Package 'vein'

Title: Vehicular Emissions Inventories
Description: Elaboration of vehicular emissions inventories, consisting in four stages, pre-processing activity data, preparing emissions factors, estimating the emissions and post-processing of emissions in maps and databases. More details in Ibarra-Espinosa et al (2018) <doi:10.5194/gmd-11-2209-2018>. Before using VEIN you need to know the vehicular composition of your study area, in other words, the combination of of type of vehicles, size and fuel of the fleet. Then, it is recommended to start with the project to download a template to create a structure of directories and scripts.
Authors: Sergio Ibarra-Espinosa [aut, cre] , Daniel Schuch [ctb] , Joao Bazzo [ctb] , Mario Gavidia-Calderón [ctb] , Karl Ropkins [ctb]
Maintainer: Sergio Ibarra-Espinosa <[email protected]>
License: MIT + file LICENSE
Version: 1.1.3
Built: 2024-09-29 06:23:26 UTC
Source: CRAN

Help Index

Construction function to add unit km

Description

add_lkm just add unit 'km' to different R objects

Usage

add_lkm(x)

Arguments

x

Object with class "data.frame", "matrix", "numeric" or "integer"

Value

Objects of class "data.frame" or "units"

See Also

Other Add distance unitts: add_miles()

Examples

## Not run: 
a <- add_lkm(rnorm(100)*10)
plot(a)
b <- add_lkm(matrix(rnorm(100)*10, ncol = 10))
print(head(b))

## End(Not run)

Construction function to add unit miles

Description

add_miles just add unit 'miles' to different R objects

Usage

add_miles(x)

Arguments

x

Object with class "data.frame", "matrix", "numeric" or "integer"

Value

Objects of class "data.frame" or "units"

See Also

Other Add distance unitts: add_lkm()

Examples

## Not run: 
a <- add_miles(rnorm(100)*10)
plot(a)
b <- add_miles(matrix(rnorm(100)*10, ncol = 10))
print(head(b))

## End(Not run)

Add polygon id to lines road network

Description

Sometimes you need to add polygon id into your streets road network. add_polid add add_polid id into your road network cropping your network by.

For instance, you have open street maps road network the you have the polygon of your regions. This function adds the id of your polygon as a new column in the streets network.

Usage

add_polid(polyg, street, by)

Arguments

polyg

sf object POLYGON or sp
street

streets road network class sf or sp
by

Character indicating the column with the id in polyg

See Also

emis_to_streets

Examples

## Not run: 
data(net)
nets <- sf::st_as_sf(net)
bb <- sf::st_as_sf(sf::st_as_sfc(sf::st_bbox(nets)))
bb$id <- "a"
a <- add_polid(polyg = bb, street = nets, by = "id")

## End(Not run)

function to add a scale to a image plot

Description

method to plot a scale in image plot.

Usage

addscale(
  z,
  zlim = range(z, na.rm = TRUE),
  col = grDevices::heat.colors(12),
  breaks = pretty(zlim),
  horiz = TRUE,
  ylim = NULL,
  xlim = NULL,
  ...
)

Arguments

z

matrix or vector
zlim

z limit
col

color
breaks

interval for the tickmarks
horiz

TRUE (default) to a horizontal scale
ylim

y limitS
xlim

x limit
...

other arguments to plot

Examples

## Not run: 
mat <- matrix(100:1,ncol = 10, byrow = F)
cor <- grDevices::heat.colors(100)
image(mat,axe = FALSE, main = "numbers from 1 to 100", col = cor)
axis(2)
addscale(mat, col = cor)

## End(Not run)

Average daily traffic (ADT) from hourly traffic data.

Description

adt calculates ADT based on hourly traffic data.

Usage

adt(
  pc,
  lcv,
  hgv,
  bus,
  mc,
  p_pc,
  p_lcv,
  p_hgv,
  p_bus,
  p_mc,
  feq_pc = 1,
  feq_lcv = 1.5,
  feq_hgv = 2,
  feq_bus = 2,
  feq_mc = 0.5
)

Arguments

pc

numeric vector for passenger cars
lcv

numeric vector for light commercial vehicles
hgv

numeric vector for heavy good vehicles or trucks
bus

numeric vector for bus
mc

numeric vector for motorcycles
p_pc

data-frame profile for passenger cars, 24 hours only.
p_lcv

data-frame profile for light commercial vehicles, 24 hours only.
p_hgv

data-frame profile for heavy good vehicles or trucks, 24 hours only.
p_bus

data-frame profile for bus, 24 hours only.
p_mc

data-frame profile for motorcycles, 24 hours only.
feq_pc

Numeric, factor equivalence
feq_lcv

Numeric, factor equivalence
feq_hgv

Numeric, factor equivalence
feq_bus

Numeric, factor equivalence
feq_mc

Numeric, factor equivalence

Value

numeric vector of total volume of traffic per link as ADT

Examples

## Not run: 
data(net)
data(pc_profile)
p1 <- pc_profile[, 1]
adt1 <- adt(pc = net$ldv*0.75,
            lcv = net$ldv*0.1,
            hgv = net$hdv,
            bus = net$hdv*0.1,
            mc = net$ldv*0.15,
            p_pc = p1,
            p_lcv = p1,
            p_hgv = p1,
            p_bus = p1,
            p_mc = p1)
head(adt1)

## End(Not run)

Applies a survival rate to numeric new vehicles

Description

age returns survived vehicles

Usage

age(x, type = "weibull", a = 14.46, b = 4.79, agemax, verbose = FALSE)

Arguments

x

Numeric; numerical vector of sales or registrations for each year
type

Character; any of "gompertz", "double_logistic", "weibull" and "weibull2"
a

Numeric; parameter of survival equation
b

Numeric; parameter of survival equation
agemax

Integer; age of oldest vehicles for that category
verbose

Logical; message with average age and total numer of vehicles regions or streets.

Value

dataframe of age distrubution of vehicles

Note

The functions age* produce distribution of the circulating fleet by age of use. The order of using these functions is:

1. If you know the distribution of the vehicles by age of use , use: my_age 2. If you know the sales of vehicles, or the registry of new vehicles, use age to apply a survival function. 3. If you know the theoretical shape of the circulating fleet and you can use age_ldv, age_hdv or age_moto. For instance, you dont know the sales or registry of vehicles, but somehow you know the shape of this curve. 4. You can use/merge/transform/dapt any of these functions.

gompertz: 1 - exp(-exp(a + b*time)), defaults PC: b = -0.137, a = 1.798, LCV: b = -0.141, a = 1.618 MCT (2006). de Gases de Efeito Estufa-Emissoes de Gases de Efeito Estufa por Fontes Moveis, no Setor Energético. Ministerio da Ciencia e Tecnologia. This curve is also used by Guo and Wang (2012, 2015) in the form: V*exp(alpha*exp(beta*E)) where V is the saturation car ownership level and E GDP per capita Huo, H., & Wang, M. (2012). Modeling future vehicle sales and stock in China. Energy Policy, 43, 17–29. doi:10.1016/j.enpol.2011.09.063 Huo, Hong, et al. "Vehicular air pollutant emissions in China: evaluation of past control policies and future perspectives." Mitigation and Adaptation Strategies for Global Change 20.5 (2015): 719-733.

double_logistic: 1/(1 + exp(a*(time + b))) + 1/(1 + exp(a*(time - b))), defaults PC: b = 21, a = 0.19, LCV: b = 15.3, a = 0.17, HGV: b = 17, a = 0.1, BUS: b = 19.1, a = 0.16 MCT (2006). de Gases de Efeito Estufa-Emissoes de Gases de Efeito Estufa por Fontes Moveis, no Setor Energético. Ministerio da Ciencia e Tecnologia.

weibull: exp(-(time/a)^b), defaults PC: b = 4.79, a = 14.46, Taxi: b = +inf, a = 5, Government and business: b = 5.33, a = 13.11 Non-operating vehicles: b = 5.08, a = 11.53 Bus: b = +inf, a = 9, non-transit bus: b = +inf, a = 5.5 Heavy HGV: b = 5.58, a = 12.8, Medium HGV: b = 5.58, a = 10.09, Light HGV: b = 5.58, a = 8.02 Hao, H., Wang, H., Ouyang, M., & Cheng, F. (2011). Vehicle survival patterns in China. Science China Technological Sciences, 54(3), 625-629.

weibull2: exp(-((time + b)/a)^b ), defaults b = 11, a = 26 Zachariadis, T., Samaras, Z., Zierock, K. H. (1995). Dynamic modeling of vehicle populations: an engineering approach for emissions calculations. Technological Forecasting and Social Change, 50(2), 135-149. Cited by Huo and Wang (2012)

See Also

Other age: age_hdv(), age_ldv(), age_moto()

Examples

## Not run: 
vehLIA <- rep(1, 25)
PV_Minia <- age(x = vehLIA)
PV_Minib <- age(x = vehLIA, type = "weibull2", b = 11, a = 26)
PV_Minic <- age(x = vehLIA, type = "double_logistic", b = 21, a = 0.19)
PV_Minid <- age(x = vehLIA, type = "gompertz", b = -0.137, a = 1.798)
dff <- data.frame(PV_Minia, PV_Minib, PV_Minic, PV_Minid)
colplot(dff)

## End(Not run)

Returns amount of vehicles at each age

Description

age_hdv returns amount of vehicles at each age

Usage

age_hdv(
  x,
  name = "age",
  a = 0.2,
  b = 17,
  agemin = 1,
  agemax = 50,
  k = 1,
  bystreet = F,
  net,
  verbose = FALSE,
  namerows,
  time
)

Arguments

x

Numeric; numerical vector of vehicles with length equal to lines features of road network
name

Character; of vehicle assigned to columns of dataframe
a

Numeric; parameter of survival equation
b

Numeric; parameter of survival equation
agemin

Integer; age of newest vehicles for that category
agemax

Integer; age of oldest vehicles for that category
k

Numeric; multiplication factor. If its length is > 1, it must match the length of x
bystreet

Logical; when TRUE it is expecting that 'a' and 'b' are numeric vectors with length equal to x
net

SpatialLinesDataFrame or Spatial Feature of "LINESTRING"
verbose

Logical; message with average age and total numer of vehicles
namerows

Any vector to be change row.names. For instance, name of regions or streets.
time

Character to be the time units as denominator, eg "1/h"

Value

dataframe of age distrubution of vehicles at each street

Note

The functions age* produce distribution of the circulating fleet by age of use. The order of using these functions is:

1. If you know the distribution of the vehicles by age of use , use: my_age 2. If you know the sales of vehicles, or the registry of new vehicles, use age to apply a survival function. 3. If you know the theoretical shape of the circulating fleet and you can use age_ldv, age_hdv or age_moto. For instance, you dont know the sales or registry of vehicles, but somehow you know the shape of this curve. 4. You can use/merge/transform/adapt any of these functions.

See Also

Other age: age_ldv(), age_moto(), age()

Examples

## Not run: 
data(net)
LT_B5 <- age_hdv(x = net$hdv,name = "LT_B5")
plot(LT_B5)
LT_B5 <- age_hdv(x = net$hdv, name = "LT_B5", net = net)
plot(LT_B5)

## End(Not run)

Returns amount of vehicles at each age

Description

age_ldv returns amount of vehicles at each age

Usage

age_ldv(
  x,
  name = "age",
  a = 1.698,
  b = -0.2,
  agemin = 1,
  agemax = 50,
  k = 1,
  bystreet = F,
  net,
  verbose = FALSE,
  namerows,
  time
)

Arguments

x

Numeric; numerical vector of vehicles with length equal to lines features of road network
name

Character; of vehicle assigned to columns of dataframe
a

Numeric; parameter of survival equation
b

Numeric; parameter of survival equation
agemin

Integer; age of newest vehicles for that category
agemax

Integer; age of oldest vehicles for that category
k

Numeric; multiplication factor. If its length is > 1, it must match the length of x
bystreet

Logical; when TRUE it is expecting that 'a' and 'b' are numeric vectors with length equal to x
net

SpatialLinesDataFrame or Spatial Feature of "LINESTRING"
verbose

Logical; message with average age and total numer of vehicles
namerows

Any vector to be change row.names. For instance, name of regions or streets.
time

Character to be the time units as denominator, eg "1/h"

Value

dataframe of age distrubution of vehicles

Note

The functions age* produce distribution of the circulating fleet by age of use. The order of using these functions is:

1. If you know the distribution of the vehicles by age of use , use: my_age 2. If you know the sales of vehicles, or the registry of new vehicles, use age to apply a survival function. 3. If you know the theoretical shape of the circulating fleet and you can use age_ldv, age_hdv or age_moto. For instance, you dont know the sales or registry of vehicles, but somehow you know the shape of this curve. 4. You can use/merge/transform/adapt any of these functions.

It consists in a Gompertz equation with default parameters from 1 national emissions inventory for green housegases in Brazil, MCT 2006

See Also

Other age: age_hdv(), age_moto(), age()

Examples

## Not run: 
data(net)
PC_E25_1400 <- age_ldv(x = net$ldv, name = "PC_E25_1400")
plot(PC_E25_1400)
PC_E25_1400 <- age_ldv(x = net$ldv, name = "PC_E25_1400", net = net)
plot(PC_E25_1400)

## End(Not run)

Returns amount of vehicles at each age

Description

age_moto returns amount of vehicles at each age

Usage

age_moto(
  x,
  name = "age",
  a = 0.2,
  b = 17,
  agemin = 1,
  agemax = 50,
  k = 1,
  bystreet = FALSE,
  net,
  verbose = FALSE,
  namerows,
  time
)

Arguments

x

Numeric; numerical vector of vehicles with length equal to lines features of road network
name

Character; of vehicle assigned to columns of dataframe
a

Numeric; parameter of survival equation
b

Numeric; parameter of survival equation
agemin

Integer; age of newest vehicles for that category
agemax

Integer; age of oldest vehicles for that category
k

Numeric; multiplication factor. If its length is > 1, it must match the length of x
bystreet

Logical; when TRUE it is expecting that 'a' and 'b' are numeric vectors with length equal to x
net

SpatialLinesDataFrame or Spatial Feature of "LINESTRING"
verbose

Logical; message with average age and total numer of vehicles
namerows

Any vector to be change row.names. For instance, name of regions or streets.
time

Character to be the time units as denominator, eg "1/h"

Value

dataframe of age distrubution of vehicles

Note

The functions age* produce distribution of the circulating fleet by age of use. The order of using these functions is:

1. If you know the distribution of the vehicles by age of use , use: my_age 2. If you know the sales of vehicles, or the registry of new vehicles, use age to apply a survival function. 3. If you know the theoretical shape of the circulating fleet and you can use age_ldv, age_hdv or age_moto. For instance, you dont know the sales or registry of vehicles, but somehow you know the shape of this curve. 4. You can use/merge/transform/adapt any of these functions.

See Also

Other age: age_hdv(), age_ldv(), age()

Examples

## Not run: 
data(net)
MOTO_E25_500 <- age_moto(x = net$ldv, name = "M_E25_500", k = 0.4)
plot(MOTO_E25_500)
MOTO_E25_500 <- age_moto(x = net$ldv, name = "M_E25_500", k = 0.4, net = net)
plot(MOTO_E25_500)

## End(Not run)

Average Weight for hourly traffic data.

Description

aw average weight form traffic.

Usage

aw(
  pc,
  lcv,
  hgv,
  bus,
  mc,
  p_pc,
  p_lcv,
  p_hgv,
  p_bus,
  p_mc,
  w_pc = 1,
  w_lcv = 3.5,
  w_hgv = 20,
  w_bus = 20,
  w_mc = 0.5,
  net
)

Arguments

pc

numeric vector for passenger cars
lcv

numeric vector for light commercial vehicles
hgv

numeric vector for heavy good vehicles or trucks
bus

numeric vector for bus
mc

numeric vector for motorcycles
p_pc

data-frame profile for passenger cars, 24 hours only.
p_lcv

data-frame profile for light commercial vehicles, 24 hours only.
p_hgv

data-frame profile for heavy good vehicles or trucks, 24 hours only.
p_bus

data-frame profile for bus, 24 hours only.
p_mc

data-frame profile for motorcycles, 24 hours only.
w_pc

Numeric, factor equivalence
w_lcv

Numeric, factor equivalence
w_hgv

Numeric, factor equivalence
w_bus

Numeric, factor equivalence
w_mc

Numeric, factor equivalence
net

SpatialLinesDataFrame or Spatial Feature of "LINESTRING"

Value

data.frame with with average weight

Examples

## Not run: 
data(net)
data(pc_profile)
p1 <- pc_profile[, 1]
aw1 <- aw(pc = net$ldv*0.75,
            lcv = net$ldv*0.1,
            hgv = net$hdv,
            bus = net$hdv*0.1,
            mc = net$ldv*0.15,
            p_pc = p1,
            p_lcv = p1,
            p_hgv = p1,
            p_bus = p1,
            p_mc = p1)
head(aw1)

## End(Not run)

Construction function for Celsius temperature

Description

celsius just add unit celsius to different R objects

Usage

celsius(x)

Arguments

x

Object with class "data.frame", "matrix", "numeric" or "integer"

Value

Objects of class "data.frame" or "units"

Examples

{
a <- celsius(rnorm(100)*10)
plot(a)
b <- celsius(matrix(rnorm(100)*10, ncol = 10))
print(head(b))
}

Check the max number of threads

Description

get_threads check the number of threads in this machine

Usage

check_nt()

Value

Integer with the max number of threads

Examples

{
  check_nt()
}

Fraction of mileage driven with a cold engine or catalizer below normal temperature

Description

This function depends length of trip and on ambient temperature. From the guidelines EMEP/EEA air pollutant emission inventory guidebook http://www.eea.europa.eu/themes/air/emep-eea-air-pollutant-emission-inventory-guidebook

Usage

cold_mileage(ltrip, ta)

Arguments

ltrip

Numeric; Length of trip. It must be in 'units' km.
ta

Numeric or data.frame; average monthly temperature Celsius. It if is a data.frame, it is convenient that each column is each month.

Note

This function is set so that values varies between 0 and 1.

Examples

## Not run: 
lkm <- units::set_units(1:10, km)
ta <- celsius(matrix(0:9, ncol = 12, nrow = 10))
a <- cold_mileage(lkm, ta)
colplot(a)

## End(Not run)

Function to plot columns of data.frames

Description

colplot plots columns of data.frame

Usage

colplot(
  df,
  cols = names(df),
  xlab = "",
  ylab = "",
  xlim = c(1, nrow(df)),
  ylim = range(unlist(df[[cols]]), na.rm = TRUE),
  main = NULL,
  theme = "black",
  col = cptcity::cpt(pal = cptcity::find_cpt("pastel")[4], n = length(names(df))),
  type = "b",
  lwd = 2,
  pch = 1:ncol(df),
  familyfont = "",
  ...
)

Arguments

df

data.frame.
cols

Character, columns of data.frame.
xlab

a label for the x axis, defaults to a description of x.
ylab

a label for the x axis, defaults to a description of x.
xlim

x limits
ylim

y limits
main

Character, a main title for the plot, see also title.
theme

Character; "black", "dark", "clean", "ink"
col

The colors for lines and points. Multiple colors can be specified so that each point can be given its own color. If there are fewer colors than points they are recycled in the standard fashion. Default are cptcity colour palette "kst_18_pastels"
type

1-character string giving the type of plot desired. The following values are possible, for details, see plot: "p" for points, "l" for lines, "b" for both points and lines, "c" for empty points joined by lines, "o" for overplotted points and lines, "s" and "S" for stair steps and "h" for histogram-like vertical lines. Finally, "n" does not produce any points or lines.
lwd

a vector of line widths, see par.
pch

plotting ‘character’, i.e., symbol to use. This can either be a single character or an integer code for one of a set of graphics symbols. The full set of S symbols is available with pch = 0:18, see the examples below. (NB: R uses circles instead of the octagons used in S.). Value pch = "." (equivalently pch = 46) is handled specially. It is a rectangle of side 0.01 inch (scaled by cex). In addition, if cex = 1 (the default), each side is at least one pixel (1/72 inch on the pdf, postscript and xfig devices). For other text symbols, cex = 1 corresponds to the default fontsize of the device, often specified by an argument pointsize. For pch in 0:25 the default size is about 75 the character height (see par("cin")).
familyfont

"Character" to specify font, default is"", options "serif", "sans", "mono" or more according device
...

plot arguments

Value

a nice plot

Note

This plot shows values > 0 by default. To plot all values, use all_values = TRUE

See Also

par

Other helpers: dmonth(), to_latex(), wide_to_long()

Examples

## Not run: 
a <- ef_cetesb("CO", c("PC_G", "PC_FE", "PC_FG", "PC_E"), agemax = 20)
colplot(df = a, ylab = "CO [g/km]", theme = "dark", type = "b")
colplot(df = a, ylab = "CO [g/km]", theme = "dark", pch = NULL, type = "b")
colplot(df = a, ylab = "CO [g/km]", theme = "clean", type = "b")
colplot(df = a, ylab = "CO [g/km]", theme = "clean", pch = NULL, type = "b")
#colplot(df = a, cols = "PC_FG", main = "EF", ylab = "CO [g/km]")
#colplot(df = a, ylab = "CO [g/km]", theme = "clean")

## End(Not run)

Description data.frame for MOVES

Description

A data.frame descriptors to use MOVES functions

Usage

data(decoder)

Format

A data frame with 69 rows and 4 columns:

CategoryField

dayID, sourceTypID, roadTypeID, pollutantID and procesID

pollutantID

Associated number

Description

Associatd description

V4

pollutants

Source

US/EPA MOVES

Number of days of the month

Description

ef_ldv_speed return the number of day sof the month

Usage

dmonth(year, month)

Arguments

year

Numeric
month

Numeric

Value

days of the month

See Also

Other helpers: colplot(), to_latex(), wide_to_long()

Examples

## Not run: 
dmonth(2022, 1)

## End(Not run)

Emissions factors for Environment Company of Sao Paulo, Brazil (CETESB)

Description

ef_cetesb returns a vector or data.frame of Brazilian emission factors.

Usage

ef_cetesb(
  p,
  veh,
  year = 2017,
  agemax = 40,
  scale = "default",
  sppm,
  full = FALSE,
  efinput,
  verbose = FALSE,
  csv
)

Arguments

p

Character;

Pollutants: "CO", "HC", "NMHC", "CH4", "NOx", "CO2", "RCHO" (aldehydes + formaldehyde), "ETOH", "PM", "N2O", "KML", "FC", "NO2", "NO", "NH3", "gD/KWH", "gCO2/KWH", "RCHO_0km" (aldehydes + formaldehyde), "PM25RES", "PM10RES", "CO_0km", "HC_0km", "NMHC_0km", "NOx_0km", "NO2_0km" ,"NO_0km", "RCHO_0km" and "ETOH_0km", "FS" (fuel sales) (g/km). If scale = "tunnel" is used, there is also "ALD" for aldehydes and "HCHO" for formaldehydes Evaporative emissions at average temperature ranges: "D_20_35", "S_20_35", "R_20_35", "D_10_25", "S_10_25", "R_10_25", "D_0_15", "S_0_15" and "R_0_15" where D means diurnal (g/day), S hot/warm soak (g/trip) and R hot/warm running losses (g/trip). THe deteriorated emission factors are calculated inside this function.
veh

Character; Vehicle categories: "PC_G", "PC_FG", "PC_FE", "PC_E", "LCV_G", "LCV_FG", "LCV_FE", "LCV_E", "LCV_D", "TRUCKS_SL", "TRUCKS_L", "TRUCKS_M", "TRUCKS_SH", "TRUCKS_H", "BUS_URBAN", "BUS_MICRO", "BUS_COACH", "BUS_ARTIC", "MC_150_G", "MC_150_500_G", "MC_500_G", "MC_150_FG", "MC_150_500_FG", "MC_500_FG", "MC_150_FE", "MC_150_500_FE", "MC_500_FE", "CICLOMOTOR", "GNV"
year

Numeric; Filter the emission factor to start from a specific base year. If project is 'constant' values above 2017 and below 1980 will be repeated
agemax

Integer; age of oldest vehicles for that category
scale

Character; values "default","tunnel" o "tunnel2018". If "tunnel", emission factors are scaled to represent EF measurements in tunnels in Sao Paulo
sppm

Numeric, sulfur (sulphur) in ppm in fuel.
full

Logical; To return a data.frame instead or a vector adding Age, Year, Brazilian emissions standards and its euro equivalents.
efinput

data.frame with efinput structure of sysdata cetesb. Allow apply deterioration for future emission factors
verbose

Logical; To show more information
csv

String with the path to download the ef in a .csv file. For instance, ef.csv

Value

A vector of Emission Factor or a data.frame

Note

new emission factors ar projects as the lates available,

The new convention for vehicles names are translated from CETESB report:

veh description
PC_G Passenger Car Gasohol (Gasoline + 27perc of anhydrous ethanol)
PC_E Passenger Car Ethanol (hydrous ethanol)
PC_FG Passenger Car Flex Gasohol (Gasoline + 27perc of anhydrous ethanol)
PC_FE Passenger Car Flex Ethanol (hydrous ethanol)
LCV_G Light Commercial Vehicle Gasohol (Gasoline + 27perc of anhydrous ethanol)
LCV_E Light Commercial Vehicle Ethanol (hydrous ethanol)
LCV_FG Light Commercial Vehicle Flex Gasohol (Gasoline + 27perc of anhydrous ethanol)
LCV_FE Light Commercial Vehicle Flex Ethanol (hydrous ethanol)
LCV_D Light Commercial Vehicle Diesel (5perc bio-diesel)
TRUCKS_SL_D Trucks Semi Light Diesel (5perc bio-diesel)
TRUCKS_L_D Trucks Light Diesel (5perc bio-diesel)
TRUCKS_M_D Trucks Medium Diesel (5perc bio-diesel)
TRUCKS_SH_D Trucks Semi Heavy Diesel (5perc bio-diesel)
TRUCKS_H_D Trucks Heavy Diesel (5perc bio-diesel)
BUS_URBAN_D Urban Bus Diesel (5perc bio-diesel)
BUS_MICRO_D Micro Urban Bus Diesel (5perc bio-diesel)
BUS_COACH_D Coach (inter-state) Bus Diesel (5perc bio-diesel)
BUS_ARTIC_D Articulated Urban Bus Diesel (5perc bio-diesel)
MC_150_G Motorcycle engine less than 150cc Gasohol (Gasoline + 27perc of anhydrous ethanol)
MC_150_500_G Motorcycle engine 150-500cc Gasohol (Gasoline + 27perc of anhydrous ethanol)
MC_500_G Motorcycle greater than 500cc Gasohol (Gasoline + 27perc of anhydrous ethanol)
MC_150_FG Flex Motorcycle engine less than 150cc Gasohol (Gasoline + 27perc of anhydrous ethanol)
MC_150_500_FG Flex Motorcycle engine 150-500cc Gasohol (Gasoline + 27perc of anhydrous ethanol)
MC_500_FG Flex Motorcycle greater than 500cc Gasohol (Gasoline + 27perc of anhydrous ethanol)
MC_150_FE Flex Motorcycle engine less than 150cc Ethanol (hydrous ethanol)
MC_150_500_FE Flex Motorcycle engine 150-500cc Ethanol (hydrous ethanol)
MC_500_FE Flex Motorcycle greater than 500cc Ethanol (hydrous ethanol)
PC_ELEC Passenger Car Electric
LCV_ELEC Light Commercial Vehicle Electric

The percentage varies of biofuels varies by law.

This emission factors are not exactly the same as the report of CETESB.

1) In this emission factors, there is also NO and NO2 based on split by published in the EMEP/EEA air pollutant emission inventory guidebook.

2) Also, the emission factors were extended till 50 years of use, repeating the oldest value.

3) CNG emission factors were expanded to other pollutants by comparison of US.EPA-AP42 emission factor: Section 1.4 Natural Gas Combustion.

In the previous versions I used the letter 'd' for deteriorated. I removed the letter 'd' internally to not break older code.

