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Biomarkers such as functional gene mRNA (transcripts) and proteins (enzymes) provide direct proof of metabolic regulation during the reductive dechlorination (RD) of chlorinated ethenes (CEs). Yet, current models to simulate their spatiotemporal variability are not flexible enough to mimic the homologous behavior of RDase functional genes. To this end, we developed new enzyme-based kinetics to model the concentrations of CEs together with the transcript and enzyme levels during RD. First, the model was calibrated to existing microcosm data on RD of cis-DCE. The model mirrored the tceA and vcrA gene expression and the production of their enzymes in Dehalococcoides spp. Considering tceA and vcrA as homologous instead of nonhomologous improved fitting of the mRNA time series. Second, CEs and biomarker patterns were explored as a proof of concept under groundwater flow conditions, considering degraders occurring in immobile and mobile states. Under both microcosm and flow conditions, biomarker-rate relationships were nonlinear hysteretic because tceA and vcrA acted as homologous genes. The mobile biomarkers additionally undergo advective-dispersive transport, which increases the nonlinearity and makes the observed patterns even more challenging to interpret. The model offers a thorough mechanistic description of RD while also allowing simulation of spatiotemporal dynamic patterns of various key biomarkers in aquifers.

Energy-efficient wastewater treatment plants (WWTPs) utilize systems like high-rate activated sludge (A-stage) system to redirect organics from wastewater are redirected into energy-rich sludge (A-sludge). Anaerobic membrane bioreactors (AnMBRs) offer lower footprint and higher effluent quality compared to conventional digesters. In this study, the biological treatment and the filtration performances of AnMBRs for A-sludge digestion under mesophilic and thermophilic conditions were comparatively evaluated through lab-scale experiments, mass balancing and dynamic modeling. Under thermophilic conditions, a higher COD fraction of the influent sludge was converted into methane gas than under mesophilic conditions (65% versus 57%). The energy balance indicated that the surplus energy recovery under thermophilic conditions was less than the additional energy required for heating the AnMBR, resulting in a more than three-fold higher net energy recovery under mesophilic conditions. Therefore, operating an AnMBR for sludge digestion under mesophilic conditions has a higher potential to improve the energy balance in WWTPs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Researchers and practitioners have extensively utilized supervised Deep Learning methods to quantify floating litter in rivers and canals. These methods require the availability of large amount of labeled data for training. The labeling work is expensive and laborious, resulting in small open datasets available in the field compared to the comprehensive datasets for computer vision, e.g., ImageNet. Fine-tuning models pre-trained on these larger datasets helps improve litter detection performances and reduces data requirements. Yet, the effectiveness of using features learned from generic datasets is limited in large-scale monitoring, where automated detection must adapt across different locations, environmental conditions, and sensor settings. To address this issue, we propose a two-stage semi-supervised learning method to detect floating litter based on the Swapping Assignments between multiple Views of the same image (SwAV). SwAV is a self-supervised learning approach that learns the underlying feature representation from unlabeled data. In the first stage, we used SwAV to pre-train a ResNet50 backbone architecture on about 100k unlabeled images. In the second stage, we added new layers to the pre-trained ResNet50 to create a Faster R-CNN architecture, and fine-tuned it with a limited number of labeled images (≈1.8k images with 2.6k annotated litter items). We developed and validated our semi-supervised floating litter detection methodology for images collected in canals and waterways of Delft (the Netherlands) and Jakarta (Indonesia). We tested for out-of-domain generalization performances in a zero-shot fashion using additional data from Ho Chi Minh City (Vietnam), Amsterdam and Groningen (the Netherlands). We benchmarked our results against the same Faster R-CNN architecture trained via supervised learning alone by fine-tuning ImageNet pre-trained weights. The findings indicate that the semi-supervised learning method matches or surpasses the supervised learning benchmark when tested on new images from the same training locations. We measured better performances when little data (≈200 images with about 300 annotated litter items) is available for fine-tuning and with respect to reducing false positive predictions. More importantly, the proposed approach demonstrates clear superiority for generalization on the unseen locations, with improvements in average precision of up to 12.7%. We attribute this superior performance to the more effective high-level feature extraction from SwAV pre-training from relevant unlabeled images. Our findings highlight a promising direction to leverage semi-supervised learning for developing foundational models, which have revolutionized artificial intelligence applications in most fields. By scaling our proposed approach with more data and compute, we can make significant strides in monitoring to address the global challenge of litter pollution in water bodies., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Author Rinze de Vries is employed by Noria Sustainable Innovators. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Structural extracellular polymeric substances (SEPS) as valuable biopolymers, can be extracted from waste activated sludge (WAS). However, the extraction yield is typically low, and detailed information on SEPS characterizations, as well as proper treatment of the sludge after SEPS extraction, remains limited. This study aimed to optimize the conditions of heating-Na 2 CO 3 extraction process to increase the yield of SEPS extracted from WAS. Subsequently, SEPS were characterized, and, for the first time, insights into their protein composition were uncovered by using proteomics. A maximum SEPS yield of 209 mg g -1 volatile solid (VS) was obtained under optimal conditions: temperature of 90 °C, heating time of 60 min, Na + dosage of 8.0 mmol/g VS, and pH required to precipitation of 4.0, which was comparable to that from the aerobic granular sludge reported in literature. Proteomics analysis unveiled that the proteins in SEPS primarily originated from microorganisms involved in nitrogen fixation and organic matter degradation, including their intracellular and membrane-associated regions. These proteins exhibited various catalytic activities and played crucial roles in aggregation processes. Besides, the process of SEPS extraction significantly enhanced volatile fatty acid (VFA) production during the anaerobic fermentation of residual WAS after SEPS extraction. A maximum VFA yield of 420 ± 14 mg COD/g VS added was observed in anaerobic fermentation of 10 d, which was 77.2 ± 0.1 % higher than that from raw sludge. Mechanism analysis revealed that SEPS extraction not only improved WAS disintegration and solubilization but also reduced the relative activity of methanogens during anaerobic fermentation. Moreover, SEPS extraction shifted the microbial population during anaerobic fermentation in the direction towards hydrolysis and acidification such as Fermentimonas sp. and Soehngenia sp. This study proposed a novel strategy based on SEPS extraction and VFA production for sludge treatment, offering potential benefits for resource recovery and improved process efficiency., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Investigating