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 2000CONTENTS

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 In side Motor Vehicles in pNEM/CO . . . . . G-1 H. Analyses Supporting Proposed Modificati ons 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 Patte rns 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-1Appendix A Distribution of Energy Expenditure Rates Associated with CHAD Location Descriptors

A-1Distributions for Energy Expenditure Rates By Ac tivity 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-2ACTIVITY 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-3ACTIVITY 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-4ACTIVITY 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-5CHAD 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-612600: 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-716700: 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-9Appendix B Fixed-Site Monitors in Colorado and California Reporting CO Data to AIRS for One or More Years Between 1993 and 1997

B-11DATE 98/06/25 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) PAGE1AMP450 AIR QUALITY SUBSYSTEMQUICK LOOK REPORT0 CARBON MONOXIDE (42101) COLORADO UNITS: 007 PPM0 PO M REP MAX 1-HR OBS> MAX 8-HR OBS>SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST 2ND 9METH__________________________________________________________________________________________________________________________________008-001-3001 1 2 ADAMS CO 78TH AVE & STEELE ST - W 93 001 8632 9.0 8.2 0 6.6 4.9 005408-001-3001 1 2 ADAMS CO 78TH AVE & STEELE ST - W 94 001 8687 9.0 8.8 0 6.4 6.3 005408-001-3001 1 2 ADAMS CO 78TH AVE & STEELE ST - W 95 001 8681 8.5 8.1 0 5.7 5.1 005408-001-3001 1 2 ADAMS CO 78TH AVE & STEELE ST - W 96 001 8712 6.2 6.2 0 3.9 3.9 005408-001-3001 1 2 ADAMS CO 78TH AVE & STEELE ST - W 97 001 8661 8.3 6.6 0 5.0 4.3 005408-001-7015 1 4 COMMERCE CITY ADAMS CO ROCKY MOUNTAIN ARSENAL 93 830 7919 7.7 6.6 0 4.3 4.0 005408-001-7015 1 4 COMMERCE CITY ADAMS CO ROCKY MOUNTAIN ARSENAL 94 830 3595 3.2 2.5 0 1.2 1.1 005408-005-0002 1 2 LITTLETON ARAPAHOE CO 8100 SO UNIVERSITY BLVD- 93 001 8589 7.0 6.5 0 4.9 3.4 005408-005-0002 1 2 LITTLETON ARAPAHOE CO 8100 SO UNIVERSITY BLVD- 94 001 8705 4.6 4.2 0 2.9 2.2 005408-005-0002 1 2 LITTLETON ARAPAHOE CO 8100 SO UNIVERSITY BLVD- 95 001 8670 3.6 3.3 0 2.6 2.1 005408-005-0002 1 2 LITTLETON ARAPAHOE CO 8100 SO UNIVERSITY BLVD- 96 001 8677 4.3 4.1 0 2.9 2.6 005408-005-0002 1 2 LITTLETON ARAPAHOE CO 8100 SO UNIVERSITY BLVD- 97 001 8463 4.3 4.0 0 3.0 2.8 005408-005-0003 1 2 ENGLEWOOD ARAPAHOE CO 3300 S. HURON ST. 93 001 8717 11.5 10.9 0 8.4 6.2 005408-005-0003 1 2 ENGLEWOOD ARAPAHOE CO 3300 S. HURON ST. 94 001 7126 7.5 7.3 0 4.9 4.0 005408-013-0009 1 2 LONGMONT BOULDER CO 440 MAIN ST. LONGMONT 93 001 8701 9.5 9.0 0 7.0 6.4 005408-013-0009 1 2 LONGMONT BOULDER CO 440 MAIN ST. LONGMONT 94 001 8557 12.6 12.2 0 6.7 6.2 005408-013-0009 1 2 LONGMONT BOULDER CO 440 MAIN ST. LONGMONT 95 001 8690 14.0 8.8 0 5.6 4.7 005408-013-0009 1 2 LONGMONT BOULDER CO 440 MAIN ST. LONGMONT 96 001 8735 11.3 9.2 0 5.5 5.5 005408-013-0009 1 2 LONGMONT BOULDER CO 440 MAIN ST. LONGMONT 97 001 8617 9.6 9.1 0 5.7 5.4 005408-013-0010 1 2 BOULDER BOULDER CO 2150 28TH STREET 93 001 353 7.6 7.6 0 5.1 4.9 005408-013-0010 1 2 BOULDER BOULDER CO 2150 28TH STREET 94 001 8639 12.4 10.5 0 6.6 6.0 005408-013-0010 1 2 BOULDER BOULDER CO 2150 28TH STREET 95 001 8608 10.6 10.3 0 5.3 5.2 005408-013-0010 1 2 BOULDER BOULDER CO 2150 28TH STREET 96 001 8576 8.5 8.4 0 5.6 4.3 005408-013-0010 1 2 BOULDER BOULDER CO 2150 28TH STREET 97 001 8697 9.0 8.2 0 5.5 3.9 005408-013-1001 1 2 BOULDER BOULDER CO 2320 MARINE ST., BOULDER 93 001 8708 10.2 8.4 0 5.5 4.1 005408-013-1001 1 2 BOULDER BOULDER CO 2320 MARINE ST., BOULDER 94 001 8565 7.8 6.0 0 2.8 2.7 005408-013-1001 1 2 BOULDER BOULDER CO 2320 MARINE ST., BOULDER 95 001 8651 8.3 8.2 0 4.1 3.7 005408-013-1001 1 2 BOULDER BOULDER CO 2320 MARINE ST., BOULDER 96 001 8669 4.5 4.3 0 2.5 2.5 005408-013-1001 1 2 BOULDER BOULDER CO 2320 MARINE ST., BOULDER 97 001 8517 7.1 6.9 0 5.1 3.3 005408-031-0002 2 1 DENVER DENVER CO 2105 BROADWAY - CAMP 93 001 8687 19.4 18.2 0 10.4 10.4 205408-031-0002 2 1 DENVER DENVER CO 2105 BROADWAY - CAMP 94 001 8700 20.4 17.1 0 9.9 8.2 105408-031-0002 2 1 DENVER DENVER CO 2105 BROADWAY - CAMP 95 001 8697 24.5 16.4 0 11.0 9.5 205408-031-0002 2 1 DENVER DENVER CO 2105 BROADWAY - CAMP 96 001 8673 21.6 16.7 0 9.0 7.3 005408-031-0002 2 1 DENVER DENVER CO 2105 BROADWAY - CAMP 97 001 8687 11.4 10.0 0 5.7 5.5 005408-031-0013 1 2 DENVER DENVER CO 14TH AND ALBION ST. NJH- 93 001 8675 18.1 14.9 0 9.1 7.8 005408-031-0013 1 2 DENVER DENVER CO 14TH AND ALBION ST. NJH- 94 001 8665 12.4 12.2 0 8.0 7.6 005408-031-0013 1 2 DENVER DENVER CO 14TH AND ALBION ST. NJH- 95 001 8647 14.6 13.6 0 8.5 6.2 005408-031-0013 1 2 DENVER DENVER CO 14TH AND ALBION ST. NJH- 96 001 8516 14.6 9.4 0 5.6 5.2 005408-031-0013 1 2 DENVER DENVER CO 14TH AND ALBION ST. NJH- 97 001 8690 11.6 10.6 0 4.8 4.7 005408-031-0014 1 1 DENVER DENVER CO 23 RD AND JULIAN_(CARRIA 93 001 8676 14.1 12.1 0 8.5 8.2 005408-031-0014 1 1 DENVER DENVER CO 23 RD AND JULIAN_(CARRIA 94 001 8543 11.2 10.9 0 9.3 7.3 005408-031-0014 1 1 DENVER DENVER CO 23 RD AND JULIAN_(CARRIA 95 001 8701 10.4 9.9 0 7.3 5.9 005408-031-0014 1 1 DENVER DENVER CO 23 RD AND JULIAN_(CARRIA 96 001 8736 9.1 8.2 0 7.3 5.7 005408-031-0014 1 1 DENVER DENVER CO 23 RD AND JULIAN_(CARRIA 97 001 8677 9.5 8.4 0 7.0 6.2 00541DATE 98/06/25 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) PAGE2AMP450 AIR QUALITY SUBSYSTEMQUICK LOOK REPORT0 CARBON MONOXIDE (42101) COLORADO UNITS: 007 PPM0 PB-2O M REP MAX 1-HR OBS> MAX 8-HR OBS>SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST 2ND 9METH__________________________________________________________________________________________________________________________________008-031-0018 1 3 DENVER DENVER CO BLAKE ST. SIDE OF SPEER 93 001 1021 16.2 15.3 0 10.4 7.7 105108-031-0018 1 3 DENVER DENVER CO BLAKE ST. SIDE OF SPEER 94 001 1591 12.2 11.6 0 7.8 6.5 005108-031-0019 1 2 DENVER DENVER CO SPEER SIDE OF SPEER & AU 93 001 997 16.2 16.1 0 10.4 7.7 105108-031-0019 1 2 DENVER DENVER CO SPEER SIDE OF SPEER & AU 94 001 3049 13.9 13.4 0 9.0 8.2 005108-031-0019 1 2 DENVER DENVER CO SPEER SIDE OF SPEER & AU 95 001 3658 15.0 14.0 0 9.7 7.1 100008-031-0019 1 2 DENVER DENVER CO SPEER SIDE OF SPEER & AU 96 001 8694 15.7 12.5 0 9.2 7.0 005408-031-0019 1 2 DENVER DENVER CO SPEER SIDE OF SPEER & AU 97 001 8354 11.2 11.2 0 6.6 6.4 005408-031-0020 1 3 DENVER DENVER CO 935 COLORADO BLVD., UCHS 94 001 1370 12.8 11.7 0 7.6 6.8 005408-031-0020 1 3 DENVER DENVER CO 935 COLORADO BLVD., UCHS 95 001 2141 11.9 10.3 0 7.4 6.0 005408-041-0004 1 2 COLORADO SPRING EL PASO CO 712 S TEJON ST 93 001 8716 11.7 10.7 0 5.6 5.0 005408-041-0004 1 2 COLORADO SPRING EL PASO CO 712 S TEJON ST 94 001 8716 12.5 10.9 0 4.4 4.2 005408-041-0004 1 2 COLORADO SPRING EL PASO CO 712 S TEJON ST 95 001 8698 10.4 9.6 0 5.2 4.7 005408-041-0004 1 2 COLORADO SPRING EL PASO CO 712 S TEJON ST 96 001 8688 10.7 9.1 0 4.7 3.8 005408-041-0004 1 2 COLORADO SPRING EL PASO CO 712 S TEJON ST 97 001 4658 10.4 8.2 0 4.7 3.9 005408-041-0006 1 2 COLORADO SPRING EL PASO CO UINTAH & I-25 93 001 8660 13.6 11.6 0 6.1 5.7 005408-041-0006 1 2 COLORADO SPRING EL PASO CO UINTAH & I-25 94 001 8578 11.7 11.4 0 5.4 4.9 005408-041-0006 1 2 COLORADO SPRING EL PASO CO UINTAH & I-25 95 001 8716 10.8 10.3 0 7.2 5.5 005408-041-0006 1 2 COLORADO SPRING EL PASO CO UINTAH & I-25 96 001 8746 11.3 11.3 0 7.6 5.0 005408-041-0006 1 2 COLORADO SPRING EL PASO CO UINTAH & I-25 97 001 8577 13.1 10.6 0 5.9 4.9 005408-041-6004 1 3 COLORADO SPRING EL PASO CO 6000 PULPIT ROCK DRIVE. 93 026 8341 4.1 3.9 0 2.1 1.8 004808-041-6004 1 3 COLORADO SPRING EL PASO CO 6000 PULPIT ROCK DRIVE. 94 026 8709 4.0 3.9 0 2.3 2.2 000008-041-6004 1 3 COLORADO SPRING EL PASO CO 6000 PULPIT ROCK DRIVE. 95 026 8724 4.2 3.8 0 2.3 2.3 009308-041-6004 1 3 COLORADO SPRING EL PASO CO 6000 PULPIT ROCK DRIVE. 96 026 8544 3.8 3.4 0 2.1 2.0 009308-041-6005 1 3 EL PASO CO 4940 S. HIGHWAY 85/87 93 026 8508 7.0 7.0 0 4.5 3.8 004808-041-6005 1 3 EL PASO CO 4940 S. HIGHWAY 85/87 94 026 8655 6.8 6.2 0 4.6 4.5 000008-041-6005 1 3 EL PASO CO 4940 S. HIGHWAY 85/87 95 026 8515 5.9 5.6 0 3.2 3.1 009308-041-6005 1 3 EL PASO CO 4940 S. HIGHWAY 85/87 96 026 3064 6.1 5.6 0 3.0 2.9 009308-041-6006 1 3 EL PASO CO 9400 CHIPITA PARK ROAD 93 026 8257 2.0 1.8 0 1.1 1.0 004808-041-6006 1 3 EL PASO CO 9400 CHIPITA PARK ROAD 94 026 7620 2.0 1.9 0 1.2 .9 000008-041-6006 1 3 EL PASO CO 9400 CHIPITA PARK ROAD 95 026 7793 3.0 2.9 0 2.4 1.9 009308-041-6006 1 3 EL PASO CO 9400 CHIPITA PARK ROAD 96 026 4148 2.1 2.1 0 1.8 1.8 009308-041-6009 1 3 EL PASO CO R.D.NIXON POWER PLANT EX 93 026 6003 1.9 1.7 0 1.0 .8 004808-041-6009 1 3 EL PASO CO R.D.NIXON POWER PLANT EX 94 026 8702 2.2 1.9 0 1.3 1.1 000008-041-6009 1 3 EL PASO CO R.D.NIXON POWER PLANT EX 95 026 8707 2.4 2.2 0 1.3 1.1 009308-041-6009 1 3 EL PASO CO R.D.NIXON POWER PLANT EX 96 026 4328 1.5 1.4 0 .8 .8 009308-041-6011 1 3 COLORADO SPRING EL PASO CO 130 WEST CACHE LA POUDRE 93 026 8584 8.7 7.9 0 4.5 3.9 004808-041-6011 1 3 COLORADO SPRING EL PASO CO 130 WEST CACHE LA POUDRE 94 026 8318 7.9 6.8 0 3.3 3.2 000008-041-6011 1 3 COLORADO SPRING EL PASO CO 130 WEST CACHE LA POUDRE 95 026 8500 7.4 7.0 0 4.0 3.7 009308-041-6011 1 3 COLORADO SPRING EL PASO CO 130 WEST CACHE LA POUDRE 96 026 8469 8.1 8.1 0 4.9 3.7 009308-041-6013 1 3 COLORADO SPRING EL PASO CO 1699 S. CORONA AVE 93 026 8619 11.0 10.1 0 5.3 4.7 005408-041-6013 1 3 COLORADO SPRING EL PASO CO 1699 S. CORONA AVE 94 026 8703 10.8 9.3 0 4.6 4.5 005408-041-6013 1 3 COLORADO SPRING EL PASO CO 1699 S. CORONA AVE 95 026 8429 9.4 8.7 0 5.0 4.0 009308-041-6013 1 3 COLORADO SPRING EL PASO CO 1699 S. CORONA AVE 96 026 4310 11.9 9.6 0 5.4 3.8 009308-041-6016 1 3 COLORADO SPRING EL PASO CO 3730 MEADOWLAND BLVD. 93 026 8593 15.1 14.2 0 6.5 5.9 00541DATE 98/06/25 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) PAGE3AMP450 AIR QUALITY SUBSYSTEMQUICK LOOK REPORT0 CARBON MONOXIDE (42101) COLORADO UNITS: 007 PPM0 PO M REP MAX 1-HR OBS> MAX 8-HR OBS>SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST 2ND 9METH__________________________________________________________________________________________________________________________________008-041-6016 1 3 COLORADO SPRING EL PASO CO 3730 MEADOWLAND BLVD. 94 026 8574 16.6 14.5 0 4.9 4.7 005408-041-6016 1 3 COLORADO SPRING EL PASO CO 3730 MEADOWLAND BLVD. 95 026 8477 15.0 12.4 0 4.9 4.5 0093B-308-041-6016 1 3 COLORADO SPRING EL PASO CO 3730 MEADOWLAND BLVD. 96 026 8682 11.0 11.0 0 4.6 4.0 009308-059-0002 1 2 ARVADA JEFFERSON CO W 57TH AVENUE AND GARRIS 93 001 8723 11.2 10.4 0 5.1 4.9 005408-059-0002 1 2 ARVADA JEFFERSON CO W 57TH AVENUE AND GARRIS 94 001 8525 10.8 10.0 0 5.2 5.0 005408-059-0002 1 2 ARVADA JEFFERSON CO W 57TH AVENUE AND GARRIS 95 001 8680 11.9 8.9 0 5.1 4.6 005408-059-0002 1 2 ARVADA JEFFERSON CO W 57TH AVENUE AND GARRIS 96 001 8724 7.9 7.2 0 4.3 4.3 005408-059-0002 1 2 ARVADA JEFFERSON CO W 57TH AVENUE AND GARRIS 97 001 8697 9.2 7.7 0 5.1 4.9 005408-069-1004 