Title:	Tools for Create Emissions for Air Quality Models
Date:	2025-01-24
Version:	0.665.9.0
Maintainer:	Daniel Schuch <underschuch@gmail.com>
Description:	Processing tools to create emissions for use in numerical air quality models. Emissions can be calculated both using emission factors and activity data (Schuch et al 2018) <doi:10.21105/joss.00662> or using pollutant inventories (Schuch et al., 2018) <doi:10.30564/jasr.v1i1.347>. Functions to process individual point emissions, line emissions and area emissions of pollutants are available as well as methods to incorporate alternative data for Spatial distribution of emissions such as satellite images (Gavidia-Calderon et. al, 2018) <doi:10.1016/j.atmosenv.2018.09.026> or openstreetmap data (Andrade et al, 2015) <doi:10.3389/fenvs.2015.00009>.
Depends:	R (≥ 3.4)
Imports:	ncdf4, units(≥ 0.5-1), raster, sf, methods, data.table
Suggests:	testthat (≥ 2.1.0), covr, lwgeom
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
URL:	https://atmoschem.github.io/EmissV/
BugReports:	https://github.com/atmoschem/EmissV/issues
NeedsCompilation:	no
Packaged:	2025-01-24 21:03:17 UTC; under
Author:	Daniel Schuch [aut, cre], Sergio Ibarra-Espinosa [aut]
Repository:	CRAN
Date/Publication:	2025-01-24 21:30:02 UTC

EmissV: Tools for Create Emissions for Air Quality Models

Description

Processing tools to create emissions for use in numerical air quality models. Emissions can be calculated both using emission factors and activity data (Schuch et al 2018) doi:10.21105/joss.00662 or using pollutant inventories (Schuch et al., 2018) doi:10.30564/jasr.v1i1.347. Functions to process individual point emissions, line emissions and area emissions of pollutants are available as well as methods to incorporate alternative data for Spatial distribution of emissions such as satellite images (Gavidia-Calderon et. al, 2018) doi:10.1016/j.atmosenv.2018.09.026 or openstreetmap data (Andrade et al, 2015) doi:10.3389/fenvs.2015.00009.

Author(s)

Maintainer: Daniel Schuch underschuch@gmail.com (ORCID)

Authors:

• Sergio Ibarra-Espinosa sergio.ibarra@usp.br (ORCID)

See Also

Useful links:

• https://atmoschem.github.io/EmissV/

• Report bugs at https://github.com/atmoschem/EmissV/issues

Distribution of emissions by area

Description

Calculate the spatial distribution by a raster masked by shape/model grid information.

Usage

areaSource(s, r, grid = NA, name = "", as_frac = FALSE, verbose = TRUE)

Arguments

Details

About the DMSP and example data https://en.wikipedia.org/wiki/Defense_Meteorological_Satellite_Program

Value

a raster object containing the spatial distribution of emissions

Source

Data avaliable https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/defense-meteorological-satellite-program

Examples

shape  <- raster::shapefile(paste(system.file("extdata", package = "EmissV"),
                            "/BR.shp",sep=""),verbose = FALSE)
shape  <- shape[22,1] # subset for Sao Paulo - BR
raster <- raster::raster(paste(system.file("extdata", package = "EmissV"),
                         "/dmsp.tiff",sep=""))
grid   <- gridInfo(paste(system.file("extdata", package = "EmissV"),"/wrfinput_d02",sep=""))
SP     <- areaSource(shape,raster,grid,name = "SPMA")

raster::plot(SP,ylab="Lat",xlab="Lon",
             main ="Spatial Distribution by Lights for Sao Paulo - Brazil")

Emissions in the format for atmospheric models

Description

Combine area sources and total emissions to model output

Usage

emission(
  inventory = NULL,
  grid,
  mm = 1,
  aerosol = FALSE,
  check = TRUE,
  total,
  pol,
  area,
  plot = FALSE,
  verbose = TRUE
)

Arguments

Format

matrix of emission

Value

a vector of emissions in MOL / mk2 h for gases and ug / m2 s for aerosols.

