File Download
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.5194/acp-20-3273-2020
- Scopus: eid_2-s2.0-85082428008
- WOS: WOS:000521159100001
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees
Title | Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees |
---|---|
Authors | |
Keywords | accuracy assessment atmospheric modeling atmospheric pollution concentration (composition) diameter |
Issue Date | 2020 |
Publisher | Copernicus 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? |
Abstract | Fine 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 Identifier | http://hdl.handle.net/10722/291077 |
ISSN | 2023 Impact Factor: 5.2 2023 SCImago Journal Rankings: 2.138 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wei, J | - |
dc.contributor.author | Li, Z | - |
dc.contributor.author | Cribb, M | - |
dc.contributor.author | Huang, W | - |
dc.contributor.author | Xue, W | - |
dc.contributor.author | Sun, L | - |
dc.contributor.author | Guo, J | - |
dc.contributor.author | Peng, Y | - |
dc.contributor.author | Li, J | - |
dc.contributor.author | Lyapustin, A | - |
dc.contributor.author | Liu, L | - |
dc.contributor.author | Wu, H | - |
dc.contributor.author | Song, Y | - |
dc.date.accessioned | 2020-11-02T05:51:14Z | - |
dc.date.available | 2020-11-02T05:51:14Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Atmospheric Chemistry and Physics, 2020, v. 20, p. 3273-3289 | - |
dc.identifier.issn | 1680-7316 | - |
dc.identifier.uri | http://hdl.handle.net/10722/291077 | - |
dc.description.abstract | Fine 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.language | eng | - |
dc.publisher | Copernicus GmbH. The Journal's web site is located at http://www.atmospheric-chemistry-and-physics.net | - |
dc.relation.ispartof | Atmospheric Chemistry and Physics | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | accuracy assessment | - |
dc.subject | atmospheric modeling | - |
dc.subject | atmospheric pollution | - |
dc.subject | concentration (composition) | - |
dc.subject | diameter | - |
dc.title | Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees | - |
dc.type | Article | - |
dc.identifier.email | Song, Y: ymsong@hku.hk | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.5194/acp-20-3273-2020 | - |
dc.identifier.scopus | eid_2-s2.0-85082428008 | - |
dc.identifier.hkuros | 318546 | - |
dc.identifier.volume | 20 | - |
dc.identifier.spage | 3273 | - |
dc.identifier.epage | 3289 | - |
dc.identifier.isi | WOS:000521159100001 | - |
dc.publisher.place | Germany | - |
dc.identifier.issnl | 1680-7316 | - |