Sunlight is a known biocide, and photodriven inactivation is an important avenue for controlling viruses in both natural and engineered systems. However, there remain significant unknowns regarding damage to viruses by sunlight, including the impact of wavelength and viral characteristics. Herein, a systematic review of the literature and meta-analysis was conducted to identify inactivation rate constants (k-values) when exposed to solar wavelengths (280–700 nm) for common human viruses and surrogates in natural and synthetic matrices. We identified 457 k-values, with 356 for nonenveloped viruses. Extracted rate constants were transformed into UV fluence-normalized k-values to isolate the most photobiologically relevant wavelengths in the solar spectrum and reported for the first time in terms of energy, rather than time, based units. Each spectral region was assessed independently, with UVB illumination reporting the highest inactivation rates, UVA contributing to inactivation both in the presence and absence of photosensitizers, and visible light demonstrating no biocidal activity. Inactivation mechanisms are reviewed identifying knowledge gaps in translating UVC mechanisms to longer wavelengths. The data compiled in this meta-analysis can be applied to inform the environmental transport of viruses, estimate solar disinfection performance in variable light conditions, or design disinfection systems based on UVA and UVB light.

Agricultural intensification has driven global biodiversity loss through land management change. However, there is no consensus on assessing the biodiversity impacts of changes in land management practices and intensity levels using life cycle assessment (LCA). This study reviews 7 expert scoring-based (ESB) and 19 biodiversity indicator-based (BIB) LCA methods used to assess biodiversity impacts, aiming to evaluate their quality and identify research needs for incorporating land management change in LCA. Overall, BIB methods outperformed ESB methods across general criteria, especially in robustness (95% higher). BIB methods assess biodiversity impacts based on land management intensity levels, whereas ESB methods emphasize specific land management practices. Neither approach fully captures biodiversity impacts across supply chains. For future studies, it is advisable to (1) model the direct (on-farm) impacts of land management change at the midpoint level; (2) establish cause-effect relationships between key land management practices and biodiversity indicators, while distinguishing between direct (on-site) and indirect (off-site) biodiversity impacts resulting from land management change; (3) characterize land-use intensity levels with specific land management practices and include the positive impacts from agroecological practices. This Review examines LCA methods for biodiversity concerning land management practices and discusses improvements to better account for the biodiversity impacts of agricultural land management.

Hydrolysis reactions comprise a widely studied class of abiotic transformation processes that impact the environmental fate of many organic contaminants. While hydrolysis rates are typically measured in buffered solutions in order to predict transformation rates in the environment, rate constants measured in laboratory buffers are often higher than values in corresponding natural water samples. In this Perspective, we summarize these discrepancies and prior explanations provided for their occurrence. Through modeling using two linear free energy relationships (i.e., the Swain–Scott and the Bro̷nsted relationships), we propose a simple but overlooked alternative explanation─namely, that hydrolysis reactions are often much more sensitive to constituents in laboratory buffers than often assumed. We suggest that buffers employed in standard practices (e.g., at 50 mM or higher concentrations recommended by regulatory guidelines) are expected to significantly catalyze many hydrolysis reactions by acting as nucleophiles or bases. Finally, we recommend strategies to successfully measure hydrolysis rates for more accurate predictions of contaminant transformation in environmental systems.

Environmental exposure is one driving factor of chronic kidney disease (CKD), yet the intrinsic molecular mechanisms are largely unexplored. As a persistent chemical, perfluorooctanesulfonate (PFOS) is regulated due to a great potential to induce multiple diseases, including renal fibrosis, a major pathological characteristic of CKD. It is hypothesized that sodium p-perfluorous nonenoxybenzenesulfonate (OBS), a typical alternative to PFOS, may also induce renal fibrosis. We observed distinct renal fibrosis in mice exposed to OBS. Metabolomics analysis showed that Nα-acetyllysine was the primary metabolite biomarker, whose level decreased greatly due to its excessive consumption by lysyloxidase (LOX). This suppressed the miR-140-5p expression, promoting upregulation of fibroblast growth factor 9 (FGF9), which activated the PI3K/Akt signaling pathway through fibroblast growth factor receptor 3 (FGFR3), thereby enhancing proliferation and activation of fibroblasts. Supplement of Nα-acetyllysine upregulated miR-140-5p expression, reduced expressions of FGF9 and FGFR3, and eventually ameliorated OBS-induced renal fibrosis. Similarly, treatment with miR-140-5p agomir and PI3K/Akt signaling pathway inhibitor LY294002 attenuated OBS-induced renal fibrosis. Taken together, OBS caused renal fibrosis through the LOX–Nα-acetyllysine–miR-140-5p–FGF9–FGFR3–PI3K/Akt–Bad–Bcl-2–fibroblast axis. The results of this study reveal a specific molecular axis for OBS to induce renal fibrosis and call for concerns in supervising the application of OBS.

