Unveiling differences in source apportionment and optical properties of wintertime carbonaceous aerosols in northern and southern Chinese Cities

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Understanding their regional variations and sources in China is essential for improving air quality control. Carbonaceous aerosols were collected during winter in four major Chinese cities—Xi'an (XA), Shijiazhuang (SJZ), Wuhan (WH), and Chongqing (CQ)—to investigate their pollution characteristics. A comprehensive analysis of various carbon fractions was conducted, including organic carbon (OC), elemental carbon (EC), and specific subfractions such as OC1 – OC4, EC1 – EC3, char-EC, and soot-EC. Using the hybrid environmental receptor model (HERM), we identified emission sources and quantified their contributions to primary total carbon (PTC) in these urban areas. The findings demonstrate substantial impacts from coal combustion during the heating season in XA and SJZ. Vehicular emissions account for a considerable proportion, particularly in SJZ, corresponding with the increase in automobile ownership in that city. In WH and CQ, emissions from industrial and residential coal utilization, especially from the steel industry, are markedly higher. Additionally, the COVID-19 pandemic results in reduced contributions from industrial emissions in WH and SJZ. We further investigate the optical characteristics of EC, revealing that the average mass absorption efficiency (MAE) values across the four cities are consistent with previous studies. Specifically, MAEs derived from different emission sources indicate higher values from biomass burning and stationary combustion in XA and CQ, whereas industrial sources result in elevated values in SJZ and WH. This study delineates the distinct characteristics of carbonaceous aerosols in northern and southern Chinese cities, providing a robust scientific basis for urban air pollution mitigation strategies. Carbonaceous aerosols Source apportionment HERM Light absorption Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Article Highlights Research comparing the pollution characteristics of carbonaceous aerosols in four cities of north and south China. Seven sources were identified and coal combustion was the most important source. The light absorption of carbonaceous aerosols is mainly affected by coal and biomass fuels and industrial sources. 1 Introduction With rapid economic growth and increased energy demands over the past decade, aerosol pollution in China’s metropolitan regions has become critically severe (Fu and Chen. 2017; Fu et al. 2014 ). Carbonaceous aerosols, comprising 20–50% of PM 2.5 mass in urban areas (Nunes and Pio 1993 ; Xue et al. 2020 ) and significantly impact climate modulation, air quality, and public health (Liu et al. 2016 ; Tshehla and Wright 2019 ). These aerosols include organic carbon (OC), elemental carbon (EC), and minor quantities of carbonate carbon (CC) (Niu et al. 2012 ). OC is further categorized into primary OC (POC) and secondary OC (SOC) (Gu et al. 2010 ). EC, predominantly generated from incomplete combustion processes, absorbs light and influences solar radiation and climate dynamics (Bisht et al. 2015 ). Urban areas frequently encounter severe pollution episodes during winter due to emissions from residential heating, industrial activities, and vehicular exhaust (Liu et al. 2015 ; Park et al. 2022 ). Therefore, understanding the constituents and pollution characteristics of carbonaceous aerosols is pivotal for source apportionment and air quality improvement. Analysis of carbonaceous constituents is instrumental in identifying pollution sources. Specifically, the spectrum characteristics of carbon facilitate qualitative source identification. For instance, Zhang et al. ( 2014 ) and Cao et al. ( 2005 ) identified motor vehicle emissions and coal combustion as primary sources in Xi'an, China, through carbonaceous aerosol analysis. Similarly, Tian et al. ( 2013 ) examined the source and seasonal variation of secondary organic carbon in PM 10 across five northern Chinese cities using the chemical mass balance (CMB) model. Moreover, ratios of OC/EC and char/soot are useful in distinguishing emission sources (Safai et al. 2014 ). Previous studies have indicated distinct OC/EC ratios for various sources: coal (~ 0.28 to 2.2), motor vehicles (~ 2.5 to 5.0), and biomass burning (~ 3.8 to 13.2) ( Schauer et al. 2002 ; Wastson et al. 2001; Zhang et al. 2007 ). In addition, Liu et al. ( 2018b ) used the positive matrix factorization (PMF) model to demonstrate that vehicle exhaust, coal combustion, and biomass burning are major pollution sources in Haikou, China, noting seasonal variations in the ratio of char-EC to soot-EC. As a significant light-absorbing component, EC has a substantial impact on global warming (Bisht et al. 2015 ). Further, Wang et al. ( 2023 ) analyzed the pollution characteristics and sources of EC in the Tibetan Plateau, underscoring its light absorption and transport, which are crucial for assessing the impacts of carbonaceous aerosols on solar radiation. This study investigates wintertime carbonaceous aerosol pollutants in four Chinese cities: Xi’an (XA), Shijiazhuang (SJZ), Wuhan (WH), and Chongqing (CQ). XA, located in the Fen-Wei Plain, endures severe air pollution due to geographical and meteorological factors, exacerbated by winter heating (Tian et al. 2022 ; Wang et al. 2015 ). Similarly, SJZ, a major city in the Beijing-Tianjin-Hebei region, faces substantial pollution challenges intensified by a recent surge in vehicle numbers (Liu et al. 2018a ). WH, a central metropolis in Central China, experiences pollution primarily from coal-fired power plants, vehicle emissions, and biomass burning (Liu et al. 2020 ; Zheng et al. 2020 ). CQ, with its robust industrial base, has observed a sharp increase in energy consumption and pollutant emissions. Its complex topography compounds these issues, leading to frequent hazy conditions (Chen et al. 2017 ). Consequently, this study aims to analyze the spatial variations and pollution characteristics of carbon components, evaluate carbonaceous aerosols using OC/EC and char/soot ratios, identify primary total carbon sources using the hybrid environmental receptor model (HERM), and assess EC’s light-absorbing characteristics and quantify the contributions of various emission sources. The findings will enhance understanding of regional variations and sources of carbonaceous aerosols in China, providing a scientific basis for urban air quality improvement. 2 Materials and methods 2.1 Study Areas This study selected four urban locations in China as sampling sites: XA and SJZ in northern China, and WH and CQ in southern China (Fig. 1 ). All sampling sites were situated in urban environments. In XA, the sampling site was located at the National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain (34.24°N, 108.87°E), within the high-tech zone southwest of the city center, surrounded by commercial and residential areas. In SJZ, the sampling site was situated in the courtyard of Hebei Sailhero Environmental Protection High-tech Co., Ltd. (38.04°N, 114.65°E), surrounded by pharmaceutical and machinery industries and near major streets. In WH, the site was positioned at the Future City Campus of the China University of Geosciences (Wuhan), within Optics Valley District, proximate to industrial areas and expressways. In CQ, the sampling site was at the Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences (29.81°N,106.55°E), surrounded by office buildings and commercial districts. Sample collection was conducted during autumn and winter. In XA, samples were collected from 15 November 2019 to 3 March 2020; in SJZ, from 12 October 2021 to 8 January 2022; in WH, from 14 October 2021 to 8 January 2022; and in CQ, from 18 January 2020 to 29 February 2020. Sampling was performed continuously for 24 h each day, starting at 8 a.m. in SJZ, WH, and CQ, and at 10 a.m. in XA. 2.2 Sampling and Analysis OC and EC were quantified using a 0.526 cm 2 quartz filter punch analyzed with a DRI model 2001 carbon analyzer (Atmoslytic, Inc., Calabasas, CA) following the thermal/optical reflectance (TOR) method under the IMPROVE_A protocol (Cao et al. 2013 ; Chow et al. 1993 ). Initially, the quartz filter was incrementally heated to 140℃ (OC1), 280℃ (OC2), 480℃ (OC3), and 580℃ (OC4) in a helium (He) atmosphere, converting particulate OC on the filter into CO 2 . Subsequently, the atmosphere was switched to an oxidizing mixture of 2% oxygen (O 2 ) and 98% He, and the temperature was elevated to 580 ℃, 780℃, and 840 ℃ to analyze EC1, EC2, and EC3, respectively. During heating, some organic carbon decomposes, forming pyrolyzed carbon (OP). According to the IMPROVE protocol, OC is defined as the sum of OC1, OC2, OC3, OC4, and OP, while EC is calculated as EC1 + EC2 + EC3 − OP. The method detection limits (MDLs) for OC and EC are 0.82 and 0.2µg cm -2 , respectively. Furthermore, EC was subdivided into char-EC (EC1 − OP) and soot-EC (EC2 + EC3) according to Han et al. ( 2007 ). To construct receptor and source profiles for the HERM model, concentrations of additional species were measured. Elements including calcium (Ca), titanium (Ti), manganese (Mn), iron (Fe), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), selenium (Se), and lead (Pb) were analyzed using energy-dispersive X-ray fluorescence (ED-XRF) spectrometry (PANalytical Epsilon 4, Almelo, The Netherlands) (Cao et al. 2012 ). Water soluble potassium (K + ) was determined through ion chromatography (IC) (Du et al. 2022 ). Detailed quality assurance and quality control (QA/QC) procedures for these analyses are described in Cao et al. ( 2003 ). 2.3 Hybrid environment receptor model (HERM) The HERM (Antony Chen and Cao 2018 ) was employed to apportion sources of carbonaceous aerosols. HERM synthesizes the PMF and CMB receptor models, allowing for the analysis of pollutant sources using comprehensive, partial, or unknown source profile information. In matrix notation, the bilinear HERM model is defined as follows: $$\:{C}_{\text{mn}}={\sum\:}_{j=1}^{J}{F}_{\text{mj}}{S}_{jn}+{E}_{mn}$$ 1 where C mn is the measured concentration of m during time n ; F mj is the source profile, representing the fractional quantity of m in source j emission; S jn represents the contribution of source j during time n ; and E mk is the model residual for m concentration measured during time k . Using an iterative conjugate gradient algorithm, HERM solves S jn and unknown F mj by minimizing the reduced chi-square (Q), defined as follows: $$\:Q={\sum\:}_{k=1}^{K}{\sum\:}_{n=1}^{N}\frac{({C}_{mn}-{\sum\:}_{j=1}^{J}{F}_{mj}{S}_{jn}{)}^{2}}{{\sigma\:}_{{C}_{mn}}^{2}+{\sum\:}_{j=1}^{J}({\sigma\:}_{{F}_{mj}}^{2}{\sigma\:}_{{S}_{jn}}^{2}+\beta\:{\delta\:}_{mj}{\sigma\:}_{{C}_{mn}}^{2}}$$ 2 where K , N , and J represent the number of samples, chemical species, and sources, respectively; δ mj is set to 0 or 1 depending on whether F mj is constrained or unknown, respectively; and β is an adjustable factor with a default value of 1. For this study, carbonaceous, elemental, and ionic components were input into HERM. A total of 17 chemical species from XA, 19 from SJZ, 18 from WH, and 20 from CQ were selected for source apportionment. Details on these species and their uncertainty calculations are provided in Text S1 in the Supplement. The HERM model analyzed solutions with 2–8 factors, assuming no prior knowledge of source profile information. The optimal solution identified five significant factors in all four cities, as detailed in Text S2 in the Supplement. 