Chapter 8 Speciation

The speciation for emissions is a very important part that deserves a only chapter on this book. VEIN initially covered speed functions emission factors of CO, HC, NOx, PM and Fuel Consumption (FC). But currently, the the pollutants covered are:

  • Criteria: “CO”, “NOx”, “HC”, “PM”, “CH4”, “NMHC”, “CO2”, “SO2”, “Pb”, “FC” (Fuel Consumption).
  • Polycyclic Aromatic Hydrocarbons (PAH) and Persisten Organic Pollutants (POP): “indeno(1,2,3-cd)pyrene”, “benzo(k)fluoranthene”, “benzo(b)fluoranthene”, “benzo(ghi)perylene”, “fluoranthene”, “benzo(a)pyrene”, “pyrene”, “perylene”, “anthanthrene”, “benzo(b)fluorene”, “benzo(e)pyrene”, “triphenylene”, “benzo(j)fluoranthene”, “dibenzo(a,j)anthacene”, “dibenzo(a,l)pyrene”, “3,6-dimethyl-phenanthrene”, “benzo(a)anthracene”, “acenaphthylene”, “acenapthene”, “fluorene”, “chrysene”, “phenanthrene”, “napthalene”, “anthracene”, “coronene”, “dibenzo(ah)anthracene”
  • Dioxins and Furans: “PCDD”, “PCDF”, “PCB”.
  • Metals: “As”, “Cd”, “Cr”, “Cu”, “Hg”, “Ni”, “Pb”, “Se”, “Zn”.
  • NMHC:
  • ALKANES: “ethane”, “propane”, “butane”, “isobutane”, “pentane”, “isopentane”, “hexane”, “heptane”, “octane”, “TWO_methylhexane”, “nonane”, “TWO_methylheptane”, “THREE_methylhexane”, “decane”, “THREE_methylheptane”, “alcanes_C10_C12”, “alkanes_C13”.
  • CYCLOALKANES: “cycloalcanes”.
  • ALKENES: “ethylene”, “propylene”, “propadiene”, “ONE_butene”, “isobutene”, “TWO_butene”, “ONE_3_butadiene”, “ONE_pentene”, “TWO_pentene”, “ONE_hexene”, “dimethylhexene”.
  • ALKYNES:“ONE_butine”, “propine”, “acetylene”.
  • ALDEHYDES: “formaldehyde”, “acetaldehyde”, “acrolein”, “benzaldehyde”, “crotonaldehyde”, “methacrolein”, “butyraldehyde”, “isobutanaldehyde”, “propionaldehyde”, “hexanal”, “i_valeraldehyde”, “valeraldehyde”, “o_tolualdehyde”, “m_tolualdehyde”, “p_tolualdehyde”.
  • KETONES: “acetone”, “methylethlketone”.
  • AROMATICS: “toluene”, “ethylbenzene”, “m_p_xylene”, “o_xylene”, “ONE_2_3_trimethylbenzene”, “ONE_2_4_trimethylbenzene”, “ONE_3_5_trimethylbenzene”, “styrene”, “benzene”, “C9”, “C10”, “C13”.

Also, some Brazilian emission factors and speciations for WRF-Chem, mechanisms: “e_eth”, “e_hc3”, “e_hc5”, “e_hc8”, “e_ol2”, “e_olt”, “e_oli”, “e_iso”, “e_tol”, “e_xyl”, “e_c2h5oh”, “e_ald”, “e_hcho”, “e_ch3oh”, “e_ket”, “E_SO4i”, “E_SO4j”, “E_NO3i”, “E_NO3j”, “E_MP2.5i”, “E_MP2.5j”, “E_ORGi”, “E_ORGj”, “E_ECi”, “E_ECj”, “H2O”.

The speciation of emissions in VEIN can be done with three ways, by selecting the pollutants in ef_speed* functions, another way explicit and the other implicit. The explicit way is by using the function speciate for the specifically available speciation. The implicit way is by adding a percentage value in the argument k in any of the functions of emission factors. The implicit way requires that the user must know the percentage of the species of pollutant.

