Evolution of atmospheric age of particles and its implications for the formation of a severe haze event in eastern China

Atmospheric age reflects how long particles have been suspended in the atmosphere, which is closely associated with the evolution of air pollutants. Severe regional haze events occur frequently in China, influencing air quality, human health, and regional climate. Previous studies have explored the characteristics of mass concentrations and compositions of fine 25 particulate matter (PM 2.5 ) during haze events, but the evolution of atmospheric age remains unclear. In this study, the age-resolved UCD/CIT model was developed and applied to simulate the concentration and age distribution of PM 2.5 during a severe regional haze episode in eastern China. The results indicated that PM 2.5 concentrations in the North China Plain (NCP) gradually accumulated due to stagnant weather conditions at the beginning stage of the haze 30 event. Accordingly, the atmospheric age of elemental carbon (EC), primary organic aerosol (POA), sulfate (SO 42− ), and secondary organic aerosol (SOA) gradually increased. The subsequent PM 2.5 concentration growth was driven by the local chemical formation of nitrate (NO 3− ) under high relative humidity. The newly formed NO 3− particles led to a decrease in the mean atmospheric age of the NO 3− particles. During the regional transport stage, aged particles 35 from the NCP moved to the downwind Yangtze River Delta (YRD) region, leading to a sharp increase in PM 2.5 concentrations and the average age of EC, POA, SO 42− , and SOA. In contrast, the average age of NO 3− and ammonium remained unchanged or even slightly decreased due to continuous local formation in the YRD region. Different evolution of the atmospheric age among these components provides a unique perspective on the formation of PM 2.5 components 40 during the regional haze event. The information can also be used for designing effective control strategies for different components of PM 2.5 .

Intensive pollutant emissions and unfavorable meteorology are two key factors controlling haze formation.NCP and YRD are two major city clusters in eastern China with intensive anthropogenic emissions.Previous studies have revealed that severe winter haze events in the NCP were initialized by the accumulation of local emissions under stable weather conditions 60 and further deteriorated by rapid secondary formation (An et al., 2019;Zheng et al., 2015b).
Polluted air masses in the NCP are rapidly eliminated by the strong prevailing northwesterly wind and moved to downwind YRD regions (Wang et al., 2021c).During the long-range transport, freshly emitted particles gradually age and mix with secondary inorganic and organic species, further influencing regional climate and air quality through aerosol-planetary 65 boundary layer (PBL) interaction (Huang et al., 2020;Zhang et al., 2021).
The atmospheric age of an air pollutant, defined as the time since it is emitted or formed, provides a unique perspective on the evolution of pollutants in the atmosphere (Wagstrom and Pandis, 2009;Ying et al., 2021;Zhang et al., 2019a).Unlike the lifetime or residence time of pollutants, atmospheric age refers to the time that a single particle remains in the atmosphere 70 at a given location and time, which can better reflect its instantaneous physical and chemical properties (Chen et al., 2017c).However, measuring and calculating the atmospheric age of air pollutants is difficult because of their chemical nonlinearity and process complexity.Previous studies have attempted to track particle age distributions by adding tracers in Lagrangian trajectory models such as FLEXPART (Stohl et al., 2003).However, due to simplified 75 chemistry, this method cannot accurately determine the age distributions of secondary species.Some other studies estimated the photochemical age of an air mass using the ratio of hydrocarbons, including toluene/benzene and ethylbenzene/benzene (Chu et al., 2021;Parrish et al., 2007).Since the oxidation rates of these hydrocarbons by hydroxyl (OH) radicals span several orders of magnitude, the hydrocarbon ratios change with photochemical aging (Chen 80 et al., 2021).By this definition, the photochemical age determines the degree of photochemical processing associated with OH radicals rather than the physical age of pollutants (Irei et al., 2016).
