2014
DOI: 10.2495/wp140121
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Development of a monitoring network of water resources in urban areas as a support for municipal environmental management

Abstract: Water resources in urban areas are often impacted by the intense land use in the cities. The development of a surface water monitoring network is a critical element of water resource sustainable management. The city of Caxias do Sul is the second largest metal mechanic industrial region in Brazil. Industrialization seriously impacts river water quality and is a challenge to public authority supervision. The development of a water monitoring network was the solution public authorities found for the municipality… Show more

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Cited by 10 publications
(12 citation statements)
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“…Continuous urbanization will result in increasing nutrient and contaminant emissions of watersheds, putting human health and ecosystems in danger [44,45]. Due to the absence of trans-scale thinking, drainage and flood protection systems mostly rely on expensive and inflexible underground solutions.…”
Section: Water Management Beyond the Traditional Pipe Systemmentioning
confidence: 99%
“…Continuous urbanization will result in increasing nutrient and contaminant emissions of watersheds, putting human health and ecosystems in danger [44,45]. Due to the absence of trans-scale thinking, drainage and flood protection systems mostly rely on expensive and inflexible underground solutions.…”
Section: Water Management Beyond the Traditional Pipe Systemmentioning
confidence: 99%
“…The objective was to give support to the monitoring process and assessment of the water quality of the water resources in the urban area. The conception of the monitoring network and its characteristics were presented by Finotti et al [1]. The monitoring network comprised 12 sampling stations, in which the flow rate and 20 water quality parameters were examined.…”
Section: Methodsmentioning
confidence: 99%
“…As the amount of data from the network was too large, the principal components analysis (PCA) was applied to indicate which quality parameters would better represent the dataset under analysis. Finotti et al [1] describe in details the results of application of PCA. These results, which are briefly presented in the results section hereof, were used as a support for the calculation of the quality index, as described below.…”
Section: Methodsmentioning
confidence: 99%
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