Science of the Total Environment 835 (2022) 155277
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Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
Non-targeted screening of volatile organic compounds in a museum in China
Using GC-Orbitrap mass spectrometry
Li Ding a,1, Luyang Wang b,c,1, Luying Nian b,c,1, Ming Tang a, Rui Yuan b,c, Anmei Shi a, Meng Shi b,c, Ying Han a,
⁎
Min Liu a, Yinping Zhang b,c, Ying Xu b,c,d,
a
National Museum of China, Beijing, China
Department of Building Science, Tsinghua University, Beijing, China
Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Beijing, China
d
Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, TX, USA
b
c
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• Non-targeted analysis (NTA) with highresolution mass spectrometry was used in
identifying volatile organic compounds
(VOCs) in a museum.
• Approximately 230 VOCs were detected,
half of which were observed at 100% frequency and found to be related to the materials and chemicals used in the museum.
• Compared with compounds in outdoor
air, indoor VOCs had a lower level of
unsaturation and more portions of chemically reduced compounds.
• Sampling adsorbents chosen may have a
large impact on results, and a single type
of adsorbent may not be enough to cover
a wide range of compounds in NTA
studies.
• Chamber emission tests suggested that
decorative materials may have been one
of the main sources of indoor VOCs in
the museum.
A R T I C L E
I N F O
Editor: Philip K. Hopke
Keywords:
Non-target screening with high-resolution mass
spectrometry
Volatile organic compounds (VOCs)
Museum environment
Air pollutants
Cultural heritage conservation
A B S T R A C T
Non-targeted analysis (NTA) was used in identifying volatile organic compounds (VOCs) in a museum in China with
the gas chromatograph (GC)−Orbitrap−mass spectrometer (MS). Approximately 230 VOCs were detected, of
which 117 were observed at 100% frequency across all sampling sites. Although some were common in indoor
environments, most of the detected VOCs were rarely reported in previous studies on museum environments. Some
of the detected VOCs were found to be associated with the materials used in furnishings and the chemicals applied
in conservation treatment. Spearman's correlation analysis showed that several classes of VOCs were well correlated,
suggesting their common sources. Compared with compounds in outdoor air, indoor VOCs had a lower level of
unsaturation and more portions of chemically reduced compounds. Hierarchical cluster analysis (HCA) were
performed. The results suggested that the sampling adsorbents chosen may have a large impact and that a single
type of adsorbent may not be sufficient to cover a wide range of compounds in NTA studies. The MonoTrap adsorbent
⁎ Corresponding author at: Department of Building Science, Tsinghua University, Beijing 100084, China.
E-mail address: xu-ying@mail.tsinghua.edu.cn (Y. Xu).
1
Li Ding, Luyang Wang, and Luying Nian contributed equally to this work as first authors.
http://dx.doi.org/10.1016/j.scitotenv.2022.155277
Received 28 January 2022; Received in revised form 8 April 2022; Accepted 10 April 2022
Available online 18 April 2022
0048-9697/© 2022 Elsevier B.V. All rights reserved.
L. Ding et al.
Science of the Total Environment 835 (2022) 155277
containing octadecylsilane (ODS) and activated carbon (AC) is suitable for aliphatic polar compounds that contain low
levels of oxygen, whereas the MonoTrap ODS and silica gel are good at sampling aliphatic and aromatic hydrocarbons
with limited polarity. Principle component analysis (PCA) showed that the indoor VOCs changed significantly at different times in the museum; this may have been caused by the removal of artifacts and refurbishment of the gallery
between sampling events. A comparison with compounds identified by chamber emission tests showed that decorative
materials may have been one of the main sources of indoor VOCs in the museum. The VOCs identified in the present
study are likely to be present in other similar museums; therefore, further examination may be warranted of their potential impacts on cultural heritage artifacts, museum personnel, and visitors.
1. Introduction
2000; Ryhl-Svendsen and Glastrup, 2002; Sánchez et al., 2020; Srivastava
et al., 2000; Tétreault, 1994). Although knowledge on the occurrence of
these VOCs in the museum environment was gained, this form of analysis
did not lend itself to conducting a comprehensive and systematic assessment of the indoor air quality in museums. The method only allowed the
detection of a limited number of compounds and ignored others, especially
unknown or emerging VOCs. Therefore, a better understanding of the composition of indoor VOCs is needed for museums to evaluate the potential
risks that indoor air poses to their collections. Since the large number of indoor VOCs include a wide range of chemicals with respect to molecular
mass, functional group distribution, and polarity at trace levels, characterization of them is challenging.
Recent developments in high-resolution mass spectrometry (HRMS)
coupled with gas or liquid chromatography have initiated new possibilities
for detecting contaminants without preselection of analytes (Bader et al.,
2016; Krauss et al., 2010). The HRMS instruments, such as Orbitrap, quadrupole time-of-flight (Q-TOF), and Fourier-transform ion cyclotron resonance (FTICR), provide high mass accuracy and resolving power to
facilitate chemical identification and the determination of trace substances
in complex matrices (Gago-Ferrero et al., 2015; Hug et al., 2014;
Schymanski et al., 2014; Schymanski et al., 2015; Wang et al., 2018).
Non-targeted analysis (NTA) with HRMS is a relatively new method that
involves the detection of analytes without a priori information (Ulrich
et al., 2018). It allows rapid characterization of hundreds to thousands of
compounds in a given sample (Sobus et al., 2018). However, compared
with the field of life sciences, development and application of NTA in the
environmental field is still in its initial stage (Ulrich et al., 2018). Although
recent NTA studies reported the presence of previously unknown contaminants in various environmental compartments, such as water, soil, aerosols,
and sediments (Carpenter and Helbling, 2018; Gago-Ferrero et al., 2015;
Getzinger et al., 2015; Hollender et al., 2017; Meng et al., 2020; Moschet
et al., 2018; Newton et al., 2017; Ouyang et al., 2017; Peng et al., 2015;
Rostkowski et al., 2019; Schymanski et al., 2014; Tian et al., 2017; Tian
et al., 2020; Wang et al., 2018; Willoughby et al., 2014; Xiao et al., 2017;
Yu et al., 2018), the research on indoor environments is very limited. Recently, NTA and multivariate data analysis were conducted to investigate
the associations between VOC pollutants in indoor environments and
sick-building syndrome (Veenaas et al., 2020a; Veenaas et al., 2020b).
NTA screening was also applied in household dust samples to identify
new chemicals, especially semi-volatile organic compounds (SVOCs),
which might have been applied to consumer products, released into indoor
environments, and accumulated in dust (Castro et al., 2019; Christia et al.,
2021).
