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FIREXEmissionsAnalysis.R
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# -------- ### All EFs ### --------
doclear=1
if (doclear == 1){ rm(list=ls()) } # clear all analysis
load('AgFires.RData')
source('speciateSpecies.R')
require(dplyr); require(plyr); require(GMCM)
R2filter = 0.70; R2filterCO = 0.90 # stricter criteria for CO as this defines the plume
R2Bot = 0.5 ; COcutoff = 400 # ppb
doprocess=0;doprocessSTEP2 =0
OHval = 5E6
lifetimecutoff = 6 #hours for short vs. long-lived
# ----- Some ggplot settings ----
fuelshapes = c(19,15,2,18,7,8,4,2,16,12,13)
fuellimits = c("corn","soybean","rice","winter wheat","grass","pile","slash","shrub","forest")
cbp1a <- c( "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00",
"#CC79A7","#000000","#999999","#CC0000")
# colorblind safe option
cbp1b = c('#377eb8','#e41a1c','#4daf4a','#f781bf','#a65628','#984ea3','#ff7f00','#ffff33')
cbp1c=c('#a50026','#d73027','#f46d43','#fdae61','#fee090','#e0f3f8','#abd9e9',
'#74add1','#4575b4','#313695')
cbp1d <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7")
cbp1 = cbp1b
# ----- +++++++++++ ANALYSIS +++++++++++++ ------
# How do emission factors change at a single fire over time, if at all?
# How do emission factors vary across various fires sampled?
# Are there metrics (i.e. MCE etc) that provide some explanatory fpower for the variation that may be present?
require(reshape); require(ggmap) ; require(OrgMassSpecR); library(readxl) ;require(plyr)# ; library(ggbrace)
require(dplyr); require(ggpubr); require(ncdf4)
# ---- EFs from Xiaoxi Liu (2016 ACP SEAC4RS rice straw)
source("xioaxi.R")
xiaoxi.avg=as.data.frame(colMeans(xiaoxi, na.rm=TRUE))
xiaoxi.sd=apply(xiaoxi, 2, sd)
xiaoxi.avg$var =rownames(xiaoxi.avg)
colnames(xiaoxi.avg) = c('mean','var')
xiaoxi.avg$sd = xiaoxi.sd
# --------- Get Andreae emission factors ------------
f2 = 'InputFiles/OtherStudies/Andreae-BB-EMFactors-14Apr2021_justtable1.csv'
andreae = read.csv(f2)
# --------- Get Akagi emission factors ---------
akagi=readxl::read_xlsx('InputFiles/OtherStudies/Akagi_acp-11-4039-2011-supplement/Tables 1-5_4.27.11.xlsx')
akagi$CropEF = as.numeric(akagi$CropEF)
akagi$SavannahEF= as.numeric(akagi$Savannah)
akagi$TemperateEF= as.numeric(akagi$Temperate)
if (doprocess == 1){
# ------ combine 5hz & 1Hz fires ----
all5hz = rbind.fill(CopperBreaks.5hz.EF,Vivian.5hz.EF,Halfpint.5hz.EF,Loretta.5hz.EF,LilDebbie.5hz.EF,Ricearoni.5hz.EF,CrawbabyHouse.5hz.EF,Crawdaddy.5hz.EF, Gumbo.5hz.EF,Jumbalaya.5hz.EF,JumbalayaJr.1hz.EF,Crouton.5hz.EF,PoBoy.5hz.EF, # aug 21
Ant.5hz.EF, Blanket.5hz.EF, Chips.5hz.EF, Dip.5hz.EF, Escargot.5hz.EF,Frisbee.5hz.EF, Guac.5hz.EF, Hamburger.5hz.EF, IPA.5hz.EF, Jello.5hz.EF, Kebab.5hz.EF,
Limoncello.5hz.EF, Mustard.5hz.EF, # aug 23
DaysLater.5hz.EF,Alien.5hz.EF,Bambi.5hz.EF,BambiJr.5hz.EF,Deadpool.5hz.EF,Elf.5hz.EF,Fargo.5hz.EF,Hellboy.5hz.EF,Invictus.5hz.EF,InvictusU.5hz.EF,# aug 26
HickoryRidge.5hz.EF, Tallgrass.5hz.EF,Boxer.5hz.EF,Chessie.5hz.EF, Dingo.5hz.EF, Elkhound.5hz.EF, # aug 29th,
Blackwaterriver.5hz.EF, WIGGINS.5hz.EF, WIGGINSNEIGHBORS.5hz.EF, ZZTop.5hz.EF, YoungMC.5hz.EF, XTC.5hz.EF, Weezer.5hz.EF, ViolentFemmes.5hz.EF, U2.5hz.EF, Toto.5hz.EF, Supertramp.5hz.EF, # Aug 30
Jaws.5hz.EF, Kingpin.5hz.EF, Leon.5hz.EF, Meatballs.5hz.EF, Nikita.5hz.EF, Oblivion.5hz.EF, OblivionJr.5hz.EF, Psycho.5hz.EF, Quarantine.5hz.EF, Ratatouille.5hz.EF, Spaceballs.5hz.EF,Tremors.5hz.EF, Up.5hz.EF, Vertigo.5hz.EF, Willow.5hz.EF,# aug 31st
Asterisk.5hz.EF, BugsBunny.5hz.EF, CharlieBrown.5hz.EF, Shawnee.5hz.EF, DaffyDuck.5hz.EF, Eeyore.5hz.EF, FatAlbert.5hz.EF, Grinch.5hz.EF, Hobbes.5hz.EF, Iago.5hz.EF, Jetson.5hz.EF, KimPossible.5hz.EF, LisaSimpson.5hz.EF, unknownKim.5hz.EF, Marge.5hz.EF, Nemo.5hz.EF,Obelix.5hz.EF,Popeye.5hz.EF,Roadrunner.5hz.EF,Spongebob.5hz.EF)# Aug 31 Akito was not a good fire, W-V & XTC-WEEZER-UNKNOWN bad fire
all5hz = all5hz[order(all5hz$variable),]
all5hz$uniqueid =(all5hz$pass/10+all5hz$transect_source_fire_ID)
#ind = which(all5hz$variable == 'CO_DACOM_DISKIN')
#tnd2 = which(duplicated(all5hz$uniqueid[ind]))
#all5hz$uniqueid[ind[tnd2]]
# ---- make 5hz VOC EF ------- this is including duplicates, so only for comparison to MCE, shouldn't be used as a total
#ind = which(all5hz$Category == 1)
#all5hz.VOC = all5hz[ind,]
#test=aggregate(all5hz.VOC$EF1, by=list(all5hz.VOC$uniqueid), FUN=sum, na.rm=TRUE)
#ind = which(test$x == 0) ; test$x[ind] = NaN # if there are zero VOCs for a fire
#test2=aggregate(all5hz.VOC, by=list(all5hz.VOC$uniqueid), FUN='mean', na.rm=TRUE)
## don't want a zero EF
#cc = colnames(all5hz)
#fullline = data.frame(matrix(vector(), length(test$Group.1), length(cc)))
#colnames(fullline) = cc
#fullline$uniqueid = test$Group.1
#fullline$EF1 = test$x
#fullline$variable = 'All5HzVOC'
#fullline$mce = test2$mce
#fullline$Category=1
#fullline$age = test2$age
#fullline$R2toX = test2$R2toX
#for (i in 1:length(fullline$fuel)){
# ind = which(fullline$uniqueid[i] == all5hz.VOC$uniqueid)
# fullline$fuel[i] = all5hz.VOC$fuel[ind[1]]
# fullline$fire[i] = all5hz.VOC$fire[ind[1]]
#}
#all5hz = rbind(all5hz, fullline)
