检索结果-南通市图书馆

维普期刊数据库博看期刊

改造国内污水管网系统需要综合考虑的四个因素:定量分析

在线全文

学校读者我要写书评

暂无评论

给水排水 2022年第10期48卷 45-55页

作者：曹业始吴军刘智晓张翼飞 KROSIS h DAIGGER G t 彭永臻中持新概念环境发展宜兴有限公司宜兴214213 扬州大学环境科学与工程学院扬州225009 北京首创生态环保集团北京100044 中持水务股份有限公司北京100192 维也纳工业大学维也纳1040 密歇根大学土木和环境工程系密歇根州48109 北京工业大学北京100124

污水管网外来水入侵、管网污水收集率、市政用水量和污水处理厂处理量是影响城市污水系统和城市水环境质量的四个主要因素。污水系统改造不仅提升污染物收集率和污水管网中污染物(如COD)浓度,同时直接影响到对受纳水体的污染物排放溢流... 详细信息

污水管网外来水入侵、管网污水收集率、市政用水量和污水处理厂处理量是影响城市污水系统和城市水环境质量的四个主要因素。污水系统改造不仅提升污染物收集率和污水管网中污染物(如COD)浓度,同时直接影响到对受纳水体的污染物排放溢流负荷。使用简化的城市污水系统模型和国内外实际数据,定量表征和讨论了四个因素对污水管网污水COD浓度和排放到受纳水体的溢流污染物负荷的影响,并就污水稀释倍数、污水处理厂处理量、污染物去除效率、排放标准和溢流排放负荷之间的多重对应关系进行了分析和讨论,根据国内现状,提出改造和升级污水管网及污水处理厂需要考虑的三点建议。

关键词：污水系统模型排水系统污水管网提质增效城市水环境污水处理厂

Adaptive signal control and coordination for urban traffic control in a connected vehicle environment: A review

在线全文

学校读者我要写书评

暂无评论

Digital Transportation and Safety

Digital transportation and Safety 2023年第2期2卷 89-111页

作者： Jiangchen Li Liqun Peng Kaizhe hou Yong tian Yulin Ma Shucai Xu tony Z.qiu College of civil Aviation Nanjing University of Aeronautics and AstronauticsNanjing 211106China tsinghua University Suzhou Automotive Research Institute Suzhou 215134JiangsuChina school of transportation and Logistics East China Jiaotong UniversityNanchang 330013China Department of civil and environmental engineering University of AlbertaEdmontonAB T6G 1H9Canada

Existing signal control systems for urban traffic are usually based on traffic flow data from fixed location *** of rapid advances in emerging vehicular communication,connected vehicle(CV)-based signal control demonstrates significant improvements over existing conventional signal control *** various CV-based signal control systems have been investigated in the past decades,these approaches still have many issues and drawbacks to *** summarize typical components and structures of these existing CV-based urban traffic signal control systems and digest several important issues from the summarized vital ***,future research directions are discussed with some *** hope this survey can facilitate the connected and automated vehicle and transportation research community to efficiently approach next-generation urban traffic signal control methods and systems.

关键词： Urban traffic signal control Adaptive signal control Signal coordination Connected vehicle-based signal control

Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Geoscience Frontiers 2021年第1期12卷 441-452页

作者： Pin Zhang Zhen-Yu Yin Yin-Fu Jin tommy htChan Fu-Ping Gao Department of civil and environmental engineering The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina school of civil engineering&Built Environment Science and Engineering FacultyQueensland University of Technology(QUT)BrisbaneQld4001Australia Key Laboratory for Mechanics in Fluid Solid Coupling Systems Institute of MechanicsChinese Academy of SciencesBeijing100190China China school of engineering Science University of Chinese Academy of SciencesBeijing100049China

Compression index Ccis an essential parameter in geotechnical design for which the effectiveness of correlation is still a *** paper suggests a novel modelling approach using machine learning(ML)*** performance of five commonly used machine learning(ML)algorithms,***-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),random forest(RF)and evolutionary polynomial regression(EPR)in predicting Cc is comprehensively investigated.A database with a total number of 311 datasets including three input variables,*** void ratio e0,liquid limit water content wL,plasticity index Ip,and one output variable Cc is first *** algorithm(GA)is used to optimize the hyper-parameters in five ML algorithms,and the average prediction error for the 10-fold cross-validation(CV)sets is set as thefitness function in the GA for enhancing the robustness of ML *** results indicate that ML models outperform empirical prediction formulations with lower prediction *** yields the lowest error followed by BPNN,ELM,EPR and *** the ranges of input variables in the database are large enough,BPNN and RF models are recommended to predict ***,if the distribution of input variables is continuous,RF model is the best ***,EPR model is recommended if the ranges of input variables are *** predicted correlations between input and output variables using five ML models show great agreement with the physical explanation.

