Pub Date : 2024-11-21DOI: 10.1016/j.watres.2024.122830
Maykoll Corrales-González, Carlos Rochera, Antonio Picazo, Antonio Camacho
Shallow saline lakes in the La Mancha Húmeda Biosphere Reserve in Central Spain show diverse degrees of cultural and natural eutrophication, prompting urgent conservation measures. This study focuses on 17 representative lakes from the site to assess seasonal nutrient dynamics and their connection to plankton metabolism (photosynthesis and respiration) during two successive hydrological periods. Effect of environmental factors was evaluated on a combination of several response variables, demonstrating that source of the nutrient inputs (ranging from natural to anthropic) had the highest influence on the nutrients stoichiometry and metabolic rates. Regarding the source of eutrophication, the model demonstrated that effects of urban wastewaters exceed that of agricultural runoff, and moreover lead to more prolonged hydroperiods and contributes to desalination. Lakes affected by wastewater inputs or surrounded by volcanic lithology showed phosphorus enrichment in both water and surface sediments. Planktonic respiration rates in these cases closely correlated with photosynthesis, suggesting the utilization of algal-derived dissolved organic matter. Conversely, wastewater-free lakes, mainly fed by runoff, accumulated uncolored, likely recalcitrant dissolved organic carbon (DOC). These lakes exhibited a better-preserved condition, characterized by higher salinity, moderate metabolic rates, and lower production/respiration ratios compared to the previous state, implying a greater dependence on allochthonous organic matter. Enhancement of management strategies, which should consider salinity, volcanic lake vulnerability, and the multifaceted impacts of wastewater, will prove more effective in the conservation and restoration of these unique and fragile ecosystems.
{"title":"Trophic status and metabolic rates of threatened shallow saline lakes in Central Spain: providing diagnostic elements for improving management strategies","authors":"Maykoll Corrales-González, Carlos Rochera, Antonio Picazo, Antonio Camacho","doi":"10.1016/j.watres.2024.122830","DOIUrl":"https://doi.org/10.1016/j.watres.2024.122830","url":null,"abstract":"Shallow saline lakes in the La Mancha Húmeda Biosphere Reserve in Central Spain show diverse degrees of cultural and natural eutrophication, prompting urgent conservation measures. This study focuses on 17 representative lakes from the site to assess seasonal nutrient dynamics and their connection to plankton metabolism (photosynthesis and respiration) during two successive hydrological periods. Effect of environmental factors was evaluated on a combination of several response variables, demonstrating that source of the nutrient inputs (ranging from natural to anthropic) had the highest influence on the nutrients stoichiometry and metabolic rates. Regarding the source of eutrophication, the model demonstrated that effects of urban wastewaters exceed that of agricultural runoff, and moreover lead to more prolonged hydroperiods and contributes to desalination. Lakes affected by wastewater inputs or surrounded by volcanic lithology showed phosphorus enrichment in both water and surface sediments. Planktonic respiration rates in these cases closely correlated with photosynthesis, suggesting the utilization of algal-derived dissolved organic matter. Conversely, wastewater-free lakes, mainly fed by runoff, accumulated uncolored, likely recalcitrant dissolved organic carbon (DOC). These lakes exhibited a better-preserved condition, characterized by higher salinity, moderate metabolic rates, and lower production/respiration ratios compared to the previous state, implying a greater dependence on allochthonous organic matter. Enhancement of management strategies, which should consider salinity, volcanic lake vulnerability, and the multifaceted impacts of wastewater, will prove more effective in the conservation and restoration of these unique and fragile ecosystems.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"81 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142678392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Climate change alters the wetting state of riparian sediments, impacting microbial community response and biogeochemical processes. Microplastics (MPs) invade nearly all ecosystems on earth, posing a significant environmental risk. However, little is known about the response mechanism of MP exposure in sediment ecosystems with different wetting states under alternating seasonal rain and drought conditions. In this study, sediments with three different wetting states were selected to explore the differential response of ecosystems to PLA MP exposure. We observed that PLA MP exposure directly affected biogeochemical processes in sediment ecosystems and induced significant changes in microbial communities. PLA MP exposure was found to alter the composition of key species and microbial functional groups in the ecosystem, resulting in a more complex, interconnected, but less stable microbial network. Our findings showed that PLA MP exposure enhances the contribution of stochastic processes, for example the dispersal limitation increasing from 7.41% to 54.32%, indicating that sediment ecosystems strive to buffer disturbances from PLA MP exposure. In addition, 87 pathogenic species were detected in our samples, with PLA MPs acting as vectors for their transmission, potentially amplifying ecosystem disturbance. Importantly, we revealed that submerged sediments may present a greater environmental risk, while alternating wet and dry sediments demonstrate greater resistance and resilience to PLA MPs pollution. Overall, this study sheds light on how sediment ecosystems respond to MP exposure, and highlights differences in sediment response mechanisms across wetting states.