1 2 FORT COLLINS LARIMER CO 708 S. MASON FT COLLINS 93 001 8698 17.3 13.8 0 7.4 6.6 005408-069-1004 1 2 FORT COLLINS LARIMER CO 708 S. MASON FT COLLINS 94 001 8703 13.6 12.1 0 7.3 6.0 005408-069-1004 1 2 FORT COLLINS LARIMER CO 708 S. MASON FT COLLINS 95 001 8699 10.6 9.8 0 5.6 5.2 005408-069-1004 1 2 FORT COLLINS LARIMER CO 708 S. MASON FT COLLINS 96 001 8597 12.7 10.9 0 5.5 5.1 005408-069-1004 1 2 FORT COLLINS LARIMER CO 708 S. MASON FT COLLINS 97 001 8708 10.3 9.2 0 5.3 5.2 005408-077-0014 1 2 GRAND JUNCTION MESA CO STOCKER STADIUM (12TH & 93 001 8383 12.0 11.2 0 6.9 6.1 005408-077-0014 1 2 GRAND JUNCTION MESA CO STOCKER STADIUM (12TH & 94 001 8367 11.6 11.6 0 7.5 6.0 005408-077-0014 1 2 GRAND JUNCTION MESA CO STOCKER STADIUM (12TH & 95 001 8209 10.0 8.7 0 5.4 5.4 005408-077-0014 1 2 GRAND JUNCTION MESA CO STOCKER STADIUM (12TH & 96 001 8754 10.5 10.5 0 7.5 5.8 005408-077-0014 1 2 GRAND JUNCTION MESA CO STOCKER STADIUM (12TH & 97 001 8550 8.5 7.8 0 6.4 5.4 005408-123-0007 1 2 GREELEY WELD CO 811 15TH ST - GREELEY 93 001 8688 10.9 10.2 0 6.1 5.8 005408-123-0007 1 2 GREELEY WELD CO 811 15TH ST - GREELEY 94 001 8707 11.3 11.1 0 6.4 5.2 005408-123-0007 1 2 GREELEY WELD CO 811 15TH ST - GREELEY 95 001 8703 10.3 9.6 0 5.7 5.3 005408-123-0007 1 2 GREELEY WELD CO 811 15TH ST - GREELEY 96 001 8739 12.3 10.7 0 7.5 7.0 005408-123-0007 1 2 GREELEY WELD CO 811 15TH ST - GREELEY 97 001 8717 9.6 8.6 0 5.3 4.8 0054B-41DATE 98/06/25 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) PAGE1AMP450 AIR QUALITY SUBSYSTEMQUICK LOOK REPORT0 CARBON MONOXIDE (42101)-METHODS: CODE COLLECTION METHOD ANALYSIS METHOD==== ============================== ===================================0 000 MULTIPLE METHODS MULTIPLE METHODS048 INSTRUMENTAL NON DISPERSIVE INFRA-RED051 INSTRUMENTAL NON DISPERSIVE INFRA-RED054 INSTRUMENTAL NON DISPERSIVE INFRA-RED093 INSTRUMENTAL GAS FILTER COORELATION CO ANALYZER1DATE 98/06/25 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) PAGE1AMP217P AIR QUALITY SUBSYSTEMAQ CLEANUP PROCESSING SUMMARY SHEET-- REPORT0 * * * PROGRAM AMP217 TERMINATED SUCCESSFULLY ON 98/06/25 AT 09:40:17 * * *B-51DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) PAGE1AMP450 AIR QUALITY SUBSYSTEMQUICK LOOK REPORT0 CARBON MONOXIDE (42101) CALIFORNIA UNITS: 007 PPM0 PO M REP MAX 1-HR OBS> MAX 8-HR OBS>SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST 2ND 9METH__________________________________________________________________________________________________________________________________006-001-0003 1 2 LIVERMORE ALAMEDA 2614 OLD 1ST ST., LIVERM 95 004 8638 5.0 4.1 0 2.3 2.3 005406-001-0003 1 2 LIVERMORE ALAMEDA 2614 OLD 1ST ST., LIVERM 96 004 8516 4.9 4.7 0 2.5 2.4 005406-001-0003 1 2 LIVERMORE ALAMEDA 2614 OLD 1ST ST., LIVERM 97 004 8257 4.6 4.4 0 2.5 2.3 005406-001-0005 1 2 OAKLAND ALAMEDA 822 ALICE ST., OAKLAND 95 004 8680 4.9 4.8 0 3.8 3.7 000806-001-0005 1 2 OAKLAND ALAMEDA 822 ALICE ST., OAKLAND 96 004 8285 6.9 5.5 0 3.9 3.8 000806-001-0005 1 2 OAKLAND ALAMEDA 822 ALICE ST., OAKLAND 97 004 8592 7.9 6.0 0 3.6 3.6 000806-001-1001 1 1 FREMONT ALAMEDA 40733 CHAPEL WAY., FREMO 95 004 8544 5.5 5.2 0 2.9 2.7 005406-001-1001 1 1 FREMONT ALAMEDA 40733 CHAPEL WAY., FREMO 96 004 8496 6.2 5.4 0 3.4 3.3 005406-001-1001 1 1 FREMONT ALAMEDA 40733 CHAPEL WAY., FREMO 97 004 8342 6.0 5.1 0 3.0 3.0 005406-005-0002 1 2 JACKSON AMADOR 201 CLINTON ROAD, JACKS 95 001 8311 9.3 3.0 0 2.6 2.2 006706-005-0002 1 2 JACKSON AMADOR 201 CLINTON ROAD, JACKS 96 001 8301 2.2 2.2 0 1.5 1.4 006706-005-0002 1 2 JACKSON AMADOR 201 CLINTON ROAD, JACKS 97 001 8352 2.8 2.7 0 1.4 1.4 006706-007-0002 1 2 CHICO BUTTE 468 MANZANITA AVE, CHICO 95 001 8247 5.8 5.5 0 3.7 3.5 006706-007-0002 1 2 CHICO BUTTE 468 MANZANITA AVE, CHICO 96 001 8318 5.3 5.1 0 3.4 3.4 006706-007-0002 1 2 CHICO BUTTE 468 MANZANITA AVE, CHICO 97 001 8335 6.8 6.1 0 3.8 3.5 006706-007-0005 1 2 CHICO BUTTE 101 SALEM ST, CHICO 95 001 8741 8.5 7.6 0 4.8 4.7 006706-007-0005 1 2 CHICO BUTTE 101 SALEM ST, CHICO 96 001 8767 8.7 7.8 0 6.1 5.3 006706-007-0005 1 2 CHICO BUTTE 101 SALEM ST, CHICO 97 001 8580 7.0 6.6 0 5.1 4.5 006706-009-0001 1 2 SAN ANDREAS CALAVERAS 501 GOLD STRIKE ROAD, SA 95 001 8230 2.1 2.1 0 1.8 1.0 006706-009-0001 1 2 SAN ANDREAS CALAVERAS 501 GOLD STRIKE ROAD, SA 96 001 8097 1.7 1.6 0 .9 .8 006706-009-0001 1 2 SAN ANDREAS CALAVERAS 501 GOLD STRIKE ROAD, SA 97 001 8108 2.1 2.0 0 1.7 1.0 006706-013-0002 1 1 CONCORD CONTRA COSTA 2975 TREAT BLVD, CONCORD 95 004 8663 5.5 4.7 0 2.9 2.7 005406-013-0002 1 1 CONCORD CONTRA COSTA 2975 TREAT BLVD, CONCORD 96 004 8608 5.6 5.4 0 2.9 2.7 005406-013-0002 1 1 CONCORD CONTRA COSTA 2975 TREAT BLVD, CONCORD 97 004 8359 5.7 5.5 0 3.0 3.0 005406-013-0003 1 2 RICHMOND CONTRA COSTA 1144 13TH ST., RICHMOND 95 004 8632 6.5 4.8 0 2.4 2.3 005406-013-0003 1 2 RICHMOND CONTRA COSTA 1144 13TH ST., RICHMOND 96 004 8676 5.0 4.8 0 2.6 2.6 005406-013-0003 1 2 RICHMOND CONTRA COSTA 1144 13TH ST., RICHMOND 97 004 2963 4.6 3.7 0 2.6 2.3 005406-013-1002 1 2 BETHEL ISLAND CONTRA COSTA 5551 BETHEL ISLAND RD, B 95 004 8484 2.8 2.2 0 1.7 1.6 005406-013-1002 1 2 BETHEL ISLAND CONTRA COSTA 5551 BETHEL ISLAND RD, B 96 004 8540 2.7 1.7 0 1.4 1.4 005406-013-1002 1 2 BETHEL ISLAND CONTRA COSTA 5551 BETHEL ISLAND RD, B 97 004 8360 1.9 1.8 0 1.4 1.4 005406-013-1003 1 2 SAN PABLO CONTRA COSTA UNIT 759 EL PORTAL SHOPP 97 004 5586 3.8 2.8 0 2.3 1.8 005406-013-3001 1 2 PITTSBURG CONTRA COSTA 583 W. 10TH ST., PITTSBU 95 004 8639 5.8 5.4 0 2.8 2.7 005406-013-3001 1 2 PITTSBURG CONTRA COSTA 583 W. 10TH ST., PITTSBU 96 004 8750 6.8 5.9 0 2.9 2.6 005406-013-3001 1 2 PITTSBURG CONTRA COSTA 583 W. 10TH ST., PITTSBU 97 004 8360 5.5 4.8 0 3.2 3.1 005406-017-0005 1 2 SOUTH LAKE TAHO EL DORADO STATELINE-4045 HWY 50, S 95 001 8205 9.3 8.7 0 6.3 5.3 006706-017-0005 1 2 SOUTH LAKE TAHO EL DORADO STATELINE-4045 HWY 50, S 96 001 8124 10.4 7.4 0 5.1 4.8 006706-017-0005 1 2 SOUTH LAKE TAHO EL DORADO STATELINE-4045 HWY 50, S 97 001 7946 7.7 7.0 0 3.8 3.6 006706-017-0010 1 2 PLACERVILLE EL DORADO 3111 GOLD NUGGET WAY, P 95 001 8258 1.6 1.5 0 1.0 1.0 006706-017-0010 1 2 PLACERVILLE EL DORADO 3111 GOLD NUGGET WAY, P 96 001 8056 1.3 1.3 0 .9 .8 006706-017-0010 1 2 PLACERVILLE EL DORADO 3111 GOLD NUGGET WAY, P 97 001 8221 1.6 1.4 0 .8 .8 006706-017-0011 1 2 SOUTH LAKE TAHO EL DORADO 3337 SANDY WAY, SOUTH L 95 001 8223 5.2 4.5 0 2.6 2.4 006706-017-0011 1 2 SOUTH LAKE TAHO EL DORADO 3337 SANDY WAY, SOUTH L 96 001 8341 4.2 4.1 0 2.4 2.3 006706-017-0011 1 2 SOUTH LAKE TAHO EL DORADO 3337 SANDY WAY, SOUTH L 97 001 7993 3.2 3.1 0 2.4 2.2 006706-019-0007 1 2 FRESNO FRESNO 4706 E. DRUMMOND ST., FR 95 069 8159 6.4 6.3 0 4.8 4.7 00111DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) PAGE2AMP450 AIR QUALITY SUBSYSTEMQUICK LOOK REPORT0 CARBON MONOXIDE (42101) CALIFORNIA UNITS: 007 PPM0 PB-6O M REP MAX 1-HR OBS> MAX 8-HR OBS>SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST 2ND 9METH__________________________________________________________________________________________________________________________________006-019-0007 1 2 FRESNO FRESNO 4706 E. DRUMMOND ST., FR 96 069 8278 6.0 5.4 0 4.4 3.6 005406-019-0007 1 2 FRESNO FRESNO 4706 E. DRUMMOND ST., FR 97 069 8185 6.3 6.1 0 3.9 3.8 005406-019-0008 1 2 FRESNO FRESNO 3425 N FIRST ST, FRESNO 95 001 8177 10.3 9.5 0 7.3 6.3 006706-019-0008 1 2 FRESNO FRESNO 3425 N FIRST ST, FRESNO 96 001 8178 10.0 9.8 0 6.8 6.7 006706-019-0008 1 2 FRESNO FRESNO 3425 N FIRST ST, FRESNO 97 001 8240 8.7 8.3 0 5.7 5.1 006706-019-0009 1 3 FRESNO FRESNO 1145 FISHER STREET, FRES 95 001 4709 12.0 11.4 0 9.1 8.3 006706-019-0009 1 3 FRESNO FRESNO 1145 FISHER STREET, FRES 96 001 3570 10.1 8.5 0 6.8 5.7 006706-019-0009 1 3 FRESNO FRESNO 1145 FISHER STREET, FRES 97 001 4076 9.9 9.7 0 7.5 7.1 006706-019-0242 1 2 FRESNO FRESNO SIERRA SKYPARK#2-BLYTHE 95 069 8258 3.8 3.3 0 2.5 2.3 001106-019-0242 1 2 FRESNO FRESNO SIERRA SKYPARK#2-BLYTHE 96 069 8294 4.3 4.1 0 3.7 2.8 005406-019-0242 1 2 FRESNO FRESNO SIERRA SKYPARK#2-BLYTHE 97 069 8210 4.1 3.9 0 2.8 2.2 005406-019-5001 1 2 CLOVIS FRESNO 908 N VILLA AVE, CLOVIS 95 069 8200 8.0 5.8 0 3.9 3.6 001106-019-5001 1 2 CLOVIS FRESNO 908 N VILLA AVE, CLOVIS 96 069 8281 6.3 6.1 0 3.5 3.5 005406-019-5001 1 2 CLOVIS FRESNO 908 N VILLA AVE, CLOVIS 97 069 8154 6.1 6.1 0 2.9 2.9 005406-025-0005 1 2 CALEXICO IMPERIAL 1029 ETHEL ST, CALEXICO 95 001 8289 32.0 29.8 0 22.9 19.7 1506706-025-0005 1 2 CALEXICO IMPERIAL 1029 ETHEL ST, CALEXICO 96 001 8106 27.0 26.2 0 22.1 14.1 906706-025-0005 1 2 CALEXICO IMPERIAL 1029 ETHEL ST, CALEXICO 97 001 8306 24.0 21.8 0 17.8 16.7 1206706-025-0006 1 2 CALEXICO IMPERIAL CALEXICO - EAST 96 001 4392 22.0 18.0 0 8.7 7.8 006706-025-0006 1 2 CALEXICO IMPERIAL CALEXICO - EAST 97 001 7771 21.0 20.6 0 16.3 9.6 206706-025-1003 1 2 EL CENTRO IMPERIAL 150 9TH ST., EL CENTRO 96 001 8784 12.0 10.0 0 6.8 6.8 001106-025-1003 1 2 EL CENTRO IMPERIAL 150 9TH ST., EL CENTRO 97 001 8702 6.0 6.0 0 3.7 3.5 001106-027-0015 1 2 BISHOP INYO 157 SHORT STREET, BISHOP 95 001 2240 4.0 4.0 0 2.0 1.8 001106-029-0010 1 2 BAKERSFIELD KERN 1128 GOLDEN STATE HIGHWA 95 069 8250 7.4 7.0 0 4.6 3.6 001106-029-0010 1 2 BAKERSFIELD KERN 1128 GOLDEN STATE HIGHWA 96 069 8138 6.2 5.6 0 3.7 3.6 005406-029-0010 1 2 BAKERSFIELD KERN 1128 GOLDEN STATE HIGHWA 97 069 8189 6.1 4.3 0 2.9 2.7 005406-029-0014 1 2 BAKERSFIELD KERN 5558 CALIFORNIA AVE, BAK 95 001 8162 7.8 7.2 0 6.2 4.9 006706-029-0014 1 2 BAKERSFIELD KERN 5558 CALIFORNIA AVE, BAK 96 001 7967 8.7 8.5 0 7.7 5.6 006706-029-0014 1 2 BAKERSFIELD KERN 5558 CALIFORNIA AVE, BAK 97 001 8035 5.2 5.2 0 3.4 3.2 006706-037-0002 1 2 AZUSA LOS ANGELES 803 N. LOREN AVE., AZUSA 95 061 8300 8.1 7.3 0 6.2 6.2 006706-037-0002 1 2 AZUSA LOS ANGELES 803 N. LOREN AVE., AZUSA 96 061 8025 6.1 5.9 0 4.1 3.9 006706-037-0002 1 2 AZUSA LOS ANGELES 803 N. LOREN AVE., AZUSA 97 061 8007 8.4 5.5 0 4.3 4.2 006706-037-0113 1 2 WEST LOS ANGELE LOS ANGELES VA HOSPITAL, WEST LOS AN 95 061 7774 7.8 7.4 0 5.7 5.6 006706-037-0113 1 2 WEST LOS ANGELE LOS ANGELES VA HOSPITAL, WEST LOS AN 96 061 8057 7.0 6.7 0 4.3 4.3 006706-037-0113 1 2 WEST LOS ANGELE LOS ANGELES VA HOSPITAL, WEST LOS AN 97 061 8360 7.3 6.4 0 4.2 4.1 006706-037-0206 1 3 DIAMOND BAR LOS ANGELES 21865 E. COPLEY DR., DIA 95 061 8357 6.9 6.1 0 5.5 4.9 006706-037-0206 1 3 DIAMOND BAR LOS ANGELES 21865 E. COPLEY DR., DIA 96 061 3073 5.5 5.4 0 4.3 3.9 006706-037-1002 1 2 BURBANK LOS ANGELES 228 W. PALM AVE., BURBAN 95 061 8291 12.5 12.5 0 11.8 11.0 506706-037-1002 1 2 BURBANK LOS ANGELES 228 W. PALM AVE., BURBAN 96 061 7696 11.6 11.0 0 9.2 8.5 006706-037-1002 1 2 BURBANK LOS ANGELES 228 W. PALM AVE., BURBAN 97 061 8025 8.8 8.6 0 7.3 7.2 006706-037-1103 1 2 LOS ANGELES LOS ANGELES 1630 N MAIN ST, LOS ANGE 95 061 8165 9.7 9.2 0 8.4 7.9 006706-037-1103 1 2 LOS ANGELES LOS ANGELES 1630 N MAIN ST, LOS ANGE 96 061 8390 10.3 10.1 0 8.4 7.5 006706-037-1103 1 2 LOS ANGELES LOS ANGELES 1630 N MAIN ST, LOS ANGE 97 061 8292 8.9 8.7 0 7.8 5.9 006706-037-1201 1 2 RESEDA LOS ANGELES 18330 GAULT ST., RESEDA 95 061 7670 13.0 11.8 0 10.3 9.4 106706-037-1201 1 2 RESEDA LOS ANGELES 18330 GAULT ST., RESEDA 96 061 8012 10.2 10.2 0 8.7 6.7 00671DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) PAGE3AMP450 