Fine Particulate Matter Emission (PM2.5) in United States
Ujjwal Shukla
Wednesday, September 17, 2014
Exploratory Data Analysis - Course Project 2
NOTE: My work and answers to the questions are at the bottom of this document.
Introduction
Fine particulate matter (PM2.5) is an ambient air pollutant for which there is strong evidence that it is harmful to human health. In the United States, the Environmental Protection Agency (EPA) is tasked with setting national ambient air quality standards for fine PM and for tracking the emissions of this pollutant into the atmosphere. Approximatly every 3 years, the EPA releases its database on emissions of PM2.5. This database is known as the National Emissions Inventory (NEI). You can read more information about the NEI at the EPA National Emissions Inventory web site.
For each year and for each type of PM source, the NEI records how many tons of PM2.5 were emitted from that source over the course of the entire year. The data that you will use for this assignment are for 1999, 2002, 2005, and 2008.
Data
The data for this assignment are available from the course web site as a single zip file:
The zip file contains two files:
PM2.5 Emissions Data (summarySCC_PM25.rds): This file contains a data frame with all of the PM2.5 emissions data for 1999, 2002, 2005, and 2008. For each year, the table contains number of tons of PM2.5 emitted from a specific type of source for the entire year. Here are the first few rows.
## fips SCC Pollutant Emissions type year
## 4 09001 10100401 PM25-PRI 15.714 POINT 1999
## 8 09001 10100404 PM25-PRI 234.178 POINT 1999
## 12 09001 10100501 PM25-PRI 0.128 POINT 1999
## 16 09001 10200401 PM25-PRI 2.036 POINT 1999
## 20 09001 10200504 PM25-PRI 0.388 POINT 1999
## 24 09001 10200602 PM25-PRI 1.490 POINT 1999fips: A five-digit number (represented as a string) indicating the U.S. countySCC: The name of the source as indicated by a digit string (see source code classification table)Pollutant: A string indicating the pollutantEmissions: Amount of PM2.5 emitted, in tonstype: The type of source (point, non-point, on-road, or non-road)year: The year of emissions recorded
Source Classification Code Table (Source_Classification_Code.rds): This table provides a mapping from the SCC digit strings int he Emissions table to the actual name of the PM2.5 source. The sources are categorized in a few different ways from more general to more specific and you may choose to explore whatever categories you think are most useful. For example, source “10100101” is known as “Ext Comb /Electric Gen /Anthracite Coal /Pulverized Coal”.
You can read each of the two files using the readRDS() function in R. For example, reading in each file can be done with the following code:
## This first line will likely take a few seconds. Be patient!
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")as long as each of those files is in your current working directory (check by calling dir() and see if those files are in the listing).
Assignment
The overall goal of this assignment is to explore the National Emissions Inventory database and see what it say about fine particulate matter pollution in the United states over the 10-year period 1999-2008. You may use any R package you want to support your analysis.
Introduction
Fine particulate matter (PM2.5) is an ambient air pollutant for which there is strong evidence that it is harmful to human health. In the United States, the Environmental Protection Agency (EPA) is tasked with setting national ambient air quality standards for fine PM and for tracking the emissions of this pollutant into the atmosphere. Approximatly every 3 years, the EPA releases its database on emissions of PM2.5. This database is known as the National Emissions Inventory (NEI). You can read more information about the NEI at the EPA National Emissions Inventory web site.
For each year and for each type of PM source, the NEI records how many tons of PM2.5 were emitted from that source over the course of the entire year. The data used are for 1999, 2002, 2005, and 2008.
Data Preparation
The first step is to maker sure the data file is downloaded and extracted
# Download archive file, if it does not exist
if(!(file.exists("summarySCC_PM25.rds") &&
file.exists("Source_Classification_Code.rds"))) {
archiveFile <- "NEI_data.zip"
if(!file.exists(archiveFile)) {
archiveURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download.file(url=archiveURL,destfile=archiveFile,method="curl")
}
unzip(archiveFile)
}We now load the NEI and SCC data frames from the .rds files.
