Agency Identifier December 1997 Development of NERL/ CHAD The National Exposure Research Laboratory Consolidated Human Activity Database National Exposure Research Laboratory Office of Research and Development U. S. Environmental Protection Agency Research Triangle Park, NC 27711 ii Agency Identifier December 1997 Development of NERL/ CHAD: The National Exposure Research Laboratory Consolidated Human Activity Database by Graham Glen, Yeshpal Lakkadi, Jo Anne Tippett, and Maria del Valle Torres ManTech Environmental Technology, Inc. Research Triangle Park, NC Contract 68 D5 0049 Work Assignment Manager Thomas McCurdy Human Exposure and Atmospheric Sciences Division National Exposure Research Laboratory Research Triangle Park, NC 27711 National Exposure Research Laboratory Office of Research and Development U. S. Environmental Protection Agency Research Triangle Park, NC 27711 iii Notice The United States Environmental Protection Agency through its Office of Research and Development managed and funded the research described here under Contract 68 D5 0049 to ManTech Environmental Technology, Inc. It has not been subjected to the Agency's peer and administrative review and therefore does not necessarily reflect the views of the Agency, and no official endorsement should be inferred. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. iv Abstract This document describes the creation and development of the Consolidated Human Activity Database ( CHAD) for the Environmental Protection Agency's National Exposure Research Laboratory. This database was created to support exposure/ intake dose/ risk assessments. The overall design incorporates comments received from potential users knowledgeable about human activity pattern research and risk assessment.

CHAD is a relational database with a graphical user interface that facilitates queries and report generation. It contains databases from previously existing human activity pattern studies, which were incorporated in two forms: ( 1) as the original raw data and ( 2) as data modified according to predefined format requirements. The latter involved development of a common activity/ location code system, compilation of background questionnaire information, and the application of data quality flags. To enhance the applicability to risk assessment, the ability to estimate metabolic activity ratios for respondents has been added.

This report covers a period from May to December 1997, and work was completed as of December 31, 1997. v Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi Acronyms and Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 Recommendations for Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3 Scoping Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3.1 Contacting Potential Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3.2 Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 4 Code Consolidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 4.1 Activity and Location Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 4.2 Standardization of Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 4.3 Questionnaire Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 5 Database Design and Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 5.1 Data Format and Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 5.2 Platform Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 5.3 Development Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 6 MET Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 7 Quality Assurance Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 7.1 Determination of Quality Flags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 7.2 QA Issues in Coding CHAD Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 8.1 California Activity Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 8.2 Resolution of Quality Flags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 8.3 Nonrandom Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Appendices A Exposure Analysts and Modelers Contacted . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 B CHAD Activity Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 C CHAD Location Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 D Occupational Codes Used in CHAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 E CHAD Questionnaire Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 F Program for Recoding NHAPS Variables into CHAD Format . . . . . . . . . . . . . . . . 56 G Modeling Multi Route Intake Dose in an Internally Consistent Manner . . . . . . . . . . . . . . . . . . . . . . . 73 H METS Distributions for CHAD Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 vi Figures 5 1 Information Formats Available Through Microsoft Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 5 2 Sample Query Data in ASCII Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 5 3 Sample Screen of CHAD Questionnaire Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 5 4 Sample Screen of Diary Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 5 5 CHAD Main Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 5 6 Sample Screen of Filter Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 5 7 Example of Query Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 6 8 Sample Screen of METS Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Tables 7 1 Quality Indicator Variables in CHAD Version of the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 7 2 ive CHAD Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 A 1 Modelers Contacted . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 B 1 HAD Activity Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 C 1 AD Location Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 H 1 for CHAD Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 vii Acronyms and Abbreviations CARB  California Air Resources Board CHAD  Consolidated Human Activity Database CHADID  A unique identification number for each individual in the CHAD database GUI  Graphical User Interface METS  Ratio of an activity specific metabolic rate to a person's resting metabolic rate NHAPS  National Human Activity Pattern Survey NHEXAS  National Human Exposure Assessment Survey PAI  Physical Activity Index PEM  Personal exposure monitor QC Variable  Quality count variable viii Acknowledgment ManTech Environmental Technology, Inc., would like to thank William C. Steen for supporting this project; James Capel and Ted Johnson for providing valuable data; and Ted Palma for his encouragement. In addition, thanks go to the numerous people contacted as sources of information and data that have enhanced this database. 1 Chapter 1 Introduction This report documents the design and development of EPA's National Exposure Research Laboratory/ Consolidated Human Activity Database ( CHAD), a comprehensive human activity database. CHAD contains data obtained from pre existing human activity studies that were collected at city, state, and national levels. The purpose of CHAD is to " provide a basis for conducting multi route, multi media exposure/ intake dose/ risk assessments" ( McCurdy, 1997a).

The database was developed in two segments. Segment 1 was begun in May 1997 and focused on initial scoping efforts, comparisons of acquired target databases, development of a code system, and creation of data quality flags. Segment 2 tasks were started in June 1997 and concentrated on platform selection, creation of the database structure, conflation of the target databases into CHAD format, application of data quality flags, incorporation of the CHAD formatted data and the original source data ( along with pre existing flags) into the database, resolution of data quality flags, identification of future variables to be added, and further scoping efforts.

This report describes the development, design, and present state of CHAD. Chapter 2 presents anticipated future work on this database. Chapter 3 presents the scoping activities that lay the groundwork for the design and development of CHAD. Chapter 4 presents the rationale and method for consolidating the codes from the various contributing databases. Chapter 5 describes the overall design of the consolidated database, a view of its infrastructure, and a discussion of the first three developmental phases of CHAD. Chapter 6 presents Phase 4 of the development, which centers on the METS data. Chapter 7 is a description of the quality assurance procedures that were implemented in the development of CHAD. Chapter 8 is a discussion of outstanding issues  the California activity codes, resolution of quality flags, and nonrandom sampling. References and appendices are included at the end of the text. 2 Chapter 2 Recommendations for Future Work As with the creation of any database system, it is expected that system test errors or other such problems will be found. Also, tasks identified previously in this document need to be completed  such as the addition of variables. Refinement of CHAD will therefore continue. The following is a brief description of some of the tasks that have been identified for future completion.

1. Once CHAD is reformatted in Oracle, it will be placed on the World Wide Web in a manner consistent with the EPA's Office of Information Resource Management standards. This will enable increased access to environmental and human activity data. Designer 2000, a web development software application, will be used to accomplish this. Users will be able to download CHAD query results and raw data batch files from the target databases.

2. Estimates of body mass ( weight in kg) will be added to CHAD.

3. Climatic data ( e. g., temperature and precipitation) from the closest available monitoring sites will be obtained for the days of human activity currently in the database. If the day of response or the location of an individual can not be determined, then seasonally and/ or spatially averaged climatic data contained in the EPA standard climate atlas will be used.

4. The METS data will be modified so that they account for post exercise " excess" oxygen consumption. This will affect only the METS expenditures equal to or greater than 3.0. In addition, CHAD will be modified so that daily physical activity for a given individual can be estimated. Ultimately, this will result in a Physical Activity Index ( PAI) value.

5. The search for more data applicable to CHAD will continue. 3 Chapter 3 Scoping Efforts 3.1 Contacting Potential Users Prior to initiating the design and development of CHAD, scoping efforts began in June 1997 to gather baseline information about human activity studies, availability of data, and requirements of potential users. These efforts began with contacting individuals known to have experience with human activity studies and/ or human exposure assessment research. In total, 33 exposure analysts and modelers were contacted via telephone or attended meetings to discuss CHAD ( Table A 1, Appendix A). Information from these contacts was recorded in a telephone log or as meeting notes. In addition, demonstrations of CHAD to potential users  both inside and outside of EPA  were given in October and November 1997. A variety of design and development suggestions were made by these potential users, most of which were incorporated into CHAD. The following are examples of the suggestions received; the chapter where each issue is discussed in this report is given in parentheses.

! A multi digit, collapsible code system should be used as the code structure for CHAD ( Chapter 4).

! The earliest " clean" version of each database should be used when possible ( because of potential data loss in later versions), and the original coding systems used in each original database should be retained in addition to the new CHAD codes ( Chapter 5).

! Data contained in CHAD should include basic background information about the participant: a first name identifier of each individual, date of birth, gender, health classes, ethnicity, smoking status, occupational code; a logical breakdown of hours in a day; and demographic status ( Chapter 5).

! CHAD should be easy to use ( Chapter 5).

! The data should be easily interpretable, well documented ( Chapter 5).

! A relational database structure should be used ( Chapter 5).

! Quality assurance checks should be incorporated that serve as indicators of data completeness and should contain the original data with flags ( Chapter 7).

! Consideration should be given to physical activity ( amount of exertion) data ( Chapter 6 METS data). 4 ! Specific information about environmental conditions should be provided ( Chapter 7).

! The availability of data on sensitive groups such as asthma sufferers should be determined ( Chapter 7).

! Data should be easily accessible; consideration should be given to storing CHAD on a Unix central processor and using NT or Unix parameters in this database ( Chapter 7).

! Compatibility with current exposure models and other databases, along with potential linkages, should be considered ( Chapter 5).

! Include ability to filter queries by state and/ or region.

! Add the capability to link to spreadsheet applications.

! Provide option to retrieve full citations for publications of target studies.

3.2 Data Acquisition This process was simultaneous with contacting the potential users. It began with identification of data that would serve as the foundation of CHAD. Existing databases from several human activity studies were targeted because of their comprehensiveness and availability of documentation. Because of the passage of time, it was difficult to locate the original databases. However, the best known sources were contacted for each. If the primary source did not have access to it, then often a second or third potential source was contacted. In some cases, it was not possible to locate the original database; second generation databases were then used instead. At the end of this process, databases were obtained from the following studies:

! A Study of Personal Exposure to Carbon Monoxide in Denver, Colorado. This study was conducted in 1983 with a total of 454 study participants ( Johnson, 1984). Each participant carried a personal exposure monitor ( PEM) and an activity diary for two consecutive 24 hour sampling periods. Approximately 900 person days of PEM and activity data were collected. Detailed background questionnaire data were also collected. The electronic data were obtained from James Capel ( Table A 1, Appendix A). The questionnaire information was incomplete, and to date, another source for the remaining questionnaire information has not been found.

! National Human Activity Pattern Study ( NHAPS). This study was conducted between October 1992 and September 1994 ( Klepeis et al., 1995), during which period 24 hour activity diaries were collected from 9386 participants. Questionnaire data were also collected. This survey was the largest and most geographically diverse ( data from 48 states) of the studies going into CHAD. It was conducted as a next day telephone interview. This method has the advantage that an interviewer can insist on filling in gaps in a diary, but suffers from the limitations of a participant's memory regarding the exact time and duration of activities. The original data for this study were obtained from William Nelson of EPA/ NERL. 5 ! Activity Patterns of California Residents. This study was conducted from October 1987 to September 1988 by the California Air Resources Board ( CARB; Wiley et al., 1991). It involved 1762 randomly selected participants aged 12 and above. Retrospective time diaries were collected via telephone for all activities occurring on the previous day. A total of 36, 918 records were collected. Questionnaire data were collected on activity locations, presence of smokers, and housing and socioeconomic characteristics. Like NHAPS, this was a next day telephone survey. The data for this study were also obtained from James Capel.

! Activity Pattern Survey for California Children. This survey was conducted by CARB from 1989 to 1990 ( Wiley, 1991). A total of 27,048 activity records were collected from 1200 children. The same methodology was used as described above, although the questionnaires and diary formats differed in certain details. James Capel also provided these data.

! Study of Carbon Monoxide Exposure of Residents of Washington, DC. This EPA sponsored investigation was carried out concurrently with the Denver study and covered 705 respondents for one day each ( Settergren et al., 1984). The version of the data acquired by for this project has incomplete questionnaire data. Also, the list of potential activity and location codes is fairly short, limiting the resolution in activity description that is possible in this study. James Capel was the source for these data.

! Human Activity Patterns in Cincinnati, Ohio. This study was funded by the Electric Power Research Institute ( EPRI) and was conducted in 1987 ( Johnson, 1989). Activity diaries were carried by 973 randomly selected participants for three consecutive 24 hour periods in March or August 1985, resulting in 2800 subject days of diary data. Detailed background questionnaires were also completed by the participants. The electronic data were provided by James Capel.

! Valdez Air Health Study. This study was conducted during 1990  91 ( Goldstein et al., 1992). A total of 289 individuals from different households were interviewed as part of a continuous monitoring program that also measured criteria pollutants, volatile organic compounds, and weather conditions. This sample covered 23% of the residences in the Valdez area. The data for this study were provided by Ted Johnson ( Table A 1, Appendix A).

! Los Angeles Area Studies: Development of Improved Methods to Measure Effective Doses of Ozone. These studies of outdoor workers exposed to oxidant pollution ( Shamoo et al., 1991), elementary and high school students exposed to oxidant pollution ( Spier et al., 1992), and ozone exposed construction workers were conducted between 1989 and 1992. The activity records from these studies cover only a work day or school day period ( approximately eight hours), as the purpose of these studies was to focus on exposures in specific microenvironments. The original data were obtained from Kenneth Clark ( Table A 1, Appendix A). Because of the limited study sizes and the difficulty of extending the diaries to a standard 24 hour period, these studies were not incorporated into the CHAD database.

Additional data source contact information is provided in a footnote in Table A 1, Appendix A. Literature references pertaining to these studies are included at the end of the text. 6 Chapter 4 Code Consolidation To conflate the selected databases and incorporate them into CHAD, a single code system was created to account for all activity and location codes across studies. This was accomplished after examining the code systems of each database and then comparing them for common categories.

4.1 Activity and Location Codes The NHAPS and California have approximately 100 different activity codes and another 100 location code, in contrast with earlier studies such as the Denver and Washington studies, which have only 10 to 20 codes each for activities and locations. In the NHAPS and California systems, a nesting format was used in which main activity categories were divided into subcategories. In some cases, these subcategories were further divided into more specific categories. This structure accommodated a specific and comprehensive documentation of participants' activities over time. Because of the strong similarity between the coding systems of the NHAPS and the California studies, use of one of these as the CHAD coding system would mean that the studies with the greatest number of diary entries would need relatively little modification. The NHAPS system is slightly more succinct than the California code system. For these reasons, the NHAPS coding system was selected as the foundation for the CHAD system.

The NHAPS system has been refined and regrouped as necessary to incorporate coding systems from other studies. In some cases, these other systems had finer categories than did NHAPS, although more often they used broader categories. Generally, all the NHAPS coding categories are retained, along with several extra ones reflecting the categories used in other studies. Thus there are more than 100 each of CHAD activity and location codes. Another regrouping put all the travel codes together in CHAD ( all starting with 18); they are not grouped together in NHAPS.

In the CHAD coding system, activities and locations are each designated by five digits. For activities, the first digit is always 1, and for locations, it is 3; this prevents activity and location codes from being confused. Subsequent digits define finer distinctions. With a five digit system, there is room to add new codes as they are needed. The resulting CHAD activity and location code systems are presented in Appendix B and Appendix C, respectively.

In retrospect, certain difficulties are evident with the NHAPS system. For instance, both indoor and outdoor ( e. g., back yard) locations are parts of the primary location category of " at home". For exposure purposes, the primary 7 distinction is the indoor/ outdoor one, while the type of building is secondary. The recoding was already underway when this issue became apparent, so no further changes to the CHAD codes were made at this time.

4.2 Standardization of Time The original studies used a variety of protocols regarding diary start time, stop time, and resolution. All of the studies incorporated into CHAD so far have nominal 24 hour ( or multiples thereof) durations and theoretical resolutions of one minute. In practice, the studies using written diaries did not start at a standard time, and they ended only approximately 24 hours later. The telephone recall surveys generally enforced a standard midnight tomidnight time frame, but seldom were able to pinpoint activity start and stop times to the nearest minute, so that five minute or in some cases only 15 minute resolution was achieved.

All diaries are standardized to an exact 24 hour length for CHAD. If the original diaries were longer, then the first 24 hours were retained. If the diaries were shorter, then extra records with missing activities and locations were added to fill the missing time. Activities that cross hourly boundaries ( i. e., that start before the top of the hour but finish after) are divided at the hour. This was motivated by the standard practice of hourly measurement of concentrations of air pollutants. A single value can be associated with each activity record in CHAD, assuming that concentration remains fixed within the hour. This assumption eases exposure modeling calculations.

Although each diary is standardized to 24 hours in length, there is no common start time that could be applied to all diaries. This has been resolved by " wrapping" the diary around so that in CHAD, all diaries begin and end at midnight. The true starting time of the original diary is indicated by the variable WRAPTIME, which is discussed in the data flagging ( Chapter 7).

The CHAD variables have been recoded with SAS programs. Quality flags were applied to the data during this process. A detailed discussion of the quality flags is presented in Chapter 7. Listings of the SAS programs may be found in Appendix F. 8 4.3 Questionnaire Data Each component study in CHAD was accompanied by a survey or questionnaire that recorded personal data from the respondent, such as age, gender, occupation, and house type. Appendix D provides a list of the occupation types used in CHAD. The list of such questions varied significantly in both length and focus, depending on the purposes of the original study. This made it difficult to consolidate the questions into a single set applicable across all studies.

