Exploring Non-Exhaust Emission Factors
2024-12-02
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:
| 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:
| 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)
#> $pollutant
#> [1] "PM10" "TSP" "PM2.5"
#>
#> $veh_type
#> [1] "Ubus Std 15 - 18 t"
#>
#> $fleet_composition
#> [1] 1
#>
#> $speed
#> 30 [km/h]
#>
#> $dist
#> 1 [km]
#>
#> $emi
#> PM10_tyre_veh_1 TSP_tyre_veh_1 PM2.5_tyre_veh_1
#> <units> <units> <units>
#> 1: 0.01873998 [g] 0.0312333 [g] 0.01311799 [g]
#>
#> $process
#> [1] "tyre"
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:
| 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)
#> $pollutant
#> [1] "PM10" "TSP" "PM2.5"
#>
#> $veh_type
#> [1] "Ubus Std 15 - 18 t"
#>
#> $fleet_composition
#> [1] 1
#>
#> $speed
#> 30 [km/h]
#>
#> $dist
#> 1 [km]
#>
#> $emi
#> PM10_brake_veh_1 TSP_brake_veh_1 PM2.5_brake_veh_1
#> <units> <units> <units>
#> 1: 0.03349245 [g] 0.03417597 [g] 0.01332863 [g]
#>
#> $process
#> [1] "brake"
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
| 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)
#> $pollutant
#> [1] "PM10" "TSP" "PM2.5"
#>
#> $veh_type
#> [1] "Ubus Std 15 - 18 t"
#>
#> $fleet_composition
#> [1] 1
#>
#> $speed
#> 30 [km/h]
#>
#> $dist
#> 1 [km]
#>
#> $emi
#> PM10_road_veh_1 TSP_road_veh_1 PM2.5_road_veh_1
#> <units> <units> <units>
#> 1: 0.038 [g] 0.076 [g] 0.02052 [g]
#>
#> $process
#> [1] "road"
Viewing Emissions
Users can also use one single function to apply for more than one
process (e.g. tire, brake and road), as shown below.
library(units)
library(ggplot2)
emis_list <- emi_europe_emep_wear(dist = units::set_units(rep(1,100),"km"),
speed = units::set_units(1:100,"km/h"),
pollutant = c("PM10","TSP","PM2.5"),
veh_type = c("Ubus Std 15 - 18 t"),
fleet_composition = c(1),
load = 0.5,
process = c("brake","tyre","road"),
as_list = TRUE)
ef_dt <- gtfs2emis::emis_to_dt(emis_list,emi_vars = "emi"
,segment_vars = "speed")
ggplot(ef_dt)+
geom_line(aes(x = as.numeric(speed),y = as.numeric(emi),color = pollutant))+
facet_wrap(facets = vars(process))+
labs(x = "Speed (km/h)",y = "Emissions (g)")+
theme_minimal()
When using the transport_model() output, users can also
visualize both hot-exhaust and non-exhaust emissions taking few more
steps. This can be done in three main stages: a) Preparing the data, b)
Creating spatial grid; c) Generating spatial and temporal
visualizations.
a) Preparing the data
library(gtfstools)
library(sf)
# read GTFS
gtfs_file <- system.file("extdata/bra_cur_gtfs.zip", package = "gtfs2emis")
gtfs <- gtfstools::read_gtfs(gtfs_file)
# keep a single trip_id to speed up this example
gtfs_small <- gtfstools::filter_by_trip_id(gtfs, trip_id ="4451136")
# run transport model
tp_model <- transport_model(gtfs_data = gtfs_small,
spatial_resolution = 100,
parallel = FALSE)
# Fleet data, using Brazilian emission model and fleet
fleet_data_ef_emep <- data.frame(veh_type = "Ubus Std 15 - 18 t",
fleet_composition = 1,
euro = "V", # for hot-exhaust emissions
fuel = "D", # for hot-exhaust emissions
tech = "SCR") # for hot-exhaust emissions
# Emission model (hot-exhaust)
emi_list_he <- emission_model(
tp_model = tp_model,
ef_model = "ef_europe_emep",
fleet_data = fleet_data_ef_emep,
pollutant = "PM10"
)
# Emission model (non-exhaust)
emi_list_ne <- emi_europe_emep_wear(
dist = tp_model$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: