# Load libraries
require('openair')
## Loading required package: openair
## Loading required package: lazyeval
## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
##
## Loading required package: maps
require('reshape2')
## Loading required package: reshape2
require('ggplot2')
## Loading required package: ggplot2
# load_odin data
## ODIN_02.
odin_02 <- read.table("/home/gustavo/data/CONA/ODIN/deployment/odin_02.data",
header=T, quote="")
odin_02$date=as.POSIXct(paste(odin_02$Date,odin_02$Time),tz='NZST')
odin_02$Time<-NULL
odin_02$Date<-NULL
odin_02$Batt<-5*odin_02$Batt/1024
summary(odin_02)
## Dust RH Temp Batt
## Min. : 0.00 Min. : 0.0 Min. :-1.200 Min. :0.0000
## 1st Qu.: 9.00 1st Qu.: 0.0 1st Qu.: 0.000 1st Qu.:0.2637
## Median : 68.00 Median : 0.0 Median : 0.000 Median :0.4004
## Mean : 52.75 Mean :23.1 Mean : 3.301 Mean :0.3589
## 3rd Qu.: 78.00 3rd Qu.:54.3 3rd Qu.: 5.300 3rd Qu.:0.4785
## Max. :258.00 Max. :86.2 Max. :23.200 Max. :1.1182
## date
## Min. :2015-08-04 09:22:19
## 1st Qu.:2015-08-22 07:22:00
## Median :2015-08-30 01:15:00
## Mean :2015-08-28 21:02:08
## 3rd Qu.:2015-09-04 15:22:30
## Max. :2015-09-08 12:19:00
## ODIN_03
odin_03 <- read.table("/home/gustavo/data/CONA/ODIN/deployment/odin_03.data",
header=T, quote="")
odin_03$date=as.POSIXct(paste(odin_03$Date,odin_03$Time),tz='NZST')
odin_03$Time<-NULL
odin_03$Date<-NULL
odin_03$Batt<-5*odin_03$Batt/1024
summary(odin_03)
## Dust RH Temp Batt
## Min. : 141.0 Min. :27.40 Min. :-1.90 Min. :0.5176
## 1st Qu.: 332.0 1st Qu.:53.50 1st Qu.: 3.10 1st Qu.:0.7520
## Median : 371.0 Median :62.30 Median : 7.50 Median :0.8057
## Mean : 369.4 Mean :62.92 Mean : 7.98 Mean :0.7895
## 3rd Qu.: 400.0 3rd Qu.:74.30 3rd Qu.:11.40 3rd Qu.:0.8398
## Max. :4736.0 Max. :96.30 Max. :28.60 Max. :3.7598
## date
## Min. :2015-08-04 08:51:35
## 1st Qu.:2015-08-08 15:13:15
## Median :2015-08-12 21:23:30
## Mean :2015-08-19 05:17:05
## 3rd Qu.:2015-09-04 04:30:45
## Max. :2015-09-08 10:44:00
## ODIN_04
odin_04 <- read.table("/home/gustavo/data/CONA/ODIN/deployment/odin_04.data",
header=T, quote="")
odin_04$date=as.POSIXct(paste(odin_04$Date,odin_04$Time),tz='NZST')
odin_04$Time<-NULL
odin_04$Date<-NULL
odin_04$Batt<-5*odin_04$Batt/1024
summary(odin_04)
## Dust RH Temp Batt
## Min. : 0 Min. :28.30 Min. :-3.000 Min. :0.3223
## 1st Qu.: 34 1st Qu.:57.70 1st Qu.: 3.800 1st Qu.:0.6494
## Median : 34 Median :68.70 Median : 7.200 Median :0.7764
## Mean : 30 Mean :68.43 Mean : 7.764 Mean :0.7364
## 3rd Qu.: 39 3rd Qu.:80.10 3rd Qu.:10.500 3rd Qu.:0.8545
## Max. :131 Max. :99.90 Max. :27.000 Max. :1.0156
## date
## Min. :2015-08-04 08:51:51
## 1st Qu.:2015-08-13 03:31:45
## Median :2015-08-21 22:16:30
## Mean :2015-08-21 22:16:30
## 3rd Qu.:2015-08-30 17:00:15
## Max. :2015-09-08 11:44:00
## ODIN_05
odin_05 <- read.table("/home/gustavo/data/CONA/ODIN/deployment/odin_05.data",
header=T, quote="")
odin_05$date=as.POSIXct(paste(odin_05$Date,odin_05$Time),tz='NZST')
odin_05$Time<-NULL
odin_05$Date<-NULL
odin_05$Batt<-5*odin_05$Batt/1024
summary(odin_05)
## Dust RH Temp Batt
## Min. : 14.00 Min. : 0.00 Min. :-2.600 Min. :0.5469
## 1st Qu.: 43.00 1st Qu.:52.00 1st Qu.: 3.900 1st Qu.:0.7520
## Median : 48.00 Median :60.40 Median : 7.500 Median :0.7861
## Mean : 48.97 Mean :60.62 Mean : 8.003 Mean :0.7782
## 3rd Qu.: 53.00 3rd Qu.:71.70 3rd Qu.:10.800 3rd Qu.:0.8154
## Max. :3735.00 Max. :99.90 Max. :29.500 Max. :1.3281
## date
## Min. :2000-01-01 00:00:01
## 1st Qu.:2015-08-13 02:56:15
## Median :2015-08-21 21:47:30
## Mean :2012-06-25 16:00:40
## 3rd Qu.:2015-08-30 16:38:45
## Max. :2015-09-08 11:28:00
## ODIN_06
odin_06 <- read.table("/home/gustavo/data/CONA/ODIN/deployment/odin_06.data",
header=T, quote="")
#force GMT as the time zone to avoid openair issues with daylight saving switches
#The actual time zone is 'NZST'
odin_06$date=as.POSIXct(paste(odin_06$Date,odin_06$Time),tz='NZST')
odin_06$Time<-NULL
odin_06$Date<-NULL
odin_06$Batt<-5*odin_06$Batt/1024
summary(odin_06)
