Back to Search Start Over

Development and Evaluation of Spatio-Temporal Air Pollution Exposure Models and Their Combinations in the Greater London Area, UK

Authors :
Konstantina Dimakopoulou
Evangelia Samoli
Antonis Analitis
Joel Schwartz
Sean Beevers
Nutthida Kitwiroon
Andrew Beddows
Benjamin Barratt
Sophia Rodopoulou
Sofia Zafeiratou
John Gulliver
Klea Katsouyanni
Source :
International Journal of Environmental Research and Public Health; Volume 19; Issue 9; Pages: 5401
Publication Year :
2022

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.

Details

ISSN :
16604601
Volume :
19
Issue :
9
Database :
OpenAIRE
Journal :
International journal of environmental research and public health
Accession number :
edsair.doi.dedup.....687b23c5aee69632b77632db57fb3097