Visualizing emission factor data
Emission fator values vary by fleet characteristics — as shown in Defining
Fleet data vignette. In this section we will use the
ef_europe_emep() function and look at three types of urban
buses (Midi, Standard and Articulated) to illustrate how emissions vary
according to vehicle type, average speed, and pollutant.
library(units)
library(gtfs2emis)
library(ggplot2)
ef_europe <- ef_europe_emep(speed = units::set_units(10:100,"km/h")
,veh_type = c("Ubus Midi <=15 t"
,"Ubus Std 15 - 18 t"
,"Ubus Artic >18 t")
,euro = c("III", "IV", "V")
,pollutant = c("PM10", "NOx")
,fuel = c("D", "D", "D")
,tech = c("-", "SCR", "SCR")
,as_list = TRUE)
names(ef_europe)
#> [1] "pollutant" "veh_type" "euro" "fuel" "tech" "process"
#> [7] "slope" "load" "speed" "EF"In the case above, the function returns a list that
contains all the relevant information for the emission factor — shown in
names(ef_europe). However, it may be useful to check the
emission factor results in a data.frame or graphic
format.
ef_europe_dt <- emis_to_dt(emi_list = ef_europe
,emi_vars = "EF"
,veh_vars = c("veh_type","euro","fuel","tech")
,pol_vars = "pollutant"
,segment_vars = c("slope","load","speed"))
head(ef_europe_dt)
#> veh_type euro fuel tech pollutant EF process
#> <char> <char> <char> <char> <char> <units> <char>
#> 1: Ubus Midi <=15 t III D - PM10 0.2559070 [g/km] hot_exhaust
#> 2: Ubus Midi <=15 t III D - PM10 0.2559070 [g/km] hot_exhaust
#> 3: Ubus Midi <=15 t III D - PM10 0.2413421 [g/km] hot_exhaust
#> 4: Ubus Midi <=15 t III D - PM10 0.2285624 [g/km] hot_exhaust
#> 5: Ubus Midi <=15 t III D - PM10 0.2172637 [g/km] hot_exhaust
#> 6: Ubus Midi <=15 t III D - PM10 0.2072074 [g/km] hot_exhaust
#> slope load speed
#> <num> <num> <units>
#> 1: 0 0.5 10 [km/h]
#> 2: 0 0.5 11 [km/h]
#> 3: 0 0.5 12 [km/h]
#> 4: 0 0.5 13 [km/h]
#> 5: 0 0.5 14 [km/h]
#> 6: 0 0.5 15 [km/h]Plotting the speed-dependent emission factors according to vehicle
type (veh_type) and euro standard (euro).
ef_europe_dt$name_fleet <- paste(ef_europe_dt$veh_type, "/ Euro"
, ef_europe_dt$euro)
# plot
ggplot(ef_europe_dt) +
geom_line(aes(x = speed,y = EF,color = name_fleet))+
labs(color = "Category / EURO")+
facet_wrap(~pollutant,scales = "free")+
theme(legend.position = "bottom")
#> Warning: The `scale_name` argument of `continuous_scale()` is deprecated as of ggplot2
#> 3.5.0.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
There are situations where the emission factor are not available for
a given input parameter. In the case of ef_europe_emep()
function, when the information on vehicle technology does not match the
existing database, the package displays a message indicating the
technology considered. Please check the message shown in the code block
below. In such case, users can either select existing data for the
combining variables (euro, tech,
veh_type, and pollutant), or accept the
assumed change in vehicle technology.
ef_europe_co2 <- ef_europe_emep(speed = units::set_units(10:100,"km/h")
,veh_type = "Ubus Std 15 - 18 t"
,euro = "VI",pollutant = "CO2"
,tech = "DPF+SCR"
,as_list = TRUE)
#> 'CO2' Emission factor not found for 'DPF+SCR' Technology and Euro 'VI'.
#> The package assumed 'SCR' Technology entry. Please check `data(ef_europe_emep_db)` for available data.The other EF functions, ef_usa_emfac(),
ef_usa_moves() and ef_brazil_cetesb() work in
a similar way. See the functions documentation for more detail.
Scaling Emission Factors: making emission factors speed-dependent
For most models (MOVES3, EMEP-EEA and EMFAC2017), emission factors
depend on a vehicle’s speed. However, the emission factors developed for
Brazil by CETESB (ef_brazil_cetesb()) do not vary by
vehicle speed. In such a case, users can “scale” or adjust the local
emission factor values to make them speed-dependent using the function
ef_scaled_euro().