## Dust RH Temp Batt
## Min. : 9.00 Min. :27.9 Min. :-2.300 Min. :0.004883
## 1st Qu.: 53.00 1st Qu.:58.4 1st Qu.: 4.100 1st Qu.:0.156250
## Median : 58.00 Median :67.9 Median : 7.500 Median :0.375977
## Mean : 55.99 Mean :67.8 Mean : 7.636 Mean :0.371918
## 3rd Qu.: 58.00 3rd Qu.:78.4 3rd Qu.:10.300 3rd Qu.:0.581055
## Max. :122.00 Max. :99.9 Max. :26.800 Max. :0.961914
## date
## Min. :2015-08-04 08:30:58
## 1st Qu.:2015-08-13 03:09:45
## Median :2015-08-21 21:50:30
## Mean :2015-08-21 21:50:52
## 3rd Qu.:2015-08-30 16:32:15
## Max. :2015-09-08 11:12:45
## ODIN_07.
odin_07 <- read.table("/home/gustavo/data/CONA/ODIN/deployment/odin_07.data",
header=T, quote="")
odin_07$date=as.POSIXct(paste(odin_07$Date,odin_07$Time),tz='NZST')
odin_07$Time<-NULL
odin_07$Date<-NULL
odin_07$Batt<-5*odin_07$Batt/1024
summary(odin_07)
## Dust RH Temp Batt
## Min. : 29.00 Min. :26.40 Min. :-3.100 Min. :0.08789
## 1st Qu.: 53.00 1st Qu.:50.20 1st Qu.: 3.800 1st Qu.:0.30273
## Median : 58.00 Median :58.60 Median : 7.300 Median :0.56641
## Mean : 59.18 Mean :57.45 Mean : 7.777 Mean :0.52134
## 3rd Qu.: 63.00 3rd Qu.:66.00 3rd Qu.:10.600 3rd Qu.:0.73730
## Max. :2021.00 Max. :77.40 Max. :28.000 Max. :1.97266
## date
## Min. :2015-08-04 08:29:47
## 1st Qu.:2015-08-13 03:18:00
## Median :2015-08-21 22:11:00
## Mean :2015-08-21 22:13:07
## 3rd Qu.:2015-08-30 17:06:00
## Max. :2015-09-08 12:02:00
# Load ECan data
download.file(url = "http://data.ecan.govt.nz/data/29/Air/Air%20quality%20data%20for%20a%20monitored%20site%20(Hourly)/CSV?SiteId=5&StartDate=14%2F08%2F2015&EndDate=10%2F09%2F2015",destfile = "ecan_data.csv",method = "curl")
system("sed -i 's/a.m./AM/g' ecan_data.csv")
system("sed -i 's/p.m./PM/g' ecan_data.csv")
ecan_data_raw <- read.csv("ecan_data.csv",stringsAsFactors=FALSE)
ecan_data_raw$date<-as.POSIXct(ecan_data_raw$DateTime,format = "%d/%m/%Y %I:%M:%S %p",tz='NZST')
ecan_data<-as.data.frame(ecan_data_raw[,c('date','PM10.FDMS','Temperature..2m')])
## Merging the data
# ECan's data was provided as 10 minute values while ODIN reports every 1 minute so before merging the data, the timebase must be homogenized
names(odin_02)<-c('Dust.02','RH.02','Temperature.02','Batt.02','date')
names(odin_03)<-c('Dust.03','RH.03','Temperature.03','Batt.03','date')
names(odin_04)<-c('Dust.04','RH.04','Temperature.04','Batt.04','date')
names(odin_05)<-c('Dust.05','RH.05','Temperature.05','Batt.05','date')
names(odin_06)<-c('Dust.06','RH.06','Temperature.06','Batt.06','date')
names(odin_07)<-c('Dust.07','RH.07','Temperature.07','Batt.07','date')
odin <- merge(odin_02,odin_03,by = 'date', all = TRUE)
odin <- merge(odin,odin_04,by='date',all=TRUE)
odin <- merge(odin,odin_05,by='date',all=TRUE)
