Nothing
R/match_ppdb.R
In PesticideLoadIndicator: Computes Danish Pesticide Load Indicator
Defines functions
compute_risk_score
match.ppdb
create.substances.table
extend.products.table
compute_HR
compute_R
extend.fate
read.excel
Documented in
compute_risk_score
match.ppdb
#' @importFrom readxl read_excel
#' @importFrom stringr str_detect
read.excel <- function(excel_file) {
table <- as.data.frame(read_excel(excel_file))
names(table) <- make.names(names(table))
table
}
extend.fate <- function(fate,ecotox)
{
required_f <- c(
"LogP",
"SCI.GROW",
"Soil.DT50.typical...days",
"Soil.DT50.lab...days",
"Soil.DT50.notes",
"Water.phase.DT50...days",
"Active",
"ID"
)
missing_f <- setdiff(required_f, names(fate))
if (length(missing_f) > 0) {
stop(paste("columns", paste(missing_f, collapse=", "), "missing in fate table"))
}
required_eco <- c(
"Bioconcentration.Factor",
"ID"
)
missing_eco <- setdiff(required_eco, names(ecotox))
if (length(missing_eco) > 0) {
stop(paste("columns", paste(missing_eco, collapse=", "), "missing in ecotox table"))
}
fate$LogP[is.na(fate$LogP)] <- 0
fate$LogP<-as.numeric(fate$LogP)
fate$LogP[is.na(fate$LogP)] <- 0
eco<- ecotox[,required_eco]
fate<- merge(fate,eco,by= "ID")
fate$BCF[fate$LogP >6 & fate$LogP !=0]<-10^((-0.2*(fate$LogP[fate$LogP >6 & fate$LogP !=0]^2))+(2.74*fate$LogP[fate$LogP >6 & fate$LogP !=0])-4.72)
fate$BCF[fate$LogP <6 & fate$LogP !=0]<-10^((0.86*fate$LogP[fate$LogP <6 & fate$LogP !=0])-0.7)
fate$BCF[fate$Bioconcentration.Factor!="" & !(is.na(fate$Bioconcentration.Factor))]<-fate$Bioconcentration.Factor[fate$Bioconcentration.Factor!="" & !(is.na(fate$Bioconcentration.Factor))]
fate$BCF[is.na(fate$BCF)]<-0
fate$BCF[fate$BCF=="Low risk"]<-0
suppressWarnings(fate$BCF<-as.numeric(fate$BCF))
fate$BCF[is.na(fate$BCF)]<-0
fate$BCF[fate$BCF>5100]<-5100
fate$SCI.GROW[fate$SCI.GROW=="Cannot be calculated"] <- 0
fate$SCI.GROW[is.na(fate$SCI.GROW)] <- 0
fate$SCI.GROW <- as.numeric(fate$SCI.GROW)
fate$SCI.GROW[fate$SCI.GROW>1000] <- 0
x<-cbind(as.numeric(fate$Soil.DT50.lab...days),as.numeric(fate$Soil.DT50.typical...days))
fate$SoilDT50<-rowMeans(x,na.rm=TRUE)
fate$stable<-ifelse(str_detect(fate$Soil.DT50.notes, "Stable"),1,0)
fate$Stable<-ifelse(str_detect(fate$Soil.DT50.notes, "stable"),1,0)
fate$stable<-fate$stable + fate$Stable
fate$SoilDT50[fate$stable!="0"]<-730
fate$SoilDT50[fate$SoilDT50==""] <- 0
fate$SoilDT50[is.na(fate$SoilDT50)] <- 0
fate$SoilDT50[fate$SoilDT50=="#N/A"] <- 0
fate$SoilDT50[fate$SoilDT50>731] <- 0
fate$SoilDT50 <- as.numeric(fate$SoilDT50)
fate$SoilDT50[fate$Soil.DT50.notes=="Both iron and phosphate naturally occur in soil. Degradation will be very slow"] <- 0
fate$SoilDT50[str_detect(fate$Active, "Copper")]<-0
fate$SoilDT50[str_detect(fate$Active, "copper")]<-0
fate$SoilDT50[str_detect(fate$Active, "Sulphur")]<-0
fate$SoilDT50[str_detect(fate$Active, "sulphur")]<-0
fate$SoilDT50[str_detect(fate$Active, "Iron")]<-0
fate$SoilDT50[str_detect(fate$Active, "iron")]<-0
fate$Water.phase.DT50...days[is.na(fate$Water.phase.DT50...days)]<-0
fate$Water.phase.DT50...days[fate$Water.phase.DT50...days==""]<-0
fate$Water.phase.DT50...days[fate$Water.phase.DT50...days=="<1"]<-0.5
fate$Water.phase.DT50...days[fate$Water.phase.DT50...days==">100"]<-100
fate$Water.phase.DT50...days[fate$Water.phase.DT50...days=="Slow, DT50 25-30 days"]<-30
fate$Water.phase.DT50...days[fate$Water.phase.DT50...days=="Stable"]<-730
fate
}
compute_R <- function(human) {
# EC.Risk.Classification
if (!('EC.Risk.Classification' %in% names(human))) {
stop('Human table needs column EC.Risk.Classification')
}
R22 <- ifelse(str_detect(human$EC.Risk.Classification, "22"), 10, 0)
R37 <- ifelse(str_detect(human$EC.Risk.Classification, "37"), 10, 0)
R38 <- ifelse(str_detect(human$EC.Risk.Classification, "38"), 10, 0)
R65 <- ifelse(str_detect(human$EC.Risk.Classification, "65"), 10, 0)
R66 <- ifelse(str_detect(human$EC.Risk.Classification, "66"), 10, 0)
R20 <- ifelse(str_detect(human$EC.Risk.Classification, "20"), 15, 0)
R21 <- ifelse(str_detect(human$EC.Risk.Classification, "21"), 15, 0)
R36 <- ifelse(str_detect(human$EC.Risk.Classification, "36"), 15, 0)
R43 <- ifelse(str_detect(human$EC.Risk.Classification, "43"), 20, 0)
R33 <- ifelse(str_detect(human$EC.Risk.Classification, "33"), 30, 0)
R67 <- ifelse(str_detect(human$EC.Risk.Classification, "67"), 30, 0)
R25 <- ifelse(str_detect(human$EC.Risk.Classification, "25"), 50, 0)
R42 <- ifelse(str_detect(human$EC.Risk.Classification, "42"), 50, 0)
R64 <- ifelse(str_detect(human$EC.Risk.Classification, "64"), 50, 0)
R23 <- ifelse(str_detect(human$EC.Risk.Classification, "23"), 70, 0)
R24 <- ifelse(str_detect(human$EC.Risk.Classification, "24"), 70, 0)
R28 <- ifelse(str_detect(human$EC.Risk.Classification, "28"), 70, 0)
R34 <- ifelse(str_detect(human$EC.Risk.Classification, "34"), 70, 0)
R40 <- ifelse(str_detect(human$EC.Risk.Classification, "40"), 70, 0)
R41 <- ifelse(str_detect(human$EC.Risk.Classification, "41"), 70, 0)
