Type:	Package
Title:	Vehicular Emissions Inventories
Version:	1.3.0
Date:	2025-03-28
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.
License:	MIT + file LICENSE
URL:	https://github.com/atmoschem/vein
BugReports:	https://github.com/atmoschem/vein/issues
LazyData:	no
Depends:	R (≥ 3.5.0)
Imports:	sf (≥ 1.0.1), data.table, units, graphics, stats, dotCall64, cptcity, grDevices
Suggests:	knitr, rmarkdown, testthat
RoxygenNote:	7.3.2
Encoding:	UTF-8
NeedsCompilation:	yes
Config/testthat/parallel:	true
Packaged:	2025-03-29 05:07:04 UTC; sergio
VignetteBuilder:	knitr
Author:	Sergio Ibarra-Espinosa [aut, cre], Daniel Schuch [ctb], Joao Bazzo [ctb], Mario Gavidia-Calderón [ctb], Karl Ropkins [ctb]
Maintainer:	Sergio Ibarra-Espinosa <zergioibarra@gmail.com>
Repository:	CRAN
Date/Publication:	2025-03-29 05:30:02 UTC

vein: 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.

Author(s)

Maintainer: Sergio Ibarra-Espinosa zergioibarra@gmail.com (ORCID)

Other contributors:

• Daniel Schuch underschuch@gmail.com (ORCID) [contributor]

• Joao Bazzo joao.bazzo@gmail.com (ORCID) [contributor]

• Mario Gavidia-Calderón mario.calderon@iag.usp.br (ORCID) [contributor]

• Karl Ropkins k.ropkins@its.leeds.ac.uk (ORCID) [contributor]

See Also

Useful links:

• https://github.com/atmoschem/vein

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

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

Value

Objects of class "EmissionFactors" or "units"

Examples

## Not run: 
#do not run
EmissionFactors(1)

## 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

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

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

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)

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

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)

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

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)
}

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

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)

Construction function to add unit km

Description

add_lkm just add unit 'km' to different R objects

Usage

add_lkm(x)

Arguments

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

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

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

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

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

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(), age_veh()

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

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(), age_ldv(), age_moto(), age_veh()

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

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(), age_hdv(), age_moto(), age_veh()

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

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(), age_hdv(), age_ldv(), age_veh()

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)

Returns amount of vehicles at each age

Description

age_veh returns amount of vehicles at each age

Usage

age_veh(
  x,
  type = "ldv",
  name = "age",
  a = if (type == "ldv") 1.698 else 0.2,
  b = if (type == "ldv") -0.2 else 17,
  agemin = 1,
  agemax = 50,
  k = 1,
  bystreet = F,
  net,
  verbose = FALSE,
  namerows,
  time
)

Arguments

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(), age_hdv(), age_ldv(), age_moto()

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)

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

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

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

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

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()

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 days of the month

Usage

dmonth(year, month)

Arguments

Value

days of the month

See Also

Other helpers: colplot(), to_latex()

Examples

## Not run: 
dmonth(2022, 1)
dmonth(Sys.Date())

## 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

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:

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

Value

An emission factor

Note

Combination of vehicles:

standard VI is assumed as V

See Also

ef_ldv_speed emis_hot_td

Other China: ef_china_det(), ef_china_h(), ef_china_hu(), ef_china_long(), ef_china_s(), ef_china_speed(), 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

Value

long data.frame

See Also

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

Value

long data.frame

See Also

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

Value

long data.frame

See Also

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

Value

long data.frame

long data.frame

See Also

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

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

Value

long data.frame

See Also

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

Value

long data.frame

See Also

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

Value

long data.frame

See Also

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

Value

long data.frame

See Also

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

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

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

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

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

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

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

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

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

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

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

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)

ef <- ef_ldv_speed(v = "LCV",
                   t = "4S",
                   cc = "<3.5",
                   f = "G",
                   p = "FC",
                   eu = c("I", "II"),
                   speed = Speed(10))

## 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

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

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

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

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)

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

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)

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

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

Value

long data.frame

See Also

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

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

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

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

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

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

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

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

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

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_veh(1:10, type = "ldv", 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

Value

long data.frame

See Also

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

Examples

{
data(net)
net <- net[1:100, ]
data(pc_profile)
x <- age_veh(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)
}

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

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

Value

emission estimation g/h

Note

silt values can vary a lot. For comparison:

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

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

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

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)

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

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

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

Note

All projects include option to apply survival functions In Sao Paulo the IM programs was functioning until 2011.

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

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)

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

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)

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

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

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

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

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

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

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

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

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

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

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

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

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

Value

dataframe of speciation in grams or mols

Note

options for spec "nmhc":

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)

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

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

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

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 and generates a .tex table, aiming to replicate the method of tablegenerator.com

Usage

to_latex(df, file, caption = "My table", label = "tab:df")

Arguments

Value

a text file with extension .tex.

See Also

vein_notes

Other helpers: colplot(), dmonth()

Examples

## Not run: 
ef <- ef_cetesb(p = "CO", veh = "PC_FG", full = T)
to_latex(ef)

## End(Not run)

Deprecated functions in package vein.

Description

The functions listed below are deprecated and will be defunct in the near future. When possible, alternative functions with similar functionality are also mentioned. Help pages for deprecated functions are available at help("-deprecated").

Usage

emis_chem(...)

long_to_wide(...)

wide_to_long(...)

invcop(...)

EmissionsList(...)

running_losses()

hot_soak()

Evaporative()

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

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

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)