Spatial Distribution of Criteria Pollutants

within Region 4

Texas A&M University, Department of Civil Engineering

Civil Engineering Application of GIS

by

Manasi Mahish

CVEN658

12/5/2010

Instructor: Dr. Francisco Olivera

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Table of Contents

Abstract ...........................................................................................................................................3

1. Introduction .........................................................................................................................3

2. Scope of the study ...............................................................................................................4

3. Data source ..........................................................................................................................4

4. General description .............................................................................................................4

5. Methodology .......................................................................................................................6

5.1 Analyze the spatial distribution of the criteria pollutants ...................................................6

a. Thiessen polygon method ..................................................................................................7

b. Interpolation method ........................................................................................................12

5.2 Comparison between two methods ..................................................................................15

5.3 Further analysis using interpolation method ...................................................................16

5.4 Assign Air Quality Index for Region4 ............................................................................19

5.5 Assign Risk Factor for Region4 ......................................................................................26

5.6 Assign Risk Factor for highway buffered zone within high risk area .............................28

6. Future scope of the study ..................................................................................................33

7. Result and conclusion .......................................................................................................33

8. Reference ..........................................................................................................................34

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Abstract

In today‟s scenario, air pollution is a huge concern. Environmental Protection Agency (EPA) has

defined 6 common pollutants as criteria pollutants which are regularly monitored and recorded.

This study focus on analyzing the spatial distribution of these criteria pollutants and determining

which area would be safer in terms of pollution. The result suggests that Dallas area has highest

risk followed by parts of Ellis, Tarrant and Denton County. Thus it is recommended to plan

further urban development away from this zone.

1. Introduction

The environment of earth including its atmosphere is undergoing continuous changes over the

past million years. However, it has been exacerbated during the last few decades as the result of

industrial revolution. In the present scenario, air pollution is one of the prime global concerns.

According to World Health Organization (WHO) thousands of people die every year due to air

pollution related disease. Numbers of automobile accidents are also related to smog formation.

Some of the worst air pollution consequences are Donora Episode (1948), London Episode

(1952), and so on; which took many lives within few days as a result of entrapment of pollutants

within the lower atmospheric layer.

In a broad sense, air pollution implies introduction of any substance into the atmosphere which

may cause harm to living beings. Pollutants may be of two types based on its origin: primary and

secondary. Primary pollutants are directly released from its sources, whereas secondary

pollutants are formed due to the reaction between primary pollutants. Environmental Protection

Agency (EPA) has defined 6 common pollutants as criteria pollutants which are particulate

matter (PM), Carbon Monoxide (CO), Sulfur Dioxide (SO2), Nitrogen Oxides (NOx), Ozone (O3)

and Lead (Pb). Among these pollutants all except Ozone is primary pollutants. Secondary

pollutant like ozone may be formed far away from the pollution source depending on the reaction

kinetics among the primary pollutants, wind direction and so on.

In this study, Region4 (according to TCEQ nomenclature) have been selected to analyze the

spatial distribution of criteria pollutants. Region4 is one of the busiest hubs of Texas, comprising

of 12 counties. The area shares a vast road length and a huge traffic volume. There are 26 air

monitoring stations distributed within the area which record the 6 criteria pollutants regularly. As

vehicular emission is one of the primary sources of air pollution, the spatial distribution of

pollutants within the urban area and around highways are of primary interest. The study also

focuses on the entire region and analyzes it using ArcGIS10.

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2. Scope of the study

i. To analyze the spatial distribution of the criteria pollutants

ii. To assign the Air Quality Index (AQI) for the entire region

iii. To assign the Risk Factor (RF) for the entire region

iv. To assign the Risk Factor (RF) for the area close to highway

v. To recommend the future urban development zone based on the analysis

3. Data source

i. Annual average air pollution data (year 2000): USEPA & TCEQ

ii. Population (year 2000): ESRI, US census bureau

iii. Road network data: TIGER

iv. County information: ESRI

4. General description

TCEQ has defined Region4 as an area which includes Dallas, Collin, Denton, Ellis, Hunt,

Kaufman, Rockwell, Tarrant, Hood, Johnson, Parker and Navarro. The total population is

approximately 5,193,600 as per 2000 census data in which majority of the population is urban.

The total area enclosed by this region is around 24,760 sq km and the total road length in the area

is more than 7640 km. So the road length is 0.3 km per square km area. On the other hand, the

area of entire Texas is around 687,390 sq km with a total road length of approximately 116,790

km, thus having only 0.17 km road length per square km area on an average (calculated from

ArcGIS 10). So the traffic volume is considerably higher at this part of the state which in turn

causing more vehicular pollution. 26 air monitoring stations are distributed across the region.

Most frequently detected pollutants within the region are NOx, Ozone and PM2.5.

