Water Research X最新文献

Micropollutants removal, residual risk, and costs for quaternary treatments in the framework of the Urban Wastewater Treatment Directive

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2025-03-15 DOI: 10.1016/j.wroa.2025.100334

Jessica Ianes, Sara Piraldi, Beatrice Cantoni, Manuela Antonelli

The revised Urban Wastewater Treatment Directive (UWWTD) imposes stringent regulations for the removal of micropollutants from urban wastewater treatment plants. The analyses conducted in this study are based on current knowledge extrapolated from literature WWTPs, to investigate the occurrence, removal and environmental risk related to the 12 target micropollutants: Amisulpride, Benzotriazole, 4,5-Methylbenzotriazole, Carbamazepine, Clarithromycin, Citalopram, Candesartan, Diclofenac, Hydrochlorothiazide, Irbesartan, Metoprolol, and Venlafaxine. The goal is to provide valuable insights into the challenges and opportunities associated with implementing quaternary treatment processes to comply with the UWWTD. Results indicate that the conventional biological treatment is insufficient, with median removal rates below 50 % for most target micropollutants (except for Benzotriazole and Irbesartan). The implementation of quaternary treatment processes, namely ozonation and activated carbon adsorption, significantly enhances WWTP micropollutants removal, with median removal efficiencies exceeding 80 % for all the target micropollutants, with Candesartan being the most recalcitrant. Environmental risk assessment reveals that some micropollutants pose a significant threat to aquatic ecosystems even with 80 % removal efficiency (Irbesartan, Candesartan, Diclofenac, and Venlafaxine), while others do not pose a risk even at WWTP influent concentrations (4,5-Methylbenzotriazole, Hydrochlorothiazide, Amisulpride, Citalopram, and Metoprolol).

Economic analysis shows that the selection of specific operating parameter values significantly impacts the cost of each treatment process, changing the economic feasibility ranking of the different treatment options.

{"title":"Micropollutants removal, residual risk, and costs for quaternary treatments in the framework of the Urban Wastewater Treatment Directive","authors":"Jessica Ianes, Sara Piraldi, Beatrice Cantoni, Manuela Antonelli","doi":"10.1016/j.wroa.2025.100334","DOIUrl":"10.1016/j.wroa.2025.100334","url":null,"abstract":"<div><div>The revised Urban Wastewater Treatment Directive (UWWTD) imposes stringent regulations for the removal of micropollutants from urban wastewater treatment plants. The analyses conducted in this study are based on current knowledge extrapolated from literature WWTPs, to investigate the occurrence, removal and environmental risk related to the 12 target micropollutants: Amisulpride, Benzotriazole, 4,5-Methylbenzotriazole, Carbamazepine, Clarithromycin, Citalopram, Candesartan, Diclofenac, Hydrochlorothiazide, Irbesartan, Metoprolol, and Venlafaxine. The goal is to provide valuable insights into the challenges and opportunities associated with implementing quaternary treatment processes to comply with the UWWTD. Results indicate that the conventional biological treatment is insufficient, with median removal rates below 50 % for most target micropollutants (except for Benzotriazole and Irbesartan). The implementation of quaternary treatment processes, namely ozonation and activated carbon adsorption, significantly enhances WWTP micropollutants removal, with median removal efficiencies exceeding 80 % for all the target micropollutants, with Candesartan being the most recalcitrant. Environmental risk assessment reveals that some micropollutants pose a significant threat to aquatic ecosystems even with 80 % removal efficiency (Irbesartan, Candesartan, Diclofenac, and Venlafaxine), while others do not pose a risk even at WWTP influent concentrations (4,5-Methylbenzotriazole, Hydrochlorothiazide, Amisulpride, Citalopram, and Metoprolol).</div><div>Economic analysis shows that the selection of specific operating parameter values significantly impacts the cost of each treatment process, changing the economic feasibility ranking of the different treatment options.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100334"},"PeriodicalIF":7.2,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploration of deep learning leak detection model across multiple smart water distribution systems: Detectable leak sizes with AMI meters

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2025-03-14 DOI: 10.1016/j.wroa.2025.100332

