Measurement report: Dual-carbon isotopic characterization of carbonaceous aerosol in Beijing and Xi’an: distinctions in primary versus secondary sources

To mitigate haze pollution in China, a better understanding of the sources of carbonaceous aerosols is required due to the complexity in multiple emissions and atmospheric processes. Here we combined the analysis of radiocarbon and the stable isotope C to investigate the sources and formation of carbonaceous aerosols collected in two Chinese megacities (Beijing and Xi’an) during severe haze events of “red alarm” level from December 2016 to January 2017. In Xi’an, liquid 15 fossil fuel combustion was the dominant source of elemental carbon (EC; 44%–57%), followed by biomass burning (25%– 29%) and coal combustion (17%–29%). In Beijing, coal combustion contributed 45%–61% of EC and biomass burning (17%– 24%) and liquid fossil fuel combustion (22%–33%) contributed less. Non-fossil sources contributed 51%–56% of organic carbon (OC) in Xi’an and fossil sources contributed 63%–69% of OC in Beijing. Secondary OC (SOC) was largely contributed by non-fossil sources in Xi’an (56 ± 6%) and by fossil sources in Beijing (75 ± 10%), especially during haze periods. The 20 fossil vs. non-fossil contributions to OC and EC did not change drastically during haze events in both Xi’an and Beijing. However, compared to clean periods, the contribution of coal combustion to EC during haze periods increased in Xi’an and decreased in Beijing. During clean periods, primary OC from biomass burning and fossil sources constituted ~70% of OC in Xi’an and ~53% of OC in Beijing. From clean to haze periods, the contribution of SOC to total OC increased in Xi’an, but decreased in Beijing, suggesting that contribution of secondary organic aerosol formation to increased OC during haze periods 25 was more efficient in Xi’an than in Beijing. In Beijing, the high SOC fraction in total OC during clean periods was mainly due to elevated contribution from non-fossil SOC. In Xi’an, a slight day-night difference was observed during clean period, with enhanced fossil contributions to OC and EC during the day. This day-night difference was negligible during severe haze periods, likely due to enhanced accumulation of pollutants under stagnant weather conditions. 30 https://doi.org/10.5194/acp-2020-455 Preprint. Discussion started: 22 June 2020 c © Author(s) 2020. CC BY 4.0 License.


Introduction
Severe haze pollution with high PM2.5 (i.e., particulate matter with aerodynamic diameter ≤ 2.5 µm) concentrations and reduced visibility occurs frequently during winter in China (An et al., 2019). Filed measurements show that carbonaceous aerosol contributes a significant fraction of PM2.5 loading during severe haze events in China Elser et al., 2016;Liu et al., 2016). Therefore, a better understanding of the sources and atmospheric processes of carbonaceous aerosols is 35 needed for mitigating haze pollution.
Carbonaceous aerosol constituents are separated into elemental carbon (EC) and organic carbon (OC), fractions differing in their thermal refractiveness with EC being thermally refractory and OC weakly refractory (Pöschl, 2003(Pöschl, , 2005Petzold et al., 2013). EC is emitted as primary particles from incomplete combustion sources (i.e., biomass burning and fossil fuel combustion). Unlike EC, OC can either be emitted as primary OC (POC) from combustion sources and non-combustion 40 sources (e.g., biogenic emissions) or formed in the atmosphere as secondary OC (SOC) via the reaction of gas precursors (Hallquist et al., 2009;Jimenez et al., 2009). The sources and abundance of different carbon fractions in carbonaceous aerosols vary considerably in different Chinese cities, as a result of complex interplay between meteorology, local and regional emissions sources, and atmospheric processes Cui et al., 2015;Tie et al., 2017;An et al., 2019). Therefore, quantification the sources of carbonaceous aerosol in China is a challenging task. 45 Radiocarbon ( 14 C) analysis of carbonaceous aerosols is the most direct and effective method to distinguish their main sources, exploiting the fact that OC and EC of fossil origins (i.e., vehicle emissions, coal combustion) do not contain 14 C (Heal, 2014;Cao et al., 2017). 14 C analysis of OC and EC separately provide a clear-cut division of carbonaceous aerosols into four major fractions: fossil OC, non-fossil OC (e.g., OC from biomass burning, biogenic emissions and cooking), fossil EC and biomassburning EC (e.g., Gustafsson et al., 2009;Szidat et al., 2009;Zotter et al., 2014;Dusek et al., 2017;Ni et al., 2018Ni et al., , 2019a. 50 For example, Liu et al. (2014) demonstrated that fossil sources including coal burning and vehicle emissions dominated EC during winter haze events in Guangzhou, China. Zhang et al. (2015) showed that the elevated carbonaceous aerosols during the severe haze event in January 2013 in China were by a large extent driven by SOC from both fossil and non-fossil precursors.
