Passive Air Samplers As a Tool for Assessing Long-Term Trends in

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Passive air samplers as a tool for assessing long-term trends in atmospheric concentrations of semivolatile organic compounds Ji#í Kalina, Martin Scheringer, Jana Boruvkova, Petr Kukucka, Petra P#ibylová, Pernilla Bohlin-Nizzetto, and Jana Klánová Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 23 May 2017 Downloaded from http://pubs.acs.org on May 25, 2017

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Environmental Science & Technology

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Passive air samplers as a tool for assessing long-

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term trends in atmospheric concentrations of

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semivolatile organic compounds

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Jiří Kalina1, Martin Scheringer*1, 2, Jana Borůvková1, Petr Kukučka1, Petra Přibylová1, Pernilla

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Bohlin-Nizzetto3, Jana Klánová1

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1 Research Centre for Toxic Compounds in the Environment RECETOX, Kamenice 5, 625 00

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Brno, Czech Republic

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2 Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland

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[email protected]

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3 Norwegian Institute for Air Research NILU, PO box 100, 2027 Kjeller, Norway

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KEYWORDS: Persistent organic pollutant, POP, passive sampling, sampling rate, active

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sampling, time series, temporal trend.

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ACS Paragon Plus Environment

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ABSTRACT: Many attempts have been made to quantify the relationship between the amount of

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persistent organic pollutants sequestered by passive air sampling devices and their actual

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concentrations in ambient air. However, this information may not be necessary for some

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applications. In this study, two sets of 30 ten-year-long time series of simultaneous passive and

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high-volume active air sampling carried out at the Košetice observatory in the Czech Republic

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were used for a comparison of temporal trends. Fifteen polyaromatic hydrocarbons, seven

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polychlorinated biphenyls and eight organochlorine pesticides were investigated. In most cases, a

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good agreement was observed between the trends derived from passive and active monitoring

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with the exception of several compounds obviously affected by sampling artifacts. Two sampling

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artifacts were observed: breakthrough of high-volume sampler filters for penta- and

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hexachlorobenzene and semi-quantitative values for PAHs with a high molecular weight. It has

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been suggested before that annually aggregated results of passive air monitoring may be used

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directly for the assessment of the long-term behavior of these compounds. The extensive set of

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long-term data used in this study allowed us to confirm this finding and to demonstrate that it is

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also possible to derive temporal trends and the compounds’ half-lives in air from the passive-

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sampling time series.


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TOC Art

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Introduction

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Passive air sampling is an increasingly common alternative to the conventional active air

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sampling of semivolatile organic compounds (SVOCs), mainly persistent organic pollutants

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(POPs) in ambient air. While active sampling relates sequestered amounts of analytes to the

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measured volume of air in order to derive chemical concentrations in air, this volume is uncertain

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for passive sampling. Therefore it is necessary to determine a sampling rate (i.e. a characteristic

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volume of air that is stripped by the passive sampling medium per unit of time). The calculation

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or estimation of compound-specific or generic sampling rates and determination of the most

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important parameters affecting these rates have been a subject of numerous studies1–5. Such

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estimations were based on calibration studies comparing the amount of analyte sequestered by a

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passive air sampler (PAS) to its air concentration derived from an active air sampler (AAS)

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employed in parallel6 or application of so called "performance reference" or "depuration"

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compounds7. The PAS geometry, the type of sampling media, and the meteorological conditions

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(temperature and wind speed) are factors affecting significantly the particle-gas and PAS-gas

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partitioning of compounds of interest, the particle sampling efficiency of PAS, and thus the

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sampling rates. Therefore reliable measurements of physicochemical properties of the analytes

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(partitioning) or simultaneous measurements of meteorological conditions are required3,8,12.

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However, even the best methods currently used to estimate sampling rates are associated with

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substantial uncertainties (published coefficients of variation (standard deviation divided by

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mean) reach values of up to 2)3,12–16. Several studies published in recent years have shown that

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primary results of passive sampling may be used to identify time trends, which would help avoid

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the calculation of the sampling rate17,18. However, these studies covered relatively few years of

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sampling and did not quantify the time trends in terms of half-lives, nor did they confirm the


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trends found in PAS-derived data by comparison with an independent sampling method such as

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active air sampling.

