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Article

Chemical Characterization of Rural Organic Aerosol in the North China Plain Using Ultrahigh-Resolution Mass Spectrometry

by
Yun Zhang
1,
Xu Gao
2,
Xingang Hou
1,
Mingyuan Liu
1,
Jiajun Han
1,* and
Hongyan Zhang
1,*
1
Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
2
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, National Observation and Research Station of Agriculture Green Development (Quzhou, Hebei), China Agricultural University, Beijing 100193, China
*
Authors to whom correspondence should be addressed.
Atmosphere 2023, 14(11), 1636; https://doi.org/10.3390/atmos14111636
Submission received: 4 September 2023 / Revised: 23 October 2023 / Accepted: 25 October 2023 / Published: 31 October 2023
(This article belongs to the Special Issue Toxicity and Health Effects of Fine Particulate Matter)
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Abstract

:
Atmospheric organic aerosol (OA) affects air quality and human health. However, compared with urban areas, the chemical composition and temporal distribution of OA in rural regions are still not well understood. In this study, one-year atmospheric particles with an aerodynamic equivalent diameter of ≤2.5 μm (PM2.5) were collected at a rural site in Quzhou County, the North China Plain (NCP), from August 2020 to July 2021. OA in PM2.5 samples were analyzed with an ultrahigh-performance liquid chromatograph (UHPLC) coupled to an ultrahigh-resolution Orbitrap mass spectrometer in negative mode (ESI−). The results show that the chemical composition and properties of OA varied in different seasons. According to the hierarchical cluster analysis, the molecular formulas of winter OA were close to those in spring, whereas the chemical composition of OA in summer and autumn was similar. The O/C ratio of summer OA was the highest at 1.21, followed by that in autumn (0.92) and spring (0.87), while the winter OA had the lowest O/C ratio of 0.64. It indicates that, compared to the other three seasons, OA underwent more intense oxidation processes in the summer. Moreover, winter OA contained more aromatic compounds with a relative peak abundance fraction of 40%, which may be related to anthropogenic sources (e.g., coal burning) in the winter in the NCP. In addition, biomass burning is considered an important source of OA in the rural region of Quzhou County, the NCP, in all seasons.

