Tree sampling as a method to assess vapor intrusion potential at a site characterized by VOC-contaminated groundwater and soil
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Abstract
Vapor intrusion (VI) by volatile organic compounds (VOCs) in the built environment presents a threat to human health. Traditional VI assessments are often time-, cost-, and labor-intensive; whereas traditional subsurface methods sample a relatively small volume in the subsurface and are difficult to collect within and near structures. Trees could provide a similar subsurface sample where roots act as the “sampler’ and are already onsite. Regression models were developed to assess the relation between PCE concentrations in over 500 tree-core samples with PCE concentrations in over 50 groundwater and 1000 soil samples collected from a tetrachloroethylene- (PCE-) contaminated Superfund site and analyzed using gas chromatography. Results indicate that in planta concentrations are significantly and positively related to PCE concentrations in groundwater samples collected at depths less than 20 m (adjusted R2 values greater than 0.80) and in soil samples (adjusted R2 values greater than 0.90). Results indicate that a 30 cm diameter tree characterizes soil concentrations at depths less than 6 m over an area of 700–1600 m2, the volume of a typical basement. These findings indicate that tree sampling may be an appropriate method to detect contamination at shallow depths at sites with VI.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Tree sampling as a method to assess vapor intrusion potential at a site characterized by VOC-contaminated groundwater and soil |
| Series title | Environmental Science & Technology |
| DOI | 10.1021/acs.est.7b02667 |
| Volume | 51 |
| Issue | 18 |
| Year Published | 2017 |
| Language | English |
| Publisher | ACS Publications |
| Contributing office(s) | Missouri Water Science Center |
| Description | 10 p. |
| First page | 10369 |
| Last page | 10378 |
| Country | United States |
| State | Missouri |
| City | Vienna |
| Google Analytic Metrics | Metrics page |