ESTIMATION OF CARBON MONOXIDE EXPOSURES

AND ASSOCIATED CARBOXYHEMOGLOBIN LEVELS

FOR RESIDENTS OF DENVER AND LOS ANGELES

USING pNEM/CO (VERSION 2.1)

APPENDICES

by

Ted Johnson, Gary Mihlan, Jacky LaPointe, and Kris Fletcher

TRJ Environmental, Inc.

713 Shadylawn Road

Chapel Hill, NC 27514

Jim Capel

1005 Demerius Street

Durham, NC 27701

Arlene Rosenbaum, Jonathan Cohen, and Pat Stiefer

ICF Consulting

101 Lucas Valley Road

San Rafael, CA 94903

Contract No. 68-D6-0064

Work Assignment Nos. 1-19, 2-24, 2-30, and 3-3

Arlene Rosenbaum, Project Manager

Prepared for

Harvey Richmond, EPA Work Assignment Manager

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Research Triangle Park, North Carolina 27711

August 2000
CONTENTS

A. Distribution of Energy Expenditure Rates Associated with CHAD Location 

Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

B. Fixed-Site Monitors in Colorado and California Reporting CO Data to AIRS 

for One or More Years Between 1993 and 1997 . . . . . . . . . . . . . . . . . . . . . . B-1

C. Distributions and Equations Used in the Ventilation Rate Algorithm . . . . . . . C-1

D. Equations for Converting Weight to Height Proposed for the 1998 Version 

pNEM/CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1

E. Algorithm for Estimating Carboxyhemoglobin Levels . . . . . . . . . . . . . . . . . . . E-1

F. Sensitive Population Estimates for Use in Carbon Monoxide Exposure 

Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F-1

G. Algorithm for Air Exchange Rates Inside Motor Vehicles in pNEM/CO . . . . . G-1

H. Analyses Supporting Proposed Modifications to the Mass Balance Algorithm 

and Gas Stove Algorithm in pNEM/CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H-1

I. Distributions for Use with the Mass Balance and Gas Stove Algorithms in

Applying pNEM/CO to Los Angeles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I-1

J. Differences in Human Activity Patterns Between Individuals With and Without

Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J-1

K. Detection of Scenario Differences in Exposure Measures of pNEM/CO

Simulations with Statistical Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K-1
Appendix A

Distribution of Energy Expenditure Rates

Associated with CHAD Location Descriptors

A-1
Distributions for Energy Expenditure Rates By Activity Code, Age, and Occupation (if applicable).

Notes:

1. Activities coded as 10... are activities with codes beginning with 10. 

2. OCC: occupational categories.

3. DN: distribution number

4. DL: distribution type (T = triangular, N = normal, U = uniform, E = exponential, P = point) 

5. Activities starting with 17... are calculated based on age. 

Age = 1 if respondent < 25 years

Age = 2 if respondent 25 - 39 years

Age = 3 if respondent > 40 years

ACTIVITY AGE OCC DN DL MEAN MED SD MIN MAX FLAG LEFT RIGHT

10... X ADMIN 4 L 1.7 1.7 0.3 1.4 2.7 0 0.16 0.01

10... X PROF 5 T 2.9 2.7 1 1.2 5.6 0 0 0

10... X

ADMSU

P 4 L 1.7 1.7 0.3 1.4 2.7 0 0.16 0.01

10... X TECH 5 T 3.3 3.3 0.4 2.5 4.5 1 0 0

10... X TRANS 4 L 3.3 3 1.5 1.3 8.4 1 0.03 0.01

10... X SALE 5 T 2.9 2.7 1 1.2 5.6 0 0 0

10... X SERV 5 T 5.2 5.3 1.4 1.6 8.4 1 0 0

10... X HSHLD 4 L 3.6 3.5 0.8 2.5 6 1 0.07 0.01

10... X

PROTE

CT 5 T 2.9 2.7 1 1.2 5.6 0 0 0

10... X PREC 5 T 3.3 3.3 0.4 2.5 4.5 1 0 0

10... X MACH 2 U 5.3 5.3 0.7 4 6.5 1 0 0

10... X FARM 4 L 7.5 7 3 3.6 17 1 0.04 0.01

10... X LABOR 5 T 8.5 8.4 2.1 3.6 13.8 1 0 0

17100 1 X 4 L 5.7 5 3 1.4 16 1 0.01 0.01

17100 2 X 1 N 5 5 2 1 9 1 0.02 0.02

17100 3 X 1 N 4.5 4.5 1.4 1.7 7.3 1 0.02 0.02

17110 1 X 4 L 3.6 3.2 1.9 1.4 10 1 0.05 0.01

17110 2 X 4 L 3.6 3.2 1.9 1.4 10 1 0.05 0.01

17110 3 X 4 L 3.4 3 1.7 1.4 9 1 0.05 0.01

17111 1 X 1 N 5.6 5.6 2.1 1.4 9.8 1 0.02 0.02

17111 2 X 1 N 5.8 5.8 2.4 1 10.6 1 0.02 0.02

17111 3 X 1 N 4.7 4.7 1.8 1.1 8.3 1 0.02 0.02

17112 1 X 2 U 3.8 3.8 1 2 5.5 1 0 0

17112 2 X 2 U 3.8 3.8 1 2 5.5 1 0 0

17112 3 X 2 U 3.5 3.5 0.9 2 5 1 00

17120 1 X 4 L 4.2 3.9 1.5 2 9 1 0.03 0.01

17120 2 X 4 L 4.2 3.9 1.5 2 9 1 0.03 0.01

17120 3 X 6 P 3.5 3.5 . . . 1 . .

17121 1 X 4 L 4.2 3.9 1.5 2 9 1 0.03 0.01

17121 2 X 4 L 4.2 3.9 1.5 2 9 1 0.03 0.01

17121 3 X 6 P 3.5 3.5 . . . 1 . .

17130 1 X 4 L 5.8 5.5 1.8 1.8 11.3 1 0 0.01

17130 2 X 1 N 5.7 5.7 1.8 2.1 9.3 1 0.02 0.02

17130 3 X 1 N 4.7 4.7 1.2 2.3 7.1 1 0.02 0.02

17131 1 X 4 L 5.8 5.5 1.8 1.8 11.3 1 0 0.01

17131 2 X 1 N 5.7 5.7 1.8 2.1 9.3 1 0.02 0.02

17131 3 X 1 N 4.7 4.7 1.2 2.3 7.1 1 0.02 0.02

A-2
ACTIVITY AGE OCC DN DL MEAN MED SD MIN MAX FLAG LEFT RIGHT

17140 1 X 1 N 5.3 5.3 1.8 1.7 8.9 1 0.02 0.02

17140 2 X 1 N 5.2 5.2 1.7 1.7 8.9 1 0.02 0.01

17140 3 X 1 N 3.8 3.8 1 1.8 5.8 1 0.02 0.02

17144 1 X 1 N 5.3 5.3 1.8 1.7 8.9 1 0.02 0.02

17144 2 X 1 N 5.2 5.2 1.7 1.7 8.9 1 0.02 0.01

17144 3 X 1 N 3.8 3.8 1 1.8 5.8 1 0.02 0.02

17180 1 X 4 L 6.6 5.9 3.2 2 17.4 1 0.01 0.01

17180 2 X 1 N 6 6 2 2 10 1 0.02 0.02

17180 3 X 1 N 4.8 4.8 1.4 2 7.6 1 0.02 0.02

10200 X X 2 U 1.8 1.8 0.4 1 2.5 0 0 0

10300 X X 2 U 1.8 1.8 0.4 1 2.5 0 0 0

11000 X X 5 T 4.7 4.6 1.3 1.5 8 1 00

11100 X X 4 L 2.6 2.5 0.5 2 4 0 0.13 0.01

11110 X X 3 E 2.8 2.5 0.9 1.9 4 0 0 0.02

11200 X X 3 E 3.4 3 1.4 2 5 1 0 0.01

11210 X X 2 U 2.5 2.5 0.1 2.3 2.7 0 0 0

11220 X X 3 E 4.1 3.5 1.9 2.2 5 1 0 0.01

11300 X X 1 N 5 5 1 2 7 1 0 0.02

11310 X X 3 E 5.3 4.5 2.7 2.6 6 1 00

11400 X X 3 E 2.2 2 0.7 1.5 4 0 0 0.02

11410 X X 6 P 2 2 . . . 0 . .

11500 X X 6 P 2 2 . . . 0 . .

11600 X X 1 N 4.5 4.5 1.5 2 8 1 0.05 0.01

11610 X X 6 P 4.5 4.5 . . . 1 . .

11620 X X 3 E 4.9 4.5 1.4 3.5 6 1 00

11630 X X 5 T 3.5 3.4 0.4 3 4.5 1 0 0

11640 X X 3 E 4.7 4.5 0.7 4 6 1 00

11650 X X 2 U 4.5 4.5 1.4 2 7 1 00

11700 X X 2 U 3.5 3.5 0.9 2 5 1 00

11800 X X 2 U 3.3 3.3 0.1 3 3.5 1 0 0

11900 X X 3 E 6.6 5.5 3.6 3 9 1 00

12000 X X 4 L 3.1 3 0.7 2.5 5 1 0.2 0.01

12100 X X 2 U 3.3 3.3 0.1 3 3.5 1 0 0

12200 X X 2 U 3.3 3.3 0.1 3 3.5 1 0 0

12300 X X 2 U 2.8 2.8 0.1 2.5 3 0 00

12400 X X 2 U 2.8 2.8 0.1 2.5 3 0 00

12500 X X 2 U 2.8 2.8 0.1 2.5 3 0 00

12600 X X 2 U 4.5 4.5 0.3 4 5 1 00

12700 X X 2 U 3.2 3.2 0.1 3 3.3 1 0 0

12800 X X 2 U 3 3 0.3 2.5 3.5 1 0 0

13000 X X 5 T 3.8 3.7 0.8 2 6 1 00

13100 X X 2 U 3.3 3.3 0.4 2.5 4 1 00

13200 X X 5 T 3.7 3.6 0.8 2 6 1 00

13210 X X 5 T 3.9 3.8 0.8 2.2 6 1 00

13220 X X 2 U 3.4 3.4 0.6 2.3 4.5 1 0 0

13230 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 0 0

13300 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 0 0

13400 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 0 0

13500 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 0 0

13600 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 0 0

A-3
ACTIVITY AGE OCC DN DL MEAN MED SD MIN MAX FLAG LEFT RIGHT

13700 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 0 0

13800 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 0 0

14000 X X 2 U 2 2 0.6 1 3 0 00

14100 X X 1 N 2 2 0.3 1 4 0 00

14110 X X 2 U 3 3 0.6 2 4 1 00

14120 X X 2 U 1.8 1.8 0.4 1 2.5 0 0 0

14200 X X 2 U 1.8 1.8 0.4 1 2.5 0 0 0

14300 X X 4 L 3.1 3 0.7 2.5 5 1 0.2 0.01

14400 X X 2 U 1.8 1.8 0.1 1.5 2 0 00

14500 X X 4 L 0.9 0.9 0.1 0.8 1.1 0 0.09 0.01

14600 X X 6 P 2.5 2.5 . . . 0 . .

14700 X X 5 T 2 2 0.4 1 2.9 0 0 0

15000 X X 4 L 1.9 1.8 0.7 1.4 4 0 0.23 0.01

15100 X X 2 U 2.1 2.1 0.4 1.4 2.8 0 0 0

15110 X X 2 U 2.3 2.3 0.4 1.5 3 0 00

15120 X X 2 U 2.1 2.1 0.4 1.4 2.8 0 0 0

15130 X X 2 U 2 2 0.3 1.4 2.5 0 0 0

15140 X X 2 U 1.8 1.8 0.2 1.4 2.2 0 0 0

15200 X X 2 U 2.2 2.2 0.5 1.4 3 0 00

15300 X X 6 P 1.8 1.8 . . . 0 . .

15400 X X 2 U 2.3 2.3 0.4 1.5 3 0 00

15500 X X 2 U 2.8 2.8 0.7 1.5 4 0 00

16000 X X 4 L 2.2 2 1.1 1 6 0 0.07 0.01

16100 X X 2 U 2.7 2.7 0.8 1.4 4 0 00

16200 X X 2 U 1.7 1.7 0.2 1.4 2 0 00

16210 X X 2 U 1.7 1.7 0.2 1.4 2 0 00

16300 X X 2 U 1.3 1.3 0.2 1 1.6 0 0 0

16400 X X 2 U 1.7 1.7 0.4 1 2.3 0 0 0

16500 X X 2 U 2.5 2.5 0.3 2 2.9 0 0 0

16600 X X 2 U 1.5 1.5 0.3 1 1.9 0 0 0

16700 X X 4 L 3.3 3 1.4 1.5 8 1 0.05 0.01

16800 X X 4 L 3.3 3 1.4 1.5 8 1 0.05 0.01

16900 X X 2 U 3.8 3.8 1.3 1.5 6 1 00

17113 X X 2 U 3 3 0.6 2 4 1 00

17114 X X 5 T 3.1 3.2 0.6 1.4 4 1 00

17122 X X 2 U 1.5 1.5 0.2 1.2 1.8 0 0 0

17141 X X 5 T 2.8 2.7 0.8 1.5 5 0 00

17142 X X 5 T 2 1.9 0.4 1.5 3 0 00

17143 X X 2 U 2.5 2.5 0.3 2 3 0 00

17150 X X 5 T 3.3 3.2 0.6 2.4 5 1 00

17160 X X 2 U 1.6 1.6 0.2 1.2 2 0 00

17170 X X 2 U 5 5 1.7 2 8 1 00

17200 X X 4 L 1.3 1.3 0.3 1 2.3 0 0.14 0.01

17210 X X 2 U 1.5 1.5 0.2 1.2 1.8 0 0 0

17211 X X 2 U . . . 1.2 . 0 0 .

17212 X X 2 U . . . 1.2 . 0 0 .

17213 X X 2 U . . . 1.2 . 0 0 .

17214 X X 2 U . . . 1.2 . 0 0 .

17215 X X 2 U . . . 1.2 . 0 0 .

17216 X X 2 U 2.7 2.7 0.8 1.4 4 0 00

A-4
ACTIVITY AGE OCC DN DL MEAN MED SD MIN MAX FLAG LEFT RIGHT

17220 X X 4 L 1.2 1.2 0.4 0.9 2.3 0 0.15 0.01

17221 X X 2 U 1.2 1.2 0.1 1 1.3 0 0 0

17222 X X 2 U 1.9 1.9 0.2 1.5 2.3 0 0 0

17223 X X 6 P 1 1 . . . 0 . .

17230 X X 2 U 1.3 1.3 0.2 1 1.6 0 0 0

17231 X X 2 U 1.3 1.3 0.2 1 1.6 0 0 0

17232 X X 2 U 1.3 1.3 0.2 1 1.6 0 0 0

17233 X X 2 U 1.3 1.3 0.2 1 1.6 0 0 0

17240 X X 2 U 1.4 1.4 0.2 1 1.8 0 0 0

17241 X X 2 U 1.4 1.4 0.2 1 1.8 0 0 0

17242 X X 2 U 1.4 1.4 0.2 1 1.8 0 0 0

17250 X X 2 U 1.2 1.2 0.1 1 1.3 0 0 0

17260 X X 2 U 1.9 1.9 0.2 1.5 2.3 0 0 0

17300 X X 2 U 1.5 1.5 0.2 1.2 1.8 0 0 0

18000 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01

18100 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01

18200 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01

18300 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01

18400 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01

18500 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01

18600 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01

18700 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01

18800 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01

18900 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01

18910 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01

18920 X X 4 L 2.3 2 1.3 1.8 7 0 0.42 0.01

A-5
CHAD Activity Codes

<10> Work and Other Income Producing Activities

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

<11> Household Activities

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 for plants

11800: care for pets/ animals

11900: other household

<12> Child Care

12000: child care, general

12100: care of baby

12200: care of child

12300: help/teach

12400: talk/read

12500: play indoors

A-6
12600: play outdoors

12700: medical care-child

12800: other child care

<13> Obtain Goods and Services

13000: obtain goods and services, general

13100: dry clean

13200: shop/ run errands, general

 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 service

13700: other repairs

13800: other services

<14> Personal Needs and Care

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

<15> Education and Professional Training

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

<16> Entertainment/ Social Activities

16000: general entertainment/ social activities

16100: attend sports events

16200: participate in social, political, or religious activities

 16210: practice religion

16300: view movie

16400: attend theater

16500: visit museums

16600: visit

A-7
16700: attend a party

16800: go to bar/ lounge

16900: other entertainment/ social events

<17> Leisure

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 computer 

 17170: participate in recess and physical education

 17180: other sports and active leisure

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 t.v.

 17230: read, general

 17231: read books

 17232: read magazine/ 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

A-8
<18> Travel

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

A-9
Appendix B

Fixed-Site Monitors in Colorado and California Reporting

CO Data to AIRS for One or More Years Between 1993 and 1997

B-1
1DATE 98/06/25 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) 

 AMP450 AIR QUALITY SUBSYSTEM QUICK LOOK REPORT0 CARBON MONOXIDE (42101) COLORADO UNITS: 007 PPM0 P O M REP MAX 1-HR OBS> MAX 8 SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST  ________________________________________________________________________________________________________________008-001-3001 1 2 ADAMS CO 78TH AVE & STEELE ST - W 93 001 8632 9.0 8.2 0 6.6  08-001-3001 1 2 ADAMS CO 78TH AVE & STEELE ST - W 94 001 8687 9.0 8.8 0 6.4  08-001-3001 1 2 ADAMS CO 78TH AVE & STEELE ST - W 95 001 8681 8.5 8.1 0 5.7  08-001-3001 1 2 ADAMS CO 78TH AVE & STEELE ST - W 96 001 8712 6.2 6.2 0 3.9  08-001-3001 1 2 ADAMS CO 78TH AVE & STEELE ST - W 97 001 8661 8.3 6.6 0 5.0  08-001-7015 1 4 COMMERCE CITY ADAMS CO ROCKY MOUNTAIN ARSENAL 93 830 7919 7.7 6.6 0 4.3  08-001-7015 1 4 COMMERCE CITY ADAMS CO ROCKY MOUNTAIN ARSENAL 94 830 3595 3.2 2.5 0 1.2  08-005-0002 1 2 LITTLETON ARAPAHOE CO 8100 SO UNIVERSITY BLVD- 93 001 8589 7.0 6.5 0 4.9  08-005-0002 1 2 LITTLETON ARAPAHOE CO 8100 SO UNIVERSITY BLVD- 94 001 8705 4.6 4.2 0 2.9  08-005-0002 1 2 LITTLETON ARAPAHOE CO 8100 SO UNIVERSITY BLVD- 95 001 8670 3.6 3.3 0 2.6  08-005-0002 1 2 LITTLETON ARAPAHOE CO 8100 SO UNIVERSITY BLVD- 96 001 8677 4.3 4.1 0 2.9  08-005-0002 1 2 LITTLETON ARAPAHOE CO 8100 SO UNIVERSITY BLVD- 97 001 8463 4.3 4.0 0 3.0  08-005-0003 1 2 ENGLEWOOD ARAPAHOE CO 3300 S. HURON ST. 93 001 8717 11.5 10.9 0 8.4  08-005-0003 1 2 ENGLEWOOD ARAPAHOE CO 3300 S. HURON ST. 94 001 7126 7.5 7.3 0 4.9  08-013-0009 1 2 LONGMONT BOULDER CO 440 MAIN ST. LONGMONT 93 001 8701 9.5 9.0 0 7.0  08-013-0009 1 2 LONGMONT BOULDER CO 440 MAIN ST. LONGMONT 94 001 8557 12.6 12.2 0 6.7  08-013-0009 1 2 LONGMONT BOULDER CO 440 MAIN ST. LONGMONT 95 001 8690 14.0 8.8 0 5.6  08-013-0009 1 2 LONGMONT BOULDER CO 440 MAIN ST. LONGMONT 96 001 8735 11.3 9.2 0 5.5  08-013-0009 1 2 LONGMONT BOULDER CO 440 MAIN ST. LONGMONT 97 001 8617 9.6 9.1 0 5.7  08-013-0010 1 2 BOULDER BOULDER CO 2150 28TH STREET 93 001 353 7.6 7.6 0 5.1  08-013-0010 1 2 BOULDER BOULDER CO 2150 28TH STREET 94 001 8639 12.4 10.5 0 6.6  08-013-0010 1 2 BOULDER BOULDER CO 2150 28TH STREET 95 001 8608 10.6 10.3 0 5.3  08-013-0010 1 2 BOULDER BOULDER CO 2150 28TH STREET 96 001 8576 8.5 8.4 0 5.6  08-013-0010 1 2 BOULDER BOULDER CO 2150 28TH STREET 97 001 8697 9.0 8.2 0 5.5  08-013-1001 1 2 BOULDER BOULDER CO 2320 MARINE ST., BOULDER 93 001 8708 10.2 8.4 0 5.5  08-013-1001 1 2 BOULDER BOULDER CO 2320 MARINE ST., BOULDER 94 001 8565 7.8 6.0 0 2.8  08-013-1001 1 2 BOULDER BOULDER CO 2320 MARINE ST., BOULDER 95 001 8651 8.3 8.2 0 4.1  08-013-1001 1 2 BOULDER BOULDER CO 2320 MARINE ST., BOULDER 96 001 8669 4.5 4.3 0 2.5  08-013-1001 1 2 BOULDER BOULDER CO 2320 MARINE ST., BOULDER 97 001 8517 7.1 6.9 0 5.1  08-031-0002 2 1 DENVER DENVER CO 2105 BROADWAY - CAMP 93 001 8687 19.4 18.2 0 10.4  08-031-0002 2 1 DENVER DENVER CO 2105 BROADWAY - CAMP 94 001 8700 20.4 17.1 0 9.9  08-031-0002 2 1 DENVER DENVER CO 2105 BROADWAY - CAMP 95 001 8697 24.5 16.4 0 11.0  08-031-0002 2 1 DENVER DENVER CO 2105 BROADWAY - CAMP 96 001 8673 21.6 16.7 0 9.0  08-031-0002 2 1 DENVER DENVER CO 2105 BROADWAY - CAMP 97 001 8687 11.4 10.0 0 5.7  08-031-0013 1 2 DENVER DENVER CO 14TH AND ALBION ST. NJH- 93 001 8675 18.1 14.9 0 9.1  08-031-0013 1 2 DENVER DENVER CO 14TH AND ALBION ST. NJH- 94 001 8665 12.4 12.2 0 8.0  08-031-0013 1 2 DENVER DENVER CO 14TH AND ALBION ST. NJH- 95 001 8647 14.6 13.6 0 8.5  08-031-0013 1 2 DENVER DENVER CO 14TH AND ALBION ST. NJH- 96 001 8516 14.6 9.4 0 5.6  08-031-0013 1 2 DENVER DENVER CO 14TH AND ALBION ST. NJH- 97 001 8690 11.6 10.6 0 4.8  08-031-0014 1 1 DENVER DENVER CO 23 RD AND JULIAN_(CARRIA 93 001 8676 14.1 12.1 0 8.5  08-031-0014 1 1 DENVER DENVER CO 23 RD AND JULIAN_(CARRIA 94 001 8543 11.2 10.9 0 9.3  08-031-0014 1 1 DENVER DENVER CO 23 RD AND JULIAN_(CARRIA 95 001 8701 10.4 9.9 0 7.3  08-031-0014 1 1 DENVER DENVER CO 23 RD AND JULIAN_(CARRIA 96 001 8736 9.1 8.2 0 7.3  08-031-0014 1 1 DENVER DENVER CO 23 RD AND JULIAN_(CARRIA 97 001 8677 9.5 8.4 0 7.0 1DATE 98/06/25 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS)  AMP450 AIR QUALITY SUBSYSTEM QUICK LOOK REPORT0 CARBON MONOXIDE (42101) COLORADO UNITS: 007 PPM0 P

B-2
 O M REP MAX 1-HR OBS> MAX 8

 SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST  ________________________________________________________________________________________________________________008-031-0018 1 3 DENVER DENVER CO BLAKE ST. SIDE OF SPEER 93 001 1021 16.2 15.3 0 10.4  08-031-0018 1 3 DENVER DENVER CO BLAKE ST. SIDE OF SPEER 94 001 1591 12.2 11.6 0 7.8  08-031-0019 1 2 DENVER DENVER CO SPEER SIDE OF SPEER & AU 93 001 997 16.2 16.1 0 10.4  08-031-0019 1 2 DENVER DENVER CO SPEER SIDE OF SPEER & AU 94 001 3049 13.9 13.4 0 9.0  08-031-0019 1 2 DENVER DENVER CO SPEER SIDE OF SPEER & AU 95 001 3658 15.0 14.0 0 9.7  08-031-0019 1 2 DENVER DENVER CO SPEER SIDE OF SPEER & AU 96 001 8694 15.7 12.5 0 9.2  08-031-0019 1 2 DENVER DENVER CO SPEER SIDE OF SPEER & AU 97 001 8354 11.2 11.2 0 6.6  08-031-0020 1 3 DENVER DENVER CO 935 COLORADO BLVD., UCHS 94 001 1370 12.8 11.7 0 7.6  08-031-0020 1 3 DENVER DENVER CO 935 COLORADO BLVD., UCHS 95 001 2141 11.9 10.3 0 7.4  08-041-0004 1 2 COLORADO SPRING EL PASO CO 712 S TEJON ST 93 001 8716 11.7 10.7 0 5.6  08-041-0004 1 2 COLORADO SPRING EL PASO CO 712 S TEJON ST 94 001 8716 12.5 10.9 0 4.4  08-041-0004 1 2 COLORADO SPRING EL PASO CO 712 S TEJON ST 95 001 8698 10.4 9.6 0 5.2  08-041-0004 1 2 COLORADO SPRING EL PASO CO 712 S TEJON ST 96 001 8688 10.7 9.1 0 4.7  08-041-0004 1 2 COLORADO SPRING EL PASO CO 712 S TEJON ST 97 001 4658 10.4 8.2 0 4.7  08-041-0006 1 2 COLORADO SPRING EL PASO CO UINTAH & I-25 93 001 8660 13.6 11.6 0 6.1  08-041-0006 1 2 COLORADO SPRING EL PASO CO UINTAH & I-25 94 001 8578 11.7 11.4 0 5.4  08-041-0006 1 2 COLORADO SPRING EL PASO CO UINTAH & I-25 95 001 8716 10.8 10.3 0 7.2  08-041-0006 1 2 COLORADO SPRING EL PASO CO UINTAH & I-25 96 001 8746 11.3 11.3 0 7.6  08-041-0006 1 2 COLORADO SPRING EL PASO CO UINTAH & I-25 97 001 8577 13.1 10.6 0 5.9  08-041-6004 1 3 COLORADO SPRING EL PASO CO 6000 PULPIT ROCK DRIVE. 93 026 8341 4.1 3.9 0 2.1  08-041-6004 1 3 COLORADO SPRING EL PASO CO 6000 PULPIT ROCK DRIVE. 94 026 8709 4.0 3.9 0 2.3  08-041-6004 1 3 COLORADO SPRING EL PASO CO 6000 PULPIT ROCK DRIVE. 95 026 8724 4.2 3.8 0 2.3  08-041-6004 1 3 COLORADO SPRING EL PASO CO 6000 PULPIT ROCK DRIVE. 96 026 8544 3.8 3.4 0 2.1  08-041-6005 1 3 EL PASO CO 4940 S. HIGHWAY 85/87 93 026 8508 7.0 7.0 0 4.5  08-041-6005 1 3 EL PASO CO 4940 S. HIGHWAY 85/87 94 026 8655 6.8 6.2 0 4.6  08-041-6005 1 3 EL PASO CO 4940 S. HIGHWAY 85/87 95 026 8515 5.9 5.6 0 3.2  08-041-6005 1 3 EL PASO CO 4940 S. HIGHWAY 85/87 96 026 3064 6.1 5.6 0 3.0  08-041-6006 1 3 EL PASO CO 9400 CHIPITA PARK ROAD 93 026 8257 2.0 1.8 0 1.1  08-041-6006 1 3 EL PASO CO 9400 CHIPITA PARK ROAD 94 026 7620 2.0 1.9 0 1.2  08-041-6006 1 3 EL PASO CO 9400 CHIPITA PARK ROAD 95 026 7793 3.0 2.9 0 2.4  08-041-6006 1 3 EL PASO CO 9400 CHIPITA PARK ROAD 96 026 4148 2.1 2.1 0 1.8  08-041-6009 1 3 EL PASO CO R.D.NIXON POWER PLANT EX 93 026 6003 1.9 1.7 0 1.0  08-041-6009 1 3 EL PASO CO R.D.NIXON POWER PLANT EX 94 026 8702 2.2 1.9 0 1.3  08-041-6009 1 3 EL PASO CO R.D.NIXON POWER PLANT EX 95 026 8707 2.4 2.2 0 1.3  08-041-6009 1 3 EL PASO CO R.D.NIXON POWER PLANT EX 96 026 4328 1.5 1.4 0 .8  08-041-6011 1 3 COLORADO SPRING EL PASO CO 130 WEST CACHE LA POUDRE 93 026 8584 8.7 7.9 0 4.5  08-041-6011 1 3 COLORADO SPRING EL PASO CO 130 WEST CACHE LA POUDRE 94 026 8318 7.9 6.8 0 3.3  08-041-6011 1 3 COLORADO SPRING EL PASO CO 130 WEST CACHE LA POUDRE 95 026 8500 7.4 7.0 0 4.0  08-041-6011 1 3 COLORADO SPRING EL PASO CO 130 WEST CACHE LA POUDRE 96 026 8469 8.1 8.1 0 4.9  08-041-6013 1 3 COLORADO SPRING EL PASO CO 1699 S. CORONA AVE 93 026 8619 11.0 10.1 0 5.3  08-041-6013 1 3 COLORADO SPRING EL PASO CO 1699 S. CORONA AVE 94 026 8703 10.8 9.3 0 4.6  08-041-6013 1 3 COLORADO SPRING EL PASO CO 1699 S. CORONA AVE 95 026 8429 9.4 8.7 0 5.0  08-041-6013 1 3 COLORADO SPRING EL PASO CO 1699 S. CORONA AVE 96 026 4310 11.9 9.6 0 5.4  08-041-6016 1 3 COLORADO SPRING EL PASO CO 3730 MEADOWLAND BLVD. 93 026 8593 15.1 14.2 0 6.5 1DATE 98/06/25 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS)  AMP450 AIR QUALITY SUBSYSTEM QUICK LOOK REPORT0 CARBON MONOXIDE (42101) COLORADO UNITS: 007 PPM0 P O M REP MAX 1-HR OBS> MAX 8 SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST  ________________________________________________________________________________________________________________008-041-6016 1 3 COLORADO SPRING EL PASO CO 3730 MEADOWLAND BLVD. 94 026 8574 16.6 14.5 0 4.9  08-041-6016 1 3 COLORADO SPRING EL PASO CO 3730 MEADOWLAND BLVD. 95 026 8477 15.0 12.4 0 4.9 

B-3
 08-041-6016 1 3 COLORADO SPRING EL PASO CO 3730 MEADOWLAND BLVD. 96 026 8682 11.0 11.0 0 4.6 

 08-059-0002 1 2 ARVADA JEFFERSON CO W 57TH AVENUE AND GARRIS 93 001 8723 11.2 10.4 0 5.1  08-059-0002 1 2 ARVADA JEFFERSON CO W 57TH AVENUE AND GARRIS 94 001 8525 10.8 10.0 0 5.2  08-059-0002 1 2 ARVADA JEFFERSON CO W 57TH AVENUE AND GARRIS 95 001 8680 11.9 8.9 0 5.1  08-059-0002 1 2 ARVADA JEFFERSON CO W 57TH AVENUE AND GARRIS 96 001 8724 7.9 7.2 0 4.3  08-059-0002 1 2 ARVADA JEFFERSON CO W 57TH AVENUE AND GARRIS 97 001 8697 9.2 7.7 0 5.1  08-069-1004 1 2 FORT COLLINS LARIMER CO 708 S. MASON FT COLLINS 93 001 8698 17.3 13.8 0 7.4  08-069-1004 1 2 FORT COLLINS LARIMER CO 708 S. MASON FT COLLINS 94 001 8703 13.6 12.1 0 7.3  08-069-1004 1 2 FORT COLLINS LARIMER CO 708 S. MASON FT COLLINS 95 001 8699 10.6 9.8 0 5.6  08-069-1004 1 2 FORT COLLINS LARIMER CO 708 S. MASON FT COLLINS 96 001 8597 12.7 10.9 0 5.5  08-069-1004 1 2 FORT COLLINS LARIMER CO 708 S. MASON FT COLLINS 97 001 8708 10.3 9.2 0 5.3  08-077-0014 1 2 GRAND JUNCTION MESA CO STOCKER STADIUM (12TH & 93 001 8383 12.0 11.2 0 6.9  08-077-0014 1 2 GRAND JUNCTION MESA CO STOCKER STADIUM (12TH & 94 001 8367 11.6 11.6 0 7.5  08-077-0014 1 2 GRAND JUNCTION MESA CO STOCKER STADIUM (12TH & 95 001 8209 10.0 8.7 0 5.4  08-077-0014 1 2 GRAND JUNCTION MESA CO STOCKER STADIUM (12TH & 96 001 8754 10.5 10.5 0 7.5  08-077-0014 1 2 GRAND JUNCTION MESA CO STOCKER STADIUM (12TH & 97 001 8550 8.5 7.8 0 6.4  08-123-0007 1 2 GREELEY WELD CO 811 15TH ST - GREELEY 93 001 8688 10.9 10.2 0 6.1  08-123-0007 1 2 GREELEY WELD CO 811 15TH ST - GREELEY 94 001 8707 11.3 11.1 0 6.4  08-123-0007 1 2 GREELEY WELD CO 811 15TH ST - GREELEY 95 001 8703 10.3 9.6 0 5.7  08-123-0007 1 2 GREELEY WELD CO 811 15TH ST - GREELEY 96 001 8739 12.3 10.7 0 7.5  08-123-0007 1 2 GREELEY WELD CO 811 15TH ST - GREELEY 97 001 8717 9.6 8.6 0 5.3 

B-4
1DATE 98/06/25 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) 

 AMP450 AIR QUALITY SUBSYSTEM QUICK LOOK REPORT0 CARBON MONOXIDE (42101)-METHODS: CODE COLLECTION METHOD ANALYSIS METHOD ==== ============================== ===================================0 000 MULTIPLE METHODS MULTIPLE METHODS 048 INSTRUMENTAL NON DISPERSIVE INFRA-RED 051 INSTRUMENTAL NON DISPERSIVE INFRA-RED 054 INSTRUMENTAL NON DISPERSIVE INFRA-RED 093 INSTRUMENTAL GAS FILTER COORELATION CO ANALYZER1DATE 98/06/25 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS)  AMP217P AIR QUALITY SUBSYSTEM AQ CLEANUP PROCESSING SUMMARY SHEET-- REPORT0 * * * PROGRAM AMP217 TERMINATED SUCCESSFULLY ON 98/06/25 AT 09:40:17 * * *

B-5
1DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) 

