The Characteristics of Heavy Ozone Pollution Episodes and Identification of the Primary Driving Factors Using a Generalized Additive Model (GAM) in an Industrial Megacity of Northern China

Tropospheric ozone is the only normal pollutant with a continuously increasing annual average concentration worldwide. In this study, data were monitored at the Nankai University Air Quality Research Supersite (NKAQRS) (38.99° N, 117.33° E) between 1 April, and 31 August from 2018 to 2020, 33 O3 episodes from 2018 to 2020 were analyzed to reveal the characteristics of O3, VOCs and OFP during O3 episodes and to evaluate the driving factors. The O3 episodes showed a decreasing trend in terms of pollution frequency, days, heavy pollution duration and peak concentration. Ethane, acetylene, cyclopentane, and methylcyclopentane were the major types in 2020, while 1-hexene was the main component in 2019. The main ozone-contributing species in 2020 were propene cyclopentane methylcyclopentane and ethylene. Alkenes were important contributors to ozone formation. Using generalized additive models (GAMs), the explanatory variables in the study are divided into environmental and meteorological factors, and 16 impact factors are selected as explanatory variables. We found that the influence of these meteorological factors on O3 pollution was nonlinear and impacted by the interaction between variables. O3 episodes were mainly driven by meteorological and precursor (NO) factors in 2018, while meteorological conditions (T), followed by precursor (NO2) were the driving factors in 2019 and 2020, suggesting that O3 episodes were mainly driven by meteorological conditions.


Introduction
Tropospheric ozone is a normal pollutant with a continuously increasing annual average concentration in the mainland China [1]. Ozone affects the healthy growth of plants, leading to a decline in crop yields, and is also hazardous to the human respiratory system, organs, immune system and tissues, which threatens health and even human life [2][3][4][5][6]. Tropospheric ozone (O 3 ) is mainly produced by the photochemical reaction of its precursors (NOx, VOCs et al.) under favorable meteorological conditions [7][8][9][10]. The concentration of ambient O 3 always shows a nonlinear correlation with these precursors, suggesting a complex physicochemical mechanism in the ozone formation process [11][12][13][14].
As key precursors of O 3 pollution, volatile organic compounds (VOCs) can be transmitted over long distances [15], and they are mostly toxic [16,17] and carcinogenic [16,17]. Under UV irradiation, VOCs react with NOx to generate ozone and enhance atmospheric oxidation [18,19]. They are one of the main contributors to regional air pollution in recent

Site Description
VOCs data were monitored at the Nankai University Air Quality Research Supersite (NKAQRS) (38.99 • N, 117.33 • E) between 1 April, and 31 August from 2018 to 2020, which is located on the Nankai University campus in the Jinnan Distinct, Tianjin (Figure 1). A student dormitory area is located to the south of the NKAQRS, and a road with relatively Atmosphere 2021, 12, 1517 3 of 13 low traffic flow is sited 20 m to the north. Easterly and southeasterly winds prevailed during the study period. According to Chinese National Ambient Air Quality Standard and Technical Regulation on Ambient Air Quality Index (AQI) of China (MEE, 2012a), we define an O 3 episode as one day or a set of continuous days longer than 2 days with a maximum daily 8 h average (MDA8) ozone concentration exceeding 160 µg m −3 . A day with mean ozone values of the maximum daily 8 h average (MDA8) exceeding 160 µg m −3 is defined as a high-O 3 day. The mean wind speed (ws), temperature (T) and the relative humidity (RH) were 1.6 ± 1.1 m/s, 23.7 ± 6.5 • C, 64.4 ± 23.1%, respectively during O 3 episodes. The meteorological conditions and pollutants were shown in Table S1.

Site Description
VOCs data were monitored at the Nankai University Air Quality Research Supersite (NKAQRS) (38.99° N, 117.33° E) between 1 April, and 31 August from 2018 to 2020, which is located on the Nankai University campus in the Jinnan Distinct, Tianjin ( Figure 1). A student dormitory area is located to the south of the NKAQRS, and a road with relatively low traffic flow is sited 20 m to the north. Easterly and southeasterly winds prevailed during the study period. According to Chinese National Ambient Air Quality Standard and Technical Regulation on Ambient Air Quality Index (AQI) of China (MEE, 2012a), we define an O3 episode as one day or a set of continuous days longer than 2 days with a maximum daily 8 h average (MDA8) ozone concentration exceeding 160 μg m −3 . A day with mean ozone values of the maximum daily 8 h average (MDA8) exceeding 160 μg m −3 is defined as a high-O3 day. The mean wind speed (ws), temperature (T) and the relative humidity (RH) were 1.6 ± 1.1 m/s, 23.7 ± 6.5 °C, 64.4 ± 23.1%, respectively during O3 episodes. The meteorological conditions and pollutants were shown in Table S1.