2.4 Calculation of mass absorption efficiency (MAE) The MAE, measured in m 2 /g, is a crucial parameter for characterizing the optical properties of EC. MAE represents the light absorption cross-section per unit mass concentration and is calculated as follows: $$\:\text{MAE}=\frac{ATN\cdot\:\frac{A}{V}}{E{C}_{s}\cdot\:C\cdot\:D\cdot\:R\left(ATN\right)}=\frac{-{ln}(\frac{{I}_{0}}{I})\cdot\:\frac{A}{V}}{E{C}_{s}\cdot\:C\cdot\:R\left(ATN\right)}$$ 3 where ATN is the attenuation calculated from the transmitted light intensities; I 0 and I are the transmitted light intensities at the beginning and end of the experiment, respectively, obtained from the original data files of the DRI Model 2001 carbon analyzer. A is the sampling area of the filter (cm 2 ); V is the sampling volume (m 3 ); EC S is the mass concentration of EC loaded on the filter (µg m − 3 ); C is a normalization factor accounting for multiple scattering, with a value of 2.14 (Bond and Bergstrom 2006 ); and R(ATN) corrects for the loading effect as a function of ATN, defined as: where f is a parameter set to 1.1 (Ram and Sarin 2009 ). 3 Results and discussion 3.1 Characteristics of carbonaceous aerosol In the four cities studied, the daily average concentrations of OC are 15.4 ± 7.5 µg m -3 in XA, 8.8 ± 3.7 µg m -3 in SJZ, 7.8 ± 3.4 µg m -3 in WH, and 14.1 ± 6.9 µg m -3 in CQ (Table 1 ). XA and CQ exhibit obviously higher concentrations than SJZ and WH. Compared with previous studies, OC concentrations have decreased across these cities (Table S1 ). OC2, OC3, and OP followed similar distribution patterns to OC, particularly in XA and CQ, where values are elevated (Fig. 2 ). This trend in XA may be associated with the heating period, during which over 30% of the days are classified as moderately or highly polluted. CQ, being a heavy industry city, consumes substantial coal during winter, leading to significant carbon emissions. All OC components are highly correlated (Fig. S3), especially between OC2 and OC3 (r = 0.83–0.96, p < 0.01). OC2 typically indicates coal combustion, while OC3 is associated with road dust and coal combustion. Table 1 The average concentrations of carbonaceous components, chemical components major chemical ratios and meteorological factors in the four cities(µg m -3 ) Species XA SJZ WH CQ TC 19.2 ± 9.3 13.0 ± 5.1 11.2 ± 4.8 17.9 ± 8.5 OC 15.4 ± 7.5 8.8 ± 3.7 7.8 ± 3.4 14.1 ± 6.9 EC 3.8 ± 2.0 4.1 ± 1.5 3.5 ± 1.4 3.8 ± 1.7 OC1 1.2 ± 0.7 0.5 ± 0.4 0.2 ± 0.1 0.5 ± 0.3 OC2 2.1 ± 1.3 1.6 ± 0.6 1.4 ± 0.6 2.3 ± 1.4 OC3 4.0 ± 2.0 3.2 ± 1.2 3.1 ± 1.4 4.0 ± 2.0 OC4 2.7 ± 1.6 2.4 ± 0.9 2.2 ± 0.9 1.6 ± 0.3 EC1 8.7 ± 5.0 5.0 ± 2.7 4.0 ± 2.1 9.1 ± 4.7 EC2 0.4 ± 0.4 0.3 ± 0.1 0.3 ± 0.1 0.3 ± 0.1 EC3 0.1 ± 0.1 0.1 ± 0.04 0.1 ± 0.1 0.1 ± 0.1 OP 5.2 ± 3.7 1.3 ± 1.5 0.9 ± 1.0 5.7 ± 3.3 char-EC 3.5 ± 1.9 3.7 ± 1.4 3.1 ± 1.3 3.4 ± 1.5 soot-EC 0.5 ± 0.4 0.4 ± 0.1 0.4 ± 0.2 0.4 ± 0.2 K + 0.882 ± 0.572 0.379 ± 0.23 0.474 ± 0.3 0.76 ± 0.671 Ca 0.607 ± 0.664 0.457 ± 0.333 0.294 ± 0.258 0.016 ± 0.013 Ti 0.024 ± 0.021 0.02 ± 0.015 0.016 ± 0.014 0.003 ± 0.003 Mn 0.040 ± 0.035 0.049 ± 0.023 0.026 ± 0.013 0.008 ± 0.005 Fe 0.690 ± 0.637 0.746 ± 0.433 0.28 ± 0.2 0.129 ± 0.075 Zn 0.166 ± 0.165 0.185 ± 0.097 0.151 ± 0.102 0.055 ± 0.079 As 0.009 ± 0.005 0.006 ± 0.003 0.008 ± 0.005 0.005 ± 0.002 Se 0.003 ± 0.002 0.004 ± 0.003 0.007 ± 0.004 0.002 ± 0.001 Pb 0.041 ± 0.024 0.029 ± 0.014 0.027 ± 0.018 0.022 ± 0.016 OC/EC 4.3 ± 1.2 2.1 ± 0.3 2.2 ± 0.3 3.7 ± 0.5 SOC 7.4 ± 4.6 2.6 ± 1.8 2.1 ± 1.4 3.9 ± 3.0 POC 8.0 ± 4.3 6.2 ± 2.2 5.7 ± 2.4 10.2 ± 4.5 char/soot 13.1 ± 11.9 9.6 ± 4.4 8.5 ± 3.5 9.4 ± 3.1 Wind Speed(m s − 1 ) 0.6 ± 0.1 2.0 ± 1.0 1.9 ± 0.8 1.0 ± 0.3 Temperature(℃) 7.9 ± 3.4 7.4 ± 5.7 12.1 ± 4.4 10.1 ± 2.0 Relative humidity(%) 58.1 ± 14.4 52.8 ± 19.1 63.3 ± 15.0 82.5 ± 7.4 Using the EC tracer method (Castro et al. 1999 ), SOC concentrations are highest in XA at 7.4 ± 4.6 µg m -3 , accounting for 47.5% of OC, followed by CQ, SJZ, and WH (Table S2). The severe SOC pollution in XA could be attributed to increased residential heating and emissions from biomass burning and coal combustion during winter (Xue et al. 2020 ). Low wind speeds averaging less than 1m s -1 and temperatures around 7.9℃ during the sampling period (Fig.S2) facilitated the conversion of semi-volatile organic compounds from gas to particle phase. Studies have demonstrated that every 10℃ rise in temperature decreases SOC concentration by approximately 18% (Odum et al. 1996 ; Pandis et al. 1992 ). Adverse weather conditions and heightened emissions of volatile organic precursors in winter exacerbated secondary pollution in XA (Wang et al. 2020 ). The formation of SOC contributes more to OC concentration in low pollution event (Hallquist M et al., 2009 ; Uttamang et al. 2023 ). When compared across cities, the SOC/OC ratio in XA (47.5%) closely approximates that in Hohhot, China (45.5%) (Liu et al. 2023a ). It is higher than those in Baotou (37.1%) (Zhou et al. 2016 ), Lvliang (23.1%) (Li et al. 2022 ), and Xiamen (22%) (Wang et al. 2021 ), but lower than those recorded in Tianjin (55%), Handan (66%) (Zhou et al. 2023 ), and Nanjing (53%) (Dai et al. 2022 ). Long-term studies of carbonaceous aerosols have shown a rising trend in SOC proportions in urban atmospheres in China, indicating that the decrease of primary source is concomitant with an increase in secondary formation (Zhou et al. 2023 ). Differences in SOC among cities may be related to distinct meteorological conditions, atmospheric oxidation capacity, and the types and concentrations of volatile organic precursors (Ji et al. 2019 ). EC and OC exhibit a strong correlation in the four cities (Fig. S3), suggesting similar sources (Cao et al. 2005 ). Daily average EC concentrations are 3.8 ± 2.0 µg m -3 in XA, 4.1 ± 1.5 µg m -3 in SJZ, 3.5 ± 1.4 µg m -3 in WH, and 3.8 ± 1.7 µg m -3 in CQ (Table 1 ). Compared to previous studies (Table S1 ), EC concentrations increase in WH due to the COVID-19 lockdowns temporarily reducing pollutant levels in the previous year (Zheng et al. 2020 ). In contrast, EC concentrations generally decline in the other three cities. The proportions of char-EC and soot-EC within the total EC vary only slightly across the four cities: 88.9% and 11.1% in XA, 90.1% and 9.9% in SJZ, 88.6% and 11.4% in WH, and 89.5% and 10.5% in CQ. Fig. S4 shows that the trends of EC and char-EC are consistent, demonstrating that EC is mainly influenced by char-EC. Char-EC primarily originates from coal combustion and biomass burning, which are significant sources of winter heating in northern cities. In contrast, heavy industrial cities in the south, such as WH and CQ, consume substantial amounts of coal industrially, despite the lack of residential heating demand. For example, in 2020, CQ’s total coal consumption reaches 88.75 million tons, accounting for 1.8% of national consumption (Chongqing Statistical Yearbook, 2021). Soot-EC, mainly resulting from motor vehicle emissions, exhibits minor variations across the cities. Given that the OC/EC ratio is influenced by secondary organic aerosol (SOA), the char-EC/soot-EC ratio serves as a more effective metric for identifying carbon aerosol sources (Bret et al. 2008 ; Han et al. 2010 ). Previous studies indicate that a char-EC/soot-EC ratio greater than 10 suggests biomass burning, less than 1 indicates motor vehicle sources, and less than 2 points to coal combustion (Cao et al. 2005 ; Chow et al. 2004 ). As shown in Fig. 3 , the char-EC/soot-EC ratios in this study are 13.1 ± 11.9 in XA, 9.6 ± 4.4 in SJZ, 8.5 ± 3.5 in WH, and 9.4 ± 3.1 in CQ, indicating a significant contribution from biomass burning, particularly in XA and SJZ. 3.2 Source apportionment of carbonaceous aerosols The HERM model was employed to analyze the source contributions of primary total carbon components (PTC) in the four cities. The detailed calculation process of PTC can be found in the Supplement (Text S3). Data collection excludes sand-dust days (13–14 February 2020) in XA. A total of 21 species, including carbonaceous, elemental, and ionic components, are selected for input into model to identify emission sources, as detailed in Text S1. Six emission sources are identified in XA, while five sources are recognized in the other three cities. These sources include biomass burning (BB), coal combustion (CC), fugitive dust (FD), industrial emissions (IE), vehicle emissions (VE), fireworks (FW), and stationary combustion sources (SCE). Figure 4 and Fig. S5 illustrate the source profiles and contributions, and Fig. 5 shows the relative contributions of each source to PTC. Biomass burning. Biomass burning is identified using K + as an effective indicator (Zhang et al. 2020 ), along with high loadings of OC1 and OP (Cao et al. 2005 ). In the four cities, significant loadings of OC1, OP, and K + are observed, with percentages ranging 69.3–81.9% for OC1, 10.0–77.7% for OP, and 38.1–66.5% for K + . This factor is identified as a biomass burning, accounting for 21.9%, 3.4%, and 34.2% of PTC in XA, SJZ, and WH, respectively. Biomass burning is a major source of pollution during winter in northern China, commonly used for heating and cooking. Due to the proximity of the SJZ sampling site to residential and industrial parks and the limited use of biomass fuels such as straw and dry wood, the contribution of biomass burning is low. In WH, biomass burning predominantly occurs in suburban areas and regions with intense agricultural activities. Fire point detection map reveals high-density distributions of fire points in these areas, indicating frequent biomass burning (Mehmood et al. 2020 ). Coal combustion. Coal combustion is characterized by high levels of OC2, As, Se, and Pb in XA, SJZ, and WH, with contributions ranging 15.5–46.0% for OC2, 42.3–57.8% for As, 35.9–74.1% for Se, and 42.8–60.4% for Pb. OC2 is generally associated with coal combustion (Xu et al. 2016 ), while As, Se, and Pb are typical trace elements found in coal combustion, significantly contributing to carbon aerosols during winter (Li et al. 2018 ). Thus, this factor is identified as coal combustion source. In XA, SJZ, and WH, coal combustion contributes 26.8%, 42.0%, and 38.8% to PTC, respectively. The data collection period in XA and SJZ coincide with the heating season when coal consumption is high. Compared to previous studies, the contribution from coal combustion to carbonaceous aerosols has decreased, partly due to recent shifts from coal to natural gas for domestic heating, driven by clean energy policies (Liu et al. 2022 ; Zhang et al. 2023 ). Conversely, in WH, a heavily industrialized city in the south, coal-fired power plants and the iron and steel industry release large amounts of particulate and gaseous pollutants. Stationary combustion source. In CQ, biomass burning and coal combustion are aggregated as a single stationary combustion source by the HERM model. This source is characterized by high levels of OC1 (72.7%), OC2 (43.3%), Pb (32.5%), As (35.1%), Se (52.6%), and K + (29.7%), contributing 33.6% to the PTC. As a major industrial city in southwest China, CQ historically has high coal consumption. However, following regional industrial structuring, coal usage has substantially declined (Feng et al. 2021 ). Fugitive dust. Fugitive dust is identified across the four cities with elevated loadings on OC4, Ca, Fe, Mn, and Ti, with their respective contribution rates ranging 9.4–51.7%, 57.9–80.9%, 38.6–63.5%, 10.3–40.8%, and 57.6–88.2%. OC4 is recognized as an indicator of road fugitive dust (Chow et al. 2004 ). Fugitive dust, primarily from soil and roads, is a significant atmospheric dust source containing elements like Ca, Al, Si, and Fe. Ca and Ti are commonly used as tracers for construction and soil dust (Gao et al. 2016 ), while Fe and Mn, often found as oxides, originate from brake wear and tire wear (Yu et al. 2019 ; Rai et al. 2020 ). These elements typically mark crustal dust, aligning with the composition of the upper continental crust (Chen et al. 2016 ). Consequently, this factor is