Toxicokinetic (TK) model provides a new approach to mechanistically elucidate the natural variation of metal handling strategy by adaptive and sensitive earthworm populations. Here, TK model was applied to explore the metal handling and resistance strategy of wild Metaphire californica with different historical exposure history through a 12-day re-exposure and another 12-day elimination incubation. M. californica populations showed different kinetic strategies for non-essential metals (Cd and Pb) and essential metals (Zn and Cu), which were closely related to their exposure history. M. californica from the most serious Cd-contaminated soil showed the fastest kinetic rates of both Cd uptake (K₁ = 0.78 gₛₒᵢₗ/gwₒᵣₘ/day) and elimination (K₂ = 0.23 day⁻¹), and also had the lowest Cd half-life (t₁/₂ = 3.01 day), which demonstrated the potential Cd-resistance of wild M. californica from Cd-contaminated soils. Besides, the comparative experiment showed totally different metal kinetics of laboratory Eisenia fetida from field M. californica, suggesting the impacts of distinct exposure history and species-specifical sensitivities. These findings provide a novel approach to identify and quantify resistance using TK model and also imply the risk of overlooking existing exposure background and interspecies extrapolation in eco-toxicological studies and risk assessments.

Biochar is often applied to paddy soils as a soil improver, as it retains nutrients and increases C sequestration; as such, it is a tool in the move towards C-neutral agriculture. Nitrogen (N) fertilizers have been excessively applied to rice paddies, particularly in small farms in China, because N is the major limiting factor for rice production. In paddy soils, dynamic changes in iron (Fe) continuously affect soil emissions of methane (CH₄) and carbon dioxide (CO₂); however, the links between Fe dynamics and greenhouse gas emissions, dissolved organic carbon (DOC), and rice yields following application of biochar remain unclear. The aims of this study were to examine the effects of two rates of nitrogen (N)-enriched biochar (4 and 8 t ha⁻¹ y⁻¹) on paddy soil C emissions and storage, rice yields, and Fe dynamics in subtropical early and late rice growing seasons. Field application of N-enriched biochar at 4 and 8 t ha⁻¹ increased C emissions in early and late rice, whereas application at 4 t ha⁻¹ significantly increased rice yields. The results of a culture experiment and a field experiment showed that the application of N-enriched biochar increased soil Fe²⁺concentration. There were positive correlations between Fe²⁺concentrations and soil CO₂, CH₄, and total C emissions, and with soil DOC concentrations. On the other way around, these correlations were negative for soil Fe³⁺concentrations. In the soil culture experiment, under the exclusion of plant growth, N-enriched biochar reduced cumulative soil emissions of CH₄ and CO₂. We conclude that moderate inputs of N-rich biochar (4 t ha⁻¹) increase rice crop yield and biomass, and soil DOC concentrations, while moderating soil cumulative C emissions, in part, by the impacts of biochar on soil Fe dynamics. We suggest that water management strategies, such as dry-wet cycles, should be employed in rice cultivation to increase Fe²⁺ oxidation for the inhibition of soil CH₄ and CO₂ production. Overall, we showed that application of 4 t ha⁻¹ of N-enriched biochar may represent a potential tool to improve sustainable food production and security, while minimizing negative environmental impacts.

Fluorescence spectroscopy is a commonly used technique to analyze dissolved organic matter in aquatic environments. Given the high sensitivity and non-destructive analysis, fluorescence has recently been used to study water-soluble organic carbon (WSOC) in atmospheric aerosols, which have substantial abundance, various sources and play an important role in climate change. Yet, current research on WSOC characterization is rather sparse and limited to a few isolated sites, making it challenging to draw fundamental and mechanistic conclusions. Here we presented a review of the fluorescence properties of atmospheric WSOC reported in various field and laboratory studies, to discuss the current advances and limitations of fluorescence applications. We highlighted that photochemical reactions and relevant aging processes have profound impacts on fluorescence properties of atmospheric WSOC, which were previously unnoticed for organic matter in aquatic environments. Furthermore, we discussed the differences in sources and chemical compositions of fluorescent components between the atmosphere and hydrosphere. We concluded that the commonly used fluorescence characteristics derived from aquatic environments may not be applicable as references for atmospheric WSOC. We emphasized that there is a need for more systematic studies on the fluorescence properties of atmospheric WSOC and to establish a more robust reference and dataset for fluorescence studies in atmosphere based on extensive source-specific experiments.

