Multi-compartmental risk assessment of heavy metal contamination in soil, plants, and wastewater: A model from Industrial Gazipur, Bangladesh
Bibliographic record
Abstract
Abstract Heavy metal contamination in industrial-agricultural regions poses global challenges, yet comprehensive risk assessment models addressing both ecological and human health impacts are scarce. This study introduces a novel multi-compartmental risk assessment framework applied to the Saldha River region of Gazipur, Bangladesh, a rapidly industrialising area experiencing significant environmental stress. Here, we analysed eight heavy metals (Cr, Pb, Cu, Fe, Mn, Zn, Ni, and Cd) in soil, wastewater, and plant samples (spinach, wild rice, and nut grass) via atomic absorption spectrophotometry (AAS). Ecological risks were evaluated through contamination factor (CF), pollution load index (PLI), and geo-accumulation index (I geo ), while human health risks were assessed using hazard indices (HI). Results revealed severe Cd contamination (enrichment factor 2563.19), indicating substantial anthropogenic influence. Correlation analysis of wastewater samples showed strong associations between metal pairs, such as Cu–Zn (0.92), Cu-Fe (0.90) and Zn-Mn (0.87), indicating common industrial sources. Transfer factor (TF) analysis in plants demonstrated substantial variability in metal uptake, with Mn and Ni showing the highest bioavailability, increasing risks to local food chains. Human health risk assessments indicated hazard indices (HI) exceeding safety thresholds for both adults and children, underscoring the urgent need for mitigation strategies. This study offers a novel, integrative framework for assessing multi-source contamination and provides critical baseline data for future environmental policy development. The model is adaptable to industrial regions worldwide, such as textile hubs in Southeast Asia or metal processing zones in Europe and North America, offering new insights into contamination pathways and risk management. Graphical Abstract
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".