There are two main philosophies on which a consolidated list can be based: Either includes those questions common to most studies ( effectively the intersection set of questions), or include all questions asked in any study ( the union of all questions). In the case of the studies in CHAD, the union would contain nearly 1000 questions per personday with the vast majority of these responses missing for any individual person. This would make CHAD unwieldy and would not effectively consolidate the data, as most questions would apply to only one of the component studies. Therefore, a small set ( around 30) of fairly common questions has been selected for CHAD, for which several ( but not necessarily all) of the original studies provided data ( Appendix E). In addition, a total of about 15 new variables have been composed and added to the CHAD data to assist the user and to help indicate data quality. These variables are described in detail in Chapter 7 of this report. 9 Chapter 5 Database Design and Development This process began in September 1997. Much of the initial database structure was based on comments received during contacts with potential users. The CHAD design was further modified after further exchange of ideas during demonstrations and presentations given in October and November 1997.

5.1 Data Format and Content One of the most common concerns from the viewpoint of potential users was that a consolidated database might not retain information of interest that was collected in only one or two of the component studies. Also, the recoding of variables into a standard format inevitably results in some information loss, as the exact phrasing of the questions ( and the allowed responses) differ somewhat from study to study. To answer these concerns, the CHAD database system contains ( 1) the CHAD consolidated database itself and ( 2) a section consisting of the original ( or raw) data obtained from the various component studies. The earliest " clean" versions of the original questionnaire and diary data and the modified data were entered into CHAD. Queries can be made of either the original data or the modified ( according to CHAD quality standards) data. The original data contain many questions or details that pertain to only one or two of the studies and therefore were not common enough to be part of a single, consolidated database. In the consolidated portion, a user can select a single search variable ( e. g., respondent's age) and apply it to all of the studies in CHAD. Also, interpretation of the variables ( e. g., activity codes) is identical for all studies, which is not the case for the original data.

Graphical user interface ( GUI) screens were developed for the CHAD data and the raw data. For the raw data, part of this process involved examining the questionnaire record sheets and grouping the variables that covered similar topics. For instance, all variables relating to bathing or showering ( i. e., BATH, BATH#) were grouped. This ensured that all of the necessary questionnaire data were entered into the database correctly and that access to the data would be convenient for future users. The modified data are augmented by a set of quality flags ( see Chapter 7) and METS variables ( see Chapter 6). Certain new variables such as the body weight of the individuals and daily temperature will be estimated and added to the database in the near future. These data were not part of the original surveys but can be estimated or obtained from other sources and statistically modified for use. Place markers for these variables are used in the first version of this database until the data estimates are obtained.

5.2 Platform Selection 10 Figure 5 1. Information formats available through Access. Access was chosen as the front end interface because it is a relational database. A relational database is a linked collection of tables, each containing data about one subject. Because the tables are related, information from more than one table can be used at a time. A data value is stored just once, and this reduces required disk storage space and reduces time required for updating and retrieving data. For example, personal data on a respondent does not need to be repeated on every line of the activity diary, because it is constant for a diary day. In this relational database, the personal data file is linked to the diary file via CHADID, which uniquely identifies a single diaryday ( Further details on CHADIDs are given in Section 5.3.) Files and reports can then be generated from both the diary file and the personal ( questionnaire) data at the same time.

The tools of Access can manage information from other desktop databases, SQL databases, or applications such as spreadsheets or word processors. Access also ( 1) provides front end development and database development; ( 2) is user friendly; ( 3) allows records to be read, added, edited, and updated; ( 4) prints data in professional print formats such as tables, forms, and reports ( Figure 5 1); ( 5) produces charts from a given set of data; ( 6) performs complex mathematical calculations; and ( 7) is cost effective.

5.3 Development Process The development process has occurred in four phases. The first three phases are described below. ( The fourth phase, focused on METS data, is presented in Chapter 6.) Again, emphasis was placed on structuring CHAD so that both questionnaire and activity data could be easily queried. The phases are described individually below.

5.3.1 Phase I: Entering Data into CHAD Tables The earliest versions of the original questionnaire and diary data, available in ASCII format, have been read into tables contained in the database. Additional tables have been created to link each field to an associated text. Questionnaire and diary information is stored in two different tables and is linked through a unique CHADID. ( See Section 5.3.3 for further details on CHADID.) Each field in a main table ( e. g., the Denver data table, shown 11 Figure 5 2. Sample query data in ASCII code format. in Figure 5 2) is linked to associated data tables; this link enables access to textual information for the specified ASCII code.

5.3.2 Phase II: Graphical User Interface Development The GUI of CHAD allows a user to browse through all records of a respondent's information. The user friendly, state of the art GUI design includes buttons and pull down menus for customizing queries. A user may access diary information for each respondent through the designated GUI screen. Also, a user may view explanations of the data codes through pull down menus.

At present, GUIs are in place for four studies ( NHAPS A and B, California adults and children, and Denver), and in the near future, access to data for each study will be provided through individual screens. The main screen for each study depicts information for one individual ( Figure 5 3). Personal information such as age, gender, ethnicity, and education may be viewed at the top portion of the screen. A user may view other questionnaire information by scrolling the bottom portion of the screen, and clicking on the " Diary Information" button brings up the diary information for that person ( Figure 5 4). Quality flag information ( discussed in Chapter 7) for the questionnaire and diary information of that person may also be viewed in the same manner. 12 Figure 5 3. Sample screen of CHAD questionnaire data. 5.3.3 Phase III: Querying CHAD Data A main menu screen was created so that the original and CHAD formatted data could be accessed from one screen ( Figure 5 5). Data for all questions common to all studies have been incorporated into CHAD. Available data from the studies may be read into CHAD tables.

Each record in the CHAD tables is identified by a unique CHADID, which consists of nine characters ( shown in F i g u r e 5  4 a s D E N 1 0 8 0 7 A ) . T h e f i r s t t h r e e c h a r a c t e r s 13 Figure 5 4. Sample screen of diary data.

Figure 5 5. CHAD main screen. represent the study source ( e. g., DEN for Denver). The next five characters represent the person's ID used in the 14 Figure 5 6. Sample screen of filter options. study, and the last character may be either A or B: A to denote the first person day and B to indicate the second person day of multiday studies.

Data may be queried by clicking the Filter Data button ( Figure 5 3). This button leads to a screen where the user can query data from options available on the screen ( Figure 5 6). A user can save the selected query in one of the boxes available on the right portion of the screen, which can then be accessed lateror used for batch queries. The queried data can be displayed on the screen, outputted in the form of an ASCII file, or printed as a report. A summary of queried data can be viewed by clicking on the Data Analysis button on the Filter Data screen ( Figure 5 6). The Data Analysis screen ( Figure 5 7) displays the number of respondents belonging to the selected criteria. This set of queried data can be further analyzed through various charts available to the user. 15 Figure 5 7. Example of query results. 16 Chapter 6 MET Data A MET is defined as the ratio of an activity specific metabolic rate to a person's resting metabolic rate. The resting metabolic rate can be estimated from personal data such as gender, age, weight, and occupation. Metabolic rates are important in the estimation of intake dose, pharmacokinetic modeling, and risk assessment. Appendix G indicates how MET values are utilized in modeling efforts that seek to quantify the intake of substances and relate this to health effects.

The MET values for specific activities are not always single point estimates, but in many cases are provided as ranges or distributions. CHAD provides for the description of MET values in the form of uniform, exponential, normal, lognormal, and triangular distributions, as well as point estimates. The distributions and their associated parameters are estimated from data reported in the literature. Table H 1 in Appendix H presents METS distributions and associated statistical information by activity code.

Figure 6 1 shows a MET Data screen, which can be accessed by clicking the METS Data button on the Diary Information Activity Monitor screen ( Figure 5 4). The user can conduct stochastic sampling of MET values from the specified distributions, and the resultant activity specific and daily total energy expenditures will be calculated. At the press of a button, a new set of randomly based MET values can be produced. To assist users who wish to emphasize large MET values in their analyses, CHAD contains a flag that indicates cases for which the median MET value is greater than or equal to 3. 17 Figure 6 1. Sample screen of METS data. 18 Chapter 7 Quality Assurance Procedures Data quality flags are used to indicate problems with data content. There are three classes of such problems that can arise in the activity database. The first is missing or blank data. This is due to periods that are not accounted for ( gaps) in the diary data, or diaries that are shorter than 24 hours, or missing questionnaire data. The second type of problem occurs if data are present but outside the allowable range. If " clean" data sets are obtained, this type of error should occur very seldom. Even so, such errors will occasionally be found. When these errors are detected, either they are corrected ( if possible), or the values are deleted. The third and most difficult type of problem to resolve occurs when the data are present and the values are within allowable range, but are deemed to be of poor quality by a researcher or analyst.

In CHAD, missing data are filled in with dummy activity records that have activity=' X' and location=' X'. No activities have been extended or inferred to fill in these gaps, although this may be done in a future version ( in which case a flag would be set to indicate that the data were changed). The second type of problem occurs infrequently and can be eliminated with careful quality control. The third type of problem is by far the most extensive, as there are literally thousands of cases of unusual or unlikely events in the database. Some of these problems can be identified by using the data quality flags added to the CHAD database.

Interviews with prospective users made clear that most users would like to have access to the unadjusted ( original) data, even if of poor quality, with the option of deleting or replacing data according to their needs. Therefore, quality flags are available to identify suspect records, but it is up to the users to determine which flags are important to them. For example, some diaries do not report any sleep time or any meals. These can be identified by using the QCSLEEP and QCMEALS flags. The user must decide whether or not the diaries are acceptable to them.

7.1 Determination of Quality Flags In CHAD, there are two main types of quality indicators. Variables whose names start with the prefix " QF" are binary quality flags of the pass/ fail type ( Table 7 1). These are set to " on" ( appearing as a filled in dot in CHAD) to indicate questionable data. The other type of quality indicators use the prefix " QC". These variables are counts ( usually of time), which indicate how often within a diary some condition was met. For example, the variable ` QCSLEEP' records the number of hours of sleep time in each diary day. This variable can then be used to select diaries that contain sleep durations within a specified range. 19 Table 7 1. Quality Indicator Variables in CHAD version of the data Name File Range Description QFACTLOC Diary 0 1 1= Inconsistent activity and location code pair QFINFER Diary 0 1 1= Record changed ( e. g. activity inferred) by analyst QFMETAB Diary 0 1 1= High (>= 3.0) METS value QFTRAVEL Summary 0 1 1= Inconsistent morning evening travel times QCSLEEP Summary 0 24 Hours of sleeping during diary day QCMISS Summary 0 1440 Minutes of time missing from diary QCACTLOC Summary 0 1440 Minutes of time with QFACTLOC= 1 QCMEALS Summary 0+ Number of separate meals in diary day QCEATIME Summary 0 1440 Minutes spent eating in diary day QCINFER Summary 0 1440 Minutes of time with QFINFER= 1 QCMETAB Summary 0 1440 Minutes of time with QFMETAB= 1 QCHEAVY Summary 0 1440 Minutes of time with heavy breathing ( HEAVYBR= 1)

QCLONG Summary 1 1440 Duration ( minutes) of longest activity in diary day There are several additional descriptive variables that have been added to the CHAD database that can be used for quality control purposes, even though their names do not start with the letter Q ( Table 7 2). For example, diaries with a small number of records ( i. e., small RECCOUNT) might be eliminated from consideration on the basis that they do not provide a detailed enough description of daily activities.

All of the following quality flags are added to the data by the SAS programs that convert the original data into CHAD form. There is one SAS program for each of the studies incorporated into CHAD. An example program ( NHAPS. SAS) is given in Appendix F. The QFACTLOC flag is set in the first data step ( DATA NHAPS1), but most of the other flagging actually takes place in the DATA SUM step of the program.

Table 7 2. Other Descriptive CHAD Variables Name File Type Description CHADID Both 9 Char Unique 9 character ID identifying each person day NDAYS Summary 1+ Total # of consecutive diary days from this person DAYNUM Summary 1 Ndays Day number ( 1= first 24 hours, 2= second 24 hrs...)

DAYTYPE Summary WD, WE WD= Weekday, WE= Weekend ( Sat or Sun) 20 RECCOUNT Summary 24+ Number of records in diary WRAPTIME Summary 0000 2359 Time at which 24 hour diary day actually started Sections 7.1.1  7.1.3 described the quality indicators that have been incorporated into CHAD.

7.1.1 Quality Flag ( QF) Variables 1. QFACTLOC  Indicates inconsistent activity location pairs in the diary.

Each record in the diary portion of the database contains both an activity code and a location code. Certain activities are not compatible with certain locations. For example, travel activities ( those with codes over 18000) should not take place in one's home ( location codes less than 31000). A list of incompatible combinations was developed and implemented. Frequency tables of flagged data were prepared for certain studies ( Denver, NHAPS, and California adults) and were used to revise the flagging criteria. Overall, about 1.7% ( 11, 292) of the activity records are flagged. This still amounts to several thousand records, so a detailed examination of flagged data will take time. Since certain activity  location combinations are unlikely but not impossible, the identification of suspect combinations will always be partly subjective, and further changes to the list may be desired in future. The total amount of time that QFACTLOC is " on" in each diary is summarized in the variable QCACTLOC.

2. QFTRAVEL  Flag indicating possible travel time inconsistencies.

One of the logical constraints on a valid activity diary is that travel to a remote location ( such as a workplace) should be roughly matched in duration by a later return trip. There are complicating factors such as traffic jams, and running errands, etc., which may result in differing travel times. Another difficulty arises from simply trying to identify trips that qualify as out and back pairs. Without examining all the diaries individually, a first attempt at flagging potential travel inconsistencies was made. The QFTRAVEL variable flags those person days that meet four conditions: ( 1) The person is employed outside the home, ( 2) the sampled day is a weekday ( Monday  Friday), ( 3) the total travel time in the morning rush hours ( 6  9 am) differs by more than a factor of 2 from the evening rush hour ( 4  7 pm) travel time, and ( 4) either the morning or evening travel time was at least 30 minutes. The travel time used is the travel time to/ from work ( Activity= 18200, if recorded) plus the time for unspecified travel ( Activity= 18000). Only the NHAPS and California studies recorded the purpose of the travel. QFTRAVEL is on in 1071 diaries.

Even with these restrictive conditions, a significant percentage of diaries are flagged ( roughly 20% for studies with only general travel, and 5% of diaries with commuting identified as such). Most of these diaries are likely to be valid, but individual examination would be necessary in order to make any decisions. A refinement of the flag definition might be possible in order to better restrict the flagging to only work commute trips, but it is not clear how best to do this since the purpose of a trip must be inferred from the diary as a whole.

3. QFINFER  Indicates that a particular diary record was inferred ( changed from the original). 21 All data in CHAD is faithful to the original raw data. If the original data contained gaps in time, or missing activity or location codes, then these show up as CHAD records with missing codes ( ACTIVITY= X and/ or LOCATION= X). At some point it may be desirable to assign inferred activities in place of the missing data. If this is done, then the QFINFER flag will be set to indicate this, and the end user will have the option of " turning off" the changes. Similarly, diaries with no missing time might be altered ( to add meals or sleep time, for instance), and the QFINFER flag would then used to indicate this condition. QFINFER= 0 for all data since no changes have yet been made. The cumulative variable QCINFER indicates the total time per diary with QFINFER= 1.

4. QFMETAB  Indicates high (> 3.0) metabolic rate factors.

A distribution of possible METS values is associated with each activity. ( See Chapter 6 for details.) The QFMETAB flag indicates those activities for which the median value in the distribution is greater than or equal to 3. These activities will have an effect on the breathing rates of subsequent activities through the phenomenon of " excess post exercise oxygen consumption" ( EPOC). In the current ( December 1997) implementation of CHAD, the METS values for these subsequent activities have not been modified, although this is planned for early 1998. The total amount of time per diary with QFMETAB set to " on" is recorded in the count variable QCMETAB. QFMETAB is on in 91370 cases, or 14.2% of the records. In each of the eight studies this flag is on between 12 18% of the time, with Valdez having the highest rate.

7.1.2 Quality Count ( QC) Variables 1. QCSLEEP  The amount of time spent sleeping.

The only two activities that are almost essential each day are sleeping and eating. For sleeping, the daily total time ( rounded to the nearest whole number of hours) is recorded in the variable QCSLEEP, which appears with the questionnaire ( summary) data. This flag does not necessarily indicate poor quality data, but may sometimes just point to unusual behavior. People may spend little or no time sleeping on a given day. Poorly recorded diaries do not necessarily underestimate sleep time. However, diaries with little or no reported sleep time should probably be examined more closely to allow the analyst to decide on their reliability. QCSLEEP is equal to zero in 304 diaries. This means 1.8% of the diaries do not record sleep time.

