Year-round record of near-surface ozone and “O3 enhancement events” (OEEs) at Dome A, East Antarctica

Dome A, the summit of the east Antarctic Ice Sheet, is an area challenging to access and is one of the harshest environments on Earth. Up until recently, long term automated observations from Dome A were only possible with very low power instruments such as a basic meteorological station. To evaluate the characteristics of near-surface O3, continuous observations were carried out in 2016. Together with observations at the Amundsen-Scott Station (South Pole – SP) and Zhongshan Station (ZS, on the southeast coast of Prydz Bay), the seasonal and diurnal O3 variabilities were investigated. The 20 results showed different patterns between coastal and inland Antarctic areas that were characterized by high concentrations in cold seasons and at night. The annual mean values at the three stations (DA, SP and ZS) were 29.2 ± 7.5 ppb, 29.9 ± 5.0 ppb and 24.1 ± 5.8 ppb, respectively. We investigated the effect of specific atmospheric processes on near-surface summer O3 variability, when O3 enhancement events (OEEs) are systematically observed at DA (average monthly frequency peaking up to 64.5% in December). As deduced by a statistical selection methodology, these O3 enhancement events (OEEs) are 25 affected by a significant interannual variability, both in their average O3 values and in their frequency. To explain part of this variability, we analyzed the OEEs as a function of specific atmospheric processes: (i) the role of synoptic-scale air mass transport over the Antarctic Plateau was explored using the Lagrangian back-trajectory analysis – Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) method and (ii) the occurrence of “deep” stratospheric intrusion events was investigated using the Lagrangian tool STEFLUX. The specific atmospheric processes, including synoptic-scale air mass 30 transport, were analysed by the HYSPLIT back-trajectory analysis and the potential source contribution function (PSCF) model. Short-range transport accounted for the O3 enhancement events (OEEs) during summer at DA, rather than efficient local production, which is consistent with previous studies of inland Antarctica. Moreover, the identification of recent (i.e., https://doi.org/10.5194/essd-2020-130 O pe n A cc es s Earth System Science Data D icu ssio n s Preprint. Discussion started: 5 August 2020 c © Author(s) 2020. CC BY 4.0 License.

production (for example, Jones et al., 2000). Moreover, this may provide an input source for the entire Antarctic region (for example, Legrand et al., 2016;Bauguitte et al., 2011). Indeed, Helmig et al. (2008a,b) provided further insight into the vigorous photochemistry and O3 production that result from the highly elevated levels of NOx in the Antarctic surface layer.
During stable atmospheric conditions, which are typically observed during low wind and fair sky conditions, O3 accumulated in the surface layer can reach up to twice its background concentration. Neff et al. (2008a) showed that shallow mixing 70 layers associated with light winds and strong surface stability can be among the dominant factors leading to high NO levels.
As shown by Cristofanelli et al. (2008) and Legrand et al. (2016), the photochemically-produced O3 in the PBL over the Antarctic Plateau can affect the O3 variability thousands of km away from the emission area, due to air mass transport.
The near-surface O3 concentrations at high-elevation sites can also be increased by the downward transport of O3-rich air from the stratosphere during deep convection and stratosphere-to-troposphere transport (STT) events. Moreover, the 75 stratospheric O3 in the polar regions can be transferred to the troposphere not only during intrusion events but also as a result of slow but prolonged subsidence (e.g., Gruzdev et al., 1993;Roscoe et al., 2004;Greenslade et al. 2017). The earliest studies, carried out by the aircraft flight NSFC-130 over the Ellsworth Mountains of Antarctica in 1978, found that mountainous terrain may induce atmospheric waves that propagate through the tropopause. The tropospheric and stratospheric air may be mixed, leading to an increase in the tropospheric O3 concentration (e.g., Robinson et al., 1983). 80 Radio soundings at the Resolute and Amundsen-Scott Stations also showed the existence of transport from the stratosphere to the troposphere, and the flux could reach up to 5×10 10 mol/cm 2 /s (e.g., Gruzdev et al., 1993). Recently, Traversi et al. (2014Traversi et al. ( , 2017 suggested that the variability of air mass transport from the stratosphere to the Antarctic Plateau could affect the nitrate content in the lower troposphere and the snowpack. Currently, the climatology of tropospheric O3 over Antarctica is relatively understudied because observations of year-round 85 near-surface O3 have been tied to manned research stations. These stations are generally located in coastal Antarctica, except for the South Pole (SP) and Dome C continental stations on the East Antarctic Plateau. Thus, the only information currently available for the vast region between the coast and plateau are spot measurements of boundary layer O3 during summer from scientific traverses (e.g., Frey et al., 2015) or airborne campaigns (e.g., Slusher et al., 2010). Moreover, the vertical profile of O3 in the troposphere cannot be measured by satellites because the high density of O3 in the stratosphere leads to the 90 inaccurate estimation of tropospheric O3 by limb-viewing sensors. Estimates of total O3 in the troposphere have been made by subtracting the stratospheric O3 column (determined by a limb-viewing sensor) from the total column of O3 (measured by a nadir-viewing sensor) (Fishman et al., 1992). In other words, tropospheric profiles cannot be obtained by satellites, and we cannot examine the spatial distribution of near-surface O3 from space. As a result of these limitations, a dearth of information exists regarding the spatial gradient of near-surface O3 across Antarctica and how it varies throughout the year.

