GlobalHighAirPollutants (GHAP) | ChinaHighAirPollutants (CHAP) | USHighAirPollutants (USHAP)

ChinaHighAirPollutants (CHAP)

• Brief Introduction

  The ChinaHighAirPollutants (CHAP) dataset refers to the long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China. It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. The CHAP dataset contains 7 major air pollutants (i.e., PM₁, PM_2.5, PM₁₀, O₃, NO₂, SO₂, and CO), PM_2.5 chemical composition (i.e., SO₄^2-, NO₃^-, NH₄⁺, Cl^-, BC, and OM), and ambient polycyclic aromatic hydrocarbons (PAHs), including 7 carcinogenic PAHs (i.e., BaA, Chr, BbF, BkF, BaP, DahA, IcdP). This CHAP dataset is public and freely open to all users!

• Open Platform

  【GitHub】, 【Zenodo】, 【国家地球系统科学数据中心】, 【国家青藏高原科学数据中心】

• Dataset summary

  

• ChinaHighPM_2.5 dataset

  [1] Big data (seamless): 1 km, 2000-2022, Daily/Monthly/Yearly (Version 4)

  Link: 【Zenodo】, 【国家地球系统科学数据中心】, 【国家青藏高原科学数据中心】

  Reference:

  Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM_2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136 (ESI Hot and Highly Cited Paper, Journal Most Cited Articles since 2019/2020, Top 100 Most Cited Chinese Papers Published in International Journals, ESSIC 2022 Best Paper Award)

  Wei, J., Li, Z., Cribb, M., Huang, W., Xue, W., Sun, L., Guo, J., Peng, Y., Li, J., Lyapustin, A., Liu, L., Wu, H., and Song, Y. Improved 1 km resolution PM_2.5 estimates across China using enhanced space-time extremely randomized trees. Atmospheric Chemistry and Physics, 2020, 20, 3273–3289. https://doi.org/10.5194/acp-20-3273-2020 (ESI Hot and Highly Cited Paper)

  [2] Himawari-8: Eastern China, 5 km, 2018, Hourly (Version 1)

  Link: 【Zenodo】

  Reference: Wei, J., Li, Z., Pinker, R., Wang, J., Sun, L., Xue, W., Li, R., and Cribb, M. Himawari-8-derived diurnal variations of ground-level PM_2.5 pollution across China using a fast space-time Light Gradient Boosting Machine (LightGBM). Atmospheric Chemistry and Physics, 2021, 21, 7863–7880. https://doi.org/10.5194/acp-21-7863-2021 (ESI Highly Cited Paper)

  

• ChinaHighPMC dataset

  Big data (seamless): 1 km, 2000-2021, Daily/Monthly/Yearly (Version 1)

  Link (2013-2020): 【Zenodo】, 【国家地球系统科学数据中心】, 【国家青藏高原科学数据中心】

  Reference: Wei, J., Li, Z., Chen, X., Li, C., Sun, Y., Wang, J., Lyapustin, A., Brasseur, G., Jiang, M., Sun, L., Wang, T., Jung, C., Qiu, B., Fang, C., Liu, X., Hao, J., Wang, Y., Zhan, M., Song, X., and Liu, Y. Separating daily 1 km PM_2.5 inorganic chemical composition in China since 2000 via deep learning integrating ground, satellite, and model data. Environmental Science & Technology, 2023, 57(46), 18282–18295. https://doi.org/10.1021/acs.est.3c00272 (ESI Highly Cited Paper)

  

• ChinaHighPM₁ dataset

  Big data (seamless): 1 km, 2000-2021, Daily/Monthly/Yearly (Version 3)

  Link: 【Zenodo】

  Reference: Wei, J., Li, Z., Guo, J., Sun, L., Huang, W., Xue, W., Fan, T, and Cribb, M. Satellite-derived 1-km-resolution PM₁ concentrations from 2014 to 2018 across China. Environmental Science & Technology, 2019, 53(22), 13265-13274. https://doi.org/10.1021/acs.est.9b03258 (ESI Hot and Highly Cited Paper)

  

• ChinaHighPM₁₀ dataset

  Big data (seamless): 1 km, 2000-2022, Daily/Monthly/Yearly (Version 4)

