Logistic mixture of multivariate regressions for analysis of water quality impacted by agrochemicals

Yongsung Joo, Keunbaik Lee, Joong Hyuk Min, Seong Taek Yun, Trevor Park

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

In this paper, we study the impacts of two representative agricultural activities, fertilizers and lime application, on water quality. Because of heavy usage of nitrogen fertilizers, nitrate (NO3 -) concentration in water is considered as one of the best indicators for agricultural pollution. The mixture of normal distributions has been widely applied with (NO3 -) concentrations to cluster water samples into two environmentally interested groups (water impacted by agrochemicals and natural background water groups). However, this method fails to yield satisfying results because it cannot distinguish low-level fertilizer impact and natural background noise. To improve performance of cluster analysis, we introduce the logistic mixture of multivariate regressions model (LMMR). In this approach, water samples are clustered based on the relationships between major element concentrations and physicochemical variables, which are different in impacted water and natural background water.

Original languageEnglish
Pages (from-to)499-514
Number of pages16
JournalEnvironmetrics
Volume18
Issue number5
DOIs
Publication statusPublished - 2007 Aug 1

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Keywords

  • Agricultural pollution
  • ECM
  • Mixture of normal distributions
  • Mixture of regressions
  • Model-based clustering
  • Water quality

ASJC Scopus subject areas

  • Environmental Science(all)
  • Environmental Chemistry
  • Statistics and Probability

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