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5. Methodology

5.1 Analyze the spatial distribution of the criteria pollutants

Steps for preparing pollutant data layer:

i. The pollution data has been extracted from USEPA website and then saved in excel

file

ii. The „xls‟ file has been opened from ArcMap using „add data‟

iii. The layer for one pollutant has been prepared by using „display XY data‟

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iv. Shape files for all the pollutants have been prepared following the aforesaid method

a. Thiessen polygon method

Theory

Thiessen polygons are polygons whose boundaries define the area that is closest to each point

relative to all other points. They are mathematically defined by the perpendicular bisectors of the

lines between all points.

The underlying assumption for considering this method is that the people living within each

polygon would experience the pollution level recorded by the monitoring station, thus the

pollution level is constant throughout the polygon.

Steps for assigning area for each monitor:

i. Separate layers have been prepared for all roads, highways and counties within

Region 4 by exporting selected data

ii. Euclidean allocation has been performed and named as „ams_alloca‟

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iii. The allocated raster data has then been converted to feature by using „raster to

polygon‟ in spatial analysis and named as „ams_ras_fe‟

iv. The selected county shapefile „reg4_county‟ has then been „clipped‟ with

„ams_ras_fe‟

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v. The area of each new polygon has been calculated using „calculate geometry‟

vi. The population density for each county in „Reg4_county‟ shape file attribute table has

been estimated by adding new field and field calculator

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vii. The Ozone pollution (O3_AMS) shapefile has been joined with the „ams_ras_fe‟

(clipped shape file) based on spatial join

viii. The new polygon shapefile has then been intersected with region county file

ix. The area for new polygons have been calculated using „calculate geometry‟

x. The new population for each polygon is calculated by adding a field and using „field

calculator‟ (population density * area)

xi. Dissolve function has been used to dissolve the polygon based on the monitoring

station FIDs, which was joined to the attribute table earlier by spatial join.

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xii. The ozone concentration and population for the entire region has been compared

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b. Interpolation method

Theory

Kriging interpolation is used for interpolating the values of a random field at an unobserved

point. The estimator is a linear combination of the observed values.

This method uses variogram to express the spatial variation, and it minimizes the error of

predicted values which are estimated by spatial distribution of the predicted values.

Ordinary Kriging assumes that the trend of the surface is unknown. Thus the ordinary method

has been adopted here.

Steps:

i. The geostatistical analysis method has been used for interpolating the pollutant

concentration within Region4

ii. Ordinary Kriging method has been used for this interpolation. By changing the number of

lags and analyzing the trend of the data set interpolated values have been predicted.

iii. The data has been exported to form a new raster layer

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iv. The Region4 county shapefile has been converted to raster by using „polygon to raster‟

conversion

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v. The raster value has been assigned as 1 using raster calculator: first by adding 1 to each

cell and then dividing the pixel value of each raster with its own value. The new raster is

named as „reg4byreg4‟

vi. Using raster calculator the interpolated raster values of ozone have been multiplied with

the „reg4byreg4‟ raster, the resulting raster file is „regmulOz2‟

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5.2 Comparison between two methods

Kriging Interpolation Thiessen Polygon

Interpolates the values within a random field Area of each polygon is defined by the boundary

which is closest to the point compared to other points

Ordinary Kriging: surface trend as unknown Pollution level is constant within the boundary of

each polygon

Accuracy depends on the recorded data set

and surface trend

Accuracy depends on the volume of recorded dataset,

with the decreased area of each polygon, accuracy

would be increasing

Trend analysis and model validation is

possible in this method

Trend analysis or model validation is not possible in

this method

Changing the lag size and excluding the

noise more accurate result can be obtained -

More accurate compared to Thiessen

method

Approximate method compared to interpolation

method

Thus Kriging interpolation technique has been adopted for further analysis

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5.3 Further analysis using Kriging interpolation method

i. Interpolation has been performed for other criteria pollutants using the same steps stated

above. The results are shown below

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Observation

For carbon monoxide there are some negative interpolated values. That has later on been

replaced by „0‟ values using raster calculator.

5.4 Assign Air Quality Index

Air Quality Index (AQI) is the presentation of the pollution level of a certain area. EPA has

converted the pollution concentration level into a ranged scale to better communicate the

significance of pollution to the general audience.

Table: AQI Table and the Equation for AQI Calculation

The AQI values are presented by certain color code as is shown in the following table.

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Table: Physical Interpretation of AQI Table

Steps:

i. Air Quality Index for ozone has first been calculated. Using the raster calculator

conditional function, the above stated equation is fitted with different range of ozone

concentration. The conditional function used is stated below.

Expression: Con(“regmuloz2” <= 0.059, 847.458*“regmuloz2”,Con(“regmuloz2” <=

0.075,3266.67* “regmuloz2”-145,Con(“regmuloz2” <= 0.095,2578.95* “regmuloz2”-

95,2578.95* “regmuloz2”-96.58)))

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ii. Using the same procedure Air Quality Index for other pollutants has been estimated.