# ----------------------------------------
all1hz = rbind.fill(CopperBreaks.1hz.EF,Vivian.1hz.EF,Halfpint.1hz.EF,Loretta.1hz.EF,LilDebbie.1hz.EF,Ricearoni.1hz.EF,CrawbabyHouse.1hz.EF,Crawdaddy.1hz.EF, Gumbo.1hz.EF,
Jumbalaya.1hz.EF,JumbalayaJr.1hz.EF,Crouton.1hz.EF,PoBoy.1hz.EF, # aug 21
Ant.1hz.EF, Blanket.1hz.EF, Chips.1hz.EF, Dip.1hz.EF, Escargot.1hz.EF,Frisbee.1hz.EF, Guac.1hz.EF, Hamburger.1hz.EF, IPA.1hz.EF, Jello.1hz.EF, Kebab.1hz.EF,
Limoncello.1hz.EF, Mustard.1hz.EF, # aug 23
DaysLater.1hz.EF,Alien.1hz.EF,Bambi.1hz.EF,BambiJr.1hz.EF,Deadpool.1hz.EF,Elf.1hz.EF,Fargo.1hz.EF,Hellboy.1hz.EF,Invictus.1hz.EF,InvictusU.1hz.EF,# aug 26
HickoryRidge.1hz.EF, Tallgrass.1hz.EF,Boxer.1hz.EF,Chessie.1hz.EF, Dingo.1hz.EF, Elkhound.1hz.EF, # aug 29th,
Blackwaterriver.1hz.EF, WIGGINS.1hz.EF, WIGGINSNEIGHBORS.1hz.EF, ZZTop.1hz.EF, YoungMC.1hz.EF, XTC.1hz.EF, Weezer.1hz.EF, ViolentFemmes.1hz.EF, U2.1hz.EF, Toto.1hz.EF, Supertramp.1hz.EF, # Aug 30
Jaws.1hz.EF, Kingpin.1hz.EF, Leon.1hz.EF, Meatballs.1hz.EF, Nikita.1hz.EF, Oblivion.1hz.EF, OblivionJr.1hz.EF,Psycho.1hz.EF, Quarantine.1hz.EF, Ratatouille.1hz.EF, Spaceballs.1hz.EF, Tremors.1hz.EF,Up.1hz.EF, Vertigo.1hz.EF, Willow.1hz.EF, # Aug 31st
Asterisk.1hz.EF, BugsBunny.1hz.EF, CharlieBrown.1hz.EF, Shawnee.1hz.EF, DaffyDuck.1hz.EF, Eeyore.1hz.EF, FatAlbert.1hz.EF, Grinch.1hz.EF, Hobbes.1hz.EF, Iago.1hz.EF, Jetson.1hz.EF, KimPossible.1hz.EF, LisaSimpson.1hz.EF, unknownKim.1hz.EF, Marge.1hz.EF, Nemo.1hz.EF,Obelix.1hz.EF,Popeye.1hz.EF, Roadrunner.1hz.EF, Spongebob.1hz.EF)# Aug 31 Akito was not a good fire, W-V & XTC-WEEZER-UNKNOWN bad fire
all1hz = all1hz[order(all1hz$variable),]
# ----- get rid of negatives ------ eventually might want to try and fix it, this is TOGA, GILMAN, BLAKE
ind = which(all1hz$ERtoX < 0)
all1hz$EF1[ind] = NaN
all1hz$ERtoX[ind] = NaN
ind = which(all1hz$ERtoCO < 0)
toinvestigate = all1hz[ind,]
all1hz$ERtoCO[ind] = NaN
all1hz$EF1CO[ind] = NaN
all1hz$uniqueid =(all1hz$pass/10+all1hz$transect_source_fire_ID)
# remove -888, Inf, negatives???
LOD = which(all1hz$maxval == -888 | all1hz$maxval == -0.888 | all1hz$maxval == -Inf | all1hz$maxval < 0)
all1hz$maxval[LOD] = NaN
tt = unique(all5hz$fuel)
# --- Plot fires analyzed ------
# Investigate O3/CO
# ind = which(allfires.1hz$fuel2 != '?' &allfires.1hz$fuel2 != '' &
# allfires.1hz$fuel2 != 'forest' &
# allfires.1hz$fuel2 != 'savannah' &
# allfires.1hz$fuel2 != 'timber' &
# allfires.1hz$fuel2 != 'Understory mixed, shrub,rice' &
# allfires.1hz$fuel2 != 'coniferous/decidous')
# O31=ggplot(allfires.1hz[ind,])+geom_point(aes(x=CO_DACOM_DISKIN, y=O3_CL_RYERSON, col=fuel2))+theme_classic()
# ind = which(allfires.1hz$fuel2 != '?' &allfires.1hz$fuel2 != '' &
# allfires.1hz$fuel2 != 'forest' &
# allfires.1hz$fuel2 != 'savannah' &
# allfires.1hz$fuel2 != 'timber' &
# allfires.1hz$fuel2 != 'Understory mixed, shrub,rice' &
# allfires.1hz$fuel2 != 'coniferous/decidous' & is.finite(allfires.1hz$NO_LIF_ROLLINS) &
# allfires.1hz$CO_DACOM_DISKIN < 1E3)
# O32=ggplot(allfires.1hz[ind,])+geom_point(aes(x=CO_DACOM_DISKIN, y=O3_CL_RYERSON, col=fire))+theme_classic()
# O32=ggplot(allfires.1hz[ind,])+geom_point(aes(x=CO_DACOM_DISKIN,y=O3_CL_RYERSON, col=NO_LIF_ROLLINS/1E3))+theme_classic()+
# scale_color_viridis_c(trans='log10')
# merge names, flags, and data so I can plot the ag fires I actually analyzed
# should probably cut though to criteria for both
# ------- How many fuels? --------
#fuels = unique(all5hz.map$fuel)
#length(unique(all5hz.map$fire))
ind = which(all1hz$Category == 1 & is.finite(as.numeric(all1hz$ERtoCO)) & as.numeric(all1hz$ERtoCO) > 0
& as.numeric(all1hz$R2toCO) > 0.75)
tmp = unique(all1hz$variable[ind])
newlist = c()
for (i in 1:length(tmp)){
tt = strsplit(tmp[i], "_")
tt = tt[[1]][1]
newlist = c(newlist,tt)
}
uniqueLIST = unique(newlist)
unique(all1hz$variable[ind])
# ------ Merge 1hz and 5hz together ------
allBOTH = merge(all5hz, all1hz, by=c('variable','fire','fuel', 'transect_source_fire_ID','mWs',
'nCs',"Start","Stop","StartO","StopO","pass","uniqueid"),
all = TRUE, suffixes = c(".5hz", ".1hz"))#, incomparables = NA) # x = 1Hz, y=5hz
allBOTH$lifetime_5hz_hr = 1/(OHval*as.numeric(allBOTH$OHrate.5hz))/60/60
allBOTH$lifetime_1hz_hr = 1/(OHval*as.numeric(allBOTH$OHrate.1hz))/60/60
allBOTH$PI = ''
for (i in 1:length(allBOTH$PI)){
tmp = strsplit(allBOTH$variable[i], '_')
tt = tmp[[1]]
allBOTH$PI[i] = tt[length(tt)]
}
ind = which(allBOTH$PI != "APEL")
allBOTH = allBOTH[ind,]
# ----------- Provide the 5hz mce, MAtoF, and 5hz CO+CH4+CO2 ERtoCO for the 1hz data --------------------------
ff = unique(allBOTH$uniqueid)
allBOTH$MCE = NaN
for (i in 1:length(ff)){
ind = which(allBOTH$uniqueid == ff[i] )
allBOTH$MCE[ind] = max(as.numeric(allBOTH$mce.5hz[ind]), na.rm=TRUE) # they should all be the same, just fill in
allBOTH$MAtoF.5hz[ind] = max(as.numeric(allBOTH$MAtoF.5hz[ind]), na.rm=TRUE) # they should all be the same, just fill in
allBOTH$TC1.5hz[ind] = max(as.numeric(allBOTH$TC1.5hz[ind]), na.rm=TRUE) # they should all be the same, just fill in)
allBOTH$TC1CO.5hz[ind] = max(as.numeric(allBOTH$TC1CO.5hz[ind]), na.rm=TRUE) # they should all be the same, just fill in)
