Water Pollution Research Research Articles

Causes and effects of contamination of water sources due to excessive use of agrochemicals and pesticides: bibliometric analysis

ABSTRACT This article conducts a bibliometric analysis of water pollution caused by agricultural pesticides, focusing on theoretical and methodological trends. The analysis uses Scopus databases and Bibliometrix and VOSviewer software tools to analyze 2,330 documents from 2013 to 2022. The research highlights the importance of environmental, agricultural, and biological sciences in addressing water pollution. The study suggests improving pesticide use, reducing fertilizer applications, and addressing the harmful impacts of contaminated fish on human health. The restriction of glyphosate use in the European Union caused economic losses for companies, and a follow-up on glyphosate use in crop applications was proposed. The study also evaluates chemical concentrations and recommends addressing aquatic ecotoxicological risks. Future research trends include increasing water use efficiency and quality, hydrogeological studies, watersheds and land use, and climate modeling and impacts. The research is unique in addressing both research trends and knowledge gaps in water pollution research in various aspects.

Efficient preparation of anisotropic cellulose sponge from cotton stalks: An excellent material for separation applications

Water pollution and solid waste resource reuse demand immediate attention and research. Here, we present a method to create anisotropic cellulose sponges from cotton stalk waste. Using the inherent structure of cotton stalks, we selectively remove lignin and hemicellulose via acid and alkali pretreatment. This process yields cellulose sponges with a natural pore structure. Our findings demonstrate that these sponges retain the original pore configuration of cotton stalks, providing excellent connectivity and compressibility due to their unique anisotropic three-dimensional structure. Moreover, these sponges exhibit exceptional super-hydrophilic and underwater super-oleophobic properties, with underwater oil contact angles exceeding 150° for all tested oils. External pressure can reduce the pore size of the cellulose sponge, facilitating the gravity-driven separation and removal of dyes and emulsions. Remarkably, removal efficiencies for Methylene Blue (MB), Congo Red (CR), water-in-oil (w/o) emulsions, and oil-in-water (o/w) emulsions exceed 99 %, 97 %, 99 %, and 99 %, respectively, highlighting superior removal and recyclability. Further investigation into the mechanisms of dye and emulsion removal employs X-ray photoelectron spectroscopy (XPS) characterization and molecular dynamics (MD) simulation. These insights lay the groundwork for the efficient recycling and resource utilization of waste cotton stalks, offering promising applications in water purification.

Optimal Controllers for the Operation of Activated Sludge Treatment Systems

Objective: Application of Optimal Control Theory to the activated sludge process in order to regulate the removal of the carbonaceous part of the organic matter. Theoretical benchmark: Dynamic models of activated sludge processes are capable of enabling the development of innovative control systems. The Optimal Control Theory, aimed at optimizing the performance of dynamic systems, allows the establishment of control systems that enhance efficiency in adapting to changes in operating conditions, ensuring satisfactory process performance. Method: The proposed control systems were established from the application of Optimal Control Theory, considering the use of the dynamic model of the activated sludge process presented by International Association on Water Pollution Research and Control. Computer simulations were used to assess the performance of the control systems proposed. Results and conclusion: The proposed control systems were capable of curbing substantially the oscillations in the concentrations of biomass, of particulate matter produced by the degradation of biomass and those of the inert particulate substrate. The reductions in the oscillations of the slowly biodegradable part of the influent were relatively smaller. The controller was quite effective in leading the wastewater treatment system sooner to the new equilibrium conditions after the imposition of the disturbances in raw effluent. Implications of research: Development of control systems for activated sludge processes to regulate the removal of the carbonaceous fraction from organic matter. Originality/value: The establishment of optimal control systems for activated sludge processes can enable greater efficiency and operational capacity, especially in industrial facilities or densely populated urban settlements, where such wastewater treatment systems are most commonly used.

Comparison of multi-DLM approaches for predicting daily runoff: evidence from the data-driven model in one of China's largest wheat production-bases.

