knitr::opts_chunk$set(echo = TRUE)

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).

Data for this project is for the years 1999, 2002, 2005, and 2008.

Synopsis

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. R package will be used to support the analysis.

Data Processing

Setting the Work Directory and Downloading EPA File From the Internet

setwd("C:/Users/fkmho/Documents/Data_Science/Exploratory_Data_Analysis")
getwd()
## [1] "C:/Users/fkmho/Documents/Data_Science/Exploratory_Data_Analysis"
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip", destfile = "FNEI_data.csv")
# Call Packages dplyr, bindrcpp & ggplot2
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.3.3
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(bindrcpp)
## Warning: package 'bindrcpp' was built under R version 3.3.3
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.3.3

Downloaded zip file contains 2 distinct RDS files Source_Classification_Code.rds and summarySCC_PM25.rds

# reading and exploring NEI data - National Emmissions Data
NEI_data <- readRDS("summarySCC_PM25.rds")
# head data
head(NEI_data)
##     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 1999
# string data
str(NEI_data)
## 'data.frame':    6497651 obs. of  6 variables:
##  $ fips     : chr  "09001" "09001" "09001" "09001" ...
##  $ SCC      : chr  "10100401" "10100404" "10100501" "10200401" ...
##  $ Pollutant: chr  "PM25-PRI" "PM25-PRI" "PM25-PRI" "PM25-PRI" ...
##  $ Emissions: num  15.714 234.178 0.128 2.036 0.388 ...
##  $ type     : chr  "POINT" "POINT" "POINT" "POINT" ...
##  $ year     : int  1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 ...
# dimension data
dim(NEI_data)
## [1] 6497651       6
# reading and exploring SCC data - Source Classification Code Data
SCC_data <- readRDS("Source_Classification_Code.rds")
# head data
head(SCC_data) 
##        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                                      
## 6
# string data
str(SCC_data)
## 'data.frame':    11717 obs. of  15 variables:
##  $ SCC                : Factor w/ 11717 levels "10100101","10100102",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ Data.Category      : Factor w/ 6 levels "Biogenic","Event",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Short.Name         : Factor w/ 11238 levels "","2,4-D Salts and Esters Prod /Process Vents, 2,4-D Recovery: Filtration",..: 3283 3284 3293 3291 3290 3294 3295 3296 3292 3289 ...
##  $ EI.Sector          : Factor w/ 59 levels "Agriculture - Crops & Livestock Dust",..: 18 18 18 18 18 18 18 18 18 18 ...
##  $ Option.Group       : Factor w/ 25 levels "","C/I Kerosene",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Option.Set         : Factor w/ 18 levels "","A","B","B1A",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ SCC.Level.One      : Factor w/ 17 levels "Brick Kilns",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ SCC.Level.Two      : Factor w/ 146 levels "","Agricultural Chemicals Production",..: 32 32 32 32 32 32 32 32 32 32 ...
##  $ SCC.Level.Three    : Factor w/ 1061 levels "","100% Biosolids (e.g., sewage sludge, manure, mixtures of these matls)",..: 88 88 156 156 156 156 156 156 156 156 ...
##  $ SCC.Level.Four     : Factor w/ 6084 levels "","(NH4)2 SO4 Acid Bath System and Evaporator",..: 4455 5583 4466 4458 1341 5246 5584 5983 4461 776 ...
##  $ Map.To             : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Last.Inventory.Year: int  NA NA NA NA NA NA NA NA NA NA ...
##  $ Created_Date       : Factor w/ 57 levels "","1/27/2000 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Revised_Date       : Factor w/ 44 levels "","1/27/2000 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Usage.Notes        : Factor w/ 21 levels ""," ","includes bleaching towers, washer hoods, filtrate tanks, vacuum pump exhausts",..: 1 1 1 1 1 1 1 1 1 1 ...
# dimension data
dim(SCC_data)
## [1] 11717    15
# aggregating NEI emmissions by year
yearly_emmissions <- aggregate(Emissions ~ year, NEI_data, sum)
# plot1.ng
cols <- c("maroon", "orange", "yellow", "Aquamarine")
barplot(height=yearly_emmissions$Emissions/1000, names.arg=yearly_emmissions$year, xlab="Year", ylab=expression('Aggregated Emission'),main=expression('Aggregated PM'[2.5]*' Emmissions by Year'), col = cols)

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.