8.1 Speed functions of VOC species

As mentioned before, the speed function emission factors, currently cover several pollutants. We firstly focus on some PAH and POP.

library(vein)
pol <- c("indeno(1,2,3-cd)pyrene", "benzo(k)fluoranthene",
         "benzo(b)fluoranthene", "benzo(ghi)perylene",
         "fluoranthene", "benzo(a)pyrene")
df <- lapply(1:length(pol), function(i){
  EmissionFactors(ef_ldv_speed("PC", "4S", "<=1400", "G",
                  "PRE", pol[i], x = 10)(10))
})
names(df) <- pol
df
## $`indeno(1,2,3-cd)pyrene`
## 1.03e-06 [g/km]
## 
## $`benzo(k)fluoranthene`
## 3e-07 [g/km]
## 
## $`benzo(b)fluoranthene`
## 8.8e-07 [g/km]
## 
## $`benzo(ghi)perylene`
## 2.9e-06 [g/km]
## 
## $fluoranthene
## 1.822e-05 [g/km]
## 
## $`benzo(a)pyrene`
## 4.8e-07 [g/km]

These pollutants come from Ntziachristos and Samaras (2016) and in this case, are not dependent on speed. I used lapply to iterate in each pollutant at 10 km/h. Now I will show the same example for dioxins and furans.

library(vein)
pol <- c("PCDD", "PCDF", "PCB")
df <- lapply(1:length(pol), function(i){
  EmissionFactors(ef_ldv_speed("PC", "4S", "<=1400", "G",
                   "PRE", pol[i], x = 10)(10))
})
names(df) <- pol
df
## $PCDD
## 1.03e-11 [g/km]
## 
## $PCDF
## 2.12e-11 [g/km]
## 
## $PCB
## 6.4e-12 [g/km]

In the case of nmhc, the estimation is:

library(vein)
pol <- c("ethane", "propane", "butane", "isobutane", "pentane")
df <- lapply(1:length(pol), function(i){
  EmissionFactors(ef_ldv_speed("PC", "4S", "<=1400", "G",
                  "PRE", pol[i], x = 10)(c(10,20,30)))
})
names(df) <- pol
df
## $ethane
## Units: [g/km]
## [1] 0.09934632 0.06062781 0.04524681
## 
## $propane
## Units: [g/km]
## [1] 0.02829865 0.01726974 0.01288849
## 
## $butane
## Units: [g/km]
## [1] 0.1746087 0.1065580 0.0795247
## 
## $isobutane
## Units: [g/km]
## [1] 0.07767076 0.04739993 0.03537478
## 
## $pentane
## Units: [g/km]
## [1] 0.10717361 0.06540455 0.04881171

Now the same example for one pollutant and several euro standards.

library(vein)
euro <- c("V", "IV", "III", "II","I", "PRE")
df <- lapply(1:length(euro), function(i){
  EmissionFactors(ef_ldv_speed("PC", "4S", "<=1400", "G",
                  euro[i], "benzene")(c(10, 30, 50)))
})
names(df) <- euro
df
## $V
## Units: [g/km]
## [1] 0.0003928819 0.0002194495 0.0001597751
## 
## $IV
## Units: [g/km]
## [1] 0.0004864655 0.0004872061 0.0005276205
## 
## $III
## Units: [g/km]
## [1] 0.0017465678 0.0008206890 0.0005928019
## 
## $II
## Units: [g/km]
## [1] 0.013332523 0.004133087 0.002363304
## 
## $I
## Units: [g/km]
## [1] 0.025656752 0.010921829 0.006979341
## 
## $PRE
## Units: [g/km]
## [1] 0.4112336 0.1872944 0.1287895

8.2 Speciate function

The arguments of the function speciate are:

args(vein::speciate)
## function (x, spec = "bcom", veh, fuel, eu, show = FALSE, 
## list = FALSE, dx)

