Emissions are usually estimated for a year, 24 hours, or one week from monday to sunday (with 168 hours). This depends on the availability of traffic data. When an air quality simulation is going to be done, they cover specific periods of time. For instance, WRF Chem emissions files support periods of time, or two emissions sets for a representative day (0-12z 12-0z). Also a WRF Chem simulation scan starts a Thursday at 00:00 UTC, cover 271 hours of simulations, but hour emissions are in local time and cover only 168 hours starting on Monday. This function tries to transform our emissions in local time to the desired UTC time, by recycling the local emissions.
emis_order(
x,
lt_emissions,
start_utc_time,
desired_length,
tz_lt = Sys.timezone(),
seconds = 0,
k = 1,
net,
verbose = TRUE
)one of the following:
Spatial object of class "Spatial". Columns are hourly emissions.
Spatial Object of class "sf". Columns are hourly emissions.
"data.frame", "matrix" or "Emissions".
In all cases, columns are hourly emissions.
Local time of the emissions at the first hour. It must be the before time of start_utc_time. For instance, if start_utc_time is 2020-02-02 00:00, and your emissions starts monday at 00:00, your lt_emissions must be 2020-01-27 00:00. The argument tz_lt will detect your current local time zone and do the rest for you.
UTC time for the desired first hour. For instance, the first hour of the namelist.input for WRF.
Integer; length to recycle or subset local emissions. For instance, the length of the WRF Chem simulations, states at namelist.input.
Character, Time zone of the local emissions. Default value is derived from Sys.timezone(), however, it accepts any other. If you enter a wrong tz, this function will show you a menu to choose one of the 697 time zones available.
Number of seconds to add
Numeric, factor.
SpatialLinesDataFrame or Spatial Feature of "LINESTRING".
Logical, to show more information, default is TRUE.
sf or data.frame
if (FALSE) { # \dontrun{
#do not run
data(net)
data(pc_profile)
data(fe2015)
data(fkm)
PC_G <- c(33491,22340,24818,31808,46458,28574,24856,28972,37818,49050,87923,
133833,138441,142682,171029,151048,115228,98664,126444,101027,
84771,55864,36306,21079,20138,17439, 7854,2215,656,1262,476,512,
1181, 4991, 3711, 5653, 7039, 5839, 4257,3824, 3068)
veh <- data.frame(PC_G = PC_G)
pc1 <- my_age(x = net$ldv, y = PC_G, name = "PC")
pcw <- temp_fact(net$ldv+net$hdv, pc_profile)
speed <- netspeed(pcw, net$ps, net$ffs, net$capacity, net$lkm, alpha = 1)
pckm <- units::set_units(fkm[[1]](1:24), "km")
pckma <- cumsum(pckm)
cod1 <- emis_det(po = "CO", cc = 1000, eu = "III", km = pckma[1:11])
cod2 <- emis_det(po = "CO", cc = 1000, eu = "I", km = pckma[12:24])
#vehicles newer than pre-euro
co1 <- fe2015[fe2015$Pollutant=="CO", ] #24 obs!!!
cod <- c(co1$PC_G[1:24]*c(cod1,cod2),co1$PC_G[25:nrow(co1)])
lef <- ef_ldv_scaled(co1, cod, v = "PC", t = "4S", cc = "<=1400",
f = "G",p = "CO", eu=co1$Euro_LDV)
E_CO <- emis(veh = pc1,lkm = net$lkm, ef = lef, speed = speed, agemax = 41,
profile = pc_profile, simplify = TRUE)
class(E_CO)
E_CO_STREETS <- emis_post(arra = E_CO, pollutant = "CO", by = "streets", net = net)
g <- make_grid(net, 1/102.47/2, 1/102.47/2) #500m in degrees
E_CO_g <- emis_grid(spobj = E_CO_STREETS, g = g, sr= 31983)
head(E_CO_g) #class sf
gr <- GriddedEmissionsArray(E_CO_g, rows = 19, cols = 23, times = 168, T)
wCO <- emis_order(x = E_CO_g,
lt_emissions = "2020-02-19 00:00",
start_utc_time = "2020-02-20 00:00",
desired_length = 241)
} # }