This study is part of a research project focusing on indoor pollutants in
the museum environment. The objectives of the present paper are to
(1) apply NTA screening to identify the tentative composition of VOC pollutants in indoor air samples collected from a museum in Beijing; (2) compare the molecular nature of the VOC elements in the museum with those in
outdoor air; (3) investigate the influences of sampling media and sampling
sites and time on the NTA results; and (4) examine the potential relationship between indoor VOCs and the display or decorative materials used in
the exhibition hall. This study is the first of its kind to apply a broad, holistic
NTA screening approach using the GC-Orbitrap MS to characterize indoor
VOCs in the museum environment. It allows scientists and conservators to
make direct use of the results to prioritize VOC contaminants that may
have impacts on cultural heritage artifacts, museum personnel, and visitors.
Museums have been used for centuries to preserve a wide variety of
artifacts of historical significance so they can be seen by visitors. The
environmental conditions in museums, such as ventilation, temperature,
humidity, light, and pollution, are therefore essential for the protection
and conservation of ancient and historical artifacts. When the conditions
are not controlled properly, damage or small changes to the collections
can be observed over a period of time and can accelerate their degradation
and shorten their lifetime (ASHRAE, 2011; Pavlogeorgatos, 2003; SharifAskari and Abu-Hijleh, 2018; Silva and Henriques, 2014). In the past, temperature, light, and water vapor have been the main environmental concerns in museums (Ferdyn-Grygierek, 2016; Marchetti et al., 2017;
Schieweck et al., 2005). Air pollutants and their effects on cultural heritage
artifacts have received increased interests in recent decades. The focus of
preventive conservation has been shifting toward indoor air pollution in
museums, with the aim of protecting cultural assets against deterioration
and providing a healthy indoor environment for museum personnel and visitors (Fenech et al., 2010; Schieweck et al., 2005).
Previous research on indoor air pollutants in museums and their effects
focused predominantly on pollutants that are generated outdoors and subsequently infiltrated indoors, such as nitrogen oxides, sulfur dioxide, hydrogen sulfide, and ozone (Ankersmit et al., 2005; Brimblecombe et al., 1992;
Hackney, 1984; Ligterink and Di Pietro, 2018; Schieweck et al., 2005).
However, there are many air pollutants that are generated indoors, of
which volatile organic compounds (VOCs) are one of the largest groups.
VOCs are organic chemicals that have a relatively high vapor pressure at
room temperature. Their primary sources are numerous, including building
materials such as wood, coatings and lacquers, plastics, textiles, rubbers,
sealants, and adhesives; cleaning products; insecticides; and cosmetics
used by visitors (Proietti et al., 2014; Samide and Smith, 2015; Sánchez
et al., 2020; Uhde and Salthammer, 2007). The artifacts may themselves
act as emission sources by releasing VOCs due to their composition or the
use of conservation and restoration products (Schieweck et al., 2005). In
addition, a variety of VOCs can be formed due to material degradations
or secondary reactions (Dupont et al., 2007; Lattuati-Derieux et al., 2006;
Ramalho et al., 2009; Schieweck, 2020; Schieweck and Salthammer,
2011). When ventilation in the museum environment is not sufficient to remove them, VOCs may accumulate within a short period of time. The
problem is likely to get even worse in display cabinets and storage areas,
where the low air exchange rate and static conditions may cause the VOC
concentration to reach the level of saturation (Grzywacz and Tennent,
1994; López-Aparicio et al., 2010; Schieweck, 2020; Schieweck and
Salthammer, 2011). However, VOCs may damage the artifacts even at
low concentrations, causing surface efflorescence on carbonate materials,
fading and degradation of pigments, depolymerization of organic-based objects such as cellulose, leather, and varnish, embrittlement, and corrosion of
metal (Bonaduce et al., 2013; Chiantore et al., 2018; Dupont and Tetreault,
2000; Hatchfield, 2002; López-Aparicio and Grašienė, 2013; Maskova et al.,
2017; Odlyha et al., 2012; Proietti et al., 2014; Ryhl-Svendsen, 2008;
Samide and Smith, 2015; Tennent et al., 1993; Tétreault et al., 2013).
Most of the previous studies primarily employed targeted analysis with
a focus on preselected VOCs, especially formaldehyde, acetaldehyde, and
organic acids (e.g., formic acid and acetic acid) (Gibson et al., 1997;
Gibson et al., 2008; Grøntoft and Marincas, 2018; Grzywacz, 2006;
Hatchfield, 2002; Pagonis et al., 2019; Raychaudhuri and Brimblecombe,
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Science of the Total Environment 835 (2022) 155277
commonly used for organic compounds with low polarity. As in field measurements, duplicate sampling was performed by drawing the chamber air
through the sorbent tubes at a flow rate of 200 mL min−1 for approximately
20 min.
The samples collected by different samplers were prepared using the
solvent extraction method. The adsorbent materials of each sampler were
placed into a centrifuge tube (United Scientific Supplies, USA). Dichloromethane (~1 mL), which has a high extraction efficiency for a wide
range of non-polar to polar compounds, was added to the centrifuge tube
containing the MonoTrap DSC18 disks or AC particles, and the polar solvent, methanol (~1 mL), was added to the tube containing the MonoTrap
DCC18 disks or silica gels. In addition, 1,4‑dichlorobenzene‑d4 (10 μL,
100 ng/ μL) was added to each sample as the internal standard. The samples
were then ultrasonically extracted for 15 min with the solvents. Extractions
were conducted in an ultrasonicator filled with clean water and ice bags to
maintain a low temperature and avoid the loss of analytes through evaporation. The extracts were then centrifuged at 12,000 rpm for 20 min at a temperature of 4 ± 0.5 °C. The supernatants were finally transferred into
amber glass vials for further analysis.
2. Materials and methods
2.1. Chemicals
Methanol, methylene chloride, and dichloromethane (ultrapure, spectroscopy grade, >99.9%) were supplied by Merck (Germany). C8-C40 alkanes and 1,4‑dichlorobenzene‑d4 standard solutions were purchased
from Accustandards Inc. (USA).
2.2. Sampling site
The sampled museum is located in the center of Beijing. It covers a land
area of 70,000 m2 and houses more than 1 million items, including ancient
and modern artifacts, rare and antiquarian books, and works of art. The
heating, ventilation, and air conditioning (HVAC) system is operated
throughout the whole building. The temperature, relative humidity, and
air change rate are strictly controlled at 22 ± 2 °C, 50 ± 5%, and 1 ±
0.1 h−1, respectively. Fig. S1 (Table and Figure numbers preceded by an
“S” are in the Supplementary Materials) shows the six different sampling locations selected in a gallery (15,000 m3) in which a temporary exhibition of
selected artifacts made of ceramics, metal, paper, textiles, and wood products was displayed. A one-week sampling event was carried out in June
2019 to collect indoor air samples. After the exhibition, a second sampling
event was conducted at the end of July 2019, when the artifacts were removed and the gallery was under renovation.