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In line with the studies by Shunthirasingham et al.17 and Gawor et al.18, we investigate whether

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it is possible to skip the conversion of passive sampling results to air concentrations and to

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compute reliable trend statistics from the primary data of the passive monitoring. Our aim is to

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show that a sufficiently long time series of passive monitoring data makes it possible to reliably

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quantify time trends of SVOCs because using annual average values eliminates the effect of

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seasonal weather conditions on the partitioning of chemicals between air and passive sampling

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media. We use time series of passive sampling data to estimate half-lives of the compounds in air

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and evaluate them by comparison with half-lives estimated from active sampling results. Finally,

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we discuss limitations of this approach by identifying chemicals for which sampling artifacts

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occur.

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Materials and methods

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Sampling and Analysis. In order to compare passive and active monitoring trends, we used data

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from a program that is part of the monitoring networks MONET (Monitoring Network of POPs

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contamination in ambient air in Europe) for the passive sampling and of EMEP (European

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Monitoring and Evaluation Programme) for the active sampling. Passive air sampling was

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conducted from October 1, 2003, to December 25, 2013, which is more than 10 years of

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continuous monitoring. The frequency of active sampling was every 7 days for 24 hours, which

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corresponds to a total number of 534 samples, each of about 600 m3 of sampled air. The

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frequency of passive sampling was every 28 days for the whole period of 28 days, which is


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equivalent to 132 samples. Active sampling data represent a concentration in units of ng/m3 of

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air, passive sampling data represent a mass of the analyte in units of ng/PUF/28 days.

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In total 30 substances were analyzed, divided in three groups: organochlorine pesticides

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(OCPs), polychlorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs). A list

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of the analytes is given in the SI. The number of concentration values is in total 16,020 for active

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sampling and 3,960 for passive sampling. Several parts of this extensive dataset have been

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published in recent works (all analytes 1996–2005 active)9, (all analytes 1996–2005 active)10,

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(PAHs 1996–2011 active)11, (OCPs 2003–2012, PAHs 2006–2012 both active and passive)12,

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(PAHs 2011–2014 active)19.

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For high-volume active sampling, a device PS-1 Tisch Environmental TE1000 (air flow: 15

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m3h-1) with a quartz fiber filter (QFF) (Whatmann, fraction > 2 µm) and polyurethane foam

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(PUF) plug (6 cm diameter, 8 cm thickness) of type N 3038 (Gumotex, Břeclav) was used up to

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31 December 2010, and from 1 January 2011 a device Digitel DPM10/30/00 Environ-sense was

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used (air flow: 30 m3h-1) with a similar quartz fiber filter (QFF) (Whatmann, fraction > 2 µm)

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and PUF plug (11 cm diameter, 10 cm thickness) of type N 3038 (Gumotex, Břeclav). For

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passive sampling, PUF disks of type T 3037 (Molitan, Břeclav) were used (15 cm diameter,

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1.5 cm thickness). The density of all PUFs was 0.0303 g·cm-3.

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Both the PUF disks from passive sampling and PUF plugs and QFF filters from active

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sampling were extracted with dichloromethane (Büchi System B-811 automatic extractor).

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Surrogate recovery standards were spiked to filters before extraction, namely d8-naphthalene,

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d10-phenanthrene, and d12-perylene for PAHs and PCBs 30 and 185 for PCBs/OCPs. Sample

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extracts were concentrated under nitrogen and then one half was used for PAHs analysis and the

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other half for PCBs/OCPs analysis.


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A clean-up was conducted in the next step, using a sulfuric acid/silica gel column eluted with

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30 mL of 1:1 hexane/dichloromethane prior to PCB/OCP analysis and a silica gel column (30 cm

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length; 1 cm i.d.; 5 g silica) eluted with 10 mL of hexane (discarded), followed by 20 mL of

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dichloromethane for PAH analysis.

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Terphenyl was used as an internal standard for PAHs and PCB-121 as an internal standard for

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PCBs/OCPs. Both passive and active samples were analyzed for PCBs and OCPs using a gas

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chromatograph coupled with a mass spectrometer (GC-MS) (HP5975) with J&W Scientific

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fused silica column (DB-5MS). Conversely, PAH were determined in all samples using a GC-

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MS (HP 6890, HP 5972 and 5973) with J&W Scientific fused silica column (DB-5MS).

