Predicting the sorption coefficients of pesticides in the real environment is a complex and challenging task. This complexity arises from the fact that multiple factors, including the characteristics of the contaminants, the composition of the sediment and the prevailing environmental conditions, can exert a significant influence on the sediment-water partition coefficient. Here, 54 commonly-used pesticides were detected in a river. Subsequently, the performance of the extended connectivity fingerprint model and the energy lattice points model in predicting sediment-water partition coefficients was compared. The results indicated that the concentration levels of pesticides in the research region followed a specific order: herbicide concentration (126.35 ± 65.68 ng/L in water and 93.02 ± 39.21 ng/g in sediment) > fungicide concentration (51.88 ± 19.91 ng/L in water and 55.26 ± 18.21 ng/g in sediment) > insecticide concentration (9.25 ± 5.24 ng/L in water and 8.18 ± 4.85 ng/g in sediment) > plant growth regulator concentration (5.68 ± 1.05 ng/L in water and 5.90 ± 2.70 ng/g in sediment). The quantitative structure-property relationship model demonstrated that, in comparison to the traditional extended connectivity fingerprint model, the accuracy of the energy lattice points model in prediction was significantly improved (r² = 0.85 vs. 0.60). This finding suggests that the energy lattice points, when used as a descriptor, can more effectively reflect the non-binding interaction characteristics between small-molecule contaminants and sediments compared to traditional model. And the energy lattice point model can be well applied to the prediction of pollutant adsorption factors in real environments.

Acetonitrile reshapes the solid–liquid interface via four synergistic effects: interfacial polarity regulation, microheterogeneous structure construction, interfacial enrichment, and diffusion pathway optimization.

Edible mushrooms are widely consumed across the globe. However, the critical lack of reliable detection methods has led to severely limited data on the contamination profiles and dietary exposure risks of Alternaria toxins in edible mushrooms. This study utilized a hydrophilic–lipophilic balanced solid-phase microextraction fiber for the detection of Alternaria toxins in edible mushrooms. The employed VIM/DVB fiber exhibited superior performance with a wide linear range (1–200 μg/kg), low limits of detection (0.01–0.26 μg/kg) and satisfactory recoveries (80.6–111.6%). Applying this method to 63 edible mushrooms (6 species) from Guiyang (Guizhou, China) revealed varying degrees of contamination, with all samples containing at least one of four target toxins (alternariol, alternariol monomethyl ether, altenuene, tentoxin). Tentoxin (1.86 ± 2.6 μg/kg, 76.2% detection frequency) and alternariol (1.61 ± 1.24 μg/kg, 55.6% detection frequency) were the predominant toxins. Toxin distribution varied by mushroom species. Lentinula edodes was prone to tentoxin contamination, while Pleurotus eryngii was even more susceptible to alternariol-tentoxin co-occurrence. Dietary risk assessment indicated that alternariol posed the highest risks, with 51% samples exceeding the toxicological threshold (2.5 ng/kg bw/day). This study provides a reliable detection tool for Alternaria toxins in edible mushrooms and highlights the signal that continuous attention needs to be paid to Alternaria toxins in edible mushrooms.