R/calcEmissionInventory.R
In pik-piam/mrcommons: MadRat commons Input Data Library
Defines functions
calcEmissionInventory
Documented in
calcEmissionInventory
#' @title calcEmisNitrogenCroplandPast
#' @description providees an emission inventory for the past, either from external data or own estimates.
#'
#' @param datasource The Inventory that shall be used. Options are CEDS, combined_CEDS_IPCC
#' (including own estimates where available), IPCC(own estimates), Nsurplus (own estimates)
#' @param targetResolution Specific mapping file to be used.
#' @param from column in mapping
#' @param to column in mapping
#'
#' @return List of magpie object with results on country level, weight on country level, unit and description.
#' @author Benjamin Leon Bodirsky, Michael S. Crawford
#' @examples
#' \dontrun{
#' calcOutput("EmisNitrogenCroplandPast")
#' }
#'
calcEmissionInventory <- function(datasource = "CEDS", targetResolution = "sectoral", from = "CEDS59", to = "Sectors") {
past <- findset("past")
if (datasource == "CEDS") {
# read CEDS emissions data from sources
bc <- readSource("CEDS", subtype = "BC")
# ch4 <- readSource("CEDS",subtype="CH4") #excluding ghg emissions, they were strange # nolint
co <- readSource("CEDS", subtype = "CO")
# co2 <- readSource("CEDS",subtype="CO2")#excluding ghg emissions, they were strange # nolint
# n2o <- readSource("CEDS",subtype="N2O") #excluding ghg emissions, they were strange # nolint
nh3 <- readSource("CEDS", subtype = "NH3")
nox <- readSource("CEDS", subtype = "NOx")
nmvoc <- readSource("CEDS", subtype = "NMVOC")
oc <- readSource("CEDS", subtype = "OC")
so2 <- readSource("CEDS", subtype = "SO2")
# identify common years (starting in 1970 at least ch4 has less years that the other gases)
y <- Reduce(intersect, list(getYears(bc),
# getYears(ch4), # nolint
getYears(co),
# getYears(co2), # nolint
# getYears(n2o), # nolint
getYears(nh3),
getYears(nox),
getYears(nmvoc),
getYears(oc),
getYears(so2)))
ceds <- mbind(bc[, y, ],
# ch4[,y,], # nolint
co[, y, ],
# co2[,y,], # nolint
# n2o[,y,], # nolint
nh3[, y, ],
nox[, y, ],
nmvoc[, y, ],
oc[, y, ],
so2[, y, ]) / 1000 # kt -> Mt # nolint
rm(bc,
# ch4,
co,
# co2,
# n2o,
nh3,
nox,
nmvoc,
oc,
so2)
# "past" may contain more years than ceds -> add missing years
missingY <- setdiff(past, y)
ceds <- add_columns(ceds, missingY, dim = 2.1)
# rename emissions according to map
map2 <- c(BC = "bc",
# CH4="ch4", # nolint
CO = "co",
# CO2="co2_c", # nolint
# N2O="n2o_n", # nolint
NH3 = "nh3_n",
NOx = "no2_n",
NMVOC = "nmhc",
OC = "oc",
SO2 = "so2")
getNames(ceds, dim = 2) <- map2[getNames(ceds, dim = 2)]
# remove third entry "kt" in data dimension
ceds <- collapseNames(ceds, collapsedim = 3)
# add nitrate leaching with zeros (to keeep a unique format with other sources)
ceds <- add_columns(ceds[, past, ], addnm = "no3_n", dim = 3.2)
ceds[, , "no3_n"] <- 0
# ceds[,,"n2o_n"]<-0 # this data seems buggy # nolint
# correct units
# ceds[,,"n2o_n"]=ceds[,,"n2o_n"]/44*28 # nolint
ceds[, , "nh3_n"] <- ceds[, , "nh3_n"] / 17 * 14
ceds[, , "no2_n"] <- ceds[, , "no2_n"] / 46 * 14
# ceds[,,"co2_c"]=ceds[,,"co2_c"]/44*12 # nolint
out <- ceds
} else if (datasource == "EDGAR6") {
edgarEmissionNames <- list("n2o",
"ch4",
"co2_excl_short", "co2_incl_short",
"nh3",
"no2",
"bc",
"co",
"oc",
# "nmvoc",
# "pm10",
# "pm25",
"so2")
