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Characterizing spatial diversity of passive sampling sites for measuring levels and trends of semivolatile organic chemicals Ji#í Kalina, Martin Scheringer, Jana Boruvkova, Petr Kukucka, Petra P#ibylová, Ond#ej Sá#ka, Lisa Melymuk, Milan Vana, and Jana Klánová Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b03414 • Publication Date (Web): 14 Aug 2018 Downloaded from http://pubs.acs.org on August 19, 2018

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Environmental Science & Technology

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Characterizing spatial diversity of passive

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sampling sites for measuring levels and trends of

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semivolatile organic chemicals

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

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Ondřej Sáňka1, Lisa Melymuk1, Milan Váňa3, Jana Klánová1

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1 Research Centre for Toxic Compounds in the Environment RECETOX,

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Kamenice 5, 625 00 Brno, Czech Republic

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2 Institute for Chemical and Bioengineering, ETH Zürich,

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8093 Zürich, Switzerland, [email protected]

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3 Czech Hydrometeorological Institute – Košetice Observatory,

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394 22 Košetice, Czech Republic

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KEYWORDS: Temporal trends, polychlorinated biphenyl, polycyclic aromatic hydrocarbon,

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organochlorine pesticide, passive air sampling, MONET.

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ACS Paragon Plus Environment

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ABSTRACT: Passive air sampling of semivolatile organic compounds (SVOCs) is a

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relatively inexpensive method that facilitates extensive campaigns with numerous sampling

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sites. An important question in the design of passive-sampling networks concerns the number

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and location of samplers. We investigate this question with the example of 17 SVOCs

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sampled at 14 background sites across the Czech Republic. More than 200 time series (length

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5–11 years) were used to characterize SVOC levels and trends in air between 2003 and 2015.

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Six polychlorinated biphenyls (PCBs), 6 polyaromatic hydrocarbons (PAHs) and 5

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organochlorine pesticides (OCPs) at 14 sites were assessed using data from the MONET

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passive sampling network. Significant decreases were found for most PCBs and OCPs

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whereas hexachlorobenzene (HCB) and most PAHs showed (mostly insignificantly)

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increases. Spatial variability was rather low for PCBs and OCPs except for

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dichlorodiphenyltrichloroethane (DDT) and rather high for PAHs. The variability of the

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SVOC levels and trends depends on characteristics of the sites including their remoteness,

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landscape, population and pollution sources. The sites can be grouped in distinct clusters,

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which helps to identify similar and, thereby, potentially redundant sites. This information is

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useful when monitoring networks need to be optimized regarding the location and number of

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sites.


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TOC Art

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Introduction

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Passive air sampling is a relatively inexpensive and efficient method for long-term

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monitoring of semivolatile organic chemicals (SVOCs) such as persistent organic pollutants

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(POPs) in air. Application of this technique enabled the development of the extensive passive

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air monitoring networks on the regional and even global scales in the last decade. This effort

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was largely driven by the Global Monitoring Plan of the Stockholm Convention. An

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important question related to the set-up of passive air sampling networks is how the

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minimum number of sampling sites can be found that are needed to represent the levels and

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trends of an SVOC in the area of interest. The representativeness of a network is defined in

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the Guidance on the Global Monitoring Plan for Persistent Organic Pollutants1 as a

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“sufficient number of sampling sites to make general conclusions about POPs trends” in the

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area.

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Many studies have demonstrated that for relatively short sampling durations (weeks or

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months) the variability between measured concentrations is rather high even in relatively

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small regions and between sites classified as background sites.2–9 Long-term monitoring

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makes it possible to reduce this variability because short-term fluctuations are averaged out.