1. Introduction

Organic aerosol (OA) makes up a large fraction (20% to 90%) of the submicron particulate mass [1,2,3]. However, the sources and atmospheric processing of OA are very uncertain. According to the origin, OA can be divided into primary aerosol and secondary aerosol. Primary aerosol is directly emitted from a natural and anthropogenic source, while secondary aerosol is formed by chemical reactions of gaseous precursors and gas-to-particle conversion [4,5,6,7,8,9,10,11]. A large number of observational data show that the secondary aerosol in the atmosphere is a great contributor to the concentration of PM2.5 [12]. It is a challenging task to analyze the chemical composition of OA. Due to the complex source and formation process, only 10% to 30% of OA has been chemically identified [13].
In the last decade, mass spectrometric techniques have been widely used in the chemical identification of OA. The Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HTOF-AMS) is commonly used to measure the elemental composition of OA and further investigate the source and atmospheric process of OA [14,15]. However, the hard ionization (i.e., electron ionization) applied in the HTOF-AMS results in the fragmentation of OA, leading to the identification of individual organic compounds in OA to be impossible. The filter inlet for gases and aerosols (FIGAERO) coupled with a high-resolution time-of-flight chemical ionization mass spectrometer (FIGAERO-TOF-CIMS) has been employed to track the evolution of gaseous and particulate components in OA [16,17], while the extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF-MS) is applied for online measurements of atmospheric aerosol particles without analyte fragmentation [18,19,20]. However, the mass resolving power of the TOF mass analyzer applied in these MS techniques is limited (i.e., up to around 4000) [19], hindering our understanding of the chemical composition of OA. In recent years, with the development of ultrahigh-resolution mass analyzers (such as Orbitrap and Fourier transform ion cyclotron resonance, FTICR) with a mass resolution power of >40,000 and a mass range of 100–500 [21], great progress has been made in the study about the chemical components of OA. When soft ionization techniques, such as atmospheric pressure chemical ionization (ACPI) and electrospray ionization (ESI) ion sources, are combined with ultrahigh-resolution mass spectrometry (UHRMS), the extremely high resolution of mass spectrometry makes it possible to simultaneously determine thousands of organic compounds in OA at a molecular level [22,23,24,25,26,27,28,29,30,31,32,33,34,35]. Tao et al. used UHRMS technology to compare and analyze the organosulfates in OA from Shanghai and Los Angeles, and found that there were great differences in the chemical composition of OA depending on the city in which it was analyzed [36]. Kourtchev et al. analyzed the oxidation products of pinene with ozone and hydroxide radicals generated in a smog chamber by using the UHRMS technique [37]. Tong et al. used the UHRMS technique to analyze the collected PM2.5 samples at two sites in England, and reported the differences of the chemical composition of PM2.5 in different regions [38]. Wang et al. analyzed OA samples collected in different seasons in an urban area of Shanghai using high performance liquid chromatography tandem ultrahigh-resolution mass spectrometry (UHPLC-UHRMS), and analyzed the seasonal variation of the chemical composition of OA in an urban area in Shanghai [28]. Wang et al. analyzed OA samples from urban areas of Changchun, Shanghai and Guangzhou using UHPLC-UHRMS, and compared the differences of OA’s chemical components in these three urban areas as well as their possible influencing factors [34]. Liu et al. investigated the difference of highly oxygenated organic molecules and low volatile compounds in SOA generated from ozonolysis of β-pinene and limonene as well as the potential mechanism by using FTICR [39]. Du et al. used LC-Orbitrap MS to identify the chemical composition of SOA formed from the photooxidation of α-pinene in an atmospheric simulation chamber [17]. Riva et al. developed a chemical ionization interface coupled to Orbitrap MS to study the highly oxygenated organic molecules generated form ozonolysis of monoterpenes in a flow tube reactor [40]. These results show that UHRMS has become a powerful tool to obtain the chemical composition of OA and understand the formation mechanism of aerosol due to its high resolution and high sensitivity.
Compared with urban areas, few studies are found on the chemical composition of organic aerosol at rural sites using UHRMS. Schmitt-Kopplin et al. analyzed the chemical composition of fine aerosol samples collected at rural sites of the Great Hungarian Plain in Hungary, as well as at Canadian rural sites, using FTICR [41]. Brüggemann et al. qualified terpenoid organosulfates in collected PM10 samples in the summertime, in rural sites of Germany and the NCP, using Orbitrap MS [42]. Kwiezinski et al. determined highly polar compounds in atmospheric particles from the rural background station in Melptiz in Germany using ion chromatography coupled to Orbitrap MS [43]. In this study, one-year PM2.5 samples were collected at a rural site in Quzhou County, which is a representative agricultural region located in the central NCP. Chemical composition of rural OA was analyzed using UHRMS and their seasonal characteristic variation was discussed.

2. Materials and Methods

2.1. Collection of PM2.5 Samples

The 24 h integrated rural PM2.5 samples were collected at a rural site in Quzhou County, Hebei Province (36.77° N, 114.96° E), from August 2020 to July 2021 using a high-volume sampler (TH-1000CII, Wuhan Tianhong Intelligence Instrumentation Facility, Wuhan, China) at a flow rate of 1.05 m3 min−1 on prebaked (prebaked temperature of 900 °C) quartz-fiber filters (Munktell MK360, 203 × 254 mm, Falun, Sweden). The concentrations of PM2.5 samples during the sampling period were 89.4–730.5 µg m−3. The filter samples were stored at −20 °C until analysis.

