Environmental Science & Technology

Hormesis is an important consideration in chemical risk assessment across most aquatic environments. Evidence suggests that hormetic responses are shaped by organismal physiological processes. Accordingly, predictions of hormesis under environmentally realistic conditions should account for physiological traits. Yet the role of these traits remains largely unexplored. This study investigated how baseline growth rate modulates antibiotic responses in the harmful cyanobacterium Microcystis and the underlying mechanisms. Results showed that at an antibiotic concentration of 200 ng L-1 (representing environmental concentration levels), slow-growing Microcystis populations exhibited an obvious growth enhancement more than 4-fold greater than that of fast-growing populations, and this enhancement was positively correlated with cellular reserves. A baseline growth rate below 0.19 day-1 emerged as the threshold below which hormetic responses were consistently observed. This study provides pioneering insights into how organisms' physiological states influence pollutant-induced hormetic responses in aquatic environments. Our findings point to a central principle for extrapolation that laboratory-derived toxicological responses may not translate directly to the field unless key physiological traits of the organisms are explicitly considered. In this regard, baseline growth rate and energy reserves may be key physiological traits for reliably predicting toxic effects at the population level. This study also makes an initial attempt to conceptualize a physiology-anchored framework for strengthening risk assessment of low-level emerging contaminants.

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with the potential to disrupt metabolic processes and contribute to carcinogenesis; however, molecular evidence linking alternative and emerging PFAS to ovarian cancer (OC) remains limited. We conducted a biomarker-based case-control study integrating exposureomics with multi-omics profiling. Plasma samples from 128 patients with OC and 128 controls were analyzed for 26 PFAS, untargeted metabolomics, lipidomics, and clinical phenotypes, while urine samples from an additional 100 patients and 100 controls underwent untargeted metabolomic and clinical profiling. Single-compound, burden-based, and mixture models were integrated with PFAS-omics association scans, cross-matrix metabolite prioritization, observational exposure-omics-clinical association-chain analyses, and repeated internal cross-validation. Higher PFAS burdens were consistently associated with OC case status, and DONA, GenX, PFNA, PFPeS, PFUdA, and PFTrDA constituted an OC-prioritized six-PFAS mixture. DONA and GenX exhibited the broadest lipid-related bridging signals, particularly across phospholipids, lysophospholipids, sphingolipids, neutral lipids, and acylcarnitines. Integrated plasma and urine analyses further identified five shared metabolites-elaidic acid, hippuric acid, betaine, corticosterone, and taurocholic acid-that provided incremental and complementary discrimination beyond reference models based on clinical variables and tumor markers in internal cross-validation. Collectively, these findings demonstrate that both alternative and emerging PFAS are associated with a distinct OC-related molecular phenotype involving coordinated lipidomic, metabolomic, and hematologic alterations, while highlighting DONA and GenX as the most prominent exposure-related signals.

Environmental exposure involves pollutant mixtures. Building on evidence that individual bisphenols alter zebrafish larval physiology and transcriptomic profiles at ng/L concentrations, we assessed the effects of specific mixtures of BPS, BPF, and BPAF without (M1) or with (M2) BPA. Experimentally, M1 and M2 induced 4.16% and 6.54% mortality in zebrafish larvae, despite the concentration addition model predicting no toxicity. Both mixtures reduced swimming-locomotor activities. These behaviors were accompanied by organ-level functional defects, as in vivo optical mapping revealed bradycardia and increased high-frequency (30-80 Hz) brain oscillations for both mixtures, while M2 additionally altered low-frequency (0.2-4 Hz) brain activity. The neurophysiological changes were accompanied by neurovascular remodeling, with in vivo two-photon microscopy revealing decreased vascular length and branching. Finally, transcriptomic analysis revealed common M1- and M2-induced alterations linked to pathways of vascular function, neuronal communication, and metabolism. M2 altered additional pathways of cardiac and neuronal signaling, consistent with the reported organ-physiology effects. We discuss the environmental relevance of mixture exposures and assess whether BPA intensifies mixture-induced responses.