Sanghoon Jun , Donghwi Jung

Numerous deep learning (DL) models have been developed for leak detection in water distribution systems (WDSs). However, significant lack of knowledge still remains concerning their detectability and the smallest detectable leak sizes across various WDSs. To address these research gaps, this study explores the performance of a DL leak detection model across eleven smart WDSs. A convolutional neural network (CNN) is employed to identify leaks using the spatially distributed pressure response images derived from the difference between advanced metering infrastructure (AMI) measurements and predictions from a well-calibrated hydraulic model (i.e., digital twin). Ten leak magnitudes are evaluated for each WDS, and three performance metrics (recall, precision, and F1 score) are calculated to assess the detectability and the detectable leak sizes of the CNN. The analysis reveals that the DL model's detection ability is highly impacted by WDS type, whether transmission- or distribution-oriented. The former networks exhibit low accuracy in identifying leaks due to the indistinguishability of pressure response images between normal and leak conditions. On the other hand, the latter networks generally achieve higher precision and recall results and can detect smaller leaks. Moreover, the smallest detectable leak sizes are more sensitive to WDS structural parameters (pipe diameter and length) than system hydraulics (system demand). Examining pipe characteristics along the leakage flow path provides most useful information in determining the detectability of leaks.

{"title":"Exploration of deep learning leak detection model across multiple smart water distribution systems: Detectable leak sizes with AMI meters","authors":"Sanghoon Jun , Donghwi Jung","doi":"10.1016/j.wroa.2025.100332","DOIUrl":"10.1016/j.wroa.2025.100332","url":null,"abstract":"<div><div>Numerous deep learning (DL) models have been developed for leak detection in water distribution systems (WDSs). However, significant lack of knowledge still remains concerning their detectability and the smallest detectable leak sizes across various WDSs. To address these research gaps, this study explores the performance of a DL leak detection model across eleven smart WDSs. A convolutional neural network (CNN) is employed to identify leaks using the spatially distributed pressure response images derived from the difference between advanced metering infrastructure (AMI) measurements and predictions from a well-calibrated hydraulic model (i.e., digital twin). Ten leak magnitudes are evaluated for each WDS, and three performance metrics (recall, precision, and F1 score) are calculated to assess the detectability and the detectable leak sizes of the CNN. The analysis reveals that the DL model's detection ability is highly impacted by WDS type, whether transmission- or distribution-oriented. The former networks exhibit low accuracy in identifying leaks due to the indistinguishability of pressure response images between normal and leak conditions. On the other hand, the latter networks generally achieve higher precision and recall results and can detect smaller leaks. Moreover, the smallest detectable leak sizes are more sensitive to WDS structural parameters (pipe diameter and length) than system hydraulics (system demand). Examining pipe characteristics along the leakage flow path provides most useful information in determining the detectability of leaks.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100332"},"PeriodicalIF":7.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using wastewater analysis to assess the health status of two distinct populations in China

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2025-03-14 DOI: 10.1016/j.wroa.2025.100335

Zhe Wang , Qiuda Zheng , Jianfa Gao , Jianan Ren , Fahad Ahmed , Yufang Chen , Cong Yang , Han Chen , Yuan Ren , Phong K. Thai

Wastewater-based epidemiology (WBE) is a powerful tool for monitoring biomarkers of human health conditions. The WBE approach could deliver robust public health data with high temporal and spatial resolution, making it highly effective for assessing the impact of public health interventions across different populations. This study applied WBE to compare substance use and explore public health implications across two distinct populations: a general urban population and a university population. Daily and weekly wastewater samples were collected from 2017 to 2018, originating from a wastewater treatment plant serving the urban catchment and a pump station encompassing 10 universities. Consumption of over-the-counter (OTC) medications, prescribed drugs, and chronic disease medications in these two populations were estimated. Additionally, previously published data on recreational substances, respiratory and allergy medications, sweeteners, stress markers, and anabolic steroids were analyzed to present a comprehensive overview of human lifestyles and health status. Our results indicated that the university population consumed more OTC painkillers, including ibuprofen and paracetamol, but fewer prescribed opioids, such as codeine and morphine, compared to the general population. In contrast, higher consumption of chronic disease medications in the urban catchment indicated poorer overall health compared to the younger university population. These findings highlight significant differences in pharmaceutical consumption patterns and associated public health profiles between younger and general populations. This study underscores the utility of WBE in identifying public health disparities and guiding targeted health interventions based on population-specific needs and behaviors.

