Difference between revisions of "Benefit-risk assessment of Baltic herring and salmon intake"

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m (→‎Calculations: exposure calculation code, nutrients not ready)
(→‎Calculations: HIA model added. Technically runs through but results are not meaningful)
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=== Calculations ===
 
=== Calculations ===
 
This section will have the actual health benefit-risk model (schematically described in the above figure) written with R. The code will utilise all variables listed in the above Dependencies section. Model results are presented as tables and figures when those are available.
 
This section will have the actual health benefit-risk model (schematically described in the above figure) written with R. The code will utilise all variables listed in the above Dependencies section. Model results are presented as tables and figures when those are available.
 +
 +
==== Bayes model for intake ====
  
 
* Model run 28.2.2017 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=KrjCTGZmB8JkCH75]
 
* Model run 28.2.2017 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=KrjCTGZmB8JkCH75]
Line 291: Line 293:
 
cat("Arrays pcd.pred, mu.pred, ans.pred stored.\n")
 
cat("Arrays pcd.pred, mu.pred, ans.pred stored.\n")
 
</rcode>
 
</rcode>
 +
 +
==== Exposure model ====
  
 
<rcode name="exposure" label="Calculate dioxin and nutrient intake (for developers only)">
 
<rcode name="exposure" label="Calculate dioxin and nutrient intake (for developers only)">
Line 340: Line 344:
 
nutconc <- melt(concddeo.pred)
 
nutconc <- melt(concddeo.pred)
  
