1. CVEN 689 Project Presentation Tim Schniedwind

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

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

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

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

6. Research Being done: TNRCC Ozone measurements for the Houston Area Ozone measurements for the Houston Area Speed Limit Issue 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’ Attempts to Calculate Air Pollution in an Area based on sources of emmission and estimated dispersion and motion of ‘pollution’ Project in Mendoza Argentina Project in Mendoza Argentina

8. Project Goals Learn GIS Learn GIS Produce at minimum a visual comparison containing: Produce at minimum a visual comparison containing: Population Dot Density Maps for Each target area Population Dot Density Maps for Each target area surface showing air pollution levels for several pollutants (both average and maximum values) 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 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) ArcMap (ArcInfo 8 line of products)Reasons: Population Dot Density done automatically, improved joins, other time savers Population Dot Density done automatically, improved joins, other time savers Location Location

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

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

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

14. Airs Locations

15. Los Angeles Los Angeles Houston Houston

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

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

18. Airs Parameter (continued) PM 10: PM 10: Measurement of Particles > 10 microns Measurement of Particles > 10 microns Particles this size cannot enter lungs Particles this size cannot enter lungs 24 hours: <155mg/m3 24 hours: <155mg/m3 SO2: SO2: Sulfur dioxide Sulfur dioxide.035ppm, hourly.035ppm, hourly CO CO Carbon Monoxide, Limit: 35.5 ppm 1hr. period 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? Which data to use to generate Dot Density Diagrams, County, Tract, or Block? Shapefiles only available as county.zip files Shapefiles only available as county.zip files Necessitated selecting individual counties that make up metropolitan area 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 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” 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 Join Metropolitan Area layer with dbf file containing Census Tract Data Use Symbology tab to Set Dot Density Parameters (simlar to legend in ArcView) Use Symbology tab to Set Dot Density Parameters (simlar to legend in ArcView)

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

24. Dot Density Chicago Each Dot represents 750 people (computational reasons) 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 Create a New Table of Monitor Values taking out values for years prior to 1999 Reason: So Join will not introduce Non Uniform Dates Reason: So Join will not introduce Non Uniform Dates Use “Select by Attributes” and “Export” from the table options menu Use “Select by Attributes” and “Export” from the table options menu Join Table with Shapefile of monitor location points 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 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 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 Method used for filling in surface based on values collected at measuring stations Part of the Spatial Analysis Package Part of the Spatial Analysis Package IDW assumes things that are closer together are more alike. 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 Gives higher weighting to those points that are closer to the location it is calculating

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

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

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