Distinct from other nontoxic phenyl-p-phenylenediamine (PPD) quinones, N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine-quinone (6PPD-Q) was recently discovered to be regioselectively metabolized to alkyl hydroxylated metabolites (alkyl–OH-6PPD-Q) in rainbow trout. It remains unknown whether the unique alkyl–OH-6PPD-Q contributes to the toxicity of 6PPD-Q. To test this, we herein synthesized chemical standards of alkyl–OH-6PPD-Q isomers and investigated their metabolic formation mechanism and toxicity. The predominant alkyl–OH-6PPD-Q was confirmed to be hydroxylated on the C₄ tertiary carbon (C₄–OH-6PPD-Q). The formation of C₄–OH-6PPD-Q was only observed in microsomal but not in cytosolic fractions of rainbow trout (O. mykiss) liver S9. A general cytochrome P450 (CYP450) inhibitor fluoxetine inhibited the formation of hydroxylated metabolites of 6PPD-Q, supporting that CYP450 catalyzed the hydroxylation. This well-explained the compound- and regio-selective formation of C₄–OH-6PPD-Q, due to the weak C–H bond on the C₄ tertiary carbon. Surprisingly, while cytotoxicity was observed for 6PPD-Q and C₃–OH-6PPD-Q in a coho salmon (O. kisutch) embryo (CSE-119) cell line, no toxicity was observed for C₄–OH-6PPD-Q. To further confirm this under physiologically relevant conditions, we fractionated 6PPD-Q metabolites formed in the liver microsome of rainbow trout. Cytotoxicity was observed for the fraction of 6PPD-Q, but not the fraction of C₄–OH-6PPD-Q. In summary, this study highlighted the C₄ tertiary carbon as the key moiety for both metabolism and toxicity of 6PPD-Q and confirmed that alkyl hydroxylation is a detoxification pathway for 6PPD-Q.

N-(1,3-Dimethylbutyl)-N′-phenyl-p-phenylenediamine-quinone (6PPD-Q), the tire rubber-derived transformation product of 6PPD, was recently discovered to cause the acute mortality of coho salmon (Oncorhynchus kisutch). Aiming to identify potential replacement antiozonants for 6PPD that do not produce toxic quinones, seven PPD-quinones with distinct side chains were synthesized to investigate their structure-related toxicities in vivo using rainbow trout (Oncorhynchus mykiss). While 6PPD-Q exerted high toxicity (96 h LC₅₀ = 0.35 μg/L), toxicity was not observed for six other PPD-quinones despite their similar structures. The fish tissue concentrations of 6PPD-Q after sublethal exposure (0.29 μg/L) were comparable to the other PPD-quinones, which indicated that bioaccumulation levels were not the reason for the selective toxicity of 6PPD-Q. Hydroxylated PPD-quinones were detected as the predominant metabolites in fish tissue. Interestingly, a single major aromatic hydroxylation metabolite was detected for the alternate PPD-quinones, but two abundant OH-6PPD-Q isomers were detected for 6PPD-Q. MS² spectra confirmed that hydroxylation occurred on the alkyl side chain for one isomer. The structurally selective toxicity of 6PPD-Q was also observed in a coho salmon (CSE-119) cell line, which further supports its intrinsic toxicity. This study reported the selective toxicity of 6PPD-Q and pinpointed the possibility for other PPDs to be applied as potential substitutes of 6PPD.

Copper (Cu) has long been a concern for human health. While previous studies have explored the toxic effects of Cu, no study is available on the relationship between the Cu redox state transformation and biotoxicity in higher organisms. In this study, we explored the gut and liver toxicity caused by the overflow of Cu(I) at low doses of Cu exposure. Here, we first elucidated the digestive and metabolic systems as the main toxic target sites by a systematic epidemiological analysis. Then, ICP-MS analysis verified that the gut and liver were the top two Cu-high-accumulated organs in zebrafish exposed to 10 and 100 μg/L waterborne Cu for 72 h. In-situ Cu(I) and Cu(II) imaging techniques demonstrated that exogenous Cu(II) was converted to Cu(I) in the zebrafish gut. Furthermore, transcriptomic sequencing revealed that the high overflow of Cu(I) induced gut toxicity by cell cycle arrest in the G phase. However, the substantial accumulation of Cu(I) disrupted the metabolism of energy source nutrients and energy supply, leading to hepatic toxicity. This study provides new insights into the toxic mechanism based on Cu redox state and emphasizes the health risks associated with Cu exposure in the digestive and metabolic systems.

Environmental DNA (eDNA) is increasingly used for species detection and biodiversity monitoring in estuary and marine environments. The dynamic nature of these environments affects eDNA distribution relative to its source organisms, complicating the interpretation of eDNA observations and challenging the field sampling design. Here, an eDNA fate and transport model, built on an ocean model with Lagrangian particle tracking, provided a spatiotemporal estimate of the rapidly diluted eDNA shed by rare targets in an estuary environment before sampling. Based on the predicted particle densities, over 70% of the preselected stations detected the target eDNA. Despite potential variations in source strength and patchy distributions, the model explained approximately 40% of the observed variation in eDNA abundance; by comparison, eDNA concentration was uncorrelated with straight-line distance from the source or with a simplified oceanographic model. Our study revealed the extent of advective transport in shaping eDNA distribution and abundance and demonstrated the utility of ocean models and particle tracking in integrating marine eDNA observations with degradation, transport, and dilution processes; thus, it suggests broader applications to enhance understanding of eDNA signals and dispersal and optimize sampling strategies in other estuarine or marine environments.