2.4. Chemical analysis with HRMS
A Thermo Scientific TRACE™ 1310 gas chromatograph (GC) coupled to
a Exactive Orbitrap™ mass spectrometer (MS) (Thermo Scientific, Bremen,
Germany) was used for sample analysis. A injection (1 μL) was performed at
280 °C with a split ratio of 1:20. Helium (99.999% purity) was used as the
carrier gas and the flow rate was set at 1.2 mL min−1. GC separation was
performed on a DB-WAX column (30 m × 0.25 mm ID, 0.25 μm film thickness, Agilent J&W) using the following temperature program: 30 °C (held
for 5 min), 10 °C/min to 90 °C (held for 5 min), and finally 10 °C/min to
240 °C (remained for 5 min).
EI was performed at 70 eV with the source and transfer line temperatures at 230 °C and 280 °C, respectively. Full scan MS acquisition mode
was applied with a m/z range of 35–400, a resolving power of 60,000 at
m/z 200 and the automatic gain control target set at 3 × 106. The maximum ion injection time was set to “AUTO.” Mass calibration was performed
before each sequence using perfluorotributylamine. During the analysis, internal mass calibration was performed by the instrument using three background ions that originated from the column bleed as lock mass
+
+
(C5H15O3Si+
3 , 207.03236; C7H21O4Si4 , 281.05115; and C9H27O5Si5 ,
355.06994) with a search window of ±2 ppm. A C8-C40 alkane series
was used for external non-isothermal retention index (RI).
2.3. Sample collection and preparation
In NTA studies, in order to cover a wide range of potential contaminants, sampling and extraction should be as non-selective as possible.
Therefore, different adsorbents and sampling methods were used.
MonoTrap DCC 18 disks (10 mm × 1 mm thickness) containing
octadecylsilane (ODS) and activated carbon (AC) and MonoTrap DSC 18
disks (10 mm × 1 mm thickness) containing only ODS were both used
for passive sampling (GL Science, Japan). According to the manufacturer
(GL Sciences, 2014), MonoTrap DCC 18 disks outperform MonoTrap DSC
18 disks in capturing polar compounds. Prior to use, the disks were cleaned
with methylene chloride and conditioned in an oven at 100 °C for 30 min to
remove any contaminants. A stainless-steel holder was then inserted into
each disk through its center hole, and the assembly was installed in a passive sampler tube (Du et al., 2013). Duplicate passive samplers were placed
at each sampling location for approximately one week. Additionally, active
sampling of indoor VOCs was also conducted using silica gel sorbent tubes
(GASTEC, Japan) which are commonly used to sample polar VOCs. Immediately before sampling, both ends of the sorbent tube were opened with a
tube tip breaker tool. The sorbent tube was then connected to a calibrated
pump with polytetrafluoroethylene (PTFE) tubing and placed into a holder.
Duplicate air sampling was conducted at a flow rate of 200 mL min−1 for
approximately 80 min at each sampling location. After sampling, both
ends of the tube were sealed with PTFE caps to avoid contamination.
Upon completion of sampling, passive or active samplers were wrapped
with aluminum foil, sealed in a zip bag, stored in a freezer at a temperature
of −80 °C, and analyzed within two days of sampling.
Chamber tests were conducted in the laboratory to identify VOCs that
were emitted from decorative materials. The test materials were provided
by the museum, which were exactly the same as those used in the exhibition area. The six types of materials studied were fabric, particle board, carpet, instant adhesive powder, medium-density fiberboard, and water-based
paint. For testing, the material was placed at the bottom of a 30-L stainlesssteel chamber in an area of approximately 0.02 m2. The chamber was operated at a temperature of 22 ± 0.5 °C and relative humidity of 50 ± 2%. The
system was first conditioned for three days, and the chamber inlets, outlets,
and gaskets were then securely closed to accumulate VOCs inside the chamber. After approximately a week, the air supply was switched on for active
sampling of the chamber air at the outlets. Both AC and silica gel sorbent
tubes were used for air sampling. Unlike silica gel, AC sorbent tubes are
2.5. HRMS data processing
The acquired EI-MS datafiles were processed using TraceFinder 5.1 software (Thermo Scientific, Bremen, Germany). Each datafile was
deconvoluted to extract individual peaks from the total ion chromatogram
(TIC) using the Deconvolution Plugin application within TraceFinder 5.1.
Peak deconvolution was processed with “all ions” option, a mass tolerance
of ±5 ppm, a signal to noise threshold of 3, a minimum TIC intensity signal
of 1 × 105, a smoothing level of 7, and an ion overlap window of 98%.
Quality of deconvoluted spectra was then manually inspected regarding
peak sharpness, cleanness, and symmetry as well as fragment patterns,
and unsatisfactory spectra were filtered out.
Identification was performed against the NIST 17 library and the
vendor-supplied GC/Orbitrap™ contaminants library. The NIST library contains EI spectra for over 260,000 compounds and the vendor-supplied library contains high-resolution spectra for over 700 compounds. The list
of tentative hits returned for each peak is scored based on a combination
of a classical search index (SI) score, high-resolution filtering (HRF)
value, and RI value, which are described in detail in the Supplementary Materials. A minimum setting of the combined score was established at 90. The
molecular formula with the highest score was selected for each peak and
the isomers were recorded. The spectrum matches were also inspected
manually. In case where there were multiple candidates with the same
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Science of the Total Environment 835 (2022) 155277
introduced by Kroll et al. (2011), is another metric used to describe the degree of oxidation of organic species. It can be obtained from Eq. (3):
highest score, the most plausible formula was selected, taking into account
of the chemical structure and the number of SciFinder® hits (https://
scifinder-n.cas.org/) that indicates the prevalence of the compound in the
literature (Rager et al., 2016). The peak abundance of each identified compound was corrected using internal standard (i.e., 1,4‑dichlorobenzene‑d4)
and blanks to reduce the impacts of matrix effect and sample preparation.
OSC ≈ 2O=C H=C
When mean values are needed for DBE, XC, OSC as well as molecular
weight (MW), and H/C and O/C ratios, weighted average values were calculated based on the number of chemical species. Considering that different
chemical species have different signal responses in the MS, the response
abundance may not represent the concentration of chemical species. Therefore, the number of chemical species was used instead of the abundance to
calculate the weighted average values. Finally, the polarity of compounds
was categorized using aA + bB (Baskaran et al., 2021), where A and B
are the Abraham descriptors for hydrogen bonding acidity and basicity of
a compound, and a and b are the respective system constants from the
poly-parameter linear free energy equation for octanol-air partitioning
(Endo and Goss, 2014). The Abraham descriptors were obtained using the
UFZ-LSER Database (Ulrich et al., 2018) or the IFSQSAR model (Brown,
2014; Brown et al., 2012), which is available on GitHub (https://github.
com/tnbrowncontam/ifsqsar), and the system constants were obtained
from Abraham et al. (2010).
