Reconstructing 6-hourly PM<sub>2.5</sub> datasets from 1960 to 2020 in China
<p>Fine particulate matter (PM<span class="inline-formula"><sub>2.5</sub></span>) has altered the radiation balance on Earth and raised environmental and health risks for decades but has only been monitored widely since 2013 in China. Historical long-term PM&l...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-07-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/14/3197/2022/essd-14-3197-2022.pdf |
| _version_ | 1811296954308624384 |
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| author | J. Zhong X. Zhang X. Zhang K. Gui J. Liao Y. Fei L. Jiang L. Guo L. Liu H. Che Y. Wang D. Wang Z. Zhou |
| author_facet | J. Zhong X. Zhang X. Zhang K. Gui J. Liao Y. Fei L. Jiang L. Guo L. Liu H. Che Y. Wang D. Wang Z. Zhou |
| author_sort | J. Zhong |
| collection | DOAJ |
| description | <p>Fine particulate matter (PM<span class="inline-formula"><sub>2.5</sub></span>) has altered the radiation balance on Earth
and raised environmental and health risks for decades but has only been
monitored widely since 2013 in China. Historical long-term PM<span class="inline-formula"><sub>2.5</sub></span>
records with high temporal resolution are essential but lacking for both
research and environmental management. Here, we reconstruct a site-based
PM<span class="inline-formula"><sub>2.5</sub></span> dataset at 6 h intervals from 1960 to 2020 that combines
long-term visibility, conventional meteorological observations, emissions,
and elevation. The PM<span class="inline-formula"><sub>2.5</sub></span> concentration at each site is
estimated based on an advanced machine learning model, LightGBM, that takes
advantage of spatial features from 20 surrounding meteorological stations.
Our model's performance is comparable to or even better than those of
previous studies in by-year cross validation (CV) (<span class="inline-formula"><i>R</i><sup>2</sup>=0.7</span>) and
spatial CV (<span class="inline-formula"><i>R</i><sup>2</sup>=0.76</span>) and is more advantageous in long-term records
and high temporal resolution. This model also reconstructs a 0.25<span class="inline-formula"><sup>∘</sup></span> <span class="inline-formula">×</span> 0.25<span class="inline-formula"><sup>∘</sup></span>, 6-hourly, gridded PM<span class="inline-formula"><sub>2.5</sub></span> dataset by
incorporating spatial features. The results show PM<span class="inline-formula"><sub>2.5</sub></span> pollution
worsens gradually or maintains before 2010 from an interdecadal scale but
mitigates in the following decade. Although the turning points vary in
different regions, PM<span class="inline-formula"><sub>2.5</sub></span> mass concentrations in key regions decreased
significantly after 2013 due to clean air actions. In particular, the annual
average value of PM<span class="inline-formula"><sub>2.5</sub></span> in 2020 is nearly the lowest since 1960. These
two PM<span class="inline-formula"><sub>2.5</sub></span> datasets (publicly available at
<a href="https://doi.org/10.5281/zenodo.6372847">https://doi.org/10.5281/zenodo.6372847</a>, Zhong et al., 2022) provide spatiotemporal variations at
high resolution, which lay the foundation for research studies associated
with air pollution, climate change, and atmospheric chemical reanalysis.</p> |
| first_indexed | 2024-04-13T05:56:53Z |
| format | Article |
| id | doaj.art-908a4c35d89d49eb93b23b0195c27a00 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-04-13T05:56:53Z |
