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I PB96159439 '

11111111111111111111111111111

CALIFORNIA RESIDENTIAL INDOOR AIR QUALITY STUDY

Volume 3

Ancillary and Exploratory Analysis

Sponsored By

Gas Research Institute

Pacific Gas and Electric Company

San Diego Gas and Electric Company

Southern California Gas Company

October 1995

Integrated Environmental Services

4199 Campus Drive, Suite 280

Irvine, CA 92715

(714) 854-1167

'\I ,( REPRODUCED BY I U.S. DEPARTMENT OF COMMERCE

i NATIONAL TECHNICAL I INFORMATION SERVICE

" SPRINGFIE.LD.'--.:-V:....:...A2=2,-,-=16c-=--1 __-, I.

....-------------------~~.,'l;~-------"'T"'------------__.

REPORT DOCUMENTATION PAGE Form Approved

OMS No, 0704·0788

PubliC rePOrt1n9 burden for this colleC110n of Informauon l~ e1tlmated to average 1 hour PE'r r~pon\e. in(~udjn9 t"'e time for re\llewing i"'tructJon". ,earcning e.rUlng data \outee1.

gall'lerrnq .n~ malnu~nln9 the d,au needed. and (emplellng and re",ew1ng the collectton of Information, \end comments regarding this burden e,t,mate or any other al.peet of tnis

~~r-'-' -1'1'\'-1'-1'1-11"1' '1-1-1-\'1-"1'\'1"'1-1-,'1"'1-1-1-1''I-II-'I"II-"~'U~:?o ~~: ~~;~~nOfl~~::;~~~~~ .~~·~~d~;;~~. ~;'>:,'~:~ ~~~~,~~~:~;e'~ f(~rio:~O~~B~W:,lh~~~I~~~ ~~~~~C 3]2 IS J.tt."on

1.[ 12. REPORT DATE 13. REPORT TYPE AND OATES COVERED

, PB96-159439 /1 October 1995 Topical Report

4. TITLE AND SUBTITLE S. FUNDING NUMBERS

California Residential Indoor Air Quality Study. GRI Contract Number(s)

Volume 3: Ancillary and Exploratory Analysis. 5091-251-2347

6, AUTHOR(S)

Wilson, A.L.; Colome, Steven D.; Tian, Yi

7. PERfORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

Integrated Environmental Services, Irvine, CA

9, SPONSORlr~G I MONITORING AGENCY NAME(S) AND ADDRESS(ES)

Gas Research Institute

8600 West Bryn Mawr Avenue

Chicago, IL 60631

1'. SUPPLEMENTARY NOTES

12a. DISTRIBUTION / AVAILABILITY STATEMENT

8, PE RF ORMIN G ORGANIZATION

REPORT NUMOER

10. SPONSORING / MONITORING

AGENCY REPORT NUMBER

GRI-93j0224.4

12b, DISTRIBUTION CODE

13. ABSTRACT (Maximum 200 words)

This report is the third in a series that analyzes a multi-pollutant database

that was collected in the field monitoring program. The database was developed

from the measurements that can be used to gain insight into levels of pollutants in

the houses and determine relative contribution of gas appliances. The first report

(GRI-93j0224.1 and .2) presented the study methodology and provided descriptive

statistics of the pollutants monitored and of other selected parameters. Volume 2

(GRI-93j0224.3) focused on analyses of factors associated with carbon monoxide and

air exchange rate. This report, volume 3, emphasizes benzene, toluene, nitrogen

dioxide, and radon concentrations which were measured by passive samplers, and

further explores carbon monoxide distributions and certain inter-pollutant

relationships of interest to the gas industry. All reports are designed to be

self-contained; however, the reader is directed to other volumes for broader

understanding of the study. Summaries of the analyses are included in the

appendixes.

14. SUBJECT HRMS 15. NUMBER OF PAGES

106 P

16. PRICE CODE

17. SECURITY CLASSIFICATION 18, SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20, LIMITATION OF ABSTRACT

OF REPORT OF THIS PAGE OF ABSTRACT "

Unclassified

NSN 7540·01·280·5500 ,-j Standard Form 298 (Rev, 2·89)

.\

Report Prepared By:

A.L. Wilson

Steven D. Colome

Yi Tian

Integrated Environmental Services

4199 Campus Drive, Suite 280

Irvine, California 92715

Tel: 714-854-1167

Fax: 714-854-1840

_..

TABLE OF CONTENTS

Table ofContents : i

List ofTables ii

List ofFigures iv

Acknowledgment v

Preface vi

Section 1 Introduction 1-1

1.1 .Background 1-1

1.2 Goals and Objectives 1-2 I

Section 2 Methodology 2-1

2.1 Residence Selection 2-1

2.2 Field Instrumentation 2-2

2.3 Field Sampling Procedure 2-4

2.4 Data Management and Analysis Methodology 2-5

Section 3 Analyses ofPassive Monitor Data 3-1

3.1 Benzene and Toluene 3-6

3.2 Nitrogen Dioxide 3-28

3.3 Radon 3-42

Section 4 CO Distributions and Extrapolations 4-1

Section 5 Mass Balance Modeling for CO, AER and Home Volume 5-1

Section 6 Exploratory Analyses .. 6-1 (/

6.1 CO and Benzene 6-1

6.2 Smoking and Benzene 6-3

6.3 Smoking and CO 6-5

Section 7 Conclusions and Recommendations 7-1

Section 8 References 8-1

Appendix A - Benzene I Toluene Data Update

Appendix B - Ambient Temperature & Analysis Files

Ii

Table Number

LIST OF TABLES

Page No.

3-1 Univariate analysis results summary 3-2

3-2 Net indoor benzene stepwise regression results: all significant variables were

available for the regression except indoor benzene, outdoor benzene and CO

eIllission rates 3-16

3-3 Mean ofnet indoor benzene contributed by categorical variables in the

stepwise regression presented in Table 3-2 3-18

3-4 Stepwise regression ofnet indoor benzene: all the air pollutant

measurements were removed 3-19

3-5 Mean ofnet indoor benzene contributed by categorical variables in the

stepwise regression presented in Table 3-4 3-20

3-6 Stepwise regression ofnet indoor toluene: all significant variables were

available for the regression except indoor and outdoor toluene,

also PG005 was removed 3-23

3-7 Mean of net indoor toluene contributed by categorical variables

in the stepwise regression presented in Table 3-6 3-24

3-8 Stepwise regression of net indoor toluene: all the air pollutant

measurements and PG005 were removed 3-25

3-9 Mean ofnet indoor toluene contributed by categorical variables

in the stepwise regression presented in Table 3-8 3-26

3-10 Stepwise regression of net indoor N02(all significant variables except CO

eIllission rates, indoor and outdoor N02) 3-37

3-11 Mean ofnet indoor N02 contributed by categorical variables in the

stepwise regression presented in Table 3-10 3-38

3-12 Stepwise regression ofnet indoor N02(without air pollutant

measurements) 3-39

ii 4
3-13 Mean of net indoorN02 contt1buted by categorical variables in the

stepwise regression presented in Table 3-12 : 3-40

3-14 Stepwise regression ofindoor radon (wi~hout PG079) 3-46

3-15 Mean ofindoor radon contributed by categorical variables 41' the stepwise

regression presented in Table 3-14 3-47

4-1 Actual measured CO descriptive statistics for 277 homes

without missing data (ppm) , 4-3

4-2 Estimated statewide CO descriptive statistics for pO homes

without missing data (ppm) , , 4-4

6-1 Unpaired t-test results petween smoking and non~smoking homes

for indoor, outdoor and net indoor benzene , 6-3

6-2 Unpaired t-test results betweep smoking and non.,s~oking homes

for indoor, outdoor and net indoor CO 6-5

/ .'

iii , "
Figure Number

LIST OF FIGURES

Page No.

4-1 Comparison ofthe actual and adjusted statewide sample for

48-hour average indoor CO 4-5

4-2 Comparison ofindoor and outdoor 48-hour average CO for

the adjusted statewide sample (130 homes) 4-5

5-1 Maximum 8-hour indoor CO distribution by appliance type 5-2

5-2 Maximum 8-hour outdoor CO distribution by appliance type 5-5

5-3 Maximum 8-hour indoor minus outdoor CO distribution

by appliance type 5-7

5-4 Home volume distribution by appliance type 5-8

5-5 Air exchange rate distribution by appliance type 5-10

5-6 Maximum 8-hour source term distribution by appliance type 5-11

5-7 Log probability plots for two groups ofhomes:

electric cooking and heating; gas cooking and heating 5-13

6-1 Outdoor benzene vs. outdoor CO 6-2

6-2 Indoor benze~ vs. indoor CO 6-2

6-3 Indoor benzene vs. outdoor CO 6-2

6-4 Correlation ofindoor and outdoor benzene by smoking for all homes 6-4

6-5 Correlation ofindoor and outdoor 48-hour average CO by cigarette

smoking for all homes 6-6

iv

ACKNOWLEDGMENT

The success ofthis study required the joint efforts ofmany individuals. We particularly

acknowledge the Customer Service Representatives who were central to the field effort. The

Steering Committee, comprised ofrepresentatives from the sponsoring organizations, provided

the overall guidance and direction ofthe work. The Committee ofTechnical Advisors provided

useful peer review and offered many suggestions for data analysis and future research.

Edward Becker

Southern California Gas Company

David Behrens, Chairman

Pacific Gas & Electric Company

Dr. Irwin Billick

Gas Research Institute

Christy Garrison

San Diego Gas & Electric Company

Gerald Akland, Chairman

U.S. Environmental Protection Agency

Atmospheric Research and Exposure

Assessment Laboratory

Dr. Wayne Ott

Stanford University and

U.S. Environmental Protection Agency

Office ofResearch and Development

Dr. Joseph Behar

U.S. Environmental Protection Agency

Exposure Assessment Research Division

John Ginnan

U.S. Environmental Protection Agency

Indoor Air Division

Dr. John Spengler

Harvard School ofPublic Health

Jim Bryant, Ron Cook, Chuck Lynch: Pacific Gas & Electric Company

Dana Cotter, Randy Morthel, Dan Thomas: Southern California Gas Company

Don Goodman, Steven Swanson: San Diego Gas & Electric Company

v

PREFACE

This report concludes the three volume report series for the California Residential Indoor

Air Quality Study sponsored by the Gas Research Institute (GRI), the Pacific Gas and

Electric Company (pG&E), the. San Diego Gas and Electric Company (SDG&E), the

Southern California Gas Company (SoCalGas) and the U.S. Environmental Protection

Agency (U.S. EPA). Volume 1 presented the study methodology and descriptive statistics

ofthe measured components and other selected parameters. Volume 2 focused on

analyses offactors associated with carbon monoxide and air exchange rate using a

systematic statistical approach. This report (Volume 3) emphasizes benzene, toluene, N02

and radon concentrations which were measured by passive samplers. CO distributions and

certain inter-pollutant relationships ofinterest to the gas industry are further explored.

The statistical approach chosen to analyze benzene, toluene, N02 and radon involved

screening each dependent variable using univariate analysis techniques in order to identify

the significant independent variables (with p < 0.05). Those significant continuous and

categorical independent variables were then listed as candidates for t~e stepwise linear

regression. This approach identified which parameters were associated with the

dependent variables. Following this initial screening, a multivariate model was developed

for each dependent variable.

With all significant variables available for the stepwise regressions, explained variance for

net indoor benzene was 59%. For net indoor toluene, the explained variance was 72%,

for net indoor N02 it was 47% and for radon it was 43%. After removing all the air

pollutant measurements from the regression candidate list, the model R2 reduced to 0.26,

0.32 and 0.36 for net indoor benzene, toluene and N02, respectively.l It should be noted

that the primary concern ofthis study was carbon monoxide, therefore, the field forms and

questionnaires were designed to focus on combustion related variables. It is satisfying to

obtain a model R2 as high as 0.72 since the study was not specifically designed for

benzene, toluene or radon.

The three utilities had approximately 10.4 million residential customers at the time ofthe

participant recruitment. Ifthe homes sampled were gathered in proportion to the number

of customers in each utility, then, based upon the territory with the lowest sampling rate,

the total number of homes would have been 136 instead of293 (75 homes from

SoCalGas, 48 homes from PG&E, and 13 homes from SDG&E). CO distributions were

compared between the actual sample of293 homes and the adjusted sample of 136 homes.

The two distributions tracked each other, with the CO arithmetic mean and median ofthe

adjusted sample slightly higher than the actual sample.

I Model R2 represents the variability explained by the multivariate model.

vi
A mass balance model was developed to evaluate the relationship ofindoor maximum 8-

hour CO with outdoor CO, air exchange rate and home volume. A parameter

proportional to the indoor source rate was calculated for each home. At the median

source rate level, homes with gas cooking and heating appliances had higher indoor CO

source terms. However, above the 90th percentile, the electric appliance homes had

source terms as high as the gas appliance homes. This suggests that quite different

strategies/approaches should be taken to lower average population exposure to CO

compared to those strategies required to reduce the number of high CO exposures.

The analyses ofthe relationship between CO and benzene revealed that indoor benzene

concentrations were not correlated with cigarette smokin~, but CO data showed a

significant smoking effect. CO conceq,trations in homes with smokers were approximately

0.5 ppm higher than CO in homes of non-smokers. Outdoor CO and benzene correlation

was high (R2=O.84) indicating there are common sources for the two pollutants, such as

automobiles.

vii
Section 1

Introduction

This report is the third and final planned volume in this report series. The first volume and

its appendices were published in May 1993 (Wilson et al., 1993a). The second volume

and its appendices were published in July 1994 (Colome et al., 1994). The Volume 1

report presented the study methodology and provided descriptive statistics ofthe

monitored gases and of other selected parameters measured in this study. In the

separately bound Volume 1 Appendices, we provided all the forms and questionnaires that

were used by the field technicians to collect information. A codebook was also developed

for this study which links the data files with the field forms and questionnaires (Wilson et

al., 1993b). Volume 2 focused on analyses offactors associated with measurements of

carbon monoxide and air exchange rate~ A systematic statistical approach was utilized to

perform the univariate and multivariate analyses on 48-hour average indoor CO, 48-hour

average indoor minus outdoor CO and air exchange rate. A limited set of analyses were

also performed on maximum 8-hour indoor CO concentrations. The current volume

focuses on the results measured by the passive samplers (i.e., benzene, toluene, N02, and

radon), as well as certain interpollutant relationships that are ofinterest to the gas

industry. This report is designed to be self-contained; however, the reader is directed to

other volumes to gain a broader understanding ofthe study.

1.1 Background

During the last decade, there have been increasing concerns about the contribution of

pollutant sources in different microenvironrnents (e.g., indoors, outdoors, and in transit) to

the total population exposure. It is now generally accepted that, to properly assess the

impact of air pollution on the general public's health, not only ambient pollutant sources

but all sources of pollution in various microenvironrnents must be taken into account. The

EPA, in its most current criteria documents for both carbon monoxide (U.S. EPA, 1991a)

and nitrogen oxides (U.S. EPA, 1993), includes evaluation of exposure and health impacts

from indoor as well as outdoor exposure. The new California Clean Air Act also places an

emphasis on microenvironmental air pollution exposures and has resulted in the California

Air Resources Board issuing two indoor air quality guidelines (CARB, 1991 and 1994).

A number ofstudies have suggested that natural gas appliances are significant contributors

to indoor air pollution (Yocom, 1982). Indoor unvented combustion appliances, such as

gas-fired ranges and unvented gas space heaters, have been associated with various

combustion pollutants. The emissions oftrace amounts of pollutants into the home as the

result of natural gas usage may include carbon monoxide (CO), nitrogen oxides (N02,

NO, HONO, RN03), radon and organic compounds such as formaldehyde and benzene.

1-1
The natural gas industry has sponsored significant indoor air quality (IAQ) research at

both national and local levels in order to develop the infonnation needed for infonned

decision making. A series oflarge-scale field studies ofindoor air quality and personal

exposures have been conducted by the gas industry beginning in 1984 in Boston (Ryan et

al., 1988) and Los Angeles (Wilson et al., 1986). Most ofthese research projects have

emphasized emissions and exposures to N02, and the results of a number ofthese studies

have appeared in the scientific peer reviewed literature. During the next year or two,

additional reports as well as publications in the peer reviewed scientific literature will be

forthcoming. The gas industry IAQ program has provided scientifically defensible data on

N02 concentrations that has placed the contribution of gas appliances in proper

perspective. These studies were recognized as significant and important efforts in the

current version ofthe N02 Criteria Document.

As a continuous effort to extend the knowledge of natural gas comJ>ustion-related

pollutants and understand how they relate to outdoor concentrations, a pilot project was

initiated in 1991 by the Gas Research Institute, Pacific Gas & Electric, San Diego Gas &

Blectric and Southern California Gas Companies. A Steering Committee was fonned by

these funding companies to oversee the project. U.S. EPA participated by providing

samplers and laboratory analyses for radon and partial funding for benzene/toluene

laboratory analyses, as well as by providing technical advisors for: the program.

The pilot project required active participation ofstaffmembers from the gas companies,

public and private sector advisors, laboratories and contractors. An important feature of

this study is the paired sampling (indoor and outdoor) ofmultiple pollutants. The primary

objective ofthis project was to better understand CO distriqutions and their relationship

with housing characteristics, applianc;e types, geographic location, and occupant-use

factors. Carbon monoxide, benzene, toluene, fonnaldehyd~, and N02 were measured

indoors and outdoors, while radon, methane and air exchange rate were measured only

indoors.

1.2 Goals and Objectives

The goal ofthis program was to assess exposure to specific pollutants and provide a proactive response to concerns apout the impact of gas appliances on residential indoor air

quality. The pilot project was divided into two phases:

Phase 1 -

Phase 2-

Fiel~ monitoring, data collecting, and reporting on methodologies

and general descriptive statistics.

Data analyses.

1-2
The objectives ofthe initial phase were:

• Determine the association that average indoor pollutant concentrations have with

building characteristics, appliance use and outdoor concentration.

• Generate the frequency distribution' of residential pollutant concentrations in 300

homes during a California winter.

• Use the results ofthe first 300 pilot homes (phase I) to assist in providing

recommendations for the need, cost and protocols for further studies.

Phase 2 ofthis project (the subject ofVolumes 2 and 3) utilized statistical data analyses to

ascertain which parameters were useful predictors ofthe measured gases and air exchange

rates. The components we attempted to predict included indoor CO, net indoor CO, air

exchange, and all the pollutants measured by the passive sampling devices. Volume 2

r-' (Colome et al., 1994) analyzed air exchange rate and carbon monoxide concentrations.

rhis report, Volume 3, continues the data analysis by presenting exploratory analyses.

.._ The topics covered in this report are~summarized as follows:

.,>E>escribe the relationship between measured benzene, toluene, NO~~'and radon

I concent.rations with building characteristics, appliance factors, meteorological

conditions, other measured pollutants, geographical location~and sampling

parameters; .'

.'-~Extrapolate the measured CO distributions from the three service territories to the

, entire State ofCalifornia;

.-:' Develop a mass balance model to predict CO concentrations from air exchange

, rate, home volume and cookinglheating type; ;~ , '

• ~',Explore the relationship among CO concentrations, measured benzene levels, and

smoking behaviors. ; .-'-- -:~, .

The next section ofthis report provides a briefsummary ofthe field sampling methods and

a detailed description ofthe data analysis approach. Analyses for benzene, toluene, N02

and radon are presented in Section 3. Section 4 discusses the extrapolation ofthe CO

distributions from the three service territories to the entire State. Exploratory analyses

between CO and selected components are presented in Sections 5 and 6. Conclusions and

recommendations are summarized in Section 7. Finally, all the cited references are listed

in Section 8.

1-3

1--

Section 2

Methodology

In the methodology section ofthe Volume 1 report (Wilson et al., 1993), we described in

detail the residence selection method, field sampling and analysis methods, field

procedures, and data management methodology. In this section, we will briefly recap the

methodologies used in this study that are relevant to this volume.