{"title":"Using wastewater analysis to assess the health status of two distinct populations in China","authors":"Zhe Wang , Qiuda Zheng , Jianfa Gao , Jianan Ren , Fahad Ahmed , Yufang Chen , Cong Yang , Han Chen , Yuan Ren , Phong K. Thai","doi":"10.1016/j.wroa.2025.100335","DOIUrl":"10.1016/j.wroa.2025.100335","url":null,"abstract":"<div><div>Wastewater-based epidemiology (WBE) is a powerful tool for monitoring biomarkers of human health conditions. The WBE approach could deliver robust public health data with high temporal and spatial resolution, making it highly effective for assessing the impact of public health interventions across different populations. This study applied WBE to compare substance use and explore public health implications across two distinct populations: a general urban population and a university population. Daily and weekly wastewater samples were collected from 2017 to 2018, originating from a wastewater treatment plant serving the urban catchment and a pump station encompassing 10 universities. Consumption of over-the-counter (OTC) medications, prescribed drugs, and chronic disease medications in these two populations were estimated. Additionally, previously published data on recreational substances, respiratory and allergy medications, sweeteners, stress markers, and anabolic steroids were analyzed to present a comprehensive overview of human lifestyles and health status. Our results indicated that the university population consumed more OTC painkillers, including ibuprofen and paracetamol, but fewer prescribed opioids, such as codeine and morphine, compared to the general population. In contrast, higher consumption of chronic disease medications in the urban catchment indicated poorer overall health compared to the younger university population. These findings highlight significant differences in pharmaceutical consumption patterns and associated public health profiles between younger and general populations. This study underscores the utility of WBE in identifying public health disparities and guiding targeted health interventions based on population-specific needs and behaviors.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100335"},"PeriodicalIF":7.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ageing underground water pipelines: Time-to-failure models, gaps and future directions

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2025-03-13 DOI: 10.1016/j.wroa.2025.100331

Beenish Bakhtawar , Tarek Zayed , Ibrahim Abdelfadeel Shaban , Nehal Elshaboury , Abdul-Mugis Yussif

Accurate prediction of the failure time of individual pipelines of a water distribution network can assist in preventing sudden bursts and leaks. Failure prediction over time can help eliminate managerial uncertainty in pipe rehabilitation and replacement decision-making. Since time-based deterioration modeling has less focus in past research, the study focuses on a critical review of the current state-of-the-art for time-to-failure/failure age models related to water pipelines. A unique unsupervised learning-based clustering framework is used to perform an in-depth and robust literature analysis. Hierarchical clustering reveals the main modeling approaches, classified as 1) physical data-based models and 2) historical data-based failure models. Critical research gaps are further explored using t-SNE and Gaussian Mixture Models based clustering. Identified gaps include fragmented modeling approaches, lack of integration between physical and data-driven models, limited data related issues, and a lack of insight on practical translation of model findings for effective utility management. Future studies can consider several integration strategies to overcome individual model limitations, use of generative AI to enrich data, IoT implementation for physical data collection, improve feature engineering and feature extraction efforts, and consider domain knowledge from hydraulic models to improve AI models. Overall, the study offers practical insights for predicting the remaining time-to-failure and service life of water pipelines.

