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Article

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

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Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece
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Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA 02115, USA
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Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA 02115, USA
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MRC Centre for Environment and Health, Imperial College London, London SE1 9NH, UK
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National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Health Impact of Environmental Hazards, Imperial College London, London SW7 2AZ, UK
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Centre for Environmental Health and Sustainability, School of Geography, Geology and the Environment, University of Leicester, University Road, Leicester LE1 7RH, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Paul B. Tchounwou
Int. J. Environ. Res. Public Health 2022, 19(9), 5401; https://doi.org/10.3390/ijerph19095401
Received: 31 March 2022 / Revised: 26 April 2022 / Accepted: 27 April 2022 / Published: 28 April 2022
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. View Full-Text
Keywords: air pollution; exposure modeling; land use regression; chemical transport models; machine learning; particulate matter air pollution; exposure modeling; land use regression; chemical transport models; machine learning; particulate matter
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MDPI and ACS Style

Dimakopoulou, K.; Samoli, E.; Analitis, A.; Schwartz, J.; Beevers, S.; Kitwiroon, N.; Beddows, A.; Barratt, B.; Rodopoulou, S.; Zafeiratou, S.; Gulliver, J.; Katsouyanni, K. Development and Evaluation of Spatio-Temporal Air Pollution Exposure Models and Their Combinations in the Greater London Area, UK. Int. J. Environ. Res. Public Health 2022, 19, 5401. https://doi.org/10.3390/ijerph19095401

AMA Style

Dimakopoulou K, Samoli E, Analitis A, Schwartz J, Beevers S, Kitwiroon N, Beddows A, Barratt B, Rodopoulou S, Zafeiratou S, Gulliver J, Katsouyanni K. Development and Evaluation of Spatio-Temporal Air Pollution Exposure Models and Their Combinations in the Greater London Area, UK. International Journal of Environmental Research and Public Health. 2022; 19(9):5401. https://doi.org/10.3390/ijerph19095401

Chicago/Turabian Style

Dimakopoulou, Konstantina, Evangelia Samoli, Antonis Analitis, Joel Schwartz, Sean Beevers, Nutthida Kitwiroon, Andrew Beddows, Benjamin Barratt, Sophia Rodopoulou, Sofia Zafeiratou, John Gulliver, and Klea Katsouyanni. 2022. "Development and Evaluation of Spatio-Temporal Air Pollution Exposure Models and Their Combinations in the Greater London Area, UK" International Journal of Environmental Research and Public Health 19, no. 9: 5401. https://doi.org/10.3390/ijerph19095401

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