the interaction between influent particles and biomass is basic and important for the biological wastewater treatment. The micro-level methods allow for this, such as the microscope image analysis method with the conventional ImageJ processing software. However, these methods are cost and time-consuming, and require a large amount of work on manual parameter tuning. To deal with this problem, we proposed a deep learning (DL) method to automatically detect and quantify microparticles free from biomass and entrapped in biomass from microscope images. Firstly, we introduced a "TU Delft-Interaction between Particles and Biomass" dataset containing labeled microscope images. Then, we built DL models using this dataset with seven state-of-the-art model architectures for a instance segmentation task, such as Mask R-CNN, Cascade Mask R-CNN, Yolact and YOLOv8. The results show that the Cascade Mask R-CNN with ResNet50 backbone achieves promising detection accuracy, with a mAP50 box and mAP50 mask of 90.6 % on the test set. Then, we benchmarked our results against the conventional ImageJ processing method. The results show that the DL method significantly outperforms the ImageJ processing method in terms of detection accuracy and processing cost. The DL method shows a 13.8 % improvement in micro-average precision, and a 21.7 % improvement in micro-average recall, compared to the ImageJ method. Moreover, the DL method can process 70 images within 1 min, while the ImageJ method costs at least 6 h. The promising performance of our method allows it to offer a potential alternative to examine the interaction between microparticles and biomass in biological wastewater treatment process in an affordable manner. This approach offers more useful insights into the treatment process, enabling further reveal the microparticles transfer in biological treatment systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Hydrometeorological processes are often assumed to be key drivers of plastic transport. However, the predominant focus on these factors overlooks the impact of anthropogenic factors, such as mismanaged plastic waste (MPW) on plastic transport variability. Here, we investigate the roles of both anthropogenic and hydrometeorological factors on plastic pollution in the Odaw catchment, Ghana. Data on macroplastic transport and density were collected at ten locations between December 2021 and December 2022. We tested for differences between the wet and dry seasons and applied a multiple regression analysis to examine the separate and combined impact of hydrometeorological variables (rainfall, discharge, and windspeed) on macroplastic transport. Additionally, we analyzed the spatial correlation in macroplastic transport/density with MPW and population density. Data collection involved visual counting of floating macroplastics at 10 river locations and counting litter at 9 riverbanks and land locations. Rainfall data was sourced from TAHMO (Trans-African Hydrometeorological Observatory), discharge was measured during field campaigns, and windspeed data sourced from a global climate data provider. We used globally modelled MPW estimates to represent anthropogenic factors. Contrary to previous studies, we found no seasonal differences in macroplastic pollution and only weak correlations were observed between the hydrometeorological variables and macroplastic transport. However, a strong correlation was observed between MPW and macroplastic pollution. We hypothesize that, the influence of hydrometeorological factors on macroplastic transport depend on the relative impact of anthropogenic factors. Our research highlights the limited role of hydrometeorology, showing the significant role of mismanaged plastic waste to field monitored macroplastic pollution variability in the catchment. This insight is essential for future research as it highlights the importance of holistically investigating both anthropogenic and hydrometeorological factors in explaining plastic transport and retention dynamics. This insight is essential for developing interventions that effectively address plastic pollution in catchments., Competing Interests: Declaration of competing interest The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Drinking water softening has primarily prioritized public health, environmental benefits, social costs and enhanced client comfort. Annually, over 35 billion cubic meters of water is softened worldwide, often utilizing three main techniques: nanofiltration, ion exchange and seeded crystallization by pellet softening. However, recent modifications in pellet softening, including changes in seeding materials and acid conditioning used post-softening, have not fully achieved desired flexibility and optimization. This highlights the need of an integral approach, as drinking water softening is just one step in the drinking water treatment chain, which includes ozonation, softening, biological active carbon filtration (BACF) and sand filtration among others. In addition, pellet softening is often practiced based on operator knowledge, lacking practical key reactor performance indicators (KPIs) for efficient control. For that reason, we propose a newly and improved integral mechanistic model designed to accurately predict (1) calcite removal rates in drinking water through seeded crystallization in pellet softening reactors, (2) the saturation of the filter bed in the subsequent treatment step, (3) values for the KPIs steering the softening efficiency. Our new mechanistic model integrates insights from hydrodynamics, thermodynamics, mass transfer kinetics, nucleation and reactor engineering, focussing on critical variables such as temperature, linear velocity, pellet particle size and saturation index with respect to calcite. Our model was validated with data from the Waternet Weesperkarspel drinking water treatment plant in Amsterdam, The Netherlands, but implies universal applicability for addressing industrial challenges beyond drinking water softening. The implementation of our model proposes five effective KPIs to optimize the softening process, chemical usage, and reactor design. The advantage of this model is that it eliminates the application of numerical methods and fills a significant gap in the field by providing predictions of the carry-over (i.e., the produced CaCO 3 fines leaving the fluidized bed) from water softening practices. With our model, the calcium removal rate is predicted with an average standard deviation (SD) of 40 % and the consequential clogging prediction of the BACF bed with an average SD of 130 %. Ultimately, our model provides crucial insights for operational management and decision-making in drinking water treatment plants, steering towards a more circular and environmentally sustainable process., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Nitrous oxide (N 2 O) emissions from wastewater treatment plants (WWTPs) exhibit significant seasonal variability, making accurate predictions with conventional biokinetic models difficult due to complex and poorly understood biochemical processes. This study addresses these challenges by exploring data-driven alternatives, using long short-term memory (LSTM) based encoder-decoder models as basis. The models were developed for future integration into a model predictive control framework, aiming to reduce N 2 O emissions by forecasting these over varying prediction horizons. The models were trained on 12 months and tested on 3 months of data from a full-scale WWTP in Amsterdam West, the Netherlands. The dataset encompasses seasonal peaks in N 2 O emissions typical for winter and spring months. The best performing model, featuring a 256-256 LSTM architecture, achieved the highest accuracy with test R 2 values up to 0.98 across prediction horizons spanning 0.5 to 6.0 h ahead. Feature importance analysis identified past N 2 O emissions, influent flowrate, NH 4 + , and dissolved oxygen (DO) in the aerobic tank as most significant inputs. The observed decreasing influence of historical N x O emissions over extended prediction horizons highlights the importance and significance of process variables for the model's performance. The model's ability to accurately forecast short-term N 2 O emissions over extended prediction horizons highlights the importance and significance of process variables for the model's performance. The model's ability to accurately forecast short-term N 2 O emissions and capture immediate trends highlights its potential for operational use in controlling emissions in WWTPs. Further research incorporating diverse datasets and biochemical process inputs related to microbial activities in the N 2 O production pathways could improve the model's accuracy for longer forecasting horizons. These findings advocate for hybridising deep learning models with biokinetic and mechanistic insights to enhance prediction accuracy and interpretability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