2.1 Residence Selection

To produce data that may be generalized to the entire population ofCalifornia, it was

necessary to select the homes in a statistically representative manner. This required that

the sample size be large enough to produce concentration estimates with reasonable

precision and to identify the major factors associated with indoor pollutant concentrations.

A randomly selected sample size of approximately 100 homes from each ofthe three

participating utilities (300 homes total) was determined to be sufficient for reaching these

goals.

The utility customer lists maintained by each utility company were identified to be the best

sources for randomly selecting the participating homes. PG&E and SDG&E had in-house

mechanisms for generating random customer listings. Their billing/marketing departments

produced listings containing names, addresses and (when available) telephone numbers for

the entire service territory, with each residential customer having an equal probability of

inclusion in the sample. Since SoCalGas did not have a similar in-house mechanism, one

was developed. Based upon the number ofresidential customers in each of21 billing

cycles (which is how SoCalGas maintains its customer files), a random number generator

was used to identify specific customers for the sample. Residences where a single gas

meter supplied more than one housing unit received a weighting factor based upon the

total number ofunits serviced by the meter. The resulting listing gave each residential

housing unit in the SoCalGas service territory an equal probability ofinclusion in the final

recruitment sample.

An objective was established that measurements should be made in at least 60% ofthe

homes that were eligible for participation in the study (i.e., the target participation rate

was 60%). High participation rates allow the results ofthis survey to be generalized to

the service territories ofPG&E, SoCalGas and SDG&E. When the project was

completed, the final sample size was 129 homes from PG&E, 75 homes from SoCalGas,

and 89 homes from SDG&E. The overall participation rate was conservatively estimated

to be 59.7% (293/491). Utility-specific participation rates were conservatively estimated

to be 64.5% for PG&E, 63.1% for SDG&E and 50.0% for SoCalGas. These percentages

2-1
are conservative since we did not eliminate homes that were ineligible for inclusion or for

which no attempt was ever made to contact.

The pilot study demonstrated that random sampling from customer listings is not only

possible but offers an attractive sampling frame frQm which to conduct a survey. Utility

customer records are current, which ensures a high degree of eligibility. Population

coverage is very high since almost every household in California receives service from one

ofthe three participating utilities. This is in coI)trast to telephone surveys which would

miss nearly 10% of all households in California that do not receive phone service and may

miss up to 25% of households in certain socio-economic groups. The utilities also have a

high level ofrecognition from their customers which reflects on the very high participation

rates obtained in this pilot effort. Less intrusive surveys (e.g., telephone interviews for

opinions or product use) can attain participation rates from 60-80% in well conducted

surveys. Sampling surveys that involve residen~al monitoring rarely attain participation

rates over 40%. This study re~ulted in the highest participation rates that we are aware of

for a study that required such a high level of participation and cooperation in making

residential measurements.

Most ofthe State ofCalifornia and a majority of all households are customers of one of

the three participating gas utilities. There are a few pockets in the State that are not

supplied by one ofthese three utilities. For example, parts of northern California are

supplied by Portland General Electric~ the Long Beach area receives its gas from a

municipal utility and is therefore not jncluded in the SoCalGas service territory. In

Section 4 ofthis report, we extrapolate the CO distributions from the three service

territories to the entire State, excluding the missing "pockets".

2.2 Field Instrumentation

The Model 190 CO dataloggers manufactured by National Draeger, Inc. were used in this

study to measure indoor aIld outdoor CO concentrations (Draeger, 1990). The device

stores one-minute average CO concentrations. For this ~tudy, the memory capacity of

each Draeger datalogger was customized so that monitoring was eXtended to the

maximum time period (up to 99 hours) ofthe plemory chips. The maximum duration of

monitoring for our customized instruments was qetermined by the variation in CO

concentration: the more variable the CO concentration the sooner all memory registers

were filled in the datalogger. In most instanc~s the datalo~gers r~corded the full 48-hour

monitoring period. In sev~ral homes, ~owever, the vari~bi!ity in CO concentration caused

the memory to fill before the end ofthe 48-hoW" monitoring time. At the conclusion of

each monitoring event, the stored data were downloaded to an ffiM compatible personal

computer through an interface box supplied by Draeger.

2-2
As part ofthis project, a number ofperfonnance evaluation studies were conducted on the

Draeger Model 190 CO monitor. The monitors were found to be temperature sensitive

and responded positively to a variety of household products containing alcohol and other

compounds (Wilson et al., 1992). An insulated Styrofoam box was designed and

manufactured for the dataloggers to minimize the negative drift observed at low

temperatures. The sensor inlet on each Draeger unit is covered by a filter housing which

contains a material to eliminate ambient dust and certain interfering gases. A special filter

housing filled with Purafil@ and activated charcoal was designed and fabricated by IES to

eliminate the effect ofinterference gases. This filter was installed on each unit instead of

the filter housing provided by National Draeger.

Organic Vapor Air Monitoring Badges supplied by Pro-Tek systems, Inc. were used to

measure benzene and toluene concentrations (pro-Tek, 1991). The badge is a passive

diffusion device containing activated charcoal and is designed to sample over 175 organic

chemicals in air. Type G-AA badges were employed in this study. A portion ofthe

deployed badges were analyzed by AtmAA, Inc. immediately after exposure, while the

remaining badges were refrigerated for about one year before the funding for the analyses

was available. The updated benzene and toluene data (including the first and second

batch) are presented in Appendix A ofthis report. Although the R2 value for benzene

indoor vs. outdoor reduced from 0.54 for the first batch to 0.38 for the full dataset, it is

mainly because there were more benzene samples with higher indoor values in the second

batch than in the first batch. The R2 value for toluene indoor vs. outdoor reduced from

0.20 to 0.15 (with one outlier removed) for the same reason. In addition, reviewing the

. plot of co-located samples for benzene (or toluene), it is very difficult to say that the

precision ofthe second batch is worse than that ofthe first batch, since the dynamic range

ofthe second batch is not the same as that ofthe first batch. Therefore, there is no strong

evidence that refrigeration ofthe sample badges for up to one year had any significant

effect on the badge perfonnance.

The U.S. EPA Laboratory at Las Vegas participated in this study by providing and

analyzing radon charcoal canisters. The canisters were only placed indoors. The sampling

mechanism for the canister can be briefly described as follows: radon in the air is adsorbed

onto the activated charcoal. Since radon decays into lead-214 and bismuth-214, both of

which emit gamma rays, gamma counts were used to determine the radon levels (D. S.

EPA, 1990a, 1990b & 1991b).

Ogawa passive samplers (Ogawa, 1992) were used to measure the nitrogen oxides

concentrations in homes, both indoors and outdoors. The sampler has two chambers, one

on each side ofthe cylinder. Each chamber holds one collection filter, which was prepared

for either N02 or NOx collection. We put one of each filter type in each sample. The

Harvard School ofPublic Health Environmental Science and Engineering laboratory

determined that their NOx results were unreliable and should be voided. Thus only N02

2-3 ./
data were reported. The Ogawa samplers were placed in 214 homes because oftheir

limited quantity.

Residential air exchange rate was measured using a perfluorocarbon tracer (PFT) system

(Dietz et al., 1986). PFT gas was released from two locations inside each home to

provide reasonably uniform PFT dispersion throughout the home. The PFT emitters were

placed 24 hours before the sampling was started, which allowed the PFT concentrations to

approach equilibrium. The PFT gas then was collected into capillary adsorption tubes

(CATs) by diffusion and the air exchange rate was determined by the collected PFT,

source emission rates, home volume and the sampling duration. The analysis ofthe CATs

was also performed by the Harvard School ofPublic Health Environmental Science and

Engineering laboratory.

Approximately 10% field blanks and 10% field duplicates (co-located samplers) were

deployed as part ofthe quality control for each measured component other than

formaldehyde. There were 20% blanks for formaldehyde. In addition, each laboratory

had its own quality control procedure including analyses ofblind zero air and span gas

samples supplied by IES, laboratory blanks, and laboratory spikes.

The results from other passive samplers that were pilot-tested, such as the carbon

monoxide passive sampler (COPS), formaldehyde "bubblers" and the methane syringe

samplers (Wilson et al, 1993a), were considered unreliable and therefore are not included

in this report.

2.3 Field Sampling Procedure

Field technician teams were formed consisting ofthe specially trained, experienced

Customer Service Represeptatives (CSRs) from each gas company. Several training

sessions were given for the CSRs to familiarize them with the field procedures and to

ensure correct deployment ofsamplers. All measurements were carried out during a

continuous 48-hour period. The CSRs recruited participants from the randomly chosen

residential units, deployed and retrieved the continuous Draeger GO monitors and all other

passive samplers. They also administered questionnaires and thoroughly inspected,

adjusted and documented all gas appliances. A thermometer recorded the minimum and

maximum temperatures inside the homes during the 48-hour sampling period; and the

initial and final temperatures at the beginning and endin~ ofthe 48-hour sampling period

were read and recorded by the CSRs.

The experience ofthe CSRs with customer relations and gas appliance operations was

invaluable to the success ofthis study. As the result ofthis pilot effort, 293 randomly

selected homes were sampled from December, 1991 to April, 1992.

2-4

\~
2.4 Data Management and Analysis Methodology

In Volume 1, an assumed exposure period of48 hours was used to carry out all the

calculations in order to expedite the publication ofthat volume. After the completion of

Volume 1, calculated pollutant concentrations reported for the passive samplers and air

exchange rates were adjusted using the actual exposure hours for each home. The

arithmetic mean ofthe actual exposure hours was 47.94 ± 1.65 hrs (std.dev.) for all 277

homes. The adjustments had minimal effects on the data distributions.

In addition, the remaining benzene and toluene sample analyses were completed since

Volume 2 was published; therefore, the descriptive tables and figures related to benzene

and toluene in Volume 1 were updated and included in the Appendix A ofthis report.

All variables collected during the field study were checked for data input errors and

organized into eleven data files, which were utilized in both Volume 2 and this report.

The eleven data files were described in Volume 2 Appendix 1. Nine out ofthe eleven data

files were directly derived from the field technician forms and questionnaires. Two other

files were created: the ambient temperature file and the analysis file. The approaches that

were taken to create these two files are documented in the Appendix B ofthis report.

A total of235 variables were hypothesized as parameters that might influence measured

pollutant concentrations. These independent variables were classified as either continuous

or categorical variables. l Out ofthe 235 variables, 79 were continuous and 156 were

categorical. A univariate analysis approach was first used to "screen" the dependent

variable, results ofthe univariate analyses were summarized and examined, then

multivariate analyses were performed. All ofthe analyses were conducted using the SAS

statistical program (SAS 1990a through d).

Univariate Analysis General Approach

Univariate analyses were performed for dependent variables using all ofthe 235

independent variables contained in the database.

For the continuous independent variables, correlation coefficients were computed with the

dependent variables. Parametric Pearson correlation coefficients and nonparametric

Spearman correlation coefficients2 were computed using SAS procedure "PROC CORR".

For the categorical independent variables, parametric and nonparametric mean comparison

tests were performed. The parametric analyses compared the means of each category.

1While all continuous variables are numerical, some numerical variables are categorical in our database.

2 The Pearson correlation is a parametric test that may be influenced by outliers; the Spearman is a rankorder nonparametric test that is influenced less by outliers but may have less power to detect associations.

2-5
When the variable has only two categories, the analysis is equivalent to a "t-test". When

comparing more than two means, an ANDVA F test indicates whether the means differ

significantly but it does not indicate which particular pair differs. Multiple comparison

methods identify specific differences among the means. The Tukey (or Tukey-Kramer)

method was used. 3 For the nonparametric tests, the Wilcoxon method was utilized.4

This method requests an analysis ofthe ranks ofthe data and corresponds to the Wilcoxon

rank-sum test (or Mann-Whitn~yU test) for a two-level variable. The method

corresponds to a Kruskal-Wallis test ifthe variable has more than two levels. The critical

value (p-value) used throughout the analyses in this report was 0.05.

Multivariate Analysis General Approach

Using the results ofthe univariate analyses, significant independent variables with p~0.05

were allowed to enter the multivariate stepwise regression models.

Continuous variables were brought into multivariate analyses directly, while categorical

variables were formatted into a series of"O/I" variabless before entering to the

multivariate analyses. One ofthe categories in each categorical variable was selected as a

"reference category" and was not entered directly into the regression. All other categories

were formatted into a set of 0/1 variables; "0" was assigned when the category was not·

present and "1" assigned when it was present. These variables were entered into ~he

multivariate analyses. The coefficient derived from the regression model is interpreted as

the change relative to the reference category attributable to that specific category. Since

the categorical variables in our data base contain anywhere between 3 to 20 individual

categories, the number ofvariables increased substantially after the formatting.

A category was selected as the reference category ifit met both ofthe following criteria:

1) the category contains at least 25 observations (about 10% ofthe homes);

2) when applicable, the category represents the lowest emission among all the

categories in that variable.

The above process assured that the multivariate analyses did not produce an overdetermined model. The reference category provided a baseline for comparison with all

other categories under that categorical variable.

The SAS multi-linear routine fits linear regression models by least-squares.6 Subsets of

independent variables that "best" predict the dependent variable can be determined by

3 This method was selected because ofthe unequal cell sizes in our datasets. This method asswnes equal

variances. The SAS procedure "PROe GLM" was used.

4 The SAS procedure "PROe NPARIWAY" was used with the "Wilcoxon" option.

S Also known as "yes/no" variables or "dummy" variables.

6 The SAS procedure used was "PROe REG".

:....

2-6
various model-selection methods. The stepwise method was selected for the analyses in

this report. This method starts with no variables in the model and adds variables that meet

the entry criteria (in this case, significant level =0.15), then keeps variables that meet the

stay criteria (in this case, significant level = 0.05). Some variables that entered into the

model earlier became "disqualified" after some additional variables entered into the model.

All ofthe independent variables that stayed in the model eventually met the 0.05

significance level.

2-7

Section 3

Analyses of Passive Monitor Data

Benzene, toluene, N02and radon were measured using passive monitors. These

measurements were systematically evaluated following the approach described in the

previous section. In order to control for the outdoor effect to better evaluate the indoor

sources ofbenzene, toluene and N02, we created the net indoor concentrations by

subtracting the outdoor from the indoor values. This is a simple and straight-forward way

to remove the outdoor effect. However, this simple subtraction may not completely (or

accurately) eIiminate the outdoor effect due to reasons such as the pollutants' decay over

time. Pollution decay will require more complicated analyses.

Univariate analyses were conducted for eight dependent variables: indoor, outdoor, and

net indoor benzene; net indoor toluene; indoor, outdoor, and net indoor N02; and indoor

radon. Analyses were carried out from univariate through multivariate analyses for four of

the eight dependent variables: net indoor benzene, net indoor toluene, net indoor N02 and

indoor radon.

An overview ofthe univariate analysis results is provided in a summary table (Table 3-1).

Discussions ofthese results, giving the directions ofthe univariate comparisons from

Table 3-1, are presented in Sections 3.1 through 3.3.

For the convenience ofinterpreting Table 3-1, the independent variables were divided into

the following ten groups:

• Building characteristics

• Cooking appliance

• Heating furnace

• Clothes dryer

• Water heater

• Meteorological factors

• Measured components

• Geographical locations

• Sampling parameters

• Occupant use characteristics

Categorical independent variables were presented first in each ofthe ten groups. Nonsignificant independent variables were not included. Table 3-1 shows associated factors

for a particular dependent variable. For instance, indoor benzene was correlated with

many cooking appliance variables. The net indoor benzene, which represents the indoor

sources ofbenzene, had very little correlation with these cooking variables. This is likely

3-1
Table 3-1. Univariate analysis results summary.

Benzene Toluene Nitrogen Dioxide Radon

Indoor Outdoor In -Out In -Out Indoor Outdoor In· Out Indoor

Bulldlna Characteristics

Buildine' descriotion ltvoe of buildino) P,N P,N P,N

Numbe~ of livine units,(occupied and vacant) at this address N P,N P P,N P,N N

Year home was oriainallv built P,N P,N P,N P,N P

Garage tvoe N P,N N P,N P,N N

Tvoe of air conditioner in home, includina no alc P,N P,N P,N P,N

Microwave present N P,N P,N P,N

Pool or SDa heater present N

Type of kitchen fan, includin!l none N P,N P,N N

Fireplace oresent P P,N N P,N P,N N

Fireplace ventin!l adeauately P,N N P,N P,N N

fireplace had a aas liahter P,N P,N P,N N

!Wood stove or wood heater present N P,N P,N N

Electric healer oresent P P,N

Area of the conditioned space in home ++ - ++ + - - ++

Hei!lht of the conditioned SDace in home +

Volume of the conditioned soace in home - ++ ++ - - - ++

Cook/na ADDllance

Cookina lvDe P,N P,N P,N P,N P,N P,N

Cookin!l appliance confiauralion P N

Cookina unit make (brand names) P,N P,N P,N P

Cookin!l ranee top bumer fuel P,N P,N P,N P,N P,N P,N

Number of ranae aas bumer oilots P,N P,N P,N N P,N N

Ranae aas bumer pilot tvoe P,N P,N P,N P,N P,N N

Number of ranae aas bumers P,N P,N P,N P,N P,N P,N

Ranae bumer name needed adiustment P,N P,N P,N P,N P,N P,N

Oven fuel P,N P,N P,N P,N P,N P,N

Number of cas oven bumers P,N P,N P,N P,N P,N P,N

Number of aas oven oilots P,N P,N P,N P,N N

Gas oven pilot lvPe N P P,N P,N P,N N

Oven cas bumer rated inout (estimated or plated) P,N P,N P,N P,N P,N N

Oven !las bumer malerial P,N P,N P,N P,N P,N N

Oven cas bumeroorts tvoe P,N P,N P,N P,N P,N P,N

Oven gas bumer name spreader P,N P,N P,N P,N P,N N

Oven bumer name needed adiustment P,N P,N P,N P P,N P.N

A seoarate broiler aas bumer N P,N P,N

Broiler gas bumer POrts lYPe P

Rancelovenlbroiler oilots needed adiustment P,N P,N P,N P,N P,N P,N

Gas leaks associated with the ranaelovenlbroiler unit P,N P,N P,N N P,N

lAce of the cookina unit . . ++ + ++

Gas oven burner input value + ++

Total eStimated oirot liaht input (ranae oven and broiler) + ++ ++ ++ - Heatino Fumace

Heating tvoe P,N N N P,N P,N P,N N

Healinc fumace make N P,N P,N N P,N N

Heatin!l furnace fuel P,N N

Fumace location P,N P,N P,N N

FumacelvDe P,N N N P,N P,N N

Fumace oilot type P,N P,N N P,N P,N

Fumace bumer rated inout value (estimated or olated) P N N P

Fumace bumer tvDe P,N

Fumace bumer material P,N P,N 'N P,N P,N P N

Fumace eauioed with a low-NOx bumer P,N P,N P.N P,N

Fumace inspection items:

