An Approach for Sustainable Management of The Balikligol Lakes, Turkey

An online water quality monitoring and management system developed for the Liming River basin in Daqing, China

Kapuas River is the largest river on the Borneo island and become the source of water for the people of this island. In Pontianak City, Kapuas River is become the source of raw water for the local water company (PDAM). To maintain product quality, PDAM Pontianak always monitor this river water quality. During the dry season or during high water, raw water quality Kapuas river becomes salty. Meanwhile, during the rainy season brings torrential river water from upstream mud and water turned into peat. To monitor water quality changes in the Kapuas river in the intake location, PDAM Pontianak has installed an online and real time water quality monitoring system using GSM technology. This paper discusses the installation process online water quality monitoring system starting from the preparation, determination of the location until the process of testing the system. The results of monitoring by the monitoring system is expected to assist the production department to determine the necessary action if there is a change of quality of raw water Kapuas river. Keywords: GSM Communication Based Online Monitoring System, Telemetry System, Intake PDAM Pontianak, Multi Probe Digital Sensor, Water Treatment Plant

Background: Zoning of the water quality based on NSFWQI index is used more than other indices. The purpose of this study to evaluate the water quality of Kashan's Ghohrood River, using National Sanitation Water Quality Index (NSFWQI) and its zoning with Geographic information system (GIS). Materials and Methods: In this study, water quality parameters of Ghohrood River are studied monthly in five different stations from October 2014 to September 2015 during 12 months in Kahsan central of Iran. Also, these data were analyzed with NSFWQI index, and finally route of river was zoned using GIS software. Results: Among the studied stations, station A had the highest and best rate of water quality by 86.87% in March. Water quality index was 60.93% in station E in August. Average studied index in stations A, B, and C in all of the months was 72-80 and in stations D and E average index was 67-69. Average index of NSFWQI had a downward slope in the warm months; so that, in the summer, the index was lower than other seasons in each station. Conclusion: Results showed that water pollution increases by increasing the distance between source of the river and station. Since the area is considered as a recreational resort especially in the spring season and the fact that around the river is used as pastures, water quality deterioration especially in D and E stations is a very important issue.

Water is an essential resource in day-to-day life. Pollution and urbanization have led to higher susceptibility of source water to contamination. There is a pressing need to develop a water quality monitoring system to preserve the quality of source water and ultimately safeguard human health. This paper proposes a low cost, wireless water quality monitoring system, wherein the quality of water stored in overhead tanks is continuously monitored. The quality of water is measured by parameters that are critical quality indicators. The data encompassing these parameters are stored in a Cloud database (in real-time) along with its timestamp. The quality of water is ascertained based on the comparison of the monitored data to standard well-established thresholds. The data, annotated with its timestamp is treated as a time-series. A univariate non-seasonal AutoRegressive Integrated Moving Average (ARIMA) model is employed to forecast individual water quality parameters. The results of forecasting is used to predict water quality deterioration. The model used is found to have mean square errors of 0.001 for pH, 0.076 for temperature and 0.001 for turbidity between the actual and forecasted values.

A Multiobjective SDSS for Management of Urbanizing Watersheds: The Case of the Lower Kaskaskia Basin, Illinois

Water quality detection plays an important role in water pollution alarm, water source pollution detection and water source diagnosis and treatment. The online water quality monitoring system based on the Internet of Things technology can monitor the water quality in real time and dynamically, and can serve the strategic requirements of the country for water resources. Currently, the water quality on-line monitoring system is mainly composed of upper computer system and lower base station in domestic researches. This mode of construction has deficiencies such as lack of channel diversity, large investment in communication network, and insufficient capacity of data. Aiming at these problems, this study attempts to use LoRa network technology, cloud architecture combined with water quality monitoring sensors to build a low cost and high reliable water quality monitoring system. Indexes of water quality are collected by intelligent sensors, and are transmitted to cloud server through LoRa network. They can be intelligently analysed by the technology of big data. The system includes both mobile and computer terminals. Indexes of water quality can be monitored timely and accurately by the system, which can further enhance the omnidirectional intelligent management of rivers. By case analysis to verify that the system has certain value of promotion and application.