Inhalation of airborne per- and polyfluoroalkyl substances (PFAS) represents an important yet understudied exposure route. While volatile organic compounds (VOCs) have been widely explored as biomarkers of toxicant exposure, their potential for early detection of PFAS-induced pulmonary toxicity remains largely unexplored. This study investigated VOC profiles in human alveolar (A549) and bronchial (BEAS-2B) epithelial cells following exposure to environmentally relevant PFAS concentrations (1, 5, and 10 ppb) for 12, 24, and 48 h. VOCs were analyzed using headspace solid-phase microextraction coupled with gas chromatography-Orbitrap mass spectrometry. We observed significant concentration- and time-dependent alterations in VOC profiles in both cell models. The principal VOCs associated with PFAS exposure include butanal, acetone, 2-butanone, 2-nonanone, (E)-2-nonenal, (E)-2-octenal, (E)-2-heptenal, 2,4-heptadienal, 2,4-nonadienal, 2,4-decadienal, 1-pentanol, and 1-hexanol. Cell type-specific regulations were observed, reflecting differential metabolic responses between transformed and non-transformed lung epithelial cells. Metabolite set enrichment analysis indicated perturbations in ketone body metabolism, consistent with altered cellular energy metabolism. Complementary qPCR analysis revealed altered expressions of CYP1A1, CYP2E1, ALDH1A1, and ALDH2, supporting a mechanistic link between PFAS exposure and oxidative stress, lipid peroxidation, and impaired aldehyde detoxification. These findings demonstrate that VOC profiling, combined with transcriptional analysis, can reveal PFAS-induced metabolic perturbations in pulmonary epithelial cells.

The anaerobic biotransformation of lindane ({gamma}-hexachlorocyclohexane), a persistent legacy pesticide and challenging subsurface contaminant was modelled using an integrated Monod biokinetics framework. Calibrated against batch experimental data, this model accounted for the dissolution of solid phase lindane and the thermodynamics of reactions involved. The study focused on a methanogenic, lindane-degrading enrichment culture (GT2) that used ethanol as the electron donor, where ethanol fermentation supplied hydrogen to support reductive dechlorination of lindane. A Dehalobacter strain was identified as the lindane-degrading organism, while a member of bacterial order Geobacterales carried out ethanol fermentation. First-order decay constants for lindane-degrading Dehalobacter were estimated from experimental data and showed variation across different growth conditions. The biokinetics model captured interactions among key microbial processes, namely lindane dechlorination, ethanol fermentation, hydrogenotrophic methanogenesis, and acetoclastic methanogenesis. Thirteen biokinetics parameters including maximum substrate utilization rates (k), half-velocity constants (Ks), and decay constants for these microbial processes were estimated through a Bayesian optimization approach. Global sensitivity analysis showed that eight parameters, primarily k and Ks, had significant linear impact on the model output, whereas responses to decay constants were more variable and potentially non-linear. This study proposed an effective modelling approach for characterizing syntrophic microbial growth within enrichment cultures and provides valuable biokinetics parameters estimates for real-world applications of anaerobic lindane bioremediation.

Biodiversity is globally threatened by chemical pollution, yet toxicity data remain unavailable for millions of species and tens of thousands of chemicals, severely limiting our ability to assess ecological impacts. Here we present TRIDENT-2, a multimodal artificial intelligence model for predicting chemical toxicity across evolutionarily diverse eukaryotic species. Trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios, TRIDENT-2 accurately predicts toxicity across Eukaryota with an average median absolute error ranging from 1.76 to 3.80. By jointly learning from chemical, biological, and experimental information, it remains accurate across broad chemical and taxonomic distances, allowing for toxicity assessment for species and chemicals beyond the current experimental evidence. Our findings demonstrate that artificial intelligence can help overcome longstanding data limitations in ecotoxicology, paving the way for improved decision-making and reducing chemical impacts on biodiversity and ecosystems.