Recent evidence suggests that there is a major switch in coastal seafloor microbial ecology already at a mildly deteriorated macrofaunal state. This knowledge is of critical value in the management and conservation of the coastal seafloor. We therefore aimed to determine the relationships between seafloor microbiota and macrofauna on a regional scale. We compared prokaryote, macrofauna, chemical, and geographical data from 1546 seafloor samples, which varied in their exposure to aquaculture activities along the Norwegian and Icelandic coasts. We found that the seafloor samples contained either a network centralized by a sulfur oxidizer (42.4% of samples, n = 656) or a network centralized by an archaeal ammonium oxidizer (44.0% of samples, n = 681). Very few samples contained neither network (9.8% of samples, n = 151) or both (3.8% of samples, n = 58). Samples with a sulfur oxidizer network had a 10-fold higher risk of macrofauna loss (odds ratios, 95% CI: 9.5 to 15.6), while those with an ammonium oxidizer network had a 10-fold lower risk (95% CI: 0.068 to 0.11). The sulfur oxidizer network was negatively correlated to distance from Norwegian aquaculture sites (Spearman rho = −0.42, p < 0.01) and was present in all Icelandic samples (n = 274). The ammonium oxidizer network was absent from Icelandic samples and positively correlated to distance from Norwegian aquaculture sites (Spearman rho = 0.67, p < 0.01). Based on 356 high-quality metagenome-assembled genomes (MAGs), we found that bicarbonate-dependent carbon fixation and low-affinity oxygen respiration were associated with the ammonium oxidizer network, while the sulfur oxidizer network was associated with ammonium retention, sulfur metabolism, and high-affinity oxygen respiration. In conclusion, our findings highlight the critical roles of microbial networks centralized by sulfur and ammonium oxidizers in mild macrofauna deterioration, which should be included as an essential part of seafloor surveillance.

It has been previously observed that rainwater input into paddy rice soils reduced the level of grain-borne arsenic, and it is hypothesized that a Fenton-like reaction triggered by interaction between rainwater-borne hydrogen peroxide and ferrous iron in paddy soils is responsible for microbially mediated impediment of As uptake by rice plants. However, this hypothesis remains untested. This study tested the hypothesis through mesocosm experiments, confirming that rainwater-borne hydrogen peroxide triggered hydroxyl radical (^•OH) generation, elevating soil redox potential, and oxidizing arsenite to less phytoavailable arsenate in soil porewater, thereby reducing As uptake by rice and As accumulation in rice grain. Comparison between two crops of rice cultivation with different fluxes of rainwater-borne hydrogen peroxide confirms that seasonal rainfall variation has an impact on accumulation of rice grain-borne arsenic, with paddy soil receiving more rainfall having a lower arsenic concentration in the rice grain compared to that receiving less rainfall. Using China’s major rice-producing region as an example, it is demonstrated that spatial variation in rainfall regime could impact the geographical distribution of rice grain-borne As at a national scale. The findings have implications for the assessment and management of the environmental risk from arsenic-contaminated rice grains.

Phthalate exposure is linked to asthma and allergic symptoms, yet their individual and combined effects on symptoms and inflammatory biomarkers, type 2 (T2) and non-T2, remain unexplored. This study examined the association of phthalate metabolites with allergic symptoms (wheeze, allergic rhinoconjunctivitis, and eczema), T2 biomarker (fraction of exhaled nitric oxide (FeNO), blood eosinophil count, and total immunoglobulin E (IgE)), and non-T2 biomarker (absolute neutrophil count (ANC)) and also their association with oxidative stress biomarkers, such as 4-hydroxynonenal, hexanoyl-lysine, and 8-hydroxy-2-deoxyguanosine. Ten urinary phthalate metabolites were measured using UPLC-MS/MS in 421 children (aged 9–12 years) from The Hokkaido Cohort, Japan. Symptoms were defined using the International Study of Asthma and Allergies in Childhood questionnaire, and biomarkers were measured in blood. Logistic regression assessed individual metabolites, while quantile-g computation and Bayesian kernel machine regression analyzed mixture effects on binary outcomes. Individual analysis showed that MnBP (mono-n-butyl phthalate) was positively associated with allergic rhinoconjunctivitis and eosinophil ≥ 300 cells/μL, while ∑DBP (dibutyl phthalate) and OH-MiNP (mono-hydroxy-isononyl phthalate) were linked with FeNO ≥ 35 ppb. DEHP (di(2-ethylhexyl) phthalate) metabolites were associated with a high prevalence of blood eosinophils ≥ 300 cells/μL. We found a positive association between phthalates and oxidative stress markers, but no link was observed between oxidative stress and inflammatory markers. Mixture analysis identified MnBP as a major contributor to the high FeNO level, with di-n-butyl phthalate (DnBP) and DEHP metabolites contributing to eosinophil count ≥ 300 cells/μL and ANC ≥ 4400 cells/μL. These findings suggest that phthalate exposure from DnBP and DEHP is associated with immune dysregulation by triggering both T2 and non-T2 inflammatory responses.