Inadeauate ventine P

Cracked firebox P,N

Firebox soot P

Forced air fumace blower door not orooerlv in olace N N N P,N P,N N

Forced air fumace interconnection or communication P,N P N P,N P,N N

Blocked or inadeauate combustion air P N

Furnace inspection: other N

Combustion products leaked into home with only Ihe oilol bumina P P,N N P,N P,N

Comb. ,products leaked into home wi furnace burner in ooeration P N

Fumace had a draft fan N P,N P

Fumace came on durina lasl two daYS N P,N

3-2
Benzene Toluene Nitrogen Dioxide Radon

Indoor Outdoor In -Out In -Out Indoor Outdoor In -Out Indoor

lAoe of the heatino fumace ++ - - ++ ++

Fumace pilot input value ++ +

Fumace bumer inout value - ++ + - - ++

Clothes Drver

Number of clothes dryers in home P,N N N P,N N P,N P,N

Clothes dryer make N N P,N P,N P,N

Clothes dryer location P,N N P,N P,N N P,N P,N

Clothes dryer fuel P,N N P,N P,N P,N P,N

Clothes dryer vented to the outside P,N

Combustion products leaked into home with the pilot bumina N N

Clothes dryer bumer rated inout (estimated or plated) P P

Clothes dryer bumer tvoe N

Clothes dryer bumer material N

Clothes dryer bumer POrt tvoe P,N N

Gas leak associated with the clothes dryer P,N P,N N P,N P,N N

Clothes dryer bumer rated input + ++

Water Heater

Number of water heaters in home P,N P,N N N N

!Water heater make N N P,N

Water heater location P,N P,N N P,N

Water healer fuel N P,N P,N P,N P,N

Water healer tvoe P,N P,N

Water heater pilot tvoe P,N N

Water heater pilot needed adiustment N

Water healer pilot needed to be relillhted N

Water heater bumer rated inout (estimated or plated) N N

Water heater bumer tvoe P,N N P,N P,N N

Water heater bumer material P P,N N

!Water heater bumer oorts type P,N N

!Water heater e<luioed with a low-NOx bumer P,N N

Water heater bumer flame needed adiustment P,N P,N N N

Water heater ventino proPertv P P,N P,N P N

Gas leak associated with the water heater N P,N N

Water heater capacity - ++ - - Iwater heater aae + - Water heater bumer rated input value - Meteoro/oalcal Factors

TemPerature in home durina last two daYS compared to naN P P P P P

Calculated heatina decree daYS ++ - ++

IAveralle indoor temPerature durinllthe samplin!l Period + - IAveralle outdoor temPerature durina the samplina Period - + + - Minimum outdoor temPerature durinothe samplin!l Period - - Indoor temPerature initial readino ++ ++ - Indoor temperature final readin!l +

Maximum indoor temPerature durinll last two days + +

Minimum indoor temPerature durina last two days - Measured ComDonents

Air exchan!le rate ++ . - ++ ++ - 48-hour averaae CO indoor ++ ++ ++ ++ ++ · 48-hour averalle CO outdoor ++ ++ - - ++ ++ - - Benzene indoor ++ ++ ++ ++ ++ ++

Benzene outdoor ++ ++ - - ++ ++ ++ - CO measured bv COPS indoor ++ ++

CO measured bv COPS outdoor - - Samolina duration ++ - Formaldehvde indoor - Formaldehvde outdoor · CO indoor minus outdoor (48-hour) ++ + ++ ++ ++ ++

Maximum 1o-minute CO indoor ++ ++ ++ ++ + - Maximum 1O-minute CO outdoor ++ ++ - - ++ ++ ++

Maximum 1-hour CO indoor ++ ++ ++ ++ ++ - Maximum 1-hour CO outdoor ++ ++ - - ++ ++ ++

Maximum 3O-minute CO indoor ++ ++ ++ ++ ++ - Maximum 3O-minute CO outdoor ++ ++ - - ++ ++ ++

Maximum 8-hour CO indoor ++ ++ ++ ++ ++ · Maximum 8-hour CO outdoor ++ ++ - - ++ ++ - NitrOQen dioxide indoor ++ ++ ++ ++ ++ - NitrOllen dioxide outdoor ++ ++ . ++ ++ - -

3-3
Benzene Toluene Nitrogen Dioxide Radon

Indoor Outdoor In -Out In -Out Indoor Outdoor In -Out Indoor

Inverse of home volume ++ - - ++ ++ ++ - Indoor radon + ++ - - ++

Toluene indoor ++ ++ ++ ++

Toluene outdoor ++ ++ - - ++ ++ - Geoaraphlcal Locations ,

ParticipatinQ home address ziPCOde (first two diaitS\ P,N P,N P,N P,N P,N

ParticiDatina home leleDhone area code P,N P,N P,N P,N P,N

Field technician names PN PN PN PN PN

Sampl/na Parameters

Dav of week samDlina started P,N P,N

Indoor sampler location P,N P N N N

Distance of the indoor samDlers to ranQe bumer +

Distance of the indoor samplers to oven vent · ++

Distance of the indoor samDiers to heatina fumace - ++ - ++

Distance of the indoor samDiers 10 oDenable outside doortwindow - +

Distance of lhe indoor samplers 10 floor ++ + ++ + - Distance of the indoor samDiers to ceilina . - ++

Dislance of the outside samplers to the nearest lias aDDlianee vent . Distance of the outside samDiers to treellarae bushes - ++

Distance of the outside samplers 10 street traffic . - - ++

Distance of the outside samplers 10 surface of lhe around · - Distance of the outside samDiers 10 side of the house - ++

Occupant Use Characteristics .

Number of DeCole lived in home durina last two days N

Number of cars parked in lhe llaraQe durinQ last 2 davs P,N P',N P,N N P,N P,N N

Used ranae/oven to heat the kitchen durina the cast two daYS P,N P N

Healed home wilh ranae/oven this year P,N P,N P,N P,N N

Left windowls) ODen durinQ the cast two daYS P,N

Cigarslciaarettesloioes smoked in the home durina last 2 daYS N

~v home remodelinalconstruc1ion in the oast Year N

Number of times cooked breakfast usinQ top burner in last 2daYs N P,N

Number of times cooked breakfast usina oven in last 2 days N N

Number of times cooked breakfast usinQ microwave in last 2 davs N P

No. ol limes cooked breakfast usina other aDDliance in last 2 davs N P,N N

Number of times cooked lunch usinll top bumer in last 2 days N N N

Number of times cooked lunch usina oven in lasl 2 daYS N

Number of times cooked dinner using microwave in last 2 daYS P,N

Number of times cooked dinner usinQ oven in last 2 days N P,N

Used an exhaust fanlhood while cookina N N N P,N P,N

Used fireDlace durina the cast two daYS P,N P,N P,N N

Smelled smoke from the firolace durina the cast two daYS P,N N P,N P N

Sex of lhe first member in lhe household P,N P,N P.N N

Race of the first member in the household P,N P,N P,N N

Sex of the second member in the household P,N P,N P,N P,N N

Race of the second member in the household P P,N P,N N

Race of the third member in the household N P,N

Race of lhe fifth member in the household N N N

lAce of the first member in the household + ++

lA.ae of the second member in the household - ++ + · ++

Gas meter initial readina . Gas meter final readinll . Household aas usaae durina Ias1two daYS ++ ++ . +

Total number of significant independent variables 60 98 83 47 115 88 83 116

Table Legend

For categorlcaJ Independent variables:

"P" • significant in parametric lest;

"N". significant in nonparametric lest;

"P,N" • significant in both parametric and nonparametric tests.

For conUnuous Independent variables:

"+" represents a possilive correlation with the d!!pendent variable and 0.0200 s p < 0.0500;

"++" represents a possitive correlation with the dependenl variable and p < 0.0200;

"." represents a negative correlation with the dePendent variable and 0.0200 Sp c 0.0500;

"-" represents a negative correla1io~ with the dependent variable and p < 0.0200.

3-4
, , .J . I \ ,f ,",",1'. ' .i ., •

due to the geographic patterns in the distribution of cooking appliances, which in tum, are

associated with outdoor benzene concentrations.

While these univariate analysis results provide very useful infonnation about the variables,

some ofthe results may seem to be confusing or misleading, such as the presence of a

microwave oven having a significant effect on indoor benzene. This is due to the

intercorrelations among the variables; one variable may be a surrogate of a few other

variables. To better understand what factors influence the pollutant concentrations, it was

necessary to carry the analyses one step further to multivariate methods.

It should also be noted that indoor pollutant concentrations reflect the source effect

outdoors; and, statistical correlations are not equivalent to causal relationships. Readers

are advised to keep these in mind while interpreting the univariate analysis results.

Individual or isolated use ofthese results may be misleading.

3-5
.~

3.1 Benzene and Toluene

3.1.1 Univariate Analysis Results

Univariate analyses were performed for indoor benzene, outdoor benzene, net indoor

benzene and net indoor toluene on each ofthe 235 independent variables. Numbers of

significant independent variables for each ofthe four dependent variables were 60, 98, 83

and 47, respectively. The following is a bullet description ofthe univariate results,

grouped under the same ten topics as shown in Table 3-1. Some discussion and

speculation are also included to assist the readers in understanding and interpreting the

univariate results.

Building Characteristics

• Indoor benzene was higher ifthe residence had a microwave oven.

• Indoor benzene was higher ifthe residence did not have an electric heater.

• Indoor benzene was positively correlated with t4e height and area ofthe

conditioned space in a home; but non-significant for the volume ofthe homes.

Cooking Appliance

• The univariate analyses on cooking variables repeatedly showed that indoor

benzene levels for gas-cooking homes were higher than for electric-cooking

homes.

• Indoor benzene was the highest ifthe ~as range or oven had no standing pilot.

• Indoor benzene for cast iron oven burners was higher than for pressed steel

oven burners.

Heating Furnace

• Indoor benzene was the highest ifthe furnace m~e was PAYNE compared

with all other makes.

• Indoor benzene was higher ifthe furnace had a draft fan. (A draft fan is likely

to increase the air exchange rate in a home, which means the higher indoor

benzene level could be the result of a higher outdoor benzene level.)

• Indoor benzene was positively correlated with tr,e furnace pilot input value.!

Clothes Dryer

• Indoor benzene was not correlated with any variables related to clothes dryers.

1 Pilot (or burner) input value is the rated heat input for a pilot (or burner), usually in the unit ofBTU/hr.

3-6

lJ
Water Heater

• Indoor benzene was the highest if a household did not have a water heater

(indicated by "not applicable" for water heater fuel). The next highest was

natural gas, followed by electric water heater.

• Indoor benzene of "water heater not venting properly" was higher than "water

heater venting properly"~ also higher than "no water heater in a home".

Meteorological Factors

• Although the parametric test indicated significance for home temperature (pvalue was less than 0.05), no significant comparisons were given.

• Indoor benzene was negatively correlated with the minimum outdoor

temperature during the two-day sampling period (the higher the outdoor

temperature, the lower the indoor benzene).

Measured Components

• Indoor benzene was positively correlated with all ofthe measured pollutants

(both indoor and outdoor) except for fonnaldehyde and radon.

Geographical Locations

• Homes in the Los Angeles area had the highest levels ofindoor benzene among

all the homes sampled. These homes had zipcodes that started with "90",

telephone area codes of"213", "310", "818", and happened to be mostly

sampled by D. Cotter of SoCalGas.

Sampling Parameters

• Indoor benzene of "indoor samplers located in the hallways" was higher than

"located in the living rooms", also higher than "located in the family rooms".

(These correlations imply that the indoor benzene sampler was measuring some

ofthe outdoor air.)

• Indoor benzene was positively correlated with the indoor samplers' distance to

the floor (the higher the samplers, the higher the indoor benzene).

Occupant Use Characteristics

• Indoor benzene was higher ifthe home did not have a garage (indicated by

"number of cars parked in the garage: not applicable").

• Indoor benzene was positively correlated with the total gas consumption in the

household during the two-day period.

There were too few significant variables in the building category to show any clear

correlation between indoor benzene and building characteristics. In the cooking category,

the pattern ofindoor benzene to cooking variables closely matches the pattern of outdoor

3-7 I'

il

-/"'LJ
benzene to these variables (shown in Table 3-1). This may imply common factors

influencing their variations. The influence can be seen in other groups as well, such as the

heating variables and sampling parameters. Since outdoor benzene concentration is a

function ofthe geographical location (for example, metropolitan Los Angeles area had

high outdoor benzene levels), the indoor benzene was also correlated with geographical

parameters.

Building Characteristics

• The univariate analyses on three buqding-related variables (building type,

number ofliving units, and garage type) clearly showed that multi-family

buildings were associated with higher outdoor benzene levels than single family

dwellings.

• Outdoor benzene was higher for homes built in the 1950s and 1960s than for

homes built in the 1970s.

• Outdoor benzene for homes without air conditioners was higher than for

homes with refrigeration type central air conditioners.

• Outdoor benzene for homes without fireplaces was higher than for homes with

fireplaces.

• Outdoor benzene for homes without wood stoves (or heaters) was higher than

for homes with wood stoves (or heaters).

• Outdoor benzene was negatively correlated with the area and volume ofthe

conditioned space in a home (larger homes had lower outdoor benzene values).

Cooking Appliance

• The univariate analyses on cooking variables repeatedly indicated that homes

with gas cooking units had higher outdoor benzene than homes with electric

cooking units; particularly gas without pilot cooking was the highest.

• Outdoor benzene was higher ifthe oven gas burner did not have a flame

spreader.

• Outdoor benzene was the highest ifthe gas oven burner was made of cast iron

compared with other materials, such as aluminum and pressed steel.

• Outdoor benzene was positively correlated with the total pilot input value of

the range/oven/broiler unit.

Heating Furnace

• Outdoor benzene of homes with gas wall furnaces was higher than those with

gas forced-air furnaces; also higher than those with electric heating.

• Outdoor benzene was the highest ifthe furnace was made by "HOLLY"

compared with all other makes.

3-8
• Outdoor benzene of homes with gas floor furnaces was higher than those with

gas forced-air furnaces.

• Outdoor benzene was higher ifthe furnace pilot type was aerated than ifit was

electronic.

• Outdoor benzene was higher ifthe furnace burner input was estimated than ifit

was read from a manufacturer's nameplate.

• Outdoor benzene was higher ifthe furnace burner was made of cast iron than if

it was made ofpressed steel. /

• Outdoor benzene was higher ifrepair referral was made for the interconnection

or communication ofthe forced-air furnace than ifno action was required.

• Outdoor benzene was higher ifcombustion product may have leaked into home

with the furnace pilot burning.

• Outdoor benzene was positively correlated with the furnace age and furnace

pilot input value, but negatively correlated with the furnace burner input value.

Clothes Dryer

• Although there were five significant variables in the clothes dryer group, all of

them indicated that homes without clothes dryers had higher outdoor benzene.

Water Heater

• !The univariate analyses on the categorical variables in the water heater file

'repeatedly showed that outdoor benzene ofhomes without water heaters was

higher than homes equipped with water heaters.

• Outdoor benzene was negatively correlated with the capacity ofthe water

heater, i.e., the larger the water heater, the lower the outdoor benzene.

Meteorological Factors

• Outdoor benzene was higher ifthe temperature in a home was cooler during

the two-day period compared with the present time (i.e., when the customer

service representative gave the second interview).

Measured Components

• Outdoor benzene was negatively correlated with the indoor formaldehyde and

radon; positively correlated with the air exchange rate and all the other

measured air pollutant (indoor and outdoor) concentrations. I

Geographical Locations

• Homes in the Los Angeles area had the highest levels ofoutdoor benzene

among all the homes that were sampled. These homes had zipcodes that

started with "90" or "91" and telephone area codes of"213", "310", "818".

Homes in this area were mostly sampled by D. Cotter and D. Thomas.

r'" /,;"

3-9 L '/

/;
Sampling Parameters

• Outdoor benzene of "indoor samplers located in the hallways" was higher than

"located in the living rooms", also higher than "located in the family rooms".

• Outdoor benzene was positively correlated with the indoor samplers' distance

to the floor and (of course) negatively correlated with the indoor samplers'

distance to the ceiling, also negatively correlated with the distance to the

heating furnace. (The relation ofindoor sampler location with outdoor

benzene was likely caused by the intercorrelation between indoor and outdoor

benzene.)

• Outdoor benzene was negatively correlated wjth the outdoor samplers'

distance to the street traffic; also negatively correlated with the outdoor

samplers' distance to the side ofthe residence. (These two findings

demonstrate the accuracy ofthe passive sampling and analysis. Outdoor

benzene level measured by the passive sampler was lower when the sampler

was further away from the sources of benzene, for example, the street traffic.)

Occupant Use Characteristics

• Outdoor benzene was the highest wnen the residence did not have a garage

(indicated by "number of cars parked in the garage: not applicable").

• Outdoor benzene was higher ifthe occupant(s) heated home with the

range/oven.

• Outdoor benzene was higher ifthe occupant(s) did not cook lunch with the

oven.

• Outdoor benzene was higher ifthe first membe~ ofthe family was black or

hispanic than ifthe first member was caucasian.

• Outdoor benzene was higher ifthere was no second member in the household

than ifthere was a male caucasian as the second member.

• Outdoor benzene was negatively correJated with the age ofthe second member

in the family.

The correlation between outdoor benzene and older gas oven (made of cast iron without

flame spreader) indicates that older homes were associated with higher outdoor benzene.

This finding is consistent with the No.2 bullet under the Building Characteristics: outdoor

benzene was higher for homes built in the 1950s and 1960s. Also, under the heating

category, we see older furnaces (with cast iron burners and without a manufacturer plate

indicating the burner input value which had to be estimated) were associated with higher

outdoor benzene levels, which in turn, means that older homes were associated with

higher outdoor benzene. In addition, outdoor benzene was positively correlated with the

furnace age (the older the furnace, the higher the outdoor benzene).

2Members in a household were interviewed and a table at the end of the Questionnaire Part B was filled

out by the customer service represep.tative. For convenience, the first person in that table is referred as

"the first member", the second person in the table "the second member", and so forth.

3-10
From the above univariate results, we can also summarize that outdoor benzene was

higher for smaller homes 'With no clothes dryers, no water heaters, and no garages. The

majority ofthese homes are apartments and trailers. They are mostly located in

metropolitan areas 'With relatively low income. However, many ofthese relationships

probably occur due to complex interrelationships among the variables. More complex

multivariate methods are needed to address these issues.

This variable was constructed by subtracting the outdoor measurement from the matched

indoor measurement ofbenzene.

Building Characteristics

• Net indoor benzene ofsingle family detached houses was higher than multifamily dwellings.

• Net indoor benzene was the highest ifthe residence had an attached garage

(side or under) compared with all other types of garages and carports.

• All three fireplace related variables indicated that net indoor benzene of

fireplace present in a home was higher than no fireplace.

• Net indoor benzene was positively correlated with the area and volume ofthe

conditioned space in a home.

Cooking Appliance

• Unlike indoor benzene which was heavily weighted on cooking related

variables, net indoor benzene was more frequently correlated with other

appliances such as clothes dryers, heating furnaces, and water heaters.

• Net indoor benzene of built-in cooking units was higher than free-standing

cooking units.

• Net indoor benzene was negatively correlated with the age ofthe cooking unit.

Heating Appliance

• Net indoor benzene was the highest for gas forced-air furnaces compared with

all other types offurnaces.

• Net indoor benzene was higher ifthe furnace make was PAYNE than ifthe

make was WilLIAMS.

• Net indoor benzene was higher ifthe furnace was located in the basement or in

the garage than ifit was located inside.

• Net indoor benzene was higher ifthe furnace pilot type was electronic than ifit

was aerated.

• Net indoor benzene was higher ifthe furnace burner material was pressed steel

than ifthe material was cast iron.

3-11
• Net indoor benzene was higher ifthe furnace was equipped with a low NOx

burner.

• Net indoor benzene was higher ifthere was no combustion product leaked into

home with the furnace pilot burning.

• Net indoor benzene was high~r ifthe furnace had a draft fan.

• Net indoor benzene was positively correlated with the furnace burner input

value, and negatively correlated with the furnace age.

Clothes Dryer

• Most significant variables related to clothes dryers indicated that net indoor

benzene in a residence with a clothes dryer was higher than without a clothes

dryer.

• Net indoor benzene was the highest ifthe clothes dryer make was GE or

KENMORE compared with all other makes.

Water Heater

• The univariate analyses ofthe variables related to water heaters repeatedly

indicated that net indoor benzene in a residence with a water heater was higher

than without a water heater.

• Net indoor benzene was positively correlated with the water heater,capacity.

