Air Pollutants EXposure Model

Volume III: Case Study of APEX 3.2 

Estimation of Carbon Monoxide Exposures in Los Angeles 

Ted Johnson 

TRJ Environmental, Inc. 

713 Shadylawn Road 

Chapel Hill, NC 27514 

Jim Capel 

1005 Demerius Street 

Durham, NC 27701 

Prepared for 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, NC 27711 

May 8, 2003 Draft — Do Not Cite or Quote 
i 

CONTENTS

Section Page

 1 Overview ........................................................................................................... 1-1 

1.1 Introduction ............................................................................................. 1-1 

1.2 Key Steps in Configuring and Performing the Apex Simulation .............. 1-1 

1.2.1 Step 1: Identify the Scope of the Analysis .................................... 1-2 

1.2.2 Step 2: Select Model Options ....................................................... 1-3 

1.2.3 Step 3: Ensure the Input Data Files Contain the Appropriate 

Information ................................................................................... 1-7 

1.2.4 Step 4: Configure the Simulation Settings .................................... 1-7 

1.2.5 Step 5: Running APEX ................................................................. 1-9 

 2 The Params File ................................................................................................ 2-1 

2.1 Input Files Section .................................................................................. 2-1 

2.2 Population Files Section ......................................................................... 2-1 

2.3 Output Files Section................................................................................ 2-4 

2.4 Job Parameter Settings Section ............................................................. 2-4 

2.5 Output Table Levels Section ................................................................... 2-9 

 3 Other Input Files ................................................................................................ 3-1 

3.1 Sector Location ....................................................................................... 3-1 

3.2 Population Data ...................................................................................... 3-2 

3.3 District Location ...................................................................................... 3-3 

3.4 Temperature Zone Location .................................................................... 3-4 

3.5 Age Group and Employment ................................................................... 3-4 

3.6 Commuting Flow ..................................................................................... 3-5 

3.7 Temperature Data ................................................................................... 3-5 

3.8 Air Quality Data ....................................................................................... 3-7 

3.9 Activity-Specific MET .............................................................................. 3-8 

3.10 Physiological Parameters ....................................................................... 3-9 

3.11 Profile Functions ................................................................................... 3-15 

3.12 Microenvironmental Mapping ................................................................ 3-19 

3.13 Personal Information ............................................................................. 3-21 

3.14 Diary Events ......................................................................................... 3-22 
ii 

Section Page

3.15 Microenvironment Descriptions ............................................................. 3-23 

3.15.1 Indoor Microenvironment 1 (Residence) .................................. 3-27 

3.15.2 Indoor Microenvironment 2 ...................................................... 3-38 

3.15.3 Indoor Microenvironment 3 ...................................................... 3-39 

3.15.4 Indoor Microenvironments 4 through 9 .................................... 3-39 

3.15.5 Outdoor Microenvironments 10 and 11 .................................... 3-39 

3.15.6 Vehicle Microenvironments 12 and 13 ..................................... 3-48 

3.15.7 Mass Transit Microenvironment 14 .......................................... 3-57 

3.14.8 Outdoor Garage Microenvironment 15 ..................................... 3-58 

 4 Output Files ....................................................................................................... 4-1 

4.1 Log File ................................................................................................... 4-1 

4.2 Hourly Exposure File ............................................................................... 4-1 

4.3 Hourly Dose File ..................................................................................... 4-1 

4.4 Profile Summary File ............................................................................... 4-6 

4.5 Microenvironment Summary File ............................................................ 4-7 

4.6 Site File ................................................................................................... 4-8 

4.7 Output Tables File .................................................................................. 4-8 

 5 References ........................................................................................................ 5-1 

Appendix A: Input Values and Other Modifications Proposed for 

Application of APEX3.1 to Los Angeles ....................................... A-1 

Appendix B: Output Tables from Case Study Run ........................................... B-1 
1 

SECTION 1 

OVERVIEW

1.1 Introduction

The Air Pollutant Exposure Model (APEX) was developed by the Office of Air Quality 

Planning and Standards as a tool for estimating human exposure to air pollution. APEX 

estimates human exposure to criteria and toxic air pollutants at the local, urban, and 

regional level using a stochastic, “microenvironmental” approach. The model randomly 

constructs demographic profiles for hypothetical individuals representative of the 

specified target population. APEX then simulates each individual’s movements through 

time and space to estimate a time series of exposures to the subject pollutant together 

with the resulting doses (optional). In typical applications, APEX accounts for homework differences in outdoor pollutant levels and for the emissions of indoor sources 

such as cigarettes and gas stoves. 

The basic steps simulated by APEX in estimating the exposure of each simulated 

individual can be summarized briefly as follows: 

(1) define the study area based on census data and the availability of 

applicable air quality and meteorological data; 

(2) generate profiles of simulated individuals consistent with the frequency of 

specified demographic factors within the modeled population; 

(3) combine diary-derived time/activity data to construct a sequence of activity 

events for each simulated individual that is consistent with the individual’s 

demographic characteristics and accounts for the effects of season, day 

type, and temperature on daily activities; 

(4) estimate hourly concentrations of the pollutant in each possible 

combination of geographic location and microenvironment using ambient 

air quality data specific to the geographic location and an appropriate 

model for relating microenvironmental concentrations to outdoor 

concentrations and the emissions of various sources (if any) within the 

microenvironment; 

(5) estimate the pollutant exposure associated with each event in the 

individual’s activity pattern and sum the event-specific exposures by hour 

to obtain a continuous time series of hourly exposures spanning the time 

period of interest; and 

(6) use an appropriate physiological model to estimate the dose associated 

with each estimated hourly exposure (currently an option for exposure 

assessments of carbon monoxide only). 
2 

Volume I of the APEX User’s Guide presents an overview of the model’s structure and 

algorithms, provides general guidance for conducting an APEX simulation, describes in 

detail the input files required to run the model, and describes the output files obtained 

from a successful run. This volume describes a representative application of APEX in 

which the model is used to estimate CO exposures and resulting carboxyhemoglobin 

(COHb) levels in Los Angeles. This section describes the key steps in setting up the 

run. 

1.2 Key Steps In Configuring and Performing the APEX Simulation

There are five general steps involved in configuring and performing an APEX simulation: 

a. Identify the scope of the analysis; 

b. Select the model options; 

c. Ensure the input data files contain the appropriate information; 

d. Configure the simulation settings; and 

e. Run APEX or TRIM.Expo-Inhalation. 

This subsection provides a brief discussion of how each step was implemented in the sample 

APEX application. 

1.2.1 Step 1: Identify the Scope of the Analysis

The first step in configuring an APEX simulation is to identify the scope of the analysis. The 

analyst must specify the pollutant, study area, modeling period, populations of interest for the 

analysis, and required output exposure and dose indicators. Table 1-1 presents these 

specifications for the sample application. The pollutant of interest is carbon monoxide (CO). 

Los Angeles has been selected as the general study area, and the exposure assessment spans 

calendar year 1997. The model will be used to estimate the CO exposures of adults (18+ years 

of age) that reside within 10 districts surrounding 10 fixed-site CO monitors in the Los Angeles 

area. APEX will provide estimates for five CO exposure indicators and for seven dose 

indicators. Dose is expressed in terms of percent carboxyhemoglobin (%COHb) levels in the 

blood. 

The scope of the APEX exposure assessment is similar to that of an assessment described in a 

report by Johnson, Mihlan, LaPointe, Fletcher, et al. (2000). In this earlier assessment, the 

pNEM/CO exposure model was used to estimate the CO exposures and resulting %COHb levels 

of adults in Los Angeles for calendar year 1997. The population of interest included adults 

residing in the same 10 districts used in the APEX assessment. As many of the input data sets 

and parameter distributions used in the APEX exposure assessment are similar or identical to 

those used in the pNEM/CO assessment, the reader is advised to acquire a copy of the cited 

pNEM/CO report for reference purposes. 

Table 1-1. Scope of the Sample Application. 
3 

Parameter Specification 

Pollutant Carbon monoxide (CO) 

General study area Los Angeles 

Modeling period Calendar year 1997 

Population of interest Adults (18+ years) residing within 10 kilometers of 10 fixed-site 

CO monitors located in the Los Angeles, Orange, and San 

Bernardino Counties of California 

Exposure indicators (see 

Table 2-4) 

TimeEXP: time spent at specified exposure level 

DM1HExp: daily maximum 1-hour exposure 

DM8HExp: daily maximum 8-hour exposure 

DAVGEXP: daily average exposure 

SAVGEXP: simulation period (1-year) average exposure 

Dose indicators 

(see Table 2-4) 

DM1HDOSE: daily maximum 1-hour %COHb 

DM8HDOSE: daily maximum 8-hour %COHb 

DMEHDOSE: daily maximum end-of-hour %COHb 

H_EHDOSE: person-hour end-of-hour %COHb 

DAVGDOSE: daily average %COHb 

SAVGDOSE: simulation period (1 year) average %COHb 

TIMEDOSE: time spent at specified %COHb 

1.2.2 Step 2: Select Model Options

After identifying the application, the user must decide which options to select for the 

analysis. There are five general categories of settings and options in APEX: 

2. General Model Settings and Options; 

3. Study Area Location; 

4. Profiles; 

5. Microenvironments; and 

6. Outputs. 

Table 1-2 describes how the option is selected and provides the settings used in the sample 

application. It also provides a brief discussion as to the rationale behind each setting. 
4 

Table 1-2. Specification of Settings and Options for the Sample Application. 

Setting/Option How Option is Selected Settings in Input Files 

for Sample Application 

Sections of 

This Guide 

Providing 

Additional 

Information 

GENERAL MODEL SETTINGS AND OPTIONS

Simulation 

start/end dates 

Specified in YYYYMMDD format using the 

START_DATE and END_DATE keywords in 

the Params file. 

START_DATE = 

19970101 

END_DATE = 19971231

Adjust for Daylight 

Saving Time (DST) 

Specified using the DSTADJUST keyword in 

the Params file. If this parameter is set to 

“YES”, then the Air Quality Data file will be 

adjusted for DST in the summer; if it is set to 

“NO”, no adjustment is made. This keyword 

should be set to “YES” if the Air Quality Data 

file is based on Standard Time yet the activity 

data are based on DST. 

The DSTADJUST = YES 

Model worker 

commuting 

Specified using the COMMUTING keyword in 

the Params file. If this keyword is set to 

“YES”, commuting to work is allowed and the 

user must provide a Commuting Flow file in 

the appropriate format and employment status 

must be specified in the Agegroup and 

Employment file; if it is set to “NO”, all workers 

are assumed to work in the home district. 

However, regardless of the setting of the 

COMMUTING keyword, the user must specify 

a Commuting Input file, even if this file is only 

a placeholder. If employment status is not 

specified in the Agegroup and Employment

file, commuting will not be modeled, 

regardless of the COMMUTING keyword 

setting in the Params file. 

COMMUTING = YES 

Model exposure 

dose 

Specified using the DODOSE keyword in the 

Params file. If this keyword is set to “YES”, 

the model will create the hourly dose file; if it is 

set to “NO”, the dose calculations will be 

suppressed and the hourly dose file will not be 

created. If DODOSE is set to “YES”, the user 

must specify the correct values for the 

ALTITUDE, COHBFACT, and COTHRESH 

keywords in the Params file. In addition, the 

user must provide the Activity-specific MET

and Physiology Data files. 

DODOSE = YES 

Air quality rollback 

adjustment (for 

estimating 

exposure in 

hypothetical control 

scenarios) 

Specified using the ROLLBACK keyword in 

the Params file. If this keyword is set to 

“YES”, the user must specify appropriate 

values for the RBTARGET and RBBACKGND 

keywords in the Params file; if it is set to “NO”, 

the value of these keywords is irrelevant, 

provided a value is entered. 

ROLLBACK = NO 
5 

Setting/Option How Option is Selected Settings in Input Files 

for Sample Application 

Sections of 

This Guide 

Providing 

Additional 

Information 

STUDY AREA LOCATION

Center of study 

area 

Specified as the latitude and longitude of the 

center of the study area in decimal degrees 

using the LATITUDE and LONGITUDE 

keywords in the Params file. 

LATITUDE = 34.0 

LONGITUDE = -118.0 

Radius of study 

area 

Specified as the distance (in km) from the 

center to the edge of the study area using the 

CITYRADIUS keyword in the Params file. 

CITYRADIUS = 300 

Restrict study area 

to selected 

counties 

Specified using the COUNTYLIST keyword in 

the Params file. If the value of this keyword is 

set to “YES”, the user must list the FIPS codes 

for the counties to which the study area will be 

restricted using the COUNTY keyword in the 

Params file. 

COUNTYLIST = YES 

County = 06037 

County = 06059 

County = 06071 

Locations of 

sectors 

Specified as sector IDs and locations (latitude 

and longitude) in the Sector Location file. 

See Figure 3-1 for partial 

list. 

Locations of air 

districts 

Specified in the District Location file. See Figure 3-3 for 

location coordinates of 10 

districts. 

Radius of air district Using the AIRRADIUS keyword in the Params

file, the user can specify the maximum 

distance (in km) that a sector can be from the 

nearest air district to remain in the study. If all 

district centers are further than AIRRADIUS

from the sector center, the sector is removed 

from the study area and is not modeled. 

AIRRADIUS = 10 

Location of 

meteorological data 

stations 

Specified as zone IDs and locations (latitude 

and longitude) in the Temperature Zone 

Location file. 

See Figure 3-4 for the 

location coordinates of 

the single meteorological 

station specified for the 

sample application. 

Radius of 

meteorological 

station coverage 

Using the ZONERADIUS keyword in the 

Params file, the user can specify the 

maximum distance (in km) from a sector to the 

nearest meteorological station. If all zone 

centers are further than ZONERADIUS from 

the sector center, the sector is removed from 

the study area and is not modeled. 

ZONERADIUS = 300 

PROFILES

Number of profiles Set to an integer using the #PROFILES

keyword in the Params file. 

#PROFILES = 1000 

Modeled 

populations 

Specified in the Params file following the 

specification of the file names. The user must 

provide a population file for each population to 

be modeled and indicate the gender and race 

associated with the file. All gender/race 

See Figure 2-2 for list of 

files providing data for 10 

population groups. 
6 

Setting/Option How Option is Selected Settings in Input Files 

for Sample Application 

Sections of 

This Guide 

Providing 

Additional 

Information 

combinations without specified population files 

are assumed to have zero populations. 

Profile function 

options 

The existing profile functions can be edited by 

the user in the Profile Functions file. This file 

can also be edited by the user to add new 

functions. 

See Table 3-1. 

Employment status Specified in the Agegroup and Employment

file for implementation of commuting. 

63 percent of people 18 to 

65 years of age are 

assumed to be employed 

Minimum and 

maximum ages for 

simulated profiles 

Specified using the AGEMIN and AGEMAX 

keywords in the Params file. 

AGEMIN = 18 

AGEMAX = 95 

Population age 

groups 

Specified in the Agegroup and Employment

file. 

See Figure 3-5. 

Size of age window The AGECUTPCT and AGE2PROBAB

keywords in the Params file are used to 

specify the window around the assigned age 

of a profile from which activity data can be 

selected. 

AGECUTPCT = 20.0 

AGE2PROBAB = 10.0 

Probabilities for 

selecting diaries 

with missing 

characteristics 

Using the MISSGENDER, MISSEMPL, and 

MISSAGE keywords in the Params file, the 

user can specify the probability that activity 

diary data with missing gender, employment 

status, or age will selected. 

MISSGENDER = 0.0 

MISSEMPL = 0.0 

MISSAGE = 0.0 

MICROENVIRONMENTS

Maximum number 

of microenvironments 

Set to an integer using the #MICRO keyword 

in the Params file; must not exceed 127. 

#MICRO = 15

Microenvironment 

definitions 

Specified in Microenvironment Mapping file See Section 3.12. 

Microenvironment 

parameter 

distributions 

Specified in the Microenvironment 

Descriptions file. 

See Section 3.15. 

OUTPUTS

Produce hourly 

outputs 

Specified using the HOURLYOUT keyword in 

the Params file. If this keyword is set to 

“YES”, the hourly exposure and dose (if 

applicable) output files are created; if it is set 

to “NO”, these files are not created. 

HOURLYOUT = YES 

Output table levels Specified using the following keywords in the 

Params file: PERCENTILES, TIMEEXP, 

DM1HEXP, DM8HEXP, DAVGEXP, 

SAVGEXP, DM1HDOSE, DM8HDOSE, 

See Figure 2-5. 
7 

DMEHDOSE, DAVGDOSE, H_EHDOSE, 

SAVGDOSE, and TIMEDOSE. These 

keywords are described in detail in Chapter 3. 

1.2.3 Step 3: Ensure the Input Data Files Contain the Appropriate Information

After deciding which model options to use and how to configure them, the next step in 

configuring an APEX simulation is to verify that the input data files contain the 

necessary data. There are 16 types of input data files required by APEX. Table 1-3 

lists each file type and a brief description of the content of the file. 

With the exception of the population data file type, for which there is one input file per 

modeled gender-race combination, there is only one input file per input data file type. 

One of these files, the Params file (see Step 4), is used to specify input and output file 

names and locations and simulation settings. Section 2 describes the contents of this 

file with respect to example application. The data contained in the remaining files varies 

depending on the options selected and the data contained in the other input files. 

Section 3 describes the contents of these files as used in the sample application. 

1.2.4 Step 4: Configure the Simulation Settings

The final step before starting an APEX simulation is to configure the Params input file. 

This file contains five sections: 

1. Input file names and locations (other than population files); 

2. Population file descriptions, names, and locations; 

3. Output file names and locations; 

4. Parameter settings; and 

5. Output table levels.

A detailed description of each of the sections of the Params file is provided in Section 2. 

Because the sample application was run in batch mode, the Params file was created by 

the user. If APEX had been run using the TRIM.Expo-Inhalation interface, this 

information would have been specified by the user within the GUI and then TRIM.ExpoInhalation would have used it to create the Params file automatically. 

Table 1-3. Overview of APEX Input Files.

Input File Name Description 

Params Specifies the overall settings (or parameters) for an APEX simulation (i.e., input 

file names, population data file names, output file names, job parameter settings, 

and output table levels). This file is the simulation control file and is the input file 

most often changed by the user. 

Sector Location* Contains the sector IDs and locations (in degrees latitude and longitude) for each 
8 

Input File Name Description 

sector. 

Population Data* Contain the number of people in each age range for each sector ID. There must 

be one Population Data file for each gender/race combination included in the 

simulation. 

District Location Includes the site ID and location (in degrees latitude and longitude) for each air 

quality monitoring/modeling location included in the simulation. 

Temperature Zone 

Location 

Includes the site ID and location (in degrees latitude and longitude) for each 

meteorological station included in the simulation. 

Agegroup and 

Employment* 

Specifies the age group ranges included in the population files and the probability 

of employment associated with each age group. 

Commuting Flow* Contains the probabilities of a worker commuting to different destination sectors 

from any given home sector. This file is only required when worker commuting is 

modeled. 

Temperature Data Contains the daily maximum/average temperature data for the meteorological 

stations and dates indicated in the Temperature Zone Location file. 

Air Quality Data Contains the hourly air concentrations for the modeled pollutant for the 

monitoring/modeling locations and dates indicated in the District Location file. 

Activity-specific 

MET* 

Provides distributional shapes and parameters for calculating a metabolic (MET) 

value for each activity code in the Diary Events file. 

Physiological 

Parameters* 

Provides tables of physiological parameters (e.g., body mass) used in calculating 

dose. 

Profile Functions Contains user-defined functions that define probabilities that simulated individuals 

will have particular characteristics (e.g., probability a simulated individual will have 

air conditioning at home). 

Microenvironment 

Mapping 

Links the diary events location codes to the APEX microenvironments in the Micro 

Descriptions file. 

Personal Info* Contains information relating to each 24-hour activity diary (e.g., day type, gender, 

age, race, occupation) from the Diary Events file. 

Diary Events* Contains the event descriptions (i.e., start time, duration, activity, and location) for 

all of the diary days in the activity database (e.g., CHAD). 

Microenvironment 

Descriptions 

Contains the definitions of the APEX microenvironments referenced in the Micro 

Mapping file. 

* The input file of this type that accompanies APEX contains data that can be applied to any study area in 

the default United States and would only be edited or replaced for an Advanced application.

A successful run of APEX produces six standard output files with the option for a 

seventh dose file. Table 1-4 lists each of the output data files and a description of the 

contents of the file. In the sample application, the names and locations, as well as the 

output table levels (e.g., output percentiles, cut-points) for these output files, were 

specified by the user in the Params file as APEX was run in batch mode). 
9 

1.2.5 Step 5: Running APEX

The final step in configuring and performing an APEX simulation is running the model. 

As APEX was run in batch mode for the sample application, this step consisted of 

running the APEX executable. If APEX had been run using TRIM.Expo-Inhalation, this 

would have required completing Step 10 described in Section 3.1.2.3, Vol 1, Users 

Guide. 

Table 1-4. Overview of APEX Output Files

Output File Name Description 

Log The Log file contains the record of the APEX model simulation as it progresses. 

 If the simulation completes successfully, the log file indicates the input files and 

parameter settings used for the simulation and reports on a number of different 

factors. If the simulation ends prematurely, the log file contains error messages 

describing the critical errors that caused the simulation to end. 

Hourly Exposure The Hourly Exposure file provides an hour-by-hour time series of exposure 

estimates for each modeled profile. 

Hourly Dose The Hourly Dose file provides an hour-by-hour time series of dose estimates for 

each modeled profile. 

Profile Summary The Profile Summary file provides a summary of each profile modeled in the 

simulation. 

Microenvironment 

Summary 

The Microenvironment Summary file provides a summary of the time and 

exposure by microenvironment for each profile modeled in the simulation. 

Output Tables The Output Tables file contains a series of tables summarizing the results of the 

simulation. The percentiles and cut-off points used in these tables are defined 

in the Params file. 

Site The Site file lists the sectors associated with each air quality site. 
1 

SECTION 2 

THE PARAMS FILE

The parameter, or Params, file is APEX's simulation control file. It names input and 

output files, sets model parameters, and controls formats of output tables. As described 

in Chapter 2 of Volume 1 (Users Guide), the Params file is created or modified directly 

by the user when running APEX in DOS batch mode. 

The Params file consists of five sections: input file names, population data file names, 

output file names, job parameter settings, and output table levels. This section presents 

the files entries used in the sample application of APEX to Los Angeles previously 

discussed in Section 1. 

2.1 Input Files Section

The input file section of Params (Figure 2-1) lists the names and path names of the 14 

non-population input files used in the APEX run. A brief description of each file can be 

found in Table 1-3. Table 2-1 provides a more detailed description of each file. Section 

3 provides additional information concerning each file together with a full or partial file 

listing for the sample application. 

INPUT FILES: 

 sectors file = C:\apex32\input\tp_geo.txt 

 districts file = C:\apex32\input\districts.txt 

 zones file = C:\apex32\input\zones.txt 

 agegroups file = C:\apex32\input\tp_minmax.txt 

 commuting file = c:\apex32\input\comm2000.txt 

 temperature file = C:\apex32\input\lamet97.txt 

 air quality file = C:\apex32\input\AirQuality.txt 

 metabolic file = C:\apex32\input\CHADMets.txt 

 physiology file = C:\apex32\input\Physiology.txt 

 distribution file = C:\apex32\input\Pfunctionsla.txt 

 microenv. file = C:\apex32\input\mP15_la.txt 

 diaryevent file = C:\apex32\input\ChadEvents.txt 

 diarysum file = C:\apex32\input\ChadQuest.txt 

 diarymap file = C:\apex32\input\micromap15.txt

Figure 2-1. Input Files Section of Params File for Sample Application. 

2.2 Population Files Section

Ten gender/race specific population data files listing Year 2000 data for all 65,443 US 

census tracts have been prepared and are provided as default files for APEX. In the 

Population Files section of the Params file, the user specifies the names and path 

names of either these default Population Data files or an alternative set of files created 

by the user. The default files were selected for the sample application. Figure 2-2 lists 

the file names and path names. There are five female files and five male files that are 
2 

Table 2-1. Descriptions of the 14 Non-Population Input Files Used by APEXa. 

Input File Keyword Description

Sector Location SECTOR Provides the IDs and locations (in degrees latitude and 

longitude) of sectors (e.g., census tracts). The file is used 

together with the user-defined CityRadius and other data to 

select the sectors within the modeled area. 

District Location DISTRICT Provides the site IDs and locations (degrees latitude and 

longitude) of air data monitoring locations. The file is used 

together with the user-defined AirRadius to define the 

geographical area covered by the air quality data. The air quality 

data from a monitoring or modeling location are used for the 

sectors (e.g., census tracts) within its covered area. Start and 

end dates indicate the dates during which the data for a 

particular location are valid. 

Temperature 

Zone Location 

ZONE Provides the site IDs and locations (degrees latitude and 

longitude) of the meteorological stations. The file is used 

together with the user-defined ZoneRadius to determine the 

area covered by the temperature data. Start and end dates 

indicate the dates during which the data for a particular location 

are valid. 

Agegroup and 

Employment 

AGEGROUP Provides a list of probabilities of employment for each age group 

in the Population Data Input files. The probabilities are used to 

determine if a simulated person (i.e., a profile) will commute to 

another census tract to work. 

Commuting Flow COMMUT Provides probabilities of a worker commuting to various 

destination census tracts from any given home tract. This file is 

only required when worker commuting is modeled. 

Temperature 

Data 

TEMPERAT Contains the daily maximum and daily average temperature data 

for the meteorological stations and dates indicated in the 

Temperature Zone Location file. Temperature is used to 

determine window positions and group activity pattern pools in 

APEX. 

Air Quality Data QUALITY Provides the hourly air quality data for the modeled pollutant for 

each air monitoring/modeling location listed in the District 

Location file. 

Activity-specific 

MET 

METABOL Provides distribution types and parameters for calculating the 

metabolic (MET) value for each activity code in the Diary Events 

file. A MET value is a dimensionless ratio of the activitydependent energy expenditure rate to the basal or resting energy 

expenditure (metabolic) rate. This file is used for calculating 

dose, not exposure concentrations. 

Physiological 

Parameters 

PHYSIOL Provides tables of certain physiological parameters (e.g., body 

mass) by age and gender. The file is used for calculating dose, 

not exposure concentrations. However, it is needed for running 

APEX even if the dose calculation is turned off. 

Profile Functions DISTRIB Provides the definitions of the following user-definable functions: 
3 

Input File Keyword Description

MaxTempCat - Binning maximum temperatures into categories 

AvgTempCat - Binning average temperatures into categories 

DiaryPools - Assigning diary pools using day of week and 

TempCat 

IDGRP - Group number for output labeling not used in internal 

calculation 

HasGasStove - Define probability of having gas stove in a 

resdience 

HasPilot - Probability of having a pilot light, based on 

HasGasStove 

AC_Home - Probability of having air conditioning at home 

AC_Car - Probability of having air conditioning in a car 

WindowPos - Probability of windows open or closed, based on 

AC_Home, MaxTempCat, and AvgTempCat

SpeedCat - Probability of average car speed categories 

Microenvironmen

t Mapping 

DIARYMAP Provides the mapping from activity location codes in Diary 

Events (e.g., from CHAD) to user-defined microenvironments in 

Micro Descriptions. 

Personal Info DIARYSUM Provides personal and other information (e.g., day type, gender, 

age, race, occupation) relating to each 24 hour activity record 

from the original activity database (e.g., CHAD). 

Diary Events DIARYEVE Provides the 24 hour event descriptions (i.e., start time, duration, 

activity, and location) for all the diary days in the original activity 

database (e.g., CHAD). This file contains the same list of diary 

IDs as the Personal Info file, in the same order. 

Microenvironmen

t Descriptions 

MICROENV Contains the definitions of the microenvironments and the 

microenvironment factors used to determine the exposure 

concentrations in user-defined microenvironments. 

a

 Other than Params and Population Data files. 

further indexed according to five race categories: White, Black, Asian, Native American 

(NatAm), and Other. 

Each Population Data file has exactly the same format and a record for each sector 

listed in the sector location file. Each record has the Sector ID, followed by a Count for 

each population age group. The number of age groups and their age ranges are 

provided in the Agegroup and Employment file. The counts are the number of a given 

age group of people living in a sector. 

POPULATION FILES: 

 pop file, Female, White = C:\apex32\input\tp_FW.txt 

 pop file, Female, Black = C:\apex32\input\tp_FB.txt 

 pop file, Female, Asian = C:\apex32\input\tp_FA.txt 

 pop file, Female, NatAm = C:\apex32\input\tp_FI.txt 

 pop file, Female, Other = C:\apex32\input\tp_FO.txt 
4 

 pop file, Male, White = C:\apex32\input\tp_MW.txt 

 pop file, Male, Black = C:\apex32\input\tp_MB.txt 

 pop file, Male, Asian = C:\apex32\input\tp_MA.txt 

 pop file, Male, NatAm = C:\apex32\input\tp_MI.txt 

 pop file, Male, Other = C:\apex32\input\tp_MO.txt

Figure 2-2. Population Files Section of Params File. 

2.3 Output Files Section

In the Output File section of the Params file, the user specifies the names and path 

names of six or seven output files, depending on whether the user specifies the optional 

Dose file. Table 1-4 provides brief descriptions of the files; Table 2-2 provides more 

detailed descriptions. Additional details and file listings for the individual output files can 

be found in Section 3. Figure 2-3 lists the output files specified for the sample 

application. 

2.4 Job Parameter Settings Section

The Job Parameter Settings section of the Params file specifies a number of different 

job parameters for APEX runs. Table 2-3 provides a description of the keyword, data 

type, and uses of these job parameters. Figure 2-4 presents the contents of this file 

section for the sample application. 

OUTPUT FILES: 

 log file = C:\apex32\output\la_log.txt 

 exposure file = C:\apex32\output\la_exp.txt 

 dose file = C:\apex32\output\la_dose.txt 

 persons file = C:\apex32\output\la_psum.txt 

 microsum file = C:\apex32\output\la_msum.txt 

 tables file = C:\apex32\output\la_tables.txt 

 site file = C:\apex32\output\la_sites.txt

Figure 2-3. Output Files Section of Params File. 
5 

Table 2-2. Output Files for APEX Model

Output file Keyword Description

Job Description 

and Log 

LOG Contains the record of the APEX model simulation as it 

progresses. If the simulation completes successfully, the log 

file indicates the input files and parameter setting used for the 

simulation, reports the number of diaries available to match 

each individual simulation, provides the total runtime of the 

simulation, reports statistical summaries for the simulated 

individuals, and includes a series of summary tables, identical 

to those found in the Output Tables file. If the simulation 

ends prematurely, the log file contains error messages 

describing the critical errors that caused the simulation to 

end. 

Hourly Exposure EXPOSURE Provides an hour-by-hour time series of exposure estimates 

for each modeled profile. 

Hourly Dose DOSE Provides an hour-by-hour time series of dose estimates for 

each modeled profile. 

Personal Summary 

Data 

PERSON Provides a summary of each profile modeled in the 

simulation. 

Microenvironment 

Summary 

MICROSUM Provides a summary of the time and exposure by 

microenvironment for each profile modeled in the simulation. 

Summary Tables TABLE Contains a series of tables summarizing the results of the 

simulation. The percentiles and cut-off points used in these 

tables are defined in the Params file. 

Site SITE Lists sectors associated with each air quality site. 

Table 2-3. Job Parameters in APEX Params File

Variable Keyword Description 

Pollutant POLL Pollutant name (for output only; not used internally). 

InUnits INPUTUNIT Pollutant concentration units used for the input data, in ppm or 

ug/m3

. 

OutUnits OUPUTUNIT Pollutant concentration units used for the output data, in ppm or 

ug/m3

. 

Location LOCATION Study area location (for output only; not used internally). 

Scenario SCENARIO Scenario description (for output only; not used internally). 

