# A ir 

# P ollutants 

# EX posure 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May 8, 2003 Draft — Do Not Cite or Quote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 Over view ........................................................................................................... 1-1

1.1 Introduc tion ............................................................................................. 1-1 1.2 Key Steps in Configuring and Pe rforming the Apex Simulati on .............. 1-1

1.2.1 Step 1: Identify the Scope of the An alysis .................................... 1-2 1.2.2 Step 2: Select Model Opti ons ....................................................... 1-3 1.2.3 Step 3: Ensure the Input Da ta Files Contain the Appropriate Informati on ................................................................................... 1-7 1.2.4 Step 4: Configure t he Simulation Se ttings .................................... 1-7 1.2.5 Step 5: Running APEX ................................................................. 1-9

2 The Para ms File ................................................................................................ 2-1

2.1 Input Files Section .................................................................................. 2-1 2.2 Population File s Secti on ......................................................................... 2-1 2.3 Output File s Secti on................................................................................ 2-4 2.4 Job Parameter Se ttings Sect ion ............................................................. 2-4 2.5 Output Table Lev els Secti on ................................................................... 2-9

3 Other I nput File s ................................................................................................ 3-1

3.1 Sector Loc ation ....................................................................................... 3-1 3.2 Population Data ...................................................................................... 3-2 3.3 District Loc ation ...................................................................................... 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 Temperatur e Data ................................................................................... 3-5 3.8 Air Qualit y Data ....................................................................................... 3-7 3.9 Activity-Spec ific ME T .............................................................................. 3-8 3.10 Physiological Pa rameters ....................................................................... 3-9 3.11 Profile Func tions ................................................................................... 3-15 3.12 Microenvironmental Mapping ................................................................ 3-19 3.13 Personal Info rmation ............................................................................. 3-21 3.14 Diary Ev ents ......................................................................................... 3-22

### ii Section Page

3.15 Microenvironment De scriptions ............................................................. 3-23

3.15.1 Indoor Microenvironm ent 1 (Residenc e) .................................. 3-27 3.15.2 Indoor Micr oenvironment 2 ...................................................... 3-38 3.15.3 Indoor Micr oenvironment 3 ...................................................... 3-39 3.15.4 Indoor Microenvir onments 4 thr ough 9 .................................... 3-39 3.15.5 Outdoor Microenv ironments 10 an d 11 .................................... 3-39 3.15.6 Vehicle Microenv ironments 12 and 13 ..................................... 3-48 3.15.7 Mass Transit Mi croenvironment 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 Su mmary File ............................................................ 4-7 4.6 Site F ile ................................................................................................... 4-8 4.7 Output Tabl es File .................................................................................. 4-8

5 Refer ences ........................................................................................................ 5-1

Appendix A: Input Values and Other Modifications Proposed for Application of APEX3.1 to Los Angel es ....................................... A-1

Appendix B: Output Tables fr om Case Study Run ........................................... B-1

###  1 SECTION 1

OVERVIEW

1.1 Introduction

The Air Pollutant Exposure Model (APEX) wa s 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 toxi c air pollutants at the local, urban, and regional level using a stochastic, “microenv ironmental” approach. The model randomly constructs demographic profiles for hypothetic al individuals representative of the specified target population. APEX then simu lates 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 c ensus data and the availability of applicable air quality and meteorological data;

(2) generate profiles of simulated indi viduals consistent with the frequency of specified demographic factors withi n 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 c onducting an APEX simulation, describes in detail the input files requir ed to run the model, and descri bes the output files obtained from a successful run. This volume descr ibes a representative application of APEX in which the model is used to estimate CO exposures and resulting carboxyhemoglobin (COHb) levels in Los Angeles. This sect ion describes the key steps in setting up the

run.

1.2 Key Steps In Configuring a nd 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 mu st decide which options to select for the analysis. There are five general categor ies 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 discu ssion as to the rationale behind each setting.

###  4 Table 1-2. Specification of Settings a nd 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 hour ly 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 lati tude and longitude of the center of the study ar ea 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 dist ance (in km) from the center to the edge of t he 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 se ctor 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 s pecified population files are assumed to have zero populations.

Profile function options The existing profile func tions 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 pr obability that activity diary data with missing gender, employment status, or age will selected. MISSGENDER = 0.0 MISSEMPL = 0.0 MISSAGE = 0.0

MICROENVIRONMENTS

Maximum numberof 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 Da ta 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 dat a files contain the necessary data. There are 16 types of i nput data files required by APEX. Table 1-3 lists each file type and a brief descrip tion 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. T he data contained in the remaining files varies depending on the options selected and the data c ontained in the other input files. Section 3 describes the contents of these f iles 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 TR IM.Expo-Inhalation interface, this information would have been specified by t he 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 degr ees 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 paramet ers 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 def ine 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 st andard 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 applicat ion, 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 samp le application, this step consisted of running the APEX executable. If APEX had been r un using TRIM.Expo-Inhalation, this would have required completing Step 10 descri bed 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 param eters, 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 out put table levels. This section presents the files entries used in the sample applic ation 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 mo re detailed description of each file. Section 3 provides additional information concerning each file together with a fu ll 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 file s listing Year 2000 data for all 65,443 US census tracts have been prepared and are provi ded 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 APEX a



### .

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 Eventsfile. 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 categor ies: 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 t he 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 prov ides a description of the keyword, data type, and uses of these job parameters. Figur e 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/m 3

.

OutUnits OUPUTUNIT Pollutant concentration units used for the output data, in ppm or ug/m 3

.

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 Y 2000 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 μ g/m 3 ). For CO, ppmfact = 1,145 (i.e., 1 ppm = 1,145 ug/m 3 ). 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 t he 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. No te that at least 100 profiles are needed to properly determine a 99 th percentile.

Exposure Cutpoints TIMEEXP This parameter specifies the exposure cu tpoints (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 maxi mum 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 maximu m 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 DM1HDOSE a This parameter specifies cut-points in %COHb for Daily Maximum 1-Hour Blood Dose. The cut-points were used to bin a ll the person-days in the simulation period.

Daily Max 8-Hour Dose Cutpoints DM8HDOSE a This parameter specifies cut-points in %COHb for Daily Maximum 8-Hour Blood Dose. The cut-points were used to bin a ll the person-days in the simulation period.

Daily Max End-ofHour Dose Cutpoints DMEHDOSE a 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_EHDOSE a 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 DAVGDOSE a This parameter specifies cut-points in %COHb for the Daily Average Blood Dose. The cut-points are used to bin all the per son/days in the simulation period.

Simulation Average Dose Cutpoints SAVEDOSE a 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 TIMEDOSE a 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 fo r CO. For other pollutants the dose calculation should be turned off.



### Figure 2-5 presents the file entries for t he sample application. Seven percentiles between 10 and 99 percent are specified. Cut points are provided for four CO exposure indicators (DM1HEXP, DM8HEXP, DAVGEXP, ANNEXP) and for seven COHb dose indicators (DM1HDOSE, DM8HDOSE, DMEXDOSE, H_EHDO SE, 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 r unning 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 app lication 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 identificati on 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 expre ssed in decimal format with at least three significant digits to prevent significant roundi ng 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 al so 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 toget her 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 si te. 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 coor dinates (latitude and longitude) of 65,443 Year 2000 US Census tracts. APEX requires that the le ft-most five characters of a sector ID be the state and county FIPS code or an alter native county-level c ode 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 f iles 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 cont aining 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 requir ed, the order of the listed si tes 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 Distr ict 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 f ile 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 ar ea 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 minimu m 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 ol dest, while the Max_Age input line lists the maximum ages of the corresponding population age groups. The Employ_Prob input line lists employment probabilities (e xpressed 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 maximu m 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 cont inues 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 cont ains 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 commu ting 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 te mperature data collect ed 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 begi n 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, ma ximum daily temperature, and average daily temperature. Consistent with inst ructions, 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 simulati on 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 Qua lity Data file prepared for the sample run. This file provides hourly air concentration dat a 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 organiz ed 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 progr am 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 t he 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 distribut ion data in the CHAD database are revised. Appendix A of the report by Johnson, Mihlan, LaP ointe, 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 spec ific to age and gender for each of the following physiological parameters.

