Difference between revisions of "EU-kalat"

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(Initiate conc_pcddf for PFAS disease burden study)
(Initiate conc_pcddf for PFAS disease burden study)
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Bayesian approach for PCDDF, PCB, OT, PFAS.
 
Bayesian approach for PCDDF, PCB, OT, PFAS.
* Model run 2021-02-07 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=gW5hy3pvCuPl13GM]
+
* Model run 2021-03-08 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=ZvJDOo7xL8d7x7EI]
  
<rcode name="pollutant_bayes" label="Initiate conc_pcddf with PFAS, OT (for developers only)" embed=0>
+
<rcode name="pollutant_bayes" label="Initiate conc_pcddf with PFAS, OT (for developers only)" embed=0 graphics=1>
 
# This is code Op_en3104/pollutant_bayes on page [[EU-kalat]]
 
# This is code Op_en3104/pollutant_bayes on page [[EU-kalat]]
 
# The code is also available at https://github.com/jtuomist/pfas/blob/main/conc_pcddf_preprocess.R
 
# The code is also available at https://github.com/jtuomist/pfas/blob/main/conc_pcddf_preprocess.R
Line 513: Line 513:
 
)
 
)
 
colnames(eu3) <- gsub("eu2Result\\.","",colnames(eu3))
 
colnames(eu3) <- gsub("eu2Result\\.","",colnames(eu3))
 +
eu3$TEQ <- eu3$PCDDF + eu3$PCB
 +
eu3$PFAS <- eu3$PFOA + eu3$PFOS
  
conl_nd <- c("PFOA","PFOS","DBT","MBT","TBT","DPhT","TPhT")
+
conl_nd <- c("PFAS","PFOA","PFOS","DBT","MBT","TBT","DPhT","TPhT")
 
eu4 <- eu3[rowSums(is.na(eu3[conl_nd]))<7 , c(1:5,match(conl_nd,colnames(eu3)))]
 
eu4 <- eu3[rowSums(is.na(eu3[conl_nd]))<7 , c(1:5,match(conl_nd,colnames(eu3)))]
 
fisl_nd <- as.character(unique(eu4$Fish))
 
fisl_nd <- as.character(unique(eu4$Fish))
Line 524: Line 526:
  
 
oprint(head(eu3))
 
oprint(head(eu3))
 +
oprint(head(eu4))
  
 
C <- length(conl)
 
C <- length(conl)
 
Fi <- length(fisl)
 
Fi <- length(fisl)
N <- 100
+
N <- 200
 
conl
 
conl
 
fisl
 
fisl
Line 588: Line 591:
 
     Fi_nd = length(fisl_nd),
 
     Fi_nd = length(fisl_nd),
 
     conc = log(conc),
 
     conc = log(conc),
     conc_nd = conc_nd,
+
     conc_nd = log(conc_nd),
 
     #    length = eu3$Length-170, # Subtract average herring size
 
     #    length = eu3$Length-170, # Subtract average herring size
 
     #    year = eu3$Year-2009, # Substract baseline year
 
     #    year = eu3$Year-2009, # Substract baseline year
Line 598: Line 601:
 
   ),
 
   ),
 
   n.chains = 4,
 
   n.chains = 4,
   n.adapt = 100
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   n.adapt = 200
 
)
 
)
  
Line 669: Line 672:
 
   facet_wrap( ~ Compound, scales="free_x")+scale_x_log10()
 
   facet_wrap( ~ Compound, scales="free_x")+scale_x_log10()
  
if(FALSE) {
+
   scatterplotMatrix(t(exp(samps.j$pred[2,1:10,,1])), main = paste("Predictions for several compounds for",
   scatterplotMatrix(t(exp(samps.j$pred[1,,,1])), main = "Predictions for all compounds for Baltic herring")
+
    names(samps.j$pred[,1,1,1])[2]))
   scatterplotMatrix(t(exp(samps.j$pred[,1,,1])), main = "Predictions for all fish species for PCDDF")
+
   scatterplotMatrix(t(exp(samps.j$pred[,70,,1])), main = paste("Predictions for all fish species for",
   scatterplotMatrix(t(samps.j$Omega[,1,1,,1]))
+
    names(samps.j$pred[1,,1,1])[70]))
   #scatterplotMatrix(t(cbind(samps.j$Omega[1,1,1,,1],samps.j$mu[1,1,,1])))
+
   scatterplotMatrix(t(samps.j$Omega[2,1:10,1,,1]), main = "Omega for several compounds in Baltic herring")
 +
 