关键词： Compressibility Clays Machine learning Optimization Random forest Genetic algorithm

Emerging contaminants:A One health perspective

在线全文

学校读者我要写书评

暂无评论

the Innovation 2024年第4期5卷 140-170,139页

作者： Fang Wang Leilei Xiang Kelvin Sze-Yin Leung Martin Elsner Ying Zhang Yuming Guo Bo Pan hongwen Sun taicheng An Guangguo Ying Bryan WBrooks Deyi hou Damian Ehelbling Jianqiang Sun hao qiu timothy MVogel Wei Zhang Yanzheng Gao Myrna JSimpson Yi Luo Scott XChang Guanyong Su Bryan MWong tzung-May Fu Dong Zhu Karl JJobst Chengjun Ge Frederic Coulon Jean Damascene harindintwali Xiankui Zeng haijun Wang Yuhao Fu Zhong Wei Rainer Lohmann Changer Chen Yang Song Concepcion Sanchez-Cid Yu Wang Ali El-Naggar Yiming Yao Yanran huang Japhet Cheuk-Fung Law Chenggang Gu huizhong Shen Yanpeng Gao Chao Qin hao Li tong Zhang Natàlia Corcoll Min Liu Daniel SAlessi hui Li Kristian KBrandt Yolanda Pico Cheng Gu Jianhua Guo Jianqiang Su Philippe Corvini Mao Ye teresa Rocha-Santos huan he Yi Yang Meiping tong Weina Zhang Fidèle Suanon Ferdi Brahushi Zhenyu Wang Syed Ahashsham Marko Virta Qingbin Yuan Gaofei Jiang Louis A.tremblay Qingwei Bu Jichun Wu Willie Peijnenburg Edward topp Xinde Cao Xin Jiang Minghui Zheng taolin Zhang Yongming Luo Lizhong Zhu Xiangdong Li DamiàBarceló Jianmin Chen Baoshan Xing Wulf Amelung Zongwei Cai Ravi Naidu Qirong Shen Janusz Pawliszyn Yong-guan Zhu Andreas Schaeffer Matthias C.Rillig Fengchang Wu Gang Yu James M.tiedje State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China University of Chinese Academy of Sciences Beijing 100049China Department of Chemistry Hong Kong Baptist UniversityHong KongChina hKBU Institute of Research and Continuing Education Shenzhen Virtual University ParkShenzhenChina technical University of Munich TUM School of Natural SciencesInstitute of Hydrochemistry85748 GarchingGermany school of Resources&Environment Northeast Agricultural UniversityHarbin 150030China Climate Air Quality Research UnitSchool of Public Health and Preventive MedicineMonash UniversityMelbourneVICAustralia Faculty of environmental Science&engineering Kunming University of Science&TechnologyKunming 650500China Ministry of Education Key Laboratory of Pollution Processes and environmental Criteria College of Environmental Science and EngineeringNankai UniversityTianjin 300350China Guangdong Key Laboratory of environmental Catalysis and health Risk Control School of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China Ministry of Education Key Laboratory of environmental theoretical Chemistry South China Normal UniversityGuangzhouGuangdong 510006China Department of environmental Science Baylor UniversityWacoTXUSA Center for Reservoir and Aquatic Systems Research(CRASR) Baylor UniversityWacoTXUSA school of Environment Tsinghua UniversityBeijing 100084China school of civil and environmental engineering Cornell UniversityIthacaNY 14853USA Key Laboratory of Microbial technology for Industrial Pollution Control of Zhejiang Province Zhejiang University of TechnologyHangzhou 310014China school of environmental Science and engineering Shanghai Jiao Tong UniversityShanghai 200240China Laboratoire d’Ecologie Microbienne Universite Claude Bernard Lyon 1UMR CNRS 5557UMR INRAE 1418VetAgro Sup69622 VilleurbanneFrance Department of Pla

environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary *** global efforts to mitigate legacy pollutants,the continuous introduction of new substances remains a major threat to both people and the *** response,global initiatives are focusing on risk assessment and regulation of emerging contaminants,as demonstrated by the ongoing efforts to establish the UN’s Intergovernmental Science-Policy Panel on Chemicals,Waste,and Pollution *** review identifies the sources and impacts of emerging contaminants on planetary health,emphasizing the importance of adopting a One health *** for monitoring and addressing these pollutants are discussed,underscoring the need for robust and socially equitable environmental policies at both regional and international *** actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

关键词： pollution planet contaminants

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

沙湖的水环境容量和污染物总量控制

水土保持通报 2019年第5期39卷 272-277页

作者：李延林邱小琮宁夏大学土木与水利工程学院宁夏银川750021 宁夏大学生命科学学院宁夏银川750021

[目的]探明宁夏回族自治区沙湖富营养化状态及计算主要污染的水环境容量,为沙湖入湖污染负荷的削减和水环境保护提供一定依据。[方法]于2015-2017年冬(1月)、春(4月)、夏(7月)、秋(10月)监测了沙湖水体理化指标。采用综合营养状态指数... 详细信息

[目的]探明宁夏回族自治区沙湖富营养化状态及计算主要污染的水环境容量,为沙湖入湖污染负荷的削减和水环境保护提供一定依据。[方法]于2015-2017年冬(1月)、春(4月)、夏(7月)、秋(10月)监测了沙湖水体理化指标。采用综合营养状态指数法对其富营养化状态进行了评价,采用沃伦威德(Vollenweider)模型和狄龙(Dillion)模型计算了4种不同水质目标情景下的高锰酸盐指数、总氮和总磷的水环境容量。[结果] 2015-2017年沙湖富营养化状态总体上为春、冬季轻度富营养化,夏、秋季中度富营养化;情景二和三水质目标下沙湖2015-2017年高锰酸盐指数,总氮和总磷水环境容量剩余量均为0;情景四水质目标下沙湖2015-2017年总磷的水环境容量剩余量均为0,高锰酸盐指数的水环境容量有剩余量,总氮的水环境容量2015年有剩余量,2016-2017年剩余量为0。在2017年水质现状的基础上,达到Ⅲ类水质要求高锰酸盐指数、总氮和总磷分别消减655.97 t,39.27 t和1.41 t,消减率分别为112.89%,45.58%和37.60%;达到Ⅳ类水质要求高锰酸盐指数、总氮和总磷分别消减0,15.83 t和0.94 t,消减率分别为0,18.37%和25.07%。[结论]沙湖水体呈现一定程度的污染,已达到富营养化状态,高锰酸盐指数、总氮和总磷的水环境容量大于目标要求的水环境容量,应该及时进行治理和保护。

关键词：沙湖富营养化综合评价水环境容量污染物总量控制

CuO-CeO2/CSAC催化剂制备及其处理染料废水

在线全文

学校读者我要写书评

暂无评论

江西师范大学学报（自然科学版） 2020年第6期44卷 584-589页

作者：邱俊明蒋柏泉南昌大学期刊社江西南昌330031 南昌大学资源环境与化工学院江西南昌330031

用自制的椰壳活性炭作为载体制备CuO-CeO2/CSAC催化剂处理酸性大红GR染料废水.正交实验优化了CuO-CeO2/CSAC制备的工艺参数,单因素和响应面联合实验优化了CuO-CeO2/CSAC处理酸性大红GR废水的工艺条件.实验结果表明:CuO-CeO2/CSAC的最佳... 详细信息

用自制的椰壳活性炭作为载体制备CuO-CeO2/CSAC催化剂处理酸性大红GR染料废水.正交实验优化了CuO-CeO2/CSAC制备的工艺参数,单因素和响应面联合实验优化了CuO-CeO2/CSAC处理酸性大红GR废水的工艺条件.实验结果表明:CuO-CeO2/CSAC的最佳制备工艺参数为τ=2.50 h,t=300.0℃,V1=15.0 mL,V2=5.0 mL,其对CODCr和色度的降解率比单组分CuO/CSAC催化剂分别提高了62.4%和69.7%.CuO-CeO2/CSAC在室温下处理模拟废水的最佳条件为τ=4.12 h,m=0.57 g,ph值为5.0,可将CODCr和色度分别从962.0 mg·L^-1和32720.0倍降解至35.2 mg·L^-1和12.7倍,相应的降解率为96.30%和99.96%.建立的分别以CODCr和色度残余质量浓度为响应值的工艺模型与实验结果吻合较好,可预测不同工艺条件下降解CODCr和色度的效果.

