Difference between revisions of "EU-kalat"

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EU-kalat is a study, where concentrations of PCDD/Fs, PCBs, PBDEs and heavy metals have been measured from fish

Question

The scope of EU-kalat study was to measure concentrations of persistent organic pollutants (POPs) including dioxin (PCDD/F), PCB and BDE in fish from Baltic sea and Finnish inland lakes and rivers. ^[1] ^[2] ^[3].

Answer

The original sample results can be acquired from Opasnet base. The study showed that levels of PCDD/Fs and PCBs depends especially on the fish species. Highest levels were on salmon and large sized herring. Levels of PCDD/Fs exceeded maximum level of 4 pg TEQ/g fw multiple times. Levels of PCDD/Fs were correlated positively with age of the fish.

Mean congener concentrations as WHO2005-TEQ in Baltic herring can be printed out with the Run code below.

+ Show code - Hide code

# This code is Op_en on page [[EU-kalat]].

library(OpasnetUtils)
library(ggplot2)

eu <- Ovariable("eu", ddata = "Op_en3104", subset = "POPs")
eu <- EvalOutput(eu)
#levels(eu$POP)
eu <- eu[eu$Fish_species == "Baltic herring" , ]

tef <- Ovariable("tef", ddata = "Op_en4017", subset = "TEF values")
tef <- EvalOutput(tef)
colnames(eu@output)[colnames(eu@output) == "POP"] <- "Congener"
levels(eu$Congener) <- gsub("HCDD", "HxCDD", levels(eu$Congener))
levels(eu$Congener) <- gsub("HCDF", "HxCDF", levels(eu$Congener))
levels(eu$Congener) <- gsub("CoPCB", "PCB", levels(eu$Congener))
#levels(eu$Congener)

euteq <- eu * tef
#head(euteq@output)
temp <- aggregate(euteq@output["Result"], by = c(euteq@output["Congener"], euteq@output["Catch_location"]), FUN = mean)

temp2 <- temp
temp2$Result <- temp2$Result / sum(temp2$Result)

cat("Congener TEQ profile of Baltic herring.\n")
oprint(temp2)
 
ggplot(temp, aes(x = Congener, y = Result, fill = Congener)) + geom_bar(stat = "identity") + facet_wrap(~ Catch_location) + 
labs(x = "Congener", y = "TEQ concentration (pg/g fat)") + theme_gray(base_size = 18) + coord_flip()

Rationale

Data

Data was collected between 2009-2010. The study contains years, tissue type, fish species, and fat content for each concentration measurement. Number of observations is 285.

There is a new study EU-kalat 3, which will produce results in 2016.

Calculations

Preprocess model 22.2.2017 [4]
- Objects used in Benefit-risk assessment of Baltic herring and salmon intake
Model run 25.1.2017 [5]

+ Show code - Hide code

# This is code Op_en3104/preprocess on page [[EU-kalat]]
library(rjags)
library(OpasnetUtils)
library(ggplot2)
library(reshape2)

eu <- Ovariable("eu", ddata = "Op_en3104", subset = "POPs")
eu <- EvalOutput(eu)
eu@output <- eu@output[c(1:4, 18, 19)] # THL code, Matrix, Congener, Fish species
eu@marginal <- eu@marginal[c(1:4, 18, 19)]
colnames(eu@output) <- c("THLcode", "Matrix", "Congener", "Fish", "euResult", "euSource")

#> unique(eu@output$Congener)
#[1] 2378TCDD     12378PeCDD   123478HCDD   123678HCDD   123789HCDD   1234678HpCDD
#[7] OCDD         2378TCDF     12378PeCDF   23478PeCDF   123478HCDF   123678HCDF  
#[13] 123789HCDF   234678HCDF   1234678HpCDF 1234789HpCDF OCDF         CoPCB77 ...     