R62 <- ifelse(str_detect(human$EC.Risk.Classification, "62"), 70, 0)
R63 <- ifelse(str_detect(human$EC.Risk.Classification, "63"), 70, 0)
R68 <- ifelse(str_detect(human$EC.Risk.Classification, "68"), 70, 0)
R26 <- ifelse(str_detect(human$EC.Risk.Classification, "26"), 100, 0)
R27 <- ifelse(str_detect(human$EC.Risk.Classification, "27"), 100, 0)
R35 <- ifelse(str_detect(human$EC.Risk.Classification, "35"), 100, 0)
R39 <- ifelse(str_detect(human$EC.Risk.Classification, "39"), 100, 0)
R45 <- ifelse(str_detect(human$EC.Risk.Classification, "45"), 100, 0)
R46 <- ifelse(str_detect(human$EC.Risk.Classification, "46"), 100, 0)
R48 <- ifelse(str_detect(human$EC.Risk.Classification, "48"), 100, 0)
R49 <- ifelse(str_detect(human$EC.Risk.Classification, "49"), 100, 0)
R60 <- ifelse(str_detect(human$EC.Risk.Classification, "60"), 100, 0)
R61 <- ifelse(str_detect(human$EC.Risk.Classification, "61"), 100, 0)
R <- (R20 + R21 + R22 + R23 + R24 + R25 + R26 + R27 + R28 + R33 + R34
+ R35 + R36 + R37 + R38 + R39 + R40 + R41 + R42 + R43 + R45 + R46
+ R48 + R49 + R60 + R61 + R62 + R63 + R64 + R65 + R66 + R67 + R68)
R
}
compute_HR <- function(human) {
# CLP.classification.2013
if (!('CLP.classification.2013' %in% names(human))) {
stop('Human table needs column CLP.classification.2013')
}
#If there are two different risk points per H, the higher one has been taken
#This happened for H300, H314, H330, H310)
H302 <- ifelse(str_detect(human$CLP.classification.2013, "302"), 10, 0)
H335 <- ifelse(str_detect(human$CLP.classification.2013, "335"), 10, 0)
H315 <- ifelse(str_detect(human$CLP.classification.2013, "315"), 10, 0)
H304 <- ifelse(str_detect(human$CLP.classification.2013, "304"), 10, 0)
H066 <- ifelse(str_detect(human$CLP.classification.2013, "066"), 10, 0)
H332 <- ifelse(str_detect(human$CLP.classification.2013, "332"), 15, 0)
H312 <- ifelse(str_detect(human$CLP.classification.2013, "312"), 15, 0)
H319 <- ifelse(str_detect(human$CLP.classification.2013, "319"), 15, 0)
H317 <- ifelse(str_detect(human$CLP.classification.2013, "317"), 20, 0)
H336 <- ifelse(str_detect(human$CLP.classification.2013, "336"), 30, 0)
H301 <- ifelse(str_detect(human$CLP.classification.2013, "301"), 50, 0)
H334 <- ifelse(str_detect(human$CLP.classification.2013, "334"), 50, 0)
H362 <- ifelse(str_detect(human$CLP.classification.2013, "362"), 50, 0)
H331 <- ifelse(str_detect(human$CLP.classification.2013, "331"), 70, 0)
H311 <- ifelse(str_detect(human$CLP.classification.2013, "311"), 70, 0)
H314 <- ifelse(str_detect(human$CLP.classification.2013, "314"), 100, 0)
H351 <- ifelse(str_detect(human$CLP.classification.2013, "351"), 70, 0)
H318 <- ifelse(str_detect(human$CLP.classification.2013, "318"), 70, 0)
H373 <- ifelse(str_detect(human$CLP.classification.2013, "373"), 70, 0)
H361 <- ifelse(str_detect(human$CLP.classification.2013, "361"), 70, 0)
H371 <- ifelse(str_detect(human$CLP.classification.2013, "371"), 70, 0)
H341 <- ifelse(str_detect(human$CLP.classification.2013, "341"), 70, 0)
H330 <- ifelse(str_detect(human$CLP.classification.2013, "330"), 100, 0)
H300 <- ifelse(str_detect(human$CLP.classification.2013, "300"), 85, 0)
H310 <- ifelse(str_detect(human$CLP.classification.2013, "310"), 100, 0)
H370 <- ifelse(str_detect(human$CLP.classification.2013, "370"), 100, 0)
H350 <- ifelse(str_detect(human$CLP.classification.2013, "350"), 100, 0)
H340 <- ifelse(str_detect(human$CLP.classification.2013, "340"), 100, 0)
H372 <- ifelse(str_detect(human$CLP.classification.2013, "372"), 100, 0)
H360 <- ifelse(str_detect(human$CLP.classification.2013, "360"), 100, 0)
HR <- (H066 + H300 + H301 + H302 + H304 + H310 + H311 + H312 + H314 + H315
+ H317 + H318 + H319 + H330 + H331 + H332 + H334 + H335 + H336 + H340
+ H341 + H350 + H351 + H360 + H361 + H362 + H370 + H371 + H372 + H373)
HR
}
extend.products.table <- function(products_table, substances_table, human, general)
{
if (!('ID' %in% names(human))) {
stop('Human table needs column ID')
}
if (!('CASS.RN' %in% names(general))) {
stop('General table needs column "CASS RN"')
}
cas.index <- match("CASS.RN", names(general))
missing.cas <- c()
for (irow in 1:nrow(products_table)) {
sum.risk.score <- 0.0
products_row = products_table[irow,]
substances_rows = substances_table[substances_table$product == products_row$product,]
for (jrow in 1:nrow(substances_rows)) {
substance_row = substances_rows[jrow,]
CAS = substance_row$CAS.number
substance = substance_row$substance
match = general[which(general[,cas.index] == CAS),]
if (nrow(match) == 0) {
missing.cas <- c(missing.cas, CAS);
if (length(missing.cas) < 11)
warning(paste("no entry for CAS", CAS, "\n"))
if (length(missing.cas) == 11)
warning("supress missing CAS matches from now on\n\n")
next
}
id <- match$ID
human_row = human[which(human$ID == id),]
if (nrow(human_row) == 0) {
warning(paste("no entry for human risk for id", id, "\n"))
next
}
if (products_row$Year <= 2012) {
sum.risk.score <- c(sum.risk.score ,compute_R(human_row))
}
else {