Meteorological Factors

• Net indoor benzene was higher ifthe temperature in a home during the two- . ,

day period was higher compared with the time when the measurement was

complete.

• Net indoor benzene was positively correlated with the heating degree day and

maximum indoor temperature during the two-day sampling period.

• Net indoor benzene was negatively correlated with the average outdoor

temperature during the two-day period.

Measured Components

• Net indoor benzene was positively correlated with the net indoor CO, radon,

indoor benzene, and indoor toluene.

• Net indoor benzene was negatively correlated with the air exchange rate,

outdoor CO, outdoor benzene, inverse ofthe home volume, and outdoor

toluene.

Geographical Locations

• Net indoor benzene in homes that were sampled by CSRs R. Morthel or 1.

Bryant was higher than in homes sampled by CSR D. Cotter.

3-12
Sampling Parameters

• Net indoor benzene was positively correlated with the indoor samplers'

distance to the furnace and positively correlated with the outdoor samplers'

distance to the side ofthe dwelling.

Occupant Use Characteristics

• Net indoor benzene was the highest ifthere were two cars parked in the garage

during the two-day period.

• Net indoor benzene was higher ifthe fireplace was used during the two-day

period.

• Net indoor benzene was positively correlated with the ages ofthe first and

second members in the family.

Contrary to what was found for outdoor benzene, net indoor benzene was higher in newer

and larger homes. Since net indoor benzene represents indoor sources of benzene with the

outdoor concentration removed, there may be indoor benzene sources in this group of

homes. The multivariate analysis described later in this section will better identify the

sources.

This variable was constructed by subtracting the outdoor measurement from the matched

indoor measurement oftoluene.

Building Characteristics

• Net indoor toluene in buildings with 20 to 49 living units was higher than in

buildings with 50 or more units; also 1 living unit was higher than 50 or more

units.

• Net indoor toluene was higher in homes that were built in 1950s and 1960s

than in homes built in 1970s.

• Net indoor toluene was the highest ifthe garages were attached (side or under)

compared with all other types ofgarages or carports (including no garage or

carport).

• Net indoor toluene was higher ifa fireplace was present in a home.

• Net indoor toluene was positively correlated with the area and volume ofthe

conditioned space in a home.

Cooking Appliance

• Net indoor toluene was negatively correlated with the age ofthe cooking unit.

No other variables were significant in this group.

3-13 L
Heating Furnace

• Net indoor toluene was the highest ifthe heating type was gas forced-air.

• Net indoor toluene was the highest ifthe furnace burner input was read from

the manufacturer identification plate.

• Net indoor toluene was the highest ifthe,furnace burner material was

aluminum.

• Net indoor toluene was the highest ifthe furnace make was PAYNE.

• Net indoor toluene was the highest ifthe furnace had a low NOx burner.

• Net indoor toluene was the highest ifthe furnace pilot type was electronic.

• Net indoor toluene was positively correlated with the furnace burner input

value and negatively correlated with the furnace age.

Clothes Dryer

• Net indoor toluene in a residence with a clothes dryer was higher than without

a clothes dryer.

• Net indoor toluene of clothes dryer located in the garage, located inside and no

clothes dryer were all higher than clothes dryer located outside.

Water Heater

• Net indoor toluene was the highest ifthe water heater was located in the

garage compared with all other locations.

• Net indoor toluene was the highest ifthe water heater burner flame needed

adjustment.

Meteorological Factors

• Net indoor toluene was positively correlated with the indoor temperature read

at the end ofthe two-day sampling period.

Measured Components

• Net indoor toluene was positively correlated with the indoor benzene, indoor

radon, and indoor toluene.

• Net indoor toluene was negatively correlated with the air exchange rate,

outdoor CO, outdoor benzene, outdoor N02, inverse ofthe home volume, and

outdoor toluene.

Geographical Locations

• There were no significant variables for net indoor toluene in this group.

Sampling Parameters

• Net indoor toluene was negatively correlated with the indoor samplers'

distance to any openable outside doors/windows; also negatively correlated

with the outdoor samplers' distance to the street traffic.

3-14
Occupant Use Characteristics

• Net indoor toluene was the highest ifthere were two cars parked in the garage

during the two-day period.

• Net indoor toluene was the highest ifthe fifth member in the household was a

caucaSIan.

• Net indoor toluene was positively correlated with the age ofthe second

member in the household.

Net indoor toluene was a very skewed distribution with a long tail extending to the right.

Most ofthe independent variables were significant only in the nonparametric tests. Had

the nonparametric tests not been performed, these significant variables could not have

been revealed. It is very important to note that net indoor toluene concentrations (i.e.,

indoor sources oftoluene) were not correlated with any geographical locations.

Since benzene and toluene were strongly correlated with each other, the net indoor

toluene followed many ofthe same patterns as did the net indoor benzene, i.e., bigger and

newer homes had higher net indoor toluene. There were a few exceptions, though. For

instance, net indoor toluene was higher for homes built in the 1950s and 1960s than for

homes built in 1970s.

3.1.2 Multivariate Analysis Results

After completing univariate analyses, multivariate analyses were carried out following the

approach described in Section 2.4. Due to the large number ofindependent variables that

we were dealing with, mean substitutions were performed for all continuous variables'

missing values in order to gain sufficient degrees offreedom for the regression analyses.

Net Indoor Benzene:

For net indoor benzene, there were 53 significant categorical variables and 30 significant

continuous variables. After formatting the categorical variables into their dichotomous

forms, there were total of 180 (30 continuous and 150 dummy) variables available for the

stepwise regression. Indoor and outdoor benzene were removed from the candidate list

because net indoor benzene was directly derived from these two variables. Results ofthis

stepwise regression is presented in Table 3-2. All variables left in the final model were

significant at the 0.05 level. The model R2 was about 0.6. Two ofthe most important

variables were indoor toluene and 48-houf average outdoor CO, each explained 23% of

the variance. Net indoor benzene was positively correlated with indoor toluene and

negatively correlated with outdoor CO.

3-15
Table 3-2. Net indoor benzene stepwise regression result: all significant variables were available

for the regression except indoor benzene, outdoor benzene, and CO emission rates.

Model R2 =0.5880

Regression

Error

Total

Degree of Freedom

13

279

292

Sum of Squares

310.66

217.71

528.37

F Value

30.62

Prob> F

0.0001

3-16 BENIO_Tl.DOC 10/6195
To better understand the categorical variables in Table 3-2, we computed the arithmetic

mean of net indoor benzene for each category in every categorical variable that had a

partial R2 of 0.01 or greater. Table 3-3 lists these variables, their categories, arithmetic

means of net indoor benzene for each category, the regression coefficients (parameter

estimate), and partial R2 values. Also indicated in the table are the reference categories

(Ref Cat.). Three ofthe four variables had positive coefficients. Net indoor benzene of

"two cars parked in the garage" was nine times higher than the reference category of "no

car parked in the garage". When the water heater burner type was "multiport/in-shot", the

net indoor benzene was seventeen times higher than the reference category of "water

heater burner type not applicable" (i.e., no water heater or electric water heater). Last but

not the least, ifthe temperature in a home during the two-day period was warmer, then the

net indoor benzene was almost four times as high as the reference category of

"temperature being the same". So far we have identified three categorical variables which

had positive correlations with the net indoor benzene.

In the next step, we intended to find out ifmore housing and appliance characteristics, can

be identified so that the net indoor benzene can be better described. Therefore, we

removed all the air pollutant measurements from the stepwise regression candidate list.

The regression results are presented in Table 3-4. This model explained 26% ofthe

variance in the net indoor benzene, with each variable's partial R2 ranging from 0.013 to

0.055. Again, arithmetic means ofthe net indoor benzene were computed for each

category in every categorical variable in this table, and the results are presented in Table 3-

5. The furnace location appeared to be a very important variable for the net indoor

benzene. One ofthe categories (located "outside") was significant in the previous

regression with a negative correlation (presented in Table 3-2), and two other categories

(located in the "garage" and "basement") were significant in this regression with positive

correlations, explaining almost 10% ofthe variance in the net indoor benzene. The

temperature and number of cars parked in the garage remain the same in the second

regression, while the "water heater burner port type" represented water heaters instead of

the "burner type" in the first regression.

From the univariate and multivariate analyses conducted thus far, we conclude that the

following are likely to be the indoor sources of benzene:

1) Natural gas (or propane gas) burning furnaces located in the basement or

garage in a home.

2) Outdoor air. This source was implied by the temperature variable which

indicates that the indoor benzene was higher when it was warmer during the

sampling period. People tend to open up their windows when it is warmer and

higher outdoor concentrations ofbenzene could increase the indoor level.

3-17
" .

Table 3-3. Mean of net indoor benzene contributed by categorical variables in the stepwise

regression presented in Table 3-2.· .

Ref. Cat.

Water heater burner

type

• Only categorical variables with >0.01 partial R-squared are listed.

•• Non-significant in the:final step ofthe regression.

3-18

0.4342

0.4489

Ref. Cat.

N/A

0.0255

0.0138

N/A

BENIO_T3.00c 10/6/95
Table 3-4. Stepwise regression of net indoor benzene: all the air pollutant measurements were

removed.

Model R2 =0.2622

Regression

Error

Total

Degree of Freedom

10

282

292

Sum of Squares

138.52

389.85

528.37

F Value

10.02

Prob > F

0.0001

3-19 BENlO_T2.00c 10/6195
Table 3-5. Mean of net indoor benzene contributed by categorical variables in the stepwise

regression presented in Table 3-4.

•• Non-significant in the final step ofthe regression.

3-20

'\ ;

1.5392

0.8368

Ref Cat.

Ref. Cat.

0.6209

Ref Cat.

0.9519

0.0402

0.0554

N/A

N/A

0.0292

N/A

0.0178

BENIO_T4.DOC 10/6195
" . . " ...':~.

3) Automobile exhaust and evaporation offuel from the cars parked in the.

garage. Also from the univariate analyses we know these garages were

attached either to the side of or under the houses.

4) Natural gas (or propane gas) burning water heaters. The reference categories

for both "water heater burner port type" and "water heater burner type" were

"not applicable". This category included homes without water heaters or with

electric water heaters; and these homes had lower net indoor benzene than

homes with gas water heaters.

Net Indoor Toluene:

For net indoor toluene, there were 24 significant categorical variables and 23 significant

continuous variables. After formatting the categorical variables into their dichotomous

forms, there was a total of 101 (23 continuous and 78 dummy) variables available for the

stepwise regression. One home (pG005) had an unusually high outdoor toluene value of

150.5 ppb. No significant source oftoluene was identified for this home on review ofthe

field forms and questionnaires. This home was removed from the dataset to avoid it

having undue influence on the multivariate results. Indoor and outdoor toluene were also

removed from the candidate list because net indoor toluene was directly derived from .

these two variables. Upon examining the result ofthe stepwise regression, we found that

the intercept was non-significant (prob>F =0.53): Therefore, the regression was run

again without an intercept. The results are presented in Table 3-6. All variables left in the

model are significant at the 0.05 level. The model R2

was 0.72.3 Two ofthe most

important variables were indoor and outdoor benzene concentrations, explaining 64% of

the variance in the net indoor toluene. The categorical variables from Table 3-6 with

partial R2 > 0.01 are summarized in Table 3-7.

Next, a multivariate model without any air pollutant measurements was derived for the net

indoor toluene. The results are presented in Table 3-8, with a model R2 of 0.32. 4 Since

all the air pollutant measurements were removed from the regression candidate list, both

indoor and outdoor benzene were not available. The first variable to appear in Table 3-8

is "Furnace Make: PAYNE" explaining 8% ofthe variance in the net indoor toluene. This

variable was the third variable in Table 3-6. Every variable in Table 3-8 has an R2 > 0.01.

Arithmetic means ofthe net indoor toluene for each category of every categorical variable

in Table 3-8 were computed and presented in Table 3-9. Based upon these results, we can

speculate that the following are indoor sources oftoluene:

1) Outdoor air.

2) A particular group of heating furnaces made by PAYNE. Homes with heating

furnaces made by PAYNE had ten times higher net indoor toluene than homes

3 The same regression with PGO05had an R-squared of 0.2668.

4 The same regression with PG005 had an R-squared of 0.1950.

3-21
without furnaces. Ifthe toluene does not come from the furnace, it is due to

some other factor which is found in c:ommon with PAYNE furnaces.

3) Automobile exha"usts from the cars par~ed in a garage lqcated underneath the

house. ". ,":'

4) Improperly vented fireplaces.

5) Gas forced-air furnaces with improperly placed blower doors.

6) Natural gas (or propane gas) burning water heaters. This source was implied

by the water heater burner flame variable ("not applicable" means no water

heaters or electric water heaters).5

5 Note that interpretation ofthe variable "water heater location" is slightly different. This variable is

answered for electric as well as gas water heaters. The "not applicable" category applies only to homes

with no water heaters.

3-22
Table 3-6. Stepwise regression ofnet indoor toluene: all significant variables were available for

the regression except indoor and outdoor toluene, also PG005 was removed.

Model R2 =0.7191

Regression

Error

Total

No Intercept

Degree of Freedom

9

283

292

Sum of Squares

7558.62

2952.32

10510.94

F Value

80.50

Prob > F

0.0001

'~¢~¢ij~:::#&m{itidBS.t:::::::::::::::[:·::::t:::::.:::·g]$$7~:: :::::::'::::::::::::':::':::P~l:4~Q:::::::::·::·::::::::::::::::~~Wq$'::: :::::::::::::::,:::::::::·:::::Q~QPQi:::::::::::::::·:::::::::::Qi~~::i:~:::

Benzene 48hr outdoor -2.5311 0.1695 223.09 0.0001 0.2313

:·:Et@i~qg::.¢.;::(ij~;;~::::::::·::: :::::::::::::::::::::::::~]2$~7::·: :::':::::::::::::::f:::::A~~4~:::::·'::::::::·:::::::.'::::::::2.Q~b,ij:;:: :::::::::::[:::::::::::::::::q~~~l:::::::::::::::::i::::I':::·::P~~Q~:::

Forced air furnace 4.4861 1.2750 12.38 0.0005 0.0148

blower door not

properly in place:

Corrected Condition

Used exhauSt fan while 1.8412 0.4063 20.53 0.0001 0.0123

cooking: sometimes

Attached side garage -1.2013 0.4158 8.35 0.0042 0.0053

:t'lr_IIIII__ill.

3-23 \ I , . '-...-
0.0205

Table 3-7. Mean ofnet indoor toluene contributed by categorical variables in the stepwise

regression result presented in Table 3-6.·

Ref. Cat.

4.2567

Ref. Cat.

• Only those categorical variables with partial R2 > 0.01 are listed.

•• Non-significant in the stepwise regression.

N/A

N/A

3-24
, \ ..

Table 3-8. Stepwise regression ofnet indoor toluene: all the air pollutant measurements and

PG005 were removed.

Model R2 =0.3177

Regression

Error

Total

Degree of Freedom

15

276

291

Sum of Squares

2304.20

4948.23

7252.44

F Value

8.57

Prob > F

0.0001

3-25
I' !'

Table 3-9. Mean ofnet indoor toluene contributed by categorical variables in the stepwise

regression result presented in Table 3-8.

Ref Cat.

•• Non-significant in the stepwise regression.

3-29

2.35

~.92

4.77

9.71

2.47

4.5828

3.7362

Ref Cat.

"--\ \

I

N/A

0.0771

0.0198

N/A
Table 3-9. Continued.

3-27

Ref Cat.

2.6387

1.9183

N/A

0.0177

0.0128
3.2 Nitrogen Dioxide

3.2.1 Univariate Analysis Results

Univariate analyses were perfonned for indoor N02, outdoor N02, and net indoor N02 on

each ofthe 235 independent variables in the database. Indoor N02 was significantly

correlated with 115 variables, outdoor N02with 88 variables, and net indoor N02with 83

variables. The following is a bullet description ofthe univariate results, grouped under the

same ten topics as shown in Table 3-1.

Building Characteristics

• Indoor N02 for buildings with 5 to 19 living units was higher than for single

unit dwellings.

• Indoor N02 for homes built during and before 1950s was higher than for

homes built during and after 1970s.

• Indoor N02 was higher for homes without garages or carports.

• Indoor N02 for homes without air conditioners was higher than for homes with

refrigeration central type air conditioners.

• Indoor N02 for homes without microwave ovens was higher than for homes

with microwave ovens.

• Indoor N02 for homes without pool or spa heaters was higher than for homes

with pool or spa heaters.

• Indoor N02 for homes without kitchen fans was higher than for homes with

kitchen fans that exhaust to outdoors.

• Indoor N02 for homes without fireplaces was higher than for homes with

fireplaces.

• Indoor N02 for homes without wood stoves or wood heaters was higher than

for homes with wood stoves or heaters.

• Indoor N02 was negatively correlated with the area and volume ofthe home.

Cooking Appliance

• Indoor N02 ofgas with pilot cooking was higher than gas without pilot

cooking~ gas with pilot was also higher than electric copking.

• Indoor N02 was the highest ifthe cooking range was made by

WEDGEWOOD compared with all other makes~ also indoor N02 ofOKEEFE

range was higher than GE range.

• Indoor N02 ofgas-fired cooking units was higher than electric units.

3-28
• Indoor N02 ofgas burners with aerated pilots was higher than gas burners with

electronic pilots.

• Indoor N02 was higher ifthe range burner flame needed adjustment.

• Indoor N02 ofgas ovens with luminous type pilots was higher than gas ovens

with electronic type pilots; also aerated pilot was higher than electronic type.

• Indoor N02 was higher ifthe oven burner input value was estimated than ifit

was read from a manufacturer plate.

• Indoor N02 was the highest ifthe oven gas burner was made of cast iron.

• Indoor N02 was higher ifthe oven gas burner had no flame spreader.

• Indoor N02 was higher ifthe oven burner flame needed adjustment.

• Indoor N02 was higher if any pilot in the rangelovenlbroiler unit needed

adjustment.

• Indoor N02 was positively correlated with the age ofthe cooking unit, the gas

oven burner input valu~, and the total range/ovenlbroiler pilot input value.

Heating Furnace

• Indoor N02 in homes with no heating furnace was higher than homes with gas

forced-air furnace and electric furnace; also indoor N02 ofgas wall furnace

was higher than gas forced-air furnace and electric furnace. _

• Indoor N02 was higher ifthe furnace was made by HOLLY or WILLIAMS;

and indoor N02 was lower ifthe furnace was made by DAY & NIGHT or

CARRIER.

• Indoor N02 was higher ifthe furnace was located inside than iflocated in the

garage.

• Indoor N02 was higher ifthe furnace pilot type was aerated than ifit was

electronic.

• Indoor N02 was higher ifthe furnace burner type was multi-port/up-shot than

ifit was multi-portfm-shot.

• Indoor N02 was higher ifthe furnace burner material was cast iron than ifit

was pressed steel.

• Indoor N02 was higher ifthe furnace was not equipped with a low NOx

burner.

• Indoor N02 was higher if, in the opinion ofthe customer service ,

representatives, combustion products may have leaked into the home with only

the furnace pilot burning (i.e., spillage suspected while burner was not in use).

• Indoor N02 was positively correlated with the furnace age, and negatively

correlated with the furnace burner input value.

Clothes Dryer

• Univariate analyses on the categorical variables related to clothes dryers

showed that homes without clothes dryers had higher indoor N02.

• Indoor N02 was positively correlated with the clothes dryer burner input value.

3-29 / I-

----1 /
Water Heater

• Indoor N02 was higher for homes without water heaters.

• Indoor N02 was higher ifthe water heater was not venting properly.

• Indoor N02 was negatively correlated with the capacity ofthe water heater,

also negatively correlated with the water heater burner input value.

Meteorological Factors

• Ifthe temperature in the home was cooler during the two test days, compared

with the temperature at the time when the measurement was completed, then

the indoor N02 was higher than ifthe temperature remained the same.