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Here, we address the question of the sufficient number of sampling sites in a network

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using a large set of long-term passive air-sampling data. The data consist of mostly

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unpublished results (only 17 of the 238 time series used here were previously published)

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from the Czech Republic. Time series from 14 sites officially classified as background sites

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according

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Hydrometeorological Institute10,11,12 were analyzed for polychlorinated biphenyls (PCBs),

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organochlorine pesticides (OCPs), and polycyclic aromatic hydrocarbons (PAHs). The length

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of the time series collected in the period 2003-2015 was between 7 and 13 years. Annually

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aggregated data for each chemical at each site were used to determine the average SVOC

to

the

Exchange-of-Information

(EoI)

classification


of

the

Czech

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concentrations and the slopes of the time trends. Using the levels and long-term trends of the

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17 SVOCs at the 14 sites, we demonstrate that it is possible to define clusters of sites with

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similar SVOC pollution patterns. This makes it possible to define a minimum number of sites

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and select a representative set of sites that is sufficient to draw conclusions about SVOC

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levels and trends in the area. Defining a set of representative sites from a larger network

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according to the approach presented here will be instrumental in securing the sustainability of

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the monitoring networks built in the last decade in support of the Global Monitoring Plan of

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the Stockholm Convention.

68 69

Materials and methods

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Sampling design. In the 2002–2015 period, SVOC passive air sampling was conducted at

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least once at 232 sites within the Monitoring Network (MONET) program.13 Out of these

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sites, 40 sites operated by the Czech Hydrometeorological Institute are considered as

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background sites according to the EoI classification based on the EU Council decision

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97/101/EC and Commission decision 2007/752/EC,10,11,12 which means they are neither

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industrial nor affected by traffic according to the detailed specification.10 A sub-set of 14

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background sites provided time series sufficiently long to be included in this analysis.

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Passive air samplers were deployed at all sites consisting of two stainless steel bowls to

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form a protective chamber in which a polyurethane foam (PUF) disc was mounted.13 The

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samplers were placed at 1.5 m height above the ground to allow the air to blow freely through

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the gap between the bowls.

81 82

Experimental section. PUF discs of 15 cm diameter and 1.5 cm thickness were used for

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sampling. After the sampling period (typically 28 days, see below) the discs were transported


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to the accredited trace analytical laboratory at RECETOX, Brno, for extraction, clean up and

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analysis. Analytical steps are described in detail by Kalina et al.14

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From the total number of 30 monitored SVOCs, 17 were selected for the analysis because

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they are not affected by sampling difficulties such as concentrations close to the detection

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limit or breakthrough.14 The selection was made in order to represent three groups of SVOCs

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with a similar number of substances: 5 OCPs, 6 indicator PCBs and 6 PAHs ranging from 3-

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ring up to 6-ring. A list of the analytes is given in Table 1. Results of the analysis were given

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in units of mass of chemical per PUF disc and the period of the exposure. Because neither the

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temporal or spatial variability assessment requires working with air concentrations in units of

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mass per air volume,14 the primary values in units of mass per PUF and 28 days were used,

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which are not affected by known shortcomings of the conversion into air concentrations15

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(see discussion). Measurement results converted into air concentrations are provided in the

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SI file Kalina_monthly_data.xlsx.

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Table 1: List of analyzed SVOCs, their abbreviations, and percentages of their values below

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the limit of quantification. OCPs

PCBs

PAHs

α-HCH

A_HCH

3% PCB 28

PCB28

2% fluorene

FLU

0%

γ-HCH

G_HCH

2% PCB 52

PCB52

3% phenanthrene

PHE

0%

p,p‘-DDE

PP_DDE

0% PCB 101

PCB101

9% pyrene

PYR

0%

pentachloro benzene

PECB

5% PCB 137

PCB137

14% chrysene

CHRY

6%

hexachloro benzene

HCB

0% PCB 153

PCB153

4% benzo(a) pyrene

BAP

48%

PCB 180

PCB180

26% benzo(ghi) perylene

BGP

56%

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Data treatment. In order to achieve robust results with the smallest possible influence of

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random effects, only the background sites with at least 5-year time series (i.e. continuous

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sampling took place in 5 consecutive calendar years) were taken for further processing. The

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five-years span encompasses also incomplete initial/final years, when the sampling started or

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ended within the year. Because these initial and final years of the time series are usually not

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fully covered by the samples, the values from incomplete initial and final years were

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excluded. This provided a set of 14 relevant sites with sampling durations from 7 to 11 years

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(2004–2014) that were used for the analysis; a list of these 14 sites is provided in Table 2.