# Remove the four with too little data (>70% BDL) and all non-PCDDF
# aggregate(eu@data$euResult, by = eu@data["POP"], FUN = function(x) mean(x == 0))
conl <- unique(eu@output$Congener)[c(1:12, 14, 15)] # 7 OCDD should be removed
eu@output <- eu@output[eu@output$Congener %in% conl , ] 

#[1] Baltic herring Sprat          Salmon         Sea trout      Vendace       
#[6] Roach          Perch          Pike           Pike-perch     Burbot        
#[11] Whitefish      Flounder       Bream          River lamprey  Cod           
#[16] Trout          Rainbow trout  Arctic char   

fisl <- unique(eu@output$Fish)[c(1:4, 6:14, 17)] 
eu@output <- eu@output[eu@output$Fish %in% fisl , ] # Remove four with too little data

eut <- eu
eut@output <- eut@output[
  eut@output$Congener == "2378TCDD" & eut@output$Matrix == "Muscle" & result(eut) != 0 , ] # Zeros cannot be used in ratio estimates
eut@output$Congener <- NULL
eut@marginal[colnames(eut@output) == "Congener"] <- NULL
eut <- log10(eu / eut)@output
conl <- conl[conl != "2378TCDD"]
eut <- eut[eut$Congener %in% conl , ]

# Analysis: a few rows disappear here, as shown by numbers per fish species. Why?
# aggregate(eut@output, by = eut@output["Fish"], FUN = length)[[2]]
# [1] 1181  141  131   55  158   51   96   30   36   40  102   49   61  180 eu
# [1] 1173  141  131   55  158   51   96   27   36   40  102   49   53  180 eu/eut
# There are samples where TCDD concenctration is zero.
#eu@data[eu@data$POP == "2378TCDD" & eu@data$euResult == 0 , c(1,4)][[1]]
#eu@data[eu@data[[1]] %in% c("09K0585", "09K0698", "09K0740", "09K0748") , c(1,3,4,18)]
# Some rows disappear because there is no TCDD measurement to compare with:
#8 Baltic herrings, 8 sprats, 3 rainbow trouts.
# Conclusion: this is ok. Total 2292 rows.

ggplot(eu@output, aes(x = euResult, colour = Fish))+geom_density()+
  facet_wrap(~ Congener) + scale_x_log10()

ggplot(eut, aes(x = Result, colour = Fish))+geom_density()+
  facet_wrap(~ Congener) 

objects.store(eu,eut)
cat("Ovariable eu and data.frame eut stored.\n")

Bayes model for dioxin concentrations

Model run 28.2.2017 [6]
Model run 28.2.2017 with corrected survey model [7]
Model run 28.2.2017 with Mu estimates [8]
Model run 1.3.2017 [9]
Model run 23.4.2017 [10] produces list conc.param and ovariable concentration
Model run 24.4.2017 [11]

+ Show code - Hide code

# This is code Op_en3014/bayes on page [[EU-kalat]]

library(OpasnetUtils)
library(reshape2)
library(rjags) # JAGS
library(ggplot2)
library(MASS) # mvrnorm
library(car) # scatterplotMatrix

objects.latest("Op_en3104", code_name = "preprocess") # [[EU-kalat]]

# Hierarchical Bayes model.

# PCDD/F concentrations in fish.
# It uses the sum of PCDD/F (Pcdsum) as the total concentration of dioxin in fish.
# Cong_j is the fraction of a congener from pcdsum.
# pcdsum is log-normally distributed. cong_j follows Dirichlet distribution.
# pcdsum depends on age of fish, fish species and catchment area, but we only have species now so other variables are omitted.
# cong_j depends on fish species.

conl <- as.character(unique(eu@output$Congener))
fisl <- sort(as.character(unique(eu@output$Fish)))
C <- length(conl)
Fi <- length(fisl)
N <- 1000
conl
fisl
fishsamples <- reshape(
  eu@output, 
  v.names = "euResult", 
  idvar = "THLcode", 
  timevar = "Congener", 
  drop = c("Matrix", "euSource"), 
  direction = "wide"
)

