Fine particulate matter variations in United States (EPA's source)

Author(s): Borja V. Sorli Sanz - borjavss@gmail.com - @borjavss -

Introduction

Fine particulate matter \( PM^(2.5) \) 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 \( PM^(2.5) \). 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.

Data Processing

The data for this assignment are available from the web site as a single zip file: Data (29Mb)

The data is provided as a compressed zip file, we should unzip it.

unzip("exdata-data-NEI_data.zip", overwrite = T)  # unziping

## Warning: error 1 in extracting from zip file

The zip file contains two files:

• \( PM^(2.5) \) 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

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 1999

• 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 \( PM^(2.5) \) 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”.

head(SCC, n = 3)

##        SCC Data.Category
## 1 10100101         Point
## 2 10100102         Point
## 3 10100201         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
##                                EI.Sector Option.Group Option.Set
## 1 Fuel Comb - Electric Generation - Coal                        
## 2 Fuel Comb - Electric Generation - Coal                        
## 3 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
##                 SCC.Level.Three
## 1               Anthracite Coal
## 2               Anthracite Coal
## 3 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
##   Created_Date Revised_Date Usage.Notes
## 1                                      
## 2                                      
## 3

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).

Process

The overall goal of this document 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. Our code downloads data automatically.

We will address the following questions/tasks in our exploratory analysis. For each question/task we will need to make a single plot.

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.

years <- c("1999", "2002", "2005", "2008")
means <- vector()  #initializing data
for (i in years) {
    means[i] <- mean(NEI$Emissions[which(NEI$year == i)], na.rm = TRUE)
}
# basic plot
barplot(means, col = rainbow(20, start = 0, end = 1), main = "Mean PM_2.5 emissions (Tons)")

1. 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.

means.baltimore <- vector()  # preparing plot data
for (i in years) {
    means.baltimore[i] <- mean(NEI$Emissions[which(NEI$year == i & NEI$fips == 
        "24510")], na.rm = TRUE)
}
# basic plot
barplot(means.baltimore, col = terrain.colors(2), main = "Mean PM_2.5 emissions (Tons)\n in Baltimore city")

1. 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.

data.baltimore <- NEI[which((NEI$fips == "24510") & (NEI$Emissions < 1000)), 
    ]  #initializing data
# plotting:
require("ggplot2")

## Loading required package: ggplot2

g2 <- ggplot(data.baltimore, aes(year, Emissions))
g2 + geom_point(aes(color = type), size = 10, alpha = 0.3) + facet_grid(. ~ 
    type) + geom_smooth(size = 2, color = "black", linetype = 1, method = "lm", 
    se = FALSE)

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

# creating plot data:
d.tmp1 <- NEI[NEI$SCC %in% SCC[grep("Coal", SCC$EI.Sector), 1], ]
d.tmp2 <- SCC[, c(1, 4)]
data.coal <- merge(d.tmp1, d.tmp2, by.x = "SCC", by.y = "SCC")[, c(4, 6, 7)]
# plotting:
require("ggplot2")
g4 <- ggplot(data.coal, aes(x = year, y = Emissions))
g4 + geom_point(aes(color = EI.Sector), size = 10, alpha = 0.3) + facet_grid(. ~ 
    EI.Sector) + geom_smooth(size = 2, color = "black", linetype = 1, method = "lm", 
    se = FALSE)

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

# reorganizing plot data:
d.tmp1 <- NEI[NEI$SCC %in% SCC[grep("Mobile", SCC$EI.Sector), 1], ]
d.tmp3 <- d.tmp1[which(d.tmp1$fips == "24510"), ]
d.tmp2 <- SCC[, c(1, 4)]
d5 <- merge(d.tmp3, d.tmp2, by.x = "SCC", by.y = "SCC")[, c(4, 6, 7)]
# removing outliers and sources out of our interest:
d5[d5$Emissions > 15, ] <- NA
d5 <- d5[which(d5$EI.Sector != "Mobile - Commercial Marine Vessels"), ]
d5 <- d5[which(d5$EI.Sector != "Mobile - Aircraft"), ]
# plotting:
require("lattice")

## Loading required package: lattice

xyplot(Emissions ~ year | EI.Sector, d5, layout = c(4, 2), ylab = "Emissions", 
    xlab = "years", panel = function(x, y) {
        panel.xyplot(x, y)
        panel.lmline(x, y, lty = 1, col = "red")
        par.strip.text = list(cex = 0.8)
    }, as.table = T)

1. 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?

# reorganizing plot data:
data <- rbind(d.baltim <- NEI[which(NEI$fips == "24510"), ], d.lac <- NEI[which(NEI$fips == 
    "06037"), ])
data$fips[which(data$fips == "24510")] <- "Baltimore City"
data$fips[which(data$fips == "06037")] <- "Los Angeles County"
names(data)[1] <- "Cities"
# plotting:
require("ggplot2")
g6 <- ggplot(data, aes(x = year, y = Emissions, fill = Cities))
g6 + geom_bar(stat = "identity", position = position_dodge())

Conclusion

Regarding Fine Particule levels (\( PM^(2.5) \)) arround US, different r open library packages and different types of graphics are used to accomplish different goals proposed. Remark that data levels are given each 3 years, for Environmental Protection Agency (EPA). These codes script could be modified to solve future questions.