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")View the data imported
head(NEI)## fips SCC Pollutant Emissions type year
## 4 09001 10100401 PM25-PRI 15.714 POINT 1999
## 8 09001 10100404 PM25-PRI 234.178 POINT 1999
## 12 09001 10100501 PM25-PRI 0.128 POINT 1999
## 16 09001 10200401 PM25-PRI 2.036 POINT 1999
## 20 09001 10200504 PM25-PRI 0.388 POINT 1999
## 24 09001 10200602 PM25-PRI 1.490 POINT 1999head(SCC)## SCC Data.Category
## 1 10100101 Point
## 2 10100102 Point
## 3 10100201 Point
## 4 10100202 Point
## 5 10100203 Point
## 6 10100204 Point
## Short.Name
## 1 Ext Comb /Electric Gen /Anthracite Coal /Pulverized Coal
## 2 Ext Comb /Electric Gen /Anthracite Coal /Traveling Grate (Overfeed) Stoker
## 3 Ext Comb /Electric Gen /Bituminous Coal /Pulverized Coal: Wet Bottom
## 4 Ext Comb /Electric Gen /Bituminous Coal /Pulverized Coal: Dry Bottom
## 5 Ext Comb /Electric Gen /Bituminous Coal /Cyclone Furnace
## 6 Ext Comb /Electric Gen /Bituminous Coal /Spreader Stoker
## EI.Sector Option.Group Option.Set
## 1 Fuel Comb - Electric Generation - Coal
## 2 Fuel Comb - Electric Generation - Coal
## 3 Fuel Comb - Electric Generation - Coal
## 4 Fuel Comb - Electric Generation - Coal
## 5 Fuel Comb - Electric Generation - Coal
## 6 Fuel Comb - Electric Generation - Coal
## SCC.Level.One SCC.Level.Two
## 1 External Combustion Boilers Electric Generation
## 2 External Combustion Boilers Electric Generation
## 3 External Combustion Boilers Electric Generation
## 4 External Combustion Boilers Electric Generation
## 5 External Combustion Boilers Electric Generation
## 6 External Combustion Boilers Electric Generation
## SCC.Level.Three
## 1 Anthracite Coal
## 2 Anthracite Coal
## 3 Bituminous/Subbituminous Coal
## 4 Bituminous/Subbituminous Coal
## 5 Bituminous/Subbituminous Coal
## 6 Bituminous/Subbituminous Coal
## SCC.Level.Four Map.To Last.Inventory.Year
## 1 Pulverized Coal NA NA
## 2 Traveling Grate (Overfeed) Stoker NA NA
## 3 Pulverized Coal: Wet Bottom (Bituminous Coal) NA NA
## 4 Pulverized Coal: Dry Bottom (Bituminous Coal) NA NA
## 5 Cyclone Furnace (Bituminous Coal) NA NA
## 6 Spreader Stoker (Bituminous Coal) NA NA
## Created_Date Revised_Date Usage.Notes
## 1
## 2
## 3
## 4
## 5
## 6Load the packages used in the exploratory analysis
library(ggplot2)
library(plyr)Further Pre-processing of the data is done.
## Converting "year", "type", "Pollutant", "SCC", "fips" to factor
colToFactor <- c("year", "type", "Pollutant","SCC","fips")
NEI[,colToFactor] <- lapply(NEI[,colToFactor], factor)
head(levels(NEI$fips))## [1] " NA" "00000" "01001" "01003" "01005" "01007"## The levels have NA as " NA", so converting that level back to NA
levels(NEI$fips)[1] = NA
NEIdata<-NEI[complete.cases(NEI),]
colSums(is.na(NEIdata))## fips SCC Pollutant Emissions type year
## 0 0 0 0 0 0Questions
Following questions and tasks are targetted by the exploratory analysis
Question 1
Have total emissions from PM2.5 decreased in the United States from 1999 to 2008? Using the base plotting system, make a plot showing the total PM2.5 emission from all sources for each of the years 1999, 2002, 2005, and 2008.
First aggregate the data in
totalEmission <- aggregate(Emissions ~ year, NEIdata, sum)
totalEmission## year Emissions
## 1 1999 7332967
## 2 2002 5635780
## 3 2005 5454703
## 4 2008 3456273Plotting the total Emissions over time using a base plotting
barplot(
(totalEmission$Emissions)/10^6,
names.arg=totalEmission$year,
xlab="Year",
ylab="PM2.5 Emissions (10^6 Tons)",
main="Total PM2.5 Emissions From All US Sources"
)
As observed from the plot, the total emissions have decreased in the US from 1999 to 2008
Question 2
Have total emissions from PM2.5 decreased in the Baltimore City, Maryland (fips == “24510”) from 1999 to 2008? Use the base plotting system to make a plot answering this question.
Subset the data for fips == “24510” and then aggregate them by summing the Emissions per years
NEIdataBaltimore<-subset(NEIdata, fips == "24510")
totalEmissionBaltimore <- aggregate(Emissions ~ year, NEIdataBaltimore, sum)
totalEmissionBaltimore## year Emissions
## 1 1999 3274
## 2 2002 2454
## 3 2005 3091
## 4 2008 1862Plotting the Total Emissions for baltimore over Time
barplot(
(totalEmissionBaltimore$Emissions)/10^6,
names.arg=totalEmissionBaltimore$year,
xlab="Year",
ylab="PM2.5 Emissions (10^6 Tons)",
main="Total PM2.5 Emissions From All Baltimore City Sources"
)
As Observed, The total PM2.5 have not continously decreased, They decreased from 1999 to 2002, but have increasedin 2005 and then decreased.