If by mistake, the user inputs one of veh names from the old convention, they are internally changed to the new convention: "SLT", "LT", "MT", "SHT","HT", "UB", "SUB", "COACH", "ARTIC", "M_G_150", "M_G_150_500", "M_G_500", "M_FG_150", "M_FG_150_500", "M_FG_500", "M_FE_150", "M_FE_150_500","M_FE_500", PC_ELEC, LCV_ELEC, TRUCKS_ELEC, BUS_ELEC, MC_150_ELEC, MC_150_500_ELEC, MC_500_ELEC

If pollutant is "SO2", it needs sppm. It is designed when veh has length 1, if it has length 2 or more, it will show a warning

Emission factor for vehicles older than the reported by CETESB were filled with las highest EF

  • Range EF from PC and LCV otto: 2018 - 1982. EF for 1981 and older as moving average.

  • Range LCV diesel : 2018 - 2006. EF for 2005 and older as moving average.

  • Range Trucks and Buse: 2018 - 1998. EF for 1997 and older as moving average.

  • Range MC Gasoline: 2018 - 2003. EF for 2002 and older as moving average.

  • Range MC Flex 150-500cc and >500cc: 2018 - 2012. EF for 2011 and older as moving average.

Currently, 2020, there are not any system for recovery of fuel vapors in Brazil. Hence, the FS takes into account the vapour that comes from the fuel tank inside the car and released into the atmosphere when injecting new fuel. There are discussions about increasing implementing stage I and II and/or ORVR these days. The ef FS is calculated by transforming g FC/km into (L/KM)*g/L with g/L 1.14 fgor gasoline and 0.37 for ethanol (CETESB, 2016). The density considered is 0.75425 for gasoline and 0.809 for ethanol (t/m^3)

CETESB emission factors did not cover evaporative emissions from motorcycles, which occur. Therefore, in the absence of better data, it was assumed the same ratio from passenger cars.

Li, Lan, et al. "Exhaust and evaporative emissions from motorcycles fueled with ethanol gasoline blends." Science of the Total Environment 502 (2015): 627-631.

If scale is used with tunnel, the references are:

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

  • Nogueira, T., de Souza, K. F., Fornaro, A., de Fatima Andrade, M., and de Carvalho, L. R. F. (2015). On-road emissions of carbonyls from vehicles powered by biofuel blends in traffic tunnels in the Metropolitan Area of Sao Paulo, Brazil. Atmospheric Environment, 108, 88-97.

  • Nogueira, T., et al (2021). In preparation (for tunnel 2018)

Emission factors for resuspension applies only with top-down approach as a experimental feature. Units are g/(streets*veh)/day. These values were derived form a bottom-up resuspension emissions from metropolitan area of Sao Paulo 2018, assuming 50000 streets

NH3 from EEA Tier 2

References

Emissoes Veiculares no Estado de Sao Paulo 2016. Technical Report. url: https://cetesb.sp.gov.br/veicular/relatorios-e-publicacoes/.

Examples

{
a <- ef_cetesb(p = "CO", veh = "PC_G")
a <- ef_cetesb(p = "NOx", veh = "TRUCKS_M_D")
a <- ef_cetesb("R_10_25", "PC_G")
a <- ef_cetesb("CO", c("PC_G", "PC_FE"))
ef_cetesb(p = "CO", veh = "PC_G", year = 1970, agemax = 40)
ef_cetesb(p = "CO", veh = "TRUCKS_L_D", year = 2018)
ef_cetesb(p = "CO", veh = "SLT", year = 2018) #  olds names
a <- ef_cetesb(p = "NMHC", veh = c("PC_G", "PC_FG", "PC_FE", "PC_E"), year = 2018, agemax = 20)
colplot(a, main = "NMHC EF", ylab = "[g/km]", xlab = "Years of use")
ef_cetesb(p = "PM25RES", veh = "PC_ELEC", year = 1970, agemax = 40)
ef_cetesb(p = "PM25RES", veh = "BUS_ELEC", year = 1970, agemax = 40)
}

Emissions factors from Chinese emissions guidelines

Description

ef_china returns emission factors as vector or data.frames. The emission factors comes from the chinese emission guidelines (v3) from the Chinese Ministry of Ecology and Environment http://www.mee.gov.cn/gkml/hbb/bgth/201407/W020140708387895271474.pdf

Usage

ef_china(
  v = "PV",
  t = "Small",
  f = "G",
  standard,
  p,
  k = 1,
  ta = celsius(15),
  humidity = 0.5,
  altitude = 1000,
  speed = Speed(30),
  baseyear_det = 2016,
  sulphur = 50,
  load_factor = 0.5,
  details = FALSE,
  correction_only = FALSE
)

Arguments

v

Character; category vehicle: "PV" for Passenger Vehicles or 'Trucks"
t

Character; sub-category of of vehicle: PV Gasoline: "Mini", "Small","Medium", "Large", "Taxi", "Motorcycles", "Moped", PV Diesel: "Mediumbus", "Largebus", "3-Wheel". Trucks: "Mini", "Light" , "Medium", "Heavy"
f

Character;fuel: "G", "D", "CNG", "ALL"
standard

Character or data.frame; "PRE", "I", "II", "III", "IV", "V". When it is a data.frame, it each row is a different region and ta, humidity, altitud, speed, sulphur and load_factor lengths have the same as the number of rows.
p

Character; pollutant: "CO", "NOx","HC", "PM", "Evaporative_driving" or "Evaporative_parking"
k

Numeric; multiplication factor
ta

Numeric; temperature of ambient in celcius degrees. When standard is a data.frame, the length must be equal to the number of rows of standard.
humidity

Numeric; relative humidity. When standard is a data.frame, the length must be equal to the number of rows of standard.
altitude

Numeric; altitude in meters. When standard is a data.frame, the length must be equal to the number of rows of standard.
speed

Numeric; altitude in km/h When standard is a data.frame, the length must be equal to the number of rows of standard.
baseyear_det

Integer; any of 2014, 2015, 2016, 2017, 2018
sulphur

Numeric; sulphur in ppm. When standard is a data.frame, the length must be equal to the number of rows of standard.
load_factor

Numeric; When standard is a data.frame, the length must be equal to the number of rows of standard.
details

Logical; When TRUE, it shows a description of the vehicle in chinese and english. Only when length standard is 1.
correction_only

Logical; When TRUE, return only correction factors.

Value

An emission factor

Note

Combination of vehicles:

v t f
PV Mini G HY
PV Bus D HY D
PV Mini CNG
PV Bus CNG
PV Mini G
PV Small G
PV Medium G
PV Large G
PV Taxi G
PV Bus G
PV Motorcycles G
PV Moped G
PV Mini D
PV Small D
PV Mediumbus D
PV Medium D
PV Largebus D
PV Bus D
PV 3-Wheel D
PV Small ALL
PV Mediumbus ALL
PV Largebus ALL
PV Taxi ALL
PV Bus ALL
Trucks Bus G
Trucks Light G
Trucks Medium G
Trucks Heavy G
Trucks Light D
Trucks Medium D
Trucks Heavy D
Trucks Low Speed D
Trucks Mini D

standard VI is assumed as V

See Also

ef_ldv_speed emis_hot_td

Other China: ef_china_det(), ef_china_hu(), ef_china_h(), ef_china_long(), ef_china_speed(), ef_china_s(), ef_china_te(), ef_china_th(), emis_china(), emis_long()

Examples

## Not run: 
# when standard is 'character'
# Checking
df_st <- rev(c(as.character(as.roman(5:1)), "PRE"))
ef_china(t = "Mini", f = "G", standard = df_st, p = "CO")
ef_china(t = "Mini", f = "G", standard = df_st, p = "HC")
ef_china(t = "Mini", f = "G", standard = df_st, p = "NOx")
ef_china(t = "Mini", f = "G", standard = df_st, p = "PM2.5")
ef_china(t = "Mini", f = "G", standard = df_st, p = "PM10")

ef_china(t = "Small", f = "G", standard = df_st, p = "CO")
ef_china(t = "Small", f = "G", standard = df_st, p = "HC")
ef_china(t = "Small", f = "G", standard = df_st, p = "NOx")
ef_china(t = "Small", f = "G", standard = df_st, p = "PM2.5")
ef_china(t = "Small", f = "G", standard = df_st, p = "PM10")


ef_china(t = "Mini",
        standard = c("PRE"),
        p = "CO",
        k = 1,
        ta = celsius(15),
        humidity = 0.5,
        altitude = 1000,
        speed = Speed(30),
        baseyear_det = 2014,
        sulphur = 50,
        load_factor = 0.5,
        details = FALSE)
ef_china(standard = c("PRE", "I"), p = "CO", correction_only = TRUE)

# when standard is 'data.frame'
df_st <- matrix(c("V", "IV", "III", "III", "II", "I", "PRE"), nrow = 2, ncol = 7, byrow = TRUE)
df_st <- as.data.frame(df_st)
a <- ef_china(standard = df_st,
              p = "PM10",
              ta = rep(celsius(15), 2),
              altitude = rep(1000, 2),
              speed = rep(Speed(30), 2),
              sulphur = rep(50, 2))
dim(a)
dim(df_st)
ef_china(standard = df_st, p = "PM2.5", ta = rep(celsius(20), 2),
altitude = rep(1501, 2), speed = rep(Speed(29), 2), sulphur = rep(50, 2))
a

# when standard, temperature and humidity are data.frames
# assuming 10 regions
df_st <- matrix(c("V", "IV", "III", "III", "II", "I", "PRE"), nrow = 10, ncol = 7, byrow = TRUE)
df_st <- as.data.frame(df_st)
df_t <- matrix(21:30, nrow = 10, ncol = 12, byrow = TRUE)
df_t <- as.data.frame(df_t)
for(i in 1:12) df_t[, i] <- celsius(df_t[, i])

# assuming 10 regions
df_h <- matrix(seq(0.4, 0.5, 0.05), nrow = 10, ncol = 12, byrow = TRUE)
df_h <- as.data.frame(df_h)
a <- ef_china(standard = df_st, p = "CO", ta = df_t, humidity = df_h,
altitude = rep(1501, 10), speed = rep(Speed(29), 10), sulphur = rep(50, 10))
a
a <- ef_china(standard = df_st, p = "PM2.5", ta = df_t, humidity = df_h,
altitude = rep(1501, 10), speed = rep(Speed(29), 10), sulphur = rep(50, 10))
a
a <- ef_china(standard = df_st, p = "PM10", ta = df_t, humidity = df_h,
altitude = rep(1501, 10), speed = rep(Speed(29), 10), sulphur = rep(50, 10))
a
dim(a)

## End(Not run)

Correction of Chinese emission factors by deterioration

Description

Correction of Chinese emission

Usage

ef_china_det(v = "PV", t = "Small", f = "G", standard, yeardet = 2015, p)

Arguments

v

Character; category vehicle: "PV" for Passenger Vehicles or 'Trucks"
t

Character; sub-category of of vehicle: PV Gasoline: "Mini", "Small","Medium", "Large", "Taxi", "Motorcycles", "Moped", PV Diesel: "Mediumbus", "Largebus", "3-Wheel". Trucks: "Mini", "Light" , "Medium", "Heavy"
f

Character;fuel: "G", "D", "CNG", "ALL"
standard

Character vector; "PRE", "I", "II", "III", "IV", "V".
yeardet

Integer; any of 2014, 2015, 2016, 2017, 2018
p

Character; pollutant: "CO", "NOx","HC", "PM", "Evaporative_driving" or "Evaporative_parking"

Value

long data.frame

See Also

Other China: ef_china_hu(), ef_china_h(), ef_china_long(), ef_china_speed(), ef_china_s(), ef_china_te(), ef_china_th(), ef_china(), emis_china(), emis_long()

Examples

{
ef_china_det(standard = "I", p = "CO")
ef_china_det(standard = c("I", "III"),
             p = "CO",
             f = "D")
}

Correction of Chinese factors by altitude

Description

Correction of Chinese emission

Usage

ef_china_h(h, v = "PV", t = "Small", f = "G", p)

Arguments

h

numeric altitude
v

Character; category vehicle: "PV" for Passenger Vehicles or 'Trucks"
t

Character; sub-category of of vehicle: PV Gasoline: "Mini", "Small","Medium", "Large", "Taxi", "Motorcycles", "Moped", PV Diesel: "Mediumbus", "Largebus", "3-Wheel". Trucks: "Mini", "Light" , "Medium", "Heavy"
f

Character;fuel: "G", "D", "CNG"
p

Character; pollutant: "CO", "NOx","HC", "PM", "Evaporative_driving" or "Evaporative_parking"

Value

long data.frame

See Also

Other China: ef_china_det(), ef_china_hu(), ef_china_long(), ef_china_speed(), ef_china_s(), ef_china_te(), ef_china_th(), ef_china(), emis_china(), emis_long()

Examples

{
ef_china_h(h = 1600, p = "CO")
}

Correction of Chinese emission factors by humidity

Description

Correction of Chinese emission

Usage

ef_china_hu(hu, v = "PV", t = "Small", f = "G", standard, p)

Arguments

hu

numeric humidity
v

Character; category vehicle: "PV" for Passenger Vehicles or 'Trucks"
t

Character; sub-category of of vehicle: PV Gasoline: "Mini", "Small","Medium", "Large", "Taxi", "Motorcycles", "Moped", PV Diesel: "Mediumbus", "Largebus", "3-Wheel". Trucks: "Mini", "Light" , "Medium", "Heavy"
f

Character;fuel: "G", "D", "CNG"
standard

Character vector; "PRE", "I", "II", "III", "IV", "V".
p

Character; pollutant: "CO", "NOx","HC", "PM", "Evaporative_driving" or "Evaporative_parking"

Value

long data.frame

See Also

Other China: ef_china_det(), ef_china_h(), ef_china_long(), ef_china_speed(), ef_china_s(), ef_china_te(), ef_china_th(), ef_china(), emis_china(), emis_long()

Examples

{
ef_china_hu(hu = 60, standard = "I", p = "CO")
}

Chinese emission factors by emissions standard

Description

Chinese emission factors in long format

Correction of Chinese emission

Usage

ef_china_long(v = "PV", t = "Small", f = "G", standard, p)

ef_china_long(v = "PV", t = "Small", f = "G", standard, p)

Arguments

v

Character; category vehicle: "PV" for Passenger Vehicles or 'Trucks"
t

Character; sub-category of of vehicle: PV Gasoline: "Mini", "Small","Medium", "Large", "Taxi", "Motorcycles", "Moped", PV Diesel: "Mediumbus", "Largebus", "3-Wheel". Trucks: "Mini", "Light" , "Medium", "Heavy"
f

Character;fuel: "G", "D", "CNG", "ALL"
standard

Character vector; "PRE", "I", "II", "III", "IV", "V".
p

Character; pollutant: "CO", "NOx","HC", "PM", "Evaporative_driving" or "Evaporative_parking"

Value

long data.frame

long data.frame

See Also

Other China: ef_china_det(), ef_china_hu(), ef_china_h(), ef_china_speed(), ef_china_s(), ef_china_te(), ef_china_th(), ef_china(), emis_china(), emis_long()

Other China: ef_china_det(), ef_china_hu(), ef_china_h(), ef_china_speed(), ef_china_s(), ef_china_te(), ef_china_th(), ef_china(), emis_china(), emis_long()

Examples

{
## Not run: 
# Do not run

## End(Not run)
}
{
ef_china_long(standard = "I", p = "CO")
}

Correction of Chinese emission factors by sulfur

Description

Correction of Chinese emission

Usage

ef_china_s(s, f = "G", standard, p)

Arguments

s

Numeric sulfur content in ppm
f

Character;fuel: "G", "D", "CNG", "ALL"
standard

Character vector; "PRE", "I", "II", "III", "IV", "V".
p

Character; pollutant: "CO", "NOx","HC", "PM", "Evaporative_driving" or "Evaporative_parking"

Value

long data.frame

See Also

Other China: ef_china_det(), ef_china_hu(), ef_china_h(), ef_china_long(), ef_china_speed(), ef_china_te(), ef_china_th(), ef_china(), emis_china(), emis_long()

Examples

{
ef_china_s(s = 1000, standard = "I", p = "CO")
}

Correction of Chinese emission factors by speed

Description

Correction of Chinese emission

Usage

ef_china_speed(speed, f = "G", standard, p, long = FALSE)

Arguments

speed

numeric speed km/h
f

Character;fuel: "G", "D", "CNG"
standard

Character vector; "PRE", "I", "II", "III", "IV", "V".
p

Character; pollutant: "CO", "NOx","HC", "PM", "Evaporative_driving" or "Evaporative_parking"
long

Logical, to process long format of ef

Value

long data.frame

See Also

Other China: ef_china_det(), ef_china_hu(), ef_china_h(), ef_china_long(), ef_china_s(), ef_china_te(), ef_china_th(), ef_china(), emis_china(), emis_long()

Examples

{
data(net)
head(ef_china_speed(speed = net$ps, standard = "I", p = "CO"))
head(ef_china_speed(speed = net$ps,
                    standard = c("II", "I"),
                    p = "NOx"))
}

Correction of Chinese emission factors by temperature

Description

Correction of Chinese emission

Usage

ef_china_te(te, v = "PV", t = "Small", f = "G", p)

Arguments

te

numeric temperature in celsius
v

Character; category vehicle: "PV" for Passenger Vehicles or 'Trucks"
t

Character; sub-category of of vehicle: PV Gasoline: "Mini", "Small","Medium", "Large", "Taxi", "Motorcycles", "Moped", PV Diesel: "Mediumbus", "Largebus", "3-Wheel". Trucks: "Mini", "Light" , "Medium", "Heavy"
f

Character;fuel: "G", "D", "CNG"
p

Character; pollutant: "CO", "NOx","HC", "PM", "Evaporative_driving" or "Evaporative_parking"

Value

long data.frame

See Also

Other China: ef_china_det(), ef_china_hu(), ef_china_h(), ef_china_long(), ef_china_speed(), ef_china_s(), ef_china_th(), ef_china(), emis_china(), emis_long()

Examples

{
data(net)
head(ef_china_te(te = net$ps,  p = "CO"))
head(ef_china_te(te = net$ps,
                 p = "NOx"))
}

Correction of Chinese factors by humidity when temperature > 24

Description

Correction of Chinese emission

Usage

ef_china_th(hu, te, v = "PV", t = "Small", f = "G", p)

Arguments

hu

numeric humidity
te

numeric temperature in celsius
v

Character; category vehicle: "PV" for Passenger Vehicles or 'Trucks"
t

Character; sub-category of of vehicle: PV Gasoline: "Mini", "Small","Medium", "Large", "Taxi", "Motorcycles", "Moped", PV Diesel: "Mediumbus", "Largebus", "3-Wheel". Trucks: "Mini", "Light" , "Medium", "Heavy"
f

Character;fuel: "G", "D", "CNG"
p

Character; pollutant: "CO", "NOx","HC", "PM", "Evaporative_driving" or "Evaporative_parking"

Value

long data.frame

See Also

Other China: ef_china_det(), ef_china_hu(), ef_china_h(), ef_china_long(), ef_china_speed(), ef_china_s(), ef_china_te(), ef_china(), emis_china(), emis_long()

Examples

{
ef_china_th(hu = 60, te = 25,  p = "CO")
}

Emissions factors from European European Environment Agency

Description

ef_cetesb returns a vector or data.frame of Brazilian emission factors.

Usage

ef_eea(
  category,
  fuel,
  segment,
  euro,
  tech,
  pol,
  mode,
  slope,
  load,
  speed,
  fcorr = rep(1, 8)
)

Arguments

category

String: "PC" (Passenger Cars), "LCV" (Light Commercial Vehicles), "TRUCKS" (Heavy Duty Trucks), "BUS" (Buses) or "MC" (Motorcycles or L-Category as in EEA 2019).
fuel

String; "G", "G HY", "G PHEV G", "G PHEV ELEC", "D", "D PHEV D", "D PHEV ELEC", "LPG BIFUEL LPG", "LPG BIFUEL G", "CNG BIFUEL CNG", "CNG BIFUEL G", "D HY D", "D HY ELEC", "CNG", "BIO D"
segment

String for type of vehicle (try different, the function will show values).
euro

String; euro standard: "PRE", "IMPROVED CONVENTIONAL", "OPEN LOOP", "ECE 15/00-01", "ECE 15/02", "ECE 15/03", "ECE 15/04". "I", "II", "III", "IV", "V", "VI A/B/C", "VI D", "VI D-TEMP", "VI D/E", "EEV".
tech

String; technology: "DPF", "DPF With S/W Update", "DPF+SCR" "EGR", "GDI", "GDI+GPF", "LNT+DPF", "PFI", "SCR".
pol

String; "CO", "NOx", "NMHC" (VOC), "PM" (PM Exhaust), "EC", "CH4", "NH3", "N2O"
mode

String; "Urban Peak", "Urban Off Peak", "Rural", "Highway", NA.
slope

Numeric; 0.00, -0.06, -0.04, -0.02, 0.02, 0.04, 0.06, or NA
load

Numeric; 0.0,0.5, 1.0 or NA
speed

Numeric; optional numeric in km/h.
fcorr

Numeric; Correction by fuel properties by euro technology. See fuel_corr. The order from first to last is "PRE", "I", "II", "III", "IV", "V", "VI", "or other VI. Default is 1

Value

Return a function depending of speed or numeric (g/km)

Examples

{
# ef_eea(category = "I DONT KNOW")
ef_eea(category = "PC",
fuel = "G",
segment = "Small",
euro = "I",
tech = NA,
pol = "CO",
mode = NA,
slope = 0,
load = 0)(10)
}

Emission Factors from EMFAC emission factors

Description

ef_emfac reads path to ef EMFAC. You must download the emission factors from EMFAC website.

Usage

ef_emfac(
  efpath,
  dg = 750,
  dd = 850,
  dhy = 750,
  dcng = 0.8,
  fill_missing = TRUE,
  verbose = TRUE
)

Arguments

efpath

Character path to EMFAC ef (g/miles)
dg

Numeric density of gasoline, default 750 kg/m3
dd

Numeric density of diesel, default 850 kg/m3
dhy

Numeric density of hybrids, default 750 kg/m3
dcng

Numeric density of CNG, default 0.8 kg/m3
fill_missing

Logical to fill and correct ef = 0
verbose

Logical, to show more information

Value

data.table with emission estimation in long format

Note

Fuel consumption must be present

Examples

## Not run: 
# do not run

## End(Not run)

Evaporative emission factor

Description

ef_evap is a lookup table with tier 2 evaporative emission factors from EMEP/EEA emisison guidelines

Usage

ef_evap(
  ef,
  v,
  cc,
  dt,
  ca,
  pollutant = "NMHC",
  k = 1,
  ltrip,
  kmday,
  show = FALSE,
  verbose = FALSE
)

Arguments

ef

Name of evaporative emission factor as *eshotc*: mean hot-soak with carburator, *eswarmc*: mean cold and warm-soak with carburator, eshotfi: mean hot-soak with fuel injection, *erhotc*: mean hot running losses with carburator, *erwarmc* mean cold and warm running losses, *erhotfi* mean hot running losses with fuel injection. Length of ef 1.
v

Type of vehicles, "PC", "Motorcycle", "Motorcycle_2S" and "Moped"
cc

Size of engine in cc. PC "<=1400", "1400_2000" and ">2000" Motorcycle_2S: "<=50". Motorcyces: ">50", "<=250", "250_750" and ">750". Only engines of >750 has canister.
dt

Character or Numeric: Average monthly temperature variation: "-5_10", "0_15", "10_25" and "20_35". This argument can vector with several elements. dt can also be data.frame, but it is recommended that the number of columns are each month. So that dt varies in each row and each column.
ca

Size of canister: "no" meaning no canister, "small", "medium" and "large".
pollutant

Character indicating any of the covered pollutants: "NMHC", "ethane", "propane", "i-butane", "n-butane", "i-pentane", "n-pentane", "2-methylpentane", "3-methylpentane", "n-hexane", "n-heptane", "propene", "trans-2-butene", "isobutene", "cis-2-butene", "1,3-butadiene", "trans-2-pentene", "cis-2-pentene", "isoprene", "propyne", "acetylene", "benzene", "toluene", "ethylbenzene", "m-xylene", "o-xylene", "1,2,4-trimethylbenzene" and "1,3,5-trimethylbenzene". Default is "NMHC"
k

multiplication factor
ltrip

Numeric; Length of trip. Experimental feature to conter g/trip and g/proced (assuming proced similar to trip) in g/km.
kmday

Numeric; average daily mileage. Experimental option to convert g/day in g/km. it is an information more solid than to know the average number of trips per day.
show

when TRUE shows row of table with respective emission factor.
verbose

Logical; To show more information

Value

emission factors in g/trip or g/proced. The object has class (g) but it order to know it is g/trip or g/proceed the argument show must by T

Note

Diurnal loses occur with daily temperature variations. Running loses occur during vehicles use. Hot soak emission occur following vehicles use.

References

Mellios G and Ntziachristos 2016. Gasoline evaporation. In: EEA, EMEP. EEA air pollutant emission inventory guidebook-2009. European Environment Agency, Copenhagen, 2009

Examples

## Not run: 
# Do not run
a <- ef_evap(ef = "eshotc", v = "PC", cc = "<=1400", dt = "0_15", ca = "no",
pollutant = "cis-2-pentene")
a <- ef_evap(ef = "ed", v = "PC", cc = "<=1400", dt = "0_15", ca = "no",
show = TRUE)
a <- ef_evap(ef = c("erhotc", "erhotc"), v = "PC", cc = "<=1400",
dt = "0_15", ca = "no",
show = TRUE)
a <- ef_evap(ef = c("erhotc", "erhotc"), v = "PC", cc = "<=1400",
 dt = "0_15", ca = "no",
show = FALSE)
a <- ef_evap(ef = "eshotc", v = "PC", cc = "<=1400", dt = "0_15", ca = "no",
show = TRUE)
ef_evap(ef = "erhotc", v = "PC", cc = "<=1400", dt = "0_15", ca = "no",
show = TRUE)
temps <- 10:20
a <- ef_evap(ef = "erhotc", v = "PC", cc = "<=1400", dt = temps, ca = "no",
show = TRUE)
dt <- matrix(rep(1:24,5), ncol = 12) # 12 months
dt <- celsius(dt)
a <- ef_evap(ef ="erhotc", v = "PC", cc = "<=1400",
dt = dt, ca = "no")
lkm <- units::set_units(10, km)
a <- ef_evap(ef ="erhotc", v = "PC", cc = "<=1400", ltrip = lkm,
dt = dt, ca = "no")

## End(Not run)

Experimental: Returns a function of Emission Factor by age of use

Description

ef_fun returns amount of vehicles at each age

Usage

ef_fun(
  ef,
  type = "logistic",
  x = 1:length(ef),
  x0 = mean(ef),
  k = 1/4,
  L = max(ef),
  verbose = TRUE
)

Arguments

ef

Numeric; numeric vector of emission factors.
type

Character; "logistic" by default so far.
x

Numeric; vector for ages of use.
x0

Numeric; the x-value of the sigmoid's midpoint,
k

Numeric; the steepness of the curve.
L

Integer; the curve's maximum value.
verbose

Logical; to show the equation.

Value

numeric vector.

References

https://en.wikipedia.org/wiki/Logistic_function

Examples

## Not run: 
CO <- ef_cetesb(p = "CO", veh = "PC_G")
ef_logit <- ef_fun(ef = CO, x0 = 27, k = 0.4, L = max(CO))
df <- data.frame(CO, ef_logit)
colplot(df)

## End(Not run)

Scaling constant with speed emission factors of Heavy Duty Vehicles

Description

ef_hdv_scaled creates a list of scaled functions of emission factors. A scaled emission factor which at a speed of the dricing cycle (SDC) gives a desired value. This function needs a dataframe with local emission factors with a columns with the name "Euro_HDV" indicating the Euro equivalence standard, assuming that there are available local emission factors for several consecutive years.