Where,

  • x: Emissions estimation
  • spec: speciation: The speciations are: “bcom”, tyre“,”break“,”road“,”iag“,”nox" and “nmhc”. ‘iag’ now includes speciation for the use of industrial and building paintings. “bcom” stands for black carbon and organic matter.
  • veh: Type of vehicle: When the spec is “bcom” or “nox” veh can be “PC”, “LCV”, HDV" or “Motorcycle”. When spec is “iag” veh can take two values depending: when the speciation is for vehicles veh accepts “veh”, eu “Evaporative”, “Liquid” or “Exhaust” and fuel “G”, “E” or “D”, when the speciation is for painting, veh is “paint” fuel or eu can be “industrial” or “building” when spec is “nmhc”, veh can be “LDV” with fuel “G” or “D” and eu “PRE”, “I”, “II”, “III”, “IV”, “V”, or “VI”. when spec is “nmhc”, veh can be “HDV” with fuel “D” and eu “PRE”, “I”, “II”, “III”, “IV”, “V”, or “VI”. when spec is “nmhc” and fuel is “LPG”, veh and eu must be “ALL”
  • fuel: Fuel. When the spec is “bcom” fuel can be “G” or “D”. When the spec is “iag” fuel can be “G”, “E” or “D”. When spec is “nox” fuel can be “G”, “D”, “LPG”, “E85” or “CNG”. Not required for “tyre”, “break” or “road”. When the spec is “nmhc” fuel can be G, D or LPG.
  • eu: Euro emission standard: “PRE”, “ECE_1501”, “ECE_1502”, “ECE_1503”, “I”, “II”, “III”, “IV”, “V”, “III-CDFP”,“IV-CDFP”,“V-CDFP”, “III-ADFP”, “IV-ADFP”,“V-ADFP” and “OPEN_LOOP”. When the spec is “iag” accept the values “Exhaust” “Evaporative” and “Liquid”. When the spec is “nox” eu can be “PRE”, “I”, “II”, “III”, “IV”, “V”, “VI”, “VIc”, “III-DPF” or “III+CRT”. Not required for “tyre”, “break” or “road”
  • show: when TRUE shows the row of the table with respective speciation
  • list: when TRUE returns a list with some elements of the list as the number species of pollutants

As shown before, there is currently 8 type of speciations.

8.3 Black carbon and organic matter

Let’s use the data inside the vein and estimate emissions. The vehicles considered are Light Trucks, assuming all HGV. We are using the 4 stage estimation with the first part arranging traffic data

library(vein)
# 1 Traffic data
data(net)
data(profiles)
data(fe2015)
lt <- age_hdv(net$ldv)
## Average age of age is 17.12
## Number of age is 1946.95 * 10^3 veh
vw <- temp_fact(net$ldv, profiles$PC_JUNE_2014) + 
  temp_fact(net$hdv, profiles$HGV_JUNE_2014)
speed <- netspeed(vw, net$ps, net$ffs, net$capacity,
                  net$lkm, alpha = 1)
# 2 Emission factors
euro <- c("V", rep("IV", 3),  
          rep("III", 7), rep("II", 7), 
          rep("I", 5), rep("PRE", 27))
lefd <- lapply(1:50, function(i) {
  ef_hdv_speed(v = "Trucks", t = "RT", g = ">32", gr = 0,
               eu = euro[i], l = 0.5, p = "PM",
               show.equation = FALSE) })
# 3 Estimate emissions
pmd <- emis(veh = lt, lkm = net$lkm, ef = lefd, speed = speed,
            profile = profiles$HGV_JUNE_2014)
## 32714.53 kg emissions in 24 hours and 7 days
# 4 process emissions
# Agreggating emissions by age of use
df <- apply(X = pmd, MARGIN = c(1,2), FUN = sum, na.rm = TRUE)
# euro
dfbcom <-   rbind(speciate(df[, 1],
                           "bcom", "PC", "D", "V"),
                  speciate(rowSums(df[, 2:4]),
                           "bcom", "PC", "D", "IV"),
                  speciate(rowSums(df[, 5:11]),
                           "bcom", "PC", "D", "III"),
                  speciate(rowSums(df[, 12:18]),
                           "bcom", "PC", "D", "II"),
                  speciate(rowSums(df[, 19:23]),
                           "bcom", "PC", "D", "I"),
                  speciate(rowSums(df[, 24:50]),
                           "bcom", "PC", "D", "PRE"))
dfbcom$id <- 1:1505 
bc <- aggregate(dfbcom$BC, by = list(dfbcom$id), sum)
om <- aggregate(dfbcom$OM, by = list(dfbcom$id), sum)
net$BC <- bc[, 2]
net$OM <- om[, 2]

