Difference between revisions of "EU-kalat"

From Testiwiki
Jump to: navigation, search
(Data uploaded using Opasnet Base Import)
(→‎Bayes model for dioxin concentrations)
 
(32 intermediate revisions by 2 users not shown)
Line 1: Line 1:
−
{{study|moderator=Olli}}
+
{{study|moderator=Arja}}
  
 +
'''EU-kalat''' is a study, where concentrations of PCDD/Fs, PCBs, PBDEs and heavy metals have been measured from fish
  
−
== Scope ==
+
== Question ==
  
−
The scope is to estimate dioxin (PCDD/F) concentrations in fish tissues. This study is part of the European Union research project <ref>[http://www.beneris.eu/ BENERIS in www]</ref> <ref>[http://en.opasnet.org/w/Beneris BENERIS in Opasnet]</ref>, in particular fish case study, Work Package3. Fish are caught from Finnish waters, the Baltic Sea, and rivers and lakes from inland areas.  
+
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. <ref name="eukalatpop">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. [http://www.evira.fi/files/attachments/fi/evira/asiakokonaisuudet/vierasaineet/eu_kalat_l.pdf]</ref>
 +
<ref name="eukalatmetallit">E-R.Venäläinen, A. Hallikainen, R. Parmanne, P.J. Vuorinen: Kotimaisen järvi- ja merikalan raskasmetallipitoisuudet. Elintarvikeviraston julkaisuja 3/2004. [http://fi.opasnet.org/fi_wiki/images/9/96/EU-kalat_1_raskasmetallit.pdf]</ref>
 +
<ref name="eukalat2">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 [http://fi.opasnet.org/fi_wiki/images/6/61/Eu_kalat_260511.pdf]</ref>.
  
−
== Definition ==
+
== Answer ==
  
 +
The original sample results can be acquired from [http://en.opasnet.org/w/Special:Opasnet_Base?id=Op_en3104 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.
 +
 +
<rcode embed=1 graphics=1>
 +
# 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()
 +
</rcode>
 +
 +
== Rationale ==
  
 
=== Data ===
 
=== Data ===
−
Data is collected between 2002-2004. The study contains years, tissue type, fish species, and fat content for each concentration measurement. Number of observations is 175.  
+
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 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=p53h4gxtFfDMOtHz]
 +
** Objects used in [[Benefit-risk assessment of Baltic herring and salmon intake]]
 +
* Model run 25.1.2017 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=wzisMQHAqcF30zcl]
 +
* Model run 22.5.2017 with new ovariables euRaw, euAll, euMain, and euRatio [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=7uTqQeaekwRFwA2J]
 +
* Model run 23.5.2017 with adjusted ovariables euRaw, eu, euRatio [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=qkseWM9rmRysGwKM]
 +
 
 +
<rcode name="preprocess" label="Preprocess (for developers only)">
 +
# This is code Op_en3104/preprocess on page [[EU-kalat]]
 +
library(OpasnetUtils)
 +
library(ggplot2)
 +
library(reshape2)
 +
 
 +
euRaw <- Ovariable("euRaw", ddata = "Op_en3104", subset = "POPs")
 +
 
 +
# levels(TEF$Group)
 +
#[1] "Chlorinated dibenzo-p-dioxins" "Chlorinated dibenzofurans"   
 +
#[3] "Mono-ortho–substituted PCBs"  "Non-ortho–substituted PCBs" 
 +
 
 +
eu <- Ovariable(
 +
  "eu",
 +
  dependencies = data.frame(
 +
    Name=c("euRaw", "TEF"),
 +
    Ident=c(NA,"Op_en4017/initiate")
 +
  ),
 +
  formula = function(...) {
 +
    eu <- euRaw[,c(1:4, 18, 19)] # THL code, Matrix, Congener, Fish species
 +
    colnames(eu@output)[1:4] <- c("THLcode", "Matrix", "Compound", "Fish")
 +
   
 +
    temp <- oapply(eu * TEF, cols = "Compound", FUN = "sum")
 +
    colnames(temp@output)[colnames(temp@output)=="Group"] <- "Compound"
 +
    eu <- combine(eu, temp)
 +
 
 +
    eu$Compound <- factor( # Compound levels are ordered based on the data table on [[TEF]]
 +
      eu$Compound,
 +
      levels = unique(c(levels(TEF$Compound), levels(eu$Compound)))
 +
    )
 +
    eu$Compound <- eu$Compound[,drop=TRUE]
 +
   