· NVO 2 

### Max ;

· Body Mass ;

· Resting Metabolic Rate (RMR); and

###  9 · Blood Volume Factor and Hemoglobin Content

.
Partial tables for the sample run can be f ound in Figures 3-10, 3-11, 3-12, and 3-13. Each table has three header lines and 202 numer ical lines of data. The first table (Figure 3-10) contains age/gender specific di stribution data on normalized lung capacity (VO 2 

### -max). Although the table provides ei ght columns of data, APEX reads only the mean and standard deviation values for NVO 2 

### Max.

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 paramet ers for the linear relationships used to estimate resting me tabolic 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 hem oglobin), and HGSTD (standard deviation of hemoglobin). APEX uses all three parameters.

The data in these tables are identical to t he 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.10Row Act Age Occ. DistType Mean Median StdDev Min Max DistNum Description1 10000 0 ADMIN LogNormal 1.7 1.7 0.3 1.4 2.7 9 Work, general2 10000 0 ADMSUP LogNormal 1.7 1.7 0.3 1.4 2.7 9 Work, general3 10000 0 FARM LogNormal 7.5 7.0 3.0 3.6 17.0 9 Work, general4 10000 0 HSHLD LogNormal 3.6 3.5 0.8 2.5 6.0 9 Work, general5 10000 0 LABOR Triangle 8.5 8.4 2.1 3.6 13.8 9 Work, general6 10000 0 MACH Uniform 5.3 5.3 0.7 4.0 6.5 9 Work, general7 10000 0 PREC Triangle 3.3 3.3 0.4 2.5 4.5 9 Work, general8 10000 0 PROF Triangle 2.9 2.7 1.0 1.2 5.6 9 Work, general9 10000 0 PROTECT Triangle 2.9 2.7 1.0 1.2 5.6 9 Work, general10 10000 0 SALE Triangle 2.9 2.7 1.0 1.2 5.6 9 Work, general11 10000 0 SERV Triangle 5.2 5.3 1.4 1.6 8.4 9 Work, general12 10000 0 TECH Triangle 3.3 3.3 0.4 2.5 4.5 9 Work, general13 10000 0 TRANS LogNormal 3.3 3.0 1.5 1.3 8.4 9 Work, general14 10000 0 X Triangle 2.9 2.7 1.0 1.2 5.6 9 Work, general15 10300 0 Any Uniform 1.8 1.8 0.4 1.0 2.5 2 Breaks16 11000 0 Any Triangle 4.7 4.6 1.3 1.5 8.0 5 Generalhousehold activities17 11100 0 Any LogNormal 2.6 2.5 0.5 2.0 4.0 4 Prepare food18 11110 0 Any Exponential 2.8 2.5 0.9 1.9 4.0 3 Prepare andclean-up food19 11200 0 Any Exponential 3.4 3.0 1.4 2.0 5.0 3 Indoor chores20 11210 0 Any Uniform 2.5 2.5 0.1 2.3 2.7 2 Clean-up food21 11220 0 Any Exponential 4.1 3.5 1.9 2.2 5.0 3 Clean houseetc.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 Para meters File Showing Partial Listings for Parameters Related to NO 2max



### .

###  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 Para meters 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 Para meters 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 Para meters File Showing Partial Listings for Parameters Related to Blood Volume and Hemoglobin Content.

###  15 3.11 Profile Functions

This file provides the user-definable f unctions required in generating profiles of simulated persons. Table 3-1 lists the pr ofile functions accessible through the Profile Functons file. Figures 3-14 through 3-18 pr esent the file entries specified for the sample run.

Four of the functions listed in Table 3-1 spec ify characteristics of the simulated person’s residence or car that may affect CO c oncentrations 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 te mperature values into temperature categories. The DiaryPools function (Figure 3-14) produces categories that account for daily maximum temperature, daily aver age 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 categor ies 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 exposur e 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 t he probability of open windows was integrated directly into the distributions characte rizing 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 Entri es 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 c haracterizing 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 Functi ons File Showing Listings for Maximum Temperature, Average Te mperature, 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 Pr ofile 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 Pr ofile 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 Pr ofile 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 Pr ofile 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 ETS a 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 databas e provides various data items that apply to the entire 24-hour period. The Pers onal 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 f ile 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 Dia ry 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 defi ned for the sample APEX run. These are the same 15 microenvironments defined for the mo st recent application of pNEM to CO. A report by Johnson, Mihlan, LaPointe, Flet cher, et al. (2000) describes the algorithms used to implement each microenvironment within the pNEM framework. To the extent practical, each microenvironment defined fo r the APEX sample run exactly duplicates the operations of the corresponding microenvir onmental algorithms in pNEM. In cases where the APEX framework did not permit an ex act 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 e ffort 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 se ction. 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 microenvironm ents. 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 met hod uses a linear relationship to calculate the pollutant concentration in the microenvir onment. 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 Mi croenvironmental Descriptions File.

In the Parameter Description section of the Micro Descriptions file, the user defines the parameters to be used in calculating pollut ant 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 ent ries for the 15 microenvironments defined for the sample run. Note that a Parame ter 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 def initions provided by Table 3-4. Additional guidance concerning the specification of ke ywords 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 follo wing 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 follo wing parameters which define the distribution of the parameters under the spec ified 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 a llows 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 histogr am or discrete probab ility 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 specif y 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 24 th 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 represent s a month of a year, beginning in January and ending in December. T he integer represents the season that a month belongs to. If this line is missi ng, all 12 months belong to season #1.

District - Area This variable is used to map air quality district s to larger areas. The number of integers in this line must match the number of air quality dist ricts 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 followi ng 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 se lected from distribution for a parameter in each hour within a time block. If NO, a r andom value is selected for a parameter for a time block and used for every hour within t he time block. The default value is NO.

ResampDays Either YES or NO. If YES, a random value is selected from a distri bution 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 r andom 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 inter nal clock, and the results will differ from one run to another. If not zero, then the specifi ed number is the actual seed, and multiple model runs with the same seed will generat e the same sequence of random numbers for the parameter (as long as t he 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 vari es 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 Pa rameters 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 s how the contents of the MicroParameters file applicable to the Indoors - Residence microenvironmen t, 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 microenvironm ent 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. Parame ters specifying proximity factor, penetration factor, and volume are also defined.

The top portion of Figure 3-23 pr esents the file entries used to specify the proximity factor (PRX). The parameter settings inst ruct APEX to select a single value for each simulated person from a lognormal distri bution 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 t he file entries for the penetration factor

###  28 (PEN). In this case, the PEN value is re sampled 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 summa ry 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 f ile entries used to determine air exchange rate (AER) in the indoors - residential microenv ironment. The values are selected from 24 lognormal distributions with parameters that vary with s eason, air conditioning status of the residence (specified by AC_Home) , and the average temperature of the day (specified by the value of the temperatur e category AVGTEMPCAT). AER values are resampled for each day and each work distric t. 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 m 3 

### , maximum equal to 957 m 3 

### , geometric mean equal to 363 m 3 

### , 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 entri es for four “ES” parameters that are

###  29 multiplied together to determine the CO em ission rate associated with environmental tobacco smoke (ETS): emission rate per cigarette ( μ g/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 val ue from the indicated lognormal distribution. The daily ME smoking status (lower por tion of Figure 3-25) is determined daily by selecting values from the indicated o ffon 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 Toba cco Smoke Emission Source in Microenvironment 1.

