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Fluorescence Properties and Chemical Composition of Fine Particles in the Background Atmosphere of North China


doi: 10.1007/s00376-022-2208-x

  • To understand the aerosol characteristics in a regional background environment, fine-particle (PM2.5, n = 228) samples were collected over a one-year period at the Shangdianzi (SDZ) station, which is a Global Atmospheric Watch regional background station in North China. The chemical and optical characteristics of PM2.5 were analyzed, including organic carbon, elemental carbon, water-soluble organic carbon, water-soluble inorganic ions, and fluorescent components of water-soluble organic matter. The source factors of major aerosol components are apportioned, and the sources of the fluorescent chromophores are further analyzed. The major chemical components of PM2.5 at SDZ were ${\rm{NO}}_3^- $, organic matter, ${\rm{SO}}_4^{2-} $, and ${\rm{NH}}_4^+ $. Annually, water-soluble organic carbon contributed 48% ± 15% to the total organic carbon. Secondary formation (52%) and fossil fuel combustion (63%) are the largest sources of water-soluble organic matter and water-insoluble organic matter, respectively. In addition, three humic-like and one protein-like matter were identified via parallel factor analysis for excitation–emission matrices. The fluorescence intensities of the components were highest in winter and lowest in summer, indicating the main impact of burning sources. This study contributes to understanding the chemical and optical characteristics of ambient aerosols in the background atmosphere.
    摘要: 地处北京市远郊区的上甸子区域大气本底站气溶胶的化学组分浓度,能很好地代表京津冀地区大气区域本底的背景浓度,同时也反映北京城区和周边地区污染物传输的影响。本文在上甸子站采集了四个季节共228个PM2.5样品,通过分析其化学组分和荧光性质,包括有机碳、元素碳、水溶性有机碳、水溶性无机离子以及水溶性有机物的荧光组分,并利用气团后向轨迹和正矩阵因数分解法解析来源,为进一步研究京津冀地区气溶胶本底浓度变化,特别是人类活动如何影响背景地区气溶胶形成提供观测依据。结果表明,上甸子PM2.5的化学组分与城市气溶胶组成基本一致,主要为NO3-、有机物、SO42-和NH4+。其中,SO42-在夏季浓度最高,其他组分浓度是秋季最高。水溶性有机碳占总有机碳的48%±15%。大气二次生成(52%)和化石燃料燃烧(63%)分别是水溶性有机物和水不溶性有机物的最大来源。运用平行因子分析法解析三维荧光光谱确定三种类腐殖酸和一种类蛋白荧光组分,荧光强度在冬季最强、夏季最弱。荧光强度的季节变化和荧光指数表明,水溶性荧光物质主要是燃烧源和二次生成的贡献,少量来源于微生物源。与冬季相比,夏季大气荧光物质更倾向于高度腐殖化或高度芳香化。研究发现,即使在上甸子这样的空气洁净地区,化石燃料燃烧和生物质燃烧等人为排放源对气溶胶的贡献也非常显著。
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  • Figure 1.  Location of the sampling site (Shangdianzi, SDZ) and 72-h backward trajectories in four seasons. The SDZ site is located approximately 150 km northeast of Beijing, on the edge of the North China Plain. (a) Autumn (October and November), (b) winter (December, January, and February), (c) spring (March and April), and (d) summer (July). The backward trajectories were calculated for an altitude of 500 m above ground. The MeteoInfo software was used for the calculation and plotting. Only major clusters with a contribution above 15% are shown.

    Figure 2.  Contributions of the chemical species to total mass concentrations of PM2.5 (EC + OM + ∑anions + ∑cations) at the SDZ site: (a) autumn, (b) winter, (c) spring, (d) summer, and (e) the annual average. The minor cations include Ca2+ and Mg2+, and the minor anions include ${\rm{NO}}_2^- $, F, ${\rm{PO}}_4^{3-} $, and MSA.

    Figure 3.  Time series of the chemical composition concentrations in PM2.5 at the SDZ site. Additional data for the minor ions are shown in Fig. S5 in the ESM.

    Figure 4.  Modeled concentrations (a) and fractional contributions (b) of different factors to the mass concentrations of WSOM and WIOM. These six source factors are resolved by the PMF analysis. The measurement results for the seasonal and annual mean concentration of WSOM and WIOM (as in Table 1) are also shown in subplot (a) as circles. Major contributions (> 10%) in the annual average are marked in the plots.

    Figure 5.  Fluorescent properties of the water-soluble organic matter (WSOM) in PM2.5 at the SDZ site. (a) Excitation–emission matrix (EEM) spectrum fingerprints of the identified fluorescent components are resolved by the PARAFAC model. The excitation and emission wavelengths of the peak intensity of each component are shown in the plots. The four components, C1, C2, C3, and C4, are named HULIS-2, HULIS-1, PLOM, and HULIS-3, respectively. (b) Seasonal variations in the fluorescence intensities of the four components and their relative fractions. (c) Seasonal variations in the humification index (HIX), fluorescence index (FI), and biological index (BIX).

    Table 1.  Seasonal variations in the mass concentrations of the chemical species in PM2.5 at SDZ. The detection limits for water-soluble inorganic ions are 0.001 µg m−3. BDL means below the detection limit.

    ComponentsAutumn (n = 54)Winter (n = 89)Spring (n = 62)Summer (n = 23)Annual (n = 228)
    RangeMean ± SDRangeMean ± SDRangeMean ± SDRangeMean ± SDRangeMean ± SD
    Carbonaceous
    Components
    (µg m–3)
    OC0.95–238.1±5.01.1–185.4±3.50.66–164.6±3.20.78–5.73.2±1.40.66–235.6±4.0
    EC0.09–3.31.0±0.730.11–1.80.66±0.410.03–2.00.57±0.400.0–0.680.39±0.190.0–3.20.70±0.53
    TC1.1–269.1±5.71.2–206.0±3.90.69–185.2±3.60.78–6.43.6±1.50.69–266.3±4.5
    WSOC0.28–123.4±2.60.66–8.42.1±1.60.51–102.7±2.00.36–4.72.3±1.10.28–122.6±2.0
    WIOC0.43–114.7±2.60.24–103.3±2.20.14–5.61.9±1.30.35–1.90.92±0.400.14–113.0±2.3
    OM1.3–3211±7.01.5–257.5±5.00.92–226.4±4.51.1–8.04.5±1.90.92–327.8±5.6
    Water-Soluble
    Inorganic Ions
    (µg m–3)
    ${\rm{SO}}_4^{2-} $0.70–225.2±5.40.49–152.4±2.20.53–164.0±3.40.16–167.1±4.70.16–224.0±4.0
    ${\rm{NO}}_3^- $0.28–6514±180.18–254.3±6.00.25–6011±130.1–103.4±3.40.10–658.4±12
    ${\rm{NH}}_4^+ $0.14–255.7±6.80.19–142.3±2.90.0–254.4±5.40.18–104.0±2.50.0–253.8±5.0
    K+0.03–1.50.44±0.340.03–3.20.31±0.390.02–1.10.29±0.230.0–0.230.08±0.060.0–3.20.31±0.33
    Ca2+BDL–4.70.78±1.0BDL–2.30.05±0.25BDL–0.610.06±0.13BDL–0.810.02±0.17BDL–4.70.23±0.60
    Na+0.09–3.20.76±0.440.15–2.30.42±0.240.23–0.880.51±0.12BDL–0.160.05±0.04BDL–3.20.49±0.33
    Cl0.19–4.21.2±0.840.0–3.80.95±0.810.09–2.30.65±0.51BDL–0.330.09±0.11BDL–4.20.85±0.78
    MSA0.0–0.390.06±0.070.0–0.120.01±0.020.0–0.070.01±0.010.0–0.040.01±0.010.0–0.390.02±0.04
    ${\rm{NO}}_2^- $0.0–1.60.45±0.430.06–1.20.26±0.180.04–1.20.29±0.230.09–0.470.32±0.10.0–1.60.32±0.28
    Mg2+0.0–0.480.12±0.140.0–0.640.01±0.070.0–0.190.02±0.030.0–0.130.04±0.030.0–0.640.05±0.09
    ${\rm{PO}}_4^{3-} $BDL–0.790.07±0.12BDL–0.630.04±0.08BDL–0.320.03±0.06BDL–0.110.04±0.03BDL–0.790.05±0.08
    F0.0–0.140.02±0.030.0–0.060.01±0.010.0–0.030.0±0.00.0–0.00.0±0.00.0–0.140.01±0.02
    DownLoad: CSV

