Sepúlveda, Andrea Castillejos, Gatti, Lais M., Kerl, Carolin F., Chennu, Arjun ORCID: and Klatt, Judith M. (2022) Arsenic speciation analysis in porewater by a novel colorimetric assay. Science of The Total Environment . p. 154155. DOI

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Arsenic is common toxic contaminant, but tracking its mobility through submerged soils is difficult because microscale processes dictate its speciation and affinity to minerals. Analyses on environmental dissolved arsenic (As) species such as arsenate and arsenite currently require highly specialized equipment and large sample volumes. In an effort to unravel arsenic dynamics in sedimentary porewater, a novel, highly sensitive, and field-usable colorimetric assay requiring 100 μL of sample was developed. Two complementary protocols are presented, suitable for sub-micromolar and micromolar ranges. Phosphate is a main interfering substance, but can be separated by measuring phosphate and arsenate under two different acidities. Arsenite is assessed by oxidation of arsenite to arsenate in the low-acidity reagent. Optimization of the protocol and spectral analyses resulted in elimination of various interferences (silicate, iron, sulfide, sulfate), and the assay is applicable across a wide range of salinities and porewater compositions. The new assay was used to study As mobilization processes through the soil of a contaminated brook. Water column sources of arsenic were limited to a modest input by a groundwater source along the flow path. In one of the sites, the arsenite and arsenate porewater profiles showed active iron-driven As redox cycling in the soil, which may play a role in arsenic mobilization and releases arsenite and arsenate into the brook water column. Low arsenic concentrations downstream from the source sites indicated arsenic retention by soil and dilution with additional sources of water. Arsenic is thus retained by the Bossegraben before it merges with larger rivers.

Document Type: Article
Programme Area: PA Not Applicable
Research affiliation: Integrated Modelling > Data Science and Technology
Refereed: Yes
Open Access Journal?: Yes
ISSN: 00489697
Date Deposited: 04 Mar 2022 09:54
Last Modified: 04 Mar 2022 09:54

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