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Data treatment. A part of the data is left-censored, i.e., includes values below the limit of

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quantification, LoQ (semiquantitative values). The LoQ is calculated as 3.3 times the limit of

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detection, LoD. The LoD, in turn, is determined for each compound as the concentration at the

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lowest point on its calibration curve where the compound is still reliably detected20. The LoQ

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was determined as 0.0025 ng/m3 for active and 0.5 ng/PUF/28 days for passive samples of PAHs

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and 0.0005 ng/m3 for active and 0.1 ng/PUF/28 days for passive samples of OCPs and PCBs. On

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average, for passive sampling 21% of the values were below the LoQ, and this proportion varies

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from 0% for some PAHs with lower molecular weight up to 65% for indeno(1,2,3-c,d)pyrene.

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There are also 9% of values below the LoQ in the active sampling data, ranging from 0% for

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PAHs with lower molecular weight up to 48% for PCB-118. For the analysis the values below

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the LoQ were replaced by half of the LoQ (compound specific).

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Because the number of semiquantitative values is relatively low for most of the compounds, all

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30 analytes were included in the statistical analysis. However, in the cases of indeno(1,2,3-


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c,d)pyrene, benzo(g,h,i)perylene, benzo(a)pyrene, PCB-118, and β-HCH the portion of values

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below LoQ exceeded 50%, which could affect the concentration averages and the resulting time

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trends. In addition, there are several effects (sampling artifacts) that influence specifically the

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passive or active sampling only, restricting their comparability. These limitations and their

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effects on the results are discussed in detail in the Results and discussion section together with

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other sampling artifacts.

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Distributions of all primary passive and active data are presented as both time plots (Figure S1 and Figure S2) and box-and-whisker plots (Figure S3 and Figure S4) in the SI.

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Annual aggregation. Given the strong seasonal changes of the concentration-time series due to

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weather conditions and variable intensity of primary emission sources (higher concentrations of

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PAHs in winter due to combustion) and revolatilization from secondary sources21–23 (higher

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concentrations of OCPs in summer due to revolatilization and agricultural activity), statistical

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evaluations that would be sensitive to seasonal fluctuations cannot be performed on the primary

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data. Therefore we averaged the values over time within each year to achieve the same

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granularity of both groups of data series with values not influenced by within-year fluctuations.

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A standard method of a unification of monitoring time series with different granularity is

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annual aggregation, usually by an arithmetic mean as suggested by UNEP24, which leads to the

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same number of values in all time series and no seasonal fluctuations in individual years. The

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annual aggregation makes it possible to avoid incomparable data caused by different rates of

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particle infiltration of the active and passive samplers. Particle phase sampling efficiency is

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about 100% for active sampling, but could be significantly lower for different types of passive

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samplers25. Because the gas-particle partitioning of SVOCs is sensitive to temperature changes,


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this leads to a variable dependence of the sampling rate on the temperature and wind velocity.

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Partitioning between the gas and particle phases is governed by the octanol-air partition

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coefficient, KOA12. If the partitioning changes because of changing environmental conditions,

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active and passive samplers provide different results during one year. This plays a role especially

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for compounds with log KOA between 8.8 and 11.3. Nevertheless, under the assumption of long-

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term stable conditions and thus similar seasonal partitioning changes, the annually aggregated

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values are similarly affected every year.

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To obtain one representative aggregated value for each year and compound, it was necessary to

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use data covering the whole year. Therefore, we excluded the data from 2003 from the analysis

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since in this year the data are available from October-December only. The data of the remaining

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ten years were aggregated to obtain 2 × 30 time series of length of 10 values, which were used

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for the subsequent statistical processing.