edgarList <- lapply(X = edgarEmissionNames, FUN = function(x) readSource("EDGAR6", subtype = x))
names(edgarList) <- edgarEmissionNames
# Find years common to all datasets
yearsPresent <- Reduce(f = intersect, x = Map(getYears, edgarList))
edgarList <- lapply(X = edgarList,
FUN = function(x) {
x <- x[, yearsPresent, ] # Eliminate incomplete temporal ranges between emissions
# Summation over dim 3.3, bio vs. fossil when appropriate
if (dimExists(3.3, x)) {
x <- dimSums(x, dim = 3.3)
}
x <- x / 1000 # kt to Mt
return(x)
})
# Consolidate "co2_excl_short" and "co2_incl_short" into one MAgPIE object
edgarCo2 <- mbind(edgarList[c("co2_excl_short", "co2_incl_short")])
edgarCo2 <- dimSums(edgarCo2, dim = 3.1)
edgarCo2 <- add_dimension(edgarCo2, dim = 3.1, nm = "co2")
edgarList <- edgarList[!names(edgarList) %in% c("co2_excl_short", "co2_incl_short")]
edgarList <- c(edgarList, list(co2 = edgarCo2))
# Unit conversion when appropriate (e.g. n2o to n2o_n)
.formatToMAgPIE <- function(x, new, conversion) {
getNames(x, dim = 1) <- new
x[, , new] <- x[, , new] * conversion
return(x)
}
edgarList[["n2o"]] <- .formatToMAgPIE(edgarList[["n2o"]], "n2o_n", (28 / 44))
edgarList[["nh3"]] <- .formatToMAgPIE(edgarList[["nh3"]], "nh3_n", (14 / 17))
edgarList[["no2"]] <- .formatToMAgPIE(edgarList[["no2"]], "no2_n", (14 / 46))
edgarList[["co2"]] <- .formatToMAgPIE(edgarList[["co2"]], "co2_c", (12 / 44))
edgar <- mbind(edgarList)
# EDGAR has incomplete temporal coverage for many countries
edgar <- toolConditionalReplace(edgar, "is.na()", 0)
# Remove names wherein all the underlying data are 0
toKeep <- lapply(X = getNames(edgar), FUN = function(x) !(all(edgar[, , x] == 0)))
edgar <- edgar[, , unlist(toKeep)]
# Filter down to the sectors relevant to MAgPIE
relevantSectors <- c("3_C_1 Emissions from biomass burning",
"3_A_1 Enteric Fermentation",
"3_A_2 Manure Management",
"3_C_7 Rice cultivations",
"3_C_4 Direct N2O Emissions from managed soils",
"3_C_5 Indirect N2O Emissions from managed soils")
edgar <- edgar[, , relevantSectors]
# Rename dimensions
names(dimnames(edgar)) <- c("Region", "Year", "pollutant.sector")
out <- edgar
} else if (datasource == "CEDS2021") {
# read CEDS emissions data from sources
ceds <- readSource("CEDS2021")
ceds <- ceds[, paste0("y", 1960:2019), ]
# add nitrate leaching with zeros (to keeep a unique format with other sources)
ceds <- add_columns(ceds[, , ], addnm = "no3_n", dim = 3.2)
ceds[, , "no3_n"] <- 0
out <- ceds
} else if (datasource %in% c("combined_CEDS_IPCC")) {
ceds <- calcOutput("EmissionInventory",
datasource = "CEDS",
targetResolution = NULL,
aggregate = FALSE)
ipcc <- calcOutput("EmissionInventory",
datasource = "IPCC",
targetResolution = NULL,
aggregate = FALSE)
# replace<-c("3B_Manure-management","3D_Soil-emissions") # nolint
# "3E_Enteric-fermentation","3F_Agricultural-residue-burning-on-fields","3D_Rice-Cultivation"
# ag_emis_in_magpie<-c("4B","4D1","4D2") # nolint
# ag_emis_in_magpie<-c("4B","4D1","4D2") # nolint
# ag_emis_in_magpie<-c("4D1") # nolint
ipcc <- add_columns(ipcc, addnm = "n2o_n", dim = 3.2)
ipcc[, , "n2o_n"] <- dimSums(ipcc[, , c("n2o_n_direct", "n2o_n_indirect")])
jointEmissions <- intersect(getNames(ipcc, dim = 2), getNames(ceds, dim = 2))
ceds[, , "3B_Manure-management"][, , jointEmissions] <- ipcc[, , "awms"][, , jointEmissions]