2.6. Data analysis
Hierarchical cluster analysis (HCA) was performed to examine the similarities of and differences in sampling sites, events, and adsorbents used,
and the abundance data were log-transformed for the analysis. The hierarchical agglomerative clustering using Ward's method was carried out, in
which the similarity between two objects is calculated using the maximum
distance. Principal component analysis (PCA) is an unsupervised exploratory technique for multivariate analysis, which is widely used for visualization of high-dimensional datasets as well as for data size reduction,
because the data can be represented as a limited number of principal components (PCs). Therefore, PCA was also applied to investigate the temporal
and spatial variations of indoor VOCs in the museum. A Shapiro-Wilk test of
normality indicated that the abundances of identified compounds were not
normally distributed. Therefore, Spearman's rank-order correlation analysis, which is a nonparametric method and has been widely used (Mazur
et al., 2021; Zhang et al., 2020), was performed to investigate the relationships between detected VOCs. To reduce the number of false positives for
multiple comparisons, Bonferroni correction to the significance level (α)
was applied to the Spearman's rank-order correlation analysis. The statistical analyses were performed using R (version 4.1.1) and OriginPro 2021
software.
The specific molecular nature of indoor air directly influences the impact they may have on the artifacts and the museum environment. Therefore, several metrics describing the chemical characteristics of VOCs in
indoor air were applied. It is recognized that the extent of unsaturation of
a molecule, mainly associated with the low H/C ratio (i.e., double, triple
bonds and aromatic structures), is an important structural feature and a
measure of its potential reactivity (Yassine et al., 2014). Therefore,
double-bond equivalent (DBE) that represents the number of double
bonds and rings in a molecule was used to assess the degree of unsaturation.
It can be expressed as:
1
DBE ¼ 1 þ ð2C H X þ N þ PÞ
2
2.7. Quality assurance and quality control
Duplicate samples were collected at all sampling sites during each sampling event. The discrepancy of chemical species between duplicate samples was less than 15%. Field and laboratory blanks were analyzed along
with the samples using the same pretreatment and chemical analysis
methods, and the features detected in the blanks were subtracted automatically by the TraceFinder software during data analysis. Methanol blanks
were run intermittently to reduce potential carryover and served as solvent
blanks. Because the NTA approach collects all accurate masses in a given
sample, the blanks provided an indication of potential background contamination in the analytical method. An internal standard was spiked into each
sample to normalize each run and estimate the tolerance of mass accuracy
and retention time drift. Furthermore, to cover a wide polarity range of
VOCs and reduce false negatives (type II errors), different sampling adsorbents and extraction solvents were used. In addition, the GC-Orbitrap-MS
instrument was automatically recalibrated during analysis by continuous
infusion into the source of the calibration solution via a calibration delivery
+
system. Three characteristic ions (C5H15O3Si+
3 , 207.03236; C7H21O4Si4 ,
+
281.05115; and C9H27O5Si5 , 355.06994) were monitored. To avoid contamination, all glassware was ultrasonically cleaned in hexane for 20 min
and conditioned at 300 °C prior to use.
(1)
where C, H, X, N, and P represent the number of carbon, hydrogen, halogen,
nitrogen, and phosphorus atoms, respectively. However, since DBE may not
accurately indicate the level of unsaturation for compounds with heteroatoms (e.g., O, N, and S), the aromaticity equivalent (XC) was also calculated using the following equation:
3½DBE ðmO þ nSÞ 2
XC ¼
DBE ðmO þ nSÞ
(3)
3. Results and discussion
3.1. Non-targeted screening of VOCs
The deconvolution of the GC-HRMS chromatograms produced 3000 to
5000 features per sample. After manual inspections for peak sharpness,
cleanness, and symmetry, as well as fragment patterns, roughly 80 of
these compounds per sample had a hit with the National Institute of
Standards and Technology (NIST) library or vendor-supplied library
(i.e., overall score ≥ 90 points). An example compound, phenol (CAS no.
108-95-2), is shown in Fig. 1a. The perfect deconvolution is indicated by
the coelution plot of the main fragments and an overall score of 95 points
(out of 99 points). The good match with the hit in the NIST library (SI
score of 855 out of 999 and HRF score of 94.8 out of 99) is shown by the
spectrum plot. In addition, the experimentally derived retention index
(RI) in the NIST library with a value of 2000 perfectly matched the
measured RI of 1992 in this study. This compound was detected in all air
samples.
The number of VOCs detected at different sampling sites during the two
sampling events is shown in Fig. 1b. Duplicate sampling was conducted,
and the number of different chemical species found in duplicate samples
(2)
where O and S represent the number of oxygen and sulfur atoms, respectively; and m and n correspond to the fraction of oxygen and sulfur atoms
involved in π-bond structure of a compound, respectively. The values of
m and n vary depending on the compound. We used m = n = 1 for conservative calculation of the XC, assuming every oxygen and sulfur atom was
considered as π-bond structure (Wang et al., 2018). If DBE ≤ mO + nS
or XC ≤ 0, then XC was defined as zero. Threshold values for XC were proposed in the literature (Wang et al., 2018; Yassine et al., 2014), where XC ≥
2.5 and XC ≥ 2.7 are the unambiguous minimum criterion for the presence
of aromatic and condensed aromatic structures in a molecule, respectively.
Use of XC value may help to improve the identification of aromatic compounds in indoor air. In addition, the carbon oxidation state (OSC),
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Fig. 1. (a) Example of phenol (CAS no. 108-95-2) detected by GC-Orbitrap-HRMS with the search index (SI) score, high-resolution filtering (HRF) value, retention index (RI)
value and overall score. (b) Number of VOCs detected at different sampling sites during two sampling events. Error bars represent the compound differences of duplicate
sampling.
(HPV). Approximately 29 of the listed VOCs were HPV chemicals and are
shown as such in the table. In addition, a SciFinder search was performed
to determine whether compounds detected in the current study had been
previously examined in indoor environments. The number of journal references generated by querying each chemical's CAS number and the term
“indoor” was recorded as a SciFinder® hit. A literature query found that
63 of the listed chemicals had been associated with an indoor environment
was within a range of 15%, indicating that the HRMS data-processing
method was consistent and effective. Similar amounts of chemicals were
collected using the three adsorbents. In the second sampling event,
MonoTrap disks containing AC and ODS and silica gel tubes detected a
slightly higher number of compounds than the MonoTrap disks containing
only ODS. During each sampling event, the total number of chemicals detected at various sampling sites was similar. However, a greater difference
was observed between sampling events, with more compounds identified
in sampling event II (224 ± 5) than in event I (211 ± 4).
3.2. VOC composition
As shown in Fig. 2, most of the detected compounds were alkanes/
-enes/-ynes, arenes, esters, heteroatoms, and naphthalenes, followed
by alcohols, ethers, ketones, aldehydes, phenols, and acids. The number
distribution of detected VOCs based on chemical classes was similar
between the two sampling events. Sample names corresponding to
each bar were listed in Fig. S2.
A total of 117 VOC compounds that had a detection frequency of 100%
across all sampling sites in both sampling events are listed in Table S1. The
table includes their general information, such as chemical formula, retention time, molecular weight, Chemical Abstracts Service (CAS) number,
chemical class, and chemical name based on the International Union of
Pure and Applied Chemistry (IUPAC) nomenclature. According to the U.S.