{"title":"Different Wetting States in Riparian Sediment Ecosystems: Response to Microplastics Exposure","authors":"Siying He, Yuhang Ye, Yajing Cui, Xiuqin Huo, Maocai Shen, Fang Li, Zhaohui Yang, Guangming Zeng, Weiping Xiong","doi":"10.1016/j.watres.2024.122823","DOIUrl":"https://doi.org/10.1016/j.watres.2024.122823","url":null,"abstract":"Climate change alters the wetting state of riparian sediments, impacting microbial community response and biogeochemical processes. Microplastics (MPs) invade nearly all ecosystems on earth, posing a significant environmental risk. However, little is known about the response mechanism of MP exposure in sediment ecosystems with different wetting states under alternating seasonal rain and drought conditions. In this study, sediments with three different wetting states were selected to explore the differential response of ecosystems to PLA MP exposure. We observed that PLA MP exposure directly affected biogeochemical processes in sediment ecosystems and induced significant changes in microbial communities. PLA MP exposure was found to alter the composition of key species and microbial functional groups in the ecosystem, resulting in a more complex, interconnected, but less stable microbial network. Our findings showed that PLA MP exposure enhances the contribution of stochastic processes, for example the dispersal limitation increasing from 7.41% to 54.32%, indicating that sediment ecosystems strive to buffer disturbances from PLA MP exposure. In addition, 87 pathogenic species were detected in our samples, with PLA MPs acting as vectors for their transmission, potentially amplifying ecosystem disturbance. Importantly, we revealed that submerged sediments may present a greater environmental risk, while alternating wet and dry sediments demonstrate greater resistance and resilience to PLA MPs pollution. Overall, this study sheds light on how sediment ecosystems respond to MP exposure, and highlights differences in sediment response mechanisms across wetting states.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"119 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Advanced suspect and non-target screening (SNTS) approach can identify a large number of potential hazardous micropollutants in groundwater, underscoring the need for pinpointing priority pollutants among detected chemicals. This present study therefore demonstrates a novel multi-criteria decision making (MCDM) framework utilizing machine learning (ML) algorithms coupled with toxicological prioritization index tool (i.e., ml_ToxPi) to rank 251 chemicals of interest in groundwater for subsequent targeted analysis. The MCDM framework integrated chemical analysis data (i.e., peak area and detection frequency), toxicity profiles (i.e., bioactivity ratio, human exposure metadata, and carcinogenicity metadata), as well as the environmental fate and transport information (i.e., octanol-water partition coefficient (log Kow), water solubility, biodegradation half-life, and soil adsorption coefficient (Koc)) for ranking the identified pollutants, and the random forest machine learning model was useful for systematically determining the weighting factors of each variable according to their variable importance scores (R2 = 0.808 and 0.778 for training and testing datasets, respectively, while RMSE = 0.042 in both cases). A total of 47 unique high priority compounds (i.e., ml_ToxPi score ≥ 0.55) were identified across the investigated sampling regions, which constituted diverse groups of compounds classified according to their chemical uses, such as alkylated polycyclic aromatic hydrocarbons (alkyl-PAHs), organophosphate flame retardants (OPFRs), parent PAHs, personal care products (PCPs), pesticides, pharmaceuticals, phenols, plasticizers, transformation product (TPs), and other industrial use chemicals. By incorporating relevant variables into the proposed ML-optimized ToxPi MCDM framework, the prioritization approach described here may be adopted in future SNTS assessment of environmental and biological media.