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To better understand the spatial variations and the source-sink mechanisms of near-surface O3 in Antarctica, near-surface O3 concentrations were measured during 2016 at Dome A (DA) and the Zhongshan Station (ZS). Together with records from https://doi.org/10.5194/essd-2020-130 the Amundsen-Scott Station (SP), we analysed specific processes that affect the intra-annual variability in surface O3 over the East Antarctic Plateau; in particular, we determined (i) the synoptic-scale air mass transport within the Antarctic interior and (ii) the role of STT transport. This study broadens the understanding of the spatial and temporal variations in the 100 near-surface O3 concentration and transport processes that impact tropospheric O3 over high plateaus.

Near-surface ozone observations
The Kunlun Station (80°25'02"S, 77°06'59"E, altitude 4087 m) is located in the DA area, on the summit of the east Antarctic Ice Sheet (Figure 1). The O3 monitor is located at the PLATO Antarctic site testing observatory. The instrument was 105 powered by the PLATO-A observatory, an improved version of the PLATO observatory described by Lawrence et al. (2009) The Zhongshan Station (69°22'12"S, 76°21'49"E, altitude 18.5 m) is located at the edge of the east Antarctic Ice Sheet ( Figure 1), where we installed a UV absorption near-surface O3 analyzer (EC9810A) for long-term near-surface O3 monitoring. The observational frequency was 3 min, and the data were transferred in real time to Beijing. Furthermore, to prevent data losses, a CR1000 data logger was used to record the data output in real time. Every three months, the O3 120 analyzer was calibrated using the EC9811 O3 calibrator, and 5 standard concentrations of O3 gas were generated for each calibration. The calibration concentration and measured concentration underwent correlation analysis, and seasonal calibration results were generated every three months. In 2016, 5 calibrations were made, and the appropriate correlation coefficients (r) were all greater than 0.9995. Preprint. Discussion started: 5 August 2020 c Author(s) 2020. CC BY 4.0 License.

Calibration process and results
Generally, the zero point, span point and operation parameters of the O3 monitor should be checked before each operation.

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The zero point should be checked regularly during continuous observation. While such regular calibration was done at the Global Atmosphere Watch (GAW) and Zhongshan Station, it was not possible at DA due to the lack of logistic support and the extreme environment. To minimize the error and evaluate the accuracy of the experiment, a UV-absorption O3 calibrator the slope of calibration curve ranges between 0.95-1.05, and the intercept ranges between -5-5 ppb. Instruments used in the calibration process include a DOA-p512-bn air compressor (USA), in addition to the Thermo 49ips O3 calibrator and the 140 Model 205 O3 monitor. Before each test, the O3 calibrator and the O3 monitor were turned on and preheated for 12 hours, and the measuring range was set to 400 ppb. We first generate a zero concentration using the Thermo 49ips and, once the analyzer response has stabilized on zero reading, we adjusted the Model 205's internal zero setting to matches the zero air source. Then, O3 airflow at 400 ppb level was generated and injected into the analyzer, and a correction factor was calculated based on the observed value, which was then loaded into the Model 205 configuration.