AIR QUALITY SUBSYSTEMQUICK LOOK REPORT0 CARBON MONOXIDE (42101) CALIFORNIA UNITS: 007 PPM0 PO M REP MAX 1-HR OBS> MAX 8-HR OBS>SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST 2ND 9METH__________________________________________________________________________________________________________________________________006-037-1201 1 2 RESEDA LOS ANGELES 18330 GAULT ST., RESEDA 97 061 8245 11.7 11.1 0 9.5 7.7 106706-037-1301 1 1 LYNWOOD LOS ANGELES 11220 LONG BEACH BLVD., 95 061 8290 16.8 16.5 0 13.8 11.6 14067B-706-037-1301 1 1 LYNWOOD LOS ANGELES 11220 LONG BEACH BLVD., 96 061 8326 22.5 21.3 0 17.5 14.5 2206706-037-1301 1 1 LYNWOOD LOS ANGELES 11220 LONG BEACH BLVD., 97 061 8302 19.2 18.8 0 17.1 15.0 1206706-037-1601 1 2 PICO RIVERA LOS ANGELES 3713 SAN GABRIEL RIVER P 95 061 8372 9.6 9.3 0 7.7 7.6 006706-037-1601 1 2 PICO RIVERA LOS ANGELES 3713 SAN GABRIEL RIVER P 96 061 8303 9.8 9.1 0 8.1 6.5 006706-037-1601 1 2 PICO RIVERA LOS ANGELES 3713 SAN GABRIEL RIVER P 97 061 7881 9.2 7.9 0 6.1 6.1 006706-037-1701 1 2 POMONA LOS ANGELES 924 N. GAREY AVE., POMON 95 061 8307 8.1 7.7 0 6.1 6.0 006706-037-1701 1 2 POMONA LOS ANGELES 924 N. GAREY AVE., POMON 96 061 8290 8.1 8.1 0 4.8 4.7 006706-037-1701 1 2 POMONA LOS ANGELES 924 N. GAREY AVE., POMON 97 061 8350 7.9 7.1 0 5.0 4.9 006706-037-2005 1 2 PASADENA LOS ANGELES 752 S. WILSON AVE., PASA 95 061 8387 11.4 11.4 0 9.1 8.6 006706-037-2005 1 2 PASADENA LOS ANGELES 752 S. WILSON AVE., PASA 96 061 8282 10.7 9.9 0 7.1 6.9 006706-037-2005 1 2 PASADENA LOS ANGELES 752 S. WILSON AVE., PASA 97 061 8250 8.1 7.7 0 6.0 5.4 006706-037-4002 1 2 LONG BEACH LOS ANGELES 3648 N. LONG BEACH BLVD. 95 061 8326 9.1 8.1 0 6.7 6.2 006706-037-4002 1 2 LONG BEACH LOS ANGELES 3648 N. LONG BEACH BLVD. 96 061 6015 9.7 9.2 0 6.8 6.5 006706-037-4002 1 2 LONG BEACH LOS ANGELES 3648 N. LONG BEACH BLVD. 97 061 8347 9.0 8.6 0 6.6 6.4 006706-037-5001 1 2 HAWTHORNE LOS ANGELES 5234 W. 120TH ST., HAWTH 95 061 8241 11.4 11.1 0 8.8 8.7 006706-037-5001 1 2 HAWTHORNE LOS ANGELES 5234 W. 120TH ST., HAWTH 96 061 8258 12.5 12.3 0 11.5 10.5 506706-037-5001 1 2 HAWTHORNE LOS ANGELES 5234 W. 120TH ST., HAWTH 97 061 8125 12.4 12.3 0 10.3 7.9 106706-037-6002 1 2 SANTA CLARITA LOS ANGELES SAN FERNANDO RD, SANTA C 95 061 8241 6.7 6.5 0 4.1 3.8 006706-037-6002 1 2 SANTA CLARITA LOS ANGELES SAN FERNANDO RD, SANTA C 96 061 8413 7.0 6.3 0 3.9 3.9 006706-037-6002 1 2 SANTA CLARITA LOS ANGELES SAN FERNANDO RD, SANTA C 97 061 8289 7.4 7.0 0 6.7 6.5 006706-037-9002 1 2 LANCASTER LOS ANGELES 315 W. PONDERA ST., LANC 95 061 8383 7.5 6.8 0 5.1 4.5 006706-037-9002 1 2 LANCASTER LOS ANGELES 315 W. PONDERA ST., LANC 96 061 8406 6.8 6.4 0 4.7 4.6 006706-037-9002 1 2 LANCASTER LOS ANGELES 315 W. PONDERA ST., LANC 97 061 7759 5.9 5.5 0 4.0 4.0 006706-041-0001 1 2 SAN RAFAEL MARIN 534 4TH ST., SAN RAFAEL 95 004 8598 6.1 5.7 0 3.2 2.9 005406-041-0001 1 2 SAN RAFAEL MARIN 534 4TH ST., SAN RAFAEL 96 004 8527 7.1 6.9 0 4.0 3.4 005406-041-0001 1 2 SAN RAFAEL MARIN 534 4TH ST., SAN RAFAEL 97 004 8266 6.0 5.6 0 2.6 2.6 005406-045-0008 1 2 UKIAH MENDOCINO 306 E. GOBBI STREET, UK 95 001 7236 5.4 5.2 0 3.2 3.2 001106-045-0008 1 2 UKIAH MENDOCINO 306 E. GOBBI STREET, UK 96 001 4052 4.8 4.2 0 2.7 2.4 001106-045-0008 1 2 UKIAH MENDOCINO 306 E. GOBBI STREET, UK 97 001 8117 4.6 4.3 0 3.2 2.8 001106-045-0009 1 2 WILLITS MENDOCINO 899 SO MAIN STREET, WIL 95 001 7861 2.5 2.3 0 1.8 1.6 001106-045-0009 1 2 WILLITS MENDOCINO 899 SO MAIN STREET, WIL 96 001 2537 3.0 3.0 0 1.5 1.5 001106-045-0009 1 2 WILLITS MENDOCINO 899 SO MAIN STREET, WIL 97 001 7955 7.4 4.2 0 3.0 2.8 001106-051-0001 1 2 MAMMOTH LAKES MONO GATEWAY HC., MAMMOTH LAK 95 001 8450 10.0 7.0 0 5.4 3.9 001106-051-0001 1 2 MAMMOTH LAKES MONO GATEWAY HC., MAMMOTH LAK 96 001 7854 6.0 6.0 0 3.0 3.0 001106-051-0001 1 2 MAMMOTH LAKES MONO GATEWAY HC., MAMMOTH LAK 97 001 8181 8.2 7.5 0 3.4 3.3 006706-053-1002 1 2 SALINAS MONTEREY II-1270 NATIVIDAD RD, SA 95 011 7906 3.2 3.0 0 1.8 1.7 001106-053-1002 1 2 SALINAS MONTEREY II-1270 NATIVIDAD RD, SA 96 011 8610 5.5 4.5 0 2.6 2.4 005106-053-1002 1 2 SALINAS MONTEREY II-1270 NATIVIDAD RD, SA 97 011 8490 4.4 3.7 0 1.8 1.7 005106-055-0003 1 2 NAPA NAPA 2552 JEFFERSON AVE., NAP 95 004 8483 7.6 6.3 0 3.5 3.3 005406-055-0003 1 2 NAPA NAPA 2552 JEFFERSON AVE., NAP 96 004 8338 5.6 5.3 0 3.8 3.8 005406-055-0003 1 2 NAPA NAPA 2552 JEFFERSON AVE., NAP 97 004 8319 5.7 5.3 0 3.9 3.9 005406-059-0001 1 1 ANAHEIM ORANGE 1610 S. HARBOR BLVD., AN 95 061 8259 9.9 9.8 0 8.0 7.3 00671DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) PAGE4AMP450 AIR QUALITY SUBSYSTEMQUICK LOOK REPORT0 CARBON MONOXIDE (42101) CALIFORNIA UNITS: 007 PPM0 PO M REP MAX 1-HR OBS> MAX 8-HR OBS>SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST 2ND 9METH__________________________________________________________________________________________________________________________________006-059-0001 1 1 ANAHEIM ORANGE 1610 S. HARBOR BLVD., AN 96 061 8298 8.9 8.9 0 7.4 6.1 006706-059-0001 1 1 ANAHEIM ORANGE 1610 S. HARBOR BLVD., AN 97 061 8354 8.4 8.2 0 5.7 5.4 006706-059-1003 1 2 COSTA MESA ORANGE 2850 MESA VERDE DR EAST, 95 061 8213 7.9 7.5 0 6.5 5.3 006706-059-1003 1 2 COSTA MESA ORANGE 2850 MESA VERDE DR EAST, 96 061 8191 8.7 8.6 0 7.2 6.6 006706-059-1003 1 2 COSTA MESA ORANGE 2850 MESA VERDE DR EAST, 97 061 8325 7.3 7.1 0 5.9 5.0 006706-059-2001 1 2 EL TORO ORANGE 23022 EL TORO RD., EL TO 95 061 8321 6.2 6.0 0 4.0 3.9 006706-059-2001 1 2 EL TORO ORANGE 23022 EL TORO RD., EL TO 96 061 8400 6.2 6.0 0 4.1 4.0 0067B-806-059-2001 1 2 EL TORO ORANGE 23022 EL TORO RD., EL TO 97 061 8385 5.5 4.6 0 3.8 3.0 006706-059-5001 1 2 LA HABRA ORANGE 621 W. LAMBERT, LA HABRA 95 061 8363 12.7 11.5 0 6.5 6.4 006706-059-5001 1 2 LA HABRA ORANGE 621 W. LAMBERT, LA HABRA 96 061 8345 12.9 12.0 0 6.9 6.3 006706-059-5001 1 2 LA HABRA ORANGE 621 W. LAMBERT, LA HABRA 97 061 8230 11.9 11.0 0 5.7 5.7 006706-061-0005 1 2 TAHOE CITY PLACER 165 RIVER ROAD, TAHOE C 95 001 4236 9.5 9.0 0 2.9 2.6 000806-061-0006 1 2 ROSEVILLE PLACER 151 NO SUNRISE BLVD, RO 95 001 8304 3.9 3.5 0 2.2 2.1 006706-061-0006 1 2 ROSEVILLE PLACER 151 NO SUNRISE BLVD, RO 96 001 8358 4.5 4.0 0 2.8 2.3 006706-061-0006 1 2 ROSEVILLE PLACER 151 NO SUNRISE BLVD, RO 97 001 8273 3.7 3.5 0 2.1 2.1 006706-061-3001 1 2 ROCKLIN PLACER 5000 ROCKLIN ROAD, ROCKL 95 001 8289 2.9 2.8 0 1.6 1.4 006706-061-3001 1 2 ROCKLIN PLACER 5000 ROCKLIN ROAD, ROCKL 96 001 3173 3.1 2.8 0 1.4 1.4 006706-065-1003 1 1 RIVERSIDE RIVERSIDE 7002 MAGNOLIA AVE., RIVE 95 061 8432 9.0 9.0 0 6.3 5.8 006706-065-1003 1 1 RIVERSIDE RIVERSIDE 7002 MAGNOLIA AVE., RIVE 96 061 8404 9.1 8.2 0 5.3 5.0 006706-065-1003 1 1 RIVERSIDE RIVERSIDE 7002 MAGNOLIA AVE., RIVE 97 061 8345 10.7 9.3 0 5.5 4.8 006706-065-5001 1 2 PALM SPRINGS RIVERSIDE FS-590 RACQUET CLUB AVE, 95 061 8258 3.3 3.1 0 1.5 1.5 006706-065-5001 1 2 PALM SPRINGS RIVERSIDE FS-590 RACQUET CLUB AVE, 96 061 8030 3.2 3.0 0 1.6 1.3 006706-065-5001 1 2 PALM SPRINGS RIVERSIDE FS-590 RACQUET CLUB AVE, 97 061 8170 2.7 2.4 0 1.3 1.3 006706-065-8001 1 2 RUBIDOUX RIVERSIDE 5888 MISSION BLVD., RUBI 95 061 8374 6.8 6.7 0 5.7 5.2 006706-065-8001 1 2 RUBIDOUX RIVERSIDE 5888 MISSION BLVD., RUBI 96 061 8311 8.5 7.4 0 5.1 4.6 006706-065-8001 1 2 RUBIDOUX RIVERSIDE 5888 MISSION BLVD., RUBI 97 061 7057 6.6 6.2 0 5.6 5.1 006706-067-0002 1 3 NORTH HIGHLANDS SACRAMENTO 7823 BLACKFOOT WAY, NORT 95 001 8068 5.1 4.8 0 3.4 3.3 006606-067-0002 1 3 NORTH HIGHLANDS SACRAMENTO 7823 BLACKFOOT WAY, NORT 96 001 8254 5.2 4.8 0 3.3 3.2 006606-067-0002 1 3 NORTH HIGHLANDS SACRAMENTO 7823 BLACKFOOT WAY, NORT 97 001 7964 5.6 5.1 0 3.6 3.2 006606-067-0006 1 2 SACRAMENTO SACRAMENTO DEL PASO-2701 AVALON DR, 95 001 8081 7.6 7.0 0 6.7 5.4 006606-067-0006 1 2 SACRAMENTO SACRAMENTO DEL PASO-2701 AVALON DR, 96 001 8302 6.7 6.7 0 6.0 5.0 000006-067-0006 1 2 SACRAMENTO SACRAMENTO DEL PASO-2701 AVALON DR, 97 001 8037 8.1 7.2 0 6.0 5.7 006606-067-0007 1 1 SACRAMENTO SACRAMENTO 3535 EL CAMINO & WATT, S 95 001 8692 8.0 7.7 0 7.4 6.5 001106-067-0007 1 1 SACRAMENTO SACRAMENTO 3535 EL CAMINO & WATT, S 96 001 8506 8.5 8.4 0 7.2 7.1 006606-067-0007 1 1 SACRAMENTO SACRAMENTO 3535 EL CAMINO & WATT, S 97 001 8577 9.5 9.0 0 7.2 6.7 006606-067-0010 1 2 SACRAMENTO SACRAMENTO 1309 T ST., SACRAMENTO, 95 001 8303 9.8 9.6 0 6.6 6.3 006706-067-0010 1 2 SACRAMENTO SACRAMENTO 1309 T ST., SACRAMENTO, 96 001 8350 8.7 8.1 0 6.8 6.8 006706-067-0010 1 2 SACRAMENTO SACRAMENTO 1309 T ST., SACRAMENTO, 97 001 8161 7.6 7.4 0 6.0 5.5 006706-067-5002 1 2 SACRAMENTO SACRAMENTO 7926 EARHART DR., SACRAM 95 001 7148 8.0 7.0 0 4.3 4.3 006606-067-5002 1 2 SACRAMENTO SACRAMENTO 7926 EARHART DR., SACRAM 96 001 8014 3.5 3.3 0 2.2 2.2 006606-067-5002 1 2 SACRAMENTO SACRAMENTO 7926 EARHART DR., SACRAM 97 001 6774 3.1 2.9 0 2.0 2.0 006606-071-0001 1 2 BARSTOW SAN BERNARDINO 200 E. BUENA VISTA, BARS 95 014 7114 6.1 3.1 0 2.1 2.1 001106-071-0001 1 2 BARSTOW SAN BERNARDINO 200 E. BUENA VISTA, BARS 96 014 8071 3.8 3.7 0 2.3 2.1 000006-071-0001 1 2 BARSTOW SAN BERNARDINO 200 E. BUENA VISTA, BARS 97 014 7712 2.8 2.5 0 1.6 1.5 00931DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) PAGE5AMP450 AIR QUALITY SUBSYSTEMQUICK LOOK REPORT0 CARBON MONOXIDE (42101) CALIFORNIA UNITS: 007 PPM0 PO M REP MAX 1-HR OBS> MAX 8-HR OBS>SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST 2ND 9METH__________________________________________________________________________________________________________________________________006-071-0012 1 3 PHELAN SAN BERNARDINO BEEKLEY & PHELAN RDS, PH 95 001 6338 1.7 1.7 0 1.5 1.3 001106-071-0012 1 3 PHELAN SAN BERNARDINO BEEKLEY & PHELAN RDS, PH 96 001 4543 2.9 1.5 0 1.3 1.0 004106-071-0014 1 2 VICTORVILLE SAN BERNARDINO 14029 AMARGOSA ROAD, VIC 95 014 2812 3.1 3.1 0 2.7 2.4 001106-071-0014 1 2 VICTORVILLE SAN BERNARDINO 14029 AMARGOSA ROAD, VIC 96 014 5686 8.4 8.2 0 7.5 6.6 000006-071-0014 1 2 VICTORVILLE SAN BERNARDINO 14029 AMARGOSA ROAD, VIC 97 014 8082 4.1 3.8 0 3.1 2.3 009306-071-0017 1 2 TWENTYNINE PALM SAN BERNARDINO 6136 ADOBE ROAD, TWENTY 95 014 2876 5.0 3.3 0 2.0 1.8 001106-071-0017 1 2 TWENTYNINE PALM SAN BERNARDINO 6136 ADOBE ROAD, TWENTY 96 014 7695 1.9 1.8 0 1.3 1.2 000806-071-0017 1 2 TWENTYNINE PALM SAN BERNARDINO 6136 ADOBE ROAD, TWENTY 97 014 2996 2.0 1.6 0 1.0 .9 000806-071-0217 1 3 MOUNT BALDY SAN BERNARDINO 6945 MT BALDY ROAD 97 001 1779 1.4 .8 0 .5 .3 006706-071-4001 1 2 HESPERIA SAN BERNARDINO 17288 OLIVE ST., HESPERI 95 014 8032 3.1 3.0 0 2.1 2.0 001106-071-4001 1 2 HESPERIA SAN BERNARDINO 17288 OLIVE ST., HESPERI 96 014 8265 3.9 3.5 0 2.2 2.1 004106-071-4001 1 2 HESPERIA SAN BERNARDINO 17288 OLIVE ST., HESPERI 97 014 8115 3.4 3.4 0 2.6 2.3 0041B-906-071-9004 1 1 SAN BERNARDINO SAN BERNARDINO 24302 4TH ST., SAN BERNA 95 061 8322 7.7 7.4 0 6.3 5.9 006706-071-9004 1 1 SAN BERNARDINO SAN BERNARDINO 24302 4TH ST., SAN BERNA 96 061 8260 5.8 5.8 0 4.5 4.3 006706-071-9004 1 1 SAN BERNARDINO SAN BERNARDINO 24302 4TH ST., SAN BERNA 97 061 7312 7.6 7.0 0 5.9 5.4 006706-073-0001 1 1 CHULA VISTA SAN DIEGO 80 E. "J" ST., CHULA VIS 95 036 8473 5.4 5.0 0 3.8 3.3 006606-073-0001 1 1 CHULA VISTA SAN DIEGO 80 E. "J" ST., CHULA VIS 96 036 