Usage

ef_hdv_scaled(df, dfcol, SDC = 34.12, v, t, g, eu, gr = 0, l = 0.5, p)

Arguments

df

deprecated
dfcol

Column of the dataframe with the local emission factors eg df$dfcol
SDC

Speed of the driving cycle
v

Category vehicle: "Coach", "Trucks" or "Ubus"
t

Sub-category of of vehicle: "3Axes", "Artic", "Midi", "RT, "Std" and "TT"
g

Gross weight of each category: "<=18", ">18", "<=15", ">15 & <=18", "<=7.5", ">7.5 & <=12", ">12 & <=14", ">14 & <=20", ">20 & <=26", ">26 & <=28", ">28 & <=32", ">32", ">20 & <=28", ">28 & <=34", ">34 & <=40", ">40 & <=50" or ">50 & <=60"
eu

Euro emission standard: "PRE", "I", "II", "III", "IV" and "V"
gr

Gradient or slope of road: -0.06, -0.04, -0.02, 0.00, 0.02. 0.04 or 0.06
l

Load of the vehicle: 0.0, 0.5 or 1.0
p

Pollutant: "CO", "FC", "NOx" or "HC"

Value

A list of scaled emission factors g/km

Note

The length of the list should be equal to the name of the age categories of a specific type of vehicle

Examples

{
# Do not run
CO <- ef_cetesb(p = "CO", veh = "TRUCKS_SL_D", full = TRUE)
lef <- ef_hdv_scaled(dfcol = CO$CO,
                     v = "Trucks",
                     t = "RT",
                     g = "<=7.5",
                     eu = CO$Euro_EqHDV,
                     gr = 0,
                     l = 0.5,
                     p = "CO")
length(lef)
ages <- c(1, 10, 20, 30, 40)
EmissionFactors(do.call("cbind",
   lapply(ages, function(i) {
       data.frame(i = lef[[i]](1:100))
}))) -> df
names(df) <- ages
colplot(df)
}

Emissions factors for Heavy Duty Vehicles based on average speed

Description

This function returns speed dependent emission factors. The emission factors comes from the guidelines EMEP/EEA air pollutant emission inventory guidebook http://www.eea.europa.eu/themes/air/emep-eea-air-pollutant-emission-inventory-guidebook

Usage

ef_hdv_speed(
  v,
  t,
  g,
  eu,
  x,
  gr = 0,
  l = 0.5,
  p,
  k = 1,
  show.equation = FALSE,
  speed,
  fcorr = rep(1, 8)
)

Arguments

v

Category vehicle: "Coach", "Trucks" or "Ubus"
t

Sub-category of of vehicle: "3Axes", "Artic", "Midi", "RT, "Std" and "TT"
g

Gross weight of each category: "<=18", ">18", "<=15", ">15 & <=18", "<=7.5", ">7.5 & <=12", ">12 & <=14", ">14 & <=20", ">20 & <=26", ">26 & <=28", ">28 & <=32", ">32", ">20 & <=28", ">28 & <=34", ">34 & <=40", ">40 & <=50" or ">50 & <=60"
eu

Euro emission standard: "PRE", "I", "II", "III", "IV", "V". Also "II+CRDPF", "III+CRDPF", "IV+CRDPF", "II+SCR", "III+SCR" and "V+SCR" for pollutants Number of particles and Active Surface.
x

Numeric; if pollutant is "SO2", it is sulfur in fuel in ppm, if is "Pb", Lead in fuel in ppm.
gr

Gradient or slope of road: -0.06, -0.04, -0.02, 0.00, 0.02. 0.04 or 0.06
l

Load of the vehicle: 0.0, 0.5 or 1.0
p

Character; pollutant: "CO", "FC", "NOx", "NO", "NO2", "HC", "PM", "NMHC", "CH4", "CO2", "SO2" or "Pb". Only when p is "SO2" pr "Pb" x is needed. See notes.
k

Multiplication factor
show.equation

Option to see or not the equation parameters
speed

Numeric; Speed to return Number of emission factor and not a function. It needs units in km/h
fcorr

Numeric; Correction by fuel properties by euro technology. See fuel_corr. The order from first to last is "PRE", "I", "II", "III", "IV", "V", VI, "VIc". Default is 1

Value

an emission factor function which depends of the average speed V g/km

Note

Pollutants (g/km): "CO", "NOx", "HC", "PM", "CH4", "NMHC", "CO2", "SO2", "Pb".

Black Carbon and Organic Matter (g/km): "BC", "OM"

PAH and POP (g/km): See speciate Dioxins and furans (g equivalent toxicity / km): See speciate

Metals (g/km): See speciate

Active Surface (cm2/km) See speciate

Total Number of particles (N/km): See speciate

The available standards for Active Surface or number of particles are: Euro II and III Euro II and III + CRDPF Euro II and III + SCR Euro IV + CRDPF Euro V + SCR

The categories Pre Euro and Euro I were assigned with the factors of Euro II and Euro III The categories euro IV and euro V were assigned with euro III + SCR

Fuel consumption for heavy VI comes from V

See Also

fuel_corr emis ef_ldv_cold speciate

Examples

## Not run: 
# Quick view
pol <- c("CO", "NOx", "HC", "NMHC", "CH4", "FC", "PM", "CO2", "SO2")
f <- sapply(1:length(pol), function(i){
print(pol[i])
ef_hdv_speed(v = "Trucks",t = "RT", g = "<=7.5", e = "II", gr = 0,
l = 0.5, p = pol[i], x = 10)(30)
})
f

V <- 0:130
ef1 <- ef_hdv_speed(v = "Trucks",t = "RT", g = "<=7.5", e = "II", gr = 0,
l = 0.5, p = "HC")
plot(1:130, ef1(1:130), pch = 16, type = "b")
euro <- c(rep("V", 5), rep("IV", 5), rep("III", 5), rep("II", 5),
          rep("I", 5), rep("PRE", 15))
lef <- lapply(1:30, function(i) {
ef_hdv_speed(v = "Trucks", t = "RT", g = ">32", gr = 0,
eu = euro[i], l = 0.5, p = "NOx",
show.equation = FALSE)(25) })
efs <- EmissionFactors(unlist(lef)) #returns 'units'
plot(efs, xlab = "age")
lines(efs, type = "l")
a <- ef_hdv_speed(v = "Trucks", t = "RT", g = ">32", gr = 0,
eu = euro, l = 0.5, p = "NOx", speed = Speed(0:125))
a$speed <- NULL
filled.contour(as.matrix(a), col = cptcity::lucky(n = 24),
xlab = "Speed", ylab = "Age")
persp(x = as.matrix(a), theta = 35, xlab = "Speed", ylab = "Age",
zlab = "NOx [g/km]", col = cptcity::lucky(), phi = 25)
aa <- ef_hdv_speed(v = "Trucks", t = "RT", g = ">32", gr = 0,
eu = rbind(euro, euro), l = 0.5, p = "NOx", speed = Speed(0:125))

## End(Not run)

Emission factors deoending on accumulated mileage

Description

ef_im calculate the theoretical emission factors of vehicles. The approache is different from including deterioration factors (emis_det) but similar, because they represent how much emits a vehicle with a normal deterioration, but that it will pass the Inspection and Manteinance program.

Usage

ef_im(ef, tc, amileage, max_amileage, max_ef, verbose = TRUE)

Arguments

ef

Numeric; emission factors of vehicles with 0 mileage (new vehicles).
tc

Numeric; rate of growth of emissions by year of use.
amileage

Numeric; Accumulated mileage by age of use.
max_amileage

Numeric; Max accumulated mileage. This means that after this value, mileage is constant.
max_ef

Numeric; Max ef. This means that after this value, ef is constant.
verbose

Logical; if you want detailed description.

Value

An emission factor of a deteriorated vehicle under normal conditions which would be approved in a inspection and mantainence program.

Examples

## Not run: 
# Do not run
# Passenger Cars PC
data(fkm)
# cumulative mileage from 1 to 50 years of use, 40:50
mil <- cumsum(fkm$KM_PC_E25(1:10))
ef_im(ef = seq(0.1, 2, 0.2), seq(0.1, 1, 0.1), mil)

## End(Not run)

Cold-Start Emissions factors for Light Duty Vehicles

Description

ef_ldv_cold returns speed functions or data.frames which depends on ambient temperature average speed. The emission factors comes from the guidelines EMEP/EEA air pollutant emission inventory guidebook http://www.eea.europa.eu/themes/air/emep-eea-air-pollutant-emission-inventory-guidebook

Usage

ef_ldv_cold(
  v = "LDV",
  ta,
  cc,
  f,
  eu,
  p,
  k = 1,
  show.equation = FALSE,
  speed,
  fcorr = rep(1, 8)
)

Arguments

v

Character; Category vehicle: "LDV"
ta

Numeric vector or data.frame; Ambient temperature. Monthly mean can be used. When ta is a data.frame, one option is that the number of rows should be the number of rows of your Vehicles data.frame. This is convenient for top-down approach when each simple feature can be a polygon, with a monthly average temperature for each simple feature. In this case, the number of columns can be the 12 months.
cc

Character; Size of engine in cc: "<=1400", "1400_2000" or ">2000"
f

Character; Type of fuel: "G", "D" or "LPG"
eu

Character or data.frame of Characters; Euro standard: "PRE", "I", "II", "III", "IV", "V", "VI" or "VIc". When 'eu' is a data.frame and 'ta' is also a data.frame both has to have the same number of rows. For instance, When you want that each simple feature or region has a different emission standard.
p

Character; Pollutant: "CO", "FC", "NOx", "HC" or "PM"
k

Numeric; Multiplication factor
show.equation

Option to see or not the equation parameters
speed

Numeric; Speed to return Number of emission factor and not a function.
fcorr

Numeric; Correction by fuel properties by euro technology. See fuel_corr. The order from first to last is "PRE", "I", "II", "III", "IV", "V", VI, "VIc". Default is 1

Value

an emission factor function which depends of the average speed V and ambient temperature. g/km

See Also

fuel_corr

Examples

## Not run: 
ef1 <- ef_ldv_cold(ta = 15, cc = "<=1400", f ="G", eu = "PRE", p = "CO",
show.equation = TRUE)
ef1(10)
speed <- Speed(10)
ef_ldv_cold(ta = 15, cc = "<=1400", f ="G", eu = "PRE", p = "CO", speed = speed)
# lets create a matrix of ef cold at different speeds and temperatures
te <- -50:50
lf <- sapply(1:length(te), function(i){
ef_ldv_cold(ta = te[i], cc = "<=1400", f ="G", eu = "I", p = "CO", speed = Speed(0:120))
})
filled.contour(lf, col= cptcity::lucky())
euros <- c("V", "V", "IV", "III", "II", "I", "PRE", "PRE")
ef_ldv_cold(ta = 10, cc = "<=1400", f ="G", eu = euros, p = "CO", speed = Speed(0))
lf <-  ef_ldv_cold(ta = 10, cc = "<=1400", f ="G", eu = euros, p = "CO", speed = Speed(0:120))
dt <- matrix(rep(2:25,5), ncol = 12) # 12 months
ef_ldv_cold(ta = dt, cc = "<=1400", f ="G", eu = "I", p = "CO", speed = Speed(0))
ef_ldv_cold(ta = dt, cc = "<=1400", f ="G", eu = euros, p = "CO", speed = Speed(34))
euros2 <- c("V", "V", "V", "IV", "IV", "IV", "III", "III")
dfe <- rbind(euros, euros2)
ef_ldv_cold(ta = 10, cc = "<=1400", f ="G", eu = dfe, p = "CO", speed = Speed(0))

ef_ldv_cold(ta = dt[1:2,], cc = "<=1400", f ="G", eu = dfe, p = "CO", speed = Speed(0))
# Fuel corrections
fcorr <- c(0.5,1,1,1,0.9,0.9,0.9,0.9)
ef1 <- ef_ldv_cold(ta = 15, cc = "<=1400", f ="G", eu = "PRE", p = "CO",
show.equation = TRUE, fcorr = fcorr)
ef_ldv_cold(ta = 10, cc = "<=1400", f ="G", eu = dfe, p = "CO", speed = Speed(0),
fcorr = fcorr)

## End(Not run)

List of cold start emission factors of Light Duty Vehicles

Description

This function creates a list of functions of cold start emission factors considering different euro emission standard to the elements of the list.

Usage

ef_ldv_cold_list(df, v = "LDV", ta, cc, f, eu, p)

Arguments

df

Dataframe with local emission factor
v

Category vehicle: "LDV"
ta

ambient temperature. Montly average van be used
cc

Size of engine in cc: <=1400", "1400_2000" and ">2000"
f

Type of fuel: "G" or "D"
eu

character vector of euro standards: "PRE", "I", "II", "III", "IV", "V", "VI" or "VIc".
p

Pollutant: "CO", "FC", "NOx", "HC" or "PM"

Value

A list of cold start emission factors g/km

Note

The length of the list should be equal to the name of the age categories of a specific type of vehicle

Examples

## Not run: 
# Do not run
df <- data.frame(age1 = c(1,1),
                 age2 = c(2,2))
eu = c("I", "PRE")
l <- ef_ldv_cold(t = 17, cc = "<=1400", f = "G",
eu = "I", p = "CO")
l_cold <- ef_ldv_cold_list(df, t = 17, cc = "<=1400", f = "G",
eu = eu, p = "CO")
length(l_cold)

## End(Not run)

Scaling constant with speed emission factors of Light Duty Vehicles

Description

This function creates a list of scaled functions of emission factors. A scaled emission factor which at a speed of the driving cycle (SDC) gives a desired value.

Usage

ef_ldv_scaled(df, dfcol, SDC = 34.12, v, t = "4S", cc, f, eu, p)

Arguments

df

deprecated
dfcol

Column of the dataframe with the local emission factors eg df$dfcol
SDC

Speed of the driving cycle
v

Category vehicle: "PC", "LCV", "Motorcycle" or "Moped
t

Sub-category of of vehicle: PC: "ECE_1501", "ECE_1502", "ECE_1503", "ECE_1504" , "IMPROVED_CONVENTIONAL", "OPEN_LOOP", "ALL", "2S" or "4S". LCV: "4S", Motorcycle: "2S" or "4S". Moped: "2S" or "4S"
cc

Size of engine in cc: PC: "<=1400", ">1400", "1400_2000", ">2000", "<=800", "<=2000". Motorcycle: ">=50" (for "2S"), "<=250", "250_750", ">=750". Moped: "<=50". LCV : "<3.5" for gross weight.
f

Type of fuel: "G", "D", "LPG" or "FH" (Full Hybrid: starts by electric motor)
eu

Euro standard: "PRE", "I", "II", "III", "III+DPF", "IV", "V", "VI", "VIc"
p

Pollutant: "CO", "FC", "NOx", "HC" or "PM". If your pollutant dfcol is based on fuel, use "FC", if it is based on "HC", use "HC".

Details

This function calls "ef_ldv_speed" and calculate the specific k value, dividing the local emission factor by the respective speed emissions factor at the speed representative of the local emission factor, e.g. If the local emission factors were tested with the FTP-75 test procedure, SDC = 34.12 km/h.

Value

A list of scaled emission factors g/km

Note

The length of the list should be equal to the name of the age categories of a specific type of vehicle. Thanks to Glauber Camponogara for the help.

See Also

ef_ldv_seed

Examples

{
CO <- ef_cetesb(p = "CO", veh = "PC_FG", full = TRUE)
lef <- ef_ldv_scaled(dfcol = CO$CO,
                     v = "PC",
                     t = "4S",
                     cc = "<=1400",
                     f = "G",
                     eu = CO$EqEuro_PC,
                     p = "CO")
length(lef)
ages <- c(1, 10, 20, 30, 40)
EmissionFactors(do.call("cbind",
   lapply(ages, function(i) {
       data.frame(i = lef[[i]](1:100))
}))) -> df
names(df) <- ages
colplot(df)
}

Emissions factors for Light Duty Vehicles and Motorcycles

Description

ef_ldv_speed returns speed dependent emission factors, data.frames or list of emission factors. The emission factors comes from the guidelines EMEP/EEA air pollutant emission inventory guidebook http://www.eea.europa.eu/themes/air/emep-eea-air-pollutant-emission-inventory-guidebook

Usage

ef_ldv_speed(
  v,
  t = "4S",
  cc,
  f,
  eu,
  p,
  x,
  k = 1,
  speed,
  show.equation = FALSE,
  fcorr = rep(1, 8)
)

Arguments

v

Character; category vehicle: "PC", "LCV", "Motorcycle" or "Moped
t

Character; sub-category of of vehicle: PC: "ECE_1501", "ECE_1502", "ECE_1503", "ECE_1504" , "IMPROVED_CONVENTIONAL", "OPEN_LOOP", "ALL", "2S" or "4S". LCV: "4S", Motorcycle: "2S" or "4S". Moped: "2S" or "4S"
cc

Character; size of engine in cc: PC: "<=1400", ">1400", "1400_2000", ">2000", "<=800", "<=2000". Motorcycle: ">=50" (for "2S"), "<=250", "250_750", ">=750". Moped: "<=50". LCV : "<3.5" for gross weight.
f

Character; type of fuel: "G", "D", "LPG" or "FH" (Gasoline Full Hybrid). Full hybrid vehicles cannot be charged from the grid and recharge; only its own engine may recharge tis batteries.
eu

Character or data.frame of characters; euro standard: "PRE", "I", "II", "III", "III+DPF", "IV", "V", "VI" or "VIc". When the pollutan is active surface or number of particles, eu can also be "III+DISI"
p

Character; pollutant: "CO", "FC", "NOx", "NO", "NO2", "HC", "PM", "NMHC", "CH4", "CO2", "SO2" or "Pb". Only when p is "SO2" pr "Pb" x is needed. Also polycyclic aromatic hydrocarbons (PAHs), persistent organi pollutants (POPs), and Number of particles and Active Surface.
x

Numeric; if pollutant is "SO2", it is sulphur in fuel in ppm, if is "Pb", Lead in fuel in ppm.
k

Numeric; multiplication factor
speed

Numeric; Speed to return Number of emission factor and not a function.
show.equation

Logical; option to see or not the equation parameters.
fcorr

Numeric; Correction by fuel properties by euro technology. See fuel_corr. The order from first to last is "PRE", "I", "II", "III", "IV", "V", VI, "VIc". Default is 1

Details

The argument of this functions have several options which results in different combinations that returns emission factors. If a combination of any option is wrong it will return an empty value. Therefore, it is important ti know the combinations.

Value

An emission factor function which depends of the average speed V g/km

Note

t = "ALL" and cc == "ALL" works for several pollutants because emission fators are the same. Some exceptions are with NOx and FC because size of engine.

Hybrid cars: the only cover "PC" and according to EMEP/EEA air pollutant emission inventory guidebook 2016 (Ntziachristos and Samaras, 2016) only for euro IV. When new literature is available, I will update these factors.

Pollutants (g/km): "CO", "NOx", "HC", "PM", "CH4", "NMHC", "CO2", "SO2", "Pb", "FC".

Black Carbon and Organic Matter (g/km): "BC", "OM"

PAH and POP (g/km): speciate Dioxins and furans(g equivalent toxicity / km): speciate Metals (g/km): speciate

NMHC (g/km): speciate

Active Surface (cm2/km): speciate"AS_urban", "AS_rural", "AS_highway"

Total Number of particles (N/km): speciate "N_urban", "N_rural", "N_highway", "N_50nm_urban", "N_50_100nm_rural", "N_100_1000nm_highway".

The available standards for Active Surface or number of particles are Euro I, II, III, III+DPF dor diesle and III+DISI for gasoline. Pre euro vehicles has the value of Euro I and euro IV, V, VI and VIc the value of euro III.

See Also

fuel_corr emis ef_ldv_cold

Examples

## Not run: 
# Passenger Cars PC
# Emission factor function
V <- 0:150
ef1 <- ef_ldv_speed(v = "PC",t = "4S", cc = "<=1400", f = "G", eu = "PRE",
p = "CO")
efs <- EmissionFactors(ef1(1:150))
plot(Speed(1:150), efs, xlab = "speed[km/h]", type = "b", pch = 16, col = "blue")

# Quick view
pol <- c("CO", "NOx", "HC", "NMHC", "CH4", "FC", "PM", "CO2", "SO2",
"1-butyne", "propyne")
f <- sapply(1:length(pol), function(i){
ef_ldv_speed("PC", "4S", "<=1400", "G", "PRE", pol[i], x = 10)(30)
})
f
# PM Characteristics
pol <- c("AS_urban", "AS_rural", "AS_highway",
"N_urban", "N_rural", "N_highway",
"N_50nm_urban", "N_50_100nm_rural", "N_100_1000nm_highway")
f <- sapply(1:length(pol), function(i){
ef_ldv_speed("PC", "4S", "<=1400", "D", "PRE", pol[i], x = 10)(30)
})
f
# PAH POP
ef_ldv_speed(v = "PC",t = "4S", cc = "<=1400", f = "G", eu = "PRE",
p = "indeno(1,2,3-cd)pyrene")(10)
ef_ldv_speed(v = "PC",t = "4S", cc = "<=1400", f = "G", eu = "PRE",
p = "napthalene")(10)

# Dioxins and Furans
ef_ldv_speed(v = "PC",t = "4S", cc = "<=1400", f = "G", eu = "PRE",
p = "PCB")(10)

# NMHC
ef_ldv_speed(v = "PC",t = "4S", cc = "<=1400", f = "G", eu = "PRE",
p = "hexane")(10)

# List of Copert emission factors for 40 years fleet of Passenger Cars.
# Assuming a euro distribution of euro V, IV, III, II, and I of
# 5 years each and the rest 15 as PRE euro:
euro <- c(rep("V", 5), rep("IV", 5), rep("III", 5), rep("II", 5),
          rep("I", 5), rep("PRE", 15))
speed <- 25
lef <- lapply(1:40, function(i) {
ef_ldv_speed(v = "PC", t = "4S", cc = "<=1400", f = "G",
          eu = euro[i], p = "CO")
ef_ldv_speed(v = "PC", t = "4S", cc = "<=1400", f = "G",
          eu = euro[i], p = "CO", show.equation = FALSE)(25) })
# to check the emission factor with a plot
efs <- EmissionFactors(unlist(lef)) #returns 'units'
plot(efs, xlab = "age")
lines(efs, type = "l")
euros <- c("VI", "V", "IV", "III", "II")
ef_ldv_speed(v = "PC", t = "4S", cc = "<=1400", f = "G",
          eu = euros, p = "CO")
a <- ef_ldv_speed(v = "PC", t = "4S", cc = "<=1400", f = "G",
          eu = euros, p = "CO", speed = Speed(0:120))
head(a)
filled.contour(as.matrix(a)[1:10, 1:length(euros)], col = cptcity::cpt(n = 18))
filled.contour(as.matrix(a)[110:120, 1:length(euros)], col = cptcity::cpt(n = 16))
filled.contour(as.matrix(a)[, 1:length(euros)], col = cptcity::cpt(n = 21))
filled.contour(as.matrix(a)[, 1:length(euros)],
col = cptcity::cpt("mpl_viridis", n = 21))
filled.contour(as.matrix(a)[, 1:length(euros)],
col = cptcity::cpt("mpl_magma", n = 21))
persp(as.matrix(a)[, 1:length(euros)], phi = 0, theta = 0)
persp(as.matrix(a)[, 1:length(euros)], phi = 25, theta = 45)
persp(as.matrix(a)[, 1:length(euros)], phi = 0, theta = 90)
persp(as.matrix(a)[, 1:length(euros)], phi = 25, theta = 90+45)
persp(as.matrix(a)[, 1:length(euros)], phi = 0, theta = 180)
new_euro <- c("VI", "VI", "V", "V", "V")
euro <- c("V", "V", "IV", "III", "II")
old_euro <- c("III", "II", "I", "PRE", "PRE")
meuros <- rbind(new_euro, euro, old_euro)
aa <- ef_ldv_speed(v = "PC", t = "4S", cc = "<=1400", f = "G",
          eu = meuros, p = "CO", speed = Speed(10:11))
# Light Commercial Vehicles
V <- 0:150
ef1 <- ef_ldv_speed(v = "LCV",t = "4S", cc = "<3.5", f = "G", eu = "PRE",
p = "CO")
efs <- EmissionFactors(ef1(1:150))
plot(Speed(1:150), efs, xlab = "speed[km/h]")
lef <- lapply(1:5, function(i) {
ef_ldv_speed(v = "LCV", t = "4S", cc = "<3.5", f = "G",
          eu = euro[i], p = "CO", show.equation = FALSE)(25) })
# to check the emission factor with a plot
efs <- EmissionFactors(unlist(lef)) #returns 'units'
plot(efs, xlab = "age")
lines(efs, type = "l")

# Motorcycles
V <- 0:150
ef1 <- ef_ldv_speed(v = "Motorcycle",t = "4S", cc = "<=250", f = "G",
eu = "PRE", p = "CO",show.equation = TRUE)
efs <- EmissionFactors(ef1(1:150))
plot(Speed(1:150), efs, xlab = "speed[km/h]")
# euro for motorcycles
eurom <- c(rep("III", 5), rep("II", 5), rep("I", 5), rep("PRE", 25))
lef <- lapply(1:30, function(i) {
ef_ldv_speed(v = "Motorcycle", t = "4S", cc = "<=250", f = "G",
eu = eurom[i], p = "CO",
show.equation = FALSE)(25) })
efs <- EmissionFactors(unlist(lef)) #returns 'units'
plot(efs, xlab = "age")
lines(efs, type = "l")
a <- ef_ldv_speed(v = "Motorcycle", t = "4S", cc = "<=250", f = "G",
eu = eurom, p = "CO", speed = Speed(0:125))
a$speed <- NULL
filled.contour(as.matrix(a), col = cptcity::lucky(),
xlab = "Speed", ylab = "Age")
persp(x = as.matrix(a), theta = 35, xlab = "Speed", ylab = "Euros",
zlab = "CO [g/km]", col = cptcity::lucky(), phi = 25)

## End(Not run)

Local Emissions factors

Description

ef_local process an data.frame delivered by the user, but adding similar funcionality and arguments as ef_cetesb, which are classification, filtering and projections

Usage

ef_local(
  p,
  veh,
  year = 2017,
  agemax = 40,
  ef,
  full = FALSE,
  project = "constant",
  verbose = TRUE
)

Arguments

p

Character; pollutant delivered by the user. the name of the column of the data.frame must be Pollutant.
veh

Character; Vehicle categories available in the data.frame provided by the user
year

Numeric; Filter the emission factor to start from a specific base year. If project is 'constant' values above 2017 and below 1980 will be repeated
agemax

Integer; age of oldest vehicles for that category
ef

data.frame, for local the emission factors. The names of the ef must be 'Age' 'Year' 'Pollutant' and all the vehicle categories...
full

Logical; To return a data.frame instead or a vector adding Age, Year, Brazilian emissions standards and its euro equivalents.
project

Character showing the method for projecting emission factors in future. Currently the only value is "constant"
verbose

Logical; To show more information

Details

returns a vector or data.frame of Brazilian emission factors.

Value

A vector of Emission Factor or a data.frame

Note

The names of the ef must be 'Age' 'Year' 'Pollutant' and all the vehicle categories...

See Also

ef_cetesb

Examples

## Not run: 
#do not run

## End(Not run)

Emissions factors of N2O and NH3

Description

ef_nitro returns emission factors as a functions of acondumulated mileage. The emission factors comes from the guidelines EMEP/EEA air pollutant emission inventory guidebook http://www.eea.europa.eu/themes/air/emep-eea-air-pollutant-emission-inventory-guidebook

Usage

ef_nitro(
  v,
  t = "Hot",
  cond = "Urban",
  cc,
  f,
  eu,
  p = "NH3",
  S = 10,
  cumileage,
  k = 1,
  show.equation = FALSE,
  fcorr = rep(1, 8)
)

Arguments

v

Category vehicle: "PC", "LCV", "Motorcycles_2S", "Motorcycles", "Trucks", "Trucks-A", "Coach" and "BUS"
t

Type: "Cold" or "Hot"
cond

"Urban", "Rural", "Highway"
cc

PC: "<=1400", "1400_2000", ">2000". LCV: "<3.5". Motorcycles: ">=50", Motorcycles_2S, "<50", ">=50". Trucks: ">3.5", "7.5_12", "12_28", "28_34". Trucks_A: ">34". BUS: "<=15", ">15 & <= 18". Coach: "<=18", ">18"
f

Type of fuel: "G", "D" or "LPG"
eu

Euro standard: "PRE", "I", "II", "III", "IV", "V", "VI", "VIc"
p

Pollutant: "N2O", "NH3"
S

Sulphur (ppm). Number.
cumileage

Numeric; Acondumulated mileage to return number of emission factor and not a function.
k

Multiplication factor
show.equation

Option to see or not the equation parameters
fcorr

Numeric; Correction by by euro technology.

