Skip to content

Commit

Permalink
fix #100
Browse files Browse the repository at this point in the history
  • Loading branch information
@Joaobazzo
Joaobazzo committed Jul 12, 2023
1 parent 95aec4f commit c15f196
Show file tree
Hide file tree
Showing 2 changed files with 112 additions and 60 deletions.
160 changes: 102 additions & 58 deletions vignettes/gtfs2emis_emission_factor.Rmd
View file
Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,11 @@ output: rmarkdown::html_vignette
urlcolor: blue
vignette: >
%\VignetteIndexEntry{Exploring Emission Factors}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
markdown:
wrap: 72
---

```{r, include = FALSE}
Expand All @@ -15,22 +18,52 @@ knitr::opts_chunk$set(
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.
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 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
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](https://cetesb.sp.gov.br/veicular/)

\- 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)

- Brazil, Environment Company of Sao Paulo --- [CETESB](https://cetesb.sp.gov.br/veicular/)
- 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 (tyre, 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.
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}
library(gtfs2emis)
Expand All @@ -50,7 +83,10 @@ ef_europe <- ef_europe_emep(speed = units::set_units(10:100,"km/h")
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.
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
Expand All @@ -61,8 +97,8 @@ ef_europe_dt <- emis_to_dt(emi_list = ef_europe
head(ef_europe_dt)
```

Plotting the speed-dependent emission factors according to vehicle type (`veh_type`) and
euro standard (`euro`).
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"
Expand All @@ -75,10 +111,15 @@ ggplot(ef_europe_dt) +
facet_wrap(~pollutant,scales = "free")+
theme(legend.position = "bottom")
```
<!-- 666 Achei esse paragrafo confuso -->
The emission factors are often produced considering data from on-board measurements, or chassis testing. 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. Users can either select existing data for the combining variables (`euro`, `tech`, `veh_type`, and `pollutant`), or accept the assumed change in vehicle technology.

<!-- 666 nao ficou indicado no texto o q esse codigo abaixo faz -->
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")
Expand All @@ -88,46 +129,43 @@ ef_europe_co2 <- ef_europe_emep(speed = units::set_units(10:100,"km/h")
,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.

<!-- ```{r} -->
<!-- ef_usa_moves_list <- ef_usa_moves(pollutant = "CO2" -->
<!-- ,model_year = 2018 -->
<!-- ,reference_year = 2019 -->
<!-- ,speed = units::set_units(20,"km/h") -->
<!-- ,fuel = "D") -->
<!-- ef_usa_emfac_list <- ef_usa_emfac(pollutant = "CO2" -->
<!-- ,model_year = 2018 -->
<!-- ,reference_year = 2019 -->
<!-- ,speed = units::set_units(20,"km/h") -->
<!-- ,fuel = "D") -->
<!-- ef_brazil_list <- ef_brazil_cetesb(pollutant = "CO2" -->
<!-- ,veh_type = "BUS_URBAN_D" -->
<!-- ,model_year = 2018) -->
<!-- ``` -->
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()`.
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:
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.
$$ 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:
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
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
2) Estimate local emission factors
```{r}
cur_local_ef <- ef_brazil_cetesb(pollutant = "CO2"
Expand All @@ -140,7 +178,7 @@ cur_local_ef_dt <- emis_to_dt(emi_list = cur_local_ef
,emi_vars = "EF")
```
3) Estimate `ef_emep_europe`
3) Estimate `ef_emep_europe`
```{r}
# Euro EF
Expand All @@ -159,9 +197,7 @@ cur_euro_ef_dt <- emis_to_dt(emi_list = cur_euro_ef
cur_euro_ef_dt$source <- "Euro EF"
```

4) Apply `ef_scaled_euro()`

4) Apply `ef_scaled_euro()`
```{r}
cur_scaled_ef <- ef_scaled_euro(ef_local = cur_local_ef$EF
Expand All @@ -179,8 +215,7 @@ cur_scaled_ef_dt <- emis_to_dt(emi_list = cur_scaled_ef
cur_scaled_ef_dt$source <- "Scaled EF"
```

5) View in ggplot2
5) View in ggplot2
```{r, fig.width=6, fig.height=5}
# rbind data
Expand All @@ -197,9 +232,10 @@ ggplot() +
# add local EF
geom_hline(aes(yintercept = cur_local_ef_dt$EF)
,colour = "black",linetype="dashed") +
geom_point(aes(x = 19,y = cur_local_ef$EF)) +
geom_point(aes(x = units::set_units(19,'km/h')
,y = cur_local_ef$EF)) +
# add local EF text
geom_text(aes(x = 19
geom_text(aes(x = units::set_units(19,'km/h')
, y = cur_local_ef_dt$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
Expand All @@ -210,21 +246,29 @@ ggplot() +
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.
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 emission factor data that included the package by using the following functions:
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)
- `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).
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).
## 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).
If you have any suggestions or want to report an error, please visit
[the package GitHub page](https://github.com/ipeaGIT/gtfs2emis/issues).
12 changes: 10 additions & 2 deletions vignettes/gtfs2emis_intro_vignette.Rmd
View file
Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,7 @@ knitr::opts_chunk$set(
One can install the development version `gtfs2emis` from Github:

```{r, eval = FALSE, message = FALSE}
library(devtools)
# From CRAN
install.packages("gtfs2emis")
Expand Down Expand Up @@ -57,9 +58,15 @@ To estimate the emission levels from a given public transport system, users need
# 4. Package Overview

Before we start, let's load a few packages we'll be using in this vignette:

```{r, message = FALSE}
```{r, message = FALSE, echo = FALSE}
library(devtools)
devtools::load_all()
```
```{r, eval = FALSE}
library(gtfs2emis)
```
```{r, message = FALSE}
library(devtools)
library(gtfstools)
library(progressr)
library(data.table)
Expand Down Expand Up @@ -182,6 +189,7 @@ names(emi_list)

Sometimes, working with data stored in a long list can be tricky. For convenience, `gtfs2emis` package has a few functions to help users analyze the results of emission estimates. For example, you can use the `emis_to_dt()` function to convert the output of `emission_model()` from a `list` to a `data.table` format:


```{r,eval = TRUE}
emi_dt <- emis_to_dt(emi_list = emi_list,
veh_vars = c("veh_type", "euro", "fuel"),
Expand Down

0 comments on commit c15f196

Please sign in to comment.