CVEN 689 Project Presentation Tim Schniedwind

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 gt 10 microns)
• Pb (lead)

17
Airs Parameters Chosen

• Ozone(O3)
• formed when VOCs react with NOx compounds in the
  presence of sunlight,
• most common in summer
• human health effects
• National Ambient Air Quality Standard
• 1hour averaging period lt .125 ppm

18
Airs Parameter (continued)

• PM 10
• Measurement of Particles gt 10 microns
• Particles this size cannot enter lungs
• 24 hours lt155mg/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 dont 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
• (.125gt 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)