Total Risk Integrated Methodology

TRIM.ExpoInhalation User's Document

Volume I: Air Pollutants Exposure Model 

(APEX, version 3) User’s Guide

Social,

Economic,

& Political

Factors

Risk

Management

Regulatory Action Decision

Exposure-Event Module

(TRIM.Expo)

Risk Characterization

Module

(TRIM.Risk)

Environmental Fate,

Transport, & Ecological

Exposure Module

(TRIM.FaTE)

APEX

Total Risk Integrated Methodology

TRIM.ExpoInhalation User's Document

Volume I: Air Pollutants Exposure Model 

(APEX, version 3) User’s Guide

Social,

Economic,

& Political

Factors

Risk

Management

Regulatory Action Decision

Exposure-Event Module

(TRIM.Expo)

Risk Characterization

Module

(TRIM.Risk)

Environmental Fate,

Transport, & Ecological

Exposure Module

(TRIM.FaTE)

APEX

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Research Triangle Park, NC 27711

Draft

April 24, 2003
This page intentionally left blank
Total Risk Integrated Methodology

TRIM.ExpoInhalation User's Document

Volume I: Air Pollutants Exposure Model 

(APEX, version 3) User’s Guide

Prepared for:

Office of Air Quality Planning and Standards

US Environmental Protection Agency

Research Triangle Park, NC

Prepared by:

ICF Consulting

Fairfax, VA

and

ManTech Environmental Technology, Inc.

Research Triangle Park, NC

Draft

April 24, 2003
DRAFT—APRIL 24, 2003 ii

Disclaimer

This document has been reviewed and approved for publication by the U.S. Environmental

Protection Agency. It does not constitute Agency policy. Mention of trade names or commercial

products is not intended to constitute endorsement or recommendation for use.
DRAFT—APRIL 24, 2003 iii

Preface

This document, the APEX User’s Guide, is part of a series of documentation for the overall Total

Risk Integrated Methodology (TRIM) modeling system, in particular the inhalation component of

the exposure module (TRIM.ExpoInhalation). The detailed documentation of TRIM’s logic,

assumptions, algorithms, equations, and input parameters is provided in comprehensive

technical support documents (TSDs) and/or user’s guidance for each of the TRIM modules. 

One of those documents is this user’s guide, which is designed primarily to assist exposure

analysts with running the Air Pollutants Exposure model (APEX), a personal computer

(PC)-based program designed to estimate human exposure to criteria and air toxic pollutants at

the local, urban, and consolidated metropolitan level. The guide currently is divided into four

volumes. Volume I, User’s Guide (this volume) is the first volume to be released. It describes

the scientific basis of the APEX model (version 3) and describes the steps involved in running

APEX for both basic and more advanced applications. Volume II, Programmer’s Guide

describes the model and computer code in more detail and thus provides advanced users with a

greater understanding of the model. Volume III, Criteria Air Pollutants Case Study and Volume

IV, Hazardous Air Pollutants Case Study illustrate the use of APEX by applying it to carbon

monoxide (criteria air pollutant) and benzene and chromium (hazardous air pollutants),

respectively. Additional volumes or revisions to these volumes may be developed as the model

is upgraded, example applications are developed, or other needs arise.

Model enhancements, bug fixes, and other changes are occasionally made to APEX, and thus

users are encouraged to revisit the download website for notices of these changes, including

those listed in a readme.txt file. Also, although APEX has been tested during development, the

number of available computer configuration options preclude the possibility of testing all

possible scenario configurations. Thus, it is possible that bugs remain in the code for some

configurations. The current version of APEX is a draft release and users should be considered

beta-testers.
DRAFT—APRIL 24, 2003 iv

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DRAFT—APRIL 24, 2003 v

Contents

Disclaimer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

1. INTRODUCTION .......................................................... 1

1.1 Organization of Volume I ................................................ 2

1.2 Background and Overview .............................................. 2

1.3 Strengths and Limitations of APEX ........................................ 4

1.3.1 Strengths ................................................... 4

1.3.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.4 Applicability .......................................................... 6

1.5 Brief History of APEX .................................................. 6

2. OVERVIEW OF MODEL DESIGN AND ALGORITHMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.1 Characterize the Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.1.1 Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.1.2 Sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.1.3 Districts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.1.4 Zones ..................................................... 14

2.2 Generate Simulated Individuals .......................................... 15

2.2.1 Demographic variables ....................................... 15

2.2.2 Residential Variables ......................................... 17

2.2.3 Physiological Profile Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2.4 Daily Varying Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.3 Construct a Sequence of Activity Events ................................... 17

2.4 Calculate Concentrations in Microenvironments ............................. 19

2.4.1 Define Microenvironments ..................................... 19

2.4.2 Calculating Concentrations in Microenvironments ................... 19

2.4.2.1 Microenvironment Parameters ......................... 19

2.4.2.2 Mass Balance Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.4.2.3 Factors Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.4.2.4 Stochastic Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.5 Determine Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.6 Determine Dose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.6.1 Ventilation Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.6.2 Carboxyhemoglobin (COHb) Determination . . . . . . . . . . . . . . . . . . . . . . . 29

3. INSTALLING APEX ....................................................... 31

3.1 Hardware and Software Requirements .................................... 31

3.2 Installing APEX to Run in MIMS ......................................... 31

3.3 Installing APEX to Run in DOS Batch Mode ................................ 35

4. RUNNING APEX ......................................................... 37

4.1 Running APEX in MIMS ................................................ 37

4.2 Running APEX in DOS Batch Mode ...................................... 40

4.3 Setting Up an APEX Simulation .......................................... 42

4.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.3.2 Detailed Steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
DRAFT—APRIL 24, 2003 vi

4.4 Overview of Input and Output Files ....................................... 44

4.4.1 Input Files ................................................. 46

4.4.2 Output Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.5 Overview of Model Settings and Options ................................... 46

5. INPUT FILES ........ . .......................... ..... ................... 55

5.1 Input File Formats .................................................... 55

5.2 Params File (Unit 10) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

5.2.1 Input and Output File List Sections of Params File . . . . . . . . . . . . . . . . . . 58

5.2.2 Population Files Sections of Params File . . . . . . . . . . . . . . . . . . . . . . . . . 61

5.2.3 Job Parameter Settings Section of Params File . . . . . . . . . . . . . . . . . . . . 63

5.2.4 Output Table Levels Sections of Params File . . . . . . . . . . . . . . . . . . . . . . 68

5.3 Sector Location File (Unit 11) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

5.4 District Location File (Unit 12) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

5.5 Temperature Zone Location File (Unit 13) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

5.6 Employment by Age Group File (Unit 14) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

5.7 Commuting Flow File (Unit 15) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

5.8 Temperature Data File (Unit 16) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

5.9 Air Quality Data File (Unit 17) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

5.10 Activity-specific MET File (Unit 18) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

5.11 Physiological Parameters File (Unit 19) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

5.12 Profile Functions File (Unit 20) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

5.13 Micro Mapping File (Unit 21) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

5.14 Personal Info File (Unit 22) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

5.15 Diary Events File (Unit 23) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

5.16 Micro Descriptions File (Unit 24) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

5.16.1 Micro Descriptions Section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

5.16.2 Parameter Description Section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

5.16.2.1 Keywords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

5.16.2.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

6. OUTPUT FILES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

6.1 Log File (Unit 25) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

6.2 Hourly Exposure File (Unit 26) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

6.3 Hourly Dose File (Unit 27) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

6.4 Profile Summary File (Unit 28) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

6.5 Microenvironment Summary File (Unit 29) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

6.6 Output Tables File (Unit 30) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

6.7 Sites File (Unit 31) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
DRAFT—APRIL 24, 2003 vii

Tables

Table 4-1. Overview of APEX Input Files ........................................ 47

Table 4-2. Overview of APEX Output Files ....................................... 48

Table 4-3. Description of Steps and Implications Involved in Changes to APEX Settings and

Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Table 5-1. Input Files for APEX Model .......................................... 59

Table 5-2. Output Files for APEX Model ......................................... 62

Table 5-3. Job Parameters in APEX Params File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Table 5-4. Output Parameter Levels in the Output Summary Table .................... 69

Table 5-6. User-definable Functions in Profile Functions File . . . . . . . . . . . . . . . . . . . . . . . . . 80

Table 5-8. Micro Parameters Required to Define a Microenvironment .................. 87

Table 5-9. Keyword Definitions for the Parameter Descriptions Section of the Micro

Descriptions File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

Table 5-10. Uses of Distribution Parameters for Each Standard Distribution Type ......... 90

Table 6-1. Definitions of Variables in Summary Tables .............................. 96

Figures

Figure 2-1. Overview of APEX ................................................. 11

Figure 2-2. Example of Study Areas, Districts, Zones, and Sectors .................... 13

Figure 2-3. Mass Balance Model ............................................... 22

Figure 4-1. General Steps to Configure and Conduct an APEX Simulation .............. 42

Figure 4-2. Steps for Setting up a “Standard” APEX Simulation ....................... 45

Figure 5-1. Example of Params File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Figure 5-2. Example Portion of Population Data File (“Wrapped” View) ................. 63

Figure 5-3. Example Portion of Sector Location File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Figure 5-4. Example of District Location File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Figure 5-5. Example of Temperature Zone Location File . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Figure 5-6. Example of Employment by Age Group File ("Wrapped" View) .............. 73

Figure 5-7. Example Portion of Commuting Flow File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Figure 5-8. Example Portion of Temperature Data File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Figure 5-9. Example Portion of Air Quality Data File (“Wrapped” View) ................. 75

Figure 5-10. Example Portion of Activity-Specific MET File . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Figure 5-11. Portions of Four Data Tables in Physiological Parameters File . . . . . . . . . . . . . 79

Figure 5-12. Example of WindowPos User-Defined Profile Function ................... 80

Figure 5-13. Example Portion of Micro Mapping File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

Figure 5-14. Example Portion of Personal Info File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Figure 5-15. Example Portion of Diary Events File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Figure 5-16. Example Portion of Micro Description File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Figure 6-1. Example Portion of Table #1 in Output Table File (“Truncated” View) ......... 94

Figure 6-2. Example of Table #3 in Output Table File ............................... 95

Figure 6-3. Example of Table #6 in the Output Table File ............................ 97
DRAFT—APRIL 24, 2003 viii

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DRAFT—APRIL 24, 2003 1

Nomenclature used throughout this User's Guide

for key model components (defined later in this

guide):

< Tips to users and other useful information

appear throughout in a shaded text box (such

as this one).

< Key terms are underlined for emphasis.

< Input file names and file types are in Italics.

< Model Variables or parameters are in regular

bold italics, especially when first used in a

section. KEYWORDS, which are how the

variable and parameter names appear in the

input files, are in uppercase bold italics.

< Input and output data are in a

single lined box, indicating that the text inside

the box is shown exactly as it exists in its

electronic form.

< Computer program and module names are

in all bold, non-italic.

1. INTRODUCTION

The Air Pollutants Exposure model (APEX) is

part of EPA’s overall Total Risk Integrated

Methodology (TRIM) model framework (EPA,

1999), in particular the inhalation exposure

component (TRIM.ExpoInhalation). TRIM is a

time series modeling system with multimedia

capabilities for assessing human health and

ecological risks from hazardous and criteria

air pollutants; it is being developed to support

evaluations with a scientifically sound,

flexible, and user-friendly methodology. The

TRIM design includes three modules:

• Environmental Fate, Transport, and

Ecological Exposure module (TRIM.FaTE);

• Human Exposure-Event module

(TRIM.Expo); and

• Risk Characterization module (TRIM.Risk).

APEX, a personal computer (PC)-based

program, is one of the tools being developed

to estimate human exposure via inhalation for

criteria and air toxic pollutants. APEX is

designed to estimate human exposure to

criteria and air toxic pollutants at the local, urban, and consolidated metropolitan level.

The TRIM.ExpoInhalation user’s document currently is divided into four volumes. Volume I, User’s

Guide (this volume) is the first to be released. It describes the scientific basis of the APEX

model and describes the steps involved in running APEX for both basic and more advanced

applications. Volume II, Programmer’s Guide describes the model and computer code in more

detail and thus provides users with a greater understanding of the model. Volume III, Criteria

Air Pollutants Case Study and Volume IV, Hazardous Air Pollutants Case Study illustrate the

use of APEX by applying it to carbon monoxide (criteria air pollutant) and benzene and

chromium (hazardous air pollutants), respectively. Additional volumes or revisions to these

volumes may be developed as the model is upgraded, example applications are developed, or

other needs arise. Users are encouraged to revisit the download website

(http://www.epa.gov/ttn/fera) for notices of these changes, including those listed in a readme.txt.

Note that APEX has been extensively reviewed. Any changes to the computer code may lead

to results that cannot be supported by this documentation.
DRAFT—APRIL 24, 2003 2

1.1 Organization of Volume I

This User’s Guide is designed for all levels of expertise, from novice to advanced, and focuses

on how to run the APEX computer model, develop the appropriate input files, and utilize the

range of options. Volume I is organized into six chapters, plus a reference section at the end:

• Chapter 1, Introduction—Describes the nomenclature used in this guide (see above box),

provides a brief overview of the conceptual model, discusses the model’s strengths,

limitations, and applicability, and provides a brief history of the model.

• Chapter 2, Overview of Model Design And Algorithms—Describes the key modeling steps,

databases, logic processes, and exposure and other equations used in APEX.

• Chapter 3, Installing APEX—Describes the hardware requirements and provides instructions

for installing APEX on a PC.

• Chapter 4, Running APEX—Provides step-by-step instructions and an overview of the basic

information needed to run the model and use the available model options.

• Chapter 5, Input Files—Provides a description of the format, data, and options for each of the

APEX input files.

• Chapter 6, Output Files—Provides a description of the format and data associated with each

of the APEX output files.

1.2 Background and Overview

This section describes some of the key terms used in this guide (see box below) and provides a

brief conceptual description of the model—additional detail is provided in Chapter 2.

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

consolidated metropolitan area level using a stochastic, “microenvironmental” approach. That

is, the model randomly selects data on a sample of hypothetical individuals in an actual

population database and simulates each individual’s movements through time and space (e.g.,

at home, in vehicles) to estimate their exposure to and, optionally, dose of the subject pollutant. 

APEX can assume people live and work in the same general area (i.e., that the ambient air

quality is the same at home and at work) or optionally can model commuting and thus exposure

at the work location for individuals who work. One caveat is that this option only applies to

people who both live and work in the study area—people who work inside the study area but

live outside of it are not modeled under this option, nor are people who live in the study area but

work outside of it.

APEX can be thought of as a simulation of a field study that would involve selecting an actual

sample of specific individuals who live in (or work and live in) a geographic area and then

continuously monitoring their activities and subsequent inhalation exposure to a specific air 
DRAFT—APRIL 24, 2003 3

Key terms used throughout this User’s Guide:

< Diary—a set of events or activities (e.g., cooking, sleeping) for an individual in a given time frame

(e.g., a day).

< District—the geographical area represented by a given set of ambient air quality data (either based

on a fixed-site monitor or output from an air quality model).

< Event—an activity (e.g., cooking) with a known starting time, duration, microenvironment, and

location (usually home or work).

< Microenvironment—a three-dimensional space in which human contact with an environmental

pollutant takes place.

< Profile—a set of characteristics that describe the person being simulated (e.g., age, gender, height,

weight, employment status, whether an owner of a gas stove or air conditioner).

< Sector—the basic geographical unit for estimating exposure to the study population (e.g., census

tract).

< Study Area—the geographical area to be modeled (e.g., metropolitan region).

< Study Area Population—total population of persons who live in the study area; when the commuting

option is used, the study area population does not include those who work outside the study area.

< Zone—similar to district, but for ambient temperature data.

pollutant during a specific period of time. The main differences between the model and an

actual field study are that in the model:

• The sample of individuals is a “virtual” sample, created by the model according to various

demographic variables and census data of relative frequencies, in order to obtain a

representative sample (to the extent possible) of the actual people in the study area;

• The activity patterns of the sampled individuals (e.g., the specification of indoor and other

microenvironments, the duration of time spent in each) are assumed by the model to be the

same as for individuals with similar demographic characteristics, according to activity data

such as diaries compiled in EPA’s Consolidated Human Activities Database (CHAD) (EPA,

2002c; McCurdy et al., 2000);

• The pollutant exposure concentrations and doses are estimated by the model using a set of

user-input ambient outdoor concentrations and information on the behavior of the pollutant in

various microenvironments; and

• Various reductions in ambient air quality levels can be simulated by either adjusting air quality

concentrations to just attain alternative ambient standards under consideration or by reducing

source emissions and obtaining resulting air quality modeling outputs that reflect these

potential emission reductions.

Thus, the model attempts to account for the most significant factors contributing to inhalation

exposure—the temporal and spatial distribution of people and pollutant concentrations
DRAFT—APRIL 24, 2003 4

throughout the study area and among the microenvironments—while also allowing the flexibility

to adjust some of these factors for regulatory assessment and other reasons.

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

are: (1) adjust the study area based on sectors (e.g., census tracts) and the availability of air

quality and weather data; (2) generate simulated individuals in the study area using censusderived frequency (probability) distributions of demographic and other variables (age, gender,

home location, work location) to randomly select and develop a personal profile for each

individual; (3) construct a sequence of activity events for each profile, taking into consideration

the demographic variables (e.g., child, adult) and day type (e.g., weekday or weekend,

temperature) being modeled; (4) calculate hourly concentrations in the microenvironments by

determining the pollutant concentration for each hour of the profile diary using ambient outdoor

concentration data supplied by the user and information on outdoor-microenvironment

relationships or dynamics supplied in data input files and incorporated into the model;

(5) estimate the pollutant exposure for each activity in the diary and then average the

concentrations by hour to obtain an hourly exposure time series; and (6) estimate the dose,

which is an optional step and one that can be conducted only for carbon monoxide currently.

After the specified number of individuals are sampled and exposures and/or doses estimated,

APEX produces a set of summary tables that indicate the distributions of exposure and dose (if

the dose option was selected) across all the profiles.

1.3 Strengths and Limitations of APEX

All models have strengths and limitations. Therefore, for each application, it is important to

carefully select the model that has the desired attributes. With this in mind, it is equally

important to understand the strengths and weaknesses of the chosen model. The following

sections provide a summary of the strengths and potential limitations of APEX.

1.3.1 Strengths

APEX simulates the movement of individuals through time and space to estimate their exposure

to a given pollutant in indoor, outdoor, and in-vehicle microenvironments. Compared to

conducting a field study that would involve identifying, interviewing, and monitoring specific

individuals in a study area, APEX provides a vastly less expensive, more timely, and more

flexible approach. Compared to other air exposure models, APEX provides a good balance in

terms of precision and resource expenditure between the more narrowly focused site-specific

model and the broadly applicable national screening-level models.

The model also allows different air quality data, exposure scenarios, and other inputs and thus

is very useful for decision making.

Another important feature of APEX is its versatility. The model is designed with a great deal of

flexibility so that detailed input data can be applied to specific applications. For example, the

supplied input data sets for this model contain information for numerous microenvironments. 

Another example is that the air quality data needed by the model can be in the form of

monitoring or modeling data. The data can be for specific locations, or political units such as

counties, or census units such as tracts, or even grid points used as air dispersion models

receptors. Furthermore, both criteria and hazardous air pollutants can be modeled.
DRAFT—APRIL 24, 2003 5

A key strength of APEX is its ability to estimate hourly exposures and doses for all simulated

individuals in the sample population from the study area. This ability allows for powerful

statistical analysis of a number of exposure characteristics (e.g., acute versus chronic exposure,

correlations with activities and demographics), many of which are provided automatically by

APEX in output tables.

APEX also estimates the exposures of workers in the geographic area where they work, in

addition to the geographic area where they live. The pollutant concentrations in these

respective locations may be very different from each other.

APEX incorporates stochastic processes representing the natural variability of personal profile

characteristics, activity patterns, and microenvironment parameters. In this way, APEX is able

to represent much of the variability in the exposure estimates resulting from the variability of the

factors effecting human exposure.

Exposure analysis with APEX has also been facilitated by the development of supplied input

files derived from the databases discussed above: national U.S. Census population and

commuting information; CHAD activity data; and microenvironment variables.

1.3.2 Limitations

A limitation of APEX is that uncertainty in the predicted distributions is not currently addressed. 

Some of the uncertainties are as follows.

• The population activity pattern data supplied with APEX (CHAD activity data) are compiled

from a number of studies in different areas, and for different seasons and years. Therefore,

the combined data set may not constitute a representative sample. Nevertheless, the largest

portion of CHAD (about 40 percent) is from a study of national scope (which could be

extracted by the user if desired to create a representative sample). Also, research has shown

that activity patterns are generally similar once you take into account age, gender, day of

week, and season/temperature.

• Commuting pattern data were derived from the 1990 U.S. Census, and these data have been

updated to 2000 proportionally based on 2000 Census data. Therefore, the commuting

pattern data may not accurately reflect current commuting patterns. Moreover, the

commuting data address only home-to-work travel. The population not employed outside the

home is assumed to always remain in the residential census tract. Furthermore, although

several of the APEX microenvironments account for time spent in travel, the travel is

assumed to always occur in basically a composite of the home and work tract. No other

provision is made for the possibility of passing through other tracts during travel. 

• APEX creates seasonal or year long sequences for a simulated individual by sampling human

activity data from more than one subject. Thus, uncertainty exists about season-long

exposure event sequences. This approach also tends to underestimate the variability from

person to person, because each simulated person essentially becomes a composite or an

“average” of several actual people in the underlying activity data (which tends to dampen the

variability). At the same time, this approach may overestimate the day to day variability for

any individual because each simulated person is represented by a sequence of potentially

dissimilar activities from different people rather than more similar activities from one person.
DRAFT—APRIL 24, 2003 6

• The model currently does not capture certain correlations among human activities that can

impact microenvironmental concentrations (e.g., cigarette smoking leading to an individual

opening a window, which in turn affects the amount of outdoor air penetrating the residence).

Also, certain aspects of the personal profiles are held constant, though in reality they change

every year (e.g., age). This is generally only an issue for simulations with long timeframes.

Other data and model limitations exist besides those identified above, including physiological,

meteorological, and those associated with estimating concentrations in microenvironments. 

EPA will continue to refine the model and data to reduce these limitations to the extent possible.

1.4 Applicability

APEX is a moderate to advanced tier air exposure model for local, urban, and consolidated

metropolitan areas. As part of TRIM, for risk assessments it generally would be used after a

simpler, more conservative model or analysis has been applied that prioritizes exposure factors

(area, scenarios, pollutants) for further investigation. APEX will provide more detailed estimates

of exposure for scenarios identified in screening activities as high priority and/or for regulatory

analyses supporting national decisions (e.g., NAAQS reviews).

APEX is appropriate for assessing both average long-term and hourly short- and long-term

inhalation exposures of the general population or a specific sub-population. The model is

designed to look at the range of inhalation exposures of different groups of people across a

population.