identified as fugitive dust, accounting for 3.7% in XA, 8.6% in SJZ, 11.6% in WH, and 5.5% in CQ of the PTC. Industrial emissions. Industrial emissions vary among the four cities, primarily consisting of metal elements identified as industrial pollutants. In XA and CQ, the predominant elements are Zn at 78.1% and 48.9%, Mn at 41.7% and 49.5%, and Fe at 28.3% and 43.8%, contributing 22.4% and 38.7% to the PTC, respectively. Zn emissions are typically associated with ferrous metal smelting industries (Chang et al. 2018 ), while Fe and Mn originate from steel manufacturing and refining industries (Zhang et al. 2018 ). In SJZ, industrial emissions are characterized by high levels of Mn (48.4%), Cr (42.7%), and Ni (37.0%), contributing 4.7% to PTC. Cr is used in various smelting processes, including tanning, electroplating, and stainless steel production (Liu et al. 2023b ). Ni emissions are linked to the oil refinery and semiconductor industries (Fernandez-Camacho et al. 2012 ). WH has significant contributions from Ni (66.9%) and Se (49.3%), accounting for 11.0% of PTC. Vehicle emissions. Vehicle emissions exhibit high loadings of EC2, EC3, and Zn across all cities, with contribution rates ranging 10.1–46.9% for EC2, 20.3–97.1% for EC3, and 7.2–68.9% for Zn. EC1 and OP are considered markers of gasoline vehicle exhaust, while EC2 and EC3 are indicative of diesel vehicle emissions (Cao et al. 2005 ). OP is primarily associated with water-soluble polar compounds in the atmosphere, commonly linked to biomass burning and gasoline vehicle exhaust. Identification of these sources typically requires integration with other factors. Elements such as Zn, Fe, Cu, and Mn serve as important tracers for automotive lubricant oils, brake pads, and tire wear (Daellenbach et al. 2020 ). Cr and Ni are emitted during the combustion of automobile fuel (Li et al. 2017 ). Additionally, this factor strongly correlates with NO 2 or NO x (R 2 = 0.45–0.78), suggesting traffic as a primary source (Zheng et al. 2020 ; Zhang et al. 2023 ). With rising vehicle ownership, vehicle emissions have become increasingly concerning. Contributions to PTC in the four cities are 15.8% in XA, 41.3% in SJZ, 4.4% in WH, and 15.3% in CQ. During the severe COVID-19 outbreak in early 2020, the sampling period in WH coincides with a decrease in traffic volume, resulting in a reduced contribution from vehicle emissions. Fireworks. During the sampling periods in XA and CQ, which include the Spring Festival, fireworks are identified as a significant source. This factor exhibits the highest loadings of Ba (76.8–98.2%), Cu (83.6% in CQ), and K + (21.1–61.4%). Ba and Cu serve as primary colorants in fireworks (Manchanda et al. 2020 ), while K + is a major component of fireworks containing 74% KNO 3 as the oxidizing agent (Drewnick et al. 2006 ). Therefore, this factor is labeled as fireworks. Contributions to PTC from fireworks in XA and CQ are 9.4% and 6.9%, respectively. The highest concentrations typically occur on New Year's Eve and New Year's Day (Kong et al. 2015 ), with peak levels during the Spring Festival recorded at 13.2 µg m -3 in XA and 9.5 µg m -3 in CQ. These findings underscore the need to enhance fireworks control during festival periods. 3.3 Mass absorption efficiency (MAE) of elemental carbon The MAE is a crucial parameter for characterizing the optical properties of EC (Xing et al. 2014 ). Figure 6 presents the analysis of light absorption coefficients and MAE data for the four cities. At 633nm, the MAE values are 10.5 ± 5.1 m 2 g -1 for XA, 5.4 ± 1.1m 2 g -1 for SJZ, 7.3 ± 1.4 m 2 g -1 for WH, and 9.6 ± 2.0 m 2 g -1 for CQ. Compared to previous studies, the MAE values for XA and CQ are higher than those reported for Nanjing (8.9 ± 2.1 m 2 g -1 ), Jinan (9.58 ± 1.83 m 2 g -1 ), Beijing (8.45 ± 1.71 m 2 g -1 ), and Yucheng (9.6 ± 1.8 m 2 g -1 ) in China (Cheng et al. 2011 ; Cui et al. 2016 ; Tian et al. 2017 ). In contrast, SJZ and WH exhibit lower MAE values compared to these cities. MAE can be influenced by various factors, including measurement methods, actual optical coefficients, corrections for pyrolysis carbonization, and related-properties of EC like coating, mixing states and chemical composition (Cheng et al. 2011 ; Ma et al. 2020 ). Figure 6 reveals a consistent trend between the light absorption coefficient b abs and EC across the cities, with mean b abs of 33.1 ± 12.6 Mm -1 in XA, 22.2 ± 7.9 Mm -1 in SJZ, 24.0 ± 8.2 Mm -1 in WH, and 33.4 ± 8.7 Mm -1 in CQ. Photochemical oxidation plays an important role in the light absorption of carbonaceous aerosols (Wang et al. 2017 ). The oxidant (O x = O 3 +NO 2 ) serves as a tracer for atmospheric aging caused by photochemical reactions (Canonaco et al. 2015 ). We analyzed the correlation between O x and MAE in the four cities to assess the impact of atmospheric oxidation capacity on the light absorption effects of EC. Figure 7 shows negative correlations between MAE and O x in XA and WH, with correlation coefficients of -0.93 and − 0.85 ( p < 0.01), respectively, while the correlations in SJZ and CQ are weaker. Fig. S6 reveals that the correlation between O 3 and MAE is weak across all cities (r=-0.08–0.19). In contrast, NO 2 and MAE demonstrate negative correlations, with coefficients of -0.32 for XA, 0.09 for SJZ, -0.53 for WH, and − 0.36 for CQ. This pattern is likely due to low O 3 concentrations and weak photochemical oxidation in winter, with oxidation in XA and WH primarily driven by NO 2 . Except in CQ, MAE values increase with increasing relative humidity (RH), exhibiting strong correlations (r = 0.42–0.64, p < 0.01) in XA, SJZ, and WH. Higher RH leads to an increase in secondary aerosols, such as sulfate and organic aerosols, generated through aqueous-phase reactions (Wu et al. 2018 ). Under high RH conditions, EC particles become coated with secondary hygroscopic components, enhancing their light absorption capabilities (Wu et al. 2016 ; Fierce et al. 2016 ). Chen et al. ( 2023 ) found that the effect of RH on light absorption is more pronounced than that caused by photochemical oxidation. Additionally, various factors, including the mixing state, aging, and different measurement methods of the light absorption coefficient, profoundly influence EC's optical properties (Wang et al. 2017 ; Ma et al. 2020 ; Wang et al. 2021 ; Cao et al. 2021 ). Using the source apportionment results, we calculated the EC contributions from different emission sources across the four cities. To analyze the MAE of EC (MAE EC ) from each source, we employed a ridge regression model encompassing sources with significant light absorption, such as biomass burning (BB EC ), coal combustion (CC EC ), vehicle emissions (VE EC ), industrial emissions (IE EC ), and stationary combustion emissions (SCE EC ) (Table 2 ). The derived MAE EC values are 4.74 ± 1.68 m 2 g -1 for BB EC , 4.74 ± 2.38 m 2 g -1 for CC EC , 3.60 ± 2.18 m 2 g -1 for VE EC , and 6.38 ± 3.44 m 2 g -1 for IE EC . These results align well with known MAE EC ranges from previous studies: 4.8–11.0 m 2 g -1 for coal combustion, 6.7–12.0 m 2 g -1 for biomass burning, and 3.4–6.5 m 2 g -1 for diesel vehicles (Cui et al. 2016 ; Wu et al. 2021 ). Table 2 The MAE of each emission sources in the four cities (m 2 g -1 ) MAE of Emission Sources XA SJZ WH CQ MAE IE 2.45 9.80 8.67 4.59 MAE VE 4.11 0.41 5.29 4.58 MAE CC 3.91 7.43 2.89 / MAE BB 4.81 3.03 6.39 / MAE SCE / / / 6.74 MAE constant 18.76 5.44 7.91 16.11 Coal combustion predominantly influences the light absorption of EC in XA, as evidenced by a strong correlation between EC from coal combustion and b abs (r = 0.52, p < 0.01) (Table S3). Similarly, EC absorption in CQ is primarily affected by fossil and biomass fuels, with a correlation of 0.69 ( p < 0.01). EC from industrial sources significantly impacts the b abs values in XA and WH, with correlations of 0.58 and 0.62 ( p < 0.01), respectively. In SJZ, EC from motor vehicle source mainly affects the b abs values, with a significant correlation (r = 0.83, p < 0.01). A recent study by Li et al. ( 2023 ) highlights that traffic emissions account for 33–48% of b abs , underscoring their significant influence on aerosol light absorption. Moreover, Cao et al. ( 2021 ) have detailed the contributions of various emission sources to the absorption enhancement of MAE, ranking them as follows: secondary aerosols (32.2%±7.7%) > industrial sources (24.7%±15.9%) > coal combustion (16.5%±2.9%) > vehicle emissions (11.7%±2.1%) > sea salt (6.5%±2.0%). 4. Conclusions This study elucidates the composition and sources of carbonaceous aerosols during winter in XA, SJZ, WH, and CQ. The average TC concentrations are 19.2 ± 9.3 µg m -3 in XA, 17.9 ± 8.5 µg m -3 in CQ, 13.0 ± 5.1 µg m -3 in SJZ, and 11.2 ± 4.8 µg m -3 in WH. Elevated concentrations of OC and its fractions are observed in XA and SJZ, primarily due to winter heating in northern areas. SOC constituted 47.5% of OC in XA, linked to unfavorable meteorological conditions such as low temperatures and stagnant air, coupled with increased emissions from coal heating. Significant regional differences in carbon component correlations underscore variations in emission sources across the cities. EC is predominantly influenced by char-EC, with biomass burning contributing obviously in XA and SJZ. The HERM model identified six emission sources in XA and five in the other cities. Contributions to PTC are as follows: biomass burning (3.4–34.2%), coal combustion (26.8–42.0%), fugitive dust (3.7–11.6%), vehicle emissions (4.4–41.3%), industrial emissions (4.7–38.7%), stationary combustion emissions (33.6% in CQ), and fireworks (6.9–9.4%). Further analysis of the optical properties reveals mean MAE values of 10.5 ± 5.1 m 2 g -1 in XA, 5.4 ± 1.1 m 2 g -1 in SJZ, 7.3 ± 1.4 m 2 g -1 in WH, and 9.6 ± 2.0 m 2 g -1 in CQ. The MAE values exhibit a positive correlation with RH and an inverse correlation with O x in XA and WH. The ridge regression model indicate that EC light absorption is mainly affected by fossil fuels and biomass burning in XA and CQ, and by industrial emissions in SJZ and WH. In conclusion, carbonaceous aerosol pollution is more pronounced in northern cities compared to southern ones. This study provides a robust scientific basis for understanding the characteristics of carbonaceous aerosols and offers insights for air quality improvement strategies. Declarations Acknowledgments This work was financially supported by the Sino-Swiss Cooperation on Air Pollution for Better Air (7F-09802.01.02) from the Swiss Agency for Development and Cooperation (SDC), the “Western Light”-Key Laboratory Cooperative Research Cross Team Project of Chinese Academy of Sciences (xbzg-zdsys-202219), the Natural Science Basic Research Program of Shaanxi (2023-JC-JQ-23), and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (Y2023110). Data availability The original data and the source apportionment results are available upon request. Conflict of interest Author Junji Cao is Editorial Board member for Aerosol Science and Engineering. Dr. Junji Cao, Qiyuan Wang, and Shaofei Kong are the editor for this special issue, involving in the conceptualization and design of the study but had no role- in data collection, analysis, or interpretation. Beside this, author André S. H. 