Note

if Inventory is provided, the firsts tree arguments are not be used by the function.

Is a good practice use the set_units(fe,your_unity), where fe is your emission factory and your_unity is usually g/km on your emission factory

the list of area must be in the same order as defined in vehicles and total emission.

just WRF-Chem is suported by now

See Also

totalEmission and areaSource

Examples

fleet  <- vehicles(example = TRUE)

EmissionFactors <- emissionFactor(example = TRUE)

TOTAL  <- totalEmission(fleet,EmissionFactors,pol = c("CO"),verbose = TRUE)

grid   <- gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d01"))
shape  <- raster::shapefile(paste0(system.file("extdata", package = "EmissV"),"/BR.shp"))
raster <- raster::raster(paste0(system.file("extdata", package = "EmissV"),"/dmsp.tiff"))

SP     <- areaSource(shape[22,1],raster,grid,name = "SP")
RJ     <- areaSource(shape[17,1],raster,grid,name = "RJ")

e_CO   <- emission(total = TOTAL,
                   pol = "CO",
                   area = list(SP = SP, RJ = RJ),
                   grid = grid,
                   mm = 28)

Tool to set-up emission factors

Description

Return a data frame for emission for multiple pollutants.

Usage

emissionFactor(
  ef,
  poluttant = names(ef),
  vnames = NA,
  unit = "g/km",
  example = FALSE,
  verbose = TRUE
)

Arguments

Value

a emission factor data frame

a emission factor data.frame for totalEmission function

See Also

areaSource and totalEmission

Examples

EF <- emissionFactor(example = TRUE)

# or the code for the same result
EF <- emissionFactor(ef = list(CO = c(1.75,10.04,0.39,0.45,0.77,1.48,1.61,0.75),
                               PM = c(0.0013,0.0,0.0010,0.0612,0.1052,0.1693,0.0,0.0)),
                     vnames = c("Light Duty Vehicles Gasohol","Light Duty Vehicles Ethanol",
                                "Light Duty Vehicles Flex","Diesel Trucks","Diesel Urban Busses",
                                "Diesel Intercity Busses","Gasohol Motorcycles",
                                "Flex Motorcycles"))

Read grid information from a NetCDF file

Description

Return a list containing information of a regular grid / domain

Usage

gridInfo(
  file = file.choose(),
  z = FALSE,
  missing_time = "1984-03-10",
  verbose = TRUE
)

Arguments

Value

a list with grid information from air quality model

Note

just WRF-Chem is suported by now

Examples

grid_d1 <- gridInfo(paste(system.file("extdata", package = "EmissV"),
                                      "/wrfinput_d01",sep=""))
grid_d2 <- gridInfo(paste(system.file("extdata", package = "EmissV"),
                                      "/wrfinput_d02",sep=""))
grid_d3 <- gridInfo(paste(system.file("extdata", package = "EmissV"),
                                      "/wrfinput_d03",sep=""))
names(grid_d1)
# for plot the shapes
shape   <- raster::shapefile(paste0(system.file("extdata", package = "EmissV"),
                                                "/BR.shp"))
raster::plot(shape,xlim = c(-55,-40),ylim = c(-30,-15), main="3 nested domains")
axis(1); axis(2); box(); grid()
lines(grid_d1$boundary, col = "red")
text(grid_d1$xlim[2],grid_d1$Ylim[1],"d1",pos=4, offset = 0.5)
lines(grid_d2$boundary, col = "red")
text(grid_d2$xlim[2],grid_d2$Ylim[1],"d2",pos=4, offset = 0.5)
lines(grid_d3$boundary, col = "red")
text(grid_d3$xlim[1],grid_d3$Ylim[2],"d3",pos=2, offset = 0.0)

Distribution of emissions by lines

Description

Create a emission distribution from 'sp' or 'sf' spatial lines data.frame or spatial lines.

There 3 modes available to create the emission grid: - using gridInfo function output (defoult) - using the patch to "wrfinput" (output from real.exe) file or "geo" for (output from geog.exe) - "sf" (and "sp") uses a grid in SpatialPolygons format

The variable is the column of the data.frame with contains the variable to be used as emissions, by defoult the idstribution taken into acount the lench distribution of lines into each grid cell and the output is normalized.