{"title":"Ageing underground water pipelines: Time-to-failure models, gaps and future directions","authors":"Beenish Bakhtawar , Tarek Zayed , Ibrahim Abdelfadeel Shaban , Nehal Elshaboury , Abdul-Mugis Yussif","doi":"10.1016/j.wroa.2025.100331","DOIUrl":"10.1016/j.wroa.2025.100331","url":null,"abstract":"<div><div>Accurate prediction of the failure time of individual pipelines of a water distribution network can assist in preventing sudden bursts and leaks. Failure prediction over time can help eliminate managerial uncertainty in pipe rehabilitation and replacement decision-making. Since time-based deterioration modeling has less focus in past research, the study focuses on a critical review of the current state-of-the-art for time-to-failure/failure age models related to water pipelines. A unique unsupervised learning-based clustering framework is used to perform an in-depth and robust literature analysis. Hierarchical clustering reveals the main modeling approaches, classified as 1) physical data-based models and 2) historical data-based failure models. Critical research gaps are further explored using t-SNE and Gaussian Mixture Models based clustering. Identified gaps include fragmented modeling approaches, lack of integration between physical and data-driven models, limited data related issues, and a lack of insight on practical translation of model findings for effective utility management. Future studies can consider several integration strategies to overcome individual model limitations, use of generative AI to enrich data, IoT implementation for physical data collection, improve feature engineering and feature extraction efforts, and consider domain knowledge from hydraulic models to improve AI models. Overall, the study offers practical insights for predicting the remaining time-to-failure and service life of water pipelines.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100331"},"PeriodicalIF":7.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leak detection in water supply networks using two-stage temporal segmentation and incremental learning for non-stationary acoustic signals

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2025-03-12 DOI: 10.1016/j.wroa.2025.100333

Xingke Ma , Yipeng Wu , Guancheng Guo , Shuming Liu , Yuexia Xu , Jingjing Fan , Hongbin Wang , Liren Xu

Acoustic detection is a primary method for identifying leaks in urban water supply networks. However, acoustic signals within pipelines are highly susceptible to dynamic interference noise. This complicates the differentiation between leak and non-leak signals. To address this challenge, this paper presents a temporal segmentation-based approach for processing acoustic signals. Specifically, the two-stage temporal segmentation approach, which applies long-term segments to isolate non-stationary characteristics and short-term segments for capturing quasi-stationary features in acoustic signals, is introduced. We then applied the CNN model to recognize the Mel spectrogram features of the two-stage segmented signals and compared its performance with other models. Results indicate that this approach enhances both the accuracy and stability of leak detection, with the model achieving an average detection accuracy of 95 %. Moreover, the model is designed as an adaptive and continuous learning model, integrating its detection outcomes and newly labeled data segments into its training dataset. In practical applications, this continuous learning capability enables the model to improve its detection efficacy over time as data volume expands.

{"title":"Leak detection in water supply networks using two-stage temporal segmentation and incremental learning for non-stationary acoustic signals","authors":"Xingke Ma , Yipeng Wu , Guancheng Guo , Shuming Liu , Yuexia Xu , Jingjing Fan , Hongbin Wang , Liren Xu","doi":"10.1016/j.wroa.2025.100333","DOIUrl":"10.1016/j.wroa.2025.100333","url":null,"abstract":"<div><div>Acoustic detection is a primary method for identifying leaks in urban water supply networks. However, acoustic signals within pipelines are highly susceptible to dynamic interference noise. This complicates the differentiation between leak and non-leak signals. To address this challenge, this paper presents a temporal segmentation-based approach for processing acoustic signals. Specifically, the two-stage temporal segmentation approach, which applies long-term segments to isolate non-stationary characteristics and short-term segments for capturing quasi-stationary features in acoustic signals, is introduced. We then applied the CNN model to recognize the Mel spectrogram features of the two-stage segmented signals and compared its performance with other models. Results indicate that this approach enhances both the accuracy and stability of leak detection, with the model achieving an average detection accuracy of 95 %. Moreover, the model is designed as an adaptive and continuous learning model, integrating its detection outcomes and newly labeled data segments into its training dataset. In practical applications, this continuous learning capability enables the model to improve its detection efficacy over time as data volume expands.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100333"},"PeriodicalIF":7.2,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fungal pretreatment as a promising approach for simultaneous recovery of phosphorus and carbon resource from garden waste: Performance and mechanism

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2025-03-10 DOI: 10.1016/j.wroa.2025.100330

