1) Satellite remote sensing of atmospheric composition (gases and aerosol particles);
2) Global atmospheric chemistry and climate change as related to energy systems.
Active research projects:
Harnessing satellite observations of methane to inform climate mitigation strategies
Methane is a potent greenhouse gas that has large human-driven emissions and breaks down in the atmospheric much quicker than carbon dioxide. Reducing methane emissions therefore represents an important opportunity for short-term climate change mitigation. Satellite observations can now pinpoint hotspots of atmospheric methane on a spatial scale of tens of meters that reflect large point sources. We propose to develop trusted methane data products, underpinned by domain-level expertise, by applying novel machine learning (ML) algorithms to the growing data volumes of satellite observations of atmosphere methane.
We are developing a framework that can automatically detect near-real-time hotspots of anomalous atmospheric methane across the globe using Sentinel-2 satellite data. First, we prepare a labelled dataset of Sentinel-2 imagery with and without anomalous atmospheric methane. We use this dataset to train and evaluate a ML model that can identify anomalous atmospheric methane in Sentinel-2 images. We then apply the resulting ML model to detect hotspots of atmospheric methane from all Sentinel-2 observations. Finally, we translate our detections of atmospheric methane hotspots into emission estimates and uncertainties that can inform mitigation strategies.
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.
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.