Notwithstanding the ability of APEX to assess hourly exposure, however, the model should not

be used to quantify episodic "high-end" inhalation exposure that results from highly localized

pollutant concentrations and/or activities that, by their nature, could result in potentially high

exposures (e.g., emergency releases, occupational exposures). Furthermore, APEX cannot

address cumulative exposure from multiple pollutants nor pollutant mixtures.

1.5 Brief History of APEX 

The APEX series of models derive from the probabilistic National Ambient Air Quality Standards

(NAAQS) Exposure Model for Carbon Monoxide (pNEM/CO). The evolution of these models

has been towards greater flexibility and accuracy. The NEM series was developed to estimate

exposure to the criteria pollutants (e.g., CO, ozone). In 1979, EPA began to develop NEM by

assembling a database of human activity patterns that could be used to estimate exposures to

outdoor pollutants (Roddin et al., 1979). The data were then combined with measured outdoor

concentrations in NEM to estimate exposures to CO (Biller et al., 1981; Johnson and Paul,

1983). In 1988, OAQPS began to incorporate probabilistic elements into the NEM methodology

and using activity pattern data based on various human activity diary studies in an early version

of probabilistic NEM for ozone (i.e., pNEM/O3). In 1991, a probabilistic version of NEM was

developed for CO (pNEM/CO) that included a one compartment mass-balance model to

estimate CO concentrations in indoor microenvironments and the application of this model to

Denver, Colorado has been documented (Johnson et al., 1992). Several newer versions of

pNEM/O3 were developed in the early to mid-1990's including applications to nine urban areas

for the general population, outdoor children, and outdoor workers (Johnson et al., 1996a,b,c). 

During 1999-2001, updated versions of pNEM/CO (versions 2.0 and 2.1) were developed that

rely on activity diary data from CHAD and enhanced algorithms for simulating gas stove usage,
DRAFT—APRIL 24, 2003 7

estimating alveolar ventilation rate (a measure of human respiration), and modeling

home-to-work commuting patterns.

The first version of APEX was essentially identical to pNEM/CO (version 2.0) except that it ran

on a PC instead of a mainframe. The APEX2 model was substantially different, particularly in

the use of a personal profile approach rather than a cohort simulation approach. APEX3

introduced a number of new features including automatic site selection from large (e.g.,

national) databases, a series of new output tables providing summary statistics, and a

thoroughly reorganized method of describing microenvironments and their parameters. Most of

the spatial and temporal constraints were removed or relaxed in APEX3.
DRAFT—APRIL 24, 2003 8

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DRAFT—APRIL 24, 2003 9

2. OVERVIEW OF MODEL DESIGN

AND ALGORITHMS

This chapter describes the key modeling steps, logic processes, and equations, and data bases

used in APEX3. It is written primarily to give exposure assessors an overview of the model from

a technical perspective and an understanding of the scientific basis of the core elements; it is

not a comprehensive description of all algorithms. See Volume II for additional detail on the

algorithms and how they are incorporated into the model, and see Johnson (2002) for additional

detail regarding the derivation and scientific basis of these and other algorithms used in

exposure modeling.

APEX is designed to simulate population exposure to criteria and air toxic pollutants at local,

urban, and regional scales. The user specifies the geographic area to be modeled and the

number of individuals to be simulated to represent this population. APEX then generates a

personal profile for each simulated person that specifies various parameter values required by

the model. The model next uses diary-derived time/activity data matched to each personal

profile to generate an exposure event sequence (also referred to as “activity pattern” or

“composite diary”) for the modeled individual that spans a specified time period, such as one

year. Each event in the sequence specifies a start time, an exposure duration, a geographic

location, a microenvironment, and an activity. Probabilistic algorithms are used to estimate the

pollutant concentration and ventilation (respiration) rate associated with each exposure event. 

The estimated pollutant concentrations account for the effects of ambient (outdoor) pollutant

concentration, penetration factor, air exchange rate, decay/deposition rate, and proximity to

emission sources, depending on the microenvironment, available data, and the estimation

method selected by the user. The ventilation rate is derived from an energy expenditure rate

estimated for the specified activity. Because the modeled individuals represent a random

sample of the population of interest, the distribution of modeled individual exposures can be

extrapolated to the larger population. The model simulation includes up to six steps:

1. Characterize the study area - APEX selects sectors (e.g., census tracts) within a study

area—and thus identifies the potentially exposed population—based on the user-defined

center and radius of the study area and availability of air quality and weather input data for

the area.

2. Generate simulated individuals - APEX stochastically generates a sample of simulated

individuals based on the census data for the study area and human profile distribution data

(such as age-specific employment probabilities). The user can specify the size of the

sample. The larger the sample, the more representative it is of the population in the study

area (but also the longer the computing time).

3. Construct a sequence of activity events - APEX constructs an exposure event sequence

(activity pattern) spanning the period of simulation for each of the simulated persons (based

on the supplied Consolidated Human Activity Database (CHAD) data, although other data

could be used).

4. Calculate hourly concentrations in microenvironments - APEX enables the user to define

microenvironments that people in a study area would visit (e.g., by grouping location codes

included in the supplied CHAD database). The model then calculates hourly

concentrations of a pollutant in each of the microenvironments for the period of simulation,
DRAFT—APRIL 24, 2003 10

based on the user-provided hourly ambient air quality data. All the hourly concentrations in

the microenvironments are re-calculated for each of simulated individuals.

5. Determine exposures - APEX assigns a concentration to each exposure event based on

the microenvironment occupied during the event and the person’s activity. These values

are averaged by clock hour to produce a sequence of hourly-average exposures spanning

the specified exposure period (typically one year). These hourly values may be further

aggregated to produce daily, monthly, and annual average exposure values. 

6. Determine doses - APEX optionally calculates hourly, daily, monthly, and annual average

dose values for each of the simulated individuals. This option currently is available only for

CO.

The model simulation continues until exposures are determined for the user-specified number of

simulated individuals. Figure 2-1 presents these steps within a schematic of the APEX model

design. The following sections provide addition detail on the key algorithms used in each of the

above simulation steps.

2.1 Characterize the Study Area

A study area in an APEX analysis consists of a set of basic geographic units called sectors

(typically defined as census tracts). The user generally provides the geographic center

(latitude/longitude) and radius of the study area. APEX calculates the distances to the center of

the study area of all the sectors included in the sector location database, and then selects the

sectors within the radius of the study area. APEX then maps the user-provided hourly air district

and daily temperature zone data to the selected sectors. The sectors identified as having

acceptable air and temperature data within the radius of the study area are selected to comprise

a final study area for the APEX simulation analysis.

The following sections describe in more detail how a study area is defined in an APEX

simulation analysis. 

2.1.1 Study Area

The APEX study area has traditionally been on the scale of a city or slightly larger metropolitan

area, although it is now possible to model larger areas such as consolidated metropolitan

statistical areas (CMSAs). Even larger study areas may be possible, depending primarily on

computing capabilities, available data, and the desired precision of the run.

The user defines an initial study area by specifying the latitude and longitude of a central point,

together with a radius. The user also has the option of providing a list of counties to be

modeled. If present, this list further restricts the area to the countries to be modeled which are

within the specified study area radius. The final study area is a function of the availability of the

user-supplied demographic data, pollutant concentration data, and the location temperature

data within the initial or intermediate study area, as determined respectively by population

sectors, air data districts, and temperature zones. Figure 2-2 and the subsections below

provide additional details about these geographical units.
DRAFT—APRIL 24, 2003 11

A simulatfollowing • Home s• Work se• Age• Gender• Race• Employ• Gas stov• Pilot ga• Air cond• Car air c• Height• Weight• Amount • Lung di• Endogen• Plus 12 

Sector/Location data

(latitude/longitude)

Defined Study Area (sectors 

within a city radius 

and with air and 

temperature data)

Population data 

(age/gender/race)

Population within 

a study area

Commuting flow data

(origin/destination

sectors)

Age-specific physiological distribution data (body weight, height, etc)

Distribution functions forprofile variables (e.g, probability of air conditioner)

Step 2

Generate N number 

of simulated 

individuals 

Site-specific

air quality and 

temperature data

Stochastic 

profile generator

Age group specific 

employmentprobabilities

Distribution functions 

for daily varying profile 

variables (e.g., window

status, car speed)

Step 1

Characterize study

area

- National

database

- Area-specific

input data

- Intermediate step

or data

- Simulation

step

- Data processor - Output data

A simulatfollowing • Home s• Work se• Age• Gender• Race• Employ• Gas stov• Pilot ga• Air cond• Car air c• Height• Weight• Amount • Lung di• Endogen• Plus 12 

Sector/Location data

(latitude/longitude)

Defined Study Area (sectors 

within a city radius 

and with air and 

temperature data)

Population data 

(age/gender/race)

Population within 

a study area

Commuting flow data

(origin/destination

sectors)

Age-specific physiological distribution data (body weight, height, etc)

Distribution functions forprofile variables (e.g, probability of air conditioner)

Step 2

Generate N number 

of simulated 

individuals 

Site-specific

air quality and 

temperature data

Stochastic 

profile generator

Age group specific 

employmentprobabilities

Distribution functions 

for daily varying profile 

variables (e.g., window

status, car speed)

Step 1

Characterize study

area

- National

database

- Area-specific

input data

- Intermediate step

or data

- Simulation

step

- Data processor - Output data

Figure 2-1a. Overview of APEX
DRAFT—APRIL 24, 2003 12

From 

previous page

Diary events/activities 

and personal information 

(e.g., from CHAD)

Hourly ambient air quality data for 

the home and work sectors

Step 3

Construct sequence of 

activity events for each

simulated individual

Select calculation methods for eachmicroenvironment:• Factors• Mass balance

Maximum/mean daily

temperature data

Activity diary pools by day 

type/temperature category

Each day in the simulation 

period, is assigned with 

activity pool # based on 

daily max/mean temperature

Hourly concentrations in all 

microenvironments during 

simulation period

The selected diary records for 

each day in the simulation 

period

Hourly concentrations an

minutes spent in each

microenvironment visited 

the simulated individua

Step 4

Calculate concentrations

in microenvironments 

for each simulated 

individual

Average exposure concentrations for simulated person:• Hourly • Daily • Monthly• Annual

Stochastic diary 

selector using 

age, gender, and 

employment

A sequence of events

(i.e., microenvironments

visited and minutes spent)

in the simulation period

Microenvironments defined by

grouping of CHAD location codes

Consolidate

location codes into 

microenvironments

Step 5

Determine exposure 

for each simulated

individual

Identify

concentrations in

microenvironments

visited

Physiological parameters 

from profile

Other physiological

parameters

From 

previous page

Diary events/activities 

and personal information 

(e.g., from CHAD)

Hourly ambient air quality data for 

the home and work sectors

Step 3

Construct sequence of 

activity events for each

simulated individual

Select calculation methods for eachmicroenvironment:• Factors• Mass balance

Maximum/mean daily

temperature data

Activity diary pools by day 

type/temperature category

Each day in the simulation 

period, is assigned with 

activity pool # based on 

daily max/mean temperature

Hourly concentrations in all 

microenvironments during 

simulation period

The selected diary records for 

each day in the simulation 

period

Hourly concentrations an

minutes spent in each

microenvironment visited 

the simulated individua

Step 4

Calculate concentrations

in microenvironments 

for each simulated 

individual

Average exposure concentrations for simulated person:• Hourly • Daily • Monthly• Annual

Stochastic diary 

selector using 

age, gender, and 

employment

A sequence of events

(i.e., microenvironments

visited and minutes spent)

in the simulation period

Microenvironments defined by

grouping of CHAD location codes

Consolidate

location codes into 

microenvironments

Step 5

Determine exposure 

for each simulated

individual

Identify

concentrations in

microenvironments

visited

Physiological parameters 

from profile

Other physiological

parameters

Figure 2-1b. Overview of APEX
DRAFT—APRIL 24, 2003 13

0 10 20 30 40

Scale in Kilometers

Parallel True to Scale: 35°

1 inch on map = 23.73 kilometers

Initial Study Area

(within City Radius

distance from Study

Area Center)

Sector

(census

tract)

Final Study Area

(dark line; the

Sectors comprising

the five Districts)

District (Air Radius

distance from Air Monitor)

Intermediate Study

Area (e.g., Charlotte,

NC CMSA)

Study Area Center City (Study Area) Radius

Zone Center

District Air Monitor

District Air Radius

Zone Radius

Zone (Zone

Radius distance

from Zone

Center) 

0 10 20 30 40

Scale in Kilometers

Parallel True to Scale: 35°

1 inch on map = 23.73 kilometers

Initial Study Area

(within City Radius

distance from Study

Area Center)

Sector

(census

tract)

Final Study Area

(dark line; the

Sectors comprising

the five Districts)

District (Air Radius

distance from Air Monitor)

Intermediate Study

Area (e.g., Charlotte,

NC CMSA)

Study Area Center City (Study Area) Radius

Zone Center

District Air Monitor

District Air Radius

Zone Radius

Zone (Zone

Radius distance

from Zone

Center) 

Figure 2-2. Example of Study Areas, Districts, Zones, and Sectors

2.1.2 Sectors

The fundamental spatial unit in APEX is called a sector. The demographic data used by the

model to create personal profiles is provided at the sector level. For each sector the model

requires demographic information representing the distribution of age, gender, race, and work

status within the study population. Each sector must have a location specified by latitude and

longitude for some representative point (e.g., geographic center). The current release of APEX

includes input files that already contain this demographic and location data for all census tracts

in the 50 United States based on the 2000 Census. This database enables the user to model

any study area in the country without having to make any changes to these input files. Finer

scales, such as census block groups, could be used if the user provided suitable population

data files. If fewer (thus larger) sectors were desired, the existing population data files could be

aggregated to larger regions such as counties.
DRAFT—APRIL 24, 2003 14

For each model run, APEX selects the sectors that meet the following conditions. First, the

sector location must be within the specified distance (city radius) of the designated study area

center (see initial study area in Figure 2-2). Second, if the user provided a specific list of

counties, then the sector must belong to one of these counties (see intermediate study area in

Figure 2-2). If no county list is provided, the initial study area is roughly circular, consisting of all

sectors with indicated locations within the specified radius. The final study area consists of the

subset of sectors in the initial study area that have suitable air quality and temperature zone

data, as described in the next two subsections.

2.1.3 Districts

The spatial units for ambient air quality data are called districts. The districts are used to assign

pollutant concentrations to the sectors and microenvironments being modeled. The ambient

outdoor air quality data must be provided by the user as an hourly time series for one location

within each district. The locations could be monitoring sites, the geographic centers of political

units such as counties, the centers of census units such as tracts, or even grid points used as

receptors by an air quality model. As with sectors, each district has a representative location

consisting of a latitude and a longitude. The user designates the effective radius of the districts

(see air radius in Figure 2-2). In APEX, all districts have the same effective radius. Districts can

reside outside of the study area.

APEX calculates the distance from each sector to each district center, and assigns the sector to

the nearest district, provided the sector’s representative location point (e.g., geographic center)

is in the district. Each sector is assigned to only one district. If the sector does not fall within

any district, the sector is deleted from the study area and is not modeled.

2.1.4 Zones

The final spatial unit in APEX is the temperature zone. At a minimum, the daily maximum

1-hour temperature is needed. Optionally, the average or other temperature for the day also

can be provided. Either or both temperature variables can be used for modeling functions such

as assigning activity diaries to personal profiles and assigning conditional probability

distributions to microenvironmental parameters. As with air quality districts, each zone has a

location (latitude and longitude), an effective radius, and start and stop dates. 

As with districts, APEX calculates the distance from each sector to each zone center and

assigns each sector to the nearest zone. If the sector is not in any zone, the sector is deleted

from the study area and is not modeled.

Thus, for the population in a given sector to be modeled, the central location of the sector must

be within a circular district, within a circular temperature zone, within the circular initial study

area, and in a county an the county list (if specified).
DRAFT—APRIL 24, 2003 15

2.2 Generate Simulated Individuals

APEX stochastically generates a user-specified number of simulated persons to represent the

population in the study area. Each simulated person is represented by a “personal profile.” 

APEX generates the simulated person or profile by probabilistically selecting values for a set of

profile variables (Table 2-1). The profile variables include:

• Demographic variables, which are generated based on the census data;

• Residential variables, which are generated based on sets of distribution data;

• Physiological variables, which are generated based on age group-specific distribution data;

and 

• Daily varying variables, which are generated based on distribution data that change daily

during the simulation period.

APEX first selects and calculates demographic, residential, and physiological variables (except

for daily values) for all the specified number of simulated individuals, and then determines

exposures and optionally doses for each simulated person. The following subsections describe

these variables in more detail.

2.2.1 Demographic variables

The values of demographic variables (gender, age, etc.) for a simulated profile are selected

probabilistically according to the following steps:

1. Calculate fraction of people in each of the gender/race combinations in the study area and

then use the fractions as probabilities to randomly select a gender/race type for a simulated

individual.

2. Calculate fraction of the selected gender/race type of people in each sector within a study

area and then use the fractions as probabilities to randomly select a home tract for the

simulated person.

3. Calculate fraction of people in each age group in the selected sector/gender/race

combination and use the fractions as probabilities to randomly select an age group for the

simulated person.

4. Randomly select a specific age within the selected age group, assuming a uniform

distribution.

5. Use the user-provided employment probability for the selected age group to randomly

determine whether a simulated person will work.

6. If the commuting option is used and a simulated person works, use the fractions of people

commuting to each of the work sectors for the selected home sector to randomly select a

work sector that a simulated person will commute to. Discard the profile if the work sector

cannot be assigned air quality or temperature data (e.g., because the sector is outside the

study area) If the commuting option is not used, assume a person who works does so in

their home sector.
DRAFT—APRIL 24, 2003 16

Table 2-1. Profile Variables in APEX

Variable

Type Profile Variables Description

Demographic 

variable

Gender Male or Female

Race White, Black, Native American, Asian, and Other

Age Age (Years)

Home sector Sector in which a simulated person lives

Work sector Sector in which a simulated person works

Employment status Indicates employment outside home

Residential

variables

Gas stove Indicates presence of gas stove

Gas pilot Indicates presence of gas pilot light

Air conditioner Indicates presence of air conditioning at home

Car air conditioner Indicates presence of air conditioning in the car

Daily varying

variables

Window position Daily window position (open or closed) during the simulation

period

Daily average car

speed

Daily average car speed during the simulation period

Daily endogenous CO

production rate

Daily endogenous CO production rate in the simulation

period

Physiological

variables

Height Height of a simulated person (in)

Weight Body weight of a simulated person (lbs)

Blood volume Blood volume of a simulated person (ml)

Lung diffusivity Lung diffusivity parameter used in COHb calculation

(ml/min/torr)

Endogenous CO

production rate #1

Endogenous (internally produced) CO production rate #1

(ml/min)

Endogenous CO

production rate #2

Endogenous CO production rate #2 (ml/min) used only for

women between ages of 12 and 50 for half the menstrual

cycle

Hemoglobin in the

blood

Amount of hemoglobin in the blood (g/ml)

Resting metabolic rate Resting metabolic activity rate (kcal/min)

Energy conversion

factor

Oxygen uptake per unit of energy expanded (liters/kcal)

Maximum permitted

MET value

Maximum metabolic activity level that can sustained for

about five minutes (dimensionless)

Starting day of

menstrual cycle

The day during the first 28 days of the simulation period that

menstruation begins; used for determining endogenous CO
DRAFT—APRIL 24, 2003 17

2.2.2 Residential Variables

The residential variables (gas stove, air conditioner, etc.) are categorical variables, that are

used to indicate whether a residence or a car associated with a simulated person has the

specified appliance or component. APEX randomly determines the result based on userspecified probabilities. For example, a user could specify probabilities of 0.3 for not having an

air conditioner and 0.7 for having an air conditioner. APEX randomly generates a value in the

range of 0 to 1, assuming a uniform distribution. If this value is larger than 0.3, the simulated

person will own an air conditioner. If the value is less than 0.3, the person will not own an air

conditioner. 

2.2.3 Physiological Profile Variables

The physiological variables (volume of blood, height etc.) are used primarily for calculating dose

(as described in Section 2.6). APEX provides gender- and age-specific normal distribution data

for lung capacity (maximum oxygen uptake), body mass, resting metabolic rate, and blood

volume. APEX randomly selects a value for each of these four variables based on the

distribution data of pre-determined age and gender for the simulated person. The energy

conversion factor is randomly selected, assuming that it is uniformly distributed in the range of

0.20-0.21 liters of oxygen per kcal. The other physiological variables are calculated from the

randomly selected values of lung capacity, body mass, resting metabolic rate, blood volume,

and demographic variables such as gender and age.

2.2.4 Daily Varying Variables

The daily varying variables are generated based on user-supplied rules and distribution data. 

For example, the probabilities for the values of window position may depend on the value for

home air conditioner and zone temperatures (average and maximum), while the speed category

is determined randomly from a set distribution for each simulation day and does not depend on

any other variables. Endogenous CO production, which is used in the evaluation of the blood

COHb level, depends on age, gender, and menstrual phase. Thus, for males it always has the

same value from day to day, while for females it may have one of two values depending on the

phase of the menstrual cycle (pre- or post-menstrual).

2.3 Construct a Sequence of Activity Events

APEX probabilistically creates a composite diary for each of the simulated persons by selecting

a 24-hour diary record—or diary day—from an activity database for each day of the simulation

period. CHAD data have been supplied with APEX for this purpose. A composite diary is a

sequence of events that simulate the movement of a modeled person through geographical

locations and microenvironments during the simulation period. Each event is defined by

geographic location, start time, duration, microenvironment visited, and an activity performed.

For the activity database, APEX currently provides a personal information file and an events file

to summarize the CHAD data. The personal information file contains the following variables:

• CHAD ID

• Day type (e.g., Monday)

• Gender
DRAFT—APRIL 24, 2003 18

• Race

• Status of employment

• Maximum temperature

• Age

• Occupation

• Count of missing time (when activity and/or location codes are missing from the activity file)

• Number of events

The events file contains the following variables:

• CHAD ID

• Start time (of the event)

• Duration

• Activity code

• Location code

The events file contains a record for each of the events indicated by the number of events in the

personal information file. Note that while CHAD data are provided with APEX, other activity

data could be used instead, as long as the input file format restrictions are met and the CHAD

coding conventions are used.

APEX develops a composite diary for each of the simulated individuals according to the

following steps:

1. Divide diary days in the CHAD database into user-defined activity pools, based on day type

and temperature. 

2. Assign an activity pool number to each day of the simulation period, based on the

user-provided daily maximum/average temperature data.

3. Calculate a selection probability for each of the diary days in each of the activity pools,

based on age/gender/employment similarity of a simulated person to a diary day.

4. Probabilistically select a diary day from diary days in the activity pool assigned to each day

of the simulation period.

5. Evaluate a MET value for each activity performed while in a CHAD location, based on the

activity-specific MET distribution data. (MET is a dimensionless ratio of the

activity-dependent energy expenditure rate to the basal or resting expenditure rate. It is

used to calculate a ventilation rate for a simulated person performing a certain activity. 