A few attempts were made to track the age distribution of aerosols using chemical transport models (CTMs) (Han and Zender, 2010;Wagstrom and Pandis, 2009;Wu et al., 2017).CTMs 85 can reproduce the evolution of pollutants in the atmosphere (including emission, transport, deposition, and chemical transformation).Zhang et al. (2019a) introduced a dynamic age-bin updating algorithm in the source-oriented University of California, Davis/California Institute of Technology (UCD/CIT) air quality model to track the age distribution of primary PM2.5.In their study, chemical variables in UCD/CIT model were expanded with one more dimension to 90 represent pollutants with different atmospheric ages.More recently, this dynamic age-bin updating algorithm was expanded to include gaseous precursors to determine the age distribution of all primary and secondary inorganic compounds in the Community Multiscale Air Quality (CMAQ) model (Ying et al., 2021).In this study, we further developed an ageresolved UCD/CIT model to track the atmospheric age distribution of various primary and 95 secondary components of PM2.5 based on the method used in the CMAQ model.Then we applied the model to investigate the evolution of the concentrations and ages of the major PM2.5 components during a typical winter haze episode in eastern China.

Description of UCD/CIT Model 100
The source-oriented UCD/CIT air quality model (Held et al., 2004;Hu et al., 2014;Hu et al., 2015;Kleeman and Cass, 2001;Ying et al., 2007;Ying and Kleeman, 2006) was used in this study to simulate air quality in eastern China.The UCD/CIT model is a 3-dimensional Eulerian regional CTM with detailed chemistry and aerosol mechanisms.Details about the fundamental algorithms used in UCD/CIT model can be found in the above references.Briefly, gas-phase 105 chemistry is modeled by the SAPRC-11 chemical mechanism (Carter and Heo, 2013).Aerosols are represented using a sectional approach with 15 log-spaced size bins encompassing 10 nm-10 µm.Thermodynamic equilibrium for inorganic aerosols is calculated by ISORROPIA (Nenes et al., 1998).Secondary organic aerosol (SOA) treatment is based on the two-product model used in the Community Multiscale Air Quality (CMAQ) model, including a total of 19 110 semi-volatile or nonvolatile species from seven precursors (Carlton et al., 2010).
In most existing air quality models, particles from diverse emission sources are mixed.
However, the UCD/CIT model applies a source-oriented framework in which primary and secondary particles from each source category are tracked separately through the calculation of all major atmospheric processes, such as advection, diffusion, deposition, and gas-particle 115 partitioning.Thus, the source contributions to regional particle concentrations can be evaluated.Zhang et al. (2019a) expanded the source-oriented UCD/CIT model to track the age distribution of elemental carbon (EC) in the atmosphere.In this study, we implemented the atmospheric age distribution modeling framework (Ying et al., 2021) The average age of particles can be calculated by (2) 130

Model setup
The age-resolved UCD/CIT model was run from 21 December 2017 to 2 January 2018, with the first 4 days as the spin-up period to minimize the impact of initial conditions.The model domain has a horizontal resolution of 36 km encompassing eastern China and a vertical structure of 16 layers with 10 layers below 1 km.Hourly meteorological inputs were generated 135 by the Weather Research Forecasting (WRF) model version 4.2 with initial and boundary conditions from the 1.0° × 1.0° National Centers for Environmental Prediction Final (NCEP FNL) operational global analyses dataset.More details on the WRF model configuration can be found in Xie et al. (2022a).Anthropogenic emissions were taken from MEIC (the Multi-resolution Emission Inventory for 140 China) v1.3 with a spatial resolution of 0.25° ×0.25° (Zheng et al., 2018).FINN (Fire INventory from NCAR) v1.5 with 1 km resolution (Wiedinmyer et al., 2011) and MEGAN (Model of Emissions of Gases and Aerosols from Nature) (Guenther et al., 2006) driven by meteorological inputs from WRF were used to provide wildfire and biogenic emissions, respectively.Total particle-phase emissions from the above mentioned sources were 145 transformed into size-resolved emissions based on measured source profiles (Kleeman et al., 2008;Robert et al., 2007a;Robert et al., 2007b).In addition, sea salt and dust emissions were calculated online within the model based on wind speed and land use type, as described in Hu et al. (2015).A total of 9 age bins were configured to determine the age distribution of particles in this study.150 The age-bin updating frequency was set to 12 h in our base simulation so that we could explicitly track particle ages up to 96 h.However, PM2.5 concentrations can grow explosively during our study period within several hours.Thus, another four simulations with age-bin updating intervals of 1, 3, 6, and 8 h were also conducted to better reflect the age distribution of particles.Results from different simulations were combined by replacing the low time 155 resolution simulations with the corresponding high time resolution results (Ying et al., 2021).
