Category: Papers

Wang et al.: A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology

Wenhao Wang October 5, 2021 0 Comments
Wenhao Wang, Xiong Liu, Jianzhao Bi, Yang Liu, A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology, Environment International, Volume 158, 2022, 106917https://doi.org/10.1016/j.envint.2021.106917 Abstract: Estimating ground-level ozone concentrations is crucial to study the adverse health effects of ozone...

Zhang et al.: A machine learning model to estimate ambient PM2.5 concentrations in industrialized highveld region of South Africa

Danlu Zhang September 29, 2021 0 Comments
Danlu Zhang, Linlin Du, Wenhao Wang, Qingyang Zhu, Jianzhao Bi, Noah Scovronick, Mogesh Naidoo, Rebecca M. Garland, Yang Liu. (2021). A machine learning model to estimate ambient PM2.5 concentrations in industrialized highveld region of South Africa. Remote Sensing of Environment, 266, 112713. Elsevier: Link Exposure to fine particulate matter (PM2.5) has been linked to a substantial...

Vu et al.: The association between asthma emergency department visits and satellite-derived PM2.5 in Lima, Peru

Bryan June 4, 2021 0 Comments
Vu, B. N., Tapia, V., Ebelt, S., Gonzales, G. F., Liu, Y., Steenland, K. (2021). The association between asthma emergency department visits and satellite-derived PM2.5 in Lima, Peru. Environmental Research, 199, 111226.  Elsevier: Link  Lima, Peru is one of the most populated and polluted cities in South America. However, epidemiological studies pertaining to air pollution in Lima, Peru have...

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...

Liang et al.: The 17-y spatiotemporal trend of PM2.5 and its mortality burden in China

Yang Liu PhD September 23, 2020 0 Comments
Estimation of the chronic health effects of PM2.5 exposure has been hindered by the lack of long-term PM2.5 data in China. To support this, high-performance machine-learning models were developed to estimate PM2.5 concentrations at 1-km resolution in China from 2000 to 2016, based on satellite data, meteorological conditions, land cover information, road networks, and air...

Wang et al.: Satellite-based assessment of the long-term efficacy of PM2.5 pollution control policies across the Taiwan Strait

Qiulun Li September 17, 2020 0 Comments
Evaluating the efficacy of air pollution control policies is an essential part of the decision-making process to develop new policies and improve existing measures.  In this analysis, we assessed the effects air pollution control policies in the Taiwan Strait Region from 2005 to 2018 using full-coverage, high-resolution PM2.5generated by a satellite-driven machine learning model. A ten-fold...

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...

Stowell et. al: Estimating PM2.5 in Southern California using satellite data: factors that affect model performance.

Qiulun Li May 18, 2020 0 Comments
In the article, the authors focus on a region where traditional satellite AOD models have not performed as well compared to other areas of the US, in order to determine which region-specific parameters have the highest impact on model accuracy. Using a two-stage linear approach, the authors identified important meteorological and land use parameters including temperature, relative humidity,...

Geng et al.: Random forest models for PM2.5 speciation using MISR data

Yang Liu PhD April 9, 2020 0 Comments
Random forest models were developed to predict ground-level daily PM2.5 speciation concentrations in California from MISR fractional AODs and other supporting data such as ground measurements, chemical transport model simulations, land use variables and meteorological fields. Sensitivity tests were also conducted to explore the influence of variable selection on model performance. Results shows...

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...