ceds[, , "3D_Soil-emissions"][, , jointEmissions] <- dimSums(ipcc[, , c("inorg_fert", "man_crop", "resid", "som", "rice", "pasture_soils")][, , jointEmissions], dim = 3.1) # nolint
out <- ceds
} else if (datasource %in% c("combined_CEDS_Nsurplus2")) {
ceds <- calcOutput("EmissionInventory",
datasource = "CEDS",
targetResolution = NULL,
aggregate = FALSE)
ipcc <- calcOutput("EmissionInventory",
datasource = "Nsurplus2",
targetResolution = NULL,
aggregate = FALSE)
# replace<-c("3B_Manure-management","3D_Soil-emissions") # nolint
# "3E_Enteric-fermentation","3F_Agricultural-residue-burning-on-fields","3D_Rice-Cultivation"
# ag_emis_in_magpie<-c("4B","4D1","4D2") # nolint
# ag_emis_in_magpie<-c("4B","4D1","4D2") # nolint
# ag_emis_in_magpie<-c("4D1") # nolint
ipcc <- add_columns(ipcc, addnm = "n2o_n", dim = 3.2)
ipcc[, , "n2o_n"] <- dimSums(ipcc[, , c("n2o_n_direct", "n2o_n_indirect")])
jointEmissions <- intersect(getNames(ipcc, dim = 2), getNames(ceds, dim = 2))
ceds[, , "3B_Manure-management"][, , jointEmissions] <- collapseNames(ipcc[, , "awms"][, , jointEmissions])
ceds[, , "3D_Soil-emissions"][, , jointEmissions] <- dimSums(ipcc[, , c("cropland_soils", "pasture_soils")][, , jointEmissions], dim = 3.1) # nolint
out <- ceds
} else if (datasource %in% c("IPCC", "Nsurplus", "Nsurplus2")) {
out <- calcOutput("EmisNitrogenPast", method = datasource, aggregate = FALSE)
} else if (datasource %in% c("combined_CEDS_PRIMAPhist")) {
if (to != "PRIMAPhist") {
stop("This datasource is only available with the PRIMAPhist sectoral mapping.
Please set argument to = 'PRIMAPhist'")
}
ceds <- calcOutput("EmissionInventory",
datasource = "CEDS",
targetResolution = NULL,
aggregate = FALSE)
primap <- readSource("PRIMAPhist", subtype = "hist")
# find years overlap
jointYears <- intersect(getYears(ceds), getYears(primap))
# aggregate ceds categories to primap categories
map <- toolGetMapping(type = "sectoral", name = "mappingCEDS59toPRIMAP.csv", where = "mappingfolder")
cedsAgg <- toolAggregate(ceds, map, dim = 3.1)
getSets(primap) <- getSets(ceds)
getSets(cedsAgg) <- getSets(ceds)
jointEmissions <- intersect(getNames(primap, dim = 2), getNames(cedsAgg, dim = 2))
# use PRIMAP data for joint emissions
cedsEmissions <- getNames(cedsAgg, dim = 2)[!(getNames(cedsAgg, dim = 2) %in% jointEmissions)]
cedsAgg <- cedsAgg[, , cedsEmissions]
out <- mbind(cedsAgg[, jointYears, ], primap[, jointYears, ])
} else {
stop("datasource unknown")
}
if (!is.null(targetResolution)) {
# aggregate and rename CEDS59 sectors to CEDS16 sectors
if (targetResolution == "sectoral") {
map <- toolGetMapping(type = "sectoral", name = "mappingCEDS59toSectors.csv", where = "mappingfolder")
} else if (targetResolution == "magpie") {
map <- toolGetMapping(type = "sectoral", name = "mappingCEDS59toMAgPIE.csv", where = "mappingfolder")
} else {
stop("Unknown target resolution \"", targetResolution, "\".")
}
# reduce ceds to available categories
out <- out[, , getNames(out, dim = 1)[getNames(out, dim = 1) %in% map[, which(names(map) == from)]]]
out <- groupAggregate(data = out, dim = 3.1, query = map, from = from, to = to)
}
return(list(x = out,
weight = NULL,
unit = "Mt",
description = "Emission inventories from various datasources. Nitrogen emissions are in N equivalents,
CO2 in C equivalents, all other in their original molecular weight.")