 +
</rcode>
 +
 +
==== Health impact model (Monte Carlo) ====
 +
 +
* Model run 13.3.2017: a simple copy of [[:op_fi:Silakan hyöty-riskiarvio]] [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=xF8zPw3M9emJd5yY]
 +
 +
<rcode graphics=1>
 +
# This is code Op_en7748/ on page [[Benefit-risk assessment of Baltic herring and salmon intake]]
 +
 +
library(OpasnetUtils)
 +
library(ggplot2)
 +
 +
exposure <- 1
 +
BW <- 70
 +
 +
solet <- opbase.data("Op_fi3831.saantioletukset")
 +
 +
############# Tämän yläpuoliset eivät ole oikeaa koodia!!!
 +
 +
#!!++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 +
## Tausta-altistus D-vitamiinille ja omega-3:lle
 +
addexposure <- EvalOutput(Ovariable("addexposure", ddata = "Op_fi3831.tausta_altistus"))
 +
#ii++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 +
 +
# Empty values ("") in indices must be replaced by NA so that Ops works correctly.
 +
levels(addexposure@output$Sukupuoli)[levels(addexposure@output$Sukupuoli) == ""] <- NA
 +
levels(addexposure@output$Exposure_agent)[levels(addexposure@output$Exposure_agent) == ""] <- NA
 +
addexposure@output <- fillna(addexposure@output, c("Sukupuoli", "Exposure_agent"))
 +
 +
sumitem <- function(
 +
  ova, #ovariable that has locations to sum
 +
  cond, # index column that contains the locations to sum
 +
  condvalue, # vector of locations to sum
 +
  sumvalue # location to be given to the rows with the sums
 +
) {
 +
  d <- ova
 +
  d@output <- d@output[d@output[[cond]] %in% condvalue , ]
 +
  d <- oapply(d, cols = cond, FUN = sum)
 +
  d@output[[cond]] <- sumvalue
 +
  ova@output <- orbind(ova, d)
 +
  return(ova)
 +
}
 +
 +
addexposure <- sumitem(addexposure, "Exposure_agent", c("PCDDF", "PCB"), "TEQ")
 +
addexposure <- sumitem(addexposure, "Exposure_agent", c("EPA", "DHA"), "Omega3")
 +
addexposure <- unkeep(addexposure, prevresults = TRUE, sources = TRUE)
 +
 +
# Make the background exposure uncertain rather than an index.
 +
 +
taustat <- Ovariable(
 +
  output = data.frame(
 +
    Iter = 1:get("N", envir = openv),
 +
    Tausta = c("Kyllä", "Ei")[sample(2, get("N", envir = openv), replace = TRUE)],
 +
    Result = 1
 +
  ),
 +
  marginal = c(TRUE, FALSE, FALSE)
 +
)
 +
 +
addexposure <- addexposure * taustat
 +
 +
exposure <- exposure + addexposure
 +
 +
# Convert mother's dioxin intake (pg/d) into child's dioxin concentration (pg/g) in fat after 6 months of breast-feeding.
 +
# For details, see [[ERF of dioxin#Dental defects]]
 +
 +
t0.5 <- Ovariable("t0.5", data = solet[solet$Muuttuja == "TEQ-puoliintumisaika" , ]["Result"])
 +
f_ing <- Ovariable("f_ing", data = solet[solet$Muuttuja == "TEQ-imeytymisosuus" , ]["Result"])
 +
f_mtoc <- Ovariable("f_mtoc", data = solet[solet$Muuttuja == "TEQ-osuus lapseen" , ]["Result"])
 +
BF <- Ovariable("BF", data = solet[solet$Muuttuja == "Imeväisen rasvamäärä" , ]["Result"])
 +
BF <- EvalOutput(BF) # LISÄTTY 13.3.
 +
temp <- exposure
 +
temp@output <- temp@output[temp@output$Exposure_agent == "TEQ" , ]
 +
temp <- log((temp * t0.5 * f_ing * f_mtoc / (log(2) * BF) ) + 1) # Actual conversion
 +
temp@output$Exposure_agent <- "logTEQ"
 +
temp <- unkeep(temp, prevresults = TRUE, sources = TRUE)
 +
exposure@output <- orbind(exposure, temp)
 +
exposure <- unkeep(exposure, prevresults = TRUE, sources = TRUE)
 +
 +
frexposed <- 1
 +
bgexposure <- addexposure
 +
date()
 +
######################################################### VÄESTÖ
 +
 +
# Tarkastellaan totcases-laskennassa aluksi yksilöriskiä, ja vasta lopussa kerrotaan yksilötulokset yksilöiden edustamien ryhmien koolla.
 +
 +
population <- Ovariable("population", data = data.frame(Result = 1))
 +
 +
# Väkimäärä. TÄTÄ KÄYTETÄÄN disincidence-ovariablen suhteuttamisessa CHD:n suhteen.
 +
 +
#!!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 +
pop <- opbase.data("Op_en2949") # [[Population of Finland]]
 +
#ii+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 +
 +
pop$Obs <- NULL
 +
pop$Observation <- NULL
 +
age <- pop$Result[pop$Age == "0-4"]
 +
pop$Result[pop$Age == "0-4"] <- 4/5 * age
 +
pop <- orbind(pop, data.frame(Age = "0", Result = 1/5 * age))
 +
levels(pop$Age)[levels(pop$Age) == "0-4"] <- "1-4"
 +
pop <- Ovariable("pop", data = pop)
 +
date()
 +
###############################################################################################
 +
# ANNOSVASTEET JA TAUTIRISKIT
 +
# dioksiinille,
 +
# omega-3-rasvahapoille,
 +
# vitamiineille (D-vitamiinille)
 +
# ja metyylielohopealle ja niiden vaikutuksille
 +
# Kuitenkaan metyylielohopeapitoisuuksia ei ole tässä malliversiossa, joten ne tippuvat pois.
 +
 +
# Annosvasteet
 +
 +
#!!+++++++++++++++++++++++ THIS CODE REPLACES ALL POLLUTANT-SPECIFIC CODES:
 +
objects.latest("Op_en2031", code_name = "initiate") # [[Exposure-response function]]
 +
 +
#!!++++++++++++++++++++++++++++++++++++++++++++++++++
 +
#objects.latest("Op_en5823", code_name = "initiate") # [[ERF of dioxin]], ovariables ERF, threshold
 +
#ii++++++++++++++++++++++++++++++++++++++++++++++++++
 +
 +
#temp1 <- ERF
 +
#temp2 <- threshold
 +
 +
#!!++++++++++++++++++++++++++++++++++++++++++++++++++
 +
#objects.latest("Op_en5830", code_name = "initiate") # [[ERF of omega-3 fatty acids]], ovariables ERF, threshold
 +
#ii++++++++++++++++++++++++++++++++++++++++++++++++++
 +
 +
#temp1@data <- orbind(ERF@data, temp1@data)
