README.md

19 de Octubre de 2016

VEIN

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What is VEIN?

Vehicular Emissions INventories (VEIN). An R package to estimate vehicular emissions. It currently covers the following pollutants in speed functions:

European emission factors for all available vehicle categories exhaust:

European emission factors speciation for evapoative emissions:

Brazilian emission factors for all available vehicle categories:

Brazilian speciation based on IAG/USP (Fátima’s group) studies:

Base emission factors from International Emission Model (IVE) for all available vehicle categories:

Emission factors from Chinese emission guidelines

System requirements

vein imports functions from spatial packages listed below. In order to install these packages, firstly the user must install the requirements mentioned here.

Packages needed

After installing system dependencies, you will need these packages:

In order to run the demo, this package is also needed:

Installation

VEIN can be installed via CRAN or github

library(devtools) 
install_github("atmoschem/vein")
library(vein)
demo(VEIN)

or

install.packages("vein")
library(vein)
demo(VEIN)

What is new?

future steps:

How does it work?

VEIN consist of: "Elaboration of vehicular emissions inventories, consisting in four stages:

  1. pre-processing activity data,
  2. preparing emissions factors,
  3. estimating the emissions and
  4. post-processing of emissions in maps and databases."

This implies the use of several functions in a coordinates ans structured way, therefore it is added the new function inventory which creates a structured set of directories and scripts to run VEIN. Please, open the file ‘main.R’ and run each line to understand VEIN. Remember, if you have doubts with any function, just type ‘?’ with the name of the function. For intance: ?inventory.

Using inventory

library(vein)
inventory(name = file.path(tempdir(), "YourCity"), 
          vehcomp = c(PC = 1, LCV = 1, HGV = 1, BUS = 1, MC = 1),
          show.dir = T, 
          show.scripts = T)
files at /tmp/RtmpEorTs8/YourCity
Directories:
 [1] "/tmp/RtmpEorTs8/YourCity"              
 [2] "/tmp/RtmpEorTs8/YourCity/ef"           
 [3] "/tmp/RtmpEorTs8/YourCity/emi"          
 [4] "/tmp/RtmpEorTs8/YourCity/emi/BUS_01"  
 [5] "/tmp/RtmpEorTs8/YourCity/emi/HGV_01"   
 [6] "/tmp/RtmpEorTs8/YourCity/emi/LCV_01"  
 [7] "/tmp/RtmpEorTs8/YourCity/emi/MC_01"    
 [8] "/tmp/RtmpEorTs8/YourCity/emi/PC_01"   
 [9] "/tmp/RtmpEorTs8/YourCity/est"          
 [10] "/tmp/RtmpEorTs8/YourCity/images"       
 [11] "/tmp/RtmpEorTs8/YourCity/network"      
 [12] "/tmp/RtmpEorTs8/YourCity/post"        
 [13] "/tmp/RtmpEorTs8/YourCity/post/df"      
 [14] "/tmp/RtmpEorTs8/YourCity/post/grids"   
 [15] "/tmp/RtmpEorTs8/YourCity/post/streets" 
 [16] "/tmp/RtmpEorTs8/YourCity/profiles"    
 [17] "/tmp/RtmpEorTs8/YourCity/veh"         
Scripts:
 [1] "est/BUS_01_input.R" 
 [2] "est/HGV_01_input.R" 
 [3] "est/LCV_01_input.R" 
 [4] "est/MC_01_input.R"  
 [5] "est/PC_01_input.R"  
 [6] "main.R"             
 [7] "post.R"            
 [8] "traffic.R"

Please, read the examples in the documentation of each function and run the demo.

1) Examples with traffic data:

  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 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.
data("net")
PC_E25_1400 <- age_ldv(x = net$ldv, name = "PC_E25_1400")
plot(PC_E25_1400, xlab = "age of use")

If you want to know the vehicles per street and by age of use, just add the net. Age functions now returns ‘sf’ objects if the net argument is present.

PC_E25_1400net <- age_ldv(x = net$ldv, name = "PC_E25_1400", net = net)
plot(PC_E25_1400net)

temporal factors and netspeed

data("net")
data("pc_profile")
pc_week <- temp_fact(net$ldv+net$hdv, pc_profile)
dfspeed <- netspeed(pc_week, net$ps, net$ffs, net$capacity, net$lkm, alpha = 1.5)
plot(dfspeed)

If you want ot check the speed at different hours by street, just add net:

dfspeednet <- netspeed(pc_week, net$ps, net$ffs, net$capacity, net$lkm,
                       alpha = 1.5, net = net)
sp::spplot(as(dfspeednet, "Spatial"),
       c("S1", "S9"), scales = list(Draw = T),
       col.regions = rev(cptcity::cpt()))

### 2) Emission Factors

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)

3) Estimation of emissions

euro <- c(rep("V", 5), rep("IV", 5), rep("III", 5), rep("II", 5),
          rep("I", 5), rep("PRE", 15))
lef <- lapply(1:40, function(i) {
ef_ldv_speed(v = "PC", t = "4S", cc = "<=1400", f = "G",
          eu = euro[i], p = "CO", show.equation = FALSE) })
E_CO <- emis(veh = PC_E25_1400, lkm = net$lkm, ef = lef, speed = dfspeed,
             profile = pc_profile)

4) Post Emissions

E_CO_DF <- emis_post(arra = E_CO,  veh = "PC", size = "<1400", fuel = "G",
pollutant = "CO", by = "veh")
E_CO_STREETS <- emis_post(arra = E_CO, pollutant = "CO", by = "streets_wide")

Grids

You can use this function in two ways

when spobj is “character”, it is a path to wrfinput file and then runs eixport::wrf_grid to create a grid based on a wrf_input file.