2.2. Sample Analysis

The filter samples were extracted and analyzed in December 2022. A detailed description of sample extraction and UHPLC-Orbitrap MS detection can be found in our previous studies [30,32,34]. In brief, a portion of filters (corresponding to ~600 μg particle mass in each extracted filter) were ultrasonicated for 30 min with 1.0–1.5 mL acetonitrile/ultrapure water (ACN/H2O) mixture (8/2, v/v) three times. After that, the extracts were combined to remove insoluble particulate matter. After being filtered through a 0.2 μm polytetrafluoroethylene (PTFE) membrane syringe filter, the extracted solution was evaporated to almost dryness with a gentle stream of nitrogen at 20 °C. Afterward, 1.0 mL of ACN/H2O (1/9, v/v) was used to redissolve the residue for subsequent analysis.
The chemical identification of organic compounds was conducted with the application of the UHRMS technique using a hybrid quadrupole-Orbitrap mass spectrometer (Orbitrap Exploris 240, Thermo Scientific, Bremen, Germany) coupled to an UHPLC system (Dionex UltiMate 3000, Thermo Scientific, Germany). The compounds in the extracts were separated with a Hypersil Gold column (C18, 50 × 2.0 mm, 1.9 μm particle size, Thermo Scientific, Germany). The mobile phase was made up of eluent A (ACN with 2% ultrapure H2O) and eluent B (ultrapure H2O with 2% ACN and 0.04% formic acid). Gradient elution was optimized at a flow rate of 0.5 mL min−1 and the detailed information of gradient elution can be found in our previous study by Wang et al. (2021) [34]. The Orbitrap was operated in a negative ion mode (ESI−) with a −3.3 kV voltage by applying a heated electrospray ionization source (HESI). The mass resolving power was 120,000 @ m/z 200, while the scanning range was 50–500 m/z. A commercial standard calibration solution Ultramark 1621 (Thermo Scientific, Germany) with a mass range of 73–1921 m/z was used for externally calibrating the mass spectrometer. In addition, a 2 mM sodium acetate solution that provided a series of negative and positive adduct ions with a low mass range was used for low m/z calibration. The extract of each sample was analyzed in triplicate with an injection volume of 10.0 μL.

2.3. Data Processing

The chromatograms and mass spectra obtained from the UHPLC-Orbitrap MS were analyzed with a non-targeted screening approach using open-source software (MZmine, version 2.53) [35]. The software automatically searched for peaks above the threshold value of 1 × 105 arbitrary units, which were significantly different from the background and then assigned them to the molecular formulas of CcHhOoNnSs with a mass tolerance of ±2 ppm. The numbers of carbon, hydrogen, oxygen, nitrogen and sulfur were set within the ranges of 1–39, 1–72, 0–20, 0–7 and 0–4, respectively. In addition, H/C, O/C, N/C and S/C ratios were constrained in the ranges of 0.3–3, 0–3, 0–1.3 and 0–0.8, respectively, to remove chemically unreasonable molecular formulas [24,30,34,44]. The detailed setting in MZmine, including the isotope search, can be found in our previous study [35].The double bond equivalent (DBE) value of a molecule, which reflects its unsaturation degree, was calculated with the following equation: DBE = (2c + 2 − h + n)/2 [3,45]. The index for aromatic compounds, named the aromaticity equivalent (XC), was calculated using the equation XC = [3(DBE − (p × o + q × s)) − 2]/[DBE − (p × o + q × s)], where p and q, respectively, indicate the fraction of oxygen and sulfur atoms involved in the π-bond structure of an individual compound [25]. In this study, p = q = 0.5 was applied in negative mode [34]. Moreover, XC ≥ 2.50 and XC ≥ 2.71 have been suggested as unambiguous minimum criteria for the presence of monoaromatic and polyaromatic compounds, respectively [30,34].

3. Results and Discussions

3.1. Hierarchical Cluster Analysis

The goal of hierarchical cluster analysis (HCA) is to build a tree diagram where the individual observations that are viewed as most similar are placed on branches that are close together. This is completed through determining the similarity between each pair of objects in a data matrix. Recently, HCA has been applied on a large non-targeted screening data set and found a strong seasonal difference in the chemical composition of OA [46,47,48,49,50]. In this study, the workflow of HCA is shown in Text S1 and Figure S1 in the Supporting Information (SI). Figure 1a shows HCA of monthly PM2.5 samples in the rural site of Quzhou County, the NCP. The horizontal and vertical dendrograms visualize the distance between different samples and compounds, respectively. The closer the dendrogram distance, the more similar the samples are. The color represents the concentration of the compound, with darker colors indicating greater concentrations. Interestingly, we found that the horizontal dendrogram clearly separated the samples into autumn OA samples (September and October samples), summer OA samples (June, July and August samples) and other OA samples, including winter and spring samples.
Meanwhile, according to the corresponding seasons, monthly PM2.5 samples were combined and divided into 4 seasonal samples including spring (i.e., March, April and May), summer (i.e., June, July and August), autumn (i.e., September, October and November) and winter (i.e., December, January and February) samples. It should be noted that, for each seasonal sample, only compounds measured in all three months were used for further analysis and their peak abundance was averaged for compounds in seasonal samples. The HCA of the seasonal samples is shown in Figure 1b, which is consistent with the HCA results of monthly samples. It shows that the samples collected in spring and winter that were gathered together showed good clustering results, which indicates that there were great similarities in the composition of organic compounds in spring and winter, while the chemical composition of summer samples were close to that of autumn samples. In addition, molecular formulas of C6H2O3S3, C2H4O6S and C2H4O2 were the most intense among the spring samples; C2H4O6S, C4H8O7S and C4H6O5 among the summer samples; C6H2O3S3, C4H6O5 and C3H6O6S among the autumn samples; and C6H2O3S3, C10H7NO3 and C12H4N6OS among the winter samples.

