LifeResearch is like riding a bicycle. To keep your balance, you must keep moving. — Albert Einstein
Research Spotlights are shown for selected recent publications only.
As illustrated in the figure below, our research addresses a conceptual system in which natural and anthropogenic activities generate emissions of air pollutants and greenhouse gases. These constituents are transported and transformed in the atmosphere, ultimately influencing solar energy generation, public health, and ecosystem health, among other impacts. In response to these environmental effects, policy interventions are implemented, which in turn can modify emissions. Our research examines this conceptual system as a whole, while also investigating its individual components.
We welcome researchers and students interested in any aspect of the conceptual system described above to join us and/or collaborate with our team in developing solutions and strategies to address these environmental challenges (see join-us)!
The work that we have done/currently do/will do:
1. Drivers of atmospheric environmental variations and their impacts on energy, public health, and ecosystem health | 大气环境变化的驱动因素及其对能源、公共健康和生态系统健康的影响
We integrate models from multiple disciplines to describe the conceptual system outlined above. Using this integrated approach, we perform counterfactual experiments to identify the socioeconomic and natural drivers behind the impacts of atmospheric environmental variations on energy, public health, and ecosystem health. The identified drivers provide guidance for effectively mitigating these environmental impacts.
Refs:
Socioeconomic drivers:
Yao, F.*, Palmer, P.I., Liu, J., Chen, H. and Wang, Y., 2025. Attribution of Solar Energy Yield Gaps due to Transboundary Particulate Matter Pollution Associated with Trade across Northeast Asia. Environmental Science & Technology, 59(29), pp.15092-15100. doi: 10.1021/acs.est.5c05935
Yao, F.* and Palmer, P.I., 2022. Source Sector Mitigation of Solar Energy Generation Losses Attributable to Particulate Matter Pollution. Environmental Science & Technology, 56(12), pp.8619-8628. doi: 10.1021/acs.est.2c01175
Liu, J.#, Yao, F.#, Chen, H.* and Zhao, H.*, 2025. Quantifying the Source–Receptor Relationships of PM2.5 Pollution and Associated Health Impacts among China, South Korea, and Japan: A Dual Perspective and an Interdisciplinary Approach. Environmental Health Perspectives, 133(3-4), p.047011. doi: 10.1289/EHP14550
EHP was taken down on 1 December 2025 due to a lack of government support. Currently, published EHP articles, including ours, remain available at PubMed Central (PMC).
Liu, J.*, Li, J. and Yao, F., 2022. Source-receptor relationship of transboundary particulate matter pollution between China, South Korea and Japan: Approaches, current understanding and limitations. Critical Reviews in Environmental Science and Technology, 52(21), pp.3896-3920. doi: 10.1080/10643389.2021.1964308
Natural drivers:
Stay tuned.
2. Satellite remote sensing of atmospheric environment and energy infrastructure | 大气环境和能源基础设施的卫星遥感监测
We use both process-based models (e.g., GEOS-Chem) and statistical and machine learning methods to track emissions (e.g., NOx) that drive atmospheric environmental conditions near the surface (e.g., PM2.5 and the urban heat island), which are most relevant to public health. We combine these ground-level environmental estimates with individual mobility and socioeconomic data to examine disparities in exposure across different population groups. The research findings inform atmospheric environmental management policies and environmental equity efforts. We are also interested in applying state-of-the-science computer vision approaches to detect energy infrastructure (e.g., solar energy panels) from satellite remote sensing imagery.
Refs:
Emissions:
Stay tuned.
Atmospheric environmental conditions (PM2.5):
Yao, F.* and Palmer, P.I., 2021. A model framework to reduce bias in ground-level PM2.5 concentrations inferred from satellite-retrieved AOD. Atmospheric Environment, 248, p.118217. doi: 10.1016/j.atmosenv.2021.118217
Yao, F., Wu, J.*, Li, W.* and Peng, J., 2019. A spatially structured adaptive two-stage model for retrieving ground-level PM2.5 concentrations from VIIRS AOD in China. ISPRS Journal of Photogrammetry and Remote Sensing, 151, pp.263-276. doi: 10.1016/j.isprsjprs.2019.03.011
Yao, F., Wu, J.*, Li, W. and Peng, J., 2019. Estimating daily PM2.5 concentrations in Beijing using 750-M VIIRS IP AOD retrievals and a nested spatiotemporal statistical model. Remote Sensing, 11(7), p.841. doi: 10.3390/rs11070841
Yao, F., Si, M., Li, W.* and Wu, J.*, 2018. A multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM2.5 concentrations over a heavily polluted region in China. Science of the Total Environment, 618, pp.819-828. doi: 10.1016/j.scitotenv.2017.08.209
Wu, J., Yao, F., Li, W.* and Si, M., 2016. VIIRS-based remote sensing estimation of ground-level PM2.5 concentrations in Beijing–Tianjin–Hebei: A spatiotemporal statistical model. Remote Sensing of Environment, 184, pp.316-328. doi: 10.1016/j.rse.2016.07.015
Supervisor as first author.
Guo, H., Li, W.*, Yao, F., Wu, J., Zhou, X., Yue, Y. and Yeh, A.G., 2020. Who are more exposed to PM2.5 pollution: A mobile phone data approach. Environment International, 143, p.105821. doi: 10.1016/j.envint.2020.105821
Atmospheric environmental conditions (urban heat island):
Wang, Y., Wang, H., Yao, F.*, Stouffs, R. and Wu, J.*, 2024. An integrated framework for jointly assessing spatiotemporal dynamics of surface urban heat island intensity and footprint: China, 2003–2020. Sustainable Cities and Society, 112, p.105601. doi: 10.1016/j.scs.2024.105601
Energy infrastructure:
Stay tuned.