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Table 2: List of background sites, their EoI classification12, spatial coordinates, and sampling

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periods. EoI classification: classification according to criteria defined under the Exchange-of-

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Information decision of the European Council.10,11 B: background, N: natural, R: countryside,

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A: agricultural, S: suburban, AN: agricultural/natural, REG: regional. Sampling period

Name of the site

Longitude Latitude EoI Letter classification

Bily Kriz, Beskydy

X

2006– B/R/N-REG 49.50261 18.53856 2015

Decinsky Sneznik, Labske piskovce

D

2006– B/R/N-REG 50.78951 14.08684 2015

Churanov, Sumava, EMEP

C

2006– B/R/N-REG 49.06844 13.61488 2015

Jesenik, Jeseniky

J

B/R/N-NCI

2006– 50.24225 17.19022 2015

Kosetice, EMEP

E

B/R/ANREG

2003– 49.57345 15.08041 2015

L

B/R/N

2005– 50.72940 14.98790 2015

M

2006– B/R/A-REG 48.79175 16.72450 2015

Liberec, Jested Mikulov, Sedlec


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2004– 50.00731 14.44620 2015

Praha, Libus

P

B/S/R

Primda, Sumava

I

2006– B/R/N-REG 49.66959 12.67785 2015

Rudolice v Horach, Krusne hory

R

2006– B/R/N-REG 50.57979 13.41922 2015

Rychory, Krkonose

Y

2006– B/R/N-REG 50.66046 15.85006 2015

Serlich, Orlicke hory

S

2006– B/R/N-REG 50.32804 16.38353 2013

Stitna nad Vlari-Popov, Planavy

T

2004– B/R/N-REG 49.04776 18.00781 2015

Svratouch

H

B/R/ANREG

2006– 49.73507 16.03413 2015

114 115

With PUF disc passive sampling, both the gas and particle phases are analyzed together,

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which yields one value for each compound within the sample. Certain fractions of the results

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were below the limit of quantification, LoQ, which leads to left-censored values. The LoQ is

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calculated as 3.3 times the limit of detection, LoD; this methodology is described in detail by

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Kalina et al.14 Percentages of values below the LoQ for individual compounds are listed in

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Table 1.

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Values below the LoQ were replaced by ½ of the LoQ as described in Kalina et al.14 The

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most frequent values below the LoQ were found for heavier PAHs, which fluctuate strongly

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between the summer and the winter. In such cases, the winter concentrations, which are

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higher by several orders of magnitude, determine the results of the annual aggregation of the

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concentrations.

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Although the typical sampling period was 28 days for almost all samples, a part of the

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samples differed in the length of the sampling interval (85% were sampled for 28 days, 3%

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for 27 days, 3% for 29 days, 1% for 26 days, 1% for 30 days, and the remaining 7% were

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sampled for different durations). It may be assumed that all sampling periods from minimum


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of 14 days up to maximal period of 98 days fall into the linear phase of the uptake curve of

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passive air sampling.15 Under these assumptions it was possible to standardize the amounts of

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the analytes sequestered by the samplers to a sampling duration of 28 days, which provides

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approximately 13 samples per year.

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Annual aggregation. Considering the strong seasonal fluctuations of the SVOC air

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concentrations,14 statistical evaluations that are sensitive to periodic changes cannot be

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performed on the primary data. Therefore, we aggregated the values annually to average out

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both the seasonal within-year fluctuations and possible random fluctuations. A standardized

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method of the annual aggregation is taking the arithmetic mean.1 As shown in our previous

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work,14 annual aggregation also avoids inconsistency of primary data caused by seasonal

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differences in sampling rates. Under the assumption of long-term stable conditions, see

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multi-year temperature data for site “E” (Kosetice) in Table S1 and Figure S1, and thus

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similar within-year changes of the sampling rates, the annually aggregated values can be used

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to establish the long-term time trend.

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Temporal trends. It may generally be assumed that SVOCs in ambient air undergo

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degradation processes that can be approximated by first-order kinetics.16–18 These reactions

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are described by compound-specific half-lives. For background sites not affected by primary

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emissions, this leads to an exponential decrease of the compounds’ levels in air.14

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The exponential trend fit was performed by using the Theil-Sen estimator, a robust tool for

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estimating trends used for regression of air pollution data.19–22 The trend was fitted as a linear

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regression on log-transformed annually aggregated data on each time series obtained for one

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SVOC at one sampling site. The assessment of significance was made for each of the 238

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time series given by all combinations of sampling sites and chemicals; each time series was

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characterized by the median level of the SVOC concentration (intercept), the half-life and

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direction of the trend (slope) and its statistical significance.