The simulated concentrations of PM2.5 and its major components from the age-resolved model show good agreement with the original UCD/CIT model (Figure S1), confirming that the dynamic age-bin updating algorithm will not change the concentration prediction.The computational burden of the age-resolved UCD/CIT model with 9 age bins is ~3 times slower 160 than the original model.
Carbonaceous components (organic carbon (OC) and EC) in four cities were analyzed with a 175 carbon analyzer (model RT-4, Sunset Laboratory Inc., USA) based on the thermal-optical transmittance method (Wang et al., 2016b).More details about the principles and operation of the above instruments can be found in the corresponding references.
The incremental mass ratio (IMR) proposed by Tan et al. (2018) was adopted in this study to determine the aerosol components that drive the particle concentration growth during the haze 180 episode.Briefly, the IMR of a certain component i ) total mass during the PM2.5 growth process: Thus, the contribution of each chemical composition to the PM2.5 increment can be calculated.185

Episode description and model evaluation
Figure 2 shows a severe regional haze episode over eastern China spanning from 25 December 2017 to 2 January 2018.The time series of PM2.5 concentrations in eastern China indicates that PM2.5 gradually accumulated in the NCP from 25-28 December 2017 under the condition of 190 low wind speed (~2 m s −1 ) and increasing RH (Figure S1), which is identified as the accumulation stage.Severe haze pollution characterized by high PM2.5 concentrations (> 150 μg m −3 ) persisted from the night of 28 December to the morning of 30 December, while the peak value of PM2.5 reached 191 μg m −3 at 10:00 LT 29 December (stabilization stage).On 30 December, a cold front formed in the NCP, where the cold air in front of the Siberian High 195 encountered the warm air from the south (Figure S2).As a result, the wind speed increased sharply from 2.5 m s −1 to 5.7 m s −1 within 6 h, followed by a steep drop in air temperature from 4.3 ℃ to −7.0 ℃ (Figure S1).Under the influence of strong northwesterly winds, a continuous movement of PM2.5 from north to south (i.e., Taiyuan, Linfen, Shijiazhuang, Zhengzhou, Nanjing, and Shanghai) occurred, and the polluted air masses dissipated quickly in the NCP 200 within several hours (dilution stage) (Figure S3).Consequently, severe haze pollution formed rapidly in the YRD during 30-31 December due to regional transport from the NCP, with the peak value of PM2.5 concentrations greater than 200 μg m −3 .
To better explore the characteristics of PM2.5 pollution in the YRD, the haze episode was divided into three stages (before, during, and after regional transport) in this study according 205 to PM2.5 concentrations and winds (Figure 2a and 2b).Before regional transport, PM2.5 concentrations in the NCP (> 250 µg m -3 ) were much higher than those in the YRD (~70 µg m -3 ).Low wind speed (~2 m s −1 ) favored the accumulation of air pollutants in the NCP.