关键词：氧化铜活性炭二氧化铈湿式催化氧化染料废水金属催化剂

Early-diverging fungal phyla:taxonomy,species concept,ecology,distribution,anthropogenic impact,and novel phylogenetic proposals

在线全文

学校读者我要写书评

暂无评论

Fungal Diversity 2021年第4期109卷 59-98页

作者： Kerstin Voigt timothy Y.James Paul M.Kirk AndréL.C.M.de A.Santiago Bruce Waldman Gareth W.Griffith Minjie Fu Renate Radek Jürgen F.h.Strassert Christian Wurzbacher Gustavo henrique Jerônimo David R.Simmons Kensuke Seto Eleni Gentekaki Vedprakash G.hurdeal Kevin D.hyde thuong t.t.Nguyen hyang Burm Lee Jena Microbial Resource Collection Leibniz Institute for Natural Product Research and Infection BiologyAdolf-Reichwein-Straße 2307745 JenaGermany Institute of Microbiology Faculty of Biological SciencesFriedrich Schiller University JenaNeugasse 2507743 JenaGermany Department of Ecology and Evolutionary Biology University of MichiganAnn ArborMI 48109USA Biodiversity Informatics and Spatial Analysis Jodrell LaboratoryRoyal Botanic Gardens KewSurrey TW93DSUK Department of Mycology Federal University of PernambucoAv.Da Engenharias/nRecifePE 50740-4600Brazil school of Biological Sciences Seoul National UniversitySeoul 08826South Korea Department of Integrative Biology Oklahoma State UniversityStillwaterOK 74078USA Institute of Biological Environmentaland Rural Sciences(IBERS)Aberystwyth UniversityAberystwyth SY233DDWalesUK Institute of Biology/Zoology Evolutionary BiologyFreie Universität Berlin14195 BerlinGermany Leibniz Institute of Freshwater Ecology and Inland Fisheries Ecosystem Research12587 BerlinGermany Department of civil Geo and Environmental EngineeringTechnical University of MunichGarchingGermany school of Science Mae Fah Luang UniversityChiang Rai 57100Thailand Center of Excellence in Fungal Research Mae Fah Luang UniversityChiang Rai 57100Thailand environmental Microbiology Lab Department of Agricultural Biological ChemistryCollege of Agriculture&Life SciencesChonnam National UniversityGwangju 61186South Korea

the increasing number of new fungal species described from all over the world along with the use of genetics to define taxa,has dramatically changed the classification system of early-diverging fungi over the past several *** number of phyla established for non-Dikarya fungi has increased from 2 to ***,to date,both the classification and phylogeny of the basal fungi are still *** this article,we review the recent taxonomy of the basal fungi and re-evaluate the relationships among early-diverging lineages of fungal *** also provide information on the ecology and distribu-tion in Mucoromycota and highlight the impact of chytrids on amphibian *** concepts in Chytridiomycota,Aphelidiomycota,Rozellomycota,Neocallimastigomycota are discussed in this *** preserve the current application of the genus Nephridiophaga(Chytridiomycota:Nephridiophagales),a new type species,Nephridiophaga blattellae,is proposed.