{"title":"Prioritization of monitoring compounds from SNTS identified organic micropollutants in contaminated groundwater using a machine learning optimized ToxPi model","authors":"Okon Dominic Ekpe, Haeran Moon, JongCheol Pyo, Jeong-Eun Oh","doi":"10.1016/j.watres.2024.122824","DOIUrl":"https://doi.org/10.1016/j.watres.2024.122824","url":null,"abstract":"Advanced suspect and non-target screening (SNTS) approach can identify a large number of potential hazardous micropollutants in groundwater, underscoring the need for pinpointing priority pollutants among detected chemicals. This present study therefore demonstrates a novel multi-criteria decision making (MCDM) framework utilizing machine learning (ML) algorithms coupled with toxicological prioritization index tool (i.e., ml_ToxPi) to rank 251 chemicals of interest in groundwater for subsequent targeted analysis. The MCDM framework integrated chemical analysis data (i.e., peak area and detection frequency), toxicity profiles (i.e., bioactivity ratio, human exposure metadata, and carcinogenicity metadata), as well as the environmental fate and transport information (i.e., octanol-water partition coefficient (log K<sub>ow</sub>), water solubility, biodegradation half-life, and soil adsorption coefficient (K<sub>oc</sub>)) for ranking the identified pollutants, and the random forest machine learning model was useful for systematically determining the weighting factors of each variable according to their variable importance scores (R<sup>2</sup> = 0.808 and 0.778 for training and testing datasets, respectively, while RMSE = 0.042 in both cases). A total of 47 unique high priority compounds (i.e., ml_ToxPi score ≥ 0.55) were identified across the investigated sampling regions, which constituted diverse groups of compounds classified according to their chemical uses, such as alkylated polycyclic aromatic hydrocarbons (alkyl-PAHs), organophosphate flame retardants (OPFRs), parent PAHs, personal care products (PCPs), pesticides, pharmaceuticals, phenols, plasticizers, transformation product (TPs), and other industrial use chemicals. By incorporating relevant variables into the proposed ML-optimized ToxPi MCDM framework, the prioritization approach described here may be adopted in future SNTS assessment of environmental and biological media.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"14 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-20DOI: 10.1016/j.watres.2024.122825
Zhigang Cao, Menghua Wang, Ronghua Ma, Hongtao Duan, Lide Jiang, Ming Shen, Kun Xue, Fenzhen Su
The Visible Infrared Imaging Radiometer Suite (VIIRS) and Ocean and Land Colour Instrument (OLCI) are two main instruments for the ocean color community to observe the global lake environment in the following decades. Despite their applications to retrieve various water optical parameters, the spatial and temporal resolutions of individual sensors cannot meet the requirements for lake monitoring effectively. To date, the possibility of complementary observations through the OLCI-VIIRS data to lake aquatic environments remains unclear. Here, we evaluated the agreement between OLCI and VIIRS-derived remote sensing reflectance (Rrs(λ)) and chlorophyll-a (Chl-a) in Chinese lakes spanning a variety of lake characteristics. We find that OLCI Rrs(λ) data generated by the NOAA Multi-Sensor Level-1 to Level-2 (MSL12) system perform satisfactory accuracy in 20 Chinese lakes with less than 30% uncertainty from 490 nm to 865 nm and show good agreements with VIIRS Rrs(λ) in more than 200 large lakes in China (> 0.90 correlation). The deep neural network algorithm outperformed several state-of-the-art algorithms in Chl-a estimates from OLCI images (23% bias). The spatial and temporal patterns of OLCI and VIIRS-derived Chl-a presented an excellent consistency with ∼20% difference, suggesting the feasibility of seamless OLCI-VIIRS observations in Chl-a for lakes. With the OLCI data and well-validated algorithm, we revealed the high-resolution maps of Chl-a in 681 lakes of larger than 10 km2 in China, which significantly filled the results in small-medium lakes where VIIRS did not observe before. This study demonstrates the reasonable agreement of OLCI-VIIRS observations in lakes and proposes an initiative to generate seamless data records in inland lakes through OLCI-VIIRS data.
