Question
What are the concentrations of persistent organic pollutants (POPs) in Baltic herring.
Answer
Answer is under work and results are preliminary.
POP concentrations in Baltic sea fish have been measured from samples collected in EU-kalat project. The original data of individual fish samples si accessible through Opasnet base. This data is used here for a Bayesian model to calculate posterior distributions (median and SD) for each congener. This data is then translated into TEQ, and can be used for health benefit assessment of Baltic herring.
Posterior congener median concentrations are presented below for each compound group (PCDD/F, PCB, BDE) analysed in EU-kalat.
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Based on the mean posterior concentrations of individual congeners, TEQs are calculated for each congener by using TEF values by WHO and plotted below.
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Rationale
This model takes in measured congener concentrations of POPs in Baltic herring in northern part of Baltic sea. Measured data is used for Bayesian model to produce posterior medians and sds for each congener and also to calculate TEQ values. Numerical results are saved as variables to Opasnetbase and result figures are presented above in the Answer section.
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## This code is Op_en2583/pop_bayes on page [[POPs_in_Baltic_herring]]
library(OpasnetUtils)
library(ggplot2)
library(rjags)
library(reshape2)
dat <- opbase.data("Op_en3104", subset = "POPs")
dat <- dat[dat$Fish_species == "Baltic herring" , ]
dat <- subset(dat,!(is.na(dat["Result"])))
dat <- dropall(dat)
levels(dat$POP) <- gsub("HCDD", "HxCDD", levels(dat$POP))
levels(dat$POP) <- gsub("HCDF", "HxCDF", levels(dat$POP))
levels(dat$POP) <- gsub("CoPCB", "PCB", levels(dat$POP))
congeners <- levels(dat$POP) #names of different congeners in data
Y <- length(congeners) #number of congeners and j's in for loop
compdat <- dat[dat$POP %in% congeners[1:Y] , ] #data for current congener
Compound <- log10(compdat$Result+1E-2) #+1E-2 because zero concentrations are not allowed
mo <- textConnection("model{
for (j in 1 : Y ) {
tau1[j] ~ dunif(0.001, 1000)
muOfCompound[j] ~ dnorm(0, 0.001)
}
for( i in 1 : N ) {
Compound[i] ~ dnorm(muOfCompound[POP[i]], tau2[i])
tau2[i] <- tau1[POP[i]]*sqrt(n[i])
}
}
")
dataList = list(
Y = Y,
Compound = Compound,
POP = as.numeric(compdat$POP),
N = length(Compound),
n = compdat$N_individuals #number of fishes in sample
)
jags <- jags.model(mo, data = dataList, n.chains = 4, n.adapt = 1000)
close(mo)
#update(jags, 1000)
out <- coda.samples(jags, c('tau1', 'muOfCompound'), 500) # Stores a posterior sample
plot(out)
Meanlogpost = c()
for (j in 1 : Y) {
logmean <- mean(out[[4]][,j]) #calculate mean of logmu for posterior (test 4)
Meanlogpost = c(Meanlogpost, logmean)
}
Sdlogpost = c()
for (j in 1 : Y) {
logsd <- sqrt(1/(mean(out[[4]][,j+Y])))
Sdlogpost = c(Sdlogpost, logsd)
}
resultsall <- data.frame(
Meanorig = aggregate(compdat$Result, compdat["POP"], mean), #calculate mean for original data
Sdorig = aggregate(compdat$Result, compdat["POP"], sd)$x, #calcaulte sd of original data
Medianorig = aggregate(compdat$Result, compdat["POP"], median)$x, #calculate median of original data
Meanlog = aggregate(Compound, compdat["POP"], mean)$x, #calculate mean for logdata
Sdlog = aggregate(Compound, compdat["POP"], sd)$x, #calculate sd for logdata
Meanlogpost = Meanlogpost,
Sdlogpost = Sdlogpost,
Medianpost = 10^Meanlogpost-1E-02,
Sdpost = 10^Sdlogpost-1E-02 #this might be incorrect
)
oprint(resultsall)
tef <- Ovariable("tef", ddata = "Op_en4017", subset = "TEF values")
tef <- EvalOutput(tef)
colnames(resultsall)[1] <- "Congener"
resultsall <- melt(resultsall)
colnames(resultsall)[3] <- "Result"
resultsall <- Ovariable("resultsall", data = resultsall)
resultsall <- EvalOutput(resultsall)
teq = resultsall * tef
objects.store(resultsall, teq)
cat("resultsall and teq stored for later use:\n", paste(ls(), collapse = ", "), "\n")
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See also
References