Usage

lineSource(
  s,
  grid,
  as_raster = FALSE,
  type = "info",
  gcol = 100,
  grow = 100,
  variable = "length",
  verbose = TRUE
)

Arguments

Value

a raster object containing the spatial distribution of emissions

Source

OpenstreetMap data avaliable https://www.openstreetmap.org/ and https://download.geofabrik.de/

See Also

gridInfo and rasterSource

Examples

# loading a shapefile with osm data for sao paulo metropolitan area
roads <- sf::st_read(paste0(system.file("extdata",package="EmissV"),"/streets.shp"))
d3    <- gridInfo(paste0(system.file("extdata", package = "EmissV"),"/wrfinput_d03"))

# calculating only for 2 streets
roadLength <- lineSource(roads[1:2,],d3,as_raster=TRUE)

# to generate a quick plot
raster::plot(roadLength,ylab="Lat", xlab="Lon",main="Length of roads")
# lines(road_lines)

Temporal profile for emissions

Description

Set of hourly profiles that represents the mean activity for each hour (local time) of the week.

LDV

Light Duty vehicles

HDV

Heavy Duty vehicles

PC_JUNE_2012

passenger cars counted in June 2012

PC_JUNE_2013

passenger cars counted in June 2013

PC_JUNE_2014

passenger cars counted in June 2014

LCV_JUNE_2012

light comercial vehicles counted in June 2012

LCV_JUNE_2013

light comercial vehicles counted in June 2013

LCV_JUNE_2014

light comercial vehicles counted in June 2014

MC_JUNE_2012

motorcycles counted in June 2012

MC_JUNE_2013

motorcycles counted in June 2013

MC_JUNE_2014

motorcycles counted in June 2014

HGV_JUNE_2012

Heavy good vehicles counted in June 2012

HGV_JUNE_2013

Heavy good vehicles counted in June 2013

HGV_JUNE_2014

Heavy good vehicles counted in June 2014

PC_JANUARY_2012

passenger cars counted in january 2012

PC_JANUARY_2013

passenger cars counted in january 2013

PC_JANUARY_2014

passenger cars counted in january 2014

LCV_JANUARY_2012

light comercial vehicles counted in january 2012

LCV_JANUARY_2013

light comercial vehicles counted in january 2013

LCV_JANUARY_2014

light comercial vehicles counted in january 2014

MC_JANUARY_2012

Motorcycles counted in january 2012

MC_JANUARY_2014

Motorcycles counted in january 2014

HGV_JANUARY_2012

Heavy good vehicles counted in january 2012

HGV_JANUARY_2013

Heavy good vehicles counted in january 2013

HGV_JANUARY_2014

Heavy good vehicles counted in january 2014

POW

Power generation emission profile

IND

Industrial emission profile

RES

Residencial emission profile

TRA

Transport emission profile

AGR

Agriculture emission profile

SHP

Emission profile for ships

SLV

Solvent use emission constant profile

WBD

Waste burning emisssion constant profile

PC_nov_2018

passenger cars at Janio Quadros on November 2018

HGV_nov_2018

heavy good vehicles at Janio Quadros on November 2018

TOTAL_nov_2018

total vehicle at Janio Quadros on November 2018

PC_out_2018

passenger cars at Anhanguera-Castello Branco on October 2018

MC_out_2018

Motorcycles cars at Anhanguera-Castello Branco on October 2018

Usage

data(perfil)

Format

A list of data frames with activity by hour and weekday.

Details

- Profiles 1 to 2 are from traffic count at São Paulo city from Perez Martínez et al (2014).

- Profiles 3 to 25 comes from traffic counted of toll stations located in São Paulo city, for summer and winters of 2012, 2013 and 2014.

- Profiles 26 to 33 are for different sectors from Oliver et al (2003).

- Profiles 34 to 36 are for volumetric mechanized traffic count at Janio Quadros tunnel on November 2018.

- Profiles 37 to 38 are for volumetric mechanized traffic count at Anhanguera-Castello Branco on October 2018.