 AMP450 AIR QUALITY SUBSYSTEM QUICK LOOK REPORT0 CARBON MONOXIDE (42101) CALIFORNIA UNITS: 007 PPM0 P O M REP MAX 1-HR OBS> MAX 8 SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST  ________________________________________________________________________________________________________________006-001-0003 1 2 LIVERMORE ALAMEDA 2614 OLD 1ST ST., LIVERM 95 004 8638 5.0 4.1 0 2.3  06-001-0003 1 2 LIVERMORE ALAMEDA 2614 OLD 1ST ST., LIVERM 96 004 8516 4.9 4.7 0 2.5  06-001-0003 1 2 LIVERMORE ALAMEDA 2614 OLD 1ST ST., LIVERM 97 004 8257 4.6 4.4 0 2.5  06-001-0005 1 2 OAKLAND ALAMEDA 822 ALICE ST., OAKLAND 95 004 8680 4.9 4.8 0 3.8  06-001-0005 1 2 OAKLAND ALAMEDA 822 ALICE ST., OAKLAND 96 004 8285 6.9 5.5 0 3.9  06-001-0005 1 2 OAKLAND ALAMEDA 822 ALICE ST., OAKLAND 97 004 8592 7.9 6.0 0 3.6  06-001-1001 1 1 FREMONT ALAMEDA 40733 CHAPEL WAY., FREMO 95 004 8544 5.5 5.2 0 2.9  06-001-1001 1 1 FREMONT ALAMEDA 40733 CHAPEL WAY., FREMO 96 004 8496 6.2 5.4 0 3.4  06-001-1001 1 1 FREMONT ALAMEDA 40733 CHAPEL WAY., FREMO 97 004 8342 6.0 5.1 0 3.0  06-005-0002 1 2 JACKSON AMADOR 201 CLINTON ROAD, JACKS 95 001 8311 9.3 3.0 0 2.6  06-005-0002 1 2 JACKSON AMADOR 201 CLINTON ROAD, JACKS 96 001 8301 2.2 2.2 0 1.5  06-005-0002 1 2 JACKSON AMADOR 201 CLINTON ROAD, JACKS 97 001 8352 2.8 2.7 0 1.4  06-007-0002 1 2 CHICO BUTTE 468 MANZANITA AVE, CHICO 95 001 8247 5.8 5.5 0 3.7  06-007-0002 1 2 CHICO BUTTE 468 MANZANITA AVE, CHICO 96 001 8318 5.3 5.1 0 3.4  06-007-0002 1 2 CHICO BUTTE 468 MANZANITA AVE, CHICO 97 001 8335 6.8 6.1 0 3.8  06-007-0005 1 2 CHICO BUTTE 101 SALEM ST, CHICO 95 001 8741 8.5 7.6 0 4.8  06-007-0005 1 2 CHICO BUTTE 101 SALEM ST, CHICO 96 001 8767 8.7 7.8 0 6.1  06-007-0005 1 2 CHICO BUTTE 101 SALEM ST, CHICO 97 001 8580 7.0 6.6 0 5.1  06-009-0001 1 2 SAN ANDREAS CALAVERAS 501 GOLD STRIKE ROAD, SA 95 001 8230 2.1 2.1 0 1.8  06-009-0001 1 2 SAN ANDREAS CALAVERAS 501 GOLD STRIKE ROAD, SA 96 001 8097 1.7 1.6 0 .9  06-009-0001 1 2 SAN ANDREAS CALAVERAS 501 GOLD STRIKE ROAD, SA 97 001 8108 2.1 2.0 0 1.7  06-013-0002 1 1 CONCORD CONTRA COSTA 2975 TREAT BLVD, CONCORD 95 004 8663 5.5 4.7 0 2.9  06-013-0002 1 1 CONCORD CONTRA COSTA 2975 TREAT BLVD, CONCORD 96 004 8608 5.6 5.4 0 2.9  06-013-0002 1 1 CONCORD CONTRA COSTA 2975 TREAT BLVD, CONCORD 97 004 8359 5.7 5.5 0 3.0  06-013-0003 1 2 RICHMOND CONTRA COSTA 1144 13TH ST., RICHMOND 95 004 8632 6.5 4.8 0 2.4  06-013-0003 1 2 RICHMOND CONTRA COSTA 1144 13TH ST., RICHMOND 96 004 8676 5.0 4.8 0 2.6  06-013-0003 1 2 RICHMOND CONTRA COSTA 1144 13TH ST., RICHMOND 97 004 2963 4.6 3.7 0 2.6  06-013-1002 1 2 BETHEL ISLAND CONTRA COSTA 5551 BETHEL ISLAND RD, B 95 004 8484 2.8 2.2 0 1.7  06-013-1002 1 2 BETHEL ISLAND CONTRA COSTA 5551 BETHEL ISLAND RD, B 96 004 8540 2.7 1.7 0 1.4  06-013-1002 1 2 BETHEL ISLAND CONTRA COSTA 5551 BETHEL ISLAND RD, B 97 004 8360 1.9 1.8 0 1.4  06-013-1003 1 2 SAN PABLO CONTRA COSTA UNIT 759 EL PORTAL SHOPP 97 004 5586 3.8 2.8 0 2.3  06-013-3001 1 2 PITTSBURG CONTRA COSTA 583 W. 10TH ST., PITTSBU 95 004 8639 5.8 5.4 0 2.8  06-013-3001 1 2 PITTSBURG CONTRA COSTA 583 W. 10TH ST., PITTSBU 96 004 8750 6.8 5.9 0 2.9  06-013-3001 1 2 PITTSBURG CONTRA COSTA 583 W. 10TH ST., PITTSBU 97 004 8360 5.5 4.8 0 3.2  06-017-0005 1 2 SOUTH LAKE TAHO EL DORADO STATELINE-4045 HWY 50, S 95 001 8205 9.3 8.7 0 6.3  06-017-0005 1 2 SOUTH LAKE TAHO EL DORADO STATELINE-4045 HWY 50, S 96 001 8124 10.4 7.4 0 5.1  06-017-0005 1 2 SOUTH LAKE TAHO EL DORADO STATELINE-4045 HWY 50, S 97 001 7946 7.7 7.0 0 3.8  06-017-0010 1 2 PLACERVILLE EL DORADO 3111 GOLD NUGGET WAY, P 95 001 8258 1.6 1.5 0 1.0  06-017-0010 1 2 PLACERVILLE EL DORADO 3111 GOLD NUGGET WAY, P 96 001 8056 1.3 1.3 0 .9  06-017-0010 1 2 PLACERVILLE EL DORADO 3111 GOLD NUGGET WAY, P 97 001 8221 1.6 1.4 0 .8  06-017-0011 1 2 SOUTH LAKE TAHO EL DORADO 3337 SANDY WAY, SOUTH L 95 001 8223 5.2 4.5 0 2.6  06-017-0011 1 2 SOUTH LAKE TAHO EL DORADO 3337 SANDY WAY, SOUTH L 96 001 8341 4.2 4.1 0 2.4  06-017-0011 1 2 SOUTH LAKE TAHO EL DORADO 3337 SANDY WAY, SOUTH L 97 001 7993 3.2 3.1 0 2.4  06-019-0007 1 2 FRESNO FRESNO 4706 E. DRUMMOND ST., FR 95 069 8159 6.4 6.3 0 4.8 1DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS)  AMP450 AIR QUALITY SUBSYSTEM QUICK LOOK REPORT0 CARBON MONOXIDE (42101) CALIFORNIA UNITS: 007 PPM0 P

B-6
 O M REP MAX 1-HR OBS> MAX 8

 SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST  ________________________________________________________________________________________________________________006-019-0007 1 2 FRESNO FRESNO 4706 E. DRUMMOND ST., FR 96 069 8278 6.0 5.4 0 4.4  06-019-0007 1 2 FRESNO FRESNO 4706 E. DRUMMOND ST., FR 97 069 8185 6.3 6.1 0 3.9  06-019-0008 1 2 FRESNO FRESNO 3425 N FIRST ST, FRESNO 95 001 8177 10.3 9.5 0 7.3  06-019-0008 1 2 FRESNO FRESNO 3425 N FIRST ST, FRESNO 96 001 8178 10.0 9.8 0 6.8  06-019-0008 1 2 FRESNO FRESNO 3425 N FIRST ST, FRESNO 97 001 8240 8.7 8.3 0 5.7  06-019-0009 1 3 FRESNO FRESNO 1145 FISHER STREET, FRES 95 001 4709 12.0 11.4 0 9.1  06-019-0009 1 3 FRESNO FRESNO 1145 FISHER STREET, FRES 96 001 3570 10.1 8.5 0 6.8  06-019-0009 1 3 FRESNO FRESNO 1145 FISHER STREET, FRES 97 001 4076 9.9 9.7 0 7.5  06-019-0242 1 2 FRESNO FRESNO SIERRA SKYPARK#2-BLYTHE 95 069 8258 3.8 3.3 0 2.5  06-019-0242 1 2 FRESNO FRESNO SIERRA SKYPARK#2-BLYTHE 96 069 8294 4.3 4.1 0 3.7  06-019-0242 1 2 FRESNO FRESNO SIERRA SKYPARK#2-BLYTHE 97 069 8210 4.1 3.9 0 2.8  06-019-5001 1 2 CLOVIS FRESNO 908 N VILLA AVE, CLOVIS 95 069 8200 8.0 5.8 0 3.9  06-019-5001 1 2 CLOVIS FRESNO 908 N VILLA AVE, CLOVIS 96 069 8281 6.3 6.1 0 3.5  06-019-5001 1 2 CLOVIS FRESNO 908 N VILLA AVE, CLOVIS 97 069 8154 6.1 6.1 0 2.9  06-025-0005 1 2 CALEXICO IMPERIAL 1029 ETHEL ST, CALEXICO 95 001 8289 32.0 29.8 0 22.9  06-025-0005 1 2 CALEXICO IMPERIAL 1029 ETHEL ST, CALEXICO 96 001 8106 27.0 26.2 0 22.1  06-025-0005 1 2 CALEXICO IMPERIAL 1029 ETHEL ST, CALEXICO 97 001 8306 24.0 21.8 0 17.8  06-025-0006 1 2 CALEXICO IMPERIAL CALEXICO - EAST 96 001 4392 22.0 18.0 0 8.7  06-025-0006 1 2 CALEXICO IMPERIAL CALEXICO - EAST 97 001 7771 21.0 20.6 0 16.3  06-025-1003 1 2 EL CENTRO IMPERIAL 150 9TH ST., EL CENTRO 96 001 8784 12.0 10.0 0 6.8  06-025-1003 1 2 EL CENTRO IMPERIAL 150 9TH ST., EL CENTRO 97 001 8702 6.0 6.0 0 3.7  06-027-0015 1 2 BISHOP INYO 157 SHORT STREET, BISHOP 95 001 2240 4.0 4.0 0 2.0  06-029-0010 1 2 BAKERSFIELD KERN 1128 GOLDEN STATE HIGHWA 95 069 8250 7.4 7.0 0 4.6  06-029-0010 1 2 BAKERSFIELD KERN 1128 GOLDEN STATE HIGHWA 96 069 8138 6.2 5.6 0 3.7  06-029-0010 1 2 BAKERSFIELD KERN 1128 GOLDEN STATE HIGHWA 97 069 8189 6.1 4.3 0 2.9  06-029-0014 1 2 BAKERSFIELD KERN 5558 CALIFORNIA AVE, BAK 95 001 8162 7.8 7.2 0 6.2  06-029-0014 1 2 BAKERSFIELD KERN 5558 CALIFORNIA AVE, BAK 96 001 7967 8.7 8.5 0 7.7  06-029-0014 1 2 BAKERSFIELD KERN 5558 CALIFORNIA AVE, BAK 97 001 8035 5.2 5.2 0 3.4  06-037-0002 1 2 AZUSA LOS ANGELES 803 N. LOREN AVE., AZUSA 95 061 8300 8.1 7.3 0 6.2  06-037-0002 1 2 AZUSA LOS ANGELES 803 N. LOREN AVE., AZUSA 96 061 8025 6.1 5.9 0 4.1  06-037-0002 1 2 AZUSA LOS ANGELES 803 N. LOREN AVE., AZUSA 97 061 8007 8.4 5.5 0 4.3  06-037-0113 1 2 WEST LOS ANGELE LOS ANGELES VA HOSPITAL, WEST LOS AN 95 061 7774 7.8 7.4 0 5.7  06-037-0113 1 2 WEST LOS ANGELE LOS ANGELES VA HOSPITAL, WEST LOS AN 96 061 8057 7.0 6.7 0 4.3  06-037-0113 1 2 WEST LOS ANGELE LOS ANGELES VA HOSPITAL, WEST LOS AN 97 061 8360 7.3 6.4 0 4.2  06-037-0206 1 3 DIAMOND BAR LOS ANGELES 21865 E. COPLEY DR., DIA 95 061 8357 6.9 6.1 0 5.5  06-037-0206 1 3 DIAMOND BAR LOS ANGELES 21865 E. COPLEY DR., DIA 96 061 3073 5.5 5.4 0 4.3  06-037-1002 1 2 BURBANK LOS ANGELES 228 W. PALM AVE., BURBAN 95 061 8291 12.5 12.5 0 11.8  06-037-1002 1 2 BURBANK LOS ANGELES 228 W. PALM AVE., BURBAN 96 061 7696 11.6 11.0 0 9.2  06-037-1002 1 2 BURBANK LOS ANGELES 228 W. PALM AVE., BURBAN 97 061 8025 8.8 8.6 0 7.3  06-037-1103 1 2 LOS ANGELES LOS ANGELES 1630 N MAIN ST, LOS ANGE 95 061 8165 9.7 9.2 0 8.4  06-037-1103 1 2 LOS ANGELES LOS ANGELES 1630 N MAIN ST, LOS ANGE 96 061 8390 10.3 10.1 0 8.4  06-037-1103 1 2 LOS ANGELES LOS ANGELES 1630 N MAIN ST, LOS ANGE 97 061 8292 8.9 8.7 0 7.8  06-037-1201 1 2 RESEDA LOS ANGELES 18330 GAULT ST., RESEDA 95 061 7670 13.0 11.8 0 10.3  06-037-1201 1 2 RESEDA LOS ANGELES 18330 GAULT ST., RESEDA 96 061 8012 10.2 10.2 0 8.7 1DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS)  AMP450 AIR QUALITY SUBSYSTEM QUICK LOOK REPORT0 CARBON MONOXIDE (42101) CALIFORNIA UNITS: 007 PPM0 P O M REP MAX 1-HR OBS> MAX 8 SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST  ________________________________________________________________________________________________________________006-037-1201 1 2 RESEDA LOS ANGELES 18330 GAULT ST., RESEDA 97 061 8245 11.7 11.1 0 9.5  06-037-1301 1 1 LYNWOOD LOS ANGELES 11220 LONG BEACH BLVD., 95 061 8290 16.8 16.5 0 13.8 

B-7
 06-037-1301 1 1 LYNWOOD LOS ANGELES 11220 LONG BEACH BLVD., 96 061 8326 22.5 21.3 0 17.5 

 06-037-1301 1 1 LYNWOOD LOS ANGELES 11220 LONG BEACH BLVD., 97 061 8302 19.2 18.8 0 17.1  06-037-1601 1 2 PICO RIVERA LOS ANGELES 3713 SAN GABRIEL RIVER P 95 061 8372 9.6 9.3 0 7.7  06-037-1601 1 2 PICO RIVERA LOS ANGELES 3713 SAN GABRIEL RIVER P 96 061 8303 9.8 9.1 0 8.1  06-037-1601 1 2 PICO RIVERA LOS ANGELES 3713 SAN GABRIEL RIVER P 97 061 7881 9.2 7.9 0 6.1  06-037-1701 1 2 POMONA LOS ANGELES 924 N. GAREY AVE., POMON 95 061 8307 8.1 7.7 0 6.1  06-037-1701 1 2 POMONA LOS ANGELES 924 N. GAREY AVE., POMON 96 061 8290 8.1 8.1 0 4.8  06-037-1701 1 2 POMONA LOS ANGELES 924 N. GAREY AVE., POMON 97 061 8350 7.9 7.1 0 5.0  06-037-2005 1 2 PASADENA LOS ANGELES 752 S. WILSON AVE., PASA 95 061 8387 11.4 11.4 0 9.1  06-037-2005 1 2 PASADENA LOS ANGELES 752 S. WILSON AVE., PASA 96 061 8282 10.7 9.9 0 7.1  06-037-2005 1 2 PASADENA LOS ANGELES 752 S. WILSON AVE., PASA 97 061 8250 8.1 7.7 0 6.0  06-037-4002 1 2 LONG BEACH LOS ANGELES 3648 N. LONG BEACH BLVD. 95 061 8326 9.1 8.1 0 6.7  06-037-4002 1 2 LONG BEACH LOS ANGELES 3648 N. LONG BEACH BLVD. 96 061 6015 9.7 9.2 0 6.8  06-037-4002 1 2 LONG BEACH LOS ANGELES 3648 N. LONG BEACH BLVD. 97 061 8347 9.0 8.6 0 6.6  06-037-5001 1 2 HAWTHORNE LOS ANGELES 5234 W. 120TH ST., HAWTH 95 061 8241 11.4 11.1 0 8.8  06-037-5001 1 2 HAWTHORNE LOS ANGELES 5234 W. 120TH ST., HAWTH 96 061 8258 12.5 12.3 0 11.5  06-037-5001 1 2 HAWTHORNE LOS ANGELES 5234 W. 120TH ST., HAWTH 97 061 8125 12.4 12.3 0 10.3  06-037-6002 1 2 SANTA CLARITA LOS ANGELES SAN FERNANDO RD, SANTA C 95 061 8241 6.7 6.5 0 4.1  06-037-6002 1 2 SANTA CLARITA LOS ANGELES SAN FERNANDO RD, SANTA C 96 061 8413 7.0 6.3 0 3.9  06-037-6002 1 2 SANTA CLARITA LOS ANGELES SAN FERNANDO RD, SANTA C 97 061 8289 7.4 7.0 0 6.7  06-037-9002 1 2 LANCASTER LOS ANGELES 315 W. PONDERA ST., LANC 95 061 8383 7.5 6.8 0 5.1  06-037-9002 1 2 LANCASTER LOS ANGELES 315 W. PONDERA ST., LANC 96 061 8406 6.8 6.4 0 4.7  06-037-9002 1 2 LANCASTER LOS ANGELES 315 W. PONDERA ST., LANC 97 061 7759 5.9 5.5 0 4.0  06-041-0001 1 2 SAN RAFAEL MARIN 534 4TH ST., SAN RAFAEL 95 004 8598 6.1 5.7 0 3.2  06-041-0001 1 2 SAN RAFAEL MARIN 534 4TH ST., SAN RAFAEL 96 004 8527 7.1 6.9 0 4.0  06-041-0001 1 2 SAN RAFAEL MARIN 534 4TH ST., SAN RAFAEL 97 004 8266 6.0 5.6 0 2.6  06-045-0008 1 2 UKIAH MENDOCINO 306 E. GOBBI STREET, UK 95 001 7236 5.4 5.2 0 3.2  06-045-0008 1 2 UKIAH MENDOCINO 306 E. GOBBI STREET, UK 96 001 4052 4.8 4.2 0 2.7  06-045-0008 1 2 UKIAH MENDOCINO 306 E. GOBBI STREET, UK 97 001 8117 4.6 4.3 0 3.2  06-045-0009 1 2 WILLITS MENDOCINO 899 SO MAIN STREET, WIL 95 001 7861 2.5 2.3 0 1.8  06-045-0009 1 2 WILLITS MENDOCINO 899 SO MAIN STREET, WIL 96 001 2537 3.0 3.0 0 1.5  06-045-0009 1 2 WILLITS MENDOCINO 899 SO MAIN STREET, WIL 97 001 7955 7.4 4.2 0 3.0  06-051-0001 1 2 MAMMOTH LAKES MONO GATEWAY HC., MAMMOTH LAK 95 001 8450 10.0 7.0 0 5.4  06-051-0001 1 2 MAMMOTH LAKES MONO GATEWAY HC., MAMMOTH LAK 96 001 7854 6.0 6.0 0 3.0  06-051-0001 1 2 MAMMOTH LAKES MONO GATEWAY HC., MAMMOTH LAK 97 001 8181 8.2 7.5 0 3.4  06-053-1002 1 2 SALINAS MONTEREY II-1270 NATIVIDAD RD, SA 95 011 7906 3.2 3.0 0 1.8  06-053-1002 1 2 SALINAS MONTEREY II-1270 NATIVIDAD RD, SA 96 011 8610 5.5 4.5 0 2.6  06-053-1002 1 2 SALINAS MONTEREY II-1270 NATIVIDAD RD, SA 97 011 8490 4.4 3.7 0 1.8  06-055-0003 1 2 NAPA NAPA 2552 JEFFERSON AVE., NAP 95 004 8483 7.6 6.3 0 3.5  06-055-0003 1 2 NAPA NAPA 2552 JEFFERSON AVE., NAP 96 004 8338 5.6 5.3 0 3.8  06-055-0003 1 2 NAPA NAPA 2552 JEFFERSON AVE., NAP 97 004 8319 5.7 5.3 0 3.9  06-059-0001 1 1 ANAHEIM ORANGE 1610 S. HARBOR BLVD., AN 95 061 8259 9.9 9.8 0 8.0 1DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS)  AMP450 AIR QUALITY SUBSYSTEM QUICK LOOK REPORT0 CARBON MONOXIDE (42101) CALIFORNIA UNITS: 007 PPM0 P O M REP MAX 1-HR OBS> MAX 8 SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST  ________________________________________________________________________________________________________________006-059-0001 1 1 ANAHEIM ORANGE 1610 S. HARBOR BLVD., AN 96 061 8298 8.9 8.9 0 7.4  06-059-0001 1 1 ANAHEIM ORANGE 1610 S. HARBOR BLVD., AN 97 061 8354 8.4 8.2 0 5.7  06-059-1003 1 2 COSTA MESA ORANGE 2850 MESA VERDE DR EAST, 95 061 8213 7.9 7.5 0 6.5  06-059-1003 1 2 COSTA MESA ORANGE 2850 MESA VERDE DR EAST, 96 061 8191 8.7 8.6 0 7.2  06-059-1003 1 2 COSTA MESA ORANGE 2850 MESA VERDE DR EAST, 97 061 8325 7.3 7.1 0 5.9  06-059-2001 1 2 EL TORO ORANGE 23022 EL TORO RD., EL TO 95 061 8321 6.2 6.0 0 4.0 

 06-059-2001 1 2 EL TORO ORANGE 23022 EL TORO RD., EL TO 96 061 8400 6.2 6.0 0 4.1 

B-8
 06-059-2001 1 2 EL TORO ORANGE 23022 EL TORO RD., EL TO 97 061 8385 5.5 4.6 0 3.8 

 06-059-5001 1 2 LA HABRA ORANGE 621 W. LAMBERT, LA HABRA 95 061 8363 12.7 11.5 0 6.5  06-059-5001 1 2 LA HABRA ORANGE 621 W. LAMBERT, LA HABRA 96 061 8345 12.9 12.0 0 6.9  06-059-5001 1 2 LA HABRA ORANGE 621 W. LAMBERT, LA HABRA 97 061 8230 11.9 11.0 0 5.7  06-061-0005 1 2 TAHOE CITY PLACER 165 RIVER ROAD, TAHOE C 95 001 4236 9.5 9.0 0 2.9  06-061-0006 1 2 ROSEVILLE PLACER 151 NO SUNRISE BLVD, RO 95 001 8304 3.9 3.5 0 2.2  06-061-0006 1 2 ROSEVILLE PLACER 151 NO SUNRISE BLVD, RO 96 001 8358 4.5 4.0 0 2.8  06-061-0006 1 2 ROSEVILLE PLACER 151 NO SUNRISE BLVD, RO 97 001 8273 3.7 3.5 0 2.1  06-061-3001 1 2 ROCKLIN PLACER 5000 ROCKLIN ROAD, ROCKL 95 001 8289 2.9 2.8 0 1.6  06-061-3001 1 2 ROCKLIN PLACER 5000 ROCKLIN ROAD, ROCKL 96 001 3173 3.1 2.8 0 1.4  06-065-1003 1 1 RIVERSIDE RIVERSIDE 7002 MAGNOLIA AVE., RIVE 95 061 8432 9.0 9.0 0 6.3  06-065-1003 1 1 RIVERSIDE RIVERSIDE 7002 MAGNOLIA AVE., RIVE 96 061 8404 9.1 8.2 0 5.3  06-065-1003 1 1 RIVERSIDE RIVERSIDE 7002 MAGNOLIA AVE., RIVE 97 061 8345 10.7 9.3 0 5.5  06-065-5001 1 2 PALM SPRINGS RIVERSIDE FS-590 RACQUET CLUB AVE, 95 061 8258 3.3 3.1 0 1.5  06-065-5001 1 2 PALM SPRINGS RIVERSIDE FS-590 RACQUET CLUB AVE, 96 061 8030 3.2 3.0 0 1.6  06-065-5001 1 2 PALM SPRINGS RIVERSIDE FS-590 RACQUET CLUB AVE, 97 061 8170 2.7 2.4 0 1.3  06-065-8001 1 2 RUBIDOUX RIVERSIDE 5888 MISSION BLVD., RUBI 95 061 8374 6.8 6.7 0 5.7  06-065-8001 1 2 RUBIDOUX RIVERSIDE 5888 MISSION BLVD., RUBI 96 061 8311 8.5 7.4 0 5.1  06-065-8001 1 2 RUBIDOUX RIVERSIDE 5888 MISSION BLVD., RUBI 97 061 7057 6.6 6.2 0 5.6  06-067-0002 1 3 NORTH HIGHLANDS SACRAMENTO 7823 BLACKFOOT WAY, NORT 95 001 8068 5.1 4.8 0 3.4  06-067-0002 1 3 NORTH HIGHLANDS SACRAMENTO 7823 BLACKFOOT WAY, NORT 96 001 8254 5.2 4.8 0 3.3  06-067-0002 1 3 NORTH HIGHLANDS SACRAMENTO 7823 BLACKFOOT WAY, NORT 97 001 7964 5.6 5.1 0 3.6  06-067-0006 1 2 SACRAMENTO SACRAMENTO DEL PASO-2701 AVALON DR, 95 001 8081 7.6 7.0 0 6.7  06-067-0006 1 2 SACRAMENTO SACRAMENTO DEL PASO-2701 AVALON DR, 96 001 8302 6.7 6.7 0 6.0  06-067-0006 1 2 SACRAMENTO SACRAMENTO DEL PASO-2701 AVALON DR, 97 001 8037 8.1 7.2 0 6.0  06-067-0007 1 1 SACRAMENTO SACRAMENTO 3535 EL CAMINO & WATT, S 95 001 8692 8.0 7.7 0 7.4  06-067-0007 1 1 SACRAMENTO SACRAMENTO 3535 EL CAMINO & WATT, S 96 001 8506 8.5 8.4 0 7.2  06-067-0007 1 1 SACRAMENTO SACRAMENTO 3535 EL CAMINO & WATT, S 97 001 8577 9.5 9.0 0 7.2  06-067-0010 1 2 SACRAMENTO SACRAMENTO 1309 T ST., SACRAMENTO, 95 001 8303 9.8 9.6 0 6.6  06-067-0010 1 2 SACRAMENTO SACRAMENTO 1309 T ST., SACRAMENTO, 96 001 8350 8.7 8.1 0 6.8  06-067-0010 1 2 SACRAMENTO SACRAMENTO 1309 T ST., SACRAMENTO, 97 001 8161 7.6 7.4 0 6.0  06-067-5002 1 2 SACRAMENTO SACRAMENTO 7926 EARHART DR., SACRAM 95 001 7148 8.0 7.0 0 4.3  06-067-5002 1 2 SACRAMENTO SACRAMENTO 7926 EARHART DR., SACRAM 96 001 8014 3.5 3.3 0 2.2  06-067-5002 1 2 SACRAMENTO SACRAMENTO 7926 EARHART DR., SACRAM 97 001 6774 3.1 2.9 0 2.0  06-071-0001 1 2 BARSTOW SAN BERNARDINO 200 E. BUENA VISTA, BARS 95 014 7114 6.1 3.1 0 2.1  06-071-0001 1 2 BARSTOW SAN BERNARDINO 200 E. BUENA VISTA, BARS 96 014 8071 3.8 3.7 0 2.3  06-071-0001 1 2 BARSTOW SAN BERNARDINO 200 E. BUENA VISTA, BARS 97 014 7712 2.8 2.5 0 1.6 1DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS)  AMP450 AIR QUALITY SUBSYSTEM QUICK LOOK REPORT0 CARBON MONOXIDE (42101) CALIFORNIA UNITS: 007 PPM0 P O M REP MAX 1-HR OBS> MAX 8 SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST  ________________________________________________________________________________________________________________006-071-0012 1 3 PHELAN SAN BERNARDINO BEEKLEY & PHELAN RDS, PH 95 001 6338 1.7 1.7 0 1.5  06-071-0012 1 3 PHELAN SAN BERNARDINO BEEKLEY & PHELAN RDS, PH 96 001 4543 2.9 1.5 0 1.3  06-071-0014 1 2 VICTORVILLE SAN BERNARDINO 14029 AMARGOSA ROAD, VIC 95 014 2812 3.1 3.1 0 2.7  06-071-0014 1 2 VICTORVILLE SAN BERNARDINO 14029 AMARGOSA ROAD, VIC 96 014 5686 8.4 8.2 0 7.5  06-071-0014 1 2 VICTORVILLE SAN BERNARDINO 14029 AMARGOSA ROAD, VIC 97 014 8082 4.1 3.8 0 3.1  06-071-0017 1 2 TWENTYNINE PALM SAN BERNARDINO 6136 ADOBE ROAD, TWENTY 95 014 2876 5.0 3.3 0 2.0  06-071-0017 1 2 TWENTYNINE PALM SAN BERNARDINO 6136 ADOBE ROAD, TWENTY 96 014 7695 1.9 1.8 0 1.3  06-071-0017 1 2 TWENTYNINE PALM SAN BERNARDINO 6136 ADOBE ROAD, TWENTY 97 014 2996 2.0 1.6 0 1.0  06-071-0217 1 3 MOUNT BALDY SAN BERNARDINO 6945 MT BALDY ROAD 97 001 1779 1.4 .8 0 .5  06-071-4001 1 2 HESPERIA SAN BERNARDINO 17288 OLIVE ST., HESPERI 95 014 8032 3.1 3.0 0 2.1  06-071-4001 1 2 HESPERIA SAN BERNARDINO 17288 OLIVE ST., HESPERI 96 014 8265 3.9 3.5 0 2.2 

 06-071-4001 1 2 HESPERIA SAN BERNARDINO 17288 OLIVE ST., HESPERI 97 014 8115 3.4 3.4 0 2.6 

B-9
 06-071-9004 1 1 SAN BERNARDINO SAN BERNARDINO 24302 4TH ST., SAN BERNA 95 061 8322 7.7 7.4 0 6.3 

 06-071-9004 1 1 SAN BERNARDINO SAN BERNARDINO 24302 4TH ST., SAN BERNA 96 061 8260 5.8 5.8 0 4.5  06-071-9004 1 1 SAN BERNARDINO SAN BERNARDINO 24302 4TH ST., SAN BERNA 97 061 7312 7.6 7.0 0 5.9  06-073-0001 1 1 CHULA VISTA SAN DIEGO 80 E. "J" ST., CHULA VIS 95 036 8473 5.4 5.0 0 3.8  06-073-0001 1 1 CHULA VISTA SAN DIEGO 80 E. "J" ST., CHULA VIS 96 036 8273 5.7 5.5 0 3.4  06-073-0001 1 1 CHULA VISTA SAN DIEGO 80 E. "J" ST., CHULA VIS 97 036 8297 5.4 5.3 0 3.8  06-073-0003 1 2 EL CAJON SAN DIEGO 1155 REDWOOD AVE., EL CA 95 036 8283 6.1 5.4 0 3.4  06-073-0003 1 2 EL CAJON SAN DIEGO 1155 REDWOOD AVE., EL CA 96 036 8273 6.3 5.9 0 4.0  06-073-0003 1 2 EL CAJON SAN DIEGO 1155 REDWOOD AVE., EL CA 97 036 8173 5.6 5.5 0 4.3  06-073-0005 1 2 OCEANSIDE SAN DIEGO 1701 MISSION AVE., OCEAN 95 036 8552 4.4 4.4 0 3.1  06-073-0005 1 2 OCEANSIDE SAN DIEGO 1701 MISSION AVE., OCEAN 96 036 8592 4.0 3.6 0 2.6  06-073-0005 1 2 OCEANSIDE SAN DIEGO 1701 MISSION AVE., OCEAN 97 036 8599 6.1 6.0 0 2.9  06-073-0006 1 2 SAN DIEGO SAN DIEGO 5555 OVERLAND AVE., SAN 95 036 8356 4.8 4.5 0 3.5  06-073-0006 1 2 SAN DIEGO SAN DIEGO 5555 OVERLAND AVE., SAN 96 036 7829 4.6 4.5 0 3.3  06-073-0006 1 2 SAN DIEGO SAN DIEGO 5555 OVERLAND AVE., SAN 97 036 8531 5.8 5.0 0 3.0  06-073-0007 1 1 SAN DIEGO SAN DIEGO 1133 UNION ST., SAN DIEG 95 036 7880 8.5 8.2 0 5.5  06-073-0007 1 1 SAN DIEGO SAN DIEGO 1133 UNION ST., SAN DIEG 96 036 8685 10.0 9.7 0 6.3  06-073-0007 1 1 SAN DIEGO SAN DIEGO 1133 UNION ST., SAN DIEG 97 036 8714 7.7 7.0 0 5.3  06-073-1002 1 2 ESCONDIDO SAN DIEGO 600 E. VALLEY PKWY., ESC 95 036 8460 9.9 9.9 0 6.0  06-073-1002 1 2 ESCONDIDO SAN DIEGO 600 E. VALLEY PKWY., ESC 96 036 8590 11.2 11.1 0 7.1  06-073-1002 1 2 ESCONDIDO SAN DIEGO 600 E. VALLEY PKWY., ESC 97 036 8572 9.3 9.3 0 4.9  06-073-1007 1 2 SAN DIEGO SAN DIEGO 330A 12TH AVE., SAN DIEG 95 036 8565 8.0 7.5 0 5.9  06-073-1007 1 2 SAN DIEGO SAN DIEGO 330A 12TH AVE., SAN DIEG 96 036 8320 7.9 7.7 0 5.4  06-073-1007 1 2 SAN DIEGO SAN DIEGO 330A 12TH AVE., SAN DIEG 97 036 8205 7.5 6.9 0 5.4  06-073-2007 1 2 OTAY MESA SAN DIEGO 1100 PASEO INTERNATIONAL 95 036 8292 8.5 7.7 0 6.3  06-073-2007 1 2 OTAY MESA SAN DIEGO 1100 PASEO INTERNATIONAL 96 036 8278 12.4 9.8 0 5.8  06-073-2007 1 2 OTAY MESA SAN DIEGO 1100 PASEO INTERNATIONAL 97 036 8263 7.7 7.4 0 4.6  06-075-0003 1 1 SAN FRANCISCO SAN FRANCISCO 939 ELLIS ST., SAN FRANC 95 004 8658 8.5 7.9 0 5.3  06-075-0003 1 1 SAN FRANCISCO SAN FRANCISCO 939 ELLIS ST., SAN FRANC 96 004 8699 8.6 7.6 0 5.6  06-075-0003 1 1 SAN FRANCISCO SAN FRANCISCO 939 ELLIS ST., SAN FRANC 97 004 8670 8.0 7.4 0 5.7  06-075-0005 1 2 SAN FRANCISCO SAN FRANCISCO 10 ARKANSAS ST., SAN FRA 95 004 8640 5.3 5.2 0 4.4  06-075-0005 1 2 SAN FRANCISCO SAN FRANCISCO 10 ARKANSAS ST., SAN FRA 96 004 8503 5.4 5.3 0 3.8 1DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS)  AMP450 AIR QUALITY SUBSYSTEM QUICK LOOK REPORT0 CARBON MONOXIDE (42101) CALIFORNIA UNITS: 007 PPM0 P O M REP MAX 1-HR OBS> MAX 8 SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST  ________________________________________________________________________________________________________________006-075-0005 1 2 SAN FRANCISCO SAN FRANCISCO 10 ARKANSAS ST., SAN FRA 97 004 8334 4.8 4.6 0 3.5  06-077-0008 1 3 STOCKTON SAN JOAQUIN 4310 CLAREMONT, STOCKTON 95 001 8078 8.7 8.5 0 6.2  06-077-0008 1 3 STOCKTON SAN JOAQUIN 4310 CLAREMONT, STOCKTON 96 001 7905 11.0 11.0 0 7.6  06-077-0008 1 3 STOCKTON SAN JOAQUIN 4310 CLAREMONT, STOCKTON 97 001 7642 6.3 6.2 0 4.2  06-077-1002 1 2 STOCKTON SAN JOAQUIN HAZELTON-HD, STOCKTON 95 001 8228 10.3 9.6 0 4.5  06-077-1002 1 2 STOCKTON SAN JOAQUIN HAZELTON-HD, STOCKTON 96 001 8288 9.4 8.7 0 6.4  06-077-1002 1 2 STOCKTON SAN JOAQUIN HAZELTON-HD, STOCKTON 97 001 8046 7.7 6.1 0 3.6  06-079-2002 1 2 SAN LUIS OBISPO SAN LUIS OBISPO 1160 MARSH ST., SAN LUIS 95 001 8363 5.7 5.7 0 3.1  06-079-2002 1 2 SAN LUIS OBISPO SAN LUIS OBISPO 1160 MARSH ST., SAN LUIS 96 001 8385 5.0 4.9 0 2.9  06-079-2002 1 2 SAN LUIS OBISPO SAN LUIS OBISPO 1160 MARSH ST., SAN LUIS 97 001 8362 6.4 6.3 0 2.6  06-081-1001 1 2 REDWOOD CITY SAN MATEO 897 BARRON AVE., REDWOOD 95 004 8677 10.1 9.3 0 3.9  06-081-1001 1 2 REDWOOD CITY SAN MATEO 897 BARRON AVE., REDWOOD 96 004 8485 8.6 7.5 0 3.6  06-081-1001 1 2 REDWOOD CITY SAN MATEO 897 BARRON AVE., REDWOOD 97 004 8343 10.7 7.1 0 4.2  06-083-0010 1 2 SANTA BARBARA SANTA BARBARA 3 W. CARRILLO ST., SANTA 95 001 8154 7.8 7.7 0 5.8  06-083-0010 1 2 SANTA BARBARA SANTA BARBARA 3 W. CARRILLO ST., SANTA 96 001 8320 12.6 8.0 0 4.9  06-083-0010 1 2 SANTA BARBARA SANTA BARBARA 3 W. CARRILLO ST., SANTA 97 001 8240 8.2 8.1 0 4.1 

