Spatial Distribution of Criteria Pollutants within Region 4

Transcription

1 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

2 Table of Contents Abstract Introduction Scope of the study Data source General description Methodology Analyze the spatial distribution of the criteria pollutants...6 a. Thiessen polygon method...7 b. Interpolation method Comparison between two methods Further analysis using interpolation method Assign Air Quality Index for Region Assign Risk Factor for Region Assign Risk Factor for highway buffered zone within high risk area Future scope of the study Result and conclusion Reference

3 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 (SO 2 ), Nitrogen Oxides (NO x ), Ozone (O 3 ) 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. 3

4 2. Scope of the study 3. Data source 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 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. 4

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6 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 6

7 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 7

8 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 8

9 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 9

10 vii. viii. The Ozone pollution (O3_AMS) shapefile has been joined with the ams_ras_fe (clipped shape file) based on spatial join 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. 10

11 xii. The ozone concentration and population for the entire region has been compared 11

12 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 12

13 iv. The Region4 county shapefile has been converted to raster by using polygon to raster conversion 13

14 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 14

15 5.2 Comparison between two methods Kriging Interpolation Interpolates the values within a random field Ordinary Kriging: surface trend as unknown Accuracy depends on the recorded data set and surface trend Trend analysis and model validation is possible in this method Changing the lag size and excluding the noise more accurate result can be obtained More accurate compared to Thiessen method Thiessen Polygon Area of each polygon is defined by the boundary which is closest to the point compared to other points Pollution level is constant within the boundary of each polygon Accuracy depends on the volume of recorded dataset, with the decreased area of each polygon, accuracy would be increasing Trend analysis or model validation is not possible in this method - Approximate method compared to interpolation method Thus Kriging interpolation technique has been adopted for further analysis 15

16 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 16

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19 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. 19

20 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, * regmuloz2,con( regmuloz2 <= 0.075, * regmuloz2-145,con( regmuloz2 <= 0.095, * regmuloz2-95, * regmuloz ))) 20

21 ii. Using the same procedure Air Quality Index for other pollutants has been estimated. 21

22 In case of CO some interpolated and AQI value was found negative. They have been assigned as zero using raster calculator 22

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24 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. 24

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26 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 AQI NOx 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 26

27 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. 27

28 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. 28

29 iii. The buffered zone has been clipped with the high risk zone and named as clipriskzone1 29

30 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 30

31 vii. Using the same method earlier done it has been converted to polygon to assign categories 31

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33 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 O 3. 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. 33

34 8. Reference ArcGIS