Ke Du, Ph.D.

Ph.D. in Environmental Engineering University of Illinois at Urbana-Champaign
M Sc in Atmospheric Environment Peking University
B.Eng in Environmental Engineering Tsinghua University

Ke Du

Areas of Research

1) Air pollution process
In this direction, Dr. Du’s research is focused on black carbon, haze, visibility, and other air quality indices addressing the issues on how rapid urbanization poses negative impact on the natural environment, and what may be cost-effective mitigation strategies from the perspective of policy-makers. For carbonaceous aerosol studies, he investigated the sources of black carbon in East Asia, studied the emission and transport of carbonaceous aerosols and the light absorbing properties of black carbon aerosols. The approaches that were taken incorporated both field measurement and mathematical modeling of the ambient and source aerosols. Such work not only builds up our knowledge in understanding the characteristics of air pollution, but serves to advise the regulatory authorities for solving challenges associated with the pollutants. For regional haze and visibility, he studied the long-term variation of visibility in Southeast China, analyzed the impact of several environmental policies on regional haze, and determined the efficacy of different policies in mitigating air pollution problems, and investigated the impact of meteorological factors on atmospheric visibility at a coastal urban site.
2) Environmental monitoring techniques
Dr. Du has devoted years in developing innovative optical and/or remote sensing technologies to measure pollutants or environmental pollution indices. This include: I) quantify the opacity of airborne particulate matters (PM) over elevated spatial and temporal scales. Opacity is an important optical property that is used by the environmental authorities to determine compliance for visible emissions from industrial sources. He invented a digital photography based method (DOM) to quantify the opacity of PM plumes from industrial point sources, which was awarded a US patent in 2008. The Office of Air Quality Planning and Standards (OAQPS) of the United States Environmental Protection Agency (USEPA) estimated that the digital camera based technology could save 200 million US dollars annually if implemented country-wide in the US; II) Apply innovative LIDAR technologies for development of methodologies to quantify PM emissions from fugitive sources, which are more important and more difficult-to-quantify. This method provides a novel approach to quantify the emissions of fugitive dust by developing an algorithm that integrates the measurements from LIDAR, Laser Transmissometers, and Fourier Transform Infrared Spectrometers to determine the mass fluxes of dust. This work was appraised (http://www.serdp.org/content/download/8726/106111/file/RC-1400-FR.pdf) by SERDP (Strategic Environmental Research and Development Program) under the US DoD (Department of Defense) and received “Research, Development, or Operational Support Team Award” from U.S. Army in 2008; III) Develop cost-effective methods for monitoring black carbon aerosol; and IV) Develop digital photographic method to quantify atmospheric visibility.

Supervising degrees

Mechanical and Manufacturing Engineering - Doctoral: Accepting Inquiries
Mechanical and Manufacturing Engineering - Masters: Accepting Inquiries
Mechanical and Manufacturing Engineering - Masters: Accepting Inquiries
Mechanical and Manufacturing Engineering - Masters: Accepting Inquiries

More information

Working with this supervisor

The ideal candidate should hold a MS degree in Atmospheric Science, Meteorology, Environmental Engineering, or other related fields; and also have experience on aerosol sampling and chemical analysis, optical properties of aerosols, source apportionment techniques, optical remote sensing techniques, air quality modeling, or climate modeling.

Contact this supervisor

Complete the following form if you are interested in working with this supervisor for your Graduate Program. All fields are required, unless indicated otherwise.

This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.