Determination of Phosphite in a Eutrophic Freshwater Lake by Suppressed Conductivity Ion Chromatography

The establishment of a sensitive and specific method for the detection of reduced phosphorus (P) is crucial for understanding P cycle. This paper presents the quantitative evidence of phosphite (P, +3) from the freshwater matrix correspondent to the typically eutrophic Lake Taihu in China. By ion chromatography coupled with gradient elution procedure, efficient separation of micromolar levels of phosphite is possible in the presence of millimolar levels of interfering ions, such as chloride, sulfate, and hydrogen carbonate in freshwater lakes. Optimal suppressed ion chromatography conditions include the use of 500 μL injection volumes and an AS11 HC analytical column heated to 30 °C. The method detection limit of 0.002 μM for phosphite was successfully applied for phosphite determination in natural water samples with recoveries ranging from 90.7 ± 3.2% to 108 ± 1.5%. Phosphite in the freshwater matrix was also verified using a two-dimensional capillary ion chromatography and ion chromatography coupled with mass spectrometry. Results confirmed the presence of phosphite in Lake Taihu ranging from 0.01 ± 0.01 to 0.17 ± 0.01 μM, which correlated to 1–10% of the phosphate. Phosphite is an important component of P and may influence biogeochemical P cycle in lakes

Tropospheric Phosphine and Its Sources in Coastal Antarctica

Earlier reports show very low concentrations of phosphine in remote air of the lower troposphere of nonpolar regions, in the low ng m-3 range during the night and in the pg m-3 range during daylight around noon. In this study, abnormally and unexpectedly high phosphine concentrations (30.0−407.8 ng m-3, 11 locations) were found in polar air samples collected on Millor Peninsula, eastern Antarctica and Fildes Peninsula, western Antarctica. The maximum concentration was measured in the atmosphere of penguin colonies. Field phosphine emission rates from four colonies were 8.99 ng m-2 h-1 (skua colony), 9.56 ng m-2 h-1 (gentoo penguin colony), 39.96 ng m-2 h-1 (seal colony) and 63.58 ng m-2 h-1 (empire penguin colony), respectively. Our air sampling sites are located downwind of two large penguin colonies, indicating that penguin colony emission is the predominant source for atmospheric PH3 on Millor Peninsula. Laboratory scale incubation of ornithogenic soils amended by penguin guanos yielded a maximum PH3 production rate of 0.58 ng kg-1 d-1 specifically at low temperature (4 °C). Significant concentrations of phosphine occur in the atmosphere of coastal Antarctica and confirm the existence of a small gaseous link in the phosphorus cycle of the Antarctic tundra ecosystem

Phosphite in Sedimentary Interstitial Water of Lake Taihu, a Large Eutrophic Shallow Lake in China

The seasonal occurrence and distribution of phosphite (HPO₃^2‑, P) in sedimentary interstitial water from Lake Taihu was monitored from 2011 to 2012 to better understand its possible link to P cycle in the eutrophic shallow lake. Phosphite concentrations ranged from < MDL to 14.32 ± 0.19 μg P/kg with a mean concentration of 1.58 ± 0.33 μg P/kg, which accounts for 5.51% total soluble P (TSP_s) in surficial sediments (0–20 cm). Spatially, the concentrations of sedimentary phosphite in the lake’s northern areas were relatively higher than those in the southern areas. Higher phosphite concentrations were always observed in seriously polluted sites. Generally, phosphite in the deeper layers (20–40 cm and 40–60 cm) showed minor fluctuations compared to that in the surficial sediments, which may be associated with the frequent exchange at the sediment–water interface. Phosphite concentrations in surficial or core sediments decreased as spring > autumn > summer > winter. Higher phosphite levels occurred in the areas with lower redox (Eh), higher P contents, and particularly when metal bonded with P to form Al–P_s and Ca–P_s. Phosphite may be an important media in the P biogeochemical cycle in Lake Taihu and contribute to its internal P transportation

Deciphering Microbe-Mediated Dissolved Organic Matter Reactome in Wastewater Treatment Plants Using Directed Paired Mass Distance

Understanding the reaction mechanism of dissolved organic matter (DOM) during wastewater biotreatment is crucial for optimal DOM control. Here, we develop a directed paired mass distance (dPMD) method that constructs a molecular network displaying the reaction pathways of DOM. It couples direction inference and PMD analysis to extract the substrate–product relationships and delta masses of potentially paired reactants directly from sequential mass spectrometry data without formula assignment. Using this method, we analyze the influent and effluent samples from the bioprocesses of 12 wastewater treatment plants (WWTPs) and build a dPMD network to characterize the core reactome of DOM. The network shows that the first step of the transformation triggers reaction cascades that diversify the DOM, but the highly overlapped subsequent reaction pathways result in similar effluent DOM compositions across WWTPs despite varied influents. Mass changes exhibit consistent gain/loss preferences (e.g., +3.995 and −16.031) but different occurrences across WWTPs. Combined with genome-centric metatranscriptomics, we reveal the associations among dPMDs, enzymes, and microbes. Most enzymes are involved in oxygenation, (de)hydrogenation, demethylation, and hydration-related reactions but with different target substrates and expressed by various taxa, as exemplified by Proteobacteria, Actinobacteria, and Nitrospirae. Therefore, a functionally diverse community is pivotal for advanced DOM degradation

