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Using satellite and low-cost sensor data for large-scale ambient air pollution prediction
Air pollution is one of the leading global health risk factors. Ambient air pollution has been traditionally monitored at regulatory stations at high instrumentation and maintenance costs. Sparse and uneven regulatory monitoring has a limited ability to reflect pollution details, especially in remote communities. This paradigm is shifting with increasingly available satellite remote sensing instruments to monitor air quality at a global scale and low-cost air sensors that enable community-level air quality monitoring. In this talk, I will present my past and ongoing research in developing statistical methods to overcome the current challenges of utilizing novel satellite and low-cost sensor data in large-scale, high-resolution ambient air pollution prediction, such as satellite data missingness and inaccuracy in low-cost sensor data. These methods particularly hold promise to advance air pollution exposure assessment that has been poorly characterized with regulatory monitoring, such as wildfire smoke estimation.


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Jianzhao Bi
@University of Washington
Jianzhao Bi is a postdoctoral fellow in the Department of Environmental & Occupational Health Sciences at the University of Washington. He earned his Ph.D. in Environmental Health Sciences at Emory University in 2020 and M.S. in Atmospheric Science at Tsinghua University in 2016. His research mainly focuses on spatiotemporal high-resolution exposure prediction and forecast of air pollution (aerosols, oxides of nitrogen, carbon monoxide, and more) based on multi-platform data from satellite instruments and low-cost air monitors as well as advanced parametric and machine learning algorithms. He is also focusing on assessing how long-term and short-term exposure to air pollution is associated with respiratory and cognitive health outcomes. He received the ISES Young Investigator Meeting Award in 2021.