• Indoor N02 was positively correlated with the two-day average indoor and

average outdoor temperatures.

• Indoor N02 was positively correlated with the indoor temperature read at the

beginning ofthe sampling and the maximum indoor temperature.

Measured Components

• Indoor N02 was positively correlated with the air exchange rate, indoor and

outdoor CO, indoor and outdoor benzene, net indoor CO, outdoor N02,

inverse ofthe home volume, outdoor toluene~ and negatively correlated with

the indoor radon.

Geographical Locations

• Indoor N02 was the highest for homes with zipcodes starting with "90".

• Indoor N02 was the highest for homes with telephone area codes of"213".

• Indoor N02 was higher for homes sampled by D. Cotter of SoCalGas than for

homes sampled by R. Cook and C. Lynch ofPG&E. (This variable was a

surrogate ofthe geographical distribution for indoor N<h).

Sampling Parameters

• Indoor N02 was positively correlated with the indoor samplers' distance to the

floor and was negatively correlated with the indoor samplers' distance to the

oven vent, to the furnace, and to the ceiling.

• Indoor N02 was negatively correlated with the outdoor samplers' distance to

trees or large bushes, to the street traffic, and to the surface ofthe ground.

Occupant Use Characteristics

• Indoor N02 was higher for homes without garages.

• Indoor N02 was higher ifthe occupant(s) used the range/oven to heat home.

• Indoor N02 was higher ifthe residence did not have an exhaust fan for

cooking.

• Indoor N02 was higher ifthe first member in the family was a female.

3-30
• Indoor N02 was higher ifthe second member in the family was a male; also

indoor N02 was higher ifthere was only one person in the household.

• Indoor N02 was higher ifthe first member in the family was black than ifit was

caucasian; also hispanic was higher than caucasian.

• Indoor N02 was higher ifthe third member in the family was hispanic than ifit

was caucaSIan.

• Indoor N02 was negatively correlated with the age ofthe second member in

the family.

There were more variables associated with N02 than with VOCs. Since the emphasis of

this study was combustion related pollutants, we gained more information about CO and

N02 related factors than any other pollutants. As shown in Table 3-1 and the above

descriptions, indoor N02 was correlated with the indoor sources as well as the outdoor

N02 levels. Low income families in older and smaller homes were associated with higher

indoor N02. Gas cooking units (especially with standing pilot) and combustion products

leaked from a burning furnace pilot were possible indoor sources ofN02 .

Building Characteristics

• Outdoor N02 was higher for homes built in 1960s or earlier than for homes

built in 1980s.

• Outdoor N02 was higher for homes without air conditioners.

• Outdoor N02 was higher for homes without wood stoves or heaters.

• Outdoor N02 was negatively correlated with the home volume.

Cooking Appliance

• Outdoor N02 for homes with gas cooking was higher than for homes with

electric cooking; particularly gas with pilot cooking was higher than electric

cooking.

• Outdoor N02 was higher ifthe gas range burner pilot was luminous than ifit

was electronic.

• Outdoor N02 was the highest ifthe oven gas burner was made of cast iron

compared with all other types ofmaterials.

• Outdoor N02was higher ifthe oven gas burner did not have a flame spreader.

• Outdoor N02 was positively correlated with the age and the total pilot input

value ofthe range/oven/broiler unit.

Heating Furnace

• Outdoor N02 of no heating furnace was higher than gas furnace, which in tum

was higher than electric furnace.

3-31 I ,

'J
• Outdoor N02 of gas wall heating was higher than electric heating.

• Outdoor N02was higher ifthe heating furnace was located inside than ifit was

located in the attic.

• Outdoor N02was higher ifthe furnace pilot type was aerated than ifit was

electronic; also aerated pilot was higher than "not applicable" (i.e., electric

furnace or no furnace).

• Outdoor N02 offurnaces not equipped with low NOx burners was higher than

electric furnaces or no furnaces, which in tum was higher than furnaces

equipped with low NOx burners.

• Outdoor N02was higher if, in the opinion ofthe customer service

representatives, the combustion product may have leaked into home with only

the furnace pilot burning (spillage suspected even when burner was not in use).

• Outdoor N02was positively correlated with the furnace age.

Clothes Dryer

• Outdoor N02 was higher ifthe home did not have a clothes dryer.

• Outdoor N02 ofhomes with gas clothes dryers was higher than homes with

electric dryers; also outdoor N02 of homes without clothes dryers was higher

than homes with electric dryers.

• Outdoor N02was positively correlated with the clothes dryer burner input

value.

Water Heater

• Outdoor N02 ofgas water heaters was higher than propane water heaters.

• Outdoor N02 ofhomes without water heaters was higher than homes with 1 or

2 water heaters.

• Outdoor N02was higher ifthe water heater burner was multi-portrm-shot than

ifit was mono-port/up-shot.

Meteorological Factors

• Outdoor N02 was positively correlated with the average outdoor temperature

and initial indoor temperature; negatively correlated with the heating degree

day.

Measured Components

• Outdoor N02was positively correlated with the air exchange rate, indoor and

outdoor CO, indoor and outdoor benzene, sampling duration, net indoor CO,

indoor N02, inverse ofthe home volume, and outdoor toluene; negatively

correlated with the indoor radon.

3-32
Geographical Locations

• Homes in the Los Angeles area had the highest levels ofoutdoor N02 among

all the homes sampled. These homes had zipcodes that started with "90" and

"91" and telephone area codes with "213" and "310". The homes were mostly

sampled by D. Cotter and D. Thomas of SoCalGas.

Sampling Parameters

• Outdoor N02 was the highest ifthe indoor samplers were located in the

hallway compared with all other indoor sampling locations.

• Outdoor N02 was positively correlated with the indoor samplers' distance to

the floor.

Occupant Use Characteristics

• Outdoor N02 was higher ifthe occupant(s) used the range/oven unit to heat

home.

• Outdoor N02 was higher ifthe first member in the family was black than ifthe

first member was caucasian..

• Outdoor N02 was higher for single person households.

• Outdoor N02 was positively correlated with the household gas usage during

the two test days.

The outdoor N02 pattern of associations had some similarities with the indoor N02

pattern. Since the outdoor N02 samplers were placed outside each residence, the

samplers actually measured the outdoor N02 concentrations in the immediate vicinity of

the homes. This is very different from the outdoor N02 concentrations obtained from

community ambient monitors. The outdoor samplers in our study could have sampled

some ofthe N02 emitted from the indoor source(s) and the indoor samplers are expected

to have measured some ofN02 emitted from nearby outdoor source(s).

Older and smaller homes that had no heating furnaces, no clothes dryers and no water

heaters were associated with higher outdoor N02. Homes in the metropolitan Los

Angeles area had the highest levels ofoutdoor N02 among all the homes sampled. Single

person households and black families tend to live in a metropolitan area where the outdoor

N02 tends to be higher.

This variable was constructed by subtracting the outdoor measurement from the matched

indoor measurement ofN02.

3-33 I'=--
Building Characteristics

• Net indoor N02for buildings with "5 to 19" living units was higher than for

buildings with "1" or "2 to 4" living units.

• Net indoor N02in homes built before 1940 was higher than in homes built

during 1960s and 1970s.

• Net indoor N02in homes without garages or carports was higher than in

homes with attached side garages.

• Net indoor N02 in homes without microwave ovens and without fireplaces was

higher.

• Net indoor N02 in homes without kitchen fans was higher than in homes with

kitchen fans that exhaust to outdoors.

• Net indoor N02was negatively correlated with the area and volume ofthe

conditioned space in a home.

Cooking Appliance

• Net indoor N02of gas with pilot cooking was higher than gas without pilot

cooking, also higher than electric cooking.

• Net indoor N02was the highest ifthe range/oven unit was made by

WEDGEWOOD compared with all other makes; also net indoor N02of

KENMORE was higher than TAPPAN and GE.

• Net indoor N02in gas cooking homes was higher than in electric cooking

homes.

• Net indoor N02was higher ifthe gas range pilots were luminous than ifthe

pilots were electronic.

• Net indoor N02was higher ifthe gas oven pilots were either luminous or

aerated than ifthe pilots were electronic.

• Net indoor N02was the highest ifthe oven gas burner material was cast iron

compared with all other types ofmaterials.

• Net indoor N02was higher ifany ofthe range/oven/broiler pilots needed

adjustment.

• Net indoor N02was positively correlated with the age ofthe cooking unit, the

oven burner input value and the total pilot input value ofthe cooking unit

(including range, oven, and broiler).

Heating Furnace

• Net indoor N02 of no heating furnace was higher than gas forced-air furnace,

and gas wall furnace was higher than gas forced-air.

• Net indoor N02was higher ifthe furnace burner was made of cast iron than if

it was made of pressed steel.

• Net indoor N02was negatively correlated with the furnace burner input value.

3-34 1 \

\-'
Clothes Dryer

• Net indoor N02 for homes without clothes dryers was higher than for homes

with clothes dryers.

Water Heater

• Net indoor N02 was higher ifthe water heater burner type was mono-port/upshot than ifit was multi-port/up-shot.

• Net indoor N02 was negatively correlated with the water heater capacity.

Meteorological Factors

• None were significant for net indoor N02.

Measured Components

• Net indoor N02 was positively correlated with the indoor CO (all averaging

times) and outdoor CO with shorter averaging times (10 min, 30 min," and 1-

hour); negatively correlated with the outdoor CO with longer averaging times

(8-hour and 48-hour) and outdoor N02.

• Net indoor N02 was positively correlated with the outdoor benzene, net indoor

CO (48-hour), indoor N02, and inverse ofthe home volume.

Geographical Locations

• Net indoor N02 was not associated with any geographical variables.

Sampling Parameters

• Net indoor N02 was positively correlated with the outdoor samplers' distance

to a tree or large bush and negatively correlated with the outdoor samplers'

distance to the nearest gas appliance vent.

Occupant Use Characteristics

• Net indoor N02 ofusing the range/oven to heat home was higher.

• Net indoor N02 of a female as the first member in the family was higher than

male as the first member.

• Net indoor N02 of a male being the second member in the family was higher

than a female being the second member.

• Net indoor N02 was negatively correlated with the total gas consumption

during the two-day sampling period.

There are several important findings in this segment we would like to point out. Net

indoor N02 was correlated with neither geographical variables, nor meteorological

variables. This indicates that indoor sources ofN02 are independent from geographical

locations as well as indoor and outdoor temperatures. Gas burning appliances, such as

cooking range/oven with standing pilots and gas wall furnaces, are possible indoor N02

3-35
sources. When not using an exhaust fan for cooking, the N02 generated indoors caused a

higher indoor N02 level than ifa cooking fan was used that exhausted to outdoors. Net

indoor N02 leveis could be reduced ifthe pilots in the cooking unit are properly adjusted.

Similar to the indoor or outdoor N02, older and smaller apartment homes were associated

with higher net indoor N02 levels. It should be noted that although net indoor N02

represent the indoor sources ofthis pollutant, the outdoor effect is not completely

removed by the simple subtraction.

3.2.2 Multivariate Analysis Results

There were 83 significant independent variables for net indoor N02 identified in the

univariate analyses. Out ofthe 83 variables, 30 were continuous and 53 were categorical.

After selecting reference categories and fonnatting each categorical variable into

dichotomous variables, there were 172 variables eligible for the stepwise regression.

Again, we removed indoor and outdoor N02 from the candidate list. The result is

summarized in Table 3-10. Model R2

was 0.47 with a positive intercept of69 ppb. The

most important variable was net indoor CO, explaining 18% ofthe total variance in net

indoor N02. The arithmetic mean ofnet indoor N02 was then computed for each

category in those categorical variables in Table 3-10 with a 0.01 or greater partial R2

, and

they were listed in Table 3-11. Cooking appliances appeared to be important for

predicting net indoor N02. Out ofthe seven variables listed in Table 3-11, six are related

to the range or oven. Variable "Range Make" entered into the stepwise regression four

times, with all four categories together explained over 9% ofthe variance in net indoor

N02.

Next, all the air pollutant measurements were removed from the candidate list, and the

stepwise regression results are presented in Table 3-12. With only the housing and

appliance related variables, we obtained a model R2 of0.36. And all ofthe variables that

stayed in the model had partial R2 greater than 0.01. Table 3-13 lists the computed mean

of net indoor N02 for each categorical variable, their parameter estimates, and partial R2

values. The multivariate analysis results were consistent with the results from the

univariate analyses. Gas cooking with standing pilot, gas wall furnace, gas water heater

with a monoport/up-shot burner and propane forced-air furnace are significant indoor

sources ofN02. Cooking appliances made by"Wedgewood" and "Kenmore" may

represent older cooking units which were associated with ~gher net indoor N02. Our

analysis results suggest that properly adjusting the pilots in the cooking appliances (to

avoid incomplete combustion) and ventilating while using the gas cooking appliances are

effective ways to reduce the net indoor N02 level.

/

3-36
Table 3-10. Stepwise regression of net indoorNOz (all significant variables except CO emission

rates, indoor and outdoor NOz).

Model RZ =0.4688

Regression

Error

Total

Degree of Freedom

18

274

292

Sum of Squares

62945.96

71337.89

134283.85

F Value

13.43

Prob> F

0.0001

Intercept 68.9127 28.5926 5.81 0.0166 N/A

:1~t::4~iiMih4BSt::pwEpp.;:::::::::::::::::!::::::.:::::::::::::·::,H :·:::::::·::·:::::::'::~~7:l$~::::·::·::::::':::.::P~QP~A::L:::::::::::..:4P~:i:~:!n::·::::::::::~iOOP!~:::::::::::::·::::P.~:17$7.i!·:

Cooking range make: Wedgewood 37.1754 7.6170 23.82 0.0001 0.0588

.:~¢#:~::qti~::'VlIB~:::':::::::::·::::::::::::::::::':::::::::::::'::.::::::n:::::::'::: :,·:,::,':::::::::::~~~741:(·::::'t:::::::::~~~7~~::: ::::::·::·::·:::::,::,::::::::9l~~:r:'::::.:::::'::::Q~~4!::::.:.:::::.:::::p.~~$~$:::

Sex ofthe second member in the 7.3637 2.2914 10.33 0.0015 0.0244

household is male

::;£¢w[C.&lfiiMjid'dt$::'::::j:::::::::::::::::::!::::::::::::·::::::::::·::'::H::::::·::.:::@1;~9S~::: :::::::::::::::::'PfS9it::: :!::::::::::::::::::::::::S;p~:::: ::.:!,::::::::::::pfPti4i:·::::!::::::::.:::::ptQ2Q65:

Oven burner flame needed 19.9428 5.3250 14.03 0.0002 0.0171

adjustment

Cooking range make: Kenmore 9.8864 4.1243 5.75 0.0172 0.0115

·:~Y~::ij.~i1::5t¢~~::¢lt~g(:ff¢ff:::·::::::::,:::::,:,::::::2Q;;Q~i7::: ::.::::::::::.:::::::"~~jA:::: ::'::·::::::::::::,::::::l:l:(QQ::::,::::::::·::::,:Q;QQ01::r:::::::::::,::'::p~Ql::~Q:::

Oven does not have a flame -17.5674 5.2930 l1.02 0.0010 0.0196

spreader

::~[ijg¢:!iHm&~1J::Dt)i~(::::::::.:,::::::::::::::,::,::,::.::,:::::::::':::::.::::J.:::::::::::::[:':::::::::::::::::::::7J~7~~:r:;::'::·:::::::::~~4g~:,::. ,::::::::::::.:::::·::'::,:::~)#7l·::::::'::·:::::p.~OOZ~::(:,:::::::.·:·]~~Qlg4.::

Heated home with range/oven 8.1536 3.4573 5.56 0.0191 0.0101

::P~~~::~i¢Bl~~¢:::apijW~:1~$i:·::iw~::4~1:::: .:n::::::::::::::::4Jj~~~r[::::·:::::::::::::~~~$~:::H:::·:::::::::::::::::::·:~:i:47::(::::::::,:::::~~pgQj:::.:: ::::!:::::::::::P~~Q7':

Building has 20 to 49 units -10.7906 4.3206 6.24 0.0131 0.0094

':'Plbtfi~~:aRf¢tl~~::l~me~::·Ni;#i.:::·::[::·::'::::::::::::::::::::::::::::::::[:::::::::~S~$QQr::·::::::::::::gJ'l ~4$::::::::::::'::::::::::':::::::::rn;Q%:::::::::::::::::·Q~OO~$::U:::::·:::;::.:P~OO9:~::

Propane forced air furnace 14.2304 6.8444 4.32 0.0385 0.0093

:eHB.kiji~:r@!~~tm!¢.11Q.lI!\:::Jttt:\i :::::t:t::::::::::jP.~:1:Sg4j::::t::::\:::t:~::~;$.Q~:~i::: :::.:;::\:::t::::t:t:\::~4!i:*~J :\i,j:::::.:;:\.p;pg$~:::: ::::::\::t::::t·:g;PP~Q:j

Building age: 1960s -4.8148 2.3452 4.21 0.0410 0.0082

3-37
Table 3-11. Mean ofnet indoor N02 contributed by categorical variables in the stepwise

regression presented in Table 3-10.·

0.0101

20.0527 0.0110

Ref. Cat.

Cooking range make 2.48 .. -9.91

-2.76

0.18

21.65 9.8864 0.0115

-4.67

3.99

11.99 10.1594 0.0090

3.62 7.4726 0.0124

-8.55 Ref. Cat. N/A

60.75 37.1754 0.0588

• Only those categorical variables with partial R-squared value of>O.OI are listed.

•• Non-significant in the stepwise regression.

3-38
Table 3-12. Stepwise regression ofnet indoor N02 (without air pollutant measurements).

Model R2 =0.3643

Regression

Error

Total

Degree of Freedom

12

280

292

Sum of Squares

48915.65

85368.20

134283.85

F Value

13.37

Prob > F

.0.0001

3-39
Table 3-13. Mean of net indoor N02 contributed by categorical variables in the stepwise

regression presented in Table 3-12.

Garage type

•• Non-significant in the stePwise regression.

3-40

-3.49

-5.65

-4.35

4.78

16.21

12.8779

Ref Cat.

28.9815

Ref Cat.

9.8757

0.0429

N/A

0.0496

N/A

0.0375
I I

Table 3-13. Continued.

• ~~:~ij~~lliJI: !ililml!~;,[mji~~t!~.I.1 i~ lill.]~ Water heater burner 1) MonoportJin-shot 3.29

type 2) MonoportJup-shot 9.32 7.7284 0.0143

3) Multiportfm-shot -2.50

4) Multiport/up-shot -5.60 Ref Cat. N/A

5) N/A 3.65

6) Up-shot 1.48 - -

'1~~11:)'[r~:!":~i~,)::illl~)f!!~~I! i!'!,II:'Il~::·!. Heated home with 1) No -0.91 Ref Cat.

range/oven 2) Yes 26.58 11.1066 0.0127

:1~1[~i~I'~~:i;rl,!~,'iil.:il!!ililijlii Clothes dryer burner 1) Estimated 7.68

input 2) N/A 3.48 5.5020 0.0118

3) Plated -6.34 Ref Cat. -

3-41
3.3 Radon

3.3.1 Univariate Analysis Results

Radon was only measured indoors, and we report both univariate and multivariate

analyses for indoor radon. There were 116 significant variables for radon, 37 continuous

and 79 categorical.

Radon univariate analysis results showed that radon was almost a "mirror image" of

indoor 48-hour CO. Also, radon had a positively skewed distribution; therefore, many of

the significant variables were detected only by the nonparametric tests. However, it is

often difficult to interpret these nonparametric test results when there are more than two

categories in a variable. The nonparametric test only indicates an overall significance for

the null hypothesis that the groups were derived from the same distribution. We have

attempted to interpret the univariate analysis results for radon. Following is the bullet

summary and description ofthe results.