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Statistical tests applied. The robust Mann-Kendall test was used to check for presence of a

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trend. This test indicates the statistical significance of a concentration decrease or increase

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without determining the magnitude of the change. The test is designed specifically for time-

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series analysis and does not presume any specific demands on the data. In addition, we also

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tested the hypothesis of a normal distribution of the concentration values for all compounds by

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the Kolmogorov-Smirnov test of normality; we obtained a negative results (p > 0.05), which

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means that normality can neither be ruled out nor expected. Thus, for our 10-year long series, the

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assumptions of the parametric Pearson test of trend are not violated. We therefore used also the

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Pearson test and the Spearman test of trend and compared their results with the Mann-Kendall

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test. Descriptions of the Pearson and Spearman tests and their results are provided in the


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Supporting Information in Table S3. Box plots illustrating the distribution of the data points in

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all time series are shown in Figures S3 and S4 in the Supporting Information.

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The Mann-Kendall test26 makes it possible to identify the temporal trend as a correlation

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(measured by the Kendall correlation coefficient, τ27) between time and the quantity of the

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analyte (concentration or mass per PUF plug).

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! = 2∙(!!!− !!!)/!∙(!−1)

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where nCP is a number of concordant pairs of time and concentration values (for earlier time

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points the concentration is lower than for later time points = increasing concentration) and nDP is

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a number of discordant pairs (for earlier time point the concentration is higher than for later one

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= decreasing concentration). The Kendall τ follows a distribution tabulated for small n and

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approximated by a normal distribution for n > 30. Thus confidence intervals and p-values can be

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computed.

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Results and discussion

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Trend identification

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Correlation coefficients τ and the resulting p-values of the Mann-Kendall test, specifying their

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statistical significance, are listed in Table 1.

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Table 1: Trend identification by Mann-Kendall test for time series derived from passive and active sampling at Košetice. Results that are significant at a 95% confidence level are highlighted in green. Results from Mann-Kendall trend test chemical

τ, active

p, active p

τ, passive

p, passive

naphthalene

0.422

0.107

0.200

0.474

acenaphthylene

0.289

0.283

-0.022

1.000


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acenaphthene

0.200

0.474

-0.111

0.721

fluorene

-0.111

0.721

-0.156

0.592

phenanthrene

-0.156

0.592

-0.244

0.371

anthracene

-0.333

0.210

-0.200

0.474

fluoranthene

0.022

1.000

0.067

0.858

pyrene

0.156

0.592

-0.022

1.000

benzo(a)anthracene

0.422

0.107

0.022

1.000

chrysene

0.156

0.592

0.244

0.371

benzo(b)fluoranthene

0.333

0.210

0.200

0.474

benzo(k)fluoranthene

0.156

0.592

-0.067

0.858

benzo(a)pyrene

0.378

0.152

0.022

1.000

indeno(123cd)pyrene

0.156

0.592

0.045

0.928

benzo(ghi)perylene

0.244

0.371

0.067

0.858

PCB 28

-0.556

0.032

-0.556

0.032

PCB 52

-0.600

0.020

-0.689

0.007

PCB 101

-0.422

0.107

-0.422

0.107

PCB 118

-0.689

0.007

-0.644

0.012

PCB 138

-0.556

0.032

-0.689

0.007

PCB 153

-0.600

0.020

-0.689

0.007

PCB 180

-0.556

0.032

-0.556

0.032

α-HCH

-0.422

0.107

-0.600

0.020

β-HCH

-0.289

0.283

-0.333

0.210

γ-HCH

-0.511

0.049

-0.778

0.002

p,p‘-DDE

-0.111

0.721

-0.244

0.371

p,p‘-DDD

-0.600

0.020

-0.600

0.02

p,p‘-DDT

-0.333

0.210

-0.289

0.283

PeCB

-0.200

0.474

-0.556

0.032

HCB

-0.067

0.858

-0.289

0.283

191


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There is generally high agreement between active and passive sampling results: in all cases

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where a significant trend was found for at least one variant of passive or active sampling, the

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orientation of the trend is the same in both active and passive sampling (only decreasing trends

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were found to be significant).

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For 27% of the chemicals, the trends from both active and passive sampling are significant,

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and in 67% of the cases both trends are insignificant. There are no cases where a trend identified

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by active sampling is not recognized by the passive sampling and only two cases where a

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significant trend was found in the passive-sampling data when it is not significant in the active-

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sampling data. These cases are PeCB and α-HCH (and for HCB in the case of the Pearson test).