 +
   scatterplotMatrix(t((samps.j$pred_nd[1,,,1])), main = paste("Predictions for several compounds for",
 +
    names(samps.j$pred_nd[,1,1,1])[1]))
 
    
 
    
   plot(coda.samples(jags, 'Omega', N))
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   #plot(coda.samples(jags, 'Omega', N))
   plot(coda.samples(jags, 'mu', N))
+
   #plot(coda.samples(jags, 'mu', N))
   plot(coda.samples(jags, 'lenp', N))
+
   #plot(coda.samples(jags, 'lenp', N))
   plot(coda.samples(jags, 'timep', N))
+
   #plot(coda.samples(jags, 'timep', N))
  plot(coda.samples(jags, 'pred', N))
+
# tst <- (coda.samples(jags, 'pred_nd', N))
}</rcode>
+
</rcode>
  
  

Revision as of 16:39, 8 March 2021


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

Dioxin concentrations in Baltic herring.

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 this link or by running the codel 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

Preprocess

  • Preprocess model 22.2.2017 [4]
  • Model run 25.1.2017 [5]
  • Model run 22.5.2017 with new ovariables euRaw, euAll, euMain, and euRatio [6]
  • Model run 23.5.2017 with adjusted ovariables euRaw, eu, euRatio [7]
  • Model run 11.10.2017: Small herring and Large herring added as new species [8]
  • Model rerun 15.11.2017 because the previous stored run was lost in update [9]
  • Model run 21.3.2018: Small and large herring replaced by actual fish length [10]
  • Model run 26.3.2018 eu2 moved here [11]

See an updated version of preprocess code for eu on Health effects of Baltic herring and salmon: a benefit-risk assessment#Code for estimating TEQ from chinese PCB7

+ Show code

Bayes model for dioxin concentrations

  • Model run 28.2.2017 [12]
  • Model run 28.2.2017 with corrected survey model [13]
  • Model run 28.2.2017 with Mu estimates [14]
  • Model run 1.3.2017 [15]
  • Model run 23.4.2017 [16] produces list conc.param and ovariable concentration
  • Model run 24.4.2017 [17]
  • Model run 19.5.2017 without ovariable concentration [18] ⇤--#: . The model does not mix well, so the results should not be used for final results. --Jouni (talk) 19:37, 19 May 2017 (UTC) (type: truth; paradigms: science: attack)
----#: . Maybe we should just estimate TEQs until the problem is fixed. --Jouni (talk) 19:37, 19 May 2017 (UTC) (type: truth; paradigms: science: comment)
  • Model run 22.5.2017 with TEQdx and TEQpcb as the only Compounds [19]
  • Model run 23.5.2017 debugged [20] [21] [22]
  • Model run 24.5.2017 TEQdx, TECpcb -> PCDDF, PCB [23]
  • Model run 11.10.2017 with small and large herring [24] (removed in update)
  • Model run 12.3.2018: bugs fixed with data used in Bayes. In addition, redundant fish species removed and Omega assumed to be the same for herring and salmon. [25]
  • Model run 22.3.2018 [26] Model does not mix well. Thinning gives little help?
  • Model run 25.3.2018 with conc.param as ovariable [27]

+ Show code

Initiate conc_pcddf for PFAS disease burden study

Bayesian approach for PCDDF, PCB, OT, PFAS.

  • Model run 2021-03-08 [28]

+ Show code


NOTE! This is not a probabilistic approach. Species and area-specific distributions should be created.

+ Show code

Initiate conc_pcddf for Goherr

  • Model run 19.5.2017 [29]
  • Model run 23.5.2017 with bugs fixed [30]
  • Model run 12.10.2017: TEQ calculation added [31]
  • Model rerun 15.11.2017 because the previous stored run was lost in update [32]
  • 12.3.2018 adjusted to match the same Omega for all fish species [33]
  • 26.3.2018 includes length and time as parameters, lengt ovariable initiated here [34]

+ Show code

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

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]