Environmental Protection Agency, chemicals that are manufactured in or
imported into the United States in amounts equal to or greater than 1 million pounds per year are considered to have a high production volume
Fig. 2. VOC compounds detected in each sample and the number distribution by
chemical class. Each bar on the y-axis represents a sample collected by each
sampling method at a specific sampling site.
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conservative and may greatly reduce the power to detect true positives
(Korthauer et al., 2019), many complex relationships between different
VOCs were still observed. The results are listed in Tables S2-S4, and the matrix correlation plots are shown in Fig. 4. The results showed that multiple
alkanes/enes/ynes, arenes, heteroatoms, and naphthalenes correlated well
between themselves within each group, with Spearman coefficients (r) in
the range of 0.70 to 0.98, indicating common sources for many of them. Although data from the literature are sparse, similar co-occurrences of alkanes
and arenes were also observed in previous studies in the museum environment (Cartechini et al., 2015; Lopez-Aparicio et al., 2010; Pagonis et al.,
2019). The co-occurrence of naphthalene compounds may be attributed
to their use as biocides or pesticides in the museum at different time periods
(Schieweck et al., 2007; Schieweck et al., 2005). Strong correlations were
also observed between different chemical classes, such as arene, naphthalene, and ester groups (r = 0.73–0.99), suggesting their common sources.
Velázquez-Gómez et al. (2019) also observed the co-occurrence of a number of arenes and naphthalenes in several museums in Spain. In addition,
dibenzofuran had presented a strong correlation with several arenes (r >
0.84), naphthalene (r > 0.85), and esters (r > 0.80). The co-occurrence of
eucalyptol and 1-methoxy-2-propyl acetate (r = 0.89) was also observed
in a previous indoor environmental study (Norris et al., 2019), and this
have been related to the use of fragrance products in the museum (U.S.
EPA, 2021). Styrene, fluorene, and 1-methylnaphthalene correlated well
with each other (r > 0.78), and a similar co-occurrence was observed in previous studies in residential homes (Chin et al., 2014; Winkle and Scheff,
2001).
in previous journal publications. The high-ranking VOCs based on
SciFinder® hits included styrene (813 hits), naphthalene (655 hits), phenanthrene (491 hits), fluorene (368 hits), acenaphthene (325 hits), biphenyl
(327 hits), mesitylene (282 hits), diethyl phthalate (267 hits), and phenol
(238 hits). Considering the lack of confirmation using reference standards
in the current study, the table should be considered as a tentative list that
can be used for future targeted analysis of VOCs in museums.
Most of the VOCs listed in Table S1 have seldom been reported in the
museum environment, with the exception of dichlorobenzene, furfural,
naphthalene, phenol, styrene, and trimethylbenzene (Alvarez-Martin
et al., 2021; Sánchez et al., 2020; Schieweck et al., 2005). However,
many of the compounds detected have been associated with materials
used for furnishing and chemicals applied in conservation treatment in
the museum environment. For example, previous studies have shown that
phenol and styrene were emitted from polyurethane foams, 1-methylethyl
benzene was released from polystyrene materials, and hydrocarbons such
as alkanes were emitted from polyethylene materials (Curran et al., 2017;
Curran et al., 2018; Kaczkowski et al., 2017). These types of polymeric materials were often used for storing and displaying heritage objects. Dichlorobenzene, furfural, naphthalene, phenoxyethanol, and propylene glycol
have been widely used as pesticides and preservatives to protect artifacts
or specimens against infestation and destruction (Alvarez-Martin et al.,
2021; Frølich et al., 1984; Höfer et al., 2015; Jochebed and Thenmozhi,
2020; Linnie and Keatinge, 2000; Makos and Hawks, 2014; Schieweck
et al., 2005). Cumene, dimethylformamide, and trimethylbenzene were
frequently used in conservation practice to clean museum objects such
as paintings, decorative arts, or archaeological materials (Dorge;
Mecklenburg et al., 2013; Museum Services Corporation, 2021; Pocobene,
2004). Triethylene glycol (mono) ethyl ether (i.e., 2-[2-(2-ethoxyethoxy)
ethoxy]ethanol in the IUPAC name), as well as other glycol ethers have historically been used in the conservation field as solvents or diluents in coatings and varnishes, adhesives, and solvent mixtures for cleaning (Ecetoc,
2005; Wheeler et al., 2013). Phenoxyethanol and propylene glycol were
also used as softening agents to maintain the flexibility of specimens and
to retard desiccation prior to fixation (Martin, 2016; Waller and
Simmons, 2003). In addition, VOCs may also be produced during the degradation of artifacts. Furfural and trimethylbenzene were detected in indoor air as a result of cellulose degradation and were present at
consistently higher concentrations in locations that contained paperbased items (Curran et al., 2018; Fenech et al., 1994; Gibson et al., 2012).
Diethyl phthalate has been one of the most commonly used plasticizers
for cellulose acetate artifacts, and its emission has been observed with
aging of cellulose acetate objects in museums (Gili et al., 2018; King
et al., 2020).
HCA was performed without a priori grouping information to further
study the similarity and variation of samples. The analysis used maximum
distances to assess which samples were similar and then organized them
into an ordered grouping, referred to as a hierarchical tree or dendrogram
(Szymanska et al., 2016). A heatmap of HCA is shown in Fig. 3, where
each column represents a sample and each row corresponds to a chemical,
with the abundance represented by color intensity. The results show that
the samples were separated into three distinct clusters by the sampling
method. The MonoTrap ODS disks captured mostly alkanes/enes/ynes,
while the MonoTrap AC and ODS disks sampled mostly esters, naphthalenes, alcohols, and ethers. In contrast, most arenes were collected through
silica gels.
The abundance of chemicals sampled using the MonoTrap ODS disks
was generally higher than that with the other two sampling methods. In addition, the samples were distinguished by the sampling event, although
grouping was not as significant as clustering by the sampling method. No
obvious clustering trend was observed based on the sampling site.
Spearman's rank-order correlation analysis was performed for all VOCs
observed with 100% detection frequency in order to evaluate the relationships between them. The Bonferroni correction was applied to control the
probability of any false positives (type I errors) caused by simultaneously
testing multiple hypotheses. Although the Bonferroni correction is highly
3.3. Comparison with outdoor air
We further compared the molecular nature of the VOC species in the
museum with outdoor air using several chemical metrics. Steimer et al.
(2020) investigated the chemical composition of outdoor aerosols in
Beijing through NTA method using ultrahigh-resolution mass spectrometry.