3.2. Monthly and Seasonal Differences of Chemical Composition of Rural OA

The main purpose of this study was to tentatively identify and compare the chemical composition of organic compounds in rural PM2.5 samples in different months and seasons. The sampling date, number of molecular formulas, relative peak abundance fraction of each elemental composition subgroup (including CHO, CHON, CHOS and CHONS) and the peak abundance-weighted average values of molecular mass, elemental ratios and DBE are listed in Table 1. Overall, 1094–1908 organic compounds in different monthly samples were determined. The largest number of organic species was observed in December (n = 1908), and followed by January (n = 1824), indicating that the collected OA during the winter season was more complex compared to the OA in other seasons. This can be explained by the large coal combustion emissions in the winter in the rural region of the NCP. In addition, the extremely low temperature during this period may have resulted in an enhanced accumulation of pollutants as well as promoted partitioning of secondary organic aerosol from the gas phase to the particle phase [30].
As shown in Table 1, a major fraction of organic species in seasonal samples were attributed to CHO and CHOS, accounting for 30–33% and 22–45%, respectively, which were higher than the relative fraction of CHON (11–27%) and CHONS (14–19%) species in terms of peak abundance. Compared with other seasons, a higher fraction (27%) of CHON compounds was observed in winter samples, which may be related to biomass burning [30,34]. In contrast, the relative abundance (45%) of CHOS compounds in the summer was much larger than (22%) in the winter. This can be explained by the frequent agricultural activities which occurred during the summer in the rural region of Quzhou County; the fuel emissions produced by agricultural machinery may lead to an increase in sulfur-containing organic compounds [51]. The results suggest a clear seasonal variation in the chemical composition of OA, which likely is due to the different ambient meteorological conditions and human activities in different seasons.
Figure 2a shows the distribution of the carbon atom number and DBE of organic compounds in 12 monthly collected PM2.5 samples in the rural site of Quzhou County, the NCP. It suggests that compounds with C ≤ 14 accounted for the major fraction (80%) and species containing 5–8 carbon atoms (for example, the oxidation products of monoterpenes [32]) exhibited the greatest peak abundance. As shown in Figure 2b, the highest relative abundance of compounds with only 2–4 carbon atoms (such as the oxidation products of glyoxal [52]) was observed in the summer, while it was at its lowest in the winter. Among the four seasons, the relative abundance of compounds with more than 9 carbon atoms in the winter was the highest. Moreover, spring samples contained the strongest abundance of compounds with C > 22, followed by winter and autumn samples, while the lowest abundance of compounds with C > 22 was observed in the summer. Compounds with more than 22 carbons were probably generated from long-chain alkanes, which were emitted from fossil fuel combustion [32]. This can be explained by the agricultural activities (such as sowing and spraying pesticide), which utilize diesel engines and residential heating with coal burning in the NCP, that occurred frequently in the spring and winter, respectively, resulting in the production of more organic compounds with higher carbon numbers [53]. With the number of carbon atoms increasing, the unsaturation of compounds usually rises up. The unsaturation of components in the summer samples was the lowest. The abundance of compounds with DBE between 0–3 was higher than that of compounds with DBE ≥ 4, especially when the carbon number was in the range of 2–8.