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After the calibration of the internal zero/span settings, a second stage of calibration was performed involving multi-point verification to check the response and stability of the analyzer. On Oct 5 th 2015 (before the instrument was shipped) and May 6 th 2017(the day that the instrument was transported back from Antarctica), a zero and 7 upscale points (0, 20, 35, 50, 65, 80, 100, 120 ppb) encompassing the full scale of the observation range (Table 2), were generated by the Thermo 49ips to test the Model 205 analyzer. Each point was observed for 15 min, during the last 10 minutes of which readings were taken every 150 minute of the calibrator and analyzer. Based on this experiment, the slope and intercept of the calibration curve were calculated by least squares. The results are shown in Table 2, it can be concluded that the slopes of the linear correction curve were 0.99936 and 1.02520, and the intercepts were 0.53861 and 0.85220l (Table 3), which fulfilled the requirements of HJ590-2010 and USEPA.
Another challenge when monitoring the atmosphere is the stability of the analyzer, which includes the analyzer's response 155 time. Similarly with the regular calibration, it could not be performed during the observation period, but it was reassuring that the Model 205 was still in good condition when we did the multi-point verification in May 2017, as shown in Table 3. The slope and intercept of the two calibration curves changed little and the standard uncertainties were small. To further test the stability, data consistency was also examined and the mean absolute deviation between two adjacent values was only 0.09 ppb. The largest difference was 0.61 ppb, indicating that the analyzer was stable and reliable.

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Before analysis, a variance test was used to remove abnormal data based on the Laida criterion method, which assumes that the records obeyed a normal distribution. The formula is , where xi is the measured value, x is the time series mean and σ is the standard deviation. After processing, 99.2%, 91.6%, and 99.5% of the hourly mean data were retained from the Amundsen-Scott Station, Zhongshan Station and Kunlun Station, respectively. which is a free software plug-in for MeteoInfo (Wang, 2014; http://meteothink.org/). The backward trajectories starting height was set at 20 m above the surface and the total run times was 120 hours for each backward trajectory, and each run 175 was performed in time intervals of 6 hours (00:00, 06:00, 12:00, 18:00).

Air mass back-trajectory calculations
The integral error part of the trajectory calculation error can be estimated by simulating the backward trajectory at the end of the forward trajectory and comparing the differences of the tracks. The starting point of the backward integration is set as (77.12°E, 80.42°S, 20m a.g.l.), the backward integration is 120 hours. Then the point reached at this time is taken as the starting point, and a forward simulation is made for 120h. In this simulation experiment, the contribution of integration error 180 to trajectory calculation error is very small within the first 72 hours. With the extension of integration time, the integration error slightly increases.