Ruizhe Zhang , Qinyuan Lu , Cong Zhang , Yifeng Chen , Dunjie Li , Wenqiang Qi , Qian Ping , Yongmei Li

Garden waste (GW), which is rich in organic matter and nutrients such as nitrogen (N) and phosphorus (P), has not been fully utilized for resource recovery. This study investigates a novel approach to recover both P and carbon source from GW by fungal pretreatment. Four types of GW—Zoysia matrella (L.) Merr (ZMM), Lolium perenne L (LPL), Platanus × acerifolia (Aiton) Willd (PAW), and Cinnamomum camphora (L.) Presl (CCP)—were subjected to alkaline, thermal, and fungal pretreatments. Results showed that fungal pretreatment was more effective than alkaline and thermal methods, especially for turfgrass GW. After 7 days of fungal pretreatment, the orthophosphate (PO₄-P) concentration in the LPL supernatant was 2.15 times that of the control. Enzyme activity and metagenomic data revealed higher abundances of lignin degrading enzymes in turfgrass GW, with laccase being the dominant enzyme. Fungi convert organic phosphorus into PO₄-P by secreting 3-phytase. Ammonium was also produced during fungal pretreatment, resulting in a weakly alkaline supernatant that promoted the precipitation of P as struvite. Consequently, a 43.51 % recovery of P from LPL as struvite with 91.3 % purity was achieved. The residual leachate served as a carbon source, achieving 78.67 % nitrate removal and 76.48 % total nitrogen removal. This study proposes a sustainable strategy for simultaneous P and carbon recovery from GW.

{"title":"Fungal pretreatment as a promising approach for simultaneous recovery of phosphorus and carbon resource from garden waste: Performance and mechanism","authors":"Ruizhe Zhang , Qinyuan Lu , Cong Zhang , Yifeng Chen , Dunjie Li , Wenqiang Qi , Qian Ping , Yongmei Li","doi":"10.1016/j.wroa.2025.100330","DOIUrl":"10.1016/j.wroa.2025.100330","url":null,"abstract":"<div><div>Garden waste (GW), which is rich in organic matter and nutrients such as nitrogen (N) and phosphorus (P), has not been fully utilized for resource recovery. This study investigates a novel approach to recover both P and carbon source from GW by fungal pretreatment. Four types of GW—<em>Zoysia matrella (</em>L.<em>) Merr</em> (ZMM), <em>Lolium perenne</em> L (LPL), <em>Platanus × acerifolia (Aiton) Willd</em> (PAW), and <em>Cinnamomum camphora (</em>L.<em>) Presl</em> (CCP)—were subjected to alkaline, thermal, and fungal pretreatments. Results showed that fungal pretreatment was more effective than alkaline and thermal methods, especially for turfgrass GW. After 7 days of fungal pretreatment, the orthophosphate (PO<sub>4</sub>-P) concentration in the LPL supernatant was 2.15 times that of the control. Enzyme activity and metagenomic data revealed higher abundances of lignin degrading enzymes in turfgrass GW, with laccase being the dominant enzyme. Fungi convert organic phosphorus into PO<sub>4</sub>-P by secreting 3-phytase. Ammonium was also produced during fungal pretreatment, resulting in a weakly alkaline supernatant that promoted the precipitation of P as struvite. Consequently, a 43.51 % recovery of P from LPL as struvite with 91.3 % purity was achieved. The residual leachate served as a carbon source, achieving 78.67 % nitrate removal and 76.48 % total nitrogen removal. This study proposes a sustainable strategy for simultaneous P and carbon recovery from GW.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100330"},"PeriodicalIF":7.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Balancing energy recovery and direct greenhouse gas emissions in wastewater treatment

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2025-03-08 DOI: 10.1016/j.wroa.2025.100328

Kaili Li , Haoran Duan , Shuting Wang , Ziping Wu , Peter Wardrop , James Lloyd , Nathali Christy , Pieter De Jong , Liu Ye