The "Three Rivers Project" is a government initiative and one of a series of catchment based water quality monitoring and management systems being developed throughout Ireland since 1997. The establishment of these multi-sectoral, basin-wide and community based systems is a response to historically perceived disjointed, legalistic and non-participative approaches to water resource management and purports to transcend the restrictions of traditional local authority administrative boundaries. The new management model embodies the concepts and objectives contained in the European Union (EU) Water Framework Directive (WFD) enacted in December 2000. Ireland, in common with many EU countries, has failed to halt decades of increasing levels of eutrophication of surface waters due principally to phosphorus loading. The "Three Rivers Project" is promoting the benefits of an integrated and cooperative approach to the management of three important river systems in Ireland, namely, the Boyne, Liffey and Suir. The project objective is to protect and improve water quality to conform with "good ecological status". The implications of the Project findings for agricultural, municipal and industrial policy are grave and one of the greatest challenges now is to organise and fund Irish River Basin Management Systems as envisaged by the WFD to continue and build on the work which the "Three Rivers Project" has undertaken.

Evaluation of physical factors that determine the suitability of a given site for a public-supply well typically involves the compilation and analysis of a large amount of data.Two factors that directly determine the suitability of a proposed site are the quantity and the chemical quality of the ground water; these in turn are influenced by many other factors, including aquifer characteristics and proximity to other wells and sources of contamination.Selected data from the U.S. Geological Survey and the New York State Department of Environmental Conservation were compiled into 26 data sets, each representing a single type of hydrogeologic, geologic, chemical, or other data.These data sets, or "coverages," were entered into a GIS (geographic information system) that can store, retrieve, analyze, and display the information.The 166.5-squaremile study area on eastern Long Island is largely undeveloped but contains a variety of land uses and is under the stresses of current development.Several computer programs were developed that enable users unfamiliar with the GIS software to extract data pertinent to the evaluation of any potential well site.The programs were not intended to make interpretations of the data, but to supply the information necessary for decisionmaking.Results indicate that the system can improve the efficiency and accuracy of such evaluations.

The importance of water quality is well understood, and it becomes even more critical when is use for drinking purposes. A case study was carried out to know the applicability of GIS tool for determining the quality of supply water. Water samples from 21 houses at different locations of Delhi were collected. Sample analysis was done for physicochemical parameters viz., pH, EC, TDS, Total Hardness, Total Alkalinity, Chloride, Fluoride, and Nitrate. The water quality data from these selected locations was analyzed using Geographical Information System (GIS) Technique. GIS software did interpolation through the inverse distance weighted (IDW) method to know the water quality (WQ) in different part of the city for various parameters mentioned above and prepare thematic maps from the analysis of water quality data as a database. These thematic maps show the distribution of different water quality parameters. Using Weighted Arithmetic Index (WAI) method, Water Quality Index is calculated. After that, the Drinking Water Quality Index (DWQI) map was generated using thematic layer, reclassification, and weight value assigned in weighted overlay tools in GIS software. Five categories viz., excellent, good, satisfactory, poor, and very poor is assign to describe DWQI. Out of all the selected locations, DWQI was good only at two locations, whereas, at the remaining sites, the DWQI was found satisfactory. However, the overall water quality was found suitable for human consumption. The analysis outcome was represented as maps that will be advantageous to know the water quality status for the area under study. The spatial database established can be a reliable technique for monitoring and managing water quality in the water supply system.

With the increasing demand for water quality monitoring and the increasing severity of water pollution, the water environmental quality in China faces a worrying situation, and water body monitoring has become an extremely important task. Now more than ever, new strategies for water quality monitoring are needed, and the rapid development of wireless monitoring technology perfectly meets people’s pursuit of low-cost, automated and real-time water quality monitoring. However, there are still problems such as few monitoring points cannot reflect the overall situation of water bodies, high artificial costs, and time-consuming analysis. This paper designs and implements a new water monitoring platform built on an Internet of Things platform. Taking water quality monitoring sensors as the core and small boats as the basic framework, it carries control, positioning, navigation equipment, uses wireless network as the data transmission means, monitors multi-point cruising sampling in different areas, realizes real-time collection, data storage and data analysis to complete related environmental monitoring. The multi-point online water quality monitoring system comprehensively applies sensor technology, Internet of Things technology and cloud computing technology to form an integrated online monitoring system. It aims to solve the problems of harsh working environment, strong repetitiveness and long sampling time of water quality monitoring tasks, and achieve fast, efficient and accurate completion of water quality monitoring tasks.