While air stripping combined with acid scrubbing remains a competitive technology for the removal and recovery of ammonia from wastewater streams, its use of strong acids is concerning. Organic acids offer promising alternatives to strong acids like sulphuric acid, but their application remains limited due to high cost. This study proposes an integration of air stripping and organic acid scrubbing with bipolar membrane electrodialysis (BPMED) to regenerate the organic acids. We compared the energy consumption and current efficiency of BPMED in recovering dissolved ammonia and regenerating sulphuric, citric, and maleic acids from synthetic scrubber effluents. Current efficiency was lower when regenerating sulphuric acid (22 %) compared to citric (47 %) and maleic acid (37 %), attributable to the competitive proton transport over ammonium across the cation exchange membrane. Organic salts functioned as buffers, reducing the concentration of free protons, resulting in higher ammonium removal efficiencies with citrate (75 %) and malate (68 %), compared to sulphate (29 %). Consequently, the energy consumption of the BPMED decreased by 54 % and 35 % while regenerating citric and maleic acids, respectively, compared to sulfuric acid. Membrane characterisation experiments showed that the electrical conductivity ranking, ammonium citrate > ammonium malate > ammonium sulphate, was mirrored by the energy consumption (kWh/kg-N recovered) ranking, ammonium sulphate (15.6) < ammonium malate (10.2) < ammonium citrate (7.2), while the permselectivity ranking, ammonium sulphate > ammonium citrate > ammonium malate, aligned with calculated charge densities. This work demonstrates the potential of combining organic acid scrubbers with BPMED for ammonium recovery from wastewater effluents with minimum chemical input., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Gladys Mutahi reports financial support was provided by Netherlands Enterprise Agency. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Release of persistent and potentially toxic per- and polyfluoroalkyl substances (PFAS) and other halogenated compounds into the aqueous environment is an emerging issue and advanced treatment methods are needed for their removal from contaminated water. Destructive treatment methods for PFAS exist, but there is a risk of incomplete degradation, resulting in creation of transformation products during treatment. This study assessed the potential of electrochemical oxidation (EO) for destruction of PFAS and other halogenated compounds, and their transformation products. Suspect and non-target screening were used to explore the chemical space of these samples and identify compounds present before and after the treatment, including transformation products. In total, 21 PFAS classes and 53 individual PFAS were identified using suspect and non-target screening, with confidence level (CL) 3d or higher. Two new classes of PFAS (FASHN and MeOH-FASA) were discovered for the first time. Suspect screening of PFAS revealed that hydro-substituted and ether PFAS could be formed during EO. A total of 12 chlorinated and two brominated compounds were also detected and confirmed with CL 1-3, with six compounds determined to be transformation products. Formation of ammonium oxidation byproducts was hypothesized as being responsible for most identified transformation products formed during EO., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Shower systems provide unique environments that are conducive to biofilm formation and the proliferation of pathogens. The water heating temperature is a delicate decision that can impact microbial growth, balancing safety and energy consumption. This study investigated the impact of different heating temperatures (39 °C, 45 °C, 51 °C and 58 °C) on the shower hose biofilm (exposed to a final water temperature of 39 °C) using controlled full-scale shower setups. Whole metagenome sequencing and metaproteomics were employed to unveil the microbial composition and protein expression profiles. Overall, the genes and enzymes associated with disinfectant resistance and biofilm formation appeared largely unaffected. However, metagenomic analysis revealed a sharp decline in the number of total (86,371 to 34,550) and unique genes (32,279 to 137) with the increase in hot water temperature, indicating a significant reduction of overall microbial complexity. None of the unique proteins were detected in the proteomics experiments, suggesting smaller variation among biofilms on the proteome level compared to genomic data. Furthermore, out of 43 pathogens detected by metagenomics, only 5 could actually be detected by metaproteomics. Most interestingly, our study indicates that 45 °C heating temperature may represent an optimal balance. It minimizes active biomass (ATP) and reduces the presence of pathogens while saving heating energy. Our study offered new insights into the impact of heating temperature on shower hose biofilm formation and proposed optimal parameters that ensure biosafety while conserving energy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Research into land subsidence caused by groundwater withdrawal is hindered by the availability of measured heads, subsidence, and forcings. In this paper, a parsimonious, linked data-driven and physics-based approach is introduced to simulate pumping-induced subsidence; the approach is intended to be applied at observation well nests. Time series analysis using response functions is applied to simulate heads in aquifers. The heads in the clay layers are simulated with a one-dimensional diffusion model, using the heads in the aquifers as boundary conditions. Finally, simulated heads in the layers are used to model land subsidence. The developed approach is applied to the city of Bangkok, Thailand, where relatively short time series of head and subsidence measurements are available at or near 23 well nests; an estimate of basin-wide pumping is available for a longer period. Despite the data scarcity, data-driven time series models at observation wells successfully simulate groundwater dynamics in aquifers with an average root mean square error (RMSE) of 2.8 m, relative to an average total range of 21 m. Simulated subsidence matches sparse (and sometimes very noisy) land subsidence measurements reasonably well with an average RMSE of 1.6 cm/year, relative to an average total range of 5.4 cm/year. Performance is not good at eight out of 23 locations, most likely because basin-wide pumping is not representative of localized pumping. Overall, this study demonstrates the potential of a parsimonious, linked data-driven, and physics-based approach to model pumping-induced subsidence in areas with limited data., (© 2024 The Author(s). Groundwater published by Wiley Periodicals LLC on behalf of National Ground Water Association.)