Species Monitoring
VOC-species data were continuously monitored at intervals of 1 hr using a GC955 series 611/811 VOC analyzer (Syntech Spectras Inc., Groningen, the Netherlands). In total, 54 VOC species designated as photochemical precursors by the United States Environmental Protection Agency (US EPA) were monitored and used in the premonitoring equipment calibration. The analyses were performed using a photo (PID) and a flame (FID) ionization detector, which ensured high sensitivity and effective identification. The GC955 series 611 and 811 devices are two separated sample and column systems, which measure high (C6-C10) and low (C2-C5) boiling-point VOC species, respectively. For the series 811, the C2-C5 VOC species in ambient air were preconcentrated on Carbosieves SIII at a temperature of −5 °C. The enriched compounds were then thermally desorbed by heating (to 270 °C) and were purged into the separation column. The target compounds were then detected by the PID and an FID. The Series 611 was used to determine C6-C10 VOC-species. Air samples were preconcentrated on Tenax GR at ambient atmospheric temperature (~30 °C). Target compounds were then desorbed at 230 °C, brought into a stripper column, and then an analysis column for separation and a PID for detection.

Species Monitoring
VOC-species data were continuously monitored at intervals of 1 hr using a GC955 series 611/811 VOC analyzer (Syntech Spectras Inc., Groningen, the Netherlands). In total, 54 VOC species designated as photochemical precursors by the United States Environmental Protection Agency (US EPA) were monitored and used in the premonitoring equipment calibration. The analyses were performed using a photo (PID) and a flame (FID) ionization detector, which ensured high sensitivity and effective identification. The GC955 series 611 and 811 devices are two separated sample and column systems, which measure high (C6-C10) and low (C2-C5) boiling-point VOC species, respectively. For the series 811, the C2-C5 VOC species in ambient air were preconcentrated on Carbosieves SIII at a temperature of −5 • C. The enriched compounds were then thermally desorbed by heating (to 270 • C) and were purged into the separation column. The target compounds were then detected by the PID and an FID. The Series 611 was used to determine C6-C10 VOC-species. Air samples were preconcentrated on Tenax GR at ambient atmospheric temperature (~30 • C). Target compounds were then desorbed at 230 • C, brought into a stripper column, and then an analysis column for separation and a PID for detection. Quality assurance and quality control measures were performed, including routine maintenance of the instrument every week, fortnightly five-point calibration and verification, daily single-point correction, and weekly filter replacement. The analyzer was calibrated using standard gas to determine the retention times and control peak areas; correlation coefficient typically varied between 0.900 and 1.000. The method detection limits (MDL) for VOC species ranged from 0.019 to 0.599 ppbv. In accordance with the detection results, we obtained data for 54 VOC species within four VOC subcategories. Wind roses during the study period are given in Figure S1 in the Supplementary Materials. The data were subject to strict quality control measures, and abnormal data were excluded from the analysis. SO 2 , NO 2 , CO data were obtained from SO 2 analyzer (API., Baltimore, ML, USA), NOx analyzer (API., Baltimore, ML, USA), CO analyzer (API., Baltimore, ML, USA) in the Nankai University Air Quality research Supersite (38 • 59 N, 117 • 20 E) in the Jinnan district of Tianjin, China.

Ozone Formation Potential (OFP)
The maximum incremental reactivity (MIR) method proposed by Carter [45] is widely used as a good indicator for comparing the ozone formation potential (OFP) of individual VOC species, and has been used to evaluate the photochemical reactivity of VOCs with OH radicals and for estimation of the contribution of individual organic compounds to ozone formation, and it is defined as follows: Here, [VOC] i is the concentration of VOC species i, OFP i is defined as the ozone formation potential of individual species i, MIR i is defined as the maximum incremental reactivity coefficient for individual species i, which is updated by Carter [46]. Additionally, OFP is defined as the ozone formation potential of the total species.