Polycyclic aromatic hydrocarbons (PAHs) are toxic and persistent pollutants that are widely distributed in the environment. PAHs are toxic to microorganisms and pose ecological risks. Bacteria encode enzymes for PAH degradation through specific genes, thereby mitigating PAH pollution. However, due to PAHs’ complexity, information on the global degradation potential, diversity, and associated risks of PAH-degrading microbes in soils is lacking. In this study, we analyzed 121 PAH-degrading genes and selected 33 as marker genes to predict the degradation potential within the soil microbiome. By constructing a Hidden Markov Model, we identified 4990 species carrying PAH-degrading genes in 40,039 soil metagenomic assembly genomes, with Burkholderiaceae and Stellaceae emerging as high-potential degraders. We demonstrated that the candidate PAH degraders predominantly emerged in artificial soil and farmland, with significantly fewer present in extreme environments, driven by factors such as average annual rainfall, organic carbon, and human modification of terrestrial systems. Furthermore, we comprehensively quantified the potential risks of each potential host in future practical applications using three indicators (antibiotic resistance genes, virulence factors, and pathogenic bacteria). We found that the degrader Stellaceae has significant application prospects. Our research will help determine the biosynthetic potential of PAH-degrading enzymes globally and further identify potential PAH-degrading bacteria at lower risk.

Polycyclic aromatic hydrocarbons (PAHs) are a class of ubiquitous environmental contaminants that can be remediated through physical, chemical, or biological means. Treatment strategies can lead to the formation of PAH-transformation products (PAH-TPs) that, despite having the potential for adverse ecological and human health effects, are unregulated and understudied in environmental monitoring and remediation. Unavailability of reference standards for PAH-TPs limits the ability to identify PAH-TPs by targeted methods. This study utilized suspect and nontarget screening approaches to identify PAH-TPs produced by a bacterial culture, Rhodococcus rhodochrous ATCC 21198, using liquid chromatography-high resolution mass spectrometry. Open-source tools were used to predict biotransformation products, predict potential PAH-TP structures from mass spectra, and estimate health hazards of potential PAH-TPs. The workflow developed in this study allowed for the tentative identification of 16 PAH-TPs (confidence levels 2a to 3), seven of which were not previously detected by targeted analysis. Several new potential transformation pathways for our bacterial pure culture were suggested by the PAH-TPs, including carboxylation, sulfonation and up to three hydroxylation reactions. A computational toxicity assessment indicated that the PAH-TPs shared many hazard characteristics with their parent compounds, including genotoxicity and endocrine disruption, highlighting the importance of considering PAH-TPs in future PAH studies.

Studies on source apportionment of indoor OPEs rarely involve multiple media and characteristic source markers on emissions. Herein, we present a novel framework for quantitative and integrative source appointment of indoor OPEs by integrating OPE concentrations in multimedia into equivalent indoor concentrations (EICs) and using characteristic source markers on indoor emissions. Utility was demonstrated by applying it to five types of microenvironments where the 13 OPEs were ubiquitous (indoor dust: 993–14000 ng/g; indoor air: 0.549–14.1 ng/m³). The paired dust and air samples were adopted to construct the “Measured-Paired-EICs” data set for source apportionments via positive matrix factorization (PMF). Moreover, an alternative model method for constructing EIC data sets from single-medium measurements was established to improve the applicability of the framework. Accordingly, “Modeled-Paired-EICs”, “Dust-Only”, and “Air-Only” data sets were constructed for comparison. The extracted factors exhibited consistent contributions for “Measured-Paired-EICs” and “Modeled-Paired-EICs” solutions (RSD: 0.14–5.4%), while the “Air-Only” solution identified incomplete factors and the “Dust-Only” solution showed errors of 40.6–262%. Specifically, PMF analysis resolved seven known sources and other unknown sources. Furthermore, heterogeneities in source identification and source contributions were observed across various exposure scenarios; the probabilistic carcinogenic risk of TCEP (up to 7.19 × 10^–7) was close to the acceptable level (1 × 10^–6) and demands further attention.

In Los Angeles, air pollution disproportionately impacts communities of color and low-income residents. Routine city-wide measurements of hazardous air pollutants (HAPs), of concern for health and contributing to urban air pollution, are notably lacking. In this study, we use the highest spatially resolved (∼2 km) measurements of emissions and concentrations ever reported of HAPs while covering a whole megacity and combine observations with US Census information. We observe higher concentrations and emissions of 17 measured HAPs, such as benzene, naphthalene, and p-chlorobenzotrifluoride (PCBTF), in California-designated Disadvantaged Communities (DACs) and census tracts with low-income Hispanics and Asians. These groups share an unequal burden from traffic-related emissions, with benzene, nitrogen oxides (NO_x), and carbon monoxide (CO) concentrations up to 60% higher. However, in DACs and census tracts with large Hispanic populations (>50%), we observe toluene-to-benzene emission ratios above 3, pointing to inequalities in other HAPs primarily caused by non-traffic emission sources such as industry and solvents. In these communities, regulatory inventories also significantly underestimate emissions. We find that efforts to address HAP inequalities and environmental justice concerns in Los Angeles will need to consider contributions from volatile chemical products, which represent a growing source of emissions driving inequalities in impacted communities.