{"title":"Seamless observations of chlorophyll-a from OLCI and VIIRS measurements in inland lakes","authors":"Zhigang Cao, Menghua Wang, Ronghua Ma, Hongtao Duan, Lide Jiang, Ming Shen, Kun Xue, Fenzhen Su","doi":"10.1016/j.watres.2024.122825","DOIUrl":"https://doi.org/10.1016/j.watres.2024.122825","url":null,"abstract":"The Visible Infrared Imaging Radiometer Suite (VIIRS) and Ocean and Land Colour Instrument (OLCI) are two main instruments for the ocean color community to observe the global lake environment in the following decades. Despite their applications to retrieve various water optical parameters, the spatial and temporal resolutions of individual sensors cannot meet the requirements for lake monitoring effectively. To date, the possibility of complementary observations through the OLCI-VIIRS data to lake aquatic environments remains unclear. Here, we evaluated the agreement between OLCI and VIIRS-derived remote sensing reflectance (<em>R<sub>rs</sub></em>(<em>λ</em>)) and chlorophyll-a (Chl-a) in Chinese lakes spanning a variety of lake characteristics. We find that OLCI <em>R<sub>rs</sub></em>(λ) data generated by the NOAA Multi-Sensor Level-1 to Level-2 (MSL12) system perform satisfactory accuracy in 20 Chinese lakes with less than 30% uncertainty from 490 nm to 865 nm and show good agreements with VIIRS <em>R<sub>rs</sub></em>(λ) in more than 200 large lakes in China (> 0.90 correlation). The deep neural network algorithm outperformed several state-of-the-art algorithms in Chl<em>-</em>a estimates from OLCI images (23% bias). The spatial and temporal patterns of OLCI and VIIRS-derived Chl-a presented an excellent consistency with ∼20% difference, suggesting the feasibility of seamless OLCI-VIIRS observations in Chl-a for lakes. With the OLCI data and well-validated algorithm, we revealed the high-resolution maps of Chl-a in 681 lakes of larger than 10 km<sup>2</sup> in China, which significantly filled the results in small-medium lakes where VIIRS did not observe before. This study demonstrates the reasonable agreement of OLCI-VIIRS observations in lakes and proposes an initiative to generate seamless data records in inland lakes through OLCI-VIIRS data.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"170 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.watres.2024.122816
Hancheng Ren, Bo Pang, Gang Zhao, Haijun Yu, Peinan Tian, Chenran Xie
Urban flooding has become a prevalent issue in cities worldwide. Urban flood dynamics differ significantly from those in natural watersheds, primarily because of the intricate drainage systems and the high spatial heterogeneity of urban surfaces, which pose considerable challenges for accurate and rapid flood simulation. In this study, an urban drainage-supervised flood model (UDFM) for urban flood simulation is proposed. The urban flood process is decoupled into drainage routing and surface flood inundation. On the basis of physical and deep learning drainage models, a hybrid module combining deep learning and dimensionality reduction algorithm is adopted to convert the 1D drainage overflow process into a high-resolution, spatiotemporal 2D pluvial flooding process. Compared with existing state-of-the-art surrogate models for rapid flood simulation, the UDFM more comprehensively and accurately represents the role of drainage systems in urban flood dynamics, providing high-resolution predictions of flood depth and velocity. When applied to a highly urbanized district in Shenzhen, UDFM-deep learning demonstrated real-time predictive capabilities and high accuracy, particularly in simulating flow velocity, with average Nash efficiency coefficients improved by 0.112 and 0.251 compared with those of a response surface model (RSM) and a low-fidelity model (LFM), respectively. These findings underscore the critical importance of drainage system overflow in urban surface flood simulations. The UDFM enhances accuracy, flexibility, interpretability, and extensibility without requiring additional physical model construction. This research introduces a novel hierarchical surrogate model structure for urban flood simulation, offering valuable insights for rapid flood warning and risk management in urban environments.
{"title":"Incorporating Dynamic Drainage Supervision into Deep Learning for Accurate Real-Time Flood Simulation in Urban Areas","authors":"Hancheng Ren, Bo Pang, Gang Zhao, Haijun Yu, Peinan Tian, Chenran Xie","doi":"10.1016/j.watres.2024.122816","DOIUrl":"https://doi.org/10.1016/j.watres.2024.122816","url":null,"abstract":"Urban flooding has become a prevalent issue in cities worldwide. Urban flood dynamics differ significantly from those in natural watersheds, primarily because of the intricate drainage systems and the high spatial heterogeneity of urban surfaces, which pose considerable challenges for accurate and rapid flood simulation. In this study, an urban drainage-supervised flood model (UDFM) for urban flood simulation is proposed. The urban flood process is decoupled into drainage routing and surface flood inundation. On the basis of physical and deep learning drainage models, a hybrid module combining deep learning and dimensionality reduction algorithm is adopted to convert the 1D drainage overflow process into a high-resolution, spatiotemporal 2D pluvial flooding process. Compared with existing state-of-the-art surrogate models for rapid flood simulation, the UDFM more comprehensively and accurately represents the role of drainage systems in urban flood dynamics, providing high-resolution predictions of flood depth and velocity. When applied to a highly urbanized district in Shenzhen, UDFM-deep learning demonstrated real-time predictive capabilities and high accuracy, particularly in simulating flow velocity, with average Nash efficiency coefficients improved by 0.112 and 0.251 compared with those of a response surface model (RSM) and a low-fidelity model (LFM), respectively. These findings underscore the critical importance of drainage system overflow in urban surface flood simulations. The UDFM enhances accuracy, flexibility, interpretability, and extensibility without requiring additional physical model construction. This research introduces a novel hierarchical surrogate model structure for urban flood simulation, offering valuable insights for rapid flood warning and risk management in urban environments.