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Additionally, a median trend was computed for each compound from all individual trends

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of the 14 sites with different levels of pollution, lengths, slopes and statistical significance. A

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method of median slope and median intercept was used to determine these median trends.

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Based on the principles of the Theil-Sen estimator on log-transformed data, this method

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computes the median of the slopes and the median of the intercepts over the 14 sites and uses

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these statistics to form a new trend. This makes it possible to compute a median half-life and

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its significance for each compound. These half-lives are listed in Table 3.

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Spatial variability. The selected 14 sites are spread over the whole area of the Czech

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Republic; they are located mainly in the countryside, at higher elevations, or in the

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surroundings of bigger municipalities. For each of the 17 compounds, these sites provide 14

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time series of different length, starting in 2007 or earlier and ending in 2010 or later. Since all

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of the time series include the year 2009, which is a middle date of the overall period (2004–

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2014), this year was chosen as a reference to determine the levels of the compounds at the

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sites. The levels were determined as the values of the Theil-Sen trend in the year 2009 in

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units of mass per PUF and 28 days.

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The interquartile ratio (IQR) was used to compare the spread of the levels of individual

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compounds among the 14 sites. The value of an interquartile ratio is computed as a difference

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between the 3rd and the 1st quartile (the interquartile range), divided by the median.

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Cluster analysis. Each of the 17 SVOCs provides two characteristics of each site: the SVOC

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level and its temporal trend. This allows us to construct a 34-dimensional (34-d) space in

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which each site is represented as a point characterized by the 17 levels and the 17 trends of

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the SVOCs (normalized values were used to handle the different magnitudes of SVOC levels

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and trends). In this space, it is possible to characterize the sites according to their proximity

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(measured by their Euclidean distance in the 34-d space) and to group them into several


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clusters; a cluster is a set of sites that are closer to one another than to sites in a different

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cluster.

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The K-means clustering method was used to define three distinct clusters of sites based on

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their mutual distances in the 34-d space.23,24 These clusters represent three typical types of

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sampling sites within the Czech Republic. A principal-component analysis was then

184

performed to project the 34-d space to a plane spanned by two dimensions to visualize the

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clusters.

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Cluster analysis over data from passive air sampling was used in earlier studies25–27 to

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separate monitoring sites influenced by different pollution sources and characterize local

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fingerprints of groups of the sites. One of these studies25 also used primary data on pollution

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levels to construct a multidimensional space for the clustering.

190 191

Results and discussion

192

The positions of the 14 background sites within the Czech Republic, along with their SVOC

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levels, trend direction and statistical significance, are represented by colored dots in Figure 1.

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Six characteristic compounds are shown to demonstrate the situation for lighter (PCB 28) and

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heavier (PCB 180) PCBs, two main types of OCPs (γ-HCH and p,p’-DDE), and a lighter

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(fluorene) and heavier (chrysene) PAHs. Maps for all 17 compounds are provided in

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Figure S2 and individual SVOC levels, half-lives and rate constants of time trends are shown

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in a colored heatmap as Table S5.


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Figure 1: Sampling sites with different trends of SVOCs in ambient air. 14 background sites

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are shown as colored dots of different size with the diameter depending logarithmically on

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the pollution level at the site. Statistically significant decrease is represented by green,

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increase by red, and insignificant change by gray color. Maps created with data from

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http://www.diva-gis.org/Data and the ArcGIS ® software, v10.5, by ESRI, Environmental

204

System Research Institute (ESRI). ArcGIS® and ArcMap™ are the intellectual property of

205

Esri and are used herein under license.


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The maps in Figure 1 show the spatial coverage and differences in SVOC levels and trends,

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but do not facilitate a direct comparison of their magnitude. These are shown in time-

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concentration plots for individual sites in Figure 2 (six of the total number of 238 plots are

209

shown) and all sites in one plot in Figure 3. The same six compounds as in Figure 1 were

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chosen for Figures 2 and 3.

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Figure 2: Individual time series of selected combinations of sites and chemicals (PCBs,

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OCPs and PAHs). Insignificant exponential trends are drawn as gray lines, significantly


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decreasing trends as green lines, and significantly increasing trends as red lines. The shaded

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area represents the 95% confidence interval of the trend.