Potential source contribution function
The observation of a secondary maximum of O3 in November-December at the inland Antarctic sites was first reported for the SP by Crawford et al. (2001), and was attributed to photochemical production induced by high NOx levels in the 185 atmospheric surface layer, which were generated by the photo-denitrification of the Antarctic snowpack (same as Davis et al., https://doi.org/10.5194/essd-2020-130  Legrand et al. (2009), proving that photochemical production of O3 in the summer takes place over a large part of the Antarctic Plateau. A further study by Legrand et al. (2016) found that the highest near-surface O3 summer values were observed within air masses that spent extensive time over the highest part of the Antarctic Plateau before arriving at DC. To investigate the possible influence of 190 synoptic-scale air mass circulation on the occurrence of OEEs at DA, 5-day HYSPLIT back-trajectories were analyzed ( Figure 9). We used the potential source contribution function (PSCF, see, e.g., Hopke et al., 1995;Brattich et al., 2017) to calculate the conditional probabilities and identify the geographical regions related to the occurrence of NOEEs and OEEs at DA ( Figure 7).
As in Yin et al. (2017), the potential source contribution function (PSCF) assumes that back trajectories arriving at times of 195 high mixing ratios likely point to significant pollution directions (Ashbaugh et al., 1985). This function was often applied to locate air masses associated with high levels of near-surface O3 at different sites (Kaiser et al., 2007;Dimitriou and Kassomenos, 2015). In this study, the PSCF was calculated using HYSPLIT trajectories. The top of the model was set to 10000 m a.s.l. The PSCF values for the grid cells in the study domain were calculated by counting the trajectory segment endpoints that terminated within each cell (Ashbaugh et al., 1985). If the total number of end points that fall in a cell is nij 200 and there are mij points for which the measured O3 parameter exceeds a criterion value selected for this parameter, then the conditional probability, the PSCF, can be determined as The concentrations of a given analyte greater than the criterion level are related to the passage of air parcels through the ijth cell during transport to the receptor site. That is, cells with high PSCF values are associated with the arrival of air parcels at 205 the receptor site, which has near-surface O3 concentrations that are higher than the criterion value. These cells are indicative of areas with 'high potential' contributions of the constituent. Identical PSCFij values can be obtained from cells with very different counts of back-trajectory points (e.g., grid cell A with ij m = 5000 and ij n = 10000 and grid cell B with ij m = 5 and ij n = 10). In this extreme situation, grid cell A has 1000 times more air parcels passing through it than grid cell B.
Because the particle count in grid cell B is sparse, the PSCF values in this cell are highly uncertain.  (3) where Nave represents the mean nij of all grid cells. The weighted PSCF values were obtained by multiplying the original PSCF values by the weighting factor.

Mean concentration
At the DA, SP, and ZS sites, the annual mean molar ratios of near-surface O3 were 29.2 ± 7.5 ppb, 29.9 ± 5.0 ppb and 24.1 ± 220 5.8 ppb, respectively; the maximum annual mean molar ratio reached 42.5 ppb, 46.4 ppb and 32.8 ppb, respectively; and the minimum annual mean molar ratios were 14.0 ppb, 10.9 ppb and 9.9 ppb, respectively. The inland stations are characterized by higher annual mean molar ratios than the coastal station.
There were also obvious differences between polar day and polar night at all stations. In Figure 2, we define the polar day and night windows by the day of year margins and have used different shading colours to identify the polar day and polar 225 night. The average molar ratios of near-surface O3 during polar night at the DA, SP and ZS sites were 34.1 ± 4.3 ppb, 31.5 ± 3.9 ppb and 28.7 ± 1.3 ppb, respectively, and much lower concentrations appeared during non-polar night, with corresponding values of 26.1 ± 7.0 ppb, 28.1 ± 5.8 ppb and 23.1 ± 5.9 ppb, respectively. Interestingly, the SP had the highest near-surface O3 concentration during non-polar night, whereas at DA the highest concentration occurred during polar night and the largest variation occurred at this site.

Seasonal variation
In this part, we define Oct-Mar as the warm season and Apr-Sept as the cold season, which is similar to the definition of polar day and night.
In agreement with previous studies (Oltmans et al., 1976;Gruzdev et al., 1993;Ghude et al., 2005), the concentrations of near-surface O3 at the three stations were high and less variable during the cold season and low and more variable during the 235 warm season (Figure 3). In Antarctica, the emissions of O3 precursors are generally less than those at mid and low latitudes, whereas ultraviolet radiation is relatively strong; thus, when solar radiation occurs, the depletion effect is much greater than the effects from photochemical reactions during the warm season (Schnell et al., 1991). As explained by previous studies, during the polar night, due to the lack of light, the photochemical reactions stop. Moreover, due to the lack of loss effect, the O3 concentration gradually increased and the fluctuations became smaller. During the polar night, the monthly variation of 240 surface O3 at ZS was lower than that at the DA but higher than that at the SP. However, due to strong UV radiation in the low latitude areas and the presence of bromine-controlled O3 depletion events in coastal areas, the ZS shows a large seasonal variations during the non-polar night (Wang et al., 2011;Prados-Roman et al., 2017). However, at the SP Station, the largest standard deviation was observed in December, similarly to the characteristics at Dome-C station (DC) from November to December (Legrand et al., 2009;Cristofanelli et al., 2018). Figures 2 and 3 indicate that the near-surface O3 showed 245 obviously larger variations at the DA than the SP during the polar night, since, due to the different geographical location, the meteorological conditions of DA and SP are different. The abnormal fluctuation of O3 concentration over the DA during the polar night may be related to its special geographical environment.
As mentioned in the introduction section, mountainous topography/mountain waves may disturb advection transport in the stratosphere and lead to downward transportation to the troposphere (Robinson et al., 1983). DA is on the summit of the east 250 Antarctic Ice Sheet, and the tropospheric depth is only~4.6 km (Liang et al., 2015), which favours exchange between the stratosphere and troposphere. However, the topography in this area is very flat and creates a disadvantage for mountain waves. Does O3 transport occur? We will analyse and discuss this question in section 4.

