Nothing
Exploring Non-Exhaust Emission Factors
In gtfs2emis: Estimating Public Transport Emissions from General Transit Feed Specification (GTFS) Data
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>")
When assessing vehicle emissions inventories for particles, one relevant step is taking into account the non-exhaust processes, such as tire, brake and road wear. The gtfs2emis incorporates the non-exhaust emissions methods from EMEP-EEA.
The following equation is employed to evaluate emissions originating
from tire and brake wear
$$
TE_i = dist \times EF_{tsp}(j) \times mf_s(i) \times SC(speed)
$$ where:
- $TE(i)$ = total emissions of pollutant $i$ (g),
- $dist$ = distance driven by each vehicle (km),
- $EF_{tsp}(j)$ = TSP (Total Suspended Particle) mass emission factor for vehicles of category $j$ (g/km),
- $mf_s(i)$ = mass fraction of TSP that can be attributed to pollutant $i$,
- $SC(speed)$ = correction factor for a mean vehicle travelling at a
given speed (-).
Tire
In the case of heavy-duty vehicles, the emission factor needs the
incorporation of vehicle size, as determined by the number of axles, and
load. These parameters are introduced into the equation as follows:
$$EFTSP^{hdv}{tire} = 0.5 \times N{axle} \times LCF_{tire} \times EFTSP^{pc}_{tire}$$
where:
- $EFTSP^{hdv}_{tire}$ = TSP emission factor for tire wear from
heavy-duty vehicles (g/km),
- $N_{axle}$ = number of vehicle axles (-),
- $LCF_{tire}$ = a load correction factor for tire wear (-),
- $EFTSP^{pc}_{tire}$ = TSP emission factor for tire wear from passenger
car vehicles (g/km).
and $$LCF_{tire} = 1.41 + (1.38 \times LF)$$
where:
- $LF$ = load factor (-), ranging from 0 for an empty bus to 1 for a fully laden one.
The function considers the following look-up table for number of vehicle
axes:
| vehicle class (j) | number of axes |
|----------------------|----------------|
| Ubus Midi \<=15 t | 2 |
| Ubus Std 15 - 18 t | 2 |
| Ubus Artic >18 t | 3 |
| Coaches Std \<=18 t | 2 |
| Coaches Artic >18 t | 3 |
The size distribution of tire wear particles are given by:
| particle size class (i) | mass fraction of TSP |
|-------------------------|----------------------|
| TSP | 1.000 |
| PM10 | 0.600 |
| PM2.5 | 0.420 |
| PM1.0 | 0.060 |
| PM0.1 | 0.048 |
Finally, the speed correction is:
- $SC_{tire}(speed) = 1.39$, when $speed < 40 km/h$;
- $SC_{tire}(speed) = -0.00974 \times speed + 1.78$, when $40 <= speed <= 90 km/h$;
- $SC_{tire}(speed) = 0.902$, when $speed > 90 km/h$.
library(gtfs2emis)
emi_europe_emep_wear(dist = units::set_units(1,"km"),
speed = units::set_units(30,"km/h"),
pollutant = c("PM10","TSP","PM2.5"),
veh_type = "Ubus Std 15 - 18 t",
fleet_composition = 1,
load = 0.5,
process = c("tyre"),
as_list = TRUE)
Brake
The heavy-duty vehicle emission factor is derived by modifying the
passenger car emission factor to conform to experimental data obtained
from heavy-duty vehicles.
$$EFTSP^{hdv}{brake} = 1.956 \times LCF{brake} \times EFTSP^{pc}_{brake}$$
where:
- $EFTSP^{hdv}_{brake}$ = heavy-duty vehicle emission factor for TSP,
- $LCF_{brake}$ = load correction factor for brake wear,
- $EFTSP^{pc}_{brake}$ = passenger car emission factor for TSP.
$$LCF_{brake} = 1 + (0.79 \times LF),$$
where:
- $LF$ = load factor (-), ranging from 0 for an empty bus to 1 for a fully laden one.
