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

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, 2000-Present, Daily/Monthly/Yearly (Version 2)

  Link: 【Zenodo】, 【Google Earth Engine】, 【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., 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

  

   

• GlobalHighO₃ dataset

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

  Link: 【Zenodo】

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

  

   

• GlobalHighNO₂ dataset

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

  Link: 【Zenodo】

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

  

   

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-Present, 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-Present, Daily/Monthly/Yearly (Version 1)

  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., 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-Present, 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-Present, 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

  [1] Big data (seamless): 1 km, 2000-Present, Daily/Monthly/Yearly (Version 2)

  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., 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)

  Yang, Z., Li, Z., Cheng, F., Lv, Q., Li, K., Zhang, T., Zhou, Y., Zhao, B., Xue, W., and Wei, J. Two-decade surface ozone (O₃) pollution in China: enhanced fine-scale estimations and environmental health implications. Remote Sensing of Environment, 2024, In Press.

  [2] Big data (seamless): 1 km, 2019, Hourly (Version 1)

  Link: 【Zenodo】

  Reference: Cheng, F., Li, Z., Yang, Z., Li, R., Wang, D., Jia, A., Li, K., Zhao, B., Wang, S., Yin, D., Li, S., Xue, W., Cribb, M., and Wei, J. First retrieval of 24-hourly 1-km-resolution gapless surface ozone (O₃) from space in China using artificial intelligence: diurnal variations and implications for air quality and phytotoxicity. Remote Sensing of Environment, 2025, 316, 114482. https://doi.org/10.1016/j.rse.2024.114482

  

• ChinaHighNO₂ dataset

  [1] Big data (seamless): 1 km, 2019-Present, Daily/Monthly/Yearly (Version 2)

  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., 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 (Version 1)

  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., 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-Present, Daily/Monthly/Yearly (Version 2)

  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】

  [2] Big data (seamless): 10 km, 2013-2018, Daily/Monthly/Yearly (Version 1)

  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., 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-Present, Daily/Monthly/Yearly (Version 2)

  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】

  [2] Big data (seamless): 10 km, 2013-2018, Daily/Monthly/Yearly (Version 1)

  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., 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: Zhang, Y., Wang, Y., Zheng, H., and Wei, J. Increased mortality risk from airborne exposure to polycyclic aromatic hydrocarbons. Journal of Hazardous Materials, 2024, 474, 134714. https://doi.org/10.1016/j.jhazmat.2024.134714

  

   

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-Present, Daily/Monthly/Yearly (Version 1)

  Link: 【Zenodo】

• USHighBC dataset

  Big data (seamless): 1 km, 2000-Present, 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.

  

   

• 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 (355)

  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)

  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 (ESI Highly Cited Paper, IF = 37.8)

  Ma, Y., Zang, E., Liu, Y., et al. Long-term exposure to wildland fire smoke PM_2.5 and mortality in the contiguous United States. PNAS, 2024, 121(40), e2403960121. https://doi.org/10.1073/pnas.2403960121

  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

  Zhang, Z., Wang, C., Lin, C., et al. Association of long-term exposure to ozone with cardiovascular mortality and its metabolic mediators: evidence from a nationwide, population-based, prospective cohort study. The Lancet Regional Health – Western Pacific, 2024, 52, 101222. https://doi.org/10.1016/j.lanwpc.2024.101222

  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)

  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 (Journal Cover Article)

  Tian, Y., Ma, Y., Wu, J., et al. Ambient PM_2.5 chemical composition and cardiovascular disease hospitalizations in China. Environmental Science & Technology, 2024, 58(37), 16327–16335. https://doi.org/10.1021/acs.est.4c05718 (Journal Cover Article)

  Zhang, Y., Chen, S., Wei, J., et al. Long-term PM₁ exposure and hypertension hospitalization: A causal inference study on a large community-based cohort in South China. Science Bulletin, 2024, 69(9), 1313-1322. https://doi.org/10.1016/j.scib.2024.03.028

   

  Impact of air pollution on public health (231)

  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, 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)

  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

  Fang, B., Wei, J., Chen, L., et al. Short-term association of particulate matter and cardiovascular disease mortality in Shanghai, China between 2003 and 2020. Frontiers in Public Health, 2024, 12, 1388069. https://doi.org/10.3389/fpubh.2024.1388069

  Fang, L., Ma, Y., Peng, Y., et al. Long-term effect of fine particulate matter constituents on reproductive hormones homeostasis in women attending assisted reproductive technologies: A population-based longitudinal study. Ecotoxicology and Environmental Safety, 2024, 284, 116915. https://doi.org/10.1016/j.ecoenv.2024.116915

  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., Zhang, W., Wei, J., et al. Medium-term exposure to size-fractioned particulate matter and asthma exacerbations in China: A longitudinal study of asthmatics with poor medication adherence. Ecotoxicology and Environmental Safety, 2024, 274, 116234. https://doi.org/10.1016/j.ecoenv.2024.116234

  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

  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

  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

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  Zhang, Z., Zhu, C., Wang, L., and Chen, Y. Effects of urban renewal on green space: Evidence from airborne particulate matter in a mega city cluster. Journal of Cleaner Production, 2024, 438, 140811. https://doi.org/10.1016/j.jclepro.2024.140811

  钟雨桐, 韦 晶, 郑月明, 阎福礼. 基于静风期污染物的PM_2.5排放清单空间精细化方法. 地球信息科学学报, 2021, 23(11), 1971-1983.

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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)