Mercury contamination in staple crops impacted by artisanal and small-scale gold mining (ASGM): stable Hg isotopes demonstrate dominance of atmospheric uptake pathway for Hg in crops
Bibliographic record
Abstract
Abstract. This study investigates mercury (Hg) biogeochemical cycling and Hg uptake mechanisms in three common staple crops at a contaminated farm (Farm1) ≈ 500 m from an artisanal and small-scale gold mining (ASGM) processing site (PS) and a background farm (Farm2; ≈ 8 km upwind) in Nigeria. We examine air, soil, and various crop tissues using total Hg (THg), Hg stable isotope, Hg speciation, and methyl-Hg (MeHg) analyses. Results show elevated gaseous elemental Hg (GEM) levels in the air (mean concentrations: 1200 ± 400 ng m−3) and soil (mean THg concentration: 2470 ± 1640 µg kg−1) at the PS, significantly higher than those at Farm1 (GEM: 54 ± 19 ng m−3; THg: 76.6 ± 59.7 µg kg−1), which are in turn significantly higher than those at the background site, Farm2 (GEM: 1.7 ng m−3; THg: 11.3 ± 8 µg kg−1). These data confirm the ASGM-derived Hg contamination at the PS and the exposures of crops at Farm1 to elevated levels of Hg in both air and soil. Aligning with Hg concentrations in air and soil, Farm1 had significantly higher THg concentrations in all crop tissues compared to Farm2. At Farm1, foliage exhibits the highest THg concentrations in tissues, particularly for peanuts and cassava (up to 385 ± 20 µg kg−1 in peanuts). These data, along with highly negative δ202Hg values in foliage and other crop tissues (indicative of light Hg isotope enrichment imparted during stomatal assimilation of Hg) demonstrate atmospheric uptake of GEM as the primary uptake pathway for Hg in these crops. We observe air-to-foliage mass-dependent enrichment factors (ε202Hg) of −2.60 ± 0.35, −2.54 ± 0.35, and −1.28 ± 0.43 ‰ for cassava, peanuts, and maize, respectively. While our two-endmember mixing model shows that Hg in crop roots is influenced by both soil (59 %–74 %) and atmospheric (26 %–41 %) uptake pathways, we suggest soil Hg in roots is largely associated with the root epidermis/cortex (external root tissues) and that little soil-derived Hg is transferred to aboveground tissues (< 7 % across all crops). The lower THg concentrations in edible parts (with the exception of cassava leaves, commonly eaten in Nigeria) indicate that even translocation from foliage to other tissues is a relatively slow process. MeHg concentrations were < 1 % across all tissues, and probable daily intakes (PDIs) for both MeHg and THg based on typical diets in Nigeria were all below reference dose thresholds, indicating these crops are generally a lower health risk to the local population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".