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Journal of Environmental Sciences 19, 167–175. https://doi.org/10.1016/S1001-0742(07)60027-8 Zhang Y, Tian J, Wang Q, Qi L, Manousakas M I, Han Y, Ran W, Sun Y, Liu H, Zhang R (2023) High-time-resolution chemical composition and source apportionment of PM 2.5 in northern Chinese cities: implications for policy. Atmospheric Chemistry and Physics 23: 9455-9471. https://doi.org/10.5194/acp-23-9455-2023 Zheng H, Kong S, Chen N, Yan Y, Liu D, Zhu B, Xu K, Cao W, Ding Q, Lan B (2020) Significant changes in the chemical compositions and sources of PM 2.5 in Wuhan since the city lockdown as COVID-19. Science of the Total Environment 739: 140000. https://doi.org/10.1016/j.scitotenv.2020.140000 Zhou H, He J, Zhao B, Zhang L, Fan Q, Lu C, Dudagula, Liu T, Yuan Y (2016) The distribution of PM 10 and PM 2.5 carbonaceous aerosol in Baotou, China. Atmospheric Research 178: 102-113. https://doi.org/10.1016/j.atmosres.2016.03.019 Zhou R-Z, Yan C-Q, Yang Q-Y, Niu H-Y, Liu J-W, Xue F-L, Chen B, Zhou T-M, Chen H-B, Liu J-Y, Jin Y-L (2023) Characteristics of wintertime carbonaceous aerosols in two typical cities in Beijing-Tianjin-Hebei region, China: Insights from multiyear measurements. Environmental Research 216: 114469. https://doi.org/10.1016/j.envres.2022.114469 Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 15 Nov, 2025 Read the published version in Aerosol Science and Engineering → Version 1 posted Reviewers agreed at journal 21 Mar, 2025 Reviewers invited by journal 21 Mar, 2025 Editor assigned by journal 22 Feb, 2025 First submitted to journal 22 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5886466","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432058683,"identity":"5c710ef8-f11f-4709-be90-72d34cd3bbf2","order_by":0,"name":"Rui Li","email":"","orcid":"","institution":"Xi'an Institute for Innovative Earth Environment Research","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Li","suffix":""},{"id":432058684,"identity":"ec122dd4-ef6f-40c9-8368-a67d9e19ecdd","order_by":1,"name":"Qiyuan 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08:33:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5886466/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5886466/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s41810-025-00359-4","type":"published","date":"2025-11-15T15:58:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79102175,"identity":"0c618a92-f63c-4ca7-8ce1-d1a657c06eb9","added_by":"auto","created_at":"2025-03-24 12:23:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":233022,"visible":true,"origin":"","legend":"\u003cp\u003eThe location of observation site in the four cities\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-5886466/v1/75cbb4781893d7a27ba47b51.png"},{"id":79102184,"identity":"92aa5b79-af35-4323-b4f1-44df6cc18079","added_by":"auto","created_at":"2025-03-24 12:24:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69201,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of carbonaceous fractions concentrations in the four cities. The box and whisker charts mean median and quartile values; the squares mean averages and the stars mean outliers\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5886466/v1/7b91ae032050c7b05e13eca5.png"},{"id":79102172,"identity":"a654db54-f94d-4e98-9ef3-a519a9aec63d","added_by":"auto","created_at":"2025-03-24 12:23:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":180571,"visible":true,"origin":"","legend":"\u003cp\u003eThe ratios of char-EC/soot-EC in the four cities\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5886466/v1/ba4369f49115a391a3fa2387.png"},{"id":79102165,"identity":"e7fae9e4-22c7-4036-8328-56a35cb4adad","added_by":"auto","created_at":"2025-03-24 12:23:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":281577,"visible":true,"origin":"","legend":"\u003cp\u003eThe source profiles and time series of each factor resolved from the HERM model in XA. Heavily colored filler bars identify the species that were mainly dominant in each factor profile. The source profiles in SJZ, WH and CQ is shown in Fig. S5\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5886466/v1/0552266fd9cc6134b695878e.png"},{"id":79102173,"identity":"6e437074-93f0-4bb1-87ee-3b6f6b5baf9c","added_by":"auto","created_at":"2025-03-24 12:23:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":125719,"visible":true,"origin":"","legend":"\u003cp\u003eThe contribution of emission sources to PTC in the four cities during the sampling period\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5886466/v1/167d8433a271fd39c862cd18.png"},{"id":79102188,"identity":"17f7f973-fa3d-4fee-acab-60be065595b1","added_by":"auto","created_at":"2025-03-24 12:24:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":314986,"visible":true,"origin":"","legend":"\u003cp\u003eThe temporal variation of EC, b\u003csub\u003eabs\u003c/sub\u003e and MAE during sampling periods in the four cities\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5886466/v1/b8bfe86e3d7b8e907e20e450.png"},{"id":79102183,"identity":"a1494cab-2443-48dc-97f0-af9bdf608ee4","added_by":"auto","created_at":"2025-03-24 12:24:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":173184,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the MAE and O\u003csub\u003ex\u003c/sub\u003e mixing ratios in the four cities. Data points are color-coded with respect to relative humidity (RH).The average values are represented by the dot in the box and the top and bottom of the box are the 75th and 25th percentiles, respectively. The dash added to the top and bottom of the box is the 90th and 10th percentiles, respectively. The red lines are the fitting of the average values\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5886466/v1/db7875cbe5596378fbdc1207.png"},{"id":96105157,"identity":"58f83a92-b659-489f-9a8a-fc0c3f5b0217","added_by":"auto","created_at":"2025-11-17 16:09:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2266043,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5886466/v1/ab4eee70-ce76-4087-9de7-3d5f641cd227.pdf"},{"id":79102180,"identity":"860d7960-5902-4948-be93-3584c3d16ee1","added_by":"auto","created_at":"2025-03-24 12:24:00","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":448263,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5886466/v1/b2e7fad64f1b775409ad9b7c.docx"}],"financialInterests":"","formattedTitle":"Unveiling differences in source apportionment and optical properties of wintertime carbonaceous aerosols in northern and southern Chinese Cities","fulltext":[{"header":"Article Highlights","content":"\u003col\u003e\n \u003cli\u003eResearch comparing the pollution characteristics of carbonaceous aerosols in four cities of north and south China.\u003c/li\u003e\n \u003cli\u003eSeven sources were identified and coal combustion was the most important source.\u003c/li\u003e\n \u003cli\u003eThe light absorption of carbonaceous aerosols is mainly affected by coal and biomass fuels and industrial sources.\u003c/li\u003e\n\u003c/ol\u003e\n"},{"header":"1 Introduction","content":"\u003cp\u003eWith rapid economic growth and increased energy demands over the past decade, aerosol pollution in China\u0026rsquo;s metropolitan regions has become critically severe (Fu and Chen. 2017; Fu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Carbonaceous aerosols, comprising 20\u0026ndash;50% of PM\u003csub\u003e2.5\u003c/sub\u003e mass in urban areas (Nunes and Pio \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Xue et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and significantly impact climate modulation, air quality, and public health (Liu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tshehla and Wright \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These aerosols include organic carbon (OC), elemental carbon (EC), and minor quantities of carbonate carbon (CC) (Niu et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). OC is further categorized into primary OC (POC) and secondary OC (SOC) (Gu et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). EC, predominantly generated from incomplete combustion processes, absorbs light and influences solar radiation and climate dynamics (Bisht et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Urban areas frequently encounter severe pollution episodes during winter due to emissions from residential heating, industrial activities, and vehicular exhaust (Liu et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Park et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, understanding the constituents and pollution characteristics of carbonaceous aerosols is pivotal for source apportionment and air quality improvement.\u003c/p\u003e \u003cp\u003eAnalysis of carbonaceous constituents is instrumental in identifying pollution sources. Specifically, the spectrum characteristics of carbon facilitate qualitative source identification. For instance, Zhang et al. (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Cao et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) identified motor vehicle emissions and coal combustion as primary sources in Xi'an, China, through carbonaceous aerosol analysis. Similarly, Tian et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) examined the source and seasonal variation of secondary organic carbon in PM\u003csub\u003e10\u003c/sub\u003e across five northern Chinese cities using the chemical mass balance (CMB) model. Moreover, ratios of OC/EC and char/soot are useful in distinguishing emission sources (Safai et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Previous studies have indicated distinct OC/EC ratios for various sources: coal (~\u0026thinsp;0.28 to 2.2), motor vehicles (~\u0026thinsp;2.5 to 5.0), and biomass burning (~\u0026thinsp;3.8 to 13.2) ( Schauer et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Wastson et al. 2001; Zhang et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In addition, Liu et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e) used the positive matrix factorization (PMF) model to demonstrate that vehicle exhaust, coal combustion, and biomass burning are major pollution sources in Haikou, China, noting seasonal variations in the ratio of char-EC to soot-EC. As a significant light-absorbing component, EC has a substantial impact on global warming (Bisht et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Further, Wang et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) analyzed the pollution characteristics and sources of EC in the Tibetan Plateau, underscoring its light absorption and transport, which are crucial for assessing the impacts of carbonaceous aerosols on solar radiation.\u003c/p\u003e \u003cp\u003eThis study investigates wintertime carbonaceous aerosol pollutants in four Chinese cities: Xi\u0026rsquo;an (XA), Shijiazhuang (SJZ), Wuhan (WH), and Chongqing (CQ). XA, located in the Fen-Wei Plain, endures severe air pollution due to geographical and meteorological factors, exacerbated by winter heating (Tian et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, SJZ, a major city in the Beijing-Tianjin-Hebei region, faces substantial pollution challenges intensified by a recent surge in vehicle numbers (Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). WH, a central metropolis in Central China, experiences pollution primarily from coal-fired power plants, vehicle emissions, and biomass burning (Liu et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). CQ, with its robust industrial base, has observed a sharp increase in energy consumption and pollutant emissions. Its complex topography compounds these issues, leading to frequent hazy conditions (Chen et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Consequently, this study aims to analyze the spatial variations and pollution characteristics of carbon components, evaluate carbonaceous aerosols using OC/EC and char/soot ratios, identify primary total carbon sources using the hybrid environmental receptor model (HERM), and assess EC\u0026rsquo;s light-absorbing characteristics and quantify the contributions of various emission sources. The findings will enhance understanding of regional variations and sources of carbonaceous aerosols in China, providing a scientific basis for urban air quality improvement.