Monte Carlo Based Multi-Media Fate Model for the Great Lakes Ecosystem Syracuse Research Corporation: Environmental Science Center Great Lakes Commission
Monte Carlo Based Multi-Media Fate Model for the Great Lakes Ecosystem Syracuse Research Corporation: Environmental Science Center Great Lakes Commission
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Monte Carlo Level III Probabilistic Calculation

Lognormal Distributions for the Half-Lives
Triangular Distributions for the Half-Lives

View Pesticide Emissions in the Great Lakes States
View 2002 Great Lakes Toxic Air Emissions Inventory

There are two choices when running the model in Monte Carlo mode: lognormal distributions for the half-lives and triangular distributions. Biodegradation data collected for a limited number of pesticides suggest that the degradation profile is typically lognormally distributed and a mean half-life and standard deviation is required to develop the distribution function. In the absence of data, risk assessors often employ triangular distributions consisting of a minimum, maximum, and most likely value. The Henry's Law constant, log Kow, and vapor pressure are also treated as log normally distributed probabilistic input parameters; however, the coefficient of variation (CV) is fixed. We based the CV for each of these parameters on data obtained from SRC databases, published papers, estimation programs, and recommendations made in the CALTOX multi-media fate model. The CV for the Henry's Law constant and vapor pressure is assumed to be 1, while the standard deviation for the log Kow is assumed to be 0.5 log units.

Lognormal distributions for the degradation half-life of several pesticides in soil are shown below. In general the coefficient of variation (ratio of standard deviation to the mean half-life) ranges from approximately 0.5 to 2.

Pesticide Mean Half-life (days) Standard Deviation (days)
Atrazine 68 77
Chlorpyrifos 66 150
2,4-D 17 26
1,3-Dichloropropene 12 18
Glyphosate 33 26
Metolachlor 41 38
Permethrin 27 23
Simazine 78 82
Terbufos 14 8
Trifluralin 200 261

View the fitted distribution functions for these chemicals.

How can I estimate the input parameters? The best method is to collect experimental data on the degradation profile of your chemical and fit the data to a lognormal distribution function. In the absence of any experimental data the model can estimate these values for you based upon your chemical's structure. However, these values in water, soil, and sediment are very approximate at best. Moreover, the half-life estimation in air only considers degradation via atmospheric oxidants and does not take into account the possibility of direct photolysis. Therefore, half-lives may be underestimated for compounds that are susceptible to direct photolysis.

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Developed by the Syracuse Research Corporation Environmental Science Center under contract to the Great Lakes Commission's Great Lakes Air Deposition (GLAD) program, supported by the U.S. EPA.

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