A critical question for effective haze mitigation is whether carbonaceous aerosols in different Chinese cities have similar characteristics during haze events. However, there are not many studies highlighting the differences in sources of primary and secondary carbonaceous aerosols between cities, especially for studies employing the analysis of 14 C or the stable isotope 13 C (e.g., Zhang et al., 2015;Andersson et al., 2015;Liu et al., 2016). In this work, we compare the severe haze events reaching 60 "red alarm" level (i.e., the highest air-quality warning level in China) in two Chinese megacities (Beijing and Xi'an) during December 2016 and January 2017. We present measurements of dual carbon isotopes (i.e., 14 C and the stable carbon isotope https://doi.org/10.5194/acp-2020-455 Preprint. Discussion started: 22 June 2020 c Author(s) 2020. CC BY 4.0 License. 13 C) for EC and OC. The sources of carbonaceous aerosols are elucidated and compared between haze and clean periods in Beijing and Xi'an, with the main objectives: (1) quantitative understanding of the difference in EC contribution from burning of biomass, coal and liquid fossil fuel (i.e., vehicle emissions) under different pollution conditions; and (2) constraint on the 65 sources of both primary and secondary OC. Furthermore, the comparison of day-time and night-time results in Xi'an yields insight into diurnal variation in sources of carbonaceous aerosols.

Concentration measurements of OC and EC
IMPROVE_A protocol (Chow et al., 2007) was implemented on a carbon analyzer (DRI Model 2001, Atmoslytic Inc., USA) for measurements of carbon concentrations. The relative standard deviations for the replicate analyses were smaller than 10 % for OC and EC. OC mass was corrected for field blanks (0.4 μg cm -2 ). EC was too small to be detected on field blanks. 80

Analysis of carbon isotope
Six samples were selected per sampling site for carbon isotope analysis (Tables S1 and S2, Fig. S1). In Xi'an, there were 4 composite samples (2 daytime + 2 nighttime) from haze days, and 2 composite samples (1 daytime + 1 nighttime) from clean days. In Beijing, five 24 h samples were selected from haze days, and 1 composite sample from two clean days. Each composite sample consists of 2 12h (for Xi'an) or 24 h (for Beijing) filter pieces with similar PM2.5 loadings. 85 2.3.1 Stable isotope 13 C Filter samples were placed in a quartz tube with CuO grains. The tube was subsequently evacuated and sealed before heating for 3h at 375 °C to remove OC. Then the EC was extracted by heating the remaining carbon for 5 h at 850 °C. The 13 C/ 12 C ratio of EC was measured by an isotope mass spectrometer (Finnigan MAT-251; Bremen, Germany) and expressed in the delta notation: 90 https://doi.org/10.5194/acp-2020-455 Preprint. Discussion started: 22 June 2020 c Author(s) 2020. CC BY 4.0 License.
δ 13 C values are usually reported in per mil (‰). ( 13 C/ 12 C)V-PDB is the 13 C/ 12 C ratio of the international standard Vienna Pee Dee Belemnite (V-PDB). A well-characterized standard was measured every working day. Duplicate analysis of δ 13 C of EC showed an analytical precision better than ± 0.3‰. This method was detailed in Ni et al., (2019b), where impacts of potential charred OC on the isolated EC were evaluated using an isotope-mass-balance based sensitivity analysis. We concluded that the 95 expected differences in δ 13 CEC is smaller than 1‰ under the assumption that the fraction of charred OC in the isolated EC is at most 20%.