The inflow and infiltration (I&I) is an issue for many urban sewer networks (USNs), which can significantly affect system functioning. Placing sensors within the USNs is a typical approach to detect large I&I event, but deploying a limited number of sensors while achieving maximum detection reliability is challenging. While some methods are available for sensor placement, they are generally heuristic search-based methods (HSBMs) and hence the resultant sensor placement strategies (SPSs) are variable over different algorithm runs or parameterizations. This paper develops a new deterministic two-stage clustering method for SPS optimization based on information entropy. Within the first stage, the Spectral Clustering method is applied to assign USN nodes to different clusters according to their joint entropy. In the second stage, the topology structure property is considered to enable further clustering for improving detection reliability. Average I&I detection reliability is used to select clusters and the optimal SPS is identified by maximizing joint entropy of all possible solutions where a single sensor is assigned to each selected cluster. The proposed method and two existing HSBMs are applied to a real USN and their performance is compared. The results obtained show that: (i) a strong correlation coefficient R (R > 0.95) is observed between joint entropy and SPS's detection reliability, which has not been revealed before, (ii) the proposed method consistently outperforms the other two approaches in efficiently offering SPSs with about 7-15 % higher detection reliability, and (iii) the proposed method provides the optimal SPS in a deterministic manner, which makes it attractive for engineering applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

High Pressure Hydrogenotrophic Denitrification (HPHD) provided a promising alternative for efficient and clean nitrate removal. In particular, the denitrification rates at low temperature could be compensated by elevated H 2 partial pressure. However, nitrite reduction was strongly inhibited while nitrate reduction was barely affected at low temperature. In this study, the nitrate reduction gradually recovered under long-term low temperature stress, while nitrite accumulation increased from 0.1 to 41.0 mg N/L. The activities of the electron transport system (ETS), nitrate reductase (NAR), and nitrite reductase (NIR) decreased by 45.8 %, 27.3 %, and 39.3 %, respectively, as the temperature dropped from 30 °C to 15 °C. Real time quantitative PCR analysis revealed that the denitrifying gene expression rather than gene abundance regulated nitrogen biotransformation. The substantial nitrite accumulation was attributed to the significant up-regulation by 54.7 % of narG gene expression and down-regulation by 73.7 % of nirS gene expression in hydrogenotrophic denitrifiers. In addition, the nirS-gene-bearing denitrifiers were more sensitive to low temperature compared to those bearing nirK gene. The dominant populations shifted from the genera Paracoccus to Hydrogenophaga under long-term low temperature stress. Overall, this study revealed the microbial mechanism of high nitrite accumulation in hydrogenotrophic denitrification at low temperature., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study quantifies the field hydraulic performance of a dual-functionality landfill cover, combining microbial methane oxidation with water diversion using a capillary barrier. The investigated 500 m 2 test field, constructed on a landfill in the Netherlands, consisted of a cover soil optimised for methane oxidation, underlain by a sandy capillary layer and a gravelly capillary block. Outflows from these layers were measured between 2009 and 2023. Average precipitation was 848 mm/a, evapotranspiration, diverted infiltration and breakthrough amounted to 504 (59.4 %), 282 (33.3 %) and 62 (7.3 %) mm/a, respectively. On average, the capillary barrier diverted 82 % of the inflow into the capillary layer. Breakthrough occurred mainly from October to March when evapotranspiration was low and the maximum water storage capacity of the cover soil was reached. During this period, inflow into the capillary barrier exceeded its diversion capacity, caused by the relatively high hydraulic conductivity of the cover soil due to its optimisation for gas transport. The diversion capacity declined drastically in the year after construction and increased again afterwards. This was attributed to suffusion of sand from the capillary layer into the capillary block and subsequent washout to greater depths or the influence of iron precipitates at the bottom of the capillary layer. The effect of a more finely grained methane oxidation layer on the hydraulic and methane oxidation performance should be investigated further. These measures could further improve the combined performance of the dual functionality landfill cover system under the given conditions of a temperate climate., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Iron (Fe 2+ ), manganese (Mn 2+ ), and ammonium (NH 4 ) oxidation processes were studied in three single media and three dual media full-scale rapid sand filters (RSFs) using reactive transport modelling (RTM) in PHREEQC and parameter estimation using PEST. Here, we present the insights gained into the spatial distribution of Fe and Mn mineral coatings in RSFs and its influence on the oxidation sequence and rates. Fe + ) oxidation processes were studied in three single media and three dual media full-scale rapid sand filters (RSFs) using reactive transport modelling (RTM) in PHREEQC and parameter estimation using PEST. Here, we present the insights gained into the spatial distribution of Fe and Mn mineral coatings in RSFs and its influence on the oxidation sequence and rates. Fe 2+ and Mn 2+ oxidation predominantly occurred simultaneously in the RSFs, contrary to the expected sequential oxidation based on Gibbs free energy calculations. During backwashing, RSF grains become fully mixed, which initiates heterogeneous Mn 2+ oxidation on Mn-coated grains that end up in the top layer. The resulting grains have a mixed Fe/Mn mineral coating, which is limiting heterogeneous Mn 2+ oxidation due to the limited Mn mineral surface available. Mixed coatings did not seem to affect Fe 2+ oxidation rates, instead oxidation rates were increasing at lower pH. We found that RSFs can be designed to spatially separate Fe 2+ and Mn 2+ oxidation. The RSF needs to consist of two layers with varying density to inhibit mixing and complete Fe 2+ oxidation. The RSF needs to consist of two layers with varying density to inhibit mixing and complete Fe 2+ oxidation is complete, and thus the ideal intersection depth of the two layers. A novel perspective is provided on how mineral coating distribution in single and dual media filters influence removal rates and the sequence of oxidation, which contributes to the design of more efficient groundwater filters.2+ oxidation is complete, and thus the ideal intersection depth of the two layers. A novel perspective is provided on how mineral coating distribution in single and dual media filters influence removal rates and the sequence of oxidation, which contributes to the design of more efficient groundwater filters., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Iron-based adsorbents are commonly used to remove arsenic (As) from water for drinking water purposes. Here, we study the role of biological As(III) oxidation on iron-based adsorbents in filters and its effect on overall As uptake. A lab-scale filter with iron oxide coated sand (IOCS), a commonly used adsorbent, was operated with water containing As(III) and As(V), while water samples were taken periodically over its height. As(III) oxidation initiated after approximately 10 days and increased to a first order rate constant of 0.09 s -1 after 57 days resulting in full oxidation of As(III) in <50 s. Consequently, the filter shifted from an As(III) to an As(V) adsorbing filter. Oxidation was not observed after inhibiting the microbial activity using sodium azide confirming its biogenic nature. This implies that As(III) oxidizing biomass can grow on iron-based adsorbents in water filters without requiring inoculation. As the experimental conditions were similar to full-scale As treatment plants, we believe that biological As(III) oxidation is widely overlooked in these systems. Occurrence of biological oxidation is, however, beneficial for removal, as at pH <8 the adsorption capacity for As(V) can be up to 10-fold higher than for As(III). With these new insights, arsenic treatment using iron-based adsorbents can be further optimized. We suggest a more robust new design with a biological active As(III) oxidizing top layer and an As(V) adsorbing bottom layer., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The performance of time series models is assessed using synthetic head series simulated with a numerical model that solves Richards' equation for variably saturated flow. Heads were simulated in a homogeneous unconfined aquifer between two parallel canals; measured daily precipitation and potential evaporation are specified at the land surface and root water uptake is simulated. The head response to a precipitation event is nonlinear and depends on the saturation degree and rainfall before and after the precipitation event while evaporation reduction occurs during summers. Synthetic series were generated for 27 years and three different soil types; the unsaturated zone thickness varies between 0 and >5 m. The synthetic head series were simulated with a linear and nonlinear time series model. Performance of a linear time series model with four parameters, using a scaled Gamma response, gave R 2 values ranging from 0.67 to 0.96. The nonlinear time series model with five parameters simulates recharge using a root zone reservoir after which the head response to recharge is simulated with a scaled Gamma response function. The nonlinear time series model was able to simulate all synthetic head series very well with R 2 values above 0.9 for almost all models. The head response of the nonlinear model to a single precipitation event compares well to the response of the variably saturated groundwater model. The provided scripts may be used to simulate synthetic head series for other climates or for systems with additional complexity to assess the performance of other data-driven models., (© 2024 The Authors. Groundwater published by Wiley Periodicals LLC on behalf of National Ground Water Association.)

