1

Children’s Health Project: Linking Asthma to PM10 in 

Central Phoenix – a report to the Arizona Department 

of Environmental Quality 

H.J.S. Fernando1, R. Dimitrova1, G. Runger2, N. Lurponglukana2, 

P. Hyde1

, B. Hedquist1, J. Anderson1

Arizona State University’s 1Center for Environmental Fluid Dynamics and 

2

Center for Health Information and Research 

February 16, 2009
2

TABLE OF CONTENTS 

List of Figures..............................................................................3 

List of Tables...............................................................................6 

Executive Summary........................................................................7 

Chapter 1: Introduction....................................................................7 

Chapter 2: PM Monitoring Networks....................................................8 

Chapter 3: Interpolation Methods......................................................13 

Chapter 4: Neutral Network.............................................................17 

Chapter 5: Air Quality Modeling.......................................................19 

Chapter 6: Neutral Networks vs. CMAQ..............................................37 

Chapter 7: Linking PM10 Concentrations.............................................40 

Chapter 8: Asthma warning systems....................................................54 

Chapter 9: Conclusions..................................................................55 

References.................................................................................56 

Appendices................................................................................59 

A. PM monitoring sites.............................................................59 

B. Neural Network..................................................................65 

C. MM5 - modeling results and discussion......................................67 

D. Definitions of statistics..........................................................73 

E. Mathematics of the case-crossover method..................................74 

Glossary.....................................................................................77 
3

LIST OF FIGURES 

Figure 2.1 PM10 monitoring sites with elevations for the Children’s Health 

Project 

10 

Figure 2.2 Spatial distribution of annual PM10 concentrations –central Phoenix 

for 2006 

12 

Figure 3.1 Comparison between IDW and Ordinary Kriging methods (Note: 

SS1 = SS = Phoenix Supersite) 

15 

Figure 3.2 Percentage differences between IDW and 

Ordinary Kriging methods – December 19, 8am 

15 

Figure 3.3 Mean Absolute Errors (MAE) for December between IDW and 

Ordinary Kriging methods – based on daily maps 

16 

Figure 3.4 Root Mean Square Error (RMSE) for December between IDW and 

Ordinary Kriging methods – based on daily maps 

16 

Figure 4.1 24-hour average PM10 predictions from the neural network versus 

observations at the Central Phoenix site for 2006 

18 

Figure 5.1 Central Phoenix study domain with monitoring locations 20 

Figure 5.2 24-hour averaged PM10 observations for 2005-2006 21 

Figure 5.3 Daily emergency department visits and admissions to hospitals of 

children ages 0-17 in Maricopa County with an asthma diagnosis for 

2005 – 2006 

22 

Figure 5.4 PM10 concentrations and asthma events (2005-2006) 23 

Figure 5.5 Predicted and observed hourly PM10 concentrations in central 

Phoenix for periods of high pollution (left) and low pollution (right) – 

from all five monitors 

24 

Figure 5.6 Predicted and observed hourly PM10 concentrations [μg/m3

] in central 

Phoenix for the two sets of monitors shown separately – high 

pollution period (November 2005) 

24 

Figure 5.7 The "soccer goal" plot measures both bias and error for central 

Phoenix 

27 

Figure 5.8 Daily Mean Bias for high pollution conditions (top) and low pollution 28 

Figure 5.9 CMAQ predictions versus observations of PM10 concentrations at 

five sites for the high pollution period of November 2005. The lines 

are the median values of the 24 hourly predictions for each day; the 

box plots are 25th to 75th percentiles of the 24 predictions 

29 

* 100

10

10 10

IDW

IDW KR

PM

PM − PM
4

Figure 5.10 CMAQ predictions versus observations of PM10 concentrations at 

five sites for the low pollution period of March 11 – April 9, 2006. 

The lines are the median values of the 24 hourly predictions for each 

day; the box plots are 25th to 75th percentiles of the 24 predictions 

30 

Figure 5.11 Hourly variation of the five-site averages, calculated and observed 

PM10 concentrations for November 2005 

31 

Figure 5.12 Contributions of different processes to PM10 concentrations for 3-

days periods in November 2005 and April 2006 

32 

Figure 5.13 The spatial distribution of the Index of Agreement between CMAQ 

and Inverse Distance Weighting (IDW) interpolated surfaces 

33 

Figure 5.14 The spatial distribution of Mean Bias between CMAQ and Inverse 

Distance Weighting (IDW) interpolated surfaces 

33 

Figure 5.15 Comparison of the daily, “modeled” PM10 concentrations with the 

observations at Central Phoenix: violet is straight CMAQ; blue is 

“CMAQ-new” -- CMAQ combined with interpolation 

34 

Figure 5.16 Comparison of the daily, “modeled” PM10 concentrations with the 

observations at West Phoenix (WP): violet is straight CMAQ; blue is 

“CMAQ-new” --CMAQ combined with interpolation 

34 

Figure 5.17 Comparison of the daily, “modeled” PM10 concentrations with the 

observations at Supersite (SS): violet is straight CMAQ; blue is 

“CMAQ-new” -- CMAQ combined with interpolation 

34 

Figure 5.18 Comparison of the daily, “modeled” PM10 concentrations with the 

observations at West 43rd Avenue (WF): violet is straight CMAQ; 

blue is “CMAQ-new” -- CMAQ combined with interpolation 

35 

Figure 5.19 Comparison of the daily, “modeled” PM10 concentrations with the 

observations at Durango Complex (DC): violet is straight CMAQ; 

blue is “CMAQ-new” -- CMAQ combined with interpolation 

35 

Figure 6.1 Time series of 24-hour average PM10 concentrations predicted by 

CMAQ and EnviNNet with observations at the Central Phoenix site 

37 

Figure 6.2 Scatter plots of hourly PM10 concentrations predicted by CMAQ 

(left) and EnviNNet with observations [μg/m3

]: November 2005, 

Central Phoenix site 

38 

Figure 6.3 Comparison of hourly PM10 concentrations predicted by Models 3 

and EnviNNet with the observations of November 5-9, 2005, Central 

Phoenix site 

39 

Figure 6.4 Comparison of hourly PM10 concentrations predicted by CMAQ and 

EnviNNet with the observations of November 25-29, 2005, Central 

Phoenix site 

39 
5

Figure 7.1 Washout periods for asthma incidences within two and five miles of a 

continuous PM10 monitor 

41 

Figure 7.2 Asthma incidences by different characteristic within two miles of 

PM10 monitoring sites 

42 

Figure 7.3 Asthma incidences by different characteristic within five miles of 

PM10 monitoring sites 

43 

Figure 7.4 Asthma incidences by zip code within two and five miles of PM10 

monitoring sites 

44 

Figure 7.5 Comparison of AZHQ and ADHS asthma incidences within two and 

five miles of PM10 monitoring sites 

45-

46 

Figure 7.6 Correlation matrix among Dailymean PM10 and lag variables 50 

Figure 7.7 Adjusted odd ratios with 95% lower and upper bound confidence 

limits for the four asthma populations 

53 

Figure 7.8 Natural logarithm of the adjusted odds ratio (Log Odds) of asthma 

incidents versus quartiles of mean PM10 concentrations 

54 

Figure A1.1 West 43rd Avenue PM10 monitoring site 61 

Figure A1.2 Greenwood PM10 monitoring site 61 

Figure A1.3 Durango Complex PM10 monitoring site 62 

Figure A1.4 Central Phoenix PM10 monitoring site 62 

Figure A1.5 West Phoenix PM10 monitoring site 63 

Figure A1.6 Supersite PM10 monitoring site 63 

Figure A1.7 South Phoenix monitoring site 64 

Figure C-1 Modeling domains used with weather predicting model MM5 66 

Figure C-2 Scatter plots of wind speed and temperature for all stations for high 

pollution episode November 2005 

68 

Figure C-3 Scatter plots of wind speed and temperature for all stations for the 

low pollution period of March 11 – April 9, 2006 

68 

Figure C-4 Comparison between predicted and observed wind speeds and 

directions at different locations – with strong synoptic winds 

69 

Figure C-5 Comparison between predicted and observed wind speeds and 

directions at different locations – with calm synoptic conditions 

70 

Figure C-6 Simulated horizontal winds at 10m and surface temperature fields 

during nocturnal (left) and daytime conditions, November 24, 2005 

71 
6

LIST OF TABLES 

Table 2.1 Central Phoenix continuous PM10 monitoring sites 9 

Table 2.2 Annual average (2006) PM10 concentrations 11 

Table 2.3 December 2007 PM10 monthly average concentrations 13 

Table 5.1 Statistical measures in comparing model-predicted hourly PM10

concentrations to observations for both study periods at different sites 

27 

Table 6.1 Statistics of CMAQ and EnviNNet predictions of hourly PM10

concentrations at the Central Phoenix site 

38 

Table 7.1 Referent selection based on time-stratified scheme within a 28-day 

stratum 

47 

Table 7.2 Model statistics for each main effect (preschool age group omitted) 50 

Table 7.3 Results for all asthma incidents 51 

Table 7.4 Results for asthma incidents excluding the preschool group 51 

Table 7.5 Result for ambulatory events only 52 

Table 7.6 Result for ambulatory events without the preschool group 52 

Table 7.7 Case-crossover statistics relating asthma incidents to PM10

concentrations in central Phoenix 

53 

Table A-1 Characteristics and images of continuous PM10 monitoring sites 59 

Table A-2 Traffic near PM monitoring sites 60 

Table C-1 Grid definitions for MM5 model 65 

Table C-2 MM5 configuration for different domains 65 

Table C-3 RMSE for wind speed at different sites 71 

Table C-4 Summary of statistical measures, in comparing observations to model 

predictions of wind speed at different sites 

72 
7

Executive summary

The effects of elevated PM10 concentrations on childhood asthma incidents (emergency room visits 

& hospital admissions) in central Phoenix, AZ were determined. A major portion of this work 

involved the application of a deterministic, numerical air quality model to produce gridded 

concentration fields, the development of a neural-network based prediction system for PM10, and 

the interpolation of PM10 concentrations from five monitoring sites to produce census-tract specific 

concentrations for the statistical health effects analyses. Through the case-crossover method, PM10

concentrations were shown to have statistically significant associations with asthma incidents in 

central Phoenix at the 95% confidence level. For children ages 5-17 an effect much stronger than in 

previous studies was determined, while children ages 0-4 exhibited virtually no effects. Previous 

studies have reported the risk of adverse health effects as a function of the change of daily mean 

PM10 from the 25th to 75th percentile; in this study that change was 36 μg/m3. This change in PM10

is associated with a 13% increase in the probability of asthma incidents among children ages 5-17. 

Within this group, neither age nor gender was significant. The lack of an effect on preschool 

children was assumed to be due to the difficulty of making accurate asthma diagnoses in the 

younger ages and/or more time spent indoors. 

Chapter 1 Introduction 

Voluminous studies on the health effects of particulate matter (PM) have been carried out. 

Atmospheric scientists deal with the conditions conducive to high PM episodes; the exposure 

community delves into the duration and severity of ambient PM concentrations to which various 

populations are subject; toxicologists and biochemists study the effects of the hazardous 

constituents of PM on human and surrogate organisms and in in vitro systems; physiologists and 

physicians conduct exposure-effect studies on humans in chambers, epidemiologists relate health 

outcomes to ambient concentrations and exposures, and modelers (including risk assessors) utilize 

various data to estimate exposure-response relationships for determining safe levels of exposure. 

Integration of such disparate disciplines is difficult, given the complexity inherent in each of the 

approaches, though the U.S. Environmental Protection Agency (EPA) and the World Health 

Organization (WHO) Air Quality Criteria Documents, the EPA Staff Papers, and the EPA Office of 

Air do make such an attempt. For issuing health warning system is sufficient to establish, if there 

be any, a reliable association between PM concentrations and health effects such as admittances due 

to respiratory (or cardiovascular) ailments to allow prediction of such events. The principal goal of 

the “Children’s Health Project” was to clarify the association of children’s health with PM10 levels 

to develop a better health warning system in Phoenix. We brought together a partnership among 

Arizona Department of Environmental Quality (ADEQ), Arizona Department of Health Services 

(ADHS), Ross & Associates, and two groups of researchers at Arizona State University: the Center 

for Health Information and Research (CHIR), and the Center for Environmental Fluid Dynamics 

(EFD). We discussed our findings with University of Arizona medical personnel and the Asthma 

Coalition. 

Work elements of the project 

• With the central portion of Phoenix as the study area, PM10 concentrations and asthma 

records were analyzed for each day in 2005 – 2006.

• PM10 concentrations were estimated with both deterministic and statistical models. 
8

• The deterministic models were the EPA’s Community Multi-scale Air Quality Modeling 

System (CMAQ) and a meteorological model called “MM5.” This grid-based weather and 

pollution simulation system relied on the most recent Western Regional Air Partnership PM 

inventories. Design days included periods of low, intermediate, and high pollution. The 

hourly and daily simulated concentrations were compared with data from five permanent 

monitoring sites with continuous PM10 instruments. 

• The first type of statistical model employed was interpolation. With the interpolation 

technique called “inverse distance weighting” (IDW), spatial concentration fields were built 

that provided a PM10 concentration for each of the 168 census tracts from measurements at 

the five central-city monitoring sites. The efficacy of this scheme was verified by deploying 

four temporary monitors for four months and by using hourly PM10 concentrations from two 

Maricopa County permanent sites whose continuous PM10 monitors began operating after 

2005. These six additional sites enabled the use of a more sophisticated interpolation 

technique called “ordinary kriging”, which produced results similar to those of the IDW. 

• The second type of statistical model was the neural network, developed by Italian scientists 

and applied to a single permanent monitoring site -- Maricopa County’s “Central Phoenix” 

site. This model is a statistical system to predict the next day’s PM10 concentrations without 

the labor and emissions-inventory intensive alternative of CMAQ. In the present work, 

however, its use was limited to estimating PM10 concentrations already recorded in 2006. 

• About 6,000 ADHS asthma events in children ages 0-18 – in a unified area within five 

miles of the five monitoring sites – comprised 75% of the Maricopa County total. These 

ADHS records of emergency room treatment and hospital admissions were cross-checked 

against an independent data set at CHIR and were found to be consistent once duplicate 

records had been eliminated. 

• The census-tract-based health data were linked with the PM10 concentrations derived from 

the IDW interpolations by a statistical method called “case-crossover”. Asthma has a strong 

seasonal component and the case-crossover analysis associates a reference time period as a 

control for each case. Consequently, each patient was treated as a matched case-referent pair 

with control exposures obtained from the same patient at different times. With an 

appropriate referent period, the case-crossover analysis controlled for long-term trends, 

seasonal effects, epidemic, and other covariates that change slowly with time such as 

behavior and diet. In addition to PM10 on the “event” day, 24-hour averages from one to 

seven days before the event were also examined (this delay is referred to as “lag time”). The 

patients were also stratified by age group, gender, and race/ethnicity, and interactions 

between air quality and these variables were explored. 

Chapter 2 PM monitoring networks

Permanent PM10 network in the central city 

Considering “metropolitan Phoenix” to include the city and all of the adjoining suburbs (but 

excluding Casa Grande and environs), five governmental agencies conduct air quality monitoring: 

• Maricopa County Department of Air Quality (MCDAQ) 

• Arizona Department of Environmental Quality – Air Quality Division (ADEQ) 

• Pinal County Air Quality Control District 
9

• Salt River Pima Maricopa Indian Community, and 

• Gila River Indian Community. 

Together, these agencies operate about 50 different sites with a variety of instruments and several 

different objectives. What the U.S EPA terms “criteria pollutants”, such as particulate matter, 

ozone, carbon monoxide, and others, may be the prime focus; but many other aspects of pollution in 

the planetary boundary layer are also monitored. For example, this monitoring includes 

meteorological parameters such as wind speed and wind direction, visibility characteristics such as 

light extinction and photographic views, and the chemical composition of particulates and gaseous 

pollutant mixtures (MCDAQ, 2007; ADEQ, 2007). 

The present work, based on the PM10 network and the asthma cases in central Phoenix, relies on the 

“continuous” meteorological and air pollutant data from the first two agencies in the list above. 

“Continuous” means that the instruments run round the clock with their meteorological parameters 

and air pollutant concentrations, including PM10, reported as hourly averages. As described later in 

this chapter, Arizona State University (ASU) and ADEQ staff set up and operated temporary PM10

continuous monitors to supplement the permanent network (Table 2.1 and Figure 2-1).

Table 2.1 Central Phoenix continuous PM10 monitoring sites

Symbol Name Major streets/city1 Duration Agency 

WF West 43rd Avenue 43rd Avenue & Broadway Rd. 

DC Durango Complex 27th Avenue & Durango Street 

WP West Phoenix 39th Avenue & Thomas Road 

CP Central Phoenix 16th Street & Roosevelt Street 

SS2 Supersite 15th Avenue & Camelback Rd. 

SP3 South Phoenix Central Ave & Broadway Rd. 