#Profiles PROFILE Number of profiles to simulate. 

#Micros MICRO Number of microenvironments defined in the Micro Mapping file 

(Unit 21) and on the Micro Descriptions file (Unit 24). 
6 

Variable Keyword Description 

#Sources SOURCE Largest number of sources in any microenvironment. 

Start_Date START_ Simulation start date in YYYYMMDD format. 

End_Date END_ Simulation end date in YYYYMMDD format. 

Latitude LATIT Latitude in decimal degrees for the centroid of the study area. 

Note that latitude south of the equator is negative. 

Longitude LONGIT Longitude in decimal degrees for the centroid of study area. 

Note that longitude west of the primary meridian is negative 

(e.g., in the United States). 

CityRadius CITYRAD Radius of study area in km. The sectors (e.g., census tracts) 

within this radius will be automatically selected for modeling. 

AirRadius AIRRAD Maximum application radius (km) of air quality data collected at 

an air monitoring station. Air quality data are applied to the 

sectors within this radius. 

ZoneRadius ZONERAD Maximum application radius (km) of daily temperature data 

collected at a weather station. 

CountyList COUNTYL Y = the study area is restricted to sectors in the listed counties 

(next variable) and within CityRadius; N = the study area is 

restricted to sectors within the specified CityRadius only. The 

default value is N. 

County COUNTY FIPS code for listed county (used only if countylist=Y). Repeat 

this line for each additional county FIPS code. 

Commuting COMMUT Y = allow a simulated profile (or person) to commute to a work 

sector (or census tract), N = No commuting. If Y, a work sector 

(e.g., census tract) is randomly selected for each simulated 

profile based on the probabilities of work sectors a person may 

travel to from a home sector. If N, then workers are assumed 

to work in their home sector. The commute database provided 

with this model is only for Y2000 Census tracts and can only be 

used with Y2000 population data. 

AgeMin AGEMIN Minimum age for simulated profiles (persons). 

AgeMax AGEMAX Maximum age for simulated profiles (persons). 

DSTAdjust DST Y = use Daylight Saving Time (DST) in summer, N = don’t use 

DST. In areas that use DST, one day per year (in April) is only 

23 hours long and another (in October) is 25 hours long. 

However, most air quality data sets are reported in Standard 

Time throughout the year. If DSTAdjust is set to Y, the first and 

last days of summer time (using different rules before 1986) are 

determined, and the concentration from 2-3 a.m. on the short 

day is duplicated, while the concentration from 2-3 a.m. on the 

long day is deleted. 

HourlyOut HOURLY Y = write hourly exposure and dose files, N = don’t unite files. 

If this flag is set to NO, the output files of hourly exposures and 
7 

Variable Keyword Description 

doses will not generated. Note that the hourly exposure or 

dose file could be as large as 200 megabytes for every 2,000 

simulated profiles (or persons). 

DoDose DODOSE Y = perform dose calculations, N = don’t perform calculations. 

If this flag is NO, the dose calculations will be turned off. This 

saves some job execution time if the user does not need dose 

calculation. Note that the physiology input file must still be 

provided, even though physiology is needed exclusively for the 

dose calculations. 

RollBack ROLLBACK Y = use air quality rollback adjustments, N = don’t use 

adjustments. Rollback adjusts the ambient air quality data 

before the exposure calculations occur. The purpose is to 

determine exposure in hypothetical scenarios where the 

ambient concentrations have been reduced by various controls. 

RbTarget RBTARGET Rollback target concentration. Use same units as InUnits. 

RbBackgnd RBBACK Rollback background concentration. Use same usits as 

InUnits. 

RbMax RBMAX Rollback maximum concentration. Use same usits as InUnits. 

PPMFactor PPM Units conversion factor (1 ppm = ppmfact μg/m3

). For CO, 

ppmfact = 1,145 (i.e., 1 ppm = 1,145 ug/m3

). It is used when 

source strengths are expressed in micrograms per hour, but 

concentrations are in parts per million (ppm), and when InUnits 

and OutUnits are in different units. 

MissGender MISSGEND Diary probability factor for missing gender. Some of the 

supplied CHAD diaries are for persons of unknown gender. All 

profiles are assigned gender, however, and the CHAD diaries 

are selected from those of the same gender or from the 

unknowns. MissGender is used as a multiplicative factor to 

reduce the selection probability. If MissGender=0, then diaries 

with missing gender will never be selected. If MissGender=1, 

then such diaries are equally likely to be selected as diaries of 

the correct gender. MissGender can also be set to values 

between zero and one. Allowing a small but nonzero value for 

MissGender expands the pool size without permitting very 

much chance of selecting a diary with missing gender. 

MissEmployment MISSEMP Diary probability factor for missing employment. Some of the 

supplied CHAD diaries are for persons of unknown 

employment status. Like MissGender, this factor lowers the 

selection probability for such diaries. If MissEmpl = 0, then 

such diaries will never be selected. 

MissAge MISSAGE Diary probability factor for missing age. Some of the supplied 

CHAD diaries are for persons of unknown age. This factor 

operates just like MissGender and MissEmpl to lower the 

selection probability for such diaries. 

AgeCutPCT AGECUT Width of main age window (%). Each simulated profile 

(person) is assigned a specific year of age. A window is 
8 

Variable Keyword Description 

created around this target age, of size equal to AgeCutPCT

percent of the target age. If the target age is 40 and 

AgeCutPCT = 25, then the age window is ten years wide (25% 

of 40) and diaries for persons from 30 to 50 years of age 

inclusive are permitted to be selected. The age window is 

always at least 1 year wide. 

Age2Probability AGE2PROB Diary probability factor for “shoulder” ages. This parameter 

allows an optional shoulder window of ages outside the primary 

age window. The shoulders have the same width in years as 

the main age window, so in the example under AgeCutPCT the 

shoulders are ages 20-29 and 51-60. The Age2Probab

parameter operates like MissAge, by suppressing the selection 

probability in the shoulders. If Age2Probab = 0 then shoulder 

ages are never selected. 

Altitude ALTITUDE Altitude of study area in feet. The altitude in feet is assumed 

constant for the study area. It is used to adjust air pressure in 

the Coburn-Forster-Kane (CFK) equation for determining blood 

COHb concentration. However, the altitude correction that was 

used in pNEM and earlier versions of APEX was in error and so 

this factor has been temporarily eliminated from the CFK 

equations in APEX3. 

COHbFactor COHBFACT Convergence parameter for COHb algorithm. This is a safety 

factor that limits the permitted error in determining the solution 

to the CFK equation. Larger factors mean greater accuracy but 

slower evaluation. Numerical tests indicate that factors in the 

range of 2 - 3 are optimal for most purposes. 

COThreshold COTHRESH CO concentration threshold for user notification. If a simulated 

individual experiences a concentration above COThresh, then 

a message is printed both to the screen and to the log file. 

Randomseed RANDOM Seed>0 is user preset, Seed=0 gets seed from clock. Note that 

only if the RandomSeed is changed between runs, two 

separate model runs of 100 profiles each time will be 

equivalent to one model run of 200 profiles.

PARAMETER SETTINGS: 

 pollutant = CO 

 InputUnits = ppm 

 OutputUnits = ppm 

 location = Los Angeles 

 scenario = as is 

 #profiles = 100 

 #micros = 15 

 #sources = 3 

 start_date = 19970101 

 end_date = 19971231 

 Latitude = 34.0 

 Longitude = -118.0 

 CityRadius = 300. 

 AirRadius = 10. 
9 

 ZoneRadius = 300. 

 CountyList = YES 

 County = 06037 

 County = 06059 

 County = 06071 

 Commuting = YES 

 AgeMin = 18 

 AgeMax = 95 

 DSTadjust = YES 

 HourlyOut = YES 

 DoDose = YES 

 rollback = NO 

 RBtarget = 5.0 

 RBbackgnd = 0.0 

 RBmax = 10.0 

 PPMFact = 1145. 

 MissGender = 0.0 

 MissEmpl = 0.0 

 MissAge = 0.0 

 AgeCutPct = 20.0 

 Age2Probab = 10.0 

 Altitude = 328. 

 COHbFact = 2.5 

 COThresh = 100. 

 RandomSeed = 10000 

Figure 2-4. Parameter Settings Section of Params File. 

2.5 Output Table Levels Section

In the Output Table Levels section of the Params file, the user can specify the value 

levels of each of 12 output parameters in the output summary tables. Each parameter 

is identified by a single keyword and the values are a list of numbers ordered from 

smallest to largest and separated by a comma. With the exception of Percentiles, the 

parameters are used to bin exposures or doses into categories in order to create output 

tables. The parameters are defined in Table 2-4. 
10 

Table 2-4. Output Parameter Levels in the Output Summary Table.

Variable Keyword Description

Percentiles PERCENTILES This parameter specifies the levels of percentile of the exposed population for 

exposure or dose in APEX output files. Note that at least 100 profiles are needed 

to properly determine a 99th percentile. 

Exposure Cutpoints TIMEEXP This parameter specifies the exposure cutpoints (ppm) for summing time spent at 

various exposure levels. The time is expressed in minutes and is summed across 

all profiles. TimeExp is used in two tables. The first table is non-cumulative, 

meaning that only the time spent at a level between one cutpoint and the next is 

totaled. The other table is cumulative, meaning that all time spent at levels above 

the cutpoint is reported, even if it is also above higher cutpoints. 

Daily Max 1-Hour 

Exposure Cutpoints 

DM1HEXP This parameter specifies the daily maximum 1-hour exposure cut-points (ppm) for 

binning all the person-days in the simulation period. 

Daily Max 8-Hour 

Exposure Cutpoints 

DM8HEXP This parameter specifies daily maximum 8-hour average exposure cut-points in 

ppm for binning all the person days in the simulation period. 

Daily Average 

Exposure Cutpoints 

DAVGEXP This parameter specifies daily average exposure cut-points (ppm) for binning all 

the person-days in the simulation period. 

Simulation Average 

Exposure Cutpoints 

SAVGEXP This parameter specifies cut-points (ppm) for average exposure over the 

simulation period. The cut points are used to bin all simulated persons created in 

a run. 

Daily Max 1-Hour 

Dose Cutpoints 

DM1HDOSEa This parameter specifies cut-points in %COHb for Daily Maximum 1-Hour Blood 

Dose. The cut-points were used to bin all the person-days in the simulation period. 

Daily Max 8-Hour 

Dose Cutpoints 

DM8HDOSEa This parameter specifies cut-points in %COHb for Daily Maximum 8-Hour Blood 

Dose. The cut-points were used to bin all the person-days in the simulation period. 

Daily Max End-ofHour Dose Cutpoints DMEHDOSEa This parameter specifies cut-points in %COHb for Daily Maximum End-of-Hour 

Blood Dose. The cut-points are used to bin all the person/days in the simulation 

period. Note that DMEHDose uses the instantaneous level at the end of each 

hour, whereas DM1HDose uses the time-averaged level over each hour. 

Person-Hour End-ofHour Dose Cutpoints H_EHDOSEa This parameter specifies cut-points in %COHb for Person-Hour End-of-Hour Blood 

Dose. The cut-points are used to bin all the person-hours in the simulation period. 

 

Daily Average Dose 

Cutpoints 

DAVGDOSEa This parameter specifies cut-points in %COHb for the Daily Average Blood Dose. 

The cut-points are used to bin all the person/days in the simulation period. 

Simulation Average 

Dose Cutpoints 

SAVEDOSEa This parameter specifies cut-points in %COHb for the Average Blood Dose over 

the entire simulation. The cut-points are used to bin all the persons (or profiles) 

created in the APEX run. 

Dose Cutpoints TIMEDOSEa This parameter specifies cut-points in %COHb for summing time spent at various 

blood dose levels. 

a

 Note that these variables are used only for summarizing dose results. The current version of APEX can only calculate doses for 

CO. For other pollutants the dose calculation should be turned off.

Figure 2-5 presents the file entries for the sample application. Seven percentiles 

between 10 and 99 percent are specified. Cutpoints are provided for four CO exposure 

indicators (DM1HEXP, DM8HEXP, DAVGEXP, ANNEXP) and for seven COHb dose 

indicators (DM1HDOSE, DM8HDOSE, DMEXDOSE, H_EHDOSE, DAVGDOSE, 

SAVGDOSE, and TIMEDOSE). 

OUTPUT TABLE LEVELS: 
11 

 Percentiles = 10, 25, 50, 75, 90, 95, 99 

 TimeExp = 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 , 60, 70, 80, 90, 100

 DM1HExp = 10, 15, 20, 25, 30, 35, 40, 45, 50, 60 

 DM8HExp = 6, 9, 12, 15, 20, 25 

 DAvgExp = 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 18, 20 

 SAvgExp = 0.5, 1, 1.25, 1.5, 1.75, 2, 2.5, 3, 4, 5, 6, 8, 10 

 DM1HDose = 0.5, 1.0, 1.5, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8,

2.9, 3.0, 4.0, 5.0, 6.0 

 DM8HDose = 0.5, 1.0, 1.5, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8,

2.9, 3.0, 4.0, 5.0, 6.0 

 DMEHDose = 0.5, 1.0, 1.5, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8,

2.9, 3.0, 4.0, 5.0, 6.0 

 H_EHDose = 0.5, 1.0, 1.5, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8,

2.9, 3.0, 4.0, 5.0, 6.0 

 DAvgDose = 0.5, 1.0, 1.5, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8,

2.9, 3.0, 4.0, 5.0, 6.0 

 SAvgDose = 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0,

2.5 

 TimeDose = 0.5, 1.0, 1.5, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8,

2.9, 3.0, 4.0, 5.0, 6.0

Figure 2-5. Output Tables Levels Section of Params File. 
1 

SECTION 3 

OTHER INPUT FILES 

Table 1-3 lists the 16 files required for running APEX. Section 2 described the setup of 

the Params file for the sample application of APEX to CO exposure in Los Angeles. 

The other 15 files prepared for the sample application are discussed in this section. 

3.1 Sector Location

Table 3-1 presents a portion of the Sector Location file developed for the sample run. 

Each row in the file lists the identification code (Sector ID) of a single census tract and 

the location of its centroid expressed by a latitude and longitude. Consistent with the 

guidance provided in Volume I of the User’s Guide, the Sector ID begins with a decimal 

and the latitude and longitude values are expressed in decimal format with at least three 

significant digits to prevent significant rounding error. As required, the sector IDs match 

the sector IDs in the Population Data files and are listed in the same order. As worker 

commute is being modeled, the sector IDs also match the sector IDs listed in the 

Commute Flow file. 

23001010100 44.093973 -70.228497 

23001010200 44.137539 -70.234280 

23001010300 44.101173 -70.232485 

23001010400 44.083873 -70.242659 

23001010500 44.085323 -70.222483 

23001010600 44.036741 -70.243874 

23001010700 44.064073 -70.268799 

23001010800 44.102272 -70.260141 

23001020100 44.095523 -70.217585 

23001020200 44.105673 -70.215177 

23001020300 44.100340 -70.204874 

23001020400 44.091106 -70.209964 

23001020500 44.087123 -70.197085 

23001020600 44.068923 -70.174723 

23001020700 44.108473 -70.190075 

23001020800 44.096123 -70.156649 

23001020900 44.126722 -70.199706 

23001030100 44.028009 -70.108295 

23001030200 44.009327 -70.056882 

23001040000 43.972505 -70.122917 

23001041000 44.049157 -70.390772 

23001041500 44.132871 -70.362689 

23001042000 44.266842 -70.245638 

23001043000 44.408579 -70.224524 

23001044000 44.446448 -70.166782 

23001045000 44.283844 -70.112543 

23001046000 44.191771 -70.138984 

23001046500 44.119007 -70.075689 

etc.

Figure 3-1. Partial Listing of Sector Location File. 
2 

The sector location file is used together with the user-specified values for CityRadius

and AirRadius to automatically select sectors to be included in the study area. APEX 

calculates the distance between the centroid of each sector listed in the file and the 

centroid specified for the study area. APEX also calculates the distance between each 

sector and the nearest air quality monitoring site. Whenever these values are less than 

the corresponding values of CityRadius and Air Radius, the sector is included in the 

study area to be used in the exposure assessment.

The Sector Location file is identical to the current default sector location file which 

contains the 11-digit ID and centroid coordinates (latitude and longitude) of 65,443 Year 

2000 US Census tracts. APEX requires that the left-most five characters of a sector ID 

be the state and county FIPS code or an alternative county-level code consistent with 

the codes used in the County list (if the study area will be limited in that way). 

3.2 Population Data

The population data are listed in the following 10 files as indicated by the Params file. 

Female, White = C:\apex32\input\tp_FW.txt 

Female, Black = C:\apex32\input\tp_FB.txt 

Female, Asian = C:\apex32\input\tp_FA.txt 

Female, NatAm = C:\apex32\input\tp_FI.txt 

Female, Other = C:\apex32\input\tp_FO.txt 

Male, White = C:\apex32\input\tp_MW.txt 

Male, Black = C:\apex32\input\tp_MB.txt 

Male, Asian = C:\apex32\input\tp_MA.txt 

Male, NatAm = C:\apex32\input\tp_MI.txt 

Male, Other = C:\apex32\input\tp_MO.txt 

Each file is specific to one combination of gender (M, F) and race (W = white, B = black, 

A = Asian, N = Native American, and O = other). Figure 3-2 provides a partial listing of 

the contents of tp_FW.txt, the file containing population data for white females. 

Consistent with the guidance in Section 4.2.2 of Volume I of the User’s Guide, each 

Population Data file used in the model run has exactly the same format and a record for 

each sector listed in the sector location file. Each record has an 11-digit Sector ID, 

followed by a Count for each population age group. The number of age groups and 

their age ranges are provided in the Agegroup and Employment file. The counts are the 

number of a given age group of people living in a sector. 

23001010100 37 45 38 31 43 13 10 29

 61 51 53 52 52 29 27 7 25 

 20 18 35 57 61 76 

23001010200 124 135 148 82 60 26 35 78

 137 148 173 187 166 138 104 34 58 

 42 59 109 120 131 190 
3 

23001010300 88 99 80 46 42 20 21 54

 108 102 110 95 86 61 59 18 20 

 15 25 19 31 24 14 

23001010400 34 55 67 38 22 5 4 24

 38 75 66 103 77 61 47 17 17 

 17 27 37 96 76 172 

23001010500 62 60 66 48 22 18 22 40

 74 84 80 54 52 52 62 14 19 

 17 26 64 52 42 22 

23001010600 71 77 109 75 31 14 17 45

 77 105 136 127 142 79 90 29 39 

 20 30 75 60 26 31 

23001010700 102 87 88 68 25 23 14 42

 99 113 139 111 135 93 66 31 34 

 29 40 66 41 44 35 

23001010800 87 108 140 101 29 10 11 40

 88 137 172 181 171 165 121 31 69 

 36 47 80 76 66 44 

23001020100 49 24 20 8 26 17 16 31

 47 31 25 27 39 36 27 6 4 

 7 14 37 45 32 37 

23001020200 45 36 27 12 24 17 21 44

 62 44 53 28 31 33 20 12 6 

 10 11 23 18 12 15 

23001020300 126 142 123 84 319 123 172 221

 209 198 196 164 124 168 117 33 65 

 61 80 151 156 179 289 

23001020400 136 133 92 67 44 35 36 92

 109 109 103 102 93 87 66 29 41 

 29 28 40 48 28 33 

23001020500 103 102 113 55 38 15 23 69

 114 141 140 124 128 133 118 47 60 

 40 73 112 110 91 58 

etc.

Figure 3-2. Population Data File (partial listing of tp_FW.txt). 

3.3 District Location

The Los Angeles study area is comprised of 10 districts centered on 10 air quality 

monitoring sites within the city. Figure 3-3 provides the Site ID, Latitude, Longitude, 

air data Start Date, and air data End Date for each of these sites. Consistent with the 

guidance in Volume I, each site ID is stored as a character string (maximum length = 

40) and the latitude and longitude values are expressed in decimal degrees. The start 

and end dates are in YYYYMMDD format and indicate a period from January 1, 1997 to 

December 31, 1997. As required, the order of the listed sites matches the order of the 

sites listed in the Air Quality Data file. 

APEX uses the District Location file to define a district surrounding each monitoring site 

that will be represented by the air quality data collected at that site. The district includes 

all sectors listed in the Sector Location file that have location coordinates (centroids) 

within the AirRadius specified for the district. A district will not be created in cases in 

which (1) the site did not provide a full air quality data set for the specified period or (2) 

the distance from the monitoring site to the city center exceeds the specified 
4 

CityRadius. All 10 districts specified in the District Location file were accepted into the 

study area for the sample run. 

 Site1 34.05 -118.46 19970101 19971231 

 Site2 34.18 -118.31 19970101 19971231 

 Site3 34.07 -118.24 19970101 19971231 

 Site4 33.93 -118.21 19970101 19971231 

 Site5 34.01 -118.06 19970101 19971231 

 Site6 34.08 -118.11 19970101 19971231 

 Site7 33.82 -118.19 19970101 19971231 

 Site8 33.93 -118.37 19970101 19971231 

 Site9 33.82 -117.92 19970101 19971231 

 Site10 33.93 -117.95 19970101 19971231

Figure 3-3. District Location File. 

3.4 Temperature Zone Location

The Temperature Zone Location file specifies the locations and time periods associated 

with meteorological data to be used by the model. The format for this file is similar to 

that of the District Location file. Each record represents one site, and contains five 

values: Site ID, Latitude, Longitude, Start Date, and End Date. The data reported by 

each site is assigned to sectors that fall within the radius (ZoneRad = 300 km) specified 

for that site. 

The file for the sample run (Figure 3-4) contains an entry for a single meteorological 

site. Because all sectors in the study area are within the 300 km radius specified for 

this site, the data from this site are applied to all sectors. 

TempSite1 34.0 -118.0 19970101 19971231

Figure 3-4. Temperature Zone Location File. 

3.5 Age Group and Employment

Twenty-three age groups were defined for the sample run. The Age Group and 

Employment file provides a minimum age, a maximum age, and an employment 

probability for each age group. The Min_Age input line is used to list the minimum ages 

of each age group from the youngest to the oldest, while the Max_Age input line lists 

the maximum ages of the corresponding population age groups. The Employ_Prob 

input line lists employment probabilities (expressed as fractions) for each population 

group in the same order. As indicated in Figure 3-5, the first group has a minimum age 

of 0, a maximum age of 4, and an employment probability of 0.0. Similarly, the fifth 

group has a minimum age of 18, a maximum age of 19, and an employment probability 

of 0.63. 
5 

Min_Age = 0 5 10 15 18 20 21 22 25 30 35 40 45 50 55 

60 62 65 67 70 75 80 85 

Max_Age = 4 9 14 17 19 20 21 24 29 34 39 44 49 54 59 

61 64 66 69 74 79 84 99 

Emp_Prob = 0.0 0.0 0.0 0.0 .63 .63 .63 .63 .63 .63 .63 .63 .63 .63 .63 

.63 .63 0.0 0.0 0.0 0.0 0.0 0.0

Figure 3-5. Age group and Employment File. 

3.6 Commuting Flow

The Commuting Flow file is used to determine the probability that a worker residing in a 

particular sector (the home sector) will commute to particular sector for work. Figure 3-

6 presents a portion of the Commuting Flow file used in the sample run. The first record 

lists the Sector ID for a home sector 01001020100 (home sector status is indicated by -

1 in the right-hand column). Records 2 through 33 each contains the Sector ID for a 

work sector and the Cumulative Fraction of the population commuting to this sector. 

The cumulative fraction for the last work sector (Record 33) associated with this home 

sector equals 1, as expected. The file continues with the entries for a new home sector 

(01001020200). The complete file for the sample run contains over 6.6 million records 

that characterize the commuting flows of home sectors. 

The Commuting Flow file for the sample run is identical to the default commuting flow 

file provided with APEX. This data base contains the 65,443 census tracts defined for 

the 2000 Census and their associated work tracts. The flows in the data base file were 

calculated from the 1994 national commuting database and then mapped to the Year 

2000 Census tracts. A report by XXXX describes the creation of this file in detail. 

3.7 Temperature Data

This file provides daily maximum temperature data collected at each of the 

meteorological sites listed in the Temperature Zone Location file. The data sets may be 

in any order. However, each site must begin with the “name” keyword input line. The 

name entered here must match a site name in the Temperature Zone Location file. 

Figure 3-7 presents the Temperaure Data file for the sample run. The file contains data 

for a single site (TempSite1) consistent with the contents of the Temperature Zone 

Location file. The data set begins with a header section with selected site information. 

The subsequent numeric lines include date, maximum daily temperature, and average 

daily temperature. Consistent with instructions, the date is expressed in YYYYMMDD 

format (for example, 20010507 is May 7, 2001), the temperature data are in Fahrenheit, 

and the data set spans the exposure simulation period. (APEX3 will ignore data that fall 

outside the exposure period). 

01001020100 -1 

01001020100 0.050060 
6 

01001020200 0.057040 

01001020300 0.097790 

01001020500 0.336685 

01001020700 0.513635 

01001020800 0.544820 

01011952200 0.550640 

01051031100 0.562280 

01073012903 0.570430 

01085981000 0.576250 

01101000100 0.620490 

01101000200 0.694990 

01101000300 0.713620 

01101000600 0.757860 

01101000700 0.770660 

01101001000 0.783470 

01101001400 0.788130 

01101001800 0.810240 

01101002000 0.814900 

01101002201 0.820666 

01101002202 0.821890 

01101002400 0.821892 

01101002800 0.828872 

01101002900 0.852152 

01101003000 0.873112 

01101003200 0.881262 

01101003301 0.887082 

01101005301 0.894062 

01101005401 0.932482 

01101005402 0.947612 

01101005403 0.954602 

01101005902 0.961580 

01101006000 1.000000 

01001020200 -1 

01001020100 0.065350 

01001020200 0.085080 

01001020300 0.177560 

01001020500 0.473735 

etc.

Figure 3-6. Partial Listing of Commuting Flow File. 

Name = TempSite1 

LA Daily Maximum 

Degrees Fahrenheit 

Start = 19970101 

End = 19971231 

Lat = 34.0 

Lon = -118.0 

19970101 68 62 

19970102 62 61 

19970103 65 60 

19970104 62 57 

19970105 59 55 

19970106 62 55 

19970107 66 57 

19970108 65 54 
7 

19970109 63 54 

19970110 58 53 

19970111 58 53 

19970112 53 52 

19970113 56 52 

19970114 57 52 

19970115 54 51 

19970116 63 54 

19970117 70 57 

19970118 75 60 

19970119 69 58 

19970120 60 55 

19970121 58 52 

19970122 58 54 

19970123 62 57 

19970124 61 57 

19970125 57 56 

19970126 61 58 

19970127 64 58 

19970128 73 61 

19970129 75 64 

19970130 76 62 

19970131 71 61

etc.

Figure 3-7. Partial Listing of Temperature Data File. 

3.8 Air Quality Data

Figure 3-8 is a partial listing of the Air Quality Data file prepared for the sample run. 

This file provides hourly air concentration data for each of the air monitoring sites listed 

in the District Location file. The data set for each site begins with a header section 

containing selected site information organized by six keywords: name of the site 

(Site1), data units (ppm), start date (19970101 = January 1, 1997), end date (19971231 

= December 31, 1997), latitude (34.050), and longitude (-118.460). (The line reading 

“Los Angeles Site1" is ignored by the program as it does not begin with a key word and 

does not contain numeric data.) Each of the subsequent numeric input lines includes 

24 Hourly Average Air Concentrations reported for a particular day, the Date of the 

day, and the number of Cumulative Hours for the day. Consistent with the guidance 

found in Volume I, all dates are expressed in the YYYYMMDD format, the hourly data 

are either comma or space delimited, and the length of each data line does not exceed 

256 characters. 

The air quality data were acquired from EPA’s Aerometric Information Retrieval System 

(AIRS). After missing values were filled in using the techniques described in Section 

3.2 of the report by Johnson, Mihlan, LaPointe, Fletcher, et al. (2000), the resulting data 

sets were reformatted to match the specified APEX input format. 

 Name = Site1 

 Los Angeles Site1 

 Units = PPM 

 Start date = 19970101 
8 

 End date = 19971231 

 Lat = 34.050 

 Lon = -118.460 

 2.1, 2.1, 2.2, 2.1, 1.9, 1.5, 1.9, 1.9, 1.5, 1.4, 1.5, 1.6, 1.7, 1.2, 1.1, 

1.2, 1.2, 1.4, 1.4, 1.4, 1.6, 1.8, 2.0, 2.0 19970101 1 

 1.9, 1.8, 1.7, 1.6, 1.4, 0.7, 0.7, 0.6, 1.0, 1.3, 1.4, 1.4, 1.2, 1.1, 0.8, 

0.7, 0.7, 0.8, 1.0, 0.7, 0.7, 0.0, 0.0, 0.0 19970102 25 

 0.0, 0.5, 0.0, 0.0, 0.5, 0.5, 0.0, 0.7, 0.8, 1.1, 0.9, 0.7, 0.7, 0.0, 0.0, 

0.0, 0.9, 0.9, 0.8, 0.8, 0.9, 0.9, 0.9, 1.3 19970103 49 

 1.5, 1.4, 1.1, 1.0, 0.6, 0.7, 0.8, 0.9, 0.9, 1.1, 1.0, 0.9, 0.8, 0.9, 0.7, 

0.6, 0.6, 0.7, 0.7, 0.7, 0.6, 0.6, 0.6, 0.6 19970104 73 

 1.1, 1.4, 1.3, 1.1, 0.9, 0.5, 0.5, 0.7, 0.6, 0.6, 0.6, 0.6, 0.5, 0.5, 0.6, 

0.6, 0.6, 0.6, 0.7, 0.7, 0.6, 0.7, 0.7, 0.7 19970105 97 

etc.

Figure 3-8. Partial Listing of Air Quality Data File. 

3.9 Activity-Specific MET

This file provides distributional shapes and parameters for calculating the MET value for 

each CHAD (or other) activity code. A MET value is a dimensionless ratio of the 

activity-dependent energy expenditure rate to the basal or resting energy expenditure 

(metabolic) rate. Figure 3-9 presents a portion of the file used in the sample run. The 

file consists of a header row followed by a line of data for each combination of activity 

code, age, and occupation. The data listed in each line include the following items. 

· Row number 

· Activity code 

· Occupation 

· Distribution type 

· Mean 

· Median 

· Standard deviation 

· Minimum 

· Maximum 

· Distribution Number 

· Description

The file used in the sample run is identical to the current default file. The user should 

not change this file unless the MET distribution data in the CHAD database are revised. 

 Appendix A of the report by Johnson, Mihlan, LaPointe, Fletcher, et al. (2000) provides 

a full listing of the MET distribution data and identifies the principal data sources. 

3.10 Physiological Parameters

This file provides a table listing data specific to age and gender for each of the following 

physiological parameters. 

· NVO2Max; 

· Body Mass; 

· Resting Metabolic Rate (RMR); and 
9 

· Blood Volume Factor and Hemoglobin Content. 

Partial tables for the sample run can be found in Figures 3-10, 3-11, 3-12, and 3-13. 

Each table has three header lines and 202 numerical lines of data. The first table 

(Figure 3-10) contains age/gender specific distribution data on normalized lung capacity 

(VO2-max). Although the table provides eight columns of data, APEX reads only the 

mean and standard deviation values for NVO2Max. 

The second table (Figure 3-11) lists body mass distribution data specific to age and 

gender. Only the data in the columns designated GM (geometric mean) and GSD 

(geometric standard deviation) are read by APEX. 

The third table (Figure 3-12) contains regression-based parameters for the linear 

relationships used to estimate resting metabolic rate (RMR). APEX reads the data 

listed in the columns labeled slope, interc (intercept) and SE (standard error of the 

residuals). 

Figure 3-13 provides the following data relating to blood volume and hemoglobin: 

BLDFAC (blood factor), HGMN (mean hemoglobin), and HGSTD (standard deviation of 

hemoglobin). APEX uses all three parameters. 