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Areas\u003c/h2\u003e \u003cp\u003eThis study selected four urban locations in China as sampling sites: XA and SJZ in northern China, and WH and CQ in southern China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All sampling sites were situated in urban environments. In XA, the sampling site was located at the National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain (34.24\u0026deg;N, 108.87\u0026deg;E), within the high-tech zone southwest of the city center, surrounded by commercial and residential areas. In SJZ, the sampling site was situated in the courtyard of Hebei Sailhero Environmental Protection High-tech Co., Ltd. (38.04\u0026deg;N, 114.65\u0026deg;E), surrounded by pharmaceutical and machinery industries and near major streets. In WH, the site was positioned at the Future City Campus of the China University of Geosciences (Wuhan), within Optics Valley District, proximate to industrial areas and expressways. In CQ, the sampling site was at the Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences (29.81\u0026deg;N,106.55\u0026deg;E), surrounded by office buildings and commercial districts.\u003c/p\u003e \u003cp\u003eSample collection was conducted during autumn and winter. In XA, samples were collected from 15 November 2019 to 3 March 2020; in SJZ, from 12 October 2021 to 8 January 2022; in WH, from 14 October 2021 to 8 January 2022; and in CQ, from 18 January 2020 to 29 February 2020. Sampling was performed continuously for 24 h each day, starting at 8 a.m. in SJZ, WH, and CQ, and at 10 a.m. in XA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sampling and Analysis\u003c/h2\u003e \u003cp\u003eOC and EC were quantified using a 0.526 cm\u003csup\u003e2\u003c/sup\u003e quartz filter punch analyzed with a DRI model 2001 carbon analyzer (Atmoslytic, Inc., Calabasas, CA) following the thermal/optical reflectance (TOR) method under the IMPROVE_A protocol (Cao et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Chow et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Initially, the quartz filter was incrementally heated to 140℃ (OC1), 280℃ (OC2), 480℃ (OC3), and 580℃ (OC4) in a helium (He) atmosphere, converting particulate OC on the filter into CO\u003csub\u003e2\u003c/sub\u003e. Subsequently, the atmosphere was switched to an oxidizing mixture of 2% oxygen (O\u003csub\u003e2\u003c/sub\u003e) and 98% He, and the temperature was elevated to 580 ℃, 780℃, and 840 ℃ to analyze EC1, EC2, and EC3, respectively. During heating, some organic carbon decomposes, forming pyrolyzed carbon (OP). According to the IMPROVE protocol, OC is defined as the sum of OC1, OC2, OC3, OC4, and OP, while EC is calculated as EC1\u0026thinsp;+\u0026thinsp;EC2\u0026thinsp;+\u0026thinsp;EC3\u0026thinsp;\u0026minus;\u0026thinsp;OP. The method detection limits (MDLs) for OC and EC are 0.82 and 0.2\u0026micro;g cm\u003csup\u003e-2\u003c/sup\u003e, respectively. Furthermore, EC was subdivided into char-EC (EC1\u0026thinsp;\u0026minus;\u0026thinsp;OP) and soot-EC (EC2\u0026thinsp;+\u0026thinsp;EC3) according to Han et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo construct receptor and source profiles for the HERM model, concentrations of additional species were measured. Elements including calcium (Ca), titanium (Ti), manganese (Mn), iron (Fe), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), selenium (Se), and lead (Pb) were analyzed using energy-dispersive X-ray fluorescence (ED-XRF) spectrometry (PANalytical Epsilon 4, Almelo, The Netherlands) (Cao et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Water soluble potassium (K\u003csup\u003e+\u003c/sup\u003e) was determined through ion chromatography (IC) (Du et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Detailed quality assurance and quality control (QA/QC) procedures for these analyses are described in Cao et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Hybrid environment receptor model (HERM)\u003c/h2\u003e \u003cp\u003eThe HERM (Antony Chen and Cao \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was employed to apportion sources of carbonaceous aerosols. HERM synthesizes the PMF and CMB receptor models, allowing for the analysis of pollutant sources using comprehensive, partial, or unknown source profile information. In matrix notation, the bilinear HERM model is defined as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{C}_{\\text{mn}}={\\sum\\:}_{j=1}^{J}{F}_{\\text{mj}}{S}_{jn}+{E}_{mn}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003emn\u003c/em\u003e\u003c/sub\u003e is the measured concentration of \u003cem\u003em\u003c/em\u003e during time \u003cem\u003en\u003c/em\u003e; \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003emj\u003c/em\u003e\u003c/sub\u003e is the source profile, representing the fractional quantity of \u003cem\u003em\u003c/em\u003e in source \u003cem\u003ej\u003c/em\u003e emission; \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003ejn\u003c/em\u003e\u003c/sub\u003e represents the contribution of source \u003cem\u003ej\u003c/em\u003e during time \u003cem\u003en\u003c/em\u003e; and \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003emk\u003c/em\u003e\u003c/sub\u003e is the model residual for \u003cem\u003em\u003c/em\u003e concentration measured during time \u003cem\u003ek\u003c/em\u003e. Using an iterative conjugate gradient algorithm, HERM solves \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003ejn\u003c/em\u003e\u003c/sub\u003e and unknown \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003emj\u003c/em\u003e\u003c/sub\u003e by minimizing the reduced chi-square (Q), defined as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:Q={\\sum\\:}_{k=1}^{K}{\\sum\\:}_{n=1}^{N}\\frac{({C}_{mn}-{\\sum\\:}_{j=1}^{J}{F}_{mj}{S}_{jn}{)}^{2}}{{\\sigma\\:}_{{C}_{mn}}^{2}+{\\sum\\:}_{j=1}^{J}({\\sigma\\:}_{{F}_{mj}}^{2}{\\sigma\\:}_{{S}_{jn}}^{2}+\\beta\\:{\\delta\\:}_{mj}{\\sigma\\:}_{{C}_{mn}}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eK\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e, and \u003cem\u003eJ\u003c/em\u003e represent the number of samples, chemical species, and sources, respectively; \u003cem\u003eδ\u003c/em\u003e\u003csub\u003e\u003cem\u003emj\u003c/em\u003e\u003c/sub\u003e is set to 0 or 1 depending on whether \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003emj\u003c/em\u003e\u003c/sub\u003e is constrained or unknown, respectively; and \u003cem\u003eβ\u003c/em\u003e is an adjustable factor with a default value of 1.\u003c/p\u003e \u003cp\u003eFor this study, carbonaceous, elemental, and ionic components were input into HERM. A total of 17 chemical species from XA, 19 from SJZ, 18 from WH, and 20 from CQ were selected for source apportionment. Details on these species and their uncertainty calculations are provided in Text S1 in the Supplement. The HERM model analyzed solutions with 2\u0026ndash;8 factors, assuming no prior knowledge of source profile information. The optimal solution identified five significant factors in all four cities, as detailed in Text S2 in the Supplement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Calculation of mass absorption efficiency (MAE)\u003c/h2\u003e \u003cp\u003eThe MAE, measured in m\u003csup\u003e2\u003c/sup\u003e/g, is a crucial parameter for characterizing the optical properties of EC. MAE represents the light absorption cross-section per unit mass concentration and is calculated as follows:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\text{MAE}=\\frac{ATN\\cdot\\:\\frac{A}{V}}{E{C}_{s}\\cdot\\:C\\cdot\\:D\\cdot\\:R\\left(ATN\\right)}=\\frac{-{ln}(\\frac{{I}_{0}}{I})\\cdot\\:\\frac{A}{V}}{E{C}_{s}\\cdot\\:C\\cdot\\:R\\left(ATN\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere ATN is the attenuation calculated from the transmitted light intensities; \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eI\u003c/em\u003e are the transmitted light intensities at the beginning and end of the experiment, respectively, obtained from the original data files of the DRI Model 2001 carbon analyzer. \u003cem\u003eA\u003c/em\u003e is the sampling area of the filter (cm\u003csup\u003e2\u003c/sup\u003e); \u003cem\u003eV\u003c/em\u003e is the sampling volume (m\u003csup\u003e3\u003c/sup\u003e); \u003cem\u003eEC\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e is the mass concentration of EC loaded on the filter (\u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e); \u003cem\u003eC\u003c/em\u003e is a normalization factor accounting for multiple scattering, with a value of 2.14 (Bond and Bergstrom \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); and \u003cem\u003eR(ATN)\u003c/em\u003e corrects for the loading effect as a function of ATN, defined as:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cem\u003ef\u003c/em\u003e is a parameter set to 1.1 (Ram and Sarin \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e"},{"header":"3 Results and discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of carbonaceous aerosol\u003c/h2\u003e \u003cp\u003eIn the four cities studied, the daily average concentrations of OC are 15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in XA, 8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in SJZ, 7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in WH, and 14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in CQ (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). XA and CQ exhibit obviously higher concentrations than SJZ and WH. Compared with previous studies, OC concentrations have decreased across these cities (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). OC2, OC3, and OP followed similar distribution patterns to OC, particularly in XA and CQ, where values are elevated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This trend in XA may be associated with the heating period, during which over 30% of the days are classified as moderately or highly polluted. CQ, being a heavy industry city, consumes substantial coal during winter, leading to significant carbon emissions. All OC components are highly correlated (Fig. S3), especially between OC2 and OC3 (r\u0026thinsp;=\u0026thinsp;0.83\u0026ndash;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). OC2 typically indicates coal combustion, while OC3 is associated with road dust and coal combustion.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe average concentrations of carbonaceous components, chemical components major chemical ratios and meteorological factors in the four cities(\u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSJZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCQ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e17.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOC1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOC2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOC3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOC4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEC1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e9.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEC2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEC3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003echar-EC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003esoot-EC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eK\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.882\u0026thinsp;\u0026plusmn;\u0026thinsp;0.