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Article

Source Apportionment of Carbonaceous Matter in Size-Segregated Aerosols at Haikou: Combustion-Related Emissions vs. Natural Emissions

by
Lingling Cao
1,2,
Li Luo
2,3,4,*,
Chen Wang
2,
Mingbin Wang
2,
Rongqiang Yang
2 and
Shuhji Kao
2,4,*
1
School of Environmental Science and Engineering, Hainan University, Haikou 570228, China
2
State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China
3
College of Marine Science and Engineering, Hainan University, Haikou 570228, China
4
Collaborative Innovation Center of Marine Science and Technology, Hainan University, Haikou 570228, China
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(22), 9859; https://doi.org/10.3390/su16229859
Submission received: 19 September 2024 / Revised: 31 October 2024 / Accepted: 8 November 2024 / Published: 12 November 2024
(This article belongs to the Special Issue Aerosol Science: Chemistry, Sources, and Formation for Sustainable Air Quality)
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Abstract

:
Air pollution can induce diseases and increase the risks of death, and it also has close links with climate change. Carbonaceous matter is an important component of aerosols, but studies quantifying the source apportionment of carbonaceous compositions in different-sized aerosols from a stable carbon isotopic perspective remain scarce. In this study, fine (particulate size < 2.5 μm) and coarse (particulate size 2.5~10 μm) particles were collected from December 2021 to February 2022 (winter) and from June to August 2022 (summer) in the tropical city of Haikou; the concentrations of water-soluble inorganic ions (WSIIs) and total carbonaceous matter (TC) and the stable carbon isotope of TC (δ13C-TC) values in both fine and coarse particles were analyzed. Higher concentrations of TC, SO42−, NO3−, and NH4+ but lower δ13C-TC values in fine particles than those in coarse particles in both winter and summer indicated that combustion-related emissions dominate fine particulate TC sources. The δ13C-TC values coupled with the stable isotope mixing model in R (SIAR) results showed that combustion-related emissions contributed 77.5% and 76.6% to the TC of fine particles in winter and summer, respectively. Additionally, the lowest δ13C-TC values were observed in summertime fine particles; plant physiological activity was identified as an important source of fine particulate TC in summer and contributed 12.4% to fine particulate TC. For coarse particles, higher δ13C-TC values and Ca2+ and Na+ concentrations but lower TC concentrations implied significant contributions from natural emissions (29.2% in winter and 44.3% in summer) to coarse particulate TC. This study underscores that instead of fossil fuels and biomass, clean energy can decrease 45–78% of aerosol TC at Haikou. In addition, our results also provide a dataset for making environmental policy and optimizing the energy structure, which further favors the sustainable development of air quality.

1. Introduction

Carbonaceous aerosols are important components of atmospheric aerosols, generally accounting for 20–50% of the total aerosol mass concentration [1,2,3,4,5]. The known sources of carbonaceous aerosols include primary carbonaceous sources (such as sea spray, mineral dust, and plant wax) and secondary aerosol formation through the oxidation of gas-phase organic components from fossil fuel combustion and biomass burning to the particulate phase [6,7]. Carbonaceous aerosols can act as cloud condensation nuclei, absorb and scatter solar radiation, and affect regional climate [8,9,10,11]. Additionally, carbonaceous aerosols can accelerate PM2.5 accumulation, reduce visibility, and adversely affect human health [12,13,14,15,16,17]. Clarifying the sources of carbonaceous aerosols can help public health authorities better assess and reduce the impact of these particles on the health of the population and help governments formulate more targeted environmental policies and regulations to ensure the rational allocation and use of resources to minimize environmental pollution and improve energy efficiency. Overall, the analysis of carbonaceous aerosol sources not only improves environmental management but also contributes to the realization of sustainable development in the broader social and economic context.
The stable carbon isotope (δ13C, δ13C = (Rsample/Rstandard − 1) × 1000, where R is the 13C/12C ratio in the sample and standard) is a valuable tool for distinguishing the potential sources of carbonaceous aerosols due to different carbonaceous emission sources having distinct δ13C values (Table S1). For example, the δ13C values from C3 plants burning (−26.0 ± 1.2‰) were lower than those from coal combustion (−24.3 ± 0.5‰) and vehicle exhaust (−24.6 ± 0.8‰) but higher than those from diesel and ship exhaust (−27.1 ± 1.8‰). In addition, our statistical results showed that δ13C-TC values in raw materials (gasoline and diesel) differed from their combusted products (exhaust, Table S1). Except for combustion-related emission sources, natural emissions, such as mineral dust, sea spray (marine), and the release of plant physiological activity (plant emission) of carbonaceous aerosols also contribute to aerosol TC, and δ13C-TC values from natural emission sources also show obvious differences (Table S1). For instance, mineral dust (−22.9 ± 2.9‰) and marine emissions (−21.9 ± 0.9‰) were higher than plant emissions (leaf waxes and BVOCs, −34.3 ± 3.5‰) (Table S1). Therefore, the differences in δ13C-TC from various sources provide valuable information for quantifying the sources of different carbonaceous aerosols.
Previous studies reported that aerosol δ13C-TC values in cities ranged from −23.6‰ to −25.3‰, and the authors concluded that coal combustion and vehicle emissions were the dominant sources of TC [18,19,20]. In contrast, studies on marine aerosols found that δ13C-TC values varied from −19.5‰ to −25.1‰. The aging process of carbonaceous aerosols and anthropogenic contributions were used to explain these differences [21,22,23]. The limited size-segregated studies showed no consistent spatiotemporal patterns for aerosol δ13C-TC values. While δ13C-TC values continued to increase from particulate sizes ≤ 0.1 μm to ≥1 μm in Vilnius city [24], a study in Lithuania found no obvious size-segregated differences in δ13C-TC values as particulate sizes increased from ≤0.1 μm to 1 μm [25]. Since aerosol chemical compositions vary with size distributions, thus providing more source apportionment than a single particle size [26,27,28], combining chemical compositions with δ13C-TC values in different-sized particles offered valuable information for the TC source investigation. However, the contributions of plant physiological activity emissions to aerosol TC in tropical island cities have not been mentioned.
Haikou is a tropical city with prevailing southeasterly winds from the South China Sea in summer and northeasterly winds from mainland China in winter. The three-year (2021–2023) annual average concentrations of PM2.5 and PM10 at Haikou were 14.7 µg/m3 and 28.0 µg/m3, which meet China Ambient Air Quality Class I Standards (15 µg/m3 for PM2.5 and 40 µg/m3 for PM10) but exceed the standards set by the World Health Organization (WHO) 2021 air quality guidelines (5 µg/m3 for PM2.5 and 15 µg/m3 for PM10). PM2.5 and PM10 concentrations in Haikou, China’s free trade port and ecological civilization pilot zone, should be close to WHO standards. TC is one of the major components of aerosols, yet few studies have focused on the source apportionment of carbonaceous in Haikou [14,29,30], let alone explored the seasonal characteristics of carbonaceous aerosols in different size distributions and quantified the contributions from emission sources, especially comparing combustion-related emissions vs. natural emissions.
Upon combining OC and EC with Positive Matrix Factorization (PMF), Liu et al. (2018) [14] reported that fossil fuel consumption and biomass burning contributed 90.5% of OC and EC. However, the contributions of sources in nature to OC and EC were ignored, let alone quantified through a stable carbon isotopic perspective. In addition, by using chemical mass balance (CMB) modeling, Fang et al. (2017) [29] reported that resuspended dust, vehicular emissions, and secondary aerosols were the main sources of PM. Similarly, Liu et al. (2017) [30] also reported that secondary sulfate, resuspended dust, and vehicular emissions were the main sources of particulate matter by model analysis. To date, there has been no study in Haikou that has analyzed the sources of carbonaceous compositions in size-segregated aerosols by using a stable carbon isotope. In this study, fine and coarse particles were sampled from Haikou during winter (December 2021 to February 2022) and summer (June 2022 to August 2022), and the total carbonaceous matter (TC), water-soluble inorganic ions (WSIIs), and stable carbon isotopic compositions of TC (δ13C-TC) were analyzed. The aim of this study was to quantify the sources of TC in different-sized aerosols using δ13C-TC and stable isotope mixing analysis in R (SIAR). Our results can assist the government in developing accurate reduction strategies to control atmospheric particulate concentrations and achieve high air quality standards in Hainan.

