Ambient ozone pollution at a coal chemical industry city in the border of Loess Plateau and Mu Us Desert: characteristics, sensitivity analysis and control strategies

Measurement data

To evaluate Yulin’s O₃ characteristics between 2017 and 2019, we obtained observational hourly concentrations of air pollutants including O₃, CO, NO₂, PM₁₀, PM_2.5, and NO, as well as meteorological parameters including temperature (T), relative humidity (RH), wind speed (WS), and wind direction (WD) from National Environmental Monitoring Stations and the Yulin Ecology and Environment Bureau. In Yulin, we chose four stations for study: the Environmental Monitoring Building (EMB, 38.25°N, 109.73°E), Experimental Middle School (EMS, 38.28°N, 109.73°E), Century Square (CS, 38.29°N, 109.48°E), and Hongshixia Gorge Eco-Park (HGEP, 38.33°N, 109.74°E). The locations of the four stations are presented in Fig. 1. There are no significant local sources close to these four sites. Ambient Air Quality Monitoring Specification (on trial) (Chinese HJ 664-2013) classified the EMB site, EMS site, and CS site as urban assessing stations and the HGEP site as an urban background station. Urban assessing stations are set up to monitor ambient air quality and changing trends in urban built-up, and participate in the assessment of urban ambient air quality. Urban background stations monitor the ambient air quality in urban areas that are not affected by local urban pollution and do not participate in the assessment of urban ambient air quality. The EMB site is surrounded by a residential area and processing, manufacturing, and coal chemical industries are located about 5 km south. The EMS site and CS site are in mixed functional areas of commerce, transportation, and residences. Although the HGEP site is regulated as an urban background station, it was surrounded by a highway during our field survey and many trucks passed through it.

To investigate the characteristics of VOCs and their contribution to O₃ formation in Yulin, we coupled online gas chromatography with a mass spectrometer and a flame ionization detector (GC-MS/FID, AC-GCMS 1000) developed by Guangzhou Hexin Instrument Co., Ltd. This was used to measure VOC levels at the EMB site from 7 July to 10 August 2019. A total of 70 VOCs (18 alkanes, 11 alkenes, one ethyne, 16 halocarbons, 14 aromatics, and 10 oxygenated VOCs (OVOCs)) were measured concurrently with 1 h resolution. The air passed over two cold traps in the separate FID and MS routes, and samples were rapidly heated and vaporized at a rate of up to 50 °C/s. The VOCs were brought into the FID and MS for online qualitative and quantitative analysis, respectively. The C₂-C₄ hydrocarbons were analyzed by the FID and the high carbon components were analyzed by the MS. To check the stability of the monitoring system, we injected the mixed standard gas (Photochemical Assessment Monitoring Stations (PAMS)) and the US Environmental Protection Agency toxic organics (TO-15)) with a concentration of 1ppb into the system seven times, and calculated the relative standard deviation (RSD) of the concentration for each component. The results showed that the RSD of all VOCs was lower than 10% (Table S1). A seven-point calibration curve was made using the standard gas external standards with the linearity (R²) of each VOC species exceeding 0.99. Another standard gas was injected daily into the instrument to test the system performance at 23:00. Therefore the data from 23:00 local time (LT) were excluded in this study.

Backward trajectory analysis

The 48 h backward trajectories from May to August 2017 to 2019 were computed using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Cohen et al., 2015; Draxler & Hess, 1998) with 6-h intervals and a start time of 00:00 LT to identify the origins of air masses 1,000 m above sea level. The meteorological data were provided by the Global Data Assimilation System of the United States National Centers for Environmental Prediction (ftp://arlftp.arlhq.noaa.gov/pub/archives/gdas1). These backward trajectories from 2017, 2018, and 2019 were analyzed using HYSPLIT, and then were categorized into a few clusters for analysis of the influence of O₃ regional transported sources.