  Link: 【Zenodo】, 【国家地球系统科学数据中心】, 【国家青藏高原科学数据中心】

  Reference: Wei, J., Li, Z., Xue, W., Sun, L., Fan, T., Liu, L., Su, T., and Cribb, M. The ChinaHighPM₁₀ dataset: generation, validation, and spatiotemporal variations from 2013 to 2019 across China. Environment International, 2021, 146, 106290. https://doi.org/10.1016/j.envint.2020.106290 (ESI Highly Cited Paper)

  

• ChinaHighO₃ dataset

  Big data (seamless): 1 km, 2000-2022, Daily/Monthly/Yearly

  Link (2000-2022, Version 2): 【Zenodo】, 【国家地球系统科学数据中心】, 【国家青藏高原科学数据中心】

  Big data (seamless): 10 km, 1979-2020, Daily/Monthly/Yearly

  Link (2013-2020, Version 1): 【Zenodo】, 【国家地球系统科学数据中心】, 【国家青藏高原科学数据中心】

  Reference:

  [1] Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O₃) pollution from 2013 to 2020 across China. Remote Sensing of Environment, 2022, 270, 112775. https://doi.org/10.1016/j.rse.2021.112775 (ESI Hot and Highly Cited Paper)

  [2] He, L., Wei, J., Wang, Y., Shang, Q., Liu, J., Yin, Y., Frankerberg, C., Jiang, J., Li, Z., and Yung, Y. Marked impacts of pollution mitigation on crop yields in China. Earth's Future, 2022, 10, e2022EF002936. https://doi.org/10.1029/2022EF002936

  

• ChinaHighNO₂ dataset

  [1] Big data (seamless): 1 km, 2019-2022, Daily/Monthly/Yearly

  Link (Version 2): 【Zenodo】, 【国家地球系统科学数据中心】, 【国家青藏高原科学数据中心】

  Reference: Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R., Dickerson, R., Lin, J., Boersma, K., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., and Wang, J. Ground-level NO₂ surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence. Environmental Science & Technology, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834 (ESI Highly Cited Paper)

  [2] Big data (seamless): 10 km, 2008-2018, Daily/Monthly/Yearly

  Link (Version 1): 【Zenodo】, 【国家地球系统科学数据中心】, 【国家青藏高原科学数据中心】

  Reference: Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO₂, SO₂, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023 (ESI Hot and Highly Cited Paper)

  

• ChinaHighSO₂ dataset

  [1] Big data (seamless): 1 km, 2019-2022, Daily/Monthly/Yearly

  Link (Version 2): 【Zenodo】, 【国家地球系统科学数据中心】, 【国家青藏高原科学数据中心】

  [2] Big data (seamless): 10 km, 2013-2018, Daily/Monthly/Yearly

  Link (Version 1): 【Zenodo】, 【国家地球系统科学数据中心】, 【国家青藏高原科学数据中心】

  Reference: Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO₂, SO₂, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023 (ESI Hot and Highly Cited Paper)

  

• ChinaHighCO dataset

  [1] Big data (seamless): 1 km, 2019-2022, Daily/Monthly/Yearly

  Link (Version 2): 【Zenodo】, 【国家地球系统科学数据中心】, 【国家青藏高原科学数据中心】

  [2] Big data (seamless): 10 km, 2013-2018, Daily/Monthly/Yearly

  Link (Version 1): 【Zenodo】, 【国家地球系统科学数据中心】, 【国家青藏高原科学数据中心】

  Reference: Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO₂, SO₂, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023 (ESI Hot and Highly Cited Paper)

  

• ChinaHighPAH dataset

  Big data (seamless): 10 km, 2013-2020, Daily/Monthly/Yearly (Version 1)

  Link: 【Zenodo】 (This dataset is only available upon request)

  Reference: Wei, J., et al. In preparation.

  

   

USHighAirPollutants (USHAP)

• Brief Introduction

  The USHighAirPollutants (USHAP) dataset refers to the long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for the Continental United States. Similarly, it is generated from the big data using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution, and now it contains PM_2.5 and BC (1 km), and more species of air pollutants will be generated in the future.