See Section 2.6.1 for details.)

6. Map the location codes in the selected diary to user-defined microenvironments.

7. Concatenate the selected diary days into a composite diary for a simulated individual.

These composite diaries are then used to calculate exposure concentrations, as described

below. 
DRAFT—APRIL 24, 2003 19

Tip. During exploratory analyses, you can

examine how a microenvironment affects overall

exposure by setting the microenvironment’s

proximity or penetration factor to zero, thus

effectively eliminating the microenvironment 

2.4 Calculate Concentrations in

Microenvironments

APEX calculates air concentrations in the various microenvironments visited by the simulated

person by using the ambient air data for the relevant sectors and the user-specified method and

parameters that are specific to each microenvironment. The subsections below describe the

technical basis and algorithms for this approach.

2.4.1 Define Microenvironments

APEX defines microenvironments by grouping the more than 100 location codes defined in the

activity (CHAD) database into a smaller subset of user-defined microenvironments amenable to

modeling. The user has control over how many microenvironments will be modeled and which

need to be defined and what CHAD (or other activity database) locations should be grouped into

each of microenvironment. Table 2-2 lists the 115 CHAD location codes included in APEX and

the microenvironment to which each currently is assigned.

2.4.2 Calculating Concentrations in Microenvironments

APEX calculates hourly concentrations of the subject air pollutant in all the microenvironments

at each hour of the simulation period for each of the simulated individuals, based on the userprovided hourly ambient air quality data specific to the geographic locations visited by the

individual. APEX provides two methods for calculating microenvironmental concentrations: the

mass balance method and the factors method. The user is required to specify a calculation

method for each of the microenvironments; some microenvironments can use one method while

the rest use the other, without restrictions. The parameters, algorithms, and stochastic

elements used in each of the methods are explained below.

2.4.2.1 Microenvironment Parameters

Table 2-3 lists and defines the parameters required by the mass balance and factors methods

(described in the next two subsections) to calculate concentrations in a microenvironment. Note

that the proximity factor is used to account for

differences in ambient concentrations

between the geographic location represented

by the ambient air quality data (e.g., a

regional fixed-site monitor) and the

geographic location of the microenvironment

(e.g., near a roadway). This factor could take

a value either greater than or less than 1. 

The penetration factor represents the fraction of a pollutant entering a microenvironment via air

exchange.

Note that concentrations in Table 2-3 and throughout the equations in the following sections

must be in the same units as the ambient air quality data, i.e., either ppm or μg/m3

 (in the

equations only the latter is shown).
DRAFT—APRIL 24, 2003 20

Table 2-2. Example of CHAD Location Codes Mapped to Microenvironments

CHAD Microenvi- CHAD MicroenviLocation ronment Location ronment

Code Description codea Code Description codea

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 4

30210 Your residence, outdoor 4

30211 ..., pool or spa 4

30219 ..., other outdoor 4

30220 Other residence, outdoor 4

30221 ..., pool or spa 4

30229 ..., other outdoor 4

30300 Residential garage or carport 4

30310 ..., indoor 4

30320 ..., outdoor 4

30330 Your garage or carport 4

30331 ..., indoor 4

30332 ..., outdoor 4

30340 Other residential garage or carport 4

30341 ..., indoor 4

30342 ..., outdoor 4

30400 Residence, none of the above 1

31000 Travel, general 2

31100 Motorized travel 2

31110 Car 2

31120 Truck 2

31121 Truck (pickup or van) 2

31122 Truck (not pickup or van) 2

31130 Motorcycle or moped 2

31140 Bus 2

31150 Train or subway 2

31160 Airplane 0

31170 Boat 2

31171 Boat, motorized 2

31172 Boat, other 2

31200 Non-motorized travel 4

31210 Walk 4

31230 In stroller or carried by adult 4

31300 Waiting for travel 4

31310 ..., bus or train stop 4

31320 ..., indoors 3

31900 Travel, other 2

31910 ..., other vehicle 2

32000 Non-residence indoor, general 3

32100 Office building/ bank/ post office 3

32200 Industrial/ factory/ warehouse 3

32300 Grocery store/ convenience store 3

32400 Shopping mall/ non-grocery store 3

32500 Bar/ night club/ bowling alley 3

32510 Bar or night club 3

32520 Bowling alley 3

32600 Repair shop 3

32610 Auto repair shop/ gas station 2

32620 Other repair shop 3

32700 Indoor gym /health club 3

32800 Childcare facility 3

32810 ..., house 3

32820 ..., commercial 3

32900 Large public building 3

32910 Auditorium/ arena/ concert hall 3

32920 Library/ courtroom/ museum/ theater 3

33100 Laundromat 3

33200 Hospital/ medical care facility 3

33300 Barber/ hair dresser/ beauty parlor 3

33400 Indoors, moving among locations 3

33500 School 3

33600 Restaurant 3

33700 Church 3

33800 Hotel/ motel 3

33900 Dry cleaners 3

34100 Indoor parking garage 2

34200 Laboratory 3

34300 Indoor, none of the above 3

35000 Non-residence outdoor, general 3

35100 Sidewalk, street 2

35110 Within 10 yards of street 2

35200 Outdoor public parking lot /garage 2

35210 ..., public garage 2

35220 ..., parking lot 2

35300 Service station/ gas station 2

35400 Construction site 4

35500 Amusement park 4

35600 Playground 4

35610 ..., school grounds 4

35620 ..., public or park 4

35700 Stadium or amphitheater 4

35800 Park/ golf course 4

35810 Park 4

35820 Golf course 4

35900 Pool/ river/ lake 4

36100 Outdoor restaurant/ picnic 4

36200 Farm 4

36300 Outdoor, none of the above 4

a

 -1 = continue using the same microenvironment as appears previously in time in the full composite diary; 0 = concentration

of zero; 1 = indoor (residence); 2 = in or near vehicle; 3 = indoor(other); 4= outdoor.
DRAFT—APRIL 24, 2003 21

Table 2-3. Parameters Required by Mass Balance and Factors Method

Method Variable Definition Units Value Range Default/ Supplied

Value

Mass

Balance 

f proximity Proximity factor unitless fproximity $ 01

f penetration Penetration factor unitless 0 # fpenetration # 11

CS Concentration source μg/m3

 or

ppm

CS $ 00

ES Emission source μg/hr ES $ 00

R removal Removal rate due to

deposition, filtration, and

chemical reaction

1/hr Rremoval $ 00

R air exchange Air exchange rate 1/hr Rair exchange $ 0 none

R mean Rremoval + Rair exchange 1/hr Rmean $ 0 Rremoval + Rair exchange

V Volume of

microenvironment

m3 V > 0 none

Factors f proximity Proximity factor unitless fproximity $ 01

f penetration Penetration factor unitless 0 # fpenetration # 11

CS Concentration source μg/m3

 or

ppm

CS $ 00

2.4.2.2 Mass Balance Method

The mass balance method assumes that an enclosed microenvironment (e.g., a residence) is a

single well-mixed box in which the air concentration is spatially uniform at any time at any

location within the box. The concentration of an air pollutant in such a microenvironment is

estimated using the following four processes (as illustrated in Figure 2-3):

• Inflow of air into the microenvironment;

• Outflow of air from the microenvironment;

• Removal of a pollutant from the microenvironment due to deposition, filtration, and chemical

 degradation; and 

• Emissions from sources of a pollutant inside the microenvironment.
DRAFT—APRIL 24, 2003 22

Air

inflow

Indoor sources

Removal due to:

•Chemical reactions

•Deposition

•Filtration

Air

outflow

Microenvironment

Air

inflow

Indoor sources

Removal due to:

•Chemical reactions

•Deposition

•Filtration

Air

outflow

Microenvironment

Figure 2-3. Mass Balance Model

Change in microenvironmental concentration due to influx of air is represented by the following

equation:

∆C (2-1) in

dC(t)

dt

Cambient f

proximity fpenetration Rair exchange == × × ×

where:

aCin = Change in microenvironmental concentration due to influx of air

(μg/m3

/hour)

C(t) = Concentration in a microenvironment at time t (μg/m3

)

t = Time

Cambient = Ambient hourly concentration (μg/m3

)

fproximity = Proximity factor (unitless)

fpenetration = Penetration factor (unitless)

Rair exchange = Air exchange rate (1/hour)

Change in microenvironmental concentration due to outflux of air is described by:

∆C (2-2) out

dC(t)

dt

 Rair exchange == × C(t)
DRAFT—APRIL 24, 2003 23

where:

aCout = Change in microenvironmental concentration due to outflux of air

(μg/m3

/hour)

Change in concentration due to deposition, filtration, and chemical degradation in a

microenvironment is simulated based on the first-order equation:

∆C (2-3) removal

dC(t)

dt (Rdeposition R filtration R chemical ) C(t) R removal == + + = × C(t)

where:

aCremoval = Change in microenvironmental concentration due to removal processes

(μg/m3

/hour)

Rdeposition = Removal rate of a pollutant from a microenvironment due to deposition

(1/hour)

Rfiltration = Removal rate of a pollutant from a microenvironment due to filtration (1/hour)

Rchemical = Removal rate of a pollutant from a microenvironment due to chemical

degradation (1/hour)

Rremoval = Removal rate of a pollutant from a microenvironment due to overall removal

(1/hour)

Sources of a pollutant from inside the microenvironment is described by the following equation:

(2-4) ∆C

source 

1

V

 ESi i 1

n

e R

mean

 CSi i 1

n

c =

=

∑ +

=

∑

where:

aCremoval = Change in microenvironmental concentration due to source emission inside a

microenvironment (μg/m3

/hour)

V = Volume of a microenvironment (m3

)

ESi = Emission rate for emission source I (μg/hour)

CSi = Emission rate for concentration source I (μg/m3

)

Rmean = Rair exchange + RRemoval (1/hour)

ne = Number of emission sources in the microenvironment

nc = Number of concentration sources in the microenvironment

(Note that concentration must be in the same units as the ambient air quality data, i.e., either

ppm or μg/m3

, although throughout these equations concentration is shown only in μg/m3. The

above equation, 2-4, is the only one that needs modification if the units are switched. 

Specifically, 1/V would be replaced by f/V, where f = 1/ppmFact. The value of ppmFact is a

user-supplied input parameter that expresses the number of μg/m3

 that equate to 1 ppm. For

CO, ppmFact=1,145.) Thus, the mass balance equation for a pollutant in a microenvironment is

described by:
DRAFT—APRIL 24, 2003 24

C equil C (t ) C combined

R

mean

 C

source

Cin

Rair exchange Rremoval

= →∞ = =

+

+

∆ ∆ ∆

(2-5) dC(t)

dt

C

source

Cin Cout Cremoval = +− − ∆ ∆∆ ∆

where:

C(t) = Concentration in a microenvironment at time t (μg/m3

)

Within the time period of an hour, aCsource and aCin can be assumed to be constant. Using

aCcombined = aCsource + aCin, Eq. (2-2), and Eq (2-3) in Eq. (2-5) leads to:

(2-6)

dC(t)

dt

 Ccombined Rair exchangeC(t) Rremoval C(t)

 Ccombined R 

meanC(t)

=− −

= −

∆

∆

Solving the differential equation Eq. (2-6) leads to:

C(t) (2-7)

C combined

Rmean

C(0) Ccombined

Rmean

 exp( R mean t = +− )















 − ∆ ∆

where:

C(0) = Concentration of a pollutant in a microenvironment at the beginning of a hour

(μg/m3

)

C(t) = Concentration of a pollutant in a microenvironment at time t within the time

period of a hour (μg/m3

).

Based on Eq. (2-7), the following three hourly concentrations in a microenvironment are

calculated:

(2-8)

C (2-9) hourly end Cequil C(0) Cequil exp ( Rmean = −− ) 



 



 −

C (2-10) hourly mean

C(t)dt

0

1

dt

0

1 Cequil (C(0) C equil )

1 exp( Rmean )

R 

mean

=

∫

∫

= +− − −

where:

Cequil = Equilibrium concentration in a microenvironment (μg/m3

)

C(0) = Concentration in a microenvironment at the beginning of each hour (μg/m3

)

Chourly end = Concentration in a microenvironment at the end of each hour (μg/m3

)
DRAFT—APRIL 24, 2003 25

Chourly mean = Hourly mean concentration in a microenvironment (μg/m3

)

Rmean = Rair exchange + Rremoval (1/hour)

At each hour time step of the simulation period, APEX uses Eq. (2-8), Eq. (2-9), and Eq. (2-10)

to calculate the hourly equilibrium, hourly ending, and hourly mean concentrations. APEX

reports hourly mean concentration as hourly concentration for a specific hour. The calculation

continues to the next hour by using C hourly end for the previous hour as C(0). 

2.4.2.3 Factors Method

The factors method is simpler than the mass balance method. It does not calculate

concentration in a microenvironment from the concentration in the previous hour, and it has

many fewer parameters. The factors method uses the following equation to calculate hourly

concentration in a microenvironment from the user-provided hourly air quality data:

C (2-11) hourly Cambient fproximity fpenetration CSi i 1

n

c

= +

=

∑

where:

Chourly = Hourly concentration in a microenvironment (μg/m3

)

Cambient = Hourly concentration in ambient environment (μg/m3

)

fproximity = Proximity factor (unitless)

fpenetration = Penetration factor (unitless)

CSi = Mean air concentration resulting from source I (μg/m3

)

nc = number of concentration sources in the microenvironment

2.4.2.4 Stochastic Elements

To account for temporal and spatial variability of the microenvironment parameters required to

calculate an hourly microenvironmental concentration APEX allows, the user to provide

distribution data to randomly select a value for each of the parameters. The following

distribution types can be specified for each model-parameter:

• Single point

• Uniform

• Normal

• Log-normal

• Triangle

• Exponential

• Off/on

• Discrete

• Histogram

Also, the user can provide distribution data for any combination of the following temporal,

spatial, and conditional variables:

• Hour in a day

• Day in a week

• Month in a year (i.e., season)
DRAFT—APRIL 24, 2003 26

• Air district

• Three conditional variables selected from the following profile variables:

< Gender

< Race

< Employment status

< Gas stove

< Gas pilot light

< Air conditioner 

< Car air conditioner

< Window position

< Maximum temperature categories

< Average temperature categories

< Car speed categories

APEX also allows the user to decide whether a new random value should be selected for a

microenvironmental factor from the same distribution data at every hour in a time block of a day

or every day in a simulation period.

2.5 Determine Exposure

APEX calculates exposure as a time series of exposure concentrations that a simulated

individual experiences during the simulation period. APEX determines the exposure by

identifying hourly concentrations and minutes spent in a sequence of microenvironments visited

according to the composite diary. The hourly exposure concentration at any clock hour during

the simulation period is determined using the following equation:

(2-12) Ci 

Chourly (j) t

(j) j 1

N

T = =

∑

















where:

Ci = Hourly exposure concentration at clock hour I of the simulation period (μg/m3

or ppm)

N = Number of events (i.e., microenvironments visited) in clock hour I of the

simulation period.

Chourly (j) = Hourly concentration in microenvironment j (μg/m3

 or ppm)

t(j) = Time spent in microenvironment j (minutes)

T = 60 minutes

From the hourly exposures, APEX calculates time series of 8-hour and daily average exposure

concentrations that a simulated individual would experience during the simulation period. APEX

then statistically summarizes and tabulates the hourly, 8-hour, and daily exposures.

2.6 Determine Dose

For some pollutants, such as CO, the toxicokinetics are sufficiently understood such that dose

can be modeled in addition to the exposure concentration. Dose may be more useful or

accurate than exposure for evaluating the effects of air pollutants because it accounts better for
DRAFT—APRIL 24, 2003 27

Definitions of dose. The term dose is often

defined as the administered or delivered amount

of a substance to the body and generally is stated

as the total mass of the chemical or the mass per

unit body weight or related denominator. But

dose actually can be subdivided as follows:

< Potential dose is the amount of chemical

contained in material ingested, air breathed, or

bulk material applied to the skin.

< Applied dose is the amount of the chemical in

contact with the major absorption boundaries,

such as the skin, lungs, and gastrointestinal

tract, and available for absorption.

< Absorbed dose is the amount of a chemical

penetrating across the absorption barrier. For

the respiratory route, the internal dose is the

amount of the chemical absorbed via the lung.

< Internal dose is a more general term denoting

the amount absorbed without respect to

specific absorption barriers or exchange

boundaries.

< Delivered dose is the amount of the chemical

available for interaction with any particular

organ or cell.

Total or expired ventilation rate, VE, also can

be calculated from VO2. In fact, APEX currently

contains the equations for calculating VE in case

they are needed for other pollutants. See

Johnson (2002) for additional information on the

derivation of VE.

differences in pollutant uptake resulting from (1) the variation in physiology and activities across

populations and (2) the variation in physiological responses to activities within an individual. 

Therefore, APEX has been designed to allow the user the option of calculating either exposure

concentration, as described in the preceding section, or also calculating dose, as described in

this section. APEX currently has been designed to calculate dose for CO. To estimate dose for

other pollutants, the development of pollutant-specific dose algorithms is required.

As seen by the box to the right, dose can

have many definitions. In APEX, dose can

be calculated for CO, in particular the

delivered dose of CO as measured by the

biotransformation product

carboxyhemoglobin (COHb)—specifically

%COHb—in the blood. The estimated

%COHb, in addition to being a more refined

measure of exposure compared to exposure

concentration, is useful for comparing to

health-based benchmarks of %COHb.

Conceptually, APEX uses a number of

factors, in particular a time series estimate of

alveolar ventilation rate (which is activity and

physiology dependent) and a time series

estimate of exposure (which indicates the

pollutant concentration in the inhaled air for

specific moments in time), to calculate

%COHb. The ventilation rate algorithm is

discussed in Section 2.6.1 and the %COHb

algorithm is discussed in Section 2.6.2. For

other factors and additional detail, see

Johnson (2002).

2.6.1 Ventilation Rate

Ventilation is a general term for the movement of air into and out of the lungs. Minute or total

ventilation is the amount of air moved in or out of the lungs per minute. Quantitatively, the

amount of air breathed in per minute (VI

) is slightly greater than the amount expired per minute

(VE). Clinically, however, this difference is

not important, and by convention minute

ventilation is always measured on an expired

sample, VE. Alveolar ventilation (VA) is the

volume of air breathed in per minute that (1)

reaches the alveoli and (2) takes part in gas

exchange. The ventilation rate needed for

the %COHb determination is this ventilation

rate, VA, and is derived for use in APEX

based on work by Adams (1998), Astrand

and Rodahl (1977), Burmaster and Crouch (1997), Esmail et al. (1995), Galetti (1959), Johnson

(1998), Joumard et al. (1981), McCurdy (2000), McCurdy et al. (2000), Schofield (1985), and

many others. Only a brief description of VA is described below; for the complete derivation, see

Johnson (2002).
DRAFT—APRIL 24, 2003 28

Conceptually, VA can be related to the oxygen ventilation rate of the individual at any given

moment. This relationship can be described as follows for a given activity event and personal

profile:

V =V CF A O OA 2 2 × , (2-13)

where

VA = Alveolar ventilation rate (ml of air/min)

VO2 = Oxygen ventilation rate (l of O2/min)

CFO2,A = Oxygen to air conversion factor (19,530 ml of air/l of O2).

VO2 in turn is related to the energy expenditure rate for the given event activity and the given

profile’s physiology in terms of oxygen ventilation per unit energy expenditure, or:

VO2 = × EE ECF (2-14)

where

EE = Energy expenditure (kcal/min)

ECF = Energy conversion factor (l of O2/kcal).

ECF is based on the physiology of the individual being modeled and is described in Section 2.2. 

EE is related to the activity-specific energy expenditure rate and the basal or resting energy

expenditure (metabolic) rate of the given profile, or:

EE = MET RMR × (2-15)

where

MET = Metabolic equivalent (dimensionless)

RMR = Resting metabolic rate (kcal/min).

RMR is based on the physiology of the individual being modeled and is described in

Section 2.2. MET—which comes from “metabolic equivalents of task”—is a dimensionless ratio

of the activity-specific energy expenditure rate to the basal or resting energy expenditure rate. 

While different people have very different basal metabolic rates, it is generally found that the

MET ratios do not exhibit as much variability. Thus, standing still might require two times the

basal energy expenditure, or two MET, for most people, with relatively little variation. Since the

basal rate is constant for each profile, it only has to be determined once and the activity-specific

MET ratio can be used to determine the absolute energy expenditure rate, EE, for each activity. 

MET is discussed in more detail below as well as in Section 2.2.

The overall equation for VA for activity I of day j for person k is:

VA (2-16) (i, j,k) = MET(i, j,k) RMR(k) ECF(k) CFO2,A × ××

APEX incorporates these concepts using a variety of other parameters, limits, and probabilistic

data and algorithms. For example, before MET can be used in APEX for a given activity and
DRAFT—APRIL 24, 2003 29

profile combination, it must be evaluated for whether it satisfies the condition that the average

level of metabolic activity over long time intervals cannot remain as high as it can for short

intervals. That is, the nominal value for the maximum MET for any individual, METmax,

represents the maximum activity level that can sustained for about five minutes. Shorter bursts

of activity can exceed this level, in part because a substantial non-aerobic contribution is usually

present. However, for longer time periods, the average MET cannot remain as high as the

nominal value for METmax. In particular, over one hour the average MET cannot exceed about

64% of METmax for any person. The average MET over a two hour time interval cannot exceed

about 54% of METmax. Similar limits exist for periods up to nine hours (33% of METmax). 

Therefore, after first determining the time series for hourly mean MET values for the entire

simulation period—which may require aggregation across several diary events or

microenvironments within each hour—APEX calculates running averages from 1 to 9 hours for

the entire simulation period. Each set of 9 averages then are compared to the respective limits

on the maximum sustainable MET averages. If the limit is exceeded, the preceding MET values

are adjusted downwards sufficiently to place the average at the limit. 

See Volume II of the User’s Guide and the above references—especially Johnson (2002)—for

additional details regarding the incorporation of this and other limits, other parameters, and

probabilistic data and algorithms used in APEX.

2.6.2 Carboxyhemoglobin (COHb) Determination

The %COHb calculation in APEX uses the time series for exposure (CO concentration in the air)

and the time series for alveolar ventilation rate, VA, as inputs (among other factors). The dose

calculation is based on the solution to the non-linear Coburn, Forster, Kane (CFK) equation, as

detailed in Johnson (2002). As pointed out by that report, the CFK equation does not have an

explicit solution, so an iterative solution or approximation is needed to determine the %COHb. 

An iterative solution, however, was determined to be unsuitable because of the model execution

time necessary (a typical model run of one calendar year represents roughly 14,000 events per

person and several thousand people, or tens of millions of diary events). Therefore, the CFK

equation is solved using a modified Taylor’s series method in which the event duration is

restricted in time (if necessary) to ensure convergence with only a few terms. This method

avoids the dangers of non-convergence that arise in some other methods.

