Question
What are the levels of persistent organic pollutants (POPs) in Baltic sea salmon?
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 is accessible through Opasnet base. This data is used here for a Bayesian model to calculate posterior concentration 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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library(OpasnetUtils)
library(ggplot2)
library(rjags)
dat <- opbase.data("Op_en3104", subset = "POPs")
dat <- dat[dat$Fish_species == "Salmon" , ]
congeners <- levels(dat$POP) #names of different congeners in data
Y <- length(congeners) #number of congeners and j's in for loop
#resultsall <-c('congener', 'meanorig', 'sdorig', 'meanlog', 'meanpost') #comined results table structure to store results of individual congener runs
compdat <- dat[dat$POP %in% congeners[1:3] , ] #data for current congener
Compound <- log(compdat$Result+1E-2) #+1E-2 because zero concentrations are not allowed
mo <- textConnection("model{
for (j in 1 : 3 ) { # for testing reduced to 3 congeners, real loop amount is Y (=79)
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])
}
}
")
# shap <- shapiro.test(compdat$Result) #test of normality
#filename <- paste("shap", j, sep="") #save shapiro test result for each congener, just for fun
#assign(filename, shap)
# if(shap$p.value < 0.05) { #if skewed, logtransformation, if not continue with original data. Or should the logtransformation be done in any case?
#Compound <- log(compdat$Result+1E-2) #+1E-2 because zero concentrations are not allowed
# } else {
# Compound <- compdat$Result
# }
dataList = list(
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)
#filename <- paste("out", j, sep="") #save posterior for each congener
#assign(filename, out)
resultsall <- data.frame(
Meanorig = aggregate(compdat$Result, compdat["POP"], mean), #calculate mean for original data
Sdorig = aggregate(compdat$Result, compdat["POP"], sd)$x, #calcaulte sd of original data
Meanlog = aggregate(Compound, compdat["POP"], mean)$x, #calculate mean for logdata
Sdlog = aggregate(Compound, compdat["POP"], sd)$x, #calculate sd for logdata
Meanpost = c(mean((out[[4]])[,1]), #calculate mean of mu for posterior (test 4)
mean((out[[4]])[,2]),
mean((out[[4]])[,3])
),
Sdpost = c(sqrt(1/(mean((out[[4]])[,4]))), #calculate mean of mu for posterior (test 4)
sqrt(1/(mean((out[[4]])[,5]))),
sqrt(1/(mean((out[[4]])[,6])))
)
)
oprint(resultsall)
ggplot(dat, aes(x = Result, colour = Catch_location))+geom_density()+scale_x_log10()
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References
Related files
<mfanonymousfilelist></mfanonymousfilelist>
- POPs in Baltic salmon. Opasnet . [1]. Accessed 20 May 2024.