• USHighPM_2.5 dataset

  Big data (seamless): 1 km, 2000-2020, Daily/Monthly/Yearly (Version 1)

  Link: 【Zenodo】

• USHighBC dataset

  Big data (seamless): 1 km, 2000-2020, Daily/Monthly/Yearly (Version 1)

  Link: 【Zenodo】

  Reference: Wei, J., Wang, J., Li, Z., Kondragunta, S., Anenberg, S., Wang, Y., Zhang, H., Diner, D., Hand, J., Lyapustin, A., Kahn, R., Colarco, P., da Silva, A., and Ichoku, C. Long-term mortality burden trends attributed to black carbon and PM_2.5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study. The Lancet Planetary Health, 2023, 7, e963–e975. https://doi.org/10.1016/S2542-5196(23)00235-8.

  

   

GlobalHighAirPollutants (GHAP)

• Brief Introduction

  The GlobalHighAirPollutants (GHAP) dataset refers to the long-term, full-coverage, high-resolution, and high-quality datasets of global ground-level air pollutants over land. Similarly, it is generated from the big data using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution, and now it contains PM_2.5 (1 km), NO₂ (1 km), and O₃ (10 km), and more species of air pollutants will be generated in the future.

• GlobalHighPM_2.5 dataset

  Big data (seamless): 1 km, 2017-2022, Daily/Monthly/Yearly (Version 1)

  Link: 【Zenodo】

  Reference: Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. First close insight into global daily gapless 1 km PM_2.5 pollution, variability, and health impact. Nature Communications, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3

  

   

• GlobalHighNO₂ dataset

  Big data (seamless): 1 km, 2018-2022, Daily/Monthly/Yearly (Version 1)

  Link: 【Zenodo】

  Reference: Wei, J., liu, S., Li, Z., et al. In preparation.

  

   

• GlobalHighO₃ dataset

  Big data (seamless): 10 km, 2000-2020, Daily/Monthly/Yearly (Version 1)

  Link: 【Zenodo】

  Reference: Wei, J., et al. In preparation.

  

   

• Dataset codes

  Data type conversion (from .nc to .tif): [1] Python code; [2] Matlab code; [3] IDL code; [4] R code

   

• News/reports on our dataset

  University of Maryland: Li and Wei Published on Air Pollution in China

  Beijing Normal University: Dr. Jing Wei released the high-resolution and high-quality air pollution dataset in China

   

• Publications using our dataset (334)

  Highlights (10)

  Lin, L., Yi, X., Liu, H., et al. The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans. Nature Medicine, 2023, 29, 1750–1759. https://doi.org/10.1038/s41591-023-02424-2 (IF = 82.9)

  Huang, W., Zhou, Y., Chen, X., et al. Individual and joint associations of long-term exposure to air pollutants and cardiopulmonary mortality: a 22-year cohort study in Northern China. The Lancet Regional Health – Western Pacific, 2023, 100776. https://doi.org/10.1016/j.lanwpc.2023.100776

  Xu, R., Huang, S., Shi, C., et al. Extreme temperature events, fine particulate matter, and myocardial infarction mortality. Circulation, 2023, 148, 312–323. https://doi.org/10.1161/CIRCULATIONAHA.122.063504 (IF = 37.8)

  Cai, M., Zhang, S., Lin, X., et al. Association of ambient particulate matter pollution of different sizes with in-hospital case fatality among stroke patients in China. Neurology, 2022, 98, 24. https://doi.org/10.1212/WNL.0000000000200546 (ESI Highly Cited Paper)

  Cai, M., Wei, J., Zhang, S., et al. Short-term air pollution exposure associated with death from kidney diseases: a nationwide time-stratifed case-crossover study in China from 2015 to 2019. BMC Medicine, 2023, 21, 32. https://doi.org/10.1186/s12916-023-02734-9 (ESI Highly Cited Paper)

  Cai, M., Lin, X., Wang, X., et al. Ambient particulate matter pollution of different sizes associated with recurrent stroke hospitalization in China: A cohort study of 1.07 million stroke patients. Science of The Total Environment, 2023, 856, 159104. https://doi.org/10.1016/j.scitotenv.2022.159104 (ESI Highly Cited Paper)

