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

Konstantina Dimakopoulou, Evangelia Samoli, Antonis Analitis, Joel Schwartz, Sean Beevers, Nutthida Kitwiroon, Andrew Beddows, Benjamin Barratt, Sophia Rodopoulou, Sofia Zafeiratou, John Gulliver and Klea Katsouyanni
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Konstantina Dimakopoulou: Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece
Evangelia Samoli: Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece
Antonis Analitis: Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece
Joel Schwartz: Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA 02115, USA
Sean Beevers: MRC Centre for Environment and Health, Imperial College London, London SE1 9NH, UK
Nutthida Kitwiroon: MRC Centre for Environment and Health, Imperial College London, London SE1 9NH, UK
Andrew Beddows: MRC Centre for Environment and Health, Imperial College London, London SE1 9NH, UK
Benjamin Barratt: MRC Centre for Environment and Health, Imperial College London, London SE1 9NH, UK
Sophia Rodopoulou: Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece
Sofia Zafeiratou: Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece
John Gulliver: Centre for Environmental Health and Sustainability, School of Geography, Geology and the Environment, University of Leicester, University Road, Leicester LE1 7RH, UK
Klea Katsouyanni: Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece

IJERPH, 2022, vol. 19, issue 9, 1-19

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 (NO 2 ), ozone (O 3 ) 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 NO 2 , 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-R 2 increased to 0.76 from 0.71 for NO 2 , 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 O 3 . The CV-R 2 obtained from the hybrid GAM framework was also increased compared to separate LUR models (CV-R 2 = 0.80 for NO 2 , 0.76 for PM10, 0.79 for PM2.5 and 0.75 for O 3 ). 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.

Keywords: air pollution; exposure modeling; land use regression; chemical transport models; machine learning; particulate matter (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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