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Combined Size and Density Fractionation of Soils for Investigations of Organo-Mineral Interactions
Published on: February 15, 2019
Soil texture and organic matter drive metal trace element retention in mangrove soils: implications for Soil
Renata Barreto Mascarenhas1, Thomas Vincent Gloaguen2, Gisele Mara Hadlich1
1Instituto de Geociências, Universidade Federal da Bahia (UFBA), Rua Barão de Jeremoabo, s/n - Ondina, Salvador, BA, Brazil.
Abstract:
Mangrove soils are highly heterogeneous environments in which grain-size and organic matter variability, and anthropogenic impacts complicate the distinction between natural concentrations and contamination by potentially toxic elements (PTEs). Soil Guideline Values (SGVs) for PTEs are unavailable for such environments: SGVs have been developed only for terrestrial ecosystems to regulate impacts from agricultural, industrial, and mining activities. This study proposes Soil Guideline Values (SVG) for metals in mangrove soils from Todos os Santos Bay, northeastern Brazil, a densely populated coastal region exposed to multiple contaminants. Two approaches were applied: (i) 75th and 90th percentile-based methods (Brazilian CONAMA Resolution 420/2009), and (ii) regression models incorporating the physicochemical variability of mangrove soils. A total of 92 soil samples were collected from three estuarine regions, representing two dominant soil types: Histosols and Gleysols. Analyses included pH, particle-size distribution, total organic carbon, nutrients, and concentrations of PTEs (USEPA 3051 A method). Histosols exhibit systematically higher concentrations of PTEs and higher SGVs than Gleysols, reaching up to 24 times higher for Co and consistently elevated concentrations of Cr and Zn, reflecting the combined effects of higher organic matter content and finer textures. Multiple regression models showed strong predictive performance (R2 = 0.71 to 0.97), highlighting the key role of soil properties in controlling PTEs cycles. These findings demonstrate that percentile-based approaches are insufficient for such heterogeneous estuarine environments, whereas using predictive models with soil attributes provides more realistic SGVs.
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