PACMAN deployment summary

Load data

The data from PACMAN does not include an ISO compliant date field so it must be constructed from the available data

library('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

library('ggplot2')
unitID<-'pacman_12'
pacman.data <- read.delim(paste0("/home/gustavo/data/CONA/PACMAN/campaign/",unitID,".txt"))
names(pacman.data)<-c('Count','Year','Month','Day','Hour','Minute','Second',
                      'Distance','Temperature_IN_C','Temperature_mV','PM_mV','CO2_mV','CO_mV','Movement','COstatus')
pacman.data$Temperature_mV <- pacman.data$Temperature_mV/1000
pacman.data$date<-ISOdatetime(year=pacman.data$Year,
                              month=pacman.data$Month,
                              day=pacman.data$Day,
                              hour=pacman.data$Hour,
                              min=pacman.data$Minute,
                              sec=pacman.data$Second,
                              tz="GMT")
dstart=as.character(min(pacman.data$date))
dend=as.character(max(pacman.data$date))

Deployment details

Parameter	Value
Date start	2015-08-18 10:58:21
Date finish	2015-08-25 14:18:35
Location	NIWA Christchurch
Unit	pacman_12

Dust correction

Using data from previous deployments of similar dust sensors (ODIN project), the raw voltage reported by PACMAN was corrected to compensate for the impact of ambient temperature on its operation.

## Dust = dustA*PM_mV + dustB*Temperature_mV + dustC ug/m3 ##

# dustA=0.8798
# dustB=-3.5372
# dustC=-289.6213
# pacman.data$Dust<-dustA*pacman.data$PM_mV + dustB*pacman.data$Temperature_mV + dustC
pacman.data$Dust<-pacman.data$PM_mV

Carbon Monoxide data

The sensor used by PACMAN operates in cycles and only one data point per cycle is valid. PACMAN reports the CO_status flag that indicates the position in the cycle that the sensor is at. The valid data point correspond to the end of the CO_status labelled 2.

nrecs=length(pacman.data$COstatus)
pacman.data$CO_ok<-FALSE
pacman.data$CO_ok[-1]=(pacman.data$COstatus[-1]==1)

Weekly variation plots

To summarize the date, we produced weekly diurnal variation plots for the measurements.

Distance

diurnal.distance<-timeVariation(pacman.data,pollutant='Distance')

ggplot(diurnal.distance$data$day.hour)+
  geom_ribbon(aes(x=hour,ymin=Lower,ymax=Upper),fill='red')+
  geom_line(aes(x=hour,y=Mean),colour='red')+
  facet_grid(~wkday)+
  ggtitle('Distance')+
  xlab('NZST hour')+
  ylab('Distance from sensor [mV]')

Temperature outside

diurnal.temp_out<-try(timeVariation(pacman.data,pollutant='Temperature_IN_C'))

if (class(diurnal.temp_out)!="try-error"){
  ggplot(diurnal.temp_out$data$day.hour)+
    geom_ribbon(aes(x=hour,ymin=Lower,ymax=Upper),fill='red')+
    geom_line(aes(x=hour,y=Mean),colour='red')+
    facet_grid(~wkday)+
    ggtitle('External temperature')+
    xlab('NZST hour')+
    ylab('External Temperature [C]')
}

Temperature inside

diurnal.temp_in<-try(timeVariation(pacman.data,pollutant='Temperature_mV'))

if (class(diurnal.temp_in)!="try-error"){
  ggplot(diurnal.temp_in$data$day.hour)+
    geom_ribbon(aes(x=hour,ymin=Lower,ymax=Upper),fill='red')+
    geom_line(aes(x=hour,y=Mean),colour='red')+
    facet_grid(~wkday)+
    ggtitle('Internal Temperature')+
    xlab('NZST hour')+
    ylab('Internal Temperature [C]')
}

Dust

diurnal.dust<-try(timeVariation(pacman.data,pollutant='Dust'))

if (class(diurnal.dust)!="try-error"){
ggplot(diurnal.dust$data$day.hour)+
  geom_ribbon(aes(x=hour,ymin=Lower,ymax=Upper),fill='red')+
  geom_line(aes(x=hour,y=Mean),colour='red')+
  facet_grid(~wkday)+
  ggtitle('Dust \n Corrected with ODIN data')+
  xlab('NZST hour')+
  ylab('Dust [ug/m3]')
}

scatterPlot(pacman.data,x='Temperature_mV',y='Dust')

Carbon Dioxide

diurnal.co2<-try(timeVariation(pacman.data,pollutant='CO2_mV'))

if (class(diurnal.co2)!="try-error"){
ggplot(diurnal.co2$data$day.hour)+
  geom_ribbon(aes(x=hour,ymin=Lower,ymax=Upper),fill='red')+
  geom_line(aes(x=hour,y=Mean),colour='red')+
  facet_grid(~wkday)+
  ggtitle('CO2')+
  xlab('NZST hour')+
  ylab('CO2 [mV]')
}

Carbon Monoxide

diurnal.co<-try(timeVariation(pacman.data,pollutant='CO_mV',plot=FALSE))

if (class(diurnal.co)!="try-error"){
ggplot(diurnal.co$data$day.hour)+
  geom_ribbon(aes(x=hour,ymin=Lower,ymax=Upper),fill='red')+
  geom_line(aes(x=hour,y=Mean),colour='red')+
  facet_grid(~wkday)+
  ggtitle('CO')+
  xlab('NZST hour')+
  ylab('CO [mV]')
}

Movement

diurnal.mov<-try(timeVariation(pacman.data,pollutant='Movement'))

if (class(diurnal.mov)!="try-error"){
ggplot(diurnal.mov$data$day.hour)+
  geom_ribbon(aes(x=hour,ymin=Lower,ymax=Upper),fill='red')+
  geom_line(aes(x=hour,y=Mean),colour='red')+
  facet_grid(~wkday)+
  ggtitle('Movement')+
  xlab('NZST hour')+
  ylab('Movement')
  }