**r~'** . --**.11~** ••• LI._I •• I._II •• LI ••**LL.~II** •.•**I.~' --~**  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 NATIONALTECHNICAL 

## I INFORMATION SERVICE " SPRINGFIE.LD.'--.:-V:....:...A 2=2,-,-=16c-=--1

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### 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'

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### 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).\



### ReportPrepared 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 DataManagement and Analysis Methodology 2-5 Section 3 Analyses ofPassive MonitorData 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 ExploratoryAnalyses
.. 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 FilesIi 

### Table Number LIST OF TABLES Page No. 3-1 Univariate analysis results summary 

### 3-2 3-2 Net indoorbenzene stepwise regression results: 

### all significant variables were available for the regression except indoorbenzene, outdoor benzene and CO eIllission rates 

### 3-16 3-3 Mean ofnet indoorbenzene contributed by categorical variables in the stepwise regression presented in Table 

### 3-2 3-18 3-4 Stepwise regression ofnet indoorbenzene: all the 

### airpollutant measurements were removed 

### 3-19 3-5 Mean ofnet indoorbenzene 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 ofnet indoortoluene contributed by categorical variables in the stepwise regression presented in Table 

### 3-6 3-24 3-8 Stepwise regression ofnet indoortoluene: 

### all the 

### airpollutant measurements and 

### PG005 were removed 

### 3-25 3-9 Mean ofnet indoortoluene contributed by categorical variables in the stepwise regression presented in Table 

### 3-8 3-26 3-10 Stepwise regression ofnet indoorN02

### (all significant variables except

## CO eIllission rates, indoor and outdoorN02 ) 3-37 

### 3-11 Mean ofnet indoorN02 contributed by categorical variables in the stepwise regression presented in Table 

### 3-10 3-38 3-12 Stepwise regression ofnet indoorN02(without 

### airpollutant measurements) 

### 3-39

ii 

# 43-13 Mean ofnet indoorN02 contt1buted by categorical variables in the stepwise regression presented in Table 

### 3-12 : 3-40 3-14 Stepwise regression ofindoorradon**(wi~hout** 

### PG079) 3-46 3-15 Mean ofindoorradon contributedby 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 indoorbenzene , 

### 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 OFFIGURES Page No. 4-1 Comparison ofthe actual and adjusted statewide sample for 48-hour average indoor CO 4-5 4-2 Comparison ofindoor and outdoor48-hour average CO for the adjusted statewide sample (130 homes) 4-5 5-1 Maximum 8-hourindoor CO distribution by appliance type 5-2 5-2 Maximum 8-hour outdoor CO distribution by appliance type 5-5 5-3 Maximum 8-hourindoorminus outdoor CO distribution by appliance type 5-7 5-4 Home volume distribution by appliance type 5-8 5-5 Air 

### exchange rate distributionby 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 Outdoorbenzene vs. outdoor CO 6-2 6-2 Indoor**benze~** 

### vs. indoor CO 6-2 6-3 Indoorbenzene vs. outdoor CO 6-2 6-4 Correlation ofindoor and outdoorbenzeneby smoking for all 

### homes 6-4 6-5 Correlation ofindoor and outdoor48-hour average CO by cigarette smoking for all 

### homes 6-6

iv

### ACKNOWLEDGMENT
The success ofthis study required thejoint 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 peerreview 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 StanfordUniversity and U.S. Environmental ProtectionAgency Office ofResearch and Development Dr. Joseph Behar U.S. Environmental Protection Agency Exposure Assessment ResearchDivision John Ginnan U.S. Environmental Protection Agency Indoor

### AirDivision Dr. John Spengler Harvard School ofPublic Health

Jim Bryant, Ron Cook, ChuckLynch: Pacific 

### Gas & Electric Company Dana Cotter, Randy Morthel, Dan Thomas: Southern California Gas Company Don Goodman, Steven Swanson: San Diego 

### Gas & Electric Company

vPREFACE This report concludes the three volume report series for the CaliforniaResidential Indoor Air Quality Study sponsored by the Gas Research Institute (GRI), the Pacific Gas and Electric Company (pG&E), the.SanDiego 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 orderto identify the significant independent variables (withp 

### < 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%. Fornet indoortoluene, the explained variance was 72%, for net indoorN02 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 studywas 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 studywas not specifically designed for benzene, toluene orradon. 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 ofcustomers in each utility, then, based upon the territory with the lowest sampling rate, the total number ofhomes 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 betweenthe actual sample of293 homes and the adjusted sample of136 homes. The two distributions tracked each other, with the CO arithmetic mean and median ofthe adjusted sample slightly higherthan the actual sample.

I Model R2 represents the variability explainedby the multivariate model.
viA 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 higherindoor 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 ofhigh CO exposures. The analyses ofthe relationship between CO and benzene revealed that indoorbenzene 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 ofnon-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 ofother 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 codebookwas 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 indoorminus outdoor CO and air exchange rate. A limited set ofanalyses 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 othervolumes to gain a broaderunderstanding 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 ofairpollution on the general public's health, not only ambient pollutant sources but all sources ofpollution 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 ofexposure 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 AirResources 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). Indoorunvented combustion appliances, such as gas-fired ranges and unvented gas space heaters, have been associated with various combustion pollutants. The emissions oftrace amounts ofpollutants into the home as the result ofnatural gas usage may include carbon monoxide (CO), nitrogen oxides (N02, NO, HONO, 

### RN03), radon and organic compounds such as formaldehyde and benzene.
1-1The 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 ofa 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 ofgas appliances in proper perspective. These studies were recognized as significant and important efforts in the current version ofthe N02 CriteriaDocument. 

### As a continuous effort to extend the knowledge ofnatural 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 betterunderstand 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 ofgas 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.

1-2The 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' ofresidential 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 Iconcent.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 majorfactors associated with indoor pollutant concentrations. A randomly selected sample size ofapproximately 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 similarin-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
2-1are 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 customerlistings is not only possible but offers an attractive sampling frame frQm which to conduct a survey. Utility customerrecords are current, which ensures a high degree ofeligibility. Population coverage is very high since almost every household in Californiareceives service from one ofthe three participatingutilities. This is in coI)trast to telephone surveys which would miss nearly 10% ofall households in Californiathat do not receive phone service and may miss up to 25% ofhouseholds in certain socio-economic groups. The utilities also have a high level ofrecognition from their customers which reflects onthe 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 over40%. This study**re~ulted** in the highest participation rates that we are aware of for a study that required such a high level ofparticipation and cooperation in making residential measurements. Most ofthe State ofCalifornia and a majority of

### all households are customers ofone 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 ofnorthern California are supplied by Portland General**Electric~** the Long Beach area receives its gas from a municipal utility and is therefore notjncluded 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. Forthis**~tudy,** the memory capacity of eachDraeger dataloggerwas customized so that monitoring was eXtended to the maximumtime 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.

2-2As part ofthis project, a number ofperfonnance evaluation studies were conducted on the DraegerModel 190 CO monitor. The monitors were found to be temperature sensitive and responded positively to a variety ofhousehold products containing alcohol and other compounds (Wilson et al., 1992). An insulated Styrofoambox 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

### AirMonitoring 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 yearbefore the funding for the analyses was available. The updated benzene and toluene data (including the first and second batch) are presented in Appendix 

### Aofthis report. Although the 

### R2 value for benzene indoor vs. outdoorreduced 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 indoorvalues in the second batch than in the first batch. The 

### R2 value for toluene indoorvs. outdoor reduced from 0.20 to 0.15 (with one outlier removed) for the same reason. In addition, reviewing the

. plot ofco-located samples for benzene (ortoluene), 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 samplerhas two chambers, one on each side ofthe cylinder. Each chamber holds one collection filter, which was prepared for eitherN02 orNOx collection. We put one ofeach filter type in each sample. The Harvard School ofPublic HealthEnvironmental Science and Engineering laboratory determined that theirNOx 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 aperfluorocarbontracer (PFT) system (Dietz et al., 1986). PFT gas was released from two locations inside eachhome to provide reasonably uniformPFT dispersionthroughout 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 theHarvard School ofPublic HealthEnvironmental 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 otherthan formaldehyde. There were 20% blanks for formaldehyde. In addition, eachlaboratory 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 customerrelations 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 orderto expedite the publication ofthat volume. Afterthe 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 1990athrough 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. ParametricPearson 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 ofeach category.
1 While all continuous variables are numerical, some numericalvariables are categorical in ourdatabase. 2ThePearson correlation is aparametric testthat maybe influencedby outliers; the Spearman is a rankordernonparametric testthat is influenced less by outliersbutmay have less powerto detect associations. 2-5When 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 particularpair 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-sumtest (or**Mann-Whitn~y** U 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 of0/1 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 afterthe 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 underthat categorical variable. The SAS multi-linear routine fits linearregression 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".