}
# Do my 1hz EFs correlate better with 5Hz if I use 5hz TC?
#ind = which(allBOTH$variable == 'Benzene_NOAAPTR_ppbv_WARNEKE')
#plot(allBOTH$EF1.5hz[ind],allBOTH$EF1.1hz[ind])
#cContentCorn = 500 # Assuming 50 % C, 500 g/kg
#allBOTH$EF1CO.1hz.5hz = NaN
#allBOTH$EF1.1hz.5hz = NaN
#allBOTH$EF1CO.1hz.5hz = cContentCorn * allBOTH$mWs/12 * allBOTH$ERtoCO.1hz /allBOTH$TC1CO.5hz
#allBOTH$EF1.1hz.5hz = cContentCorn * allBOTH$mWs/12 * allBOTH$ERtoX.1hz /allBOTH$TC1.5hz
#ind = which(allBOTH$variable == 'Benzene_NOAAPTR_ppbv_WARNEKE')
#points(allBOTH$EF1.5hz[ind],allBOTH$EF1.1hz.5hz[ind], col='red' ) # barely improves#
# ---- Cut data to > 400 ppb where CO to CO2 R2 > 0.9
dofilter=1
if (dofilter==1){
ind = which(allBOTH$variable == 'CO_DACOM_DISKIN' &
as.numeric(allBOTH$R2toX.5hz) >= R2filterCO & as.numeric(allBOTH$maxval.5hz) > COcutoff)
goodpasses = allBOTH$uniqueid[ind]
cc = c()
for (i in 1:length(goodpasses)){
ind = which(allBOTH$uniqueid == goodpasses[i])
cc = c(cc, ind)
}
allBOTH.filter = allBOTH[cc,]
}
allBOTH.filter$OHrate.1hz=as.numeric(allBOTH.filter$OHrate.1hz)
allBOTH.filter$OHrate.5hz=as.numeric(allBOTH.filter$OHrate.5hz)
allBOTH.filter$maxval.5hz=as.numeric(allBOTH.filter$maxval.5hz)
allBOTH.filter$maxval.1hz=as.numeric(allBOTH.filter$maxval.1hz)
allBOTH.filter$Category.5hz= as.numeric(allBOTH.filter$Category.5hz)
allBOTH.filter$Category.1hz= as.numeric(allBOTH.filter$Category.1hz)
allBOTH.filter$BGSpecies.5hz= as.numeric(allBOTH.filter$BGSpecies.5hz)
allBOTH.filter$BGSpecies.1hz= as.numeric(allBOTH.filter$BGSpecies.1hz)
allBOTH.filter$BGX.1hz = as.numeric(allBOTH.filter$BGX.1hz)
allBOTH.filter$BGX.5hz = as.numeric(allBOTH.filter$BGX.5hz)
allBOTH.filter$BGCO.1hz = as.numeric(allBOTH.filter$BGCO.1hz)
allBOTH.filter$BGCO.5hz = as.numeric(allBOTH.filter$BGCO.5hz)
allBOTH.filter$intercept.1hz = as.numeric(allBOTH.filter$intercept.1hz)
allBOTH.filter$intercept.5hz = as.numeric(allBOTH.filter$intercept.5hz)
# Provide same MCE to all passes from 5hz
ff = unique(allBOTH.filter$uniqueid) # need to redo this
allBOTH.filter$MCE = NaN
for (i in 1:length(ff)){
ind = which(allBOTH.filter$uniqueid == ff[i] )
allBOTH.filter$MCE[ind] = max(as.numeric(allBOTH.filter$mce.5hz[ind]), na.rm=TRUE) # they should all be the same, just fill in
}
# Remove EF and ER below R2 filter
ind = which(round(allBOTH.filter$R2toCO.5hz, digits = 2) < R2filter)
allBOTH.filter$EF1CO.5hz[ind] = NaN
allBOTH.filter$ERtoCO.5hz[ind] = NaN
allBOTH.filter$MCE[ind] = NaN
ind = which(round(allBOTH.filter$R2toCO.1hz, digits=2) < R2filter)
allBOTH.filter$EF1CO.1hz[ind] = NaN
allBOTH.filter$ERtoCO.1hz[ind] = NaN
allBOTH.filter$MCE[ind] = NaN
# start with 5hz
allBOTH.filter$FinalEF = allBOTH.filter$EF1CO.5hz
allBOTH.filter$FinalERtoCO = allBOTH.filter$ERtoCO.5hz
allBOTH.filter$FinalR2 = allBOTH.filter$R2toCO.5hz
ind = which(!is.finite(allBOTH.filter$FinalEF) & is.finite(allBOTH.filter$EF1CO.1hz))
allBOTH.filter$FinalEF[ind] = allBOTH.filter$EF1CO.1hz[ind]
ind = which(!is.finite(allBOTH.filter$FinalERtoCO) & is.finite(allBOTH.filter$ERtoCO.1hz))
allBOTH.filter$FinalERtoCO[ind] = allBOTH.filter$ERtoCO.1hz[ind]
allBOTH.filter$FinalR2[ind] = allBOTH.filter$R2toCO.1hz[ind]
# If R2 is between 0.5 and 0.75, assume we can use the integration method
# actually don't
doINT = 0
if (doINT == 1){
ind = which(!is.finite(allBOTH.filter$FinalEF) & allBOTH.filter$R2toCO.5hz > R2Bot &
allBOTH.filter$R2toCO.5hz < R2filter & is.finite(allBOTH.filter$EF1COintfill.5hz))
allBOTH.filter$FinalEF[ind] = allBOTH.filter$EF1COintfill.5hz[ind]
ind = which(!is.finite(allBOTH.filter$FinalERtoCO) & allBOTH.filter$R2toCO.5hz > R2Bot &
allBOTH.filter$R2toCO.5hz < R2filter & is.finite(allBOTH.filter$ERtoCOintfill.5hz))
allBOTH.filter$FinalERtoCO[ind] = allBOTH.filter$ERtoCOintfill.5hz[ind]
ind = which(!is.finite(allBOTH.filter$FinalEF) & allBOTH.filter$R2toCO.1hz > R2Bot &
allBOTH.filter$R2toCO.1hz < R2filter & is.finite(allBOTH.filter$EF1COintfill.1hz))
allBOTH.filter$FinalEF[ind] = allBOTH.filter$EF1COintfill.1hz[ind]
ind = which(!is.finite(allBOTH.filter$FinalERtoCO) & allBOTH.filter$R2toCO.1hz > R2Bot &
allBOTH.filter$R2toCO.1hz < R2filter & is.finite(allBOTH.filter$ERtoCOintfill.1hz))
allBOTH.filter$FinalERtoCO[ind] = allBOTH.filter$ERtoCOintfill.1hz[ind]
}
# ----------- Now, choose EFs that had the best correlation to either CO2 or CO --------
#allBOTH.filter$ChosenEF.5hz = NaN; allBOTH.filter$ChosenEF.R2.5hz = NaN
#allBOTH.filter$ChosenEF.1hz = NaN; allBOTH.filter$ChosenEF.R2.1hz = NaN
# Pick highest correlation for either the CO or CO2 EF
#for (i in 1:length(allBOTH.filter$variable)){
# Pick for 5hz data
# tmpR = c(as.numeric(allBOTH.filter$R2toX.5hz[i]),as.numeric(allBOTH.filter$R2toCO.5hz[i]))
# tmpEF= c(as.numeric(allBOTH.filter$EF1.5hz[i]),as.numeric(allBOTH.filter$EF1CO.5hz[i]))
# # Ok, actually I just want to use CO
# tmpR = c(as.numeric(allBOTH.filter$R2toCO.5hz[i]))
# tmpEF= c(as.numeric(allBOTH.filter$EF1CO.5hz[i]))
# ind = which(is.finite(tmpR))
# if (length(ind) > 0){
# ind2 = which(tmpR == max(tmpR, na.rm=TRUE))
# if (length(ind2) ==1){
# allBOTH.filter$ChosenEF.5hz[i] = tmpEF[ind2]
# allBOTH.filter$ChosenEF.R2.5hz[i] = tmpR[ind2]
# } else{
# allBOTH.filter$ChosenEF.5hz[i] = mean(tmpEF[ind2], na.rm=TRUE)
# allBOTH.filter$ChosenEF.R2.5hz[i] = mean(tmpR[ind2], na.rm=TRUE)
# }
# }
#
# # Pick for 1hz data
# tmpR = c(as.numeric(allBOTH.filter$R2toX.1hz[i]),as.numeric(allBOTH.filter$R2toCO.1hz[i]))
# tmpEF= c(as.numeric(allBOTH.filter$EF1.1hz[i]),as.numeric(allBOTH.filter$EF1CO.1hz[i]))
# Ok, actually I just want to use CO
# tmpR = c(as.numeric(allBOTH.filter$R2toCO.1hz[i]))
# tmpEF= c(as.numeric(allBOTH.filter$EF1CO.1hz[i]))
# ind = which(is.finite(tmpR))
# if (length(ind) > 0){
# ind2 = which(tmpR == max(tmpR, na.rm=TRUE))
# if (length(ind2) ==1){
# allBOTH.filter$ChosenEF.1hz[i] = tmpEF[ind2]
# allBOTH.filter$ChosenEF.R2.1hz[i] = tmpR[ind2]
# } else{
#if (max(tmpEF[ind2],na.rm=TRUE) < 0){print(c("i",i,tmpEF[ind2]))}