The occurrence of smoking during a particu lar 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 par ameters 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 multiplie r term (K), annual fuel usage by burners (AUB), burner emission factor (EFBURN), seas onal adjustment factor (Y), on/off status during specified block hour, and duration of us e 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 pers ons 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, maximu m = 3,730,000 kilojoules, geometric mean = 1,730,000 kilojoules, and geomet ric 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 distri bution. 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 det ermining 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 det ermining whether a gas stove is on or off during a particular hour. The distributional ent ries 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 r egardless 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 duri ng 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 defi ned in Figure 3-32. These parameters account for annual fuel use by pilot light (AUP), a multip lier term (K), and emission factor for burners (EFBURN). Values for the AUP parameter ar e 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 parameter s; 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, Microenvir onment 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 val ue of PRX is selected from the indicated lognormal distribution for each simulated pers on. 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 distributi on 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. T here 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 pers on. PEN values are selected every hour from another lognormal distribution. A val ue 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 th rough A-23 of Appendix A and in Table 4-8 of the report by Johnson, Mihlan, LaP ointe, Fletcher, et al. (2000).

3.15.4 Indoor Microenvironments 4 through 9

Table 3-2 lists six additional indoor micr oenvironments (4 through 9) used in the application of APEX to Los Angeles. The f ile 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 ca se, a single value of PRX is sampled from a lognormal distribution for each simu lated person. PEN values are sampled hourly from a lognormal distribution. T hese distributions are discussed on pages 85 through 88 of Appendix A. Values of AER ar e sampled from a lognormal distribution for the home and work districts. The distribut ion 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 “Out doors - 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. Figur es 3-41 and 3-42 present the parameters of these distributions. These values we re 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 (V ehicle - 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 VO L 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 ( μ g/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 di stribution. The daily ME smoking status is determined daily by selecting values from the indicated off/on probability function.

Consistent with Microenvironm ent 1, the occurrence of smoking in a car during a particular hour is determined by an “on/o ff” 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 t hat smoking duration is determined by a point estimate that va ries by block hour (Figure 3-46).

Microenvironment 13 (Vehicle - Truck) is model ed 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 ( μ g/h), daily ME smoking status (1 = yes, 0 = no), smoking during hour (1 = yes, 0 = no), and smoking dur ation during hour (min). The file entries for emission rate per cigarette and daily ME sm oking status specific to trucks (Figure 3- 48) are identical to those used for cars. Howe ver, 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 estimate s for smoking duration in Figure 3-50.

The distributions used in simulating t he vehicle microenvironments 12 and 13 were obtained from pages A-5 through A-7 (smo king 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 Toba cco 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 Toba cco 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 pr ovides 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 l ognormal distribution at hourly intervals. AER values are sampled from a unifo rm 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 se ven output files listed in Table 1-4. This section provides a brief descrip tion 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 cont ains 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 associ ated with the run. Figure 4-2 lists 26 job par ameters, including the number of profiles generated, number of microenvironments def ined, number of sources simulated, minimum and maximum age of simulated people, information used in defining districts, the random seed specif ied, 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 fi nal study area, the air quality districts, population statistics by sector, data related to METS (energy expenditu re rates), various microenvironmental parameters, demographic characteristics of each generated profile, counts of various demographic factors, and t he 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 prov ides 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 parti al 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 fo r 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 condi tioning 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 Su mmary 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 pers on, the mean pollutant concentration (“MeanConc”) experienced in the microenvironm ent by the person, and the maximum concentration associated with an exposure ev ent (“MaxConc”) in the microenvironment. Figure 4-6 provides a partial listing of this f ile; it shows the file entries for Person #1 and Person #2. The complete file contains listi ngs for all 1000 simulated people. Note that the listings for each person include t he 15 microenvironments defined for the assessment (numbered 1 through 15) and a “Zer oExposure” (No. 0) microenvironment that is used to tabulate exposures when t he 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 ai r 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 qua lity site and between the sector and the meteorological station. Fi gure 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 t he results of the simulation with respect to number of minutes spent in each microenvir onment at specified CO concentrations, daily maximum 1-hour and 8-hour CO exposur es, daily average and yearly-average COexposures, 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.Expo Inhalation 

### 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., Flet cher, 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 St andards 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 fo rmat 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 pN EM/CO in applications of this kind. To make the comparison as valid as possibl e, 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 es timates previously obtained by the pNEM exposure assessment.

As part of this effort, the authors revi ewed 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 modeli ng process as to the effort required to make the element consistent with the co rresponding element in the pNEM/CO analysis of Los Angeles. We also noted cases in wh ich the current internal structure of APEX3 prevented the user from implem enting 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 memor andum 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 t hat APEX3 could not implement a number of the more complex probabilistic algorithm s 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 descr ibed 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 a ssessments. Ted Johnson provided the following five recommendations to John Langstaff on May 30, 2002.

6. Enable APEX3 to resample source term distri butions 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 w ill 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 ( μ g/m 3 

### -- with each value being selected according to a different sampling frequency (hourly, dail y, 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 distributi on. 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 dura tion, 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 = (n smokers 

### )(n cigs/smoker/hr 

### )(CO cigarette 

### ) (1)

in which S is the emission rate in μ g/hr, n smokers 

### is the number of smokers present, n cigs/smoker/hr 

### is the number of cigarettes smoked per hour per smoker, and CO cigarette 

### 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 n smokers 

### n cigs/smoker/hr 

### CO cigarette



### GM, μ g/hr GSD Min, μ g/hr Max, μ g/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

μ g/hr (i.e., 1 smoker x 2 cigarettes per smoker per hour x 71,400 μ g/hr per cigarette). The corresponding minimum and maximum values are 92,800 μ g/hr and 219,800 μ g/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 CO mass 

### (m,h) -- the CO mass emitted by passive smoking during hour h in ME m -- can be calculated by the following expression.

CO mass 

### (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 th e distribution options currently included in
APEX3.1.

We have implemented an alternative approach in which the following equation is substituted for Equation 2.

CO mass 

### (m,h) = O(m,d) x O(m,h) x D avg 

### (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 P 0 

### (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 P 0 

### (m,h) and varies with hour of day and microenvironment. Values of O(m,h) are sampled hourly.

D avg 

### (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). D avg 

### (m,h) is a point estimate that varies with microenvironment and hour of day.

Estimates of P 0 

### (m,d), P 0 

### (m,h), and D avg 

### (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, P 0 

### (m,d) was set equal to the ratio of these two values.

Number of person-days in ME Microenvironment No passive smoking Total P 0 

### (m,d)

Residence 6841 8044 0.8504
Car 5746 6191 0.9281 Truck 896 974 0.9199

Each of the three P 0 

### (m,d) values exceeds 0.85, indicating that passive smoking occurs relatively infrequently in these ME’s.

Table 2 lists estimates of P 0 

### (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 P 0 

### (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 D avg 

### (m,h) by time of day for the three ME’s. Each value was calculated by the expression

D avg 

### (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
P 0 

### (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 diffe rentiated between gas stoves with and without electronic ignitions and provided a s eparate algorithm for each stove type. The Monte Carlo algorithm described in Subs ection 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 a ssumed to have continuously burning pilot lights. The mass of CO emitted by a gas st ove 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, ERB URN is the hourly burn emission rate in mg Table 2. Average probability that passive smoking will not occur [P 0 

### (m,h)] for the indicated combinat ion of microenvironment and hour. Based on person-days in which subjec t reported that passive smoking occurred sometime during the day in the indicated microenvironment.