2. Materials and Methods

2.1. Sample Collection

The sampling site was on the roof of an eight-floor building at Hainan University, Haidian campus (20.06° N, 110.32° E) in Haikou (Figure 1). Fine and coarse particles were collected during winter (December 2021 to February 2022) and summer (June 2022 to August 2022) using a high-volume air sampler equipped with a PM2.5 impactor. The sampling durations were 24 or 48 h. Before sampling, the quartz membranes (TISSUQUARTZ 2500QAT-UP, PALL Corporation, New York, NY, USA) were combusted at 450 °C for 6 h. After sampling, the samples were frozen at −20 °C until chemical and isotopic analysis. Additionally, to identify δ13C-TC values from specific point sources, we sampled PM2.5 from an underground parking lot by using a low-volume automated air sampler. The concentrations of air pollutants and meteorological parameters during our sampling periods are listed in Table S2.

2.2. Chemical Concentrations and δ13C-TC Value Analysis

Water-soluble inorganic ions (including NO3−, SO42−, Cl−, Na+, Mg2+, K+, Ca2+, and NH4+) were measured using ion chromatography (DIONEX AQ–1100, CS12A for cations, and AS22 for anions, Dionex Aquion RFIC, ThermoFisher, Sunnyvale, CA, USA). Briefly, a portion of the filter was extracted three times with ultrapure water (18.2 Ω). The extracts were filtered through a membrane filter (Millex-GP,0.22 μm, Merck Millipore Ltd, Cork, IRELAND). The analytical error for duplicate analyses was less than 5%. The concentrations of water-soluble inorganic ions were corrected for field blanks. Detailed analytical procedures can be found in the research work by Wang et al. (2024) [31].
The TC concentrations and δ13C-TC values in fine and coarse particles were determined using an elemental analyzer (Flash 2000 HT, Thermo Fisher Scientific Inc, Bremen, Germany) coupled with an isotope ratio mass spectrometer (MAT 253 Plus, Thermo Fisher Scientific Inc.). In brief, a 2.83 cm2 filter was cut and packed into a tin cup, which was then placed into an autosampler equipped with an elemental analyzer. The tin-cup-packed samples were heated in a quartz tube at 1020 °C inside the elemental analyzer [32,33]. All carbonaceous matter was oxidized into CO2, which was then transferred to an isotope ratio mass spectrometer through a ConFlo IV interface to measure CO2 concentrations and δ13C-TC. The TC concentration was calculated based on the linear relationship between the weight of C-content and the measured peak area of international reference materials (Figure 2a). The δ13C-TC values were calibrated using international reference materials USGS40L, USGS61, USGS62, USGS64, and USGS65 (Figure 2b), and the analytical precision was less than ±0.35‰ for δ13C-TC.

2.3. Models

2.3.1. Air Mass Backward Trajectory Analysis

The Geographic Information System software (MeteoInfo,1.5.6) with the Trajectory Statistics plugin (Trajstat v1.5.3) was employed to conduct air mass backward trajectories during the sampling period. For details on the model, refer to the MeteoInfo website (http://meteothink.org/index.html, accessed on 18 September 2024). The original dataset was downloaded from the National Oceanic and Atmospheric Administration (NOAA) (https://www.ready.noaa.gov/data/archives/gdas1/, accessed on 18 September 2024). The height of the trajectories was set at 100 m, and the backward periods were 3 days. Detailed information about the calculation can be found in Wang et al. (2009) [34] and Yadav (2022) [35].

2.3.2. SIAR

To quantify the relative contributions of different emission sources to aerosol TC, the stable isotope mixing model in R (SIAR, version 4.3.1) was employed, and the calculation following Equation (1) is as follows:
X i j = ∑ k = 1 p g i k f j k + e i j
where Xij is the value of j tracer in sample i, p is the number of sources assumed, gik represents the source contribution of source k to sample i, fjk represents the tracer values of species j in source k, and eij is the residual. The detailed set of SIAR parameters can be found elsewhere [36,37,38,39].

3. Results and Discussion

3.1. Seasonal Differences in Concentrations of Fine and Coarse Particles

Overall, concentrations of PM2.5, PM10, and O3 in winter were significantly higher than those in summer (Table S2), suggesting the coordinated pollution of atmospheric particulate matter and O3 during winter in Haikou. Similarly, concentrations of NO3−, NH4+, and SO42− were also higher in winter than in summer for both fine and coarse particles (Table 1). The Pearson correlation coefficients among NO3−, NH4+, and SO42− in winter, both in fine and coarse particles and compared to those in summer, suggested secondary formation dominates NO3−, NH4+, and SO42− production in winter (Figure S1). However, concentrations of SO2 and NO2 in winter were similar to those in summer (Table S2); the observed higher levels of NO3− and SO42− in winter than in summer can be explained by the following two factors. First, nearly all winter air mass backward trajectories originated from southern China and the coastal regions of southern China (Figure 1a), where there were higher concentrations of NO3− and SO42− (2.8~20.6 µg/m3 for NO3− and 5.5~28.9 µg/m3 for SO42− [40,41,42,43]) than those (0.48~3.2 µg/m3 for NO3− and 1.4~4.1 µg/m3 for SO42−; Table 1) in Haikou. Therefore, the direct transportation of NO3− and SO42− from southern China contributed to NO3− and SO42− in Haikou during winter. Second, higher winter O3 concentrations suggest stronger atmospheric oxidation capacity [44], which accelerates the oxidation of gaseous SO2 and NO2 to particulate SO42− and NO3− in Haikou.
Whether in winter or summer, fine particulate TC concentrations (6.8 ± 3.0 µg/m3 in winter and 3.7 ± 1.0 µg/m3 in summer) were higher than coarse particle concentrations (1.1 ± 0.6 µg/m3 in winter and 1.2 ± 0.6 µg/m3 in summer; Table 1), which was consistent with previous studies of size-segregated TC in Thailand [45,46] and São Paulo [4]. In addition, the mass percentages of TC in fine particles (0.37 ± 0.16 in winter and 0.45 ± 0.13 in summer) were significantly higher than in coarse particles (0.03 ± 0.02 in winter and 0.11 ± 0.05 in summer), suggesting the significant contribution of TC to atmospheric fine particles (Table 1). The better Pearson correlation coefficients among TC, NO3−, NH4+, and SO42− in fine particles for those in coarse particles in both winter and summer (Figure S1) suggest their similar sources and formation pathways in winter. Actually, NO3−, NH4+, and SO42− in fine particles have been widely attributed to secondary aerosol formation, and their precursors are primarily sourced from combustion-related emissions [47,48,49]. Thus, TC in fine particles mainly originates from secondary aerosol formation and combustion-related emissions in Haikou. For coarse particles, TC concentrations in summer (1.2 ± 0.6 µg/m3) were similar to those in winter (1.1 ± 0.6 µg/m3), with TC accounting for only 3% to 11% of the mass of coarse particles (Table 1), indicating a smaller contribution of TC to coarse particles in Haikou.
In addition, the fine particulate TC concentrations in winter (6.8 ± 3.0 µg/m3) were higher than those in summer (3.7 ± 1.0 µg/m3). The TC concentrations in Haikou were lower than most urban TC concentrations but higher than the observations on the island (Table S3), indicating the strong influence of anthropogenic activities on aerosol TC. Higher TC concentrations in winter than in summer have been widely contributed to the heating in winter [23,50,51,52]. In addition, TC concentrations in Haikou were lower than those in Hong Kong and Guangzhou [53], which where close to Haikou, which indicated the contributions of TC in PM2.5 from Chinese land to Haikou along with the atmospheric transportation. Haikou is a tourist city with few industrial enterprises and no heating season, so the seasonal emission strength of TC in Haikou should be relatively stable. The higher TC concentrations in wintertime fine particles in Haikou were influenced by transportation from southern China, as wintertime air mass backward trajectories (Figure 1a) passed through high TC concentration regions (e.g., Guangzhou: 10.9 ± 6.1 µg/m3 [52] and Xiamen: 16.4 ± 1.2 µg/m3 [50]). In contrast, the air mass backward trajectories in summer Haikou originated from the South China Sea (Figure 1b). Previous studies of total suspended particles (TSPs) in the South China Sea reported lower TC concentrations (2.4 ± 1.5~3.5 ± 1.5 µg/m3, [23,51]) than those (fine + coarse) in Haikou (4.9 ± 1.2 µg/m3).