Observational-based model

The OBM was developed by Cardelino & Chameides (1995) and was built on the newest version of the Master Chemical Mechanism (MCM v3.3.1; http://mcm.leeds.ac.uk/MCM/), a near-explicit mechanism describing the oxidation reactions of 146 primary VOCs together with the latest inorganic chemistry from the International Union of Pure and Applied Chemistry evaluation (He et al., 2020). Hourly concentrations of VOCs and four trace gases (SO₂, CO, NO, and NO₂), as well as hourly meteorological parameters (temperature and relative humidity), were used as inputs to constrain the model (Lyu et al., 2019). Moreover, this model can be employed to assess the sensitivity of O₃ photochemical production by calculating the relative incremental reactivity (RIR) and changing the concentrations of its precursors without detailed or accurate knowledge of these emissions (He et al., 2019): ${\displaystyle RIR\left(X\right)=\frac{\left[P_{O_3}\left(X\right)-P_{O_3}\left(X-\Delta X\right)\right]∕P_{O_3}\left(X\right)}{\Delta S\left(X\right)∕S\left(X\right)}}$ where X is a precursor of O₃ and △X represents the change in X concentrations; P(O₃) is the O₃ net production rate, which is calculated by the OBM; S(X) is the measured concentration of precursor X, and △S(X)/S(X) represents the relative change of S(X) (20% S(X) in this study).

Positive matrix factorization model

The positive matrix factorization (PMF) model is used to interpret source types and contributions based on VOC concentrations obtained from receptor point measurements (Paatero & Tapper, 1994). It has been used for the source apportionment of VOCs (Sun et al., 2020). In this study, we used PMF 5.0 to identify and quantify the main VOC sources in Yulin. A detailed description of the PMF model can be found in the EPA PMF 5.0 User Guide. Briefly, the ambient VOC observations matrix (x) was decomposed into two matrices: source profile (f) and source contribution (g): ${\displaystyle x_{ij}=\sum _{k=1}^p{g}_{ik}{f}_{kj}+e_{ij}}$ where, x_ij is the concentration of species j measured in sample i; g_ik is the contribution of the source k to the sample i; f_kj isthe profile of the species i in the source k; e_ij is the residual of species j in sample i; and p represents the total number of sources (Fan et al., 2020). In this study, we measured 70 VOC species and divided these species into three categories according to their signal-to-noise ratio (S/N) and detection limit (BDL). In general, species were classified as strong if the S/N was >2, weak if the S/N was between 0.2 and 2 or if the BDL percentage was >50%, and bad if the S/N was <0.2 or if the BDL percentage was >60% (Hui et al., 2020). Ultimately, 46 VOC species were selected and served as inputs of the PMF model.

In the PMF model, Q was the sum of the squared residual weighted by the inverse of their respective measurement uncertainty and in order to minimize the value. Q can be described as: ${\displaystyle Q=\sum _{i=1}^n\sum _{j=1}^m\left(\frac{X_{ij}-\sum _{k=1}^p{g}_{ik}{e}_{kj}}{S_{ij}}\right)}$ Ideally, the modeled Q value should eventually approach the expected Q values (Q_exp), which are equal to the degree of freedom of the model solution (n ×m –p(n+m) (Yan et al., 2016a).

Temporal O₃ distribution characteristics

According to the Chinese Technical regulation for ambient air quality assessment (on trial) (Chinese HJ 663-2013), 1-hour average O₃ concentrations and daily maximum 8-hour average O₃ concentrations (MDA8) are employed to describe hourly and daily O₃ variations, respectively. The 90th percentile of daily maximum 8-h average of O₃ concentrations (MDA8-90) is employed to evaluate monthly and annual O₃concentration levels. In this study, we used both MDA8 and maximum daily 1-hour average O₃ concentrations (MDA1) to present daily mean and extreme O₃ concentrations. The Ambient Air Quality Standard (Chinese GB3095-2012) regulates the Grade II Standard of MDA8 and MDA1 O₃, and are 160 µg/m³(about 75 ppbv) and 200 µg/m³ (about 93 ppbv), respectively. We defined the day when MDA8 or MDA1 O₃ levels exceeded the Grade II Standard as O₃ pollution day. Moreover, we also evaluated the diurnal, monthly, and annual variations observed in Yulin. The HGEP site was an urban background station which, according to technical regulation for the selection of ambient air quality monitoring stations (on trial) in China, are not included in the evaluation of a city’s air quality. Therefore, the HGEP site data were not included in the description of ambient O₃ changes in Yulin.