sum.risk.score <- c(sum.risk.score, compute_HR(human_row))
}
}
products_table[irow, "sum.risk.score"] <- max(sum.risk.score, na.rm=T)
products_table[irow, "reference.sum.risk.scores"] <- 300
}
# remove duplicates
missing.cas <- union(missing.cas, missing.cas)
if (length(missing) > 0) {
txt <- paste(missing.cas, collapse=", ")
warning(paste("\nthe CAS numbers", txt, "caused problems, please fix this\n\n"))
}
products_table
}
create.substances.table <- function(input_table, general, fate, ecotox) {
if (!('CASS.RN' %in% names(general))) {
stop('General table needs column "CASS RN"')
}
cas.index <- match("CASS.RN", names(general))
required.fate <- c("ID", "SCI.GROW", "Water.phase.DT50...days","Active")
missing <- setdiff(required.fate, names(fate))
if (length(missing) > 0) {
stop(paste("columns", paste(missing, collapse=", "), "missing in fate table"))
}
required.ecotox <- c("ID",
"Birds...Acute.LD50.mg.kg",
"Mammals...Acute.Oral.LD50.mg.kg.BW.day",
"Fish...Acute.96hr.LC50.mg.l",
"Aquatic.Invertebrates...Acute.48hr.EC50.mg.l",
"Algae...Acute.72hr.EC50.Growth.mg.l",
"Aquatic.Plants...Acute.7d.EC50.mg.l",
"Earthworms...Acute.14d.LC50.mg.kg",
"Honeybees...Contact.acute.48hr.LD50.ug.per.bee",
"Honeybees...Oral.Acute.48hr.LD50.ug.per.bee",
"Fish...Chronic.21d.NOEC.mg.l",
"Aquatic.Invertebrates...Chronic.21d.NOEC.mg.l",
"Earthworms...Chronic.NOEC..Reproduction.mg.kg",
"Bioconcentration.Factor")
missing <- setdiff(required.ecotox, names(ecotox))
if (length(missing) > 0) {
stop(paste("columns", paste(missing, collapse=", "), "missing in ecotox table"))
}
names(input_table)<-make.names(names(input_table))
required.input <- c("substance", "product", "concentration")
missing <- setdiff(required.input, names(input_table))
if (length(missing) > 0) {
stop(paste("columns", paste(missing, collapse=", "), "missing in substances table"))
}
fate <- extend.fate(fate,ecotox)
col_names <- required_columns_substances
result <- data.frame(matrix(NA, ncol = length(col_names)),
stringsAsFactors=FALSE)
names(result) <- col_names
missing.cas <- c()
missing.ecotox <- c()
for (irow in 1:nrow(input_table)) {
row = input_table[irow,]
CAS = row$CAS.number
substance = row$substance
if (CAS == "" || substance == "" )
next
match = general[which(general[, cas.index] == CAS),]
if (nrow(match) == 0) {
if (!(CAS %in% missing.cas)) {
missing.cas <- c(missing.cas, CAS);
if (length(missing.cas) < 11)
warning(paste("no entry for CAS", CAS, "\n"))
if (length(missing.cas) == 11)
message("supress missing CAS matches from now on\n\n")
}
next
}
id <- match$ID
fate_row <- fate[which(fate$ID == id),]
ecotox_row <- ecotox[which(ecotox$ID == id),]
for (name in required.ecotox)
if (is.na(ecotox_row[name])) {
if (!(CAS %in% missing.ecotox)) {
missing.ecotox <- c(missing.ecotox, CAS);
if (length(missing.ecotox) < 11)
warning(paste("entry", name, "for CAS", CAS, "is NAN in ecotox\n"))
if (length(missing.ecotox) == 11)
message("supress missing ecotox data from now on\n\n")
}
next
}
if (is.na(ecotox_row$Honeybees...Contact.acute.48hr.LD50.ug.per.bee)) {
ecotox_honeybees <- ecotox_row$Honeybees...Oral.Acute.48hr.LD50.ug.per.bee
}
else
ecotox_honeybees <-
mean(c(as.numeric(ecotox_row$Honeybees...Contact.acute.48hr.LD50.ug.per.bee),
as.numeric(ecotox_row$Honeybees...Oral.Acute.48hr.LD50.ug.per.bee)),na.rm=T)
new_row <- c(
row$substance,
row$product,
row$concentration,
fate_row$SCI.GROW,
10.91,
20, # row$Load.Factor.SCI,
fate_row$BCF,
5100,
2.5, # row$Load.Factor.BCF,
fate_row$SoilDT50,
354,
2.5, # row$Load.Factor.SoilDT50,
ecotox_row$Birds...Acute.LD50.mg.kg,
49,
1, # row$Load.Factor.Birds,
ecotox_row$Mammals...Acute.Oral.LD50.mg.kg.BW.day,
20,
1, # row$Load.Factor.Mammals,
ecotox_row$Fish...Acute.96hr.LC50.mg.l,
0.00021,
30, # row$Load.Factor.Fish,
ecotox_row$Aquatic.Invertebrates...Acute.48hr.EC50.mg.l,
0.0003,
30, # row$Load.Factor.Aquatic.Invertebrates,
ecotox_row$Algae...Acute.72hr.EC50.Growth.mg.l,
0.000025,
3, # row$Load.Factor.Algae,
ecotox_row$Aquatic.Plants...Acute.7d.EC50.mg.l,
0.00036,
3, # row$Load.Factor.Aquatic.Plants,
ecotox_row$Earthworms...Acute.14d.LC50.mg.kg,
3.4,
2, # row$Load.Factor.Earthworms,
ecotox_honeybees,
0.02,
100, # row$Load.Factor.Bees,
ecotox_row$Fish...Chronic.21d.NOEC.mg.l,
0.000115,
3, # row$Load.Factor.Fish.Chronic,
fate_row$Water.phase.DT50...days,
ecotox_row$Aquatic.Invertebrates...Chronic.21d.NOEC.mg.l,
0.000115,
3, # row$Load.Factor.Aquatic.Invertebrates.Chronic,
ecotox_row$Earthworms...Chronic.NOEC..Reproduction.mg.kg,
0.2,
2 # row$Load.Factor.Earthworms.Chronic
)
result[nrow(result) + 1, ] <- new_row
}
problematic.cas <- union(missing.cas, missing.ecotox)
if (length(problematic.cas) > 0) {
txt <- paste(problematic.cas, collapse=", ")
warning(paste("\nthe CAS numbers", txt, "caused problems, please fix this\n\n"))
}
result;
}
#' @title Expend tables with information on ecotoxicity, fate (and human health) properties from PPDB
#'
#' @param products Dataframe with raw pesticide application data.
