Environmental Health Criteria 214

HUMAN EXPOSURE ASSESSMENT
Table 15. (continued)
cooking fuel; data on gender, resolution; time of
age, parental education day of week; time o
Location Pollutant Participant characteristics Survey characterist
California, VOCs 188 people; representative; February-March 1984
USA oversampled high-exposure follow-up: 52 in Ma
occupation; data on age, 1984 and 51 in Feb
gender, race, socio-economic March 1987; 24-h re
status and proximity to VOC diary; activity
sources questionnaire
Boston, NO 2 325 (winter), 298 (summer) 1986; diary and
Massachusetts, ages 8 and above; questionnaire;
USA representative; stratified 15-30-min resolutio
by range type; no personal sample; time of yea
data of week; time of da
Los Angeles NO 2 620 people ages 8 and above May 1987-May 1988; d
and Orange sampled two 24-h periods; 65 questionnaire; 15-m
Counties; sampled eight cycles; resolution; two-day
representative; data on age, time of year; day o
gender, work status time of day
Albuquerque, NO 2 1000+ infants; stratified January 1988-Decembe
New Mexico, by range type; data on every 2 months for
USA child's health and parents' first 18 months of
socio-economic and 60 min; time of yea
demographic characteristics day of week; time o
reporting a smoker present in the home was associated with increased
indoor concentrations and personal exposures of aromatic compounds;
visiting dry cleaners, self-reports of proximity to smokers, pesticide
use, exposure to solvent, degreasing compounds, and odorous chemicals,
and employment status in certain occupations (e.g., paint, chemical or
plastics plants) were associated with increased personal exposure to
several VOCs (Wallace et al., 1985, 1986, 1988). Occupational exposure
may be an important component of total exposure for some individuals
or sub-populations.
2. Time-activity data allow modelling of human exposure to
pollutants for which personal monitors are not yet available or are
very expensive, or for which exposure is a function of multiple
pathways. Total exposure can be simulated from information on the time
spent doing various activities and/or in specific locations, coupled
with knowledge about the likely range of pollutant concentrations in
each situation. For example, the models SHAPE (Ott et al., 1988), NEM
(Johnson et al., 1990), SIMSYS (Sexton & Ryan, 1988), and REHEX (Hall
et al., 1992) are currently being used to estimate exposure to carbon
monoxide, ozone, particulates, sulfur dioxide and nitrogen dioxide.
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