3.2. δ13C-TC Values from Different Emission Sources: A Comparison of Datasets

Fine particulate δ13C-TC values in an underground parking lot in this observation ranged from −29.2 to −26.1‰ (−28.5 ± 1.0‰; Table S1), which were lower than those in previous studies in a tunnel (−25.0 ± 0.3 ~ −25.9 ± 0.3‰ [54,55]) but higher than those in fuel oil (including both gasoline and diesel, −29.8~−31.6‰ [56]). In tunnels, engines operate at high speeds, and fuel oil is almost entirely burned; thus, fine particulate δ13C-TC in tunnels is derived from completely combusted fuel oil. In contrast, in underground parking, the ignition of most vehicles has just been turned on, and the fine particles contain both incompletely burned fuel oil and the product of combustion. This explains our observed fine particulate δ13C-TC values (−29.2~−26.1‰) in the underground parking lot falling between fuel oil (−30.1~−26.6‰) and the combustion products (−25.9~−22.5‰, Figure 3a). A similar study on aerosol δ13C-TC values from ship emissions also found that aerosol δ13C-TC values during cruising periods (−26.8‰) were significantly higher than those observed in ports or docks (−31.7 to −30.4‰) [57]. In addition, our statistical results also showed that δ13C-TC values from ship emissions in ports or docks were close to those of fuel oil (−31.9 to −25.8‰; Figure 3b).
To analyze the potential sources of aerosol TC using δ13C-TC values, we refined the distributions of δ13C-TC values from fossil fuel combustion and biomass burning (Figure 3a–d), including both raw materials and their combustion products. The results showed that for liquid fuels, the δ13C-TC values in the raw materials of gasoline (−28.0 ± 1.1‰) and diesel (−29.5 ± 2.2‰) were significantly lower than those in combustion products (−24.6 ± 0.8‰ for vehicle exhaust and −27.1 ± 1.8‰ for ship exhaust), displaying a bimodal distribution (Figure 3a,b). For solid fuels, the δ13C-TC values in the raw materials of coal (−25.2 ± 1.4‰) and C3 plants (−26.1 ± 1.3‰) were similar to those in products from coal combustion (−24.3 ± 0.5‰) and biomass burning (−26.0 ± 1.2‰), displaying a unimodal distribution (Figure 3c,d). The δ13C-TC values in natural sources displayed a unimodal distribution (Figure 3e). The highest δ13C-TC values were recorded in surface seawater dissolved organic carbon (ranging from −20.3 to −24.7‰, with an average of −21.9 ± 0.9‰) and mineral dust (varying from −14.6 to −26.7‰, with an average of −22.9 ± 2.9‰). In contrast, the lowest δ13C-TC values were found in leaf waxes from plant physiological activity (ranging from −15.3 to −45.4‰, with an average of −34.3 ± 3.5‰).

3.3. Differences in δ13C-TC Values in Fine and Coarse Particles

3.3.1. Size Differences in δ13C-TC Values in Fine and Coarse Particles

The fine particulate δ13C-TC values (−27.5‰ to −24.3‰, −26.0 ± 0.6‰) were lower than those in coarse particles (−26.6‰ to −23.2‰, −25.2 ± 0.8‰) throughout our observations (Figure 4). Similar patterns in the size distribution of δ13C-TC values have also been reported in previous studies, with lower δ13C-TC values in fine particles (−27.4 ± 1.2‰) than coarse particles (−25.4 ± 0.8‰, Figure 4), which indicated the different sources of TC in fine and coarse particles. Masalaite et al. (2015) reported that δ13C-TC values in fine particles (Dp < 1.0 μm, −28.0 ± 0.9‰) were significantly lower than those in coarse particles (Dp > 1.0 μm, −25.6 ± 1‰), and the authors suggested that the lower δ13C-TC values in fine particles were influenced by fossil fuel combustion but the sources of TC in coarse particles were not documented [24]. Garbaras et al. (2009) found that δ13C-TC values in accumulation mode particles (Dp = 0.1~1.8 μm, −26.7‰ to −30.4‰) were significantly lower than those in coarse mode particles (Dp = 1.8~18 μm, −22.8 to −26.3‰), and the authors attributed the higher δ13C-TC in the coarse particles to the influence of dust from surrounding areas [25].
In addition to sources, the carbon isotopic fractionation during secondary organic aerosol formation and carbonaceous aging in the atmosphere have also been used to explain the differences in aerosol δ13C-TC [58,59,60]. The carbon isotopic fractionation includes chemical dynamic fractionation (e.g., the oxidation of gas-phase VOCs to particulate carbon by OH radicals, which induces δ13C values in products that were 5.8‰ lighter than their precursors [59]) and thermal equilibrium fractionation (e.g., photochemical aging of organic carbon aerosols, which can enrich 5‰ δ13C in products [58]). Due to the lack of systematic studies on δ13C fractionation in carbonaceous aerosols, we cannot determine the total δ13C fractionation factor during secondary organic aerosol formation and carbonaceous aging in the atmosphere. Thus, the effects of carbon isotopic fractionation on aerosol δ13C-TC values were not considered in this study.
Currently, the known sources of aerosol TC include combustion-related sources (vehicle and ship exhaust, coal combustion, and biomass burning) and natural sources (mineral dust and marine and plant emissions). The variations in the relative contributions of different emission sources to aerosol TC determine δ13C-TC values in fine and coarse particles. The δ13C-TC values (−27.5‰ to −24.3‰, −26.0 ± 0.6‰) in this study within the ranges of δ13C-TC values from combustion-related sources (Figure 3a–d) suggest that fossil fuel combustion and biomass burning were the main sources of fine particulate TC. At the same time, the enrichment of δ13C-TC values (−26.6‰ to −23.2‰, −25.2 ± 0.8‰) in coarse particles suggested increasing contributions from mineral dust and marine emissions to TC in coarse particles [25,61].

3.3.2. Seasonal Variations of δ13C-TC in Fine and Coarse Particles

In fine particles, the δ13C-TC values in winter (−25.7 ± 0.5‰) were higher than those in summer (−26.4 ± 0.5‰) in our observations (Figure 5). Similar seasonal patterns have been reported in previously published datasets for both inland and coastal cities (Figure 6a). The higher δ13C-TC values in wintertime fine particles than in summer have been generally attributed to the increase in fossil fuel consumption in winter, as fine particulate δ13C-TC values in winter were close to those from fossil fuel combustion products [12,18,62]. The lower δ13C-TC values in fine particles in summer suggested the presence of a specific emission source with extremely low δ13C-TC values. Based on statistical δ13C values from different emission sources (Figure 3), the extremely low δ13C-TC values in summer fine particles were likely sourced from plant emissions, including the direct emission of leaf wax (with a δ13C value of −35.0 ± 3.2‰ for C3 plants [63]) into fine particles and the emission of volatile organic compounds (e.g., isoprene, with δ13C values ranging from −29.2 ± 0.6‰ to −27.7 ± 2.0‰ [64]), which were further oxidized into carbonaceous aerosols in the atmosphere.
For coarse particles, seasonal δ13C-TC values showed an opposite trend to fine particles, with lower δ13C-TC values in winter (−25.4 ± 0.6‰) than in summer (−24.7 ± 0.8‰) (Figure 5). Due to the lack of studies on seasonal variations of δ13C-TC values in coarse particles, we only compared seasonal differences in coarse particulate δ13C-TC values with published δ13C-TC values in TSPs or PM10. The statistical results showed no consistent seasonal patterns for δ13C-TC values in previous TSP or PM10 datasets (Figure 6b). In our observations, coarse particulate was defined as particulate size from 2.5 μm to 10 μm, while in previous TSP or PM10 datasets, included both fine (particulate size < 2.5 μm) and coarse (particulate size > 2.5 μm) particles. Due to the mass percentages of TC in fine particles were 37~45%, which is even higher than those (3~11%) in coarse particles (Table 1); thus, fine particulate δ13C-TC values can directly influence the δ13C-TC values in TSPs or PM10. Additionally, nearly all the air mass backward trajectories in summer originated from the ocean and passed through Hainan Island (Figure 1b). The higher δ13C values in coarse particles, along with the high concentrations of Na+ and Cl− (indicators of sea salt in coarse particles in marine atmospheres [65]) and Ca2+ (a widely used indicator of mineral dust [66]), suggested the increasing contributions of marine and mineral dust to the TC in coarse particles in summer in Haikou.
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Figure 5. Seasonal variation of δ13C-TC in fine and coarse particles in Haikou City.
Figure 5. Seasonal variation of δ13C-TC in fine and coarse particles in Haikou City.
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Figure 6. Comparison of seasonal variations of δ13C-TC in (a) fine and (b) coarse particles. BJ (Beijing [67]), TJ (Tianjin [68]), TY (Taiyuan [62]), XA (Xi’an [18]), ND (New Delhi [19]), IB (Bhopal, India [35]), IM (Mysuru, India [35]), SH (Shanghai [69]), NB (Ningbo [69]), HK (Haikou; this study), GZ (Guangzhou [23]), HI (Hokkaido Island [70]), YX (Yongxing Island [23]), JI (Jeju Island [71]), OI (Okinawa Island [22]). Details of the sampling sites are provided in Supplementary Information Figure S2.
Figure 6. Comparison of seasonal variations of δ13C-TC in (a) fine and (b) coarse particles. BJ (Beijing [67]), TJ (Tianjin [68]), TY (Taiyuan [62]), XA (Xi’an [18]), ND (New Delhi [19]), IB (Bhopal, India [35]), IM (Mysuru, India [35]), SH (Shanghai [69]), NB (Ningbo [69]), HK (Haikou; this study), GZ (Guangzhou [23]), HI (Hokkaido Island [70]), YX (Yongxing Island [23]), JI (Jeju Island [71]), OI (Okinawa Island [22]). Details of the sampling sites are provided in Supplementary Information Figure S2.
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3.4. Source Apportionment of TC by δ13C-TC and SIAR