#' @param substances Dataframe describing active ingredients of the applied pesticide products and their CAS number.
#' @param folder Folder with exported xlsx files from PPDB containing information on active ingredient properties.
#' @param healthrisk_off Compute the Human Health risk sum score from the PPDb (default off).
#' @return Adds Ecotoxicity and Fate properties of active substances
#' needed to compute the Pesticide Load Indicator
#' to user-provided substance and product data frames.
#' Properties are based on information from the Pesticide Properties Database (PPDB),
#' which has to be provided by the user in Excel format (license required).
#' Note that the function can optionally also retrieve the
#' sum of risk scores for Human Health from the PPDB, based on
#' active ingredient-level risk phrases in the PPDB.
#' This is not recommended.
#' Best practice is to compute the sum of risk scores
#' based on risk phrases of the respective pesticide product
#' (see Kudsk et al., 2018 for weighing of respective risk phrases).
#' Product label information cannot be retrieved from the PPDB as labels
#' might be country-specific. Check national pesticide databases for
#' this information.
#' Note that you have to add teh reference value for sum.risk.scores, as follows,
#' if you select healthrisk_off=TRUE:
#' products$reference.sum.risk.scores <- 300
#'
#' @export
#
match.ppdb <- function(substances, products, folder, healthrisk_off=TRUE) {
suppressWarnings({
human <- read.excel(file.path(folder, "Human.xlsx"))
general <- read.excel(file.path(folder, "General.xlsx"))
fate <- read.excel(file.path(folder, "Fate.xlsx"))
ecotox <- read.excel(file.path(folder, "Ecotox.xlsx"))
})
general <- general[!duplicated(general$CASS.RN),]
general <- general[!is.na(general$CASS.RN),]
products <- extend.products.table(products, substances, human, general)
if(healthrisk_off) products["sum.risk.score"]<-0
substances <- create.substances.table(substances, general, fate, ecotox)
return(list(products=products, substances=substances))
}
#' @title Calculates the sum of risk scores from a list of H-phrases and expands the table
#'
#' @param table Dataframe with H-phrases on product level.
#' @param var_name Name of the variable that contains the information (string) on H-phrases.
#' For example "H317; H411" or "H410, H411".
#' @return Adds a variable indicating the sum of risk scores
#' needed to compute the Pesticide Load Indicator
#' Check national pesticide databases for information on product labels of pesticides
#' (information on H-phrases of each product).
#'
#' @export
#
compute_risk_score <- function(table,var_name) {
for ( i in 1:dim(table)[1])
{
#If there are two different risk points per H, the higher one has been taken
#This happened for H300, H314, H330, H310)
H302 <- ifelse(str_detect(table[i,var_name], "302"), 10, 0)
H335 <- ifelse(str_detect(table[i,var_name], "335"), 10, 0)
H315 <- ifelse(str_detect(table[i,var_name], "315"), 10, 0)
H304 <- ifelse(str_detect(table[i,var_name], "304"), 10, 0)
H066 <- ifelse(str_detect(table[i,var_name], "066"), 10, 0)
H332 <- ifelse(str_detect(table[i,var_name], "332"), 15, 0)
H312 <- ifelse(str_detect(table[i,var_name], "312"), 15, 0)
H319 <- ifelse(str_detect(table[i,var_name], "319"), 15, 0)
H317 <- ifelse(str_detect(table[i,var_name], "317"), 20, 0)
H336 <- ifelse(str_detect(table[i,var_name], "336"), 30, 0)
H301 <- ifelse(str_detect(table[i,var_name], "301"), 50, 0)
H334 <- ifelse(str_detect(table[i,var_name], "334"), 50, 0)
H362 <- ifelse(str_detect(table[i,var_name], "362"), 50, 0)
H331 <- ifelse(str_detect(table[i,var_name], "331"), 70, 0)
H311 <- ifelse(str_detect(table[i,var_name], "311"), 70, 0)
H314 <- ifelse(str_detect(table[i,var_name], "314"), 100, 0)
H351 <- ifelse(str_detect(table[i,var_name], "351"), 70, 0)
H318 <- ifelse(str_detect(table[i,var_name], "318"), 70, 0)
H373 <- ifelse(str_detect(table[i,var_name], "373"), 70, 0)
H361 <- ifelse(str_detect(table[i,var_name], "361"), 70, 0)
H371 <- ifelse(str_detect(table[i,var_name], "371"), 70, 0)
H341 <- ifelse(str_detect(table[i,var_name], "341"), 70, 0)
H330 <- ifelse(str_detect(table[i,var_name], "330"), 100, 0)
H300 <- ifelse(str_detect(table[i,var_name], "300"), 85, 0)
H310 <- ifelse(str_detect(table[i,var_name], "310"), 100, 0)
H370 <- ifelse(str_detect(table[i,var_name], "370"), 100, 0)
H350 <- ifelse(str_detect(table[i,var_name], "350"), 100, 0)
H340 <- ifelse(str_detect(table[i,var_name], "340"), 100, 0)
H372 <- ifelse(str_detect(table[i,var_name], "372"), 100, 0)
H360 <- ifelse(str_detect(table[i,var_name], "360"), 100, 0)
HR <- (H066 + H300 + H301 + H302 + H304 + H310 + H311 + H312 + H314 + H315
+ H317 + H318 + H319 + H330 + H331 + H332 + H334 + H335 + H336 + H340
+ H341 + H350 + H351 + H360 + H361 + H362 + H370 + H371 + H372 + H373)
HR
table[i,"sum.risk.score"] <- HR
}
table
}
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PesticideLoadIndicator documentation built on Oct. 24, 2022, 5:07 p.m.
