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.

No abstract

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.

No abstract

Quantitative assessment of metal sources in sediments is essential for implementation of source control and remediation strategies. This study investigated metal contamination in sediments to assess potential ecological risks and quantify pollutant sources of metals (Cu, Zn, Pb, Cd, Cr, Co and Ni) in the Wen-Rui Tang River watershed. Total and fraction analysis indicated high pollution levels of metals. Zinc and Cd posed high ecological risk based on the risk assessment code, with the highest ecological risk found in the southwestern of the watershed. The positive matrix factorization (PMF) model was highly effective in predicting total metal concentrations and identified three contributing metal sources. An agricultural source (factor 1) contributed highly to Cu (74.1%) and Zn (42.5%), and was most prominent in the west and south-central portions of the watershed. Cd (93.5%) showed a high weighting with industrial sources (factor 2) with a hot spot in the southwest. Factor 3 was identified as a mixed natural and vehicle traffic source that showed large contribution to Cr (65.2%), Ni (63.9%) and Pb (50.7%). Spatial analysis indicated a consistent pattern between PMF-identified factors and suspected metal sources at the watershed scale demonstrating the efficacy of the PMF modeling approach for watershed analysis.

Minerals are ubiquitous in the natural environment and have close contact with microorganisms. In various scenarios, microorganisms that harbor extracellular electron transfer (EET) capabilities have evolved a series of beneficial strategies through the mutual exchange of electrons with extracellular minerals to enhance survival and metabolism. These electron exchange interactions are highly relevant to the cycling of elements in the epigeosphere and have a profound significance in bioelectrochemical engineering applications. In this review, we summarize recent advances related to the effects of different minerals that facilitate the EET process and discuss the underlying mechanisms and outlooks for future applications. The promotional effects of minerals arise from their redox-active ability, electrical conductivity and photocatalytic capability. In mineral-promoted EET processes, various responses have concurrently arisen in microorganisms, such as stretching of electrically conductive pili (e-pili), upregulated expression of outer-membrane cytochromes (Cyts) and production of specific enzymes, and secretion of extracellular polymeric substances (EPSs). This review synthesizes the understanding of electron exchange mechanisms between microorganisms and minerals and highlights potential applications in development of renewable energy production and pollutant remediation, which are topics of particular significance to future exploitation of biotechnology.

• CdS NPs assists hybrid microbialphotoelectrochemical processes. • CdS NPs display desirable biocompatibility with microorganisms. • CdS NPs-immobilized biohybrids display a potential for environmental application.

• Key riverine nitrate contributors were identified in a drinking water source. • MCMC model quantified the spatial variation of individual nitrate pollution sources. • Uncertainty, denoting variation strength for source contributions, was quantified.