The data in these tables are identical to the data contained in the current default file. 

Section 5 of the report by Johnson, Mihlan, LaPointe, Fletcher, et al. (2000) discusses 

the sources of these data. 
10 

Row Act Age Occ. DistType Mean Median StdDev Min Max DistNu

 1 10000 0 ADMIN LogNormal 1.7 1.7 0.3 1.4 2.7 9  2 10000 0 ADMSUP LogNormal 1.7 1.7 0.3 1.4 2.7 9  3 10000 0 FARM LogNormal 7.5 7.0 3.0 3.6 17.0 9  4 10000 0 HSHLD LogNormal 3.6 3.5 0.8 2.5 6.0 9  5 10000 0 LABOR Triangle 8.5 8.4 2.1 3.6 13.8 9  6 10000 0 MACH Uniform 5.3 5.3 0.7 4.0 6.5 9  7 10000 0 PREC Triangle 3.3 3.3 0.4 2.5 4.5 9  8 10000 0 PROF Triangle 2.9 2.7 1.0 1.2 5.6 9  9 10000 0 PROTECT Triangle 2.9 2.7 1.0 1.2 5.6 9  10 10000 0 SALE Triangle 2.9 2.7 1.0 1.2 5.6 9  11 10000 0 SERV Triangle 5.2 5.3 1.4 1.6 8.4 9  12 10000 0 TECH Triangle 3.3 3.3 0.4 2.5 4.5 9  13 10000 0 TRANS LogNormal 3.3 3.0 1.5 1.3 8.4 9  14 10000 0 X Triangle 2.9 2.7 1.0 1.2 5.6 9  15 10300 0 Any Uniform 1.8 1.8 0.4 1.0 2.5 2  16 11000 0 Any Triangle 4.7 4.6 1.3 1.5 8.0 5 household activities  17 11100 0 Any LogNormal 2.6 2.5 0.5 2.0 4.0 4  18 11110 0 Any Exponential 2.8 2.5 0.9 1.9 4.0 3 clean-up food  19 11200 0 Any Exponential 3.4 3.0 1.4 2.0 5.0 3  20 11210 0 Any Uniform 2.5 2.5 0.1 2.3 2.7 2  21 11220 0 Any Exponential 4.1 3.5 1.9 2.2 5.0 3 etc. 

Figure 3-9. Partial Listing of Activity-Specific MET File. 
11 

Males age 0-100, then females age 0-100 (last revised 6-11-98) 

 NVO2max distribution 

 Age Source Distr Mean SD Lower Upper Assumptions 

 0 1 Normal 44.0 5.2 33.7 54.3 2-yr-old mean 

 1 1 Normal 44.0 5.2 33.7 54.3 2-yr-old mean 

 2 1 Normal 44.0 5.2 33.7 54.3 CV = 6.9/57.9 

 3 1 Normal 46.0 5.5 35.3 56.7 CV = 6.9/57.9 

 4 1 Normal 48.0 5.7 36.8 59.2 CV = 6.9/57.9 

 5 1 Normal 50.0 6.0 38.3 61.7 CV = 6.9/57.9 

 6 1 Normal 52.0 6.2 39.9 64.1 CV = 6.9/57.9 

 7 1 Normal 54.0 6.4 41.4 66.6 CV = 6.9/57.9 

 8 1 Normal 56.0 6.7 42.9 69.1 CV = 6.9/57.9 

 9 3g Normal 57.9 6.9 44.4 71.4 

 10 3g Normal 57.9 6.9 44.4 71.4 

 11 3b Normal 45.4 8.1 29.6 61.2 

 12 3b Normal 47.4 8.1 31.5 63.3 

 13 3b Normal 46.0 7.0 32.3 59.7 

 14 3b Normal 45.7 4.3 37.4 54.0 

 15 3b Normal 47.5 4.7 38.3 56.7 

 16 1 Normal 55.0 5.4 44.4 65.6 CV = 4.69/47.5 

 17 1 Normal 53.0 5.2 42.7 63.3 

 18 1 Normal 50.0 4.9 40.3 59.7 

 19 1 Normal 50.0 4.9 40.3 59.7 

 20 3a Normal 58.6 4.5 49.8 67.4 

 21 3c Normal 54.5 7.6 39.6 69.4 

 22 3c Normal 54.5 7.6 39.6 69.4 

 23 3c Normal 54.5 7.6 39.6 69.4 

 24 3c Normal 54.5 7.6 39.6 69.4 

 25 3c Normal 54.5 7.6 39.6 69.4 

 26 3c Normal 54.5 7.6 39.6 69.4 

 27 3c Normal 54.5 7.6 39.6 69.4 

 28 3a Normal 58.6 4.5 49.8 67.4 

 29 3a Normal 58.6 4.5 49.8 67.4 

 30 3a Normal 39.8 7.3 25.5 54.1 

etc. 

Figure 3-10. Section of the Physiological Parameters File Showing Partial Listings for 

Parameters Related to NO2max. 
12 

Males age 0-100, then females age 0-100 (last revised 6-11-98) 

 Body mass distribution, kg 

 Age Source Distr GM GSD Lower Upper Assumptions 

 0 4 LN 9.3 1.141 7.2 12.0 

 1 4 LN 11.7 1.126 9.3 14.8 

 2 4 LN 13.5 1.127 10.7 17.1 

 3 4 LN 15.6 1.121 12.5 19.5 

 4 4 LN 17.6 1.142 13.6 22.8 

 5 4 LN 19.9 1.148 15.2 26.1 

 6 4 LN 22.9 1.156 17.2 30.4 

 7 4 LN 24.8 1.163 18.4 33.3 

 8 4 LN 27.9 1.198 19.6 39.8 

 9 4 LN 30.9 1.179 22.4 42.7 

 10 4 LN 36.2 1.215 24.7 53.0 

 11 4 LN 40.0 1.287 24.4 65.6 

 12 4 LN 43.8 1.251 28.2 67.9 

 13 4 LN 48.4 1.240 31.7 73.8 

 14 4 LN 55.7 1.198 39.1 79.4 

 15 4 LN 59.7 1.172 43.7 81.5 

 16 4 LN 66.7 1.183 48.0 92.7 

 17 4 LN 66.0 1.182 47.6 91.6 

 18 4 LN 70.1 1.172 51.4 95.7 

 19 4 LN 70.8 1.166 52.4 95.7 

 20 4 LN 76.7 1.190 54.5 107.9 

 21 4 LN 76.7 1.190 54.5 107.9 

 22 4 LN 76.7 1.190 54.5 107.9 

 23 4 LN 76.7 1.190 54.5 107.9 

 24 4 LN 76.7 1.190 54.5 107.9 

 25 4 LN 76.7 1.190 54.5 107.9 

 26 4 LN 76.7 1.190 54.5 107.9 

 27 4 LN 76.7 1.190 54.5 107.9 

 28 4 LN 76.7 1.190 54.5 107.9 

 29 4 LN 76.7 1.190 54.5 107.9 

 30 4 LN 76.7 1.190 54.5 107.9

etc.

Figure 3-11. Section of the Physiological Parameters File Showing Partial Listings for 

Parameters Related to Body Mass. 
13 

Males age 0-100 then females age 0-100 (last revised 6-11-98) 

 Regression equation Estimate for RMR 

 Age Source DV IV Slope Interc SE Units med. wgt 

 0 R47g BMR BM 0.244 -0.127 0.290 MJ/day 2.1 

 1 R47g BMR BM 0.244 -0.127 0.290 MJ/day 2.7 

 2 R47g BMR BM 0.244 -0.127 0.280 MJ/day 3.2 

 3 R47h BMR BM 0.095 2.110 0.280 MJ/day 3.6 

 4 R47h BMR BM 0.095 2.110 0.280 MJ/day 3.8 

 5 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.0 

 6 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.3 

 7 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.5 

 8 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.8 

 9 R47h BMR BM 0.095 2.110 0.280 MJ/day 5.0 

 10 R47i BMR BM 0.074 2.754 0.440 MJ/day 5.4 

 11 R47i BMR BM 0.074 2.754 0.440 MJ/day 5.7 

 12 R47i BMR BM 0.074 2.754 0.440 MJ/day 6.0 

 13 R47i BMR BM 0.074 2.754 0.440 MJ/day 6.3 

 14 R47i BMR BM 0.074 2.754 0.440 MJ/day 6.9 

 15 R47i BMR BM 0.074 2.754 0.440 MJ/day 7.2 

 16 R47i BMR BM 0.074 2.754 0.440 MJ/day 7.7 

 17 R47i BMR BM 0.074 2.754 0.440 MJ/day 7.6 

 18 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.3 

 19 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.4 

 20 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 21 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 22 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 23 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 24 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 25 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 26 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 27 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 28 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 29 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 30 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

etc.

Figure 3-12. Section of the Physiological Parameters File Showing Partial Listings for 

Parameters Related to Resting Metabolic Rate (RMR). 
14 

Males age 0-100 then females age 0-100 

Blood Volume factor and Hemoglobin content 

 Age BLDFAC HGMN HGSTD 

 0 17.0 16.0 1.0 

 1 17.0 16.0 1.0 

 2 17.0 16.0 1.0 

 3 17.0 16.0 1.0 

 4 17.0 16.0 1.0 

 5 17.0 16.0 1.0 

 6 17.0 16.0 1.0 

 7 17.0 16.0 1.0 

 8 17.0 16.0 1.0 

 9 17.0 16.0 1.0 

 10 17.0 16.0 1.0 

 11 17.0 16.0 1.0 

 12 17.0 16.0 1.0 

 13 17.0 16.0 1.0 

 14 17.0 16.0 1.0 

 15 17.0 16.0 1.0 

 16 17.0 16.0 1.0 

 17 17.0 16.0 1.0 

 18 17.0 16.0 1.0 

 19 20.4 15.3 1.0 

 20 20.4 15.3 1.0 

 21 20.4 15.3 1.0 

 22 20.4 15.3 1.0 

 23 20.4 15.3 1.0 

 24 20.4 15.3 1.0 

 25 20.4 15.3 1.0 

 26 20.4 15.3 1.0 

 27 20.4 15.3 1.0 

 28 20.4 15.3 1.0 

 29 20.4 15.3 1.0 

 30 20.4 15.3 1.0

etc.

Figure 3-13. Section of the Physiological Parameters File Showing Partial Listings for 

Parameters Related to Blood Volume and Hemoglobin Content. 
15 

3.11 Profile Functions

This file provides the user-definable functions required in generating profiles of 

simulated persons. Table 3-1 lists the profile functions accessible through the Profile 

Functons file. Figures 3-14 through 3-18 present the file entries specified for the 

sample run. 

Four of the functions listed in Table 3-1 specify characteristics of the simulated person’s 

residence or car that may affect CO concentrations in that microenvironment: 

HasGasStove (gas stove in residence), HasPilot (pilot light in residence), AC_Home

(air conditioner in residence), and AC_Car (air conditioner in car). Each of these 

functions is assigned one of two values (1 = yes, 2 = no) based on the values of other 

variables and/or on the value of number randomly selected from a uniform (0,1) 

distribution. Consider the file entries for HasPilot found in Figure 3-15. 

HasPilot 

!Probability of having a pilot light on gas stove 

TABLE 

INPUT 1 INTVALUE 2 "HasGasStove" 

1 2 

INPUT 2 CONDITIONAL 4 "Pilot Light Probabilities" 

.51 .49 

0.0 1.0 

RESULT INTEGER 2 "Yes/No" 

1 2 

#

The value of HasPilot is assigned a value of 1 (“yes”) when the value of HasGasStove

equals 1 and the number randomly selected from the uniform distribution is between 0 

and 0.51. Otherwise, the value of HasPilot is zero (“no”). 

Two of the functions (MaxTempCat and AveTempCat) in Figure 3-14 are used to 

convert daily maximum and daily average temperature values into temperature 

categories. The DiaryPools function (Figure 3-14) produces categories that account for 

daily maximum temperature, daily average temperature, and day of the week. 

The IDGRP function is used to assign each simulated person to a demographic group 

based on the values of Age, Gender, and Employed. The entries for this function in 

Figure 3-14 produce demographic group categories that are consistent with the 

demographic groups employed in the pNEM/CO exposure model [Johnson, Mihlan, 

LaPointe, Fletcher, et al. (2000)]. IDGRP is used solely to label APEX output for the 

purposes of comparing APEX and pNEM exposure estimates; it does not affect the 

internal workings of APEX. 

The WindowPos function determines whether windows in the residence are open or 

closed based on air conditioning status (AC_Home), maximum temperature of the day 

(MaxTemp), and average temperature of the day (AvgTemp). This function was not 

used in the sample application, as the probability of open windows was integrated 

directly into the distributions characterizing air exchange rate according to the 
16 

methodology described on pages A-15 through A-18 of Appendix A. The entries for the 

WindowPos function in Figure 3-17 are thus place holders. 

Table 3-1. Explanations of File Entries Included in the Profile Functions File. 

Function Explanation 

Figure 

showing file 

entries for 

sample run 

Interpretation of file entries 

MaxTempCat Maximum temperature 

category (1 or 2) 

3-14 Assigns an integer value to MaxTempCat

based on MaxTemp value 

AveTempCat Average temperature 

category (1 or 2) 

3-14 Assigns an integer value to AveTempCat

based on AvgTemp value 

DiaryPools Diary Pool (1, 2, 3, ..., 8) 3-14 Assigns an integer value to DiaryPools

based on values of MaxTemp, AvgTemp, 

and DayOfWeek 

IDGRP pNEM group number used for 

labeling output (1, 2, 3, ... 11) 

3-14 Assigns an integer value to IDGRP based 

on values of Age, Gender, and Employed

HasGasStove Residence has gas stove (1 

= yes, 2 = no) 

3-15 Assigns an integer value to HasGasStove

based on value randomly selected from 

uniform (0,1) distribution 

HasPilot Residence has gas pilot light 

(1 = yes, 2 = no) 

3-15 Assigns an integer value to HasPilot

based on value of HasGasStove and 

value randomly selected from uniform (0,1) 

distribution 

AC_Home Residence has air 

conditioning system (1 = yes, 

2 = no) 

3-16 Assigns an integer value to AC_Home

based on value randomly selected from 

uniform (0,1) distribution 

AC_Car Car has air conditioning 

system (1 = yes, 2 = no) 

3-16 Assigns an integer value to AC_Car based 

on value randomly selected from uniform 

(0,1) distribution 

WindowPos Window position in residence 

(1 = yes, 2 = no) 

3-17 Assigns an integer value to WindowPos

based on the values of AC_Home, 

MaxTemp, and AvgTemp — not used in 

case study 

SpeedCat Vehicle speed category (1, 2, 

or 3) 

3-18 Assigns an integer value to SpeedCat 

based on value randomly selected from 

uniform (0,1) distribution — not used in 

case study

SpeedCat provides the speed category (1 = speed less than 20 mph, 2 = 20 to 40 mph, 

3 = 40+ mph) that can be used to determine air exchange rates for cars. This function 

was not used in the case study, as the probability distribution of vehicle speed was 

integrated directly into the distributions characterizing air exchange rate according to the 

methodology described on pages A-17 through A-19 of Appendix A. Figure 3-17 shows 
17 

the place-holder values entered for the SpeedCat function. 

MaxTempCat 

! Not used, placeholders 

TABLE 

INPUT 1 INTRANGE 1 "MaxTemp" 

RESULT INTEGER 1 "TempCatM" 

1 

# 

AvgTempCat 

!Temperature ranges (categories) 

!Deg Fahrenheit 

TABLE 

INPUT 1 INTRANGE 3 "AvgTemp" 

63 76 

RESULT INTEGER 3 "TempCatA" 

1 2 3 

# 

DiaryPools 

!Group activity diaries into pools 

TABLE 

INPUT 1 INTRANGE 3 "MaxTemp" 

55 84 

INPUT 2 INTRANGE 1 "AvgTemp" 

INPUT 3 INTVALUE 7 "DayOfWeek" 

1 2 3 4 5 6 7 

RESULT INTEGER 21 "Pool number" 

4 5 6 

1 2 3 

1 2 3 

1 2 3 

1 2 3 

1 2 3 

4 5 6 

# 

IDGRP 

!Demographic Group assignments for pNEM comparisons 

TABLE 

INPUT 1 INTRANGE 4 "Age" 

18 45 65 

INPUT 2 INTVALUE 2 "Gender" 

1 2 

INPUT 3 INTVALUE 2 "Employed" 

1 2 

RESULT INTEGER 16 "Demographic Group" 

1 2 4 6 

1 7 9 11 

1 3 5 6 

1 8 10 11 

# 

Figure 3-14. Section of the Profile Functions File Showing Listings for Maximum 

Temperature, Average Temperature, Diary Pools, and IDGRP. 

HasGasStove 

!Probability of having a gas stove at home 

TABLE 

INPUT 1 PROBABILITY 2 "Gas Stove probabilities" 

.79 .21 

RESULT INTEGER 2 "Yes/No" 
18 

1 2 

# 

HasPilot 

!Probability of having a pilot light on gas stove 

TABLE 

INPUT 1 INTVALUE 2 "HasGasStove" 

1 2 

INPUT 2 CONDITIONAL 4 "Pilot Light Probabilities" 

.51 .49 

0.0 1.0 

RESULT INTEGER 2 "Yes/No" 

1 2 

#

Figure 3-15. Section of the Profile Functions File Showing Listings for Gas Stove and 

Pilot Light Sections. 

AC_Home 

!Has air conditioning at home 

TABLE 

INPUT 1 PROBABILITY 2 "A/C probabilities" 

.53 .47 

RESULT INTEGER 2 "Yes/No" 

1 2 

# 

AC_Car 

!Has air conditioning in car 

TABLE 

INPUT 1 PROBABILITY 2 "A/C probabilities" 

.85 .15 

RESULT INTEGER 2 "Yes/No" 

1 2 

# 

Figure 3-16. Section of the Profile Functions File Showing Listings for Home and Car 

Air Conditioning. 

WindowPos 

! Placeholders, not used 

TABLE 

INPUT 1 INTVALUE 2 "AC_Home" 

1 2 

INPUT 2 INTRANGE 1 "MaxTemp" 

INPUT 3 INTRANGE 1 "AvgTemp" 

INPUT 4 CONDITIONAL 4 

1.0 0.0 

1.0 0.0 

RESULT INTEGER 2 

1 2 

# 

Figure 3-17. Section of the Profile Functions File Showing “Place-Holder” Listings for 

Window Position. 
19 

SpeedCat 

! Placeholders, not used 

TABLE 

INPUT 1 PROBABILITY 1 "Speed probabilities" 

1.0 

RESULT INTEGER 1 "Not used" 

1 

# 

Figure 3-18. Section of the Profile Functions File Showing “Place Holder” Listings for 

Speed Category. 

3.12 Microenvironmental Mapping

This file assigns each of the Location Codes used in the CHAD activity database to 

one of the 15 Microenvironments defined for APEX (Table 3-2). Figure 3-19 presents 

the first 35 lines of the file used in the sample run. The complete contents of the file can 

be found in Table 2-4 of the report by Johnson, Mihlan, LaPointe, Fletcher, et al. (2002). 

 The file begins with two header lines followed by a keyword line for each of the 115 

location codes appearing in the CHAD database. Each keyword line begins with the 

location code followed by a short description, an “=”, and an integer that designates one 

of the microenvironments defined in the Micro Description file. (A value of -1 for 

microenvironment code directs APEX to use whichever microenvironment was 

previously in use in the composite diary time series.) APEX reads only the location 

codes and the corresponding codes for the APEX microenvironment. 
20 

Table 3-2. Methodology Used to Estimate Carbon Monoxide Concentrations in Each 

Microenvironment Defined for Sample Application of APEX to Los Angles. 

 

Microenvironment 

Activity diary locations included 

in microenvironment 

CO sources 

simulated in model 

run 

APEX modeling 

method 

Code 

General 

location Specific location 

1 Indoors Residence Indoors - residence Outdoor CO 

Gas stoves 

Gas pilot lights 

ETSa

Mass balance 

2 Indoors Nonresidence A Service station 

Auto repair 

Outdoor CO Mass balance 

3 Indoors Nonresidence B Other repair shop 

Shopping mall 

Outdoor CO Mass balance 

4 Indoors Nonresidence C Restaurant Outdoor CO 

ETS 

Mass balance 

5 Indoors Nonresidence D Bar Outdoor CO 

ETS 

Mass balance 

6 Indoors Nonresidence E Other indoor location 

Auditorium 

Outdoor CO Mass balance 

7 Indoors Nonresidence F Store 

Office 

Other public building 

Outdoor CO Mass balance 

8 Indoors Nonresidence G Health care facility 

School 

Church 

Manufacturing facility 

Outdoor CO Mass balance 

9 Indoors Residential garage Residential garage Outdoor CO Mass balance 

10 Outdoors Near road Near road 

Bicycle 

Motorcycle 

Outdoor CO Factors 

11 Outdoors Other locations Outdoor residential garage 

Construction site 

Residential grounds 

School grounds 

Sports arena 

Park or golf course 

Other outdoor location 

Outdoor CO Factors 

12 Vehicle Automobile Automobile Outdoor CO 

ETS 

Mass balance 

13 Vehicle Truck Truck Outdoor CO 

ETS 

Mass balance 

14 Vehicle Mass transit vehicles Bus 

Train/subway 

Other vehicle 

Outdoor CO Mass balance 

15 Outdoor Public parking or 

fueling facility 

Indoor parking garage 

Outdoor parking garage 

Outdoor parking lot 

Outdoor service station 

Outdoor CO Factors

a

Environmental tobacco smoke.
21 

CHAD Loc. Description APEX micro 

--------- ------------------------------------------------- 

X No data = -1 

U Uncertain of correct code = -1 

30000 Residence, general = 1 

30010 Your residence = 1 

30020 Other residence = 1 

30100 Residence, indoor = 1 

30120 Your residence, indoor = 1 

30121 ..., kitchen = 1 

30122 ..., living room or family room = 1 

30123 ..., dining room = 1 

30124 ..., bathroom = 1 

30125 ..., bedroom = 1 

30126 ..., study or office = 1 

30127 ..., basement = 1 

30128 ..., utility or laundry room = 1 

30129 ..., other indoor = 1 

30130 Other residence, indoor = 1 

30131 ..., kitchen = 1 

30132 ..., living room or family room = 1 

30133 ..., dining room = 1 

30134 ..., bathroom = 1 

30135 ..., bedroom = 1 

30136 ..., study or office = 1 

30137 ..., basement = 1 

30138 ..., utility or laundry room = 1 

30139 ..., other indoor = 1 

30200 Residence, outdoor = 11 

30210 Your residence, outdoor = 11 

30211 ..., pool or spa = 11 

30219 ..., other outdoor = 11 

30220 Other residence, outdoor = 11 

30221 ..., pool or spa = 11 

30229 ..., other outdoor = 11 

etc. 

Figure 3-19. Partial Listing of Microenvironmental Mapping File. 

3.13 Personal Information

The current CHAD activity database was used as the source of activity data for the 

sample run. This database contains 16,175 24-hour activity patterns based on data 

acquired from various activity diary studies. In addition to a time series of activities 

spanning each 24-hour diary period, the database provides various data items that 

apply to the entire 24-hour period. The Personal Information file includes the following 

data items of this type. 

· CHAD ID; 

· Day Type; 

· Gender; 

· Age; 

· Race; 

· Employment Status; 
22 

· Maximum Temperature; 

· Occupation; 

· Count of Missing Time; and 

· Record Count. 

In the sample run, the Personal Information file is identical to the Diary Questionnaire 

file produced by the Access version of the CHAD database. Figure 3-20 presents the 

first 31 lines of this file. 

BAL97001A,TUE,F,W,N,77,43,34,X,45,29 

BAL97001B,WED,F,W,N,77,51,41,X,135,28 

BAL97001C,THU,F,W,N,77,57,47,X,15,30 

BAL97001D,FRI,F,W,N,77,45,36,X,0,28 

BAL97001E,TUE,F,W,N,77,47,39,X,0,27 

BAL97001F,WED,F,W,N,77,36,29,X,0,28 

BAL97001G,THU,F,W,N,77,38,29,X,0,26 

BAL97001H,FRI,F,W,N,77,43,36,X,0,28 

BAL97001I,TUE,F,W,N,77,41,39,X,15,28 

BAL97001J,WED,F,W,N,77,54,44,X,15,28 

BAL97001K,THU,F,W,N,77,48,40,X,0,30 

BAL97001L,FRI,F,W,N,77,42,36,X,30,30 

BAL97006A,WED,M,W,N,80,51,41,X,0,31 

BAL97006B,THU,M,W,N,80,57,47,X,60,36 

BAL97006C,FRI,M,W,N,80,45,36,X,75,31 

BAL97006D,TUE,M,W,N,80,47,39,X,15,33 

BAL97006E,WED,M,W,N,80,36,29,X,30,31 

BAL97006F,THU,M,W,N,80,38,29,X,210,34 

BAL97006G,FRI,M,W,N,80,43,36,X,165,30 

BAL97006H,TUE,M,W,N,80,41,39,X,45,31 

BAL97006I,WED,M,W,N,80,54,44,X,60,34 

BAL97006J,THU,M,W,N,80,48,40,X,15,31 

BAL97008A,TUE,F,W,N,88,43,34,X,0,31 

BAL97008B,WED,F,W,N,88,51,41,X,60,27 

BAL97008C,THU,F,W,N,88,57,47,X,345,33 

BAL97008D,FRI,F,W,N,88,45,36,X,90,28 

BAL97008E,TUE,F,W,N,88,47,39,X,30,29 

BAL97008F,WED,F,W,N,88,36,29,X,90,26 

BAL97008G,TUE,F,W,N,88,41,39,X,270,32 

BAL97008H,WED,F,W,N,88,54,44,X,150,31 

BAL97008I,THU,F,W,N,88,48,40,X,465,35 

etc.

Figure 3-20. Partial Listing of Personal Information File. 

3.14 Diary Events 

As discussed in Section 3.13, the CHAD database provides a sequence of activity 

events for each 24-hour diary period. The Diary Events file provides the following 

information for each event in the CHAD database. 

· CHAD ID; 

· Event Start Time; 

· Event Duration; 
23 

· Activity Code; and 

· Location Code. 

Figure 3-21 presents the first 29 lines of the Diary Events file used in the sample run. 

This file is identical to the current default Diary Events file; it was generated from the 

CHAD database at the same time as the Personal Information file to ensure that the 

CHAD IDs are listed in the same order in each file. The event durations range from 1 to 

60 minutes and account for all 24 hours of each diary day (midnight to midnight). 

BAL97001A,0000,60,14500,30125, 

BAL97001A,0100,60,14500,30125, 

BAL97001A,0200,60,14500,30125, 

BAL97001A,0300,60,14500,30125, 

BAL97001A,0400,60,14500,30125, 

BAL97001A,0500,60,14500,30125, 

BAL97001A,0600,60,14500,30125, 

BAL97001A,0700,30,14500,30125, 

BAL97001A,0730,30,14400,30121, 

BAL97001A,0800,60,16000,30122, 

BAL97001A,0900,60,14500,30125, 

BAL97001A,1000,30,14500,30125, 

BAL97001A,1030,30,X ,X , 

BAL97001A,1100,45,14500,30125, 

BAL97001A,1145,15,X ,X , 

BAL97001A,1200,60,14500,30125, 

BAL97001A,1300,60,14500,30125, 

BAL97001A,1400,60,14500,30125, 

BAL97001A,1500,60,16000,30122, 

BAL97001A,1600,60,14600,30125, 

BAL97001A,1700,15,14600,30125, 

BAL97001A,1715,45,14400,30123, 

BAL97001A,1800,45,14400,30123, 

BAL97001A,1845,15,16000,30122, 

BAL97001A,1900,60,16000,30122, 

BAL97001A,2000,60,17223,30125, 

BAL97001A,2100,60,17223,30125, 

BAL97001A,2200,60,17223,30125, 

BAL97001A,2300,60,17223,30125, 

etc.

Figure 3-21. Partial Listing of Diary Events File. 

3.15 Microenvironment Descriptions

Table 3-2 lists the 15 microenvironments defined for the sample APEX run. These are 

the same 15 microenvironments defined for the most recent application of pNEM to CO. 

 A report by Johnson, Mihlan, LaPointe, Fletcher, et al. (2000) describes the algorithms 

used to implement each microenvironment within the pNEM framework. To the extent 

practical, each microenvironment defined for the APEX sample run exactly duplicates 

the operations of the corresponding microenvironmental algorithms in pNEM. In cases 

where the APEX framework did not permit an exact duplication of a particular pNEM 
24 

microenvironment, analysts were able to develop distributional inputs for APEX 

parameters that produced an acceptable approximation of the corresponding pNEM 

algorithms. The technical memorandum by Johnson and Capel (August 22, 2002) in 

Appendix A summarizes this developmental effort and provides tabulations of the 

resulting distributional parameters. 

The Micro Descriptions file consists of two main sections: the Micro Descriptions 

section and the Parameter Descriptions section. Figure 3-22 presents the Micro 

Descriptions section used in the sample run. The file consists of a header followed by a 

line of data for each of the 15 microenvironments. The data for each microenvironment 

includes the Microenvironment Number, the Name of the microenvironment, and the 

Calculation Method to be applied to the microenvironment. The Microenvironment 

Numbers correspond to the microenvironment numbers in the Micro Mapping file. The 

Calculation Method is either MASSBAL or FACTORS. The MASSBAL method uses a 

mass balance model to calculate the pollutant concentration in the microenvironment for 

specified time intervals. The FACTORS method uses a linear relationship to calculate 

the pollutant concentration in the microenvironment. Volume I of the User’s Guide 

provides the generic equations used to implement each method. 

! First section - micro descriptions 

Micro Name Method 

1 Residence MASSBAL 

2 insideoA MASSBAL 

3 inside_oB MASSBAL 

4 Restaurant MASSBAL 

5 Bar MASSBAL 

6 inside_oE MASSBAL 

7 inside_oF MASSBAL 

8 inside_oG MASSBAL 

9 indoor_garage MASSBAL 

10 Outdoorn_road FACTORS 

11 Outdoor_other FACTORS 

12 Car MASSBAL 

13 Truck MASSBAL 

14 MT_Vehicle MASSBAL 

15 Outdoor_garage FACTORS 

Figure 3-22. Methods Section of Microenvironmental Descriptions File. 

In the Parameter Description section of the Micro Descriptions file, the user defines the 

parameters to be used in calculating pollutant concentrations in each microenvironment. 

 Table 3-3 lists the types of parameters that can be defined for each microenvironment. 

 It also indicates the default value that APEX will use if the user does not provide a 

value. 

Figures 3-23 through 3-52 provide the file entries for the 15 microenvironments defined 

for the sample run. Note that a Parameter Description section is provided for each 
25 

parameter consisting of keywords and data. The specification of keywords for the 

sample run is consistent with the Keyword definitions provided by Table 3-4. Additional 

guidance concerning the specification of keywords can be found in Volume I. 

Table 3-3. Micro Parameters Required to Define a Microenvironment.

Calculation 

Method 

Parameter Type Code Units Default Value 

FACTORS Proximity PR None 1 

Penetration PE None 1 

Csource CS parts per million 0 

MASSBAL Proximity PR None 1 

Penetration PE None 1 

Csource CS parts per million 0 

DecayRate DE per hour 0 

AIrExRate AE Air changes per hour - 

Volume VO Cubic meters - 

MeanR MR Per hour AirExRate+DecayRate 

ESource ES Micrograms per hour 0 

The 13 keyword lines are followed by a data line for each possible combination of the 

user-specified cases of the following seven indexing parameters: 

· Block — time block 

· Daytype — day time 

· Season — season 

· Area — area 

· C1 — conditional variable # 1 

· C2 — conditional variable # 2 

· C3 — conditional variable # 3 

Each data line provides values for the following parameters which define the distribution 

of the parameters under the specified set of conditions. 

