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Characterization of Natural and Affected Environments

Patterns of personal exposure to urban pollutants using personal passive samplers and GC×GC/ToF-MS Carlos A Manzano, Nathan Gray Dodder, Eunha Hoh, and Raul G. E. Morales Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b06220 • Publication Date (Web): 21 Dec 2018 Downloaded from http://pubs.acs.org on December 27, 2018

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Environmental Science & Technology

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Patterns of personal exposure to urban pollutants using personal passive samplers and

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GC×GC/ToF-MS

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Carlos A. Manzano,1,2, * Nathan G. Dodder,2,3 Eunha Hoh,2 Raul Morales1

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1Center

for Environmental Science, Faculty of Science, Universidad de Chile, Santiago, Chile

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2School

of Public Health, San Diego State University, San Diego, CA, USA

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3San

Diego State University Research Foundation, San Diego, CA, USA

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Abstract

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The performance of silicon wristband passive samplers (WB), combined with comprehensive two-

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dimensional gas-chromatography/time-of-flight mass-spectrometry (GC×GC/ToF-MS), for the analysis

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of urban derived pollutants in the personal environment was evaluated. Cumulative 5-day exposure

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samples from 27 individuals in areas with different geographical/socioeconomic characteristics within the

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Santiago Metropolitan Region (Chile) were collected during winter and summer (2016-2017). Samples

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were extracted without cleanup/fractionation and analyzed using targeted and nontargeted methods. The

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quantified semi-volatile organic compounds (SVOCs, n=33) (targeted analysis), and tentatively identified

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features (n=595-1,011) (nontargeted analysis) were classified according to their use/source. Seasonal

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differences were observed in the targeted analysis, while seasonal and spatial differences were observed

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in the nontargeted analysis. Higher concentrations of combustion products were observed in winter, while

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higher concentrations of consumer products were found in summer. Spatial differences were observed in

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hierarchical clustering analysis of the nontargeted data, with distinct clusters corresponding to specific

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sub-regions of the urban area. Results from this study provide spatial and seasonal distributions of urban

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pollutants within an urban area and establish the utility of linking WB with nontargeted analysis as a tool

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to identify and prioritize new exposures to urban contaminants at the local/community level.

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Keywords. Wristbands, nontargeted, urban pollutants, fingerprint, GC×GC/ToF-MS

ACS Paragon Plus Environment


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1. Introduction

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Urban air pollution, connected to rapidly rising urban populations, is a top environmental concern and

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has been recognized as a global challenge in the 21st century.1-3 It is well-known that residents of urban

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settlements are exposed to a complex mixture of pollutants of natural and synthetic origin.4-8 Pollutants

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originating from different sources, such as dietary or smoking habits, consumer products, and

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heating/cooking fuels and their combustion products may be part of this complex mixture. Although some

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urban derived pollutants have been associated with adverse health effects,9-11 there is little or no

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information about a potentially vast number of pollutants to which urban populations are exposed.

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Therefore, there is a need to derive a comprehensive description of urban pollutants at the local level (i.e.,

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within cities and communities).

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Most environmental research and urban monitoring efforts have historically used stationary active

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sampling methods to measure a limited number of pollutants believed to pose potential risk.12 Some

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advantages of using active sampling methods include the ability to provide reliable quantitative

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concentration data, identify high concentration peak episodes, and provide high temporal resolution.13-15

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Government-operated air quality monitoring networks have been built worldwide based on this

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approach.16-19 Active air sampling has been used in our study area (Santiago, Chile) since the 1990s.19

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However, estimating personal exposure using stationary active samplers may have limitations due to the

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proximity effect, in which contaminant sources near the subject could cause higher exposures that might

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not be captured by monitoring stations.20 Additionally, active monitoring networks represent mostly

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outdoor environment, thus indoor sources of pollutants (i.e., where people spend ~80-90% of their day21,

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22)

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tested to minimize these uncertainties and improve on exposure studies,23 but often they can be a burden

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for study participants because of pump noise, weight and equipment malfunction, in addition to their

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relatively high cost.24

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tend to be underrepresented. Personal active samplers (e.g., air monitoring backpacks) have been

Passive sampling technology represents an attractive alternative to active sampling, does not require external energy, and is generally low-cost.25 However, some disadvantages of using passive sampling


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techniques include a low temporal resolution (due of their lower sampling rate), unsuitability for short-

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term monitoring and difficulties with automation.13, 25 In passive sampling, chemicals are sequestered

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from the environment through passive diffusion into a receiving phase until equilibrium is reached.

