In this study, a novel three-dimensional biofilm electrode reactor (3D-BER) with a graphene oxide (GO)–modified cathode was developed to enhance the denitrification performance of secondary effluent from wastewater treatment plants (SEWTPs). The effects of different HRTs and currents on the 3D-BER were explored. The results indicated that at the optimal HRT of 4 h and current of 350 mA/m2, the 3D-BER with GO-modified cathode had a higher denitrification rate (2.40 ± 0.1 mg TN/L/h) and less accumulation of intermediate products, especially with 3.34% TN molar conversion to N2O. The GO-modified cathode offered a large biocompatible specific surface area and enhanced the conductivity, which favored microbial growth and increased electron transfer efficiency and extracellular enzyme activities. Moreover, the activity of nitrite reductase increased more than that of nitrate reductase to accelerate nitrite reduction, thus facilitating the denitrification process. The proposed 3D-BER provided an effective solution to elevate tertiary denitrification in the SEWTP.
{"title":"Enhanced denitrification by graphene oxide–modified cathode for the secondary effluent of wastewater treatment plants in three-dimensional biofilm electrode reactors","authors":"Ying Xue, Chaojie Zhang, Sibo Li, Qi Zhou, Xuefei Zhou, Yalei Zhang","doi":"10.2166/wst.2024.179","DOIUrl":"https://doi.org/10.2166/wst.2024.179","url":null,"abstract":"\u0000 \u0000 In this study, a novel three-dimensional biofilm electrode reactor (3D-BER) with a graphene oxide (GO)–modified cathode was developed to enhance the denitrification performance of secondary effluent from wastewater treatment plants (SEWTPs). The effects of different HRTs and currents on the 3D-BER were explored. The results indicated that at the optimal HRT of 4 h and current of 350 mA/m2, the 3D-BER with GO-modified cathode had a higher denitrification rate (2.40 ± 0.1 mg TN/L/h) and less accumulation of intermediate products, especially with 3.34% TN molar conversion to N2O. The GO-modified cathode offered a large biocompatible specific surface area and enhanced the conductivity, which favored microbial growth and increased electron transfer efficiency and extracellular enzyme activities. Moreover, the activity of nitrite reductase increased more than that of nitrate reductase to accelerate nitrite reduction, thus facilitating the denitrification process. The proposed 3D-BER provided an effective solution to elevate tertiary denitrification in the SEWTP.","PeriodicalId":505935,"journal":{"name":"Water Science & Technology","volume":"31 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141271557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Regueiro-Picallo, Alma Schellart, Henriette Jensen, Jeroen Langeveld, M. Viklander, Lian Lundy
Enhancing sediment accumulation monitoring techniques in sewers will enable a better understanding of the build-up processes to develop improved cleaning strategies. Thermal sensors provide a solution to sediment depth estimation by passively monitoring temperature fluctuations in the wastewater and sediment beds, which allows evaluation of the heat-transfer processes in sewer pipes. This study analyses the influence of the flow conditions on heat-transfer processes at the water–sediment interface during dry weather flow conditions. For this purpose, an experimental campaign was performed by establishing different flow, temperature patterns, and sediment depth conditions in an annular flume, which ensured stable flow and room-temperature conditions. Numerical simulations were also performed to assess the impact of flow conditions on the relationships between sediment depth and harmonic parameters derived from wastewater and sediment-bed temperature patterns. Results show that heat transfer between water and sediment occurred instantaneously for velocities greater than 0.1 m/s, and that sediment depth estimations using temperature-based systems were barely sensitive to velocities between 0.1 and 0.4 m/s. A depth estimation accuracy of ±7 mm was achieved. This confirms the ability of using temperature sensors to monitor sediment build-up in sewers under dry weather conditions, without the need for flow monitoring.