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Figure 3: Exponential regression of time series of selected PCBs, OCPs and PAHs.

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Insignificant trends are drawn as gray lines, significantly decreasing trends as green lines, and

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significantly increasing trends as red lines (the only significant increase was observed for

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chrysene). Letters indicate sites as in Table 2. The median trend computed by the method of


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median slope and median intercept is depicted as a thick darker line. Six representative

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compounds were selected, for others see Figure S3 in the Supporting Information.

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Exponential trends, median levels of concentrations and half-lives of the median trends

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computed for the 17 compounds by the method of median slope and median intercept are

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listed in Table 3. Interquartile ratios are shown to indicate the variance of the levels. More

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detailed values of SVOC levels and trends are provided in Table S5.

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Table 3: Median trends, half-lives, and median levels with their interquartile ratio.

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Statistically significantly decreasing trends are highlighted in green, increasing in red. compound

fluorene

overall exponential trend (∆T = years counted from 2004) [ng/PUF/28 days]

median annual increase level (2009) [%] [ng/ PUF/28 days]

interquartile ratio [–]

overall half-life [year]

9,58 × exp(-0.02 × ∆T)

-2.3

233.9

0.74

30.5

13.75 × exp(-0.04 × ∆T)

-4.0

651.1

0.78

17.2

6.78 × exp(-0.01 × ∆T)

-1.2

93.2

1.59

59.2

chrysene

3.33 × exp(0.06 × ∆T)

+6.4

7.86

2.05

-10.8

benzo(a)pyrene

1.05 × exp(0.03 × ∆T)

+3.4

0.77

0.93

-20.5

benzo(ghi)perylene

0.90 × exp(0.02 × ∆T)

+1.9

0.63

0.82

-37.3

α-HCH

0.77 × exp(-0.12 × ∆T)

-11.4

1.92

0.35

6.09

γ-HCH

1.54 × exp(-0.11 × ∆T)

-21.0

3.36

0.41

3.30

p,p‘-DDE

1.53 × exp(-0.02 × ∆T)

-1.9

3.27

1.20

36.3

pentachlorobenzene

0.88 × exp(-0.07 × ∆T)

-6.9

1.58

0.22

10.1

hexachlorobenzene

2.84 × exp(0.03 × ∆T)

+2.5

8.38

0.17

-27.5

PCB 28

0.65 × exp(-0.09 × ∆T)

-9.4

1.04

0.41

7.41

PCB 52

0.45 × exp(-0.15 × ∆T)

-15.2

0.85

0.45

4.56

PCB 101

0.42 × exp(-0.10 × ∆T)

-9.9

0.40

0.98

7.03

phenanthrene pyrene


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PCB 138

0.28 × exp(-0.15 × ∆T)

-14.6

0.26

0.69

4.74

PCB 153

0.40 × exp(-0.13 × ∆T)

-13.0

0.49

0.70

5.32

PCB 180

0.28 × exp(-0.09 × ∆T)

-9.0

0.15

0.93

7.69

228 229

PAHs. The range of PAH levels among all sites is rather high compared to the other

230

compounds, namely up to two orders of magnitude (IQR 0.73–2.05). The lowest level was

231

found for BGP at site C (0.383 ng/PUF/28 days), the highest for PHE at site P (1.251

232

ng/PUF/28 days).

233

The sites D, T, H and P showed the highest levels for most of the PAHs. While site D is

234

presumably affected by sources in Germany,28 the site P located on southern edge of the

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capital city of Prague could possibly be influenced by car traffic (total length of roads within

236

5 km diameter around the site is 999 km compared to median of 170 km over the whole set of

237

sites, for details see Table S3 (note that this site is still officially classified as a background

238

site and is not directly affected by local traffic emissions12). An explanation for increased

239

levels of PAHs for all the sites D, T, H and P is provided by an observed linear relationship

240

between the logarithm of population and the logarithm of lignite consumption which is the

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most common solid fuel used for domestic heating in the Czech Republic: data from 145

242

Czech municipalities provided an expected annual consumption of approx. 2,000 Mg/year of

243

lignite for the sites T and H (2,700 Mg/year for D and 7,300 Mg/year for P), see Table S4 and

244

text on p. S7 in the SI. This is a significantly higher amount than the median estimated

245

consumption of the remaining sites, which is approx. 1,000 Mg. For details see Table S4.

