I am a postdoctoral scholar at Stanford University, working with Marshall Burke as a part of the ECHO (Environmental Change and Human Outcomes) Lab. My research interest is in environmental and energy policies with a global focus on issues involving air pollution, climate change and energy systems. I use causal inference, machine learning, and atmospheric chemistry modeling to study the sustainability challenges at the intersection of energy, pollution and climate using real-world data.
I received my PhD degree from MIT’s Institute for Data, Systems, and Society on September 2021, advised by Noelle Selin. I also worked closely with my committee members: Valerie Karplus, Cory Zigler and Colette Heald. I received bachelor degrees in environmental sciences and economics from Peking University in Beijing.
Honors & Awards
Outstanding Student Presentation Awards (OSPA), American Geophysical Union Fall Meeting (2021)
Fellow, Martin Family Society of Fellows for Sustainability (2020)
Young Scientists Summer Program, IIASA (2019)
Marshall Burke, Postdoctoral Faculty Sponsor
Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States.
2022; 8 (48): eabn8762
Understanding impacts of renewable energy on air quality and associated human exposures is essential for informing future policy. We estimate the impacts of U.S. wind power on air quality and pollution exposure disparities using hourly data from 2011 to 2017 and detailed atmospheric chemistry modeling. Wind power associated with renewable portfolio standards in 2014 resulted in $2.0 billion in health benefits from improved air quality. A total of 29% and 32% of these health benefits accrued to racial/ethnic minority and low-income populations respectively, below a 2021 target by the Biden administration that 40% of the overall benefits of future federal investments flow to disadvantaged communities. Wind power worsened exposure disparities among racial and income groups in some states but improved them in others. Health benefits could be up to $8.4 billion if displacement of fossil fuel generators prioritized those with higher health damages. However, strategies that maximize total health benefits would not mitigate pollution disparities, suggesting that more targeted measures are needed.
View details for DOI 10.1126/sciadv.abn8762
View details for PubMedID 36459553
Daily Local-Level Estimates of Ambient Wildfire Smoke PM2.5 for the Contiguous US.
Environmental science & technology
Smoke from wildfires is a growing health risk across the US. Understanding the spatial and temporal patterns of such exposure and its population health impacts requires separating smoke-driven pollutants from non-smoke pollutants and a long time series to quantify patterns and measure health impacts. We develop a parsimonious and accurate machine learning model of daily wildfire-driven PM2.5 concentrations using a combination of ground, satellite, and reanalysis data sources that are easy to update. We apply our model across the contiguous US from 2006 to 2020, generating daily estimates of smoke PM2.5 over a 10 km-by-10 km grid and use these data to characterize levels and trends in smoke PM2.5. Smoke contributions to daily PM2.5 concentrations have increased by up to 5 mug/m3 in the Western US over the last decade, reversing decades of policy-driven improvements in overall air quality, with concentrations growing fastest for higher income populations and predominantly Hispanic populations. The number of people in locations with at least 1 day of smoke PM2.5 above 100 mug/m3 per year has increased 27-fold over the last decade, including nearly 25 million people in 2020 alone. Our data set can bolster efforts to comprehensively understand the drivers and societal impacts of trends and extremes in wildfire smoke.
View details for DOI 10.1021/acs.est.2c02934
View details for PubMedID 36134580
Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions
ATMOSPHERIC CHEMISTRY AND PHYSICS
2022; 22 (16): 10551-10566
View details for DOI 10.5194/acp-22-10551-2022
View details for Web of Science ID 000841772800001
Using snapshot measurements to identify high-emitting vehicles
ENVIRONMENTAL RESEARCH LETTERS
2022; 17 (4)
View details for DOI 10.1088/1748-9326/ac5c9e
View details for Web of Science ID 000773098200001
Improving Evaluation of Energy Policies with Multiple Goals: Comparing Ex Ante and Ex Post Approaches
ENVIRONMENTAL SCIENCE & TECHNOLOGY
2020; 54 (24): 15584-15593
Sustainability policies are often motivated by the potential to achieve multiple goals, such as simultaneously mitigating the climate change and air quality impacts of energy use. Ex ante analysis is used prospectively to inform policy decisions by estimating a policy's impact on multiple objectives. In contrast, ex post analysis of impacts that may have multiple causes can retrospectively evaluate the effectiveness of policies. Ex ante analyses are rarely compared with ex post evaluations of the same policy. These comparisons can assess the realism of assumptions in ex ante methods and reveal opportunities for improving prospective analyses. We illustrate the benefits of such a comparison by examining a case of two energy policies in China. Using ex post analysis, we estimate the impacts of two policies, one that targets energy intensity and another that imposes quantitative targets on SO2 emissions, on energy use and pollution outcomes in two major energy-intensive industrial sectors (cement, iron and steel) in China. We find that the ex post effects of the energy intensity policy on both energy and pollution outcomes are very limited on average, while the effects of the SO2 emissions policy are large. Compared with ex ante analysis, ex post estimates of benefits of the energy intensity policy are on average smaller, and differ by location in both sign and magnitude. Accounting for firm-level heterogeneity in production processes and policy responses, as well as the use of empirically grounded counterfactual baselines, can improve the realism of ex ante analysis and thus provide a more reliable basis for policy design.
View details for DOI 10.1021/acs.est.0c01381
View details for Web of Science ID 000600100400003
View details for PubMedID 33263386
The contribution of the Beijing, Tianjin and Hebei region's iron and steel industry to local air pollution in winter
2019; 245: 1095-1106
The Beijing, Tianjin and Hebei region (BTH) in China is a highly populated area that has recently experienced frequent haze episodes in winter. With high production capacities, the iron and steel industry (ISI) has long been a key source of air pollutants in BTH and is thus considered responsible for the degradation of local air quality. Here, we conducted a cross-disciplinary research combining the Weather Research and Forecasting with Chemistry (WRF/Chem) model, the multiregional input-output model (MRIO) and the health assessment model to explore the impacts of the ISI on air pollution in the BTH region in January 2012. Our results show large increases in air pollution due to direct ISI emissions, with up to a 90 μg/m3 monthly average of fine particulate matter (PM2.5) and sulfur dioxide (SO2) in eastern Tangshan and western Handan. In addition to direct emissions, the ISI has induced large quantities of indirect emissions from upstream sectors (e.g., the electricity and transportation sectors), leading to PM2.5, SO2 and NOx increases of 2-10 μg/m3 in BTH. Considering the direct and indirect emissions, we estimated that 275 (233-313) PM2.5-related mortalities occurred in January, and approximately 42% of these premature deaths occurred in Tangshan. A high rate of premature deaths also occurred in urban Beijing due to its high population density. Revealing the great health burden caused by the ISI, our results underscore the necessity for the Chinese government to reduce air pollutant emissions from the ISI and its upstream industries in BTH.
View details for DOI 10.1016/j.envpol.2018.11.088
View details for Web of Science ID 000457511900117
View details for PubMedID 30682744