Human metabolism converts inorganic arsenic to the pentavalent methylated species MMA(V) and DMA(V), the forms most people excrete, and the forms long read as the end of a detoxification pathway. Whether a transporter sets how much of these metabolites reaches the genome has not been tested in a mammalian cell. We expressed human AQP3, AQP7, AQP9 or AQP10 in HEK293T and MRC5-SV40 cells and measured gamma-H2AX by flow cytometry across dose series of As(V), MMA(V) and DMA(V), pairing every aquaporin with a GFP-Tubulin control and an untransfected mock acquired in the same replicate. As(V) was inactive in HEK293T cells and only weakly active in MRC5-SV40 cells to 20 micromolar, and both methylated species damaged DNA only in the millimolar range, DMA(V) being the more potent of the two in both cell lines. Against that weak baseline, AQP9 and AQP10 raised DMA(V)-induced gamma-H2AX in HEK293T cells by roughly 17 percentage points over the matched control, more than doubling the damage the same exposure produced in control cells, whereas AQP3 and AQP7 changed it not at all. AQP9 alone remained active with MMA(V). The ranking held in MRC5-SV40 fibroblasts at one-sixth the size, and within single wells the damage rose with the amount of AQP9 a cell carried while the control was flat. Aquaglyceroporins therefore discriminate among arsenic species, and AQP9 and AQP10 turn a weakly genotoxic metabolite into a substantially more genotoxic one.

Wastewater sequencing using probe-capture enrichment of public health-relevant viruses is a powerful, non-intrusive approach to monitor infectious diseases, but performance depends on sample type and processing approach. We compared two processing pipelines from wastewater influent samples collected at three timepoints across three wastewater treatment facilities. Each sample was split for parallel processing, with viruses concentrated (1) directly from whole influent using nano-sized affinity particles and (2) by settling and dewatering the solids fraction. Combined DNA/RNA extracts were prepared into libraries using a pre-commercial Illumina protocol, then probe-capture enrichment was performed with Illumina Viral Surveillance Panel (VSP) v1 (66 targets) and v2 (242 targets). Sequencing depth was increased for VSP v2 to accommodate the expanded target set. On average, 160 +/- 146 viral species were detected per sample, with wastewater fraction (sample type) explaining 19% of the variance in viral abundances. Solids contained a higher abundance of DNA viruses compared to RNA viruses, while influent exhibited the inverse. Metagenomic analysis of the VSP v2 sequencing data detected more viral species in influent (median 358) than in solids (median 184), but probe-capture enrichment produced more comparable target-virus readouts across fractions, supporting the use of either fraction for wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) has become a cornerstone of public health surveillance, yet routine regulatory monitoring remains largely focused on respiratory viruses and selecting antimicrobial resistant bacteria. Emerging foodborne parasites, such as Cyclospora cayetanensis, remain excluded from standard environmental surveillance panels despite increasing domestic outbreaks. This study presents a laboratory-based pilot protocol evaluating the feasibility of extracting and quantifying C. cayetanensis from urban wastewater across the Chicago metropolitan area. Using sample concentration via centrifugation and mechanical bead-beating, automated magnetic bead nucleic acid extraction, and digital PCR (dPCR), we demonstrate high-sensitivity detection in municipal influents and sewer nodes. These findings establish a proof-of-concept laboratory methodology to support the expansion of regulatory public health surveillance frameworks to include non-routine protozoan pathogens.

Current insecticide toxicology still lacks an integrated understanding of how lethal and sublethal exposure shape organismal performance across biologically and environmentally relevant contexts. In particular, insecticide susceptibility may vary with physiological state, environmental conditions, and recent exposure history, factors that are only partially captured by standardized toxicity assessments. Here, using Drosophila melanogaster, we systematically compared responses to three insecticides with distinct modes of action - acetamiprid, chlorantraniliprole, and deltamethrin - across developmental stages, sexes, adult ages, and ambient temperatures. Insecticide sensitivity varied substantially across these conditions, with temperature modulating toxicity in a compound-dependent manner. Notably, deltamethrin caused substantial lethality at concentrations more than 10,000-fold below approved field application rates. Beyond these context-dependent differences in acute toxicity, sublethal pre-exposure to each insecticide rapidly reduced lethality during a subsequent challenge with the same compound, with increased tolerance already detectable after 24 h. Importantly, these exposure conditions that enhanced insecticide tolerance were associated with reduced resistance to environmental challenges, including heat and nutrient starvation, revealing a potential trade-off in organismal stress resilience. Acetamiprid exposure further altered reproductive performance in a concentration-dependent manner, with higher exposure reducing egg production while increasing developmental success among the resulting offspring, such that the number of offspring reaching adulthood remained largely unchanged. Together, these findings demonstrate that insecticide susceptibility is highly context dependent and can be rapidly modified by recent exposure history. We identify short-term, compound-specific, and potentially costly tolerance as an underappreciated consequence of insecticide exposure and highlight the importance of incorporating biological context, environmental conditions, and sublethal exposure history into pesticide risk assessment.