246

In contrast, the lowest levels of PAHs were sampled at site C in the Sumava mountains in

247

southern Bohemia and at sites J and S in the mountains in the northern part of the country.

248

These mountains are far from cities and industrial centers with large areas covered by

249

coniferous forests.


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The trend of PAHs pollution is in most cases insignificant, usually with negative estimated

251

half-lives, which means that both a concentration increase or decrease are possible. The only

252

significant increase was detected for chrysene at site E with a 10-years time series available.

253

The median trend calculated from all 14 sites reveals a statistically significant increase for

254

chrysene and decreases for fluorene and phenanthrene. For several PAHs the initial phase of

255

the time series reveals decreasing tendencies and changes to insignificant trends in the later

256

phase.

257

OCPs. In the cases of HCHs, PECB and HCB, the range of the levels is rather low with IQRs

258

between 0.17 and 0.41, which is less than one order of magnitude. For p,p’-DDE the spread

259

of the levels is intermediate with an IQR of 1.19, which is approximately one order of

260

magnitude. The highest levels of all 7 OCPs were detected in the same group of sites (D, H,

261

P, T) as for PAHs. High levels of p,p’-DDE were also found at site M. The lowest HCH

262

levels were found at mountainous sites such as C, L, S and X in the Sumava and northern

263

mountains.

264

At several sites, the half-lives of HCB and p,p’-DDE were detected as negative with

265

insignificantly increasing trends or as positive but with half-lives longer than 10 years. The

266

other OCPs were decreasing with different significance at all 14 sites, for γ-HCH significant

267

decreases were found at all 14 sites. Half-lives were between 2.3 and 11.5 years with the

268

exception of sites E, M and X with longer half-lives for PECB. This could be due to an

269

agricultural influence on sites E and M (63% (site E) and 67% (site M) of the area within 5

270

km diameter is agricultural land, compared to the median of 5% over all 14 sites, see Table

271

S3). The median trend showed a statistically significant decrease for both HCHs and PECB,

272

an insignificant trend for p,p’-DDE, and a significant increase for HCB.

273

PCBs. The range of PCBs is relatively low with IQRs between 0.41 and 0.97, which

274

corresponds to a variance of less than one order of magnitude. The highest levels of all 6


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PCBs were found at site D, which could be affected by long-range transport from industrial

276

areas of northern Germany28 (2.2 ng/PUF/28 days for PCB28) and P (2.4 ng/PUF/28 days for

277

PCB28) in the suburb of the capital, Prague. The lowest levels of all PCBs were detected at

278

the mountainous sites in the northern part of the country (J, L, S, X).

279

With the only exception of PCB101 at site M, all PCBs were decreasing and their median

280

half-lives were about 6 years. The highest median half-life was detected for PCB 180 (7.7

281

years), the lowest for PCB 52 (4.6 years). These findings are in good accordance with many

282

previously published results.14,16–18 All 6 PCBs decreased significantly at site T, most of them

283

decreased significantly also at site P.

284

Clustering

285

The K-means clustering method was used to identify clusters of similar sites based on the

286

levels and trends of all 17 chemicals at all 14 sites. Three characteristic clusters were

287

identified, representing three types of sites that are typical by their pollution levels and trends

288

of the SVOCs. The results of the clustering with a characterization of the clusters and their

289

differences in SVOC levels and trends are provided in Table 4.

290 291

Table 4: Clusters of similar sites identified by the K-mean clustering method.

Number Character of the of the cluster cluster

Sites within the cluster

OCPs

PCBs

PAHs

1 (red)

Rural sites with C, E, I, M, increasing levels R, Y (rural/ of PAHs agricultural)

Mostly moderate levels and slow, mainly insignificant decrease.

Moderate levels and slow, mainly insignificant decrease.

Low levels, (sometimes insignificantly) increasing.

2 (green)

Mountain sites with low levels of all SVOC types and decreasing

Low levels and insignificant decrease except of HCB.

Low levels and partially significant decrease.

Low and (mostly insignificantly) decreasing levels.