The size distribution of brake wear particles are given by:
| particle size class (i) | mass fraction of TSP |
|-------------------------|----------------------|
| TSP | 1.000 |
| PM10 | 0.980 |
| PM2.5 | 0.390 |
| PM1.0 | 0.100 |
| PM0.1 | 0.080 |
Finally, the speed correction is:
- $SC_{brake}(speed) = 1.67$, when $speed < 40 km/h$;
- $SC_{brake}(speed) = -0.0270 \times speed + 2.75$, when $40 <= speed <= 95 km/h$;
- $SC_{brake}(speed) = 0.185$, when $speed > 95 km/h$.
emi_europe_emep_wear(dist = units::set_units(1,"km"),
speed = units::set_units(30,"km/h"),
pollutant = c("PM10","TSP","PM2.5"),
veh_type = "Ubus Std 15 - 18 t",
fleet_composition = 1,
load = 0.5,
process = c("brake"),
as_list = TRUE)
Road Wear
Emissions are calculated according to the equation:
$$TE(i) = dist \times EF^{road}{tsp}(j) \times mf{road}$$
where:
- $TE(i)$ = total emissions of pollutant i (g),
- $dist$ = total distance driven by vehicles in category j (km),
- $EF^{road}_{tsp}$ = TSP mass emission factor from road wear for vehicles j (0.0760 g/km),
- $mf_{road}$ = mass fraction of TSP that can be attributed to particle size class i (-).
The following table shows the size distribution of road surface wear
particles
| particle size class (i) | mass fraction of TSP |
|-------------------------|----------------------|
| TSP | 1.00 |
| PM10 | 0.50 |
| PM2.5 | 0.27 |
emi_europe_emep_wear(dist = units::set_units(1,"km"),
speed = units::set_units(30,"km/h"),
pollutant = c("PM10","TSP","PM2.5"),
veh_type = "Ubus Std 15 - 18 t",
fleet_composition = 1,
load = 0.5,
process = c("road"),
as_list = TRUE)
Viewing Emissions
Users can also use one single function to apply for more than one process (e.g. tire, brake and road), as shown below.
library(units)
library(ggplot2)
emis_list <- emi_europe_emep_wear(dist = units::set_units(rep(1,100),"km"),
speed = units::set_units(1:100,"km/h"),
pollutant = c("PM10","TSP","PM2.5"),
veh_type = c("Ubus Std 15 - 18 t"),
fleet_composition = c(1),
load = 0.5,
process = c("brake","tyre","road"),
as_list = TRUE)
ef_dt <- gtfs2emis::emis_to_dt(emis_list,emi_vars = "emi"
,segment_vars = "speed")
ggplot(ef_dt)+
geom_line(aes(x = as.numeric(speed),y = as.numeric(emi),color = pollutant))+
facet_wrap(facets = vars(process))+
labs(x = "Speed (km/h)",y = "Emissions (g)")+
theme_minimal()
When using the transport_model() output, users can also visualize both hot-exhaust and non-exhaust emissions taking few more steps. This can be done in three main stages: a) Preparing the data, b) Creating spatial grid; c) Generating spatial and temporal visualizations.
a) Preparing the data
library(gtfstools)
library(sf)
# read GTFS
gtfs_file <- system.file("extdata/bra_cur_gtfs.zip", package = "gtfs2emis")
gtfs <- gtfstools::read_gtfs(gtfs_file)
# keep a single trip_id to speed up this example
gtfs_small <- gtfstools::filter_by_trip_id(gtfs, trip_id ="4451136")
# run transport model
tp_model <- transport_model(gtfs_data = gtfs_small,
spatial_resolution = 100,
parallel = FALSE)
# Fleet data, using Brazilian emission model and fleet
fleet_data_ef_emep <- data.frame(veh_type = "Ubus Std 15 - 18 t",
fleet_composition = 1,
euro = "V", # for hot-exhaust emissions
fuel = "D", # for hot-exhaust emissions
tech = "SCR") # for hot-exhaust emissions
# Emission model (hot-exhaust)
emi_list_he <- emission_model(
tp_model = tp_model,
ef_model = "ef_europe_emep",
fleet_data = fleet_data_ef_emep,
pollutant = "PM10"
)
# Emission model (non-exhaust)
emi_list_ne <- emi_europe_emep_wear(
dist = tp_model$dist,
speed = tp_model$speed,
pollutant = "PM10",
veh_type = c("Ubus Std 15 - 18 t"),
fleet_composition = c(1),
load = 0.5,
process = c("brake","tyre","road"),
as_list = TRUE)