Deciphering Microbe-Mediated Dissolved Organic Matter Reactome in Wastewater Treatment Plants Using Directed Paired Mass Distance

Understanding the reaction mechanism of dissolved organic matter (DOM) during wastewater biotreatment is crucial for optimal DOM control. Here, we develop a directed paired mass distance (dPMD) method that constructs a molecular network displaying the reaction pathways of DOM. It couples direction inference and PMD analysis to extract the substrate–product relationships and delta masses of potentially paired reactants directly from sequential mass spectrometry data without formula assignment. Using this method, we analyze the influent and effluent samples from the bioprocesses of 12 wastewater treatment plants (WWTPs) and build a dPMD network to characterize the core reactome of DOM. The network shows that the first step of the transformation triggers reaction cascades that diversify the DOM, but the highly overlapped subsequent reaction pathways result in similar effluent DOM compositions across WWTPs despite varied influents. Mass changes exhibit consistent gain/loss preferences (e.g., +3.995 and −16.031) but different occurrences across WWTPs. Combined with genome-centric metatranscriptomics, we reveal the associations among dPMDs, enzymes, and microbes. Most enzymes are involved in oxygenation, (de)hydrogenation, demethylation, and hydration-related reactions but with different target substrates and expressed by various taxa, as exemplified by Proteobacteria, Actinobacteria, and Nitrospirae. Therefore, a functionally diverse community is pivotal for advanced DOM degradation

A Graph Neural Network Model with a Transparent Decision-Making Process Defines the Applicability Domain for Environmental Estrogen Screening

The application of deep learning (DL) models for screening environmental estrogens (EEs) for the sound management of chemicals has garnered significant attention. However, the currently available DL model for screening EEs lacks both a transparent decision-making process and effective applicability domain (AD) characterization, making the reliability of its prediction results uncertain and limiting its practical applications. To address this issue, a graph neural network (GNN) model was developed to screen EEs, achieving accuracy rates of 88.9% and 92.5% on the internal and external test sets, respectively. The decision-making process of the GNN model was explored through the network-like similarity graphs (NSGs) based on the model features (FT). We discovered that the accuracy of the predictions is dependent on the feature distribution of compounds in NSGs. An AD characterization method called ADFT was proposed, which excludes predictions falling outside of the model’s prediction range, leading to a 15% improvement in the F1 score of the GNN model. The GNN model with the AD method may serve as an efficient tool for screening EEs, identifying 800 potential EEs in the Inventory of Existing Chemical Substances of China. Additionally, this study offers new insights into comprehending the decision-making process of DL models

A Graph Neural Network Model with a Transparent Decision-Making Process Defines the Applicability Domain for Environmental Estrogen Screening

The application of deep learning (DL) models for screening environmental estrogens (EEs) for the sound management of chemicals has garnered significant attention. However, the currently available DL model for screening EEs lacks both a transparent decision-making process and effective applicability domain (AD) characterization, making the reliability of its prediction results uncertain and limiting its practical applications. To address this issue, a graph neural network (GNN) model was developed to screen EEs, achieving accuracy rates of 88.9% and 92.5% on the internal and external test sets, respectively. The decision-making process of the GNN model was explored through the network-like similarity graphs (NSGs) based on the model features (FT). We discovered that the accuracy of the predictions is dependent on the feature distribution of compounds in NSGs. An AD characterization method called ADFT was proposed, which excludes predictions falling outside of the model’s prediction range, leading to a 15% improvement in the F1 score of the GNN model. The GNN model with the AD method may serve as an efficient tool for screening EEs, identifying 800 potential EEs in the Inventory of Existing Chemical Substances of China. Additionally, this study offers new insights into comprehending the decision-making process of DL models

Uptake and Accumulation of Polystyrene Microplastics in Zebrafish (Danio rerio) and Toxic Effects in Liver

Microplastics have become emerging contaminants, causing widespread concern about their potential toxic effects. In this study, the uptake and tissue accumulation of polystyrene microplastics (PS-MPs) in zebrafish were detected, and the toxic effects in liver were investigated. The results showed that after 7 days of exposure, 5 μm diameter MPs accumulated in fish gills, liver, and gut, while 20 μm diameter MPs accumulated only in fish gills and gut. Histopathological analysis showed that both 5 μm and 70 nm PS-MPs caused inflammation and lipid accumulation in fish liver. PS-MPs also induced significantly increased activities of superoxide dismutase and catalase, indicating that oxidative stress was induced after treatment with MPs. In addition, metabolomic analysis suggested that exposure to MPs induced alterations of metabolic profiles in fish liver and disturbed the lipid and energy metabolism. These findings provide new insights into the toxic effects of MPs on fish