The 4th Annual
NC
Undergraduate Summer
Research Symposium
Statistics VIGRE
Traineeship
Abstracts are listed in
alphabetical order by the last name of the corresponding author.
Each year, wildfires destroy millions of acres of
land in the
Using a regression model with lightning and fire data from June and July 1998,
it can be shown that the total number of fires in an area is related to several
of the lightning characteristics.
The most prominent finding, so far, has been the possible discrepancies we have
found among the data. Fire cause, location, and ignition time are
extremely hard to pinpoint. Fires can smolder for weeks or months before
spreading into a noticeable fire. Lightning detection also presents a
location accuracy problem. The strokes can touch ground several
kilometers away from one another. This makes it difficult to identify the
precise location where a lightning strike caused a fire.
PM 2.5 is particulate matter that is 2.5 micrometers
or less in aerodynamic diameter and is caused by various processes that emit
particles into the air. These particles, over time, have been linked to
cardiovascular and respiratory diseases especially in younger and older
individuals. Thus there is a large concern of how well the people that
are being exposed to various amounts of this pollutant are being informed about
it so they can take the necessary steps to protect themselves. The EPA’s
AQI (Air Quality Index) entails 6 levels of health concern that are provided
for citizens via television and newspaper. Two databases were examined in
this study, the Air Quality System (AQS) database consisting of quality assured
Federal Reference Method fine PM data, and the AirNow
database which consists of continuous fine PM measurements provided to EPA on a
real time basis used to predict the AQI. Comprehensive exploratory statistical
analyses and visualizations were conducted to examine the causes of the variation
between the AQS and the AirNow databases.
Discrepancies were found in the concentrations reported to AirNow
and AQS. On some occasions the AirNow database
reported 2-3 AQI levels above or below what was reported to the AQS
database. The various locations and seasons of where and when these
discrepancies in the AirNow occurred were
found. These findings suggest a difference in the way the AirNow values and AQS values in the various states are
being reported to the EPA. Developing a quality control procedure to pair
the “problem sites” with sites that consistently report data accurately may
lead to an early warning system to detect data that has not been properly
adjusted at a “problem” site. It is critically important that the general
public be accurately informed regarding air quality so they can properly plan
their daily activities.
Why do we need emission standards? We need them
to help keep industries from producing excessive amounts of harmful
chemicals. These standards are enforced by a regulating company that
monitors the chemicals emitted from pollution sources. To help monitor
these pollution levels, there is usually a limit or emission standard that
cannot be exceeded. These can be in the form of a rate or a maximum
concentration amount. This research project dealt with a particular
industry that was monitored for a month giving hourly observations of Nitrogen
Oxide concentrations from a stack. These hourly observations can be
changed into many different useful variables. There are two types of
averages that were used. The first being rolling averages that use three,
eight, and twenty-four hour periods to come up with an average every
hour. Second are block averages that happen in three, eight, and
twenty-four hour blocks. For example, a three hour block average is only
computed for 8 three hour periods in a day. Daily maxes are also valuable
for controlling certain types of pollution. All of these different
indicators were examined. Different distributions were constructed to
characterize each indicator. There are known parametric distributions and
also nonparametric distributions. The goodness of fit of each of
these known distributions can be tested with the help of some statistical
software. These fitted distributions should characterize the whole
population of concentrations instead of just our sample. From this, we
can obtain the standard from the higher percentiles of the fitted distribution,
such as the 95th or 99th percentiles. These provide us with a limit that
we know should not be exceeded more than a certain percentage of the
time. Using this limit, the monitoring agency has a method of deciding
when an industry is exceeding the acceptable amount of pollution.
The best way to determine bone density is
to have a bone mass measurement (called bone mineral density or BMD
test). Bone mineral density measurements are obtained annually in many
sites throughout the country. These measures provide an indication of
bone strength and predisposition to sustain fracture. BMD in hip is
examined in this longitudinal study of 636 women taken from Swan (Study of
Women’s health Across the Nation) data and a collection of seven variables was
recorded. The different variables are number of days of collection (totdays), creatinine adjusted progestrone (pdgadj), bone
mineral density (bmd_tot), day, body mass index (bmi), cohid, and age.After analysis using a regression model with Swan data,
I found that body mass index is the most predictive of BMD.
|
Kalendra, Eric J. |
|
|
Home Institution: |
North Carolina State
University |
|
Program: |
Statistics VIGRE
Traineeship |
|
Department(s): |
Statistics |
|
Research |
Kevin Gross/Statistics |
|
Title of Presentation: |
Estimating the
Abundance of Endangered Butterfly Species from Time Series of Count Data |
Several butterfly species with discrete generations
are rare or endangered. For these species, a model based on Manly (1974)
and Zonneveld (1991) can be used to estimate both
total population size and death rate from transect count data. Here, we
study the coverage rates of confidence intervals for estimated population size
produced by the Manly-Zonneveld model. When
butterfly population dynamics are deterministic, actual coverage rates of
confidence intervals are close to the nominal 95% level. However, under
the more reasonable assumption of stochastic population dynamics, confidence
interval coverage rates are unacceptably low. We propose a parametric
bootstrap as an alternative for generating confidence intervals, and show that
the resulting coverage rates are much closer to the nominal 95%.
This procedure improves the Manly-Zonneveld by
quantifying the uncertainty in estimated butterfly abundance more accurately.
EPA created the Toxic Release Inventory (TRI) in
response to the “Emergency Planning and Community Right-To-Know Act” of
1986. The public can access the TRI Explorer on EPA’s website. The TRI
Explore provides citizens with the means to identify industry reported routine
releases of over 650 chemical materials. The database spans the years
1988 to 2002 and includes more than 20 industry categories. The intent of
the TRI Explorer is to facilitate communication between the government, the
public and industry so that all parties can work together to identify potential
problems, set realistic goals and evaluated progress. One of the
chemicals that TRI database contains is styrene. Our objective is to use
the TRI data to create a computer program that compares
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