Meanwhile, southeasterly winds prevailed in the coastal areas of the YRD, bringing less polluted air masses.In the following 1-2 days, eastern China was under the control of strong 210 northwesterly winds (4-5 m s −1 ) due to the cold front, and the heavily polluted air masses gradually moved from north to south (Figure 2d).After the cold front passes, high pressure controls the YRD, leading to subsidence and trapping PM2.5 in the PBL.Thus, high concentrations of PM2.5 occurred in the YRD with low wind speed, especially in Jiangsu and Shanghai (Figure 2e).215 The UCD/CIT model well reproduces the observed temporal variations of hourly PM2.5 concentrations averaged over the NCP and the YRD during this haze episode with a high correlation coefficient (R > 0.85) and a low bias (NMB < 15%) (Figure 2b).High PM2.5 concentrations (> 150 μg m −3 ) with low wind speed over southern Hebei, Shandong, Henan, northern Jiangsu, and Anhui provinces are well captured by the model (Figure S4).The 220 simulated PM2.5 compositions (SO4 2− , NO3 − , NH4 + , EC, and organic matter (OM)) also agree well with the daily-averaged measurements in Beijing, Jinan, Nanjing, and Shanghai (Figure S5), with model performance statistics comparable to those in other studies (Shi et al., 2017;Hu et al., 2016;Zhang et al., 2019b).Detailed model evaluation can be found in the Supporting Information.225 growth process in all four cities, while the mass fraction of EC and OM in PM2.5 decreased.In Beijing and Jinan, located in the NCP, the daily averaged SNA concentrations increased from 230 10 and 22 µg m -3 on 25 December to 110 and 157 µg m -3 on 29 December, and their mass fraction in PM2.5 increased from ~40% to ~75%.During 25-29 December, the NCP region was under the control of a uniform pressure field with low horizontal winds (Figure S2), and pollutants gradually accumulated under such stagnant conditions.The observed RH gradually increased, and the maximum exceeded 80% on 28-29 December, which facilitated the 235 chemical formation of secondary aerosols and accelerated the hygroscopic growth of particles (Cheng et al., 2016;Sun et al., 2014;Yang et al., 2015).Process analysis also indicates that chemical formation is the driving process for the growth of SNA in the NCP, with its net production rate ~3 times larger than that of vertical mixing and horizontal advection during the accumulation stage (Figure 4a, b).For YRD cities, Nanjing and Shanghai, daily SNA 240 concentrations increased sharply by 3-6 times within two days (30-31 December) and accounted for 78% of the peak PM2.5.Horizontal advection played a dominant role during the explosive growth of air pollutants, with a maximum production rate of 8.1 and 2.7 µg m -3 h -1 for NO3 − and SO4 2− respectively (Figure 4c, d).The chemical process also contributed obviously to NO3 − and SO4 2− in Nanjing during the regional transport, indicating the continuous 245 local formation in the YRD.

Evolution of particle chemical compositions
NO3 − exhibited the highest levels among SNA in all four cities.The peak value of NO3 − was 49, 57, 80, and 51 µg m -3 for Beijing, Jinan, Nanjing, and Shanghai, respectively, contributing to 25-41% of PM2.5 mass concentrations.The IMR of NO3 − (29-33%) was much higher than that of other components in Beijing, Nanjing, and Shanghai (Figure 3e), indicating that NO3 − 250 was the driving component during the PM2.5 growth process.In Jinan, the IMR of SO4 2− (26%) was slightly higher than that of NO3 − (24%).Nevertheless, the mass fraction of NO3 − in PM2.5 during the PM2.5 growth process (25-29 December) was 26%, significantly larger than that of SO4 2− (16%) and NH4 + (15%).The higher fraction of NO3 − in PM2.5 and its dominant contribution to the PM2.5 growth process have also been pointed out by recent observation and 255 modeling studies conducted in eastern China during winter haze periods (Shao et al., 2018

Evolution of particle age distribution
The age distribution evolutions of the major PM2.5 compositions (EC, SO4 2− , NO3 − , NH4 + , POA, and SOA) in Beijing and Shanghai are illustrated in Figures 5 and 6. High concentrations of 260 EC, POA, and SO4 2− typically occurred at low atmospheric ages, with obvious diurnal variations in both cities.The bimodal distribution of fresh particles was mainly related to the variations in local emissions and the evolution of PBL.Because the shallow PBL at night was not conducive to the diffusion of air pollutants, particles emitted during evening rush hours remained in the atmosphere longer than those emitted in the morning.Several hours after being 265 released, the concentrations of particles dropped rapidly due to the atmospheric dilution process such as advection and deposition.Nevertheless, under stable weather conditions with low wind speeds, the dilution effect was weak, and EC, POA, and SO4 2− particles could accumulate in the atmosphere for a longer time.This can be seen in Beijing from 25-29 December with a gradually increasing mean atmospheric age of EC, POA, and SO4 2− .During this time, relatively 270 high concentrations of aged EC, POA, and SO4 2− particles (with atmospheric age > 24 h) together with large contributions from fresh particles (with atmospheric age < 24 h) can also be observed (e.g., 12:00 to 16:00 LT 28 December).On 30 December, aged EC, POA, and SO4 2− particles in Beijing were removed sharply by strong northwesterly wind, leading to a steep decrease (from ~40 h to less than 6 h) in their mean atmospheric age.Subsequently, in 275 Shanghai, the concentration of aged particles increased rapidly during the period from 14:00 LT on 30 December to 02:00 LT on 31 December, indicating the influence of regional transport.