2. QCMISS  Missing diary time ( less than 24 hours).

The CHAD database is divided into person days that contain exactly 24 hours of consecutive diary data. In some cases, the original data did not span a full 24 hours, or else it contained one or more gaps ( time intervals) with no recorded activities. The total amount ( in minutes) of such time is recorded in the QCMISS variable, so that incomplete diaries can be eliminated by filtering. QCMISS is greater than zero in 876 diaries; 5.2% are incomplete ( have less than 24 hours).

3. QCMEALS  Number of meals in the diary day.

A meal is defined as one or more consecutive records of eating activity. Two meals must be separated by at least one non eating activity. Roughly 6% of the diaries do not report any eating on that particular day. Examination 22 of the diaries shows that in some cases eating is not reported because it is subsumed under some other activity ( e. g., visiting a friend or relative for a very long period of time). Eating may also take place while watching TV or traveling, for example. There are a few diaries that report excessive eating ( more than six hours per day). The diaries are flagged but are not excluded because of unusual eating patterns. The analyst must decide on the appropriateness of using any of these diaries. QCMEALS is equal to zero in 1684 diaries which is 9.9%.

4. QCLONG  The duration of the longest activity in the diary.

Some diaries report a single activity ( meaning that both the ACTIVITY and LOCATION codes do not change) lasting 10 or 12 hours or more. The duration in minutes of the longest such activity is recorded in the variable QCLONG. Diaries with large values ( say, > 600) for QCLONG could be examined to determine whether or not the diary appears complete.

The remaining QC variables provide time summaries of other flags. This allows diaries to be excluded on the basis of thresholds of time flagged in each category.

5. QCACTLOC  Total time ( minutes) in diary with QFACTLOC flag set to " on".

6. QCEATIME  Total time ( minutes) in diary spent eating.

7. QCINFER  Total time ( minutes) in diary with QFINFER set to " on".

8. QCMETAB  Total time ( minutes) in diary with QFMETAB set to " on".

9. QCHEAVY  Total time ( minutes) in diary with HEAVYBR= 1 ( Heavy breathing).

The NHAPS and Cincinnati studies asked the respondents to identify those activities accompanied by heavy breathing. The definition of heavy breathing is therefore subjective and not consistent across individuals or across studies. Nevertheless, this flag is provided so that the user can quickly find those diaries that are likely to represent active individuals.

7.1.3 Other Variables 1. RECCOUNT  The number of individual activity records in a diary day.

A small number of reported activities over a 24 hour period may be a sign of poor or unreliable diaries. Some activity databases delete diaries that contain fewer than some specified number of entries ( records), but any specific number of activities would represent an arbitrary standard. Using the RECCOUNT variable as a filter, the CHAD user can select any desired minimum number. Note that all diaries have at least 24 records because there is one or more for every hour. ( See Chapter 4 for a discussion of the rationale for subdividing records at each hour boundary.)

2. WRAPTIME  The time at which the 24 hour diary actually started. 23 For consistency, all of the CHAD diaries are organized into a midnight to midnight form. However, some studies ( e. g., Denver) did not have a standard starting time. In such cases, a consecutive 24 hour period was taken, starting at the actual start time, and the part up until midnight was " chopped off" and added back on at the other end. The WRAPTIME variable indicates if this has been done, so the end user could " unwrap" the diary into its original form if desired. WRAPTIME indicates the actual start time of the diary in military time, so it has the value ' 0000' for diaries that were never wrapped ( always midnight to midnight), and can range up to a value of 2359. For wrapped diaries, the date and the weekday given in CHAD represent the day that contained more than 12 hours of time.

7.2 QA Issues in Coding CHAD Variables QA issues in coding CHAD variables include completeness of time of the activity diaries, coding of uncertain activities, and coding of missing activities or nonsequential activities. These issues are described below.

7.2.1 Completeness of Time of the Activity Diaries The activity data in each study should reflect activities for a 24 hour period. However, we found that records may have missing activity data such that a complete 24 hour period was not represented. Also, in three major studies ( Denver, Washington, and Cincinnati) the start and end times were not consistent from person to person. In CHAD, all diaries are exactly 24 hours long and start at midnight. As explained above, the variable WRAPTIME indicates the true diary starting time. Two sequencing numbers are provided: RECNUM indicates the new midnight to midnight order, while SEQ indicates the original chronological sequence.

The Denver study collected two diary days from each participant, that is, 48 continuous hours. The two diary days from each respondent were first recombined into one 48 hour diary. If data were available for less than 45 hours, then only one diary day was retained. The number of consecutive diary days from the same respondent is indicated by the variable NDAYS. In most case of the original data, there was some time overlap between the first and second days. This was due to the extending or adding of records, often with missing activity codes, so that the diary days either started or ended on an hour. These records were removed from the data, and a stop time was set exactly 24 hours after the first record started. The second diary day then starts immediately and continues another 24 hours. If the second day was too short, then additional records were added with the activity and location codes set to missing ( i. e., ` X'). These missing values may perhaps be filled in at a later date. The different diary days from the same individual are indicated in two ways: ( 1) The leftmost eight characters in the CHADID are the same, and the ninth ( rightmost) is A for the first diary day, B for the second, and so on; and ( 2) the DAYNUM variable on the personal summary screen indicates the day number numerically.

7.2.2 Coding of Uncertain Activities In a number of diary records in the target databases, the applicable activity code was uncertain. The code used for these activities in the Denver database is 15. For the data standardization described above, the new code assigned to these activities in the CHAD portion of the Denver data is " U". This will be done for all the databases being added. Too many instances of the 15 code would result in a record with a large amount of activity time unaccounted for. It was decided that records with more than eight hours total ( over two days) of activity time 24 coded as 15 would be excluded from entry in CHAD. Activities coded as U are distinct from activities not recorded at all, since other data may have been provided ( such as the location, the presence of smokers, etc.). Often, U signifies a combination of activities ( such as eating and TV watching) for which the respondent was uncertain of the proper coding method.

7.2.3 Coding of Missing Activities or Nonsequential Activities In a number of instances, blank spaces were found in some of the diary data, indicating missing data. All such time gaps were filled in with new activities by using " X" for both the activity and location code. In some cases there were time overlaps, usually because two diary days extended over each other. In most cases this was easy to resolve, but in 35 cases a specific decision was made on the best method of removing the overlap. These cases are explicitly written in the SAS program named DENVER. SAS, which is used to transform the raw Denver data into the CHAD form. Note that most studies ( including NHAPS and California) did not have any gaps or overlaps in their diaries. 25 Chapter 8 Discussion 8.1 California Activity Codes The version of the California adult survey ( 1988) that was obtained by ManTech contains certain activity codes that are not included in the study documentation. Telephone conversations with Susan Lum of the California Air Resources Board have revealed that our documentation is identical to the " official" version, but that our electronic data are not. Several draft versions of the database were developed by various researchers, and evidently we have one of these. Our version nevertheless contains a finer breakdown ( more codes) than does the final version. Use of these extra codes would require mapping the 100 undocumented codes into the nearest CHAD codes; some cases would require new CHAD codes. These tasks were beyond the scope of this project. An alternative would be to request the official version of the California data; the CHAD user could then view the text description corresponding to each activity to determine any finer breakdown of activity categories.

8.2 Resolution of Quality Flags The data incorporated into CHAD have been assigned quality flags as described in Chapter 7. As a result, several thousand diaries have been flagged for one reason or another. So far, no changes have been made to the flagged diaries. To resolve the points being flagged, an analyst would have to examine each flagged diary in detail to determine whether changes should be made. This is a very labor intensive process since so many diaries are involved. For the first ( beta test) version of CHAD, it will be left to the end user to " correct" any flagged records in the database. A feature for modifying records should be added to the CHAD user interface to facilitate this process.

8.3 Nonrandom Sampling Users may wish to obtain " random" activity diaries from CHAD that meet certain conditions. However, the respondents sampled in CHAD are not randomly distributed. Some cities or states are oversampled because entire studies were carried out in those places. Other studies disqualified large groups of people such as smokers, or non English speakers, or people without telephones. Many surveys were age restricted, or they preferentially sampled certain target groups ( such as asthmatics in Cincinnati). As a result, users should be cautioned against using CHAD to draw random individuals who represent Americans as a whole. One solution to this problem, which is outside the scope of this project, would be to create a set of statistical weights for CHAD to reflect the probability of sampling. 26 References Deyling, K. 1997. Development of a Consistent & Comprehensive Data Base for Assessing Multipathway/ Intake Dose in Children & Others: Part I. Consolidated Report, prepared under Work Assignment 39 of Contract 68 D5 0049 for the U. S. Environmental Protection Agency, National Exposure Research Laboratory, Research Triangle Park, NC.

Goldstein, B., R. Tardiff, G. Hoffnagle, and R. Kester. 1992. Valdez Air Health Study: Summary Report. Prepared for Alyeska Pipeline Service Company, Anchorage, AK.

Johnson, T. 1984. Study of Personal Exposure to Carbon Monoxide in Denver, Colorado. Prepared for U. S. Environmental Protection Agency, Environmental Monitoring Systems Laboratory, Research Triangle Park, NC.

Johnson, T., J. Capel, and L. Wijnberg. 1986. Selected Data Analyses Relating to Studies of Personal Carbon Monoxide Exposure in Denver and Washington, DC. Prepared for U. S. Environmental Protection Agency, Environmental Monitoring Systems Laboratory, Research Triangle Park, NC.

Johnson, T. 1989. Human Activity Patterns in Cincinnati, Ohio. Final Report. Prepared for Electric Power Research Institute, Health Studies Program, Palo Alto, CA.

Klepeis, N., A. Tsang, and J. Behar. 1995. Analysis of the National Human Activity Pattern Survey ( NHAPS) Respondents from a Standpoint of Exposure Assessment. Final Report. Prepared for U. S. Environmental Protection Agency, National Exposure Research Laboratory, Las Vegas, NV.

Linn, W., C. Spier, and J. Hackney. 1993. Activity Patterns in Ozone Exposed Construction Workers. Journal of Occupational Medicine and Toxicology, 2: 1. pp. 1 14.

McCurdy, T. 1997a. Work Assignment 39 of Contract 68 D5 0049. U. S. Environmental Protection Agency, National Exposure Research Laboratory, Research Triangle Park, NC.

McCurdy, T. 1997b. Modeling Multi route Intake Dose in an Internally Consistent Manner. U. S. Environmental Protection Agency, National Exposure Research Laboratory, Research Triangle Park, NC. 27 Paul, R., J. Yoder, T. Johnson, and J. Capel. 1991. Documentation for the Multi City Activity Data Base. Prepared for U. S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC.

Settergren, S. K., T. D. Hartwell, and C. A. Clayton. 1984. Study of Carbon Monoxide Exposure of Residents of Washington, DC.: Additional Analyses. Prepared for U. S. Environmental Protection Agency, Environmental Monitoring Systems Laboratory, Research Triangle Park, NC.

Shamoo, D., T. Johnson, S. Trim, D. Little, W. Linn, and J. Hackney. 1991. Activity Patterns in a Panel of Outdoor Workers Exposed to Oxidant Pollution. Journal of Exposure Analysis and Environmental Epidemiology, 1: 4. pp. 423 438.

Spier, C., D. Little, S. Trim, T. Johnson, W. Linn, and J. Hackney. 1992. Activity Patterns in Elementary and High School Students Exposed to Oxidant Pollution. Journal of Exposure Analysis and Environmental Epidemiology, 2: 3. pp. 277 293.

Wiley, J., J. Robinson, T. Piazza, K. Garrett, K. Cirksena, Y. Cheng, and G. Martin. 1991. Activity Patterns of California Residents. Final Report. Prepared for California Air Resources Board, Research Division, Sacramento, CA.

Wiley, J. 1991. The Study of Children's Activity Patterns. Final Report. Prepared for California Air Resources Board, Research Division, Sacramento, CA. 28 Appendix A Exposure Analysts and Modelers Contacted Table A 1. Exposure Analysts and Modelers Contacted*

Contact* Location Area of Expertise Gerry Ackland Research Triangle Institute Denver and Washington study Maurice Berry NERL Dietary exposure assessment James Capel* Independent Contractor Ten City Study Kenneth Clark* LAREI Los Angeles study Curtis Dary NERL Human activity bibliography George Duggan OAQPS pNEM model Michael Dusetzina OAQPS pNEM model Debra Edwards HED Human activity pattern research William Engelmann Lockheed NHAPS Jeff Evans OPP Health effects research Michael Firestone OPPTS Health effects research Steve Hern NERL THERDbase Alan Huber NERL NHAPS Steve Hui CARB Air pollution research Stephanie Irene HED Human activity pattern research John Irwin OAQPS Multicity study Ted Johnson* Independent Contractor Los Angeles and Valdez Neil Klepeis Lockheed NHAPS Michael Lebowitz Arizona State University Allergy studies and NHEXAS Michael McCoy Formerly of IT Los Angeles and Valdez Table A 1. Exposure Analysts and Modelers Contacted*

Contact* Location Area of Expertise 29 Maria Morandi University of Texas Houston Asthma study William Nelson* NERL NHAPS Al Nielson OPP Health effects research Joan Novak NERL MODELS3 Mary O'Rourke Arizona State University Allergy studies and NHEXAS Ted Palma OAQPS Target study documentation James Quackenboss NERL THERDbase Harvey Richmond OAQPS pNEM Ingrid Sunzenauer OPP Health effects research Amy Vasu OAQPS TRIM Karen Whitby OPP Health effects research Ed Zager OPP Health effects research Michael Zelenka NERL NHAPS * Abbreviations used: HED  Health Effects Division ( OPPTS/ OPP/ HED) NERL  National Exposure Research Laboratory NHAPS  National Human Activity Pattern Survey NHEXAS  National Human Exposure Assessment Survey OAQPS  Office of Air Quality Planning and Standards OPP  Office of Pesticides Programs ( OPPTS/ OPP) OPPTS  Office of Prevention, Pesticides, and Toxic Substances pNEM  Probabilistic National Ambient Air Quality Standards Exposure Model THERDbase  Total Human Exposure Relational Database and Advanced Simulation Environment TRIM  Total Risk Integrated Model * These contact persons were also sources of data used for CHAD, as shown below. James Capel 919 967 8008 Denver, Washington, Cincinnati, and California Kenneth Clark 562 401 7561 Los Angeles Ted Johnson 919 929 8266 Valdez William Nelson 919 541 3184 NHAPS 30 Appendix B CHAD Activity Codes 31 Table B 1. CHAD Activity Codes Code Denver Washington Cincinnati California 1988 California 1990 NHAPS < 10> Work and Other Income Producing Activities 10000: work and other income producing activities, general 10100: work, general 2, 22 10110: work, general, for organizational activities 19 60 10111: work for professional/ union organizations 60 60 10112: work for special interest identity organizations 61 61 10113: work for political party and civic participation 62 62 10114: work for volunteer/ helping organizations 63 63 10115: work of/ for religious groups 64 64 10116: work for fraternal organizations 66 66 10117: work for child/ youth/ family organizations 67 67 10118: work for other organizations 68 68 10120: work, income related only 2 2 1 5 01 10130: work, secondary ( income related) 5 05 Table B 1. CHAD Activity Codes Code Denver Washington Cincinnati California 1988 California 1990 NHAPS 32 10200: unemployment 2 2 02 10300: breaks 8 08 < 11> Household Activities 11000: general household activities 11100: prepare food 3 3, 23 10 10 10 11110: prepare and clean up food 8 11200: indoor chores 5, 25 11210: clean up food 11 11 11 11220: clean house 12 12 12 11300: outdoor chores 6 6, 26 11 13 11310: clean outdoors 13 13 11400: care of clothes 4 4, 24 9 14 14 14 11410: wash clothes 149 11500: build a fire 169 169 11600: repair, general 11610: repair of boat 166 166 11620: paint home/ room 167 167 11630: repair/ maintain car 15 15 15 Table B 1. CHAD Activity Codes Code Denver Washington Cincinnati California 1988 California 1990 NHAPS 33 11640: home repairs 16 11650: other repairs 16 16 11700: care for plants 17 17 17 11800: care for pets/ animals 18 19 18 11900: other household 10 19 18 19 < 12> Child Care 12000: child care, general 12 12100: care of baby 20 20 20 12200: care of child 21 21 21 12300: help/ teach 22 22 22 12400: talk/ read 23 23 23 12500: play indoors 24 24 24 12600: play outdoors 25 25 25 12700: medical care child 26 26 26 12800: other child care 27 27 27 < 13> Obtain Goods and Services 13000: obtain goods and services, general 13100: dry clean 28 28 Table B 1. CHAD Activity Codes Code Denver Washington Cincinnati California 1988 California 1990 NHAPS 34 13200: shop/ run errands, general 7 7, 27 13 301 13210: shop for food 30 30 30 13220: shop for clothes or household goods 31 31 31 13230: run errands 38 38 38 13300: obtain personal care service 32 32 32 13400: obtain medical service 33 33 33 13500: obtain government/ financial services 34 34 34 13600: obtain car service 35 35 35 13700: other repairs 36 36 36 13800: other services 37 37 37 < 14> Personal Needs and Care 14000: personal needs and care, general 8, 28 14100: shower, bathe, personal hygiene 40 40 14110: shower, bathe 40 14120: personal hygiene 44 14200: medical care 41 41 41 14300: help and care 42 42 42 Table B 1. CHAD Activity Codes Code Denver Washington Cincinnati California 1988 California 1990 NHAPS 35 14400: eat 8 13, 33 15 43, 44, 6 43, 44, 6, 711 43 14500: sleep or nap 9 11, 31 16 45,46 45,46 45 14600: dress, groom 47 47 47 14700: other personal needs 10 17 48 < 15> Education and Professional Training 15000: general education and professional training 12, 32 15100: attend full time school 50 50 50 15110: attend day care 3 52 15120: attend K 12 4 15130: attend college or trade school 5 15140: attend adult education and special training 6 15200: attend other classes 51 51 51 15300: do homework 7 54 54, 549 54 15400: use library 55 55 55 15500: other education 56 56 56 < 16> Entertainment/ Social Activities 16000: general entertainment/ social activities 16100: attend sports events 21 70 70 70 Table B 1. CHAD Activity Codes Code Denver Washington Cincinnati California 1988 California 1990 NHAPS 36 16200: participate in social, political, or religious activities 11 15 16210: practice religion 18 65 65 16300: view movie 72 72 72 16400: attend theater 73 73 73 16500: visit museums 24 74 74 74 16600: visit 26 75 75 75 16700: attend a party 25 76 76 76 16800: go to bar/ lounge 23 77 77 77 16900: other entertainment/ social events 22 78,71 71,78 71, 78 < 17> Leisure 17000: leisure, general 9, 29 17100: participate in sports and active leisure 14, 34 17110: participate in sports 28 80 80 80 17111: hunting, fishing, hiking 29 17112: golf 801 801 17113: bowling/ pool/ ping pong/