8273 5.7 5.5 0 3.4 3.4 006606-073-0001 1 1 CHULA VISTA SAN DIEGO 80 E. "J" ST., CHULA VIS 97 036 8297 5.4 5.3 0 3.8 2.8 006606-073-0003 1 2 EL CAJON SAN DIEGO 1155 REDWOOD AVE., EL CA 95 036 8283 6.1 5.4 0 3.4 3.4 006606-073-0003 1 2 EL CAJON SAN DIEGO 1155 REDWOOD AVE., EL CA 96 036 8273 6.3 5.9 0 4.0 3.9 006606-073-0003 1 2 EL CAJON SAN DIEGO 1155 REDWOOD AVE., EL CA 97 036 8173 5.6 5.5 0 4.3 3.9 006606-073-0005 1 2 OCEANSIDE SAN DIEGO 1701 MISSION AVE., OCEAN 95 036 8552 4.4 4.4 0 3.1 3.1 006606-073-0005 1 2 OCEANSIDE SAN DIEGO 1701 MISSION AVE., OCEAN 96 036 8592 4.0 3.6 0 2.6 2.5 006606-073-0005 1 2 OCEANSIDE SAN DIEGO 1701 MISSION AVE., OCEAN 97 036 8599 6.1 6.0 0 2.9 2.7 006606-073-0006 1 2 SAN DIEGO SAN DIEGO 5555 OVERLAND AVE., SAN 95 036 8356 4.8 4.5 0 3.5 3.5 006606-073-0006 1 2 SAN DIEGO SAN DIEGO 5555 OVERLAND AVE., SAN 96 036 7829 4.6 4.5 0 3.3 3.1 006606-073-0006 1 2 SAN DIEGO SAN DIEGO 5555 OVERLAND AVE., SAN 97 036 8531 5.8 5.0 0 3.0 2.8 006606-073-0007 1 1 SAN DIEGO SAN DIEGO 1133 UNION ST., SAN DIEG 95 036 7880 8.5 8.2 0 5.5 5.0 006606-073-0007 1 1 SAN DIEGO SAN DIEGO 1133 UNION ST., SAN DIEG 96 036 8685 10.0 9.7 0 6.3 6.0 006606-073-0007 1 1 SAN DIEGO SAN DIEGO 1133 UNION ST., SAN DIEG 97 036 8714 7.7 7.0 0 5.3 4.7 006606-073-1002 1 2 ESCONDIDO SAN DIEGO 600 E. VALLEY PKWY., ESC 95 036 8460 9.9 9.9 0 6.0 5.5 008806-073-1002 1 2 ESCONDIDO SAN DIEGO 600 E. VALLEY PKWY., ESC 96 036 8590 11.2 11.1 0 7.1 6.0 008806-073-1002 1 2 ESCONDIDO SAN DIEGO 600 E. VALLEY PKWY., ESC 97 036 8572 9.3 9.3 0 4.9 4.7 008806-073-1007 1 2 SAN DIEGO SAN DIEGO 330A 12TH AVE., SAN DIEG 95 036 8565 8.0 7.5 0 5.9 5.4 006606-073-1007 1 2 SAN DIEGO SAN DIEGO 330A 12TH AVE., SAN DIEG 96 036 8320 7.9 7.7 0 5.4 4.6 006606-073-1007 1 2 SAN DIEGO SAN DIEGO 330A 12TH AVE., SAN DIEG 97 036 8205 7.5 6.9 0 5.4 4.9 006606-073-2007 1 2 OTAY MESA SAN DIEGO 1100 PASEO INTERNATIONAL 95 036 8292 8.5 7.7 0 6.3 4.5 006606-073-2007 1 2 OTAY MESA SAN DIEGO 1100 PASEO INTERNATIONAL 96 036 8278 12.4 9.8 0 5.8 4.8 006606-073-2007 1 2 OTAY MESA SAN DIEGO 1100 PASEO INTERNATIONAL 97 036 8263 7.7 7.4 0 4.6 4.2 006606-075-0003 1 1 SAN FRANCISCO SAN FRANCISCO 939 ELLIS ST., SAN FRANC 95 004 8658 8.5 7.9 0 5.3 5.0 005406-075-0003 1 1 SAN FRANCISCO SAN FRANCISCO 939 ELLIS ST., SAN FRANC 96 004 8699 8.6 7.6 0 5.6 5.1 005406-075-0003 1 1 SAN FRANCISCO SAN FRANCISCO 939 ELLIS ST., SAN FRANC 97 004 8670 8.0 7.4 0 5.7 3.9 005406-075-0005 1 2 SAN FRANCISCO SAN FRANCISCO 10 ARKANSAS ST., SAN FRA 95 004 8640 5.3 5.2 0 4.4 3.2 005406-075-0005 1 2 SAN FRANCISCO SAN FRANCISCO 10 ARKANSAS ST., SAN FRA 96 004 8503 5.4 5.3 0 3.8 3.7 00541DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) PAGE6AMP450 AIR QUALITY SUBSYSTEMQUICK LOOK REPORT0 CARBON MONOXIDE (42101) CALIFORNIA UNITS: 007 PPM0 PO M REP MAX 1-HR OBS> MAX 8-HR OBS>SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST 2ND 9METH__________________________________________________________________________________________________________________________________006-075-0005 1 2 SAN FRANCISCO SAN FRANCISCO 10 ARKANSAS ST., SAN FRA 97 004 8334 4.8 4.6 0 3.5 3.1 005406-077-0008 1 3 STOCKTON SAN JOAQUIN 4310 CLAREMONT, STOCKTON 95 001 8078 8.7 8.5 0 6.2 5.2 006706-077-0008 1 3 STOCKTON SAN JOAQUIN 4310 CLAREMONT, STOCKTON 96 001 7905 11.0 11.0 0 7.6 6.7 006706-077-0008 1 3 STOCKTON SAN JOAQUIN 4310 CLAREMONT, STOCKTON 97 001 7642 6.3 6.2 0 4.2 3.9 006706-077-1002 1 2 STOCKTON SAN JOAQUIN HAZELTON-HD, STOCKTON 95 001 8228 10.3 9.6 0 4.5 4.4 005406-077-1002 1 2 STOCKTON SAN JOAQUIN HAZELTON-HD, STOCKTON 96 001 8288 9.4 8.7 0 6.4 5.3 005406-077-1002 1 2 STOCKTON SAN JOAQUIN HAZELTON-HD, STOCKTON 97 001 8046 7.7 6.1 0 3.6 3.4 005406-079-2002 1 2 SAN LUIS OBISPO SAN LUIS OBISPO 1160 MARSH ST., SAN LUIS 95 001 8363 5.7 5.7 0 3.1 2.4 006706-079-2002 1 2 SAN LUIS OBISPO SAN LUIS OBISPO 1160 MARSH ST., SAN LUIS 96 001 8385 5.0 4.9 0 2.9 2.3 006706-079-2002 1 2 SAN LUIS OBISPO SAN LUIS OBISPO 1160 MARSH ST., SAN LUIS 97 001 8362 6.4 6.3 0 2.6 2.3 006706-081-1001 1 2 REDWOOD CITY SAN MATEO 897 BARRON AVE., REDWOOD 95 004 8677 10.1 9.3 0 3.9 3.5 005406-081-1001 1 2 REDWOOD CITY SAN MATEO 897 BARRON AVE., REDWOOD 96 004 8485 8.6 7.5 0 3.6 3.4 005406-081-1001 1 2 REDWOOD CITY SAN MATEO 897 BARRON AVE., REDWOOD 97 004 8343 10.7 7.1 0 4.2 3.8 005406-083-0010 1 2 SANTA BARBARA SANTA BARBARA 3 W. CARRILLO ST., SANTA 95 001 8154 7.8 7.7 0 5.8 4.9 006706-083-0010 1 2 SANTA BARBARA SANTA BARBARA 3 W. CARRILLO ST., SANTA 96 001 8320 12.6 8.0 0 4.9 4.5 006706-083-0010 1 2 SANTA BARBARA SANTA BARBARA 3 W. CARRILLO ST., SANTA 97 001 8240 8.2 8.1 0 4.1 3.8 006706-083-1025 1 4 CAPITAN SANTA BARBARA LFC #1-LAS FLORES CANYON 95 909 7823 1.0 1.0 0 1.0 .9 0054B-1006-083-1025 1 4 CAPITAN SANTA BARBARA LFC #1-LAS FLORES CANYON 96 909 7893 1.3 1.2 0 .9 .8 005406-083-1025 1 4 CAPITAN SANTA BARBARA LFC #1-LAS FLORES CANYON 97 909 8164 1.2 .9 0 .7 .7 005406-083-2004 1 2 LOMPOC SANTA BARBARA 128 S 'H' ST, LOMPOC 95 017 8296 3.7 3.4 0 1.7 1.4 005106-083-2004 1 2 LOMPOC SANTA BARBARA 128 S 'H' ST, LOMPOC 96 017 8240 3.6 3.4 0 1.9 1.5 000006-083-2004 1 2 LOMPOC SANTA BARBARA 128 S 'H' ST, LOMPOC 97 017 8201 3.6 3.4 0 2.0 1.5 005406-083-2011 1 2 GOLETA SANTA BARBARA 380 N FAIRVIEW AVENUE, G 95 017 8269 4.0 3.7 0 1.8 1.7 004106-083-2011 1 2 GOLETA SANTA BARBARA 380 N FAIRVIEW AVENUE, G 96 017 7612 3.4 3.0 0 1.6 1.5 004106-083-2011 1 2 GOLETA SANTA BARBARA 380 N FAIRVIEW AVENUE, G 97 017 8275 3.5 3.5 0 2.0 1.6 004106-083-4003 1 4 VANDENBERG AFB SANTA BARBARA STS POWER PLANT, VANDENB 95 046 4804 2.4 1.6 0 1.2 1.2 006706-083-4003 1 4 VANDENBERG AFB SANTA BARBARA STS POWER PLANT, VANDENB 96 046 8072 1.4 .9 0 .7 .6 006706-083-4003 1 4 VANDENBERG AFB SANTA BARBARA STS POWER PLANT, VANDENB 97 046 7916 1.1 1.0 0 .5 .5 006706-083-5001 1 4 VANDENBERG AFB SANTA BARBARA WATT RD, VANDENBERG AFB 95 046 5234 2.2 2.1 0 1.9 1.4 005106-083-5001 1 4 VANDENBERG AFB SANTA BARBARA WATT RD, VANDENBERG AFB 96 046 8202 .7 .7 0 .6 .6 005106-083-5001 1 4 VANDENBERG AFB SANTA BARBARA WATT RD, VANDENBERG AFB 97 046 1344 .8 .7 0 .7 .5 005106-085-0002 1 2 GILROY SANTA CLARA 9TH & PRINCEVILLE, GILRO 95 004 1753 3.6 3.5 0 2.0 1.9 005406-085-0004 1 1 SAN JOSE SANTA CLARA 120B N 4TH ST, SAN JOSE 95 004 8339 8.9 8.3 0 5.8 5.8 005406-085-0004 1 1 SAN JOSE SANTA CLARA 120B N 4TH ST, SAN JOSE 96 004 8366 8.8 8.6 0 7.0 5.8 005406-085-0004 1 1 SAN JOSE SANTA CLARA 120B N 4TH ST, SAN JOSE 97 004 8316 9.9 8.5 0 6.1 5.6 005406-085-0004 2 2 SAN JOSE SANTA CLARA 120B N 4TH ST, SAN JOSE 95 004 8154 8.6 8.0 0 5.4 5.4 005406-085-0004 2 2 SAN JOSE SANTA CLARA 120B N 4TH ST, SAN JOSE 96 004 8277 8.4 8.3 0 6.5 5.5 005406-085-0004 2 2 SAN JOSE SANTA CLARA 120B N 4TH ST, SAN JOSE 97 004 8309 9.1 8.1 0 5.8 5.1 005406-085-2004 1 2 SAN JOSE SANTA CLARA 1866 W SAN CARLOS ST, SA 95 004 2520 7.3 6.4 0 3.9 3.7 005406-087-0003 1 3 DAVENPORT SANTA CRUZ FIRE DEPT, DAVENPORT 95 011 7744 1.4 1.1 0 .9 .8 001106-087-0003 1 3 DAVENPORT SANTA CRUZ FIRE DEPT, DAVENPORT 96 011 8355 3.0 3.0 0 .9 .7 005106-087-0003 1 3 DAVENPORT SANTA CRUZ FIRE DEPT, DAVENPORT 97 011 8329 .9 .8 0 .7 .7 005106-095-0004 1 2 VALLEJO SOLANO 304 TUOLUMNE ST., VALLEJ 95 004 8491 7.0 6.8 0 5.3 5.1 005406-095-0004 1 2 VALLEJO SOLANO 304 TUOLUMNE ST., VALLEJ 96 004 8435 6.4 6.2 0 4.9 4.5 00541DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) PAGE7AMP450 AIR QUALITY SUBSYSTEMQUICK LOOK REPORT0 CARBON MONOXIDE (42101) CALIFORNIA UNITS: 007 PPM0 PO M REP MAX 1-HR OBS> MAX 8-HR OBS>SITE ID C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST 2ND 9METH__________________________________________________________________________________________________________________________________006-095-0004 1 2 VALLEJO SOLANO 304 TUOLUMNE ST., VALLEJ 97 004 8329 6.5 6.3 0 4.9 4.9 005406-097-0003 1 2 SANTA ROSA SONOMA 837 5TH ST., SANTA ROSA 95 004 8700 4.9 4.2 0 2.8 2.4 005406-097-0003 1 2 SANTA ROSA SONOMA 837 5TH ST., SANTA ROSA 96 004 8201 5.6 4.4 0 3.0 3.0 005406-097-0003 1 2 SANTA ROSA SONOMA 837 5TH ST., SANTA ROSA 97 004 8264 5.4 5.0 0 3.3 3.1 005406-099-0005 1 2 MODESTO STANISLAUS 814 14TH ST., MODESTO 95 001 8308 11.4 9.5 0 5.7 5.4 006706-099-0005 1 2 MODESTO STANISLAUS 814 14TH ST., MODESTO 96 001 8311 9.2 9.0 0 6.5 5.6 006706-099-0005 1 2 MODESTO STANISLAUS 814 14TH ST., MODESTO 97 001 8316 7.1 7.1 0 5.0 4.2 006706-099-0006 1 2 TURLOCK STANISLAUS 900 S MINARET STREET, T 95 069 8243 4.1 4.0 0 3.4 3.0 001106-099-0006 1 2 TURLOCK STANISLAUS 900 S MINARET STREET, T 96 069 8301 5.1 5.0 0 3.2 3.0 005406-099-0006 1 2 TURLOCK STANISLAUS 900 S MINARET STREET, T 97 069 7607 5.2 4.9 0 3.9 3.2 005406-101-0003 1 2 YUBA CITY SUTTER 773 ALMOND ST, YUBA CITY 95 001 8207 7.5 6.9 0 4.7 4.1 006706-101-0003 1 2 YUBA CITY SUTTER 773 ALMOND ST, YUBA CITY 96 001 8325 7.7 6.9 0 4.7 4.1 006706-101-0003 1 2 YUBA CITY SUTTER 773 ALMOND ST, YUBA CITY 97 001 8205 6.1 6.0 0 4.1 3.9 006706-107-2002 1 2 VISALIA TULARE 310 N CHURCH ST, VISALIA 95 001 8002 9.3 9.2 0 4.4 4.2 005406-107-2002 1 2 VISALIA TULARE 310 N CHURCH ST, VISALIA 96 001 8210 5.3 5.1 0 4.0 3.9 005406-107-2002 1 2 VISALIA TULARE 310 N CHURCH ST, VISALIA 97 001 8294 7.3 6.8 0 4.1 3.5 005406-109-0005 1 2 SONORA TUOLUMNE 251 S BARRETTA, SONORA, 95 001 8229 3.9 3.9 0 3.4 2.6 006706-109-0005 1 2 SONORA TUOLUMNE 251 S BARRETTA, SONORA, 96 001 8337 4.5 4.1 0 2.6 2.5 006706-109-0005 1 2 SONORA TUOLUMNE 251 S BARRETTA, SONORA, 97 001 8318 6.6 3.2 0 1.9 1.9 006706-111-2002 1 2 SIMI VALLEY VENTURA 5400 COCHRAN STREET, SIM 95 019 7766 8.9 7.5 0 4.3 3.9 006706-111-2002 1 2 SIMI VALLEY VENTURA 5400 COCHRAN STREET, SIM 96 019 8005 7.8 6.7 0 3.4 3.3 006706-111-2002 1 2 SIMI VALLEY VENTURA 5400 COCHRAN STREET, SIM 97 019 8114 7.4 7.1 0 3.8 3.2 0000B-1106-111-3001 1 2 EL RIO VENTURA RIO MESA SCHOOL, EL RIO 95 019 7741 2.9 2.8 0 2.4 2.4 005106-111-3001 1 2 EL RIO VENTURA RIO MESA SCHOOL, EL RIO 96 019 8235 2.2 2.0 0 1.5 1.4 005106-111-3001 1 2 EL RIO VENTURA RIO MESA SCHOOL, EL RIO 97 019 8065 2.6 2.2 0 1.9 1.5 006706-113-0004 1 3 DAVIS YOLO UC DAVIS-CAMPUS, DAVIS 96 001 4908 2.4 2.2 0 1.8 1.3 006706-113-0004 1 3 DAVIS YOLO UC DAVIS-CAMPUS, DAVIS 97 001 8285 2.8 2.7 0 1.8 1.5 006706-113-0006 1 2 DAVIS YOLO 23 RUSSEL BLVD, DAVIS 95 001 1416 5.3 5.3 0 3.1 2.9 00111DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) PAGE1AMP450 AIR QUALITY SUBSYSTEMQUICK LOOK REPORT0 CARBON MONOXIDE (42101)-METHODS: CODE COLLECTION METHOD ANALYSIS METHOD==== ============================== ===================================0 000 MULTIPLE METHODS MULTIPLE METHODS008 INSTRUMENTAL NON DISPERSIVE INFRA-RED011 INSTRUMENTAL NONDISPERSIVE INFRA-RED041 INSTRUMENTAL NON DISPERSIVE INFRA-RED051 INSTRUMENTAL NON DISPERSIVE INFRA-RED054 INSTRUMENTAL NON DISPERSIVE INFRA-RED066 INSTRUMENTAL NON DISPERSIVE INFRA-RED067 INSTRUMENTAL NON DISPERSIVE INFRA-RED088 INSTRUMENTAL NON-DISPERSIVE INFARED PHOTOMETRY093 INSTRUMENTAL GAS FILTER COORELATION CO ANALYZER1DATE 99/03/09 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) PAGE1AMP217P AIR QUALITY SUBSYSTEMAQ CLEANUP PROCESSING SUMMARY SHEET-- REPORTRECORD USER RECORD/REPORT SELECT/REQUESTB-12Appendix C Distributions and Equations Used in the Ventilation Rate Algorithm

