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<title>Decreases in Fine Particle Air Pollution Between 1999 and 2012</title>
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<h1>Decreases in Fine Particle Air Pollution Between 1999 and 2012</h1>
<h2>Synopsis</h2>
<p>In this report we aim to describe the changes in fine particle (PM<sub>2.5</sub>) outdoor air pollution in the United States between the years 1999 and 2012. Our overall hypothesis is that out door PM<sub>2.5</sub> has decreased on average across the U.S. due to nationwide regulatory requirements arising from the Clean Air Act. To investigate this hypothesis, we obtained PM<sub>2.5</sub> data from the U.S. Environmental Protection Agency which is collected from monitors sited across the U.S. We specifically obtained data for the years 1999 and 2012 (the most recent complete year available). From these data, we found that, on average across the U.S., levels of PM<sub>2.5</sub> have decreased between 1999 and 2012. At one individual monitor, we found that levels have decreased and that the variability of PM<sub>2.5</sub> has decreased. Most individual states also experienced decreases in PM<sub>2.5</sub>, although some states saw increases.</p>
<h2>Loading and Processing the Raw Data</h2>
<p>From the <a href="http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm">EPA Air Quality System</a> we obtained data on fine particulate matter air pollution (PM<sub>2.5</sub>) that is monitored across the U.S. as part of the nationwide PM monitoring network. We obtained the files for the years <a href="http://www.epa.gov/ttn/airs/airsaqs/detaildata/501files/Rd_501_88101_1999.Zip">1999</a> and <a href="http://www.epa.gov/ttn/airs/airsaqs/detaildata/501files/RD_501_88101_2012%5B1%5D.zip">2012</a>.</p>
<h3>Reading in the 1999 data</h3>
<p>We first read in the 1999 data from the raw text file included in the zip archive. The data is a delimited file were fields are delimited with the <code>|</code> character adn missing values are coded as blank fields. We skip some commented lines in the beginning of the file and initially we do not read the header data.</p>
<pre><code class="r">pm0 <- read.table("pm25_data/RD_501_88101_1999-0.txt", comment.char = "#",
header = FALSE, sep = "|", na.strings = "")
</code></pre>
<p>After reading in the 1999 we check the first few rows (there are 117,421) rows in this dataset. </p>
<pre><code class="r">dim(pm0)
</code></pre>
<pre><code>## [1] 117421 28
</code></pre>
<pre><code class="r">head(pm0[, 1:13])
</code></pre>
<pre><code>## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13
## 1 RD I 1 27 1 88101 1 7 105 120 19990103 00:00 NA
## 2 RD I 1 27 1 88101 1 7 105 120 19990106 00:00 NA
## 3 RD I 1 27 1 88101 1 7 105 120 19990109 00:00 NA
## 4 RD I 1 27 1 88101 1 7 105 120 19990112 00:00 8.841
## 5 RD I 1 27 1 88101 1 7 105 120 19990115 00:00 14.920
## 6 RD I 1 27 1 88101 1 7 105 120 19990118 00:00 3.878
</code></pre>
<p>We then attach the column headers to the dataset and make sure that they are properly formated for R data frames.</p>
<pre><code class="r">cnames <- readLines("pm25_data/RD_501_88101_1999-0.txt", 1)
cnames <- strsplit(cnames, "|", fixed = TRUE)
names(pm0) <- make.names(cnames[[1]]) ## Ensure names are properly formatted
head(pm0[, 1:13])
</code></pre>
<pre><code>## X..RD Action.Code State.Code County.Code Site.ID Parameter POC
## 1 RD I 1 27 1 88101 1
## 2 RD I 1 27 1 88101 1
## 3 RD I 1 27 1 88101 1
## 4 RD I 1 27 1 88101 1
## 5 RD I 1 27 1 88101 1
## 6 RD I 1 27 1 88101 1
## Sample.Duration Unit Method Date Start.Time Sample.Value
## 1 7 105 120 19990103 00:00 NA
## 2 7 105 120 19990106 00:00 NA
## 3 7 105 120 19990109 00:00 NA
## 4 7 105 120 19990112 00:00 8.841
## 5 7 105 120 19990115 00:00 14.920
## 6 7 105 120 19990118 00:00 3.878
</code></pre>
<p>The column we are interested in is the <code>Sample.Value</code> column which contains the PM<sub>2.5</sub> measurements. Here we extract that column and print a brief summary.</p>
<pre><code class="r">x0 <- pm0$Sample.Value
summary(x0)
</code></pre>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 7 12 14 18 157 13217
</code></pre>
<p>Missing values are a common problem with environmental data and so we check to se what proportion of the observations are missing (i.e. coded as <code>NA</code>).</p>
<pre><code class="r">mean(is.na(x0)) ## Are missing values important here?