201

These discrepancies are most likely caused by sampling artifacts, as discussed below. The

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results of the Pearson and Spearman tests listed in the Supporting Information are almost

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identical with those from the Mann-Kendall test.

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Linear trends, exponential trends and half-lives estimation

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The simplest method of a trend quantification is a linear fit that specifies a constant slope of the

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trend. Slopes were estimated separately for the time series of active and passive sampling by

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means of least squares optimization. As in the trend identification above (Table 1), there were no

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significant trends for any of the PAHs. For all other chemicals (except for α-HCH and HCB),

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active and passive sampling gave the same results for significant or insignificant trends. α-HCH

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and HCB showed significant trends in the passive sampling, but not in the active sampling data.

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Detailed results including plots are provided in the Supporting Information as Table S5 and

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Figure S6.


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Next, we performed an exponential fit, which makes it possible to check for a first-order

215

chemical kinetics governing the concentrations and to estimate the half-lives of the compounds

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in ambient air. The underlying differential equation describing the concentration-time trend is !" = −! ∙ ! !"

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where c denotes the concentration, t time and k a rate constant, indicating a fraction of

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pollutant loss per unit of time. This corresponds to a situation of negligible emissions together

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with ongoing loss processes of the pollutants such as degradation, deposition and transport9,28–31.

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The exponential trend fit was performed by least-squares optimization, providing results of

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relative decrease, expressed as a half-life, t1/2, and its significance. The half-life is expressed as

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follows: !!/! =

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ln 2 !

Characteristics of the exponential trend models are listed in Table 2 and the results of the

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exponential fits are plotted in Figure S6 in the Supporting Information.

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Table 2: Results of exponential trend fitting. Green-colored cells indicate statistically significant

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results (p < 0.05). For fits that are not significant, the confidence intervals include positive values

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(range above upper bound) and negative values (range below lower bound).

pollutant

active

passive

half-life [years]

half-life [years]

(95% confidence interval)

p

(95% confidence interval)

p

naphthalene

-11.5 (87.8; -5.4)

0.076

-32.81 (12.7; -7.2)

0.539

acenaphthylene

-12.6 (16.6; -4.6)

0.226

64.56 (4.9; -5.8)

0.855

acenaphthene

-19.8 (32.3; -7.6)

0.191 -177.63 (16.2; -13.7)

0.852


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fluorene

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391.0 (12.2; -13.1)

0.942

70.94 (11.6; -17.3)

0.663

68.3 (9.9; -14.0)

0.706

65.29 (11.6; -17.9)

0.633

anthracene

28.36 (7.1; -14.3)

0.462

12.51 (4.6; -17.1)

0.219

fluoranthene

-25.7 (19.1; -7.7)

0.354

-33.22 (18.2; -8.7)

0.439

pyrene

-37.0 (13.4; -7.8)

0.558

-141.87 (10.0; -8.8)

0.883

benzo(a)anthracene

-10.5 (67.1; -4.9)

0.081

-23.75 (6.7; -4.3)

0.624

chrysene

-19.7 (16.9; -6.2)

0.319

-14.44 (22.5; -5.5)

0.198

benzo(b)fluoranthene

-12.6 (46.2; -5.6)

0.108

-18.91 (7.0; -4.0)

0.550

benzo(k)fluoranthene

-36.8 (13.6; -7.8)

0.551

140.07 (5.0; -5.4)

0.934

benzo(a)pyrene

-12.8 (46.0; -5.6)

0.109

67.78 (3.5; -3.9)

0.903

indeno(123cd)pyrene

-11.8 (28.6; -4.9)

0.141

-32.21 (2.5; -2.2)

0.871

benzo(ghi)perylene

-11.7 (29.3; -4.9)

0.138

31.27 (2.2; -2.6)

0.865

PCB 28

6.7 (3.8; 29.7)

0.018

8.0 (4.4; 42.8)

0.022

PCB 52

3.1 (2.1; 6.3)

0.002

3.1 (2.2; 5.1)

0.000

PCB 101

4.3 (2.2; 119.2)

0.044

3.8 (2.1; 21.8)

0.023

PCB 118

3.4 (2.4; 6.1)

