Author: Jianzhao Bi

Bi et al.: Characterizing outdoor infiltration and indoor contribution of PM2.5 with PurpleAir data

Jianzhao Bi April 12, 2021 0 Comments
Bi, J., Wallace, L. A., Sarnat, J. A., & Liu, Y. (2021). Characterizing outdoor infiltration and indoor contribution of PM2.5 with citizen-based low-cost monitoring data. Environmental Pollution, 276, 116763. ResearchGate (full text): Link       Elsevier: Link Research in quantifying infiltration factors (Finf), the fraction of outdoor PM2.5 that infiltrates indoors, has...

Bi et al.: Temporal changes in short-term associations between cardiorespiratory ED visits and PM2.5

Jianzhao Bi July 21, 2020 0 Comments
Temporal changes in short-term associations between cardiorespiratory emergency department visits and PM2.5 in Los Angeles, 2005 to 2016 Emissions control programs targeting certain air pollution sources may alter PM2.5 composition as well as the risk of adverse health outcomes associated with PM2.5 (as a mixture). In this analysis, we examined temporal changes in the risk of emergency...

Bi et al.: Incorporating low-cost sensor data into large-scale, high-resolution PM2.5 modeling

Jianzhao Bi January 27, 2020 0 Comments
Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5 Modeling at a Large Spatial Scale A spatially varying calibration and a down-weighting strategy have been proposed to incorporate volunteer-generated low-cost air quality data from PurpleAir sensors into large-scale PM2.5 exposure assessment while minimizing the negative impacts of their significant uncertainty. The inclusion...

Bi et al.: Contribution of low-cost sensor measurements to the prediction of PM2.5 levels

Jianzhao Bi October 21, 2019 0 Comments
Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment, while emerging low-cost sensors have the potential to fill in the gaps. In this study, the contribution of low-cost sensor measurements to high-resolution PM2.5...

Bi et al. Impacts of snow and cloud covers on satellite-derived PM2⁠.5 levels

Jianzhao Bi December 9, 2018 0 Comments
We developed an AOD gap-filling model with the consideration of snow and cloud covers and a PM2.5 prediction model based on the gap-filled AOD to estimate full-coverage and high-resolution PM2⁠.5 in New York State. It indicated the significance of snow fraction in the AOD gap-filling process and demonstrated the ability of the AOD-based prediction model to reflect detailed PM2.5 emission...