End time of hour P 0 (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 hou r) of smoking when and if it occurs [D avg 

### (m,h)] for the indicated combinat ion of microenvironment and hour.

End time of hour D avg 

### (m,h) by microenvironment and hour

Indoors - residence In car In truck

1 0.9182 0.9603 1.0000

2 0.9602 1.0000 1.0000 a



### 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 P 0 

### (m,d) Residence: 0.8504 Car: 0.9281 Truck: 0.9199

O(m,h) Hourly OffOn P 0 

### (m,h) See Table 2

D avg 

### (m,h) Hourly Point estimate Constant See Table 3

S(m)

μ g/hr Annual Lognormal GM 142,800

GSD 1.3

Min 92,800

Max 219,800

per hour, and ERPILOT is the hourly pilot light em ission 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 wit h 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 bur ners in kilojoules, 365.2 is the number of hours per year that the bur ners are operated assuming 60 minutes of use per day (Table 5), EFBURN is the burner emission fact or 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 P 0 (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 devia tion = 1.48. The value of AUB was bounded by 0.8 and 3.73 million kilojoules. These value represent the 2.5 th 

### and 97.5 th 

### percentiles of the lognormal distribution.

The seasonal adjustment factor (Y  ) was determined by the equation

Y  (j) = 1.00 - (0.190){sin[-1.616 + (2 π )(j)/365]}. (8)

in which j is the Julian date. This equat ion 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 st andard deviation = 2.77. Values of EFBURN were not permitted to exceed 0.400 mg/kilojoule.

Consistent with the above discussion, the fo llowing 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 random ly selected from a lognormal distribution with geometric mean = 2.76 milli on 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 y ear. These bounds correspond to the 2.5 th 

### and 97.5 th 

### percentiles, respectively, of the specified lognormal distribution.

The value of MASSCO(h) as determined by E quation 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 wa s 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/C O 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: CO mass 

### =[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 t hat O(h) will equal zero is P 0 

### (h) and varies with hour of day. Table 5 lists values of P 0 

### (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: CO mass 

### =(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 milli on kilojoules and geometric standard deviation = 1.48. The value of AUB is bounded by 0.8 and 3.73 million kilojoules (the 2.5 th 

### and 97.5 th 

### 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 dist ribution 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 CO mass 

### values for gas stoves with pilot lights and for gas stoves wit h electric ignitions. The inclusion of the pilot light and burners in the same algorit hm 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 t hat 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 algorit hm, we assumed that the hourly CO mass 

### value for pilot lights could be calculated by the following equation

pilot lights: CO mass 

### = (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 va lue 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.) T able 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 re spect 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 pr obabilistic 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 “c losed 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 probabilit y 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 pr ovided by pNEM/CO. The user may also specify a set of ranges for daily average temper ature. For each combination of AC type and temperature range, the user may prov ide 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. Consis tent 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 thes e deficiencies in APEX3.1. We used the Crystal Ball (tm) software package to dev elop a stand-alone Monte Carlo program (WIN_SIM1.XLS) that duplic ated 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 P 0 

### (h) See Table 5

M(h) minutes Hourly Point estimate Constant See Table 5

AUB kilojoules Annual Lognormal GM 1.73 x 10 6



### GSD 1.48

Min 0.8 x 10 6



### Max 3.73 x 10 6



### 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 E quation 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 10 6



### GSD 1.84

Min 0.84 x 10 6



### Max 9.12 x 10 6



### 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 ex treme (minimum and maximum) values permitted by the pNEM/CO algorithm. We consi dered these latter “absolute” minimum and maximum values to be the appropriate bounds for the AER values to be generated by APEX3. Consequently, the parameters specifi ed for the APEX3 runs included the GM and GSD values listed in Table 9 for eac h 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 vehicl es. 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 tem perature. Another algorithm determines vehicle speed probabilistically. AER is then determined probabilistically as a function of vent status and speed. The overall procedur e is relatively complex and cannot be implemented directly using only the conditional variables available within APEX3.1.

To address this deficiency, we again used t he 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 implem ented 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 abs ent) and temperature range; the results were considered representative of both cars and tru cks 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 fo r the APEX3.1 runs included the GM and GSD values listed in Table 10 for each AC-t emperature classification together with the associated extreme values in the two far-right columns.

The reader should note that we did not empl oy 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 withi n the stand-alone Monte Carlo program.

We also needed to provide the APEX3.1 mass balance model with separate distributions of passenger cab volumes fo r 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 fr om 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. Howe ver, we found that lognormal distributions provided an acceptable fit to the pNEM/CO hi stograms for automobiles and trucks. The Table 9. Lognormal Distributions Fit to Ai r 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 GM a GSD a Minimum Maximum Minimum a Maximum a

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 Si mulating 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 GM a GSD a Minimum Maximum Minimum a Maximum a

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 param eters specified for the APEX3.1 runs included the GM and GSD values listed in Tabl e 11 for a particular ME together with the associated extreme values in the two far-right columns.

Table 11. Lognormal Distributions Fit to V ehicle 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 (m 3 

### ) GSD Minimum (m 3 

### ) Maximum (m 3 

### )

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

CO out 

### (c,m,d,h) = M(m) x L(c, m, d) x T(c,m,d,h) x [CO mon 

### (d,h)] A 

### (13)

in which

CO out 

### (c,m,d,h) = outdoor CO concentration (ppm) for cohort c with respect to micro-environment m in dist rict 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 (hel d 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,

CO mon 

### (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 y ear-long sequence of outdoor one-hour CO concentrations for each combination of cohor t (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 (GM L 

### ) equal to 1.0 and geometric standard deviation (GSD L 

### ) equal to 1.5232. The natural logar ithms of this distribution can be characterized by a normal distri bution with an arithmetic mean ( μ L 

### ) equal to zero and an arithmetic standard deviation ( σ L 

### ) 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 di stribution with geometric mean (GM T 

### ) equal to 1.0 and geometric standard deviation (GSD T 

### ) equal to 1.6289. The natural logarithms of this distribution follow a normal di stribution with an ar ithmetic mean ( μ T 

### ) equal to zero and an arithmetic standard deviation ( σ T 

### ) equal to 0.4879.

The CO out 

### (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. CO mon 

### (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 concent ration affecting the indoor concentration is determined by the formula

CO(outdoor contrib) = Ambient * Proximity * Penetration

We can represent Proximity and Penetrati on as distributions in APEX3.1. These distributions can be sampled hourly, daily, or yearly. Let us make the following substitutions:

Ambient = [CO mon 

### (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 [CO mon 

### (d,h)] A



### which is identical to Equation 13.

To obtain results from APEX3.1 that are co mparable to pNEM/CO, we preprocessed the hourly monitor-derived data assigned to the di strict containing the microenvironment using the formula

Ambient = [CO mon 

### (d,h)] 0.621



### in which CO mon 

### (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 st andard deviation equal to 1.5232. The natural logarithms of this distribution were characterized by a normal distribution with an arithmetic mean ( μ L 

### ) equal to ln[M(m)] and an arithmetic standard deviation ( σ L 

### ) equal to 0.4208. Consistent with the pNEM/CO algorithm, Proximity values were not permitted to fall below the 5 th 

### percentile of the specifi ed distribution or above the 95 th 

### percentile of the distribution. Table 12 lists the par ameter values applicable to each ME.

We randomly selected a value for Penetration for each hour from a lognormal distribution with geometric mean (GM T 

### ) equal to 1.0 and geometric standard deviation (GSD T 

### ) equal to 1.6289. As indicated above, the natural logarithms of this distribution followed a normal distributi on with an arithmetic mean ( μ T 

