POPs in Baltic salmon

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Question

What are the levels of persistent organic pollutants (POPs) in Baltic sea salmon?

Answer

POP concentrations in Baltic sea fish have been measured from samples collected in EU-kalat project.

+ Show code- Hide code 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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POPs in Baltic salmon. Opasnet . [1]. Accessed 03 Apr 2025.