Radiocarbon
OC and EC in PM2.5 samples were converted to CO2 using an aerosol combustion system (ACS; Dusek et al., 2014), subsequently reduced to graphite (de Rooij et al., 2010) before 14 C measurements can be conducted with the accelerator mass 100 spectrometer (AMS) at CIO (van der Plicht et al., 2000). The temperature protocol for OC and EC combustion has been detailed in Zenker et al. (2017) and Ni et al. (2018), and is summarized in Fig. S2. To remove possible interfering gas (e.g., NOx, halogen and water vapor) from CO2, a reduction oven filled with copper grains and silver, a dry ice-ethanol bath and a flask filled with phosphorus pentoxide are installed on the ACS.
Fraction modern (F 14 C) is used to report the 14 C data (Reimer et al., 2004). F 14 C relates the 14 C/ 12 C ratio of a sample to the 105 ratio of the unperturbed atmosphere in the reference year 1950: . (2) Both ratios are normalized to δ 13 C of -25‰ to remove the effect of isotope fractionation. Practically, ( 14 C/ 12 C)1950, [-25] equals to the 14 C/ 12 C ratio of an oxalic acid standard (OXII) multiplied by a factor of 0.7459. Contamination during graphitization and AMS measurements was quantified from the measured F 14 C of standards (OXII with known F 14 C of 1.3407 and 110 Rommenhöller with F 14 C=0) processed in the same way as samples. The resulting estimated dead and modern contamination were used to correct the 14 C data according to Santos et al. (2007). The reliability of data correction was further verified by measuring two secondary standards (i.e., IAEA-C7 and-C8) on the same wheel of samples. The measured values of IAEA-C7 (0.495 ± 0.008) and IAEA-C8 (0.154 ± 0.007) agree with their respective consensus value (0.4953 ± 0.0012 and 0.1503 ± 0.0017) within uncertainties. 115

Source apportionment methods
F 14 C is larger than the fraction of non-fossil carbon (i.e., fnf(OC) for OC, fbb(EC) for EC) due to the large release of 14 C into the atmosphere from the nuclear bomb tests in 1960s. To eliminate this effect, F 14 C is divided by F 14 C of non-fossil sources (F 14 Cnf). F 14 Cnf is estimated as 1.09 ± 0.05 for OC and 1.10 ± 0.05 for EC (see details in Ni et al., 2019b), using a tree growth https://doi.org/10.5194/acp-2020-455 Preprint. Discussion started: 22 June 2020 c Author(s) 2020. CC BY 4.0 License. model and the contemporary atmospheric 14 CO2 over the past years (Lewis et al., 2004;Mohn et al., 2008;Levin et al., 2010), 120 with the assumption that biomass-burning OC and biogenic OC contribute to 85% and 15% of total OC, respectively. Once fnf(OC) and fnf(EC) are known, carbon concentrations can be apportioned into EC and OC from non-fossil sources (ECbb, OCnf) and fossil sources (ECfossil, OCfossil) (Eq. 3-6 in Table 1). OCnf and OCfossil are further divided into POC from biomass burning (POCbb), other non-fossil OC (OCo,nf) (Eq. 7-8), primary and secondary fossil OC (POCfossil and SOCfossil, respectively; Eq. 9-10). POCbb and POCfossil are estimated using EC as a tracer of primary emissions (i.e., the EC tracer method; Turpin and 125 Huntzicker, 1995). Based on OCo,nf and SOCfossil, total SOC and the fraction of fossil carbon in SOC (ffossil(SOC)) are estimated using Eq. (11-12). OCo,nf mainly includes SOC of non-fossil origins (SOCnf), primary biogenic OC and cooking OC. OCo,nf is approximately SOCnf, as contributions of primary biogenic sources and cooking to OCo,nf are likely small (Hu et al., 2010;Guo et al., 2012). If cooking is prominent, OCo,nf is an overestimate of SOCnf. To estimate the uncertainties of the source apportionment results, a Monte Carlo simulation (n=10000) using Eq. (3-12) was carried out as described in Supplement S2. 130 The 14 C source apportionment results are presented in Tables S3 and S4.