The δ13C-TC combined with SIAR results showed that combustion-related sources (coal combustion, vehicle and ship exhaust, and biomass burning) were the dominant contributors to fine particulate TC in both winter (77.5%) and summer (76.6%) in Haikou (Figure 7a,c). Those results were supported by the high concentrations of SO42− and NO3− (most of the fine particulate SO42− and NO3− are formed secondarily from SO2 and NOx emitted by fossil fuel combustion [72]) and K+ (an indicator of biomass burning [73]) in fine particles (Table 1). Additionally, the fractional contributions of plant physiological activity emissions to fine particulate TC increased significantly from 7.8% in winter (Figure 7a) to 12.4% in summer (Figure 7c), indicating that plant emissions are a non-negligible source of TC in fine particles during summer. For coarse particles, the fractional contributions of combustion-related sources to TC in winter (70.8%; Figure 7b) were significantly higher than in summer (55.7%; Figure 7d), indicating that combustion-related sources were also the main contributors to coarse particulate TC in winter. The contributions of mineral dust and marine sources to TC increased from 11 to 14.7% in fine particles to 22.6–39.4% in coarse particles. The increasing trend is similar to the mass percentages of Na+, Cl−, and Ca2+ relative to total water-soluble ionic concentrations (Table 1), indicating the increased contribution of marine and mineral dust to coarse particles.

4. Conclusions

To quantify the sources of size-segregated aerosol TC, the size distribution aerosols were collected in Haikou in winter and summer, and the concentrations of TC, water-soluble inorganic ions, and δ13C-TC values in both fine and coarse particles during winter and summer were analyzed. The δ13C-TC values of fine particles in an underground parking lot were measured, and δ13C-TC values from different emission sources were also compared. Fine particulate TC concentrations were significantly higher in winter than in summer, while no seasonal differences were found for coarse particulate TC concentrations. The δ13C-TC values in fine particles were lower than in coarse particles, suggesting a significant contribution of combustion-related sources to fine particulate TC. Additionally, plant emissions contributed 12.4% to summer TC in fine particles, highlighting the non-negligible influence of biogenic sources on fine particulate TC during summer. This study provides a detailed investigation into the sources and characteristics of carbonaceous aerosols in Haikou, China. These findings are important for developing effective air quality management strategies that address the diverse sources of carbonaceous aerosols. However, the overlap of δ13C values from emission sources and the sole dataset of δ13C-TC increased the uncertainties of the SIAR model. More refined studies, such as timing analysis of the δ13C-TC, δ13C-OC, and δ13C-EC, may provide more reliable source apportionment.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su16229859/s1. Figure S1: Pearson correlation coefficients (R2) of WSIIs, TC, and δ13C-TC in fine and coarse particles during both winter and summer in Haikou City; Figure S2: Sites for seasonal comparison of δ13C-TC. Yellow points represent the sampling sites from this study, and black points represent sites from previous studies; Table S1: The δ13C-TC (‰) signatures of potential sources from around the world [22,54,55,57,63,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95]; Table S2: Air pollutants and meteorological parameters during the sampling period; Table S3: Comparison of mass concentrations (μg m−3) of TC with previous studies [18,19,22,50,53,68,96,97].

