emis_hot_td estimates cld start emissions with a top-down appraoch. This is, annual or monthly emissions or region. Especifically, the emissions are esitmated for row of the simple feature (row of the spatial feature).

In general was designed so that each simple feature is a region with different average monthly temperature. This funcion, as other in this package, adapts to the class of the input data. providing flexibility to the user.

emis_hot_td(veh, lkm, ef, pro_month, params, verbose = FALSE)

Arguments

veh

"Vehicles" data-frame or spatial feature, wwhere columns are the age distribution of that vehicle. and rows each simple feature or region. The number of rows is equal to the number of streets link

lkm

Numeric; mileage by the age of use of each vehicle.

ef

Numeric or data.frame; emission factors. When it is a data.frame number of rows can be for each region, or also, each region repeated along 12 months. For instance, if you have 10 regions the number of rows of ef can also be 120 (10 * 120). when you have emission factors that varies with month, see ef_china.

pro_month

Numeric or data.frame; montly profile to distribuite annual mileage in each month. When it is a data.frame, each region (row) can have a different monthly profile.

params

List of parameters; Add columns with information to returning data.frame

verbose

Logical; To show more information

Value

Emissions data.frame

See also

Examples

{ # Do not run euros <- c("V", "V", "IV", "III", "II", "I", "PRE", "PRE") efh <- ef_ldv_speed(v = "PC", t = "4S", cc = "<=1400", f = "G", eu = euros, p = "CO", speed = Speed(34)) lkm <- units::as_units(18:11, "km")*1000 veh_month <- c(rep(8, 1), rep(10, 5), 9, rep(10, 5)) veh <- age_ldv(1:10, agemax = 8) a <- emis_hot_td(veh = veh, lkm = lkm, ef = EmissionFactors(as.numeric(efh[, 1:8])), verbose = TRUE) head(a) plot(aggregate(a$emissions, by = list(a$age), sum)$x,type ="b") emis_hot_td(veh = veh, lkm = lkm, ef = EmissionFactors(as.numeric(efh[, 1:8])), verbose = TRUE, params = list(paste0("data_", 1:10), "moredata")) aa <- emis_hot_td(veh = veh, lkm = lkm, ef = EmissionFactors(as.numeric(efh[, 1:8])), pro_month = veh_month, verbose = TRUE) head(aa) aa <- emis_hot_td(veh = veh, lkm = lkm, ef = EmissionFactors(as.numeric(efh[, 1:8])), pro_month = veh_month, verbose = FALSE, params = list(paste0("data_", 1:10), "moredata")) print(aa) }
#> Estimation without monthly profile
#> 'ef' is numeric
#> Sum of emissions: 6090769
#> Estimation without monthly profile
#> 'ef' is numeric
#> Adding names to params
#> Sum of emissions: 6090769
#> Estimation with monthly profile
#> Assuming you have the same emission factors in each simple feature
#> 'pro_month' is numeric and 'ef' is numeric
#> Sum of emissions: 6090769 #> emissions rows age month P_1 P_2 #> 1 27.03706 [g] 1 1 1 data_1 moredata #> 2 54.07412 [g] 2 1 1 data_2 moredata #> 3 81.11118 [g] 3 1 1 data_3 moredata #> 4 108.14824 [g] 4 1 1 data_4 moredata #> 5 135.18530 [g] 5 1 1 data_5 moredata #> ... and more 955 rows