 +
#temp2@data <- orbind(threshold@data, temp2@data)
 +
 +
#!!++++++++++++++++++++++++++++++++++++++++++++++++++
 +
#objects.latest("Op_en6866", code_name = "initiate") # [[ERFs of vitamins]], ovariables ERF, threshold
 +
#ii++++++++++++++++++++++++++++++++++++++++++++++++++
 +
 +
#temp1@data <- orbind(ERF@data, temp1@data)
 +
#temp2@data <- orbind(threshold@data, temp2@data)
 +
 +
#!!++++++++++++++++++++++++++++++++++++++++++++++++++
 +
#objects.latest("Op_en5825", code_name = "initiate") # [[ERF of methylmercury]], ovariables ERF, threshold
 +
#ii++++++++++++++++++++++++++++++++++++++++++++++++++
 +
 +
#ERF@data <- orbind(ERF@data, temp1@data)
 +
#threshold@data <- orbind(threshold@data, temp2@data)
 +
 +
# Drop ERF rows that are not used in this assessment.
 +
 +
erfpois <- paste(ERF@data$Exposure_agent, ERF@data$Trait, ERF@data$ERF_parameter, ERF@data$Scaling) %in%
 +
  c(
 +
    "MeHg Child's IQ ERS BW",
 +
    "DHA Child's intelligence ERS BW",
 +
    "Omega3 Coronary heart disease RR None",
 +
    "Omega3 CHD Relative Hill None",
 +
    "logTEQ Developmental dental defects incl. agenesis ERS None",
 +
    "TEQ Cancer, total CSF BW",
 +
    "logTEQ Tooth defect ERS None" # Poistetaan Seveso koska suomalainen ERF uskottavampi
 +
  )
 +
ERF@data <- ERF@data[!erfpois , ]
 +
 +
# Drop nuisance indices because they use a lot of memory in oapply.
 +
dropp <- c("Response_metric", "Exposure_route", "Exposure_metric", "Exposure_unit")
 +
ERF <- unkeep(EvalOutput(ERF), dropp, sources = TRUE)
 +
threshold <- unkeep(EvalOutput(threshold), dropp, sources = TRUE)
 +
 +
 +
######################################## Tautiriski
 +
#!!++++++++++++++++++++++++++++++++++++++++++++++++++
 +
objects.latest("Op_en5917", code_name = "initiate") # [[:op_en:Disease risk]] ovariable disincidence
 +
#ii++++++++++++++++++++++++++++++++++++++++++++++++++
 +
 +
disincidence <- EvalOutput(disincidence) # TEMPORARY 13.3.
 +
 +
disincidence <- disincidence / 100000 # Change from 1/100000py to 1/py.
 +
disincidence <- unkeep(disincidence, cols = c("Age", "Sex", "Population", "Unit", "Response_metric"))
 +
# Näitä indeksejä ei tarvita tässä arvioinnissa.
 +
disincidence@output <- disincidence@output[disincidence@output$Response != "CHD" , ] # Tulee ikävakioidusta alta.
 +
 +
############################################################################
 +
# Ikävakioidut kuolemansyyt
 +
 +
# Sairastuvuus (tarkemmin sanottuna [[Kuolemansyyt Suomessa]])
 +
#!!++++++++++++++++++++++++++++++++++++++++++++++++++++
 +
syy <- Ovariable(
 +
  "syy",
 +
  data = opbase.data(
 +
    "Op_fi4558",
 +
    subset = "2012",
 +
    include = list(Kuolemansyy = c(
 +
      "04-21 Syövät (C00-C97)",
 +
      "27 Iskeemiset sydäntaudit (I20-I25)",
 +
      "29 Aivoverisuonien sairaudet (I60-I69)"
 +
    )
 +
    )
 +
  )
 +
)
 +
 +
#ii++++++++++++++++++++++++++++++++++++++++++++++++++++
 +
syy@data$Ikä <- gsub(" ", "", syy@data$Ikä)
 +
colnames(syy@data)[colnames(syy@data) == "Ikä"] <- "Age"
 +
 +
# Syöpää ei haluta linkata ikäryhmittäin ERF:iin, vaan sitä käytetään muodostamaan diskontattu elinaikainen
 +
# odotettu eliniän menetys jos kuolee syöpään tietyn ikäisenä. Ks. myöhemmin.
 +
 +
väli <- Ovariable(output = data.frame(
 +
  Kuolemansyy = c(
 +
    rep("27 Iskeemiset sydäntaudit (I20-I25)", 2),
 +
    "04-21 Syövät (C00-C97)",
 +
    "29 Aivoverisuonien sairaudet (I60-I69)"
 +
  ),
 +
  Response = c("CHD", "CHD2", "Syövät", "Stroke"), # Syövät nimetään hassusti, koska sen EI haluta linkkautuvan ERF:iin.
 +
  # Lisäksi Tehdään kaksinkertainen sydäntaululistaus, koska on kaksi ERFiä: Mozaffarian ja Cohen.
 +
  Result = 1
 +
))
 +
 +
pop <- EvalOutput(pop)
 +
 +
syyt <- syy / (pop / 2) * väli # Pop ei ole sukupuolen mukaan jaoteltu mutta syy on, joten pistetään kahtia.
 +
 +
disincidence <- disincidence * Ovariable(output = data.frame(Result = 1), marginal = FALSE)
 +
marginals <- union(colnames(syyt@output)[syyt@marginal], colnames(disincidence@output)[disincidence@marginal])
 +
syyt@output <- orbind(disincidence, syyt)
 +
syyt@marginal <- colnames(syyt@output) %in% marginals
 +
syyt <- unkeep(syyt, cols = c("Kuolemansyy", "Observation"), prevresults = TRUE, sources = TRUE)
 +
syyt@output$Sukupuoli[syyt@output$Sukupuoli == "Miehet"] <- "Mies" # Ei ole faktori vaan character
 +
syyt@output$Sukupuoli[syyt@output$Sukupuoli == "Naiset"] <- "Nainen"
 +
 +
disincidence <- syyt # Nyt sisältää sekä alkuperäisen disincidencen että ikäluokittaisen CHD:n
 +
date()
 +
###################################################################################
 +
#### HIA-ovariablet
 +
 +
#!!++++++++++++++++++++++++++++++++++++++++++++++++++
 +
objects.latest("Op_en2261", code_name = "initiate") # [[:op_en:Health impact assessment]] ovariables dose, RR, totcases (AF)
 +
#ii++++++++++++++++++++++++++++++++++++++++++++++++++
 +
 +
dose <- unkeep(EvalOutput(dose), prevresults = TRUE, sources = TRUE)
 +
 +
RR <- unkeep(EvalOutput(RR), prevresults = TRUE, sources = TRUE)
 +
 +
totcases <- EvalOutput(totcases)
 +
date()
 +
 +
ggplot(totcases@output, aes(x = Response, weight = totcasesResult, fill = Sukupuoli))+
 +
  geom_bar()
 +
 +
##################################################################################
 +
# Tapauskohtaiset postprosessoinnit
 +
 +
if(FALSE) {
 +
 
