Passive Air Samplers As a Tool for Assessing Long-Term Trends in Atmospheric Concentrations of Semivolatile Organic Compounds

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Authors

KALINA Jiří SCHERINGER Martin BORŮVKOVÁ Jana KUKUČKA Petr PŘIBYLOVÁ Petra BOHLIN-NIZZETTO Pernilla KLÁNOVÁ Jana

Year of publication 2017
Type Article in Periodical
Magazine / Source ENVIRONMENTAL SCIENCE AND TECHNOLOGY
MU Faculty or unit

Faculty of Science

Citation
Web http://pubs.acs.org/doi/10.1021/acs.est.7b02319
Doi http://dx.doi.org/10.1021/acs.est.7b02319
Field Informatics
Keywords passive sampling; passive sampler; long-term trends; air pollution; persistent pollutants; semivolatile organic compounds
Description Many attempts have been made to quantify the relationship between the amount of persistent organic pollutants sequestered by passive air sampling devices and their actual concentrations in ambient air. However, this information may not be necessary for some applications. In this study, two sets of 30 ten-year-long time, series of simultaneous passive and high-volume active air sampling carried out at the Koss'etice observatory in the Czech Republic were used for a comparison of temporal trends. Fifteen polyaromatic hydrocarbons, seven polychlorinated biphenyls and eight organochlorine pesticides were investigated. In most cases, a good agreement was observed between the trends derived 'from passive and active monitoring with the exception of several compounds obviously affected by sampling artifacts. Two sampling artifacts were observed: breakthrough of high-volume sampler filters for penta- and hexachlorobenzene and semiquantitative values for PAIR with a high molecular weight. It has been suggested before that annually aggregated results of passive air monitoring may be used directly for the assessment of the long-term behavior of these compounds. The extensive set of longterm data used in this study allowed us to confirm this finding and to demonstrate that it is also possible to derive temporal trends and the compounds' half-lives in air from the passive-sampling time series.
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