Value

an emission factor function which depends on the acondumulated mileage, or an EmissionFactor

Note

if length of eu is bigger than 1, cumileage can have values of length 1 or length equal to length of eu

Examples

## Not run: 
efe10 <- ef_nitro(v = "PC", t = "Hot", cond = "Urban", f = "G", cc = "<=1400",
eu = "III", p = "NH3", S = 10,
show.equation = FALSE)
efe50 <- ef_nitro(v = "PC", t = "Hot", cond = "Urban", f = "G", cc = "<=1400",
eu = "III", p = "NH3", S = 50,
show.equation = TRUE)
efe10(10)
efe50(10)
efe10 <- ef_nitro(v = "PC", t = "Hot", cond = "Urban", f = "G", cc = "<=1400",
eu = "III", p = "NH3", S = 10, cumileage = units::set_units(25000, "km"))

## End(Not run)

Emissions factors from tyre, break and road surface wear

Description

ef_wear estimates wear emissions. The sources are tyres, breaks and road surface.

Usage

ef_wear(
  wear,
  type,
  pol = "TSP",
  speed,
  load = 0.5,
  axle = 2,
  road = "urban",
  verbose = FALSE
)

Arguments

wear

Character; type of wear: "tyre" (or "tire"), "break" (or "brake") and "road"
type

Character; type of vehicle: "2W", "MC", "Motorcycle", "PC", "LCV", 'HDV", "BUS", "TRUCKS"
pol

Character; pollutant: "TSP", "PM10", "PM2.5", "PM1" and "PM0.1"
speed

Data.frame of speeds
load

Load of the HDV
axle

Number of axle of the HDV
road

Type of road "urban", "rural", "motorway". Only applies when type is "E6DV" or "BEV"
verbose

Logical to show more information. Only applies when type is "E6DV" or "BEV"

Value

emission factors grams/km

References

Ntziachristos and Boulter 2016. Automobile tyre and break wear and road abrasion. In: EEA, EMEP. EEA air pollutant emission inventory guidebook-2009. European Environment Agency, Copenhagen, 2016

When type is "E6DV" or "BEV": Tivey J., Davies H., Levine J., Zietsman J., Bartington S., Ibarra-Espinosa S., Ropkins K. 2022. Meta Analysis as Early Evidence on the Particulate Emissions Impact of EURO VI to Battery Electric Bus Fleet Transitions. Paper under development.

Examples

{
data(net)
data(pc_profile)
pc_week <- temp_fact(net$ldv+net$hdv, pc_profile)
df <- netspeed(pc_week, net$ps, net$ffs, net$capacity, net$lkm, alpha = 1)
ef <- ef_wear(wear = "tyre", type = "PC", pol = "PM10", speed = df)

ef_wear(wear = "tyre",
        type = c("E6DV"),
        pol = "PM10",
        verbose = TRUE)

ef_wear(wear = "tyre",
        type = c("E6DV"),
        pol = "PM10",
        verbose = FALSE)

}

Emission factor that incorporates the effect of high emitters

Description

ef_whe return weighted emission factors of vehicles considering that one part of the fleet has a normal deterioration and another has a deteriorated fleet that would be rejected in a inspection and mantainence program but it is still in circulation. This emission factor might be applicable in cities without a inspection and mantainence program and with Weighted emission factors considering that part of the fleet are high emitters.

Usage

ef_whe(efhe, phe, ef)

Arguments

efhe

Numeric; Emission factors of high emitters vehicles. This vehicles would be rejected in a inspection and mantainnence program.
phe

Numeric; Percentage of high emitters.
ef

Numeric; Emission factors deteriorated vehicles under normal conditions. These vehicles would be approved in a inspection and mantainence program.

Value

An emission factor by annual mileage.

Examples

{
# Do not run
# Let's say high emitter is 5 times the normal ef.
co_efhe <- ef_cetesb(p = "COd", "PC_G") * 5
# Let's say that the perfil of high emitters increases linearly
# till 30 years and after that percentage is constant
perc <- c(seq(0.01, 0.3, 0.01), rep(0.3, 10))
# Now, lets use our ef with normal deterioration
co_ef_normal <- ef_cetesb(p = "COd", "PC_G")
efd <- ef_whe(efhe = co_efhe,
              phe = perc,
              ef = co_ef_normal)
# now, we can plot the three ef
colplot(data.frame(co_efhe, co_ef_normal, efd))
}

Estimation of emissions

Description

emis estimates vehicular emissions as the product of the vehicles on a road, length of the road, emission factor avaliated at the respective speed. E=VEH∗LENGTH∗EF(speed)E = VEH*LENGTH*EF(speed)

Usage

emis(
  veh,
  lkm,
  ef,
  speed,
  agemax = ifelse(is.data.frame(veh), ncol(veh), ncol(veh[[1]])),
  profile,
  simplify = FALSE,
  fortran = FALSE,
  hour = nrow(profile),
  day = ncol(profile),
  verbose = FALSE,
  nt = ifelse(check_nt() == 1, 1, check_nt()/2)
)

Arguments

veh

"Vehicles" data-frame or list of "Vehicles" data-frame. Each data-frame as number of columns matching the age distribution of that ype of vehicle. The number of rows is equal to the number of streets link. If this is a list, the length of the list is the vehicles for each hour.
lkm

Length of each link in km
ef

List of functions of emission factors
speed

Speed data-frame with number of columns as hours. The default value is 34km/h
agemax

Age of oldest vehicles for that category
profile

Dataframe or Matrix with nrows equal to 24 and ncol 7 day of the week
simplify

Logical; to determine if EmissionsArray should les dimensions, being streets, vehicle categories and hours or default (streets, vehicle categories, hours and days). Default is FALSE to avoid break old code, but the recommendation is that new estimations use this parameter as TRUE
fortran

Logical; to try the fortran calculation when speed is not used. I will add fortran for EmissionFactorsList soon.
hour

Number of considered hours in estimation. Default value is number of rows of argument profile
day

Number of considered days in estimation
verbose

Logical; To show more information
nt

Integer; Number of threads wich must be lower than max available. See check_nt. Only when fortran = TRUE

Value

If the user applies a top-down approach, the resulting units will be according its own data. For instance, if the vehicles are veh/day, the units of the emissions implicitly will be g/day.

Examples

## Not run: 
# Do not run
data(net)
data(pc_profile)
data(profiles)
data(fe2015)
data(fkm)
PC_G <- c(
  33491, 22340, 24818, 31808, 46458, 28574, 24856, 28972, 37818, 49050, 87923,
  133833, 138441, 142682, 171029, 151048, 115228, 98664, 126444, 101027,
  84771, 55864, 36306, 21079, 20138, 17439, 7854, 2215, 656, 1262, 476, 512,
  1181, 4991, 3711, 5653, 7039, 5839, 4257, 3824, 3068
)
pc1 <- my_age(x = net$ldv, y = PC_G, name = "PC")

# Estimation for morning rush hour and local emission factors and speed
speed <- data.frame(S8 = net$ps)
lef <- EmissionFactorsList(ef_cetesb("CO", "PC_G", agemax = ncol(pc1)))
system.time(E_CO <- emis(veh = pc1, lkm = net$lkm, ef = lef, speed = speed))
system.time(E_CO_2 <- emis(veh = pc1, lkm = net$lkm, ef = lef, speed = speed, simplify = TRUE))
identical(E_CO, E_CO_2)

# Estimation for morning rush hour and local emission factors without speed
lef <- ef_cetesb("CO", "PC_G", agemax = ncol(pc1))
system.time(E_CO <- emis(veh = pc1, lkm = net$lkm, ef = lef))
system.time(E_CO_2 <- emis(veh = pc1, lkm = net$lkm, ef = lef, fortran = TRUE))
identical(E_CO, E_CO_2)

# Estimation for 168 hour and local factors and speed
pcw <- temp_fact(net$ldv + net$hdv, pc_profile)
speed <- netspeed(pcw, net$ps, net$ffs, net$capacity, net$lkm, alpha = 1)
lef <- EmissionFactorsList(ef_cetesb("CO", "PC_G", agemax = ncol(pc1)))
system.time(
  E_CO <- emis(
    veh = pc1,
    lkm = net$lkm,
    ef = lef,
    speed = speed,
    profile = profiles$PC_JUNE_2014
  )
)
system.time(
  E_CO_2 <- emis(
    veh = pc1,
    lkm = net$lkm,
    ef = lef,
    speed = speed,
    profile = profiles$PC_JUNE_2014,
    simplify = TRUE
  )
)

# Estimation for 168 hour and local factors and without speed
lef <- ef_cetesb("CO", "PC_G", agemax = ncol(pc1))
system.time(
  E_CO <- emis(
    veh = pc1,
    lkm = net$lkm,
    ef = lef,
    profile = profiles$PC_JUNE_2014
  )
)
sum(E_CO)
system.time(
  E_CO_2 <- emis(
    veh = pc1,
    lkm = net$lkm,
    ef = lef,
    profile = profiles$PC_JUNE_2014,
    fortran = TRUE
  )
)
sum(E_CO)
system.time(
  E_CO_3 <- emis(
    veh = pc1,
    lkm = net$lkm,
    ef = lef,
    profile = profiles$PC_JUNE_2014,
    simplify = TRUE
  )
)
sum(E_CO)
system.time(
  E_CO_4 <- emis(
    veh = pc1,
    lkm = net$lkm,
    ef = lef,
    profile = profiles$PC_JUNE_2014,
    simplify = TRUE,
    fortran = TRUE
  )
)
sum(E_CO)
identical(round(E_CO, 2), round(E_CO_2, 2))
identical(round(E_CO_3, 2), round(E_CO_4, 2))
identical(round(E_CO_3[, , 1], 2), round(E_CO_4[, , 1], 2))
dim(E_CO_3)
dim(E_CO_4)
# but
a <- unlist(lapply(1:41, function(i) {
  unlist(lapply(1:168, function(j) {
    identical(E_CO_3[, i, j], E_CO_4[, i, j])
  }))
}))
unique(a)

# Estimation with list of vehicles
lpc <- list(pc1, pc1)
lef <- EmissionFactorsList(ef_cetesb("CO", "PC_G", agemax = ncol(pc1)))
E_COv2 <- emis(veh = lpc, lkm = net$lkm, ef = lef, speed = speed)

# top down
veh <- age_ldv(x = net$ldv[1:4], name = "PC_E25_1400", agemax = 4)
mil <- fkm$KM_PC_E25(1:4)
ef <- ef_cetesb("COd", "PC_G")[1:4]
emis(veh, units::set_units(mil, "km"), ef)

# group online
bus1 <- age_hdv(30, agemax = 4)
veh <- bus1
lkm <- units::set_units(400, "km")
speed <- 40
efco <- ef_cetesb("COd", "UB", agemax = 4)
lef <- ef_hdv_scaled(
  dfcol = as.numeric(efco),
  v = "Ubus",
  t = "Std",
  g = ">15 & <=18",
  eu = rep("IV", 4),
  gr = 0,
  l = 0.5,
  p = "CO"
)
for (i in 1:length(lef)) print(lef[[i]](10))
(a <- emis(veh = bus1, lkm = lkm, ef = efco, verbose = TRUE))
(b <- emis(veh = bus1, lkm = lkm, ef = efco, verbose = TRUE, fortran = TRUE))

## End(Not run)

(in development, needs checks) Aggregate emissions by lumped groups in chemical mechanism

Description

emis_chem aggregates emissions by chemical mechanism and convert grams to mol. This function reads all hydrocarbos and respective criteria polluants specified in ef_ldv_speed and ef_hdv_speed.

Usage

emis_chem(dfe, mechanism, colby, long = FALSE)

Arguments

dfe

data.frame with column 'emissions' in grams and 'pollutant' in long format. It is supposed that each line is the pollution of some region. Then the 'coldby' argument is for include the name of the region.
mechanism

Character, "SAPRC", "RACM", "RADM2", "CBMZ", "MOZART", "SAPRC99", "CB05", "CB06CMAQ", "CB05CMAQ", "RACM2CMAQ", "SAPRC99CMAQ", "SAPRC07CMAQ", "SAPRC07A", "RADM2_SORG", "CBMZ_MOSAIC", "CPTEC", "GOCART_CPTEC", "MOZEM", "MOZCEM", "CAMMAM", "MOZMEM", "MOZC_T1_EM", "CB05_OPT1", "CB05_OPT2", "CRIMECH"
colby

Character indicating column name for aggregating extra column. For instance, region or province.
long

Logical. Do you want data in long format?

Value

data.frame with lumped groups by chemical mechanism. It transform emissions in grams to mol.

Note

This feature is experimental and the mapping of pollutants and lumped species may change in future. This function is converting the intial data.frame input into data.table. To have a comprehensive speciation is necessary enter with a data.frame with colum 'emission' in long format including another column named 'pollutant' with species of NMHC, CO, NO, NO2, NH3, SO2, PM2.5 and coarse PM10.

Groups derived from gases has units 'mol' and from aersols 'g'. The aersol units for WRF-Chem are ug/m^2/s while for CMAQ and CAMx are g/s. So, leaving the units just in g, allow to make further change while providing flexibility for several models. TODO: Enter with wide data.frame, with each line as a each street, each column for pollutant

See Also

ef_ldv_speed ef_hdv_speed speciate ef_evap

Examples

## Not run: 
# CO
df <- data.frame(emission = Emissions(1:10))
df$pollutant = "CO"
emis_chem(dfe = df, "CBMZ_MOSAIC")
# hexanal
df$pollutant = "hexanal"
emis_chem(df, "CBMZ_MOSAIC")
# propadiene and NO2
df2 <- df1 <- df
df1$pollutant = "propadiene"
df2$pollutant = "NO2"
(dfe <- rbind(df1, df2))
emis_chem(dfe, "CBMZ_MOSAIC")
dfe$region <- rep(letters[1:2], 10)
emis_chem(dfe, "CBMZ_MOSAIC", "region")
emis_chem(dfe, "CBMZ_MOSAIC", "region", TRUE)

## End(Not run)

Aggregate emissions by lumped groups in chemical mechanism

Description

emis_chem2 aggregates VOC emissions by chemical mechanism and convert grams to mol.

Usage

emis_chem2(df, mech, nx, na.rm = FALSE)

Arguments

df

data.frame with emissions including columns "id" and "pol".
mech

Character, "CB4", "CB05", "S99", "S7","CS7", "S7T", "S11", "S11D","S16C","S18B","RADM2", "RACM2","MOZT1", "CBMZ", "CB05opt2"
nx

Character, colnames for emissions data, for instance "V1", "V2"...
na.rm

Logical, to remove lines with NA from group

Value

data.frame with lumped groups by chemical mechanism.

Note

  • CB05: "ALD" "ALDX" "ETH" "HC3" "HC5" "HC8" "HCHO" "KET" "OL2" "OLI" "OLT" "TOL" "XYL"

  • CB05opt2: "ALD2" "ALDX" "BENZENE" "ETH" "ETHA" "FORM" "IOLE" "OLE" "PAR" "TOL" "XYL"

  • RADM2: "ALD" "ETH" "HC3" "HC5" "HC8" "HCHO" "KET" "MACR" "OL2" "OLI" "OLT" "TOL" "XYL"

  • RACM2: ACD" "ACE" "ACT" "ALD" "BALD" "BEN" "DIEN" "ETE" "ETH" "HC3" "HC5" "HC8" "HCHO" "MACR" "MEK" "OLI" "OLT" "TOL" "UALD" "XYM" "XYO" "XYP"

  • CB4: "ALD2" "ETH" "FORM" "OLE" "PAR" "TOL" "XYL"

  • S99: "ACET" "ALK1" "ALK2" "ALK3" "ALK4" "ALK5" "ARO1NBZ" "ARO2" "BALD" "BENZENE" "CCHO" "ETHENE" "HCHO" "IPROD" "MACR" "MEK" "OLE1" "OLE2" "RCHO"

  • CB4: "ACET" "ACYE" "ALK1" "ALK2" "ALK3" "ALK4" "ALK5" "ARO1" "ARO2" "BALD" "BENZ" "CCHO" "ETHE" "HCHO" "IPRD" "MACR" "MEK" "OLE1" "OLE2" "RCHO"

  • CS7: "ALK3" "ALK4" "ARO1" "ARO2" "CCHO" "ETHE" "HCHO" "IPRD" "NROG" "OLE1" "OLE2" "PRD2" "RCHO"

  • S7: "ACET" "ACYE" "ALK1" "ALK2" "ALK3" "ALK4" "ALK5" "ARO1" "ARO2" "BALD" "BENZ" "CCHO" "ETHE" "HCHO" "IPRD" "MACR" "MEK" "OLE1" "OLE2" "RCHO"

  • S7T: "13BDE" "ACET" "ACRO" "ACYE" "ALK1" "ALK2" "ALK3" "ALK4" "ALK5" "ARO1" "ARO2" "B124" "BALD" "BENZ" "CCHO" "ETHE" "HCHO" "IPRD" "MACR" "MEK" "MXYL" "OLE1" "OLE2" "OXYL" "PRPE" "PXYL" "RCHO" "TOLU"

  • S11: "ACET" "ACYL" "ALK1" "ALK2" "ALK3" "ALK4" "ALK5" "ARO1" "ARO2" "BALD" "BENZ" "CCHO" "ETHE" "HCHO" "IPRD" "MACR" "MEK" "OLE1" "OLE2" "RCHO"

  • S11D: "ACET" "ACRO" "ACYL" "ALLENE" "BALD" "BENZ" "BUTDE13" "BUTENE1" "C2BENZ" "C2BUTE" "C2PENT" "C4RCHO1" "CCHO" "CROTALD" "ETACTYL" "ETHANE" "ETHE" "HCHO" "HEXENE1" "ISOBUTEN" "M2C3" "M2C4" "M2C6" "M2C7" "M3C6" "M3C7" "MACR" "MEACTYL" "MEK" "MXYLENE" "NC1" "NC4" NC5" "NC6" "NC7" "NC8" "NC9" "OLE2" "OTH2" "OTH4" "OTH5" "OXYLENE" "PENTEN1" "PROPALD" "PROPANE" "PROPENE" "PXYLENE" "RCHO" "STYRENE" "TMB123" "TMB124" "TMB135" "TOLUENE"

  • S16C:"ACET" "ACETL" "ACRO" "ACYLS" "ALK3" "ALK4" "ALK5" "BALD" "BENZ" "BUT13" "BZ123" "BZ124" "BZ135" "C2BEN" "ETCHO" "ETHAN" "ETHEN" "HCHO" "MACR" "MECHO" "MEK" "MXYL" "NC4" "OLE1" "OLE2" "OLE3" "OLE4" "OLEA1" "OTH1" "OTH3" "OTH4" "OXYL" "PROP" "PROPE" "PXYL" "RCHO" "STYRS" "TOLU"

  • S18B:"ACET" "ACETL" "ACRO" "ACYLS" "ALK3" "ALK4" "ALK5" "BALD" "BENZ" "BUT13" "BZ123" "BZ124" "BZ135" "C2BEN" "ETCHO" "ETHAN" "ETHEN" "HCHO" "MACR" "MECHO" "MEK" "MXYL" "NC4" "OLE1" "OLE2" "OLE3" "OLE4" "OLEA1" "OTH1" "OTH3" "OTH4" "OXYL" "PROP" "PROPE" "PXYL" "RCHO" "STYRS" "TOLU"

References

Carter, W. P. (2015). Development of a database for chemical mechanism assignments for volatile organic emissions. Journal of the Air & Waste Management Association, 65(10), 1171-1184.

See Also

speciate

Examples

{
id <-1:2
df <- data.frame(V1 = 1:2, V2 = 1:2)
dx <- speciate(x = df,
               spec = "nmhc",
               fuel = "E25",
               veh = "LDV",
               eu = "Exhaust")
dx$id <- rep(id, length(unique(dx$pol)))
names(dx)
vocE25EX <- emis_chem2(df = dx,
                       mech = "CB05",
                       nx = c("V1", "V2"))
}

Estimation with Chinese factors

Description

Emissions estimates

Usage

emis_china(
  x,
  lkm,
  tfs,
  v = "PV",
  t = "Small",
  f = "G",
  standard,
  s,
  speed,
  te,
  hu,
  h,
  yeardet = 2016,
  p,
  verbose = TRUE,
  array = FALSE
)

Arguments

x

Vehicles data.frame
lkm

Length of each link in km
tfs

temporal factor
v

Character; category vehicle: "PV" for Passenger Vehicles or 'Trucks"
t

Character; sub-category of of vehicle: PV Gasoline: "Mini", "Small","Medium", "Large", "Taxi", "Motorcycles", "Moped", PV Diesel: "Mediumbus", "Largebus", "3-Wheel". Trucks: "Mini", "Light" , "Medium", "Heavy"
f

Character;fuel: "G", "D", "CNG", "ALL"
standard

Character vector; "PRE", "I", "II", "III", "IV", "V".
s

Sulhur in ppm
speed

Speed (length nrow x)
te

Temperature (length tfs)
hu

Humidity (length tfs)
h

Altitude (length nrow x)
yeardet

Year, default 2016
p

Character; pollutant: "CO", "NOx","HC", "PM", "Evaporative_driving" or "Evaporative_parking"
verbose

Logical to show more info
array

Logical to return EmissionsArray or not

Value

long data.frame

See Also

Other China: ef_china_det(), ef_china_hu(), ef_china_h(), ef_china_long(), ef_china_speed(), ef_china_s(), ef_china_te(), ef_china_th(), ef_china(), emis_long()

Examples

{
ef_china_h(h = 1600, p = "CO")
}

Estimation of cold start emissions hourly for the of the week

Description

emis_cold emissions are estimated as the product of the vehicles on a road, length of the road, emission factor evaluated at the respective speed. The estimation considers the beta parameter, the fraction of mileage driven

Usage

emis_cold(
  veh,
  lkm,
  ef,
  efcold,
  beta,
  speed = 34,
  agemax = if (!inherits(x = veh, what = "list")) {
     ncol(veh)
 } else {
    
    ncol(veh[[1]])
 },
  profile,
  simplify = FALSE,
  hour = nrow(profile),
  day = ncol(profile),
  array = TRUE,
  verbose = FALSE
)

Arguments

veh

"Vehicles" data-frame or list of "Vehicles" data-frame. Each data-frame as number of columns matching the age distribution of that type of vehicle. The number of rows is equal to the number of streets link
lkm

Length of each link
ef

List of functions of emission factors of vehicular categories
efcold

List of functions of cold start emission factors of vehicular categories
beta

Dataframe with the hourly cold-start distribution to each day of the period. Number of rows are hours and columns are days
speed

Speed data-frame with number of columns as hours
agemax

Age of oldest vehicles for that category
profile

Numerical or dataframe with nrows equal to 24 and ncol 7 day of the week
simplify

Logical; to determine if EmissionsArray should les dimensions, being streets, vehicle categories and hours or default (streets, vehicle categories, hours and days). Default is FALSE to avoid break old code, but the recommendation is that new estimations use this parameter as TRUE
hour

Number of considered hours in estimation
day

Number of considered days in estimation
array

Deprecated! emis_cold returns only arrays. When TRUE and veh is not a list, expects a profile as a dataframe producing an array with dimensions (streets x columns x hours x days)
verbose

Logical; To show more information

Value

EmissionsArray g/h

Examples

## Not run: 
# Do not run
data(net)
data(pc_profile)
data(fe2015)
data(fkm)
data(pc_cold)
pcf <- as.data.frame(cbind(pc_cold,pc_cold,pc_cold,pc_cold,pc_cold,pc_cold,
pc_cold))
PC_G <- c(33491,22340,24818,31808,46458,28574,24856,28972,37818,49050,87923,
          133833,138441,142682,171029,151048,115228,98664,126444,101027,
          84771,55864,36306,21079,20138,17439, 7854,2215,656,1262,476,512,
          1181, 4991, 3711, 5653, 7039, 5839, 4257,3824, 3068)
veh <- data.frame(PC_G = PC_G)
pc1 <- my_age(x = net$ldv, y = PC_G, name = "PC")
pcw <- temp_fact(net$ldv+net$hdv, pc_profile)
speed <- netspeed(pcw, net$ps, net$ffs, net$capacity, net$lkm, alpha = 1)
pckm <- units::set_units(fkm[[1]](1:24), "km"); pckma <- cumsum(pckm)
cod1 <- emis_det(po = "CO", cc = 1000, eu = "III", km = pckma[1:11])
cod2 <- emis_det(po = "CO", cc = 1000, eu = "I", km = pckma[12:24])
#vehicles newer than pre-euro
co1 <- fe2015[fe2015$Pollutant=="CO", ] #24 obs!!!
cod <- c(co1$PC_G[1:24]*c(cod1,cod2),co1$PC_G[25:nrow(co1)])
lef <- ef_ldv_scaled(co1, cod, v = "PC", cc = "<=1400",
                     f = "G",p = "CO", eu=co1$Euro_LDV)
# Mohtly average temperature 18 Celcius degrees
lefec <- ef_ldv_cold_list(df = co1, ta = 18, cc = "<=1400", f = "G",
                          eu = co1$Euro_LDV, p = "CO" )
lefec <- c(lefec,lefec[length(lefec)], lefec[length(lefec)],
           lefec[length(lefec)], lefec[length(lefec)],
           lefec[length(lefec)])
length(lefec) == ncol(pc1)
#emis change length of 'ef' to match ncol of 'veh'
class(lefec)
PC_CO_COLD <- emis_cold(veh = pc1,
                        lkm = net$lkm,
                        ef = lef,
                        efcold = lefec,
                        beta = pcf,
                        speed = speed,
                        profile = pc_profile)
class(PC_CO_COLD)
plot(PC_CO_COLD)
lpc <- list(pc1, pc1)
PC_CO_COLDv2 <- emis_cold(veh = pc1,
                          lkm = net$lkm,
                          ef = lef,
                          efcold = lefec,
                          beta = pcf,
                          speed = speed,
                          profile = pc_profile,
                          hour = 2,
                          day = 1)

## End(Not run)

Estimation of cold start emissions with top-down approach

Description

emis_cold_td estimates cld start emissions with a top-down appraoch. This is, annual or monthly emissions or region. Especifically, the emissions are esitmated for row of the simple feature (row of the spatial feature).

In general was designed so that each simple feature is a region with different average monthly temperature. This funcion, as other in this package, adapts to the class of the input data. providing flexibility to the user.