GR3 Greenwood 27th Avenue & I-10 

Permanent MCDAQ or 

ADEQ 

MRV Maryvale 59th Ave & Indian School Rd. 

VGC Valley Garden Center 15th Avenue & McDowell Rd. 

WVR Weaver’s Auto 

Service 

29th Street & Thomas Road 

CSA Community Service 

of AZ 

59th Ave. & Glendale Avenue, 

Glendale, AZ 

Temporary: 

Nov. 2007 – 

March 2008 

ASU/ADEQ

1. All except CSA are in Phoenix, Arizona. 

2. SS, Supersite, is ADEQ; other permanent sites are MCDAQ. 

3. Used only in the ordinary kriging interpolation, along with the other permanent and all temporary sites; not 

used in the inverse distance weighting interpolation for 2005 – 2006, because continuous PM10 monitoring did 

not begin until January 2006. 

Each of these sites has its unique combination of local emission sources and its own susceptibility 

to urban transport that together, along with the desert background, result in the measured 

concentrations. Each has its own degree of representativeness: i.e. how far away from the monitor 

do the measured concentrations actually prevail? The permanent county sites, fully described in 

Maricopa 2007, and the ADEQ Supersite, are also characterized in Appendix A. What follows is a 

discussion of how their average PM10 concentrations fit into the spatial distribution of PM10

concentrations throughout metropolitan Phoenix. 
10

SP

GR

WVR

VGS

CSA

MRV

SP

GR

WVR

VGS

CSA

MRV

Figure 2.1. PM10 monitoring sites with elevations for the Children’s Health Project. The permanent 

monitors are black, the temporary – blue. 

The PM10 variation among all of the metropolitan sites can be seen in their 2006 annual averages, 

given in Table 2.2 and also expressed as a percentage of the lowest urban concentration site, Mesa. 

The central city sites, from WF to CP, have concentrations considerably higher than Mesa’s (from 

38 to 162% higher), varying widely among themselves. The PM10 gradients are steepest here, 

tapering off in the more outlying portions of the central city. 

Shown in Figure 2.2, these concentrations have their highest values and steepest gradients in southcentral Phoenix, with almost all of the highest ones being south of I-10. As one moves west of 75th

Ave, north of Bethany Home Rd, east of 36th St., and south of Dobbins Rd, the prevailing 

concentrations generally fall to the mid to low 30’s, or even lower in the case of the Phoenix 

Mountain Preserve of the South Mountains. Because measured PM10 concentrations exhibit high 

sensitivities to localized emissions; however, elevated concentrations can occur in the vicinity of 

such sources as major earthmoving projects or intensively tilled agricultural lands. Two such 

examples of these PM10 discontinuities in metropolitan Phoenix are Higley in the southeast and 

Buckeye in the far west. The former’s concentrations are elevated above the values expected with 

its distance from the city center by emissions from major earthmoving activities from road 

construction, commercial building, and residential housing developments, as well as from continued 

farming. The latter’s concentrations are elevated from the same source categories, with the 

agricultural activities being the most influential. The greatest uncertainty in this concentration field 

lies to the west, but even this has been diminished by including measurements from a discontinued 
11

county site called “Maryvale”, at 61st Avenue and Encanto Boulevard, normalized to those of 

Central Phoenix (the former is 1.7% higher than the latter). 

Table 2.2. Annual average (2006) PM10 concentrations (μg/m3

) 

(bold sites used in the present work) 

Symbol Site name 

Location or type 

[PM10] 

% 

from 

Mesa 

WF West 43rd Avenue Central 79.9 162 

DC Durango Complex Central 69.2 127 

HI Higley Suburban 60.6 99 

SP South Phoenix Central 55.0 80 

BU Buckeye Urban fringe 53.2 74 

GR Greenwood Central 51.7 70 

WP West Phoenix Central 49.8 63 

MV1 Maryvale Central 42.7 40 

CP Central Phoenix Central 42.0 38 

GL Glendale Suburban 36.3 15 

SSC South Scottsdale Suburban 35.0 19 

NP North Phoenix Suburban 34.4 13 

SS2 Supersite Central 32.9 8 

DY Dysart Urban fringe 32.3 6 

ME Mesa Suburban 30.5 0 

ES3 Estrella Park (1995-2004) Urban fringe 30.2 -1 

AJ4 Apache Junction Urban fringe 23.6 -23 

PV5 Palo Verde (1997-2004) Urban fringe 21.9 -28 

AO6 Ajo (1995 – 2003) Remote small town 20.2 -34 

OP7 Organ Pipe (1995-2004) Background 10.6 -65 

1. MV, Maryvale, a discontinued county site at 61st Avenue and Encanto Blvd. The 2006 annual average is 

estimated by the MV/CP long-term average. 

2. SS, Supersite, 2006 average was estimated by the SS/CP long-term average. 

3. ES, Estrella Park, a discontinued ADEQ site, near Estrella Parkway and the Gila River, 18 mi (29 km) westsouthwest of downtown (Van Buren, Grand, 7th Avenue intersection) 

4. AJ, Apache Junction, a Pinal County site, 32 miles (51 km) east of downtown 

5. PV, Palo Verde, a discontinued ADEQ site, 43 mi (69 km) west of downtown 

6. AO, Ajo, Arizona, an ADEQ site, 83 mi (133 km) south-southwest (SSW) of downtown

7. OP, Organ Pipe National Monument, a National Park Service site, 105 mi (169 km) SSW of downtown

Although the two highest concentration sites, West 43rd Avenue (WF) and the Durango Complex 

(DC) garner most of the regulatory attention, the Greenwood site deserves special mention, as well. 

Unlike the top two, whose elevated concentrations can in part be attributed to the somewhat 

localized windblown and mechanically generated PM10 emissions along the Salt River, Greenwood, 

named for the cemetery across the street, is situated in a residential neighborhood with little in the 

way of fugitive dust emissions. Instead, this monitor is affected by heavy traffic on nearby paved 

roads. (Traffic on paved roads generates PM10 emissions by resuspending soil particles from the 

road surface and by exhaust emissions, with the former having ten times the weight of the latter.) 

From the monitor inlet it is 10 meters east to the busy arterial of 27th Avenue (19,000 vehicles on an 

average weekday (ADT)), 905 m east to I-17 (112,000), and 85 m north to Interstate 10 (229,000 

ADT). PM10 concentrations from this monitor, with suitable adjustments for wind direction and 

traffic volume, could be assigned to all of the freeway corridors in the central city. 
12

xxxxxxxv

Figure 2.2. Spatial distribution of annual PM10 concentrations –central Phoenix for 2006 

Temporary PM10 sites for this project 

Given the five permanent monitoring sites with complete records in 2005 – 2006, and considering 

that the Greenwood and South Phoenix continuous PM10 monitors began operation after 2005, 

additional continuous PM10 monitors were thought desirable for this project. ASU and ADEQ staff 

established four temporary sites and operated them from November 2007 through March 2008. 

These additional concentrations, with the Greenwood and South Phoenix sites included as well, 

provided the 11 points required for the more sophisticated interpolation technique called “ordinary 

kriging” (the simpler technique is inverse distance weighting, or IDW). The temporary sites were 

chosen to fill the gaps in the geographic coverage of the permanent PM10 network and to be placed 

in areas with moderate to high numbers of asthma cases. These four temporary sites are shown as 

three-initial labels in Table 2.1 and Figure 2.1, with additional details given in Appendix A. Even 

though the PM10 concentration fields built with the 11 monitors did not differ much from those 

based on the original five, the efforts involved were considered well worth while. Without these six 

additional sites, the fidelity of the interpolated fields based on only the five permanent monitors 

would have remained too uncertain for comfort. 

The PM10 concentrations at these four temporary sites were generally lower than those at the 

permanent sites (Table 2-3). 
13

Table 2.3. December 2007 PM10 monthly average concentrations (μg/m3) 

Chapter 3 Interpolation methods 

Introduction 

Interpolating air pollutant measurements to produce spatial concentration fields has a rich history on 

all scales -- urban, regional, and global. What distinguishes the present work is the nature of the 

pollutant. Instead of gaseous air pollutants or fine particles that mimic gases (particles 2.5 microns 

and smaller, “PM2.5”), this project concerns particles 10 microns and smaller, or “PM10”. In 

metropolitan Phoenix the coarse fraction of the aerosol (2.5 – 10 microns) comprises two thirds of 

the mass of PM10 and consists of particles that are 90% geological. Even the fine fraction has an 

appreciable geological component (20%), in addition to the usual secondary and combustion 

particles. Speciated aerosol measurements show that somewhat less than one fourth of the regulated 

PM10 comes from the better understood and more homogeneously distributed emission sources such 

as fuel combustion from motor vehicles, industrial combustion, and food cooking, as well as the 

secondary components of nitrate and sulfate. Most of the remaining three quarters, virtually all 

geological, comes from paved road dust, construction dust, windblown dust, and unpaved road dust. 

It is precisely this geological, or “soil”, or “crustal” component that defies efforts to build precise 

emission inventories, and, with a few notable exceptions, including the present work, makes it 

difficult to simulate with numerical models on an urban scale. Because the continuous PM2.5

monitoring network in 2005-2006 was limited to two stations, PM10 was the choice for this project. 

Practical constraints make it impossible to obtain data at every desired point. Thus, interpolation is 

important and fundamental to graphing, analyzing and understanding the spatial distribution of air 

pollution throughout a metropolitan area. Interpolation methods selected in this study were inverse 

distance weighting and ordinary kriging, the former with the five permanent PM10 sites in 2005-

2006; the latter, with the expanded network from December 2007 – February 2008. Hourly 

concentrations were converted to daily 24-hour averages for the daily spatial concentration fields. In 

addition, select hours were also examined on a case by case basis. Some consideration was given to 

using emission or traffic estimates to adjust the interpolated values, but this was rejected. 

Inverse distance weighting 

Inverse distance weighting (IDW) is one of the simplest interpolation methods. A neighborhood in 

the vicinity of the interpolated point is identified and a weighted average is taken of the observation 

Label Name Type [PM10] 

DC Durango Complex 49.6 

WF West 43rd Avenue 46.1 

GR Greenwood 42.9 

SP South Phoenix 39.6 

WP West Phoenix 

Permanent 

38.2 

CSA CSA of Glendale Temporary 33.7 

CP Central Phoenix Permanent 32.8 

VGC Valley Garden Center 31.4 

MRV Maryvale Temporary 28.5 

SS Supersite Permanent 28.3 

WVR Weaver’s Auto Service Temporary 21.8 
14

values within it. The weights are a decreasing function of distance. The user has control over the 

mathematical form of the weighting function, the size of the neighborhood (expressed as a radius or 

a number of points), in addition to other options. In this project, IDW was used to interpolate the 

PM10 concentrations of 2005-2006 from the five permanent monitoring sites with an automated 

function in Geographic Information Systems (GIS). Within ArcGIS 9.2 software, the Spatial 

Analyst extension tool was used to create IDW interpolations within the study area. Within Spatial 

Analyst, a pull-down menu is found in which the user can input the specific information pertaining 

to the dataset utilized. In the pull-down menu, default input parameters were selected. This included 

selecting PM10 for the z-value, a power value of two, a variable search radius with 12 points, and 

the search radius distance value unspecified. Once the proper data files were selected in the pulldown menu, the GIS software then automatically created a temporary interpolation raster, based on 

the PM10 value at each of the five monitor sites on the specific day or hour selected. (A “raster” is a 

scanning pattern of parallel lines that form the display of an image projected on a display screen.) 

Next, the temporary interpolation raster files were made permanent by saving each hourly file in the 

table of contents section of ArcMap, the principal graphical user interface to create maps in ArcGIS. 

Finally, the interpolated surface value tables were aggregated by census tracts in Excel. Census tract 

shapefiles, containing the new information from these interpolations, were then finalized as 

graphics in ArcMap. 

Ordinary kriging 

Ordinary kriging is a geostatistical approach to modeling. Instead of weighting nearby data points 

by some power of their inverted distance, it relies on the spatial correlation structure of the data to 

determine the weighting values. This is a more rigorous approach than IDW, as correlation between 

data points determines the estimated value at an unsampled point. Ordinary kriging makes the 

assumption of normality among the data points, as well as an unknown constant trend. This is 

unlike simple or universal kriging, which assume a known constant trend and/or model. 

The word “kriging”is synonymous with “optimal prediction”. Kriging methods, including ordinary 

kriging, utilize a variogram model to express the spatial variation of data points, and then minimize 

their error. 

For this project, the Spatial Analyst extension in ArcGIS (the same used for IDW analysis) was 

chosen as the optimal automated method to create kriged surfaces of PM10. After selecting an 

hourly file containing PM10 for the z-value, the spherical semivariogram model was selected in the 

pull-down menu as the best way to create a kriged surface with the least amount of error. The 

advanced parameters tab, which allows for input on the lag size, major range, partial sill, and nugget 

size, was not used. Default options, including a variable search radius with 12 neighbors, and an 

unspecified search distance, were used to create temporary interpolated raster files. As was the case 

with IDW, interpolated files were first made permanent, and were then aggregated into census tract 

shapefiles in Excel and ArcMap, to create maps of kriging values within study area census tracts. 

Comparison between IDW and ordinary kriging 

Each interpolation method has its advantages and disadvantages. Although ordinary kriging is more 

rigorous and better at predicting surfaces at a distance from known data points, IDW can be utilized 

with fewer data points. For example, ordinary kriging performs best with a large number of data 

points, ideally above 50, although as few as 10 can be used. IDW on the other hand, can create a 

prediction surface, though based mainly on nearest neighbors, with as few as 3-4 points within a 

given area. Figure 3.1 illustrates results from the two methods. Five PM10 station values were used 

in IDW on the left; 11 stations were used in ordinary kriging on the right. Figure 3.2 shows the 
15

percentage differences between the two methods (PM10IDW-PM10KR)/PM10IDW. In Figures 3.3 and 

3.4, statistical comparisons between the two interpolation methods reveal better agreement close to 

the central part of the study area, in close proximity to the five permanent PM10 monitors. Greater 

disagreement between the two models occurs in the outer edges of the study area, in census tracts 

close to the temporary PM10 monitors. 

Figure 3.1. Comparison between IDW and Ordinary Kriging methods 

Figure 3.2. Percentage differences between IDW and Ordinary Kriging 

methods – December 19, 2007, 8am 

* 100

PM10

PM10 PM10

IDW

IDW − KR
16

Figure 3.3. Mean Absolute Errors (MAE) for December between IDW and Ordinary Kriging 

methods – based on daily maps 

Figure 3.4. Root Mean Square Error (RMSE) for December between IDW and Ordinary Kriging 

methods – based on daily maps (see Appendix D for mathematical definitions of MAE and RMSE) 
17

Chapter 4 Neural network 

Introduction 

Traditionally, the term “neural network” referred to a circuit of biological neurons. The modern 

usage of the term often refers to networks made up of artificial neurons or “nodes.” These networks 

consist of interconnecting artificial neurons such as programming constructs that mimic the 

properties of biological neurons. Neural networks have been developed and used widely in both 

applied mathematics and applied science, including predictive modeling based on a time series of 

measurements – its application in the present work. Other application areas include system 

identification and control (vehicle control, process control), game-playing and decision making 

(backgammon, chess, racing), pattern recognition (radar systems, face identification, object 

recognition), sequence recognition (gesture, speech, handwritten text recognition), medical 

diagnosis, financial applications, data mining, and e-mail spam filtering. These networks are always 

imbued with the capability of “learning” through “training,” with a variety of different methods 

employed. Although this may appear somewhat metaphysical, it really boils down to a set of data 

and predictive equations, which, exercised daily, acquires “knowledge” and an improved predictive 

ability through the simple acquisition and prediction of additional data. In this project the neural 

network’s application was limited to the prediction of historical PM10 concentrations. An 

operational neural network, on the other hand, would make the daily predictions of tomorrow’s air 

pollution. This network would acquire more “skill” with time as additional daily relationships 

between PM10 concentrations and meteorological variables were encountered, processed, and 

predicted. 

In the present work, an artificial neural network called “EnviNNet” was developed and applied to 

the PM10 monitoring record at the Central Phoenix (CP) site, with the goal of constructing an air 

pollution predictive system that might complement the deterministic system of the grid-based air 

quality model, discussed in Chapter 5. Four steps were necessary: 

1. Assemble hourly records of PM10 and meteorological variables from one monitoring site, 

preferably for three years or more (only one year was examined in this project). 

2. Derive and calculate statistical relationships between the PM10 concentrations and the 

meteorological measurements. 

3. Run a meteorological predictive model such as MM5 (see Chapter 5) for each day of the 

historical record examined, determining quantitatively how well its predictions matched the 

measurements. 

4. Build the artificial neural network and the meteorological modeling system such that 

predictions of PM10 are forthcoming for each day of the historical record. Compare these 

predictions with the measurements. 

Should an organization wish to apply the neural network in a genuine air pollution predictive 

system, then the requisite software would need to be installed, the data gathering mechanisms built, 

the meteorological model would be set up for daily operational forecasts, and considerable staff 

training would have to take place. The remainder of this chapter discusses some aspects of these 

artificial neural networks and describes how the EnviNNet was built. 

