---
title: "Exploring Non-Exhaust Emission Factors"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
urlcolor: blue
vignette: >
%\VignetteIndexEntry{Exploring Non-Exhaust Emission Factors}
\usepackage[utf8]{inputenc}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
markdown:
wrap: 72
---
```{r, include = FALSE}
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](https://www.eea.europa.eu//publications/emep-eea-guidebook-2019).
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 $speed > 90 km/h$.
```{r}
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 $speed > 95 km/h$.
```{r, message = FALSE}
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 |
```{r, message = FALSE}
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.
```{r, message = FALSE,fig.height=3, fig.width=8}
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
```{r, message = FALSE}
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$tp_model <- tp_model
```
### b) Creating spatial grid
```{r, message = FALSE}
# create spatial grid
grid <- sf::st_make_grid(
x = sf::st_make_valid(tp_model)
, cellsize = 0.25 / 200
, crs= 4326
, what = "polygons"
, square = FALSE
)
# grid (hot-exhaust)
emi_grid_he <- emis_grid( emi_list_he,grid,time_resolution = 'day'
,aggregate = TRUE)
setDT(emi_grid_he)
pol_names <- setdiff(names(emi_grid_he),"geometry")
emi_grid_he_dt <- melt(emi_grid_he,measure.vars = pol_names,id.vars = "geometry")
emi_grid_he_dt <- sf::st_as_sf(emi_grid_he_dt)
# grid (non-exhaust)
emi_grid_ne <- emis_grid( emi_list_ne,grid,time_resolution = 'day'
,aggregate = TRUE)
setDT(emi_grid_ne)
pol_names <- setdiff(names(emi_grid_ne),"geometry")
emi_grid_ne_dt <- melt(emi_grid_ne,measure.vars = pol_names,id.vars = "geometry")
emi_grid_ne_dt <- sf::st_as_sf(emi_grid_ne_dt)
# bind grid
emi_grid_dt <- data.table::rbindlist(l = list(emi_grid_he_dt,emi_grid_ne_dt))
emi_grid_sf <- sf::st_as_sf(emi_grid_dt)
```
### c) Generating spatial and temporal patterns
```{r, message = FALSE,fig.height=3, fig.width=6}
# plot
library(ggplot2)
ggplot(emi_grid_sf) +
geom_sf(aes(fill= as.numeric(value)), color=NA) +
geom_sf(data = tp_model$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
```{r, message = FALSE,fig.height=3, fig.width=6}
# 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](https://www.eea.europa.eu//publications/emep-eea-guidebook-2019).
## Report a bug
If you have any suggestions or want to report an error, please visit
[the package GitHub page](https://github.com/ipeaGIT/gtfs2emis/issues).