:.... 

### 2-6various 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 (inthis 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 earlierbecame "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 ofPassive 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 orderto control for the outdoor effect to better evaluatethe indoor sources ofbenzene, toluene and N02 , we created the net indoor concentrations by subtracting the outdoorfrom 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 indoorbenzene; net indoortoluene; indoor, outdoor, and net indoorN02 ; and indoor radon. Analyses were carried out from univariate through multivariate analyses for four of the eight dependent variables: net indoorbenzene, net indoortoluene, net indoorN02 and indoorradon. 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. Forthe convenience ofinterpreting Table 3-1, the independent variables were divided into the following ten groups: • Building characteristics
• Cooking appliance
• Heating furnace
• Clothes dryer
• Waterheater
• Meteorological factors
• Measured components
• Geographical locations
• Sampling parameters • Occupantuse characteristics Categorical independent variables were presented first in each ofthe ten groups. Nonsignificant independentvariables were not included. Table 3-1 shows associated factors for a particular dependent variable. For instance, indoorbenzene was correlated with many cooking appliance variables. The net indoorbenzene, which represents the indoor sources ofbenzene, had very little correlationwith these cooking variables. This is likely

3-1Table 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 orwood heater present N P,N P,N N Electric healer oresent P P,N Area ofthe 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 Numberof 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 ofaas 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 ofthe 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 blowerdoor 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 3-2Benzene 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 dryervented 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 bumertvoe 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 + ++ WaterHeater Number ofwater 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 healertvoe 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 bumertvoe 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 bumerflame needed adiustment P,N P,N N N Water heaterventino 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 indoortemPerature durinllthe samplin!l Period + 

## - IAveralle outdoortemPerature durina the samplina Period 

## - + + 

## - Minimum outdoor temPerature durinothe samplin!l Period 

## - - Indoor temPerature initial readino ++ ++ 

## - Indoor temperature final readin!l + Maximum indoortemPerature durinll lasttwo days + + Minimum indoortemPerature durina last two days 

## - Measured ComDonents Air exchan!le rate ++ 

### . 

# - ++ ++ 

## - 48-hour averaae CO indoor ++ ++ ++ ++ ++ 

### · 48-houraveralle 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-hourCO 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 ++ ++ 

### . ++ ++ 

## - - 3-3Benzene 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 ++ ++ 

## - - ++ ++ 

# - GeoaraphlcalLocations , 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 ofweek samDlina started P,N P,N Indoor sampler location P,N P N N N Distance ofthe indoor samDlers to ranQe bumer + Distance of the indoor samplers to oven vent · ++ Distance of the indoor samDiers to heatina fumace 

## - ++ 

## - ++ Distance ofthe indoorsamDiers 10 oDenable outside doortwindow 

## - + Distance of lhe indoorsamplers 10 floor ++ + ++ + 

## - Distance ofthe indoor samDiers to ceilina . 

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

## - ++ Distance ofthe outside samplers 10 street traffic . 

## - - ++ Distance of the outside samplers 10 surface of lhe around · 

## - Distance of the outside samDiers 10 side ofthe 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 oftimes cooked breakfast usinQ top burner in last 2daYs N P,N Number oftimes cooked breakfast usina oven in last 2 days N N Number oftimes cooked breakfast usinQ microwave in last 2 davs N P No. ollimes cooked breakfast usina other aDDliance in last 2 davs N P,N N Number oftimes cooked lunch usinll top bumer in last 2 days N N N Number oftimes cooked lunch usina oven in lasl 2 daYS N Number oftimes cooked dinner using microwave in last 2 daYS P,N Number oftimes 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 ofthe second member in the household P,N P,N P,N P,N N Race ofthe second member in the household P P,N P,N N Race ofthe 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 ForcategorlcaJIndependentvariables: "P" • significant in parametric lest; "N". significant in nonparametric lest; "P,N" • significant in both parametric and nonparametric tests. ForconUnuous Independentvariables: "+" 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 0.0200Sp 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 ofcooking appliances, which in

### tum, are associated with outdoorbenzene 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 ofa microwave oven having a significant effect on indoorbenzene. This is due to the intercorrelations among the variables; one variable may be a surrogate ofa few other variables. To betterunderstand 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 indoorbenzene, outdoorbenzene, 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 abullet 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 inunderstanding and interpreting the univariate results.

Building Characteristics
• Indoorbenzene was higher 

### ifthe residence had a microwave oven. • Indoorbenzene was higher 

### ifthe residence did not have an electric heater.
• Indoorbenzene 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.
• Indoorbenzene for cast iron ovenburners was higherthan for pressed steel ovenburners. Heating Furnace
• Indoor benzenewas the highest 

### ifthe furnace**m~e** was PAYNE compared with all other makes.
• Indoorbenzene 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 ofa higher outdoorbenzene level.)
• Indoor benzene was positively correlated with tr,e furnace pilot input value.! Clothes Dryer
• Indoorbenzene was not correlated with any variables related to clothes dryers.

1 Pilot (orburner) inputvalue is the rated heat inputfor a pilot (orburner), usually in the unit ofBTU /hr. 3-6 lJWaterHeater
• Indoorbenzene was the highest 

### ifa 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 higherthan "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 higherthe outdoor temperature, the lowerthe indoorbenzene). 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 ofindoorbenzene 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 ofSoCalGas. Sampling Parameters
• Indoorbenzene of"indoor samplers located in the hallways" was higher than "located in the living rooms", also higherthan "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 higherthe samplers, the higher the indoor benzene). Occupant Use Characteristics
• Indoorbenzene was higher 

### ifthe home did not have a garage (indicated by "number ofcars 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 correlationbetween indoorbenzene and building characteristics. In the cooking category, the pattern ofindoor benzene to cooking variables closely matches the pattern ofoutdoor
3-7 I' il -/"'LJ

### benzene to these variables (shown in Table 3-1). This may imply common factors influencing theirvariations. The influence canbe seen in othergroups as well, such as the heating variables and sampling parameters. Since outdoorbenzene concentrationis a function ofthe geographical location (for example, metropolitanLos Angeles area had high outdoorbenzene levels), the indoorbenzene was also correlatedwith geographical parameters.