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Passive samplers act over long periods of time to provide time-weighted average concentrations, they are

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resistant to accidental or extreme variations of concentrations and are suitable for long-term monitoring of

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complex mixtures.25-29 Recent studies have demonstrated the effectiveness of silicon wristbands (WB) as

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personal passive sampler devices to capture atmospheric semi-volatile organic compounds (SVOCs) with

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log octanol-air partition coefficient ranging from 2.1-13.7.30 Additionally, WB may capture pollutants

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through contact with skin, which may include pollutants from air, water, and personal care products, and

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thus could be more representative of the totality of the personal exposure to urban pollutants.31, 32 This is

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relevant as it has been estimated that SVOC uptake by skin absorption can even exceed uptake by

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inhalation.33 WB provide the potential for a comprehensive assessment of personal exposure by capturing

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multiple exposure pathways over a time period, which include workplace, indoor and outdoor exposures

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through inhalation and dermal absorption.34 WB have been used in studies ranging from 48 hour to one

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month exposure periods to measure pesticides,35-38 chemicals used in personal care products,36, 39 flame

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retardants,31, 40 and other organic pollutants in urban, rural, outdoor and indoor settings.41-44 Although

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personal exposures are likely composed of multiple classes of contaminants, with a large variability

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observed among subjects,45 most of these studies have focused on target analytes, which often includes

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extensive sample cleanup and fractionation.

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Comprehensive two-dimensional gas chromatography (GC×GC/ToF-MS) has gained wide

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implementation in the analysis of complex environmental samples since pioneered nearly 26 years ago.46

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The theoretical peak capacity of GC×GC has been determined as being an order of magnitude higher than

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conventional GC methods,47 and has been effectively used for the simultaneous analysis of multiple

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classes of chemicals in complex environmental matrices: snow samples, soil, sediments and biota.48-57

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In this study, the performance of low-cost WB samplers combined with GC×GC/ToF-MS with targeted

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and nontargeted approaches was evaluated for the measurement of urban derived pollutants in the



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personal environment during 5-days exposures. We tested two hypotheses: 1) WB passive samplers

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combined with multiple chemical analysis based on GC×GC/ToF-MS can be used as a tool to identify

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spatial and seasonal differences in exposure to urban contaminants in various sub-regions of the same

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metropolitan area, 2) That nontargeted analysis can detect exposure patterns unobserved by a 47-

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compound targeted-analysis alone. This work is a methodological advancement in the use of WB passive

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samplers to assess urban contaminant exposure. The results from this study provided preliminary spatial

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and seasonal distributions of urban contaminants in the Santiago Metropolitan Region and can be used for

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future prioritization efforts to characterize urban contaminants at the local level.

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2. Materials and Methods

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Study design. The protocols for recruitment of volunteers, collection, processing and analysis of samples

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were pre-approved by the Ethical Committee of the Faculty of Sciences of the University of Chile

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(approved and signed on 12/21/2015). Volunteers were recruited from three high school institutions of

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different residential areas of the Santiago Metropolitan Region located at >12 km from each other: Las

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Condes (sampling area ‘LC’: 33°25’28” S; 70°33’16” W), Maipu (sampling area ‘M’: 33°29’57” S;

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70°45’32” W) and Puente Alto (sampling area ‘PA’: 33°36’41” S; 70°34’14” W) (Figure 1). According to

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the latest Chilean National Population Census, sampling area LC is mainly inhabited by upper-middle to

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high-income families (projected 2018 population of 288,228 people), sampling area M is mainly

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associated with middle to upper-middle income families (projected 2018 population of 563,449), whereas

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sampling area PA is mostly inhabited by low-middle to middle income families (projected 2018

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population of 633,021).58 The selected residential areas have been reported to be affected differently by

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urban expansion over the last 30 years. Land use studies have demonstrated that agricultural areas have

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experienced high losses mainly due to new urban areas (i.e., housing and roads). However, the spread of

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these new urban areas was not evenly distributed, as it was limited by the Andes mountain range to the

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east. Therefore, the southern areas of the city experienced generalized urban expansion (sampling area M


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and PA), compared to the northern areas (sampling area LC).59 These new developments have produced

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segregated communities with poor connectivity and detrimental environmental and socio-economic