Considering its wide usage in atmospheric applications (Gardner and Dorling 1998), a type of 

neural network called “Multi Layer Perceptron (MLP)” was employed. The MLP structure consists 

of an interconnected system of nodes (neurons) within a hidden layer that employs non-linear 
18

continuous transfer functions connecting input and output vectors. (Appendix B provides more 

details.) 

Phoenix case study 

As the first step, PM10 and meteorological data from the Central Phoenix (CP) monitoring station 

were assembled. EnviNNet was trained using a selected set of appropriately pre-processed historic 

time series data at CP, whereupon it was used to predict PM10 in the hindcasting mode. The input 

vector is made of the following variables: 

1. Pollution data - These constitute the endogenous component of the NN (A component or 

variable is called endogenous if it is explained within the model in which it appears). Time 

lags are selected by optimizing the data reconstruction efficiency of the NN, so as to achieve 

the best trade-off between sensitivity and specificity. Since these are multi-step operations, the 

measured pollutant levels are used in a rolling mode to generate the reconstructed values. 

2. Meteorological measurements - These comprise the exogenous component of the NN, and 

have a modulating effect on PM10 concentrations (exogenous refers to an action or object 

coming from outside a modeling system). The time lags were selected as before, and the 

balance between data reconstruction efficacy and network efficiency was optimized. 

3. Statistical and descriptive indicators - These represent the behaviour of the “Central 

Phoenix” neighbourhood via classes of input parameters that account for aggregated 

homogeneous spatio-temporal bands of microclimatic factors and air pollutant concentrations. 

The latter are dependent on the wind velocity, wind direction, and emissions specified as either 

weekday or weekend/holidays. 

The meteorological and emissions characteristics of low, moderate, and elevated PM10

concentrations at CP have to be accounted for in developing the NN, which makes the training 

strategy critical for reliable predictions. Since daily PM10 concentrations vary widely throughout the 

month and year, specific input models had to be built by combining time section data of different 

years to make the NN robust and adaptive to the local climate. The data for training came from 

selected four-to-six-month windows, as well as from time periods that show noteworthy patterns 

such as exceptional events. 

Input variables selected for training are: 

• Historical series of PM10 and meteorological data collected at CP during 2005-2006, prior to 

the dates selected for hindcasting; 

• A selection of days based on analysis and correlations of historical series representing PM10

trends in the time window selected; 

• Noteworthy patterns observed outside the time window of training (e.g. significant PM10

peaks representative of exceptional events). 

In this phase, the intrinsic parameters of the NN were estimated by numeric optimization until a 

satisfactory convergence was obtained. The weights and time lags so identified naturally depend on 

the site (e.g. local weather and neighbourhood emission characteristics). The most important aspect 

of this particular training is to obtain a non-linear dynamic relationship that connects input data with 

PM10 output so that the NN can be operated in the hindcasting mode. 
19

Results

The neural-network system gave dependable results for the Central Phoenix monitoring site. These 

results, compared with those of CMAQ in Chapter 6, are illustrated in Figure 4.1. PM10 predictions 

in arid climates are difficult and a correlation coefficient of 0.38 is better than average. 

0 50 100 150 200 250

Observed PM10 concentrations [μg/m3]

0

50

100

150

200

250

Modeled PM10 concentrations [μg/m3]

 November, 2005

R2=1

R2=0.38

Figure 4.1. 24-hour average PM10 predictions from the neural network versus observations at the 

Central Phoenix site for 2006 

Chapter 5 Air Quality Modeling

Introduction 

This chapter describes the deterministic, grid-based numerical models employed in simulating the 

central Phoenix PM10 concentrations. The three kinds of models are meteorological, emissions, and 

air quality. In addition, near the chapter’s end, the air quality model results are discussed in light of 

the interpolated concentration fields of Chapter 3. 

The MM5/SMOKE/CMAQ modeling system was employed to simulate PM10 concentrations in 

central Phoenix. This widely used, sophisticated platform has been adopted as a regulatory tool. The 

predictive system consists of three integrated models: the Pennsylvania State University and 

National Center for Atmospheric Research (NCAR) Mesoscale Meteorological Model - MM5 v3.7 

(Grell and Dudia, 1994) for simulating the weather, the Sparse Matrix Operator Kernel Emissions – 

SMOKE v2.2 model for emissions processing (Carolina Environmental Program, 2005). and the 

Community Multiscale Air Quality Model – CMAQ v.4.5 (Byun and Ching, 1999) for simulating 

pollutant concentrations. 

MM5 is a terrain-following model that simulates mesoscale atmospheric circulation. It has been 

applied to a broad spectrum of studies, including land-sea breezes, mountain-valley circulation, and 

real-time weather forecasting. The data for model initialization and lateral boundary conditions 

were obtained from the operational NCEP/Eta model (40km grid resolution) and NCEP global 

surface and upper air observations every three and six hours, respectively. (“NCEP” is the National 
20

Weather Service’s National Center for Environmental Prediction and “Eta” is one of the global 

weather models.) Three-dimensional analysis and surface nudging with observed data were used to 

generate grid-based meteorological fields. MM5 provided the meteorological input to CMAQ. 

Appendix C contains further modeling details. 

The SMOKE model produces gridded, hourly emissions for CMAQ, using meteorological fields 

from MM5 and a host of emission estimating techniques. Temporally, spatially, and chemically 

resolved model-ready emissions were prepared using the recent Western Regional Air Partnership 

(WRAP) emissions inventory (including the BRAVO-1999 study for the Mexico emissions 

inventory). 

Developed by EPA in the 1990s, CMAQ is a multi-pollutant, multi-scale air quality model that 

contains techniques for simulating all atmospheric and land processes that affect the transport, 

chemical reactions, and deposition of atmospheric pollutants and/or their precursors on both 

regional and urban scales. It is designed as a science-based modeling tool for handling all the major 

pollutants such as ozone and particulate matter. This “Eulerian” or grid-based model produces 

hourly concentrations of a number of pollutants in the frame of a fixed, three-dimensional grid with 

uniformly sized horizontal grid cells and variable vertical layer thicknesses. This model has 

undergone numerous applications and performance evaluations, in Phoenix, in Arizona (Yuma and 

Douglas), in the Western United States, and throughout the world. 

Modeling domains 

The modeling domain was based on a Lambert Projection centered at (97°W, 40°N), with three 

nested domains having 36, 12 and 4km grids to predict meteorology, emissions, and air quality, 

respectively. In the nested simulations, the results obtained from the outer domain were used as 

initial and boundary conditions for the inner domain (Figure 5.1). Vertically 29 levels were 

specified with the layer closest to the ground being seven meters thick to capture boundary-layer 

processes. The inner domain with 4x4km grids was centered in the Salt River Valley in central 

Phoenix. 

Supersite

West Phoenix

Durango Complex

West 43rd Avenue

Central Phoenix

Supersite

West Phoenix

Durango Complex

West 43rd Avenue

Central Phoenix

Figure 5.1. Modeling domains for MM5/SMOKE/CMAQ simulations 
21

Modeling periods 

The modeling periods selected were constrained by the available air quality and health data in 2004-

2006. As was described in Chapter 2, there are five monitors in the study area with hourly PM10

concentrations, but only two with hourly PM2.5 concentrations. Therefore, the study examined only 

the PM10 concentrations. One part of the project was to develop and test different interpolation 

techniques to build spatial concentration fields. These interpolation methods require from five to 

ten monitoring sites, another reason that the PM2.5 data could not be used. In 2004 the sites of West 

Phoenix and West 43rd Avenue had incomplete records, restricting the analysis to 2005 and 2006. 

The 24-hour averages of PM10 are shown for the five monitors in Figure 5.2, whose names and 

designations are given below: 

CP – Central Phoenix 

DC – Durango Complex 

WF – West 43rd Avenue 

WP – West Phoenix 

SS – Supersite 

Different periods with high and low pollution levels were selected for analysis. 

• High pollution (above the National Ambient Air Quality Standard of 150μg/m3

 for a 24-

hour average); November 2005; December 12-19, 2005; January 10-20, 2006; November 2-16, 

2006 

• Low pollution (less than 75μg/m3); September 3-15, 2005; August 2005; March 11 - April 

9, 2006; August, 2006 

Figure 5.2. 24-hour averaged PM10 observations for 2005-2006 

The daily counts of emergency department visits and hospital admissions with an asthma diagnosis 

in Maricopa County, shown in Figure 5.3, were divided into four categories: 

• Worst: greater than the sum of the average and the standard deviation; 

• Bad: between the annual average and the sum of the average and the standard deviation; 

• Moderate: between the average and the difference between this average and the standard 

deviation; 

• Good: less than the difference between the average and the standard deviation. 

0

75

150

225

1 366 Julian day

PM10 concentrations

CP DC WF WP SS

2005 2006

0

75

150

225

1 366 Julian day

PM10 concentrations

CP DC WF WP SS

2005 2006

μg/m3
22

01/01/05 06/30/05 12/27/05 06/25/06

Day of the year

0

10

20

30

40

Asthma events

Figure 5.3. Daily emergency department visits and admissions to hospitals of children ages 0-17 in 

Maricopa County with an asthma diagnosis for 2005 – 2006 

The average number of daily asthma visits is 9 with a range from 1 to 31. The seasonal pattern is 

virtually identical for both years. The good category occurs almost exclusively in July; the worst 

category is mostly limited to a period from the last week of October until March 1, with a hiatus 

from December 20 to January 20, when the level of admissions stays in the moderate range. 

Simple statistical analyses of the 24-hour PM10 concentrations and daily asthma visits were carried 

out to help select the modeling periods. Curves in Figure 5.4 illustrate the variations throughout the 

two years, with a seasonal periodicity in both PM10 and asthma being quite evident. The periods 

when the two data sets diverge are well displayed. The highest number of asthma incidents and 

elevated PM10 concentrations occurred together in November 2005 through January 2006. A 

decrease in asthma incidents near the end of December, while PM10 concentrations remain elevated, 

may be due to behavioral changes during the Christmas holidays. November 2005 had the highest 

number of asthma incidents; this month was selected to represent “high pollution” conditions. It was 

more difficult to select the period of low pollution because of incoherence in both data sets. The 

lowest PM10 concentrations were recorded in August, while the lowest numbers of asthma incidents 

came in July. One period with both low PM10 levels and low numbers of asthma cases was found in 

March-April 2006. This period was chosen to represent “low pollution” conditions. 

The modeling was conducted for the two selected periods: 

• November 2005, with high pollution, and 

• March 11 - April 9, 2006, with low pollution. 

Modeling runs were carried out with 31 hours of spin-up time for the weather predictions and 24 

hours for the air quality modeling. “Spin-up” time means that the model run is begun before the day 

(or period) of interest. This allows its calculations to begin before the period of interest, reducing 

the influence of the initial conditions on the model results. 

average 

} standard 

 deviation
23

01/01/05 06/30/05 12/27/05 06/25/06

Day of the year

0

10

20

30

40

Asthma events

0

50

100

150

200

250

PM10 concentration [

μg/m

3] Figure 5.4. PM10 concentrations and asthma events (2005-2006) 

Modeling results and discussion 

Numerical simulations were carried out with the MM5/SMOKE/CMAQ modeling system for both 

high and low pollution periods and were compared with observations from the five permanent air 

monitoring stations. This model validation was necessary to estimate the model’s performance in 

the study region. Air pollutant concentrations depend on meteorological conditions such as wind 

and rainfall, as well as on the strength and patterns of air pollutant emissions. Consequently, in any 

air quality model’s performance evaluation, one must take into account the uncertainties in both the 

emission and meteorological models. 

Model runs for two time periods: November 2005 and March 11 - April 9, 2006, show reasonable 

results in comparison with the observations. The scatter plots of simulated hourly PM10

concentrations versus the observations at all five locations in central Phoenix are shown for both 

periods with their corresponding regression coefficients (Fig. 5.5). 

CMAQ generally overestimated the low (<50μg/m3

) and underestimated the elevated (>150μg/m3) 

observed PM10 concentrations for both periods, with better correspondence between observed and 

modeled data for the high pollution period. Elevated PM10 levels were a frequent occurrence at the 

Durango Complex (DC) and West 43th Avenue (WF) sites, well above the other three sites in 

winter. For this reason the model results were also compared with the measurements in two groups: 

• CP, WP, and SS, the three lower concentration sites, and 

• DC and WF, the two higher concentration sites (Figure 5.6). 

November, 2005 March 11 - April 9, 2006 
24

0 100 200 300 400 500

Observed PM10 concentrations [μg/m3]

0

50

100

150

200

250

Modeled PM10 concentrations [μg/m3]

 November, 2005

R2=1

R2=0.16

0 100 200 300 400 500

Observed PM10 concentrations [μg/m3]

0

50

100

150

200

250

Modeled PM10 concentrations [μg/m3]

 March 11 - April 9, 2006

R2=1

R2=0.12

Figure 5.5. Simulated and observed hourly PM10 concentrations in central Phoenix for periods of 

high pollution (left) and low pollution (right) – from all five monitors 

The coefficients of determination, which quantify the goodness of the fit and are 1.0 for perfection, 

were less than 0.2 for all five sites within the study domain with the 4km grids. Perhaps this level of 

performance can be attributed to various inadequacies of the emissions inventory. Even a perfect 

description of the boundary layer air flow from the meteorological model would not produce 

excellent modeled air pollutant concentrations as long as the estimated PM10 emissions were 

inaccurate in magnitude, space, and time. As the WRAP emission inventories came in 36 and 12km 

grids, the 4km emissions for the central Phoenix model runs were obtained by setting them equal to 

one ninth of the 12km emissions (there are nine 4km grids within a 12x12km square). 

PM10 - November, 2005

R2 = 0.2078

R2

 = 1

R2

 = 0.33

R2

 = 0.2544

0

100

200

0 100 200 300 400 500

Observed [ug/m3]

Predicted [ug/m3]

CP

WP

SS

PM10 - November, 2005

R2 = 0.2056

R2 = 1

R2 = 0.1625

0

100

200

0 100 200 300 400 500

Observed [ug/m3]

Predicted [ug/m3]

DC

WF

Figure 5.6. Predicted and observed hourly PM10 concentrations in central Phoenix for the two sets 

of monitors shown separately – high pollution period (November 2005) 
25

One explanation for the elevated PM10 concentrations measured at the DC and WF monitors is that 

weekday “industrial” and roadway emissions add to the concentration loading already high from 

urban transport. The air quality model’s inability to simulate these higher concentrations, 

demonstrated by the near-horizontal angle of the DC and WF regression lines in the right figure 

above, may lie with an insufficient emission inventory. One should keep in mind that while CMAQ 

has produced excellent simulations for ozone and chemically speciated fine particulates, whose 

atmospheric behavior mimics that of gaseous pollutants its ability to simulate PM10 has not been so 

good. The reasons for this lack-luster performance are many, and include: 

• a single, spatially uniform emission value throughout a single grid – at complete odds with 

the spatial variability of real-world emissions; 

• deposition phenomena that depend on the particle size distribution, as different emission 

sources produce particles with different distributions; The average particle size of the total 

mass of PM10 in central Phoenix is from five to seven microns, large enough to be deposited 

somewhat rapidly to the ground, in contrast to the smaller particles comprising the fine 

fraction (smaller than 2.5 microns), which remain suspended considerably longer. 

• the sporadic and unknown temporal distribution of most fugitive sources that cannot be 

captured by any emissions inventory; and 

• the heterogeneous spatial distribution of ambient PM10, exemplified by monitors influenced 

by localized emissions that vary widely in their distance from the monitor, in their timing, in 

their release height, and in their magnitude. 

Despite its shortcomings, a deterministic model such as MM5/CMAQ offers three advantages over 

its statistically based competitors, providing: 

1. nearly complete geographic coverage, albeit limited to the size of the grid, and adequate 

temporal coverage in the form of hourly averages, with simulated values of both 

meteorological variables and air pollutant concentrations calculated for each and every grid 

cell; 

2. air quality predictions years or decades into the future, necessarily dependent on the 

reliability of a future-year emissions inventory; and 

3. a way to quantify the benefits of specific air pollution control strategies, accomplished by 

modifying a future, base-case emissions inventory to reflect the anticipated emission 

reductions from the controls. 

While only the first advantage is of relevance to the Children’s Health project, the other two should 

be considered in any general assessment of the utility of deterministic models. 

