Exploring Emission Factors
Emission factor models tell us the
mass of pollutants that are expected to be emitted by a given vehicle
given a few characteristics such as vehicle age, type, fuel, technology,
speed, and distance traveled. Various environmental agencies develop
these functional relations based on data collected from local
measurements. Understanding how emission factor data work is very
important to understand how the emission estimates of a given vehicle or
public transport system are influenced by the methodological choices of
which emission factor model should be used. This vignette helps users
explore the emission factors data available in the
gtfs2emis package.
Available emission factor models
The gtfs2emis package currently includes hot-exhaust
emission factor data from four environmental agencies. Reports with
detailed information and methods on how these emission factor data were
originally calculated can be found on the agencies’ websites in the
links below
Hot-exhaust emissions
- Brazil, Environment Company of Sao Paulo — [CETESB]
- United States, Environmental Protection Agency — MOVES3 Model
- United States, California Air Resources Board — EMFAC2017 model
- Europe, European Environment Agency — EMEP-EEA
Wear emissions (tire, brake and road wear)
- Europe, European Environment Agency — EMEP-EEA
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 EFscaled(V) is the scaled emission factor for each street link, EFlocal is the local emission factor, EFeuro(V) and EFeuro(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 EFlocal, EFscaled(V), and the EFeuro(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.
Checking gtfs2emis imported data
Users can have a closer look to the hot-exhaust emission factor data included in the package by using the following functions:
data(ef_brazil_cetesb)from Environment Company of Sao Paulo, Brazil (CETESB)data(ef_usa_moves)from MOtor Vehicle Emission Simulator (MOVES)data(ef_usa_emfac)from California Air Resources Board (EMFAC Model)data(ef_europe_emep)from European Environment Agency (EMEP/EEA)
The data presented on the agencies website and software was
downloaded and pre-processed in gtfs2emis to be easily read
by the emission factor functions. Users can also access the scripts used
to process raw data in the gtfs2emis
GitHub repository.
Report a bug
If you have any suggestions or want to report an error, please visit the package GitHub page.