Mapping soil contamination enables the delineation of areas where protection measures are needed. Traditional soil sampling on a grid pattern followed by chemical analysis and geostatistical interpolation methods (GIMs), such as Kriging interpolation, can be costly, slow and not well-suited to highly heterogeneous soil environments. Here we propose a novel method to map soil contamination by combining high-resolution aerial imaging (HRAI) with machine learning algorithms. To support model establishment and validation, 1068 soil samples were collected from an arsenic (As) contaminated area in Zhongxiang, Hubei province, China. The average arsenic concentration was 39.88 mg/kg (SD = 213.70 mg/kg), with individual sample points determined as low risk (66.9%), medium risk (29.4%), or high risk (3.7%), respectively. Then, identified features were extracted from a HRAI image of the study area. Four machine learning algorithms were developed to predict As risk levels, including (i) support vector machine (SVM), (ii) multi-layer perceptron (MLP), (iii) random forest (RF), and (iii) extreme random forest (ERF). Among these, we found that the ERF algorithm performed best overall and that its prediction performance was generally better than that of traditional Kriging interpolation. The accuracy of ERF in test area 1 reached 0.87, performing better than RF (0.81), MLP (0.78) and SVM (0.77). The F1-score of ERF for discerning high-risk points in test area 1 was as high as 0.8. The complexity of the distribution of points with different risk levels was a decisive factor in model prediction ability. Identified features in the study area associated with fertilizer factories had the most important contribution to the ERF model. This study demonstrates that HRAI combined with machine learning has good potential to predict As soil risk levels.

The entirety of the sediment bed in lake Tyrifjorden, Norway, is contaminated by per- and polyfluoroalkyl substances (PFAS). A factory producing paper products and a fire station were investigated as possible sources. Fire station emissions were dominated by the eight carbon perfluoroalkyl sulfonic acid (PFSA), perfluorooctanesulfonic acid (PFOS), from aqueous film forming foams. Factory emissions contained PFOS, PFOS precursors (preFOS and SAmPAP), long chained fluorotelomer sulfonates (FTS), and perfluoroalkyl carboxylic acids (PFCA). Concentrations and profiles in sediments and biota indicated that emissions originating from the factory were the main source of pollution in the lake, while no clear indication of fire station emissions was found. Ratios of linear-to branched-PFOS increased with distance from the factory, indicating that isomer profiles can be used to trace a point source. A dated sediment core contained higher concentrations in older sediments and indicated that two different PFAS products have been used at the factory, referred to here as Scotchban and FTS mixture. Modelling, based on the sediment concentrations, indicated that 42–189 tons Scotchban, and 2.4–15.6 tons FTS mixture, were emitted. Production of paper products may be a major PFAS point source, that has generally been overlooked. It is hypothesized that paper fibres released from such facilities are important vectors for PFAS transport in the aquatic environment.

Development of non-noble metal cluster catalysts, aiming at concurrently high activity and stability, for emission control systems has been challenging because of sintering and overcoating of clusters on the support. In this work, we reported the role of well-dispersed copper nanoclusters supported on TiO₂ in CO oxidation under industrially relevant operating conditions. The catalyst containing 0.15 wt% Cu on TiO₂ (0.15 CT) exhibited a high dispersion (59.1%), a large specific surface area (381 m²/gCᵤ), a small particle size (1.77 nm), and abundant active sites (75.8% Cu₂O). The CO oxidation activity measured by the turnover frequency (TOF) was found to be enhanced from 0.60 × 10⁻³ to 3.22 × 10⁻³ molCO·molCᵤ⁻¹·s⁻¹ as the copper loading decreased from 5 to 0.15 wt%. A CO conversion of approximately 60% was still observed in the supported cluster catalyst with a Cu loading of 5 wt% at 240 °C. No deactivation was observed for catalysts with low copper loading (0.15 and 0.30 CT) after 8 h of time-on-stream, which compares favorably with less stable Au cluster-based catalysts reported in the literature. In contrast, catalysts with high copper loading (0.75 and 5 CT) showed deactivation over time, which was ascribed to the increase in copper particle size due to metal cluster agglomeration. This study elucidated the size-activity threshold of TiO₂-supported Cu cluster catalysts. It also demonstrated the potential of the supported Cu cluster catalyst at a typical temperature range of diesel engines at light-load. The supported Cu cluster catalyst could be a promising alternative to noble metal cluster catalysts for emission control systems.

Dissolved organic matter (DOM) and dissolved ions are two integral parameters to affect the environmental fate of As in different ways. Numerous studies chose surrogate of DOM, humic substances (HSs), to investigate the As complexation behavior. However, microbial secretion (protein and polysaccharide) was also considered for a great proportion in surface aquatic system, and its effect was still not fully understood. The present research distinguished the As complexation behavior with different DOM components (HSs, protein, polysaccharide and synthetic organic matter) in natural and simulated water samples. The results indicated that different DOM components exhibited various binding capacities for As. HSs showed the strongest affinity for As, followed by long-chain compounds (polysaccharide and synthetic organic matter) and proteins. In water source, HSs were probably the primary parameter for As complexation. In eutrophic water system, however, polysaccharide maybe the main DOM component to bind As. Cationic bridge function was prone to occur in the presence of HSs, but not observed in the presence of protein. PO₄³⁻ competed for binding sites with As, consequently decreasing the As complexation with all the DOM components. The research implied that a comprehensive and meticulous analyses of DOM fractions and coexist ions are the prerequisite to understanding the behavior of As (or other pollutants) in different natural aquatic systems.

