Question
What are the average emission factors of cars and other vehicles on air pollutants? Air pollutants include PM2.5 and CO2. Emission factors are expressed as g emission per km driven. Factors are given separately for different car types.
Answer
Rationale
Data
Data comes from Lipasto [1] and reflects the 2016 situation. (accessed 11.12.2018). Numbers are for combined street+road (assuming 27 % street).
Emission factors for road transport(g/km)Obs | Vehicle | PM2.5 | CO2 | CO2eq | Description |
---|
1 | bus | 0.11 | 998 | 1014 | Half-full diesel bus on streets |
2 | minibus | 0.1 | 806 | 816 | An empty diesel bus on streets |
3 | gasoline car | 0.0026 | 159 | 159 | |
4 | gasoline hybrid | 0.00208 | 127.2 | 127.2 | Assumes 20 % better energy efficiency |
5 | hybrid with gasoline | 0.00104 | 63.6 | 63.6 | Assumes 40 % gasoline and zero emissions from electricity |
6 | diesel car | 0.023 | 139 | 141 | |
7 | diesel hybrid | 0.0184 | 111.2 | 112.8 | Assumes 20 % better energy efficiency |
8 | hybrid with diesel | 0.0092 | 55.6 | 56.4 | Assumes 40 % diesel and zero emissions from electricity |
9 | electric car | 0.000684 | 36.252 | 36.252 | Assumes 0.19 kWh/km electricity from national grid. See Emission factors for burning processes. c(1,53000) * (1/1000)/(1/3.6)*0.19 # mg/MJ-->g/km |
10 | gasoline CNG car | 0.0014 | 66.6 | 70.1 | Assumes 100 % gas |
11 | gasoline ethanol car | 0.0014 | 30 | 30 | Assumes 100 % flexifuel |
12 | other car | 0.0026 | 159 | 159 | Assumes gasoline car |
13 | Bike | -0.0013 | -80 | -80 | Assumes compensatory reduction in car driving (50 % of gasoline car emission) |
14 | vans | 0.070278554 | 180.64732 | 183.48244 | Indirectly calculated from Lipasto |
15 | buses | 0.044720934 | 1093.78679 | 1102.20248 | Indirectly calculated from Lipasto |
16 | trucks | 0.081325425 | 1222.68957 | 1229.59117 | Indirectly calculated from Lipasto |
17 | motorcycles | 0.027740079 | 106.64706 | 111.74124 | Indirectly calculated from Lipasto |
18 | mopeds | 0.031830000 | 60.82314 | 61.90407 | Indirectly calculated from Lipasto |
19 | microcars | 0.027520000 | 92.78745 | 93.72712 | Indirectly calculated from Lipasto |
Fine particle and carbon dioxide unit emissions for average vehicles. Fine particle emissions are taken from the Lipasto model[1] using average (mixed gasoline and diesel) values for personal car and diesel EURO3 (applied since 2000) values for composite vahicles. For CO2, typical emissions of a new car were used based on the Finnish Vehicle Administration AKE.[2] The following vehicles are used as typical examples of the class:
8-seat vehicle: Toyota Hiace 2.5 D4D 100 4 door long DX bus
4-seat vehicle: Toyota Corolla 2.0 90 D4D Linea Terra 5 door Hatchback (diesel)
Car: Toyota Corolla 1.6 VVT-i Linea Terra 5ov Hatchback (gasoline)[3]
Relative emission decrease rate(%/a)Obs | Vehicle | PM2.5 | CO2 | CO2eq | Description |
---|
1 | bus | 5 | 0.5 | 0.5 | Guesstimates based on historical Lipasto numbers |
2 | minibus | 5 | 0.5 | 0.5 | Guesstimates based on historical Lipasto numbers |
3 | gasoline car | 5 | 0.5 | 0.5 | Guesstimates based on historical Lipasto numbers |
4 | gasoline hybrid | 5 | 0.5 | 0.5 | Guesstimates based on historical Lipasto numbers |
5 | hybrid with gasoline | 5 | 0.5 | 0.5 | Guesstimates based on historical Lipasto numbers |
6 | diesel car | 5 | 0.5 | 0.5 | Guesstimates based on historical Lipasto numbers |
7 | diesel hybrid | 5 | 0.5 | 0.5 | Guesstimates based on historical Lipasto numbers |
8 | hybrid with diesel | 5 | 0.5 | 0.5 | Guesstimates based on historical Lipasto numbers |
9 | electric car | 5 | 0.5 | 0.5 | Guesstimates based on historical Lipasto numbers |
10 | gasoline CNG car | 5 | 0.5 | 0.5 | Guesstimates based on historical Lipasto numbers |
11 | gasoline ethanol car | 5 | 0.5 | 0.5 | Guesstimates based on historical Lipasto numbers |
12 | other car | 5 | 0.5 | 0.5 | Guesstimates based on historical Lipasto numbers |
