Literature DB >> 23974534

Application of the positive matrix factorization approach to identify heavy metal sources in sediments. A case study on the Mexican Pacific Coast.

C González-Macías1, G Sánchez-Reyna, L Salazar-Coria, I Schifter.   

Abstract

During the last two decades, sediments collected in different sources of water bodies of the Tehuantepec Basin, located in the southeast of the Mexican Pacific Coast, showed that concentrations of heavy metals may pose a risk to the environment and human health. The extractable organic matter, geoaccumulation index, and enrichment factors were quantified for arsenic, cadmium, copper, chromium, nickel, lead, vanadium, zinc, and the fine-grained sediment fraction. The non-parametric SiZer method was applied to assess the statistical significance of the reconstructed metal variation along time. This inference method appears to be particularly natural and well suited to temperature and other environmental reconstructions. In this approach, a collection of smooth of the reconstructed metal concentrations is considered simultaneously, and inferences about the significance of the metal trends can be made with respect to time. Hence, the database represents a consolidated set of available and validated water and sediment data of an urban industrialized area, which is very useful as case study site. The positive matrix factorization approach was used in identification and source apportionment of the anthropogenic heavy metals in the sediments. Regionally, metals and organic matter are depleted relative to crustal abundance in a range of 45-55 %, while there is an inorganic enrichment from lithogenous/anthropogenic sources of around 40 %. Only extractable organic matter, Pb, As, and Cd can be related with non-crustal sources, suggesting that additional input cannot be explained by local runoff or erosion processes.

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Year:  2013        PMID: 23974534     DOI: 10.1007/s10661-013-3375-0

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


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