Environmental Health Criteria 214

HUMAN EXPOSURE ASSESSMENT
participate or not because of the variables that the study is designed
to predict. As shown in the European multi-city study EXPOLIS, the
subjects in Basel ready to participate had lower traffic density
around their homes than non-participants (Oglesby, 1998). The
potential for misrepresenting the exposure distribution must,
therefore, be considered because there is no method for quantifying
the direction and/or extent of the bias with respect to high-exposure
behaviours.
The representativeness of the activity data collected may also be
influenced by the increased burden imposed upon participants by
exposure assessment studies. Epidemiologists and social scientists
have found that participation rates and compliance with instructions
may decline with increasing study periods, longer questionnaires, more
complicated questions and more complex tasks. Whitmore (1988)
speculated that the higher than average refusal rates experienced in
air pollution exposure studies are related to the burden associated
with carrying monitors and completing activity diaries. This has been
shown in the European multi-city EXPOLIS study in Grenoble where
participants had different time activity patterns in days with
personal exposure monitors compared to days when only time-activity
data was collected (Boudet et al., 1997).
5.3.2 Validity and reliability
Survey researchers in a number of fields have raised questions
about the validity of data collected via self-administered surveys:
i.e., is the instrument measuring what is intended (Laporte et al.,
1985). Data validity is of particular importance when trying to link
measured exposure with a given day's activity diary. The error
introduced by an inaccurate diary affects both efforts to explain the
contribution of certain activities to personal exposure and efforts to
estimate the distribution of personal exposure from time-weighted
microenvironmental measurements. The relationship between the degree
of error in the diary and the degree of error in the predictive model
depends upon the concentration in the microenvironment and the total
time spent there. Neglecting to report even short-duration activities
in high-concentration microenvironments will have more effect than
underestimating a similar amount of time in a low-concentration
microenvironment in which a large portion of the day is spent.
Scientists who use activity pattern data have raised a variety of
concerns about the effects of inadvertent and/or deliberate errors in
reporting. For instance, activity diary data may be compromised by
participants' misunderstanding of the definitions of various locations
(microenvironments). Discussions with participants have revealed the
potential for confusion about: How far is "far from home?" Is a
"parking garage" inside or outside? Is "walking" a light- or
medium-exertion activity? (Schwab et al., 1991, 1992).
To a certain extent, the quality of the data can be controlled
during data collection. Detailed instructions can improve participant
compliance. Field and laboratory pretesting of the survey instrument
and instructions, important components of the survey design process,
can yield improvements in protocol and clearer definitions of survey
terminology such as distinctions between microenvironmental categories
(Bercini, 1992). Extensive training of participants in keeping the
diary can be expensive, but detailed reference sheets and one-on-one
sessions can greatly improve data quality. One of the more
time-consuming but necessary steps is reviewing the returned diaries
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