· Shape — distribution type 

· Min — minimum; 

· Max — maximum; 

· Par1 -- parameter 1 for distribution; 

· Par2.-- parameter 2 for distribution. 
26 

Shape represents the distribution type. APEX3 allows the user to specify one of the six 

distribution types (Point, Uniform, Normal, Lognormal, Triangle, and Exponential) or as 

a custom distribution specified by a histogram or discrete probability distribution. 

Table 3-4. Keyword Definitions for the Parameter Descriptions Section of the 

Micro Descriptions File.

Keyword Description 

Micro Number This number must match the micro number in the Microenvironment Description section. 

Parameter Type A parameter code such as PR (Proximity) and PE (Penetration) provided in Table X-XX 

should be used to specify a parameter type. 

Hours - Block This variable is used to map hours of a day to different time blocks. The input line always 

contains a list of 24 integers, representing 24 hours a day. The first hour is midnight to 

1.a.m. and the 24th is 11 p.m. to midnight. If this line is missing, the default value is that all 

24 hours are in a single time block - block #1. 

Weekday - Daytype This variable is used to map days in a week to different day types. Seven integers must be 

given in this input line. The position of an integer in the input line represents a day, 

beginning on Sunday and ending on Saturday. The integer represents the day type a day 

belongs to. If this variable is not defined, all days of a week will belong to daytype #1. 

Month - Season This variable is used to map months of a year to different seasons. Twelve integers must 

be given in this input line. The position of an integer represents a month of a year, 

beginning in January and ending in December. The integer represents the season that a 

month belongs to. If this line is missing, all 12 months belong to season #1. 

District - Area This variable is used to map air quality districts to larger areas. The number of integers in 

this line must match the number of air quality districts in the study area. Note: this variable 

is optional for most applications. The user can delete this line or place the same number 

of 1 in this line as the number of air districts. 

Condition # 1 Choice for the first conditional variable. If not used, this line may either be omitted or the 

value set to zero. APEX accepts the following variables as conditional variable : Gender, 

Race, Employed, GasStove, GasPilot, AC_Home, AC_Car, WindowPos, TempCat, 

SpeedCat. 

Condition # 2 Choice for the second conditional variable. 

Condition # 3 Choice for the third conditional variable. 

ResampHours Either YES or NO. If YES, a random value is selected from distribution for a parameter in 

each hour within a time block. If NO, a random value is selected for a parameter for a 

time block and used for every hour within the time block. The default value is NO. 

ResampDays Either YES or NO. If YES, a random value is selected from a distribution for a parameter 

for each day within a day type. If NO, a random value is selected for a day type and used 

for every day within the same day type. The default is NO. 

ResampWork Either YES or NO. If YES, a separate set of random values is selected from a distribution 

for the workplace. If NO, the same set of random values are used (for the same day and 

hour) both for home and at work. The default is YES. 

RandomSeed Either zero or a positive integer up to about 2.1 billion. If zero, the random number seed 

for a parameter is determined from the internal clock, and the results will differ from one 

run to another. If not zero, then the specified number is the actual seed, and multiple 

model runs with the same seed will generate the same sequence of random numbers for 

the parameter (as long as the MP definition is unchanged). The default value is zero.
27 

Each distribution is defined by the values of two parameters (Par1 and Par2); the 

identify of these parameters varies with the distribution as indicated by Table 3-5. Note 

that the Min and Max parameters are optional for some distributions and required for 

others. 

Table 3-5. Uses of Distribution Parameters for Each Distribution Type

Shape Min Max Par1 Par2 

Point Required Not used Not used Not used 

Uniform Required Required Not used Not used 

Normal Optional Optional Mean – required 

Standard deviation – 

required 

Lognormal Optional Optional 

Geometric mean – 

required 

Geometric standard 

deviation – Required 

Triangle Required Required Mode – Required Not used 

Exponential Required Optional Mean – Required Not used

The following subsections describe the file sections associated with each 

microenvironment in more detail. 

3.15.1 Indoor Microenvironment 1 (Residence)

Figures 3-23 through 3-32 show the contents of the MicroParameters file applicable to 

the Indoors - Residence microenvironment, the most complex of the 15 

microenvironments defined for the sample run. As indicated by its entry in Figure 3-22, 

CO concentrations in this microenvironment are calculated using the mass balance 

method. The file provides parameters for modeling three emission sources (gas stoves, 

gas pilot lights, and environmental tobacco smoke) and for simulating the effects of 

open windows on air exchange rates. Parameters specifying proximity factor, 

penetration factor, and volume are also defined. 

The top portion of Figure 3-23 presents the file entries used to specify the proximity 

factor (PRX). The parameter settings instruct APEX to select a single value for each 

simulated person from a lognormal distribution with minimum = 0.5175, maximum = 

2.0661, Part1 (geometric mean) = 1.034, and Part2 (geometric standard deviation) = 

1.5232. 

The bottom portion of Figure 3-23 presents the file entries for the penetration factor 
28 

(PEN). In this case, the PEN value is resampled from a lognormal distribution for each 

hour, day, and work district (see page XXX of REF for discussion of work district 

resampling). The lognormal distribution has a minimum of 0.4482, a maximum of 

2.2313, a geometric mean of 1.0, and a geometric standard deviation of 1.6289. Pages 

A-20 through A-23 of Appendix A provide a summary of the methods used to derive the 

distributions of PRX and PEN. Note that all values are dimensionless. 

The top portion of Figure 3-24 presents the file entries used to determine air exchange 

rate (AER) in the indoors - residential microenvironment. The values are selected from 

24 lognormal distributions with parameters that vary with season, air conditioning status 

of the residence (specified by AC_Home), and the average temperature of the day 

(specified by the value of the temperature category AVGTEMPCAT). AER values are 

resampled for each day and each work district. The 24 AER distributions were obtained 

from Table 9 of Appendix A. 

The lower section of Figure 3-24 presents the file entries that determine the volume of 

the residence. A single value is selected for each simulated person from a lognormal 

distribution with minimum equal to 138 m3

, maximum equal to 957 m3, geometric mean 

equal to 363 m3

, and geometric standard deviation equal to 1.64. The derivation of this 

distribution from census data specific to Los Angeles is discussed in Section 4.5.1 of the 

report by Johnson, Mihlan, LaPointe, Fletcher, et al. (2000). 

Micro number = 1 

Parameter Type = PRX 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .5175 2.0661 1.034 

1.5232 

Micro number = 1 

Parameter Type = PEN 

ResampHours = YES 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4482 2.2313 1.0 1.6289

Figure 3-23. Sections of the Microenvironmental Descriptions File Defining the PRX 

and PEN Parameters Used in Modeling in Microenvironment 1. 

Figures 3-25 through 3-27 present file entries for four “ES” parameters that are 
29 

multiplied together to determine the CO emission rate associated with environmental 

tobacco smoke (ETS): emission rate per cigarette (μg/h), smoking in ME during day(1 = 

yes, 0 = no), smoking during hour (1 = yes, 0 = no), and smoking duration during hour 

(min). The emission rate per cigarette (upper portion of Figure 3-25) is determined once 

for each simulated person by selecting a value from the indicated lognormal distribution. 

 The daily ME smoking status (lower portion of Figure 3-25) is determined daily by 

selecting values from the indicated offon probability function distribution. The 

distributions are discussed on pages A-5 through A-7 of Appendix A. 

Micro number = 1 

Parameter Type = AER 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 2 2 2 3 3 3 4 4 4 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = AC_HOME 

Condition # 2 = AVGTEMPCAT 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .1430 3.16 .5560 1.7563

1 1 1 1 1 2 1 LogNormal .1430 3.16 .9436 1.7718

1 1 1 1 1 3 1 LogNormal .1430 3.16 .7928 1.8261

1 1 1 1 2 1 1 LogNormal .1430 3.16 .5544 1.7365

1 1 1 1 2 2 1 LogNormal .1430 3.16 1.1972 1.5376

1 1 1 1 2 3 1 LogNormal .1430 3.16 1.2657 1.4871

1 1 2 1 1 1 1 LogNormal .1670 3.16 .6628 1.7447

1 1 2 1 1 2 1 LogNormal .1670 3.16 1.0049 1.6950

1 1 2 1 1 3 1 LogNormal .1670 3.16 .8612 1.7565

1 1 2 1 2 1 1 LogNormal .1670 3.16 .6568 1.7635

1 1 2 1 2 2 1 LogNormal .1670 3.16 1.2511 1.4948

1 1 2 1 2 3 1 LogNormal .1670 3.16 1.2925 1.4673

1 1 3 1 1 1 1 LogNormal .1760 6.296 1.0821 2.1231

1 1 3 1 1 2 1 LogNormal .1760 6.296 1.2653 1.6827

1 1 3 1 1 3 1 LogNormal .1760 6.296 1.1999 1.8541

1 1 3 1 2 1 1 LogNormal .1760 6.296 1.0885 2.1312

1 1 3 1 2 2 1 LogNormal .1760 6.296 1.3206 1.4959

1 1 3 1 2 3 1 LogNormal .1760 6.296 1.3416 1.4751

1 1 4 1 1 1 1 LogNormal .1510 3.16 .6615 1.8172

1 1 4 1 1 2 1 LogNormal .1510 3.16 1.0107 1.6974

1 1 4 1 1 3 1 LogNormal .1510 3.16 .8587 1.7828

1 1 4 1 2 1 1 LogNormal .1510 3.16 .6593 1.8241

1 1 4 1 2 2 1 LogNormal .1510 3.16 1.2352 1.5162

1 1 4 1 2 3 1 LogNormal .1510 3.16 1.2943 1.4894

Micro number = 1 

Parameter Type = VOL 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 Lognormal 138. 957.0 363.0 1.64

Figure 3-24. Sections of the Microenvironmental Descriptions File Defining the Air 

Exchange Rate and Volume Parameters Used in Modeling in 
30 

Microenvironment 1. 

Micro number = 1 

Parameter Type = ES ! Cigarette smoke, micrograms per hour, S(m) term

Source number = 1 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 92800. 219800. 142800. 1.3 

Micro number = 1 ! Residence 

Parameter Type = ES ! Daily ME smoking, Off/On 

Source number = 1 

ResampHours = NO 

ResampDays = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 OffOn 0.8504 0.1496 . .

Figure 3-25. Sections of the Microenvironmental Descriptions File Defining the 

Cigarette Emission Rate and Daily ME Smoking Parameters Used in 

Modeling the Environmental Tobacco Smoke Emission Source in 

Microenvironment 1. 

The occurrence of smoking during a particular hour is determined by an “on/off” 

probability function defined by two parameters (Min and Max) that vary by block hour 

(Figure 3-26). New values for these parameters are resampled on both an hourly and 

daily basis. Smoking duration is also determined by an on/off probability function with 

parameters that vary by block hour (Figure 3-27). Pages A-5 through A-7 of Appendix A 

describe the methods used to derive the on/off distribution parameters for the 

occurrence and duration parameters. 

The emission rate from gas stoves is determined by the product of the six ES 

parameters defined in Figures 3-28 through 3-31 (see Equation 11 in Appendix A). 

These parameters account for a multiplier term (K), annual fuel usage by burners 

(AUB), burner emission factor (EFBURN), seasonal adjustment factor (Y), on/off status 

during specified block hour, and duration of use during specified block hour. 

As indicated in Figure 3-28, the K term is a constant (0.00004564) that does not vary — 

the same value is used for all simulated persons during all time periods. If GasStove = 

1 (residence has gas stove), a value for AUB is selected from a lognormal distribution 

with minimum = 800,000 kilojoules, maximum = 3,730,000 kilojoules, geometric mean = 

1,730,000 kilojoules, and geometric standard deviation = 1.48. Otherwise, AUB equals 

zero. EFBURN is determined in a similar manner. If GasStove = 1, then EFBURN is 

selected from the specified lognormal distribution. Otherwise, EFBURN = zero. These 

parameter values are consistent with the entries in Table 7 of Appendix A. 

Figure 3-29 presents the file entries for determining the Y parameter. This parameter 
31 

adjusts the gas stove emission rate for seasonal variations. A point estimate (constant) 

is provided for each month of the year as specified in Table 6 of Appendix A. 

Micro number = 1 

Parameter Type = ES ! Cigarette smoke, Hourly Off/On of people 

exposed 

Source number = 1 

Hours - Block = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 24 

ResampHours = YES 

ResampDays = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 OffOn 0.8502 0.1498 . . 

2 1 1 1 1 1 1 OffOn 0.8744 0.1256 . . 

3 1 1 1 1 1 1 OffOn 0.8951 0.1049 . . 

4 1 1 1 1 1 1 OffOn 0.9097 0.0903 . . 

5 1 1 1 1 1 1 OffOn 0.9103 0.0897 . . 

6 1 1 1 1 1 1 OffOn 0.9042 0.0958 . . 

7 1 1 1 1 1 1 OffOn 0.8883 0.1117 . . 

8 1 1 1 1 1 1 OffOn 0.8556 0.1444 . . 

9 1 1 1 1 1 1 OffOn 0.8278 0.1722 . . 

10 1 1 1 1 1 1 OffOn 0.7852 0.2148 . . 

11 1 1 1 1 1 1 OffOn 0.7449 0.2551 . . 

12 1 1 1 1 1 1 OffOn 0.7150 0.2850 . . 

13 1 1 1 1 1 1 OffOn 0.6401 0.3599 . . 

14 1 1 1 1 1 1 OffOn 0.6231 0.3769 . . 

15 1 1 1 1 1 1 OffOn 0.6216 0.3784 . . 

16 1 1 1 1 1 1 OffOn 0.6092 0.3908 . . 

17 1 1 1 1 1 1 OffOn 0.5876 0.4124 . . 

18 1 1 1 1 1 1 OffOn 0.5608 0.4392 . . 

19 1 1 1 1 1 1 OffOn 0.5062 0.4938 . . 

20 1 1 1 1 1 1 OffOn 0.4850 0.5150 . . 

21 1 1 1 1 1 1 OffOn 0.4828 0.5172 . . 

22 1 1 1 1 1 1 OffOn 0.5417 0.4583 . . 

23 1 1 1 1 1 1 OffOn 0.6330 0.3670 . . 

24 1 1 1 1 1 1 OffOn 0.7583 0.2417 . .

Figure 3-26. Section of the Microenvironmental Descriptions File Defining the Smoking 

Occurrence Parameter Used in Modeling the Environmental Tobacco 

Smoke Emission Source in Microenvironment 1. 

Micro number = 1 

Parameter Type = ES ! Cigarette smoke duration 

Source number = 1 

Hours - Block = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 24 

ResampHours = YES 

ResampDays = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 Point 0.9182 . . . 

2 1 1 1 1 1 1 Point 0.9602 . . . 

3 1 1 1 1 1 1 Point 0.9634 . . . 

4 1 1 1 1 1 1 Point 0.9824 . . . 

5 1 1 1 1 1 1 Point 0.9492 . . . 

6 1 1 1 1 1 1 Point 0.8693 . . . 
32 

7 1 1 1 1 1 1 Point 0.8491 . . . 

8 1 1 1 1 1 1 Point 0.7910 . . . 

9 1 1 1 1 1 1 Point 0.7979 . . . 

10 1 1 1 1 1 1 Point 0.8105 . . . 

11 1 1 1 1 1 1 Point 0.8508 . . . 

12 1 1 1 1 1 1 Point 0.8849 . . . 

13 1 1 1 1 1 1 Point 0.8533 . . . 

14 1 1 1 1 1 1 Point 0.9089 . . . 

15 1 1 1 1 1 1 Point 0.9129 . . . 

16 1 1 1 1 1 1 Point 0.9190 . . . 

17 1 1 1 1 1 1 Point 0.9029 . . . 

18 1 1 1 1 1 1 Point 0.8267 . . . 

19 1 1 1 1 1 1 Point 0.7830 . . . 

20 1 1 1 1 1 1 Point 0.8296 . . . 

21 1 1 1 1 1 1 Point 0.8776 . . . 

22 1 1 1 1 1 1 Point 0.8825 . . . 

23 1 1 1 1 1 1 Point 0.8588 . . . 

24 1 1 1 1 1 1 Point 0.8420 . . .

Figure 3-27. Section of the Microenvironmental Descriptions File Defining the Smoking 

Duration Parameter Used in Modeling the Environmental Tobacco Smoke 

Emission Source in Microenvironment 1. 
33 

Micro number = 1 ! In residence 

Parameter Type = ES ! Gas stove K Term 

Source number = 2 

Condition # 1 = 0 

ResampHours = NO 

ResampDays = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 Point .00004564 . . . 

Micro number = 1 ! In residence 

Parameter Type = ES ! Gas stove AUB Term 

Source number = 2 

Condition # 1 = GasStove 

ResampHours = NO 

ResampDays = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 Lognormal 800000. 3730000. 1730000. 

 1.48 

1 1 1 1 2 1 1 Point 0. . . . 

Micro number = 1 ! In residence 

Parameter Type = ES ! Gas stove EFBURN Term 

Source number = 2 

Condition # 1 = GasStove 

ResampHours = NO 

ResampDays = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 Lognormal 0. 400. 29.4 2.77 

1 1 1 1 2 1 1 Point 0. . . .

Figure 3-28. Sections of the Microenvironmental Descriptions File Defining the K, AUB, 

and EFBURN Parameters Used in Modeling the Gas Stove Emission 

Source in Microenvironment 1. 
34 

Micro number = 1 ! In residence 

Parameter Type = ES ! Gas stove Y Term 

Source number = 2 

Month - Season = 1 2 3 4 5 6 7 8 9 10 11 12 

Condition # 1 = 0 

ResampHours = NO 

ResampDays = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 Point 1.1828 . . . 

1 1 2 1 1 1 1 Point 1.1404 . . . 

1 1 3 1 1 1 1 Point 1.0630 . . . 

1 1 4 1 1 1 1 Point 0.9658 . . . 

1 1 5 1 1 1 1 Point 0.8779 . . . 

1 1 6 1 1 1 1 Point 0.8227 . . . 

1 1 7 1 1 1 1 Point 0.8155 . . . 

1 1 8 1 1 1 1 Point 0.8589 . . . 

1 1 9 1 1 1 1 Point 0.9400 . . . 

1 1 10 1 1 1 1 Point 1.0373 . . . 

1 1 11 1 1 1 1 Point 1.1246 . . . 

1 1 12 1 1 1 1 Point 1.1782 . . .

Figure 3-29. Section of the Microenvironmental Descriptions File Defining the Y 

Parameter Used in Modeling the Gas Stove Emission Source in 

Microenvironment 1. 

Figure 3-30 presents the file entries for determining whether a gas stove is on or off 

during a particular hour. The distributional entries vary by hour and by gas stove status 

(indexed by the C1 parameter). If GasStove = 1 (i.e., residence has gas stove), then a 

determination of stove use is made for each hour using the OffOn probability function. 

For example, the probability of the stove being on when hour = 1 is 0.025 (2.5 percent). 

 If GasStove = 2, the gas stove will always be off, as the parameter values for OffOn 

function in each of these cases is 1.0, 0 regardless of hour. The values entered into 

this file were obtained from Table 5 of Appendix A. 

When a gas stove is determined to be on during a particular hour, the file presented in 

Figure 3-31 is used to determine the duration of stove use. A point estimate (30 or 60 

minutes) is provided for each hour of the day consistent with Table 5 of Appendix A. 

Condition #1 is set to zero to indicate that Condition #1 will not be specified. 

The emission rate from pilot lights used on gas stoves is determined by the product of 

the three ES parameters defined in Figure 3-32. These parameters account for annual 

fuel use by pilot light (AUP), a multiplier term (K), and emission factor for burners 

(EFBURN). Values for the AUP parameter are selected from the specified lognormal 

distribution when the stove has a gas pilot light (GasPilot = 1) and set equal to zero 

otherwise (GasPilot = 2). The K parameter is equal to a point estimate (1.142E-4) when 

GasPilot = 1 and to zero when GasPilot = 2. EFBURN is selected from the specified 

lognormal distribution when GasPilot = 1 and to zero when GasPilot = 2. Resampling is 

not performed for any of these parameters; consequently, the product of the three 

parameters is constant over all hours for a particular simulated person. The parameter 

values and distributions were taken from Table 8 of Appendix A. 
35 

Micro number = 1 ! In residence 

Parameter Type = ES ! Gas stove, OffOn using P0 

Source number = 2 

Hours - Block = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 

Condition # 1 = GasStove 

ResampHours = YES 

ResampDays = YES 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 OffOn 0.975 0.025 . . 

2 1 1 1 1 1 1 OffOn 0.977 0.023 . . 

3 1 1 1 1 1 1 OffOn 0.977 0.023 . . 

4 1 1 1 1 1 1 OffOn 0.977 0.023 . . 

5 1 1 1 1 1 1 OffOn 0.977 0.023 . . 

6 1 1 1 1 1 1 OffOn 0.974 0.026 . . 

7 1 1 1 1 1 1 OffOn 0.951 0.049 . . 

8 1 1 1 1 1 1 OffOn 0.942 0.058 . . 

9 1 1 1 1 1 1 OffOn 0.919 0.081 . . 

10 1 1 1 1 1 1 OffOn 0.927 0.073 . . 

11 1 1 1 1 1 1 OffOn 0.938 0.062 . . 

12 1 1 1 1 1 1 OffOn 0.925 0.075 . . 

13 1 1 1 1 1 1 OffOn 0.915 0.085 . . 

14 1 1 1 1 1 1 OffOn 0.929 0.071 . . 

15 1 1 1 1 1 1 OffOn 0.933 0.067 . . 

16 1 1 1 1 1 1 OffOn 0.936 0.064 . . 

17 1 1 1 1 1 1 OffOn 0.893 0.107 . . 

18 1 1 1 1 1 1 OffOn 0.870 0.130 . . 

19 1 1 1 1 1 1 OffOn 0.909 0.091 . . 

20 1 1 1 1 1 1 OffOn 0.942 0.058 . . 

21 1 1 1 1 1 1 OffOn 0.948 0.052 . . 

22 1 1 1 1 1 1 OffOn 0.953 0.047 . . 

23 1 1 1 1 1 1 OffOn 0.960 0.040 . . 

24 1 1 1 1 1 1 OffOn 0.965 0.035 . . 

1 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

2 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

3 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

4 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

5 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

6 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

7 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

8 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

9 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

10 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

11 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

12 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

13 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

14 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

15 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

16 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

17 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

18 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

19 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

20 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

21 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

22 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

23 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

24 1 1 1 2 1 1 OffOn 1.0 0.0 . .

Figure 3-30. Section of the Microenvironmental Descriptions File Defining the On/Off 

Parameter Used in Modeling the Gas Stove Emission Source in 

Microenvironment 1. 

Micro number = 1 ! In residence 

Parameter Type = ES ! Gas stove duration M(h) 

Source number = 2 
36 

Hours - Block = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 24 

Condition # 1 = 0 

ResampHours = YES 

ResampDays = YES 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 Point 30.0 . . . 

2 1 1 1 1 1 1 Point 30.0 . . . 

3 1 1 1 1 1 1 Point 30.0 . . . 

4 1 1 1 1 1 1 Point 30.0 . . . 

5 1 1 1 1 1 1 Point 30.0 . . . 

6 1 1 1 1 1 1 Point 30.0 . . . 

7 1 1 1 1 1 1 Point 30.0 . . . 

8 1 1 1 1 1 1 Point 30.0 . . . 

9 1 1 1 1 1 1 Point 30.0 . . . 

10 1 1 1 1 1 1 Point 30.0 . . . 

11 1 1 1 1 1 1 Point 30.0 . . . 

12 1 1 1 1 1 1 Point 30.0 . . . 

13 1 1 1 1 1 1 Point 30.0 . . . 

14 1 1 1 1 1 1 Point 30.0 . . . 

15 1 1 1 1 1 1 Point 60.0 . . . 

16 1 1 1 1 1 1 Point 60.0 . . . 

17 1 1 1 1 1 1 Point 60.0 . . . 

18 1 1 1 1 1 1 Point 60.0 . . . 

19 1 1 1 1 1 1 Point 60.0 . . . 

20 1 1 1 1 1 1 Point 60.0 . . . 

21 1 1 1 1 1 1 Point 60.0 . . . 

22 1 1 1 1 1 1 Point 60.0 . . . 

23 1 1 1 1 1 1 Point 30.0 . . . 

24 1 1 1 1 1 1 Point 30.0 . . .

Figure 3-31. Section of the Microenvironmental Descriptions File Defining the Duration 

Parameter Used in Modeling the Gas Stove Emission Source in 

Microenvironment 1. 

Micro number = 1 

Parameter Type = ES ! Pilot light AUP Term 

Source number = 3 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = GasPilot 

ResampHours = NO 

ResampDays = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 Lognormal 840000. 9120000. 2760000. 

 1.84 

1 1 1 1 2 1 1 OffOn 1.0 0.0 . . 

Micro number = 1 

Parameter Type = ES ! Pilot light K Term 

Source number = 3 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = GasPilot 

ResampHours = NO 

ResampDays = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 Point 0.0001142 . . . 

1 1 1 1 2 1 1 OffOn 1.0 0.0 . . 
37 

Micro number = 1 ! In residence 

Parameter Type = ES ! Pilot light EFBURN Term 

Source number = 3 

Condition # 1 = GasPilot 

ResampHours = NO 

ResampDays = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 Lognormal 0. 400. 29.4 2.77 

1 1 1 1 2 1 1 Point 0. . . .

Figure 3-32. Section of the Microenvironmental Descriptions File Defining the AUP, K, 

and EFBURN Parameters Used in Modeling the Pilot Light Emission 

Source in Microenvironment 1. 
38 

3.15.2 Indoor Microenvironment 2

As indicated in Table 3-2, Microenvironment 2 includes two indoor non-residential 

locations: service stations and automobile repair shops. The mass balance method is 

used to estimate CO concentrations in this microenvironment. As there are assumed to 

be no major emission sources in the microenvironment, the mass balance model 

contains only three parameters (PRX, PEN, and AER). Figure 3-33 provides the input 

entries for these parameters. A single value of PRX is selected from the indicated 

lognormal distribution for each simulated person. PEN values are selected every hour 

from another lognormal distribution. An AER value is selected from the indicated 

lognormal distribution for each district (home and work). 

Micro number = 2 

Parameter Type = PRX 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 1.4864 5.9345 2.970 

1.5232 

Micro number = 2 

Parameter Type = PEN 

ResampHours = YES 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4482 2.2313 1.0 

1.6289 

Micro number = 2 

Parameter Type = AER 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 0.34 4.5 1.24 1.93

Figure 3-33. Section of the Microenvironmental Descriptions File Defining the PRX, 

PEN, and AER Parameters Used in Modeling Microenvironment 2. 
39 

The methods used to develop the PRX and PEN distributions are discussed on pages 

85 through 88 of Appendix A. The AER distribution is taken from Table 4-8 of the report 

by Johnson, Mihlan, LaPointe, Fletcher, et al. (2000). 

3.15.3 Indoor Microenvironment 3 

Microenvironment 3 includes another set of non-residential indoor locations (other repair 

shop and shopping mall). The mass balance method is again used to estimate CO 

concentrations in this microenvironment. There are no major emission sources; Figure 

3-34 provides the input entries for the three parameters (PRX, PEN, and AER) included 

in the mass balance model. A single value of PRX is selected from the indicated 

lognormal distribution for each simulated person. PEN values are selected every hour 

from another lognormal distribution. A value for AER is selected from the indicated 

lognormal distribution for both the home and work districts. The parameters for these 

distributions can be found on pages A-20 through A-23 of Appendix A and in Table 4-8 

of the report by Johnson, Mihlan, LaPointe, Fletcher, et al. (2000). 

3.15.4 Indoor Microenvironments 4 through 9 

Table 3-2 lists six additional indoor microenvironments (4 through 9) used in the 

application of APEX to Los Angeles. The file entries for these microenvironments are 

presented in Figures 3-35 through 3-40. The entries are similar in that each 

microenvironment is modeled using the mass balance method that includes only three 

parameters (PRX, PEN, and AER). In each case, a single value of PRX is sampled 

from a lognormal distribution for each simulated person. PEN values are sampled 

hourly from a lognormal distribution. These distributions are discussed on pages 85 

through 88 of Appendix A. Values of AER are sampled from a lognormal distribution for 

the home and work districts. The distribution parameters for AER were obtained from 

Table 4-8 of the report by Johnson, Mihlan, LaPointe, Fletcher, et al. (2000). 

3.15.5 Outdoor Microenvironments 10 and 11

Microenvironments 10 and 11 are labeled “Outdoors - Near Road” and “Outdoors - 

Other,” respectively. Each is modeled using the FACTORS method with two 

parameters (PRX and PEN). In each case, a single value of PRX is selected from the 

specified lognormal distribution for each sampled person and PEN is selected hourly 

from another lognormal distribution. Figures 3-41 and 3-42 present the parameters of 

these distributions. These values were obtained from pages A-20 through A-23 of 

Appendix A. 

Micro number = 3 

Parameter Type = PRX 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 
40 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .6071 2.4237 1.213 

1.5232 

Micro number = 3 

Parameter Type = PEN 

ResampHours = YES 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4482 2.2313 1.0 

1.6289 

Micro number = 3 

Parameter Type = AER 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 0.34 4.5 1.24 1.93

Figure 3-34. Section of the Microenvironmental Descriptions File Defining the PRX, 

PEN, and AER Parameters Used in Modeling Microenvironment 3. 

Micro number = 4 

Parameter Type = PRX 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .6071 2.4237 1.213 

1.5232 

Micro number = 4 

Parameter Type = PEN 

ResampHours = YES 
41 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4482 2.2313 1.0 

1.6289 

Micro number = 4 

Parameter Type = AER 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 0.34 4.5 1.24 1.93

Figure 3-35. Section of the Microenvironmental Descriptions File Defining the PRX, 

PEN, and AER Parameters Used in Modeling Microenvironment 4. 
42 

Micro number = 5 

Parameter Type = PRX 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .6071 2.4237 1.213 

1.5232 

Micro number = 5 

Parameter Type = PEN 

ResampHours = YES 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4482 2.2313 1.0 

1.6289 

Micro number = 5 

Parameter Type = AER 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 0.34 4.5 1.24 1.93

Figure 3-36. Section of the Microenvironmental Descriptions File Defining the PRX, 

PEN, and AER Parameters Used in Modeling Microenvironment 5. 
43 

Micro number = 6 

Parameter Type = PRX 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .6071 2.4237 1.213 

1.5232 

Micro number = 6 

Parameter Type = PEN 

ResampHours = YES 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4482 2.2313 1.0 

1.6289 

Micro number = 6 

Parameter Type = AER 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 0.34 4.5 1.24 1.93

Figure 3-37. Section of the Microenvironmental Descriptions File Defining the PRX, 

PEN, and AER Parameters Used in Modeling Microenvironment 6. 
44 

Micro number = 7 

Parameter Type = PRX 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .6071 2.4237 1.213 

1.5232 

Micro number = 7 

Parameter Type = PEN 

ResampHours = YES 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4482 2.2313 1.0 

1.6289 

Micro number = 7 

Parameter Type = AER 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 0.34 4.5 1.24 1.93

Figure 3-38. Section of the Microenvironmental Descriptions File Defining the PRX, 

PEN, and AER Parameters Used in Modeling Microenvironment 7. 
45 

Micro number = 8 

Parameter Type = PRX 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4950 1.9762 0.989 

1.5232 

Micro number = 8 

Parameter Type = PEN 

ResampHours = YES 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4482 2.2313 1.0 

1.6289 

Micro number = 8 

Parameter Type = AER 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 0.38 4.83 1.36 1.91

Figure 3-39. Section of the Microenvironmental Descriptions File Defining the PRX, 

PEN, and AER Parameters Used in Modeling Microenvironment 8. 
46 

Micro number = 9 

Parameter Type = PRX 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .5175 2.0661 1.034 

1.5232 

Micro number = 9 

Parameter Type = PEN 

ResampHours = YES 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4482 2.2313 1.0 

1.6289 

Micro number = 9 

Parameter Type = AER 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 0.34 4.5 1.24 1.93

Figure 3-40. Section of the Microenvironmental Descriptions File Defining the PRX, 

PEN, and AER Parameters Used in Modeling Microenvironment 9. 
47 

Micro number = 10 

Parameter Type = PRX 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .8042 3.211 1.607 1.5232

Micro number = 10 

Parameter Type = PEN 

ResampHours = YES 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4482 2.2313 1.0 

1.6289

Figure 3-41. Section of the Microenvironmental Descriptions File Defining the PRX and 

PEN Parameters Used in Modeling Microenvironment 10. 

