Aerosol-associated non-polar organic compounds (NPOCs),
including 15 polycyclic aromatic hydrocarbons (PAHs), 30
In recent years, severe atmospheric pollution characterized by haze episodes has been
a recurring problem in developing countries, affecting visibility, optical radiation
and human health (Yadav et al., 2013; Wang et al., 2015; Shen et al., 2015;
Sulong et al., 2017). China has experienced numerous severe and long-lasting
haze episodes since winter in 2013, which has affected over 600 million local
residents and covered a quarter of the country's land area (Huang et al.,
2014; Hao and Liu, 2015). In essence, a haze episode is caused by the
distribution of particulate matter with different sizes in atmosphere, leading
to a decrease in visibility (Xie et al., 2017). Carbonaceous aerosols contain a
large amount of particulate matter, accounting for 30–50 % of
Since carbonaceous aerosols can affect the ambient environment significantly, it
is crucial to investigate aerosol-associated organic compounds. Because
non-polar organic compounds (NPOCs) can provide specific information on the
identification of aerosol sources, they are now of special interest to
researchers (Rajput and Sarin, 2014; Wang et al., 2016).
PAHs are typical semi-volatile organic compounds, which can partition between
gas and particle phases in the ambient atmosphere (He and Balasubramanian, 2009;
Ma et al., 2011). Recently, research has shown that
NPOCs are typically assumed to be stable and nonreactive (Feng et al., 2006;
Ma et al., 2011). However, recent research has shown that NPOCs can be
oxidized by
To develop strategies for controlling atmospheric pollution caused by
particulate matter, receptor-based models (e.g., positive matrix
factorization, PMF) have been widely applied to quantitatively apportion
sources of particulate matter (Wang et al., 2009; Li et al., 2016; Huang et
al., 2017). However, the output factors of the receptor model are not necessarily
emission sources, because there exist some atmospheric processes like
photodegradation or gas–particle partitioning. Considering the gas–particle
partitioning of NPOCs, Xie et al. (2013) adopted both gaseous and particulate
NPOCs in the PMF model and successfully extracted seven factors. More recently,
Wang et al. (2016) used data of NPOCs combined with those of
organic/elemental compounds (OC/EC), inorganic compounds and elemental
compounds as input for the PMF model, and they found that total
(gas
In this paper, we conducted a comprehensive study on
Jiujiang is located at 113
Location of aerosol sampling sites in Jiujiang, eastern China.
Five
Detailed description of the six sampling sites in this study.
The sampling site of size-specific aerosols was located at a five-story
building of the Jiujiang environmental monitoring station (EM site). Airborne
particle samples were collected for 23 h using a nano-micro-orifice uniform
deposition impactor (MOUDI) sampler (model 122R, MSP Corp, USA), at an air
flow rate of 30 L min
A total of 57 NPOC species (Table S1 and Fig. S1 in the Supplement) were
identified by using an in-injection port
thermal desorption unit (TDU, Shimadzu, Japan),
coupled with gas chromatography–mass spectrometry (GC–MS, QP2010 Plus, Shimadzu, Japan). Compared with the
traditional solvent extraction method, the TD–GC–MS method (Ho and Yu,
2004; Ho et al., 2008) has advantages such as solvent and sample filtration,
labor saving, and less contamination from solvent impurities. A filter
aliquot (1 cm
The sample processing time in TD tube was set to 45 min, and the TD tube was
electronically cooled to
OC and EC were analyzed (a round punch of 0.538 cm
Elemental compositions, including Na, K, Ca, Mg, P, Fe, Ti, Al, Pb, Cu and
Zn, were determined by energy dispersive X-ray fluorescence (ED-XRF)
spectrometry (Epsilon 5, the Netherlands). Water-soluble inorganic ions,
including cations (
Prior to sampling in each site, the five
The NPOCs standards used were the National Institute of Standards and
Technology (NIST, USA) Standard Reference Materials (SRM), including SRM
2260A, SRM 1494 and SRM 2266 for 15 PAHs, 30
Recovery experiments were conducted to improve the desorption of targeted compounds from filters and experimental detection. The analytical recovery was calculated by spiking a known amount of the SRM solution to blank filter, and most compounds were recovered with a recovery efficiency > 90 % except for several light-molecular-weight species. The accuracy of the method was evaluated by the reproducibility of the standard and selected samples were ascertained by processing in quintuplicate; the results suggest the analytical precision was better than 5 %.