GAM
GAM have a nonlinear relationship between the corresponding variables and predictors using a smooth function, where i indicates the ith hour's observation. k refers to the type of impact factors. f k (x) are smooth functions of the data. The element g(µ i ) is the "link" function, which specifies the relationship between the linear formulation on the right side of Equation (1) and the response µ i . Nonlinear functions f k (x) are used to represent the complex relationship between ozone and impact factors. ε i is the residual [47]. In this study, we considered hourly O 3 concentration as the response variable, and hourly value of the relevant influencing factors, which were divided into environmental and meteorological factors in this study, as the explanatory variable. The GAM model check is mainly used to evaluate the quality of the proposed optimal model through the gam.check function in the R language mgcv package. In addition, we used the adjusted R 2 and variance interpretation rate to evaluate the quality of the fitted GAM. The higher the adjusted R 2 and variance interpretation rate are, the better the model fitting effect are. (279 µg /m 3 ) in the JJJUA [48]. The MAD8 peak concentration from 2014 to 2018 was observed (297 µg /m 3 ) in Shijiazhuang [49]. It can be seen that although the value of ozone concentration in 2020 was not the lowest, the O 3 episodes showed a decreasing trend in terms of pollution frequency, days, heavy pollution duration and peak concentration. This has a lot to do with the unprecedented reduction in air pollution emissions caused by the various containment measures taken by the Chinese government during the period of COVID-19 outbreak [50][51][52]. episodes in 2020 was 66.4 ppbv, which was higher than 2018 (61.9 ppbv) and lower than 2019 (75.5 ppbv). The frequency of O3 episodes were 15, 17, 4 and the number of days were 29, 28, and 5 d in 2018, 2019 and 2020, respectively ( Figure 2). The longest duration of O3 episodes was 2 days in 2020, while the longest were 6~8 d in 2018 and 2019. The total hours of O3 episodes with an hourly value greater than 160 μg/m 3 were 209, 191, and 30 h, and the hourly peak concentrations were 344, 258 and 211 μg/m 3 in 2018, 2019 and 2020, respectively. The hourly peak concentrations in 2017 was observed (279 μg /m 3 ) in the JJJUA [48]. The MAD8 peak concentration from 2014 to 2018 was observed (297 μg /m 3 ) in Shijiazhuang [49]. It can be seen that although the value of ozone concentration in 2020 was not the lowest, the O3 episodes showed a decreasing trend in terms of pollution frequency, days, heavy pollution duration and peak concentration. This has a lot to do with the unprecedented reduction in air pollution emissions caused by the various containment measures taken by the Chinese government during the period of COVID-19 outbreak [50][51][52]. The average value of NO2 were 25, 19, and 15 ppbv during O3 episodes in 2018, 2019 and 2020, respectively, showing a decreasing trend. From the diurnal variation of pollutants during O3 episodes (Figure 3), we can see that CO and SO2 in 2020 were 1.7 ppbv and 3.3 ppbv, respectively, which is significantly higher than previous years.  (Figure 3), we can see that CO and SO 2 in 2020 were 1.7 ppbv and 3.3 ppbv, respectively, which is significantly higher than previous years.
