Two-Dimensional Mapping of Arsenic Concentration and Speciation with Diffusive Equilibrium in Thin-Film Gels.
Castillejos Sepúlveda, Andrea, Metzger, Edouard, Littmann, Sten, Taubner, Heidi, Chennu, Arjun ORCID: https://orcid.org/0000-0002-0389-5589, Gatti, Lais, de Beer, Dirk and Klatt, Judith M. (2023) Two-Dimensional Mapping of Arsenic Concentration and Speciation with Diffusive Equilibrium in Thin-Film Gels. Environmental Science & Technology, 57 (21). pp. 8107-8117. DOI https://doi.org/10.1021/acs.est.3c00887.
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Abstract
We present a new approach combining diffusive equilibrium in thin-film gels and spectrophotometric methods to determine the spatial distribution of arsenite, arsenate, and phosphate at submillimeter resolution. The method relies on the simultaneous deployment of three gel probes. Each retrieved gel is exposed to malachite green reagent gels differing in acidity and oxidant addition, leading to green coloration dependent on analyte speciation and concentration. Hyperspectral images of the gels enable mapping the three analytes in the 2.5–20 μM range. This method was applied in a contaminated brook in the Harz mountains, Germany, together with established mapping of dissolved iron. The use of two-dimensional (2D) gel probes was compared to traditional porewater extraction. The gels revealed banded porewater patterns on a mm-scale, which were undetectable using traditional methods. Small-scale correlation analyses of arsenic and iron microstructures in the gels suggested active iron-driven local redox cycling of arsenic. Overall, the results indicate continuous net release of arsenic from contaminant particles and deepen our understanding of arsenate transformation under anaerobic conditions. This study is the first fine-scale 2D characterization of arsenic speciation in porewater and represents a crucial step toward understanding the transfer and redox cycling of arsenic in contaminated sediment/soil ecosystems.
Document Type: | Article |
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Programme Area: | PA3 |
Research affiliation: | Integrated Modelling > Data Science and Technology |
Refereed: | Yes |
Open Access Journal?: | No |
DOI: | https://doi.org/10.1021/acs.est.3c00887 |
ISSN: | 0013-936X |
Date Deposited: | 20 Sep 2023 14:56 |
Last Modified: | 20 Sep 2023 14:56 |
URI: | http://cris.leibniz-zmt.de/id/eprint/5251 |
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