Environmental biosurveillance requires technologies that can rapidly translate genomic information into sensitive, specific, and deployable detection tools. Here, we present the first proof-of-concept application of SENTINEL (Smart Environmental Nucleic-acid Tracking using Inference from Neural-networks for Early-warning Localization), integrating artificial-intelligence-guided target discovery, isothermal amplification, CRISPR-based detection, and environmental DNA. Using Rattus exulans, R. norvegicus, and R. rattus as a challenging model of closely related invasive vertebrates, SENTINEL achieved species-specific discrimination without detectable cross-reactivity and sensitivities of 10 aM for plasmid DNA and 10 fg/uL for genomic DNA. Selected assays also showed reproducible semi-quantitative performance and successful eDNA validation. This first application of CRISPR-based environmental biosurveillance to terrestrial invasive vertebrates demonstrates a transferable framework for converting sequence data into field-deployable assays, with potential applications across biosecurity, conservation, agriculture, and environmental monitoring.

Current environmental biosurveillance infrastructure excels at tracking known biological threats but is limited in its ability to proactively identify potential pre-emergent pathogens (PEPs). To address this gap, we developed a microfluidics-based Capture-Enrichment pipeline that isolates individual environmental cells via microdroplet encapsulation, preserving community biodiversity while enabling iterative selection against human sera. When applied to urban wastewater, this approach successfully recovered pathogens, commensals, and PEPs that fell below the detection limits of conventional amplicon sequencing. Genomic analysis revealed that wastewater-derived ESKAPE pathogens cluster closely with clinical isolates, showing that the pipeline captures clinically relevant threats. Furthermore, isolated PEPs exhibited known and emerging phenotypes associated with early-stage pathogenesis and antimicrobial resistance. These findings demonstrate that targeting PEPs can fundamentally expand the scope and quality of biosurveillance by providing a scalable strategy to identify, characterize, and close important gaps in our understanding of emerging microbial pathogens.

Long-term PM2.5 exposure is associated with increased mortality and is usually represented by annual mean concentration, which does not capture how concentrations are distributed across days. We tested whether the prespecified PM2.5 magnitude-rank index (PMRI), a summary of within-year daily concentration structure, improves mortality prediction beyond the annual mean. Daily modeled PM2.5 estimates were linked to age-adjusted mortality rates for 27,289 county-years from 3,064 U.S. counties during 2003-2011. PMRI is the largest k for which at least k days reached k micrograms per cubic meter. County-grouped 10-fold cross-validation compared models with annual mean alone versus annual mean plus PMRI, with additional geographic, temporal, cause-specific, and specification analyses. Annual mean PM2.5 had the highest standalone predictive performance. Adding PMRI increased county-held-out R-squared from 0.0802 to 0.1391 (increase in R-squared = 0.0589; 95% CI, 0.0499-0.0677) and reduced RMSE and MAE. Improvement persisted across geographic and temporal validations and all six cause-specific mortality outcomes. Within-year PM2.5 exposure structure therefore contains reproducible mortality-predictive information not captured by annual mean concentration.

Background: Privacy curtains represent a uniquely difficult infection control surface because they are frequently touched by patients, visitors, and healthcare workers, yet are difficult to disinfect using routine environmental cleaning protocols. We sought to quantify the performance of a novel automated aerosolized hydrogen peroxide system against pathogens on privacy curtains. Methods: This study was performed in an enclosed office space ({approx} 65 m^3), approximating a patient exam room. Four Bacillus atrophaeus (ATCC 9372) biological indicators (BIs) were affixed to a standard polyester privacy curtain. A single one-minute fogging phase was followed by a ten-minute contact/aeration phase (total = 11 min). Four corresponding chemical indicators measured peak airborne aerosolized hydrogen peroxide concentration, and two control BIs were placed outside of the room. Results: All four BIs demonstrated complete inactivation (no growth after seven-day incubation), representing [≥] 4-log10 spore reduction. Peak dosages reached 42 ppm nearest the aerosolized hydrogen peroxide jet outlet, with the lowest value at the near point on the back side of the curtain reaching 27 ppm (exceeding the target dosage of 25 ppm). Control exposures failed to achieve sterilization. Conclusions: A next-generation aerosolized hydrogen peroxide dry-fogging disinfection system achieved full inactivation of resistant spores on both sides of privacy curtains within a short 11-minute automated cycle.

