This package provides tools to create emissions (with a focus on vehicular emissions) for use in numeric air quality models such as WRF-Chem.
EmissV import functions from ncdf4 for reading model information, raster and sf to process grinded/geographic information and units. These packages need some aditional libraries:
The following steps are required for installation on Ubuntu:
sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable --yes
sudo apt-get --yes --force-yes update -qq
# netcdf dependencies:
sudo apt-get install --yes libnetcdf-dev netcdf-bin
# units/udunits2 dependency:
sudo apt-get install --yes libudunits2-dev
# sf dependencies (without libudunits2-dev):
sudo apt-get install --yes libgdal-dev libgeos-dev libproj-devThe following steps are required for installation on Fedora:
sudo dnf update
# netcdf dependencies:
sudo yum install netcdf-devel
# units/udunits2 dependency:
sudo yum install udunits2-devel
# sf dependencies (without libudunits2-dev):
sudo yum install gdal-devel proj-devel proj-epsg proj-nad geos-develNo additional steps for windows installation.
Detailed instructions can be found at netcdf, libudunits2-dev and sf developers page.
To conda (miniconda / anaconda)
First create a new environment called rspatial (or a better name):
conda create -n rspatial -y
conda activate rspatialand to install some requisites:
conda install -c conda-forge r-sf -y
conda install -c conda-forge r-rgdal -y
conda install -c conda-forge r-lwgeom -y
conda install -c conda-forge r-raster -yTo install the CRAN version (0.665.5.2):
install.packages("EmissV")To install the development version (0.665.5.3) using remotes:
require("remotes")
remotes::install_github("atmoschem/EmissV")or to install the development version (0.665.5.3) using devtools:
require("devtools")
devtools::install_github("atmoschem/EmissV")EmissV can be used to process emissions of atmospheric pollutants and green house gases from inventories such as EDGAR, RCP, GAINS and other datasets in NetCDF format, the GEIA-ACCENT and ECCAD emission data portal makes available some of these inventories. You can verify the supported format with:
EmissV::read()To generate a simple emission it's a straightforward process in 4 steps:
library(EmissV)
### 1. download the EDGAR Netcdf using the function get_edgar from the eixport R-package
### or from the http://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/EDGAR/datasets/v50_AP/
### EDGAR 5.0 website and unzip inside a temporary directory
# create the temporary directory to download the data
dir.create(file.path(tempdir(), "EDGAR"))
# download the total emissions of NOx from EDGAR v50_AP for 2015
eixport::get_edgar(dataset = "v50_AP",
pol = 'NOx',
sector = "TOTALS",
year = 2015,
type = 'nc', ask = FALSE, copyright = FALSE,
destpath = file.path(tempdir(), "EDGAR"))
# unzip the file
unzip(zipfile = paste0(file.path(tempdir(), "EDGAR"),'/v50_NOx_2015.0.1x0.1.zip'),
exdir = paste0(file.path(tempdir(), "EDGAR")))
### 2. read the emissions (using the spec argument to split NOx into NO and NO2)
NOx <- read(paste0(file.path(tempdir(), "EDGAR"),'/v50_NOx_2015.0.1x0.1.nc'),
version = 'EDGAR',
spec = c(E_NO = 0.9 , # optional, 90% of NOx used to NO
E_NO2 = 0.1 )) # optional, 10% of NOx uset to NO2
### 3. get the information from a WRF grid from a initial conditions file (wrfinput)
g <- gridInfo(paste(system.file("extdata", package = "EmissV"),"/wrfinput_d01",sep=""))
### 4. calculate the emissions for grid g
NO <- emission(grid = g, inventory = NOx$E_NO, pol = "NO", mm = 30.01, plot = T)
NO2 <- emission(grid = g, inventory = NOx$E_NO2,pol = "NO2",mm = 46.0055, plot = T)The next step is to save the emission in a emission file, the next example show how to save emissions using the eixport R-package:
library(eixport)
### create a temporary folder for emissions
dir.create(file.path(tempdir(), "EMISSION"))
### create the emision file
wrf_create(wrfinput_dir = system.file("extdata", package = "EmissV"),
wrfchemi_dir = file.path(tempdir(), "EMISSION"),
domains = 1)
### get the file path of the emission file
emis_file <- list.files(path = file.path(tempdir(), "EMISSION"),
pattern = "wrfchemi_d01",
full.names = TRUE)
### save the emission
wrf_put(NO, file = emis_file, name = "E_NO", verbose = TRUE)
wrf_put(NO2, file = emis_file, name = "E_NO2", verbose = TRUE)Check the wrf_create, wrf_put and to_wrf to more information and customize for your application.
NOTE: The emission file must be compatible with the WRF-Chem options (many arguments are the same as the namelist.input from WRF) check the eixport R-Package documentation and the WRF-Chem manual for more information.
Other R-packages are available to write netcdf such as ncdf4, RNetCDF, tidync are available on CRAN. Other languages such as NCL leanguage and the Python package wrf-python, and preprocessor anthro_emiss are aternative to write NetCDF files.
In EmissV the vehicular emissions are estimated by a top-down approach, i.e. the emissions are calculated using the statistical description of the fleet at avaliable level (National, Estadual, City, etc).The following steps show an example workflow for calculating vehicular emissions, these emissions are initially temporally and spatially disaggregated, and then distributed spatially and temporally.
