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Volume 12
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Correction published on 9 September 2021, see Remote Sens. 2021, 13(18), 3588.
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Article

A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain

by
Rochelle Schneider
1,2,3,*,
Ana M. Vicedo-Cabrera
4,5,
Francesco Sera
1,
Pierre Masselot
1,
Massimo Stafoggia
6,
Kees de Hoogh
7,8,
Itai Kloog
9,
Stefan Reis
10,11,
Massimo Vieno
10 and
Antonio Gasparrini
1,2,12
1
Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London WC1H 9SH, UK
2
The Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1H 9SH, UK
3
European Centre for Medium-Range Weather Forecast (ECMWF), Shinfield Rd, Reading RG2 9AX, UK
4
Institute of Social and Preventive Medicine, University of Bern, 3012 Bern, Switzerland
5
Oeschger Center for Climate Change Research, University of Bern, 3012 Bern, Switzerland
6
Department of Epidemiology, Lazio Regional Health Service, 00147 Rome, Italy
7
Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland
8
University of Basel, Petersplatz 1, 4051 Basel, Switzerland
9
Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva P.O. Box 653, Israel
10
UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Edinburgh, Midlothian EH26 0QB, UK
11
University of Exeter Medical School, Knowledge Spa, Truro TR1 3HD, UK
12
Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
 
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(22), 3803; https://doi.org/10.3390/rs12223803
Submission received: 18 September 2020 / Revised: 30 October 2020 / Accepted: 2 November 2020 / Published: 20 November 2020
(This article belongs to the Special Issue The Use of Earth Observations for Exposure Assessment in Epidemiological Studies)
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Abstract

Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This study aims to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM2.5) levels across Great Britain between 2008–2018. This high-resolution model consists of random forest (RF) algorithms applied in four stages. Stage-1 augments monitor-PM2.5 series using co-located PM10 measures. Stage-2 imputes missing satellite aerosol optical depth observations using atmospheric reanalysis models. Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM2.5. Stage-4 applies Stage-3 models to estimate daily PM2.5 concentrations over a 1 km grid. The RF architecture performed well in all stages, with results from Stage-3 showing an average cross-validated R2 of 0.767 and minimal bias. The model performed better over the temporal scale when compared to the spatial component, but both presented good accuracy with an R2 of 0.795 and 0.658, respectively. These findings indicate that direct satellite observations must be integrated with other satellite-based products and geospatial variables to derive reliable estimates of air pollution exposure. The high spatio-temporal resolution and the relatively high precision allow these estimates (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposure to PM2.5.
Keywords: fine particulate matter; aerosol optical depth; satellite; reanalysis; machine learning; random forest fine particulate matter; aerosol optical depth; satellite; reanalysis; machine learning; random forest
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MDPI and ACS Style

Schneider, R.; Vicedo-Cabrera, A.M.; Sera, F.; Masselot, P.; Stafoggia, M.; de Hoogh, K.; Kloog, I.; Reis, S.; Vieno, M.; Gasparrini, A. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain. Remote Sens. 2020, 12, 3803. https://doi.org/10.3390/rs12223803

AMA Style

Schneider R, Vicedo-Cabrera AM, Sera F, Masselot P, Stafoggia M, de Hoogh K, Kloog I, Reis S, Vieno M, Gasparrini A. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain. Remote Sensing. 2020; 12(22):3803. https://doi.org/10.3390/rs12223803

Chicago/Turabian Style

Schneider, Rochelle, Ana M. Vicedo-Cabrera, Francesco Sera, Pierre Masselot, Massimo Stafoggia, Kees de Hoogh, Itai Kloog, Stefan Reis, Massimo Vieno, and Antonio Gasparrini. 2020. "A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain" Remote Sensing 12, no. 22: 3803. https://doi.org/10.3390/rs12223803

APA Style

Schneider, R., Vicedo-Cabrera, A. M., Sera, F., Masselot, P., Stafoggia, M., de Hoogh, K., Kloog, I., Reis, S., Vieno, M., & Gasparrini, A. (2020). A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain. Remote Sensing, 12(22), 3803. https://doi.org/10.3390/rs12223803

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