Building Characteristics
• The univariate analyses onthree buqding-related variables (building type, number ofliving units, and garage type) clearly showed that multi-family buildings were associated with higher outdoorbenzene levels than single family dwellings.
• Outdoorbenzene was higherfor homes built inthe 1950s and 1960s than for homes built inthe 1970s.
• Outdoorbenzene for homes without air conditioners was higherthan for homes with refrigeration type central air conditioners.
• Outdoor benzene for homes without fireplaces was higherthanfor homes with fireplaces.
• Outdoorbenzene for homes without wood stoves (orheaters) was higherthan for homes with wood stoves (orheaters).
• Outdoorbenzene was negatively correlated with the area and volume ofthe conditioned space in a home (largerhomes had lower outdoorbenzene values). Cooking Appliance
• The univariate analyses on cooking variables repeatedly indicated that homes with gas cooking units had higher outdoorbenzene than homes with electric cooking units; particularlygas without pilot cooking was the highest.
• Outdoor benzenewas higher if

### the oven gas burnerdid not have aflame spreader.
• Outdoorbenzene was the highest if

### the gas ovenburnerwas made ofcast iron compared with other materials, such as aluminum and pressed steel.
• Outdoorbenzene was positively correlated with the total pilot input value of the range/oven/broilerunit. Heating Furnace
• Outdoorbenzene ofhomes with gas wall furnaces was higherthan those with gas forced-air furnaces; also higherthan those with electric heating.
• Outdoorbenzenewas the highest if

### the furnace was made by "HOLLY" compared with all othermakes.
3-8• Outdoor benzene ofhomes with gas floor furnaces was higher than those with gas forced-air furnaces.
• Outdoorbenzene was higher ifthe furnace pilot type was aerated than ifit was electronic.
• Outdoorbenzene was higher ifthe furnace burner input was estimated than ifit was read from a manufacturer's nameplate.
• Outdoorbenzene was higher ifthe furnace burnerwas made ofcast 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 ifcombustionproduct 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 outdoorbenzene. WaterHeater
• !The univariate analyses on the categorical variables in the water heater file 'repeatedly showed that outdoorbenzene ofhomes without water heaters was higher than homes equipped with water heaters.
• Outdoorbenzene was negatively correlated with the capacity ofthe water heater, i.e., the largerthe waterheater, the lowerthe outdoorbenzene. Meteorological Factors
• Outdoorbenzene 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
• Outdoorbenzene was negatively correlated with the indoorformaldehyde 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 ofoutdoorbenzene 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
• Outdoorbenzene of"indoor samplers located in the hallways" was higher than "located 

### in the living rooms", also higher than "located in the family rooms".
• Outdoorbenzene was positively correlated with the indoor samplers' distance to the floor and (ofcourse) 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 intercorrelationbetween indoor and outdoor benzene.)
• Outdoorbenzene 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 ofbenzene, for example, the street traffic.) Occupant Use Characteristics
• Outdoorbenzene was the highest wnenthe residence did not have a garage (indicated by "number ofcars parked 

### in the garage: not applicable").
• Outdoorbenzene was higher ifthe occupant(s) heated home with the range/oven.
• Outdoorbenzene was higher ifthe occupant(s) did not cook lunch with the oven. • Outdoorbenzene was higher ifthe first**membe~** ofthe family was black or hispanic than ifthe first member was caucasian.
• Outdoorbenzene was higher ifthere was no second member in the household than ifthere was a male caucasian as the second member.
• Outdoorbenzene was negatively correJated with the age ofthe second member in the family. The correlation between outdoorbenzene and older gas oven (made ofcast 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 olderfurnaces (with cast ironburners and without a manufacturer plate indicating the burner input value which had to be estimated) were associated with higher outdoorbenzene levels, which in turn, means that older homes were associated with higher outdoorbenzene. 

### In addition, outdoorbenzene was positively correlated with the furnace age (the older the furnace, the higherthe outdoorbenzene).

2Members in a household were interviewed andatable atthe end ofthe QuestionnairePartB was filled outby the customer service represep.tative. Forconvenience, the firstperson in thattable is referred as "thefirst member", the secondperson in the table "the second member", and so forth. 3-10Fromthe above univariate results, we can also summarize that outdoor benzene was higher for smallerhomes '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 indoorbenzene was the highest 

### ifthe residence had an attached garage (side or under) compared with all othertypes ofgarages and carports.
• All three fireplace related variables indicated that net indoorbenzene of fireplace present in a home was higher

### than no fireplace.
• Net indoorbenzene 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 indoorbenzene was more frequently correlated with other appliances such as clothes dryers, heating furnaces, and waterheaters.
• Net indoorbenzene of built-in cooking units was higher than free-standing cooking units.
• Net indoorbenzene 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 indoorbenzene was higher 

### ifthe furnace make was PAYNE than 

### ifthe make was 

### WilLIAMS.
• Net indoorbenzene was higher 

### ifthe furnace was located in the basement or in the garage than ifit was located inside.
• Net indoorbenzene was higher 

### ifthe furnace pilottype was electronic than 

### ifit was aerated.
• Net indoorbenzene was higher 

### ifthe furnace burnermaterial was pressed steel than ifthe material was cast iron.


### 3-11• Net indoorbenzene 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 higherthan without a clothes dryer.
• Net indoor benzene was the highest ifthe clothes dryer make was GE or KENMORE compared with all other makes. WaterHeater
• The univariate analyses ofthe variables related to water heaters repeatedly indicated that net indoorbenzene in a residence with awater heaterwas higher than without a water heater.
• Net indoor benzene was positively correlated with the water heater,capacity. Meteorological Factors
• Net indoorbenzene was higher ifthe temperature in a home during the two-

. , day period was higher compared with the time whenthe 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 correlatedwiththe average outdoor temperature during the two-day period. Measured Components
• Net indoor benzene was positively correlated with the net indoor CO, radon, indoorbenzene, and indoortoluene.
• Net indoorbenzene was negatively correlated with the 

### air exchange rate, outdoor CO, outdoorbenzene, 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 CSRD. Cotter.



### 3-12Sampling 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 outdoorbenzene, net indoor benzene was higher in newer and largerhomes. Since net indoorbenzene represents indoor sources ofbenzene with the outdoor concentration removed, there maybe indoorbenzene 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 indoortoluene in buildings with 20 to 49 living units was higherthan in buildings with 50 or more units; also 1 living unit was higherthan 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 orunder) compared with all othertypes ofgarages or carports (including no garage or carport).
• Net indoor toluene was higher ifa fireplace was present in a home.
• Net indoortoluene was positively correlated withthe area and volume ofthe conditioned space in a home. Cooking Appliance
• Net indoortoluene was negatively correlated with the age ofthe cooking unit. No other variables were significant in this group.

3-13 

# LHeating Furnace
• Net indoortoluene was the highest ifthe heating type was gas forced-air.
• Net indoortoluene 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 indoortoluene 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 withthe furnace burner input value and negatively correlated with the furnace age. Clothes Dryer
• Net indoor toluene in a residence with a clothes dryer was higherthan without a clothes dryer.
• Net indoortoluene ofclothes dryer located in the garage, located inside and no clothes dryer were 

### all higher than clothes dryer located outside. WaterHeater
• Net indoor toluene was the highest ifthe water heaterwas located in the garage compared with 

### all other locations.
• Net indoor toluene was the highest 

### ifthe water heaterburner 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 indoortoluene.
• Net indoortoluene was negatively correlated with the air exchange rate, outdoor CO, outdoorbenzene, outdoorN02, inverse ofthe home volume, and outdoortoluene. Geographical Locations
• There were no significant variables for net indoor toluene in this group. Sampling Parameters
• Net indoortoluene was negatively correlated withthe indoor samplers' distance to any openable outside doors/windows; also negatively correlated with the outdoor samplers' distance to the street traffic.


### 3-14Occupant 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 indoorbenzene, i.e., bigger and newer homes had higher net indoor toluene. There were a few exceptions, though. For instance, net indoor toluene was higherfor 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 orderto gain sufficient degrees offreedom for the regression analyses. 

### NetIndoorBenzene: For net indoorbenzene, there were 53 significant categorical variables and 30 significant continuous variables. After formatting the categorical variables into their dichotomous forms, there were total of180 (30 continuous and 150 dummy) variables available for the stepwise regression. Indoor and outdoorbenzene were removed from the candidate 

### list because net indoorbenzene 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-houfaverage outdoor CO, each explained 23% of the variance. Net indoor benzene was positively correlated with indoortoluene and negatively correlated with outdoor CO.

3-15Table 3-2. Net indoor benzene stepwise regression result: 

### all significant variables were available for the regression except indoorbenzene, outdoorbenzene, and CO emission rates.