I. Total: emission of pollutants is estimated from the fleet, use and emission factors and for the interest area (cities, states, countries, etc).
library(EmissV)
fleet <- vehicles(example = T)
# using a example of vehicles (DETRAN 2016 data and SP vahicle distribution):
# Category Type Fuel Use SP ...
# Light Duty Vehicles Gasohol LDV_E25 LDV E25 41 km/d 11624342 ...
# Light Duty Vehicles Ethanol LDV_E100 LDV E100 41 km/d 874627 ...
# Light Duty Vehicles Flex LDV_F LDV FLEX 41 km/d 9845022 ...
# Diesel Trucks TRUCKS_B5 TRUCKS B5 110 km/d 710634 ...
# Diesel Urban Busses CBUS_B5 BUS B5 165 km/d 792630 ...
# Diesel Intercity Busses MBUS_B5 BUS B5 165 km/d 21865 ...
# Gasohol Motorcycles MOTO_E25 MOTO E25 140 km/d 3227921 ...
# Flex Motorcycles MOTO_F MOTO FLEX 140 km/d 235056 ...
fleet <- fleet[,c(-6,-8,-9)] # dropping RJ, PR and SC
EF <- emissionFactor(example = T)
# using a example emission factor (values calculated from CETESB 2015):
# CO PM
# Light duty Vehicles Gasohol 1.75 g/km 0.0013 g/km
# Light Duty Vehicles Ethanol 10.04 g/km 0.0000 g/km
# Light Duty Vehicles Flex 0.39 g/km 0.0010 g/km
# Diesel trucks 0.45 g/km 0.0612 g/km
# Diesel urban busses 0.77 g/km 0.1052 g/km
# Diesel intercity busses 1.48 g/km 0.1693 g/km
# Gasohol motorcycles 1.61 g/km 0.0000 g/km
# Flex motorcycles 0.75 g/km 0.0000 g/km
TOTAL <- totalEmission(fleet,EF,pol = c("CO"),verbose = T)
# Total of CO : 1128297.0993334 t year-1II. Spatial distribution: The package has functions to read information from tables, georeferenced images (tiff), shapefiles (sh), OpenStreet maps (osm), global inventories in NetCDF format (nc) to calculate point, line and area sources.
raster <- raster::raster(paste(system.file("extdata", package = "EmissV"),
"/dmsp.tiff",sep=""))
grid <- gridInfo(paste(system.file("extdata", package = "EmissV"),
"/wrfinput_d02",sep=""))
# Grid information from: .../EmissV/extdata/wrfinput_d02
shape <- raster::shapefile(paste(system.file("extdata", package = "EmissV"),
"/BR.shp",sep=""),verbose = F)[12,1]
Minas_Gerais <- areaSource(shape,raster,grid,name = "Minas Gerais")
# processing Minas Gerais area ...
# fraction of Minas Gerais area inside the domain = 0.0145921494236101
shape <- raster::shapefile(paste(system.file("extdata", package = "EmissV"),
"/BR.shp",sep=""),verbose = F)[22,1]
Sao_Paulo <- areaSource(shape,raster,grid,name = "Sao Paulo")
# processing Sao Paulo area ...
# fraction of Sao Paulo area inside the domain = 0.474658563750987
sp::spplot(raster::merge(drop_units(TOTAL$CO[[1]]) * Sao_Paulo,
drop_units(TOTAL$CO[[2]]) * Minas_Gerais),
scales = list(draw=TRUE),ylab="Lat",xlab="Lon",
main=list(label="Emissions of CO [g/d]"),
col.regions = c("#031638","#001E48","#002756","#003062",
"#003A6E","#004579","#005084","#005C8E",
"#006897","#0074A1","#0081AA","#008FB3",
"#009EBD","#00AFC8","#00C2D6","#00E3F0"))
III. Emission calculation: calculate the final emission from all different sources and converts to model units and resolution.
CO_emissions <- emission(total = TOTAL,
pol = "CO",
area = list(SP = Sao_Paulo, MG = Minas_Gerais),
grid = grid,
mm = 28,
plot = T)
# calculating emissions for CO using molar mass = 28 ...
IV. Temporal distribution: the package has a set of hourly profiles that represent the mean activity for each day of the week calculated from traffic counts of toll stations located in São Paulo city.
data(perfil)
names(perfil)The package has additional functions for read netcdf data, create line and point sources (with plume rise) and to estimate the total emissions of of volatile organic compounds from exhaust (through the exhaust pipe), liquid (carter and evaporative) and vapor (fuel transfer operations).
Functions:
- read: read global inventories in netcdf format
- vehicles: tool to set-up vehicle data.table
- emissionFactor: tool to set-up emission factors data.table
- gridInfo: read grid information from a NetCDF file
- pointSource: emissions from point sources
- plumeRise: calculate plume rise
- rasterSource: distribution of emissions by a georeferenced image
- lineSource: distribution of emissions by line vectors
- areaSource: distribution of emissions by region
- totalEmission: total emissions
- emission: Emissions to atmospheric models
- speciation: Speciation of emissions in different compounds
Sample datasets:
- Species: species mapping tables
- Perfil: vehicle counting profile for vehicular activity
- Sample of an image of persistent lights of the Defense Meteorological Satellite Program (DMSP)
- CETESB 2015 emission factors as
emissionFactor(example=T) - DETRAN 2016 data and SP vahicle distribution as
vehicles(example=T) - Shapefiles for Brazil states
Bug reports, suggestions, and code contributions are all welcome. Please see CONTRIBUTING.md for details. Note that this project adopt the Contributor Code of Conduct and by participating in this project you agree to abide by its terms.
EmissV is published under the terms of the MIT License. Copyright (c) 2018 Daniel Schuch.
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