A disadvantage of CMAQ is that, as a deterministic model, it has none of the interpolation 

techniques that could better fit its output to the observed data. Furthermore, the emissions data and 

meteorological data that drive CMAQ each have considerable uncertainties, the former to a far 

greater degree than the latter. These uncertainties, as well as those inherent in its embedded science, 

cause it to predict better for longer time scales (monthly, annual) than for shorter times (1-hour or 

24-hour); and to be the most error prone with smaller grid cells. The "soccer goal" plot, 

recommended by USEPA (Guidance on the Use of Models, 2007), is a convenient way to visualize 
26

model performance, as both bias and errors are shown on a single plot. Two statistics -- the Mean 

Fractional Error and the Mean Fractional Bias -- for both the high and low pollution periods are 

plotted for the different sites in Figure 5.7. As bias and error approach 0, the points are plotted 

closer to or within the "goal" represented here by the dashed boxes. (See Appendix D for the 

mathematical definitions of these and other statistics.) The plot shows reasonable model 

performance, with only a single site being out of the goal area -- West 43th Avenue for the high 

pollution period. 

0

25

50

75

100

125

-90 -60 -30 0 30 60 90

Mean Bias

Mean Absolute Error

0

25

50

75

100

125

High pollution Low pollution

goal area

WF

DC

WP CP SS

WF

DC WP SS

CP

0

25

50

75

100

125

-90 -60 -30 0 30 60 90

Mean Bias

Mean Absolute Error

0

25

50

75

100

125

High pollution Low pollution

goal area

WF

DC

WP CP SS

WF

DC WP SS

CP

goal area

WF

DC

WP CP SS

WF

DC WP SS

CP

Figure 5.7. The "soccer goal" plot measures both bias and error for central Phoenix 

In addition to this visual estimate of performance, the different statistics that were calculated to 

estimate the model performance for the study periods also show reasonable model performance 

(Table 5.1). 

Of all these statistics, perhaps the most telling one is the Index of Agreement (IA): its values range 

from 0.46 to 0.72, with perfect agreement being 1.0. The best IA was achieved at WP during the 

winter and at DC during the spring. The better known Coefficients of Determination (RSQ), also 

known as the linear “correlation coefficient” abbreviated as “R2

, ranges from 0.10 to 0.33, 

indicative of a slightly positive correlation. This weak correlation is often found in matching 

numerically modeled concentrations with the measured ones in air pollution studies, especially for 

PM10. The Mean Absolute Errors (MAE) are all less than 30 except for DC and WF in November 

2005. The local emissions not taken into account with sufficient spatial and temporal accuracy in 

the emissions inventory contribute to this disagreement. CMAQ overestimated the PM10

concentrations at CP and SS: the Modified Coefficient of Determination (RSQ*) is greater than 1; 

but it underestimated the observations at WF and DC, where RSQ* is less than 1 for both high and 

low pollution periods. At West Phoenix (WP) the model under-estimated the higher concentrations 

measured in November 2005 but over-estimated the lower ones in March-April 2006. The Root 

Mean Square Errors (RMSE) ranged from 26 to 42, except for the two problematic sites of WF and 

DC in November 2005, which had anomalously high values about 90. 
27

Table 5.1. Statistical measures in comparing model-predicted hourly PM10 concentrations to 

observations for both study periods at different sites 

Monitor MAE RMSE RSQ RSQ* IA

Perfect agreement 0 na 1.00 na 1

Central Phoenix 26.12 34.39 0.21 1.45 0.68

West Phoenux 27.81 41.99 0.33 0.60 0.72

Super Site 23.48 30.06 0.25 3.30 0.62

West 43rd Avenue 62.21 89.85 0.16 0.06 0.51

Durango Cpmplex 58.49 85.80 0.21 0.68 0.55

Central Phoenix 25.00 33.06 0.10 4.28 0.46

West Phoenux 22.78 30.25 0.16 2.39 0.59

Super Site 22.19 27.15 0.12 5.01 0.47

West 43rd Avenue 25.31 26.47 0.17 0.42 0.60

Durango Cpmplex 23.94 35.86 0.25 0.54 0.68

 November 2005

 March 11- April 9, 2006

Abreviation: MAE - Mean Absolute Error; RMSE - Root Mean Square Error; RSQ - Coefficient of Determination (aka 

R2

); RSQ* - Modified Coefficient of Determination2; IA - Index of Agreement 2

Modified Coefficient of Determination: values greater than 1 mean the model overestimated (orange); values less than 

1 mean the model underestimated (green) 

The health data are available only on a daily basis, and consequently, the prediction of accurate 24-

hour averages of PM10 was of greater importance than the hourly values. Figure 3 shows the 24-

hour averaged Mean Bias for both high and low pollution periods. The difference between daily 

calculated and observed concentrations can be substantial. While the average MB for 30 days was 

in the range of -15 to 15, except at WF and DC in November (see Fig. 5.7), the 24-hour averaged 

MB doubled for some days and is almost zero for the others, as can be seen from Figure 5.8. 

 

The model underestimations (the negative MB) were most pronounced at Durango Complex and 

West 43rd Avenue, and a weekly periodicity can be found for the MB. The weekday industrial and 

roadway emissions add to already-high concentration loading from urban transport. These two 

regulatory problematic sites, which represent the worst PM10 air quality in metropolitan Phoenix, 

have relatively high frequencies of violations of the NAAQS, in part because of somewhat dense 

localized emissions from the extractive and material handling industries along the Salt River. Some 

of these sporadic and unknown temporally distributed fugitive sources cannot be captured by an 

emissions inventory, and they contribute to the heterogeneous spatial distribution of ambient PM10

and hence to prediction errors. The average particle size of the total mass of PM10 in central 

Phoenix is from five to seven microns, large enough to be deposited on the ground somewhat 

rapidly, in contrast to the smaller particles comprising the fine fraction (smaller than 2.5 microns) 

that remains suspended for considerably longer times. The aerosol module in CMAQ considers both 

PM2.5 and PM10, but represents the particle size distribution as the superposition of three lognormal 

sub-distributions called modes. The model takes into account only one mode of the "coarse 

particles" (2.5-10μm) and assigns the same deposition velocity. 
28

-150

-125

-100

-75

-50

-25

0

25

50

Mean Bias

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Day

November, 2005

CP DC WF WP SS

-150

-125

-100

-75

-50

-25

0

25

50

Mean Bias

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Day

March 11 - April 9, 2006

CP DC WF WP SS

Figure 5.8. Daily Mean Bias for high pollution conditions (top) and low pollution 

Figures 5.9 and 5.10 show the CMAQ predictions compared with 24-hour averaged observations for 

both high and low pollution periods. The calculated data were extracted from the grid cell where the 

corresponding monitor is located. A good correspondence between observed and calculated data for 

WP and CP sites can be found in November 2005 (Fig. 5.9). The model overestimates the 24-hour 

averaged observed values at SS and strongly underestimates the values at WF and DC sites. The 

same behavior can be found for the low pollution case (Fig. 5.10). CMAQ overestimates the 24-

hour averaged measured values at SS, CP, WP and underestimates the values at WF and DC sites 

for several days. Nonetheless, the model's predicted values generally follow the trends of the 

observations. In spite of MM5’s inability to simulate the higher wind speeds for several days, the 

model did predict some higher wind speeds, reflected by the low PM10 concentrations calculated by 

CMAQ for 11, 26-27 of November 2005, March 30, and 2, 5-6 of April, 2006. 
29

0 10 20 30

0

50

100

150 CP

Day

PM10 concentration (μg/m3)

0 10 20 30

0

50

100

150 WP

Day

PM10 concentration (μg/m3)

0 10 20 30

0

50

100

150 DC

Day

PM10 concentration (μg/m3)

0 10 20 30

0

50

100

150 WF

Day

PM10 concentration (μg/m3)

0 10 20 30

0

50

100

150 SS

Day

PM10 concentration (μg/m3)

Observation

CMAQ

Observation

CMAQ

Figure 5.9. CMAQ predictions versus 

observations of PM10 concentrations at five sites 

for the high pollution period of November 2005. 

The lines are the median values of the 24 hourly 

predictions for each day; the box plots are 25th to 

75th percentiles of the 24 predictions. 
30

0 10 20 30

0

50

100

150 CP

Day

PM10 concentration (μg/m3)

0 10 20 30

0

50

100

150 WP

Day

PM10 concentration (μg/m3)

0 10 20 30

0

50

100

150 DC

Day

PM10 concentration (μg/m3)

0 10 20 30

0

50

100

150 WP

Day

PM10 concentration (μg/m3)

0 10 20 30

0

50

100

150 SS

Day

PM10 concentration (μg/m3)

Observation

CMAQ

Observation

CMAQ

Figure 5.10. CMAQ predictions versus 

observations of PM10 concentrations at five sites 

for the low pollution period of March 11 – April 

9, 2006. The lines are the median values of the 

24 hourly predictions for each day; the box plots 

are 25th to 75th percentiles of the 24 predictions. 
31

The PM10 concentrations were averaged for the five sites for the entire period for every hour. 

Figure 5.11 presents the averaged concentrations from the five monitors in central Phoenix with the 

spatially-averaged PM10 concentrations from the numerical modeling. 

0

50

100

150

0 2 4 6 8 10 12 14 16 18 20 22

Time [h]

Observed Calculated

Figure 5.11. Hourly variation of the five-site averages, calculated and observed PM10 concentrations 

for November 2005 

The model is able to produce the morning and evening maxima at the right times, in agreement with 

the observations, but it underestimates the magnitude of the peaks. There are at least two 

explanations for this lack of agreement. First, consider the weather-predicting MM5 model, which 

is in need of some modifications itself. Its predicted wind fields have speeds considerably higher 

than the measured values during the morning and evening transitions (for more details see 

Appendix C). Provided that the wind speeds remain below the dust resuspension threshold - roughly 

15 to 20 miles per hour (7 to 10 meters per second) - higher wind speeds generally lead to lower 

PM10 concentrations; hence, the underestimation. Second, consider that MM5 does not differentiate 

among the various types of urban land surface. The treatment of urban land in modeling grids 

results in too uniform a surface roughness and other properties, which are not representative of the 

local-scale variation. Sub-categories are needed for the so called "urban" land characteristics. These 

unrealistic uniformities can cause incorrect transport of pollutants, as well as too little or too much 

vertical mixing, both of which affect their resultant concentrations. Especially for stable conditions, 

which can last for several days, improvements in the application of this model to metropolitan 

Phoenix would be helpful. 

CMAQ contains “accurate” and “state-of-the-science” parameterizations of atmospheric processes 

affecting transport, transformation, and deposition of pollutants. Process analysis is a useful tool for 

understanding the contributions of different physical processes to the total PM10 concentrations. 

Through process analysis the contribution that each phenomenon makes to the resultant PM10

concentration can be quantified (Fig. 5.12). The emissions (EMIS), dry deposition (DDEP), and 

vertical diffusion (VDIF) give a joint contribution ten times greater than the combined contributions 

of advection (horizontal HADV + vertical VADV), horizontal diffusion (HDIF), atmospheric 

chemistry (CHEM), aerosol composition (AERO), and cloud chemistry (CLDS) combined. 

Inadequate parameterizations of both the diffusion and the depositional processes in the planetary 

boundary layer, combined with uncertain emission inventories, may be responsible for the model’s 

performance. 

μg/m3
32

Process Analysis - November 7-9, 2005 

-800

-600

-400

-200

0

200

400

600

800

0 12 24 36 48 60 72

time [h]

Concentration rate [ug/m3]

-60

-40

-20

0

20

40

60

DDEP EMIS VDIF AERO CLDS

HADV VADV HDIF CHEM PM10

Process Analysis - April 1-3, 2006 

-800

-600

-400

-200

0

200

400

600

800

0 12 24 36 48 60 72

time [h]

Concentration rate [ug/m3]

-60

-40

-20

0

20

40

60

DDEP EMIS VDIF AERO CLDS

HADV VADV HDIF CHEM PM10

Figure 5.12. Contributions of different processes to PM10 concentrations for 3-days periods in 

November 2005 and April 2006: the values are averaged from several grid cells in central Phoenix, 

with EMIS, DDEP, and VDIF plotted on the left Y-axis, with all others on the right 

One reason for the differences between the modelled and observed PM10 concentrations is that 

measured point values cannot represent the larger grid-based volumes of 36, 12, or 4 km 

surrounding the monitoring sites, given the large spatial inhomogeneities already noted. The spatial 

distribution of the Index of Agreement (IA) between the interpolated and modeled PM10 

concentrations is shown in Figure 5.13 for November 2005. The interpolated surfaces were 

constructed from the observations and mapped into census tract concentrations, from which the 

average value for each modeling grid cell was calculated. The map shows the IA between the 

interpolated PM10 concentrations from the IDW and the CMAQ estimates for each 4 km grid cell. 

A very good correspondence can be found with the IA greater than 0.5 for the whole domain. The 

modeled data fit very well to the interpolated surfaces (IA between 0.6 - 0.7) in the northeast part 

(WP, CP, SS) and some disagreement can be seen at the south-west (less than 0.5). The CMAQ 

model overestimates the PM10 concentrations in the northeast around CP and SS, unlike in the 

EMIS, DDEP, VDIF

EMIS, DDEP, VDIF
33

southwest around WF and DC, where it generally underestimates the observations, as can be seen 

from the map of Mean Bias for November 2005 (Figure 5.14). 

Figure 5.13. The spatial distribution of the Index of Agreement between CMAQ and Inverse 

Distance Weighting (IDW) interpolated surfaces 

Figure 5.14. The spatial distribution of Mean Bias between CMAQ and Inverse Distance Weighting 

(IDW) interpolated surfaces 
34

One way to improve the numerical predictions for the central part of the study area would be to 

combine CMAQ and IDW surfaces. Application of the Daily Mean Biases to CMAQ predictions 

produces a more realistic pattern of PM10 concentrations and reduces the disagreement between 

calculated and observed data. Examples of this technique are shown in Figures 5.14-5.18 for all five 

permanent sites for November 2005. 

ErrBar=25%

0

20

40

60

80

100

120

0 5 10 15 20 25 30

Day of November, 2005

PM10 concentration 

CP-obs CMAQ CMAQ-new

Figure 5.15. Comparison of the daily, “simulated” PM10 concentrations with the observations at 

Central Phoenix: violet is straight CMAQ; blue is “CMAQ-new” - CMAQ combined with 

interpolation 

ErrBar=25%

0

50

100

150

0 5 10 15 20 25 30

Day of November, 2005

PM10 concentration 

WP-obs CMAQ CMAQ-new

Figure 5.16. Comparison of the daily, “simulated” PM10 concentrations with the observations at 

West Phoenix (WP): violet is straight CMAQ; blue is “CMAQ-new” - CMAQ combined with 

interpolation 

ErrBar=25%

0

20

40

60

80

100

0 5 10 15 20 25 30

Day of November, 2005

PM10 concentration 

SS-obs CMAQ CMAQ-new

Figure 5.17. Comparison of the daily, “simulated” PM10 concentrations with the observations at 

Supersite (SS): violet is straight CMAQ; blue is “CMAQ-new” - CMAQ combined with 

interpolation. 
35

ErrBar=25%

0

50

100

150

200

250

300

0 5 10 15 20 25 30

Day of November, 2005

PM10 concentration 

WF-obs CMAQ CMAQ-new

Figure 5.18. Comparison of the daily, “simulated” PM10 concentrations with the observations at 

West 43rd Avenue (WF): violet is straight CMAQ; blue is “CMAQ-new” - CMAQ combined with 

interpolation 

ErrBar=25%

0

50

100

150

200

250

300

0 5 10 15 20 25 30

Day of November, 2005

PM10 concentration 

DC-obs CMAQ CMAQ-new

Figure 5.19. Comparison of the daily, “simulated” PM10 concentrations with the observations at 

Durango Complex (DC): violet is straight CMAQ; blue is “CMAQ-new” - CMAQ combined with 

interpolation 

Although this "nudging" technique can be applied to historical periods, it cannot be of help in 

forecasting, as the observations that would provide the interpolated surfaces are yet to be made. 

Any improved health warning system would have to rely either on a statistical-based prediction 

such as the neural-network EnviNNet or on CMAQ or another deterministic model prediction 

driven by the meteorological model’s predicted weather for the next day. 

Conclusions 

For the meteorological fields: 

• MM5 fails to predict high wind speeds, perhaps due to the inconsistency in global model 

predictions used as input and boundary conditions. 

• MM5 slightly overestimates the low wind speeds during the generally stable conditions of 

the night and early morning; furthermore, it cannot faithfully simulate rapid changes of wind 

direction. 

• MM5 does accurately simulate the temperature field, albeit with slight underestimates of 

nocturnal temperatures. 
36

For the PM10 concentration fields:

• CMAQ generally underestimates the higher observed PM10 concentrations and 

overestimates the lower ones. 

• CMAQ captures the diurnal variation of PM10 concentrations. 

• CMAQ especially underestimates the ately simulates accurate such as the neural-network 

based EnviNNet or on a CMAQ or CMAQ-type deterministic model driven byelevated PM10 

concentrations at West 43rd Avenue and the Durango Complex in November 2005. Dust 

emissions in the vicinity of the monitors, but inadequately accounted for in the emissions 

inventory, are the likely explanation. 

• CMAQ adequately simulates the surface PM10 distribution in the central Phoenix study area 

for the needs of this project. 