Now we have spatial objects of Back Carbon

sp::spplot(net, "BC", scales = list(draw = T),
           main = "Black Carbon [g/168h]",
           col.regions = rev(cptcity::cpt()))
Black Carbon [g/168h]

Figure 8.1: Black Carbon [g/168h]

moreover, Organic Matter

sp::spplot(net, "OM", scales = list(draw = T),
           main = "Organic Matter [g/168h]",
           col.regions = rev(cptcity::cpt()))
Organic Matter [g/168h]

Figure 8.2: Organic Matter [g/168h]

If you are interested in speciate total emissions by age of use not spatial emissions, you could use emis_post with by = "veh" and then aggregate the emissions by standard. Then speciate PM by standard:

dfpm <- emis_post(arra = pmd,  veh = "LT", 
                  size = "small", fuel = "D",
                  pollutant = "PM", by = "veh")
dfpm$euro <- euro
dfpmeuro <- aggregate(dfpm$g, by = list(dfpm$euro), sum)
names(dfpmeuro) <- c("euro", "g")
bcom <- as.data.frame(sapply(1:6, function(i){
  speciate(dfpmeuro$g[i], "bcom", "PC", "D", dfpmeuro$euro[i])
}))
names(bcom) <- dfpmeuro$euro

And the resulting speciation is:

Table 8.1: Black Carbon and Organic Matter [g/168h]
BC OM
I 6826527.4 [g/h] 2730611.0 [g/h]
II 5313757.9 [g/h] 1222164.3 [g/h]
III 3321183.0 [g/h] 498177.5 [g/h]
IV 155078.7 [g/h] 20160.2 [g/h]
PRE 6700527.8 [g/h] 4690369.5 [g/h]
V 10369.3 [g/h] 20738.5 [g/h]

8.4 Tyre wear, breaks wear and road abrasion

Tire, break and road abrasions come from Ntziachristos and Boulter (2009). Tires consist of a complex mixture of rubber, which after the user starts to degrade. Indeed, when the driving cycle is more aggressive, higher is tire mass emitted from the tire, with tire wear emission factors higher in HDV than LDV. Break wear emits mass and with more aggressive driving, more emissions.

The input are wear emissions in Total Suspended Particles (TSP) ,as explained in section @ref(#ew). The speciation consists in 60% PM10, 42% PM2.5, 6% PM1 and 4.8% PM0.1. Now, a simple example for TP adspeciation:

library(vein)
data(net)
data(profiles)
pro <- profiles$PC_JUNE_2012[, 1] # 24 hours
pc_week <- temp_fact(net$ldv+net$hdv, pro)
df <- netspeed(pc_week, net$ps, net$ffs, net$capacity,
               net$lkm, alpha = 1)
ef <- ef_wear(wear = "tyre", type = "PC", speed = df)
emit <- emis_wear(veh = age_ldv(net$ldv, 
                                name = "VEH"), speed = df,
                  lkm = net$lkm, ef = ef, profile =  pro)
## Average age of VEH is 11.17
## Number of VEH is 1946.95 * 10^3 veh
emib <- emis_wear(what = "break",
                  veh = age_ldv(net$ldv, name = "VEH"),
                  lkm = net$lkm, ef = ef,
                  profile =  pro, speed = df)
## Average age of VEH is 11.17
## Number of VEH is 1946.95 * 10^3 veh
# emit
# emib
emitp <- emis_post(arra = emit, veh = "PC",
                   size = "ALL", fuel = "G",
                   pollutant = "TSP", by = "veh")
emibp <- emis_post(arra = emib, veh = "PC",
                   size = "ALL", fuel = "G",
                   pollutant = "TSP", by = "veh")