 +
    return(eu)
 +
  }
 +
)
 +
 
 +
euRatio <- Ovariable(
 +
  "euRatio",
 +
  dependencies = data.frame(Name=c("eu")),
 +
  formula = function(...) {
 +
    euRatio <- eu[
 +
      eu$Compound == "2378TCDD" & eu$Matrix == "Muscle" & result(eu) != 0 , ] # Zeros cannot be used in ratio estimates
 +
    euRatio$Compound <- NULL
 +
    euRatio <- log10(eu / euRatio)@output
 +
    euRatio <- euRatio[!euRatio$Compound %in% c("2378TCDD", "2378-TCDD", "TCDD") , ]
 +
    return(euRatio)
 +
  }
 +
)
 +
 
 +
# 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.
 +
 
 +
################## Data for the main congeners and species only
 +
 
 +
#> unique(eu$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 PCDDFs with too little data (>70% BDL) and all non-PCDDF
 +
# aggregate(eu@data$euResult, by = eu@data["POP"], FUN = function(x) mean(x == 0))
 +
 
 +
#[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 
 +
 
 +
indices <- list(
 +
  Compound.TEQ2 = c("PCDDF", "PCB"),
 +
  Compound.PCDDF14 = as.character(unique(euRaw@data$POP)[c(1:12, 14, 15)]), # 7 OCDD should be removed
 +
  Fish.Fish14 = as.character(unique(euRaw@data$Fish_species)[c(1:4, 6:14, 17)])
 +
)
 +
 
 +
# conl
 +
#[1] "2378TCDD"    "12378PeCDD"  "123478HCDD"  "123678HCDD"  "123789HCDD" 
 +
#[6] "1234678HpCDD" "OCDD"        "2378TCDF"    "12378PeCDF"  "23478PeCDF" 
 +
#[11] "123478HCDF"  "123678HCDF"  "234678HCDF"  "1234678HpCDF"
 +
#> fisl
 +
#[1] "Baltic herring" "Sprat"          "Salmon"        "Sea trout"   
 +
#[5] "Roach"          "Perch"          "Pike"          "Pike-perch"   
 +
#[9] "Burbot"        "Whitefish"      "Flounder"      "Bream"       
 +
#[13] "River lamprey"  "Rainbow trout"
 +
 
 +
objects.store(euRaw, eu, euRatio, indices)
 +
cat("Ovariables euRaw, eu, and euRatio and list indices stored.\n")
 +
</rcode>
 +
 
 +
==== Bayes model for dioxin concentrations ====
 +
 
 +
* Model run 28.2.2017 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=KrjCTGZmB8JkCH75]
 +
* Model run 28.2.2017 with corrected survey model [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=ZeO0SdlshPgOjqdL]
 +
* Model run 28.2.2017 with Mu estimates [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=TwY2bAIiWr037zqb]
 +
* Model run 1.3.2017 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=3Xu19vkWK1lyWVg3]
 +
* Model run 23.4.2017 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=8DnCPAKsMxGALkjs] produces list conc.param and ovariable concentration
 +
* Model run 24.4.2017 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=ujtyawudKqJ7mmjn]
 +
* Model run 19.5.2017 without ovariable concentration [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=bXhdwkBaQQi1LTcu] {{attack|# |The model does not mix well, so the results should not be used for final results.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 19:37, 19 May 2017 (UTC)}}
 +
::{{comment|# |Maybe we should just estimate TEQs until the problem is fixed.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 19:37, 19 May 2017 (UTC)}}
 +
* Model run 22.5.2017 with TEQdx and TEQpcb as the only Compounds [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=2vTgALXXTzLgd4l1]
 +
* Model run 23.5.2017 debugged [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=rMSAZy6PSKzKhHwp] [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=1P7ZPBbghEfisEcH] [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=BcZDhfjpv3fa4IRU]
 +
* Model run 24.5.2017 TEQdx, TECpcb -> PCDDF, PCB [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=kNNzEMTSD4N2f0Yy]
 +
 
 +
<rcode name="bayes" label="Sample Bayes model (for developers only)" graphics=1>
 +
# This is code Op_en3104/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]] eu, euRatio, indices
 +
 
 +
eu2 <- eu <- EvalOutput(eu)
 +
 
 +
conl <- indices$Compound.TEQ2
 +
fisl <- indices$Fish.Fish14
 +
C <- length(conl)
 +
Fi <- length(fisl)
 +
N <- 1000
 +
conl
 +
fisl
 +
 
