Electron affinities (EAs) and free energies for electron attachment (DeltaGo(a,298K)) have been directly calculated for 45 polynuclear aromatic hydrocarbons (PAHs) and related molecules by a variety of theoretical methods, with standard regression errors of about 0.07 eV (mean unsigned error = 0.05 eV) at the B3LYP/6-31 + G(d,p) level and larger errors with HF or MP2 methods or using Koopmans' Theorem. Comparison of gas-phase free energies with solution-phase reduction potentials provides a measure of solvation energy differences between the radical anion and neutral PAH. A simple Born-charging model approximates the solvation effects on the radical anions, leading to a good correlation with experimental solvation energy differences. This is used to estimate unknown or questionable EAs from reduction potentials. Two independent methods are used to predict DeltaGo(a,298K) values: (1) based upon DFT methods, or (2) based upon reduction potentials and the Born model. They suggest reassignments or a resolution of conflicting experimental EAs for nearly one-half (17 of 38) of the PAH molecules for which experimental EAs have been reported. For the antiaromatic molecules, 1,3,5-tri-tert-butylpentalene and the dithia-substituted cyclobutadiene 1, the reduction potentials lead to estimated EAs close to those expected from DFT calculations and provide a basis for the prediction of the EAs and reduction potentials of pentalene and cyclobutadiene. The Born model has been used to relate the electrostatic solvation energies of PAH and hydrocarbon radical anions, and spherical halide anions, alkali metal cations, and ammonium ions to effective ionic radii from DFT electron-density envelopes. The Born model used for PAHs has been successfully extended here to quantitatively explain the solvation energy of the C60 radical anion.

Agricultural pesticides are being transported by air large distances to remote mountain areas and have been implicated as a cause for recent population declines of several amphibian species in such locations. Largely unmeasured, however, are the magnitude and temporal variation of pesticide concentrations in these areas, and the relationship between pesticide use and pesticide appearance in the montane environment. We addressed these topics in the southern Sierra Nevada mountains, California, by sampling water weekly or monthly from four alpine lakes from mid-June to mid-October 2003. The lakes were 46-83 km from the nearest pesticide sources in the intensively cultivated San Joaquin Valley. Four of 41 target pesticide analytes were evaluated for temporal patterns: endosulfan, propargite, dacthal, and simazine. Concentrations were very low, approximately 1 ng/L or less, at all times. The temporal patterns in concentrations differed among the four pesticides, whereas the temporal pattern for each pesticide was similar among the four lakes. For the two pesticides applied abundantly in the San Joaquin Valley during the sampling period, endosulfan and propargite, temporal variation in concentrations corresponded strikingly with application rates in the Valley with lag times of 1-2 weeks. A finer-scale analysis suggests that a large fraction of these two pesticides reaching the lakes originated in localized upwind areas within the Valley.

The United States (US) Environmental Protection Agency (EPA)'s SPECIATE database contains speciated particulate matter (PM) and volatile organic compound (VOC) emissions profiles. Emissions profiles from anthropogenic combustion, industry, wildfires, and agricultural sources among others are key inputs for creating chemically-resolved emissions inventories for air quality modeling. While the database and its use for air quality modeling are routinely updated and evaluated, this work sets out to systematically prioritize future improvements and communicate speciation data needs to the research community. We first identify the most prominent profiles (PM and VOC) used in the EPA's 2014 emissions modeling platform based on PM mass and VOC mass and reactivity. It is important to note that the on-road profiles were excluded from this analysis since speciation for these profiles is computed internally in the MOVES model. We then investigate these profiles further for quality and to determine whether they were being appropriately matched to source types while also considering regional variability of speciated pollutants. We then applied a quantitative needs assessment ranking system which rates the profile based on age, appropriateness (i.e. is the profile being used appropriately), prevalence in the EPA modeling platform and the quality of the reference. Our analysis shows that the highest ranked profiles (e.g. profile assignments with the highest priority for updates) include PM 2.5 profiles for fires (prescribed, agricultural and wild) and VOC profiles for crude oil storage tanks and residential wood combustion of pine wood. Top ranked profiles may indicate either that there are problems with the currently available source testing or that current mappings of profiles to source categories within EPA's modeling platform need improvement. Through this process, we have identified 29 emissions sourcecategories that would benefit from updated mapping. Many of these

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