Abstract
Water quality indices that describe the status of water are commonly used in freshwater vulnerability assessment. The design of river water quality monitoring programs has always been a complex process and despite the numerous methodologies employed by experts, there is still no generally accepted, holistic and practical approach to support all the phases and elements related. Here, a Geographical Information System (GIS)-based multicriteria decision analysis approach was adopted so as to contribute to the design of the national network for monitoring of water quality parameters in Greece that will additionally fulfill the urgent needs for an operational, real-time monitoring of the water resources. During this cost-effective and easily applied procedure the high priority areas were defined by taking into consideration the most important conditioning factors that impose pressures on rivers and the special conditions that increase the need for monitoring locally. The areas of increased need for automatic monitoring of water quality parameters are highlighted and the output map is validated. The sites in high priority areas are proposed for the installation of automatic monitoring stations and the installation and maintenance budget is presented. Finally, the proposed network is contrasted with the current automatic monitoring network in Greece.
Highlights
Abundant and high quality water resources have always been linked to human survival and socioeconomic development [1], while the access of people to safe freshwater has commonly risen conflicts [2]
The pairwise criteria comparison based on the Analytic Hierarchy Process (AHP) approach resulted to the preference matrix with assigned preference values and calculated weights (Table 6), and to a consistency ratio CR of 0.018
Most of the methodologies propose the selection of the water quality monitoring sites based on based on statistical approaches, such as principal component analysis, principal factor analysis, statistical approaches, such as principal component analysis, principal factor analysis, canonical canonical correlation analysis, correlation analysis, cluster analysis, regression analysis, artificial neural correlation analysis, correlation analysis, cluster analysis, regression analysis, artificial neural networks, maintenance of variance extension, and matter element analysis (e.g., [30,31,32,33,92,93,94])
Summary
Abundant and high quality water resources have always been linked to human survival and socioeconomic development [1], while the access of people to safe freshwater has commonly risen conflicts [2]. A water quality variable is any physical, chemical, or biological property that influences the suitability of water for natural ecological systems or the use by humans, and the term water quality is linked to the suitability of water for a particular purpose [3]. Historical observations of water quality variables provide the irreplaceable means for understanding the behavior of water resource systems, while real-time data are essential for assessing their current status and making short-term predictions concerning the water availability and the associated risks.
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