</code></pre>
<pre><code>## [1] 0.1126
</code></pre>
<p>Because the proportion of missing values is relatively low (0.1126), we choose to ignore missing values for now.</p>
<h3>Reading in the 2012 data</h3>
<p>We then read in the 2012 data in the same manner in which we read the 1999 data (the data files are in the same format). </p>
<pre><code class="r">pm1 <- read.table("pm25_data/RD_501_88101_2012-0.txt", comment.char = "#",
header = FALSE, sep = "|", na.strings = "", nrow = 1304290)
</code></pre>
<p>We also set the column names (they are the same ast the 1999 dataset) and extract the <code>Sample.Value</code> column from this dataset.</p>
<pre><code class="r">names(pm1) <- make.names(cnames[[1]])
x1 <- pm1$Sample.Value
</code></pre>
<h2>Results</h2>
<h3>Entire U.S. analysis</h3>
<p>In order to show aggregate changes in PM across the entire monitoring network, we can make boxplots of all monitor values in 1999 and 2012. Here, we take the log of the PM values to adjust for the skew in the data.</p>
<pre><code class="r">boxplot(log2(x0), log2(x1))
</code></pre>
<pre><code>## Warning: NaNs produced
## Warning: Outlier (-Inf) in boxplot 1 is not drawn
## Warning: Outlier (-Inf) in boxplot 2 is not drawn
</code></pre>
<p><img 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" alt="plot of chunk boxplot log values"/> </p>
<pre><code class="r">summary(x0)
</code></pre>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 7 12 14 18 157 13217
</code></pre>
<pre><code class="r">summary(x1)
</code></pre>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -10 4 8 9 12 909 73133
</code></pre>
<p>Interestingly, from the summary of <code>x1</code> it appears there are some negative values of PM, which in general should not occur. We can investigate that somewhat to see if there is anything we should worry about.</p>
<pre><code class="r">negative <- x1 < 0
mean(negative, na.rm = T)
</code></pre>
<pre><code>## [1] 0.0215
</code></pre>
<p>There is a relatively small proportion of values that are negative, which is perhaps reassuring. In order to investigate this a step further we can extract the date of each measurement from the original data frame. The idea here is that perhaps negative values occur more often in some parts of the year than other parts. However, the original data are formatted as character strings so we convert them to R's <code>Date</code> format for easier manipulation.</p>
<pre><code class="r">dates <- pm1$Date
dates <- as.Date(as.character(dates), "%Y%m%d")
</code></pre>
<p>We can then extract the month from each of the dates with negative values and attempt to identify when negative values occur most often.</p>
<pre><code class="r">missing.months <- month.name[as.POSIXlt(dates)$mon + 1]
tab <- table(factor(missing.months, levels = month.name))
round(100 * tab/sum(tab))
</code></pre>
<pre><code>##
## January February March April May June July
## 15 13 15 13 14 13 8
## August September October November December
## 6 3 0 0 0
</code></pre>
<p>From the table above it appears that bulk of the negative values occur in the first six months of the year (January–June). However, beyond that simple observation, it is not clear why the negative values occur. That said, given the relatively low proportion of negative values, we will ignore them for now.</p>
<h3>Changes in PM levels at an individual monitor</h3>