Micro number = 11 

Parameter Type = PRX 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .7187 2.8693 1.436 

1.5232 

Micro number = 11 

Parameter Type = PEN 

ResampHours = YES 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4482 2.2313 1.0 1.6289

Figure 3-42. Section of the Microenvironmental Descriptions File Defining the PRX and 

PEN Parameters Used in Modeling Microenvironment 11. 
48 

3.15.6 Vehicle Microenvironments 12 and 13

Microenvironments 12 (Vehicle - Car) and 13 (Vehicle - Truck) are each modeled using 

the mass balance method to account for air exchange rate and emissions from 

environmental tobacco smoke. Figure 3-43 provides file entries for PRX, PEN, AER, 

and VOL (enclosed volume) applicable to cars. PRX, PEN, and VOL are each selected 

from a lognormal distribution; PRX and VOL are selected once per simulated person 

while PEN is selected each hour. AER is more complex: values are selected daily from 

lognormal distributions that vary with air conditioning status (AC_CAR) and average 

temperature category (AVGTEMPCAT). 

Cigarette emission rate is the product of the four ES terms defined in Figures 3-44 

through 3-46: emission rate per cigarette (μg/h), smoking in ME during day (1 = yes, 0 

= no), smoking during hour (1 = yes, 0 = no), and smoking duration during hour (min). 

The emission rate per cigarette is determined once for each simulated person by 

selecting value from the indicated lognormal distribution. The daily ME smoking status 

is determined daily by selecting values from the indicated off/on probability function. 

Consistent with Microenvironment 1, the occurrence of smoking in a car during a 

particular hour is determined by an “on/off” probability function defined by two 

parameters (Min and Max) that vary by block hour (Figure 3-45). New values for these 

parameters are resampled on both an hourly and daily basis. However, the car 

microenvironment differs from the residential microenvironment in that smoking duration 

is determined by a point estimate that varies by block hour (Figure 3-46). 

Microenvironment 13 (Vehicle - Truck) is modeled in a manner very similar to that used 

for Microenvironment 12 (Vehicle - Car). In both cases, the mass balance method is 

employed to account for air exchange rate and environmental tobacco smoke. Figure 

3-47 presents the file entries for PRX, PEN, AER, and VOL for trucks. Only the entries 

for VOL differ from the car-specific entries in Figure 3-43. In this case, VOL is selected 

from a lognormal distribution with minimum = 1.52, maximum = 2.25, geometric mean = 

1.8649, and geometric standard deviation = 1.2304. 

Cigarette emission rate is the product of the same four ES terms used for cars: 

emission rate per cigarette (μg/h), daily ME smoking status (1 = yes, 0 = no), smoking 

during hour (1 = yes, 0 = no), and smoking duration during hour (min). The file entries 

for emission rate per cigarette and daily ME smoking status specific to trucks (Figure 3-

48) are identical to those used for cars. However, the truck file entries differ from the 

car entries with respect to the Min and Max values specified for the smoking Off/On 

function in Figure 3-49 and the point estimates for smoking duration in Figure 3-50. 

The distributions used in simulating the vehicle microenvironments 12 and 13 were 

obtained from pages A-5 through A-7 (smoking parameters) and pages A-17 through A23 (PRX, PEN, AER, and volume) of Appendix A. 

Micro number = 12 

Parameter Type = PRX 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 
49 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 1.5114 6.0344 3.020 

1.5232 

Micro number = 12 

Parameter Type = PEN 

ResampHours = YES 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4482 2.2313 1.0 

1.6289 

Micro number = 12 ! Car 

Parameter Type = AER 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = AC_car 

Condition # 2 = AVGTEMPCAT 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = YES 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 1.0525 202.71 5.9465 1.9711

1 1 1 1 1 2 1 LogNormal 1.0525 202.71 16.2355 2.8134

1 1 1 1 1 3 1 LogNormal 1.0525 202.71 9.4915 2.6939

1 1 1 1 2 1 1 LogNormal 1.0525 202.71 6.4039 2.0550

1 1 1 1 2 2 1 LogNormal 1.0525 202.71 33.5219 1.8984

1 1 1 1 2 3 1 LogNormal 1.0525 202.71 37.5848 1.6916

Micro number = 12 

Parameter Type = VOL 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 Lognormal 1.93 4.81 2.7548 1.1506

Figure 3-43. Section of the Microenvironmental Descriptions File Defining the PRX, 

PEN, AER, and VOL Parameters Used in Modeling Microenvironment 12. 

Micro number = 12 ! Car 

Parameter Type = ES ! Cigarette smoke, micrograms per hour, S(m) term

Source number = 1 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 92800. 219800. 142800. 1.3 
50 

Micro number = 12 ! Car 

Parameter Type = ES ! Daily ME smoking status, Off/On 

Source number = 1 

ResampHours = NO 

ResampDays = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 OffOn 0.9281 0.0719 . .

Figure 3-44. Sections of the Microenvironmental Descriptions File Defining the 

Cigarette Emission Rate and Daily ME Smoking Parameters Used in 

Modeling the Environmental Tobacco Smoke Emission Source in 

Microenvironment 12. 
51 

Micro number = 12 ! Cars 

Parameter Type = ES ! Cigarette smoke, Off/On 

Source number = 1 

Hours - Block = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 24 

ResampHours = YES 

ResampDays = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 OffOn 0.3750 0.6250 . . 

2 1 1 1 1 1 1 OffOn 0.4783 0.5217 . . 

3 1 1 1 1 1 1 OffOn 0.5455 0.4545 . . 

4 1 1 1 1 1 1 OffOn 0.5000 0.5000 . . 

5 1 1 1 1 1 1 OffOn 0.3333 0.6667 . . 

6 1 1 1 1 1 1 OffOn 0.6111 0.3889 . . 

7 1 1 1 1 1 1 OffOn 0.6441 0.3559 . . 

8 1 1 1 1 1 1 OffOn 0.6333 0.3667 . . 

9 1 1 1 1 1 1 OffOn 0.7143 0.2857 . . 

10 1 1 1 1 1 1 OffOn 0.5532 0.4468 . . 

11 1 1 1 1 1 1 OffOn 0.5045 0.4955 . . 

12 1 1 1 1 1 1 OffOn 0.5461 0.4539 . . 

13 1 1 1 1 1 1 OffOn 0.4857 0.5143 . . 

14 1 1 1 1 1 1 OffOn 0.5241 0.4759 . . 

15 1 1 1 1 1 1 OffOn 0.5429 0.4571 . . 

16 1 1 1 1 1 1 OffOn 0.5494 0.4506 . . 

17 1 1 1 1 1 1 OffOn 0.5141 0.4859 . . 

18 1 1 1 1 1 1 OffOn 0.4555 0.5445 . . 

19 1 1 1 1 1 1 OffOn 0.4785 0.5215 . . 

20 1 1 1 1 1 1 OffOn 0.4902 0.5098 . . 

21 1 1 1 1 1 1 OffOn 0.4741 0.5259 . . 

22 1 1 1 1 1 1 OffOn 0.4565 0.5435 . . 

23 1 1 1 1 1 1 OffOn 0.4487 0.5513 . . 

24 1 1 1 1 1 1 OffOn 0.5306 0.4694 . . 

Figure 3-45. Section of the Microenvironmental Descriptions File Defining the Smoking 

Occurrence Parameter Used in Modeling the Environmental Tobacco 

Smoke Emission Source in Microenvironment 12. 

Micro number = 12 ! Cars 

Parameter Type = ES ! Cigarette smoke duration 

Source number = 1 

Hours - Block = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 24 

ResampHours = YES 

ResampDays = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 Point 0.9603 . . . 

2 1 1 1 1 1 1 Point 1.0000 . . . 

3 1 1 1 1 1 1 Point 1.0000 . . . 

4 1 1 1 1 1 1 Point 1.0000 . . . 

5 1 1 1 1 1 1 Point 1.0000 . . . 

6 1 1 1 1 1 1 Point 1.0000 . . . 

7 1 1 1 1 1 1 Point 0.9614 . . . 

8 1 1 1 1 1 1 Point 0.9222 . . . 

9 1 1 1 1 1 1 Point 0.8724 . . . 

10 1 1 1 1 1 1 Point 0.9338 . . . 

11 1 1 1 1 1 1 Point 0.9843 . . . 

12 1 1 1 1 1 1 Point 0.9625 . . . 

13 1 1 1 1 1 1 Point 0.9471 . . . 
52 

14 1 1 1 1 1 1 Point 0.9619 . . . 

15 1 1 1 1 1 1 Point 0.9625 . . . 

16 1 1 1 1 1 1 Point 0.9448 . . . 

17 1 1 1 1 1 1 Point 0.9256 . . . 

18 1 1 1 1 1 1 Point 0.9038 . . . 

19 1 1 1 1 1 1 Point 0.9321 . . . 

20 1 1 1 1 1 1 Point 0.9465 . . . 

21 1 1 1 1 1 1 Point 0.9779 . . . 

22 1 1 1 1 1 1 Point 0.9814 . . . 

23 1 1 1 1 1 1 Point 0.9648 . . . 

24 1 1 1 1 1 1 Point 0.9638 . . . 

Figure 3-46. Section of the Microenvironmental Descriptions File Defining the Smoking 

Duration Parameter Used in Modeling the Environmental Tobacco Smoke 

Emission Source in Microenvironment 12. 
53 

Micro number = 13 ! Trucks 

Parameter Type = PRX 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 1.5114 6.0344 3.020 

1.5232 

Micro number = 13 

Parameter Type = PEN 

ResampHours = YES 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4482 2.2313 1.0 

1.6289

Micro number = 13 ! Truck 

Parameter Type = AER 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = AC_car 

Condition # 2 = AVGTEMPCAT 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = YES 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 1.0525 202.71 5.9465 1.9711

1 1 1 1 1 2 1 LogNormal 1.0525 202.71 16.2355 2.8134

1 1 1 1 1 3 1 LogNormal 1.0525 202.71 9.4915 2.6939

1 1 1 1 2 1 1 LogNormal 1.0525 202.71 6.4039 2.0550

1 1 1 1 2 2 1 LogNormal 1.0525 202.71 33.5219 1.8984

1 1 1 1 2 3 1 LogNormal 1.0525 202.71 37.5848 1.6916

Micro number = 13 

Parameter Type = VOL 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 Lognormal 1.52 2.25 1.8649 1.2304

Figure 3-47. Section of the Microenvironmental Descriptions File Defining the PRX, 

PEN, AER, and VOL Parameters Used in Modeling Microenvironment 13. 
54 

Micro number = 13 ! 

Parameter Type = ES ! Cigarette smoke, micrograms per hour, S(m) term

Source number = 1 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 92800. 219800. 142800. 1.3 

Micro number = 13 ! Truck 

Parameter Type = ES ! Daily ME smoking status, Off/On 

Source number = 1 

ResampHours = NO 

ResampDays = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 OffOn 0.9199 0.0801 . .

Figure 3-48. Sections of the Microenvironmental Descriptions File Defining the 

Cigarette Emission Rate and Daily ME Smoking Parameters Used in 

Modeling the Environmental Tobacco Smoke Emission Source in 

Microenvironment 13. 
55 

Micro number = 13 ! Trucks 

Parameter Type = ES ! Cigarette smoke, Off/On Hourly 

Source number = 1 

Hours - Block = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 24 

ResampHours = YES 

ResampDays = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 OffOn 0.5000 0.5000 . . 

2 1 1 1 1 1 1 OffOn 1.0000 0.0000 . . 

3 1 1 1 1 1 1 OffOn 0.0000 1.0000 . . 

4 1 1 1 1 1 1 OffOn 0.0000 1.0000 . . 

5 1 1 1 1 1 1 OffOn 0.5000 0.5000 . . 

6 1 1 1 1 1 1 OffOn 0.4444 0.5556 . . 

7 1 1 1 1 1 1 OffOn 0.4286 0.5714 . . 

8 1 1 1 1 1 1 OffOn 0.3636 0.6364 . . 

9 1 1 1 1 1 1 OffOn 0.5000 0.5000 . . 

10 1 1 1 1 1 1 OffOn 0.3333 0.6667 . . 

11 1 1 1 1 1 1 OffOn 0.3200 0.6800 . . 

12 1 1 1 1 1 1 OffOn 0.3333 0.6667 . . 

13 1 1 1 1 1 1 OffOn 0.4231 0.5769 . . 

14 1 1 1 1 1 1 OffOn 0.5000 0.5000 . . 

15 1 1 1 1 1 1 OffOn 0.2308 0.7692 . . 

16 1 1 1 1 1 1 OffOn 0.3571 0.6429 . . 

17 1 1 1 1 1 1 OffOn 0.3667 0.6333 . . 

18 1 1 1 1 1 1 OffOn 0.3871 0.6129 . . 

19 1 1 1 1 1 1 OffOn 0.3913 0.6087 . . 

20 1 1 1 1 1 1 OffOn 0.1875 0.8125 . . 

21 1 1 1 1 1 1 OffOn 0.4000 0.6000 . . 

22 1 1 1 1 1 1 OffOn 0.4545 0.5455 . . 

23 1 1 1 1 1 1 OffOn 0.2500 0.7500 . . 

24 1 1 1 1 1 1 OffOn 0.2500 0.7500 . .

Figure 3-49. Section of the Microenvironmental Descriptions File Defining the Smoking 

Occurrence Parameter Used in Modeling the Environmental Tobacco 

Smoke Emission Source in Microenvironment 13. 

Micro number = 13 ! Trucks 

Parameter Type = ES ! Cigarette smoke duration 

Source number = 1 

Hours - Block = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

23 24 

ResampHours = YES 

ResampDays = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 Point 1.0000 . . . 

2 1 1 1 1 1 1 Point 1.0000 . . . 

3 1 1 1 1 1 1 Point 1.0000 . . . 

4 1 1 1 1 1 1 Point 1.0000 . . . 

5 1 1 1 1 1 1 Point 0.6250 . . . 

6 1 1 1 1 1 1 Point 1.0000 . . . 

7 1 1 1 1 1 1 Point 0.9583 . . . 

8 1 1 1 1 1 1 Point 0.9781 . . . 

9 1 1 1 1 1 1 Point 0.9929 . . . 

10 1 1 1 1 1 1 Point 0.8494 . . . 

11 1 1 1 1 1 1 Point 1.0000 . . . 

12 1 1 1 1 1 1 Point 1.0000 . . . 

13 1 1 1 1 1 1 Point 1.0000 . . . 

14 1 1 1 1 1 1 Point 0.9181 . . . 

15 1 1 1 1 1 1 Point 0.9755 . . . 
56 

16 1 1 1 1 1 1 Point 1.0000 . . . 

17 1 1 1 1 1 1 Point 0.9098 . . . 

18 1 1 1 1 1 1 Point 0.8929 . . . 

19 1 1 1 1 1 1 Point 1.0000 . . . 

20 1 1 1 1 1 1 Point 0.9184 . . . 

21 1 1 1 1 1 1 Point 1.0000 . . . 

22 1 1 1 1 1 1 Point 1.0000 . . . 

23 1 1 1 1 1 1 Point 0.7777 . . . 

24 1 1 1 1 1 1 Point 0.9723 . . .

Figure 3-50. Section of the Microenvironmental Descriptions File Defining the Smoking 

Duration Parameter Used in Modeling the Environmental Tobacco Smoke 

Emission Source in Microenvironment 13. 
57 

3.15.7 Mass Transit Microenvironment 14 

Microenvironment 14 includes all mass transit vehicles. The mass balance method is 

used to account for the effects of air exchange rate in this microenvironment; no 

emission sources are modeled. Figure 3-51 provides the file entries for PRX, PEN, and 

AER. One PRX value is sampled from a lognormal distribution for each simulated 

person, and PEN values are sampled from a lognormal distribution at hourly intervals. 

AER values are sampled from a uniform distribution with minimum = 1.8 h-1 and 

maximum = 5.6 1.8 h-1 on a daily basis. These entries are consistent with the 

parameters proposed on pages A-17 through A-23 of Appendix A. 

Micro number = 14 

Parameter Type = PRX 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 1.5114 6.0344 3.020 

1.5232 

Micro number = 14 

Parameter Type = PEN 

ResampHours = YES 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4482 2.2313 1.0 

1.6289 

Micro number = 14 

Parameter Type = AER 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 Uniform 1.8 5.6 . .

Figure 3-51. Section of the Microenvironmental Descriptions File Defining the PRX, 

PEN, and AER Parameters Used in Modeling Microenvironment 14. 
58 

3.14.8 Outdoor Garage Microenvironment 15 

Microenvironment 15 (outdoor - garage) is modeled using the FACTORS method with 

two parameters (PRX and PEN), the same approach employed for the other two 

outdoor microenvironments (10: outdoors, near road and 11: outdoors, other). As 

indicated by Figure 3-52, a single value of PRX is selected from the specified lognormal 

distribution for each simulated person and PEN is selected hourly from another 

lognormal distribution. The values were obtained from pages A-17 through A-23 of 

Appendix A. 

Micro number = 15 

Parameter Type = PRX 

Hours - Block = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 

Weekday - Daytype = 1 1 1 1 1 1 1 

Month - Season = 1 1 1 1 1 1 1 1 1 1 1 1 

District- Area = 1 1 1 1 1 1 1 1 1 1 

Condition # 1 = 0 

Condition # 2 = 0 

Condition # 3 = 0 

ResampHours = NO 

ResampDays = NO 

ResampWork = NO 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal 1.4864 5.9345 2.970 

1.5232 

Micro number = 15 

Parameter Type = PEN 

ResampHours = YES 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Min Max Par1 Par2 

1 1 1 1 1 1 1 LogNormal .4482 2.2313 1.0 1.6289

Figure 3-52. Section of the Microenvironmental Descriptions File Defining the PRX and 

PEN Parameters Used in Modeling Microenvironment 15. 
1 

SECTION 4 

OUTPUT FILES 

The sample application of APEX produced the seven output files listed in Table 1-4. 

This section provides a brief description of the contents of each file. 

4.1 Log File 

The Log file contains the record of the APEX model simulation as it progresses. If the 

simulation completes successfully, the log file indicates the input files and parameter 

settings used for the simulation and reports on a number of different factors. If the 

simulation ends prematurely, the log file contains error messages describing the critical 

errors that caused the simulation to end. 

Figures 4-1 and 4-2 are sections of the Log file obtained from the sample application, 

which completed successfully. Figure 4-1 lists the study area location, pollutant, input 

and output concentration units, scenario (“as is” = no adjustment of air quality data), and 

start and end dates for the simulation period. It also lists the names of the input and 

output files associated with the run. Figure 4-2 lists 26 job parameters, including the 

number of profiles generated, number of microenvironments defined, number of sources 

simulated, minimum and maximum age of simulated people, information used in 

defining districts, the random seed specified, and various parameters listed in the 

Params file. 

Due to its extreme length, the remainder of the Log file is not shown here. It lists the 

counties included in the study area, the names of the population input files, user-defined 

distributions, the sectors included in the final study area, the air quality districts, 

population statistics by sector, data related to METS (energy expenditure rates), various 

microenvironmental parameters, demographic characteristics of each generated profile, 

counts of various demographic factors, and the output tables. The output tables are 

also included in a separate file (see Section 4.7). 

4.2 Hourly Exposure File

The Hourly Exposure file provides an hour-by-hour time series of exposure estimates 

for each modeled profile. Figure 4-3, which provides a partial listing of this file, shows 

the hourly average CO exposures for Person #1 for the first 12 days of the simulation 

period. The complete file provides data for each of 1000 simulated people over the 

entire calendar year 1997. 

4.3 Hourly Dose File

The format of the Hourly Exposure file, which provides an hour-by-hour time series of 

dose (%COHb) estimates for each modeled profile, is similar to that of the Hourly 

Exposure Output File. Figure 4-3 provides a partial listing of the file, showing the hourly 
2 

dose values for Person #1 for the first 12 days of the simulation period. 

 Log file for APEX3.23 model run 

 Location = Los Angeles 

 Pollutant = CO 

 InputUnits = ppm 

 OutputUnits = ppm 

 Scenario = as is 

 Start date = 19970101 

 End date = 19971231 

 

 File names used for this run: 

 Input: 

 Pop. Sectors = C:\apex32\input\tp_geo.txt 

 Air Districts = C:\apex32\input\districts.txt 

 Temperature Zones = C:\apex32\input\zones.txt 

 Temperature Data = C:\apex32\input\lamet97.txt 

 Air Quality Data = C:\apex32\input\AirQuality.txt 

 METS distributions = C:\apex32\input\CHADMets.txt 

 Physiology = C:\apex32\input\Physiology.txt 

 Age & Employment = C:\apex32\input\tp_minmax.txt 

 Commuting = c:\apex32\input\comm2000.txt 

 Distributions = C:\apex32\input\Pfunctionsla.txt 

 Location Mapping = C:\apex32\input\micromap15.txt 

 Diary Events = C:\apex32\input\ChadEvents.txt 

 Diary Questionaire = C:\apex32\input\ChadQuest.txt 

 Microenvironments = C:\apex32\input\mP15_la.txt 

 Output: 

 Log = C:\apex32\output\la_log.txt 

 Hourly Exposure = C:\apex32\output\la_exp.txt 

 Hourly Dose = C:\apex32\output\la_dose.txt 

 Profile Summary = C:\apex32\output\la_psum.txt 

 Micro. Summary = C:\apex32\output\la_msum.txt 

 Summary tables = C:\apex32\output\la_tables.txt 

 Site list = C:\apex32\output\la_sites.txt

Figure 4-1. Section of the Log Output File Listing Run Identification Information, Input 

File Names, and Output File Names. 

Job Parameters: 

 # Personal profiles = 1000 

 # Microenvironments = 15 

 # Enumerated Sources = 3 

 Age minimum = 18 

 Age maximum = 95 

 City center latitude = 34.00000 

 City center longitude = -118.0000 

 Study area radius (km) = 300.0000 

 List of counties = Yes 

 Commuting implemented = Yes 

 Air district radius = 10.00000 

 Temp. zone radius = 300.0000 

 Daylight Savings adjust = Yes 

 Save hourly output = Yes 

 Calculate doses = Yes 

 Rollback = No 

 Rollback factor = 1.000000 
3 

 Rollback background = 0.0000000E+00 

 Missing gender probab. = 0.0000000E+00 

 Missing age probab. = 0.0000000E+00 

 Missing employ. probab. = 0.0000000E+00 

 Age window width, pct. = 20.00000 

 Age shoulder probab. = 10.00000 

 Altitude in feet = 328.0000 

 Debug Level = 0 

 Random number seed = 10000

Figure 4-2. Section of the Log Output File Listing Job Parameters. 
4 

 Person Day# Year Mn Dy Wkd Tmp Hour1 Hour2 Hour3 Hour4 

 Hour5 Hour6 Hour7 Hour8 Hour9 Hour10 Hour11 

Hour12 Hour13 Hour14 Hour15 Hour16 Hour17 Hour18 Hour19

 Hour20 Hour21 Hour22 Hour23 Hour24 

 1 1 1997 1 1 WED 68 0.396E+01 0.455E+01 0.398E+01 0.445E+01 

0.486E+01 0.541E+01 0.526E+01 0.501E+01 0.376E+01 0.351E+01 0.293E+01 

0.243E+01 0.215E+01 0.187E+01 0.146E+01 0.131E+01 0.120E+01 0.371E+01 

0.223E+01 0.308E+01 0.380E+01 0.297E+01 0.326E+01 0.288E+01 

 1 2 1997 1 2 THU 62 0.266E+01 0.317E+01 0.303E+01 0.306E+01 

0.306E+01 0.285E+01 0.287E+01 0.206E+01 0.112E+01 0.146E+01 0.156E+01 

0.366E+01 0.479E+01 0.354E+01 0.287E+01 0.213E+01 0.232E+01 0.278E+01 

0.241E+01 0.268E+01 0.225E+01 0.219E+01 0.236E+01 0.223E+01 

 1 3 1997 1 3 FRI 65 0.197E+01 0.167E+01 0.155E+01 0.148E+01 

0.269E+01 0.568E+00 0.358E+00 0.466E+00 0.692E+00 0.108E+01 0.136E+01 

0.118E+01 0.962E+00 0.912E+00 0.942E+00 0.303E+01 0.218E+01 0.185E+01 

0.242E+01 0.198E+01 0.241E+01 0.283E+01 0.293E+01 0.347E+01 

 1 4 1997 1 4 SAT 62 0.365E+01 0.351E+01 0.317E+01 0.219E+01 

0.211E+01 0.157E+01 0.486E+00 0.462E+00 0.892E+00 0.106E+01 0.757E+00 

0.477E+00 0.300E+00 0.573E+00 0.794E+00 0.935E+00 0.107E+01 0.191E+01 

0.142E+01 0.142E+01 0.273E+01 0.178E+01 0.214E+01 0.209E+01 

 1 5 1997 1 5 SUN 59 0.232E+01 0.254E+01 0.210E+01 0.177E+01 

0.204E+01 0.224E+01 0.202E+01 0.197E+01 0.187E+01 0.187E+01 0.257E+01 

0.176E+01 0.176E+01 0.188E+01 0.209E+01 0.681E+00 0.673E+00 0.625E+00 

0.532E+00 0.255E+01 0.178E+01 0.203E+01 0.255E+01 0.266E+01 

 1 6 1997 1 6 MON 62 0.129E+01 0.121E+01 0.103E+01 0.988E+00 

0.130E+01 0.138E+01 0.145E+01 0.197E+01 0.149E+01 0.130E+01 0.108E+01 

0.748E+00 0.450E+00 0.670E+00 0.183E+01 0.460E+00 0.150E+01 0.168E+01 

0.220E+01 0.173E+01 0.138E+01 0.133E+01 0.128E+01 0.135E+01 

 1 7 1997 1 7 TUE 66 0.149E+01 0.164E+01 0.188E+01 0.236E+01 

0.247E+01 0.240E+01 0.277E+01 0.672E+01 0.313E+01 0.111E+01 0.966E+00 

0.831E+00 0.649E+00 0.833E+00 0.986E+00 0.100E+01 0.116E+01 0.252E+01 

0.176E+01 0.195E+01 0.207E+01 0.219E+01 0.238E+01 0.233E+01 

 1 8 1997 1 8 WED 65 0.214E+01 0.166E+01 0.133E+01 0.142E+01 

0.185E+01 0.177E+01 0.319E+01 0.210E+01 0.314E+01 0.309E+01 0.311E+01 

0.235E+01 0.275E+01 0.270E+01 0.118E+01 0.262E+01 0.954E+00 0.107E+01 

0.122E+01 0.106E+01 0.776E+00 0.116E+01 0.472E+01 0.459E+01 

 1 9 1997 1 9 THU 63 0.413E+01 0.354E+01 0.339E+01 0.384E+01 

0.344E+01 0.676E+01 0.345E+01 0.420E+01 0.531E+01 0.535E+01 0.452E+01 

0.350E+01 0.207E+01 0.194E+01 0.155E+01 0.142E+01 0.173E+01 0.573E+01 

0.400E+01 0.408E+01 0.407E+01 0.450E+01 0.473E+01 0.519E+01 

 1 10 1997 1 10 FRI 58 0.889E+01 0.741E+01 0.735E+01 0.581E+01 

0.388E+01 0.297E+01 0.314E+01 0.449E+01 0.686E+01 0.657E+01 0.479E+01 

0.377E+01 0.542E+01 0.447E+01 0.237E+01 0.290E+01 0.490E+01 0.265E+01 

0.327E+01 0.334E+01 0.303E+01 0.414E+01 0.216E+01 0.418E+01 

 1 11 1997 1 11 SAT 58 0.441E+01 0.477E+01 0.468E+01 0.451E+01 

0.499E+01 0.518E+01 0.483E+01 0.450E+01 0.411E+01 0.422E+01 0.437E+01 

0.357E+01 0.156E+01 0.273E+01 0.224E+01 0.128E+01 0.178E+01 0.213E+01 

0.141E+01 0.297E+01 0.331E+01 0.106E+01 0.291E+01 0.450E+01 

 1 12 1997 1 12 SUN 53 0.544E+01 0.420E+01 0.249E+01 0.189E+01 

0.161E+01 0.251E+01 0.343E+01 0.357E+01 0.278E+01 0.275E+01 0.191E+01 

0.207E+01 0.222E+01 0.269E+01 0.331E+01 0.339E+01 0.301E+01 0.221E+01 

0.320E+01 0.182E+01 0.330E+01 0.348E+01 0.234E+01 0.158E+01 

etc. 

