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Polycyclic aromatic hydrocarbons (PAHs) undergo transformation reactions with atmospheric photochemical oxidants, such as hydroxyl radicals (OH center dot), nitrogen oxides (NOx), and ozone (O-3). The most common PAH-transformation products (PAH-TPs) are nitrated, oxygenated, and hydroxylated PAHs (NPAHs, OPAHs, and OHPAHs, respectively), some of which are known to pose potential human health concerns. We sampled four theoretical approaches for predicting the location of reactive sites on PAHs (i.e., the carbon where atmospheric oxidants attack), and hence the chemoselectivity of the PAHs. All computed results are based on density functional theory (B3LYP/6-31G(d) optimized structures and energies). The four approaches are (1) Clar's prediction of aromatic resonance structures, (2) thermodynamic stability of all OHPAH adduct intermediates, (3) computed atomic charges (Natural Bond order, ChelpG, and Mulliken) at each carbon on the PAH, and (4) average local ionization energy (ALIE) at atom or bond sites. To evaluate the accuracy of these approaches, the predicted PAH-TPs were compared to published laboratory observations of major NPAH, OPAH, and OHPAH products in both gas and particle phases. We found that the Clar's resonance structures were able to predict the least stable rings on the PAHs but did not offer insights in terms of which individual carbon is most reactive. The OHPAH adduct thermodynamics and the ALIE approaches were the most accurate when compared to laboratory data, showing great potential for predicting the formation of previously unstudied PAH-TPs that are likely to form in the atmosphere., Funding Agencies:National Institute of Environmental Health Sciences (NIEHS) P30ES00210 P42ES016465 National Institutes of Health (NIH) National Science Foundation (NSF) AGS-1411214 OSU Stone Family NSF CHE-1352663 Johnson Research Fellowship OSU Department of Chemistry through the Dorothy Ramon Barnes Fellowship NIEHS T32ES007060

Non-targeted analysis of environmental samples, using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC x GC/ToF-MS), poses significant data analysis challenges due to the large number of possible analytes. Non-targeted data analysis of complex mixtures is prone to human bias and is laborious, particularly for comparative environmental samples such as contaminated soil pre- and post-bioremediation. To address this research bottleneck, we developed OCTpy, a Python (TM) script that acts as a data reduction filter to automate GC x GC/ToF-MS data analysis from LECO (R) ChromaTOF (R) software and facilitates selection of analytes of interest based on peak area comparison between comparative samples. We used data from polycyclic aromatic hydrocarbon (PAH) contaminated soil, pre- and post-bioremediation, to assess the effectiveness of OCTpy in facilitating the selection of analytes that have formed or degraded following treatment. Using datasets from the soil extracts pre- and post-bioremediation, OCTpy selected, on average, 18% of the initial suggested analytes generated by the LECO (R) ChromaTOF (R) software Statistical Compare feature. Based on this list, 63-100% of the candidate analytes identified by a highly trained individual were also selected by OCTpy. This process was accomplished in several minutes per sample, whereas manual data analysis took several hours per sample. OCTpy automates the analysis of complex mixtures of comparative samples, reduces the potential for human error during heavy data handling and decreases data analysis time by at least tenfold. (C) 2018 Elsevier B.V. All rights reserved.