter
PM2.5
Public, Environmental & Occupational Health
Science & Technology
Publication Status
Published
Coverage Spatial
Switzerland
Article Number
5401
Date Publish Online
2022-04-28