Community Health Risk Assessment of Total Suspended Particulates near a Cement Plant in Maros Regency, Indonesia
Bibliographic record
Abstract
BACKGROUND: Cement plants generate particulate matter (PM) across processes from raw material preparation to packaging. The presence of total suspended particulates (TSP) coming out of the stack causes a high accumulation of dust in residential areas. Human exposure to TSP could affect human health and wellbeing. OBJECTIVES: The present study aims to evaluate concentrations of TSP and to estimate the health risks of TSP exposure through the inhalation pathway in communities surrounding a private cement industry in Maros regency, Indonesia. METHODS: Total suspended particulates were collected using a high-volume air sampler (HVAS) at five locations. Samples were taken by grab sampling for 24 hours. The SCREEN3 program was used to view the maximum range and distribution of pollutants based on the geographical, stack profiles and meteorological factors in the study area. Hazard quotient (HQ) was used to estimate non-carcinogenic risks of TSP in surrounding communities. RESULTS: . This maximum value exceeds the minimum acceptable level from Canadian National Ambient Air Quality Objectives (C-NAAQOs). The non-carcinogenic risks from the inhalation pathway were low except for location 3 (HQ>1) across all locations. CONCLUSIONS: The cement plant may significantly contribute to total TSP concentrations in air and may potentially have adverse effects on human health. Communities near the cement plant are vulnerable to TSP exposure and measures are needed to reduce TSP in Maros regency, Indonesia. PARTICIPANT CONSENT: Obtained. ETHICS APPROVAL: This study was approved by the Health Research Ethics Committee of Hasanuddin University with protocol number 28920093022. COMPETING INTERESTS: The authors declare no competing financial interests.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".