1) Create a grid using make_grid.The spobj is the spatial net. The size of the grid has the size of the net. You have to specify the grid spacing. 2) Create a grid using a path to wrfinput file instead a net. The grid will have the size of the wrf_input. You don’t have to specify the grid spacing.

data(net)
E_CO_STREETSnet <- emis_post(arra = E_CO, pollutant = "CO", by = "streets_wide",
                             net = net)
g <- make_grid(net, 1/102.47/2) #500m in degrees
E_CO_g <- emis_grid(spobj = E_CO_STREETSnet, g = g, sr= 31983)
plot(E_CO_g["V9"], axes = T, pal = cptcity::cpt(colorRampPalette = T, rev = T),
main = "CO [g/h] emissions road transportation in Sao Paulo, Brazil")

At this step, you can feed you grid with emissions from other sources!

Creating a WRFChem Input file using eixport:

  1. Create a grid using make_grid and a wrfinput file
  2. Run emis_grid to grid your emissions.
  3. Create a GriddedEmissionsArray.
  4. Create a wrfchem input file [eixport::wrf_create](https://atmoschem.github.io/eixport/reference/wrf_create.html.
  5. Put the GriddedEmissionsArray into the wrf chem input file using eixport::wrf_put.
library(eixport)
dir.create(file.path(tempdir(), "EMISS"))
wrf_create(wrfinput_dir         = system.file("extdata", package = "eixport"),
           wrfchemi_dir         = file.path(tempdir(), "EMISS"),
           domains              = 2,
           frames_per_auxinput5 = 1, #hours
           auxinput5_interval_m = 60,
           verbose              = TRUE)
path_to_wrfi <- paste0(system.file("extdata", package = "eixport"), "/wrfinput_d02")
path_to_wrfc <- list.files(file.path(tempdir(), "EMISS"), full.names = TRUE)[1]
gwrf <- eixport::wrf_grid(path_to_wrfi)
E_CO_gwrf <- emis_grid(spobj = E_CO_STREETSnet, g = gwrf)
gr <- GriddedEmissionsArray(E_CO_gwrf, rows = 51, cols = 63, times = 1)
eixport::wrf_put(file = path_to_wrfc, name = "E_CO", POL = gr)

Creating a WRFChem Input file using AS4WRF

  1. Create a grid using make_grid and your net.
  2. Run emis_grid to grid your emissions.
  3. Run eixport::to_as4wrf to create a data.frame the specifications for AS4WRF.ncl.
  4. Export data.frame to a text.file. Recall that AS4WRF requires all the lumped species.
  5. Contact the developer of AS4WRF Angel Vara [email protected] to get a copy and run AS4WRF.ncl.

Thanks and enjoy VEIN!

Citation

If you use VEIN, please, cite it (BIBTEX, ENDNOTE):

Ibarra-Espinosa, S., Ynoue, R., O’Sullivan, S., Pebesma, E., Andrade, M. D. F., and Osses, M.: VEIN v0.2.2: an R package for bottom-up vehicular emissions inventories, Geosci. Model Dev., 11, 2209-2229, https://doi.org/10.5194/gmd-11-2209-2018, 2018.

@article{gmd-11-2209-2018,
author = {Ibarra-Espinosa, S. and Ynoue, R. and O'Sullivan, S. and Pebesma, E. and Andrade, M. D. F. and Osses, M.},
title = {VEIN v0.2.2: an R package for bottom--up vehicular emissions inventories},
journal = {Geoscientific Model Development},
volume = {11},
year = {2018},
number = {6},
pages = {2209--2229},
url = {https://www.geosci-model-dev.net/11/2209/2018/},
doi = {10.5194/gmd-11-2209-2018}
}

Communications, doubts etc

Issues

If you encounter any issues while using VEIN, please submit your issues to: https://github.com/atmoschem/vein/issues/

If you have any suggestions just let me know to [email protected].

Contributing

Please, read this guide. Contributions of all sorts are welcome, issues and pull requests are the preferred ways of sharing them. When contributing pull requests, please follow the Google’s R Style Guide. This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

Note for non-english and anaconda users

Sometimes you need to install R and all dependencies and a way for doing that is using anaconda. Well, as my system is in portuguese, after installing R using anaconda it changed the decimal character to ‘,’. In order to change it back to english meaning decimal separator as ‘.’, I added this variable into the .bashrc

nano ~/.bashrc
export Lang=C

More

More details on StackOverflow

You can learn more about VEIN reading the documentation in PDF, online, reading the book online, or buy it in Kindle or Paperback



ibarraespinosa/vein documentation built on Jan. 26, 2020, 9:15 p.m.