pinball 802 802 17114: yoga 803 803 Table B 1. CHAD Activity Codes Code Denver Washington Cincinnati California 1988 California 1990 NHAPS 37 17120: participate in outdoor leisure 81 17121: play, unspecified 811 17122: passive, sitting 81 17130: exercise 82 17131: walk, bike, or jog ( not in transit) 13 30, 31 82 82 17140: create art, music, participate in hobbies 32 17141: participate in hobbies 83 83 83 17142: create domestic crafts 84 84 84 17143: create art 85 85 85 17144: perform music/ drama/ dance 86 86 86 17150: play games 87 87 87 17160: use of computer 88 88 17170: participate in recess and physical education 27 17180: other sports and active leisure 33 17200: participate in passive leisure 17210: watch 17211: watch adult at work 8 Table B 1. CHAD Activity Codes Code Denver Washington Cincinnati California 1988 California 1990 NHAPS 38 17212: watch someone provide child care 28 17213: watch personal care 48 17214: watch education 58 17215: watch organizational activities 68 17216: watch recreation 88 17220: listen to radio/ listen to recorded music/ watch t. v. 35 17221: listen to radio 90 90 90 17222: listen to recordedmusic 92 92 92 17223: watch t. v. 91, 914 91, 914, 915 91 17230: read, general 34 17231: read books 93, 939 93, 934, 937,

938 93 17232: read magazine/ not ascertained 94 94, 944 94 17233: read newspaper 95, 954 95, 954 95 17240: converse/ write 36 17241: converse 96 96 96 17242: write for leisure/ pleasure/

paperwork 97, 971 97, 971 97 Table B 1. CHAD Activity Codes Code Denver Washington Cincinnati California 1988 California 1990 NHAPS 39 17250: think and relax 37 98 98 17260: other passive leisure 38 17300: other leisure 14 98, 875, 877,

879 < 18> Travel 18000: travel, general 1 1, 21 1 18100: travel during work 3 3 03 18200: travel to/ from work 9 9 09 18300: travel for child care 29 29 29 18400: travel for goods and services 39 39 39 18500: travel for personal care 49 49 49 18600: travel for education 59 59 59 18700: travel for organizational activity 69 69 69 18800: travel for event/ social activity 79 79 79 18900: travel for leisure 18910: travel for active leisure 89 89 89 18920: travel for passive leisure 99 99 99 40 Appendix C CHAD Location Codes 41 Table C 1. CHAD Location Codes Code Denver Washington Cincinnati California 1988 California 1990 NHAPS < 30> Home 30000: residence, general 30010: your residence 30020: other's residence 32 30100: residence, indoor 2 0200 30120: your residence, indoor 3 30121: kitchen 1 1 101 30122: living room/ family room 2 2 102 30123: dining room 3 3 103 30124: bathroom 4 4 104 30125: bedroom 5 5 105 30126: study/ office 6 6 106 30127: basement 8 8 108 30128: utility room/ laundry room 9 9 110 30129: other indoor 30130: other's residence, indoor 4 30131: kitchen 3201 201 30132: living room/ family room 3202 202 30133: dining room 3203 203 30134: bathroom 3204 204 30135: bedroom 3205 205 Table C 1. CHAD Location Codes Code Denver Washington Cincinnati California 1988 California 1990 NHAPS 42 30136: study/ office 3206 206 30137: basement 3208 208 30138: utility room/ laundry room 3209 210 30139: other indoor 30200: residence, outdoor 76 0883 60 30210: your residence, outdoor 30211: pool, spa 10 10 111 30219: other outdoor 11 11 112 30220: other's residence, outdoor 30221: pool, spa 3210 211 30229: other outdoor 3211 212 30300: garage 30310: indoor garage 38 30320: outdoor garage 55 30330: your garage 7 7 107 30331: indoor garage 51 0661 30332: outdoor garage 71 0881 30340: other's garage 3207 207 30341: indoor garage 30342: outdoor garage 30400: other, residence 13 Table C 1. CHAD Location Codes Code Denver Washington Cincinnati California 1988 California 1990 NHAPS 43 < 31> Travel 31000: travel, general 1 0100 31100: motorized travel 31110: car 1 51 51 301 31120: truck 28 31121: truck ( pickup or van) 30 52 52 302 31122: truck ( other than pickup or van) 57 57 303 31130: motorcycle/ moped/ motorized scooter 31 60 60 304 31140: bus 29 55 55 305 31150: train/ subway/ rapid transit 32 56 56, 69, 70 310 31160: airplane 33 58 58 311 31170: boat 312 31171: motorized boat 34 31172: unmotorized boat 35 31200: non motorized travel 31210: walk 53 53 306 31220: bicycle/ skateboard/

rollerskates 59 59 307 31230: in a stroller or carried by an adult 63 308 31300: waiting Table C 1. CHAD Location Codes Code Denver Washington Cincinnati California 1988 California 1990 NHAPS 44 31310: wait for bus, train, ride ( at stop) 54 54 313 31320: wait for travel, indoors 314 31900: travel, other 61 300, 320 31910: other vehicle 36 < 32 34> Other Indoor 32000: other indoor, general 6, 50 32100: office building/ bank/ post office 3 0300 5 21 21 401 32200: industrial plant/ factory/ warehouse 53 6 22 22 402 32300: grocery store/ convenience store 4 0400 8 23 23 403 32400: shopping mall/ non grocery store 58 0664 48 24 24 404 32500: bar/ night club/ bowling alley 405 32510: bar/ night club 52 29 29 32520: bowling alley 51 32600: repair shop 32610: auto repair shop/ gas station 54 40 33 33 406 32620: other repair shop 55 41 418 32700: indoor gym/ sports or health club 42 31 31 407 32800: childcare facility 32810: childcare facility, house 253 32820: childcare facility, commercial 255 32900: public building/ library/ museum/ theater 26 26 408 Table C 1. CHAD Location Codes Code Denver Washington Cincinnati California 1988 California 1990 NHAPS 45 32910: auditorium, sport's arena, concert hall 56 0662 43 32920: library, courtroom, museum,

theater 61 44, 50, 45 33100: Laundromat 409 33200: hospital/ health care facility/ doctor's office 59 0666 49 27 27 410 33300: beauty parlor/ barber shop/ hair dresser's 37 37 411 33400: at work: no specific location, moving among locations 38 412 33500: school 60 0665 7 25 251 413 33600: restaurant 5 0500 46 28 28 414 33700: church 57 0663 47 30 30 415 33800: hotel/ motel 35 35 416 33900: dry cleaners 36 36 417 34100: parking garage 52 0661 39 419 34200: laboratory 0667 34200: other, indoor ( specify) 62 0669 53, 9 39 39 400, 420 < 35 36> Other Outdoor 35000: other outdoor, general 8, 70 34, 11 35100: sidewalk/ street/ neighborhood 501 35110: within 10 yards of street 7 0700 10 35200: public garage/ parking lot 0881 35210: public garage 72 56 Table C 1. CHAD Location Codes Code Denver Washington Cincinnati California 1988 California 1990 NHAPS 46 35220: parking lot 73 57 502 35300: service station/ gas station 74 0885 58 503 35400: construction site 75 0882 59 504 35500: amusement park 38 35600: school grounds/ playground 505 35610: school grounds 77 61 35620: playground 62 34 35700: sports stadium and amphitheater 78 63 506 35800: park/ golf course 79 0884 64 507 35810: park 35820: golf course 35900: pool, river, lake 508 36100: restaurant, picnic 510 36200: farm 511 36300: other outdoor ( specify) 0889 67, 11 40 40 500, 520 47 Appendix D Occupational Codes Used in CHAD ( Based on U. S. Census Bureau occupational code categories)

005 Administrators and officials, public administration 007 Financial managers 008 Personnel and labor relations managers 009 Purchasing managers 013 Managers, marketing, advertising, and public relations 014 Administrators, education 015 Managers, medicine and health 016 Managers, properties and real estate 019 Managers and administrators, not elsewhere coded( n. e. c.) 023 Accountants and auditors 025 Other financial officers 026 Management analysts 027 Personnel, training, and labor relations specialists 029 Buyers, wholesale and retail, except farm products 037 Management related occupations, n. e. c. 055 Electrical and electronic engineers 059 Engineers, n. e. c. 064 Computer systems analysts ans scientists 065 Operations and systems researchers and analysts 067 Statistician 075 Geologists and geodesists 077 Agricultural and food scientists 078 Biological and life scientists 084 Physicians 095 Registered nurses 096 Pharmacists 097 Dietitians 124 Political science teachers 137 Art, drama, and music teachers 138 Physical education teachers 139 Education teachers 143 English teachers 148 Trade and industrial teachers 155 Teachers, prekindergarden and kindergarden 156 Teachers, elementary school 157 Teachers, secondary school 158 Teachers, special education 159 Teachers, n. e. c. 163 Counselors, educational and vocational 164 Librarians 48 173 Urban planners 174 Social workers 176 Clergy 178 Lawyers 185 Designers 186 Musicians and composers 189 Photographers 195 Editors and reporters 198 Announcers 203 Clinical lab technologists and technicians 207 Licensed practical nurses 208 Health technologists and technicians, n. e. c. 213 Electrical and electronic technicians 216 Engineering technicians, n. e. c. 228 Broadcast equipment operators 229 Computer programmers 235 Technicians, n. e. c. 243 Supervisors and proprietors, sales occupations 253 Insurance sales occupations 254 Real estate sales occupations 255 Securities and financial services sales occupations 257 Sales occupations, other business services 259 Sales representatives, mining, manufacturing and wholesale 263 Sales workers, motor vehicles and boats 268 Sales workers, hardware and building supplies 274 Sales workers, other other commodities 275 Sales counter clerks 276 Cashiers 277 Street and door to door sales workers 278 News vendors 303 Supervisors, general office 309 Peripheral equipment operators 313 Secretaries 315 Typists 319 Receptionists 323 Information clerks, n. e. c. 336 Records clerks 337 Bookkeepers, accounting, and auditing clerks 354 Postal clerks, except mail carriers 357 Messengers 375 Insurance adjusters, examiners, and insvestigators 376 Investigators and adjusters, except insurance 377 Eligibility clerks, social welfare 378 Bill and account collectors 379 General office clerks 385 Data entry keyers 406 Child care workers, private household 49 418 Police detectives, public services 426 Guards and police, except public service 427 Protective services occupations, n. e. c. 434 Bartenders 435 Waiters and waitresses 436 Cooks, except short order 444 Miscellaneous food preparation occupations 446 Heath aides, except nursing 447 Nursing aides, orderlies, and attendants 449 Maids and housemen 453 Janitors and cleaners 467 Homemaker 468 Child care workers 495 Forestry workers, except logging 503 Supervisors, mechanics, and repairers 506 Auto mechanic apprentices 507 Bus, truck, and stationery engine mechanics 508 Aircraft engine mechanics 509 Small engine repairers 514 Auto body and related repairers 516 Heavy equipment mechanics 518 Industrial machinery repairers 529 Telephone installers and repairers 534 Heating, air conditioning, and refregeration mechanics 535 Camera, watch, and musical instrument repairers 536 Locksmiths and safe repairers 539 Mechanical controls and valve repairers 555 Supervisors, electricians and power transmission installers 558 Supervisors, construction occupations, n. e. c. 567 Carpenters 577 Electrical power installers and repairers 588 Concrete and terrazzo finishers 599 Construction trades, n. e. c. 617 Mining occupations, n. e. c. 633 Supervisors, production occupations 637 Machinists 644 Precision grinders, fitters, and tool sharpeners 657 Cabinet makers and bench carpenters 677 Optical goods workers 678 Dental lab and medical appliance technicians 696 Stationary engineers 734 Printing machine operators 736 Typesetters and compositors 743 Textile cutting machine operators 744 Textile sewing machine operators 763 Roasting and baking machine operators, food 765 Folding machine operators 50 774 Photographic process machine operators 777 Miscellaneous machine operators, n. e. c. 779 Machine operators, not specified 787 Hand molding, casting, and forming occupations 796 Production inspectors, checkers, and examiners 804 Truck drivers, heavy 805 Truck drivers, light 856 Industrial truck and tractor equipment operators 869 Construction laborers 877 Stock handlers and baggers 885 Garage and service station related occupations 888 Hand packers and packagers 889 Laborers, except construction 998 Not employed 999 Retired 51 Appendix E CHAD Questionnaire Information 1. Which of the following best describes your living quarters?

2. How are your living quarters heated?

3. Which fuel or energy source is used most for heating in your living quarters?

4. Does your house have an attached garage?

5. Do you use a gas stove in your house?

6a. Are you employed outside the home?

6b. If employed, do you work part time or full time?