Runoff forecasting is extremely important for various activities of water pollution research and agricultural. Data-driven models have been proved an effective approach in predicting daily runoff when combining deep learning methods (DLM). However, predicting accuracy of daily runoff still need improved. Here, we firstly proposed a combined model of Gate Recurrent Unit (GRU) and Residual Network (ResNet) and compared with one shallow learning method (Back Propagation Neural Network, BPNN) and one deep learning method (GRU) with data from 2010 to 2020 in three stations in daily runoff forecasting in the Yiluo River watershed. The results showed that the combined model with precipitation data and runoff data as input has the highest prediction accuracy (NSE = 0.9325, 0.8735, 0.9186, respectively). Input data with precipitation have higher prediction accuracy than that without. The performance of the model was better in the dry season than the wet season. The topographic and geomorphic factors may also the main factors affecting runoff forecast. Those results of this study can provide useful strategies to predict short runoff and manage watershed scale water resources especially in the important agriculture region.

Evolution of neural network to deep learning in prediction of air, water pollution and its Indian context.

The scenario of developed and developing countries nowadays is disturbed due to modern living style which affects environment, wildlife and natural habitat. Environmental quality has become or is a subject of major concern as it is responsible for health hazard of mankind and animals. Measurements and prediction of hazardous parameters in different fields of environment is a recent research topic for safety and betterment of people as well as nature. Pollution in nature is an after-effect of civilization. To combat the damage already happened, some processes should be evolved for measurement and prediction of pollution in various fields. Researchers of all over the world are active to find out ways of predicting such hazard. In this paper, application of neural network and deep learningalgorithms is chosen for air pollution and water pollution cases. The purpose of this review isto reveal how family of neural network algorithms has applied on these two pollution parameters. In this paper, importance is given on algorithm, and datasets used for air and water pollution as well as the predicted parameters have also been noted for ease of future development. One major concern of this paper is Indian context of air and water pollution research, and the research potential presents in this area using Indian dataset. Another aspect for including both air and water pollutions in one review paper is to generate an idea of artificial neural network and deep learning techniques which can be cross applicable for future purpose.

Pollution impact on metal and biomarker responses in intestinal cytosol of freshwater fish.

In the present study, essential and nonessential metal content and biomarker responses were investigated in the intestine of fish collected from the areas polluted by mining. Our objective was to determine metal and biomarker levels in tissue responsible for dietary intake, which is rarely studied in water pollution research. The study was conducted in the Bregalnica River, reference location, and in the Zletovska and Kriva Rivers (the Republic of North Macedonia), which are directly influenced by the active mines Zletovo and Toranica, respectively. Biological responses were analyzed in Vardar chub (Squalius vardarensis; Karaman, 1928), using for the first time intestinal cytosol as a potentially toxic cell fraction, since metal sensitivity is mostly associated with cytosol. Cytosolic metal levels were higher in fish under the influence of mining (Tl, Li, Cs, Mo, Sr, Cd, Rb, and Cu in the Zletovska River and Cr, Pb, and Se in the Kriva River compared to the Bregalnica River in both seasons). The same trend was evident for total proteins, biomarkers of general stress, and metallothioneins, biomarkers of metal exposure, indicating cellular disturbances in the intestine, the primary site of dietary metal uptake. The association of cytosolic Cu and Cd at all locations pointed to similar pathways and homeostasis of these metallothionein-binding metals. Comparison with other indicator tissues showed that metal concentrations were higher in the intestine of fish from mining-affected areas than in the liver and gills. In general, these results indicated the importance of dietary metal pathways, and cytosolic metal fraction in assessing pollution impacts in freshwater ecosystems.

Predicting lake water quality index with sensitivity-uncertainty analysis using deep learning algorithms

Regular monitoring and assessment of water quality is essential to maintain the quality of lake water. A commonly used method for assessing water quality is the Water Quality Index (WQI). Artificial intelligence (AI) can predict the WQI rapidly and more accurately than conventional methods. In this study, a stacking ensemble model based on Deep Learning (DL) was proposed to predict the WQI by integrating three models: Gradient Boosting Machine (GBM), Generalized Linear Model (GLM) and Neural Network (NN). The performance of this model was compared with that of Deep Dense Neural Network (DNN) and Convolutional Neural Network (CNN). The inclusion of a DNN model, which was used for the first time in water pollution research to perform sensitivity and uncertainty analysis in predicting WQI, added a new dimension to the workflow. The mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R2) were used to evaluate the performance of the WQI prediction models. The results showed that the WQI of the water samples ranged from 90.75 to 145.29, indicating poor to very poor water quality. The validation results showed that the stack model was superior to all other models in terms of prediction accuracy, with MSE of 25.77, RMSE of 5.07, MAE of 3.5 and R2 of 0.98. The DNN-based sensitivity analysis showed that pH and turbidity significantly affect the WQI and should be monitored to minimise water pollution. The uncertainty analysis indicated that electrical conductivity and total dissolved solids have the highest uncertainty in predicting WQI. This study provides researchers, decision makers and water scientists with accurate information on water quality and enables the implementation of clean production processes to reduce pollution and improve water quality.