Different diagnostic parameters were adopted in this study to explore natural
and anthropogenic contributions. The parameters include the carbon preference
index (CPI), the carbon number of the most abundant CPI is defined as the ratio of the total concentration of odd WNA % is calculated as Eq. (2). Note that the negative value of
[C Aerosol-associated ACL can indicate emissions of MDRs for PAH source apportionment include the ANT
Gas–particle partitioning is an important mechanism that affects the fate and
transport of NPOCs (Pankow, 1994; Kim et al., 2011). To understand the
partitioning behavior of NPOCs, we evaluated the distribution of NPOCs
between gas and particle phases in the atmosphere. The gas–particle
coefficient
The total concentration (
Also, Junge–Pankow model was
further used to investigate gas–particle partitioning. In this model, the
ratio (
The statistical summary and the abundance of measured
A total of 57 NPOCs were identified in this study (Table 2), including 30
When comparing with other NPOCs measurements in China, Li et al. (2013)
reported a similar level in that the
daily concentration of
Comparison of NPOC concentrations between Jiujiang
and other areas (ng m
The percentiles of NPOCs and
PAHs are ubiquitous pollutants of the environment. They are originated from
natural and anthropogenic sources such as biomass burning, vehicle exhausts,
residential heating, waste incineration and industrial emissions. The
Due to the vapor-pressure-dependent partitioning, two- and seven-ring PAHs distributed mainly in the gas and particle phases, respectively. However, PAHs with three to six rings appeared in both gas and particle phases through gas–particle partitioning. Moreover, FLU-PYR-CHR (chrysene) and BaA-BaP congeners of the four-ring PAHs often indicate diesel vehicle and biomass combustion (Yadav et al., 2013), respectively, while the five-ring BkF is considered a marker of vehicle tracer. The total percent contribution of four- and five-ring PAHs was 67.9 % in this study, which suggests vehicle exhaust, biomass burning and fossil fuel combustion have mixed effects on local atmospheric pollution.
MDRs of atmospheric PAHs with similar molecular weight have been widely used
as a useful tool for aerosol source identification. In this study, the
ANT
Concentration profiles of NPOCs.
Unique signatures of
Our analysis showed that the middle-chain-length
Plant wax
Hopanes and steranes are usually found in crude oil and engine oil,
and subsequently in vehicle exhausts from unburned lubricating oil residues.
They are regarded as markers of fossil fuel combustion. The concentration
profile of hopanes and alkanes is shown in Fig. 3c. Their total
concentrations ranged from 1.1 to 20.5 ng m
The predominant hopane analogs were
Particulate matter within 13 size fractions was collected. The size-specific
distribution of NPOCs was then obtained (Fig. 4). The mean
The concentrations of
The size-specific distribution of hopanes and steranes is illustrated in
Fig. 4c. Hopanes and steranes were the most abundant in the following five
fractions: 0.56–1.00 (2.9), 0.32–0.56 (2.5), 0.18–0.32 (1.8), 9.9–18
(1.2) and 0.10–0.18
Moreover, our recent research (Han et al., 2018) found that the organic
compounds carrier, OC/EC, displayed a unimodal distribution in the fraction
of 0.56–1.0
Mean-normalized size-specific distribution of NPOCs in the collected
Photochemical oxidation has great influences on the mass concentration and
size-specific distribution of NPOCs, as well as on their removal and atmospheric
fate (May et al., 2012). Photochemical decay could cause the ambient data to
be distributed along a line emanating from the source profile in the ratio–ratio
plot, with increasing photochemical age (Robinson et al., 2006; Yu et al.
2011). EC shares common origins with PAHs and hopanes but they are subject to
photodegradation. In this study, two pairs of EC normalized PAHs and hopanes
(Fig. 5), namely IcdP/BghiP and C29-
Most of the EC normalized IcdP/BghiP data points were distributed along a
line (Fig. 5a), implying ambient PAHs underwent photochemical degradation and
were influenced by vehicle emissions and coal combustion. It was reported that the
free ends of C–C scission products of PAHs remain tethered together, which
prevent fragmentation and help in forming more functional groups from the
reactions with OH
In Fig. 5b, most of the data are linearly distributed, implying the
photochemical decay of C29-
Ratio–ratio plots of two pairs of characterized species (IcdP/BghiP
and C29-
An important aspect of atmospheric NPOCs is their gas–particle partitioning
behavior, which has effects on their fate and size-specific occurrence. The
particle-phase fraction (
Average particle-phase fractions (
Source apportionment analysis involves techniques that can be used to identify source species and their unique contributions, which are critical to making policies for controlling pollution. It is typically assumed the molecular markers are stable in the ambient environment, i.e., being nonreactive and nonvolatile (May et al., 2012). However, as discussed above, many organic markers can be oxidized over atmospherically relevant timescales, as well as partition between gas and particle phases. If the data of NPOCs in the single particle phase are directly used as input for the receptor model, this may confound the aerosol factors.