 06-083-1025 1 4 CAPITAN SANTA BARBARA LFC #1-LAS FLORES CANYON 95 909 7823 1.0 1.0 0 1.0 

B-10
 06-083-1025 1 4 CAPITAN SANTA BARBARA LFC #1-LAS FLORES CANYON 96 909 7893 1.3 1.2 0 .9 

 06-083-1025 1 4 CAPITAN SANTA BARBARA LFC #1-LAS FLORES CANYON 97 909 8164 1.2 .9 0 .7  06-083-2004 1 2 LOMPOC SANTA BARBARA 128 S 'H' ST, LOMPOC 95 017 8296 3.7 3.4 0 1.7  06-083-2004 1 2 LOMPOC SANTA BARBARA 128 S 'H' ST, LOMPOC 96 017 8240 3.6 3.4 0 1.9  06-083-2004 1 2 LOMPOC SANTA BARBARA 128 S 'H' ST, LOMPOC 97 017 8201 3.6 3.4 0 2.0  06-083-2011 1 2 GOLETA SANTA BARBARA 380 N FAIRVIEW AVENUE, G 95 017 8269 4.0 3.7 0 1.8  06-083-2011 1 2 GOLETA SANTA BARBARA 380 N FAIRVIEW AVENUE, G 96 017 7612 3.4 3.0 0 1.6  06-083-2011 1 2 GOLETA SANTA BARBARA 380 N FAIRVIEW AVENUE, G 97 017 8275 3.5 3.5 0 2.0  06-083-4003 1 4 VANDENBERG AFB SANTA BARBARA STS POWER PLANT, VANDENB 95 046 4804 2.4 1.6 0 1.2  06-083-4003 1 4 VANDENBERG AFB SANTA BARBARA STS POWER PLANT, VANDENB 96 046 8072 1.4 .9 0 .7  06-083-4003 1 4 VANDENBERG AFB SANTA BARBARA STS POWER PLANT, VANDENB 97 046 7916 1.1 1.0 0 .5  06-083-5001 1 4 VANDENBERG AFB SANTA BARBARA WATT RD, VANDENBERG AFB 95 046 5234 2.2 2.1 0 1.9  06-083-5001 1 4 VANDENBERG AFB SANTA BARBARA WATT RD, VANDENBERG AFB 96 046 8202 .7 .7 0 .6  06-083-5001 1 4 VANDENBERG AFB SANTA BARBARA WATT RD, VANDENBERG AFB 97 046 1344 .8 .7 0 .7  06-085-0002 1 2 GILROY SANTA CLARA 9TH & PRINCEVILLE, GILRO 95 004 1753 3.6 3.5 0 2.0  06-085-0004 1 1 SAN JOSE SANTA CLARA 120B N 4TH ST, SAN JOSE 95 004 8339 8.9 8.3 0 5.8  06-085-0004 1 1 SAN JOSE SANTA CLARA 120B N 4TH ST, SAN JOSE 96 004 8366 8.8 8.6 0 7.0  06-085-0004 1 1 SAN JOSE SANTA CLARA 120B N 4TH ST, SAN JOSE 97 004 8316 9.9 8.5 0 6.1  06-085-0004 2 2 SAN JOSE SANTA CLARA 120B N 4TH ST, SAN JOSE 95 004 8154 8.6 8.0 0 5.4  06-085-0004 2 2 SAN JOSE SANTA CLARA 120B N 4TH ST, SAN JOSE 96 004 8277 8.4 8.3 0 6.5  06-085-0004 2 2 SAN JOSE SANTA CLARA 120B N 4TH ST, SAN JOSE 97 004 8309 9.1 8.1 0 5.8  06-085-2004 1 2 SAN JOSE SANTA CLARA 1866 W SAN CARLOS ST, SA 95 004 2520 7.3 6.4 0 3.9  06-087-0003 1 3 DAVENPORT SANTA CRUZ FIRE DEPT, DAVENPORT 95 011 7744 1.4 1.1 0 .9  06-087-0003 1 3 DAVENPORT SANTA CRUZ FIRE DEPT, DAVENPORT 96 011 8355 3.0 3.0 0 .9  06-087-0003 1 3 DAVENPORT SANTA CRUZ FIRE DEPT, DAVENPORT 97 011 8329 .9 .8 0 .7  06-095-0004 1 2 VALLEJO SOLANO 304 TUOLUMNE ST., VALLEJ 95 004 8491 7.0 6.8 0 5.3  06-095-0004 1 2 VALLEJO SOLANO 304 TUOLUMNE ST., VALLEJ 96 004 8435 6.4 6.2 0 4.9 1DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS)  AMP450 AIR QUALITY SUBSYSTEM QUICK LOOK REPORT0 CARBON MONOXIDE (42101) CALIFORNIA UNITS: 007 PPM0 P O M REP MAX 1-HR OBS> MAX 8 SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST  ________________________________________________________________________________________________________________006-095-0004 1 2 VALLEJO SOLANO 304 TUOLUMNE ST., VALLEJ 97 004 8329 6.5 6.3 0 4.9  06-097-0003 1 2 SANTA ROSA SONOMA 837 5TH ST., SANTA ROSA 95 004 8700 4.9 4.2 0 2.8  06-097-0003 1 2 SANTA ROSA SONOMA 837 5TH ST., SANTA ROSA 96 004 8201 5.6 4.4 0 3.0  06-097-0003 1 2 SANTA ROSA SONOMA 837 5TH ST., SANTA ROSA 97 004 8264 5.4 5.0 0 3.3  06-099-0005 1 2 MODESTO STANISLAUS 814 14TH ST., MODESTO 95 001 8308 11.4 9.5 0 5.7  06-099-0005 1 2 MODESTO STANISLAUS 814 14TH ST., MODESTO 96 001 8311 9.2 9.0 0 6.5  06-099-0005 1 2 MODESTO STANISLAUS 814 14TH ST., MODESTO 97 001 8316 7.1 7.1 0 5.0  06-099-0006 1 2 TURLOCK STANISLAUS 900 S MINARET STREET, T 95 069 8243 4.1 4.0 0 3.4  06-099-0006 1 2 TURLOCK STANISLAUS 900 S MINARET STREET, T 96 069 8301 5.1 5.0 0 3.2  06-099-0006 1 2 TURLOCK STANISLAUS 900 S MINARET STREET, T 97 069 7607 5.2 4.9 0 3.9  06-101-0003 1 2 YUBA CITY SUTTER 773 ALMOND ST, YUBA CITY 95 001 8207 7.5 6.9 0 4.7  06-101-0003 1 2 YUBA CITY SUTTER 773 ALMOND ST, YUBA CITY 96 001 8325 7.7 6.9 0 4.7  06-101-0003 1 2 YUBA CITY SUTTER 773 ALMOND ST, YUBA CITY 97 001 8205 6.1 6.0 0 4.1  06-107-2002 1 2 VISALIA TULARE 310 N CHURCH ST, VISALIA 95 001 8002 9.3 9.2 0 4.4  06-107-2002 1 2 VISALIA TULARE 310 N CHURCH ST, VISALIA 96 001 8210 5.3 5.1 0 4.0  06-107-2002 1 2 VISALIA TULARE 310 N CHURCH ST, VISALIA 97 001 8294 7.3 6.8 0 4.1  06-109-0005 1 2 SONORA TUOLUMNE 251 S BARRETTA, SONORA, 95 001 8229 3.9 3.9 0 3.4  06-109-0005 1 2 SONORA TUOLUMNE 251 S BARRETTA, SONORA, 96 001 8337 4.5 4.1 0 2.6  06-109-0005 1 2 SONORA TUOLUMNE 251 S BARRETTA, SONORA, 97 001 8318 6.6 3.2 0 1.9  06-111-2002 1 2 SIMI VALLEY VENTURA 5400 COCHRAN STREET, SIM 95 019 7766 8.9 7.5 0 4.3  06-111-2002 1 2 SIMI VALLEY VENTURA 5400 COCHRAN STREET, SIM 96 019 8005 7.8 6.7 0 3.4 

 06-111-2002 1 2 SIMI VALLEY VENTURA 5400 COCHRAN STREET, SIM 97 019 8114 7.4 7.1 0 3.8 

B-11
 06-111-3001 1 2 EL RIO VENTURA RIO MESA SCHOOL, EL RIO 95 019 7741 2.9 2.8 0 2.4 

 06-111-3001 1 2 EL RIO VENTURA RIO MESA SCHOOL, EL RIO 96 019 8235 2.2 2.0 0 1.5  06-111-3001 1 2 EL RIO VENTURA RIO MESA SCHOOL, EL RIO 97 019 8065 2.6 2.2 0 1.9  06-113-0004 1 3 DAVIS YOLO UC DAVIS-CAMPUS, DAVIS 96 001 4908 2.4 2.2 0 1.8  06-113-0004 1 3 DAVIS YOLO UC DAVIS-CAMPUS, DAVIS 97 001 8285 2.8 2.7 0 1.8  06-113-0006 1 2 DAVIS YOLO 23 RUSSEL BLVD, DAVIS 95 001 1416 5.3 5.3 0 3.1 1DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS)  AMP450 AIR QUALITY SUBSYSTEM QUICK LOOK REPORT0 CARBON MONOXIDE (42101)-METHODS: CODE COLLECTION METHOD ANALYSIS METHOD ==== ============================== ===================================0 000 MULTIPLE METHODS MULTIPLE METHODS 008 INSTRUMENTAL NON DISPERSIVE INFRA-RED 011 INSTRUMENTAL NONDISPERSIVE INFRA-RED 041 INSTRUMENTAL NON DISPERSIVE INFRA-RED 051 INSTRUMENTAL NON DISPERSIVE INFRA-RED 054 INSTRUMENTAL NON DISPERSIVE INFRA-RED 066 INSTRUMENTAL NON DISPERSIVE INFRA-RED 067 INSTRUMENTAL NON DISPERSIVE INFRA-RED 088 INSTRUMENTAL NON-DISPERSIVE INFARED PHOTOMETRY 093 INSTRUMENTAL GAS FILTER COORELATION CO ANALYZER1DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS)  AMP217P AIR QUALITY SUBSYSTEM AQ CLEANUP PROCESSING SUMMARY SHEET-- REPORT RECORD USER RECORD/REPORT SELECT/REQUEST

B-12
Appendix C

Distributions and Equations Used in the Ventilation Rate Algorithm

C-1
Each table in Appendix C is specific to parameter and gender (e.g., NVO2max values for

males). The tables which list distributions include the following data items 

Age: age of person in years

Source: source of data (see Table C-1)

Distr: distribution of data [normal, lognormal (LN), or uniform]

Mean: arithmetic mean for normal distributions SD: arithmetic standard deviation 

GM: geometric mean of lognormal distribution

GSD: geometric standard deviation of lognormal distribution

Lower bound: smallest value permitted 

Upper bound: largest value permitted

Assumptions: special assumptions used in developing distribution parameters 

The tables which provide equations for estimating RMR include the following data items

Age: age of person in years

Source: source of data (see Table C-1)

DV: dependent variable of regression equation

IV: independent variable of regression equation

Slope: slope of regression equation (estimate of “a” in Equation 5-10)

Interc: intercept of regression equation (estimate of “b” in Equation 5-10)

SE: standard error of regression residuals (estimate of σe in Equation 5-10)

Assumptions: special assumptions used in developing equation parameters

The codes listed under “source” are informal identification codes developed by analysts. 

The following table relates these codes to tables provided in Section 5. 

C-2
Table C-1. Explanation of Codes Listed Under “Source” in Appendix C Tables. 

Code Listed in “Source”

Column of Table in

Appendix C

Referenced Table in

This Report Original References

1 a Astrand and Rodahl (1977),

Mercier et al. (1991).

2 a Astrand and Rodahl (1977),

Astrand (1960). 

3a Table 5-4 Astrand (1960)

3c Table 5-4 Katch and Park (1975)

3d Table 5-4 Heil et al. (1995)

4 Table 5-5 Brainard and Burmaster

(1992), Burmaster et al.

(1994).

5 Table 5-5 Esmail, Bhambhani, and

Brintnell (1995). 

R47d - R47f, R47j - R47l Table 5-6 Schofield (1985) a

Figure 9-13 of Astrand and Rodahl (1977) provides estimates of VO2max-to-weight ratio

that are specific to age and gender. The mean NVO2max values for males and females

aged 18 and 19 years listed in this appendix were based on these ratios. The standard

deviation specified for males 18 and 19 (4.9 ml min-1 kg-1) was estimated by assuming

the coefficient of variation was equal to 0.099, a value derived from data presented by

Mercier et al. (1991) for males aged 15 years. The standard deviation for females 18

and 19 (4.8 ml min-1 kg-1) was estimated by assuming the coefficient of variation was

equal to 0.118, a value derived from data presented by Astrand (1960) for females 20 to

29 years. 

C-3
NVO2max - Males

Males (last revised 6-11-98)

NVO2max distribution

Age Source Distr Mean SD Lower Upper Assumptions

18 1 Normal 50.0 4.9 40.3 59.7

19 1 Normal 50.0 4.9 40.3 59.7

20 3a Normal 58.6 4.5 49.8 67.4

21 3c Normal 54.5 7.6 39.6 69.4

22 3c Normal 54.5 7.6 39.6 69.4

23 3c Normal 54.5 7.6 39.6 69.4

24 3c Normal 54.5 7.6 39.6 69.4

25 3c Normal 54.5 7.6 39.6 69.4

26 3c Normal 54.5 7.6 39.6 69.4

27 3c Normal 54.5 7.6 39.6 69.4

28 3a Normal 58.6 4.5 49.8 67.4

29 3a Normal 58.6 4.5 49.8 67.4

30 3a Normal 39.8 7.3 25.5 54.1

31 3a Normal 39.8 7.3 25.5 54.1

32 3a Normal 39.8 7.3 25.5 54.1

33 3a Normal 39.8 7.3 25.5 54.1

34 3a Normal 39.8 7.3 25.5 54.1

35 3a Normal 39.8 7.3 25.5 54.1

36 3a Normal 39.8 7.3 25.5 54.1

37 3a Normal 39.8 7.3 25.5 54.1

38 3a Normal 39.8 7.3 25.5 54.1

39 3a Normal 39.8 7.3 25.5 54.1

40 3a Normal 39.2 5.5 28.4 50.0

41 3a Normal 39.2 5.5 28.4 50.0

42 3a Normal 39.2 5.5 28.4 50.0

43 3a Normal 39.2 5.5 28.4 50.0

44 3a Normal 39.2 5.5 28.4 50.0

45 3a Normal 39.2 5.5 28.4 50.0

46 3a Normal 39.2 5.5 28.4 50.0

47 3a Normal 39.2 5.5 28.4 50.0

48 3a Normal 39.2 5.5 28.4 50.0

49 3a Normal 39.2 5.5 28.4 50.0

50 3a Normal 33.1 4.9 23.5 42.7

51 3a Normal 33.1 4.9 23.5 42.7

52 3a Normal 33.1 4.9 23.5 42.7

53 3a Normal 33.1 4.9 23.5 42.7

54 3a Normal 33.1 4.9 23.5 42.7

55 3a Normal 33.1 4.9 23.5 42.7

56 3a Normal 33.1 4.9 23.5 42.7

57 3a Normal 33.1 4.9 23.5 42.7

58 3a Normal 33.1 4.9 23.5 42.7

59 3a Normal 33.1 4.9 23.5 42.7

60 3a Normal 31.4 5.3 21.0 41.8

61 3a Normal 31.4 5.3 21.0 41.8

62 3a Normal 31.4 5.3 21.0 41.8

C-4
NVO2max distribution

Age Source Distr Mean SD Lower Upper Assumptions

63 3a Normal 31.4 5.3 21.0 41.8

64 3a Normal 31.4 5.3 21.0 41.8

65 3a Normal 31.4 5.3 21.0 41.8

66 3a Normal 31.4 5.3 21.0 41.8

67 3a Normal 31.4 5.3 21.0 41.8

68 3a Normal 31.4 5.3 21.0 41.8

69 3a Normal 31.4 5.3 21.0 41.8

70 3d Normal 27.2 5.7 16.1 38.3

71 3d Normal 27.2 5.7 16.1 38.3

72 3d Normal 27.2 5.7 16.1 38.3

73 3d Normal 27.2 5.7 16.1 38.3

74 3d Normal 27.2 5.7 16.1 38.3

75 3d Normal 27.2 5.7 16.1 38.3

76 3d Normal 27.2 5.7 16.1 38.3

77 3d Normal 27.2 5.7 16.1 38.3

78 3d Normal 27.2 5.7 16.1 38.3

79 3d Normal 27.2 5.7 16.1 38.3

80 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

81 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

82 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

83 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

84 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

85 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

86 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

87 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

88 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

89 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

90 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

91 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

92 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

93 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

94 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

95 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

96 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

97 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

98 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

99 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

100 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

C-5
NVO2max - Females

Females (last revised 6-11-98)

NVO2max distribution

Age Source Distr Mean SD Lower Upper Assumptions

18 2 Normal 41.0 4.8 31.5 50.5 CV = 4.7/39.9

19 2 Normal 41.0 4.8 31.5 50.5 CV = 4.7/39.9

20 3a Normal 39.9 4.7 30.7 49.1

21 3a Normal 39.9 4.7 30.7 49.1

22 3a Normal 39.9 4.7 30.7 49.1

23 3a Normal 39.9 4.7 30.7 49.1

24 3a Normal 39.9 4.7 30.7 49.1

25 3a Normal 39.9 4.7 30.7 49.1

26 3a Normal 39.9 4.7 30.7 49.1

27 3a Normal 39.9 4.7 30.7 49.1

28 3a Normal 39.9 4.7 30.7 49.1

29 3a Normal 39.9 4.7 30.7 49.1

30 3a Normal 37.3 5.2 27.1 47.5

31 3a Normal 37.3 5.2 27.1 47.5

32 3a Normal 37.3 5.2 27.1 47.5

33 3a Normal 37.3 5.2 27.1 47.5

34 3a Normal 37.3 5.2 27.1 47.5

35 3a Normal 37.3 5.2 27.1 47.5

36 3a Normal 37.3 5.2 27.1 47.5

37 3a Normal 37.3 5.2 27.1 47.5

38 3a Normal 37.3 5.2 27.1 47.5

39 3a Normal 37.3 5.2 27.1 47.5

40 3a Normal 32.5 2.7 27.2 37.8

41 3a Normal 32.5 2.7 27.2 37.8

42 3a Normal 32.5 2.7 27.2 37.8

43 3a Normal 32.5 2.7 27.2 37.8

44 3a Normal 32.5 2.7 27.2 37.8

45 3a Normal 32.5 2.7 27.2 37.8

46 3a Normal 32.5 2.7 27.2 37.8

47 3a Normal 32.5 2.7 27.2 37.8

48 3a Normal 32.5 2.7 27.2 37.8

49 3a Normal 32.5 2.7 27.2 37.8

50 3a Normal 28.4 2.7 23.1 33.7

51 3a Normal 28.4 2.7 23.1 33.7

52 3a Normal 28.4 2.7 23.1 33.7

53 3a Normal 28.4 2.7 23.1 33.7

54 3a Normal 28.4 2.7 23.1 33.7

55 3a Normal 28.4 2.7 23.1 33.7

56 3a Normal 28.4 2.7 23.1 33.7

57 3a Normal 28.4 2.7 23.1 33.7

58 3a Normal 28.4 2.7 23.1 33.7

59 3a Normal 28.4 2.7 23.1 33.7

60 3a Normal 30.7 8.0 15.1 46.3

61 3a Normal 30.7 8.0 15.1 46.3

62 3a Normal 30.7 8.0 15.1 46.3

63 3a Normal 30.7 8.0 15.1 46.3

C-6
NVO2max distribution

Age Source Distr Mean SD Lower Upper Assumptions

64 3a Normal 30.7 8.0 15.1 46.3

65 3a Normal 30.7 8.0 15.1 46.3

66 3d Normal 30.7 8.0 15.1 46.3

67 3d Normal 30.7 8.0 15.1 46.3

68 3d Normal 30.7 8.0 15.1 46.3

69 3d Normal 30.7 8.0 15.1 46.3

70 3d Normal 27.2 5.7 16.1 38.3

71 3d Normal 27.2 5.7 16.1 38.3

72 3d Normal 27.2 5.7 16.1 38.3

73 3d Normal 27.2 5.7 16.1 38.3

74 3d Normal 27.2 5.7 16.1 38.3

75 3d Normal 27.2 5.7 16.1 38.3

76 3d Normal 27.2 5.7 16.1 38.3

77 3d Normal 27.2 5.7 16.1 38.3

78 3d Normal 27.2 5.7 16.1 38.3

79 3d Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

80 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

81 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

82 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

83 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

84 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

85 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

86 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

87 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

88 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

89 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

90 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

91 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

92 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

93 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

94 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

95 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

96 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

97 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

98 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

99 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

100 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies

C-7
Body Mass - Males

Males (last revised 6-11-98)

Body mass distribution, kg

Age Source Distr GM GSD Lower Upper Assumptions

18 4 LN 70.1 1.172 51.4 95.7

19 4 LN 70.8 1.166 52.4 95.7

20 4 LN 76.7 1.190 54.5 107.9

21 4 LN 76.7 1.190 54.5 107.9

22 4 LN 76.7 1.190 54.5 107.9

23 4 LN 76.7 1.190 54.5 107.9

24 4 LN 76.7 1.190 54.5 107.9

25 4 LN 76.7 1.190 54.5 107.9

26 4 LN 76.7 1.190 54.5 107.9

27 4 LN 76.7 1.190 54.5 107.9

28 4 LN 76.7 1.190 54.5 107.9

29 4 LN 76.7 1.190 54.5 107.9

30 4 LN 76.7 1.190 54.5 107.9

31 4 LN 76.7 1.190 54.5 107.9

32 4 LN 76.7 1.190 54.5 107.9

33 4 LN 76.7 1.190 54.5 107.9

34 4 LN 76.7 1.190 54.5 107.9

35 4 LN 76.7 1.190 54.5 107.9

36 4 LN 76.7 1.190 54.5 107.9

37 4 LN 76.7 1.190 54.5 107.9

38 4 LN 76.7 1.190 54.5 107.9

39 4 LN 76.7 1.190 54.5 107.9

40 4 LN 76.7 1.190 54.5 107.9

41 4 LN 76.7 1.190 54.5 107.9

42 4 LN 76.7 1.190 54.5 107.9

43 4 LN 76.7 1.190 54.5 107.9

44 4 LN 76.7 1.190 54.5 107.9

45 4 LN 76.7 1.190 54.5 107.9

46 4 LN 76.7 1.190 54.5 107.9

47 4 LN 76.7 1.190 54.5 107.9

48 4 LN 76.7 1.190 54.5 107.9

49 4 LN 76.7 1.190 54.5 107.9

50 4 LN 76.7 1.190 54.5 107.9

51 4 LN 76.7 1.190 54.5 107.9

52 4 LN 76.7 1.190 54.5 107.9

53 4 LN 76.7 1.190 54.5 107.9

54 4 LN 76.7 1.190 54.5 107.9

55 4 LN 76.7 1.190 54.5 107.9

56 4 LN 76.7 1.190 54.5 107.9

57 4 LN 76.7 1.190 54.5 107.9

58 4 LN 76.7 1.190 54.5 107.9

59 4 LN 76.7 1.190 54.5 107.9

60 4 LN 76.7 1.190 54.5 107.9

61 4 LN 76.7 1.190 54.5 107.9

62 4 LN 76.7 1.190 54.5 107.9

63 4 LN 76.7 1.190 54.5 107.9

64 4 LN 76.7 1.190 54.5 107.9

C-8
Body mass distribution, kg

Age Source Distr GM GSD Lower Upper Assumptions

65 4 LN 76.7 1.190 54.5 107.9

66 4 LN 76.7 1.190 54.5 107.9

67 4 LN 76.7 1.190 54.5 107.9

68 4 LN 76.7 1.190 54.5 107.9

69 4 LN 76.7 1.190 54.5 107.9

70 4 LN 76.7 1.190 54.5 107.9

71 4 LN 76.7 1.190 54.5 107.9

72 4 LN 76.7 1.190 54.5 107.9

73 4 LN 76.7 1.190 54.5 107.9

74 4 LN 76.7 1.190 54.5 107.9

75 4 LN 76.7 1.190 54.5 107.9

76 4 LN 76.7 1.190 54.5 107.9

77 4 LN 76.7 1.190 54.5 107.9

78 4 LN 76.7 1.190 54.5 107.9

79 4 LN 76.7 1.190 54.5 107.9

80 4 LN 76.7 1.190 54.5 107.9

81 4 LN 76.7 1.190 54.5 107.9

82 4 LN 76.7 1.190 54.5 107.9

83 4 LN 76.7 1.190 54.5 107.9

84 4 LN 76.7 1.190 54.5 107.9

85 4 LN 76.7 1.190 54.5 107.9

86 4 LN 76.7 1.190 54.5 107.9

87 4 LN 76.7 1.190 54.5 107.9

88 4 LN 76.7 1.190 54.5 107.9

89 4 LN 76.7 1.190 54.5 107.9

90 4 LN 76.7 1.190 54.5 107.9

91 4 LN 76.7 1.190 54.5 107.9

92 4 LN 76.7 1.190 54.5 107.9

93 4 LN 76.7 1.190 54.5 107.9

94 4 LN 76.7 1.190 54.5 107.9

95 4 LN 76.7 1.190 54.5 107.9

96 4 LN 76.7 1.190 54.5 107.9

97 4 LN 76.7 1.190 54.5 107.9

98 4 LN 76.7 1.190 54.5 107.9

99 4 LN 76.7 1.190 54.5 107.9

100 4 LN 76.7 1.190 54.5 107.9

C-9
Body Mass - Females

Females (last revised 6-11-98)

Body mass distribution, kg

Age Source Distr GM GSD Lower Upper Assumptions

18 4 LN 58.6 1.158 44.0 78.1

19 4 LN 60.3 1.161 45.0 80.8

20 4 LN 64.7 1.220 43.8 95.5

21 4 LN 64.7 1.220 43.8 95.5

22 4 LN 64.7 1.220 43.8 95.5

23 4 LN 64.7 1.220 43.8 95.5

24 4 LN 64.7 1.220 43.8 95.5

25 4 LN 64.7 1.220 43.8 95.5

26 4 LN 64.7 1.220 43.8 95.5

27 4 LN 64.7 1.220 43.8 95.5

28 4 LN 64.7 1.220 43.8 95.5

29 4 LN 64.7 1.220 43.8 95.5

30 4 LN 64.7 1.220 43.8 95.5

31 4 LN 64.7 1.220 43.8 95.5

32 4 LN 64.7 1.220 43.8 95.5

33 4 LN 64.7 1.220 43.8 95.5

34 4 LN 64.7 1.220 43.8 95.5

35 4 LN 64.7 1.220 43.8 95.5

36 4 LN 64.7 1.220 43.8 95.5

37 4 LN 64.7 1.220 43.8 95.5

38 4 LN 64.7 1.220 43.8 95.5

39 4 LN 64.7 1.220 43.8 95.5

40 4 LN 64.7 1.220 43.8 95.5

41 4 LN 64.7 1.220 43.8 95.5

42 4 LN 64.7 1.220 43.8 95.5

43 4 LN 64.7 1.220 43.8 95.5

44 4 LN 64.7 1.220 43.8 95.5

45 4 LN 64.7 1.220 43.8 95.5

46 4 LN 64.7 1.220 43.8 95.5

47 4 LN 64.7 1.220 43.8 95.5

48 4 LN 64.7 1.220 43.8 95.5

49 4 LN 64.7 1.220 43.8 95.5

50 4 LN 64.7 1.220 43.8 95.5

51 4 LN 64.7 1.220 43.8 95.5

52 4 LN 64.7 1.220 43.8 95.5

53 4 LN 64.7 1.220 43.8 95.5

54 4 LN 64.7 1.220 43.8 95.5

55 4 LN 64.7 1.220 43.8 95.5

56 4 LN 64.7 1.220 43.8 95.5

57 4 LN 64.7 1.220 43.8 95.5

C-10
Body mass distribution, kg

Age Source Distr GM GSD Lower Upper Assumptions

58 4 LN 64.7 1.220 43.8 95.5

59 4 LN 64.7 1.220 43.8 95.5

60 4 LN 64.7 1.220 43.8 95.5

61 4 LN 64.7 1.220 43.8 95.5

62 4 LN 64.7 1.220 43.8 95.5

63 4 LN 64.7 1.220 43.8 95.5

64 4 LN 64.7 1.220 43.8 95.5

65 4 LN 64.7 1.220 43.8 95.5

66 4 LN 64.7 1.220 43.8 95.5

67 4 LN 64.7 1.220 43.8 95.5

68 4 LN 64.7 1.220 43.8 95.5

69 4 LN 64.7 1.220 43.8 95.5

70 4 LN 64.7 1.220 43.8 95.5

71 4 LN 64.7 1.220 43.8 95.5

72 4 LN 64.7 1.220 43.8 95.5

73 4 LN 64.7 1.220 43.8 95.5

74 4 LN 64.7 1.220 43.8 95.5

75 4 LN 64.7 1.220 43.8 95.5

76 4 LN 64.7 1.220 43.8 95.5

77 4 LN 64.7 1.220 43.8 95.5

78 4 LN 64.7 1.220 43.8 95.5

79 4 LN 64.7 1.220 43.8 95.5

80 4 LN 64.7 1.220 43.8 95.5

81 4 LN 64.7 1.220 43.8 95.5

82 4 LN 64.7 1.220 43.8 95.5

83 4 LN 64.7 1.220 43.8 95.5

84 4 LN 64.7 1.220 43.8 95.5

85 4 LN 64.7 1.220 43.8 95.5

86 4 LN 64.7 1.220 43.8 95.5

87 4 LN 64.7 1.220 43.8 95.5

88 4 LN 64.7 1.220 43.8 95.5

89 4 LN 64.7 1.220 43.8 95.5

90 4 LN 64.7 1.220 43.8 95.5

91 4 LN 64.7 1.220 43.8 95.5

92 4 LN 64.7 1.220 43.8 95.5

93 4 LN 64.7 1.220 43.8 95.5

94 4 LN 64.7 1.220 43.8 95.5

95 4 LN 64.7 1.220 43.8 95.5

96 4 LN 64.7 1.220 43.8 95.5

97 4 LN 64.7 1.220 43.8 95.5

98 4 LN 64.7 1.220 43.8 95.5

99 4 LN 64.7 1.220 43.8 95.5

C-11
Body mass distribution, kg

Age Source Distr GM GSD Lower Upper Assumptions

100 4 LN 64.7 1.220 43.8 95.5

C-12
ECF - Males

Males (last revised 6-11-98)

ECF

Age Source Distr Lower Upper Assumptions

18 5 Uniform 0.20 0.21

19 5 Uniform 0.20 0.21

20 5 Uniform 0.20 0.21

21 5 Uniform 0.20 0.21

22 5 Uniform 0.20 0.21

23 5 Uniform 0.20 0.21

24 5 Uniform 0.20 0.21

25 5 Uniform 0.20 0.21

26 5 Uniform 0.20 0.21

27 5 Uniform 0.20 0.21

28 5 Uniform 0.20 0.21

29 5 Uniform 0.20 0.21

30 5 Uniform 0.20 0.21

31 5 Uniform 0.20 0.21

32 5 Uniform 0.20 0.21

33 5 Uniform 0.20 0.21

34 5 Uniform 0.20 0.21

35 5 Uniform 0.20 0.21

36 5 Uniform 0.20 0.21

37 5 Uniform 0.20 0.21

38 5 Uniform 0.20 0.21

39 5 Uniform 0.20 0.21

40 5 Uniform 0.20 0.21

41 5 Uniform 0.20 0.21

42 5 Uniform 0.20 0.21

43 5 Uniform 0.20 0.21

44 5 Uniform 0.20 0.21

45 5 Uniform 0.20 0.21

46 5 Uniform 0.20 0.21

47 5 Uniform 0.20 0.21

48 5 Uniform 0.20 0.21

49 5 Uniform 0.20 0.21

50 5 Uniform 0.20 0.21

51 5 Uniform 0.20 0.21

52 5 Uniform 0.20 0.21

53 5 Uniform 0.20 0.21

54 5 Uniform 0.20 0.21

55 5 Uniform 0.20 0.21

56 5 Uniform 0.20 0.21

57 5 Uniform 0.20 0.21

C-13
ECF

Age Source Distr Lower Upper Assumptions

58 5 Uniform 0.20 0.21

59 5 Uniform 0.20 0.21

60 5 Uniform 0.20 0.21

61 5 Uniform 0.20 0.21

62 5 Uniform 0.20 0.21

63 5 Uniform 0.20 0.21

64 5 Uniform 0.20 0.21

65 5 Uniform 0.20 0.21

66 5 Uniform 0.20 0.21

67 5 Uniform 0.20 0.21

68 5 Uniform 0.20 0.21

69 5 Uniform 0.20 0.21

70 5 Uniform 0.20 0.21

71 5 Uniform 0.20 0.21

72 5 Uniform 0.20 0.21

73 5 Uniform 0.20 0.21

74 5 Uniform 0.20 0.21

75 5 Uniform 0.20 0.21

76 5 Uniform 0.20 0.21

77 5 Uniform 0.20 0.21

78 5 Uniform 0.20 0.21

79 5 Uniform 0.20 0.21

80 5 Uniform 0.20 0.21

81 5 Uniform 0.20 0.21

82 5 Uniform 0.20 0.21

83 5 Uniform 0.20 0.21

84 5 Uniform 0.20 0.21

85 5 Uniform 0.20 0.21

86 5 Uniform 0.20 0.21

87 5 Uniform 0.20 0.21

88 5 Uniform 0.20 0.21

89 5 Uniform 0.20 0.21

90 5 Uniform 0.20 0.21

91 5 Uniform 0.20 0.21

92 5 Uniform 0.20 0.21

93 5 Uniform 0.20 0.21

94 5 Uniform 0.20 0.21

95 5 Uniform 0.20 0.21

96 5 Uniform 0.20 0.21

97 5 Uniform 0.20 0.21

98 5 Uniform 0.20 0.21

99 5 Uniform 0.20 0.21

C-14
ECF

Age Source Distr Lower Upper Assumptions

100 5 Uniform 0.20 0.21

C-15
 ECF - Females

Females (last revised 6-11-98)

ECF

Age Source Distr Lower Upper Assumptions

18 5 Uniform 0.20 0.21

19 5 Uniform 0.20 0.21

20 5 Uniform 0.20 0.21

21 5 Uniform 0.20 0.21

22 5 Uniform 0.20 0.21

23 5 Uniform 0.20 0.21

24 5 Uniform 0.20 0.21

25 5 Uniform 0.20 0.21

26 5 Uniform 0.20 0.21

27 5 Uniform 0.20 0.21

28 5 Uniform 0.20 0.21

29 5 Uniform 0.20 0.21

30 5 Uniform 0.20 0.21

31 5 Uniform 0.20 0.21

32 5 Uniform 0.20 0.21

33 5 Uniform 0.20 0.21

34 5 Uniform 0.20 0.21

35 5 Uniform 0.20 0.21

36 5 Uniform 0.20 0.21

37 5 Uniform 0.20 0.21

38 5 Uniform 0.20 0.21

39 5 Uniform 0.20 0.21

40 5 Uniform 0.20 0.21

41 5 Uniform 0.20 0.21

42 5 Uniform 0.20 0.21

43 5 Uniform 0.20 0.21

44 5 Uniform 0.20 0.21

45 5 Uniform 0.20 0.21

46 5 Uniform 0.20 0.21

47 5 Uniform 0.20 0.21

48 5 Uniform 0.20 0.21

49 5 Uniform 0.20 0.21

50 5 Uniform 0.20 0.21

51 5 Uniform 0.20 0.21

52 5 Uniform 0.20 0.21

53 5 Uniform 0.20 0.21

54 5 Uniform 0.20 0.21

55 5 Uniform 0.20 0.21

56 5 Uniform 0.20 0.21

57 5 Uniform 0.20 0.21

C-16
ECF

Age Source Distr Lower Upper Assumptions

58 5 Uniform 0.20 0.21

59 5 Uniform 0.20 0.21

60 5 Uniform 0.20 0.21

61 5 Uniform 0.20 0.21

62 5 Uniform 0.20 0.21

63 5 Uniform 0.20 0.21

64 5 Uniform 0.20 0.21

65 5 Uniform 0.20 0.21

66 5 Uniform 0.20 0.21

67 5 Uniform 0.20 0.21

68 5 Uniform 0.20 0.21

69 5 Uniform 0.20 0.21

70 5 Uniform 0.20 0.21

71 5 Uniform 0.20 0.21

72 5 Uniform 0.20 0.21

73 5 Uniform 0.20 0.21

74 5 Uniform 0.20 0.21

75 5 Uniform 0.20 0.21

76 5 Uniform 0.20 0.21

77 5 Uniform 0.20 0.21

78 5 Uniform 0.20 0.21

79 5 Uniform 0.20 0.21

80 5 Uniform 0.20 0.21

81 5 Uniform 0.20 0.21

82 5 Uniform 0.20 0.21

83 5 Uniform 0.20 0.21

84 5 Uniform 0.20 0.21

85 5 Uniform 0.20 0.21

86 5 Uniform 0.20 0.21

87 5 Uniform 0.20 0.21

88 5 Uniform 0.20 0.21

89 5 Uniform 0.20 0.21

90 5 Uniform 0.20 0.21

91 5 Uniform 0.20 0.21

92 5 Uniform 0.20 0.21

93 5 Uniform 0.20 0.21

94 5 Uniform 0.20 0.21

95 5 Uniform 0.20 0.21

96 5 Uniform 0.20 0.21

97 5 Uniform 0.20 0.21

98 5 Uniform 0.20 0.21

99 5 Uniform 0.20 0.21

C-17
ECF

Age Source Distr Lower Upper Assumptions

100 5 Uniform 0.20 0.21

C-18
RMR - Males

Males (last revised 6-11-98)

Regression equation Estimate for

Age Source DV IV Slope Interc SE Units

median

weight Assumptions

18 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.3

19 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.4

20 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7

21 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7

22 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7

23 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7

24 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7

25 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7

26 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7

27 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7

28 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7

29 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7

30 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

31 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

32 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

33 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

34 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

35 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

36 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

37 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

38 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

39 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

40 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

41 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

42 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

43 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

44 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

45 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

46 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

47 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

48 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

49 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

50 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

51 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

52 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

53 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

54 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

55 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

56 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

C-19
Regression equation Estimate for

Age Source DV IV Slope Interc SE Units

median

weight Assumptions

57 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

58 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

59 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

60 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

61 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

62 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

63 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

64 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

65 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

66 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

67 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

68 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

69 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

70 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3

71 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

72 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

73 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

74 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

75 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

76 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

77 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

78 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

79 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

80 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

81 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

82 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

83 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

84 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

85 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

86 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

87 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

88 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

89 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

90 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

91 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

92 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

93 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

94 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

95 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

96 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

97 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

C-20
Regression equation Estimate for

Age Source DV IV Slope Interc SE Units

median

weight Assumptions

98 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

99 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

100 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2

C-21
RMR - Females

Females (last revised 6-11-98)

Regression equation Estimate for

Age Source DV IV Slope Interc SE Units

median

weight Assumptions

18 R47d BMR BM 0.062 2.036 0.500 MJ/day 5.7

19 R47d BMR BM 0.062 2.036 0.500 MJ/day 5.8

20 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0

21 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0

22 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0

23 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0

24 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0

25 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0

26 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0

27 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0

28 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0

29 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0

30 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

31 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

32 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

33 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

34 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

35 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

36 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

37 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

38 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

39 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

40 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

41 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

42 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

43 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

44 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

45 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

46 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

47 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

48 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

49 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

50 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

51 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

52 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

53 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

54 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

55 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

56 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

C-22
Regression equation Estimate for

Age Source DV IV Slope Interc SE Units

median

weight Assumptions

57 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

58 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

59 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7

60 R47e BMR BM 0.038 2.755 0.450 MJ/day 5.2

61 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

62 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

63 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

64 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

65 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

66 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

67 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

68 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

69 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

70 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

71 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

72 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

73 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

74 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

75 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

76 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

77 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

78 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

79 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

80 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

81 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

82 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

83 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

84 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

85 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

86 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

87 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

88 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

89 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

90 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

91 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

92 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

93 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

94 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

95 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

96 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

97 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

C-23
Regression equation Estimate for

Age Source DV IV Slope Interc SE Units

median

weight Assumptions

98 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

99 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

100 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2

C-24
Appendix D

Equations for Converting Weight to Height

Proposed for the 1998 Version of pNEM/CO

D-1
Memorandum

To: Arlene Rosenbaum

Systems Applications International

From: Ted Johnson

TRJ Environmental, Inc.