As shown in Table 1, the mean MDA8 O₃ concentration, MDA8-90 O₃ concentration, and pollution days all showed a rising trend from 2017 to 2019. Previous studies found that the average MDA8 O₃ concentration for China’s 31 capital cities in 2013 and 2017 was 61 and 76 ppbv, respectively (Zeng et al., 2019). In the YRD, the average MDA8 O₃ concentration was 78 ppbv in 2018 (Li et al., 2019). In Lanzhou, China, the average MDA8 O₃ concentration from 2013 to 2015 was 68.26 µg/m³ (about 32 ppbv) (Wang et al., 2017b). The average MDA8 O₃ concentration in the GZP was 96 µg/m³ (about 45 ppbv) in 2019 (http://sthjt.shaanxi.gov.cn/zfxxgk/hjzl/hjzkgb/20201130/64307.html). Generally, O₃ concentrations in Yulin were lower than developed areas such as cities in the YRD, comparable to the national average level and the GZP, and higher than Lanzhou.

The O₃ pollution days all occurred between May and August in 2017 and 2018. One unanticipated finding was that O₃ pollution days also appeared in April and September in 2019. The T, RH, and WS averages in April and September 2019 were 15 °C and 19 °C, 43% and 65%, and 1.7 m/s and 1.3 m/s, respectively. The T, RH, and WS averages in April and September 2017 to 2019 were 14 °C and 18 °C, 41% and 61%, and 1.8 m/s and 1.5 m/s, respectively (Table S2). The meteorological conditions in April and September 2019 were not significantly different from those in 2017 and 2018. Two O₃ pollution days occurred consecutively from 18 to 19 April 2019. On 18 April, the O₃ concentration peaked at 19:00 LT, decreased to 51.3 ppbv the next day at 5:00, and then started to increase again. Relatively high O₃ concentration at night could be explained by regional transportation. Six consecutive O₃ pollution days occurred from 3 to 8 September 2019. Unlike the O₃ pollution days in April, regional transportation was not the primary reason for this O₃ pollution episode. The average NO₂ concentrations from 3 to 8 September were 20, 22, 27, 28, 22, and 21 ppbv, respectively, which were higher than the average concentrations on adjacent days, such as 2 September (16.6 ppbv) and 9 September (12.8 ppbv). This suggests that the continued increase in O₃ concentration may have been closely related to the increase in nitrogen dioxide concentration.

Figure 2A displays the monthly O₃ variations. The highest MDA8-90 O₃ concentrations in 2017, 2018, and 2019 were 40, 39, and 39 ppbv, respectively. The highest MDA8-90 O₃ concentration appeared in May 2017 and June 2019. MDA8-90 O₃ concentrations in May to July 2017-2019 exceeded the Grade II Standard. Additionally, the MDA8-90 O₃ concentration in April and September 2019 also exceeded the standard. Monthly O₃ variations reflected an obvious seasonal cycle, as O₃ concentrations were high in summer and spring and low in winter and autumn. Some areas in north China also show this kind of seasonal cycle (Fang et al., 2020; Yan et al., 2016b; Wang et al., 2012; Li et al., 2020a). This seasonal O₃ variation was largely influenced by meteorological conditions (Wang et al., 2020b; Zheng et al., 2019).

The diurnal variation of O₃ concentrations is driven by photochemical reactions, meteorological conditions, the transport of O₃ and its precursors, and surface deposition (Crutzen, 2016; David & Nair, 2011). The diurnal variation in Yulin from 2017-2019, similar to many cities in northern China, shows a typical single peak curve: a valley in early morning and a peak in afternoon (Fig. 2B; Wang et al., 2012; Yu et al., 2020b; Li et al., 2020a). It was apparent that the O₃ concentrations were highest in summer, then spring, autumn, and winter. The O₃ concentration peaks were 60, 67, 43, and 34 ppbv in spring, summer, autumn, and winter, respectively. The peaks occurred at 16:00 LT in spring, autumn, and winter, but at 15:00 LT in summer. The solar radiation on Loess Plateau is intense, especially in summer, which can lead to more efficient O₃ production (Hu et al., 2014).