3.3. Chemical Characteristics of Rural OA at Different Seasons

Figure 3 shows that H/C and O/C ratios of OA at different months and peak abundance-weighted average values of the elemental ratio of OA in different seasons, respectively. During a yearly cycle, a clear regular change was observed in the O/C ratio, which increased from April to August 2020 and then gradually decreased from August 2020 to February 2021. Moreover, among the four seasons, the organic compounds in the summer samples had the largest O/C ratio (1.21), followed by autumn (0.92), spring (0.87) and winter (0.64) samples, showing the following order of the oxidation degree of the samples: summer > autumn > spring > winter. Interestingly, we found that the relative peak abundance fractions of compounds with O/C ≥ 0.6 (which are considered to be highly oxidized molecules [27]) are 74%, 71% and 74% in June, July and August, respectively. The average value of the relative fraction of the highly oxidized compounds in the summer was 73%, which was much higher than the relative fraction of the samples in the other seasons, showing that summer OA samples had a higher degree of oxidation. This agrees with the observation made in Figure S2, which shows that more compounds with a low retention time (retention time ≤ 6 min), i.e., polar compounds with polar functional groups (such as hydroxy and carboxyl), were observed in the summer compared to the other seasonal samples.
As shown in Figure 3, the H/C ratio presented a similar trend to the O/C ratio; however, the highest value of O/C was observed in August. For seasonal results, the H/C ratio of OA in the summer samples was 1.60, which is higher than that in spring (1.29), autumn (1.37) and winter samples (1.20). The van Krevelen (VK) diagram plot of the H/C ratio as a function of the O/C ratio often describes the overall compositional characteristics of the complex organic mixture [54,55]. Compared with the other seasonal samples, the summer OA samples contained more compounds with both a high H/C ratio (≥1.5) and a high O/C ratio (≥0.5) as shown in the VK diagrams in Figure S3. In addition, the summer OA samples had the lowest value of DBE (2.96) and the lowest number of carbon atoms (6.83). This suggests that the organic compounds were more oxidized in the summer, and may have undergone intense oxidation and aging processes [27,34]. On the contrary, the lowest of the O/C and H/C ratios was 0.64 and 1.20, respectively, and these values were observed in the winter OA samples, which showed the highest values of DBE (5.50) and the largest number of carbon atoms (9.10). This result is in strong agreement with the observation that was made, which is that more compounds are attributed to low-oxygen-containing aromatic hydrocarbons with a low H/C ratio (≤1.0) and a low O/C ratio (≤0.5) in the winter (see Figure S3). In addition, the highly oxidized molecules accounted for 40% of the relative peak abundance fraction in the winter samples, which was lower than in the samples from other seasons. This suggests that the winter OA samples were more unsaturated and experienced a lesser degree of oxidation. Furthermore, comparing the spring and autumn samples, the autumn OA samples had a lower molecular mass, DBE and carbon atom number, but higher O/C and H/C. It is considered that the oxidation degree of the samples in autumn was higher in comparison to spring. The results indicate that oxidation characteristics of organic compounds vary in different seasons, which may be affected by different temperatures, humidity, solar radiation intensity and human activities [56,57,58].
Besides oxygenation characteristics, the aromaticity of organic species exhibited remarkable differences between the four seasons, as shown in Figure 4. The size of the circles is proportional to the fourth root of the peak abundance of each formula, and reduces the area difference of the circles since the peak abundance can vary by orders of magnitude. Organic compounds observed in this study were mainly assigned to non-aromatic compounds with XC < 2.50 (gray), spanning a large region of carbon atoms and DBE values, indicating that the OA samples in the rural site of Quzhou County, the NCP, were dominated by aliphatic compounds in all seasons. The relative peak abundance fraction of aliphatic compounds in total organic species was 89% in summer, 77% in spring, 78% in autumn and 60% in winter. Moreover, a total of 11–40% of organic compounds (in terms of peak abundance) were assigned to monoaromatics (2.50 < XC ≤ 2.71, light blue) or polyaromatics (XC ≥ 2.71, dark blue) in seasonal samples, which lie in the region of C atoms ≥ 3 and DBE values ≥ 5. The largest relative fractions of monoaromatics (22%) and polyaromatics (18%) were observed in the winter, indicating that rural OA during the wintertime was highly affected by anthropogenic precursors (which may be related to coal-fired heating in the northern winter [53]. Furthermore, in spring and autumn samples, the relative fraction of monoaromatics was 10% and 11%, respectively, while that of polyaromatics was 13% and 11%, respectively. In contrast, the relative fraction of mono- and polyaromatic compounds in the summer samples was 6% and 5%, respectively, which were obviously lower compared to that in the other seasons. This indicates that summer OA samples possessed a lower degree of unsaturation and aromaticity. The results suggest that there are clear differences in terms of the distribution of aliphatic compounds and aromatics in OA from four seasons, likely due to the different OA sources [30,59].