Considering that organic aerosol was an intermediate, which was often
formed from gas-phase precursors and ultimately returned, in part, to gasphase products (Jimenez et al., 2009), their data were used in the comparison. Although their sampling and analytical instruments may have resulted
in the inclusion of more compounds in a wider range, significant differences
in chemical characteristics between indoor and outdoor air were still observed due to their different emission sources and environmental conditions. Fig. 5a shows the value of the double-bond equivalent (DBE) as a
function of the carbon number of compounds detected. The numberweighted average DBE in indoor air (4.3) was considerably lower than in
outdoor air (7.3). Since the DBE represents the number of double bonds
and rings in a molecule, the results suggested that there were more unsaturated compounds in the atmosphere than in indoor air. Many unsaturated
compounds in the museum were aromatics, with XC values greater than
or equal to 2.5 (Fig. 5b) (Yassine et al., 2014). Although the indoor VOCs
were more saturated than those in outdoor air, their concentrations in indoor environment could be much higher than those observed outdoors as
found in previous studies (Price et al., 2019; Weschler and Carslaw,
2018). Reactions between unsaturated indoor VOCs and oxidants
(e.g., O3 and OH radicals) may generate a large number of complex products in the gas phase (Fan et al., 2003; Weschler and Carslaw, 2018). Additionally, some unsaturated indoor VOCs may also originate from these
chemical transformations or from oxidations of squalene or aromatic fragments indoors (Arata, 2020; Avery et al., 2019).
The carbon oxidation state (OSC) and carbon number provide insights
into the degree of oxidation and chemical aging (Kroll et al., 2011). As
shown in Fig. 5c, the majority of the VOCs in the museum consisted of reduced compounds (OSC < −0.75) with low carbon numbers, and the
number-weighted average OSC value was −1.2. In comparison, over
80% of the outdoor air consisted of relatively more oxidized compounds
(OSC ≥ −0.75), with a number-weighted average OSC value of −0.28.
The outdoor air had significantly more oxidized compounds compared
with indoor air in the museum. We believe that the result is reasonable
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Science of the Total Environment 835 (2022) 155277
Fig. 3. Heatmap of hierarchical clustering analysis (HCA) of VOCs with 100% detection frequency. Values represent the abundance of detected compounds in each sample
(mean abundance was used due to duplicate measurements). The colors in the first, second, and third rows and the first column represent different sampling sites, sampling
events, sampling methods, and chemical classes, respectively. The bottom row is a unique site identifier, where the first letter is the sampling method (O = MonoTrap ODS;
A = MonoTrap AC and ODS; and S = silica gel), the second letter indicates whether it is sampling event I or II, and the third letter represents the sampling site.
because the concentration levels of oxidants, such as ozone, chlorine, nitrate, and hydroxyl radicals, are typically higher outdoors than indoors,
along with the photolytic action of sunlight (Bianchi et al., 2019), leading
to more oxidation processes. Data on the museum environment is very limited in the literature. Price et al. (2019) measured the chemical composition
of indoor air at an art museum in Colorado and found that more than 80%
of the carbon in the air was highly reduced (OSC < −0.5), volatile, and had
low carbon numbers; this was similar to the findings of the current study.
3.4. Effects of sampling adsorbents
The use of different adsorbent media for air sampling may greatly affect
the NTA results, as the nature of the adsorbents determines the range of
compounds to be extracted. The Venn diagram (Fig. 6a) shows the number
percentage of VOCs measured by each type of adsorbent, as well as those
found in one or more of the adsorbents. Of all the VOCs with a detection frequency of 100%, silica gel collected the largest fraction (43%), while the
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Science of the Total Environment 835 (2022) 155277
Fig. 4. Spearman's rank-order correlation matrix for VOCs observed with 100% detection frequency using (a) the MonoTrap ODS, (b) MonoTrap AC and ODS, and (c) silica
gel. Only significant values are shown (Bonferroni corrected p-value <0.00005). The darker the color, the higher the correlation coefficient.
MonoTrap AC and ODS collected more low-oxygen-containing aliphatic
compounds (region C) than other compounds, while the MonoTrap ODS
and the silica gel were good at sampling aliphatic and aromatic hydrocarbons (regions A and D). For polar compounds, the former outperformed
the latter two adsorbents. The AC component is believed to contribute considerably to the adsorption of polar compounds in the MonoTrap AC and
ODS adsorbent (GL Sciences). Although the AC surface itself generally has
no polarity, introduction of certain functional groups on the carbon surface
can improve the adsorption of AC for polar compounds (Goto et al., 2015).
Overall, the results suggest that different sampling adsorbents should be
considered and carefully selected in order to analyze compounds with a
wide range of chemical properties when performing NTA.
MonoTrap AC and ODS and MonoTrap ODS identified 38% and 35%, respectively. Only 3% of VOCs were found in all three adsorbents, suggesting
that a single type of adsorbent may not provide sufficient screening to detect a wide range of compounds.
The number of VOCs detected in each chemical class using different adsorbents is shown in the histogram (Fig. 6b). Most alkanes/enes/ynes were
captured by the MonoTrap ODS, most esters and alcohols were sampled
using the MonoTrap AC and ODS, and most arenes were collected by the silica gels. In addition, in order to investigate the nature of VOCs retained by
each type of adsorbent, the molecular formula of each detected VOC was
plotted on a van Krevelen (vK) diagram based on its presence in different
adsorbents (Fig. 6c). The vK diagram has been widely used in studies to obtain an estimation of the main compound categories present in environmental samples (Rivas-Ubach et al., 2018). According to the H/C and O/C
ratios, organic compounds were divided into four categories
(i.e., aromatic hydrocarbons, low-oxygen-containing aromatic hydrocarbons, low-oxygen-containing aliphatic compounds, and aliphatic compounds) in the vK diagrams in Fig. 6c, corresponding to regions A to D,
respectively. Although the four regions (i.e., the H/C and O/C boundaries)
may not accurately define the compound categories and can vary among
studies (Rivas-Ubach et al., 2018), the results still clearly show that the
3.5. Temporal and spatial variations
A clear separation of sampling events I and II was observed through PCA
(Fig. 7). The first two PCs accounted for 83.3%, 49.9%, and 64.6%, respectively, under different sampling methods. For air samples collected using
the MonoTrap ODS disks, no overlapping distribution was observed, indicating that the two sampling events were very different. The loading plot
of the PCA (Fig. S3) showed that some compounds such as alkanes/enes/
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L. Ding et al.
Science of the Total Environment 835 (2022) 155277
have remained, and some new VOCs may have been introduced. The size
of the ellipse that represents the 95% confidence interval was larger for
sampling event II than event I when using the MonoTrap ODS disks and
the silica gel, indicating more variability of sampling event II. In addition,
it was not possible to visually differentiate samples between sampling
sites using the PCA (Fig. S4). It suggested that the VOC composition of air
samples at each sampling site was similar. Therefore, significant temporal
changes of indoor VOCs in the museum were observed during sampling,
but there were no obvious spatial variations. The results agreed well with
the HCA, in which clustering trends were observed based on sampling
methods and events rather than sampling sites.