Author Contributions

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

Funding

This research was funded by the Hainan Province Science and Technology Special Foundation, Grant No ZDYF2022SHFZ095, the Innovational Fund for Scientific and Technological Personnel of Hainan Province, Grant No KJRC2023B04, the Collaborative Innovation Center Foundation of Hainan University, Grant No XTCX2022HYB06, and the National Natural Science Foundation of China, Grant No 42263001.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Chen, Y.; Tian, C.; Feng, Y.; Zhi, G.; Li, J.; Zhang, G. Measurements of emission factors of PM2.5, OC, EC, and BC for household stoves of coal combustion in China. Atmos. Environ. 2015, 109, 190–196. [Google Scholar] [CrossRef]
  2. Kanakidou, M.; Seinfeld, J.H.; Pandis, S.N.; Barnes, I.; Dentener, F.J.; Facchini, M.C.; Van Dingenen, R.; Ervens, B.; Nenes, A.; Nielsen, C.J.; et al. Organic aerosol and global climate modelling: A review. Atmos. Chem. Phys. 2005, 5, 1053–1123. [Google Scholar] [CrossRef]
  3. Putaud, J.-P.; Van Dingenen, R.; Alastuey, A.; Bauer, H.; Birmili, W.; Cyrys, J.; Flentje, H.; Fuzzi, S.; Gehrig, R.; Hansson, H.C.; et al. A European aerosol phenomenology–3: Physical and chemical characteristics of particulate matter from 60 rural, urban, and kerbside sites across Europe. Atmos. Environ. 2010, 44, 1308–1320. [Google Scholar] [CrossRef]
  4. Souza, D.Z.; Vasconcellos, P.C.; Lee, H.; Aurela, M.; Saarnio, K.; Teinilä, K.; Hillamo, R. Composition of PM2.5 and PM10 Collected at Urban Sites in Brazil. Aerosol Air Qual. Res. 2014, 14, 168–176. [Google Scholar] [CrossRef]
  5. Zhou, L.; Liu, G.; Shen, M.; Hu, R.; Liu, Y. Source identification of heavy metals and stable carbon isotope in indoor dust from different functional areas in Hefei, China. Sci. Total Environ. 2020, 710, 135599. [Google Scholar] [CrossRef] [PubMed]
  6. Annapurna, S.M.; Anitha, M.; Sutha Kumar, L. Composition and source based aerosol classification using machine learning algorithms. Adv. Space Res. 2024, 73, 474–497. [Google Scholar] [CrossRef]
  7. Fuzzi, S.; Baltensperger, U.; Carslaw, K.; Decesari, S.; Denier Van Der Gon, H.; Facchini, M.C.; Fowler, D.; Koren, I.; Langford, B.; Lohmann, U.; et al. Particulate matter, air quality and climate: Lessons learned and future needs. Atmos. Chem. Phys. 2015, 15, 8217–8299. [Google Scholar] [CrossRef]
  8. Chow, J.C.; Watson, J.G.; Mauderly, J.L.; Costa, D.L.; Wyzga, R.E.; Vedal, S.; Hidy, G.M.; Altshuler, S.L.; Marrack, D.; Heuss, J.M.; et al. Health Effects of Fine Particulate Air Pollution: Lines that Connect. J. Air Waste Manag. Assoc. 2006, 56, 1368–1380. [Google Scholar] [CrossRef]
  9. Menon, S.; Hansen, J.; Nazarenko, L.; Luo, Y. Climate Effects of Black Carbon Aerosols in China and India. Science 2002, 297, 2250–2253. [Google Scholar] [CrossRef]
  10. Ramanathan, V.; Crutzen, P.J.; Kiehl, J.T.; Rosenfeld, D. Aerosols, Climate, and the Hydrological Cycle. Science 2001, 294, 2119–2124. [Google Scholar] [CrossRef]
  11. Li, D.D.; Wang, C.; Chan, Q.N.; Yeoh, G.H. Soot: A review of computational models at different length scales. Exp. Comput. Multiph. Flow 2023, 5, 1–14. [Google Scholar] [CrossRef]
  12. Bai, H.; Liu, X.; Liu, X.; Zhang, C.; Mu, L.; Peng, L. Carbon isotope seasonal characteristics of fine carbonaceous aerosol in Jinzhong City, Shanxi Province, China. Atmos. Environ. 2021, 246, 118164. [Google Scholar] [CrossRef]
  13. Chow, J.C.; Bachmann, J.D.; Wierman, S.S.G.; Mathai, C.V.; Malm, W.C.; White, W.H.; Mueller, P.K.; Kumar, N.; Watson, J.G. Visibility: Science and Regulation. J. Air Waste Manag. Assoc. 2002, 52, 973–999. [Google Scholar] [CrossRef]
  14. Liu, B.; Zhang, J.; Wang, L.; Liang, D.; Cheng, Y.; Wu, J.; Bi, X.; Feng, Y.; Zhang, Y.; Yang, H. Characteristics and sources of the fine carbonaceous aerosols in Haikou, China. Atmos. Res. 2018, 199, 103–112. [Google Scholar] [CrossRef]
  15. Samet, J.M.; Zeger, S.L.; Dominici, F.; Curriero, F.; Coursac, I.; Dockery, D.W.; Schwartz, J.; Zanobetti, A. National Morbidity, Mortality, and Air Pollution Study. Part II: Morbidity and Mortality from Air Pollution in the United States. Res. Rep. Health Eff. Inst. 2000, 94 Pt 2, 5–79. [Google Scholar] [PubMed]
  16. Wessels, A.; Birmili, W.; Albrecht, C.; Hellack, B.; Jermann, E.; Wick, G.; Harrison, R.M.; Schins, R.P.F. Oxidant Generation and Toxicity of Size-Fractionated Ambient Particles in Human Lung Epithelial Cells. Environ. Sci. Technol. 2010, 44, 3539–3545. [Google Scholar] [CrossRef]
  17. Yoo, S.-J.; Kurokawa, A.; Matsunaga, K.; Ito, K. Spatial distributions of airborne transmission risk on commuter buses: Numerical case study using computational fluid and particle dynamics with computer-simulated persons. Exp. Comput. Multiph. Flow 2023, 5, 304–318. [Google Scholar] [CrossRef]
  18. Cao, J.; Chow, J.C.; Tao, J.; Lee, S.; Watson, J.G.; Ho, K.; Wang, G.; Zhu, C.; Han, Y. Stable carbon isotopes in aerosols from Chinese cities: Influence of fossil fuels. Atmos. Environ. 2011, 45, 1359–1363. [Google Scholar] [CrossRef]
  19. Sharma, S.K.; Karapurkar, S.G.; Shenoy, D.M.; Mandal, T.K. Stable carbon and nitrogen isotopic characteristics of PM2.5 and PM10 in Delhi, India. J. Atmos. Chem. 2022, 79, 67–79. [Google Scholar] [CrossRef]
  20. Wang, S.; Pavuluri, C.M.; Ren, L.; Fu, P.; Zhang, Y.L.; Liu, C.Q. Implications for biomass/coal combustion emissions and secondary formation of carbonaceous aerosols in North China. RSC Adv. 2018, 8, 38108–38117. [Google Scholar] [CrossRef]
  21. Bikkina, P.; Bikkina, S.; Kawamura, K.; Sarma, V.V.S.S.; Deshmukh, D.K. Unraveling the sources of atmospheric organic aerosols over the Arabian Sea: Insights from the stable carbon and nitrogen isotopic composition. Sci. Total Environ. 2022, 827, 154260. [Google Scholar] [CrossRef] [PubMed]
  22. Kunwar, B.; Kawamura, K.; Zhu, C. Stable carbon and nitrogen isotopic compositions of ambient aerosols collected from Okinawa Island in the western North Pacific Rim, an outflow region of Asian dusts and pollutants. Atmos. Environ. 2016, 131, 243–253. [Google Scholar] [CrossRef]
  23. Xiao, H.-W.; Xiao, H.-Y.; Luo, L.; Zhang, Z.-Y.; Huang, Q.-W.; Sun, Q.-B.; Zeng, Z. Stable carbon and nitrogen isotope compositions of bulk aerosol samples over the South China Sea. Atmos. Environ. 2018, 193, 1–10. [Google Scholar] [CrossRef]
  24. Masalaite, A.; Remeikis, V.; Garbaras, A.; Dudoitis, V.; Ulevicius, V.; Ceburnis, D. Elucidating carbonaceous aerosol sources by the stable carbon δ13CTC ratio in size-segregated particles. Atmos. Res. 2015, 158, 1–12. [Google Scholar] [CrossRef]
  25. Garbaras, A.; Rimšelytė, I.; Kvietkus, K.; Remeikis, V. δ13C values in size-segregated atmospheric carbonaceous aerosols at a rural site in Lithuania. Lith. J. Phys. 2009, 49, 229–236. [Google Scholar] [CrossRef]
  26. Chen, H.-Y.; Chen, L.-D.; Chiang, Z.-Y.; Hung, C.-C.; Lin, F.-J.; Chou, W.-C.; Gong, G.-C.; Wen, L.-S. Size fractionation and molecular composition of water-soluble inorganic and organic nitrogen in aerosols of a coastal environment. J. Geophys. Res. 2010, 115, D22307. [Google Scholar] [CrossRef]
  27. Luo, L.; Liao, T.; Zhang, X.; Wu, Y.; Li, J.; Zhang, R.; Zheng, Z.; Kao, S.-J. Quantifying the formation pathways of nitrate in size-segregated aerosols during winter haze pollution. Gondwana Res. 2023, 115, 71–80. [Google Scholar] [CrossRef]