# forming Baltimore data which will be NEI_data subset
baltdata <- NEI_data[NEI_data$fips=="24510", ]
# Baltimore yearly emmisisons data
baltYrEmm <- aggregate(Emissions ~ year, baltdata, sum)
# plot2.png
cols1 <- c("maroon", "yellow", "orange", "Aquamarine")
barplot(height=baltYrEmm$Emissions/1000, names.arg=baltYrEmm$year, xlab="Year", ylab=expression('Aggregated Emission'),main=expression('Baltimore Aggregated PM'[2.5]*' Emmissions by Year'), col = cols1)

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.

# Baltimore yearly emmisisons data
baltYrTypEmm <- aggregate(Emissions ~ year+ type, baltdata, sum)
# plot3.png
chart <- ggplot(baltYrTypEmm, aes(year, Emissions, color = type))
chart <- chart + geom_line() +
  xlab("year") +
  ylab(expression('Total Emissions')) +
  ggtitle('Total Baltimore Emissions [2.5]* From 1999 to 2008')
print(chart)

Across the United States, how have emissions from coal combustion-related sources changed from 1999-2008?

# plot4.png
chart1 <- ggplot(baltYrTypEmm, aes(factor(year), Emissions))
chart1 <- chart + geom_bar(stat="identity") +
  xlab("year") +  
  ylab(expression('Total Emissions')) +
  ggtitle('Total [2.5]* Coal Emissions From 1999 to 2008')
print(chart1)

How have emissions from motor vehicle sources changed from 1999-2008 in Baltimore City?

# plot5.png
# Type: ON-ROAD, Fips = "24510" Baltimore Motor Vehicle PM[2.5]* Emissions from 1999 to 2008
chart <- ggplot(baltYrTypEmm, aes(factor(year), Emissions))
chart <- chart + geom_bar(stat="identity") +
  xlab("year") +
  ylab(expression('Total Emissions')) +
  ggtitle('Baltimore Motor Vehicle PM[2.5] Emissions From 1999 to 2008')
print(chart)

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?

# Comparing Baltimore, MD-24510 and Los Angeles, CA-06037
baltYrTypEmmFips <- summarise(group_by(filter(NEI_data, NEI_data$fips == "24510"& type == 'ON-ROAD'), year), Emissions=sum(Emissions))
laYrTypEmmFips <- summarise(group_by(filter(NEI_data, NEI_data$fips == "06037"& type == 'ON-ROAD'), year), Emissions=sum(Emissions))

baltYrTypEmmFips$County <- "Baltimore City, MD"
laYrTypEmmFips$County <- "Los Angeles County, CA"

baltLaEmissions <- rbind(baltYrTypEmmFips, laYrTypEmmFips)
# plot6.png
# Type: ON-ROAD, Fips = 24510 for Baltimore, MD Motor Vehicle PM[2.5]* Emissions Against Los Angeles, CA Fips = 06037  from 1999 to 2008
ggplot(baltLaEmissions, aes(x=factor(year), y=Emissions, fill=County,label = round(Emissions,2))) +
    geom_bar(stat="identity") + 
    facet_grid(County~., scales="free") +
    ylab(expression("total PM"[2.5]*" emissions in tons")) + 
    xlab("year") +
    ggtitle(expression("Baltimore City vs Los Angeles County Motor vehicle emission in tons"))+
    geom_label(aes(fill = County),colour = "yellow", fontface = "bold")

knitr::opts_chunk$set(echo = TRUE)