Construction of Surface Water Pollution Prediction Model Based on Machine Learning

Existing prediction models have low prediction accuracy for surface water pollution with many influencing factors. Taking algal bloom prediction as the entry point of surface water pollution research, LASSO-LARS algorithm is adopted to select the main factors affecting algal bloom variables. At the same time, combined with BP neural network in machine learning, a surface water pollution prediction model based on machine learning is proposed. The results show that the proposed method can accurately predict the algal blooms by using the BP neural network algal bloom prediction model, where the LASSO-LARS algorithm is used to select the variables, such as water temperature, pH, transparency, conductivity, dissolved oxygen, ammonia nitrogen, and chlorophyll a, as model inputs, and the relative error of prediction is less than 5.2%. Thus, the proposed method has certain validity and practical application value.

A review of artificial neural network techniques for environmental issues prediction

The smarter world needs more efforts to purposeful manage and usage of technologies, science, artificial intelligence, and artificial neural networks, as their product. One of the main tools to facilitate collecting beneficial information is reviewing the publications of the target domain. To this end, the present paper attempts to categorize recent trends and innovations in the application of artificial neural networks in water quality and pollution research. The most prevalent methods for water pollution and quality prediction modeling during 2011–2020 were BPNN and normal MLP.. Conducting and comparing other analytical methods such as ANN, RBFNN, LSTM, and CNN to find accurate results are in the next orders. Moreover, along with the emerging and development of the internet of things (IoT), it was observed that IoT as a real-time technology for collecting relevant data, which appeared in the water quality analysis since 2016–2017, was used increasingly in 2019 and 2020. Furthermore, reviewed papers revealed that pH, chemical oxygen demand, and biochemical oxygen demand were the most water quality indicators. Regionally, drivers in China were the most investigated sites during the last decade, and then India and Turkey were the second and third place.

Recent Advances on Water Pollution Research in Africa: A Critical Review

Africa is favored with characteristic and natural water resources, but misuse of these asserts has been driven to extraordinary contamination of the environment. Populace development and urbanization due to improvements have moreover contributed gigantically to the present water contamination in Africa. The principal causes of contamination for surface waterways are untreated effluents released into the environment by humans and machines. Several African countries have set targets for emissions released into surface waters, but it’s unclear whether they’re being followed. Despite the fact that groundwater sources such as wells and boreholes are important sources of drinking water for Africans, the natural water quality of these groundwater sources is, for the most part, moo within the vicinity due to close proximity to sterile areas. Distinguished sources of water contaminations in Africa incorporate agrarian activities, mining, roadside outflows, wastes from industries and workshops, dumps and e-wastes. Oil leaks are a major problem in oil-rich African countries like Nigeria and Angola. Agrarian practices had the least impact on soil in Africa, whereas e-waste reuse has the highest quantities of Pb, Cu, and Zn (>1%). In Africa, there is a need for effective natural toxin control. This paper analyzes and summarizes the recent scholarship on water pollution on the African continent. It gives the past and current overview of the research breakthrough among the African scholars and scholars from the diaspora working on water pollution.

A glance at one decade of water pollution research in Iranian environmental health journals

