(Received 18 October 2010; accepted 29 April 2011)
Published Online: 2011
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Understanding the relationship among allergies, asthma, and indoor air allergen triggers increasingly require use of standardized and evidence based exposure assessment methods. Exposure assessment for common indoor allergens, however, is often limited by use of surface in place of air sampling for a variety of reasons. Chief among them is that many indoor allergens settle to the ground quickly after being released. Another reason for the wide-spread use of surface over air sampling for indoor air allergens is that air sampling is typically performed over a limited time frame and plausibly cannot capture a composite of exposure that a surface sample can. Protocols have been developed to collect surface borne allergen in several US wide-national studies, and while these samplers may provide an adequate means to correlate composite dust mass or allergens to some specific health outcomes, there is still a lack of evidence to support their adoption on the basis of several criteria which stem from theoretical and evidence based considerations. To become more valuable tools to risk assessment, epidemiological studies, and environmental intervention, surface sampling methods should be constructed using basic principles of particle behavior on surfaces as well as in the air. Criteria need to be developed from these principles and studies that address these criteria should be used or developed to enable the creation of performance-based standards. This paper is a review of the literature, which highlights surface sampling methods that have taken this theory- or evidence-based approach. After the review, a discussion is then developed on the current state-of-the-art surface sampling for indoor allergens. Recommendations are suggested for both future theoretical or empirical work necessary for devising performance standards for sampling and collecting surface borne-allergens.
Lewis, Roger D.
Saint Louis University School of Public Health,
Washington University School of Engineering,
Stock #: JAI103514