EU-kalat: Difference between revisions

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* Model run 2021-03-08 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=VpSUS4pfGavspLG9] with the fish needed in PFAS assessment
* Model run 2021-03-08 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=VpSUS4pfGavspLG9] with the fish needed in PFAS assessment
* Model run 2021-03-12 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=Lc9KWY7r1tTuGWVD] using euw
* Model run 2021-03-12 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=Lc9KWY7r1tTuGWVD] using euw
* Model run 2021-03-13 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=MfdpHgFZClUyGIpC] with location parameter for PFAS


<rcode name="pollutant_bayes" label="Initiate conc_param with PCDDF, PFAS, OT (for developers only)" embed=0 graphics=1>
<rcode name="pollutant_bayes" label="Initiate conc_param with PCDDF, PFAS, OT (for developers only)" embed=0 graphics=1>
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# Catchment year affects all species similarly.  
# Catchment year affects all species similarly.  


euw <- euw[!colnames(euw) %in% c("MPhT","DOT","BDE138")] # No values > 0
eu3 <- euw[!colnames(euw) %in% c("MPhT","DOT","BDE138")] # No values > 0


eu3 <- euw[euw$Matrix == "Muscle" , ]
eu3 <- eu3[eu3$Matrix == "Muscle" , ]
eu3$Locat <- ifelse(eu3$Location=="Porvoo",2,
                      ifelse(eu3$Location=="Helsinki, Vanhankaupunginlahti Bay",3,1))
locl <- c("Finland","Porvoo","Helsinki")


#conl_nd <- c("PFAS","PFOA","PFOS","DBT","MBT","TBT","DPhT","TPhT")
#conl_nd <- c("PFAS","PFOA","PFOS","DBT","MBT","TBT","DPhT","TPhT")
conl_nd <- c("PFAS","PFOS","TBT")
conl_nd <- c("PFAS","PFOS") # TBT would drop Porvoo measurements
fisl <- fisl_nd <- c("Baltic herring","Bream","Flounder","Perch","Roach","Salmon","Whitefish")
fisl <- fisl_nd <- c("Baltic herring","Bream","Flounder","Perch","Roach","Salmon","Whitefish")


eu4 <- eu3[rowSums(is.na(eu3[conl_nd]))<length(conl_nd) & eu3$Fish %in% fisl_nd ,
eu4 <- eu3[rowSums(is.na(eu3[conl_nd]))<length(conl_nd) & eu3$Fish %in% fisl_nd ,
           c(1:5,match(conl_nd,colnames(eu3)))]
           c(1:5,match(c("Locat",conl_nd),colnames(eu3)))]
#fisl_nd <- as.character(unique(eu4$Fish))


conc_nd <- add_loq(eu4[eu4$Fish %in% fisl_nd , 6:ncol(eu4)])
conc_nd <- add_loq(eu4[conl_nd])


conl <- c("TEQ","PCDDF","PCB") # setdiff(colnames(eu3)[-(1:5)], conl_nd)
conl <- c("TEQ","PCDDF","PCB") # setdiff(colnames(eu3)[-(1:5)], conl_nd)
eu3 <- eu3[!is.na(eu3$PCDDF) & eu3$Fish %in% fisl , c(1:5, match(conl,colnames(eu3)))]
eu3 <- eu3[!is.na(eu3$PCDDF) & eu3$Fish %in% fisl , c(1:5, match(conl,colnames(eu3)))]
#conl <- colnames(eu3)[-(1:5)]
#fisl <- as.character(unique(eu3$Fish))


oprint(head(eu3))
oprint(head(eu3))
Line 628: Line 629:
fisl_nd
fisl_nd


conc <- add_loq(eu3[rowSums(is.na(eu3))==0 , 6:ncol(eu3)]) # Remove rows with missing data.
eu3 <- eu3[rowSums(is.na(eu3))==0,]
conc <- add_loq(eu3[conl]) # Remove rows with missing data.


