Sensors are being utilized to increasing degrees in all forms of industry. Researchers and industrial practitioners in all fields seek to obtain a better understanding of appropriate processes so as to improve quality of service and efficiency. The quality of water is no exception, and the water industry is faced with a wide array of water quality issues being present world-wide. Thus, the need for sensors to tackle this diverse subject is paramount. The aim of this book is to combine, for the first time, international expertise in the area of water quality monitoring using smart sensors and systems in order that a better understanding of the challenges faced and solutions posed may be available to all in a single text.
[ANGLÈS] Automated Measuring Stations for river quality monitoring have been used to generate on-line measurements of water quality variables. Even though a high degree of automation has been implemented at the levels of measurements, maintenance and control, data management and the resulting access to errors and uncertainties. Besides managing a monitoring program, there is the responsibility to ensure high quality data with data and methods that are broadly accessible, and as cost-e ective as possible. Moreover, higher assurance is needed to analyse, interpret, and apply data that are being collected so as to form monitoring networks that are designed to promote comparability of data across sites and across scales of space and time. monEAU (monitoring of water, "eau" in French) is the next generation of water quality monitoring networks. This project has as vision to provide some tools to get a exible and open modular system, a high quality/performance database, remote use, automatic data quality assessment, user-friendly and user-oriented software, and proactive and exible maintenance. In the context of the project, the objective of this thesis was to e ciently monitor, collect and manipulate water quality data such that unreliable data due to anomalies in sensors, insu cient maintenance tasks, severe environmental conditions and other external factors can be minimized. To achieve these goals it was required to maintain and keep the monitoring stations working, collect the data, analyse samples in the laboratory to verify sensor data, create an appropriate database, import all collected data to a database, and nally export the data to statistics software and data manipulation programs for posterior analysis.
Water Quality Monitoring and Management: Basis, Technology and Case Studies presents recent innovations in operations management for water quality monitoring. It highlights the cost of using and choosing smart sensors with advanced engineering approaches that have been applied in water quality monitoring management, including area coverage planning and sequential scheduling. In parallel, the book covers newly introduced technologies like bulk data handling techniques, IoT of agriculture, and compliance with environmental considerations. Presented from a system engineering perspective, the book includes aspects on advanced optimization, system and platform, Wireless Sensor Network, selection of river water quality, groundwater quality detection, and more. It will be an ideal resource for students, researchers and those working daily in agriculture who must maintain acceptable water quality. Discusses field operations research and application in water science Includes detection methods and case analysis for water quality management Encompasses rivers, lakes, seas and groundwater Covers water for agriculture, aquaculture, drinking and industrial uses
Water can be considered as one of the basic elements supporting life and the natural environment, a primary component for industry, a consumer item for humans and animals and a vector for domestic and industrial pollution. Various European Directives already provide a framework for the control of aquatic substances, the quality of bathing, surface and drinking water and effluent control. Such regulatory measures are closely related to analytical measurements. In order to comply with these regulations on a permanent basis, treatment plants and distribution companies, as well as water users and polluters, need to implement automated measuring techniques, such as sensors and other analytical tools in continuous or sequential mode to obtain suitable alarm systems and facilitate the management of water resources and decision making processes. This approach is in continuous evolution within the European Union. In view of the consequences of wrong measurements, it is vital that reliable quality control systems be achieved and maintained. At present, only a small range of analytical parameters can be measured automatically, it is therefore necessary to develop and validate new methods to extend the list of parameters. The development of new methodologies requires that the most recent know-how in fields such as optics, electrochemistry, biochemistry, chemometrics and others be shared by experts. In order to identify possible actions to be undertaken in the field of standards, measurements and testing for monitoring water quality, the Standards Measurements and Testing (SMT) Programme of the European Union, organised a European workshop that was held in Nancy 29-31 May 1997. This workshop bought together researchers and industrial users with the aim to make an overview of the present state-of-the-art, to consider possible improvements in existing techniques and the need and possibilities of developing new advanced technologies.
In 1997, New York City adopted a mammoth watershed agreement to protect its drinking water and avoid filtration of its large upstate surface water supply. Shortly thereafter, the NRC began an analysis of the agreement's scientific validity. The resulting book finds New York City's watershed agreement to be a good template for proactive watershed management that, if properly implemented, will maintain high water quality. However, it cautions that the agreement is not a guarantee of permanent filtration avoidance because of changing regulations, uncertainties regarding pollution sources, advances in treatment technologies, and natural variations in watershed conditions. The book recommends that New York City place its highest priority on pathogenic microorganisms in the watershed and direct its resources toward improving methods for detecting pathogens, understanding pathogen transport and fate, and demonstrating that best management practices will remove pathogens. Other recommendations, which are broadly applicable to surface water supplies across the country, target buffer zones, stormwater management, water quality monitoring, and effluent trading.
Water quality monitoring stations are used to measure water quality at high frequency. For effective data management, the quality of the data must be evaluated. In a previously developed univariate method both outliers and faults were detected in the data measured by these stations by using exponential smoothing models that give one-step ahead forecasts and their confidence intervals. In the present study, the outlier detection step of the univariate method is improved by identifying an auto-regressive moving average model for a moving window of data and forecasting one-step ahead. The turbidity data measured at the inlet of a municipal treatment plant in Denmark is used as case study to compare the performance of the use of the two models. The results show that the forecasts made by the new model are more accurate. Also, inclusion of the new forecasting model in the univariate method shows satisfactory performance for detecting outliers and faults in the case study data.
This guidebook, now thoroughly updated and revised in its second edition, gives comprehensive advice on the designing and setting up of monitoring programmes for the purpose of providing valid data for water quality assessments in all types of freshwater bodies. It is clearly and concisely written in order to provide the essential information for all agencies and individuals responsible for the water quality.