As a result, the mean atmospheric age of EC, POA, and SO4 2− increased from 3-6 h to 47-52 h.
Similar to that of EC, POA, and SO4 2− , the average age of SOA gradually increased during the 280 accumulation stage in the NCP region, and then decreased sharply on 30 December due to the sweeping effect of the strong northwesterly wind.In YRD, the average age of SOA before the regional transport was larger than that of EC and POA, although its concentrations were relatively low.During the regional transport, the average age of SOA increased from ~20 h to ~60 h within several hours.Oligomers of anthropogenic SOA (AOLGA), xylene, toluene, long-285 chain alkanes, and monoterpenes were found to be the most important precursors, contributing over 95% of the total SOA in Beijing, Jinan, Nanjing, and Shanghai (Figure S7 and S8).The contribution of AOLGA increased with atmospheric age in all four cities, while the contributions of xylene, toluene, and long-chain alkanes decreased with age.In the NCP cities, the contribution of AOLGA to total SOA concentrations increased from 35% in the 290 accumulation stage to 48% in the stabilization stage.This is because semi-volatile SOA would not immediately form AOLGA after being released into the atmosphere, and the oligomerization reactions take time.
The mean atmospheric age of NO3 − and NH4 + did not show an increasing trend and even 295 decreased on some occasions during the accumulation stage in Beijing and Jinan (Figures S9   and S10).Such a low atmospheric age was mainly due to the continuous formation of secondary NH4NO3 particles locally (Figure S11), which increased the concentrations of fresh particles and decreased the mean age of NO3 − and NH4 + .High concentrations of fresh NO3 − combined with a moderate contribution of aged NO3 − can also be seen during the explosive 300 growth stage (e.g., 16:00 to 23:00 LT 30 December 2017) in Nanjing and Shanghai, indicating that the continuous local formation with additional help from regional transport together contribute to the high concentrations of NO3 − .The average atmospheric age of NO3 − in Jinan, Nanjing, and Shanghai decrease significantly with RH and low average ages are often observed with a high concentration of NO3 − (Figure S12).This confirms our speculation that the rapid 305 chemical formation of NO3 − under high RH conditions (Figure S1 the NCP under slow wind speed (~2 m s −1 ).The average age of EC, SO4 2− , NO3 − , and SOA was approximately 30-50 h, 50-60 h, 20-45 h, and 50-60 h respectively, larger than that in the YRD (15-30 h for EC, 30-50 h for SO4 2− , 8-24 h for NO3 − , and 35-45 h for SOA).An obvious vertical gradient of particle age occurred in the YRD, with lower age near the surface and 315 higher age aloft.Due to the accumulation of aged pollutants in the atmosphere under stable weather conditions, the vertical distribution of particle ages was more uniform in the NCP.
When the cold front passed through, polluted air masses carrying aged particles gradually moved from the NCP to the YRD under strong northwest wind (>5 m s −1 ).During this period, high PM2.5 concentrations occurred in eastern China and vertically extended up to 1.2 km, 320 forced by the upward motion along the cold front.For EC, SO4 2− , and SOA, the average age in the YRD increased significantly within the whole PBL due to the regional transport from the NCP.The maximum age reached 48, 60, and 65 h for EC, SO4 2− , and SOA, respectively.For NO3 − , the highest concentration reached 180 µg m -3 for regions between Jinan and Nanjing at 08:00 LT on 30 December 2017, which was 2 times the peak value in the NCP before the 325 regional transport (~90 µg m -3 ).Moreover, the average age of NO3 − was relatively low (6-24 h) for high-concentration NO3 − particles, indicating the significant contribution from the local chemical formation.
