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A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM 2.5 Concentrations across Great Britain.
- Source :
-
Remote sensing [Remote Sens (Basel)] 2020 Nov 20; Vol. 12 (22), pp. 3803. Date of Electronic Publication: 2020 Nov 20 (Print Publication: 2020). - Publication Year :
- 2020
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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 (PM <subscript>2.5</subscript> ) 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-PM <subscript>2.5</subscript> series using co-located PM <subscript>10</subscript> 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 PM <subscript>2.5</subscript> . Stage-4 applies Stage-3 models to estimate daily PM <subscript>2.5</subscript> concentrations over a 1 km grid. The RF architecture performed well in all stages, with results from Stage-3 showing an average cross-validated R <superscript>2</superscript> 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 R <superscript>2</superscript> 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 PM <subscript>2.5</subscript> .<br />Competing Interests: Conflicts of Interest: The authors declare no conflict of interest.
Details
- Language :
- English
- ISSN :
- 2072-4292
- Volume :
- 12
- Issue :
- 22
- Database :
- MEDLINE
- Journal :
- Remote sensing
- Publication Type :
- Academic Journal
- Accession number :
- 33408882
- Full Text :
- https://doi.org/10.3390/rs12223803