Diurnal variation
To characterize the typical monthly O3 diurnal variations at the three stations, we analysed the mean diurnal variations of O3 255 at the three stations ( Figure 4) and the standard deviation of the mean diurnal variations (Figure 5). At the DA site, the mean diurnal concentrations for each month were relatively steady, with the standard deviation of the mean diurnal concentration for each month being lower than 0.4 ppb. At the SP, the mean diurnal concentrations were less variable as well. Except for December, the standard deviation of the mean diurnal concentration was lower than 0.3 ppb. At ZS, except for October, the standard deviation of the mean diurnal concentration was greater than that at the other two stations. In particular, the 260 standard deviation of the mean diurnal concentration of ZS in September, November and December exceeded 0.5 ppb. The mean diurnal variations in different time periods were not obvious, and the mean diurnal concentrations of the three stations fluctuated within a range of less than 1 ppb, indicating that daily photochemistry reactions were not the dominant factor in near-surface O3 at the three stations. The magnitude of the diurnal variation was low, which is similar to the variations found at other Antarctic stations (Gruzdev et al., 1993;Ghude et al., 2005;Oltmans et al., 2008).

Identification of OEEs
Our method to select the days characterized by OEEs is based on the procedure used in Cristofanelli et al. (2018). First, a sinusoidal fit is used to calculate the O3 annual cycle not affected by the OEEs, then a probability density function (PDF) of the deviations from the sinusoidal fit is calculated, with the application of a Gaussian fit to the obtained PDF. As reported in 270 Giostra et al. (2011), the deviations from the Gaussian distribution (calculated by using the Origin 9© statistical tool) can be used to identify observations affected by non-background variability. We computed the further Gaussian fitting of PDF points beyond 1 σ (standard deviation) of the Gaussian PDF, and determined the non-background O3 daily values that may be affected by "anomalous" O3 enhancement. The intersection of the two fitting curves is taken as our screening threshold ( In total, 42 days at DA were found to be affected by anomalous OEEs: 14.3% in January, 2.4% in May, 14.3% in June, 4.8% in July, 11.9% in August, 4.8% in November and 47.6% in December (Figure 6e, blue bars). This result clearly indicates that half of the anomalous days occurred in December, followed by January and June. At SP, 36 days with OEEs were found in 2016: 44.4% in January, 30.6% in November, and 25% in December (Figure 6d, grey bars). Apparently, OEEs occur only in 280 summertime at this measurement site. ZS was characterized by more days with OEEs: 53 days in April (34.0%), followed by September (18.9%), January (13.2%), October (11.3%), November (11.3%), December (5.7%) March (3.8%) and May (1.9%) (Figure 6f, yellow bars).
From the results above, SP was characterized by concentrated OEE occurrences, and ZS had the most scattered OEEs pattern.
In addition, all OEEs at SP and ZS occurred during the Antarctic warm season, and no OEEs were present during the polar 285 night, similarly to the pattern observed at DC (Cristofanelli et al., 2018). In contrast, the OEEs also occurred during the polar night in DA, and the number of OEE occurrence days accounted for up to 33% of the total number of events throughout the year. Previous studies (e.g., Legrand et al., 2016;Cristofanelli et al., 2018) carried out in DC showed that the O3 variability at DC could be associated with processes occurring at long temporal scales. In addition, the accumulation of photochemically produced O3 during transport of air masses was the main reason for OEEs, whereas the stratospheric 290 intrusion events had only a minor influence on OEEs (up to 3%). This finding cannot explain the temporal occurrence pattern of OEEs at DA. To determine the unknown cause, we investigated the synoptic-scale air mass transport and the STT occurrence at the measurement site.