# Find the level of quantification for dinterval function
LOQ <- unlist(lapply(fishsamples[3:ncol(fishsamples)], FUN = function(x) min(x[x!=0])))
names(LOQ) <- conl
cong <- data.matrix(fishsamples[3:ncol(fishsamples)])
cong <- sapply(1:length(LOQ), FUN = function(x) ifelse(cong[,x]==0, 0.5*LOQ[x], cong[,x]))

mod <- textConnection("
  model{
    for(i in 1:S) { # s = fish sample
      #        below.LOQ[i,j] ~ dinterval(-cong[i,j], -LOQ[j])
      cong[i,1:C] ~ dmnorm(mu[fis[i],], Omega[fis[i],,])
    }
    for(i in 1:Fi) { # Fi = fish species
      for(j in 1:C) { 
        mu[i,j] ~ dnorm(mu1[j], tau1[j])
      }
      Omega[i,1:C,1:C] ~ dwish(Omega0[1:C,1:C],S)
      pred[i,1:C] ~ dmnorm(mu[i,], Omega[i,,]) # Model prediction
    }
    for(i in 1:C) { # C = Compound
      mu1[i] ~ dnorm(0, 0.0001)
      tau1[i] ~ dunif(0,10000)
      pred1[i] ~ dnorm(mu1[i], tau1[i])
    }
    Omega1[1:C,1:C] ~ dwish(Omega0[1:C,1:C],S)
  }
")

jags <- jags.model(
  mod,
  data = list(
    S = nrow(fishsamples),
    C = C,
    Fi = Fi,
    cong = log(cong),
    #    LOQ = LOQ,
    #    below.LOQ = is.na(cong)*1,
    fis = match(fishsamples$Fish, fisl),
    Omega0 = diag(C)/100000
  ),
  n.chains = 4,
  n.adapt = 100
)

update(jags, 100)

samps.j <- jags.samples(
  jags, 
  c('mu', 'Omega', 'pred', 'mu1', 'Omega1', 'tau1', 'pred1'), 
  N
)
dimnames(samps.j$mu) <- list(Fish = fisl, Compound = conl, Iter = 1:N, Chain = 1:4)
dimnames(samps.j$mu1) <- list(Compound = conl, Iter = 1:N, Chain = 1:4)
dimnames(samps.j$pred) <- list(Fish = fisl, Compound = conl, Iter = 1:N, Chain = 1:4)
dimnames(samps.j$mu1) <- list(Compound = conl, Iter = 1:N, Chain = 1:4)
dimnames(samps.j$tau1) <- list(Compound = conl, Iter = 1:N, Chain = 1:4)
dimnames(samps.j$pred1) <- list(Compound = conl, Iter = 1:N, Chain = 1:4)
dimnames(samps.j$Omega) <- list(Fish = fisl, Compound = conl, Compound2 = conl, Iter=1:N, Chain=1:4)
dimnames(samps.j$Omega1) <- list(Compound = conl, Compound2 = conl, Iter=1:N, Chain=1:4)

##### conc.param contains expected values of the distribution parameters from the model
conc.param <- list(
  mu = apply(samps.j$mu[,,,1], MARGIN = 1:2, FUN = mean),
  Omega = apply(samps.j$Omega[,,,,1], MARGIN = 1:3, FUN = mean),
  pred.mean = apply(samps.j$pred[,,,1], MARGIN = 1:2, FUN = mean),
  pred.sd = apply(samps.j$pred[,,,1], MARGIN = 1:2, FUN = sd),
  mu1 = apply(samps.j$mu1[,,1], MARGIN = 1, FUN = mean),
  tau1 = apply(samps.j$tau1[,,1], MARGIN = 1, FUN = mean),
  pred1.mean = apply(samps.j$pred1[,,1], MARGIN = 1, FUN = mean),
  pred1.sd = apply(samps.j$pred1[,,1], MARGIN = 1, FUN = sd)
)

concentration <- Ovariable(
  "concentration",
  dependencies = data.frame(Name = "conc.param"),
  formula = function(...) {
    jsp <- lapply(
      1:length(conc.param$mu[,1]),
      FUN = function(x) {
        temp <- exp(mvrnorm(openv$N, conc.param$mu[x,], conc.param$Omega[x,,]))
        dimnames(temp) <- c(list(Iter = 1:openv$N), dimnames(conc.param$mu)[2])
        return(temp)
      }
    )
    names(jsp) <- dimnames(conc.param$mu)[[1]]
    jsp <- melt(jsp, value.name = "Result")
    colnames(jsp)[colnames(jsp)=="L1"] <- "Fish" # Convert automatic name to meaningful
    jsp <- Ovariable(output=jsp, marginal = colnames(jsp) != "Result")
    return(jsp)
  }
)

objects.store(concentration, conc.param, samps.j)
cat("Ovariable concentration and lists conc.params and samps.j stored.\n")