7. Are you attending school?

8. Do you have asthma?

9. Shave you been around pesticides lately?

10. Which U. S. Bureau of Census Occupational Group does your main occupation fall under?

11. What is your household's total income before taxes?

12. Do you smoke?

13. Do you have air conditioning?

14. State 15. What is your zipcode?

16. County 52 Appendix F Program for Recoding NHAPS Variables into CHAD Format * Program for reading diary data and translating into CHAD form ; * Original PROGRAM NAME= C:\ WA50\ MCCURDY\ PROGRAMS\ TESTACTIV 8 22 97 ; * This program operates on the NHAPS data ; * Latest changes made by Graham Glen on December 29, 1997. ;

RUN; OPTIONS OBS= MAX; LIBNAME OUT ' E:\ WA50\ SASDATA'; TITLE ' NHAPS DATA';

* Read questionnaire data ( only the variables needed for CHAD);

DATA QST1; INFILE ' D:\ NHAPS\ DATA\ MAIN. DAT' PAD;

LENGTH CHADID $ 9 OCCUP $ 7 PID $ 6 COUNTY $ 20 STATE $ 14 AGEGROUP JOBHOURS ZIPCODE DUMMY5 $ 5 WRAPTIME DUMMY4 $ 4 AGE WEIGHT TEMPERAT PREF $ 3 HOUSING DAYTYPE HEATING FUEL EDUCAT INCOME $ 2 GARAGE GASSTOVE GENDER RACE STUDENT ASTHMA HEARTLUN SMOKER SMOKER2 PESTICID EMPLOYED FULLTIME AIRCOND $ 1;

KEEP CHADID PID DAYNUM NDAYS AGEGROUP JOBHOURS ZIPCODE AGE HOUSING DAYTYPE HEATING FUEL EDUCAT WRAPTIME GARAGE GASSTOVE GENDER RACE STUDENT ASTHMA HEARTLUN SMOKER SMOKER2 PESTICID EMPLOYED FULLTIME OCCUP INCOME STATE AIRCOND WEIGHT TEMPERAT COUNTY;

INPUT # 1 @ 1 PID $ CHAR6. AB 11 STATEN 25 26 # 2 SMOK2 20 21 SMOKY 23 CIGS 25 CIGARS 27 # 3 BIRTH 47 48 EMPL 50 UNEMP 52 WORKH 58 59 RACEN 73 HISP 75 GRADE 77 78 SEX 80 # 4 ASTH 10 ANGINA 12 RESP 14 ZIP 47 51 HOUSE 56 ATTGAR 68 GSTOV 76 # 5 HEAT 10 FUELN 12 DAYTYP 42 OTHSMOK 60 # 6 PEST 22 # 7 FURNY 49 FUELY 51 WSTOVE 55 KERO 57 FIRE 61 # 20;

* Use prefix NHA for NHAPS Air questionnaire, and NHW for Water questionnaire;

DUMMY5= 10000+_ N_; IF ( AB= 1) THEN PREF=' NHA'; ELSE IF ( AB= 2) THEN PREF=' NHW'; ELSE PREF=' NHX'; CHADID= PREF || DUMMY5 || ' A';

PID= TRANSLATE( PID,' 0',' ');

DAYNUM= 1; NDAYS = 1;

IF ( DAYTYP= 1) THEN DAYTYPE=' WD'; ELSE 53 IF ( DAYTYP= 2) THEN DAYTYPE=' WE'; ELSE DAYTYPE=' X' ;

* All NHAPS diaries start at midnight; WRAPTIME=' 0000';

IF ( HOUSE= 1) THEN HOUSING=' AP'; ELSE IF ( HOUSE= 2) THEN HOUSING=' SF'; ELSE IF ( HOUSE= 3) THEN HOUSING=' MF'; ELSE IF ( HOUSE= 4) THEN HOUSING=' O' ; ELSE HOUSING=' X' ;

IF ( AB= 1) THEN DO; IF ( FURNY= 1) THEN HEATING=' VB'; ELSE IF ( FUELY= 2) THEN HEATING=' E' ; ELSE IF ( WSTOVE= 1) THEN HEATING=' UV'; ELSE IF ( KERO= 1) THEN HEATING=' UV'; ELSE IF ( FIRE= 1) THEN HEATING=' UV'; ELSE HEATING=' X' ; END; IF ( AB= 2) THEN DO; IF ( HEAT= 1) THEN HEATING=' VB'; ELSE IF ( HEAT= 2) THEN HEATING=' S' ; ELSE IF ( HEAT= 3) THEN HEATING=' UV'; ELSE IF ( HEAT= 7) THEN DO; IF ( FUELN= 1) THEN HEATING=' UV'; ELSE IF ( FUELN= 2) THEN HEATING=' E' ; ELSE IF ( FUELN= 3) THEN HEATING=' UV'; ELSE IF ( FUELN= 4) THEN HEATING=' UV'; ELSE IF ( FUELN= 5) THEN HEATING=' UV'; ELSE IF ( FUELN= 6) THEN HEATING=' UV'; ELSE IF ( FUELN= 7) THEN HEATING=' E' ; ELSE HEATING=' O' ; END; ELSE HEATING=' X'; END;

IF ( AB= 1) THEN DO; IF ( FUELY= 1) THEN FUEL=' G' ; ELSE IF ( FUELY= 2) THEN FUEL=' ES'; ELSE IF ( FUELY= 3) THEN FUEL=' OK'; ELSE IF ( FUELY= 4) THEN FUEL=' C' ; ELSE IF ( FUELY= 5) THEN FUEL=' OK'; ELSE IF ( FUELY= 6) THEN FUEL=' W' ; ELSE IF ( FUELY= 7) THEN FUEL=' ES'; ELSE IF ( WSTOVE= 1) THEN FUEL=' W' ; ELSE IF ( KERO= 1) THEN FUEL=' OK'; ELSE FUEL=' X' ; END; IF ( AB= 2) THEN DO; IF ( FUELN= 1) THEN FUEL=' G' ; ELSE IF ( FUELN= 2) THEN FUEL=' ES'; ELSE IF ( FUELN= 3) THEN FUEL=' OK'; ELSE IF ( FUELN= 4) THEN FUEL=' OK'; ELSE IF ( FUELN= 5) THEN FUEL=' C' ; ELSE IF ( FUELN= 6) THEN FUEL=' W' ; ELSE IF ( FUELN= 7) THEN FUEL=' ES'; ELSE IF ( FUELN= 0) THEN FUEL=' O' ; ELSE FUEL=' X' ; END;

IF ( ATTGAR= 0) THEN GARAGE=' N'; ELSE IF ( ATTGAR= 1) THEN GARAGE=' Y'; ELSE GARAGE=' X'; 54 IF ( GSTOV= 0) THEN GASSTOVE=' N'; ELSE IF ( GSTOV= 1) THEN GASSTOVE=' Y'; ELSE GASSTOVE=' X';

IF ( SEX= 1) THEN GENDER=' M'; ELSE IF ( SEX= 2) THEN GENDER=' F'; ELSE GENDER=' X';

IF ( RACEN= 1) THEN RACE=' W'; ELSE IF ( RACEN= 2) THEN RACE=' B'; ELSE IF ( RACEN= 3) THEN RACE=' A'; ELSE IF ( RACEN= 4) THEN RACE=' O'; ELSE IF ( RACEN= 5) THEN RACE=' H'; ELSE IF ( HISP= 1) THEN RACE=' H'; ELSE RACE=' X';

IF ( GRADE< 1) THEN EDUCAT=' N' ; ELSE IF ( GRADE< 8) THEN EDUCAT=' SE'; ELSE IF ( GRADE= 8) THEN EDUCAT=' E' ; ELSE IF ( GRADE< 12) THEN EDUCAT=' SH'; ELSE IF ( GRADE= 12) THEN EDUCAT=' H' ; ELSE IF ( GRADE< 16) THEN EDUCAT=' SC'; ELSE IF ( GRADE= 16) THEN EDUCAT=' C' ; ELSE IF ( GRADE= 17) THEN EDUCAT=' SG'; ELSE IF ( GRADE= 18) THEN EDUCAT=' G' ; ELSE EDUCAT=' X' ;

* Only the year of birth was recorded. Age is calculated depending on the ; * quarter in which the survey was conducted. ; Q= SUBSTR( PID, 6,1); IF ( BIRTH= 99) THEN AGE=' X'; ELSE DO; NAGE=( 92 BIRTH)+ FLOOR( Q/ 4); IF ( NAGE>= 0) THEN AGE= NAGE; ELSE AGE=' 0'; END;

IF ( NAGE=.) THEN AGEGROUP=' X' ; ELSE IF ( NAGE< 5) THEN AGEGROUP=' 0_ 4' ; ELSE IF ( NAGE< 15) THEN AGEGROUP=' 5_ 14' ; ELSE IF ( NAGE< 19) THEN AGEGROUP=' 15_ 18'; ELSE IF ( NAGE< 65) THEN AGEGROUP=' 19_ 64'; ELSE AGEGROUP='>= 65' ;

IF ( EMPL= 1) THEN EMPLOYED=' Y'; ELSE IF ( EMPL= 2) THEN EMPLOYED=' Y'; ELSE IF ( EMPL= 3) THEN EMPLOYED=' N'; ELSE EMPLOYED=' X';

IF ( EMPL= 1) THEN FULLTIME=' Y'; ELSE IF ( EMPL= 2) THEN FULLTIME=' N'; ELSE IF ( EMPL= 3) THEN FULLTIME=' N'; ELSE FULLTIME=' X';

IF ( EMPL = 3 ) THEN JOBHOURS=' 0' ; ELSE IF ( WORKH< 10) THEN JOBHOURS=' 0_ 9' ; ELSE IF ( WORKH< 20) THEN JOBHOURS=' 10_ 19'; ELSE IF ( WORKH< 30) THEN JOBHOURS=' 20_ 29'; ELSE IF ( WORKH< 40) THEN JOBHOURS=' 30_ 39'; ELSE IF ( WORKH< 50) THEN JOBHOURS=' 40_ 49'; ELSE IF ( WORKH< 80) THEN JOBHOURS=' 50_ 79'; ELSE IF ( WORKH< 87) THEN JOBHOURS=' 80_' ; ELSE JOBHOURS=' X' ; 55 IF ( AGEGROUP=' 5_ 14') THEN STUDENT=' Y'; ELSE IF ( UNEMP= 4) THEN STUDENT=' Y'; ELSE IF ( UNEMP< 8) THEN STUDENT=' N'; ELSE IF ( FULLTIME=' Y') THEN STUDENT=' N'; ELSE STUDENT=' X';

OCCUP=' X';

IF ( ASTH= 0) THEN ASTHMA=' N'; ELSE IF ( ASTH= 1) THEN ASTHMA=' Y'; ELSE ASTHMA=' X';

IF ( ASTH= 0 AND ANGINA= 0 AND RESP= 0) THEN HEARTLUN=' N'; ELSE IF ( ASTH= 1 OR ANGINA= 1 OR RESP= 1) THEN HEARTLUN=' Y'; ELSE HEARTLUN=' X';

IF ( SMOKY= 0 AND CIGARS= 0) THEN SMOKER=' N'; ELSE IF ( SMOKY= 1 OR CIGARS= 1) THEN SMOKER=' Y'; ELSE SMOKER=' X';

IF ( AB= 1) THEN DO; IF ( OTHSMOK= 0) THEN SMOKER2=' N'; ELSE IF ( OTHSMOK= 1) THEN SMOKER2=' Y'; ELSE SMOKER2=' X'; END; IF ( AB= 2) THEN DO; IF ( SMOK2= 0 ) THEN SMOKER2=' N'; ELSE IF ( SMOK2< 11) THEN SMOKER2=' Y'; ELSE SMOKER2=' X'; END;

INCOME=' X';

IF ( ZIP=. ) THEN ZIPCODE=' X'; ELSE IF ( ZIP= 99999) THEN ZIPCODE=' X'; ELSE IF ( ZIP> 9999 ) THEN ZIPCODE= ZIP; ELSE DO; DUMMY4= ZIP; ZIPCODE=' 0' || DUMMY4; END;

IF ( PEST= 0) THEN PESTICID=' N'; ELSE IF ( PEST= 1) THEN PESTICID=' Y'; ELSE PESTICID=' X';

IF ( STATEN= 1) THEN STATE=' Alabama' ; ELSE IF ( STATEN= 3) THEN STATE=' Arizona' ; ELSE IF ( STATEN= 4) THEN STATE=' Arkansas' ; ELSE IF ( STATEN= 5) THEN STATE=' California' ; ELSE IF ( STATEN= 6) THEN STATE=' Colorado' ; ELSE IF ( STATEN= 7) THEN STATE=' Connecticut' ; ELSE IF ( STATEN= 8) THEN STATE=' Delaware' ; ELSE IF ( STATEN= 9) THEN STATE=' Dist. Columbia'; ELSE IF ( STATEN= 10) THEN STATE=' Florida' ; ELSE IF ( STATEN= 11) THEN STATE=' Georgia' ; ELSE IF ( STATEN= 13) THEN STATE=' Idaho' ; ELSE IF ( STATEN= 14) THEN STATE=' Illinois' ; ELSE IF ( STATEN= 15) THEN STATE=' Indiana' ; ELSE IF ( STATEN= 16) THEN STATE=' Iowa' ; ELSE IF ( STATEN= 17) THEN STATE=' Kansas' ; ELSE IF ( STATEN= 18) THEN STATE=' Kentucky' ; ELSE IF ( STATEN= 19) THEN STATE=' Louisiana' ; ELSE IF ( STATEN= 20) THEN STATE=' Maine' ; ELSE IF ( STATEN= 21) THEN STATE=' Maryland' ; ELSE 56 IF ( STATEN= 22) THEN STATE=' Massachusetts' ; ELSE IF ( STATEN= 23) THEN STATE=' Michigan' ; ELSE IF ( STATEN= 24) THEN STATE=' Minnesota' ; ELSE IF ( STATEN= 25) THEN STATE=' Mississippi' ; ELSE IF ( STATEN= 26) THEN STATE=' Missouri' ; ELSE IF ( STATEN= 27) THEN STATE=' Montana' ; ELSE IF ( STATEN= 28) THEN STATE=' Nebraska' ; ELSE IF ( STATEN= 29) THEN STATE=' Nevada' ; ELSE IF ( STATEN= 30) THEN STATE=' New Hampshire' ; ELSE IF ( STATEN= 31) THEN STATE=' New Jersey' ; ELSE IF ( STATEN= 32) THEN STATE=' New Mexico' ; ELSE IF ( STATEN= 33) THEN STATE=' New York' ; ELSE IF ( STATEN= 34) THEN STATE=' North Carolina'; ELSE IF ( STATEN= 35) THEN STATE=' North Dakota' ; ELSE IF ( STATEN= 36) THEN STATE=' Ohio' ; ELSE IF ( STATEN= 37) THEN STATE=' Oklahoma' ; ELSE IF ( STATEN= 38) THEN STATE=' Oregon' ; ELSE IF ( STATEN= 39) THEN STATE=' Pennsylvania' ; ELSE IF ( STATEN= 41) THEN STATE=' Rhode Island' ; ELSE IF ( STATEN= 42) THEN STATE=' South Carolina'; ELSE IF ( STATEN= 43) THEN STATE=' South Dakota' ; ELSE IF ( STATEN= 44) THEN STATE=' Tennessee' ; ELSE IF ( STATEN= 45) THEN STATE=' Texas' ; ELSE IF ( STATEN= 46) THEN STATE=' Utah' ; ELSE IF ( STATEN= 47) THEN STATE=' Vermont' ; ELSE IF ( STATEN= 49) THEN STATE=' Virginia' ; ELSE IF ( STATEN= 50) THEN STATE=' Washington' ; ELSE IF ( STATEN= 51) THEN STATE=' West Virginia' ; ELSE IF ( STATEN= 52) THEN STATE=' Wisconsin' ; ELSE IF ( STATEN= 53) THEN STATE=' Wyoming' ; ELSE STATE=' X' ;

AIRCOND =' X'; WEIGHT =' X'; TEMPERAT=' X'; COUNTY =' X';

RUN;

PROC SORT DATA= QST1; BY PID; RUN;

* Now process the diary data.;

DATA NHAPS1; INFILE ' D:\ NHAPS\ DATA\ DIARY. DAT' PAD;

LENGTH PID $ 6 STARTIME ENDTIME A4 L4 $ 4 ACTTEXT DESCRIP $ 49 ACTIVITY LOCATION A5 L5 $ 5 WEEKDAY A3 L3 $ 3 A2 L2 $ 2 GASUSE SMOKING2 HEAVYBR QFACTLOC QFINFER QFMETAB $ 1;

KEEP PID STARTIME DURATION ENDTIME ACTIVITY LOCATION DESCRIP GASUSE SMOKING2 HEAVYBR QFACTLOC QFINFER QFMETAB MONTH DAY YEAR WEEKDAY HOUR MINUTE ENDHOUR ENDMIN;

INPUT @ 1 PID $ CHAR6. MONTH 9 10 DAY 11 12 YEAR 13 14 STARTIME 15 18 ENDTIME 19 22 BREATH 25 ACTTEXT 26 74 ACTIV 75 77 LOCATE 78 80 SMOKING 81 DURATION 82 85 WKDAY 87 HOUR 15 16 MINUTE 17 18 ENDHOUR 19 20 ENDMIN 21 22;

IF YEAR=. THEN DELETE; IF MONTH=. THEN DELETE; IF DAY=. THEN DELETE; IF HOUR=. THEN DELETE; 57 IF MINUTE=. THEN DELETE; IF DURATION= 0 THEN DELETE;

* Convert to 4 digit representation for YEAR; YEAR= YEAR+ 1900;

IF WKDAY= 1 THEN WEEKDAY=' MON'; ELSE IF WKDAY= 2 THEN WEEKDAY=' TUE'; ELSE IF WKDAY= 3 THEN WEEKDAY=' WED'; ELSE IF WKDAY= 4 THEN WEEKDAY=' THU'; ELSE IF WKDAY= 5 THEN WEEKDAY=' FRI'; ELSE IF WKDAY= 6 THEN WEEKDAY=' SAT'; ELSE IF WKDAY= 7 THEN WEEKDAY=' SUN'; ELSE WEEKDAY=' X';

