---
title: 'Exploring Emission Factors'
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
urlcolor: blue
vignette: >
%\VignetteIndexEntry{Exploring Emission Factors}
\usepackage[utf8]{inputenc}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
markdown:
wrap: 72
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>")
```
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](https://www.epa.gov/moves)
\- United States, California Air Resources Board --- [EMFAC2017
model](https://arb.ca.gov/emfac/emissions-inventory)
\- Europe, European Environment Agency ---
[EMEP-EEA](https://www.eea.europa.eu//publications/emep-eea-guidebook-2019)
***Wear emissions (tire, brake and road wear)***
\- Europe, European Environment Agency ---
[EMEP-EEA](https://www.eea.europa.eu//publications/emep-eea-guidebook-2019)
## Visualizing emission factor data
Emission fator values vary by fleet characteristics --- as shown in
[Defining Fleet data
vignette](https://ipeagit.github.io/gtfs2emis/articles/gtfs2emis_fleet_data.html).
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.
```{r, message = FALSE, echo = FALSE}
#library(devtools)
#devtools::load_all()
```
```{r, eval = FALSE,message = FALSE,}
#library(gtfs2emis)
```
```{r, message = FALSE}
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)
```
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.
```{r}
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)
```
Plotting the speed-dependent emission factors according to vehicle type
(`veh_type`) and euro standard (`euro`).
```{r, fig.height=4, fig.width=7}
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")
```
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.
```{r}
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)
```
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 $EF_{scaled}(V)$ is the scaled emission factor for each street
link, $EF_{local}$ is the local emission factor, $EF_{euro}(V)$ and
$EF_{euro}(SDC)$ 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
$EF_{local}$, $EF_{scaled}(V)$, and the $EF_{euro}(V)$ that is used as
the reference To plot these data, we need six quick steps:
1) Build a `data.frame` of fleet indicating the correspondence between
the fleet characteristic in the local and European emission models
```{r}
fleet_filepath <- system.file("extdata/bra_cur_fleet.txt", package = "gtfs2emis")
cur_fleet <- read.table(fleet_filepath,header = TRUE, sep = ",", nrows = 1)
cur_fleet
```
2) Estimate local emission factors
```{r}
cur_local_ef <- ef_brazil_cetesb(pollutant = "CO2"
,veh_type = cur_fleet$type_name_br
,model_year = cur_fleet$year)
head(cur_local_ef)
# convert Local EF to data.frame
cur_local_ef_dt <- emis_to_dt(emi_list = cur_local_ef
,emi_vars = "EF")
```
3) Estimate `ef_emep_europe`
```{r}
# 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$source <- "Euro EF"
```
4) Apply `ef_scaled_euro()`
```{r}
cur_scaled_ef <- ef_scaled_euro(ef_local = cur_local_ef$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$source <- "Scaled EF"
```
5) View in ggplot2
```{r, fig.width=6, fig.height=5}
# rbind data
cur_ef <- rbind(cur_euro_ef_dt, cur_scaled_ef_dt)
cur_ef$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](https://github.com/ipeaGIT/gtfs2emis/tree/master/data-raw).
## Learn more
Check out our extra guides:
- [Exploring Non-Exhaust Emission Factors](https://ipeagit.github.io/gtfs2emis/articles/gtfs2emis_non_exhaust_ef.html)
## 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).