Spatial O₃ distribution characteristics

The four Yulin sites from most south to most north are the EMB, EMS, CS, and HGEP. Figure 3 shows that O₃ concentrations decreased spatially from south to north between 2017 and 2018. In 2017, the EMB site had the highest O₃ concentration, followed by the CS site, then the EMS site, and the HGEP site. In 2018, the O₃ concentrations decreased from south to north. In 2019, the EMB site had the highest O₃ concentration, followed by the HGEP site, then the EMS site, and the CS site, implying that the central region had lower O₃ concentrations than the northern and southern regions. The EMB site had the highest concentrations between 2017 and 2019. This suggested that Yulin’s O₃ concentration was greatly affected by industry. Diurnal and monthly variations at the four sites showed the same trend (Fig. S2). The diurnal variation curve showed that the lowest concentrations appeared at 7:00 LT in the morning and the highest concentrations appeared at 16:00 LT in the afternoon. The distance between these sites is less than 5 km. Therefore, different pollution sources can explain the differences in O₃ concentrations at the four sites.

The HGEP site’s O₃ concentration increased from 2017 to 2019 but was lower than that of Yulin’s urban area. The monthly and daily changes were consistent with those of the urban area. However, the O₃ concentrations were lower at night at the HGEP site than at the other sites (Fig. S2A). As mentioned above, the HGEP site is a traffic-intensive area, and vehicles emit NO that can titrate and lower O₃ levels.

Overview of O₃ pollution during the VOC sampling period

To further reveal the impact of O₃local formation, we collected VOC samples at the EMB site from 7 July to 10 August 2019. Figure 4 shows the time series of the meteorological factors and NO₂, CO, PM_2.5, PM₁₀, VOC, and O₃ concentrations in Yulin during the VOC sampling period. The average values are listed in Table S3. We regarded consecutive MDA8 O₃ pollution days as one O₃ episode period (EP). There were a total of six O₃ episode periods (EP1: 11-14 July, EP2: 20 July, EP3: 25-26 July, EP4: 28 July, EP5: 30 July-2 August, and EP6: 10 August) observed during the sampling period. During the sampling period, the average MDA8 O₃ concentration was 72 ± 9 ppbv with a range from 50 ppbv to 85 ppbv, and 83 ± 2, 75, 82 ± 3, 77, 79 ± 3 and 84 ppbv, respectively, from EP1 to EP6. The average concentration of total VOCs was 26 ± 12 ppbv with a wide range from 6 to 90 ppbv, and 30, 41, 28, 27, 31, and 13 ppbv, respectively, in the EPs. The meteorological factors of these six EPs (Table S3) were beneficial to in situ photochemical processes. NO₂ and total volatile organic compound (TVOC) concentrations were higher than in the non-pollution period. This indicated an increase in local precursor emissions. The valley values on 26 July (EP3), 28 July (EP4), 2 August (EP5), and 10 August (EP6) were 31, 22 50, and 33 ppbv, respectively. Compared to the average valley value during the VOC sampling period (19 ppbv) and the median value (16 ppbv), these valley values were relatively high. This result implied that EP3, EP4, EP5, and EP6 were also affected by regional transportation. The 48 h backward trajectories when hourly O₃ concentrations were over 75 ppbv are shown in Fig. S3. Most of the backward trajectories of EP1 and EP2 came from local and southwest areas, respectively, and there were no O₃ pollution areas along the trajectories. Most of the backward trajectories of EP3, EP4, and EP5 came from the Fenwei Plain, and O₃ pollution also occurred in the cities along the trajectories (Table S4). However, during EP6, no pollution was found along the trajectories. This O₃ EP was complex and the data cannot be explained. Determining whether the changes in O₃ concentration were caused by transportation, meteorological conditions, or anthropogenic sources will be our focus in future studies.