BackgroundDue to the importance of water, the negative effects of water pollution on human health and the lack of proper knowledge of the state of research in this regard, the purpose of this study was to analyse scientific publications on water pollution in Iranian journals of environmental health.MethodsThis cross-sectional study was conducted using a scientometric method over a ten-year period (2008–2017) on articles published in five specialised environmental health journals emphasising the issue of water pollution. Data were collected through a researcher-based checklist using the International Committee of Medical Journal Editors (ICMJE) and the World Association of Medical Editors (WAME) recommendations. Data processing was done using descriptive statistics and VOSviewer software.ResultsA review of 1276 articles in specialised environmental health journals revealed that 33.7% of the articles were in the field of water. Physical and chemical factors in water were the main subjects of 21.4% of studies, and temperature and turbidity were the most important physical factors. Microbial agents in groundwater and surface water were the main subjects of 14.1% of the articles. The findings show that 55 of 430 articles related to water were written with contributions from 20 countries (including Iran), with the most contributions from researchers from Asia.ConclusionsGood conditions do not exist to investigate the status of articles on water pollution in specialised environmental health journals in Iran over a ten-year period. Health policymakers should therefore provide mechanisms to encourage national researchers, especially environmental health professionals, by setting national research priorities on quality and water pollution to conduct studies in this regard.

基于文献计量的生态系统观测研究网络长期观测数据应用研究

PDF HTML阅读 XML下载 导出引用 引用提醒 基于文献计量的生态系统观测研究网络长期观测数据应用研究 DOI: 10.5846/stxb201805221121 作者: 作者单位: 中国科学院地理科学与资源研究所生态系统网络观测与模拟重点实验室 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划（2017YFC0503803） Using bibliometrics to analyze the application of long-term observation data of ecosystem research networks Author: Affiliation: Key Laboratory of Ecosystem Network Observation and Modeling,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:基于CNKI 数据库，采用文献计量和知识图谱的方法，通过对应用生态系统观测研究网络长期定位观测数据的文献进行分析，探讨长期观测数据的应用领域、具体用途、用户特点及不同生态站数据的应用状况与研究主题，以期为提高生态系统观测研究网络长期观测数据的共享服务能力、充分发挥长期观测数据的价值提供参考。分析结果表明：生态系统观测研究网络长期观测数据受到越来越多学者的关注，其应用学科领域以林业、农业基础科学为主，同时不断扩展到其他学科中，呈多元化态势；数据主要在生态系统服务研究、模型模拟、人工林研究、水污染研究、生物多样性研究、小麦玉米研究、土壤水分研究等方面发挥作用；数据的主要用户群体为高等院校和科研院所，不同机构应用长期观测数据开展的研究各有侧重；各生态站的长期观测数据能够为揭示其所代表生态区和生态系统类型的生态系统结构与功能、能量流动与养分循环的变化规律，分析主要生态环境问题的现状、动态变化及驱动机制等方面提供重要支撑。最后，对生态系统观测研究网络长期观测数据应用的相关方面提出几点建议：（1）健全数据引用机制，制定相应的科学数据引用和著录标准；（2）发挥生态网络长期观测数据优势，开展专题数据产品的生产，充分开发生态网络长期观测数据的潜在价值；（3）加大和稳定生态站的经费投入，提高生态站的观测能力和水平，同时还要完善、优化生态站布局。 Abstract:The ecosystem research network is a data-intensive science platform for field observation and research, whose major goal is long-term monitoring and networked study of ecosystems. Since 1998, field stations have continuously measured and recorded more than 300 monitoring variations in hydrological, pedological, atmospheric, and biological elements of major terrestrial and aquatic ecosystems in China, such as cropland, forest, grassland, desert, marshes, lakes, and bays according to standard monitoring protocols, and large amounts of long-term observation data have been collected. These data are crucial resources for research on ecology and other relevant disciplines; thus, a national treasure. Many efforts have been made to share the data. To ensure the value of long-term observation data and improve data sharing services, a bibliometric analysis has been applied to obtain an overview of the situation and characteristics using the CNKI database. A total of 160 documents published between 2001 and 2018 related to the subject have been retrieved and analyzed according to five main aspects: publication years, subject areas, author keywords, institutions, and field stations. The results indicated that the long-term observation data of the ecosystem research network has attracted more and more attention from researchers. The major research areas of data application are forestry and basic agricultural science, and continue to expand into other disciplines. The data is mainly used in ecosystem service research, model simulation, plantation research, water pollution research, biodiversity research, wheat and corn research, and soil moisture research to support the spatial pattern analysis and environmental drive mechanisms of ecosystem processes, and the discovery and verification of commonality laws of different ecosystems. Two main user groups of long-term observation data can be distinguished: universities and research institutes. The main user institutions include institutes affiliated to the Chinese Academy of Sciences, forestry and agricultural universities, comprehensive universities, normal universities, and institutes affiliated to the China Academy of Forestry Sciences; different institutions vary in data usage combined with their geographical location, research direction, and dominant disciplines. The long-term observation data of each field station could provide important support for revealing the rules of ecosystem structure and functions, energy flow and materials cycles, and analyzing the current situation, dynamic change, and driving mechanisms of the main ecological and environmental problems of the ecological zone and ecosystem that the field station represents. Some suggestions have been offered for the application of long-term observation data of the ecosystem research network such as to (i) standardize scientific data citation to improve the data citation mechanism of the ecosystem research network, (ii) develop thematic data products by analyzing and mining the data to provide the full advantage of long-term observation data, and (iii) increase the investment in field stations to improve the ability and level of observation, and optimize the layout of field stations. 参考文献 相似文献 引证文献