C-1Each table in Appendix C is specific to parameter and gender (e.g., NVO 2max values for males). The tables which list distribut ions 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 distribut ions SD: arithmetic standard deviation GM: geometric mean of lognormal distribution GSD: geometric standard deviati on 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 es timating 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 (e stimate of “a” in Equation 5-10) Interc: intercept of regression equation (estimate of “b” in Equation 5-10) SE: standard error of regre ssion residuals (estimate of σ e 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-2Table C-1. Explanation of Codes List ed 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 VO 2max -to-weight ratio that are specific to age and gender. The mean NVO 2max 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 pr esented by Astrand (1960) for females 20 to 29 years.

C-3NVO 2max - Males

Males (last revised 6-11-98) NVO 2max 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-4NVO 2max 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-5NVO 2max - Females

Females (last revised 6-11-98) NVO 2max 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-6NVO 2max 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-7Body 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-8Body 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-9Body 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-10Body 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-11Body mass distribution, kg Age Source Distr GM GSD Lower Upper Assumptions 100 4 LN 64.7 1.220 43.8 95.5

C-12ECF - 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-13ECF 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-14ECF Age Source Distr Lower Upper Assumptions 100 5 Uniform 0.20 0.21

C-15ECF - 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-16ECF 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-17ECF Age Source Distr Lower Upper Assumptions 100 5 Uniform 0.20 0.21

C-18RMR - 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-19Regression 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-20Regression 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-21RMR - 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-22Regression 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-23Regression 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-24Appendix D Equations for Converting Weight to Height Proposed for the 1998 Version of pNEM/CO

D-1Memorandum
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 t he Probabilistic NAAQS Exposure Model for Carbon Monoxide (pNEM/CO). These m odifications will include enhancements to the algorithm which provides a “physiologi cal profile” for each person simulated by pNEM/CO. The enhanced algorithm will use pr obabilistic techniques to generate values for all physiological parameters required by two other pNEM/CO algorithms: (1) the algorithm that estimates ventilation (resp iratory) rate and (2) the algorithm that estimates carboxyhemoglobin (COHb) level. This memorandum provides equations which can be incorporated into the physiologi cal 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 (V b ) as a function of weight and height by the following equations
Men: V b = (20.4)(W) + (0.00683)(H 3 ) - 30 (1)
Women: V b = (14.6)(W) + (0.00678)(H 3 ) - 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 = A 0 + (A 1 )(height) + (z)(S.E.) (3)
where the values for A 0 , A 1 , and S.E. were obtained from Table 14 of Johnson et al.

D-2(1992) according to the gender and age group of t he 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 lognorma l distributions of weight for all combinations of gender and age where age is giv en in one-year increments from zero to 100. For these reasons, we would prefer t hat 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 probabilisti c 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 lognorma l distributions proposed for weight were obtained from an article by Burmaster and Cr ouch (1997). In an article published five years earlier, Brainard and Burmaster (1992) provide a method for sampling weight and height simultaneously from a bivariate di stribution based on data obtained from the second National Health and Nutrition Exam ination Survey (NHANES II). Although Brainard and Burmaster do not explicitly pr ovide a method for estimating height given weight, statistical results presented in the ar ticle indirectly suggest that height could be determined from weight using a r egression-derived equation of the form height = a 0 + (a 1 )[ln(weight)] + e (4) in which e is a normally-distributed random term with mean = zero and standard deviation = σ e . Ideally, the values of a 0, a 1, and σ e 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 male s (18 to 74 years) associated with each of 210 combinations of height (inches) and wei ght (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 categor ies 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 weight ed 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-3The R 2 value was 0.1469.
To estimate σ e , the standard deviation of e, we used the equation

σ e = [ Σ (O ij - E ij ) 2 (M ij )/(total males)] 0.5 (6)
in which O ij was the observed (listed) height associated with cell ij , E ij was the estimated height for cell ij based on the regression equation, M ij was the number of males listed in Table 1 for cell ij , and total males was the total number of males in all cells. Equation 6 produced the estimate σ e = 2.38 inches. To test the regression results, we perform ed a sample calculation using Equation 5 and σ e = 2.38 inches. For weight = 175 lb, we obtained height = 68.88 ± 2.38 inches. Based on this estimate, we would expect T able 1 to show a mode (most frequent value) for 175 lbs near 68.88 inches, with approximat ely 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 lb s. 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 fe males 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 year s) associated with each of 270 combinations of height and weight. Brainard and Burmast er 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 categorie s). 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 t he 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 t he 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 R 2 value for females was low (0.0477) relative to the value obtained above for males (0.1469), but was still statistically significant.
To estimate σ e , the standard deviation of e, we used the equation

σ e = [ Σ (O ij - E ij ) 2 (F ij )/(total females)] 0.5 (8)
in which O ij was the observed (listed) height associated with cell ij , E ij was the estimated height for cell ij based on the regression equation, F ij was the number of females listed in Table 2 for cell ij , and total females is the total number of females in all cells. Equation 8 yielded an estimate of σ e = 2.48 inches for females, a value consistent with the value of

D-42.38 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 anal yses described above were performed using weighting factors (i.e., values of M ij and F ij ) 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 σ e are larger than the estimates we would have obtained from an analysis in which the values of M ij and F ij were determined for individual weights rather than for weight ranges. Consequently, the values of σ e presented above should be considered conservative in the sense that they indi cate 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 dat a 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 equat ions in the literature. To facilitate ongoing model testing, Jim Capel has inco rporated 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. Tabl e 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-5References

Brainard, J., and D. E. Burmaster. 1992. “Biv ariate 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, Resear ch Triangle Park, North Carolina.

