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<title>Exploratory Data Analysis - Course Project 2</title>
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<h1>Exploratory Data Analysis - Course Project 2</h1>
<p><strong>NOTE: My work and answers to the questions are at the bottom of this document.</strong></p>
<h1>Introduction</h1>
<p>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). You can read more information about the NEI at the EPA National <a href="http://www.epa.gov/ttn/chief/eiinformation.html">Emissions Inventory web site</a>.</p>
<p>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.</p>
<h1>Data</h1>
<p>The data for this assignment are available from the course web site as a single zip file:</p>
<ul>
<li><a href="https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip">Data for Peer Assessment [29Mb]</a></li>
</ul>
<p>The zip file contains two files:</p>
<p>PM2.5 Emissions Data (<code>summarySCC_PM25.rds</code>): 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.</p>
<pre><code>## 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
</code></pre>
<ul>
<li><code>fips</code>: A five-digit number (represented as a string) indicating the U.S. county</li>
<li><code>SCC</code>: The name of the source as indicated by a digit string (see source code classification table)</li>
<li><code>Pollutant</code>: A string indicating the pollutant</li>
<li><code>Emissions</code>: Amount of PM2.5 emitted, in tons</li>
<li><code>type</code>: The type of source (point, non-point, on-road, or non-road)</li>
<li><code>year</code>: The year of emissions recorded</li>
</ul>
<p>Source Classification Code Table (<code>Source_Classification_Code.rds</code>): This table provides a mapping from the SCC digit strings int he 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”.</p>
<p>You can read each of the two files using the <code>readRDS()</code> function in R. For example, reading in each file can be done with the following code:</p>
<pre><code>## This first line will likely take a few seconds. Be patient!
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
</code></pre>
<p>as long as each of those files is in your current working directory (check by calling <code>dir()</code> and see if those files are in the listing).</p>
<h1>Assignment</h1>
<p>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 may use any R package you want to support your analysis.</p>
<h2>Making and Submitting Plots</h2>
<p>For each plot you should</p>
<ul>
<li>Construct the plot and save it to a PNG file.</li>
<li>Create a separate R code file (plot1.R, plot2.R, etc.) that constructs the corresponding plot, i.e. code in plot1.R constructs the plot1.png plot. Your code file should include code for reading the data so that the plot can be fully reproduced. You should also include the code that creates the PNG file. Only include the code for a single plot (i.e. plot1.R should only include code for producing plot1.png)</li>
<li>Upload the PNG file on the Assignment submission page</li>
<li>Copy and paste the R code from the corresponding R file into the text box at the appropriate point in the peer assessment.</li>
</ul>
<p>In preparation we first ensure the data sets archive is downloaded and extracted.</p>
<p>We now load the NEI and SCC data frames from the .rds files.</p>
<pre><code class="r">NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
</code></pre>
<h2>Questions</h2>
<p>You must address the following questions and tasks in your exploratory analysis. For each question/task you will need to make a single plot. Unless specified, you can use any plotting system in R to make your plot.</p>
<h3>Question 1</h3>
<p>First we'll aggregate the total PM2.5 emission from all sources for each of the years 1999, 2002, 2005, and 2008.</p>
<pre><code class="r">aggTotals <- aggregate(Emissions ~ year,NEI, sum)
</code></pre>
<p>Using the base plotting system, now we plot the total PM2.5 Emission from all sources,</p>
<pre><code class="r">barplot(
(aggTotals$Emissions)/10^6,
names.arg=aggTotals$year,
xlab="Year",
ylab="PM2.5 Emissions (10^6 Tons)",
main="Total PM2.5 Emissions From All US Sources"
)
</code></pre>
<p><img 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" alt="plot of chunk plot1"/> </p>
<p><strong>Have total emissions from PM2.5 decreased in the United States from 1999 to 2008?</strong></p>
<p>As we can see from the plot, total emissions have decreased in the US from 1999 to 2008.</p>
<h3>Question 2</h3>
<p>First we aggregate total emissions from PM2.5 for Baltimore City, Maryland (fips=“24510”) from 1999 to 2008.</p>
<pre><code class="r">baltimoreNEI <- NEI[NEI$fips=="24510",]
aggTotalsBaltimore <- aggregate(Emissions ~ year, baltimoreNEI,sum)
</code></pre>
<p>Now we use the base plotting system to make a plot of this data,</p>
<pre><code class="r">barplot(
aggTotalsBaltimore$Emissions,
names.arg=aggTotalsBaltimore$year,
xlab="Year",
ylab="PM2.5 Emissions (Tons)",
main="Total PM2.5 Emissions From All Baltimore City Sources"
)
</code></pre>