    Table 2.  Seasonal variations in the ratios between the species and the fluorescent indices in PM2.5 at SDZ.

    ParameterAutumn (n = 54)Winter (n = 89)Spring (n = 62)Summer (n = 23)Annual (n = 228)
    RangeMean ± SDRangeMean ± SDRangeMean ± SDRangeMean ± SDRangeMean± SD
    OC/EC5.5–228.7±2.65.9–178.2±1.66.2–208.4±2.14.2–148.8±2.54.2–228.4±2.1
    EC/TC0.04–0.150.11±0.020.06–0.140.11±0.020.05–0.140.11±0.020.0–0.190.10±0.040.0–0.190.11±0.02
    WSOC/OC*0.14–0.600.40±0.090.21–0.780.40±0.110.44–0.820.60±0.090.46–0.820.70±0.100.14–0.820.48±0.15
    +/∑0.92–1.31.1±0.120.76–1.70.99±0.140.06–1.41.0±0.250.51–1.31.1±0.140.06–1.71.0±0.18
    ${\rm{NO}}_3^- $/${\rm{SO}}_4^{2-} $0.21–5.42.2±1.50.14–4.51.4±1.10.23–6.92.3±1.50.04–1.70.55±0.510.04–6.91.8±1.4
    K+/EC0.10–1.00.44±0.200.12–4.60.46±0.550.13–2.00.52±0.280.08–0.370.20±0.080.08–4.60.45±0.40
    HIX1.0–3.72.2 ± 0.630.96–2.81.6 ± 0.521.6–4.32.6 ± 0.611.8–7.73.4 ± 1.20.96–7.72.2 ± 0.89
    FI1.2–1.51.4 ± 0.081.2–1.61.4 ± 0.091.2–1.41.3 ± 0.051.2–1.41.3 ± 0.041.2–1.61.4 ± 0.09
    BIX0.75–1.41.0 ± 0.170.99–1.41.2 ± 0.120.82–1.31.0 ± 0.110.71–1.00.87 ± 0.100.71–1.41.1 ± 0.18
    * WSOC/OC = WSOM/OM.
    DownLoad: CSV

    Table 3.  Correlation coefficients (r) between the six source factors of WSOM and the fluorescent components and the indices measured in the water extracts in PM2.5 at the Shangdianzi (SDZ) site in (a) autumn, (b) winter, (c) spring, and (d) summer. Correlation coefficients r ≥ 0.50 or r ≤ –0.50 are highlighted in bold.

    Source factorsHULIS-1HULIS-2HULIS-3PLOMHIXFIBIX
    Autumn
    Secondary formation-10.620.29−0.11−0.080.57−0.44−0.57
    Secondary formation-20.350.11−0.18−0.150.51−0.35−0.53
    Biomass burning0.630.630.320.280.05−0.11−0.01
    Fossil fuel combustion0.660.710.700.68−0.110.140.20
    Dust0.510.13−0.06−0.080.36−0.31−0.35
    Industry−0.02−0.12−0.05−0.080.22−0.18−0.29
    Winter
    Secondary formation-10.890.800.310.140.60−0.54−0.56
    Secondary formation-20.580.490.05−0.100.60−0.47−0.50
    Biomass burning0.790.680.280.140.51−0.46−0.51
    Fossil fuel combustion0.600.750.800.77−0.160.090.07
    Dust0.020.030.040.07−0.040.01−0.02
    Industry−0.19−0.120.080.19−0.310.300.25
    Spring
    Secondary formation-10.870.740.210.350.66−0.42−0.44
    Secondary formation-20.730.570.070.200.70−0.45−0.54
    Biomass burning0.710.790.740.810.00−0.060.10
    Fossil fuel combustion0.420.330.180.250.24−0.18−0.21
    Dust0.360.280.240.200.12−0.15−0.11
    Industry−0.55−0.59−0.38−0.46−0.220.220.09
    Summer
    Secondary formation-10.870.82−0.040.76−0.01−0.33−0.42
    Secondary formation-20.770.67−0.190.370.14−0.39−0.87
    Biomass burning0.790.70−0.080.580.12−0.45−0.44
    Fossil fuel combustion−0.14−0.010.030.01−0.240.400.22
    Dust−0.21−0.170.21−0.200.000.150.12
    Industry−0.21−0.180.16−0.250.010.150.05
    Notes: Autumn: t-test is p ≤ 0.0001 for the correction where r is ≥ 0.51. Winter: t-test is p < 0.0001 for the correction where r is ≥ 0.51. Spring: t-test is p < 0.0001 for the correction where r is ≥ 0.57. Summer: t-test is p < 0.0001 for the correction where r is > 0.67; t-test is 0.0001 < p < 0.01 for the correction where r is from 0.58 to 0.67.
    DownLoad: CSV
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Manuscript received: 05 August 2022
Manuscript revised: 25 November 2022
Manuscript accepted: 13 December 2022
通讯作者: 陈斌, bchen63@163.com
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Fluorescence Properties and Chemical Composition of Fine Particles in the Background Atmosphere of North China

    Corresponding author: Pingqing FU, fupingqing@tju.edu.cn
  • 1. State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 2. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
  • 3. Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
  • 4. University of Chinese Academy of Sciences, Beijing 100049, China

Abstract: To understand the aerosol characteristics in a regional background environment, fine-particle (PM2.5, n = 228) samples were collected over a one-year period at the Shangdianzi (SDZ) station, which is a Global Atmospheric Watch regional background station in North China. The chemical and optical characteristics of PM2.5 were analyzed, including organic carbon, elemental carbon, water-soluble organic carbon, water-soluble inorganic ions, and fluorescent components of water-soluble organic matter. The source factors of major aerosol components are apportioned, and the sources of the fluorescent chromophores are further analyzed. The major chemical components of PM2.5 at SDZ were ${\rm{NO}}_3^- $, organic matter, ${\rm{SO}}_4^{2-} $, and ${\rm{NH}}_4^+ $. Annually, water-soluble organic carbon contributed 48% ± 15% to the total organic carbon. Secondary formation (52%) and fossil fuel combustion (63%) are the largest sources of water-soluble organic matter and water-insoluble organic matter, respectively. In addition, three humic-like and one protein-like matter were identified via parallel factor analysis for excitation–emission matrices. The fluorescence intensities of the components were highest in winter and lowest in summer, indicating the main impact of burning sources. This study contributes to understanding the chemical and optical characteristics of ambient aerosols in the background atmosphere.