The dual carbon isotope signatures of EC were used in a Bayesian Markov chain Monte Carlo (MCMC) scheme (Andersson, 2011), to conduct the mass-balance three source apportionment of EC (e.g., Andersson et al., 2015;Winiger et al., 2016Winiger et al., , 2017Fang et al., 2017Fang et al., , 2018. That is, the F 14 C and δ 13 C of ambient EC (F 14 C(EC) and δ 13 CEC) can be explained by burning of biomass (bb), coal (coal) and liquid fossil fuel (liq.fossil; i.e., vehicle emissions): 135  (Andersson et al., 2015;Ni et al., 2018; and references therein). Uncertainties in F 14 C and δ 13 C source 140 signatures and the measured F 14 C(EC) and δ 13 CEC are considered in the MCMC technique (Parnell et al., 2010(Parnell et al., , 2013. MCMC outputs are the posterior PDFs for fbb, fcoal and fliq.fossil (i.e., the relative contribution of each source to EC). The median and interquartile range (25th-75th percentile) are used as the best estimate and the uncertainties, respectively.

Fossil and non-fossil contributions to EC and OC 145
During the measurement periods, the highest daily mass concentrations of PM2.5 in Xi'an (~250-420 µg m -3 ) and Beijing in both cities during several haze periods, and compared them to clean periods, with PM2.5 concentrations below 100 µg m -3 in Xi'an and below 20 µg m -3 in Beijing. In Xi'an, even during clean periods we defined here, daily PM2.5 concentrations were 150 higher than the Chinese pollution standard of 75 μg m -3 , reflecting severe air quality problems. PM2.5, OC and EC concentrations during haze periods were > 2 times higher in Xi'an and > 5 times higher in Beijing than those during clean periods, respectively. OC/EC ratios in Xi'an slightly decreased from ~4 during haze periods to ~3 during clean periods, while OC/EC ratios in Beijing were lower during haze periods (~3) than clean periods (~4). This reflects different sources and formation mechanisms of haze pollution in the two cities. In Xi'an, we collected day and night PM2.5 samples. No consistent 155 day-night variations in concentrations of PM2.5, OC and EC (Figs. 1 and S1) were observed. This is resulted from diurnal cycle of human activities (e.g., traffic, usage of biomass and coal for heating or cooking) and the development of planetary boundary layer height which controls the vertical mixing and dilution of pollutants.
Radiocarbon ( 14 C) in EC and OC was measured to distinguish their fossil (mainly coal burning and traffic emissions) and nonfossil sources (mainly biomass burning). The most important contributor to EC was fossil fuel combustion, both in Xi'an and 160 Beijing, contributing 73 ± 2% in Xi'an and 80 ± 3% in Beijing. The remaining EC arose from biomass burning (27 ± 2% in Xi'an and 20 ± 3% in Beijing; Fig. 1). In Xi'an, the fraction of biomass-burning EC in total EC (fbb(EC)) was largely constant during haze and clean periods (range: 25%-29%), regardless of the wide concentration range of EC from biomass burning (ECbb, 1.8-6.4 μg m -3 ) and fossil fuel combustion (ECfossil, 4.3-18 μg m -3 ). This suggests that the increase in ECfossil and ECbb concentrations during haze periods in Xi'an is likely caused by the enhanced emissions from both fossil fuel and biomass 165 burning by a similar factor and due to meteorological conditions favoring the accumulation of particulate air pollutions. fbb (EC) values in Beijing (20 ± 3% with a range of 17%-24%) were consistently smaller than those in Xi'an (range: 25%-29%), showing that fossil sources contribute more strongly to EC in Beijing. Moreover, during haze periods in Beijing, fbb (EC) increased with increasing total EC concentrations (Fig. 2).
