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Arsenic species in weathering mine tailings and biogenic solids at the Lava Cap Mine Superfund Site, Nevada City, CA.

Foster AL, Ashley RP, Rytuba JJ - Geochem. Trans. (2011)

Bottom Line: Linear combination, least-squares fits constrained in part by PCA results were then used to quantify arsenic speciation in XAFS spectra of tailings and biogenic solids.The highest dissolved arsenic concentrations were found in Lost Lake porewater and in a groundwater-fed pond in the tailings deposition area.The stability of both primary and secondary As phases is likely to be at a minimum under cyclic wet-dry conditions.

View Article: PubMed Central - HTML - PubMed

Affiliation: U,S, Geological Survey, 345 Middlefield Rd,, MS 901 Menlo Park, CA, 94025, USA. afoster@usgs.gov.

ABSTRACT

Background: A realistic estimation of the health risk of human exposure to solid-phase arsenic (As) derived from historic mining operations is a major challenge to redevelopment of California's famed "Mother Lode" region. Arsenic, a known carcinogen, occurs in multiple solid forms that vary in bioaccessibility. X-ray absorption fine-structure spectroscopy (XAFS) was used to identify and quantify the forms of As in mine wastes and biogenic solids at the Lava Cap Mine Superfund (LCMS) site, a historic "Mother Lode" gold mine. Principal component analysis (PCA) was used to assess variance within water chemistry, solids chemistry, and XAFS spectral datasets. Linear combination, least-squares fits constrained in part by PCA results were then used to quantify arsenic speciation in XAFS spectra of tailings and biogenic solids.

Results: The highest dissolved arsenic concentrations were found in Lost Lake porewater and in a groundwater-fed pond in the tailings deposition area. Iron, dissolved oxygen, alkalinity, specific conductivity, and As were the major variables in the water chemistry PCA. Arsenic was, on average, 14 times more concentrated in biologically-produced iron (hydr)oxide than in mine tailings. Phosphorous, manganese, calcium, aluminum, and As were the major variables in the solids chemistry PCA. Linear combination fits to XAFS spectra indicate that arsenopyrite (FeAsS), the dominant form of As in ore material, remains abundant (average: 65%) in minimally-weathered ore samples and water-saturated tailings at the bottom of Lost Lake. However, tailings that underwent drying and wetting cycles contain an average of only 30% arsenopyrite. The predominant products of arsenopyrite weathering were identified by XAFS to be As-bearing Fe (hydr)oxide and arseniosiderite (Ca2Fe(AsO4)3O3•3H2O). Existence of the former species is not in question, but the presence of the latter species was not confirmed by additional measurements, so its identification is less certain. The linear combination, least-squares fits totals of several samples deviate by more than ± 20% from 100%, suggesting that additional phases may be present that were not identified or evaluated in this study.

Conclusions: Sub- to anoxic conditions minimize dissolution of arsenopyrite at the LCMS site, but may accelerate the dissolution of As-bearing secondary iron phases such as Fe3+-oxyhydroxides and arseniosiderite, if sufficient organic matter is present to spur anaerobic microbial activity. Oxidizing, dry conditions favor the stabilization of secondary phases, while promoting oxidative breakdown of the primary sulfides. The stability of both primary and secondary As phases is likely to be at a minimum under cyclic wet-dry conditions. Biogenic iron (hydr)oxide flocs can sequester significant amounts of arsenic; this property may be useful for treatment of perpetual sources of As such as mine adit water, but the fate of As associated with natural accumulations of floc material needs to be assessed.

No MeSH data available.