关键词： Early-diverging fungi Evolution Nuclear large subunit(LSU/28S) Nuclear small subunit(SSU/18S) Molecular phylogeny

太阳山湿地浮游植物优势功能群季节演替特征及驱动因子分析

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

中国环境监测 2023年第4期39卷 133-146页

作者：李霖赵睿智邱小琮宁夏大学土木与水利工程学院宁夏银川750021 宁夏大学生命科学学院宁夏银川750021

为探明太阳山湿地浮游植物优势功能群季节演替规律及其主要驱动因子,于2019年4月(春季)、7月(夏季)、10月(秋季)和2020年1月(冬季)采样分析了太阳山湿地浮游植物的种类组成、优势种、丰度、生物量及季节变化,同时测定了水环境理化因子指... 详细信息

为探明太阳山湿地浮游植物优势功能群季节演替规律及其主要驱动因子,于2019年4月(春季)、7月(夏季)、10月(秋季)和2020年1月(冬季)采样分析了太阳山湿地浮游植物的种类组成、优势种、丰度、生物量及季节变化,同时测定了水环境理化因子指标,采用冗余分析方法研究了浮游植物优势功能群的优势度、丰度与水环境因子之间的关系。结果表明:太阳山湿地浮游植物可分为22个功能类群;优势功能群的季节演替和空间分异特征明显,存在一定的规律性。春、秋、冬3个季节的浮游植物以硅藻门为主,夏季以绿藻门和蓝藻门为主。春季优势功能群主要为D、C、P,以硅藻门种类为主;夏季优势功能群主要为J、Lo、tC、M、h1,以硅藻门、绿藻门、蓝藻门种类为主;秋季优势功能群主要为D、S1、MP,以硅藻门、绿藻门种类为主;冬季优势功能群主要为D、X3,以硅藻门种类为主。影响太阳山湿地浮游植物优势功能群季节演替的水环境因子有水温(Wt)、ph、溶解氧(DO)、透明度(SD)、盐度(Sal)、氮磷营养元素含量、化学需氧量(COD_(Cr))和高锰酸盐指数(COD_(Mn))。4个湖区浮游植物优势功能群的时空差异与水环境因子密切相关,其中,西湖区浮游植物优势功能群的季节演替驱动因子为ph、DO、Wt、总磷(tP),东湖区为ph、DO、Wt、氮磷营养元素含量,南湖区为ph、DO、COD_(Cr)、五日生化需氧量(BOD_(5)),小南湖区为ph、DO、Wt、BOD_(5)、COD_(Cr)、tP。ph、DO、Wt、BOD_(5)、SD等水环境因子的季节差异以及tP、tN、氨氮(Nh_(3)-N)、COD_(Mn)等水环境因子的湖区差异是太阳山湿地浮游植物优势功能群出现季节演替的主要原因。

关键词：优势功能群丰度优势度水环境因子冗余分析

用于沼气发酵系统的聚光型太阳能加热装置研究

在线全文

学校读者我要写书评

暂无评论

西北农林科技大学学报（自然科学版） 2011年第5期39卷 85-90页

作者：张铎邱凌邓媛方西北农林科技大学机械与电子工程学院陕西杨凌712100 农业部沼气西北分中心陕西杨凌712100

【目的】研究用于沼气发酵系统的聚光型太阳能(Parabolic trough solar concentrate,PtC)加热装置,为解决寒冷地区沼气工程冬季加热保温问题提供参考。【方法】根据PtC的特点,设计了一种用于沼气发酵系统的太阳能加热装置,对该装置的结... 详细信息

【目的】研究用于沼气发酵系统的聚光型太阳能(Parabolic trough solar concentrate,PtC)加热装置,为解决寒冷地区沼气工程冬季加热保温问题提供参考。【方法】根据PtC的特点,设计了一种用于沼气发酵系统的太阳能加热装置,对该装置的结构及工作原理进行了分析说明,介绍了该装置的设计步骤及相关参数的计算方法,并将该装置用于陕西省淳化县润镇中学的沼气发酵系统,分析其能源效益,进行实际应用研究。【结果】介绍了聚光型太阳能沼气加热系统的主要组成部分,总结了该系统主要部分的设计及参数选择,为该系统的建设提供了理论依据。将PtC加热装置用于淳化县润镇中学已有的生态校园沼气系统,结果表明,聚光型太阳能具有良好的能源及环保效应,该系统在淳化县润镇中学的全年太阳能产出量为855 360～1 166 400 kW.h,相当于标准煤307.93～419.9 t,可减少二氧化碳排放788.95～1 075.83 t。【结论】聚光型太阳能作为沼气工程的一种新型加热方式,在理论及实践上都是可行的。

关键词：沼气太阳能聚光型太阳能