However, in Beijing, OCnf (12 ± 5 μg m -3 ; 3-19 μg m -3 ) was significantly lower than OCfossil (24 ± 10 μg m -3 ; 4-33 μg m -3 ) (pvalue = 0.001). Consequently, the relative contribution of OCnf to total OC (fnf(OC)) was much higher in Xi'an (average 54 ± 2 %) than in Beijing (34 ± 3%). fnf(OC) in both cities was considerably higher than the corresponding fbb(EC) for all samples (Fig. 1). The main reason for larger fnf(OC) than fbb(EC) is that primary OC/EC ratios from biomass burning emissions are 175 higher than those from fossil sources. So even though biomass burning contributes a small portion of EC, its contribution to primary OC will much higher. In addition, other non-combustion sources (e.g., biogenic emissions, cooking fumes) and secondary formation contribute only to OC, but not to EC.
In this study, the ffossil ( (Fang et al., 2017). A slightly higher ffossil(EC) in urban Beijing was observed during February 2010 (Chen et al., 2013). Despite the slight variation of ffossil(EC) over time, ffossil(EC) in Beijing is generally higher than that in Xi'an (Fig. 3b). The presented overall average ffossil(OC) for winter 2016/2017 in Beijing (66 ± 3%) was higher than that in Xi'an (46 ± 3%), consistent with 185 previously reported ffossil(OC) in Beijing and Xi'an (Zhang et al., 2015;Ni et al., 2019a). Lower ffossil(OC) values in winter were reported for Chongqing (24%), and higher ffossil(OC) was observed in Taiyuan (71%) during winter 2013/2014 (Ni et al., 2019a). The comparison of ffossil(EC) and ffossil(OC) in different Chinese cities indicates that the relative importance of fossil sources in carbonaceous aerosols vary spatially, and can change over the years. In Xi'an, clean periods showed a slight daynight difference with increased contributions of fossil sources to EC and OC during the day. During haze periods, especially 190 the 2 nd haze event (XH_day2, XH_night2), this day-night difference disappeared, which suggests a long residence time of the pollution particles in the urban atmosphere during haze events.

Fossil EC apportioned by stable carbon isotopes: coal vs. liquid fossil fuel
Besides F 14 C, the δ 13 C of EC adds additional dimension where fossil EC can be distinguished into EC from burning of coal and of liquid fossil fuel (i.e., vehicle emissions). Considerable geographical differences in δ 13 CEC signatures were observed, In both Xi'an and Beijing, moderate differences exist in δ 13 CEC between clean and haze days, pointing to a shift in combustion 200 sources. In Xi'an, δ 13 CEC during clean periods (~−25.5‰) was slightly depleted compared to that during haze periods (−25.0‰ to −24.4‰), whereas Beijing exhibited more enriched δ 13 CEC during clean periods (−23.4‰) than during haze periods (−24.4‰ to −24.1‰). This suggests a moderate increase in coal combustion contribution to EC in Xi'an during haze days and a decrease in Beijing. In Xi'an, no strong day-night difference in δ 13 CEC was observed, with the largest absolute differences of 0.5‰ between XH_day1 and XH_night1. The day-night differences are small relative to the uncertainties of the potential sources, 205 for example, the endmember range for coal combustion is more uncertain (± 1.3‰). The small day-night differences in δ 13 CEC reflect well-mixed EC emissions. The Bayesian MCMC model takes into account the uncertainties of the δ 13 C and F 14 C endmembers and statistically apportions EC into the fraction of biomass burning (fbb), coal combustion (fcoal) and liquid fossil fuel combustion (fliq.fossil). The MCMCderived fbb is in principle the same as the 14 C-based fbb(EC) (Fig. S3). The MCMC results (Fig. 4) show that there were no 210 strong day-night differences in EC sources during haze and clean periods in Xi'an. Liquid fossil fuel combustion was the most important contributor to EC in Xi'an, with increased contribution during clean periods. In Beijing, coal combustion was the dominant source of EC, with the relative contribution ranging from 48% (median; 31%−61%, interquartile range) during haze https://doi.org/10.5194/acp-2020-455 Preprint. Discussion started: 22 June 2020 c Author(s) 2020. CC BY 4.0 License. periods to 61% (45%−71%) during clean periods. fbb was fairly constant between haze and clean periods with respect to fcoal and fliq.fossil for all samples. In Xi'an, fbb was comparable to fcoal during haze days, and larger than fcoal during clean days. In 215 Beijing, biomass-burning EC was the smallest fraction in total EC, with smaller fbb than fcoal during both haze and clean days.