Data-driven metamodels reproduce the input-output mapping of physics-based models while significantly reducing simulation times. Such techniques are widely used in the design, control, and optimization of water distribution systems. Recent research highlights the potential of metamodels based on Graph Neural Networks as they efficiently leverage graph-structured characteristics of water distribution systems. Furthermore, these metamodels possess inductive biases that facilitate generalization to unseen topologies. Transferable metamodels are particularly advantageous for problems that require an efficient evaluation of many alternative layouts or when training data is scarce. However, the transferability of metamodels based on GNNs remains limited, due to the lack of representation of physical processes that occur on edge level, i.e. pipes. To address this limitation, our work introduces Edge-Based Graph Neural Networks, which extend the set of inductive biases and represent link-level processes in more detail than traditional Graph Neural Networks. Such an architecture is theoretically related to the constraints of mass conservation at the junctions. To verify our approach, we test the suitability of the edge-based network to estimate pipe flowrates and nodal pressures emulating steady-state EPANET simulations. We first compare the effectiveness of the metamodels on several benchmark water distribution systems against Graph Neural Networks. Then, we explore transferability by evaluating the performance on unseen systems. For each configuration, we calculate model performance metrics, such as coefficient of determination and speed-up with respect to the original numerical model. Our results show that the proposed method captures the pipe-level physical processes more accurately than node-based models. When tested on unseen water networks with a similar distribution of demands, our model retains a good generalization performance with a coefficient of determination of up to 0.98 for flowrates and up to 0.95 for predicted heads. Further developments could include simultaneous derivation of pressures and flowrates., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Rivers represent one of the main conduits for the delivery of plastics to the sea, while also functioning as reservoirs for plastic retention. In tropical regions, rivers are exposed to both high levels of plastic pollution and invasion of water hyacinths. This aquatic plant forms dense patches at the river surface that drift due to winds and currents. Recent work suggests that water hyacinths play a crucial role in influencing plastic transport, by efficiently trapping the majority of surface plastic within their patches. However, a comprehensive understanding of the interaction between water hyacinths and plastics is still lacking. We hypothesize that the properties relevant to plastic transport change due to their trapping in water hyacinth patches. In particular, the length scale, defined as the characteristic size of the transported material, is a key property in understanding how materials move within rivers. Here, we show that water hyacinth patches trap on average 54%-77% of all observed surface plastics at the measurement site (Saigon river, Vietnam). Both temporally and spatially, we found that plastic and water hyacinth presence co-occur. The formation of plastic-plant aggregates carries significant implications for both clean-up and monitoring purposes, as these aggregates can be detected from space and need to be jointly removed. In addition, the length scale of trapped plastics (∼4.0 m) was found to be forty times larger than that of open water plastics (∼0.1 m). The implications of this increased length scale for plastic transport dynamics are yet to be fully understood, calling for further investigation into travel distances and trajectories. The effects of plastic trapping likely extend to other key properties of plastic-plant aggregates, such as effective buoyancy and mass. Given the prevalence of plant invasion and plastic pollution in rivers worldwide, this research offers valuable insights into the complex environmental challenges faced by numerous rivers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Wastewater resources can be used to produce microbial protein for animal feed or organic fertiliser, conserving food chain resources. This investigation hasemployed thefermented sewage to photoheterotrophically grown purple non-sulfur bacteria (PNSB) in a 2.5 m 3 pilot-scaleraceway-pond with infrared light to produce proteinaceous biomass. Fermented sewage with synthetic media consisting of sodium acetate and propionic acids at a surface-to-volume (S/V) ratio of 10 m 2 /m 3 removed 89%, 93%, and 81% of chemical oxygen demand, ammonium nitrogen, and orthophosphate, respectively; whereas respective removal in fermented sewage alone without synthetic media was 73%, 73%, and 72% during batch operation of 120 h. The biomass yield of 0.88-0.95 g COD biomass /g COD removed with protein content of 40.3 ± 0.3%-43.9 ± 0.2% w/w was obtained for fermented sewage with synthetic media. The results revealed enhanced possibility of scaling-up the raceway reactor to recover resources from municipal wastewater and enable simultaneous high-rate PNSB single-cell protein production., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Large-sample hydrology datasets have become increasingly available, contributing to significant scientific advances. However, in Europe, only a few such datasets have been published, capturing only a fraction of the wealth of information from national data providers in terms of available spatial density and temporal extent. We present "EStreams", an extensive dataset of hydro-climatic variables and landscape descriptors and a catalogue of openly available stream records for 17,130 European catchments. Spanning up to 120 years, the dataset includes streamflow indices, catchment-aggregated hydro-climatic signatures and landscape attributes (topography, soils, geology, vegetation and landcover). The catalogue provides detailed descriptions that allow users to directly access streamflow data sources, overcoming challenges related to data redistribution policies, language barriers and varied data portal structures. EStreams also provides Python scripts for data retrieval, aggregation and processing, making it dynamic in contrast to static datasets. This approach enables users to update their data as new records become available. Our goal is to extend current large-sample datasets and further integrate hydro-climatic and landscape data across Europe., (© 2024. The Author(s).)

Photodegradation of microplastics (MPs) induced by sunlight plays a crucial role in determining their transport, fate, and impacts in aquatic environments. Dissolved black carbon (DBC), originating from pyrolyzed carbon, can potentially mediate the photodegradation of MPs owing to its potent photosensitization capacity. This study examined the impact of pyrolyzed wood derived DBC (5 mg C/L) on the photodegradation of polystyrene (PS) MPs in aquatic solutions under UV radiation. It revealed that the photodegradation of PS MPs primarily occurred at the benzene ring rather than the aliphatic segments due to the fast attack of hydroxyl radical (•OH) and singlet oxygen ( 1 O 2 ) on the benzene ring. The photosensitivity of DBC accelerated the degradation of PS MPs, primarily attributed to the increased production of •OH, 1 O 2 , and triplet-excited state DBC ( 3 DBC*). Notably, DBC-mediated photodegradation was related to its molecular weight (MW) and chemical properties. Low MW DBC (<3 kDa) containing more carbonyl groups generated more •OH and 1 O 2 , accelerating the photodegradation of MPs. Nevertheless, higher aromatic phenols in high MW DBC (>30 kDa) scavenged •OH and generated more O 2 • - , inhibiting the photodegradation of MPs. Overall, this study offered valuable insights into UV-induced photodegradation of MPs and highlighted potential impacts of DBC on the transformation of MPs.