* Recode activities using new CHAD codes; IF ACTIV= 60 THEN ACTIVITY=' 10111'; ELSE IF ACTIV= 61 THEN ACTIVITY=' 10112'; ELSE IF ACTIV= 62 THEN ACTIVITY=' 10113'; ELSE IF ACTIV= 63 THEN ACTIVITY=' 10114'; ELSE IF ACTIV= 64 THEN ACTIVITY=' 10115'; ELSE IF ACTIV= 66 THEN ACTIVITY=' 10116'; ELSE IF ACTIV= 67 THEN ACTIVITY=' 10117'; ELSE IF ACTIV= 68 THEN ACTIVITY=' 10118'; ELSE IF ACTIV= 01 THEN ACTIVITY=' 10120'; ELSE IF ACTIV= 05 THEN ACTIVITY=' 10120'; ELSE IF ACTIV= 02 THEN ACTIVITY=' 10200'; ELSE IF ACTIV= 08 THEN ACTIVITY=' 10300'; ELSE IF ACTIV= 10 THEN ACTIVITY=' 11100'; ELSE IF ACTIV= 11 THEN ACTIVITY=' 11210'; ELSE IF ACTIV= 12 THEN ACTIVITY=' 11220'; ELSE IF ACTIV= 13 THEN ACTIVITY=' 11300'; ELSE IF ACTIV= 14 THEN ACTIVITY=' 11400'; ELSE IF ACTIV= 15 THEN ACTIVITY=' 11630'; ELSE IF ACTIV= 16 THEN ACTIVITY=' 11650'; ELSE IF ACTIV= 17 THEN ACTIVITY=' 11700'; ELSE IF ACTIV= 18 THEN ACTIVITY=' 11800'; ELSE IF ACTIV= 19 THEN ACTIVITY=' 11900'; ELSE IF ACTIV= 20 THEN ACTIVITY=' 12100'; ELSE IF ACTIV= 21 THEN ACTIVITY=' 12200'; ELSE IF ACTIV= 22 THEN ACTIVITY=' 12300'; ELSE IF ACTIV= 23 THEN ACTIVITY=' 12400'; ELSE IF ACTIV= 24 THEN ACTIVITY=' 12500'; ELSE IF ACTIV= 25 THEN ACTIVITY=' 12600'; ELSE IF ACTIV= 26 THEN ACTIVITY=' 12700'; ELSE IF ACTIV= 27 THEN ACTIVITY=' 12800'; ELSE IF ACTIV= 28 THEN ACTIVITY=' 13100'; ELSE IF ACTIV= 30 THEN ACTIVITY=' 13210'; ELSE IF ACTIV= 31 THEN ACTIVITY=' 13220'; ELSE IF ACTIV= 38 THEN ACTIVITY=' 13230'; ELSE IF ACTIV= 32 THEN ACTIVITY=' 13300'; ELSE IF ACTIV= 33 THEN ACTIVITY=' 13400'; ELSE IF ACTIV= 34 THEN ACTIVITY=' 13500'; ELSE IF ACTIV= 35 THEN ACTIVITY=' 13600'; ELSE IF ACTIV= 36 THEN ACTIVITY=' 13700'; ELSE IF ACTIV= 37 THEN ACTIVITY=' 13800'; ELSE IF ACTIV= 40 THEN ACTIVITY=' 14110'; ELSE IF ACTIV= 44 THEN ACTIVITY=' 14120'; ELSE IF ACTIV= 41 THEN ACTIVITY=' 14200'; ELSE IF ACTIV= 42 THEN ACTIVITY=' 14300'; ELSE IF ACTIV= 43 THEN ACTIVITY=' 14400'; ELSE IF ACTIV= 45 THEN ACTIVITY=' 14500'; ELSE IF ACTIV= 47 THEN ACTIVITY=' 14600'; ELSE IF ACTIV= 48 THEN ACTIVITY=' 14700'; ELSE 58 IF ACTIV= 50 THEN ACTIVITY=' 15100'; ELSE IF ACTIV= 51 THEN ACTIVITY=' 15200'; ELSE IF ACTIV= 54 THEN ACTIVITY=' 15300'; ELSE IF ACTIV= 55 THEN ACTIVITY=' 15400'; ELSE IF ACTIV= 56 THEN ACTIVITY=' 15500'; ELSE IF ACTIV= 70 THEN ACTIVITY=' 16100'; ELSE IF ACTIV= 65 THEN ACTIVITY=' 16210'; ELSE IF ACTIV= 72 THEN ACTIVITY=' 16300'; ELSE IF ACTIV= 73 THEN ACTIVITY=' 16400'; ELSE IF ACTIV= 74 THEN ACTIVITY=' 16500'; ELSE IF ACTIV= 75 THEN ACTIVITY=' 16600'; ELSE IF ACTIV= 76 THEN ACTIVITY=' 16700'; ELSE IF ACTIV= 77 THEN ACTIVITY=' 16800'; ELSE IF ACTIV= 71 THEN ACTIVITY=' 16900'; ELSE IF ACTIV= 78 THEN ACTIVITY=' 16900'; ELSE IF ACTIV= 80 THEN ACTIVITY=' 17110'; ELSE IF ACTIV= 81 THEN ACTIVITY=' 17122'; ELSE IF ACTIV= 82 THEN ACTIVITY=' 17130'; ELSE IF ACTIV= 83 THEN ACTIVITY=' 17141'; ELSE IF ACTIV= 84 THEN ACTIVITY=' 17142'; ELSE IF ACTIV= 85 THEN ACTIVITY=' 17143'; ELSE IF ACTIV= 86 THEN ACTIVITY=' 17144'; ELSE IF ACTIV= 87 THEN ACTIVITY=' 17150'; ELSE IF ACTIV= 88 THEN ACTIVITY=' 17160'; ELSE IF ACTIV= 90 THEN ACTIVITY=' 17221'; ELSE IF ACTIV= 92 THEN ACTIVITY=' 17222'; ELSE IF ACTIV= 91 THEN ACTIVITY=' 17223'; ELSE IF ACTIV= 93 THEN ACTIVITY=' 17231'; ELSE IF ACTIV= 94 THEN ACTIVITY=' 17232'; ELSE IF ACTIV= 95 THEN ACTIVITY=' 17233'; ELSE IF ACTIV= 96 THEN ACTIVITY=' 17241'; ELSE IF ACTIV= 97 THEN ACTIVITY=' 17242'; ELSE IF ACTIV= 98 THEN ACTIVITY=' 17250'; ELSE IF ACTIV= 03 THEN ACTIVITY=' 18100'; ELSE IF ACTIV= 09 THEN ACTIVITY=' 18200'; ELSE IF ACTIV= 29 THEN ACTIVITY=' 18300'; ELSE IF ACTIV= 39 THEN ACTIVITY=' 18400'; ELSE IF ACTIV= 49 THEN ACTIVITY=' 18500'; ELSE IF ACTIV= 59 THEN ACTIVITY=' 18600'; ELSE IF ACTIV= 69 THEN ACTIVITY=' 18700'; ELSE IF ACTIV= 79 THEN ACTIVITY=' 18800'; ELSE IF ACTIV= 89 THEN ACTIVITY=' 18910'; ELSE IF ACTIV= 99 THEN ACTIVITY=' 18920'; ELSE ACTIVITY=' X';

* Recode locations ( microenvironments) into CHAD codes; IF LOCATE= 100 THEN LOCATION=' 30010'; ELSE IF LOCATE= 114 THEN LOCATION=' 30010'; ELSE IF LOCATE= 120 THEN LOCATION=' 30010'; ELSE IF LOCATE= 199 THEN LOCATION=' 30010'; ELSE IF LOCATE= 200 THEN LOCATION=' 30020'; ELSE IF LOCATE= 214 THEN LOCATION=' 30020'; ELSE IF LOCATE= 220 THEN LOCATION=' 30020'; ELSE IF LOCATE= 299 THEN LOCATION=' 30020'; ELSE IF LOCATE= 113 THEN LOCATION=' 30120'; ELSE IF LOCATE= 101 THEN LOCATION=' 30121'; ELSE IF LOCATE= 102 THEN LOCATION=' 30122'; ELSE IF LOCATE= 103 THEN LOCATION=' 30123'; ELSE IF LOCATE= 104 THEN LOCATION=' 30124'; ELSE IF LOCATE= 105 THEN LOCATION=' 30125'; ELSE IF LOCATE= 106 THEN LOCATION=' 30126'; ELSE IF LOCATE= 108 THEN LOCATION=' 30127'; ELSE IF LOCATE= 110 THEN LOCATION=' 30128'; ELSE IF LOCATE= 213 THEN LOCATION=' 30130'; ELSE 59 IF LOCATE= 201 THEN LOCATION=' 30131'; ELSE IF LOCATE= 202 THEN LOCATION=' 30132'; ELSE IF LOCATE= 203 THEN LOCATION=' 30133'; ELSE IF LOCATE= 204 THEN LOCATION=' 30134'; ELSE IF LOCATE= 205 THEN LOCATION=' 30135'; ELSE IF LOCATE= 206 THEN LOCATION=' 30136'; ELSE IF LOCATE= 208 THEN LOCATION=' 30137'; ELSE IF LOCATE= 210 THEN LOCATION=' 30138'; ELSE IF LOCATE= 111 THEN LOCATION=' 30211'; ELSE IF LOCATE= 112 THEN LOCATION=' 30219'; ELSE IF LOCATE= 211 THEN LOCATION=' 30221'; ELSE IF LOCATE= 212 THEN LOCATION=' 30229'; ELSE IF LOCATE= 107 THEN LOCATION=' 30330'; ELSE IF LOCATE= 207 THEN LOCATION=' 30340'; ELSE IF LOCATE= 399 THEN LOCATION=' 31000'; ELSE IF LOCATE= 301 THEN LOCATION=' 31110'; ELSE IF LOCATE= 302 THEN LOCATION=' 31121'; ELSE IF LOCATE= 303 THEN LOCATION=' 31122'; ELSE IF LOCATE= 304 THEN LOCATION=' 31130'; ELSE IF LOCATE= 305 THEN LOCATION=' 31140'; ELSE IF LOCATE= 310 THEN LOCATION=' 31150'; ELSE IF LOCATE= 311 THEN LOCATION=' 31160'; ELSE IF LOCATE= 312 THEN LOCATION=' 31170'; ELSE IF LOCATE= 306 THEN LOCATION=' 31210'; ELSE IF LOCATE= 307 THEN LOCATION=' 31220'; ELSE IF LOCATE= 308 THEN LOCATION=' 31230'; ELSE IF LOCATE= 313 THEN LOCATION=' 31310'; ELSE IF LOCATE= 314 THEN LOCATION=' 31320'; ELSE IF LOCATE= 300 THEN LOCATION=' 31900'; ELSE IF LOCATE= 320 THEN LOCATION=' 31900'; ELSE IF LOCATE= 499 THEN LOCATION=' 32000'; ELSE IF LOCATE= 401 THEN LOCATION=' 32100'; ELSE IF LOCATE= 402 THEN LOCATION=' 32200'; ELSE IF LOCATE= 403 THEN LOCATION=' 32300'; ELSE IF LOCATE= 404 THEN LOCATION=' 32400'; ELSE IF LOCATE= 405 THEN LOCATION=' 32500'; ELSE IF LOCATE= 406 THEN LOCATION=' 32610'; ELSE IF LOCATE= 418 THEN LOCATION=' 32620'; ELSE IF LOCATE= 407 THEN LOCATION=' 32700'; ELSE IF LOCATE= 408 THEN LOCATION=' 32900'; ELSE IF LOCATE= 409 THEN LOCATION=' 33100'; ELSE IF LOCATE= 410 THEN LOCATION=' 33200'; ELSE IF LOCATE= 411 THEN LOCATION=' 33300'; ELSE IF LOCATE= 412 THEN LOCATION=' 33400'; ELSE IF LOCATE= 413 THEN LOCATION=' 33500'; ELSE IF LOCATE= 414 THEN LOCATION=' 33600'; ELSE IF LOCATE= 415 THEN LOCATION=' 33700'; ELSE IF LOCATE= 416 THEN LOCATION=' 33800'; ELSE IF LOCATE= 417 THEN LOCATION=' 33900'; ELSE IF LOCATE= 419 THEN LOCATION=' 34100'; ELSE IF LOCATE= 400 THEN LOCATION=' 34200'; ELSE IF LOCATE= 420 THEN LOCATION=' 34200'; ELSE IF LOCATE= 599 THEN LOCATION=' 35000'; ELSE IF LOCATE= 501 THEN LOCATION=' 35100'; ELSE IF LOCATE= 502 THEN LOCATION=' 35220'; ELSE IF LOCATE= 503 THEN LOCATION=' 35300'; ELSE IF LOCATE= 504 THEN LOCATION=' 35400'; ELSE IF LOCATE= 505 THEN LOCATION=' 35600'; ELSE IF LOCATE= 506 THEN LOCATION=' 35700'; ELSE IF LOCATE= 507 THEN LOCATION=' 35800'; ELSE IF LOCATE= 508 THEN LOCATION=' 35900'; ELSE IF LOCATE= 510 THEN LOCATION=' 36100'; ELSE IF LOCATE= 511 THEN LOCATION=' 36200'; ELSE IF LOCATE= 500 THEN LOCATION=' 36300'; ELSE 60 IF LOCATE= 520 THEN LOCATION=' 36300'; ELSE LOCATION=' X';

* Create substrings of activity and location codes. For example, A2 is the ; * first ( leftmost) two digits of the activity code. If A2=' 18' then the ; * activity is some form of travel. The coding system is tree based so that ; * this type of grouping becomes fairly natural. ;

A5= ACTIVITY; L5= LOCATION; A4= SUBSTR( A5,1,4); L4= SUBSTR( L5,1,4); A3= SUBSTR( A4,1,3); L3= SUBSTR( L4,1,3); A2= SUBSTR( A3,1,2); L2= SUBSTR( L3,1,2);

* Set the quality flag QFACTLOC to identify suspect activity location pairs; QFACTLOC=' 0'; IF NOT ( A2=' X' OR L2=' X') THEN DO;

IF ( A3=' 110' & L2>' 30') THEN QFACTLOC=' 1'; IF ( A3=' 111' & ( L5=' 30124' OR L5=' 30134')) THEN QFACTLOC=' 1'; IF ( A3=' 112' & ( L2>' 30' OR L3=' 302')) THEN QFACTLOC=' 1'; IF ( A3=' 113' & L3=' 301') THEN QFACTLOC=' 1'; IF ( A3=' 113' & L2>' 30' & L3 NE ' 351') THEN QFACTLOC=' 1'; IF ( A3=' 119' & L2>' 30' & L3 NE ' 351' & L3 NE ' 311') THEN QFACTLOC=' 1'; IF ( A3=' 125' & ( L3=' 302' OR L2=' 35')) THEN QFACTLOC=' 1'; IF ( A3=' 126' & ( L3=' 301' OR L2=' 32')) THEN QFACTLOC=' 1'; IF ( A3=' 131' & L3 NE ' 339') THEN QFACTLOC=' 1'; IF ( A3=' 134' & L3 NE ' 332') THEN QFACTLOC=' 1'; IF ( A3=' 135' & NOT ( L3 IN (' 321',' 329',' 311',' 324'))) THEN QFACTLOC=' 1'; IF ( A3=' 136' & L4 NE ' 3261' & L3 NE ' 353') THEN QFACTLOC=' 1'; IF ( A3=' 1410' & L2>' 30' & NOT ( L3 IN (' 327',' 338',' 359'))) THEN QFACTLOC=' 1'; IF ( A4=' 1410' & L2=' 30' & NOT ( L5 IN (' 30124',' 30125',' 30134'))) THEN QFACTLOC=' 1'; IF ( A3=' 1411' & L2>' 30' & NOT ( L3 IN (' 327',' 338',' 359'))) THEN QFACTLOC=' 1'; IF ( A4=' 1411' & L2=' 30' & NOT ( L5 IN (' 30124',' 30125',' 30134'))) THEN QFACTLOC=' 1'; IF ( A4=' 1510' & NOT ( L3 IN (' 335',' 320',' 329',' 301',' 356'))) THEN QFACTLOC=' 1'; IF ( A4=' 1510' & L3=' 301' & L4 NE ' 3013') THEN QFACTLOC=' 1'; IF ( A4=' 1511' & NOT ( L3 IN (' 328',' 300',' 301'))) THEN QFACTLOC=' 1'; IF ( A4=' 1512' & NOT ( L3 IN (' 335',' 356',' 329'))) THEN QFACTLOC=' 1'; IF ( A4=' 1513' & NOT ( L3 IN (' 335',' 320',' 329'))) THEN QFACTLOC=' 1'; IF ( A4=' 1514' & NOT ( L3 IN (' 335',' 320',' 329'))) THEN QFACTLOC=' 1'; IF ( A3=' 154' & NOT ( L3 IN (' 320',' 335',' 329'))) THEN QFACTLOC=' 1'; IF ( A2=' 16' & L2=' 31') THEN QFACTLOC=' 1'; IF ( A3=' 161' & L3<' 325' & L3 NE ' 320') THEN QFACTLOC=' 1'; IF ( A3=' 161' & ( L3=' 326' OR L3=' 328')) THEN QFACTLOC=' 1'; IF ( A3=' 161' & L2=' 33' & L3 NE ' 335') THEN QFACTLOC=' 1'; IF ( A3=' 161' & L2=' 35' & L3<' 354') THEN QFACTLOC=' 1'; IF ( A3=' 164' & NOT ( L3=' 329' OR L3=' 320')) THEN QFACTLOC=' 1'; IF ( A3=' 165' & NOT ( L3=' 329' OR L3=' 320')) THEN QFACTLOC=' 1'; IF ( A3=' 168' & NOT ( L3 IN (' 320',' 325',' 336'))) THEN QFACTLOC=' 1'; IF ( A2=' 17' & A4 NE ' 1772' & L2=' 31' & L3 NE ' 312') THEN QFACTLOC=' 1'; IF ( A3=' 171' & L2=' 31' & L3 NE ' 312') THEN QFACTLOC=' 1'; IF ( A5=' 17111' & NOT ( L3 IN (' 350',' 359',' 363'))) THEN QFACTLOC=' 1'; IF ( A5=' 17112' & NOT ( L3=' 350' OR L3=' 358')) THEN QFACTLOC=' 1'; IF ( A5=' 17113' & ( L2=' 31' OR L2>=' 33')) THEN QFACTLOC=' 1'; IF ( A5=' 17113' & L2=' 32' & NOT ( L3 IN (' 320',' 325',' 327'))) THEN QFACTLOC=' 1'; IF ( A4=' 1712' & NOT ( L3=' 302' OR L2=' 35')) THEN QFACTLOC=' 1'; IF ( A5=' 17131' & L2<' 32' & L3 NE ' 302') THEN QFACTLOC=' 1'; IF ( A5=' 17131' & L2=' 32' & NOT ( L3=' 327' OR L3=' 320')) THEN QFACTLOC=' 1'; IF ( A4=' 1717' & NOT ( L3 IN (' 320',' 335',' 350',' 356'))) THEN QFACTLOC=' 1'; IF ( A4=' 1718' & L2=' 30' & NOT ( L3=' 302' OR L3=' 301')) THEN QFACTLOC=' 1'; IF ( A4=' 1718' & L2=' 32' & NOT ( L3=' 327' OR L3=' 320')) THEN QFACTLOC=' 1'; IF ( A5=' 17223' & ( L3=' 302' OR L2=' 35')) THEN QFACTLOC=' 1'; IF ( A2=' 18' & L2 NE ' 31' & L5 NE ' 35110') THEN QFACTLOC=' 1'; END; 61 * Remove extraneous characters from the text description of the activity; DESCRIP= COMPRESS( ACTTEXT,' 0123456789');

