Evaluation of changes in effluent quality from industrial complexes on the Korean nationwide scale using a self-organizing map.
One of the major issues related to the environment in the 21st century is sustainable development. The innovative economic growth policy has supported relatively successful economic development, but poor environmental conservation efforts, have consequently resulted in serious water quality pollution issues. Hence, assessments of water quality and health are fundamental processes towards conserving and restoring aquatic ecosystems. In this study, we characterized spatial and temporal changes in water quality (specifically physico-chemical variables plus priority and non-priority pollutants) of discharges from industrial complexes on a national scale in Korea. The data were provided by the Water Quality Monitoring Program operated by the Ministry of Environment, Korea and were measured from 1989 to 2008 on a monthly basis at 61 effluent monitoring sites located at industrial complexes. Analysis of monthly and annual changes in water quality, using the seasonal Mann-Kendall test, indicated an improvement in water quality, which was inferred from a continuous increase in dissolved oxygen and decrease in other water quality factors. A Self-Organizing Map, which is an unsupervised artificial neural network, also indicated an improvement of effluent water quality, by showing spatial and temporal differences in the effluent water quality as well as in the occurrence of priority pollutants. Finally, our results suggested that continued long-term monitoring is necessary to establish plans and policies for wastewater management and health assessment.
Department of Biology, Kyung Hee University, Dongdaemun, Seoul 130-701, Korea. mjbae@khu.ac.kr
Mi-Jung
MJ
Bae
Jun-Su
JS
Kim
Young-Seuk
YS
Park
D001151:Arsenic / Q000032:analysis; D057919:Biological Oxygen Demand Analysis; D004755:Enterobacteriaceae / Q000302:isolation & purification; D004784:Environmental Monitoring; D007220:Industrial Waste; D007723:Korea; D019216:Metals, Heavy / Q000032:analysis; D016571:Neural Networks, Computer; D009584:Nitrogen / Q000032:analysis; D019800:Phenol / Q000032:analysis; D010710:Phosphates / Q000032:analysis; D014865:Waste Disposal, Fluid; D014874:Water Pollutants, Chemical / Q000032:analysis*; D060753:Water Quality
D007220:Industrial Waste; D019216:Metals, Heavy; D010710:Phosphates; D014874:Water Pollutants, Chemical; D019800:Phenol; D001151:Arsenic; D009584:Nitrogen