{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T19:56:19Z","timestamp":1783108579844,"version":"3.54.6"},"reference-count":55,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,20]],"date-time":"2020-11-20T00:00:00Z","timestamp":1605830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MR\/M022625\/1"],"award-info":[{"award-number":["MR\/M022625\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000270","name":"Natural Environment Research Council UK","doi-asserted-by":"crossref","award":["NE\/R009384\/1"],"award-info":[{"award-number":["NE\/R009384\/1"]}],"id":[{"id":"10.13039\/501100000270","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000270","name":"Natural Environment Research Council UK","doi-asserted-by":"crossref","award":["NE\/R016429\/1"],"award-info":[{"award-number":["NE\/R016429\/1"]}],"id":[{"id":"10.13039\/501100000270","id-type":"DOI","asserted-by":"crossref"}]},{"name":"European Union\u2019s Horizon 2020 Project Exhaustion","award":["820655"],"award-info":[{"award-number":["820655"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u20132018. 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.<\/jats:p>","DOI":"10.3390\/rs12223803","type":"journal-article","created":{"date-parts":[[2020,11,20]],"date-time":"2020-11-20T09:46:18Z","timestamp":1605865578000},"page":"3803","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2905-0154","authenticated-orcid":false,"given":"Rochelle","family":"Schneider","sequence":"first","affiliation":[{"name":"Department of Public Health, Environments and Society, London School of Hygiene &amp; Tropical Medicine, London WC1H 9SH, UK"},{"name":"The Centre on Climate Change and Planetary Health, London School of Hygiene &amp; Tropical Medicine, London WC1H 9SH, UK"},{"name":"European Centre for Medium-Range Weather Forecast (ECMWF), Shinfield Rd, Reading RG2 9AX, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6982-8867","authenticated-orcid":false,"given":"Ana","family":"Vicedo-Cabrera","sequence":"additional","affiliation":[{"name":"Institute of Social and Preventive Medicine, University of Bern, 3012 Bern, Switzerland"},{"name":"Oeschger Center for Climate Change Research, University of Bern, 3012 Bern, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8890-6848","authenticated-orcid":false,"given":"Francesco","family":"Sera","sequence":"additional","affiliation":[{"name":"Department of Public Health, Environments and Society, London School of Hygiene &amp; Tropical Medicine, London WC1H 9SH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7326-1290","authenticated-orcid":false,"given":"Pierre","family":"Masselot","sequence":"additional","affiliation":[{"name":"Department of Public Health, Environments and Society, London School of Hygiene &amp; Tropical Medicine, London WC1H 9SH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massimo","family":"Stafoggia","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Lazio Regional Health Service, 00147 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5974-2007","authenticated-orcid":false,"given":"Kees","family":"de Hoogh","sequence":"additional","affiliation":[{"name":"Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland"},{"name":"University of Basel, Petersplatz 1, 4051 Basel, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Itai","family":"Kloog","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva P.O. 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Word Health Organization (WHO). Available online: https:\/\/www.who.int\/health-topics\/air-pollution#tab=tab_1."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1056\/NEJMoa1817364","article-title":"Ambient particulate air pollution and daily mortality in 652 cities","volume":"381","author":"Liu","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.envint.2014.11.011","article-title":"Short-term effects of particulate matter constituents on daily hospitalizations and mortality in five South-European cities: Results from the MED-PARTICLES project","volume":"75","author":"Jacquemin","year":"2015","journal-title":"Environ. Int."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.envint.2015.11.007","article-title":"Particulate matter air pollution components and risk for lung cancer","volume":"87","author":"Beelen","year":"2016","journal-title":"Environ. Int."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envint.2019.104953","article-title":"Spatial variations in ambient ultrafine particle concentrations and risk of congenital heart defects","volume":"130","author":"Lavigne","year":"2019","journal-title":"Environ. Int."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1164\/rccm.201810-1976OC","article-title":"Spatiotemporal variations in ambient ultrafine particles and the incidence of childhood asthma","volume":"199","author":"Lavigne","year":"2019","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_7","unstructured":"(2020, March 20). NASA Earth Observations, Available online: https:\/\/neo.sci.gsfc.nasa.gov\/view.php?datasetId=MODAL2_M_AER_OD."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1289\/ehp.0901623","article-title":"Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: Development and application","volume":"118","author":"Martin","year":"2010","journal-title":"Environ. Health Perspect."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5304","DOI":"10.1016\/j.atmosenv.2006.04.044","article-title":"Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe","volume":"40","author":"Koelemeijer","year":"2006","journal-title":"Atmos. Environ."