odin <- merge(odin,odin_06,by='date',all=TRUE)
odin <- merge(odin,odin_07,by='date',all=TRUE)
odin <- selectByDate(odin,start = '2015-08-15',end = '2015-09-10')
## Warning in checkPrep(mydata, vars, "default", remove.calm = FALSE,
## strip.white = FALSE): Detected data with Daylight Saving Time, converting
## to UTC/GMT
odin.10min<-timeAverage(odin,avg.time='10 min')
all_merged.1min<-merge(odin,ecan_data,by='date',all=TRUE)
all_merged.10min<-timeAverage(all_merged.1min,avg.time = '10 min')
timePlot(all_merged.10min,pollutant = c('Temperature..2m',
'Temperature.02',
'Temperature.03',
'Temperature.04',
'Temperature.05',
'Temperature.06',
'Temperature.07'))
## Time sync
lag_test=ccf(all_merged.10min$Temperature.05,
all_merged.10min$Temperature..2m,
na.action=na.pass,
lag.max=100,
type='covariance',
ylab='Correlation',
main='Temperature correlation as function of clock lag')
odin_lag=lag_test$lag[which.max(lag_test$acf)]
# correct timing
odin$date=odin$date-odin_lag*10*60
odin.10min<-timeAverage(odin,avg.time='10 min')
all_merged.10min<-merge(odin.10min,ecan_data,by='date',all=TRUE)
lag_test=ccf(all_merged.10min$Temperature.05,
all_merged.10min$Temperature..2m,
na.action=na.pass,
lag.max=100,
type='covariance',
ylab='Correlation',
main='Temperature correlation as function of clock lag')
## Remove drift from ODIN raw data
# Estimate the baseline from a simple linear regression
all_merged.10min$ODIN_drift.02<-predict(lm(all_merged.10min$Dust.02~seq(all_merged.10min$Dust.02)),newdata = all_merged.10min)
all_merged.10min$ODIN_drift.03<-predict(lm(all_merged.10min$Dust.03~seq(all_merged.10min$Dust.03)),newdata = all_merged.10min)
all_merged.10min$ODIN_drift.04<-predict(lm(all_merged.10min$Dust.04~seq(all_merged.10min$Dust.04)),newdata = all_merged.10min)
all_merged.10min$ODIN_drift.05<-predict(lm(all_merged.10min$Dust.05~seq(all_merged.10min$Dust.05)),newdata = all_merged.10min)
all_merged.10min$ODIN_drift.06<-predict(lm(all_merged.10min$Dust.06~seq(all_merged.10min$Dust.06)),newdata = all_merged.10min)
all_merged.10min$ODIN_drift.07<-predict(lm(all_merged.10min$Dust.07~seq(all_merged.10min$Dust.07)),newdata = all_merged.10min)
# Remove the baseline drift from the raw ODIN data
all_merged.10min$Dust.02.raw <- all_merged.10min$Dust.02
all_merged.10min$Dust.02.detrend<-all_merged.10min$Dust.02.raw - all_merged.10min$ODIN_drift.02
all_merged.10min$Dust.03.raw <- all_merged.10min$Dust.03
all_merged.10min$Dust.03.detrend<-all_merged.10min$Dust.03.raw - all_merged.10min$ODIN_drift.03
all_merged.10min$Dust.04.raw <- all_merged.10min$Dust.04
all_merged.10min$Dust.04.detrend<-all_merged.10min$Dust.04.raw - all_merged.10min$ODIN_drift.04
all_merged.10min$Dust.05.raw <- all_merged.10min$Dust.05