Three difficulties plague the application of a deterministic system such as MM5/SMOKE/CMAQ to 

air quality surveillance and management: 

• First, these models need precise initial conditions, boundary conditions, and emissions to 

provide reasonable predictions; 

• Second, they are computationally demanding and somewhat labor-intensive to operate, 

troubleshoot, update, and maintain; and 

• Third, the meteorological model MM5 is actually designed to predict larger-scale, regional 

phenomena, not the more local-scale characteristics that prevail in and around specific urban 

air pollution monitoring sites. 

As done in this project, the importance of the initial conditions can be reduced through spin-up 

times. As also done in this project, the importance of boundary conditions can be diminished 

through choosing larger modeling domains. The importance of spatially and temporally accurate 

hourly emissions, however, cannot be overlooked: their contribution to the performance of air 

quality model predictions is paramount. In particular, airsheds in arid and semiarid areas, including 

most of the southwestern U.S. and much of its intermountain West, are subject to pervasive fugitive 

emissions of dust under all meteorological conditions and to windblown dust under more turbulent 

and stormy conditions. Locally suspended dust from widespread fugitive emissions is a major 

component of everyday particulate matter concentrations in this region. Production of soil dust 

aerosols depends on the nature of the mechanical disturbance, the wind energy, and the soil surface 

properties. Improvements in quantifying both human-caused, mechanically generated emissions, as 

well as windblown dust emissions, would improve deterministic model performance in the study 

area. 

The computational demands of deterministic models cannot be casually dismissed, in spite of the 

recent, substantial improvements in speed and power stemming from parallelization and other 

techniques. Accurate weather forecasting on the synoptic scale of one to five days is now a facet of 

our daily lives; still, the computational time needed for more precise predictions on the local scale 

remains about equal to the typical real-world time of the local phenomena themselves. These 

phenomena include thermally driven upslope and valley winds, stagnation, extremely low wind 

speeds, stratification, and canalization effects – all of which last for hours and require equal hours 

of simulation. Considering human exposure, in spite of the general lack of short-term health 
37

standards (let’s say from one to six hours), the higher concentrations occurring in a few to several 

hours may be as important or even more so than the exposures of 24 hours or longer. 

Meteorology’s critical control of air pollutant concentrations is well known: the emission and 

chemical transformation of particles, their transport and dispersion – both horizontal and vertical -- 

and their loss through dry and wet deposition are all consequences of the weather. During the 

meteorological simulations of this project, it has become evident that the MM5 model would benefit 

from a number of modifications. For example, disagreements between a measured wind vector 

(speed and direction) and the MM5 volume-averaged predictions for the 36, 12, or 4 km grids are 

commonly encountered. MM5 is not scientifically formulated to reproduce the exact wind speed 

and direction at each measurement point; rather, it has been designed to generate sub-regional and 

regional wind, temperature, and moisture fields that are internally consistent and strictly satisfy the 

laws of mass, momentum, and energy conservation. Conversely, local wind measurements are 

seldom sited in such a manner as to be representative of a large volume of space surrounding the 

observation site. In the complex terrain of metropolitan Phoenix and its surrounding mountains, 

because of terrain forcing and orographic effects, the MM5 volume-averaged meteorological 

estimates have greater uncertainties than in flat terrain. 

Chapter 6 Neutral Network versus CMAQ Predictions 

For these comparisons November 2005 was selected, considering that the late fall and winter 

months tend to have the highest PM10 concentrations and the most hospital visits for respiratory 

illnesses. EnviNNet predictions were limited to a single site, Central Phoenix. Furthermore, this 

November has both high and low PM10 days, including two periods of elevated PM10 – November 

10 and November 22-23, for which statistics and graphics have been produced. The daily averaged 

values are presented first, given that the health data for comparison with PM10 are available on a 

daily basis and that the National Ambient Air Quality Standard is expressed as a 24-hour average, 

midnight to midnight. The data are from the Maricopa County Air Quality Department, whose staff 

has verified their accuracy through quality control procedures and has reported them to EPA’s 

AIRS system. Figure 6.1 shows the time series of these daily predictions by EnviNNet and CMAQ 

in comparison with the daily observations; Figure 6.2 presents comparisons of the hourly 

observations from November 2005 with the two models. 

0

20

40

60

80

100

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 Day

PM10 concentration 

[ug/m3]

EnviNNet CMAQ Observations

Figure 6.1. Time series of 24-hour average PM10 concentrations predicted by CMAQ and EnviNNet 

with observations at the Central Phoenix site 
38

0 50 100 150 200 250

Observed PM10 concentrations [μg/m3]

0

50

100

150

200

250

Modeled PM10 concentrations [μg/m3]

 November, 2005

R2=1

R2=0.21

0 50 100 150 200 250

Observed PM10 concentrations [μg/m3]

0

50

100

150

200

250

Modeled PM10 concentrations [μg/m3]

 November, 2005

R2=1

R2=0.38

Figure 6.2. Scatter plots of hourly PM10 concentrations predicted by CMAQ (left) and EnviNNet 

with observations: November 2005, Central Phoenix site 

Predicting the higher PM10 concentrations well is more important than doing well on the low ones. 

To this end the model’s predictions for elevated PM10 concentrations were evaluated for the two 

high pollution periods of November 6-10 and November 22-26. The results are shown in Figures 

6.3 and 6.4; the performance measures for the three time periods and the two models are in Table 

6.1, are defined in Appendix D, and are discussed below. 

The calculations were made for one month and for the two five-day periods with elevated PM10

concentrations. The Index of Agreement (IA) is greater than 0.6, which shows good correspondence 

between the calculated and observed data. Generally, EnviNNet gives better IA in comparison with 

CMAQ for all periods considered here. The Mean Absolute Errors (MAE) are less than 32 for 

CMAQ and less than 24 for EnviNNet. The Root Mean Square Errors (RMSE) are in the range of 

25 – 40 for different periods. The statistical model EnviNnet yields smaller errors than CMAQ. 

Table 6.1. Statistics of CMAQ and EnviNNet predictions of hourly PM10 concentrations at the 

Central Phoenix site 

Model MAE RMSE RSQ RSQ* IA Period

CMAQ 26.12 34.40 0.21 1.45 0.68 November

EnviNNet 19.01 25.02 0.39 0.82 0.77 November

CMAQ 32.24 40.18 0.14 1.05 0.61 6-10 Nov.

EnviNNet 20.39 28.04 0.42 0.75 0.79 6-10 Nov.

CMAQ 29.33 39.17 0.10 1.19 0.60 22-26 Nov.

EnviNNet 24.26 34.01 0.13 0.78 0.61 22-26 Nov.

Abreviation: MAE - Mean Absolute Error; RMSE - Root Mean Square Error; RSQ - Coefficient of Determination (aka 

R2); RSQ* - Modified Coefficient of Determination2; IA - Index of Agreement 2

Modified Coefficient of Determination: values greater than 1 mean the model overestimated (orange); values less than 

1 mean the model underestimated (green) 
39

November 6-10, 2005

0

50

100

150

200

250

0 12 24 36 48 60 72 84 96 108 120

Time [h]

PM10 concentration 

[ug/m3]

Observed CMAQ EnvirNNet

Figure 6.3. Comparison of hourly PM10 concentrations predicted by Models 3 and EnviNNet with 

the observations of November 6-10, 2005, Central Phoenix site 

November 22-26, 2005

0

50

100

150

200

250

0 12 24 36 48 60 72 84 96 108 120

Time [h]

PM10 concentration 

[ug/m3]

Observed CMAQ EnvirNNet

Figure 6.4. Comparison of hourly PM10 concentrations predicted by CMAQ and EnviNNet with the 

observations of November 22-26, 2005, Central Phoenix site 

The neural network satisfactorily predicts the PM10 peaks in contrast to CMAQ, which has problems 

capturing them satisfactorily. Although the deterministic model may have given much better 

predictions under ideal initial, boundary and pollution inventory conditions with all scales of motion 

fully resolved, the present status of the model is far from this state. In addition, CMAQ cannot 

resolve scales smaller than the size of the grid, which in this case is 4x4 km. 

Conclusions

As a workable alternative to the deterministic MM5/CMAQ, the neural network proved to be 

somewhat better than CMAQ in predicting the moderate to high PM10 concentrations. Its principal 

advantage in an asthma warning system is that it would not require an emissions inventory or the 

daily computational and staff demands of the grid-based, deterministic modeling system. The neural 

network is much easier and quicker to use than CMAQ and could be partially automated for issuing 

health warnings. Its shortcoming lies in its limited geographical coverage, especially for pollutants 

with steep concentration gradients such as PM10. In addition, only one site was examined, 

suggesting that similar work at additional sites would be advisable before any claims of area-wide 

viability could be supported. 
40

Chapter 7 Linking asthma with PM10 concentrations 

Introduction 

The goal of the health effects analysis is to determine whether asthma incidents (primarily 

emergency department (ER) visits and hospital admissions with a diagnosis of asthma) can be 

quantitatively linked to the air quality measurements obtained from Maricopa County, ADEQ, and 

ASU monitoring sites. The primary health data are the asthma incident reports from the Arizona 

Department of Health Services (ADHS). This data includes hospitalization admissions and 

emergency room visits from throughout the state as well as patient demographics e.g., gender, age, 

etc. 

The health effects analysis was conducted by the Center for Health Information & Research 

(CHIR), part of the School of Computing and Informatics in Arizona State University’s Ira A. 

Fulton School of Engineering. CHIR is a multidisciplinary team whose interests are health care, 

clinical quality, the health care workforce, occupational illness and injury, medical malpractice, 

health care economics, and disability. CHIR is home of the Arizona Health Query (AZHQ), a 

community/university database effort. More than fifty organizations voluntarily share their data 

with ASU to create this integrated database. Each partnership is governed by contracts that comply 

with the federal Health Insurance Portability and Accountability Act (HIPAA), and stringent 

security is maintained to avoid unauthorized disclosures. AZHQ contains health information on 

millions of Arizona adults and children and provides the unique capability of tracking patients 

across health care systems and over time. As the data system continues to expand to include more 

services, CHIR efforts are focused on building additional partnerships with large employers, third 

party administrators, and physician groups. The ADHS routinely reports its data to the AZHQ. 

AZHQ data has already been utilized to study the respiratory effects of agricultural burning in 

northern Mexico and southwest Arizona on Yuma County, AZ children study funded by the 

Southwest Center for Environmental Research and Policy (SCERP). Using AZHQ, we examined 

the frequency of health care visits, emergency department utilization’ and hospitalizations for 

respiratory illnesses for children ages 0-17 during the burning and compared this frequency with the 

prevalence of respiratory illness before and after exposure to agricultural burning. A separate study 

was conducted that linked asthma incidents to air quality in the Casa Grande area of Pinal County, 

AZ.

Data 

In the present work, the ADHS and CHIR conducted a high-level scan of air quality monitoring data 

for PM10 and asthma incidents from hospital discharge data [and ER admission data] to determine 

the relationship, if any, between poor air quality and higher rates of asthma admissions. Preliminary 

analyses also helped researchers define the demographic groups, sample size, and data cleaning 

strategies that were subsequently applied in the final analyses of the work being reported here. 

Based on these preliminary results and discussions, research focused on ADHS asthma incident data 

occurring within the 168 census tract areas of central Phoenix from January 1, 2005 through 

December 31, 2006, a period for which complete air monitoring records were available. All asthma 

events within five miles of a continuous PM10 monitor were included. 

In addition to the standard cleaning applied by CHIR to correct errors, additional cleaning steps 

were taken. For example, there were cases where the same patient went to an emergency room and 

was later admitted to the hospital. This single patient had multiple records on the same day with his 
41

status changing from emergency to inpatient. Such redundant records were deleted to leave but a 

single asthma incident. Also, those subsequent medical encounters occurring sufficiently long after 

the initial visit were considered to be a distinct incident. This time period is commonly referred to 

as a “washout” period. We explored several washout periods in this study. Figure 7.1 shows the 

change in the number of reported incidents for selected washout periods. As the incident changes 

are small for washout periods of seven days or greater, we selected a washout period of seven days 

in this work. If the same person had multiple records within the seven days, only the initial record 

was kept. 

Figure 7.1. Washout periods for asthma incidents within two and five miles of a continuous PM10

monitor 

Asthma incidents were described by age group, day of the week, month, and place of service 

(emergency department and inpatient admission), resulting in a dataset containing about 2,000 

events for the two-mile radius and 6,000 events for the five-mile radius for children aged 0-17 

years. See Figures 7.2 - 7.5. 

Gap (days)

Number of cases

1 7 14 21 28

11000

10000

9000

8000

7000

6000

5000

4000

3000

Variable

2-mile

5-mile

ADHS - Number of Records
42

 

 (a) Asthma incidents by age group (b) Asthma incidents by day of week 

 

 (c) Asthma incidents by month (d) Asthma incidents by place of service 

 (e) Asthma incidents by age group and time 

Figure 7.2. Asthma incidents by different characteristic within two miles of PM10 monitoring sites 
43

 

 (a) Asthma incidents by age group (b) Asthma incidents by day of week 

 

 (c) Asthma incidents by month (d) Asthma incidents by place of service 

 

 

 (e) Asthma incidents by age group and time 

Figure 7.3. Asthma incidents by different characteristic within five miles of PM10 monitoring sites 
44

 

(a) Asthma incidents by zip code 

 

 (b) Asthma incidents by zip code and place of service 

(c) Asthma incidents by age group and place of service 

Figure 7.4. Asthma incidents by zip code within two and five miles of PM10 monitoring sites 

Age Ambulatory Emergency Inpatient Missing Other

Group AZHQ ADHS AZHQ ADHS AZHQ ADHS AZHQ ADHS AZHQ ADHS

0 - 4 810 - 184 685 95 155 1 - 4 -

5 - 9 868 - 185 580 57 117 7 - 3 -

10 - 14 574 - 107 338 29 64 3 - 5 -

15 - 17 184 - 47 115 6 11 2 - 3 -

18 - 24 173 - 110 222 25 33 - - 1 -

25 - 34 192 - 113 216 27 32 4 - 2 -

35 - 44 262 - 120 227 34 45 4 - 4 -

45 - 54 211 - 73 160 48 69 1 - 5 -

55 - 64 176 - 51 94 35 85 1 - 5 -

65 - 74 99 - 22 57 17 41 - - - -

75+ 183 - 24 23 21 31 - - 3 -

Total 3732 - 1036 2717 394 683 23 - 35 -

ZIP Ambulatory Emergency Inpatient Missing Other

AZHQ ADHS AZHQ ADHS AZHQ ADHS AZHQ ADHS AZHQ ADHS

85003 108 - 47 93 23 25 - - 2 -

85004 54 - 15 61 13 22 - - 1 -

85006 441 - 145 428 54 86 - - 2 -

85009 938 - 257 594 79 128 3 - 4 -

85019 347 - 100 326 32 89 - - 7 -

85031 546 - 131 336 55 83 5 - 11 -

85041 753 - 218 553 79 165 12 - 2 -

85043 267 - 69 218 32 47 2 - 5 -

85339 278 - 54 108 27 38 1 - 1 -

Total 3732 - 1036 2717 394 683 23 - 35 -

0

200

400

600

800

1000

1200

1400

123456789

ADHS

AZHQ

No. ZIP ADHS AZHQ

1 85003 118 180

2 85004 83 83

3 85006 514 642

4 85009 722 1281

5 85019 415 486

6 85031 419 748

7 85041 718 1064

8 85043 265 375

9 85339 146 361

Total 3400 5220
45

 (a) Asthma incidents by zip code 

 

(b) Asthma incidents by zip code and place of service 

No. ZIP ADHS AZHQ

1 85003 118 180

2 85004 83 83

3 85006 514 642

4 85007 331 461

5 85008 1049 1205

6 85009 722 1281

7 85012 102 323

8 85013 231 342

9 85014 325 423

10 85015 596 993

11 85016 421 434

12 85017 578 736

13 85018 310 389

14 85019 415 486

15 85031 419 748

16 85033 821 1213

17 85034 224 226

18 85035 633 1018

19 85040 624 1022

20 85041 718 1064

21 85043 265 375

22 85301 788 1509

23 85339 146 361

Total 10433 15514

0

200

400

600

800

1000

1200

1400

1600

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

ADHS

AZHQ

ZIP Ambulatory Emergency Inpatient Missing Other

AZHQ ADHS AZHQ ADHS AZHQ ADHS AZHQ ADHS AZHQ ADHS

85003 108 - 47 93 23 25 - - 2 -

85004 54 - 15 61 13 22 - - 1 -

85006 441 - 145 428 54 86 - - 2 -

85007 302 - 112 269 41 62 1 - 5 -

85008 806 - 265 861 114 188 10 - 10 -

85009 938 - 257 594 79 128 3 - 4 -

85012 257 - 42 72 23 30 - - 1 -

85013 195 - 102 195 42 36 - - 3 -

85014 251 - 105 274 63 51 1 - 3 -

85015 647 - 246 514 86 82 2 - 12 -

85016 308 - 78 332 38 89 2 - 8 -

85017 494 - 175 473 59 105 1 - 7 -

85018 262 - 67 235 56 75 1 - 3 -

85019 347 - 100 326 32 89 - - 7 -

85031 546 - 131 336 55 83 5 - 11 -

85033 891 - 240 663 64 158 7 - 11 -

85034 161 - 49 184 13 40 - - 3 -

85035 757 - 191 534 58 99 3 - 9 -

85040 713 - 207 508 86 116 7 - 9 -

85041 753 - 218 553 79 165 12 - 2 -

85043 267 - 69 218 32 47 2 - 5 -

85301 1031 - 343 602 115 186 7 - 13 -

85339 278 - 54 108 27 38 1 - 1 -

Total 10807 - 3258 8433 1252 2000 65 - 132 -
46

 

 (c) Asthma incidents by age group and place of service 

Figure 7.5. Comparison of AZHQ and ADHS asthma incidents within two and five miles of PM10

monitoring sites 

Analysis Objectives 

The processed asthma incidence data by census tract were linked with the PM10 concentrations 

interpolated from the five continuous monitors. In addition to the daily PM10 concentrations on the 

asthma “event” day, 24-hour averages of PM10 from one to seven days before the event were also 

examined in the statistical analysis (these variables are referred to as “lags”). To detect the effects 

of PM10 for each asthma incident while accounting for the confounding variables of seasonality, day 

of the week, patient-level covariates (e.g., age, gender, ethnicity), and the presence of other air 

pollutants, a case-crossover analysis was conducted. This type of analysis associates a reference 

time period as a referent (or control) for each case. Consequently, each patient was treated as a 

matched case-referent pair with control exposures obtained from the same patient in different time 

periods. The time-stratified design used a 28-day period or “stratum size” with three referents 

selected from the same day of the week as the case. With an appropriate time-stratified period, the 

case-crossover analysis controlled for long-term trends, seasonal effects, and various epidemiologic 

covariates that change slowly with time by design (e.g., lifestyle behaviors, diet). 