  Xu, H., Guo, B., Qian, W., et al. Dietary pattern and long-term effects of particulate matter on blood pressure: a large cross-sectional study in Chinese adults. Hypertension, 2021, 78, 184–194. https://doi.org/10.1161/HYPERTENSIONAHA.121.17205 (Journal High Impact Paper)

  Guo, J., Zhou, J., Han, R., et al. Association of short-term co-exposure to particulate matter and ozone with mortality risk. Environmental Science & Technology, 2023, 57(42), 15825–15834. https://doi.org/10.1021/acs.est.3c04056 (Supplementary Cover Article)

  Zhang, Y., Wei, J., Shi, Y., et al. Effects of early-life exposure to submicron particulate air pollution on asthma development in Chinese preschool children. Journal of Allergy and Clinical Immunology, 2021, 148, 771-782. https://doi.org/10.1016/j.jaci.2021.02.030

  Ge, E., Gao, J., Wei, X., et al. Effect modification of greenness on PM_2.5 associated with all-cause mortality in a multidrug resistant tuberculosis cohort. Thorax, 2022, 77, 1202-1209. http://dx.doi.org/10.1136/thoraxjnl-2020-216819

   

  Impact of air pollution on public health (210)

  Ai, B., Zhang, S., Fu, J., et al. Associations between green space surrounding kindergartens and hyperactivity behaviors among Chinese preschool children. Future, 2023, 1(1), 27-37. https://doi.org/10.3390/future1010005

  Ao, L., Zhou, J., Han, M., et al. The joint effects of physical activity and air pollution on type 2 diabetes in older adults. BMC Geriatrics, 2022, 22, 472. https://doi.org/10.1186/s12877-022-03139-8

  Bai, H., Yan, R., Gao, W., et al. Spatial representativeness of PM_2.5 monitoring stations and its implication for health assessment. Air Quality, Atmosphere & Health, 2022. https://doi.org/10.1007/s11869-022-01202-2

  Cai, X., Ni, H., Wang, Q., et al. Sperm quality decline associated with gaseous pollutant exposure: Evidence from a large cohort multicenter study. Journal of Hazardous Materials, 2023, 460, 132330. https://doi.org/10.1016/j.jhazmat.2023.132330

  Cao, Y., Zang, T., Qiu, T., et al. Does PM₁ exposure during pregnancy impact the gut microbiota of mothers and neonates?. Environmental Research, 2023, 231, 116304. https://doi.org/10.1016/j.envres.2023.116304

  Chen, L., Jia, Y., Guo, Y., et al. Residential greenness associated with decreased risk of metabolic- dysfunction-associated fatty liver disease: Evidence from a large population-based epidemiological study. Ecotoxicology and Environmental Safety, 2023, 249, 114338. https://doi.org/10.1016/j.ecoenv.2022.114338

  Chen, L., Xie, J., Ma, T., et al. Greenness alleviates the effects of ambient particulate matter on the risks of high blood pressure in children and adolescents. Science of The Total Environment, 2022, 812, 152431. https://doi.org/10.1016/j.scitotenv.2021.152431

  Chen, L., Gao, D., Ma, T., et al. Could greenness modify the effects of physical activity and air pollutants on overweight and obesity among children and adolescents? Science of The Total Environment, 2022, 832, 155117. https://doi.org/10.1016/j.scitotenv.2022.155117

  Chen, L., Jia, Y., Guo, Y., et al. Could greenness modify the effects of physical activity and air pollutants on overweight and obesity among children and adolescents? Ecotoxicology and Environmental Safety, 2023, 149, 114338. https://doi.org/10.1016/j.ecoenv.2022.114338

  Chen, L., Gao, D., Ma, T., et al. Ambient gaseous pollutant exposure and incidence of visual impairment among children and adolescents: fndings from a longitudinal, two‑center cohort study in China. Environmental Science and Pollution Research, 2022, 29, 73262-73270. https://doi.org/10.1007/s11356-022-20025-3

