Earth Science Asked on December 15, 2020
MUNICH is the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) (Kim et al., 2018) and VEIN is the Vehicular Emissions INventory model (Ibarra-Espinosa et al, 2018), an R package.
VEIN estimates vehicular emissions at street level, generating useful inputs for the MUNICH model, however, some pre-processing is necessary. Therefore, the model eixport was created to help in this processing.
As a maintainer I receive many emails asking how to generate inputs for the MUNICH model
First, download a project with vein and estimate vehicular emissions. Some projects are here: https://atmoschem.github.io/vein/reference/get_project.html
library(vein)
get_project(directory = "awesomecity")
Then open the file main.Rproj with Rstudio and source the file main.R
Then, run the following code
library(data.table)
library(eixport)
library(sf)
#> Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
library(writexl)
library(vein)
library(units)
#> udunits system database from /usr/share/xml/udunits
# epsg 31983, projected UTM
co <- readRDS("post/streets/CO.rds")
st_crs(co)
#> Coordinate Reference System:
#> User input: EPSG:31983
#> wkt:
#> PROJCS["SIRGAS 2000 / UTM zone 23S",
#> GEOGCS["SIRGAS 2000",
#> DATUM["Sistema_de_Referencia_Geocentrico_para_las_AmericaS_2000",
#> SPHEROID["GRS 1980",6378137,298.257222101,
#> AUTHORITY["EPSG","7019"]],
#> TOWGS84[0,0,0,0,0,0,0],
#> AUTHORITY["EPSG","6674"]],
#> PRIMEM["Greenwich",0,
#> AUTHORITY["EPSG","8901"]],
#> UNIT["degree",0.0174532925199433,
#> AUTHORITY["EPSG","9122"]],
#> AUTHORITY["EPSG","4674"]],
#> PROJECTION["Transverse_Mercator"],
#> PARAMETER["latitude_of_origin",0],
#> PARAMETER["central_meridian",-45],
#> PARAMETER["scale_factor",0.9996],
#> PARAMETER["false_easting",500000],
#> PARAMETER["false_northing",10000000],
#> UNIT["metre",1,
#> AUTHORITY["EPSG","9001"]],
#> AXIS["Easting",EAST],
#> AXIS["Northing",NORTH],
#> AUTHORITY["EPSG","31983"]]
head(co, 1) # units g/h
#> Simple feature collection with 1 feature and 25 fields
#> geometry type: LINESTRING
#> dimension: XY
#> bbox: xmin: 328001.4 ymin: 7391129 xmax: 328036.5 ymax: 7391204
#> projected CRS: SIRGAS 2000 / UTM zone 23S
#> id V1 V2 V3 V4
#> 1 1 [1/h] 275.088 [g/h] 140.0241 [g/h] 84.63286 [g/h] 86.81989 [g/h]
#> V5 V6 V7 V8 V9
#> 1 195.8425 [g/h] 680.7458 [g/h] 1423.767 [g/h] 1719 [g/h] 1723.265 [g/h]
#> V10 V11 V12 V13 V14
#> 1 1748.996 [g/h] 1600.077 [g/h] 1448.586 [g/h] 1378.825 [g/h] 1551.5 [g/h]
#> V15 V16 V17 V18 V19
#> 1 1556.648 [g/h] 1538.186 [g/h] 1698.133 [g/h] 1655.548 [g/h] 1601.138 [g/h]
#> V20 V21 V22 V23 V24
#> 1 1398.703 [g/h] 941.4398 [g/h] 709.3241 [g/h] 581.2601 [g/h] 407.1127 [g/h]
#> geometry
#> 1 LINESTRING (328001.4 739120...