### ) equal to zero and an arithmetic standard deviation ( σ T 

### ) equal to 0.4879. In agreement with the pNEM/CO

###  23 algorithm, Penetration values were not permitted to fall below the 5 th 

### percentile of the specified distribution (0.4482) or above the 95 th 

### percentile of the distribution (2.2313).

It should be noted that we did not use the P enetration 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 pollutant s during the transfer of outdoor air to a microenvironment. We used the Penetration fact or 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 pr oduct 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 tw o 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 s eason 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 bot h the input activity data file and the input meteorology data file. This approach was f easible because daily maximum temperature is only used for pooling activity data; the tem perature 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 te mperatures 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 cu t-points for the APEX diary pools were set at 60, 70, and 80 degrees which effectivel y 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 (5 th PCT) Maxmum (95 th 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2Exposure: Minutes in each Exposure interval ( ppm ), by microenvironment, for N = 1000 ProfilesLevel: 0.0000 5.0000 10.0000 15.0000 20.0000 25.0000 30.0000 35.0000 40.0000 45.000050.0000 60.0000 70.0000 80.0000 90.0000 100.0000Micro --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------0 Minutes 314229. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00000 Tot_% 0.0598 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00001 Minutes 358505573. 2694985. 40624. 1885. 180. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00001 Tot_% 68.2088 0.5127 0.0077 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00002 Minutes 1301180. 285114. 27651. 3967. 922. 60. 15. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00002 Tot_% 0.2476 0.0542 0.0053 0.0008 0.0002 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00003 Minutes 7031167. 30376. 660. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00003 Tot_% 1.3377 0.0058 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00004 Minutes 8402559. 48021. 424. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00004 Tot_% 1.5987 0.0091 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00005 Minutes 2394348. 27252. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00005 Tot_% 0.4555 0.0052 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00006 Minutes 452006. 2723. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00006 Tot_% 0.0860 0.0005 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00007 Minutes 43119149. 423064. 5853. 174. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00007 Tot_% 8.2038 0.0805 0.0011 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00008 Minutes 30834768. 122268. 805. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00008 Tot_% 5.8666 0.0233 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00009 Minutes 1466647. 6545. 60. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00009 Tot_% 0.2790 0.0012 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000010 Minutes 5782897. 185578. 8935. 488. 120. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000010 Tot_% 1.1002 0.0353 0.0017 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000011 Minutes 28585389. 667580. 29089. 2461. 364. 75. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000011 Tot_% 5.4386 0.1270 0.0055 0.0005 0.0001 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000012 Minutes 19101250. 4938477. 672933. 106858. 19813. 3145. 708. 237. 120. 0.0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000012 Tot_% 3.6342 0.9396 0.1280 0.0203 0.0038 0.0006 0.0001 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000013 Minutes 3310523. 947833. 161637. 41091. 10860. 3162. 1270. 703. 110. 54.57. 0. 15. 0. 0. 0.13 Row_% 73.9399 21.1697 3.6101 0.9178 0.2426 0.0706 0.0284 0.0157 0.0025 0.00120.0013 0.0000 0.0003 0.0000 0.0000 0.000013 Tot_% 0.6299 0.1803 0.0308 0.0078 0.0021 0.0006 0.0002 0.0001 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000014 Minutes 1453591. 376279. 48466. 5279. 450. 210. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000014 Tot_% 0.2766 0.0716 0.0092 0.0010 0.0001 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000015 Minutes 1299144. 253123. 28303. 4431. 814. 425. 189. 60. 90. 60.0. 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 0.00380.0000 0.0000 0.0000 0.0000 0.0000 0.000015 Tot_% 0.2472 0.0482 0.0054 0.0008 0.0002 0.0001 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.0000---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------3Exposure: Minutes at or above each Exposure level ( ppm ), by microenvironment, for N = 1000 ProfilesLevel: 0.0000 5.0000 10.0000 15.0000 20.0000 25.0000 30.0000 35.0000 40.0000 45.000050.0000 60.0000 70.0000 80.0000 90.0000 100.0000Micro --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------0 Minutes 314229. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00000 Tot_% 0.0598 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00001 Minutes 361243232. 2737674. 42689. 2065. 180. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00001 Tot_% 68.7297 0.5209 0.0081 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00002 Minutes 1618909. 317729. 32615. 4964. 997. 75. 15. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00002 Tot_% 0.3080 0.0605 0.0062 0.0009 0.0002 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00003 Minutes 7062203. 31036. 660. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00003 Tot_% 1.3436 0.0059 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00004 Minutes 8451004. 48445. 424. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00004 Tot_% 1.6079 0.0092 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00005 Minutes 2421600. 27252. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00005 Tot_% 0.4607 0.0052 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00006 Minutes 454729. 2723. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00006 Tot_% 0.0865 0.0005 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00007 Minutes 43548240. 429091. 6027. 174. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00007 Tot_% 8.2854 0.0816 0.0011 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00008 Minutes 30957840. 123073. 805. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00008 Tot_% 5.8900 0.0234 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00009 Minutes 1473252. 6605. 60. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.00009 Tot_% 0.2803 0.0013 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000010 Minutes 5978018. 195121. 9543. 608. 120. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000010 Tot_% 1.1374 0.0371 0.0018 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000011 Minutes 29284958. 699569. 31989. 2900. 439. 75. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000011 Tot_% 5.5717 0.1331 0.0061 0.0006 0.0001 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000012 Minutes 24843540. 5742291. 803814. 130881. 24023. 4210. 1065. 357. 120. 0.0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000012 Tot_% 4.7267 1.0925 0.1529 0.0249 0.0046 0.0008 0.0002 0.0001 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000013 Minutes 4477315. 1166792. 218959. 57322. 16231. 5371. 2209. 939. 236. 126.72. 15. 15. 0. 0. 0.13 Row_% 100.0000 26.0601 4.8904 1.2803 0.3625 0.1200 0.0493 0.0210 0.0053 0.00280.0016 0.0003 0.0003 0.0000 0.0000 0.000013 Tot_% 0.8518 0.2220 0.0417 0.0109 0.0031 0.0010 0.0004 0.0002 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000014 Minutes 1884275. 430684. 54405. 5939. 660. 210. 0. 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 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000014 Tot_% 0.3585 0.0819 0.0104 0.0011 0.0001 0.0000 0.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.000015 Minutes 1586639. 