3.4. Rural OA Source Apportionment

Figure 5 shows the Oxidation State of Carbon (OSc) as a function of the carbon number for samples collected during the spring, summer, autumn and winter, which can be used to identify potential sources of organic aerosol components. According to previous studies [3,28,37], the molecules with an OSc between −1 and −2 with 18 or more carbon atoms are suggested to be associated with hydrocarbon-like organic aerosol (HOA). The molecules with an OSc between −1.25 and −0.25 with 7–23 carbon atoms are associated with biomass burning organic aerosol (BBOA) that is directly emitted into the atmosphere. The molecules with an OSc between −0.5 and + 0.25 with 5–18 carbon atoms are associated with semi-volatile oxygenated organic aerosol (SV-OOA), while the molecules with an OSc between +0.25 and +1.0 with 4–13 carbon atoms are associated with low-volatility oxygenated organic aerosol (LV-OOA). As shown in Figure 5, the identified organic compounds in different seasonal samples had an intense distribution in four circles, which illustrates the diversity and complexity of the sources of atmospheric OA in the rural site of Quzhou County, the NCP. A large amount of BBOA was found in all of the seasons, indicating that biomass combustion events occurred frequently [30]. This observation agrees well with the previous studies, showing that biomass burning is an important OA source in rural regions [53,59]. Interestingly, more BBOA-associated molecules were obtained in both the summer and winter samples, which may be related to the summer wheat harvest in the NCP region and the burning of materials for heat in the winter, respectively [59,60,61]. In addition, more molecules attributed to LV-OOA and SV-OOA were observed in the summer compared to such compounds in the other seasons. Since LV-OOA compounds are often produced through multiple oxidation steps, it indicates that summer OA compounds may experience more intense atmospheric oxidation and aging [59,62,63].

4. Conclusions

In this study, we applied the UHPLC-Orbitrap MS for the analysis of organic compounds in PM2.5 samples collected from Augus 2020 to July 2021 at a rural site of Quzhou County, the NCP. In total, 1094–1908 organic compounds were observed in the seasonal samples. The results suggest that the chemical composition of these compounds vary during different seasons. PM2.5 samples from the spring and winter seasons were similar in terms of PM2.5 concentration and chemical composition, while summer OA samples had a similar concentration and chemical composition to autumn OA samples. Furthermore, the peak abundance-weighted method was used to illustrate the difference in chemical formulas assigned with the Orbitrap MS. We found that the chemical characteristics of organic compounds in the summer were significantly different from those in the winter. Summer OA shows a higher degree of oxidation and saturation with a high O/C ratio (1.21) and a low DBE value (2.96), which may be caused by high solar radiation and high temperatures in the summer. In contrast, the winter OA compounds are less oxidized and more unsaturated and have a low O/C ratio (0.64) and a high DBE value (5.50). Moreover, a greater relative fraction of aromatic compounds was observed in the winter samples, indicating that winter OA is more affected by anthropogenic precursors from combustion activities. In addition, a large amount of BBOA was found in all of the seasons, indicating that biomass burning is an important OA source in the rural region of Quzhou County, the NCP. To further quantify the sources of OA in the rural site of Quzhou County, the NCP, an online measurement campaign with the application of the Aerodyne aerosol mass spectrometer could be carried out in future studies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos14111636/s1, Text S1: Workflow of hierarchical cluster analysis (HCA); Figure S1: The three-dimensional plot of MZmine non-targeted analysis of UHPLC-UHRMS raw file. The first dimensional shows retention time of individual compounds. The second dimensional shows m/z of individual compounds. The third dimensional illustrates the intensity of individual organic compounds.; Figure S2: The retention time of organic compounds in PM2.5 samples collected from (a) spring, (b) summer, (c) autumn, and (d) winter. The dotted line divides the compounds located into three regions (retention time ≤ 2 min, 2 < retention time ≤ 6 min and retention time > 6 min). The value shows the percentage of compounds located in each retention time region; Figure S3: The van Krevelen diagrams for individual compounds in PM2.5 samples collected from (a) spring, (b) summer, (c) autumn, and (d) winter. The size of the bubbles indicates the fourth root of the intensity of each compound. The ‘A’ and ‘B’ regions refer to aliphatic compounds and low-oxygen-containing aromatic hydrocarbons in organic aerosol, respectively.