  Chen, N., Ma, L., Zhang, Y., et al. Association of household solid fuel use and long-term exposure to ambient air pollution with estimated 10-year high cardiovascular disease risk among postmenopausal women. Environmental Pollution, 2024, 342, 123091. https://doi.org/10.1016/j.envpol.2023.123091

  Chen, Q., Chen, Q., Wang, Q., et al. Particulate matter and ozone might trigger deaths from chronic ischemic heart disease. Ecotoxicology and Environmental Safety, 2022, 242, 113931. https://doi.org/10.1016/j.ecoenv.2022.113931

  Chen, S., Zhang, Y., Wang, Y., et al. Long-term particulate matter exposure and the risk of neurological hospitalization: Evidence from causal inference of a large longitudinal cohort in South China. Chemosphere, 2023, 345, 140397. https://doi.org/10.1016/j.chemosphere.2023.140397

  Chen, Y., Hu, Y., Li, R., et al. Association of residential greenness with chronotype among children Science of The Total Environment, 2023, 903, 166011. https://doi.org/10.1016/j.scitotenv.2023.166011

  Cheng, J., Zheng, H., Wei, J., et al. Short-term residential exposure to air pollution and risk of acute myocardial infarction deaths at home in China. Environmental Science and Pollution Research, 2023. https://doi.org/10.1007/s11356-023-27813-5

  Deng, P., Tang, H., Zhu, L., et al. Association of long-term ambient fine particulate matter (PM_2.5) and incident non-alcoholic fatty liver disease in Chinese adults. Environmental Pollution, 2023, 329, 121666. https://doi.org/10.1016/j.envpol.2023.121666

  Deng, X., Wang, Q., Shi, C., et al. Heat wave exposure and semen quality in sperm donation volunteers: A retrospective longitudinal study in south China. Environmental Research, 2023, 236, 116665. https://doi.org/10.1016/j.envres.2023.116665

  Duan, J., Li, Y., Li, S., et al. Association of long-term ambient fine particulate matter (PM_2.5) and incident CKD: A prospective cohort study in China. American Journal of Kidney Diseases, 2023, 80(5), 638-647.e1. https://doi.org/10.1016/j.envres.2023.115405

  Duan, X., Zhao, W., Yang, B., et al. Association of residential greenness with obstructive sleep apnea among Chinese old adults and the mediation role of PM_2.5 and leisure-time physical activity. Science of The Total Environment, 2024, 915, 170120. https://doi.org/10.1016/j.scitotenv.2024.170120

  Fan, Z., Li, Y., Wei, J., et al. Long-term exposure to fine particulate matter and site-specific cancer mortality: A difference-in-differences analysis in Jiangsu province, China. Environmental Research, 2023, 222, 115405. https://doi.org/10.1016/j.envres.2023.115405

  Feng, C., Yu, B., Fei, T., et al. Association between residential greenness and all-cause mortality and the joint mediation effect of air pollutants among old people with disability: A prospective cohort study Science of The Total Environment, 2023, 857, 159604. https://doi.org/10.1016/j.scitotenv.2022.159604

  Feng, S., Meng, Q., Guo, B., et al. Joint exposure to air pollution, ambient temperature and residential greenness and their association with metabolic syndrome (MetS): A large population-based study among Chinese adults. Environmental Research, 2022, 214, 113699. https://doi.org/10.1016/j.envres.2022.113699

  Feng, Y., Wei, J., Hu, M., et al. Lagged effects of exposure to air pollutants on the risk of pulmonary tuberculosis in a highly polluted region. International Journal of Environmental Research and Public Health, 2022, 19, 5752. https://doi.org/10.3390/ijerph19095752

  Fu, J., Lin, Q., Ai, B., et al. Associations between maternal exposure to air pollution during pregnancy and trajectories of infant growth: A birth cohort study. Ecotoxicology and Environmental Safety, 2024, 269, 115792. https://doi.org/10.1016/j.ecoenv.2023.115792

  Geng, L., Yang, Y., Chen, Y., et al. Association between ambient temperature exposure and pregnancy outcomes in patients undergoing in vitro fertilization in Shanghai, China: a retrospective cohort study Human Reproduction, 2023, dead192. https://doi.org/10.1093/humrep/dead192