Usage

emis_cold_td(
  veh,
  lkm,
  ef,
  efcold,
  beta,
  pro_month,
  params,
  verbose = FALSE,
  fortran = FALSE,
  nt = ifelse(check_nt() == 1, 1, check_nt()/2)
)

Arguments

veh

"Vehicles" data-frame or spatial feature, wwhere columns are the age distribution of that vehicle. and rows each simple feature or region. The number of rows is equal to the number of streets link
lkm

Numeric; mileage by the age of use of each vehicle.
ef

Numeric; emission factor with
efcold

Data.frame. When it is a data.frame, each column is for each type of vehicle by age of use, rows are are each simple feature. When you have emission factors for each month, the order should a data.frame ina long format, as rurned by ef_ldv_cold.
beta

Data.frame with the fraction of cold starts. The rows are the fraction for each spatial feature or subregion, the columns are the age of use of vehicle.
pro_month

Numeric; montly profile to distribuite annual mileage in each month.
params

List of parameters; Add columns with information to returning data.frame
verbose

Logical; To show more information
fortran

Logical; to try the fortran calculation.
nt

Integer; Number of threads wich must be lower than max available. See check_nt. Only when fortran = TRUE

Value

Emissions data.frame

See Also

ef_ldv_cold

Examples

## Not run: 
# Do not run
veh <- age_ldv(1:10, agemax = 8)
euros <- c("V", "V", "IV", "III", "II", "I", "PRE", "PRE")
dt <- matrix(rep(2:25, 5), ncol = 12, nrow = 10) # 12 months, 10 rows
row.names(dt) <- paste0("Simple_Feature_", 1:10)
efc <- ef_ldv_cold(ta = dt, cc = "<=1400", f = "G", eu = euros, p = "CO", speed = Speed(34))
efh <- ef_ldv_speed(
  v = "PC", t = "4S", cc = "<=1400", f = "G",
  eu = euros, p = "CO", speed = Speed(runif(nrow(veh), 15, 40))
)
lkm <- units::as_units(18:11, "km") * 1000
cold_lkm <- cold_mileage(ltrip = units::as_units(20, "km"), ta = celsius(dt))
names(cold_lkm) <- paste0("Month_", 1:12)
veh_month <- c(rep(8, 1), rep(10, 5), 9, rep(10, 5))
system.time(
  a <- emis_cold_td(
    veh = veh,
    lkm = lkm,
    ef = efh[1, ],
    efcold = efc[1:10, ],
    beta = cold_lkm[, 1],
    verbose = TRUE
  )
)
system.time(
  a2 <- emis_cold_td(
    veh = veh,
    lkm = lkm,
    ef = efh[1, ],
    efcold = efc[1:10, ],
    beta = cold_lkm[, 1],
    verbose = TRUE,
    fortran = TRUE
  )
) # emistd2coldf.f95
a$emissions <- round(a$emissions, 8)
a2$emissions <- round(a2$emissions, 8)
identical(a, a2)

# Adding parameters
emis_cold_td(
  veh = veh,
  lkm = lkm,
  ef = efh[1, ],
  efcold = efc[1:10, ],
  beta = cold_lkm[, 1],
  verbose = TRUE,
  params = list(
    paste0("data_", 1:10),
    "moredata"
  )
)
system.time(
  aa <- emis_cold_td(
    veh = veh,
    lkm = lkm,
    ef = efh,
    efcold = efc,
    beta = cold_lkm,
    pro_month = veh_month,
    verbose = TRUE
  )
)
system.time(
  aa2 <- emis_cold_td(
    veh = veh,
    lkm = lkm,
    ef = efh,
    efcold = efc,
    beta = cold_lkm,
    pro_month = veh_month,
    verbose = TRUE,
    fortran = TRUE
  )
) # emistd5coldf.f95
aa$emissions <- round(aa$emissions, 8)
aa2$emissions <- round(aa2$emissions, 8)
identical(aa, aa2)

## End(Not run)

Determine deterioration factors for urban conditions

Description

emis_det returns deterioration factors. The emission factors comes from the guidelines for developing emission factors of the EMEP/EEA air pollutant emission inventory guidebook http://www.eea.europa.eu/themes/air/emep-eea-air-pollutant-emission-inventory-guidebook This function subset an internal database of emission factors with each argument

Usage

emis_det(
  po,
  cc,
  eu,
  speed = Speed(18.9),
  km,
  verbose = FALSE,
  show.equation = FALSE
)

Arguments

po

Character; Pollutant "CO", "NOx" or "HC"
cc

Character; Size of engine in cc covering "<=1400", "1400_2000" or ">2000"
eu

Character; Euro standard: "I", "II", "III", "III", "IV", "V", "VI", "VIc"
speed

Numeric; Speed to return Number of emission factor and not a function. It needs units in km/h
km

Numeric; accumulated mileage in km.
verbose

Logical; To show more information
show.equation

Option to see or not the equation parameters

Value

It returns a numeric vector representing the increase in emissions due to normal deterioring

Note

The deterioration factors functions are available for technologies euro "II", "III" and "IV". In order to cover all euro technologies, this function assumes that the deterioration function of "III" and "IV" applies for "V", "VI" and "VIc". However, as these technologies are relative new, accumulated milage is low and hence, deteerioration factors small.

Examples

## Not run: 
data(fkm)
pckm <- fkm[[1]](1:24); pckma <- cumsum(pckm)
km <- units::set_units(pckma[1:11], km)
# length eu = length km = 1
emis_det(po = "CO", cc = "<=1400", eu = "III", km = km[5], show.equation = TRUE)
# length eu = length km = 1, length speed > 1
emis_det(po = "CO", cc = "<=1400", eu = "III", km = km[5], speed = Speed(1:10))
# length km != length eu error
# (cod1 <- emis_det(po = "CO", cc = "<=1400", eu = c("III", "IV"), speed = Speed(30),
# km = km[4]))
# length eu = 1 length km > 1
emis_det(po = "CO", cc = "<=1400", eu = "III", km = km)
# length eu = 2, length km = 2 (if different length, error!)
(cod1 <- emis_det(po = "CO", cc = "<=1400", eu = c("III", "IV"), km = km[4:5]))
# length eu = 2, length km = 2, length speed > 1
(cod1 <- emis_det(po = "CO", cc = "<=1400", eu = c("III", "IV"), speed = Speed(0:130),
km = km[4:5]))
euros <- c("V","V","V", "IV", "IV", "IV", "III", "III", "III", "III")
# length eu = 2, length km = 2, length speed > 1
(cod1 <- emis_det(po = "CO", cc = "<=1400", eu = euros, speed = Speed(1:100),
km = km[1:10]))
cod1 <- as.matrix(cod1[, 1:11])
filled.contour(cod1, col = cptcity::cpt(6277, n = 20))
filled.contour(cod1, col = cptcity::lucky(n = 19))
euro <- c(rep("V", 5), rep("IV", 5), "III")
euros <- rbind(euro, euro)
(cod1 <- emis_det(po = "CO", cc = "<=1400", eu = euros, km = km))

## End(Not run)

Allocate emissions into spatial objects (street emis to grid)

Description

emis_dist allocates emissions proportionally to each feature. "Spatial" objects are converter to "sf" objects. Currently, 'LINESTRING' or 'MULTILINESTRING' supported. The emissions are distributed in each street.

Usage

emis_dist(gy, spobj, pro, osm, verbose = FALSE)

Arguments

gy

Numeric; a unique total (top-down)
spobj

A spatial dataframe of class "sp" or "sf". When class is "sp" it is transformed to "sf".
pro

Matrix or data-frame profiles, for instance, pc_profile.
osm

Numeric; vector of length 5, for instance, c(5, 3, 2, 1, 1). The first element covers 'motorway' and 'motorway_link. The second element covers 'trunk' and 'trunk_link'. The third element covers 'primary' and 'primary_link'. The fourth element covers 'secondary' and 'secondary_link'. The fifth element covers 'tertiary' and 'tertiary_link'.
verbose

Logical; to show more info.

Note

When spobj is a 'Spatial' object (class of sp), they are converted into 'sf'.

Examples

## Not run: 
data(net)
data(pc_profile)
po <- 1000
t1 <- emis_dist(gy = po, spobj = net)
head(t1)
sum(t1$gy)
#t1 <- emis_dist(gy = po, spobj = net, osm = c(5, 3, 2, 1, 1) )
t1 <- emis_dist(gy = po, spobj = net, pro = pc_profile)

## End(Not run)

Emission calculation based on EMFAC emission factors

Description

emis_emfac estimates emissions based on an emission factors database from EMFAC.You must download the emission factors from EMFAC website.

Usage

emis_emfac(
  ef,
  veh,
  lkm,
  tfs,
  speed,
  vehname,
  pol = "CO_RUNEX",
  modelyear = 2021:1982,
  vkm = TRUE,
  verbose = TRUE
)

Arguments

ef

data.frame or character path to EMFAC ef (g/miles)
veh

Vehicles data.frame
lkm

Distance per street-link in miles
tfs

vector to project activity by hour
speed

Speed data.frame in miles/hour
vehname

numeric vector for heavy good vehicles or trucks
pol

character, "CO_RUNEX"
modelyear

numeric vector, 2021:1982
vkm

logical, to return vkm
verbose

logical, to show more information

Value

data.table with emission estimation in long format

Note

Emission factors must be in g/miles

Examples

## Not run: 
# do not run

## End(Not run)

Estimation of evaporative emissions

Description

emis_evap estimates evaporative emissions from EMEP/EEA emisison guidelines

Usage

emis_evap(
  veh,
  x,
  ed,
  hotfi,
  hotc,
  warmc,
  carb = 0,
  p,
  params,
  pro_month,
  verbose = FALSE
)

Arguments

veh

Numeric or data.frame of Vehicles with untis 'veh'.
x

Numeric which can be either, daily mileage by age of use with units 'lkm', number of trips or number of proc. When it has units 'lkm', all the emission factors must be in 'g/km'. When ed is in g/day, x it is the number of days (without units). When hotfi, hotc or warmc are in g/trip, x it is the number of trips (without units). When hotfi, hotc or warmc are in g/proced, x it is the number of proced (without units).
ed

average daily evaporative emissions. If x has units 'lkm', the units of ed must be 'g/km', other case, this are simply g/day (without units).
hotfi

average hot running losses or soak evaporative factor for vehicles with fuel injection and returnless fuel systems. If x has units 'lkm', the units of ed must be 'g/km', other case, this is simply g/trip or g/proced
hotc

average running losses or soak evaporative factor for vehicles with carburetor or fuel return system for vehicles with fuel injection and returnless fuel systems. If x has units 'lkm', the units of ed must be 'g/km',
warmc

average cold and warm running losses or soak evaporative factor for vehicles with carburetor or fuel return system for vehicles with fuel injection and returnless fuel systems. If x has units 'lkm', the units of ed must be 'g/km',
carb

fraction of gasoline vehicles with carburetor or fuel return system.
p

Fraction of trips finished with hot engine
params

Character; Add columns with information to returning data.frame
pro_month

Numeric; monthly profile to distribute annual mileage in each month.
verbose

Logical; To show more information

Value

numeric vector of emission estimation in grams

Note

When veh is a "Vehicles" data.frame, emission factors are evaluated till the number of columns of veh. For instance, if the length of the emission factor is 20 but the number of columns of veh is 10, the 10 first emission factors are used.

References

Mellios G and Ntziachristos 2016. Gasoline evaporation. In: EEA, EMEP. EEA air pollutant emission inventory guidebook-2009. European Environment Agency, Copenhagen, 2009

See Also

ef_evap

Examples

## Not run: 
(a <- Vehicles(1:10))
(lkm <- units::as_units(1:10, "km"))
(ef <- EmissionFactors(1:10))
(ev <- emis_evap(veh = a, x = lkm, hotfi = ef))

## End(Not run)

Estimation of evaporative emissions 2

Description

emis_evap performs the estimation of evaporative emissions from EMEP/EEA emission guidelines with Tier 2.

Usage

emis_evap2(
  veh,
  name,
  size,
  fuel,
  aged,
  nd4,
  nd3,
  nd2,
  nd1,
  hs_nd4,
  hs_nd3,
  hs_nd2,
  hs_nd1,
  rl_nd4,
  rl_nd3,
  rl_nd2,
  rl_nd1,
  d_nd4,
  d_nd3,
  d_nd2,
  d_nd1
)

Arguments

veh

Total number of vehicles by age of use. If is a list of 'Vehicles' data-frames, it will sum the columns of the eight element of the list representing the 8th hour. It was chosen this hour because it is morning rush hour but the user can adapt the data to this function
name

Character of type of vehicle
size

Character of size of vehicle
fuel

Character of fuel of vehicle
aged

Age distribution vector. E.g.: 1:40
nd4

Number of days with temperature between 20 and 35 Celsius degrees
nd3

Number of days with temperature between 10 and 25 Celsius degrees
nd2

Number of days with temperature between 0 and 15 Celsius degrees
nd1

Number of days with temperature between -5 and 10 Celsius degrees
hs_nd4

average daily hot-soak evaporative emissions for days with temperature between 20 and 35 Celsius degrees
hs_nd3

average daily hot-soak evaporative emissions for days with temperature between 10 and 25 Celsius degrees
hs_nd2

average daily hot-soak evaporative emissions for days with temperature between 0 and 15 Celsius degrees
hs_nd1

average daily hot-soak evaporative emissions for days with temperature between -5 and 10 Celsius degrees
rl_nd4

average daily running losses evaporative emissions for days with temperature between 20 and 35 Celsius degrees
rl_nd3

average daily running losses evaporative emissions for days with temperature between 10 and 25 Celsius degrees
rl_nd2

average daily running losses evaporative emissions for days with temperature between 0 and 15 Celsius degrees
rl_nd1

average daily running losses evaporative emissions for days with temperature between -5 and 10 Celsius degrees
d_nd4

average daily diurnal evaporative emissions for days with temperature between 20 and 35 Celsius degrees
d_nd3

average daily diurnal evaporative emissions for days with temperature between 10 and 25 Celsius degrees
d_nd2

average daily diurnal evaporative emissions for days with temperature between 0 and 15 Celsius degrees
d_nd1

average daily diurnal evaporative emissions for days with temperature between -5 and 10 Celsius degrees

Value

dataframe of emission estimation in grams/days

References

Mellios G and Ntziachristos 2016. Gasoline evaporation. In: EEA, EMEP. EEA air pollutant emission inventory guidebook-2009. European Environment Agency, Copenhagen, 2009

Examples

## Not run: 
data(net)
PC_G <- c(33491,22340,24818,31808,46458,28574,24856,28972,37818,49050,87923,
          133833,138441,142682,171029,151048,115228,98664,126444,101027,
          84771,55864,36306,21079,20138,17439, 7854,2215,656,1262,476,512,
          1181, 4991, 3711, 5653, 7039, 5839, 4257,3824, 3068)
veh <- data.frame(PC_G = PC_G)
pc1 <- my_age(x = net$ldv, y = PC_G, name = "PC")
ef1 <- ef_evap(ef = "erhotc",v = "PC", cc = "<=1400", dt = "0_15", ca = "no")
dfe <- emis_evap2(veh = pc1,
                 name = "PC",
                 size = "<=1400",
                 fuel = "G",
                 aged = 1:ncol(pc1),
                 nd4 = 10,
                 nd3 = 4,
                 nd2 = 2,
                 nd1 = 1,
                 hs_nd4 = ef1*1:ncol(pc1),
                 hs_nd3 = ef1*1:ncol(pc1),
                 hs_nd2 = ef1*1:ncol(pc1),
                 hs_nd1 = ef1*1:ncol(pc1),
                 d_nd4 = ef1*1:ncol(pc1),
                 d_nd3 = ef1*1:ncol(pc1),
                 d_nd2 = ef1*1:ncol(pc1),
                 d_nd1 = ef1*1:ncol(pc1),
                 rl_nd4 = ef1*1:ncol(pc1),
                 rl_nd3 = ef1*1:ncol(pc1),
                 rl_nd2 = ef1*1:ncol(pc1),
                 rl_nd1 = ef1*1:ncol(pc1))
lpc <- list(pc1, pc1, pc1, pc1,
            pc1, pc1, pc1, pc1)
dfe <- emis_evap2(veh = lpc,
                 name = "PC",
                 size = "<=1400",
                 fuel = "G",
                 aged = 1:ncol(pc1),
                 nd4 = 10,
                 nd3 = 4,
                 nd2 = 2,
                 nd1 = 1,
                 hs_nd4 = ef1*1:ncol(pc1),
                 hs_nd3 = ef1*1:ncol(pc1),
                 hs_nd2 = ef1*1:ncol(pc1),
                 hs_nd1 = ef1*1:ncol(pc1),
                 d_nd4 = ef1*1:ncol(pc1),
                 d_nd3 = ef1*1:ncol(pc1),
                 d_nd2 = ef1*1:ncol(pc1),
                 d_nd1 = ef1*1:ncol(pc1),
                 rl_nd4 = ef1*1:ncol(pc1),
                 rl_nd3 = ef1*1:ncol(pc1),
                 rl_nd2 = ef1*1:ncol(pc1),
                 rl_nd1 = ef1*1:ncol(pc1))

## End(Not run)

Allocate emissions into a grid returning point emissions or flux

Description

emis_grid allocates emissions proportionally to each grid cell. The process is performed by the intersection between geometries and the grid. It means that requires "sr" according to your location for the projection. It is assumed that spobj is a Spatial*DataFrame or an "sf" with the pollutants in data. This function returns an object of class "sf".

It is

Usage

emis_grid(spobj = net, g, sr, type = "lines", FN = "sum", flux = TRUE, k = 1)

Arguments

spobj

A spatial dataframe of class "sp" or "sf". When class is "sp" it is transformed to "sf".
g

A grid with class "SpatialPolygonsDataFrame" or "sf".
sr

Spatial reference e.g: 31983. It is required if spobj and g are not projected. Please, see http://spatialreference.org/.
type

type of geometry: "lines", "points" or "polygons".
FN

Character indicating the function. Default is "sum"
flux

Logical, if TRUE, it return flux (mass / area / time (implicit)) in a polygon grid, if false, mass / time (implicit) as points, in a similar fashion as EDGAR provide data.
k

Numeric to multiply emissions

Note

1) If flux = TRUE (default), emissions are flux = mass / area / time (implicit), as polygons. If flux = FALSE, emissions are mass / time (implicit), as points. Time untis are not displayed because each use can have different time units for instance, year, month, hour second, etc.

2) Therefore, it is good practice to have time units in 'spobj'. This implies that spobj MUST include units!.

3) In order to check the sum of the emissions, you must calculate the grid-area in km^2 and multiply by each column of the resulting emissions grid, and then sum.

4) If FN = "sum", is mass conservative!.

Examples

## Not run: 
data(net)
g <- make_grid(net, 1/102.47/2) #500m in degrees
names(net)
netsf <- sf::st_as_sf(net)
netg <- emis_grid(spobj = netsf[, c("ldv", "hdv")], g = g, sr= 31983)
plot(netg["ldv"],
     axes = TRUE,
     graticule = TRUE,
     bg = "black",
     lty = 0)
g <- sf::st_make_grid(net, 1/102.47/2, square = FALSE) #500m in degrees
g <- st_sf(i  =1, geometry = g)
netg <- emis_grid(spobj = netsf[, c("ldv", "hdv")], g = g, sr= 31983)
plot(netg["ldv"],
     axes = TRUE,
     graticule = TRUE,
     bg = "black",
     lty = 0)
plot(netg["hdv"], axes = TRUE)
netg <- emis_grid(spobj = netsf[, c("ldv", "hdv")], g = g, sr= 31983, FN = "mean")
plot(netg["ldv"], axes = TRUE)
plot(netg["hdv"], axes = TRUE)
netg <- emis_grid(spobj = netsf[, c("ldv", "hdv")], g = g, sr= 31983, flux = FALSE)
plot(netg["ldv"],
     axes = TRUE,
     pch = 16,
     pal = cptcity::cpt(colorRampPalette= TRUE,
                        rev = TRUE),
     cex = 3)

## End(Not run)

Estimation of hot exhaust emissions with a top-down approach

Description

emis_hot_td estimates cold start emissions with a top-down appraoch. This is, annual or monthly emissions or region. Especifically, the emissions are estimated for the row of the simple feature (row of the spatial feature).

In general was designed so that each simple feature is a region with different average monthly temperature. This function, as others in this package, adapts to the class of the input data. providing flexibility to the user.

Usage

emis_hot_td(
  veh,
  lkm,
  ef,
  pro_month,
  params,
  verbose = FALSE,
  fortran = FALSE,
  nt = ifelse(check_nt() == 1, 1, check_nt()/2)
)

Arguments

veh

"Vehicles" data-frame or spatial feature, where columns are the age distribution of that vehicle. and rows each simple feature or region.
lkm

Numeric; mileage by the age of use of each vehicle.
ef

Numeric or data.frame; emission factors. When it is a data.frame number of rows can be for each region, or also, each region repeated along 12 months. For instance, if you have 10 regions the number of rows of ef can also be 120 (10 * 120). when you have emission factors that varies with month, see ef_china.
pro_month

Numeric or data.frame; monthly profile to distribute annual mileage in each month. When it is a data.frame, each region (row) can have a different monthly profile.
params

List of parameters; Add columns with information to returning data.frame
verbose

Logical; To show more information
fortran

Logical; to try the fortran calculation.
nt

Integer; Number of threads which must be lower than max available. See check_nt. Only when fortran = TRUE

Details

List to make easier to use this function.

  1. 'pro_month' is data.frame AND rows of 'ef' and 'veh' are equal.

  2. 'pro_month' is numeric AND rows of 'ef' and 'veh' are equal.

  3. 'pro_month' is data.frame AND rows of 'ef' is 12X rows of 'veh'.

  4. 'pro_month' is numeric AND rows of 'ef' is 12X rows of 'veh'.

  5. ‘pro_month' is data,frame AND class of 'ef' is ’units'.

  6. ‘pro_month' is numeric AND class of 'ef' is ’units'.

  7. NO ‘pro_month' AND class of 'ef' is ’units'.

  8. NO 'pro_month' AND 'ef' is data.frame.

  9. 'pro_month' is numeric AND rows of 'ef' is 12 (monthly 'ef').

Value

Emissions data.frame

See Also

ef_ldv_speed ef_china

Examples

## Not run: 
# Do not run
euros <- c("V", "V", "IV", "III", "II", "I", "PRE", "PRE")
efh <- ef_ldv_speed(
  v = "PC", t = "4S", cc = "<=1400", f = "G",
  eu = euros, p = "CO", speed = Speed(34)
)
lkm <- units::as_units(c(20:13), "km") * 1000
veh <- age_ldv(1:10, agemax = 8)
system.time(
  a <- emis_hot_td(
    veh = veh,
    lkm = lkm,
    ef = EmissionFactors(as.numeric(efh[, 1:8])),
    verbose = TRUE
  )
)
system.time(
  a2 <- emis_hot_td(
    veh = veh,
    lkm = lkm,
    ef = EmissionFactors(as.numeric(efh[, 1:8])),
    verbose = TRUE,
    fortran = TRUE
  )
) # emistd7f.f95
identical(a, a2)

# adding columns
emis_hot_td(
  veh = veh,
  lkm = lkm,
  ef = EmissionFactors(as.numeric(efh[, 1:8])),
  verbose = TRUE,
  params = list(paste0("data_", 1:10), "moredata")
)

# monthly profile (numeric) with numeric ef
veh_month <- c(rep(8, 1), rep(10, 5), 9, rep(10, 5))
system.time(
  aa <- emis_hot_td(
    veh = veh,
    lkm = lkm,
    ef = EmissionFactors(as.numeric(efh[, 1:8])),
    pro_month = veh_month,
    verbose = TRUE
  )
)
system.time(
  aa2 <- emis_hot_td(
    veh = veh,
    lkm = lkm,
    ef = EmissionFactors(as.numeric(efh[, 1:8])),
    pro_month = veh_month,
    verbose = TRUE,
    fortran = TRUE
  )
) # emistd5f.f95
aa$emissions <- round(aa$emissions, 8)
aa2$emissions <- round(aa2$emissions, 8)
identical(aa, aa2)

# monthly profile (numeric) with data.frame ef
veh_month <- c(rep(8, 1), rep(10, 5), 9, rep(10, 5))
def <- matrix(EmissionFactors(as.numeric(efh[, 1:8])),
  nrow = nrow(veh), ncol = ncol(veh), byrow = TRUE
)
def <- EmissionFactors(def)
system.time(
  aa <- emis_hot_td(
    veh = veh,
    lkm = lkm,
    ef = def,
    pro_month = veh_month,
    verbose = TRUE
  )
)
system.time(
  aa2 <- emis_hot_td(
    veh = veh,
    lkm = lkm,
    ef = def,
    pro_month = veh_month,
    verbose = TRUE,
    fortran = TRUE
  )
) # emistd1f.f95
aa$emissions <- round(aa$emissions, 8)
aa2$emissions <- round(aa2$emissions, 8)
identical(aa, aa2)

# monthly profile (data.frame)
dfm <- matrix(c(rep(8, 1), rep(10, 5), 9, rep(10, 5)),
  nrow = 10, ncol = 12,
  byrow = TRUE
)
system.time(
  aa <- emis_hot_td(
    veh = veh,
    lkm = lkm,
    ef = EmissionFactors(as.numeric(efh[, 1:8])),
    pro_month = dfm,
    verbose = TRUE
  )
)
system.time(
  aa2 <- emis_hot_td(
    veh = veh,
    lkm = lkm,
    ef = EmissionFactors(as.numeric(efh[, 1:8])),
    pro_month = dfm,
    verbose = TRUE,
    fortran = TRUE
  )
) # emistd6f.f95
aa$emissions <- round(aa$emissions, 2)
aa2$emissions <- round(aa2$emissions, 2)
identical(aa, aa2)

# Suppose that we have a EmissionsFactor data.frame with number of rows for each month
# number of rows are 10 regions
# number of columns are 12 months
tem <- runif(n = 6 * 10, min = -10, max = 35)
temp <- c(rev(tem[order(tem)]), tem[order(tem)])
plot(temp)
dftemp <- celsius(matrix(temp, ncol = 12))
dfef <- ef_evap(
  ef = c(rep("eshotfi", 8)),
  v = "PC",
  cc = "<=1400",
  dt = dftemp,
  show = F,
  ca = "small",
  ltrip = units::set_units(10, km),
  pollutant = "NMHC"
)
dim(dfef) # 120 rows and 9 columns, 8 ef (g/km) and 1 for month
system.time(
  aa <- emis_hot_td(
    veh = veh,
    lkm = lkm,
    ef = dfef,
    pro_month = veh_month,
    verbose = TRUE
  )
)
system.time(
  aa2 <- emis_hot_td(
    veh = veh,
    lkm = lkm,
    ef = dfef,
    pro_month = veh_month,
    verbose = TRUE,
    fortran = TRUE
  )
) # emistd3f.f95
aa$emissions <- round(aa$emissions, 2)
aa2$emissions <- round(aa2$emissions, 2)
identical(aa, aa2)
plot(aggregate(aa$emissions, by = list(aa$month), sum)$x)

# Suppose that we have a EmissionsFactor data.frame with number of rows for each month
# monthly profile (data.frame)
system.time(
  aa <- emis_hot_td(
    veh = veh,
    lkm = lkm,
    ef = dfef,
    pro_month = dfm,
    verbose = TRUE
  )
)
system.time(
  aa2 <- emis_hot_td(
    veh = veh,
    lkm = lkm,
    ef = dfef,
    pro_month = dfm,
    verbose = TRUE,
    fortran = TRUE
  )
) # emistd4f.f95
aa$emissions <- round(aa$emissions, 8)
aa2$emissions <- round(aa2$emissions, 8)
identical(aa, aa2)
plot(aggregate(aa$emissions, by = list(aa$month), sum)$x)

## End(Not run)

Estimation with long format

Description

Emissions estimates

Usage

emis_long(x, lkm, ef, tfs, speed, verbose = TRUE, array = FALSE)

Arguments

x

Vehicles data.frame. x repeats down for each hour
lkm

Length of each link in km. lkm repeats down for each hour
ef

data.frame. ef repeats down for each hour
tfs

temporal factor
speed

Speed data.frame (nrow x)
verbose

Logical to show more info
array

Logical to return EmissionsArray or not

Value

long data.frame

See Also

Other China: ef_china_det(), ef_china_hu(), ef_china_h(), ef_china_long(), ef_china_speed(), ef_china_s(), ef_china_te(), ef_china_th(), ef_china(), emis_china()

Examples

{
data(net)
net <- net[1:100, ]
data(pc_profile)
x <- age_ldv(net$ldv)
pc_week <- temp_fact(net$ldv+net$hdv, pc_profile[[1]])
df <- netspeed(pc_week,
               net$ps,
               net$ffs,
               net$capacity,
               net$lkm,
               alpha = 1)

s  <- do.call("rbind",lapply(1:ncol(df), function(i) {
 as.data.frame(replicate(ncol(x), df[, i]))
}))

ef <- ef_wear(wear = "tyre",
              type = "PC",
              pol = "PM10",
              speed = as.data.frame(s))

e <- emis_long(x = x,
               lkm = net$lkm,
               ef = ef,
               tfs = pc_profile[[1]],
               speed = df)

ae <- emis_long(x = x,
               lkm = net$lkm,
               ef = ef,
               tfs = pc_profile[[1]],
               speed = df,
               array = TRUE)
}

Merge several emissions files returning data-frames or 'sf' of lines

Description

emis_merge reads rds files and returns a data-frame or an object of 'spatial feature' of streets, merging several files.

