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Article: Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees

TitleImproved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees
Authors
Keywordsaccuracy assessment
atmospheric modeling
atmospheric pollution
concentration (composition)
diameter
Issue Date2020
PublisherCopernicus GmbH. The Journal's web site is located at http://www.atmospheric-chemistry-and-physics.net
Citation
Atmospheric Chemistry and Physics, 2020, v. 20, p. 3273-3289 How to Cite?
AbstractFine particulate matter with aerodynamic diameters ≤2.5 µm (PM2.5) has adverse effects on human health and the atmospheric environment. The estimation of surface PM2.5 concentrations has made intensive use of satellite-derived aerosol products. However, it has been a great challenge to obtain high-quality and high-resolution PM2.5 data from both ground and satellite observations, which is essential to monitor air pollution over small-scale areas such as metropolitan regions. Here, the space–time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PM2.5 estimates across China. To this end, the newly released Moderate Resolution Imaging Spectroradiometer Multi-Angle Implementation of Atmospheric Correction AOD product, along with meteorological, topographical and land-use data and pollution emissions, was input to the STET model, and daily 1 km PM2.5 maps for 2018 covering mainland China were produced. The STET model performed well, with a high out-of-sample (out-of-station) cross-validation coefficient of determination (R2) of 0.89 (0.88), a low root-mean-square error of 10.33 (10.93) µg m−3, a small mean absolute error of 6.69 (7.15) µg m−3 and a small mean relative error of 21.28 % (23.69 %). In particular, the model captured well the PM2.5 concentrations at both regional and individual site scales. The North China Plain, the Sichuan Basin and Xinjiang Province always featured high PM2.5 pollution levels, especially in winter. The STET model outperformed most models presented in previous related studies, with a strong predictive power (e.g., monthly R2=0.80), which can be used to estimate historical PM2.5 records. More importantly, this study provides a new approach for obtaining high-resolution and high-quality PM2.5 dataset across mainland China (i.e., ChinaHighPM2.5), important for air pollution studies focused on urban areas.
Persistent Identifierhttp://hdl.handle.net/10722/291077
ISSN
2020 Impact Factor: 6.133
2020 SCImago Journal Rankings: 2.622
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWei, J-
dc.contributor.authorLi, Z-
dc.contributor.authorCribb, M-
dc.contributor.authorHuang, W-
dc.contributor.authorXue, W-
dc.contributor.authorSun, L-
dc.contributor.authorGuo, J-
dc.contributor.authorPeng, Y-
dc.contributor.authorLi, J-
dc.contributor.authorLyapustin, A-
dc.contributor.authorLiu, L-
dc.contributor.authorWu, H-
dc.contributor.authorSong, Y-
dc.date.accessioned2020-11-02T05:51:14Z-
dc.date.available2020-11-02T05:51:14Z-
dc.date.issued2020-
dc.identifier.citationAtmospheric Chemistry and Physics, 2020, v. 20, p. 3273-3289-
dc.identifier.issn1680-7316-
dc.identifier.urihttp://hdl.handle.net/10722/291077-
dc.description.abstractFine particulate matter with aerodynamic diameters ≤2.5 µm (PM2.5) has adverse effects on human health and the atmospheric environment. The estimation of surface PM2.5 concentrations has made intensive use of satellite-derived aerosol products. However, it has been a great challenge to obtain high-quality and high-resolution PM2.5 data from both ground and satellite observations, which is essential to monitor air pollution over small-scale areas such as metropolitan regions. Here, the space–time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PM2.5 estimates across China. To this end, the newly released Moderate Resolution Imaging Spectroradiometer Multi-Angle Implementation of Atmospheric Correction AOD product, along with meteorological, topographical and land-use data and pollution emissions, was input to the STET model, and daily 1 km PM2.5 maps for 2018 covering mainland China were produced. The STET model performed well, with a high out-of-sample (out-of-station) cross-validation coefficient of determination (R2) of 0.89 (0.88), a low root-mean-square error of 10.33 (10.93) µg m−3, a small mean absolute error of 6.69 (7.15) µg m−3 and a small mean relative error of 21.28 % (23.69 %). In particular, the model captured well the PM2.5 concentrations at both regional and individual site scales. The North China Plain, the Sichuan Basin and Xinjiang Province always featured high PM2.5 pollution levels, especially in winter. The STET model outperformed most models presented in previous related studies, with a strong predictive power (e.g., monthly R2=0.80), which can be used to estimate historical PM2.5 records. More importantly, this study provides a new approach for obtaining high-resolution and high-quality PM2.5 dataset across mainland China (i.e., ChinaHighPM2.5), important for air pollution studies focused on urban areas.-
dc.languageeng-
dc.publisherCopernicus GmbH. The Journal's web site is located at http://www.atmospheric-chemistry-and-physics.net-
dc.relation.ispartofAtmospheric Chemistry and Physics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectaccuracy assessment-
dc.subjectatmospheric modeling-
dc.subjectatmospheric pollution-
dc.subjectconcentration (composition)-
dc.subjectdiameter-
dc.titleImproved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees-
dc.typeArticle-
dc.identifier.emailSong, Y: ymsong@hku.hk-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5194/acp-20-3273-2020-
dc.identifier.scopuseid_2-s2.0-85082428008-
dc.identifier.hkuros318546-
dc.identifier.volume20-
dc.identifier.spage3273-
dc.identifier.epage3289-
dc.identifier.isiWOS:000521159100001-
dc.publisher.placeGermany-
dc.identifier.issnl1680-7316-

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