# for cans, R2 is both 1. But the ratio to CO seems to work better
# if (tmpR[1] == 1 & tmpR[2] == 1 & allBOTH.filter$PI == 'APEL'){
# allBOTH.filter$ChosenEF.1hz[i] = as.numeric(allBOTH.filter$EF1CO.1hz[i])
# allBOTH.filter$ChosenEF.R2.1hz[i] = as.numeric(allBOTH.filter$R2toCO.1hz[i])
# }
# if (tmpR[1] == 1 & tmpR[2] == 1 & allBOTH.filter$PI == 'ppt'){
# allBOTH.filter$ChosenEF.1hz[i] = as.numeric(allBOTH.filter$EF1CO.1hz[i])
# allBOTH.filter$ChosenEF.R2.1hz[i] = as.numeric(allBOTH.filter$R2toCO.1hz[i])
# }
# if (tmpR[1] == 1 & tmpR[2] == 1 & allBOTH.filter$PI == 'BLAKE'){
# allBOTH.filter$ChosenEF.1hz[i] = as.numeric(allBOTH.filter$EF1CO.1hz[i])
# allBOTH.filter$ChosenEF.R2.1hz[i] = as.numeric(allBOTH.filter$R2toCO.1hz[i])
# }
# if (tmpR[1] == 1 & tmpR[2] == 1 & allBOTH.filter$PI == 'GILMAN'){
# allBOTH.filter$ChosenEF.1hz[i] = as.numeric(allBOTH.filter$EF1CO.1hz[i])
# allBOTH.filter$ChosenEF.R2.1hz[i] = as.numeric(allBOTH.filter$R2toCO.1hz[i])
# }
# }
# }
# if (allBOTH.filter$variable[i] == 'NO2_ACES_WOMACK'){
# print(c(i,allBOTH.filter$variable[i],allBOTH.filter$ChosenEF.5hz[i],allBOTH.filter$ChosenEF.1hz[i]))
# }
#}
ff = unique(allBOTH.filter$uniqueid)
gg = unique(allBOTH.filter$variable)
for (i in 1:length(ff)){
for (j in 1:length(gg)){
ind = which(allBOTH.filter$uniqueid == ff[i] & allBOTH.filter$variable == gg[j])
allBOTH.filter$Category.5hz[ind] = max(c(as.numeric(allBOTH.filter$Category.1hz[ind]),as.numeric(allBOTH.filter$Category.5hz[ind])), na.rm=TRUE) # they should all be the same, just fill in
}
}
# I think I need to get rid of plumes at 1hz < 400 ppb CO too for WAS
ind = which(allBOTH.filter$variable == 'CO_DACOM_DISKIN' & as.numeric(allBOTH.filter$maxval.1hz) < COcutoff)
badpasses= allBOTH.filter$uniqueid[ind]
ind = which(allBOTH.filter$PI == 'BLAKE' & allBOTH.filter$uniqueid %in% badpasses) # WAS
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$PI == 'ppt' & allBOTH.filter$uniqueid %in% badpasses) # TOGA
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$PI == 'GILMAN' & allBOTH.filter$uniqueid %in% badpasses) # iWAS
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
save(allBOTH.filter, file='AllBOTH.filter.RData')
} else{
load('AllBOTH.filter.RData')
}# end do process
# ---- Remove very strange outliers -----
# TOGA struggles with Wiggins-neighbor pass 2 & Limoncello 3
ind = which(allBOTH.filter$fire == 'Wiggins-neighbor' & allBOTH.filter$pass == 2 & allBOTH.filter$PI == 'ppt')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$fire == 'Limoncello' & allBOTH.filter$pass == 3 & allBOTH.filter$PI == 'ppt')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
# iWAS struggles with Ratatouille pass 2
ind = which(allBOTH.filter$fire == 'Ratatouille' & allBOTH.filter$pass == 2 & allBOTH.filter$PI == 'GILMAN')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
if (doprocessSTEP2 == 1){
# ----------- Get rid of emission factors at 1hz and 5hz that had poor correlation ----------
#ind = which(allBOTH.filter$ChosenEF.R2.1hz < R2filter)
#allBOTH.filter$ChosenEF.1hz[ind] = NaN
#ind = which(allBOTH.filter$ChosenEF.R2.5hz < R2filter)
#allBOTH.filter$ChosenEF.5hz[ind] = NaN
# ------ Make a column just of Final EF where first we use 5hz
#allBOTH.filter$FinalEF = allBOTH.filter$ChosenEF.5hz
#allBOTH.filter$FinalR2 = allBOTH.filter$ChosenEF.R2.5hz
#ind = which(is.na(allBOTH.filter$FinalEF) & is.finite(allBOTH.filter$ChosenEF.1hz))
#allBOTH.filter$FinalEF[ind] = allBOTH.filter$ChosenEF.1hz[ind]
#allBOTH.filter$FinalR2[ind] = allBOTH.filter$ChosenEF.R2.1hz[ind]
# ------ Make a column just of Final ERtoCO where first we use 5hz
#allBOTH.filter$FinalERtoCO = allBOTH.filter$ERtoCO.5hz
#ind = which(is.na(allBOTH.filter$FinalERtoCO) & is.finite(allBOTH.filter$ERtoCO.1hz))
#allBOTH.filter$FinalERtoCO[ind] = allBOTH.filter$ERtoCO.1hz[ind]
# ---- Can have finite ER to CO but NaN EF if CH4 or CO2 are missing
# ---------------remove these for consistency
#ind = which(!is.finite(allBOTH.filter$FinalEF))
#allBOTH.filter$FinalERtoCO[ind] = NaN
allBOTH.filter$kind = allBOTH.filter$kind.5hz
allBOTH.filter$formula = allBOTH.filter$formula.5hz
allBOTH.filter$names = allBOTH.filter$names.5hz
allBOTH.filter$lifetime = allBOTH.filter$lifetime_5hz_hr
ind = which(is.na(allBOTH.filter$lifetime))
allBOTH.filter$lifetime[ind]= allBOTH.filter$lifetime_1hz_hr[ind]
ind = which(is.na(allBOTH.filter$kind))
allBOTH.filter$kind[ind] = allBOTH.filter$kind.1hz[ind]
ind = which(is.na(allBOTH.filter$formula))
allBOTH.filter$formula[ind] = allBOTH.filter$formula.1hz[ind]
ind = which(is.na(allBOTH.filter$names))
allBOTH.filter$names[ind] = allBOTH.filter$names.1hz[ind]
# get rid of Inf
ind = which(allBOTH.filter$maxval.5hz == -Inf)
allBOTH.filter$maxval.5hz[ind] = NaN
# Probs should get rid of negative emission factors, however need to go back and check these
ind = which(allBOTH.filter$ERtoCO.1hz < 0 & allBOTH.filter$Category.1hz == 1)
allBOTH.filter$EF1CO.1hz[ind] = NaN
allBOTH.filter$ERtoCO.1hz[ind] = NaN
allBOTH.filter.allfuels = allBOTH.filter
# provide the dominant fuel class for the 1hz data
# ff = unique(allBOTH.filter.allfuels$uniqueid)
# for (i in 1:length(ff)){
# ind = which(allBOTH.filter.allfuels$uniqueid == ff[i] )
# allBOTH.filter.allfuels$transect_dominant_fuel.5hz[ind] = max(allBOTH.filter.allfuels$transect_dominant_fuel.1hz[ind], na.rm=TRUE) # they should all be the same, just fill in
# allBOTH.filter.allfuels$transect_fuel_class.5hz[ind] = max(allBOTH.filter.allfuels$transect_fuel_class.1hz[ind], na.rm=TRUE) # they should all be the same, just fill in
# allBOTH.filter.allfuels$transect_fuel_confidence.5hz[ind] = max(allBOTH.filter.allfuels$transect_fuel_confidence.1hz[ind], na.rm=TRUE) # they should all be the same, just fill in
# }
# Dominant fuel class
#1 Forest
#2 Savanna
#3 Shrubland
#4 Grassland
#5 Cropland
#6 Pile
#7 Slash
#8 Understory
#9 Urban/Barren
# ----- For all EFs that are NaN, set MCE to NaN -----
ind = which(is.na(allBOTH.filter.allfuels$FinalEF))
allBOTH.filter.allfuels$MCE[ind] = NaN
# ------- get rid of non-ag fuels ------------
# Use the new flags from Amber
#ind = which(allBOTH.filter.allfuels$transect_fuel_class.5hz >= 3 &
# allBOTH.filter.allfuels$transect_fuel_class.5hz <= 7)
ind = which(allBOTH.filter.allfuels$fuel != '?' & allBOTH.filter.allfuels$fuel != 'house')
# lets keep all the fuels but '?' for now, and filter after calculating more stuff
#ind = which(allBOTH.filter.allfuels$fuel == 'slash' | allBOTH.filter.allfuels$fuel == 'pile' |
# allBOTH.filter.allfuels$fuel == 'shrub' |
# allBOTH.filter.allfuels$fuel == 'grass' |
# allBOTH.filter.allfuels$fuel == 'rice' | allBOTH.filter.allfuels$fuel == 'corn' |
# allBOTH.filter.allfuels$fuel == 'soybean' |
# allBOTH.filter.allfuels$fuel == 'winter wheat' )
allBOTH.filter = allBOTH.filter.allfuels[ind,]
# check for zeros that will mess up averages
ind = which(allBOTH.filter$ERtoCO.1hz == 0)