Usage

emis_merge(
  pol = "CO",
  what = "STREETS.rds",
  streets = T,
  net,
  FN = "sum",
  ignore,
  path = "emi",
  crs,
  under = "after",
  as_list = FALSE,
  k = 1,
  verbose = TRUE
)

Arguments

pol

Character. Pollutant.
what

Character. Word to search the emissions names, "STREETS", "DF" or whatever name. It is important to include the extension .'rds'. For instance, If you have several files "XX_CO_STREETS.rds", what should be "STREETS.rds"
streets

Logical. If true, emis_merge will read the street emissions created with emis_post by "streets_wide", returning an object with class 'sf'. If false, it will read the emissions data-frame and rbind them.
net

'Spatial feature' or 'SpatialLinesDataFrame' with the streets. It is expected #' that the number of rows is equal to the number of rows of street emissions. If #' not, the function will stop.
FN

Character indicating the function. Default is "sum"
ignore

Character; Which pollutants or other charavter would you like to remove?
path

Character. Path where emissions are located
crs

coordinate reference system in numeric format from http://spatialreference.org/ to transform/project spatial data using sf::st_transform
under

"Character"; "after" when you stored your pollutant x as 'X_' "before" when '_X' and "none" for merging directly the files.
as_list

"Logical"; for returning the results as list or not.
k

factor
verbose

Logical to display more information or not. Default is TRUE

Value

'Spatial feature' of lines or a dataframe of emissions

Examples

## Not run: 
# Do not run


## End(Not run)

Re-order the emission to match specific hours and days

Description

Emissions are usually estimated for a year, 24 hours, or one week from monday to sunday (with 168 hours). This depends on the availability of traffic data. When an air quality simulation is going to be done, they cover specific periods of time. For instance, WRF Chem emissions files support periods of time, or two emissions sets for a representative day (0-12z 12-0z). Also a WRF Chem simulation scan starts a Thursday at 00:00 UTC, cover 271 hours of simulations, but hour emissions are in local time and cover only 168 hours starting on Monday. This function tries to transform our emissions in local time to the desired UTC time, by recycling the local emissions.

Usage

emis_order(
  x,
  lt_emissions,
  start_utc_time,
  desired_length,
  tz_lt = Sys.timezone(),
  seconds = 0,
  k = 1,
  net,
  verbose = TRUE
)

Arguments

x

one of the following:

  • Spatial object of class "Spatial". Columns are hourly emissions.

  • Spatial Object of class "sf". Columns are hourly emissions.

  • "data.frame", "matrix" or "Emissions".

In all cases, columns are hourly emissions.
lt_emissions

Local time of the emissions at the first hour. It must be the before time of start_utc_time. For instance, if start_utc_time is 2020-02-02 00:00, and your emissions starts monday at 00:00, your lt_emissions must be 2020-01-27 00:00. The argument tz_lt will detect your current local time zone and do the rest for you.
start_utc_time

UTC time for the desired first hour. For instance, the first hour of the namelist.input for WRF.
desired_length

Integer; length to recycle or subset local emissions. For instance, the length of the WRF Chem simulations, states at namelist.input.
tz_lt

Character, Time zone of the local emissions. Default value is derived from Sys.timezone(), however, it accepts any other. If you enter a wrong tz, this function will show you a menu to choose one of the 697 time zones available.
seconds

Number of seconds to add
k

Numeric, factor.
net

SpatialLinesDataFrame or Spatial Feature of "LINESTRING".
verbose

Logical, to show more information, default is TRUE.

Value

sf or data.frame

See Also

GriddedEmissionsArray

Examples

## Not run: 
#do not run
data(net)
data(pc_profile)
data(fe2015)
data(fkm)
PC_G <- c(33491,22340,24818,31808,46458,28574,24856,28972,37818,49050,87923,
          133833,138441,142682,171029,151048,115228,98664,126444,101027,
          84771,55864,36306,21079,20138,17439, 7854,2215,656,1262,476,512,
          1181, 4991, 3711, 5653, 7039, 5839, 4257,3824, 3068)
veh <- data.frame(PC_G = PC_G)
pc1 <- my_age(x = net$ldv, y = PC_G, name = "PC")
pcw <- temp_fact(net$ldv+net$hdv, pc_profile)
speed <- netspeed(pcw, net$ps, net$ffs, net$capacity, net$lkm, alpha = 1)
pckm <- units::set_units(fkm[[1]](1:24), "km")
pckma <- cumsum(pckm)
cod1 <- emis_det(po = "CO", cc = 1000, eu = "III", km = pckma[1:11])
cod2 <- emis_det(po = "CO", cc = 1000, eu = "I", km = pckma[12:24])
#vehicles newer than pre-euro
co1 <- fe2015[fe2015$Pollutant=="CO", ] #24 obs!!!
cod <- c(co1$PC_G[1:24]*c(cod1,cod2),co1$PC_G[25:nrow(co1)])
lef <- ef_ldv_scaled(co1, cod, v = "PC", t = "4S", cc = "<=1400",
                     f = "G",p = "CO", eu=co1$Euro_LDV)
E_CO <- emis(veh = pc1,lkm = net$lkm, ef = lef, speed = speed, agemax = 41,
              profile = pc_profile, simplify = TRUE)
class(E_CO)
E_CO_STREETS <- emis_post(arra = E_CO, pollutant = "CO", by = "streets", net = net)
g <- make_grid(net, 1/102.47/2, 1/102.47/2) #500m in degrees
E_CO_g <- emis_grid(spobj = E_CO_STREETS, g = g, sr= 31983)
head(E_CO_g) #class sf
gr <- GriddedEmissionsArray(E_CO_g, rows = 19, cols = 23, times = 168, T)
wCO <- emis_order(x = E_CO_g,
                   lt_emissions = "2020-02-19 00:00",
                   start_utc_time = "2020-02-20 00:00",
                   desired_length = 241)

## End(Not run)

Estimation of resuspension emissions from paved roads

Description

emis_paved estimates vehicular emissions from paved roads. The vehicular emissions are estimated as the product of the vehicles on a road, length of the road, emission factor from AP42 13.2.1 Paved roads. It is assumed dry hours and annual aggregation should consider moisture factor. It depends on Average Daily Traffic (ADT)

Usage

emis_paved(
  veh,
  adt,
  lkm,
  k = 0.62,
  sL1 = 0.6,
  sL2 = 0.2,
  sL3 = 0.06,
  sL4 = 0.03,
  W,
  net = net
)

Arguments

veh

Numeric vector with length of elements equals to number of streets It is an array with dimenssions number of streets x hours of day x days of week
adt

Numeric vector of with Average Daily Traffic (ADT)
lkm

Length of each link
k

K_PM30 = 3.23 (g/vkm), K_PM15 = 0.77 (g/vkm), K_PM10 = 0.62 (g/vkm) and K_PM2.5 = 0.15 (g/vkm).
sL1

Silt loading (g/m2) for roads with ADT <= 500
sL2

Silt loading (g/m2) for roads with ADT > 500 and <= 5000
sL3

Silt loading (g/m2) for roads with ADT > 5000 and <= 1000
sL4

Silt loading (g/m2) for roads with ADT > 10000
W

array of dimensions of veh. It consists in the hourly averaged weight of traffic fleet in each road
net

SpatialLinesDataFrame or Spatial Feature of "LINESTRING"

Value

emission estimation g/h

Note

silt values can vary a lot. For comparison:

ADT US-EPA g/m2 CENMA (Chile) g/m2
< 500 0.6 2.4
500-5000 0.2 0.7
5000-1000 0.06 0.6
>10000 0.03 0.3

References

EPA, 2016. Emission factor documentation for AP-42. Section 13.2.1, Paved Roads. https://www3.epa.gov/ttn/chief/ap42/ch13/final/c13s0201.pdf

CENMA Chile: Actualizacion de inventario de emisiones de contaminntes atmosfericos RM 2020 Universidad de Chile#'

Examples

## Not run: 
# Do not run
veh <- matrix(1000, nrow = 10,ncol = 10)
W <- veh*1.5
lkm <-  1:10
ADT <-1000:1010
emi  <- emis_paved(veh = veh, adt = ADT, lkm = lkm, k = 0.65, W = W)
class(emi)
head(emi)

## End(Not run)

Post emissions

Description

emis_post simplify emissions estimated as total per type category of vehicle or by street. It reads EmissionsArray and Emissions classes. It can return a dataframe with hourly emissions at each street, or a database with emissions by vehicular category, hour, including size, fuel and other characteristics.

Usage

emis_post(arra, veh, size, fuel, pollutant, by = "veh", net, type_emi, k = 1)

Arguments

arra

Array of emissions 4d: streets x category of vehicles x hours x days or 3d: streets x category of vehicles x hours
veh

Character, type of vehicle
size

Character, size or weight
fuel

Character, fuel
pollutant

Pollutant
by

Type of output, "veh" for total vehicular category , "streets_narrow" or "streets". "streets" returns a dataframe with rows as number of streets and columns the hours as days*hours considered, e.g. 168 columns as the hours of a whole week and "streets repeats the row number of streets by hour and day of the week
net

SpatialLinesDataFrame or Spatial Feature of "LINESTRING". Only when by = 'streets_wide'
type_emi

Character, type of emissions(exhaust, evaporative, etc)
k

Numeric, factor

Note

This function depends on EmissionsArray objests which currently has 4 dimensions. However, a future version of VEIN will produce EmissionsArray with 3 dimensiones and his fungeorge soros drugsction also will change. This change will be made in order to not produce inconsistencies with previous versions, therefore, if the user count with an EmissionsArry with 4 dimension, it will be able to use this function.

Examples

## Not run: 
# Do not run
data(net)
data(pc_profile)
data(fe2015)
data(fkm)
PC_G <- c(33491,22340,24818,31808,46458,28574,24856,28972,37818,49050,87923,
          133833,138441,142682,171029,151048,115228,98664,126444,101027,
          84771,55864,36306,21079,20138,17439, 7854,2215,656,1262,476,512,
          1181, 4991, 3711, 5653, 7039, 5839, 4257,3824, 3068)
pc1 <- my_age(x = net$ldv, y = PC_G, name = "PC")
# Estimation for morning rush hour and local emission factors
speed <- data.frame(S8 = net$ps)
p1h <- matrix(1)
lef <- EmissionFactorsList(fe2015[fe2015$Pollutant=="CO", "PC_G"])
E_CO <- emis(veh = pc1,lkm = net$lkm, ef = lef, speed = speed,
             profile = p1h)
E_CO_STREETS <- emis_post(arra = E_CO, pollutant = "CO", by = "streets_wide")
summary(E_CO_STREETS)
E_CO_STREETSsf <- emis_post(arra = E_CO, pollutant = "CO",
                           by = "streets", net = net)
summary(E_CO_STREETSsf)
plot(E_CO_STREETSsf, main = "CO emissions (g/h)")
# arguments required: arra, veh, size, fuel, pollutant ad by
E_CO_DF <- emis_post(arra = E_CO,  veh = "PC", size = "<1400", fuel = "G",
pollutant = "CO", by = "veh")
# Estimation 168 hours
pc1 <- my_age(x = net$ldv, y = PC_G, name = "PC")
pcw <- temp_fact(net$ldv+net$hdv, pc_profile)
speed <- netspeed(pcw, net$ps, net$ffs, net$capacity, net$lkm, alpha = 1)
pckm <- units::set_units(fkm[[1]](1:24),"km"); pckma <- cumsum(pckm)
cod1 <- emis_det(po = "CO", cc = 1000, eu = "III", km = pckma[1:11])
cod2 <- emis_det(po = "CO", cc = 1000, eu = "I", km = pckma[12:24])
#vehicles newer than pre-euro
co1 <- fe2015[fe2015$Pollutant=="CO", ] #24 obs!!!
cod <- c(co1$PC_G[1:24]*c(cod1,cod2),co1$PC_G[25:nrow(co1)])
lef <- ef_ldv_scaled(dfcol = cod, v = "PC",  cc = "<=1400",
                     f = "G",p = "CO", eu=co1$Euro_LDV)
E_CO <- emis(veh = pc1,lkm = net$lkm, ef = lef, speed = speed, agemax = 41,
             profile = pc_profile)
# arguments required: arra, pollutant ad by
E_CO_STREETS <- emis_post(arra = E_CO, pollutant = "CO", by = "streets")
summary(E_CO_STREETS)
# arguments required: arra, veh, size, fuel, pollutant ad by
E_CO_DF <- emis_post(arra = E_CO,  veh = "PC", size = "<1400", fuel = "G",
pollutant = "CO", by = "veh")
head(E_CO_DF)
# recreating 24 profile
lpc <-list(pc1*0.2, pc1*0.1, pc1*0.1, pc1*0.2, pc1*0.5, pc1*0.8,
           pc1, pc1*1.1, pc1,
           pc1*0.8, pc1*0.5, pc1*0.5,
           pc1*0.5, pc1*0.5, pc1*0.5, pc1*0.8,
           pc1, pc1*1.1, pc1,
           pc1*0.8, pc1*0.5, pc1*0.3, pc1*0.2, pc1*0.1)
E_COv2 <- emis(veh = lpc,  lkm = net$lkm, ef = lef, speed = speed[, 1:24],
            agemax = 41, hour = 24, day = 1)
plot(E_COv2)
E_CO_DFv2 <- emis_post(arra = E_COv2,
                       veh = "PC",
                       size = "<1400",
                       fuel = "G",
                       type_emi = "Exhaust",
                       pollutant = "CO", by = "veh")
head(E_CO_DFv2)

## End(Not run)

Emis to streets distribute top-down emissions into streets

Description

emis_to_streets allocates emissions proportionally to each feature. "Spatial" objects are converter to "sf" objects. Currently, 'LINESTRING' or 'MULTILINESTRING' supported. The emissions are distributed in each street.

Usage

emis_to_streets(streets, dfemis, by = "ID", stpro, verbose = TRUE)

Arguments

streets

sf object with geometry 'LINESTRING' or 'MULTILINESTRING'. Or SpatialLinesDataFrame
dfemis

data.frame with emissions
by

Character indicating the columns that must be present in both 'street' and 'dfemis'
stpro

data.frame with two columns, category of streets and value. The name of the first column must be "stpro" and the sf streets must also have a column with the nam "stpro" indicating the category of streets. The second column must have the name "VAL" indicating the associated values to each category of street
verbose

Logical; to show more info.

Note

When spobj is a 'Spatial' object (class of sp), they are converted into 'sf'.

See Also

add_polid

Examples

## Not run: 
data(net)
stpro = data.frame(stpro = as.character(unique(net$tstreet)),
                   VAL = 1:9)
dnet <- net["ldv"]
dnet$stpro <- as.character(net$tstreet)
dnet$ID <- "A"
df2 <- data.frame(BC = 10, CO = 20, ID = "A")
ste <- emis_to_streets(streets = dnet, dfemis = df2)
sum(ste$ldv)
sum(net$ldv)
sum(ste$BC)
sum(df2$BC)
ste2 <- emis_to_streets(streets = dnet, dfemis = df2, stpro = stpro)
sum(ste2$ldv)
sum(net$ldv)
sum(ste2$BC)
sum(df2$BC)

## End(Not run)

Emission estimation from tyre, brake and road surface wear

Description

emis_wear estimates wear emissions. The sources are tyres, breaks and road surface.

Usage

emis_wear(
  veh,
  lkm,
  ef,
  what = "tyre",
  speed,
  agemax = ncol(veh),
  profile,
  hour = nrow(profile),
  day = ncol(profile)
)

Arguments

veh

Object of class "Vehicles"
lkm

Length of the road in km.
ef

list of emission factor functions class "EmissionFactorsList", length equals to hours.
what

Character for indicating "tyre", "break" or "road"
speed

Speed data-frame with number of columns as hours
agemax

Age of oldest vehicles for that category
profile

Numerical or dataframe with nrows equal to 24 and ncol 7 day of the week
hour

Number of considered hours in estimation
day

Number of considered days in estimation

Value

emission estimation g/h

References

Ntziachristos and Boulter 2016. Automobile tyre and break wear and road abrasion. In: EEA, EMEP. EEA air pollutant emission inventory guidebook-2009. European Environment Agency, Copenhagen, 2016

Examples

## Not run: 
data(net)
data(pc_profile)
pc_week <- temp_fact(net$ldv[1:10] + net$hdv[1:10], pc_profile[, 1])
df <- netspeed(pc_week, net$ps[1:10], net$ffs[1:10],
              net$capacity[1:10], net$lkm[1:10], alpha = 1)
ef <- ef_wear(wear = "tyre", type = "PC", pol = "PM10", speed = df)
emi <- emis_wear(veh = age_ldv(net$ldv[1:10], name = "VEH"),
                 lkm = net$lkm[1:10], ef = ef, speed = df,
                 profile = pc_profile[, 1])
emi

## End(Not run)

Construction function for class "EmissionFactors"

Description

EmissionFactors returns a transformed object with class "EmissionFactors" and units g/km.

Usage

EmissionFactors(x, mass = "g", dist = "km", ...)

## S3 method for class 'EmissionFactors'
print(x, ...)

## S3 method for class 'EmissionFactors'
summary(object, ...)

## S3 method for class 'EmissionFactors'
plot(
  x,
  pal = "mpl_viridis",
  rev = TRUE,
  fig1 = c(0, 0.8, 0, 0.8),
  fig2 = c(0, 0.8, 0.55, 1),
  fig3 = c(0.7, 1, 0, 0.8),
  mai1 = c(0.2, 0.82, 0.82, 0.42),
  mai2 = c(1.3, 0.82, 0.82, 0.42),
  mai3 = c(0.7, 0.62, 0.82, 0.42),
  bias = 1.5,
  ...
)

Arguments

x

Object with class "data.frame", "matrix" or "numeric"
mass

Character to be the time units as numerator, default "g" for grams
dist

String indicating the units of the resulting distance in speed.
...

ignored
object

object with class "EmissionFactors'
pal

Palette of colors available or the number of the position
rev

Logical; to internally revert order of rgb color vectors.
fig1

par parameters for fig, par.
fig2

par parameters for fig, par.
fig3

par parameters for fig, par.
mai1

par parameters for mai, par.
mai2

par parameters for mai, par.
mai3

par parameters for mai, par.
bias

positive number. Higher values give more widely spaced colors at the high end.

Value

Objects of class "EmissionFactors" or "units"

Examples

## Not run: 
data(fe2015)
names(fe2015)
class(fe2015)
df <- fe2015[fe2015$Pollutant=="CO", c(ncol(fe2015)-1,ncol(fe2015))]
ef1 <- EmissionFactors(df)
class(ef1)
summary(ef1)
plot(ef1)
print(ef1)

## End(Not run)

Construction function for class "EmissionFactorsList"

Description

EmissionFactorsList returns a transformed object with class"EmissionsFactorsList".

Usage

EmissionFactorsList(x, ...)

## S3 method for class 'EmissionFactorsList'
print(x, ..., default = FALSE)

## S3 method for class 'EmissionFactorsList'
summary(object, ...)

## S3 method for class 'EmissionFactorsList'
plot(x, ...)

Arguments

x

Object with class "list"
...

ignored
default

Logical value. When TRUE prints default list, when FALSE prints messages with description of list
object

Object with class "EmissionFactorsList"

Value

Objects of class "EmissionFactorsList"

Examples

## Not run: 
data(fe2015)
names(fe2015)
class(fe2015)
df <- fe2015[fe2015$Pollutant=="CO", c(ncol(fe2015)-1,ncol(fe2015))]
ef1 <- EmissionFactorsList(df)
class(ef1)
length(ef1)
length(ef1[[1]])
summary(ef1)
ef1

## End(Not run)

Construction function for class "Emissions"

Description

Emissions returns a transformed object with class "Emissions". The type of objects supported are of classes "matrix", "data.frame" and "numeric". If the class of the object is "matrix" this function returns a dataframe.

Usage

Emissions(x, mass = "g", time, ...)

## S3 method for class 'Emissions'
print(x, ...)

## S3 method for class 'Emissions'
summary(object, ...)

## S3 method for class 'Emissions'
plot(
  x,
  pal = "colo_angelafaye_Coloured_sky_in",
  rev = FALSE,
  fig1 = c(0, 0.8, 0, 0.8),
  fig2 = c(0, 0.8, 0.55, 1),
  fig3 = c(0.7, 1, 0, 0.8),
  mai1 = c(0.2, 0.82, 0.82, 0.42),
  mai2 = c(1.3, 0.82, 0.82, 0.42),
  mai3 = c(0.7, 0.72, 0.82, 0.42),
  main = NULL,
  bias = 1.5,
  ...
)

Arguments

x

Object with class "data.frame", "matrix" or "numeric"
mass

Character to be the time units as numerator, default "g" for grams
time

Character to be the time units as denominator, eg "h"
...

ignored
object

object with class "Emissions"
pal

Palette of colors available or the number of the position
rev

Logical; to internally revert order of rgb color vectors.
fig1

par parameters for fig, par.
fig2

par parameters for fig, par.
fig3

par parameters for fig, par.
mai1

par parameters for mai, par.
mai2

par parameters for mai, par.
mai3

par parameters for mai, par.
main

title of plot
bias

positive number. Higher values give more widely spaced colors at the high end.

Value

Objects of class "Emissions" or "units"

Examples

## Not run: 
data(net)
data(pc_profile)
data(fe2015)
data(fkm)
PC_G <- c(33491,22340,24818,31808,46458,28574,24856,28972,37818,49050,87923,
          133833,138441,142682,171029,151048,115228,98664,126444,101027,
          84771,55864,36306,21079,20138,17439, 7854,2215,656,1262,476,512,
          1181, 4991, 3711, 5653, 7039, 5839, 4257,3824, 3068)
veh <- data.frame(PC_G = PC_G)
pc1 <- my_age(x = net$ldv, y = PC_G, name = "PC")
pcw <- temp_fact(net$ldv+net$hdv, pc_profile)
speed <- netspeed(pcw, net$ps, net$ffs, net$capacity, net$lkm, alpha = 1)
pckm <- units::as_units(fkm[[1]](1:24), "km"); pckma <- cumsum(pckm)
cod1 <- emis_det(po = "CO", cc = 1000, eu = "III", km = pckma[1:11])
cod2 <- emis_det(po = "CO", cc = 1000, eu = "I", km = pckma[12:24])
#vehicles newer than pre-euro
co1 <- fe2015[fe2015$Pollutant=="CO", ] #24 obs!!!
cod <- c(co1$PC_G[1:24]*c(cod1,cod2),co1$PC_G[25:nrow(co1)])
lef <- ef_ldv_scaled(co1, cod, v = "PC",  cc = "<=1400",
                     f = "G", p = "CO", eu=co1$Euro_LDV)
E_CO <- emis(veh = pc1,lkm = net$lkm, ef = lef, speed = speed, agemax = 41,
             profile = pc_profile)
dim(E_CO) # streets x vehicle categories x hours x days
class(E_CO)
plot(E_CO)
####
Emissions(1)
Emissions(1, time = "h")

## End(Not run)

Construction function for class "EmissionsArray"

Description

EmissionsArray returns a transformed object with class "EmissionsArray" with 4 dimensions.

Usage

EmissionsArray(x, ...)

## S3 method for class 'EmissionsArray'
print(x, ...)

## S3 method for class 'EmissionsArray'
summary(object, ...)

## S3 method for class 'EmissionsArray'
plot(x, main = "average emissions", ...)

Arguments

x

Object with class "data.frame", "matrix" or "numeric"
...

ignored
object

object with class "EmissionsArray'
main

Title for plot

Value

Objects of class "EmissionsArray"

Note

Future version of this function will return an Array of 3 dimensions.

Examples

## Not run: 
data(net)
data(pc_profile)
data(fe2015)
data(fkm)
PC_G <- c(33491,22340,24818,31808,46458,28574,24856,28972,37818,49050,87923,
          133833,138441,142682,171029,151048,115228,98664,126444,101027,
          84771,55864,36306,21079,20138,17439, 7854,2215,656,1262,476,512,
          1181, 4991, 3711, 5653, 7039, 5839, 4257,3824, 3068)
veh <- data.frame(PC_G = PC_G)
pc1 <- my_age(x = net$ldv, y = PC_G, name = "PC")
pcw <- temp_fact(net$ldv+net$hdv, pc_profile)
speed <- netspeed(pcw, net$ps, net$ffs, net$capacity, net$lkm, alpha = 1)
pckm <- units::set_units(fkm[[1]](1:24), "km"); pckma <- cumsum(pckm)
cod1 <- emis_det(po = "CO", cc = 1000, eu = "III", km = pckma[1:11])
cod2 <- emis_det(po = "CO", cc = 1000, eu = "I", km = pckma[12:24])
#vehicles newer than pre-euro
co1 <- fe2015[fe2015$Pollutant=="CO", ] #24 obs!!!
cod <- c(co1$PC_G[1:24]*c(cod1,cod2),co1$PC_G[25:nrow(co1)])
lef <- ef_ldv_scaled(co1, cod, v = "PC", cc = "<=1400",
                     f = "G",p = "CO", eu=co1$Euro_LDV)
E_CO <- emis(veh = pc1,lkm = net$lkm, ef = lef, speed = speed, agemax = 41,
             profile = pc_profile, simplify = TRUE)
class(E_CO)
summary(E_CO)
E_CO
plot(E_CO)
lpc <- list(pc1, pc1)
E_COv2 <- emis(veh = lpc,lkm = net$lkm, ef = lef, speed = speed, agemax = 41,
             profile = pc_profile, hour = 2, day = 1)

## End(Not run)

Emission factors from Environmental Agency of Sao Paulo CETESB

Description

A dataset containing emission factors from CETESB and its equivalency with EURO

Usage

data(fe2015)

Format

A data frame with 288 rows and 12 variables:

Age

Age of use

Year

Year of emission factor

Pollutant

Pollutants included: "CH4", "CO", "CO2", "HC", "N2O", "NMHC", "NOx", and "PM"

Proconve_LDV

Proconve emission standard: "PP", "L1", "L2", "L3", "L4", "L5", "L6"

t_Euro_LDV

Euro emission standard equivalence: "PRE_ECE", "I", "II", "III","IV", "V"

Euro_LDV

Euro emission standard equivalence: "PRE_ECE", "I", "II", "III","IV", "V"

Proconve_HDV

Proconve emission standard: "PP", "P1", "P2", "P3", "P4", "P5", "P7"

Euro_HDV

Euro emission standard equivalence: "PRE", "I", "II", "III", "V"

PC_G

CETESB emission standard for Passenger Cars with Gasoline (g/km)

LT

CETESB emission standard for Light Trucks with Diesel (g/km)

Source

CETESB

List of functions of mileage in km fro Brazilian fleet

Description

Functions from CETESB: Antonio de Castro Bruni and Marcelo Pereira Bales. 2013. Curvas de intensidade de uso por tipo de veiculo automotor da frota da cidade de Sao Paulo This functions depends on the age of use of the vehicle

Usage

data(fkm)

Format

A data frame with 288 rows and 12 variables:

KM_PC_E25

Mileage in km of Passenger Cars using Gasoline with 25% Ethanol

KM_PC_E100

Mileage in km of Passenger Cars using Ethanol 100%

KM_PC_FLEX

Mileage in km of Passenger Cars using Flex engines

KM_LCV_E25

Mileage in km of Light Commercial Vehicles using Gasoline with 25% Ethanol

KM_LCV_FLEX

Mileage in km of Light Commercial Vehicles using Flex

KM_PC_B5

Mileage in km of Passenger Cars using Diesel with 5% biodiesel

KM_TRUCKS_B5

Mileage in km of Trucks using Diesel with 5% biodiesel

KM_BUS_B5

Mileage in km of Bus using Diesel with 5% biodiesel

KM_LCV_B5

Mileage in km of Light Commercial Vehicles using Diesel with 5% biodiesel

KM_SBUS_B5

Mileage in km of Small Bus using Diesel with 5% biodiesel

KM_ATRUCKS_B5

Mileage in km of Articulated Trucks using Diesel with 5% biodiesel

KM_MOTO_E25

Mileage in km of Motorcycles using Gasoline with 25% Ethanol

KM_LDV_GNV

Mileage in km of Light Duty Vehicles using Natural Gas

Source

CETESB

Correction due Fuel effects

Description

Take into account the effect of better fuels on vehicles with older technology. If the ratio is less than 1, return 1. It means that it is nota degradation function.