Compared with earlier observations in Xi'an ( Fig. 3b), we found that the δ 13 CEC values in January 2017 from this study are comparable with wintertime δ 13 CEC in 2015/2016 (Ni et al., 2019b), but much more depleted than wintertime δ 13 CEC in (Ni et al., 2018 and January 2003 (Cao et al., 2011). This suggests that fossil sources of EC in Xi'an have changed in the past decade, with decreasing relative contribution from coal combustion. This is in line with recent changes in energy 225 use, and the decreasing enrichment factors of As and Pb (e.g., indicators of coal combustion) in Xi'an, as documented in recent studies (Xu et al., 2016). As shown in Fig. 3b, in Beijing, variations in δ 13 CEC from January 2003 (Cao et al., 2011) to January 2017 (this study) are much narrower than those in Xi'an, indicating that EC combustion sources did not change significantly throughout the years in Beijing. Our δ 13 CEC values overlap with those in January 2014 (Fang et al., 2017) and fall into the range of reported δ 13 CEC values in urban Beijing (Cao et al., 2011;Chen et al., 2013) and the regional receptor site of Beijing 230 (Andersson et al., 2015;Fang et al., 2017).

Primary and Secondary OC
As explained in Sect. 2.4, OCnf and OCfossil are apportioned into primary (POCbb, POCfossil) and secondary OC (OCo,nf, SOCfossil; OCo,nf is used as an approximation of SOCnf, or can be regarded as an upper limit of SOCnf if cooking is a prominent OC source. 235 In Xi'an, both ratios of OCo,nf/POCbb and SOCfossil/POCfossil increased during haze periods (Fig. 5a). OCo,nf/POCbb ratio increased by 2.5 times from 0.33-0.46 during clean periods to 0.86-1.1 during haze periods, in contrast to SOCfossil/POCfossil increased by 1.5 times from 0.46-0.50 to 0.62-0.78. This underlines that haze episodes in Xi'an were mainly caused by additional SOC formation, with larger contribution from non-fossil sources than fossil sources. As shown in Fig. 5b, the contribution of SOC (i.e., SOC ≅ OCo,nf + SOCfossil) to OC increased from clean periods (28%-32%) to haze periods (44%-240 48%), mainly resulted from increased contribution of OCo,nf to total OC (i.e., from 14%-16% to 26%-29%). In Xi'an, the daynight difference was larger during clean periods with less SOC at night for both absolute concentration and relative contribution to total OC (Figs. 5b, 5c).
In contrast, Beijing had the opposite variation trends of OCo,nf/POCbb and SOCfossil/POCfossil from clean to haze periods.