allBOTH.filter$ERtoCO.1hz[ind] = NaN
allBOTH.filter$EF1CO.1hz[ind] = NaN
# ---- SET USEME ----
# 1: include in the table and total VOC EF
# 2: include in the table not total EF
# -1 : Dont report at all, not an emission
# 0: Dont include in the table but include for analysis of MCE dependence etc.
allBOTH.filter$USEME = 1
# No longer reported by CALTECH
ind1 = which(allBOTH.filter$names == 'C4 Hydroxyperoxide')
allBOTH.filter$USEME[ind1] = 0
# ------ These PM1 species don't correlate with CO ------
ind1 = which(allBOTH.filter$variable =="Iodine_JIMENEZ")
allBOTH.filter$USEME[ind1] = -1
ind1 = which(allBOTH.filter$variable == "ClO4_JIMENEZ")
allBOTH.filter$USEME[ind1] = -1
ind1 = which(allBOTH.filter$variable =="Bromine_JIMENEZ")
allBOTH.filter$USEME[ind1] = -1
ind1 = which(allBOTH.filter$variable == "Seasalt_JIMENEZ")
allBOTH.filter$USEME[ind1] = -1
ind1 = which(allBOTH.filter$variable == "MSA_JIMENEZ")
allBOTH.filter$USEME[ind1] = -1
# ---------------- Get PM1 EF --------------------------------
ind1 = which(allBOTH.filter$variable == 'OC_JIMENEZ')
oc = allBOTH.filter[ind1,]
oa = oc
oa$FinalEF = oa$FinalEF*oa$OAtoOC.5hz
oa$FinalERtoCO = oa$FinalERtoCO*oa$OAtoOC.5hz
oa$names = 'Organic aerosol'
oa$variable = 'OA_JIMENEZ'
ind1 = which(allBOTH.filter$variable == 'BC_SCHWARZ')
bc = allBOTH.filter[ind1,]
ind1 = which(allBOTH.filter$variable == 'Ammonium_JIMENEZ')
ammonium= allBOTH.filter[ind1,]
ind1 = which(allBOTH.filter$variable == "Sulfate_JIMENEZ")
sulf = allBOTH.filter[ind1,]
ind1 = which(allBOTH.filter$variable == "Nitrate_JIMENEZ")
nit = allBOTH.filter[ind1,]
ind1 = which(allBOTH.filter$variable == "NR_Chloride_JIMENEZ")
nrcl = allBOTH.filter[ind1,]
ind1 = which(allBOTH.filter$variable == "Potassium_JIMENEZ")
pot = allBOTH.filter[ind1,]
for (i in 1:length(oc$variable)){
newline = oc[i,]
vars = c(oa$FinalEF[i],bc$FinalEF[i],ammonium$FinalEF[i],
sulf$FinalEF[i],nit$FinalEF[i],nrcl$FinalEF[i], pot$FinalEF[i])
varsER = c(oc$FinalERtoCO[i],bc$FinalERtoCO[i],ammonium$FinalERtoCO[i],
sulf$FinalERtoCO[i],nit$FinalERtoCO[i],nrcl$FinalERtoCO[i],pot$FinalERtoCO[i])
# only sum PM1 if we have OC
newline$FinalEF = NaN
if (is.finite(vars[1])){newline$FinalEF = sum(vars, na.rm=TRUE)}
if (is.finite(vars[1])){newline$FinalERtoCO = sum(varsER, na.rm=TRUE)}
newline$variable = 'PM1'
newline$names = 'PM1'
newline$formula = 'PM1'
# give it a big MW so its in the right order
newline$mWs= 500
allBOTH.filter = rbind(allBOTH.filter,newline)
}
# also append OA
allBOTH.filter = rbind(allBOTH.filter,oa)
# --- actually don't do this
dobeckyoutliers = 0
if (dobeckyoutliers == 1){
# ---- Get rid of outlier points from Becky ------
ind = which(allBOTH.filter$fire == "U2" & allBOTH.filter$variable == 'nButane_ppt')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$fire == "U2" & allBOTH.filter$variable == 'nPentane_ppt')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$fire == "Supertramp" & allBOTH.filter$variable == 'nButane_ppt')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$fire == "Supertramp" & allBOTH.filter$variable == 'nPentane_ppt')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
#ind = which(allBOTH.filter$fire == "Blackwater" & allBOTH.filter$variable == 'nButane_ppt')
#allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
#ind = which(allBOTH.filter$fire == "Blackwater" & allBOTH.filter$variable == 'nPentane_ppt')
#allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
#ind = which(allBOTH.filter$fire == "Blackwater" & allBOTH.filter$variable == 'iButane_ppt')
#allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
#ind = which(allBOTH.filter$fire == "Blackwater" & allBOTH.filter$variable == 'iButene1Butene_ppt')
#allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$fire == "Willow" & allBOTH.filter$names == 'n-Butane')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$fire == "Willow" & allBOTH.filter$variable == 'n-Pentane')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$fire == "Willow" & allBOTH.filter$variable == 'Isobutane')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$fire == "Willow" & allBOTH.filter$variable == 'i-Butene/1Butene')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$fire == "BugsBunny" & allBOTH.filter$variable == 'n-Butane')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$fire == "BugsBunny" & allBOTH.filter$variable == 'n-Pentane')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$fire == "BugsBunny" & allBOTH.filter$variable == 'Isobutane')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$fire == "BugsBunny" & allBOTH.filter$variable == 'i-Butene/1Butene')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$fire == "BugsBunny" & allBOTH.filter$variable == '2-Methylpentane')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$fire == "BugsBunny" & allBOTH.filter$variable == '3-Methylpentane')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
ind = which(allBOTH.filter$fire == "FatAlbert" & allBOTH.filter$variable == 'n-Pentane')
allBOTH.filter$FinalEF[ind] = NaN; allBOTH.filter$FinalERtoCO[ind] = NaN
# just for plotting
ind = which(was.all$fire == "U2" )
was.all$nButane_WAS_BLAKE[ind] = NaN
was.all$nPentane_WAS_BLAKE[ind] = NaN
ind = which(was.all$fire == "Supertramp" )
was.all$nButane_WAS_BLAKE[ind] = NaN
was.all$nPentane_WAS_BLAKE[ind] = NaN
ind = which(was.all$fire == "Willow" )
was.all$nButane_WAS_BLAKE[ind] = NaN
was.all$nPentane_WAS_BLAKE[ind] = NaN
was.all$iButane_WAS_BLAKE[ind] = NaN
was.all$iButene_WAS_BLAKE[ind] = NaN
ind = which(was.all$fire == "BugsBunny" )
was.all$nButane_WAS_BLAKE[ind] = NaN
was.all$nPentane_WAS_BLAKE[ind] = NaN
was.all$iButane_WAS_BLAKE[ind] = NaN
was.all$iButene_WAS_BLAKE[ind] = NaN
was.all$x2MePentane_WAS_BLAKE[ind] = NaN
was.all$x3MePentane_WAS_BLAKE[ind] = NaN
ind = which(was.all$fire == "FatAlbert" )
was.all$nPentane_WAS_BLAKE[ind] = NaN
}
# ------- Removing these measurements entirely
# No emission factors come out for these species
ind = which(allBOTH.filter$formula == 'C2H4O3S')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$formula == 'MSA')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$formula == 'Na')
allBOTH.filter$USEME[ind] = -1
# Don't correlate with CO
ind = which(allBOTH.filter$formula == 'BrO')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$formula == 'BrCl')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$formula == 'BrCN')
allBOTH.filter$USEME[ind] = -1
# ---------- These TOGA species don't correlate with CO
ind = which(allBOTH.filter$variable == 'iPropONO2_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CHBr3_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable =='CHBrCl2_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CH3CCl3_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CHCl3_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'HFC134a_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'HCFC142b_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'HCFC141b_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CHBr2Cl_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CH2ClI_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'LimoneneD3Carene_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'Propane_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'Propene_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'MBO_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'iButONO2and2ButONO2_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'C2H5OH_ppt')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CH2ClCH2Cl_ppt' )
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'HCFC22_ppt' )
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CH2Cl2_ppt' ) # TOGA doesn't correlate with CO but WAS does
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'C2Cl4_ppt' ) #
allBOTH.filter$USEME[ind] = -1
