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- Kar, Sandeep, et al.
(author)
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Health risks for human intake of aquacultural fish : Arsenic bioaccumulation and contamination
- 2011
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In: Journal of Environmental Science and Health. Part A. - : Informa UK Limited. - 1093-4529 .- 1532-4117. ; 46:11, s. 1266-1273
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Journal article (peer-reviewed)abstract
- Aquacultural tilapia (Oreochromis mossambicus L.) and shrimp (Penaeus monodon L.) from groundwater-cultured ponds in southwestern Taiwan were analyzed to estimate arsenic (As) bioaccumulation and the potential health risk to human intake. Most of aquacultural ponds exhibited higher arsenic than maximum allowed concentrations (50 mu g L(-1)) in pond water of Taiwan. Arsenic levels in tilapia in Budai, Yichu and Beimen were 0.92 +/- 0.52 mu g g(-1), 0.93 +/- 0.19 mu g g(-1) and 0.76 +/- 0.03 mu g g(-1), respectively and in shrimp was 0.36 +/- 0.01 mu g g(-1) in Beimen. Total arsenic in tilapia is highly correlated (R(2) = 0.80) with total arsenic concentration of pond water. Total arsenic in fish showed high correlation with that in bone (R(2) = 0.98), head (R(2) = 0.97) and tissue (R(2) = 0.96). Organic arsenic species (DMA) was found higher relative to inorganic species of As(III) and As(V). The average percent contribution of inorganic arsenic to total arsenic in fish samples was 12.5% and ranged between 11.7 to 14.2%. Bioaccumulation factors (BAFs) for total arsenic in fish ranged from 10.3 to 22.1, whereas BAF for inorganic arsenic ranged from 1.33 to 2.82. The mean human health cancer risk associated with the ingestion of inorganic arsenic in the fish was estimated at 2.36 x 10(-4) +/- 0.99 x 10(-4), which is over 200 times greater than a de Minimus cancer risk of 1 x 10(-6). The mean human health hazard quotient associated with ingesting inorganic arsenic in the fish was 1.22 +/- 0.52, indicating that expected human exposure exceeds the reference dose for non-cancer health effects by 22%. These results suggest that the inhabitants in this region are being subjected to moderately elevated arsenic exposure through the consumption of tilapia and shrimp raised in aquaculture ponds.
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- Liu, Chia-Chuan, et al.
(author)
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Biogeochemical interactions among the arsenic, iron, humic substances, and microbes in mud volcanoes in southern Taiwan
- 2011
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In: Journal of Environmental Science and Health. Part A. - : Informa UK Limited. - 1093-4529 .- 1532-4117. ; 46:11, s. 1218-1230
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Journal article (peer-reviewed)abstract
- Fluid and mud samples collected from Hsiaokunshui (HKS), Wushanting (WST), Yenshuikeng (YSK), Kunshuiping (KSP), Liyushan (LYS), and Sinyangnyuhu (SYNH) mud volcanoes of southwestern Taiwan were characterized for major ions, humic substances (HS) and trace elements concentrations. The relationship between the release of arsenic (As) and activities of sulfate-reducing bacteria has been assessed to understand relevant geochemical processes in the mud volcanoes. Arsenic (0.02-0.06 mg/L) and humic substances (4.13 x 10(-4) to 1.64 x 10(-3) mM) in the fluids of mud volcanoes showed a positive correlation (r = 0.99, p < 0.05) except in Liyushan mud volcano. Arsenic and iron in mud sediments formed two separate groups i) high As, but low Fe in HKS, WST, and SYNH; and ii) low As, but high Fe in the YSK, KSP, and LYS mud volcanoes. The Eh(S.H.E). values of the mud volcano liquids were characterized by mild to strongly reducing conditions. The HKS, SYNH, and WST mud volcanoes (near the Chishan Fault) belongs to strong reducing environment (-33 to -116 mV), whereas the LYS, YSK, and KSP mud volcanoes located near the coastal plain are under mild reducing environment (-11 to 172 mV). At low Eh values mud volcanoes, saturation index (SI) values of poorly crystalline phases such as amorphous ferric hydroxide indicate understaturation, whereas saturation is reached in relatively high Eh(S.H.E.) values mud volcanoes. Arsenic contents in sediments are low, presumably due to its release to fluids (As/Fe ratio in YSK, KSP, and LYS sediment: 4.86 x 10(-4)-6.20 x 10(-4)). At low Eh(S.H.E.) values (mild to strong reducing environment), arsenic may co-precipitate with sulfides as a consequence of sulfate reduction (As/Fe ratios in WST, HKS, and SYNH sediments: 0.42-0.69).
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- Schweinsberg, Martin, et al.
(author)
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Same data, different conclusions : Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis
- 2021
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In: Organizational Behavior and Human Decision Processes. - : Elsevier BV. - 0749-5978 .- 1095-9920. ; 165, s. 228-249
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Journal article (peer-reviewed)abstract
- In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists' gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for orga-nizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed.
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