Will I choke? – when is the best time for outdoor activities on Penang Island

Key points of post

• To assess whether air pollution index (API) levels in Penang during 2014 are unhealthy
• To assess how API varies seasonally and diurnally

Last year our blog post on air quality on Penang Island was particularly popular. In that post we used a comparatively crude ‘visibility index’ collected at Penang International Airport since 2001.  The data suggested that pollution (visibility) had gotten worse since 2001 and that it tended to be worse in June and July.

We discovered recently, however, that the Malaysian Government’s Department of the Environment (DOE) collects much more detailed data on air pollution at 52 stations across the country, see http://bit.ly/1wUMxYG.  Conveniently, one of the stations is located at Universiti Sains Malaysia (USM) and we've managed to acquire this data scraped off the DOE site by Keith Rozario (nice work Keith!) using python and MySQL. He provides a nice summary of the nationwide API data from August 2013 to February 2015 and the link to all the scripts he used (on github) to scrape the data on his site - please see http://bit.ly/1EnwTLs. He was also generous enough to make a zipped copy of the dataset available for the public to use under the Creative Commons Attribution 4.0 license.

Inspired by an analysis (http://bit.ly/1B6o2eM) by ‘Typhoon Tony’ from Taiwan, we sought to investigate the magnitude of air pollution on Penang Island, and how it varies seasonally and diurnally. 

Air pollution is typically quantified using the air pollution index (API) which is calculated from the following five major air pollutants: sulphur dioxide, nitrogen dioxide, carbon monoxide, particulate matter, and ozone, for details see http://bit.ly/1wUMxYG. APIs between 101 and 200 are considered to be ‘unhealthy’ especially for those with existing heart and/or lung problems.

So how does Penang stack up? We’re always hearing how terrible pollution is on the island, but is it actually true ? In the ‘calendar’ plot below, we merged the API data with hourly observations on wind-speed and direction from Weather Underground.  The seasonality in API is clear. We had high API values in February and March 2014, but the highest were in June and July which corresponds to the south-west monsoon transporting smoke from forest fires in Sumatera.  

We summarize diurnal variability in API (and to a certain extent seasonality too) in the ‘rose’ plot below.  It suggests that only rarely can air pollution on the island be considered ‘unhealthy’ since it is typically below 100.  If you are, however, concerned your best bet for strenuous outdoor activities is during the night or in the morning up until 1pm when levels of API start to increase.  The least healthy time of day to be out is rush hour between 4pm and 6pm. You may also want to avoid February, March, June and July!

Do note, however, that the API monitoring station at USM is relatively isolated from the island’s urban pell-mell and may not be a good overall reflection/summary of air pollution on the island. 

The R code used to produce the plots shown above are provided below. We are currently working on trying to scrape the API data off the Malaysian DOE website using R (which would make things a lot more convenient!) and once we've managed to do that, we will update the code accordingly.

# Load required libraries
 
library(ggplot2)
library(reshape)
library(lubridate)
library(zoo)
library(openair)
library(scales)
library(grid)
library(extrafont)
library(weatherData)
 
loadfonts()
 
# Set working directory
 
setwd("D:/Projects/AP/ReadingsByRegion/")
 
# Read data from csv file 
 
df <- read.table('USM.csv',header=F,sep=":")
 
# Rename columns
 
colnames(df) <- c("station_id","station_name","dates","hour","api","unk","unk1")
 
# Remove last 2 columns - not relevant information
 
df <- df[-c(6,7)]
 
# Add minute column
 
df$minute <- as.character("00")
 
df$time <- as.character(paste(df$hour,df$minute,sep = ":" ))
 
df$date <- as.POSIXct(paste(df$dates,df$time, format = "%Y-%m-%d %H:%M" ))
 
# Convert 0 to NA
 
df$api[df$api == 0] <- NA
 
# Date and time columns
 
df$year<-as.numeric(as.POSIXlt(df$date)$year+1900)
df$month<-as.numeric(as.POSIXlt(df$date)$mon+1)
df$monthf<-factor(df$month,levels=as.character(1:12),labels=c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"),ordered=TRUE)
df$weekday = as.numeric(format(as.POSIXlt(df$date),"%u"))
df$yearmonth<-as.yearmon(df$dates)
df$yearmonthf<-factor(df$yearmonth)
df$week <- as.numeric(format(as.POSIXlt(df$date),"%W"))
df$day = strftime(df$date,'%A')
df$jday <- strptime(df$date, "%Y-%m-%d")$yday+1
df$hour <- as.numeric(format(strptime(df$date, format = "%Y-%m-%d %H:%M"),format = "%H"))
 
