POPs in Baltic salmon

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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 salmon.

Posterior congener median concentrations are presented below for each compound group (PCDD/F, PCB, BDE) analysed in EU-kalat.

You can print out the numerical results of prior and posterior congener concentrations below. In addition the possible updated versions of the above figures are printed out.

+ Show code- Hide code library(OpasnetUtils) library(ggplot2) library(reshape2) objects.latest("Op_en7486", "pop_bayes") oprint(dcast(resultsall@output, Congener~variable, fill=0, value.var = "resultsallResult")) ggplot(subset(resultsall@output, grepl("PCB", Congener) & grepl("Medianpost", variable)), aes(x = Congener, y = resultsallResult, fill = Congener)) + geom_bar(stat = "identity") + labs(x = "Congener", y = "Concentration (pg/g fat)") + coord_flip() + ggtitle("Median posterior concentrations of PCB's in Baltic salmon") ggplot(subset(resultsall@output, grepl("CD", Congener) & grepl("Medianpost", variable)), aes(x = Congener, y = resultsallResult, fill = Congener)) + geom_bar(stat = "identity") + labs(x = "Congener", y = "Concentration (pg/g fat)") + coord_flip() + ggtitle("Median posterior concentrations of PCDD/F's in Baltic salmon") ggplot(subset(resultsall@output, grepl("BDE", Congener) & grepl("Medianpost", variable)), aes(x = Congener, y = resultsallResult, fill = Congener)) + geom_bar(stat = "identity") + labs(x = "Congener", y = "Concentration (pg/g fat)") + coord_flip() + ggtitle("Median posterior concentrations of BDE's in Baltic salmon") ggplot(subset(teq@output, grepl("Medianpost", variable)), aes(x = Congener, y = Result, fill = Congener)) + geom_bar(stat = "identity") + labs(x = "Congener", y = "TEQ (pg/g fat)") + coord_flip() + ggtitle("Median posterior TEQ of POPs in Baltic salmon")

Rationale

This model takes in measured congener concentrations of POPs in Baltic salmon in northern part of Baltic sea (Bothnian Bay, Bothnian Sea, Åland Sea, Gulf of Finland). 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.

+ Show code- Hide code ## This code is Op_en????/pop_bayes on page [[POPs_in_Baltic_salmon]] library(OpasnetUtils) library(ggplot2) library(rjags) library(reshape2) dat <- opbase.data("Op_en3104", subset = "POPs") dat <- dat[dat$Fish_species == "Salmon" , ] dat <- subset(dat,!(is.na(dat["Result"]))) dat <- dropall(dat) levels(dat$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")

Related files

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POPs in Baltic salmon. Opasnet . [1]. Accessed 03 Apr 2025.