The overall goal of this project 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 language compiler
- R base graphic devices
- Tidyverse library packages
- RMarkdown library package
- Knitr library package
- Raw data set: Environmental Protection Agency National Emissions Inventory
- CODEBOOK.md: step-by-step book explaining the code processing.
- plotn.png: the plotted files
- plotn.R: the scripts to produce the plotted images
- script.Rmd: the script to compile the project from source
Inside the project root folder, execute the following
R
library(rmarkdown)
rmarkdown::render("script.Rmd", "html_document")
Pick libraries to use in the project
library(tidyverse)## ── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.0 ✓ purrr 0.3.3
## ✓ tibble 3.0.0 ✓ dplyr 0.8.5
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
Download and extract data set from EPA National Emissions Inventory (USA)
download.file(url = "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip", destfile = "mydata.zip")
unzip("mydata.zip", exdir = "./data")
file.remove("mydata.zip")Read tables into the Environment
# National Emissions Inventory (NEI)
nei <- read_rds("./data/summarySCC_PM25.rds")
# Source Classification Code (SCC)
scc <- read_rds("./data/Source_Classification_Code.rds")See a glimpse of the content
glimpse(nei)## Rows: 6,497,651
## Columns: 6
## $ fips <chr> "09001", "09001", "09001", "09001", "09001", "09001", "0900…
## $ SCC <chr> "10100401", "10100404", "10100501", "10200401", "10200504",…
## $ Pollutant <chr> "PM25-PRI", "PM25-PRI", "PM25-PRI", "PM25-PRI", "PM25-PRI",…
## $ Emissions <dbl> 15.714, 234.178, 0.128, 2.036, 0.388, 1.490, 0.200, 0.081, …
## $ type <chr> "POINT", "POINT", "POINT", "POINT", "POINT", "POINT", "POIN…
## $ year <int> 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999,…
glimpse(scc)## Rows: 11,717
## Columns: 15
## $ SCC <fct> 10100101, 10100102, 10100201, 10100202, 10100203,…
## $ Data.Category <fct> Point, Point, Point, Point, Point, Point, Point, …
## $ Short.Name <fct> "Ext Comb /Electric Gen /Anthracite Coal /Pulveri…
## $ EI.Sector <fct> Fuel Comb - Electric Generation - Coal, Fuel Comb…
## $ Option.Group <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,
## $ Option.Set <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,
## $ SCC.Level.One <fct> External Combustion Boilers, External Combustion …
## $ SCC.Level.Two <fct> Electric Generation, Electric Generation, Electri…
## $ SCC.Level.Three <fct> Anthracite Coal, Anthracite Coal, Bituminous/Subb…
## $ SCC.Level.Four <fct> "Pulverized Coal", "Traveling Grate (Overfeed) St…
## $ Map.To <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ Last.Inventory.Year <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ Created_Date <fct> , , , , , , , , , , , , , , , , , , , 6/6/2003 0:…
## $ Revised_Date <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,
## $ Usage.Notes <fct> , , , , , , , , , , , , , , , , , , , , , , , , ,
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.
Look for years tracked in inventory
unique(nei$year)## [1] 1999 2002 2005 2008
Filter total emissions for each year
total_emissions <- nei %>%
group_by(year) %>%
summarize(
sum_emissions = sum(Emissions)
)
total_emissions## # A tibble: 4 x 2
## year sum_emissions
## <int> <dbl>
## 1 1999 7332967.
## 2 2002 5635780.
## 3 2005 5454703.
## 4 2008 3464206.
Change column sum_emissions to integer
total_emissions <- total_emissions %>%
mutate_at(vars(sum_emissions), as.integer)
total_emissions## # A tibble: 4 x 2
## year sum_emissions
## <int> <int>
## 1 1999 7332966
## 2 2002 5635780
## 3 2005 5454703
## 4 2008 3464205
Make a barplot with the sum of emissions by each year
par(mfrow = c(1, 1))
plot(
x = total_emissions$year,
y = total_emissions$sum_emissions,
type = "l",
lwd = 2,
col = "blue",
ylim = c(0, 8e06),
xlab = "Year",
ylab = expression("Total PM"[2.5]*" emission (ton)"),
main = "Particulate Matter Emissions"
)
abline(
lm(
data = total_emissions,
formula = sum_emissions ~ year
),
lty = 2
)