Enteric viruses are important contaminants of surface waters and a significant burden on public health. In aquatic ecosystems, the stability of these pathogens is differentially impacted by abiotic and biotic stressors, which can exert inactivating effects. Here, we investigated the fate of two enteroviruses, echovirus 11 (E11) and coxsackievirus B5 (CVB5), and one adenovirus, human adenovirus 2 (HAdV2), in lakewater and explored the mechanisms underlying their microbial inactivation. By combining infectivity assays, genome quantification, and capsid integrity analysis, we characterized virus-specific inactivation kinetics and mechanisms and examined the relationship between infectivity loss and capsid structural integrity. We observed rapid, intermediate, and negligible decay for HAdV2, E11, and CVB5, respectively, and confirmed that microbial proteases contribute to their inactivation. In addition, we revealed that loss of capsid structural integrity drives E11 inactivation, but not HAdV2 inactivation. Genome decay did not consistently correlate with loss of infectivity, highlighting the limitations of genome-based detection for assessing the presence of infectious viruses. These findings provide new insights into the mechanisms governing virus inactivation in aquatic environments and emphasize the importance of understanding viral fate when interpreting molecular detection data to assess virus-associated microbial risks.

Experimental assessment of developmental neurotoxicity (DNT) is costly, time-consuming, and difficult to scale across large chemical inventories. New approach methodologies (NAMs), including computational approaches based on machine learning, deep learning, and large language models (LLMs), offer new ways to screen and prioritize chemicals. However, these emerging computational approaches have largely focused on predicting toxicity endpoints, with less attention to the mechanisms underlying those predictions. For DNT assessment, understanding this mechanistic basis is equally important. In this study, we integrated LLM inference with the adverse outcome pathway (AOP) framework to connect DNT predictions with plausible mechanisms. We constructed an AOP-informed benchmark linking ChEMBL target-activity measurements for 5,861 compounds to 14 AOPs representing five neurodevelopmental outcomes and evaluated ten LLMs for the prediction of molecular initiating events (MIEs), key events (KEs), key event relationships (KERs), and adverse outcomes (AOs), as well as the generation of mechanistic explanations. Explanation quality was evaluated using both a naive LLM judge and a rubric-based LLM judge. Across the zero-shot models, the highest observed macro-F1 scores were 0.818 for MIEs and 0.463 for AOs, but only 0.056 for KEs and 0.034 for KERs, identifying downstream pathway reconstruction as the principal challenge. Few-shot prompting substantially improved performance, with Gemini 3.8 Flash under four-shot prompting achieving macro-F1 scores of 0.956 for AOs, 0.813 for MIEs, 0.492 for KEs, and 0.457 for KERs. Both LLM-judge configurations indicated improved mechanistic explanation quality with few-shot examples, although strong outcome prediction did not consistently ensure recovery of the recorded pathway. Our results demonstrate the potential of AOP-informed LLMs to support DNT assessment by linking predicted neurodevelopmental outcomes to plausible biological mechanisms and enabling predictions to be evaluated at both the outcome and pathway levels.

Wastewater-based epidemiology uses both targeted and non-targeted bioanalytical approaches to detect and track disease-causing microbes in wastewater. Targeted approaches such as quantitative polymerase chain reaction (qPCR) remain widely used while sequencing-based approaches have slowly gained popularity. However, concordance between established targeted molecular assays and sequencing approaches remains incompletely characterized. To assess the validity of sequencing as an alternative to qPCR, we compared outcomes of both methods targeting five respiratory viruses in wastewater samples collected across 16 Texas cities over 4 years (May 2022-July 2026) of continuous sampling. We found positive correlations between qPCR and sequencing measurements, with both methods independently correlating with statewide clinical emergency department visits. qPCR positivity also increased with genomic sequencing coverage, and sequencing demonstrated greater potential than qPCR as an early respiratory virus outbreak detection method. Our findings support metagenomic sequencing as a sensitive and highly specific alternative to targeted qPCR for wastewater surveillance, and reinforce its utility towards long-term longitudinal monitoring of respiratory viruses.