Note

The profile is normalized by days (but is balanced for a complete week) it means diary_emission x profile = hourly_emission.

References

Pérez-Martínez, P. J., Miranda, R. M., Nogueira, T., Guardani, M. L., Fornaro, A., Ynoue, R., & Andrade, M. F. (2014). Emission factors of air pollutants from vehicles measured inside road tunnels in São Paulo: case study comparison. International Journal of Environmental Science and Technology, 11(8), 2155-2168.

Olivier, J., J. Peters, C. Granier, G. Pétron, J.F. Müller, and S. Wallens, Present and future surface emissions of atmospheric compounds, POET Report #2, EU project EVK2-1999-00011, 2003.

Examples

# load the data
data(perfil)

# function to simple view
plot.perfil <- function(per = perfil$LDV, text="", color = "#0000FFBB"){
  plot(per[,1],ty = "l", ylim = range(per),axe = FALSE,
       xlab = "hour",ylab = "Intensity",main = text,col=color)
  for(i in 2:7){
    lines(per[,i],col = color)
  }
  for(i in 1:7){
    points(per[,i],col = "black", pch = 20)
  }
  axis(1,at=0.5+c(0,6,12,18,24),labels = c("00:00","06:00","12:00","18:00","00:00"))
  axis(2)
  box()
}

# view all profiles in perfil data
for(i in 1:length(names(perfil))){
  cat(paste("profile",i,names(perfil)[i],"\n"))
  plot.perfil(perfil[[i]],names(perfil)[i])
}

Calculate plume rise height.

Description

Calculate the maximum height of rise based on Brigs (1975), the height is calculated using different formulations depending on stability and wind conditions.

Usage

plumeRise(df, imax = 10, ermax = 1/100, Hmax = TRUE, verbose = TRUE)

Arguments

Format

data.frame with the input, rise (m) and effective higt (m)

Details

The input data.frame must contains the folloging colluns:

- z: height of the emission (m)

- r: source raius (m)

- Ve: emission velocity (m/s)

- Te: emission temperature (K)

- ws: wind speed (m/s)

- Temp: ambient temperature (K)

- h: height of the Atmospheric Boundary Layer-ABL (m)

- L: Monin-Obuhkov Lench (m)

- dtdz: lapse ration of potential temperature, used only for stable ABL (K/m)

- Ustar: atriction velocity, used only for neutral ABL (m/s)

- Wstar: scale of convectie velocity, used only for convective ABL (m/s)

Addcitionaly some combination of ws, Wstar and Ustar can produce inacurate results, Weil (1979) propose a geometric limit of 0.62 * (h - Hs) for the rise value.

Value

a data.frame with effective height of emissions for pointSource function

References

The plume rise formulas are from Brigs (1975):"Brigs, G. A. Plume rise predictions, Lectures on Air Pollution and Environmental Impact Analyses. Amer. Meteor. Soc. p. 59-111, 1975." and Arya 1999: "Arya, S.P., 1999, Air Pollution Meteorology and Dispersion, Oxford University Press, New York, 310 p."

The limits are from Weil (1979): "WEIL, J.C. Assessmet of plume rise and dispersion models using LIDAR data, PPSP-MP-24. Prepared by Environmental Center, Martin Marietta Corporation, for Maryland Department of Natural Resources. 1979."

The example is data from a chimney of the Candiota thermoelectric powerplant from Arabage et al (2006) "Arabage, M. C.; Degrazia, G. A.; Moraes O. L. Simulação euleriana da dispersão local da pluma de poluente atmosférico de Candiota-RS. Revista Brasileira de Meteorologia, v.21, n.2, p. 153-160, 2006."