Author Contributions

Conceptualization, Y.Z.; methodology, Y.Z.; software, X.G.; validation, X.H. and M.L.; formal analysis, Y.Z. and X.G.; investigation, Y.Z., X.G., X.H. and M.L.; resources, J.H. and H.Z.; data curation, Y.Z., X.G., X.H. and M.L.; writing—original draft preparation, Y.Z.; writing—review and editing, Y.Z., J.H. and H.Z.; visualization, X.G.; supervision, J.H. and H.Z.; project administration, J.H.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (NSFC, grant no. 42207125) and the 2115 Talent Development Program of China Agricultural University.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to partly data applied in other unpublished manuscripts.

Acknowledgments

We thank the four anonymous reviewers for their constructive comments that led to the improvements of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Atmosphere 14 01636 g001
Figure 1. Hierarchical cluster analysis (HCA) of PM2.5 samples in 12 months (a) and 4 seasons (b) in the rural site of Quzhou County, the NCP. The closer the dendrograms are in the horizontal and vertical directions, the more similar the chemical composition of the samples is. Darker colors represent higher concentrations of compounds.
Figure 1. Hierarchical cluster analysis (HCA) of PM2.5 samples in 12 months (a) and 4 seasons (b) in the rural site of Quzhou County, the NCP. The closer the dendrograms are in the horizontal and vertical directions, the more similar the chemical composition of the samples is. Darker colors represent higher concentrations of compounds.
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Figure 2. Distribution of the carbon number and DBE of monthly samples (a) and seasonal samples (b). The colors indicate different carbon number distributions and DBE subgroups.
Figure 2. Distribution of the carbon number and DBE of monthly samples (a) and seasonal samples (b). The colors indicate different carbon number distributions and DBE subgroups.
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Figure 3. Plots of the O/C (red) and H/C (blue) values of organic compounds in monthly and seasonal PM samples collected in the rural site of Quzhou County, the NCP, from August 2020 to July 2021. The solid circle refers to the H/C (red) or O/C (blue) observed in each PM2.5 sample, while the hollow circle refers to the average H/C (red) or O/C (blue) observed in each seasonal PM2.5 sample.
Figure 3. Plots of the O/C (red) and H/C (blue) values of organic compounds in monthly and seasonal PM samples collected in the rural site of Quzhou County, the NCP, from August 2020 to July 2021. The solid circle refers to the H/C (red) or O/C (blue) observed in each PM2.5 sample, while the hollow circle refers to the average H/C (red) or O/C (blue) observed in each seasonal PM2.5 sample.
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Figure 4. Double bond equivalent (DBE) versus carbon number of organic compounds for the four seasons, i.e., (a) spring, (b) summer, (c) autumn and (d) winter. The color bar denotes the aromaticity equivalent (gray (XC < 2.50), light blue (2.50 ≤ XC < 2.71) and dark blue (XC ≥ 2.71)). The size of the circles corresponds to the power of the fourth root of the peak abundance of individual compounds. The pie charts represent the percentage of each XC category.
Figure 4. Double bond equivalent (DBE) versus carbon number of organic compounds for the four seasons, i.e., (a) spring, (b) summer, (c) autumn and (d) winter. The color bar denotes the aromaticity equivalent (gray (XC < 2.50), light blue (2.50 ≤ XC < 2.71) and dark blue (XC ≥ 2.71)). The size of the circles corresponds to the power of the fourth root of the peak abundance of individual compounds. The pie charts represent the percentage of each XC category.