emi_list_ne$tp_model <- tp_model
b) Creating spatial grid
# create spatial grid
grid <- sf::st_make_grid(
x = sf::st_make_valid(tp_model)
, cellsize = 0.25 / 200
, crs= 4326
, what = "polygons"
, square = FALSE
)
# grid (hot-exhaust)
emi_grid_he <- emis_grid( emi_list_he,grid,time_resolution = 'day'
,aggregate = TRUE)
setDT(emi_grid_he)
pol_names <- setdiff(names(emi_grid_he),"geometry")
emi_grid_he_dt <- melt(emi_grid_he,measure.vars = pol_names,id.vars = "geometry")
emi_grid_he_dt <- sf::st_as_sf(emi_grid_he_dt)
# grid (non-exhaust)
emi_grid_ne <- emis_grid( emi_list_ne,grid,time_resolution = 'day'
,aggregate = TRUE)
setDT(emi_grid_ne)
pol_names <- setdiff(names(emi_grid_ne),"geometry")
emi_grid_ne_dt <- melt(emi_grid_ne,measure.vars = pol_names,id.vars = "geometry")
emi_grid_ne_dt <- sf::st_as_sf(emi_grid_ne_dt)
# bind grid
emi_grid_dt <- data.table::rbindlist(l = list(emi_grid_he_dt,emi_grid_ne_dt))
emi_grid_sf <- sf::st_as_sf(emi_grid_dt)
c) Generating spatial and temporal patterns
# plot
library(ggplot2)
ggplot(emi_grid_sf) +
geom_sf(aes(fill= as.numeric(value)), color=NA) +
geom_sf(data = tp_model$geometry,color = "black")+
scale_fill_continuous(type = "viridis")+
labs(fill = "PM10 (g)")+
facet_wrap(facets = vars(variable),nrow = 1)+
theme_void()
The total emissions can be also viewed in bar graphics
# Emissions by time
emi_time_he <- emis_summary(emi_list_he,by = "time")
emi_time_ne <- emis_summary(emi_list_ne,by = "time")
emi_time <- data.table::rbindlist(l = list(emi_time_he,emi_time_ne))
ggplot(emi_time)+
geom_col(aes(x = process,y = as.numeric(emi),fill = as.numeric(emi)))+
scale_fill_continuous(type = "viridis")+
labs(fill = "PM10 level",y = "Emissions (g)")+
theme_minimal()
References
EMEP/EEA data and reports can be accessed in the following links:
- 2019 edition EMEP-EEA.
Report a bug
If you have any suggestions or want to report an error, please visit
the package GitHub page.
Try the gtfs2emis package in your browser
Any scripts or data that you put into this service are public.
gtfs2emis documentation built on April 4, 2025, 12:36 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.
knitr::opts_chunk$set( collapse = TRUE, comment = "#>")
When assessing vehicle emissions inventories for particles, one relevant step is taking into account the non-exhaust processes, such as tire, brake and road wear. The gtfs2emis incorporates the non-exhaust emissions methods from EMEP-EEA.
The following equation is employed to evaluate emissions originating from tire and brake wear
$$ TE_i = dist \times EF_{tsp}(j) \times mf_s(i) \times SC(speed) $$ where:
- $TE(i)$ = total emissions of pollutant $i$ (g),
- $dist$ = distance driven by each vehicle (km),
- $EF_{tsp}(j)$ = TSP (Total Suspended Particle) mass emission factor for vehicles of category $j$ (g/km),
- $mf_s(i)$ = mass fraction of TSP that can be attributed to pollutant $i$,
- $SC(speed)$ = correction factor for a mean vehicle travelling at a given speed (-).
Tire
In the case of heavy-duty vehicles, the emission factor needs the incorporation of vehicle size, as determined by the number of axles, and load. These parameters are introduced into the equation as follows:
$$EFTSP^{hdv}{tire} = 0.5 \times N{axle} \times LCF_{tire} \times EFTSP^{pc}_{tire}$$
where:
- $EFTSP^{hdv}_{tire}$ = TSP emission factor for tire wear from heavy-duty vehicles (g/km),
- $N_{axle}$ = number of vehicle axles (-),
- $LCF_{tire}$ = a load correction factor for tire wear (-),
- $EFTSP^{pc}_{tire}$ = TSP emission factor for tire wear from passenger car vehicles (g/km).
and $$LCF_{tire} = 1.41 + (1.38 \times LF)$$
where:
- $LF$ = load factor (-), ranging from 0 for an empty bus to 1 for a fully laden one.