Date: September 29, 1998 (Rev. 1.2)

Project: EPA Work Assignment 1-19

Memo No. 5: Equations for Converting Weight to Height Proposed for the

1998 Version of pNEM/CO 

Work Assignment 1-19 of EPA Contract No. 68-D6-0064 directs the ICF project team to 

propose and implement modifications to the Probabilistic NAAQS Exposure Model for

Carbon Monoxide (pNEM/CO). These modifications will include enhancements to the

algorithm which provides a “physiological profile” for each person simulated by

pNEM/CO. The enhanced algorithm will use probabilistic techniques to generate values

for all physiological parameters required by two other pNEM/CO algorithms: (1) the

algorithm that estimates ventilation (respiratory) rate and (2) the algorithm that

estimates carboxyhemoglobin (COHb) level. This memorandum provides equations

which can be incorporated into the physiological profile algorithm to estimate height

given a predetermined value for weight. 

Background

In the 1992 version of pNEM/CO (Johnson et al., 1992), the COHb algorithm estimates

blood volume (Vb) as a function of weight and height by the following equations

Men: Vb = (20.4)(W) + (0.00683)(H3

) - 30 (1)

Women: Vb = (14.6)(W) + (0.00678)(H3

) - 30. (2)

W is weight in pounds, and H is height in inches. We propose to use this same method

to determine blood volume in the 1998 version of pNEM/CO. Note that Equations 1

and 2 require estimates of weight and height specific to the simulated person. 

In the 1992 version of pNEM/CO, height was selected from one of six normal

distributions which varied according to age and gender (see Table 14 of Johnson et al.

1992). The value for weight was then determined by the equation

weight = A0 + (A1)(height) + (z)(S.E.) (3)

where the values for A0, A1, and S.E. were obtained from Table 14 of Johnson et al.

D-2
(1992) according to the gender and age group of the simulated person. The value of z

was randomly selected from a unit normal distribution. 

In the 1998 version of pNEM/CO, weight will be used in both the ventilation and COHb

algorithms, whereas height will be used only in the COHb algorithm. As discussed in

Memorandum No. 3, we have developed lognormal distributions of weight for all

combinations of gender and age where age is given in one-year increments from zero to

100. For these reasons, we would prefer that the physiological profile algorithm first

select a value of weight according to gender and age and then let that value determine

a corresponding value of height. The 1998 version of pNEM/CO will initially be applied

to adults in Denver, Colorado. Consequently, the physiological profile algorithm

currently requires gender-specific probabilistic equations which predict height as a

function of weight for people ages 18 years and older. 

Equations for Estimating Height from Weight

As noted in Memorandum No. 3, the lognormal distributions proposed for weight were

obtained from an article by Burmaster and Crouch (1997). In an article published five

years earlier, Brainard and Burmaster (1992) provide a method for sampling weight and

height simultaneously from a bivariate distribution based on data obtained from the

second National Health and Nutrition Examination Survey (NHANES II). Although

Brainard and Burmaster do not explicitly provide a method for estimating height given

weight, statistical results presented in the article indirectly suggest that height could be

determined from weight using a regression-derived equation of the form

height = a0 + (a1)[ln(weight)] + e (4)

in which e is a normally-distributed random term with mean = zero and standard

deviation = σe. Ideally, the values of a0, a1, and σe would be specific to age and gender. 

Table 1 is a facsimile of a table appearing in the article by Brainard and Burmaster

(1992). This table lists the number of males (18 to 74 years) associated with each of

210 combinations of height (inches) and weight (lbs). The authors suggest that an

analysis of weight vs. height based on these data be limited to the 132 cells that fall

within the shaded “box” (13 height categories times 12 weight categories). 

Consequently, we set up a data file containing 132 records, each corresponding to one

of the cells in the included in the box. Each record listed the indicated height value, the

midpoint of the indicated weight range (e.g., 115 = midpoint of 110 to 119 range), and

the number of males associated with the cell. 

As indicated in Equation 4, Brainard and Burmaster recommend relating height to

ln(weight). Consequently, we performed a weighted linear regression of the data file

using height as the dependent variable, ln(weight) as the independent variable, and

number of males as a weighting factor. The resulting regression equation for males (18

to 74 years) was

height, inches = 34.43 in + (6.67)[ln(weight,lbs)] + e. (5)

D-3
The R2

 value was 0.1469. 

To estimate σe, the standard deviation of e, we used the equation

σe = [Σ(Oij - Eij)

2

(Mij)/(total males)]0.5 (6)

in which Oij was the observed (listed) height associated with cellij, Eij was the estimated

height for cellij based on the regression equation, Mij was the number of males listed in

Table 1 for cellij, and total males was the total number of males in all cells. Equation 6

produced the estimate σe = 2.38 inches. 

To test the regression results, we performed a sample calculation using Equation 5 and

σe = 2.38 inches. For weight = 175 lb, we obtained height = 68.88 ± 2.38 inches. 

Based on this estimate, we would expect Table 1 to show a mode (most frequent value)

for 175 lbs near 68.88 inches, with approximately 68.3 percent of the values falling

between 68.88 - 2.38 = 66.5 inches and 68.88 + 2.38 = 71.26 inches. Consistent with

our estimate, Table 1 shows a mode of 69 inches in the height values listed for the

weight range (170 to 179 lb) centered on 175 lbs. Furthermore, 68.7 percent of the

males in the weight range have heights that fall between 67 and 71 inches. 

We repeated the analysis using the data for females provided by Table 2 of Brainard

and Burmaster (1992). Table 2 of this memo presents a facsimile of this table which

lists the number of females (18 to 74 years) associated with each of 270 combinations

of height and weight. Brainard and Burmaster suggest that an analysis of weight vs.

height based on these data be limited to the 208 cells that fall within the shaded “box”

(16 height categories times 13 weight categories). Consistent with the earlier analysis,

we set up a data file containing these 208 records in which each record listed the

indicated height value, the midpoint of the indicated weight range (e.g., 115 = midpoint

of 110 to 119 range), and the number of females associated with the cell. 

We performed a weighted linear regression of the resulting data file using height as the

dependent variable, ln(weight) as the independent variable, and number of females as a

weighting factor. The resulting regression equation for females (18 to 74 years) was

height, in = 48.07 in + (3.07)[ln(weight,lb)] + e. (7)

The R2

 value for females was low (0.0477) relative to the value obtained above for

males (0.1469), but was still statistically significant. 

To estimate σe, the standard deviation of e, we used the equation

σe = [Σ(Oij - Eij)

2

(Fij)/(total females)]0.5 (8)

in which Oij was the observed (listed) height associated with cellij, Eij was the estimated

height for cellij based on the regression equation, Fij was the number of females listed in

Table 2 for cellij, and total females is the total number of females in all cells. Equation 8

yielded an estimate of σe = 2.48 inches for females, a value consistent with the value of

D-4
2.38 inches obtained previously for males. 

We performed a sample calculation to test the regression results. For weight = 145 lb,

Equation 7 yielded an estimate of height = 63.35 in ± 2.48 in. Based on this estimate,

we would expect Figure 2 to show a mode for 145 lb near 63.35 inches, with

approximately 68.3 percent of the values falling between 63.35 - 2.48 = 60.87 inches

and 63.35 + 2.48 = 65.83 inches. Figure 2 shows a height mode for the corresponding

weight range (140 to 149 lb) at 63 in, with 75 percent of the females falling between 61

in and 66 in. 

It should be noted that the regression analyses described above were performed using

weighting factors (i.e., values of Mij and Fij) which pertained to ranges of weight values

(e.g., 140 to 149 lbs) rather than to individual weight values. Although each weight

range was represented by its midpoint in the regression analyses, the resulting

estimates of σe are larger than the estimates we would have obtained from an analysis

in which the values of Mij and Fij were determined for individual weights rather than for 

weight ranges. Consequently, the values of σe presented above should be considered

conservative in the sense that they indicate an overly wide distribution of possible

heights for a given weight. 

In addition, it should be noted that the data used in the regression analyses (Figures 1

and 2) represent subjects 18 to 74 years of age. We intend to apply the regression 

results to all adult cohorts in 1998 pNEM/CO analyses. As these cohorts include

simulated persons 18 to 100 years of age, we will occasionally apply the results to

simulated people who fall outside the range of data used in the regression analyses. 

Although we believe the proposed equations are adequate for use in pNEM/CO, we will

continue to look for alternative estimation equations in the literature. To facilitate ongoing model testing, Jim Capel has incorporated the proposed equations into the 1998

version of pNEM/CO. Table 3 presents a subroutine for performing the required

calculations. 

Parameters To Be Generated by the Physiological Profile Algorithm

At this stage in the developmental process, we need a “master list” of the components

which make up the physiological profile. Table 4 lists all adult physiological parameters

which are currently required by the COHb and ventilation rate algorithms in the 1998

version of pNEM/CO. This table may change as we revise these algorithms. 

D-5
References

Brainard, J., and D. E. Burmaster. 1992. “Bivariate Distributions for Height and Weight

of Men and Women in the United States,” Risk Analysis, Vol. 12, No. 2, pp. 267 - 275.

Burmaster, D.E., and E.A.C. Crouch. 1997. “Lognormal Distributions for Body Weight

as a Function of Age for Males and Females in the United States,” Risk Analysis, Vol.

17, No. 4, pp. 499 - 508. 

Johnson, T., J. Capel, R. Paul, and L. Wijnberg. 1992. Estimation of Carbon

Monoxide Exposures and Associated Carboxyhemoglobin Levels in Denver

Residents Using a Probabilistic Version of NEM. Report prepared by International

Technology Air Quality Services under EPA Contract No. 68-D0-0062. U.S.

Environmental Protection Agency, Research Triangle Park, North Carolina. 

D-6
Table 1. Number of Men 18-74 Years of Age by Weight and Height, 1976 - 1980 (Number of Pers

Height,

inchesc

Weight, lbsb

< 110 110-

119

120--

129

130-

139

140-

149

150-

159

160-

169

170-

179

180-

189

190-

199

200-

209

210-

219

2

< 62 41 70 100 42 110 38 69 24 8 19 10 

62 38 34 94 102 196 73 35 33 48 15 63 66 65 195 197 286 136 113 98 33 29 3 64 33 110 237 381 376 413 181 231 106 62 7 8 65 53 191 177 578 806 820 556 363 269 161 154 30 66 50 131 457 555 843 910 986 547 515 252 105 58 67 12 102 324 780 1087 1237 1174 1181 801 429 319 135 68 29 77 319 743 1127 1351 1625 1328 1152 686 390 284 69 7 11 322 488 960 1169 1547 1436 1286 747 750 390 70 4 37 104 455 900 1041 1450 1313 1334 710 479 441 71 32 22 242 453 911 818 1103 868 692 481 377 72 9 19 67 217 392 716 831 765 696 436 216 73 20 41 228 356 322 483 370 306 190 74 7 42 76 73 203 270 243 .191 156 

$ 75 47 24 245 168 121 173 58 

True

total

333 869 2377 4692 7402 8842 9723 9258 8106 5217 3806 2356 

aSource: Table I of Brainard and Burmaster (1992). bHeight without shoes. cWeight with clothes, estimated as ranging from 0.20 - 0.62 lb. dNumber of cells scaled up to reflect sizie of population; only 9983 men actually examined. eShaded cells contain data used in statistical analysis described in text. 

D-7
D-8
Table 2. Number of Women 18-74 Years of Age by Weight and Height, 1976 - 1980 (Number of P

Thousands)b. 

Height,

inb

Weight, lbsc

< 90 90-99 100-

109

110-

119

120--

129

130-

139

140-

149

150-

159

160-

169

170-

179

180-

189

190-

199

200-

209

< 55 7 8 11 25 7 7 15 

55 13 4 26 12 31 13 8 56 31 57 12 41 55 25 44 25 6 57 44 91 107 90 55 115 76 26 24 9 18 58 93 164 132 338 317 147 78 120 35 68 27 14 34 59 50 196 262 552 342 365 297 201 123 116 69 46 30 60 86 267 538 621 722 775 451 334 261 239 128 99 54 61 12 368 754 1286 1355 1089 877 807 439 308 269 240 123 62 14 258 938 1660 1899 1306 1117 728 583 448 305 227 130 63 32 165 843 1729 1776 1600 1565 1006 817 655 477 357 277 64 30 531 1168 1653 1936 1475 950 741 513 404 274 117 65 64 283 873 1582 2162 1183 1201 693 396 455 269 156 66 10 76 705 804 1365 902 696 509 255 193 213 116 67 32 188 514 740 605 336 338 381 275 155 106 68 10 85 213 488 369 336 193 41 99 95 82 69 33 98 135 266 125 214 119 43 28 70 6 38 56 52 19 46 25 3 

$ 71 16 16 55 42 30 28 15 4 

True

total

362 1677 4572 9363 11,420 12,328 9435 7023 5047 3621 2753 2081 1232 

aSource: Table II of Brainard and Burmaster (1992). bHeight without shoes. cWeight with clothes, estimated as ranging from 0.20 - 0.62 lb. 

D-9
dNumber of cells scaled up to reflect sizie of population; only 10,339 women actually examined. eShaded cells contain data used in statistical analysis de

D-10
Table 3. Subroutine for Determining Adult Height Values in the Physiological

Parameter Algorithm.

Estimation of Adult Height Given Weight (Version 1.0)

1. In previous steps, the “physiological profile” program has determined age and

gender of simulated person and selected a value for weight in kilograms

(wgt_kg) from the body mass distribution specified for the age and gender. 

2. Convert wgt_kg to wgt_lb by equation: wgt_lb = (2.2046)(wgt_kg). 

3. Select random value (z) from unit normal distribution with mean = 0 and

standard deviation = 1. 

4. If male: hgt_inches = 34.43 inches + (6.67)[ln(wgt_lb)] + (2.38 inches)(z). 

5. If female: hgt_inches = 48.07 inches + (3.07)[ln(wgt_lb)] + (2.48 inches)(z). 

6. If height in cm is required, convert hgt_inches to hgt_cm by equation: 

 hgt_cm = (2.54)(hgt_inches). 

D-11
Table 4. Physiological Parameters for Adults To Be Included in the 1998 Version of

pNEM/CO.

Parameter Algorithm(s) Containing

Parameter

Other Parameters Required

for Calculating Parameter

Age COHb

Ventilation rate

Gender COHb

Ventilation rate

Weight (body mass) COHb

Ventilation rate

Gender

Age

Height COHb Weight

Gender

Menstrual phase COHb Gender

Age

Blood volume COHb Gender

Weight

Height

Total hemoglobin content of the blood COHb Gender

Age

Pulmonary CO diffusion rate COHb Gender

Height 

Age

Endogenous CO production rate COhb Gender

Menstrual phase

Resting metabolic rate (RMR) Ventilation rate Gender

Age

Weight (body mass)

Energy conversion factor (ECF) Ventilation rate Gender

MAXRATIO: VER at VO2max Ventilation rate Gender

Age

SUBRATIO: VER at 65% of VO2max Ventilation rate Gender

Age

MAXRATIO

VO2max Ventilation rate NVO2max

Weight (body mass)

NVO2max Ventilation rate Gender

Age

D-12
Appendix E

Algorithm for Estimating Carboxyhemoglobin Levels 

E-1
COHB MODULE FOR pNEM/CO

H. M. Richmond and T. R. Johnson

February 1999

This appendix describes the probabilistic COHb module and discusses its basis. The

approach described here is based primarily on the COHb module described by Biller and

Richmond in an Appendix to the 1992 version of pNEM/CO (Johnson et al., 1992).

I. THE BASE PHYSIOLOGICAL MODEL FOR COMPUTING COHb LEVELS

The COHB module in the original CO-NEM (Johnson and Paul, 1983) used as its basic

model the differential equation derived by Coburn, Forster, and Kane (1965) which described the

dynamic relationship between instantaneous blood levels of COHb, inspired CO, and other

physiological variables. This model, which will be referred to here as the CFK model, continues

to be the most widely-used method for estimating COHb and is the basic model for COHb

computations in pNEM/CO. The CFK model is described in Section II. 

The CFK model describes the rate of change of COHb blood levels as a function of the

following quantities: 

 1. Inspired CO pressure

 2. COHb level

 3. Oxyhemoglobin (O2Hb) level

 4. Hemoglobin (Hb) content of blood

 5. Blood volume

 6. Alveolar ventilation rate

 7. Endogenous CO production rate

 8. Mean pulmonary capillary oxygen pressure

 9. Pulmonary diffusion rate of CO

10. Haldane coefficient (M)

11. Barometric pressure

12. Vapor pressure of water at body temperature (47 torr)

If all of the listed quantities except COHb level are constant over some time interval, the CFK

equation has a linear form over the interval and is readily integrated. The solution to the linear

form gives reasonably accurate results for lower levels of COHb. However, CO and oxygen

compete for the available hemoglobin and are, therefore, not independent of each other. If this

dependency is taken into account, the resulting differential equation is no longer linear. Peterson

and Stewart (1975) proposed a heuristic approach to account for with this dependency which

assumed the linear form and then adjusted the O2Hb level iteratively based on the assumption of

a linear relationship between COHB and O2Hb. This approach was used in the COHb module of

the original CO-NEM. Alternatively, it is possible to determine COHb at any time by numerical

integration of the nonlinear CFK equation (e.g. by use of the Runge-Kutta method) if one

assumes a particular relationship between COHb and O2Hb. Muller and Barton (1987)

E-2
demonstrated that assuming a linear relationship between COHb and O2Hb leads to a form of the

CFK equation equivalent to the Michaelis-Menton kinetic model which is analytically

integrable. However, the analytical solution in this case cannot be solved explicitly for COHb. 

Muller and Barton demonstrated a binary search method for determining the COHb value. 

The COHb module for pNEM/CO employs a linear relationship between COHb and

O2Hb which is consistent with the basic assumptions of the CFK model but differs from the

linear forms used by other modelers. The Muller and Barton (1987) solution is employed. 

However, instead of the simple binary search described in the Muller and Barton paper, a

combination of the binary search and Newton-Raphson root finding methods was used to solve

for COHb (Press et al., 1986). Using the Muller and Barton solution increased computation time

compared to the Peterson-Stewart method but was shown to be faster than fourth order RungeKutta numerical integration. 

II. The CFK Model For Estimation Of Carboxyhemoglobin

Table 1 defines the variables which appear in the equations of this section. Coburn,

Forster, and Kane (1965) derived the following differential equation governing COHb levels in

the blood upon exposure to CO. 

(Eq. 1)    

 

d COHb

dt

P

BV

P COHb

MBV O Hb

I

b

CO

b

CO

 

V 

V

CO

b

2

2

where

(Eq. 2)   B

D

P P

L V

B HO

CO A

 

1  2



If the only quantity in this equation that can vary with time is [COHb], the CFK equation

is linear and can be readily integrated. However, since oxygen (O2) and CO compete for the

available HB, [COHb] and [O2Hb] must be related. Increasing [COHb] will result in decreasing

[O2Hb]. Thus the CFK equation is not linear and requires the relationship between the two

quantities to be known if it is to be accurately integrated over a wide range of COHb levels. 

Various linear relationships between [COHb] and [O2Hb] have been used (See Marcus,

1980; McCartney, 1990; Muller and Barton, 1987; and Tikuisis et al., 1987). A relationship not

previously used follows directly from the basic assumptions of the CFK model. The CFK model

employs the Haldane coefficient, which is the equilibrium constant associated with the following

reaction representing the replacement of O2 in O2Hb by CO: 

(Eq. 3) CO + O2Hb = O2 + COHb

Table 1. Definitions Of CFK Model Variables

E-3
Variable Definition

t Time from start of an exposure event, min

[COHb] Concentration of carboxyhemoglobin (COHb) in blood at 

time, t, ml CO per ml blood at STPD

[O2Hb] Concentration of oxyhemoglobin (O2Hb) in blood at time t, 

ml O2 per ml blood at STPD

[RHb] Concentration of reduced hemoglobin in blood as

equivalent

ml CO per ml of blood at STPD

[COHb]o [COHb] at t = 0

[THb]0 [RHb] + [COHb] + [O2Hb]

%[COHb] [COHb] expressed as percent of [RHb]0

%[O2Hb] [O2Hb] expressed as percent of [RHb]0

%[COHb]0 %[COHb] at t = 0

%[COHB]4 %[COHb] at t = 4

PIco Pressure of inspired CO in air saturated with water vapor at

 body temperature, torr

PC Mean pulmonary capillary CO pressure, torr CO

 

PC Mean pulmonary capillary O2 pressure, torr O2

PB Barometric pressure, torr

PH O Vapor pressure of water at body temperature, torr (47 torr) 2

VA Alveolar ventilation rate, ml/min STPD

VCO Endogenous CO production rate, ml/min STPD

Table 1. Definitions Of CFK Model Variables (Continued)

E-4
Variable Definition

DL Pulmonary CO diffusion rate, ml/min/torr STPD CO

M Haldane coefficient

k Equilibrium constant for reaction O2 + RHb = O2Hb

Vb Blood volume, ml

 

Hb Total hemoglobin in blood, g/100ml

%MetHb Methemoglobin as weight percent of Hb

E-5
The following equation, the Haldane relationship, applies approximately at equilibrium

conditions. 

(Eq. 4)

 

 

P COHb

P O Hb

M

CO

CO

2

2



The Haldane coefficient, M, is the chemical equilibrium constant for reaction (3)

The above reaction can also be viewed as the difference between two competing

chemical reactions:

(Eq. 5) CO + RHb = COHb

(Eq. 6) O2 + RHb = O2Hb

Subtracting (6) from (5) yields (3). If (3) is in equilibrium, then (5) and (6) are in equilibrium. 

If k is the equilibrium constant for (6) then: 

(Eq. 7)  

 

O Hb

P RHb k

CO

2

2



It is known that an individual breathing air free of CO for an extended period will have

about 97% of the reactive hemoglobin tied up as O2Hb and the rest (3%) as RHb. It is also

known that at one atmosphere barometric pressure the mean pulmonary capillary oxygen

pressure is approximately 100 torr. Substituting into (7) yields 0.32 as the approximate value of

k at body temperature. From mass balance considerations: 

(Eq. 8) [O2Hb] + [COHb] + [RHb] = [THb]o

Eliminating [RHb] between (7) and (8) and solving for [O2Hb] yields:

(Eq. 9)   O Hb      

kP

kP THb COHb

CO

CO

2 0 2

2 1   

This equation is the desired linear relationship. It has the same form as a relationship

given without explanation by McCartney (1990), but replaces the constant in the McCartney

equation by the term in (9) involving the mean pulmonary capillary oxygen pressure and the

equilibrium constant k. Substituting (9) into (1) yields a CFK equation free of [O2Hb] and fully

consistent with Coburn, Forster, and Kane’s original derivation. 

E-6
(Eq. 10)    

  

d COHb

dt

V

V

P

BV

COHb

THb COHb

kP

kMBV

CO

b

I

b o

C

b

CO O

  

 



1 2

In working with the CFK model it is convenient to express COHb as a percent of [RHb]0. 

Multiplying (10) by 100 and dividing by [RHb]0:

(Eq.11)  

 

 

 

 

 

d COHb

dt THb

V

V

P

BV

COHb

COHb

kP

o k RHb MBV

CO

b

I

b

C

o b

CO % O  %

%

 





 



  

 100

100

100 1 2

Equation (11) can be written in the form suggested by Muller and Barton (1987):

(Eq.12)    

 

d COHb

dt C C

COHb

COHb o

% %

%    1

100

where

(Eq.13)   C THb

V

V

P

BV o

o

CO

b

I

b

CO  





 



 100 

(Eq.14)

 

  C

kP

k THb MBV

c

o b

O

1

100 1 2





Given values for the atmospheric pressure and the physiological variables in equations

(12) - (14), the value of %[COHb] at time t can be found by numerical integration using such

techniques as the fourth order Runge-Kutta method (Press et al., 1986).

Muller and Barton (1987) demonstrated that an equation of the form of (12) is equivalent

to a Michaelis-Menton kinetics model which is integrable. Integration yields: 

(Eq.15)              

        

   



C C t COHb COHb COHb

COHb COHb

COHb COHb 0 1 0

0

% % % ln 100 0

% %

% %

The equation for %[COHb]4 is obtained by setting equation (12) equal to zero and solving for

%[COHb] which is now equal to %[COHb]4:

E-7
(Eq.16)     % COHb

C

C C   

100 0

0 1

Equation (15) cannot be solved explicitly for %[COHb]. The Muller and Barton paper suggests

the binary search method as one way to find the value of %[COHb]. Press and coauthors (1986)

contend a combination of the binary search and Newton-Raphson methods is faster on average. 

III. Application of the Basic COHb Model in pNEM/CO

Description of pNEM/CO

pNEM/CO follows the daily activities over an extended period of a finite set of cohorts

residing within a given geographic area. The period may be a single season or a calendar year. 

Each cohort is defined as a group of people with similar demographic and physiological

characteristics who are likely to follow similar activity patterns. (Smokers are typically omitted

from the cohorts included in pNEM/CO assessments, as cigarette smoking dominates their

exposure to CO.) The exposure of each cohort is represented by a continuous sequence of

exposure events which span the time period of interest. Each exposure event represents a time

interval of 60 minutes or less during which the individual resides in a single environment and

engages in a single activity. To permit calculation of hourly average exposures, exposure events

are not permitted to fall in more than one clock hour. Consequently, the passage from one

exposure event to the next is indicated by a change in microenvironment, activity, or clock hour. 

Algorithms within pNEM/CO calculates an average CO concentration for each exposure event

according to the time, district, and microenvironment specified for the event. As the exposure

events for a cohort are contiguous, the model can combine these concentrations to output

distributions of one-hour and running eight-hour exposures for each cohort. The exposures

calculated for individual cohorts can then be weighted according to their estimated populations

to produce exposure distributions for larger population groups of particular interest. 

To treat the daily behavior of cohorts probabilistically, each cohort is identified

according to home district, work district, demographic group, and the use of cooking fuel in the

residence. Currently seven demographic groups are used to distinguish cohorts by sex and age. 

A set of pools of 24-hour activity patterns is used based on activity data drawn from the

Consolidated Human Activity Data Base (CHAD) which is described in Section 2.3 of this

report. These patterns are twenty-four hour samplings of the behavior of real people. The

patterns in each pool represent the same demographic cohort group, day type (weekday, weekend

day), and ambient temperature range. Each pattern consists of a 24-hour set of contiguous

exposure events defined by event start time, duration, microenvironment, and activity. For a

given cohort a daily pattern is randomly sampled from the appropriate pool each day during the

period of the computation.

From the description in the preceding paragraph, it is apparent that while the cohort

represents a single demographic group, the activity pattern selected to represent each 24-hour

period is obtained from a different member of the group. This feature has an impact on the

design of the COHb module. 

E-8
The COHb Module

The COHb module of pNEM/CO employs the Muller and Barton (1987) integration of

the CFK model as represented by equations (12)-(14) to compute the COHb level of a cohort at

the end of each exposure event. To perform this computation, the COHb module requires

information on each of the quantities listed in the section describing the CFK model. In addition,

the COHb level at the beginning of the exposure event must be known. This latter quantity is

usually the COHb level computed at the end of the previous contiguous exposure event. To

obtain the initial COHb at the start of the exposure period, the computation is started one day

before the beginning of the period. The effect of the initial COHb value on the end value is

negligible after about 15 hours. The program stores the COHb levels at the end of each clock

hour and outputs distributions of COHb levels for the sensitive population. 

Assignment of CFK Model Input Data for an Exposure Event

Section IV describes the equations and procedures used by the pNEM/CO COHb module

to obtain the values of the input variables for equations (2) and (13) through (16). A brief

overview is given here.

The actual inspired CO level can change significantly during an exposure event. The

model supplies an average exposure concentration for the event, which is used as the CO input. 

The time constant for the change in COHb is sufficiently large that the use of concentrations

based on averaging times up to one hour can be used in place of the instantaneous concentrations

over the averaging time period with little loss of accuracy in estimating the COHb level at the

end of the exposure event. Furthermore, applying the average concentrations to a contiguous

sequence of exposure events does not cause an accumulation of error. 

The COHb model presently used in pNEM/CO does not account for changing barometric

pressure. It uses a constant barometric pressure which is a function of the average elevation of

an area above sea level. The pressure at sea level is taken to be 760 torr. 

The remaining input variables to the CFK model are all physiological parameters. While

the Haldane coefficient, the equilibrium constant k, and average pulmonary capillary oxygen

pressure are treated as having the same constant values for all cohorts, the remaining

physiological input variables will vary among individuals. The next section describes the

methods used to generate the various physiological input variables for each combination of

cohort and calendar day processed by pNEM/CO. 

IV. Computation of Input Data for the COHb Module

As discussed in the previous section and in Sections 2.43 and 2.44 of the main body of

this report, the algorithms used to estimate VE and COHb require values for various

physiological parameters such as body mass, blood volume, and pulmonary diffusion rate. Table

2 provides a complete list of these parameters. A special algorithm within pNEM

probabilistically generates a value for each parameter on the list (collectively referred to as a

“physiological profile”) for each combination of cohort and calender day processed by

E-9
pNEM/CO. Figure 1 is a flow diagram showing the process by which each physiological profile

was generated. Each of the generated physiological profiles is internally consistent, in that the

functional relationships among the various parameters are maintained. For example, blood

volume is determined as a function of weight and height, where height is estimated as a function

of weight. Weight in turn is selected from a distribution specific to gender and age. Table 2

provides a brief summary of the method used to estimate values for each parameter in the

application of pNEM/CO to Denver.

For each cohort, as defined above, pNEM/CO computes exposure for a contiguous

sequence of exposure events spanning the total time period of the computation. This multi-day

sequence of exposure events is determined by random sampling day-long event sequences from a

set of pools of 24-hour activity patterns. An individual 24-hour pattern in one of these pools is

referred to as a unit exposure sequence (UES). Each pool consists of a collection of UESs which

are specific to the cohort demographic group, day type, and average daily temperature. 

A UES is a contiguous set of exposure events spanning 24 hours. Each event is

characterized by start time, duration in minutes, home/work status, microenvironment, and

activity. All exposure events are constrained to occur entirely within a clock hour. The UESs

start at 7:00 p.m. and end at 7:00 p.m. the following day. 

The CFK model within the COHb module is called for each exposure event. For each

event it requires the following data. 

Time duration of event, min

Inspired CO partial pressure averaged over the event, torr

Percent COHb at the start of the event

Alveolar ventilation rate, ml/min STPD

Average pulmonary capillary oxygen pressure, torr

Haldane Coefficient

Equilibrium constant for the reaction of O2

Atmospheric pressure, torr

Blood volume, ml

Total potential reduced hemoglobin content of blood, ml CO/ml STPD

Pulmonary CO diffusion rate, ml/min/torr STPD

Endogenous CO production rate, ml/min STPD

Given these data as inputs, the module computes the percent COHb at the end of the exposure

event. This value is used by the module as the initial percent COHb for the next contiguous

exposure event. The main program retains only those COHb values at the end of each clock

hour. 

Some of the above data do not change during a pNEM/CO computer run and, therefore,

need to be supplied to the computer program only once at the start. Some of the data vary with

the cohort and therefore need to be supplied at the beginning of each activity day. Other data

tend to change with the exposure event and therefore need to be supplied for each new exposure

event. 

E-10
Table 2. Parameters Included in Physiological Profile for Adults in Version 2.0 of pNEM/CO.

Parameter

Algorithm(s)

Containing

Parameter

Other Parameters

Required for

Calculating

Parameter

Method Used to Estimate Parameter 

Age COHb

Ventilation rate 

Demographic group Randomly selected from population-weighted distribution s

group

Gender COHb

Ventilation rate

Demographic group Randomly selected from population-weighted distribution s

group

Weight (body mass) COHb

Ventilation rate

GenderAge

Randomly selected from population-weighted distribution sbased on Brainard and Burmaster (1992).

Height COHb Weight

Gender

Estimated by the following equations: males: height = 34.43 inches + (6.67)[ln(weight)] + (2 females: height = 48.07 inches + (3.07)[ln(weight)] + (2The z term was randomly selected from a unit normal [N(0,height (inches), weight (lbs). The estimation equations are based on the results of a statis

(1998) of height and weight data provided by Brainard and 

Menstrual phase COHb Gender

Age

If gender = female, menstrual phase was randomly assigned 

cycles according to the following age-specific probabilities.  Age < 12 or >50: 100% premenstrual Age 12 through 50: 50% premenstrual, 50% postmenstr

E-11
Parameter

Algorithm(s)

Containing

Parameter

Other Parameters

Required for

Calculating

Parameter

Method Used to Estimate Parameter 

Blood volume COHb Gender

WeightHeight

Blood volume (Vb) was determined according to gender by 

which are based on work by Allen et al. (1956) which was munits used for height and weight.

 Men: Vb = (20.4)(weight) + (0.00683)(H3) - 30

 Women: Vb = (14.6)(weight) + (0.00678)(H3) - 30

Units: blood volume (ml), weight (lbs), height (inches).

Hemoglobin content ofthe blood, Hb

COHb Gender

Age

Randomly selected from normal distribution with arithmetic arithmetic standard deviation (ASD) determined by gender 

obtained from the 1976-1980 NHANES study (USDHHS, 1 Males, 18 - 44: AM = 15.3, ASD = 1.0 Males, 45 - 64: AM = 15.1, ASD = 1.2 Males, 65+: AM = 14.8, ASD = 1.4 Females, 18 - 44: AM = 13.3, ASD = 1.1 Females, 45 - 64: AM = 13.6, ASD = 1.2 Females, 65+: AM = 13.7, ASD = 1.2Units: grams of Hb per deciliter of blood

Pulmonary CO diffusionrate, DLCO

COHb Gender

Height Age

Pulmonary CO diffusion rate (DL) was determined accordinage according to the following equations obtained from a pa

and modified to conform to the units used in the COHb mod Males: = (0.361)(height) - (0.232)(age) + 16.3 ml

DLCO

 Females: = (0.556)(height) - (0.115)(age) - 5.97 m

DLCO

Units: (ml/min/torr), height (inches), age (years). DLCO

E-12
Parameter

Algorithm(s)

Containing

Parameter

Other Parameters

Required for

Calculating

Parameter

Method Used to Estimate Parameter 

Endogenous COproduction rate

COHb Gender

AgeMenstrual phase

Endogenous CO production rate was randomly selected from distribution with geometric mean (GM) and geometric standdetermined according to the following equations specific to menstrual phase.  Males, 18 - 64: GM = 0.473, GSD = 1.316 Males, 65+: GM = 0.473, GSD = 1.316 Females, 18 - 64, premenstrual: GM = 0.497, GSD = 1.45 Females, 18 - 64, postmenstrual: GM = 0.311, GSD = 1.4 Females, 65+: GM = 0.497, GSD = 1.459 Units: GM (ml/hr), GSD (dimensionless).

Resting metabolic rate(RMR)

Ventilation rate Gender

AgeWeight (body mass)

See Section 5.4 of the main body of this report. 

Energy conversionfactor (ECF)

Ventilation rate Gender See Section 5.4 of the main body of this report.

NVO2max Ventilation rate Gender

Age

See Section 5.4 of the main body of this report.

VO2max Ventilation rate NVO2max

Weight (body mass)

See Section 5.4 of the main body of this report.

E-13
Figure 1. Flow Diagram for Physiological Profile Generator.

Input Data Supplied at Start of the pNEM/CO Computation

Demographic Group

Gender Weight Age

Height

Menstrual Phase

Endogenous CO

Production Rate

Blood Volume

Pulmonary CO

Diffusion Rate

Maximum Normalized

Oxygen Uptake Rate

(NVO2max)

Maximum Oxygen

Uptake Rate (VO2max)

Resting Metabolic

Rate (RMR)

Total Hemoglobin

Content

of the Blood

Energy Conversion

Factor (ECF)

E-14
Barometric Pressure

A constant barometric pressure is assumed for the study area based on the average height

above sea level: 

(Eq. 17) P Altitude B    760 0 0000386 exp .  

where altitude is the average height (in feet) of the study area above sea level (USEPA, 1978). 

The altitude was set at 5183 feet for Denver and 328 feet for Los Angeles. 

Average Pulmonary Capillary Oxygen Pressure

The equation employed is based on an approximation used by Peterson and Stewart

(1975) in which the 49 torr is subtracted from the partial pressure of inspired oxygen. This leads

to the following approximate relationship:

(Eq.18) P P C B   O2

  0 209 47 49 .

The constant 0.209 is the mole fraction of O2 in dry air. The constant 47 is the vapor pressure of

water at body temperature. This expression was used in an investigation of the CFK equation by

Tikuisis et al. (1987). Modelers have tended to use the value 100 torr. Equation (18) gives the

value 100 torr for a barometric pressure of 760 torr. 

Haldane Coefficient

The value of 218 has been used for the Haldane coefficient. Measured values in the

range 210 to 270 have been reported in the literature. Modelers have tended to use values in the

range 210 to 240. In the early 1980's, the Clean Air Scientific Advisory Committee (CASAC)

expressed the opinion to EPA (Friedlander, 1982) that the most careful work done in this area

was that by Rodkey (1969), who determined a value of 218. This value was used in the COHb

module of the earlier CO NEM version. Other modelers using values in the range 218 to 220 are

Peterson and Stewart, 1970; Marcus, 1980; Collier and Goldsmith, 1983; Muller and Barton,

1987. As the value 218 falls within the range currently used by modelers, EPA analysts have

decided to continue using value in pNEM/CO.