)
}
pik-piam/mrcommons documentation built on Dec. 8, 2024, 7:23 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions calcEmissionInventory
Documented in calcEmissionInventory
#' @title calcEmisNitrogenCroplandPast
#' @description providees an emission inventory for the past, either from external data or own estimates.
#'
#' @param datasource The Inventory that shall be used. Options are CEDS, combined_CEDS_IPCC
#' (including own estimates where available), IPCC(own estimates), Nsurplus (own estimates)
#' @param targetResolution Specific mapping file to be used.
#' @param from column in mapping
#' @param to column in mapping
#'
#' @return List of magpie object with results on country level, weight on country level, unit and description.
#' @author Benjamin Leon Bodirsky, Michael S. Crawford
#' @examples
#' \dontrun{
#' calcOutput("EmisNitrogenCroplandPast")
#' }
#'
calcEmissionInventory <- function(datasource = "CEDS", targetResolution = "sectoral", from = "CEDS59", to = "Sectors") {
past <- findset("past")
if (datasource == "CEDS") {
# read CEDS emissions data from sources
bc <- readSource("CEDS", subtype = "BC")
# ch4 <- readSource("CEDS",subtype="CH4") #excluding ghg emissions, they were strange # nolint
co <- readSource("CEDS", subtype = "CO")
# co2 <- readSource("CEDS",subtype="CO2")#excluding ghg emissions, they were strange # nolint
# n2o <- readSource("CEDS",subtype="N2O") #excluding ghg emissions, they were strange # nolint
nh3 <- readSource("CEDS", subtype = "NH3")
nox <- readSource("CEDS", subtype = "NOx")
nmvoc <- readSource("CEDS", subtype = "NMVOC")
oc <- readSource("CEDS", subtype = "OC")
so2 <- readSource("CEDS", subtype = "SO2")
# identify common years (starting in 1970 at least ch4 has less years that the other gases)
y <- Reduce(intersect, list(getYears(bc),
# getYears(ch4), # nolint
getYears(co),
# getYears(co2), # nolint
# getYears(n2o), # nolint
getYears(nh3),
getYears(nox),
getYears(nmvoc),
getYears(oc),
getYears(so2)))
ceds <- mbind(bc[, y, ],
# ch4[,y,], # nolint
co[, y, ],
# co2[,y,], # nolint
# n2o[,y,], # nolint
nh3[, y, ],
nox[, y, ],
nmvoc[, y, ],
oc[, y, ],
so2[, y, ]) / 1000 # kt -> Mt # nolint
rm(bc,
# ch4,
co,
# co2,
# n2o,
nh3,
nox,
nmvoc,
oc,
so2)
# "past" may contain more years than ceds -> add missing years
missingY <- setdiff(past, y)
ceds <- add_columns(ceds, missingY, dim = 2.1)
# rename emissions according to map
map2 <- c(BC = "bc",
# CH4="ch4", # nolint
CO = "co",
# CO2="co2_c", # nolint
# N2O="n2o_n", # nolint
NH3 = "nh3_n",
NOx = "no2_n",
NMVOC = "nmhc",
OC = "oc",
SO2 = "so2")
getNames(ceds, dim = 2) <- map2[getNames(ceds, dim = 2)]
# remove third entry "kt" in data dimension
ceds <- collapseNames(ceds, collapsedim = 3)
# add nitrate leaching with zeros (to keeep a unique format with other sources)
ceds <- add_columns(ceds[, past, ], addnm = "no3_n", dim = 3.2)
ceds[, , "no3_n"] <- 0
# ceds[,,"n2o_n"]<-0 # this data seems buggy # nolint
# correct units
# ceds[,,"n2o_n"]=ceds[,,"n2o_n"]/44*28 # nolint
ceds[, , "nh3_n"] <- ceds[, , "nh3_n"] / 17 * 14
ceds[, , "no2_n"] <- ceds[, , "no2_n"] / 46 * 14
# ceds[,,"co2_c"]=ceds[,,"co2_c"]/44*12 # nolint
out <- ceds
} else if (datasource == "EDGAR6") {
edgarEmissionNames <- list("n2o",
"ch4",
"co2_excl_short", "co2_incl_short",
"nh3",
"no2",
"bc",
"co",
"oc",
# "nmvoc",
# "pm10",
# "pm25",
"so2")