Related in: MedlinePlus

Variance scatterplots of PCA results for Group 1 (ore, tailings, and Lost Lake sediments; top panel) and Group 2 (microbial mats/flocs; bottom panel), illustrating their utility as model-independent means of examining spectral variance. Each point represents one sample EXAFS spectrum in a coordinate space defined by the product of the first eigenvector (principal component, v1) and its sample-specific first eigenvalues (w1,i ) on the x-axis, plotted against the product of the second eigenvector (v2) and its sample-specific second eigenvalues (w2,i ) on the y-axis. Axes are dimensionless. Outlying samples have been labeled for reference
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Figure 3: Variance scatterplots of PCA results for Group 1 (ore, tailings, and Lost Lake sediments; top panel) and Group 2 (microbial mats/flocs; bottom panel), illustrating their utility as model-independent means of examining spectral variance. Each point represents one sample EXAFS spectrum in a coordinate space defined by the product of the first eigenvector (principal component, v1) and its sample-specific first eigenvalues (w1,i ) on the x-axis, plotted against the product of the second eigenvector (v2) and its sample-specific second eigenvalues (w2,i ) on the y-axis. Axes are dimensionless. Outlying samples have been labeled for reference

Mentions: The top panel in Figure 3 displays the variance plot derived from a PCA on Group 1 EXAFS spectra (ore, tailings, and tailings-rich mud samples. It separated the spectra into three subgroups: the first contained the only sample of pyrite-rich ore (97-LCD3) and the second contained 2 samples of dry tailings (99-LL4-ss2 and 99-LL2-ss2). The third subgroup contained 8 samples: one ore sample (97-LCD1), 2 samples collected at the shorelines of Lost Lake (99-LL4-ss1) and Frog Pond (00-LL2-ss), and 5 subaqueous bottom sediment samples (99-LL2-ss1, 99-LL12-cs1, 99-LL12-cs2, 99-LL6-cs, and 00-LL5-cs). PCA on the 11 samples of Group 1 identified 3 definitive PCs (Figure 4, top panel). Visual analysis of the individual components (Figure 4, bottom left) suggests that the first 3 are clearly principal: together they accounted for a total of 69% of the variance within Group 1 and are clearly low in noise. Using the number of components required to reconstruct spectra as an indicator, 4 components are suggested, due to inability to reconstruct the features of spectrum LL5. Even when 4 components were used, spectral reconstructions did vary in quality among samples, with the single largest factor being the noise content of the unknown spectrum. Based on both pieces of information, the number of principal components in the group 1 PCA was determined to be 4. With the addition of the 4th component, 76% of the spectral variance was accounted for.


Arsenic species in weathering mine tailings and biogenic solids at the Lava Cap Mine Superfund Site, Nevada City, CA.

Foster AL, Ashley RP, Rytuba JJ - Geochem. Trans. (2011)

Variance scatterplots of PCA results for Group 1 (ore, tailings, and Lost Lake sediments; top panel) and Group 2 (microbial mats/flocs; bottom panel), illustrating their utility as model-independent means of examining spectral variance. Each point represents one sample EXAFS spectrum in a coordinate space defined by the product of the first eigenvector (principal component, v1) and its sample-specific first eigenvalues (w1,i ) on the x-axis, plotted against the product of the second eigenvector (v2) and its sample-specific second eigenvalues (w2,i ) on the y-axis. Axes are dimensionless. Outlying samples have been labeled for reference
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3037876&req=5

Figure 3: Variance scatterplots of PCA results for Group 1 (ore, tailings, and Lost Lake sediments; top panel) and Group 2 (microbial mats/flocs; bottom panel), illustrating their utility as model-independent means of examining spectral variance. Each point represents one sample EXAFS spectrum in a coordinate space defined by the product of the first eigenvector (principal component, v1) and its sample-specific first eigenvalues (w1,i ) on the x-axis, plotted against the product of the second eigenvector (v2) and its sample-specific second eigenvalues (w2,i ) on the y-axis. Axes are dimensionless. Outlying samples have been labeled for reference
Mentions: The top panel in Figure 3 displays the variance plot derived from a PCA on Group 1 EXAFS spectra (ore, tailings, and tailings-rich mud samples. It separated the spectra into three subgroups: the first contained the only sample of pyrite-rich ore (97-LCD3) and the second contained 2 samples of dry tailings (99-LL4-ss2 and 99-LL2-ss2). The third subgroup contained 8 samples: one ore sample (97-LCD1), 2 samples collected at the shorelines of Lost Lake (99-LL4-ss1) and Frog Pond (00-LL2-ss), and 5 subaqueous bottom sediment samples (99-LL2-ss1, 99-LL12-cs1, 99-LL12-cs2, 99-LL6-cs, and 00-LL5-cs). PCA on the 11 samples of Group 1 identified 3 definitive PCs (Figure 4, top panel). Visual analysis of the individual components (Figure 4, bottom left) suggests that the first 3 are clearly principal: together they accounted for a total of 69% of the variance within Group 1 and are clearly low in noise. Using the number of components required to reconstruct spectra as an indicator, 4 components are suggested, due to inability to reconstruct the features of spectrum LL5. Even when 4 components were used, spectral reconstructions did vary in quality among samples, with the single largest factor being the noise content of the unknown spectrum. Based on both pieces of information, the number of principal components in the group 1 PCA was determined to be 4. With the addition of the 4th component, 76% of the spectral variance was accounted for.