The age distribution of the major PM2.5 chemical compositions (EC, SO4 2− , NO3 − , NH4 + , and SOA) in Beijing, Jinan, Nanjing, and Shanghai is shown in Figure 9. SO4 2− and SOA exhibited 330 larger atmospheric age than the other three species, with a maximum average age of 84 and 81 h, respectively.In the NCP cities, Beijing and Jinan, more aged particles occurred in the stabilization stage.The average age in Beijing was 45, 76, 55, 61, and 71 h for EC, SO4 2− , NO3 − , NH4 + , and SOA respectively, higher than that in the accumulation and dilution stages.It is worth noting that a large fraction (50-60%) of fresh NO3 − and NH4 + particles with an age of 335 less than 12 h occurred in Jinan during the stabilization stage, indicating a rapid local formation.
In the YRD cities, Nanjing and Shanghai, the mass fraction of aged EC, SO4 2− , and SOA particles increased significantly during the regional transport, their average ages were even larger than that in the NCP.This is mainly because of the strong northwesterly wind that brought abundant aged particles from the NCP.NO3 − and NH4 + showed smaller atmospheric 340 age than SO4 2− and SOA, with an average age of 20-30 h during the regional transport.Fresh NO3 − and NH4 + particles with atmospheric age of less than 24 h account for more than 70% of the total mass.

Figures 10 and S15
show the size distribution of the major PM2.5 chemical compositions in Beijing and Shanghai.Both EC and POA exhibited bimodal distributions, with a fine-mode 345 peak at 0.2-0.4μm and a coarse-mode peak at 1-4 μm, respectively.SOA, SO4 2− , NO3 − , and NH4 + were mainly concentrated in the fine mode, with a peak at 3-4 μm.The size distribution of particles with different atmospheric ages was quite different.Aged particles were mainly concentrated in a larger size range, especially for SOA, SO4 2− , and NO3 − .For example, SO4 2− with a diameter >0.4 μm in both Beijing and Shanghai showed an atmospheric age of >96 h.350 When the accumulation stage evolved into the stabilization stage, the size of SOA, SO4 2− , and NO3 − slightly increased in Beijing, while that of EC and POA remained almost unchanged.In Shanghai, NO3 − and NH4 + were mainly concentrated in the size range of 0.1-0.3μm before the regional transport.Their dominant size increased to 0.3-0.7 μm during the regional transport.

Discussion 355
Our results indicate that the atmospheric age of EC, POA, SO4 2− and SOA increased gradually during the accumulation stage in the NCP due to air stagnation.The regional transport from the NCP to the YRD brought in high concentrations of aged primary particles, such as EC and POA.As a result, the simulated average atmospheric age of EC was ~40 h during the regional transport, which was much higher than the 'experimental' aging time scale to achieve complete 360 morphology modification and absorption enhancement of BC in Beijing (4.6 h) and Houston (18 h) (Peng et al., 2016).It could be speculated that the aged EC or POA particles are coated continuously by the newly formed fresh SNA particles along the transport route, which could further enhance the light absorption of particles (Bond et al., 2013).Using transmission electron microscopy (TEM), Zhang et al. (2021) observed abundant spherical primary OM 365 particles coated with secondary aerosols in the YRD during the regional transport, which is consistent with our findings.Previous studies have confirmed the crucial role of aerosol-PBL interaction in altering the vertical structure of PBL and the formation and accumulation of haze in eastern China (Huang et al., 2020;Li et al., 2017).Thus, the potential absorption enhancement of aged black or brown carbon particles during the regional transport could 370 amplify the aerosol-PBL interactions and further exacerbate air pollution.
