In MazamaScience/AirSensor2: Process and Display Data from Air Quality Sensors

knitr::opts_chunk$set(fig.width = 7, fig.height = 5)

Test PurpleAir QC REport

Champaign Urbana on 2023-09-11

# AirSensor2 package
library(AirSensor2)

# Set user's PurpleAir_API_READ_KEY
source('/Users/jonathancallahan/Projects/MazamaScience/AirSensor2/global_vars.R')

# Initialize spatial datasets
initializeMazamaSpatialUtils()

# Create a new 'pas' object for Okanogan county
champaign_pas <-
  pas_createNew(
    api_key = PurpleAir_API_READ_KEY,
    countryCodes = "US",
    stateCodes = "IL",
    counties = c("Champaign"),
    lookbackDays = 365,         # all sensors from the past year
    location_type = 0           # outdoor sensors only
  )

pas <-
  champaign_pas %>%
  pas_filter(sensor_index == "152934")

PurpleAir Sensor #152934 -- Poplar & Holts

pas %>% pas_leaflet()

# Poplar & Holts sensor occasionally has some very high values

# Create raw pat object
pat <-
  pat_create(
    api_key = PurpleAir_API_READ_KEY,
    pas = champaign_pas,
    sensor_index = "152934",
    startdate = "2023-09-01",
    enddate = "2023-09-12",
    timezone = "UTC",
    verbose = TRUE
  )

tbl <- pat$data

Sensor Electronics

#plot(tbl[,c(1,2:5)], pch = 15, cex = 0.5, main = "Sensor Electronics")
layout(matrix(seq(4)))
plot(tbl[,c(1,3)], pch = 15, cex = 0.5, main = "uptime")
plot(tbl[,c(1,5)], pch = 15, cex = 0.5, main = "memory")
plot(tbl[,c(1,2)], pch = 15, cex = 0.5, main = "wifi strength")
plot(tbl[,c(1,4)], pch = 15, cex = 0.5, main = "latency")
layout(1)

Atmospheric Variables

#plot(tbl[,c(1,6:8)], pch = 15, cex = 0.5, main = "Atmospheric Variables")
layout(matrix(seq(3)))
plot(tbl[,c(1,6)], pch = 15, cex = 0.5, main = "humidity")
plot(tbl[,c(1,7)], pch = 15, cex = 0.5, main = "temperature")
plot(tbl[,c(1,8)], pch = 15, cex = 0.5, main = "pressure")
layout(1)

PM2.5 "atm"

#plot(tbl[,c(1,12:14)], pch = 15, cex = 0.5, main = "PM2.5 'atm'")
plot(tbl[,c(1,12)], pch = 15, cex = 0.5, col = "transparent", main = "pm2.5_atm")
points(tbl[,c(1,13)], pch = 15, cex = 0.5, col = adjustcolor("blue", 0.3))
points(tbl[,c(1,14)], pch = 15, cex = 0.5, col = adjustcolor("red", 0.3))

PM2.5 AB Correlation

correlation <- cor(tbl$pm2.5_atm_a, tbl$pm2.5_atm_b) %>% round(3)
title <- sprintf("A/B Channel Correlation = %0.3f", correlation)
plot(tbl[,c(13:14)], pch = 15, cex = 0.5, main = title)