  Guan, Q., Zhu, C., Zhang, G., et al. Association of land urbanization and type 2 diabetes mellitus prevalence and mediation of greenness and physical activity in Chinese adults Environmental Pollution, 2023, 337, 122579. https://doi.org/10.1016/j.envpol.2023.122579

  Gui, Z., Ji, W., Wang, Y., et al. Severer air pollution, poorer cognitive function: Findings from 176,345 elders in Northwestern China Ecotoxicology and Environmental Safety, 2024, 271, 116008. https://doi.org/10.1016/j.ecoenv.2024.116008

  Guo, T., Chen, X., Wei, J., et al. Unveiling causal connections: Long-term particulate matter exposure and type 2 diabetes mellitus mortality in Southern China. Ecotoxicology and Environmental Safety, 2024, 274, 116212. https://doi.org/10.1016/j.ecoenv.2024.116212

  Guo, H., Wei, J., Li, X., et al. Do socioeconomic factors modify the effects of PM1 and SO2 on lung cancer incidence in China? Science of the Total Environment, 2021, 756, 143998. https://doi.org/10.1016/j.scitotenv.2020.143998

  Guo, H., Liu, J., and Wei, J. Ambient ozone, PM₁ and female lung cancer incidence in 436 Chinese counties. International Journal of Environmental Research and Public Health, 2021, 18(19), 10386. https://doi.org/10.3390/ijerph181910386

  Guo, H., Li, X., Wei, J., et al. Smaller particular matter, larger risk of female lung cancer incidence? Evidence from 436 Chinese counties. BMC Public Health, 2022, 22, 344. https://doi.org/10.1186/s12889-022-12622-1

  Guo, H., Li, X., Li, W., et al. Climatic modification effects on the association between PM1 and lung cancer incidence in China. BMC Public Health, 2021, 21, 880. https://doi.org/10.1186/s12889-021-10912-8

  Guo, Q., Zhao, Y., Zhao, J., et al. Physical activity attenuated the associations between ambient air pollutants and metabolic syndrome (MetS): A nationwide study across 28 provinces. Environmental Pollution, 2023, 315, 120348. https://doi.org/10.1016/j.envpol.2022.120348

  Guo, X., Su, W., Wang, X. et al. Assessing the effects of air pollution and residential greenness on frailty in older adults: a prospective cohort study from China. Environmental Science and Pollution Research , 2024, 31, 9091–9105. https://doi.org/10.1007/s11356-023-31741-9

  Han, S., Zhang, F., Yu, H., et al. Systemic inflammation accelerates the adverse effects of air pollution on metabolic syndrome: Findings from the China health and Retirement Longitudinal Study (CHARLS). Environmental Research, 2022, 215, 114340. https://doi.org/10.1016/j.envres.2022.114340

  Hao, Z., He, C., Li, J., et al. Long-term exposure to ambient fine particles and heart rate in Northwestern China: Findings from 1.8 million adults of the Kashgar Prospective Cohort Study (KPCS). Atmosphere, 2023, 14, 394. https://doi.org/10.3390/atmos14020394

  He, F., Wei, J., Dong, Y., et al. Associations of ambient temperature with mortality for ischemic and hemorrhagic stroke and the modification effects of greenness in Shandong Province, China. Science of The Total Environment, 2022, 851, 158046. https://doi.org/10.1016/j.scitotenv.2022.158046

  Huang, Z., Cao, C., Xu, M., and Yang, X. Impact of environmental exposure on chronic diseases in China and assessment of population health vulnerability. ISPRS International Journal of Geo-Information, 2023, 12, 155. https://doi.org/10.3390/ijgi12040155

  Hu, K., Zhang, Z., Li, Y., et al. Urban overall and visible greenness and diabetes among older adults in China. Landscape and Urban Planning, 2023, 240, 104881. https://doi.org/10.1016/j.landurbplan.2023.104881

  Hu, K., Li, W., Zhang, Y., et al. Association between outdoor artificial light at night and sleep duration among older adults in China: A cross-sectional study. Environmental Research, 2022, 212, 113343. https://doi.org/10.1016/j.envres.2022.113343