Role of synoptic-scale air mass transport
During NOEEs, the air masses arriving at DA mainly come from the west and east of DA, and the 3-D clusters show that the 295 air masses travelled over the Antarctic plateau before reaching DA (Fig. 8b). The difference in the number of the three cluster trajectories is small, and the difference in the corresponding cluster average concentrations is not large. Using the PSCF results, we have identified air masses associated with higher surface ozone at DA during NOEEs (Fig. 8a). The Antarctic Plateau to the east and west of DA had high PSCF weight values (Figure 7), which shows that, during NOEEs, the potential source area of surface O3 for DA is mainly in the inland plateaus in the east and west, and the area of high PSCF 300 weight values distribution in the east is more larger than other directions.
Compared with NOEEs, the clustering results of trajectories during OEEs have different characteristics. In OEEs, the air masses that arrived at DA were predominantly from the north and from the west, and the 3-D clusters indicated that the 73% of the air mass trajectories came from the area north of DA (red line in Fig. 9a). The average concentrations of the three clusters differ greatly (Fig. 9c), but they are all higher than those obtained for NOEEs. It should be noted that 68% of Line-2 305 cluster (green line in Fig. 9a) occurred during the polar night (Fig. 10) and had a high average O3 concentration (reached 36.3 ppb). This shows that the OEEs of the polar night are more affected by the high value O3 air masses over the plateau west of DA than those during the polar day. Using the PSCF results, during OEEs, we did not find a large area of high WPSCF value, the high WPSCF value only appeared in the east and the north of DA over a limited area. However, independently on the polar day or on the polar night, the Line-1 cluster trajectory accounted for more than 60% during OEEs.

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In addition, the short distance of Line-1 cluster trajectory indicates that the air mass transport speed is slow, which is conducive to the accumulation of O3 along the way. It can be seen from Figure 9b that the characteristic values of backward trajectory clustering during OEEs are mostly lower than 200 m a.g.l. (supporting the role of snow as the source of near surface O3). As Fiebig et al. (2014) have proposed, the increase of O3 values in the near surface of central Antarctica may also be related to the transport of free tropospheric air and aged pollution plumes from low latitudes. In addition, Figure 11 315 shows that the average O3 growth rate reached 0.29 ppb/h during OEEs, while the average O3 growth rate was -0.06 ppb/h during NOEEs (Figure 11). The statistical scatter distribution showed that 97% of OEEs occurred when the wind speed was lower than 4 m/s. The overall average wind speed during OEEs is also significantly lower than that of NOEEs. As on a compiled stratosphere-to-troposphere exchange climatology, making use of the ERA-Interim reanalysis dataset from the ECMWF, and a refined version of a well-established Lagrangian methodology. STEFLUX is able to detect stratospheric intrusion events on a regional scale, and it has the advantage of retaining additional information concerning the pathway of stratosphere-affected air masses, such as the location of tropopause crossing and other meteorological parameters along the trajectories.

