Abstract
European Community policy concerning water is placing increasing demands on the acquisition of information about the quality of aquatic environments. The cost of this information has led to a reflection on the rationalization of monitoring networks and, therefore, on the economic value of information produced by these networks. The aim of this article is to contribute to this reflection. To do so, we used the Bayesian framework to define the value of additional information in relation to the following three parameters: initial assumptions (prior probabilities) on the states of nature, costs linked to a poor decision (error costs) and accuracy of additional information. We then analyzed the impact of these parameters on this value, particularly the combined role of prior probabilities and error costs that increased or decreased the value of information depending on the initial uncertainty level. We then illustrated the results using a case study of a stream in the Bas-Rhin department in France.
Highlights
Water quality monitoring can be defined as the acquisition of quantitative and representative information about the physical, chemical and biological characteristics of a water body over time and space [1]
Concerning the value of information generated by water quality monitoring networks, Bouzit, Graveline and Maton [18] present three case studies to assess the value of new pollution measurement techniques, the first aimed at identifying the origin of the pollution through nitrates in groundwater, the second by defining the contribution of two sources to the presence of pesticides in a water table, and the third by detecting accidental toxic pollution in the Rhine
The major contribution of this work is the theoretical modeling of the problem, enabling us to better understand the role of each parameter on the value of information, namely: initial assumptions on the states of nature, costs linked to a poor decision and accuracy of additional information
Summary
Water quality monitoring can be defined as the acquisition of quantitative and representative information about the physical, chemical and biological characteristics of a water body over time and space [1]. Diop out any ex-post evaluation of the soundness of the initial choice [2], leading to the production of data that provided little information [3] It has only been in recent decades that a reflection on the design of monitoring networks has emerged in order to better reflect specific problems such as eutrophication, salinization, acidification and microbial or heavy metal contamination [2]. Concerning the value of information generated by water quality monitoring networks, Bouzit, Graveline and Maton [18] present three case studies to assess the value of new pollution measurement techniques, the first aimed at identifying the origin of the pollution through nitrates in groundwater, the second by defining the contribution of two sources to the presence of pesticides in a water table, and the third by detecting accidental toxic pollution in the Rhine.
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