<p><img 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" alt="plot of chunk plot2"/> </p>
<p><strong>Have total emissions from PM2.5 decreased in the Baltimore City, Maryland (fips == “24510”) from 1999 to 2008?</strong></p>
<p>Overall total emissions from PM2.5 have decreased in Baltimore City, Maryland from 1999 to 2008.</p>
<h3>Question 3</h3>
<p>Using the ggplot2 plotting system,</p>
<pre><code class="r">library(ggplot2)
ggp <- ggplot(baltimoreNEI,aes(factor(year),Emissions,fill=type)) +
geom_bar(stat="identity") +
theme_bw() + guides(fill=FALSE)+
facet_grid(.~type,scales = "free",space="free") +
labs(x="year", y=expression("Total PM"[2.5]*" Emission (Tons)")) +
labs(title=expression("PM"[2.5]*" Emissions, Baltimore City 1999-2008 by Source Type"))
print(ggp)
</code></pre>
<p><img 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" alt="plot of chunk plot3"/> </p>
<p><strong>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?</strong></p>
<p>The <code>non-road</code>, <code>nonpoint</code>, <code>on-road</code> source types have all seen decreased emissions overall from 1999-2008 in Baltimore City.</p>
<p><strong>Which have seen increases in emissions from 1999–2008?</strong></p>
<p>The <code>point</code> source saw a slight increase overall from 1999-2008. Also note that the <code>point</code> source saw a significant increase until 2005 at which point it decreases again by 2008 to just above the starting values. </p>
<p>(Note that I did not catch this originally as I started off with a log scale on Emissions)</p>
<h3>Question 4</h3>
<p>First we subset coal combustion source factors NEI data.</p>
<pre><code class="r"># Subset coal combustion related NEI data
combustionRelated <- grepl("comb", SCC$SCC.Level.One, ignore.case=TRUE)
coalRelated <- grepl("coal", SCC$SCC.Level.Four, ignore.case=TRUE)
coalCombustion <- (combustionRelated & coalRelated)
combustionSCC <- SCC[coalCombustion,]$SCC
combustionNEI <- NEI[NEI$SCC %in% combustionSCC,]
</code></pre>
<p>Note: The SCC levels go from generic to specific. We assume that coal combustion related SCC records are those where SCC.Level.One contains the substring 'comb' and SCC.Level.Four contains the substring 'coal'.</p>
<pre><code class="r">library(ggplot2)
ggp <- ggplot(combustionNEI,aes(factor(year),Emissions/10^5)) +
geom_bar(stat="identity",fill="grey",width=0.75) +
theme_bw() + guides(fill=FALSE) +
labs(x="year", y=expression("Total PM"[2.5]*" Emission (10^5 Tons)")) +
labs(title=expression("PM"[2.5]*" Coal Combustion Source Emissions Across US from 1999-2008"))
print(ggp)
</code></pre>
<p><img 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" alt="plot of chunk plot4"/> </p>
<p><strong>Across the United States, how have emissions from coal combustion-related sources changed from 1999–2008?</strong></p>
<p>Emissions from coal combustion related sources have decreased from 6 * 10<sup>6</sup> to below 4 * 10<sup>6</sup> from 1999-2008.</p>
<p>Eg. Emissions from coal combustion related sources have decreased by about 1/3 from 1999-2008!</p>
<h3>Question 5</h3>
<p>First we subset the motor vehicles, which we assume is anything like Motor Vehicle in SCC.Level.Two.</p>
<pre><code class="r">vehicles <- grepl("vehicle", SCC$SCC.Level.Two, ignore.case=TRUE)
vehiclesSCC <- SCC[vehicles,]$SCC
vehiclesNEI <- NEI[NEI$SCC %in% vehiclesSCC,]
</code></pre>
<p>Next we subset for motor vehicles in Baltimore,</p>
<pre><code class="r">baltimoreVehiclesNEI <- vehiclesNEI[vehiclesNEI$fips==24510,]
</code></pre>
<p>Finally we plot using ggplot2,</p>
<pre><code class="r">library(ggplot2)
ggp <- ggplot(baltimoreVehiclesNEI,aes(factor(year),Emissions)) +
geom_bar(stat="identity",fill="grey",width=0.75) +
theme_bw() + guides(fill=FALSE) +
labs(x="year", y=expression("Total PM"[2.5]*" Emission (10^5 Tons)")) +
labs(title=expression("PM"[2.5]*" Motor Vehicle Source Emissions in Baltimore from 1999-2008"))
print(ggp)
</code></pre>
<p><img 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" alt="plot of chunk plot5"/> </p>
<p><strong>How have emissions from motor vehicle sources changed from 1999–2008 in Baltimore City?</strong></p>
<p>Emissions from motor vehicle sources have dropped from 1999-2008 in Baltimore City!</p>
<h3>Question 6</h3>
<p>Comparing emissions from motor vehicle sources in Baltimore City (fips == “24510”) with emissions from motor vehicle sources in Los Angeles County, California (fips == “06037”),</p>
<pre><code class="r">vehiclesBaltimoreNEI <- vehiclesNEI[vehiclesNEI$fips == 24510,]
vehiclesBaltimoreNEI$city <- "Baltimore City"
vehiclesLANEI <- vehiclesNEI[vehiclesNEI$fips=="06037",]
vehiclesLANEI$city <- "Los Angeles County"
bothNEI <- rbind(vehiclesBaltimoreNEI,vehiclesLANEI)
</code></pre>
<p>Now we plot using the ggplot2 system,</p>
<pre><code class="r">library(ggplot2)
ggp <- ggplot(bothNEI, aes(x=factor(year), y=Emissions, fill=city)) +
geom_bar(aes(fill=year),stat="identity") +
facet_grid(scales="free", space="free", .~city) +
guides(fill=FALSE) + theme_bw() +