Analysis Methods 

Background 

The case-crossover design, similar to a crossover and matched-pair case-control studies, was 

developed to study transient short-term exposure effects on the risk of rare acute events (Maclure, 

1991). The case is a person with the event of interest at a certain time called the “hazard period”, 

while the control (called the referent) is the same person at a different time called the “control 

period.” The key feature of this design is that each case serves as its own control. The exposure 

information for each subject during the hazard period is compared to the exposure information 

during the control period, defined as the referent period for that event. With an appropriate referent 

Age Ambulatory Emergency Inpatient Missing Other

Group AZHQ ADHS AZHQ ADHS AZHQ ADHS AZHQ ADHS AZHQ ADHS

0 - 4 2456 - 533 1966 301 446 2 - 6 -

5 - 9 2387 - 479 1006 157 167 12 - 14 -

10 - 14 1572 - 320 332 90 34 7 - 16 -

15 - 17 513 - 134 663 17 78 5 - 11 -

18 - 24 496 - 308 899 64 112 4 - 5 -

25 - 34 631 - 445 815 86 165 13 - 12 -

35 - 44 794 - 414 584 129 256 10 - 28 -

45 - 54 786 - 311 1674 151 323 7 - 15 -

55 - 64 554 - 149 278 122 202 3 - 12 -

65 - 74 314 - 86 139 68 113 1 - 4 -

75+ 304 - 79 77 67 104 1 - 9 -

Total 10807 - 3258 8433 1252 2000 65 - 132 -
47

period, the case-crossover analysis controls for long term trends, seasonal effects, epidemic, and 

other covariates that change slowly with time by design. 

In the bidirectional approach Bateson and Schwartz (1999, 2001) have examined methods to sample 

for the referent to reduce the bias by selecting from either side of the event Also, disjoint referent 

periods have been recommended for bias concerns (Levy et al., 2001). With a control close in time 

to the event, these methods avoid much of the confounding due to subject differences and other 

long-term effects such as seasonality. However, a balance must be maintained between a control too 

close in time that generates autocorrelation and a control too far apart which confounds the longterm effects. Previous work considered alternatives within a few weeks of the event. Neas et al. 

(1999) studied daily mortality in Philadelphia where the case period was the 48 hour period ending 

at midnight on the day of death (Schwartz and Dockery, 1992) and the control period was the day of 

week seven, 14, or 21 days before and after the case period. Medina-Ramon et al. (2006) used a 

matching scheme from Bateson and Schwartz (1999, 2001) and a time-stratified partition by 

Lumley and Levy (2000) that chose control days only in the same month as the hospital admission 

for chronic obstructive pulmonary disease (COPD). Lin et al. (2005) calculated a one to seven day 

exposure average ending on the admission date as the exposure in the case period with control 

periods of two weeks before and after the admission date. Peel et al. (2005) studied the hospital 

admissions associated with ambient air pollution levels and respiratory health effects. The case 

period of three-day moving averages of pollutant concentration was selected within two weeks of 

the case period. The average was the average of pollutant concentrations on the same day as the 

visit, one day before, and two days before. 

Method of the present work 

We chose to select the reference period based on a time-stratified scheme because it can control 

confounding effects from seasonality and time trends. The referents are also selected within a 28-

day stratum at seven, 14, and 21 days before or after the case, provided they occur in the same 

stratum shown in Figure 7.6. Each case has three controls. 

Table 7.1. Referent selection based on time-stratified scheme within a 28-day stratum 

Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 

Stratum S M T W TH F SA S M T W TH F SA S M T W TH F SA S M T W TH F SA

1 

2 

3 

 

While the case-crossover method has been widely used to examine a transient effect of an 

intermittent exposure on an infrequent acute disease, the same problem can be analyzed by other 

methods. Szyszkowicz (2006) applied a generalized linear mixed models (GLMM) and used days of 

the week to construct clusters to study the effects of air pollution on daily emergency department 

(ED) visits in Vancouver, Canada. Each cluster contained four or five days of the same day of the 

week. We conducted preliminary analyses with this approach but did not find it as effective as the 

case-crossover method. Mathematical details of the case-crossover method employed in the present 

work can be found in Appendix B. 

Results 

In the analysis, we considered many potential effects. The first primary effect was the 24-hour 

average PM10 concentration from the Inverse Distance Weighting (IDW) interpolation of the 

observations on the day of the asthma event. The second primary effect, called the “lag”, was the 

PM10 concentration two to six days before the event, the “lag” being the delay in days between the 

Event Day = Case Referent Day = Control 
48

exposure and the health effect. For example, a two-day lag is the average PM10 of the previous two 

days, abbreviated as “lag2”. 

Secondary effects were the covariates of age, gender, ethnicity, and place of service. Place of 

service is a categorical variable with two values: emergency and inpatient. Age is encoded into four 

categories: 0 - 4, 5 - 9, 10 - 14, and 15 -17 years. Ethnicity followed U. S. Census Bureau 

definitions. Preliminary analysis detected no differences between places of service; therefore, this 

variable was not considered further. Even though the case-crossover design does control for the 

main effects of these patient-level covariates, we nonetheless also considered the interactions of 

these covariates with the event-day PM10 concentration and its various lags. For example, we 

considered interactions such as: 

age*event day PM10, 

age*meanlag2, 

age*meanlag3, 

age*meanlag4, 

age*meanlag5, 

age*meanlag6, 

gender*event day PM10, 

gender *meanlag2, 

gender *meanlag3, 

gender *meanlag4, 

gender *meanlag5, 

and gender*meanlag6. 

The analysis considered all subsets of these covariates through conditional logistic regression. The 

event day PM10, also called “Dailymean”, and all lag variables were highly correlated as shown in 

the scatter plots of Figure 7.7 and the statistics of Table 7.1. These and the other statistics that 

appear in the remainder of this chapter are explained below, as the relationships between an 

increased probability of an asthma incident and PM10 : 

• Variable 

• Coefficient 

• Chi Square 

• p-value 

• Standard Error (SE) 

• Adjusted Odds Ratio (Adjusted OR*) 

• 95% Confidence Interval (95% CI) 

• Interquartile Range (IQR) 

Variable: 

The mean PM10 concentration on the day of the asthma incident, or the average concentration of the 

“lag” days before the incident. 

Coefficient:

The slope of a line obtained using linear least squares fitting is called the regression coefficient, or, 

for short, just “Coefficient”. In Table 7.1 the first five coefficients are similar, indicative of 
49

generally similar relationships between the “Dailymean PM10“ and the lagged PM10. The last 

variable, “Lag6”, has a coefficient different from the others, suggesting a different least squares 

relationship. In Tables 7.2 – 7.5 the “Coefficient” has a similar meaning, although it comes from 

the more complex “conditional logistic regressions” of the case-crossover method. These 

coefficients do vary considerably – from 0.0016 to 0.0055 – indicating somewhat different 

relationships between the increased probability of an asthma event and the event-day PM10

concentrations among the four different cases. 

Chi Square:

Generally speaking, the chi-square test examines whether two variables are independent: in the case 

of Table 7.1, the two variables are: 

1. the PM10 concentration on the day of the asthma incident, and 

2. the average concentration of the “lag” days before the incident. 

The “Chi Square” values of Table 7.1 exceed the five percent significance level of 3.84, meaning 

that with an uncertainty of five percent, the effects of the lags differ insignificantly from the effects 

of the PM10 concentration on the day of the asthma incident (called “Dailymean” in the table). 

p-value: 

In statistical hypothesis testing, the p-value is the probability of obtaining a result at least as 

extreme as the one that was actually observed, given that the null hypothesis is true. Generally, one 

rejects the null hypothesis if the p-value is smaller than or equal to the significance level. If the level 

is 0.05, then the results are only 5% likely to be as extraordinary as just seen, given that the null 

hypothesis is true. In Table 7.1 the p-values are all less than the significance level of 0.05; thus, the 

null hypothesis that the lagged concentrations differ from the event-day concentrations is rejected. 

In Tables 7.2 – 7.5, all p-values are also less than the significance level of 0.05, indicating that the 

PM10 concentration on the day of the asthma incident was significantly associated with it. 

Standard Error (SE):

The standard error of a statistical analysis is the estimated standard deviation of the error in that 

analysis. Specifically, it estimates the standard deviation of the difference between the measured or 

estimated values and the true values. Notice that the true value of the standard deviation is usually 

unknown and the use of the term standard error carries with it the idea that an estimate of this 

unknown quantity is being used. It also carries with it the idea that it measures not the standard 

deviation of the estimate itself but the standard deviation of the error in the estimate, and these can 

be very different. Standard errors of the first three cases in Tables 7.2 – 7.4 are similar, but the one 

for the fourth case – the ambulatory events excluding preschoolers – is roughly twice that of the 

first three, indicating more uncertainty for this case. 

Adjusted Odds Ratio (Adjusted OR*): 

While its mathematical form is in Appendix E, this odds ratio can be readily interpreted as a 

measure of how likely or unlikely it is for an asthma incident to be associated with a Dailymean 

PM10 concentration. Odds ratios greater than 1.0 mean that the asthma event is more likely to be 

associated with the PM10 concentration; less than 1.0, less likely; and 1.0, equally likely. 

95% Confidence Interval (95% CI):

In statistics, a confidence interval (CI) is an interval estimate of a population parameter. Instead of 

estimating the parameter by a single value, an interval likely to include the parameter is given. 
50

Thus, confidence intervals, such as the 95% confidence interval from the case-crossover statistical 

analysis of the present work, indicate the reliability of a estimate: the wider the interval, the lower 

the confidence. 

Interquartile Range (IQR): 

The difference between the 75th and 25th percentiles of the daily mean PM10 concentrations in 

μg/m3

. 

Figure 7.6. Correlation matrix among Dailymean PM10 and lag variables 

Table 7.2. Model statistics for each main effect (preschool age group omitted) 

Case-crossover: final results 

The case-crossover statistics are now presented and discussed for four different samples of the 

childhood asthma population: 

1. all events, ages 0 -17, 

2. events excluding ages 0-4, the preschool group, 

Variable Coefficient Chi Square P-Value

Dailymean 0.00331 11.7859 0.0006 

Lag2 0.00350 9.3428 0.0022 

Lag3 0.00303 5.7093 0.0169 

Lag4 0.00312 5.0621 0.0245 

Lag5 0.00380 6.4476 0.0111 

Lag6 0.00465 8.4119 0.0037 
51

3. all ambulatory events, ages 0 – 17, and 

4. ambulatory events excluding ages 0-4, the preschool group. 

All events, ages 0 -17

Because of the high positive correlations between the Dailymean and its lags, further analysis was 

restricted to the Dailymean as the principal covariate of PM10. All subsets regression indicated that 

the Dailymean alone was a sufficient covariate for asthma incidents. A statistically significant 

relationship was detected between Dailymean and asthma incidents. The adjusted odds ratio and 

95% confidence interval are shown in Table 7.2 

Table 7.3. Results for all asthma incidents 

Variable Coefficient Standard 

Error 

p-value Adjusted OR* 95% CI 

Dailymean PM10 0.00248 0.0007507 0.0010 1.093 (1.061, 1.153) 

*Adjusted OR* is the odds ratio adjusted by the approximate interquartile range of 36: (Q3 - Q1) ≈ (71-35) ≈ 36 μg/m3

The change in daily average PM10 from the 25th to 75th percentile is the interquartile range (IQR). 

For the data analyzed here the IQR = 36 μg/m3. Every additional 36 μg/m3 of daily mean PM10

increases the odds ratio of an asthma incident for a person under 18 years old by 9. The 95% 

confidence interval suggests that there could be as little as 6% or as high as 15% increased risk of 

an asthma incident. 

There was also evidence of an interaction between age and Dailymean. The research group 

considered an analysis to compare the 0-4 age group to the others because asthma is more difficult 

to detect in the youngest age group. Further analysis detected a stronger relationship between 

Dailymean and asthma incidents when the 0-4 age group “preschoolers”) was excluded. 

Events excluding ages 0-4, the preschool group

The preschool (0-4 years old) group was excluded and the analysis was repeated. All subsets 

regression again indicated that Dailymean alone was a sufficient covariate for asthma incidents. The 

adjusted odds ratio and 95% confidence interval are shown in Table 7.3 

Table 7.4. Results for asthma incidents excluding the preschool group 

Variable Coefficient SE p-value Adjusted OR* 95% CI 

Dailymean PM10 0.00331 0.0009629 0.0006 1.127 (1.053, 1.206) 

*Adjusted OR* is the odds ratio adjusted by approximate interquartile range of 36: (Q3 - Q1) ≈ (71-35) ≈ 36 μg/m3

Every additional 36 μg/m3 of daily mean PM10 increases the odds ratio of an asthma incident for a 

person between 5 to 17 years old by 13%. The 95% confidence interval suggests that there could be 

as little as 5% or as high as 21% increased risk of an asthma incident. 

All ambulatory events, ages 0 – 17

The ADHS data base contains emergency and inpatient records. There are also a large number of 

asthma incidents by ambulatory place of service in the AZHQ database. We conducted an analysis 

from the AZHQ database with only ambulatory data to compare to the previous ADHS results. The 

adjusted odds ratio and 95% confidence interval are shown in Table 7.4 
52

Table 7.5. Results for ambulatory events only 

Variable Coefficient SE p-value Adjusted OR* 95% CI 

Dailymean PM10 0.00163 0.0008124 0.0448 1.060 (1.001, 1.123) 

*Adjusted OR* is the odds ratio adjusted by the approximate interquartile range of 36: (Q3 - Q1) ≈ (71-35) ≈ 36 μg/m3 

The adjusted odds ratio is 1.060. The confidence interval suggests that there could be as little as 

0.1% or as high as 12% increased risk of an asthma incident. The results from AZHQ and ADHS 

showed similar effects. 

Ambulatory events excluding ages 0-4, the preschool group

The analysis of the AZHQ ambulatory events was repeated excluding the preschool (0-4 years old) 

group. The odds ratio and 95% confidence interval are shown in Table 7.5 

Table 7.6. Results for ambulatory events without the preschool group 

*Adjusted OR* is the odds ratio adjusted by the approximate interquartile range of 36: (Q3 - Q1) ≈ (71-35) ≈ 36 μg/m3

The adjusted odds ratio is 1.219. The confidence interval suggests that there could be as little as 8% 

or as high as 38% increased risk of an asthma incident. The results from AZHQ and ADHS data 

were similar. 

Case-crossover summary 

Assembled in Table 7.6, the results from the four childhood asthma populations exhibit certain 

differences. That the coefficients vary considerably indicate four somewhat different relationships 

between asthma and event-day PM10 concentration. As the standard errors are all similar except for 

the fourth case, this case is considered the most uncertain by this criterion. Among the p-values, 

however, it is the third case that is the most uncertain, although still statistically significant at the 

five percent confidence level. The adjusted odd ratios show that in all four cases the risk of asthma 

increases with an increase in PM10 concentration of 36 μg/m3. The increased risk is highest for the 

case of ambulatory events excluding preschoolers and lowest for all ambulatory events, but these 

are the two cases of greatest uncertainty, based on the standard error of the former and p-value of 

the latter. Of greater importance in this work, the adjusted odd ratios of the first two cases -- all 

events with and without preschoolers – are accompanied by low standard errors and by p-values far 

less than the significance level of 0.05. The increased risk of asthma from an incremental gain of 

36 μg/m3

 of daily-averaged PM10 is 9% for all children 0-17 years of age and 13% for children 5-17 

years of age. 