3.6. Chamber tests
Chamber tests were conducted in the laboratory to identify VOCs that
were emitted from decorative materials used in the exhibition area. Six
types of materials were studied, including fabric, particle board, carpet, instant adhesive powder, medium-density fiberboard, and water-based paint.
A comparison of compounds identified by the emission tests and the field
measurements clearly shows that decorative materials were one of the
main sources of indoor VOCs in the museum. Approximately 48 VOC species emitted from the decorative materials were detected in indoor air in
the museum (Table 1 and Fig. S5). Among them, styrene (CAS no. 10042-5), phenol (CAS no. 108-95-2), mesitylene (CAS no. 108-67-8), 2,6-ditert-butyl-4-methylphenol (CAS no. 128-37-0), and 1-methoxy-2-propyl
acetate (CAS no. 108-65-6) were also found in previous emission studies
of sealants, furniture board, and lacquers (Wilke et al., 2021). In addition,
the functional use of the detected chemicals is shown in Table 1, with a
number of VOCs being used as fragrances, solvents, surfactants, and biocides. Some VOCs, such as 2-propylene glycol (CAS no. 57-55-6), phenol
(CAS no. 108-95-2), and naphthalene (CAS no. 91-20-3), were detected
from most of the tested materials, suggesting their widespread use. Interestingly, fabrics emitted the most types of VOCs, while water-based paint released the fewest. During the manufacture of fabrics, some VOCs could be
used as additives, such as oleyl alcohol (CAS no. 143-28-2) as a surfactant
(Acosta et al., 2003). However, given that the fabric material tested had
been used in the museum, the various VOCs released from the fabric
could also be attributed to the re-emission of compounds that had accumulated in the fabric.
3.7. Limitations
There are several limitations in our study. First, when processing the
raw data from HRMS, it was necessary to manually perform certain steps,
such as inspection of peak sharpness, cleanness, and symmetry, identification of fragment patterns, and determination of compounds from lists of
candidates. These steps are highly subjective, and the expertise and experience of the analyst may have affected the results (Kutarna et al., 2021;
Schulze et al., 2020; Schymanski et al., 2015). In addition, a minimum
score of 90 based on the combination of SI, HRF, and RI values was used
to make final decisions on whether a match was a true analytical positive.
Although it helped to reduce false positives (i.e., type I errors), it may
also filter out compounds that were present, resulting in false negatives
(i.e., type II errors). Second, isomers were not differentiated, which is a
common challenge in NTA studies, although the structural differences of
isomers may lead to significant differences in their physical and chemical
properties. Third, chemical reference standards were not used to confirm
and quantify the hundreds of detected compounds due to their unavailability and high cost, similar to a number of NTA screening studies (Hedgespeth
et al., 2019; Wang et al., 2018; Willoughby et al., 2014). Therefore, the
identified VOCs should be considered a tentative list that can be used for future targeted analysis of chemicals in the museum environment. Next, silica
gel tubes sampled air within a short period of time in an active way, while
MonoTrap disks collected time-integrated air samples for a week passively.
The potential impact was not considered when analyzing the data, although
it might be minimal due to the continued operation of building HVAC
Fig. 5. Plots of the (a) double-bond equivalent (DBE), (b) aromaticity equivalent
(XC), and (c) carbon oxidation state (OSC) as a function of carbon number for the
detected indoor VOCs in the museum and compounds in outdoor air reported in
the literature. The size of bubbles represents the number of compounds. Some of
the identified VOCs were marked in the figure, including diethyl phthalate,
furfural, naphthalene, phenol, and styrene.
ynes, heteroatoms, naphthalenes, and alcohols were most responsible for
the observed difference. In contrast, the data points obtained by the
MonoTrap AC and ODS disks and the silica gel had overlapping distributions, suggesting that VOCs collected using these two sampling methods
were partially similar between the two sampling events. The top ranked
compounds that contributed to the PCs included arenes, esters, heteroatoms, naphthalenes, and alcohols. We believe that the results are reasonable. Considering that sampling event II was carried out during the
removal of artifacts and the refurbishment of the gallery, some VOCs may
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Science of the Total Environment 835 (2022) 155277
MonoTrap (ODS)
MonoTrap (AC and ODS)
(a)
Silica gel
(b)
(c)
Fig. 6. (a) Venn diagram showing the relative number distributions of VOCs present in any of the three types of adsorbents. Areas of overlap represent the percentages of
VOCs that appear in both (or all three) of the adsorbents. (b) Histogram of the number of VOCs detected in each chemical class using different adsorbents. (c) Van
Krevelen diagrams for VOCs measured by the three types of adsorbents. Regions A to D refer to aromatic hydrocarbons with low H/C (<1.0) and zero O/C ratio (Line A),
low-oxygen-containing aromatic hydrocarbons with low H/C ratio (<1.0) and low O/C ratio (≤0.5) (Area B), low-oxygen-containing aliphatic compounds with high H/C
ratio (≥1.0) and low O/C ratio (≤0.5) (area C), and aliphatic compounds with high H/C ratio (≥1.0) and zero O/C ratio (Line D), respectively. Pie charts showing the
number distribution (%) of VOCs in the four regions.
may influence NTA results, more extraction methods and more types of
solvents could be included to avoid the loss of compounds in future studies.
system and the resulting stable environmental conditions in the gallery. Additionally, atmospheric organic aerosol data were used when comparing
indoor and outdoor air, because no raw data of NTA studies on outdoor
gas-phase VOCs in Beijing were found, although organic aerosol was an intermediate, which was often formed from gas-phase precursors and
ultimately returned, in part, to gas-phase products (Jimenez et al., 2009).
Finally, ultrasonication with dichloromethane and methanol was used for
extraction in the present study. Since extraction approaches and solvents
4. Conclusions
This study is the first of its kind to comprehensively characterize VOCs
in a museum environment using NTA screening approach with the GCOrbitrap-MS. Approximately 230 VOCs were identified, of which 117
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Science of the Total Environment 835 (2022) 155277
Fig. 7. Principal component analysis (PCA) of detected VOCs. Two-dimensional PCA score plots reveal separation in VOC samples induced by different sampling events.