Saxitoxins (STXs), a group of closely related neurotoxins, are among the most potent natural toxins known. While genes encoding STX biosynthesis have been observed in Lake Erie, the organism(s) responsible for producing STXs in the Laurentian Great Lakes have not been identified. We identified a full suite of STX biosynthesis genes in a Dolichospermum metagenome-assembled genome (MAG). The content of sxt genes suggest that this organism can produce STX, decarbamoyl and deoxy-decarbamoyl saxitoxins, and other congeners. The absence of sxtX indicates this organism is unable to produce neosaxitoxin, a potent congener. However, a distinct, lower abundance sxt operon from an unidentified organism did contain sxtX, indicating neosaxitoxin biosynthesis potential. Metatranscriptomic data confirmed STX biosynthesis gene expression. We also recovered highly similar Dolichospermum MAGs lacking sxt genes, implying gene loss or horizontal gene transfer. sxtA was detected by quantitative polymerase chain reaction during 47 of 76 sampling dates between 2015 and 2019, demonstrating higher sensitivity than metagenomic approaches. sxtA gene abundance was positively correlated with temperature and particulate nitrogen:phosphorus ratio and negatively correlated with ammonium concentration. All Dolichospermum MAGs had genes required for nitrogen fixation. Collectively, this study provides a foundation for understanding potential new threats to Lake Erie water quality.

The property of groundwater dissolved organic matter (DOM) subjected to anthropogenic groundwater recharge (AGR) might be affected by the water quality disparity between surface water and natural groundwater. However, the diverse molecular scenarios of groundwater DOM under uneven recharging levels remain largely unexplored. We combined molecular characteristics, carbon isotopic signatures of organic molecules, and end-member mixing analysis to explore the sensitivity and potential tracking capabilities of DOM to AGR along with recharging gradients. Our findings suggested that AGR enriched groundwater with diverse, saturated, labile, and sulfur-rich molecules, amplifying DOM abundance and intensity, which intensified with recharge gradients. Additionally, S-containing molecules and their indicators like CHOS% (with threshold values of 7.82%) exhibited high sensitivity and predictive power for AGR recognition. The major signatures (diversity, saturated degree, and stability) indicated by ¹³C-containing molecules were similar to the whole molecular pool. Notably, specific molecules (C₁₂H₁₀O₅S and C₁₅H₁₆O₁₂), although not detected in all groundwater samples, exhibit robust stability or favorable solubility, rendering them potential candidates as AGR-sensitive molecules. The R_13C/12C ratio of ¹³C-containing C₁₉H₂₄O₅ emerged as the most robust tracer, exhibiting a strong correlation with the recharge ratio and the smallest deviation from the theoretical mixing line, signifying its optimal suitability for precise groundwater DOM source apportionment. This study offers novel insights into AGR impacts and contributes to fostering a harmonious balance between human activities and water resource sustainability.

The application of aqueous film-forming foams (AFFFs) has caused considerable per- and polyfluoroalkyl substances (PFAS) pollution in the environment. Soil serves as a long-term source of PFAS for the adjacent groundwater and surface water, but the lack of extractable organofluorine (EOF) mass balance data in the AFFF-impacted soils may lead to an underestimation of PFAS contamination. This study analyzed ten surface soil samples from three AFFF-impacted sites in Sweden, using alkaline extraction followed by acidic extraction. The alkaline and acidic fractions were subjected to further cleanup and analyzed separately for target, suspect screening, and EOF analysis to evaluate the extraction efficiencies of different PFAS in the soil samples and reveal PFAS remaining unknown in the AFFF-impacted soils. Total target PFAS concentrations ranged from 33.0 to 2.40 × 10⁴ ng/g dry weight. Thirty-six PFAS were identified using suspect screening. Considerable amounts of zwitterionic and cationic PFAS (up to 58%) were identified in the acidic extraction fraction, while >95% of anionic PFAS were found in the alkaline extraction fraction. EOF mass balance analysis was conducted on AFFF-impacted soils for the first time. The high proportion of unexplained organofluorine (up to 65%) indicated the necessity for future investigation of the unknown PFAS in AFFF-impacted soils to comprehensively understand their fate and risk.

Perfluorooctanesulfonate (PFOS) migration from vadose zone sources to groundwater is determined by multiple interfacial retention processes and their dependency on hydrochemistry. This study investigates the impact of air–water and mineral–water interfacial retention on PFOS transport under different hydrochemical conditions to assess their adsorption magnitudes and feedback dynamics as a function of ionic strength. Flow-through experiments were conducted in unsaturated quartz and goethite-coated quartz sands equilibrated with different background electrolyte concentrations to distinguish between air–water and goethite–water interfacial adsorption contributions to PFOS retardation. Measurements of PFOS breakthrough curves at column outlets allowed tracking the differences in spatio-temporal evolution of the PFOS plumes between the two porous media. Process-based reactive transport simulations, incorporating a thermodynamic framework of mass-action reactions accounting for multiple interfacial retention processes, allowed the quantitative interpretation of physical and geochemical processes. Experimental and modeling results reveal that multiprocess retention causes nonideal PFOS transport, with plume retardation and spatio-temporal mass transfer between the different phases determined by the relative contribution of the individual retention processes and their electrostatic interplay driven by solution counterions. These findings illuminate the interplay between air–water and mineral–water sorption and emphasize the need for reactive transport simulators implementing interdependent interfacial retention processes, influenced by water chemistry conditions.

