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This article in JEQ

  1. Vol. 43 No. 2, p. 753-762
     
    Received: June 08, 2013
    Published: June 23, 2014


    * Corresponding author(s): k.a.koziol@gmail.com
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doi:10.2134/jeq2013.06.0225

Reducing Monitoring Costs in Industrially Contaminated Rivers: Cluster and Regression Analysis Approach

  1. M. Rumana,
  2. E. Olkowskab,
  3. K. Kozioł *cd,
  4. D. Absalona,
  5. M. Matysika and
  6. Ż. Polkowskab
  1. a Earth Sciences Faculty, Univ. of Silesia, ul. Będzińska 60, 41-200 Sosnowiec, Poland
    b Gdańsk Univ. of Technology, ul. G. Narutowicza 11/12, 80-233 Gdańsk, Poland
    c Dep. of Geography, Univ. of Sheffield, Winter Street, S10 2TN Sheffield, UK
    d Univ. Centre in Svalbard (UNIS), P.O. Box 156, N-9171 Longyearbyen, Svalbard, Norway

Abstract

Monitoring contamination in river water is an expensive procedure, particularly for developing countries where pollution is a significant problem. This study was conducted to provide a pollution monitoring strategy that reduces the cost of laboratory analysis. The new monitoring strategy was designed as a result of cluster and regression analysis on field data collected from an industrially influenced river. Pollution sources in the study site were coal mining, metallurgy, chemical industry, and metropolitan sewage. This river resembles those in other areas of the world, including developing countries where environmental monitoring is financially constrained. Data were collected on variability of contaminant concentrations during four seasons at the same points on tributaries of the river. The variables described in the study are pH, electrical conductivity, inorganic ions, trace elements, and selected organic pollutants. These variables were divided into groups using cluster analysis. These groups were then tested using regression models to identify how the behavior of one variable changes in relation to another. It was found that up to 86.8% of variability of one parameter could be determined by another in the dataset. We adopted 60, 65, and 70% determination levels (R2) for accepting a regression model. As a result, monitoring could be reduced by 15 (60% level) and 10 variables (65 and 70%) out of 43, which comprises 35 and 23% of the monitored variable total. Cost reduction would be most effective if trace elements or organic pollutants were excluded from monitoring because these are the constituents most expensive to analyze.

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