2000 Progress Report: Testing of a Model to Predict Human Exposures to Aldehydes Arising from Mobile and Point SourcesEPA Grant Number: R826787
Title: Testing of a Model to Predict Human Exposures to Aldehydes Arising from Mobile and Point Sources
Investigators: Raymer, James H. , Akland, Gerald G. , Clayton, C. Andrew , Johnson, Ted , Pellizzari, Edo D.
Current Investigators: Raymer, James H. , Akland, Gerald G. , Clayton, C. Andrew , Johnson, Ted , Michael, L. C. , Pellizzari, Edo D.
Institution: Desert Research Institute , TRJ Environmental Inc.
EPA Project Officer: Chung, Serena
Project Period: October 1, 1998 through September 30, 2002 (Extended to September 30, 2003)
Project Period Covered by this Report: October 1, 1999 through September 30, 2000
Project Amount: $629,841
RFA: Urban Air Toxics (1998) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Air
The overall objective of this research project is to estimate human exposure to target aldehydes (formaldehyde, acetaldehyde, acrolein, propionaldehyde, butyraldehyde, crotonaldehyde, glyoxal, and methylglyoxal) by means of microenvironmental and personal exposure monitoring for two urban areas. The main hypothesis to be tested is that a mathematical model (pHAP) can be used to predict personal exposure distribution to aldehydes. Additional hypotheses to be tested are that: (1) personal exposure levels of aldehydes exceed outdoor concentrations; (2) indoor aldehyde concentrations exceed outdoor concentrations; and (3) the composition of oxygenated fuel results in significant differences in population exposures to aldehydes.
Sample analysis continued for the Sacramento phase of the study. A preliminary analysis of formaldehyde and acetaldehyde data was conducted. A total of 191 formaldehyde concentrations and 192 acetaldehyde concentrations were judged as acceptable for statistical analysis. Formaldehyde concentrations ranged from 2.3 µg/m3 to 73 µg/m3, with an arithmetic mean of 13 µg/m3; acetaldehyde concentrations ranged from 2.1 µg/m3 to 320 µg/m3, with an arithmetic mean of 34 µg/m3. The activity diary codes for "location" were used to identify the principal microenvironment represented by each concentration. Some of the sampling periods included a few minutes in a second microenvironment, usually the technician's car. Each of the 18 microenvironments sampled was classified by a general category (e.g., indoors) and a specific category (e.g., residence).
The five smallest arithmetic mean values measured for formaldehyde are all associated with outdoor microenvironments: park/golf (5.3 µg/m3), school grounds (5.5 µg/m3), parking lot (6.6 µg/m3), service station (6.6 µg/m3), and sports arena (6.8 µg/m3). In contrast, the five largest arithmetic mean values are all associated with indoor microenvironments: service station (19 g/m3), residence (21 µg/m3), beauty salon (24 µg/m3), church (25 µg/m3), and store (27 µg/m3). The means for the "vehicle-car" and "outdoors-near road" microenvironments are similar (8.4 µg/m3 and 8.8 µg/m3, respectively).
For acetaldehyde, the five smallest mean values again are associated with outdoor microenvironments: not specified (3.2 µg/m3), other location (12 µg/m3), park/golf (13 µg/m3), service station (13 µg/m3), and parking lot (14 µg/m3). The highest outdoor mean is associated with the near-road microenvironment (17 µg/m3). The five largest means are associated with indoor or vehicle microenvironments: church (25 µg/m3), vehicle-car (26 µg/m3), store (35 µg/m3), residence (66 µg/m3), and restaurant (78 µg/m3).
In addition to microenvironment, the comprehensive database contained a large number of parameters relating to the geographic location, store type (grocery, building supplies, drugstore, etc.), indoor sources (smokers, alcoholic beverages, wood products, etc.), ventilation conditions (air conditioning and window position), traffic counts, subject activities (drinking alcoholic beverages, using solvents, etc.), and other special conditions reported by the subject (e.g., forest fires). Researchers performed a series of step-wise linear regression (SLR) analyses on the comprehensive database to identify factors that could be used to predict formaldehyde and acetaldehyde concentrations.
The regression equation for formaldehyde includes 10 variables that provide a cumulative R2 value of 0.650. Each of the selected variables is binary in value; i.e., it equals one when the indicated condition exists, and zero otherwise. All of the regression coefficients are positive, indicating that the occurrence of each condition tends to increase formaldehyde concentrations. The results suggest that formaldehyde concentrations are increased by the indoors-store microenvironment (particularly, stores selling auto parts, furniture, and building supplies), the indoors-residence microenvironment, solvent use, and smoke from forest fires.
The regression equation for acetaldehyde includes five binary variables, each with a positive regression coefficient. The results suggest that acetaldehyde concentrations are increased by air conditioning, drinking alcoholic beverages, visiting grocery stores, spending time in the residence, and eating. The cumulative R2 value is 0.378 for the SLR of the acetaldehyde concentrations.
The above results should be considered preliminary because additional data are being processed for inclusion in the database. These data include pollutant concentrations measured at local fixed-site monitors, meteorological data, and global positioning system (GPS) data.
Scripts were designed for the Milwaukee phase of the study. Participants were recruited for the Milwaukee phase using the same approach as for the Sacramento phase. A total of 33 participants were enrolled into and completed the Milwaukee study. Sample analysis is underway.
Future activities planned for Project Year 3 are to: (1) complete sample analysis, quality assurance (QA) check data, and create a database of Milwaukee results; (2) receive data on air exchange and aldehyde QA for both cities; (3) transfer questionnaire data to a database; (4) QA Milwaukee field data; and (5) begin data analysis.