Source apportionment for contaminated soils using multivariate statistical methods

Parra, Sonnia; Bravo, Manuel; Quiroz, Waldo; Moreno, Teresa; Karanasiou, Angeliki; Font, Oriol; Vidal, Víctor; Cereceda-Balic, Francisco

doi:10.1016/j.chemolab.2014.08.003

Cited by 32 publications

(18 citation statements)

References 37 publications

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“…The activities in this industrial complex could be important potential sources of discharge of trace elements to marine ecosystem. Several studies on contamination of soils has been conducted in this area (Chiang, 1985;González and Berqvist, 1986;González and Ite, 1992;Ginocchio, 2000Ginocchio, , 2004Parra et al, 2014) but a systematic study on the level of trace element contamination in marine sediments along the Quintero Bay has not been carried out.…”

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confidence: 97%

Distribution and pollution assessment of trace elements in marine sediments in the Quintero Bay (Chile)

Parra

Bravo

Quiroz

et al. 2015

Marine Pollution Bulletin

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confidence: 97%

Distribution and pollution assessment of trace elements in marine sediments in the Quintero Bay (Chile)

Parra

Bravo

Quiroz

et al. 2015

Marine Pollution Bulletin

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“…The Los Maitenes wetland is subject to the local wind patterns determined by the contrast between the Quintero bay, and inland along an west‐east direction. The combined effect of source‐proximity and wind direction has been systematically indicated as the main driver for spatial variations in the content of elements in local rainfalls (Cereceda‐Balic et al., 2020), vegetation (Gorena et al., 2020) and topsoils (De Gregori et al., 2003; Parra et al., 2014b; Salmani‐Ghabeshi et al., 2015; Tapia‐Gatica et al., 2020). Indeed, independent geostatistical models (González et al., 2014; Tapia‐Gatica et al., 2020; Tume et al., 2019) consistently reproduce lower concentrations of metals around the northern tip of the Quintero bay and higher concentrations in sites proximal to VIA (e.g., La Greda and Los Maitenes towns, Figure 1a).…”

Section: Resultsmentioning

confidence: 99%

A Cross‐Cutting Approach for Relating Anthropocene, Environmental Injustice and Sacrifice Zones

et al. 2022

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The Anthropocene is an uneven phenomenon. Accelerated shifts in the functioning of the Earth System are mainly driven by the production and consumption of wealthy economies. Social, environmental and health costs of such industrialization, however, bear on low‐income communities inhabiting severely degraded territories by polluting activities (i.e., sacrifice zones). How global, national and local socio‐economic and governance processes have interacted in perpetuating socio‐environmental inequalities in these territories has been rarely explored. Here, we develop an historical quantitative approach integrating a novel chemostratigraphic record, data on policy making, and socio‐economic trends to evaluate the feedback relationship between environmental injustice and Anthropocene in sacrifice zones. We specifically outline a case study for the Puchuncaví valley ‐one of the most emblematic sacrifice zones from Chile‐. We verify an ever‐growing burden of heavy metals and metalloids over the past five decades paced by the staggering expansion of local industrial activities, which has ultimately been spurred by national and transnational market forces. Local poverty levels have declined concomitantly, but this path toward social equality is marginal as costs of pollution have grown through time. Indeed, national and international pollution control actions appear insufficient in mitigating the cumulative impact brought by highly toxic elements. Thus, our sub‐decadal reconstruction for pollution trends over the past 136 years from a sediment record, emerges as a science‐based tool for informing the discussion on Anthropocene governance. Furthermore, it helps to advance in the assessment of environmental inequality in societal models that prioritize economic growth to the detriment of socio‐environmental security.

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“…There has been much research on the pollution status, distribution, risk assessment and source of heavy metals in soil [10][11][12]. Some methods have been suggested to evaluate heavy metal pollution level, such as pollution index (PI), geo-accumulation index (I geo ) contamination factor (CF), Nemerow integrated pollution index (NIPI), and pollution load index (PLI) [4,[13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Source identification is mainly used to qualitatively determine the source of pollutants, such as correlation analysis, cluster analysis, PCA and other methods [22,25]. Source apportionment can not only determine the main pollution sources, but also quantify the contribution of pollution sources [10]. At present, the most common source-apportionment methods for heavy metals include the chemical mass balance (CMB) model [26], positive matrix factor (PMF) model [27], UNMIX model [28], the absolute principle-component scores multiple linear regression (APCS-MLR) model [29], etc.…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment and Source Apportionment of Heavy Metals in Soils from Handan City

et al. 2021

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Soil-heavy metals are potentially harmful to the ecosystem and human health. Quantifying heavy metals sources is conducive to pollution control. In this study, 64 surface-soil samples were collected in Handan city. Cr, Mn, Ni, Cu, Zn, Cd and Pb were determined; then, their spatial distribution in the sampling area was drawn by ArcGIS. The pollution index (PI) method, geo-accumulation index (Igeo) method, Nemerow integrated pollution index (NIPI) and pollution load index (PLI) were used to evaluate the pollution level of heavy metals in surface soil; then, an ecological and health risk assessment of soil-heavy metals was carried out. Combined with the spatial distribution, correlation analysis, cluster analysis, PCA and PMF model, the pollution sources of heavy metals in soil were identified and apportioned. The results showed that the average content of Cd was nearly ten times that of the background limit, which was the most serious among the studied metals. In terms of non-carcinogenic risk, Cr had the highest value, followed by Pb. In terms of carcinogenic risk, Cd, Cr, and Ni had an acceptable or tolerable risk. Three pollution sources were identified by cluster analysis and PCA, including traffic sources with Cu, Pb and Cd as main loads, industrial sources with Mn, Cd and Zn as main loads, and natural sources with Cr and Ni as main loads. The PMF model analyzed three main factors: traffic source (17.61%), natural source (28.62%) and industrial source (53.77%). The source categories and the main load elements obtained from the source apportionment results were consistent with the source identification results.

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Source apportionment for contaminated soils using multivariate statistical methods

Cited by 32 publications

References 37 publications

Distribution and pollution assessment of trace elements in marine sediments in the Quintero Bay (Chile)

Distribution and pollution assessment of trace elements in marine sediments in the Quintero Bay (Chile)

A Cross‐Cutting Approach for Relating Anthropocene, Environmental Injustice and Sacrifice Zones

Risk Assessment and Source Apportionment of Heavy Metals in Soils from Handan City

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