As the mathematical derivation in the above report is very detailed, only the main results are

presented here. First, it should be noted that the literature discusses two forms of the CFK

equation, namely a linear and a non-linear form. The linear form itself is an approximation that

allows an explicit solution, but is not accurate under all conditions. The non-linear form is

considered to be more correct and is the one being discussed here.

Restricting %COHb(t) to between 0 and 100 (percent), the CFK equation takes the form of the

following differential equation:

%COHb (t) C (2-17) oC1

%COHb(t)

100 %COHb(t) ′ = −× −

where C0 and C1 are constants over the duration of one event that depend on physical and

physiological parameters, including VA, and on the CO concentration in the air.

Time zero represents the start of the current event. The concentration %COHb(0) (at time zero)

is assumed to be known. The first derivative, %COHb(0), can easily be found from the above

equation. The solution %COHb(t) is a smoothly varying function of time without sudden

discontinuities or changes in slope. It therefore can be expanded in a Taylor’s series about
DRAFT—APRIL 24, 2003 30

t = 0, which should converge fairly rapidly. One simplification is to rescale the time variable to

the unitless parameter z:

(2-18) z (C C ) t

(100 D D )

0 1

0 0

= + ×

× ×

where

D (2-19) 1 %COHb(0)

100 0 = −

The Taylor’s series up to the fourth order term is:

(2-20)

T (z) %COHb(0) 100 D Dz 100 A D Dz

2

100 A D D (A 2D) z

6

100 A D D (A 8DA 6D ) z

24

4 0

1 0

2

10 1

3

10 1

2

1

2 4

= + ××−

×× ×

+

× × ×× − × − × × ×× − + ×

where

A (2-21)

C

C +C 1

1

0 1

=

DD A (2-22) = − 0 1

For typical values for the constants C0, C1, and %COHb(0), convergence occurs for z<1. For

z values below this limit but still close to one, the convergence is slow, so the terms beyond

fourth order would be needed if high accuracy were desired. It is found that z<1 generally

corresponds to %COHb(0) values below 40 to 50% for one-hour events.

The z value is proportional to t. In APEX, an event can have a duration of no more than one

hour. For one hour events there are conditions where z is close to one and convergence of the

Taylor’s series may require more than four terms. However, it is not necessary to evaluate the

entire hour in one step. By dividing the event into shorter subevents, each will have a smaller

z value. For example, if a one-hour activity diary event has initial conditions that correspond to

z=0.9, then by dividing this into three 20 minute subevents, each will have a z value around 0.3. 

Actually, the first subevent will have exactly z=0.3. The others will be slightly different as the

initial conditions for those subevents will have changed slightly. Thus, dose accumulates the

average %COHb level over the subevents. At the end of all subevents, the average dose is the

accumulated dose divided by the number of subevents, while the final value of the dose is

simply the final value of COHb itself. These values are saved and the next diary event is

processed. 
DRAFT—APRIL 24, 2003 31

3. INSTALLING APEX

This chapter provides the requirements and instructions for installing APEX on a personal

computer (PC). Section 3.1 describes the hardware and software requirements for installing

APEX. Section 3.2 describes the steps involved in a typical installation of APEX with the

Multimedia Integrated Modeling System (MIMS) graphical user interface (GUI). Section 3.3

describes the steps involved in installing APEX to run in DOS batch mode.

3.1 Hardware and Software Requirements

To use APEX, the user’s PC should be equipped with at least:

• 256 MB of RAM;

• 600 MHz or faster processor; and

• 450 MB of available hard drive space.

PCs with less RAM and slower processors will also work, but performance (e.g., model run

time) will be less than optimal.

APEX is compatible with the following computer operating systems:

• Windows XP

• Windows 2000

• Windows NT

• Windows 95/98

However, Windows XP, Windows 2000, and Windows NT are the recommended operating

systems for running APEX because they best meet the memory requirements of the model. As

APEX was developed using Java, it can be run under a number of other operating systems,

such as Linux and Solaris; however, APEX has not been fully implemented in any nonWindows-based operating systems to date.

3.2 Installing APEX to Run in MIMS

The installation of APEX with the MIMS GUI consists of the following six steps. Note that the

first five steps can be completed in any order; however, Step 6 must be completed after Step

(5).

1. Download and unzip the APEX project file

Users should download the MIMS project for APEX from the internet

(http://www.epa.gov/ttn/fera) to their hard drive. This zip file, which contains a single file in BIN

format, should then be unzipped into the directory in which APEX will be installed (e.g.,

C:\APEX) using extraction software (e.g., WinZip). This directory will be referred to as the

“Default Directory.”

2. Install the Java Runtime Environment

The Java Runtime Environment (JRE) is installed by completing the following steps.
DRAFT—APRIL 24, 2003 32

• Download the JRE file from http://java.sun.com/j2se/1.3/download.html (select the

appropriate Windows download from the “JRE” column). Important note: During testing,

several issues have been identified with running APEX in MIMS using JRE version 1.4. If

you encounter problems and are using JRE version 1.4, consider switching to JRE version

1.3.

• Close all open applications.

• Run the downloaded JRE file (e.g., j2re-1_3_1_04-windows-i586.exe) and follow the

instructions on the screen.

• Reboot the computer.

• To free up hard drive space, delete the downloaded JRE file.

3. Download and unzip the APEX executable

Users should download the APEX executable from the internet (http://www.epa.gov/ttn/fera) to

their hard drive. This zip file should then be unzipped into the Default Directory using extraction

software (e.g., WinZip).

4. Download and unzip the APEX national input databases

Users should download the following national input databases from the internet 

(http://www.epa.gov/ttn/fera) to their hard drive.

• Consolidated Human Activity Database Files (4 MB zipped). This database contains

activity diaries, as well as metabolic and physiological parameter data.

• Population Files (28 MB zipped). This databases contains 2000 tract-level U.S. Census

counts, tract locations, and national employment probabilities by age.

• Commuting Database (36 MB zipped). This database contains adult commuting patterns

based on the 1990 U.S. Census, updated to year 2000 population.

These zip files should then be unzipped into the Default Directory using extraction software

(e.g., WinZip). These databases can be used for most applications in the United States;

however, users can optionally use alternative databases (e.g., if the user wishes to model a

subpopulation). More details on these databases can be found in Chapter 5.

5. Install MIMS

Users must install the Multimedia Integrated Modeling System (MIMS) to run APEX in MIMS. 

For instructions on installing MIMS, refer to http://www.epa.gov/ttn/fera.
DRAFT—APRIL 24, 2003 33

6. Import the APEX project file into MIMS

To import the APEX project file into MIMS, the user must complete the following steps.

• Start MIMS as described in the MIMS installation instructions. The “Project Selection

Window” will open.

• From Project Selection Window, select “Import Project” from the File menu.
DRAFT—APRIL 24, 2003 34

• After selecting “Import Project,” a file browser will appear. Use this browser to select the

APEX project file (with extension “.BIN”). After selecting the file, click “Import.”

• In the “Import Project” window, enter a name for APEX project (e.g., “TRIM Inhalation

Exposure Assessment”) and click “OK.”

• After importing the project, the Project Selection Window will include the new APEX project. 

To open the model, double-click on the project name.
DRAFT—APRIL 24, 2003 35

3.3 Installing APEX to Run in DOS Batch Mode

To install APEX to run in DOS batch model, the user must download the zip files containing the

APEX executable and national input database files from http://www.epa.gov/ttn/fera. After

downloading these four zip files (i.e., one zip file containing the executable and three zip files

containing the national input databases and other supplied data), the user must unzip them

using extraction software (e.g., WinZip). These files can be unzipped anywhere on the hard

drive; however, the user must ensure that there is at least 450 MB of available hard drive space

in the target location for the national input database files. The user should note the name and

location of all of the unzipped files for later reference.
DRAFT—APRIL 24, 2003 36

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DRAFT—APRIL 24, 2003 37

What’s a “unit number”? The

APEX computer code uses a unit

number to refer to a specific input or

output file. For example, Unit 10 is

the Params file (which specifies the

overall settings, or parameters, for

an APEX simulation—see Chapter

5). Thus, if a unit number is

requested during an APEX run, then

that means that one of the input files

could not be found. The user should

consult the appropriate section of

this user’s guide. Unit numbers

have been included in section

headings for easy reference.

4. RUNNING APEX

This chapter describes the steps involved in setting up

and running an APEX simulation, the purpose and

content of the input and output files, and the available

model options. This chapter is organized as follows:

Section 4.1 Running APEX in MIMS

Section 4.2 Running APEX in DOS Batch Mode

Section 4.3 Setting Up an APEX Simulation

Section 4.4 Overview of APEX Input and Output Files

Section 4.5 Overview of Model Settings and Options

Chapters 5 and 6 provide a detailed description of the

input and output files, respectively.

4.1 Running APEX in MIMS

After installing APEX in MIMS as described in Section 3.2, the user can run the model by

starting MIMS and either:

• Selecting the APEX project from the “Available Projects” list in the “Project Selection

Window” and then selecting “Open Project” from the File menu, or

• Double-clicking on the APEX project in the “Available Projects” list in the “Project Selection

Window.”

Either of these methods will bring up a window named with the name of the project (e.g., “TRIM

Inhalation Exposure Assessment”). This window is comprised of a drop-down menu of

“Category” types, a list of the items associated with the selected type, and five buttons that

correspond to the actions that can be performed on these items. There are three “Category”

types listed in the drop-down menu: Scenario, Domain Object, and Default External Module. 

The user should refer to the MIMS guidance for detailed descriptions of these “Category” types.
DRAFT—APRIL 24, 2003 38

To configure and run an application using APEX in MIMS, the user must complete the following

steps.

1. Select “Scenario” from the “Category” drop-down menu. The list of scenarios

included in the project will appear in the space below the drop-down menu. 

Initially, this list will include a generic scenario for U.S. locations (i.e., “Exposure

Assessment w/APEX”) that can be used to develop a new scenario specific to a

user’s application and two example application scenarios (i.e., “Exposure

Assessment for Benzene (Houston test case)” and “Exposure Assessment for

Chromium (Houston test case)”). Users wishing to run either of the example

applications should open the example scenario (by either double-clicking on the

scenario name or selecting the scenario name and clicking the “Open” button)

and then skip to Step (3). Users wishing to configure a new application should

proceed to Step (2).

2. Select the “Exposure Assessment w/APEX” scenario and click the “Duplicate”

button. The “Rename Library Member” pop-up window will appear. Enter a

name for the application scenario (e.g., “New Scenario”) and click “OK.” The

new scenario will then appear in the list of scenarios. Open the new scenario by

either double-clicking on the scenario name or selecting the scenario name and

clicking the “Open” button.

3. Select “Expand All” from the “Object” drop-down menu in the “Graph View of

Scenario: <Scenario Name>” panel. Select “Calculate Inhalation Exposure:

APEX” in the “Graph View of Scenario: <Scenario Name>” panel. When

selected, the text will change to bold italics.
DRAFT—APRIL 24, 2003 39

4. Double-click on “Calculate Inhalation Exposure: APEX" in the “Graph View of

Scenario: <Scenario Name>” panel. The “Module Instance” window will appear.

5. Set the necessary parameter values in the “Module Instance” window. These

parameters correspond to the variables in the Params file (described in Chapter

5). The mapping between the parameter names in the “Module Instance”

window and the variable names in the Params file is provided in Tables 5-1

through 5-4 in Chapter 5. After setting the necessary parameter values, close

the Module Instance window by clicking on the “X” in the upper right-hand corner

of the window.
DRAFT—APRIL 24, 2003 40

6. In the “Parameter Table” at the bottom of the Scenario Window, press the leftmost button to expand the list of parameters. If necessary, check the “Create

Output Directories” parameter. When selected, the option allows MIMS to create

any required output directories if they do not exist when the simulation starts. If

the user specifies an output directory that already exists, this option does not

need to be selected. Set the “Default Directory” to correspond to directory

containing the APEX executable (i.e., the Default Directory from the installation). 

Set the remaining scenario settings as desired. Refer to the MIMS User Guide

for more details about these parameters.

7. To start the simulation, select “Execute All” from the Scenario drop-down menu

of the Scenario window.

4.2 Running APEX in DOS Batch Mode

The APEX executable can be run in DOS batch mode, meaning that the model’s executable

(.exe) and Params files are specified in a single user-created file (referred to as a “batch” file)

that is submitted to the operating system for job execution. When running APEX in DOS batch

mode, there is no GUI. To run APEX in DOS batch mode, the user must complete the following

steps.
DRAFT—APRIL 24, 2003 41

Additional Options for Running APEX Outside of MIMS or DOS Batch Mode

1. Typing the file name of the APEX executable at the prompt in a DOS window. APEX will prompt the

user for the “Unit(10)” file, at which time the user would input the location and name of the Params file.

2. Double-clicking on the APEX executable in Explorer. APEX will prompt the user for the “Unit(10)” file, at

which time the user would input the location and name of the Params file.

3. Typing the file names of the APEX executable and Params file at the prompt in a DOS window. 

1. Create the APEX batch file

To create an APEX batch file, open a new file in a text editor (e.g., Notepad). On the first line of

this file, enter the file path and name of the APEX executable followed by a space and the file

path and name of the Params file. For example,

In this example the APEX executable (i.e., APEX3.exe) is located in the “C:\APEX” directory

and the Params file (i.e., Params.txt) is located in the “C:\APEX\input” directory. After entering

this line, save the file. The file can be named anything, provided it ends with the extension

“.bat” (e.g., APEX_Batch.bat).

2. Run the APEX batch file

APEX can be run using the batch file in any of the following ways.

• Double-clicking on the batch file in Explorer

• Typing the batch file name in a DOS window

• Selecting “Run” from the Start menu and entering the file path and name of the batch file

• Creating a shortcut to the batch file on the desktop by selecting the batch file in Explorer,

right-clicking the mouse, and selecting “Create Shortcut” from the menu. A shortcut file will

be created and this file can be dragged onto the desktop and optionally renamed. To run

APEX, double-click on this shortcut.

In addition to running in DOS batch mode, there are several other ways to run APEX outside of

the MIMS framework (described in the text box below). Refer to MS-DOS documentation for

additional information on these alternative options.
DRAFT—APRIL 24, 2003 42

Figure 4-1. General Steps to

Configure and Conduct an

APEX Simulation

4.3 Setting Up an APEX Simulation

This section describes the steps involved in performing an APEX simulation. In particular, this

section provides an overview of these steps followed by more detailed descriptions.

4.3.1 Overview

Figure 4-1 lists the five general steps involved in configuring

and performing an APEX simulation. These steps are

described below. Because each of these steps builds on the

previous one, the user should proceed through these steps in

this order.

1. Identify the Scope of the Analysis

The first step in configuring an APEX simulation is to identify

the scope of the analysis. This step involves selecting the

study area, modeling period, pollutant, and populations of

interest for the analysis.

2. Select Model Options

After identifying the scope of the analysis, the user must decide

which options to select. To determine the appropriate options

for the application, the user must answer questions such as the

following.

• Do I want to model worker commuting?

• How many profiles (or what percentage of the population)

do I want to model?

• How many microenvironments do I want to model?

• How should I define my microenvironments?

• Which model settings should I select (e.g., model Daylight

Savings Time? use air quality controls, or “rollbacks”?)

• What types of outputs do I want from the model?

Each of these options is described in more detail in Section 4.4.

3. Set Up Input Files

After deciding which model options to use and how to configure

them, the next step in configuring an APEX simulation is to set

up the input data files with the necessary data. There are 16

types of input data files required by APEX. With the exception

of the population data file type, there is only one input file per input data file type. One of these

files, the Params file (see next step), is used to specify input and output file names and

locations and simulation settings. However, when running APEX in MIMS, the Params file is

generated by the model (see next step), as opposed to being supplied by the user (as is the

case when APEX is run in DOS batch mode). The remaining files contain the input data

necessary to run APEX. The data contained in these remaining files varies depending on the
DRAFT—APRIL 24, 2003 43

options selected and the data contained in the other input files. The relationships between the

input files (e.g., if you change one input file, does it impact any of the others?) is described in

Table 4-3 in Section 4.5 and Chapter 5.

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:

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

• Population file descriptions, names, and locations;

• Output file names and locations;

• Parameter settings; and

• Output table levels.

A detailed description of each of the sections of the Params file is provided in Chapter 5.

When running APEX using the DOS batch mode, the Params file must be created by the user. 

When running APEX in MIMS, this information is specified by the user within the GUI and then

the model uses it to create the Params file automatically. The steps involved is specifying these

settings in the MIMS GUI are described in Section 4.1.

5. Running APEX

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

is run in DOS batch mode, this step involves running the APEX executable. If APEX is run

using MIMS, it involves completing Step (7) in Section 4.1.

4.3.2 Detailed Steps

The usual manner for preparing an APEX model run is to start with an existing application and

modify it as necessary (e.g., change simulation settings, input file content, or output file names). 

Because there are many different ways to run APEX, there is no single list of steps that will work

for every analysis. Users should always review the Params file (when running APEX in DOS

batch mode) or the settings in the MIMS scenario (when running APEX in MIMS) to ensure the

file locations and simulation settings are correct. The key factor in determining the effort

required to prepare an analysis, however, is the degree to which the remaining input files need

to be modified. In light of this, APEX applications can be grouped into two categories: standard

and advanced.

Standard applications are designed to use the national-scale input database files accompanying

APEX, to the extent possible. That is, these applications define sectors as census tracts, define

the age groupings as in the accompanying data files, and use the profile functions, commuting

data, population data, dose input data (if dose is modeled), microenvironment mapping, and

CHAD activity data provided with APEX. These settings are defined in the following input files:

• Sector Location file;

• Population Data files;

• Employment by Age Group file;

• Commuting file;

• Activity-specific MET file;
DRAFT—APRIL 24, 2003 44

• Physiological Parameter file;

• Profile Functions file

• Microenvironment Mapping file;

• Personal Info file; and

• Diary Events file.

Thus, for the first run of a standard application for a given study area, pollutant, etc., the user

generally would only need to edit or create the following input files (in addition to the Params file

or settings in the MIMS scenario):

• District Location file;

• Temperature Zone Location file;

• Temperature Data file;

• Air Quality Data file;

• Profile Functions file;

• Micro Mapping file; and

• Microenvironment Descriptions file.

Subsequent runs for the same study area, pollutant, etc. could involve as little as a single

parameter change (e.g., the simulation start or stop dates) in the Params file (when running

APEX in DOS batch mode) or in the MIMS GUI (when running APEX in MIMS) – the most basic

standard application – or could involve changes in all of the same input files edited as indicated

above for the first run. Figure 4-2 contains a flow chart that describes each of the steps a user

must complete to configure and perform a standard APEX simulation.

Advanced applications involve modifying or replacing the national-scale input database files

accompanying APEX. These types of applications allow the user to perform analyses that are

specifically tailored to unique needs, such as use in a country other than the U.S. or using a

finer (or coarser) spatial resolution. Depending on the application, the user may need to edit or

replace all of the input data files. The steps required for an advanced application depend on

how much the application deviates from a standard application. Users should be aware that

advanced applications can require significantly more time to develop input data.

4.4 Overview of Input and Output Files

This section provides a brief overview of the input and output files associated with APEX. (For

more detailed descriptions of the input and output files, refer to Chapters 5 and 6, respectively.) 

All of the input and output files used by APEX are ASCII text files, which allows them to be read

and/or modified by the user using any text editor. Note, however, that certain files, such as the

commuting input file and possibly the exposure and dose output files, may be very large (over

100 megabytes) and difficult for some text editors to handle.
DRAFT—APRIL 24, 2003 45

Figure 4-2. Steps for Setting up a “Standard” APEX Simulation
DRAFT—APRIL 24, 2003 46

4.4.1 Input Files

There are 16 types of input data files required by APEX. Table 4-1 lists each file type and a

brief description of the content of the file. With the exception of the population data file type,

there is only one input file per input data type. When APEX is run in DOS batch mode, the

Params file will typically be edited prior to each simulation. When running APEX in MIMS, the

user will edit the inputs and settings for each simulation in the MIMS GUI and the model will

create the Params file based on the provided information. The remaining input data files may

need to be edited for a particular simulation depending on the type of application (i.e., standard

or advanced) and the options selected for the simulation (e.g., commuting, dose estimates). 

(Table 4-3 in Section 4.5 provides an explanation of the input data file changes required with

changes to the different model options.)

Note that several of the APEX input files use a “keyword” approach. APEX processes these

files and searches for particular keywords followed by an equal sign and one or more values for

the keyword. Chapter 5 provides a detailed description of the keyword approach and its syntax.

4.4.2 Output Files

There are seven types of output files from APEX. Table 4-2 lists each of the output data files

and a description of the content of the file. The names and locations, as well as the output table

levels (e.g., output percentiles, cut-points) for these output files, are specified by the user in the

Params file (when running APEX in DOS batch mode) or in the MIMS GUI (when running APEX

in MIMS). If an output file is specified with the same name and location as an existing file, the

old files are overwritten. Therefore, if the user wishes to conduct a series of model runs, the

output files for each run should be named differently or the output should be copied elsewhere

before the next model run is submitted.

4.5 Overview of Model Settings and Options

This section describes the settings and options available in APEX. There are five general

categories of settings and options in APEX:

• General Model Settings and Options;

• Study Area Location;

• Profiles;

• Microenvironments; and

• Outputs.

Table 4-3 describes all of the settings and options in each of these categories, how the settings

and options are selected or changed, and the impact of changing a setting or option on the

other input files. Note that the impact of these settings and options on input files should be

distinguished from the changes in certain input files that may significantly affect other input files

(e.g., the effect that changes in Population Data would have on Commuting Flow). As many of

the settings and options impact multiple APEX input files, it is important for the user to pay close

attention to the impacts that changing the various settings and options can have on the different

input files. The table includes the settings and selected options in the input data files

accompanying APEX to help the user understand the settings for a standard application and
DRAFT—APRIL 24, 2003 47

Table 4-1. 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

sector. 

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

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

simulation.

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

quality monitoring/modeling location included in the simulation.

Temperature Zone

Location

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

meteorological station included in the simulation.

Employment by

Age Group*

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 and optionally average (or other) temperature data

for the meteorological stations and dates indicated in the Temperature Zone

Location file.

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

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

Activity-specific

MET*

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

value for each activity code in the Diary Events file. 

Physiological

Parameters*

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

dose.

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

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

air conditioning at home). 

Microenvironment

Mapping

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

Descriptions file.

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

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

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

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

Microenvironment

Descriptions

Contains the definitions of the APEX microenvironments referenced in the Micro

Mapping file.

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

the United States and would only be edited or replaced for an Advanced application.
DRAFT—APRIL 24, 2003 48

Table 4-2. 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, if doses are modeled.

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.

Sites The Sites file lists the sectors, districts, and zones in the study area, and

identifies the mapping between them.