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Defines functions compute_risk_score match.ppdb create.substances.table extend.products.table compute_HR compute_R extend.fate read.excel
Documented in compute_risk_score match.ppdb
#' @importFrom readxl read_excel
#' @importFrom stringr str_detect
read.excel <- function(excel_file) {
table <- as.data.frame(read_excel(excel_file))
names(table) <- make.names(names(table))
table
}
extend.fate <- function(fate,ecotox)
{
required_f <- c(
"LogP",
"SCI.GROW",
"Soil.DT50.typical...days",
"Soil.DT50.lab...days",
"Soil.DT50.notes",
"Water.phase.DT50...days",
"Active",
"ID"
)
missing_f <- setdiff(required_f, names(fate))
if (length(missing_f) > 0) {
stop(paste("columns", paste(missing_f, collapse=", "), "missing in fate table"))
}
required_eco <- c(
"Bioconcentration.Factor",
"ID"
)
missing_eco <- setdiff(required_eco, names(ecotox))
if (length(missing_eco) > 0) {
stop(paste("columns", paste(missing_eco, collapse=", "), "missing in ecotox table"))
}
fate$LogP[is.na(fate$LogP)] <- 0
fate$LogP<-as.numeric(fate$LogP)
fate$LogP[is.na(fate$LogP)] <- 0
eco<- ecotox[,required_eco]
fate<- merge(fate,eco,by= "ID")
fate$BCF[fate$LogP >6 & fate$LogP !=0]<-10^((-0.2*(fate$LogP[fate$LogP >6 & fate$LogP !=0]^2))+(2.74*fate$LogP[fate$LogP >6 & fate$LogP !=0])-4.72)
fate$BCF[fate$LogP <6 & fate$LogP !=0]<-10^((0.86*fate$LogP[fate$LogP <6 & fate$LogP !=0])-0.7)
fate$BCF[fate$Bioconcentration.Factor!="" & !(is.na(fate$Bioconcentration.Factor))]<-fate$Bioconcentration.Factor[fate$Bioconcentration.Factor!="" & !(is.na(fate$Bioconcentration.Factor))]
fate$BCF[is.na(fate$BCF)]<-0
fate$BCF[fate$BCF=="Low risk"]<-0
suppressWarnings(fate$BCF<-as.numeric(fate$BCF))
fate$BCF[is.na(fate$BCF)]<-0
fate$BCF[fate$BCF>5100]<-5100
fate$SCI.GROW[fate$SCI.GROW=="Cannot be calculated"] <- 0
fate$SCI.GROW[is.na(fate$SCI.GROW)] <- 0
fate$SCI.GROW <- as.numeric(fate$SCI.GROW)
fate$SCI.GROW[fate$SCI.GROW>1000] <- 0
x<-cbind(as.numeric(fate$Soil.DT50.lab...days),as.numeric(fate$Soil.DT50.typical...days))
fate$SoilDT50<-rowMeans(x,na.rm=TRUE)
fate$stable<-ifelse(str_detect(fate$Soil.DT50.notes, "Stable"),1,0)
fate$Stable<-ifelse(str_detect(fate$Soil.DT50.notes, "stable"),1,0)
fate$stable<-fate$stable + fate$Stable
fate$SoilDT50[fate$stable!="0"]<-730
fate$SoilDT50[fate$SoilDT50==""] <- 0
fate$SoilDT50[is.na(fate$SoilDT50)] <- 0
fate$SoilDT50[fate$SoilDT50=="#N/A"] <- 0
fate$SoilDT50[fate$SoilDT50>731] <- 0
fate$SoilDT50 <- as.numeric(fate$SoilDT50)
fate$SoilDT50[fate$Soil.DT50.notes=="Both iron and phosphate naturally occur in soil. Degradation will be very slow"] <- 0
fate$SoilDT50[str_detect(fate$Active, "Copper")]<-0
fate$SoilDT50[str_detect(fate$Active, "copper")]<-0
fate$SoilDT50[str_detect(fate$Active, "Sulphur")]<-0
fate$SoilDT50[str_detect(fate$Active, "sulphur")]<-0
fate$SoilDT50[str_detect(fate$Active, "Iron")]<-0
fate$SoilDT50[str_detect(fate$Active, "iron")]<-0
fate$Water.phase.DT50...days[is.na(fate$Water.phase.DT50...days)]<-0
fate$Water.phase.DT50...days[fate$Water.phase.DT50...days==""]<-0
fate$Water.phase.DT50...days[fate$Water.phase.DT50...days=="<1"]<-0.5
fate$Water.phase.DT50...days[fate$Water.phase.DT50...days==">100"]<-100
fate$Water.phase.DT50...days[fate$Water.phase.DT50...days=="Slow, DT50 25-30 days"]<-30
fate$Water.phase.DT50...days[fate$Water.phase.DT50...days=="Stable"]<-730
fate
}
compute_R <- function(human) {
# EC.Risk.Classification
if (!('EC.Risk.Classification' %in% names(human))) {
stop('Human table needs column EC.Risk.Classification')
}
R22 <- ifelse(str_detect(human$EC.Risk.Classification, "22"), 10, 0)
R37 <- ifelse(str_detect(human$EC.Risk.Classification, "37"), 10, 0)
R38 <- ifelse(str_detect(human$EC.Risk.Classification, "38"), 10, 0)
R65 <- ifelse(str_detect(human$EC.Risk.Classification, "65"), 10, 0)
R66 <- ifelse(str_detect(human$EC.Risk.Classification, "66"), 10, 0)
R20 <- ifelse(str_detect(human$EC.Risk.Classification, "20"), 15, 0)
R21 <- ifelse(str_detect(human$EC.Risk.Classification, "21"), 15, 0)
R36 <- ifelse(str_detect(human$EC.Risk.Classification, "36"), 15, 0)
R43 <- ifelse(str_detect(human$EC.Risk.Classification, "43"), 20, 0)
R33 <- ifelse(str_detect(human$EC.Risk.Classification, "33"), 30, 0)
R67 <- ifelse(str_detect(human$EC.Risk.Classification, "67"), 30, 0)
R25 <- ifelse(str_detect(human$EC.Risk.Classification, "25"), 50, 0)
R42 <- ifelse(str_detect(human$EC.Risk.Classification, "42"), 50, 0)
R64 <- ifelse(str_detect(human$EC.Risk.Classification, "64"), 50, 0)
R23 <- ifelse(str_detect(human$EC.Risk.Classification, "23"), 70, 0)
R24 <- ifelse(str_detect(human$EC.Risk.Classification, "24"), 70, 0)
R28 <- ifelse(str_detect(human$EC.Risk.Classification, "28"), 70, 0)
R34 <- ifelse(str_detect(human$EC.Risk.Classification, "34"), 70, 0)
R40 <- ifelse(str_detect(human$EC.Risk.Classification, "40"), 70, 0)
R41 <- ifelse(str_detect(human$EC.Risk.Classification, "41"), 70, 0)