edgarList <- lapply(X = edgarEmissionNames, FUN = function(x) readSource("EDGAR6", subtype = x))
names(edgarList) <- edgarEmissionNames
# Find years common to all datasets
yearsPresent <- Reduce(f = intersect, x = Map(getYears, edgarList))
edgarList <- lapply(X = edgarList,
FUN = function(x) {
x <- x[, yearsPresent, ] # Eliminate incomplete temporal ranges between emissions
# Summation over dim 3.3, bio vs. fossil when appropriate
if (dimExists(3.3, x)) {
x <- dimSums(x, dim = 3.3)
}
x <- x / 1000 # kt to Mt
return(x)
})
# Consolidate "co2_excl_short" and "co2_incl_short" into one MAgPIE object
edgarCo2 <- mbind(edgarList[c("co2_excl_short", "co2_incl_short")])
edgarCo2 <- dimSums(edgarCo2, dim = 3.1)
edgarCo2 <- add_dimension(edgarCo2, dim = 3.1, nm = "co2")
edgarList <- edgarList[!names(edgarList) %in% c("co2_excl_short", "co2_incl_short")]
edgarList <- c(edgarList, list(co2 = edgarCo2))
# Unit conversion when appropriate (e.g. n2o to n2o_n)
.formatToMAgPIE <- function(x, new, conversion) {
getNames(x, dim = 1) <- new
x[, , new] <- x[, , new] * conversion
return(x)
}
edgarList[["n2o"]] <- .formatToMAgPIE(edgarList[["n2o"]], "n2o_n", (28 / 44))
edgarList[["nh3"]] <- .formatToMAgPIE(edgarList[["nh3"]], "nh3_n", (14 / 17))
edgarList[["no2"]] <- .formatToMAgPIE(edgarList[["no2"]], "no2_n", (14 / 46))
edgarList[["co2"]] <- .formatToMAgPIE(edgarList[["co2"]], "co2_c", (12 / 44))
edgar <- mbind(edgarList)
# EDGAR has incomplete temporal coverage for many countries
edgar <- toolConditionalReplace(edgar, "is.na()", 0)
# Remove names wherein all the underlying data are 0
toKeep <- lapply(X = getNames(edgar), FUN = function(x) !(all(edgar[, , x] == 0)))
edgar <- edgar[, , unlist(toKeep)]
# Filter down to the sectors relevant to MAgPIE
relevantSectors <- c("3_C_1 Emissions from biomass burning",
"3_A_1 Enteric Fermentation",
"3_A_2 Manure Management",
"3_C_7 Rice cultivations",
"3_C_4 Direct N2O Emissions from managed soils",
"3_C_5 Indirect N2O Emissions from managed soils")
edgar <- edgar[, , relevantSectors]
# Rename dimensions
names(dimnames(edgar)) <- c("Region", "Year", "pollutant.sector")
out <- edgar
} else if (datasource == "CEDS2021") {
# read CEDS emissions data from sources
ceds <- readSource("CEDS2021")
ceds <- ceds[, paste0("y", 1960:2019), ]
# add nitrate leaching with zeros (to keeep a unique format with other sources)
ceds <- add_columns(ceds[, , ], addnm = "no3_n", dim = 3.2)
ceds[, , "no3_n"] <- 0
out <- ceds
} else if (datasource %in% c("combined_CEDS_IPCC")) {
ceds <- calcOutput("EmissionInventory",
datasource = "CEDS",
targetResolution = NULL,
aggregate = FALSE)
ipcc <- calcOutput("EmissionInventory",
datasource = "IPCC",
targetResolution = NULL,
aggregate = FALSE)
# replace<-c("3B_Manure-management","3D_Soil-emissions") # nolint
# "3E_Enteric-fermentation","3F_Agricultural-residue-burning-on-fields","3D_Rice-Cultivation"
# ag_emis_in_magpie<-c("4B","4D1","4D2") # nolint
# ag_emis_in_magpie<-c("4B","4D1","4D2") # nolint
# ag_emis_in_magpie<-c("4D1") # nolint
ipcc <- add_columns(ipcc, addnm = "n2o_n", dim = 3.2)
ipcc[, , "n2o_n"] <- dimSums(ipcc[, , c("n2o_n_direct", "n2o_n_indirect")])
jointEmissions <- intersect(getNames(ipcc, dim = 2), getNames(ceds, dim = 2))
ceds[, , "3B_Manure-management"][, , jointEmissions] <- ipcc[, , "awms"][, , jointEmissions]