Usage

fuel_corr(
  euro,
  g = c(e100 = 52, aro = 39, o2 = 0.4, e150 = 86, olefin = 10, s = 165),
  d = c(den = 840, pah = 9, cn = 51, t95 = 350, s = 400)
)

Arguments

euro

Character; Euro standards ("PRE", "I", "II", "III", "IV", "V", VI, "VIc")
g

Numeric; vector with parameters of gasoline with the names: e100(vol. (sulphur, ppm)
d

Numeric; vector with parameters for diesel with the names: den (density at 15 Celsius degrees kg/m3), pah ( (Back end distillation in Celsius degrees) and s (sulphur, ppm)

Value

A list with the correction of emission factors.

Note

This function cannot be used to account for deterioration, therefore, it is restricted to values between 0 and 1. Parameters for gasoline (g):

O2 = Oxygenates in

S = Sulphur content in ppm

ARO = Aromatics content in

OLEFIN = Olefins content in

E100 = Mid range volatility in

E150 = Tail-end volatility in

Parameters for diesel (d):

DEN = Density at 15 C (kg/m3)

S = Sulphur content in ppm

PAH = Aromatics content in

CN = Cetane number

T95 = Back-end distillation in o C.

Examples

## Not run: 
f <- fuel_corr(euro = "I")
names(f)

## End(Not run)

Get ef reference data

Description

Get the reference data used to build the emission factor (ef) model applied by vein.

Usage

get_ef_ref(ref)

Arguments

ref

Character; The ef model required (e.g. "eea" for ef_eea)

Note

This function is a shortcut to access unexported ef model information in vein.

Examples

## Not run: 
get_ef_ref("eea")

## End(Not run)

Download vein project

Description

get_project downloads a project for running vein. The projects are available on Github.com/atmoschem/vein/projects

Usage

get_project(directory, case, url)

Arguments

directory

Character; Path to an existing or a new directory to be created.
case

Character; One of of the following:

case Description EF Notes
emislacovid Bottom-up March 2020 CETESB .rds
brazil_bu_chem Bottom-up chemical mechanisms CETESB+tunnel .rds
brazil_bu_chem_streets Bottom-up chemical mechanisms for streets and MUNICH CETESB+tunnel .rds
brazil_td_chem Top-down with chemical mechanisms CETESB .csv and .rds
brazil_country Top down CETESB+tunnel .rds
brazil_countryv2 Top down CETESB+tunnel .rds
masp2020 Bottom-down CETESB+tunnel csv and.rds
sebr_cb05co2 Top-down SP, MG and RJ CETESB+tunnel .rds
amazon2014 Top-down Amazon CETESB+tunnel csv and.rds
curitiba Bottom-down +GTFS CETESB+tunnel csv and.rds
ecuador_td Top-down. Renamed ecuador_td_im EEA csv and.rds
ecuador_td_hot_month Top-down EEA csv and.rds
moves_bu Bottom-up US/EPA MOVES csv and.rds (requires MOVES >=3.0 on Windows)
manizales_bu Bottom-up chemical mechanisms EEA csv, csv.gz, .rds
eu_bu_chem Bottom-up chemical mechanisms EEA 2019 .rds
eu_bu_chem_simple Bottom-up chemical mechanisms 7 veh EEA 2019 .rds
china_bu_chem Bottom-up chemical mechanisms MEE China .rds
china_bu_chem_1h Bottom-up chemical mechanisms MEE China .rds
url

String, with the URL to download VEIN project

Note

All projects include option to apply survival functions

brazil_bu_chem covers "brazil", "brazil_bu", "brasil_bu", "brazil_bu_chem", "brazil_bu_csvgz", "brazil_bu_csv", "brazil_bu_cb05", "brazil_mech", "brazil_bu_chem_month", "brazil_bu_chem_im" "brazil_bu_chem_streets_im" (type <- 'streets') "brazil_bu_chem_streets" (type <- 'streets')

brazil_td_chem covers "brazil_td_chem_im"

sebr_cb05co2 covers "sebr_cb05co2_im"

ecuador_td covers "ecuador_td", "ecuador_td_hot", "ecuador_td_im"

In any case, if you find any error, please, send a pull request in github.

In Sao Paulo the IM programs was functioning until 2011.

brazil_countryv2 has scripts updated

Examples

## Not run: 
#do not run
get_project("awesomecity", case = "brazil_bu_chem")

## End(Not run)

Allocate emissions gridded emissions into streets (grid to emis street)

Description

grid_emis it is sort of the opposite of emis_grid. It allocates gridded emissions into streets. This function applies emis_dist into each grid cell using lapply. This function is in development and pull request are welcome.

Usage

grid_emis(spobj, g, top_down = FALSE, sr, pro, char, verbose = FALSE)

Arguments

spobj

A spatial dataframe of class "sp" or "sf". When class is "sp" it is transformed to "sf".
g

A grid with class "SpatialPolygonsDataFrame" or "sf". This grid includes the total emissions with the column "emission". If the profile is going to be used, the column 'emission' must include the sum of the emissions for each profile. For instance, if profile covers the hourly emissions, the column 'emission' bust be the sum of the hourly emissions.
top_down

Logical; requires emissions named 'emissions' and allows to apply profile factors. If your data is hourly emissions or a spatial grid with several emissions at different hours, being each hour a column, it is better to use top_down = FALSE. In this way all the hourly emissions are considered, however, each hourly emissions has to have the name "V" and the number of the hour like "V1"
sr

Spatial reference e.g: 31983. It is required if spobj and g are not projected. Please, see http://spatialreference.org/.
pro

Numeric, Matrix or data-frame profiles, for instance, pc_profile.
char

Character, name of the first letter of hourly emissions. New variables in R start with the letter "V", for your hourly emissions might start with the letter "h". This option applies when top_down is FALSE. For instance, if your hourly emissions are: "h1", "h2", "h3"... 'char“ can be "h"
verbose

Logical; to show more info.

Note

Your gridded emissions might have flux units (mass / area / time(implicit)) You must multiply your emissions with the area to return to the original units.

Examples

## Not run: 
data(net)
data(pc_profile)
data(fkm)
PC_G <- c(33491,22340,24818,31808,46458,28574,24856,28972,37818,49050,87923,
133833,138441,142682,171029,151048,115228,98664,126444,101027,
       84771,55864,36306,21079,20138,17439, 7854,2215,656,1262,476,512,
1181, 4991, 3711, 5653, 7039, 5839, 4257,3824, 3068)
pc1 <- my_age(x = net$ldv, y = PC_G, name = "PC")
# Estimation for morning rush hour and local emission factors
lef <- EmissionFactorsList(ef_cetesb("CO", "PC_G"))
E_CO <- emis(veh = pc1,lkm = net$lkm, ef = lef,
            profile = 1, speed = Speed(1))
E_CO_STREETS <- emis_post(arra = E_CO, by = "streets", net = net)

g <- make_grid(net, 1/102.47/2) #500m in degrees

gCO <- emis_grid(spobj = E_CO_STREETS, g = g)
gCO$emission <- gCO$V1
area <- sf::st_area(gCO)
area <- units::set_units(area, "km^2") #Check units!
gCO$emission <- gCO$emission*area
#
\dontrun{
#do not run
library(osmdata)
library(sf)
osm <- osmdata_sf(
add_osm_feature(
opq(bbox = st_bbox(gCO)),
key = 'highway'))$osm_lines[, c("highway")]
st <- c("motorway", "motorway_link", "trunk", "trunk_link",
"primary", "primary_link", "secondary", "secondary_link",
"tertiary", "tertiary_link")
osm <- osm[osm$highway %in% st, ]
plot(osm, axes = T)
# top_down requires name `emissions` into gCO`
xnet <- grid_emis(osm, gCO, top_down = TRUE)
plot(xnet, axes = T)
# bottom_up requires that emissions are named `V` plus the hour like `V1`
xnet <- grid_emis(osm, gCO,top_down= FALSE)
plot(xnet["V1"], axes = T)
}

## End(Not run)

Construction function for class "GriddedEmissionsArray"

Description

GriddedEmissionsArray returns a transformed object with class "EmissionsArray" with 4 dimensions.

Usage

GriddedEmissionsArray(x, ..., cols, rows, times = ncol(x), rotate = "default")

## S3 method for class 'GriddedEmissionsArray'
print(x, ...)

## S3 method for class 'GriddedEmissionsArray'
summary(object, ...)

## S3 method for class 'GriddedEmissionsArray'
plot(x, ..., times = 1)

Arguments

x

Object with class "SpatialPolygonDataFrame", "sf" "data.frame" or "matrix"
...

ignored
cols

Number of columns
rows

Number of rows
times

Number of times
rotate

Character, rotate array:"default", "left", "right", "cols","rows", "both", "br", "colsbr", "rowsbr", "bothbr". br means starting a matrix byrow
object

object with class "EmissionsArray'

Value

Objects of class "GriddedEmissionsArray"

Examples

## Not run: 
data(net)
data(pc_profile)
data(fe2015)
data(fkm)
PC_G <- c(33491,22340,24818,31808,46458,28574,24856,28972,37818,49050,87923,
          133833,138441,142682,171029,151048,115228,98664,126444,101027,
          84771,55864,36306,21079,20138,17439, 7854,2215,656,1262,476,512,
          1181, 4991, 3711, 5653, 7039, 5839, 4257,3824, 3068)
veh <- data.frame(PC_G = PC_G)
pc1 <- my_age(x = net$ldv, y = PC_G, name = "PC")
pcw <- temp_fact(net$ldv+net$hdv, pc_profile)
speed <- netspeed(pcw, net$ps, net$ffs, net$capacity, net$lkm, alpha = 1)
pckm <- units::set_units(fkm[[1]](1:24), "km")
pckma <- cumsum(pckm)
cod1 <- emis_det(po = "CO", cc = 1000, eu = "III", km = pckma[1:11])
cod2 <- emis_det(po = "CO", cc = 1000, eu = "I", km = pckma[12:24])
#vehicles newer than pre-euro
co1 <- fe2015[fe2015$Pollutant=="CO", ] #24 obs!!!
cod <- c(co1$PC_G[1:24]*c(cod1,cod2),co1$PC_G[25:nrow(co1)])
lef <- ef_ldv_scaled(co1, cod, v = "PC", t = "4S", cc = "<=1400",
                     f = "G",p = "CO", eu=co1$Euro_LDV)
E_CO <- emis(veh = pc1,lkm = net$lkm, ef = lef, speed = speed, agemax = 41,
              profile = pc_profile, simplify = TRUE)
class(E_CO)
E_CO_STREETS <- emis_post(arra = E_CO, pollutant = "CO", by = "streets",
                          net = net, k = units::set_units(1, "1/h"))
g <- make_grid(net, 1/102.47/2, 1/102.47/2) #500m in degrees
E_CO_g <- emis_grid(spobj = E_CO_STREETS, g = g, sr= 31983)
plot(E_CO_g["V9"])
# check all
rots <- c("default", "left", "right",
          "cols","rows", "both",
          "br", "colsbr", "rowsbr", "bothbr")
oldpar <- par()
par(mfrow = c(2,5))
lg <- lapply(seq_along(rots), function(i){
            x <- GriddedEmissionsArray(E_CO_g,
                                rows = 19,
                                cols = 23,
                                times = 168,
                                rotate = rots[i])
         plot(x, main = rots[i])
        })

par(mfrow = c(1,1))

## End(Not run)

Helper function to copy and zip projects

Description

invcop help to copy and zip projects

Usage

invcop(
  in_name = getwd(),
  out_name,
  all = FALSE,
  main = TRUE,
  ef = TRUE,
  est = TRUE,
  network = TRUE,
  veh_rds = FALSE,
  veh_csv = TRUE,
  zip = TRUE
)

Arguments

in_name

Character; Name of current project.
out_name

Character; Name of output project.
all

Logical; copy ALL (and for once) or not.
main

Logical; copy or not.
ef

Logical; copy or not.
est

Logical; copy or not.
network

Logical; copy or not.
veh_rds

Logical; copy or not.
veh_csv

Logical; copy or not.
zip

Logical; zip or not.

Value

emission estimation g/h

Note

This function was created to copy and zip project without the emis.

Examples

## Not run: 
# Do not run

## End(Not run)

Inventory function.

Description

inventory produces an structure of directories and scripts in order to run vein. It is required to know the vehicular composition of the fleet.

Usage

inventory(
  name,
  vehcomp = c(PC = 1, LCV = 1, HGV = 1, BUS = 1, MC = 1),
  show.main = FALSE,
  scripts = TRUE,
  show.dir = FALSE,
  show.scripts = FALSE,
  clear = TRUE,
  rush.hour = FALSE,
  showWarnings = FALSE
)

Arguments

name

Character, path to new main directory for running vein. NO BLANK SPACES
vehcomp

Vehicular composition of the fleet. It is required a named numerical vector with the names "PC", "LCV", "HGV", "BUS" and "MC". In the case that there are no vehicles for one category of the composition, the name should be included with the number zero, for example, PC = 0. The maximum number allowed is 99 per category.
show.main

Logical; Do you want to see the new main.R file?
scripts

Logical Do you want to generate or no R scripts?
show.dir

Logical value for printing the created directories.
show.scripts

Logical value for printing the created scripts.
clear

Logical value for removing recursively the directory and create another one.
rush.hour

Logical, to create a template for morning rush hour.
showWarnings

Logical, showWarnings?

Value

Structure of directories and scripts for automating the compilation of vehicular emissions inventory. The structure can be used with another type of sources of emissions. The structure of the directories is: daily, ef, emi, est, images, network and veh. This structure is a suggestion and the user can use another. ' ef: it is for storing the emission factors data-frame, similar to data(fe2015) but including one column for each of the categories of the vehicular composition. For instance, if PC = 5, there should be 5 columns with emission factors in this file. If LCV = 5, another 5 columns should be present, and so on.

emi: Directory for saving the estimates. It is suggested to use .rds extension instead of .rda.

est: Directory with subdirectories matching the vehicular composition for storing the scripts named input.R.

images: Directory for saving images.

network: Directory for saving the road network with the required attributes. This file will include the vehicular flow per street to be used by age* functions.

veh: Directory for storing the distribution by age of use of each category of the vehicular composition. Those are data-frames with number of columns with the age distribution and number of rows as the number of streets. The class of these objects is "Vehicles". Future versions of vein will generate Vehicles objects with the explicit spatial component.

The name of the scripts and directories are based on the vehicular composition, however, there is included a file named main.R which is just an R script to estimate all the emissions. It is important to note that the user must add the emission factors for other pollutants. Also, this function creates the scripts input.R where the user must specify the inputs for the estimation of emissions of each category. Also, there is a file called traffic.R to generate objects of class "Vehicles". The user can rename these scripts.

Examples

## Not run: 
name = file.path(tempdir(), "YourCity")
inventory(name = name)

## End(Not run)

Transform data.frame from long to wide format

Description

long_to_wide transform data.frame from long to wide format

Usage

long_to_wide(
  df,
  column_with_new_names = names(df)[1],
  column_with_data = "emission",
  column_fixed,
  net
)

Arguments

df

data.frame with three column.
column_with_new_names

Character, column that has new column names
column_with_data

Character column with data
column_fixed

Character, column that will remain fixed
net

To return a sf

Value

wide data.frame.

See Also

emis_hot_td emis_cold_td wide_to_long

Examples

## Not run: 
df <- data.frame(pollutant = rep(c("CO", "propadiene", "NO2"), 10),
                 emission = vein::Emissions(1:30),
                 region = rep(letters[1:2], 15))
df
long_to_wide(df)
long_to_wide(df, column_fixed = "region")

## End(Not run)

Creates rectangular grid for emission allocation

Description

make_grid creates a sf grid of polygons. The spatial reference is taken from the spatial object.

Usage

make_grid(spobj, width, height = width, crs = 3857)

Arguments

spobj

A spatial object of class sp or sf.
width

Width of grid cell. It is recommended to use projected values.
height

Height of grid cell.
crs

coordinate reference system in numeric format from http://spatialreference.org/ to transform/project spatial data using sf::st_transform. The default value is 3857, Pseudo Mercator

Value

A grid of polygons class 'sf'

Examples

## Not run: 
data(net)
grid <- make_grid(net, width = 0.5/102.47) #500 mts
plot(grid, axes = TRUE) #class sf
# make grid now returns warnings for crs with form +init...
#grid <- make_grid(net, width = 0.5/102.47) #500 mts


## End(Not run)

MOVES emission factors

Description

moves_ef reads and filter MOVES data.frame of emission factors.

Usage

moves_ef(
  ef,
  vehicles,
  source_type_id = 21,
  process_id = 1,
  fuel_type_id = 1,
  pollutant_id = 2,
  road_type_id = 5,
  speed_bin
)

Arguments

ef

emission factors from EmissionRates_running exported from MOVES
vehicles

Name of category, with length equal to fuel_type_id and other with id
source_type_id

Number to identify type of vehicle as defined by MOVES.
process_id

Number to identify emission process defined by MOVES.
fuel_type_id

Number to identify type of fuel as defined by MOVES.
pollutant_id

Number to identify type of pollutant as defined by MOVES.
road_type_id

Number to identify type of road as defined by MOVES.
speed_bin

Data.frame or vector of avgSpeedBinID as defined by MOVES.

Value

EmissionFactors data.frame

Note

'decoder' shows a decoder for MOVES to identify

Examples

{
data(decoder)
decoder
}

MOVES estimation of using rates per distance

Description

moves_rpd estimates running exhaust emissions using MOVES emission factors.

Usage

moves_rpd(
  veh,
  lkm,
  ef,
  fuel_type,
  speed_bin,
  profile,
  source_type_id = 21,
  fuel_type_id = 1,
  pollutant_id = 91,
  road_type_id = 5,
  process_id = 1,
  vehicle = NULL,
  vehicle_type = NULL,
  fuel_subtype = NULL,
  net,
  path_all,
  verbose = FALSE
)

Arguments

veh

"Vehicles" data-frame or list of "Vehicles" data-frame. Each data-frame as number of columns matching the age distribution of that ype of vehicle. The number of rows is equal to the number of streets link.
lkm

Length of each link in miles
ef

emission factors from EmissionRates_running exported from MOVES
fuel_type

Data.frame of fuelSubtypeID exported by MOVES.
speed_bin

Data.frame or vector of avgSpeedBinID as defined by MOVES.
profile

Data.frame or Matrix with nrows equal to 24 and ncol 7 day of the week
source_type_id

Number to identify type of vehicle as defined by MOVES.
fuel_type_id

Number to identify type of fuel as defined by MOVES.
pollutant_id

Number to identify type of pollutant as defined by MOVES.
road_type_id

Number to identify type of road as defined by MOVES.
process_id

Number to identify type of pollutant as defined by MOVES.
vehicle

Character, type of vehicle
vehicle_type

Character, subtype of vehicle
fuel_subtype

Character, subtype of vehicle
net

Road network class sf
path_all

Character to export whole estimation. It is not recommended since it is usually too heavy.
verbose

Logical; To show more information. Not implemented yet

Value

a list with emissions at each street and data.base aggregated by categories. See link{emis_post}

Note

'decoder' shows a decoder for MOVES

Examples

{
data(decoder)
decoder
}

MOVES estimation of using rates per distance by model year

Description

moves_rpdy estimates running exhaust emissions using MOVES emission factors.

Usage

moves_rpdy(
  veh,
  lkm,
  ef,
  source_type_id = 21,
  fuel_type_id = 1,
  pollutant_id = 91,
  road_type_id = 5,
  fuel_type,
  speed_bin,
  profile,
  vehicle,
  vehicle_type,
  fuel_subtype,
  process_id,
  net,
  path_all,
  verbose = FALSE
)

Arguments

veh

"Vehicles" data-frame or list of "Vehicles" data-frame. Each data-frame as number of columns matching the age distribution of that ype of vehicle. The number of rows is equal to the number of streets link.
lkm

Length of each link in miles
ef

emission factors from EmissionRates_running exported from MOVES
source_type_id

Number to identify type of vehicle as defined by MOVES.
fuel_type_id

Number to identify type of fuel as defined by MOVES.
pollutant_id

Number to identify type of pollutant as defined by MOVES.
road_type_id

Number to identify type of road as defined by MOVES.
fuel_type

Data.frame of fuelSubtypeID exported by MOVES.
speed_bin

Data.frame or vector of avgSpeedBinID as defined by MOVES.
profile

Data.frame or Matrix with nrows equal to 24 and ncol 7 day of the week
vehicle

Character, type of vehicle
vehicle_type

Character, subtype of vehicle
fuel_subtype

Character, subtype of vehicle
process_id

Character, processID
net

Road network class sf
path_all

Character to export whole estimation. It is not recommended since it is usually too heavy.
verbose

Logical; To show more information. Not implemented yet

Value

a list with emissions at each street and data.base aggregated by categories. See link{emis_post}

Note

'decoder' shows a decoder for MOVES

Examples

{
data(decoder)
decoder
}

MOVES estimation of using rates per distance by model year

Description

moves_rpdy_meta estimates running exhaust emissions using MOVES emission factors.

Usage

moves_rpdy_meta(
  metadata,
  lkm,
  ef,
  fuel_type,
  speed_bin,
  profile,
  agemax = 31,
  net,
  simplify = TRUE,
  verbose = FALSE
)

Arguments

metadata

data.frame with the metadata for a vein project for MOVES.
lkm

Length of each link in miles
ef

emission factors from EmissionRates_running exported from MOVES
fuel_type

Data.frame of fuelSubtypeID exported by MOVES.
speed_bin

Data.frame or vector of avgSpeedBinID as defined by MOVES.
profile

Data.frame or Matrix with nrows equal to 24 and ncol 7 day of the week
agemax

Integer; max age for the fleet, assuming the same for all vehicles.
net

Road network class sf
simplify

Logical, to return the whole object or processed by streets and veh
verbose

Logical; To show more information. Not implemented yet

Value

a list with emissions at each street and data.base aggregated by categories.

Note

The idea is the user enter with emissions factors by pollutant

Examples

{
data(decoder)
decoder
}

MOVES estimation of using rates per distance by model year

Description

moves_rpdy_sf estimates running exhaust emissions using MOVES emission factors.

Usage

moves_rpdy_sf(
  veh,
  lkm,
  ef,
  speed_bin,
  profile,
  source_type_id = 21,
  vehicle = NULL,
  vehicle_type = NULL,
  fuel_subtype = NULL,
  path_all,
  verbose = FALSE
)

Arguments

veh

"Vehicles" data-frame or list of "Vehicles" data-frame. Each data-frame as number of columns matching the age distribution of that ype of vehicle. The number of rows is equal to the number of streets link.
lkm

Length of each link in miles
ef

emission factors from EmissionRates_running exported from MOVES filtered by sourceTypeID and fuelTypeID.
speed_bin

Data.frame or vector of avgSpeedBinID as defined by MOVES.
profile

numeric vector of normalized traffic for the morning rush hour
source_type_id

Number to identify type of vehicle as defined by MOVES.
vehicle

Character, type of vehicle
vehicle_type

Character, subtype of vehicle
fuel_subtype

Character, subtype of vehicle
path_all

Character to export whole estimation. It is not recommended since it is usually too heavy.
verbose

Logical; To show more information. Not implemented yet

Value

a list with emissions at each street and data.base aggregated by categories. See link{emis_post}

Note

'decoder' shows a decoder for MOVES

Examples

{
data(decoder)
decoder
}

MOVES estimation of using rates per start by model year

Description

moves_rpsy_meta estimates running exhaust emissions using MOVES emission factors.

Usage

moves_rpsy_meta(
  metadata,
  lkm,
  ef,
  fuel_type,
  profile,
  agemax = 31,
  net,
  simplify = TRUE,
  verbose = FALSE,
  colk,
  colkt = F
)

Arguments

metadata

data.frame with the metadata for a vein project for MOVES.
lkm

Length of each link in miles
ef

emission factors from EmissionRates_running exported from MOVES
fuel_type

Data.frame of fuelSubtypeID exported by MOVES.
profile

Data.frame or Matrix with nrows equal to 24 and ncol 7 day of the week
agemax

Integer; max age for the fleet, assuming the same for all vehicles.
net

Road network class sf
simplify

Logical, to return the whole object or processed by streets and veh
verbose

Logical; To show more information. Not implemented yet
colk

Character identifying a column in 'metadata' to multiply the emission factor
colkt

Logical, TRUE if 'colk' is used

Value

a list with emissions at each street and data.base aggregated by categories.

Note

The idea is the user enter with emissions factors by pollutant

Examples

{
data(decoder)
decoder
}

MOVES estimation of using rates per start by model year

Description

moves_rpsy_sf estimates running exhaust emissions using MOVES emission factors.

Usage

moves_rpsy_sf(
  veh,
  lkm,
  ef,
  profile,
  source_type_id = 21,
  vehicle = NULL,
  vehicle_type = NULL,
  fuel_subtype = NULL,
  net,
  path_all,
  verbose = FALSE
)

Arguments

veh

"Vehicles" data-frame or list of "Vehicles" data-frame. Each data-frame as number of columns matching the age distribution of that type of vehicle. The number of rows is equal to the number of streets link.
lkm

Length of each link in miles
ef

emission factors from EmissionRates_running exported from MOVES filtered by sourceTypeID and fuelTypeID.
profile

numeric vector of normalized traffic for the morning rush hour
source_type_id

Number to identify type of vehicle as defined by MOVES.
vehicle

Character, type of vehicle
vehicle_type

Character, subtype of vehicle
fuel_subtype

Character, subtype of vehicle
net

Road network class sf
path_all

Character to export whole estimation. It is not recommended since it is usually too heavy.
verbose

Logical; To show more information. Not implemented yet

Value

a list with emissions at each street and data.base aggregated by categories. See link{emis_post}

Note

'decoder' shows a decoder for MOVES

Examples

{
data(decoder)
decoder
}

Return speed bins according to US/EPA MOVES model

Description

speed_moves return an object of average speed bins as defined by US EPA MOVES. The input must be speed as miles/h (mph)

Usage

moves_speed(x, net)

Arguments

x

Object with class, "sf", "data.frame", "matrix" or "numeric" with speeds in miles/h (mph)
net

optional spatial dataframe of class "sf". it is transformed to "sf".

Examples

{
data(net)
net$mph <- units::set_units(net$ps, "miles/h")
net$speed_bins <- moves_speed(net$mph)
head(net)
moves_speed(net["ps"])
}

Returns amount of vehicles at each age

Description

my_age returns amount of vehicles at each age using a numeric vector.

Usage

my_age(
  x,
  y,
  agemax,
  name = "vehicle",
  k = 1,
  pro_street,
  net,
  verbose = FALSE,
  namerows
)

Arguments

x

Numeric; vehicles by street (or spatial feature).
y

Numeric or data.frame; when pro_street is not available, y must be 'numeric', else, a 'data.frame'. The names of the columns of this data.frame must be the same as the elements of pro_street and each column must have a profile of age of use of vehicle. When 'y' is 'numeric' the vehicles has the same age distribution to all streets. When 'y' is a data.frame, the distribution by age of use varies the streets.
agemax

Integer; age of oldest vehicles for that category
name

Character; of vehicle assigned to columns of dataframe.
k

Integer; multiplication factor. If its length is > 1, it must match the length of x
pro_street

Character; each category of profile for each street. The length of this character vector must be equal to the length of 'x'. The names of the data.frame 'y' must have the same content of 'pro_street'
net

SpatialLinesDataFrame or Spatial Feature of "LINESTRING"
verbose

Logical; message with average age and total number of vehicles.
namerows

Any vector to be change row.names. For instance, the name of regions or streets.