Commercial toilet bowl cleaning tablets were examined in laboratory systems to characterize their release of active halogens and their potential to form trihalomethanes (THMs) when combined with synthetic sewage. Active halogens (e.g., HOCl, HOBr, and reactive halamines) were quantified via derivatization with 1,3,5-trimethoxybenzene prior to analysis by liquid chromatography. The effects of several variables on halogen release profiles were examined, including pH, ionic strength, temperature, tank solution volume, flushing frequency, and tablet brand. Changes in pH resulted in modest or no appreciable changes in halogen release profiles. Release of active halogens increased as ionic strength decreased and as temperature increased. Tank volume, flushing frequency, and tablet brand had pronounced impacts on halogen release profiles. Maximum measured active chlorine and bromine concentrations in toilet tank water were 189 mg/L as Cl₂ and 164 mg/L as Cl₂, respectively. Active halogens persisted in toilet bowl water for >24 h. When toilet-tablet-treated water was combined with synthetic sewage, THMs formed at up to 219 ppb with bromine incorporation factors up to 2.86. Active halogens and highly brominated THMs released into wastewater from toilet tablets could have implications for downstream microbial ecology, septic system performance, and overall water quality.

The marine environment is grappling with microplastic (MP) pollution, necessitating an understanding of its distribution patterns, influencing factors, and potential ecological risks. However, the vast area of the ocean and budgetary constraints make conducting comprehensive surveys to assess MP pollution impractical. Interpretable machine learning (ML) offers an effective solution. Herein, we used four ML algorithms based on MP data calibrated to the size range of 20–5000 μm and considered various factors to construct a robust predictive ML model of marine MP distribution. Interpretation of the ML model indicated that biogeochemical and anthropogenic factors substantially influence global marine MP pollution, while atmospheric and physical factors exert lesser effects. However, the extent of the influence of each factor may vary within specific marine regions and their underlying mechanisms may differ across regions. The predicted results indicated that the global marine MP concentrations ranged from 0.176 to 27.055 particles/m³ and that MPs in the 20–5000-μm size range did not pose a potential ecological risk. The interpretable ML framework developed in this study covered MP data preprocessing, MP distribution prediction, and interpretation of the influencing factors of MPs, providing an essential reference for marine MP pollution management and decision making.

Coastal wetlands function as critical retention zones for environmental microplastics, potentially accelerating their degradation through unique hydrological conditions. This study conducted a comprehensive 24-month in situ experiment at the Chongming Dongtan National Nature Reserve, examining the weathering processes of five morphologically distinct polyethylene (PE), polypropylene (PP), and polystyrene (PS) microplastics. Quarterly analyses revealed progressive surface deterioration in all microplastics after initial exposure, followed by polymer-specific fragmentation patterns and environmental pollutant adherence. Surface elemental analysis showed rising O/C ratios, with intertidal zones exhibiting higher variance (0.0014–0.0096 vs 0.0006–0.0028 supratidal). Carbonyl index (CI) displayed fluctuating increases, with PS showing the highest CI rise (75.75%/year intertidal vs 61.77%/year supratidal). Systematic comparisons identified three weathering determinants: enhanced intertidal degradation from mechanical-photochemical synergy; spherical particles degrading faster than films via larger surface area; and polymer vulnerabilities dictating PS > PP > PE degradation rates. These findings demonstrate that microplastic weathering in coastal wetlands is collectively governed by hydrological conditions, particle morphology, and polymer composition, providing crucial quantitative parameters for assessing environmental persistence and ecological risks in these sensitive transition ecosystems.

In this study, we introduce PFASorptionML, a novel machine learning (ML) tool developed to predict solid–liquid distribution coefficients (K_d) for per- and polyfluoroalkyl substances (PFAS) in soils. Leveraging a data set of 1,274 K_d entries for PFAS in soils and sediments, including compounds such as trifluoroacetate, cationic, and zwitterionic PFAS, and neutral fluorotelomer alcohols, the model incorporates PFAS-specific properties such as molecular weight, hydrophobicity, and pK_a, alongside soil characteristics like pH, texture, organic carbon content, and cation exchange capacity. Sensitivity analysis reveals that molecular weight, hydrophobicity, and organic carbon content are the most significant factors influencing sorption behavior, while charge density and mineral soil fraction have comparatively minor effects. The model demonstrates high predictive performance, with RPD values exceeding 3.16 across validation data sets, outperforming existing tools in accuracy and scope. Notably, PFAS chain length and functional group variability significantly influence K_d, with longer chain lengths and higher hydrophobicity positively correlating with K_d. By integrating location-specific soil repository data, the model enables the generation of spatial K_d maps for selected PFAS species. These capabilities are implemented in the online platform PFASorptionML, providing researchers and practitioners with a valuable resource for conducting environmental risk assessments of PFAS contamination in soils.

The increasing amount of sewage sludge generated during wastewater treatment poses both growing management challenge and environmental issues. Sludge with many co-occurring contaminants is often destined to land application which raises concern regarding human and environmental health. It is also a good integrator in time and space and can provide valuable information on consumption pattern and change over time. Here, we have conducted suspect and nontarget screening (SNTS) in sludge from 29 wastewater treatment plants (WWTPs) covering 30% of the Swiss population. Over 500 contaminants were identified and up to 382 quantified, with concentrations ranging from a few ng/g to several thousand ng/g, which translated into total annual loads of approximately 5 g of micropollutants per Swiss citizen. The distribution of detected substances was dominated by pharmaceuticals in terms of number of compounds (>250) and personal care products in terms of concentration (e.g., 75 μg/g for linoleic acid). Homologous series analysis revealed the presence of multiple classes of surfactants among those compounds with the highest signal intensities in sludge. Principal component analysis and hierarchical clustering showed that spatial distribution of contaminants across Switzerland was not homogeneous, while Pearson correlation indicated that changes can be attributed to different anaerobic digestion times in WWTPs.