The estimation covered 24 hours and for simplicity, we are assuming a year with 365 similar days and dividing by 1 million to have t/y. Hence,the speciation is:

library(ggplot2)
tyrew <- speciate(x =  unclass(emitp$g), spec = "tyre")
breakw <- speciate(x =  unclass(emibp$g), spec = "brake")
df <- data.frame(Emis = c(sapply(tyrew, sum),
                          sapply(breakw, sum))*365/1000000)
df$Pollutant <- factor(x = names(tyrew), levels = names(tyrew))
df$Emissions <- c(rep("Tyre", 4), rep("Brake", 4))
ggplot(df, aes(x = Pollutant, y = Emis, fill = Emissions)) +
  geom_bar(stat = "identity", position = "dodge")+
  labs(x = NULL, y = "[t/y]")
Break and Tyre emissions (t/y)

Figure 8.3: Break and Tyre emissions (t/y)

It is interesting to see that break emissions are higher than tire wear emissions, and the smaller the diameter, higher the difference, as shown:

Table 8.2: Ratio between break and tire wear emissions
x
PM10 1.6593167
PM2.5 0.9433433
PM1 1.6931803
PM0.1 1.6931803

The procedure can be used with emis_post with by = streets_wide to obtain the spatial speciation.

8.5 \(NO\) and \(NO_2\)

The speciation of \(NO_X\) into \(NO\) and \(NO2\) depends on the type of vehicle, fuel, and euro standard. The following example will consist in comparing a Gasoline passenger car and a Diesel truck.

library(vein)
data(net)
data(profiles)
propc <- profiles$PC_JUNE_2012[, 1] # 24 hours
prolt <- profiles$PC_JUNE_2012[, 1] # 24 hours
pweek <- temp_fact(net$ldv+net$hdv, pro)
df <- netspeed(pc_week, net$ps, net$ffs, 
               net$capacity, net$lkm, alpha = 1)
euro <- c("V", rep("IV", 3),  
          rep("III", 7), rep("II", 7),
          rep("I", 5), rep("PRE", 27))
lefpc <- lapply(1:50, function(i) {
  ef_ldv_speed(v = "PC", t = "4S", cc = "<=1400", f = "G",
               eu = euro[i], p = "NOx", 
               show.equation = FALSE) })
leflt <- lapply(1:50, function(i) {
  ef_hdv_speed(v = "Trucks", t = "RT", 
               g = ">32", gr = 0,
               eu = euro[i], l = 0.5, p = "NOx", 
               show.equation = FALSE) 
  })
emipc <- emis(veh = age_ldv(net$ldv),
          lkm = net$lkm, ef = lefpc, speed = df,
          profile = profiles$PC_JUNE_2014[, 1])*365/1000000
## Average age of age is 11.17
## Number of age is 1946.95 * 10^3 veh
## 4097.62 kg emissions in 24 hours and 1 days
emilt <- emis(veh = age_ldv(net$hdv),lkm = net$lkm, 
          ef = leflt, speed = df,
         profile = profiles$HGV_JUNE_2014[, 1])*365/1000000
## Average age of age is 11.17
## Number of age is 148.12 * 10^3 veh
## 14612.67 kg emissions in 24 hours and 1 days

Then, once the emissions are estimated, we will speciate the emissions by euro standard. The estimation covered 24 hours, and for simplicity, we are assuming a year with 365 similar days and dividing by 1 million to have t/y.

df <- rowSums(apply(X = emipc, MARGIN = c(2,3), 
                    FUN = sum, na.rm = TRUE))
dfpc <-   rbind(speciate(df[1], "nox", "PC", "G", "V"),
                speciate(df[2:4], "nox", "PC", "G", "V"),
                speciate(df[5:11], "nox", "PC", "G", "V"),
                speciate(df[12:18], "nox", "PC", "G", "V"),
                speciate(df[19:23], "nox", "PC", "G", "V"),
                speciate(df[24:50], "nox", "PC", "G", "V"))
df <- rowSums(apply(X = emipc, MARGIN = c(2,3), 
                    FUN = sum, na.rm = TRUE))
dfhdv <-   rbind(speciate(df[1], "nox", "HDV", "D", "V"),
                 speciate(df[2:4], "nox", "HDV", "D", "V"),
                 speciate(df[5:11], "nox", "HDV", "D", "V"),
                 speciate(df[12:18], "nox", "HDV", "D", "V"),
                 speciate(df[19:23], "nox", "HDV", "D", "V"),
                 speciate(df[24:50], "nox", "HDV", "D", "V"))

df <- data.frame(rbind(sapply(dfpc, sum), sapply(dfhdv, sum)))
row.names(df) <- c("PC", "HGV")
knitr::kable(df, caption = "NO2 and NO speciation (t/y)")
Table 8.3: NO2 and NO speciation (t/y)
NO2 NO
PC 44.8689 1450.761
HGV 179.4756 1316.154

