David Klavens



Project title: HPLC Analysis of 1-Nitropyrene as a Method for the Measurement of Diesel Particulate Matter

Degree: MS (Thesis) | Program: Industrial Hygiene & Safety (IH&S) | Project type: Thesis/Dissertation
Completed in: 2005 | Faculty advisor: Christopher D. Simpson

Abstract:

Exposure to diesel exhaust (DE) is associated with a variety of detrimental short-term and chronic health effects in humans. This includes increasing evidence from epidemiological studies and animal research that an association between diesel exhaust and lung cancer exists. Recent estimates of environmental levels of diesel particulate matter overlap the lower range of occupational time-weighted exposures for jobs that are associated with increased health risks due to elevated DE exposure. This has raised concern among scientists, regulators and public health professionals. For the last ten years elemental carbon (EC) has been the preferred marker for DE and its detection and measurement is the basis for NIOSH Method 5040, Elemental Carbon (Diesel Particulate). Uncertainty exists because EC is emitted by a wide range of combustion sources making source apportionment a difficult problem. This research proposes that 1-Nitropyrene (1-NP), a nitro-polycyclic aromatic hydrocarbon, is a more specific marker for DE and can greatly enhance the accuracy of measuring DE exposures and environmental concentrations. HPLC with fluorescence detection was used to analyze 1-NP in fine particulate matter collected from three studies: the Kenworth Truck factory in Renton, WA, diesel school buses and school children who commute to school on these busses in the Seattle, WA area, and a specially designed diesel exposure facility at the University of Washington, Seattle, WA. Concurrent EC samples were collected and analyzed when possible. The use of 1-NP for the detection of diesel exhaust was shown to be a practical method with certain qualifications. It was not possible to derive any quantitative relationship between 1-NP and EC measurements based on the available data.