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Changes of heavy metals in Pollutant Release and Transfer Registers (PRTRs) in Korea.

Kwon YS, Bae MJ, Park YS - Int J Environ Res Public Health (2014)

Bottom Line: From the database, we selected nine heavy metals (Pb, Cd, Mn, Sb, Cu, Zn, Cr, Sn, and Ni) and compared the differences in their effluent for different types of industries.The heavy metal effluents released into freshwater ecosystems were classified into four clusters through the learning process of the self-organizing map.Cluster 1 was characterized by the relatively higher effluent volumes of heavy metals, whereas cluster 4 had lower effluent volumes.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Kyung Hee University, Seoul 130-701, Korea. davy3021@hanmail.net.

ABSTRACT
Industrial effluent containing heavy metals discharged into streams may pose high toxicity risks to aquatic organisms and to human health. Therefore, it is important to understand how to change the amount of effluent with heavy metals discharged from industries into open aquatic ecosystems both for effective management of heavy metals and to foster sustainable ecosystems. This study was conducted to characterize the release of heavy metals from industries based on the Pollutant Release and Transfer Registers database in Korea from 1999 to 2010. From the database, we selected nine heavy metals (Pb, Cd, Mn, Sb, Cu, Zn, Cr, Sn, and Ni) and compared the differences in their effluent for different types of industries. The heavy metal effluents released into freshwater ecosystems were classified into four clusters through the learning process of the self-organizing map. Cluster 1 was characterized by the relatively higher effluent volumes of heavy metals, whereas cluster 4 had lower effluent volumes. The different patterns of the effluent volumes in heavy metals were closely associated with the differences of industrial types, and the changes of effluents of heavy metals reflected the changes in regulations and laws for aquatic ecosystem management.

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Differences in effluent of heavy metals for each industry types among the four clusters defined by the SOM. Different letters indicate significant differences between the clusters based on Dunn’s test after a Kruskal–Wallis test (p < 0.05). Error bars indicate standard error.
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ijerph-11-02381-f007: Differences in effluent of heavy metals for each industry types among the four clusters defined by the SOM. Different letters indicate significant differences between the clusters based on Dunn’s test after a Kruskal–Wallis test (p < 0.05). Error bars indicate standard error.

Mentions: The effluent volumes of nine heavy metals were significantly different among clusters (Dunn’s test, p < 0.05) (Figure 6). The effluent volumes of Pb, Cd, Mn, and Sb were significantly higher in cluster 1 than in other clusters, and those of Cd and Sb showed no significant differences among three different clusters (clusters 2-4) (Dunn’s test, p < 0.05). Meanwhile the effluents of Cr, Ni, and Cu were the highest in cluster 2 and the lowest in cluster 4, with the exception of Cu (Dunn’s test, p < 0.05). Similarly, the effluents of heavy metals from manufactures of chemical products and metal products were high in cluster 1, whereas those from manufactures of textiles, electronic components, transport equipment, and others were high in cluster 2 (Figure 7). Clusters 3 and 4 were characterized by small effluents of heavy metals. Meanwhile, effluent volumes from manufactures of food products, non-metallic mineral products, and machinery and equipment were not significantly different among clusters (Dunn’s test, p > 0.05). The number of industries also differed between clusters (Table 2). The number of industries of different industrial types was significantly higher in clusters 1 and 2 than in cluster 4 (Dunn’s test, p < 0.05).


Changes of heavy metals in Pollutant Release and Transfer Registers (PRTRs) in Korea.

Kwon YS, Bae MJ, Park YS - Int J Environ Res Public Health (2014)

Differences in effluent of heavy metals for each industry types among the four clusters defined by the SOM. Different letters indicate significant differences between the clusters based on Dunn’s test after a Kruskal–Wallis test (p < 0.05). Error bars indicate standard error.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3986981&req=5

ijerph-11-02381-f007: Differences in effluent of heavy metals for each industry types among the four clusters defined by the SOM. Different letters indicate significant differences between the clusters based on Dunn’s test after a Kruskal–Wallis test (p < 0.05). Error bars indicate standard error.
Mentions: The effluent volumes of nine heavy metals were significantly different among clusters (Dunn’s test, p < 0.05) (Figure 6). The effluent volumes of Pb, Cd, Mn, and Sb were significantly higher in cluster 1 than in other clusters, and those of Cd and Sb showed no significant differences among three different clusters (clusters 2-4) (Dunn’s test, p < 0.05). Meanwhile the effluents of Cr, Ni, and Cu were the highest in cluster 2 and the lowest in cluster 4, with the exception of Cu (Dunn’s test, p < 0.05). Similarly, the effluents of heavy metals from manufactures of chemical products and metal products were high in cluster 1, whereas those from manufactures of textiles, electronic components, transport equipment, and others were high in cluster 2 (Figure 7). Clusters 3 and 4 were characterized by small effluents of heavy metals. Meanwhile, effluent volumes from manufactures of food products, non-metallic mineral products, and machinery and equipment were not significantly different among clusters (Dunn’s test, p > 0.05). The number of industries also differed between clusters (Table 2). The number of industries of different industrial types was significantly higher in clusters 1 and 2 than in cluster 4 (Dunn’s test, p < 0.05).

Bottom Line: From the database, we selected nine heavy metals (Pb, Cd, Mn, Sb, Cu, Zn, Cr, Sn, and Ni) and compared the differences in their effluent for different types of industries.The heavy metal effluents released into freshwater ecosystems were classified into four clusters through the learning process of the self-organizing map.Cluster 1 was characterized by the relatively higher effluent volumes of heavy metals, whereas cluster 4 had lower effluent volumes.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Kyung Hee University, Seoul 130-701, Korea. davy3021@hanmail.net.

ABSTRACT
Industrial effluent containing heavy metals discharged into streams may pose high toxicity risks to aquatic organisms and to human health. Therefore, it is important to understand how to change the amount of effluent with heavy metals discharged from industries into open aquatic ecosystems both for effective management of heavy metals and to foster sustainable ecosystems. This study was conducted to characterize the release of heavy metals from industries based on the Pollutant Release and Transfer Registers database in Korea from 1999 to 2010. From the database, we selected nine heavy metals (Pb, Cd, Mn, Sb, Cu, Zn, Cr, Sn, and Ni) and compared the differences in their effluent for different types of industries. The heavy metal effluents released into freshwater ecosystems were classified into four clusters through the learning process of the self-organizing map. Cluster 1 was characterized by the relatively higher effluent volumes of heavy metals, whereas cluster 4 had lower effluent volumes. The different patterns of the effluent volumes in heavy metals were closely associated with the differences of industrial types, and the changes of effluents of heavy metals reflected the changes in regulations and laws for aquatic ecosystem management.

Show MeSH
Related in: MedlinePlus