Ellipses represent the 95% confidence interval.
used in future studies to confirm and prioritize VOC contaminants that
may largely contribute to the overall abundance and have potential impacts
on cultural heritage artifacts, museum personnel, and visitors.
were detected at 100% frequency across all sampling sites in both sampling
events. Although some were common in indoor environments, most of the
detected VOC compounds were rarely reported in the museum environment. They were found to be associated with the materials used in furnishings and the chemicals applied in conservation treatment. Spearman's rankorder correlation analysis showed that several classes of VOCs were well
correlated within each group, as well as between different chemical classes,
suggesting their common sources. Compared with outdoor air, indoor VOCs
(with a DBE value of 4.3 and OSC value of −1.2) had a lower level of
unsaturation and more portions of reduced compounds, which was attributed to the differences in environmental conditions, oxidant levels, and
emission sources between indoor and outdoor air. The results of HCA suggested that a single type of adsorbent may not be enough to cover a wide
range of compounds in NTA studies. The MonoTrap AC and ODS adsorbent
is suitable for aliphatic polar compounds that contain low levels of oxygen,
while the MonoTrap ODS and silica gel are good at sampling aliphatic and
aromatic hydrocarbons with limited polarity. The results of PCA also
showed that there were significant temporal changes of indoor VOCs in
the museum, which may have been caused by the removal of artifacts
and refurbishment of the gallery between sampling events, but no obvious
spatial variations were observed. A comparison with compounds identified
by chamber emission tests showed that decorative materials are possibly
one of the main sources of indoor VOCs in the museum. We expected that
many of the VOCs detected in the present study are likely to be present in
other similar museum environments. Therefore, the results can be directly
CRediT authorship contribution statement
Li Ding: Formal analysis, Writing – original draft. Luyang Wang: Methodology, Investigation, Data curation. Luying Nian: Formal analysis, Data
curation, Visualization, Writing – original draft. Ming Tang: Project
administration, Resources. Rui Yuan: Project administration. Anmei Shi:
Investigation, Data curation. Meng Shi: Investigation. Ying Han: Investigation, Resources. Min Liu: Investigation, Resources. Yinping Zhang:
Supervision. Ying Xu: Conceptualization, Methodology, Supervision,
Writing – original draft, Writing – review & editing, Funding acquisition.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the
work reported in this paper.
Acknowledgments
Publication of this work was supported by the National Key R&D
Program of China (No. 2020YFC1522102) and the National Natural
11
12
1002-17-1
1560-86-7
17301-30-3
17301-32-5
17302-01-1
2213-23-2
52670-34-5
54833-48-6
55499-02-0
563-16-6
61142-37-8
62016-19-7
74645-98-0
100-42-5
108-67-8
1889-67-4
246-02-6
2801-84-5
3075-84-1
3910-35-8
527-53-7
700-12-9
86-73-7
874-41-9
Alkane/ene/yne C12H26
C20H42
C13H28
C13H28
C10H22
C9H20
C12H26
C21H44
C12H24
C8H18
C12H24
C11H24
C15H32
C8H8
C9H12
C18H22
C14H10
C12H26
C16H18
C18H20
C10H14
C11H16
C13H10
C10H14
Arene
CAS number
Chemical
formula
Chemical class
1-Ethyl-2,4-dimethylbenzene
3-Methylpentan-2-yl cyclohexane
6-Ethyl-2-methyloctane
2,7,10-Trimethyldodecane
Ethenylbenzene
1,3,5-Trimethylbenzene
1,1′-(2,3-Dimethylbutane-2,3-diyl)dibenzene
Benzo[a]azulene
2,4-Dimethyldecane
2,2′,5,5’-Tetramethyl-1,1′-biphenyl
1,1,3-Trimethyl-3-phenyl-2,3-dihydro-1H-indene
1,2,3,5-Tetramethylbenzene
1,2,3,4,5-Pentamethylbenzene
Fluorene
(3E)-2,2-Dimethyldec-3-ene
3,3-Dimethylhexane
2,9-Dimethyldecane
2-Methylnonadecane
3,8-Dimethylundecane
4,7-Dimethylundecane
3-Ethyl-3-methylheptane
2,4-Dimethylheptane
2,3,6,7-Tetramethyloctane
2,6,10,15-Tetramethylheptadecane
Chemical name
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●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Carpet Instant
adhesive
powder
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
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●
Fabric Particle
board
●
●
●
●
●
●
●
●
Medium-density
fiberboard
●
Water-based
paint
Table 1
VOCs detected both in the museum and in the chamber emission test of decorative materials. Black dots indicate the presence of a specific compound in a material.
Fragrance b
Not available
Component in human breath e and in polyethylene
terephthalate/polyethylene foil f
Not available
Component in human breath g
Catalyst b
Fragrance, flavorant a
Fragrance, catalyst, flavorant a
Fragrance b
Not available
Not available
Not available
Fragrance b
Fragrance, flavorant, colorant a
Fragrance b
Plastic additives b
Fragrance b
Lubricating agent, emulsion stabilizer, surfactant a
Not available
Catalyst b
Component in polyethylene c
Component in polypropylene d
Fragrance, catalyst, flavorant a
Catalyst b
Chemical usage
L. Ding et al.
Science of the Total Environment 835 (2022) 155277
13
h
g
f
e
d
c
b
a
C6H12O3
C17H34O2
C14H26O4
C12H24O3
C9H8O3
C18H24O4
C12H14O4
C7H5NS
C12H12
C12H12
C11H10
C10H8
C8H18O4
C18H36O
C4H10O2
C3H8O2
C10H20O
C10H18O
C15H24O2
C5H10O
C10H16O
C15H22O2
C6H6O
C15H24O
108-65-6
112-39-0
141-04-8
244074-78-0
55153-12-3
84-64-0
84-66-2
95-16-9
1127-76-0
575-41-7
90-12-0
91-20-3
112-50-5
143-28-2
19132-06-0
57-55-6
4457-62-9
470-82-6
10396-80-2
107-87-9
464-49-3
1620-98-0
108-95-2
128-37-0
1-Methoxypropan-2-yl acetate
Methyl hexadecanoate
Bis(2-methylpropyl) hexanedioate
2-Methylpropyl 3-hydroxy-2,2,4-trimethylpentanoate
Phenacyl formate
Butyl cyclohexyl benzene-1,2-dicarboxylate
Diethyl phthalate
Benzothiazole
1-Ethylnaphthalene
1,3-Dimethylnaphthalene
1-Methylnaphthalene
Naphthalene
Triethylene glycol monoethyl ether
(9Z)-Octadec-9-en-1-ol
(2S,3S)-Butane-2,3-diol
Propane-1,2-diol
2,5-Dipropyloxolane
1,8-Cineol
2,6-Di-tert-butyl-4-hydroxy-4-methylcyclohexa-2,5-dien-1-one
Pentan-2-one
D-Camphor
3,5-Di-tert-butyl-4-hydroxybenzaldehyde
Phenol
2,6-Di-tert-butyl-4-methylphenol
●
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Fragrance, UV absorber a
Emollient, lubricating agent, emulsifier a
Fragrance, flavorant, emollient a
Not available
Component in biopolymer h
Plastic additives b
Fragrance, preservative, UV absorber a
Catalyst, flavorant, biocide a
Not available
Not available
Fragrance, flavorant, antioxidant a
Colorant, fragrance, flavorant a
Crosslinker, biocide, catalyst a
Fragrance, surfactant, emollient a
Not available
Skin conditioner, humectant, reducer a
Not available
Fragrance a
Drinking water chemicals b
Flavorant, fragrance, humectant a
Fragrance, flavorant, colorant a
Antioxidant, UV absorber, heat stabilizer a
Hair dye, preservative, biocide a
Antioxidant, UV absorber, heat stabilizer a
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Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.scitotenv.2022.155277.
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