The function considers the following look-up table for number of vehicle axes:
| vehicle class (j) | number of axes | |----------------------|----------------| | Ubus Midi \<=15 t | 2 | | Ubus Std 15 - 18 t | 2 | | Ubus Artic >18 t | 3 | | Coaches Std \<=18 t | 2 | | Coaches Artic >18 t | 3 |
The size distribution of tire wear particles are given by:
| particle size class (i) | mass fraction of TSP | |-------------------------|----------------------| | TSP | 1.000 | | PM10 | 0.600 | | PM2.5 | 0.420 | | PM1.0 | 0.060 | | PM0.1 | 0.048 |
Finally, the speed correction is:
- $SC_{tire}(speed) = 1.39$, when $speed < 40 km/h$;
- $SC_{tire}(speed) = -0.00974 \times speed + 1.78$, when $40 <= speed <= 90 km/h$;
- $SC_{tire}(speed) = 0.902$, when $speed > 90 km/h$.
library(gtfs2emis) emi_europe_emep_wear(dist = units::set_units(1,"km"), speed = units::set_units(30,"km/h"), pollutant = c("PM10","TSP","PM2.5"), veh_type = "Ubus Std 15 - 18 t", fleet_composition = 1, load = 0.5, process = c("tyre"), as_list = TRUE)
Brake
The heavy-duty vehicle emission factor is derived by modifying the passenger car emission factor to conform to experimental data obtained from heavy-duty vehicles.
$$EFTSP^{hdv}{brake} = 1.956 \times LCF{brake} \times EFTSP^{pc}_{brake}$$
where:
- $EFTSP^{hdv}_{brake}$ = heavy-duty vehicle emission factor for TSP,
- $LCF_{brake}$ = load correction factor for brake wear,
- $EFTSP^{pc}_{brake}$ = passenger car emission factor for TSP.
$$LCF_{brake} = 1 + (0.79 \times LF),$$
where:
- $LF$ = load factor (-), ranging from 0 for an empty bus to 1 for a fully laden one.
The size distribution of brake wear particles are given by:
| particle size class (i) | mass fraction of TSP | |-------------------------|----------------------| | TSP | 1.000 | | PM10 | 0.980 | | PM2.5 | 0.390 | | PM1.0 | 0.100 | | PM0.1 | 0.080 |
Finally, the speed correction is:
- $SC_{brake}(speed) = 1.67$, when $speed < 40 km/h$;
- $SC_{brake}(speed) = -0.0270 \times speed + 2.75$, when $40 <= speed <= 95 km/h$;
- $SC_{brake}(speed) = 0.185$, when $speed > 95 km/h$.
emi_europe_emep_wear(dist = units::set_units(1,"km"), speed = units::set_units(30,"km/h"), pollutant = c("PM10","TSP","PM2.5"), veh_type = "Ubus Std 15 - 18 t", fleet_composition = 1, load = 0.5, process = c("brake"), as_list = TRUE)
Road Wear
Emissions are calculated according to the equation:
$$TE(i) = dist \times EF^{road}{tsp}(j) \times mf{road}$$
where:
- $TE(i)$ = total emissions of pollutant i (g),
- $dist$ = total distance driven by vehicles in category j (km),
- $EF^{road}_{tsp}$ = TSP mass emission factor from road wear for vehicles j (0.0760 g/km),
- $mf_{road}$ = mass fraction of TSP that can be attributed to particle size class i (-).
The following table shows the size distribution of road surface wear particles
| particle size class (i) | mass fraction of TSP | |-------------------------|----------------------| | TSP | 1.00 | | PM10 | 0.50 | | PM2.5 | 0.27 |
emi_europe_emep_wear(dist = units::set_units(1,"km"), speed = units::set_units(30,"km/h"), pollutant = c("PM10","TSP","PM2.5"), veh_type = "Ubus Std 15 - 18 t", fleet_composition = 1, load = 0.5, process = c("road"), as_list = TRUE)
Viewing Emissions
Users can also use one single function to apply for more than one process (e.g. tire, brake and road), as shown below.