摘要: 地处北京市远郊区的上甸子区域大气本底站气溶胶的化学组分浓度,能很好地代表京津冀地区大气区域本底的背景浓度,同时也反映北京城区和周边地区污染物传输的影响。本文在上甸子站采集了四个季节共228个PM2.5样品,通过分析其化学组分和荧光性质,包括有机碳、元素碳、水溶性有机碳、水溶性无机离子以及水溶性有机物的荧光组分,并利用气团后向轨迹和正矩阵因数分解法解析来源,为进一步研究京津冀地区气溶胶本底浓度变化,特别是人类活动如何影响背景地区气溶胶形成提供观测依据。结果表明,上甸子PM2.5的化学组分与城市气溶胶组成基本一致,主要为NO3-、有机物、SO42-和NH4+。其中,SO42-在夏季浓度最高,其他组分浓度是秋季最高。水溶性有机碳占总有机碳的48%±15%。大气二次生成(52%)和化石燃料燃烧(63%)分别是水溶性有机物和水不溶性有机物的最大来源。运用平行因子分析法解析三维荧光光谱确定三种类腐殖酸和一种类蛋白荧光组分,荧光强度在冬季最强、夏季最弱。荧光强度的季节变化和荧光指数表明,水溶性荧光物质主要是燃烧源和二次生成的贡献,少量来源于微生物源。与冬季相比,夏季大气荧光物质更倾向于高度腐殖化或高度芳香化。研究发现,即使在上甸子这样的空气洁净地区,化石燃料燃烧和生物质燃烧等人为排放源对气溶胶的贡献也非常显著。

    • Atmospheric particulate matter not only affects atmospheric visibility, regional air quality, and the Earth’s radiation balance but also adversely affects human health (Pöschl, 2005; Glasius and Goldstein, 2016; Finlayson-Pitts et al., 2020). A previous study has revealed that human deaths caused by fine particulate matter (PM2.5) in the atmosphere accounted for 12% of all reported deaths (Zou et al., 2019), for example, death from asthma (Reynolds, 2020). Further exploring the chemical components, seasonal variations, and sources of fine aerosol particles is necessary to address the risks of air pollution in humans (Li et al., 2021). However, the chemical composition of atmospheric aerosols is highly complex in terms of inorganic and organic species (Goldstein and Galbally, 2007; Zhang et al., 2015; Huang et al., 2021). Inorganic components comprise water-soluble inorganic ions (WSIIs) and water-insoluble components (e.g., mineral elements and heavy metals), whereas organic components comprise a large spectrum of organic species (Zheng et al., 2005; Wang et al., 2006; Fu et al., 2008; Cong et al., 2015; Zhao et al., 2018). The chemical compositions of the particles vary with the source.

      Most studies have focused on the characteristics, chemical composition, sources, and formation of urban and rural aerosols, as well as the impact of surrounding areas on urban aerosols under different meteorological conditions. However, only a few studies have studied the aerosol formation in relatively clean areas in China (Wu et al., 2021b). Human activities have distinctly impacted remote environments, even polar regions, via atmospheric transportation (Mu et al., 2018; Ye et al., 2021; Yue et al., 2022b). A recent study has revealed that nitrogen oxides (NOx) emitted in nearby cities can promote the formation of biogenic secondary organic aerosols (SOA), exhibiting an increase of 60%–200% (up to 400%) in the Amazon rainforest (Shrivastava et al., 2019). Therefore, studying the aerosol characteristics in clean regions is imperative but has received little attention thus far.

      Anthropogenic emissions in the North China Plain (NCP) are typical and have been comprehensively studied (Lang et al., 2017; Zhao et al., 2017; Qi et al., 2018; Ji et al., 2019; Xu et al., 2019; Tang et al., 2020; Wu et al., 2020; Khan et al., 2021; Su et al., 2021). However, few studies have focused on the effect of anthropogenic emissions in the NCP on ambient aerosol formation in neighboring clean environments. In this study, fine aerosol (PM2.5) samples were collected at Shangdianzi (SDZ), which is an atmospheric background station on the northern edge of the NCP, during all four seasons from 2017 to 2018. This station is ideal for representing regional background tropospheric aerosol concentrations in the Beijing–Tianjin–Hebei (BTH) metropolitan area (Zhao et al., 2013b; Yang et al., 2017; Pu et al., 2020). This study can provide an observational reference basis for understanding how anthropogenic activities affect background aerosols.

      Herein, we present laboratory measurements of the chemical compositions of ambient aerosols, including organic carbon (OC), elemental carbon (EC), water-soluble organic carbon (WSOC), and WSIIs, as well as fluorescence characteristics (excitation-emission matrix, EEM) of the water-soluble organic matter (WSOM) of background aerosols. Potential sources were identified based on the dominant chemical components through correlations, positive matrix factorization (PMF), and backward trajectories of air masses. Fluorescence spectroscopy and fluorescence indices have been used to examine the sources and properties of organic matter (OM) to further reveal the possible chromophores in WSOM (Chen et al., 2016b, 2017; Yan and Kim, 2017; Zhao et al., 2019; Li et al., 2022; Zhan et al., 2022). This study reports the aerosol characteristics including the dominant chemical compositions, seasonal variations, fluorescence properties, and sources of major organic matter at the regional SDZ station.

    2.   Materials and methods
    • PM2.5 samples were obtained from the SDZ station [40°39′N, 117°07′E, 293.9 m a.s.l. (above sea level); Fig. 1], which is a regional background monitoring station belonging to the Global Atmospheric Watch (GAW) program initiated by the World Meteorological Organization. The station is 150 km northeast of the downtown area of Beijing and close to the northern edge of the NCP, located on the slope of a hill in the suburb of the Miyun District of Beijing. As a major source of municipal water for Beijing, Miyun Reservoir is shielded from industrial activities as an “ecological conservation zone” (Yan et al., 2012b). Crop farming and horticultural farming are the major economic activities. The SDZ is approximately 50 km north of Miyun Town and is primarily surrounded by hills, croplands, orchards, and forests with sporadic and insignificant sources of anthropogenic pollution in the surrounding villages within a radius of 30 km from the station (Yan et al., 2012b; Yang et al., 2017). In this region, the prevailing monsoon blows from the southeast in summer and northwest in winter, whereas westerly winds typically dominate in both spring and autumn (Hänel et al., 2012). The pollutants from urban Beijing and its adjacent areas, such as Tianjin, Tangshan in Hebei Province, and Taiyuan and Datong in Shanxi Province, may be easily transported to the SDZ by westerly and southeasterly winds. Therefore, the observation site can be considered as a regional background site characteristic of atmospheric components in the northern NCP, particularly in the BTH region (Li et al., 2016; Yang et al., 2017).

      Figure 1.  Location of the sampling site (Shangdianzi, SDZ) and 72-h backward trajectories in four seasons. The SDZ site is located approximately 150 km northeast of Beijing, on the edge of the North China Plain. (a) Autumn (October and November), (b) winter (December, January, and February), (c) spring (March and April), and (d) summer (July). The backward trajectories were calculated for an altitude of 500 m above ground. The MeteoInfo software was used for the calculation and plotting. Only major clusters with a contribution above 15% are shown.