Zooplankton are critical indicators of pressures impacting freshwater ecosystems. We analyzed the response of zooplankton communities across different sub-catchment types-headwaters, natural, urban, urban-agricultural, and agricultural-within the Łyna river-lake system in Northern Poland. Using taxonomic groups and functional traits (body size, feeding strategies), we applied Partial Least Squares Regression (PLS-R) to elucidate the relationships between environmental conditions, land use, and zooplankton metacommunity structure. Two-Way Cluster Analysis (TWCA) identified local subsets with characteristic patterns, while Indicator Species Analysis (ISA) determined area-specific taxa. The natural river zone exhibited significant habitat heterogeneity and feeding niches, whereas urban areas created functional homogenization of zooplankton, dominated by small, broad-diet microphages. Agricultural areas promoted diversity among large filter feeders (Crustacea), active suctors (Rotifera), and amoebae (Protozoa). However, intensified agricultural activities, substantially diminished the zooplankton population, biomass, taxonomic richness, and overall ecosystem functionality. The impact of land cover change is more pronounced at small-scale sub-catchments than at the catchment level as a whole. Therefore, assessing these impacts requires detailed spatial and temporal analysis at the sub-catchment level to identify the most affected areas. This study introduces a new sub-catchment-based perspective on ecosystem health assessment and underscores the zooplankton's role as robust indicators of ecological change., (© 2024. The Author(s).)

Treated drinking water is delivered to customers through drinking water distribution systems (DWDSs). Although studies have focused on exploring the microbial ecology of DWDSs, knowledge about the effects of different water treatments on the bacterial community of biofilm and loose deposits in DWDS is limited. This study assessed the effects of additional treatments on the bacterial communities developed in 10 months' old pilot DWDSs. The results showed a similar bacterial community in the pipe-wall biofilm, which was dominated by Novosphingobium spp. (20-82 %) and Sphingomonas spp. (11-53 %), regardless of the treatment applied. The bacterial communities that were retained in the distribution systems (including pipe-wall biofilm and loose deposits) were similar to the particle-associated bacteria (PAB) in the corresponding supply water. The additional treatments showed clear effects of the removal and/or introduction of particles. The genera Aeromonas spp., Clostridium spp., Legionella spp., and Pseudomonas spp., which contain opportunistic pathogenic species, were only detected among the PAB in ion exchange system. Our study demonstrated that the biofilm community is consistent across treatments, and the contribution from bacteria in loose deposits is important but can be controlled by removing particles. These findings offer more insight into the origin and development of microbial ecology in DWDSs and suggest paths for further research on the possibility of managing the microbial ecology in distribution systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Rivers are one of the main conduits that deliver plastic from land into the sea, and also act as reservoirs for plastic retention. Yet, our understanding of the extent of river exposure to plastic pollution remains limited. In particular, there has been no comprehensive quantification of the contributions from different river compartments, such as the water surface, water column, riverbank and floodplain to the overall river plastic transport and storage. This study aims to provide an initial quantification of these contributions. We first identified the main relevant transport processes for each river compartment considered. We then estimated the transport and storage terms, by harmonizing available observations on surface, suspended and floodplain plastic. We applied our approach to two river sections in The Netherlands, with a focus on macroplastics (≥2.5 cm). Our analysis revealed that for the studied river sections, suspended plastics account for over 96% of item transport within the river channel, while their relative contribution to mass transport is only 30%-37% (depending on the river section considered). Surface plastics predominantly consisted of heavier items (mean mass: 7.1 g/#), whereas suspended plastics were dominated by lighter fragments (mean mass: 0.1 g/#). Additionally, the majority (98%) of plastic mass was stored within the floodplains, with the river channel accounting for only 2% of the total storage. Our study developed a harmonized approach for quantifying plastic transport and storage across different river compartments, providing a replicable methodology applicable to different regions. Our findings emphasize the importance of systematic monitoring programs across river compartments for comprehensive insights into riverine plastic pollution., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Competing Interests: The authors declare no competing financial interest.

European catfish is a large-bodied apex predator, a key species in native areas, but invasive in others where it negatively impacts local aquatic fauna necessitates catfish regulation. However, traditional ichthyological methods face challenges in capturing it. The study presents a detailed description of the efficient long-line method, refined through 48 sampling campaigns across twelve European water bodies. This method proves cost-effective and technically undemanding, requiring an average of 5.6 bait fish to catch one European catfish per day. The long-lines outperform other techniques, with the highest Biomass per unit effort (BPUE) of 6.205 kg of catfish per man-hour and minimal by-catch (0.276 kg per man-hour). In contrast, fyke nets, the second most efficient method, achieve a BPUE of 0.621 kg of catfish per man-hour with 3.953 kg of by-catch per man-hour. To optimize long-line catches, a 15 m distance between branch lines and regular relocation is recommended. Live fish is the most effective bait with no significant differences observed among species. However, earthworms, a less controversial alternative, are also efficient, especially for smaller catfish. Our recapture approach using various ichthyological methods revealed no hook avoidance behavior by catfish after a previous catch or avoidance by a certain part of the population. The long-line method is suitable for population regulation, scientific research, and conservation efforts and is the most effective means of capturing live European catfish., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Bacillus nitratireducens was isolated from textile effluent and showed high tolerance to chromium (Cr), reaching up to a 1000 mg/L MIC value. This research was aimed at utilizing biosorbents from live and dead cells of B. nitratireducens to remove Cr from an aqueous solution. A batch biosorption test was performed, and mechanisms analysis was approached by an adsorption-desorption test, SEM-EDS, and FTIR analysis. Cr removal by dead cells in 25, 50, and 100 mg/L of Cr were 58.99 ± 0.7%, 69.8 ± 0.2%, and 82.87 ± 0.11%, respectively, while that by live cells was 73.08 ± 1.9%, 80.27 ± 6.33%, and 86.17 ± 1.93%, respectively. Live cells showed significantly higher Cr removal and adsorption capacities as compared to dead cells. In all concentrations, absorption contributed more than adsorption to the Cr removal by both live and dead cells. Absorption of Cr was subjected to occur due to passive mechanisms in dead cells while involving some active mechanisms in live cells. SEM-EDS confirmed the detection of Cr on the cell surface, while FTIR revealed the shifting of some peaks after the biosorption test, suggesting interactions between Cr and functional groups. Further TEM analysis is suggested to be conducted as a future approach to reveal the inner structure of cells and confirm the involvement of absorption mechanisms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