{"title":"Flow rate influence on sediment depth estimation in sewers using temperature sensors","authors":"M. Regueiro-Picallo, Alma Schellart, Henriette Jensen, Jeroen Langeveld, M. Viklander, Lian Lundy","doi":"10.2166/wst.2024.193","DOIUrl":"https://doi.org/10.2166/wst.2024.193","url":null,"abstract":"\u0000 Enhancing sediment accumulation monitoring techniques in sewers will enable a better understanding of the build-up processes to develop improved cleaning strategies. Thermal sensors provide a solution to sediment depth estimation by passively monitoring temperature fluctuations in the wastewater and sediment beds, which allows evaluation of the heat-transfer processes in sewer pipes. This study analyses the influence of the flow conditions on heat-transfer processes at the water–sediment interface during dry weather flow conditions. For this purpose, an experimental campaign was performed by establishing different flow, temperature patterns, and sediment depth conditions in an annular flume, which ensured stable flow and room-temperature conditions. Numerical simulations were also performed to assess the impact of flow conditions on the relationships between sediment depth and harmonic parameters derived from wastewater and sediment-bed temperature patterns. Results show that heat transfer between water and sediment occurred instantaneously for velocities greater than 0.1 m/s, and that sediment depth estimations using temperature-based systems were barely sensitive to velocities between 0.1 and 0.4 m/s. A depth estimation accuracy of ±7 mm was achieved. This confirms the ability of using temperature sensors to monitor sediment build-up in sewers under dry weather conditions, without the need for flow monitoring.","PeriodicalId":505935,"journal":{"name":"Water Science & Technology","volume":"51 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141269642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elif İnce, M. Ince, Furkan Durmaz, Handenur Yasar, Yasin Abdullah Uslu
Anoxic–oxic membrane bioreactor (A2O-MBR), one of the biological systems requently used for the treatment of coking wastewater, increased under the influence of the growing iron and steel industry; however it cannot meet the discharge standards set by the Ministry of Environment, Urbanization and Climate Change of the Republic of Türkiye (CSB) due to recalcitrant pollutants in the wastewater. Advanced treatment of coking wastewater treated in A2O-MBR to meet the standards of the Ministry; nanofiltration (NF) (two different membranes and different pressures), powder activated carbon-microfiltration (PAC-MF), and PAC-NF (two different membranes and five different PAC concentrations) were investigated. In addition to the parameters determined by the Ministry, other parameters (ammonium, thiocyanate (SCN−), hydrogen cyanide (HCN), dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), color) were also investigated to evaluate the performance of flux and treatment efficiency of the hybrid process. According to the results, chemical oxygen demand (COD) in the NF process, COD, and total cyanide (T-CN) in the PAC-MF process could not meet the discharge standards. In the PAC-NF hybrid system, XN45 met the discharge standards in all parameters (COD = 96 ± 1.88 mg/L, T-CN = <0.02 mg/L, phenol = <0.05 mg/L), with a 78% recovery rate at 0.5 g/L PAC concentration.
{"title":"Further treatment of coking wastewater treated in A2O-MBR by the nanofiltration-powder activated carbon hybrid system","authors":"Elif İnce, M. Ince, Furkan Durmaz, Handenur Yasar, Yasin Abdullah Uslu","doi":"10.2166/wst.2024.091","DOIUrl":"https://doi.org/10.2166/wst.2024.091","url":null,"abstract":"\u0000 Anoxic–oxic membrane bioreactor (A2O-MBR), one of the biological systems requently used for the treatment of coking wastewater, increased under the influence of the growing iron and steel industry; however it cannot meet the discharge standards set by the Ministry of Environment, Urbanization and Climate Change of the Republic of Türkiye (CSB) due to recalcitrant pollutants in the wastewater. Advanced treatment of coking wastewater treated in A2O-MBR to meet the standards of the Ministry; nanofiltration (NF) (two different membranes and different pressures), powder activated carbon-microfiltration (PAC-MF), and PAC-NF (two different membranes and five different PAC concentrations) were investigated. In addition to the parameters determined by the Ministry, other parameters (ammonium, thiocyanate (SCN−), hydrogen cyanide (HCN), dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), color) were also investigated to evaluate the performance of flux and treatment efficiency of the hybrid process. According to the results, chemical oxygen demand (COD) in the NF process, COD, and total cyanide (T-CN) in the PAC-MF process could not meet the discharge standards. In the PAC-NF hybrid system, XN45 met the discharge standards in all parameters (COD = 96 ± 1.88 mg/L, T-CN = <0.02 mg/L, phenol = <0.05 mg/L), with a 78% recovery rate at 0.5 g/L PAC concentration.","PeriodicalId":505935,"journal":{"name":"Water Science & Technology","volume":"6 5‐6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Alhamami, Emmanuel Falude, Ahmed Osman Ibrahim, Y. Dodo, Okpakhalu Livingston Daniel, Farruh Atamurotov
This study employs diverse machine learning models, including classic artificial neural network (ANN), hybrid ANN models, and the imperialist competitive algorithm and emotional artificial neural network (EANN), to predict crucial parameters such as fresh water production and vapor temperatures. Evaluation metrics reveal the integrated ANN-ICA model outperforms the classic ANN, achieving a remarkable 20% reduction in mean squared error (MSE). The emotional artificial neural network (EANN) demonstrates superior accuracy, attaining an impressive 99% coefficient of determination (R2) in predicting freshwater production and vapor temperatures. The comprehensive comparative analysis extends to environmental assessments, displaying the solar desalination system's compatibility with renewable energy sources. Results highlight the potential for the proposed system to conserve water resources and reduce environmental impact, with a substantial decrease in total dissolved solids (TDS) from over 6,000 ppm to below 50 ppm. The findings underscore the efficacy of machine learning models in optimizing solar-driven desalination systems, providing valuable insights into their capabilities for addressing water scarcity challenges and contributing to the global shift toward sustainable and environmentally friendly water production methods.