<p>So far we have examined the change in PM levels on average across the country. One issue with the previous analysis is that the monitoring network could have changed in the time period between 1999 and 2012. So if for some reason in 2012 there are more monitors concentrated in cleaner parts of the country than there were in 1999, it might appear the PM levels decreased when in fact they didn't. In this section we will focus on a single monitor in New York State to see if PM levels <em>at that monitor</em> decreased from 1999 to 2012. </p>
<p>Our first task is to identify a monitor in New York State that has data in 1999 and 2012 (not all monitors operated during both time periods). First we subset the data frames to only include data from New York (<code>State.Code == 36</code>) and only include the <code>County.Code</code> and the <code>Site.ID</code> (i.e. monitor number) variables.</p>
<pre><code class="r">site0 <- unique(subset(pm0, State.Code == 36, c(County.Code, Site.ID)))
site1 <- unique(subset(pm1, State.Code == 36, c(County.Code, Site.ID)))
</code></pre>
<p>Then we create a new variable that combines the county code and the site ID into a single string.</p>
<pre><code class="r">site0 <- paste(site0[, 1], site0[, 2], sep = ".")
site1 <- paste(site1[, 1], site1[, 2], sep = ".")
str(site0)
</code></pre>
<pre><code>## chr [1:33] "1.5" "1.12" "5.73" "5.80" "5.83" "5.110" ...
</code></pre>
<pre><code class="r">str(site1)
</code></pre>
<pre><code>## chr [1:18] "1.5" "1.12" "5.80" "5.133" "13.11" "29.5" ...
</code></pre>
<p>Finaly, we want the intersection between the sites present in 1999 and 2012 so that we might choose a monitor that has data in both periods.</p>
<pre><code class="r">both <- intersect(site0, site1)
print(both)
</code></pre>
<pre><code>## [1] "1.5" "1.12" "5.80" "13.11" "29.5" "31.3" "63.2008"
## [8] "67.1015" "85.55" "101.3"
</code></pre>
<p>Here (above) we can see that there are 10 monitors that were operating in both time periods. However, rather than choose one at random, it might best to choose one that had a reasonable amount of data in each year.</p>
<pre><code class="r">## Find how many observations available at each monitor
pm0$county.site <- with(pm0, paste(County.Code, Site.ID, sep = "."))
pm1$county.site <- with(pm1, paste(County.Code, Site.ID, sep = "."))
cnt0 <- subset(pm0, State.Code == 36 & county.site %in% both)
cnt1 <- subset(pm1, State.Code == 36 & county.site %in% both)
</code></pre>
<p>Now that we have subsetted the original data frames to only include the data from the monitors that overlap between 1999 and 2012, we can split the data frames and count the number of observations at each monitor to see which ones have the most observations.</p>
<pre><code class="r">sapply(split(cnt0, cnt0$county.site), nrow) ## 1999
</code></pre>
<pre><code>## 1.12 1.5 101.3 13.11 29.5 31.3 5.80 63.2008 67.1015
## 61 122 152 61 61 183 61 122 122
## 85.55
## 7
</code></pre>
<pre><code class="r">sapply(split(cnt1, cnt1$county.site), nrow) ## 2012
</code></pre>
<pre><code>## 1.12 1.5 101.3 13.11 29.5 31.3 5.80 63.2008 67.1015
## 31 64 31 31 33 15 31 30 31
## 85.55
## 31
</code></pre>
<p>A number of monitors seem suitable from the output, but we will focus here on County 63 and site ID 2008. </p>
<pre><code class="r">both.county <- 63
both.id <- 2008
## Choose county 63 and side ID 2008
pm1sub <- subset(pm1, State.Code == 36 & County.Code == both.county & Site.ID ==
both.id)
pm0sub <- subset(pm0, State.Code == 36 & County.Code == both.county & Site.ID ==
