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CVEN 689 Project Presentation Tim Schniedwind

CVEN 689 Project Presentation Tim Schniedwind. Introduction to Project. Correlation Between Air Pollution and Population Density in Metropolitan Areas Target Areas New York Chicago Houston Los Angeles. Air Pollution Research. Covers Scale from Global Warming to Air inside Homes

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CVEN 689 Project Presentation Tim Schniedwind

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  1. CVEN 689Project PresentationTim Schniedwind

  2. Introduction to Project • Correlation Between Air Pollution and Population Density in Metropolitan Areas • Target Areas • New York • Chicago • Houston • Los Angeles

  3. Air Pollution Research • Covers Scale from Global Warming to Air inside Homes • Produced by Factories(point source), Cars(non point source),stoves etc…

  4. Impacts of Air Pollution • Local(near source) Impacts to Human Health • As it disperses: Impacts to Environment • Acid Rain • Holes in the Ozone Layer • Human Health issues

  5. Importance for Urban Planning • Mass Transportation vs. Expanding Existing Highways or Building New ones • Urban Sprawl • Does More Spread Out = More Pollution

  6. Research Being done: TNRCC • Ozone measurements for the Houston Area • Speed Limit Issue

  7. Air Pollution Models • Attempts to Calculate Air Pollution in an Area based on sources of emmission and estimated dispersion and motion of ‘pollution’ • Project in Mendoza Argentina

  8. Project Goals • Learn GIS • Produce at minimum a visual comparison containing: • Population Dot Density Maps for Each target area • surface showing air pollution levels for several pollutants (both average and maximum values) • Take more of a public policy approach than scientific or engineering perspective

  9. Dot Density Example

  10. Development Platform • ArcMap (ArcInfo 8 line of products) Reasons: • Population Dot Density done automatically, improved joins, other time savers • Location

  11. Location: Home vs. School Grad Students: Open 9-5

  12. Data Acquisition: Census Data • www.census.gov • Just .txt files, many per state • www.Geographynetwork.com • Census 2000 data • County, Tract, Block • Shapefile Data available on County Level • Tables with Census Data available on Statewide Level

  13. EPA Air Pollution Data • EPA Airs Monitoring System • Yearly data 1994-1999 • Averages and Peak Values • Shapefile with monitoring locations (.e00 format) • .dbf table containing the measurements for each monitoring location

  14. Airs Locations

  15. Airs Locations • Los Angeles • Houston

  16. AIRS Parameters • Carbon Monoxide • NO2 • SO2 • O3(Ozone) • PM10 (Particulate Matter > 10 microns) • Pb (lead)

  17. Airs Parameters Chosen • Ozone(O3): • formed when VOC’s react with NOx compounds in the presence of sunlight, • most common in summer • human health effects • National Ambient Air Quality Standard: • 1hour averaging period < .125 ppm

  18. Airs Parameter (continued) • PM 10: • Measurement of Particles > 10 microns • Particles this size cannot enter lungs • 24 hours: <155mg/m3 • SO2: • Sulfur dioxide • .035ppm, hourly • CO • Carbon Monoxide, Limit: 35.5 ppm 1hr. period

  19. Methodology: Dot Density Diagrams • Which data to use to generate Dot Density Diagrams, County, Tract, or Block? • Shapefiles only available as county .zip files • Necessitated selecting individual counties that make up metropolitan area

  20. Tract or Block

  21. Chicago Area Tracts

  22. Dot Density Steps • Select Counties and Download Tract shapefiles(and associated files) as well as dbf containing census data on the tract level • Merge(geoprocessing wizard) County Layers together to form one layer containing all the tracts in the “metropolitan area” • Join Metropolitan Area layer with dbf file containing Census Tract Data • Use Symbology tab to Set Dot Density Parameters (simlar to legend in ArcView)

  23. Dot Density Example • Houston Area, Each Dot Represents 150 people

  24. Dot Density Chicago • Each Dot represents 750 people (computational reasons)

  25. Methodology: Creating a Surface • Create a New Table of Monitor Values taking out values for years prior to 1999 • Reason: So Join will not introduce Non Uniform Dates • Use “Select by Attributes” and “Export” from the table options menu • Join Table with Shapefile of monitor location points

  26. Methodology continued • Trim out those stations that don’t measure pollution levels for the particular surface being created • Use Spatial Analyst (inverse distance weighting) to Interpolate A Grid from Monitor Point data

  27. Inverse Distance Weighting • Method used for filling in surface based on values collected at measuring stations • Part of the Spatial Analysis Package • IDW assumes things that are closer together are more alike. • Gives higher weighting to those points that are closer to the location it is calculating

  28. Surface Example • Houston, Ozone Max 1 Hr Values: • (.125> violates federal regulations)

  29. Future Plans • Pull Everything Together so that it can be analyzed Visually • Draw Conclusions • Numerical Analysis?

  30. Questions • (please help me, I need to fill 13 more minutes)

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