Another interesting finding is that the atmospheric age of NO3 − remained unchanged or even slightly decreased during the regional transport from NCP to YRD, contrary to the age evolution of SO4 2− .This indicates that SO4 2− is mainly formed upwind and then transported to YRD, while there is a large fraction of NO3 − is formed locally in YRD.SO4 2− concentrations 375 have been dramatically reduced during the last decade due to desulfurization devices vigorously promoted in coal-fired facilities, and NO3 − has become the dominant inorganic component of PM2.5 in most regions of Eastern China (Sun et al., 2022).Our previous study for January 2013 suggested that NOx emissions from local sources and adjacent Jiangsu province contributed to nearly 30% of NO3 − in Shanghai, respectively (Xie et al., 2021).380 Therefore, NO3 − reduction can be achieved by cooperative emission controls within the YRD region.Surely, emission reduction actions should be taken a few days in advance to mitigate severe haze pollution under unfavorable weather conditions.This study is subject to a few limitations.The UCD/CIT model includes SO4 2− formation mechanisms through the gas phase oxidation of SO2 by OH radicals and the in-cloud aqueous 385 oxidation.However, recent field observations indicated a large contribution from other pathways during winter haze events in China, such as manganese-catalyzed oxidation on aerosol surfaces (Wang et al., 2021b), and aqueous oxidation of SO2 by NO2 (Cheng et al., 2016;Wang et al., 2016a).The missing mechanism in the current model leads to a substantial underestimation of SO4 2− (42.3%), which will further affect the age distribution of SO4 2− 390 particles.Since SOA is universally underestimated in current CTMs (Hu et al., 2017), uncertainties may also occur with SOA.Additionally, the discretization of atmospheric age in our model can lead to some uncertainties, especially for the calculation of average atmospheric age (Xie et al., 2022a).Thus, in this study, we run five different simulations with ∆τ of 1, 3, 6,

Conclusions
In this study, the age-resolved UCD/CIT model was used to investigate the age distribution of PM2.5 during a severe regional haze episode in eastern China in December 2017.Comparison with surface observation shows that the model reasonably captured the spatiotemporal 400 variations of PM2.5 and its major chemical compositions.Our results indicate that at the beginning stage of the haze event (25-29 December 2017), the stagnant weather conditions characterized by weak surface wind and high RH facilitated the accumulation and secondary formation of air pollutants, leading to increased PM2.5 concentrations in the NCP region.NO3 − was found to be the dominant chemical composition during this haze episode, contributing to 405 ~30% of PM2.5 concentration growth in Beijing, Jinan, Nanjing, and Shanghai.Both the concentration and atmospheric age of EC, POA, SO4 2− and SOA increased gradually during the accumulation stage in the NCP due to weakened atmospheric diffusion capacity, while the atmospheric age of NO3 − and NH4 + remained unchanged because of continuous local formation.During the regional transport stage (30 December), a cold front moved from north to south, 410 bringing aged particles from the NCP to the YRD region and increasing PM2.5 concentrations rapidly within hours.Accordingly, the average atmospheric age of EC, POA, SO4 2− and SOA particles in the YRD increased from 5-20 h to 50-60 h.In contrast, continuous local chemical formation resulted in an unexpected decrease in the atmospheric age of NO3 − and NH4 + in the YRD, although the concentrations of aged particles with old atmospheric age increased due to 415 regional transport.The age information provided in this study enhances our understanding of the formation mechanism of haze events and helps design cost-effective control strategies for different PM2.5 components.
) leads to a high concentration of fresh NO3 − and decreases the mean age of NO3 − .The vertical cross sections of the concentrations and mean ages for EC and NO3 − along the transport route from Beijing to Shanghai (white solid line in Figure 1) are shown in Figures 7 and 8, while those for SO4 2− and SOA are illustrated in Figures S13 and S14.Before the 310 regional transport (e.g., 16:00 LT 28 December 2017), aged particles mainly accumulated in https://doi.org/10.5194/acp-2023-11Preprint.Discussion started: 16 March 2023 c Author(s) 2023.CC BY 4.0 License.

Figure
Figure 1.Modeling domains and the locations of the observation stations.Black crosses represent the weather stations and the blue dots represent the air quality stations.Four main cities (BJ: Beijing, JN: Figure 1.Modeling domains and the locations of the observation stations.Black crosses represent the weather stations and the blue dots represent the air quality stations.Four main cities (BJ: Beijing, JN: 685

Figure 9 .Figure 10 .
Figure 9.The mass fractional contributions of different age bins to EC, SO4 2− , NO3 − , NH4 + , and SOA in Beijing, Jinan, Nanjing, and Shanghai.The red circle with a black dot indicates the average 730 atmospheric age (in hours, right y-axis).A, S, and D indicate the accumulation, stabilization, and dilution stage in Beijing and Jinan; B, D, and A represent the period before, during, and after regional transport in Nanjing and Shanghai.
age bin would be moved to the higher age bin successively.Particles in the last age bin represent 125 those older than the highest explicit age.The average age of particles in the ith age bin ( i