Concern over the contamination of freshwater ecosystems with carbon black (CB) is increasing. Here, the toxicity of CB to phytoplankton (Chlorella pyrenoidosa) was evaluated; upon exposure, the median effective concentration for 72 h was 23.4 mg/L. CB underwent significant photooxidation during 15 days of light irradiation, although phototransformation was generally completed by day 7. Algal growth inhibition induced by phototransformed CB (TCB) at 1 mg/L was 64.1% greater than that induced by parent CB. Mechanistically, 1) phototransformation triggered the release of highly toxic byproducts, which inhibited algal growth by 18.9%; 2) metabolomic results demonstrate that the suppression of carbon and nitrogen assimilation in algal cells induced by TCB was 13.2–53.7% greater than that induced by CB; 3) TCB exhibited reactive oxygen species production ability, which triggered more significant algal membrane damage. A full-factorial experiment (2⁶⁺¹ runs) showed that the combined effect of temperature and suspended mineral particles, as well as electrical conductivity, was the primary environmental factor that mediated CB and TCB toxicity, respectively. The predicted toxicity of CB and TCB in Taihu Lake exhibited significant regional distribution, and TCB posed a greater environmental risk in aquatic ecosystems than CB. These findings highlight the importance of particulate contaminant transformation and environmental factors when evaluating their environmental risk.

Polychlorinated biphenyls (PCBs) are pervasive pollutants that pose risks to ecosystems and human health. Microbial reductive dehalogenation plays crucial roles in attenuating PCBs, but comprehensive insights into PCB dechlorination pathways, reactivity, and governing factors are limited by the vast number of congeners and costly experimental approaches. We address this challenge by establishing a high-throughput in vitro assay approach of reductive dehalogenation (HINVARD), which increases dechlorination test throughput by 30-fold and enhances reagents and cell utilization efficiency by over 10-fold compared to conventional assay methods. Using HINVARD, we screened 61 PCB congeners across 9 enrichment cultures and 3 Dehalococcoides isolates, identifying active dechlorination of 31–44 congeners. Results showed that PCB congener properties (chlorine substitution patterns, steric hindrance, and solubility) primarily determine the dechlorination potential, leading to consistent reactivity trends across cultures. In contrast, different organohalide-respiring bacteria catalyzed distinct dechlorination pathways, preferentially removing para- or meta-chlorines. Structural modeling of reductive dehalogenases revealed unique binding orientations governing substrate specificity, offering molecular insights into these pathways. This study provides a high-efficiency strategy for investigating microbial reductive dehalogenation, yielding the first comprehensive understanding of PCB dechlorination patterns and mechanisms. These findings guide the design of tailored microbial consortia for effective PCB bioremediation.

The plastisphere is a potential contributor to global antimicrobial resistance (AMR), posing potential threats to public and environmental health. However, comprehensively quantifying the contribution of microplastics with different biodegradability to AMR is lacking. In this study, we systematically quantified AMR risk mediated by biodegradable and nonbiodegradable microplastics using abundance-based methods and a custom AMR risk ranking framework that includes antimicrobial resistance genes (ARGs) abundance, mobility, and host pathogenicity. Our results demonstrated that biodegradable microplastics exhibited higher AMR risk compared to that of nonbiodegradable plastics. Key resistance genes, including those for multidrug, bacitracin, and aminoglycoside resistance, were predominant. Machine learning analysis identified cell motility as the most significant signature associated with AMR risk, highlighting its potential role in promoting ARGs dissemination. In addition, biodegradable microplastics promoted oxidative stress and SOS responses, which likely enhanced horizontal gene transfer (HGT) and AMR. Metagenome-assembled genomes (MAGs) analysis uncovered the colocalization of microplastic degradation genes, ARGs, and virulence factors (VFs), further supporting the elevated risk in biodegradable plastisphere. The proximity of ARGs to mobile genetic elements (MGEs) suggests that microplastic degradation processes might favor ARGs mobility. These findings would contribute critical insights into AMR dissemination in the plastisphere, emphasizing the need for integrated environmental and public health strategies under the context of One Health.

Identifying primary estrogen receptor (ER) agonists in municipal sewage is essential for ensuring the health of aquatic environments. Given the complex and variable chemical composition of sewage, the predominant ER agonists remain unclear. High-resolution mass spectrometry (HRMS)-based models have been developed to predict compound bioactivity in complex matrices, but further optimization is needed to effectively bridge HRMS features with ER agonists. To address this challenge, an FT-GNN (fragmentation tree-based graph neural network) model was proposed. Given limited data and class imbalance, data augmentation was performed using model predictions within the applicability domain (AD) and oversampling technique (OTE). Model development results demonstrated that integrating the FT-GNN with data augmentation improved the balanced accuracy (bACC) value by 6%–31%. The developed model, with a high bACC to identify more true ER agonists, efficiently classified tens of thousands of unidentified HRMS features in sewage, reducing postprocessing workload in nontargeted screening. Analysis of ER agonist transformation during sewage treatment revealed the anaerobic stage as key to both their removal and formation. Estrogenic effect balance analysis suggests that α-E2 and 9,11-didehydroestriol may be two previously overlooked key ER agonists. Collectively, the development and application of the FT-GNN model are crucial advancements toward credible tracking and efficient control of estrogenic risks in water.