# These iWAS species dont correlate with CO
ind = which(allBOTH.filter$variable == 'CycHexane_NOAAiWAS_GILMAN')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'x3MePentane_NOAAiWAS_GILMAN')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'x224TriMePentane_NOAAiWAS_GILMAN')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'x22DiMeButane_NOAAiWAS_GILMAN')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CHCl3_NOAAiWAS_GILMAN')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'C2Cl4_NOAAiWAS_GILMAN')
allBOTH.filter$USEME[ind] = -1
#These WAS species dont correlate with CO
ind = which(allBOTH.filter$variable == 'C2Cl4_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CHBrCl2_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'C2HCl3_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CHCl3_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'Limonene_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'ClBenzene_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'H1211_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CFC11_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CFC12_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'x234TrimePentane_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CH2ClCH2Cl_WAS_BLAKE' )
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CCl4_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CH3CCl3_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CFC12_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'HFC134a_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'HFC152a_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'HCFC142b_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'HCFC141b_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'HFC365mfc_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'x2MePentane_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CFC114_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'x3MePentane_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'H1301_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'H2402_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CHBr2Cl_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CFC113_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable =='HCFC22_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'x23Dimebutane_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'CHBr3_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable =='x2PentONO2_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable =='x2ButONO2_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable =='x3PentONO2_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'iPropONO2_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable == 'x3Me2ButONO2_WAS_BLAKE')
allBOTH.filter$USEME[ind] = -1
# ind = which(allBOTH.filter$variable =='CycPentane_WAS_BLAKE')
# allBOTH.filter$USEME[ind] = 0
# For these species, set USEME == 2 to use in table, not total VOC
ind2 = which(allBOTH.filter$variable == 'MVK_ppt')
allBOTH.filter$USEME[ind2] = 2
ind3 = which(allBOTH.filter$variable== 'MAC_ppt')
allBOTH.filter$USEME[ind3] = 2
ind4 = which(allBOTH.filter$variable== 'x2Butenals_ppt')
allBOTH.filter$USEME[ind4] = 2
ind2 = which(allBOTH.filter$variable== 'Propanal_ppt')
allBOTH.filter$USEME[ind2] = 2
ind3 = which(allBOTH.filter$variable== 'Acetone_ppt')
allBOTH.filter$USEME[ind3] =2
ind3 = which(allBOTH.filter$variable== 'CRESOL_WENNBERG')
allBOTH.filter$USEME[ind3] =2
# ----- These species just really don't correlate in my opinion!
ind = which(allBOTH.filter$variable =='Cl2_NOAACIMS_VERES')
allBOTH.filter$USEME[ind] = -1
ind = which(allBOTH.filter$variable =='ISOPN_WENNBERG')
allBOTH.filter$USEME[ind] = -1
# Speciate C9 aromatics with Blake (C9H12) - maybe not enough, so just dont include the speciated in the total VOC
ind = which(allBOTH.filter$variable== 'C9Aromatics_NOAAPTR_ppbv_WARNEKE')
allBOTH.filter$USEME[ind] = 1
ind2 = which(allBOTH.filter$names== '1,2,4-Trimethylbenzene')
allBOTH.filter$USEME[ind2] = 2
ind3 = which(allBOTH.filter$names== '1,3,5-trimethylbenzene' | allBOTH.filter$names== '1,3,5-Trimethylbenzene' )
allBOTH.filter$USEME[ind3] = 2
ind4 = which(allBOTH.filter$names== 'i-Propylbenzene')
allBOTH.filter$USEME[ind4] = 2
ind5 = which(allBOTH.filter$names== 'n-Propylbenzene')
allBOTH.filter$USEME[ind5] = 2
ind6 = which(allBOTH.filter$names== '2-Ethyltoluene')
allBOTH.filter$USEME[ind6] = 2
ind7 = which(allBOTH.filter$names== '3-Ethyltoluene')
allBOTH.filter$USEME[ind7] = 2
ind8 = which(allBOTH.filter$names== '4-Ethyltoluene')
allBOTH.filter$USEME[ind8] = 2
# Toga CH2Br2 seems weird - maybe use blake?
ind = which(allBOTH.filter$variable == 'CH2Br2_ppt')
allBOTH.filter$USEME[ind] =-1
# -- Actually just use TOGA furan, methyl furan, and furfural per GIGI's paper
ind = which(allBOTH.filter$names == ' Furan and fragments' & allBOTH.filter$PI == 'WARNEKE' )
allBOTH.filter$USEME[ind] = 0
ind = which(allBOTH.filter$names == 'sum of 2-methylfuran 3-methylfuran and fragments' & allBOTH.filter$PI == 'WARNEKE' )
allBOTH.filter$USEME[ind] = 0
# -- Warneke has the fragments so keep it for total VOC for and furfural
ind = which(allBOTH.filter$formula == 'C5H4O2' & allBOTH.filter$PI == 'WARNEKE' )
allBOTH.filter$USEME[ind] = 0
# don't use BLAKE MVK and MACR at all
ind = which(allBOTH.filter$variable == 'MVK_WAS_BLAKE')
allBOTH.filter$USEME[ind] = 0
ind = which(allBOTH.filter$variable == 'MAC_WAS_BLAKE')
allBOTH.filter$USEME[ind] = 0
# -- Warneke - keep for total VOC
ind = which(allBOTH.filter$names == 'Acetone')
allBOTH.filter$USEME[ind] = 0
ind = which(allBOTH.filter$names == 'Propanal')
allBOTH.filter$USEME[ind] = 0
ind = which(allBOTH.filter$names == 'Acetone/Propanal' & allBOTH.filter$PI != 'WARNEKE')
allBOTH.filter$USEME[ind] = 0
ind = which(allBOTH.filter$PI == 'STCLAIR')
allBOTH.filter$USEME[ind] = 0
ind = which(allBOTH.filter$names == 'NOy')
allBOTH.filter$USEME[ind] = 0
# all measurements agree so just keep WARNEKE
ind = which(allBOTH.filter$names == 'Benzene' & allBOTH.filter$PI != 'WARNEKE')
allBOTH.filter$USEME[ind] = 0
ind = which(allBOTH.filter$names == 'Toluene' & allBOTH.filter$PI != 'WARNEKE')
allBOTH.filter$USEME[ind] = 0
# ---- Weird toluene outlier
ind = which(allBOTH.filter$variable == 'Toluene_NOAAPTR_ppbv_WARNEKE' & allBOTH.filter$FinalEF > 2)
allBOTH.filter$FinalEF[ind] = allBOTH.filter$EF1CO.5hz[ind]
allBOTH.filter$FinalERtoCO[ind] = allBOTH.filter$ERtoCO.5hz[ind]
ind = which(allBOTH.filter$names == 'Acetaldehyde' & allBOTH.filter$PI != 'WARNEKE')
allBOTH.filter$USEME[ind] = 0
ind = which(allBOTH.filter$names == 'Acrolein' & allBOTH.filter$PI != 'WARNEKE') #APEL and WARNEKE agree, BLAKE is low
allBOTH.filter$USEME[ind] = 0
# Don't use WARNEKE or TOGA CH2O
ind = which(allBOTH.filter$names == 'Formaldehyde' & allBOTH.filter$PI == 'ppt')
allBOTH.filter$USEME[ind] = 0
ind = which(allBOTH.filter$names == 'Formaldehyde' & allBOTH.filter$PI == 'WARNEKE')
allBOTH.filter$USEME[ind] = 0
# All but WAS agree so just keep warneke for MEK, but average iWAS and TOGA for table
#allBOTH.filter = mergelines(allBOTH.filter, 'MEK_ppt','MEK_NOAAiWAS_GILMAN')
ind = which(allBOTH.filter$names == 'MEK/ 2-methyl propanal' & allBOTH.filter$PI == 'WARNEKE')
allBOTH.filter$USEME[ind] = 1
ind = which(allBOTH.filter$names == 'Methyl ethyl ketone' & allBOTH.filter$PI == 'GILMAN')#
allBOTH.filter$USEME[ind] = 0
ind = which(allBOTH.filter$names == 'Methyl ethyl ketone' & allBOTH.filter$PI == 'BLAKE' )
allBOTH.filter$USEME[ind] = 0
ind = which(allBOTH.filter$names == 'Methyl ethyl ketone' & allBOTH.filter$PI == 'ppt')
allBOTH.filter$USEME[ind] = 0
ind = which(allBOTH.filter$names == 'Methyl ethyl ketone' & allBOTH.filter$PI != 'WARNEKE' & allBOTH.filter$PI != 'GILMAN' &
allBOTH.filter$PI != 'ppt' & allBOTH.filter$PI != 'BLAKE')
