Chapter 1 Introduction

1.1 Definitions and sources of information

Developing emissions inventories is a complex task. Therefore, before entering into the details of the functions, it is good to provide some initial definitions.

A good starting point is a book “The Art of Emissions Inventorying” (Pulles and Heslinga 2010). This book provides a general description of what is an emissions inventory. One of the early sources of information about emissions inventories and emissions factors are the Environmental Protection Agency (U.S.EPA) and its compilation of emission factors AP42 (https://www.epa.gov/air-emissions-factors-and-quantification). A third source of information is the European EMEP/EEA air pollutant emission inventory guidebook (https://www.eea.europa.eu/themes/air/emep-eea-air-pollutant-emission-inventory-guidebook/).

An emissions inventory is the compilation of all mass emitted into the atmosphere by activities in a defined region during a lapse of time. The Eq. ((1.1) shows the general form (McGlade and Vidic 2009):

\[\begin{equation} Emis{pol}=\sum_{Act} Act \cdot EF_{Act, pol} \tag{1.1} \end{equation}\]

Where Emis are the emissions, pol the pollutants, Act the activities and EF the emission factors. There are two main kinds of inventories based on the application; one is for policy application and the other for scientific application (Pulles and Heslinga 2010).

  • Inventories for policy application include the National Greenhouse Gas Inventory for the parties under the United Nations Framework on Climate Change (UNFCCC 2017).
  • Emissions inventories with scientific applications include the Emission Database for Global Atmospheric Research EDGAR (EJ-JRC/PBL 2016).

Emissions inventories can also be multimedia inventories, such as the Pollutant Release and Transfer Registers (PRTR) which include pollutants released to air, water, and soil and transferred off-site for treatment or disposal (http://www.oecd.org/chemicalsafety/pollutant-release-transfer-register/). However, the type of inventory covered in this book includes only the emissions released into the atmosphere.

1.1.1 Approaches

It is necessary to show some definitions of vehicular emissions inventories approaches, which in this case comes from the European Emissions Guidelines (Ntziachristos and Samaras 2016):

  • Top-down uses input activity traffic data as fleet statistics, fuel consumption, representative speeds, and country balances. The emissions factors are based on representative speeds. These inventories are also known as static.
  • Bottom-up uses using input activity traffic data from traffic counts, traffic simulations, vehicle composition, speed recordings and length of roads. The emission factors are speed or/and acceleration functions. These inventories are also known as dynamic.
  • Reconciliation Both approaches must be reconciled in urban emissions inventories with the comparison of total mileage, cold start mileage or emission factors to ensure that the comparison with the total fuel consumption in the study area is satisfactory. Another point of calibration is that the estimation of fuel consumption must match the fuel sales in your study area.

1.2 Installation

The VEIN R-package can be installed from Comprehensive R Archive Network CRAN directly:

# CRAN
install.packages("vein")

VEIN can be also installed from github:

library(devtools)
install_github("atmoschem/vein")

The GitHub installation requires that the devtools package (Wickham and Chang 2017) must be installed, including dependencies.

1.3 Required R packages and dependencies

VEIN imports functions from other R packages. Hence, these packages are necessary::

  • sf is a package that provides simple features access for R (Pebesma 2017). It depends on the libraries GEOS (http://trac.osgeo.org/geos), ‘Geospatial’ Data Abstraction Library (‘GDAL’) (http://www.gdal.org/) also, PROJ (http://proj4.org/).
  • data.table is a package is an extension of data.frame class, providing high-speed operations (Dowle and Srinivasan 2017).
  • sp. This is a package that provides classes and methods for spatial data (Pebesma and Bivand 2005) Considering points, lines, polygons, and grids. Few functions of ‘sp’ are imported, most of them come from sf. eixport is a package (Ibarra-Espinosa, Schuch, and de Freitas 2017) that creates inputs for atmospheric models (Ibarra-Espinosa, Schuch, and Dias de Freitas 2018).
  • units. It is a package for measurement units in R (Pebesma, Mailund, and Hiebert 2016). This package depends on the library udunits2 (http://www.unidata.ucar.edu/software/udunits/). I
install.packages(c("sf", "data.table", "sp", "eixport", "units"))

You have to install the package sf, including its library dependencies into your operating system.