all_merged.10min$Dust.05.detrend<-all_merged.10min$Dust.05.raw - all_merged.10min$ODIN_drift.05
all_merged.10min$Dust.06.raw <- all_merged.10min$Dust.06
all_merged.10min$Dust.06.detrend<-all_merged.10min$Dust.06.raw - all_merged.10min$ODIN_drift.06
all_merged.10min$Dust.07.raw <- all_merged.10min$Dust.07
all_merged.10min$Dust.07.detrend<-all_merged.10min$Dust.07.raw - all_merged.10min$ODIN_drift.07
## Testing not correcting drift
all_merged.10min$Dust.02.detrend<-all_merged.10min$Dust.02.raw
all_merged.10min$Dust.03.detrend<-all_merged.10min$Dust.03.raw
all_merged.10min$Dust.04.detrend<-all_merged.10min$Dust.04.raw
all_merged.10min$Dust.05.detrend<-all_merged.10min$Dust.05.raw
all_merged.10min$Dust.06.detrend<-all_merged.10min$Dust.06.raw
# all_merged.10min$Dust.07.detrend<-all_merged.10min$Dust.07.raw
## Remove ODIN_02 data that doesn't have valid temperature with it
no_TRH_sensor.02<-(all_merged.10min$RH.02==0)|(is.na(all_merged.10min$RH.02))
all_merged.10min$Temperature.02[no_TRH_sensor.02] <- NA
## Calculate the temperature interference
all_merged.10min$Temperature.02.bin<-cut(all_merged.10min$Temperature.02,breaks = c(0,5,10,15,20,25),labels = c('2.5','7.5','12.5','17.5','22.5'))
all_merged.10min$Temperature.03.bin<-cut(all_merged.10min$Temperature.03,breaks = c(0,5,10,15,20,25),labels = c('2.5','7.5','12.5','17.5','22.5'))
all_merged.10min$Temperature.04.bin<-cut(all_merged.10min$Temperature.04,breaks = c(0,5,10,15,20,25),labels = c('2.5','7.5','12.5','17.5','22.5'))
all_merged.10min$Temperature.05.bin<-cut(all_merged.10min$Temperature.05,breaks = c(0,5,10,15,20,25),labels = c('2.5','7.5','12.5','17.5','22.5'))
all_merged.10min$Temperature.06.bin<-cut(all_merged.10min$Temperature.06,breaks = c(0,5,10,15,20,25),labels = c('2.5','7.5','12.5','17.5','22.5'))
all_merged.10min$Temperature.07.bin<-cut(all_merged.10min$Temperature.07,breaks = c(0,5,10,15,20,25),labels = c('2.5','7.5','12.5','17.5','22.5'))
Temp <- c(2.5,7.5,12.5,17.5,22.5)
Dust.02<-tapply(all_merged.10min$Dust.02.detrend,all_merged.10min$Temperature.02.bin,quantile,0.25)
Dust.03<-tapply(all_merged.10min$Dust.03.detrend,all_merged.10min$Temperature.03.bin,quantile,0.25)
Dust.04<-tapply(all_merged.10min$Dust.04.detrend,all_merged.10min$Temperature.04.bin,quantile,0.25)
Dust.05<-tapply(all_merged.10min$Dust.05.detrend,all_merged.10min$Temperature.05.bin,quantile,0.25)
Dust.06<-tapply(all_merged.10min$Dust.06.detrend,all_merged.10min$Temperature.06.bin,quantile,0.25)
Dust.07<-tapply(all_merged.10min$Dust.07.detrend,all_merged.10min$Temperature.07.bin,quantile,0.25)
TC_Dust.02 <- data.frame(Dust.02.detrend = Dust.02,Temperature.02 = Temp)
TC_Dust.03 <- data.frame(Dust.03.detrend = Dust.03,Temperature.03 = Temp)
TC_Dust.04 <- data.frame(Dust.04.detrend = Dust.04,Temperature.04 = Temp)
