Geospatial mapping of lead and mercury contamination hotspots in tropical coastal sediments and their correlation with reproductive impairment in commercially harvested bivalves and finfish
Bibliographic record
Abstract
Centuries of religious, agricultural, and industrial activity along the Ganga at Varanasi have deposited metals in riverbed and floodplain sediments, yet spatial mapping of these deposits and their biological consequences remained incomplete before 2012. This research combined geospatial analysis of lead (Pb) and mercury (Hg) in sediments with reproductive health assessment of freshwater bivalves (Lamellidens marginalis) and commercially harvested finfish (Labeo rohita) at five stations along a 24-km stretch of the Ganga near Varanasi during January to December 2009. Sediment samples were collected monthly from surficial deposits (top 10 cm) at each station and analysed by cold-vapour atomic absorption spectrophotometry (Hg) and flame AAS (Pb). Spatial interpolation was performed using inverse-distance weighting in a geographic information system to generate contamination hotspot maps. Lead concentrations ranged from 21.6 mg/kg dry weight at the upstream reference station during the monsoon to 94.3 mg/kg at the industrial-zone station during the pre-monsoon, exceeding the Canadian Interim Sediment Quality Guideline (ISQG) of 50 mg/kg at three of five stations for at least part of the year. Mercury ranged from 32 µg/kg to 189 µg/kg, surpassing the ISQG of 150 µg/kg at the industrial station during three pre-monsoon months. Geospatial maps identified a persistent contamination hotspot centred on the industrial outfall zone that expanded laterally into adjoining floodplain soils during the monsoon recession. Gonadosomatic Index (GSI) in L. marginalis at the most contaminated station (pre-monsoon mean: 7.3%) was 43% lower than at the reference station (12.8%), and histological examination of gonadal tissue revealed elevated rates of oocyte atresia (38.7% vs. 11.4%) and spermatogenic arrest. In L. rohita, GSI declined from 8.4% at the reference station to 3.4% at the industrial station, and the proportion of females at advanced maturity stages fell from 62% to 23%. Sediment Pb correlated negatively with bivalve GSI (r = -0.89, p<0.001) and sediment Hg correlated negatively with finfish GSI (r = -0.92, p<0.001). These results map, for the first time in this river segment, the spatial extent of metal contamination and connect it directly to reproductive impairment in organisms that support both ecological function and local livelihoods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".