Figure 4-3. Partial Listing of the Hourly Exposure Output File. 
5 

 Person Day# Year Mn Dy Wkd Tmp Hour1 Hour2 Hour3 Hour4 

 Hour5 Hour6 Hour7 Hour8 Hour9 Hour10 Hour11 

Hour12 Hour13 Hour14 Hour15 Hour16 Hour17 Hour18 Hour19

 Hour20 Hour21 Hour22 Hour23 Hour24 

 1 1 1997 1 1 WED 68 0.573E+00 0.619E+00 0.658E+00 0.692E+00 

0.730E+00 0.772E+00 0.813E+00 0.841E+00 0.820E+00 0.785E+00 0.750E+00 

0.713E+00 0.675E+00 0.619E+00 0.560E+00 0.508E+00 0.470E+00 0.487E+00 

0.518E+00 0.528E+00 0.563E+00 0.585E+00 0.579E+00 0.558E+00 

 1 2 1997 1 2 THU 62 0.554E+00 0.573E+00 0.595E+00 0.615E+00 

0.633E+00 0.646E+00 0.650E+00 0.627E+00 0.519E+00 0.441E+00 0.401E+00 

0.438E+00 0.531E+00 0.598E+00 0.583E+00 0.543E+00 0.511E+00 0.508E+00 

0.522E+00 0.521E+00 0.520E+00 0.516E+00 0.530E+00 0.543E+00 

 1 3 1997 1 3 FRI 65 0.547E+00 0.545E+00 0.541E+00 0.533E+00 

0.535E+00 0.503E+00 0.421E+00 0.326E+00 0.257E+00 0.241E+00 0.257E+00 

0.265E+00 0.257E+00 0.251E+00 0.251E+00 0.278E+00 0.349E+00 0.364E+00 

0.382E+00 0.400E+00 0.422E+00 0.455E+00 0.489E+00 0.526E+00 

 1 4 1997 1 4 SAT 62 0.564E+00 0.597E+00 0.622E+00 0.627E+00 

0.589E+00 0.557E+00 0.452E+00 0.376E+00 0.341E+00 0.330E+00 0.302E+00 

0.272E+00 0.246E+00 0.227E+00 0.222E+00 0.225E+00 0.242E+00 0.272E+00 

0.285E+00 0.291E+00 0.333E+00 0.368E+00 0.394E+00 0.420E+00 

 1 5 1997 1 5 SUN 59 0.446E+00 0.473E+00 0.495E+00 0.506E+00 

0.515E+00 0.528E+00 0.537E+00 0.542E+00 0.543E+00 0.523E+00 0.479E+00 

0.459E+00 0.440E+00 0.444E+00 0.436E+00 0.379E+00 0.300E+00 0.262E+00 

0.242E+00 0.271E+00 0.321E+00 0.345E+00 0.377E+00 0.416E+00 

 1 6 1997 1 6 MON 62 0.433E+00 0.433E+00 0.435E+00 0.438E+00 

0.438E+00 0.439E+00 0.442E+00 0.444E+00 0.428E+00 0.376E+00 0.344E+00 

0.314E+00 0.290E+00 0.265E+00 0.278E+00 0.276E+00 0.280E+00 0.299E+00 

0.332E+00 0.364E+00 0.374E+00 0.383E+00 0.376E+00 0.371E+00 

 1 7 1997 1 7 TUE 66 0.384E+00 0.401E+00 0.420E+00 0.443E+00 

0.469E+00 0.491E+00 0.504E+00 0.610E+00 0.700E+00 0.582E+00 0.474E+00 

0.389E+00 0.330E+00 0.294E+00 0.274E+00 0.263E+00 0.258E+00 0.324E+00 

0.357E+00 0.382E+00 0.408E+00 0.426E+00 0.449E+00 0.472E+00 

 1 8 1997 1 8 WED 65 0.491E+00 0.498E+00 0.497E+00 0.497E+00 

0.499E+00 0.501E+00 0.512E+00 0.538E+00 0.554E+00 0.579E+00 0.599E+00 

0.563E+00 0.548E+00 0.564E+00 0.540E+00 0.443E+00 0.407E+00 0.355E+00 

0.333E+00 0.322E+00 0.303E+00 0.283E+00 0.361E+00 0.468E+00 

 1 9 1997 1 9 THU 63 0.541E+00 0.584E+00 0.612E+00 0.640E+00 

0.660E+00 0.725E+00 0.765E+00 0.737E+00 0.760E+00 0.807E+00 0.817E+00 

0.789E+00 0.736E+00 0.644E+00 0.571E+00 0.503E+00 0.450E+00 0.517E+00 

0.606E+00 0.642E+00 0.674E+00 0.706E+00 0.742E+00 0.779E+00 

 1 10 1997 1 10 FRI 58 0.907E+00 0.104E+01 0.110E+01 0.112E+01 

0.110E+01 0.106E+01 0.102E+01 0.995E+00 0.101E+01 0.105E+01 0.105E+01 

0.102E+01 0.973E+00 0.958E+00 0.815E+00 0.666E+00 0.690E+00 0.642E+00 

0.622E+00 0.626E+00 0.626E+00 0.643E+00 0.638E+00 0.640E+00 

 1 11 1997 1 11 SAT 58 0.677E+00 0.716E+00 0.752E+00 0.780E+00 

0.808E+00 0.838E+00 0.863E+00 0.880E+00 0.888E+00 0.889E+00 0.890E+00 

0.866E+00 0.765E+00 0.689E+00 0.627E+00 0.531E+00 0.464E+00 0.437E+00 

0.430E+00 0.439E+00 0.497E+00 0.383E+00 0.409E+00 0.454E+00 

 1 12 1997 1 12 SUN 53 0.526E+00 0.595E+00 0.616E+00 0.611E+00 

0.600E+00 0.597E+00 0.612E+00 0.637E+00 0.654E+00 0.652E+00 0.632E+00 

0.604E+00 0.584E+00 0.577E+00 0.587E+00 0.603E+00 0.625E+00 0.606E+00 

0.594E+00 0.570E+00 0.563E+00 0.590E+00 0.598E+00 0.579E+00 

etc. 

Figure 4-4. Partial Listing of the Hourly Dose Output File. 
6 

4.4 Profile Summary File

The Profile Summary files provides values for the parameters that define the profile of 

each simulated person. The file includes information on location [home sector, work 

sector (if applicable), temperature zone], demographic characteristics (demographic 

group, age, gender, race, employment status), potential impacts of selected CO sources 

(gas stoves, gas pilot lights), air conditioning status (home and car), and physiology 

(height, weight, hemoglobin, lung diffusivity factor (Diffday), blood volume, hemoglobin 

factor, and endogenous CO production rates. The file also lists selected exposure and 

dose statistics for the simulated person (e.g., average exposure). Figure 4-5, which 

provides a partial listing of the Profile Summary File, contains data for the first 12 

simulated people. 

 Person HSect WSect HDis WDis Zone DGRP Age Gender Race Empl Stove Pilot

ACHom ACCar Height Weight Hemoglob Diffday BloodVol HemFac Endgn1 

 Endgn2 #Events AvgExp AvgDose MaxExp MaxDose 

 1 265 1041 3 7 1 2 23 Male Asian Works NoGas NoPlt

HasAC HasAC 71.840 181.064 15.261 36.898 6196.00 0.000000 0.008649 

0.008649 13515 0.2294E+01 0.5027E+00 0.1371E+02 0.1607E+01 

 2 725 725 6 6 1 11 73 Female White NoWrk Gas NoPlt

NoAC HasAC 72.033 205.433 13.057 25.685 5503.40 0.000000 0.007359 

0.004605 14307 0.1656E+01 0.4834E+00 0.2176E+02 0.1399E+01 

 3 141 124 3 3 1 4 46 Male White Works NoGas NoPlt

NoAC HasAC 72.328 160.865 14.316 31.738 5835.91 0.000000 0.008666 

0.008666 13548 0.2022E+01 0.5210E+00 0.1437E+02 0.1328E+01 

 4 952 220 4 3 1 7 20 Female Black Works NoGas NoPlt

HasAC HasAC 63.536 145.562 12.745 27.056 3834.20 0.000000 0.006243 

0.003907 14239 0.2105E+01 0.4780E+00 0.2014E+02 0.1719E+01 

 5 303 303 3 3 1 10 63 Female Black NoWrk Gas Pilot

NoAC HasAC 64.051 152.439 12.514 22.398 3977.24 0.000000 0.010846 

0.006787 14016 0.1642E+01 0.6789E+00 0.7837E+01 0.1220E+01 

 6 63 63 2 2 1 11 67 Female White NoWrk Gas Pilot

NoAC HasAC 65.733 131.633 14.259 22.873 3817.54 0.000000 0.011777 

0.007369 13794 0.1301E+01 0.6644E+00 0.8062E+01 0.1073E+01 

 7 1322 557 8 4 1 2 44 Male White Works NoGas NoPlt

HasAC NoAC 69.362 150.053 16.660 31.132 5310.30 0.000000 0.006965 

0.006965 13924 0.1366E+01 0.4230E+00 0.1392E+02 0.1045E+01 

 8 309 309 1 1 1 8 27 Female Other NoWrk NoGas NoPlt

NoAC HasAC 62.577 188.909 13.683 25.718 4389.46 0.000000 0.008844 

0.005534 16255 0.1499E+01 0.4519E+00 0.1033E+02 0.9775E+00 

 9 564 564 7 7 1 10 50 Female White NoWrk Gas NoPlt

HasAC HasAC 61.507 157.488 15.540 22.478 3846.91 0.000000 0.007035 

0.004402 14760 0.1913E+01 0.4739E+00 0.1015E+02 0.1051E+01 

 10 504 216 1 3 1 7 36 Female White Works Gas NoPlt

HasAC HasAC 62.792 120.094 13.654 24.802 3401.92 0.000000 0.010476 

0.006555 14144 0.1324E+01 0.4911E+00 0.1686E+02 0.1338E+01 

 11 1343 1343 1 1 1 8 40 Female White NoWrk Gas NoPlt

NoAC NoAC 62.409 168.251 14.881 24.130 4074.54 0.000000 0.011140 

0.006971 14918 0.8140E+00 0.3905E+00 0.1373E+02 0.1065E+01 

 12 990 990 4 4 1 3 39 Male White NoWrk Gas NoPlt

NoAC NoAC 68.799 160.720 14.330 32.089 5472.88 0.000000 0.011435 

0.011435 13542 0.1467E+01 0.5281E+00 0.1129E+02 0.1219E+01 

etc. 

Figure 4-5. Partial Listing of Profile Summary File. 
7 

4.5 Microenvironment Summary File

The Microenvironment Summary file lists the number of minutes spent in each 

microenvironment by each simulated person, the mean pollutant concentration 

(“MeanConc”) experienced in the microenvironment by the person, and the maximum 

concentration associated with an exposure event (“MaxConc”) in the microenvironment. 

 Figure 4-6 provides a partial listing of this file; it shows the file entries for Person #1 and 

Person #2. The complete file contains listings for all 1000 simulated people. Note that 

the listings for each person include the 15 microenvironments defined for the 

assessment (numbered 1 through 15) and a “ZeroExposure” (No. 0) microenvironment 

that is used to tabulate exposures when the microenvironment is not defined. All 

exposures in Microenvironment No. 0 are assigned a CO concentration of zero. 

 

 Person Micro# Micro Name Minutes MeanConc MaxConc 

 1 0 ZeroExposure 1500 0.000000 0.000000 

 1 1 Residence 305604 2.447296 10.019948 

 1 2 insideoA 5554 3.371410 12.486727 

 1 3 inside_oB 3907 1.432879 3.816188 

 1 4 Restaurant 7640 0.819590 2.420799 

 1 5 Bar 2961 1.087247 2.346964 

 1 6 inside_oE 0 0.000000 0.000000 

 1 7 inside_oF 67769 1.840998 7.350540 

 1 8 inside_oG 42306 1.653526 5.184237 

 1 9 indoor_garag 2051 2.145931 4.520467 

 1 10 Outdoorn_roa 9593 1.534676 6.586651 

 1 11 Outdoor_othe 35977 1.572160 7.693139 

 1 12 Car 26714 3.864897 13.708302 

 1 13 Truck 11272 3.940155 19.973602 

 1 14 MT_Vehicle 1043 2.650021 5.707592 

 1 15 Outdoor_gara 1709 1.924787 8.388002 

 2 0 ZeroExposure 0 0.000000 0.000000 

 2 1 Residence 425943 1.525181 7.282927 

 2 2 insideoA 41 8.844779 10.956852 

 2 3 inside_oB 7389 1.740446 5.172035 

 2 4 Restaurant 7100 0.967166 2.668336 

 2 5 Bar 567 1.178537 1.974309 

 2 6 inside_oE 163 1.490694 1.677743 

 2 7 inside_oF 14253 1.793276 5.376172 

 2 8 inside_oG 19091 1.363949 4.757785 

 2 9 indoor_garag 183 1.416974 2.198241 

 2 10 Outdoorn_roa 4778 2.028392 8.512715 

 2 11 Outdoor_othe 18968 0.767300 2.717813 

 2 12 Car 24487 4.423753 18.410887 

 2 13 Truck 1544 7.430559 21.757242 

 2 14 MT_Vehicle 349 4.535996 8.335470 

 2 15 Outdoor_gara 744 2.851016 5.999593 

etc. 

Figure 4-6. Partial Listing of Microenvironment Summary File. 
8 

4.6 Site File

The Site File lists each sector used in the APEX assessment. The entries for each 

sector include the name and coordinates of air quality monitoring site that defines the 

district containing the sector and the name and coordinates of the meteorological station 

that determines the temperature data to be used in the district. The file also lists the 

distances between the sector and the air quality site and between the sector and the 

meteorological station. Figure 4-7 provides a partial listing of this file. 

 sector# latitude longitude sectorname air# airdistance 

airlatitude airlongitude airname tem# temdistance 

temlatitude temlongitude temname 

 1 34.2592 -118.2919 06037101110 2 8.9333 

 34.1800 -118.3100 Site2 1 39.3682 34.0000 

 -118.0000 TempSite1 

 2 34.2663 -118.2894 06037101120 2 9.7509 

 34.1800 -118.3100 Site2 1 39.7885 34.0000 

 -118.0000 TempSite1 

 3 34.2518 -118.2915 06037101210 2 8.1365 

 34.1800 -118.3100 Site2 1 38.7504 34.0000 

 -118.0000 TempSite1 

 4 34.2516 -118.2816 06037101220 2 8.3517 

 34.1800 -118.3100 Site2 1 38.1013 34.0000 

 -118.0000 TempSite1 

 5 34.2477 -118.2707 06037101300 2 8.3303 

 34.1800 -118.3100 Site2 1 37.1030 34.0000 

 -118.0000 TempSite1 

 6 34.2458 -118.2900 06037101400 2 7.5163 

 34.1800 -118.3100 Site2 1 38.1748 34.0000 

 -118.0000 TempSite1 

 7 34.2335 -118.3498 06037102101 2 6.9686 

 34.1800 -118.3100 Site2 1 41.3627 34.0000 

 -118.0000 TempSite1 

 8 34.2156 -118.3476 06037102102 2 5.2486 

 34.1800 -118.3100 Site2 1 39.9892 34.0000 

 -118.0000 TempSite1 

 9 34.2624 -118.3083 06037103102 2 9.1269 

 34.1800 -118.3100 Site2 1 40.6679 34.0000 

 -118.0000 TempSite1 

 10 34.2621 -118.3524 06037103300 2 9.8998 

 34.1800 -118.3100 Site2 1 43.5886 34.0000 

 -118.0000 TempSite1 

 11 34.2569 -118.3145 06037103400 2 8.5311 

 34.1800 -118.3100 Site2 1 40.6436 34.0000 

 -118.0000 TempSite1 

 12 34.2241 -118.4038 06037121010 2 9.9253 

 34.1800 -118.3100 Site2 1 44.7674 34.0000 

 -118.0000 TempSite1 

etc.

Figure 4-7. Partial Listing of Site File. 

4.7 Output Tables File

Appendix B contains the 14 tables included in the Output Tables File produced by the 

case study run. These tables summarize the results of the simulation with respect to 

number of minutes spent in each microenvironment at specified CO concentrations, 

daily maximum 1-hour and 8-hour CO exposures, daily average and yearly-average CO 

exposures, daily maximum 1-hour and 8-hour %COHb levels, daily maximum end-ofhour %COHb levels, daily average and yearly-average %COHb levels, and minutes at 
9 

specified %COHb levels. The percentiles and cut-off points used in these tables were 

defined in the Params file. 
1 

SECTION 5 

REFERENCES 

Total Risk Integrated Methodology. TRIM.ExpoInhalation User’s Document. Volume I: Air 

Pollutants Exposure Model (APEX, Version 3) User’s Guide. Office of Air Quality 

Planning and Standards, U.S. Environmental Protection Agency, Research Triangle 

Park, NC. April 24, 2003. Draft. 

Johnson, T., Mihlan, G., LaPointe, J., Fletcher, K., Rosenbaum, A., Cohen, J., and 

Stiefer, P. Estimation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO (Version 

2.1). Report prepared under Contract No. 68-D6-0064 for the Office of Air Quality 

Planning and Standards, U.S. Environmental Protection Agency, Research Triangle 

Park, NC, June 19, 2000. 
1 

APPENDIX A

Input Values and Other Modifications Proposed for 

Application of APEX3.1 to Los Angeles
2 

MEMORANDUM

To: Steve Fudge 

EC/R Incorporated 

From: Ted Johnson (TRJ Environmental, Inc.) 

Jim Capel (consultant) 

Date: August 22, 2002 (Revision 7) 

Re: Input Values and Other Modifications Proposed for Application of APEX3.1 to 

Los Angeles 

The Office of Air Quality Planning and Standards within the U.S. Environmental 

Protection Agency (EPA) is currently planning to (1) set up and apply the APEX 

exposure model to the Los Angeles study area used in recent pNEM/CO assessments, 

(2) obtain estimates of carbon monoxide (CO) exposures and resulting carboxyhemoglobin (COHb) levels comparable in format to the pNEM/CO estimates, and (3) 

compare the estimates obtained from the two models with the goal of determining 

whether APEX is an acceptable alternative to pNEM/CO in applications of this kind. To 

make the comparison as valid as possible, EPA proposes that the input modeling 

parameters, internal operations, and output formats of APEX be as consistent as 

possible with those used in the pNEM/CO assessment. 

Work Assignment No. 1-7 of EPA Contract No. 68-D0-1076 directed the EC/R team to 

(1) use the current version of APEX (APEX3) to perform an exposure 

assessment for Los Angeles (LA) using inputs that were developed for a 

pNEM/CO exposure assessment of LA and 

(2) compare the results with exposure estimates previously obtained by the 

pNEM exposure assessment. 

As part of this effort, the authors reviewed the input requirements of APEX3, the 

methods used within APEX3 to process these inputs, and the content and format of the 

exposure estimates produced by the model. In conducting this review, we classified 

each significant element within the APEX3 modeling process as to the effort required to 

make the element consistent with the corresponding element in the pNEM/CO analysis 

of Los Angeles. We also noted cases in which the current internal structure of APEX3 

prevented the user from implementing algorithmic operations that could be carried out in 

the current version of pNEM/CO. In each of these cases, we proposed a method to 

circumvent the problem through the use of an external probabilistic program. 
3 

Our memorandum dated July 11, 2002, summarizes our review of APEX3 and our 

proposals for applying APEX3 to Los Angeles. Where relevant, the memorandum 

includes material from an earlier memorandum dated September 30, 2001, prepared by 

J. Capel, T. Johnson, J. Cohen, and A. Rosenbaum under EPA Contract No. 68-D6-

0064. 

In the memorandum of July 11, we noted that APEX3 could not implement a number of 

the more complex probabilistic algorithms found in pNEM/CO. We described an 

approach in which these algorithms would be implemented by programs external to 

APEX3 and the results then used as input distributions for APEX3. This approach was 

implemented through a work effort beginning in March 2002 and completed in May 

2002. The results of this work are described in an earlier draft of this memorandum 

(Version 3) available from TRJ. At the time of this effort, it was our understanding that 

APEX3 would not be enhanced prior to its application to LA. 

Subsequent to the completion of the work summarized in the draft memorandum, EPA 

requested that Ted Johnson identify possible changes to APEX3 that would facilitate its 

use in performing pNEM-type exposure assessments. Ted Johnson provided the 

following five recommendations to John Langstaff on May 30, 2002. 

6. Enable APEX3 to resample source term distributions and the Pzero function at different 

frequencies. For example, gas stove emission rate could be sampled yearly and the Pzero 

function for gas stove operation could be implemented hourly. This enhancement was 

initially proposed during a conference call with EPA and ManTech on May 21, 2002. 

7. Enable the user to specify two or more temperature variables (e.g., daily maximum and 

daily average) in defining temperature categories. 

8. Enable the user of APEX to create two or more distributions applicable to the source 

terms of the mass balance model such that the distributions can be (1) sampled at 

different frequencies and (2) the sampled values from each distribution can be multiplied 

or added together to produce a resultant emission rate value. The user should also be 

able to specify a separate Pzero function for each distribution with its own sampling 

frequency. This enhancement would permit analysts to estimate the CO mass emitted by 

a gas stove during a particular hour as the product of stove use time during the hour 

(Distribution A, values selected hourly) x hourly CO emission rate (Distribution B, 

values selected yearly). Stove on/off conditions would be determined by the Pzero 

function. 

9. Enable the user to define distributions using histogram-type data (i.e, fraction of 

distribution falling in user-defined ranges). 

10. Add an output table showing cumulative person-hours of end-of-hour dose (COHb) 

levels for area population. 

We were notified on July 1, 2002, that EPA had directed ManTech to implement 

recommendation nos. 1, 2, and 3 by the beginning of August and that we should plan on using 
4 

this enhanced version of APEX to perform the exposure assessment of LA. 

On August 6, 2002, we received Version 3.1 of APEX (APEX3.1) and a description of the 

program enhancements made by ManTech. We reviewed APEX3.1 and evaluated each of the 

“work-arounds” described in the earlier version of this memorandum in light of APEX3.1's new 

capabilities. We determined that the proposed fixes for passive smoking and gas stoves needed 

to be revised to take advantage of various program enhancements. We confirmed that the other 

fixes proposed in the earlier memorandum were still valid. This latest version of the 

memorandum provides a description of each fix as it is currently being implemented in 

APEX3.1. 

Pertinent Enhancements Incorporated Into APEX3.1

APEX3.1 incorporates three enhancements that will facilitate its use in performing pNEM-style 

exposure assessments. 

Multiplication of Source Terms with Varying Sampling Frequencies. Unlike APEX3, 

APEX3.1 permits the user to define an emission source in a particular microenvironment 

(ME) as the product of two or more terms, each term with its own distribution and 

sampling frequency. Consequently, it is now possible to calculate an hourly emission 

rate as the product of values randomly selected from distributions representing (1) 

probability of emission source use (yes/no) during the hour, (2) duration of use (minutes) 

during the hour, and (3) emission rate (μg/m3

 -- with each value being selected according 

to a different sampling frequency (hourly, daily, yearly, etc.). APEX3 was structurally 

incapable of performing such a calculation internally. 

New distribution (OffOn). In APEX3, the user had the option of specifying a Pzero 

function for each source term. The Pzero function would determine whether the source 

was on or off during a particular time interval. If the source was on, a value would be 

selected from the emission rate distribution. Unfortunately, Pzero and the associated 

emission source distribution had to be sampled at the same frequency -- a restriction that 

severely limited the use of Pzero in implementing the more complex pNEM/CO 

algorithms. APEX3.1 bypasses this problem by introducing a new distribution function 

(OffOn) that can be sampled independently of any other source term. 

Flexibility in specifying temperature variable. In APEX3, many of the operations 

involving a daily temperature variable were constrained to use a specific variable 

“hardwired” to the operation. APEX3.1 permits the user to use either of two temperature 

variables to control such operations. In the application of APEX3.1 to LA, the variables 

will be daily maximum temperature and daily average temperature. 

The following sections describe how these program enhancements are being used to implement 

various pNEM/CO algorithms within APEX3.1. 

Passive Smoking

In applying Version 2.1 of pNEM/CO to Los Angeles, the design team assumed that passive 
5 

smoking occurred in three enclosed ME’s (residence, car, and truck). The pNEM/CO model 

determined passive smoking events based on codes for smoking status present in the activity 

pattern (exposure event sequences) associated with each cohort. Consistent with APEX3, 

APEX3.1 does not permit the use of activity patterns in determining passive smoking events. 

Instead, APEX3.1 allows the user to specify the probability that passive smoking will occur 

during an hour, the distribution of smoking duration, and the distribution for the CO emission 

rates associated with these passive smoking events. 

In pNEM/CO, the emission rate per hour from passive smoking is calculated by the formula 

S = (nsmokers)(ncigs/smoker/hr)(COcigarette) (1) 

in which S is the emission rate in μg/hr, nsmokers is the number of smokers present, ncigs/smoker/hr is 

the number of cigarettes smoked per hour per smoker, and COcigarette is the mass of CO emitted 

per cigarette. Table 1 lists the distributions used for each parameter in each of the three 

microenvironments. 

Table 1. Parameters of Distributions Used in pNEM/CO Passive Smoking Algorithm. 

ME nsmokers ncigs/smoker/hr

COcigarette

GM, μg/hr GSD Min, μg/hr Max, μg/hr 

1: indoors - 

residence 

1 2 71,400 1.3 46,400 109,900 

12: in car 1 2 71,400 1.3 46,400 109,900 

13: in truck 1 2 71,400 1.3 46,400 109,900 

The three sets of parameters are identical. In each case, the distribution of hourly emission rates 

can be represented by a lognormal distribution with a geometric mean (GM) equal to 142,800 

μg/hr (i.e., 1 smoker x 2 cigarettes per smoker per hour x 71,400 μg/hr per cigarette). The 

corresponding minimum and maximum values are 92,800 μg/hr and 219,800 μg/hr, respectively. 

 The geometric standard deviation (GSD) remains the same (1.3). 

Ideally, we would program APEX3.1 to generate hourly emission rates for passive smoking 

using the above distribution and then adjust each value by the number of minutes of passive 

smoking occurring in the hour. The adjustment would be performed as follows. Let S(m) equal 

the value for hourly emission rate selected for ME m from the specified lognormal distribution 

(GM = 142,800, GSD = 1.3, min = 92,800, max = 219,800). This value remains constant for the 

entire exposure period. Let R(m,h) equal the fraction of hour h during which passive smoking 

occurs. Then COmass(m,h) -- the CO mass emitted by passive smoking during hour h in ME m -- 

can be calculated by the following expression. 

COmass(m,h) = R(m,h) x S(m). (2) 
6 

Unfortunately, R(m,h) is typically a “U-shaped” distribution with modes at zero and 1. Such a 

distribution cannot be closely fit by any of the distribution options currently included in 

APEX3.1. 

We have implemented an alternative approach in which the following equation is substituted for 

Equation 2. 

COmass(m,h) = O(m,d) x O(m,h) x Davg(m,h) x S(m). (3) 

In Equation 3, O(m,d) is equal to 0 when no smoking is expected to occur in the ME during the 

day and equal to 1 when smoking is expected to occur at least once during the day. The 

probability that O(m,d) will equal zero is P0(m,d) and varies with day and microenvironment. 

When O(m,d) = 1, the O(m,h) term determines whether passive smoking occurs in the ME 

during hour h; that is, O(m,h) is equal to 0 if no smoking occurs during the hour and equal to 1 if 

smoking occurs during the hour. The probability that O(m,h) will equal zero is P0(m,h) and 

varies with hour of day and microenvironment. Values of O(m,h) are sampled hourly. 

Davg(m,h) is the average smoking duration when and if smoking occurs and is thus calculated 

using only non-zero duration values (expressed as fractions of hour). Davg(m,h) is a point 

estimate that varies with microenvironment and hour of day. 

Estimates of P0(m,d), P0(m,h), and Davg(m,h) for the three relevant ME’s (residence, car, and 

truck) were obtained from statistical analyses of the CHAD data to be used in the Los Angeles 

APEX3.1 application. We began by calculating (1) the number of person-days in which each 

ME was visited and no passive smoking was reported and (2) the total number of days in which 

each ME was visited. As indicated below, P0(m,d) was set equal to the ratio of these two values. 

 

 Number of person-days in ME

Microenvironment No passive smoking Total P0(m,d)

Residence 6841 8044 0.8504 

Car 5746 6191 0.9281 

Truck 896 974 0.9199 

Each of the three P0(m,d) values exceeds 0.85, indicating that passive smoking occurs relatively 

infrequently in these ME’s. 

Table 2 lists estimates of P0(m,h) by time of day for the three ME’s. These values were obtained 

from an analysis restricted to those person-days in which passive smoking occurred sometime 

during the day in the specified ME. For example, the values for residence were based on the 

1203 person-days (i.e., 8044 - 6841 person-days) in which subjects reported passive smoking at 

least once during the day in the residential ME. The value of P0(m,h) listed in Table 2 for hour 1 

(0.8502) was calculated as the ratio of the number of person-hours in the indicated time interval 

during which no passive smoking occurred (965) divided by the total number of person-hours in 
7 

the time interval (1135). 

Table 3 provides estimates for Davg(m,h) by time of day for the three ME’s. Each value was 

calculated by the expression 

Davg(m,h) = Mean[Smoke(m,h)/Total(m,h)], (4) 

in which Total(m,h) was the total number of minutes spent in the indicated ME during hour h 

and Smoke(m,h) was the number of minutes of passive smoking in the ME during hour h. Note 

that only non-zero values of the quotient Smoke(m,h)/Total(m,h) were averaged to obtain the 

listed values. We calculated these values using the same CHAD data set used to estimate the 

P0(m,h) values discussed above. 

Table 4 provides a summary of the parameter values to be used in implementing this algorithm 

in APEX3.1. 

Gas Stove Operation

The program enhancements incorporated into APEX3.1 have enabled us to faithfully simulate 

the operation of the pNEM/CO algorithm for gas stoves with one minor simplication. This 

section first describes the pNEM/CO algorithm and then shows how this algorithm was 

implemented within the APEX3.1 framework. 

pNEM/CO Version. Version 2.1 of pNEM/CO differentiated between gas stoves with 

and without electronic ignitions and provided a separate algorithm for each stove type. 

The Monte Carlo algorithm described in Subsection 4.4.4 of Johnson, Mihlan, LaPointe 

et al. (2000) was used to randomly determine pilot light status for each cohort defined 

as having gas stoves. 

Gas stoves without electronic ignition were assumed to have continuously burning pilot 

lights. The mass of CO emitted by a gas stove with a pilot light during a particular hour 

(h) was estimated by the equation 

MASSCO(h) = (ERBURN)[M(h)]/60 + (ERPILOT)(1 hr) (5) 

where MASSCO(h) is expressed in mg, ERBURN is the hourly burn emission rate in mg 

Table 2. Average probability that passive smoking will not occur 

[P0(m,h)] for the indicated combination of microenvironment and hour. 

Based on person-days in which subject reported that passive smoking 

occurred sometime during the day in the indicated microenvironment. 

End time of hour 

P0(m,h) by microenvironment and hour 

Indoors - residence In car In truck 

1 0.8502 0.3750 0.5000 

2 0.8744 0.4783 1.0000 
8 

3 0.8951 0.5455 0.0000 

4 0.9097 0.5000 0.0000 

5 0.9103 0.3333 0.5000 

6 0.9042 0.6111 0.4444 

7 0.8883 0.6441 0.4286 

8 0.8556 0.6333 0.3636 

9 0.8278 0.7143 0.5000 

10 0.7852 0.5532 0.3333 

11 0.7449 0.5045 0.3200 

12 0.7150 0.5461 0.3333 

13 0.6401 0.4857 0.4231 

14 0.6231 0.5241 0.5000 

15 0.6216 0.5429 0.2308 

16 0.6092 0.5494 0.3571 

17 0.5876 0.5141 0.3667 

18 0.5608 0.4555 0.3871 

19 0.5062 0.4785 0.3913 

20 0.4850 0.4902 0.1875 

21 0.4828 0.4741 0.4000 

22 0.5417 0.4565 0.4545 

23 0.6330 0.4487 0.2500 

24 0.7583 0.5306 0.2500
9 

Table 3. Average duration (fraction of hour) of smoking when and if it occurs 

[Davg(m,h)] for the indicated combination of microenvironment and hour. 

End time of hour 

Davg(m,h) by microenvironment and hour 

Indoors - residence In car In truck 

1 0.9182 0.9603 1.0000 

2 0.9602 1.0000 1.0000a

3 0.9634 1.0000 1.0000 

4 0.9824 1.0000 1.0000 

5 0.9492 1.0000 0.6250 

6 0.8693 1.0000 1.0000 

7 0.8491 0.9614 0.9583 

8 0.7910 0.9222 0.9781 

9 0.7979 0.8724 0.9929 

10 0.8105 0.9338 0.8494 

11 0.8508 0.9843 1.0000 

12 0.8849 0.9625 1.0000 

13 0.8533 0.9471 1.0000 

14 0.9089 0.9619 0.9181 

15 0.9129 0.9625 0.9755 

16 0.9190 0.9448 1.0000 

17 0.9029 0.9256 0.9098 

18 0.8267 0.9038 0.8929 

19 0.7830 0.9321 1.0000 

20 0.8296 0.9465 0.9184 

21 0.8776 0.9779 1.0000 

22 0.8825 0.9814 1.0000 

23 0.8588 0.9648 0.7777 

24 0.8420 0.9638 0.9723 
10 

a

Assumed value (no CHAD data available for this time interval). 
11 

Table 4. Parameter Values for Use in Equation 3 (Passive Smoking). 

Term Selection frequency Distribution Parameter Value 

O(m,d) Daily OffOn P0(m,d) Residence: 0.8504 

Car: 0.9281 

Truck: 0.9199 

O(m,h) Hourly OffOn P0(m,h) See Table 2 

Davg(m,h) Hourly Point estimate Constant See Table 3 

S(m) 

μg/hr 

Annual Lognormal GM 142,800 

GSD 1.3 

Min 92,800 

Max 219,800 

per hour, and ERPILOT is the hourly pilot light emission rate in mg per hour. M(h) is the 

duration of burner use during hour h expressed in minutes. The pilot light is assumed to 

be on continuously during the one hour period. 

The mass of CO emitted by a gas stove with electronic ignition was estimated by the 

equation 

MASSCO(h) = (ERBURN)[M(h)]/60; (6) 

i.e., the ERPILOT term in Equation 5 was set equal to zero. 