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"174 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integrated application of hydrological models and best management practices (BMPs) serves as a pivotal decision-making tool for managing nonpoint source (NPS) pollution in watersheds. Optimizing and selecting BMP options are critical challenges in managing NPS pollution, as these processes are typically computationally expensive and involve mixed discrete-continuous decision variables. Our study integrated a novel method, the multi-objective adaptive surrogate modeling-based optimization for constrained hybrid problems (MO-ASMOCH), with the distributed Soil and Water Assessment Tool (SWAT) model to efficiently optimize the deployment of BMPs in the Four Lakes watershed of China. We compared the optimization results with those obtained using the traditional non-dominated sorting genetic algorithm (NSGA-II) method. Our results demonstrate that MO-ASMOCH significantly outperforms NSGA-II in computational efficiency, achieving comparable Pareto-optimal solutions with just 1,150 model evaluations compared to NSGA-II's requirement of 10,000 model evaluations. This demonstrates that MO-ASMOCH is a more efficient optimization algorithm for BMP optimization problems with both discrete and continuous decision variables. We selected representative scenarios to calculate in-lake concentrations of total phosphorus (TP) and total nitrogen (TN) pollutant loads. The largest reduction scenario could reduce TN and TP loads by 18.3% and 20.7%, respectively, at a cost of 1.54 × 108 Chinese Yuan. Under this scenario, the water quality classification level of TN improves from inferior Class V to Class IV-V, while TP attains Class III throughout the year. The methods of this study could enhance our capability to manage NPS pollution in watersheds effectively and provide targeted decision-making insights for environmental management practices.
{"title":"Surrogate modelling-based multi-objective optimization for best management practices of nonpoint source pollution","authors":"Aoyun Long, Ruochen Sun, Xiyezi Mao, Qingyun Duan, Mengtian Wu","doi":"10.1016/j.watres.2024.122788","DOIUrl":"https://doi.org/10.1016/j.watres.2024.122788","url":null,"abstract":"The integrated application of hydrological models and best management practices (BMPs) serves as a pivotal decision-making tool for managing nonpoint source (NPS) pollution in watersheds. Optimizing and selecting BMP options are critical challenges in managing NPS pollution, as these processes are typically computationally expensive and involve mixed discrete-continuous decision variables. Our study integrated a novel method, the multi-objective adaptive surrogate modeling-based optimization for constrained hybrid problems (MO-ASMOCH), with the distributed Soil and Water Assessment Tool (SWAT) model to efficiently optimize the deployment of BMPs in the Four Lakes watershed of China. We compared the optimization results with those obtained using the traditional non-dominated sorting genetic algorithm (NSGA-II) method. Our results demonstrate that MO-ASMOCH significantly outperforms NSGA-II in computational efficiency, achieving comparable Pareto-optimal solutions with just 1,150 model evaluations compared to NSGA-II's requirement of 10,000 model evaluations. This demonstrates that MO-ASMOCH is a more efficient optimization algorithm for BMP optimization problems with both discrete and continuous decision variables. We selected representative scenarios to calculate in-lake concentrations of total phosphorus (TP) and total nitrogen (TN) pollutant loads. The largest reduction scenario could reduce TN and TP loads by 18.3% and 20.7%, respectively, at a cost of 1.54 × 10<sup>8</sup> Chinese Yuan. Under this scenario, the water quality classification level of TN improves from inferior Class V to Class IV-V, while TP attains Class III throughout the year. The methods of this study could enhance our capability to manage NPS pollution in watersheds effectively and provide targeted decision-making insights for environmental management practices.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"11 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.watres.2024.122820
Lizhi He, Ming Zhang, Jiahao Li, Qingdong Duan, Daoyong Zhang, Xiangliang Pan
ClO2 has been ever-increasingly used as an alternative disinfectant to alleviate antibiotic resistance risk in aquaculture. However, the feasibility of ClO2 disinfection in reducing antibiotic resistance has not been clarified yet. We comparatively explored the aggregation mechanisms and their effect on extracellular DNA (exDNA) partition and settlement in disinfected aquaculture waters and natural waters. In contrast to the unimodal aggregation in natural non-aquaculture waters, a unique bimodal size distribution pattern of micron-sized aggregates was found in aquaculture waters regardless of the disinfectants (macro-aggregates – 200-700 μm in diameter and micro-aggregates – 2-200 μm in diameter). The bimodal aggregates had 2-4 orders of magnitude higher content of Ferron cations and enriched hundred-fold exDNA in aquaculture waters than in natural waters. ExDNA was adsorbed on the surface of aggregates and conglutinated mainly by carbohydrates and coagulative cations. Macro-aggregates had lower fractal dimension but greater sedimentation velocities compared with micro-aggregates. Polylithionite was the key ballast mineral facilitating fast sedimentation of aggregates in aquaculture waters. Loading more antibiotic resistance genes and mobile gene elements, the aquaculture aggregates sank more rapidly from water to sediments than the natural-water aggregates. It indicates that disinfection with ClO2 or antibiotics facilitated the spatial transfer of antibiotic resistance risk with high horizontal transfer potential from water column to sediment through forming bimodal aggregates. These findings imply that the adoption of antibiotic alternatives such as the oxidant of ClO2 is far from sufficient to alleviate antibiotic resistance in aquaculture.