# The model assumes that all fish groups have the same Omega but mu varies.
# The model assumes that all fish groups have the same Omega but mu varies.
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       for(j in 1:C_nd) {
       for(j in 1:C_nd) {
         conc_nd[i,j] ~ dnorm(muind_nd[i,j], tau_nd[j])
         conc_nd[i,j] ~ dnorm(muind_nd[i,j], tau_nd[j])
         muind_nd[i,j] <- mu_nd[fis_nd[i],j] #+ lenp[fis[i]]*length[i] + timep*year[i]
         muind_nd[i,j] <- mu_nd[fis_nd[i],j] + mulocat[locat[i]] #+ lenp[fis[i]]*length[i] + timep*year[i]
       }
       }
     }
     }
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     }
     }
     # Non-dioxins
     # Non-dioxins
    mulocat[1] <- 0
    mulocat[2] ~ dnorm(0,0.001)
    mulocat[3] ~ dnorm(0,0.001)
     for(j in 1:C_nd) {
     for(j in 1:C_nd) {
       tau_nd[j] ~ dgamma(0.001,0.001)
       tau_nd[j] ~ dgamma(0.001,0.001)
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     conc = log(conc),
     conc = log(conc),
     conc_nd = log(conc_nd),
     conc_nd = log(conc_nd),
    locat = eu4$Locat,
     #    length = eu3$Length-170, # Subtract average herring size
     #    length = eu3$Length-170, # Subtract average herring size
     #    year = eu3$Year-2009, # Substract baseline year
     #    year = eu3$Year-2009, # Substract baseline year
Line 708: Line 714:
     'pred_nd',
     'pred_nd',
     'mu_nd',
     'mu_nd',
     'tau_nd'
     'tau_nd',
    'mulocat'
   ),  
   ),  
   thin=thin,
   thin=thin,
Line 721: Line 728:
dimnames(samps.j$tau_nd) <- list(Compound = conl_nd, Iter = 1:N, Chain = 1:4)
dimnames(samps.j$tau_nd) <- list(Compound = conl_nd, Iter = 1:N, Chain = 1:4)
#dimnames(samps.j$timep) <- list(Dummy = "time", Iter = 1:N, Chain = 1:4)
#dimnames(samps.j$timep) <- list(Dummy = "time", Iter = 1:N, Chain = 1:4)
dimnames(samps.j$mulocat) <- list(Location = locl, Iter = 1:N, Chain = 1:4)


##### conc_param contains expected values of the distribution parameters from the model
##### conc_param contains expected values of the distribution parameters from the model
Line 777: Line 785:


scatterplotMatrix(t((samps.j$pred_nd[1,,,1])), main = paste("Predictions for several compounds for",
scatterplotMatrix(t((samps.j$pred_nd[1,,,1])), main = paste("Predictions for several compounds for",
                                                            names(samps.j$pred_nd[,1,1,1])[1]))
scatterplotMatrix(t((samps.j$mulocat[,,1])), main = paste("Predictions for location average difference",
                                                             names(samps.j$pred_nd[,1,1,1])[1]))
                                                             names(samps.j$pred_nd[,1,1,1])[1]))


Line 785: Line 796:
plot(coda.samples(jags, 'pred', N*thin, thin))
plot(coda.samples(jags, 'pred', N*thin, thin))
plot(coda.samples(jags, 'mu_nd', N*thin, thin))
plot(coda.samples(jags, 'mu_nd', N*thin, thin))
plot(coda.samples(jags, 'mulocat', N*thin, thin))
tst <- (coda.samples(jags, 'pred', N))
tst <- (coda.samples(jags, 'pred', N))
</rcode>
</rcode>

Revision as of 05:45, 13 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

This code is similar to preprocess but is better and includes PFAS concentrations from op_fi:PFAS-yhdisteiden tautitaakka. It produces data.frame euw that is the EU-kalat + PFAS data in wide format and, for PFAS but not EU-kalat, a sampled value for measurements below the level of quantification.

+ Show code

Bayesian approach for PCDDF, PCB, OT, PFAS.

  • Model run 2021-03-08 [28]
  • Model run 2021-03-08 [29] with the fish needed in PFAS assessment
  • Model run 2021-03-12 [30] using euw
  • Model run 2021-03-13 [31] with location parameter for PFAS

+ Show code

+ Show code

Initiate conc_pcddf for Goherr

  • Model run 19.5.2017 [32]
  • Model run 23.5.2017 with bugs fixed [33]
  • Model run 12.10.2017: TEQ calculation added [34]
  • Model rerun 15.11.2017 because the previous stored run was lost in update [35]
  • 12.3.2018 adjusted to match the same Omega for all fish species [36]
  • 26.3.2018 includes length and time as parameters, lengt ovariable initiated here [37]

+ 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]