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.379\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.474\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.607\u0026thinsp;\u0026plusmn;\u0026thinsp;0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.457\u0026thinsp;\u0026plusmn;\u0026thinsp;0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.294\u0026thinsp;\u0026plusmn;\u0026thinsp;0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.016\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.024\u0026thinsp;\u0026plusmn;\u0026thinsp;0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.016\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.003\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.040\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.049\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.026\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.008\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.690\u0026thinsp;\u0026plusmn;\u0026thinsp;0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.746\u0026thinsp;\u0026plusmn;\u0026thinsp;0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.129\u0026thinsp;\u0026plusmn;\u0026thinsp;0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.166\u0026thinsp;\u0026plusmn;\u0026thinsp;0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.185\u0026thinsp;\u0026plusmn;\u0026thinsp;0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.151\u0026thinsp;\u0026plusmn;\u0026thinsp;0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.055\u0026thinsp;\u0026plusmn;\u0026thinsp;0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.009\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.006\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.008\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.003\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.004\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.007\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.041\u0026thinsp;\u0026plusmn;\u0026thinsp;0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.029\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.027\u0026thinsp;\u0026plusmn;\u0026thinsp;0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.022\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOC/EC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePOC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003echar/soot\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWind Speed(m s\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTemperature(℃)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRelative humidity(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e58.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e52.8\u0026thinsp;\u0026plusmn;\u0026thinsp;19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e63.3\u0026thinsp;\u0026plusmn;\u0026thinsp;15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e82.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing the EC tracer method (Castro et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), SOC concentrations are highest in XA at 7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e, accounting for 47.5% of OC, followed by CQ, SJZ, and WH (Table S2). The severe SOC pollution in XA could be attributed to increased residential heating and emissions from biomass burning and coal combustion during winter (Xue et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Low wind speeds averaging less than 1m s\u003csup\u003e-1\u003c/sup\u003e and temperatures around 7.9℃ during the sampling period (Fig.S2) facilitated the conversion of semi-volatile organic compounds from gas to particle phase. Studies have demonstrated that every 10℃ rise in temperature decreases SOC concentration by approximately 18% (Odum et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Pandis et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Adverse weather conditions and heightened emissions of volatile organic precursors in winter exacerbated secondary pollution in XA (Wang et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The formation of SOC contributes more to OC concentration in low pollution event (Hallquist M et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Uttamang et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). When compared across cities, the SOC/OC ratio in XA (47.5%) closely approximates that in Hohhot, China (45.5%) (Liu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). It is higher than those in Baotou (37.1%) (Zhou et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Lvliang (23.1%) (Li et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and Xiamen (22%) (Wang et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), but lower than those recorded in Tianjin (55%), Handan (66%) (Zhou et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and Nanjing (53%) (Dai et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Long-term studies of carbonaceous aerosols have shown a rising trend in SOC proportions in urban atmospheres in China, indicating that the decrease of primary source is concomitant with an increase in secondary formation (Zhou et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Differences in SOC among cities may be related to distinct meteorological conditions, atmospheric oxidation capacity, and the types and concentrations of volatile organic precursors (Ji et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEC and OC exhibit a strong correlation in the four cities (Fig. S3), suggesting similar sources (Cao et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Daily average EC concentrations are 3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in XA, 4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in SJZ, 3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in WH, and 3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in CQ (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Compared to previous studies (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), EC concentrations increase in WH due to the COVID-19 lockdowns temporarily reducing pollutant levels in the previous year (Zheng et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, EC concentrations generally decline in the other three cities.\u003c/p\u003e \u003cp\u003eThe proportions of char-EC and soot-EC within the total EC vary only slightly across the four cities: 88.9% and 11.1% in XA, 90.1% and 9.9% in SJZ, 88.6% and 11.4% in WH, and 89.5% and 10.5% in CQ. Fig. S4 shows that the trends of EC and char-EC are consistent, demonstrating that EC is mainly influenced by char-EC. Char-EC primarily originates from coal combustion and biomass burning, which are significant sources of winter heating in northern cities. In contrast, heavy industrial cities in the south, such as WH and CQ, consume substantial amounts of coal industrially, despite the lack of residential heating demand. For example, in 2020, CQ\u0026rsquo;s total coal consumption reaches 88.75\u0026nbsp;million tons, accounting for 1.8% of national consumption (Chongqing Statistical Yearbook, 2021). Soot-EC, mainly resulting from motor vehicle emissions, exhibits minor variations across the cities.\u003c/p\u003e \u003cp\u003eGiven that the OC/EC ratio is influenced by secondary organic aerosol (SOA), the char-EC/soot-EC ratio serves as a more effective metric for identifying carbon aerosol sources (Bret et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Han et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Previous studies indicate that a char-EC/soot-EC ratio greater than 10 suggests biomass burning, less than 1 indicates motor vehicle sources, and less than 2 points to coal combustion (Cao et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Chow et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the char-EC/soot-EC ratios in this study are 13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9 in XA, 9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 in SJZ, 8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5 in WH, and 9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1 in CQ, indicating a significant contribution from biomass burning, particularly in XA and SJZ.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Source apportionment of carbonaceous aerosols\u003c/h2\u003e \u003cp\u003eThe HERM model was employed to analyze the source contributions of primary total carbon components (PTC) in the four cities. The detailed calculation process of PTC can be found in the Supplement (Text S3). Data collection excludes sand-dust days (13\u0026ndash;14 February 2020) in XA. A total of 21 species, including carbonaceous, elemental, and ionic components, are selected for input into model to identify emission sources, as detailed in Text S1. Six emission sources are identified in XA, while five sources are recognized in the other three cities. These sources include biomass burning (BB), coal combustion (CC), fugitive dust (FD), industrial emissions (IE), vehicle emissions (VE), fireworks (FW), and stationary combustion sources (SCE). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. S5 illustrate the source profiles and contributions, and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the relative contributions of each source to PTC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eBiomass burning.\u003c/b\u003e Biomass burning is identified using K\u003csup\u003e+\u003c/sup\u003e as an effective indicator (Zhang et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), along with high loadings of OC1 and OP (Cao et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In the four cities, significant loadings of OC1, OP, and K\u003csup\u003e+\u003c/sup\u003e are observed, with percentages ranging 69.3\u0026ndash;81.9% for OC1, 10.0\u0026ndash;77.7% for OP, and 38.1\u0026ndash;66.5% for K\u003csup\u003e+\u003c/sup\u003e. This factor is identified as a biomass burning, accounting for 21.9%, 3.4%, and 34.2% of PTC in XA, SJZ, and WH, respectively. Biomass burning is a major source of pollution during winter in northern China, commonly used for heating and cooking. Due to the proximity of the SJZ sampling site to residential and industrial parks and the limited use of biomass fuels such as straw and dry wood, the contribution of biomass burning is low. In WH, biomass burning predominantly occurs in suburban areas and regions with intense agricultural activities. Fire point detection map reveals high-density distributions of fire points in these areas, indicating frequent biomass burning (Mehmood et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCoal combustion.\u003c/b\u003e Coal combustion is characterized by high levels of OC2, As, Se, and Pb in XA, SJZ, and WH, with contributions ranging 15.5\u0026ndash;46.0% for OC2, 42.3\u0026ndash;57.8% for As, 35.9\u0026ndash;74.1% for Se, and 42.8\u0026ndash;60.4% for Pb. OC2 is generally associated with coal combustion (Xu et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), while As, Se, and Pb are typical trace elements found in coal combustion, significantly contributing to carbon aerosols during winter (Li et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, this factor is identified as coal combustion source. In XA, SJZ, and WH, coal combustion contributes 26.8%, 42.0%, and 38.8% to PTC, respectively. The data collection period in XA and SJZ coincide with the heating season when coal consumption is high. Compared to previous studies, the contribution from coal combustion to carbonaceous aerosols has decreased, partly due to recent shifts from coal to natural gas for domestic heating, driven by clean energy policies (Liu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conversely, in WH, a heavily industrialized city in the south, coal-fired power plants and the iron and steel industry release large amounts of particulate and gaseous pollutants.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStationary combustion source.