Equilibrium Constant for the Reaction of O2 and RHb

This quantity was estimated in Section II to have the value 0.32 based on the observation

that %[RHb] is about 3% in individuals breathing air which is free of CO and a value of 100 torr

for . PCO2

Total Reduced Hemoglobin in the Absence of O2 and CO 

The quantity [THb]0 is expressed as equivalent milliliters of O2 or CO at STPD per

milliliter of blood. Total Hb blood levels are customarily expressed as grams per deciliter of

E-15
blood. The total Hb level in the absence of COHb and O2Hb would consist principally of RHb

which can react with O2 or CO and MetHb which cannot. Total Hb blood levels also tend to be

higher in people living at higher altitudes. To relate [THb]0 to Hb, it is therefore necessary to

correct for the MetHb present, adjust for the effect of altitude, and convert to equivalent

milliliters of CO at STPD. The later conversion is based on the observation that a gram of

reduced Hb can react with a maximum of 1.39 ml of O2 or CO at STPD. The application of

these three factors yields the equation: 

(Eq. 23)       THb Hb MetHb

HbAlt Altitude

0 139 100

1

100

  

 

. %

where HbAlt is a regression constant. Hb in equation (23) is a sea level value. Hb level in a

human population is normally distributed with the mean Hb and standard deviation both

dependent on gender and age class (see entry in Table 2 for the distributions of Hb by age and

gender). Given the hemoglobin content of the blood based on the distributions listed in Table 2,

[THb]0 is calculated using equation (23). The weight percent MetHB, %MetHB, is taken to be

0.5% of the weight of Hb (Muller and Barton, 1987). 

The altitude correction factor, HbAlt, was developed by application of simple regression

analyses to Hb data obtained in 17 U.S. cities (USEPA, 1973). 

Men: 0.000161 S.E. = 0.000064

Women: 0.000115 S.E. = 0.000043

Two cities (Phoenix and Houston) were eliminated in the regression analysis because the

measured Hb levels were substantially below that of the other cities. The altitude factor is small. 

It predicts about a 5% increase in Hb for residents of Denver over that for people living at sea

level. 

Determination of Weight

Body mass or weight (in kg) was determined by fitting lognormal distributions to data

organized by age and gender based on work by Brainard and Burmaster (1992) and Burmaster et

al. (1994). Table 5-5 in the main body of this report summarizes the parameters for the

lognormal distributions obtained.

Determination of Height

The following equations were used to estimate height as a function of gender and weight. 

(Eq. 24) males: height = 34.43 inches + (6.67)[ln(weight)] + (2.38 inches)(z)

(Eq. 25) females: height = 48.07 inches + (3.07)[ln(weight)] + (2.48 inches)(z)

The z term was randomly selected from a unit normal [N(0,1)] distribution. 

Equations 24 and 25 are based on the results of a statistical analysis by Johnson (1998) of height

E-16
    V geom mean geom S D CO . . .. . z

  0 01667

and weight data provided by Brainard and Burmaster (1992). 

Base Pulmonary Diffusion Rate of CO

A base lung diffusivity of CO for the cohort is calculated as follows: 

(Eq. 26) Men: D height age L

CO  0 361 0 232 16 3 . ..    

(Eq. 27) Women: D height age L

CO  0556 0115 597 . ..    

where height is in inches and age is in years. 

The regression equations were obtained from a paper by Salorinne (1976) and modified

to conform to the units used in the COHb module. The Salorinne data were obtained for nonexercising individuals. Tikuisis et al. (1992), working with eleven male subjects at various

exercise levels, showed significant increase in lung diffusivity of CO with increasing alveolar

ventilation rate. Regression analyses of data provided by Tikuisis for the individual subjects in

the study showed the relationship to be linear. From this relationship and the heights and ages of

the subjects in the Tikuisis et al. study, it was determined that the Salorinne equations for male

subjects correspond to an alveolar ventilation rate of 6.69 l/min STPD. In the absence of other

data it is assumed that this same value applies to women. Thus, for each twenty-four hour period

equations (26) and (27) are used to compute lung diffusion rates of CO for a base case alveolar

ventilation rate of 6.69 1/min STPD. As will be seen, this value is adjusted to account for the

actual ventilation rate experienced by the cohort during each individual exposure event. 

Endogenous Rate of CO Production

The endogenous CO production rates taken from a number of sources show the rate to be

distributed lognormally in the population (see Appendix for data and sources). The distribution

is different for men and women. For a woman there is a further difference depending on whether

she is in her premenstrual or postmenstrual phase. Table 2 presents these distributions classified

by class, gender, and menstrual phase. 

For each male cohort, pNEM/CO specifies a single value for endogenous CO production

rate and uses it for all days of the year. For each female cohort between 18 and 64 years of age,

pNEM/CO specifies one value of endogenous CO production rate to represent premenstrual days

and one value to represent postmenstrual days. Females cohorts under 12 years and older than

50 are assumed to be premenstrual; consequently, pNEM/CO specifies a single value for

endogenous CO production rate to be used for all days of the year. The specified values are

randomly selected from the appropriate distributions presented in Table 2. A random number, z,

is sampled from the standardized normal distribution, N(0,1) to make each selection. The

appropriate endogenous CO production rate is then obtained from: 

(Eq. 28)

E-17
The constant term converts ml/hr to ml/min. 

A probabilistic algorithm within pNEM/CO assigns a menstrual phase to each day of the

year for females cohorts aged 12 to 50 years. The algorithm randomly assigns a number between

1 and 28 to January 1. The number is increased by one for each successive day until number 28

is reached. The next day is numbered 1 and the 28-day numbering cycle is repeated until each 

day of the year has been assigned a number between 1 and 28. Days numbered 1 through 14 are

identified as post-menstrual days; days numbered 15 through 28 are identified as pre-menstrual

days. 

INPUT DATA SUPPLIED WITH EACH EXPOSURE EVENT

Duration of Exposure Event

The duration of the exposure event in minutes is supplied by the main program to the

COHb module.

Partial Pressure of Inspired Carbon Monoxide

The main program supplies the inspired CO concentration averaged over the duration of

the exposure expressed as ppm. This quantity is converted to pressure via: 

(Eq. 29) P CO P  I b CO

    ( ) 47 10 6

Initial Percent COHb Level at Start of Exposure Event

The program retains the percent COHb computed at the end of the previous exposure

event and uses this value as the initial percent COHb for the present event. The starting COHb

at the beginning of an activity day is the final COHb level at the end of the preceding activity

day. This latter procedure is used for the first activity day of the overall computation since the

program starts the day before the overall period covered by the pNEM/CO computation. 

Alveolar Ventilation Rate

The main program supplies the COHb module with ventilation rate derived from the

algorithm discussed in Section 2.4.3 in the main body of this report. 

Adjusted Pulmonary Diffusion Rate of CO

Given the alveolar ventilation rate for the exposure event the associated adjusted

pulmonary diffusion rate can be calculated from: 

(Eq. 30) D Adjusted D Base V     L L A CO CO

  0 000845 565 .  .

E-18
(See discussion of base pulmonary diffusion rate.)

E-19
V. REFERENCES

Biller, W. F. and H.M. Richmond. 1982. “Sensitivity Analysis on Coburn Model Predictions of

COHb Levels Associated with Alternative CO Standards.” Report to Strategies and Air

Standards Division of the Office of Air quality Planning and Standards, U.S. Environmental

Protection Agency, Research Triangle Park, NC, November, 1982. 

Brouillard, R. P., M.E. Contrad, and T.A. Bensinger. 1975. Blood Vol.45, pp. 67-69.

Brainard, J. and D. Burmaster. 1992. “Bivariate Distributions for Height and Weight of Men

and Women in the United States,” Risk Analysis, Vol. 12, No.2, pp. 267-275.

Burke, P. D., F.L. Rodkey, T.F. Blaschke, H.A. Collison, and J.G. Waggoner. 1974. 

“Comparison of Plasma Bilirubin Turnover and Carbon Monoxide Production in Man.” J. Lab.

Clin. Med. Vol. 83, pp. 29-37.

Coburn, R. F., R.E. Forster, and R.B. Kane. 1965. “Considerations of the Physiology and

Variables That Determine Blood Carboxyhemoglobin Concentration in Man.” J. Clin. Invest.,

Vol. 44, pp. 1899-1910. 

Coburn, R. F., W.S. Blakemore, and R.E. Forster. 1963. “Endogenous Carbon Monoxide

Production in Man.” J. Clin. Invest., Vol. 42, pp.1172-1178.

Coltman, C. A., and G.M. Dudley III. 1969. “The Relationship Between Endogenous Carbon

Monoxide and Total Heme Mass in Normal and Abnormal Subjects. Am. J. Med. Sci. Vol.

258, pp. 374-385.

Collier, C. and J.R. Goldsmith. 1983. “Interactions of Carbon Monoxide and Hemoglobin at

High Altitude.” Atmos. Environ. Vol. 17, pp. 723-728.

Delivoria-Papadoppulos, M., R.F. Coburn, and R.E. Forster. 1974. “Cyclic Variation of Rate of

Carbon Monoxide Production in Normal Women.” J. Appl. Physiol. Vol. 36, pp. 49-71.

Friedlander, S. K. 1982. “Letter from The Clean Air Scientific Advisory Committee to USEPA

Administrator.” August 31, 1982. 

Hartwell, T. D., C.A. Clayton, R.M. Richie, R.W. Whitmore, H.S. Zelon, S.M. Jones, and D.A.

Whitehurst. 1984. “A Study of Carbon Monoxide Exposure of Residents of Washington, D. C.

and Denver, Colorado.” EPA-600/s4-84-031. U.S. Environmental Protection Agency, Research

Triangle Park, NC. 

Johnson, T. and R.A. Paul. 1983. “The NAAQS Model (NEM) Applied to Carbon Monoxide.” 

EPA 450/5-83-003, U.S. Environmental Protection Agency, Research Triangle Park, NC.

Johnson, T. 1987. “A Study of Human Activity Patterns in Cincinnati, Ohio.” Prepared by PEI

Associates, Inc. for the Electric Power Research Institute, Palo Alto, CA.

E-20
Johnson, T. 1998. “Memo No. 5: Equations for Converting Weight to Height Proposed for the

1998 Version of pNEM/CO.” Memorandum Submitted to U.S. Environmental Protection

Agency. TRJ Environmental, Inc., 713 Shadylawn Road, Chapel Hill, North Carolina 27514. 

Luomanmaki, K. and R.F. Coburn. 1969. “Effects of Metabolism and Distribution of Carbon

Monoxide on Blood and Body Stores.” Am. J. Physiol. Vol. 217, No.2, pp. 354-362.

Lynch, S. R. and A.L. Moede. 1972. “Variation in the Rate of Endogenous Carbon Monoxide

Production in Normal Human Beings.” J. Lab. Clin. Med. Vol. 79, pp. 85-95.

Marcus, A. H. 1980. “Mathematical Models for Carboxyhemoglobin.” Atmos. Environ. Vol.

14, pp. 841-844.

McCartney, M. L. 1990. “Sensitivity Analysis Applied to Coburn-forster-kane Models of

Carboxhemoglobin Formation.” Am. Ind. Hyg. Assoc. J. Vol. 51, No. 3, pp.169-177.

Merke, C., E. Cavallin-Stahl, and B. Lundh. 1975. “Carbon Monoxide Production and

Reticulocyte Count in Normal Women: Effect of Contraceptive Drugs and Smoking.” Acta

Med. Scan. Vol. 198, pp.155-160.

Muller, K. E. and C.N. Barton. 1987. “A Nonlinear Version of the Coburn, Forster and Kane

Model of Blood Carboxyhemoglobin.” Atmos. Environ. Vol. 21, pp.1963-1967.

Peterson, J. E. and R.D. Stewart. 1975. “Predicting the Carboxhemoglobin Levels Resulting

from Carbon Monoxide Exposures.” J. Appl. Physiol. Vol. 39, pp. 633-638.

Press, W. H., B.P. Flannery, S.A. Teukolsky, and W.T. Vettering. 1986. NUMERICAL

RECIPES, Cambridge University Press. 

Salorinne, Y. 1976. “Single-breath Pulmonary Diffusing Capacity.” Scand. J. Resp. Diseases

Supplementum 96.

Tikuisis, P., H.D. Madill., B.J. Gill, W.F. Lewis, K.M. Cox, and D.M. Kane, D. M. (1987) A

critical analysis of the use of the CFK equation in predicting COHb formation. Am. Ind. Hyg.

Assoc. J. 48(3):208-213.

Tikuisis, P., Kane, D. M., McLellan, T. M., Buick, F., and S.M. Fairburn. 1992. “Rate of

Formation of Carboxyhemoglobin in Exercising Humans Exposed to Carbon Monoxide.” J.

Appli. Physiol. Vol. 72, No. 4.

U.S. Department of Health and Human Services. 1982. “Hematological and Nutritional

Biochemistry Reference Data for Persons 6 Months-74 Years of Age: United States, 1976-80.” 

DHHS Publication No. (PHS)83-1682.

U.S. Environmental Protection Agency. 1978. “Altitude as a Factor in Air Pollution.” EPA600/9-78-015, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

E-21
U. S. Environmental Protection Agency. 1990. “Air Quality Criteria for Carbon Monoxide.” 

EPA-600/8-90-045A, U.S. Environmental Protection Agency, Research Triangle Park, NC.

Werner, B. and J. Lindahl. 1980. “Endogenous Carbon Monoxide Production after Bicycle

Exercise in Healthy Subjects and in Patients with Hereditary Spherocytosis.” Scan. J. Lab.

Invest. Vol. 40, pp. 319-324.

E-22
Table 3. Literature Data Used to Derive Geometric Mean and Standard Deviation

Lognormal Distribution of Endogenous Co Production Rate

ENDOGENOUS CO PRODUCTION RATE FOR MEN

 Vco

(ml/hr) REFERENCE

0.35 Coburn et al., 1963

0.35 “

0.4 “

0.39 “

0.43 “

0.35 “

0.51 “

0.42 “

0.57 “

0.45 “

0.4 Lynch and Moede, 1972

0.81 “

0.26 “

0.65 “

0.51 “

0.62 “

0.44 “

0.43 Berk et al., 1974

0.58 “

0.52 “

0.59 “

0.8 “

0.72 “

0.54 “

E-23
ENDOGENOUS CO PRODUCTION RATE FOR MEN

 Vco

(ml/hr) REFERENCE

0.45 Delivoria-Papadopoules et al., 1974

0.26 “

0.6 “

0.45 “

0.39 “

0.4 “

0.81 Brouillard et al., 1975

0.57 “

0.33 “

0.7 “

0.58 “

0.38 “

0.51 “

0.55 “

0.37 “

0.49 “

0.45 “

0.5 “

0.33 “

0.45 “

0.36 “

0.54 Werner and Lindahl, 1980

0.76 “

0.48 “

0.31 “

0.7 “

E-24
ENDOGENOUS CO PRODUCTION RATE FOR MEN

 Vco

(ml/hr) REFERENCE

0.36 “

0.65 “

0.38 Luomanmaki and Coburn, 1969

0.42 “

0.41 “

0.54 “

0.38 “

0.72 Lynch and Moede, 1972

0.37 “

0.23 “

0.33 “

0.42 “

0.44 “

0.29 “

0.48 “

0.57 Delivoria-Papadopoulos et al., 1974

0.54 “

0.72 “

0.99 “

0.48 “

0.53 “

0.43 “

0.64 Merke et al., 1975

0.86 “

0.35 “

0.52 “

E-25
ENDOGENOUS CO PRODUCTION RATE FOR MEN

 Vco

(ml/hr) REFERENCE

0.8 “

0.54 “

0.68 “

0.28 “

0.48 Lynch and Moede, 1972

0.23 “

0.25 “

0.2 “

0.22 “

0.15 “

0.21 “

0.23 Delivoria-Papadopoulos et al., 1974

0.51 “

0.34 “

0.41 “

0.26 “

0.16 “

0.3 “

0.4 Merke et al., 1975

0.47 “

0.23 “

0.24 “

0.55 “ 

0.32 “

0.43 “

0.35 “

E-26
E-27
Appendix F

Sensitive Population Estimates for Use in Carbon Monoxide

Exposure Estimates

F-1
August 25, 1998

MEMORANDUM

SUBJECT: Sensitive Population Estimates for Use in Carbon Monoxide Exposure Analysis

FROM: Harvey M. Richmond

Risk and Exposure Assessment Group (MD-15)

TO: The files

This memo documents the procedure and resulting estimates for obtaining estimated

prevalence of ischemic heart disease in the United States population by age and sex. The

probabilistic NAAQS Exposure Model for CO (pNEM/CO) divides the population into distinct

subpopulation groups based on age, sex, where individuals live and work, and whether

individuals live in a gas stove home or not. While pNEM/CO is generally run for the entire adult

population, EPA is most interested in exposure and carboxyhemoglobin estimates for the

population which has been identified as the most sensitive population group, namely, individuals

with diagnosed or undiagnosed ischemic heart disease (IHD). Prevalence estimates of diagnosed

IHD are available based on the National Health Interview Survey broken down by age and sex

(Adams and Marano, 1995). The estimated prevalence of diagnosed IHD for children (age 0-17)

is 0.01 percent. According to this survey, approximately 8.0 million individuals are estimated to

have diagnosed IHD in the civilian, non-institutionalized population. These estimates do not

include individuals in the military or individuals in nursing homes or other institutions. In

addition, as many as three to four million persons have been estimated to have silent ischemia or

undiagnosed IHD (American Heart Association, 1990). 

In order to provide estimates for use in pNEM/CO, I have used a best estimate of 3.5

million persons for undiagnosed IHD and have assumed that the prevalence for this group would

be distributed in the same way as diagnosed IHD by age and sex. Tables 1 and 2 (see

attachments) provide prevalence estimates for diagnosed, undiagnosed, and total IHD by age and

sex.

Attachments

cc: J. Capel

D. McKee

J. Raub

A. Rosenbaum 

F-2
Attachment 1

Table 1. Estimated Prevalence of Ischemic Heart Disease in the Male U.S. Population

for 1994 (in percent)*

Age <45 Age 45-64 Age > 64

Diagnosed IHD 0.38 8.19 19.2

Undiagnosed IHD 0.17 3.60 8.45

Total IHD 0.55 11.8 27.7

*For the civilian, non-institutionalized population.

Table 2. Estimated Prevalence of Ischemic Heart Disease in the Female U.S. Population

for 1994 (in percent)*

Age < 45 Age 45-64 Age > 64

Diagnosed IHD 0.13 3.25 12.3

Undiagnosed IHD 0.06 1.43 5.41

Total IHD 0.19 4.68 17.7

*For the civilian, non-institutionalized population.

References

1. Adams, P.F., and Marano, M.A. Current Estimates from the National Health Interview

Survey, 1994, National Center for Health Statistics, Vital Health Stat 10 (193), 1995. 

II. American Heart Association, 1990 Heart and Stroke Facts, American Heart Association,

Dallas, TX, 1990, p.13.

F-3
Appendix G

Algorithm for Air Exchange Rates Inside Motor Vehicles in pNEM/CO

G-1
M E M O R A N D U M

TO: Harvey Richmond

FROM: Jonathan Cohen, Ted Johnson, and Arlene Rosenbaum

DATE: November 17, 1999

SUBJECT: EPA 68-DO-0062 Work Assignment 3-03: Algorithm for Air Exchange

Rates Inside Motor Vehicles in pNEM/CO

Background and Summary

Work Assignment 2-30 of EPA Contract No. 68-D6-0064 directed the ICF project team to 

update the pNEM/CO algorithm used to estimate air exchange rates in motor vehicles. Under

Work Assignment 3-03 of the same contract, the team were requested to revise the speed

distributions to reflect trip mean speeds rather than second by second speeds. This memorandum

presents some alternative approaches for this part of the pNEM/CO model. We first briefly

summarize how the mass balance model in pNEM/CO uses air exchange rates and summarize

the current statistical model for simulating vehicular air exchange rates. We then discuss the

sensitivity of the model to the air exchange rates, demonstrating that once the exchange rate

exceeds 10 hr-1, the pNEM/CO models are not very sensitive to the exact value of the air

exchange rate. Data supplied by a California Air Resources Board (CARB) study (Rodes et al,

1998) show that the measured exchange rates are all above 20 hr-1 when the vehicle vents are

open (and the windows are open or closed), but were measured to be as low as 2 hr-1 when the

vehicle vents are closed (at very low speeds). Thus the application of the study data to

pNEM/CO requires a reasonably accurate estimate of the frequency of driving with vents closed.

Finally we present our proposed approach: Using the data from the CARB study, air exchange

rates for a given speed and vent status (open or closed) are simulated from a log-normal

distribution. The speed is simulated using trip mean speed distributions developed from the

Spokane-Baltimore instrumented vehicle study (Cohen et al, 1994). To determine whether vents

are open or closed, if there is no smoking present, we propose to use the same pNEM/CO

algorithm that estimates the probability of having windows open or closed in a residence, as a

function of daily average temperature and of whether or not an air conditioner is available.

Obviously, behavior patterns for opening windows in residences are not the same as those for

opening vents in vehicles, but there does not seem to be any available good data directly

applicable for vehicles. To complete the simulation, the probability of having a functioning air

conditioner is estimated using the results of an EPA Office of Mobile Sources study (Koupal,

1998).

An important indoor CO source is environmental tobacco smoke (ETS). The previous version of

G-2
pNEM/CO includes additive increments for ETS exposures in several micro-environments, but

no ETS impacts for the inside vehicle micro-environment. The model is currently being revised

to add a mass balance model for ETS inside vehicles with a one minute averaging time. A

constant emission rate based on an assumption of a single smoker smoking 2 cigarettes per hour

will be assumed over the time period that the activity pattern indicates an in-vehicle environment

with smoking present. Over this time period when there is smoking within the vehicle microenvironment, it will also be assumed that the vehicle vents are open, instead of applying the

residential windows closed or open algorithm. See the memorandum by Rosenbaum, Johnson,

and Cohen (1999) for some discussion on approaches for modeling ETS in various pNEM/CO

micro-environments. 

Note: After reviewing this memorandum, EPA directed that Version 2.1 of pNEM/CO treat

vehicle window status using the same algorithm based on daily average temperature and the

availability of air conditioning, regardless of whether or not smokers were present. This

decision was made because of the lack of available data relating smoking in vehicles and

window status. A sensitivity analysis was conducted which examined the impact on the results of

assuming that vehicle windows were always open when the activity data indicated that a smoker

was present. See Section 7 for a discussion of the results of this sensitivity analysis. 

The Mass Balance Model (Assuming No ETS Contribution) 

The pNEM/CO methodology includes a mass-balance model which is used to estimate CO

concentrations when a cohort is assigned to an indoor or motor vehicle microenvironment. The

mass-balance model is based on the generalized mass-balance model presented by Nagda,

Rector, and Koontz (1987). As originally proposed, this model assumed that pollutant

concentration decays indoors at a constant rate. For use in pNEM/CO, the Nagda model was

revised to incorporate an alternative assumption that the indoor decay rate is proportional to the

indoor concentration. The resulting model can be expressed by the differential equation 

(1)

dC

dt F C

S

cV

m C FC qFC

cV

in

B out in d in

in      ( ) 1  

in which 

Cin = Indoor concentration (units: mass/volume)

FB = Fraction of outdoor concentration intercepted by the enclosure

(dimensionless fraction)

Fd = Pollutant decay coefficient (1/time)

ν = Air exchange rate (1/time)

Cout = Outdoor concentration (mass/volume)

G-3
S = Indoor generation rate (mass/time)

cV = Effective indoor volume where c is a dimensionless fraction (volume)

m = Mixing factor (dimensionless fraction)

q = Flow rate through air-cleaning device (volume/time)

F = Efficiency of the air-cleaning device (dimensionless fraction)

(This summary does not incorporate the adjustment for ETS impacts described above.) 

As CO is a nonreactive pollutant, it is reasonable to assume 1) that the enclosure does not

intercept any of the CO as it moves indoors, 2) that the CO does not decay once it enters the

enclosure, and 3) that no CO is removed by air-filtration devices. Under these assumptions, the

parameters FB, Fd, and F in Equation 1 would be set equal to zero. If the additional assumptions

are made that c and m are each equal to 1, the resulting differential equation is

(2)

dC

dt C

S

V

C in

out in    

It can be shown that this equation has the following exact solution:

C t kC t t kC t t k (3) in in out () ( ) ( )      12 3  

where

k e (4) t

1  

k e (5) t

2   1 

kS e V (6) t

3   ( )/ 1   

and Δt is a fixed time interval. Based on this relationship, the average indoor pollutant

concentration of hour h [Cin(h)] can be calculated by the expression

G-4
Cin(h) = a1Cin(h - 1) + a2Cout(h) + a3 (7)

where Cin(h - 1) is the indoor concentration at the end of the preceding hour and Cout(h) is the

average outdoor concentration during hour h. The other variables appearing in Equation 7 are

defined by the following equations:

a zh (8) 1  ( ),

a zh (9) 2   1 ( ),

a (10)

S

V 3   1 z h



[ ( )]

zh e ( ) ( )/   (11)  1  

Equation 7 was used to construct a sequence of hourly average values for each combination of

microenvironment (indoor and motor vehicle) and exposure district.

In constructing each sequence, the value of Cout for a particular hour was set equal to the value

for outdoor concentration determined for that hour by the algorithm described in Subsection

2.4.1 of the pNEM/CO report. A value for air exchange rate (ν) was selected from a userspecified distribution each day at midnight and held constant for the entire day. This procedure

was consistent with the procedure used to construct the exposure event sequence (EES) for each

cohort. As discussed in Section 2 of the pNEM/CO report, each EES consisted of a series of

person-days selected from an activity diary data base. Each person-day spanned a 24-hour

period from midnight to midnight. 

The term S/V represents the contributions of indoor sources to indoor levels. This term was

included in the mass-balance equation when the microenvironment was indoors - residence and

the cohort was characterized as using natural gas for cooking. The term was assumed to equal

zero in the motor vehicle microenvironment. 

Current Treatment of the Motor Vehicle Microenvironment in pNEM/CO

A point estimate of 36 air changes per hour was used for in-vehicle locations in the 1992 version

of pNEM/CO. This value was obtained from Hayes (1991) based on his analysis of data

presented by Peterson and Sabersky (1975). 

In a study reported by Ott, Switzer, and Willis (1994), researchers measured an AER value of

13.1 air changes per hour in a car moving at 20 mph with windows closed. AER values of 67 to

120 were measured in the car at the same speed with windows open. 

During the preparation of the most recent draft of the pNEM/CO report, Peggy Jenkins (1998)

indicated that the California Air Resources Board would provide additional data on AER in

G-5
vehicles in the near future. We expected that these data would provide a better basis for

determining an AER distribution for vehicles. During the interim period, we modeled air

exchange rates in vehicles by sampling from a lognormal distribution with geometric mean =

39.7 and geometric standard deviation = 1.76. This approach was based on the assumption that

the AER distribution has a lognormal distribution and that the 2.5th and 97.5th percentiles of this

distribution correspond to the lowest and highest reported AER values for vehicles (13 and 120

air changes per hour, respectively). 

The remainder of this memorandum discusses potential improvements to this approach based on

the data that were recently provided by CARB (Rodes et al, 1998). 

Air Exchange Rate Data Provided by CARB

Table 1 lists the data provided by the CARB (Rodes et al, 1998). There are 11 AER values

measured under test conditions which varied the ventilation setting, vehicle speed, and vehicle

type. (The draft version of the CARB report shows that multiple measurements were made for

some vehicle/vent combinations - our analysis used the average values given in their final

report). Three of the 11 values were obtained from a 91 Caprice which was tested under all three

ventilation conditions. Unfortunately, this vehicle was tested at only one speed (55 mph). The

97 Taurus was tested at two ventilation settings and one speed (55 mph). The 97 Explorer was

tested at the same two ventilation settings and at all three speeds. Note that the none of Taurus

and Explorer values represent conditions with windows open. There is only one value for

windows open -- the 91 Caprice driven at 55 mph. For the statistical analysis, we grouped this

special case of vent open and windows (partially) open with the other cases of vent open and

windows closed so that we only considered two ventilation conditions (open or closed).

Sensitivity of pNEM/CO to Air Exchange Rate

To help determine the best approach for using the CARB data to revise the pNEM/CO model, we

first considered whether or not the measured vehicular air exchange rates were high enough that

detailed modeling of the exchange rate would not be needed. The following analysis shows that

with vents open, the model predictions are not very sensitive to the exact value of the AER, since

the AER is relatively high. However, with vents closed, the AER can be low enough to have an

important impact (i.e. less than about 10 per hr), for some vehicles at low speeds. Thus the

statistical modeling discussed in the final section of this memorandum is important for treating

these cases of low AER.

Effect of Air Exchange Rate on Inside/Outside Ratio

Of the 11 AER values in Table 1, 9 values exceed 10 hr-1. The two low values occurred with

vents closed at low speeds. The analysis below suggests that once an AER exceeds about 10 hr-1

there is almost instantaneous mixing of inside and outside air. Under these conditions, the

inside/outside ratio for a one-hour averaging time is essentially 1.0 and it doesn’t really matter

whether the AER value is 10 hr-1 or 100 hr-1. 

G-6
Equation 7 provides an estimate of the average indoor pollutant concentration of hour h [Cin(h)]

given values for the indoor concentration at the end of the preceding hour [Cin(h - 1)] and the

average outdoor concentration during hour h [Cout(h)]. In pNEM/CO, we assume that the source

term S is equal to zero. Under these circumstances, the a3 term in Equation 7 is equal to zero and

the equation can be expressed as

Cin(h) = a1Cin(h - 1) + a2Cout(h). (12)

Consider the situation where the instantaneous concentration inside the vehicle at the end of the

previous hour is zero [i.e., a1Cin(h - 1) = 0]. Equation 12 can now be expressed as

 Cin(h) = a2Cout(h). (13)

Remember that Cin(h) is the average concentration inside the vehicle during hour h and that

Cout(h) is the average concentration outside the vehicle during hour h. The ratio of these two

quantities can be estimated by rearranging Equation 13 to obtain 

Cin(h)/Cout(h) = a2 = 1 - z(h) = 1 - (1 - e-ν)/ν. (14)

Note that the ratio is solely a function of the air exchange rate (ν). 

Table 2 lists values of the inside/outside ratio calculated by Equation 14 for air exchange rates

between 0.1 hr-1 and 100 hr-1. The air exchange rates form a geometric series in which each air

exchange rate is approximately 59 percent larger than the preceding value. 

Note that the inside/outside ratio exceeds 0.9 when the air exchange rate for the hour exceeds 10. 

Consider a vehicle with an inside CO concentration equal to zero at time t = 0. The vehicle is

driven for an hour under conditions in which the outside concentration is held constant at a nonzero value (say at 10 ppm). The inside concentration will rise from zero to some final value at t

= 1 hour. According to Equation 9, the average inside concentration during the hour (the value

that pNEM/CO uses in exposure estimates) will be about 90 percent of the outside concentration. 

If the outside value is 10 ppm, the average inside value will be 9 ppm. Note that we would overestimate the average inside concentration by only 11 percent if we assumed the average inside

concentration during the hour was equal to the average outside concentration. 

We can calculate a similar error value for other air exchange rates. Table 2 lists these error

values for air exchange rates between 0.1 hr-1 and 100 hr-1. The following table lists the error

values for air exchange rates between 10 hr-1 and 100 hr-1. 

G-7
Air exchange rate Inside/outside ratio Error (%) in assuming inside = outside

 10.0 0.900 11.1

15.8 0.937 6.7

25.1 0.960 4.2

39.8 0.975 2.6

63.1 0.984 1.6

100 0.990 1.0

All of the AER values listed in Table 1 for vent open (windows closed or open) and speed = 55

mph exceed 39.8 hr-1. Consequently, we could assume that inside = outside for these cases and

make less than a 2.6 percent error. 

The smallest AER value listed for vent open (windows closed or open) is 20.7 hr-1. This value

was measured in the 97 Explorer when speed = 0 and windows were closed. The error of

assuming inside = outside associated with this AER is around 5 percent. 

Note that the 97 Explorer has the smallest AER of the tested vehicles when the speed = 55 mph,

the vent is open, and windows are closed. This suggests (1) that the 97 Explorer is the “tightest”

of the three tested vehicles under these ventilation conditions and (2) that the other two vehicles

are likely to have AER values greater than 20.7 at speed = 0. Consequently, we may be justified

in assuming that AER > 20 hr-1 when vents are open and speed = 0, regardless of tested vehicle. 

When AER > 20 hr-1, the error in assuming inside = outside is 5 percent or less. 

This line of reasoning suggests that we should focus our analysis on the “vent closed” situation,

as this is the only condition likely to produce inside concentrations which are significantly less

than outside concentrations. The average AER value measured in the Caprice when the vent

was closed and speed = 55 mph was 39 hr-1. Although no AER values are reported for the

Caprice for slower speeds when the vent was closed, the 1997 Explorer data shows that at slower

speeds, AER values below 10 hr-1 occur with vents closed, and these cases are ones for which

serious errors would be introduced by assuming perfect exchange between inside and outside air:

Measured AER from CARB study for 1997 Explorer with vent closed, low fan speed. 

Speed AER (hr-1) Cin/Cout Error %

0 1.8 0.54 85

35 5.6 0.82 21

55 13.5 0.93 8

The data for the Explorer shows that AER increases with increasing vehicle speed. 

Proposed Algorithm

G-8
This section describes the steps of our proposed algorithm to update the current pNEM/CO

algorithm for estimating AER inside motor vehicles. We first present the various steps of the

algorithm that, in turn, simulates air conditioner availability, whether vents are open or closed

(based on the presence or absence of smoking, ambient temperature, and air conditioner

availability), vehicle speed, and finally AER (based on vent status and vehicle speed). We then

describe the data analysis used to justify each step of the simulation. 

1. Temperature. For each day, determine daily average temperature (DAT) from a

supplementary temperature file. 

2. Air Conditioner Availability. Select a random number (RN1) between zero and 1. 

If RN1 is 0.85 or below, then assume an air conditioner is available. Otherwise

assume an air conditioner is unavailable (i.e., the vehicle does not have an air

conditioner or the air conditioner is not functional).

3. Vent Status.

If the activity pattern indicates that a smoker is present, then assume the vents are

open (see note on page G-3). 

Otherwise, if the activity pattern indicates that a smoker is not present, apply the

residential window status algorithm as described in the pNEM/CO project report. 

This algorithm determines window status based on AC system and the daily

average temperature according to the probabilities listed in Table 3. For the

current purpose, vehicles with functional air conditioners are equated to rooms in

residences with room air conditioning systems, and vehicles with vents open are

equated to residences with windows open:

a) For each day, determine daily average temperature from step 1 and air

conditioning (AC) system availability from step 2. Select RN2 between

zero and 1.

b) Assume step a specified 65 degrees and functional AC. RN2 will be

evaluated against percentage values listed in Table 3 for functional AC -

medium temperature range (i.e., 12.0, 44.2, and 43.8).

c) If RN2 < 0.120, vents are always closed. AER value is selected from the

"vents closed" AER distribution.

d) If 0.230 < RN2 < 0.562, vents are always open. AER value is selected

from the "vents open" AER distribution.

e) If 0.562 < RN2, vents are open for 61.8 percent of the time (see last

column). To deal with this case, assign a 0.618 probability for the vents

G-9
being open. (An alternative approach, equivalent on average, would be to

apply probabilities of 0.618 and 0.382 to AER estimates with vents open

and vents closed, as in the residential windows algorithm). Select RN3

between zero and 1. If RN3 < 0.618, assume the vents are open. Otherwise

assume the vents are closed.

4. Speed. Select a random number (RN4) between zero and 1. Use this random

number to select a vehicle speed using the distribution given in Table 4. 

a) If RN4 < 0.0462, speed = 0 mph.

b) If 0.0462 < RN4 < 0.0462 + 0.0662 = 0.1124, speed = 5 mph.

c) If 0.1124 < RN4 < 0.1124 + 0.1276 = 0.2400, speed = 10 mph.

d) Etc. Final case is 0.9988 < RN4, speed = 60 mph. 

5. Air Exchange Rate. Simulate AER from a log-normal distribution. Vent status

and speed were simulated in steps 3 and 4.

a) Compute mean (of the logarithms) using the formulae

If vent is closed, μ = 3.37311 !2.46213 + 0.03696 × speed

If vent is open, μ = 3.37311 + 0.01798 × speed

 

b) Variance (of the logarithms) = σ2

 = 0.27323

c) Simulate Z from a standard normal distribution

d) AER = exp(μ + σZ)

Air Conditioner Availability.

The analysis of air conditioner availability is based on an EPA Office of Mobile Sources report 

(Koupal, 1998). Figure 1 taken from Koupal (1998) shows the estimated percentage of LDVs

(light duty vehicles, i.e., cars) and LDTs (light duty trucks) that have air conditioning systems by

model year. For cars, the percentage increases from 88 to 98 % during 1992 to 2000 model

years. For trucks, the percentage increases from 80 to 98 % during 1992 to 2000 model years.

The appropriate percentage for the pNEM/CO model would, in principle, depend on the calendar

year modeled, since the fleet distribution (percentage of on road fleet for each vehicle class and

model year) changes continually. Based on Figure 1, a single value of 90 % base penetration for

both cars and trucks is proposed, since this approximately represents the current on road fleet.

However, even if a vehicle originally had an air conditioning system, that system may have

G-10
malfunctioned and may not have been repaired. Figure 2, also taken from Koupal (1998) shows

that between 0 and 7 % of systems are estimated to be malfunctioning, depending upon vehicle

age. A reasonably representative malfunction rate taken from this figure is 5 %. Thus the

percentage of the on-road fleet with functioning air conditioning systems can be estimated as 85

%. Step 1 assumes 85 % availability.