{"title":"Solar desalination system for fresh water production performance estimation in net-zero energy consumption building: a comparative study on various machine learning models","authors":"A. Alhamami, Emmanuel Falude, Ahmed Osman Ibrahim, Y. Dodo, Okpakhalu Livingston Daniel, Farruh Atamurotov","doi":"10.2166/wst.2024.092","DOIUrl":"https://doi.org/10.2166/wst.2024.092","url":null,"abstract":"\u0000 This study employs diverse machine learning models, including classic artificial neural network (ANN), hybrid ANN models, and the imperialist competitive algorithm and emotional artificial neural network (EANN), to predict crucial parameters such as fresh water production and vapor temperatures. Evaluation metrics reveal the integrated ANN-ICA model outperforms the classic ANN, achieving a remarkable 20% reduction in mean squared error (MSE). The emotional artificial neural network (EANN) demonstrates superior accuracy, attaining an impressive 99% coefficient of determination (R2) in predicting freshwater production and vapor temperatures. The comprehensive comparative analysis extends to environmental assessments, displaying the solar desalination system's compatibility with renewable energy sources. Results highlight the potential for the proposed system to conserve water resources and reduce environmental impact, with a substantial decrease in total dissolved solids (TDS) from over 6,000 ppm to below 50 ppm. The findings underscore the efficacy of machine learning models in optimizing solar-driven desalination systems, providing valuable insights into their capabilities for addressing water scarcity challenges and contributing to the global shift toward sustainable and environmentally friendly water production methods.","PeriodicalId":505935,"journal":{"name":"Water Science & Technology","volume":"14 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140227191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sahand Iman Shayan, Steve Youssef, P. van der Steen, Qiong Zhang, S. Ergas
The algal–bacterial shortcut nitrogen removal (ABSNR) process can be used to treat high ammonia strength wastewaters without external aeration. However, prior algal–bacterial SNR studies have been conducted under fixed light/dark periods that were not representative of natural light conditions. In this study, laboratory-scale photo-sequencing batch reactors (PSBRs) were used to treat anaerobic digester sidestream under varying light intensities that mimicked summer and winter conditions in Tampa, FL (USA). A dynamic mathematical model was developed for the ABSNR process, which was calibrated and validated using data sets from the laboratory PSBRs. The model elucidated the dynamics of algal and bacterial biomass growth under natural illumination conditions as well as transformation processes for nitrogen species, oxygen, organic and inorganic carbon. A full-scale PSBR with a 1.2 m depth, a 6-day hydraulic retention time (HRT) and a 10-day solids retention time (SRT) was simulated for treatment of anaerobic digester sidestream. The full-scale PSBR could achieve >90% ammonia removal, significantly reducing the nitrogen load to the mainstream wastewater treatment plant. The dynamic simulation showed that ABSNR process can help wastewater treatment facilities meet stringent nitrogen removal standards with low energy inputs.
