Abstract
It is hypothesized that PM2.5 with high oxidative potential (OP) can catalytically generate reactive oxygen species (ROS) in excess of the body’s antioxidant capacity, leading to oxidative stress. Therefore, two advanced methods for conducting source apportionment, along with field experiments characterizing air quality, are used to identify the sources of PM2.5 with high OP and relate them to acute health effects. The field study measured OP of ambient water-soluble PM2.5 using a dithiothreitol (DTT) assay at four sites across the Southeastern United States from June 2012 to June 2013. Source apportionment was performed on collocated speciated PM2.5 samples using the Chemical Mass Balance Method with ensemble-trained profiles in Atlanta, GA and CMAQ-DDM for Atlanta and all other measurement sites (Yorkville, GA, Centerville, AL, and Birmingham, AL). Source-OP relationships were investigated using least squares linear regression. The model for Atlanta, GA was applied to PM2.5 source impacts from 1998–2010 to estimate long-term trends in ambient PM2.5 OP for use in population-level acute epidemiologic studies. Biomass burning contributes the largest fraction to total historical OP in Atlanta, followed by light-duty gasoline vehicles and heavy-duty diesel vehicles (43, 22 and 17%, respectively). Results find significant associations between estimated OP and emergency department visits related to congestive heart failure and asthma/wheezing attacks, supporting the hypothesis that PM2.5 health effects are, in part, due to oxidative stress and that OP is a useful indicator of PM2.5 health impacts. Finally, controlling PM2.5 sources with high OP, like biomass burning, may help prevent acute health effects.
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Acknowledgements
This work was funded, in part, by U.S. Environmental Protection Agency under Grants RD834799, RD83096001, RD83107601 and RD83215901. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the US EPA. Further, the US EPA does not endorse the purchase of any commercial products or services mentioned in the publication. Observational data and logistical support was provided by Atmospheric Research & Analysis, Inc.
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Questioner: Peter Viaene.
Question: Is there any specific reason why you couldn’t/didn’t include all source types? Isn’t there a risk of missing indirect effects these other sources could have on your results?
Answer: For the source apportionment step, we used all available sources with profiles for CMB in Atlanta and 15 relevant sources developed for CMAQ-DDM. In the regression modelling, there are two main reasons that sources were removed from the model, one statistical and one physical. First, we wanted to avoid overfitting of the model by removing coefficients that were not statistically significant. Second, we wanted to investigate which sources contributed the most to OP and thus be used to simulate OP of ambient PM2.5 by removing sources without significant impact and analyzing how the results changed. Removing these sources did not change the R2 of the model significantly.
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Bates, J.T. et al. (2018). Source Impacts on and Cardiorespiratory Effects of Reactive Oxygen Species Generated by Water-Soluble PM2.5 Across the Eastern United States. In: Mensink, C., Kallos, G. (eds) Air Pollution Modeling and its Application XXV. ITM 2016. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-57645-9_79
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DOI: https://doi.org/10.1007/978-3-319-57645-9_79
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