Implementation of Chemical Health, Safety, and Environmental Risk Assessment in Laboratories: A Case-Series Study
Bibliographic record
Abstract
Introduction: Characterizing risks associated with laboratory activities in universities may improve health, safety, and environmental management and reduce work-related diseases and accidents. This study aimed to develop and implement a chemical risk assessment method to determine and prioritize more hazardous chemicals in the academic laboratories. Methods: A case-series study was conducted at five academic laboratories and research facilities of an Iranian medical sciences university in 2021. A risk assessment was developed and implemented in three phases to identify, evaluate, and classify potential risks and hazards. The approach provided an innovative tool for evaluating and prioritizing risks in chemical laboratories. Hazards were classified on a five-level scale. The technique reviewed both quantitative and qualitative data and pieces of evidence using Laboratory Safety Guidance (OSHA), Occupational Hazard Datasheet (ILO), the standards of the American Conference of Governmental Industrial Hygienists (ACGIH), International Agency for Research on Cancer (IARC), and National Fire Protection Agency (NFPA) codes. Results: Overall, the frequency of risks rated from "moderate" to "very high" levels was determined for the health hazards (9.3%), environmental hazards (35.2%), and safety hazards (20.4%). Hydrochloric acid had a high consumption rate in laboratory operations and received the highest risk levels in terms of potential hazards to employees' health and the environment. Nitric acid, Sulfuric acid, Formaldehyde, and Sodium hydroxide were assessed as potential health hazards. Moreover, Ethanol and Sulfuric acid were recognized as safety hazards. We observed adequate security provisions and procedures in academic laboratory operations. However, the lack of awareness concerning health, safety, environmental chemical hazards, and inappropriate sewage disposal systems contributed to the increasing levels of laboratory risk. Conclusions: Chemicals used in laboratory activities generate workplace and environmental hazards that must be assessed, managed, and risk mitigated. Developing a method of rating health, safety, and environmental risks related to laboratory chemicals may assist in defining and understanding potential hazards. Our assessment suggested the need for improving the risk perception of individuals involved in handling chemicals to prevent exposure from workplace duties and environmental pollution hazards.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".