### Model R2

## =0.5880 Regression Error Total 

### Degree ofFreedom 13 279 292 

### Sum ofSquares 310.66 217.71 528.37 

### F Value 30.62 

### Prob> F 0.0001

3-16 BENIO_Tl.DOC 10/6195To betterunderstand the categorical variables in Table 3-2, we computed the arithmetic mean ofnet indoor benzene for each category in every categorical variable that had a partial R2 of0.01 or greater. Table 3-3 lists these variables, their categories, arithmetic means ofnet 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 higherthan the reference category of"no car parked in the garage". When the waterheaterburnertype was "multiport/in-shot", the net indoorbenzene 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 indoorbenzene 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 indoorbenzene. In the next step, we intended to find out 

### ifmore housing and appliance characteristics,can be identified so that the net indoorbenzene 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 indoorbenzene. The temperature and number ofcars parked 

### in the garage remain the same in the second regression, while the "water heaterburner port type" represented water heaters instead of the "burnertype" 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 ofbenzene: 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 indoorbenzene was higher when it was warmer during the sampling period. People tend to openup their windows when it is warmer and higher outdoor concentrations ofbenzene could increase the indoor level.



### 3-17"
.

Table 3-3. Mean ofnet indoorbenzene 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 of

# the regression. 

### 3-18 0.4342

0.4489 

### Ref. Cat. N/A 

### 0.0255

0.0138 N/A

BENIO_T3.00c 10/6/95Table 3-4. Stepwise regression ofnet indoorbenzene: all the air pollutant measurements were removed.


### Model R2

## =0.2622 Regression Error Total 

### Degree ofFreedom 10 282 292 

### Sum ofSquares 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 ofnet indoorbenzene 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 inthe. garage. Also from the univariate analyses we know these garages were attached eitherto the side oforunderthe houses. 4) Natural gas (or propane gas) burning water heaters. The reference categories for both "waterheater burnerport type" and "waterheaterburner type" were "not applicable". This category included homes without water heaters orwith electric water heaters; and these homes had lower net indoor benzene than homes with gas water heaters. 

### NetIndoor Toluene: Fornet indoor toluene, there were 24 significant categorical variables and 23 significant continuous variables. After formatting the categorical variables into their dichotomous forms, there was atotal of101 (23 continuous and 78 dummy) variables available for the stepwise regression. One home (pG005) had an unusually high outdoortoluene 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 onthe multivariate results. Indoor and outdoortoluene 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 outdoorbenzene 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 of0.32.4 Since all the air pollutant measurements were removed from the regression candidate list, both indoor and outdoorbenzene were not available. The first variable to appear in Table 3-8 is "Furnace Make: PAYNE" explaining 8% ofthe variance inthe 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 ofevery 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 ofheating furnaces made by PAYNE. Homes with heating furnaces made by PAYNE had ten times higher net indoortoluene than homes

3The same regression with PGO05had an R-squared of0.2668. 4The same regression with PG005 had an R-squared of0.1950. 3-21without 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 heaterburnerflame variable ("not applicable" means no water heaters or electric water heaters).5

5 Note that interpretation ofthe variable "waterheater 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. 

### 3-22Table 

### 3-6. Stepwise regression ofnet indoortoluene: 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 ofFreedom 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 withpartial R2 > 0.01 are listed. •• Non-significant in the stepwise regression. 

### N/A

N/A

3-24, \ ..



### Table 3-8. Stepwise regression ofnet indoortoluene: all 

### the 

### airpollutant measurements and PG005 were removed.
Model R2

## =0.3177 Regression Error Total Degree ofFreedom 15 276 291 Sum ofSquares 2304.20 4948.23 7252.44 

### F Value 8.57 Prob > 

### F 0.0001

3-25I' !'

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 higherthan for single unit dwellings.
• Indoor 

### N02 for homes built during and before 1950s was higherthan 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 higherthan 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 higherthan for homes with pool or spa heaters.
• Indoor 

### N02 for homes without kitchen fans was higherthan for homes with kitchen fans that exhaust to outdoors.
• Indoor 

### N02 for homes without fireplaces was higherthan 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 higherthan 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 higherthan GE range. • Indoor 

### N02 ofgas-fired cooking units was higherthan electric units.

3-28• IndoorN02 ofgas burners with aerated pilots was higherthan gas burners with electronic pilots.
• IndoorN02 was higher 

### ifthe range burnerflame needed adjustment.
• IndoorN02 ofgas ovens with luminous type pilots was higher than gas ovens with electronic type pilots; also aerated pilot was higherthan electronic type.
• IndoorN02 was higher ifthe oven burner input value was estimated than ifit was read from a manufacturer plate.
• IndoorN02 was the highest ifthe oven gas burner was made ofcast iron.
• IndoorN02 was higher ifthe oven gas burner had no flame spreader.
• IndoorN02 was higher 

### ifthe oven burnerflame needed adjustment.
• IndoorN02 was higher 

### ifany pilot in the rangelovenlbroilerunit needed adjustment.
• IndoorN02 was positively correlated with the age ofthe cooking unit, the gas ovenburnerinput

## valu~, and the total range/ovenlbroiler pilot input value. Heating Furnace
• IndoorN02 in homes with no heating furnace was higherthan homes with gas forced-air furnace and electric furnace; also indoorN02 ofgas wall furnace was higher than gas forced-air furnace and electric furnace.

_ • IndoorN02 was higher 

### ifthe furnace was made by HOLLY orWILLIAMS; and indoorN02 was lower ifthe furnace was made by DAY & NIGHT or CARRIER.
• IndoorN02was higher ifthe furnace was located inside than iflocated in the garage. • IndoorN02 was higher 

### ifthe furnace pilot type was aerated than ifit was electronic.
• IndoorN02was higher 

### ifthe furnace burner type was multi-port/up-shot than ifit was multi-portfm-shot.
• IndoorN02 was higher 

### ifthe furnace burner material was cast iron than ifit was pressed steel.
• IndoorN02was higher 

### ifthe furnace was not equipped with a low NOx burner.
• IndoorN02 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).
• IndoorN02 was positively correlated with the furnace age, and negatively correlated with the furnace burnerinput value. Clothes Dryer
• Univariate analyses on the categorical variables related to clothes dryers showed that homes without clothes dryers had higher indoorN02. • IndoorN02 was positively correlated with the clothes dryerburnerinput value.
3-29 

# / I- 

# ----1 /Water Heater
• Indoor 

### N02was higher for homes without waterheaters.
• Indoor 

### N02 was higher 

### ifthe water heaterwas not venting properly.
• Indoor 

### N02 was negatively correlated with the capacity ofthe water heater, also negatively correlated with the water heaterburnerinput 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 higherthan 

### ifthe temperature remained the same. • Indoor 

### N02 was positively correlated with the two-day average indoor and average outdoortemperatures.
• Indoor 

### N02 was positively correlated with the indoortemperature read at the beginning ofthe sampling and the maximum indoortemperature. Measured Components
• Indoor 

### N02 was positively correlated with the 

### air exchange rate, indoor and outdoor CO, indoor and outdoorbenzene, 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 ofSoCalGas 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 correlatedwith 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 streettraffic, 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

### N02was higher 

### ifthe first member in the family was a female.

3-30• IndoorN02 was higherifthe second member in the family was a male; also indoorN02 was higher 

### ifthere was only one person in the household.
• IndoorN02 was higherifthe first member in the family was black than if

### it was caucasian; also hispanic was higherthan caucasian.
• IndoorN02 was higherifthe third member in the family was hispanic than ifit was caucaSIan.
• IndoorN02 was negatively correlated with the age ofthe second member in the family. There were more variables associated withN02 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 otherpollutants. As shown in Table 3-1 and the above descriptions, indoorN02 was correlated with the indoor sources as well as the outdoor N02 levels. Low income families in older and smallerhomes were associated with higher indoorN02. Gas cooking units (especially with standing pilot) and combustion products leaked from aburning furnace pilot were possible indoor sources ofN02 .

