Under pressure: Daphnia magna's responses to 4-chloroaniline exposure and climate-induced temperature rise.

Nickel and binary metal mixture responses in Daphnia magna: Molecular fingerprints and (sub)organismal effects

A diverse range of emerging contaminants (ECs) coexist in aquatic environments, and a deep understanding of the interactions among these ECs is crucial for evaluating their behavior. This study investigates the interactions between polystyrene nanoplastic (PSNP), 2,2',5,5'-tetrachlorobiphenyl (PCB), 4-hydroxy-2,2',5,5'-tetrachlorobiphenyl (OHPCB), bisphenol S (BPS), phenanthrene (PHE), sulfamethazine (SM2), dichlorodiphenyltrichloroethane (DDT), and perfluorooctanesulfonate (PFOS) in water, as well as the impact of natural organic matter (NOM) on their interactions. The results indicate that pristine PSNPs are more likely to adsorb nonpolar ECs, whereas aged PSNPs preferentially adsorb polar ECs with oxygenated functional groups and that the adsorption of hydrophilic ECs on the PSNP promotes the adsorption of hydrophobic ECs. The presence of NOM significantly altered the adsorption mechanism of PSNP toward ECs. For the adsorption of ECs on pristine PSNP, the interaction between ECs and NOM facilitated their migration to pristine PSNP, and the ECs initially bound to NOM transferred to the surface of pristine PSNP. Further analysis indicates that the conformational changes of NOM upon adsorption, along with the resultant alterations in the interaction intensities of PSNP-ECs and NOM-ECs, are the key mechanisms driving the transfer of ECs. Through a "bridge" effect and a "protective layer" effect that impede the diffusion of ECs, NOM significantly enhances the adsorption stability of ECs on pristine PSNP. For aged PSNP, however, NOM reduces the adsorption efficiency of PSNP toward certain ECs, such as PCB and DDT, through a competitive adsorption mechanism. These results provide new insights for evaluating the environmental behavior of ECs.

Chapter 17 - Chemosensing technology for rapid detection of emerging contaminants

Emerging contaminants (ECs) can exert irreversible health impacts on humans, even at trace concentrations. Currently, nontargeted screening of ECs has been developed for their assessment, which requires sophisticated instrumentation. Although satellite remote sensing is a cost-effective technology for water quality assessment, accurately measuring ECs in a river-to-ocean continuum remains a significant challenge due to their trace levels. To address this challenge, we innovate a strategy utilizing satellite remote sensing to achieve high-resolution nontargeted EC screening. By employing DOM as an intermediary variable, bridging the gap between satellite remote sensing and ECs in river-to-ocean continua. DOM, including the total sum of ECs, reflects their distribution and spectral sensitivity, enabling satellite sensing to capture their unique fingerprints. In this study, this strategy has enhanced the accuracy of nontargeted EC screening from 32.2 to 95.7% using machine learning. Interpretable machine learning causal inference and SHAP models reveal that shortwave infrared (SWIR) S2-B11 is crucial for EC screening while emphasizing the importance of avoiding multicollinearity with similar SWIR band S2-B12. Additionally, the band reflectance is influenced by the proportion of polarity-related heterogeneity in the ECs. Furthermore, we developed a real-time remote sensing surveillance system featuring interactive maps for nontargeted screening of ECs and GPT-based contamination interpretation.