The  [58,59]. Isoprene was mainly derived from biological sources, indicating that rubber, plastics and other chemical manufacturing industries were important sources, and the impact of biological emissions may show a downward trend.
The top ten substances of VOCs concentrations are shown in Figure 4. The major VOCs components during O 3 episodes were essentially consistent in 2018 and 2020, and most of the components were low-carbon alkanes. During O 3 episodes, propane, ethane, acetylene and i-pentane were the major species in 2018, and ethane, acetylene, cyclopentane and methylcyclopentane were the major species in 2020. Additionally, 1-hexene was the main component in 2019, followed by propane and cyclopentane.
The top ten substances of VOCs concentrations are shown in Figure 4. The major VOCs components during O3 episodes were essentially consistent in 2018 and 2020, and most of the components were low-carbon alkanes. During O3 episodes, propane, ethane, acetylene and i-pentane were the major species in 2018, and ethane, acetylene, cyclopentane and methylcyclopentane were the major species in 2020. Additionally, 1-hexene was the main component in 2019, followed by propane and cyclopentane.   Figure 4. TOP 10 VOCs species in this study during O3 episodes.
During the O3 episodes in 2018 ( Figure 5), the concentration of O3 was high from 12:00 to 19:00, and the concentration of propane and i-pentane in this period first decreased significantly, then increased sharply after 14:00, and then decreased and leveled off after 16:00. In 2019, the concentration of O3 was higher from 14:00 to 20:00, and the concentration of 1-hexene and propane first decreased significantly during this time period, then increased after 20:00. In 2020, the concentration of O3 was higher from 12:00 to 21:00; the concentration of methylcyclopentane first decreased significantly during this time period, The value of OFP during O 3 episodes from 2018 to 2020 are shown in Figure S3. The top ten OFP species are shown in Figure S4, where the main ozone-contributing species were propene (4.68 ppbv), isoprene (4.08 ppbv), and ethylene (2.58 ppbv) in 2018. The main ozone-contributing species in 2020 were propene (6.11 ppbv), cyclopentane (4.30 ppbv), methylcyclopentane (3.55 ppbv) and ethylene (3.18 ppbv), and alkenes were the important contributor to ozone formation. The top ten substances of OFP in 2019 were significantly different, and the main ozone-contributing species were 1-hexene (17.72 ppbv), isoprene (11.83 ppbv), propene (6.25 ppbv) and cyclopentane (4.83 ppbv).
During the O 3 episodes in 2018 ( Figure 5), the concentration of O 3 was high from 12:00 to 19:00, and the concentration of propane and i-pentane in this period first decreased significantly, then increased sharply after 14:00, and then decreased and leveled off after 16:00. In 2019, the concentration of O 3 was higher from 14:00 to 20:00, and the concentration of 1-hexene and propane first decreased significantly during this time period, then increased after 20:00. In 2020, the concentration of O 3 was higher from 12:00 to 21:00; the concentration of methylcyclopentane first decreased significantly during this time period, and increased sharply after 16:00, then decreased and stabilized after 19:00, and acetylene increased sharply after 18:00. Other major VOCs species did not show an obvious trend during this period.

