J
Jianzhao Bi
Researcher at University of Washington
Publications - 33
Citations - 573
Jianzhao Bi is an academic researcher from University of Washington. The author has contributed to research in topics: Medicine & Environmental science. The author has an hindex of 8, co-authored 23 publications receiving 207 citations. Previous affiliations of Jianzhao Bi include Emory University & Tsinghua University.
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Journal ArticleDOI
Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5 Modeling at a Large Spatial Scale.
TL;DR: In this article, a downweighting strategy was developed to optimize the use of low-cost sensor data in PM2.5 estimation, which reduced the systematic bias to ∼0 μg/m3 and residual errors by 36%.
Journal ArticleDOI
Impacts of snow and cloud covers on satellite-derived PM2.5 levels.
Jianzhao Bi,Jessica H. Belle,Yujie Wang,Yujie Wang,Alexei Lyapustin,Alexei Lyapustin,Avani Wildani,Yang Liu +7 more
TL;DR: The impacts of snow and cloud covers on AOD and PM2.5 predictions are examined and the proposed methods can be generalized to the areas with extensive snow/cloud covers and large proportions of missing satellite AOD data for predicting PM2-5 levels with high resolutions and complete coverage.
Journal ArticleDOI
Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA
Jianzhao Bi,Jennifer D. Stowell,Edmund Seto,Paul English,Mohammad Z. Al-Hamdan,Patrick L. Kinney,Frank R. Freedman,Yang Liu +7 more
TL;DR: The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 prediction with high spatiotemporal resolutions based on statistical models.
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Calibration of low-cost PurpleAir outdoor monitors using an improved method of calculating PM2.5
TL;DR: A transparent and reproducible alternative method (ALT) of calculating PM2.5 from the particle numbers in three size categories was used in place of the estimates provided by Plantower, the manufacturer of the sensors used in PurpleAir monitors as discussed by the authors.
Journal ArticleDOI
Developing an advanced PM2.5 exposure model in Lima, Peru
Bryan N. Vu,Odon Sánchez,Jianzhao Bi,Qingyang Xiao,Nadia N. Hansel,William Checkley,Gustavo F. Gonzales,Kyle Steenland,Yang Liu +8 more
TL;DR: An advanced machine learning model is developed to estimate daily PM2.5 concentrations at a 1 km2 spatial resolution in Lima, Peru from 2010 to 2016 using a random forest model against ground measurements from 16 monitoring stations, showing good precision and accuracy from the model.