Qian DI, Sc.D., Principle Investigator, Assitant Professor

2007-2011 School of Earth and Space Science, Peking University Bachelor of Science

2008-2011 China Center for Economic Research, Peking University Bachelor of Economics (double major)

2011-2013 Department of Geography, Pennsylvania State University Master of Science

2013-2015 Department of Epidemiology, Harvard T.H. Chan School of Public Health Master of Science

2015-2018 Department of Environmental Health, Harvard T.H. Chan School of Public Health Doctor of Science

2015-2018 Department of Epidemiology, Harvard T.H. Chan School of Public Health Doctor of Science

2018-2018 Department of Epidemiology, Harvard T.H. Chan School of Public Health  Postdoctoral Research Fellow

2019–2020 School of Medicine, Tsinghua University Principal Investigator, Assistant Professor

2020–2021 Vanke School of Public Health, Tsinghua University Principal Investigator, Assistant Professor

2021–2022 Vanke School of Public Health, Tsinghua University Principal Investigator, Asscoiate Professor

Courses

Undergraduate course: Earth, Environment, Human Society, and Human Health (00960012-90)

Graduate source: Environmental Health Issue at Global Scale (80960003-0）

Graduate source: How to read health-related articles critically（80960121-0）

Graduate source: Introduction to Environmental Health: From Concepts to Application (84001193-0)

Graduate source: Environmental Health Issues from a Global Perspective (84001203-0)

Research Overview

My study began with creating datasets on air pollution (particulate matter, ozone, and NO2) and temperature (maximal, minimal and mean temperatures). Lacking available datasets on air pollution is always one major limitation of public health and epidemiological study. To deal with the data limitation, I have used satellite data that quantify the light attenuation due to air pollution and applied machine learning to predict air pollution levels, with tens of millions of records training the model. In additional to satellite data, other geographic data such as land coverage data, meteorological data, and simulation data have been used as predictors of air pollution. This big data approach has demonstrated good performance and predicted daily air pollution for every square kilometer in the U.S for 13 years, with high accuracy.

With high-resolution air pollution data and Medicare beneficiaries’ information, my colleagues and I studied the association between air pollution and mortality among 60 million older adults in the U.S., especially in “clean” areas where air quality reached the standards of Environmental Protection Agency (EPA). We found that there is a significant association between mortality and air pollution at very low levels, even below the current air quality standards. Moreover, the same increase in air pollution leads to higher mortality increase among African Americans. In other words, the current EPA’s air quality standards are not stringent enough. Some subgroups, such as people of color, are particularly more vulnerable to air pollution than the general population. We published our results in the New England Journal of Medicine (NEJM) and Journal of the American Medical Association (JAMA).

I have been translating my science into social impact. The NEJM study received extensive media coveragefrom several countries, including National Public Radio, CBS, the New York Times, Reuters, and the Washington Times, and was mentioned in a Congressional hearing in Washington D.C. I also wrote some articles to Chinese audience on how to use U.S. experience to resolve China’s environmental problems. Some of my articles went viral on WeChat, a mobile communication app famous in China, and provoked tens of thousands of reads and comments.

Academic Award

2011 National Scholarship

2015-2017 Harvard Graduate Consortium on Energy & Environment

2018 Excellent Prize of Chinese Government Award for outstanding students abroad (granted 10 students globally)

Keywords:

Environmental Health, Epidemiology, Air Pollution, Machine Learning, Geographic Information System, Remote Sensing

Selected Publications

1. Di, Qian, Lingzhen Dai, Yun Wang, Antonella Zanobetti, Christine Choirat, Joel D. Schwartz, and Francesca Dominici. "Association of short-term exposure to air pollution with mortality in older adults." JAMA318, no. 24 (2017): 2446-2456.

2. Di, Qian, Yan Wang, Antonella Zanobetti, Yun Wang, Petros Koutrakis, Christine Choirat, Francesca Dominici, and Joel D. Schwartz. "Air pollution and mortality in the Medicare population." New England Journal of Medicine 376, no. 26 (2017): 2513-2522.

3. Di, Qian, Sebastian Rowland, Petros Koutrakis, and Joel Schwartz. "A hybrid model for spatially and temporally resolved ozone exposures in the continental United States." Journal of the Air & Waste Management Association 67, no. 1 (2017): 39-52.

4. Di, Qian, Petros Koutrakis, and Joel Schwartz. "A hybrid prediction model for PM2. 5 mass and components using a chemical transport model and land use regression." Atmospheric Environment 131 (2016): 390-399.

5. Di, Qian, ItaiKloog, Petros Koutrakis, Alexei Lyapustin, Yujie Wang, and Joel Schwartz. "Assessing PM2. 5 exposures with high spatiotemporal resolution across the continental United States." Environmental science & technology 50, no. 9 (2016): 4712-4721.

Contact Information

To perspective students: if you are interested in public health, environmental health, climate change, and have experience in animal study/biostatistics/machine learning, don’t hesitate to contact me. Postdoc positions are also available.

Email: qiandi@tsinghua.edu.cn

Personal Webpage

https://scholar.google.com/citations?user=BpMY1OkAAAAJ