Polycyclic aromatic hydrocarbons (PAHs) are a class of persistent organic pollutants of water, and their determination at trace levels in the aquatic ecosystems is essential. In this work, an ultrasound-assisted dispersive liquid–liquid microextraction (DLLME) procedure was suggested utilizing a binary dispersive agent for recovery of different molecular weight polycyclic aromatic hydrocarbons (PAHs) from waters. The detection was carried out by gas chromatography–mass spectrometry (GC-MS) as well as high-performance liquid chromatography with fluorescence and diode-array detection (HPLC-FD/PDA). The method was optimized for the extraction of analytes with respect to the mixture composition, ratios of components, ultrasonication time and centrifugation parameters. The analytical schemes for PAHs extraction from water samples using different ratios of extraction and dispersive solvents are reported. The mixture consisting of chloroform and methanol was applied for the extraction of PAHs containing two or three fused aromatic rings; the mixture of chloroform and acetonitrile is suitable for PAHs containing more than four aromatic rings. The mixture of chloroform:acetone + acetonitrile was applied in the universal scheme and allowed for the simultaneous extraction of 20 PAHs with different structures. The developed sample preparation schemes were combined with GC-MS and HPLC-FD/PDA, which allowed us to determine the analytes at low concentrations (from 0.0002 µg/L) with the recoveries exceeding 80% and relative standard deviations of about 8%. The developed methods for the determination of 20 PAHs were applied to the analysis of water samples from the Karasun Lake (Krasnodar), Azov Sea (Temryuk) and Black Sea (Sochi).

Polycyclic aromatic hydrocarbons (PAHs) are a class of compounds which have been known to be carcinogenic, teratogenic and mutagenic as well as act as pollutants of environmental objects. The determination of PAHs in complex matrices is difficult, and it is very important to use an efficient sample pretreatment technique. A sample preparation technique was developed involving extractive freezing-out and centrifugation of the samples for the determination of polycyclic aromatic hydrocarbons (PAHs) in soils and bottom sediments using gas chromatography – mass spectrometry (GC–MS). Sochi soils (The Imereti Lowlands), turf, sea bottom sediments (Azov Sea, The Temryuk Bay), river bottom sediment (Kurchansky estuary) and Caio Romano (Cuba) island sand were selected as the objects for the research. Soils and bottom sediments which contained no determined PAHs were used as model samples. The conditions of sample preparation have been optimized, and the extraction effects of acetonitrile with water on the PAHs recoveries have been investigated. It was found that for the determination of the compounds consisting from two to four fused aromatic rings such as naphthalene, 2-methylnaphthalene, acenaphthylene, biphenyl, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene and pyrene, the extraction occurred when the extractive mixture contained 15% acetonitrile. The proposed method detection limits of individual compounds ranged from 0.83 to 0.92 µg/kg. The extractive mixture containing 50% acetonitrile was proposed for the determination of 20 PAHs such as naphthalene, 2-methylnaphthalene, acenaphthene, biphenyl, acenaphthylene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benz[a]anthracene, chrysene, triphenylene, benz[b]fluoranthene, benz[k]fluoranthene, benz[e]pyrene, benz[a]pyrene, indene[1,2,3–c,d]pyrene, dibenz[a,h]anthracene, benz[g,h,i]perylene. The extraction of PAHs in these conditions demonstrated the recoveries from 61% to 97%. As matter of fact, the lower recoveries of PAHs, that contained four or more fused aromatic rings, have been caused by the sorption in the cracks of the solid aqueous phase. The extraction, clear-up of extract and concentration were realized as one step of the sample pretreatment. As a result, the rapid and express technique of the sample preparation with combined GC-MS were proposed for the PAHs determination in soils and bottom sediments. This method’s limits of individual PAHs quantitation ranged from 1 to 5 µg/kg, and these were lower that the maximum permissible concentration.

Background: Selecting optimal extractant solvents for liquid-liquid extraction (LLE) of polycyclic aromatic hydrocarbons (PAHs) requires balancing interdependent physicochemical factors across 20 priority analytes and multiple solvent classes. Systematic experimental screening is time-and resource-intensive, while published machine learning approaches have not combined quantum-chemical descriptors with stacking ensemble architectures for this task. Results: A two-level stacking ensemble was developed, integrating gradient boosting (CatBoost), ensemble decision trees (Random Forest (RF)), and a hybrid multilayer perceptron with bidirectional long short-term memory (MLP+BiLSTM) as base learners, with a linear meta-model trained via 5-fold out-of-fold (OOF) cross-validation. The feature space comprised 18 physicochemical and DFT-derived quantum-chemical descriptors. Across 10 independent runs, CatBoost delivered the highest predictive accuracy (R² = 0.896 ± 0.032, mean absolute error (MAE) = 8.77 ± 1.58%) and lowest variability, outperforming MLP+BiLSTM by ΔR² = 0.109. The stacking ensemble exceeded the best direct-application baseline by ΔR² = +0.117 on a challenging data split (p = 2.4∙10^(-5)). Solvent physicochemical descriptors-solvation cavity volume (15.8%), dielectric constant (14.4%), and boiling point (13.3%)-collectively dominated feature importance (57.4%), consistent with solvation theory. Significance: The proposed methodology enables in silico screening of candidate solvents, reducing experimental effort by an estimated 70-80% during analytical method development. The stacking framework with OOF cross-validation is transferable to other solvent-selection tasks in analytical chemistry, including mobile-phase optimisation in chromatography and sorbent design for solid-phase extraction.