Quantitative analysis of global research trend on water pollution

The present study analysed quantitative analysis of 51347 contributions on water pollution research during 2000-2014 based on the total publication output, its growth rate, quality of papers published and rank of India in the global context. This paper discusses year wise research output, growth rate, document wise classification of papers, subject wise distribution of articles, language wise, country wise and institutions wise contributions and also prolific authors have been analyzed. The study analysed that, 75.03% of the research papers was in the form of articles followed by 16.67% conference papers. Subject wise analysis shows that, highest 65.41% publication output came from Environmental Science and 14.40% from, Engineering. The study also investigated that majority of the publications are published from United States (23.53%), followed by China (15.31%) and India (5.69%).

Erratum

Biotechnology and BioengineeringVolume 114, Issue 12 p. 2955-2955 ErratumFree Access Erratum First published: 24 October 2017 https://doi.org/10.1002/bit.26401AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat The following error was published in [1]. Mohamed Mahmoud's full affiliations were not listed. In addition to those listed, the author is also affiliated with: Water Pollution Research Department, National Research Centre, 33 El Bohouth St., Dokki, Cairo 12311, Egypt. We apologize for this error. Reference Mahmoud M, Parameswaran P, Torres CI, Rittmann BE. 2017. Electrochemical techniques reveal that total ammonium stress increases electron flow to anode respiration in mixed-species bacterial anode biofilms. Biotechnol Bioeng 114: 1151– 1159. doi: 10.1002/bit.26246 Volume114, Issue12December 2017Pages 2955-2955 ReferencesRelatedInformation

Response to “Comment on ‘Enhanced Biosorptive Remediation of Hexavalent Chromium Using Chemotailored Biomass of a Novel Soil Isolate Bacillus aryabhattai ITBHU02: Process Variables Optimization through Artificial Neural Network Linked Genetic Algorithm’”

ADVERTISEMENT RETURN TO ISSUEPREVCorrespondenceResponse to “Comment on ‘Enhanced Biosorptive Remediation of Hexavalent Chromium Using Chemotailored Biomass of a Novel Soil Isolate Bacillus aryabhattai ITBHU02: Process Variables Optimization through Artificial Neural Network Linked Genetic Algorithm’”Devendra Kumar Verma†, Syed Hadi Hasan‡, Devendra Kumar Singh‡, Shalini Singh†, and Yogendra Singh*†View Author Information† †School of Biochemical Engineering and ‡Water Pollution Research Laboratory, Department of Applied Chemistry, Indian Institute of Technology (Banaras Hindu University), Varanasi−221005 (U.P.), India*Tel.: +91 9450283646. E-mail: yogendrasingh.rs.bce @iitbhu.ac.inCite this: Ind. Eng. Chem. Res. 2014, 53, 17, 7271–7272Publication Date (Web):April 15, 2014Publication History Published online15 April 2014Published inissue 30 April 2014https://doi.org/10.1021/ie501357uCopyright © 2014 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views295Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (142 KB) Get e-AlertsSUBJECTS:Bacteria,Biomass,Chromium,Neural networks,Soils Get e-Alerts

Effect of Divalent Metals on the Molecular Structure of Protein: Modeling and Spectroscopic Approaches