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Figure 5. Carbon oxidation state plot for organic compounds in PM2.5 samples from the four seasons, i.e., (a) spring, (b) summer, (c) autumn and (d) winter, in the rural site of Quzhou County, the NCP. The black dash oval areas are marked as hydrocarbon-like organic aerosol (HOA), biomass burning organic aerosol (BBOA), semi-volatile oxygenated organic aerosol (SV-OOA) and low-volatility oxygenated organic aerosol (LV-OOA) and correspond accordingly to these compounds in the figure, as shown.
Figure 5. Carbon oxidation state plot for organic compounds in PM2.5 samples from the four seasons, i.e., (a) spring, (b) summer, (c) autumn and (d) winter, in the rural site of Quzhou County, the NCP. The black dash oval areas are marked as hydrocarbon-like organic aerosol (HOA), biomass burning organic aerosol (BBOA), semi-volatile oxygenated organic aerosol (SV-OOA) and low-volatility oxygenated organic aerosol (LV-OOA) and correspond accordingly to these compounds in the figure, as shown.
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Table 1. The sampling date, number of molecular formulas, relative peak abundance fraction (RF) of each subgroup, the peak abundance-weighted average values of molecular mass (MW), elemental ratios and double bond equivalent (DBE) for detected organic compounds in each month and different seasons.
Table 1. The sampling date, number of molecular formulas, relative peak abundance fraction (RF) of each subgroup, the peak abundance-weighted average values of molecular mass (MW), elemental ratios and double bond equivalent (DBE) for detected organic compounds in each month and different seasons.
Sample IDSampling DateMolecular Formulas
Number		RFCHO (%)RFCHON (%)RFCHOS (%)RFCHONS (%)Molecular Mass (Da)H/CO/CDBE
Mar *11 April 25150130153817211 ± 11.31 ± 0.000.90 ± 0.014.01 ± 0.02
Apr *15 May 24109432163418231 ± 11.21 ± 0.000.80 ± 0.005.02 ± 0.03
May *12 May 22124931153915231 ± 11.35 ± 0.010.92 ± 0.014.53 ± 0.02
Spring 128131153717225 ± 101.29 ± 0.060.87 ± 0.054.52 ± 0.41
Jun *15 February 25161331104613210 ± 11.63 ± 0.011.19 ± 0.012.83 ± 0.01
Jul *14 April 22178730114415212 ± 11.61 ± 0.011.21 ± 0.012.95 ± 0.01
Aug **11 January 25147330114514211 ± 11.56 ± 0.011.23 ± 0.013.11 ± 0.01
Summer 162430114514211 ± 11.60 ± 0.031.21 ± 0.022.96 ± 0.11
Sep **11 January 25140139143512216 ± 11.50 ± 0.011.07 ± 0.013.67 ± 0.01
Oct **24 January 24164935202817217 ± 01.39 ± 0.000.88 ± 0.004.13 ± 0.01
Nov **14 May 23147825193323224 ± 11.21 ± 0.000.81 ± 0.004.82 ± 0.02
Autumn 150933183217219 ± 41.37 ± 0.120.92 ± 0.114.21 ± 0.47
Dec **15 January 23190836212518222 ± 11.19 ± 0.000.65 ± 0.005.54 ± 0.02
Jan *12 March 21182435301817212 ± 11.21 ± 0.000.63 ± 0.005.46 ± 0.02
Feb *16 June 25132025282522234 ± 11.19 ± 0.000.63 ± 0.005.49 ± 0.02
Winter 168432272219223 ± 91.20 ± 0.010.64 ± 0.015.50 ± 0.03
Note: * The sampling year was 2021. ** The sampling year was 2020.
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Zhang, Y.; Gao, X.; Hou, X.; Liu, M.; Han, J.; Zhang, H. Chemical Characterization of Rural Organic Aerosol in the North China Plain Using Ultrahigh-Resolution Mass Spectrometry. Atmosphere 2023, 14, 1636. https://doi.org/10.3390/atmos14111636

AMA Style

Zhang Y, Gao X, Hou X, Liu M, Han J, Zhang H. Chemical Characterization of Rural Organic Aerosol in the North China Plain Using Ultrahigh-Resolution Mass Spectrometry. Atmosphere. 2023; 14(11):1636. https://doi.org/10.3390/atmos14111636

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Zhang, Yun, Xu Gao, Xingang Hou, Mingyuan Liu, Jiajun Han, and Hongyan Zhang. 2023. "Chemical Characterization of Rural Organic Aerosol in the North China Plain Using Ultrahigh-Resolution Mass Spectrometry" Atmosphere 14, no. 11: 1636. https://doi.org/10.3390/atmos14111636

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