\u003c/b\u003e In CQ, biomass burning and coal combustion are aggregated as a single stationary combustion source by the HERM model. This source is characterized by high levels of OC1 (72.7%), OC2 (43.3%), Pb (32.5%), As (35.1%), Se (52.6%), and K\u003csup\u003e+\u003c/sup\u003e (29.7%), contributing 33.6% to the PTC. As a major industrial city in southwest China, CQ historically has high coal consumption. However, following regional industrial structuring, coal usage has substantially declined (Feng et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFugitive dust.\u003c/b\u003e Fugitive dust is identified across the four cities with elevated loadings on OC4, Ca, Fe, Mn, and Ti, with their respective contribution rates ranging 9.4\u0026ndash;51.7%, 57.9\u0026ndash;80.9%, 38.6\u0026ndash;63.5%, 10.3\u0026ndash;40.8%, and 57.6\u0026ndash;88.2%. OC4 is recognized as an indicator of road fugitive dust (Chow et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Fugitive dust, primarily from soil and roads, is a significant atmospheric dust source containing elements like Ca, Al, Si, and Fe. Ca and Ti are commonly used as tracers for construction and soil dust (Gao et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), while Fe and Mn, often found as oxides, originate from brake wear and tire wear (Yu et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rai et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These elements typically mark crustal dust, aligning with the composition of the upper continental crust (Chen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Consequently, this factor is identified as fugitive dust, accounting for 3.7% in XA, 8.6% in SJZ, 11.6% in WH, and 5.5% in CQ of the PTC.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIndustrial emissions.\u003c/b\u003e Industrial emissions vary among the four cities, primarily consisting of metal elements identified as industrial pollutants. In XA and CQ, the predominant elements are Zn at 78.1% and 48.9%, Mn at 41.7% and 49.5%, and Fe at 28.3% and 43.8%, contributing 22.4% and 38.7% to the PTC, respectively. Zn emissions are typically associated with ferrous metal smelting industries (Chang et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), while Fe and Mn originate from steel manufacturing and refining industries (Zhang et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In SJZ, industrial emissions are characterized by high levels of Mn (48.4%), Cr (42.7%), and Ni (37.0%), contributing 4.7% to PTC. Cr is used in various smelting processes, including tanning, electroplating, and stainless steel production (Liu et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Ni emissions are linked to the oil refinery and semiconductor industries (Fernandez-Camacho et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). WH has significant contributions from Ni (66.9%) and Se (49.3%), accounting for 11.0% of PTC.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVehicle emissions.\u003c/b\u003e Vehicle emissions exhibit high loadings of EC2, EC3, and Zn across all cities, with contribution rates ranging 10.1\u0026ndash;46.9% for EC2, 20.3\u0026ndash;97.1% for EC3, and 7.2\u0026ndash;68.9% for Zn. EC1 and OP are considered markers of gasoline vehicle exhaust, while EC2 and EC3 are indicative of diesel vehicle emissions (Cao et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). OP is primarily associated with water-soluble polar compounds in the atmosphere, commonly linked to biomass burning and gasoline vehicle exhaust. Identification of these sources typically requires integration with other factors. Elements such as Zn, Fe, Cu, and Mn serve as important tracers for automotive lubricant oils, brake pads, and tire wear (Daellenbach et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Cr and Ni are emitted during the combustion of automobile fuel (Li et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, this factor strongly correlates with NO\u003csub\u003e2\u003c/sub\u003e or NO\u003csub\u003ex\u003c/sub\u003e (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.45\u0026ndash;0.78), suggesting traffic as a primary source (Zheng et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). With rising vehicle ownership, vehicle emissions have become increasingly concerning. Contributions to PTC in the four cities are 15.8% in XA, 41.3% in SJZ, 4.4% in WH, and 15.3% in CQ. During the severe COVID-19 outbreak in early 2020, the sampling period in WH coincides with a decrease in traffic volume, resulting in a reduced contribution from vehicle emissions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFireworks.\u003c/b\u003e During the sampling periods in XA and CQ, which include the Spring Festival, fireworks are identified as a significant source. This factor exhibits the highest loadings of Ba (76.8\u0026ndash;98.2%), Cu (83.6% in CQ), and K\u003csup\u003e+\u003c/sup\u003e (21.1\u0026ndash;61.4%). Ba and Cu serve as primary colorants in fireworks (Manchanda et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while K\u003csup\u003e+\u003c/sup\u003e is a major component of fireworks containing 74% KNO\u003csub\u003e3\u003c/sub\u003e as the oxidizing agent (Drewnick et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Therefore, this factor is labeled as fireworks. Contributions to PTC from fireworks in XA and CQ are 9.4% and 6.9%, respectively. The highest concentrations typically occur on New Year's Eve and New Year's Day (Kong et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), with peak levels during the Spring Festival recorded at 13.2 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in XA and 9.5 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in CQ. These findings underscore the need to enhance fireworks control during festival periods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Mass absorption efficiency (MAE) of elemental carbon\u003c/h2\u003e \u003cp\u003eThe MAE is a crucial parameter for characterizing the optical properties of EC (Xing et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the analysis of light absorption coefficients and MAE data for the four cities. At 633nm, the MAE values are 10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e for XA, 5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e for SJZ, 7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e for WH, and 9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e for CQ. Compared to previous studies, the MAE values for XA and CQ are higher than those reported for Nanjing (8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e), Jinan (9.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e), Beijing (8.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e), and Yucheng (9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e) in China (Cheng et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cui et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tian et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In contrast, SJZ and WH exhibit lower MAE values compared to these cities. MAE can be influenced by various factors, including measurement methods, actual optical coefficients, corrections for pyrolysis carbonization, and related-properties of EC like coating, mixing states and chemical composition (Cheng et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e reveals a consistent trend between the light absorption coefficient b\u003csub\u003eabs\u003c/sub\u003e and EC across the cities, with mean b\u003csub\u003eabs\u003c/sub\u003e of 33.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6 Mm\u003csup\u003e-1\u003c/sup\u003e in XA, 22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9 Mm\u003csup\u003e-1\u003c/sup\u003e in SJZ, 24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2 Mm\u003csup\u003e-1\u003c/sup\u003e in WH, and 33.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7 Mm\u003csup\u003e-1\u003c/sup\u003e in CQ.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePhotochemical oxidation plays an important role in the light absorption of carbonaceous aerosols (Wang et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The oxidant (O\u003csub\u003ex\u003c/sub\u003e= O\u003csub\u003e3\u003c/sub\u003e+NO\u003csub\u003e2\u003c/sub\u003e) serves as a tracer for atmospheric aging caused by photochemical reactions (Canonaco et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). We analyzed the correlation between O\u003csub\u003ex\u003c/sub\u003e and MAE in the four cities to assess the impact of atmospheric oxidation capacity on the light absorption effects of EC. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows negative correlations between MAE and O\u003csub\u003ex\u003c/sub\u003e in XA and WH, with correlation coefficients of -0.93 and \u0026minus;\u0026thinsp;0.85 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), respectively, while the correlations in SJZ and CQ are weaker. Fig. S6 reveals that the correlation between O\u003csub\u003e3\u003c/sub\u003e and MAE is weak across all cities (r=-0.08\u0026ndash;0.19). In contrast, NO\u003csub\u003e2\u003c/sub\u003e and MAE demonstrate negative correlations, with coefficients of -0.32 for XA, 0.09 for SJZ, -0.53 for WH, and \u0026minus;\u0026thinsp;0.36 for CQ. This pattern is likely due to low O\u003csub\u003e3\u003c/sub\u003e concentrations and weak photochemical oxidation in winter, with oxidation in XA and WH primarily driven by NO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExcept in CQ, MAE values increase with increasing relative humidity (RH), exhibiting strong correlations (r\u0026thinsp;=\u0026thinsp;0.42\u0026ndash;0.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in XA, SJZ, and WH. Higher RH leads to an increase in secondary aerosols, such as sulfate and organic aerosols, generated through aqueous-phase reactions (Wu et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Under high RH conditions, EC particles become coated with secondary hygroscopic components, enhancing their light absorption capabilities (Wu et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fierce et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Chen et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that the effect of RH on light absorption is more pronounced than that caused by photochemical oxidation. Additionally, various factors, including the mixing state, aging, and different measurement methods of the light absorption coefficient, profoundly influence EC's optical properties (Wang et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cao et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsing the source apportionment results, we calculated the EC contributions from different emission sources across the four cities. To analyze the MAE of EC (MAE\u003csub\u003eEC\u003c/sub\u003e) from each source, we employed a ridge regression model encompassing sources with significant light absorption, such as biomass burning (BB\u003csub\u003eEC\u003c/sub\u003e), coal combustion (CC\u003csub\u003eEC\u003c/sub\u003e), vehicle emissions (VE\u003csub\u003eEC\u003c/sub\u003e), industrial emissions (IE\u003csub\u003eEC\u003c/sub\u003e), and stationary combustion emissions (SCE\u003csub\u003eEC\u003c/sub\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The derived MAE\u003csub\u003eEC\u003c/sub\u003e values are 4.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e for BB\u003csub\u003eEC\u003c/sub\u003e, 4.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e for CC\u003csub\u003eEC\u003c/sub\u003e, 3.