Difference between revisions of "EU-kalat"

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(→‎Rationale: hierarchical Bayes model for congener profiles in fish species)
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There is a new study EU-kalat 3, which will produce results in 2016.
 
There is a new study EU-kalat 3, which will produce results in 2016.
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 +
=== Calculations ===
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 +
Model run 22.1.2017 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=9fsWjm4HGRYW9mon]
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<rcode graphics=1>
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# This is code Op_en3104/ on page [[EU-kalat]]
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library(rjags)
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library(OpasnetUtils)
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library(ggplot2)
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library(reshape2)
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eu <- Ovariable("eu", ddata = "Op_en3104", subset = "POPs")
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eu <- EvalOutput(eu)
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eu@output <- eu@output[c(1:4, 18, 19)] # THL code, Matrix, Congener, Fish species
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eu@marginal <- eu@marginal[c(1:4, 18, 19)]
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eu@output <- eu@output[result(eu) != 0 , ] # Zeros cannot be used in ratio estimates
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colnames(eu@output)[colnames(eu@output) == "POP"] <- "Congener"
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colnames(eu@output)[colnames(eu@output) == "Fish_species"] <- "Fish"
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#> unique(eu@output$Congener)
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#[1] 2378TCDD    12378PeCDD  123478HCDD  123678HCDD  123789HCDD  1234678HpCDD
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#[7] OCDD        2378TCDF    12378PeCDF  23478PeCDF  123478HCDF  123678HCDF 
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#[13] 123789HCDF  234678HCDF  1234678HpCDF 1234789HpCDF OCDF        CoPCB77 ...   
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# Remove the four with too little data (>70% BDL) and all non-PCDDF
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# aggregate(eu@data$euResult, by = eu@data["POP"], FUN = function(x) mean(x == 0))
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conl <- unique(eu@output$Congener)[c(1:12, 14, 15)] # 7 OCDD should be removed
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eu@output <- eu@output[eu@output$Congener %in% conl , ]
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#[1] Baltic herring Sprat          Salmon        Sea trout      Vendace     
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#[6] Roach          Perch          Pike          Pike-perch    Burbot       
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#[11] Whitefish      Flounder      Bream          River lamprey  Cod         
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#[16] Trout          Rainbow trout  Arctic char 
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fisl <- unique(eu@output$Fish)[c(1:4, 6:14, 17)]
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eu@output <- eu@output[eu@output$Fish %in% fisl , ] # Remove four with too little data
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eut <- eu
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eut@output <- eut@output[
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  eut@output$Congener == "2378TCDD" & eut@output$Matrix == "Muscle"  , ]
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eut@output$Congener <- NULL
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eut@marginal[colnames(eut@output) == "Congener"] <- NULL
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eut <- log10(eu / eut)@output
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conl <- conl[conl != "2378TCDD"]
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eut <- eut[eut$Congener %in% conl , ]
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# Analysis: a few rows disappear here, as shown by numbers per fish species. Why?
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# aggregate(eut@output, by = eut@output["Fish"], FUN = length)[[2]]
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# [1] 1181  141  131  55  158  51  96  30  36  40  102  49  61  180 eu
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# [1] 1173  141  131  55  158  51  96  27  36  40  102  49  53  180 eu/eut
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# There are samples where TCDD concenctration is zero.
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#eu@data[eu@data$POP == "2378TCDD" & eu@data$euResult == 0 , c(1,4)][[1]]
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#eu@data[eu@data[[1]] %in% c("09K0585", "09K0698", "09K0740", "09K0748") , c(1,3,4,18)]
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# Some rows disappear because there is no TCDD measurement to compare with:
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#8 Baltic herrings, 8 sprats, 3 rainbow trouts.
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# Conclusion: this is ok. Total 2292 rows.
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ggplot(eut, aes(x = Result, colour = Fish))+geom_density()+
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  facet_wrap(~ Congener)
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modf <- textConnection("
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  model{
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    for(i in 1:N) { # i = observation
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      eutt[i] ~ dnorm(mu[con[i],fis[i]], tau[con[i],fis[i]])
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    }
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    for(j in 1:J) { # j = congener
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      for(k in 1:K) { # k = fish species
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        mu[j,k] ~ dunif(-3,3)
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        tau[j,k] <- pow(sigma[j,k], -2)
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        sigma[j,k] ~ dunif(0, 10)
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        pred[j,k] ~ dnorm(mu[j,k], tau[j,k]) # Model prediction
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      }
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    }
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  }
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")
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# A binomial distribution is assumed for bins of answer choices.
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jags <- jags.model(
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  modf,
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  data = list(
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    N = nrow(eut),
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    J = length(conl),
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    K = length(fisl),
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    eutt = eut$Result,
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    con = match(eut$Congener, conl),
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    fis = match(eut$Fish, fisl)
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  ),
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  n.chains = 4,
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  n.adapt = 100
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)
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update(jags, 1000)
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samps <- jags.samples(jags, c('mu', 'pred'), 1000)
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pl <- melt(array(samps$pred, dim = c(length(conl), length(fisl), 1000, 4),
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  dimnames = list(
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  Congener = conl,
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  Fish = fisl,
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  Iter = 1:1000,
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  Seed = 1:4
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  )
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))
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ggplot(pl, aes(x = value, colour = Fish))+geom_density(size = 1) +
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  facet_wrap(~ Congener, scale = "free_y") + coord_cartesian(xlim = c(-1.5,1.5))
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samps.coda <- coda.samples(jags, c('mu', 'pred'), 1000)
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out <- data.frame(
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  Congener = conl,
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  Fish = rep(fisl, each = length(conl)),
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  Param = rep(c("mu", "pred"), each = length(conl)*length(fisl)),
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  Value = (summary(samps.coda)[[1]][,1])
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)
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oprint(out)
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ggplot(out[out$Param == "mu", ], aes(x = Congener, y = Value, colour = Fish, group = Fish)) +
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  geom_line() + labs(title = "Congener amounts in fish compared with TCDD", y = "Log_10 ratio")
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if(FALSE){
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  tef <- Ovariable("tef", ddata = "Op_en4017", subset = "TEF values")
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  tef <- EvalOutput(tef)
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  #levels(eu$Congener) <- gsub("HCDD", "HxCDD", levels(eu$Congener))
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  #levels(eu$Congener) <- gsub("HCDF", "HxCDF", levels(eu$Congener))
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  #levels(eu$Congener) <- gsub("CoPCB", "PCB", levels(eu$Congener))
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  euteq <- eu * tef
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} # IF FALSE
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</rcode>
  
 
==See also==
 
==See also==

Revision as of 11:44, 22 January 2017


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

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

Model run 22.1.2017 [4]

+ Show code

See also

References

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