287495. 34372. 6069. 1638. 824. 399. 210. 150. 60.0. 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 0.00380.0000 0.0000 0.0000 0.0000 0.0000 0.000015 Tot_% 0.3019 0.0547 0.0065 0.0012 0.0003 0.0002 0.0001 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.0000 0.0000 0.0000---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------4Exposure: Person-days at or above each Daily Maximum 1-Hour Exposure level ( ppm ), for N = 1000 Profiles. Area Population= 6592124Level: 0.000 10.000 15.000 20.000 25.000 30.000 35.000 40.000 45.000 50.00060.000--------------------------------------------------------------------------------------------------------------------------------------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 0.000E+00#Meet (Pop): 6592124 6012017 2999416 1061332 303238 145027 65921 26368 6592 00%Meet (Pop): 100.000 91.200 45.500 16.100 4.600 2.200 1.000 0.400 0.100 0.0000.000Mean : 365.000 5.897 0.884 0.189 0.048 0.023 0.010 0.004 0.001 0.0000.000Std.Dev. : 0.000 6.147 1.292 0.473 0.223 0.157 0.100 0.063 0.032 0.0000.000CV : 0.000 1.042 1.462 2.501 4.647 6.805 9.955 15.788 31.623 0.0000.000Minimum : 365.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.00010.0 %ile : 365.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.00025.0 %ile : 365.000 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.00050.0 %ile : 365.000 4.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.00075.0 %ile : 365.000 8.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.00090.0 %ile : 365.000 13.900 3.000 1.000 0.000 0.000 0.000 0.000 0.000 0.0000.00095.0 %ile : 365.000 19.000 4.000 1.000 0.000 0.000 0.000 0.000 0.000 0.0000.00099.0 %ile : 365.000 28.000 5.000 2.000 1.000 1.000 0.990 0.000 0.000 0.0000.000Maximum : 365.000 41.000 7.000 3.000 2.000 2.000 1.000 1.000 1.000 0.0000.000Mean (%) : 100.000 1.616 0.242 0.052 0.013 0.006 0.003 0.001 0.000 0.0000.000Min (%) : 100.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000Max (%) : 100.000 11.233 1.918 0.822 0.548 0.548 0.274 0.274 0.274 0.0000.000Counts(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 0.000E+00#Meet (Sim): 1000 912 455 161 46 22 10 4 1 00--------------------------------------------------------------------------------------------------------------------------------------5Exposure: Person-days at or above each Daily Maximum 8-Hour Exposure level ( ppm ), for N = 1000Profiles. Area Population = 6592124Level: 0.000 6.000 9.000 12.000 15.000 20.000 25.000----------------------------------------------------------------------------------------------Counts(Pop): 0.241E+10 0.244E+08 0.217E+07 0.396E+06 0.989E+05 0.659E+04 0.000E+00#Meet (Pop): 6592124 4574934 1206359 336198 85698 6592 0%Meet (Pop): 100.000 69.400 18.300 5.100 1.300 0.100 0.000Mean : 365.000 3.702 0.329 0.060 0.015 0.001 0.000Std.Dev. : 0.000 7.724 1.019 0.277 0.137 0.032 0.000CV : 0.000 2.086 3.096 4.609 9.139 31.623 0.000Minimum : 365.000 0.000 0.000 0.000 0.000 0.000 0.00010.0 %ile : 365.000 0.000 0.000 0.000 0.000 0.000 0.00025.0 %ile : 365.000 0.000 0.000 0.000 0.000 0.000 0.00050.0 %ile : 365.000 2.000 0.000 0.000 0.000 0.000 0.00075.0 %ile : 365.000 4.000 0.000 0.000 0.000 0.000 0.00090.0 %ile : 365.000 8.000 1.000 0.000 0.000 0.000 0.00095.0 %ile : 365.000 14.000 2.000 1.000 0.000 0.000 0.00099.0 %ile : 365.000 44.990 5.000 1.000 1.000 0.000 0.000Maximum : 365.000 81.000 14.000 3.000 2.000 1.000 0.000Mean (%) : 100.000 1.014 0.090 0.016 0.004 0.000 0.000Min (%) : 100.000 0.000 0.000 0.000 0.000 0.000 0.000Max (%) : 100.000 22.192 3.836 0.822 0.548 0.274 0.000Counts(Sim): 0.365E+06 0.370E+04 0.329E+03 0.600E+02 0.150E+02 0.100E+01 0.000E+00#Meet (Sim): 1000 694 183 51 13 1 0----------------------------------------------------------------------------------------------6Exposure: Person-days at or above each Daily Average Exposure level ( ppm ), for N = 1000 Profiles. Area Population = 6592124Level: 0.000 2.000 4.000 5.000 6.000 7.000 8.000 9.000 10.000 11.00012.000 14.000 16.000 18.000 20.000------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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 0.000E+00 0.000E+00 0.000E+00#Meet (Pop): 6592124 6592124 3876169 1713952 718542 342790 145027 46145 19776 131846592 0 0 0 0%Meet (Pop): 100.000 100.000 58.800 26.000 10.900 5.200 2.200 0.700 0.300 0.2000.100 0.000 0.000 0.000 0.000Mean : 365.000 85.168 3.736 0.857 0.236 0.068 0.025 0.007 0.003 0.0020.001 0.000 0.000 0.000 0.000Std.Dev. : 0.000 59.431 9.664 3.428 1.132 0.346 0.180 0.083 0.055 0.0450.032 0.000 0.000 0.000 0.000CV : 0.000 0.698 2.587 4.000 4.797 5.084 7.201 11.916 18.239 22.34931.623 0.000 0.000 0.000 0.000Minimum : 365.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.00010.0 %ile : 365.000 19.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.00025.0 %ile : 365.000 37.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.00050.0 %ile : 365.000 71.500 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.00075.0 %ile : 365.000 120.750 3.000 1.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.00090.0 %ile : 365.000 168.000 9.000 2.000 1.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.00095.0 %ile : 365.000 200.850 16.000 3.000 1.000 1.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.00099.0 %ile : 365.000 253.980 58.940 17.990 5.000 1.000 1.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000Maximum : 365.000 324.000 110.000 48.000 16.000 5.000 3.000 1.000 1.000 1.0001.000 0.000 0.000 0.000 0.000Mean (%) : 100.000 23.334 1.024 0.235 0.065 0.019 0.007 0.002 0.001 0.0010.000 0.000 0.000 0.000 0.000Min (%) : 100.000 0.274 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000Max (%) : 100.000 88.767 30.137 13.151 4.384 1.370 0.822 0.274 0.274 0.2740.274 0.000 0.000 0.000 0.000Counts(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 0.000E+00 0.000E+00 0.000E+00#Meet (Sim): 1000 1000 588 260 109 52 22 7 3 21 0 0 0 0------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Exposure: Persons at or above each Overall Average Exposure level ( ppm ), for N = 1000 Profiles. Area Population = 6592124Level: 0.000 0.500 1.000 1.250 1.500 1.750 2.000 2.500 3.000 4.0005.000 6.000 8.000 10.000--------------------------------------------------------------------------------------------------------------------------------------------------------------------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 0.000E+00 0.000E+00#Meet (Pop): 6592124 6592124 6381176 5188002 3381760 1839203 876752 112066 19776 00 0 0 0%Meet (Pop): 100.000 100.000 96.800 78.700 51.300 27.900 13.300 1.700 0.300 0.0000.000 0.000 0.000 0.000Counts(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 0.000E+00 0.000E+00#Meet (Sim): 1000 1000 968 787 513 279 133 17 3 00 0 0 0--------------------------------------------------------------------------------------------------------------------------------------------------------------------7Dose: Person-days at or above each Daily Max End-of-Hour Dose level (%COHb), 1000 Profiles. Area Population = 6592124Level: 0.000 0.500 1.000 1.500 2.000 2.100 2.200 2.300 2.400 2.5002.600 2.700 2.800 2.900 3.000 4.000 5.000 6.000------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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 0.659E+04 0.000E+00 0.000E+00#Meet (Pop): 6592124 6592124 6163636 2478639 639436 520778 415304 290053 191172 151619118658 92290 65921 52737 52737 6592 0 0%Meet (Pop): 100.000 100.000 93.500 37.600 9.700 7.900 6.300 4.400 2.900 2.3001.800 1.400 1.000 0.800 0.800 0.100 0.000 0.000Mean : 365.000 245.162 20.636 1.514 0.128 0.093 0.072 0.053 0.033 0.0240.018 0.014 0.010 0.008 0.008 0.001 0.000 0.000Std.Dev. : 0.000 91.661 45.182 10.324 0.496 0.353 0.295 0.265 0.200 0.1600.133 0.118 0.100 0.089 0.089 0.032 0.000 0.000CV : 0.000 0.374 2.189 6.819 3.874 3.794 4.094 5.001 6.057 6.6477.390 8.396 9.955 11.141 11.141 31.623 0.000 0.000Minimum : 365.000 24.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00010.0 %ile : 365.000 113.100 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00025.0 %ile : 365.000 169.000 3.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00050.0 %ile : 365.000 253.000 7.