allBOTH.filter$USEME[ind] = 2
# Stategy - maybe average the 'short' measurements?
# ------ Make averages of specific species, set USME for individuals == 0 -----------
# --- Average Fried and Hanisco HCHO
allBOTH.filter = mergelines(allBOTH.filter, 'CH2O_CAMS_pptv_FRIED','CH2O_ISAF_HANISCO')
# --- Average Ryerson and Rollins NO
allBOTH.filter = mergelines(allBOTH.filter, 'NO_LIF_ROLLINS','NO_RYERSON')
# --- Average Ryerson and Womack NO2
allBOTH.filter = mergelines(allBOTH.filter, 'NO2_ACES_WOMACK','NO2_RYERSON')
# --- Average VERES and WOMACK HONO
allBOTH.filter = mergelines(allBOTH.filter, 'HNO2_ACES_WOMACK','HNO2_NOAACIMS_VERES')
# --- Average GILMAN AND BLAKE ethene
allBOTH.filter = mergelines(allBOTH.filter, 'Ethene_NOAAiWAS_GILMAN','Ethene_WAS_BLAKE')
# --- Average GILMAN AND BLAKE Propene
allBOTH.filter = mergelines(allBOTH.filter, 'Propene_NOAAiWAS_GILMAN','Propene_WAS_BLAKE')
# --- Average GILMAN AND BLAKE i-Butene
allBOTH.filter = mergelines(allBOTH.filter, 'iButene_NOAAiWAS_GILMAN','iButene_WAS_BLAKE')
# --- Average GILMAN AND BLAKE trans-2-Butene
allBOTH.filter = mergelines(allBOTH.filter, 't2butene_NOAAiWAS_GILMAN','t2Butene_WAS_BLAKE')
# --- Average GILMAN AND BLAKE cis-2-Butene
allBOTH.filter = mergelines(allBOTH.filter, 'c2Butene_NOAAiWAS_GILMAN','c2Butene_WAS_BLAKE')
# --- Average GILMAN AND BLAKE 1-Butene
allBOTH.filter = mergelines(allBOTH.filter, 'x1Butene_NOAAiWAS_GILMAN','x1Butene_WAS_BLAKE')
# --- Average GILMAN AND BLAKE 1-Pentene
allBOTH.filter = mergelines(allBOTH.filter, 'x1Pentene_NOAAiWAS_GILMAN','x1Pentene_WAS_BLAKE')
# --- Average GILMAN AND BLAKE t2-Pentene
allBOTH.filter = mergelines(allBOTH.filter, 't2Pentene_NOAAiWAS_GILMAN','t2Pentene_WAS_BLAKE')
# --- Average GILMAN AND BLAKE c2-Pentene
allBOTH.filter = mergelines(allBOTH.filter, 'c2Pentene_NOAAiWAS_GILMAN','c2Pentene_WAS_BLAKE')
# --- Average GILMAN AND BLAKE 1,3-pentadiene
allBOTH.filter = mergelines(allBOTH.filter, 't13Pentadiene_NOAAiWAS_GILMAN','x13Pentadienes_WAS_BLAKE')
# --- Average GILMAN AND BLAKE 2-methyl-1-butene
allBOTH.filter = mergelines(allBOTH.filter, 'X2Me1Butene_WAS_BLAKE','x2Me1Butene_NOAAiWAS_GILMAN')
# --- Average GILMAN AND BLAKE 3-methyl-1-butene
allBOTH.filter = mergelines(allBOTH.filter, 'X3Me1Butene_WAS_BLAKE','x3Me1Butene_NOAAiWAS_GILMAN')
# --- Average GILMAN AND BLAKE Methylcyclopentane
allBOTH.filter = mergelines(allBOTH.filter, 'MeCycPentane_NOAAiWAS_GILMAN','MeCycPentane_WAS_BLAKE')
# --- Average GILMAN AND BLAKE Methylcyclohexane
allBOTH.filter = mergelines(allBOTH.filter, 'MeCycHexane_NOAAiWAS_GILMAN','MeCycHexane_WAS_BLAKE')
# --- Average Toga AND BLAKE Camphene
allBOTH.filter = mergelines(allBOTH.filter, 'Camphene_WAS_BLAKE','Camphene_ppt')
# --- Average Toga AND BLAKE Isobutanal # actually just use TOGA
ind = which(allBOTH.filter$variable == 'iButanal_WAS_BLAKE')
allBOTH.filter$USEME[ind] = 0
# allBOTH.filter = mergelines(allBOTH.filter, 'iButanal_WAS_BLAKE','iButanal_ppt')
# --- Average Toga AND BLAKE Butanal # actually just use TOGA
#allBOTH.filter = mergelines(allBOTH.filter, 'Butanal_WAS_BLAKE','Butanal_ppt')
ind = which(allBOTH.filter$variable == 'Butanal_WAS_BLAKE')
allBOTH.filter$USEME[ind] = 0
# --- Average Toga AND BLAKE CH3I
allBOTH.filter = mergelines(allBOTH.filter, 'CH3I_WAS_BLAKE','CH3I_ppt')
# --- Average Toga AND BLAKE CH2Cl2 - does seem emitted from corn
allBOTH.filter = mergelines(allBOTH.filter, 'CH2Cl2_WAS_BLAKE','CH2Cl2_ppt')
# --- Average Toga AND BLAKE ethono2
allBOTH.filter = mergelines(allBOTH.filter, 'EthONO2_WAS_BLAKE','EthONO2_ppt')
# --- Average Toga AND BLAKE meono2
allBOTH.filter = mergelines(allBOTH.filter, 'MeONO2_WAS_BLAKE','MeONO2_ppt')
# --- Average GILMAN AND BLAKE Cyclohexane
# actually, iwas cyclohexane is bad, just use blake
ind = which(allBOTH.filter$variable == 'CycHexane_NOAAiWAS_GILMAN')
allBOTH.filter$USEME[ind] = 0
#allBOTH.filter = mergelines(allBOTH.filter, 'CycHexane_WAS_BLAKE','CycHexane_NOAAiWAS_GILMAN')
# --- Average GILMAN AND BLAKE n-Decane
allBOTH.filter = mergelines(allBOTH.filter, 'nDecane_NOAAiWAS_GILMAN','nDecane_WAS_BLAKE')
# --- Average GILMAN AND BLAKE ethyne
allBOTH.filter = mergelines(allBOTH.filter, 'Ethyne_NOAAiWAS_GILMAN','Ethyne_WAS_BLAKE')
# --- Average GILMAN, BLAKE, APEL 3-methylpentane
# GILMAN is bad, dont use
ind = which(allBOTH.filter$variable == 'x3MePentane_NOAAiWAS_GILMAN')