labs(x="year", y=expression("Total PM"[2.5]*" Emission (Kilo-Tons)")) +
labs(title=expression("PM"[2.5]*" Motor Vehicle Source Emissions in Baltimore & LA, 1999-2008"))
print(ggp)
</code></pre>
<p><img 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lKIUxnRnBnVZ3LROTNq/CiPRJ3qx7Bj0Nj7UX/DY9V9yptRp7Lkhgj8JemXaEdCF9VY0nW5hu6rPlXnQw02NRDdMIivu9Q5LcoV2uWvhedDE71rxQeu5efNTTLzfP/73/dm2dB8JhO9Uz6Bm7jkzfQyBcu5yRFB165dA5ntHdSymOgLCdcVkOCQQw7JoJfZz028CFwP1l/LNtE39PyPf/xjPw7tKqCMn9kHMg1p7Mz1gDK3nLIKXMWcOXcWAD8mHB2v0xwHmSzlZKIXP1fxZt7RuNIvf/lLfx4dn3SK05vhXGWaeVbxctaCINtEr/FCp4gyz8m0uMYaa/i0zlzMOrj//vsD14v38oidxho1ZBM61ygJXOUROGUTXgoki8xxMstly6CHXMEOXO/HP6+xfMVV43Oh0xilhgZkxpPTOFzfvn0DpyAC19P3fF2PLnw8cErMP++UU+Za9EAmesmu8b/sP1cJ+kdD5porIae8Lrlc5e3P9e/www/PpGM0DVyjLnAVSeY5DdW4BpA/lzlbY5xiLeeUT+B6+T4e2WPwDeU/5X3N8RAPOdd48DKKp1zbtm0zJk6dX3HFFUFostR51EXlD+MenR+idFZ8c7nQRO8aGYH+NEdHrDRPQ/ELnbOaBcrroQvzaihvPhN9Lvnipo1rPAZOsdcxS6sMbr311l4UmaIlr2tEhKJ5OTVPo74ymXkwcqD4KA6Kv9LFNRIid3MfNmSiV9pqGFJpJ6fyobzrerK5PWzg6tSpU31cXQMh8+To0aN9uoXDfMqHbhKef05xaOz9TEB5Dv70pz/5MivTupzqJWexqPOGyrjydJz7esY13Lyfqo+Uvq4T5N8t5l/+qZLO91p2Modp1rVafZrhO3jwYN8yktkk6tQ61OxMtZCPPvpob/aqr/eunox6LGrJ53JRU4pak07x5HrMX4sTrr4IUM9XpvTQucJlbpww05IPr+s37vMymeWbKSvT0F577eXNtApLPXHNnNVM29DpWL05WQRCpxnVmonrMqO/JFnVOwudU86Znl54Tb/q9esdN/Etc1mmernoEIJ6KOq5qCWtIYbQqSWv2czqledy6knrTzOmZbYdN26c76WpR6Ves/x0FZDv8YXvq+evvKP46DiOcwo885h6bzK1yYwnp5m2oUVCvXn1GNWiD+OhHl/Y68vumYSeqiUv60u2U6s/dLIWyHQpp7yu8KPyK+/q65Bsp/IhC4SGaJT2+pP5XE5pqK8mXMPXc3QTycxVoH4mtXqaoYub/8QlnHmsMqI/WXnklE7qxd97773eiqKyGJ2hHoaV61cyduvWLXNLM8Jlcs7nFI4sULJu6GsbmdfVcxYjsVMvyjWqfdrJvK68qnyhHmKhrpC0kSVIFgjly9Dtu+++JutayFzySdbQNVQmJXfUqb5S2sqqo3wuS4WGZ1SOTjvtNG91iss+6q+sjWLpJixm6inlSZnaXYMr+mjRx24+kLe0ykKgOkoWHOVzDQ9p2KGx92XRCjlLSFkhovw0RKhhE+UflQs53VcdGHU6D2f3N3RfM+eVFhr6c506X9/+4he/8HWFrDWFulQreJnmVMDjuNBcHlYsUhbZThlGmVYFTspIlXTU6ZMJFTaZYeVkmpbZN59rKFw1KOQ0TyDqNthgA6/4o9d0HPd5NXgacjKjKpNpTEtxVSNIQwOhk+JVhlWBCp1rqfoCHJriVHFGnT6XCZV/9LpMqmpUNFSZyCwqxaiCFw1XSiksZFF/dayGnszLUoKKg/5UaNzEFW/OlhlecdHnLmFB1Hsyi0phaFgidFHZpRCynT6pDJ3M/KHpLrwW/io8NT5Ds3Z43VmV8n5+qDioAsjnshkqjRSP0Ok8l5PykILQ/AGluczdUvias6FPfaTwNMYsk6TMlBu6xp2GAKIN5rj5T/kk6qTsQ7Yar1SF7awufo6FxnA1fquy2ZDL9leN2OgwU6731eiMppuGSlQ+FE+VT8VZ5fh73/ueb7CpcRLOscjlX75rhaSN8kjYUAv9FHNxUkNXLiq3zhsqk9lzTjQcoTKqXznFKxz3VlnNltc/FOOfs6b4T5LdrPbM0yovKudqIIV1ZOZmEQeqhzVcIUWs4RgpRX1GGs67aOx9DR+qDIdOdaHKjvg7K6TddtttPv+7CZnhI37IQDoi6lRnOeuxv6SGe777YV0l/+U0hKc4aU4FCt4jKe6fJmdoYo7G3lWINCkrrKzko8YCNWlEFZqcepqalKKxuNC5Wee+QlCLP65rKFy1gFX56dv0aGHXuRJfLsx0Oo7zvJ6L41TRqYegnrsyc3Rynd5Xo0Ot5ugEGI1/aaJZdkXSUHhirp6W5hGElbR6u5pEpHG40EkJy29xD3uXuqcxb93L5ZQeej6cfBM+o8aYKh1VllKc6k1L9rA3rPSUTM4k6nsEei/aotcEqnxOhVr+RZ1a/FIUYqdGkAqyLDlyakhIqZViglM0zLjH4q3K/rrrrvOVmGTRuKwsEVLwmnimyZL6U89PY7dSftF0aGz+U69Y1hU1LKTQda6GtSaYxlHwceOa77nQ6qe8qDRS+JdeeqlvbOg91QvqRTfUcMgXRpx7yiPK19F5GTpX/qnPMlhomZRijJZVcZdSvuyyy3zvNI6c2c+ozCgNVWeosRA68VKZUH2hfNNYp/pXf25YMOOV0imcoNbY+6rbcjlZIDQZWJ3A7PF2nWu+iBoBYUNaDWOli1xD92UBy67HZAVVwy3qZy65cl1rUpPscgEIr6lXp96WzFPhJLLwnn7Vog+Vu8yoUvahIgqfU8tUk7BUKf7xj3/0Zr3wXn2/DYUrU5MylBvL870rVTjqeWoykEyqcuqdqVWo3qAmLTX0fH2yZF9Xw0J+qbC7cc0VTMNS+FLobhzKKz61zjVJq5jZ4FIWUvJSeGIoa4h61uIeFhTJp2NZFjRRS+Eo06sBItOcKuRcTsMuUlqauKLCpkpNFaVmw8rUrMpS76t1rUl3arWLrxoWUvaa2KS0lizqXUoRa9jEjS/mCi5z7YgjjvATIyWrhnVkctVQixoMbh6Er+w0SU8NC6WrTKIKX42q+pxkkz/Zf0qfxjqZB2X+D3staviIt/v0yMunfC1uYq4/cZAlKeri5Nfo89nH6nXJ9CkOUu4KR3Ko4VAupwaMeMpErZnXGgbR8JoscOrFypKjyYRykinslYa96KhcoeJV4zDaGIw+E/dY1hz5I2Uo1uqZypKmdKjPFVomlUcVhhoRGiKR4tIkQikV9WCVJ+pzKiPZ+VB1gCYlqtEQ1k/h+2Kp8BQHhaUyobrDfb4aPlLQr2RU2Q6trUo7NSyUf+Qaez+XMLJwSH411FXfhPHXREA5mfGlpMMvBtRI0CTS0JLb0H3lPcVBcZGTTpG1TGkerQf9zTj/XAFKnQsn2WV/B58d0XCSXXhdkxkcxMD1Tvwl12rSQHKdSXauoAVubDLQBL5s58wo/htM14INXGXt/7Kf0Xmh4WrCjBaycQXET3RyreDAzfrPeK3vu50pzcuq75Ebel6T7OJ+/y2GYuIaPZnwogdOefkJbq5A+19NBtRkFzlNenIVc/Tx4MQTTwxcwffXohOQdMG1uP03pZq45irKwDUufFzEU+ngKiL/nitYgSaEaRKgJhRpUpdrhPh79f0TLz0nhvJLk5fkh1OsmVec8g+0YIYmXDlFEzizf50FLDQ5Tu/pvlP2flKfZJVzhdr7Kz+iTnnCVTT+W1ZN3tKknNBpMpizIniZXAMi6NevX6C8W59zjQ0fhuTP/tPkKLlczOW3M2NmvHWm90B5QC6aBko314Pz6z9IZqVBdNKj+wzQT0DTPcVb/FQe9J7kCWVvKP9pkp1rbGXk0YG+FXYVp7/mhgkC1wvzi4VokqIz5wb1fUMdlT9X3DVBzg091AkrPAkn2YUslc+Vrm7cO3BKI3wsUB53DRm/GI5rTAea/Km0cpW2fyY6yc71Uv09+ekac3X45pIvX9rIc03UFG9NcFTec0NLgRvT9eE6xe8nDvuTyL98ZTLyWObQ9Xp9/lTZUHlS2qiMacKY64FnnoseSKaQW/TXWXN8GXINjejjmWPXmPX1ieLllLz3w83hydyv7yDXJDs9K9lVx4iNm3sRuPHwOl409n4dz9yJJjhG4xseK/+Gzilnn5+VV5y1JTPRMO591zn06aA4hXVvdNJk6E+cX7WQcTEJqCJT4Xet3Qbf0OIZqpBL6VzvIXA99ZxeqmLJzgT5ns/pSZEXFbYaRc7cXaQPdV8Tu6jirXv3uzPXgwpcj+G7CzGO5K/reWRWM8z1isJX5ZPLKY6Kq+Ic16lCdr3ieh93Y3QrpF29D1fghuKYT17lQbFvyDU2/+lrEbGpFldIHlf+CRVxY+WXP846V1D5KrRMKs3fc8o3WoZ17KwQjRU/7/tSwGpoNcYprvrqpz7X2Pv1+ZvvusJUmuk3l2vovtjr64DG8m+mwF0rBNcAAZcg/ntimaxlZoo6IXQtLT9Wp2+ONVlJE3Q0OUmLm+AgAAEIQKAuAQ11akjIWTb8kEjdu5yVggAKPiZFjUs5M7lt6MaJQ6fJW/p8Ropc9zUuqzFkjUNqApYWz6lvZnfoB78QgAAEIACBchBAwZeBqiaQuDGqMviMlxCAAAQgAIF4BFDw8TjxFAQgAAEIQKCmCKR6oRt90oKDAARKT0DDUNGVEPWZH9N5Ss8ZHyGgT5/1V4xLtYLXt4TRlcmKAcQ7EIDAigS02Ep0SVh9o63vnHEQgEBpCWi1Qm0CVIxLtYLXQi35Fg0pBhjvQAAC/1twKMpBZU2Lw+AgAIHSEtAXWsU6mtzFkuM9CEAAAhCAQBUTQMFXceIgGgQgAAEIQKBYAij4Ysml+D3tLKUNHLShhnby0oYv+dwpp5zib2tnJ63DrM04otvb5nu3FPe0dr22lpWsWm9eO6LJ6VdbAGtN8H+7jVRwECgnAZWbMO+VIhyt6a79GErltL+DW264aO+09r7WjtdWslrvQ/KVymlhMFzpCaDgS8+05n3UJgraFEGbpGhDFW04oc11QqcVqLRRROi005GcNlXQphja/MatOR/e9r/RjTmi74YPuaUZw8PMr8IJXa77uueWD/ZbvmorR20oo13HtLWiNr3QFrHa9U+b8IQbUoT+8QuBUhNQudHXBLlcrvyrZ7VCZi7nlqf15U733RKyKzwSLU/hzVxhRK/JT21dGnXR+7qushktd9Fn1WjXQl/aDU4bULn9ATJbs+q5bL+i72Yf69no81osDFd6AqmeZFd6XE3PRxX4cLKiPoNSIZcS1/a52r5WrXk5bSWqRsCYMWO8ctduevrTvuvahU3Pa6tIrfynhYC0h7K2knSbSPhVALWLmNuMxW9Jq16GtpxUz1sVibZ5VcWk8GVVCLdeVLjaRU1bwYYrBmrXN72jyZXaGUvhujXE/baSaqhoWUztzqUtQeWvZqcWO0NV4eMgkI+AlFhYZvSVgba81VbU2o1Mn/GqcawVMvv27VvHG+1C5jZH8mVMDWftcqm8rF32tFObyoYasNpZ8b///a/f+U1fNkg5a7dCLZetlTaj5UZlLHTyf+zYsSalr3C0/LasXbJ06fNHyRiWbb2j8LQ6Z7h1s54544wzMg0ULcmtsqgJYdpmun///t5P7b4oJ0uEdsd78803/dbXClfxUf0gq5/KuywCaszss88+fgdHWQRVr2jHOFxxBOjBF8ct9W+pF6zKQxWSKhT1yqWYpdRVOUl5R7eF1T7HqhTc7l2ejQqwnCoYbWEpS4D2sJdpz20wkdl6Vy13KXRtV6nCrd623nU7dPmegrbyVNiqWLS9q5YEjjq906VLl+glv/2rvtNWg0B+qcJQI0Bb0u6www6+UaHr2uJRe8LjIFAuAtpOVA1S5W81TrXVqPLeK6+84hWo8r7bNWyF4LUFsrar1d7mbpe+jGVA5WP06NG+Ia2te+WkyN3ukN56pYatev1S4PnKjfZqd7sK+q1VtdW0lK0axLKEqWyHDeZQMG0Nu84664Sn/ldfTahBrUb7q6++ahdffLFvaEhRSwbFM3ThcWixuOSSS/z2yKoTtGWzPgWT1dBt5uW3JNZ7EyZM8OehH/wWToAefOHMmsQbajUPGDDAx1W93rvvvtvclrEZs9yaa66Zab3nA6Lev/YTl1NPfu211/bH6mmE5kL1puXcVq0mhS7ntuf0vzpXK17r/MupsRF18jvcizm87nZhqnf9A/UuZGJU5dSrVy9vnQjf4xcCpSagPebVWJWThUp5WWVCDWEpRClHzXeJOrcrpD377LO+Qa3rsqJpnQH12DfbbLPMAkNqxMpJOYdWLSlmme/zlRsNJajXL8uCnJSvFPgJJ5xg2u9c4+zqgUetCmpEux3b/PPhP/W8Nf9F1yWXnHr2Utbq7UcXPgoVu55ROZdTwyZ7OEDWNA21qW6Q/xoSwBVPgB588eyazJtSwDInahxblYda35rQlj2WLhNktFALUHTxk+zvOd1+497cH/qjyi/sjYfvde/e3Svi888/30+gk6Ug6tQzf/HFF31Fo+vyS7396BhlVC41HGRu1KQe9ZBwECgnAeXP1157zQfxzjvveOuShrg0R+Tyyy/3PV41oKNOvVo1pjXUpD/18u+//37/SFguos+rjIRhvPzyy/5WvnKjXrfKmSamqlzJsqWGsnr9I0eONJnVNVlQZvPQtW/f3lsEZDYPnYa61JjWgkdvvPGGvyxFrsa4evuqC1QOdU3xDV2uOIT31Pjp3bu3XXnllb6REV7ntzgC9OCL45b6t2Sil/lcSlkb52jsT04z6lXhaGxRFUV0UpF6KHpu1KhRsfnILKdKRZWAevjqVU+cODHzvgq7GhbnnHOO79Foe8moU8WjmfPqjaghIuU9ZMgQ3/IPe/2dO3f244Pjxo2zwYMHe7OfFDy9gyhJjktBICw38ku92nBISmPQUnKnn366aSxcvW5dU29a4+VRJ/O8hsFCpzFyKcvoRNfwnn5VhqSUNXylBq569g2VG5UjlVOt9CmlKjO5TPpnnXWWt5KpLKsnHnUq25JL1jw1+GUtGDRokK8jttxyS/+uvqA5wo3nK45qQGuyrRryDS2CpN68Og4qy5JFwxkjRoyIBs9xEQRSvdmMWr3KXLjSEpASra/AhhVMoSGqpR+a6nO929B9vaNKJ9uEH/olM6QqSZkQNWlJvQgpe1xxBFSuQlOrfNCy0PXlieJCSNdb6rVHJ7kpdiorakAXu854SOixxx6znj17mhq7sgpononM+XINlZvs+yojarTXV45CP9UoUFmKOpnbVa50L3Qqk4p3vl57+GxYd8i8r0l7pfxEMAyjFn+VFur4FOPqplAxPvBOkyOQryIPxwULhZJPucuvhu7rmXyVUljpyPQnU6msEDgIVIpAtnJXuMWWlWyZ1di64IIL/OWOHTvWmTjaULnJvq8GR75ypECy3wnlydVQaciv8F39ioeGJzRJL5ytH73PceEEUt2Dv/feexvMrIUj4w0IQGDdddf1XyaEJMaPH1+n5xZe5xcCEGgcAVlmZJUpxqW6B69JIoJTaSdzcNhjrHTYpQ5PJjv9xTGxlTrscviXprRJKi7KD5p/EXUqa0ns3JgUg2jcS3mcpvgQl9LkjOzJyYX4mloFr0pIM0WTGGfV9+KamJYGp3E1/RViaqvmeKcpbZKKi8ZK9dlW1EnhDx06NHqpIseaDyLzdxoa1KqztDJjduOpIiDLEEhS+bMMUfGTIbXGQGOUbbFy3XfffcW+anwmVzQ6XoQABCAAAQhULwEUfPWmDZJBAAIQgAAEiiaAgi8aHS9CAAIQgAAEqpcACr560wbJIAABCEAAAkUTQMEXjY4XIQABCEAAAtVLAAVfvWmDZBCAAAQgAIGiCaDgi0bHixCAAAQgAIHqJYCCr960QTIIQAACEIBA0QRQ8EWj40UIQAACEIBA9RJAwVdv2iAZBCAAAQhAoGgCKPii0fEiBCAAAQhAoHoJoOCrN22QDAIQgAAEIFA0ARR80eh4EQIQgAAEIFC9BFDw1Zs2SAYBCEAAAhAomgAKvmh0vAgBCEAAAhCoXgIo+OpNGySDAAQgAAEIFE0ABV80Ol6EAAQgAAEIVC+BltUrGpJBAAIQgEA5Cfzwl38vp/dl9XvaVT8tq/9p8JwefBpSkThAAAIQgAAEsgig4LOAcAoBCEAAAhBIAwEUfBpSkThAAAIQgAAEsgig4LOAcAoBCEAAAhBIAwEUfBpSkThAAAIQgAAEsggwiz4LCKcQgAAE8hHYoP9x+W5X7b0PHr22amVDsPIQoAdfHq74CgEIQAACEEiUAAo+UfwEDgEIQAACECgPARR8ebjiKwQgAAEIQCBRAij4RPETOAQgAAEIQKA8BFDw5eGKrxCAAAQgAIFECaDgE8VP4BCAAAQgAIHyEEDBl4crvkIAAhCAAAQSJYCCTxQ/gUMAAhCAAATKQyDVC90sX77cli5dWh5yeXxNKtw8IhV9a9myZaa/JDgWLXSeF9OUNknFRXkhCII6lHWeRB4J82a2PHWE48QTSCJ9yom+kvEJ81mzZs3KGaWcfjcmb9ODz4mUixCAAAQgAIHaJpDqHnzz5s2tZcvKRzGpcMuRFdV6VE8xCY7liE+a0iapuCg/ZPdkdJ5EHmnRooUPV7+4/ASSSJ/8EjXubiXjE+az7HzfuBjEe7sxYdKDj8eYpyAAAQhAAAI1RQAFX1PJhbAQgAAEIACBeARQ8PE48RQEIAABCECgpgig4GsquRAWAhCAAAQgEI8ACj4eJ56CAAQgAAEI1BQBFHxNJRfCQgACEIAABOIRQMHH48RTEIAABCAAgZoigIKvqeRCWAhAAAIQgEA8Aij4eJx4CgIQgAAEIFBTBFDwNZVcCAsBCEAAAhCIRwAFH48TT0EAAhCAAARqigAKvqaSC2EhAAEIQAAC8Qig4ONx4ikIQAACEIBATRFAwddUciEsBCAAAQhAIB4BFHw8TjwFAQhAAAIQqCkCKPiaSi6EhQAEIAABCMQjgIKPx4mnIAABCEAAAjVFAAVfU8mFsBCAAAQgAIF4BFDw8TjxFAQgAAEIQKCmCKDgayq5EBYCEIAABCAQjwAKPh4nnoIABCAAAQjUFAEUfE0lF8JCAAIQgAAE4hFAwcfjxFMQgAAEIACBmiKAgq+p5EJYCEAAAhCAQDwCKPh4nHgKAhCAAAQgUFMEUPA1lVwICwEIQAACEIhHAAUfjxNPQQACEIAABGqKQMuakhZhIQABCEAAAvUQWLY8qOdO4y4vc97K72bNGudPfW+3aF4ej1Hw9RHnOgQgAAEI1BSBgbcuLJO8LZy/i8rkt9mEw1cti9+Y6MuCFU8hAAEIQAACyRJAwSfLn9AhAAEIQAACZSGAgi8LVjyFAAQgAAEIJEsABZ8sf0KHAAQgAAEIlIUACr4sWPEUAhCAAAQgkCwBFHyy/AkdAhCAAAQgUBYCKPiyYMVTCEAAAhCAQLIEUPDJ8id0CEAAAhCAQFkIoODLghVPIQABCEAAAskSQMEny5/QIQABCEAAAmUhgIIvC1Y8hQAEIAABCCRLAAWfLH9ChwAEIAABCJSFAAq+LFjxFAIQgAAEIJAsARR8svwJHQIQgAAEIFAWAij4smDFUwhAAAIQgECyBNgPPln+hA6BJkGgw/d71WQ8Z7/3fE3KjdAQEAF68OQDCEAAAhCAQAoJoOBTmKhECQIQgAAEIFBRBf/ll1/aueeea3vttZddffXVnv7ixYvt2GOPtV122cXfC5Pksssus/79+9vBBx9sc+fO9Zffffdd23///a1fv3523333hY/yCwEIQAACEIBAFoGKKvjjjjvOBgwYYA