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00075.0 %ile : 365.000 333.000 18.000 1.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00090.0 %ile : 365.000 364.000 42.000 2.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00095.0 %ile : 365.000 365.000 85.900 4.000 1.000 1.000 1.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00099.0 %ile : 365.000 365.000 308.940 16.000 2.000 2.000 1.000 1.000 1.000 1.0001.000 1.000 0.990 0.000 0.000 0.000 0.000 0.000Maximum : 365.000 365.000 365.000 228.000 8.000 5.000 3.000 3.000 2.000 2.0001.000 1.000 1.000 1.000 1.000 1.000 0.000 0.000Mean (%) : 100.000 67.168 5.654 0.415 0.035 0.025 0.020 0.015 0.009 0.0070.005 0.004 0.003 0.002 0.002 0.000 0.000 0.000Min (%) : 100.000 6.575 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Max (%) : 100.000 100.000 100.000 62.466 2.192 1.370 0.822 0.822 0.548 0.5480.274 0.274 0.274 0.274 0.274 0.274 0.000 0.000Counts(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 0.100E+01 0.000E+00 0.000E+00#Meet (Sim): 1000 1000 935 376 97 79 63 44 29 2318 14 10 8 8 1 0 0------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Dose: Person-days at or above each Daily Max 1-Hour Dose level (%COHb), 1000 Profiles. Area Population = 6592124Level: 0.000 0.500 1.000 1.500 2.000 2.100 2.200 2.300 2.400 2.5002.600 2.700 2.800 2.900 3.000 4.000 5.000 6.000------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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 0.000E+00 0.000E+00 0.000E+00#Meet (Pop): 6592124 6592124 6051570 2234730 573515 421896 323014 243909 171395 11865885698 65921 52737 46145 32961 0 0 0%Meet (Pop): 100.000 100.000 91.800 33.900 8.700 6.400 4.900 3.700 2.600 1.8001.300 1.000 0.800 0.700 0.500 0.000 0.000 0.000Mean : 365.000 241.262 19.342 1.382 0.111 0.078 0.058 0.044 0.029 0.0200.014 0.010 0.008 0.007 0.005 0.000 0.000 0.000Std.Dev. : 0.000 93.014 44.246 9.972 0.432 0.335 0.273 0.241 0.185 0.1540.126 0.100 0.089 0.083 0.071 0.000 0.000 0.000CV : 0.000 0.386 2.288 7.216 3.894 4.291 4.713 5.479 6.376 7.6858.984 9.955 11.141 11.916 14.114 0.000 0.000 0.000Minimum : 365.000 22.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00010.0 %ile : 365.000 110.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00025.0 %ile : 365.000 164.000 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00050.0 %ile : 365.000 247.000 6.500 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00075.0 %ile : 365.000 332.000 15.000 1.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00090.0 %ile : 365.000 364.000 39.000 2.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00095.0 %ile : 365.000 365.000 82.850 3.000 1.000 1.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00099.0 %ile : 365.000 365.000 301.940 14.000 2.000 2.000 1.000 1.000 1.000 1.0001.000 0.990 0.000 0.000 0.000 0.000 0.000 0.000Maximum : 365.000 365.000 365.000 221.000 6.000 5.000 3.000 3.000 2.000 2.0002.000 1.000 1.000 1.000 1.000 0.000 0.000 0.000Mean (%) : 100.000 66.099 5.299 0.379 0.030 0.021 0.016 0.012 0.008 0.0050.004 0.003 0.002 0.002 0.001 0.000 0.000 0.000Min (%) : 100.000 6.027 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Max (%) : 100.000 100.000 100.000 60.548 1.644 1.370 0.822 0.822 0.548 0.5480.548 0.274 0.274 0.274 0.274 0.000 0.000 0.000Counts(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 0.000E+00 0.000E+00 0.000E+00#Meet (Sim): 1000 1000 918 339 87 64 49 37 26 1813 10 8 7 5 0 0 0------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------8Dose: Person-days at or above each Daily Max 8-Hour Dose level (%COHb), 1000 Profiles. Area Population = 6592124Level: 0.000 0.500 1.000 1.500 2.000 2.100 2.200 2.300 2.400 2.5002.600 2.700 2.800 2.900 3.000 4.000 5.000 6.000------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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 0.000E+00 0.000E+00 0.000E+00#Meet (Pop): 6592124 6592124 4944093 929489 151619 92290 59329 39553 32961 2636826368 19776 13184 13184 13184 0 0 0%Meet (Pop): 100.000 100.000 75.000 14.100 2.300 1.400 0.900 0.600 0.500 0.4000.400 0.300 0.200 0.200 0.200 0.000 0.000 0.000Mean : 365.000 213.780 12.022 0.676 0.030 0.018 0.012 0.008 0.007 0.0060.006 0.004 0.003 0.003 0.002 0.000 0.000 0.000Std.Dev. : 0.000 100.680 37.437 7.226 0.226 0.166 0.134 0.109 0.105 0.1000.100 0.077 0.071 0.071 0.045 0.000 0.000 0.000CV : 0.000 0.471 3.114 10.689 7.539 9.247 11.141 13.663 14.957 16.64516.645 19.349 23.561 23.561 22.349 0.000 0.000 0.000Minimum : 365.000 8.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00010.0 %ile : 365.000 76.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00025.0 %ile : 365.000 128.250 0.250 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00050.0 %ile : 365.000 212.500 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00075.0 %ile : 365.000 307.000 7.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00090.0 %ile : 365.000 360.900 21.000 1.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00095.0 %ile : 365.000 365.000 52.950 2.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00099.0 %ile : 365.000 365.000 231.910 6.990 1.000 1.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Maximum : 365.000 365.000 365.000 172.000 4.000 3.000 2.000 2.000 2.000 2.0002.000 2.000 2.000 2.000 1.000 0.000 0.000 0.000Mean (%) : 100.000 58.570 3.294 0.185 0.008 0.005 0.003 0.002 0.002 0.0020.002 0.001 0.001 0.001 0.001 0.000 0.000 0.000Min (%) : 100.000 2.192 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Max (%) : 100.000 100.000 100.000 47.123 1.096 0.822 0.548 0.548 0.548 0.5480.548 0.548 0.548 0.548 0.274 0.000 0.000 0.000Counts(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 0.000E+00 0.000E+00 0.000E+00#Meet (Sim): 1000 1000 750 141 23 14 9 6 5 44 3 2 2 2 0 0 0------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Dose: Person-days at or above each Daily Average Dose level (%COHb), 1000 Profiles. Area Population = 6592124Level: 0.000 0.500 1.000 1.500 2.000 2.100 2.200 2.300 2.400 2.5002.600 2.700 2.800 2.900 3.000 4.000 5.000 6.000------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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 0.000E+00 0.000E+00 0.000E+00#Meet (Pop): 6592124 6578940 1839203 105474 6592 0 0 0 0 00 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 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Mean : 365.000 144.949 3.417 0.078 0.001 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Std.Dev. : 0.000 106.166 22.100 1.237 0.032 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000CV : 0.000 0.732 6.468 15.855 31.623 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Minimum : 365.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00010.0 %ile : 365.000 23.100 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00025.0 %ile : 365.000 57.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00050.0 %ile : 365.000 118.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00075.0 %ile : 365.000 217.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00090.0 %ile : 365.000 324.000 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00095.0 %ile : 365.000 360.000 8.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00099.0 %ile : 365.000 365.000 84.910 1.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Maximum : 365.000 365.000 365.000 30.000 1.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Mean (%) : 100.000 39.712 0.936 0.021 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Min (%) : 100.