allBOTH.filter$USEME[ind] = 0
# Blake shows a negative dependence on CO...
ind = which(allBOTH.filter$variable == 'x3MePentane_WAS_BLAKE')
allBOTH.filter$USEME[ind] = 0
# allBOTH.filter = mergelines3(allBOTH.filter, 'x3MePentane_ppt',','')
# --- Average GILMAN, BLAKE, APEL 2-methylpentane
allBOTH.filter = mergelines3(allBOTH.filter, 'x2MePentane_ppt','x2MePentane_WAS_BLAKE','x2MePentane_NOAAiWAS_GILMAN')
# --- Average GILMAN, BLAKE, APEL n-hexane
allBOTH.filter = mergelines3(allBOTH.filter, 'nHexane_ppt','nHexane_WAS_BLAKE','nHexane_NOAAiWAS_GILMAN')
# --- Average BLAKE, APEL n-heptane
allBOTH.filter = mergelines(allBOTH.filter, 'nHeptane_ppt','nHeptane_WAS_BLAKE')
# --- Average GILMAN, BLAKE, APEL m,p-xylene
allBOTH.filter = mergelines3(allBOTH.filter, 'mpXylene_ppt','mpXylene_WAS_BLAKE','mpXylene_NOAAiWAS_GILMAN')
# --- Average GILMAN, BLAKE, APEL o-xylene
allBOTH.filter = mergelines3(allBOTH.filter, 'oXylene_ppt','oXylene_WAS_BLAKE','oXylene_NOAAiWAS_GILMAN')
# --- Average GILMAN, BLAKE, APEL ethylbenzene
allBOTH.filter = mergelines3(allBOTH.filter, 'EthBenzene_ppt','EthBenzene_WAS_BLAKE','EthBenzene_NOAAiWAS_GILMAN')
# --- Average WARNEKE, BLAKE, APEL Nitromethane
# allBOTH.filter = mergelines3(allBOTH.filter, 'CH3NO2_NOAAPTR_ppbv_WARNEKE','Nitromethane_WAS_BLAKE','Nitromethane_ppt')
# TOGA nitromethane is way low compaared to WAS, which is within 30% of WARNEKE. USE Warneke
ind = which(allBOTH.filter$variable == 'Nitromethane_WAS_BLAKE' | allBOTH.filter$variable == 'Nitromethane_ppt')
allBOTH.filter$USEME[ind] = 0
ind = which(allBOTH.filter$variable == 'CH3NO2_NOAAPTR_ppbv_WARNEKE')
allBOTH.filter$USEME[ind] = 1
# --- Average GBlake, TOGA, CH3Br
allBOTH.filter = mergelines(allBOTH.filter, 'CH3Br_ppt','CH3Br_WAS_BLAKE')
# --- Average GILMAN BLAKE, APEL 2,2,4-Trimethylpentane
allBOTH.filter = mergelines3(allBOTH.filter, 'x224TrimePentane_ppt','x224TrimePentane_WAS_BLAKE','x224TriMePentane_NOAAiWAS_GILMAN')
# --- Average GILMAN, BLAKE, APEL octane
allBOTH.filter = mergelines3(allBOTH.filter, 'nOctane_ppt','nOctane_WAS_BLAKE','nOctane_NOAAiWAS_GILMAN')
# --- Average GILMAN AND BLAKE nonane
allBOTH.filter = mergelines(allBOTH.filter, 'nNonane_NOAAiWAS_GILMAN','nNonane_WAS_BLAKE')
# --- Average GILMAN, BLAKE, APEL alphapinene
allBOTH.filter = mergelines3(allBOTH.filter, 'aPinene_ppt','aPinene_WAS_BLAKE','aPinene_NOAAiWAS_GILMAN')
# --- Average BLAKE, APEL isopropanol
allBOTH.filter = mergelines(allBOTH.filter, 'iPropanol_ppt','iPropanol_WAS_BLAKE')
# --- Average BLAKE, APEL methyl acetate - actually just use TOGA
ind = which(allBOTH.filter$variable == 'MeAcetate_WAS_BLAKE')
allBOTH.filter$USEME == 0
# allBOTH.filter = mergelines(allBOTH.filter, 'MeAcetate_ppt','MeAcetate_WAS_BLAKE')
# Use warneke for the total VOC, but keep for table
ind = which(allBOTH.filter$variable == 'MeAcetate_ppt')
allBOTH.filter$USEME[ind] == 2
# For betapinene/myrcene, lets add was together, then average with TOGA
ind = which(allBOTH.filter$variable == 'Myrcene_WAS_BLAKE')
ind2 = which(allBOTH.filter$variable == 'bPinene_WAS_BLAKE')
tmp1 = allBOTH.filter[ind,]; tmp2 = allBOTH.filter[ind2,]
tmp1$FinalEF = rowSums(cbind(tmp1$FinalEF,tmp2$FinalEF), na.rm=TRUE)
tmp1$FinalERtoCO = rowSums(cbind(tmp1$FinalERtoCO,tmp2$FinalERtoCO), na.rm=TRUE)
tmp1$variable = 'bPinene/Myrcene_WAS_BLAKE'
tmp1$names = 'beta-Pinene/Myrcene'
allBOTH.filter = rbind(allBOTH.filter,tmp1)
# have zeros though now instead of NaNs
ind = which(allBOTH.filter$variable == 'bPinene/Myrcene_WAS_BLAKE' & allBOTH.filter$FinalEF == 0.0)
allBOTH.filter$FinalEF[ind] = NaN
allBOTH.filter = mergelines(allBOTH.filter, 'bPinene/Myrcene_WAS_BLAKE',
'bPineneMyrcene_ppt')
ind = which(allBOTH.filter$variable == 'bPinene/Myrcene_WAS_BLAKE' |
allBOTH.filter$variable =='bPineneMyrcene_ppt')
allBOTH.filter$USEME[ind] = 0
# --- Average VERES and WARNEKE HCOOH
allBOTH.filter = mergelines(allBOTH.filter, 'HCOOH_NOAACIMS_VERES','HCOOH_NOAAPTR_ppbv_WARNEKE')
# --- Average GILMAN, BLAKE, APEL nbutane
allBOTH.filter = mergelines3(allBOTH.filter, 'nButane_ppt','nButane_WAS_BLAKE','nButane_NOAAiWAS_GILMAN')
# --- Average GILMAN, BLAKE, APEL ibutane
allBOTH.filter = mergelines3(allBOTH.filter, 'iButane_ppt','iButane_WAS_BLAKE','iButane_NOAAiWAS_GILMAN')
# --- Average APEL and GILMAN methylformate
allBOTH.filter = mergelines(allBOTH.filter, 'MeFormate_NOAAiWAS_GILMAN','MeFormate_ppt')
# --- Average GILMAN, BLAKE, APEL ipentane
allBOTH.filter = mergelines3(allBOTH.filter, 'iPentane_ppt','iPentane_WAS_BLAKE','iPentane_NOAAiWAS_GILMAN')
# --- Average GILMAN, BLAKE, APEL npentane
allBOTH.filter = mergelines3(allBOTH.filter, 'nPentane_ppt','nPentane_WAS_BLAKE','nPentane_NOAAiWAS_GILMAN')
# --- Average GILMAN AND BLAKE 22-dimethylbutane
allBOTH.filter = mergelines(allBOTH.filter, 'x22DiMeButane_NOAAiWAS_GILMAN','x22Dimebutane_WAS_BLAKE')
# --- Average Apel AND BLAKE ethynlbenzene
allBOTH.filter = mergelines(allBOTH.filter, 'EthynylBenzene_ppt','EthynylBenzene_WAS_BLAKE')
# --- Average Apel AND BLAKE tricyclene
allBOTH.filter = mergelines(allBOTH.filter, 'Tricyclene_ppt','Tricyclene_WAS_BLAKE')
# --- Average warneke, veres, HNCO
allBOTH.filter = mergelines(allBOTH.filter, 'HNCO_NOAACIMS_VERES','HNCO_NOAAPTR_ppbv_WARNEKE')
# --- Average wennberg, gilman, toga acrylonitrile
allBOTH.filter = mergelines4(allBOTH.filter,'Acrylonitrile_NOAAPTR_ppbv_WARNEKE', 'Acrylonitrile_ppt','Acrylonitrile_NOAAiWAS_GILMAN', 'Acrylonitrile_WAS_BLAKE')
# --- Average wennberg, veres, warneke, and apel hydrogen cyanide - Ok, per Lu Xu, don't use WARNEKE as it may have a water interference
# water intereference is now fixed
allBOTH.filter = mergelines4(allBOTH.filter, 'HCN_NOAACIMS_VERES','HCN_WENNBERG','HCN_ppt','HCN_NOAAPTR_ppbv_WARNEKE')
# --- Average Apel AND BLAKE propionitrile
allBOTH.filter = mergelines(allBOTH.filter, 'PropNitrile_ppt','PropNitrile_WAS_BLAKE')
# --- Average Apel AND BLAKE DMS
allBOTH.filter = mergelines(allBOTH.filter, 'PropNitrile_ppt','PropNitrile_WAS_BLAKE')
# --- Average BLAKE and APEL 2-methylfuran ***actually just use TOGA
ind = which(allBOTH.filter$variable == 'x2MeFuran_WAS_BLAKE')
allBOTH.filter$USEME[ind] = 0