Value

dataframe of age distribution of vehicles.

Note

The functions age* produce distribution of the circulating fleet by age of use. The order of using these functions is:

1. If you know the distribution of the vehicles by age of use , use: my_age 2. If you know the sales of vehicles, or (the regis)*better) the registry of new vehicles, use age to apply a survival function. 3. If you know the theoretical shape of the circulating fleet and you can use age_ldv, age_hdv or age_moto. For instance, you dont know the sales or registry of vehicles, but somehow you know the shape of this curve. 4. You can use/merge/transform/adapt any of these functions.

Examples

## Not run: 
data(net)
dpc <- c(seq(1,20,3), 20:10)
PC_E25_1400 <- my_age(x = net$ldv, y = dpc, name = "PC_E25_1400")
class(PC_E25_1400)
plot(PC_E25_1400)
PC_E25_1400sf <- my_age(x = net$ldv, y = dpc, name = "PC_E25_1400", net = net)
class(PC_E25_1400sf)
plot(PC_E25_1400sf)
PC_E25_1400nsf <- sf::st_set_geometry(PC_E25_1400sf, NULL)
class(PC_E25_1400nsf)
yy <- data.frame(a = 1:5, b = 5:1)    # perfiles por categoria de calle
pro_street <- c("a", "b", "a")         # categorias de cada calle
x <- c(100,5000, 3)                               # vehiculos
my_age(x = x, y =  yy, pro_street = pro_street)

## End(Not run)

Road network of the west part of Sao Paulo city

Description

This dataset is an sf class object with roads from a traffic simulation made by CET Sao Paulo, Brazil

Usage

data(net)

Format

A Spatial data.frame (sf) with 1796 rows and 1 variables:

ldv

Light Duty Vehicles (veh/h)

hdv

Heavy Duty Vehicles (veh/h)

lkm

Length of the link (km)

ps

Peak Speed (km/h)

ffs

Free Flow Speed (km/h)

tstreet

Type of street

lanes

Number of lanes per link

capacity

Capacity of vehicles in each link (1/h)

tmin

Time for travelling each link (min)

geometry

geometry

Calculate speeds of traffic network

Description

netspeed Creates a dataframe of speeds for different hours and each link based on morning rush traffic data

Usage

netspeed(
  q = 1,
  ps,
  ffs,
  cap,
  lkm,
  alpha = 0.15,
  beta = 4,
  net,
  scheme = FALSE,
  dist = "km"
)

Arguments

q

Data-frame of traffic flow to each hour (veh/h)
ps

Peak speed (km/h)
ffs

Free flow speed (km/h)
cap

Capacity of link (veh/h)
lkm

Distance of link (km)
alpha

Parameter of BPR curves
beta

Parameter of BPR curves
net

SpatialLinesDataFrame or Spatial Feature of "LINESTRING"
scheme

Logical to create a Speed data-frame with 24 hours and a default profile. It needs ffs and ps:
dist

String indicating the units of the resulting distance in speed. Default is units from peak speed 'ps'

00:00-06:00 ffs
06:00-07:00 average between ffs and ps
07:00-10:00 ps
10:00-17:00 average between ffs and ps
17:00-20:00 ps
20:00-22:00 average between ffs and ps
22:00-00:00 ffs

Value

dataframe speeds with units or sf.

Examples

{
data(net)
data(pc_profile)
pc_week <- temp_fact(net$ldv+net$hdv, pc_profile)
df <- netspeed(pc_week, net$ps, net$ffs, net$capacity, net$lkm, alpha = 1)
class(df)
plot(df) #plot of the average speed at each hour, +- sd
# net$ps <- units::set_units(net$ps, "miles/h")
# net$ffs <- units::set_units(net$ffs, "miles/h")
# df <- netspeed(pc_week, net$ps, net$ffs, net$capacity, net$lkm, alpha = 1)
# class(df)
# plot(df) #plot of the average speed at each hour, +- sd
# df <- netspeed(ps = net$ps, ffs = net$ffs, scheme = TRUE)
# class(df)
# plot(df) #plot of the average speed at each hour, +- sd
# dfsf <- netspeed(ps = net$ps, ffs = net$ffs, scheme = TRUE, net = net)
# class(dfsf)
# head(dfsf)
# plot(dfsf, pal = cptcity::lucky(colorRampPalette = TRUE, rev = TRUE),
# key.pos = 1, max.plot = 9)
}

Profile of Vehicle start patterns

Description

This dataset is a dataframe with percentage of hourly starts with a lapse of 6 hours with engine turned off. Data source is: Lents J., Davis N., Nikkila N., Osses M. 2004. Sao Paulo vehicle activity study. ISSRC. www.issrc.org

Usage

data(pc_cold)

Format

A data frame with 24 rows and 1 variables:

V1

24 hours profile vehicle starts for Monday

Profile of traffic data 24 hours 7 n days of the week

Description

This dataset is a dataframe with traffic activity normalized monday 08:00-09:00. This data is normalized at 08:00-09:00. It comes from data of toll stations near Sao Paulo City. The source is ARTESP (www.artesp.com.br)

Usage

data(pc_profile)

Format

A data frame with 24 rows and 7 variables:

V1

24 hours profile for Monday

V2

24 hours profile for Tuesday

V3

24 hours profile for Wednesday

V4

24 hours profile for Thursday

V5

24 hours profile for Friday

V6

24 hours profile for Saturday

V7

24 hours profile for Sunday

Data.frame with pollutants names and molar mass used in VEIN

Description

This dataset also includes MIR, MOIR and EBIR is Carter SAPRC07.xls https://www.engr.ucr.edu/~carter/SAPRC/

Usage

data(pollutants)

Format

A data frame with 148 rows and 10 variables:

n

Number for each pollutant, from 1 to 132

group1

classification for pollutants including "NMHC", "PAH", "METALS", "PM", "criteria" and "PCDD"

group2

A sub classification for pollutants including "alkenes", "alkynes", "aromatics", "alkanes", "PAH",, "aldehydes", "ketones", "METALS", "PM_char", "criteria", "cycloalkanes", "NMHC", "PCDD", "PM10", "PM2.5"

pollutant

1 of the 132 pollutants covered

CAS

CAS Registry Number

g_mol

molar mass

MIR

Maximum incremental Reactivity (gm O3 / gm VOC)

MOIR

Reactivity (gm O3 / gm VOC)

EBIR

Reactivity (gm O3 / gm VOC)

notes

Inform some assumption for molar mass

Profile of traffic data 24 hours 7 n days of the week

Description

This dataset is n a list of data-frames with traffic activity normalized monday 08:00-09:00. It comes from data of toll stations near Sao Paulo City. The source is ARTESP (www.artesp.com.br) for months January and June and years 2012, 2013 and 2014. The type of vehicles covered are PC, LCV, MC and HGV.

Usage

data(pc_profile)

Format

A list of data-frames with 24 rows and 7 variables:

PC_JUNE_2012

168 hours

PC_JUNE_2013

168 hours

PC_JUNE_2014

168 hours

LCV_JUNE_2012

168 hours

LCV_JUNE_2013

168 hours

LCV_JUNE_2014

168 hours

MC_JUNE_2012

168 hours

MC_JUNE_2013

168 hours

MC_JUNE_2014

168 hours

HGV_JUNE_2012

168 hours

HGV_JUNE_2013

168 hours

HGV_JUNE_2014

168 hours

PC_JANUARY_2012

168 hours

PC_JANUARY_2013

168 hours

PC_JANUARY_2014

168 hours

LCV_JANUARY_2012

168 hours

LCV_JANUARY_2013

168 hours

LCV_JANUARY_2014

168 hours

MC_JANUARY_2012

168 hours

MC_JANUARY_2014

168 hours

HGV_JANUARY_2012

168 hours

HGV_JANUARY_2013

168 hours

HGV_JANUARY_2014

168 hours

Remove units

Description

remove_units Remove units from sf, data.frames, matrix or units.

Usage

remove_units(x, verbose = FALSE)

Arguments

x

Object with class "sf", "data.frame", "matrix" or "units"
verbose

Logical, to print more information

Value

"sf", data.frame", "matrix" or numeric

Examples

## Not run: 
ef1 <- ef_cetesb(p = "CO", c("PC_G", "PC_FE"))
class(ef1)
sapply(ef1, class)
(a <- remove_units(ef1))

## End(Not run)

Speciation of emissions

Description

speciate separates emissions in different compounds. It covers black carbon and organic matter from particulate matter. Soon it will be added more speciations

Usage

speciate(
  x = 1,
  spec = "bcom",
  veh,
  fuel,
  eu,
  list = FALSE,
  pmpar,
  verbose = FALSE
)

Arguments

x

Emissions estimation
spec

The speciations are:

  • "bcom": Splits PM2.5 in black carbon and organic matter.

  • "tyre" or "tire": Splits PM in PM10, PM2.5, PM1 and PM0.1.

  • "brake": Splits PM in PM10, PM2.5, PM1 and PM0.1.

  • "road": Splits PM in PM10 and PM2.5.

  • "nox": Splits NOx in NO and NO2.

  • "nmhc": Splits NMHC in compounds, see ef_ldv_speed.

  • "voc": Splits NMHC in voc groups according EDGAR.

  • "pmiag", "pmneu", "pmneu2", "pm2023": Splits PM in groups, see note below.

veh

Type of vehicle:

  • "bcom": veh can be "PC", "LCV", HDV" or "Motorcycle".

  • "tyre" or "tire": not necessary.

  • "brake": not necessary.

  • "road": not necessary.

  • "nox": veh can be "PC", "LCV", HDV" or "Motorcycle".

  • "nmhc":see below

  • ""pmiag", "pmneu", "pmneu2", "pm2023": not necessary.

fuel

Fuel.

  • "bcom": "G" or "D".

  • "tyre" or "tire": not necessary.

  • "brake": not necessary.

  • "road": not necessary.

  • "nox": "G", "D", "LPG", "E85" or "CNG".

  • "nmhc":see below

  • "pmiag", "pmneu", "pmneu2", "pm2023": not necessary.

eu

Emission standard

  • "bcom": "G" or "D".

  • "tyre" or "tire": not necessary.

  • "brake": not necessary.

  • "road": not necessary.

  • "nox": "G", "D", "LPG", "E85" or "CNG".

  • "nmhc":see below

  • "pmiag", "pmneu", "pmneu2", "pm2023": not necessary.

list

when TRUE returns a list with number of elements of the list as the number species of pollutants
pmpar

Numeric vector for PM speciation eg: c(e_so4i = 0.0077, e_so4j = 0.0623, e_no3i = 0.00247, e_no3j = 0.01053, e_pm25i = 0.1, e_pm25j = 0.3, e_orgi = 0.0304, e_orgj = 0.1296, e_eci = 0.056, e_ecj = 0.024, h2o = 0.277) These are default values. however, when this argument is present, new values are used.
verbose

Logical to show more information

Value

dataframe of speciation in grams or mols

Note

options for spec "nmhc":

veh fuel eu
LDV G PRE
LDV G I
LDV D all
HDV D all
LDV LPG all
LDV G Evaporative
LDV E25 Evaporative
LDV E100 Evaporative
LDV E25 Exhaust
LDV E100 Exhaust
HDV B5 Exhaust
LDV E85 Exhaust
LDV E85 Evaporative
LDV CNG Exhaust
ALL E100 Liquid
ALL G Liquid
ALL E25 Liquid
ALL ALL OM
LDV G OM-001
LDV D OM-002
HDV D OM-003
MC G OM-004
ALL LPG OM-005
LDV G OM-001-001
LDV G OM-001-002
LDV G OM-001-003
LDV G OM-001-004
LDV G OM-001-005
LDV G OM-001-006
LDV G OM-001-007
LDV D OM-002-001
LDV D OM-002-002
LDV D OM-002-003
LDV D OM-002-004
LDV D OM-002-005
LDV D OM-002-006
HDV D OM-003-001
HDV D OM-003-002
HDV D OM-003-003
HDV D OM-003-004
HDV D OM-003-005
HDV D OM-003-006
MC G OM-004-001
MC G OM-004-002
MC G OM-004-003
ALL ALL urban
ALL ALL highway

after eu = OM, all profiles are Chinese # the following specs will be removed soon

  • "iag_racm": ethanol emissions added in hc3.

  • "iag" or "iag_cb05": Splits NMHC by CB05 (WRF exb05_opt1) group .

  • "petroiag_cb05": Splits NMHC by CB05 (WRF exb05_opt1) group .

  • "iag_cb05v2": Splits NMHC by CB05 (WRF exb05_opt2) group .

  • "neu_cb05": Splits NMHC by CB05 (WRF exb05_opt2) group alternative.

  • "petroiag_cb05v2": Splits NMHC by CB05 (WRF exb05_opt2) group alternative.

spec "pmiag" speciate pm2.5 into e_so4i, e_so4j, e_no3i, e_no3j, e_mp2.5i, e_mp2.5j, e_orgi, e_orgj, e_eci, e_ecj and h2o. Reference: Rafee, S.: Estudo numerico do impacto das emissoes veiculares e fixas da cidade de Manaus nas concentracoes de poluentes atmosfericos da regiao amazonica, Master thesis, Londrina: Universidade Tecnologica Federal do Parana, 2015.

specs: "neu_cb05", "pmneu" and "pmneu2" provided by Daniel Schuch, from Northeastern University. "pm2023" provided by Iara da Silva; Leila D. Martins

Speciation with fuels "E25", "E100" and "B5" made by Prof. Leila Martins (UTFPR), represents BRAZILIAN fuel

pmiag2 pass the mass only on j fraction

spec "voc" splits nmhc into the 25 VOC groups according: Huang et al 2019, "Speciation of anthropogenic emissions of non-methane volatile organic compounds: a global gridded data set for 1970-2012" ACP. Speciation In development.

References

"bcom": Ntziachristos and Zamaras. 2016. Passenger cars, light commercial trucks, heavy-duty vehicles including buses and motorcycles. In: EEA, EMEP. EEA air pollutant emission inventory guidebook-2009. European Environment Agency, Copenhagen, 2016

"tyre", "brake" and "road": Ntziachristos and Boulter 2016. Automobile tyre and brake wear and road abrasion. In: EEA, EMEP. EEA air pollutant emission inventory guidebook-2009. European Environment Agency, Copenhagen, 2016

"iag": Ibarra-Espinosa S. Air pollution modeling in Sao Paulo using bottom-up vehicular emissions inventories. 2017. PhD thesis. Instituto de Astronomia, Geofisica e Ciencias Atmosfericas, Universidade de Sao Paulo, Sao Paulo, page 88. Speciate EPA: https://cfpub.epa.gov/speciate/. : K. Sexton, H. Westberg, "Ambient hydrocarbon and ozone measurements downwind of a large automotive painting plant" Environ. Sci. Tchnol. 14:329 (1980).P.A. Scheff, R.A. Schauer, James J., Kleeman, Mike J., Cass, Glen R., Characterization and Control of Organic Compounds Emitted from Air Pollution Sources, Final Report, Contract 93-329, prepared for California Air Resources Board Research Division, Sacramento, CA, April 1998. 2004 NPRI National Databases as of April 25, 2006, http://www.ec.gc.ca/pdb/npri/npri_dat_rep_e.cfm. Memorandum Proposed procedures for preparing composite speciation profiles using Environment Canada s National Pollutant Release Inventory (NPRI) for stationary sources, prepared by Ying Hsu and Randy Strait of E.H. Pechan Associates, Inc. for David Niemi, Marc Deslauriers, and Lisa Graham of Environment Canada, September 26, 2006.

Examples

## Not run: 
# Do not run
pm <- rnorm(n = 100, mean = 400, sd = 2)
(df <- speciate(pm, veh = "PC", fuel = "G", eu = "I"))
(df <- speciate(pm, spec = "brake", veh = "PC", fuel = "G", eu = "I"))
(dfa <- speciate(pm, spec = "iag", veh = "veh", fuel = "G", eu = "Exhaust"))
(dfb <- speciate(pm, spec = "iag_cb05v2", veh = "veh", fuel = "G", eu = "Exhaust"))
(dfb <- speciate(pm, spec = "neu_cb05", veh = "veh", fuel = "G", eu = "Exhaust"))
pm <- units::set_units(pm, "g/km^2/h")
#(dfb <- speciate(as.data.frame(pm), spec = "pmiag", veh = "veh", fuel = "G", eu = "Exhaust"))
#(dfb <- speciate(as.data.frame(pm), spec = "pmneu", veh = "veh", fuel = "G", eu = "Exhaust"))
#(dfb <- speciate(as.data.frame(pm), spec = "pmneu2", veh = "veh", fuel = "G", eu = "Exhaust"))
# new
(pah <- speciate(spec = "pah", veh = "LDV", fuel = "G", eu = "I"))
(xs <- speciate(spec = "pcdd", veh = "LDV", fuel = "G", eu = "I"))
(xs <- speciate(spec = "pmchar", veh = "LDV", fuel = "G", eu = "I"))
(xs <- speciate(spec = "metals", veh = "LDV", fuel = "G", eu = "all"))

## End(Not run)

Construction function for class "Speed"

Description

Speed returns a transformed object with class "Speed" and units km/h. This function includes two arguments, distance and time. Therefore, it is possible to change the units of the speed to "m" to "s" for example. This function returns a data.frame with units for speed. When this function is applied to numeric vectors it adds class "units".

Usage

Speed(x, ..., dist = "km", time = "h")

## S3 method for class 'Speed'
print(x, ...)

## S3 method for class 'Speed'
summary(object, ...)

## S3 method for class 'Speed'
plot(
  x,
  pal = "mpl_inferno",
  rev = FALSE,
  fig1 = c(0, 0.8, 0, 0.8),
  fig2 = c(0, 0.8, 0.55, 1),
  fig3 = c(0.7, 1, 0, 0.8),
  mai1 = c(1, 0.82, 0.82, 0.42),
  mai2 = c(1.8, 0.82, 0.5, 0.42),
  mai3 = c(1, 1, 0.82, 0.2),
  bias = 1.5,
  ...
)

Arguments

x

Object with class "data.frame", "matrix" or "numeric"
...

ignored Default is units is "km"
dist

String indicating the units of the resulting distance in speed.
time

Character to be the time units as denominator, default is "h"
object

Object with class "Speed"
pal

Palette of colors available or the number of the position
rev

Logical; to internally revert order of rgb color vectors.
fig1

par parameters for fig, par.
fig2

par parameters for fig, par.
fig3

par parameters for fig, par.
mai1

par parameters for mai, par.
mai2

par parameters for mai, par.
mai3

par parameters for mai, par.
bias

positive number. Higher values give more widely spaced colors at the high end.

Value

Constructor for class "Speed" or "units"

Note

default time unit for speed is hour

See Also

units

Examples

{
data(net)
data(pc_profile)
speed <- Speed(net$ps)
class(speed)
plot(speed, type = "l")
pc_week <- temp_fact(net$ldv+net$hdv, pc_profile)
df <- netspeed(pc_week, net$ps, net$ffs, net$capacity, net$lkm)
summary(df)
plot(df)
# changing to miles
net$ps <- units::set_units(net$ps, "miles/h")
net$ffs <- units::set_units(net$ffs, "miles/h")
net$lkm <- units::set_units(net$lkm, "miles")
df <- netspeed(pc_week, net$ps, net$ffs, net$capacity, net$lkm, dist = "miles")
plot(df)
}

Split street emissions based on a grid

Description

split_emis split street emissions into a grid.

Usage

split_emis(net, distance, add_column, verbose = TRUE)

Arguments

net

A spatial dataframe of class "sp" or "sf". When class is "sp" it is transformed to "sf" with emissions.
distance

Numeric distance or a grid with class "sf".
add_column

Character indicating name of column of distance. For instance, if distance is an sf object, and you wand to add one extra column to the resulting object.
verbose

Logical, to show more information.

Examples

## Not run: 
data(net)
g <- make_grid(net, 1/102.47/2) #500m in degrees
names(net)
dim(net)
netsf <- sf::st_as_sf(net)[, "ldv"]
x <- split_emis(net = netsf, distance = g)
dim(x)
g$A <- rep(letters, length = 20)[1:nrow(g)]
g$B <- rev(g$A)
netsf <- sf::st_as_sf(net)[, c("ldv", "hdv")]
xx <- split_emis(netsf, g, add_column = c("A", "B"))

## End(Not run)

Expansion of hourly traffic data

Description

temp_fact is a matrix multiplication between traffic and hourly expansion data-frames to obtain a data-frame of traffic at each link to every hour

Usage

temp_fact(q, pro, net, time)

Arguments

q

Numeric; traffic data per each link
pro

Numeric; expansion factors data-frames
net

SpatialLinesDataFrame or Spatial Feature of "LINESTRING"
time

Character to be the time units as denominator, eg "1/h"

Value

data-frames of expanded traffic or sf.

Examples

## Not run: 
# Do not run
data(net)
data(pc_profile)
pc_week <- temp_fact(net$ldv+net$hdv, pc_profile)
plot(pc_week)
pc_weeksf <- temp_fact(net$ldv+net$hdv, pc_profile, net = net)
plot(pc_weeksf)

## End(Not run)

Expanded Vehicles data.frame by hour

Description

temp_veh multiplies vehicles with temporal factor

Usage

temp_veh(x, tfs, array = FALSE)

Arguments

x

Vehicles data.frame
tfs

temporal factor
array

Logical, to return an array

Value

data.table

See Also

temp_fact

Examples

## Not run: 
data(net)
data(pc_profile)
x <- age_ldv(x = net$ldv)
dx <- temp_veh(x = x, tfs = pc_profile[[1]])
plot(Vehicles(as.data.frame(dx[, 1:50])))
dx2 <- temp_veh(x = x,
                tfs = pc_profile[[1]],
                array = TRUE)
plot(EmissionsArray(dx2))

## End(Not run)

creates a .tex a table from a data.frame

Description

to_latex reads a data.frme an dgenerates a .tex table, aiming to replicate the method of tablegenerator.com

Usage

to_latex(df, file, caption = "My table", label = "tab:df")

Arguments

df

data.frame with three column.
file

Character, name of new .tex file
caption

Character caption of table
label

Character, label of table

Value

a text file with extension .tex.

See Also

vein_notes long_to_wide

Other helpers: colplot(), dmonth(), wide_to_long()

Examples

## Not run: 
df <- data.frame(pollutant = rep(c("CO", "propadiene", "NO2"), 10),
                 emission = vein::Emissions(1:30),
                 region = rep(letters[1:2], 15))
df
long_to_wide(df)
(df2 <- long_to_wide(df, column_fixed = "region"))
to_latex(df2)
to_latex(long_to_wide(df, column_fixed = "region"),
file = paste0(tempfile(), ".tex"))

## End(Not run)

Construction function for class "Vehicles"

Description

Vehicles returns a tranformed object with class "Vehicles" and units 'veh'. The type of objects supported are of classes "matrix", "data.frame", "numeric" and "array". If the object is a matrix it is converted to data.frame. If the object is "numeric" it is converted to class "units".

Usage

Vehicles(x, ..., time = NULL)

## S3 method for class 'Vehicles'
print(x, ...)

## S3 method for class 'Vehicles'
summary(object, ...)

## S3 method for class 'Vehicles'
plot(
  x,
  pal = "colo_lightningmccarl_into_the_night",
  rev = TRUE,
  bk = NULL,
  fig1 = c(0, 0.8, 0, 0.8),
  fig2 = c(0, 0.8, 0.55, 1),
  fig3 = c(0.7, 1, 0, 0.8),
  mai1 = c(1, 0.82, 0.82, 0.42),
  mai2 = c(1.8, 0.82, 0.5, 0.42),
  mai3 = c(1, 1, 0.82, 0.2),
  bias = 1.5,
  ...
)

Arguments

x

Object with class "Vehicles"
...

ignored
time

Character to be the time units as denominator, eg "1/h"
object

Object with class "Vehicles"
pal

Palette of colors available or the number of the position
rev

Logical; to internally revert order of rgb color vectors.
bk

Break points in sorted order to indicate the intervals for assigning the colors.
fig1

par parameters for fig, par.
fig2

par parameters for fig, par.
fig3

par parameters for fig, par.
mai1

par parameters for mai, par.
mai2

par parameters for mai, par.
mai3

par parameters for mai, par.
bias

positive number. Higher values give more widely spaced colors at the high end.

Value

Objects of class "Vehicles" or "units"

Examples

## Not run: 
lt <- rnorm(100, 300, 10)
class(lt)
vlt <- Vehicles(lt)
class(vlt)
plot(vlt)
LT_B5 <- age_hdv(x = lt,name = "LT_B5")
summary(LT_B5)
plot(LT_B5)

## End(Not run)

Notes with sysinfo

Description

vein_notes creates aa text file '.txt' for writting technical notes about this emissions inventory

Usage

vein_notes(
  notes,
  file = "README",
  yourname = Sys.info()["login"],
  title = "Notes for this VEIN run",
  approach = "Top Down",
  traffic = "Your traffic information",
  composition = "Your traffic information",
  ef = "Your information about emission factors",
  cold_start = "Your information about cold starts",
  evaporative = "Your information about evaporative emission factors",
  standards = "Your information about standards",
  mileage = "Your information about mileage"
)

Arguments

notes

Character; vector of notes.
file

Character; Name of the file. The function will generate a file with an extension '.txt'.
yourname

Character; Name of the inventor compiler.
title

Character; Title of this file. For instance: "Vehicular Emissions Inventory of Region XX, Base year XX"
approach

Character; vector of notes.
traffic

Character; vector of notes.
composition

Character; vector of notes.
ef

Character; vector of notes.
cold_start

Character; vector of notes.
evaporative

Character; vector of notes.
standards

Character; vector of notes.
mileage

Character; vector of notes.

Value

Writes a text file.

Examples

## Not run: 
#do not run
a <- "delete"
f <- vein_notes("notes", file = a)
file.remove(f)

## End(Not run)

Estimation of VKM

Description

vkm consists in the product of the number of vehicles and the distance driven by these vehicles in km. This function reads hourly vehicles and then extrapolates the vehicles

Usage

vkm(
  veh,
  lkm,
  profile,
  hour = nrow(profile),
  day = ncol(profile),
  array = TRUE,
  as_df = TRUE
)

Arguments

veh

Numeric vector with number of vehicles per street
lkm

Length of each link (km)
profile

Numerical or dataframe with nrows equal to 24 and ncol 7 day of the week
hour

Number of considered hours in estimation
day

Number of considered days in estimation
array

When FALSE produces a dataframe of the estimation. When TRUE expects a profile as a dataframe producing an array with dimensions (streets x hours x days)
as_df

Logical; when TRUE transform returning array in data.frame (streets x hour*days)

Value

emission estimation of vkm

Examples

## Not run: 
# Do not run
pc <- lkm <- abs(rnorm(10,1,1))*100
pro <- matrix(abs(rnorm(24*7,0.5,1)), ncol=7, nrow=24)
vkms  <- vkm(veh = pc, lkm = lkm, profile = pro)
class(vkms)
dim(vkms)
vkms2  <- vkm(veh = pc, lkm = lkm, profile = pro, as_df = FALSE)
class(vkms2)
dim(vkms2)

## End(Not run)

Transform data.frame from wide to long format

Description

wide_to_long transform data.frame from wide to long format

Usage

wide_to_long(df, column_with_data = names(df), column_fixed, geometry)

Arguments

df

data.frame with three column.
column_with_data

Character column with data
column_fixed

Character, column that will remain fixed
geometry

To return a sf

Value

long data.frame.

See Also

emis_hot_td emis_cold_td long_to_wide

Other helpers: colplot(), dmonth(), to_latex()

Examples

## Not run: 
data(net)
net <- sf::st_set_geometry(net, NULL)
df <- wide_to_long(df = net)
head(df)

## End(Not run)