Probabilistic Spill Occurrence Simulations and Quantitative Water Quality Risk Analysis for Chemical Spill Management
Bibliographic record
Abstract
Thousands of inland chemical spills occur as a result of accidents or natural disasters each year in the world and threaten human health and the environment. More than 700 recorded inland chemical spills involving more than 1,000 types of chemical occur every year in Southern Ontario, resulting in multiple environmental impacts. Eleven regional municipalities involving 77 municipalities had experienced chemical spills in the period of 1988-2007. The majority of these chemical spills occurred at industrial plants, while pipe/hose leaks accounted for the highest proportion of total chemical spills, resulting in the largest portion of chemical spills causing surface water impacts. A comprehensive spill management planning framework is proposed to facilitate the development of municipal spill prevention, control, and emergency response plans. In order to develop a spill management framework, simulation models termed MMCS (MATLAB-based Monto Carlo Simulation) and EMMCS (Extended MMCS) that characterizes temporal and spatial randomness and quantifies statistical uncertainty have also been developed. The MMCS model simulates the probabilistic quantifiable occurrences of inland chemical spills by time, magnitude, and location based on North America Industry Classification System (NAICS) codes, while the EMMCS model quantifies the risk of drinking water quality violation due to inland chemical spills. The models can also quantify aleatory and epistemic uncertainties through integrated bootstrap resampling technique. Benzene spills into the St. Clair River Areas of Concern are used as a case study to demonstrate the models. The probabilistic occurrences of various NAICS codes are found to be 1.2 to 5.1 over a 10-year period. The violation-causing NAICS-based spill occurrences and the associated risks of drinking water quality impairments at the Ontario‘s intakes are found to be less than 1.4 and 37%, respectively. No drinking water quality is found to be impaired at the Michigan intakes. Uncertainty analysis indicates that simulated spill characteristics can be described by lognormal distributions and the NAICS-based risks of violation at the Ontario‘s intakes are Weibull distributed. A hypothetical case, benzene spills in the Mimico Creek watershed is used to investigate the possibility of spill characteristic transfer from one area to another area.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".