2. Have total emissions from PM2.5 decreased in the Baltimore City, Maryland (fips==“24510”) from 1999 to 2008?
Filter sum of emissions in Baltimore City
nei_baltimore <- nei %>%
filter(fips == "24510") %>%
group_by(year) %>%
summarize(
sum_emissions = sum(Emissions)
)
nei_baltimore## # A tibble: 4 x 2
## year sum_emissions
## <int> <dbl>
## 1 1999 3274.
## 2 2002 2454.
## 3 2005 3091.
## 4 2008 1862.
Set the canvas to 1 plot and plot the results with a tendency line
par(mfrow = c(1, 1))
plot(
x = nei_baltimore$year,
y = nei_baltimore$sum_emissions,
type = "l",
lwd = 2,
col = "green",
ylim = c(0, 3500),
xlab = "Year",
ylab = expression("Total PM"[2.5]*" emission (ton)"),
main = "Particulate Matter Emissions in Baltimore"
)
abline(
lm(
data = nei_baltimore,
formula = sum_emissions ~ year
),
lty = 2
)
3. Of the four types of sources indicated by the type (point, nonpoint, onroad, nonroad) variable, which of these four sources have seen decreases in emissions from 1999–2008 for Baltimore City? Which have seen increases in emissions from 1999–2008?
Change NEI data set to work as a tibble (compatible with the tidyverse) and filter emissions from Baltimore by each type
nei <- as_tibble(nei)
pm_type_baltimore <- nei %>%
filter(fips == "24510") %>%
group_by(type, year) %>%
summarize(
emissions = sum(Emissions)
) %>%
arrange(year, type)Plot the results
ggplot(pm_type_baltimore, aes(year, emissions, group = type, color = type)) +
geom_point(na.rm = TRUE) +
geom_smooth() +
labs(
x = "Year",
y = expression("Total PM"[2.5]*" emission (ton)"),
color = "Type",
title = "Emissions in Baltimore City",
subtitle = "(1999-2008)",
shape = ""
) +
geom_smooth(
method = "lm",
lty = 2,
se = FALSE,
lwd = 0.2
) +
theme_minimal()
4. Across the United States, how have emissions from coal combustion-related sources changed from 1999–2008?
Set SCC as tibble.
Filter coal occurrences in SCC and use it as parameter to filter coal occurrences in NEI.
From filtered NEI get the sum of coal emissions by year and type.
tscc <- as.tibble(scc)## Warning: `as.tibble()` is deprecated as of tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
scc_coal <- tscc %>%
filter(grepl("coal", Short.Name, ignore.case = TRUE))
total_coal <- nei %>%
filter(SCC %in% scc_coal$SCC) %>%
group_by(year, type) %>%
summarize(
emissions = sum(Emissions)
)
print(total_coal)## # A tibble: 8 x 3
## # Groups: year [4]
## year type emissions
## <int> <chr> <dbl>
## 1 1999 NONPOINT 18683.
## 2 1999 POINT 583941.
## 3 2002 NONPOINT 68365.
## 4 2002 POINT 496575.
## 5 2005 NONPOINT 68365.
## 6 2005 POINT 501290.
## 7 2008 NONPOINT 8230.
## 8 2008 POINT 349854.
Plot the results in scatterplot and reggression line models
ggplot(total_coal, aes(year, emissions, group = type, color = type)) +
geom_point(na.rm = TRUE) +
geom_smooth(
method = "lm",
lty = 2,
se = FALSE,
color = "grey",
lwd = .5
) +
geom_smooth() +
labs(
x = "Year",
y = "CCE (ton)",
color = "Type",
title = "Total Coal Emissions",
subtitle = "(1999-2008)"
) +
theme_minimal()
scc_vehicle <- tscc %>%
filter(grepl("vehicle", SCC.Level.Two, ignore.case = TRUE))
total_vehicles_baltimore <- nei %>%
filter(SCC %in% scc_vehicle$SCC, fips == "24510") %>%
group_by(year, type) %>%
summarize(
emissions = sum(Emissions)
)