With the global concerns on antibiotic resistance (AR) as a public health issue, it is pivotal to have data exchange platforms for studies on antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in the environment. For this purpose, the NORMAN Association is hosting the NORMAN ARB&ARG database, which was developed within the European project ANSWER. The present article provides an overview on the database functionalities, the extraction and the contribution of data to the database. In this study, AR data from three studies from China and Nepal were extracted and imported into the NORMAN ARB&ARG in addition to the existing AR data from 11 studies (mainly European studies) on the database. This feasibility study demonstrates how the scientific community can share their data on AR to generate an international evidence base to inform AR mitigation strategies. The open and FAIR data are of high potential relevance for regulatory applications, including the development of emission limit values / environmental quality standards in relation to AR. The growth in sharing of data and analytical methods will foster collaboration on risk management of AR worldwide, and facilitate the harmonization in the effort for identification and surveillance of critical hotspots of AR. The NORMAN ARB&ARG database is publicly available at: https://www.norman-network.com/nds/bacteria/., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The study covered a small, shallow lake, intensively used for recreation (sailing, tourist services and port infrastructure). This study aimed to determine the spatial differentiation of bottom sediments and the potential for phosphorus release in five zones, differing mainly in the type of recreation, depth, direct catchment management, shoreline management and macrophyte presence. The results were used to propose protective and restoration measures to improve the water quality of the studied lake. The innovation in the study was the detailed analysis of bottom sediments, which can be a significant source of pollution besides the external load from the catchment and tourist pressure, in the planned management of this ecosystem. Examination of the physicochemical properties of the bottom sediments showed a clear variation in both composition and potential for internal phosphorus loading. The sediments from the profundal zone, where the most boating activity was observed, together with the sediments from the shallow zone where the boats dock (mooring zone), had the highest potential to supply phosphorus to the bottom waters. This fact was demonstrated by the highest total phosphorus (TP) concentrations in sediments (up to 1.32 mgPg -1 DW) and the content of the most mobile fractions (up to 33%). The other zones associated with the marina, fuel zone, tributary and canal were not significant sources of phosphorus to the ecosystem. Based on the above results, a restoration method involving the removal of bottom sediments from the bottom zone was proposed, supported, of course, by protective measures in the catchment (maintaining a buffer zone around the lake and limiting the inflow of pollutants with tributary waters). The proposed measures with sustainable tourist pressure should improve water quality and thus contribute to protecting this valuable natural landscape., (© 2024. The Author(s).)

A full-scale high-rate cascade anaerobic digestion (CAD) system was evaluated for its ability to enhance enzymatic sludge hydrolysis. The system included a newly built digester, innovatively divided into three pie-shaped compartments (500 m 3 each), followed by an existing, larger digester (1500 m 3 ). The system treated a mixture of waste activated sludge and primary sludge, achieving a stable total chemical oxygen demand reduction efficiency (56.1 ± 6.8 %), and enhanced sludge hydrolytic enzyme activities at a 14.5-day total solids retention time (SRT). High-throughput sequencing data revealed a consistent microbial community across reactors, dominated by consortia that govern hydrolysis and acidogenesis. Despite relatively short SRTs in the initial reactors of the CAD system, acetoclastic methanogens belonging to Methanosaeta became the most abundant archaea. ‬‬‬‬‬‬‬‬‬‬‬‬‬ This study proves that the CAD system achieves stable sludge reduction, accelerates enzymatic hydrolysis at full-scale, and paves the way for its industrialization in municipal waste sewage sludge treatment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Constructed wetlands (CWs) have been proven to effectively immobilize plastic particles. However, little is known about the differences in the impact of varying sized plastic particles on nitrous oxide (N 2 O) release, as well as the intervention mechanisms in CWs. Here, we built a lab-scale wetland model and introduced plastic particles of macro-, micro-, and nano-size at 100 μg/L for 370 days. The results showed that plastic particles of all sizes reduced N 2 O release in CWs, with the degrees being the strongest for the Nano group, followed by Micro and Macro groups. Meanwhile, 15 N- and 18 O-tracing experiment revealed that the ammoxidation process contributed the most N 2 O production, followed by denitrification. While for every N 2 O-releasing process, the contributing proportion of N 2 O in nitrification-coupled denitrification were most significantly cut down under exposing to macro-sized plastics and had an obvious increase in nitrifier denitrification in all groups, respectively. Finally, we revealed the three mechanism pathways of N 2 O release reduction with macro-, micro-, and nano-sized plastics by impacting carbon assimilation (RubisCO activity), ammonia oxidation (gene amo abundance and HAO activity), and N-ion transmembrane and reductase activities, respectively. Our findings thus provided novel insights into the potential effects of plastic particles in CWs as an eco-technology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Past and  recent applications of 2,4,6-trinitrotoluene (TNT) in military and civilian industries have led to contamination of soil and marine ecosystems. Among various TNT remediation techniques, biological remediation is widely accepted for its sustainability, low cost, and scalable applications. This study was designed to isolate a fungus strain from a TNT-contaminated soil to investigate its tolerance to and potential for removal of TNT. Thus, a soil column with a history of periodic TNT amendment was used to isolate dominant strains of fungi Fusarium solani isolate, which is not commonly reported for TNT mineralization and was found predominant in the subsurface layer of the TNT-amended soil. F. solani was investigated for TNT concentration tolerance at 30, 70, and 100 mg/L on agar plates and for TNT removal in liquid cultures at the same given concentrations. F. solani activity was compared with that of a reference soil-born fungus that has been intensively studied for TNT removal (Phanerochaete chrysosporium) obtained from the American Type Culture Collection. On agar media, F. solani showed a larger colony diameter than P. chrysosporium at similar TNT concentrations, indicating its high potential to tolerate toxic levels of TNT as found in contaminated sites. In the liquid culture medium, F. solani was able to significantly produce higher biomass than P. chrysosporium in all TNT concentrations. The TNT removal percentage from the liquid culture at the highest TNT concentration of 100 mg/L reached about 85% with F. solani, while P. chrysosporium was no better than 25% at the end of an 84-h incubation period. Results indicate a significant potential of using F. solani in the bioremediation of polluted TNT soils that overcome the high concentration barrier in the field. However, further investigation is needed to identify enzymatic potential and the most effective applications and possible limitations of this method on a large scale., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Increasing wildfire frequency, a consequence of global climate change, releases incomplete combustion byproducts such as aquatic pyrogenic dissolved organic matter (DOM) and black carbon (DBC) into waters, posing a threat to water security. In August 2022, a series of severe wildfires occurred in Chongqing, China. Samples from seven locations along the Yangtze and Jialing Rivers revealed DBC, quantified by the benzene poly(carboxylic acid) (BPCA) method, comprising 9.5-19.2% of dissolved organic carbon (DOC). High concentrations of BPCA-DBC with significant polycondensation were detected near wildfire areas, likely due to atmospheric deposition driven by wind. Furthermore, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) revealed that wildfires were associated with an increase in condensed aromatics, proteins, and unsaturated hydrocarbons, along with a decrease in lignins. The condensed aromatics primarily consisted of dissolved black nitrogen (DBN), contributing to abundant high-nitrogen-containing compounds in locations highly affected by wildfires. Meanwhile, wildfires potentially induced the input of recalcitrant sulfur-containing protein-like compounds, characterized by high oxidation, aliphatic nature, saturation, and low aromaticity. Overall, this study revealed the appearance of recalcitrant DBC and dissolved organic sulfur in river waters following wildfire events, offering novel insights into the potential impacts of wildfires on water quality and environmental biogeochemistry.