Building Characteristics

• A number of variables related to building characteristics indicated that radon in

single family detached houses was higher than in buildings for two or more

families.

• Radon in homes with central refrigeration air conditioners was higher than in

homes without air conditioners.

• Radon in homes with microwave ovens, kitchen fans, fireplaces and wood

stoves (or heaters) was higher than in homes without these items.

• Radon was positively correlated with the area and volume ofthe conditioned

space in a home.

Cooking Appliance

• Univariate analyses on several cooking related variables showed that radon in

homes with electric cooking appliances was higher than in homes with gas

cooking appliances.

• Radon was higher for built-in cooking units than for free-standing units.

• Radon was the highest ifthe cooking appliance was made by WHIRLPOOL

compared with all other makes.

• Radon was negatively correlated with the total pilot input value ofthe cooking

unit (including range, oven and broiler).

3-42
,I' ,

Heating Furnace

• Radon was the highest ifthe furnace in a home was propane or natural gas

forced-air furnace compared with all other types offurnaces.

• Radon was higher ifthe furnace was located outside than iflocated inside.

• Radon was higher ifthe furnace burner material was aluminum than ifit was

cast iron.

• Radon was positively correlated with the furnace burner input value.

Clothes Dryer.

• Radon was higher in homes with clothes dryers than in homes without clothes

dryers.

• Radon was the highest ifthe clothes dryer was made by GE compared with all

other makes.

• Radon was the highest ifthe clothes dryer was located in the basement

compared with all other locations.

• Radon of electric clothes dryers was higher than no clothes dryers.

"

Water Heater

• Radon was higher in homes with water heaters than in homes without water

heaters.

• Radon was the highest ifthe water heater was made by RHEEM compared

with all other makes.

• Radon was the highest ifthe water heater was located in the basement

compared with all other locations.

• Radon was the highest ifthe water heater fuel was electricity compared with all

other types offuels.

• Radon was higher ifthe water heater burner type was multi-port/up-shot than

ifit was multi-portfm-shot.

• Radon was the highest ifthe water heater burner was made of pressed steel

(these are not electric water heaters).

• Radon was higher ifthe water heater burner port was ribbon type than ifit was

slotted.

• Radon was the highest ifthe water heater was equipped with a low NOx

burner.

• Radon was negatively correlated with the age ofthe water heater.

Meteorological Factors

• Ifthe temperature in the home was cooler during the two test days, compared

with the temperature at the time when the measurement was completed, then

the indoor radon was higher than ifthe temperature remained the same.

3-43
• Radon was positively correlated with t~e heating degree day, but negatively

correlated with the average outdoor temperature and minimum outdoor

temperature during the two-day testing period.

• Radon was negatively correlated with the average, initial, and minimum indoor

temperatures during the two-day measurement period.

Measured Components

• Radon was negatively correlated with the air exchange rate, indoor and

outdoor 48-hour average CO, maximum IO-min, 30-min, I-hr and 8-hr indoor

CO, outdoor benzene, outdoor fonnaldehyde, indoor and outdoor N(h,

inverse ofthe home volume, and outdoor toluene.

Geographical Locations

• Radon was higher in homes located in the area where the zipcodes started with

"95" and the telephone area code was "916".

Sampling Parameters

• Radon was the highest ifthe home sampling was started on a Friday compared

with started on any other day ofthe week.

• Radon was the highest ifthe indoor samplers were located in the family room

compared with all other locations.

• Radon was positively correlated with the indoor samplers' distance to the

range burner, oven vent, furnace, any openable outside door/window, and to

the ceiling; also positively correlated with the outdoor samplers' distance to the

street traffic.

• Radon was negatively correlated with the indoor samplers' distance to the

floor, and outdoor samplers' distance to the surface ofthe ground.

Occupant Use Characteristics

• Radon was higher ifthe occupant(s) did not use the range/oven to heat home.

• Radon was higher ifthe occupant(s) did not open the window(s) during the

two-day period.

• Radon was higher ifthe occupant(s) did not smoke.

• Radon was higher ifbreakfast was cooked on the stove once than ifit was

cooked twice during the two-day period.

• Radon was higher ifdinner was cooked in the microwave oven once than ifit

was not cooked in the microwave during the two-day period.

• Radon was higher ifthe first member ofthe family was male.

• Radon was higher ifthe first two members ofthe family were caucasian than if

they were Asian.

• Radon was positively correlated with the ages ofthe first two members ofthe

family.

3-44 \\
'.' " - i • 1 '

"

• Radon was positively correlated with the total gas consumption during the two

day sampling period.

From these univariate analyses we see that indoor radon behaved differently from all the

pollutants presented earlier in this section. Homes with the following types of appliances

had higher radon indoors: electric cooking units, propane or natural gas forced-air heating

furnaces, electric clothes dryers, electric or newer gas water heaters. Apparently, large

single family houses with basements had the highest indoor radon among all types of

dwellings. The geographical region where the zipcodes started with "95" and telephone

area code was "916" was associated with higher indoor radon levels than any other

regions. Finally, the analysis results showed that opening the windows (or increase the air

exchange) is an effective way to reduce the indoor concentration ofradon.

3.3.2 Multivariate Analysis Results

After completion ofthe univariate analyses for radon, the 79 significant categorical

variables were fonnatted into dichotomous variables, resulting in a total of265 variables

eligible for the stepwise regression. The final step ofthe regression identified a model

which had an R2 of 0.33. However, examining the regression result and residuals to the

model we felt that the regression was trying to predict one high level home (pG079).

Therefore, we completed a regression with home PG079 removed from the dataset.

Results ofthis regression were summarized in Table 3-14. A higher model R2 of0.43 was

obtained. The most important variable for radon was the air exchange rate which

explained 11% ofthe variance. The higher the air exchange rate in a residence the lower

the indoor concentration ofradon. The first categorical variable to enter the regression

was the telephone area code ofthe participating homes.

Arithmetic means ofradon concentrations were computed and presented in Table 3-15 for

the significant categorical variables in Table 3-14. The radon arithmetic mean for homes

with the telephone area code of"916" was 2.24 pCiIL and for "209" was 1.46 pCiIL.

These two area codes represent several communities in Northern California near the Sierra

Foothills, which is known as an area in the State with higher concentrations ofradon.

3-45
Table 3-14. Stepwise regression ofindoor radon (without PG079).

Model R:Z = 0.4335

Regression

Error

Total

Degree of Freedom

14

277

291

Sum of Squares

52.94

69.19

122.13

F Value

15.14

Prob >F

0.0001

3-46

\

\
Table 3-15. Mean ofindoor radon contributed by categorical variables in the stepwise regression

presented in Table 3-14.-

Ref. Cat. N/A

Ref. Cat. N/A

Participant telephone

areacode

• Only those categorical variables with partial R-squared value of >0.01 are listed.

•• Non-~ignificant in the stepwise regression.

0.5917

-- 0.0961

3-47
Table 3-15. Continued.

•

Any gas leak

,--,

1) No

•••

0.74 Ref. Cat. N/A

associated with 2) N/A 1.33 0.1808 0.0165

ran e/oven/broiler

••1111

3-48

r,,\

\ / \

\.- '

.
:':,o.r,-

Section 4

CO Distributions and Extrapolations

In this Section, the CO distributions from this study are extrapolated to the entire State of

.California. As described in Section 2.1 ofthis report and in the Volume 1 report,

participants were selected from a random list ofresidential customers from the

participating utilities. Each utility had a target of 100 homes. This procedure led to a

sample that is representative ofthe residential customers of each utility but may not be

representative ofthe entire State in the aggregate.

The major factors that may cause the sample not to be representative ofthe entire State

are:

• Some areas ofthe State are not serviced by the three utilities.

• All SoCalGas homes have gas service but the other two territories contain

some homes without gas serVice (i.e., they are all electric homes).

• More homes are located in the SoCalGas' territory than the other two.

The 1990 census reported a statewide total of 11.18 million housing units in the State. At

the time ofthis survey about two years later (December 1991), the three utilities had

approximately 10.36 million residential customers. The three utilities are likely to have

accounted for more than 80 percent ofthe State residences during the survey.

The 1990 census also indicated that approximately 81 percent ofthe residences in Los

Angeles County heated their homes with utility natural gas which would imply that a large

majority ofthe homes in the County were available to be included in the sample.

Assuming that the residential customers from the three utilities represent the State of

f~~~~;tt , t 't Th.1

SoCalGas

••

5.72 55%

I.N~II. 75 26% seIVlce em ory. e

table on the right lists PG&E 3.67 35% 129 44%

the total number and SDG&E 0.97 9% 89 -30%

percentage of Total 10.36 100% 293 100%

residential customers in each service territory and the numbers and percentages ofhomes

in our sample. SoCalGas has about 55 percent ofthe residential customers among the

three utilities but only 26 percent in our sample.

4-1 ( /

.~ ~/" 'I'v .(
SoCalGas

Ifthe homes sampled were assigned in proportion to the number of customers in each

utility, without exceeding the actual number

of homes sampled in any area, then the

number of homes per territory would have

been as listed in the table on the right.

48 35%

13 9%

Total 136 100%

SDG&E

PG&E

A statewide sample ofhomes was prepared.

First, all 75 home identification numbers

were selected from the 75 homes in the

SoCalGas list. Next, forty eight homes were selected from the PG&E sample and 13 were

selected from the SDG&E sample by random sampling without replacement.

The resulting list of 136 homes is adjusted for the number ofresidential customers in each

utility. This home list was used to create adjusted data analysis files.

Tables 4-1 and 4-2 list the descriptive statistics for carbon monoxide using the actual

sample and the adjusted sample, respectively. The actual sample of293 homes resulted in

277 without missing CO data. The adjusted sample resulted in 130 homes with no missing

CO data.

Figure 4-1 shows a comparison ofthe 48-hour indoor CO distributions for the actual

sample and the adjusted statewide sample in log-probability format. A slight difference is

observed between the medians ofthe two curves, with the slopes being essentially equal.

Figure 4-2 shows two CO distributions for the 130 adjusted statewide homes: the 48-hour

indoor and 48-hour outdoor CO log-probability plots.

j

4-2

;" r- I .\.

\.\

\
I

W

Table 4-1. Actual measured CO descriptive statistics for 277 homes without missing data (ppm).

Parametfihi\:\\:/C:( .... .....

,:::.:::·;:::.'·.:';:·'::.;::· ..,:.::·.::.::::::i):·:,..:..... :.:...:.(

Arithmetic Mean I 1.61 5.21 4.81 4.51 2.91 1.01 5.51 4.31 

Standard Error 1 0.11 0.31 0.31 0.31 0.21 0.11 0.41 0.31 

Median I 1.21 3.51 3.11 3.01 2.01 0.81 3.31 2.91 

Mode I 1.01 2.01 2.01 2.01 1.01 0.11 2.01 2.01 

Standard Deviation I 1.71 5.51 4.81 4.41 2.91 1.21 6.71 4.4\ 

Percentiles

') --~~

Maximum

95th

76th

50th

25th

5th

Minimum

12.9 37.9 36.7 35.8 23.5 10.8 68.7 31.5 

4.3 15.1 14.2 13.2 8.3 2.7 16.1 12.3 

1.8 6.6 6.0 5.8 3.4 1.3 6.1 5.2 

1.2 3.5 3.1 3.0 2.0 0.8 3.3 2.9 

0.7 2.0 2.0 2.0 1.2 0.3 2.0 1.9 

0.1 1.0 1.0 1.0 0.5 0.1 1.1 1.0 

0.0 0.1 0.0 0.0 0.0 0.0 0.2 0.1 
Table 4-2. Estimated statewide CO descriptive statistics for 130 homes without missing data (ppm

1.

//

ejr~m~l~r.:r:\)::;·;:i:

~ : ~:i:j-:-:;~;~~t~; ~~~~1~t ;~~~;;~:~;t~~~~~~~;~~~~;~~\ ..:.~;;:;;; ;;:;:~:; -.-.

Arithmetic Mean

Standard Error

Median

Mode

Standard Deviation

Percentiles

Maximum

95th

75th

50th

25th

5th

Minimum

2.01 6.51 6.1/ 5.71 3.61 1.41 6.71 5.41 

0.21 0.61 0.51 0.51 0.31 0.11 0.71 0.51 

1.41 4.81 4.21 4.01 2.81 1.01 4.31 3.61 

1.01 2.01 2.01 2.01 1.01 0.11 2.01 2.01 

2.01 6.41 5.81 5.41 3.51 1.71 7.91 5.51 

12.9 37.9 36.7 35.8 23.5 10.8 68.7 31.5 

5.8 18.6 18.6 17.7 10.4 4.6 18.4 16.3 

2.4 7.5 7.0 6.8 4.5 1.5 7.7 6.7 

1.4 4.8 4.2 4.0 2.8 1.0 4.3 3.6 

0.8 2.5 2.2 2.1 1.4 0.4 2.1 2.0 

0.3 1.0 1.0 1.0 0.9 0.1 1.1 1.0 

0.0 0.1 0.0 0.0 0.0 0.0 0.9 0.3 
,--

48-HOUR AVERAGE INDOOR CO

Actual and Estimated Statewide

o ActuaJ (277) • Statewide (130)

20

10 o· 0

.. II>'

fP

_-w...

0.1

0.1 5 10 30 50 70

Percen1age of Homes Less Than

90 95 99 99.9

Figure 4-1. Comparison ofthe actual and adjusted statewide sample for 48-hour average

indoor CO.

48-HOUR AVERAGE CO STATEWIDE

Indoor and Outdoor (130 homes)

o Indoor • OUtdoor

0

.

. ...dJB'

-" - -

,

20

10

0.1

0.1 5 10 30 50 70

Percen1age of Homes Less Than

90 95 99 99.9

Figure 4-2. Comparison ofindoor and outdoor 48-hour average CO for the adjusted

statewide sample (130 homes).

4-5

Section 5

Mass Balance Modeling for CO, AER and Home Volume

This section develops a mass balance model ofCO in an effort to increase understanding

ofthe indoor sources ofCO.

Data presented in Volume 1 showed that thirteen homes had indoor CO concentrations

above the 8-hour NAAQS of 9 ppm, which is not to be exceeded more than once per year.

Only one home had a CO value in excess ofthe I-hour NAAQS of35 ppm. Therefore,

we selected the 8-hour averaging time as the most critical time period relative to the health

based standards. It should be emphasized that the 8-hour maximum values discussed in

this section were measured over a single 48-hour period. It is quite probable that both the

number of homes with exceedances would have increased ifwe had sampled for a longer

period oftime.

The major combustion appliances included in our study were cooking units, heating units,

water heaters, clothes dryers and fireplaces. Certainly, anyone ofthese appliances if

malfunctioning, installed improperly, misused or subject to backdrafting has the potential

.to cause elevated CO indoors. In Volume 2, we evaluated the homes with elevated CO

and noted that gas cooking units, gas heating units and gas fired fireplaces were each

suspected to have caused some ofthe elevated CO. Automobile exhausts from attached

garages and carports were also suspected to have contributed to the elevated CO in some

homes.

The analysis presented in this section focuses on the two major appliance categories that

have been associated with increased indoor CO: cooking and heating. The cooking

appliances have been separated into three categories: electric, gas without a standing pilot

light (GwoP), and gas with a standing pilot light (GwP). Heating units were also divided

into three categories: electric, gas forced air (FA), and gas wall furnaces.

Figure 5-1 shows the log-probability distributions for the maximum 8-hour indoor CO

concentrations1 for all homes in the study. The figure has three sections, each containing

the full set ofhomes. The top section shows all homes in a single distribution. These are

subsetted in the mid-section to show homes divided into three cooking categories. The

bottom section shows all homes divided into three heating categories. For the number of

homes in our sample, the lognormal distribution is best evaluated between the 10th and

90th percentiles. Also, given the accuracy and precision of our CO instrumentation,

values above 1 ppm are much more accurate than the lower values. Therefore, we

1 All averaging concentrations in this report are running averages. For instance, the maximum 8-hour

indoor CO concentration is actually the maximum 8-hour running average indoor CO concentration.

5-1 f j
8-Hr Maximum Inside CO

100

. . - 10 ;"'"

'E V co.

a

8 ". ./ -' 1 :-

.: -

--- 0.1

0.001 0.1 1 5 10 30 50 70 90 95 99 99.9 99.999

Percontage '"H_t..a Then

8-Hr Maximum Inside CO

By Cooking Type

+ EIedric CJ GwoP • GwP

100

.. ". c. _

10 CO 'E . co. k / a -,,,~-

8 -'=.

. .-....rF""..... 1 .. cx::o-

. o 0 :-

f-

.- 0.1

0.001 0.1 1 5 10 30 50 70 90 95 99 99.9 99.999

~"'H_t..aThen

8-Hr Maximum Inside CO

By Healing Type

+ EIec:Iric: CJ FA • WaH

100

. 0

o·

10 "po 0

'Eco.

a

0

0 .- .....,..~

1 8' . . ... ooco p.

0 . 0.1

0.001 0,1 1 5 10 30 50 70 90 95 99 99.9 99.999

Pe!<:en1age '"H_t..a Then

Figure 5-1. Maximum 8-hour indoor CO distribution by appliance type.

5-2
,'1

recommend ofuse the portion ofthe log-probability distribution that is above 1 ppm and

between the 10th and 90th percentiles to extrapolate to the higher CO concentrations.

Figure 5-1 indicates that homes with gas cooking appliances (GwoP and GwP) have

higher 8-hour indoor CO concentrations than homes with electrical cooking appliances.

Furthermore, homes with gas wall units,have higher 8-hour indoor CO concentrations than

either FA or electrically heated homes. This trend appears to be consistent throughout the

distribution, also through the 90th or 95th percentiles. It should be noted that these plots

show association with appliance types but not necessariIy causation. For example, homes

with electrical cooking or electric heating appliances have some ofthe highest indoor CO

values but we are not implying that CO is emitted from those appliances, only that those

homes have some other indoor CO source.

Other combustion sources (e.g., outside air, automobiles in garages, fireplaces, smoking)

contribute to indoor CO concentrations as well. Simple pairwise comparisons such as

these plots can provide insight but can also be misleading. It is also important to note that

correlations exist among the major variables. For example, very few homes have electric

cooking with gas wall heaters; while many homes with gas forced-air furnaces and gas

water heaters have electric cooking appliances. A further discussion ofthese appliance

combinations will be provided at the end ofthis section.

Figure 5-1 is illustrative but does not account for outside CO and may be misleading with

respect to the effect ofthe gas appliances. We can use a simple mass balance model to

help understand the impact of indoor combustion sources by inclusion ofthree important

factors: outdoor CO, house volume, and air exchange rates.

The following mass balance equation may be used to predict the change in indoor

pollutant concentrations, assuming a well mixed single zone structure (Ott, 1995).

where

c=

Co =

t=

p=

s=

v=

a=

k=

dc =paco dt + (slv) dt - (a+k)c dt,

final indoor pollutant concentration (Jlg/m3);

outdoor pollutant concentration (Jlglm3);

time (hrs);

penetration factor (unitIess);

emission rate (Jlglhr);

dwelling unit volume (m3);

air exchange rate (hr°l);

deposition rate (hr°l).

5-3

(1)

/
Assuming Co, p, a, s, v and k are constant over the time period ofinterest, equation (1) can

be solved for c(t) to give:

c(t) = -----

a+k

+

s(l_e-t(a+k»

v(a + k)

+ c(O)e-t(a+k) (2)

where c(O) == initial concentration at t = O.

Since carbon monoxide (CO) has low reactivity, we can assume that p = 1 and k = O.