0.001

3.9 (2.3; 13.3)

0.011

PCB 138

3.1 (1.9; 10.5)

0.011

2.6 (1.7; 5.7)

0.003

PCB 153

4.4 (2.5; 17.0)

0.014

3.6 (2.0; 14.1)

0.015

PCB 180

3.6 (2.1; 14.9)

0.016

3.4 (2.0; 9.8)

0.008

α-HCH

9.3 (4.4; -80.1)

0.073

5.7 (3.5; 16.9)

0.008

β-HCH

2.2 (1.1; 32.0)

0.038

5.2 (1.9; -6.9)

0.224

γ-HCH

3.0 (1.5; -139.8)

0.054

4.6 (3.3; 7.6)

0.001

p,p‘-DDE

67.5 (10.1; -14.3)

0.697

19.6 (6.2; -16.7)

0.319

p,p‘-DDD

3.9 (2.3; 12.3)

0.010

4.38 (2.9; 8.5)

0.001

p,p‘-DDT

5.8 (2.4; -14.9)

0.136

6.62 (2.5; -10.3)

0.198

22.4 (7.6; -23.9)

0.269

9.92 (5.17; 123.0)

0.036

phenanthrene

PeCB


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HCB

12.8 (3.6; -8.9)

0.392

6.3 (2.9; -38.2)

0.083

228 229

As Table 2 shows, there is a similar pattern of correspondence of statistically significant trends

230

from active and passive sampling as in the trend identification (Table 1). There is also an

231

accordance of the estimated half-life values between the active and the passive sampling. Except

232

for γ-HCH, in all cases the passive-sampling half-lives are located in the 95% confidence

233

interval of the active-sampling estimates and vice versa. For 30% of the chemicals, both trends

234

are significant, for 60% both trends are insignificant, there is one case (3%; β-HCH) with a

235

significant trend from active sampling only, and there are two cases (7%; α-HCH, PeCB) with

236

significant trends from passive sampling only. However, even in these cases, the estimated half-

237

lives fall in the 95% confidence interval of their counterparts. Negative values of the half-lives

238

represent increasing trends, but these are in all cases statistically insignificant.

239

The concentrations of PCBs in ambient air approximately follow a first-order kinetics; this has

240

been observed at different sites in earlier work28–30 and is also visible from our results. Moreover,

241

for all PCBs assessed, the half-lives of PCBs in ambient air are very similar for the active and

242

passive trends, see Figure 1. Our estimated half-lives vary from 3.1 years for active sampling and

243

2.6 years for passive sampling (PCB 138) to 6.7 years for active sampling and 8.0 years for

244

passive sampling (PCB 28), which is in good agreement with similar results estimated at sites in

245

Germany28, UK29 and US30, with median half-lives (based on values from 10 sites) from 5.2 to

246

8.3 years (the shortes half-life was 2.3 years, the longest 330 years) for the same set of PCBs.

247


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248 249

Figure 1: Ranges of half-lives of PCBs in ambient air determined in Košetice and at other

250

sites28,29. Crosshairs: individual half-lives from the literature; red squares: medians over the

251

literature-derived half-lives; dots: half-lives estimated in Košetice from passive (orange) and

252

active (green) sampling.

253 254

For the PAHs, the exponential fit yields virtually straight lines (Figure S6), which suggests that

255

the PAH concentrations do not follow first-order kinetics. PAH concentrations are, in addition to

256

loss processes, controlled also by ongoing emissions, as confirmed by other research works11,32.

257

For all assessed PAHs the trends were not significant (oscillating) and for half of them even

258

increasing, which is in accordance with the latest results from Central Europe34.

259

Half-life estimates for OCPs are shown in Figure 2. For substances where both trends are

260

significant (γ-HCH and DDD, see Table 2), the estimates from passive and active sampling are

261

close (orange and green dots). For substances with greater differences between the two estimates,

262

no (p,p’-DDE, HCB) or only one (β-HCH, PeCB) significant trend was observed. Finally, DDT

263

has no significant trend, but very similar half-life estimates, and α-HCH has one significant trend

264

(passive sampling) and the half-life estimates are relatively close. Half-life data from the


16

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265

literature are mainly available for α- and γ-HCH35–38; for both HCHs, our estimates are within the

266

range spanned by the earlier data. Moreover, the difference between the passive and active

267

sampling estimates is less than the range spanned by all data.

