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Related Concept Videos

Pharmaceutical Poisoning: Potential Scenarios01:26

Pharmaceutical Poisoning: Potential Scenarios

Pharmaceutical poisoning can occur through various channels, impacting an estimated 2 million hospitalized patients in the U.S. annually with serious adverse drug responses. These scenarios encompass both therapeutic uses, such as drug toxicity, where even standard dosages can lead to severe central nervous system depression, and non-therapeutic exposures, including accidental ingestion by children, and environmental and occupational exposures.Unintentional poisonings often involve exploratory...
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Design Example: Creating a Hydraulic Model of a Dam Spillway

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Sampling Plans01:23

Sampling Plans

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Spontaneity

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Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
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Related Experiment Video

Updated: May 7, 2026

Chemical Analysis of Water-accommodated Fractions of Crude Oil Spills Using TIMS-FT-ICR MS
08:17

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Published on: March 3, 2017

Probabilistic spill occurrence simulation for chemical spills management.

Weihua Cao1, James Li, Darko Joksimovic

  • 1Department of Civil Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario, Canada M5B 2K3.

Journal of Hazardous Materials
|October 8, 2013
PubMed
Summary

A new Monte Carlo simulation model quantifies inland chemical spill risks. This tool aids water quality management by predicting spill occurrences, time, and magnitude, improving decision-making for environmental protection.

Keywords:
Chemical spillsMonte Carlo simulationProbabilistic occurrenceSpill managementUncertainty analysis

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Risk Assessment

Background:

  • Inland chemical spills significantly threaten global water quality.
  • Effective spill management requires sophisticated models addressing temporal, spatial randomness, and data uncertainty.
  • Probabilistic models are crucial for informed decision-making in spill response.

Purpose of the Study:

  • To present a MATLAB-based Monte Carlo simulation (MMCS) model for inland chemical spills.
  • To simulate probabilistic spill occurrences, including time, magnitude, and location.
  • To quantify model uncertainties using bootstrap resampling.

Main Methods:

  • Developed a MATLAB-based Monte Carlo simulation (MMCS) model.
  • Utilized North America Industry Classification System (NAICS) codes for spill characterization.
  • Integrated bootstrap resampling to quantify aleatory and epistemic uncertainties.
  • Applied the model to benzene spills in the St. Clair River area for a 10-year simulation.

Main Results:

  • Simulated spill occurrences, timing, and expected mass for benzene spills.
  • Uncertainty analysis revealed spill characteristics follow lognormal distributions with positive skewness.
  • Generated spill time series suitable for quantitative risk analysis of water quality impacts.

Conclusions:

  • The MMCS model provides a robust framework for probabilistic inland chemical spill simulation.
  • The model aids in understanding and managing water quality risks associated with chemical spills.
  • Governments can utilize the MMCS model to prioritize chemical spill risks and inform policy.