When using the EMEP-EEA model as a reference, the scaled emission factor varies according to vehicle’s speed following the expression:
\[ EF_{scaled} (V) = EF_{local} * \frac{EF_{euro}(V)}{EF_{euro}(SDC)}, \] where \(EF_{scaled}(V)\) is the scaled emission factor for each street link, \(EF_{local}\) is the local emission factor, \(EF_{euro}(V)\) and \(EF_{euro}(SDC)\) are the EMEP/EEA emission factor the speed of V and the average urban driving speed SDC, respectively.
The scaled behavior of EF can be verified graphically when we plot the \(EF_{local}\), \(EF_{scaled}(V)\), and the \(EF_{euro}(V)\) that is used as the reference To plot these data, we need six quick steps:
- Build a
data.frameof fleet indicating the correspondence between the fleet characteristic in the local and European emission models
fleet_filepath <- system.file("extdata/bra_cur_fleet.txt", package = "gtfs2emis")
cur_fleet <- read.table(fleet_filepath,header = TRUE, sep = ",", nrows = 1)
cur_fleet
#> year euro shape_id type_name_br veh_type total fuel
#> 1 2006 III 1849 BUS_URBAN_D Ubus Std 15 - 18 t 2 D- Estimate local emission factors
cur_local_ef <- ef_brazil_cetesb(pollutant = "CO2"
,veh_type = cur_fleet$type_name_br
,model_year = cur_fleet$year)
head(cur_local_ef)
#> $pollutant
#> [1] "CO2"
#>
#> $veh_type
#> [1] "BUS_URBAN_D"
#>
#> $model_year
#> [1] 2006
#>
#> $EF
#> Units: [g/km]
#> CO2_2006
#> [1,] 1385.626
#>
#> $process
#> [1] "hot_exhaust"
# convert Local EF to data.frame
cur_local_ef_dt <- emis_to_dt(emi_list = cur_local_ef
,emi_vars = "EF")- Estimate
ef_emep_europe
# Euro EF
cur_euro_ef <- ef_europe_emep(speed = units::set_units(10:100,"km/h")
,veh_type = cur_fleet$veh_type
,euro = cur_fleet$euro
,pollutant = "CO2"
,tech = "-"
)
# convert to data.frame
cur_euro_ef_dt <- emis_to_dt(emi_list = cur_euro_ef
,emi_vars = "EF"
,veh_vars = c("veh_type","euro","fuel","tech")
,segment_vars = "speed")
cur_euro_ef_dt$source <- "Euro EF"- Apply
ef_scaled_euro()
cur_scaled_ef <- ef_scaled_euro(ef_local = cur_local_ef$EF
,speed = units::set_units(10:100,"km/h")
,veh_type = cur_fleet$veh_type
,euro = cur_fleet$euro
,pollutant = "CO2"
,tech = "-"
)
# convert to data.frame
cur_scaled_ef_dt <- emis_to_dt(emi_list = cur_scaled_ef
,emi_vars = "EF"
,veh_vars = c("veh_type","euro","fuel","tech")
,segment_vars = "speed")
cur_scaled_ef_dt$source <- "Scaled EF"- View in ggplot2
# rbind data
cur_ef <- rbind(cur_euro_ef_dt, cur_scaled_ef_dt)
cur_ef$source <- factor(cur_ef$source
,levels = c("Scaled EF", "Euro EF"))
# plot
ggplot() +
# add scaled and euro EF
geom_line(data = cur_ef
,aes(x = speed,y = EF
,group = source,color = source))+
# add local EF
geom_hline(aes(yintercept = cur_local_ef_dt$EF)
,colour = "black",linetype="dashed") +
geom_point(aes(x = units::set_units(19,'km/h')
,y = cur_local_ef$EF)) +
# add local EF text
geom_text(aes(x = units::set_units(19,'km/h')
, y = cur_local_ef_dt$EF)
,label = sprintf('Local EF = %s g/km at 19 km/h',round(cur_local_ef_dt$EF,1))
,hjust = 0,nudge_y = 100,nudge_x = 1
,size = 3,fontface = 1)+
# configs plots
scale_color_manual(values=c("red","blue"))+
coord_cartesian(ylim = c(0,max(cur_scaled_ef_dt$EF)))+
labs(color = NULL)
In this case, the scaled_EF has the same value of
local_EF (dashed line) when speed = 19 km/h,
and a similar decaying behavior as Euro_EF as speed
decreases.