  28. Tian, S.; Pan, Y.; Liu, Z.; Wen, T.; Wang, Y. Size-resolved aerosol chemical analysis of extreme haze pollution events during early 2013 in urban Beijing, China. J. Hazard. Mater. 2014, 279, 452–460. [Google Scholar] [CrossRef]
  29. Fang, X.; Bi, X.; Xu, H.; Wu, J.; Zhang, Y.; Feng, Y. Source apportionment of ambient PM10 and PM2.5 in Haikou, China. Atmos. Res. 2017, 190, 1–9. [Google Scholar] [CrossRef]
  30. Liu, B.; Li, T.; Yang, J.; Wu, J.; Wang, J.; Gao, J.; Bi, X.; Feng, Y.; Zhang, Y.; Yang, H. Source apportionment and a novel approach of estimating regional contributions to ambient PM2.5 in Haikou, China. Environ. Pollut. 2017, 223, 334–345. [Google Scholar] [CrossRef]
  31. Wang, C.; Luo, L.; Xu, Z.; Liu, S.; Li, Y.; Ni, Y.; Kao, S.J. Assessment of Secondary Sulfate Aqueous-Phase Formation Pathways in the Tropical Island City of Haikou: A Chemical Kinetic Perspective. Toxics 2024, 12, 105. [Google Scholar] [CrossRef] [PubMed]
  32. Chen, P.; Wang, G.; Han, J.; Liu, X.; Liu, M. δ13C difference between plants and soil organic matter along the eastern slope of Mount Gongga. Chin. Sci. Bull. 2010, 55, 55–62. [Google Scholar] [CrossRef]
  33. Narukawa, M.; Kawamura, K.; Li, S.-M.; Bottenheim, J.W. Stable carbon isotopic ratios and ionic composition of the high-Arctic aerosols: An increase in δ13C values from winter to spring. J. Geophys. Res. 2008, 113, D02312. [Google Scholar] [CrossRef]
  34. Wang, Y.Q.; Zhang, X.Y.; Draxler, R.R. TrajStat: GIS-based software that uses various trajectory statistical analysis methods to identify potential sources from long-term air pollution measurement data. Environ. Model. Softw. 2009, 24, 938–939. [Google Scholar] [CrossRef]
  35. Yadav, K.; Sunder Raman, R.; Bhardwaj, A.; Paul, D.; Gupta, T.; Shukla, D.; Laxmi Prasad, S.V.; Lokesh, K.S.; Venkatesh, P. Tracing the predominant sources of carbon in PM2.5 using δ13C values together with OC/EC and select inorganic ions over two COALESCE locations. Chemosphere 2022, 308, 136420. [Google Scholar] [CrossRef]
  36. Jackson, A.L.; Inger, R.; Bearhop, S.; Parnell, A. Erroneous behaviour of MixSIR, a recently published Bayesian isotope mixing model: A discussion of Moore & Semmens (2008). Ecol. Lett. 2009, 12, E1–E5. [Google Scholar] [CrossRef] [PubMed]
  37. Moore, J.W.; Semmens, B.X. Incorporating uncertainty and prior information into stable isotope mixing models. Ecol. Lett. 2008, 11, 470–480. [Google Scholar] [CrossRef]
  38. Parnell, A.C.; Inger, R.; Bearhop, S.; Jackson, A.L. Source Partitioning Using Stable Isotopes: Coping with Too Much Variation. PLoS ONE 2010, 5, e9672. [Google Scholar] [CrossRef]
  39. Stock, B.C.; Jackson, A.L.; Ward, E.J.; Parnell, A.C.; Phillips, D.L.; Semmens, B.X. Analyzing mixing systems using a new generation of Bayesian tracer mixing models. PeerJ 2018, 6, e5096. [Google Scholar] [CrossRef]
  40. Huang, H.; Zou, C.; Cao, J.; Tsang, P.; Zhu, F.; Yu, C.; Xue, S. Water-soluble Ions in PM2.5 on the Qianhu Campus of Nanchang University, Nanchang City: Indoor-Outdoor Distribution and Source Implications. Aerosol Air Qual. Res. 2012, 12, 435–443. [Google Scholar] [CrossRef]
  41. Liu, J.; Wu, D.; Fan, S.; Mao, X.; Chen, H. A one-year, on-line, multi-site observational study on water-soluble inorganic ions in PM2.5 over the Pearl River Delta region, China. Sci. Total Environ. 2017, 601, 1720–1732. [Google Scholar] [CrossRef] [PubMed]
  42. Wu, X.; Deng, J.; Chen, J.; Hong, Y.; Xu, L.; Yin, L.; Du, W.; Hong, Z.; Dai, N.; Yuan, C.-S. Characteristics of Water-Soluble Inorganic Components and Acidity of PM2.5 in a Coastal City of China. Aerosol Air Qual. Res. 2017, 17, 2152–2164. [Google Scholar] [CrossRef]
  43. Xiong, C.; Yu, S.; Chen, X.; Li, Z.; Zhang, Y.; Li, M.; Liu, W.; Li, P.; Seinfeld, J.H. Dominant Contributions of Secondary Aerosols and Vehicle Emissions to Water-Soluble Inorganic Ions of PM2.5 in an Urban Site in the Metropolitan Hangzhou, China. Atmosphere 2021, 12, 1529. [Google Scholar] [CrossRef]
  44. Chen, S.; Wang, H.; Lu, K.; Zeng, L.; Hu, M.; Zhang, Y. The trend of surface ozone in Beijing from 2013 to 2019: Indications of the persisting strong atmospheric oxidation capacity. Atmos. Environ. 2020, 242, 117801. [Google Scholar] [CrossRef]
  45. Phairuang, W.; Suwattiga, P.; Chetiyanukornkul, T.; Hongtieab, S.; Limpaseni, W.; Ikemori, F.; Hata, M.; Furuuchi, M. The influence of the open burning of agricultural biomass and forest fires in Thailand on the carbonaceous components in size-fractionated particles. Environ. Pollut. 2019, 247, 238–247. [Google Scholar] [CrossRef]
  46. Phairuang, W.; Inerb, M.; Furuuchi, M.; Hata, M.; Tekasakul, S.; Tekasakul, P. Size-fractionated carbonaceous aerosols down to PM0.1 in southern Thailand: Local and long-range transport effects. Environ. Pollut. 2020, 260, 114031. [Google Scholar] [CrossRef]
  47. Li, X.; Wang, L.; Ji, D.; Wen, T.; Pan, Y.; Sun, Y.; Wang, Y. Characterization of the size-segregated water-soluble inorganic ions in the Jing-Jin-Ji urban agglomeration: Spatial/temporal variability, size distribution and sources. Atmos. Environ. 2013, 77, 250–259. [Google Scholar] [CrossRef]
  48. Zhang, T.; Cao, J.J.; Tie, X.X.; Shen, Z.X.; Liu, S.X.; Ding, H.; Li, W.T. Water-soluble ions in atmospheric aerosols measured in Xi’an, China: Seasonal variations and sources. Atmos. Res. 2011, 102, 110–119. [Google Scholar] [CrossRef]
  49. Zhao, Z.-Y.; Cao, F.; Fan, M.-Y.; Zhang, W.-Q.; Zhai, X.-Y.; Wang, Q.; Zhang, Y.-L. Coal and biomass burning as major emissions of NOX in Northeast China: Implication from dual isotopes analysis of fine nitrate aerosols. Atmos. Environ. 2020, 242, 117762. [Google Scholar] [CrossRef]
  50. Niu, Z.; Zhang, F.; Chen, J.; Yin, L.; Wang, S.; Xu, L. Carbonaceous species in PM2.5 in the coastal urban agglomeration in the Western Taiwan Strait Region, China. Atmos. Res. 2013, 122, 102–110. [Google Scholar] [CrossRef]
  51. Song, J.; Zhao, Y.; Zhang, Y.; Fu, P.; Zheng, L.; Yuan, Q.; Wang, S.; Huang, X.; Xu, W.; Cao, Z.; et al. Influence of biomass burning on atmospheric aerosols over the western South China Sea: Insights from ions, carbonaceous fractions and stable carbon isotope ratios. Environ. Pollut. 2018, 242, 1800–1809. [Google Scholar] [CrossRef] [PubMed]
  52. Huang, J.; Zhang, Z.; Tao, J.; Zhang, L.; Nie, F.; Fei, L. Source apportionment of carbonaceous aerosols using hourly data and implications for reducing PM2.5 in the Pearl River Delta region of South China. Environ. Res. 2022, 210, 112960. [Google Scholar] [CrossRef] [PubMed]
  53. Duan, J.; Tan, J.; Cheng, D.; Bi, X.; Deng, W.; Sheng, G.; Fu, J.; Wong, M.H. Sources and Characteristics of carbonaceous aerosol in two largest cities in Pearl River Delta Region, China. Atmos. Environ. 2007, 41, 2895–2903. [Google Scholar] [CrossRef]
  54. Dai, S.; Bi, X.; Chan, L.Y.; He, J.; Wang, B.; Wang, X.; Peng, P.; Sheng, G.; Fu, J. Chemical and stable carbon isotopic composition of PM2.5 from on-road vehicle emissions in the PRD region and implications for vehicle emission control policy. Atmos. Chem. Phys. 2015, 15, 3097–3108. [Google Scholar] [CrossRef]
  55. Yao, P.; Huang, R.-J.; Ni, H.; Kairys, N.; Yang, L.; Meijer, H.A.J.; Dusek, U. 13C signatures of aerosol organic and elemental carbon from major combustion sources in China compared to worldwide estimates. Sci. Total Environ. 2022, 810, 151284. [Google Scholar] [CrossRef]