Ubuntu

sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable
sudo apt-get update
sudo apt-get install libudunits2-dev libgdal-dev libgeos-dev 
libproj-dev

Fedora

sudo dnf install gdal-devel proj-devel proj-epsg proj-nad 
geos-devel udunits2-devel

Debian and other

Dockerfiles from rocker geospatial.

Windows Via Rtools.

MAC OS

brew unlink gdal
brew tap osgeo/osgeo4mac && brew tap --repair
brew install proj
brew install geos
brew install udunits
brew install gdal2 --with-armadillo --with-complete 
--with-libkml --with-unsupported
brew link --force gdal2

The R package eixport imports function from ncdf4 (Pierce 2017), therefore the library NetCDF must be installed. For Ubuntu, do:

sudo apt install libnetcdf-dev  netcdf-bin

1.4 Data inside the VEIN model

There are several datasets available inside the model:

1.4.1 Emission factors from CETESB

  • fe2015: Emission factors from the Environmental Agency of Sao Paulo (CETESB). Pollutants included: “CH4”, “CO”, “CO2”, “HC”, “N2O”, “NMHC”, “NOx” and “PM”. The type of vehicles included is Passenger Cars (PC). Please note that I added a new function ef_cetesb in version 0.4.4 which covers all CETESB emission factors.

usage:

library(vein, quietly = T)
data(fe2015)
Table 1.1: Emission factors from CETESB in VEIN model
Age Year Pollutant PC_G LT
1 2015 CO 0.171 0.027
2 2014 CO 0.216 0.012
3 2013 CO 0.237 0.012
4 2012 CO 0.273 0.005
5 2011 CO 0.275 0.379
6 2010 CO 0.204 0.416

1.4.2 Mileage functions of Brazilian Fleet

  • fkm: A list of functions of mileage in km for the Brazilian fleet. It includes mileage functions based on more than 2 million recordings of vehicles (Bruni and Bales 2013). It consists of a list of age functions. The type of cars are Passenger Cars (PC), Light Commercial Vehicles (LCV), Small Busses (SBUS), Trucks, Articulated Trucks (ATRUCKS), Motorcycles (MOTO) and Light vehicles (LDV). The fuels are Gasoline using 25% of ethanol (E25), Ethanol 100% (E100) and Diesel with 5% of biodiesel (B5). There are also vehicles with flex engines which can run either with E25, E100 or any mixture in between (Giroldo et al. 2005). The Fig. (1.1 shows the mileage of PC using fuel E25. To see the names of the dataset fkm, uncomment the line by deleting the symbol #.

usage:

library(vein, quietly = T)
data(fkm)
# names(fkm)
age <- 1:50
mileage <- fkm$KM_PC_E25(1:50) 
par(mar = c(4, 4, .1, .1))
plot(y = mileage, x = age, pch = 19, type = "b")
Mileage of PC using E25

Figure 1.1: Mileage of PC using E25

  • net: Road network of the west part of Sao Paulo city. It consistes in a SpatialLinesDataFrame (class of sp) with the attributes ldv (light duty vehicles, \(1 \cdot h^{-1}\)), hdv (heavy duty vehicles, \(1 \cdot h^{-1}\)), ps (peak speed, \(km \cdot h^{-1}\)), ffs (free flow speed, \(km \cdot h^{-1}\)), tstreet (type of street), lanes (number of lanes), capacity (capacity fo vehicles at each link, 1/h) and tmin (time for travelling each link, min). The Fig. (4 shows more details.

1.4.3 Temporal factors for Passenger Cars

Temporal factors (TF) are the matrix of hourly traffic data normalized for the hour of interest, usually the morning rush hour 08:00-09:00 in local time (LT) (S. Ibarra-Espinosa et al. 2018). This dataset covers 168 hours of one week, from traffic counts of toll stations near the center of the city of SãoPaulo, Brazil. Fig. (1.2 shows the temporal factors for PC.

usage:

library(vein, quietly = T)
data(pc_profile)
tf <- unlist(pc_profile)
hours <- 1:168
par(mar = c(4, 4, .1, .1))
plot(y = tf, x = hours, pch = 16, type = "b")
Temporal Factors for PC

Figure 1.2: Temporal Factors for PC

1.4.4 Profile for cold starts of Passenger Cars

I included a profile of hourly percentage of cold starts of Passenger Cars. This data covers 24 hours of a working day, and it is based cold start recordings during the implementation of the International Vehicle Emissions (IVE) model (Davis et al. 2005) in São Paulo (Lents et al. 2004). The Fig. (1.3 shows the cold-start profile.