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e for VE\u003csub\u003eEC\u003c/sub\u003e, and 6.38\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e for IE\u003csub\u003eEC\u003c/sub\u003e. These results align well with known MAE\u003csub\u003eEC\u003c/sub\u003e ranges from previous studies: 4.8\u0026ndash;11.0 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e for coal combustion, 6.7\u0026ndash;12.0 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e for biomass burning, and 3.4\u0026ndash;6.5 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e for diesel vehicles (Cui et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe MAE of each emission sources in the four cities (m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAE of Emission Sources\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSJZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCQ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMAE\u003c/b\u003e\u003csub\u003e\u003cb\u003eIE\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMAE\u003c/b\u003e\u003csub\u003e\u003cb\u003eVE\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMAE\u003c/b\u003e\u003csub\u003e\u003cb\u003eCC\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMAE\u003c/b\u003e\u003csub\u003e\u003cb\u003eBB\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMAE\u003c/b\u003e\u003csub\u003e\u003cb\u003eSCE\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMAE\u003c/b\u003e\u003csub\u003e\u003cb\u003econstant\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCoal combustion predominantly influences the light absorption of EC in XA, as evidenced by a strong correlation between EC from coal combustion and b\u003csub\u003eabs\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table S3). Similarly, EC absorption in CQ is primarily affected by fossil and biomass fuels, with a correlation of 0.69 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). EC from industrial sources significantly impacts the b\u003csub\u003eabs\u003c/sub\u003e values in XA and WH, with correlations of 0.58 and 0.62 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), respectively. In SJZ, EC from motor vehicle source mainly affects the b\u003csub\u003eabs\u003c/sub\u003e values, with a significant correlation (r\u0026thinsp;=\u0026thinsp;0.83, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). A recent study by Li et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlights that traffic emissions account for 33\u0026ndash;48% of b\u003csub\u003eabs\u003c/sub\u003e, underscoring their significant influence on aerosol light absorption. Moreover, Cao et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have detailed the contributions of various emission sources to the absorption enhancement of MAE, ranking them as follows: secondary aerosols (32.2%\u0026plusmn;7.7%)\u0026thinsp;\u0026gt;\u0026thinsp;industrial sources (24.7%\u0026plusmn;15.9%)\u0026thinsp;\u0026gt;\u0026thinsp;coal combustion (16.5%\u0026plusmn;2.9%)\u0026thinsp;\u0026gt;\u0026thinsp;vehicle emissions (11.7%\u0026plusmn;2.1%)\u0026thinsp;\u0026gt;\u0026thinsp;sea salt (6.5%\u0026plusmn;2.0%).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis study elucidates the composition and sources of carbonaceous aerosols during winter in XA, SJZ, WH, and CQ. The average TC concentrations are 19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in XA, 17.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in CQ, 13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in SJZ, and 11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in WH. Elevated concentrations of OC and its fractions are observed in XA and SJZ, primarily due to winter heating in northern areas. SOC constituted 47.5% of OC in XA, linked to unfavorable meteorological conditions such as low temperatures and stagnant air, coupled with increased emissions from coal heating. Significant regional differences in carbon component correlations underscore variations in emission sources across the cities. EC is predominantly influenced by char-EC, with biomass burning contributing obviously in XA and SJZ.\u003c/p\u003e \u003cp\u003eThe HERM model identified six emission sources in XA and five in the other cities. Contributions to PTC are as follows: biomass burning (3.4\u0026ndash;34.2%), coal combustion (26.8\u0026ndash;42.0%), fugitive dust (3.7\u0026ndash;11.6%), vehicle emissions (4.4\u0026ndash;41.3%), industrial emissions (4.7\u0026ndash;38.7%), stationary combustion emissions (33.6% in CQ), and fireworks (6.9\u0026ndash;9.4%).\u003c/p\u003e \u003cp\u003eFurther analysis of the optical properties reveals mean MAE values of 10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e in XA, 5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e in SJZ, 7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e in WH, and 9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e in CQ. The MAE values exhibit a positive correlation with RH and an inverse correlation with O\u003csub\u003ex\u003c/sub\u003e in XA and WH. The ridge regression model indicate that EC light absorption is mainly affected by fossil fuels and biomass burning in XA and CQ, and by industrial emissions in SJZ and WH. In conclusion, carbonaceous aerosol pollution is more pronounced in northern cities compared to southern ones. This study provides a robust scientific basis for understanding the characteristics of carbonaceous aerosols and offers insights for air quality improvement strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the Sino-Swiss Cooperation on Air Pollution for Better Air (7F-09802.01.02) from the Swiss Agency for Development and Cooperation (SDC), the\u0026nbsp;\u0026ldquo;Western Light\u0026rdquo;-Key Laboratory Cooperative Research Cross Team Project of Chinese Academy of Sciences (xbzg-zdsys-202219), the Natural Science Basic Research Program of Shaanxi (2023-JC-JQ-23), and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (Y2023110).\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe original data and the source apportionment results are available upon request.\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eAuthor Junji Cao is Editorial Board member for Aerosol Science and Engineering. Dr. Junji Cao, Qiyuan Wang, and Shaofei Kong are the editor for this special issue, involving in the conceptualization and design of the study but had no role- in data collection, analysis, or interpretation. Beside this, author Andr\u0026eacute; S. H. Pr\u0026eacute;v\u0026oacute;t, Yuemei Han and Jie Tian are also the guest editor for this special issue. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eCRediT authorship contribution statement\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRui Li:\u003c/strong\u003e Writing-original draft, Writing-review \u0026amp; editing; \u003cstrong\u003eQiyuan Wang:\u003c/strong\u003e Conceptualization, Writing-review \u0026amp; editing, Supervision, Funding acquisition; \u003cstrong\u003eJunji Cao:\u0026nbsp;\u003c/strong\u003eConceptualization, Writing-review \u0026amp; editing, Supervision, Funding acquisition; \u003cstrong\u003eAndr\u0026eacute; S. H. Pr\u0026eacute;v\u0026ocirc;t\u003c/strong\u003e: Supervision, review; \u003cstrong\u003eLu Qi, Yang Chen\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Shaofei Kong:\u003c/strong\u003e Data analysis and interpretation; \u003cstrong\u003eSuixin Liu\u003c/strong\u003e, \u003cstrong\u003eJie Tian\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eYong Zhang:\u0026nbsp;\u003c/strong\u003efield measurements;\u003cstrong\u003e\u0026nbsp;Yuemei Han, TingTing Wu, Jin Wang\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Julian Shi\u003c/strong\u003e: Investigation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAntony Chen L-W, Cao J (2018) PM\u003csub\u003e2.5\u003c/sub\u003e source apportionment using a hybrid environmental receptor model. 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Environmental Research 216: 114469. https://doi.org/10.1016/j.envres.2022.114469\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"aerosol-science-and-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"asen","sideBox":"Learn more about [Aerosol Science and Engineering](https://link.springer.com/journal/41810)","snPcode":"41810","submissionUrl":"https://www.editorialmanager.com/asen/default2.aspx","title":"Aerosol Science and Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Carbonaceous aerosols, Source apportionment, HERM, Light absorption","lastPublishedDoi":"10.21203/rs.3.rs-5886466/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5886466/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCarbonaceous aerosols adversely affect air quality, visibility and public health. Understanding their regional variations and sources in China is essential for improving air quality control. Carbonaceous aerosols were collected during winter in four major Chinese cities\u0026mdash;Xi'an (XA), Shijiazhuang (SJZ), Wuhan (WH), and Chongqing (CQ)\u0026mdash;to investigate their pollution characteristics. A comprehensive analysis of various carbon fractions was conducted, including organic carbon (OC), elemental carbon (EC), and specific subfractions such as OC1 \u0026ndash; OC4, EC1 \u0026ndash; EC3, char-EC, and soot-EC. Using the hybrid environmental receptor model (HERM), we identified emission sources and quantified their contributions to primary total carbon (PTC) in these urban areas. The findings demonstrate substantial impacts from coal combustion during the heating season in XA and SJZ. Vehicular emissions account for a considerable proportion, particularly in SJZ, corresponding with the increase in automobile ownership in that city. In WH and CQ, emissions from industrial and residential coal utilization, especially from the steel industry, are markedly higher. Additionally, the COVID-19 pandemic results in reduced contributions from industrial emissions in WH and SJZ. We further investigate the optical characteristics of EC, revealing that the average mass absorption efficiency (MAE) values across the four cities are consistent with previous studies. Specifically, MAEs derived from different emission sources indicate higher values from biomass burning and stationary combustion in XA and CQ, whereas industrial sources result in elevated values in SJZ and WH. This study delineates the distinct characteristics of carbonaceous aerosols in northern and southern Chinese cities, providing a robust scientific basis for urban air pollution mitigation strategies.\u003c/p\u003e","manuscriptTitle":"Unveiling differences in source apportionment and optical properties of wintertime carbonaceous aerosols in northern and southern Chinese Cities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-24 12:23:33","doi":"10.21203/rs.3.rs-5886466/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-03-21T23:19:44+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-21T11:11:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-22T09:18:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Aerosol Science and Engineering","date":"2025-01-23T03:33:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"aerosol-science-and-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"asen","sideBox":"Learn more about [Aerosol Science and Engineering](https://link.springer.com/journal/41810)","snPcode":"41810","submissionUrl":"https://www.editorialmanager.com/asen/default2.aspx","title":"Aerosol Science and Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a2ecee37-8c78-43f8-b91b-d26f5850ed65","owner":[],"postedDate":"March 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:04:18+00:00","versionOfRecord":{"articleIdentity":"rs-5886466","link":"https://doi.org/10.1007/s41810-025-00359-4","journal":{"identity":"aerosol-science-and-engineering","isVorOnly":false,"title":"Aerosol Science and Engineering"},"publishedOn":"2025-11-15 15:58:42","publishedOnDateReadable":"November 15th, 2025"},"versionCreatedAt":"2025-03-24 12:23:33","video":"","vorDoi":"10.1007/s41810-025-00359-4","vorDoiUrl":"https://doi.org/10.1007/s41810-025-00359-4","workflowStages":[]},"version":"v1","identity":"rs-5886466","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5886466","identity":"rs-5886466","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}