Among the nitrated and oxygenated polycyclic aromatic hydrocarbons (NPAHs and OPAHs) are some of the most hazardous substances to public health, mainly because of their carcinogenicity and oxidative potential. Despite these concerns, the concentrations and fate of NPAHs and OPAHs in the atmospheric environment are largely unknown. Ambient air concentrations of 18 NPAHs, 5 quinones, and 5 other OPAHs were determined at two urban and one regional background sites in central Europe. At one of the urban sites, the total (gas and particulate) concentrations of Σ10OPAHs were 10.0 ± 9.2 ng/m3 in winter and 3.5 ± 1.6 ng/m3 in summer. The gradient to the regional background site exceeded 1 order of magnitude. Σ18NPAH concentrations were typically 1 order of magnitude lower than OPAHs. Among OPAHs, 9-fluorenone and (9,10)-anthraquinone were the most abundant species, accompanied by benzanthrone in winter. (9,10)-Anthraquinone represented two-thirds of quinones. We found that a large fraction of the target substance particulate mass was carried by submicrometer particles. The derived inhalation bioaccessibility in the PM10 size fraction is found to be ≈5% of the total ambient concentration of OPAHs and up to ≈2% for NPAHs. For 9-fluorenone and (9,10)-anthraquinone, up to 86 and 18%, respectively, were found at the rural site. Our results indicate that water solubility could function as a limiting factor for bioaccessibility of inhaled particulate NPAHs and OPAHs, without considerable effect of surfactant lipids and proteins in the lung lining fluid.

Polycyclic aromatic hydrocarbons (PAHs) are common atmospheric pollutants and known to cause adverse health effects. Nitrated PAHs (NPAHs) are formed in combustion activities and by nitration of PAHs in the atmosphere and may be equally or more toxic, but their spatial and temporal distribution in the atmosphere is not well characterized. Using the global EMAC model with atmospheric chemistry and surface compartments coupled, we investigate the formation, abundance, and fate of two secondarily formed NPAHs, 2-nitrofluoranthene (2-NFLT) and 2-nitropyrene (2-NPYR). The default reactivity scenario, the model with the simplest interpretation of parameters from the literature, tends to overestimate both absolute concentrations and NPAH/PAH ratios at observational sites. Sensitivity scenarios indicate that NO2-dependent NPAH formation leads to better agreement between measured and predicted NPAH concentrations and that photodegradation is the most important loss process of 2-NFLT and 2-NPYR. The highest concentrations of 2-NFLT and 2-NPYR are found in regions with strong PAH emissions, but because of continued secondary formation from the PAH precursors, these two NPAHs are predicted to be spread across the globe.

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Abstract. Polycyclic aromatic hydrocarbons (PAHs) and their alkylated (RPAHs), nitrated (NPAHs) and oxygenated (OPAHs) derivatives are air pollutants. Many of these substances are long-lived, can undergo long-range atmospheric transport and adversely affect human health upon exposure. However, the occurrence and fate of these air pollutants have hardly been studied in the marine atmosphere. In this study, we report the atmospheric concentrations over the Mediterranean Sea, the Red Sea, the Arabian Sea, the Gulf of Oman and the Arabian Gulf, determined during the AQABA (Air Quality and Climate Change in the Arabian Basin) project, a comprehensive ship-borne campaign in summer 2017. The average concentrations of ∑26PAHs, ∑19RPAHs, ∑11OPAHs and ∑17NPAHs, in the gas and particulate phases, were 2.99 ± 3.35 ng m−3, 0.83 ± 0.87 ng m−3, 0.24 ± 0.25 ng m−3 and 4.34 ± 7.37 pg m−3, respectively. The Arabian Sea region was the cleanest for all substance classes, with concentrations among the lowest ever reported. Over the Mediterranean Sea, we found the highest average burden of ∑26PAHs and ∑11OPAHs, while the ∑17NPAHs were most abundant over the Arabian Gulf (known also as the Persian Gulf). 1,4-Naphthoquinone (1,4-O2NAP) followed by 9-fluorenone and 9,10-anthraquinone were the most abundant studied OPAHs in most samples. The NPAH composition pattern varied significantly across the regions, with 2-nitronaphthalene (2-NNAP) being the most abundant NPAH. According to source apportionment investigations, the main sources of PAH derivatives in the region were ship exhaust emissions, residual oil combustion and continental pollution. All OPAHs and NPAHs except 2-nitrofluoranthene (2-NFLT), which were frequently detected during the campaign, showed elevated concentrations in fresh shipping emissions. In contrast, 2-NFLT and 2-nitropyrene (2-NPYR) were highly abundant in aged shipping emissions due to secondary formation. Apart from 2-NFLT and 2-NPYR, benz(a)anthracene-7,12-dione and 2-NNAP also had significant photochemical sources. Another finding was that the highest concentrations of PAHs, OPAHs and NPAHs were found in the sub-micrometre fraction of particulate matter (PM1).

Quinones are chemical compounds commonly found in air particulate matter (PM).Their redox activity can generate reactive oxygen species (ROS) and contribute to the oxidative potential (OP) of PM leading to adverse health effects of aerosols. The quinones' OP and ability to form ROS are linked to their reduction potential (RP, measured in volts), a metric for the tendency to lose electrons in redox reactions. Here, we use convolutional neural networks (CNN) as quantitative structure-activity relationship (QSAR) models to relate the one-electron RP of quinones to their molecular structure. For CNN training and testing, a data set of more than 100,000 quinones with associated RP values derived from density functional theory calculations was encoded in simplified molecular input line entry system (SMILES). The best performing CNN model achieved a root mean square error (RMSE) of 0.115 V for an independent test data set and outperformed linear regression models fitted on common molecular descriptors (≥ 0.140 V RMSE). Augmentation methods were newly adapted or applied to support CNN training with smaller data sets, improving RMSE by up to approximately 37% for a data set of 321 molecules. Adjusted for solvent effects, the CNN-derived RP predictions showed good agreement with experimental data.Using the newly developed method, we identified a subset of atmospherically relevant quinones that are likely to have a high OP and play a role in aerosol health effects, which remains to be further elucidated by experimental studies. We suggest to use the presented machine learning approach in further investigations of atmospheric aerosol chemistry and health effects as well as other studies that require a target-oriented screening of the properties and effects of large classes of substances.