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We applied STEFLUX to assess the possible contribution of STT to near-surface O3 variability in the DA region (i.e., STEFLUX "target box", for further details on the methodology see Putero et al., 2016), and for identifying the measurement periods possibly affected by "deep" STT events (i.e., stratospheric air masses transferred down to the lower troposphere).
For this work, we set the top lid of the box at 500 hPa, and the following geographical boundaries: 79-82°S, and 76-79°E.
A "deep" STT event at Kunlun Station was determined if at least 1 stratospheric trajectory crossed the 3-D target box. The possible occurrence of stratospheric intrusion events, and their role in affecting the variability of near-surface O3 and tropospheric air-chemistry in Antarctica has been investigated in several studies (Murayama et al., 1992;Roscoe, 2004;Stohl and Sodemann, 2010;Mihalikova and Kirkwood, 2013;Traversi et al., 2014;Traversi et al., 2017;Cristofanelli et al., 2018). To provide a systematic assessment of the possible influence of "deep" STT events to the near-surface O3 variability 345 at Kunlun Station, we used the STEFLUX tool (see Sect. 4.3.1). Figure 12 shows the distribution of the occurrence of "deep" STT events over DA during the year. Although it is difficult to see a clear seasonal cycle, due to the low frequency of "deep" STT events, our results are in agreement with previous studies, indicating STT influence of up to 2% on a monthly basis (Stohl and Sodemann, 2010;Cristofanelli et al., 2018). According to our STEFLUX outputs, the highest frequency of "deep" STT events was observed in May and August (1.1%). The frequency of occurrence of "deep" STT events identified by 350 STEFLUX at Kunlun Station is about one order of magnitude lower than the occurrence of OEEs. Thus, a direct link of STT with OEEs interannual variability is unlikely, as also reported for DC station (Cristofanelli et al., 2018). Nevertheless, STT events can be a source of nitrates for the Antarctic atmosphere through different processes, thus indirectly affecting near-surface O3 concentrations and favouring the presence of OEEs (Traversi et al., 2014;.

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Based on the in-situ monitoring data during 2016 at DA, the variation, formation, and decay mechanisms of near-surface O3 were studied and compared with those at SP and ZS stations. The annual mean concentrations of near-surface O3 at the DA, SP and ZS sites were 29.2 ± 7.5 ppb, 29.9 ± 5.0 ppb, and 24.1 ± 5.8 ppb, respectively. The near-surface O3 concentrations were clearly higher in winter/polar night, with small fluctuations, than in the other seasons, which is different from the patterns observed at low latitudes. The O3 in inland areas was also higher than over the coast.

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The diurnal variations showed nonsignificant regular patterns, and the range of the average diurnal concentration fluctuation was less than 1 ppb at all three stations. These findings suggest that the synoptic transport somehow controls the overall O3 variability, as has been shown at the Amundsen-Scott and DC stations (Neff et al., 2008b;Cristofanelli et al., 2018).
At Kunlun station, it is unlikely that there is a direct relationship between STT and OEEs. The frequency of deep STT events identified by STEFLUX is about an order of magnitude lower than OEEs, and reaches its highest frequency (1.1%) in May 365 and August. As deduced by the STEFLUX application, "deep" STT events play a marginal role in steering the occurrence of OEEs at DA via "direct" transport of O3 from the stratosphere/the free troposphere, to the surface. As explained in Cristofanelli et al. (2018), this can be related to an underestimation of STT "young" (i.e., < 4-day old) events by STEFLUX, or to insufficient spatial and vertical resolution from ERA-Interim to fully resolve the complex STT transport in the Antarctic atmosphere (Mihalikova and Kirkwood, 2013). Despite this, STT can still represent a source of nitrates for the 370 Antarctic snowpack, thus possibly affecting summer photochemical O3 production. Therefore, it is important to carry out further studies to better assess these processes.
The characteristics and mechanisms of near-surface O3 revealed in this paper have important implications for better understanding the formation and decay processes of near-surface O3 in Antarctica, especially over the plateau areas.
Nevertheless, the lack of observations restricted our ability to amass more information. Long-term sustained observations at 375 Dome A, Dome C, Dome F, SP, Vostok, and other locations, would greatly help in the future. In addition, the atmospheric chemical models are also valuable (Lin et al., 2017;Xu et al., 2018). In the future, we will compare and analyze different atmospheric chemical models and methods to obtain a more accurate analysis of the OEEs in Antarctica.

Data availability
All data presented in this paper are available in https://doi.org/10.5281/zenodo.3923517 (Ding et al., 2020). The data set