R62 <- ifelse(str_detect(human$EC.Risk.Classification, "62"), 70, 0)
R63 <- ifelse(str_detect(human$EC.Risk.Classification, "63"), 70, 0)
R68 <- ifelse(str_detect(human$EC.Risk.Classification, "68"), 70, 0)
R26 <- ifelse(str_detect(human$EC.Risk.Classification, "26"), 100, 0)
R27 <- ifelse(str_detect(human$EC.Risk.Classification, "27"), 100, 0)
R35 <- ifelse(str_detect(human$EC.Risk.Classification, "35"), 100, 0)
R39 <- ifelse(str_detect(human$EC.Risk.Classification, "39"), 100, 0)
R45 <- ifelse(str_detect(human$EC.Risk.Classification, "45"), 100, 0)
R46 <- ifelse(str_detect(human$EC.Risk.Classification, "46"), 100, 0)
R48 <- ifelse(str_detect(human$EC.Risk.Classification, "48"), 100, 0)
R49 <- ifelse(str_detect(human$EC.Risk.Classification, "49"), 100, 0)
R60 <- ifelse(str_detect(human$EC.Risk.Classification, "60"), 100, 0)
R61 <- ifelse(str_detect(human$EC.Risk.Classification, "61"), 100, 0)
R <- (R20 + R21 + R22 + R23 + R24 + R25 + R26 + R27 + R28 + R33 + R34
+ R35 + R36 + R37 + R38 + R39 + R40 + R41 + R42 + R43 + R45 + R46
+ R48 + R49 + R60 + R61 + R62 + R63 + R64 + R65 + R66 + R67 + R68)
R
}
compute_HR <- function(human) {
# CLP.classification.2013
if (!('CLP.classification.2013' %in% names(human))) {
stop('Human table needs column CLP.classification.2013')
}
#If there are two different risk points per H, the higher one has been taken
#This happened for H300, H314, H330, H310)
H302 <- ifelse(str_detect(human$CLP.classification.2013, "302"), 10, 0)
H335 <- ifelse(str_detect(human$CLP.classification.2013, "335"), 10, 0)
H315 <- ifelse(str_detect(human$CLP.classification.2013, "315"), 10, 0)
H304 <- ifelse(str_detect(human$CLP.classification.2013, "304"), 10, 0)
H066 <- ifelse(str_detect(human$CLP.classification.2013, "066"), 10, 0)
H332 <- ifelse(str_detect(human$CLP.classification.2013, "332"), 15, 0)
H312 <- ifelse(str_detect(human$CLP.classification.2013, "312"), 15, 0)
H319 <- ifelse(str_detect(human$CLP.classification.2013, "319"), 15, 0)
H317 <- ifelse(str_detect(human$CLP.classification.2013, "317"), 20, 0)
H336 <- ifelse(str_detect(human$CLP.classification.2013, "336"), 30, 0)
H301 <- ifelse(str_detect(human$CLP.classification.2013, "301"), 50, 0)
H334 <- ifelse(str_detect(human$CLP.classification.2013, "334"), 50, 0)
H362 <- ifelse(str_detect(human$CLP.classification.2013, "362"), 50, 0)
H331 <- ifelse(str_detect(human$CLP.classification.2013, "331"), 70, 0)
H311 <- ifelse(str_detect(human$CLP.classification.2013, "311"), 70, 0)
H314 <- ifelse(str_detect(human$CLP.classification.2013, "314"), 100, 0)
H351 <- ifelse(str_detect(human$CLP.classification.2013, "351"), 70, 0)
H318 <- ifelse(str_detect(human$CLP.classification.2013, "318"), 70, 0)
H373 <- ifelse(str_detect(human$CLP.classification.2013, "373"), 70, 0)
H361 <- ifelse(str_detect(human$CLP.classification.2013, "361"), 70, 0)
H371 <- ifelse(str_detect(human$CLP.classification.2013, "371"), 70, 0)
H341 <- ifelse(str_detect(human$CLP.classification.2013, "341"), 70, 0)
H330 <- ifelse(str_detect(human$CLP.classification.2013, "330"), 100, 0)
H300 <- ifelse(str_detect(human$CLP.classification.2013, "300"), 85, 0)
H310 <- ifelse(str_detect(human$CLP.classification.2013, "310"), 100, 0)
H370 <- ifelse(str_detect(human$CLP.classification.2013, "370"), 100, 0)
H350 <- ifelse(str_detect(human$CLP.classification.2013, "350"), 100, 0)
H340 <- ifelse(str_detect(human$CLP.classification.2013, "340"), 100, 0)
H372 <- ifelse(str_detect(human$CLP.classification.2013, "372"), 100, 0)
H360 <- ifelse(str_detect(human$CLP.classification.2013, "360"), 100, 0)
HR <- (H066 + H300 + H301 + H302 + H304 + H310 + H311 + H312 + H314 + H315
+ H317 + H318 + H319 + H330 + H331 + H332 + H334 + H335 + H336 + H340
+ H341 + H350 + H351 + H360 + H361 + H362 + H370 + H371 + H372 + H373)
HR
}
extend.products.table <- function(products_table, substances_table, human, general)
{
if (!('ID' %in% names(human))) {
stop('Human table needs column ID')
}
if (!('CASS.RN' %in% names(general))) {
stop('General table needs column "CASS RN"')
}
cas.index <- match("CASS.RN", names(general))
missing.cas <- c()
for (irow in 1:nrow(products_table)) {
sum.risk.score <- 0.0
products_row = products_table[irow,]
substances_rows = substances_table[substances_table$product == products_row$product,]
for (jrow in 1:nrow(substances_rows)) {
substance_row = substances_rows[jrow,]
CAS = substance_row$CAS.number
substance = substance_row$substance
match = general[which(general[,cas.index] == CAS),]
if (nrow(match) == 0) {
missing.cas <- c(missing.cas, CAS);
if (length(missing.cas) < 11)
warning(paste("no entry for CAS", CAS, "\n"))
if (length(missing.cas) == 11)
warning("supress missing CAS matches from now on\n\n")
next
}
id <- match$ID
human_row = human[which(human$ID == id),]
if (nrow(human_row) == 0) {
warning(paste("no entry for human risk for id", id, "\n"))
next
}
if (products_row$Year <= 2012) {
sum.risk.score <- c(sum.risk.score ,compute_R(human_row))
}
else {