D-6Table 1. Number of Men 18-74 Years of Age by Weightand Height, 1976 - 1980 (Number of Persons in Thousands)a.Height,inchescWeight, lbsb< 110 110-119120--129130-139140-149150-159160-169170-179180-189190-199200-209210-219220-229$230 Truetotal< 62 41 70 100 42 110 38 69 24 8 19 10 11 54262 38 34 94 102 196 73 35 33 48 15 66863 66 65 195 197 286 136 113 98 33 29 3 122164 33 110 237 381 376 413 181 231 106 62 7 8 30 217565 53 191 177 578 806 820 556 363 269 161 154 30 30 25 421366 50 131 457 555 843 910 986 547 515 252 105 58 43 83 553567 12 102 324 780 1087 1237 1174 1181 801 429 319 135 154 245 798068 29 77 319 743 1127 1351 1625 1328 1152 686 390 284 250 205 956669 7 11 322 488 960 1169 1547 1436 1286 747 750 390 155 310 957870 4 37 104 455 900 1041 1450 1313 1334 710 479 441 252 347 886771 32 22 242 453 911 818 1103 868 692 481 377 217 500 671672 9 19 67 217 392 716 831 765 696 436 216 251 404 501973 20 41 228 356 322 483 370 306 190 203 226 274574 7 42 76 73 203 270 243 .191 156 84 119 1464$75 47 24 245 168 121 173 58 121 306 1263Truetotal333 869 2377 4692 7402 8842 9723 9258 8106 5217 3806 2356 1801 2770 67,552aSource: Table I of Brainard and Burmaster (1992).bHeight without shoes.cWeight with clothes, estimatedas ranging from 0.20 - 0.62 lb.dNumber of cells scaled up to reflect sizie ofpopulation; only 9983 men actually examined.eShaded cells contain data used in statistical analysis described in text.D-7D-8Table 2. Number of Women 18-74 Years of Age byWeight and Height, 1976 - 1980 (Number of Persons inThousands)b.Height,inbWeight, lbsc< 90 90-99 100-109110-119120--129130-139140-149150-159160-169170-179180-189190-199200-209210-219$230 Truetotal< 55 7 8 11 25 7 7 158055 13 4 26 12 31 13 8 10756 31 57 12 41 55 25 44 25 6 29657 44 91 107 90 55 115 76 26 24 9 18 4 36 69558 93 164 132 338 317 147 78 120 35 68 27 14 34 3 42 161259 50 196 262 552 342 365 297 201 123 116 69 46 30 31 268060 86 267 538 621 722 775 451 334 261 239 128 99 54 30 40 464561 12 368 754 1286 1355 1089 877 807 439 308 269 240 123 110 164 820162 14 258 938 1660 1899 1306 1117 728 583 448 305 227 130 117 218 994863 32 165 843 1729 1776 1600 1565 1006 817 655 477 357 277 151 283 11,73364 30 531 1168 1653 1936 1475 950 741 513 404 274 117 198 280 10,27065 64 283 873 1582 2162 1183 1201 693 396 455 269 156 109 516 994266 10 76 705 804 1365 902 696 509 255 193 213 116 84 253 618167 32 188 514 740 605 336 338 381 275 155 106 67 253 399068 10 85 213 488 369 336 193 41 99 95 82 14 106 213169 33 98 135 266 125 214 119 43 28 93 115470 6 38 56 52 19 46 25 3 245$71 16 16 55 42 30 28 15 4 51 257Truetotal362 1677 4572 9363 11,420 12,328 9435 7023 5047 3621 2753 2081 1232 887 236674,167aSource: Table II of Brainard and Burmaster (1992).bHeight without shoes.cWeight with clothes, estimatedas ranging from 0.20 - 0.62 lb.D-9dNumber of cells scaled up to reflect sizie ofpopulation; only 10,339 women actually examined.eShaded cells contain data used in statistical analysis described in text.D-10Table 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 “physiologica l profile” program has determined age and gender of simulated person and selected a value for weight in kilograms (wgt_kg) from the body mass distri bution specified for the age and gender. 2. Convert wgt_kg to wgt_lb by equati on: 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, conver t hgt_inches to hgt_cm by equation: hgt_cm = (2.54)(hgt_inches).

D-11Table 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 VO 2max Ventilation rate Gender Age

SUBRATIO: VER at 65% of VO 2max Ventilation rate Gender Age MAXRATIO
VO 2max Ventilation rate NVO 2max Weight (body mass)
NVO 2max Ventilation rate Gender Age

D-12Appendix E Algorithm for Estimating Carboxyhemoglobin Levels

E-1COHB 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 (O 2 Hb) 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, theref ore, 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 O 2 Hb level iteratively based on the assumption of a linear relationship between COHB and O 2 Hb. 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 O 2 Hb. Muller and Barton (1987)
E-2demonstrated that assuming a linear relationship between COHb and O 2 Hb 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 O 2 Hb 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 timecompared 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 V L B HO

A CO 

###    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 (O 2 ) and CO compete for the available HB, [COHb] and [O 2 Hb] must be related. Increasing [COHb] will result in decreasing [O 2 Hb]. 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 [O 2 Hb] have been used (See Marcus, 1980; McCartney, 1990; Muller and Barton, 1987; a nd 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 O 2 in O 2 Hb by CO: (Eq. 3) CO + O 2 Hb = O 2 + COHb


### Table 1. Definitions Of CFK Model Variables

E-3Variable 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 [O 2 Hb] Concentration of oxyhemoglobin (O 2 Hb) in blood at time t, ml O 2 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] + [O 2 Hb] %[COHb] [COHb] expressed as percent of [RHb] 0 %[O 2 Hb] [O 2 Hb] expressed as percent of [RHb] 0 %[COHb] 0 %[COHb] at t = 0 %[COHB]

4 %[COHb] at t = 4
P I co Pressure of inspired CO in air saturated with water vapor at body temperature, torr
Mean pulmonary capillary CO pressure, torr 

### P C CO Mean pulmonary capillary O2 pressure, torr 

### P C O 2 Barometric pressure, torr 

### P

B Vapor pressure of water at body temperature, torr (47 torr) 

### P

H O

2
Alveolar ventilation rate, ml/min STPD 

###  V

A
Endogenous CO production rate, ml/min STPD 

###  V

CO 

### Table 1. Definitions Of CFK Model Variables (Continued)

E-4Variable Definition
Pulmonary CO diffusion rate, ml/min/torr STPD 

### D L CO
M Haldane coefficient k Equilibrium constant for reaction O 2 + RHb = O 2 Hb

V b Blood volume, ml Hb Total hemoglobin in blood, g/100ml %MetHb Methemoglobin as weight percent of Hb

E-5The 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) O 2 + RHb = O 2 Hb 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 O 2 Hb 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) [O 2 Hb] + [COHb] + [RHb] = [THb] o Eliminating [RHb] between (7) and (8) and solving for [O 2 Hb] 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 [O 2 Hb] 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 k RHb MBV o 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 0 100 0 % % % ln % % % %

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 a nd 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, demogr aphic 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-8The 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 fo r 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 V E 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-9pNEM/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 O 2 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-10Table 2. Parameters Included in Physiological Profile for Adults in Version 2.0 of pNEM/CO.ParameterAlgorithm(s)ContainingParameterOther ParametersRequired forCalculatingParameterMethod Used to Estimate Parameter ValueAge COHbVentilation rateDemographic group Randomly selected from population-weighted distribution specific to demographicgroupGender COHbVentilation rateDemographic group Randomly selected from population-weighted distribution specific to demographicgroupWeight (body mass) COHbVentilation rateGenderAgeRandomly selected from population-weighteddistribution specific to age and genderbased on Brainard and Burmaster (1992).Height COHb WeightGenderEstimated by the following equations:males: height = 34.43 inches + (6.67)[ln(weight)] + (2.38 inches)(z)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. Units:height (inches), weight (lbs).The estimation equations are based on the results of a statistical analysis by Johnson(1998) of height and weight data provided by Brainard and Burmaster (1992).Menstrual phase COHb GenderAgeIf gender = female, menstrual phase wasrandomly assigned in alternating 14-daycycles according to the following age-specific probabilities.Age < 12 or >50: 100% premenstrualAge 12 through 50: 50% premenstrual, 50% postmenstrual.E-11ParameterAlgorithm(s)ContainingParameterOther ParametersRequired forCalculatingParameterMethod Used to Estimate Parameter ValueBlood volume COHb GenderWeightHeightBlood volume (Vb) was determined according to gender by the following equationswhich are based on work by Allen et al. (1956) which was modified to accept theunits used for height and weight.Men: Vb= (20.4)(weight) + (0.00683)(H3) - 30Women: Vb= (14.6)(weight) + (0.00678)(H3) - 30Units: blood volume (ml), weight (lbs), height (inches).Hemoglobin content ofthe blood, HbCOHb GenderAgeRandomly selected from normal distribution with arithmetic mean (AM) andarithmetic standard deviation (ASD) determined by gender and agebased on dataobtained from the 1976-1980 NHANES study (USDHHS, 1982) as follows.Males, 18 - 44: AM = 15.3, ASD = 1.0Males, 45 - 64: AM = 15.1, ASD = 1.2Males, 65+: AM = 14.8, ASD = 1.4Females, 18 - 44: AM = 13.3, ASD = 1.1Females, 45 - 64: AM = 13.6, ASD = 1.2Females, 65+: AM = 13.7, ASD = 1.2Units: grams of Hb per deciliter of bloodPulmonary CO diffusionrate,DLCOCOHb GenderHeightAgePulmonary CO diffusion rate (DL) was determined according to gender, height, andage according to the following equationsobtained from a paper by Salorinne (1976)and modified to conform to the units used in the COHb module.Males: = (0.361)(height) - (0.232)(age) + 16.3 ml/min/torrDLCOFemales: = (0.556)(height) - (0.115)(age) - 5.97 ml/min/torrDLCOUnits: (ml/min/torr), height (inches), age (years).DLCOE-12ParameterAlgorithm(s)ContainingParameterOther ParametersRequired forCalculatingParameterMethod Used to Estimate Parameter ValueEndogenous COproduction rateCOHb GenderAgeMenstrual phaseEndogenous CO production rate was randomly selected from a lognormaldistribution with geometric mean (GM) and geometric standard deviation (GSD)determined according to the following equations specific to age, gender, andmenstrual phase.Males, 18 - 64: GM = 0.473, GSD = 1.316Males, 65+: GM = 0.473, GSD = 1.316Females, 18 - 64, premenstrual: GM = 0.497, GSD = 1.459Females, 18 - 64, postmenstrual: GM = 0.311, GSD = 1.459Females, 65+: GM = 0.497, GSD = 1.459Units: GM (ml/hr), GSD (dimensionless).Resting metabolic rate(RMR)Ventilation rate GenderAgeWeight (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.NVO2maxVentilation rate GenderAgeSee Section 5.4 of the main body of this report.VO2maxVentilation rate NVO2maxWeight (body mass)See Section 5.4 of the main body of this report.E-13Figure 1. Flow Diagram for Physiological Profile Generator. Input Data Supplied at Start of the pNEM/CO Computation Demographic Group

Age Gender Weight

Height

Menstrual Phase

Endogenous CO Production Rate

Blood Volume

Pulmonary CO Diffusion Rate

Maximum Normalized Oxygen Uptake Rate (NVO 2max )

Maximum Oxygen Uptake Rate (VO 2max ) Resting Metabolic Rate (RMR) Total Hemoglobin Content of the Blood

Energy Conversion Factor (ECF)

E-14Barometric 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

O 2 

### 0 209 47 49

  .

The constant 0.209 is the mole fraction of O 2 in dry air. The constant 47 is the vapor pressure of water at body temperature. This expression was us ed 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 O 2 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 breathi ng air which is free of CO and a value of 100 torr for . 

### P C O 2 Total Reduced Hemoglobin in the Absence of O 2 and CO The quantity [THb] 0 is expressed as equivalent milliliters of O 2 or CO at STPD per milliliter of blood. Total Hb blood levels are customarily expressed as grams per deciliter of
E-15blood. The total Hb level in the absence of COHb and O 2 Hb would consist principally of RHb which can react with O 2 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 O 2 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 inch es + (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 modifiedto conform to the units used in the COHb module. The Salorinne data were obtained for nonexercising individuals. Tikuisis et al. (1992), wo rking 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 l ung 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 th ere 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 pr oduction 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 distribu tions 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-17The 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-19V. 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. “Inter actions 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 Protecti on 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-20Johnson, T. 1998. “Memo No. 5: Equations for C onverting 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. “Effect s 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: E ffect 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 Exercisi ng Humans Exposed to Carbon Monoxide.” J. Appli. Physiol. Vol. 72, No. 4. U.S. Department of Health and Human Se rvices. 1982. “Hematological and Nutritional Biochemistry Reference Data for Persons 6 M onths-74 Years of Age: United States, 1976-80.” DHHS Publication No. (PHS)83-1682. U.S. Environmental Protection Agency. 1978. “A ltitude as a Factor in Air Pollution.” EPA600/9-78-015, U.S. Environmental Protection Agency, Research Triangle Park, NC.

E-21U. S. Environmental Protection Agency. 1990. “Air Quality Criteria for Carbon Monoxide.” EPA-600/8-90-045A, U.S. Environmental Protec tion 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-22Table 3. Literature Data Used to Derive Geometric Mean and Standard Deviation Lognormal Distribution of Endogenous Co Production Rate

ENDOGENOUS CO PRODUCTION RATE FOR MEN
V co (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-23ENDOGENOUS CO PRODUCTION RATE FOR MEN
V co (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-24ENDOGENOUS CO PRODUCTION RATE FOR MEN
V co (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-25ENDOGENOUS CO PRODUCTION RATE FOR MEN
V co (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-26E-27Appendix F Sensitive Population Estimates for Use in Carbon Monoxide Exposure Estimates

F-1August 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-2Attachment 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-3Appendix 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 Ai r 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 (ope n 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-2pNEM/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 environmentwith 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 dir ected 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

C in = Indoor concentration (units: mass/volume)

F B = Fraction of outdoor concentration intercepted by the enclosure (dimensionless fraction)

F d = Pollutant decay coefficient (1/time)

ν = Air exchange rate (1/time)

C out = Outdoor concentration (mass/volume)

G-3S = 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 F B , F d , 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:

(3) 

### C t kC t t kC t t k

in in out



### () ( ) ( )   

  12 3 

###  

where
(4) 

### k e t

1 

###    

(5) 

### k e t

2 

### 1

    

(6) 

### kS e V t 3 

### 1



###   ( )/  

### 



and Δ t is a fixed time interval. Based on this relationship, the average indoor pollutant concentration of hour h [ C in (h) ] can be calculated by the expression

G-4C in (h) = a 1 C in (h - 1) + a 2 C out (h) + a 3 (7)

where C in (h - 1) is the indoor concentration at the end of the preceding hour and C out (h) is the average outdoor concentration during hour h. The other variables appearing in Equation 7 are defined by the following equations:
(8) 

### a zh

1 

###  ( ),

(9) 

### a zh

2 

### 1

  ( ),
(10) 

### a S V z h 3 

### 1

   [ ( )]

(11) 

### zh e

( ) ( )/    

### 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 C out 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 Vehi cle 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-5vehicles 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 2.5 th and 97.5 th 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 vehicl e 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-6Equation 7 provides an estimate of the average indoor pollutant concentration of hour h [ C in (h) ] given values for the indoor concentration at the end of the preceding hour [C in (h - 1)] and the average outdoor concentration during hour h [ C out (h) ]. In pNEM/CO, we assume that the source term S is equal to zero. Under these circumstances, the a 3 term in Equation 7 is equal to zero and the equation can be expressed as

C in (h) = a 1 C in (h - 1) + a 2 C out (h)

. (12)

Consider the situation where the instantaneous concentration inside the vehicle at the end of the previous hour is zero [i.e., a 1 C in (h - 1) = 0]. Equation 12 can now be expressed as

C in (h) = a 2 C out (h)

. (13)

Remember that C in (h) is the average concentration inside the vehicle during hour h and that C out (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

C in (h)/C out (h) = a 2 = 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 isdriven 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-7Air 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 clos ed, 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 Explor er with vent closed, low fan speed.

