Development of Mitigation Strategies for Preventing Firewater Runoff into Aquatic Systems from Chemical Storage and Warehousing Facilities
Bibliographic record
Abstract
This study investigates the environmental risks associated with contaminated firefighting water runoff from hazardous chemical storage facilities. A case study of sixty-three facilities in Gyeonggi Province, Korea, was conducted using a quantitative methodology. Firefighting water generation was estimated for different accident scenarios, and mitigation factors such as hydrants, sprinkler systems, and on-site personnel were considered to adjust discharge volumes. For the representative case facility, the predicted volume was 12,960 tons, reduced to 9,072 tons after accounting for early response capacity. However, on-site storage capacity was only 138 tons, resulting in an estimated external discharge of 8,934 tons. Risk assessment matrices incorporating discharge volume, containment distance, chemical inventory, and ecotoxicity indicated a “high” risk level. These findings were consistent across multiple facilities, demonstrating the broader applicability of the methodology. Comparison with international regulations, including NFPA 30 (United States), CEPA 1999 (Canada), WHG §62 (Germany), and UK guidance, confirmed the importance of structural prevention and institutional coordination. The results emphasize that firefighting water management should extend beyond individual facilities to regional institutional frameworks. This study provides a quantitative basis for strengthening institutional systems for environmental safety and offers practical implications for preventing secondary water pollution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".