Author: Yang Liu PhD

Dr. Liu has 20 years of experience on studying the spatial and temporal characteristics of air pollution using remote sensing data from various satellite instruments. His research interests also include satellite aerosol retrieval and product design, the potential impacts of global climate change on public health, GIS and spatial statistics. Dr. Liu has been funded by NASA, CDC, NIH, EPA, HEI, and WHO to apply satellite data in air quality modeling and study the impact of climate change on air quality and human health using remote sensing and model simulations. Specifically, he has been working with the Oak Ridge National Lab to generate statistically and dynamically downscaled high-resolution regional climate projections under various RCPs. Using these simulation results, Dr. Liu conducted research to estimate spatially resolved population risks associated with extreme heat, wildfires, ambient air pollution such as ozone and PM2.5, and infectious diseases that can be attributed to future climate change. He has published 200+ peer-reviewed journal articles and contributed to two books in these areas. He is a member of the NASA MAIA and Terra MISR science team, the NASA Aura science team, a PI member of the NASA Air Quality Applied Science Team (AQAST) and the following Health and Air Quality Applied Science Team (HAQAST). He is a member of the Scientific Steering Committee of the WHO Platform on Air Quality and Health, and a GBD expert on ambient air pollution.

Gupta et al.: Boosting for regression transfer via importance sampling

Yang Liu PhD July 28, 2023 0 Comments

Gupta S, Bi J, Liu Y, Wildani A. 2023. Boosting for regression transfer via importance sampling. Int J Data Sci Anal. https://doi.org/10.1007/s41060-023-00414-8. Instance transfer learning methodologies are extremely efficient for continuous-valued, regression datasets. However, these methodologies can suffer negative transfer due to distribution shifts between the training and test data as well...

Bi et al.: Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast

Yang Liu PhD January 13, 2022 0 Comments

Forecasting ambient PM2.5 concentrations with spatiotemporal coverage is key to alerting decision makers of pollution episodes and preventing detrimental public exposure. In this study, we developed a PM2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product, NASA’s Goddard Earth Observing System “Composition...

Stowell et al.: Asthma exacerbation due to climate change-induced wildfire smoke in the Western US

Yang Liu PhD January 10, 2022 0 Comments

Climate change and human activities have drastically altered the natural wildfire balance in the Western US and increased population health risks due to exposure to pollutants from fire smoke. Using dynamically downscaled climate model projections, we estimated additional asthma emergency room visits and hospitalizations due to exposure to smoke fine particulate matter (PM2.5) in the Western US...

Emory contributes to Lancet Countdown global report and US country brief

Yang Liu PhD December 21, 2020 0 Comments

RSPH reported our group's contribution to the 2020 Lancet Countdown global report in estimating population direct exposure to wildfires and population risks to wildfires. Since 2019, We have served as a reviewer of the Lancet Countdown US Country Brief and Emory has been an official sponsor of the Lancet Countdown US launch.

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...

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...

She et al.: Hourly PM2.5 levels during heavy winter episodes in the Yangtze River Delta

Yang Liu PhD October 6, 2019 0 Comments

In this publication, we quantitatively investigated the feasibility of using the aerosol optical depth (AOD) data retrieved by the Geostationary Ocean Color Imager (GOCI) to estimate hourly PM2.5 concentrations during winter haze episodes in the Yangtze River Delta (YRD). We developed a three-stage spatial statistical model, using GOCI AOD and fine mode fraction, as well as corresponding...

Murray et al.: new method on PM2.5 Bayesian Ensemble Models

Yang Liu PhD August 29, 2019 0 Comments

We develop a method to combine PM2.5 estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical models. While most previous methods utilize AOD or CTM separately, we aim to leverage advantages offered by both data sources in terms of resolution and coverage using Bayesian ensemble averaging. Our approach differs from previous...

Our work on satellite-based UV exposure in the US got reported by NASA Observatory

Yang Liu PhD August 8, 2019 0 Comments

This project was funded by the NASA Applied Science Program to generate secondary data product using NASA satellite data for societal benefits. Led by Emory, the research team include researchers from CDC and University of Iowa. The full publication led by CDC researchers can be found here.

Zhou et al.: Compilation and spatio-temporal analysis of publicly available total solar and UV irradiance data in the United States

Yang Liu PhD August 2, 2019 0 Comments

Skin cancer is the most common type of cancer in the United States, the majority of which is caused by overexposure to ultraviolet (UV) irradiance, which is one component of sunlight. Our group worked with University of Iowa and the National Environmental Public Health Tracking Program at CDC to develop and disseminate county-level daily UV irradiance (2005 - 2015) and total solar irradiance...