8.6 Volatile organic compounds: nmhc

nmhc was shown earlier in this chapter, but they can also be used to speciate objects, resulting in all species at once. The speciation of NMHC must be applied to NMHC. However, the emission guidelines of Ntziachristos and Samaras (2016) does not show specific emission factor of NMHC and the suggested procedure is subtract the \(CH_4\) from the \(HC\). I included this aspect in VEIN with ef_speed* functions to return NMHC directly.

library(vein)
data(net)
data(profiles)
propc <- profiles$PC_JUNE_2012[, 1] # 24 hours
pweek <- temp_fact(net$ldv+net$hdv, propc)
df <- netspeed(pweek, net$ps, net$ffs, net$capacity,
               net$lkm, alpha = 1)
euro <- c("V", rep("IV", 3),  
          rep("III", 7), rep("II", 7),
          rep("I", 5), rep("PRE", 27))
lefpc <- lapply(1:50, function(i) {
  ef_ldv_speed(v = "PC", t = "4S", cc = "<=1400", f = "G",
               eu = euro[i], p = "NMHC",
               show.equation = FALSE)})
euro <- c("V", rep("IV", 3), rep("III", 7),
          rep("II", 7), rep("I", 5), rep("PRE", 27))
lefpc <- lapply(1:length(euro), function(i) {
  ef_ldv_speed(v = "PC", t = "4S", cc = "<=1400", f = "G",
               eu = euro[i], p = "HC",
               show.equation = FALSE) })
enmhc <- emis(veh = age_ldv(net$ldv), lkm = net$lkm,
              ef = lefpc, speed = df,
              profile = profiles$PC_JUNE_2014[, 1])
## Average age of age is 11.17
## Number of age is 1946.95 * 10^3 veh
## 3030.65 kg emissions in 24 hours and 1 days
df_enmhc <- emis_post(arra = enmhc, veh = "PC",
                      size = "ALL", fuel = "G",
                      pollutant = "NMHC", by = "veh")
# assuming all euro I
spec <- speciate(x = as.numeric(df_enmhc$g),
                 spec = "nmhc", veh = "LDV",
                 fuel = "G", eu = "I")
names(spec)
##  [1] "ethane"                   "propane"                 
##  [3] "butane"                   "isobutane"               
##  [5] "pentane"                  "isopentane"              
##  [7] "hexane"                   "heptane"                 
##  [9] "octane"                   "TWO_methylhexane"        
## [11] "nonane"                   "TWO_methylheptane"       
## [13] "THREE_methylhexane"       "decane"                  
## [15] "THREE_methylheptane"      "alcanes_C10_C12"         
## [17] "alkanes_C13"              "cycloalcanes"            
## [19] "ethylene"                 "propylene"               
## [21] "propadiene"               "ONE_butene"              
## [23] "isobutene"                "TWO_butene"              
## [25] "ONE_3_butadiene"          "ONE_pentene"             
## [27] "TWO_pentene"              "ONE_hexene"              
## [29] "dimethylhexene"           "ONE_butine"              
## [31] "propine"                  "acetylene"               
## [33] "formaldehyde"             "acetaldehyde"            
## [35] "acrolein"                 "benzaldehyde"            
## [37] "crotonaldehyde"           "methacrolein"            
## [39] "butyraldehyde"            "isobutanaldehyde"        
## [41] "propionaldehyde"          "hexanal"                 
## [43] "i_valeraldehyde"          "valeraldehyde"           
## [45] "o_tolualdehyde"           "m_tolualdehyde"          
## [47] "p_tolualdehyde"           "acetone"                 
## [49] "methylethlketone"         "toluene"                 
## [51] "ethylbenzene"             "m_p_xylene"              
## [53] "o_xylene"                 "ONE_2_3_trimethylbenzene"
## [55] "ONE_2_4_trimethylbenzene" "ONE_3_5_trimethylbenzene"
## [57] "styrene"                  "benzene"                 
## [59] "C9"                       "C10"                     
## [61] "C13"

8.7 The speciation iag

The Carbon Bond Mechanism Z (1999) is a lumped-structure mechanism.