Evaluation of the Influencing Factors on O 3 Episodes
Relevant research shows that the concentration of ozone is related to CO, SO 2 , NO, NO 2 , acetylene, alkenes, aromatics, alkanes, wd(wind direction), ws(wind speed), T(temperature), RH(relative humidity), P(average air pressure), pre(precipitation), vis(visbility), TSRI(Total solar radiation intensity) and other influencing factors. Spearman's correlation analysis was conducted between hourly ozone and the corresponding influencing factors during O 3 episodes from 2018 to 2020 (Figures S5-S7). The explanatory variables in the study are divided into environmental and meteorological factors. Environmental factors include CO, SO 2 , NO, NO 2 , acetylene, alkenes, aromatics and alkanes; meteorological factors include wd, ws, T, RH, P, Pre, TSRI, vis. A total of 16 impact factors were selected as explanatory variables, and O 3 concentration as the response variable, whose distributions are all normal ( Figure S8). The multifactor correlation analysis was carried out through the GAM model, and the effective data was totaled into 1475 groups. and increased sharply after 16:00, then decreased and stabilized after 19:00, and acetylene increased sharply after 18:00. Other major VOCs species did not show an obvious trend during this period.

Evaluation of the Influencing Factors on O3 Episodes
Relevant research shows that the concentration of ozone is related to CO, SO2, NO, NO2, acetylene, alkenes, aromatics, alkanes, wd(wind direction), ws(wind speed), T(temperature), RH(relative humidity), P(average air pressure), pre(precipitation), vis(visbility), TSRI(Total solar radiation intensity) and other influencing factors. Spearman's correlation analysis was conducted between hourly ozone and the corresponding influencing factors during O3 episodes from 2018 to 2020 (Figures S5-S7). The explanatory variables in the study are divided into environmental and meteorological factors. Environmental factors include CO, SO2, NO, NO2, acetylene, alkenes, aromatics and alkanes; meteorological factors include wd, ws, T, RH, P, Pre, TSRI, vis. A total of 16 impact factors were selected as explanatory variables, and O3 concentration as the response variable, whose distributions are all normal ( Figure S8). The multifactor correlation analysis was carried out through the GAM model, and the effective data was totaled into 1475 groups.
Multivariate analysis showed that retention factor significantly affected the change of O3 concentration at the level of p < 0.001, which was statistically significant. Factors that did not pass the significance analysis of TVOC were deleted, and O3 was taken as the explanatory variable used to reconstruct the multifactor GAM model until all variables passed the significance test. Factors which were included in the final GAM model in different years are listed in Table S3. The R 2 of the multifactor GAM model during O3 episodes from 2018 to 2020 were 0.92, 0.90 and 0.98, respectively, and the deviances explained were 94%, 90.8% and 98.9%. The model-fitting effect in Table S3 was better.
The gam.check function was used through the mgcv program package of R version 4.0.2 to evaluate the fitting effect of the multifactor model ( Figures S9-S11). From the model residual QQ diagram, the points were approximately distributed in a straight line. From the residual histogram, the residual mean was close to 0, and the frequency distribution was centered at 0. The closer to 0, the higher the frequency, and the two sides were essentially symmetrical. The model residuals were approximately normally distributed. From the scatter plot of residuals and predicted values, the points were essentially randomly distributed, indicating that the residuals were not related to the predicted values. Judging from the scatter plot of the observed values and the fitted values, the two basically had a straight-line distribution of y = x, indicating that the response variable after the model was fitted has a higher degree of matching with the fitted value. In conclusion, the multifactor model proposed in this paper had a good fitting effect.
The F value reflects the relative importance of each explanatory variable of the model to the dependent variable [63]. During O3 episodes in 2018, vis (16.  (Table S3). T (81.8), NO2 (39.8), Multivariate analysis showed that retention factor significantly affected the change of O 3 concentration at the level of p < 0.001, which was statistically significant. Factors that did not pass the significance analysis of TVOC were deleted, and O 3 was taken as the explanatory variable used to reconstruct the multifactor GAM model until all variables passed the significance test. Factors which were included in the final GAM model in different years are listed in Table S3. The R 2 of the multifactor GAM model during O 3 episodes from 2018 to 2020 were 0.92, 0.90 and 0.98, respectively, and the deviances explained were 94%, 90.8% and 98.9%. The model-fitting effect in Table S3 was better.
The gam.check function was used through the mgcv program package of R version 4.0.2 to evaluate the fitting effect of the multifactor model ( Figures S9-S11). From the model residual QQ diagram, the points were approximately distributed in a straight line. From the residual histogram, the residual mean was close to 0, and the frequency distribution was centered at 0. The closer to 0, the higher the frequency, and the two sides were essentially symmetrical. The model residuals were approximately normally distributed. From the scatter plot of residuals and predicted values, the points were essentially randomly distributed, indicating that the residuals were not related to the predicted values. Judging from the scatter plot of the observed values and the fitted values, the two basically had a straight-line distribution of y = x, indicating that the response variable after the model was fitted has a higher degree of matching with the fitted value. In conclusion, the multifactor model proposed in this paper had a good fitting effect.
The F value reflects the relative importance of each explanatory variable of the model to the dependent variable [63]. Multifactor correlation analysis was performed through the GAM model to obtain the impact effect map of the influencing factors (Figure 6), and the specific influence of each influencing factor on O 3 concentration were analyzed. T was the factor that had the greatest influence on O 3 during O 3 episodes in 2019 and 2020, which was much higher than other factors. O 3 and T had a mainly nonlinear and positive correlation, with O 3 episodes mainly occurring when T was greater than 20 • C, and ozone concentration increased with the rise of temperature. Further, there was an obvious inflection point, and when the temperature was higher, the increase in ozone concentration was more obvious, while the trend tends to be flat. In 2018, the effect of T on ozone was not obvious at 20-35 • C, and the ozone concentration showed an increasing trend with the increase in temperature after Atmosphere 2021, 12, 1517 9 of 13 35 • C. High temperatures can enhance solar radiation and reduce cloud cover, which in turn increases the intensity of photochemical reactions, leading to an increase in ozone concentration [64].