IF ( SMOKING> 0 AND SMOKING< 8) THEN SMOKING2=' Y'; ELSE IF ( SMOKING= 0) THEN SMOKING2=' N'; ELSE SMOKING2=' X';

IF ( BREATH= 1) THEN HEAVYBR=' Y'; ELSE IF ( BREATH= 0) THEN HEAVYBR=' N'; ELSE HEAVYBR=' X';

GASUSE =' X'; QFINFER=' 0'; QFMETAB=' 0';

RUN;

PROC SORT DATA= NHAPS1; BY PID; RUN;

DATA OCCUPS; SET QST1; BY PID; KEEP PID OCCUP; RUN;

DATA NHAPS1A; MERGE NHAPS1( IN= IN1) OCCUPS ( IN= IN2); BY PID; IF ( IN1= 0) THEN DELETE; RUN;

PROC SORT DATA= NHAPS1A; BY PID; RUN;

* Check to see if all diary days have exactly 1440 minutes;

PROC MEANS DATA= NHAPS1 NOPRINT; BY PID; VAR DURATION; OUTPUT OUT= DURS SUM= DURSUM; RUN;

PROC MEANS DATA= DURS; VAR DURSUM; RUN;

* Output records that do not meet standard assumptions about time;

DATA BADRECS; SET NHAPS1; BY PID; LENGTH LASTEND $ 4; RETAIN LASTEND ' 0000'; IF ( ACTIVITY=' X' OR LOCATION=' X') THEN OUTPUT; IF ( FIRST. PID AND STARTIME NE ' 0000') THEN OUTPUT; 62 IF ( FIRST. PID) THEN LASTEND= STARTIME; IF ( STARTIME NE LASTEND) THEN OUTPUT; LASTEND= ENDTIME; IF ( HOUR< 0 OR HOUR> 23 OR MINUTE< 0 OR MINUTE> 59) THEN OUTPUT; IF (( 60* HOUR+ MINUTE+ DURATION) NE ( 60* ENDHOUR+ ENDMIN)) THEN OUTPUT; RUN;

* Break activities at hour boundaries;

DATA NHAPS2; SET NHAPS1A; BY PID; LENGTH ENDT $ 4 ENDHR ENDMN $ 2 DUMMY1 QFMETAB $ 1; DROP ENDH ENDM DUMMY1 ENDHR ENDMN ENDT; RETAIN RECNUM 0;

QFMETAB=' 0'; IF ACTIVITY IN (' 11000',' 11200',' 11220',' 11300',' 11310',' 11600',' 11610', ' 11620',' 11630',' 11640',' 11650',' 11700',' 11800',' 11900',' 12000', ' 12100',' 12200',' 12600',' 12700',' 12800',' 13000',' 13100',' 13200', ' 13210',' 13220',' 13230',' 13300',' 13400',' 13500',' 13600',' 13700', ' 13800',' 14110',' 14300',' 16700',' 16800',' 16900',' 17113',' 17114', ' 17170',' 17100',' 17110',' 17111',' 17112',' 17120',' 17121',' 17130', ' 17131',' 17140',' 17144',' 17180') THEN QFMETAB=' 1'; IF ( ACTIVITY>=' 10000' & ACTIVITY<' 10200') THEN IF OCCUP IN (' PROTECT',' FARM',' PREC',' MACH',' TRANS',' TECH') THEN QFMETAB=' 1';

IF ( FIRST. PID) THEN RECNUM= 0; chop: IF ( ENDHOUR= HOUR OR ( ENDHOUR= HOUR+ 1 AND ENDMIN= 0)) THEN DO; RECNUM= RECNUM+ 1; OUTPUT; END; ELSE DO; ENDH= ENDHOUR; ENDM= ENDMIN; ENDT= ENDTIME; ENDHOUR= HOUR+ 1; ENDMIN= 0; DURATION= 60 MINUTE; IF ( ENDMIN> 9) THEN ENDMN= ENDMIN; ELSE DO; DUMMY1= ENDMIN; ENDMN=' 0' || DUMMY1; END; IF ( ENDHOUR> 9) THEN ENDHR= ENDHOUR; ELSE DO; DUMMY1= ENDHOUR; ENDHR=' 0' || DUMMY1; END; ENDTIME= ENDHR || ENDMN; RECNUM= RECNUM+ 1; OUTPUT;

STARTIME= ENDTIME; 63 HOUR= ENDHOUR; MINUTE= ENDMIN; DURATION= 60*( ENDH HOUR)+( ENDM MINUTE); ENDHOUR= ENDH; ENDMIN= ENDM; ENDTIME= ENDT; GO TO CHOP; END;

RUN;

PROC SORT DATA= NHAPS2; BY PID RECNUM; RUN;

* Check the new data set ( with breaks at each hour) to ensure that no new ; * problems have arisen. ;

DATA BADRECS; SET NHAPS2; BY PID; LENGTH LASTEND $ 4; RETAIN LASTEND ' 0000'; IF ( ACTIVITY=' X' OR LOCATION=' X') THEN OUTPUT; IF ( FIRST. PID AND STARTIME NE ' 0000') THEN OUTPUT; IF ( FIRST. PID) THEN LASTEND= STARTIME; IF ( STARTIME NE LASTEND) THEN OUTPUT; LASTEND= ENDTIME; IF ( HOUR< 0 OR HOUR> 23 OR MINUTE< 0 OR MINUTE> 59) THEN OUTPUT; IF (( 60* HOUR+ MINUTE+ DURATION) NE ( 60* ENDHOUR+ ENDMIN)) THEN OUTPUT; IF ( DURATION> 60) THEN OUTPUT; IF ( ENDHOUR NE HOUR) THEN DO; IF (( ENDHOUR NE HOUR+ 1) AND ( ENDMIN NE 0)) THEN OUTPUT; END; RUN;

* Sum information across each diary day to get daily totals;

DATA SUM; SET NHAPS2; BY PID;

LENGTH LASTACT LASTLOC $ 5;

KEEP PID RECCOUNT QCACTLOC QCHEAVY QCINFER QCMETAB QCMISS QCSLEEP TRAVMOR TRAVEVE QFTRAVEL YEAR MONTH DAY WEEKDAY QCMEALS QCEATIME QCLONG;

RETAIN QCACTLOC QCHEAVY QCINFER QCMETAB QCMISS QCSLEEP TRAVMOR TRAVEVE RECCOUNT QCMEALS QCEATIME LASTEAT QCLONG CURDUR 0 LASTACT LASTLOC;

IF ( FIRST. PID) THEN DO; RECCOUNT = 0; QCACTLOC= 0; QCHEAVY = 0; QCINFER = 0; 64 QCMETAB = 0; QCMISS = 0; QCSLEEP = 0; TRAVMOR = 0; TRAVEVE = 0; QCEATIME= 0; QCMEALS = 0; LASTEAT = 0; QCLONG = 0; CURDUR = 0; LASTACT =' 0'; LASTLOC =' 0'; END; RECCOUNT= RECCOUNT+ 1; IF ( QFACTLOC=' 1') THEN QCACTLOC= QCACTLOC+ DURATION; IF ( HEAVYBR =' Y') THEN QCHEAVY = QCHEAVY + DURATION; IF ( QFINFER =' 1') THEN QCINFER = QCINFER + DURATION; IF ( QFMETAB =' 1') THEN QCMETAB = QCMETAB + DURATION; IF ( ACTIVITY=' 14500') THEN QCSLEEP = QCSLEEP + DURATION; IF ( ACTIVITY=' X' OR LOCATION=' X') THEN QCMISS = QCMISS + DURATION; IF ( ACTIVITY=' 14400') THEN DO; QCEATIME= QCEATIME+ DURATION; IF ( LASTEAT= 0) THEN QCMEALS= QCMEALS+ 1; LASTEAT= 1; END; ELSE LASTEAT= 0; IF ( ACTIVITY=' 18000' OR ACTIVITY=' 18200') THEN DO; IF ( HOUR IN ( 6,7,8)) THEN TRAVMOR = TRAVMOR + DURATION; IF ( HOUR IN ( 16,17,18)) THEN TRAVEVE = TRAVEVE + DURATION; END; IF (( ACTIVITY NE LASTACT) OR ( LOCATION NE LASTLOC)) THEN CURDUR= 0; CURDUR= CURDUR+ DURATION; IF ( QCLONG< CURDUR) THEN QCLONG= CURDUR; LASTACT= ACTIVITY; LASTLOC= LOCATION;

IF ( LAST. PID) THEN DO; QCSLEEP= FLOOR(( QCSLEEP+ 30)/ 60); QFTRAVEL= 0; IF (( TRAVMOR> 2* TRAVEVE) & ( TRAVMOR> 30)) THEN QFTRAVEL= 1; IF (( TRAVEVE> 2* TRAVMOR) & ( TRAVEVE> 30)) THEN QFTRAVEL= 1; IF ( WEEKDAY=' SAT' OR WEEKDAY=' SUN') THEN QFTRAVEL= 0; OUTPUT; END; RUN;

PROC SORT DATA= SUM; BY PID; RUN;

PROC CONTENTS DATA= SUM; RUN; PROC CONTENTS DATA= QST1; RUN;

* Merge the data from the NHAPS questionnaire with the summary data calculated ; * from the diaries. ;

DATA QST2; 65 MERGE SUM( IN= IN1) QST1( IN= IN2); BY PID; LENGTH NEWTYPE $ 2; DROP NEWTYPE; IF ( IN1= 0) THEN DELETE; IF ( IN2= 0) THEN DELETE; NEWTYPE=' WD'; IF ( WEEKDAY=' SAT' OR WEEKDAY=' SUN') THEN NEWTYPE=' WE'; IF ( NEWTYPE NE DAYTYPE) THEN PUT ' BAD DAYTYPE ' PID DAYTYPE NEWTYPE; RUN;

* The CHADID was determined from the questionnaire data ( depending on which ; * type ( air or water) was administered. However, the CHADID must be written ; * back onto the diaries. ;

DATA CHADID; SET QST1; KEEP CHADID PID; RUN;

PROC SORT DATA= CHADID; BY PID; RUN;

* The CHAD version of the diaries is stored in the SAS dataset NHPDIARY ; * and is also written to an ASCII file for input into the database. ;

DATA OUT. NHPDIARY; MERGE NHAPS2 CHADID; BY PID; DROP HOUR MINUTE ENDHOUR ENDMIN YEAR MONTH DAY WEEKDAY;

FILE '\\ METISRV0\ AEAR\ WA50\ CHADDATA\ NHPDIARY. TXT';

SEQ= RECNUM; IF ( ENDTIME=' 2400') THEN ENDTIME=' 0000';

PUT CHADID 1 9 RECNUM 11 13 SEQ 15 17 STARTIME 19 22 DURATION 24 25 ENDTIME 27 30 ACTIVITY 32 36 LOCATION 38 42 GASUSE 44 SMOKING2 46 QFACTLOC 48 QFINFER 50 QFMETAB 52 HEAVYBR 54 DESCRIP 56 104;

RUN;

* The CHAD questionnaire data is written in both SAS and ASCII form to two files ; * named NHPSURV ( one in SASDATA directory and the other in CHADDATA). ;

DATA OUT. NHPSURV; SET QST2;

FILE '\\ METISRV0\ AEAR\ WA50\ CHADDATA\ NHPSURV. TXT'; 66 PUT CHADID 1 9 PID 15 21 DAYNUM 23 NDAYS 25 YEAR 27 30 MONTH 32 33 DAY 35 36 WEEKDAY 38 40 RECCOUNT 42 44 WRAPTIME 46 49 QCSLEEP 51 52 QFTRAVEL 54 QCMISS 56 59 HOUSING 61 62 HEATING 64 65 FUEL 67 68 GARAGE 70 GASSTOVE 72 GENDER 74 RACE 76 EDUCAT 78 80 AGE 82 84 AGEGROUP 86 90 EMPLOYED 92 FULLTIME 94 95 JOBHOURS 97 101 STUDENT 103 ASTHMA 105 SMOKER 107 SMOKER2 109 INCOME 111 112 ZIPCODE 114 118 PESTICID 120 AIRCOND 122 HEARTLUN 124 WEIGHT 130 132 TEMPERAT 134 136 DAYTYPE 138 139 OCCUP 141 147 QCHEAVY 149 152 QCINFER 154 157 QCMETAB 159 162 QCACTLOC 164 167 STATE 169 182 COUNTY 184 203 QCMEALS 205 206 QCEATIME 208 211 QCLONG 213 216;

RUN;

PROC FREQ DATA= NHAPS2; TABLES QFACTLOC QFMETAB QFINFER; RUN;

DATA STATS; SET QST2; KEEP QCA QCMT QCI QFT QCS QCML QCE QCH QCMS; IF ( QCACTLOC> 0) THEN QCA = 1; ELSE QCA = 0; IF ( QCMETAB > 0) THEN QCMT= 1; ELSE QCMT= 0; IF ( QCINFER > 0) THEN QCI = 1; ELSE QCI = 0; IF ( QCSLEEP > 0) THEN QCS = 1; ELSE QCS = 0; IF ( QCMEALS > 0) THEN QCML= 1; ELSE QCML= 0; IF ( QCEATIME> 0) THEN QCE = 1; ELSE QCE = 0; IF ( QCHEAVY > 0) THEN QCH = 1; ELSE QCH = 0; IF ( QCMISS > 0) THEN QCMS= 1; ELSE QCMS= 0; QFT= QFTRAVEL; RUN;

PROC FREQ DATA= STATS; TABLES QCA QCMT QCI QFT QCS QCML QCE QCH QCMS; RUN;

* End of processing of NHAPS data; 67 Appendix G Modeling Multi Route Intake Dose in an Internally Consistent Manner C:\ 1MCCURDY\ PAPERS\ BMR. CONS T. McCurdy: October 23, 1997 MODELING MULTI ROUTE INTAKE DOSE IN AN INTERNALLY CONSISTENT MANNER Fundamental Approach The focus of a risk assessment to environmental contaminants should be an individual, since each individual differs widely in his or her activities, health status, metabolism, distribution, storage, and elimination of xenobiotic substances. Human health effects arise when the amount and pattern of dose received at a target organ the dose rate over time exceed's that organ's ability to " defend" itself during some defined time period. In general, timeweighted dose metrics do not provide relevant information necessary to understand or predict these effects.

The amount and pattern of dose received at a target organ is related in a complex biochemical manner to the amount/ pattern of dose taken into the body ( intake dose) via the respiratory tract, the gastrointestinal system, and through the skin. Thus, in a total human health risk assessment, it is essential to understand the intake dose pattern associated with breathing, ingestion both food and liquids and dermal absorption. The various media air, liquids, and physical surfaces interact with the main entry pathways into the body so it is necessary to assess multi media, multi pathway " total" exposure in order to fully understand human health impact associated with environmental pollutants.

Understanding the time pattern of dose received implies that the time series of dose and exposure be evaluated. This leads directly to an activity specific approach that " tracks" individuals in time and space as they go through their daily activities. Since mediaspecific exposure and dose assessments do not maintain the correlated nature of an individual's bodily processes, it is 68 necessary to use a metric suitable for multi pathway modeling. Such a metric is a MET.

MET: Metabolic Equivalent of Work A MET is defined to be the ratio of an activity specific metabolic rate to a person's resting metabolic rate ( RMR). Since a MET is a ratio, it is unitless.

