Ambient Air Pollutant

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. Approximately 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.

Review Criteria

For each question
1. Does the plot appear to address the question being asked?
2. Is the submitted code appropriate for construction of the submitted plot?

Data

The data for this assignment are available from the web site as a single zip file: Data for Assessment

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: A five-digit number (represented as a string) indicating the U.S. county
SCC: The name of the source as indicated by a digit string (see source code classification table)
Pollutant: A string indicating the pollutant
Emissions: Amount of PM2.5 emitted, in tons
type: 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 in the 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 readr::read_rds() function in R. For example, reading in each file can be done with the following code:

nei<-read_rds("summarySCC_PM25.rds")
scc<-read_rds("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 must use ggplot2 to produce your plots

Questions

You must address the following questions and tasks in your exploratory analysis. For each question/task you will need to make a single plot. You must use ggplot2 to make your plot.

Question 1 Have total emissions from PM2.5 decreased in the United States from 1999 to 2008? Make a plot showing the total PM2.5 emission from all sources for each of the years 1999, 2002, 2005, and 2008.

pblm1<-nei %>% 
  group_by(year) %>%
  summarize(emissions=mean(Emissions))

ggplot(data=pblm1, mapping=aes(x=year,y=emissions)) + 
    geom_line(linetype="dashed", color="#bcbddc") +
    geom_point(color="#756bb1") +
  labs(subtitle='PM2.5 emissions have decreased in the US')

Question 2. Have total emissions from PM2.5 decreased in San Antonio, Texas (fips==“48029”) from 1999 to 2008?

pblm2<-nei %>% 
  group_by(year) %>%
  filter(fips=="48029") %>%
  summarize(emissions=mean(Emissions))

ggplot(data=pblm2, mapping=aes(x=year,y=emissions)) + 
  geom_line(linetype="dashed", color="#bcbddc") +
  geom_point(color="#756bb1") +
  labs(subtitle='PM2.5 emissions have decreased in Bexar Co.')

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 San Antonio? Which have seen increases in emissions from 1999–2008?

pblm3<-nei %>%
  group_by(type,year) %>%
  summarize(emissions=mean(Emissions))
  
ggplot(data=pblm3, mapping=aes(x=year,y=emissions,group=type)) + 
  geom_line(aes(color=type)) +
  geom_point(aes(color=type)) +
  scale_color_brewer(palette="Dark2") +
  theme(legend.position="bottom") +
  labs(subtitle='PM2.5 emissions have decreased for all types')

Question 4. Across the United States, how have emissions from coal combustion-related sources changed from 1999–2008?

find_coal<-scc[grepl("Coal",scc$Short.Name),]
pblm4<-nei %>%
  filter(SCC %in% find_coal$SCC)

ggplot(data=pblm4,aes(x=as.factor(year),y=Emissions,fill=type)) +
  geom_bar(stat="identity") +
  labs(title="Coal related emmisions") +
  theme(legend.position="bottom") +
  scale_color_brewer(palette="Dark2") 

Question 5. How have emissions from motor vehicle sources changed from 1999–2008 in San Antonio?

pblm5<-nei %>%
  filter(fips=="48029" & type=="ON-ROAD") %>%
  group_by(type, year, fips) %>%
  summarize(Emissions=sum(Emissions))

ggplot(data=pblm5, mapping=aes(x=year,y=Emissions)) + 
  geom_line(linetype="dashed", color="#bcbddc") +
  geom_point(color="#756bb1") +
  labs(subtitle='Motor Vehicle emissions in Bexar Co.')

Question 6. Compare emissions from motor vehicle sources in San Antonio 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?

pblm6<-nei %>%
  filter(fips %in% c("48029","06037") & type=="ON-ROAD") %>%
  group_by(type, year, fips) %>%
  summarize(Emissions=sum(Emissions))

ggplot(data=pblm6, mapping=aes(x=year,y=Emissions,group=fips)) + 
  geom_line(aes(color=fips)) +
  geom_point(aes(color=fips)) +
  scale_color_brewer(palette="Dark2") +
  theme(legend.position="bottom") +
  labs(subtitle='Vehicle emmisions in Bexar Co. have decreased more than LA Co.')