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impacts.60

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At least five volunteers were recruited from each sampling area at the start of the study. The criteria for

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participation were that the volunteer must travel 100 based on peak abundance (Table S4). The results were further processed using the statistical

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compare add-on within ChromaTOF. Statistical compare is a post-data processing software package used

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for the analysis of complex GC×GC chromatograms. It facilitates data mining and performs analyte

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alignment based on retention time and mass spectral matches to make comparisons between groups of

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samples.61 The WB extracts were divided into 7 groups according to their sampling location (i.e.,

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sampling areas M, PA and LC during winter and summer, and blanks), and statistical compare parameters

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were set conservatively to search for features with S/N>100 and present in 100% of WB in each group

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(i.e., sampling area/season), therefore reducing personal variability (Table S4). The peak areas of the

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resulting features were normalized to that of an internal standard (d12-chrysene) spiked before extraction,

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and those showing ratios below 50:1 with respect to the average normalized peak area of the blanks were

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manually excluded. Tentative peak identifications were assigned based on mass spectral matching to the

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NIST 2011 MS library v2.0, with a minimum required similarity match of 500 (50%). Features below

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50% similarity were reported as ‘unknown’. Note that the main objective was to screen for features

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unique to each group of samples using tentative identifications with ≥ 50% similarity scores; and not to

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confirm chemical identities, in which case a higher similarity score threshold would have been

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appropriate.

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Hierarchical clustering analysis on the log-transformed full data set (i.e., log10([abundance]+1)) with

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Ward variance minimization was used for data interpretation and to identify closely related samples

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without forcing associations between samples or analytes. Heatmaps were generated using Python’s

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seaborn package v0.9.0.62

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3. Results and Discussion


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Thirty WBs (n=30, worn by 30 people) were initially deployed. However, not all participants completed

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the exposure study. One WB was lost, and another participant withdrew from the study, both from

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sampling area LC, leaving it with only 3 WBs in winter and summer. Conversely, one extra WB (6 total)

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was deployed in sampling area PA during the summer at the request of the participants. The participants

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were composed of 15 women (56%) and 12 men (44%), ranging from 15-17 years old. The participant’s

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evaluation of the experiment (on a scale from 1 to 10, with 10 being “very comfortable”) was 8.5±1.5

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(Figure S1), which shows the participant’s willingness to wear WB samplers. All participants in this study

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were high school students, therefore reducing variation due to occupational settings, and spent ~33% of

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their time in school (given that the average number of hours in the school day in Chile is 8 hours). The

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average commute reported was 3.6±2 min (50:1 with respect to the average of the blanks were included.

Combustion Products

Consumer Products

Industrial Products

Plasticizers

Natural Products

Others

Unknowns

Total

MW PAW LCW

115 105 148

180 170 229

121 125 169

23 17 23

80 71 102

130 139 197

74 56 143

723 683 1011

Summer MS PAS LCS

83 94 119

153 169 229

120 105 165

24 16 26

73 65 102

110 107 168

58 39 100

621 595 909

Sampling site

Winter


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Figure Captions.

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Figure 1. Sampling area location in the Santiago Metropolitan Region. Sampling area M: ‘Maipu’ is

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located to the southwest, while sampling areas PA: ‘Puente Alto’ and LC: ‘Las Condes’ are located to the

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eastern edge at higher altitude (~600 m above sea level).

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Figure 2. SVOCs quantified on WB after the 5-day exposure periods during the winter and summer

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seasons. SVOCs were grouped in the x-axis according their use. The color scale corresponds to

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concentrations measured in ng/g WB. Winter and summer samples are identified in the y-axis

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Figure 3. Winter and summer averaged concentrations (log10 ng/g WB) of SVOCs on WB (n=13 for

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winter, n=14 for summer) after the 5-day of exposure periods. Dashed lines separate groups of

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compounds according to their use, and error bars show the 95% confidence interval of the mean.

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Statistically significant seasonal differences are shown with an asterisk (*).

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Figure 4. Hierarchical clustering analysis of normalized peak areas of chromatographic features present

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in 100% of the WB in each sampling area, and with abundances >50:1 compared to the average in the

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blanks. Clusters are represented by colored panels in the y-axis and were assigned by selecting the 7

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highest level dendrogram nodes.

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Figure 1


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Figure 2.



Figure 3.


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Figure 4.



For TOC only


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Patterns of Personal Exposure to Urban Pollutants ... - ACS Publications

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