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Small-scale distribution of copper in a Technosol horizon studied by nano-scale secondary ion mass spectrometry.
Rapid Communications in Mass Spectrometry : RCM 2018 June 16
RATIONALE: In contaminated soil, copper (Cu) is commonly distributed among various very small particles. To enlighten the qualitative distribution of Cu in a contaminated Technosol (a soil developed from deposited technogenic material) on the sub-micron scale, we used nano-scale secondary ion mass spectrometry (NanoSIMS).
METHODS: We studied seven areas (up to 40 μm × 40 μm) on a thin section of a soil horizon by NanoSIMS, measuring 12 C- , 18 O- , 32 S- , 63 Cu- and 56 Fe16 O- . We evaluated the NanoSIMS measurements with a novel digital image processing tool to enlighten the composition of measured areas and thus the distribution of Cu at the sub-micron scale. Image processing comprised spatial and spectral smoothing, normalization, endmember extraction and supervised classification.
RESULTS: Copper was present in all areas studied on the thin section in hotspots. 63 Cu- was never the ion with the highest number of mean-normalized counts (MNCs). In classes indicating Cu accumulation, Fe or S had the highest MNCs with mostly small values for O, pointing to the presence of Cu in sulfides. Copper adsorbed on Fe oxides was also indicated. Direct interaction of Cu with organic matter was less important. Copper-containing minerals were rather adjacent to or surrounded by an organic matrix.
CONCLUSIONS: The combination of NanoSIMS analyses with digital image processing gave us new insights into the distribution of Cu in contaminated soil. We suggest this combination as a new powerful tool for the identification of ionic contaminants in soil and other solid phases in the environment.
METHODS: We studied seven areas (up to 40 μm × 40 μm) on a thin section of a soil horizon by NanoSIMS, measuring 12 C- , 18 O- , 32 S- , 63 Cu- and 56 Fe16 O- . We evaluated the NanoSIMS measurements with a novel digital image processing tool to enlighten the composition of measured areas and thus the distribution of Cu at the sub-micron scale. Image processing comprised spatial and spectral smoothing, normalization, endmember extraction and supervised classification.
RESULTS: Copper was present in all areas studied on the thin section in hotspots. 63 Cu- was never the ion with the highest number of mean-normalized counts (MNCs). In classes indicating Cu accumulation, Fe or S had the highest MNCs with mostly small values for O, pointing to the presence of Cu in sulfides. Copper adsorbed on Fe oxides was also indicated. Direct interaction of Cu with organic matter was less important. Copper-containing minerals were rather adjacent to or surrounded by an organic matrix.
CONCLUSIONS: The combination of NanoSIMS analyses with digital image processing gave us new insights into the distribution of Cu in contaminated soil. We suggest this combination as a new powerful tool for the identification of ionic contaminants in soil and other solid phases in the environment.
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