268

269 270

Figure 2: Ranges of half-lives of OCP air concentrations in Košetice and other sites.35–38

271

Individual half-lives are depicted as crosshairs, medians over all values are depicted as red

272

squares and values estimated in Košetice are depicted as green dots for active and orange dots for

273

passive sampling.

274 275

Sampling artifacts

276

There are two groups of compounds that show systematic differences between the trends derived

277

from the active sampling and passive sampling time series. The first group includes the most

278

volatile compounds, which are mostly in the gas-phase due to their low octanol-air partition

279

coefficient (log KOA at 25 °C about 7 and lower): naphthalene, acenaphthylene, acenaphthene,

280

and to some extent possibly also pentachlorobenzene and hexachlorobenzene. For the trends of


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281

the three light PAHs, there is typically a slower concentration decrease (or even an increase) in

282

the trends from the active sampling than in the trends from the passive sampling.

283

The second group includes three higher-molecular-weight PAHs with high PUF-air partition

284

coefficients, which are mostly bound to particles (log KOA at 25 °C about 9 and higher), namely

285

indeno(1,2,3-c,d)pyrene, benzo(a)pyrene and benzo(g,h,i)perylene. Due to the relatively low

286

particle infiltration efficiency of the deployed passive samplers25, the amount of these

287

compounds sequestered by the samplers during a 28-day period is relatively low, near to the

288

quantification limit (0.5 ng/PUF/28 days).

289

These two groups of compounds are related to two different sampling artifacts. The artifacts

290

affect either the active or the passive sampling time series and, thereby, make the time series

291

incomparable.

292

1. Semiquantitative values: a significant number of semiquantitative values (left-

293

censored values lower than the limit of quantification of the analytical method) and

294

their substitution by ½ of the limit can influence the annually aggregated values and the

295

resulting trend39. This was the case for passive sampling of indeno(1,2,3-c,d)pyrene,

296

benzo(a)pyrene and benzo(g,h,i)perylene, which are present almost entirely in the

297

particle phase and are, therefore, only partially sequestered by the passive sampler.

298

Average concentrations of these compounds were close to the quantification limit and

299

the number of semiquantitative results was too high to provide meaningful results (see

300

Table S7 in the SI). Regardless of the censoring, trends of these compounds were

301

insignificant for both the active and the passive sampling.

302


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303

2. Breakthrough. This occurs for compounds for which the sorption capacity of the PUF

304

plug in the high volume active air sampler is not sufficient with respect to the total

305

volume of air passing through the sampler. Latest research40 shows that PAHs with low

306

molecular weight (2 and 3 rings), PeCB and HCB tend to show breakthrough even for

307

ca. 700 m3 of sampled air with a PUF plug of ca. 1,000 cm3. This is more probable

308

when higher concentrations are present (typically in summer for OCPs because of

309

higher temperatures and in winter for PAHs because of higher emissions).

310

The effect of the breakthrough is loss of material from the sample and, therefore, a

311

reduction of both the concentration raw data and the annually aggregated concentration

312

values.

313

Regarding breakthrough effects in our active-sampling data, the most important

314

incident was the change from a PS-1 sampler, deployed from 2004 to 2010, to a Digitel

315

sampler deployed from 2011 to 2013. The Digitel sampler deploys a PUF plug with a

316

volume of 950 cm3 (11 cm diameter, 10 cm thickness) compared to 226 cm3 (6 cm

317

diameter, 8 cm thickness) of the older PS-1 PUF plug made of the same material. The

318

airflow per unit area of the Digitel sampler is 1.7 times lower than for the PS-1 (see the

319

calculation in the SI). Together with the greater thickness of the Digitel plug (i.e.