library(units) library(ggplot2) emis_list <- emi_europe_emep_wear(dist = units::set_units(rep(1,100),"km"), speed = units::set_units(1:100,"km/h"), pollutant = c("PM10","TSP","PM2.5"), veh_type = c("Ubus Std 15 - 18 t"), fleet_composition = c(1), load = 0.5, process = c("brake","tyre","road"), as_list = TRUE) ef_dt <- gtfs2emis::emis_to_dt(emis_list,emi_vars = "emi" ,segment_vars = "speed") ggplot(ef_dt)+ geom_line(aes(x = as.numeric(speed),y = as.numeric(emi),color = pollutant))+ facet_wrap(facets = vars(process))+ labs(x = "Speed (km/h)",y = "Emissions (g)")+ theme_minimal()
When using the transport_model() output, users can also visualize both hot-exhaust and non-exhaust emissions taking few more steps. This can be done in three main stages: a) Preparing the data, b) Creating spatial grid; c) Generating spatial and temporal visualizations.
a) Preparing the data
library(gtfstools) library(sf) # read GTFS gtfs_file <- system.file("extdata/bra_cur_gtfs.zip", package = "gtfs2emis") gtfs <- gtfstools::read_gtfs(gtfs_file) # keep a single trip_id to speed up this example gtfs_small <- gtfstools::filter_by_trip_id(gtfs, trip_id ="4451136") # run transport model tp_model <- transport_model(gtfs_data = gtfs_small, spatial_resolution = 100, parallel = FALSE) # Fleet data, using Brazilian emission model and fleet fleet_data_ef_emep <- data.frame(veh_type = "Ubus Std 15 - 18 t", fleet_composition = 1, euro = "V", # for hot-exhaust emissions fuel = "D", # for hot-exhaust emissions tech = "SCR") # for hot-exhaust emissions # Emission model (hot-exhaust) emi_list_he <- emission_model( tp_model = tp_model, ef_model = "ef_europe_emep", fleet_data = fleet_data_ef_emep, pollutant = "PM10" ) # Emission model (non-exhaust) emi_list_ne <- emi_europe_emep_wear( dist = tp_model$dist, speed = tp_model$speed, pollutant = "PM10", veh_type = c("Ubus Std 15 - 18 t"), fleet_composition = c(1), load = 0.5, process = c("brake","tyre","road"), as_list = TRUE) emi_list_ne$tp_model <- tp_model
b) Creating spatial grid
# create spatial grid grid <- sf::st_make_grid( x = sf::st_make_valid(tp_model) , cellsize = 0.25 / 200 , crs= 4326 , what = "polygons" , square = FALSE ) # grid (hot-exhaust) emi_grid_he <- emis_grid( emi_list_he,grid,time_resolution = 'day' ,aggregate = TRUE) setDT(emi_grid_he) pol_names <- setdiff(names(emi_grid_he),"geometry") emi_grid_he_dt <- melt(emi_grid_he,measure.vars = pol_names,id.vars = "geometry") emi_grid_he_dt <- sf::st_as_sf(emi_grid_he_dt) # grid (non-exhaust) emi_grid_ne <- emis_grid( emi_list_ne,grid,time_resolution = 'day' ,aggregate = TRUE) setDT(emi_grid_ne) pol_names <- setdiff(names(emi_grid_ne),"geometry") emi_grid_ne_dt <- melt(emi_grid_ne,measure.vars = pol_names,id.vars = "geometry") emi_grid_ne_dt <- sf::st_as_sf(emi_grid_ne_dt) # bind grid emi_grid_dt <- data.table::rbindlist(l = list(emi_grid_he_dt,emi_grid_ne_dt)) emi_grid_sf <- sf::st_as_sf(emi_grid_dt)
c) Generating spatial and temporal patterns
# plot library(ggplot2) ggplot(emi_grid_sf) + geom_sf(aes(fill= as.numeric(value)), color=NA) + geom_sf(data = tp_model$geometry,color = "black")+ scale_fill_continuous(type = "viridis")+ labs(fill = "PM10 (g)")+ facet_wrap(facets = vars(variable),nrow = 1)+ theme_void()
The total emissions can be also viewed in bar graphics
# Emissions by time emi_time_he <- emis_summary(emi_list_he,by = "time") emi_time_ne <- emis_summary(emi_list_ne,by = "time") emi_time <- data.table::rbindlist(l = list(emi_time_he,emi_time_ne)) ggplot(emi_time)+ geom_col(aes(x = process,y = as.numeric(emi),fill = as.numeric(emi)))+ scale_fill_continuous(type = "viridis")+ labs(fill = "PM10 level",y = "Emissions (g)")+ theme_minimal()
References
EMEP/EEA data and reports can be accessed in the following links:
- 2019 edition EMEP-EEA.
Report a bug
If you have any suggestions or want to report an error, please visit the package GitHub page.
Try the gtfs2emis package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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