888IA98cQTNnPmTLv00kuta9euNmnSJHvrrbfskUcesaeeesrff/jhh23HHXe0Cy+80Is9fPhwO/vss+3ee++18847z+bMmZMVHU4hAAEIQAACEBCBik2yW7ZsmVfgK6+8st155512/fXX26qrrmpTpkzxx61atbIDDjjAHnvsMevQoYMNGTLEWrZs6Xvw6vHLzZgxw3r27OmP+/TpY88++6xvMOjC22+/bVdccYW/p3/NmjXzFoCFCxdmrlXqYMmSJZZEuOWIn9Jt6dKlnmc5/K+0n2lKm6TionCXL19eJ+mUT9KS56MRS1Oc0hQXpVGa4pMvLtllLZo/GzqumIL//PPPTX/XXHONdevWzXbYYQd74YUX7OOPP7Y11ljDyynFrl79/Pnz/TO62L59e5s1a5bNnj3b1DgIXfhseN66dWtbZ511wlOvkKTkW7RokblWqYOkwi1H/IIgsObNmyfCsRzxSVPaJBUXVTgKO+qSkiUqQzmOk6g/yhEP+ZmmuNQfn6XlwldWf/OlTXZZK0SQiin4Nm3a+Fa/FLx65jLHT5w40dq1a2fz5s3zyluKfa211vIKRdfkFixYYJ06dbK2bdv64zBy4bPh+YYbbmgjR44MT02Kafz48SbFX2mn+CURbjniGWautMQnTWmTZFzCfBHmOTUC05JHwjjpN01xSlNc6k+b2lTw+dImu6xF82dDxxUbg5ciVw/7m2++8TJ9+umntvrqq3uT+zPPPOOvaey9e/fuOa+pMltllVV8b17KW71/jd3jIAABCEAAAhBYkUDFevAKWr33ESNGmGbTd+nSxZvp119/fTvppJNszJgxfkxek+dkAtRku7333ts+++wzGzt2rJd89OjRdsghh9iiRYts4MCB3o8Vo8QVCEAAAhCAAAQqquB33nln05/M7ppgJ7feeuv5WfGaZKAeupzGI/QZXfSarvft29f/ScFHx+N1DwcBCEAAAhCAwHcEKmai/y5Iyyj36LVQuTd0TfdR7lFKHEMAAhCAAARWJJCIgl9RDK5AAAIQgAAEIFBKAij4UtLELwhAAAIQgECVEEDBV0lCIAYEIAABCECglARQ8KWkiV8QgAAEIACBKiGAgq+ShEAMCEAAAhCAQCkJoOBLSRO/IAABCEAAAlVCAAVfJQmBGBCAAAQgAIFSEkDBl5ImfkEAAhCAAASqhAAKvkoSAjEgAAEIQAACpSSAgi8lTfyCAAQgAAEIVAkBFHyVJARiQAACEIAABEpJAAVfSpr4BQEIQAACEKgSAij4KkkIxIAABCAAAQiUkgAKvpQ08QsCEIAABCBQJQRQ8FWSEIgBAQhAAAIQKCUBFHwpaeIXBCAAAQhAoEoIoOCrJCEQAwIQgAAEIFBKAij4UtLELwhAAAIQgECVEEDBV0lCIAYEIAABCECglARQ8KWkiV8QgAAEIACBKiGAgq+ShEAMCEAAAhCAQCkJoOBLSRO/IAABCEAAAlVCAAVfJQmBGBCAAAQgAIFSEkDBl5ImfkEAAhCAAASqhAAKvkoSAjEgAAEIQAACpSSAgi8lTfyCAAQgAAEIVAkBFHyVJARiQAACEIAABEpJAAVfSpr4BQEIQAACEKgSAij4KkkIxIAABCAAAQiUkgAKvpQ08QsCEIAABCBQJQRaFiPHzJkz7cUXX7TXXnvNvv76a9t6661tm222sfXWW68Y73gHAhCAAAQgAIESEyioBy9lfsYZZ1iPHj3skksu8Qp+1qxZds0119jOO+9sPXv2tEcffbTEIuIdBCAAAQhAAAKFEojdg58yZYpddNFFdvzxx9vvf/97a9Wq1QphvfLKKzZ69Gi744477Prrr1/hPhcgAAEIQAACEKgMgdgKvlu3bjZ+/Pi8UumZW2+91WTCx0EAAhCAAAQgkByB2Cb69u3b15Hy/fff9+evv/6677W/+uqrmfsdO3bMHHMAAQhAAAIQgEDlCcRW8FHRTjvtNDviiCPs22+/tf79+9sTTzxhgwYNso8++ij6GMcQgAAEIAABCCREoCgFf+edd9q4ceNs0qRJ1qFDB3vggQfskEMOsYkTJyYUDYKFAAQgAAEIQCBKoGAFHwSBLV261FZbbTUbO3as7b///t6/xYsXW9u2baN+cwwBCEAAAhCAQEIEYk+yC+Vr1qyZ9e7d2wYPHmxTp0715vkbb7zRbrnlFhs5cmT4GL8QgAAEIAABCCRIoOAevGS97bbbbOjQod4kv+WWW9q6665r06ZN8+b6BONC0BCAAAQgAAEI/D+Bgnvwek/K/Oabb7bPPvvMli9fnoF5+eWX2x577JE55wACEIAABCAAgWQIFKXgNYP+yCOPtH79+lnLlt95sfHGGycTC0KFAAQgAAEIQKAOge+0c53L9Z9okt0333zjl6zVeDwOAhCAAAQgAIHqI1DwGLyU+u6772433XSTaeY8DgIQgAAEIACB6iNQsIJXFObOnWtHHXWUrbnmmrb55ptn/h566KHqiyESQQACEIAABJoggYJN9GI0atSonJ/EVdsYvCYA6pv9Srukwi1HPJctW2b6S4JjOeKTprRJKi7KCxqqi7pwfYzotTQcpyXfKy3SFJe0xSdf2mSXtULKVVEKXtvFqnL54IMPvJl+k002sebNizIGFCJrUc8mNU8gqXCLgpTnJcUj/MvzWE3dSkvaCHoScakvzPqu11TmyBI2TXFKU1ySyvtZ2aNkp+VKm6IU/PTp0/138K+99pq1adPGtwwvvfRSO/bYY0sW4VJ4pEZHixYtSuFVQX4kFW5BQsZ8WA25NMWHuMRM+DyPqUxlV0g6T6Ks5RGzJLfSFKc0xUWJm6b45ItLdlkrJGMX1e0+5phj/Ep2n3/+uX311Vf28MMPe7O9dpbDQQACEIAABCCQPIGievBS5BMmTLDWrVv7GOy00042bNgwmzJlim2xxRbJxwoJIAABCEAAAk2cQFE9+K222speeumlOugmT55s7ANfBwknEIAABCAAgcQIFNWDP/7442233XbzK9mtv/76vjffvn1723vvvROLCAFDAAIQgAAEIPAdgdg9+CVLltjs2bP9m9poRnvBb7vttt5Mf8YZZ/id5Vq1avWdzxxBAAIQgAAEIJAYgdg9eG0Ne+6559rjjz/uhe3Vq5fpDwcBCEAAAhCAQPURiN2Drz7RkQgCEIAABCAAgfoIxO7By4M5c+ZkevC5PNQM+k6dOuW6xTUIQAACEIAABCpIoCAF/+abb9qhhx5ar3jXXnutDRo0qN773IAABCAAAQhAoDIEClLwvXv3ztuDr4zIhAIBCEAAAhCAQEMEGINviBD3IQABCEAAAjVIILaCX2ONNWy77barwSgiMgQgAAEIQKDpEYit4LWD3B//+Mc6hLSa3YUXXljnGicQgAAEIAABCCRPILaCzyXqhx9+aP/6179y3eIaBCAAAQhAAAIJEmiUgk9QboKGAAQgAAEIQCAPgUYpeK1DP3DgwDzecwsCEIAABCAAgSQIFK3gly9fbu3atbN9993XdIyDAAQgAAEIQKB6CBSl4KdPn249e/a0TTbZxM+sb9u2rV133XXVEyskgQAEIAABCDRxAkUp+GOOOcYGDx5sn3/+uX311Vf28MMP26hRo+z1119v4jiJPgQgAAEIQKA6CBS0kl0oshT5hAkT/FaxurbTTjvZsGHDbMqUKab16HEQgAAEIAABCCRLoKge/FZbbWX6Bj7qJk+ebB07doxe4hgCEIAABCAAgYQIFNWDP/7442233Xazfv36mWbSqzffvn1723vvvROKBsFCAAIQgAAEIBAlUFQPfujQoTZp0iTbdtttvZn+jDPOsKlTp1qrVq2ifnMMAQhAAAIQgEBCBIrqwUvWXr16+b+E5CZYCEAAAhCAAATyEIit4J977jk78sgj83hldvHFF9uAAQPyPsNNCEAAAhCAAATKTyC2gt90003tmmuuySvR5ptvnvc+NyEAAQhAAAIQqAyB2Ar+zTfftOHDh/vZ83379rUZM2asIOEVV1xhe+yxxwrXuQABCEAAAhCAQGUJxFbw+r79L3/5i5fuyiuvtCVLlqwg6UYbbbTCNS5AAAIQgAAEIFB5ArEV/Pvvv29t2rTxEnbv3n0FSW+99Vb74osvbM8991zhHhcgAAEIQAACEKgsgdifyS1YsMB23XVXe++99+pIOH/+fDv88MNN38avueaade5xAgEIQAACEIBAMgRiK/jtt9/efvGLX1j//v3to48+8tJqNbsf/vCH9sYbb9i0adP8xjPJRINQIQABCEAAAhCIEoit4PXSmWeeaUcffbTvyV900UV+Dfqf/OQn9uSTT5pm2eMgAAEIQAACEKgOArHH4ENxzzrrLH+o1evuv/9+22effcJb/EIAAhCAAAQgUCUEYiv4IAhs2bJlXuxf/epXfhb9Oeec43vxWoderkWLFtasWTN/zD8IQAACEIAABJIjEFvB//vf//bj79midurUKXPprrvusgMOOCBzzgEEIAABCEAAAskQiK3gd9xxR/vggw/ySsl2sXnxcBMCEIAABCBQMQKxFfxbb71la6yxhq233np5hfv222/toYceYmw+LyVuQgACEIAABMpLIPYs+nbt2tnpp59uv/zlL/3WsEuXLs1ItnjxYv+p3OjRo02f082ePTtzjwMIQAACEIAABCpPIHYPfoMNNrA77rjDJk+ebCNHjrTnn3/eOnToYCuvvLJ9/PHH/jO5QYMG+furr7565WNCiBCAAAQgAAEIZAjEVvDhG7vssos9/vjjfkb9O++8Y19//bVttdVWtsoqq4SP8AsBCEAAAhCAQMIEClbwobz6JI7FbUIa/EIAAhCAAASqi0DsMfjqEhtpIAABCEAAAhDIRwAFn48O9yAAAQhAAAI1SgAFX6MJh9gQgAAEIACBfASKUvBTp061H/3oR9a1a1fbbLPNMn/6/h0HAQhAAAIQgEDyBIqaZHfEEUfYkUceaf369bOWLb/zYuONN04+RkgAAQhAAAIQgIB9p51jwtCmM998841pNzk2lokJjccgAAEIQAACFSZQsIleSn333Xe3m266ybSCXTHu/PPPtxtvvNG/Kj+OPfZY0/f15557bsa7yy67zG9uc/DBB9vcuXP99Xfffdf2339/bzm47777Ms9yAAEIQAACEIBAXQIFK3i9LoV71FFH2Zprrmmbb7555i/OGPxTTz1lV199tV8gR35deumlfix/0qRJpvXuH3nkEdMzTzzxhD388MOmTW4uvPBCPWrDhw+3s88+2+69914777zzbM6cOf46/yAAAQhAAAIQqEugYBO9Xh81apRfrrauV2YNjcHPmzfPfve739lpp51my5cv969PmTLFrr/+emvVqpXfavaxxx7zS+AOGTLEj++rB7/XXnv5Z2fMmGE9e/b0x3369LFnn33WBgwY4M9fe+01u+CCC/yx/snSID/mz5+fuVapA1klkgi3HPFbtmyZad8BDc2kwaUpbZKKy5IlSzLlN8wTyidpyfNhnPSbpjilKS71p02zaPLVzHG+tAl1ZTGRKUrB9+jRwxdwbR+rSmaTTTax5s0bNgaMGDHCm+H/85//ZMzuWsdeu9TJaW37mTNn+kLVrVs3f619+/Y2a9Ysv4GN1r0PXfhseK7NcHbYYYfw1Ct4rba30korZa5V6kAVYBLhliN+Uu5qLKUlPmlKm6Tiovygv6hT+U9LHonGK01xSlNclEa541PcsHE0zZM4zh2X/0mSXdYKka8oBT99+nQbOnSoqdfcpk0b38OTqV1j6fW5Rx991KZNm2ZqHMj8rm1lBw4caFLM6tlLeasVs9Zaa/nGgq7JLViwwDp16mRt27b1x6H/4bPhubaxlQk/dOpxjh8/vs4s//BeuX9V2UW/Lih3eOX0XxzVgkxLfNKUNknFRfkhu9LReVrySLQ8pSlOaYqL0ih3fGpTweeOy/9yYnZZi+bPho4b7nbn8OGYY46xwYMH2+eff25fffWVHyuX2f7111/P8fT/Lm244YamHrx63quuuqrfnKZ169be5P7MM8/4hzT23r1795zXBEAb2qg3L6Xzwgsv+LH7egPkBgQgAAEIQKAJEyiqBy9FPmHCBJOClttpp51s2LBhpvH0LbbYIifOjTbayPQnp963JurpXHvMn3TSSTZmzBiv+DV5Tj0ETbbbe++97bPPPrOxY8f697Tf/CGHHGKLFi3yvf8uXbr46/yDAAQgAAEIQKAugaIUvLaHfemll2y77bbL+KZ94k8++eTMeb6Do48+OnNbpnXNil+4cGFmy1mNnWumffSaXujbt6//k4KPjsdnPOMAAhCAAAQgAAFPoCgFf/zxx9tuu+3mv0dff/31fW9ek+HU4y7W5dpPPtc1+Y9yL5Yy70EAAhCAQFMhUNQYvCbY6bv1bbfd1pvptaqd1qfXp244CEAAAhCAAASSJ1BUD15i9+rVy/8lHwUkgAAEIAABCEAgm0DsHrxmum+99db+fY2Fb7rppiv8xVnJLlsAziEAAQhAAAIQKD2B2D14zY7/y1/+4iW48sorTYtsZLtwlnz2dc4hAAEIQAACEKgsgdg9eC00Ey4Tq2/VtQ79Ntts42e+azxeK/HoG3ccBCAAAQhAAALJE4it4KOiai157Qmv1ej69+/vV6YbNGiQffTRR9HHOIYABCAAAQhAICECRSn4O++808aNG+dn0qvX/sADD/gFaCZOnJhQNAgWAhCAAAQgAIEogYIVvJaJ1QYkq622ml9hTvuzy2nTGZnxcRCAAAQgAAEIJE8g9iS7UFQtfN+7d2+/Fr2+fdfGMTfeeKPdcsstObeQDd/jFwIQgAAEIACByhEouAcv0W677Ta/m5xM8ltuuaWtu+66fqc4JtlVLuEICQIQgAAEIJCPQFEKXrvB9evXzy90o41nXnnlFZszZ06+cLgHAQhAAAIQgEAFCRSl4JlFX8EUIigIQAACEIBAEQSKUvDMoi+CNK9AAAIQgAAEKkigYAXPLPoKpg5BQQACEIAABIokwCz6IsHxGgQgAAEIQKCaCRTcg1dkmEVfzUmKbBCAAAQgAAGz2D147SZ37LHH2ksvvWR77rmnzZgxYwV+V1xxhe2xxx4rXOcCBCAAAQhAAAKVJRBbwbObXGUThtAgAAEIQAACjSEQW8Fn7yan797ffPNN06S70EWPw2v8QgACEIAABCBQeQKxFXxUtN/+9rc2ZswY69y5s7Vo0SJz65JLLrEBAwZkzjmAAAQgAAEIQCAZAkUpeK09//bbb/s94ZMRm1AhAAEIQAACEMhHoKhZ9Ouss47Nmzcvn7/cgwAEIAABCEAgQQJF9eCvuuoq23777f1s+o4dO2bE/9nPfmbdu3fPnHMAAQhAAAIQgEAyBIpS8CNGjMjsIte8+XdGgOh4fDLRIVQIQAACEIAABESgKAU/ffp0++STT6x169ZQhAAEIAABCECgCgl81/0uQLhdd93VtBc8DgIQgAAEIACB6iRQlIJftGiRDRo0yDT+vvnmm2f+HnrooeqMJVJBAAIQgAAEmhiBokz0o0aNspEjR66AauONN17hGhcgAAEIQAACEKg8gdgKfsGCBfbll1/aBhtsYD169FhB0smTJ9vcuXOtQ4cOK9zjAgQgAAEIQAAClSUQ20SvzWYOO+ywjHS/+c1v7J577smcX3bZZaZncBCAAAQgAAEIJE8gtoLPFlW7yc2ePTv7MucQgAAEIAABCFQBgaIVfBXIjggQgAAEIAABCNRDAAVfDxguQwACEIAABGqZQOxJdork4sWL7YsvvvDxXbhwoZ9UF55/++23tcwB2SEAAQhAAAKpIlCQgn/66af9FrEhgdtvv91OPfXU8NSOOOKIzDEHEIAABCAAAQgkRyC2gt9ll118jz2fqCuvvHK+29yDAAQgAAEIQKBCBGIreG0ks9pqq1VILIKBAAQgAAEIQKAxBJhk1xh6vAsBCEAAAhCoUgIo+CpNGMSCAAQgAAEINIZAoxT8kiVLbNmyZT58bUCj5Wo1ux4HAQhAAAIQgECyBBql4K+55hqbNGmS/3Sub9++9uCDD9ruu+9uM2fOTDZWhA4BCEAAAhBo4gQapeBDdmPHjrXf/e53dtFFF9lf//pXu+GGG8Jb/EIAAhCAAAQgkACBRil4zawfM2aMvf766zZt2jSTyf6WW26xH/zgBwlEhSAhAAEIQAACEAgJxP5MLnwh+jt8+HDbZ5997MUXX7SlS5fa/Pnz7fvf/77tt99+0cc4hgAEIAABCECgwgQapeAl63rrref/QrkPP/zw8JBfCEAAAhCAAAQSItBoBZ8t9/PPP2/aSnbw4MHZtyp+rhn+GjaotEsq3HLEU5YZ/SXBsRzxSVPaJBUX5YUgCOokj87TkkeiEUtTnNIUF6VRmuKTLy7ZZS2aPxs6brSC1+dxn376qW200UY+rI4dO3qF0FDAlbjfrFkza968UdMMihIzqXCLEraBl8Qv/Gvg0Zq4naa0SSouucpUUrKUO9Plimu5wyyX/2mKixilKT7likujFPx7771nRx11lOsP/HQAACEeSURBVF/CtkuXLn7C3ZdffmkTJkyw3r17lyufxvZX0DQRsNIuqXDLEc/ly5f7gpQEx3LEJ01pk1RclBek0LNdWvJINF5pilOa4qI0SlN88sUlV1mL5tF8x43q3t5///122WWX2QMPPGA9evSwE088MV9Y3IMABCAAAQhAoEIEGqXgu3fvbjfeeKN98803duyxx/oZ9PoeHgcBCEAAAhCAQLIEGmWi33XXXW3x4sU2a9YsW3311W3EiBG29tprJxsjQocABCAAAQhAwGIreM3ymz179grIttlmG3/tiy++sHbt2tnBBx+8wjNcgAAEIAABCECgsgRiK/ipU6da//7980p311132QEHHJD3GW5CAAIQgAAEIFB+ArEVvD6D22uvveztt9/2K9UddNBB1q1btzoSNma2Xx2POIEABCAAAQhAoFEEYk+y22CDDWzcuHH29NNP28Ybb2ynnnqq9ezZ0y644ALT53Io90alAy9DAAIQgAAESkogtoIPQ+3QoYMdeeSR9sgjj/jv3TW5btiwYf6795dffjl8jF8IQAACEIAABBIkULCCj8qqFez0p6Vpv/32W9PSmTgIQAACEIAABJInEHsMPhT1pZdesrvvvts0oW6llVayAw880JvuN9tss/ARfiEAAQhAAAIQSJhAbAWvPd/33XdfP9Yupa5V7LbccsuExSd4CEAAAhCAAARyEYhtov/8889t+vTp9s4779j555/vl6Zt2bKlRf/uueeeXGFwDQIQgAAEIACBChOI3YPfcccd7eOPP84r3hprrJH3PjchAAEIQAACEKgMgdgKXuPtnTt39uPt8+bNsyFDhvhd5CojJqFAAAIQgAAEIFAIgdgmenm633772fHHH29/+tOf/JrzkydPLiQsnoUABCAAAQhAoEIEYit4LWbz5JNP2htvvGHTpk2zk08+2W8VWyE5CQYCEIAABCAAgQIIxFbwn3zyiW266aamhW7kdt99d3v//ff9Mf8gAAEIQAACEKguArEVvHaTa9WqVUZ6KfqFCxdmzjmAAAQgAAEIQKB6CMSeZCeRly5danPmzPHSz50715YvX54518U2bdrUaQT4B/kHAQhAAAIQgEDFCRSk4DUGH5roQ0mj52wXG1LhFwIQgAAEIJAsgdgKfuedd7ZZs2bllXa11VbLe5+bEIAABCAAAQhUhkBsBa8V61jIpjKJQigQgAAEIACBxhKIPcmusQHxPgQgAAEIQAAClSOAgq8ca0KCAAQgAAEIVIwACr5iqAkIAhCAAAQgUDkCscfgn3vuOTvyyCPzSnbxxRfbgAED8j7DTQhAAAIQgAAEyk8gtoLXKnbXXHNNXok233zzvPe5CQEIQAACEIBAZQjEVvDt2rWznXbaKa9UixYtynufmxCAAAQgAAEIVIZAUWPwkyZNsl122cW6detmW221lXXt2tVvJfvggw9WRmpCgQAEIAABCEAgL4HYPfioL9oy9rDDDrPXX3/dK3gtcHPbbbf5PeKjz3EMAQhAAAIQgEAyBAruwQdBYF9//bWNHDnS7yi3ePFiO/HEE23XXXe1hx56KJlYECoEIAABCEAAAnUIFKzgmzVr5jeVkZLv0aOHTZ061XuoVe4+/PDDOp5zAgEIQAACEIBAMgSKMtEfd9xx1r17d3v33Xf9nvAHHnigPfroo/b0008nEwtChQAEIAABCECgDoGCe/B6W9/Djx8/3rQ+vSbcabLdzTffbO3bt6/jOScQgAAEIAABCCRDoCAFr8/g9NevXz/Td/E67ty5s40YMcJuueUWGzduXDKxIFQIQAACEIAABOoQKEjBDxo0yFZZZRV79dVX/a+O9adZ9E888YTtuOOOdTznBAIQgAAEIACBZAgUpOAnTpxoS5YssYMOOsj/6lh/S5cutY8++sj36pOJBqFCAAIQgAAEIBAlUJCC1wx6jbvffvvt1rx5c6/U33nnnah/HEMAAhCAAAQgUAUEClLwobzTp0+3nj172iabbGLbbbedtW3b1q677rrwNr8QgAAEIAABCCRMoCgFf8wxx9jgwYPt888/t6+++soefvhhGzVqlF/ZLuH4EDwEIAABCEAAAo5AUd/Ba4naCRMmWOvWrT1EbUIzbNgwmzJlim2xxRaAhQAEIAABCEAgYQJF9eC1wcxLL71UR/TJkydbx44d61zjBAIQgAAEIACBZAgU1YPXZjO77bab/x5+/fXX9715LXKz9957JxMLQoUABCAAAQhAoA6B2D14fQ43e/Zs//LQoUP9CnbbbrutN9OfccYZfk36Vq1a1fGcEwhAAAIQgAAEkiEQuwevTWXOPfdce/zxx72kvXr1Mv3hIAABCEAAAhCoPgKxe/DVJzoSQQACEIAABCBQH4HYPXh5MGfOnEwPPpeHmkHfqVOnXLe4BgEIQAACEIBABQkUpODffPNNO/TQQ+sV79prrzWtV1+fe/755+3SSy+1+fPn2z777GM///nPbfHixXbCCSfYG2+8Yf379/fDAHr/sssus7Fjx9raa6/tF9HRYjranvb000+3WbNm2Yknnmj77bdffUFxHQIQgAAEINCkCRSk4Hv37p23B98QyVNOOcUvc7vuuuv6Wfg//vGP/XnXrl3tqquussMOO8weeeSRzOY1WkBHjYYLL7zQ/vCHP9jw4cPt/PPPN83c1yx+NQjYorYh6tyHAAQgAIGmSKAgBd9YQFrD/nvf+573Ruvaf/bZZ35xnOuvv940A/+AAw6wxx57zDp06GBDhgzx694ffPDBttdee/l3ZsyY4ZfI1UmfPn3s2WeftQEDBvh7L7/8so0cOdIf65/Wyj/88MNt7ty5mWuVOtA2uopfGtyyZcss/EtDfNKUNknFRV/ULF++vE520IZTSZS1OkKU4SRNcUpTXJTUuePTogy5oPxe5o7L/8JV/Vusi63g11hjDb/ufLEB6b1QuV988cVeiWsd+48//tjkt5wU+8yZM70Jv1u3bv6aeugyyesTvZVXXtlfiz4bXpApX7vchU4KVo2GNm3ahJcq9qvKL4lwyxFBVdyq0LUtcBpcmtImqbhoWC27AatNqNKS56P5PE1xSlNclEa547Momnw1c5w7Lv8TX53VYl1sBd+jRw/TX2Od1qx/7bXXvGlefrVr187mzZvnlbfG5tdaay3f+9Y1uQULFviJexqD13HowmfD886dO9shhxwSnloQBDZ+/HjvV+ZihQ5U+TUmUSokZqxgFJe0xSdNaZNEXBRmtoJXZkpClliZuBEPpSlOaYpL2vJbvrTJVdbiZunimwZxQ4g8J+X+xRdf2N///vfMOvbale6ZZ57xTz311FPWvXt3b4bPvqYegnqR6s1Leb/wwgumsXscBCAAAQhAAAIrEojdg1/x1cKufPrpp3beeef5zWi22WYb//Lo0aP9rPiTTjrJxowZY6uuuqp/RqZHTbbT0rcap9dsejk9r166xh4HDhxoXbp08df5BwEIQAACEIBAXQIVU/CaOa+x3Fzu3nvvtYULF2bGeVu0aGFXX311nWt6r2/fvv5PCj46Hp/LT65BAAIQgAAEmjKBipro84HONYkr1zX5gXLPR5J7EIAABCAAATcvBggQgAAEIAABCKSPAAo+fWlKjCAAAQhAAAL04MkDEIAABCAAgTQSoAefxlQlThCAAAQg0OQJoOCbfBYAAAQgAAEIpJEACj6NqUqcIAABCECgyRNAwTf5LAAACEAAAhBIIwEUfBpTlThBAAIQgECTJ4CCb/JZAAAQgAAEIJBGAij4NKYqcYIABCAAgSZPAAXf5LMAACAAAQhAII0EUPBpTFXiBAEIQAACTZ4ACr7JZwEAQAACEIBAGgmg4NOYqsQJAhCAAASaPAEUfJPPAgCAAAQgAIE0EkDBpzFViRMEIAABCDR5Aij4Jp8FAAABCEAAAmkkgIJPY6oSJwhAAAIQaPIEUPBNPgsAAAIQgAAE0kgABZ/GVCVOEIAABCDQ5Amg4Jt8FgAABCAAAQikkQAKPo2pSpwgAAEIQKDJE0DBN/ksAAAIQAACEEgjARR8GlOVOEEAAhCAQJMngIJv8lkAABCAAAQgkEYCKPg0pipxggAEIACBJk8ABd/kswAAIAABCEAgjQRQ8GlMVeIEAQhAAAJNngAKvslnAQBAAAIQgEAaCaDg05iqxAkCEIAABJo8ARR8k88CAIAABCAAgTQSQMGnMVWJEwQgAAEINHkCKPgmnwUAAAEIQAACaSSAgk9jqhInCEAAAhBo8gRQ8E0+CwAAAhCAAATSSAAFn8ZUJU4QgAAEINDkCaDgm3wWAAAEIAABCKSRAAo+jalKnCAAAQhAoMkTQME3+SwAAAhAAAIQSCMBFHwaU5U4QQACEIBAkyeAgm/yWQAAEIAABCCQRgIo+DSmKnGCAAQgAIEmTwAF3+SzAAAgAAEIQCCNBFDwaUxV4gQBCEAAAk2eAAq+yWcBAEAAAhCAQBoJtExjpMI4LVu2zJYsWRKeVuw3qXDLEcGlS5ea/pLgWI74pCltkoqL8kIQBHWSR+dpySPRiKUpTmmKi9IoTfHJF5fsshbNnw0dp1rBN2/e3Fq0aNEQg5LfTyrckkfEeajMtXz58kQ4liM+aUqbpOKSq0w1a9YsNXkkmu9yxTV6v5aO0xQXcc8dn8p36EqRB3LHpfE+p1rBq9JRJVhpl1S45Yin4pK2+CSRJ8qVNknERWEqT2S7JGTJlqHU52mKU5rionROU3zyxSVXWYubzyuv/eJKxnMQgAAEIAABCBRNAAVfNDpehAAEIAABCFQvARR89aYNkkEAAhCAAASKJoCCLxodL0IAAhCAAASqlwAKvnrTBskgAAEIQAACRRNAwReNjhchAAEIQAAC1UsABV+9aYNkEIAABCAAgaIJoOCLRseLEIAABCAAgeolgIKv3rRBMghAAAIQgEDRBFDwRaPjRQhAAAIQgED1EkDBV2/aIBkEIAABCECgaAIo+KLR8SIEIAABCECgegmg4Ks3bZAMAhCAAAQgUDQBFHzR6HgRAhCAAAQgUL0EUPDVmzZIBgEIQAACECiaAAq+aHS8CAEIQAACEKheAij46k0bJIMABCAAAQgUTQAFXzQ6XoQABCAAAQhULwEUfPWmDZJBAAIQgAAEiiaAgi8aHS9CAAIQgAAEqpcACr560wbJIAABCEAAAkUTQMEXjY4XIQABCEAAAtVLAAVfvWmDZBCAAAQgAIGiCaDgi0bHixCAAAQgAIHqJYCCr960QTIIQAACEIBA0QRQ8EWj40UIQAACEIBA9RJAwVdv2iAZBCAAAQhAoGgCKPii0fEiBCAAAQhAoHoJtKxe0cov2Q233F2WQBYtWmQrr7xyWfyWp0cddkDZ/MZjCEAAAhBIB4EmreBPP+eimkxFFHxNJhtCQwACEKgoAUz0FcVNYBCAAAQgAIHKEEDBV4YzoUAAAhCAAAQqSgAFX1HcBAYBCEAAAhCoDAEUfGU4EwoEIAABCECgogRQ8BXFTWAQgAAEIACByhBAwVeGM6FAAAIQgAAEKkoABV9R3AQGAQhAAAIQqAwBFHxlOBMKBCAAAQhAoKIEUPAVxU1gEIAABCAAgcoQaNIr2VUGcWVCuej6f5QloOXLl9uy5cusVctWZfH/10cPWcHfqa9+ssK1Ul1YsHCBrbrK3FJ5V8efPlt1qXPOCQQgAIEkCaDgk6RfwrCvvmNCCX2rnFe5FPyJ106pnAAlDGnaVT8toW94BQEIQKBxBDDRN44fb0MAAhCAAASqkgA9+KpMFoRKE4E/TllQlugsWdzMWrUuj98S+PSdVy2L3HgKAQhUhgAKvjKcCaUJE5j4brki36JcHnt/T9+5rN7jOQQgUGYCmOjLDBjvIQABCEAAAkkQQMEnQZ0wIQABCEAAAmUmgIIvM2C8hwAEIAABCCRBoKYU/Lvvvmv777+/9evXz+67774keBEmBCAAAQhAoCYI1NQku+HDh9v5559v66+/vu22227Wv39/a9++fU2ARkgIQAACEIBAJQnUlIKfMWOG9ezZ0/Pp06ePPfvsszZgwAB//sILL9hJJ52UYde8eXM78cQT7ZtvvslcS8tBmuLUNOJSU8UsU0zqS5slS5aYVjiMOl2r7/noc7V2nKY4pSkuyke545OusqZ4Llu2TD9FuZqhMXv2bFt55ZUzkezQoYPNnDkzc77hhhvamWeemTnXgSqh1Vdfvc616Mns956PnpbsWBkvX7glCyji0QePXhs5K92hKm79rbpq5b6JLueKcEmkzYTDS5ceUZ+SiIvCX7x4sakBHXWtWrXKm+fLVdbmz5/v64UWLcr7yWA0ruUqa0EQ2Lx586xt27bR4Mp6TFmLh3fu3Lm22mqrWbNmzeK9UMKnGpO3a0bBK9MvWPDdoh4q2GuttVYG45prrmmDBg3KnKuwjB8/PnPOAQQgAAEIQKApEajbDK/imLds2dJWWWUVmzVrlkl5yyTftWvXKpYY0SAAAQhAAALJEaiZHrwQjR492g455BBbtGiRDRw40Lp0Yfeu5LIOIUMAAhCAQDUTqCkF37dvX9OfFHx0PL6aASMbBCAAAQhAIAkCNWOij8JBuUdpcAwBCEAAAhBYkUBNKvgVo8EVCEAAAhCAAASiBFDwURocQwACEIAABFJCAAWfkoQkGhCAAAQgAIEoARR8lAbHEIAABCAAgZQQQMGnJCGJBgQgAAEIQCBKAAUfpcExBCAAAQhAICUEUPApSUiiAQEIQAACEIgSQMFHaXAMAQhAAAIQSAkBFHxKEpJoQAACEIAABKIEUPBRGhxDAAIQgAAEUkIABZ+ShCQaEIAABCAAgSgBFHyUBscQgAAEIACBlBCoqd3kCmHerFkz+/LLL+2uu+4q5LWSPPvOO+/YWmutZW3bti2Jf0l6Iobave973/tekmKUJOzFixfb9OnTbcsttyyJf0l78vbbb9t6661nq666akVFCYLAvv/979cJ89tvv02krH344YfWpk0bW3PNNevIU4sn33zzjc2cOdM22mijWhS/jszKI6+88opttdVW1rx57fcj33vvPVtjjTWsXbt2deJZiRPpkmJdM5cQQbEv815uAtrS9tRTT7W999479wM1dPXSSy+11157zW688cYakjq3qO+++65tu+22NmvWrNwP1NjV7t2725gxY2znnXeuMclLJ+7BBx/s43/88ceXztOEfFJnROVswoQJCUlQumDV4NOunwsXLkzF1t577rmnHX744fbTn/60dJAq4FPtN60qAIkgIAABCEAAArVGAAVfaymGvBCAAAQgAIEYBFDwMSAV+ojG//WXBkdcqjcV05Q2xVJOE4M0xkVxSoOr1bRhDD4NuY84QAACEIAABLII0IPPAsIpBCAAAQhAIA0EUPBpSEXiAAEIQAACEMgigILPAhLndNy4cXb22WdnHv3HP/5hgwcPtgMOOMD0Dbycvh8/+eSTbY899rBLLrnEX1u+fLmNHj3aXzv22GP99+X+RkL//v73v9uQIUPswAMPtMmTJ3sp9CnZ/vvvb/369bP77rsvI9lll11m/fv3N32WNHfuXH891/uZFyp88Pzzz3vZ9tlnH7vpppsyoeeS++GHH7aBAwfaj3/8Y3vzzTf9s/o0aYcddsj8ffrppxk/kjg499xzba+99rJjjjnGPvjgAy9CLrnrS6+LL77Y58nhw4fbnDlzkohCScLMLmsvvfSST2d9gvrQQw/5MGqhrOXKn1qXQfXALrvsYkrv0OXKs7neD5+v9O8XX3xh+ixR+fOss84yxUMul9y58qfSK1rWHnzwwUpHoU54cctafelV1WVN38Hj4hO46KKLArfIR/DLX/7Sv+QWpwh69OgRfP3114FbDCHYfvvt/XXXAAiuueaawCn14KijjgpcRRVMnDgxOOSQQ4KlS5cGt956a+C+lY8fcImfdAtqBL179w7c96qBjrfZZhsvq2uQBNOmTfPXevbsGcyePTt48skng6FDhwZLliwJrrzyysAVan8/1/slFjO2d3369Ancoiee7Y9+9KPAKcWcciu9evXq5ePllEXgviH3YZx55pnB/fffH7hFffxf7IDL8OC//vWv4LjjjvM+u4aHzz/1yZ0rvV599dXAKUD/vvKg8mwtuuyypjg4ZRi4dRmCBQsW+Pw7Y8aMoNrLmuTOlT8vuOCC4E9/+lPgFEfgvq8OXAMuZ56t731dT8L9+te/Du644w4ftMrN3/72t3rlzpU/ladHjBiRKWvLli1LIho+zELKWq70qvayRg++Tluu4ROtmHX11VdnHnzuuef84imrr766bbjhhuYUol/c4fHHH/c9KM2+VG/4iSeeMF3TggktWrQwp4T8tYxHFT5YbbXV7IEHHrDWrVv7hShcRWmyMOjXKXa/MpirlOzZZ5+1qVOn+p5+y5Ytfe/pscces/rer3A0MsHdfvvtfrU9sRXzzz77LKfc6rErfu3btzfXMDPXMPM9kBdffNGnndI26R6vejdOufm4rbLKKvbJJ594S0MuuXOllxYYUS/rrbfeMlcB1eyKitllTYunqPe3xRZbmLholbSXX37ZlytZ0Kq1rCkhc+XPKVOm2E9+8hNr1aqVt/6pXOUqa/W97zNIAv9OOukkcw1+H3KYP+uTO1f+VFlbaaWV7PLLL/crSya50l0hZS1XelV7WUPBF1hAjjzySJOiC91OO+3kFbVM3K5X7s2pUi6qcC688EJfAbkWrsnkq2vXX3+9ycyoFeJUCSflVMC0BKLMTocddpidccYZpqUylWFD16FDB7905scff+yXadR1KUatBJfrfSnXpFy4lK7MZZJ7u+22s1xyR6+F8dHyoJLdNemtc+fOpobNvHnzkoqKXw5TDUYp9hNPPNFGjRpVJy6h3P/9739zplenTp182sqMOmnSJD+0klhkGhFwdllTntMSuTfffLNXhM4ililX1VzWhKCh/JmvrNX3fiPQNurVddZZx9eB//73v+2ee+7xwwzRchXWEers5KpPnPXFN8a6detmRx99tG+gNUqgRryspWfjlDXVEdE4hulV7WXtO03VCEhN+VVVOvfee69X3JtuuqlXDuuuu66vmJ0Zy2644QY/pq21jLVM6siRI+3Pf/6zX8b2hRdeSBSdlpHUvIFdd93VTjjhBHNDB6bCF7r58+d7RaFCECo83Vemlst+P3wvqV8pQi2rq96SXC65o9f0jOLQsWNHGz9+vE69c0MUprHfgw46KLxU8V/ll/3228/3cpRvNO8hTAMJI7llMcqVXspfanj+6le/stdff92P48t6lAb317/+1Zcf7SmgvKu1+DUvpNrLmtjXlz+lBMOypt5smM7Rspbr/STTU/MffvOb39g///lPX86i5SqUW3tx5MqfSq/QOfO8L69a3jspF6esqY4I4xhNr2ova/TgG5mrVPG6sVvfI9dkNVW8UvpaV9qNx5sbszZNNHHjvqaJMnpeZmAVZJmIk3Iyx++7775eiZ1yyileDFkmZHJTD129WTVAunbt6k3azzzzjH/mqaeeMq2Bnuv9pOKicFV5yiKiiX8adpCTSTtbbm00IwuK4ifzvOKheGvSXVgZybSt3kVSTtYemUClzDQBSy6X3NpkJld6afhElZGcKqNKb0bjAy7TP629f/rpp9vvf/97U0NMZvpqL2tCETd/5sqz9b1fJsQNevvII49466QmfXbp0sU/n0vu+uoTN/5uGtqUq5WypjolVxyrvqz5mQb8K4iAy+CZSXZ60fV+/SSZ3XffPXBK3PulCVxulnZw6KGHBs5U6q85BRK4MfjAtWADZ673E/P8jQT+3XnnnYGr+IOtt9468/fVV18FzuwWKB6uRZ2ZnKVJgYqDZHa9ycCNqwX1vZ9AVAJnyg5cZRK4hkcmLpqwlEtuyecaWIFT6H6ynSY+yjmzb6AJQW6+ROBmNvtrSf0Tazd8komL+6rBi5JL7lzp5RqRgbM+BD/72c98mmmSZK267LLmGj2Bm70duMZp4Ia7fLSqvazVlz8/+uijwFlpfJ2gNHaNzZx5tr73k0pT1+gP3I53mfypyWf1lbVc+VOTeFXHaCKoypvqnaRcIWUtV3pVe1ljJTvfjmz8P/X+cvWUcl3Pda3xEpTWB20RGx0/k++yTqjHWGsul9yae6AeRnSCj3rzelbbj1aryyW3ZE1TejXEXgyUbkq/qMtVrnJdi75TDce58meua9Uga0My1Cd3rvwpa2Y1b6ldX1nLFcdc1xpiVYn7KPhKUCYMCEAAAhCAQIUJMAZfYeAEBwEIQAACEKgEARR8JSgTBgQgAAEIQKDCBFDwFQZOcBCAAAQgAIFKEEDBV4IyYUAAAhCAAAQqTAAFX2HgBAeBaiagb5vDZUglpxY/0kI7WhtBy8Rq8Z1wmd9wgyI9p9XltGaCvgv+4Q9/mPnOWUvu6pt1LUijJU5xEIBA5Qig4CvHmpAgUPUEdtxxR79Lm5bJldP66FrkQ+vC//znP/cL6Gg9f+2UOGzYMP+MVpXTrnVaQdB9K+wXdQp3W9TiQ1pz/KqrrjK30ZJ/nn8QgEBlCKDgK8OZUCBQEwTUA9eqftoCWe7uu+82t9OZucVI/LKkWv5W6z1oS2Etyfyf//zHr9+vFQO10pe+T9dmMNpkJHTag0GrJmp/ABwEIFA5Aij4yrEmJAjUBAEpdO2voHXCx44d60322mhDu7X179/fNttsM/+nzW7cKnl+sRK3sqG/tvnmm/t3tWhQ6GSex0EAApUnUHcpqMqHT4gQgECVEVAP/qijjvIb7mhcXbuHyUSv9e1feeUVvzmPRNaYvK7dcsstflcxNQq0NrwaBW6f8EysktxlMCMEBxBoggTowTfBRCfKEMhHQEsUu/XevZLWBkpyGofXroPa7EW9c22JLFO8xuNlvt9kk028cndrivtNcpYsWZIvCO5BAAIVIICCrwBkgoBArRHQVrkywWusPXTqld92221+T3ZNxjvttNP8zoLa/lMmfO2OqF3vtHOixuC1DjwOAhBIjgBr0SfHnpAhULUExo0bZzfccIM98MADK8go07z2x9aYfNTpU7oOHTrU2cAnep9jCECgsgQYg68sb0KDQFUTkIn917/+td13333e1J5L2E6dOuW67Mfpc97gIgQgkAgBTPSJYCdQCFQnAfXKNanuuuuusz59+lSnkEgFAQjEIoCJPhYmHoIABCAAAQjUFgF68LWVXkgLAQhAAAIQiEUABR8LEw9BAAIQgAAEaosACr620gtpIQABCEAAArEIoOBjYeIhCEAAAhCAQG0RQMHXVnohLQQgAAEIQCAWARR8LEw8BAEIQAACEKgtAv8HZrENnTJ1NAIAAAAASUVORK5CYII=" alt="plot of chunk plot6"/> </p>
<p><strong>Which city has seen greater changes over time in motor vehicle emissions?</strong></p>
<p>Los Angeles County has seen the greatest changes over time in motor vehicle emissions.</p>
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