{"title":"Aquaculture oxidant (ClO2) or antibiotic disinfection induces unique bimodal aggregation and boosts exDNA sedimentation: A disinfection-driven great spatial shift of antibiotic resistance risk","authors":"Lizhi He, Ming Zhang, Jiahao Li, Qingdong Duan, Daoyong Zhang, Xiangliang Pan","doi":"10.1016/j.watres.2024.122820","DOIUrl":"https://doi.org/10.1016/j.watres.2024.122820","url":null,"abstract":"ClO<sub>2</sub> has been ever-increasingly used as an alternative disinfectant to alleviate antibiotic resistance risk in aquaculture. However, the feasibility of ClO<sub>2</sub> disinfection in reducing antibiotic resistance has not been clarified yet. We comparatively explored the aggregation mechanisms and their effect on extracellular DNA (exDNA) partition and settlement in disinfected aquaculture waters and natural waters. In contrast to the unimodal aggregation in natural non-aquaculture waters, a unique bimodal size distribution pattern of micron-sized aggregates was found in aquaculture waters regardless of the disinfectants (macro-aggregates – 200-700 μm in diameter and micro-aggregates – 2-200 μm in diameter). The bimodal aggregates had 2-4 orders of magnitude higher content of Ferron cations and enriched hundred-fold exDNA in aquaculture waters than in natural waters. ExDNA was adsorbed on the surface of aggregates and conglutinated mainly by carbohydrates and coagulative cations. Macro-aggregates had lower fractal dimension but greater sedimentation velocities compared with micro-aggregates. Polylithionite was the key ballast mineral facilitating fast sedimentation of aggregates in aquaculture waters. Loading more antibiotic resistance genes and mobile gene elements, the aquaculture aggregates sank more rapidly from water to sediments than the natural-water aggregates. It indicates that disinfection with ClO<sub>2</sub> or antibiotics facilitated the spatial transfer of antibiotic resistance risk with high horizontal transfer potential from water column to sediment through forming bimodal aggregates. These findings imply that the adoption of antibiotic alternatives such as the oxidant of ClO<sub>2</sub> is far from sufficient to alleviate antibiotic resistance in aquaculture.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"18 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.watres.2024.122821
Xinjie Shi, Wanzhu Li, Baoli Wang, Na Liu, Xia Liang, Meiling Yang, Cong-Qiang Liu
The production of both methane (CH4) and refractory dissolved organic matter (RDOM) depends on microbial consortia in inland waters, and it is unclear yet the link of these two processes and the underlying microbial regulation mechanisms. Therefore, a large-scale survey was conducted in China's inland waters, with the measurement of CH4 concentrations, DOM chemical composition, microbial community composition, and relative environmental parameters mainly by chromatographic, optical, mass spectrometric, and high-throughput sequencing analyses, to clarify the abovementioned questions. Here, we found a synchronous production of CH4 and RDOM linked by microbial consortia in inland waters. The increasing microbial cooperation driven by the keystone taxa (mainly Fluviicola and Polynucleobacter) could promote the transformation of labile DOM into RDOM and meanwhile benefit methanogenic microbial communities to produce CH4. As such, CH4 and RDOM showed consistent spatial differences, which were mainly influenced by total nitrogen and dissolved oxygen concentrations. This finding deepened the understanding of microbial-driven carbon transformation and will help to more accurately evaluate the carbon source-sink relationship in inland waters.