{"title":"Algal-bacterial shortcut nitrogen removal model with seasonal light variations","authors":"Sahand Iman Shayan, Steve Youssef, P. van der Steen, Qiong Zhang, S. Ergas","doi":"10.2166/wst.2024.090","DOIUrl":"https://doi.org/10.2166/wst.2024.090","url":null,"abstract":"\u0000 \u0000 The algal–bacterial shortcut nitrogen removal (ABSNR) process can be used to treat high ammonia strength wastewaters without external aeration. However, prior algal–bacterial SNR studies have been conducted under fixed light/dark periods that were not representative of natural light conditions. In this study, laboratory-scale photo-sequencing batch reactors (PSBRs) were used to treat anaerobic digester sidestream under varying light intensities that mimicked summer and winter conditions in Tampa, FL (USA). A dynamic mathematical model was developed for the ABSNR process, which was calibrated and validated using data sets from the laboratory PSBRs. The model elucidated the dynamics of algal and bacterial biomass growth under natural illumination conditions as well as transformation processes for nitrogen species, oxygen, organic and inorganic carbon. A full-scale PSBR with a 1.2 m depth, a 6-day hydraulic retention time (HRT) and a 10-day solids retention time (SRT) was simulated for treatment of anaerobic digester sidestream. The full-scale PSBR could achieve >90% ammonia removal, significantly reducing the nitrogen load to the mainstream wastewater treatment plant. The dynamic simulation showed that ABSNR process can help wastewater treatment facilities meet stringent nitrogen removal standards with low energy inputs.","PeriodicalId":505935,"journal":{"name":"Water Science & Technology","volume":"27 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Withdrawn: Emerging progress of carbon nanomaterials in wastewater treatment: synthesis and utilization","authors":"Yuansha Xie, Wu Chen, Jie Dai","doi":"10.2166/wst.2024.085","DOIUrl":"https://doi.org/10.2166/wst.2024.085","url":null,"abstract":"","PeriodicalId":505935,"journal":{"name":"Water Science & Technology","volume":"12 6‐7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ping Xu, Ke Wang, Xue Fu, Zhuangzhuang Liu, Yilin Song
The conventional building drainage system was constructed based on the theory of two-phase flow involving water and air. However, the drainage system contained a more intricate three-phase flow, encompassing water, air, and solids, which was relatively overlooked in research. This study addressed the impact of solids on pressure fluctuations, air flow rates, and hydraulic jump fullness within the drainage system, considering three factors: the mass factor, cross-section factor, and viscosity. The investigation was conducted within a single-stack system using both experimental methods and CFD simulations. The findings revealed a positive correlation between both positive and negative pressures and above three factors. The mass factor and the cross-section factor had a more significant impact on the negative pressure of the system. The maximum growth rates of negative pressure extremes under different mass and cross-section factors reached 7.72 and 16.52%, respectively. In contrast, the viscosity of fecal sludge had a slightly higher effect on the positive pressure fluctuation of the drainage system, with the maximum growth rate of positive pressure extremes at 3.41%.
{"title":"Influence and mechanism of solids on the air pressure fluctuations on the building drainage system","authors":"Ping Xu, Ke Wang, Xue Fu, Zhuangzhuang Liu, Yilin Song","doi":"10.2166/wst.2024.088","DOIUrl":"https://doi.org/10.2166/wst.2024.088","url":null,"abstract":"\u0000 \u0000 The conventional building drainage system was constructed based on the theory of two-phase flow involving water and air. However, the drainage system contained a more intricate three-phase flow, encompassing water, air, and solids, which was relatively overlooked in research. This study addressed the impact of solids on pressure fluctuations, air flow rates, and hydraulic jump fullness within the drainage system, considering three factors: the mass factor, cross-section factor, and viscosity. The investigation was conducted within a single-stack system using both experimental methods and CFD simulations. The findings revealed a positive correlation between both positive and negative pressures and above three factors. The mass factor and the cross-section factor had a more significant impact on the negative pressure of the system. The maximum growth rates of negative pressure extremes under different mass and cross-section factors reached 7.72 and 16.52%, respectively. In contrast, the viscosity of fecal sludge had a slightly higher effect on the positive pressure fluctuation of the drainage system, with the maximum growth rate of positive pressure extremes at 3.41%.","PeriodicalId":505935,"journal":{"name":"Water Science & Technology","volume":"22 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kan Chen, Xiaofei Shi, Zhihao Zhang, Shijun Chen, Ji Ma, Tong Zheng, Leonardo Alfonso
The water reuse facilities of industrial parks face the challenge of managing a growing variety of wastewater sources as their inlet water. Typically, this clustering outcome is designed by engineers with extensive expertise. This paper presents an innovative application of unsupervised learning methods to classify inlet water in Chinese water reuse stations, aiming to reduce reliance on engineer experience. The concept of ‘water quality distance’ was incorporated into three unsupervised learning clustering algorithms (K-means, DBSCAN, and AGNES), which were validated through six case studies. Of the six cases, three were employed to illustrate the feasibility of the unsupervised learning clustering algorithm. The results indicated that the clustering algorithm exhibited greater stability and excellence compared to both artificial clustering and ChatGPT-based clustering. The remaining three cases were utilized to showcase the reliability of the three clustering algorithms. The findings revealed that the AGNES algorithm demonstrated superior potential application ability. The average purity in six cases of K-means, DBSCAN, and AGNES were 0.947, 0.852, and 0.955, respectively.