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.

how deviating from these settings can significantly increase the effort required to setup the

required input files. Throughout this table, “keywords” (or variables or parameters) are

referenced in indicating how different options can be selected. See Chapters 5 and 6 for

additional details on the keywords and input files, how to change them, and how they interact

with other keywords and files. Also, see Section 1.3.2 for limitations on the use of this model,

especially in terms of the number of setting options it has available.
DRAFT—APRIL 24, 2003 49

Table 4-3. Description of Steps and Implications Involved in Changes to APEX Settings and 

Setting/Option How Option is Selected Impact of Changing Def

on Other Input F

GENERAL MODEL SETTINGS AND OPTIONSSimulation start/end dates Specified in YYYYMMDD format (e.g., 19960704 is July 4,

1996) using the START_DATE and END_DATE keywords in

the Params file. The user must define the appropriate startand end dates for an application.

The indicated start and end dates musranges included in the District LocationLocation, Temperature Data, and Air Qfiles may contain additional data before and end dates, but must contain data fbetween the specified start and end da

Adjust for Daylight SavingTime (DST)

Specified using the DSTADJUST keyword in the Params file. If this parameter is set to “YES”, then the Air Quality Data filewill be adjusted for DST in the summer; if it is set to “NO”, noadjustment is made. This keyword should be set to “YES” ifthe Air Quality Data file is based on Standard Time yet the

activity data are based on DST.

Changing this setting means that the Abased on DST (it typically is in Standaactivity data are based on Standard Tidata are based on DST). Regardless (hourly exposure and dose) for all simuexactly 24 hours, and all input activity dexactly 24 hours. 

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 specifiedin the Employment by Age Group file; if it is set to “NO”, allworkers are assumed to work at home and the user is notrequired to provide a Commuting Flow file. If employmentstatus is not specified in the Employment by Age Group file,commuting will not be modeled, regardless of theCOMMUTING keyword setting in the Params file. TheCommuting Flow file accompanying APEX containscommuting flows between all census tracts from the 2000Census. These commuting data are sufficient for allapplications where the sectors are defined as census tracts.

If the user changes the value of the CO

“NO”, there are no additional changes chooses to use sectors other than cenCommuting Flow file(in addition to a numust be created corresponding to the 
Table 4-3. Description of Steps and Implications Involved in Changes to APEX Settings and Optio

Setting/Option How Option is Selected Impact of Changing Def

on Other Input F

DRAFT—APRIL 24, 2003 50

Model exposure dose Specified using the DODOSE keyword in the Params file. If

this keyword is set to “YES”, the model will create the hourlydose file; if it is set to “NO”, the dose calculations will besuppressed and the hourly dose file will not be created. If

DODOSE is set to “YES”, the user must specify the correctvalues for the ALTITUDE, COHBFACT, and COTHRESH

keywords in the Params file. Regardless of the setting for

DODOSE, the user must provide the Activity-specific MET and

Physiology Data files; however, if DODOSE is set to “NO,” thecontent of these files does not impact the results. 

The Activity-specific MET file accompachanged if (1) the underlying MET distdatabase are changed or (2) activity daprovided in the CHAD database are usor replace the Physiology Data file proprovided the new or revised file is in th

Air quality rollbackadjustment (for estimatingexposure in hypotheticalcontrol scenarios)

Specified using the ROLLBACK keyword in the Params file. 

If this keyword is set to “YES”, the user must specifyappropriate values for the RBTARGET, RBBACKGND , and

RBMAX keywords in the Params file; if it is set to “NO”, valuesare not required for these keywords (and any present will beignored).

If the ROLLBACK keyword is changed 

file accompanying APEX, the RBTARG

and RBMAX keywords must be set to 

STUDY AREA LOCATIONCenter of study area Specified as the latitude and longitude of the center of the

study area in decimal degrees using the LATITUDE and

LONGITUDE keywords in the Params file. The user must

always define the appropriate study area center for anapplication.

If the study area is changed, the user mfollowing files contain appropriate data Sector Location file (unless the include

Location file, Temperature Zone Locat

Data file, and Air Quality Data file.

Radius of study area Specified as the distance (in km) from the center to the edge

of the study area using the CITYRADIUS keyword in theParams file. The user must always define the appropriatestudy area radius for an application.

If the study area is changed, the user mfollowing files contain appropriate data Sector Location file (unless the includeLocation file, Temperature Zone Locat

Data file, and Air Quality Data file.
Table 4-3. Description of Steps and Implications Involved in Changes to APEX Settings and Optio

Setting/Option How Option is Selected Impact of Changing Def

on Other Input F

DRAFT—APRIL 24, 2003 51

Restrict study area toselected counties

Specified using the COUNTYLIST keyword in the Params file. 

If the value of this keyword is set to “YES”, the user must listthe FIPS code (or other relevant portion of the sector ID if thesupplied sector files are not used) for the counties to which thestudy area will be restricted using the COUNTY keyword in theParams file. The county IDs for all census tracts in the 2000Census are included in the Sector Location file accompanying

APEX, thus allowing the user to select counties in the Paramsfile without making changes to the included Sector Location

file.

If the user does not use the included S

must ensure that the new Sector Loca

county ID for each sector as part of the appropriate format.

Locations of sectors Specified as sector IDs and locations (latitude and longitude)

in the Sector Location file. The Sector Location file

accompanying APEX use the census tracts from the 2000Census as sectors. This file also specifies the countyassociated with each sector (via the first five characters of thesector ID, which are the county FIPS codes in the supplieddata), which allows the user to restrict the study area toselected counties. In most cases, the user will not need to

change this setting as it provides sectors with the necessarypopulation and commuting data for the entire United States.

Sectors identified in Sector Location filidentified in Population Data files. If thcensus tracts from the 2000 Census, t

accompanying APEX will be sufficient. in the commuting file must be included 

file and the Population Data files; if secCensus tracts are used, the user must file compatible with these sectors. In ato restrict the study area to selected co

Location file must include the county IDas part of the sector IDs in the proper ffile).

Locations of air districts Specified in the District Location file. The user must always

define the appropriate air districts for an application. The usermust always define the appropriate air districts for anapplication.

The locations of the air districts must bcan provide reasonable estimates of aand study period included in the analys

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 befrom 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. Users must always define an appropriate value for thiskeyword based on their application.

The radius of the air districts must be swill include the sectors the user would analysis.
Table 4-3. Description of Steps and Implications Involved in Changes to APEX Settings and Optio

Setting/Option How Option is Selected Impact of Changing Def

on Other Input F

DRAFT—APRIL 24, 2003 52

Location of meteorologicaldata stations

Specified as zone IDs and locations (latitude and longitude) inthe Temperature Zone Location file. In the absence of a

national database of temperature data, the user must always

define the location(s) of meteorological stations for anapplication.

Temperature data for each meteorologspecified in the Temperature Zone Loc

provided in the Temperature Data file.

Radius of meteorologicalstation coverage

Using the ZONERADIUS keyword in the Params file, the user

can specify the maximum distance (in km) from a sector to thenearest meteorological station. If all zone centers are further

than ZONERADIUS from the sector center, the sector isremoved from the study area and is not modeled. Users mustdefine an appropriate value for this keyword based on theirapplication.

The radius of the zones must be selec

include the sectors the user would like analysis.

PROFILESNumber of profiles Set to an integer using the #PROFILES keyword in the

Params file. Users must define an appropriate value for thiskeyword based on their application.

None.

Modeled populations Specified in the Params file following the specification of the

file names. The user must provide a population file for eachpopulation to be modeled and indicate the gender and raceassociated with the file. All gender/race combinations without

specified population files are assumed to have zeropopulations. Users can select from the sets of availablePopulation Data files accompanying APEX (i.e., the national

population files or the files specific to the Houston exampleapplications), or generate their own.

If the user wishes to model a subpopusupply alternative Population Data files counts. The sector IDs in these populacorrespond to sector IDs provided in th

Profile function options The existing profile functions can be edited by the user in the

Profile Functions file. This file can also be edited by the userto add new functions. The Profile Functions fileaccompanying APEX contains nine functions that can beedited by the user as necessary for an application.

None, if the user edits one of the existi

Employment status Specified in the Employment by Age Group file for

implementation of commuting. The Employment by AgeGroup file accompanying APEX should be sufficient for allapplications where sectors are defined as census tracts.

None.
Table 4-3. Description of Steps and Implications Involved in Changes to APEX Settings and Optio

Setting/Option How Option is Selected Impact of Changing Def

on Other Input F

DRAFT—APRIL 24, 2003 53

Minimum and maximumages for simulated profiles

Specified using the AGEMIN and AGEMAX keywords in the

Params file.

None.

Modeled age groups Specified in the Employment by Age Group file. The

Employment by Age Group file accompanying APEX defines

the age groups as single years up to 99 and three groupsbeyond, and thus should be sufficient for all applicationswhere sectors are defined as census tracts.

The age groups specified in the Emplo

must match the age groups used in the the user wishes to change the age groby Age Group file, the associated chanto the Population Data files. 

Size of age window The AGECUTPCT and AGE2PROBAB keywords in the

Params file are used to specify the window around theassigned age of a profile from which activity data can beselected.

None.

Probabilities for selectingdiaries with missingcharacteristics

Using the MISSGENDER, MISSEMPL, and MISSAGEkeywords in the Params file, the user can specify the

probability that activity diary data with missing gender,employment status, or age will selected.

None.

MICROENVIRONMENTSMaximum number of microenvironments Set to an integer using the #MICRO keyword in the Params file; must not exceed 127. Number of APEX microenvironments i

Mapping and Microenvironment Descrexceed the specified value in the Para

Microenvironmentdefinitions

Specified in the Microenvironment Descriptions file. The usermust develop data relevant to a particular application prior toperforming an APEX simulation.

Each location referenced in the activity must be mapped to one of the microenthe Microenvironment Descriptions file 

Microenvironment Mapping file.

OUTPUTSProduce 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”, thesefiles are not created.

None.
Table 4-3. Description of Steps and Implications Involved in Changes to APEX Settings and Optio

Setting/Option How Option is Selected Impact of Changing Def

on Other Input F

DRAFT—APRIL 24, 2003 54

Output table levels Specified using the following keywords in the Params file:

PERCENTILES, EXPTIME, DM1HEXP, DM8HEXP,

DAVGEXP, ANNEXP, DM1HDOSE, DM8HDOSE,

H_EHDOSE, DMEHDOSE, DAVGDOSE, ANNDOSE, and

DOSETIME. These keywords are described in detail inChapter 5.

None.
DRAFT—APRIL 24, 2003 55

5. INPUT FILES

This chapter provides the details necessary for accessing, creating, and modifying, as needed,

the APEX input files. Addressed are input file format, the Params (or control) input file, and the

population and other input files. The user should have a general understanding of how the

APEX model works (from Chapter 2) and a clear understanding of the relationships between the

various input files (from Chapter 4—in particular Table 4-3) before using this chapter.

5.1 Input File Formats

APEX requires the following input files to run:

• A parameters file;

• 14 other input files; and 

• A population file for each combination of gender/race to be modeled.

All input files are “ASCII text” files that can be edited on a text editor such as Notepad. Each

input line of these files is categorized into one of four types:

1. Keyword (or variable or parameter) line: Keywords are used in the Params file to indicate to

APEX where the input files are located and what values should be assigned to certain

variables. A keyword line always contains an “=” sign. The part of the line to the left of “=”

is called the “keyword” and the part to the right is called the “value.” The keyword must start

with a letter and must match the spelling sought by the program code, after which the

keyword may contain other letters, blanks, or commas. APEX uses the keyword to locate

and set the input values. The values may be character, logical, or numeric values, or file

names.

2. Numeric line: Any line beginning with a digit (0-9) is recognized as a numerical data line by

APEX. Non-digits may appear later in a numeric line.

3. Character line: A line that begins with a character but does not contain an “=” sign is

recognized as a character data line.

4. Comment line: Any blank lines and any lines beginning with “!” generally are regarded as

comment lines by APEX and used only by the user to help document the input file data. 

Comment lines inserted in the middle of a block of data, however, can be unpredictable. 

That is, if the computer code is expecting to read a long series of numbers without a break,

then comments may break the flow. If in doubt, use comments only where they are used in

the supplied input files.

The keywords and input values are not case sensitive. Also, each line on an input file is

processed independently by APEX. Continuation of data values across multiple lines is not

permitted unless specifically noted for a particular file. APEX uses “list” (or “free”) format for

most input values (the Physiological Parameters input file—Section 5.11—is the exception). 

This means that the values or data do not have to be fixed in specific positions on an input line. 

Multiple items on an input line can be separated by either a blank or comma. The various site

names and similar inputs, however, should not contain internal blanks, as these will be

interpreted as delimiters between input fields. This does not apply to keyword lines, as those
DRAFT—APRIL 24, 2003 56

Tip. Keep a copy of the Params file associated

with each simulation to provide a record of the

input and output files and model settings

associated with the simulation and to make it

easier to run the model again based on different

input data.

lines have only two fields (separated by the “=” sign), so either or both sides may contain

internal blanks.

The following sections discuss the details of APEX input files and provide several examples. 

Note that these example files in this and the next chapter are provided for illustration only! They

have been developed solely for the purpose of highlighting various aspects and options of

APEX. These example files are not necessarily related to each other nor to the supplied data

files that can be downloaded separately from this user’s guide. In many cases these examples

are only portions of the necessary input files. Thus, these example files will not work as an

actual set of input files. Users are encouraged to view the EPA-supplied input files

(downloaded separately) for a complete and related set of input files. Also, Volumes III and

IV—which provide APEX case studies—are associated with complete and related sets of input

(and output) files.

5.2 Params File (Unit 10)

The parameter, or Params, file is APEX's

simulation control file. The Params file

names input and output files, sets model

parameters, and controls formats of output

files. APEX only processes keyword lines in

this file. Any other types of input lines are

ignored. As described in Chapter 4, the

Params file is created or modified directly by

the user when running APEX in DOS batch mode. Conversely, when running TRIM.ExpoInhalation,

the Params file is created or modified automatically based on the parameter settings provided

by the user in the TRIM.ExpoInhalation GUI. Although the remainder of this section is written from

the perspective of a user configuring a Params file to run APEX in DOS batch mode, users can

refer to the keyword tables in this section (i.e., Tables 5-1 through 5-4) for a description of how

the TRIM.ExpoInhalation parameters relate to the keywords in the Params file. That is, each of

these tables contain a column titled “TRIM.Expo-Inh Parameter Name” that indicates the

parameter name in TRIM.ExpoInhalation that corresponds to the keyword in the Params file. With

this information, users can determine how to set the parameter values in the TRIM.ExpoInhalation

GUI.

When accessing in DOS batch mode, the following rules should be used:

• Keywords (or parameters or variable names) are placed to the left of the equal sign in a

keyword line;

• Parameter values are to the right of the equal sign;

• Lines may appear in any order;

• Lines may be omitted if defaults are acceptable;

• Only one equal sign allowed per keyword line;

• Anything after an exclamation mark in a line is ignored; and

• Any line without an equal sign is ignored.

To ensure that the keywords are input properly, the user may want to use the example file as a

template. The user only needs to change values to the right of the “=” sign on the key word line. 

An example of the Params file is provided in Figure 5-1.
DRAFT—APRIL 24, 2003 57

Figure 5-1. Example of Params Filea

INPUT FILES:

 sectors file = C:\APEX3\input\tp_geo.txt

 districts file = C:\APEX3\input\districts_houston61.txt

 zones file = C:\APEX3\input\zones_houston.txt

 employment file = C:\APEX3\input\employment.txt

 commuting file = c:\APEX3\input\comm2000.txt

 temperature file = C:\APEX3\input\temperatures_houston.txt

 air quality file = C:\APEX3\input\APEXHoustonAir61.txt

 metabolic file = C:\APEX3\input\CHADMets.txt

 physiology file = C:\APEX3\input\Physiology.txt

 distribution file = C:\APEX3\input\distrib_houston.txt 

 microenv. file = C:\APEX3\input\MP_houston.txt

 diaryevent file = C:\APEX3\input\CHADEvents.txt

 diarysum file = C:\APEX3\input\CHADQuest.txt

 diarymap file = C:\APEX3\input\micromap34.txt

POPULATION FILES:

 pop file, Female, White = C:\APEX3\input\tp_FW.txt

 pop file, Female, Black = C:\APEX3\input\tp_FB.txt

 pop file, Female, Asian = C:\APEX3\input\tp_FA.txt

 pop file, Female, NatAm = C:\APEX3\input\tp_FN.txt

 pop file, Female, Other = C:\APEX3\input\tp_FO.txt

 pop file, Male, White = C:\APEX3\input\tp_MW.txt

 pop file, Male, Black = C:\APEX3\input\tp_MB.txt

 pop file, Male, Asian = C:\APEX3\input\tp_MA.txt

 pop file, Male, NatAm = C:\APEX3\input\tp_MN.txt

 pop file, Male, Other = C:\APEX3\input\tp_MO.txt

OUTPUT FILES: 

 log file = C:\APEX3\output\log.txt

 exposure file = C:\APEX3\output\exp.txt

 dose file = C:\APEX3\output\dose.txt

 persons file = C:\APEX3\output\psum.txt

 microsum file = C:\APEX3\output\msum.txt

 tables file = C:\APEX3\output\tablesb.txt

 sites file = C:\APEX3\output\sites.txt

PARAMETER SETTINGS: 

 pollutant = Benzene

 inputunit = ppm 

 outputunit = ppm

 location = Houston

 scenario = test

 #profiles = 20

 #micros = 4

 #sources = 3

 start_date = 19960101

 end_date = 19961231

 Latitude = 29.75533

 Longitude = -95.18716

 CityRadius = 20

 AirRadius = 0.5

 ZoneRadius = 300.
DRAFT—APRIL 24, 2003 58

Figure 5-1. Example of Params File (continued)

 CountyList = YES

 County = 48201

 County = 48157

 Commuting = YES

 AgeMin = 0

 AgeMax = 99

 DSTadjust = YES

 HourlyOut = NO

 DoDose = NO

 rollback = NO

 RBtarget = 5.0

 RBbackgnd = 0.0

 RBmax = 10.0

 PPMFact = 1145.

 MissGender = 0.1

 MissEmpl = 0.1

 MissAge = 0.1

 AgeCutPct = 25.0

 Age2Probab = 0.1

 Altitude = 90

 COHbFact = 2.5

 COThresh = 100.

 DebugLevel = 0

 RandomSeed = 547862400

 

OUTPUT TABLE LEVELS:

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

 TimeEXP = 2, 4, 6, 8, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60 

 DM1HExp = 5, 10, 20, 30, 40, 50, 75

 DM8HExp = 3, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 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, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 4.0, 5.0, 6.0

 DM8HDose = 0.5, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 4.0, 5.0, 6.0

 H_EHDose = 0.5, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 4.0, 5.0, 6.0

 DMEHDose = 0.5, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 4.0, 5.0, 6.0

 DAvgDose = 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 4.0, 5.0

 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, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 4.0, 5.0, 6.0

a

 Note that COHbFact and COthresh parameters are needed only for CO dose calculation. For other pollutants,

DoDose should be set to NO. APEX currently only includes dose algorithms for CO.

There are five sections in this example (these same sections are recommended for all Params

files): input file, population files, output files, parameter settings, and output table levels. The

details of each section are discussed below.

5.2.1 Input and Output File List Sections of Params File

In the Input File section of the parameter file, the user needs to specify the names and path

names of 14 non-population input files. Table 5-1 provides a brief description of these files. 

For a new scenario or standard application, the user needs to modify or replace the files of units

12, 13, 16, and 17. (See the introduction to Chapter 4 for a description of “Unit number”
DRAFT—APRIL 24, 2003 59

Table 5-1. Input Files for APEX Modela

Input File Keyword APEX in MIMS

Parameter

Name

Unit

#

Uses

Sector Location SECTOR Input File -

Sector

Locations

11 Provides the IDs and locations (in degrees

latitude and longitude) of sectors (e.g.,

census tracts). The file is used along with the

user-defined CityRadius and other data to

select the sectors within the modeled area.

District Location DISTRICT Input File -

District

Locations

12 Provides the site IDs and locations (degrees

latitude and longitude) of air quality

monitoring or modeling locations. The file is

used along 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 Input File -

Meteorological

Zone Locations

13 Provides the site IDs and locations (degrees

latitude and longitude) of the meteorological

stations. The file is used along with the userdefined 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.

Employment by

Age Group

EMPLOYMENT

(or 

AGEGROUP)

Input File -

Employment

14 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 Input File -

Census

Commuting

Flow

15 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 Input File -

Temperature

Data

16 Contains the daily maximum and optionally

average (or other) 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 Input File - Air

Quality Data

17 Provides the hourly air quality data for the

modeled pollutant for each air

monitoring/modeling location listed in the

District Location file. 
Table 5-1. Input Files for APEX Modela

 (continued)

Input File Keyword APEX in MIMS

Parameter

Name

Unit

#

Uses

DRAFT—APRIL 24, 2003 60

Activity-specific

MET

METABOL Input File -

Activity

Specific METS

18 Provides distribution types and parameters for

calculating the metabolic (MET) value for

each activity code in the Diary Events file. A

MET value is a dimensionless ratio of the

activity-dependent energy expenditure rate to

the basal or resting energy expenditure

(metabolic) rate. This file is used for

calculating dose, not exposure

concentrations. It is needed for running

APEX even if the dose calculation is turned

off.

Physiological

Parameters

PHYSIOL Input File -

Physiological

Parameters

19 Provides tables of certain physiological

parameters (e.g., body mass) by age and

gender. The file is used for calculating dose,

not exposure concentrations. It is needed for

running APEX even if the dose calculation is

turned off.

Profile Functions DISTRIB Input File -

Profile

Functions

20 Provides the definitions of the following userdefinable functions:

MaxTempCat - Binning daily 1-hour

maximum temperatures into categories

AvgTempCat - Binning daily mean

temperatures into categories

DiaryPools - Assigning diary pools using day

of week, MaxTempCat, and AvgTempCat

IDGRP - Group number for output labeling not

used in internal calculation

HasGasStove - Define probability of having

gas stove in a residence

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

Microenvironment

Mapping

DIARYMAP Input File -

Microenvironm

ent Mapping

21 Provides the mapping from activity location

codes in Diary Events (e.g., from CHAD) to

user-defined microenvironments in Micro

Descriptions.

Personal Info DIARYSUM Input File -

Personal Info

22 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).
Table 5-1. Input Files for APEX Modela

 (continued)

Input File Keyword APEX in MIMS

Parameter

Name

Unit

#

Uses

DRAFT—APRIL 24, 2003 61

Diary Events DIARYEVE Input File -

Diary Events

23 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. 

Microenvironment

Descriptions

MICROENV Input File -

Microenvironm

ent

Descriptions

24 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.

and how it is used by APEX; see the tables below for which input and output files have been

assigned which unit number.) If the user wants to redefine the set of microenvironments, the

files of units 21 and 24 should also be changed. Similarly, the user may want to redefine the

various distributions used in APEX (unit 20). The details of these input files are provided in the

subsequent sections of this chapter.