sum.risk.score <- c(sum.risk.score, compute_HR(human_row))
}
}
products_table[irow, "sum.risk.score"] <- max(sum.risk.score, na.rm=T)
products_table[irow, "reference.sum.risk.scores"] <- 300
}
# remove duplicates
missing.cas <- union(missing.cas, missing.cas)
if (length(missing) > 0) {
txt <- paste(missing.cas, collapse=", ")
warning(paste("\nthe CAS numbers", txt, "caused problems, please fix this\n\n"))
}
products_table
}
create.substances.table <- function(input_table, general, fate, ecotox) {
if (!('CASS.RN' %in% names(general))) {
stop('General table needs column "CASS RN"')
}
cas.index <- match("CASS.RN", names(general))
required.fate <- c("ID", "SCI.GROW", "Water.phase.DT50...days","Active")
missing <- setdiff(required.fate, names(fate))
if (length(missing) > 0) {
stop(paste("columns", paste(missing, collapse=", "), "missing in fate table"))
}
required.ecotox <- c("ID",
"Birds...Acute.LD50.mg.kg",
"Mammals...Acute.Oral.LD50.mg.kg.BW.day",
"Fish...Acute.96hr.LC50.mg.l",
"Aquatic.Invertebrates...Acute.48hr.EC50.mg.l",
"Algae...Acute.72hr.EC50.Growth.mg.l",
"Aquatic.Plants...Acute.7d.EC50.mg.l",
"Earthworms...Acute.14d.LC50.mg.kg",
"Honeybees...Contact.acute.48hr.LD50.ug.per.bee",
"Honeybees...Oral.Acute.48hr.LD50.ug.per.bee",
"Fish...Chronic.21d.NOEC.mg.l",
"Aquatic.Invertebrates...Chronic.21d.NOEC.mg.l",
"Earthworms...Chronic.NOEC..Reproduction.mg.kg",
"Bioconcentration.Factor")
missing <- setdiff(required.ecotox, names(ecotox))
if (length(missing) > 0) {
stop(paste("columns", paste(missing, collapse=", "), "missing in ecotox table"))
}
names(input_table)<-make.names(names(input_table))
required.input <- c("substance", "product", "concentration")
missing <- setdiff(required.input, names(input_table))
if (length(missing) > 0) {
stop(paste("columns", paste(missing, collapse=", "), "missing in substances table"))
}
fate <- extend.fate(fate,ecotox)
col_names <- required_columns_substances
result <- data.frame(matrix(NA, ncol = length(col_names)),
stringsAsFactors=FALSE)
names(result) <- col_names
missing.cas <- c()
missing.ecotox <- c()
for (irow in 1:nrow(input_table)) {
row = input_table[irow,]
CAS = row$CAS.number
substance = row$substance
if (CAS == "" || substance == "" )
next
match = general[which(general[, cas.index] == CAS),]
if (nrow(match) == 0) {
if (!(CAS %in% missing.cas)) {
missing.cas <- c(missing.cas, CAS);
if (length(missing.cas) < 11)
warning(paste("no entry for CAS", CAS, "\n"))
if (length(missing.cas) == 11)
message("supress missing CAS matches from now on\n\n")
}
next
}
id <- match$ID
fate_row <- fate[which(fate$ID == id),]
ecotox_row <- ecotox[which(ecotox$ID == id),]
for (name in required.ecotox)
if (is.na(ecotox_row[name])) {
if (!(CAS %in% missing.ecotox)) {
missing.ecotox <- c(missing.ecotox, CAS);
if (length(missing.ecotox) < 11)
warning(paste("entry", name, "for CAS", CAS, "is NAN in ecotox\n"))
if (length(missing.ecotox) == 11)
message("supress missing ecotox data from now on\n\n")
}
next
}
if (is.na(ecotox_row$Honeybees...Contact.acute.48hr.LD50.ug.per.bee)) {
ecotox_honeybees <- ecotox_row$Honeybees...Oral.Acute.48hr.LD50.ug.per.bee
}
else
ecotox_honeybees <-
mean(c(as.numeric(ecotox_row$Honeybees...Contact.acute.48hr.LD50.ug.per.bee),
as.numeric(ecotox_row$Honeybees...Oral.Acute.48hr.LD50.ug.per.bee)),na.rm=T)
new_row <- c(
row$substance,
row$product,
row$concentration,
fate_row$SCI.GROW,
10.91,
20, # row$Load.Factor.SCI,
fate_row$BCF,
5100,
2.5, # row$Load.Factor.BCF,
fate_row$SoilDT50,
354,
2.5, # row$Load.Factor.SoilDT50,
ecotox_row$Birds...Acute.LD50.mg.kg,
49,
1, # row$Load.Factor.Birds,
ecotox_row$Mammals...Acute.Oral.LD50.mg.kg.BW.day,
20,
1, # row$Load.Factor.Mammals,
ecotox_row$Fish...Acute.96hr.LC50.mg.l,
0.00021,
30, # row$Load.Factor.Fish,
ecotox_row$Aquatic.Invertebrates...Acute.48hr.EC50.mg.l,
0.0003,
30, # row$Load.Factor.Aquatic.Invertebrates,
ecotox_row$Algae...Acute.72hr.EC50.Growth.mg.l,
0.000025,
3, # row$Load.Factor.Algae,
ecotox_row$Aquatic.Plants...Acute.7d.EC50.mg.l,
0.00036,
3, # row$Load.Factor.Aquatic.Plants,
ecotox_row$Earthworms...Acute.14d.LC50.mg.kg,
3.4,
2, # row$Load.Factor.Earthworms,
ecotox_honeybees,
0.02,
100, # row$Load.Factor.Bees,
ecotox_row$Fish...Chronic.21d.NOEC.mg.l,
0.000115,
3, # row$Load.Factor.Fish.Chronic,
fate_row$Water.phase.DT50...days,
ecotox_row$Aquatic.Invertebrates...Chronic.21d.NOEC.mg.l,
0.000115,
3, # row$Load.Factor.Aquatic.Invertebrates.Chronic,
ecotox_row$Earthworms...Chronic.NOEC..Reproduction.mg.kg,
0.2,
2 # row$Load.Factor.Earthworms.Chronic
)
result[nrow(result) + 1, ] <- new_row
}
problematic.cas <- union(missing.cas, missing.ecotox)
if (length(problematic.cas) > 0) {
txt <- paste(problematic.cas, collapse=", ")
warning(paste("\nthe CAS numbers", txt, "caused problems, please fix this\n\n"))
}
result;
}
#' @title Expend tables with information on ecotoxicity, fate (and human health) properties from PPDB
#'
#' @param products Dataframe with raw pesticide application data.