{"title":"Using unsupervised learning to classify inlet water for more stable design of water reuse in industrial parks","authors":"Kan Chen, Xiaofei Shi, Zhihao Zhang, Shijun Chen, Ji Ma, Tong Zheng, Leonardo Alfonso","doi":"10.2166/wst.2024.087","DOIUrl":"https://doi.org/10.2166/wst.2024.087","url":null,"abstract":"\u0000 The water reuse facilities of industrial parks face the challenge of managing a growing variety of wastewater sources as their inlet water. Typically, this clustering outcome is designed by engineers with extensive expertise. This paper presents an innovative application of unsupervised learning methods to classify inlet water in Chinese water reuse stations, aiming to reduce reliance on engineer experience. The concept of ‘water quality distance’ was incorporated into three unsupervised learning clustering algorithms (K-means, DBSCAN, and AGNES), which were validated through six case studies. Of the six cases, three were employed to illustrate the feasibility of the unsupervised learning clustering algorithm. The results indicated that the clustering algorithm exhibited greater stability and excellence compared to both artificial clustering and ChatGPT-based clustering. The remaining three cases were utilized to showcase the reliability of the three clustering algorithms. The findings revealed that the AGNES algorithm demonstrated superior potential application ability. The average purity in six cases of K-means, DBSCAN, and AGNES were 0.947, 0.852, and 0.955, respectively.","PeriodicalId":505935,"journal":{"name":"Water Science & Technology","volume":"55 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140231230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigated the potential of waste materials from wastewater treatment plants (WWTPs) to serve as an alternative lipid feedstock for biodiesel production. The average lipid recoveries from fat balls (46.4%) and primary scum (49.5–54.5%) were higher than the lipid recovery of primary sludge (15.8–16.4%). The yield of biodiesel produced from the extracted lipids ranged from 5.7 to 20.1%. There were considerable site- and season-dependent variations in the characteristics of the lipid waste materials. Radiocarbon analysis indicated the presence of fossil-derived carbon (26.0–42.0%) in the biodiesel obtained from wastewater lipids. Finally, we estimated the potential for biodiesel production from WWTP-derived lipids; about 9,053.0 metric tons of biodiesel per year could be produced from fat balls and primary scum in Japan, potentially satisfying 32% of Japan's current biodiesel demand. The results indicate that lipid-rich materials from WWTPs represent a valuable alternative feedstock for biodiesel production.