Variable Coefficient SE p-value Adjusted OR* 95% CI 

Dailymean PM10 0.00551 0.00172 0.0013 1.219 (1.080, 1.377 
53

Table 7.7. Case-crossover statistics relating asthma incidents to PM10 concentrations in central 

Phoenix 

Case Variable Coefficient Standard 

Error 

p-value Adjusted 

 OR* 

95% CI 

All events Dailymean 

PM10

0.0025 0.00075 0.0010 1.093 (1.061, 1.153) 

All events 

w/o ages 0-4 

Dailymean 

PM10

0.0033 0.00096 0.0006 1.127 (1.053, 1.206) 

Ambulatory 

events 

Dailymean 

PM10

0.0016 0.00081 0.0448 1.060 (1.001 1.123) 

Ambulatory 

events w/o 

ages 0-4 

Dailymean 

PM10

0.0055 0.00172 0.0013 1.219 (1.080, 1.377) 

0

5

10

15

20

25

30

35

40

1234

Case

Adjusted odds ratios (OR*) as percentages

95% CI-lower

OR*

95% CI-upper

case 1: all asthma events

case 2: all events without preschoolers

case 3: ambulatory asthma events

case 4: ambulatory events without preschoolers

Figure 7.7. Adjusted odd ratios expressed as percentages with 95% lower and upper bound 

confidence limits for the four asthma populations

Model Validation 

The regression analysis is based on a linear relationship between the Dailymean exposure and 

response. A model without any linear or monotonic assumptions determined the relationship 

between asthma incidents and Dailymean PM10. Divided into quartiles, the PM10 concentrations 

were linked to asthma events by the indicator variables in the regression models with the first 

quartile as the baseline. The natural logarithm of the adjusted odds ratio of an asthma incident, “log 

odds” of the y-axis, plotted against the exposure quartile in Figure 7.8, illustrates the monotonic, 

nearly linear relationship of the log odds versus the quartiles of the Dailymean PM10. 
54

Log Odds VS Grouped PM10

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

Q1=35 Q2=55 Q3=71 Q4=273

PM10

Log Odds

Figure 7.8. Natural logarithm of the adjusted odds ratio (Log Odds) of asthma incidents versus 

quartiles of mean PM10 concentrations 

Chapter 8 Asthma warning systems 

Predicting air pollutant concentrations has been one of the mainstays of the Air Quality Division of 

the Arizona Department of Environmental Quality since the early 1990s. These predictions have 

been for ozone in Phoenix and for particulate matter in Phoenix and Yuma, with more specialized 

predictions for various towns and cities during the forest fire and prescribed burning seasons. In 

general, predictions of this kind can be done in three ways, presented here in hypothetically 

decreasing degrees of rigor, reliability, and labor: 

1. Apply deterministic grid-based meteorological and air quality models, coupled with a 

complete, model-ready emissions inventory, to predict concentrations of several different 

pollutants throughout a particular urban area or region. One example of this is a predictive 

system for the eastern United States for both ozone and fine particulates. Another example 

is in Seattle, Washington, where predictions of gaseous, particulate, and air toxic pollutants 

are made throughout the Seattle-Portland region. Predictions for both of these progressive 

systems are distributed to the news media and are made available through the internet. The 

predictions are a result of first, having an adequate emissions inventory that is ready for the 

air quality model; second, running a prognostic meteorological model such as MM5 to 

predict tomorrow’s meteorological fields of temperature, winds, and so forth; and third, 

running an air quality model such as the Community Multiscale Air Quality system 

(CMAQ) to provide the grid-based concentrations of pollutants. Among the products of the 

Seattle system are color-coded concentration maps for the next day’s pollutants that can be 

viewed through the internet on home (and business) computers. 

2. Apply a statistical model that relates historical pollutant levels to measured meteorological 

variables at a specific air pollutant monitoring site to predict tomorrow’s air quality. 

Though neither computationally nor labor intensive as the first method, this system still 

requires full-scale meteorological modeling with a grid-based prognostic model. This 

meteorological modeling is first done for the historical period to derive quantitative 

relationships between the measured and simulated variables such as mixing height, wind 
55

speed, and temperature. In constructing the system as a whole, the next step is to determine 

from the historical record the statistical relationship between measured meteorological 

variables and pollutant concentrations at a specific monitoring site. With this relationship, 

and with the link between the meteorological model’s predictive performance and the 

measured variables, a daily operational system can then be put into place. It works as 

follows and entails considerable automation. First, the prognostic meteorological model is 

run for the next day, producing tomorrow’s meteorological fields of importance in air 

pollution. Second, these predicted fields are aligned with those from the historical record 

and their corresponding air pollutant concentrations. Third, tomorrow’s air pollution is 

predicted statistically from the predicted weather and the correspondence between the 

historical weather and measured air pollution. Into this system an additional component can 

be incorporated that adds the daily pollutant-weather measurements and simulated weather 

to the historical records in such a way that the system en toto effectively improves it 

performance with each additional day’s prediction. This last wrinkle is subsumed into a 

mathematical-statistical construct called a “neural network.” As described in chapters 4 and 

6 of this report, the historical portion of this work has already been done for PM10 for 

Maricopa County’s Central Phoenix monitoring site. Based on one year of measurements, 

the system’s performance for November 2005 PM10 concentrations was slightly better than 

the deterministic CMAQ model’s. 

3. The third kind of predictive system, and the one employed by Air Quality Division 

meteorologists for over ten years, can be characterized as “manual forecasting.” In this 

method the meteorologist scrutinizes different National Weather Service and other 

numerical models that predict a host of variables influencing pollutant concentrations. This 

scrutiny extends to regional and national weather maps and charts that are of help in 

predicting long-distance transport. This staff person synthesizes the various predictions, in 

part based on how well they have performed. The next step is to relate the most recent two 

or three days’ pollutant concentrations to the measured weather variables, and, in many 

cases, to consult the historical records to understand how these relationships have led to 

specific levels of an air pollutant. Often pollutant concentrations are assembled from broad 

swaths of Arizona and southern California, as well as from greater Phoenix. Because the 

prediction does not have to be made until noon, the staff person will usually examine the 

morning’s hourly concentrations to determine how quickly (or slowly) the present day’s air 

pollution is developing. Finally, with all this information and with genuine seat-of-the-pants 

knowledge and intuition, the forecaster issues tomorrow’s predictions for ozone or 

particulates. 

Since asthma has been clearly linked with PM10 concentrations, not only in the present work but 

also in numerous studies worldwide, an effective asthma warning system would consist of two 

parts: 

• one (or more than one) of the three predictive systems just described, and 

• an automated, or semi-automated communication network that would not only advise the 

news media on tomorrow’s PM10 outlook but would also send this prediction directly to 

interested citizens by phone, fax, text, and/or e-mail. These citizens would include parents of 

asthmatic children, school nurses and other school officials, public health nurses, and, anyone 

of any age or occupation who wished to receive the notices because of respiratory sensitivity 

in themselves, their immediate families, relatives, friends, or neighbors. 
56

Chapter 9 Conclusions 

Major findings on asthma and PM10

• PM10 concentrations have a statistically significant association with asthma incidents in 

central Phoenix at the 95% confidence level. 

• For children ages 5-17 an increase in the daily mean PM10 from the 25th to the 75th percentile 

- 36 μg/m3 in this study - is associated with a 13% increase in the probability of asthma 

incidents, an effect much stronger than in previous studies. 

• The effects of age and gender were insignificant. 

Additional findings 

• CMAQ generally underestimated the higher PM10 concentrations and overestimated the 

lower, with the better correlations for the higher concentrations. 

• The better correlations in the higher concentration regimes, despite their under predictions, 

still bode well for the viability of the predictive tool to warn asthma populations in central 

Phoenix for these reasons: 

o The under predictions were most pronounced at the two maximum concentration 

sites of Durango Complex and West 43rd Avenue, which represent the worst PM10 air 

quality in metropolitan Phoenix. 

o Any predictive system that comes close to predicting these peak concentrations 

provides adequate protection for asthmatic children in the rest of greater Phoenix. 

• Despite some shortcomings, the meteorological model MM5 performed adequately for the 

needs of this project, even though it 

o failed to simulate the higher wind speeds; 

o overestimated the low wind speeds during stable conditions at night and in the early 

morning; 

o could not capture the rapid changes of wind direction during the morning and 

evening transitions. 

• Because the uncertainty in emissions far exceeds that of the meteorological fields or of the 

dynamics and chemistry inherent in the model, the construction, maintenance, and periodic 

updating of an adequate model-ready PM10 emissions inventory ought to receive a higher 

priority and more resources. 

• The Neural Network, which proved to be somewhat better than CMAQ in predicting PM10

concentrations, might be a complementary system for the “manual forecasting” now in place. 

• Because the PM10 concentration fields from inverse distance weighting (IDW) and the more 

sophisticated ordinary kriging were similar, only the former were used to produce the censustract-specific concentrations for the health analysis. 
57

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59

Appendix A 

PM monitoring sites 

Table A-1. Characteristics and images of continuous PM10 monitoring sites 

Symbol Name Objective1 Scale2 Height3 Cover4 

Permanent sites (all MC, except SS, which is ADEQ) 

Gravel 

WF Paved West 43rd

Avenue 

#1 

#3 

Middle/ 

Neighborhood 5 

Desert, bare dirt 

Paved 

Bare dirt DC Durango 

Complex 

#1 

#3 

Middle/ 

Neighborhood 3.9 

Paved, bare dirt, 

homes, offices 

Gravel 

WP West Phoenix #2 Neighborhood 3.6 Paved 

Lawns/Houses 

Paved 

Paved CP Central Phoenix #2 Neighborhood 11.3 Commercial & 

residential 

Gravel 

SS Supersite #2 Neighborhood 4.4 Lawns 

Homes, apartments 

Paved 

GR Greenwood #2 Micro/Middle 4.4 Paved, lawns 

Freeways 

Paved parking lot 

Pavement, dirt lot SP South Phoenix #2 

#3 Neighborhood 4.0 Commercial & 

residential 

Temporary sites (ASU), November 2007 – March 2008 

Lawn 

MRV Maryvale #2 Neighborhood 4.4 Golf Coarse 

Homes 

Lawns 

VGC Homes Valley Garden 

Center #2 Neighborhood 2.0 

Garden Area 

Paved 

WVR Lawns, homes Weaver’s Auto 

Service #2 Neighborhood 2.0 

Lawns, apartments 

Gravel 

CSA Paved Community 

Service AZ #2 Neighborhood 4.4 

Homes 

1. Objectives 

1. Determine highest concentrations expected to occur in the area covered by the network. 

2. Determine representative concentrations in areas of high population density. 

3. Determine the impact on ambient pollution levels of significant sources or source categories. 

2. Choices of spatial measurement scales (radius from site) 

Micro Scale 0 to 100 meters 

Middle Scale 100 to 500 meters 

Neighborhood Scale 0.5 to 4 kilometers 

Urban Scale 4 to 50 kilometers 
60

Regional Scale 10 to 100s of kilometers 

Note: In addition to the scales specified in the table, all sites have PM10 concentrations with some (unknown) 

degree of urban contributions from transport and with 10 ug/m3 of desert background on an annual basis. 

3. Height of instrument inlet above ground in meters 

4. Predominant ground surface in 

(1) immediate vicinity of the monitor (0 – 25 meters); 

(2) 25 – 100 meters; and 

(3) 100 – 500 meters. 

Table A-2. Traffic near PM monitoring sites 

Symbol Name Streets Distance1/ 

Direction 

Traffic2 

WF West 43rd Ave. Broadway Road 37, S 8,000 

27th Avenue 78, E 9,000 DC Durango 

Complex Durango Street 325, N 9,000 

39th Avenue 30, W 2,000 

WP Earll Drive 25, N <1,000 

West Phoenix 

Thomas Road 360, S 32,000 

16th Street 91, W 24,000 

Roosevelt Street 75, N 12,000 

I-10 – E-W portion 450, N 276,000 CP 

Central Phoenix 

I-10 – N-S portion 865, E 297,000 

17th Avenue 11, E <1,000 

15th Avenue 425, E 17,000 

Camelback Road 635, N 40,000 

19th Avenue 370, W 17,000 

SS Supersite 

I-17 1525, W 208,000 

27th Avenue 10, E 19,000 

GR I-10 85, N 229,000 

Greenwood 

I-17 850, E 101,000 

Broadway Road 360, N 25,000 

SP Central Avenue 200, E 16,000 

South Phoenix 

7th Avenue 680, W 22,000 

51st Avenue 115, E 30,000 MRV Maryvale Indian School Road 300, S 34,000 

15th Avenue 30, W 13,000 VGC Valley Garden 

Center McDowell Road 267, S 21,000 

29th Street 33, W <500 WVR Weaver’s Auto 

Service Thomas Road 75, S 38,000 

59th Avenue 36, E 31,000 

CSA 

Community 

Service AZ Glendale Ave./Grand 

Ave/59th Ave Intersection 

375, NNE 73,000 

1 Distance in meters and direction towards the roadway 

2 Average daily traffic for weekdays: vehicles per day
61

Figure A1.1. West 43rd Avenue PM10 monitoring site 

Figure A1.2. Greenwood PM10 monitoring site 
62

Figure A1.3. Durango Complex PM10 monitoring site 

Figure A1.4. Central Phoenix PM10 monitoring site 
63

Figure A1.5. West Phoenix PM10 monitoring site 

Figure A1.6. Supersite PM10 monitoring site 
64

Figure A1.7. South Phoenix monitoring site 
65

Appendix B 

Neural Network 

This appendix goes with Chapter 4 and explains some of the details of the network’s construction. 

Bearing in mind that the main purpose of EnviNNet is the prediction of PM10, the following design 

choices were made: 

 

• architecture: three-layer MLP network, with the number of hidden nodes selected to reliably 

rebuild data from a test data-set; 

 

• topology: full connection between layers and no connection between neurons in the same layer; 

• transfer function: exponential, to ensure positive functions at the output node, and hyperbolic 

tangent for hidden nodes, with the latter an excellent compromise for a non-linear function both 

globally and locally; 

• information flow: feed-forward; 

• representation of input variables: standardization of inputs to eliminate problems due to 

different measurement scales for different predictors; 

• reconstruction of the output signal: through linear combination and exponentialization of the 

outputs of hidden nodes; and 

• training method: parameters are learned or estimated through the conjugate-gradient method. It 

is preferred over the back-propagation method as it exploits both first-order (gradient) and 

second-order (curvature) data during optimization of the objective function. 

Mathematically, the three-layer neural network has the following form: 

y = f( ) φ(x,w) 

where x represents input data, w the coefficients (parameters estimated by learning), f the activation 

functions from layers 2 to 3 and φ the activation functions from layers 1 to 2. The choice of f and 

φ determines the output; for example, if φ is hyperbolic tangent and f linear, an input of 

meteorological and pollution variables are transformed to an output with both negative and positive 

values, but if f is exponential the output can have only positive values. As such, an exponential 

function for the output and a hyperbolic tangent activation function for the hidden neurons were 

selected. 
66

Appendix C

MM5 - modeling results and discussion 

This appendix goes with Chapter 5 and explains some of the more detailed aspects of the 

meteorological modeling. 

The modeling domain was based on a Lambert Projection, centered at (97°W, 40°N), and three 

nested domains with 36, 12 and 4km grids were utilized. In the nested simulations, the results 

obtained for the outer domain were used as initial and boundary conditions for the inner domain. 

Vertically 29 levels were applied with the layer closest to the ground being seven meters to capture 

the surface-layer processes. The parent domain for MM5 covered the entire North American 

continent with 36km grids, the inner domain with 4x4km grids, was centered in Phoenix and its 

surrounding mountains (Figure C-1). 