 +
### Muutetaan exposure yksikköön g /d kaikkien Exposure_agentien osalta.
 +
skaala <- Ovariable(
 +
  output = data.frame(
 +
    Exposure_unit = "g /d",
 +
    Exposure_agent = c("Vitamin_D", "EPA", "DHA", "Omega3", "PCDDF", "PCB", "TEQ", "MeHg"),
 +
    Result = c(1E-6, 1E-3, 1E-3, 1E-3, 1E-12, 1E-12, 1E-12, 1E-6)),
 +
  marginal = c(FALSE, TRUE, FALSE)
 +
)
 +
 +
tiedot <- rivit * taustat * Ovariable(
 +
  output = data.frame(
 +
    silakka[c("Rivi", "Sukupuoli", "Age", "Hedelm", "Ikä", "Paino", "Silakkamäärä")],
 +
    Result = 1
 +
  ),
 +
  marginal = c(FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE)
 +
)
 +
tiedot <- unkeep(tiedot, cols = "Rivi", prevresults = TRUE, sources = TRUE)
 +
 +
date()
 +
 +
exposure <- exposure * skaala * tiedot
 +
amount <- amount * tiedot
 +
concentration <- unkeep(concentration, prevresults = TRUE, sources = TRUE) * skaala
 +
 +
#Etsitään hedelmällisessä iässä olevat naiset ja lasten ÄO-vaste ja korjataan synnytyksen todennäköisyydellä.
 +
 +
indivrisk <- totcases
 +
 +
indivrisk <- indivrisk * tiedot
 +
 +
result(indivrisk) <- result(indivrisk) * ifelse(
 +
  indivrisk@output$Trait %in% c("Child's IQ", "Tooth defect", "Dental defect"),
 +
  ifelse(
 +
    indivrisk@output$Sukupuoli == "Nainen" & indivrisk@output$Hedelm == "TRUE",
 +
    0.1, # Probability of birth during a year.
 +
    0
 +
  ),
 +
  1
 +
)
 +
 +
#ages <- c(
 +
# "0", "1-4", "5-9", "10-14", "15-19", "20-24",
 +
# "25-29", "30-34", "35-39", "40-44", "45-49", "50-54",
 +
# "55-59", "60-64", "65-69", "70-74", "75-79"
 +
#)
 +
indivrisk@output$Age <- factor(indivrisk@output$Age, levels = ages, ordered = TRUE)
 +
 +
indivrisk@output$Iter <- as.numeric(as.character(indivrisk@output$Iter))
 +
 +
#!!+++++++++++++++++++++++++++++++++++++++++++++++++
 +
objects.store(indivrisk, pop, exposure, amount, ERF, threshold, silakka, disincidence, concentration, rivit)
 +
#ii+++++++++++++++++++++++++++++++++++++++++++++++++
 +
cat("indivrisk, pop, exposure, amount, ERF, threshold, silakka, disincidence, concentration, rivit stored. \n")
 +
date()
 +
 +
####################################3
 +
 +
for(i in c("RR", "disincidence", "totcases", "ERF", "threshold")) {
 +
  print(levels(get(i)@output$Response))
 +
}
 +
} # if(FALSE)
 