J, L, S, X (remote/ mountainous)


18

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levels of PAHs 3 (blue)

Sites with increased levels of all SVOC types

D, H, P, T (suburban/ rural)

Increased levels with different trends.

High and partially significantly decreasing levels.

High and rather stable levels.

292 293

In order to visualize the results of the K-mean clustering, a principal-component analysis

294

was conducted, which rotates the 34-d space and projects it into the plane of the 1st and 2nd

295

dimension, see Figure 4. In our case, these two dimensions explain in total 61% of the overall

296

variability of the dataset.

297

298 299

Figure 4: Clusters found in the 34-d space after rotation by PCA and projection onto the

300

plane spanned by the first two principal components. The three different clusters are depicted

301

by different colors. Similar sites are close to each other in the 34-d space, but not necessarily


19


Page 20 of 29

302

in reality (see map in Figure 5). The first principal component is depicted on the x-axis,

303

correlating positively with the levels of all three substance groups, PAHs, PCBs and OCPs,

304

and explaining 42% of the variability; the second principal component is depicted on the y-

305

axis, correlating negatively with PAHs decrease rate constants. It explains 19% of the

306

variability. An overall centroid site is depicted as the yellow dot in the middle of 34-d space.

307 308

Figure 5: Location of sites from the three different clusters within the Czech Republic. Sites

309

from the cluster with increased SVOC pollution levels are shown in blue (cluster 3),

310

northern-mountain sites with decreasing PAHs levels in green (cluster 2), and rural sites with

311

moderate levels of SVOCs and even increasing levels of PAHs in red (cluster 1). Map from

312

https://www.czso.cz/csu/czso/13-2108-06-v_letech_2000_az_2005-h000.

313

The first principal component explains 42% of the total variability and correlates positively

314

with most SVOC concentrations. In fact, the cluster with the highest levels of PAHs (cluster

315

3) reveals also the highest levels of OCPs and PCBs and vice versa (cluster 1). The second

316

principal component, explaining another 19% of the total variability between the sites, is

317

positively correlated with the decrease in (mainly the lighter) PAHs (higher positive values of


20

Page 21 of 29


318

PC2 means faster decrease, lower negative values of PC2 means faster increase). This means

319

PAHs are the most variable compounds between the sites, contributing mostly to the

320

differentiation of the clusters.

321

Sites D, H, P and T form the cluster with the highest pollution levels in OCPs, PCBs as

322

well as PAHs (cluster 3, blue). The remaining 10 sites are less polluted, with their main

323

difference in the PAHs half-lives. The cluster of sites J, L, S and X (cluster 2, green)

324

represents the least polluted sites with a clear and relatively fast decrease in PAHs levels

325

(half-lives around 10 years), compared to the cluster of sites C, E, I, M, R and Y (cluster 1,

326

red) with a slow increase in PAHs levels (median doubling time around 40 years).

327

The same coloring of the clusters is used in Figure 5, showing the distribution of the

328

clusters over the Czech Republic. The lowest levels of most SVOCs and decreasing PAH

329

trends are typical for the green cluster of sites in mountainous areas in the northern part of the

330

country (cluster 2). (The site Y (red, cluster 1) also located in that area has specific features

331

with lighter PAHs decreasing (FLU, PHE, PYR) and heavier ones increasing (CHRY, BAP,

332

BGP).) Considering a northwest-to-southeast prevailing wind direction, these sites are not

333

affected by PAH sources from the Czech Republic, but may be influenced by sources in

334

Germany and Poland.28 OCP and PCB levels at the sites of the green cluster are also the

335

lowest ones, which could be related to the high elevation of the sites within this cluster

336

(average elevation 840 m).

337

The blue cluster of sites with the highest PAH, OCP and PCB levels (cluster 3) is probably

338

affected by emissions from nearby settlements. These sites have the lowest elevation (550 m

339

on average) and lie close to densely populated areas (note that they are nevertheless officially

340

classified as background sites12). The levels of all SVOCs are relatively high and they change

341

only slowly with rather stable levels mainly for heavier PAHs.


21


Page 22 of 29

342

The red cluster (cluster 1) contains moderately polluted sites in mostly rural areas differing

343

from cluster 2 (green) mainly due to increasing levels of PAHs and slower decrease of OCPs.

