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Abstract. Polycyclic aromatic hydrocarbons (PAHs) are not declining in Arctic air despite reductions in their global emissions. In Svalbard, the Longyearbyen coal-fired power plant is considered to be one of the major local sources of PAHs. Power plant stack emissions and ambient air samples, collected simultaneously at 1 km (UNIS) and 6 km (Adventdalen) transect distance, were analysed (gaseous and particulate phases separately) for 22 nitro-PAHs, 8 oxy-PAHs, and 16 parent PAHs by gas chromatography in combination with single quadrupole electron capture negative ionization mass spectrometry (GC-ECNI-MS) and gas chromatography in combination with triple quadrupole electron ionization mass spectrometry (GC-EI-MS/MS). Results confirm low levels of PAH emissions (∑16 PAHs =1.5 µg kg−1 coal) from the power plant. Phenanthrene, 9,10-anthraquinone, 9-fluorenone, fluorene, fluoranthene, and pyrene accounted for 85 % of the plant emission (not including naphthalene). A dilution effect was observed for the transect ambient air samples: 1.26±0.16 and 0.63±0.14 ng m−3 were the sum of all 47 PAH derivatives for UNIS and Adventdalen, respectively. The PAH profile was homogeneous for these recipient stations with phenanthrene and 9-fluorenone being most abundant. Multivariate statistical analysis confirmed coal combustion and vehicle and marine traffic as the predominant sources of PAHs. Secondary atmospheric formation of 9-nitroanthracene and 2+3-nitrofluoranthene was evaluated and concluded. PAHs partitioning between gaseous and particulate phases showed a strong dependence on ambient temperatures and humidity. The present study contributes important data which can be utilized to eliminate uncertainties in model predictions that aim to assess the extent and impacts of Arctic atmospheric contaminants.

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In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects-voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds (575 h) if it was annotated manually. For the validation of the proposed technique, a sub-dataset-Voice, is annotated manually as well as with the proposed technique. Cohen's Kappa statistics is used to evaluate the degree of agreement between the two annotations. The high Kappa value (i.e., 0.9571%) shows the high level of agreement between the two. This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost. This research also contributes in consolidating the guidelines for the manual annotation process.

Local point sources (mainly firefighting stations) and diffuse sources contributed to the exposure of PFAS to the marine food web near Longyearbyen on Svalbard. Certain PFOS substitutes showed a bioaccumulation potential in marine invertebrates.