Apply hourly aggregation QC using "AB_O4" algorithm

Creates a pm25 timeseries by averaging aggregated data from the A and B channels and applying the following QC logic:

1. Create pm25 by averaging the A and B channel aggregation means

2. Invalidate data where: (min_count < 20)

3. Invalidate data where: (A/B hourly difference > 5 AND A/B hourly percent difference > 70%)

4. Invalidate data where: (A/B hourly data recovery < 90%)

PurpleAirQC_hourly_AB_03(pat = NULL, min_count = 20, returnAllColumns = FALSE)

Arguments

pat

A PurpleAir timeseries object.

min_count

Aggregation bins with fewer than min_count measurements will be marked as NA.

returnAllColumns

Logical specifying whether to return all columns of statistical data generated for QC algorithm or just the final pm25 result.

Value

Data frame with columns datetime and pm25.

Note

Purple Air II sensors reporting after the June, 2019 firmware upgrade report data every 2 minutes or 30 measurements per hour. The default setting of min_count = 20 is equivalent to a required data recovery rate of 67%.

Examples

# \donttest{
library(AirSensor)

df_00 <- 
  example_pat %>%
  pat_qc() %>%
  PurpleAirQC_hourly_AB_00()
  
df_01 <- 
  example_pat %>%
  pat_qc() %>%
  PurpleAirQC_hourly_AB_01()
  
df_02 <- 
  example_pat %>%
  pat_qc() %>%
  PurpleAirQC_hourly_AB_02()

layout(matrix(seq(2)))

plot(df_00, pch = 16, cex = 0.8, col = "red")
points(df_01, pch = 16, cex = 0.8, col = "black")
title("example_pat_failure_A -- PurpleAirQC_hourly_AB_01")

plot(df_00, pch = 16, cex = 0.8, col = "red")
points(df_02, pch = 16, cex = 0.8, col = "black")
title("example_pat_failure_A -- PurpleAirQC_hourly_AB_02")


layout(1)
# }