Microplastics (MPs) in marine and terrestrial environments have been intensively studied, but the dynamics of airborne MPs remains limited. Existing studies on atmospheric MPs are mostly derived from collection of atmospheric deposition, whereas direct measurements of airborne MPs are scarce. However, the abundance of airborne MPs is more relevant for evaluating human inhalation exposure risk. Herein, airborne MPs in indoor and outdoor environments from urban and rural areas of a coastal city in eastern China were investigated. MP concentrations (mean±SD) in indoor air (1583 ± 1180 n/m 3 ) were an order of magnitude higher than outdoor air (189 ± 85 n/m 3 ), and airborne MP concentrations in urban areas (224 ± 70 n/m 3 ) were higher than rural areas (101 ± 47 n/m 3 ). MPs smaller than 100 µm dominated airborne MPs, and the predominant shape of airborne MPs was fragments, as opposed to fibers. The larger MP size fractions contained a higher proportion of fibers, whereas the smaller size fractions were nearly exclusively composed of fragments. The health risk caused by ubiquitous airborne MPs should not be discounted as the maximum annual outdoor exposure of airborne MPs can reach 1 million/year, while indoor exposure may be even higher due to higher indoor airborne MP concentrations.

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Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the ruralsuburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R 2 ), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R 2 , and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.

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