344

Most of the sites are located in southern parts of the Czech Republic with possible influences

345

of agricultural areas.

346

If a representative set of monitoring sites is to be set up, it is necessary to have at least one

347

site from each of the three clusters. This means that three is also the lowest possible number

348

of sampling sites when one intends to optimize the number of samplers while capturing most

349

of the variability of the SVOC levels and temporal trends found for the Czech Republic.

350

Using the primary values in units of mass per PUF and 28 days encompasses several

351

sources of uncertainty but also avoids possible shortcomings of the conversion into air

352

concentrations14,15. The main sources of uncertainty are different environmental conditions

353

(mostly temperature) changing between both seasons and sites. The seasonal changes of the

354

sampling rates are mostly averaged out by the annual aggregation of the data and the trends

355

from the passive samplers agree very well with trends determined with active samplers.14

356

Regarding the spatial differences, we used the recalculation model by Harner29 in

357

combination with site-specific temperatures to estimate sampling rates for all 14 sites. Long-

358

term monthly temperature averages30 were used to calculate the sampling rates; this showed

359

that differences between the sites were for most of the compounds less than 12% (with the

360

exceptions of PCB 138, PCB 180 and BAP, for which the differences are still below 30%),

361

see the SI file Kalina_monthly_data.xlsx. These uncertainties are small compared to the

362

differences of measured SVOC levels, see interquartile ratios above.

363 364

Environmental significance

365

Based on differences in pollution levels and trends, a set of 14 passive air monitoring sites in

366

the Czech Republic was classified into three clusters. There is a cluster of sites with high and


22

Page 23 of 29


367

stable levels of pollution of all three groups of compounds and two clusters with lower levels

368

of SVOCs, differing in whether PAH levels are increasing or decreasing. For the cluster with

369

lowest SVOC levels a further decrease in PAH levels is typical whereas for the rest of the

370

sites PAHs are increasing.

371

Our results show that passive air sampling makes it possible to define clusters of

372

background sites with similar pollution profiles, which can help to optimize present

373

monitoring networks. In the case of the Czech Republic, three clusters of different type were

374

found (remote/mountain, rural/agricultural and sub-urban/rural), but the approach presented

375

here can be applied similarly to other passive sampling networks with long-term data series.

376

These data are currently being generated under the umbrella of the Global Monitoring Plan

377

(GMP) of the Stockholm Convention31. The networks included in the GMP cover areas on

378

larger (regional or continental) scales where other types of background sites (marine, polar,

379

remote) can also be included. A cluster analysis as presented here will reveal whether there

380

are several sites that are sufficiently similar so that they form a cluster and some of them may

381

be removed from the network or whether all sites present in the network are needed. In the

382

current discussion about the sustainability of sampling networks under the GMP (driven by

383

the costs of their long-term maintenance) it is highly important to develop strategies for

384

minimizing the sampling effort while the information gain from the networks is not

385

substantially reduced. The work presented in this study is a contribution to this process.

386 387

Acknowledgement

388

This work was supported by the Czech Ministry of Education, Youth and Sports (RECETOX

389

RI: LM2015051, ACTRIS_CZ RI: LM2015037) and the European Structural and Investment

390

Funds

(RECETOX

RI:

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


ACTRIS_CZ

-

RI:

23


Page 24 of 29

391

CZ.02.1.01/0.0/0.0/16_013/0001315, and CETOCOEN PLUS: CZ.02.1.01/0.0/0.0/15_003/

392

0000469).

393

Supporting Information

394

Average daily temperatures in Kosetice (Table S1); average daily temperatures in Kosetice

395

(Figure S1); list of sites suitable for trend estimation, their spatial coordinates, and sampling

396

periods (Table S2); characteristics of the sites including elevation, density of traffic networks,

397

expected PAHs emissions (Table S3); expected lignite consumption in the vicinity of the sites

398

(Table S4); sampling sites of different trends (Figure S2); exponential regression of time

399

series of selected PCBs, OCPs and PAHs (Figure S3); median SVOC levels, half-lives and

400

rate constants of SVOC time trends at individual sites (Table S5) (PDF).

401

Monthly averages of primary data from passive monitoring, estimated concentrations in air

402

and average sampling rates (XLS).

403 404

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