both.id)
</code></pre>
<p>Now we plot the time series data of PM for the monitor in both years.</p>
<pre><code class="r">dates1 <- as.Date(as.character(pm1sub$Date), "%Y%m%d")
x1sub <- pm1sub$Sample.Value
dates0 <- as.Date(as.character(pm0sub$Date), "%Y%m%d")
x0sub <- pm0sub$Sample.Value
## Find global range
rng <- range(x0sub, x1sub, na.rm = T)
par(mfrow = c(1, 2), mar = c(4, 5, 2, 1))
plot(dates0, x0sub, pch = 20, ylim = rng, xlab = "", ylab = expression(PM[2.5] *
" (" * mu * g/m^3 * ")"))
abline(h = median(x0sub, na.rm = T))
plot(dates1, x1sub, pch = 20, ylim = rng, xlab = "", ylab = expression(PM[2.5] *
" (" * mu * g/m^3 * ")"))
abline(h = median(x1sub, na.rm = T))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-10"/> </p>
<p>From the plot above, we can that median levels of PM (horizontal solid line) have decreased a little from 10.45 in 1999 to 8.29 in 2012. However, perhaps more interesting is that the variation (spread) in the PM values in 2012 is much smaller than it was in 1999. This suggest that not only are median levels of PM lower in 2012, but that there are fewer large spikes from day to day. One issue with the data here is that the 1999 data are from July through December while the 2012 data are recorded in January through April. It would have been better if we'd had full-year data for both years as there could be some seasonal confounding going on.</p>
<h3>Changes in state-wide PM levels</h3>
<p>Although ambient air quality standards are set at the federal level in the U.S. and hence affect the entire country, the actual reduction and management of PM is left to the individual states. States that are not “in attainment” have to develop a plan to reduce PM so that that the are in attainment (eventually). Therefore, it might be useful to examine changes in PM at the state level. This analysis falls somewhere in between looking at the entire country all at once and looking at an individual monitor.</p>
<p>What we do here is calculate the mean of PM for each state in 1999 and 2012.</p>
<pre><code class="r">mn0 <- with(pm0, tapply(Sample.Value, State.Code, mean, na.rm = TRUE)) ## 1999
mn1 <- with(pm1, tapply(Sample.Value, State.Code, mean, na.rm = TRUE)) ## 2012
## Make separate data frames for states / years
d0 <- data.frame(state = names(mn0), mean = mn0)
d1 <- data.frame(state = names(mn1), mean = mn1)
mrg <- merge(d0, d1, by = "state")
head(mrg)
</code></pre>
<pre><code>## state mean.x mean.y
## 1 1 19.956 10.126
## 2 10 14.493 11.236
## 3 11 15.787 11.992
## 4 12 11.137 8.240
## 5 13 19.943 11.321
## 6 15 4.862 8.749
</code></pre>
<p>Now make a plot that shows the 1999 state-wide means in one “column” and the 2012 state-wide means in another columns. We then draw a line connecting the means for each year in the same state to highlight the trend.</p>
<pre><code class="r">par(mfrow = c(1, 1))
rng <- range(mrg[, 2], mrg[, 3])
with(mrg, plot(rep(1, 52), mrg[, 2], xlim = c(0.5, 2.5), ylim = rng, xaxt = "n",
xlab = "", ylab = "State-wide Mean PM"))
with(mrg, points(rep(2, 52), mrg[, 3]))
segments(rep(1, 52), mrg[, 2], rep(2, 52), mrg[, 3])
axis(1, c(1, 2), c("1999", "2012"))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-12"/> </p>
<p>From the plot above we can see that many states have decreased the average PM levels from 1999 to 2012 (although a few states actually increased their levels). </p>
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