Citing:

**“it is currently unfeasible to treat the organic species individually in a regional or global chemistry model for three primary reasons:**

- (1) limited computational resources, - (2) lack of detailed speciated emissions inventories, and - (3) lack of kinetic and mechanistic information for all the species and their reaction products.

Thus there exists a need for a condensed mechanism that is capable of describing the tropospheric hydrocarbon chemistry with reasonable accuracy, sensitivity, and speed at regional to global scales.

The research in this aspect is intense and out of the scope of this book. However, it is vital that the user who intends to performs an atmospheric simulation to understand this why it is essential to speciate the emissions and what represent each group.

A popular air quality model nowadays is the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) (Grell et al. 2005) https://ruc.noaa.gov/wrf/wrf-chem/and the Emissions Guide (https://ruc.noaa.gov/wrf/wrf-chem/Emission_guide.pdf).

The speciation iag splits the NMHC into the lumped groups for the mechanism CMB-Z. The name iag comes from the Institute of Astronomy, Geophysics and Atmospheric Sciences (IAG, http://www.iag.usp.br/) from the University of São Paulo (USP). The Department of Atmospheric Sciences (DAC) of IAG has measure and model air quality models for several years. There are several scientific productions such as: Nogueira et al. (2015), Hoshyaripour et al. (2016), Andrade et al. (2017), Freitas et al. (2005), Martins et al. (2006), Pérez-Martinez et al. (2014), Ulke and Andrade (2001), Vivanco and Fátima Andrade (2006), Boian and Andrade (2012), Fatima Andrade et al. (2012), Andrade et al. (2015), Vara-Vela et al. (2016), Rafee (2015), Ibarra-Espinosa et al. (2017). The following figure shows an air quality simulation over South East Brazil from DAC/IAG/USP.

http://www.lapat.iag.usp.br/aerossol/wrf9/index.php

Figure 8.4: http://www.lapat.iag.usp.br/aerossol/wrf9/index.php

Some of these papers show measurements in tunnels and fuel. The speciation iag it is based on these experiments, and the last versions, covers an update during 2015 so that the exhaust emissions speciation is more representative of Brazilian gasoline, which means?

** THE SPECIATION IAG IS BASED ON BRAZILIAN MEASUREMENTS**

Moreover, the user who wants to apply it outside Brazil should be ensured, that the fleet and fuel are compatible. As that will be hard to accomplish, I can say that this speciation is for and to be used in Brazil, even more, in São Paulo.

The currently speciation iag is:

Table 8.4: Speciation for NMHC by type of fuel and process mol/100g (Ibarra-Espinosa, S., 2017)
NMHC GEX GEV GLIQ EEX EEV ELIQ DEX
e_eth 0.23 0.02 0.00 0.00 0.00 0.00 0.00
e_hc3 0.36 0.24 0.02 0.00 0.00 0.00 0.05
e_hc5 0.13 0.45 0.16 0.00 0.00 0.00 0.06
e_hc8 0.06 0.00 0.19 0.05 0.00 0.00 0.30
e_ol2 0.48 0.04 0.00 0.13 0.00 0.00 0.32
e_olt 0.20 0.20 0.08 0.00 0.00 0.00 0.39
e_oli 0.13 0.46 0.18 0.00 0.00 0.00 0.00
e_iso 0.01 0.00 0.00 0.00 0.00 0.00 0.00
e_tol 0.12 0.08 0.05 0.01 0.00 0.00 0.24
e_xyl 0.22 0.00 0.12 0.01 0.00 0.00 0.01
e_c2h5oh 0.31 0.35 0.61 1.52 2.17 2.17 0.00
e_hcho 0.11 0.00 0.00 0.13 0.00 0.00 0.32

Where G is the gasohol, or gasoline blended with 25% of Ethanol, E is 100% ethanol and D is diesel with 5% of biodiesel. EX means exhaust, EV are the evaporative emissions and LIQ the liquid emissions.

