Articles | Volume 21, issue 24
https://doi.org/10.5194/acp-21-18375-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-18375-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Dec 2021
Research article |
| 17 Dec 2021
| 17 Dec 2021
The spatiotemporal relationship between PM2.5 and aerosol optical depth in China: influencing factors and implications for satellite PM2.5 estimations using MAIAC aerosol optical depth
Qingqing He
Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong SAR, China
School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, China
Mengya Wang
Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong SAR, China
Asian School of the Environment, Nanyang Technological University, Singapore
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
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Cited
16 citations as recorded by crossref.
- Full-coverage 1-km estimates and spatiotemporal trends of aerosol optical depth over Taiwan from 2003 to 2019 W. Wang et al. 10.1016/j.apr.2022.101579
- Evaluation and comparison of MODIS aerosol optical depth retrieval algorithms over Brazil A. Rudke et al. 10.1016/j.atmosenv.2023.120130
- Mapping the seamless hourly surface visibility in China: a real-time retrieval framework using a machine-learning-based stacked ensemble model X. Zhang et al. 10.1038/s41612-024-00617-1
- Variation of Aerosol Optical Depth Measured by Sun Photometer at a Rural Site near Beijing during the 2017–2019 Period X. Wu et al. 10.3390/rs14122908
- Transboundary Wildfire Smoke and Expressed Sentiment: Evidence from Twitter R. Du et al. 10.2139/ssrn.4066034
- Assessing the impact of a waste incinerator on the environment using the MAIAC-AOD and AERMOD models A. Hongthong & S. Nakapan 10.3389/fenvs.2023.1240705
- Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak A. Rudke et al. 10.1016/j.rse.2023.113514
- Effects of Anthropogenic Emission Control and Meteorology Changes on the Inter-Annual Variations of PM2.5–AOD Relationship in China L. Qi et al. 10.3390/rs14184683
- Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations T. Mi et al. 10.1016/j.gsf.2023.101686
- Estimation of Regional Ground-Level PM2.5 Concentrations Directly from Satellite Top-of-Atmosphere Reflectance Using A Hybrid Learning Model Y. Feng et al. 10.3390/rs14112714
- Establishment of aerosol optical depth dataset in the Sichuan Basin by the random forest approach M. Jiang et al. 10.1016/j.apr.2022.101394
- Changing PM2.5 and related meteorology over India from 1950–2014: a new perspective from a chemistry-climate model ensemble S. Hancock et al. 10.1088/2752-5295/acb22a
- Transboundary vegetation fire smoke and expressed sentiment: Evidence from Twitter R. Du et al. 10.1016/j.jeem.2024.102928
- Aerosol optical depth climatology from the high-resolution MAIAC product over Europe: differences between major European cities and their surrounding environments L. Di Antonio et al. 10.5194/acp-23-12455-2023
- Spatiotemporally continuous estimates of daily 1-km PM2.5 concentrations and their long-term exposure in China from 2000 to 2020 Q. He et al. 10.1016/j.jenvman.2023.118145
- Enhancing the reliability of hindcast modeling for air pollution using history-informed machine learning and satellite remote sensing in China Q. He et al. 10.1016/j.atmosenv.2023.119994
16 citations as recorded by crossref.
- Full-coverage 1-km estimates and spatiotemporal trends of aerosol optical depth over Taiwan from 2003 to 2019 W. Wang et al. 10.1016/j.apr.2022.101579
- Evaluation and comparison of MODIS aerosol optical depth retrieval algorithms over Brazil A. Rudke et al. 10.1016/j.atmosenv.2023.120130
- Mapping the seamless hourly surface visibility in China: a real-time retrieval framework using a machine-learning-based stacked ensemble model X. Zhang et al. 10.1038/s41612-024-00617-1
- Variation of Aerosol Optical Depth Measured by Sun Photometer at a Rural Site near Beijing during the 2017–2019 Period X. Wu et al. 10.3390/rs14122908
- Transboundary Wildfire Smoke and Expressed Sentiment: Evidence from Twitter R. Du et al. 10.2139/ssrn.4066034
- Assessing the impact of a waste incinerator on the environment using the MAIAC-AOD and AERMOD models A. Hongthong & S. Nakapan 10.3389/fenvs.2023.1240705
- Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak A. Rudke et al. 10.1016/j.rse.2023.113514
- Effects of Anthropogenic Emission Control and Meteorology Changes on the Inter-Annual Variations of PM2.5–AOD Relationship in China L. Qi et al. 10.3390/rs14184683
- Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations T. Mi et al. 10.1016/j.gsf.2023.101686
- Estimation of Regional Ground-Level PM2.5 Concentrations Directly from Satellite Top-of-Atmosphere Reflectance Using A Hybrid Learning Model Y. Feng et al. 10.3390/rs14112714
- Establishment of aerosol optical depth dataset in the Sichuan Basin by the random forest approach M. Jiang et al. 10.1016/j.apr.2022.101394
- Changing PM2.5 and related meteorology over India from 1950–2014: a new perspective from a chemistry-climate model ensemble S. Hancock et al. 10.1088/2752-5295/acb22a
- Transboundary vegetation fire smoke and expressed sentiment: Evidence from Twitter R. Du et al. 10.1016/j.jeem.2024.102928
- Aerosol optical depth climatology from the high-resolution MAIAC product over Europe: differences between major European cities and their surrounding environments L. Di Antonio et al. 10.5194/acp-23-12455-2023
- Spatiotemporally continuous estimates of daily 1-km PM2.5 concentrations and their long-term exposure in China from 2000 to 2020 Q. He et al. 10.1016/j.jenvman.2023.118145
- Enhancing the reliability of hindcast modeling for air pollution using history-informed machine learning and satellite remote sensing in China Q. He et al. 10.1016/j.atmosenv.2023.119994
Latest update: 24 Apr 2024
Short summary
We explore the spatiotemporal relationship between PM2.5 and AOD over China using a multi-scale analysis with MODIS MAIAC 1 km aerosol observations and ground measurements. The impact factors (vertical distribution, relative humidity and terrain) on the relationship are quantitatively studied. Our results provide significant information on PM2.5 and AOD, which is informative for mapping high-resolution PM2.5 and furthering the understanding of aerosol properties and the PM2.5 pollution status.
We explore the spatiotemporal relationship between PM2.5 and AOD over China using a multi-scale...
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