Building Characteristics
• OutdoorN02 was higher for homes built in 1960s or earlierthan for homes built in 1980s.
• OutdoorN02 was higher for homes without 

### air conditioners.
• OutdoorN02 was higher for homes without wood stoves or heaters.
• OutdoorN02 was negatively correlated with the home volume. Cooking Appliance
• OutdoorN02 for homes with gas cooking was higherthan for homes with electric cooking; particularly gas with pilot cooking was higher than electric cooking.
• OutdoorN02 was higher 

### ifthe gas range burner pilot was luminous than ifit was electronic.
• OutdoorN02 was the highest ifthe oven gas burner was made ofcast iron compared with all other types ofmaterials.
• OutdoorN02was higher ifthe oven gas burner did not have a flame spreader.
• OutdoorN02 was positively correlated with the age and the total pilot input value ofthe range/oven/broilerunit. Heating Furnace
• OutdoorN02 ofno heating furnace was higher than gas furnace, which in tum was higher than electric furnace.
3-31 I , 'J• OutdoorN02of gas wall heating was higherthan electric heating.
• OutdoorN02was higher 

### ifthe heating furnace was located inside than 

### if

### it was located in the attic.
• OutdoorN02was 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).
• OutdoorN02 offurnaces not equipped with low NOx burners was higherthan electric furnaces or no furnaces, which in tum was higher than furnaces equipped with low NOx burners.
• OutdoorN02was higher 

### if, in the opinion ofthe customer service representatives, the combustion product may have leaked into home with only the furnace pilotburning (spillage suspected even when burnerwas not 

### in use).
• OutdoorN02was positively correlated with the furnace age. Clothes Dryer
• OutdoorN02 was higher 

### ifthe home did not have a clothes dryer.
• OutdoorN02ofhomes with gas clothes dryers was higherthan homes with electric dryers; also outdoorN02ofhomes without clothes dryers was higher than homes with electric dryers.
• OutdoorN02was positively correlated with the clothes dryer burner input value. WaterHeater
• OutdoorN02ofgas water heaters was higher than propane water heaters.
• OutdoorN02ofhomes without water heaters was higher 

### than homes with 1 or 

### 2 water heaters.
• OutdoorN02was higher 

### ifthe water heater burnerwas multi-portrm-shot than ifit was mono-port/up-shot. Meteorological Factors
• OutdoorN02 was positively correlated with the average outdoortemperature and initial indoor temperature; negatively correlated with the heating degree day. Measured Components
• OutdoorN02was positively correlated with the air exchange rate, indoor and outdoor CO, indoor and outdoor benzene, sampling duration, net indoor CO, indoorN02 , inverse ofthe home volume, and outdoortoluene; negatively correlated with the indoor radon.



### 3-32Geographical Locations
• Homes in the Los Angeles areahad the highest levels ofoutdoorN02 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 ofSoCalGas. Sampling Parameters
• OutdoorN02 was the highest ifthe indoor samplers were located in the hallway compared with all otherindoor sampling locations.
• OutdoorN02 was positively correlated with the indoor samplers' distance to the floor. Occupant Use Characteristics
• OutdoorN02 was higher 

### ifthe occupant(s) used the range/oven unit to heat home.
• OutdoorN02 was higher ifthe first member in the family was blackthan ifthe first member was caucasian
.. • OutdoorN02 was higher for single person households.
• OutdoorN02was positively correlated with the household gas usage during the two test days. The outdoorN02 pattern ofassociations had some similarities with the indoorN02 pattern. Since the outdoorN02 samplers were placed outside each residence, the samplers actually measured the outdoorN02 concentrations in the immediate vicinity of the homes. This is very different from the outdoorN02 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 outdoorN02. Homes in the metropolitanLos Angeles areahad the highest levels ofoutdoorN02 among all the homes sampled. Single person households and blackfamilies 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 indoorN02for buildings with "5 to 19" living units was higher than for buildings with "1" or 

### "2 to 4" living units.
• Net indoorN02in homes built before 1940 was higherthan in homes built during 1960s and 1970s.
• Net indoorN02in homes without garages or carports was higher than in homes with attached side garages. • Net indoorN02 in homes without microwave ovens and without fireplaces was higher.
• Net indoorN02 in homes without kitchen fans was higherthan in homes with kitchen fans that exhaust to outdoors.
• Net indoorN02was negatively correlated with the area and volume ofthe conditioned space in a home. Cooking Appliance
• Net indoorN02of gas with pilot cooking was higherthan gas without pilot cooking, also higher than electric cooking.
• Net indoorN02was the highest 

### ifthe range/oven unit was made by WEDGEWOOD compared with all other makes; also net indoorN02of KENMORE was higherthan TAPPAN and GE.
• Net indoorN02in gas cooking homes was higher than in electric cooking homes.
• Net indoorN02was higher 

### ifthe gas range pilots were luminous than 

### ifthe pilots were electronic.
• Net indoorN02was higher ifthe gas oven pilots were either luminous or aerated than 

### ifthe pilots were electronic.
• Net indoorN02was the highest 

### ifthe oven gas burner material was cast iron compared with all other types ofmaterials.
• Net indoorN02was higher 

### ifany ofthe range/oven/broilerpilots needed adjustment.
• Net indoorN02was positively correlated with the age ofthe cooking unit, the ovenburner input value and the total pilot inputvalue ofthe cooking unit (including range, oven, and broiler). Heating Furnace
• Net indoorN02ofno heating furnace was higherthan gas forced-air furnace, and gas wall furnace was higherthan gas forced-air.
• Net indoorN02was higher ifthe furnace burnerwas made ofcast iron than 

### if it was made ofpressed steel.
• Net indoorN02was negatively correlated with the furnace burner input value.

3-34 1 \ 

# \-'Clothes Dryer
• Net indoorN02 for homes without clothes dryers was higher than for homes with clothes dryers. Water Heater
• Net indoorN02 was higher 

### ifthe water heaterburnertype was mono-port/upshot than ifit was multi-port/up-shot.
• Net indoorN02 was negatively correlated with the water heater capacity. Meteorological Factors
• None were significant for net indoorN02. Measured Components
• Net indoorN02 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 outdoorN02. • Net indoorN02 was positively correlated with the outdoorbenzene, net indoor CO (48-hour), indoorN02, and inverse ofthe home volume. Geographical Locations
• Net indoor N02 was not associated with any geographical variables. Sampling Parameters
• Net indoorN02was 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 indoorN02 ofusing the range/oven to heat home was higher.
• Net indoorN02 ofa female as the first memberin the family was higherthan male as the first member.
• Net indoorN02 ofa male being the second memberin the family was higher than a female being the second member.
• Net indoorN02 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 outdoortemperatures. Gas burning appliances, such as cooking range/oven with standing pilots and gas wall furnaces, are possible indoorN02


### 3-35sources. When not using an exhaust fan for cooking,the N02 generated indoors caused a higher indoorN02 level than ifa cooking fan was used that exhausted to outdoors. Net indoorN02 leveis could be reduced ifthe pilots in the cooking unit are properly adjusted. Similarto the indoor or outdoorN02, older and smaller apartment homes were associated with higher net indoorN02 levels. It should be noted that although net indoorN02 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 indoorN02 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 outdoorN02 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 indoorN02. The arithmetic mean ofnet indoorN02 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 indoorN02. Out ofthe seven variables listed in Table 3-11, six are related to the range or oven. Variable "RangeMake" 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 greaterthan 0.01. Table 3-13 lists the computed mean ofnet indoorN02 for each categorical variable, their parameter estimates, and partial R2 values. The multivariate analysis results were consistent withthe 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 indoorN02. 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 indoorN02 level.