In both equations, M(h) is zero for each hour in which the algorithm assigns the stove a 

status of "off." If the stove status is "on" for a particular hour, M(h) is assigned a value 

of 30 or 60 minutes according to Table 5. 

ERBURN was determined by the equation 

ERBURN = (AUB/365.2)(EFBURN)(Y) (7) 

where AUB is the annual fuel usage of the burners in kilojoules, 365.2 is the number of 

hours per year that the burners are operated assuming 60 minutes of use per day 

(Table 5), EFBURN is the burner emission factor in mg of CO per kilojoule, and Y is an 

adjustment factor which varies sinusoidally throughout the year. The values of AUB and 

EFBURN are specific to study area; Y varies with the Julian date. 

In the application of pNEM/CO to Los Angeles, the values of AUB required by Equation 

7 were randomly selected from a lognormal distribution with geometric mean = 1.73 
12 

Table 5. Probability of Gas Stove Use by Clock Hour and Assumed Burner 

Operation Period for pNEM/CO and APEX3.1 Applications 

Clock hour 

AP(h) 

 probability that gas 

stove will operate during 

hour 

P0(h) = 1 - AP(h) 

probability that gas 

stove will not operate 

during hour 

M(h): assumed burner 

operation period, 

minutes 

1 0.025 0.975 30 

2 0.023 0.977 30 

3 0.023 0.977 30 

4 0.023 0.977 30 

5 0.023 0.977 30 

6 0.026 0.974 30 

7 0.049 0.951 30 

8 0.058 0.942 30 

9 0.081 0.919 30 

10 0.073 0.927 30 

11 0.062 0.938 30 

12 0.075 0.925 30 

13 0.085 0.915 30 

14 0.071 0.929 30 

15 0.067 0.933 60 

16 0.064 0.936 60 

17 0.107 0.893 60 

18 0.130 0.870 60 

19 0.091 0.909 60 

20 0.058 0.942 60 

21 0.052 0.948 60 

22 0.047 0.953 60 

23 0.040 0.960 30 

24 0.035 0.965 30
13 

million kilojoules and geometric standard deviation = 1.48. The value of AUB was 

bounded by 0.8 and 3.73 million kilojoules. These value represent the 2.5th and 97.5th

percentiles of the lognormal distribution. 

The seasonal adjustment factor (Y) was determined by the equation 

Y(j) = 1.00 - (0.190){sin[-1.616 + (2π)(j)/365]}. (8) 

in which j is the Julian date. This equation was derived by Johnson (1998) from a 

sinusoidal pattern observed in a study conducted by NIGAS (Wilkes and Koontz, 1995). 

 The NIGAS data indicate that gas use in the winter is approximately 46 percent higher 

than in the summer. 

Values of EFBURN were randomly selected from a lognormal distribution with geometric 

mean = 0.0294 mg/kilojoule and geometric standard deviation = 2.77. Values of 

EFBURN were not permitted to exceed 0.400 mg/kilojoule. 

Consistent with the above discussion, the following algorithm was used to estimate a 

value of ERBURN for each 24-hour period. 

1. Randomly select a value for AUB from a lognormal distribution with geometric 

mean = 1.73 million kilojoules per year and geometric standard deviation = 1.48. 

 

2. Randomly select a value of EFBURN from a lognormal distribution with geometric mean = 0.0294 mg/kilojoule and geometric standard deviation = 

2.77. 

3. Go to the next day of the current sequence. The Julian date of this day is j. 

1. Use Equation 8 to calculate Y(j). 

4. ERBURN = (EFBURN)(AUB)(Y)/365.2 for all hours of the day. 

5. Go to Step 3. Repeat for all days of the year. 

The resulting daily values of ERBURN were inserted into Equation 7 as required. 

The ERPILOT value in Equation 5 was determined by the equation 

ERPILOT = (AUP/8760)(EFPILOT) (9) 

where AUP is the annual fuel usage by all pilot lights in kilojoules, 8760 is the number of hours 

per year that the pilot lights are in operation, and EFPILOT is the pilot light emission factor in 

mg of CO per kilojoule. The value of AUP was held constant over the entire year and was 

randomly selected from a specified lognormal distribution. The

value of EFPILOT was assumed to be constant over the entire year and was set equal 
14 

to the value determined for EFBURN in Equation 7. 

Values of AUP for Los Angeles were randomly selected from a lognormal distribution 

with geometric mean = 2.76 million kilojoules per year and geometric standard deviation 

= 1.84. Values of AUP were not permitted to fall below 0.84 million kilojoules per year 

or above 9.12 million kilojoules per year. These bounds correspond to the 2.5th and 

97.5th percentiles, respectively, of the specified lognormal distribution. 

The value of MASSCO(h) as determined by Equation 5 or 6 was used as the value of S 

for hour h in the mass balance model, regardless of the value of M(h). This approach 

permitted the use of the hourly average exact solution to the mass-balance equation. 

The practical result of this simplification was a slight smoothing in the simulated hour-tohour variation in indoor CO concentrations with respect to the pattern which would be 

simulated by a model with finer time resolution. 

APEX3.1 Version. We have implemented the pNEM/CO algorithm described above “as 

is” in APEX3.1 with one minor simplification. Rather than using Equation 8 to determine 

a value of Y for each calendar day, we specify an average value of Y for each calendar 

month. This approach is necessary because APEX3.1 does not permit the user to vary 

distributional parameters such as Y on a daily basis. 

The pNEM/CO algorithm for gas stoves with electronic ignition (i.e., burners only) can 

be expressed as 

burners: COmass =[O(h)](AUB/365.2)(EFBURN)(Y)[M(h)]/60. (10) 

O(h) is equal to 0 if no cooking occurs during the hour and equal to 1 if cooking occurs 

during the hour. The probability that O(h) will equal zero is P0(h) and varies with hour of 

day. Table 5 lists values of P0(h) which are equal to 1 - AP(h). Note that values of O(h) 

are sampled hourly. 

Equation 10 can be simplified to the following expression: 

burners: COmass =(K)[O(h)](AUB)(EFBURN)(Y)[M(h)]. (11) 

K equals 4.564 x 10-5. Inserting the Julian dates 1 through 365 into Equation 8 yields 

the monthly average values for Y listed in Table 6. 

Table 5 lists the M(h) value assigned to each hour in pNEM/CO. These same values 

were used in APEX3.1. 

Consistent with the application of pNEM/CO to Los Angeles, the value of AUB required 

by Equation 11 is randomly selected on an annual basis from a lognormal distribution 

with geometric mean = 1.73 million kilojoules and geometric standard deviation = 1.48. 

The value of AUB is bounded by 0.8 and 3.73 million kilojoules (the 2.5th and 97.5th

percentiles of the lognormal distribution). Values of EFBURN are likewise randomly 

Table 6. Average values of Y by month. 
15 

Month Average Y

January 1.1828 

February 1.1404 

March 1.0630 

April 0.9658 

May 0.8779 

June 0.8227 

July 0.8155 

August 0.8589 

September 0.9400 

October 1.0373 

November 1.1246 

December 1.1782 

selected annually from a lognormal distribution with geometric mean = 0.0294 

mg/kilojoule and geometric standard deviation = 2.77. Values of EFBURN are not 

permitted to exceed 0.400 mg/kilojoule. Table 7 provides a summary of these 

parameter values. 

Version 2.1 of pNEM/CO provides separate algorithms for estimating COmass values for 

gas stoves with pilot lights and for gas stoves with electric ignitions. The inclusion of the 

pilot light and burners in the same algorithm enabled us to use the same randomlyselected value for the emission factors of the burners and pilot light of a particular 

simulated stove. Thus a stove with “dirty” burners would be assigned a “dirty” pilot light. 

In APEX3.1, we have elected to treat pilot lights as a separate CO source activated 

whenever (1) the personal profile indicates that the individual had a gas stove and (2) a 

pilot light conditional variable determines that the gas stove had a pilot light. The pilot 

light conditional variable in APEX3.1 was set to assign pilot lights to gas stoves for 51 

percent of the generated individuals, based on the prevalence rate employed in the 

application of pNEM/CO to Los Angeles. 

Consistent with the pNEM/CO algorithm, we assumed that the hourly COmass value for 

pilot lights could be calculated by the following equation 

pilot lights: COmass = (K)(AUP)(EFPILOT) (12) 
16 

in which K = 1/8760 = 1.142 x 10-4 and AUP is selected from a bounded lognormal 

distribution (GM = 2.76E6 kilojoules, GSD = 1.84, minimum = 0.84E6 kilojoules, and 

maximum = 9.12E6 kilojoules). Note that the APEX3.1 method differs from pNEM/CO 

in that we were not able to set the value of EFPILOT to the value determined for 

EFBURN above. (As noted previously, the pNEM/CO algorithm matches EFPILOT to 

EFBURN for gas stoves with pilot lights.) Table 8 provides a summary of the parameter 

values used in simulating gas stove operation in APEX3.1. 

Air Exchange Rates in Residences

APEX3.1 and pNEM/CO differ significantly with respect to the methods used to estimate 

air exchange rate (AER) in the “indoor - residential” microenvironment. In the 

application of pNEM/CO to Los Angeles, a set of air conditioning (AC) probability 

fractions were used city-wide to determine which of three AC categories applied to a 

particular residence (32% central AC, 21% window units, and 47% none). After the AC 

category was determined, pNEM/CO used a probabilistic algorithm to determine window 

position (open all day, closed all day, mixed) as a function of AC system and daily 

average temperature. The algorithm then used window position to determine the 

method by which air exchange rate was determined (select value from an “open 

window” distribution, select value from a “closed window” distribution, or calculate a 

weighted average of values selected from the open and closed window distributions). 

The closed window distribution of AER varied by season; a single distribution was used 

to represent AER for open windows. 

APEX3.1 also provides inputs for “AC probability fractions” as a conditional variable. 

However, APEX3.1 provides only two AC status types (essentially “AC present” and 

“AC absent”) rather than the three types provided by pNEM/CO. The user may also 

specify a set of ranges for daily average temperature. For each combination of AC type 

and temperature range, the user may provide a probability for the windows being open 

or closed. A distribution of air exchange rates may be linked to the window position 

(open or closed) and to the season. Consistent with the input data, APEX3.1 randomly 

determines air conditioning availability and then checks the temperature to determine 

the window position. A value for air exchange rate is then selected from the appropriate 

distribution. Unlike pNEM/CO, the APEX3.1 procedure for determining residential air 

exchange rates (1) restricts AC type to either available or not and (2) allows for only two 

window positions - open all day or closed all day. 

We developed an external “fix” to address these deficiencies in APEX3.1. We used the 

Crystal Ball (tm) software package to develop a stand-alone Monte Carlo program 

(WIN_SIM1.XLS) that duplicated the algorithm in pNEM/CO that determines window 

status and resulting AER. As the pNEM/CO algorithm provides AER’s for three AC 

categories (central, room, and none), we added a final step to the algorithm that 

randomly selected between the values generated for central and room AC according to 

the prevalence of these categories in Los Angeles. The selected AER values were 

assigned to the APEX3.1 category “AC present.” The AER values generated for the 

pNEM/CO category “none” were assigned to the APEX3.1 category “AC absent.” Using 

this program, we generated 5000 representative AER values for each combination of 
17 

Table 7. Parameter Values for Use in Equation 11 (Gas Stove Burners). 

Term 

Selection 

frequency Distribution Parameter Value 

K Annual Point estimate Constant 4.564 x 10-5

O(h) Hourly OffOn P0(h) See Table 5 

M(h) 

minutes 

Hourly Point estimate Constant See Table 5 

AUB 

kilojoules 

Annual Lognormal GM 1.73 x 106

GSD 1.48 

Min 0.8 x 106

Max 3.73 x 106

EFBURN 

mg/kilojoule 

Annual Lognormal GM 0.0294 

GSD 2.77 

Min 0 

Max 0.400 

Y’ Monthly Point estimate Constant See Table 6 

Table 8. Parameter Values for Use in Equation 12 (Gas Stove Pilot Lights). 

Term 

Selection 

frequency Distribution Parameter Value 

K Annual Point estimate Constant 1.142 x 10-4

AUP 

kilojoules 

Annual Lognormal GM 2.76 x 106

GSD 1.84 

Min 0.84 x 106

Max 9.12 x 106

EFBURN 

mg/kilojoule 

Annual — use 

same value 

selected for 

burners 

Lognormal GM 0.0294 

GSD 2.77 

Min 0 
18 

Max 0.400 

season, AC category (present or absent), and temperature range. Table 9 lists the 

parameters of the lognormal distribution fit to the AER values generated for each 

classification. Table 9 also lists the extreme (minimum and maximum) values permitted 

by the pNEM/CO algorithm. We considered these latter “absolute” minimum and 

maximum values to be the appropriate bounds for the AER values to be generated by 

APEX3. Consequently, the parameters specified for the APEX3 runs included the GM 

and GSD values listed in Table 9 for each season-AC-temperature classification 

together with the associated extreme values in the two far-right columns. 

Air Exchange Rates and Volumes for Automobiles and Trucks

Table 4-4 in the pNEM/CO project report (Johnson et al, 2000) presents the algorithm 

used to determine air exchange rate in vehicles. This algorithm incorporates the 

residential window status algorithm as a means of determining vent status as a function 

of vehicle AC system and daily average temperature. Another algorithm determines 

vehicle speed probabilistically. AER is then determined probabilistically as a function of 

vent status and speed. The overall procedure is relatively complex and cannot be 

implemented directly using only the conditional variables available within APEX3.1. 

To address this deficiency, we again used the Crystal Ball (tm) software to develop a 

stand-alone Monte Carlo program (VEH_WIN_SIM1.XLS) that duplicated the necessary 

algorithms in pNEM/CO. Specifically, the program implemented the probabilistic 

algorithms that pNEM/CO uses to determine vent status, vehicle speed, and resulting 

AER. We used this program to generate 5000 representative AER values that could be 

classified by AC category (present or absent) and temperature range; the results were 

considered representative of both cars and trucks as the algorithm does not differentiate 

between these two classes of vehicles. We then fit a lognormal distribution to the 

values generated within each classification to obtain the parameters listed in Table 10. 

Table 10 also lists the extreme (minimum and maximum) AER values permitted by the 

pNEM/CO algorithm. We considered these latter “absolute” minimum and maximum 

values to be the appropriate bounds for the AER values to be generated by APEX3.1. 

Consequently, the parameters specified for the APEX3.1 runs included the GM and 

GSD values listed in Table 10 for each AC-temperature classification together with the 

associated extreme values in the two far-right columns. 

The reader should note that we did not employ the speed-related conditional variable 

provided by APEX3.1 (SpeedCat), as this variable does not provide the number of 

levels required by the algorithm described in Table 4-4. Instead, we incorporated the 

Table 4-4 speed algorithm directly within the stand-alone Monte Carlo program. 

We also needed to provide the APEX3.1 mass balance model with separate 

distributions of passenger cab volumes for automobile and truck volumes consistent 

with those used in the pNEM/CO mass balance model. In pNEM/CO, a probabilistic 

algorithm is used to select vehicle volumes from histograms specific to vehicle type. As 

noted in our previous memorandum, APEX3.1 does not have the capability of randomly 
19 

selecting values from histograms. However, we found that lognormal distributions 

provided an acceptable fit to the pNEM/CO histograms for automobiles and trucks. The 

Table 9. Lognormal Distributions Fit to Air Exchange Rate (AER) Values Generated 

by External Monte Carlo Program Simulating pNEM/CO Algorithm for 

Residential Microenvironment. 

Classification of generated AER 

values 

Parameters of lognormal distribution fit to 5000 

generated AER values 

Extreme values 

permitted by pNEM/CO 

algorithm 

Season 

AC 

status 

Temperature 

range GMa GSDa Minimum Maximum Minimuma Maximuma

1 Yes Low 0.5560 1.7563 0.1438 2.5211 0.1430 3.16 

Medium 0.9436 1.7718 0.1435 3.1462 0.1430 3.16 

High 0.7928 1.8261 0.1443 3.1418 0.1430 3.16 

No Low 0.5544 1.7365 0.1430 2.5098 0.1430 3.16 

Medium 1.1972 1.5376 0.1520 3.1544 0.1430 3.16 

High 1.2657 1.4871 0.1530 3.1500 0.1430 3.16 

2 Yes Low 0.6628 1.7447 0.1674 3.0069 0.1670 3.16 

Medium 1.0049 1.6950 0.1675 3.1494 0.1670 3.16 

High 0.8612 1.7565 0.1676 3.1221 0.1670 3.16 

No Low 0.6568 1.7635 0.1674 3.0198 0.1670 3.16 

Medium 1.2511 1.4948 0.1782 3.1535 0.1670 3.16 

High 1.2925 1.4673 0.1792 3.1550 0.1670 3.16 

3 Yes Low 1.0821 2.1231 0.1763 6.2796 0.1760 6.296 

Medium 1.2653 1.6827 0.1822 5.9429 0.1760 6.296 

High 1.1999 1.8541 0.1761 6.2695 0.1760 6.296 

No Low 1.0885 2.1312 0.1766 6.2808 0.1760 6.296 

Medium 1.3206 1.4959 0.1853 5.8217 0.1760 6.296 

High 1.3416 1.4751 0.2098 6.2589 0.1760 6.296 

4 Yes Low 0.6615 1.8172 0.1513 3.0885 0.1510 3.16 

Medium 1.0107 1.6974 0.1527 3.1494 0.1510 3.16 

High 0.8587 1.7828 0.1514 3.1271 0.1510 3.16 

No High 0.6593 1.8241 0.1521 3.0054 0.1510 3.16 

Medium 1.2352 1.5162 0.1533 3.1377 0.1510 3.16 

Low 1.2943 1.4894 0.1738 3.1585 0.1510 3.16 

a

Value specified for APEX3.1 runs. 
20 

Table 10. Lognormal Distributions Fit to Air Exchange Rate (AER) Values Generated 

by External Monte Carlo Program Simulating pNEM/CO Algorithm for Car 

and Truck Microenvironments. 

Classification of 

generated AER values 

Parameters of lognormal distribution fit to 5000 

generated AER values 

Extreme values permitted 

by pNEM/CO algorithm 

AC 

status 

Temperature 

range GMa GSDa Minimum Maximum Minimuma Maximuma

Yes Low 5.9465 1.9711 1.0767 126.01 1.0525 202.71 

Medium 16.2355 2.8134 1.0539 164.09 1.0525 202.71 

High 9.4915 2.6939 1.0707 145.47 1.0525 202.71 

No Low 6.4039 2.0550 1.1067 112.55 1.0525 202.71 

Medium 33.5219 1.8984 1.2205 176.21 1.0525 202.71 

High 37.5848 1.6916 1.4605 150.78 1.0525 202.71

a

Value specified for APEX3.1 runs. 

resulting parameters are listed in Table 11. Table 11 also lists the extreme (minimum 

and maximum) volume values permitted by the pNEM/CO algorithm for automobiles 

and trucks. Consistent with our approach to bounding the AER distributions, we 

considered these values to be the appropriate bounds for the volume values to be 

generated by APEX3.1. Consequently, the parameters specified for the APEX3.1 runs 

included the GM and GSD values listed in Table 11 for a particular ME together with the 

associated extreme values in the two far-right columns. 

Table 11. Lognormal Distributions Fit to Vehicle Volume Data Used by pNEM/CO 

Algorithm. 

Vehicle type 

Parameters of lognormal 

distribution fit to histogram of 

volumes used by pNEM/CO 

Extreme values permitted by 

pNEM/CO 

GM (m3) GSD Minimum (m3) Maximum (m3) 

Automobiles 2.7548 1.1506 1.93 4.81 

Trucks 1.8649 1.2304 1.52 2.25 
21 

Calculation of Outdoor CO Concentration

Version 2.1 of pNEM/CO determined the hourly outdoor CO concentration required by 

each mass-balance algorithm through a Monte Carlo process based on the equation 

COout(c,m,d,h) = M(m) x L(c, m, d) x T(c,m,d,h) x [COmon(d,h)]A (13) 

in which 

COout(c,m,d,h) = outdoor CO concentration (ppm) for cohort c with respect to 

micro-environment m in district d during hour h, 

M(m) = multiplier (> 0) specific to microenvironment m, 

L(c,m,d) = location multiplier (> 0) specific to cohort c, microenvironment m, and district d (held constant for all hours), 

T(c,m,d,h) = time-of-day multiplier (> 0) specific to cohort c, 

microenvironment m, district d, and hour h, 

COmon(d,h) = monitor-derived CO concentration (ppm) for hour h in district 

d, and 

A = exponent (A > 0). 

This equation was used to generate a year-long sequence of outdoor one-hour CO 

concentrations for each combination of cohort (c), microenvironment (m), and district 

(d). The exponent A was set equal to 0.621 and held constant for all sequences. The 

value of M(m) varied only with microenvironment as indicated in Table 2-6 of the project 

report by Johnson et al. (2000). 

A value of the location factor L(c, m, d) was specified for each individual sequence and 

held constant for all hours in the sequence. The value was randomly selected from a 

lognormal distribution with geometric mean (GML) equal to 1.0 and geometric standard 

deviation (GSDL) equal to 1.5232. The natural logarithms of this distribution can be 

characterized by a normal distribution with an arithmetic mean (μL) equal to zero and an 

arithmetic standard deviation (σL) equal to 0.4208. 

A value of the time-of-day factor T(c, m, d, h) was randomly selected for each hour 

within a sequence from a lognormal distribution with geometric mean (GMT) equal to 1.0 

and geometric standard deviation (GSDT) equal to 1.6289. The natural logarithms of 

this distribution follow a normal distribution with an arithmetic mean (μT) equal to zero 

and an arithmetic standard deviation (σT) equal to 0.4879. 

The COout(c, m, d, h) term is interpreted as the outdoor CO concentration in the 

immediate vicinity of microenvironment m in district d during hour h. COmon(d, h) is the 

CO concentration reported for hour h by a nearby fixed-site monitor selected to 
22 

represent district d. 

The mass balance model in pNEM/CO includes a penetration factor that is set equal to 

1.0 for CO. Consequently, there is no change in CO concentration as ambient (outdoor) 

air moves into a microenvironment. 

In APEX3.1, the portion of the outdoor concentration affecting the indoor concentration 

is determined by the formula 

CO(outdoor contrib) = Ambient * Proximity * Penetration 

We can represent Proximity and Penetration as distributions in APEX3.1. These 

distributions can be sampled hourly, daily, or yearly. Let us make the following 

substitutions: 

Ambient = [COmon(d,h)]A

Proximity = M(m) x L(c, m, d) 

Penetration = T(c,m,d,h). 

We obtain 

CO(outdoor contrib) = M(m) x L(c, m, d) x T(c,m,d,h) x [COmon(d,h)]A

which is identical to Equation 13. 

To obtain results from APEX3.1 that are comparable to pNEM/CO, we preprocessed the 

hourly monitor-derived data assigned to the district containing the microenvironment 

using the formula 

Ambient = [COmon(d,h)]0.621

in which COmon(d,h) is expressed in ppm. For each profile, a value for the Proximity

term was selected for each microenvironment from a lognormal distribution with 

geometric mean equal to M(m) and geometric standard deviation equal to 1.5232. The 

natural logarithms of this distribution were characterized by a normal distribution with an 

arithmetic mean (μL) equal to ln[M(m)] and an arithmetic standard deviation (σL) equal to 

0.4208. Consistent with the pNEM/CO algorithm, Proximity values were not permitted 

to fall below the 5th percentile of the specified distribution or above the 95th percentile of 

the distribution. Table 12 lists the parameter values applicable to each ME. 

We randomly selected a value for Penetration for each hour from a lognormal 

distribution with geometric mean (GMT) equal to 1.0 and geometric standard deviation 

(GSDT) equal to 1.6289. As indicated above, the natural logarithms of this distribution 

followed a normal distribution with an arithmetic mean (μT) equal to zero and an 

arithmetic standard deviation (σT) equal to 0.4879. In agreement with the pNEM/CO 
23 

algorithm, Penetration values were not permitted to fall below the 5th percentile of the 

specified distribution (0.4482) or above the 95th percentile of the distribution (2.2313). 

It should be noted that we did not use the Penetration factor according to its intended 

purpose. As discussed in the APEX3 user’s guide, the Penetration factor is typically 

used to account for removal of pollutants during the transfer of outdoor air to a 

microenvironment. We used the Penetration factor to represent the T(c,m,d,h) term in 

Equation 13 because Penetration is the only APEX3.1 parameter available for this 

purpose, given that Proximity is being used to represent the product of M(m) and 

L(c,m,d). We could not represent the product of M(m), L(c,m,d), and T(c,m,d,h) by a 

single term because L(c,m,d) and T(c,m,d,h) have different averaging times (day vs. 

hour). 

Treatment of Temperature Data

Activity data in pNEM were pooled by two temperature categories, two day types 

(weekday/weekend), and two seasons. In the summer season, the temperature 

categories were determined by whether or not the maximum temperature was less than 

84 degrees Fahrenheit. The non-summer season temperature categories were 

determined by whether or not the daily maximum temperatures were less than 55 

degrees. APEX3.1 permits pooling activity data by temperature and day of week but 

not by season. To match the pNEM method for pooling activity data, we manipulated 

the daily maximum temperature entries in both the input activity data file and the input 

meteorology data file. This approach was feasible because daily maximum temperature 

is only used for pooling activity data; the temperature values are not used directly in any 

exposure-related calculation. For non-summer days, all temperatures below 55 

degrees were reset to 50 degrees and all temperatures equal to or greater than 55 

degrees were set to 71 degrees. For summer days, all temperatures below 84 were 

reset to 69 degrees and all temperatures equal to or greater than 84 degrees were set 

to 85 degrees. The maximum temperature cut-points for the APEX diary pools were set 

at 60, 70, and 80 degrees which effectively matched the pNEM pooling by season and 

temperature category. 
24 

Table 12. Parameters of Bounded Lognormal Distributions Defined for Proximity 

Factors Used in Application of APEX3 to Los Angeles. 

Microenvironment 

Activity diary locations 

included in 

microenvironment 

Parameters of bounded lognormal distribution 

Code 

General 

location Specific location 

Geometric 

mean 

Geometric 

standard 

deviation 

Minimum 

(5th PCT) 

Maxmum 

(95th PCT) 

1 Indoors Residence Indoors - residence 1.034 1.5232 0.5175 2.0661 

2 Indoors Nonresidence A Service station 

Auto repair 

2.970 1.5232 1.4864 5.9345 

3 Indoors Nonresidence B Other repair shop 

Shopping mall 

1.213 1.5232 0.6071 2.4237 

4 Indoors Nonresidence C Restaurant 1.213 1.5232 0.6071 2.4237 

5 Indoors Nonresidence D Bar 1.213 1.5232 0.6071 2.4237 

6 Indoors Nonresidence E Other indoor location 

Auditorium 

1.213 1.5232 0.6071 2.4237 

7 Indoors Nonresidence F Store 

Office 

Other public building 

1.213 1.5232 0.6071 2.4237 

8 Indoors Nonresidence G Health care facility 

School 

Church 

Manufacturing facility 

0.989 1.5232 0.4950 1.9762 

9 Indoors Residential 

garage 

Residential garage 1.034 1.5232 0.5175 2.0661 

10 Outdoors Near road Near road 

Bicycle 

Motorcycle 

1.607 1.5232 0.8042 3.2110 

11 Outdoors Other locations Outdoor res. garage 

Construction site 

Residential grounds 

School grounds 

Sports arena 

Park or golf course 

Other outdoor location 

1.436 1.5232 0.7187 2.8693 

12 Vehicle Automobile Automobile 3.020 1.5232 1.5114 6.0344 

13 Vehicle Truck Truck 3.020 1.5232 1.5114 6.0344 

14 Vehicle Mass transit 

vehicles 

Bus 

Train/subway 

Other vehicle 

3.020 1.5232 1.5114 6.0344 

15 Outdoor Public parking or 

fueling facility 

Indoor parking garage 

Outdoor parking 

garage 

Outdoor parking lot 

Outdoor service station 

2.970 1.5232 1.4864 5.9345
1 

APPENDIX B

OUTPUT TABLES FROM CASE STUDY RUN
2 

Exposure: Minutes in each Exposure interval ( ppm ), by microenvironment, for N = 1000 Profiles  Level: 0.0000 5.0000 10.0000 15.0000 20.0000 25.0000 30.0000 35.0000 40.0000 45.0000 50.0000 60.0000 70.0000 80.0000 90.0000 10 Micro -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 0 Minutes 314229. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  0 Row_% 100.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  0 Tot_% 0.0598 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  1 Minutes 358505573. 2694985. 40624. 1885. 180. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  1 Row_% 99.2422 0.7460 0.0112 0.0005 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  1 Tot_% 68.2088 0.5127 0.0077 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  2 Minutes 1301180. 285114. 27651. 3967. 922. 60. 15. 0. 0. 0. 0. 0. 0. 0. 0.  2 Row_% 80.3739 17.6115 1.7080 0.2450 0.0570 0.0037 0.0009 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  2 Tot_% 0.2476 0.0542 0.0053 0.0008 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  3 Minutes 7031167. 30376. 660. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  3 Row_% 99.5605 0.4301 0.0093 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  3 Tot_% 1.3377 0.0058 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  4 Minutes 8402559. 48021. 424. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  4 Row_% 99.4268 0.5682 0.0050 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  4 Tot_% 1.5987 0.0091 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  5 Minutes 2394348. 27252. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  5 Row_% 98.8746 1.1254 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  5 Tot_% 0.4555 0.0052 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  6 Minutes 452006. 2723. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  6 Row_% 99.4012 0.5988 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  6 Tot_% 0.0860 0.0005 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  7 Minutes 43119149. 423064. 5853. 174. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  7 Row_% 99.0147 0.9715 0.0134 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  7 Tot_% 8.2038 0.0805 0.0011 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  8 Minutes 30834768. 122268. 805. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  8 Row_% 99.6025 0.3950 0.0026 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  8 Tot_% 5.8666 0.0233 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  9 Minutes 1466647. 6545. 60. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  9 Row_% 99.5517 0.4443 0.0041 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  9 Tot_% 0.2790 0.0012 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  10 Minutes 5782897. 185578. 8935. 488. 120. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  10 Row_% 96.7360 3.1043 0.1495 0.0082 0.0020 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  10 Tot_% 1.1002 0.0353 0.0017 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  11 Minutes 28585389. 667580. 29089. 2461. 364. 75. 0. 0. 0. 0. 0. 0. 0. 0. 0.  11 Row_% 97.6112 2.2796 0.0993 0.0084 0.0012 0.0003 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  11 Tot_% 5.4386 0.1270 0.0055 0.0005 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  12 Minutes 19101250. 4938477. 672933. 106858. 19813. 3145. 708. 237. 120. 0. 0. 0. 0. 0. 0.  12 Row_% 76.8862 19.8783 2.7087 0.4301 0.0798 0.0127 0.0028 0.0010 0.0005 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  12 Tot_% 3.6342 0.9396 0.1280 0.0203 0.0038 0.0006 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  13 Minutes 3310523. 947833. 161637. 41091. 10860. 3162. 1270. 703. 110. 54. 57. 0. 15. 0. 0.  13 Row_% 73.9399 21.1697 3.6101 0.9178 0.2426 0.0706 0.0284 0.0157 0.0025 0.0012 0.0013 0.0000 0.0003 0.0000 0.0000  13 Tot_% 0.6299 0.1803 0.0308 0.0078 0.0021 0.0006 0.0002 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  14 Minutes 1453591. 376279. 48466. 5279. 450. 210. 0. 0. 0. 0. 0. 0. 0. 0. 0.  14 Row_% 77.1433 19.9694 2.5721 0.2802 0.0239 0.0111 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  14 Tot_% 0.2766 0.0716 0.0092 0.0010 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  15 Minutes 1299144. 253123. 28303. 4431. 814. 425. 189. 60. 90. 60. 0. 0. 0. 0. 0.  15 Row_% 81.8803 15.9534 1.7838 0.2793 0.0513 0.0268 0.0119 0.0038 0.0057 0.0038 0.0000 0.0000 0.0000 0.0000 0.0000  15 Tot_% 0.2472 0.0482 0.0054 0.0008 0.0002 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
3 