Examples

candiota <- matrix(c(150,1,20,420,3.11,273.15 + 3.16,200,-34.86,3.11,0.33,
                     150,1,20,420,3.81,273.15 + 4.69,300,-34.83,3.81,0.40,
                     150,1,20,420,3.23,273.15 + 5.53,400,-24.43,3.23,0.48,
                     150,1,20,420,3.47,273.15 + 6.41,500,-15.15,3.48,0.52,
                     150,1,20,420,3.37,273.15 + 6.35,600, -8.85,3.37,2.30,
                     150,1,20,420,3.69,273.15 + 5.93,800,-10.08,3.69,2.80,
                     150,1,20,420,3.59,273.15 + 6.08,800, -7.23,3.49,1.57,
                     150,1,20,420,4.14,273.15 + 6.53,900,-28.12,4.14,0.97),
                     ncol = 10, byrow = TRUE)
candiota <- data.frame(candiota)
names(candiota) <- c("z","r","Ve","Te","ws","Temp","h","L","Ustar","Wstar")
row.names(candiota) <- c("08:00","09:00",paste(10:15,":00",sep=""))
candiota <- plumeRise(candiota,Hmax = TRUE)
print(candiota)

Emissions from point sources

Description

Transform a set of points into a grinded output

Usage

pointSource(emissions, grid, verbose = TRUE)

Arguments

Value

a raster

See Also

gridInfo and rasterSource

Examples

d1 <- gridInfo(paste(system.file("extdata", package = "EmissV"),"/wrfinput_d01",sep=""))

p = data.frame(lat      = c(-22,-22,-23.5),
               lon      = c(-46,-48,-47  ),
               z        = c(0  ,  0,  0  ),
               emission = c(666,444,111  ) )

p_emissions <- pointSource(emissions = p, grid = d1)

raster::plot(p_emissions,ylab="Lat", xlab="Lon",
             main = "3 point sources for domain d1")

Distribution of emissions by a georeferenced image

Description

Calculate the spatial distribution by a raster

Usage

rasterSource(r, grid, nlevels = "all", conservative = TRUE, verbose = TRUE)

Arguments

Details

About the DMSP and example data https://en.wikipedia.org/wiki/Defense_Meteorological_Satellite_Program

Value

Returns a matrix

Source

Exemple data is a low resolution cutting from image of persistent lights of the Defense Meteorological Satellite Program (DMSP) https://pt.wikipedia.org/wiki/Defense_Meteorological_Satellite_Program

Data avaliable https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/defense-meteorological-satellite-program

See Also

gridInfo and lineSource

Examples

grid  <- gridInfo(paste(system.file("extdata", package = "EmissV"),"/wrfinput_d01",sep=""))
x     <- raster::raster(paste(system.file("extdata", package = "EmissV"),"/dmsp.tiff",sep=""))
test  <- rasterSource(x, grid)
image(test, axe = FALSE, main = "Spatial distribution by Persistent Nocturnal Lights from DMSP")

Read NetCDF data from global inventories

Description

Read data from global inventories. Several files can be read to produce one emission output and/or can be splitted into several species

Usage

read(
  file = file.choose(),
  version = NA,
  coef = rep(1, length(file)),
  spec = NULL,
  year = 1,
  month = 1,
  hour = 1,
  categories,
  reproject = TRUE,
  as_raster = TRUE,
  skip_missing = FALSE,
  verbose = TRUE
)

Arguments

Value

Matrix or raster

Note

for EDGAR (all versions), GAINS, RCP and MACCTITY, please use flux (kg m-2 s-1) NetCDF file.

Source

Read abbout EDGAR at http://edgar.jrc.ec.europa.eu and MACCITY at http://accent.aero.jussieu.fr/MACC_metadata.php

More info for EDGARv8.1 https://edgar.jrc.ec.europa.eu/dataset_ap81 for short live species and https://edgar.jrc.ec.europa.eu/dataset_ghg80 for GHG

References

Janssens-Maenhout, G., Dentener, F., Van Aardenne, J., Monni, S., Pagliari, V., Orlandini, L., ... & Wankmüller, R. (2012). EDGAR-HTAP: a harmonized gridded air pollution emission dataset based on national inventories. European Commission Joint Research Centre Institute for Environment and Sustainability. JRC 68434 UR 25229 EUR 25229, ISBN 978-92-79-23123-0.