ceds[, , "3D_Soil-emissions"][, , jointEmissions] <- dimSums(ipcc[, , c("inorg_fert", "man_crop", "resid", "som", "rice", "pasture_soils")][, , jointEmissions], dim = 3.1) # nolint
out <- ceds
} else if (datasource %in% c("combined_CEDS_Nsurplus2")) {
ceds <- calcOutput("EmissionInventory",
datasource = "CEDS",
targetResolution = NULL,
aggregate = FALSE)
ipcc <- calcOutput("EmissionInventory",
datasource = "Nsurplus2",
targetResolution = NULL,
aggregate = FALSE)
# replace<-c("3B_Manure-management","3D_Soil-emissions") # nolint
# "3E_Enteric-fermentation","3F_Agricultural-residue-burning-on-fields","3D_Rice-Cultivation"
# ag_emis_in_magpie<-c("4B","4D1","4D2") # nolint
# ag_emis_in_magpie<-c("4B","4D1","4D2") # nolint
# ag_emis_in_magpie<-c("4D1") # nolint
ipcc <- add_columns(ipcc, addnm = "n2o_n", dim = 3.2)
ipcc[, , "n2o_n"] <- dimSums(ipcc[, , c("n2o_n_direct", "n2o_n_indirect")])
jointEmissions <- intersect(getNames(ipcc, dim = 2), getNames(ceds, dim = 2))
ceds[, , "3B_Manure-management"][, , jointEmissions] <- collapseNames(ipcc[, , "awms"][, , jointEmissions])
ceds[, , "3D_Soil-emissions"][, , jointEmissions] <- dimSums(ipcc[, , c("cropland_soils", "pasture_soils")][, , jointEmissions], dim = 3.1) # nolint
out <- ceds
} else if (datasource %in% c("IPCC", "Nsurplus", "Nsurplus2")) {
out <- calcOutput("EmisNitrogenPast", method = datasource, aggregate = FALSE)
} else if (datasource %in% c("combined_CEDS_PRIMAPhist")) {
if (to != "PRIMAPhist") {
stop("This datasource is only available with the PRIMAPhist sectoral mapping.
Please set argument to = 'PRIMAPhist'")
}
ceds <- calcOutput("EmissionInventory",
datasource = "CEDS",
targetResolution = NULL,
aggregate = FALSE)
primap <- readSource("PRIMAPhist", subtype = "hist")
# find years overlap
jointYears <- intersect(getYears(ceds), getYears(primap))
# aggregate ceds categories to primap categories
map <- toolGetMapping(type = "sectoral", name = "mappingCEDS59toPRIMAP.csv", where = "mappingfolder")
cedsAgg <- toolAggregate(ceds, map, dim = 3.1)
getSets(primap) <- getSets(ceds)
getSets(cedsAgg) <- getSets(ceds)
jointEmissions <- intersect(getNames(primap, dim = 2), getNames(cedsAgg, dim = 2))
# use PRIMAP data for joint emissions
cedsEmissions <- getNames(cedsAgg, dim = 2)[!(getNames(cedsAgg, dim = 2) %in% jointEmissions)]
cedsAgg <- cedsAgg[, , cedsEmissions]
out <- mbind(cedsAgg[, jointYears, ], primap[, jointYears, ])
} else {
stop("datasource unknown")
}
if (!is.null(targetResolution)) {
# aggregate and rename CEDS59 sectors to CEDS16 sectors
if (targetResolution == "sectoral") {
map <- toolGetMapping(type = "sectoral", name = "mappingCEDS59toSectors.csv", where = "mappingfolder")
} else if (targetResolution == "magpie") {
map <- toolGetMapping(type = "sectoral", name = "mappingCEDS59toMAgPIE.csv", where = "mappingfolder")
} else {
stop("Unknown target resolution \"", targetResolution, "\".")
}
# reduce ceds to available categories
out <- out[, , getNames(out, dim = 1)[getNames(out, dim = 1) %in% map[, which(names(map) == from)]]]
out <- groupAggregate(data = out, dim = 3.1, query = map, from = from, to = to)
}
return(list(x = out,
weight = NULL,
unit = "Mt",
description = "Emission inventories from various datasources. Nitrogen emissions are in N equivalents,
CO2 in C equivalents, all other in their original molecular weight.")
)
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.