Difference between revisions of "Concentrations of PCBs in fish"
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== Scope == | == Scope == | ||
− | '''Concentrations of PCBs in fish '''describes concentrations of polychlorinated biphenyls (PCBs) in fish. Concentrations of marine and freshwater fish species are studied separately. | + | '''Concentrations of PCBs in fish '''describes average concentrations of polychlorinated biphenyls (PCBs) in fish. Concentrations of marine and freshwater fish species are studied separately. |
== Definition == | == Definition == | ||
Line 10: | Line 10: | ||
=== Data === | === Data === | ||
− | Probability distributions of | + | Probability distributions of average PCB concentration (dioxin-like PCB congeners) in various fish species were extracted from <ref>http://www.pyrkilo.fi/beneris/index.php/Image:Analytical_data_of_foods-Finland_for_BENERIS_hk_ak.xls</ref> where concentrations of 37 PCB congeners found in marine and freshwater fish samples were collected. Average PCB concentration was estimated by the arithmetic mean. Because of small number of measurements per species the non-parametric bootstrap procedure was used to create the probability distribution of the arithmetic mean. |
+ | For fish species with n concentration measurements the procedure employed can be summarized as follows: | ||
+ | # Draw m samples of size n with replacement from the original sample. These samples are called bootstrap samples. | ||
+ | # Calculate the sample mean for each of the bootstrap samples. | ||
+ | # Make the density histogram of bootstrap means. | ||
+ | # Find a parametric probability density function that matches the histogram best. | ||
+ | |||
+ | As a result a set of parametric distributions was obtained that characterize the uncertainty in the average concentration of PCBs in various fish species. | ||
+ | The parameters of these distributions are summarized below in results part. | ||
+ | |||
+ | The number of bootstrap replicates m was chosen to be n^n or 2000 (if the numerical evaluation of all n^n samples was impossible). | ||
<br> | <br> | ||
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=== Unit === | === Unit === | ||
− | + | pg/g fw WHO-TEQ <br> | |
=== Formula === | === Formula === | ||
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== Result == | == Result == | ||
− | + | Distributions used: | |
+ | Normal(mean,standard deviation) | ||
+ | Beta(alfa,beta) | ||
+ | Gamma(alfa,beta) | ||
+ | Lognormal(mu,sigma) | ||
{| border="1" | {| border="1" | ||
|- | |- | ||
| '''Fish species''' | | '''Fish species''' | ||
− | | ''' | + | | '''Distribution(parameters)''' |
|- | |- | ||
| Baltic Herring | | Baltic Herring | ||
− | | | + | | Normal(2.8797,0.2818) |
|- | |- | ||
| Vendace(inland) | | Vendace(inland) | ||
− | | | + | | Normal(0.8767,0.1559) |
|- | |- | ||
| Vendace(sea) | | Vendace(sea) | ||
− | | | + | | Normal(0.4468,0.0816) |
|- | |- | ||
| Whitefish(inland) | | Whitefish(inland) | ||
− | | | + | | Normal(0.9605,0.271) |
|- | |- | ||
| Whitefish(sea) | | Whitefish(sea) | ||
− | | | + | | Lognormal(0.7341,0.097) |
|- | |- | ||
| Pike(inland) | | Pike(inland) | ||
− | | | + | | Gamma(0.1081,5.1975) |
|- | |- | ||
| Pike(sea) | | Pike(sea) | ||
− | | | + | | Normal(1.0544,0.1604) |
|- | |- | ||
| Perch(inland) | | Perch(inland) | ||
− | | | + | | Beta(7.9494,8.298) |
|- | |- | ||
| Perch(sea) | | Perch(sea) | ||
− | | | + | | Normal(2.3615,0.3956) |
|- | |- | ||
| Atlantic Salmon | | Atlantic Salmon | ||
− | | | + | | Normal(11.2524,1.515) |
|- | |- | ||
| Pike-perch(inland) | | Pike-perch(inland) | ||
− | | | + | | Normal(0.6929,0.1551) |
|- | |- | ||
| Pike-perch(sea) | | Pike-perch(sea) | ||
− | | | + | | Normal(1.4828,0.2169) |
|} | |} | ||
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Contents
Scope
Concentrations of PCBs in fish describes average concentrations of polychlorinated biphenyls (PCBs) in fish. Concentrations of marine and freshwater fish species are studied separately.
Definition
Data
Probability distributions of average PCB concentration (dioxin-like PCB congeners) in various fish species were extracted from [1] where concentrations of 37 PCB congeners found in marine and freshwater fish samples were collected. Average PCB concentration was estimated by the arithmetic mean. Because of small number of measurements per species the non-parametric bootstrap procedure was used to create the probability distribution of the arithmetic mean. For fish species with n concentration measurements the procedure employed can be summarized as follows:
- Draw m samples of size n with replacement from the original sample. These samples are called bootstrap samples.
- Calculate the sample mean for each of the bootstrap samples.
- Make the density histogram of bootstrap means.
- Find a parametric probability density function that matches the histogram best.
As a result a set of parametric distributions was obtained that characterize the uncertainty in the average concentration of PCBs in various fish species. The parameters of these distributions are summarized below in results part.
The number of bootstrap replicates m was chosen to be n^n or 2000 (if the numerical evaluation of all n^n samples was impossible).
Causality
Unit
pg/g fw WHO-TEQ
Formula
Result
Distributions used: Normal(mean,standard deviation) Beta(alfa,beta) Gamma(alfa,beta) Lognormal(mu,sigma)
Fish species | Distribution(parameters) |
Baltic Herring | Normal(2.8797,0.2818) |
Vendace(inland) | Normal(0.8767,0.1559) |
Vendace(sea) | Normal(0.4468,0.0816) |
Whitefish(inland) | Normal(0.9605,0.271) |
Whitefish(sea) | Lognormal(0.7341,0.097) |
Pike(inland) | Gamma(0.1081,5.1975) |
Pike(sea) | Normal(1.0544,0.1604) |
Perch(inland) | Beta(7.9494,8.298) |
Perch(sea) | Normal(2.3615,0.3956) |
Atlantic Salmon | Normal(11.2524,1.515) |
Pike-perch(inland) | Normal(0.6929,0.1551) |
Pike-perch(sea) | Normal(1.4828,0.2169) |
See also