Hanan Elhaes1, Nahla M. Elkashef2, Fagr Kh. Abdel-Gawad3 4, Ahmed Shabaan4, and Medhat Ibrahim5 ∗ 1Faculty of Women for Arts, Science and Education, Physics Department, Ain Shams University, 11757 Cairo, Egypt 2Faculty of Science, Physics Department, Suez Canal University, Ismailia, Egypt 3Centre of Excellence for Advanced Science (CEAS), Water Pollution Research Department, National Research Centre, 12311 Dokki, Cairo, Egypt 4Water Pollution Research Department, National Research Centre, 12311 Dokki, Cairo, Egypt 5Spectroscopy Department, National Research Center, 12311 Dokki, Cairo, Egypt

Acute Toxicity of Indo-Sulpin to a fresh water Gastropod Thira Lineate Changes in Rate of Respiration

Toxicity of sumithion to the fresh water field crab has been studied by Bhagya Laxmi and Ramamurti (1981), Aness (1975) and Verma et al., (1978) observed the toxicity of some organophosphate compounds. The oxygen consumption has been estimated by Alam and Lomte(1984) after exposing the gastropods. Viviparous bengalensis in zink sulphate Lowry, O. H.; Rosenbroughi, N. J.; Farr,A. L. and Randall, R. J. et. al. (1951, 1983) have observed the effects of carbaryl on oxygen consumption of the fish, Channa punctatus Chaudari.et.al. (1988) have observed rate of oxygen consumption. Considering the studies on pesticide pollution on the fresh water gastropod Thiara on the fresh water gastropod Thiara Lineata the present investigation has under taken with the view to understand the acute toxicity and the effect on the rate of oxygen consumption. Organophosphate compounds are now extensively use in place of organochloride compounds on account of their lesser residuals toxicity, but the prevention of aqutic animals' mortality remains an important object of water pollution research. Kaviraj and konar (1982) have studied the effects of heavy metals on accute toxicity of mercury, chromium and cadmium and cadmium to the fish. Tyllapia Mossambica. Mane and Akarte (1987) have studied the effects of cythion or Malathian on the estuarine Clam Catalasia optima.

The Methods of Soil Heavy Metal Pollution Evaluation Using GRA

To assess the soil heavy metal pollution, there are so many methods, such as the mono pollution-factor, the comprehensive Nemerow pollution-factor and so on. Grey Relational Analysis (GRA) is one of the major contents of gray system theory. The method of GRA was used in the application of environmental impact assessment, such as water pollution research. In this paper, we designed the new practical method of the heavy metal pollution evaluation, which is focus on six soil heavy metals (Cd, Hg, Pb, Cr, Cu, Zn). According to the consenting range of environment level by the National Environmental Standard (GB15618-1995), we have done our researches.

Modeling a bench-scale alternating aerobic/anoxic activated sludge system for nitrogen removal using a modified ASM1

The Activated Sludge Model No. 1 (ASM1), developed by The International Association of Water Pollution Research and Control, was applied to model dynamics of NH4 +, and NO3 − in a bench scale alternating aerobic-anoxic (AAA) activated sludge system for nitrogen removal. The model was modified by eliminating inert soluble COD (SI) and inert particulate COD (XI) from the model's state variables as these two variables are not involved in any biological reaction and are not readily measurable with conventional routine COD analysis. It was assumed that the soluble COD and particulate COD of wastewater represent readily biodegradable COD (SS) and slowly biodegradable (XS) in the model, respectively. In addition, alkalinity was also removed from the model, since alkalinity of an AAA system remains stable due to the cyclic modes of the system. Even with the elimination of the three state variables and the assumption made, the model could reasonably predict the NH4 + and NO3 − dynamics of the AAA system, and effluent NH4 + and NO3 − concentrations with adjustment of only a few kinetic parameters. Compared to the original ASM1, it is expected that the modified ASM1 presented in this study can be more easily utilized by engineers in designing or operating an AAA system in practice, since it requires simple characterization of wastewater COD.

Development and application of some renovated technologies for municipal wastewater treatment in China

China has been experiencing fast economic development in recent decades at the cost of serious environ- mental deterioration. Wastewater discharge, especially municipal wastewater discharge, and non-point pollution sources are becoming the major water pollution source and research focus. Great efforts have been made on water pollu- tion control and a number of renovated technologies and pro- cesses for municipal wastewater treatment and reclamation as well as non-point pollution control have been developed and applied in China. This paper discusses the development and application of the appropriate technologies, including natural treatment systems, anaerobic biological treatment, biofilm reactors and wastewater reclamation technologies, for water pollution control in the country.