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Environ Sci Pollut Res Int. 2016 May;23(10):9774-90. doi: 10.1007/s11356-016-6155-x. Epub 2016 Feb 6.

Characterization and source identification of pollutants in runoff from a mixed land use watershed using ordination analyses.

Author information

1
Department of Civil and Environmental Engineering, Dongguk University-Seoul, Seoul, 100-715, South Korea.
2
Department of Environmental Engineering and Energy, Myongji University, Yongin, Gyeonggi-do, 499-728, South Korea.
3
Department of Civil and Environmental Engineering, Dongguk University-Seoul, Seoul, 100-715, South Korea. joohyon@dongguk.edu.

Abstract

While identification of critical pollutant sources is the key initial step for cost-effective runoff management, it is challenging due to the highly uncertain nature of runoff pollution, especially during a storm event. To identify critical sources and their quantitative contributions to runoff pollution (especially focusing on phosphorous), two ordination methods were used in this study: principal component analysis (PCA) and positive matrix factorization (PMF). For the ordination analyses, we used runoff quality data for 14 storm events, including data for phosphorus, 11 heavy metal species, and eight ionic species measured at the outlets of subcatchments with different land use compositions in a mixed land use watershed. Five factors as sources of runoff pollutants were identified by PCA: agrochemicals, groundwater, native soils, domestic sewage, and urban sources (building materials and automotive activities). PMF identified similar factors to those identified by PCA, with more detailed source mechanisms for groundwater (i.e., nitrate leaching and cation exchange) and urban sources (vehicle components/motor oils/building materials and vehicle exhausts), confirming the sources identified by PCA. PMF was further used to quantify contributions of the identified sources to the water quality. Based on the results, agrochemicals and automotive activities were the two dominant and ubiquitous phosphorus sources (39-61 and 16-47 %, respectively) in the study area, regardless of land use types.

KEYWORDS:

Land use; Ordination analysis; Positive matrix factorization; Principal component analysis; Runoff; Source identification

PMID:
26850099
DOI:
10.1007/s11356-016-6155-x
[Indexed for MEDLINE]

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