 
##### Creating roseplot of pollutant magnitude over 24 hours #####
 
# Subset data for 2014
 
df2014 <- subset(df,year==2014)
 
# Set Color Table
 
colortable = c("#99FFFF", "#00FFFF", "#00FF00", "#CCFF33", "#FFFF00",
               "#FFCC00", "#FF6600", "#FF3333", "#FF33CC", "#660033")
 
# Cut data into ten parts
 
STN = "USM"
Time_of_Day = df2014$hour[df2014$station_name==STN]
mag = cut_number(round(df2014$api,100)[df2014$station_name==STN],n = 10)
rosedata = data.frame(dir=Time_of_Day,mag=mag)
 
# Plot rose chart
 
rc <- ggplot(rosedata,aes(x=Time_of_Day,fill=mag))+  geom_bar(binwidth = 1, drop = TRUE) +
      coord_polar() + xlab("") + ylab("") + 
      scale_x_continuous(breaks = seq(0,23),
      labels=c("12.00am","1:00am","2:00am","3:00am","4:00am","5:00am","6:00am",
      "7:00am","8:00am","9:00am","10:00am","11:00am","12:00pm","1:00pm",
      "2:00pm","3:00pm","4:00pm","5:00pm","6:00pm","7:00pm","8:00pm","9:00pm",
      "10:00pm","11:00pm")) +
      ggtitle("\nDaily API readings in 2014") +  scale_fill_manual(values=colortable,name="API Reading")+
      theme(panel.background=element_blank(),
      axis.title.y=element_text(size=10,hjust=0.75,colour="grey20"),
      axis.title.x=element_text(size=7,colour="grey20"),
      legend.title=element_text(size=13,colour="grey20",face="bold",family="Myriad Pro"),
      legend.text=element_text(size=11,colour="grey20",face="bold",family="Myriad Pro"),
      panel.grid=element_blank(),
      axis.ticks=element_blank(),
      axis.text.y=element_text(size=10,colour="grey20",family="Clear Sans"),
      axis.text.x=element_text(size=10,colour="grey20",face="bold",family="Clear Sans"),
      plot.title = element_text(lineheight=1, face="bold",size = 20, colour = "grey20",family="Graphik-Black"),
      plot.margin = unit(c(-0.5,0.25,-0.25,0.25), "in"),
      legend.key.width=unit(c(0.2,0.2),"in"))
 
rc
 
# Save plot to png
 
ggsave(file="D:/rc.png",dpi=300,w=10,h=8,unit="in",type="cairo-png")
 
 
### Reading weather data from Weather Underground using weatherData library ####
 
# Get weather data for 2014
 
we <- getWeatherForDate("WMKP", "2014-01-01","2015-12-31", opt_detailed=T, opt_custom_columns=T, custom_columns=c(1,6,7,8,9,13,14))
 
# Rename columns
 
colnames(we) <- c("date","date_myt","vis_km","wda","ws","gspd_kmh","wd","date_utc")
 
# Create date and time columns
 
we$date<-as.POSIXct(we$date)
we$hour <- as.numeric(format(strptime(we$date, format = "%Y-%m-%d %H:%M"),format = "%H"))
we$min <- as.numeric(format(strptime(we$date, format = "%Y-%m-%d %H:%M"),format = "%M"))
 
# Only use data on the hour
 
we_df<- subset(we, min == 0)
 
# Remove duplicate data
 
we_df$date[duplicated(we_df$date)]
 
# Merge API and weather data
 
we_api <- merge(we_df, df2014, by=c("date","date")) 
 
# Reformat yearmonth column for use in openair library
 
we_api$yearmonth <- as.factor(we_api$yearmonth)
 
# Reformat wind speed column
 
we_api$ws[we_api$ws == "Calm"] <- 0
we_api$ws <- as.numeric(we_api$ws)
 
# Plot 'calendar plot' and save as png
 
png(filename = "D:/Projects/AP/USM_API_2014_CalendarPlot.png",height=8,width=10,
    bg = "white",units='in', res = 400, family = "",  type = "cairo-png")
 
calendarPlot(we_api, pollutant = "api",year="2014",
             main = "Daily average API readings at USM\n
             with wind-speed scaled wind direction overlay (2014)\n",
             key.header = "API\n(Air Pollutant Index)",
             cols="heat", annotate = "ws")
 
dev.off()

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