Figure C-1. Modeling domains used with weather predicting model MM5

The number of rows (running west-east), columns (running south-north) and the definition of the xy origin (i.e. the southwest corner) are listed in Table C-1. For each domain the values indicated as 

the x-y origin are the distances in meters of the domain's southwest corner from the center of the 

parent domain (97°W, 40°N). 
67

 Table C-1. Grid definitions for MM5 model 

South-west corner (in m) Model Columns 

Dot (Cross)1

Rows 

Dot (Cross)1 x Origin Y Origin 

MM5 – 36km 130 (129) 94 (93) -2304000 -1656000 

MM5 – 12km 77 (76) 77 (76) -1836000 -972000 

MM5 – 4km 83 (82) 83 (82) -1648000 -908000 

1

Dot nodes are defined at grid cell vertices; cross nodes – at grid cell centers; 

A model validation was necessary to estimate the model performance in the study region. Without a 

doubt concentration fields depend on the meteorological conditions (wind, temperature, rainfall, 

humidity, and so forth). So it is very important to have good performance for the meteorological 

flow field. Several tests were made for different physical components to achieve better model 

predictions. The sensitivity tests (three days were performed for each case - November 2005 and 

April 2006) were carried out specifically to solve temperature and humidity problems in the 

southwestern US. It was found in earlier studies (WGA, 2006; Morris et al., 2004; Kemball-Cook et 

al., 2004) that the model underestimates the amplitude of the diurnal temperature cycle and greatly 

overestimates the humidity during the summer. The difficulties associated with the parameterization 

of turbulence in the stable nocturnal planetary boundary layer (PBL) have been overcome by the use 

of a modified Medium Range Forecast (MRF) scheme (Lee et al., 2005). MRF is high resolution 

PBL based on non-local K-theory with an additional counter gradient term that incorporates the 

contribution of large-scale eddies to the total flux. A unique aspect of this parameterization is its 

stability-dependant turbulent Prandtl number that allows momentum to be transported by the 

internal waves, while heat diffusion is impeded by the stratification. The new schemes showed an 

improvement in predictions, particularly for nocturnal near-surface temperatures. Surface wind 

predictions also improved slightly, but not to the extent of the temperature predictions. The physical 

options ultimately chosen are shown into the Table C-2. 

Table C-2. MM5 configuration for different domains * 

Grid Analysis FDDA 

resolution Radiation LSM PBL Cumulus Microphysics 3D Surface 

D1 – 36km RRTM 5 Layers MRF mod. Betts-Miller Mixed Phase W/T/H W/T/H 

D2 – 12km RRTM 5 Layers MRF mod. Grell Mixed Phase W/T/H W/T/H 

D3 – 4km RRTM 5 Layers MRF mod. None Mixed Phase W/T/H W/T/H 

* Abbreviations: LSM – land surface model; PBL – planetary boundary layer; FDDA – four dimensional data 

assimilation; RRTM – rapid radiative transfer model; MRF mod. – Medium Range Forecast scheme - modified; W/T/H 

– wind/temperature/humidity. 

Numerical calculations were completed for both 30-day periods – November 2005 and March 11--

April 9, 2006. The model evaluations have been made for all three domains, but outcomes only for 

the innermost one are presented in this report. The scatter plots based on hourly wind speeds and 

temperature are shown for two different cases with high and low pollution (Figures C-2 and C-3) 

with their corresponding regression coefficients. The conclusion for both cases is that we achieve 

excellent model performance for temperature but only average predictions for wind fields. The 

model was not able to capture events with high synoptic wind speeds (Figure C-4), because of the 

inconsistency of the input data from the operational NCEP/ETA global model. The model predicted 

wind speeds considerably higher than the measured values during the night and early morning 

periods (Figure C-5), when stagnation with low wind speeds occurs, especially in the northeast 

valley. The model was able to predict the wind direction for a strong synoptic wind (Figure C-4), 

but the disagreement between predicted and observed directions is quite pronounced for the case 
68

with low synoptic wind conditions (Figure C-5). The change in the wind direction with 12 hours 

periodicity is well displayed within the model predictions (Figure C-5). Mesoscale thermal 

circulation induced by the diurnal heating-cooling cycle of valley and slope flows, with downslope/down-valley flows occurring at night and up-valley/up-slope flows during the day, was well 

simulated by the model, especially under low synoptic wind conditions (Figure C-6). 

0 2 4 6 8 10 12 14 16 18 20

Observed wind speed [m/s]

0

2

4

6

8

10

Modeled 

wind speed [m/s]

 November, 2005

R2=1

R2=0.3

-5 0 5 10 15 20 25 30 35 40

Observed temperature [°C]

-5

0

5

10

15

20

25

30

35

40

Modeled temperature [°C]

 November, 2005

R2=1

R2=0.88

Figure C-2. Scatter plots of wind speed and temperature for all stations for high pollution episode 

November 2005 

0 2 4 6 8 10 12 14 16 18 20

Observed wind speed [m/s]

0

2

4

6

8

10

Modeled 

wind speed [m/s]

 March 11 - April 9, 2006

R2=1

R2=0.4

-5 0 5 10 15 20 25 30 35 40

Observed temperature [°C]

-5

0

5

10

15

20

25

30

35

40

Modeled temperature [°C]

 March 11 - April 9, 2006

R2=1

R2=0.84

Figure C-3. Scatter plots of wind speed and temperature for all stations for the low pollution period 

of March 11 – April 9, 2006 
69

November 26-29, 2005

0

5

10

15

0 12 24 36 48 60 72

time [h]

wind speed [m/s]

CP - obs CP - mod GR - obs GR - mod

WP - obs WP - mod DC - obs DC - mod

SP -obs SP - mod WF - obs WF - mod

0

90

180

270

360

0 12 24 36 48 60 72

time [h]

wind direction [deg]

CP - obs CP - mod GR -obs GR - mod

WP - obs WP - mod DC - obs DC - mod

SP - obs SP - mod WF - obs WF - mod

Figure C-4. Comparison between predicted and observed wind speeds and directions at different 

locations – with strong synoptic winds 
70

November 21-24, 2005

0

2

4

6

0 12 24 36 48 60 72

time [h]

wind speed [m/s]

CP - obs CP - mod GR - obs GR - mod

WP - obs WP - mod DC - obs DC - mod

SP -obs SP - mod WF - obs WF - mod

0

90

180

270

360

0 12 24 36 48 60 72

time [h]

wind direction [deg]

CP - obs CP - mod GR -obs GR - mod

WP - obs WP - mod DC - obs DC - mod

SP - obs SP - mod WF - obs WF - mod

Figure C-5. Comparison between predicted and observed wind speeds and directions at different

locations – with calm synoptic conditions
71

24 November, 00h

Main

flow

24 November, 12h

Main

flow

Figure C-6. Simulated horizontal winds at 10m and surface temperature fields during nocturnal 

(left) and daytime conditions, November 24, 2005

The Root Mean Squared Error (RMSE) is given at different locations within the study domain in 

Table C-3. The model gives better predictions for wind speeds less than 6m/s but the errors double 

for the higher wind speeds. The model provides wind flow better in the southern part (DC, SP) than 

the northern and central parts (CP, WP, GR) of the study domain. The worst model performance is 

for WF, where the influence of local effects from the Salt River channel and the South Mountains 

appears to be beyond the model’s grasp. The MM5 model gives better performance for the winter 

period (November 2005) than the spring period (March-April 2006), as the RMSE for the CP and 

DC sites in the former are twice that of the latter. 

Table C-3. RMSE for wind speed at different sites* 

Range (m/s) CP WP GR WF DC SP 

November, 2005

All 2.56 3.25 3.1 4.0 1.95 3.32 

<6 1.6 1.52 1.56 1.5 1.33 1.34 

>6 6.17 4.52 3.87 6.37 4.96 5.13 

March 11 – April 9, 2006 

All 5.19 5.18 - 5.21 4.28 - 

<6 1.9 1.76 - 1.92 1.78 - 

>6 6.1 4.63 - 6.06 5.72 - 

* Abbreviations: CP – Central Phoenix; WP – West Phoenix; GR – Greenwood; WF – West 43rd Avenue; DC – 

Durango complex; SP – South Phoenix. 

Additional statistics are shown in Table C-4, giving the model's performance at different locations 

in central Phoenix for both high and low pollution periods. The following indicators were used for 

performance evaluation (see Appendix 1 for details). 
72

Table C-4. Summary of statistical measures, in comparing observations to model predictions of 

wind speed at different sites* 

Site MAE RMSE RSQ IA MFB [%] 

November 2005 

CP 1.02 2.56 0.36 0.55 -25.65 

WP 0.12 3.25 0.36 0.67 7.74 

GR 0.01 3.09 0.37 0.68 23.17 

WF 0.35 3.98 0.38 0.60 -36.36 

DC 0.20 1.95 0.34 0.70 -8.81 

SP 0.25 3.32 0.33 0.70 5.03 

March 11- April 9, 2006 

CP 2.02 2.56 0.62 0.60 -52.82 

WP 1.87 3.25 0.58 0.64 -47.39 

WF 1.92 3.98 0.63 0.64 -53.63 

DC 1.10 1.95 0.46 0.68 -2.42 

* Abbreviations: CP – Central Phoenix; WP – West Phoenix; GR – Greenwood; WF – West 43rd Avenue; DC – 

Durango complex; SP – South Phoenix; MAE - Mean Absolute Error, RMSE - Root Mean Square Error, RSQ - 

Coefficient of Determination (aka R2), IA – Index of Agreement, MFB - Mean Fractional Bias 
73

Appendix D 

Definitions of statistics: 

The following indicators were used for performance evaluation. Here P is the predicted value, O the 

measured value, and P and O the mean values. 

∑ P - O

N

1

MAE= N

i=1 i i (Mean Absolute Error) 

∑(P - O ) N

1

MB= N

i=1 i i (Mean Bias) 

N

∑(P - O )

RMSE =

N

i=1 i i

 (Root Mean Square Error) 

[ ]

∑ ∑ (P - P) (O - O)

∑(P - P)(O - O)

R = N

i=1

N

i=1

2

i

2

i

N 2

i=1 i i 2

 (RSQ Coefficient of determination) 

∑(O - O)

∑(P - O)

R = N

i=1

2

i

N

i=1

2

i 2

 (RSQ* Coefficient of determination) 

∑(|P -O|+|O -O|)

∑(P -O) IA =1

i i

i 2

2

 (IA Index of Agreement) 

∑ .100% (P + O )

(P - O )

N

2

MFB = N

i=1 i i

i i

 (Mean Fractional Bias) 

∑ .100%

P + O

P O

N

2

MFB = N

i=1

i i

i i

 (Mean Fractional Error) 

.100%

∑O

∑(P - O )

NMB = N

i=1 i

N

i=1 i i

 (Normalized Mean Bias) 

.100%

∑O

∑ P - O

NME = N

i=1 i

N

i=1 i i

 (Normalized Mean Error) 
74

Appendix E 

Mathematics of the case-crossover method 

This appendix begins with a general discussion of the “odds ratio”, the typical statistic employed in 

reporting the results from the case-crossover method. The appendix continues with a discussion of 

conditional logistic regression, and it concludes with the specific mathematical formulation of the 

odds ratio used in the present work. 

The odds ratio is a measure of effect size particularly important in logistic regression. 

It is defined as the ratio of the odds of an event occurring in one group to the odds of it occurring in 

another group, or to a sample-based estimate of that ratio. These groups might be men and women, 

an experimental group and a control group, or any other dichotomous classification. If the 

probabilities of the event in each of the groups are p (first group) and q (second group), then the 

odds ratio is: 

An odds ratio of 1 indicates that the condition or event under study is equally likely in both groups. 

An odds ratio greater than 1 indicates that the condition or event is more likely in the first group. 

And an odds ratio less than 1 indicates that the condition or event is less likely in the first group. 

The odds ratio must be greater than or equal to zero. As the odds of the first group approaches zero, 

the odds ratio approaches zero. As the odds of the second group approaches zero, the odds ratio 

approaches positive infinity. 

For example, suppose that in a sample of 100 men, 90 have drunk wine in the previous week, while 

in a sample of 100 women only 20 have drunk wine in the same period. The odds of a man drinking 

wine are 90 to 10, or 9:1, while the odds of a woman drinking wine are only 20 to 80, or 1:4 = 

0.25:1. The odds ratio is thus 9/0.25, or 36, showing that men are much more likely to drink wine 

than women. Using the above formula for the calculation yields the same result: 

The above example also shows how odds ratios are sometimes sensitive in stating relative positions: 

in this sample men are 90/20 = 4.5 times more likely to have drunk wine than women, but have 36 

times the odds. The logarithm of the odds ratio, the difference of the logits of the probabilities, 

tempers this effect, and also makes the measure symmetric with respect to the ordering of groups. 

For example, using natural logarithms, an odds ratio of 36/1 maps to 3.584, and an odds ratio of 

1/36 maps to −3.584. 

The increased use of logistic regression in medical and social science research means that the odds 

ratio is commonly used as a means of expressing the results in some forms of clinical trials, in 

survey research, and in epidemiology, such as in case-control studies. It is often abbreviated "OR" 

in reports. When data from multiple surveys is combined, it will often be expressed as "pooled OR". 
75

Conditional logistic regression has been widely applied to model stratified data, such as casecrossover analysis and case-control analysis (Lin et al. 2005, Neas et al. 1999, Figuerias et al. 2005, 

and Redelmeier and Tibishirani 1997). Logistic regression is a form of regression used when the 

response is binary. Let x be a vector of k predictor variables (or covariates). Let Y be a binary 

response (y = 0 or y = 1). The conditional probability that Y = 1 is P(Y = 1|x) = p(x). The logit of the 

logistic regression model is 

p p f ( ) = β + β x +...+ β x x 0 1 1

so the logistic regression model is 

( )

( )

1 ( ) f x

f x

e

e

p

+

x =

Let consider the system that has a binary response y and a two-level covariate x, x = 0 or x = 1. The 

logit transformation is defined in terms of p(x) as 

f x [ ] p x ( ) p x x 0 1 ( ) = ln ( ) 1− ( ) = β + β . 

The conditional logistic regression works in similar fashion as regular logistic regression. It 

considers the stratification structure in the data specifying which individuals belong to which strata. 

Suppose there are K strata with nk subjects in the th k stratum, where k = 1, 2, 3,..., K There are 

n1k case subjects, n0k control subjects, and nk = n1k + n0k (Hosmer and Lemeshow 2000). The 

logistic regression model can be shown as 

β x

β x

x + ′

+ ′

+ = k

k

e

e

pk 0

0

1 ( ) β

β

where is a nuisance parameter with the contribution of all terms constant within the th k stratum, 

( , ,..., ) = β1 β 2 β k β′ are the vector of coefficients of covariates, x. 

To analyze case-crossover design using conditional logistic regression, the PROC PHREG from 

SAS® statistical software package was used. Conditional logistic regression can be performed by 

the PHREG procedure by using the discrete logistic model and forming a stratum for each matched 

set. The dummy survival times are needed to be created so that all the cases in a matched set have 

the same event time value, and the corresponding controls are censored at later times. 

From the logistic regression model defined earlier. The odds of y = 1 when the covariates have 

values x1 is defined as p(1)/(1-p(1)) and the odds of y = 0 when the covariates have values x0 is 

defined as p(0)/(1- p(0)). The odds ratio (OR) is defined as the odds for x1 to the odds for x0, odds 

ratios (OR) can be shown to be 
76

[ ]

[ ]

( ) 0 1 0 1

0

0 1

0 0

0

0 1 0 1

0 1

1

1

1

1

1

1

( 0 ) 1 ( 0 )

( 1 ) 1 ( 1 ) OR

β β β β

β

β β

β β

β

β β β β

β β

e e

e

e

e e

e

e e

e

p p

p p

= = =

⎟

⎠

⎞ ⎜

⎝

⎛

+ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

⎟

⎠

⎞ ⎜

⎝

⎛

+ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+ =

−

− =

+ −

+

+ +

+

The odds ratio is an associate measure of how much more likely (an odds ratio > 1), unlikely (an 

odds ratio < 1), or equally likely (an odds ratio = 1) it is for the response to present under x1 than 

under conditions x0. 
77

GLOSSARY 

ADEQ Arizona Department of Environmental Quality 

ADHS Arizona Department of Health Services 

ADT Average Daily Traffic (vehicles per day) 

AQS Air Quality System, EPA's archive of ambient air pollution data 

AZHQ Arizona Health Query 

CEFD Center for Environmental Fluid Dynamics at ASU 

CHIR Center for Health Information and Research at ASU 

CMAQ Community Multi-scale Air Quality modeling system 

COPD Chronic Obstructive Pulmonary Disease 

EPA US Environmental Protection Agency 

Eta The global weather model of the National Weather Service 

GIS Geographic Information System 

HC Hospital Counts 

HIPAA Health Insurance Portability and Accountability Act 

IDW Inverse Distance Weighting (a numerical interpolation technique) 

IQR Interquartile Range 

LST Local Standard Time 

MAE Mean Absolute Error 

MM5 Mesoscale Meteorological Model, version 5 

MCDAQ Maricopa County Department of Air Quality 

μg/m3 Micrograms per cubic meter, the units of concentration for PM 

MLP Multi-Layer Perceptron 

NCAR National Center for Atmospheric Research 

NCEP National Center for Environmental Prediction 
78

NN Neural Network 

PM Airborne Particulate Matter 

PM10 Particulate Matter 10 microns and smaller, aka “inhalable” 

PM2.5 Particulate Matter 2.5 microns and smaller, aka “fine particulates” 

PM2.5-10 Particulate Matter 2.5 to 10 micros, aka “coarse particulates” 

[PM10] Airborne concentration of PM10 

RMSE Root Mean Square Error 

SCERP The Southwest Consortium for Environmental Research and Policy 

SMOKE Sparse Matrix Operator Kernal Emissions, software to build modelready air pollutant emission fields