{"title":"Keystone taxa drive the synchronous production of methane and refractory dissolved organic matter in inland waters","authors":"Xinjie Shi, Wanzhu Li, Baoli Wang, Na Liu, Xia Liang, Meiling Yang, Cong-Qiang Liu","doi":"10.1016/j.watres.2024.122821","DOIUrl":"https://doi.org/10.1016/j.watres.2024.122821","url":null,"abstract":"The production of both methane (CH4) and refractory dissolved organic matter (RDOM) depends on microbial consortia in inland waters, and it is unclear yet the link of these two processes and the underlying microbial regulation mechanisms. Therefore, a large-scale survey was conducted in China's inland waters, with the measurement of CH4 concentrations, DOM chemical composition, microbial community composition, and relative environmental parameters mainly by chromatographic, optical, mass spectrometric, and high-throughput sequencing analyses, to clarify the abovementioned questions. Here, we found a synchronous production of CH4 and RDOM linked by microbial consortia in inland waters. The increasing microbial cooperation driven by the keystone taxa (mainly Fluviicola and Polynucleobacter) could promote the transformation of labile DOM into RDOM and meanwhile benefit methanogenic microbial communities to produce CH4. As such, CH4 and RDOM showed consistent spatial differences, which were mainly influenced by total nitrogen and dissolved oxygen concentrations. This finding deepened the understanding of microbial-driven carbon transformation and will help to more accurately evaluate the carbon source-sink relationship in inland waters.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"69 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.watres.2024.122815
Jinsheng Huang, Waqar Muhammad Ashraf, Talha Ansar, Muhammad Mujtaba Abbas, Mehdi Tlija, Yingying Tang, Yunxue Guo, Wei Zhang
The contamination of water by arsenic (As) poses a substantial environmental challenge with far-reaching influence on human health. Accurately predicting adsorption capacities of arsenite (As(III)) and arsenate (As(V)) on different materials is crucial for the remediation and reuse of contaminated water. Nonetheless, predicting the optimal As adsorption on various materials while considering process energy consumption continues to pose a persistent challenge. Literature data regarding the As adsorption on diverse materials were collected and employed to train machine learning models (ML), such as CatBoost, XGBoost, and LGBoost. These models were utilized to predict both As(III) and As(V) adsorption on a variety of materials using their reaction parameters, structural properties, and composition. The CatBoost model exhibited superior accuracy, achieving a coefficient of determination (R²) of 0.99 and a root mean square error (RMSE) of 1.24 for As(III), and an R² of 0.99 and RMSE of 5.50 for As(V). The initial As(III) and As(V) concentrations were proved to be the primary factors influencing adsorption, accounting for 27.9% and 26.6% of the variance for As(III) and As(V) individually. The genetic optimization led optimisation process, considering the low energy consumption, determined maximum adsorption capacities of 291.66 mg/g for As(III) and 271.56 mg/g for As(V), using C-Layered Double Hydroxide with reduced graphene oxide and chitosan combined with rice straw biochar, respectively. To further facilitate the process design for different real-life applications, the trained ML models are embedded into a web-app that the user can use to estimate the As(III) and As(V) adsorption under different design conditions. The utilization of ML for the energy-efficient As(III) and As(V) adsorption is deemed essential for advancing the treatment of inorganic As in aquatic settings. This approach facilitates the identification of optimal adsorption conditions for As in various material-amended waters, while also enabling the timely detection of As-contaminated water.
{"title":"Optimisation led energy-efficient arsenite and arsenate adsorption on various materials with machine learning","authors":"Jinsheng Huang, Waqar Muhammad Ashraf, Talha Ansar, Muhammad Mujtaba Abbas, Mehdi Tlija, Yingying Tang, Yunxue Guo, Wei Zhang","doi":"10.1016/j.watres.2024.122815","DOIUrl":"https://doi.org/10.1016/j.watres.2024.122815","url":null,"abstract":"The contamination of water by arsenic (As) poses a substantial environmental challenge with far-reaching influence on human health. Accurately predicting adsorption capacities of arsenite (As(III)) and arsenate (As(V)) on different materials is crucial for the remediation and reuse of contaminated water. Nonetheless, predicting the optimal As adsorption on various materials while considering process energy consumption continues to pose a persistent challenge. Literature data regarding the As adsorption on diverse materials were collected and employed to train machine learning models (ML), such as CatBoost, XGBoost, and LGBoost. These models were utilized to predict both As(III) and As(V) adsorption on a variety of materials using their reaction parameters, structural properties, and composition. The CatBoost model exhibited superior accuracy, achieving a coefficient of determination (R²) of 0.99 and a root mean square error (RMSE) of 1.24 for As(III), and an R² of 0.99 and RMSE of 5.50 for As(V). The initial As(III) and As(V) concentrations were proved to be the primary factors influencing adsorption, accounting for 27.9% and 26.6% of the variance for As(III) and As(V) individually. The genetic optimization led optimisation