Characterizing Spatial Diversity of Passive Sampling Sites for Measuring Levels and Trends of Semivolatile Organic Chemicals
| Autor: | Lisa Melymuk, Petr Kukučka, Martin Scheringer, Jana Borůvková, Petra Přibylová, Ondřej Sáňka, Milan Váňa, Jiří Kalina, Jana Klánová |
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| Rok vydání: | 2018 |
| Předmět: |
Pollutant
Air Pollutants Air sampling 010504 meteorology & atmospheric sciences Organic chemicals Organochlorine pesticide Sampling (statistics) General Chemistry Hexachlorobenzene 010501 environmental sciences Polychlorinated Biphenyls 01 natural sciences chemistry.chemical_compound chemistry Environmental chemistry Hydrocarbons Chlorinated Environmental Chemistry Environmental science Spatial variability Pesticides Polycyclic Aromatic Hydrocarbons Czech Republic Environmental Monitoring 0105 earth and related environmental sciences Passive sampling |
| Zdroj: | Environmental Science & Technology. 52:10599-10608 |
| ISSN: | 1520-5851 0013-936X |
| DOI: | 10.1021/acs.est.8b03414 |
| Popis: | Passive air sampling of semivolatile organic compounds (SVOCs) is a relatively inexpensive method that facilitates extensive campaigns with numerous sampling sites. An important question in the design of passive-sampling networks concerns the number and location of samplers. We investigate this question with the example of 17 SVOCs sampled at 14 background sites across the Czech Republic. More than 200 time series (length 5-11 years) were used to characterize SVOC levels and trends in air between 2003 and 2015. Six polychlorinated biphenyls (PCBs), 6 polyaromatic hydrocarbons (PAHs), and 5 organochlorine pesticides (OCPs) at 14 sites were assessed using data from the MONET passive sampling network. Significant decreases were found for most PCBs and OCPs whereas hexachlorobenzene (HCB) and most PAHs showed (mostly insignificant) increases. Spatial variability was rather low for PCBs and OCPs except for dichlorodiphenyltrichloroethane (DDT) and rather high for PAHs. The variability of the SVOC levels and trends depends on characteristics of the sites including their remoteness, landscape, population, and pollution sources. The sites can be grouped in distinct clusters, which helps to identify similar and, thereby, potentially redundant sites. This information is useful when monitoring networks need to be optimized regarding the location and number of sites. |
| Databáze: | OpenAIRE |
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