Speed AER (hr -1 ) C in /C out 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-8This 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-9being 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-nor mal 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) = σ 2 = 0.27323
c) Simulate Z from a standard normal distribution
d) AER = exp( μ + σ Z)

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 (percenta ge 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-10malfunctioned 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 drivi ng 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 pa rallel 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-11Finally, 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 st arts 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) + β O × speed × I(open) + β C × 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 , β O , and β C 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, β O = 0.01798, and β C = 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-12The 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 nationa l 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-13Petersen, 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. Clayt on, 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-14Table 1. Air Exchange Rate Data.

Test conditions Air Exchange Rate (hr -1 )

Ventilation settings Vehicle speed, mph a 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-15Table 2. Ratios of Hourly Average Values of Inside Concentration to Outside Concentrations.

Air exchange rate, hr -1 Ratio a = C in (h)/C out (h) Error in assuming C in (h) = C out (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 C in (h) is the average concentration inside the vehicle during hour h, and C out (h) is the average concentration outside the vehicle during hour h. Ratios based on assumptions that instantaneous value of C in at beginning of hour h equals zero, air exch ange rate is constant during hour h, and C out is constant during hour h.

G-16Table 3. Estimated Percentage of Time With Indicated Vent Ratios by Air Conditioning System And Temperature Range

Air conditioning system Temperature range a Percentage of time with indicated vent ratio b 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 repor t. Equates residences with vehicles.
a Low: 31 E to 62 E F.

Medium: 63 E to 75 E F. High: 76 E F and above. b Ratio = (minutes vent open)/(minutes spent in vehicle).

G-17Table 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-180 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 YearPercent equipped with A/C

LDV's LDT's Figure 2 - Proposed Base Market Penetration Estimates Figure 1. From Koupal (1998).

G-190.0% 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 A ge (Y ears )Rate of Unrepaired Malfunctions

R -134a Freon (1995 or later scenario year) Figure 3 - Proposed Rate of Unrepaired Malfunctions Figure 2. From Koupal(1998).

G-20G-21G-22G-23Appendix H Analyses Supporting Proposed Modifications to the Mass Balance Algorithm and Gas Stove Algorithm in pNEM/CO

H-1Memorandum

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 t he Probabilistic NAAQS Exposure Model for Carbon Monoxide (pNEM/CO). In earlier work performed under EPA Contract No. 68- D3-0094, TRJ Environmental, Inc., (TRJ) assi sted IT Corporation in developing a set of recommendations for modifying two com ponents of pNEM/CO: the mass balance algorithm and the gas stove algorithm. T he 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. Environment al 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 es timates 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 incorpor ate the seasonal variation in gas stove burner use into the model. This sect ion presents a method which meets these requirements. A report by the Gas Research Institute (G RI) indicates that the arithmetic mean of burner gas use for the NIGAS survey was 21.8 therms = 2180 ft 3 per year (n = 57) with an arithmetic standard deviati on of 8.9 therms = 890 ft 3 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-2GM = AM/[(ASD/AM) 2 + 1] 0.5 (1) GSD = exp{ln[(ASD/AM) 2 + 1]} 0.5 (2) in which GM = geometric mean, GSD = geom etric standard deviation, AM = arithmetic mean, and ASD = arithmetic standard deviation. Making the appropriate substitutions, one obtains GM = 2018 ft 3 per year and GSD = 1.48 (dimensionless). The GM must be converted to kilojoules fo r 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 D enver is 1048 kilojoules per cubic feet. Applying this conversion factor, we obtain 2018 ft 3 per year x 1048 kilojoules/ft 3 = 2,114,864 kilojoules per year = 2.11 million kilojoules per year. GEOMET provides the following equation for estimating daily gas use by burners (ft 3 /day) when the date is converted to number of weeks since May 1: y(x) = 6.37 - (1.21)[sin(0.45 + 2 π x/52)] (3)

in which

x = number of weeks since May 1 y = mean gas stove consumption for week x, excluding pilot lights (ft 3 /day). The following normalized version of this equation produces a mean value of 1.0: Y’(x) = 1.00 - (0.190)[sin(0.45 + 2 π x/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 + 2 π m/365)]. (5) There are 17 weeks (120 days) from J anuary 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 + (2 π )(j - 120)/365]} or (6) Y’(j) = 1.00 - (0.190){sin[-1.616 + (2 π )(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 calc ulating ERBURN, perform the following steps:

1. Go to the next day of the current sequence. The Julian date of this day is j.

H-32. 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 m ean = 2.11 million kilojoules per year and geometric standard deviation = 1.48. 3. Y’(j) = 1.00 - (0.190){sin[-1.616 + (2 π )(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 proj ect team to base all estimates of fuel use by pilot lights on the NIGAS study di scussed 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 arit hmetic mean of burner gas use for the NIGAS survey was 21.8 therms (n = 57) wit h an arithmetic standar d 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 (18.3 2 - 8.9 2 = 255.7) provides an estimate of the variance of fuel usage by pilot lights. The square root of the esti mated 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 ft 3 and an arithmetic standard deviation of 16.0 therms = 1600 ft 3

. The ratio of standard deviation to mean is 16.0/35.3 = 0.45, suggesting that the underlying distribution is skewed. If we assu me 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 ft 3 per year and GSD = 1.84 (dimensionless). Applying the ft 3 -to-kilojoule conversion factor cited above, we calcul ate the geometric mean to be 3215 ft 3 per year x 1048 kilojoules/ft 3 = 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 di rected the project team to use the data from Reference 8 of the Law letter to devel op 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 Analysi s, Vol. 15, No. 4, 1995) which lists distributions of AER by season and c limatic 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-4Season 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 r egression-based equation could be used to predict the geometric mean values with n > 20 in Table 1 with an R 2 value of 0.851 (adjusted R 2 = 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 estima te 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 R 2 value of 0.857 (adjusted R 2 = 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-5Similar analyses of the geometric standard dev iation (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. Cons equently, 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 followi ng 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 dire cted 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 m easured 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 val ues 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 distri butions 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 fo r 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 2.5 th and 97.5 th percentiles of the specified logno rmal 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 t he 1992 pNEM/CO model, we will determine the distribution for living area (i.e., square foot age) and assume a constant 8-foot ceiling

H-6height. Table 3 lists frequency data from the CBHS for Denver and Los Angeles (another possible study area for pNEM/CO analyse s). 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, m 3 : 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 m 3 and a standard deviation of 1.62. The new geometric mean (436 m 3 ) 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 distribut ions proposed in this memorandum. The lower and upper bounds for values to be selected from each distribution are initially set at the 2.5 th and 97.5 th percentiles of the distribution. These bounds may be changed after further analysis of the applicable data bas es. 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-7Table 1. Air Exchange Rates for Residences, Windows Closed (Murray and Burmaster, 1995)

Region States Season a 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 5 b (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 2 b (1.261) na
4 23 b (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 18 b (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-8Table 2. Data for Air Exchange Rates Measured During Scripted House Study When One or More Windows Were Open (Johnson, Weaver,and Mozier, 1997)StarttimeOfficeDoor AOfficeWindow COfficeDoor DBedroomDoor EBedroomWindow FGreat RoomWindow GGreat RoomWindow IHeat DraftconditionsaAER,hr-16 am Closed OPEN Open Open Closed Closed Closed Off no 1.619 am Closed OPEN Open Open Closed Closed OPEN Off yes 2.9210 am Closed OPEN CLOSED Open Closed Closed OPEN Off no 0.8011 am Closed OPEN Open Open Closed Closed Closed Off no 2.136 pm Closed OPEN CLOSED Open Closed Closed Closed Off no 0.967 pm Closed OPEN Open Open Closed Closed Closed Off no 1.228 pm Closed OPEN Open Open Closed Closed OPEN Off yes 1.359 pm Closed OPEN CLOSED Open Closed Closed Closed Off no 0.784 am Closed OPEN CLOSED Open Closed Closed Closed ON no 1.37Variedduringtest?No Yes Yes No No No Yes Yes Yes Seenote ba“Yes” indicates that air had unobstructed flow path from open Office Window C to open Great Window I.bOpen window AER statistics: geometric mean = 1.336 hr-1, geometric standard deviation = 1.550.H-9Table 3. Statistics on Square F ootage 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
Total a 419.1 -- 1232.1 --

a Omits 38,500 units which did not report square footage values.

H-10Table 4. Summary of Distributions Proposed in this Memorandum.

Quantity City Distribution Parameter a 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
GSD b 1.48
Lower bound 0.98 million c Upper bound 4.55 million d Annual fuel use by gas stove pilot light, kJ/yr Denver Lognormal GM 3.37 million
GSD b 1.84
Lower bound 1.02 million c Upper bound 11.13 million d Air exchange rate -- windows closed (Season 1), h -1 Denver Lognormal GM 0.450
GSD b 1.960
Lower bound 0.120 c Upper bound 1.683 d Air exchange rate -- windows closed (Season 2), h -1 Denver Lognormal GM 0.308
GSD b 2.241
Lower bound 0.063 c Upper bound 1.498 d Air exchange rate -- windows closed (Season 3), h -1 Denver Lognormal GM 0.653
GSD b 2.010
Lower bound 0.166 c Upper bound 2.566 d Air exchange rate -- windows closed (Season 4), h -1 Denver Lognormal GM 0.309
GSD b 1.716
Lower bound 0.107 c Upper bound 0.890 d Air exchange rate -- windows open, h -1 Any Lognormal GM 1.34
GSD b 1.55
Lower bound 0.57 c Upper bound 3.16 d

H-11Quantity City Distribution Parameter a Value Residential volumes, m 3 Denver Lognormal GM 436
GSD b 1.62
Lower bound 169 c Upper bound 1122 d Residential volumes, m 3 Los Angeles Lognormal GM 363
GSD b 1.64
Lower bound 138 c Upper bound 957 d

a GM = geometric mean, GSD = geometric standard deviation. b GSD is a dimensionless quantity.

c Lower bound = 2.5 th percentile of proposed distribution. d Upper bound = 97.5 th percentile of proposed distribution.

H-12Appendix I

Distributions for Use with the M ass Balance and Gas Stove Algorithms in Applying pNEM/CO to Los Angeles

I-1Memorandum

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 Vers ion 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 tw o 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 estima tes of fuel use by gas stove burners on data obtained from a survey performed by Nort hern Illinois Gas Company (NIGAS). This survey included 752 gas appliances in 354 single-family residences. Harvey Richmond also requested that analysts in corporate the seasonal variat ion in gas stove burner use into the model. This section presents a me thod 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 t he Gas Research Institute (GRI) indicates that the arithmetic mean of burner gas use for the NIGAS survey was 21.8 therms = 2180 ft 3 per year (n = 57) with an arithmetic standard deviation of 8.9 therms = 890 ft 3 per year (unadjusted for age of occupants). The ratio of standard deviation to mean is 0.41. This value suggests that the underlyi ng 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-2in which GM = geometric mean, GSD = geom etric standard deviation, AM = arithmetic mean, and ASD = arithmetic standard deviation. Making the appropriate substitutions, one obtains GM = 2018 ft 3 per year and GSD = 1.48 (dimensionless). The GM must be converted to kilojoules fo r 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 ft 3 per year x 1048 kilojoules/ft 3 = 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 bet ween the population sampled by NIGAS and the Los Angeles study area population. The resu lting estimate for the GM is 1.73 million kilojoules. The GSD is unchanged. Wilkes and Koontz (1995) provides the follo wing equation for estimating daily gas use by burners (ft 3 /day) when the date is converted to number of weeks since May 1: y(x) = 6.37 - (1.21)[sin(0.45 + 2 π x/52)] (3)

in which

x = number of weeks since May 1 y = mean gas stove consumption for week x, excluding pilot lights (ft 3 /day). The following normalized version of this equation produces a mean value of 1.0: Y’(x) = 1.00 - (0.190)[sin(0.45 + 2 π x/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 + 2 π m/365)]. (5) There are 17 weeks (120 days) from J anuary 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 + (2 π )(j - 120)/365]} or (6) Y’(j) = 1.00 - (0.190){sin[-1.616 + (2 π )(j)/365]}. (7)
As before, EFBURN is defined as the CO emission factor for stove burners for a

I-3particular hour. To use Equation 7 in calc ulating 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 m ean = 1.73 million kilojoules per year and geometric standard deviation = 1.48. 7. Y’(j) = 1.00 - (0.190){sin[-1.616 + (2 π )(j)/365]}. 8. ERBURN = (EFBURN)(AUB)(Y’)/365.2 for all hours of day. The distribution of EFBURN values previous ly 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 proj ect team to base all estimates of fuel use by pilot lights on the NIGAS study discuss ed above. The report by Menkedick et al. (1993) indicates that the NIGAS study m easured total gas usage (burners plus pilot lights) for 33 stoves and reported an arit hmetic mean of 57.1 therms and a standard deviation of 18.3 therms. As discuss ed 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 (18.3 2 - 8.9 2 = 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 distri bution of pilot light usage has an arithmetic mean of 35.3 therms = 3530 ft 3 and an arithmetic standard deviation of 16.0 therms = 1600 ft 3

. The ratio of standard deviation to mean is 16.0/35.3 = 0.45, suggesting that the underlying distribution is skewed. If we assu me 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 ft 3 per year and GSD = 1.84 (dimensionless). Applying the ft 3 -to-kilojoule conversion factor cited above, we calcul ate the geometric mean to be 3215 ft 3 per year x 1048 kilojoules/ft 3 = 3,369,320 kilojoules per year = 3.37 million kilojoules per year. If we apply the adjustment factor for Los A ngeles (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 applicat ion of the adjustment factor to the NIGAS pilot light data assumes that pilot light us e is proportionally lower in Los Angeles, an assumption which cannot be currently verified.

I-4Development of Los Angeles Adjustment Factor (0.82) Pat Stiefer has obtained gas stove data specif ic 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 t he 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 t herms representing a reasonable overall estimate. Note that these values specify to tal gas use by the stove; the CEC did not provide separate estimates for burners and pilot lights. As indicated above, the NIGAS study repor ted 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 samp led 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 adjus tment 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 Housi ng 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 t he census tracts included in the study area defined for Los Angeles pNEM/CO analysis. T he remaining census tracts are located just outside Los Angeles County in Orange C ounty 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 repres ented 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 lis ted in Table 1 for LADWP (78.6 percent) and BDG (78.4 percent). Taken together, the th ree estimates support an estimate of about 79 percent for the gas stove prevalence rate.

I-5Air Exchange Rates for Residences with Windows Closed In applying pNEM/CO to Los Angeles, we requi re distributions for air exchange rates in residences with windows closed. In our prev ious work applying pNEM/CO to Denver, we based the distributions on a journal arti cle 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. Para meters 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 be tter estimates for Season 4, we conducted a series of statistical analyses. We f ound that the following regression-based equation could be used to predict the geometric mean values with n > 20 in Table 3 with an R 2 value of 0.857 (adjusted R 2 = 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 va lue listed in Table 3 for Season 2 (0.619), the season with the most similar temperature c onditions (i.e., Season 4 = Fall, Season 2 = Spring). Consequently, I suggest that we use th is estimate (0.607) for Season 4 rather than the listed value (0.416). Similar analyses of the geometric standard dev iation (GSD) values found that season

I-6and 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 th ree 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 fo llowing seasonal distributions be used to represent the AER values of Los A ngeles 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 al so 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 fr om the draft “scripted house” report by Johnson, Weaver, and Mozier (1997). As r eported in the September 23 memorandum, analysis of the data suggested that we coul d 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 lo wer and upper bounds of 0.57 and 3.15. I recommend we use this distribution in t he 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) repr esenting 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 prov ided 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-7Assuming 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, m 3 : 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 pN EM/CO to Denver. Consistent with this approach, I recommend that we use a lognormal distribution with GM = 363 m 3 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 attac hed map shows the CO monitors which define home districts in Los Angeles and selected el evations. 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 distri butions 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 2.5 th and 97.5 th 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 va lues 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 m onoxide concentration and upper bounds of indexing intervals

I-8Table 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 previ ously 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 repor t) 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, Repor t 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 Housi ng 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 Housi ng 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 Housi ng Reports, Report No. H170/95-7. U.S. Department of Commerce. California Energy Commission. 1998. “R esidential Demand Forecast Model: 1998 Baseline Energy Outlook.” Demand Analysis Offi ce, California Energy Commission.
Johnson, T., M. Weaver, J. Mozier, et al. 1998. “Variation of Residential Air Exchange Rates Under Scripted Ventilation Conditi ons.” 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. “Analyse s Supporting Proposed Modifications to the

I-9Mass Balance Algorithm and Gas Stove Al gorithm 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 Ba telle 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 Para metric Distributions by Season and Climatic Region.” Risk Analysis , Vol. 15, No. 4. U.S. Department of Commerce, Bureau of t he 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 t he 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 t he 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. “Anal ysis of Sampling Strategies for Estimating Annual Average Indoor NO 2 Concentrations in Residences with Gas Stoves.” Journal of the Air and Waste Management Association , Vol. 46, pp. 853 - 860.