Achieving net-zero emissions is a critical goal for the water industry. This study provides a comprehensive evaluation of energy recovery and direct greenhouse gas (GHG) emissions from a full-scale wastewater treatment plant (WWTP), highlighting the important balance between carbon capture and emissions reduction. Long-term monitoring results revealed that upstream carbon capture, while recovering significant energy for carbon offset (40 % of total emission), stimulated downstream nitrous oxide (N₂O) emissions, a major contributor to Scope 1 emissions. In response, integrated mitigation strategies were developed using mechanistic modelling, incorporating process optimizations (adjusting split ratios, DO setpoints, and mixing ratio) and retrofitting solution (raw wastewater diversion). The identified strategies reduced N₂O emissions by 50 % and the overall carbon footprint by 40 %, despite a 31 % decrease in energy recovery, compared with the baseline case (N₂O emission factor: 1.31 % of TKN load, net emissions: 354.29 kg CO₂-e/ML, and energy recovery: 386.02 kg CO₂-e/ML). The findings demonstrated the need for a holistic assessment of carbon capture, energy recovery, and GHG emissions across the entire treatment process. The outcome offers actionable insights for improving WWTP operations towards net-zero emissions.

{"title":"Balancing energy recovery and direct greenhouse gas emissions in wastewater treatment","authors":"Kaili Li , Haoran Duan , Shuting Wang , Ziping Wu , Peter Wardrop , James Lloyd , Nathali Christy , Pieter De Jong , Liu Ye","doi":"10.1016/j.wroa.2025.100328","DOIUrl":"10.1016/j.wroa.2025.100328","url":null,"abstract":"<div><div>Achieving net-zero emissions is a critical goal for the water industry. This study provides a comprehensive evaluation of energy recovery and direct greenhouse gas (GHG) emissions from a full-scale wastewater treatment plant (WWTP), highlighting the important balance between carbon capture and emissions reduction. Long-term monitoring results revealed that upstream carbon capture, while recovering significant energy for carbon offset (40 % of total emission), stimulated downstream nitrous oxide (N<sub>2</sub>O) emissions, a major contributor to Scope 1 emissions. In response, integrated mitigation strategies were developed using mechanistic modelling, incorporating process optimizations (adjusting split ratios, DO setpoints, and mixing ratio) and retrofitting solution (raw wastewater diversion). The identified strategies reduced N<sub>2</sub>O emissions by 50 % and the overall carbon footprint by 40 %, despite a 31 % decrease in energy recovery, compared with the baseline case (N<sub>2</sub>O emission factor: 1.31 % of TKN load, net emissions: 354.29 kg CO<sub>2</sub>-e/ML, and energy recovery: 386.02 kg CO<sub>2</sub>-e/ML). The findings demonstrated the need for a holistic assessment of carbon capture, energy recovery, and GHG emissions across the entire treatment process. The outcome offers actionable insights for improving WWTP operations towards net-zero emissions.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"28 ","pages":"Article 100328"},"PeriodicalIF":7.2,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated social development on analyzing the distribution, risk and source apportionment of antibiotics pollution in mountainous rivers

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2025-03-01 DOI: 10.1016/j.wroa.2025.100327

Wei Fan , Minjie Yang , Ying Shao , Dongjun Shen , Liang Ao , Zhongli Chen

Antibiotics, as the widespread drugs stimulate the evolution of antibiotic resistance, threatening human and ecosystem health worldwide. However, studies rarely conducted in rivers among the regional scale with diverse economic development, which limits the management efficient of antibiotic control. Therefore, we investigated the concentration, distribution, risk and source apportionment of 54 antibiotics in 9 mountainous rivers, where the economic social development divers among their watersheds in Chongqing, China. The results showed that the concentrations of antibiotics detected in surface water, effluent of wastewater treatment plants, hospital, livestock and aquaculture sewage were 0.13–290 ng/L, 2.17–590 ng/L, 6.58–2.16 × 10⁵ ng/L, 4.5–7.4 × 10⁵ ng/L and 4.41–7.49 × 10³ ng/L, respectively. The order of total antibiotic concentrations along the investigated rivers was Laixi River > Changshou Lake > Longxi River > Fujiang River > Jialing River > Qiongjiang River > Yangtze River > Wujiang River > Qijiang River. The risk quotient indicates that ofloxacin and lincomycin exhibited high risk. Through Mantel test and correlation analysis screened antibiotics associated with anthropogenic factors. Municipal wastewater had a positive impact on QNs. The positive matrix factorization model was used to identify the main sources of antibiotics in surface water with special focus on the Longxi River, revealing the livestock and aquaculture were main contributions, respectively. The ToxPi method was employed to prioritize antibiotics in surface water, and seven compounds were recommended as priority chemicals of concern in the future. This work provides a valuable regional scale dataset of antibiotics in the mountainous rivers, which promises valuable insights for controlling antibiotic contamination.