  Hu, M., Wei, J., Hu, Y., et al. Long-term effect of submicronic particulate matter (PM₁) and intermodal particulate matter (PM_1-2.5) on incident dyslipidemia in China: A nationwide 5-year cohort study. Environmental Research, 2023, 216, 114860. https://doi.org/10.1016/j.envres.2022.114860

  Hu, X., Nie, Z., Ou, Y., et al. Long-term exposure to ambient air pollution, circadian syndrome and cardiovascular disease: A nationwide study in China. Science of The Total Environment, 2023, 868, 161696. https://doi.org/10.1016/j.scitotenv.2023.161696

  Hu, Y., Chen, Y., Liu, S., et al. Residential greenspace and childhood asthma: An intra-city study. Science of The Total Environment, 2023, 857, 159792. https://doi.org/10.1016/j.scitotenv.2022.159792

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  Wang, L., Zhang, Z., and Gu, Z. Demarcation of coordinated prevention and control regions in the Yangtze River Delta based on spatio-temporal variations in PM_2.5 and O₃ cncentrations. Atmosphere, 2022, 13(8), 1300. https://doi.org/10.3390/atmos13081300

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  Xue, W., Li, X., Yang, Z., and Wei, J. Are house prices affected by PM_2.5 pollution? Evidence from Beijing, China. International Journal of Environmental Research and Public Health, 2022, 19, 8461. https://doi.org/10.3390/ijerph19148461

  Xue, W., Zhang, J., Zhong, C., et al. Spatiotemporal PM_2.5 variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region. Journal of Cleaner Production, 2021, 279, 123742. https://doi.org/10.1016/j.jclepro.2020.123742 (ESI Highly Cited Paper)

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  Yao, L., Kong, S., Nemitz, E., et al. Improving below-cloud scavenging coefficients of sulfate, nitrate, and ammonium in PM_2.5 and implications for numerical simulation and air pollution control. Journal of Geophysical Research: Atmospheres, 2024, 129, e2023JD039487. https://doi.org/10.1029/2023JD039487

  Yao, J., Wu, S., Cao, Y., et al. Dry deposition effect of urban green spaces on ambient particulate matter pollution in China. Science of The Total Environment, 2023, 900, 165830. https://doi.org/10.1016/j.scitotenv.2023.165830

  Zeng, Y., Sui, X., Ma, C., et al. Industrial heat source-related PM_2.5 concentration estimates and analysis using new three-stage model in the Beijing–Tianjin–Hebei region. Atmosphere, 2024, 15, 131. https://doi.org/10.3390/atmos15010131

  Zhang, H., Wang, Z., Chai, J., and Wilson, J. Land use\cover change and influencing factors inside the urban development boundary of different level cities: A case study in Hubei Province, China. Heliyon, 2022, e10408. https://doi.org/10.1016/j.heliyon.2022.e10408

  Zhang, L., Fang, C., Zhao, R., et al. Spatial–temporal evolution and driving force analysis of eco-quality in urban agglomerations in China. Science of The Total Environment, 2023, 866, 161465. https://doi.org/10.1016/j.scitotenv.2023.161465

  Zhang, L., Zhao, N., Zhang, W., and Wilson, J. Changes in long-term PM_2.5 pollution in the urban and suburban areas of China’s three largest urban agglomerations from 2000 to 2020. Remote Sensing, 2022, 14(7), 1716. https://doi.org/10.3390/rs14071716

  Zhang, W., Li, Y., Zheng, C., and Zhu, Y. Surface urban heat island effect and its driving factors for all the cities in China: Based on a new batch processing method. Ecological Indicators, 2023, 146, 109818. https://doi.org/10.1016/j.ecolind.2022.109818

  Zhang, Y., Yang, Y., Chen, J., and Shi, M. Spatiotemporal heterogeneity of the relationships between PM_2.5 concentrations and their drivers in China's coastal ports. Journal of Environmental Management, 2023, 345, 118698. https://doi.org/10.1016/j.jenvman.2023.118698

  戴刘新, 张 莹, 李正强, 漏嗣佳. 中国近地面PM_2.5质量浓度卫星遥感数据集比较及历史趋势分析. 大气与环境光学学报, 2022, 17(6), 613-629.