usage:

library(vein, quietly = T)
data(pc_cold)
cold <- unlist(pc_cold)
hours <- 1:24
par(mar = c(4, 4, .1, .1))
plot(y = cold, x = hours, pch = 16, type = "b")
Cold-starts profile for PC

Figure 1.3: Cold-starts profile for PC

1.5 Acknowledgements

Martin Ramacher, Ph.D. Student Chemistry Transport Modelling, Institute for Coastal Research, Helmholtz-Zentrum Geesthacht, Germany - Proofreading. Júlio Barboza Chiquetto, Ph.D. in Physical Geography, Department of Geography, University of São Paulo, Brazil - Proofreading. Leila Droprinchinski Martins, Professor (Associate) Federal Technological University of Parana, Brazil - Comments.

References

Pulles, Tim, and Dick Heslinga. 2010. “The Art of Emission Inventorying.” TNO, Utrecht.

McGlade, J, and S Vidic. 2009. “EMEP/Eea Air Pollutant Emission Inventory Guidebook 2009: Technical Guidance to Prepare National Emission Inventories.” Technical report 9/2009, EEA, Copenhagen, Denmark.

UNFCCC. 2017. Greenhouse Gas Inventory Data. http://unfccc.int/ghg_data/items/3800.php.

EJ-JRC/PBL. 2016. “Emission Database for Global Atmospheric Research (Edgar), Release Edgar V4.3.1_v2 (1970 - 2010).” http://edgar.jrc.ec.europa.eu.

Ntziachristos, L, and Z Samaras. 2016. “EMEP/Eea Emission Inventory Guidebook; Road Transport: Passenger Cars, Light Commercial Trucks, Heavy-Duty Vehicles Including Buses and Motorcycles.” European Environment Agency, Copenhagen.

Wickham, Hadley, and Winston Chang. 2017. Devtools: Tools to Make Developing R Packages Easier. https://CRAN.R-project.org/package=devtools.

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Ibarra-Espinosa, Sergio, Daniel Schuch, and Edmilson Dias de Freitas. 2017. Eixport: An R Package to Export Emissions to Atmospheric Models. https://CRAN.R-project.org/package=eixport.

Ibarra-Espinosa, Sergio, Daniel Schuch, and Edmilson Dias de Freitas. 2018. “Eixport: An R Package to Export Emissions to Atmospheric Models.” The Journal of Open Source Software. doi:10.21105/joss.00607.

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Pierce, David. 2017. Ncdf4: Interface to Unidata netCDF (Version 4 or Earlier) Format Data Files. https://CRAN.R-project.org/package=ncdf4.

Bruni, Antonio De Castro, and Marcelo Pereira Bales. 2013. “Curvas de Intensidade de Uso Por Tipo de Veículo Automotor Da Frota Da Cidade de São Paulo.” CETESB.

Giroldo, Mauro Berti, Edward Werninghaus, Eugenio Coelho, and William Makant. 2005. “Development of 1.6l Flex Fuel Engine for Brazilian Market.” In SAE Technical Paper. SAE International. doi:10.4271/2005-01-4130.

Ibarra-Espinosa, S., R. Ynoue, S. O’Sullivan, E. Pebesma, M. D. F. Andrade, and M. Osses. 2017. “VEIN V0.2.2: An R Package for Bottom-up Vehicular Emissions Inventories.” Geoscientific Model Development Discussions 2017: 1–29. doi:10.5194/gmd-2017-193.

2018. “VEIN V0.2.2: An R Package for Bottom–up Vehicular Emissions Inventories.” Geoscientific Model Development 11 (6): 2209–29. doi:10.5194/gmd-11-2209-2018.

Davis, Nicole, James Lents, Mauricio Osses, Nick Nikkila, and Matthew Barth. 2005. “Part 3: Developing Countries: Development and Application of an International Vehicle Emissions Model.” Transportation Research Record: Journal of the Transportation Research Board 8 (1939). Transportation Research Board of the National Academies: 155–65.

Lents, James, Nicole Davis, Nick Nikkila, and Mauricio Osses. 2004. “São Paulo Vehicle Activity Study.” 605 South Palm Street, Suite C, La Habra, CA 90631, USA: International Sustainable System Research Center.