},{"key":"ref_10","first-page":"1","article-title":"Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach","volume":"114","author":"Gupta","year":"2009","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.atmosenv.2013.04.024","article-title":"Application of the deletion\/substitution\/addition algorithm to selecting land use regression models for interpolating air pollution measurements in California","volume":"77","author":"Beckerman","year":"2013","journal-title":"Atmos. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.atmosenv.2009.11.016","article-title":"Comparison of land-use regression models between Great Britain and the Netherlands","volume":"44","author":"Vienneau","year":"2010","journal-title":"Atmos. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1016\/j.envpol.2017.10.025","article-title":"Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland","volume":"233","author":"Stafoggia","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6267","DOI":"10.1016\/j.atmosenv.2011.08.066","article-title":"Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements","volume":"45","author":"Kloog","year":"2011","journal-title":"Atmos. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.atmosenv.2015.10.004","article-title":"Estimating daily PM2.5 and PM10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data","volume":"122","author":"Kloog","year":"2015","journal-title":"Atmos. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7991","DOI":"10.5194\/acp-11-7991-2011","article-title":"A novel calibration approach of MODIS AOD data to predict PM2. 5 concentrations","volume":"11","author":"Lee","year":"2011","journal-title":"Atmos. Chem. Phys."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.envint.2016.11.024","article-title":"Estimation of daily PM10 concentrations in Italy (2006\u20132012) using finely resolved satellite data, land use variables and meteorology","volume":"99","author":"Stafoggia","year":"2016","journal-title":"Environ. Int."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.scitotenv.2018.04.251","article-title":"A machine learning method to estimate PM2.5 concentrations across China with remote sensing meteorological and land use information","volume":"636","author":"Chen","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envint.2019.104909","article-title":"An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution","volume":"130","author":"Di","year":"2019","journal-title":"Environ. Int."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.envint.2019.01.016","article-title":"Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013\u20132015, using a spatiotemporal land-use random-forest model","volume":"124","author":"Stafoggia","year":"2019","journal-title":"Environ. Int."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yazdi, M.D., Kuang, Z., Dimakopoulou, K., Barratt, B., Suel, E., Amini, H., Lyapustin, A., Katsouyanni, K., and Schwartz, J. (2020). Predicting Fine Particulate Matter (PM2.5) in the Greater London Area: An Ensemble Approach using Machine Learning Methods. Remote Sens., 12.","DOI":"10.3390\/rs12060914"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2019.111221","article-title":"Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach","volume":"231","author":"Wei","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.atmosenv.2019.01.027","article-title":"Extreme gradient boosting model to estimate PM2.5 concentrations with missing-filled satellite data in China","volume":"202","author":"Chen","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.atmosenv.2017.02.023","article-title":"Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm","volume":"155","author":"Zhan","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Van der Laan, M.J., and Rose, S. (2011). Super Learning. Targeted Learning: Causal Inference for Observational and Experimental Data, Springer.","DOI":"10.1007\/978-1-4419-9782-1"},{"key":"ref_26","unstructured":"Office for National Statistics (ONS) (2020, April 01). Available online: https:\/\/www.ons.gov.uk\/peoplepopulationandcommunity\/populationandmigration\/populationestimates."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1127\/0941-2948\/2006\/0130","article-title":"World Map of the K\u00f6ppen-Geiger climate classification updated","volume":"15","author":"Kottek","year":"2006","journal-title":"Meteorol. Z."},{"key":"ref_28","unstructured":"(2020, April 01). Digimap. Available online: https:\/\/digimap.edina.ac.uk\/webhelp\/os\/data_information\/os_data_issues\/grid_references.htm."},{"key":"ref_29","unstructured":"(2020, May 25). Openair R Package. Available online: https:\/\/cran.r-project.org\/web\/packages\/openair\/openair.pdf."},{"key":"ref_30","unstructured":"Lyapustin, A., and Wang, Y. (2020, May 28). MCD19A2 MODIS\/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1km SIN Grid V006. 2018, distributed by NASA EOSDIS Land Processes DAAC. Available online: https:\/\/doi.org\/10.5067\/MODIS\/MCD19A2.006."},{"key":"ref_31","unstructured":"Bozzo, A., Remy, S., Benedetti, A., Flemming, J., Bechtold, P., Rodwell, M.J., and Morcrette, J.J. (2017). Implementation of a CAMS-Based Aerosol Climatology in the IFSA, European Centre for Medium-Range Weather Forecasts. Available online: https:\/\/www.ecmwf.int\/sites\/default\/files\/elibrary\/2017\/17219-implementation-cams-based-aerosol-climatology-ifs.pdf."},{"key":"ref_32","unstructured":"(2020, July 13). European Modelling and Evaluation Programme for the UK (EMEP4UK). Available online: http:\/\/www.emep4uk.ceh.ac.uk\/."