Increasing aquaculture cultivation produces large quantities of wastewater. If not handled properly, it can have negative impacts on the environment. Constructed wetlands (CWs) are one of the phytoremediation methods that can be applied to treat aquaculture effluent. This research was aimed at determining the performance of Cyperus rotundus in removing COD, BOD, TSS, turbidity, ammonia, nitrate, nitrite, and phosphate from the batch CW system. Treatment was carried out for 30 days with variations in the number of plants (10, 15, and 20) and variations in media height (10, 12, and 14 cm). The result showed that aquaculture effluent contains high levels of organic compounds and nutrients, and C. rotundus can grow and thrive in 100% of aquaculture effluent. Besides that, the use of C. rotundus in CWs with the effect of numbers of plants and media height showed performance of COD, BOD, TSS, turbidity, ammonia, nitrate, nitrite, and phosphate with 70, 79, 90, 96, 64, 82, 92, and 48% of removal efficacy, respectively. There was no negative impact observed on C. rotundus growth after exposure to aquaculture effluent, as indicated by the increase in wet weight, dry weight, and growth rate when compared to the control. Thus, adding aquaculture effluent to CWs planted with C. rotundus supports the growth and development of plants while also performing phytoremediation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

A new type of bio-composite material is being produced from water-recovered resources such as cellulose fibres from wastewater, calcite from the drinking water softening process, and grass and reed from waterboard sites. These raw materials may be contaminated with pathogens and chemicals such as Escherichia coli , heavy metals, and resin compounds. A novel risk assessment framework is proposed here, addressing human health risks during the production of new bio-composite materials. The developed framework consists of a combination of existing risk assessment methods and is based on three main steps: hazard identification, qualitative risk mapping, and quantitative risk assessment. The HAZOP and Event Tree Analysis methodologies were used for hazard identification and risk mapping stages. Then, human health risks were quantitatively assessed using quantitative chemical risk assessment, evaluating cancer and non-cancer risk, and quantitative microbial risk assessment. The deterministic and the stochastic approaches were performed for this purpose. The contamination of raw materials may pose human health concerns, resulting in cancer risk above the threshold. Microbial risk is also above the safety threshold. Additional analysis would be significant as future research to better assess the microbial risk in biocomposite production. The framework has been effectively used for chemical and microbial risk assessment., Competing Interests: The authors declare there is no conflict., (© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).)

A biokinetic model based on BioWin's Activated Sludge Digestion Model (ASDM) coupled with a nitrous oxide (N 2 O) model was setup and calibrated for a full-scale wastewater treatment plant (WWTP) Amsterdam West, in the Netherlands. The model was calibrated using one year of continuous data to predict the seasonal variations of N 2 O emissions in the gaseous phase. This, according to our best knowledge, is the most complete full-scale data set used to date for this purpose. The results obtained suggest that the currently available biokinetic model predicted the winter, summer, and autumn N 2 O emissions well but failed to satisfactorily simulate the spring peak. During the calibration process, it was found that the nitrifier denitrification pathway could explain the observed emissions during all seasons while a combination of the nitrifier denitrification and incomplete heterotrophic denitrification pathways seemed to be dominant during the emissions peak observed during the spring season. Specifically, kinetic parameters related to free nitrous acid (FNA) displayed significant sensitivity leading to increased N 2 O production. The obtained values of two kinetic parameters, i.e., the FNA half-saturation during ammonia oxidising bacteria (AOB) denitrification and the FNA inhibition concentration related to heterotrophic denitrification, suggested a strong influence of the FNA bulk concentration on the N 2 O emissions and the observed seasonal variations. Based on the suboptimal performance and limitations of the biokinetic model, further research is needed to better understand the biochemical processes behind the seasonal peak and the influence of FNA., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Valley Fever is a respiratory disease caused by inhalation of arthroconidia, a type of spore produced by fungi within the genus Coccidioides spp. which are found in dry, hot ecosystems of the Western Hemisphere. A quantitative microbial risk assessment (QMRA) for the disease has not yet been performed due to a lack of dose-response models and a scarcity of quantitative occurrence data from environmental samples. A literature review was performed to gather data on experimental animal dosing studies, environmental occurrence, human disease outbreaks, and meteorological associations. As a result, a risk framework is presented with information for parameterizing QMRA models for Coccidioides spp., with eight new dose-response models proposed. A probabilistic QMRA was conducted for a Southwestern US agricultural case study, evaluating eight scenarios related to farming occupational exposures. Median daily workday risks for developing severe Valley Fever ranged from 2.53 × 10 -7 (planting by hand while wearing an N95 facemask) to 1.33 × 10 -3 (machine harvesting while not wearing a facemask). The literature review and QMRA synthesis confirmed that exposure to aerosolized arthroconidia has the potential to result in high attack rates but highlighted that the mechanistic relationships between environmental conditions and disease remain poorly understood. Recommendations for Valley Fever risk assessment research needs in order to reduce disease risks are discussed, including interventions for farmers., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Kerry Hamilton and other faculty at QMRA workshop (Charles Haas) reports financial support and travel were provided by NIH., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The excessive use and accumulation of water-soluble polymers (WSPs, known as "liquid plastics") in the environment can pose potential risks to both ecosystems and human health, but the environmental fate of WSPs remains unclear. Here, the adsorption behavior of WSPs with different molecular weight on kaolinite (Kaol) and montmorillonite (Mt) were examined. The results showed that the adsorption of PEG and PVP on minerals were controlled by hydrogen bond and van der Waals force. The Fourier transform infrared (FTIR) spectra and two-dimensional correlation spectroscopy (2D-COS) analysis revealed that there were interactions between the Al-O and Si-O groups of the minerals and the polar O- or N-containing functional groups as well as the alkyl groups of PEG and PVP. The adsorption characteristics of WSPs were closely related to their molecular weight and the pore size of minerals. Due to the relatively large mesopore size of Kaol, both PEG and PVP were absorbed into inner spaces, for which the adsorption capacity increased with molecular weight of the polymers. For Mt, all types of PEG could enter its micropores, while PVP with larger molecular weights appeared to be confined externally, leading to a decrease in the adsorption capacity of PVP with increasing molecular weight. The findings of this study provide a theoretical basis for scientific evaluation of environmental processes of WSPs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)