      Using an air sampler (High-volume TISCH, Cleves, USA) with a constant airflow rate (1.13 m3 min–1), PM2.5 samples were obtained on filters (quartz fiber, Pallflex, 20 cm × 25 cm), which were pre-combusted at 450°C for 6 h. The sampling was performed in four periods: 20 October–30 November 2017 (autumn), 1 December 2017–28 February 2018 (winter), 1 March–1 May 2018 (spring), and 12 July–31 July 2018 (summer). Field blank samples were also prepared by placing the prebaked filters on the sampler for several seconds to half a minute, without pumping. In total, 228 field samples and 5 blanks were collected. Following sampling, all filters were individually packed with annealed aluminum foil and stored in darkness at –20°C until the chemical analysis. To avoid positive artifacts, all tweezers used for sampling were cleaned with ultrapure Milli-Q water, methanol, and a mixture of methanol and dichloromethane (1:2 in volume), respectively.

    • OC and EC were examined using a thermal/optical carbon analyzer (Sunset Laboratory Inc., USA) following the temperature program set by the National Institute for Occupational Safety and Health, assuming that the carbonate carbon loading is negligible. The parameters typically change under different ambient conditions (e.g., temperature), leading to inaccurate measurement results. Instrumental parameters, particularly the calibration constant (µg carbon), were calibrated before each measurement. The sucrose standard (5 µg C µL−1) with 4 µL (20 µg C) was trialed three times to adjust the calibration parameters before starting the measurement. A gradient volume (2–10 µL) of sucrose solution was used to correct the constant when the relative error was greater than 5%. A punch filter with a 14-mm diameter was placed in a transparent quartz tube filter and was then steadily heated to 850°C in pure He, cooled down to 530°C, and then subsequently warmed to 870°C in the He/O2 mixture. One field blank was analyzed for every other 10 samples and subtracted from the sample concentrations to correct for OC and EC concentrations. The limit of detection was 0.20 µg cm−2, and the uncertainty was ±10% for both OC and EC. The analytical error in the triplicate analysis of sucrose standards was controlled at less than 5% for both OC and EC.

      WSOC was examined using a total organic carbon (TOC) analyzer (TOC-L CPH, Shimadzu, Japan). In brief, the filtered sample (two disks with 24-mm diameter) was extracted with 15 mL of ultrapure water (Millipore, MilliQIntegral5, USA) under ultra-sonication for 30 min at a low temperature with the application of an ice bag. The water extracts were then transferred via a PVDF syringe filter (Millipore Millex-GV, 0.22 µm) to sample tubes. The aforementioned extraction bottles, syringe filters, and sample tubes were made of glass and pre-combusted at 450°C in a muffle furnace for 6 h to remove artificial carbon. Linear regression of the potassium hydrogen phthalate standard (20 µg C mL−1) was used to quantify the WSOC concentration with a coefficient of determination (R2) ≥0.999. Ultrapure Milli-Q water was used to clean the instrument pipeline before the measurement until the WSOC concentration of the water was less than or equal to 0.09. During the measurement, ultrapure Milli-Q water was injected into every other 10 samples to prevent cross-contamination. The total carbon (TC) was equal to the sum of OC and EC. The organic matter (OM) is typically calculated by multiplying the OC concentration by a factor of 1.4, which is identical to the magnitude of the value adopted in the previous study at SDZ (Yan et al., 2012b). Water-insoluble organic carbon (WIOC) was calculated as the difference between OC and WSOC. The analytical reliability was maintained within 8% in duplicate analysis.

    • The WSIIs were determined via ion chromatography (DIONEX ICS-1100, Thermo Scientific, USA). In short, a small portion of the filtrate (two disks with 18-mm diameter) was extracted with 5 mL ultrapure water under ultra-sonication for 30 min at a low temperature with the application of an ice bag and then filtered using a syringe filter (0.22 µm, SCAA-214, ANPEL, Shanghai). All procedures were performed twice. The final solution was a mixture of the two extracts, in which seven anions (${\rm{NO}}_3^- $, ${\rm{SO}}_4^{2-}$, Cl, ${\rm{NO}}_2^- $, F, ${\rm{PO}}_4^{3-} $, and MSA) and five cations (${\rm{NH}}_4^+ $, K+, Na+, Ca2+, and Mg2+) were analyzed. Anions were separated using a column of IonPacTM AS11-HC (4 mm × 250 mm, Thermo Dionex, USA) equipped with an ASRS 300 self-regenerating suppressor (Thermo Dionex, USA). Anions were eluted using 30 mM NaOH at a flow rate of 1.5 mL min−1. Cations were separated using an IonPac CS12A column (4 mm × 250 mm, Thermo Dionex, USA) equipped with a CSRS 300 self-regenerating suppressor (Thermo Dionex, USA). The cations were eluted using 20 mM methanesulfonic acid at a flow rate of 1.0 mL min−1. Ultrapure Milli-Q water was injected into every other 10 samples to clean the instrument pipeline during measurement. The concentrations of the ions were determined via linear regression analysis of the standard (o2si smart solutions, USA) for each anion and cation with R2 ≥ 0.999.

    • We applied the positive matrix factorization (PMF) analysis to apportion the major aerosol composition [Table S1 in the electronic supplementary material (ESM)] to various source factors by using the EPA (Environmental Protection Agency, U.S.) PMF (v.5.0.). The principle of PMF has been well-documented in previous reports (Paatero and Tapper, 1994; Polissar et al., 1998). Briefly, The PMF analysis model is defined as X = G × F + E, where X is the matrix of measured major aerosol concentrations, G is the contribution factor matrix, F is the composition factor matrix, and E is the matrix of residuals.

      Here, these species are put into the PMF model: EC, WSOM, WIOM, Cl, ${\rm{SO}}_4^{2-} $, ${\rm{NO}}_3^- $, Na+, ${\rm{NH}}_4^+$, K+, Mg2+, and Ca2+. The number of factors was determined based on the factor profile, with consideration of the change of the Qtrue/Qexp with the number of factors (Brown et al., 2015). Qtrue is the sum of the squared scaled residuals; Qexp equals (number of non-weak data values in X) − (total number of elements in G and F) (Brown et al., 2015). Six factors were resolved and identified based on typical tracers (Fig. S1 in the ESM). The PMF model captures the variation of the input species well, as shown by the correlations between the observed and the modeled concentrations of the species (Table S1). We here focused on the source apportionment of WSOM and WIOM. Further, the source factors of WSOM are used to facilitate the understanding of the complex fluorescent components.

    • UV–Vis absorption spectra were measured in the range of 200−800 nm with an interval of 5 nm using a UV–Vis spectrophotometer (UV-3600, Shimadzu, Japan). Before scanning the sample extracts, Milli-Q water was used for the baseline correction. Fluorescence properties were measured using a Fluoromax-4 fluorometer (Horiba) at a constant temperature of approximately 20°C. The EEMs were obtained in the specified range in which the excitation wavelength was 240–455 nm and the emission wavelength was 290–550 nm with intervals of 5 nm and 2 nm, respectively. Following the program developed by Murphy et al. (2013), the EEMs were calibrated and normalized to the Raman peaks of Milli-Q water. The fluorescence intensity is reported in Raman units (RU L m–3) (Lawaetz and Stedmon, 2009). Parallel factor analysis (PARAFAC) for EEMs was performed with non-negativity constraints in MATLAB (drEEM toolbox 0.1.0 version). From the two- to ten-component PARAFAC models, the four-component model was selected, as the residuals decreased when the number of components increased from three to four (Fig. S2 in the ESM). Moreover, by comparing the spectra of the components, the four-component model was found to be more reasonable (Fig. S3 in the ESM). It can satisfy the evaluation of the model fit via the split-half method with the S4C6T3 style to correct the number of components (Fig. S4 in the ESM). This four-component resolution can explain 99.6% of the variations within the dataset.