{"title":"Valorization of fat balls and primary scum from wastewater treatment: a promising renewable lipid feedstock for biodiesel production","authors":"Febrian Rizkianto, K. Oshita, Masaki Takaoka","doi":"10.2166/wst.2024.089","DOIUrl":"https://doi.org/10.2166/wst.2024.089","url":null,"abstract":"\u0000 \u0000 We investigated the potential of waste materials from wastewater treatment plants (WWTPs) to serve as an alternative lipid feedstock for biodiesel production. The average lipid recoveries from fat balls (46.4%) and primary scum (49.5–54.5%) were higher than the lipid recovery of primary sludge (15.8–16.4%). The yield of biodiesel produced from the extracted lipids ranged from 5.7 to 20.1%. There were considerable site- and season-dependent variations in the characteristics of the lipid waste materials. Radiocarbon analysis indicated the presence of fossil-derived carbon (26.0–42.0%) in the biodiesel obtained from wastewater lipids. Finally, we estimated the potential for biodiesel production from WWTP-derived lipids; about 9,053.0 metric tons of biodiesel per year could be produced from fat balls and primary scum in Japan, potentially satisfying 32% of Japan's current biodiesel demand. The results indicate that lipid-rich materials from WWTPs represent a valuable alternative feedstock for biodiesel production.","PeriodicalId":505935,"journal":{"name":"Water Science & Technology","volume":"108 8‐10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140228349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haotian Li, Jun Chen, Liguo Cao, Wei Liu, Zheng Duan
This study compared two different methods, the satellite altimetry-based and DEM (digital elevation model)-based, for estimating lake water volume changes. We focused on 34 lakes in China as the testing sites to compare the two methods for lake water volume changes from 2005 to 2020. The satellite altimetry-based method used water levels provided by the DAHITI (Database for Hydrological Time Series of Inland Waters) data and surface areas derived from Landsat imagery. The DEM-based method used the SRTM DEM data in combination with Landsat-derived lake extents. Our results showed a high degree of consistency in lake water volume changes estimated between the two methods (R2 < 0.90), but each method has its limitations. In terms of temporal coverage, the satellite altimetry-based method with the DAHITI data is limited by missing water level data in certain periods. The performance of the DEM-based method in extracting lake shore boundaries in regions with flat terrains (slope <1.5°) is not satisfactory. The DEM-based method has complete regional applicability (100%) in the Tibetan Plateau (TP) Lake Region, yet its effectiveness drops significantly in the Xinjiang and Eastern China Plain Lake Regions, with applicability rates of 50 and 40%, respectively.
本研究比较了基于卫星测高和基于 DEM（数字高程模型）的两种估算湖泊水量变化的不同方法。我们以中国的 34 个湖泊为测试点，比较了这两种方法对 2005 年至 2020 年湖泊水量变化的影响。基于卫星测高法的方法使用了由 DAHITI（内陆水域水文时间序列数据库）数据提供的水位和大地卫星图像得出的湖面面积。基于 DEM 的方法使用了 SRTM DEM 数据和 Landsat 导出的湖泊面积。结果表明，两种方法估算的湖泊水量变化高度一致（R2 < 0.90），但每种方法都有其局限性。就时间覆盖范围而言，基于卫星测高法的 DAHITI 数据因某些时段的水位数据缺失而受到限制。基于 DEM 的方法在提取地形平坦地区（坡度小于 1.5°）的湖岸边界时性能不尽人意。基于 DEM 的方法在青藏高原（TP）湖区具有完全的区域适用性（100%），但在新疆和华东平原湖区的有效性明显下降，适用率分别为 50%和 40%。
{"title":"A comparative study of satellite altimetry-based and DEM-based methods for estimating lake water volume changes","authors":"Haotian Li, Jun Chen, Liguo Cao, Wei Liu, Zheng Duan","doi":"10.2166/wst.2024.086","DOIUrl":"https://doi.org/10.2166/wst.2024.086","url":null,"abstract":"\u0000 This study compared two different methods, the satellite altimetry-based and DEM (digital elevation model)-based, for estimating lake water volume changes. We focused on 34 lakes in China as the testing sites to compare the two methods for lake water volume changes from 2005 to 2020. The satellite altimetry-based method used water levels provided by the DAHITI (Database for Hydrological Time Series of Inland Waters) data and surface areas derived from Landsat imagery. The DEM-based method used the SRTM DEM data in combination with Landsat-derived lake extents. Our results showed a high degree of consistency in lake water volume changes estimated between the two methods (R2 < 0.90), but each method has its limitations. In terms of temporal coverage, the satellite altimetry-based method with the DAHITI data is limited by missing water level data in certain periods. The performance of the DEM-based method in extracting lake shore boundaries in regions with flat terrains (slope <1.5°) is not satisfactory. The DEM-based method has complete regional applicability (100%) in the Tibetan Plateau (TP) Lake Region, yet its effectiveness drops significantly in the Xinjiang and Eastern China Plain Lake Regions, with applicability rates of 50 and 40%, respectively.","PeriodicalId":505935,"journal":{"name":"Water Science & Technology","volume":"54 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140231199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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