Conclusions
1. O3 episodes from 2018 to 2020 were analyzed in this study, The ozone concentration of O3 episodes in 2020 was 66.4 ppbv, which was higher than 2018 (61.9 ppbv) and lower than 2019 (75.5 ppbv), although the value of ozone concentration in 2020 was not the lowest, the O3 episodes showed a decreasing trend in terms of pollution frequency, days, heavy pollution duration and peak concentration. The average value of NO2 during O3 episodes showed a decreasing trend, CO and SO2 in 2020 were 1.7 ppbv and 3.3 ppbv, respectively, significantly higher than previous years.
2. The major VOCs components during O3 episodes were essentially consistent in 2018 and 2020, and most of the components were low-carbon alkanes. Propane, ethane, acetylene and i-pentane were the major species in 2018, ethane, acetylene, cyclopentane, and methylcyclopentane were the major species in 2020; 1-hexene was the main component in 2019. Above all, LPG/natural gas emissions were important sources in 2018, and both LPG/natural gas and gasoline volatilization/emissions were important sources in 2019 and 2020. NO 2 was a factor that has a greater impact on O 3 during O 3 episodes in 2019 and 2020, second only to T. O 3 and NO 2 were mainly nonlinear and negatively correlated, and the O 3 concentration decreases with the increase of NO 2 concentration, and the confidence interval of NO 2 concentration was relatively narrow, indicating that there was a significant negative effect. NOx emission controls in China have been motivated mainly by the goal of decreasing nitrate PM 2 . 5 , and further controls were expected in the future (http://env.people.com.cn/n1/2020/0515/c1010-31710781.html, accessed on 20 August 2021). NO had a greater impact on O 3 during O 3 episodes in 2018 and 2019. When the NO concentration was lower than 10 ppbv, the O 3 concentration showed a decreasing trend, but when the NO concentration was high, the overall impact trend was positive. This may be due to the role of HO 2 in promoting the oxidation of NO to NO 2 in the high-concentration environment [65], and photolysis generating O 3 and increasing its concentration. Under certain conditions, nitrogen oxides, nitric oxide and VOCs generate ozone through photochemical reactions, which increase the ozone concentration and cause higher ozone pollution.
Vis was the factor that had the greatest impact on O 3 during O 3 episodes in 2018, and it also had a greater impact on O 3 in 2019 and 2020. Visibility may be affected by several factors such as rain or thunderstorms, haze, fog and mist. The O 3 concentration and vis were mainly nonlinear and negatively correlated in 2018 and 2019. As the vis increased, the O 3 concentration gradually decreased. Vis showed a nonlinear, positive correlation at 10-20 km in 2020, and a nonlinear, negative correlation after 20 km. RH was a factor that had a great impact on O 3 during O 3 episodes in 2018-it was second only to vis. The O 3 concentration was mainly nonlinearly related to RH. When RH < 60%, the effect of RH on O 3 concentration did not change significantly; when RH > 60%, the O 3 concentration decreased with the increase in RH, and the decrease range was larger. TVOC (alkenes, aromatic hydrocarbons, alkynes, alkanes) had relatively low effects on O 3 concentration, and some had not passed the GAM significance test. Compared with meteorological factors, their impact on ozone was very small.

Conclusions
1. O 3 episodes from 2018 to 2020 were analyzed in this study, The ozone concentration of O 3 episodes in 2020 was 66.4 ppbv, which was higher than 2018 (61.9 ppbv) and lower than 2019 (75.5 ppbv), although the value of ozone concentration in 2020 was not the lowest, the O 3 episodes showed a decreasing trend in terms of pollution frequency, days, heavy pollution duration and peak concentration. The average value of NO 2 during O 3 episodes showed a decreasing trend, CO and SO 2 in 2020 were 1.7 ppbv and 3.3 ppbv, respectively, significantly higher than previous years.
2. The major VOCs components during O 3 episodes were essentially consistent in 2018 and 2020, and most of the components were low-carbon alkanes. Propane, ethane, acetylene and i-pentane were the major species in 2018, ethane, acetylene, cyclopentane, and methylcyclopentane were the major species in 2020; 1-hexene was the main component in 2019. Above all, LPG/natural gas emissions were important sources in 2018, and both LPG/natural gas and gasoline volatilization/emissions were important sources in 2019 and 2020.
4. Based on the GAM results, O 3 episodes was mainly driven by meteorological and precursor (NO) factors in 2018, while meteorological conditions (T), followed by precursor (NO 2 ) were the main driving factors in 2019 and 2020. Different factors had different driving impact on the O 3 episodes. T had a nonlinear and positive impact, while NO 2 had a nonlinear and negative impact.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/atmos12111517/s1, Figure S1: Wind roses during study period in 2018(a), 2019(b), 2020(c) and during O 3 episodes from 2018 to 2020(d), Figure S2: The mean concentrations of VOCs during the O 3 episodes in this study, Figure S3:The mean OFP during the O 3 episodes in this study, Figure S4: TOP 10 OFP species in this study during O 3 episodes, Figure S5 Table S1: Statistics analysis for meteorological parameters and pollutants during the the study period, Table S2: O 3 episodes between 1th April, and 31th August from 2018 to 2020, Table S3: Estimated degree of freedom (Edf), degree of reference (Ref. df), P-value, F-value (which measures the relative importance of smoothed variable) for the smoothed variables in the GAM model.