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. | ||
eu3 <- euw[!colnames(euw) %in% c("MPhT","DOT","BDE138")] # No values > 0 | |||
eu3 <- | 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" | 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)))] | ||
conc_nd <- add_loq(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)))] | ||
oprint(head(eu3)) | oprint(head(eu3)) | ||
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fisl_nd | fisl_nd | ||
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$Year-2009, # Substract baseline year | ||
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'pred_nd', | 'pred_nd', | ||
'mu_nd', | 'mu_nd', | ||
'tau_nd' | 'tau_nd', | ||
'mulocat' | |||
), | ), | ||
thin=thin, | thin=thin, | ||
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dimnames(samps.j$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 | ||
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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])) | ||
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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
This page is a study.
The page identifier is Op_en3104 |
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Moderator:Arja (see all) |
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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 this link or by running the codel below.
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]
- Objects used in Benefit-risk assessment of Baltic herring and salmon intake
- 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
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)
- 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]
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.
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
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]
⇤--#: . 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
- ↑ 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]