Vent Status.

The proposed algorithm is most sensitive to the estimated probabilities of having vents open or

closed, since the CARB data shows that the AER is very sensitive to the vent status. Data

directly giving estimates of the fraction of driving with vents open or closed does not seem to be

currently available. The closest set of data to this objective seems to be a Phoenix study

referenced in Koupal (1998) that gave the measured percentages of driving time that the

compressor was in use for a vehicle driven in Phoenix and the ambient temperature during the

summer. However, these data were not useful for the current purpose, since a) having vents open

is not the same as having air conditioning on, and b) the ambient temperatures were

unrepresentatively high (compared to the rest of the nation). 

Our proposed approach for periods when smoking is present is to assume that the vents remain

open. Since the pNEM/CO model is used to estimate CO exposures for non-smokers, it is not

unreasonable to assume that the non-smoker would want to have the windows or vents open

while smoking is present. However, this simplified approach does not allow the vent status to

depend on the ambient temperature or the availability of an air conditioner in this situation of

ETS exposure. 

Our proposed approach for periods when smoking is not present uses the residential window

open algorithm from pNEM/CO as a surrogate for vehicular vents being open. This assumes that

a person would decide to open a car vent under the same temperature and air conditioning

system availability conditions as a person in a residence deciding whether or not to open

windows in a room where there was a room air conditioning system. An alternative approach

might be to use the windows open rates for residences with central air conditioning systems, but

we assume that the availability of a local air conditioning system in a room rather than the

availability of central air conditioning is more parallel to the availability of air conditioning in a

vehicle. Turning on the air conditioner in a vehicle is more comparable to turning on the room

air conditioner in that room as opposed to overriding the default settings of a central air

conditioning system (possibly with the controls in another room.) This approach is, of course, a

large approximation.

One important consideration is that a vehicle driver or passenger can use the air conditioning

system either with vents open (to condition incoming air) or with vents closed (to condition the

indoor air), which is not the same as the situation for a residence. A possibly more realistic

approach might be to assume that at high temperatures, if the probability model indicates the

vehicle vents are closed, the model actually has the vents open for a few minutes (to quickly cool

down the hot vehicle using the cooler outside air) and then has the vents closed. Implementation

of that approach is not recommended since the air exchange rate algorithm will still have

substantial uncertainty despite the extra model complexity. 

G-11
Finally, note that Koupal (1998) argued that as well as the ambient temperature, the humidity is 

an important factor in the decision to use the air conditioner. A more sophisticated approach

might model the decision to open or close vehicle vents based on the availability of an air

conditioner, the ambient temperature, and the humidity. However, the humidity data for each

simulated day are not readily available, nor are data that could be used to model vent status as a

function of air conditioner availability, ambient temperature, and humidity.

Speed.

The speed distributions were originally proposed to be taken from Sierra Research’s analysis

(Carlson and Austin, 1997) of the 1992 instrumented vehicle study carried out in Spokane,

Baltimore, and Atlanta, to assist in EPA’s review of the Federal Test Procedure. In each city, a

sample of vehicles were instrumented so that second-by-second speeds (and other information)

could be recorded. The regular driver of that vehicle drove it for about one week with the

instrumentation on. Since the Rodes et al (1998) study measured average air exchange rates for

cruising at selected fixed speeds, instead of instantaneous speeds, the algorithm was later revised

to use a distribution of trip mean speeds developed by ICF / Systems Applications International

from the Spokane-Baltimore subset of the same instrumented vehicle study database (Cohen et

al, 1994). The analysis by Cohen et al (1994) computed the distribution of the trip mean speed,

where a trip (referred to as an “event” in the cited report) was defined as any continuous period

of second by second records starting with a valid speed measurement and followed by at least 19

seconds of missing or invalid speed measurements. The requirement for a 19 second gap was

designed so that short engine stalls or false starts would not be counted as a trip but would

instead be included as part of a longer trip. The distributions used for pNEM/CO were based

only on the vehicles installed with a 3-parameter data logger, rather than a 6-parameter data

logger; the set of vehicles that could be fitted with the 3-parameter logger are more

representative of the on-road fleet The trip mean speed distributions are shown in Table 4.

Air Exchange Rate.

For a given speed and vent status, the distribution of AER was estimated based on the CARB

study data (Rodes et al, 1998). Using SAS, and the data in Table 1, a mixed model was fitted of

the form:

log(AER) = vehicle + α × I(closed) + βO × speed × I(open) + βC × speed × I(closed) + Error

I(closed) = 1 if vents are closed, = 0 if vents are open.

I(open) = 1 if vents are open, = 0 if vents are closed.

The parameters α, βO, and βC are fixed effects showing that the log(AER) varies linearly with

speed, but has a different intercept and slope depending upon whether or not the vents were

open. The fitted model had α = -2.46213, βO = 0.01798, and βC = 0.03696. The estimated slope

for the vent open case is one half the slope for the vent closed case. The value of α is the

difference between the intercepts for vents open and vents closed.

G-12
The vehicle effect was assumed to be a normally distributed variable. Thus, instead of estimating

separate fixed effects for the three vehicles, the statistical model assumes that these three

vehicles were a random sample from the national fleet, each with an associated vehicle effect

drawn from this normal distribution. The fitted model had a vehicle effect with mean 3.37311

and variance 0.22966. The estimated mean, 3.37311 is the mean value of the log(AER) for a

stationary vehicle with vents open. 

Finally, the error term was assumed to be a normally distributed variable with mean zero. The

estimated error variance was 0.04357, giving a total variance of 0.22966 + 0.04357 = 0.27323

(including the vehicle to vehicle variability and the within vehicle variability).

Step 5 of the algorithm applies this statistical model to simulate air exchange rate from vehicle

speed and vent status. The fitted model for the relationship between the air exchange rate and the

vent status and speed is plotted in Figures 3 (raw scale) and 4 (log scale). The value on the y axis

is the predicted value of the air exchange rate, i.e., the exponential power of the expected value

of log(AER). For a given speed and vent status, the fitted log-normal mixed model assumes that

the air exchange rate is drawn from a log-normal distribution centered at the predicted value

(symmetrically distributed on the log scale).

References

Carlson, T. R. and T. C. Austin. 1997. Development of Speed Correction Cycles. Sierra

Research, Inc. SR-97-04-01. 

Cohen, J. P., A. M. Noda, R. K. Iwamiya, A. K. Pollack, and T. L. Darlington. 1994.

Comparison of Driving Patterns Between the Spokane/Baltimore 3- and 6- Parameter

Instrumented Data and Several Driving Cycles. Systems Applications International.

SYSAPP94-94/020. 

Hayes, S. R. 1991. “Use of an Outdoor Air Quality Model (IAQM) to Estimate Indoor ozone

Levels.” Journal of the Air and Waste Management Association, Vol. 41, pp. 161 - 170. 

Jenkins, Peggy. 1998. Personal Communication with Ted Johnson. California Air Resources

Board, Sacramento, California. 

Koupal, J. 1998. Air Conditioning Activity Effects in MOBILE6. EPA Office of Mobile

Sources. M6.AE.001.

Nagda, N. L., H. E. Rector, and M. D. Koontz. 1987. Guidelines for Monitoring Indoor Air

Quality, Hemisphere Publishing Corporation, Washington, D.C. 

Ott, W., P. Switzer, and N. Willis. 1994. “Carbon Monoxide Exposures Inside an Automobile

Traveling on an Urban Arterial Highway.” Journal of the Air and Waste Management

Association, Vol. 44, pp. 1010 - 1018. 

G-13
Petersen, G. A., and R. H. Sabersky. 1975. “Measurements of Pollutants Inside an

Automobile.” Journal of the Air Pollution Control Association, Volume 25, No. 10, pp. 1028

- 1032. 

Rodes, C., L. Sheldon, D. Whitaker, A. Clayton, K. Fitzgerald, J. Flanagan, F. Digenova, S.

Hering, and C. Frazier. 1998. Measuring Concentrations of Selected Air Pollutants Inside

California Vehicles. Air Resources Board. Report No. 95-339. 

Rosenbaum, A. S., T. Johnson, and J. Cohen, 1999. Options for Incorporating Passive

Smoking Into pNEM/CO With a Mass Balance Approach. ICF Consulting. July 16, 1999.

Memorandum to Harvey Richmond, EPA.

G-14
Table 1. Air Exchange Rate Data.

Test conditions Air Exchange Rate (hr-1)

Ventilation settings

Vehicle speed,

mpha 91 Caprice 97 Taurus 97 Explorer

vent closed

low fan speed

55 39 14 13.5

35 5.6

0 1.8

vent open

low fan speed

55 98 76 55.5

35 35.7

0 20.7

vent open

low fan speed

front windows 1/3 open

55 160

a

The vehicle speed was constantly maintained throughout the AER measurement.

Reference: Rodes, C., L. Sheldon, D. Whitaker, A. Clayton, K. Fitzgerald, J. Flanagan, F.

DiGenova, S. Hering, C. Frazier. 1998. Measuring Concentrations of Selected Air Pollutants

Inside California Vehicles. Air Resources Board. Report No. 95-339. Final report. 

G-15
Table 2. Ratios of Hourly Average Values of Inside Concentration to Outside

Concentrations. 

Air exchange rate, hr-1 Ratioa

 = Cin(h)/Cout(h)

Error in assuming 

Cin(h) = Cout(h), percent

0.10 0.048 1967

0.16 0.075 1229

0.25 0.116 764

0.40 0.175 471

0.63 0.258 287

1.00 0.368 172

1.58 0.498 101

2.51 0.634 58

3.98 0.754 33

6.31 0.842 19

10.00 0.900 11.1

15.85 0.936 6.7

25.12 0.960 4.1

39.81 0.974 2.6

63.10 0.984 1.6

100.00 0.990 1.0

a

Cin(h) is the average concentration inside the vehicle during hour h, and Cout(h) is the average

concentration outside the vehicle during hour h. Ratios based on assumptions that instantaneous

value of Cin at beginning of hour h equals zero, air exchange rate is constant during hour h, and

Cout is constant during hour h. 

G-16
Table 3. Estimated Percentage of Time With Indicated Vent Ratios by Air Conditioning

System And Temperature Range

Air

conditioning

system

Temperature

rangea

Percentage of time with indicated vent

ratiob Mean of ratios

not equal to 0

or 1 Ratio = 0 Ratio = 1 0 < Ratio =

<1

Functional Low

Medium

High

73.2

12.0

17.1

2.0

44.2

34.3

24.7

43.8

48.6

0.316

0.618

0.521

No air

conditioning

or not

functional

Low

Medium

High

80.0

4.7

1.4

1.0

59.1

70.8

19.0

36.2

27.8

0.276

0.716

0.774

Based on Table 4-3 of pNEM/CO project report. Equates residences with vehicles.

a Low: 31E to 62EF.

Medium: 63E to 75EF.

High: 76EF and above.

b Ratio = (minutes vent open)/(minutes spent in vehicle).

G-17
Table 4. Vehicle trip mean speed distribution.

Speed (mph) Frequency (%)

0 4.62

5 6.62

10 12.76

15 21.75

20 21.52

25 16.12

30 7.57

35 4.10

40 2.18

45 1.46

50 0.91

55 0.27

60 0.12

Reference: Cohen et al (1994). Table 2-17. For computational convenience, and to better reflect

the likely experimental conditions of the Rodes et al (1998) study, the distribution in the cited

Table 2-17 has been aggregated to 5 mph increments from the original 1 mph increments. The

tabulated speeds 0, 5, 10, 15, . . . correspond to the measured speed ranges 0 to 2.5, 2.6 to 7.5,

7.6 to 12.5, 12.6 to 17.5, . . . 

G-18
0

10

20

30

40

50

60

70

80

90

100

72- 74 76 78 80 82 84 86 88 90 92 94 96 98 00+

Model Year

Percent equipped with A/C

LDV's LDT's

Figure 2 - Proposed Base Market Penetration Estimates

Figure 1. From Koupal (1998).

G-19
0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

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

Vehicle Age (Years)

Rate of Unre

paired Malfunctions

R-134a Freon (1995 or later scenario year)

Figure 3 - Proposed Rate of Unrepaired Malfunctions

Figure 2. From Koupal(1998).

G-20
G-21
G-22
G-23
Appendix H

Analyses Supporting Proposed Modifications to the Mass Balance 

Algorithm and Gas Stove Algorithm in pNEM/CO

H-1
Memorandum

To: Arlene Rosenbaum

Systems Applications International

From: Ted Johnson

TRJ Environmental, Inc.

Date: September 23, 1998 (Rev. 1.2)

Project: EPA Work Assignment 1-19

Memo No. 1: Analyses Supporting Proposed Modifications to the Mass Balance

Algorithm and Gas Stove Algorithm in pNEM/CO 

Work Assignment 1-19 of EPA Contract No. 68-D6-0064 directs the ICF project team to 

propose and implement modifications to the Probabilistic NAAQS Exposure Model for

Carbon Monoxide (pNEM/CO). In earlier work performed under EPA Contract No. 68-

D3-0094, TRJ Environmental, Inc., (TRJ) assisted IT Corporation in developing a set of

recommendations for modifying two components of pNEM/CO: the mass balance

algorithm and the gas stove algorithm. The proposed modifications are summarized in

two letters (dated June 9 and June 12, 1998) from Andy Law of IT Corporation to

Harvey Richmond of the U.S. Environmental Protection Agency. This memorandum

describes a series of analyses performed by TRJ in support of these modifications. 

Seasonal Variation for Fuel Use by Gas Stove Burners

Based on recommendations made by analysts at IT Corporation under a prior work

assignment, Harvey Richmond of EPA’s Risk and Exposure Assessment Group (REAG)

directed the IT project team to base all estimates of fuel use by gas stove burners on

data obtained from a survey performed by Northern Illinois Gas Company (NIGAS). 

This survey included 752 gas appliances in 354 single-family residences. Harvey

Richmond also requested that analysts incorporate the seasonal variation in gas stove

burner use into the model. This section presents a method which meets these

requirements. 

A report by the Gas Research Institute (GRI) indicates that the arithmetic mean of

burner gas use for the NIGAS survey was 21.8 therms = 2180 ft3

 per year (n = 57) with

an arithmetic standard deviation of 8.9 therms = 890 ft3 per year (unadjusted for age of

occupants). The ratio of standard deviation to mean is 0.41. This value suggests that

the underlying distribution is skewed, as the ratio for normal distributions fit to nonnegative data is typically less than 0.33. If we assume that the distribution is

lognormal, the corresponding values for geometric mean and standard deviation can be

calculated by the expressions 

H-2
GM = AM/[(ASD/AM)2

 + 1]0.5 (1)

GSD = exp{ln[(ASD/AM)2

 + 1]}0.5 (2)

in which GM = geometric mean, GSD = geometric standard deviation, AM = arithmetic

mean, and ASD = arithmetic standard deviation. Making the appropriate substitutions,

one obtains GM = 2018 ft3 per year and GSD = 1.48 (dimensionless). 

The GM must be converted to kilojoules for use in the pNEM/CO mass balance model. 

The 1992 pNEM/CO report notes that the Gas Research Institute estimates that the

average heating value of natural gas in Denver is 1048 kilojoules per cubic feet. 

Applying this conversion factor, we obtain 2018 ft3 per year x 1048 kilojoules/ft3 =

2,114,864 kilojoules per year = 2.11 million kilojoules per year. 

GEOMET provides the following equation for estimating daily gas use by burners

(ft3

/day) when the date is converted to number of weeks since May 1: 

y(x) = 6.37 - (1.21)[sin(0.45 + 2πx/52)] (3)

in which 

x = number of weeks since May 1

y = mean gas stove consumption for week x, excluding pilot lights (ft3/day).

The following normalized version of this equation produces a mean value of 1.0: 

Y’(x) = 1.00 - (0.190)[sin(0.45 + 2πx/52)]. (4)

Equation 4 can be converted to the following equation which uses m = days since May 1

rather than x = weeks since May 1. 

Y’(m) = 1.00 - (0.190)[sin(0.45 + 2πm/365)]. (5)

There are 17 weeks (120 days) from January 1 through April 30 and 35 weeks (245

days) from May 1 through December 31. The following versions of Equation 5 can be

used with j = Julian date (i.e., j = 1 for January 1):

Y’(j) = 1.00 - (0.190){sin[0.45 + (2π)(j - 120)/365]} or (6)

Y’(j) = 1.00 - (0.190){sin[-1.616 + (2π)(j)/365]}. (7)

As before, EFBURN is defined as the CO emission factor for stove burners for a

particular hour. To use Equation 7 in calculating ERBURN, perform the following steps:

1. Go to the next day of the current sequence. The Julian date of this day is j. 

H-3
2. Obtain value for AUB = annual fuel usage of burners from an appropriate

distribution. As indicated above, we can assume that AUB follows a lognormal

distribution with geometric mean = 2.11 million kilojoules per year and geometric

standard deviation = 1.48. 

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

4. ERBURN = (EFBURN)(AUB)(Y’)/365.2 for all hours of day. 

Fuel Use by Gas Stove Pilot Lights

Based on recommendations made by analysts at IT Corporation under a prior work

assignment, Harvey Richmond directed the project team to base all estimates of fuel

use by pilot lights on the NIGAS study discussed above. The NIGAS study measured

total gas usage (burners plus pilot lights) for 33 stoves and reported an arithmetic mean

of 57.1 therms and a standard deviation of 18.3 therms. As discussed above, the study

measured gas usage by burners only for arithmetic mean of burner gas use for the

NIGAS survey was 21.8 therms (n = 57) with an arithmetic standard deviation of 8.9

therms. The difference in means between the two samples, 57.1 - 21.8 = 35.3, provides

an estimate of the mean fuel usage for pilot lights only. The corresponding difference in

variances (18.32

 - 8.92 = 255.7) provides an estimate of the variance of fuel usage by

pilot lights. The square root of the estimated variance provides an estimate of the

standard deviation (16.0). Consequently, we can estimate that the distribution of pilot

light usage has an arithmetic mean of 35.3 therms = 3530 ft3 and an arithmetic standard

deviation of 16.0 therms = 1600 ft3. 

The ratio of standard deviation to mean is 16.0/35.3 = 0.45, suggesting that the

underlying distribution is skewed. If we assume that the distribution is lognormal, the

corresponding values for geometric mean and standard deviation can be calculated by

Equations 1 and 2 above. Making the appropriate substitutions, we obtain GM = 3215

ft3 per year and GSD = 1.84 (dimensionless). Applying the ft3-to-kilojoule conversion

factor cited above, we calculate the geometric mean to be 3215 ft3

 per year x 1048

kilojoules/ft3

 = 3,369,320 kilojoules per year = 3.37 million kilojoules per year. The

GSD is unchanged. 

Air Exchange Rates for Residences with Windows Closed

In the May 4 memorandum, Harvey Richmond directed the project team to use the data

from Reference 8 of the Law letter to develop distributions for the region and seasons

that are included in the analysis for Denver. Reference 8 is a journal article by D. M.

Murray and D. E. Burmaster (Risk Analysis, Vol. 15, No. 4, 1995) which lists

distributions of AER by season and climatic region based on data compiled by

Brookhaven National Laboratory (BNL). Table 1 is an excerpt of the data from Murray

and Burmaster which has been reformatted for easier analysis. The data applicable to

Denver can be found under Region 2, which includes the State of Colorado. 

Parameters for the lognormal distribution are provided for each of four seasons and for

all seasons combined. The four seasons and associated sample sizes are listed below. 

H-4
Season 1 (n = 428): December, January, February 

Season 2 (n = 43): March, April, May 

Season 3 (n = 2): June, July, August 

Season 4 (n = 23): September, October, November. 

The indicated sample sizes are considered adequate for characterizing lognormal

distributions appropriate for Seasons 1 and 2. The sample size for Season 4 (n = 23) is

considered marginal, and the sample size for Season 3 (n = 2) is obviously inadequate. 

To provide better estimates for Seasons 3 and 4, we conducted a series of statistical

analyses. We found that the following regression-based equation could be used to

predict the geometric mean values with n > 20 in Table 1 with an R2 value of 0.851

(adjusted R2 = 0.778). 

GM = 0.627 + (0.298)(S3) - (0.344)(R1) - (0.272)(R2) - (0.241)(R3)

where S3 equals 1 for Season 3, 0 otherwise. 

R1 equals 1 for Region 1, 0 otherwise.

R2 equals 1 for Region 2, 0 otherwise.

R3 equals 1 for Region 3, 0 otherwise.

Applied to Season 3 in Region 2, this equation produces the estimate of GM = 0.627 +

(0.298)(1) - (0.272)(1) = 0.653. This estimate is consistent with the GM values for

Season 3 in Regions 1, 3, and 4 which tend to be larger than the GM values for the

other three seasons. 

The above equation is not applicable to Season 4, as it does not include the variable

S4. Additional analyses found that the following applicable equation could be used to

predict the geometric mean values with n > 20 in Table 1 with an R2

 value of 0.857

(adjusted R2 = 0.715). 

GM = 0.923 - (0.310)(S1) - (0.278)(S2) - (0.316)(S4) - (0.338)(R1) - (0.266)(R2) -

(0.236)(R3)

where S1 equals 1 for Season 1, 0 otherwise. 

S2 equals 1 for Season 2, 0 otherwise.

S4 equals 1 for Season 4, 0 otherwise.

R1 equals 1 for Region 1, 0 otherwise.

R2 equals 1 for Region 2, 0 otherwise.

R3 equals 1 for Region 3, 0 otherwise.

Applied to Season 4 in Region 2, this equation produces the estimate of GM = 0.923 -

(0.316)(1) - (0.266)(1) = 0.341. As this estimate (0.341) is consistent with the GM

value listed in Table 1 for Season 3 (0.309), we can make a case for using either value. 

I suggest that we use the listed value (0.309). 

H-5
Similar analyses of the geometric standard deviation (GSD) values found that season

and region were not good predictors of the GSD values in Table 1, which tended to fall

within a relatively narrow range. Consequently, I suggest that we use the average

GSD for Region 2 (2.010) as the GSD for Season 3 in Region 2 and the listed GSD

(1.716) for Season 4.

In summary, I am proposing that the following seasonal distributions be used represent

the AER values of Denver residences with windows closed. 

Season Months Geometric mean Geometric std. dev.

 1 Dec, Jan, Feb 0.450 1.960

 2 Mar, Apr, May 0.308 2.241

 3 Jun, Jul, Aug 0.653 2.010

 4 Sep, Oct, Nov 0.309 1.716

Note: Following the completion of the analysis described above, ICF became aware of

potential errors in the BNL database analyzed by Murray and Burmaster. If the errors

are significant, the analysis presented above may need to be revised when BNL issues

a corrected database. 

Air Exchange Rates for Residences with Windows Open

In the May 4 memorandum, Harvey Richmond directed the project team to use the draft

“scripted house” report by Johnson, Weaver, and Mozier (1997) to develop a uniform

distribution for the air exchange rates (AER) of residences with windows open. This

report provides 24 hourly-average AERs measured in a residence under varying

conditions. Table 2 is a listing of the nine hourly-average AER values measured when

one or more windows were open. The values range from 0.78 to 2.92, appropriate

lower and upper limits for a uniform distribution fit to the data. 

I also evaluated the normal and lognormal distributions as models for the data. These

data analyses indicated that the AER values could be reasonably characterized by a

lognormal distribution with geometric mean = 1.336 hr-1 and geometric standard

deviation = 1.550 (dimensionless). (The Wilk-Shapiro statistics for goodness-of-fit were

0.877 for the normal distribution and 0.960 for the lognormal distribution, where 1.000

indicates a perfect fit .) I recommend that we use the specified lognormal distribution in

pNEM/CO with lower and upper bounds of 0.57 and 3.15. These bounds correspond to

the 2.5th and 97.5th percentiles of the specified lognormal distribution and include the full

range of observed values. 

Residential Volumes

The May 4 memorandum directs analysts to use the 1995 Census Bureau Housing

Survey (CBHS) to develop a distribution for volumes of residences in Denver. 

Consistent with the approach used in the 1992 pNEM/CO model, we will determine the

distribution for living area (i.e., square footage) and assume a constant 8-foot ceiling

H-6
height. Table 3 lists frequency data from the CBHS for Denver and Los Angeles

(another possible study area for pNEM/CO analyses). Plots of these statistics indicate

that the data are closely fit by lognormal distributions with the following values for

geometric mean and geometric standard deviation. 

Geometric mean Geometric std. dev. Median

Square footage: Denver 1926 1.62 2020

Los Angeles 1604 1.64 1651

The values listed under median were provided in the CBHS listings and agree closely

with the estimated geometric means. (The geometric mean of a “perfect” lognormal

distribution is equal to its median). 

Assuming an eight-foot ceiling and using 1 cubic meter = 35.315 cubic feet, the

residential volumes can be modeled by lognormal distributions with the following

parameters. 

Geometric mean Geometric std. dev.

Volume, m3: Denver 436 1.62

Los Angeles 363 1.64

The lognormal distribution used for Denver in the 1992 version of pNEM/CO had a

geometric mean of 321 m3 and a standard deviation of 1.62. The new geometric mean

(436 m3

) represents an increase of 36 percent in the average residential volume for

Denver residences. The geometric standard deviation is essentially unchanged. 

Summary Table

Table 4 presents a summary of the distributions proposed in this memorandum. The

lower and upper bounds for values to be selected from each distribution are initially set

at the 2.5th and 97.5th percentiles of the distribution. These bounds may be changed

after further analysis of the applicable data bases. Note also that the analysis of air

exchange rates for residences with windows closed may need to revised when BNL

issues a corrected database. 

H-7
Table 1. Air Exchange Rates for Residences, Windows Closed (Murray and Burmaster, 1995)

Region States Seasona Sample

size

Lognormal distribution fit to air exchange

rate data

Geometric mean Geometric standard

deviation

All All All 2844 0.528 2.273

1 1139 0.433 2.026

2 1051 0.503 2.079

3 529 1.008 2.479

4 125 0.316 1.895

1 ID, MN, MT, NH, NY1,

VT, WI

All 467 0.314 2.038

1 161 0.271 2.223

2 254 0.364 1.872

3 5b (0.643) 2.088

4 47 0.216 1.749

2 CO, CT, IL, NJ, NY2,

OH, PA, WA (includes

Denver and New York)

All 496 0.430 2.010

1 428 0.450 1.960

2 43 0.308 2.241

3 2b (1.261) na

4 23b (0.309) (1.716)

3 CA3, MD, OR, WA All 332 0.439 1.996

1 96 0.384 1.802

2 165 0.448 2.186

3 34 0.555 1.844

4 37 0.455 1.573

4 AZ, CA4, FL, TX

(includes Los Angeles)

All 1549 0.687 2.243

1 454 0.507 1.910

2 589 0.619 1.950

3 488 1.054 2.489

4 18b (0.416) (2.034)

a

 Season 1: December, January, February; Season 2: March, April, May; Season 3: June, July, August; 

 Season 4: September, October, November. b

 Sample size < 25. 

H-8
Table 2. Data for Air Exchange Rates Measured During Scripted House Study When One or More Windows Were O

and Mozier, 1997)

Start

time

Office

Door A

Office

Window C

Office

Door D

Bedroom

Door E

Bedroom

Window F

Great Room

Window G

Great Room

Window I

Heat 

6 am Closed OPEN Open Open Closed Closed Closed Off 9 am Closed OPEN Open Open Closed Closed OPEN Off 

10 am Closed OPEN CLOSED Open Closed Closed OPEN Off 11 am Closed OPEN Open Open Closed Closed Closed Off 

6 pm Closed OPEN CLOSED Open Closed Closed Closed Off 7 pm Closed OPEN Open Open Closed Closed Closed Off 8 pm Closed OPEN Open Open Closed Closed OPEN Off 9 pm Closed OPEN CLOSED Open Closed Closed Closed Off 4 am Closed OPEN CLOSED Open Closed Closed Closed ON 

Varied

during

test?

No Yes Yes No No No Yes Yes 

a “Yes” indicates that air had unobstructed flow path from open Office Window C to open Great Window I. b Open window AER statistics: geometric mean = 1.336 hr-1, geometric standard deviation = 1.550.

H-9
Table 3. Statistics on Square Footage of Occupied Units. 

Range of

square footage

for occupied

units

Denver Los Angeles

Number in

thousands

Cumulative

percent

Number in

thousands

Cumulative

percent

less than 500 1.0 0.2 20.7 1.7

500 to 749 9.3 2.5 37.0 4.7

750 to 999 34.1 10.6 104.9 13.2

1,000 to 1,499 75.6 28.6 354.8 42.0

1,500 to 1,999 86.0 49.2 326.1 68.5

2,000 to 2,499 86.7 69.8 199.6 84.7

2,500 to 2,999 49.8 81.7 66.8 90.1

3,000 to 3,999 53.4 94.5 77.1 96.3

4,000+ 23.2 100.00 45.1 100.0

Totala 419.1 -- 1232.1 --

a

 Omits 38,500 units which did not report square footage values. 

H-10
Table 4. Summary of Distributions Proposed in this Memorandum. 

Quantity City Distribution Parametera Value

Annual fuel use by gas stove

burners, kJ/yr (Use Equation 7 and

four-step algorithm to determine

seasonal patterns in gas use.)

Denver Lognormal GM 2.11 million

GSDb 1.48

Lower bound 0.98 millionc

Upper bound 4.55 milliond

Annual fuel use by gas stove pilot

light, kJ/yr

Denver Lognormal GM 3.37 million

GSDb 1.84

Lower bound 1.02 millionc

Upper bound 11.13 milliond

Air exchange rate -- windows

closed (Season 1), h-1

Denver Lognormal GM 0.450

GSDb 1.960

Lower bound 0.120c

Upper bound 1.683d

Air exchange rate -- windows

closed (Season 2), h-1

Denver Lognormal GM 0.308

GSDb 2.241

Lower bound 0.063c

Upper bound 1.498d

Air exchange rate -- windows

closed (Season 3), h-1

Denver Lognormal GM 0.653

GSDb 2.010

Lower bound 0.166c

Upper bound 2.566d

Air exchange rate -- windows

closed (Season 4), h-1

Denver Lognormal GM 0.309

GSDb 1.716

Lower bound 0.107c

Upper bound 0.890d

Air exchange rate -- windows open,

h-1

Any Lognormal GM 1.34

GSDb 1.55

Lower bound 0.57c

Upper bound 3.16d

H-11
Quantity City Distribution Parametera Value

Residential volumes, m3 Denver Lognormal GM 436

GSDb 1.62

Lower bound 169c

Upper bound 1122d

Residential volumes, m3 Los

Angeles

Lognormal GM 363

GSDb 1.64

Lower bound 138c

Upper bound 957d

a

 GM = geometric mean, GSD = geometric standard deviation. 

b

 GSD is a dimensionless quantity. 

c

 Lower bound = 2.5th percentile of proposed distribution. d

 Upper bound = 97.5th percentile of proposed distribution. 

H-12
Appendix I

Distributions for Use with the Mass Balance and Gas Stove Algorithms

in Applying pNEM/CO to Los Angeles

I-1
Memorandum

To: Arlene Rosenbaum

Systems Applications International

From: Ted Johnson

TRJ Environmental, Inc.

Date: April 21, 1999 (Rev. 1.6)

Project: EPA Work Assignment 2-30

Memo No. 2: Distributions for Use with the Mass Balance and Gas Stove

Algorithms in Applying pNEM/CO to Los Angeles

Work Assignment 2-30 of EPA Contract No. 68-D6-0064 directs the ICF project team to 

provide input data necessary to apply Version 2.0 of the Probabilistic NAAQS Exposure

Model for Carbon Monoxide (pNEM/CO) to Los Angeles. This memorandum provides

some of the input data required by two components of pNEM/CO: the mass balance

algorithm and the gas stove algorithm. 

Seasonal Variation for Fuel Use by Gas Stove Burners

Based on recommendations made by analysts at IT Corporation under a prior work

assignment, Harvey Richmond of EPA’s Risk and Exposure Assessment Group (REAG)

directed the project team to base all estimates of fuel use by gas stove burners on data

obtained from a survey performed by Northern Illinois Gas Company (NIGAS). This

survey included 752 gas appliances in 354 single-family residences. Harvey Richmond

also requested that analysts incorporate the seasonal variation in gas stove burner use

into the model. This section presents a method which meets these requirements. The

application of this method to Denver was discussed previously in my memorandum of

September 23, 1998.

A report by the Menkedick et al. (1993) for the Gas Research Institute (GRI) indicates

that the arithmetic mean of burner gas use for the NIGAS survey was 21.8 therms =

2180 ft3

 per year (n = 57) with an arithmetic standard deviation of 8.9 therms = 890 ft3

per year (unadjusted for age of occupants). The ratio of standard deviation to mean is

0.41. This value suggests that the underlying distribution is skewed, as the ratio for

normal distributions fit to non-negative data is typically less than 0.33. If we assume

that the distribution is lognormal, the corresponding values for geometric mean and

standard deviation can be calculated by the expressions 

GM = AM/[(ASD/AM)2

 + 1]0.5 (1)

GSD = exp{ln[(ASD/AM)2

 + 1]}0.5 (2)

I-2
in which GM = geometric mean, GSD = geometric standard deviation, AM = arithmetic

mean, and ASD = arithmetic standard deviation. Making the appropriate substitutions,

one obtains GM = 2018 ft3 per year and GSD = 1.48 (dimensionless). 

The GM must be converted to kilojoules for use in the pNEM/CO mass balance model. 

The 1992 pNEM/CO report notes that the Gas Research Institute estimates that the

average heating value of natural gas in Denver is 1048 kilojoules per cubic feet. IN a

personal communication, the California Energy Commission told Pat Stiefer that the all

western natural gas should have the same heating value. Assuming that the Denver

conversion factor also applies to Los Angeles, we obtain 2018 ft3 per year x 1048

kilojoules/ft3 = 2,114,864 kilojoules per year = 2.11 million kilojoules per year. 

As described below, we are proposing that the 2.11 million kilojoules value by multiplied

by 0.82 to adjust for the differences between the population sampled by NIGAS and the

Los Angeles study area population. The resulting estimate for the GM is 1.73 million

kilojoules. The GSD is unchanged. 

Wilkes and Koontz (1995) provides the following equation for estimating daily gas use

by burners (ft3

/day) when the date is converted to number of weeks since May 1: 

y(x) = 6.37 - (1.21)[sin(0.45 + 2πx/52)] (3)

in which 

x = number of weeks since May 1

y = mean gas stove consumption for week x, excluding pilot lights (ft3/day).

The following normalized version of this equation produces a mean value of 1.0: 

Y’(x) = 1.00 - (0.190)[sin(0.45 + 2πx/52)]. (4)

Equation 4 can be converted to the following equation which uses m = days since May 1

rather than x = weeks since May 1. 

Y’(m) = 1.00 - (0.190)[sin(0.45 + 2πm/365)]. (5)

There are 17 weeks (120 days) from January 1 through April 30 and 35 weeks (245

days) from May 1 through December 31. The following versions of Equation 5 can be

used with j = Julian date (i.e., j = 1 for January 1):

Y’(j) = 1.00 - (0.190){sin[0.45 + (2π)(j - 120)/365]} or (6)

Y’(j) = 1.00 - (0.190){sin[-1.616 + (2π)(j)/365]}. (7)

As before, EFBURN is defined as the CO emission factor for stove burners for a

I-3
particular hour. To use Equation 7 in calculating ERBURN, perform the following steps:

5. Go to the next day of the current sequence. The Julian date of this day is j. 

6. Obtain value for AUB = annual fuel usage of burners from an appropriate

distribution. As indicated above, we can assume that AUB follows a lognormal

distribution with geometric mean = 1.73 million kilojoules per year and geometric

standard deviation = 1.48. 

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

8. ERBURN = (EFBURN)(AUB)(Y’)/365.2 for all hours of day. 

The distribution of EFBURN values previously used in the Denver analysis would be

applied to Los Angeles. 

Fuel Use by Gas Stove Pilot Lights

Based on recommendations made by analysts at IT Corporation under a prior work

assignment, Harvey Richmond directed the project team to base all estimates of fuel

use by pilot lights on the NIGAS study discussed above. The report by Menkedick et al.

(1993) indicates that the NIGAS study measured total gas usage (burners plus pilot

lights) for 33 stoves and reported an arithmetic mean of 57.1 therms and a standard

deviation of 18.3 therms. As discussed above, the study measured gas usage by

burners only for arithmetic mean of burner gas use for the NIGAS survey was 21.8

therms (n = 57) with an arithmetic standard deviation of 8.9 therms. The difference in

means between the two samples, 57.1 - 21.8 = 35.3, provides an estimate of the mean

fuel usage for pilot lights only. The corresponding difference in variances (18.32 - 8.92 =

255.7) provides an estimate of the variance of fuel usage by pilot lights. The square

root of the estimated variance provides an estimate of the standard deviation (16.0). 

Consequently, we can estimate that the distribution of pilot light usage has an arithmetic

mean of 35.3 therms = 3530 ft3

 and an arithmetic standard deviation of 16.0 therms =

1600 ft3

. 

The ratio of standard deviation to mean is 16.0/35.3 = 0.45, suggesting that the

underlying distribution is skewed. If we assume that the distribution is lognormal, the

corresponding values for geometric mean and standard deviation can be calculated by

Equations 1 and 2 above. Making the appropriate substitutions, we obtain GM = 3215

ft3

 per year and GSD = 1.84 (dimensionless). Applying the ft3-to-kilojoule conversion

factor cited above, we calculate the geometric mean to be 3215 ft3 per year x 1048

kilojoules/ft3

 = 3,369,320 kilojoules per year = 3.37 million kilojoules per year. 

If we apply the adjustment factor for Los Angeles (0.82) to 3.37 million kilojoules, we

obtain an estimate of the GM for pilot lights of 2.76 million kilojoules per stove. The

GSD remains equal to 1.84. Note that application of the adjustment factor to the NIGAS

pilot light data assumes that pilot light use is proportionally lower in Los Angeles, an

assumption which cannot be currently verified. 

