Near Real-Time (NRT)

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    ChinaHighAirPollutants (CHAP)

  • Brief Introduction

    The ChinaHighAirPollutants (CHAP) dataset refers to the long-term [historical (HIS) and near real-time (NRT)], full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China. It is generated from big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence to account for the spatiotemporal heterogeneity of air pollution. The CHAP dataset includes 7 major air pollutants (i.e., PM1, PM2.5, PM10, O3, NO2, SO2, and CO), as well as PM2.5 chemical composition (i.e., SO42-, 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 available 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】

     

    • CHAP Historical (HIS) Data

  • Dataset summary

  • ChinaHighPM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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, 2025, 317, 114459. https://doi.org/10.1016/j.rse.2024.114459

    [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

     

  • ChinaHighTEM dataset

    Big data (seamless): 1 km, 2003-Present, Daily Average/Maximum/Minimum (Version 1)

    TEMavg: 【Zenodo】，TEMmin: 【Zenodo】， TEMmax: 【Zenodo】

    Reference: Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022. Scientific Data, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z

     

  • Publications using our dataset (400+)

    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

    Liu, H., Lei, J., Liu, Y., et al. Hospital admissions attributable to reduced air pollution due to clean-air policies in China. Nature Medicine, 2025. https://doi.org/10.1038/s41591-025-03515-y

    Qu, W., Hua, H., Yang, T., et al. Delayed leaf green-up is associated with fine particulate air pollution in China. Nature Communications, 2025, 16, 3406. https://doi.org/10.1038/s41467-025-58710-9

    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)

    He, Q., Lang, X., Shen, H., et al. Impact of extreme temperature on congenital heart disease mortality: a population-based nationwide case-crossover study. The Lancet Regional Health – Western Pacific, 2024, 53, 101244. https://doi.org/10.1016/j.lanwpc.2024.101244

    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

    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 PM2.5 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)

    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)