Environmental Health Risk Assessment Due to Exposure to Mercury in Artisanal and Small-Scale Gold Mining Area of Lebak District
Bibliographic record
Abstract
In Indonesia it is estimated that there are around 250,000 artisanal and small-scale gold mining (ASGM) and generally use mercury for amalgamation process and then release it to the environment during gold refining process. This study aims to analyze mercury levels in the environment around ASGM in Lebaksitu Sub-District, Lebak District, Banten Province and identify hazardous exposure that may occur. The study design used was descriptive observational with Environmental Health Risk Assessment (EHRA) method. Environmental data taken include water and food samples. Social-demographic and dietary interviews were conducted. The study population was 72 residents of Lebaksitu Sub-District obtained through sample size formula and selected by simple random sampling. The study was conducted from April to May 2017. Exposure assessment is an important part of risk assessment. Exposure is a process that causes contact with environmental hazards such as risk agents, as a bridge connecting 'hazards' to 'risks'. Exposure analysis needs to consider all routes (inhalation, ingestion, absorption) and media (air, water, soil, food, drinking water) so that the total intake can be calculated. Exposure route analysis usually generate a critical pathway, the dominant exposure path. This pathway concerns which environmental media is the vehicle of risk agent and how it enters the body. Once a critical pathway is found, other possibility pathways contribution may be small and can be ignored. Mercury is a toxic pollutant that bioaccumulated and biomagnetic continuously through the food chain. The levels of mercury at the research sites on rice, fish, and vegetables have average of 0.027 mg/kg; 0.283 mg/kg; and 0.410 mg/kg. The calculation of risk assessment obtained value of risk quotient (RQ) of 3.79 (RQ>1). The results of this calculation of risk assessment showed that mercury content in samples of rice, fish, and vegetables originating from Lebaksitu Sub-District potentially cause a health risk for the community surrounding the gold mining area who consume it.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".