TC_Dust.05 <- data.frame(Dust.05.detrend = Dust.05,Temperature.05 = Temp)
TC_Dust.06 <- data.frame(Dust.06.detrend = Dust.06,Temperature.06 = Temp)
TC_Dust.07 <- data.frame(Dust.07.detrend = Dust.07,Temperature.07 = Temp)
# Now we calculate the linear regression for the minimum dust response in each temperature bin and subtract it from the detrended data
summary(odin.02_T<-lm(data = TC_Dust.02,Dust.02.detrend~Temperature.02))
##
## Call:
## lm(formula = Dust.02.detrend ~ Temperature.02, data = TC_Dust.02)
##
## Residuals:
## 2.5 7.5 12.5 17.5 22.5
## -0.8700 0.1475 1.3650 0.3075 -0.9500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60.09125 1.00035 60.07 1.02e-05 ***
## Temperature.02 1.10150 0.06966 15.81 0.00055 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.101 on 3 degrees of freedom
## Multiple R-squared: 0.9881, Adjusted R-squared: 0.9842
## F-statistic: 250.1 on 1 and 3 DF, p-value: 0.0005498
summary(odin.03_T<-lm(data = TC_Dust.03,Dust.03.detrend~Temperature.03))
##
## Call:
## lm(formula = Dust.03.detrend ~ Temperature.03, data = TC_Dust.03)
##
## Residuals:
## 2.5 7.5 12.5 17.5 22.5
## -8.335 13.985 -9.470 10.325 -6.505
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 258.3325 11.7688 21.951 0.000207 ***
## Temperature.03 4.6910 0.8195 5.724 0.010581 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.96 on 3 degrees of freedom
## Multiple R-squared: 0.9161, Adjusted R-squared: 0.8882
## F-statistic: 32.77 on 1 and 3 DF, p-value: 0.01058
summary(odin.04_T<-lm(data = TC_Dust.04,Dust.04.detrend~Temperature.04))
##
## Call:
## lm(formula = Dust.04.detrend ~ Temperature.04, data = TC_Dust.04)
##
## Residuals:
## 2.5 7.5 12.5 17.5 22.5
## 0.01 -0.10 0.19 -0.12 0.02
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.935000 0.129518 254.29 1.34e-07 ***
## Temperature.04 0.422000 0.009018 46.79 2.15e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1426 on 3 degrees of freedom
## Multiple R-squared: 0.9986, Adjusted R-squared: 0.9982
## F-statistic: 2190 on 1 and 3 DF, p-value: 2.149e-05
summary(odin.05_T<-lm(data = TC_Dust.05,Dust.05.detrend~Temperature.05))
##
## Call:
## lm(formula = Dust.05.detrend ~ Temperature.05, data = TC_Dust.05)
##
## Residuals:
## 2.5 7.5 12.5 17.5 22.5
## -0.090 0.290 -0.055 -0.400 0.255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.19250 0.29676 145.5 7.15e-07 ***
## Temperature.05 0.46900 0.02066 22.7 0.000187 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3267 on 3 degrees of freedom
## Multiple R-squared: 0.9942, Adjusted R-squared: 0.9923
## F-statistic: 515.1 on 1 and 3 DF, p-value: 0.0001873
summary(odin.06_T<-lm(data = TC_Dust.06,Dust.06.detrend~Temperature.06))
