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Focus on Student Research: Chang-Fu Wu

Exposure Assessment Using Open-path Fourier Transform Infrared Spectroscopy
(from SPHCM's Spotlight on Research, Issue 5:Summer 2001)

Chang-Fu Wu
PhD, Industrial Hygiene '02

Dissertation: Applying Optical Remote Sensing Techniques to Evaluate Personal Exposure

Advisor: Michael G. Yost, PhD

More Information:
Optical Remote Sensing Laboratory

Related Publications:
Hashmonay RA, Yost MG, Chang-Fu Wu. Computed Tomography of Air Pollutants Using Radial Scanning Path-Integrated Optical Remote Sensing. Atmospheric Environment 1999, 33(3), 267-74.

Exposure assessment is an important process for evaluating risk from occupational hazards. This information can be used to determine if administrative controls, engineering controls and respiratory protection, or other programs are needed to protect workers from adverse health effects of airborne contaminants. The most common methods to evaluate personal exposures utilize either sampling pumps with appropriate collecting media or passive sampling devices. Usually these air samples are collected for a full working shift and then interpreted and compared to the 8-hour Time Weighted Average (TWA) exposure limits.

However, in the context of providing a warning system to prevent short-term (e.g. 15 minutes) over exposures, conventional sampling methods can have several limitations. Obtaining exposure data with conventional approaches requires changing the collecting media many times during the sampling period. These samples usually need laboratory analysis and do not provide real-time information. When
acutely toxic agents are present, the time lag between sampling and analysis is unacceptable. Although some modern real-time monitors may evaluate short-term exposure levels in situ, each type of contaminant usually needs a specific type of detector. In other words, considerable prior knowledge about the chemical properties of the hazards is required, knowledge that may not always be available.

To overcome those problems, Chang-Fu Wu, a doctoral student in the Department of Environmental Health, has proposed using open-path Fourier transform infrared (OPFTIR) spectroscopy to monitor personal exposure. The OPFTIR instrument sends out infrared light along a fixed beam path to measure chemicals in the air over distances up to 1000 meters. The collected light can provide information on composition and concentration of many air contaminants. For industrial-hygiene exposure assessments, applying OPFTIR can: 1) identify and quantify mixtures of airborne compounds with low detection limits; 2) provide continuous and real-time information; 3) measure agents in situ without the need for sampling devices; and 4) provide a warning
system to prevent further exposure.

Data collected by OP-FTIR instruments are in the form of a path-integrated concentration. With data along only one fixed beam path, it is unclear if the contaminant is uniformly distributed along the path or highly concentrated in a small area. In the lab, it has been demonstrated that air contaminants can be rapidly mapped using data from multiple beam paths and applying a novel radial scanning technique with computed tomography (CT) algorithms. This creates a
reconstruction of the air pollutants, somewhat like CT scanning that is used in medical imaging.

The proposed sampling approach involves two major concepts. First, the concentration profile over space during the sampling period is needed. It is obtained from the CTFTIR reconstructions. Second, it is necessary to know the person’s position in real-time, which can be determined by applying the technique of motion tracking either with image analysis or with the Global Positioning System. A series of simulation studies was conducted to demonstrate the feasibility of this sampling approach. The results showed that the correlation between the expected and reconstructed exposure levels can be very high (r2>0.8). Currently, Chang-Fu and others in the lab are collecting experimental data in a ventilation chamber to test the system in controlled conditions. If all goes well, the experiments will soon be finished, and there will be field data to validate this theory.

 


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