A central question in ecology and evolution is determining when environmental challenges translate into stress and biological harm. Human-derived pollutants are often assumed to be inherently damaging; however, their effects may depend strongly on ecological and physiological context. Microplastics exemplify this complexity, eliciting heterogeneous responses across taxa rather than uniform toxicity. Here, we synthesise evidence from 14 meta-analyses encompassing 9,101 effect sizes to identify the conditions under which microplastic exposure leads to damage, neutrality, compensation, or stimulation. These syntheses reveal clear negative effects alongside frequent neutral outcomes and occasional stimulatory responses. Microplastic effects vary with particle characteristics and exposure conditions and are strongly moderated by species-specific traits and life stage. We further argue that plasticity in the gut and in behavioural and physiological traits may buffer microplastic effects. Emerging evidence also indicates that biofilm colonisation alters the physicochemical and biological properties of microplastics, potentially modifying their ecological interactions and toxicity profiles. By reframing microplastics as context-dependent challenges rather than universally toxic agents, we provide a conceptual framework for understanding animal responses under realistic ecological conditions and identifying the biological and environmental contexts in which microplastic exposure is most likely to result in harm.

The unprecedented rise in greenhouse gases such as carbon dioxide (CO2), and their detrimental effects on the atmosphere have intensified the current climate emergency. This challenge has inspired the development of novel carbon capture and utilisation (CCU) technologies, specifically microbial electrosynthesis (MES), for the bioelectrochemical fixation of CO2 into commodity chemical compounds (CCCs) using microbial biocatalysts. In this study, we systematically enriched and maintained robust mixed microbial communities of electroactive bacteria (EAB) from wastewater treatment sludge to serve as biocatalysts for MES. The MES performance of these biocatalysts under different operational conditions in bioelectrochemical systems (BES) reactors were investigated. BES reactors with enriched biocatalysts incubated and maintained under controlled conditions produced higher CCCs yields than those developed at ambient temperatures, while strict anaerobic operations further improved the MES efficiency compared to aerobic conditions. At an applied potential of -1000 mV vs Ag/AgCl, CO2 conversion reached up to 88.11%, significantly outperforming the lower potential (-600 mV) operation. These findings underscore the importance of systematic development of robust and stable biocatalysts, as well as maintaining strict anaerobic conditions, for achieving efficient MES performance, thereby demonstrating the potential of our approach for scalable industrial CCU applications.

Wastewater contains a complex mixture of nucleic acids derived from human cells, microorganisms, diets, and agricultural or industrial inputs. Metatranscriptomics has the potential to broaden the understanding of wastewater viromes, but its utility is constrained by high-abundance RNA molecules that prevent the detection of lower-abundance signals, including human pathogens. Two major sources of this interfering background signal are ribosomal RNAs (rRNAs) from various organisms, and plant-infecting tobamoviruses from human diets, which are often the most abundant RNA viruses in wastewater. These rRNA molecules and RNA genomes can account for upwards of 95% of total RNA sequences, thereby obscuring the detection of lower-abundance viruses of public health importance. This study evaluated whether combined depletion of ribosomal RNAs and genomic RNA from abundant plant tobamoviruses can improve the recovery of underrepresented viral sequences from wastewater. RNA extracts from a large municipal wastewater treatment plant in Calgary, Canada, were processed with or without probe-based depletion of rRNAs and eight abundant tobamoviruses. Metatranscriptomes revealed that depletion reduced rRNA reads by 90%. Reads assigned to tobamoviruses, which accounted for up to 25% of viral reads in untreated samples, were reduced to <2% of viral reads after depletion. This decrease in undesired RNA signal led to an order of magnitude increase in viral read signals and improved assembled viral genome quality. Furthermore, the number of reads assigned to human and animal pathogens increased 42-fold after depletion, enabling the detection of important pathogens like influenza, RSV, and hepatitis A, which were undecteable in untreated samples. Together, these findings demonstrate that targeted depletion of abundant background RNAs improves the detection of low abundance viruses, and enhances the utility of metatranscriptomic approaches for wastewater viral surveillance.