##
## Call:
## lm(formula = Dust.06.detrend ~ Temperature.06, data = TC_Dust.06)
##
## Residuals:
## 2.5 7.5 12.5 17.5 22.5
## -0.10 0.35 -0.20 -0.25 0.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 50.32500 0.27500 183.00 3.6e-07 ***
## Temperature.06 0.51000 0.01915 26.63 0.000116 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3028 on 3 degrees of freedom
## Multiple R-squared: 0.9958, Adjusted R-squared: 0.9944
## F-statistic: 709.4 on 1 and 3 DF, p-value: 0.0001161
summary(odin.07_T<-lm(data = TC_Dust.07,Dust.07.detrend~Temperature.07))
##
## Call:
## lm(formula = Dust.07.detrend ~ Temperature.07, data = TC_Dust.07)
##
## Residuals:
## 2.5 7.5 12.5 17.5 22.5
## -0.34099 0.10404 0.57362 -0.09539 -0.24128
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.02053 0.37940 -21.14 0.000232 ***
## Temperature.07 0.74162 0.02642 28.07 9.92e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4177 on 3 degrees of freedom
## Multiple R-squared: 0.9962, Adjusted R-squared: 0.9949
## F-statistic: 788.1 on 1 and 3 DF, p-value: 9.923e-05
all_merged.10min$Dust.02.corr <- all_merged.10min$Dust.02.detrend - predict(odin.02_T,newdata = all_merged.10min)
all_merged.10min$Dust.03.corr <- all_merged.10min$Dust.03.detrend - predict(odin.03_T,newdata = all_merged.10min)
all_merged.10min$Dust.04.corr <- all_merged.10min$Dust.04.detrend - predict(odin.04_T,newdata = all_merged.10min)
all_merged.10min$Dust.05.corr <- all_merged.10min$Dust.05.detrend - predict(odin.05_T,newdata = all_merged.10min)
all_merged.10min$Dust.06.corr <- all_merged.10min$Dust.06.detrend - predict(odin.06_T,newdata = all_merged.10min)
all_merged.10min$Dust.07.corr <- all_merged.10min$Dust.07.detrend - predict(odin.07_T,newdata = all_merged.10min)
timePlot(all_merged.10min,pollutant = c('PM10.FDMS',
'Dust.02.corr',
'Dust.03.corr',
'Dust.04.corr',
'Dust.05.corr',
'Dust.06.corr',
'Dust.07.corr'
)
,name.pol = c('PM10.FDMS',
'ODIN.02',
'ODIN.03',
'ODIN.04',
'ODIN.05',
'ODIN.06',
'ODIN.07')
,ylab = 'Sensor Response'
,group = FALSE
,avg.time = '60 min'
,main = '60 min')
timePlot(all_merged.10min,pollutant = c('PM10.FDMS'
,'Dust.02.corr'
,'Dust.03.corr'
,'Dust.04.corr'
,'Dust.05.corr'
,'Dust.06.corr'
,'Dust.07.corr')
,group = TRUE
,normalise = 'mean'
,avg.time = '1 day'
,main = '24 hour time series'
)
TV_norm <- timeVariation(all_merged.10min,pollutant = c('PM10.FDMS'
,'Dust.02.corr'
,'Dust.03.corr'
,'Dust.04.corr'
,'Dust.05.corr'
,'Dust.06.corr'
,'Dust.07.corr'
)
,normalise = TRUE
,plot = FALSE)
ggplot(TV_norm$data$hour)+
geom_ribbon(aes(x=hour,ymin=Lower,ymax=Upper),fill='red', alpha = 0.3)+
geom_line(aes(x=hour,y=Mean))+
facet_grid(variable~.)+
ggtitle('Average diurnal variation')+
xlab('NZST hour')+
ylab('Normalised value')