      In addition, three fluorescence indices were calculated from the information contained in the EEMs. The humification index (HIX) is the ratio of H/P, assuming that H is the fluorescence intensity excited at 255 nm for the emission spectrum ranging from 436 nm to 480 nm, and P is the fluorescence intensity excited at 255 nm for the emission spectrum ranging from 300 nm to 344 nm. The fluorescence index (FI) is the ratio of the emission intensities at 450 nm to 500 nm with excitation at 370 nm. The biological index (BIX) is defined as the ratio of 310 nm/380 nm to 310 nm/430 nm (excitation/emission) (Yue et al., 2016, 2017). The fluorescence parameters are presented in Table S2.

    • Backward trajectory clustering was employed to trace the history of air masses with similar sources and transportation (Wang, 2014). The MeteoInfo model was adopted to analyze the trajectories of air masses at the SDZ during the sampling period. For each sample, 72-h backward trajectories were calculated at an elevation of 500 m using meteorological data from the Global Data Assimilation System of the National Oceanic and Atmospheric Administration. Every trajectory was computed every 6 h (including 0000, 0600, 1200, and 1800 LST, LST = UTC+8) for each day. The relative contributions of different air masses arriving in the SDZ were obtained through a cluster analysis of air mass trajectories for each season. In particular, clusters with a major contribution are given in detail.

    3.   Results and discussion
    • Figure 1 shows the 72-h backward air mass trajectories in the four seasons. The SDZ is highly affected by northwest air masses transported from Mongolia and Inner Mongolia during autumn, winter, and spring. This site is also affected by southwestern air masses traveling over Datong (a significant coal production site) and urban Beijing in autumn. In spring, 21% of the air masses originate from the NCP. In summer, the site is influenced by the regions to the southeast (Shandong Province, Hebei Province, and Tianjin); 32% of the air masses originate from the nearby BTH metropolitan area, which is densely populated with high SO2 and NOx levels from agricultural and industrial activities (Xu et al., 2019; Khan et al., 2021; Su et al., 2021).

    • The statistics of the concentrations of carbonaceous components and WSIIs in PM2.5 at the SDZ are summarized in Table 1. TC was the sum of OC and EC, with an average of 6.3 ± 4.5 µg m–3. OC ranged from 0.66 µg m–3 to 23 µg m–3, with an average of 5.6 ± 4.0 µg m–3. EC ranged from 0.0 µg m–3 to 3.2 µg m–3, with an average of 0.70 ± 0.53 µg m–3. The average OC and EC concentrations at SDZ were approximately two to eight times lower than those in the adjacent cities within the NCP region (Beijing: 11 µg m–3 and 3.4 µg m–3; Tianjin: 12 µg m–3 and 3.1 µg m–3; Shijiazhuang: 23 µg m–3 and 5.4 µg m–3; Tangshan: 12 µg m–3 and 3.5 µg m–3, respectively) (Ji et al., 2019). The total WSIIs (${\rm{NO}}_3^- $, ${\rm{SO}}_4^{2-} $, Cl, ${\rm{NO}}_2^- $, F, ${\rm{PO}}_4^{3-} $, MSA, ${\rm{NH}}_4^+ $, K+, Na+, Ca2+, and Mg2+) varied from 1.1 µg m–3 to 118 µg m–3 with an average of 19 µg m–3. Secondary inorganic ions ${\rm{NO}}_3^{-} $, ${\rm{SO}}_4^{2-} $, and ${\rm{NH}}_4^+ $ were the major WSIIs, contributing 34%, 27%, and 19% to the total WSIIs mass, respectively.

      ComponentsAutumn (n = 54)Winter (n = 89)Spring (n = 62)Summer (n = 23)Annual (n = 228)
      RangeMean ± SDRangeMean ± SDRangeMean ± SDRangeMean ± SDRangeMean ± SD
      Carbonaceous
      Components
      (µg m–3)
      OC0.95–238.1±5.01.1–185.4±3.50.66–164.6±3.20.78–5.73.2±1.40.66–235.6±4.0
      EC0.09–3.31.0±0.730.11–1.80.66±0.410.03–2.00.57±0.400.0–0.680.39±0.190.0–3.20.70±0.53
      TC1.1–269.1±5.71.2–206.0±3.90.69–185.2±3.60.78–6.43.6±1.50.69–266.3±4.5
      WSOC0.28–123.4±2.60.66–8.42.1±1.60.51–102.7±2.00.36–4.72.3±1.10.28–122.6±2.0
      WIOC0.43–114.7±2.60.24–103.3±2.20.14–5.61.9±1.30.35–1.90.92±0.400.14–113.0±2.3
      OM1.3–3211±7.01.5–257.5±5.00.92–226.4±4.51.1–8.04.5±1.90.92–327.8±5.6
      Water-Soluble
      Inorganic Ions
      (µg m–3)
      ${\rm{SO}}_4^{2-} $0.70–225.2±5.40.49–152.4±2.20.53–164.0±3.40.16–167.1±4.70.16–224.0±4.0
      ${\rm{NO}}_3^- $0.28–6514±180.18–254.3±6.00.25–6011±130.1–103.4±3.40.10–658.4±12
      ${\rm{NH}}_4^+ $0.14–255.7±6.80.19–142.3±2.90.0–254.4±5.40.18–104.0±2.50.0–253.8±5.0
      K+0.03–1.50.44±0.340.03–3.20.31±0.390.02–1.10.29±0.230.0–0.230.08±0.060.0–3.20.31±0.33
      Ca2+BDL–4.70.78±1.0BDL–2.30.05±0.25BDL–0.610.06±0.13BDL–0.810.02±0.17BDL–4.70.23±0.60
      Na+0.09–3.20.76±0.440.15–2.30.42±0.240.23–0.880.51±0.12BDL–0.160.05±0.04BDL–3.20.49±0.33
      Cl0.19–4.21.2±0.840.0–3.80.95±0.810.09–2.30.65±0.51BDL–0.330.09±0.11BDL–4.20.85±0.78
      MSA0.0–0.390.06±0.070.0–0.120.01±0.020.0–0.070.01±0.010.0–0.040.01±0.010.0–0.390.02±0.04
      ${\rm{NO}}_2^- $0.0–1.60.45±0.430.06–1.20.26±0.180.04–1.20.29±0.230.09–0.470.32±0.10.0–1.60.32±0.28
      Mg2+0.0–0.480.12±0.140.0–0.640.01±0.070.0–0.190.02±0.030.0–0.130.04±0.030.0–0.640.05±0.09
      ${\rm{PO}}_4^{3-} $BDL–0.790.07±0.12BDL–0.630.04±0.08BDL–0.320.03±0.06BDL–0.110.04±0.03BDL–0.790.05±0.08
      F0.0–0.140.02±0.030.0–0.060.01±0.010.0–0.030.0±0.00.0–0.00.0±0.00.0–0.140.01±0.02

      Table 1.  Seasonal variations in the mass concentrations of the chemical species in PM2.5 at SDZ. The detection limits for water-soluble inorganic ions are 0.001 µg m−3. BDL means below the detection limit.