</rcode>
 
</rcode>
  

Revision as of 09:18, 13 March 2017



Scope

This assessment is part of the WP5 work in Goherr project. Purpose is to evaluate health benefits and risks caused of eating Baltic herring and salmon in four Baltic sea countries (Denmark, Estonia, Finland and Sweden). This assessment is currently on-going.

Question

What are the current population level health benefits and risks of eating Baltic herring and salmon in Finland, Estonia, Denmark and Sweden? How would the health effects change in the future, if consumption of Baltic herring and salmon changes due to actions caused by different management scenarios of Baltic sea fish stocks?

Intended use and users

Results of this assessment are used to inform policy makers about the health impacts of fish. Further, this assessment will be combined with the results of the other Goherr WPs to produce estimates of future health impacts of Baltic fish related to different policy options. Especially, results of this assessment will be used as input in the decision support model built in Goherr WP6.

Participants

  • National institute for health and welfare (THL)
  • Goherr project group

Boundaries

  • Four baltic sea countries (Denmark, Estonia, Finland, Sweden)
  • Current situation (fish use year 2016, pollutant levels in fish year 2010?)
  • Estimation for future (year 2020?)

Decisions and scenarios

Management scenarios developed in Goherr WP3 frames the following boundaries to the use and consumption of Baltic herring and salmon as human food. Effect of these scenarios to the dioxin levels and the human food use will be evalauted quantitatively and feed into the health benefit-risk model to assess the health effect changes.

  • Scenario 1: “Transformation to sustainability”
    • Hazardous substances, including dioxins, are gradually flushed out and the dioxin levels in Baltic herring are below or close to the maximum allowable level.
    • Fish stocks are allowed to recover to levels, which makes maximum sustainable yield possible and increases the total catches of wild caught fish. The catches of salmon by commercial fisheries has stabilized at low level, while the share of recreational catch increases slightly.
    • The use of the Baltic herring catch for food increases. A regional proactive management plan for the use of catch has increased the capacity of the fishing fleets to fish herring for food and through product development and joint marketing, have increased consumer demand for Baltic herring.
  • Scenario 2: “Business-as-usual”
    • The commercial catches of salmon continue to decrease. The demand for top predatory species, such as salmon and cod remains high, while the demand for herring decreased further as a result of demographic changes.
    • Most of the herring catch are used for fish meal and oil production in the region.
    • The use of Baltic herring from the southern parts of the Baltic Sea where the dioxin contents are not likely to exceed the maximum allowable level, are prioritised for human consumption. In the absence of the demand in many of the Baltic Sea countries, majority of the herring intended for direct human consumption are exported to Russia.
  • Scenario 3: “Inequality”
    • The nutrient and dioxins levels continue to decrease slowly.
    • The commercial catches of salmon have decreased further as the general attitudes favour recreational fishing, which has also resulted in decreased demand.
    • The herring catches have increased slightly, but the availability of herring suitable for human consumption remains low due to both, dioxin levels that remain above the maximum allowable limit in the northern Baltic Sea and the poor capacity to fish for food.
    • The use of the catch varies between countries. In Estonia, for example, where the whole catch has been traditionally used for human consumption, there is no significant change in this respect, but in Finland, Sweden and Denmark, herring fishing is predominantly feed directed.
  • Scenario 4: “Transformation to protectionism”
    • The level of hazardous substances also increases as emission sources are not adequately addressed.
    • Commercial salmon fisheries disappears almost completely from the Baltic Sea, although restocking keeps small scale fisheries going.
    • Many of the Baltic herring stocks are also fished above the maximum sustainable yield and total catches are declining.
    • Owing to the growing dioxin levels detected in herring, majority of the catch is used for aquaculture.