Therefore equation (2) reduces to:

c(t) = co(1-e-at) + --- + c(O)e-at

va

(3)

A limiting assumption ofthese equations is that the air exchange rate (a), outdoor

pollutant concentration (co) and source emission rate (s) remain constant over the time

period (t). We know, however, that these parameters may vary substantially during short

time intervals. For instance, air exchange rates vary diurnally as a function of ambient

wind speeds and window/door management. Outdoor CO changes rapidly near streets

and parking lots, thus one sampling location may not be representative ofthe complete

dwelling unit envelop. And while source emission rates for standing pilot lights should be

fairly constant~ however, other source rates such as smoking and cooking are variable.

For an 8-hour time period, the term e-at is small for most of our observed air exchange

rates. High initial indoor CO may be important in only a few homes with low air exchange

rates. We have ignored the influence ofthe initial concentration on the 8-hour maximum

value since the term e-at rapidly dampens the impact ofthe initial concentration. The three

remaining mass balance factors other than the indoor emission sources are the average

outdoor CO for that same 8-hour period, the air exchange rate and the home volume.

The maximum 8-hour running average outdoor CO (for any time period within the 48-

hours) is shown in Figure 5-2 for different cooking and heating types. It is apparent that

the 8-hour outdoor CO distribution is higher for gas cooking appliances and for gas wall

units. At the 90th percentile there are differences ofover 5 ppm for different appliance

types.

The outside CO must be considered when evaluating the indoor sources, especially ifthe

gas appliances are to be compared with the electric appliances. Equation (3) above would

imply that a simple subtraction (indoor minus outdoor CO) may be an appropriate means

to compensate approximately for the different range of outdoor CO values noted for the

major appliances. This simple subtraction implies that we are neglecting the temporal

5-4 \

I -,j,

" ,
8-Hr Maximum Outside CO

100

0 0

00

10 I

'E I"

,,; ...

a

8 V 1 -" ----- ..::-

- 0.1

0.001 0.1 1 5 10 30 50 70 90 95 99 99.9 99.999

Percemage 01 Homes Less Than

8-Hr Maximum Outside CO

By Cooking Type

+ EIedric CJ GwoP • GwP

100 F

-

D •" 0

10 ll'il 'E...

.so ..... . 8

-e: 1 ~~_. ~~.

.. -~- . I0.1

0.001 0.1 1 5 10 30 50 70 90 96 (99 99.9 99.999

Pen:8ntBge of Ho..- I.8ss Than

8-Hr Maximum Outside CO

By Heating Type

+ EIecIric CJ FA • WeD

100

. . " 10 ... D

'E... ""

-- a pD. . 8 . 1 - ...~~-

coIr-

...~- .., . 0.1

0.001 0.1 1 5 10 30 50 70 90 95 99 99.9 . 99.999

PeR:entBge 01 Homos Less Than

Figure 5-2. Maximum 8-hour outdoor CO distribution by appliance type.

5-5
variations ofthe outdoor CO and the air exchange rate. Furthennore, the effect ofthe

initial indoor CO prior to the 8-hour time period is assumed to be small relative to the 8-

hour maximum indoor CO. These assumptions should not be biased toward a particular

housing characteristic or appliance type. The indoor minus outdoor CO value is our

attempt to show the effect of all indoor sources ofCO, including attached garages,

smoking or other combustion sources.

Figure 5-3 shows the log-probability distribution ofthe simple subtraction ofthe indoor

and outdoor CO values. The time period selected for the subtraction was based upon the

maximum running averages ofthe difference between 10-minute indoor and outdoor CO.

That is, the CO data were summarized for each ten minute period for each home. Those

ten minute values were subtracted to yield 6 values per hour. Running 8-hour averages of

the 10-minute differences were calculated for each home and the maximum was then

selected.

In Figure 5-3 (top chart), the log-probability distribution is a straight line between the 50th

percentile through the 95th percentile. The downward bias below the 30th percentile is

thought to be due to the CO instrument accuracy and precision at the low concentration

levels. Differences below 1 ppm are considered to be within the noise range ofthe

instruments. The outside instruments were subject to more downward drift than the inside

instruments due to lower outdoor temperature. Several 8-hour differences were

calculated to be negative or zero. The effect of combustion sources below 1 ppm over 8

hours is too small to be determined accurately using our techniques. Given the increased

error due to subtraction, it is still reasonable to conclude that the 1-0 data are generally

lognonnally distributed. The trend line between the 50th and 90th percentiles is a

- reasonable region for deriving the log-probability parameters.

Compared with the indoor CO presented in Figure 5-1, the indoor minus outdoor

callculation shown in Figure 5-3 resulted in the GwoP and electric cooking distributions

being closer to each other. Figure 5-3 also suggests that the effect ofindoor sources in

homes with electric heating may be as high as the gas heating homes in the upper 5th

percentile.

The mass balance equation states that home volume and air exchange rates are also

important considerations in evaluating the potential relative magnitude ofindoor sources.

A small source in a small volume home with the windows closed may have a larger effect

than a large source in a large house with the windows open. The next two figures plot

those two parameters as a function ofmajor appliance type. Figure 5-4 shows a

remarkable difference in home volumes for the various appliance types. Homes with gas

cooking and pilot lights are smaller and homes with forced air furnaces tend to be much

larger. Therefore, using the indoor minus outdoor value without also correcting for

volume could lead to large errors in judging the magnitude ofthe indoor sources.

5-6

./ .....-\ 'j/

J '
Indoor Minus Outdoor 8-Hr Max CO

100

. . 'E 10

.eo

... ",.-- 8

2 Vl/ 1 ~-

--- -- 0.1

0.001 0.1 1 5 10 30 50 70 90 95 99 99.9 99.999

Percen1llge 01 Homes Leos Than

Indoor Minus Outdoor B-Hr Max CO

By Cooking Type

+ EIec:lric CI GwoP • GwP

100

. . 'E 10 F c ... . .eo

~~ ~.

8 t;.: ... 2

J"!. V' 1

~-_"'! -- .

• co-_ => . po - cc _

0.1

0.001 0.1 1 5 10 30 50 70 90 95 99 99.9 99.999

Pe"",nlBgo 01 Homes L.eos Then

Indoor Minus Outdoor 8-Hr Max CO

By Heating Type

+ EIec:lric CJ FA • WoO

100

. 0

'E 10 • 0 ... . 0

.eo . 8 pc 2 .- 1

--~IJ"" • ..;:m ..I-- 0.1

0.001 0,' 1 5 10 30 50 70 90 95 99 99.9 99.999

Percen1llge 01 Homes l.8s8 Than

Figure 5-3. Maximum 8-hour indoor minus outdoor CO distribution by appliance type.

5-7
Home Volumes

100000

. .

I- ""... :[

t......... 10000

I.....V

",

0

..- 1000

0.001 0.1 1 5 10 30 50 70 90 95 99 99.9 99.999

Pelcentage of Homes Less Then

Home Volume

By Cooking Type

+ EIecIric Cl GwoP • GwP

100000

. .. 0

,.,...."

:[ ""c . .0

.2 10000 -V' ..-

0 ..' o •

. . . 0

1000

0.001 0.1 1 5 10 30 50 70 90 95 99 99.9 99.999

Pen:en1llge of Homes Less Than

Home Volume

By Healing Type

+ EIecIric Cl FA • WeD

100000

0

C

~a:; i:<Jcc

. . :[ . ,

l-- _. . .0

.2 10000 ~r

'"1,...9 t;;:: .... 0 o o~ . .:;..- ..

.0

. . 1000

0.001 0.1 1 5 10 30 50 70 90 95 99 99.9 99.999

Pelcentage 01 Homes Less Then

Figure 5-4. Home volume distribution by appliance type.

5-8
Figure 5-5 shows the log-probability distribution for the air exchange rates. The

differences in the air exchange rates for the major appliance types are dramatic. For

homes with GwP cooking, the slope ofthe distribution is higher than for the other two

cooking types. Wall unit homes have much higher air exchange rate distributions than

electric or FA unit homes. It is interesting to note the similarity of air exchange rates for

FA and electrically heated homes. This may be due to many electric heating units using

mechanical air circulation systems similar to gas forced-air units. These observations are

important because air exchange has to be considered to understand the relative magnitude

ofthe indoor sources for different major appliances.

Using the mass balance model, ifwe knew the indoor and outdoor CO temporal profiles as

well as the air exchange rate temporal profile, we could solve the equation for the indoor

source strength for short time periods when the model assumptions were not significantly

violated. Unfortunately, our data set is not that complete. For short time periods (less

than 1 hour) it is unreasonable to assume that the home volume is well mixed and that one

monitor in the home would adequately represent the entire house. Also, we do not have

the air exchange rate estimates for time periods shorter than 48 hours.

Even though all ofthe data are not available to directly solve for indoor source strengths,

we can use the mass balance equation to infer proportional relationships. We have defined

a parameter, total source tenn, to better understand the relative impact ofindoor sources

as follows:

Total Source Terrns.Hr= (Indoor CO-Outdoor CO) 8-Hr x (Air Exchange Rate) x (Volume).

That parameter was calculated for each home and the log-probability distribution is shown

in Figure 5-6. The top plot indicates a nearly straight line distribution from the 50th to the

90th percentile. The downward bend below the 50th percentile is due to the downward

bend seen in the indoor minus outdoor distributions in Figure 5-3. It appears that the

total source term distribution above the 90th percentile can best be estimated using the

data between the 50th and the 90th percentiles.

At the median, the relative total source tenns would imply that homes with gas ranges

have indoor sources that are about twice as high as homes with electric cooking. Also, at

the median, it is clear that homes with wall furnaces have higher indoor source rates than

homes with FA units. Also, homes with FA units have higher median source strengths

than homes with electrical heating. However, above the 90th percentile, a completely

different profile emerges. The cooking unit is no longer a distinguishing parameter as

indicated by the merging ofthe trend lines for the three cooking appliance types. The

homes with gas heating total source terms tend to merge together above the 90th

percentile~ but surprisingly, the electrically heated homes appear to have higher potential

source rates.

5-9
Air Exchange Rates

10

. . ,.. '=' :l:

! ". It 1

V w

<

V ..,.r

..-- 0.1

0.001 0.1 1 ·5 10 30 50 70 90 95 99 99.9 99.999

Percentage of Homes Lese Then

Air Exchange Rate

By Cooking Type

+ Electric CJ GwoP • GwP

10

.. '=' .. a :l: "

! " . "" a .. . It 1 w ".... < .J.

J':JP "

e·· .. ..~~

.. .. 0.1

0.001 0.1 1 5 10 30 50 70 90 95 99 99.9 99.999

PercentBge of Homes Lese Than

Air Exchange Rate

By Heating Type

+ EIecIric CJ FA • WeU

10

. ; .. i .. . ... ! ...r- ......

. It 1 or !Ii! ~' ",.-,.,-",

. . ...- ."",pa!

.... 0.1

0.001 0.1 1 5 10 30 50 70 90 95 99 99.9 99.999

Pe_ge 01 Homes Lese Than

Figure 5-5. Air exchange rate distribution (48-hour average) by appliance type.

5-10
8-Hr Max Source Term

10000

'::' 0

l 00 0

1000 ,.,

:c

a:

w

,/'V

6' ./ 0

Q0;:. 100 f

, 10

0.001 0,1 I 5 10 30 50 70 90 95 99 99.9 99.999

Pe<oontBge 01 H_ LMs Than

8-Hr Max Source Term

By Cooking Type

+ EIec:lric CI GwoP • GwP

10000

'::' . l .- ..J 1000

§2 o~

it:

W -<#"~ - :c .,,-

8 J Q 100

~~ 0;:. V 0

c' . '" 10

0.001 0.1 I 5 10 30 50 70 90 95 99 99.9 99.999

Percenlage '" H..... Less Than

8-Hr Max Source Term

By Heating Type

+ EIec:lric CI FA • w.n

10000

'::' - .c

r .. C

..J 1000 ,...rziO

a:

w

<

Q 100 -~. ?

V 0;:. 0

_..- .

co .- . 6' 10

0.001 0.1 1 5 10 30 50 70 90 95 99 99.9 99.999

Percentoge 01 H_ I.ese Than

Figure 5-6. Maximum 8-hour source term distribution by appliance type.

5-11 I'

//''''

.~ .....
On closer examination ofthe homes at the upper ends ofthese distributions we conclude

that this finding is not spurious. The home with the highest total source term (SCOB) had

an electric stove and an electric heating unit but they also used the gas fired fireplace with

the damper closed. The second highest total source term home (pGO08) was an all

electric home that used a wood-burning fireplace and the occupants were smokers. The

homes were also divided into two major groups: 34 homes with both electric cooking and

heating appliances; and, 112 homes with both gas cooking and gas heating appliances.

Figure 5-7 shows the log-probability distribution for those two groups. The earlier

patterns are still apparent. At the median, homes with gas cooking and heating appliances

have relative total source terms two to three times larger than for homes with electric

heating and electric cooking. However, around the 90th percentile, the two distributions

appear to merge. Ifthe trend continues for electric heating between the 50th to the 90th,

it is clear that those homes would have higher indoor total source tenns for CO above the

95th percentile. This is not to imply that electric appliances caused the elevated CO,

rather it suggests that electric homes may have other less common sources ofindoor CO.

These findings may be significant for developing public policy regarding reducing human

exposure to CO. Quite different strategies may be required to lower average exposure

compared to reducing the frequency of occurrence ofhigh exposures.

5-12
8-Hr Max Source Term

By Cooking and Heating Units

10000

+ Eledric Cook & Heat • Gas Cook & Heat

-::-

.s::.

C;

S.

..J 1000

0

>4

c:

/ W

<

5-

()

q 100 '-"

.

.-..

- + , ~~ +

./

,..,... ++ +

+

+

.- +

10

0.001 0.1 1 5 10 .30 50 70 90 95 99 99.9 99.999

Percentage of Homes Less Than

Figure 5-7. Log-probability plots for two groups of homes: electric cooking and heating;

gas cooking and heating.

5-13

Section 6

Exploratory'Analyses

This section is a compilation of exploratory analyses related to CO and benzene conducted

during this study. We noted a correlation between CO and benzene during initial analyses.

To explore this observation we present three linear regressions in Section 6.1 that show

the relationship ofindoor and outdoor concentrations of benzene and CO. Since cigarette

smoking is known to release trace amounts ofboth CO and benzene, in Sections 6.2 and

6.3 we evaluate the relationships between occupant smoking and the measured

concentrations of benzene and CO. The latter analyses evaluate the differences between

smoking and non-smoking homes.

6.1 CO and Benzene

Correlations between 48-hour average CO and benzene (both indoor and outdoor) were

derived from the full data set and are presented in the form ofscatter plots (Figures 6-1

through 6-3). The highest correlation is between outdoor benzene and outdoor CO

concentrations (R2 = 0.84). The R2between indoor benzene and indoor CO was 0.35, and

between indoor benzene and outdoor CO it was 0.32. The strong correlation between

outdoor benzene and outdoor CO indicates that there is likely to be a common outdoor

source for the two pollutants. The indoor values ofthe two pollutants follow the same

general pattern but with weaker associations as shown in Figure 6-2. The weakening of

the CO to benzene relation is most likely due to the influence ofindoor sources for either

one ofthese gases. From Figure 6-3 we can identify seven outlier homes that were

located in areas with very low ambient CO that had high indoor benzene levels, suggesting

the presence ofindoor benzene sources.

The analysis implies potential common outdoor sources ofCO and benzene (R2= 0.84).

The lower correlation between indoor benzene and indoor CO (R2=O.32) implies that there

are additional sources and/or chemical reactions indoors.

6-1
12 14

•

•

•

2 4 6 8 10

Outdoor Benzene (ppb)

Slope=O.70 Intercept=-O.32

r-squared=O.84 N=156

o

(J

'-o

o

"C-:::s

o

14 ..,------------------------,

- 12 E

8: 10

- 8

6

4

2

o

o

Figure 6-1. Outdoor benzene vs. 48-hour average outdoor CO.

14

•

12 • -

E

Co 10 •

Co Slope=O.53 Intercept=O.21 • - 8 r-squared=O.35 N=159

0

(J • • '- 6

0

•

0

"C 4

oS •

2 •

• • •

0 •

0 2 4 6 8 10 12 14

Indoor Benzene (ppb)

Figure 6-2. Indoor benzene vs. 48-hour average indoor CO.

12 14

Slope=O.85 Intercept=1.85

r-squared=O.32 N=157

4 6 8 10

Outdoor CO (ppm)

• •

2

2

O~--+----I---+----+------'f---~--~

o

14 ..,------------------------,

12

Q) ••

ai10 .•

N

ai 8

OJ

5 6

.g4

c

Figure 6-3. Indoor benzene vs. 48-hour average outdoor CO.

6-2
6.2 Smoking and Benzene

Correlations ofindoor and outdoor benzene concentrations were computed and plotted in

Figure 6-4 for smoking and non-smoking homes. Regression parameters were also

summarized in the figure for the two groups of homes. As shown in Figure 6-4, the

highest indoor benzene was observed in non-smoking homes. Out of 14 homes with

indoor benzene above 6 ppb only one was a smoking home. It is difficult to conclude

from this figure that smoking contributes to higher indoor benzene. Simple statistical tests

were conducted between the smoking and non-smoking homes. For this analysis the same

univariate approach was utilized as described in Section 2.4. The dependent variables

were indoor benzene, outdoor benzene and indoor minus outdoor benzene. There was

only one independent variable in this first anaysis: cigarette smoking (asked in

Questionnaire B). There were only two categories for the independent variable (yes or

no), the test was equivalent to a t-test as summarized in Table 6-1.

Table 6-1. Unpaired t-test results (p-values) between smoking and non-smoking homes

for indoor benzene, outdoor benzene and indoor minus outdoor benzene.

Parametric Test

Non- arametric Test

0.45

0.43

0.77

0.47

0.56

0.22

All six tests were highly non-significant, indicating that this analysis did not detect any

smoking effect related to benzene. Next, we removed the three outlier homes located on

the upper left-hand comer in Figure 6-4 and repeated the above six t-tests. Results

remained non-significant.

Three additional paired t-tests were carried out between indoor and outdoor benzene for

three groups: all homes, smoking homes and non-smoking homes. The purpose of

conducting these tests was to see ifthe indoor and outdoor benzene concentrations

measured in this study were from different distributions. The t-test results showed that the

two distributions were significantly different (p<0.0005) for all three groups. This

confinns the observation that average benzene concentrations indoors were higher than

outdoors.

6-3

r~ ... I

- / .. ,/
Figure 6-4. Correlation of indoor and outdoor 48-hour average

benzene by smoking for all homes

12.0 ..,-------------------------------_

o o

o

o

10.0

4.0

, 0

o "

o Non-smoking

--Slope=O.66 Int.=1.28 r-squared=O.56

N=44

----- --- Slope=O.72Int.=1.38 r-squared=O.36

N=112

• Smoking

o

o

o

o

o

o

o 0

•

•.0

o

o

o

o

. -~,-

0,'- 0 - 0 ,

.0 c9 r;p .,.

• _,' 0 0 cT' •

-"'r#c~ 0 . oil CO 0

cP 0 0

2.0

-.c

c. 8.0

c.

-Q)

c

c

Q) m 6.0

'-

.c

co

"I:t

'-o

o

"C

.E

2.0 4.0 6.0 8.0 10.0 12.0

0.0 +------+------+-----+-----1------+---------1

0.0

Outdoor 48hr Benzene (ppb)

6-4 BENSMKAL Chart 1
6.3 Smoking and CO

Since we are unable to find an effect ofsmoking on benzene, we further explored the

smoking relationships for carbon monoxide. Correlations of 48-hour average indoor and

outdoor CO concentrations were computed and plotted in Figure 6-5 for smoking and

non-smoking homes, respectively. Regression parameters were also summarized in the

figure for the two groups of homes. Indoor CO ofsmoking homes was generally higher

than that of non-smoking homes. We performed the same t-tests (as described above for

benzene) on indoor CO, outdoor CO, indoor minus outdoor CO with cigarette smoking as

the independent variable. Results are summarized in Table 6-2.