      The major chemical components were ${\rm{NO}}_3^- $ > OM > ${\rm{SO}}_4^{2-} $ > ${\rm{NH}}_4^+ $ > EC, according to their average concentrations (Fig. 2). The annual fraction of the three major ions (60%) was approximately twice that of the carbonaceous components (OM + EC: 32%). Consistent with other observations in the BTH region (Khan et al., 2021; Su et al., 2021), ${\rm{NO}}_3^- $ was significantly higher than NH4+, likely because of some additional ${\rm{NO}}_3^- $ sources. For example, the heterogeneous reaction HNO3 +NaCl→NaNO3 can provide an additional ${\rm{NO}}_3^- $ in ambient aerosol particles (Wu et al., 2006).

      Figure 2.  Contributions of the chemical species to total mass concentrations of PM2.5 (EC + OM + ∑anions + ∑cations) at the SDZ site: (a) autumn, (b) winter, (c) spring, (d) summer, and (e) the annual average. The minor cations include Ca2+ and Mg2+, and the minor anions include ${\rm{NO}}_2^- $, F, ${\rm{PO}}_4^{3-} $, and MSA.

      Figure 3 and Table 1 show that the chemical components exhibited seasonal variations. All the carbonaceous components and most of the ions had the highest concentrations in autumn compared to other seasons. This result is inconsistent with the previous study at SDZ, in which the highest concentration was observed during spring or winter (Li et al., 2016). However, the ${\rm{SO}}_4^{2-} $ concentration peaked in summer, followed by autumn, and reached its lowest level in winter. The ${\rm{NO}}_3^- $ concentration in autumn was about four times higher than that in summer. The seasonal contribution of each component to the total aerosol concentration (EC + OM + ∑cations +∑anions) is shown in Fig. 2. ${\rm{NO}}_3^- $ was a major contributor, accounting for 39% in spring and 35% in autumn, followed by OM, accounting for > 20% in all seasons and as high as 39% in winter. ${\rm{SO}}_4^{2-} $ was a major contributor in summer with a contribution of 35%, but it contributed only 12% in autumn and winter.

      Figure 3.  Time series of the chemical composition concentrations in PM2.5 at the SDZ site. Additional data for the minor ions are shown in Fig. S5 in the ESM.

      Secondary ions (${\rm{NO}}_3^- $, ${\rm{SO}}_4^{2-} $, and NH4+) strongly correlated with each other (r ≥ 0.71, p ≤ 0.0001) during the observation period, except for ${\rm{NO}}_3^- $ and ${\rm{SO}}_4^{2-} $ during summer (r = 0.31) (Table S3 in the ESM). This suggests that these three major secondary inorganics have the same source or come from a similar reaction generation process. In all seasons, WSOC, ${\rm{SO}}_4^{2-} $, ${\rm{NO}}_3^- $, and NH4+ positively correlated with EC (r ≥ 0.52, p ≤ 0.0001), indicating that primary anthropogenic activities like burning fossil fuel and biomass are strong sources of the precursors to secondary organic and inorganic components (Pavuluri et al., 2011). In the following, the sources of organic matter are further discussed in the PMF analysis.

      From the PMF source apportionment, six factors were resolved including two secondary formation factors, biomass burning, fossil fuel combustion, industry, and dust (Fig. S1). Figure 4 shows the fractional contributions of different factors to the mass concentrations of WSOM and WIOM. Annually, secondary formation (52%) and fossil fuel combustion (63%) are the largest sources of WSOM and WIOM, respectively. Fossil fuel combustion also contributes 1/3 of WSOM. Biomass burning is also nonnegligible, accounting for ~10% of both WSOM and WIOM. Seasonally, the secondary formation can represent ~90% of WSOM in summer, and even in winter it is ~1/3. Fossil fuel combustion is the dominant source of WIOM in each season, with a contribution between ~ 45% to 75%, while in summer, the two secondary factors as a whole are counterpart contributors to WIOM. Except in summer, biomass burning contributes to around 10%–15% of both WSOM and WIOM.

      Figure 4.  Modeled concentrations (a) and fractional contributions (b) of different factors to the mass concentrations of WSOM and WIOM. These six source factors are resolved by the PMF analysis. The measurement results for the seasonal and annual mean concentration of WSOM and WIOM (as in Table 1) are also shown in subplot (a) as circles. Major contributions (> 10%) in the annual average are marked in the plots.

      As shown by the source apportionment of WSOM and WIOM, the concentration of WSOM in winter and summer is similar. But the concentration of WIOM is about four times higher in winter than in summer. Thus, the WSOM/OM ratio decreased from summer to winter, from ~0.7 to ~0.4. Besides, the increase of WIOM from summer to winter is largely driven by fossil fuel combustion and biomass burning, totally contributing to ~90% of WIOM in winter.

      Table S4 in the ESM compares the concentrations of carbonaceous components and major WSIIs between the measurements performed at the SDZ (this study and previous measurements dating back to a decade ago) and other GAW atmosphere background stations. The summary included global background stations at Mt. Waliguanshan (WLG, 36°17′N, 100°54′E, 3816 m a.s.l.) and Mt. Cimone (CMN, 44°12′N, 10°42′E, 2165 m a.s.l.) and regional background stations in Akdala located in northwestern China (AKD, 47°06′N, 87°58′E, 562 m a.s.l.), Lin' an in eastern China (LA, 38°18′N, 119°44′E, 131 m a.s.l.), and Mt. Longfengshan in northeastern China (LFS, 44°44′N, 127°36′E, 331 m a.s.l.). First, the annual average concentrations of carbonaceous components (OC and EC) at the SDZ decreased from 2009 to 2018. A remarkable downward atmospheric deposition of OC was also observed in rural regions of the NCP during 2016–20 (Cao et al., 2022). In addition, the concentration of the major ions (${\rm{SO}}_4^{2-} $, ${\rm{NO}}_3^- $, ${\rm{NH}}_4^+ $, and Cl) decreased from 2009 to 2015. From 2015 to 2018, the ${\rm{SO}}_4^{2-} $ concentration decreased, ${\rm{NH}}_4^+ $ concentration was stable, and ${\rm{NO}}_3^- $ and Cl- concentrations increased (Yan et al., 2012a, b; Zhao et al., 2013a; Li et al., 2016). Second, comparing these background stations, for each season, the concentrations of the carbonaceous components and ions were considerably higher at the regional background stations (AKD, LA, LFS, and SDZ) than at the global stations (WLG and CMN) (Yang et al., 1996; Qu et al., 2008, 2009; Zhao et al., 2013a; Carbone et al., 2014; Li et al., 2015), suggesting that global background stations are less affected by anthropogenic emissions than the regional counterparts.

    • The average WSOC concentration was 2.6 µg m–3 (0.28–12 µg m–3), accounting for 48% of the OC; WIOC represented the other half (52%; average: 3.0 µg m−3) (Table 1). The contribution of WSOM to OM was highest in summer (70%), followed by spring (60%) (Table 2). The measurement of the EEM spectra together with the post-PARAFAC analysis was used to characterize the chromophores in WSOM (Chen et al., 2016b; Chen et al., 2017; Yan and Kim, 2017). Fluorescence intensity as a semi-quantitative measure of the abundance of fluorescent chromophores in atmospheric samples (Yan and Kim, 2017; Wu et al., 2021a; Chen et al., 2022) is reported and used in correlation analyses.