Exposure: Minutes at or above each Exposure level ( ppm ), by microenvironment, for N = 1000 Profiles  Level: 0.0000 5.0000 10.0000 15.0000 20.0000 25.0000 30.0000 35.0000 40.0000 45.0000 50.0000 60.0000 70.0000 80.0000 90.0000 10 Micro -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 0 Minutes 314229. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  0 Row_% 100.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  0 Tot_% 0.0598 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  1 Minutes 361243232. 2737674. 42689. 2065. 180. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  1 Row_% 100.0000 0.7578 0.0118 0.0006 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  1 Tot_% 68.7297 0.5209 0.0081 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  2 Minutes 1618909. 317729. 32615. 4964. 997. 75. 15. 0. 0. 0. 0. 0. 0. 0. 0.  2 Row_% 100.0000 19.6261 2.0146 0.3066 0.0616 0.0046 0.0009 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  2 Tot_% 0.3080 0.0605 0.0062 0.0009 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  3 Minutes 7062203. 31036. 660. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  3 Row_% 100.0000 0.4395 0.0093 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  3 Tot_% 1.3436 0.0059 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  4 Minutes 8451004. 48445. 424. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  4 Row_% 100.0000 0.5732 0.0050 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  4 Tot_% 1.6079 0.0092 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  5 Minutes 2421600. 27252. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  5 Row_% 100.0000 1.1254 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  5 Tot_% 0.4607 0.0052 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  6 Minutes 454729. 2723. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  6 Row_% 100.0000 0.5988 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  6 Tot_% 0.0865 0.0005 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  7 Minutes 43548240. 429091. 6027. 174. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  7 Row_% 100.0000 0.9853 0.0138 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  7 Tot_% 8.2854 0.0816 0.0011 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  8 Minutes 30957840. 123073. 805. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  8 Row_% 100.0000 0.3976 0.0026 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  8 Tot_% 5.8900 0.0234 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  9 Minutes 1473252. 6605. 60. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  9 Row_% 100.0000 0.4483 0.0041 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  9 Tot_% 0.2803 0.0013 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  10 Minutes 5978018. 195121. 9543. 608. 120. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  10 Row_% 100.0000 3.2640 0.1596 0.0102 0.0020 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  10 Tot_% 1.1374 0.0371 0.0018 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  11 Minutes 29284958. 699569. 31989. 2900. 439. 75. 0. 0. 0. 0. 0. 0. 0. 0. 0.  11 Row_% 100.0000 2.3888 0.1092 0.0099 0.0015 0.0003 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  11 Tot_% 5.5717 0.1331 0.0061 0.0006 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  12 Minutes 24843540. 5742291. 803814. 130881. 24023. 4210. 1065. 357. 120. 0. 0. 0. 0. 0. 0.  12 Row_% 100.0000 23.1138 3.2355 0.5268 0.0967 0.0169 0.0043 0.0014 0.0005 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  12 Tot_% 4.7267 1.0925 0.1529 0.0249 0.0046 0.0008 0.0002 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  13 Minutes 4477315. 1166792. 218959. 57322. 16231. 5371. 2209. 939. 236. 126. 72. 15. 15. 0. 0.  13 Row_% 100.0000 26.0601 4.8904 1.2803 0.3625 0.1200 0.0493 0.0210 0.0053 0.0028 0.0016 0.0003 0.0003 0.0000 0.0000  13 Tot_% 0.8518 0.2220 0.0417 0.0109 0.0031 0.0010 0.0004 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  14 Minutes 1884275. 430684. 54405. 5939. 660. 210. 0. 0. 0. 0. 0. 0. 0. 0. 0.  14 Row_% 100.0000 22.8567 2.8873 0.3152 0.0350 0.0111 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  14 Tot_% 0.3585 0.0819 0.0104 0.0011 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000  15 Minutes 1586639. 287495. 34372. 6069. 1638. 824. 399. 210. 150. 60. 0. 0. 0. 0. 0.  15 Row_% 100.0000 18.1197 2.1663 0.3825 0.1032 0.0519 0.0251 0.0132 0.0095 0.0038 0.0000 0.0000 0.0000 0.0000 0.0000  15 Tot_% 0.3019 0.0547 0.0065 0.0012 0.0003 0.0002 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 

 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
4 

Exposure: Person-days at or above each Daily Maximum 1-Hour Exposure level ( ppm ), for N = 1000 Profiles. Area Population  Level: 0.000 10.000 15.000 20.000 25.000 30.000 35.000 40.000 45.000 50.000  ------------------------------------------------------------------------------------------------------------------------------ Counts(Pop): 0.241E+10 0.389E+08 0.583E+07 0.125E+07 0.316E+06 0.152E+06 0.659E+05 0.264E+05 0.659E+04 0.000E+00 0 #Meet (Pop): 6592124 6012017 2999416 1061332 303238 145027 65921 26368 6592 0  %Meet (Pop): 100.000 91.200 45.500 16.100 4.600 2.200 1.000 0.400 0.100 0.000  Mean : 365.000 5.897 0.884 0.189 0.048 0.023 0.010 0.004 0.001 0.000  Std.Dev. : 0.000 6.147 1.292 0.473 0.223 0.157 0.100 0.063 0.032 0.000  CV : 0.000 1.042 1.462 2.501 4.647 6.805 9.955 15.788 31.623 0.000  Minimum : 365.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  10.0 %ile : 365.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  25.0 %ile : 365.000 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  50.0 %ile : 365.000 4.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  75.0 %ile : 365.000 8.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  90.0 %ile : 365.000 13.900 3.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000  95.0 %ile : 365.000 19.000 4.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000  99.0 %ile : 365.000 28.000 5.000 2.000 1.000 1.000 0.990 0.000 0.000 0.000  Maximum : 365.000 41.000 7.000 3.000 2.000 2.000 1.000 1.000 1.000 0.000  Mean (%) : 100.000 1.616 0.242 0.052 0.013 0.006 0.003 0.001 0.000 0.000  Min (%) : 100.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Max (%) : 100.000 11.233 1.918 0.822 0.548 0.548 0.274 0.274 0.274 0.000  Counts(Sim): 0.365E+06 0.590E+04 0.884E+03 0.189E+03 0.480E+02 0.230E+02 0.100E+02 0.400E+01 0.100E+01 0.000E+00 0 #Meet (Sim): 1000 912 455 161 46 22 10 4 1 0  ------------------------------------------------------------------------------------------------------------------------------
5 

Exposure: Person-days at or above each Daily Maximum 8-Hour Exposure level ( ppm ), for N Profiles. Area Population = 6592124  Level: 0.000 6.000 9.000 12.000 15.000 20.000  ---------------------------------------------------------------------------------------- Counts(Pop): 0.241E+10 0.244E+08 0.217E+07 0.396E+06 0.989E+05 0.659E+04 0.0 #Meet (Pop): 6592124 4574934 1206359 336198 85698 6592  %Meet (Pop): 100.000 69.400 18.300 5.100 1.300 0.100  Mean : 365.000 3.702 0.329 0.060 0.015 0.001  Std.Dev. : 0.000 7.724 1.019 0.277 0.137 0.032  CV : 0.000 2.086 3.096 4.609 9.139 31.623  Minimum : 365.000 0.000 0.000 0.000 0.000 0.000  10.0 %ile : 365.000 0.000 0.000 0.000 0.000 0.000  25.0 %ile : 365.000 0.000 0.000 0.000 0.000 0.000  50.0 %ile : 365.000 2.000 0.000 0.000 0.000 0.000  75.0 %ile : 365.000 4.000 0.000 0.000 0.000 0.000  90.0 %ile : 365.000 8.000 1.000 0.000 0.000 0.000  95.0 %ile : 365.000 14.000 2.000 1.000 0.000 0.000  99.0 %ile : 365.000 44.990 5.000 1.000 1.000 0.000  Maximum : 365.000 81.000 14.000 3.000 2.000 1.000  Mean (%) : 100.000 1.014 0.090 0.016 0.004 0.000  Min (%) : 100.000 0.000 0.000 0.000 0.000 0.000  Max (%) : 100.000 22.192 3.836 0.822 0.548 0.274  Counts(Sim): 0.365E+06 0.370E+04 0.329E+03 0.600E+02 0.150E+02 0.100E+01 0.0 #Meet (Sim): 1000 694 183 51 13 1  ----------------------------------------------------------------------------------------
6 

Exposure: Person-days at or above each Daily Average Exposure level ( ppm ), for N = 1000 Profiles. Area Population = 6592124  Level: 0.000 2.000 4.000 5.000 6.000 7.000 8.000 9.000 10.000 11.000 12.000 14.000  ---------------------------------------------------------------------------------------------------------------------------------------------------- Counts(Pop): 0.241E+10 0.561E+09 0.246E+08 0.565E+07 0.156E+07 0.448E+06 0.165E+06 0.461E+05 0.198E+05 0.132E+05 0.659E+04 0.000E+00 0.0 #Meet (Pop): 6592124 6592124 3876169 1713952 718542 342790 145027 46145 19776 13184 6592 0  %Meet (Pop): 100.000 100.000 58.800 26.000 10.900 5.200 2.200 0.700 0.300 0.200 0.100 0.000  Mean : 365.000 85.168 3.736 0.857 0.236 0.068 0.025 0.007 0.003 0.002 0.001 0.000  Std.Dev. : 0.000 59.431 9.664 3.428 1.132 0.346 0.180 0.083 0.055 0.045 0.032 0.000  CV : 0.000 0.698 2.587 4.000 4.797 5.084 7.201 11.916 18.239 22.349 31.623 0.000  Minimum : 365.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  10.0 %ile : 365.000 19.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  25.0 %ile : 365.000 37.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  50.0 %ile : 365.000 71.500 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  75.0 %ile : 365.000 120.750 3.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  90.0 %ile : 365.000 168.000 9.000 2.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  95.0 %ile : 365.000 200.850 16.000 3.000 1.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000  99.0 %ile : 365.000 253.980 58.940 17.990 5.000 1.000 1.000 0.000 0.000 0.000 0.000 0.000  Maximum : 365.000 324.000 110.000 48.000 16.000 5.000 3.000 1.000 1.000 1.000 1.000 0.000  Mean (%) : 100.000 23.334 1.024 0.235 0.065 0.019 0.007 0.002 0.001 0.001 0.000 0.000  Min (%) : 100.000 0.274 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Max (%) : 100.000 88.767 30.137 13.151 4.384 1.370 0.822 0.274 0.274 0.274 0.274 0.000  Counts(Sim): 0.365E+06 0.852E+05 0.374E+04 0.857E+03 0.236E+03 0.680E+02 0.250E+02 0.700E+01 0.300E+01 0.200E+01 0.100E+01 0.000E+00 0.0 #Meet (Sim): 1000 1000 588 260 109 52 22 7 3 2 1 0  ----------------------------------------------------------------------------------------------------------------------------------------------------Exposure: Persons at or above each Overall Average Exposure level ( ppm ), for N = 1000 Profiles. Area Population = 6592124  Level: 0.000 0.500 1.000 1.250 1.500 1.750 2.000 2.500 3.000 4.000 5.000 6.000  ---------------------------------------------------------------------------------------------------------------------------------------------------- Counts(Pop): 0.659E+07 0.659E+07 0.638E+07 0.519E+07 0.338E+07 0.184E+07 0.877E+06 0.112E+06 0.198E+05 0.000E+00 0.000E+00 0.000E+00 0.0 #Meet (Pop): 6592124 6592124 6381176 5188002 3381760 1839203 876752 112066 19776 0 0 0  %Meet (Pop): 100.000 100.000 96.800 78.700 51.300 27.900 13.300 1.700 0.300 0.000 0.000 0.000  Counts(Sim): 0.100E+04 0.100E+04 0.968E+03 0.787E+03 0.513E+03 0.279E+03 0.133E+03 0.170E+02 0.300E+01 0.000E+00 0.000E+00 0.000E+00 0.0 #Meet (Sim): 1000 1000 968 787 513 279 133 17 3 0 0 0  ----------------------------------------------------------------------------------------------------------------------------------------------------
7 

Dose: Person-days at or above each Daily Max End-of-Hour Dose level (%COHb), 1000 Profiles. Area Population = 6592124  Level: 0.000 0.500 1.000 1.500 2.000 2.100 2.200 2.300 2.400 2.500 2.600 2.700 2.800 2.900 3.000  --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Counts(Pop): 0.241E+10 0.162E+10 0.136E+09 0.998E+07 0.844E+06 0.613E+06 0.475E+06 0.349E+06 0.218E+06 0.158E+06 0.119E+06 0.923E+05 0.659E+05 0.527E+05 0.527E+05 0. #Meet (Pop): 6592124 6592124 6163636 2478639 639436 520778 415304 290053 191172 151619 118658 92290 65921 52737 52737  %Meet (Pop): 100.000 100.000 93.500 37.600 9.700 7.900 6.300 4.400 2.900 2.300 1.800 1.400 1.000 0.800 0.800  Mean : 365.000 245.162 20.636 1.514 0.128 0.093 0.072 0.053 0.033 0.024 0.018 0.014 0.010 0.008 0.008  Std.Dev. : 0.000 91.661 45.182 10.324 0.496 0.353 0.295 0.265 0.200 0.160 0.133 0.118 0.100 0.089 0.089  CV : 0.000 0.374 2.189 6.819 3.874 3.794 4.094 5.001 6.057 6.647 7.390 8.396 9.955 11.141 11.141  Minimum : 365.000 24.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  10.0 %ile : 365.000 113.100 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  25.0 %ile : 365.000 169.000 3.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  50.0 %ile : 365.000 253.000 7.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  75.0 %ile : 365.000 333.000 18.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  90.0 %ile : 365.000 364.000 42.000 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  95.0 %ile : 365.000 365.000 85.900 4.000 1.000 1.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  99.0 %ile : 365.000 365.000 308.940 16.000 2.000 2.000 1.000 1.000 1.000 1.000 1.000 1.000 0.990 0.000 0.000  Maximum : 365.000 365.000 365.000 228.000 8.000 5.000 3.000 3.000 2.000 2.000 1.000 1.000 1.000 1.000 1.000  Mean (%) : 100.000 67.168 5.654 0.415 0.035 0.025 0.020 0.015 0.009 0.007 0.005 0.004 0.003 0.002 0.002  Min (%) : 100.000 6.575 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Max (%) : 100.000 100.000 100.000 62.466 2.192 1.370 0.822 0.822 0.548 0.548 0.274 0.274 0.274 0.274 0.274  Counts(Sim): 0.365E+06 0.245E+06 0.206E+05 0.151E+04 0.128E+03 0.930E+02 0.720E+02 0.530E+02 0.330E+02 0.240E+02 0.180E+02 0.140E+02 0.100E+02 0.800E+01 0.800E+01 0. #Meet (Sim): 1000 1000 935 376 97 79 63 44 29 23 18 14 10 8 8  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Dose: Person-days at or above each Daily Max 1-Hour Dose level (%COHb), 1000 Profiles. Area Population = 6592124  Level: 0.000 0.500 1.000 1.500 2.000 2.100 2.200 2.300 2.400 2.500 2.600 2.700 2.800 2.900 3.000  --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Counts(Pop): 0.241E+10 0.159E+10 0.128E+09 0.911E+07 0.732E+06 0.514E+06 0.382E+06 0.290E+06 0.191E+06 0.132E+06 0.923E+05 0.659E+05 0.527E+05 0.461E+05 0.330E+05 0. #Meet (Pop): 6592124 6592124 6051570 2234730 573515 421896 323014 243909 171395 118658 85698 65921 52737 46145 32961  %Meet (Pop): 100.000 100.000 91.800 33.900 8.700 6.400 4.900 3.700 2.600 1.800 1.300 1.000 0.800 0.700 0.500  Mean : 365.000 241.262 19.342 1.382 0.111 0.078 0.058 0.044 0.029 0.020 0.014 0.010 0.008 0.007 0.005  Std.Dev. : 0.000 93.014 44.246 9.972 0.432 0.335 0.273 0.241 0.185 0.154 0.126 0.100 0.089 0.083 0.071  CV : 0.000 0.386 2.288 7.216 3.894 4.291 4.713 5.479 6.376 7.685 8.984 9.955 11.141 11.916 14.114  Minimum : 365.000 22.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  10.0 %ile : 365.000 110.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  25.0 %ile : 365.000 164.000 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  50.0 %ile : 365.000 247.000 6.500 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  75.0 %ile : 365.000 332.000 15.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  90.0 %ile : 365.000 364.000 39.000 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  95.0 %ile : 365.000 365.000 82.850 3.000 1.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  99.0 %ile : 365.000 365.000 301.940 14.000 2.000 2.000 1.000 1.000 1.000 1.000 1.000 0.990 0.000 0.000 0.000  Maximum : 365.000 365.000 365.000 221.000 6.000 5.000 3.000 3.000 2.000 2.000 2.000 1.000 1.000 1.000 1.000  Mean (%) : 100.000 66.099 5.299 0.379 0.030 0.021 0.016 0.012 0.008 0.005 0.004 0.003 0.002 0.002 0.001  Min (%) : 100.000 6.027 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Max (%) : 100.000 100.000 100.000 60.548 1.644 1.370 0.822 0.822 0.548 0.548 0.548 0.274 0.274 0.274 0.274  Counts(Sim): 0.365E+06 0.241E+06 0.193E+05 0.138E+04 0.111E+03 0.780E+02 0.580E+02 0.440E+02 0.290E+02 0.200E+02 0.140E+02 0.100E+02 0.800E+01 0.700E+01 0.500E+01 0. #Meet (Sim): 1000 1000 918 339 87 64 49 37 26 18 13 10 8 7 5  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
8 

Dose: Person-days at or above each Daily Max 8-Hour Dose level (%COHb), 1000 Profiles. Area Population = 6592124  Level: 0.000 0.500 1.000 1.500 2.000 2.100 2.200 2.300 2.400 2.500 2.600 2.700 2.800 2.900 3.000  --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Counts(Pop): 0.241E+10 0.141E+10 0.793E+08 0.446E+07 0.198E+06 0.119E+06 0.791E+05 0.527E+05 0.461E+05 0.396E+05 0.396E+05 0.264E+05 0.198E+05 0.198E+05 0.132E+05 0. #Meet (Pop): 6592124 6592124 4944093 929489 151619 92290 59329 39553 32961 26368 26368 19776 13184 13184 13184  %Meet (Pop): 100.000 100.000 75.000 14.100 2.300 1.400 0.900 0.600 0.500 0.400 0.400 0.300 0.200 0.200 0.200  Mean : 365.000 213.780 12.022 0.676 0.030 0.018 0.012 0.008 0.007 0.006 0.006 0.004 0.003 0.003 0.002  Std.Dev. : 0.000 100.680 37.437 7.226 0.226 0.166 0.134 0.109 0.105 0.100 0.100 0.077 0.071 0.071 0.045  CV : 0.000 0.471 3.114 10.689 7.539 9.247 11.141 13.663 14.957 16.645 16.645 19.349 23.561 23.561 22.349  Minimum : 365.000 8.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  10.0 %ile : 365.000 76.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  25.0 %ile : 365.000 128.250 0.250 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  50.0 %ile : 365.000 212.500 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  75.0 %ile : 365.000 307.000 7.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  90.0 %ile : 365.000 360.900 21.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  95.0 %ile : 365.000 365.000 52.950 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  99.0 %ile : 365.000 365.000 231.910 6.990 1.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Maximum : 365.000 365.000 365.000 172.000 4.000 3.000 2.000 2.000 2.000 2.000 2.000 2.000 2.000 2.000 1.000  Mean (%) : 100.000 58.570 3.294 0.185 0.008 0.005 0.003 0.002 0.002 0.002 0.002 0.001 0.001 0.001 0.001  Min (%) : 100.000 2.192 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Max (%) : 100.000 100.000 100.000 47.123 1.096 0.822 0.548 0.548 0.548 0.548 0.548 0.548 0.548 0.548 0.274  Counts(Sim): 0.365E+06 0.214E+06 0.120E+05 0.676E+03 0.300E+02 0.180E+02 0.120E+02 0.800E+01 0.700E+01 0.600E+01 0.600E+01 0.400E+01 0.300E+01 0.300E+01 0.200E+01 0. #Meet (Sim): 1000 1000 750 141 23 14 9 6 5 4 4 3 2 2 2  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Dose: Person-days at or above each Daily Average Dose level (%COHb), 1000 Profiles. Area Population = 6592124  Level: 0.000 0.500 1.000 1.500 2.000 2.100 2.200 2.300 2.400 2.500 2.600 2.700 2.800 2.900 3.000  --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Counts(Pop): 0.241E+10 0.956E+09 0.225E+08 0.514E+06 0.659E+04 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0. #Meet (Pop): 6592124 6578940 1839203 105474 6592 0 0 0 0 0 0 0 0 0 0  %Meet (Pop): 100.000 99.800 27.900 1.600 0.100 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Mean : 365.000 144.949 3.417 0.078 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Std.Dev. : 0.000 106.166 22.100 1.237 0.032 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  CV : 0.000 0.732 6.468 15.855 31.623 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Minimum : 365.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  10.0 %ile : 365.000 23.100 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  25.0 %ile : 365.000 57.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  50.0 %ile : 365.000 118.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  75.0 %ile : 365.000 217.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  90.0 %ile : 365.000 324.000 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  95.0 %ile : 365.000 360.000 8.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  99.0 %ile : 365.000 365.000 84.910 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Maximum : 365.000 365.000 365.000 30.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Mean (%) : 100.000 39.712 0.936 0.021 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Min (%) : 100.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Max (%) : 100.000 100.000 100.000 8.219 0.274 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Counts(Sim): 0.365E+06 0.145E+06 0.342E+04 0.780E+02 0.100E+01 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0. #Meet (Sim): 1000 998 279 16 1 0 0 0 0 0 0 0 0 0 0  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
9 

Dose: Persons at or above each Overall Average Dose level (%COHb), 1000 Profiles. Area Population = 6592124  Level: 0.000 0.400 0.500 0.600 0.700 0.800 0.900 1.000 1.200 1.400 1.600 1.800  ---------------------------------------------------------------------------------------------------------------------------------------------------- Counts(Pop): 0.659E+07 0.514E+07 0.241E+07 0.903E+06 0.330E+06 0.145E+06 0.725E+05 0.330E+05 0.659E+04 0.000E+00 0.000E+00 0.000E+00 0.0 #Meet (Pop): 6592124 5135265 2412717 903121 329606 145027 72513 32961 6592 0 0 0  %Meet (Pop): 100.000 77.900 36.600 13.700 5.000 2.200 1.100 0.500 0.100 0.000 0.000 0.000  Counts(Sim): 0.100E+04 0.779E+03 0.366E+03 0.137E+03 0.500E+02 0.220E+02 0.110E+02 0.500E+01 0.100E+01 0.000E+00 0.000E+00 0.000E+00 0.0 #Meet (Sim): 1000 779 366 137 50 22 11 5 1 0 0 0  ----------------------------------------------------------------------------------------------------------------------------------------------------Dose: Person-hours at or above each End-of-Hour Dose level (%COHb), 1000 Profiles. Area Population = 6592124  Level: 0.000 0.500 1.000 1.500 2.000 2.100 2.200 2.300 2.400 2.500 2.600 2.700 2.800 2.900 3.000  --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Counts(Pop): 0.577E+11 0.228E+11 0.927E+09 0.474E+08 0.249E+07 0.171E+07 0.121E+07 0.824E+06 0.593E+06 0.402E+06 0.310E+06 0.231E+06 0.165E+06 0.132E+06 0.112E+06 0. #Meet (Pop): 6592124 6592124 6163636 2478639 639436 520778 415304 290053 191172 151619 118658 92290 65921 52737 52737  %Meet (Pop): 100.000 100.000 93.500 37.600 9.700 7.900 6.300 4.400 2.900 2.300 1.800 1.400 1.000 0.800 0.800  Mean : 8760.000 3461.836 140.638 7.191 0.377 0.260 0.183 0.125 0.090 0.061 0.047 0.035 0.025 0.020 0.017  Std.Dev. : 0.000 2272.073 541.346 75.138 1.870 1.292 0.981 0.791 0.683 0.546 0.442 0.366 0.307 0.279 0.230  CV : 0.000 0.656 3.849 10.449 4.960 4.970 5.361 6.330 7.588 8.943 9.395 10.455 12.294 13.935 13.512  Minimum : 8760.000 89.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  10.0 %ile : 8760.000 820.000 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  25.0 %ile : 8760.000 1613.000 8.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  50.0 %ile : 8760.000 2976.500 24.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  75.0 %ile : 8760.000 4980.750 69.000 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  90.0 %ile : 8760.000 7046.900 208.800 8.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  95.0 %ile : 8760.000 7939.000 536.900 13.950 3.000 2.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  99.0 %ile : 8760.000 8711.960 2842.810 60.990 7.000 6.000 4.990 3.990 3.000 2.000 1.990 1.000 0.990 0.000 0.000  Maximum : 8760.000 8760.000 8595.000 1786.000 34.000 23.000 13.000 13.000 12.000 10.000 8.000 7.000 6.000 6.000 5.000  Mean (%) : 100.000 39.519 1.605 0.082 0.004 0.003 0.002 0.001 0.001 0.001 0.001 0.000 0.000 0.000 0.000  Min (%) : 100.000 1.016 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Max (%) : 100.000 100.000 98.116 20.388 0.388 0.263 0.148 0.148 0.137 0.114 0.091 0.080 0.068 0.068 0.057  Counts(Sim): 0.876E+07 0.346E+07 0.141E+06 0.719E+04 0.377E+03 0.260E+03 0.183E+03 0.125E+03 0.900E+02 0.610E+02 0.470E+02 0.350E+02 0.250E+02 0.200E+02 0.170E+02 0. #Meet (Sim): 1000 1000 935 376 97 79 63 44 29 23 18 14 10 8 8  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
10 

Dose: Minutes in each Dose interval (%COHb), for N = 1000 Profiles. Area Population = 6592124  Level: 0.000 0.500 1.000 1.500 2.000 2.100 2.200 2.300 2.400 2.500 2.600 2.700 2.800 2.900 3.000  --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Counts(Pop): 0.210E+13 0.131E+13 0.521E+11 0.262E+10 0.469E+08 0.294E+08 0.205E+08 0.125E+08 0.920E+07 0.715E+07 0.247E+07 0.287E+07 0.162E+07 0.158E+07 0.646E+07 0. #Meet (Pop): 6559163 6592124 6110899 2399533 468041 388935 257093 224132 158211 105474 46145 46145 39553 26368 46145  %Meet (Pop): 99.500 100.000 92.700 36.400 7.100 5.900 3.900 3.400 2.400 1.600 0.700 0.700 0.600 0.400 0.700  Mean : 317946.305 199327.565 7907.805 396.980 7.120 4.456 3.110 1.898 1.396 1.085 0.375 0.435 0.245 0.240 0.980  Std.Dev. : 136622.314 125925.466 29286.295 4372.778 53.951 28.005 21.530 13.173 10.807 10.889 5.340 5.698 3.877 3.789 14.462  CV : 0.430 0.632 3.703 11.015 7.577 6.285 6.923 6.940 7.741 10.036 14.239 13.099 15.824 15.788 14.757  Minimum : 0.000 5127.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  10.0 %ile : 102455.703 47822.801 70.500 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  25.0 %ile : 227028.500 95559.500 420.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  50.0 %ile : 347509.500 176487.500 1305.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  75.0 %ile : 429048.250 291608.750 3780.250 120.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  90.0 %ile : 476939.094 395781.500 12081.300 360.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  95.0 %ile : 492759.812 433531.562 31660.551 624.850 53.800 25.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  99.0 %ile : 506778.781 482174.781 165513.125 3599.460 120.000 120.000 94.950 60.000 60.000 60.000 0.000 0.000 0.000 0.000 0.000  Maximum : 520323.000 510049.000 410554.000 105046.000 1387.000 660.000 457.000 275.000 120.000 240.000 120.000 120.000 95.000 60.000 300.000  Mean (%) : 60.492 37.924 1.505 0.076 0.001 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Min (%) : 0.000 0.975 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Max (%) : 98.996 97.041 78.111 19.986 0.264 0.126 0.087 0.052 0.023 0.046 0.023 0.023 0.018 0.011 0.057  Counts(Sim): 0.318E+09 0.199E+09 0.791E+07 0.397E+06 0.712E+04 0.446E+04 0.311E+04 0.190E+04 0.140E+04 0.108E+04 0.375E+03 0.435E+03 0.245E+03 0.240E+03 0.980E+03 0. #Meet (Sim): 995 1000 927 364 71 59 39 34 24 16 7 7 6 4 7  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Dose: Minutes at or above each Dose level (%COHb), for N = 1000 Profiles. Area Population = 6592124  Level: 0.000 0.500 1.000 1.500 2.000 2.100 2.200 2.300 2.400 2.500 2.600 2.700 2.800 2.900 3.000  --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Counts(Pop): 0.346E+13 0.137E+13 0.549E+11 0.276E+10 0.141E+09 0.938E+08 0.644E+08 0.439E+08 0.314E+08 0.222E+08 0.150E+08 0.126E+08 0.969E+07 0.808E+07 0.649E+07 0. #Meet (Pop): 6592124 6592124 6110899 2399533 632844 481225 388935 283461 197764 131842 98882 79105 65921 46145 46145  %Meet (Pop): 100.000 100.000 92.700 36.400 9.600 7.300 5.900 4.300 3.000 2.000 1.500 1.200 1.000 0.700 0.700  Mean : 525600.000 207653.695 8326.130 418.325 21.345 14.225 9.769 6.659 4.761 3.365 2.280 1.905 1.470 1.225 0.985  Std.Dev. : 0.000 136622.314 32427.048 4427.003 109.242 75.833 55.660 44.835 39.065 33.200 25.175 21.956 18.113 17.282 14.523  CV : 0.000 0.658 3.895 10.583 5.118 5.331 5.698 6.733 8.205 9.866 11.042 11.526 12.322 14.108 14.744  Minimum : 525600.000 5277.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  10.0 %ile : 525600.000 48660.898 70.500 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  25.0 %ile : 525600.000 96551.750 435.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  50.0 %ile : 525600.000 178090.500 1380.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  75.0 %ile : 525600.000 298571.500 4020.000 120.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  90.0 %ile : 525600.000 423144.312 12323.100 480.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  95.0 %ile : 525600.000 476259.156 31727.051 804.000 134.500 89.250 30.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  99.0 %ile : 525600.000 523107.000 169456.891 3628.410 420.000 344.850 276.930 180.000 120.000 120.000 59.700 59.700 9.900 0.000 0.000  Maximum : 525600.000 525600.000 516080.000 105526.000 1980.000 1392.000 732.000 720.000 720.000 660.000 480.000 420.000 360.000 360.000 300.000  Mean (%) : 100.000 39.508 1.584 0.080 0.004 0.003 0.002 0.001 0.001 0.001 0.000 0.000 0.000 0.000 0.000  Min (%) : 100.000 1.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  Max (%) : 100.000 100.000 98.189 20.077 0.377 0.265 0.139 0.137 0.137 0.126 0.091 0.080 0.068 0.068 0.057  Counts(Sim): 0.526E+09 0.208E+09 0.833E+07 0.418E+06 0.213E+05 0.142E+05 0.977E+04 0.666E+04 0.476E+04 0.336E+04 0.228E+04 0.190E+04 0.147E+04 0.122E+04 0.985E+03 0. #Meet (Sim): 1000 1000 927 364 96 73 59 43 30 20 15 12 10 7 7  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------