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In case of CO some interpolated and AQI value was found negative. They have been

assigned as „zero‟ using raster calculator

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iii. Overall Air Quality Index for the region has been calculated using the conditional

function of raster calculator.

Expression: Con("aqioz" > "AQI_PM25","aqioz", Con("AQI_PM25" >

"AQISO2","AQI_PM25", Con("AQISO2" > "AQIPM10","AQISO2",Con("AQIPM10" >

"AQICO2","AQIPM10","AQICO2"))))

iv. Categorization has been performed based on the overall AQI value of the region.

v. Using raster calculator AQI12 raster has been created based on value range of AQIall and

assigned value 1 and 2.

vi. AQI12 raster has been converted to polygon using „raster to polygon‟ and named

„aqi_feat‟.

vii. A new field has been added and based on the gridcode category has been assigned as

„Unhealthy (Sensitive People)‟ and „Unhealthy‟ using editor.

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Observation

Lead has not been considered for the AQI estimation by the EPA suggested method. Thus the

AQI has not been determined for Lead in this study. Also the lead concentration was well within

the acceptable limit

The highest concentration of NOx is well below the range wherefrom AQINOx estimation starts,

thus AQI has not been calculated for NOx as well.

The AQI of the region is mostly influenced by the ozone pollution level. The interpolation could

have been more accurate with a large recorded dataset. Part of the area with high AQI also has

high population density.

5.5 Assign Risk Factor

Theory

The Risk Factor is a function of both pollution concentration as well as population density.

Assuming the relation is a linear function.

Risk Factor (RF) = f(pollution concentration, population density)

RF = Constant (k) * pollution concentration * population density; (assuming k = 1/50,000)

Steps:

i. The AMS_polygon has been converted to raster based on the population density and

named as poly_ams_ras

ii. Using raster calculator population density of all the polygons has been multiplied with

the overall AQI.

Risk factor is calculated as AQIall * poly_ams_ras / 50000, and the new raster is RF1

iii. Following the similar procedure stated earlier, a new shape file for risk factor is created

with „category‟ field

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Observation

Some parts of the area may have very high risk compared to other parts of the area even though

they have lower AQI values and vice versa.

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5.6 Assign Risk Factor for highway buffered zone within high risk area

Theory

The automobile may be a significant source of air pollution especially within the urban area

where the traffic load is huge. Thus the area around highway has higher risk compared to other

parts of the region. The risk factor here also includes distance from the highway which can be

considered as the source of the pollution.

Risk Factor (RF) = f(pollution concentration, population density, distance from the highway)

RF =

*constant(k) (k is assumed 1 here)

Steps:

i. „Selected moderate to high‟ to „high‟ risk zones of Region 4 and exported to create a new

shape file named „highriskzone‟

ii. Highway buffering zones have been created by using multiple ring buffers from 0.5km to

3km with 0.5km ring width.

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iii. The buffered zone has been clipped with the high risk zone and named as „clipriskzone1‟

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iv. Polygon to raster conversion has been performed for „clipriskzone1‟ based on its distance

from the highway and named as „clipriskras‟.

v. Risk factor for the buffered area has been estimated using the raster calculator

considering the effects of AQI, population density and distance from the highway

Expression: RF1 * poly_ams_ras / clipriskras

vi. The result is shown below

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vii. Using the same method earlier done it has been converted to polygon to assign categories

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Observation

Buffered highway area in Dallas is under high risk compared to other parts.

6. Future scope of the study

a. To relate the study with the traffic volume

b. To study the effect of the meteorological parameters (e.g. Rainfall, Temperature, Wind

Direction and Velocity) on the spatial distribution of the pollutants

c. To include the landuse data in urban development planning study

7. Result and Conclusion

a. The major pollutants are Ozone and PM2.5.

b. During the 1970s leaded gasoline was phased out. Since then Pb pollution is not a big

concern except from specific industry sources. The Pb pollution within this region is well

within the acceptable range. Also EPA does not include Pb for AQI estimation.

c. The entire region has an air quality beyond the acceptable range (AQIacceptable=100).

d. Although Ozone is a secondary pollutant and is a byproduct of reaction between NOx and

Volatile Organic Carbons (VOCs), it is often of more concern compared to NOx within

this region. That may be due to trans-boundary transport phenomenon of the secondary

pollutants like O3.

e. The population density is one of the driving force in the risk factor assessment, thus even

comparatively lesser AQI value region may be riskier in case it has higher population

density.

f. The relation between the risk factor function and the variables has been assumed linear.

In reality it may be different. Thus depending on the relation, the risk factor may produce

different result for the area.

g. The people living nearby the highways are at higher risk compared to others.

h. Further urban development should be implemented within the green zone shown in the

risk factor map of Region 4.

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8. Reference

ArcGIS10

http://www.tceq.state.tx.us/

http://epa.gov/air/data/monvals.html

http://www.esri.com/mapmuseum/index.html

http://www.esri.com

http://dot.state.tx.us/

http://library.tamu.edu/about/collections/map-gis-collections-services