Lamarque, J.-F., Bond, T. C., Eyring, V., Granier, C., Heil, A., Klimont, Z., Lee, D., Liousse, C., Mieville, A., Owen, B., Schultz, M. G., Shindell, D., Smith, S. J., Stehfest, E., Van Aardenne, J., Cooper, O. R., Kainuma, M., Mahowald, N., McConnell, J. R., Naik, V., Riahi, K., and van Vuuren, D. P.: Historical (1850-2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application, Atmos. Chem. Phys., 10, 7017-7039, doi:10.5194/acp-10-7017-2010, 2010.

Z Klimont, S. J. Smith and J Cofala The last decade of global anthropogenic sulfur dioxide: 2000–2011 emissions Environmental Research Letters 8, 014003, 2013

Gurney, Kevin R., Jianming Liang, Risa Patarasuk, Yang Song, Jianhua Huang, and Geoffrey Roest (2019) The Vulcan Version 3.0 High-Resolution Fossil Fuel CO2 Emissions for the United States. Nature Scientific Data.

See Also

rasterSource and gridInfo

species

Examples

folder <- file.path(tempdir(), "EDGARv8.1")
dir.create(folder)

url     <- "https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/EDGAR/datasets"
dataset <- "v81_FT2022_AP_new/NOx/TOTALS/flx_nc"
file    <- "v8.1_FT2022_AP_NOx_2022_TOTALS_flx_nc.zip"

download.file(paste0(url,"/",dataset,"/",file), paste0(folder,"/",file))

unzip(paste0(folder,"/",file),exdir = folder)

nox  <- read(file    = dir(path=folder, pattern="flx\\.nc", full.names=TRUE),
             version = "EDGARv8",
             spec    = c(E_NO  = 0.9 ,   # 90% of NOx is NO
                         E_NO2 = 0.1 ))  # 10% of NOx is NO2
# creating a color scale
cor <- colorRampPalette(colors = c(c("#031057", "#0522FC",
                                     "#7E0AFA", "#EF0AFF",
                                     "#FFA530", "#FFF957")))
raster::plot(nox$E_NO, xlab="Latitude", ylab="Longitude",
             col = cor(12),zlim = c(-6.5e-7,1.4e-5),
             main="TOTAL NO emissions from EDGARv8.1 (in g / m2 s)")

d1  <- gridInfo(paste(system.file("extdata", package = "EmissV"),"/wrfinput_d01",sep=""))
NO  <- emission(grid = d1, inventory = nox$E_NO, pol = "NO", mm = 30.01,plot = TRUE)

Speciation of emissions in different compounds

Description

Distribute the total mass of estimated emissions into model species.

Usage

speciation(total, spec = NULL, verbose = TRUE)

Arguments

Value

Return a list with the daily total emission by interest area (cityes, states, countries, etc).

See Also

species

Examples

veic <- vehicles(example = TRUE)
EmissionFactors <- emissionFactor(example = TRUE)
TOTAL <- totalEmission(veic,EmissionFactors,pol = "PM")
pm_iag <- c(E_PM25I = 0.0509200,
            E_PM25J = 0.1527600,
            E_ECI   = 0.1196620,
            E_ECJ   = 0.0076380,
            E_ORGI  = 0.0534660,
            E_ORGJ  = 0.2279340,
            E_SO4I  = 0.0063784,
            E_SO4J  = 0.0405216,
            E_NO3J  = 0.0024656,
            E_NO3I  = 0.0082544,
            E_PM10  = 0.3300000)
PM <- speciation(TOTAL,pm_iag)

Species mapping tables

Description

Set of tables for speciation:

voc_radm2_mic

Volatile organic compounds for RADM2

voc_cbmz_mic

Volatile organic compounds for CBMZ

voc_moz_mic

Volatile organic compounds for MOZART

voc_saprc99_mic

volatile organic compounds for SAPRC99

veicularvoc_radm2_iag

Vehicular volatile organic compounds for RADM2 (MOL)

veicularvoc_cbmz_iag

Vehicular volatile organic compounds for CBMZ (MOL)

veicularvoc_moz_iag

Vehicular volatile organic compounds for MOZART (MOL)

veicularvoc_saprc99_iag

Vehicular volatile organic compounds for SAPRC99 (MOL)

pm_madesorgan_iag

Particulate matter for made/sorgan

pm25_madesorgan_iag

Fine particulate matter for made/sorgan

nox_iag

Nox split Perez Martínez et al (2014)

nox_bcom

Nox split usin Ntziachristos and Zamaras (2016)

voc_radm2_edgar432

Volatile organic compounds species from EDGAR 4.3.2 for RADM2 (MOL)

voc_moz_edgar432

Volatile organic compounds species from EDGAR 4.3.2 for MOZART (MOL)