#' @param substances Dataframe describing active ingredients of the applied pesticide products and their CAS number.
#' @param folder Folder with exported xlsx files from PPDB containing information on active ingredient properties.
#' @param healthrisk_off Compute the Human Health risk sum score from the PPDb (default off).
#' @return Adds Ecotoxicity and Fate properties of active substances
#' needed to compute the Pesticide Load Indicator
#' to user-provided substance and product data frames.
#' Properties are based on information from the Pesticide Properties Database (PPDB),
#' which has to be provided by the user in Excel format (license required).
#' Note that the function can optionally also retrieve the
#' sum of risk scores for Human Health from the PPDB, based on
#' active ingredient-level risk phrases in the PPDB.
#' This is not recommended.
#' Best practice is to compute the sum of risk scores
#' based on risk phrases of the respective pesticide product
#' (see Kudsk et al., 2018 for weighing of respective risk phrases).
#' Product label information cannot be retrieved from the PPDB as labels
#' might be country-specific. Check national pesticide databases for
#' this information.
#' Note that you have to add teh reference value for sum.risk.scores, as follows,
#' if you select healthrisk_off=TRUE:
#' products$reference.sum.risk.scores <- 300
#'
#' @export
#
match.ppdb <- function(substances, products, folder, healthrisk_off=TRUE) {
suppressWarnings({
human <- read.excel(file.path(folder, "Human.xlsx"))
general <- read.excel(file.path(folder, "General.xlsx"))
fate <- read.excel(file.path(folder, "Fate.xlsx"))
ecotox <- read.excel(file.path(folder, "Ecotox.xlsx"))
})
general <- general[!duplicated(general$CASS.RN),]
general <- general[!is.na(general$CASS.RN),]
products <- extend.products.table(products, substances, human, general)
if(healthrisk_off) products["sum.risk.score"]<-0
substances <- create.substances.table(substances, general, fate, ecotox)
return(list(products=products, substances=substances))
}
#' @title Calculates the sum of risk scores from a list of H-phrases and expands the table
#'
#' @param table Dataframe with H-phrases on product level.
#' @param var_name Name of the variable that contains the information (string) on H-phrases.
#' For example "H317; H411" or "H410, H411".
#' @return Adds a variable indicating the sum of risk scores
#' needed to compute the Pesticide Load Indicator
#' Check national pesticide databases for information on product labels of pesticides
#' (information on H-phrases of each product).
#'
#' @export
#
compute_risk_score <- function(table,var_name) {
for ( i in 1:dim(table)[1])
{
#If there are two different risk points per H, the higher one has been taken
#This happened for H300, H314, H330, H310)
H302 <- ifelse(str_detect(table[i,var_name], "302"), 10, 0)
H335 <- ifelse(str_detect(table[i,var_name], "335"), 10, 0)
H315 <- ifelse(str_detect(table[i,var_name], "315"), 10, 0)
H304 <- ifelse(str_detect(table[i,var_name], "304"), 10, 0)
H066 <- ifelse(str_detect(table[i,var_name], "066"), 10, 0)
H332 <- ifelse(str_detect(table[i,var_name], "332"), 15, 0)
H312 <- ifelse(str_detect(table[i,var_name], "312"), 15, 0)
H319 <- ifelse(str_detect(table[i,var_name], "319"), 15, 0)
H317 <- ifelse(str_detect(table[i,var_name], "317"), 20, 0)
H336 <- ifelse(str_detect(table[i,var_name], "336"), 30, 0)
H301 <- ifelse(str_detect(table[i,var_name], "301"), 50, 0)
H334 <- ifelse(str_detect(table[i,var_name], "334"), 50, 0)
H362 <- ifelse(str_detect(table[i,var_name], "362"), 50, 0)
H331 <- ifelse(str_detect(table[i,var_name], "331"), 70, 0)
H311 <- ifelse(str_detect(table[i,var_name], "311"), 70, 0)
H314 <- ifelse(str_detect(table[i,var_name], "314"), 100, 0)
H351 <- ifelse(str_detect(table[i,var_name], "351"), 70, 0)
H318 <- ifelse(str_detect(table[i,var_name], "318"), 70, 0)
H373 <- ifelse(str_detect(table[i,var_name], "373"), 70, 0)
H361 <- ifelse(str_detect(table[i,var_name], "361"), 70, 0)
H371 <- ifelse(str_detect(table[i,var_name], "371"), 70, 0)
H341 <- ifelse(str_detect(table[i,var_name], "341"), 70, 0)
H330 <- ifelse(str_detect(table[i,var_name], "330"), 100, 0)
H300 <- ifelse(str_detect(table[i,var_name], "300"), 85, 0)
H310 <- ifelse(str_detect(table[i,var_name], "310"), 100, 0)
H370 <- ifelse(str_detect(table[i,var_name], "370"), 100, 0)
H350 <- ifelse(str_detect(table[i,var_name], "350"), 100, 0)
H340 <- ifelse(str_detect(table[i,var_name], "340"), 100, 0)
H372 <- ifelse(str_detect(table[i,var_name], "372"), 100, 0)
H360 <- ifelse(str_detect(table[i,var_name], "360"), 100, 0)
HR <- (H066 + H300 + H301 + H302 + H304 + H310 + H311 + H312 + H314 + H315
+ H317 + H318 + H319 + H330 + H331 + H332 + H334 + H335 + H336 + H340
+ H341 + H350 + H351 + H360 + H361 + H362 + H370 + H371 + H372 + H373)
HR
table[i,"sum.risk.score"] <- HR
}
table
}
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