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Max (%) : 100.000 100.000 100.000 8.219 0.274 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Counts(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 0.000E+00 0.000E+00 0.000E+00#Meet (Sim): 1000 998 279 16 1 0 0 0 0 00 0 0 0 0 0 0 0------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------9Dose: Persons at or above each Overall Average Dose level (%COHb), 1000 Profiles. Area Population = 6592124Level: 0.000 0.400 0.500 0.600 0.700 0.800 0.900 1.000 1.200 1.4001.600 1.800 2.000 2.500--------------------------------------------------------------------------------------------------------------------------------------------------------------------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 0.000E+00 0.000E+00#Meet (Pop): 6592124 5135265 2412717 903121 329606 145027 72513 32961 6592 00 0 0 0%Meet (Pop): 100.000 77.900 36.600 13.700 5.000 2.200 1.100 0.500 0.100 0.0000.000 0.000 0.000 0.000Counts(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 0.000E+00 0.000E+00#Meet (Sim): 1000 779 366 137 50 22 11 5 1 00 0 0 0--------------------------------------------------------------------------------------------------------------------------------------------------------------------Dose: Person-hours at or above each End-of-Hour Dose level (%COHb), 1000 Profiles. Area Population = 6592124Level: 0.000 0.500 1.000 1.500 2.000 2.100 2.200 2.300 2.400 2.5002.600 2.700 2.800 2.900 3.000 4.000 5.000 6.000------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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 0.659E+04 0.000E+00 0.000E+00#Meet (Pop): 6592124 6592124 6163636 2478639 639436 520778 415304 290053 191172 151619118658 92290 65921 52737 52737 6592 0 0%Meet (Pop): 100.000 100.000 93.500 37.600 9.700 7.900 6.300 4.400 2.900 2.3001.800 1.400 1.000 0.800 0.800 0.100 0.000 0.000Mean : 8760.000 3461.836 140.638 7.191 0.377 0.260 0.183 0.125 0.090 0.0610.047 0.035 0.025 0.020 0.017 0.001 0.000 0.000Std.Dev. : 0.000 2272.073 541.346 75.138 1.870 1.292 0.981 0.791 0.683 0.5460.442 0.366 0.307 0.279 0.230 0.032 0.000 0.000CV : 0.000 0.656 3.849 10.449 4.960 4.970 5.361 6.330 7.588 8.9439.395 10.455 12.294 13.935 13.512 31.623 0.000 0.000Minimum : 8760.000 89.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00010.0 %ile : 8760.000 820.000 2.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00025.0 %ile : 8760.000 1613.000 8.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00050.0 %ile : 8760.000 2976.500 24.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00075.0 %ile : 8760.000 4980.750 69.000 2.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00090.0 %ile : 8760.000 7046.900 208.800 8.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00095.0 %ile : 8760.000 7939.000 536.900 13.950 3.000 2.000 1.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00099.0 %ile : 8760.000 8711.960 2842.810 60.990 7.000 6.000 4.990 3.990 3.000 2.0001.990 1.000 0.990 0.000 0.000 0.000 0.000 0.000Maximum : 8760.000 8760.000 8595.000 1786.000 34.000 23.000 13.000 13.000 12.000 10.0008.000 7.000 6.000 6.000 5.000 1.000 0.000 0.000Mean (%) : 100.000 39.519 1.605 0.082 0.004 0.003 0.002 0.001 0.001 0.0010.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000Min (%) : 100.000 1.016 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Max (%) : 100.000 100.000 98.116 20.388 0.388 0.263 0.148 0.148 0.137 0.1140.091 0.080 0.068 0.068 0.057 0.011 0.000 0.000Counts(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 0.100E+01 0.000E+00 0.000E+00#Meet (Sim): 1000 1000 935 376 97 79 63 44 29 2318 14 10 8 8 1 0 0------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------10Dose: Minutes in each Dose interval (%COHb), for N = 1000 Profiles. Area Population = 6592124Level: 0.000 0.500 1.000 1.500 2.000 2.100 2.200 2.300 2.400 2.5002.600 2.700 2.800 2.900 3.000 4.000 5.000 6.000------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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 0.330E+05 0.000E+00 0.000E+00#Meet (Pop): 6559163 6592124 6110899 2399533 468041 388935 257093 224132 158211 10547446145 46145 39553 26368 46145 6592 0 0%Meet (Pop): 99.500 100.000 92.700 36.400 7.100 5.900 3.900 3.400 2.400 1.6000.700 0.700 0.600 0.400 0.700 0.100 0.000 0.000Mean : 317946.305 199327.565 7907.805 396.980 7.120 4.456 3.110 1.898 1.396 1.0850.375 0.435 0.245 0.240 0.980 0.005 0.000 0.000Std.Dev. : 136622.314 125925.466 29286.295 4372.778 53.951 28.005 21.530 13.173 10.807 10.8895.340 5.698 3.877 3.789 14.462 0.158 0.000 0.000CV : 0.430 0.632 3.703 11.015 7.577 6.285 6.923 6.940 7.741 10.03614.239 13.099 15.824 15.788 14.757 31.623 0.000 0.000Minimum : 0.000 5127.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00010.0 %ile : 102455.703 47822.801 70.500 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00025.0 %ile : 227028.500 95559.500 420.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00050.0 %ile : 347509.500 176487.500 1305.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00075.0 %ile : 429048.250 291608.750 3780.250 120.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00090.0 %ile : 476939.094 395781.500 12081.300 360.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00095.0 %ile : 492759.812 433531.562 31660.551 624.850 53.800 25.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00099.0 %ile : 506778.781 482174.781 165513.125 3599.460 120.000 120.000 94.950 60.000 60.000 60.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Maximum : 520323.000 510049.000 410554.000 105046.000 1387.000 660.000 457.000 275.000 120.000 240.000120.000 120.000 95.000 60.000 300.000 5.000 0.000 0.000Mean (%) : 60.492 37.924 1.505 0.076 0.001 0.001 0.001 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Min (%) : 0.000 0.975 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Max (%) : 98.996 97.041 78.111 19.986 0.264 0.126 0.087 0.052 0.023 0.0460.023 0.023 0.018 0.011 0.057 0.001 0.000 0.000Counts(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 0.500E+01 0.000E+00 0.000E+00#Meet (Sim): 995 1000 927 364 71 59 39 34 24 167 7 6 4 7 1 0 0------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Dose: Minutes at or above each Dose level (%COHb), for N = 1000 Profiles. Area Population = 6592124Level: 0.000 0.500 1.000 1.500 2.000 2.100 2.200 2.300 2.400 2.5002.600 2.700 2.800 2.900 3.000 4.000 5.000 6.000------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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 0.330E+05 0.000E+00 0.000E+00#Meet (Pop): 6592124 6592124 6110899 2399533 632844 481225 388935 283461 197764 13184298882 79105 65921 46145 46145 6592 0 0%Meet (Pop): 100.000 100.000 92.700 36.400 9.600 7.300 5.900 4.300 3.000 2.0001.500 1.200 1.000 0.700 0.700 0.100 0.000 0.000Mean : 525600.000 207653.695 8326.130 418.325 21.345 14.225 9.769 6.659 4.761 3.3652.280 1.905 1.470 1.225 0.985 0.005 0.000 0.000Std.Dev. : 0.000 136622.314 32427.048 4427.003 109.242 75.833 55.660 44.835 39.065 33.20025.175 21.956 18.113 17.282 14.523 0.158 0.000 0.000CV : 0.000 0.658 3.895 10.583 5.118 5.331 5.698 6.733 8.205 9.86611.042 11.526 12.322 14.108 14.744 31.623 0.000 0.000Minimum : 525600.000 5277.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00010.0 %ile : 525600.000 48660.898 70.500 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00025.0 %ile : 525600.000 96551.750 435.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00050.0 %ile : 525600.000 178090.500 1380.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00075.0 %ile : 525600.000 298571.500 4020.000 120.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00090.0 %ile : 525600.000 423144.312 12323.100 480.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00095.0 %ile : 525600.000 476259.156 31727.051 804.000 134.500 89.250 30.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00099.0 %ile : 525600.000 523107.000 169456.891 3628.410 420.000 344.850 276.930 180.000 120.000 120.00059.700 59.700 9.900 0.000 0.000 0.000 0.000 0.000Maximum : 525600.000 525600.000 516080.000 105526.000 1980.000 1392.000 732.000 720.000 720.000 660.000480.000 420.000 360.000 360.000 300.000 5.000 0.000 0.000Mean (%) : 100.000 39.508 1.584 0.080 0.004 0.003 0.002 0.001 0.001 0.0010.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Min (%) : 100.000 1.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Max (%) : 100.000 100.000 98.189 20.077 0.377 0.265 0.139 0.137 0.137 0.1260.091 0.080 0.068 0.068 0.057 0.001 0.000 0.000Counts(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 0.500E+01 0.000E+00 0.000E+00#Meet (Sim): 1000 1000 927 364 96 73 59 43 30 2015 12 10 7 7 1 0 0------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------