Question 3
Of the four types of sources indicated by the type (point, nonpoint, onroad, nonroad) variable, which of these four sources have seen decreases in emissions from 1999-2008 for Baltimore City? Which have seen increases in emissions from 1999-2008? Use the ggplot2 plotting system to make a plot answer this question.
g<-ggplot(aes(x = year, y = Emissions, fill=type), data=NEIdataBaltimore)
g+geom_bar(stat="identity")+
facet_grid(.~type)+
labs(x="year", y=expression("Total PM"[2.5]*" Emission (Tons)")) +
labs(title=expression("PM"[2.5]*" Emissions, Baltimore City 1999-2008 by Source Type"))+
guides(fill=FALSE)
As seen from the graphs, the “NON-ROAD”, “NONPOINT” and “ON-ROAD” type of sources have shown a decrease in the total PM2.5 Emissions. “POINT” type of source, shows the increase in the total PM2.5 emissions from 1999-2005 but again a decrease in 2008
Question 4
Across the United States, how have emissions from coal combustion-related sources changed from 1999-2008?
## making the names in the SCC dataframe pretty by removing \\. in all the names
names(SCC)<-gsub("\\.","", names(SCC))Note: The SCC levels go from generic to specific. We assume that coal combustion related SCC records are those where SCC.Level.One contains the substring ‘comb’ and SCC.Level.Four contains the substring ‘coal’.
SCCcombustion<-grepl(pattern = "comb", SCC$SCCLevelOne, ignore.case = TRUE)
SCCCoal<-grepl(pattern = "coal", SCC$SCCLevelFour, ignore.case = TRUE)
## extracting the SCC in
SCCCoalCombustionSCC<-SCC[SCCcombustion & SCCCoal,]$SCC
NIECoalCombustionValues<-NEIdata[NEIdata$SCC %in% SCCCoalCombustionSCC,]
NIECoalCombustionTotalEm<-aggregate(Emissions~year, NIECoalCombustionValues, sum)Plotting the subset of NEI data with SCC matched with coal and combustion.
g<-ggplot(aes(year, Emissions/10^5), data=NIECoalCombustionTotalEm)
g+geom_bar(stat="identity",fill="grey",width=0.75) +
guides(fill=FALSE) +
labs(x="year", y=expression("Total PM"[2.5]*" Emission (10^5 Tons)")) +
labs(title=expression("PM"[2.5]*" Coal Combustion Source Emissions Across US from 1999-2008"))
As is viewed in the graph, Coal cumbustion is showing a decreasing trend with a sligh increase from 2002-2005, and then a decrease after
Question 5
How have emissions from motor vehicle sources changed from 1999-2008 in Baltimore City?
First we subset the motor vehicles, which we assume is anything like Vehicle in EISector column
SCCvehicle<-grepl(pattern = "vehicle", SCC$EISector, ignore.case = TRUE)
SCCvehicleSCC <- SCC[SCCvehicle,]$SCC
## using this boolean vector get the interested rows from the baltimore data
NEIvehicleSSC <- NEIdata[NEIdata$SCC %in% SCCvehicleSCC, ]
NEIvehicleBaltimore <- subset(NEIvehicleSSC, fips == "24510")
NIEvehicleBaltimoreTotEm<-aggregate(Emissions~year, NEIvehicleBaltimore, sum)Plotting the year-Emissions
g<-ggplot(aes(year, Emissions/10^5), data=NIEvehicleBaltimoreTotEm)
g+geom_bar(stat="identity",fill="grey",width=0.75) +
guides(fill=FALSE) +
labs(x="year", y=expression("Total PM"[2.5]*" Emission (10^5 Tons)")) +
labs(title=expression("PM"[2.5]*" Motor Vehicle Source Emissions in Baltimore from 1999-2008"))
Question 6
Compare emissions from motor vehicle sources in Baltimore City with emissions from motor vehicle sources in Los Angeles County, California (fips == “06037”). Which city has seen greater changes over time in motor vehicle emissions?
Subset the VehiclesSSC based on fips and add a column for the city name, then combine the two data frame to generate data for both cities
NEIvehicleBalti<-subset(NEIvehicleSSC, fips == "24510")
NEIvehicleBalti$city <- "Baltimore City"
NEIvehiclela<-subset(NEIvehicleSSC, fips == "06037")
NEIvehiclela$city <- "Los Angeles County"
NEIBothCity <- rbind(NEIvehicleBalti, NEIvehiclela)Plot the result by facets for each city
ggplot(NEIBothCity, aes(x=year, y=Emissions, fill=city)) +
geom_bar(aes(fill=year),stat="identity") +
facet_grid(.~city) +
guides(fill=FALSE) + theme_bw() +
labs(x="year", y=expression("Total PM"[2.5]*" Emission (Kilo-Tons)")) +
labs(title=expression("PM"[2.5]*" Motor Vehicle Source Emissions in Baltimore & LA, 1999-2008"))
To View the maximum change in the emission levels
aggregateEmissions <- aggregate(Emissions~city+year, data=NEIBothCity, sum)
aggregate(Emissions~city, data=aggregateEmissions, range)## city Emissions.1 Emissions.2
## 1 Baltimore City 88.28 346.82
## 2 Los Angeles County 3931.12 4601.41So it is observed that Los Angeles County has seen greater changes over time in vehicle emissions