Note that the keyword FILE must appear (with a blank space before it) right after each of the file

keywords and before the “=”. If any of these 14 files are not found at the specified locations,

then APEX will prompt the user for the pathname of the missing file using the unit #. 

In the Output File section of the Params file, the user needs to specify the names and path

names of six and optionally seven output files. If the user turns off the dose calculation, hourly

exposure, or dose outputs, the corresponding output files will not be created. Table 5-2

describes the output files. The details of these output files are further explained in Chapter 6.

5.2.2 Population Files Sections of Params File

In the Population Files section of the Params file, the user needs to specify the names and path

names of either the 10 supplied Population Data files or any Population Data files the user

creates. The number of population files could change, depending on how the user classifies the

population. 

Similar to the other input files, the keywords POP and FILE must appear at the beginning of the

keyword part of the keyword input line in Params, followed by a comma and Gender and

another comma and Race. Gender must be either male or female and it can be shortened to

M or F. Currently, to correspond to the supplied data, the races must be White, Black, Asian,

NatAm, or Other, which may be shortened to W, B, A, N, or O. It is not necessary to specify all

genders and race combinations for APEX to run. However, the model assumes that any

missing gender/race combinations have zero population and it does not give any warning

messages.
DRAFT—APRIL 24, 2003 62

Table 5-2. Output Files for APEX Model

Output file Keyword APEX in MIMS

Parameter Name

Unit # Description

Log LOG Output File - Log 25 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 Output File -

Hourly Exposure

Estimates

26 Provides an hour-by-hour time series of

exposure estimates for each modeled

profile.

Hourly Dose DOSE Output File -

Hourly COHb Dose

Estimates

27 Provides an hour-by-hour time series of

dose estimates for each modeled profile.

Personal

Summary Data

PERSON Output File -

Person Summary

28 Provides a summary of each profile

modeled in the simulation.

Microenvironment

Summary

MICROSUM Output File -

Microenvironment

Summary

29 Provides a summary of the time and

exposure by microenvironment for each

profile modeled in the simulation.

Summary

Tables

TABLE Output File -

Tables

30 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.

Sites SITES Output File - Site

Mapping

31 The Sites file lists the sectors, districts,

and zones in the study area, and

identifies the mapping between them.

Each Population Data file used in a model run must have 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 (youngest first). The number of age groups and their age

ranges are provided in the Employment by Age Group file (Unit 14). The counts are the number

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

Ten gender/race specific population data files for all 65,443 Year 2000 Census tracts have been

prepared and provided with APEX. These population files should be adequate for most

purposes. Figure 5-2 provides an example of a portion of a Population Data File. The first

column, sector ID, is the census tract FIPS code. The 23 numbers that follow are the

population counts for each of the 23 defined age groups starting with the youngest.
DRAFT—APRIL 24, 2003 63

Figure 5-2. Example Portion of Population Data File (“Wrapped” View)

01001020100 44 75 79 52 18 13 7 32 60 61 90 87 68 70 56

 18 23 14 19 23 17 7 15

01001020200 77 86 61 42 25 17 9 47 76 68 60 86 71 73 51

 24 19 19 21 34 21 17 23

01001020300 130 125 141 75 41 12 21 53 117 134 155 139 106 91 80

 32 44 31 35 68 48 46 76

01001020400 129 172 166 87 60 24 21 52 121 144 196 191 145 150 146

 58 114 63 73 115 70 38 39

01001020500 239 281 241 138 57 20 23 84 195 205 394 282 206 188 141

 54 65 33 53 58 54 41 16

5.2.3 Job Parameter Settings Section of Params File

In the Job Parameter Settings section of the Params file, the user can specify a number of

different job parameters for APEX runs. Table 5-3 provides a description of the keyword, data

type, and uses of these job parameters. As with Input and Output Files, the keyword is the part

of the Params input line that is necessary to allow APEX to identify the parameter. Data type is

either integer (I), real (R), or character (C)). Each character variable has a specified length: input

values longer than allowed will be truncated to this length, and values shorter than allowed are

simply padded with blanks. In all cases except County, if the same keyword appears more

than once, the last occurrence overwrites the others.

Table 5-3. Job Parameters in APEX Params File

Variable Keyword APEX in MIMS

Parameter

Name

Type

(Length)

Description

Pollutant POLL Pollutant C(40) Pollutant name (for output only; not

used internally).

InUnits INPUTUNIT Input

Concentration

Units

C(40) Pollutant concentration units used for

the input data, in ppm or ug/m3

.

OutUnits OUPUTUNIT Output

Concentration

Units

C(40) Pollutant concentration units used for

the output data, in ppm or ug/m3

.

Location LOCATION Location C(40) Study area location (for output only; not

used internally).

Scenario SCENARIO Scenario C(40) Scenario description (for output only;

not used internally).

#Profiles PROFILE Number of

Profiles

Integer Number of profiles to simulate.

#Micros MICRO Maximum

Microenvironm

ent Number

Integer Number of microenvironments defined

in the Micro Mapping file (Unit 21) and

on the Micro Descriptions file (Unit 24).

#Sources SOURCE Number of

Sources

Integer Largest number of sources in any

microenvironment.

Start_Date START_ Start Date Integer Simulation start date in YYYYMMDD

format (e.g., 19960704 for July 4,

1996).
Table 5-3. Job Parameters in APEX Params File (continued)

Variable Keyword APEX in MIMS

Parameter

Name

Type

(Length)

Description

DRAFT—APRIL 24, 2003 64

End_Date END_ End Date Integer Simulation end date in YYYYMMDD

format.

Latitude LATIT Latitude Real Latitude in decimal degrees for the

center of the study area. Note that

latitude south of the equator is

negative.

Longitude LONGIT Longitude Real Longitude in decimal degrees for the

center of study area. Note that

longitude west of the primary meridian

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

CityRadius CITYRAD Study Area

Radius

Real Radius of study area in km. The

sectors (e.g., census tracts) with

representative locations within this

radius will be automatically selected for

modeling.

AirRadius AIRRAD Air Quality

District Radius

Real Maximum application radius (km) of air

quality data collected at an air

monitoring station or modeled at that

location. Air quality data are applied to

the sectors within this radius.

ZoneRadius ZONERAD Meteorological

Zone Radius

Real Maximum application radius (km) of

daily temperature data collected at a

weather station.

CountyList COUNTYL Use County

List?

C(1) 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 County FIPS

Code

C(5) FIPS code for listed county (or other

relevant portion of the sector ID if the

supplied sector files are not used). 

County is used only if countylist=Y. 

Repeat this line for each additional

county code.

Commuting COMMUT Include

Commuting to

Work?

C(1) Y = allow a simulated profile (or

person) to commute to a work sector

(or census tract), N = No commuting. 

If Y, a work sector (e.g., census tract)

is randomly selected for each

simulated profile based on the

probabilities of work sectors a person

may travel to from a home sector. If N,

then workers are assumed to work in

their home sector. The commute

database provided with this model is

only for Y2000 Census tracts and can

only be used with Y2000 population

data.

AgeMin AGEMIN Minimum Age Integer Minimum age for simulated profiles

(persons).
Table 5-3. Job Parameters in APEX Params File (continued)

Variable Keyword APEX in MIMS

Parameter

Name

Type

(Length)

Description

DRAFT—APRIL 24, 2003 65

AgeMax AGEMAX Maximum Age Integer Maximum age for simulated profiles

(persons).

DSTAdjust DST Use Daylight

Savings Time?

C(1) 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. Regardless

of this setting, the output (hourly

exposure and dose) for all simulated

days will contain exactly 24 hours, and

all input activity diaries must contain

exactly 24 hours. 

HourlyOut HOURLY Write Hourly

Output?

C(1) Y = write hourly exposure and dose

files, N = don’t write files. If this flag is

set to NO, the output files of hourly

exposures and doses will not be

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 Do COHb Dose

Calculations?

C(1) 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 Do Rollback

Adjustments?

C(1) 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

Real Rollback target concentration. Use

same units as InUnits.

RbBackgnd RBBACK Rollback

background

concentration

Real Rollback background concentration. 

Use same units as InUnits.
Table 5-3. Job Parameters in APEX Params File (continued)

Variable Keyword APEX in MIMS

Parameter

Name

Type

(Length)

Description

DRAFT—APRIL 24, 2003 66

RbMax RBMAX Rollback

maximum

concentration

Real Rollback maximum concentration. Use

same units as InUnits.

PPMFactor PPM PPM to ug/m3

Factor

Real Units conversion factor (1 ppm =

ppmfact :g/m3

). For CO, ppmfact =

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

). It is

used when source strengths are

expressed in micrograms per hour, but

concentrations are in parts per million

(ppm), and when InUnits and

OutUnits are in different units. 

MissGender MISSGEND Missing Gender

Diary

Probability

Factor

Real 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

(which would cause APEX to stop

prematurely).

MissEmployment MISSEMP Missing

Employment

Diary

Probability

Factor

Real 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 Missing Age

Diary

Probability

Factor

Real 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.
Table 5-3. Job Parameters in APEX Params File (continued)

Variable Keyword APEX in MIMS

Parameter

Name

Type

(Length)

Description

DRAFT—APRIL 24, 2003 67

AgeCutPCT AGECUT Primary Age

Window Width

Real Width of main age window (%). Each

simulated profile (person) is assigned a

specific year of age. A window is

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 Shoulder Age

Window Width

Real 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 Real Altitude of study area in feet. The

altitude in feet is assumed constant for

the study area. It is used in the

Coburn-Forster-Kane (CFK) equation

for determining blood COHb

concentration. Note, however, that the

altitude correction that was used in

pNEM and earlier versions of APEX

was found to be in error and so this

factor has been temporarily eliminated

from the CFK equations. The use of

altitude to adjust for air pressure as a

function of altitude still exists.

COHbFactor COHBFACT COHb

Convergence

Factor

Real 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.
Table 5-3. Job Parameters in APEX Params File (continued)

Variable Keyword APEX in MIMS

Parameter

Name

Type

(Length)

Description

DRAFT—APRIL 24, 2003 68

COThreshold COTHRESH CO Notification

Threshold

Real 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.

DebugLevel DEBUG Debug Level Integer A value of one results in more

information being written to the log file

than for a value of zero.

Randomseed RANDOM Random Seed Integer Seed>0 is user preset, Seed=0 gets

seed from clock. If RandomSeed is

changed between runs (using 0 for

both runs or using two different nonzero numbers), two separate model

runs of 100 profiles each time will be

equivalent to one model run of 200

profiles. Otherwise, the same 100

profiles will be generated over again. 

Control of the random number seeds is

an important part of using APEX for

sensitivity analysis. For example,

when performing multiple runs with

slightly different inputs, it may be

convenient to sample the same set of

profiles, activity diaries, and

microenvironmental concentrations, in

order to prevent stochastic differences

between the runs from obscuring the

differences due to the changed input.

5.2.4 Output Table Levels Sections of Params File

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

each of 12 output parameters in the output summary tables. Each parameter is identified by a

single keyword and the values are a list of numbers ordered from smallest to largest and

separated by a comma. All the values are read as real numbers, although the decimal points

are optional if the values happen to be integers. Items in each list must be separated by

commas. Except for the Percentiles, all of other parameters are used to bin exposures or

doses into categories in order to create output tables. Note that there is always one more bin

than there are number of values in the list, since the first bin is below the first value in the list

and the last bin is above the last number. The specific meanings of the parameters are

explained in Table 5-4.
DRAFT—APRIL 24, 2003 69

Table 5-4. Output Parameter Levels in the Output Summary Table

Variable Keyword APEX in MIMS

Parameter

Name

Data

Type

Description

Percentiles PERCENTILES Table -

Percentiles

Real This parameter specifies the levels of

percentile of the exposed population for

exposure or dose in APEX output files. Values

can include up to one digit beyond the decimal

point (e.g. the 99.5 or 99.9 percentile). 

Percentiles can only be distinguished from

nearby ones if there are enough profiles in the

model run. For example, at least 100 profiles

are needed to properly determine a 99th

percentile, and unless at least 200 profiles are

used, the 99.5 and 99.9 percentiles will both

report the results for the highest individual

profile.

Exposure

Cutpoints

TIMEEXP Table -

Exposure

Cutpoints

Real This parameter specifies the exposure

cutpoints (ppm) for summing time spent at

various exposure levels. The time is

expressed in minutes and is summed across

all profiles. TimeExp is used in two tables. 

The first table is non-cumulative, meaning that

only the time spent at a level between one

cutpoint and the next is totaled. The other

table is cumulative, meaning that all time spent

at levels above the cutpoint is reported, even if

it is also above higher cutpoints. For both

tables, the second dimension is provided by

the microenvironment number.

Daily Max 1-

Hour

Exposure

Cutpoints

DM1HEXP Table - Daily

Max 1-Hour

Exposure

Cutpoints

Real This parameter specifies the daily maximum 1-

hour exposure cut-points (ppm) for binning all

the person-days in the simulation period.

Daily Max 8-

Hour

Exposure

Cutpoints

DM8HEXP Table - Daily

Max 8-Hour

Exposure

Cutpoints

Real This parameter specifies daily maximum 8-

hour average exposure cut-points in ppm for

binning all the person days in the simulation

period. It is similar to DM1HExp except for the

longer averaging time. Typically, DM8HExp

values are about 1/3 of DM1HExp values. 

Daily

Average

Exposure

Cutpoints

DAVGEXP Table - Daily

Average

Exposure

Cutpoints

Real This parameter specifies daily average

exposure cut-points (ppm) for binning all the

person-days in the simulation period. 

Simulation

Average

Exposure

Cutpoints

SAVGEXP Table -

Simulation

Average

Exposure

Cutpoints

Real This parameter specifies cut-points (ppm) for

average exposure over the simulation period. 

The cut points are used to bin all simulated

persons created in a run. 

Daily Max 1-

Hour Dose

Cutpoints

DM1HDOSEa Table - Daily

Max 1-Hour

COHb Dose

Cutpoints

Real This parameter specifies cutpoints in %COHb

for Daily Maximum 1-Hour Blood Dose. The

cut points were used to bin all the person-days

in the simulation period.
Table 5-4. Output Parameter Levels in the Output Summary Table (continued)

Variable Keyword APEX in MIMS

Parameter

Name

Data

Type

Description

DRAFT—APRIL 24, 2003 70

Daily Max 8-

Hour Dose

Cutpoints

DM8HDOSEa Table - Daily

Max 8-Hour

COHb Dose

Cutpoints

Real This parameter specifies cutpoints in %COHb

for Daily Maximum 8-Hour Blood Dose. The

cut points were used to bin all the person-days

in the simulation period.

Daily Max

End-of-hour

Dose

Cutpoints

DMEHDOSEa Table - Daily

Max End-ofHour COHb

Dose Cutpoints

Real This parameter specifies cutpoints 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. 

These two statistics usually track each other

fairly closely. DMEHDose is reported since it

is the dose statistic calculated in the pneum.

model.

Hourly

End-of-hour

Dose

H_EHDOSEa Table - Number

of Hours

End-of-Hour

Dose

Cutpoints

Real Similar to DMEHDose, except that instead of

using just the highest single end-of-hour dose

on each day, it collects results for all 24

end-of-hour doses on each day. As with the

other keywords, the values specified here refer

to the cutpoints in %COHb used for tabulating

the dose results.

Daily

Average

Dose

Cutpoints

DAVGDOSEa Table - Daily

Average COHb

Dose Cutpoints

Real This parameter specifies cutpoints in %COHb

for the Daily Average Blood Dose. The cutpoints are used to bin all the person/days in

the simulation period. 

Simulation

Average

Dose

Cutpoints

SAVEDOSEa Table -

Simulation

Average COHb

Dose Cutpoints

Real This parameter specifies cutpoints in %COHb

for the Average Blood Dose over the entire

simulation. The cut-points are used to bin all

the persons (or profiles) created in the APEX

run.

Dose

Cutpoints

TIMEDOSEa Table - COHb

Dose Cutpoints

Real This parameter specifies cut-points in %COHb

for summing time spent at various blood dose

levels. Apart from the statistic, the tables

resemble the Time Exp tables. 

a

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

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

5.3 Sector Location File (Unit 11)

The Sector Location file provides the latitude and longitude of a representative location such as

the geographic center of all the sectors (e.g., census tracts) to be included in the Population

Data files. The file includes only numeric lines. Each line includes a Sector ID (starting with a

digit), Latitude, and Longitude. The sector IDs must match and be in the same order as the

sector IDs in the Population Data files. In addition, the sector ID must match the sector IDs in

the Commute Flow file as well (if worker commute is being modeled). 
DRAFT—APRIL 24, 2003 71

The sector location file is used along with the user-specified CityRadius to automatically select

sectors (or census tracts) within the study area (after also addressing an optional county test

and ensuring suitable air district and temperature zone data). APEX calculates the distance

between the location of a sector and the center of the study area and then compares it with the

CityRadius. All the sectors with a distance less than the city radius will be selected and

included in the exposure assessment.

The current default sector location file contains the 11-digit ID and latitudes and longitudes of

65,443 Year 2000 US Census tracts. An example of this file is provided in Figure 5-3. APEX

expects that the left most five characters of a sector ID should be the state and county FIPS

code or at least whatever county-level code is used in the County list (if the study area will be

limited in that way). 

The latitude and longitude should be in decimal degrees. At least three significant digits should

be provided after the decimal point to prevent significant rounding error. Note that the longitude

west of the primary meridian (e.g., United States locations) should be negative.

Figure 5-3. Example Portion of Sector Location File

01001020100 32.470986 -86.487033

01001020200 32.466056 -86.472934

01001020300 32.474035 -86.457764

01001020400 32.466794 -86.445569

01001020500 32.454933 -86.425025

5.4 District Location File (Unit 12)

The District Location file provides the Site ID, Latitude, Longitude, air data Start Date, and air

data End Date for all air quality (modeling or monitoring) sites included in the Air Quality Data

file (Section 5.9). The site ID is stored as a character string (up to length 40). Latitude and

longitude are in decimal degrees. The start and end dates are in YYYYMMDD format (for

example, 19951231 is December 31, 1995). This file contains only character or comment lines. 

The order of the listed sites must match the order of the sites in the Air Quality Data file. See

Figure 5-4 for an example of the District Location file.

Figure 5-4. Example of District Location File

 Site1 29.76112 -95.27645 19960101 19961231

 Site2 29.81707 -95.27897 19960101 19961231

 Site3 29.83677 -95.25692 19960101 19961231

 Site4 29.82384 -95.19532 19960101 19961231

 Site5 29.80085 -95.25586 19960101 19961231
DRAFT—APRIL 24, 2003 72

APEX uses the District Location file to

determine the “district” or geographical area

represented by the set of ambient air quality

data collected at a specified location. APEX

first compares the start and end dates for

each air quality site with the start and end

dates for the APEX exposure simulation. 

Only the sites with air quality data covering

the entire simulation period are accepted. Air

quality data before or after the simulation

period are simply ignored.

APEX also calculates the distance of a district location from the study area center and

compares it with the sum of CityRadius and AirRadius. This allows air quality data to be used

from a nearby (and the nearest) district even if the district’s location is outside the study area. 

Only the sites with a distance less than this sum are used.

APEX then calculates the distances of a site from the locations of sectors (e.g., census tracts). 

Sectors with distances less than AirRadius will be mapped to an air site. Based on this

mapping, APEX will use each set of air quality data in the Air Quality file only for the sectors

within its AirRadius. APEX assigns the sector to the nearest district. Each sector is assigned to

only one district.

5.5 Temperature Zone Location File (Unit 13)

The format and use of the Temperature Zone Location file is exactly like 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 site selection process is analogous to that

described above for the District Location file. The file is used to map the set of temperature

data collected at a weather station to sectors within its zone radius for exposure calculations. 

An example file is provided in Figure 5-5. Similar to air quality districts, zones within the sum of

CityRadius and ZoneRadius are used.

Figure 5-5. Example of Temperature Zone Location File

TempSite1 39.742 -105.045 19950101 19951231

TempSite2 39.103 -105.521 19950101 19951231

5.6 Employment by Age Group File (Unit 14)

This file provides employment probabilities for all the age groups included in the Population

Data files. The file contains only three records: Min_Age, Max_Age, and Employ_Prob. The

format for each record is a keyword input line with a keyword, an “=”, and a list of values. 

APEX only reads the first three letters of each input line as keywords (i.e., MIN, MAX, and

EMP), although the user can spell out the record names in full if desired. The Min_Age input

line is used to list the minimum ages of each age group from the youngest to the oldest, while

the Max_Age input line to list the maximum ages of the corresponding population age groups. 

Tip. Not all districts on the air quality input file

need sectors assigned to them. Such districts are

simply not included in the modeling. This feature

allows the user to prepare an input file in the

simplest manner, perhaps containing more

districts than are necessary. For example, a

single input file could be prepared for all districts

in a given state. This same input file could then

be run on several study areas in the state without

having to alter the air quality input file. 
DRAFT—APRIL 24, 2003 73

The Employ_Prob input line lists employment probabilities for each population group. The

probabilities should be a real value between 0 and 1. See Figure 5-6 for an example of this file.

Figure 5-6. Example of Employment by Age Group File ("Wrapped" View)

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

Employ_prob = 0.0 0.0 0.1 0.5 0.6 0.8 0.9 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.8

 0.5 0.5 0.2 0.1 0.0 0.0

5.7 Commuting Flow File (Unit 15)

This file provides cumulative fractions of the population in a home sector commuting to different

work sectors. An example of a portion of this file is provided in Figure 5-7. Each record

contains a Sector ID and a Cumulative Fraction of the population commuting to this sector. 

The home sector is indicated by a negative value of -1 for cumulative fraction. The subsequent

records provide the Work Tract IDs and the Cumulative Fractions. The cumulative fraction

for the last work sector should always be 1. APEX uses this file to determine which work sector

a simulated individual may commute to by using the cumulative fractions as commuting

probabilities.

If the sectors used in the simulation are Year 2000 Census tracts, the default commuting flow

file provided with APEX should be used. This data base file contains all the 65,443 year 2000

Census tracts and their associated work tracts. The mean number of associated work tracts

per home tract is 104, with a minimum of one and a maximum of 829. The flows in the data

base file were calculated from the 1994 national commuting database and then mapped to the

Year 2000 Census tracts. 

5.8 Temperature Data File (Unit 16)

This file provides daily maximum and optionally average (or other) temperature data collected at

the sites listed in the Temperature Zone Location file (Unit 13). Only keyword or numeric input

lines are processed and other types of input lines are ignored in this file. The data set from

each site begins with a header section with selected site information (see Figure 5-8). The

subsequent numeric lines include Date, Maximum Temperature (Fahrenheit), and other

temperature data such as mean or minimum daily temperature (if present).