Table 6-2. Unpaired t-test results (p-values) between smoking and non-smoking homes

for indoor CO, outdoor CO and indoor minus outdoor CO. Indoor CO and net Indoor

CO were higher for homes with smokers.

Parametric Test

Non- arametric Test

0.0235

0.0021

0.9810

0.3717

0.0005

0.0003

The tests on indoor and net indoor CO were significant. Arithmetic means ofindoor CO

for smoking and non-smoking homes were 1.92 ppm and 1.42 ppm, respectively; and

arithmetic means of net indoor CO for smoking and non-smoking homes were 0.94 ppm

and 0.41 ppm, respectively. These results indicate that cigarette smoking is associated

with an approximate increase of0.5 ppm ofCO indoors.

6-5
Figure 6-5. Correlation of indoor and outdoor 48-hour average CO

by cigarette smoking for all homes

14 ..,-----------------------------,

•

12 •

-

E 10

c.

c.

-

0

0

G) 8

C)

ns

'-G)

0

'- 6

co

'-0 0 0

" 0

c

•

4

2

o

o

o o

,9,

o

, ,

o Non-smoking

-------- Slope=O.93Int.=O.49 r-squared=O.63

N=190

• Smoking

--Slope=1.37 Int.=O.56 r-squared=O.52

N=85

o 2 4 6 8 10 12 14

Outdoor 48hr Average CO (ppm)

6-6 \. \

'\ \

\\

COSMKALL Chart 1
Section 7

Conclusions and Recommendations

The following is a summary of conclusions derived only from analyses presented in this

volume. We also discuss recommendations for further evaluations ofthe data set,

integration of Volume I, 2 and 3 and comparisons to other field studies.

Outdoor Concentrations

Outdoor concentrations ofCO, benzene, toluene and N02 were all found to significantly

influence the corresponding concentrations indoors. The single most important factor

influencing variability ofindoor concentrations was outdoor concentrations. Therefore,

outdoor ambient air quality has a significant role in exposures during at home time

.periods. It should be noted that at the higher indoor concentrations (i.e., the upper

percentiles ofthe distributions) the observed indoor concentrations appear to be

independent ofthe outdoor concentrations due to the later's relatively low contribution to

the total.

Benzene

Approximately 60 percent ofthe sample had indoor benzene concentrations higher than

outdoor. However, outdoor benzene was found to be the single most important variable

(R2=0.38) affecting indoor benzene concentrations. The stepwise linear regression ofnet

indoor benzene resulted in thirteen significant variables accounting for 59% ofthe overall

variability. After all the air pollutant measurements were removed from the candidate list,

the model R2 reduced to 0.26 which indicates unaccounted sources ofindoor benzene.

Parameters relating to gas furnaces entered into the regression while parameters relating

to gas cooking and smoking did not. The unaccounted variation in the regression could be

due to a number ofreasons including: poor fit to the linear model, unidentified consumer

product usage and/or insufficient field data for less frequently encountered sources of

benzene.

Toluene

Approximately 80 percent ofthe sample had indoor toluene concentrations higher than

outdoor, indicating the presence ofindoor sources in most residences. Unlike benzene,

indoor toluene was not very dependent upon outdoor concentrations (R2=o.01).1 The

stepwise linear regression of net indoor toluene resulted in fifteen significant variables

accounting for a fraction ofthe overall variability (R2=O.32 with one outlying data point

removed). Only two ofthese 15 variables had entered into the benzene regression,

indicating the presence oftoluene sources that were not common to benzene. However,

1 or R2=O.lS with one outlying data point removed.

7-1 ,

,

I_~
since indoor benzene accounted for a substantial portion ofthe explained toluene

variation, the benzene sources also released toluene.

Nntrogen Dioxide

Approximately 38 percent ofthe residences had higher indoor N02values than outdoor.

Outdoor N02 was found to be the single most important variable (R?=O.22) affecting

indoor N02concentrations. The stepwise regression resulted in a 12-variable model for

net indoor N02(R2=O.36). Gas cooking appliance with standing pilot was the most

significant variable in the stepwise regression ofthe net indoor N02(R2=O.12).

Radon

One out of 261 samples ofindoor radon was above the EPA action level of4 pCiIL. The

stepwise linear regression ofindoor radon resulted in fourteen significant variables

accounting for almost half ofthe overall variability (R2=O.43 with one outlying data point

removed). Air exchange rate was the first variable to enter into the stepwise regression

explaining indoor radon levels (R2=O.11). Higher air exchange rates tend to lower the

radon concentrations.

Statewide Extrapolation

The CO distributions were adjusted to weight the samples in each gas company service

territory to be proportional to the total number ofresidential customers in that territory.

This weighting allowed extrapolation to the entire State. The adjusted statewide

distribution ofCO tends to be higher than our original sample due to the higher indoor and

outdoor values in the Southern California Gas territory, which was underrepresented in

the sample.

Mass Balance Modeling of CO

Indoor air quality models are frequently based upon a mass balance equation. In the mass

balance model, the important variables are outdoor CO, air exchange rate, source strength,

and dwelling volume. These variables in turn are dependent on housing characteristics and

combustion appliance types and use patterns. The interdependence among the variables

makes it difficult to interpret regression and factor analysis results. For example, data

analyses in Volume 2 show that residences with gas-fired wall furnaces tend to be small,

have gas ranges with standing gas pilots, and are located in areas with elevated outdoor

CO.

Using the mass balance equation, a parameter was calculated for each home to estimate

the relative total indoor emissions ofCO during the 8-hour time period during which the

maximum indoor CO was observed. The estimated "source term" represents the total of

aU emissions into the home, not the emissions from any particular combustion source(s),

during any given time period. The median total source term for residences with gas

heating and cooking appliances was approxima~ely three times larger than for residences

7-2
with electric cooking and heating. Above the 95th percentile, the total source tenns could

not be differentiated by appliance type. This indicates that residences with the highest

emission rates occur independent ofthe specific cooking or heating appliance type.

Potential sources ofthese higher indoor emissions not related to cooking or heating

include automobile exhaust from attached garages, fireplaces and other combustion

sources.

CO and Benzene Interrelation

Outdoor benzene and CO are highly correlated (R?=O.84) which indicates a common

source such as automobiles.. However, the indoor correlation ofthese two gases is much

lower (R2=0.35) indicating that their indoor sources differ and/or indicating different

indoor reaction chemistry.

Smoking

Indoor and net indoor CO both increased slightly with reported residential smoking.

However, neither indoor nor net indoor benzene were associated in this data set with

smoking in the residence.

Recommendations

This study of nearly 300 residences clearly shows the general relation ofindoor CO with

respect to major appliances, housing characteristics and outdoor CO. Data analyses have

been presented in three separate volumes that summarized certain portions ofthe study.

Interpretation ofthe multiple pollutant relationships to gain further insights into source

relations and integration ofthe three volumes into one executive summary are suggested.

It is also suggested that other similar field studies be reviewed, summarized and their

findings compared in the summary document.

The findings in this study represent a 2-day snapshot of a randomly selected sample of

California residences during a single season. However, it is difficult to extrapolate the 2-

day snapshot to a longer averaging time and to address policy-relevant issues such as the

frequency ofoccurrence of elevated concentrations within the population. A 12-month

longitudinal field study of a subset ofthe residences from this pilot program would

provide better insight into the proportion ofresidences experiencing elevated

concentrations and the frequency of elevated concentrations.

Furthennore, the results may be unique to California and its housing characteristics,

ambient pollutant levels, climate and appliance use patterns. Similar types ofstudies

should be conducted in other areas ofthe country and over alI seasons.

7-3 C

"'

_.-<, ;:,1' 'I " r I -

,

Section 8

References

CARB (1991). "Supplement to Indoor Air Quality Guideline No.1, Formaldehyde in the

Home". State ofCalifornia Air Resources Board, Research Division.

CARB (1994). "Supplement to Indoor Air Quality Guideline No.2, Combustion

Pollutants in Your Home". California Environmental Protection Agency Air Resources

Board, Research Division.

Colome SD, Wilson AL and Tian Y (1994). "California Residential Indoor Air Quality

Study, Volume 2, Carbon Monoxide and Air Exchange Rate: A Univariate & Multivariate

Analysis". Report prepared for the Gas Research Institute, Pacific Gas & Electric Co.,

San Diego Gas & Electric Co., Southern California Gas Company.

Dietz RN, Goodrich RW, Cote EA and Wieser RF (1986). "Detailed Description and

Performance ofA Passive Perfluorocarbon Tracer System for Building Ventilation and Air

Exchange Measurements". Special Technical Publication 904, ASTM, pp203-264.

Draeger (1990). "Model 190 Datalogger, CO (Rev. 3)". National Draeger, Inc., 101

Technology Drive, P.O.Box 120, Pittsburgh, PA 15230-0120.

OGAWA (1992). ''NO - N02 Simultaneous Sampling Protocol Using Ogawa Sampler,

Version 1.2". Ogawa & Company, USA, Inc., 1230 S.E. 7th Ave., Pompano Beach,

Florida 33060.

Ott WR (1995). Environmental Statistics and Data Analysis. CRC Press, Inc., 2000

Corporate Blvd.,N.W., Boca Raton, Florida 33431.

Pro-Tek (1991). "Air Monitoring Badges Catalog and Price List". Pro-Tek Systems,

Inc., 95 Brownstone Avenue, Portland, CT 06480.

Ryan PB, Sczek ML, Spengler JD and Billick ill (1988). "The Boston Residential N02

Characterization Study - n. Survey Methodology and Population Concentration

Estimates." Atmos. Environ. 22:2115.

SAS (1990a). SAS Language: Reference. Version 6. First Edition. SAS Institute Inc.,

Cary, North Carolina.

SAS (1990b). SAS Procedures Guide. Version 6, Third Edition. SAS Institute Inc.,

Cary, North Carolina.

SAS (1990c). SAS/STAT User's Guide. Version 6. Fourth Edition. Volume 1. SAS

Institute Inc., Cary, North Carolina.

8-1
SAS (1990d). SAS/STAT User's Guide. Version 6. Fourth Edition. Volume 2. SAS

Institute Inc., Cary, North Carolina.

u.s. EPA (1990a). "Charcoal Canister Quality Assurance Data Management System

User's Guide". Prepared by Computer Sciences Corporation, Las Vegas, Nevada.

U.S. EPA (1990b). "Charcoal Canister Database Management System User's Guide".

Prepared by Computer Sciences Corporation, Las Vegas, Nevada.

U.S. EPA (1991a). Air Quality Criteria for Carbon Monoxide. EPN600/8-901045F, U.S.

Environmental Protection Agency, Environmental Criteria and Assessment Office,

Research Triangle Park, North Carolina.

U.S. EPA (1991b). "Radon Standard Operating Procedure (SQP) No. 15, Charcoal

Canister Analyzer System Descriptions". EPA QRIA-LVF.

U.S. EPA (1993). Air Quality Criteria for Oxides ofNitrogen. EPA 600/8-91/049aF,

U.S~ Environmental Protection Agency, Washington D.C.

Wilson AL, Colome SD, Baker PE and Becker EW (1986). "Residential Indoor Air

Quality Characterization Study ofNitrogen Dioxide, Phase I, Volume 2: Final Report".

EngineeringlResearch, Southern California Gas Company, Los Angeles.

Wilson AL, Kleinman M, Spicer CW and Hedrick R (1992). "Common household

products as sources ofinterference in the electrochemical detection of carbon monoxide:

Problem identification and resolution". Paper presented at the 85th Annual Air & Waste

Management Association Meeting, Kansas City, Missouri.

Wilson AL, Colome SD and Tian Y (1993a). "California Residential Indoor Air Quality

Study, Volume 1, Methodology and Descriptive Statistics". Report prepared for the Gas

Research Institute, Pacific Gas & Electric Co., San Diego Gas & Electric Co., Southern

California Gas Company.

Wilson AL, Colome SD and Tian Y (1993b). "California Residential Indoor Air Quality

Study, Codebook", Prepared for the Gas Research Institute, Pacific Gas & Electric Co.,

San Diego Gas & Electric Co., Southern California Gas Company.'

Yocom JE (1982). "Indoor-Outdoor Air Quality Relationships: A Critical Review". J. of

Air Poll. Control Assoc., May:509-518.

8-2
\,

Appendix A

Benzene / Toluene Data Update

.Contents:

1. Descriptive statistics for 48-hour average benzene (Ppb), full data set

2. Descriptive statistics for 48-hour average toluene (Ppb), full data set

3. Scatter plot ofbenzene co-located samples

4. Scatter plot oftoluene co-located samples

5. Benzene indoor vs. outdoor, full data set

6. Toluene indoor vs. outdoor, full data set and one outlier removed

Descriptive statistics for 48-hour average benzene (ppb), full data set.

~.:·:::}·:::.:::·:·ea~Erft~ffi~~:.:: .:..:..(:.:....... . ....:..... "':::::'::. ..: .

••·:.·.aijtd~6i·· JhdijH~:· ...}.. biltd~()r\ihdH6r

Arithmetic Mean

Standard Error

Mode

Standard Deviation

Count

Second Highest

Second Lowest

Percentiles

2.7 2.0 2.3 1.5 4.3 . 3.6 2.3 1.6

0.2 0.2 0.2 0.1 0.5 0.6 0.3 0.1

#N/A 1.4 #N/A 1.4 #N/A #N/A #N/A #N/A

2.2 1.9 1.9 0.8 2.7 3.3 1.8 0.9

161 160 81 80 35 35 45 45

11.0 11.4 11.0 3.9 9.3 11.4 7.4 3.9

0.6 0.3 0.7 0.3 0.7 0.5 0.7 0.6

.'

Maximum 11.2 11.8 11.2 4.1 11.0 11.8 10.5 4.4

95th 7.6 5.5 4.7 3.2 9.1 11.0 4.6 2.9

75th 3.3 2.3 2.8 1.8 5.7 4.8 2.6 2.0

Median 2.1 1.3 1.8 1.2 3.5 2.8 1.7 1.3

25th 1.2 0.9 1.1 0.9 2.6 0.9 1.2 1.0r

5th 0.7 0.6 0.7 0.5 0.8 0.6 1.0 0.7

Minimum 0.5 0.3 0.5 0.3 0.6 0.4 0.6 0.6
Descriptive statistics for 48-hour average toluene, full data set.

IQutdQor::llha6Qr}::IOotaij~t.Olllidb:6f:··:··lomab:&t··

··:::.···::.~q"€a~Wii·······:I$ija~IG~$·Hdmei: ::-I$PGic~H~ffi~$:.·.: 6miib6f:IHdij~t::·{ . .

Arithmetic Mean I 8.31 5.41 7.41 5.2

Standard Error I 0.5/ 1.0/ 0.71 1.9

Mode I #N/A I 2.21 #N/A I 2.2

Standard Deviation I 6.71 12.71 6.01 16.9

Count I 1611 1601 811 80

Second Highest I 34.51 30.71 30.51 30.5

Second Lowest I 1.41 0.41 1.41 0.5

10.9 8.0 7.8 3.8

1.2 1.3 1.1 0.6

#N/A #N/A #N/A #N/A

7.2 7.7 7.1 4.0

35 35 45 45

27.2 23.6 28.7 13.5

2.0 1.0 1.9 1.0

Percentiles

Maximum 36.0 150.5 34.5 150.5 32.6 30.7 36.0 22.7

95th 21.7 14.6 16.3 8.6 23.6 23.3 20.7 11.9

75th 11.0 5.0 9.0 4.1 12.9 10.4 9.4 4.3

Median 6.1 2.6 5.8 2.5 10.1 6.2 5.1 2.4

25th 4.0 1.6 4.0 1.7 5.8 1.5 3.4 1.6

5th 1.9 0.9 1.7 0.8 2.1 1.0 2.3 1.2

Minimum 1.1 0.1 1.1 0.1 1.8 0.6 1.8 0.4

~-.SJ
Benzene Co-located Sampies

12 .,---------------------------------"

10

•

, , ,

. ,' ,

8

.cQ.

Q.

-en

CI) 6 -

eu

.~

Q.

::::s

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4

2

, ,

o '

o

,,

2

, ,

, ,

o

4

o

,

, ,

, ,

, , , , ,

6

Samples (ppb)

,, ,

8

• Batch 1

o Batch 2

-------- 1:1 Line

10 12

BENZ_CUP.xLC 10/6195
Toluene Co-located Samples

35 ...,..--------------------------------,

•

30

• Batch 1

o Batch 2

-------- 1:1 Line

• , , ,

, , .

25

.c8: 20

-fA

Q)

-tU

CJ

c. 15

C •

o

10

5

o

5 10 15 20 25 30 35

O+-----+-----+-----+-----+----+-----Ir------i

o

Samples (ppb)

TOL_DUPS.xLC 1016195
48-hour Average Benzene Indoor vs. Outdoor, Full Dataset

12.0 Slope=0.71 Intercept=1.33

• r-squared=0.38 N=160

• •

•

10.0

•

• - 8.0

.Q •

Q.

Q.

••

• -

0

0 •

"C

•

6.0 •

.E

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c: • • G)

•

N • •

c:

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••

m 4.0 •

•• •

2.0

2.0 4.0 6.0 8.0 10.0 12.0

0.0 +-----+-----+-----+-----+-----+---------i

0.0

Benzene Outdoor (ppb)

btallivo.xls chart1
48-hour Average Toluene Indoor vs. Outdoor. Full Dataset

20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0

0.0 +----f------+----+----+------+----+----+-----i

0.0

40.0

• Slope=0.06 Intercept=7.92

35.0 • r-squared=0.01 N=160

•

30.0 •

•

• •

.cc. 25.0

c.

-

0 • •

0

"C 20.0 I .5

(1) ••

c

(1) •

'0= 15.0

I10.0

•

5.0

•

Toluene Outdoor (ppb)

btallivo.x1s chart2

,/
48-hour Average Toluene Indoor vs. Outdoor, with One Outlier

Removed

5.0

5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0

0.0 +----+----+------/----+----+-----+-----+------1

0.0

Toluene Outdoor (ppb)

............ btallivoJds chart3

Appendix B

Ambient Temperature & Analysis Files

The ambient temperature file contains four temperature related variables: minimum

ambient temperature (min_tout), average ambient temperature (avg_tout), average indoor

temperature (avg_tin) and heating degree days (deg_day). These variables were derived in

the following manner:

The highest and lowest outdoor temperatures for 3 consecutive days covering the

48-hour monitoring period for each home were extracted from newspapers, which

published meteorological data obtained typically from monitoring stations located

in municipal airports. The closest monitoring station to each home was identified,

and the temperature data ofthat monitoring station were assigned to that home.

The highest and lowest outdoor temperatures for the 3 consecutive days were

named as DAYIR, DAY2R, DAY3R, DAYIL, DAY2L, and DAY3L. The

outdoor minimum temperature (min_tout) was simply the lowest ofDAYIL,

DAY2L, and DAY3L. The average outdoor temperature (avg_tout) for each home

was calculated as:

avg_tout = [DAYIH + DAY2H + DAY3H + DAYIL + DAY2L + DAY3L] /6 .

Heating degree days (deg_day) was calculated using the following formula:

The fourth variable in the ambient temperature file is average indoor temperature

(avg_tin). A minimax thermometer was placed next to all the other indoor

samplers in each home. Four temperatures were recorded by the CSRs for each

home: the minimum (Tmin ), the maximum (Tmax), the initial (Tinitia1) and the final

(Tfinal) temperatures. The average indoor temperature was calculated as follows:

avg_tin =[(Tmin + Tmax) /2 + Tinitial + Tfina1 ] /3.

The analysis file was. created by consolidating the indoor and outdoor CO concentrations

(with different averaging times), all the indoor and outdoor passive measurement results

(actual exposure hours adjusted), and certain derived variables such as the inverse ofthe

home volume (lN), CO emission rates for various averaging times, heating type and

cooking type.