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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:
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.