The Cellular Energy Allocation (CEA) methodology wasdeveloped as biomarker technique to assess the effectof toxic stress on the energy budget of testorganisms. This short-term assay is based on thebiochemical assessment of changes in the energyreserves (total carbohydrate, protein and lipidcontent) and the energy consumption (electrontransport activity). The CEA methodology was evaluatedusing Daphnia magna juveniles exposed for 96hto sublethal lindane and mercury chlorideconcentrations. The ecological relevance of the CEAassay was assessed by comparing the sub-organismalresponse with population level parameters (obtainedfrom 21 day life table experiments) such as theintrinsic rate of natural increase (rm) and themean total offspring per female. Two differentmethodologies were used to assess the effect levels:the no (lowest) observed effect level (NOAECs-LOAECs)approach and the regression-based approach. Bothtoxicants caused a significant decrease in the netenergy budget of D. magna, with a LowestObserved (Adverse) Effect Concentration (LOAEC) of0.18 mg/l and 5.6 µg/l for lindane andHgCl2,respectively. Changes in the lipid content of theorganisms were detected at toxicant concentrationslower than those affecting the total carbohydrate andprotein content. Toxicant specific effects wereobserved on the electron transport activity. Comparison of the CEA results with those of thepopulation level tests revealed that for mercury theCEA based LOAEC was a three times lower than thatbased on rm and the total brood size(18 µg/l). For lindane the CEA based LOAEC was twotimes lower than the LOAEC based on rm(0.32 mg/l) but was higher than that based on thetotal number of offspring produced (0.1 mg/l). Using the regression-based approach, EC10 valueswere calculated using three parameter sigmoid orlogistic models. Comparison between the CEA andrm based EC10 values demonstrates that forboth chemicals similar effect concentrations areobtained: the CEA-based EC10 (0.20 mg/l) forlindane is 1.5 times higher than the rm-basedEC10 threshold (0.13 mg/l), while for mercury thebiomarker-based EC10 value (9 µg/l) was 1.4times lower than the population-based EC10 value(12.5 µg/l). From these results, we suggest that the short-term CEAassay may be useful for predicting long-term effectsat the population level. The consequences of theobserved effects on the energy budget of the testorganism are discussed in the context of the effectsemerging at the population and community level.

The cellular energy allocation (CEA) methodology was used to assess the adverse effects of toxic stress on the energy budget of test organisms. This biochemical assay is quantified by determining changes in the available energy reserves, Ea (total carbohydrate, protein, and lipid content) and the energy consumption, Ec (electron transport activity). The CEA methodology was fully explored by using neonates of Daphnia magna exposed for 96 h to six model toxicants (CdCl2, K2Cr2O7, tributyltin chloride, linear alkylbenzene sulfonic acid, sodium pentachlorophenolate, and 2,4-dichlorophenoxyacetic acid). To evaluate the ecological relevance of the CEA parameter, we compared the suborganismal responses with population-level parameters (obtained from 21-d life-table experiments) such as the intrinsic rate of natural increase (rm) and the mean total offspring per female. The observed reductions in CEA values were both the result of a decrease in Ea and an increase in Ec. From all individual CEA components analyzed, the lipid reserve criterion was the most sensitive endpoint studied. Both the CEA-based lowest-observed-adverse-effect concentration (LOAEC) values and the effective concentration of 10% (EC10) values were significantly (p < 0.05) and linearly correlated with the chronic (21-d) LOAEC and EC10 values based on growth, survival, and reproduction. This relationship demonstrates the usefulness of the methodology to predict long-term effects. Furthermore, significant (p < 0.0001) sigmoid relationships between the 96-h CEA value (expressed as percentage relative to the control) and population-level effects were observed.

This review identifies understudied areas of emerging contaminant (EC) research in wastewaters and the environment, and recommends direction for future monitoring. Non-regulated trace organic ECs including pharmaceuticals, illicit drugs and personal care products are focused on due to ongoing policy initiatives and the expectant broadening of environmental legislation. These ECs are ubiquitous in the aquatic environment, mainly derived from the discharge of municipal wastewater effluents. Their presence is of concern due to the possible ecological impact (e.g., endocrine disruption) to biota within the environment. To better understand their fate in wastewaters and in the environment, a standardised approach to sampling is needed. This ensures representative data is attained and facilitates a better understanding of spatial and temporal trends of EC occurrence. During wastewater treatment, there is a lack of suspended particulate matter analysis due to further preparation requirements and a lack of good analytical approaches. This results in the under-reporting of several ECs entering wastewater treatment works (WwTWs) and the aquatic environment. Also, sludge can act as a concentrating medium for some chemicals during wastewater treatment. The majority of treated sludge is applied directly to agricultural land without analysis for ECs. As a result there is a paucity of information on the fate of ECs in soils and consequently, there has been no driver to investigate the toxicity to exposed terrestrial organisms. Therefore a more holistic approach to environmental monitoring is required, such that the fate and impact of ECs in all exposed environmental compartments are studied. The traditional analytical approach of applying targeted screening with low resolution mass spectrometry (e.g., triple quadrupoles) results in numerous chemicals such as transformation products going undetected. These can exhibit similar toxicity to the parent EC, demonstrating the necessity of using an integrated analytical approach which compliments targeted and non-targeted screening with biological assays to measure ecological impact. With respect to current toxicity testing protocols, failure to consider the enantiomeric distribution of chiral compounds found in the environment, and the possible toxicological differences between enantiomers is concerning. Such information is essential for the development of more accurate environmental risk assessment.

: Emerging Contaminants (ECs) have no existing peer-reviewed toxicity values or health standards or the existing standards are being re-evaluated due to new science. The Department of Defense has developed a three-tiered process for over-the-horizon scanning for ECs, conducting impact assessments in five DoD functional areas, and development of risk management options. The five functional areas are: (1) Environmental, Safety and Health, (2) Mission/Readiness, (3) Acquisition, (4) Operation and Maintenance of DoD Assets, and (5) Cleanup. This presentation will describe the national and international trends related to risk assessment, analytical procedures, and overall chemical management. It will then provide an update on DoD's EC Program to include the status of DoD?s EC watch and action lists, results of impact assessments, and on-going and planned risk management actions for chemicals and materials that have high risks for DoD. These risk management actions have been approved by DoD?s cross-functional EC Governance Council. Attendees will become informed about the nature of risks and issues posed by ECs and DoD?s initiative to address these risks and issues.

Performance and mechanisms of reactive substrates in constructed wetlands: Emerging contaminant removal and greenhouse gas mitigation—A comprehensive review

Emerging contaminant gradients drive deterministic assembly and cooperative networks of sediment bacterial community in China's second largest freshwater lake.

The impact of nutritional and environmental stressors on the immune response, oxidative stress and energy use of rainbow trout (Oncorhynchus mykiss).

The study investigates the role of redox conditions and food-to-microorganism (F/M) ratio on emerging contaminants (ECs) attenuation in a laboratory-scale system. A Modified Ludzack Ettinger process integrated with a polyvinyl alcohol (PVA) gel-based biofilm reactor following the anaerobic tank was employed. Twenty ECs covering a wide range of physico-chemical properties were monitored across four treatment zones - aerobic, PVA, anoxic, and anaerobic reactors to understand the role of different redox conditions in removing ECs. Overall, the system achieved an average EC removal of 87%, with 9 out of 20 compounds removed by >80% and between 50 and 80%. The ECs removal contribution followed the trend: aerobic (42.7%) > PVA (33.4%) > anoxic (25.7%) > anaerobic (19.3%). The analysis revealed higher solid-water partition coefficients (Kd) in the settled sludge compared to the treatment reactors, with values varying based on the compound's chemical properties. The mass balance analysis showed biodegradation as the primary removal mechanism. Of the total EC mass load of 3.68, 0.5 and 0.022 g d-1 was detected in the final effluent and sludge, respectively. Importantly, a strong negative correlation (r2 = 0.83) was observed between the F/M ratio and EC removal efficiency, highlighting its critical role in process optimization.

Ozone has demonstrated high efficacy in depredating emerging contaminants (ECs) during drinking water treatment. However, traditional quantitative structure-activation relationship (QSAR) models often fall short in effectively normalizing and characterizing diverse molecular structures, thereby limiting their predictive accuracy for the removal of various ECs. This study uses embedded molecular structure vectors generated by a graph neural network (GNN), combined with functional group prompts, as inputs to a feedforward neural network. A data set of 28 ECs and 542 data points, representing diverse molecular structures and physiochemical properties, was built to predict the residual rate of ECs (REC) in ozonation oxidation. Compared to traditional QSAR models, the GNN-based molecular structure embedded methods significantly improve prediction accuracy. The resulting KANO-EC model achieved an R2 of 0.97 for REC, demonstrating its ability to capture complex structural features. Moreover, KANO-EC maintains exceptional interpretability, elucidating key functional groups (e.g., carbonyls, hydroxyls, aromatic rings, and amines) involved in the oxidation mechanism. This study presents the KANO-EC model as a novel approach for predicting the ozonation removal efficiency of current and potential ECs. The model also provides valuable insights for developing efficient control strategies for ensuring the long-term safety and sustainability of drinking water supplies.

Emerging contaminant degradation and removal in algal wastewater treatment ponds: Identifying the research gaps

Occurrence and distribution of emerging contaminants in mine-impacted lake water and potential use as co-tracers of anthropogenic activity in the subarctic region, Northwest Territories, Canada