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The International Year of Statistics (Statistics2013)
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Department of

Statistics

NCSU Dept of Statistics
5109 SAS Hall
2311 Stinson Drive
Raleigh, NC 27695-8203

Tel: (919) 515-2528
Fax: (919) 515-7591

Computation for Undergraduates in Statistics Program


2008 - 2009 Projects


Application of autoregressive modeling to determine the impact of reductions in nitrogen oxides emissions at electric utilities and their impact on fine partical matter.

Background: Particulate matter (PM) is the general term used for a mixture of solid particles and liquid droplets found in the air. Some particles and liquid droplets are large or dark enough to be seen as soot or smoke. Others are so small that they can be detected only with an electron microscope. PM2.5 describes the "fine" particles that are less than or equal to 2.5 micrometers in diameter. Course particles are greater than 2.5, but less than or equal to 10 micrometers in diameter. A particle 10 micrometers in diameter is about one-seventh the diameter of a human hair. PM can be emitted directly or form secondarily in the atmosphere. "Primary" particles, such as dust from roads or elemental carbon (soot) from wood combustion, are emitted directly into the atmosphere. "Secondary" particles are formed in the atmosphere from primary gaseous emissions, such as sulfate, formed from SO2 emissions from power plants and industrial facilities, and nitrates, formed from NOx emissions from power plants, automobiles, and other types of combustion sources. The Nitrogen Oxides State Implementation Plan Call (NOx SIP Call) was implemented to reduce ground level ozone. Did it have an impact on PM2.5 levels?

Purpose: This project will determine the impact of the electrical utility NOx emission reductions at selected PM2.5 sites in North Carolina and other states.

  1. Are their reductions in PM2.5 levels at sites in North Carolina and other locations that can be explained by NOx reductions at utilities?
  2. Applying autoregressive modeling methods to 2000-2006 PM2.5 and meteorological data can we identify common diurnal, weekly, seasonal and multi-year trends? How do they compare with trends in ozone?
  3. Using "finger" and "bull's eye" plots can the direction and source of the high PM2.5 levels be identified?

Data Sources: Ambient air quality and meteorological data will be provided by the North Carolina Department of Environment and Natural Resources. The ozone air quality data is measured at NCDENR air monitoring sites and the meteorology data comes from nearby airports. Other data will be provided by the Maryland Department of the Environment.

Client: Mr. George Bridgers and Ms. Bebhinn Do, NCDENR Air Division

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