I-4
Development of Los Angeles Adjustment Factor (0.82)

Pat Stiefer has obtained gas stove data specific to Los Angeles which provides a basis

for adjusting the NIGAS-derived values for application to Los Angeles. Table 1 lists

data for six utility districts provided by the Demand Analysis Office of the California

Energy Commission (CEC, 1998). Three of the districts span the Los Angeles study

area (LADWP, BDG, and SCE). The average gas use per stove in these three districts

ranges from 45.0 to 49.2 therms with 47 therms representing a reasonable overall

estimate. Note that these values specify total gas use by the stove; the CEC did not

provide separate estimates for burners and pilot lights. 

As indicated above, the NIGAS study reported an average of total gas usage of 57.1

therms per stove based on 33 stoves. If we assume that Los Angeles residents use

less gas per stove than the population sampled by NIGAS, we can calculate an

appropriate adjustment ratio by the expression

Adjustment ratio = (47 therms)/(57.1 therms) = 0.82. (8)

The adjusted GM for burner use would be 2.11 million kilojoules x 0.82 = 1.73 million

kilojoules. The corresponding GSD of 1.48 would be unchanged, as this is a

dimensionless quantity. Using the same adjustment approach, the GM for pilot light use

would be estimated as 3.37 million kilojoules x 0.82 = 2.76 million kilojoules. Again, the

GSD would remain unchanged at 1.84. 

Prevalence of Gas Stoves

According to Jim Capel, we estimated that 19.6 percent of the Denver households used

gas stoves for cooking and applied this single prevalence rate to all home districts. 

The demographic data listed in Table 2 provide a basis for estimating a corresponding 

prevalence rate for the Los Angeles study area. The data were compiled by Pat Stiefer

from three reports (U.S. Department of Commerce, 1996a, 1996b, 1996c) listing

statistics obtained from American Housing Survey (AHS). The area labeled Los

Angeles in Table 2 includes the Los Angeles - Long Beach PMSA (i.e., Los Angeles

County), a region which contains most of the census tracts included in the study area

defined for Los Angeles pNEM/CO analysis. The remaining census tracts are located

just outside Los Angeles County in Orange County or in Riverside County. Because of

their similar demographics and close proximity to Los Angeles County, these bordering

census tracts are likely to be better represented by the prevalence rate of Los Angeles

County (79.8 percent) than by rates listed for Orange County (61.9 percent) or Riverside

County (77.4 percent). 

The 79.8 percent prevalence rate for Los Angeles County obtained from the AHS is

consistent with the gas stove use rates listed in Table 1 for LADWP (78.6 percent) and

BDG (78.4 percent). Taken together, the three estimates support an estimate of about

79 percent for the gas stove prevalence rate. 

I-5
Air Exchange Rates for Residences with Windows Closed

In applying pNEM/CO to Los Angeles, we require distributions for air exchange rates in

residences with windows closed. In our previous work applying pNEM/CO to Denver,

we based the distributions on a journal article by D. M. Murray and D. E. Burmaster

(1995). This article lists distributions of AER by season and climatic region based on

data compiled by Brookhaven National Laboratory (BNL). Table 3 is an excerpt of the

data from Murray and Burmaster which has been reformatted for easier analysis. The

data applicable to Los Angeles can be found under Region 4, which includes the portion

of California containing Los Angeles. Parameters for the lognormal distribution are

provided for each of four seasons and for all seasons combined. The four seasons and

associated sample sizes are listed below. 

Season 1 (n = 454): December, January, February 

Season 2 (n = 589): March, April, May 

Season 3 (n = 488): June, July, August 

Season 4 (n = 18): September, October, November. 

The indicated sample sizes are considered adequate for characterizing lognormal

distributions appropriate for Seasons 1, 2, and 3. The sample size for Season 4 (n =

18) is considered marginal. To provide better estimates for Season 4, we conducted a

series of statistical analyses. We found that the following regression-based equation

could be used to predict the geometric mean values with n > 20 in Table 3 with an R2

value of 0.857 (adjusted R2 = 0.715). 

GM = 0.923 - (0.310)(S1) - (0.278)(S2) - (0.316)(S4) - (0.338)(R1) - (0.266)(R2) -

(0.236)(R3)

where S1 equals 1 for Season 1, 0 otherwise. 

S2 equals 1 for Season 2, 0 otherwise.

S4 equals 1 for Season 4, 0 otherwise.

R1 equals 1 for Region 1, 0 otherwise.

R2 equals 1 for Region 2, 0 otherwise.

R3 equals 1 for Region 3, 0 otherwise.

Applied to Season 4 in Region 4, this equation produces the estimate of 

GM = 0.923 - (0.316)(1) = 0.607. 

This estimate is consistent with the GM value listed in Table 3 for Season 2 (0.619), the

season with the most similar temperature conditions (i.e., Season 4 = Fall, Season 2 = 

Spring). Consequently, I suggest that we use this estimate (0.607) for Season 4 rather

than the listed value (0.416). 

Similar analyses of the geometric standard deviation (GSD) values found that season

I-6
and region were not good predictors of the GSD values in Table 3, which tended to fall

within a relatively narrow range. As the listed value for Season 4 in Region 4 (2.034)

falls within the range of the other three seasons and appears to be a reasonable

estimate, I suggest we use this value as the GSD for Season 4 in Los Angeles. 

In summary, I am proposing that the following seasonal distributions be used to

represent the AER values of Los Angeles residences with windows closed. 

Season Months Geometric mean Geometric std. dev.

 1 Dec, Jan, Feb 0.507 1.910

 2 Mar, Apr, May 0.619 1.950

 3 Jun, Jul, Aug 1.054 2.489

 4 Sep, Oct, Nov 0.607 2.034

Air Exchange Rates for Residences with Windows Open

In applying pNEM/CO to Los Angeles, we also require a distribution for air exchange

rates in residences with windows open. In our previous work applying pNEM/CO to

Denver, we based this distribution on data from the draft “scripted house” report by

Johnson, Weaver, and Mozier (1997). As reported in the September 23 memorandum, 

analysis of the data suggested that we could represent AER values with windows open

by a lognormal distribution with geometric mean = 1.336 hr-1 and geometric standard

deviation = 1.550 (dimensionless) with lower and upper bounds of 0.57 and 3.15. I

recommend we use this distribution in the pNEM/CO analysis of Los Angeles until we

can acquire better data. 

Residential Volumes

Table 4 is taken from my September 23 memorandum. It presents data obtained from

the 1995 American Housing Survey (AHS) representing the distribution for volumes of

residences in Denver and Los Angeles. As discussed in my September 23

memorandum, plots of these statistics indicate that the data are closely fit by lognormal

distributions with the following values for geometric mean and geometric standard

deviation. 

Geometric mean Geometric std. dev. Median

Square footage: Denver 1926 1.62 2020

Los Angeles 1604 1.64 1651

The values listed under “median” were provided in the AHS listings and agree closely

with the estimated geometric means. (The geometric mean of a “perfect” lognormal

distribution is equal to its median). 

I-7
Assuming an eight-foot ceiling and using 1 cubic meter = 35.315 cubic feet, the

residential volumes can be modeled by lognormal distributions with the following

parameters. 

Geometric mean Geometric std. dev.

Volume, m3: Denver 436 1.62

Los Angeles 363 1.64

We previously used a lognormal distribution with GM = 436 and GSD = 1.62 to

represent residential volume in applying pNEM/CO to Denver. Consistent with this

approach, I recommend that we use a lognormal distribution with GM = 363 m3 and

GSD = 1.64 for residential volume in applying pNEM/CO to Los Angeles. 

Altitude Above Sea Level

The COHb algorithm in pNEM/CO requires an estimate of altitude above sea level

specific to the study area. The attached map shows the CO monitors which define

home districts in Los Angeles and selected elevations. The elevation listings nearest to

the 10 monitors range from 57 feet near West Los Angeles (No. 1) to 861 feet near

Pasadena (No. 6). The most central listed elevation is 320 feet near Los Angeles (No.

3). I recommend that we use a value of 100 meters (328 feet) as an average for the

area. 

Summary Table

Table 5 presents a summary of the distributions and point estimates proposed in this

memorandum for Los Angeles. The lower and upper bounds for values to be selected

from each distribution are initially set at the 2.5th and 97.5th percentiles of the

distribution. These bounds may be changed after further analysis of the applicable data

bases. 

Table 5 is limited to parameters which have values for Los Angeles that differ from the

values used previously in applying pNEM/CO to Denver. The following parameters

(organized by table where they are listed in the current draft of pNEM/CO report) are not

discussed in this memorandum, as I am recommending that the values used previously

for Denver be applied to both cities. 

Table 2-5 (entire table)

Non-zero passive smoking increments

Table 2-6 (entire table)

Initial values for previous outdoor carbon monoxide concentration and 

upper bounds of indexing intervals

I-8
Table 2-8 (entire table)

Percentage of persons with ischemic heart disease by demographic group

Table 4-1

Burner emission factor

Air exchange rate, residence - windows open

Table 4-2 (entire table)

Air exchange rate, enclosed, nonresidential microenvironments

Table 4-3 (entire table)

Window ratios

Table 4-5 (entire table)

Probability of gas stove operation

Assumed burner operation period

In addition, the physiological factors used previously to estimate ventilation rates in the

Denver analysis (see Section 5, Appendix A, and Appendix C) should be applied to both

cities. With the exception of altitude above sea level, the COHb parameters used

previously (Appendix E of the project report) should also be applied to both cities. 

References

Bureau of Census. 1995. “American Housing Survey for the Denver Metropolitan Area

in 1995.” Current Housing Reports, Report No. H170/95-46. U.S. Department of

Commerce. 

Bureau of Census. 1996a. “American Housing Survey for the Anaheim-Santa Ana

Metropolitan Area in 1995.” Current Housing Reports, Report No. H170/95-46. U.S.

Department of Commerce. 

Bureau of Census. 1996b. “American Housing Survey for the Riverside-Santa Ana 

Metropolitan Area in 1995.” Current Housing Reports, Report No. H170/95-37. U.S.

Department of Commerce. 

Bureau of Census. 1996c. “American Housing Survey for the Los Angeles-Long Beach

Metropolitan Area in 1995.” Current Housing Reports, Report No. H170/95-7. U.S.

Department of Commerce. 

California Energy Commission. 1998. “Residential Demand Forecast Model: 1998

Baseline Energy Outlook.” Demand Analysis Office, California Energy Commission. 

Johnson, T., M. Weaver, J. Mozier, et al. 1998. “Variation of Residential Air Exchange

Rates Under Scripted Ventilation Conditions.” Presented at the International

Symposium on the Measurements of Toxic and Related Pollutants, Air and Waste

management Association, September 1 - 3, 1998, Cary, North Carolina. 

Johnson, T. September 23, 1998. “Analyses Supporting Proposed Modifications to the

I-9
Mass Balance Algorithm and Gas Stove Algorithm in pNEM/CO.” Memorandum

prepared by TRJ Environmental, Inc., under EPA Contract No. 68-D6-0064. 

Menkedick, J., N. Niemuth, P. Hartford, and D. K. Landstrom. 1993. Metered Ranges,

Cooktops, and Ovens in the Northern Illinois Gas Residential Load Study Data

Base. GRI Report No. 93/0204. Prepared by Batelle for the Gas Research Institute,

8600 West Bryn Mawr Avenue, Chicago, IL 60631. 

Murray, D. M., and D. E. Burmaster. 1995. “Residential Air Exchange Rates in the

United States: Empirical and Estimated Parametric Distributions by Season and Climatic

Region.” Risk Analysis, Vol. 15, No. 4. 

U.S. Department of Commerce, Bureau of the Census and U. S. Department of Housing

and Urban Development. 1996a. American Housing Survey for the Anaheim-Santa

Ana Metropolitan Area in 1994. Current Housing Reports H170/94-2. 

U.S. Department of Commerce, Bureau of the Census and U. S. Department of Housing

and Urban Development. 1996b. American Housing Survey for the Los AngelesLong Beach Metropolitan Area in 1995. Current Housing Reports H170/95-7. 

U.S. Department of Commerce, Bureau of the Census and U. S. Department of Housing

and Urban Development. 1996c. American Housing Survey for the Riverside-San

Bernardino-Ontario Metropolitan Area in 1994. Current Housing Reports H170/94-

37. 

Wilkes, C.R., and M.D. Koontz. 1995. “Analysis of Sampling Strategies for Estimating

Annual Average Indoor NO2 Concentrations in Residences with Gas Stoves.” Journal

of the Air and Waste Management Association, Vol. 46, pp. 853 - 860. 

I-10
Table 1. Gas Stove Data Provided by Demand Analysis Office of the California Energy Commissio

Utility district Approximate

geographic area

Percent

cooking w/gas

Number of

gas stoves

Therms used by 

stoves (millions

Pacific Gas and Electric

(PG&E)

northwest of LosAngeles

33.0 1,399,887 67.8 

Sacramento Municipal

Utility Division (SMUD)

Sacramento 30.5 130,319 6.2 

Southern California

Edison (SCE)

South coast outsidedowntown LosAngeles

68.6 2,772,522 131 

Los Angeles Department

of Water and Power

(LADWP)

Los Angeles 78.6 982,891 48.4 

San Diego Gas and

Electric (SDG&E)

San Diego 46.5 486,537 20.5 

Combined Municipal

Districts of Burbank,

Glendale, and Pasadena

(BDG)

Burbank, Glendale,Pasadena

78.4 131,111 5.9 

aCalculated by Ted Johnson. 

I-11
Table 2. Fuel Use Statistics from the American Housing Survey (1996). 

Statistics Anaheim (1994)a Los Angeles (1995)b Riverside (1994)c

Total Housing Units

(HU)

918,000 3,276,000 1,121,400

Total HU w/cooking

fuel

915,500 3,165,200 1,101,900

Total HU w/natural

gas cooking fuel

568,700 2,614,000 867,500

Prevalence of HU

w/gas cooking fueld

61.9 percent 79.8 percent 77.4 percent

a

Anaheim = Orange County PMSA (includes all of Orange County).

b

Los Angeles = Los Angeles - Long Beach PMSA (includes all of Los Angeles County.

c

Riverside = Riverside-San Bernardino PMSA (Includes all of Riverside and San

Bernardino Counties). 

d

Prevalence rate = 100 x (total HU w/natural gas cooking fuel)/(total housing units). 

I-12
Table 3. Air Exchange Rates for Residences, Windows Closed (Murray and Burmaster, 1995)

Region States Seasona Sample

size

Lognormal distribution fit to air exchange

rate data

Geometric mean Geometric standard

deviation

All All All 2844 0.528 2.273

1 1139 0.433 2.026

2 1051 0.503 2.079

3 529 1.008 2.479

4 125 0.316 1.895

1 ID, MN, MT, NH, NY1,

VT, WI

All 467 0.314 2.038

1 161 0.271 2.223

2 254 0.364 1.872

3 5b (0.643) 2.088

4 47 0.216 1.749

2 CO, CT, IL, NJ, NY2,

OH, PA, WA (includes

Denver and New York)

All 496 0.430 2.010

1 428 0.450 1.960

2 43 0.308 2.241

3 2b (1.261) na

4 23b (0.309) (1.716)

3 CA3, MD, OR, WA All 332 0.439 1.996

1 96 0.384 1.802

2 165 0.448 2.186

3 34 0.555 1.844

4 37 0.455 1.573

4 AZ, CA4, FL, TX

(includes Los Angeles)

All 1549 0.687 2.243

1 454 0.507 1.910

2 589 0.619 1.950

3 488 1.054 2.489

4 18b (0.416) (2.034)

a

 Season 1: December, January, February; Season 2: March, April, May; Season 3: June, July, August; 

 Season 4: September, October, November. b

 Sample size < 25. 

I-13
Table 4. Statistics on Square Footage of Occupied Units. 

Range of

square footage

for occupied

units

Denver Los Angeles

Number in

thousands

Cumulative

percent

Number in

thousands

Cumulative

percent

less than 500 1.0 0.2 20.7 1.7

500 to 749 9.3 2.5 37.0 4.7

750 to 999 34.1 10.6 104.9 13.2

1,000 to 1,499 75.6 28.6 354.8 42.0

1,500 to 1,999 86.0 49.2 326.1 68.5

2,000 to 2,499 86.7 69.8 199.6 84.7

2,500 to 2,999 49.8 81.7 66.8 90.1

3,000 to 3,999 53.4 94.5 77.1 96.3

4,000+ 23.2 100.00 45.1 100.0

Totala 419.1 -- 1232.1 --

a

 Omits 38,500 units which did not report square footage values. 

I-14
Table 5. Summary of Parameter Estimates Proposed for Los Angeles in this

Memorandum. 

Quantity Distribution Parametera Value

Annual fuel use by gas stove

burners, kJ/yr (Use Equation 7 and

four-step algorithm to determine

seasonal patterns in gas use.)

Lognormal GM 1.73 million

GSDb 1.48

Lower bound 0.80 millionc

Upper bound 3.73 milliond

Annual fuel use by gas stove pilot

light, kJ/yr

Lognormal GM 2.76 million

GSDb 1.84

Lower bound 0.84 millionc

Upper bound 9.12 milliond

Gas stove prevalence rate Point estimate Point estimate 79 percent

Air exchange rate -- windows closed

(Season 1), h-1

Lognormal GM 0.507

GSDb 1.910

Lower bound 0.143c

Upper bound 1.802d

Air exchange rate -- windows closed

(Season 2), h-1

Lognormal GM 0.619

GSDb 1.950

Lower bound 0.167c

Upper bound 2.292d

Air exchange rate -- windows closed

(Season 3), h-1

Lognormal GM 1.054

GSDb 2.489

Lower bound 0.176c

Upper bound 6.296d

Air exchange rate -- windows closed

(Season 4), h-1

Lognormal GM 0.607

GSDb 2.034

Lower bound 0.151c

Upper bound 2.441d

I-15
Quantity Distribution Parametera Value

Air exchange rate -- windows open,

h-1

Lognormal GM 1.34

GSDb 1.55

Lower bound 0.57c

Upper bound 3.16d

Residential volumes, m3 Lognormal GM 363

GSDb 1.64

Lower bound 138c

Upper bound 957d

Altitude above sea level, m Point estimate Point estimate 100

a

 GM = geometric mean, GSD = geometric standard deviation. 

b

 GSD is a dimensionless quantity. 

c

 Lower bound = 2.5th percentile of proposed distribution.

d

 Upper bound = 97.5th percentile of proposed distribution. 

I-16
Appendix J

Differences in Human Activity Patterns Between Individuals

With and Without Cardiovascular Disease

J-1
M E M O R A N D U M

TO: Harvey Richmond

FROM: Jonathan Cohen, Sergey Nikiforov, and Arlene Rosenbaum

DATE: January 15, 1999

SUBJECT: EPA 68-DO-0062 Work Assignment 2-24: Task 2: Evaluation of

Differences in Human Activity Patterns Between Individuals With or

Without Cardiovascular Disease

EVALUATION OF DIFFERENCES IN HUMAN ACTIVITY PATTERNS BETWEEN

INDIVIDUALS WITH OR WITHOUT CARDIOVASCULAR DISEASE

SUMMARY

Activity pattern data from the National Human Activity Pattern Survey were used to compare

activity patterns and exertion distributions between subjects with or without angina. The diary

survey provided a 24-hour diary of activities. Exertion rates for each person in the survey were

simulated 100 times. For each person, the body weight was simulated from a log-normal

distribution specific to the age and gender. The resting metabolic rate was simulated using a

regression against body weight, with coefficients depending on age and gender. Finally, the

exertion rate was simulated for each activity and person by multiplying the simulated resting

metabolic rate by a MET exertion ratio with a distribution specific to each type of activity. The

current version of the probabilistic NAAQS Exposure Model for Carbon Monoxide (pNEM/CO),

described in Johnson (1998), begins with the same set of physiological equations and statistical

distributions for probabilistic simulation of exposure. The pNEM/CO model uses the much

broader Consolidated Human Activity Data Base (CHAD) and simulates additional

physiological variables, such as the ventilation rate. The description of the relevant probabilistic

and physiological equations in this memorandum is largely based on Johnson (1998); see that

memorandum for more detailed information.

Differences between angina and non-angina subjects were evaluated for several summary

statistics: average and 95th percentile of the maximum daily 8-hour exertion, percentage of time

spent outdoors or in a vehicle, average percentage of time at light, moderate or heavy exertion

J-2
levels. Age and gender have very significant effects on these summary statistics of activity and

exertion. Since angina patients tend to be much older and tend to include more females than the

general population, it is very important to adjust for age and gender effects when comparing

angina and non-angina groups. Otherwise, one cannot distinguish between the angina effect and

the effects of age and gender. Statistical analyses comparing angina to non-angina subjects were

performed, adjusting for age and gender either by stratification (comparing subjects in a given

age/gender subgroup), or by fitting a general linear model (with separate terms for age, gender,

and angina effects and their interactions). These analyses showed that, overall, angina subjects

tended to have less extreme exertion levels. More specifically, the maximum 8-hour exertion

energies tended to be lower, as did the percentages of time above moderate or high exertion rate

thresholds. The percentages of time spent outdoors or in a vehicle were generally not statistically

significantly different between angina and non-angina subjects.

The large sample of NHAPS subjects produced, in many cases, statistically significant

differences in the exertion rate summaries between angina and non-angina subjects. However,

those differences were generally numerically small compared to the mean values. Therefore we

conclude that the differences in activity and exertion between angina and non-angina subjects,

although statistically significant, are not large enough to severely impact the validity of

pNEM/CO modeling results that do not adjust for an angina/non-angina difference.

METHODOLOGY

For these analyses we used the National Human Activity Pattern Survey (NHAPS) database, a

telephone survey of human activity patterns conducted for the USEPA between October 1992

and September 1994 by the Survey Research Center at the University of Maryland. See Klepeis

et al. (1996, 1998) and Tsang and Klepeis (1996) for more details about the NHAPS study and

various statistical analyses of those data. The NHAPS data (Triplett, 1996) are included in

CHAD. (Other CHAD studies did not include questions about cardiovascular disease and so

could not be used for these analyses comparing angina and non-angina respondents.) A

nationally representative sample of 9,386 respondents completed a detailed diary listing all their

activities and locations over a 24-hour period (either from the previous day or a previous

weekend day). A few respondents did not state their age and/or gender and their data was not

used in our analysis. Our analysis used 9,149 of the surveys. Respondents were also asked

demographic questions, including age and gender, and health questions, including whether or not

they have been told by a doctor that they have angina: 243 respondents (2.6 percent) had angina.

Respondents were asked about employment status (e.g. full-time, part-time, or unemployed) but

not about their occupation. Other follow-up questions (not used in our analyses) related to the

respondent’s exposure to either water or air pollution on the diary day. For each household, the

respondent was randomly selected to be either the adult or child (under 18) with the next

birthday; an adult provided proxy responses for a child.

The EPA report (Klepeis, Tsang and Behar, 1996), Section 3, shows that the sample is

reasonably representative of the national population with respect to gender and age distributions.

The NHAPS population slightly underrepresented males (46 % NHAPS compared to 49 % from

the 1990 Census). The fraction of weekend (Saturday or Sunday) respondents was 33 %, close to

J-3
the desired ratio of 2:7, but Thursdays, Fridays and Saturdays were underrepresented. The Fall

season was significantly underrepresented. The database includes weights to adjust for varying

selection probabilities, due to differences in the numbers of adults or children in a selected

household, the numbers of non-business phones in a household, the numbers of non-business

telephones in each census region, and to the survey stratification between weekend or weekdays

and between children and adults. Based on discussions with the EPA WAM, it was decided that

the weights would not be used in these analyses; the raw, unweighted data would be treated as an

approximately simple random sample. Note that the statistical weights: 1) were not used in the

pNEM/CO exposure modeling effort, 2) could not be used to accurately estimate standard errors

of weighted means, and 3) were close to 1 for most respondents.

In pNEM/CO, each activity is assigned a probability distribution of the exertion rate (kilocalories per minute). For this analysis, the 24-hour sequence of exertion rates was simulated 100

times for each person in the NHAPS sample; the sequence of activities is fixed but the simulated

exertion rates vary. Following both CHAD and the exposure modeling methodology currently

used in pNEM/CO, a constant simulated exertion rate is assumed throughout the time period of

each listed activity in the 24-hour diary. If the individual repeats the same activity at a later time,

with other activities intervening, the exertion rate is simulated again. SAS statistical software

was used for the simulations and for the statistical analysis. 

The assigned exertion rate distribution depends upon the type of activity, and the occupation,

age, gender, and body weight of the respondent. The exertion rate (kilo-calories/minute =

kcal/min), also referred to as average energy expenditure rate, EE, is defined as the product

EE = MET x RMR.

MET is the metabolic equivalent of work, a dimensionless ratio (i.e., exertion compared to the

resting metabolic rate) specific to each activity, and, in some cases, to an age group. RMR is the

resting metabolic rate (kcal/min), approximately equal to the basal metabolic rate. We used the

same set of MET statistical distributions supplied by Tom McCurdy that are currently used in

pNEM/CO (and CHAD). For the work activity “at main job,” the MET distribution depends on

the occupation. Since occupation was not recorded in NHAPS, we followed the pNEM

methodology and randomly selected the occupation based on census fractions of persons in each

activity. The same occupation is assumed throughout a simulated person-day (in case the person

repeats the work activity), but is randomly selected again for the next simulated person-day.

Note that this procedure may bias the comparison between angina and non-angina subjects, since

the distribution of occupation is expected to differ between angina subjects and the general

population.

A single RMR value was simulated to represent each person-day. Thus the same person would

have 100 simulated RMRs, one for each of the 100 days simulated. This reflects the assumption

that each person represents the activity pattern for a group of persons with the same age and

gender. As in pNEM/CO, RMR was simulated from a normal distribution where the mean is of

the form a + b (Body Mass), and the standard deviation is the constant . The values of a, b, and

 are the values derived by Schofield (1985) for 12 age/gender combinations (this assumes basal

metabolic rate is equivalent to resting metabolic rate). In turn, the body mass was simulated

J-4
using the log-normal distributions estimated by Brainard and Burmaster (1992) and Burmaster

and Crouch (1994). The parameters of the log-normal distributions depend on age and gender.

The statistical analysis used the following summary statistics of the activity and simulated

exertion patterns for each person in the NHAPS study. The selection of these summary statistics

was based on recommendations from the EPA WAM:

 Average maximum 8-hour energy expenditure. For each 8-hour period in a simulated personday, starting every 10 minutes, integrate the simulated EE to give the energy expenditure in

Mcal (millions of calories), i.e. sum the products of activity time and energy expenditure

rate. For each simulated day, compute the maximum 8-hour energy expenditure, treating the

simulated day in circular fashion so that the respondent is assumed to repeat exactly the same

activity and exertion rate patterns on the day after the diary day. For example, the simulated

activities for the period starting at 10 pm are assumed to follow the reported sequence of

activities for the diary day from 10 pm to midnight and then the reported sequence from the

beginning of the diary day until 6 am. To represent a typical value for the selected person,

compute the average maximum 8-hour energy expenditure across the 100 simulations.

 95th percentile maximum 8-hour energy expenditure. As in the last bullet, compute the

maximum 8-hour energy expenditure for each simulated day. To represent an extreme value

for the selected person, compute the fifth highest maximum 8-hour energy expenditure

among the 100 simulations.

 Percentage time spent outdoors. This number is the same for all simulations, since the

activity patterns are held constant.

 Percentage time spent in a vehicle. This number is the same for all simulations, since the

activity patterns are held constant.

 Percentage time spent outdoors or in a vehicle. This number is the same for all simulations,

since the activity patterns are held constant.

 Average percentage time with exertion rate above 2.39 kcal/min. For each simulated personday, the percentage of that day with an EE (rate) above the threshold level of 2.39 kcal/min

was computed; then, this percentage was averaged over the 100 simulations for that person.

The statistic estimates the percentage time spent at or above the threshold exertion rate level

over a long period, assuming the daily activity pattern was the same every day. The threshold

of 2.39 kcal/min, which equals 0.010 MJ/min, represents “light” exertion (see below).

 Average percentage time with exertion rate above 5.97 kcal/min. The threshold of 5.97

kcal/min, which equals 0.025 MJ/min, represents “moderate” exertion (see below).

· Average percentage time with exertion rate above 9.55 kcal/min. The threshold of 9.55

kcal/min, which equals 0.040 MJ/min, represents “heavy” exertion (see below).

J-5
The exertion rate thresholds used for these analysis were originally defined as 0.010, 0.025, and

0.040 mega-joules per minute, but were converted into the more commonly used calorie units (1

joule equals 0.2388 calories). For purposes of exposure assessment, exertion categories (i.e.,

light, moderate, or heavy exertion) are more usefully defined by the ventilation rate VE (liters air

per minute) rather than the energy expenditure rate EE (kilo-calories per minute). For the EPA’s

Ozone Criteria Document, the Environmental Criteria and Assessment Office categorized VE

into ranges of 0-23, 24-43, 44-63, and 64+ liters of air per minute to define light, moderate,

heavy, and very heavy exertion, respectively (based on a reference male adult with body weight

70 kg). To convert from EE to VE, EE is first multiplied by an energy conversion factor, ECF, to

give the oxygen uptake rate VO2 (liters of oxygen per minute). ECF varies across the

population, but is approximately 0.2 liters oxygen per kcal (Esmail, Bhambhani, and Brintnell,

1995). The “ventilatory equivalent rate” (VER) is the dimensionless ratio of VE (liters per

minute) divided by VO2 (liters per minute) and has typical values from about 24 for light

exertion to about 32 for peak exertion. Thus the selected energy expenditure rates are

approximately equivalent to the following ventilation rates:

EE = 0.010 MJ/min = 2.39 kcal/min:

VE = EE  ECF  VER = 2.39  0.2  24 = 11.5 liters/min = light exertion

EE = 0.025 MJ/min = 5.97 kcal/min:

VE = EE  ECF  VER = 5.97  0.2  28 = 33.4 liters/min = moderate exertion

EE = 0.040 MJ/min = 9.55 kcal/min:

VE = EE  ECF  VER = 9.55  0.2  32 = 61.1 liters/min = heavy exertion

The selected summary statistics were computed for each of the 243 angina subjects and 8,906

non-angina subjects in the NHAPS study. A statistical analysis compared the distributions of

these summary statistics for persons with and without angina. For each summary statistic we

compared the mean values between the angina and non-angina groups using standard t tests. The

significance level (p-value) for the difference in means was computed using the SmithSatterthwaite procedure, that tests for no difference in population means assuming that the two

populations are normally distributed but may have different variances. P-values at or below 0.05

denote significant differences at the five percent level of significance. By the central limit

theorem, the p-values for the t test comparisons should be reasonably accurate for the large

samples used in the overall analyses, even if the normality assumption does not hold, but the pvalues will be less accurate for the analyses of specific gender and age subgroups. We also

compared variances using a standard F test, that assumes normality of the two populations.

Since the normality assumption may not be a sufficiently good approximation, we also applied

two non-parametric tests that do not require specific parametric distributions. The nonparametric Wilcoxon test, also known as the Mann-Whitney-Wilcoxon test or the Rank Sum

Test, was used to compare the central tendencies of the two distributions. This test assumes only

that the populations have the same distributional shape, which may or may not be the normal

distribution, but the distribution of values for angina population might be shifted by some

constant value, and thus might have a different median than the non-angina population. The

J-6
Kolmogorov-Smirnov test was used to evaluate any possible differences between the two

distributions, whether due to differences in means, medians, variances, or any other features of

the distribution. This test uses the maximum absolute difference between the two cumulative

distribution functions, assuming only that these distributions are continuous.

The mean, variance, median, and distribution function comparisons were made for all persons

combined, separately for males and females, and then separately for four age groups within the

male and female subgroups. Age groupings were chosen to include approximately 25 percent of

angina subjects in each group. Separate comparisons for males and females are needed to

distinguish whether any overall differences in exertion or activity are explained by the fact that

angina subjects are more likely to be female than in the general population. Since activity

patterns and exertion rates differ between males and females, any overall difference between the

angina and non-angina groups might be explained by the greater propensity for females to get

angina, rather than the direct effect of angina. Similarly, the subsetting by age group evaluates

the effect of the different age distributions for angina subjects compared to the general

population (angina subjects tend to be much older). This statistical analysis does not, and cannot,

address questions as to whether the angina causes the change in exertion or activity patterns, or

vice versa. We only examine whether or not the summary statistics of activity and exertion

patterns are different for the two populations.

A general linear model approach was also used as an alternative method of adjusting for the

effects of age and gender on the angina/non-angina comparison. We focused attention on a

relatively simple statistical model with cubic terms in age (a simple linear function of age fitted

poorly), gender, interactions between age and gender, and a single term for the effect of angina:

Summary Statistic = I(male){ + (age) + (age)2

 + (age)3}

+ I(female){ + (age) + (age)2

 + (age)3}

+ I(angina) + error

where: I(male) = 1 for males, 0 for females; I(female) = 1 for females, 0 for males; I(angina) = 1

for persons having angina, 0 for persons not having angina. The errors are assumed to be

normally distributed, statistically independent, and have mean zero and some constant variance.

This statistical model assumes that the expected value of the summary statistic is a cubic

function of age, but is a different function for males and females. The selected model has the

same coefficient for the cubic term for males and females, but different coefficients for the

intercept, linear, and quadratic effects. The model also assumes that having angina changes the

mean by a constant amount, which is the same factor for all age groups and both genders. A

more sophisticated model might allow for interactions between angina and the age and gender

variables, to allow for the possibility that the angina effect varies by gender and/or age. Note,

however, that our statistical analysis clearly showed that age and gender were much more

significant predictors of exertion patterns than the angina indicator, explaining most of the

variability in the summary statistics.

Project resources were insufficient for a detailed exploration of alternative statistical models. We

J-7
tried using logarithmic transformations to improve the model fit, but could not reasonably use

such models in view of the large number of cases where the observed summary statistic was zero

(the logarithm is then undefined). The model fit for the selected model (without taking

logarithms) varied with the summary statistic. R squared goodness-of-fit statistics were

extremely low, less than 0.05, for the percentages of time spent outdoors and/or in a vehicle. For

the summary statistics based on the maximum 8-hour exertion and the percentages of time above

exertion rate thresholds, the R squared statistics ranged from a poor fit, 0.25, to a fairly good fit,

0.48. The cases of poor fitting models may be because the selected statistical models poorly

represent the relationship between age, gender, and angina and the activity/exertion summary

statistic and/or because the activity/exertion pattern varies substantially between people of the

same age, gender, and angina status.

RESULTS

Age, Gender, and Angina Disease Distributions

Table 1 shows the number of subjects with or without angina by gender and by age group. The

four age groups were chosen to have approximately the same numbers of angina subjects. The

strong association between angina and age is illustrated by the fact that 52/243 = 21 % of angina

subjects are under 55 but 6877/8906 = 77 % of non-angina subjects are under 55. Angina

subjects tend to be significantly older than the general population. The association between

angina and gender is weaker. 103/243 = 42.3 % of angina subjects are male, but 4116/8906 =

46.2 % of non-angina subjects are male.

Overall Comparisons of Activity and Exertion Summary Statistics between Angina and

Non-Angina Subjects

Table 2 compares the means between the angina and non-angina subjects, without stratification

by age or gender. The average and 95th percentile of the maximum eight hour exertion has a

statistically significantly lower mean for angina subjects. Furthermore, for each of the exertion

levels 2.39, 5.97, and 9.55 kcal/min (0.010, 0.025, and 0.040 MJ/min), the mean percentage of

time above each level was statistically significantly lower for the angina subjects. Non-angina

subjects spend an average of 2.8 percent of their time doing activities requiring moderate or

higher levels of exertion, defined by exertion rates above 5.97 kcal/min (0.025 MJ/min); angina

subjects spend an average of 2.2 percent of their time doing such activities. All subjects spend

over 75 percent of time in light or sedentary activities, with extertion rates below 2.39 kcal/min,

including sleeping. All these exertion distribution comparisons show that angina subjects tend to

do activities with less exertion than the general population. However, since the summary

analyses in Table 2 do not take into account the marked differences between the age and gender

distributions of angina and non-angina subjects, the lower exertion rates could be associated with

the tendency for angina subjects to be older (and female) rather than the disease itself. The

average percentages of time spent outdoors are nearly identical, and are not statistically

significantly different between angina and non-angina subjects, but angina subjects spend

statistically significantly less time in vehicles (4.5 % rather than 5.5 %, on average).

Table 2 compares the standard deviations using a F-test based on the variance ratio for angina vs.

J-8
non-angina subjects. In most cases the F tests show statistically significantly different variances

(and, therefore, standard deviations).

Table 2 also uses the non-parametric Wilcoxon test to compare the central tendencies of the two

distributions without the normality assumption required by the T test. Corresponding to the T

test comparisons, the Wilcoxon test finds that the angina and non-angina distributions are

significantly different in almost all cases; the angina subjects have a lower median value for each

of the selected summary statistics. Exceptions are for the average maximum 8-hour exertion, just

significant at the 7 % level, and the percentage of time spent outdoors, which has a nonsignificant p-value of 22 %.

Finally, Table 2 compares the distribution functions using the Kolmogorov-Smironov test. The

distributions are statistically significantly different at the five and one percent levels in all cases

except for the percentage of time spent outdoors, which shows no significant difference. For that

variable, the T and Wilcoxon tests showed no statistically significant differences in central

tendency although the F test showed a statistically significant difference in the population

variances. If the population variances are different, so are the two distribution functions. The

discrepancy between the F and Kolmogorov-Smirnov tests is partly explained by the fact that the

F test is very sensitive to the assumption of normal distributions, whereas the KolmogorovSmirnov test only requires the distributions to be continuous. (Both tests assume that the mean

and variances are constant for each population, which is inconsistent with the variation of the

means and variances with age and gender shown in the stratified analyses in Tables 3 and 4.) The

discrepancy is also partly explained by the fact that the Kolmogorov-Smirnov test is less

powerful (less likely to detect a difference) than the other tests, because it makes the fewest

assumptions and considers the widest class of alternative hypotheses.