In this study, we evaluated the distribution of up to forty-three antibiotics and 4 metabolites residues in different environmental compartments of an urban river receiving both diffuse and point sources of pollution. This is the first study to assess the fate of different antibiotic families in water, biofilms and sediments simultaneously under a real urban river scenario. Solid phase extraction, bead-beating disruption and pressurized liquid extraction were applied for sample preparation of water, biofilm and sediment respectively, followed by the quantification of target antibiotics by UPLC-ESI-MS/MS. Twelve antibiotics belonging to eight chemical families were detected in Suquía River samples (67% positive samples). Sites downstream the WWTP discharge were the most polluted ones. Concentrations of positive samples ranged 0.003-0.29 µg L⁻¹ in water (max. cephalexin), 2-652 µg kg⁻¹d.w. in biofilm (max. ciprofloxacin) and 2-34 µg kg⁻¹d.w. in sediment (max. ofloxacin). Fluoroquinolones, macrolides and trimethoprim were the most frequently detected antibiotics in the three compartments. However cephalexin was the prevalent antibiotic in water. Antibiotics exhibited preference for their accumulation from water into biofilms rather than in sediments (bioaccumulation factors > 1,000 L kg⁻¹d.w. in biofilms, while pseudo-partition coefficients in sediments < 1,000 L kg⁻¹d.w.). Downstream the WWTP there was an association of antibiotics levels in biofilms with ash-free dry weight, opposite to chlorophyll-a (indicative of heterotrophic communities). Cephalexin and clarithromycin in river water were found to pose high risk for the aquatic ecosystem, while ciprofloxacin presented high risk for development of antimicrobial resistance. This study contributes to the understanding of the fate and distribution of antibiotic pollution in urban rivers, reveals biofilm accumulation as an important environmental fate, and calls for attention to government authorities to manage identified highly risk antibiotics.

Fine particulate matter (PM₂.₅) has attracted extensive attention because of its baneful influence on human health and the environment. However, the sparse distribution of PM₂.₅ measuring stations limits its application to public utility and scientific research, which can be remedied by satellite observations. Therefore, we developed a Geo-intelligent long short-term network (Geoi-LSTM) to estimate hourly ground-level PM₂.₅ concentrations in 2017 in Wuhan Urban Agglomeration (WUA). We conducted contrast experiments to verify the effectiveness of our model and explored the optimal modeling strategy. It turned out that Geoi-LSTM with TOA reflectance, meteorological conditions, and NDVI as inputs performs best. The station-based cross-validation R², root mean squared error and mean absolute error are 0.82, 15.44 μg/m³, 10.63 μg/m³, respectively. Based on model results, we revealed spatiotemporal characteristics of PM₂.₅ in WUA. Generally speaking, during the day, PM₂.₅ concentration remained stable at a relatively high level in the morning and decreased continuously in the afternoon. While during the year, PM₂.₅ concentrations were highest in winter, lowest in summer, and in-between in spring and autumn. Combined with meteorological conditions, we further analyzed the whole process of a PM₂.₅ pollution event. Finally, we discussed the loss in removing clouds-covered pixels and compared our model with several popular models. Overall, our results can reflect hourly PM₂.₅ concentrations seamlessly and accurately with a spatial resolution of 5 km, which benefits PM₂.₅ exposure evaluations and policy regulations.

Ozone-induced changes in the relationship between photosynthesis (Aₙ) and stomatal conductance (gₛ) vary among species, leading to inconsistent water use efficiency (WUE) responses to elevated ozone (O₃). Thus, few vegetation models can accurately simulate the effects of O₃ on WUE. Here, we conducted an experiment exposing two differently O₃-sensitive species (Cotinus coggygria and Magnolia denudata) to five O₃ concentrations and investigated the impact of O₃ exposure on predicted WUE using a coupled Aₙ-gₛ model. We found that increases in stomatal O₃ uptake caused linear reductions in the maximum rates of Rubisco carboxylation (Vcₘₐₓ) and electron transport (Jₘₐₓ) in both species. In addition, a negative linear correlation between O₃-induced changes in the minimal gₛ of the stomatal model (g₀) derived from the theory of optimal stomatal behavior and light-saturated photosynthesis was found in the O₃-sensitive M. denudata. When the O₃ dose-based responses of Vcₘₐₓ and Jₘₐₓ were included in a coupled Aₙ-gₛ model, simulated Aₙ under elevated O₃ were in good agreement with observations in both species. For M. denudata, incorporating the O₃ response of g₀ into the coupled model further improved the accuracy of the simulated gₛ and WUE. In conclusion, the modified Vcₘₐₓ, Jₘₐₓ and g₀ method presented here provides a foundation for improving the prediction for O₃-induced changes in Aₙ, gₛ and WUE.