River run: an interactive GIS and dynamic graphing website for decision support and exploratory data analysis of water quality parameters of the lower Cape Fear river

SA Water is a State owned organisation that owns and manages South Australia's water supplies, providing reliable drinking water to nearly 1.4 million South Australians. A major issue affecting SA Water's ability to manage water quality effectively has been the difficulty accessing water quality information which has been stored in separate, generally inaccessible databases with poor reporting and decision support capability. To improve SA Water's ability to make timely and effective decisions regarding water quality, an integrated business system has been developed which provides water managers with direct access to comprehensive water quality information. The system includes improved field data collection units which incorporate a barcode system; sample point images and workflow support tools; an integrated water quality data warehouse with automated standard and ad hoc reporting capabilities; a geographical information system containing comprehensive coverages of natural resources and system infrastructure information; and water incident exception reporting and incident management support through a corporate incident management system. Major benefits of the system will include improved management of public health risk through quicker and more accurate reporting of incidents; improved customer confidence in SA Water; improved knowledge capture and visibility of water quality information; increased efficiency of capital utilisation and better understanding of system performance through spatial representation of data and trending of results. WaterScope can also be used and shared by data partners and regulators, making optimal use of the State's limited water quality data sets. It can also be made available commercially to other water management organisations. Future challenges include the integration of wastewater and recycled water data, linking of continuous (on-line) water quality data and links to water demand management systems.

Chapter 12 - Water Quality Monitoring in Aquaculture

Geographic Information System (GIS) and Remote Sensing (RS) are useful tools in environmental monitoring, evaluation and analysis for various sectors including agriculture. This paper reviews the applications of GIS, RS and the integration of both techniques in the agricultural field, in general, and Controlled Environment Agriculture (CEA), in particular. More emphasis is given to their applications in arid areas and Oman is taken as a case study. GIS techniques have been used in the mapping of soil and water quality, spatial assessment for water quantity stress, land suitability, pest and disease distribution of crops as well as delineating and generating database management systems (DBMS) for protected cultivations. In Oman, GIS was only employed to analyse the spatio-temporal dynamics of land use changes as affected by external factors and greenhouses as an example in northern part. RS was also utilised to map the changes in land cover and their uses, detect and map soil salinity, and monitor agricultural droughts. In CEA, RS was utilised for mapping, detection and classification of greenhouses through aerial images and satellites. In Oman, negligible study was documented on the use of RS techniques in the CEA field. The integration of both techniques has proven its capability in mapping, evaluating and managing natural resources and greenhouse distribution and generating database management system in agriculture and CEA fields. Sophisticated geostatistical analysis models based on Multi-criteria analysis using Fuzzy-logic and Analytic Hierarchy Process could be a good platform for trade-off analysis for land suitability analysis and optimal location of CEA in challenging agriculture like Oman.

Monitoring environmental water quality in an efficient, cheap, and sustainable way can better serve the country's strategic requirements for water resources and water ecological protection. This paper takes the Shaanxi section of the Weihe River Basin as a pilot project and aims to use the Internet of Things technology to develop water quality monitoring sensors, so as to realize the construction of low-cost, high-reliability water quality monitoring demonstration applications. First of all, we established the design of the water quality collection terminal, designed the low-power water quality sensor node, supported the Internet of Things protocol and the collection of various water quality parameters, and used networking for data transmission. Secondly, we use the ant colony algorithm-based system clustering model to obtain a cluster map of water quality monitoring tasks in a certain section of the Weihe River Basin. We take the task clustering graph as an example for analysis, optimize the monitoring model through the ant colony algorithm, and obtain the weight of the optimization index. The weight of the scheduled task limit of the monitoring point becomes larger, so the release of the monitoring task mainly affects the limit of the scheduled task of the monitoring point. Through the above work, we designed and implemented a set of online water quality monitoring system based on the Internet of Things and data mining technology. The system can provide reference for large-scale water resource protection and water environment governance.