OCo,nf/POCbb ratios during clean periods (1.3) were on average five times higher than those during haze periods (0.18-0.33), and SOCfossil/POCfossil ratios during clean periods (0.71) were slightly higher than those during haze periods (0.41-0.64). This suggests that in Beijing the increased OC concentrations during haze periods were mainly derived from elevated concentrations of POCbb and POCfossil. As shown in Fig. 5b, high SOC contribution to total OC was observed during clean periods, mainly due to elevated contribution from OCo,nf. In winter, the OCo,nf is not likely attributed to biogenic OC, because the biogenic emissions are very low. As a result, the elevated contribution from OCo,nf to OC during clean periods in Beijing could be 250 attributed to regional sources. During clean periods, concentrations of OC and OCo,nf are small, and the measured carbon concentrations can reflect regional sources, which are dominated by secondary sources due to long-range transport. It could also be that contribution of cooking OC to OCo,nf can be noticeable during clean conditions. The fossil fraction of the total SOC can be defined as ffossil(SOC) = SOCfossil/SOC. In Xi'an around half of SOC was derived from fossil sources (ffossil(SOC) = 44 ± 6%), whereas ffossil(SOC) = 75 ± 10% in Beijing. Using a similar approach with this 255 study, Zhang et al. (2015) also found that Beijing had higher ffossil(SOC) (48%−63%) than in Xi'an (30%-35%). These findings suggest the important contribution of fossil sources to SOC in Beijing and non-fossil sources in Xi'an. ffossil (SOC) in Beijing increased during haze periods, whereas the opposite trend was found in Xi'an (Fig. 6). During haze periods in Beijing, ffossil(SOC) overlapped with ffossil(EC), and was clearly higher than ffossil(OC).

Differences between the fractions of non-fossil carbon in OC and EC 260
The differences between fnf(OC) and fbb(EC) were smaller in Beijing, ranging from 11% to 20%, compared to 25%-29% in Xi'an. To better understand what governs the differences, we express fnf(OC) in terms of fossil to biomass burning ratio in EC and primary OC/EC emissions ratios. Starting from the formulas of fbb(EC) and fnf ( ~ 1). However, the fossil source coal combustion has a higher primary OC to EC ratio than vehicle emissions (i.e., rcoal>rvehicle). Therefore, in a city where biomass burning and coal combustion are the dominant pollution sources, fnf(OC) and fbb(EC) will be more similar than in a city where the main sources are biomass burning and vehicle emissions.
Compared to Xi'an, Beijing had significantly smaller differences between fbb(EC) and fnf(OC) (Fig. 1), which was also observed 275 in previous studies during the haze event in January 2013 (Zhang et al., 2015). This suggests either strong contribution from coal combustion in Beijing or large secondary formation from fossil sources, or both. The stronger contribution of coal combustion to OC in Beijing than in Xi'an was a direct consequence of a larger proportion of coal combustion in EC in Beijing, as demonstrated by the Bayesian MCMC results of EC (Sect. 3.2). The latter was further validated by the variation of SOC.
The ffossil(SOC) in Beijing was higher than that in Xi'an, despite the variations between haze and clean periods (Sect. 3.3). 280 Furthermore, as shown in Fig. 1, unlike Xi'an where the differences between fnf(OC) and fbb(EC) were relatively constant for all samples, in Beijing the differences between fnf(OC) and fbb(EC) were smaller during haze periods than clean periods, caused by decreased fbb(EC) and slightly increased fnf(OC) during clean periods. This might indicate a higher relative contribution from coal combustion and/or fossil-dominated SOC during haze periods in Beijing. However, the Bayesian MCMC results of EC show the opposite, i.e., in Beijing the contribution of coal combustion to EC was lower during haze periods than during 285 clean periods (Sect. 3.2). Therefore, the only possible explanation is that, during haze periods in Beijing, SOC was dominated by fossil sources. This is validated by considerable higher ffossil(SOC) during haze periods (76%−81%) than during clean periods (~55%; Sect. 3.3).

Conclusion
In this study the sources of carbonaceous aerosol were quantified using a dual-carbon isotopic approach for PM2.5 samples 290 collected in urban Xi'an and Beijing reaching "red alarm" level during December 2016 and January 2017. The 14 C results showed that fossil sources dominated EC, contributing on average 73 ± 2 % of EC in Xi'an and 80 ± 3% of EC in Beijing. The remaining EC was attributed to biomass burning. In Xi'an, fbb(EC) was fairly constant during haze and clean periods, despite the wide variation of EC concentrations. However, in Beijing, fbb(EC) increased with increasing EC concentrations.