},{"key":"ref_33","first-page":"12","article-title":"The UK particulate matter air pollution episode of March\u2013April 2014: More than Saharan dust","volume":"11","author":"Vieno","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7963","DOI":"10.5194\/acp-10-7963-2010","article-title":"Modelling surface ozone during the 2003 heat-wave in the UK","volume":"10","author":"Vieno","year":"2010","journal-title":"Atmos. Chem. Phys."},{"key":"ref_35","unstructured":"(2020, May 28). ERA 5 Global Climate Reanalysis. Available online: https:\/\/cds.climate.copernicus.eu\/cdsapp#!\/dataset\/reanalysis-era5-single-levels?tab=overview."},{"key":"ref_36","unstructured":"(2020, May 28). ERA 5 Land Global Climate Reanalysis. Available online: https:\/\/cds.climate.copernicus.eu\/cdsapp#!\/dataset\/reanalysis-era5-land?tab=overview."},{"key":"ref_37","unstructured":"(2020, May 28). UERRA Regional Reanalysis. Available online: https:\/\/cds.climate.copernicus.eu\/cdsapp#!\/dataset\/reanalysis-uerra-europe-soil-levels?tab=overview."},{"key":"ref_38","unstructured":"Didan, K. (2020, November 03). MOD13A3 MODIS\/Terra Vegetation Indices Monthly L3 Global 1 km SIN Grid V006 [Data set]. NASA EOSDIS LP DAAC, 2015, Available online: https:\/\/lpdaac.usgs.gov\/products\/mod13a3v006\/."},{"key":"ref_39","unstructured":"Copernicus Land Monitoring Service (CLMS) (2020, May 29). Available online: https:\/\/land.copernicus.eu\/pan-european."},{"key":"ref_40","unstructured":"Earth Observation Group (EOG) (2020, July 01). Available online: https:\/\/ngdc.noaa.gov\/eog\/viirs\/download_dnb_composites.html."},{"key":"ref_41","unstructured":"(2020, May 29). Ordnance Survey Open Roads. Available online: https:\/\/www.ordnancesurvey.co.uk\/documents\/os-open-roads-user-guide.pdf."},{"key":"ref_42","unstructured":"Civil Aviation Authority (CAA) (2020, May 29). Available online: caa.co.uk\/home."},{"key":"ref_43","unstructured":"UK Data Service (2020, May 29). Available online: https:\/\/www.ukdataservice.ac.uk\/."},{"key":"ref_44","first-page":"1","article-title":"Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data","volume":"88","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An introduction to statistical learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_46","unstructured":"Department for Environment, Food & Rural Affairs (DEFRA) (2020, May 25). Fine Particulate Matter (PM2.5) in the UK 2012, Available online: https:\/\/www.gov.uk\/government\/publications\/fine-particulate-matter-pm2-5-in-the-uk."},{"key":"ref_47","unstructured":"DEFRA (2020, July 13). Modelled Background Pollution Data, Available online: https:\/\/uk-air.defra.gov.uk\/data\/pcm-data."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"353","DOI":"10.5194\/gmd-6-353-2013","article-title":"Air quality modelling using the Met Office Unified Model (AQUM OS24-26): Model description and initial evaluation","volume":"6","author":"Savage","year":"2013","journal-title":"Geosci. Model Dev."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"11221","DOI":"10.5194\/acp-18-11221-2018","article-title":"Air quality simulations for London using a coupled regional-to-local modelling system","volume":"18","author":"Hood","year":"2018","journal-title":"Atmos. Chem. Phys."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.5194\/gmd-10-1767-2017","article-title":"Spatiotemporal evaluation of EMEP4UK-WRF v4.3 atmospheric chemistry transport simulations of health-related metrics for NO2, O3, PM10, and PM2.\u20095 for 2001\u20132010","volume":"10","author":"Lin","year":"2017","journal-title":"Geosci. Model Dev."},{"key":"ref_51","unstructured":"Brookes, D.M., Stedman, J.R., Grice, S.E., Kent, A.J., Walker, H.L., Cooke, S.L., Vincent, K.J., Lingard, J.J.N., Bush, T.J., and Abbott, J. (2020, July 06). UK Air Quality Modelling under the Air Quality Directive (2008\/50\/EC) for 2010 Covering the Following Air Quality Pollutants: SO2, NOx, NO2, PM10, PM2.5, Lead, Benzene, CO, and Ozone. Report for the Department for Environment, Food and Rural Affairs (Defra), Welsh Government, Scottish Government and the Department of the Environment in Northern Ireland. AEA report. AEAT\/ENV\/R\/3215 Issue 1, Available online: http:\/\/uk-air.defra.gov.uk\/reports\/cat09\/1204301513_AQD2010mapsrep_master_v0.pdf."},{"key":"ref_52","unstructured":"Air Quality Expert Group (AQEG) (2020, July 06). Mitigation of United Kingdom PM2.5 Concentrations 2013, Available online: https:\/\/uk-air.defra.gov.uk\/assets\/documents\/reports\/cat11\/1508060903_DEF-PB14161_Mitigation_of_UK_PM25.pdf."},{"key":"ref_53","unstructured":"European Space Agency (2020, October 15). Copernicus Sentinel-5 Precursor Mission. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/missions\/sentinel-5p."},{"key":"ref_54","unstructured":"European Space Agency (2020, October 16). Copernicus Sentinel-4 Mission. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/missions\/sentinel-4."},{"key":"ref_55","unstructured":"European Space Agency (2020, October 16). Copernicus Sentinel-5 Mission. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/missions\/sentinel-5."}],"updated-by":[{"DOI":"10.3390\/rs13183588","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2020,11,20]],"date-time":"2020-11-20T00:00:00Z","timestamp":1605830400000}}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3803\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T13:54:27Z","timestamp":1754229267000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3803"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,20]]},"references-count":55,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12223803"],"URL":"https:\/\/doi.org\/10.3390\/rs12223803","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.07.19.20157396","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,20]]}}}