320

higher number of theoretical chromatography plates), this results in reduced risk of

321

breakthrough40 for the Digitel and thus considerably fewer breakthrough events

322

between 2011 and 2013. Accordingly, the trend derived from active sampling is

323

affected by unrealistically low results caused by breakthrough in the initial phase

324

(2004–2010). This is visible for NAP, ACY and ACE in Figure 3; there is an apparent

325

effect of low sorption capacity in the initial period (2004–2010). Whereas the low


19


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326

values remain similar from 2004 to 2013, the highest peaks are clearly lower in the

327

earlier period (2004–2010), see also Figures S1 and S2 in the SI.

328

For PeCB and HCB, the highest concentrations in the first three years are probably

329

affected by breakthrough; afterwards (2007–2013) the concentrations are generally

330

lower (as it is confirmed by the passive sampling results) and breakthrough is no longer

331

an issue.

332

Figure 3: Primary active sampling data for compounds tending to show breakthrough.

333


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334

Breakthrough can be avoided by shorter periods of active air sampling, by using more efficient

335

sorbents (e.g. XAD) or by using PUF plugs of higher sorption capacity, i.e. plugs with larger

336

surface (as from 2011).

337 338

Environmental Significance

339

The effectiveness evaluation of the Stockholm Convention on POPs based on Article 16 has been

340

the main driving force for rapid development of passive air sampling techniques in the last

341

decade. In order to obtain reliable temporal trend data, the Global Monitoring Plan (GMP) was

342

established under the SC. While active air samplers could only provide accurate time-resolved

343

data from very few monitoring sites across the globe due to the high costs and logistic

344

constraints, passive samplers offer a simple and cheap alternative allowing for establishment of

345

sustainable air monitoring programs. Several global and regional large-scale efforts (GAPS,

346

MONET, LAPAN) stem from this advancing technology and generate data as required under the

347

GMP. However, it has been found that interpretation of the resulting data is not straightforward

348

as there are several ways how to translate amounts of chemicals sequestered by the passive

349

sampling devices into relevant air concentrations. Numerous studies introduced these calibration

350

methods revealing at the same time their uncertainties. In this paper, we demonstrate that for an

351

assessment of the long-term trends this step can be avoided and long-term trends can be derived

352

directly from concentrations of target chemicals in sampling media. This approach further

353

reduces the costs of the long-term programs by avoiding using labeled performance reference

354

compounds or parallel active samplers and reduces uncertainties of the resulting temporal trends.

355

The approach demonstrated in this study is applicable in all cases where the actual air

356

concentration of a pollutant is not an ultimate goal of the analysis, i.e. where the goal is


21


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357

identification of long-term trends and possibly half-lives. Whereas high-volume active air

358

samplers may be prone to breakthrough during a 24-hour deployment, the linear uptake phase of

359

the PUF plugs in the Digitel samplers is not exceeded when the samplers are deployed for 28

360

days. This renders passive samplers a suitable tool for long-term assessment of lighter (gas-

361

phase) semivolatile organic compounds.

362 363 364

ASSOCIATED CONTENT The Supporting Information is available free of charge on the ACS Publications website at

365

DOI: XXX

366

Time plots of primary data from active (Figure S1) and passive (Figure S2) sampling in

367

Košetice, box-and-whisker plots of primary data from active (Figure S3) and passive (Figure S4)

368

sampling in Košetice, tables of annually aggregated values of ambient air concentrations of

369

target compounds derived from active (Table S1) and passive (Table S2) monitoring in Košetice,

370

description of Pearson and Spearman tests of trend and their results on annually aggregated data

371

(Table S3), description and numeric results of linear trends (Table S4 and Figure S5), similar

372

plots for exponential trend (Figure S6) and comparison of half-lives of the target compounds

373

between Košetice and several sites from other studies.

374

AUTHOR INFORMATION

375

Corresponding Author

376

*Email: [email protected] Phone: +420 549496698

377

Notes

378

The authors declare no competing financial interest.


22

Page 23 of 31


379

ACKNOWLEDGMENT

380

This work was supported by the RECETOX and ACTRIS research infrastructures (Ministry of

381

Education, Youth and Sports of the Czech Republic, LM2015051, LM2015037 and

382

CZ.02.1.01/0.0/0.0/16_013/0001761),

383

(CZ.02.1.01/0.0/0.0/15_003/0000469), and by a grant from Iceland, Liechtenstein and Norway

384

(EHP-CZ02-OV-1-029-2015).

385

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