 +
replaces <- list(
 +
  c("Chlorinated dibenzo-p-dioxins", "PCDDF"),
 +
  c("Chlorinated dibenzofurans", "PCDDF"),
 +
  c("Mono-ortho-substituted PCBs", "PCB"),
 +
  c("Non-ortho-substituted PCBs", "PCB")
 +
)
 +
 
 +
for(i in 1:length(replaces)) {
 +
  levels(eu2$Compound)[levels(eu2$Compound)==replaces[[i]][1]] <- replaces[[i]][2]
 +
}
 +
 
 +
eu2 <- unkeep(eu2, prevresults = TRUE, sources = TRUE)
 +
eu2 <- oapply(eu2, cols = "TEFversion", FUN = "sum") # This goes wrong if > 1 TEFversion
 +
 
 +
# Hierarchical Bayes model.
 +
 
 +
# PCDD/F concentrations in fish.
 +
# It uses the TEQ sum of PCDD/F (PCDDF) as the total concentration
 +
# of dioxin and PCB respectively for PCB in fish.
 +
# PCDDF depends on age of fish, fish species and catchment area,
 +
# but we only have species now so other variables are omitted.
 +
# cong depends on fish species.
 +
 
 +
eu3 <- eu2[eu2$Compound %in% conl & eu2$Fish %in% fisl & eu2$Matrix == "Muscle" , ]
 +
eu3 <- reshape(
 +
  eu3@output,
 +
  v.names = "euResult",
 +
  idvar = "THLcode",
 +
  timevar = "Compound",
 +
  drop = c("Matrix"),
 +
  direction = "wide"
 +
)
 +
 
 +
oprint(head(eu3))
 +
 
 +
#> colnames(eu3)
 +
#[1] "THLcode"        "Fish"            "euResult.PCDDF"  "euResult.PCB"
 +
 
 +
# Find the level of quantification for dinterval function
 +
LOQ <- unlist(lapply(eu3[3:ncol(eu3)], FUN = function(x) min(x[x!=0])))
 +
names(LOQ) <- conl
 +
cong <- data.matrix(eu3[3:ncol(eu3)])
 +
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(eu3),
 +
    C = C,
 +
    Fi = Fi,
 +
    cong = log(cong),
 +
    fis = match(eu3$Fish, fisl),
 +
    Omega0 = diag(C)/100000
 +
  ),
 +
  n.chains = 4,
 +
  n.adapt = 100
 +
)
 +
 
 +
update(jags, 100)
 +
 
 +
samps.j <- jags.samples(
 +
  jags,
 +
  c(
 +
    'mu', # mean by fish and compound
 +
    'Omega', # precision matrix by fish and compound
 +
    'pred', # predicted concentration by fish and compound
 +
    #    'mu1', # mean prior for mu by compound
 +
    'Omega1', # precision matrix by compound
 +
    #    'tau1', # precision for prior of all mu
 +
    'pred1' # predicted concentration by compound
 +
  ),
 +
  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$pred) <- list(Fish = fisl, Compound = conl, Iter = 1:N, Chain = 1:4)
 +
#dimnames(samps.j$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)
 +
)
 +
 
 +
 
 +
objects.store(conc.param, samps.j)
 +
cat("Lists conc.params and samps.j stored.\n")
 +
 
 +
######################3
 +
 
 +
cat("Descriptive statistics:\n")
 +
 
 +
# Leave only the main fish species and congeners and remove others
 +
conl <- indices$Compound.PCDDF14
 +
eu <- eu[eu$Compound %in% conl & eu$Fish %in% fisl , ]
 +
 
 +
oprint(summary(
 +
  eu,
 +
  marginals = c("Fish", "Compound"), # Matrix is always 'Muscle'
 +
  function_names = c("mean", "sd")
 +
))
 +
 
 +
euRatio <- EvalOutput(euRatio)
 +
 
 +
oprint(summary(
 +
  euRatio,
 +
  marginals = c("Fish", "Compound"), # Matrix is always 'Muscle'
 +
  function_names = c("mean", "sd")
 +
))
 +
 
 +
ggplot(eu@output, aes(x = euResult, colour=Compound))+geom_density()+
 +
  facet_wrap( ~ Fish, scales = "free_y")+scale_x_log10()
 +
#stat_ellipse()
 +
 
 +
ggplot(euRatio@output, aes(x = euRatioResult, colour = Compound))+geom_density()+
 +
  facet_wrap(~ Fish, scales = "free_y")
 +
 
 +
ggplot(melt(exp(samps.j$pred[,,,1])), aes(x=value, colour=Compound))+geom_density()+
 +
  facet_wrap( ~ Fish,scales = "free_y")+scale_x_log10()
 +
 