Complementing 14 C with δ 13 C in a Bayesian MCMC approach allows for separation of fossil sources of EC into coal 295 combustion and liquid fossil fuel combustion. The MCMC results in Xi'an suggest that liquid fossil fuel combustion contributed 44%-49% of EC during haze periods, and 54%-57% of EC during clean periods. In Beijing, coal combustion was the dominate fossil source of EC, with decreasing contribution to EC from clean periods (~61%) to haze periods (~48%).
https://doi.org/10.5194/acp-2020-455 Preprint. Discussion started: 22 June 2020 c Author(s) 2020. CC BY 4.0 License. 14 C measurements of OC showed that the contribution of non-fossil sources to OC was larger than that to EC, and was on average 54 ± 2 % in Xi'an and 34 ± 3% in Beijing. The differences between non-fossil fraction in OC and EC were smaller in 300 Beijing and larger in Xi'an. This suggests strong contribution from coal combustion or large secondary formation from fossil sources, or both in Beijing. In Xi'an, the fraction of SOC in total OC was larger during haze periods than during clean periods, mainly due to increased SOC from non-fossil sources. Beijing showed the opposite trends with a larger fraction of SOC in total OC during clean periods than during haze periods, mainly due to elevated contribution from non-fossil SOC during clean periods. 305 SOC was dominated by non-fossil sources in Xi'an but by fossil sources in Beijing, especially during haze periods. The relative contribution of fossil sources to SOC (ffossil(SOC)) was consistently higher in Beijing than in Xi'an. In Beijing, ffossil(SOC) was higher during haze periods (76%-81%) than during clean periods (55%), whereas an opposite trend was found in Xi'an, with ffossil(SOC) increasing from ~39%-43% during haze periods to ~52% during clean periods. In Xi'an, a slight day-night difference was found during clean periods, with increasing fossil contribution to OC and EC during the day and less SOC at 310 night. During strong haze, this day-night difference was negligible, suggesting a large accumulation under stagnant weather conditions during the severe haze periods.
Data availability. Data used to support the findings in this study are archived at the Institute of Earth Environment, Chinese Academy of Sciences, and are available on request from the corresponding author.
Competing interests. The authors declare that they have no conflict of interest. 315 Author contributions. RJH and UD designed the study. Isotope measurements were made by HN, MMC, and JG. Data analysis and interpretation were made by HN, MMC, RJH, and UD. HN wrote the paper with contributions from all co-authors.  fraction of non-fossil carbon in OC and EC (fnf(OC) and fbb(EC), respectively) for daytime and nighttime PM2.5 samples in Xi'an, and 24h-integrated PM2.5 samples in Beijing during haze and clean periods. Uncertainties of 14 C-apportioned fnf(OC) and fbb(EC) are indicated but are too small to be visible. The data are shown in Table S3.
https://doi.org/10.5194/acp-2020-455 Preprint. Discussion started: 22 June 2020 c Author(s) 2020. CC BY 4.0 License. Comparison with previous observations in Xi'an and Beijing, where BTH-Beijing is a regional receptor site of Beijing, located at 100 km southwest of Beijing. Samples from Cao et al. (2011) are placed on the x-axis, because no 14 C data were available. The expected 14 C and δ 13 C endmember ranges for emissions from C3 480 plant burning, liquid fossil fuel burning and coal burning are shown as green, black and brown bars, respectively. The δ 13 C source signatures are indicated as mean ± SD (Sect. 2.4). The δ 13 C signature of corn stalk burning (i.e., C4 plant; −16.4 ± 1.4 ‰) is also indicated.
https://doi.org/10.5194/acp-2020-455 Preprint. Discussion started: 22 June 2020 c Author(s) 2020. CC BY 4.0 License. concentrations. Averaged fraction (b) and concentration (c) of POCbb, OCo,nf, POCfossil and SOCfossil in total OC during haze and clean periods in Xi'an and Beijing, China. The data are given in Table S4. and ffossil(OC) are indicated but are too small to be visible.