# example of polygon as buffer at center
st_bbox(co) %>%
st_as_sfc() %>%
st_centroid() %>%
st_buffer(dist = 2000) -> polygon
# if you have another polygon, read it
# polygon <- st_read("/path/to/polygon.gpkg")
cob <- st_intersection(co, polygon)
#> Warning: attribute variables are assumed to be spatially constant throughout all
#> geometries
# Remove ID
co$id <- NULL
# split on vertex conserving mass
cos <- st_explode(cob)
#> Sum: 16542955.31
dim(cos)
#> [1] 372 27
# it adds length LKM and LKM2
# Should we remove streets shorter than 10 mts?
cos <- cos[as.numeric(cos$LKM2) > 10, ]
# remove LKM and LKM2
cos$LKM <- cos$LKM2 <- NULL
# We need to add units back, g/h
cos <- vein::Emissions(cos, time = "1/h")
head(cos, 1)
#> Simple feature collection with 1 feature and 24 fields
#> geometry type: LINESTRING
#> dimension: XY
#> bbox: xmin: 331916.3 ymin: 7385767 xmax: 331922.9 ymax: 7385775
#> projected CRS: SIRGAS 2000 / UTM zone 23S
#> V1 V2 V3 V4 V5
#> 1 59.38562 [g/h] 30.22821 [g/h] 18.27042 [g/h] 18.74256 [g/h] 42.27821 [g/h]
#> V6 V7 V8 V9 V10
#> 1 146.9585 [g/h] 307.3609 [g/h] 371.0953 [g/h] 372.0159 [g/h] 377.5708 [g/h]
#> V11 V12 V13 V14 V15
#> 1 345.4224 [g/h] 312.7188 [g/h] 297.6588 [g/h] 334.9356 [g/h] 336.0469 [g/h]
#> V16 V17 V18 V19 V20
#> 1 332.0614 [g/h] 366.5907 [g/h] 357.3973 [g/h] 345.6514 [g/h] 301.95 [g/h]
#> V21 V22 V23 V24
#> 1 203.2367 [g/h] 153.1279 [g/h] 125.4816 [g/h] 87.88691 [g/h]
#> geometry
#> 1 LINESTRING (331916.3 738577...
# now we transform for the REQUIRED UNITS FOR MUNICH
# ug/km/h
# we have 24 hours in this case
hours <- paste0("V", 1:24)
for (i in seq_along(hours)) {
cos[[hours[i]]] <- set_units(cos[[hours[i]]], ug / h)
cos[[hours[i]]] <- cos[[hours[i]]] / set_units(st_length(cos), km)
}
plot(cos["V1"], axes = T)
etm <- to_munich(sdf = cos)
names(etm)
#> [1] "Emissions" "Street"
head(etm$Emissions, 1)
#> i idbrin typo xa ya xb yb V1
#> 1 1 1 0 -46.64773 -23.62993 -46.64766 -23.63001 5462199660 [ug/h/km]
#> V2 V3 V4
#> 1 2780345076 [ug/h/km] 1680485867 [ug/h/km] 1723911918 [ug/h/km]
#> V5 V6 V7
#> 1 3888685780 [ug/h/km] 13517016236 [ug/h/km] 28270586844 [ug/h/km]
#> V8 V9 V10
#> 1 34132780053 [ug/h/km] 34217462106 [ug/h/km] 34728386913 [ug/h/km]
#> V11 V12 V13
#> 1 31771425772 [ug/h/km] 28763397272 [ug/h/km] 27378203813 [ug/h/km]
#> V14 V15 V16
#> 1 30806871891 [ug/h/km] 30909088139 [ug/h/km] 30542507720 [ug/h/km]
#> V17 V18 V19
#> 1 33718452534 [ug/h/km] 32872865695 [ug/h/km] 31792493506 [ug/h/km]
#> V20 V21 V22
#> 1 27772900557 [ug/h/km] 18693404826 [ug/h/km] 14084471885 [ug/h/km]
#> V23 V24
#> 1 11541609851 [ug/h/km] 8083705065 [ug/h/km]
head(etm$Street, 1)
#> i length width height
#> 1 1 10.87211 [m] 0 30
# to export to CSV which supports bit64
data.table::fwrite(
etm$Emissions,
paste0(basename(getwd()), "_Emissions.csv")
)
data.table::fwrite(
etm$Street,
paste0(basename(getwd()), "_Streets.csv")
)
# to export to Excel
writexl::write_xlsx(
x = etm$Emissions,
path = paste0(basename(getwd()), "_Emissions.xlsx")
)
writexl::write_xlsx(
x = etm$Street,
path = paste0(basename(getwd()), "_Streets.xlsx")
)
Created on 2020-10-14 by the reprex package (v0.3.0)
Answered by Sergio on December 15, 2020
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