{"title":"Integrated social development on analyzing the distribution, risk and source apportionment of antibiotics pollution in mountainous rivers","authors":"Wei Fan , Minjie Yang , Ying Shao , Dongjun Shen , Liang Ao , Zhongli Chen","doi":"10.1016/j.wroa.2025.100327","DOIUrl":"10.1016/j.wroa.2025.100327","url":null,"abstract":"<div><div>Antibiotics, as the widespread drugs stimulate the evolution of antibiotic resistance, threatening human and ecosystem health worldwide. However, studies rarely conducted in rivers among the regional scale with diverse economic development, which limits the management efficient of antibiotic control. Therefore, we investigated the concentration, distribution, risk and source apportionment of 54 antibiotics in 9 mountainous rivers, where the economic social development divers among their watersheds in Chongqing, China. The results showed that the concentrations of antibiotics detected in surface water, effluent of wastewater treatment plants, hospital, livestock and aquaculture sewage were 0.13–290 ng/L, 2.17–590 ng/L, 6.58–2.16 × 10<sup>5</sup> ng/L, 4.5–7.4 × 10<sup>5</sup> ng/L and 4.41–7.49 × 10<sup>3</sup> ng/L, respectively. The order of total antibiotic concentrations along the investigated rivers was Laixi River > Changshou Lake > Longxi River > Fujiang River > Jialing River > Qiongjiang River > Yangtze River > Wujiang River > Qijiang River. The risk quotient indicates that ofloxacin and lincomycin exhibited high risk. Through Mantel test and correlation analysis screened antibiotics associated with anthropogenic factors. Municipal wastewater had a positive impact on QNs. The positive matrix factorization model was used to identify the main sources of antibiotics in surface water with special focus on the Longxi River, revealing the livestock and aquaculture were main contributions, respectively. The ToxPi method was employed to prioritize antibiotics in surface water, and seven compounds were recommended as priority chemicals of concern in the future. This work provides a valuable regional scale dataset of antibiotics in the mountainous rivers, which promises valuable insights for controlling antibiotic contamination.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"28 ","pages":"Article 100327"},"PeriodicalIF":7.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Upgrading waste activated sludge into valuable biosolids via an integrated biochemical approach

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2025-02-27 DOI: 10.1016/j.wroa.2025.100325

Xi Lu , Zhiyao Wang , Zheng Kong , Haoran Duan , Zhiqiang Zuo , Zhetai Hu , Min Zheng , Shihu Hu

Sludge management is a significant challenge for water utilities worldwide. High costs are largely driven by poor digestibility and dewaterability, while elevated concentrations of pathogens and toxic metals limit the safe and beneficial reuse of sludge. This study proposes an innovative integrated biochemical method that concurrently enhances sludge digestibility, facilitates toxic metal removal, improves dewaterability, and achieves pathogen reduction. In a laboratory-scale aerobic sludge digester processing waste-activated sludge, in-situ sludge acidification was achieved within 35–40 days, reaching a pH of approximately 2.6. This acidification was driven by the natural cultivation of acid-tolerant ammonia-oxidizing bacteria (Ca. Nitrosoglobus), which generate protons by oxidizing ammonium released from the sludge. Sludge acidification resulted in significant improvements, including volatile solids (VS) destruction (49 ± 6 %), pathogen reduction (∼4 log reduction), enhanced dewaterability (demonstrated by three methods), and toxic metal solubilization. While most toxic metals were solubilized to meet the Grade A biosolid standard (the highest biosolids standard qualified for unrestricted and safe use), copper only met the Grade B standard. To address this, a low dose of nitrite (5 mg N/L) was added to the acidified sludge (pH 2.2, adjusted with a small amount of acid) for 5 h, successfully solubilizing copper and upgrading the sludge to Grade A standards. Overall, this study demonstrates the potential of in-situ sludge acidification combined with minimal nitrite and acid addition as an efficient approach for improving multiple aspects of sludge management.