Bottom Line: Linear combination, least-squares fits constrained in part by PCA results were then used to quantify arsenic speciation in XAFS spectra of tailings and biogenic solids.The highest dissolved arsenic concentrations were found in Lost Lake porewater and in a groundwater-fed pond in the tailings deposition area.The stability of both primary and secondary As phases is likely to be at a minimum under cyclic wet-dry conditions.

View Article: PubMed Central - HTML - PubMed

Affiliation: U,S, Geological Survey, 345 Middlefield Rd,, MS 901 Menlo Park, CA, 94025, USA. afoster@usgs.gov.

ABSTRACT

Background: A realistic estimation of the health risk of human exposure to solid-phase arsenic (As) derived from historic mining operations is a major challenge to redevelopment of California's famed "Mother Lode" region. Arsenic, a known carcinogen, occurs in multiple solid forms that vary in bioaccessibility. X-ray absorption fine-structure spectroscopy (XAFS) was used to identify and quantify the forms of As in mine wastes and biogenic solids at the Lava Cap Mine Superfund (LCMS) site, a historic "Mother Lode" gold mine. Principal component analysis (PCA) was used to assess variance within water chemistry, solids chemistry, and XAFS spectral datasets. Linear combination, least-squares fits constrained in part by PCA results were then used to quantify arsenic speciation in XAFS spectra of tailings and biogenic solids.

Results: The highest dissolved arsenic concentrations were found in Lost Lake porewater and in a groundwater-fed pond in the tailings deposition area. Iron, dissolved oxygen, alkalinity, specific conductivity, and As were the major variables in the water chemistry PCA. Arsenic was, on average, 14 times more concentrated in biologically-produced iron (hydr)oxide than in mine tailings. Phosphorous, manganese, calcium, aluminum, and As were the major variables in the solids chemistry PCA. Linear combination fits to XAFS spectra indicate that arsenopyrite (FeAsS), the dominant form of As in ore material, remains abundant (average: 65%) in minimally-weathered ore samples and water-saturated tailings at the bottom of Lost Lake. However, tailings that underwent drying and wetting cycles contain an average of only 30% arsenopyrite. The predominant products of arsenopyrite weathering were identified by XAFS to be As-bearing Fe (hydr)oxide and arseniosiderite (Ca2Fe(AsO4)3O3•3H2O). Existence of the former species is not in question, but the presence of the latter species was not confirmed by additional measurements, so its identification is less certain. The linear combination, least-squares fits totals of several samples deviate by more than ± 20% from 100%, suggesting that additional phases may be present that were not identified or evaluated in this study.

Conclusions: Sub- to anoxic conditions minimize dissolution of arsenopyrite at the LCMS site, but may accelerate the dissolution of As-bearing secondary iron phases such as Fe3+-oxyhydroxides and arseniosiderite, if sufficient organic matter is present to spur anaerobic microbial activity. Oxidizing, dry conditions favor the stabilization of secondary phases, while promoting oxidative breakdown of the primary sulfides. The stability of both primary and secondary As phases is likely to be at a minimum under cyclic wet-dry conditions. Biogenic iron (hydr)oxide flocs can sequester significant amounts of arsenic; this property may be useful for treatment of perpetual sources of As such as mine adit water, but the fate of As associated with natural accumulations of floc material needs to be assessed.

No MeSH data available.


Related in: MedlinePlus