- Volatile organic compounds species map from 1 to 4 are from Li et al (2014) taken into account several sources of pollutants.

- Volatile organic compounds from vehicular activity species map 5 to 8 is a by fuel and emission process from USP-IAG tunel experiments (Rafee et al., 2017) emited by the process of exhaust (through the exhaust pipe), liquid (carter and evaporative) and vapor (fuel transfer operations).

- Particulate matter speciesmap for made/sorgan emissions 9 and 10.

- Nox split using Perez Martínez et al (2014) data (11).

- Nox split using mean of Ntziachristos and Zamaras (2016) data (12).

- Volatile organic compounds species map 13 and 14 are the corespondence from EDGAR 4.3.2 VOC specialization to RADM2 and MOZART.

Usage

data(species)

Format

List of numeric vectors with the 'names()' of the species and the values of each species.

Details

iag-voc: After estimating all the emissions of NMHC, it was used the speciation presented in (RAFEE et al., 2017). This speciation is based on tunnel measurements in São Paulo, depends on the type of fuel (E25, E100 and B5) and provides the mass of each chemical compound as mol/g. This speciation splits the NMHC from evaporative, liquid and exhaust emissions of E25, E100 and B5, into minimum compounds required for the Carbon Bond Mechanism (CBMZ) (ZAVERI; PETERS, 1999). Atmospheric simulations using the same pollutants in Brazil have resulted in good agreement with observations (ANDRADE et al., 2015).

iag-pm: data tunnel experiments at São Paulo in Perez Martínez et al (2014)

iag-nox: common NOx split for São Paulo Metropolitan area.

bcom-nox: mean of Ntziachristos and Zamaras (2016) data.

mic: from Li et al (2014).

edgar: Edgar 4.3.2 emissions Crippa et al. (2018).

Note

The units are mass ratio (mass/mass) or MOL (MOL), this last case do not change the default 'mm' into 'emission()' function.

References

Li, M., Zhang, Q., Streets, D. G., He, K. B., Cheng, Y. F., Emmons, L. K., ... & Su, H. (2014). Mapping Asian anthropogenic emissions of non-methane volatile organic compounds to multiple chemical mechanisms. Atmos. Chem. Phys, 14(11), 5617-5638.

Huang, G., Brook, R., Crippa, M., Janssens-Maenhout, G., Schieberle, C., Dore, C., ... & Friedrich, R. (2017). Speciation of anthropogenic emissions of non-methane volatile organic compounds: a global gridded data set for 1970–2012. Atmospheric Chemistry and Physics, 17(12), 7683.

Abou Rafee, S. A., Martins, L. D., Kawashima, A. B., Almeida, D. S., Morais, M. V. B., Souza, R. V. A., Oliveira, M. B. L., Souza, R. A. F., Medeiros, A. S. S., Urbina, V., Freitas, E. D., Martin, S. T., and Martins, J. A.: Contributions of mobile, stationary and biogenic sources to air pollution in the Amazon rainforest: a numerical study with the WRF-Chem model, Atmos. Chem. Phys., 17, 7977-7995, https://doi.org/10.5194/acp-17-7977-2017, 2017.

Martins, L. D., Andrade, M. F. D., Freitas, E., Pretto, A., Gatti, L. V., Junior, O. M. A., et al. (2006). Emission factors for gas-powered vehicles traveling through road tunnels in Sao Paulo, Brazil. Environ. Sci. Technol. 40, 6722–6729. doi: 10.1021/es052441u

Pérez-Martínez, P. J., Miranda, R. M., Nogueira, T., Guardani, M. L., Fornaro, A., Ynoue, R., & Andrade, M. F. (2014). Emission factors of air pollutants from vehicles measured inside road tunnels in São Paulo: case study comparison. International Journal of Environmental Science and Technology, 11(8), 2155-2168.

