Probabilistic spill occurrence simulation for chemical spills management

Abstract

Inland chemical spills pose a great threat to water quality in worldwide area. A sophisticated probabilistic spill-event model that characterizes temporal and spatial randomness and quantifies statistical uncertainty due to limited spill data is a major component in spill management and associated decision making. This paper presents a MATLAB-based Monte Carlo simulation (MMCS) model for simulating the probabilistic quantifiable occurrences of inland chemical spills by time, magnitude, and location based on North America Industry Classification System codes. The model's aleatory and epistemic uncertainties were quantified through integrated bootstrap resampling technique. Benzene spills in the St. Clair River area of concern were used as a case to demonstrate the model by simulating spill occurrences, occurrence time, and mass expected for a 10-year period. Uncertainty analysis indicates that simulated spill characteristics can be described by lognormal distributions with positive skewness. The simulated spill time series will enable a quantitative risk analysis for water quality impairments due to the spills. The MMCS model can also help governments to evaluate their priority list of spilled chemicals.

Keywords: Chemical spills; Monte Carlo simulation; Probabilistic occurrence; Spill management; Uncertainty analysis.