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引用次数: 0

Comparative analysis of synergistic effects of phosphorus enrichment and hydroxyapatite nucleation on anammox granular sludge

IF 7.2 2区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research X

Pub Date : 2025-02-27 DOI: 10.1016/j.wroa.2025.100326

Yanjun Zhu , Dong Li , Huiping Zeng , Sibo Fu , Yuliang Zhu , Jifang Zheng , Sen Yang , Songyue Li , Jie Zhang

Combined nutrient removal and phosphorus recovery are pivotal for advancing sustainable wastewater treatment technologies. Despite the proven efficacy of anammox granular sludge systems in nutrient removal, the long-term structural development, microbial dynamics, and resource recovery potential of these systems remain insufficiently understood, particularly under municipal wastewater conditions at the microscopic level. Addressing this gap is critical to optimizing reactor performance and enhancing sustainability. In this study, we investigated the impact of phosphorus enrichment on granular sludge characteristics using two reactors. The phosphorus-free reactor (R1) facilitated the rapid formation of hollow granular sludge, with particle sizes growing rapidly—yet with significant variability—and total nitrogen (TN) removal efficiencies ranging between 74.86 % and 86.24 %. In contrast, the phosphorus-enriched reactor (R2) was supplemented to promote hydroxyapatite (HAP) nucleation, resulting in slower but more organized growth of dense granules with a narrower size distribution and consistently higher nitrogen removal efficiencies (75.17 %–91.97 %). Notably, phosphorus removal efficiency in R2 peaked when granules reached approximately 800 μm, attributable to HAP-mediated adsorption and precipitation. Furthermore, the relative abundance of key anammox bacteria, such as Candidatus Brocadia, increased dramatically from 1.28 % in R1 to 18.32 % in R2, while Denitratisoma also proliferated. Despite structural differences, similar amounts of floating sludge were observed in both reactors, likely due to gas entrapment and extracellular polymeric substances (EPS) production. These findings demonstrate that phosphorus enrichment not only enhances nitrogen removal and phosphorus recovery but also modulates microbial community composition and granule morphology.

营养物去除和磷回收相结合是推进可持续废水处理技术的关键。尽管厌氧颗粒污泥系统在去除营养物方面的功效已得到证实，但人们对这些系统的长期结构发展、微生物动态和资源回收潜力的了解仍然不够，特别是在城市污水条件下的微观层面。填补这一空白对于优化反应器性能和提高可持续性至关重要。在这项研究中，我们使用两个反应器研究了磷富集对颗粒污泥特性的影响。无磷反应器（R1）有利于中空颗粒污泥的快速形成，颗粒尺寸快速增长，但变化很大，总氮（TN）去除率介于 74.86 % 和 86.24 % 之间。相比之下，在富磷反应器（R2）中添加了促进羟基磷灰石（HAP）成核的添加剂，因此致密颗粒的生长速度较慢，但更有组织，粒度分布较窄，脱氮效率一直较高（75.17 %-91.97 %）。值得注意的是，当颗粒达到约 800 μm 时，R2 的除磷效率达到峰值，这归因于 HAP 介导的吸附和沉淀。此外，关键的厌氧菌（如 Candidatus Brocadia）的相对丰度从 R1 的 1.28% 急剧增加到 R2 的 18.32%，同时 Denitratisoma 也大量繁殖。尽管结构上存在差异，但在两个反应器中观察到的漂浮污泥数量相似，这可能是由于气体截留和胞外聚合物质（EPS）的产生。这些研究结果表明，磷富集不仅能提高脱氮和磷回收率，还能调节微生物群落组成和颗粒形态。

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引用次数: 0