      ParameterAutumn (n = 54)Winter (n = 89)Spring (n = 62)Summer (n = 23)Annual (n = 228)
      RangeMean ± SDRangeMean ± SDRangeMean ± SDRangeMean ± SDRangeMean± SD
      OC/EC5.5–228.7±2.65.9–178.2±1.66.2–208.4±2.14.2–148.8±2.54.2–228.4±2.1
      EC/TC0.04–0.150.11±0.020.06–0.140.11±0.020.05–0.140.11±0.020.0–0.190.10±0.040.0–0.190.11±0.02
      WSOC/OC*0.14–0.600.40±0.090.21–0.780.40±0.110.44–0.820.60±0.090.46–0.820.70±0.100.14–0.820.48±0.15
      +/∑0.92–1.31.1±0.120.76–1.70.99±0.140.06–1.41.0±0.250.51–1.31.1±0.140.06–1.71.0±0.18
      ${\rm{NO}}_3^- $/${\rm{SO}}_4^{2-} $0.21–5.42.2±1.50.14–4.51.4±1.10.23–6.92.3±1.50.04–1.70.55±0.510.04–6.91.8±1.4
      K+/EC0.10–1.00.44±0.200.12–4.60.46±0.550.13–2.00.52±0.280.08–0.370.20±0.080.08–4.60.45±0.40
      HIX1.0–3.72.2 ± 0.630.96–2.81.6 ± 0.521.6–4.32.6 ± 0.611.8–7.73.4 ± 1.20.96–7.72.2 ± 0.89
      FI1.2–1.51.4 ± 0.081.2–1.61.4 ± 0.091.2–1.41.3 ± 0.051.2–1.41.3 ± 0.041.2–1.61.4 ± 0.09
      BIX0.75–1.41.0 ± 0.170.99–1.41.2 ± 0.120.82–1.31.0 ± 0.110.71–1.00.87 ± 0.100.71–1.41.1 ± 0.18
      * WSOC/OC = WSOM/OM.

      Table 2.  Seasonal variations in the ratios between the species and the fluorescent indices in PM2.5 at SDZ.

      In this study, we used the EEM-PARAFAC method to identify and quantify the chromophoric components of PM2.5. Four fluorescence components (C1, C2, C3, and C4) were resolved (Fig. 5a). The identities of the chromophores were determined by EEM spectra regarding those in previous reports (Fu et al., 2015; Yue et al., 2016; Chen et al., 2019; Wu et al., 2019; Zhang et al., 2021). C1 (ex/em = 240 nm/396 nm), C2 (ex/em = 255 nm/472 nm), and C3 (ex/em = 245 nm/390 nm), with typical fluorescence peaks, are identified as humic-like substances (HULIS) with previous reports (Fu et al., 2015; Yue et al., 2016; Chen et al., 2019) and are denoted as HULIS-2, HULIS-1, and HULIS-3, respectively. HULIS-1 chromophores with long emission wavelengths originate from large conjugated systems that may contain heteroatoms in their highly aromatic conjugate structures (Chen et al., 2016a). According to Chen et al. (2016b), a HULIS component with a longer emission wavelength is potentially more oxygenated, suggesting secondary sources. This is indicated by the strong correlations of HULIS-1 and -2 with secondary formation factors, which is absent for HULIS-3 (Table 3). Besides, the burning factors (biomass burning and/or fossil fuel combustion) and their tracers (EC, Cl-, and K+) also correlate well with these three fluorescent components (Table 3 and Table S3), suggesting these primary emissions are also important sources to them.

      Figure 5.  Fluorescent properties of the water-soluble organic matter (WSOM) in PM2.5 at the SDZ site. (a) Excitation–emission matrix (EEM) spectrum fingerprints of the identified fluorescent components are resolved by the PARAFAC model. The excitation and emission wavelengths of the peak intensity of each component are shown in the plots. The four components, C1, C2, C3, and C4, are named HULIS-2, HULIS-1, PLOM, and HULIS-3, respectively. (b) Seasonal variations in the fluorescence intensities of the four components and their relative fractions. (c) Seasonal variations in the humification index (HIX), fluorescence index (FI), and biological index (BIX).

      Source factorsHULIS-1HULIS-2HULIS-3PLOMHIXFIBIX
      Autumn
      Secondary formation-10.620.29−0.11−0.080.57−0.44−0.57
      Secondary formation-20.350.11−0.18−0.150.51−0.35−0.53
      Biomass burning0.630.630.320.280.05−0.11−0.01
      Fossil fuel combustion0.660.710.700.68−0.110.140.20
      Dust0.510.13−0.06−0.080.36−0.31−0.35
      Industry−0.02−0.12−0.05−0.080.22−0.18−0.29
      Winter
      Secondary formation-10.890.800.310.140.60−0.54−0.56
      Secondary formation-20.580.490.05−0.100.60−0.47−0.50
      Biomass burning0.790.680.280.140.51−0.46−0.51
      Fossil fuel combustion0.600.750.800.77−0.160.090.07
      Dust0.020.030.040.07−0.040.01−0.02
      Industry−0.19−0.120.080.19−0.310.300.25
      Spring
      Secondary formation-10.870.740.210.350.66−0.42−0.44
      Secondary formation-20.730.570.070.200.70−0.45−0.54
      Biomass burning0.710.790.740.810.00−0.060.10
      Fossil fuel combustion0.420.330.180.250.24−0.18−0.21
      Dust0.360.280.240.200.12−0.15−0.11
      Industry−0.55−0.59−0.38−0.46−0.220.220.09
      Summer
      Secondary formation-10.870.82−0.040.76−0.01−0.33−0.42
      Secondary formation-20.770.67−0.190.370.14−0.39−0.87
      Biomass burning0.790.70−0.080.580.12−0.45−0.44
      Fossil fuel combustion−0.14−0.010.030.01−0.240.400.22
      Dust−0.21−0.170.21−0.200.000.150.12
      Industry−0.21−0.180.16−0.250.010.150.05
      Notes: Autumn: t-test is p ≤ 0.0001 for the correction where r is ≥ 0.51. Winter: t-test is p < 0.0001 for the correction where r is ≥ 0.51. Spring: t-test is p < 0.0001 for the correction where r is ≥ 0.57. Summer: t-test is p < 0.0001 for the correction where r is > 0.67; t-test is 0.0001 < p < 0.01 for the correction where r is from 0.58 to 0.67.

      Table 3.  Correlation coefficients (r) between the six source factors of WSOM and the fluorescent components and the indices measured in the water extracts in PM2.5 at the Shangdianzi (SDZ) site in (a) autumn, (b) winter, (c) spring, and (d) summer. Correlation coefficients r ≥ 0.50 or r ≤ –0.50 are highlighted in bold.

      C4 (ex/em = 270 nm/336 nm) is termed PLOM because its EEM spectra are similar to those of amino acids (Ohno and Bro, 2006; Wu et al., 2019; Zhang et al., 2021). But it should be noted that nitrogen-free nonbiological components (non-proteinaceous matter) can also contribute to C4 (Coble, 2007; Chen et al., 2016b, 2021). Here, we did not resolve a source factor for primary bioaerosols, which may be due to a relatively low contribution of bioaerosols in PM2.5. It also should be noted that the biomass burning factor and the dust factor can also include co-emitted bioaerosols (Yue et al., 2019, 2022a), although the impact might be minor here. A higher correlation of PLOM with fossil fuel combustion (autumn and winter) and the biomass burning factor (spring and summer) reflect the non-biological contribution (Table 3). The fluorescence intensity of PLOM in both summer and spring is lower than in winter (Fig. 5b) and also reflects a significant non-biological contribution.