/ 3-36

### Table 3-10. Stepwise regression ofnet indoorNOz(all 

### significantvariables except CO emission rates, indoor and outdoorNO

### z ).
Model RZ

## =0.4688 Regression Error Total Degree ofFreedom 18 274 292 Sum ofSquares 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 inthe 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 burnerflame 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 aflame -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

3-37Table 3-11. Mean ofnet indoorN02 contributed by categorical variables in the stepwise regression presented in Table 3-10.·

0.0101 0.0110 20.0527 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 categoricalvariables withpartialR-squared value of>O.OI are listed
. •• Non-significant in the stepwise regression. 3-38Table 3-12. Stepwise regression ofnet indoorN02 (without 

### air pollutant measurements).
Model R2 

## =0.3643 Regression Error Total Degree ofFreedom 12 280 292 Sum ofSquares 48915.65 85368.20 134283.85 F Value 

### 13.37 Prob > 

### F .0.0001



### 3-39

### Table 3-13. Mean ofnet indoorN02 

### contributed by categorical variables inthe stepwise regression presented in Table 3-12.

Garagetype

•• 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 0.0375I 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.

-

3-413.3 

### Radon
3.3.1 Univariate Analysis Results Radon was only measured indoors, and we report bothunivariate 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 "mirrorimage" of indoor 48-hour CO. Also, radon had apositively skewed distribution; therefore, many of the significantvariables were detected only by the nonparametric tests. However, it is often difficultto 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 ofvariables related to building characteristics indicated that radon in single family detached houses was higher than inbuildings for two or more families.
• Radon in homes with central refrigeration air conditioners was higherthan in homes without air conditioners.
• Radon in homes with microwave ovens, kitchen fans, fireplaces and wood stoves (or heaters) was higher than in homes withoutthese 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 othermakes.
• 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 othertypes 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 dryerwas 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 ofelectric clothes dryers was higher than no clothes dryers. 

# " WaterHeater
• Radon was higher in homes with water heaters than in homes without water heaters.
• Radon was the highest 

### ifthe water heaterwas made byRHEEM compared with all other makes.
• Radon was the highest 

### ifthe waterheaterwas located in the basement compared with all other locations.
• Radon was the highest 

### ifthe waterheater fuel was electricity compared with all othertypes offuels.
• Radon was higher 

### ifthe water heater burnertype was multi-port/up-shot than 

### ifit was multi-portfm-shot.
• Radon was the highest 

### ifthe water heater burner was made ofpressed steel (these are not electric water heaters).
• Radon was higher 

### ifthe water heaterburner portwas ribbon type than 

### ifit was slotted.
• Radon was the highest 

### ifthe water heater was equipped with a lowNOx burner.
• Radon was negatively correlated with the age ofthe waterheater. 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 higherthan 

### 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 outdoorN(h, inverse ofthe home volume, and outdoortoluene. 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 indoorradon behaved differently from all the pollutants presented earlier in this section. Homes with the following types ofappliances had higher radon indoors: electric cooking units, propane or natural gas forced-air heating furnaces, electric clothes dryers, electric or newer gas waterheaters. Apparently, large single family houses with basements had the highest indoorradon among all types of dwellings. The geographical region where the zipcodes started with "95" and telephone area code was "916" was associated with higher indoorradon 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 atotal of265 variables eligible for the stepwise regression. The final step ofthe regression identified a model which had an R2 of0.33. 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 

### airexchange rate 

### in a residence the lower the indoor concentration ofradon. The first categorical variable to enterthe 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 inNorthern California near the Sierra Foothills, which is known as an areain the State with higher concentrations ofradon.

3-45Table 3-14. Stepwise regression ofindoorradon (without PG079).
Model R:Z = 0.4335 Regression Error 

### Total Degree ofFreedom 14 277 291 Sum ofSquares 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 extrapolatedto 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 ofeach 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 othertwo 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 theirhomes 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 

# .1••**I.N~II.** , t 't Th SoCalGas 5.72 55% 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 ofcustomers in each utility, without exceeding the actual number ofhomes sampled in any area, then the number ofhomes per territory would have been as listed in the table on the right. 48 35% 13 9% 136 100% Total 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 of136 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 3.8 Standard Error 1 0.11 0.31 0.31 0.31 0.21 0.11 0.41 0.31 0.2 Median 

### I 1.21 3.51 3.11 3.01 2.01 0.81 3.31 2.91 2.6 Mode 

### I 1.01 2.01 2.01 2.01 1.01 0.11 2.01 2.01 1.0 Standard Deviation I 1.71 5.51 4.81 4.41 2.91 1.21 6.71 4.4\ 4.0 Percentiles Max8hr 2.0 0.1 1.4 1.0 2.4



# ')**--~~** Maximum 95th 76th 50th 25th 5th Minimum 12.9 37.9 36.7 35.8 23.5 10.8 68.7 31.5 27.3 17.3 4.3 15.1 14.2 13.2 8.3 2.7 16.1 12.3 10.6 6.3 1.8 6.6 6.0 5.8 3.4 1.3 6.1 5.2 4.8 2.2' 1.2 3.5 3.1 3.0 2.0 0.8 3.3 2.9 2.6 1.4 0.7 2.0 2.0 2.0 1.2 0.3 2.0 1.9 1.5 0.9 0.1 1.0 1.0 1.0 0.5 0.1 1.1 1.0 1.0 0.3 0.0 0.1 0.0 0.0 0.0 0.0 0.2 0.1 0.0 0.0

ALLCOMP.XLS

### 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 4.91 2.7 0.21 0.61 0.51 0.51 0.31 0.11 0.71 0.51 0.41 0.3 1.41 4.81 4.21 4.01 2.81 1.01 4.31 3.61 3.21 1.8 1.01 2.01 2.01 2.01 1.01 0.11 2.01 2.01 1.01 1.0 2.01 6.41 5.81 5.41 3.51 1.71 7.91 5.51 5.11 3.1 12.9 37.9 36.7 35.8 23.5 10.8 68.7 31.5 27.3 17.3

5.8 18.6 18.6 17.7 10.4 4.6 18.4 16.3 15.3 9.2 2.4 7.5 7.0 6.8 4.5 1.5 7.7 6.7 5.9 3.0 1.4 4.8 4.2 4.0 2.8 1.0 4.3 3.6 3.2 1.8 0.8 2.5 2.2 2.1 1.4 0.4 2.1 2.0 1.8 1.0 0.3 1.0 1.0 1.0 0.9 0.1 1.1 1.0 1.0 0.2 0.0 0.1 0.0 0.0 0.0 0.0 0.9 0.3 0.2 0.0

ALLCOMP.XLS,-- 

### 48-HOUR AVERAGE INDOOR CO Actual and Estimated Statewide o ActuaJ (277) • Statewide (130)
20 10 o· 0



# .. II>' fP _-w

# ...

0.10.1 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

0.10.1 5 10 30 50 70 Percen1age ofHomes Less Than 90 95 99 99.9
Figure 4-2. Comparison ofindoor and outdoor48-hour average CO for the adjusted statewide sample (130 homes). 

### 4-5

### Section 5 Mass Balance Modeling for CO, AER andHome 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-hourNAAQS of9 ppm, which is notto be exceeded more than once per year. Only one home had a CO value in excess ofthe I-hourNAAQS 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 ofhomes 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-hourindoor 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 ofour CO instrumentation, values above 1 ppm are much more accurate than the lowervalues. Therefore, we

1 All averaging concentrations in this report are running averages. Forinstance, the maximum 8-hour indoor CO concentration is actuallythe maximum 8-hourrunning average indoorCO concentration. 5-1 

### f 

### j8-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 'E co. a 0 

# .- 0**.....,..~** 1 8' 

# . . ... oocop. 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-hourindoor CO distribution by appliancetype. 

### 5-2,'1



### recommend ofuse the portion ofthe log-probability distribution that is above 1 ppm and betweenthe 10th and 90thpercentiles to extrapolate to the higher CO concentrations. Figure 5-1 indicates that homes with gas cooking appliances (GwoP and GwP) have higher 8-hourindoor CO concentrations than homes with electrical cooking appliances. Furthermore, homes with gas wall units,have higher 8-hourindoor CO concentrations than eitherFA or electrically heated homes. This trend appears to be consistentthroughout the distribution, also throughthe 90th or 95thpercentiles. 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 thatthose 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. Simplepairwise comparisons such as these plots can provide insightbut can also be misleading. It 