Stratified Comparisons of Activity and Exertion Summary Statistics between Angina and

Non-Angina Subjects

Tables 3 and 4 provide the same statistical comparisons as Table 2, stratified by gender and age

group. The results show the mean values for the selected summary statistics are not consistently

lower for each age and gender subgroup of angina subjects. For example, Table 2 showed that

the angina subjects had a lower overall mean value of the average maximum 8-hour exertion

than the non-angina subjects. Tables 3 and 4 show the mean is actually higher for angina

subjects 0-54 of either gender and for males 75 or older. The mean average maximum 8-hour

exertions are consistently higher for males of all age groups, with or without angina, compared

to females. Similar patterns are found for the 95th percentile of the maximum 8-hour exertion.

The comparisons of the percentages of time spent outdoors or in a vehicle also vary across age

and gender subgroups. The largest, and most surprising, angina vs. non-angina difference is for

the mean percentage of time spent outdoors by 0-54 year old males: angina subjects have a mean

of 17 % compared to the mean of 9 % for non-angina subjects. However the angina subjects in

the 55-64 and 65-74 age groups of either gender spend less time outdoors, on average, than nonangina subjects.

The Table 3 and 4 comparisons of the mean percentages of time above the light, moderate or

J-9
high exertion levels show a variety of patterns for different age groups, genders, and exertion

levels. 

Comparisons of Activity and Exertion Summary Statistics between Angina and NonAngina Subjects Adjusted for Age and Gender Differences

Table 5 gives the results of the fitted general linear model. As explained above, the fitted model

assumes that for each gender, the average value of the summary statistic is a cubic function of

age. Furthermore, having angina changes the expected value by a fixed amount, which is

assumed to be the same value for every age and gender. This angina effect is the coefficient

reported in the table, together with its standard error and p-value. P-values less than or equal to

0.05 indicate summary statistics where the angina effect was statistically significant at the 5

percent significance level. The angina coefficient can be thought of as the effect of angina after

adjusting for age and gender. The effects of age and gender are not reported, but in all cases were

extremely statistically significant compared to the angina effect.

Table 5 also reports the R squared goodness-of-fit statistic, which is the squared correlation

between the observed and predicted values. R squared values vary from 0 (the worst possible fit)

to 1 (a perfect fit), and are often interpreted as the fraction of the variability in the dependent

variable (summary statistic) that is explained by the regression model.

The first two rows of Table 5 show that the angina effect on the average and 95th percentile

maximum 8-hour exertion is a statistically significant reduction (at the 6 and 1 % levels,

respectively) for angina subjects compared to non-angina subjects. However, these reductions of

0.04 Mcal and 0.16 Mcal are small when compared to the overall mean values of 1.4 and 2.3

Mcal (non-angina subjects) reported in Table 2. The next three rows show that angina subjects

tend to spend a little more time (0.7 percentage points) outdoors and a little less time (0.5

percentage points) in a vehicle compared to non-angina subjects; those differences are not

statistically significant. The last four rows show that angina subjects tend to spend less time at

moderate or high levels of exertion, after adjusting for age and gender, although the differences

are at most 1 percentage point and are not statistically significant. For example, the unadjusted

average percentage time above 2.39 kcal/min (0.010 MJ/min) was 23.5 % for non-angina

subjects (Table 2), and the effect of angina is to reduce the expected percentage of time by 0.7.

As shown in Tables 3 and 4, this is due to average reductions of up to 5 percentage points for

ages 55 and older but increases of 6 (males) and 2 (females) percentage points for the 0-54 age

group.

R squared goodness-of-fit statistics were extremely low, 0.05 or less, for the percentages of time

spent outdoors and/or in a vehicle. Thus the regression models for those percentages give very

poor predictions. There are two possible reasons for this. First, the combination of age, gender,

and angina status may be strongly associated with the percentages of time spent outdoors or in a

vehicle but the assumed form of the regression model may poorly represent the functional

relationship. Second, the combination of age, gender, and angina status may be poorly associated

with the percentages of time spent outdoors or in a vehicle so that those activity percentages vary

mainly with the effects of factors other than age, gender, and angina status. In either case, those

regression models are not recommended for use in predicting the activity percentages.

J-10
For the summary statistics based on the maximum 8-hour exertion and the percentages of time

above exertion rate thresholds, the R squared statistics ranged from a poor fit, 0.25, to a

reasonably good fit, 0.48. As above, the cases of poor fitting models may be because the selected

statistical models poorly represent the relationship between age, gender, and angina and the

activity/exertion summary statistic and/or because the activity/exertion pattern varies

substantially between people of the same age, gender, and angina status. Alternative general

linear models, or the more sophisticated generalized linear models, could be developed to

improve the predictive ability of the statistical models.

REFERENCES

Brainard, J., and Burmaster, D. E. 1992. “Bivariate Distributions for Height and Weight of Men

and Women in the United States.” Risk Analysis 12:2, pp. 267-275.

Burmaster, D. E., and Crouch, E. A. C. 1994. “Lognormal Distributions of Body Weight as a

Function of Age for Males and Females in the United States.” Risk Analysis 17:4, pp. 499-508.

Esmail, Bhambhani, and Brintnell. 1995. “Gender Differences in Work Performance on the

Baltimore Therapeutic Equipment Work Simulator.” Amer. J. Occup. Therapy 49, pp. 405-411. 

Johnson, T. R, 1998. Proposed Probabilistic Algorithm for Estimating Ventilation Rate in the

1998 Version of pNEM/CO. Memorandum submitted to EPA (September 25, 1998).

Klepeis, N. E., Tsang, A. E., and Behar, J. V. 1996. Analysis of the National Human Activity

Pattern Survey (NHAPS) Respondents from a Standpoint of Exposure Assessment. EPA/600/R96/074.

Klepeis, N. E., Nelson, W. C., Tsang, A. M., Robinson, J. P., Hern, S. C., Engelmann, W. H.,

and Behar, J. V. 1998. “The National Human Activity Pattern Survey (NHAPS): Data Collection

Methodology and Selected Results.” Submitted to Journal of Exposure Analysis and

Environmental Epidemiology.

Schofield, W. N. 1985. “Predicting Basal Metabolic Rate, New Standards, and Review of

Previous Work.” Hum Nutr Clin Nutr. 39C (Supp 1), pp. 5-41.

Triplett, T. 1996. National Human Activity Pattern Survey, CD-ROM Version 2.0. University of

Maryland Survey Research Center.

Tsang, A. M. and Klepeis, N. E. 1996. Descriptive Statistics Tables from a Detailed Analysis of

the National Human Activity Pattern Survey (NHAPS) Data. EPA/600/R-96/148.

J-11
J-12
Table 1. Distribution of subjects according to their age, gender and disease status

 Gender Age group

 MalesAngina(%) Non-angina(%) All

 FemalesAngina(%) Non-angina(%) All

 AllAngina(%) Non-angina

0-54 35 (1.0) 3307 (98.9) 3342 17 (0.5) 3570 (95.5) 3587 52 (0.8) 6877 (99.2) 55-64 28 (6.5) 400 (93.5) 428 28 (5.4 ) 491 (94.6) 519 56 (5.9) 891 (94.1) 65-74 23 (7.9) 267 (92.1) 290 48 (9.6) 450 (91.4) 498 71 (9.0) 717 (91.0) 75+ 17 (10.7) 142 (89.3) 159 47 (14.4) 279 (85.6) 326 64 (13.2) 421 (86.8) Total 103 (2.4) 4116 (97.6) 4219 140 (2.8) 4790 (97.2) 4930 243 (2.6) 8906 (97.4) 

J-13
Table 2. Statistical Tests for the Association between Angina and Various Variables Representing Physica

Variable T Test Comparison of

Means Mean Mean P-valueAngina Non-angina 

F Test Comparison of Standard DeviationsSt. Dev. St. Dev. P-valueAngina Non-angina

WilcoxonTest P-value

Average maximum 8hrexertion (Mcal)

1.28 1.40 0.00 0.48 0.49 0.68 0.00 

Ninety fifth percentile ofmaximum 8hr exertion (Mcal)

1.87 2.25 0.00 0.97 1.13 0.00 0.00 

Percentage of time spentoutdoors

6.73 6.74 0.99 12.87 11.63 0.02 0.22 

Percentage of time spent invehicle

4.55 5.55 0.01 6.19 7.13 0.00 0.00 

Percentage of time spentoutdoors or in vehicle

11.27 12.29 0.27 14.33 13.45 0.15 0.00 

Average percentage of timewith exertion above 2.39kcal/min = 0.010MJ/min (light) 

19.98 23.53 0.00 13.56 13.78 0.75 0.00 

Average percentage of timewith exertion above 5.97kcal/min = 0.025 MJ/ min (moderate)

2.17 2.78 0.01 3.68 3.56 0.46 0.00 

Average percentage of timewith exertion above 9.55kcal/min = 0.040 MJ/ min (heavy)

0.213 0.406 0.00 0.554 0.761 0.00 0.00 

J-14
Table 3. Statistical Tests for the Association between Angina and Various Variables Representing Physic

MalesVariable Age

Group

T Test Comparison ofMeans Mean Mean P-valueAngina Non-angina 

F Test Comparison of Standard DeviationsSt. Dev. St. Dev. P-valueAngina Non-angina 

WilcoxonTest P-value

Average maximum8hr exertion (Mcal)

0-5455-6465-7475+

1.85 1.59 0.001.48 1.77 0.001.39 1.49 0.361.27 1.20 0.52

0.49 0.55 0.340.48 0.48 0.860.47 0.48 0.960.44 0.42 0.65

 0.02 0.01 0.41 0.51 

Ninety fifthpercentile ofmaximum 8hrexertion (Mcal)

0-5455-6465-7475+

2.94 2.68 0.172.40 2.90 0.041.91 2.17 0.141.73 1.67 0.71

1.06 1.30 0.131.21 1.14 0.610.78 0.94 0.310.69 0.76 0.68 

 0.22 0.02 0.26 0.55 

Percentage of timespent outdoors

0-5455-6465-7475+

16.86 8.85 0.029.28 10.02 0.796.43 10.40 0.138.23 7.09 0.72

19.16 13.75 0.0014.26 13.86 0.7811.38 14.34 0.2012.63 10.05 0.16

 0.01 0.65 0.12 0.58

Percentage of timespent in vehicle

0-5455-6465-7475+

5.96 6.09 0.923.99 6.87 0.00 7.20 5.88 0.512.29 3.34 0.14

7.55 8.10 0.633.78 9.63 0.009.04 7.80 0.292.54 3.94 0.05 

 0.89 0.18 0.82 0.44

J-15
Table 3. Statistical Tests for the Association between Angina and Various Variables Representing Physic

MalesVariable Age

Group

T Test Comparison ofMeans Mean Mean P-valueAngina Non-angina 

F Test Comparison of Standard DeviationsSt. Dev. St. Dev. P-valueAngina Non-angina 

WilcoxonTest P-value

Percentage of timespent outdoors or invehicle

0-5455-6465-7475+

22.83 14.94 0.0213.27 16.89 0.2413.63 16.29 0.4510.51 10.43 0.98 

19.28 15.52 0.0515.30 16.18 0.7615.87 15.91 1.0012.86 10.39 0.19 

 0.02 0.17 0.19 0.69

Average percentageof time with exertionabove 2.39 kcal/min= 0.010MJ/min(light)

0-5455-6465-7475+

34.76 27.78 0.0024.60 30.07 0.0621.92 23.32 0.6318.24 15.87 0.41

13.33 15.18 0.3414.51 12.03 0.1413.23 12.48 0.6411.13 10.37 0.63 

 0.02 0.06 0.67 0.39

Average percentageof time with exertionabove 5.97 kcal/min= 0.025 MJ/ min(moderate)

0-5455-6465-7475+

6.63 4.46 0.053.43 5.44 0.012.27 3.27 0.082.02 1.62 0.53

6.37 4.31 0.003.55 4.41 0.172.47 3.46 0.062.42 2.41 0.92

 0.02 0.01 0.20 0.43

Average percentageof time with exertionabove 9.55 kcal/min= 0.040 MJ/ min(heavy)

0-5455-6465-7475+

0.662 0.735 0.590.565 0.846 0.190.155 0.388 0.010.132 0.157 0.79

0.792 0.986 0.111.068 1.222 0.400.361 0.716 0.000.331 0.512 0.05 

 0.55 0.05 0.06 0.55 

J-16
Table 4. Statistical Tests for the Association between Angina and Various Variables Representing Physic

FemalesVariable Age

Group

T Test Comparison ofMeans Mean Mean P-valueAngina Non-angina 

F Test Comparison of Standard DeviationsSt. Dev. St. Dev. P-valueAngina Non-angina 

WilcoxonTest P-value

Average maximum8hr exertion (Mcal)

0-5455-6465-7475+

1.30 1.27 0.691.21 1.27 0.331.05 1.10 0.290.96 0.98 0.63 

0.31 0.38 0.340.32 0.33 1.000.30 0.31 0.940.33 0.30 0.34 

 0.72 0.56 0.31 0.44

Ninety fifthpercentile ofmaximum 8hrexertion (Mcal)

0-5455-6465-7475+

1.98 2.01 0.861.79 1.92 0.411.42 1.51 0.261.27 1.31 0.59

0.82 0.91 0.690.80 0.77 0.680.53 0.57 0.570.56 0.52 0.43 

 0.99 0.33 0.31 0.43 

Percentage of timespent outdoors

0-5455-6465-7475+

3.64 5.11 0.424.31 4.59 0.882.84 4.14 0.143.79 2.27 0.40 

7.29 9.58 0.209.53 8.22 0.235.51 7.34 0.0212.04 4.60 0.00 

 0.43 0.23 0.49 0.76

Percentage of timespent in vehicle

0-5455-6465-7475+

4.54 5.35 0.554.21 5.60 0.355.26 4.15 0.232.82 2.94 0.86

5.48 5.98 0.727.53 7.23 0.715.94 6.18 0.774.37 4.34 0.91

 0.40 0.06 0.15 0.72 

Percentage of time 0-54 8.18 10.46 0.36 9.93 11.27 0.57 0.18 

J-17
Table 4. Statistical Tests for the Association between Angina and Various Variables Representing Physic

FemalesVariable Age

Group

T Test Comparison ofMeans Mean Mean P-valueAngina Non-angina 

F Test Comparison of Standard DeviationsSt. Dev. St. Dev. P-valueAngina Non-angina 

WilcoxonTest P-value

spent outdoors or invehicle

55-6465-7475+

8.52 10.18 0.488.10 8.29 0.886.61 5.21 0.45

12.15 10.42 0.228.21 9.38 0.2612.36 6.25 0.00 

 0.04 0.99 0.77

Average percentageof time with exertionabove 2.39 kcal/min= 0.010MJ/min(light)

0-5455-6465-7475+

23.50 21.40 0.4919.04 21.04 0.3313.97 15.46 0.2611.31 11.84 0.73 

12.28 12.15 0.8610.34 10.47 1.008.45 9.53 0.319.75 8.61 0.24 

 0.41 0.51 0.43 0.51

Average percentageof time with exertionabove 5.97 kcal/min= 0.025 MJ/ min(moderate)

0-5455-6465-7475+

1.44 1.59 0.710.79 1.31 0.050.65 0.68 0.870.75 0.43 0.23

1.66 1.97 0.441.27 2.07 0.001.35 1.56 0.221.73 1.31 0.01

 0.73 0.04 0.51 0.84

Average percentageof time with exertionabove 9.55 kcal/min= 0.040 MJ/ min(heavy)

0-5455-6465-7475+

0.101 0.184 0.060.095 0.093 0.960.028 0.030 0.890.025 0.016 0.44 

0.170 0.324 0.000.277 0.198 0.010.076 0.110 0.000.075 0.077 0.90

 0.63 0.67 0.85 0.28 

J-18
Table 5. General Linear Models for the Association between Angina and Various Variables Representing 

Variable Angina Coefficient1 Standard Error P-value 

Average maximum 8hr exertion (Mcal) -0.0445 0.0237 0.0608 Ninety fifth percentile of maximum 8hr exertion (Mcal) -0.1553 0.0581 0.0075 Percentage of time spent outdoors +0.6975 0.7648 0.3618 Percentage of time spent in vehicle -0.4805 0.4679 0.3045 Percentage of time spent outdoors or in vehicle +0.2170 0.8777 0.8047 Average percentage of time with exertion above 2.39kcal/min = 0.010 MJ/min (light)

-0.7359 0.6996 0.2929 

Average percentage of time with exertion above 5.97kcal/min = 0.025 MJ/min (moderate)

-0.1730 0.1910 0.3650 

Average percentage of time with exertion above 9.55kcal/min = 0.040 MJ/min (heavy)

-0.0933 0.0439 0.0334 

1. The angina coefficient is the expected difference (angina minus non-angina) between the summary statistic for angina subjects of the same age and gender.

J-19
xxx

Appendix K

Detection of Scenario Differences in Exposure Measures 

of pNEM/CO Simulations with Statistical Tests

K-1
TECHNICAL MEMORANDUM

To: Harvey Richmond, US EPA

From: Jonathan Cohen and Arlene Rosenbaum

Date: March 31, 2000

Re: Detection of scenario differences in exposure measures of pNEM/CO

simulations with statistical tests

BACKGROUND

The objective of this task was to compare exposure results for selected recent scenario

simulations of pNEM/CO, using statistical methods to determine whether significant

differences could be detected. To accomplish this objective results of pNEM/CO

simulations for the following scenarios were examined:

4. Los Angeles, “As-Is”, Indoor Sources Included

5. Los Angeles, “Attainment”, Indoor Sources Included

6. Los Angeles, “Sensitivity”, Indoor Sources Included

7. Los Angeles, “As-Is”, Indoor Sources Omitted

8. Los Angeles, “Attainment”, Indoor Sources Omitted

Scenario 1 was compared with scenarios 2 and 3; while scenario 4 was compared with

scenario 5. The comparison of 1 with 2 and the comparison of 4 with 5 are assessments

of the impact of changes in the fixed site outdoor concentrations on estimates of

exposure concentrations. The comparison of 1 with 3 is an assessment of the impact of

assumptions about window status in vehicles during smoking events on estimates of

exposure concentrations.

For each scenario modeled with pNEM/CO, 10 simulations were conducted. For each

set of 10 simulations, an identical set of initiation values (or “seeds”) was specified for

the random number generator used to make the Monte Carlo selections. Thus, the 10

simulations for each scenario are matched to the 10 simulations for every other

scenario. That is, in spite of the random variation incorporated into the simulations, the

first simulation of each scenario differs from the first simulation of every other scenario

only by the change in the input data. For example, the only difference between the first

simulation of the “as-is” scenario for Los Angeles (indoor sources included) and the first

simulation of the “attainment” scenario for Los Angeles (indoor sources included) is the

input data for the fixed site outdoor concentrations. This matching format allows us to

more easily assess the variation due to changing any particular factor in the exposure

assessment by keeping it separate from the variation due to the Monte Carlo process. 

Because the only difference between the as-is and attainment simulations is a decrease

in the fixed site outdoor concentrations, and because the fixed site outdoor

concentrations influence the resulting exposure concentrations according to the

K-2
modeling algorithms, for each attainment simulation every exposure measure will be

less than or equal to the corresponding exposure measure for the as-is simulations.

That is, we know that the exposure measures resulting from the attainment simulations

will be lower by some amount than (or equal to) the corresponding measures resulting

from the as-is simulations. This is an important point to keep in mind when interpreting

some of the statistical tests discussed below. 

RESULTS

Tables 1a - 1d present the results of statistical comparisons of the as-is and attainment

simulations for Los Angeles with indoor source included (Scenarios 1 and 2). Tables 2a

- 2d show the same information for the as-is and attainment simulations for Los Angeles

with indoor sources omitted (Scenarios 4 and 5); and tables 3a - 3d for the as-is and

sensitivity simulations for Los Angeles with indoor sources included (Scenarios 1 and 3). 

In each table, the second column shows the Spearman correlation coefficient for the 

given exposure measure for the 10 pairs of matched simulations. The Spearman

correlation coefficient, often referred to as Spearman’s rho, is the usual product-moment

correlation coefficient computed for the ranks of the 10 runs, i.e., the run with lowest

value for each case is given rank 1 and the run with the highest value for each case is

given rank 10. This non-parametric measure of correlation is less sensitive to the form

of the underlying statistical distribution, but has a similar interpretation: values close to 1

indicate that the results for the as-is runs are almost in the same order as the runs for

the attainment (or sensitivity) cases, and values close to zero are consistent with

independence. Since we know that these simulations were matched to have the same

initiation random seeds, a high positive correlation coefficient can be interpreted to

mean that the run-to-run random variation is much larger than the effect of interest

(between the as-is case and the other cases); a correlation coefficient close to one

means that the impact of changing scenarios hardly changed the order of the results for

the 10 runs. If the correlation coefficient were small or negative, this would mean that

the run-to-run random variation is much smaller than the effect of interest.

In tables 1a - 1d, with a few exceptions, the correlation coefficients for the sets of

matched simulations appear to be high for each exposure measure. This suggests that

with indoor sources included the impact of changes in the fixed site outdoor

concentrations is not great enough to substantially modify the relative magnitudes of the

exposure measures from simulation to simulation. This contrasts with tables 2a - 2d,

where there is poor and even negative correlation for several measures. This results

from the greater influence of changes in outdoor concentrations on simulated exposure

measures when indoor sources are not accounted for. 

The third column in each table shows the mean difference for the 10 matched pairs. The

fourth column shows the p-value for a one-sided paired t-test. A one-sided test is used

because we know that the results for the attainment simulations cannot be higher than

those for the as-is simulations. The paired t test compares the distribution of the

arithmetic differences with zero, assuming that the 10 differences are independently

K-3
drawn from a normal distribution with an unknown, but constant variance1

. The p-value

is the probability of getting the mean difference actually obtained, or greater, for a

sample of 10 differences, if the distribution of differences had a true mean of zero. A low

p-value indicates that the results obtained would be highly unlikely if the true average

difference were zero. 

In order to interpret the meaning of the p-values in this context, it is helpful to keep in

mind that we know that the true mean difference, as would be measured by a

hypothetically infinite number of runs, is positive (although not necessarily large enough

to be important), since this follows from the definition of the as-is, attainment, and

sensitivity simulations. The detection of this known difference by the t-test will depend

on (a) the average size of the difference, (b) the standard deviation of the difference,

and (c) the number of simulations. Therefore, the p-value is a measure of the ability of

the Monte Carlo process to detect this difference based only on ten simulations. In other

words, a p-value below 0.05 indicates that the average difference between the two

scenarios is large enough to be detected at the five percent significance level with 10

simulations. A p-value above 0.05 indicates that the observed set of differences are not

large enough (or not similar enough) for the average scenario difference to be

demonstrably above zero based on a five percent statistical test with only 10

simulations. 

For the as-is/attainment comparisons, both with and without indoor sources (tables 1a –

1d and 2a – 2d), the t-test generally shows statistically significant differences (> 95%

confidence) in exposure measures at the lower CO and COHb levels, but often not at

the higher levels. This is because the run-to-run variability of the person-days, personhours, or persons above those high levels is relatively large (since these exposures are

rare), and the mean difference is relatively small, and, therefore, less likely to be

detected. In contrast, the paired t-test shows statistically significant differences at all

exposure levels for the as-is/sensitivity comparisons, indicating that these differences

are large enough at all levels to be detected with 10 simulations.

The fifth column shows the p-value for a Wilcoxon test. The Wilcoxon test is a nonparametric version of the t test designed to avoid the need for assumptions about the

form of the distribution. Assuming only that the 10 differences are independently drawn

from some common distribution, the Wilcoxon test addresses whether the median

difference is zero , i.e. whether positive and negative differences are equally likely2

.

(The t test made the additional requirement that the distribution is normal). If the true

distribution is normal, then the Wilcoxon test will be less likely to detect an effect.

However, if the true distribution is not normal, then for many possible distributions, the

Wilcoxon test will be more likely to detect an effect. Except at the very highest levels,

1

 The arithmetic mean difference is divided by an estimate of its standard deviation and

compared to a t distribution percentile.

2

 The Wilcoxon test statistic is based on ranking the non-zero unsigned differences from 1 to k

(the number of non-zero differences) and then calculating the sum of the ranks that were

assigned to the positive differences. A high rank sum is evidence that the true median is positive.

K-4
the Wilcoxon test p-values were consistently lower than the t test p-values, signifying a

higher level of confidence. This is expected in view of the fact that in every case all the

differences were positive or zero, indicating a high probability that the median difference

is positive.

Finally, the sixth column shows the number of matched simulations that would have to

be conducted to have a 95% probability of detecting a difference with 95% confidence,

assuming that the true average difference and true standard deviation of the difference

are both identical to our estimates derived from the 10 matched simulations that we

actually conducted. This calculation was carried using the same assumptions as for a

paired t test3. Under the given assumptions, the probability of detecting a difference with

95 % confidence was computed using the non-central t distribution, and the smallest

size needed to make the detection probability 95% or greater is reported. Note that

although a p-value of 0.05 or less may have been obtained for the particular 10

simulations that were conducted for this study, this does not imply there is a 95%

probability of detecting a difference with 10 simulations. Rather, it is generally the case

that if the p-value is close to 0.05, then the probability of detecting a difference in

another set of 10 simulations is only about 50%, assuming the estimated and true

means and standard deviations are equal.

The results in the sixth column can be used to evaluate whether the mean scenario

difference is comparable to the run-to-run variation from 10 simulations. If the minimum

sample size is much above 10, then 10 simulations are insufficient for the difference to

be detected with 95% probability. However, any positive difference, however small, can

be detected at any selected confidence level and with any selected probability of

detection, as long as the number of simulations is large enough.

3

 For each sample size, n, a difference will be detected with 95 % confidence if the mean

difference divided by its estimated standard deviation exceeds the 95th percentile of a t

distribution with n-1 degrees of freedom. This calculation estimates the probability of detecting

any non-zero difference, rather than the much smaller probability that the difference is

significantly greater than our estimate from the 10 matched runs.

K-5
Table 1a. Comparison of Los Angeles “As – is” and “Attainment” Cases, Indoor

Sources Included:

Cumulative Person-days at 1-hour Daily Maximum Exposure Concentration

CO (ppm) $ Correlation

coeff

Mean Δ:

(as-is -

att)

p-value:

paired ttest

p-value:

Wilcoxon test

sample size for 5%

sig t-test

(95 % probability)

60 1.00 6 0.041 0.031 30

50 0.99 196 0.110 0.001 64

45 0.81 476 0.087 0.002 52

40 0.72 427 0.049 0.001 33

35 0.90 1,032 0.001 0.001 7

30 0.96 3,983 0.000 0.001 4

25 0.82 14,141 0.000 0.001 3

20 0.66 43,369 0.000 0.001 3

15 0.81 198,472 0.000 0.001 2

10 0.75 1,087,175 0.000 0.001 2

0 0

Table 1b. Comparison of Los Angeles “As – is” and “Attainment” Cases, Indoor

Sources Included:

Cumulative Person-days at 8-hour Daily Maximum Exposure Concentration

CO (ppm) $ Correlation

coeff

Mean Δ:

(as-is -

att)

p-value:

paired ttest

p-value:

Wilcoxon test

sample size for 5%

sig t-test

(95 % probability)

25 0.99 983 0.106 0.001 62

20 0.96 1,549 0.011 0.001 16

15 0.99 5,498 0.000 0.001 6

12 0.71 14,689 0.000 0.001 4

9 0.36 69,830 0.000 0.001 3

6 0.88 708,002 0.000 0.001 3

0 0

K-6
Table 1c. Comparison of Los Angeles “As – is” and “Attainment” Cases, Indoor

Sources Included:

Cumulative Number of Persons at 1-hour Daily Max COHb Levels 

COHb (%) $ Correlation

coeff

Mean Δ:

(as-is -

att)

p-value:

paired t-test

p-value:

Wilcoxon test

sample size for 5%

sig t-test

(95 % probability)

6.0 0.96 212 0.096 0.004 56

5.0 0.88 217 0.106 0.001 62

4.0 1.00 258 0.013 0.001 17

3.0 0.98 1,901 0.001 0.001 8

2.9 0.95 2,869 0.007 0.001 14

2.8 0.92 2,707 0.000 0.001 5

2.7 0.94 2,396 0.001 0.001 8

2.6 0.90 3,113 0.001 0.001 8

2.5 0.87 3,965 0.000 0.001 6

2.4 0.94 6,939 0.000 0.001 4

2.3 0.83 6,526 0.000 0.001 5

2.2 0.83 8,051 0.000 0.001 5

2.1 0.95 9,565 0.000 0.001 3

2.0 0.90 10,610 0.000 0.001 3

1.5 0.18 41,396 0.000 0.001 3

1.0 0.39 38,705 0.000 0.001 3

0.5 0

0.0 0

K-7
Table 1d. Comparison of Los Angeles “As – is” and “Attainment” Cases, Indoor

Sources Included:

Cumulative Person-hours at 1-hour COHb Levels During CO Season

COHb (%) $ Correlation

coeff

Mean Δ:

(as-is - att)

p-value:

paired ttest

p-value:

Wilcoxon test

sample size for 5%

sig t-test

(95 % probability)

6.0 0.93 586 0.065 0.001 41

5.0 0.96 1,154 0.019 0.001 20

4.0 0.99 2,746 0.000 0.001 7

3.0 0.94 10,904 0.001 0.001 7

2.9 0.96 12,984 0.000 0.001 5

2.8 0.95 14,600 0.000 0.001 4

2.7 0.96 17,104 0.000 0.001 5

2.6 0.98 20,195 0.000 0.001 4

2.5 0.98 19,638 0.000 0.001 3

2.4 1.00 27,780 0.000 0.001 3

2.3 0.99 37,125 0.000 0.001 3

2.2 0.98 49,220 0.000 0.001 3

2.1 0.77 70,615 0.000 0.001 4

2.0 0.99 121,069 0.001 0.001 7

1.5 0.98 1,105,897 0.000 0.001 5

1.0 0.95 21,549,252 0.000 0.001 3

0.5 1.00 249,479,677 0.000 0.001 2

0.0 0

K-8
Table 2a. Comparison of Los Angeles “As – is” and “Attainment” Cases, Indoor

Sources Omitted:

Cumulative Person-days at 1-hour Daily Maximum Exposure Concentration

CO (ppm) $ Correlation

coeff

Mean Δ:

(as-is -

att)

p-value:

paired ttest

p-value:

Wilcoxon test

sample size for 5%

sig t-test

(95 % probability)

60 0

50 0

45 6 0.167 0.25 106

40 251 0.158 0.031 98

35 0.34 279 0.133 0.001 79

30 0.30 1,714 0.002 0.001 9

25 -0.21 7,617 0.000 0.001 4

20 0.54 33,290 0.000 0.001 3

15 0.73 175,757 0.000 0.001 2

10 0.59 1,054,924 0.000 0.001 2

0 0

Table 2b. Comparison of Los Angeles “As – is” and “Attainment” Cases, Indoor

Sources Omitted:

Cumulative Person-days at 8-hour Daily Maximum Exposure Concentration

CO (ppm) $ Correlation

coeff

Mean Δ:

(as-is -

att)

p-value:

paired ttest

p-value:

Wilcoxon test

sample size for 5%

sig t-test

(95 % probability)

25 0

20 4 0.098 0.063 57

15 0.65 1,133 0.005 0.001 12

12 0.07 6,984 0.000 0.001 4

9 0.29 53,654 0.000 0.001 3

6 0.64 672,361 0.000 0.001 3

0 0

K-9
Table 2c. Comparison of Los Angeles “As – is” and “Attainment” Cases, Indoor

Sources Omitted:

Cumulative Number of Persons at 1-hour Daily Max COHb Levels 

COHb (%) $ Correlation

coeff

Mean Δ:

(as-is -

att)

p-value:

paired t-test

p-value:

Wilcoxon test

sample size for 5%

sig t-test

(95 % probability)

6.0 0

5.0 0

4.0 0.3 0.172 0.500 110

3.0 0.29 57 0.011 0.002 16

2.9 0.17 81 0.003 0.001 10

2.8 0.24 181 0.024 0.001 23

2.7 0.11 553 0.023 0.001 22

2.6 -0.10 686 0.010 0.001 16

2.5 -0.05 1,341 0.003 0.001 10

2.4 0.73 1,623 0.001 0.001 7

2.3 0.34 2,290 0.001 0.001 9

2.2 -0.02 3,777 0.000 0.001 5

2.1 -0.20 5,618 0.000 0.001 4

2.0 0.13 7,049 0.000 0.001 4

1.5 0.36 47,700 0.000 0.001 3

1.0 0.34 77,524 0.000 0.001 3

0.5 0

0.0 0

K-10
Table 2d. Comparison of Los Angeles “As – is” and “Attainment” Cases, Indoor

Sources Omitted:

Cumulative Person-hours at 1-hour COHb Levels 

COHb (%) $ Correlation

coeff

Mean Δ:

(as-is - att)

p-value:

paired ttest

p-value:

Wilcoxon test

sample size for 5%

sig t-test

(95 % probability)

6.0 0

5.0 0

4.0 0.3 0.172 0.500 110

3.0 -0.07 96 0.009 0.002 15

2.9 -0.30 132 0.002 0.001 9

2.8 -0.20 352 0.036 0.001 28

2.7 -0.08 811 0.018 0.001 20

2.6 -0.15 1,084 0.014 0.001 18

2.5 -0.23 2,419 0.013 0.001 17

2.4 0.63 3,204 0.004 0.001 12

2.3 0.90 5,937 0.001 0.001 8

2.2 0.92 11,682 0.000 0.001 6

2.1 0.69 28,997 0.003 0.001 11

2.0 0.83 68,286 0.016 0.001 19

1.5 0.83 1,802,078 0.049 0.001 34

1.0 0.80 23,100,237 0.000 0.001 5

0.5 0.51 268,317,881 0.000 0.001 3

0.0 0

K-11
Table 3a. Comparison of Los Angeles “As – is” and “Sensitivity” Cases, Indoor

Sources Included:

Cumulative Person-days at 1-hour Daily Maximum Exposure Concentration

CO (ppm) $ Correlation

coeff

Mean Δ:

(as-is -

sens)

p-value:

paired ttest

p-value:

Wilcoxon test

sample size for 5%

sig t-test

(95 % probability)

60 0.54 131 0.002 0.001 9

50 0.62 481 0.000 0.001 4

45 0.74 861 0.000 0.001 4

40 0.73 1,654 0.000 0.001 3

35 0.98 3,261 0.000 0.001 3

30 0.83 7,852 0.000 0.001 3

25 0.93 17,342 0.000 0.001 3

20 0.59 39,540 0.000 0.001 3

15 0.90 91,908 0.000 0.001 2

10 0.95 203,499 0.000 0.001 2

0 0

K-12
Table 3b. Comparison of Los Angeles “As – is” and “Sensitivity” Cases, Indoor

Sources Included:

Cumulative Person-days at 8-hour Daily Maximum Exposure Concentration

CO (ppm) $ Correlation

coeff

Mean Δ:

(as-is

-sens)

p-value:

paired ttest

p-value:

Wilcoxon test

sample size for 5%

sig t-test

(95 % probability)

25 0.99 3,250 0.000 0.001 4

20 0.99 5,355 0.000 0.001 3

15 0.99 10,787 0.000 0.001 3

12 0.89 18,152 0.000 0.001 3

9 0.99 36,534 0.000 0.001 2

6 1.00 84,541 0.000 0.001 2

0 0

K-13
Table 3c. Comparison of Los Angeles “As – is” and “Sensitivity” Cases, Indoor Sources

Included:

Cumulative Number of Persons at 1-hour Daily Max COHb Levels 

COHb (%) $ Correlation

coeff

Mean Δ:

(as-is -

sens)

p-value:

paired ttest

p-value:

Wilcoxon test

sample size for 5%

sig t-test

(95 % probability)

6.0 0.77 393 0.000 0.001 6

5.0 0.85 1,096 0.000 0.001 4

4.0 0.99 2,459 0.000 0.001 4

3.0 0.96 6,044 0.000 0.001 4

2.9 0.96 7,109 0.000 0.001 4

2.8 0.90 7,703 0.000 0.001 3

2.7 0.85 8,560 0.000 0.001 4

2.6 0.87 9,748 0.000 0.001 3

2.5 0.76 11,333 0.000 0.001 3

2.4 0.88 12,584 0.000 0.001 3

2.3 0.93 14,168 0.000 0.001 3

2.2 0.98 15,040 0.000 0.001 3

2.1 0.95 17,019 0.000 0.001 3

2.0 0.90 18,957 0.000 0.001 3

1.5 0.96 27,339 0.000 0.001 3

1.0 0.73 6,489 0.000 0.001 4

0.5 0

0.0 0

K-14
Table 3d. Comparison of Los Angeles “As – is” and “Sensitivity” Cases, Indoor

Sources Included:

Cumulative Person-hours at 1-hour COHb Levels During CO Season

COHb (%) $ Correlation

coeff

Mean Δ:

(as-is -

sens)

p-value:

paired ttest

p-value:

Wilcoxon test

sample size for 5%

sig t-test

(95 % probability)

6.0 0.73 2,349 0.000 0.001 6

5.0 0.93 6,049 0.000 0.001 5

4.0 0.95 15,882 0.000 0.001 4

3.0 0.99 38,869 0.000 0.001 3

2.9 0.96 43,939 0.000 0.001 3

2.8 0.99 49,051 0.000 0.001 3

2.7 0.96 55,720 0.000 0.001 3

2.6 0.95 63,130 0.000 0.001 3

2.5 0.96 71,410 0.000 0.001 3

2.4 0.87 80,246 0.000 0.001 3

2.3 0.93 91,687 0.000 0.001 3

2.2 0.95 105,191 0.000 0.001 3

2.1 0.94 120,752 0.000 0.001 3

2.0 0.99 138,678 0.000 0.001 3

1.5 1.00 350,873 0.000 0.001 2

1.0 1.00 1,114,447 0.000 0.001 2

0.5 1.00 2,008,799 0.000 0.001 2

0.0 0

K-15