# Predictions for all congeners of fish1 (Baltic herring)
scatterplotMatrix(t(samps.j$pred[1,,,1]))
# Means for all congeners of the generic fish
scatterplotMatrix(t(samps.j$mu1[,,1]))
# Prediction for all congeners of the generic fish
scatterplotMatrix(t(samps.j$pred1[,,1]))
# Predictions for all fish species for TCDD
scatterplotMatrix(t(samps.j$pred[,1,,1]))
# Predictions for pike for omegas and PeCDD
scatterplotMatrix(t(samps.j$Omega[6,2,,,1]))

concentration <- EvalOutput(concentration)

ggplot(melt(exp(samps.j$pred[,,,1])), aes(x=value, colour=Compound))+geom_density()+
  facet_wrap( ~ Fish,scales = "free_y")+scale_x_log10()
ggplot(eu@output, aes(x = euResult, colour=Congener))+geom_density()+
  facet_wrap( ~ Fish, scales = "free_y")+scale_x_log10()
#stat_ellipse()

coda.j <- coda.samples(
  jags, 
  c('mu', 'pred', 'omega1', 'pred1'), 
  N
)

plot(coda.j)

References

↑ A. Hallikainen, H. Kiviranta, P. Isosaari, T. Vartiainen, R. Parmanne, P.J. Vuorinen: Kotimaisen järvi- ja merikalan dioksiinien, furaanien, dioksiinien kaltaisten PCB-yhdisteiden ja polybromattujen difenyylieettereiden pitoisuudet. Elintarvikeviraston julkaisuja 1/2004. [1]
↑ E-R.Venäläinen, A. Hallikainen, R. Parmanne, P.J. Vuorinen: Kotimaisen järvi- ja merikalan raskasmetallipitoisuudet. Elintarvikeviraston julkaisuja 3/2004. [2]
↑ Anja Hallikainen, Riikka Airaksinen, Panu Rantakokko, Jani Koponen, Jaakko Mannio, Pekka J. Vuorinen, Timo Jääskeläinen, Hannu Kiviranta. Itämeren kalan ja muun kotimaisen kalan ympäristömyrkyt: PCDD/F-, PCB-, PBDE-, PFC- ja OT-yhdisteet. Eviran tutkimuksia 2/2011. ISSN 1797-2981 ISBN 978-952-225-083-4 [3]

[eukalatpop-1] A. Hallikainen, H. Kiviranta, P. Isosaari, T. Vartiainen, R. Parmanne, P.J. Vuorinen: Kotimaisen järvi- ja merikalan dioksiinien, furaanien, dioksiinien kaltaisten PCB-yhdisteiden ja polybromattujen difenyylieettereiden pitoisuudet. Elintarvikeviraston julkaisuja 1/2004. [1]

[eukalatmetallit-2] E-R.Venäläinen, A. Hallikainen, R. Parmanne, P.J. Vuorinen: Kotimaisen järvi- ja merikalan raskasmetallipitoisuudet. Elintarvikeviraston julkaisuja 3/2004. [2]

[eukalat2-3] Anja Hallikainen, Riikka Airaksinen, Panu Rantakokko, Jani Koponen, Jaakko Mannio, Pekka J. Vuorinen, Timo Jääskeläinen, Hannu Kiviranta. Itämeren kalan ja muun kotimaisen kalan ympäristömyrkyt: PCDD/F-, PCB-, PBDE-, PFC- ja OT-yhdisteet. Eviran tutkimuksia 2/2011. ISSN 1797-2981 ISBN 978-952-225-083-4 [3]

[1]

[2]

[3]

Difference between revisions of "EU-kalat"

Revision as of 20:09, 24 April 2017

Contents

Question

Answer

Rationale

Data

Calculations

Bayes model for dioxin concentrations

See also

References

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