Timing

  • Model development during 2016 and 2017
  • First set of results in March 2017, draft publication in March 2018

Answer

This section will be updated as soon as preliminary results are available

Results

Conclusions

Rationale

Error creating thumbnail: Unable to save thumbnail to destination
Schematic picture of the health benefit-risk model for Baltic herring and salmon intake.

Stakeholders

  • Policy makers
    • Food safety authorities
    • Fisheries management
  • Researchers
    • Food safety
    • Health
  • NGO's
    • WWF
    • Active consumers
    • Marine Stewardship Council
  • Baltic sea fishers and producers?

Dependencies

Content of all variable pages below are preliminary and will be updated when this assessment proceeds.

Analyses

Indices

  • Country (Denmark, Estonia, Finland, Sweden)
  • Year (current, future)
  • Sex
  • Age (categories?)
  • Fish species (Baltic herring, Baltic salmon)
  • Health end-point (ICD-10 code?, name? something else? --# : This need to be matched for disability weight, duration of disease, background incidence and disease burden data, dose-responses --Arja (talk) 09:39, 22 September 2016 (UTC)
  • Compound (pollutant, fatty acid) --# : This needs to be matched with dose-responses --Arja (talk) 09:39, 22 September 2016 (UTC)

Calculations

This section will have the actual health benefit-risk model (schematically described in the above figure) written with R. The code will utilise all variables listed in the above Dependencies section. Model results are presented as tables and figures when those are available.

Bayes model for intake

  • Model run 28.2.2017 [1]
  • Model run 28.2.2017 with corrected survey model [2]
  • Model run 28.2.2017 with Mu estimates [3]
  • Model run 1.3.2017 [4]

+ Show code

Exposure model

+ Show code

Health impact model (Monte Carlo)

+ Show code

Plot concentrations and survey

  • Requires codes Op_en7748/bayes and indirectly Op_en7748/preprocess.
  • Model run 1.3.2017 [6]

+ Show code

References


Keywords

See also

Goherr Research project 2015-2018: Integrated governance of Baltic herring and salmon stocks involving stakeholders
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Goherr public website

Workpackages including task description and follow-up:
WP1 Management · WP2 Sociocultural use, value and goverrnance of Baltic salmon and herring · WP3 Scenarios and management objectives · WP4 Linking fish physiology to food production and bioaccumulation of dioxin · WP5 Linking the health of the Baltic Sea with health of humans: Dioxin · WP6 Building a decision support model for integrated governance · WP7 Dissemination

Other relevant pages in Opasnet: GOHERR assessment · Relevant literature

Relevant data: Exposure response functions of dioxins · Fish consumption in Sweden · POP concentrations in Baltic sea fish · Exposure response functions of Omega3 fatty acids

Relevant methods: Health impact assessment · OpasnetBaseUtils‎ · Modelling in Opasnet

Relevant assessments: Benefit-risk assessment of Baltic herring · Benefit-risk assessment on farmed salmon · Benefit-risk assessment of methyl mercury and omega-3 fatty acids in fish · Benefit-risk assessment of fish consumption for Beneris · Benefit-risk assessment of Baltic herring (in Finnish)

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Error creating thumbnail: Unable to save thumbnail to destination

http://www.bonusportal.org/ http://www.bonusprojects.org/bonusprojects

Related files

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