The arguments of the function speciate take the following form:

  • x: Emissions estimation, it is recommended that x are griddded emissions from NMHC.
  • spec: iag. Alternatively, you can put any name of the following: “e_eth”, “e_hc3”, “e_hc5”, “e_hc8”, “e_ol2”, “e_olt”, “e_oli”, “e_iso”, “e_tol”, “e_xyl”, “e_c2h5oh”, “e_hcho”, “e_ch3oh”, “e_ket”
  • veh: When spec is “iag” veh can take two values depending: when the speciation is for vehicles veh accepts “veh”, eu “Evaporative”, “Liquid” or“Exhaust”
  • fuel: “G”, “E” or “D”.
  • eu: “Exhaust” “Evaporative” or “Liquid”.
  • show: Let this FALSE.
  • list: Let this TRUE so that the result is a list, and each element of the list a lumped specie.

Example:

# Do not run
x <- data.frame(x = rnorm(n = 100, mean = 400, sd = 2))
dfa <- speciate(x, spec = "e_eth", veh = "veh",
                fuel = "G", eu = "Exhaust")
head(dfa)
##       e_eth
## 1 0.9259756
## 2 0.9259793
## 3 0.9288054
## 4 0.9270968
## 5 0.9202770
## 6 0.9375046
df <- speciate(x, spec = "iag", veh = "veh", fuel = "G",
               eu = "Exhaust", list = TRUE)
# names(df)
names(df)[1]
## [1] "e_eth"
df[[1]][1,]
## 0.9259756 [mol/h]

8.8 The speciation pmiag

The speciation pmiagis also based on IAG studies and applied only for PM2.5 emissions. The speciation splits the PM2.5 in percentages as follow:

Table 8.5: Speciation of PM2.5
E_SO4i 0.00770
E_SO4j 0.06230
E_NO3i 0.00247
E_NO3j 0.01053
E_MP2.5i 0.10000
E_MP2.5j 0.30000
E_ORGi 0.03040
E_ORGj 0.12960
E_ECi 0.05600
E_ECj 0.02400

When running this speciation, the only two required arguments are x and spec. Also, There is a message that this speciation applies only in gridded emissions, because the unit must be in flux \(g/km^2/h\), and internally are transformed to the required unit $ g/m^2/s$ as:

\[ \frac{g}{(km)^2*h}*\frac{10^6 \mu g}{g}*(\frac{km}{1000m})^2*\frac{h}{3600s}\frac{1}{dx^2}\]

dx is the length of the grid cell. Example:

library(vein)
data(net)
pm <- data.frame(pm = rnorm(n = length(net),
                            mean = 400, sd = 2))
net@data <- pm
g <- make_grid(net, 1/102.47/2) # 500 m
## Number of lon points: 23
## Number of lat points: 19
gx <- emis_grid(net, g)
## Sum of street emissions 601957.37
## Sum of gridded emissions 601957.37
df <- speciate(gx, spec = "pmiag", veh = "veh", fuel = "G",
               eu = "Exhaust", list = FALSE, dx = 0.5)
## For emissions grid only, emissions must be in g/(Xkm^2)/h
## PM.2.5-10 must be calculated as substraction of PM10-PM2.5 to enter this variable into WRF

Uncomment to see more details.

# names(df)
# head(df)
df[1, 1:6]
##   e_so4i e_so4j e_no3i e_no3j e_pm2.5i e_pm2.5j
## 1 0.0014 0.0116  5e-04  0.002   0.0186   0.0559

References

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Ntziachristos, L, and P Boulter. 2009. “EMEP/Eea Emission Inventory Guidebook; Road Transport: Automobile Tyre and Break Wear and Road Abrasion.” European Environment Agency, Copenhagen.

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