Rapid Methods to Estimate Potential Exposure to Semivolatile Organic Compounds in the Indoor Environment

Publication: Research - peer-reviewJournal article – Annual report year: 2012

View graph of relations

A systematic and efficient strategy is needed to assess and manage potential risks to human health that arise from the manufacture and use of thousands of chemicals. Among available tools for rapid assessment of large numbers of chemicals, significant gaps are associated with the capability to evaluate exposures that occur indoors. For semivolatile organic compounds (SVOCs), exposure is strongly influenced by the types of products in which these SVOCs occur. We propose methods for obtaining screening-level estimates for two primary SVOC source classes: additives in products used indoors and ingredients in products sprayed or applied to interior surfaces. Accounting for product use, emission characteristics, and the properties of the SVOCs, we estimate exposure via inhalation of SVOCs in the gas-phase, inhalation of SVOCs sorbed to airborne particles, ingestion of SVOCs sorbed to dust, and dermal sorption of SVOCs from the air into the blood. We also evaluate how exposure to the general public will change if chemical substitutions are made. Further development of a comprehensive set of models including the other SVOC-containing products and the other SVOC exposure pathways, together with appropriate methods for estimating or measuring the key parameters (in particular, the gas-phase concentration in equilibrium with the material-phase concentration of the SVOC in the product, or y0), is needed. When combined with rapid toxicity estimates, screening-level exposure estimates can contribute to health-risk-based prioritization of a wide range of chemicals of concern.
Original languageEnglish
JournalEnvironmental Science & Technology (Washington)
Issue number20
Pages (from-to)11171-11178
StatePublished - 2012
CitationsWeb of Science® Times Cited: 68
Download as:
Download as PDF
Select render style:
Download as HTML
Select render style:
Download as Word
Select render style:

ID: 12309483