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Scope

Working Group 8 aims to estimate the ground-level air pollutants with those harmful to human health using big data and artificial intelligence, and highlight the aspects from the perspective of data science or application by producing high-resolution and high-quality air pollution, climate, and health data sets.

Co-Chairs

  Bin Zou, Central South University
Steve Hung Lam Yim, Nanyang Technological University

Secretary

Jing Wei, University of Maryland

Invited Experts, alphabetical by First Name

Gholamreza Goudarzi, Ahvaz Jundishapur University of Medical Sciences

Guannan Geng, Tsinghua University

Hung Chak Ho, The University of Hong Kong

Kai Chen, Yale University

Kaixu Bai, East China Normal University

Md Firoz Khan,Bangladesh North South University

Meng Gao, Hong Kong Baptist University

Peng Jia, Wuhan University

Qiangqiang Yuan, Wuhan University

Yuanjian Yang, Nanjing University of Information Science and Technology

Zongwei Ma, Nanjing University

References

Bai, K., Li, K., Ma, M., Li, K., Li, Z., Guo, J., Chang, N., Tan, Z., and Han, D. LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion. Earth System Science Data, 2022, 14, 907–927.

Bai, K., Li, K., Guo, J., and Chang, N. Multiscale and multisource data fusion for full-coverage PM2.5 concentration mapping: Can spatial pattern recognition come with modeling accuracy? ISPRS Journal of Photogrammetry and Remote Sensing. 2022, 184, 31–44.

Chen, K., Vicedo-Cabrera, A., Dubrow, R. Projections of ambient temperature- and air pollution-related mortality burden under combined climate change and population aging scenarios: A review. Current Environmental Health Reports, 2020, 7(3):243-255.

Chen, K., Wang, M., Huang, C., Kinney, P., Anastas, P. Air pollution reduction and mortality benefit during the COVID-19 outbreak in China. The Lancet Planetary Health, 2020, 4, e210-e212.

Feng H., Zou B., Wang J., and Gu X. Dominant variables of global air pollution-climate interaction: Geographic insight. Ecological Indicators, 2019, 99(2019): 251-260.

Geng, G., Zheng, Y., Zhang, Q., Xue, T., Zhao, H., Tong, D., Zheng, B., Li, M., Liu, F., Hong, C., He, K., and Davis, S. Drivers of PM2.5 air pollution deaths in China 2002-2017. Nature Geoscience, 2021, 14(9): 645-650.

Geng, G., Xiao, Q., Liu, S., Liu, X., Cheng, J., Zheng, Y., Xue, T., Tong, D., Zheng, B., Peng, Y.,  Huang, X., He, K., and Zhang, Q. Tracking Ai r Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion. Environmental Science & Technology, 2021, 55(17): 12106-12115.

Ho, H., Man, S., Lin, Y., Shi, W., & Chan, T. Spatiotemporal influence of temperature, air quality, and urban environment on cause-specific mortality during hazy days. Environment International, 2018, 112, 10-22.

Ma, Z., Hu, X., Sayer, A. M., Levy, R., Zhang, Q., Xue, Y., et al. Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004–2013. Environmental Health Perspectives, 2016, 124(2), 184-192.

Ma, Z., Dey, S., Christopher, S., Liu, R., Bi, J., Balyan, P., and Liu, Y. A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data. Remote Sensing of Environment, 2022, 269, 112827.

Xiao, X., Xu, Y., Zhang, X., Wang, F., Lu, X., Cai, Z., Brasseur, G., and Gao, M. Amplified upward trend of the joint occurrences of heat and ozone extremes in China over 2013–20. Bulletin of the American Meteorological Society, 2022, 103(5), E1330-E1342.

Xu, Y., Ho, H., Wong, M., Deng, C., Shi, Y., Chan, T., and Knudby, A. Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM2.5. Environmental Pollution, 2018, 242, 1417-1426.

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

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

Zhou, C., Gao, M., Li, J., Bai, K., Tang, X., Lu, X., Liu, C., Wang, Z. and Guo, Y., 2022. Optimal planning of air quality-monitoring sites for better depiction of PM2.5 pollution across China. ACS Environmental Au, 2022. https://doi.org/10.1021/acsenvironau.1c00051

Zong, L., Yang, Y., Xia, H., Gao, M., Sun, Z., Zheng, Z., Li, X., Ning, G., Li, Y., and Lolli, S. Joint occurrence of heatwaves and ozone pollution and increased health risks in Beijing, China: role of synoptic weather pattern and urbanization, Atmospheric Chemistry and Physics, 2022, 22, 6523–6538.

Zou, B., Li S., Lin Y., Wang B., Cao S., Zhao X., Peng F., Qin N., Guo Q., Feng H., Matthew C., Xu S., and Duan X., Efforts in reducing air pollution exposure risk in China: State versus individuals. Environment International, 2020, 137, 105504.

Working Groups
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