Articles | Volume 12, issue 4
https://doi.org/10.5194/essd-12-3067-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-3067-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
25 Nov 2020
Data description paper |
| 25 Nov 2020
| 25 Nov 2020
A homogenized daily in situ PM2.5 concentration dataset from the national air quality monitoring network in China
Kaixu Bai
Key Laboratory of Geographic Information Science (Ministry of
Education), East China Normal University, Shanghai, China
Institute of Eco-Chongming, 20 Cuiniao Rd., Chongming, Shanghai, China
School of Geographic Sciences, East China Normal University, Shanghai,
China
Ke Li
School of Geographic Sciences, East China Normal University, Shanghai,
China
Chengbo Wu
School of Geographic Sciences, East China Normal University, Shanghai,
China
Ni-Bin Chang
Department of Civil, Environmental, and Construction Engineering,
University of Central Florida, Orlando, FL, USA
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing, China
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Cited
14 citations as recorded by crossref.
- Reconstruction of daily haze data across China between 1961 and 2020 Y. Yu et al. 10.1002/joc.7552
- Regional source apportionment of trace metals in fine particulate matter using an observation-constrained hybrid model K. Liao et al. 10.1038/s41612-023-00393-4
- Estimating spatio-temporal variability of aerosol pollution in Yunnan Province, China F. Zhou et al. 10.1016/j.apr.2022.101450
- Multiscale and multisource data fusion for full-coverage PM2.5 concentration mapping: Can spatial pattern recognition come with modeling accuracy? K. Bai et al. 10.1016/j.isprsjprs.2021.12.002
- Construction of homogenized daily surface air temperature for the city of Tianjin during 1887–2019 P. Si et al. 10.5194/essd-13-2211-2021
- LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion K. Bai et al. 10.5194/essd-14-907-2022
- Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion G. Geng et al. 10.1021/acs.est.1c01863
- Prenatal exposure to fine particulate matter chemical constituents and the risk of stillbirth and the mediating role of pregnancy complications: A cohort study T. Shi et al. 10.1016/j.chemosphere.2023.140858
- Optimizing air quality monitoring spatial layout by maximizing the coverage of the population in Beijing–Tianjin–Hebei and surrounding areas J. Xi et al. 10.1016/j.scitotenv.2024.177029
- Using machine learning approach to reproduce the measured feature and understand the model-to-measurement discrepancy of atmospheric formaldehyde H. Yin et al. 10.1016/j.scitotenv.2022.158271
- Development of a data-driven three-dimensional PM2.5 forecast model based on machine learning algorithms Z. Han et al. 10.1016/j.eti.2024.103930
- Spatiotemporal variations and trends of air quality in major cities in Guizhou F. Lu et al. 10.3389/fenvs.2023.1254390
- Impact of near-surface turbulence on PM2.5 concentration in Chengdu during the COVID-19 pandemic X. Xia et al. 10.1016/j.atmosenv.2021.118848
- Clarifying Relationship between PM2.5 Concentrations and Spatiotemporal Predictors Using Multi-Way Partial Dependence Plots H. Shi et al. 10.3390/rs15020358
14 citations as recorded by crossref.
- Reconstruction of daily haze data across China between 1961 and 2020 Y. Yu et al. 10.1002/joc.7552
- Regional source apportionment of trace metals in fine particulate matter using an observation-constrained hybrid model K. Liao et al. 10.1038/s41612-023-00393-4
- Estimating spatio-temporal variability of aerosol pollution in Yunnan Province, China F. Zhou et al. 10.1016/j.apr.2022.101450
- Multiscale and multisource data fusion for full-coverage PM2.5 concentration mapping: Can spatial pattern recognition come with modeling accuracy? K. Bai et al. 10.1016/j.isprsjprs.2021.12.002
- Construction of homogenized daily surface air temperature for the city of Tianjin during 1887–2019 P. Si et al. 10.5194/essd-13-2211-2021
- LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion K. Bai et al. 10.5194/essd-14-907-2022
- Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion G. Geng et al. 10.1021/acs.est.1c01863
- Prenatal exposure to fine particulate matter chemical constituents and the risk of stillbirth and the mediating role of pregnancy complications: A cohort study T. Shi et al. 10.1016/j.chemosphere.2023.140858
- Optimizing air quality monitoring spatial layout by maximizing the coverage of the population in Beijing–Tianjin–Hebei and surrounding areas J. Xi et al. 10.1016/j.scitotenv.2024.177029
- Using machine learning approach to reproduce the measured feature and understand the model-to-measurement discrepancy of atmospheric formaldehyde H. Yin et al. 10.1016/j.scitotenv.2022.158271
- Development of a data-driven three-dimensional PM2.5 forecast model based on machine learning algorithms Z. Han et al. 10.1016/j.eti.2024.103930
- Spatiotemporal variations and trends of air quality in major cities in Guizhou F. Lu et al. 10.3389/fenvs.2023.1254390
- Impact of near-surface turbulence on PM2.5 concentration in Chengdu during the COVID-19 pandemic X. Xia et al. 10.1016/j.atmosenv.2021.118848
- Clarifying Relationship between PM2.5 Concentrations and Spatiotemporal Predictors Using Multi-Way Partial Dependence Plots H. Shi et al. 10.3390/rs15020358
Latest update: 13 Dec 2024
Short summary
PM2.5 data from the national air quality monitoring network in China suffered from significant inconsistency and inhomogeneity issues. To create a coherent PM2.5 concentration dataset to advance our understanding of haze pollution and its impact on weather and climate, we homogenized this PM2.5 dataset between 2015 and 2019 after filling in the data gaps. The homogenized PM2.5 data is found to better characterize the variation of aerosol in space and time compared to the original dataset.
PM2.5 data from the national air quality monitoring network in China suffered from significant...
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