  56. Garbaras, A.; Garbarienė, I.; Bučinskas, L.; Šapolaitė, J.; Ežerinskis, Ž.; Matijošius, J.; Rimkus, A.; Remeikis, V. Characterization of particulate matter emissions from internal combustion engines using δ13C values: Impact of engine operation conditions and fuel type on PM10 isotopic composition. Atmos. Pollut. Res 2023, 14, 101868. [Google Scholar] [CrossRef]
  57. Yao, P.; Chianese, E.; Kairys, N.; Holzinger, R.; Materić, D.; Sirignano, C.; Riccio, A.; Ni, H.; Huang, R.-J.; Dusek, U. A large contribution of methyl siloxanes to particulate matter from ship emissions. Environ. Int. 2022, 165, 107324. [Google Scholar] [CrossRef]
  58. Bosch, C.; Andersson, A.; Kirillova, E.N.; Budhavant, K.; Tiwari, S.; Praveen, P.; Russell, L.M.; Beres, N.D.; Ramanathan, V.; Gustafsson, Ö. Source-diagnostic dual-isotope composition and optical properties of water-soluble organic carbon and elemental carbon in the South Asian outflow intercepted over the Indian Ocean. J. Geophys. Res.-Atmos. 2014, 119, 11743–11759. [Google Scholar] [CrossRef]
  59. Irei, S.; Huang, L.; Collin, F.; Zhang, W.; Hastie, D.; Rudolph, J. Flow reactor studies of the stable carbon isotope composition of secondary particulate organic matter generated by OH-radical induced reactions of toluene. Atmos. Environ. 2006, 40, 5858–5867. [Google Scholar] [CrossRef]
  60. Pavuluri, C.M.; Kawamura, K. Enrichment of 13C in diacids and related compounds during photochemical processing of aqueous aerosols: New proxy for organic aerosols aging. Sci. Rep. 2016, 6, 36467. [Google Scholar] [CrossRef]
  61. Miyazaki, Y.; Kawamura, K.; Jung, J.; Furutani, H.; Uematsu, M. Latitudinal distributions of organic nitrogen and organic carbon in marine aerosols over the western North Pacific. Atmos. Chem. Phys. 2011, 11, 3037–3049. [Google Scholar] [CrossRef]
  62. Liu, X.; Li, X.; Bai, H.; Mu, L.; Li, Y.; Zhang, D. Stable carbon isotopic compositions and source apportionment of the carbonaceous components in PM2.5 in Taiyuan, China. Atmos. Environ. 2021, 261, 118601. [Google Scholar] [CrossRef]
  63. Liu, J.; Zhao, J.; He, D.; Huang, X.; Jiang, C.; Yan, H.; Lin, G.; An, Z. Effects of plant types on terrestrial leaf wax long-chain n-alkane biomarkers: Implications and paleo applications. Earth-Sci. Rev. 2022, 235, 104248. [Google Scholar] [CrossRef]
  64. Goldstein, A.H.; Shaw, S.L. Isotopes of Volatile Organic Compounds: An Emerging Approach for Studying Atmospheric Budgets and Chemistry. Chem. Rev. 2003, 103, 5025–5048. [Google Scholar] [CrossRef]
  65. Xiao, H.-W.; Xiao, H.-Y.; Luo, L.; Shen, C.-Y.; Long, A.-M.; Chen, L.; Long, Z.-H.; Li, D.-N. Atmospheric aerosol compositions over the South China Sea: Temporal variability and source apportionment. Atmos. Chem. Phys. 2017, 17, 3199–3214. [Google Scholar] [CrossRef]
  66. Luo, L.; Yao, X.H.; Gao, H.W.; Hsu, S.C.; Li, J.W.; Kao, S.J. Nitrogen speciation in various types of aerosols in spring over the northwestern Pacific Ocean. Atmos. Chem. Phys. 2016, 16, 325–341. [Google Scholar] [CrossRef]
  67. Schleicher, N.J.; Yu, Y.; Cen, K.; Chai, F.; Chen, Y.; Wang, S.; Norra, S. Source identification and seasonal variations of carbonaceous aerosols in Beijing—A stable isotope approach. In Urban Environment, Proceedings of the 11th Urban Environment Symposium (UES), Karlsruhe, Germany, 16–19 September 2012; Springer: Dordrecht, The Netherlands, 2013; pp. 263–270. [Google Scholar] [CrossRef]
  68. Dong, Z.; Pavuluri, C.M.; Xu, Z.; Wang, Y.; Li, P.; Fu, P.; Liu, C.-Q. Measurement report: Chemical components and 13C and 15N isotope ratios of fine aerosols over Tianjin, North China: Year-round observations. Atmos. Chem. Phys. 2023, 23, 2119–2143. [Google Scholar] [CrossRef]
  69. Zhang, H.; Hong, Z.; Wei, L.; Thornton, B.; Hong, Y.; Chen, J.; Zhang, X. Stable Isotopes Unravel the Sources and Transport of PM2.5 in the Yangtze River Delta, China. Atmosphere 2023, 14, 1120. [Google Scholar] [CrossRef]
  70. Pavuluri, C.M.; Kawamura, K. Seasonal changes in TC and WSOC and their 13C isotope ratios in Northeast Asian aerosols: Land surface–biosphere–atmosphere interactions. Acta Geochim. 2017, 36, 355–358. [Google Scholar] [CrossRef]
  71. Kundu, S.; Kawamura, K. Seasonal variations of stable carbon isotopic composition of bulk aerosol carbon from Gosan site, Jeju Island in the East China Sea. Atmos. Environ. 2014, 94, 316–322. [Google Scholar] [CrossRef]
  72. Wang, X.; Wei, W.; Cheng, S.; Zhang, H.; Yao, S. Source estimation of SO42− and NO3− based on monitoring-modeling approach during winter and summer seasons in Beijing and Tangshan, China. Atmos. Environ. 2019, 214, 116849. [Google Scholar] [CrossRef]
  73. Tang, W.; Llort, J.; Weis, J.; Perron, M.M.G.; Basart, S.; Li, Z.; Sathyendranath, S.; Jackson, T.; Rodriguez, E.S.; Proemse, B.C.; et al. Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires. Nature 2021, 597, 370–375. [Google Scholar] [CrossRef] [PubMed]
  74. López-Veneroni, D. The stable carbon isotope composition of PM2.5 and PM10 in Mexico City Metropolitan Area air. Atmos. Environ. 2009, 43, 4491–4502. [Google Scholar] [CrossRef]
  75. Mašalaitė, A.; Garbaras, A.; Remeikis, V. Stable isotopes in environmental investigations. Lith. J. Phys. 2012, 52, 261–268. [Google Scholar] [CrossRef]
  76. Bush, S.E.; Pataki, D.E.; Ehleringer, J.R. Sources of variation in δ13C of fossil fuel emissions in Salt Lake City, USA. Appl. Geochem. 2007, 22, 715–723. [Google Scholar] [CrossRef]
  77. Ancelet, T.; Davy, P.K.; Trompetter, W.J.; Markwitz, A.; Weatherburn, D.C. Carbonaceous aerosols in an urban tunnel. Atmos. Environ. 2007, 45, 4463–4469. [Google Scholar] [CrossRef]
  78. Widory, D.; Roy, S.; Le Moullec, Y.; Goupil, G.; Cocherie, A.; Guerrot, C. The origin of atmospheric particles in Paris: A view through carbon and lead isotopes. Atmos. Environ. 2004, 38, 953–961. [Google Scholar] [CrossRef]
  79. Górka, M.; Sauer, P.E.; Lewicka-Szczebak, D.; Jędrysek, M.-O. Carbon isotope signature of dissolved inorganic carbon (DIC) in precipitation and atmospheric CO2. Environ. Pollut. 2011, 159, 294–301. [Google Scholar] [CrossRef]
  80. Stelmach, W.; Szarlip, P.; Trembaczowski, A.; Bieganowski, A.; Kuzyakov, Y. Suppression of soil organic matter decomposition by gasoline and diesel as assessed by 13C natural abundance. Eur. J. Soil Biol. 2016, 73, 8–14. [Google Scholar] [CrossRef]
  81. Suto, N.; Kawashima, H. Global mapping of carbon isotope ratios in coal. J. Geochem. Explor. 2016, 167, 12–19. [Google Scholar] [CrossRef]
  82. Widory, D. Combustibles, fuels and their combustion products: A view through carbon isotopes. Combust. Theor. Model. 2006, 10, 831–841. [Google Scholar] [CrossRef]
  83. Moura, J.M.S.; Martens, C.S.; Moreira, M.Z.; Lima, R.L.; Sampaio, I.C.G.; Mendlovitz, H.P.; Menton, M.C. Spatial and seasonal variations in the stable carbon isotopic composition of methane in stream sediments of eastern Amazonia. Tellus B Chem. Phys. Meteorol. 2008, 60, 21–31. [Google Scholar] [CrossRef]
  84. Martinsson, J.; Andersson, A.; Sporre, M.K.; Friberg, J.; Kristensson, A.; Swietlicki, E.; Olsson, P.-A.; Stenström, K.E. Evaluation of δ13C in Carbonaceous Aerosol Source Apportionment at a Rural Measurement Site. Aerosol Air Qual. Res. 2017, 17, 2081–2094. [Google Scholar] [CrossRef]
  85. Zeller, B.; Brechet, C.; Maurice, J.-P.; Le Tacon, F. 13C and 15N isotopic fractionation in trees, soils and fungi in a natural forest stand and a Norway spruce plantation. Ann. For. Sci. 2007, 64, 419–429. [Google Scholar] [CrossRef]