total_vehicles_baltimore## # A tibble: 8 x 3
## # Groups: year [4]
## year type emissions
## <int> <chr> <dbl>
## 1 1999 NON-ROAD 57.0
## 2 1999 ON-ROAD 347.
## 3 2002 NON-ROAD 57.7
## 4 2002 ON-ROAD 134.
## 5 2005 NON-ROAD 55.0
## 6 2005 ON-ROAD 130.
## 7 2008 NON-ROAD 50.0
## 8 2008 ON-ROAD 88.3
ggplot(total_vehicles_baltimore, aes(year, emissions, group = type, color = type)) +
geom_point(na.rm = TRUE) +
geom_smooth(
method = "lm",
lty = 2,
se = FALSE,
color = "grey",
lwd = .5
) +
geom_smooth() +
labs(
x = "Year",
y = expression("Total PM"[2.5]*" emission (ton)"),
color = "Type",
title = "Total Vehicle Emissions in Baltimore City",
subtitle = "(1999-2008)"
) +
theme_minimal()
6. Compare emissions from motor vehicle sources in Baltimore City with emissions from motor vehicle sources in Los Angeles County, California (fips==“06037”). Which city has seen greater changes over time in motor vehicle emissions?
Set the total Vehicle emissions in NEI by using the SCC vehicles vector
total_vehicles_ba_la <- nei %>%
filter(SCC %in% scc_vehicle$SCC, fips == c("24510", "06037")) %>%
group_by(fips, year, type) %>%
summarize(
emissions = sum(Emissions)
) %>%
mutate(
city = if_else(fips == "24510", "Baltimore", "Los Angeles")
)## Warning in fips == c("24510", "06037"): longer object length is not a multiple
## of shorter object length
glimpse(total_vehicles_ba_la)## Rows: 18
## Columns: 5
## Groups: fips, year [8]
## $ fips <chr> "06037", "06037", "06037", "06037", "06037", "06037", "0603…
## $ year <int> 1999, 1999, 2002, 2002, 2002, 2005, 2005, 2008, 2008, 2008,…
## $ type <chr> "NON-ROAD", "ON-ROAD", "NON-ROAD", "ON-ROAD", "POINT", "NON…
## $ emissions <dbl> 511.39000, 1677.64000, 1635.26000, 1861.42297, 0.08000, 156…
## $ city <chr> "Los Angeles", "Los Angeles", "Los Angeles", "Los Angeles",…
Plot disaggregate
ggplot(total_vehicles_ba_la, aes(year, emissions, group = type, color = type)) +
geom_point(na.rm = TRUE) +
geom_line(aes(year, emissions)) +
facet_grid(city ~ .) +
geom_smooth(
method = "lm",
lty = 2,
se = FALSE,
color = "grey",
lwd = .5
) +
labs(
x = "Year",
y = expression("Total PM"[2.5]*" emission (ton)"),
color = "Type",
title = "Disaggregate Vehicle Emissions Baltimore x Los Angeles",
subtitle = "(1999-2008)"
)
Using the same method, aggregate the sum of emissions of emissions by each county
sum_vehicles_ba_la <- nei %>%
filter(SCC %in% scc_vehicle$SCC, fips == c("24510", "06037")) %>%
group_by(fips, year) %>%
summarize(
emissions = sum(Emissions)
) %>%
mutate(
city = if_else(fips == "24510", "Baltimore", "Los Angeles")
) %>%
arrange(year)## Warning in fips == c("24510", "06037"): longer object length is not a multiple
## of shorter object length
sum_vehicles_ba_la## # A tibble: 8 x 4
## # Groups: fips [2]
## fips year emissions city
## <chr> <int> <dbl> <chr>
## 1 06037 1999 2189. Los Angeles
## 2 24510 1999 237. Baltimore
## 3 06037 2002 3497. Los Angeles
## 4 24510 2002 81.7 Baltimore
## 5 06037 2005 3522. Los Angeles
## 6 24510 2005 85.1 Baltimore
## 7 06037 2008 2879. Los Angeles
## 8 24510 2008 54.1 Baltimore
Plot the total vehicle emissions comparison
ggplot(sum_vehicles_ba_la, aes(year, emissions, group = city, color = city)) +
geom_point(na.rm = TRUE) +
geom_smooth(aes(year, emissions)) +
geom_smooth(
method = "lm",
lty = 2,
se = FALSE,
color = "grey",
lwd = .5
) +
labs(
x = "Year",
y = expression("Total PM"[2.5]*" emission (ton)"),
color = "County",
title = "Total Vehicle Emissions Baltimore x Los Angeles",
subtitle = "(1999-2008)"
) +
theme_minimal()