      The fluorescence intensities of these four fluorescent components in PM2.5 greatly varied between seasons (Table S2 and Fig. 5b). All these four fluorescent components exhibited similar seasonal variations, with the highest intensity in winter/autumn and the lowest in summer. On the other hand, the concentration of WSOM in winter and summer is similar, but with a dominant secondary source in summer while the main contributions of fossil fuel and biomass burning occurred in winter (Fig. 4). Collectively, these results indicate that the fluorescence components are more abundant (or of higher intensity) per mass of WSOM from the burning sources than from the secondary formation. In summer, the contributions of HULIS-2 (49%, Fig. 5b) and HULIS-1 (31%, Fig. 5b) to total fluorescence intensity are high, which is due to two factors: 1) secondary source as a major source of them (Table 3); and 2) the dominance of secondary formation in the contribution to WSOM (~90%, Fig. 4). Whereas in winter, the relative contributions of PLOM (34%) and HULIS-3 (28%) to total fluorescence intensity increase compared to the summer condition (Fig. 5b), which is because: 1) fossil fuel combustion is their main source; and 2) the contribution of fossil fuel combustion in WSOM is dominant (50%, Fig. 4). With the relative increase of PLOM and HULIS-3, the fractional contributions of HULIS-2 and HULIS-1 to total fluorescence intensity cannot be as much as in winter.

    • To further investigate the potential sources of chromophoric components in WSOM, fluorescence indices were calculated based on the ratios of fluorescence intensity in specific spectral regions (section 2.3). Three fluorescence indices are typically used to track the origins, transformation, and chemical processing of atmospheric aerosols: HIX, FI, and BIX (Fu et al., 2015; Xie et al., 2016; Yue et al., 2016, 2019; Tang et al., 2021). The HIX, FI, and BIX variations are plotted in Fig. 5c and presented in Table 2.

      The HIX is an index used to evaluate the maturation of humification (Zsolnay et al., 1999; Fu et al., 2015). A high HIX (>10) indicates heavily humified or aromatic organics, primarily of terrestrial origin, whereas a low HIX (< 4.0) indicates OM of microbial origin (Zsolnay et al., 1999; McKnight et al., 2001; Ohno and Bro, 2006; Huguet et al., 2009; Lee et al., 2013; Fu et al., 2015; Qin et al., 2018; Yue et al., 2019). HIX ranged from 0.96 to 7.7 with an average of 2.2 ± 0.89, which was lower than that in urban aerosols (7.1) (Xie et al., 2020) but comparable to that at a suburb site (1.6 in winter) (Yi et al., 2020), in forest environments (2.4 both in autumn and spring) (Xie et al., 2016), and at a mountain site (2.4 and 1.1 for fine and coarse aerosols, respectively) (Yue et al., 2019) (Table 2). The low annual average HIX indicates that fluorophores in water-soluble PM2.5 are primarily contributed by microbial-derived OM. As presented in Table 2 and Fig. 5c, the HIX exhibits seasonal variations in the following order: summer (average: 3.4) > spring (2.6) > autumn (2.2) > winter (1.6). This indicates that compared to summer, atmospheric chromophores tend to be either less humified or less aromatic in winter, which may be associated with the higher fraction of secondary formation in winter (Fig. 4 and Table 3).

      BIX is an index used to measure the contribution of biological sources (Huguet et al., 2009; Xie et al., 2020). The increase in BIX corresponds to the enhanced contribution of microbially derived organics (Fu et al., 2015). A high BIX (> 1.0) indicates a significant biological or WSOM of microbial origin, whereas a low BIX (< 0.60) suggests a scarce biological origin (Huguet et al., 2009; Fu et al., 2015; Yue et al., 2016, 2019). As presented in Table 2, BIX varies between 0.71 and 1.4 with an average of 1.1 ± 0.18, comparable to that in Xi’an suburbs (1.2–1.4 in winter) (Yi et al., 2020). BIX was slightly higher in SDZ than in Nanjing urban areas (0.88) (Xie et al., 2020). A high BIX implies a predominately biological PM2.5 source. The highest and lowest BIX occurred in winter (1.2 ± 0.12) and summer (0.87 ± 0.10), respectively (Table 2). This is consistent with the magnitude of HIX (low value corresponds to high microbial sources) and another FI index (Table 2 and Fig. 5c). A higher FI also indicates more microbial sources and lower aromaticity (McKnight et al., 2001; Fu et al., 2015; Yue et al., 2016; Chen et al., 2021). The relatively low biological contribution to the fluorophores in the WSOM in summer is likely because the fraction of SOA in the WSOM is more enhanced in this season, as suggested by the strong negative correlation of BIX with secondary factor (Table 3) and the dominance of this factor in the WSOM in summer (Fig. 4).

    4.   Conclusions
    • This study analyzed the concentrations and sources of chemical components and chromophores in year-round fine aerosols over SDZ, a regional background station in northern China. The major chemical component was ${\rm{NO}}_3^- $, suggesting the influence of anthropogenic emissions. All carbonaceous components (OC, EC, and WSOC) and most of the ions had the highest concentrations in autumn, whereas the ${\rm{SO}}_4^{2-} $ concentration peaked in summer. The OC and EC concentrations, with an average of 5.6 ± 4.0 µg m–3 and 0.70 ± 0.53 µg m–3, respectively, were significantly lower than those in the adjacent cities within the NCP. During the recent ten-year period 2009–18, the OC, EC, and ${\rm{SO}}_4^{2-} $ concentrations decreased at this regional background site.

      Six source factors, i.e., two secondary formation factors, biomass burning, fossil fuel combustion, industry, and dust, were derived in the positive matrix factorization (PMF) analysis to further apportion the major aerosol composition. Over the year, secondary formation (52%) and fossil fuel combustion (63%) are the largest sources of WSOM and WIOM, respectively. Moreover, the secondary formation could contribute approximately 90% to WSOM in summer, fossil fuel combustion almost dominates the WIOM from spring to winter, while biomass burning contributes little (10%–15%) to both WSOM and WIOM.

      Four types of chromophores were identified via EEM-PARAFAC: three HULIS and one PLOM. The fluorescence intensities of the four fluorescent components exhibited similar seasonal variations, which were highest in winter and lowest in summer. Among the four chromophore types, HULIS-2 and PLOM were the most abundant fluorophores in all samples, whereas HULIS-1 was the weakest fluorophore in all four seasons. The seasonal variation of fluorescence intensity of PLOM and high correlation with fossil fuel combustion and the biomass burning factor reflects the significant non-biological contribution. A low HIX (< 4.0) indicated freshly introduced, less-humified fluorescent WSOM generated from the plant biomass. In addition, our results suggest that atmospheric chromophores at the background site had a higher degree of humification or aromaticity in summer than in winter.

      Acknowledgements. This study was supported by the National Natural Science Foundation of China (Grant Nos. 42130513 and 41625014) and the National Key Research and Development Program of China (Grant No. 2019YFA0606801). The authors declare that they have no conflicts of interest. The data used in this study are listed in tables, figures, and supplementary materials.

      Electronic supplementary material: Supplementary material is available in the online version of this article at https://doi.org/10.1007/s00376-022-2208-x.

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