The date should be in YYYYMMDD format (e.g., 20010507 is May 7, 2001) and the temperature

data should be in Fahrenheit. Each data set should cover the exposure simulation period. A

data set can include more days than the exposure simulation period. APEX only uses the data

within the simulation period. Thus, the user may prepare a file with a full year or many years of

data for each site and then use the same temperature file for a series of different simulation

periods.
DRAFT—APRIL 24, 2003 74

Figure 5-7. Example Portion of Commuting Flow File

01001020100 -1

01001020100 0.050060

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

Figure 5-8. Example Portion of Temperature Data File

Name = TempSite1 

Denver Daily Maximum and Mean Temperatures

Degrees Fahrenheit

Start = 19950101

End = 19951231

Lat = 39.742

Lon = -105.045

19950101 41 17

19950102 25 20

19950103 20 14

19950104 20 10

19950105 36 23
DRAFT—APRIL 24, 2003 75

The data sets may be in any order. However, each site must begin with the “name” keyword

input line. APEX matches a site name in the Temperature Zone Location file with the data set

site name to locate its data in this file. If desired, the user could add more comment or

character lines in the header section of a data set.

This file should contain data sets for the sites and time duration indicated on the Temperature

Zone Location file (Unit 13). Any missing dates within the simulation period are written to the

log file.

5.9 Air Quality Data File (Unit 17)

This file provides hourly air concentration data for air sites listed in the District Location file. 

Only keyword or numeric input lines are processed and other types of input lines are ignored in

this file. The data set for each site begins with a header section containing selected site

information (see Figure 5-9). Each of the subsequent numeric input lines includes 24 Hourly

Average Air Concentrations and a Date. The date should be in YYYYMMDD format (e.g.,

20010507 is May 7, 2001). Air quality data should be in units of parts per million (ppm) or

ug/m3

. Hourly data could be either comma or space delimited. In the example file, the number

of cumulative hours for each day also is included (for quality control)—APEX ignores this

column. Note that the length of each data line in air quality file should not exceed 256

characters.

Air data sets can be in any order. APEX locates the air data set by matching a site name in the

District Location file with the site name in this file. Any missing dates within the simulation

period will be written to the log file.

Figure 5-9. Example Portion of Air Quality Data File (“Wrapped” View)

Name = #48201212100

 Houston TX Modeling Receptor Location # 1

 Units = ppm

 Start Date = 19960101

 End Date = 19961231

 Lat = 29.76113

 Lon = -95.27644

0.00, 0.65, 0.57, 0.45, 0.87, 1.24, 3.46, 3.89, 2.88, 0.00, 1.20, 1.59, 1.89,

 1.04, 1.21, 1.25, 1.08, 1.05, 1.01, 0.86, 0.71, 0.55, 0.58, 0.52 19960101 1

0.29, 0.23, 0.22, 0.17, 0.28, 0.38, 1.00, 1.57, 1.52, 1.14, 1.05, 1.03, 0.96,

 1.20, 1.03, 1.02, 1.53, 3.38, 4.20, 4.30, 2.79, 1.01, 0.52, 0.56 19960102 25

0.25, 0.31, 0.16, 0.29, 0.98, 2.21, 4.76, 6.33, 3.10, 1.45, 1.61, 1.31, 0.54,

 0.59, 1.01, 1.44, 1.08, 0.00, 2.62, 0.00, 3.58, 3.04, 2.64, 2.06 19960103 49

1.53, 1.19, 0.93, 0.00, 0.00, 1.74, 7.25, 6.17, 2.62, 0.59, 0.63, 0.78, 0.37,

 0.79, 0.71, 0.82, 1.00, 0.82, 1.79, 6.20, 3.10, 3.74, 0.00, 3.84 19960104 73

1.68, 0.00, 0.00, 0.00, 0.68, 1.54, 5.81, 5.56, 0.00, 4.00, 4.23, 2.29, 2.83,

 2.63, 3.13, 5.98, 2.35, 1.72, 1.56, 1.98, 1.59, 0.82, 0.52, 0.47 19960105 97

0.60, 0.59, 0.32, 0.23, 0.17, 0.21, 0.56, 0.67, 0.88, 1.07, 1.01, 1.53, 1.35,

 1.24, 0.97, 1.04, 0.97, 0.88, 1.10, 0.74, 0.60, 0.50, 0.75, 0.55 19960106 121

0.70, 0.27, 0.23, 0.23, 0.23, 0.24, 0.41, 0.59, 0.56, 0.69, 0.62, 0.66, 0.97,

 0.91, 1.07, 1.04, 1.05, 1.34, 3.24, 3.87, 5.36, 1.92, 3.00, 3.46 19960107 145
DRAFT—APRIL 24, 2003 76

5.10 Activity-specific MET File (Unit 18)

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. The file is

inherited from APEX2 unchanged. The user should not change this file unless the MET

distribution data in the CHAD database are revised.

Each data line in this file provides the following information in list format:

• MET Distribution Number;

• Activity Code from supplied CHAD data (see Table 5-5);

• Age Category;

• Occupation;

• Distribution Type;

• Mean;

• Median;

• Standard Deviation;

• Minimum;

• Maximum; and

• Description of Activity Type.

A portion of this file is shown in Figure 5-10.

Figure 5-10. Example Portion of Activity-Specific MET File

1 10000 0 ADMIN LogNormal 1.7 1.7 0.3 1.4 2.7 9 Work, general

2 10000 0 ADMSUP LogNormal 1.7 1.7 0.3 1.4 2.7 9 Work, general

3 10000 0 FARM LogNormal 7.5 7.0 3.0 3.6 17.0 9 Work, general

4 10000 0 HSHLD LogNormal 3.6 3.5 0.8 2.5 6.0 9 Work, general

5 10000 0 LABOR Triangle 8.5 8.4 2.1 3.6 13.8 9 Work, general

5.11 Physiological Parameters File (Unit 19)

This file provides four tables of age/gender specific data for the following physiological

parameters (also see Figure 5-11):

• NVO2Max;

• Body Mass;

• Resting Metabolic Rate (RMR); and

• Blood Volume Factor and Hemoglobin Content.

This file requires the data to be in fixed column positions. Each table has three header lines

and 202 numerical lines of data. The first table contains age/gender specific distribution data on

normalized lung capacity (VO2-max). APEX only reads the Mean NVO2Max in columns 24-30 in

F7.1 Fortran format and the NVO2Max standard deviation in columns 31-37 (F7.1). 
DRAFT—APRIL 24, 2003 77

Table 5-5. CHAD Activity Codes

CHAD CHAD 

Activity Activity 

Code Description Code Description

10000 Work and other income producing activities,

general

10100 Work, General

10110 Work, general, for organizational activities

10111 Work for professional/union organizations

10112 Work for special interest identity

organizations

10113 Work for political party and civic

participation

10114 Work for volunteer/ helping organizations

10115 Work of/for religious groups

10116 Work for fraternal organizations

10117 Work for child / youth / family organizations

10118 Work for other organizations

10120 Work, income-related only

10130 Work, secondary (income-related)

10200 Unemployment

10300 Breaks

11000 General household activities

11100 Prepare food

11110 Prepare and clean-up food

11200 Indoor chores

11210 Clean-up food

11220 Clean house

11300 Outdoor chores

11310 Clean outdoors

11400 Care of clothes

11410 Wash clothes

11500 Build a fire

11600 Repair, general

11610 Repair of boat

11620 Paint home / room

11630 Repair / maintain car

11640 Home repairs

11650 Other repairs

11700 Care of plants

11800 Care for pets/animals

11900 Other household

12000 Child care, general

12100 Care of baby

12200 Care of child

12300 Help / teach

12400 Talk /read

12500 Play indoors

12600 Play outdoors

12700 Medical care-child

12800 Other child care

13000 Obtain goods and services, general

13100 Dry clean

13200 Shop / run errands

13210 Shop for food

13220 Shop for clothes or household goods

13230 Run errands

13300 Obtain personal care service

13400 Obtain medical service

13500 Obtain government / financial services

13600 Obtain car services

13700 Other repairs

13800 Other services

14000 Personal needs and care, general

14100 Shower, bathe, personal hygiene

14110 Shower, bathe

14120 Personal hygiene

14200 Medical care

14300 Help and care

14400 Eat

14500 Sleep or nap

14600 dress, groom

14700 Other personal needs

15000 General education and professional training

15100 Attend full-time school

15110 Attend day-care

15120 Attend K-12

15130 Attend college or trade school

15140 Attend adult education and special training

15200 Attend other classes

15300 Do homework

15400 Use library

15500 Other education

16000 General entertainment / social activities

16100 Attend sports events

16200 Participate in social, political, or religious

activities

16210 Practice religion

16300 Watch movie

16400 Attend theater

16500 Visit museums

16600 Visit

16700 Attend a party

16800 Go to bar / lounge

16900 Other entertainment / social events

17000 Leisure, general

17100 Participate in sports and active leisure

17110 Participate in sports

17111 Hunting, fishing, hiking

17112 Golf

17113 Bowling / pool / ping pong / pinball

17114 Yoga

17120 Participate in outdoor leisure

17121 Play, unspecified

17122 Passive, sitting

17130 Exercise

17131 Walk, bike, or jog (not in transit)

17140 Create art, music, participate in hobbies

17141 Participate in hobbies

17142 Create domestic crafts

17143 Create art

17144 Perform music / drama / dance

17150 Play games

17160 Use of computers

17170 Participate in recess and physical education

17180 Other sports and active leisure
Table 5-5. CHAD Activity Codes (continued)

CHAD CHAD 

Activity Activity 

Code Description Code Description

DRAFT—APRIL 24, 2003 78

17200 Participate in passive leisure

17210 Watch

17211 Watch adult at work

17212 Watch someone provide childcare

17213 Watch personal care

17214 Watch education

17215 Watch organizational activities

17216 Watch recreation

17220 Listen to radio/listen to recorded

music/watch T.V.

17221 Listen to radio

17222 listen to recorded music

17223 Watch TV

17230 Read, general

17231 Read books

17232 Read magazines / not ascertained

17233 Read newspaper

17240 Converse / write

17241 Converse

17242 Write for leisure / pleasure / paperwork

17250 Think and relax

17260 Other passive leisure

17300 Other leisure

18000 Travel, general

18100 Travel during work

18200 Travel to/from work

18300 Travel for child care

18400 Travel for goods and services

18500 Travel for personal care

18600 Travel for education

18700 Travel for organizational activity

18800 Travel for event / social activity

18900 Travel for leisure

18910 Travel for active leisure

18920 Travel for passive leisure

U Unknown

X Missing

The second table contains age/gender specific body mass (kg) distribution data. APEX only

reads the mean body mass in columns 22-30 (F9.1) and the body mass standard deviation in

columns 31-38 (F8.3). 

The third table contains regression parameters for fitting the resting metabolic rate. APEX

reads the slope in columns 30-38 (F9.3), the intercept in columns 39-47 (F9.3), and the residual

error in columns 48-54 (F7.3). 

The fourth table contains blood parameters. APEX reads the blood factor in columns 7-15

(F9.1), the hemoglobin mean in columns 16-24 (F9.1), and the hemoglobin standard deviation in

columns 25-32 (F8.1).

5.12 Profile Functions File (Unit 20)

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

persons. The profile functions that can be defined in this file are summarized in Table 5-6. 

Note that all user-definable functions must be included in this file except those identified as not

required in Table 5-6. Also, note that when preparing or editing a profile functions file, be

careful not to use Tab to separate the items on a line. APEX is explicitly searching for blanks

(spaces) as delimiters, and fails to recognize Tabs as such. An example of the user-defined

function for WindowPos is provided in Figure 5-12. The box after this example provides a

general description of the procedures for defining a function.
DRAFT—APRIL 24, 2003 79

Figure 5-11. Portions of Four Data Tables in Physiological Parameters File

(Columns in this view are not in their required positions)

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

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

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

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
DRAFT—APRIL 24, 2003 80

Users may enter additional functions for use with the microenvironmental concentration

calculations. These additional functions may be given any names that start with “M” (except for

“MaxTempCat”, already used for another purpose). These functions are used if they are

referenced on the Micro Descriptions input file (unit 24). The user also can define functions in

this input file that are not actually referenced by the model. This could be useful if the user has

two versions of some function and for convenience would like to store the definitions of both in

the same file. One function would then be given the function name expected by APEX and the

other would be given some related name. After the model is run, the functions could be

interchanged simply by switching function names, and the job run again. This would permit a

comparison of the effects of the two versions of the function.

Table 5-6. User-definable Functions in Profile Functions File

Function Purpose

MaxTempCat Binning daily 1-hour maximum temperatures into categories

AvgTempCat Binning daily average temperatures into categories

Diary Pools Assigning diary pools using day of week, MaxTempCat, and AvgTempCat

IDGRP pneum. group number (for output labeling, not used internally in APEX)

HasGasStove Probability of having a gas stove

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 car

WindowPos Probability of windows being open or closed, based on AC_Home, MaxTempCat, and

AvgTempCat

SpeedCat Probability of average speed categories for vehicles

M1S1I2 (not required) Cigarette emissions micro #1, block #2 in ug/hr

M1S1I1 (not required) Cigarette emissions micro #1, block #1 in ug/hr

M2S1 (not required) Cigarette emissions micro #2 in ppm

Figure 5-12. Example of WindowPos User-Defined Profile Function

WindowPos

! Home windows open(1) or closed (2)

TABLE

INPUT 1 INTVALUE 2 "AC_Home"

1 2

INPUT 2 INTRANGE 3 "MaxTemp"

56 80

INPUT 3 INTRANGE 1 "AvgTemp"

INPUT 4 CONDITIONAL 12

0.2 0.8

0.2 0.8 

0.5 0.5

0.7 0.3

0.1 0.9

0.9 0.1

RESULT INTEGER 2

1 2
DRAFT—APRIL 24, 2003 81

General procedures for defining a profile function:

1. A function definition begins with its name on the first input line.

2. Add as many comment lines as necessary to describe the profile function or units of the involved parameters.

3. A function type—Table or Continuous—is specified on the subsequent input line. At the present time, APEX

can only recognize the function type of Table. 

4. For the function type Table, the number of subsequent input lines varies with the number of input variable

required to define a function. At least two input lines are needed for each input variable of the function. In

addition, at least two lines are also needed for the function result. For each input variable (table dimension),

the first line starts with the keyword INPUT, followed by the indexing number of Input Variable in the function,

the Type of Input Variable, and the Number of Values allowed for the input variable. At the end of this

input line, the user may add comments in double quotes to explain input variables. The type of input variables

must be one of the following six types:

• conditional, 

• probability, 

• realrange, 

• intrange,

• intvalue, or 

• intindex. 

“Conditional” refers to conditional probabilities that depend on the values of other input variables. “Probability”

means fixed probabilities for each outcome, not dependent on the input data. “Realrange” means a set of

discrete categories, each consisting of a range of real numbers. “Intrange” is similar, except each category

consists of a range of integers. “Intvalue” means that each possible value that the input variable may take on

is listed. “Intindex” means that the input variable is integer and is to be used to index the table directly (e.g., a

value of 3 means use the third cell along that dimension). For each input variable, the remaining lines

(beyond the first) contain the data. 

5. After all the input variables are specified, the next line must contain the keyword RESULT, followed by a type

(either integer or real) and the number of possible results. 

6. List the results in order in subsequent lines.

7. End the profile function with a new line that has a # sign.

5.13 Micro Mapping File (Unit 21)

This file provides the mapping of the Location Codes (e.g., for CHAD) to Microenvironments

defined in APEX. An example of a portion of this file is provided in Figure 5-13. This file only

allows comment lines and keyword input lines, except for the first two header lines. Each

keyword input line begins with a location code followed by a short description, an “=”, and an

integer that designates a microenvironment defined in the Micro Description file. APEX only

reads the location codes and the code for the APEX microenvironments.

The supplied file contains 115 CHAD location codes. The user must assign each location code

to microenvironments defined in the Micro Description file by specifying the microenvironment

number in the APEX Micro column. The user could place a zero in the APEX Micro column if no

exposure occurs in a CHAD microenvironment location, or -1 if the CHAD locations are ‘U’ or

‘X’ (unknown). The value of -1 means that APEX should use whichever microenvironment was

previously in use in the composite diary time series. See Table 2-2 for a description of the

CHAD location codes supplied with APEX and mapped in the Micro Description file.
DRAFT—APRIL 24, 2003 82

Figure 5-13. Example Portion of Micro Mapping File

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 = 4

30210 Your residence, outdoor = 4

30211 ..., pool or spa = 4

30219 ..., other outdoor = 4

30220 Other residence, outdoor = 4

30221 ..., pool or spa = 4

30229 ..., other outdoor = 4

30300 Residential garage or carport = 4 

30310 ..., indoor = 4 

30320 ..., outdoor = 4

30330 Your garage or carport = 4 

30331 ..., indoor = 4

30332 ..., outdoor = 4

30340 Other residential garage or carport = 4

30341 ..., indoor = 4

30342 ..., outdoor = 4

30400 Residence, none of the above = 1

5.14 Personal Info File (Unit 22)

This file provides the personal information component of each 24-hour activity diary (Figure 5-

14). Each record contains values for the following variables:

• CHAD ID;

• Day Type (MON, TUE, etc., Missing (X));

• Gender (Male (M), Female (F), Missing (X));

• Race (White (W), Black (B), Asian (A), Hispanic (H), Other (O), Information not available

(X));

• Employment Status (Yes (Y), No (N), Missing (X));

• Maximum Temperature (degrees C);
DRAFT—APRIL 24, 2003 83

• Age (Years);

• Occupation (see Table 5-7);

• Count of Missing Time (the total number of minutes associated with events in the Diary

Events file for which the activity and/or location codes are missing); and 

• Record Count.

This file is identical to the Diary Questionnaire file produced by the Access® version of the

CHAD database (see next section). The user should not change this input file unless the CHAD

database has changed or other activity data are to be used instead. If the latter, the input file

format restrictions must be met and the CHAD coding conventions used. Note that this file has

only one record per CHAD ID, whereas the CHAD Diary Events file has Record Count of

records per CHAD ID.

Figure 5-14. Example Portion of Personal Info File

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

BAL97008F,WED,F,W,N, 88, 36,X , 90, 26
DRAFT—APRIL 24, 2003 84

Table 5-7. CHAD Occupation Codes

Code Description 

ADMIN Executive, administrative, and managerial

PROF Professional

TECH Technicians

SALE Sales

ADMSUP Administrative support

HSHLD Private household

PROTECT Protective services

SERV Service

FARM Farming, forestry, and fishing

PREC Precision production, craft, and repair

MACH Machine operators, assemblers, and inspectors

TRANS Transportation and material moving

LABOR Handling, equipment cleaners, helpers, and laborers

X Missing

5.15 Diary Events File (Unit 23)

This file provides descriptions of events occurring hourly or in shorter duration in each day for all

the diary days in the CHAD database. Each record includes the following variables:

• CHAD ID;

• Event Start Time (the time the event began; HHMM, with 0000 representing midnight);

• Event Duration (the duration of the event, in minutes);

• Activity Code (see Table 5-5); and

• Location Code (see Table 2-2).

This file should be generated from the CHAD database at the same time as the Personal Info

(questionnaire) file to ensure that the CHAD IDs are in the same order. Each diary day begins

and ends at midnight and there should be exactly twenty-four hours of data per diary. See

Figure 5-15 for an example of a portion of this file. And see the previous section on the

Personal Info file if user-supplied date are to be provided.
DRAFT—APRIL 24, 2003 85

Figure 5-15. Example Portion of Diary Events File

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,

5.16 Micro Descriptions File (Unit 24)

The Micro Descriptions file provides microenvironment definitions and parameters required to

calculate pollutant concentrations in microenvironments. As described below, the Micro

Descriptions file has two sections: Micro Descriptions and Parameter Descriptions. A portion of

an example file is provided in Figure 5-16.

5.16.1 Micro Descriptions Section

In the Micro Descriptions section of the Micro Descriptions file, the user specifies a

Microenvironment Number, a Name, and a Calculation Method for each microenvironment,

as shown in Figure 5-16. The microenvironment number cannot exceed the number of

microenvironments specified in the Params file and nor can it exceed 127. It also has to

correspond with each of the microenvironment numbers in the Micro Mapping file. A

microenvironment name may be a word up to 12 characters. The calculation method could be

either MASSBAL or FACTORS. In the MASSBAL method, the concentration in a

microenvironment is calculated using a mass balance approach, while in the FACTORS method

the microenvironment concentration is assumed to be a linear function of ambient

concentration.
DRAFT—APRIL 24, 2003 86

Figure 5-16. Example Portion of Micro Description File

! First section - micro descriptions

Micro Name Method

1 Residence MASSBAL

2 Car MASSBAL

3 InsideOther FACTORS

4 Outside FACTORS

! Second section - parameter descriptions

Micro number = 1

Parameter Type = PRX

Hours - Block = 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 

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 

Condition # 1 = 0

Condition # 2 = 0

Condition # 3 = 0

ResampHours = NO

ResampDays = YES

ResampWork = YES

RandomSeed = 0

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

1 1 1 1 1 1 1 Normal 0. 2. 1.2 0.4

2 1 1 1 1 1 1 Point 2. . .

1 1 2 1 1 1 1 Lognormal 0. 2. 1.2 1.5

2 1 2 1 1 1 1 Exponential 1. . 2. .

1 1 3 1 1 1 1 Triangle 0. 3. 2. .

2 1 3 1 1 1 1 Normal 1. 2.5 1.5 0.5

1 1 4 1 1 1 1 Uniform 0. 1.6 . .

2 1 4 1 1 1 1 Lognormal 1. . 3. 1.4

Micro number = 1

Parameter Type = AER

Hours - Block = 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 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 

Condition # 1 = AC_HOME

Condition # 2 = 0

Condition # 3 = 0

ResampHours = NO

ResampDays = NO

ResampWork = YES

RandomSeed = 0

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

1 1 1 1 1 1 1 Uniform 0.5 4. . .

2 1 1 1 1 1 1 Normal 0. . 2.5 1.0

1 1 1 1 2 1 1 Point 3. . . .

2 1 1 1 2 1 1 Triangle 1. 5. 2. .

5.16.2 Parameter Description Section

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

parameters for each microenvironment, depending in the calculation method: three parameters

for the FACTORS method and eight parameters for the MASSBAL method. These parameters

are required for calculating concentrations in microenvironments. If a default or supplied value

(in Table 5-8) is acceptable, the user does not have to define the parameters further. A

Parameter Description section is needed for each parameter and consists of keywords and
DRAFT—APRIL 24, 2003 87

data. The following two sections provide additional details on the parameter keywords and

data.