Natural polyphenols can be oxidized into reactive quinones, which might play a key role in the removal of specific contaminants in natural polyphenol-related advanced oxidation processes (AOPs). In this study, peracetic acid (PAA) was employed in combination with natural protocatechuic acid (PCA) to remove sulfonamide antibiotics (SAs) from water. More than 95% removal of sulfamethoxazole (SMX) and other SAs was observed in the PCA/PAA system, and neutral pH conditions (5.0–8.0) were more conducive to the removal of SMX. The PCA/PAA system exhibited a great anti-interference ability against complex water matrices. ortho-Quinone, generated from the oxidation of PCA by PAA, played a dominant role in the SMX removal. Electrons tended to transfer from SMX to the generated ortho-quinones and form covalent bonds, resulting in the production of less toxic oligomers via the overlooked polymerization pathway. A reduction in the toxicity of the SMX solution was found following treatment with the PCA/PAA system. More interestingly, several polyphenols structurally related to PCA could also facilitate SMX removal using PAA as the oxidant. Overall, this study proposes a novel strategy for developing reactive quinones dominated AOPs with robust anti-interference performance, as well as enhances the understanding of contaminant removal via an overlooked polymerization pathway in natural polyphenol-related AOPs.

Catalysts operating at low temperatures are imperative for the denitrification of flue gases from various non-electric industries. Current catalysts typically exhibit significant activity for the selective catalytic reduction of NO_x with NH₃ (NH₃–SCR) at temperatures above 100 °C. Here, we observed for the first time remarkable room-temperature NH₃–SCR activity on the surface of carbon-based catalysts. The oxidation of NO to NO₂ by surface C–O–C groups at room temperature serves as a key initial step, triggering a fast SCR reaction. The C–OH groups, as active sites, directly participate in the elementary reaction pathways of the SCR through an H migration process, significantly reducing the energy barrier of the rate-determining step to below 1 eV, thereby enabling fast SCR to occur at room temperature. The room-temperature SCR and its novel reaction mechanism reported in this study would inevitably expand the research boundaries of NO_x abatement.

High-valent iron species [Fe(V) and Fe(IV)] exhibit remarkable oxidative activity in environmental chemistry. However, the distinctions between the properties of Fe(V) and Fe(IV) remain poorly understood due to the challenges of distinguishing them. Herein, using pyrophosphate as a model ligand, we comprehensively investigated the influence of oxo-ligands on the reactivity of high-valent iron(VI, V, IV) species. An innovative strategy to selectively generate Fe(IV) using the Fe(VI)-initiated system was proposed, enabling an in-depth investigation of the interaction between Fe(IV) and pyrophosphate. The results reveal that pyrophosphate strongly inhibits Fe(V) oxidation, while it has minimal impact on the reactivity of Fe(VI) and Fe(IV). Based on ligand field theory, pyrophosphate complexation can induce iron 3d orbital resplitting, leading to spin electron rearrangement. Specifically, the hexa-coordinated Fe(V)-oxo complex ligated by pyrophosphate exhibits higher orbital energy, reducing its stability and effective collisions with contaminants, whereas, the potential Jahn–Teller distortion of the Fe(IV)-oxo complex could enhance its stability and preserve its significant reactivity. Given its selective inhibition of Fe(V) oxidation, pyrophosphate can emerge as a promising targeted quenching agent for Fe(V) species. This study provides valuable theoretical insights to guide the identification and characterization of intermediate iron species in iron-based oxidation processes.

Electrochemical pH-swing systems have demonstrated significant potential across diverse applications, including chemical production, carbon capture, and water treatment. However, conventional systems predominantly depend on costly ion exchange membranes, which are often plagued by fouling and scaling challenges. Here, we introduce an ingenious electrochemically assisted calcium silicate (EACS) system capable of achieving a rapid pH swing from 8.5 to 10 within 1 h through the in situ utilization of H⁺ and OH^– ions, eliminating the need for membranes. The EACS system incorporates a novel 3D-printed porous basket holder designed to house calcium silicate particles and a rod-shaped Ru–Ir anode. Under closed-circuit conditions, the packed calcium silicate reacts with H⁺ generated at the anode, releasing Ca²⁺ into bulk solution, while OH^– produced at the cathode accumulates, resulting in an elevated bulk pH. This mechanism enables the EACS system to achieve exceptional phosphorus recovery efficiency (88.4%–96.6%) from various waste streams, with energy consumption as low as 24.4 kWh kg P^–1. Long-term continuous flow experiments demonstrate that periodic replacement of depleted silicate minerals sustains system efficiency and stability. Furthermore, comparative analysis reveals that while carbonate and silicate minerals are functionally viable, silicate minerals exhibit superior performance in removal kinetics, product purity, and reduced carbon emissions. Notably, the effluent from the EACS system, enriched with Ca²⁺ and characterized by a high pH, exhibits potential for direct air carbon capture. The proposed EACS system offers a transformative approach to environmental remediation and industrial applications, leveraging the fundamental principle of pH-swing to open new avenues for sustainable solutions.