### is also important to note that correlations exist among the majorvariables. For example, very few homes have electric cooking with gas wall heaters; while many homes with gas forced-air furnaces and gas waterheaters have electric cooking appliances. Afurther discussion ofthese appliance combinations will 

### be provided at the end ofthis section. Figure 5-1 is illustrativebut does not account for outside CO and maybe misleading with respect to the effect ofthe gas appliances. We canuse a simple mass balance model to help understand the impact of indoorcombustion sources by inclusion ofthree important factors: outdoorCO, house volume, and air exchange rates. The following mass balance equation may be used to predictthe change in indoor pollutant concentrations, assuming awell 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); 

### outdoorpollutant concentration (Jlglm3); 

### time (hrs); penetration factor (unitIess); emissionrate (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 overthe 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 overthe 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 ofambient 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-hourtime period, the term e-at is small for most ofour 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-atrapidly dampens the impact ofthe initial concentration. The three remaining mass balance factors otherthan 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-hourrunning 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) maybe an appropriate means to compensate approximately for the different range ofoutdoor 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 

### I" 'E 

### ,,; ... a 

# V 8 

### -" 1 

# ----- ..::-



# - 0.1

0.001 0.1 1 5 10 30 50 70 90 95 99 99.9 99.999 Percemage 01 Homes LessThan 8-Hr Maximum Outside CO By Cooking Type + EIedric CJ GwoP 

## • GwP
100 F

- " 0 D • 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 "" ... 

# -- pD. a 

# . 8 

# .- 1 

# ..**~~-** 

# . co

# ...**~-**

Ir- .., 

# . 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-houroutdoor CO distribution by appliance type. 

### 5-5

### variations ofthe outdoor CO and the air exchangerate. Furthennore, the effect ofthe initial indoor CO priorto the 8-hourtime period is assumed to be small relative to the 8- hourmaximum indoor CO. These assumptions should not be biasedtoward aparticular housing characteristic or appliancetype. The indoorminus outdoor CO value is our attempt to showthe effect ofall 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 uponthe maximum running averages ofthe difference between 10-minuteindoor and outdoor CO. That is, the CO datawere summarized for each ten minute period for each home. Those tenminute values were subtractedto yield 6values perhour. 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 betweenthe 50th percentile through the 95thpercentile. The downward bias belowthe 30th percentile is thought to be due to the CO instrument accuracy and precision atthe low concentration levels. Differences below 1 ppm are considered to be within the noise range ofthe instruments. The outside instruments were subjectto more downward 

### drift than the inside instruments due to lower outdoortemperature. Several 8-hourdifferences were calculated to be negative or zero. The effect ofcombustion sources below 1 ppm over 8 hours is too small to be determined accuratelyusing ourtechniques. Given the increased error due to subtraction, it is still reasonableto conclude that the 1-0 

### data are generally lognonnally distributed. The trend line betweenthe 50th and 90thpercentiles is a

- reasonable region for deriving the log-probabilityparameters. Comparedwiththe indoor CO presented in Figure 5-1, the indoorminus outdoor callculation shown inFigure 5-3 resulted in the GwoP and electric cooking distributions being closerto each other. Figure 5-3 also suggests thatthe effect ofindoor sources in homes with electric heating may be as high as the gas heating homes inthe upper 5th percentile. The mass balance equation states that home volume and air exchangerates are also important considerations in evaluating the potential relative magnitude ofindoor sources. A small source in a small volume home withthe windows closed may have a larger effect than a large source in alarge house withthe windows open. The next two figures plot those two parameters as afunction 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 smallerand homes with forced airfurnaces tend to be much larger. Therefore, using the indoorminus outdoorvalue without also correcting for volume could lead to large errors injudgingthe magnitude ofthe indoor sources.
5-6 ./

.....-\ 'j/ J 'Indoor Minus Outdoor 8-Hr Max CO

100



# . 

# . 'E 10 

# - ... ",.- .eo 8 

# V 2 

## l/ 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-hourindoorminus outdoorCO distribution by appliance type. 5-7Home Volumes
100000



# . . I- "".

# .. :[ 

### t......... 10000 

# V I
..... ", 

# - . . 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 LessThan 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 appliancetype. 5-8Figure 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 orFAunit homes. It is interesting to note the similarity ofair exchange rates for FA and electrically heated homes. This may be due to many electric heating units using mechanical 

### air circulation systems similarto 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 monitorin 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 equationto infer proportional relationships. We have defined a parameter, total source tenn, to betterunderstand the relative impact ofindoor sources as follows: Total Source Terrns.Hr= (Indoor CO-Outdoor CO) 8-Hrx (AirExchange 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 50thto the 90th percentile. The downward bend below the 50th percentile is due to the downward bend seen in the indoorminus outdoor distributions inFigure 5-3. It appears that the total source term distribution above the 90th percentile can best be estimated using the data betweenthe 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 clearthat homes with wall furnaces have higher indoor source rates than homes with FAunits. Also, homes with FAunits have highermedian source strengths than homes with electrical heating. However, above the 90th percentile, a completely different profile emerges. The cookingunit 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.
5-9Air 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 ofHomes LeseThen 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 

# ,., 

# V a: w :c 

### ,/' 6' 

### ./ 0 Q 100 0;:. 

# f

, 10 0.001 0,1 I 5 10 30 50 70 90 95 99 99.9 99.999 Pe<oontBge01 H_ LMsThan 8-Hr Max Source Term By Cooking Type + EIec:lric CI GwoP 

## • GwP 10000

'::' 

# . l 

# .

# - ..J 1000**o~** §2 

# - it: 

# -**<#"~** W 

## .,,- :c 

### 8 J Q 100

# ~~ V 0;:. 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.eseThan 

### 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 majorgroups: 34 homes withboth 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 clearthat those homes would have higher indoortotal 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 ofoccurrence ofhigh exposures.

5-12

## 8-Hr Max Source Term By Cooking and Heating Units

10000 

### + Eledric Cook & Heat 

# • Gas Cook & Heat

-::- .s::. C; 

### S. 1000 ..J 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 ofhomes: electric cooking and heating; gas cooking and heating.

5-13

### Section 6 Exploratory'Analyses
This section is a compilation ofexploratory 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 linearregressions in Section 6.1 that show the relationship ofindoor and outdoor concentrations ofbenzene 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 ofbenzene 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 ofscatterplots (Figures 6-1 through 6-3). The highest correlation is between outdoorbenzene and outdoor CO concentrations (R2 = 0.84). The 

### R2between indoorbenzene and indoor CO was 0.35, and between indoor benzene and outdoor CO it was 0.32. The strong correlation between outdoorbenzene and outdoor CO indicates that there is likely to be a common outdoor source for the two pollutants. The indoorvalues ofthe two pollutants follow the same general patternbut 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. FromFigure 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 indoorbenzene and indoor CO (R2=O.32) implies that there are additional sources and/or chemical reactions indoors.

6-1

### 14 12 

# •


# • •

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 

# oo Figure 6-1. Outdoorbenzene vs. 48-hour average outdoor CO. 

### 14 

# • 12 

# • 

# - E 

### 10 

# • Co Slope=O.53 Intercept=O.21 

# • Co 

# - 8 r-squared=O.35 N=159 

### 0 (J 

# • • '- 

### 6 

# • 0 0 

### 4 "C 

### oS 

# • 2 

# • • • • 0 

# • 0 

### 2 4 6 8 

### 10 12 14 Indoor Benzene 

### (ppb) Figure 6-2. Indoorbenzene vs. 48-hour average indoor CO.



### 14 

### 12 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. Indoorbenzene vs. 48-hour average outdoor CO. 6-2

### 6.2 Smoking and Benzene
Correlations ofindoor and outdoorbenzene 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 ofhomes. 

### As shown in Figure 6-4, the highest indoor benzene was observed in non-smoking homes. Out of 

### 14 homes with indoorbenzene 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 betweenthe smoking and non-smoking homes. Forthis analysis the same univariate approach was utilized as described in Section 

### 2.4. The dependent variables were indoorbenzene, outdoorbenzene and indoor minus outdoorbenzene. 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 indoorbenzene, outdoorbenzene and indoor minus outdoorbenzene.


