Abstract
Soils regulate the environmental impacts of trace elements, but direct measurements of reaction mechanisms in these complex, multi-component systems can be challenging. The objective of this work was to develop approaches for assessing effects of co-localized geochemical matrix elements on the accumulation and chemical speciation of arsenate applied to a soil matrix. Synchrotron X-ray fluorescence microprobe (μ-XRF) images collected across 100μm × 100μm and 10μm × 10μm regions of a naturally weathered soil sand-grain coating before and after treatment with As(V) solution showed strong positive partial correlations (r′ = 0.77 and 0.64, respectively) between accumulated As and soil Fe, with weaker partial correlations (r′ > 0.1) between As and Ca, and As and Zn in the larger image. Spatial and non-spatial regression models revealed a dominant contribution of Fe and minor contributions of Ca and Ti in predicting accumulated As, depending on the size of the sample area analyzed. Time-of-flight secondary ion mass spectrometry analysis of an area of the sand grain showed a significant correlation (r = 0.51) between Fe and Al, so effects of Fe versus Al (hydr)oxides on accumulated As could not be separated. Fitting results from 25AsK-edge microscale X-ray absorption near-edge structure (μ-XANES) spectra collected across a separate 10μm × 10μm region showed ∼60% variation in proportions of Fe(III) and Al(III)-bound As(V) standards, and fits to μ-XANES spectra collected across the 100μm × 100μm region were more variable. Consistent with insights from studies on model systems, the results obtained here indicate a dominance of Fe and possibly Al (hydr)oxides in controlling As(V) accumulation within microsites of the soil matrix analyzed, but the analyses inferred minor augmentation from co-localized Ti, Ca and possibly Zn.
| Original language | English |
|---|---|
| Pages (from-to) | 1967-1979 |
| Number of pages | 13 |
| Journal | Journal of Synchrotron Radiation |
| Volume | 26 |
| DOIs | |
| State | Published - Nov 1 2019 |
Keywords
- arsenic
- multi-component complexity
- partial correlation
- reactive microsites
- spatial regression
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