  何利杰, 杜梦晴, 韦 晶, 万 庆, 何培培, 王伦澈. 长江中游城市群土地利用变化对PM_2.5污染的影响及空间溢出效应. 长江流域资源与环境, 2023, 32(05), 1018-1029.

  蒋子琪, 王旭红, 冯子豪, 等. 西安市热岛效应与污岛效应的相互作用研究. 干旱区研究, 2022, (6), 1768-1781.

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  赵彦云, 陆香怡, 王 汶. 低碳城市的CO₂与PM_2.5减排协同效应分析. 中国环境科学, 2023, 43(1), 465-476.

   

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  Cai, Z., Sorte, F., Chen, Y., et al. The surface urban heat island effect decreases bird diversity in Chinese cities. Science of The Total Environment, 2023, 902, 166200. https://doi.org/10.1016/j.scitotenv.2023.166200

  Cheng, Y., Kong, S., Yao, L., et al. Multiyear emissions of carbonaceous aerosols from cooking, fireworks, sacrificial incense, joss paper burning, and barbecue as well as their key driving forces in China. Earth System Science Data, 2022, 14, 4757–4775. https://doi.org/10.5194/essd-14-4757-2022

  Chu, W., Zhang, C., Zhao, Y., Li, R., and Wu, P. Spatiotemporally continuous reconstruction of retrieved PM_2.5 data using an autogeoi-stacking model in the Beijing-Tianjin-Hebei region, China. IEEE Transactions on Geoscience and Remote Sensing, 2023. https://doi.org/10.3390/rs14184432

  Dong, X., Ye, Y., Yang, R., and Li, X. Planning for green infrastructure based on integration of multi-driving factors: A case study in pilot site of sponge city. Sustainable Cities and Society, 2023, 93, 104549. https://doi.org/10.1016/j.scs.2023.104549

  Fan, Y., Zheng, J., Xu, W., et al. Spatiotemporal occurrence and characteristics of microplastics in the urban road dust in a megacity, eastern China. Journal of Hazardous Materials, 2024, 468, 133733. https://doi.org/10.1016/j.jhazmat.2024.133733

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  He, L., Wei, J., Wang, Y., et al. Marked impacts of pollution mitigation on crop yields in China. Earth's Future, 2022, 10, e2022EF002936. https://doi.org/10.1029/2022EF002936

  Hu, Z., Huang, L., Zhai, X., et al. Quantifying individual PM_2.5 exposure with human mobility inferred from mobile phone data. Sustainability, 2024, 16, 184. https://doi.org/10.3390/su16010184

  Huang, Y., Ma, L., and Cao, J. Exploring spatial heterogeneity in the high-speed rail impact on air quality. Journal of Transport Geography, 2023, 106, 103498. https://doi.org/10.1016/j.jtrangeo.2022.103498

  Lei, C., Xu, X., Ma, Y., et al. Full coverage estimation of the PM_2.5 concentration across China based on an adaptive spatiotemporal approach. Land, 2022, 14(18), 4432. https://doi.org/10.1109/TGRS.2022.3213797

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  Meng, J., Han, W., and Yuan, C. Seasonal and multi-scale difference of the relationship between built-up land landscape pattern and PM_2.5 concentration distribution in Nanjing. Ecological Indicators, 2023, 156, 111079. https://doi.org/10.1016/j.ecolind.2023.111079

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  Zhang, X., Wang, H., Ning, X., et al. Identification of metropolitan area boundaries based on comprehensive spatial linkages of cities: A case study of the Beijing–Tianjin–Hebei region. ISPRS International Journal of Geo-Information, 2022, 11, 396. https://doi.org/10.3390/ijgi11070396

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Welcome to use our dataset for related scientific research! Note that this dataset is continuously updated, and if you need longer-period or higher temporal-resolution (e.g., hourly, daily or monthly) data, please contact me (weijing_rs@163.com; weijing.rs@gmail.com).

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• Other related air pollution datasets

  [1] Global monthly merged Satellite-AERONET AOD product, Peking University (Dr. Jing Li)

  [2] Tracking Air Pollution in China (TAP), Tsinghua University (Dr. Qiang Zhang and Dr. Guannan Geng)