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

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.

Source apportionment of PAHs in surface sediments using positive matrix factorization combined with GIS for the estuarine area of the Yangtze River, China

“…The simplest method of obtaining such a matrix is to use the analytical or method uncertainties that correspond to each species concentration value as calculated in the User Guide (Norris et al, 2008). PMF requires that all concentrations and uncertainty values should be positive values; therefore, the data below detection limits (DL) and missing data (MD) must be omitted or replaced with appropriate substitute values (Gonzalez-Macias et al, 2014). In the concentration matrix, values below DL were replaced with DL/2.…”

Source Apportionment of Heavy Metal Pollution in Agricultural Soils around the Poyang Lake Region Using UNMIX Model

“…Cd was a common impurity in Zn and Pb ores, generated mostly during the production of Zn. The smelting production was mainly to recover Zn and Pb, and Cd, which cannot be recycled [40], was released into the environment. In light of the survey, there was a Yinshan lead-zinc mine in Dexing City, southeast of Poyang Lake District, which was usually accompanied by a high Cd content.…”

Mixed response of trace element concentrations in fluvial sediments to a flash flood in a former mining area

“…Developed by Paatero and Tapper in 1994 [66], it was initially mainly used to investigate atmospheric pollutants [60,[67][68][69][70][71][72][73][74][75][76]. However, in recent years, it has been successfully applied to a variety of fields like groundwater [77], streamwater [78], rainwater [79], provenance analysis [80], and especially trace elements in sediments [62,[81][82][83][84][85][86][87]. PMF, which can be implemented relying on robust statistics [58,88,89], does not require any a priori knowledge of the emission profiles [56,82].…”