ANDRADE, M. d. F. et al. Air quality forecasting system for southeastern brazil. Frontiers in Environmental Science, Frontiers, v. 3, p. 1–12, 2015.

Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., Aardenne, J. A. V., ... & Janssens-Maenhout, G. (2018). Gridded emissions of air pollutants for the period 1970–2012 within EDGAR v4.3.2. Earth System Science Data, 10(4), 1987-2013.

See Also

speciation and read

Examples

# load the mapping tables
data(species)
# names of eath mapping tables
for(i in 1:length(names(species)))
    cat(paste0("specie map ",i," ",names(species)[i],"\n"))
# names of species contained in the (first) mapping table
names(species[[1]])
# The first mapping table / species and values
species[1]

Calculate total emissions

Description

Caculate the total emission with:

Emission(pollutant) = sum( Vehicles(n) * Km_day_use(n) * Emission_Factor(n,pollutant) )

where n is the type of the veicle

Usage

totalEmission(v, ef, pol, verbose = TRUE)

Arguments

Value

Return a list with the daily total emission by interest area (cityes, states, countries, etc).

Note

the units (set_units("value",unit) where the recomended unit is g/d) must be used to make the ef data.frame

See Also

rasterSource, lineSource and emission

Examples

veic <- vehicles(example = TRUE)

EmissionFactors <- emissionFactor(example = TRUE)

TOTAL <- totalEmission(veic,EmissionFactors,pol = c("CO","PM"))

Tool to set-up vehicle data table

Description

Return a data frame with 4 columns (vehicle category, type, fuel and avarage kilometers driven) and an aditional column with the number of vehicles for each interest area (cityes, states, countries, etc).

Average daily kilometres driven are defined by vehicle type:

- LDV (Light duty Vehicles) 41 km / day

- TRUCKS (Trucks) 110 km / day

- BUS (Busses) 165 km / day

- MOTO (motorcycles and other vehicles) 140 km / day

The number of vehicles are defined by the distribution of vehicles by vehicle classs and the total number of vehicles by area.

Usage

vehicles(
  total_v,
  area_name = names(total_v),
  distribution,
  type,
  category = NA,
  fuel = NA,
  vnames = NA,
  example = FALSE,
  verbose = TRUE
)

Arguments

Value

a fleet distribution data.frame for totalEmission function

Note

total_v and area_name must have the same length.

distribution, type, category (if used), fuel (if used) and vnames (if used) must have the same length.

See Also

areaSource and totalEmission

Examples

fleet <- vehicles(example = TRUE)

# or the code bellow for the same result
# DETRAN 2016 data for total number of vehicles for 5 Brazilian states (Sao Paulo,
# Rio de Janeiro, Minas Gerais, Parana and Santa Catarina)
# vahicle distribution of Sao Paulo

fleet <- vehicles(total_v = c(27332101, 6377484, 10277988, 7140439, 4772160),
                  area_name = c("SP", "RJ", "MG", "PR", "SC"),
                  distribution = c( 0.4253, 0.0320, 0.3602, 0.0260,
                                   0.0290, 0.0008, 0.1181, 0.0086),
                  category =  c("LDV_E25","LDV_E100","LDV_F","TRUCKS_B5",
                                "CBUS_B5","MBUS_B5","MOTO_E25","MOTO_F"),
                  type = c("LDV", "LDV", "LDV","TRUCKS",
                          "BUS","BUS","MOTO", "MOTO"),
                  fuel = c("E25", "E100", "FLEX","B5",
                           "B5","B5","E25", "FLEX"),
                  vnames = c("Light duty Vehicles Gasohol","Light Duty Vehicles Ethanol",
                             "Light Duty Vehicles Flex","Diesel trucks","Diesel urban busses",
                             "Diesel intercity busses","Gasohol motorcycles",
                             "Flex motorcycles"))