Table 5-8. Micro Parameters Required to Define a Microenvironment

Calculation

Method

Parameter Type Code Units Default/Supplied Value

FACTORS Proximity PR None 1

Penetration PE None 1

Csource CS ppm or μg/m3

 (depends

on InputUnits)

0

MASSBAL Proximity PR None 1

Penetration PE None 1

Csource CS ppm or μg/m3

 (depends

on InputUnits)

0

DecayRate DE 1/hr 0

AIrExRate AE Air changes/hr none

Volume VO m3

 none

MeanR MR 1/hr AirExRate+DecayRate

ESource ES μg/m3 0

5.16.2.1 Keywords

The user needs to specify values for the 13 parameters on keyword input lines, as shown in

Figure 5-16. A brief description of each parameter is provided in Table 5-9. The keyword list

needs to end with a character line that is the header line for the data section and begins with the

word Block, which APEX recognizes as an indicator for ending of the keyword section. The

Esource and Csource terms, unlike the others, may have multiple definitions within the same

microenvironment. These terms are then added or multiplied together, depending on the source

numbering. If source terms are multiplied, then only the final product must have the proper

units. This feature allows the source terms to be expressed in any suitable units, with the units

conversion expressed as a multiplicative factor.
DRAFT—APRIL 24, 2003 88

Table 5-9. Keyword Definitions for the Parameter Descriptions Section of the

Micro Descriptions File

Keyword Description

Micro Number These numbers must match the micro numbers in the Microenvironment Description

section.

Parameter Type A parameter code such as PR (Proximity) and PE (Penetration) provided in Table 5-8

should be used to specify a parameter type.

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

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

1.a.m. and the 24th is 11 p.m. to midnight. The position of an integer in the input line

represents the hour in a day. The integer represents the number of a time block that a

hour belongs to. The hours in a time block do not need to be consecutive, nor does a

time block have to have the same number of hours. If this line is missing, the default

value is that all 24 hours are in a single time block - block #1.

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

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

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

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

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

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

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

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

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

this line must match the number of air quality districts in the study area. This variable is a

holdover from APEX2 and should not be used unless really necessary. The user could

delete this line or place the same number of 1 in this line as the number of air districts.

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

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

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

AvgTempCat, SpeedCat. 

Condition # 2 Choice for the second conditional variable.

Condition # 3 Choice for the third conditional variable. 

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

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

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

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

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

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

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

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

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

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

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

run to another. If not zero, then Seed = (RandomSeed x 232) + RandomSeed. Multiple

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

the parameters (as long as the MP definition is unchanged). The default value is zero.
DRAFT—APRIL 24, 2003 89

5.16.2.2 Data

The data are sets of distribution data for all possible combinations of the user-specified cases of

the following seven indexing parameters:

• Block — time block

• Daytype — day time

• Season — season

• Area — area

• C1 — conditional variable # 1

• C2 — conditional variable # 2

• C3 — conditional variable # 3

Note that the cases of each indexing parameter must be represented by integers ranging from 1

to the maximum number of cases for an indexing parameter. The number of cases for a

indexing parameter is specified in the keyword section.

In the example file in Figure 5-16, there are two time blocks and four types of seasons for the

PRX parameter of microenvironment 1. Thus, the user needs eight data lines in the data

section to specify eight sets of distribution data. In the second parameter description section,

there are two time blocks and C1 specified as AC-Home. Since AC-Home has two options—off

(1) or on (2)—the user needs four data lines to specify distribution data in the data section. 

The meaning of integers for conditional variables can could be found in the Profile Function file

(Unit 20).

The user needs to specify the following parameters for a set of distribution data:

• Shape — distribution type

• Min — minimum;

• Max — maximum;

• Par1; and 

• Par2.

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

distribution types—point, uniform, normal, lognormal, triangle, and exponential—plus userdefined functions. APEX only examines the first letter of a distribution type. The user could

specify a shape value by using P, U, N, L, T, and E or spelling out the name of a distribution

type. The letter M is for the user-defined functions.

A real value must be specified for a required parameter. The parameters that are not used for

specifying a distribution should be marked with a period (see Figure 5-16).

The optional Min and Max parameters are used only when the user wants to set the limits to

selected random values from a distribution. If the optional parameters are not used, they should

be marked with a period as well. Note that the Par1 and Par2 represent different parameters for

different distribution types. 

Table 5-10 lists the required and optional parameters for specifying each type of distribution.
DRAFT—APRIL 24, 2003 90

Table 5-10. Uses of Distribution Parameters for Each Standard 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 Required (Standard Deviation)

Lognormal Optional Optional Geometric Mean –

Required

Geometric Standard Deviation

– Required

Triangle Required Required Mode – Required Not used

Exponential Required Optional Mean – Required No used
DRAFT—APRIL 24, 2003 91

6. OUTPUT FILES

APEX produces the following six output files:

1. Log file;

2. Hourly Exposure file;

3. Hourly Dose file;

4. Profile Summary file;

5. Microenvironment Summary file;

6. Output Table file; and

7. Sites file.

Most of these output files can be opened and reviewed using a text editor. The larger files,

Hourly Exposure and Hourly Dose, may need a spreadsheet or other program. Details of each

of these output files are provided in the following sections. Additional discussion and a

description of how these files were set up in the Params file can be found in Sections 5.2.1 and

5.2.4.

6.1 Log File (Unit 25)

The Log file records the following information as a model run progresses:

• Input files used;

• Model parameters used;

• Number of diaries available to match each simulated person (or profile);

• Model execution time (sec.);

• Sectors in the study area;

• Air districts in the study area;

• Temperature zones in the study area:

• Mappings of sectors to air districts and temperature zones;

• Statistical summaries of each simulated person (or profile); and 

• Output summary tables.

If a model run stops abnormally, a error message will be written to the log file. The user should

review the Log file after a run to ensure that a model run is executed and terminated normally

and the output results are valid. Note that output summary tables in this file are exactly the

same as the tables in the Output Table file.

6.2 Hourly Exposure File (Unit 26)

The Hourly Exposure file contains hourly time series of exposure concentrations for each

simulated person or profile. Each record provides 24 hourly exposure values for a simulation

day in the following format:

• Profile number—Sequential index number for simulated individual

• Day number—Sequential index number for the day of the simulation

• Year—Part of the date for the simulated day

• Month—Part of the date for the simulated day

• Day—Part of the date for the simulated day
DRAFT—APRIL 24, 2003 92

• Hour 1—Mean exposure concentration from midnight to 1 a.m. for the profile on that day

• Hour 2, etc.—Mean exposure concentration from 1 a.m. to 2 a.m., etc.

• Hour 24—Mean exposure concentration from 11 p.m. to midnight 

The units of exposure concentrations are the same as those of the air quality data. Note that

the hourly exposure file could be very large if a large number of profiles are simulated. The user

could block generation of the hourly exposure file by setting the HourlyOut parameter to NO in

the parameter file.

6.3 Hourly Dose File (Unit 27)

The Hourly Dose file contains hourly time series of doses for each simulated person or profile. 

In APEX, dose is defined as the percent of carboxyhemoglobin (%COHB) in the blood. Thus,

as currently formulated, APEX calculates dose only for carbon monoxide.

Each record in Hourly Dose provides 24 hourly dose values for a simulation day in the same

format as the Hourly Exposure file, except the hourly values are in dose rather than

concentration.

The hourly dose file could be very large if a large number of profiles are simulated. The user

could block generation of the hourly dose file by setting the HourlyOut parameter to NO in the

parameter file.

6.4 Profile Summary File (Unit 28)

This file provides a summary of profile characteristics and exposure/dose for each simulated

person. Each record contains values for the following variables for each simulated individual:

• Profile number—Sequential index number for simulated individual

• HSect—Sector in which the person lives (home)

• WSect—Sector in which the person works (=HSect for non-workers)

• HDis—Air quality district for HSect 

• WDis—Air quality district for WSect

• Zone—Temperature zone for HSect 

• DGRP—Demographic group # (1-11) as defined in pNEM/CO

• Age—Age (years)

• Gender—Male or female

• Race—Such as White, Black, Asian, Native American (NatAm), Other (depending on pop.

files)

• Employed—Indicates employment outside the home

• Stove—Indicates the presence of a gas stove in the home

• Pilot—Indicates the presence of a gas pilot light

• ACHom—Indicates the presence of air conditioning in the home

• ACCar—Indicates the presence of air conditioning in the car

• Height—Person height (inches)

• Weight—Body mass (pounds)

• Hemoglob—The amount of hemoglobin in the blood (g/ml)

• DiffDay—A lung diffusivity parameter used in the COHB calculation (ml/min/torr)

• BloodVol—The volume of blood in the body (ml)

• HemFac—The change in the hemoglobin level with altitude (1/ft)
DRAFT—APRIL 24, 2003 93

• Endgn1—Endogenous CO production rate (ml/min)

• Endgn2—Endogenous CO production rate (ml/min) used only for women between ages of

12 and 50 for half the menstrual cycle

• #Events—Number of diary events covering the simulation period

• AvgExp—Mean exposure concentration over the simulation (ppm or μg/m3

, as specified in

Params file)

• AvgDose—Mean dose over the simulation (blood %COHB level) 

• MaxExp—Maximum 1-hour exposure concentration over the simulation (ppm or μg/m3

, as

specified in Params file)

• MaxDose—Maximum 1-hour average dose over the simulation (blood %COHB level) 

The file can be opened using a text editor or imported into a spreadsheet or other file type.

6.5 Microenvironment Summary File (Unit 29)

This file provides the amount of time spent, mean exposure concentration, and maximum

exposure concentration in each microenvironment during the period of simulation, for each

simulated person. Each record in the file contains the following variables:

• Profile number—Sequential index number for simulated individual

• Microenvironment—Sequential index number for each microenvironment

• Minutes—Total time spent in the microenvironment by the profile

• MeanConc—Average (mean) concentration during the time spent in the microenvironment

(ppm or μg/m3

, as specified in Params file)

• MaxConc—Maximum concentration during the time spent in the microenvironment (ppm or

μg/m3

, as specified in Params file)

6.6 Output Tables File (Unit 30)

This file provides 14 summary tables. The first six are exposure summary tables:

1. Non-cumulative person-minutes at each exposure range by microenvironment

2. Cumulative person-minutes at each exposure level by microenvironment

3. Cumulative person-days of daily max 1-hour exposure levels

4. Cumulative person-days of daily max 8-hour exposure levels

5. Cumulative person-days of daily average exposure levels

6. Cumulative # persons at each overall (simulation) average exposure level

The remaining eight are dose summary tables:

1. Cumulative person-days of daily max end-of-hour dose levels

2. Cumulative person-days of daily max 1-hour average dose levels

3. Cumulative person-days of daily max 8-hour average dose levels

4. Cumulative person-days of daily average dose levels

5. Cumulative # persons at each overall (simulation) average dose level

6. Cumulative person-hours at each end-of-hour dose level

7. Non-cumulative person-minutes at each dose level

8. Cumulative person-minutes at each dose level
DRAFT—APRIL 24, 2003 94

There are two types of tables in this file: cumulative and non-cumulative time tables. The

cumulative time tables summarize the minutes or days spent by simulated individuals when the

exposure concentration in a microenvironment is equal to or greater than various threshold

levels. The non-cumulative time tables summarize the minutes or days spent by simulated

individuals when exposure is within various ranges. 

In the follow sections, each of the 14 summary tables is discussed in more detail.

Table #1—Non-cumulative minutes at each exposure range by microenvironment

This table lists the total minutes spent by all simulated persons in each microenvironment when

exposure concentration is within various ranges. The bounds of a range are specified at the top

of each column and the top of the next column to the right (Figure 6-1) . For each

microenvironment, the table provides three rows of data for the following three variables:

• Minutes—The number of person-minutes summed over all the simulated persons that are

spent in the specified microenvironment and that fall within the exposure concentration

range bounded by the values indicated at the top of the column and the top of the next

column to the right;

• Row_%—The percent of the minutes spent in the specified microenvironment that fall within

the exposure concentration range; and

• Tot_%—the percent of the total minutes that are spent in the microenvironment and that fall

within the exposure concentration range. 

Figure 6-1. Example Portion of Table #1 in Output Table File (“Truncated” View)a

Non-cumulative minutes at each exposure level by microenvironment, for

N = 2000 Profiles

 

 Level: 0.0000 2.0000 4.0000 6.0000 8.0000 

 Micro ---------------------------------------------------------------------

 0 Minutes 0. 0. 0. 0. 0.

 0 Row_% NaN NaN NaN NaN NaN 

 0 Tot_% 0.0000 0.0000 0.0000 0.0000 0.0000 

 1 Minutes 37602112. 35900088. 32791732. 26165204. 18736580. 

 1 Row_% 21.2988 20.3347 18.5741 14.8206 10.6129 

 1 Tot_% 1.2713 1.2138 1.1087 0.8846 0.6335

 2 Minutes 3911714. 2669300. 1258124. 678414. 431163. 

 2 Row_% 40.9246 27.9264 13.1626 7.0976 4.5109 

 2 Tot_% 0.1323 0.0902 0.0425 0.0229 0.0146 

 3 Minutes 951714. 823961. 593331. 397681. 262075. 

 3 Row_% 28.5896 24.7519 17.8237 11.9464 7.8728 

 3 Tot_% 0.0322 0.0279 0.0201 0.0134 0.0089

a

Because this table is truncated (i.e., it actually extends to the right), not all columns are visible and

Row_% does not add to 100%.
DRAFT—APRIL 24, 2003 95

Table #2—Cumulative minutes at each exposure level by microenvironment

This table is similar to Table #1, except that it reports the cumulative person-minutes that are

spent in a microenvironment with an exposure concentration that equals or exceeds the value

indicated at the top of the column.

Tables #3—Cumulative Person-days of Daily Max 1-Hour Exposure levels 

This table provides a statistical summary of the cumulative person-days, for both simulated

persons and the population in the study area, with a daily maximum 1-hour (hourly) average

exposure concentration that equals or exceeds the value indicated at the top of the column

(Figure 6-2). The definitions of variables in Table #3 are provided in Table 6-1.

Figure 6-2. Example of Table #3 in Output Table File

 Cumulative Person-days of Daily Max 1-Hour Exposure levels for N = 2000 Profiles, 

with Area Population = 44732 

 Level: 0.000 5.000 10.000 20.000 30.000 40.000 

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

 Counts(Pop): 0.164E+08 0.158E+08 0.803E+07 0.845E+06 0.311E+06 0.155E+06 

 #Meet(Pop) : 44732 44732 44732 44732 44732 42741

 %Meet(Pop) : 100.000 100.000 100.000 100.000 100.000 95.550

 Mean : 366.000 353.592 179.612 18.880 6.945 3.462 

 Std.Dev. : 0.000 28.169 116.865 9.762 4.155 2.572 

 CV : 0.000 0.080 0.651 0.517 0.598 0.743

 Minimum : 366.000 197.000 27.000 2.000 1.000 0.000 

 10.0 %ile : 366.000 321.000 62.000 9.000 3.000 1.000 

 25.0 %ile : 366.000 360.000 81.000 12.000 4.000 2.000 

 50.0 %ile : 366.000 365.000 127.000 17.000 6.000 3.000 

 75.0 %ile : 366.000 366.000 315.000 23.000 9.000 5.000 

 90.0 %ile : 366.000 366.000 363.000 31.900 12.000 7.000 

 95.0 %ile : 366.000 366.000 365.000 39.000 15.000 9.000 

 99.0 %ile : 366.000 366.000 366.000 52.000 21.000 12.990 

 Maximum : 366.000 366.000 366.000 74.000 27.000 18.000 

 Mean (%) : 100.000 96.610 49.074 5.158 1.898 0.946 

 Min (%) : 100.000 53.825 7.377 0.546 0.273 0.000 

 Max (%) : 100.000 100.000 100.000 20.219 7.377 4.918 

 Counts(Sim): 0.732E+06 0.707E+06 0.359E+06 0.378E+05 0.139E+05 0.692E+04 

 #Meet(Sim) : 2000 2000 2000 2000 2000 1911

Table #4—Cumulative person-days of daily max 8-hour exposure levels

This table provides a statistical summary of the cumulative person-days, for both simulated

persons and the population in the study area, with a daily maximum 8-hour average exposure

concentration that equals or exceeds specified levels. The table is the same as Table #3

(Figure 6-2) except that the exposure metric is the daily max 8-hour average exposure

concentration.
DRAFT—APRIL 24, 2003 96

Table 6-1. Definitions of Variables in Summary Tables

Counts(pop) The number of study area person-days for which the exposure metric is equal to or

greater than the level specified at the top of each column of the table.

#Meet(pop) The number of persons in the study area whose exposure metric is equal to or

greater than the level specified at the top of each column at least once during the

period of simulation.

%Meet(pop) The percent of the population in the study area whose exposure metric is equal to or

greater than the level specified at the top of each column at least once during the

period of simulation.

Mean The average of the number of days for each simulated person for which the exposure

metric is equal to or greater than the level specified at the top of each column.

Std. Dev. The standard deviation of the number of days for each simulated person for which

exposure metric is equal to or greater than the level specified at the top of each

column.

CV The coefficient of variation, which is the ratio of the standard deviation to the mean.

Minimum The minimum number of days for any simulated person for which the exposure

metric is equal to or exceeds the level specified at the top of each column.

<n>%ile The nth percentile value of the number of days for each simulated person for which

the exposure metric is equal to or exceeds the level specified at the top of each

column.

Maximum The maximum number of days for any simulated person for which the exposure

metric is equal to or exceeds the level specified at the top of each column.

Mean(%) The average percent of days during the period of simulation for which a simulated

person’s exposure metric is equal to or exceeds the level specified at the top of each

column. (This variable is calculated by dividing the Mean by the total number of days

in the simulation period and then multiplying by 100.)

Minimum(%) This variable represents the minimum percent of days during the period of simulation,

in which a simulated person experience exposure at or above a level specified at the

top row of each column of a table. This variable is calculated by dividing the

Minimum by the total number of days in the simulation period and then multiplying by

100.

Maximum(%) The maximum percent of days during the period of simulation for which a simulated

person’s exposure metric is equal to or exceeds the level specified at the top of each

column. (This variable is calculated by dividing the Maximum by the total number of

days in the simulation period and then multiplying by 100.)

Counts(Sim) The number of simulated person-days for which the exposure metric is equal to or

exceeds the level specified at the top of each column.

#Meet(Sim) The number of simulated persons for whom the exposure metric is equal to or

exceeds the level specified at the top of each column at least once during the period

of simulation.
DRAFT—APRIL 24, 2003 97

Note that Table #7 through #13 are

produced only for CO dose calculation. 

For other pollutants, dose calculation

should be turned off.

Table #5—Cumulative person-days of daily average exposure levels

This table provides a statistical summary of the cumulative person-days, for both simulated

persons and the population in the study area, with a daily average exposure concentration that

equals or exceeds specified levels. The table is the same as Table #3 (Figure 6-2) except that

exposure metric is the daily average exposure concentration.

Table #6—Cumulative number of persons at each overall average exposure level

This table provides a statistical summary of cumulative numbers of both simulated persons and 

people in the study area whose overall average exposure concentrations equal or exceed

specified levels. The overall average exposure concentration is the average of hourly exposure

concentrations over the whole period of simulation. An example of this table is provided in

Figure 6-3. The definitions of the variables in this example can be found in Table 6-1.

Figure 6-3. Example of Table #6 in the Output Table File

 Cumulative # Persons at each Overall Average Exposure level for N = 2000 Profiles,

with Area Population = 44732 

 Level: 0.000 0.500 1.000 1.250 1.500 1.750 

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

 Counts(Pop): 0.447E+05 0.447E+05 0.447E+05 0.447E+05 0.447E+05 0.447E+05 

 #Meet(Pop) : 44732 44732 44732 44732 44732 44732 

 %Meet(Pop) : 100.000 100.000 100.000 100.000 100.000 100.000

 Counts(Sim): 0.200E+04 0.200E+04 0.200E+04 0.200E+04 0.200E+04 0.200E+04 

 #Meet(Sim) : 2000 2000 2000 2000 2000 2000

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

Table #7—Cumulative person-days of daily max

end-of-hour dose levels

This table provides a statistical summary of the

cumulative person-days for both simulated persons

and the population in the study area, for which the

daily maximum end-of-hour dose is equal to or exceeds specified levels. The format of the table

is the same as Table #3 (Figure 6-2). The definitions of the variables in this table can be found

in Table 6-1.

Table #8—Cumulative person-days of daily max 1-hour dose levels

This table provides a statistical summary of the cumulative person-days, for both simulated

persons and the population in the study area, for which the daily maximum 1-hour average dose

is equal to or exceeds specified levels. The format of the table is the same as Table #3 (Figure

6-2). The definitions of the variables in this table can be found in Table 6-1.

Table #9—Cumulative person-days of daily max 8-hour dose levels

This table provides a statistical summary of the cumulative person-days, for both simulated

persons and the population in the study area, for which the daily maximum 8-hour average dose

is equal to or exceeds specified levels. The format of the table is the same as Table #3 (Figure

6-2). The definitions of variables in this table can be found in Table 6-1.
DRAFT—APRIL 24, 2003 98

Table #10—Cumulative person-days of daily average dose levels

This table provides a statistical summary of the cumulative person-days, for both simulated

persons and the population in the study area, for which the daily average dose is equal to or

exceeds specified levels. The format of the table is the same as Table #3 (Figure 6-2). The

definitions of the variables in the table can be found in Table 6-1.

Table #11—Cumulative # persons at each overall average dose level

This table provides a statistical summary of cumulative numbers of both simulated persons and

the people in the study area whose overall average doses are equal to or exceed a specified

level. The overall average dose is the average of hourly dose levels over the whole period of

simulation. The format of this table is the same as Table #6 (Figure 6-3). The definitions of the

variables in this table can be found in Table 6-1.

Table #12—Cumulative person-hours at each end-of-hour dose level

This table provides a statistical summary of the number of person-hours, for both simulated

persons and the population in the study area, for which each end-of-hour dose level is equal to

or exceeds specified levels. The format of this table is the same as Table #3 (Figure 6-2). The

definitions of the variables in the table can be found in Table 6-1, except that the time units are

hours rather than days.

Table #13—Non-cumulative minutes at each dose level

This table provides a statistical summary of cumulative person-minutes, for both simulated

persons and the population in the study area, for which the dose (i.e., blood %COHb level) is

within a specified range. The bounds of the dose range are specified by the levels at the top of

each column and the top of the next column to the right. The format of this table is similar to

the exposure summary table, Table #3 (Figure 6-2). The definitions of the variables in this table

are similar to those found in Table 6-1, except that the time units are minutes rather than days. 

Table #14—Cumulative minutes at each dose level

This table provides a statistical summary of cumulative person-minutes spent by both simulated

persons and the population in the study area, for which the dose (i.e., blood %COHb level) is

equal to or exceeds specified levels. The format of this table is the same as Table #3 (Figure 6-

2). The definitions of the variables in this table are similar to those found in Table 6-1, except

that the time units are minutes rather than days

6.7 Sites File (Unit 31)

The Sites output file lists the sectors, districts, and zones in the study area, and identifies the

mapping between them. Thus, each record contains the following:

• Sector#—Sector ID

• Latitude—Sector latitude

• Longitude—Sector longitude

• Sectorname—Sector name

• Air#—Air district ID
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• Airdistance—Distance from district to sector

• Airlatitude—Air district latitude

• Airlongitude—Air district longitude

• Airname—Air district name

• Tem#—Tempurature zone ID

• Temdistance—Distance from zone to sector

• Temlatitude—Zone latitude

• Temlongitude—Zone longitude

• Temname—Zone name
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