  86. Weiguo, L.; Xiahong, F.; Youfeng, N.; Qingle, Z.; Yunning, C.; Zhisheng, A.N. δ13C variation of C3 and C4 plants across an Asian monsoon rainfall gradient in arid northwestern China. Glob. Chang. Biol. 2005, 11, 1094–1100. [Google Scholar] [CrossRef]
  87. Zhao, L.; Xiao, H.; Cheng, G.; Liu, X.; Yang, Q.; Yin, L.; Li, C. Correlation between δ13C and δ15N in C4 and C3 plants of natural and artificial sand-binding microhabitats in the Tengger Desert of China. Ecol. Inform. 2010, 5, 177–186. [Google Scholar] [CrossRef]
  88. Turekian, V.C.; Macko, S.; Ballentine, D.; Swap, R.J.; Garstang, M. Causes of bulk carbon and nitrogen isotopic fractionations in the products of vegetation burns: Laboratory studies. Chem. Geol. 1998, 152, 181–192. [Google Scholar] [CrossRef]
  89. Das, O.; Wang, Y.; Hsieh, Y.-P. Chemical and carbon isotopic characteristics of ash and smoke derived from burning of C3 and C4 grasses. Org. Geochem. 2010, 41, 263–269. [Google Scholar] [CrossRef]
  90. Haberstroh, S.; Kreuzwieser, J.; Boeddeker, H.; Eiblmeier, M.; Gutte, H.; Lobo-do-Vale, R.; Caldeira, M.C.; Werner, C. Natural Carbon Isotope Composition Distinguishes Compound Groups of Biogenic Volatile Organic Compounds (BVOC) in Two Mediterranean Woody Species. Front. For. Glob. Chang. 2019, 2, 55. [Google Scholar] [CrossRef]
  91. Li, L.; Ren, L.; Ren, H.; Yue, S.; Xie, Q.; Zhao, W.; Kang, M.; Li, J.; Wang, Z.; Sun, Y.; et al. Molecular characterization and seasonal variation in primary and secondary organic aerosols in Beijing, China. J. Geophys. Res. 2018, 123, 12–394. [Google Scholar] [CrossRef]
  92. Kawamura, K.; Kobayashi, M.; Tsubonuma, N.; Mochida, M.; Watanabe, T.; Lee, M. Organic and inorganic compositions of marine aerosols from East Asia: Seasonal variations of water-soluble dicarboxylic acids, major ions, total carbon and nitrogen, and stable C and N isotopic composition. In The Geochemical Society Special Publications; Elsevier: Amsterdam, The Netherlands, 2004; Volume 9, pp. 243–265. [Google Scholar] [CrossRef]
  93. Ceburnis, D.; Garbaras, A.; Szidat, S.; Rinaldi, M.; Fahrni, S.; Perron, N.; Wacker, L.; Leinert, S.; Remeikis, V.; Facchini, M.C.; et al. Quantification of the carbonaceous matter origin in submicron marine aerosol by 13C and 14C isotope analysis. Atmos. Chem. Phys. 2011, 11, 8593–8606. [Google Scholar] [CrossRef]
  94. Crocker, D.R.; Hernandez, R.E.; Huang, H.D.; Pendergraft, M.A.; Cao, R.; Dai, J.; Morris, C.K.; Deane, G.B.; Prather, K.A.; Thiemens, M.H. Biological Influence on δ13C and Organic Composition of Nascent Sea Spray Aerosol. ACS Earth Space Chem. 2020, 4, 1686–1699. [Google Scholar] [CrossRef]
  95. Fry, B.; Hopkinson, C.S., Jr.; Nolin, A.; Wainright, S.C. 13C/12C composition of marine dissolved organic carbon. Chem. Geol. 1998, 152, 113–118. [Google Scholar] [CrossRef]
  96. Khan, M.F.; Shirasuna, Y.; Hirano, K.; Masunaga, S. Characterization of PM2.5, PM2.5–10 and PM>10 in ambient air, Yokohama, Japan. Atmos. Res. 2010, 96, 159–172. [Google Scholar] [CrossRef]
  97. Ni, H.; Huang, R.-J.; Cao, J.; Liu, W.; Zhang, T.; Wang, M.; Meijer, H.A.J.; Dusek, U. Source apportionment of carbonaceous aerosols in Xi’an, China: Insights from a full year of measurements of radiocarbon and the stable isotope 13C. Atmos. Chem. Phys. 2018, 18, 16363–16383. [Google Scholar] [CrossRef]
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Figure 1. The three-day air mass backward trajectories at Haikou in 100 m above ground level during the sampling period.
Figure 1. The three-day air mass backward trajectories at Haikou in 100 m above ground level during the sampling period.
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Figure 2. The relationship between (a) C-content and measured peak area and (b) measured and standard δ13C in reference material.
Figure 2. The relationship between (a) C-content and measured peak area and (b) measured and standard δ13C in reference material.
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Figure 3. Histogram of statistical δ13C values in TC. The data were obtained from Table S1.
Figure 3. Histogram of statistical δ13C values in TC. The data were obtained from Table S1.
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Figure 4. Comparison of δ13C-TC values in fine and coarse particles from this study and previous studies [24,25]. There are significant differences in categories with different letters. The significance was set at 0.05.
Figure 4. Comparison of δ13C-TC values in fine and coarse particles from this study and previous studies [24,25]. There are significant differences in categories with different letters. The significance was set at 0.05.
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Figure 7. SIAR model outputs of proportional contributions from potential sources of carbonaceous aerosols for each season in both fine and coarse particles in Haikou City.
Figure 7. SIAR model outputs of proportional contributions from potential sources of carbonaceous aerosols for each season in both fine and coarse particles in Haikou City.
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Table 1. Concentrations of water-soluble inorganic ions (WSIIs) and total carbonaceous matter (TC), mass ratios of TC to fine and coarse particles, and stable carbon isotopic compositions of TC (δ13C-TC).
Table 1. Concentrations of water-soluble inorganic ions (WSIIs) and total carbonaceous matter (TC), mass ratios of TC to fine and coarse particles, and stable carbon isotopic compositions of TC (δ13C-TC).
WinterSummer
FineCoarseFineCoarse
Ave.Std.Ave.Std.Ave.Std.Ave.Std.
SO42− (µg/m3)4.11.91.10.521.40.680.420.13
NO3− (µg/m3)3.22.91.40.910.480.310.350.15
NH4+ (µg/m3)1.91.30.070.090.340.230.000.00
Ca2+ (µg/m3)0.240.190.520.290.300.120.260.07
Na+ (µg/m3)0.360.190.750.570.150.070.230.10
Cl− (µg/m3)0.610.331.301.000.160.110.360.22
K+ (µg/m3)0.230.140.040.010.080.030.020.01
Mg2+ (µg/m3)0.050.030.080.040.030.010.040.01
TC (µg/m3)6.83.01.10.63.71.01.20.6
TC/PM0.370.160.030.020.450.130.110.05
δ13C-TC (‰)−25.70.5−25.40.6−26.40.5−24.70.8
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Cao, L.; Luo, L.; Wang, C.; Wang, M.; Yang, R.; Kao, S. Source Apportionment of Carbonaceous Matter in Size-Segregated Aerosols at Haikou: Combustion-Related Emissions vs. Natural Emissions. Sustainability 2024, 16, 9859. https://doi.org/10.3390/su16229859

AMA Style

Cao L, Luo L, Wang C, Wang M, Yang R, Kao S. Source Apportionment of Carbonaceous Matter in Size-Segregated Aerosols at Haikou: Combustion-Related Emissions vs. Natural Emissions. Sustainability. 2024; 16(22):9859. https://doi.org/10.3390/su16229859

Chicago/Turabian Style

Cao, Lingling, Li Luo, Chen Wang, Mingbin Wang, Rongqiang Yang, and Shuhji Kao. 2024. "Source Apportionment of Carbonaceous Matter in Size-Segregated Aerosols at Haikou: Combustion-Related Emissions vs. Natural Emissions" Sustainability 16, no. 22: 9859. https://doi.org/10.3390/su16229859

APA Style

Cao, L., Luo, L., Wang, C., Wang, M., Yang, R., & Kao, S. (2024). Source Apportionment of Carbonaceous Matter in Size-Segregated Aerosols at Haikou: Combustion-Related Emissions vs. Natural Emissions. Sustainability, 16(22), 9859. https://doi.org/10.3390/su16229859

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