Additionally, individual organic tracers, elemental species, inorganic ions
and OC/EC have been demonstrated to be able to provide source apportionment
of aerosols. To explore the impact of gas–particle partitioning on
Factor 1 (Fig. 7a) was characterized by a significant presence of Al, Ca, Mg,
Ti and Fe, which are regarded as good indicators of dust (including
construction dust, geological dust and road dust) (Wang et al., 2015). These
elements are the major elements of dust sand, usually accumulated in the
coarse mode particles. Geological dust typically contains high concentrations
of crustal elements, including Fe and Mn. Hence, this factor was regard as
“dust”, with percent contributions of 8.90 and 11.0 % under
PMF
Factor 2 (Fig. 7b) was characterized by the significant presence of Cu, Mn, Zn, Pb, BkF, BbF, BaF and BaP. Mn, Zn and As are related to emissions from steel production, brick, ceramic and glass-making factories (Li et al., 2016; Sulong et al., 2017). Cu mainly originates from non-ferrous metal production and smelting factories. BkF, BbF and BaP are typical markers of emissions from the coke industry. Several large-scale industrial parks are located in Jiujiang, e.g., Shacheng Industrial Park and Jiujiang Comprehensive Industrial Park in the northern and southern areas, respectively. Therefore, factor 2 was associated with industrial emission.
Factor 3 (Fig. 7c) was characterized by large fractions of
high molecular weight (HMW)
PAHs (IcdP, BghiP, DahA and COR), as well as relatively high fractions of
hopanes and steranes. BghiP and COR are excellent tracers of vehicle
exhausts. Hopanes and steranes are related to exhausts from heavy-duty
vehicles with diesel engines (Wang et al., 2016). As mentioned above, there
were over 700 000 motor vehicles in Jiujiang in 2015, among which about
1/15 were mainly powered by diesel engines. Therefore, factor 3 was
identified as vehicle-related exhausts, with percent contributions of 12.5
and 15.0 % under PMF
Factor 4 (Fig. 7d) was characterized by the presence of well-documented
indicators of secondary aerosol formation, such as
Factor 5 (Fig. 7e) was characterized by a significant presence of
Factor 6 (Fig. 7f) was characterized by high percentage of
Factor 7 (Fig. 7g) was characterized by high fraction of Ni and V, which are excellent tracers of exhausts from ship and heavy-duty diesel vehicles. In fact, Jiujiang harbor is among the 10 busiest harbors in the Yangtze River, whose port cargo throughput is 59 million t per year. Hence, factor 7 was identified as shipping and diesel exhausts.
Factor 8 (Fig. 7h) was characterized by a high load of short-chain
Source profiles of eight sources resolved by PMF.
As stated above, using the data of the single particle phase as input data
for the PMF model could lead to uncertainty in results, which was related to
gas–particle partitioning of NPOCs in the mathematical solution. This
influence could be reduced by adding the predicted gaseous NPOC
concentrations when the measured gaseous NPOC data were not available. In
the present study, the eight extracted factors showed similar source profiles
between PMF
Despite the fact that the current study could not predict gas-phase NPOCs with high
accuracy, the source apportionment result extracted by PMF
In this work, we confirmed that using the total (gas
Low temperature promotes NPOCs adsorbing/absorbing onto aerosols, while photochemical degradation of NPOCs is relatively weak in the cold season. Moreover, photochemical reactions would reduce the abundances of organic markers depend on species, significantly altering the relative contribution of different sources extracted by linear source inversion. Compared with the long-term investigation, this study was mainly focused on the cold season, which leads to a relatively high abundance of particle NPOCs with small variation.
The limitation for the PMF model was that it could not identify a potential source without a preexisting tracer. Also, the relative small number of measurements might lead to some uncertainty in source apportionment. In the future, more source tracer data need to be included for the calculation of potential contributions.
NPOCs are typical molecular markers for source identification, which attract
researchers' interest worldwide. A total of 57
For size distribution, PAHs and
The data presented in this article are available from the authors upon request (jpcheng@sjtu.edu.cn)
The supplement related to this article is available online at:
DH mainly conducted the sampling, sample analysis work, as well as manuscript writing and revision. JC provided direct funding and helped with manuscript revision. SEMC and SAES helped with sampling quality control and laboratory analysis, respectively. Other authors helped this work by sampling and analysis.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Regional transport and transformation of air pollution in eastern China”. It is not associated with a conference.
This study was financially supported by the National Natural Science Foundation of China (no. 21577090 and no. 21777094) and Jiujiang Committee of Science and Technology (grant no. JXTCJJ2016130099). We thank the Jiujiang Environmental Protection Agency and Jiujiang environmental monitoring station for coordinating the sampling process and for their valuable contribution to field measurements. We appreciate Yajuan Zhou (Instrumental Analysis Center, Shanghai Jiao Tong University) for her assistance in experimental analysis. Edited by: Aijun Ding Reviewed by: four anonymous referees