process, considering the low energy consumption, determined maximum adsorption capacities of 291.66 mg/g for As(III) and 271.56 mg/g for As(V), using C-Layered Double Hydroxide with reduced graphene oxide and chitosan combined with rice straw biochar, respectively. To further facilitate the process design for different real-life applications, the trained ML models are embedded into a web-app that the user can use to estimate the As(III) and As(V) adsorption under different design conditions. The utilization of ML for the energy-efficient As(III) and As(V) adsorption is deemed essential for advancing the treatment of inorganic As in aquatic settings. This approach facilitates the identification of optimal adsorption conditions for As in various material-amended waters, while also enabling the timely detection of As-contaminated water.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"99 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142671103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.watres.2024.122814
Md Noim Imtiazy, Andrew M. Paterson, Scott N. Higgins, Huaxia Yao, Daniel Houle, Jeff J. Hudson
The increase in dissolved organic carbon (DOC) concentrations in freshwater systems has received considerable attention due to its implications for drinking water treatment and numerous limnological processes. While past studies have documented the influence of recovery from acidification and climate change on long-term DOC trends, the emerging importance of these explanatory factors remains less understood. In addition, few studies have followed up on recent trends in sites that have undergone increases in DOC. Using a dataset from 1980 to 2020, we investigated interannual variations in DOC and dissolved organic nitrogen (DON) in 49 lakes across four eastern Canadian regions with a history of increases in DOC. We identified recent shifts in DOC patterns using LOESS smoothing and piecewise regression. We observed a stabilizing pattern or even a decrease (p < 0.001) in high acidification regions (Dorset and Nova Scotia), where increases in DOC were previously documented. At the low acid deposition region, IISD-Experimental Lakes Area, an increasing pattern in DOC stabilized in the early 2000s; however, DOC appears to be increasing again in recent years (p = 0.03). Our analysis identified precipitation and SO4 deposition as the primary explanatory variables for DOC patterns (explaining 56–71% of variance). However, because acid deposition has declined substantially, climate and local watershed factors are becoming increasingly influential, leading to the emergence of new DOC patterns. Long-term changes in DOC and DON were not always synchronous, as these were often correlated with different factors (e.g., DON with ammonium deposition). This resulted in observable shifts in DOC:DON ratios, indicative of changes in dissolved organic matter (DOM) composition. We underscore the importance of ongoing monitoring in diverse regions because of the changing nature of environmental variables and new emerging trends.
{"title":"Has lake brownification ceased? Stabilization, re-browning, and other factors associated with dissolved organic matter trends in eastern Canadian lakes","authors":"Md Noim Imtiazy, Andrew M. Paterson, Scott N. Higgins, Huaxia Yao, Daniel Houle, Jeff J. Hudson","doi":"10.1016/j.watres.2024.122814","DOIUrl":"https://doi.org/10.1016/j.watres.2024.122814","url":null,"abstract":"The increase in dissolved organic carbon (DOC) concentrations in freshwater systems has received considerable attention due to its implications for drinking water treatment and numerous limnological processes. While past studies have documented the influence of recovery from acidification and climate change on long-term DOC trends, the emerging importance of these explanatory factors remains less understood. In addition, few studies have followed up on recent trends in sites that have undergone increases in DOC. Using a dataset from 1980 to 2020, we investigated interannual variations in DOC and dissolved organic nitrogen (DON) in 49 lakes across four eastern Canadian regions with a history of increases in DOC. We identified recent shifts in DOC patterns using LOESS smoothing and piecewise regression. We observed a stabilizing pattern or even a decrease (p < 0.001) in high acidification regions (Dorset and Nova Scotia), where increases in DOC were previously documented. At the low acid deposition region, IISD-Experimental Lakes Area, an increasing pattern in DOC stabilized in the early 2000s; however, DOC appears to be increasing again in recent years (p = 0.03). Our analysis identified precipitation and SO<sub>4</sub> deposition as the primary explanatory variables for DOC patterns (explaining 56–71% of variance). However, because acid deposition has declined substantially, climate and local watershed factors are becoming increasingly influential, leading to the emergence of new DOC patterns. Long-term changes in DOC and DON were not always synchronous, as these were often correlated with different factors (e.g., DON with ammonium deposition). This resulted in observable shifts in DOC:DON ratios, indicative of changes in dissolved organic matter (DOM) composition. We underscore the importance of ongoing monitoring in diverse regions because of the changing nature of environmental variables and new emerging trends.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"22 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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