13 | Bike | 5 | 0.5 | 0.5 | Guesstimates based on historical Lipasto numbers |
Calculations
+ Show code- Hide code
# This is code Op_en2989/emissionFactor on page [[Emission factors of cars on air pollutants]].
library(OpasnetUtils)
library(ggplot2)
library(reshape2)
objects.latest("Op_en7925",code_name="preprocess") # [[:File:Finnish road traffic emissions by Liisa.zip]] emissionRT
ggplot(emissionRT, aes(x=Consumption, y=CO2,colour=Location))+geom_point()+
facet_wrap(~Vehicle, scales="free")
# coord_cartesian(xlim=c(0,500),ylim=c(0,10))
emissionRT$EF.HC <- emissionRT$HC / emissionRT$Consumption
ggplot(emissionRT, aes(x = Vehicle, y = EF.HC, colour=Location))+geom_point()
# Units: CO [t], HC [t], NOx [t], PM [t], CH4 [t], N2O [t], SO2 [t],
# CO2 [t], CO2eq [t], Consumption [t], Energy [TJ], Mileage [Mkm]
dat <- melt(
emissionRT,
id.vars=c("Municipality","Source","Vehicle","Location","EF.HC","Consumption"),
variable.name = "Pollutant",
value.name = "Result"
)
dat <- dat[!(dat$Vehicle=="vans" & dat$Location=="street" & dat$Pollutant=="HC" & dat$EF.HC>0.007),]
reg <- list()
for(i in unique(dat$Vehicle)) {
for(j in unique(dat$Location)) {
for(k in unique(dat$Pollutant)) {
tmpdat <- dat[dat$Vehicle==i & dat$Location==j & dat$Pollutant==k,]
if(nrow(tmpdat)>0) {
tmp <- lm(Result ~ Consumption, data = tmpdat)
reg <- rbind(
reg,
cbind(
Vehicle = i,
Location = j,
Pollutant = k,
as.data.frame(summary(tmp)[[4]])
)
)
}
}
}
}
#Vehicle Estimate Std. Error t value Pr(>|t|)
#(Intercept) private cars 4.100480e+01 1.660571e+00 2.469319e+01 6.604968e-93
#Consumption private cars 4.039917e-02 2.289063e-04 1.764878e+02 0.000000e+00
#(Intercept)1 vans 1.777582e-05 2.382803e-04 7.460046e-02 9.405580e-01
#Consumption1 vans 4.269940e-02 2.063648e-07 2.069123e+05 0.000000e+00
#(Intercept)2 buses 4.038413e-01 1.960740e-02 2.059637e+01 2.250162e-71
#Consumption2 buses 4.282124e-02 2.060304e-05 2.078395e+03 0.000000e+00
#(Intercept)3 trucks 4.321900e-01 1.517886e-02 2.847315e+01 9.705433e-113
#Consumption3 trucks 4.266974e-02 4.031930e-06 1.058296e+04 0.000000e+00
#(Intercept)4 motorcycles -3.309552e-15 1.259225e-15 -2.628245e+00 9.035057e-03
#Consumption4 motorcycles 4.142677e-02 3.941630e-18 1.051006e+16 0.000000e+00
#(Intercept)5 mopeds -4.136940e-16 2.328126e-16 -1.776940e+00 7.661557e-02
#Consumption5 mopeds 4.142677e-02 4.417103e-18 9.378721e+15 0.000000e+00
#(Intercept)6 microcars -1.292794e-15 1.457366e-16 -8.870755e+00 7.319877e-17
#Consumption6 microcars 4.335997e-02 5.737516e-18 7.557272e+15 0.000000e+00
# Conclusions:
# Consumption = constant * Energy within Vehicle groups
# BUT: Strange small intercepts: for private cars and buses they are fairly large causing deviation in the relation.
# For trucks makes slight variation. Therefore, we just drop the intercept and use the constant.
# Standard errors are so small that they are meaningless
# HC: vans on streets have two different emission factors. The larger is only on small
# municipalities. The larger is less likely, so the smaller is used.
# CO2 and all other pollutants = constant * Consumption
tmp <- reg[grepl("Consumption",rownames(reg)),1:4]
colnames(tmp)[4] <- "Result"
emissionFactor <- Ovariable(
"emissionFactor",
data = tmp[!tmp$Pollutant %in% c("Energy","Mileage"),]
)
emissionFactor@meta$unit <- "ton/ton"
energyFactor <- Ovariable(
"energyFactor",
data = tmp[tmp$Pollutant == "Energy", colnames(tmp) != "Pollutant"]
)
energyFactor@meta$unit <- "TJ/ton"
mileageFactor <- Ovariable(
"mileageFactor",
data = tmp[tmp$Pollutant == "Mileage", colnames(tmp) != "Pollutant"]
)
mileageFactor@meta$unit <- "Gm/ton"
objects.store(emissionFactor, energyFactor, mileageFactor)
cat("Ovariables emissionFactor, energyFactor, mileageFactor stored.\n")
emissionFactor <- EvalOutput(emissionFactor)
ggplot(emissionFactor@output, aes(x=Vehicle, y=emissionFactorResult, colour=Location))+
geom_point()+
facet_wrap(~ Pollutant, scales="free_y")+
theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0))
energyFactor <- EvalOutput(energyFactor)
mileageFactor <- EvalOutput(mileageFactor)
| |
See also
References
- ↑ Lipasto
- ↑ Autorekisterikeskus AKE: Uuden auton kulutustiedot. EKOAKE, huhtikuu 2003.
- ↑ Pääkaupunkiseudun julkaisusarja B1999: 5. Vaihtoehtoisten polttoaineiden käyttömahdollisuudet joukkoliikentessä Pääkaupunkiseudulla. Taulukko 3, Keskusta ja esikaupunki.