I-10Table 1. Gas Stove Data Provided by Demand Analysis Office of the California Energy Commission.Utility district Approximategeographic areaPercentcooking w/gasNumber ofgas stovesTherms used by allstoves (millions)Therms perstoveaPacific Gas and Electric(PG&E)northwest of LosAngeles33.0 1,399,887 67.8 48.4Sacramento MunicipalUtility Division (SMUD)Sacramento 30.5 130,319 6.2 47.6Southern CaliforniaEdison (SCE)South coast outsidedowntown LosAngeles68.6 2,772,522 131 47.3Los Angeles Departmentof Water and Power(LADWP)Los Angeles 78.6 982,891 48.4 49.2San Diego Gas andElectric (SDG&E)San Diego 46.5 486,537 20.5 42.1Combined MunicipalDistricts of Burbank,Glendale, and Pasadena(BDG)Burbank, Glendale,Pasadena78.4 131,111 5.9 45.0aCalculated by Ted Johnson.I-11Table 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 fuel d 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 PM SA (Includes all of Riverside and San Bernardino Counties). d Prevalence rate = 100 x (total HU w/natural gas cooking fuel)/(total housing units).

I-12Table 3. Air Exchange Rates for Residences, Windows Closed (Murray and Burmaster, 1995)

Region States Season a 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 5 b (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 2 b (1.261) na
4 23 b (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 18 b (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-13Table 4. Statistics on Square F ootage 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
Total a 419.1 -- 1232.1 --

a Omits 38,500 units which did not report square footage values.

I-14Table 5. Summary of Parameter Estimates Proposed for Los Angeles in this Memorandum.

Quantity Distribution Parameter a 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
GSD b 1.48
Lower bound 0.80 million c Upper bound 3.73 million d Annual fuel use by gas stove pilot light, kJ/yr Lognormal GM 2.76 million
GSD b 1.84
Lower bound 0.84 million c Upper bound 9.12 million d Gas stove prevalence rate Point estimate Point estimate 79 percent Air exchange rate -- windows closed (Season 1), h -1 Lognormal GM 0.507
GSD b 1.910
Lower bound 0.143 c Upper bound 1.802 d Air exchange rate -- windows closed (Season 2), h -1 Lognormal GM 0.619
GSD b 1.950
Lower bound 0.167 c Upper bound 2.292 d Air exchange rate -- windows closed (Season 3), h -1 Lognormal GM 1.054
GSD b 2.489
Lower bound 0.176 c Upper bound 6.296 d Air exchange rate -- windows closed (Season 4), h -1 Lognormal GM 0.607
GSD b 2.034
Lower bound 0.151 c Upper bound 2.441 d

I-15Quantity Distribution Parameter a Value Air exchange rate -- windows open, h -1 Lognormal GM 1.34
GSD b 1.55
Lower bound 0.57 c Upper bound 3.16 d Residential volumes, m 3 Lognormal GM 363
GSD b 1.64
Lower bound 138 c Upper bound 957 d 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 = 2.5 th percentile of proposed distribution. d Upper bound = 97.5 th percentile of proposed distribution.

I-16Appendix 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 coefficien ts 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 95 th 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-2levels. 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 analys es 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 adju st 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 que stions 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 th eir 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-3the 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-4using 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.

 95 th 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-5The 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) a nd 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 statisti cal 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. Thesignificance 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 percen t level of significance. By the central limit theorem, the p-values for the t test comparisons should be reasonably accurate for the largesamples 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 appliedtwo 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 diffe rent median than the non-angina population. The

J-6Kolmogorov-Smirnov test was used to evaluate any possible differences between the two distributions, whether due to differences in mean s, 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 explaine d by the greater propensity for females to get angina, rather than the direct effect of angi na. 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-7tried 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 approximate ly 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 95 th 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 th eir 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-8non-angina subjects. In most cases the F tests s how 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, justsignificant 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 f unctions 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 differe nt, so are the two distribution functions. The discrepancy between the F and Kolmogorov-Smirnov tests is partly explained by the fact that theF 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 Exert ion 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 95 th 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 surp rising, 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 inthe 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-9high 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 95 th 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 sta tistically 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-10For 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 Meta bolic 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-11J-12Table 1. Distribution of subjects according to their age, gender and disease statusGenderAge groupMalesAngina(%) Non-angina(%) AllFemalesAngina(%) Non-angina(%) AllAllAngina(%) Non-angina(%) All0-54 35 (1.0) 3307 (98.9) 3342 17 (0.5)3570 (95.5) 3587 52 (0.8) 6877 (99.2) 692955-64 28 (6.5) 400 (93.5) 428 28 (5.4 )491 (94.6) 519 56 (5.9) 891 (94.1) 94765-74 23 (7.9) 267 (92.1) 290 48 (9.6)450 (91.4) 498 71 (9.0) 717 (91.0) 78875+ 17 (10.7) 142 (89.3) 159 47 (14.4)279 (85.6) 326 64 (13.2) 421 (86.8) 485Total 103 (2.4) 4116 (97.6) 4219 140 (2.8)4790 (97.2) 4930 243 (2.6) 8906 (97.4) 9149J-13Table 2. Statistical Tests for the Association between Anginaand Various Variables Representing Physical Exertion. AllVariable T Test Comparison ofMeansMean Mean P-valueAngina Non-anginaF Test Comparison ofStandard DeviationsSt. Dev. St. Dev. P-valueAngina Non-anginaWilcoxonTestP-valueKolmogorovSmirnov TestP-valueAverage maximum 8hrexertion (Mcal)1.28 1.40 0.00 0.48 0.49 0.68 0.00 0.00Ninety fifth percentile ofmaximum 8hr exertion (Mcal)1.87 2.25 0.00 0.97 1.13 0.00 0.00 0.00Percentage of time spentoutdoors6.73 6.74 0.99 12.87 11.63 0.02 0.22 0.23Percentage of time spent invehicle4.55 5.55 0.01 6.19 7.13 0.00 0.00 0.00Percentage of time spentoutdoors or in vehicle11.27 12.29 0.27 14.33 13.45 0.15 0.00 0.00Average 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 0.00Average 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 0.00Average 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 0.00J-14Table 3. Statistical Tests for the Association between Angina and Various Variables Representing Physical Exertion.MalesVariable AgeGroupT Test Comparison ofMeansMean Mean P-valueAngina Non-anginaF Test Comparison ofStandard DeviationsSt. Dev. St. Dev. P-valueAngina Non-anginaWilcoxonTestP-valueKolmogorovSmirnov TestP-valueAverage 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 0.520.49 0.55 0.340.48 0.48 0.860.47 0.48 0.960.44 0.42 0.650.020.010.410.510.020.020.820.95Ninety 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 0.711.06 1.30 0.131.21 1.14 0.610.78 0.94 0.310.69 0.76 0.680.220.020.260.550.060.030.630.88Percentage of timespent outdoors0-5455-6465-7475+16.86 8.85 0.029.28 10.02 0.796.43 10.40 0.138.23 7.09 0.7219.16 13.75 0.0014.26 13.86 0.7811.38 14.34 0.2012.63 10.05 0.160.010.650.120.580.011.000.130.66Percentage of timespent in vehicle0-5455-6465-7475+5.96 6.09 0.923.99 6.87 0.007.20 5.88 0.512.29 3.34 0.147.55 8.10 0.633.78 9.63 0.009.04 7.80 0.292.54 3.94 0.050.890.180.820.440.520.190.930.54J-15Table 3. Statistical Tests for the Association between Angina and Various Variables Representing Physical Exertion.MalesVariable AgeGroupT Test Comparison ofMeansMean Mean P-valueAngina Non-anginaF Test Comparison ofStandard DeviationsSt. Dev. St. Dev. P-valueAngina Non-anginaWilcoxonTestP-valueKolmogorovSmirnov TestP-valuePercentage of timespent outdoors or invehicle0-5455-6465-7475+22.83 14.94 0.0213.27 16.89 0.2413.63 16.29 0.4510.51 10.43 0.9819.28 15.52 0.0515.30 16.18 0.7615.87 15.91 1.0012.86 10.39 0.190.020.170.190.690.020.220.290.41Average 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 0.4113.33 15.18 0.3414.51 12.03 0.1413.23 12.48 0.6411.13 10.37 0.630.020.060.670.390.050.140.840.74Average 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 0.536.37 4.31 0.003.55 4.41 0.172.47 3.46 0.062.42 2.41 0.920.020.010.200.430.010.010.400.59Average 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 0.790.792 0.986 0.111.068 1.222 0.400.361 0.716 0.000.331 0.512 0.050.550.050.060.550.150.170.040.96J-16Table 4. Statistical Tests for the Association between Angina and Various Variables Representing Physical Exertion.FemalesVariable AgeGroupT Test Comparison ofMeansMean Mean P-valueAngina Non-anginaF Test Comparison ofStandard DeviationsSt. Dev. St. Dev. P-valueAngina Non-anginaWilcoxonTestP-valueKolmogorovSmirnov TestP-valueAverage 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 0.630.31 0.38 0.340.32 0.33 1.000.30 0.31 0.940.33 0.30 0.340.720.560.310.440.730.220.440.66Ninety 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 0.590.82 0.91 0.690.80 0.77 0.680.53 0.57 0.570.56 0.52 0.430.990.330.310.430.860.280.620.47Percentage of timespent outdoors0-5455-6465-7475+3.64 5.11 0.424.31 4.59 0.882.84 4.14 0.143.79 2.27 0.407.29 9.58 0.209.53 8.22 0.235.51 7.34 0.0212.04 4.60 0.000.430.230.490.760.910.630.531.00Percentage of timespent in vehicle0-5455-6465-7475+4.54 5.35 0.554.21 5.60 0.355.26 4.15 0.232.82 2.94 0.865.48 5.98 0.727.53 7.23 0.715.94 6.18 0.774.37 4.34 0.910.400.060.150.720.350.120.190.96Percentage of time 0-54 8.18 10.46 0.36 9.93 11.27 0.57 0.18 0.27J-17Table 4. Statistical Tests for the Association between Angina and Various Variables Representing Physical Exertion.FemalesVariable AgeGroupT Test Comparison ofMeansMean Mean P-valueAngina Non-anginaF Test Comparison ofStandard DeviationsSt. Dev. St. Dev. P-valueAngina Non-anginaWilcoxonTestP-valueKolmogorovSmirnov TestP-valuespent outdoors or invehicle55-6465-7475+8.52 10.18 0.488.10 8.29 0.886.61 5.21 0.4512.15 10.42 0.228.21 9.38 0.2612.36 6.25 0.000.040.990.770.050.860.97Average 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 0.7312.28 12.15 0.8610.34 10.47 1.008.45 9.53 0.319.75 8.61 0.240.410.510.430.510.690.540.760.89Average 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 0.231.66 1.97 0.441.27 2.07 0.001.35 1.56 0.221.73 1.31 0.010.730.040.510.840.740.060.880.67Average 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 0.440.170 0.324 0.000.277 0.198 0.010.076 0.110 0.000.075 0.077 0.900.630.670.850.280.801.000.990.83J-18Table 5. General Linear Models for the Association between Angina and Various Variables Representing Physical Exertion.VariableAnginaCoefficient1StandardErrorP-value R squaredAverage maximum 8hr exertion (Mcal) -0.0445 0.0237 0.0608 0.4819Ninety fifth percentile of maximum 8hr exertion (Mcal) -0.1553 0.0581 0.0075 0.4114Percentage of time spent outdoors +0.6975 0.7648 0.3618 0.0388Percentage of time spent in vehicle -0.4805 0.4679 0.3045 0.0325Percentage of time spent outdoors or in vehicle +0.2170 0.8777 0.8047 0.0520Average percentage of time with exertion above 2.39kcal/min = 0.010 MJ/min (light)-0.7359 0.6996 0.2929 0.4239Average percentage of time with exertion above 5.97kcal/min = 0.025 MJ/min (moderate)-0.1730 0.1910 0.3650 0.3570Average percentage of time with exertion above 9.55kcal/min = 0.040 MJ/min (heavy)-0.0933 0.0439 0.0334 0.24941. The angina coefficient is the expected difference (angina minus non-angina) between the summary statistic for angina and nonangina subjects of the same age and gender.J-19xxx

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 statistica l methods to determine whether significant differences could be detected. To accomp lish 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 si te 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 in itiation values (or “seeds”) was specified for the random number generator used to make t he 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 vari ation 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 exam ple, 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 concentra tions. This matching format allows us to more easily assess the variation due to changi ng 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 concentra tions, and because the fixed site outdoor concentrations influence the resulting ex posure concentrations according to the
K-2modeling algorithms, for each attainment simulation every exposure measure will be less than or equal to the corresponding expos ure 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 impor tant point to keep in mind when interpreting some of the statistical tests discussed below.

RESULTS Tables 1a - 1d present the results of statis tical 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 wit h indoor sources included (Scenarios 1 and 3).
In each table, the second column shows t he 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 ran ks 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 dist ribution, but has a similar inte rpretation: values close to 1 indicate that the results for the as-is runs ar e 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 case s); 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 we re 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 substantia lly modify the relative magnitudes of the exposure measures from simulation to simu lation. This contrasts with tables 2a - 2d, where there is poor and even negative correlati on 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 a ttainment simulations cannot be higher than those for the as-is simulations. The paired t test compares the distribution of the arithmetic differences with zero, assu ming that the 10 differences are independently

K-3drawn from a normal distribution wit h an unknown, but constant variance 1 . The p-value is the probability of getting the mean differ ence 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-val ues 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 t hat the average difference between the two scenarios is large enough to be detected at t he five percent significance level with 10 simulations. A p-value above 0.05 indicates t hat 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 per cent 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 stat istically 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 re latively 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 co mparisons, 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 t hat the 10 differences are independently drawn from some common distribution, the W ilcoxon test addresses whether the median difference is zero , i.e. whether positiv e and negative differences are equally likely 2

. (The t test made the additional requirement t hat 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 norma l, then for many possible distributions, the Wilcoxon test will be more likely to detect an e ffect. 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-4the 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, indicati ng 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 det ecting a difference with 95% confidence, assuming that the true average difference and true standard deviation of the difference are both identical to our estimates deriv ed from the 10 matched simulations that we actually conducted. This calculation was carried using the same assumptions as for a paired t test 3 . Under the given assumptions, the pr obability of detecting a difference with 95 % confidence was computed using the non-c entral 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 ma y 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 simu lations. 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 va riation from 10 simulations. If the minimum sample size is much above 10, then 10 simula tions 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 95 th 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-5Table 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-6Table 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-7Table 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-8Table 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-9Table 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-10Table 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-11Table 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-12Table 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-13Table 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-14Table 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