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Composition and Integrity of PAHs, Nitro-PAHs, Hopanes and Steranes In Diesel Exhaust Particulate Matter
Lei Huang1, Stanislav V Bohac2, Sergei M Chernyak1
1Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA.
Abstract:
Diesel exhaust particulate matter contains many semivolatile organic compounds (SVOCs) of environmental and health significance. This study investigates the composition, emission rates, and integrity of 25 SVOCs, including polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs (NPAHs), and diesel biomarkers hopanes and steranes. Diesel engine particulate matter (PM), generated using an engine test bench, three engine conditions, and ultra-low sulfur diesel (ULSD), was collected on borosilicate glass fiber filters. Under high engine load, the PM emission rate was 0.102 g/kWh, and emission rate of ΣPAHs (10 compounds), ΣNPAHs (6 compounds), Σhopanes (2 compounds), and Σsteranes (2 compounds) were 2.52, 0.351, 0.02 ~ 2 and 1μg/kWh, respectively. Storage losses were evaluated for three cases: conditioning filters in clean air at 25 °C and 33% relative humidity (RH) for 24 h; storing filter samples (without extraction) wrapped in aluminum foil at 4 °C for up to one month; and storing filter extracts in glass vials capped with Teflon crimp seals at 4 °C for up to six months. After conditioning filters for 24 h, 30% of the more volatile PAHs were lost, but lower volatility NPAHs, hopanes and steranes showed negligible changes. Storing wrapped filters and extracts at 4 °C for up to one month did not lead to significant losses, but storing extracts for five months led to significant losses of PAHs and NPAHs; hopanes and steranes demonstrated greater integrity. These results suggest that even relatively brief filter conditioning periods, needed for gravimetric measurements of PM mass, and extended storage of filter extracts can lead to underestimates of SVOC concentrations. Thus, SVOC sampling and analysis protocols should utilize stringent criteria and performance checks to identify and limit possible biases occurring during filter and extract processing.
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