 +
ggplot(melt(exp(samps.j$pred1[,,1])), aes(x=value, colour=Compound))+geom_density()+
 +
  scale_x_log10()
 +
 
 +
 
 +
scatterplotMatrix(t(samps.j$pred[1,,,1]), main = "Predictions for all compounds for Baltic herring")
 +
## scatterplotMatrix(t(samps.j$mu1[,,1]), main = "Means for all compounds of the generic fish")
 +
scatterplotMatrix(t(samps.j$pred1[,,1]), main = "Prediction for all compounds of the generic fish")
 +
scatterplotMatrix(t(samps.j$pred[,1,,1]), main = "Predictions for all fish species for PCDDF")
 +
scatterplotMatrix(t(samps.j$Omega[6,2,,,1]), main = "Predictions of Omega for pike and PCB")
 +
 
 +
coda.j <- coda.samples(
 +
  jags,
 +
  c('mu', 'pred', 'Omega', 'pred1'),
 +
  N
 +
)
 +
 
 +
plot(coda.j)
 +
</rcode>
 +
 
 +
'''Initiate conc_pcddf
 +
 
 +
* Model run 19.5.2017 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=ystfGN6yfNwWNfnq]
 +
* Model run 23.5.2017 with bugs fixed [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=8iYF4GXFO9bUnld4]
 +
 
 +
<rcode name="initiate" label="Initiate conc_pcddf (for developers only)">
 +
# This is code Op_en3104/initiate on page [[EU-kalat]]
  
 +
library(OpasnetUtils)
  
−
=== Causality ===
+
conc_pcddf <- Ovariable(
 +
  "conc_pcddf",
 +
  dependencies = data.frame(Name = "conc.param", Ident = "Op_en3104/bayes"),
 +
  formula = function(...) {
 +
    require(MASS)
 +
    require(reshape2)
 +
    jsp <- lapply(1:length(conc.param$mu[, 1]), FUN = function(x) {
 +
      temp <- exp(mvrnorm(
 +
        openv$N,
 +
        conc.param$mu[x, ],
 +
        solve(conc.param$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"
 +
    jsp <- Ovariable(
 +
      output = jsp,
 +
      marginal = colnames(jsp) != "Result"
 +
    )
 +
    return(jsp)
 +
  }
 +
)
  
−
This is original data, no upstream variables
+
objects.store(conc_pcddf)
 +
cat("Ovariable conc_pcddf stored.\n")
 +
</rcode>
  
−
== Result ==
+
{{attack|# |These codes should be coherent with [[POPs in Baltic herring]].|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 12:14, 7 June 2017 (UTC)}}
−
The results can be acquired using [[Opasnet base]] connection from a small white window in this article. {{attack|# |Data cannot be accessed from this page. Where can it be found?|--[[User:Arja|Arja]] ([[User talk:Arja|talk]]) 11:48, 23 April 2015 (UTC)}}
 
  
 
==See also==
 
==See also==
Line 25: Line 414:
 
==References==
 
==References==
  
−
<references/><!-- __OBI_TS:1432042360 -->
+
<references/><!-- __OBI_TS:1432108926 -->

Latest revision as of 07:26, 9 June 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

+ Show code

Bayes model for dioxin concentrations

  • Model run 28.2.2017 [8]
  • Model run 28.2.2017 with corrected survey model [9]
  • Model run 28.2.2017 with Mu estimates [10]
  • Model run 1.3.2017 [11]
  • Model run 23.4.2017 [12] produces list conc.param and ovariable concentration
  • Model run 24.4.2017 [13]
  • Model run 19.5.2017 without ovariable concentration [14] ⇤# : The model does not mix well, so the results should not be used for final results. --Jouni (talk) 19:37, 19 May 2017 (UTC)
--# : Maybe we should just estimate TEQs until the problem is fixed. --Jouni (talk) 19:37, 19 May 2017 (UTC)
  • Model run 22.5.2017 with TEQdx and TEQpcb as the only Compounds [15]
  • Model run 23.5.2017 debugged [16] [17] [18]
  • Model run 24.5.2017 TEQdx, TECpcb -> PCDDF, PCB [19]

+ Show code

Initiate conc_pcddf

  • Model run 19.5.2017 [20]
  • Model run 23.5.2017 with bugs fixed [21]

+ Show code

⇤# : These codes should be coherent with POPs in Baltic herring. --Jouni (talk) 12:14, 7 June 2017 (UTC)

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]
Retrieved from "http://www.opasnet.org/testiwiki/index.php?title=EU-kalat&oldid=40926"