| publishDate | 2022-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-908a4c35d89d49eb93b23b0195c27a002022-12-22T02:59:36ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162022-07-01143197321110.5194/essd-14-3197-2022Reconstructing 6-hourly PM<sub>2.5</sub> datasets from 1960 to 2020 in ChinaJ. Zhong0X. Zhang1X. Zhang2K. Gui3J. Liao4Y. Fei5L. Jiang6L. Guo7L. Liu8H. Che9Y. Wang10D. Wang11Z. Zhou12State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaState Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaCenter for Excellence in Regional Atmospheric Environment, IUE, Chinese Academy of Sciences, Xiamen 361021, ChinaState Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaNational Meteorological Information Center, Beijing 100081, ChinaNational Meteorological Information Center, Beijing 100081, ChinaEarth System Numerical Prediction Center, Beijing 100081, ChinaState Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaDepartment of Earth System Science, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaState Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaState Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaNational Meteorological Information Center, Beijing 100081, China<p>Fine particulate matter (PM<span class="inline-formula"><sub>2.5</sub></span>) has altered the radiation balance on Earth and raised environmental and health risks for decades but has only been monitored widely since 2013 in China. Historical long-term PM<span class="inline-formula"><sub>2.5</sub></span> records with high temporal resolution are essential but lacking for both research and environmental management. Here, we reconstruct a site-based PM<span class="inline-formula"><sub>2.5</sub></span> dataset at 6 h intervals from 1960 to 2020 that combines long-term visibility, conventional meteorological observations, emissions, and elevation. The PM<span class="inline-formula"><sub>2.5</sub></span> concentration at each site is estimated based on an advanced machine learning model, LightGBM, that takes advantage of spatial features from 20 surrounding meteorological stations. Our model's performance is comparable to or even better than those of previous studies in by-year cross validation (CV) (<span class="inline-formula"><i>R</i><sup>2</sup>=0.7</span>) and spatial CV (<span class="inline-formula"><i>R</i><sup>2</sup>=0.76</span>) and is more advantageous in long-term records and high temporal resolution. This model also reconstructs a 0.25<span class="inline-formula"><sup>∘</sup></span> <span class="inline-formula">×</span> 0.25<span class="inline-formula"><sup>∘</sup></span>, 6-hourly, gridded PM<span class="inline-formula"><sub>2.5</sub></span> dataset by incorporating spatial features. The results show PM<span class="inline-formula"><sub>2.5</sub></span> pollution worsens gradually or maintains before 2010 from an interdecadal scale but mitigates in the following decade. Although the turning points vary in different regions, PM<span class="inline-formula"><sub>2.5</sub></span> mass concentrations in key regions decreased significantly after 2013 due to clean air actions. In particular, the annual average value of PM<span class="inline-formula"><sub>2.5</sub></span> in 2020 is nearly the lowest since 1960. These two PM<span class="inline-formula"><sub>2.5</sub></span> datasets (publicly available at <a href="https://doi.org/10.5281/zenodo.6372847">https://doi.org/10.5281/zenodo.6372847</a>, Zhong et al., 2022) provide spatiotemporal variations at high resolution, which lay the foundation for research studies associated with air pollution, climate change, and atmospheric chemical reanalysis.</p>https://essd.copernicus.org/articles/14/3197/2022/essd-14-3197-2022.pdf |
| spellingShingle | J. Zhong X. Zhang X. Zhang K. Gui J. Liao Y. Fei L. Jiang L. Guo L. Liu H. Che Y. Wang D. Wang Z. Zhou Reconstructing 6-hourly PM<sub>2.5</sub> datasets from 1960 to 2020 in China Earth System Science Data |
| title | Reconstructing 6-hourly PM<sub>2.5</sub> datasets from 1960 to 2020 in China |
| title_full | Reconstructing 6-hourly PM<sub>2.5</sub> datasets from 1960 to 2020 in China |
| title_fullStr | Reconstructing 6-hourly PM<sub>2.5</sub> datasets from 1960 to 2020 in China |
| title_full_unstemmed | Reconstructing 6-hourly PM<sub>2.5</sub> datasets from 1960 to 2020 in China |
| title_short | Reconstructing 6-hourly PM<sub>2.5</sub> datasets from 1960 to 2020 in China |
| title_sort | reconstructing 6 hourly pm sub 2 5 sub datasets from 1960 to 2020 in china |
| url | https://essd.copernicus.org/articles/14/3197/2022/essd-14-3197-2022.pdf |
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