There are scores of articles providing estimates of METS for specific activities ( METa). These will be provided in NERL's " Consolidated Human Activity Database" ( CHAD) in the most disaggregated way possible; at the minimum and especially for low MET activities a deterministic " best estimate" will be provided. Otherwise, an estimated METa range or distribution parameters of the METa will be provided.

CHAD will also ( in early 1998) provide post high exercise adjusted METS to mimic the " oxygen debt" phenomenon ( higher than expected oxygen consumption after exercise to compensate for the oxygen used during anaerobic breakdown of adenosine triphosphate ( ATP).

Modeling Inhalation Intake Dose Using METS 1. For a particular individual sampled from CHAD to represent a specific age/ gender cohort, obtain an estimate of his or her body weight ( body mass: BM) in kg. Said estimates will be provided in CHAD, but could be ignored by the user and obtained some other way.

2. Predict RMR ( in units of kcal min 1 kg 1) using a regression approach using two or more anthropogenic variables ( age, gender, BM) as independent variables. There are over 100 relevant equations found in the literature. Add variability for a particular individual by adding on an " error term" ( e: x )= 0, F, from the literature) using Monte Carlo sampling. Multiply RMR by 1,440 and by BM to obtain daily resting metabolism ( RMD):

RMD = ( RMR + e ) * 1,440 * BM 3. Obtain activity specific estimates of metabolism energy expenditure ( EE) as: METa * RMR * BM; we will call this EEa. Its unit is kcal min 1.

4. Obtain activity specific oxygen consumption by converting the EEa to mL O 2 using a distribution of energy oxygen conversion factors found in the literature. ( The age/ gender specific estimate of BM obtained above minimizes the spread of this distribution but does not eliminate it.) The converted variable is V 0 O2a. 69 5. Convert V 0 O2a to V 0 Ea using a distribution of V 0 E to V 0 O 2 ratios found in the literature. ( The pNEM model uses these now.) A summary of such ratios specific to age/ gender classes is available. V 0 Ea, when multiplied by the activity specific pollutant concentration in the microenvironment provides the estimate of inhalation intake dose for the activity. Steps 4 and 5 easily could be conflated into a single conversion distribution if so desired.

6. Obtain total daily energy expenditure ( EED) by simply summing all EEa's for the day.

Modeling Daily Fluid & Water Ingestion Intake Dose It is assumed here that there is an estimate of the amount of xenobiotic substance of interest in fluids and water consumed by the individual. The estimate could be either microenvironmentspecific or on a space free basis. If it is the former, then EEa would be used to develop the fluid/ water intake dose estimate. The following assumes that only daily estimates of the xenobiotic substance are available.

7. Convert EED to daily fluid and water consumption ( FWID) using relationships found in the literature; the relationship is fairly linear for an individual. FWID is the estimate of daily fluid/ water intake needed for fluid ingestion modeling.

Modeling Daily Food Ingestion Intake Dose As above, it is assumed that there is an estimate of the xenobiotic substance in the individual's daily food intake either specific to a food type or to a daily aggregate ( or proportion) of food types.

8. Convert EED to daily food consumption ( in kcal) directly, assuming that the individual's daily energy intake ( EID) equals his or her daily energy expenditure. This implies that there is no daily loss or gain in body mass weight. If body mass change is considered in the assessment, EID must be adjusted for the change.

Use age, gender, and cultural specific nutritional proportions found in the dietary literature to disaggregate EID into the amount of protein, carbohydrates, and fat consumed. Alternatively, use a " food basket" approach to estimate the amount and type of food consumed given EID. These approaches will provide an estimate of food ingestion needed for risk assessment modeling.

Modeling Dermal Absorption Intake Dose 70 The only other major route of multi pathway modeling usually discussed is dermal absorption. This route is not well handled using an energy expenditure approach, although for a particular activity, dermal absorption probably is monotonically related to expenditure ( as more activity a priori increases the surface area of skin available for contact given a steady state concentration of the substance in the microenvironment). This subject needs more research in order to develop usable metabolic or energy expenditure to dermal uptake relationships.

Classifying Individuals by Activity Levels Divide EED by RMD to obtain what clinical nutritionists call a person's Physical Activity Index ( PAI; it is unitless). The PAI can be used to classify individuals into activity classes for separate exposure modeling assessment using the following PAI categories:

Sedentary < 1.50 Normal 1.50  1.75 Active > 1.75 For example, the PAI could be used to identify active children or exercising adults. CHAD will also include ( in 1998) an estimate of a person's PAI, but obviously it could be ignored by a user. 71 Appendix H METS Distributions for CHAD Activities Table H 1. METS Distributions for CHAD Activities ACTIVITY AGE OCC. DN DL MEAN MED SD MIN MAX FLAG 10... X ADMIN 4 L 1.8 1.7 0.1 1.4 2.7 0 10... X PROF 4 L 1.8 1.7 0.1 1.4 2.7 0 10... X ADMSUP 4 L 1.8 1.7 0.1 1.4 2.7 0 10... X TECH 4 L 3.9 3.0 0.3 1.3 8.4 1 10... X TRANS 4 L 3.9 3.0 0.3 1.3 8.4 1 10... X SALE 5 T 2.9 2.7 1.0 1.2 5.6 0 10... X SERV 5 T 2.9 2.7 1.0 1.2 5.6 0 10... X HSHLD 5 T 2.9 2.7 1.0 1.2 5.6 0 10... X PROTECT 5 T 3.3 3.3 0.4 2.5 4.5 1 10... X PREC 4 L 3.6 3.5 0.0 2.5 6.0 1 10... X MACH 5 T 5.2 5.3 1.4 1.6 8.4 1 10... X FARM 5 T 8.5 8.4 2.1 3.6 13.8 1 17100 1 X 4 L . 5.0 . 1.4 16.0 1 17100 2 X 1 N 5.0 5.0 2.0 1.0 9.0 1 17100 3 X 1 N 4.5 4.5 1.4 1.7 7.3 1 17110 1 X 4 L . 3.2 . 1.4 10.0 1 17110 2 X 4 L . 3.2 . 1.4 10.0 1 17110 3 X 4 L . 3.0 . 1.4 9.0 1 17111 1 X 1 N 5.6 5.6 2.1 1.4 9.8 1 17111 2 X 1 N 5.8 5.8 2.4 1.0 10.6 1 17111 3 X 1 N 4.7 4.7 1.8 1.1 8.3 1 17112 1 X 2 U 3.8 3.8 1.0 2.0 5.5 1 17112 2 X 2 U 3.8 3.8 1.0 2.0 5.5 1 17112 3 X 2 U 3.5 3.5 0.9 2.0 5.0 1 17120 1 X 4 L . 3.9 . 2.0 9.0 1 17120 2 X 4 L . 3.9 . 2.0 9.0 1 17120 3 X 6 P 3.5 3.5 . . . 1 17121 1 X 4 L . 3.9 . 2.0 9.0 1 17121 2 X 4 L . 3.9 . 2.0 9.0 1 17121 3 X 6 P 3.5 3.5 . . . 1 17130 1 X 4 L . 5.5 . 1.8 11.3 1 17130 2 X 1 N 5.7 5.7 1.8 2.1 9.3 1 17130 3 X 1 N 4.7 4.7 1.2 2.3 7.1 1 17131 1 X 4 L . 5.5 . 1.8 11.3 1 17131 2 X 1 N 5.7 5.7 1.8 2.1 9.3 1 17131 3 X 1 N 4.7 4.7 1.2 2.3 7.1 1 17140 1 X 1 N 5.3 5.3 1.8 1.7 8.9 1 17140 2 X 1 N 5.2 5.2 1.7 1.7 8.9 1 72 17140 3 X 1 N 3.8 3.8 1.0 1.8 5.8 1 17144 1 X 1 N 5.3 5.3 1.8 1.7 8.9 1 17144 2 X 1 N 5.2 5.2 1.7 1.7 8.9 1 17144 3 X 1 N 3.8 3.8 1.0 1.8 5.8 1 17180 1 X 4 L . 5.9 . 2.0 17.4 1 17180 2 X 1 N 6.0 6.0 2.0 2.0 10.0 1 17180 3 X 1 N 4.8 4.8 1.4 2.0 7.6 1 10200 X X 2 U 1.8 1.8 0.4 1.0 2.5 0 10300 X X 2 U 1.8 1.8 0.4 1.0 2.5 0 11000 X X 5 T 4.7 4.6 1.3 1.5 8.0 1 11100 X X 4 L . 2.5 . 2.0 4.0 0 11110 X X 3 E 2.8 2.5 0.9 1.9 4.0 0 11200 X X 3 E 3.4 3.0 1.4 2.0 5.0 1 11210 X X 2 U 2.5 2.5 0.1 2.3 2.7 0 11220 X X 3 E 4.1 3.5 1.9 2.2 5.0 1 11300 X X 1 N 5.0 5.0 1.0 2.0 7.0 1 11310 X X 3 E 5.3 4.5 2.7 2.6 6.0 1 11400 X X 3 E 2.2 2.0 0.7 1.5 4.0 0 11410 X X 6 P 2.0 2.0 . . . 0 11500 X X 6 P 2.0 2.0 . . . 0 11600 X X 1 N 4.5 4.5 1.5 2.0 8.0 1 11610 X X 6 P 4.5 4.5 . . . 1 11620 X X 3 E 4.9 4.5 1.4 3.5 6.0 1 11630 X X 5 T 3.5 3.4 0.4 3.0 4.5 1 11640 X X 3 E 4.7 4.5 0.7 4.0 6.0 1 11650 X X 2 U 4.5 4.5 1.4 2.0 7.0 1 11700 X X 2 U 3.5 3.5 0.9 2.0 5.0 1 11800 X X 2 U 3.3 3.3 0.1 3.0 3.5 1 11900 X X 3 E 6.6 5.5 3.6 3.0 9.0 1 12000 X X 4 L . 3.0 . 2.5 5.0 1 12100 X X 2 U 3.3 3.3 0.1 3.0 3.5 1 12200 X X 2 U 3.3 3.3 0.1 3.0 3.5 1 12300 X X 2 U 2.8 2.8 0.1 2.5 3.0 0 12400 X X 2 U 2.8 2.8 0.1 2.5 3.0 0 12500 X X 2 U 2.8 2.8 0.1 2.5 3.0 0 12600 X X 2 U 4.5 4.5 0.3 4.0 5.0 1 12700 X X 2 U 3.2 3.2 0.1 3.0 3.3 1 12800 X X 2 U 3.0 3.0 0.3 2.5 3.5 1 13000 X X 5 T 3.8 3.7 0.8 2.0 6.0 1 13100 X X 2 U 3.3 3.3 0.4 2.5 4.0 1 13200 X X 5 T 3.7 3.6 0.8 2.0 6.0 1 13210 X X 5 T 3.9 3.8 0.8 2.2 6.0 1 13220 X X 2 U 3.4 3.4 0.6 2.3 4.5 1 13230 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 13300 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 13400 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 13500 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 13600 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 13700 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 13800 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 14000 X X 2 U 2.0 2.0 0.6 1.0 3.0 0 14100 X X 1 N 2.0 2.0 0.3 1.0 4.0 0 14110 X X 2 U 3.0 3.0 0.6 2.0 4.0 1 14120 X X 2 U 1.8 1.8 0.4 1.0 2.5 0 14200 X X 2 U 1.8 1.8 0.4 1.0 2.5 0 14300 X X 4 L . 3.0 . 2.5 5.0 1 14400 X X 2 U 1.8 1.8 0.1 1.5 2.0 0 73 14500 X X 4 L . 0.9 . 0.8 1.1 0 14600 X X 6 P 2.5 2.5 . . . 0 14700 X X 5 T 2.0 2.0 0.4 1.0 2.9 0 15000 X X 4 L . 1.8 . 1.4 4.0 0 15100 X X 2 U 2.1 2.1 0.4 1.4 2.8 0 15110 X X 2 U 2.3 2.3 0.4 1.5 3.0 0 15120 X X 2 U 2.1 2.1 0.4 1.4 2.8 0 15130 X X 2 U 2.0 2.0 0.3 1.4 2.5 0 15140 X X 2 U 1.8 1.8 0.2 1.4 2.2 0 15200 X X 2 U 2.2 2.2 0.5 1.4 3.0 0 15300 X X 6 P 1.8 1.8 . . . 0 15400 X X 2 U 2.3 2.3 0.4 1.5 3.0 0 15500 X X 2 U 2.8 2.8 0.7 1.5 4.0 0 16000 X X 4 L . 2.0 . 1.0 6.0 0 16100 X X 2 U 2.7 2.7 0.8 1.4 4.0 0 16200 X X 2 U 1.7 1.7 0.2 1.4 2.0 0 16210 X X 2 U 1.7 1.7 0.2 1.4 2.0 0 16300 X X 2 U 1.3 1.3 0.2 1.0 1.6 0 16400 X X 2 U 1.7 1.7 0.4 1.0 2.3 0 16500 X X 2 U 2.5 2.5 0.3 2.0 2.9 0 16600 X X 2 U 1.5 1.5 0.3 1.0 1.9 0 16700 X X 4 L . 3.0 . 1.5 8.0 1 16800 X X 4 L . 3.0 . 1.5 8.0 1 16900 X X 2 U 3.8 3.8 1.3 1.5 6.0 1 17113 X X 2 U 3.0 3.0 0.6 2.0 4.0 1 17114 X X 5 T 3.1 3.2 0.6 1.4 4.0 1 17122 X X 2 U 1.5 1.5 0.2 1.2 1.8 0 17141 X X 5 T 2.8 2.7 0.8 1.5 5.0 0 17142 X X 5 T 2.0 1.9 0.4 1.5 3.0 0 17143 X X 2 U 2.5 2.5 0.3 2.0 3.0 0 17150 X X 5 T 3.3 3.2 0.6 2.4 5.0 1 17160 X X 2 U 1.6 1.6 0.2 1.2 2.0 0 17170 X X 2 U 5.0 5.0 1.7 2.0 8.0 1 17200 X X 4 L . 1.3 . 1.0 2.3 0 17210 X X 2 U 1.5 1.5 0.2 1.2 1.8 0 17211 X X 2 U . . . 1.2 . 0 17212 X X 2 U . . . 1.2 . 0 17213 X X 2 U . . . 1.2 . 0 17214 X X 2 U . . . 1.2 . 0 17215 X X 2 U . . . 1.2 . 0 17216 X X 2 U 2.7 2.7 0.8 1.4 4.0 0 17220 X X 4 L . 1.2 . 0.9 2.3 0 17221 X X 2 U 1.2 1.2 0.1 1.0 1.3 0 17222 X X 2 U 1.9 1.9 0.2 1.5 2.3 0 17223 X X 6 P 1.0 1.0 . . . 0 17230 X X 2 U 1.3 1.3 0.2 1.0 1.6 0 17231 X X 2 U 1.3 1.3 0.2 1.0 1.6 0 17232 X X 2 U 1.3 1.3 0.2 1.0 1.6 0 17233 X X 2 U 1.3 1.3 0.2 1.0 1.6 0 17240 X X 2 U 1.4 1.4 0.2 1.0 1.8 0 17241 X X 2 U 1.4 1.4 0.2 1.0 1.8 0 17242 X X 2 U 1.4 1.4 0.2 1.0 1.8 0 17250 X X 2 U 1.2 1.2 0.1 1.0 1.3 0 17260 X X 2 U 1.9 1.9 0.2 1.5 2.3 0 17300 X X 2 U 1.5 1.5 0.2 1.2 1.8 0 18000 X X 4 L . 2.0 . 1.0 7.0 0 18100 X X 4 L . 2.0 . 1.0 7.0 0 74 18200 X X 4 L . 2.0 . 1.0 7.0 0 18300 X X 4 L . 2.0 . 1.0 7.0 0 18400 X X 4 L . 2.0 . 1.0 7.0 0 18500 X X 4 L . 2.0 . 1.0 7.0 0 18600 X X 4 L . 2.0 . 1.0 7.0 0 18700 X X 4 L . 2.0 . 1.0 7.0 0 18800 X X 4 L . 2.0 . 1.0 7.0 0 18900 X X 4 L . 2.0 . 1.0 7.0 0 18910 X X 4 L . 2.0 . 1.0 7.0 0 18920 X X 4 L . 2.0 . 1.8 7.0 0 Key for activity distribution file.

ACTIVITY = activity code ( activity 10... refers to a set of activity values, rather than a single value) AGE = age group ( 1= < 25; 2= 25 39; 3= > 40; X = missing) OCC. = occupation ( X= missing) DN = indicator for distribution type ( 1  normal; 2  uniform; 3  exponential; 4  lognormal; 5  triangular; 6  point) DL = indicator for distribution type ( first letter of distribution name) MEAN = mean value MED = median value SD = standard deviation MIN = lower bound MAX = upper bound FLAG = indicator of high median ( 0 if MED < 3; 1, otherwise)

Missing numeric values are indicated by a single decimal point.

Further information about the activity codes can be found in Table in Appendix .