### 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 outbetween indoor and outdoorbenzene for three groups: all homes, smoking homes and non-smoking homes. The purpose of conducting these tests was to see ifthe indoor and outdoorbenzene 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 higherthan 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

12.0 10.0 8.0 6.0 4.0 2.0 0.0 

### +------+------+-----+-----1------+---------1 0.0 

### Outdoor 48hr Benzene (ppb)



### 6-4 BENSMKALChart 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 of48-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 ofhomes. Indoor CO ofsmoking homes was generally higher than that ofnon-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 indoorminus 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 ofnet 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 •



# - 10 E 

### 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 ofconclusions derived only from analyses presented in this volume. We also discuss recommendations for further evaluations ofthe data set, integration ofVolume 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 significantrole 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 appearto 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, outdoorbenzene was found to be the single most important variable 

### (R2=0.38) affecting indoor benzene concentrations. The stepwise linearregression ofnet indoorbenzene 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 R2reduced 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, indoortoluene was not very dependent upon outdoor concentrations 

### (R2=o.01).1 The stepwise linear regression ofnet indoortoluene 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 pointremoved.7-1

, ,**I_~**

### since indoorbenzene accounted for a substantial portion ofthe explained toluene variation, the benzene sources also released toluene. Nntrogen Dioxide 

### Approximately 38 percent ofthe residences had higher indoorN02values than outdoor. OutdoorN02 was found to be the single most importantvariable (R?=O.22) affecting indoorN02concentrations. The stepwiseregression resulted in a 12-variable model for net indoorN02(R2=O.36). Gas cooking appliance with standing pilotwas the most significant variable inthe stepwise regression ofthe net indoorN02(R2=O.12). Radon 

### One out of261 samples ofindoorradon was abovethe EPA action level of4 pCiIL. The stepwise linearregression ofindoorradon resulted in fourteen significantvariables accounting for almost halfofthe overall variability (R2=O.43 with one outlying datapoint removed). Air exchange rate was the first variable to enterinto the stepwise regression explaining indoorradon levels (R2=O.11). Higher air exchangerates 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 extrapolationto the entire State. The adjusted statewide distribution ofCO tends to be higher than 

### ouroriginal sample due to the higher indoor and outdoorvalues in the Southern California Gas 

### territory, whichwas underrepresented in the sample. Mass Balance Modeling ofCO 

### Indoor airquality models are frequently basedupon amass balance equation. In the mass balance model, the important variables are outdoor CO, air exchange rate, source strength, and dwelling volume. These variables inturn 

### are dependent on housing characteristics and combustion appliance types and use patterns. The interdependence among the variables makes it difficultto 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 parameterwas calculated for each hometo estimate the relative total indoor emissions ofCO during the 8-hourtime period during which the maximumindoor CO was observed. The estimated "sourceterm" represents the total of aU emissions into the home, notthe emissions from any particular combustion source(s), during any giventime period. The median total sourcetermfor residences with gas heating and cooking appliances was**approxima~ely** 

### threetimes largerthan for residences
7-2with 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 Outdoorbenzene 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 nornet indoorbenzene were associated in this data set with smoking in the residence. 

### Recommendations This study ofnearly 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 similarfield studies be reviewed, summarized and their findings compared in the summary document. The findings 

### in this study represent a 2-day snapshot ofa randomly selected sample of Californiaresidences 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 ofelevated concentrations within the population. A 12-month longitudinal field study ofa subset ofthe residences from this pilot program would provide betterinsight into the proportion ofresidences experiencing elevated concentrations and the frequency ofelevated 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 GuidelineNo.1, Formaldehyde 

### in the Home". State ofCalifornia 

### Air Resources Board, Research Division. CARB (1994). "Supplement to Indoor 

### Air Quality GuidelineNo.2, Combustion Pollutants in YourHome". CaliforniaEnvironmental Protection Agency 

### AirResources Board, Research Division. Colome SD, Wilson AL and Tian Y (1994). "CaliforniaResidential Indoor 

### Air Quality Study, Volume 2, CarbonMonoxide and 

### AirExchange Rate: A Univariate & Multivariate Analysis". Report prepared for the Gas Research Institute, Pacific Gas & Electric Co., SanDiego Gas & Electric Co., Southern California Gas Company. Dietz RN, Goodrich RW, CoteEA and WieserRF (1986). "Detailed Description and Performance ofAPassive 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. OttWR (1995). Environmental Statistics and Data Analysis. CRC Press, Inc., 2000 Corporate Blvd.,N.W., BocaRaton, Florida 33431. Pro-Tek (1991). 

### "AirMonitoring Badges Catalog and PriceList". Pro-Tek Systems, Inc., 95 Brownstone Avenue, Portland, CT 06480. Ryan PB, Sczek 

### ML, SpenglerJD and Billick ill (1988). "The Boston Residential N02 Characterization Study

- 

### n. SurveyMethodology 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. FourthEdition. Volume 1. SAS Institute Inc., Cary, North Carolina.
8-1SAS (1990d). SAS/STAT User's Guide. Version 6. FourthEdition. Volume 2. SAS Institute Inc., Cary, North Carolina. 

# u.s. EPA (1990a). "Charcoal Canister Quality Assurance DataManagement System User's Guide". Prepared by Computer Sciences Corporation, Las Vegas, Nevada. U.S. EPA (1990b). "Charcoal CanisterDatabase 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 SystemDescriptions". 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, BakerPE and BeckerEW (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 HedrickR (1992). "Common household products as sources ofinterference 

### in the electrochemical detection ofcarbon 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). "CaliforniaResidential Indoor 

### Air Quality Study, Codebook", Prepared for the Gas Research Institute, Pacific Gas & Electric Co., San Diego Gas & Electric Co., Southern California Gas Company.' YocomJE (1982). "Indoor-Outdoor

### Air Quality Relationships: A Critical Review". J. 

### of AirPoll. ControlAssoc., 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 averagetoluene (Ppb), full data set 3. Scatterplot ofbenzene co-located samples 4. Scatter plot oftoluene co-located samples 5. Benzene indoor vs. outdoor, full data set 6. Toluene indoorvs. outdoor, full data set and one outlier removedDescriptive 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 1.0 r 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

DSTATBEN.XLS 10/6195Descriptive 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

DSTATTOL.XLS 10/6/95

## Benzene Co-located Sampies
12 .,---------------------------------"

10 

# • , , , , 

# . ' ,
8



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### Q. ::::s C 4

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### o ,
, ,

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### 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



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### c. 15 C 

# • o 10

5 o
35 30 25 20 15 10 5 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 

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## m 4.0 

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# • •
2.0

12.0 10.0 8.0 6.0 4.0 2.0 0.0 

### +-----+-----+-----+-----+-----+---------i 0.0 

### Benzene Outdoor (ppb)

btallivo.xls chart1

### 48-hour Average Toluene Indoorvs. Outdoor. Full Dataset

160.0 140.0 120.0 100.0 80.0 60.0 40.0 20.0 0.0 

### +----f------+----+----+------+----+----+-----i 0.0 40.0


# • Slope=0.06 Intercept=7.92 35.0 

# • r-squared=0.01 N=160 

# • 30.0 

# • •



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### c. c. 

# - 0 

# • • 0 "C 20.0 

## I .5 (1) 

## •• c (1) 

# • = '0 15.0I10.0 

# • 5.0 

# •

Toluene Outdoor (ppb)

btallivo.x1s chart2 ,/48-hour Average Toluene Indoorvs. Outdoor, with One Outlier Removed

5.0

40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 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 outdoortemperatures 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 outdoortemperature (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 indoortemperature (avg_tin). A minimax thermometerwas placed next to all the other indoor samplers in each home. Fourtemperatures 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 consolidatingthe indoor and outdoor CO concentrations (with different averaging times), all the indoor and outdoorpassive 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.