Different VOC species are capable of influencing O₃ formation potential (OFP), which can be estimated using Maximum Incremental Reactivity (MIR) (Carter, 2009). The OH reactivity concept is useful when estimating VOC O₃ production because VOC degradation caused by OH oxidation eventually leads to net O₃ production (Tan et al., 2018). OH reactivity is the sum of the products of precursor O₃ concentrations and the reaction rate constants between O₃ precursors and OH (Atkinson & Arey, 2004). The percentage contributions of alkanes, alkenes, ethyne, halocarbons, aromatics, oxygenated VOCs, and isoprene to the mixing ratio, L_OH, and OFP of total VOCs are shown in Fig. 5. Among these, alkanes were the most abundant VOC species, accounting for 46.2%, followed by OVOCs (26.9%), alkenes (10.1%), aromatics (7.8%), ethyne (4.3%), halocarbons (4.3%), and isoprene (0.4%). Although alkanes were the most abundant VOC species in Yulin, the proportions of L_OH (12.7%) and OFP (14.0%) were relatively small. Alkenes (31.8%), OVOCs (31.7%), and aromatics (21.3%) were the top three VOC species in terms of OFP proportion. L_OH and OFP showed good consistency, and alkenes (44%), OVOCs (23.5%), and aromatics (12.8%) had the top three proportions of L_OH. In previous studies, aromatics accounted for the highest OFP and L_OH proportions in the PRD (Yu et al., 2020a; Tan et al., 2019). In Xi’an, the capital of Shaanxi Province, alkenes have been shown to have the highest proportion of L_OH and OFP (Song et al., 2020). The OFP of sampling sites around Lanzhou were mainly from aromatics, which accounted for 46.3% (Wu et al., 2019), due to the petrochemical industry there. Due to its abundant coal resources, Yulin has many coking plants. The exhaust gas from coking plants contains a lot of alkenes, the key species in O₃ formation, and aromatics (Zhang et al., 2020c).

The impact of meteorological parameters on O₃ concentration

Liu & Wang (2020) used the Community Multiscale Air Quality (CMAQ) modeling system and found that the impact of meteorological factors on O₃ trends varied by region and by year. Between 2013 and 2017, O₃ concentrations in China were affected by meteorological factors, which showed comparable or even greater effects than anthropogenic emissions. Meteorological conditions such as T, RH, WS, and WR directly affect surface O₃ concentrations via changes in chemical reaction rates, dilution, wet and dry removal, and transport flux, or indirectly via changes in natural emissions (Lu, Zhang & Shen, 2019b; Li et al., 2020c). We focused on the relationship between meteorological parameters (T, RH, WS, and WR) and O₃ concentrations between May to August from 2017 to 2019 because O₃ pollution days mainly occurred during this period. The correlational analysis results are shown in Table 2. Table S5 shows the total number of days, number of MDA8 and MDA1 O₃ pollution days, and MDA8 and MDA1 O₃ exceedance probabilities in different ranges of T, RH, WS, and WR. The O₃ exceedance probability was calculated using the percentage of the number of O₃ pollution days across the total number of days. The influence of wind speed on ground O₃ concentrations was insignificant (−0.088). This result matches those in earlier studies (Chen et al., 2020; Fan et al., 2020).

T directly impacts chemical kinetic rates and the mechanistic pathway for O₃ formation (Atkinson, 1990). High RH days were always associated with more cloud cover, which can reduce photochemistry (Kavassalis & Murphy, 2017). The correlation coefficients between T and MDA8 O₃ and MDA1 O₃ were 0.547 and 0.519, respectively. Between RH and MDA8 O₃ and MDA1 O₃, the correlation coefficients were −0.286 and −0.250, respectively (Table 2). O₃ was positively correlated with T and negatively correlated with RH. Further statistical test results are shown in Table S5. The MDA8 (MDA1) O₃ exceedance probability of T in the ranges of 10−20 °C, 20−30 °C, and over 30 °C were 0.8% (0%), 27.7% (6.6%), and 81.82% (9.1%), respectively, and that of RH in the ranges of 20–40%, 40–60%, 60–80%, and 80–100% were 22.6% (6.1%), 31.3% (9.0%), 19.3% (2.3%), and 1.04% (0.0%), respectively. These results confirm that there is a correlation between O₃ and T and RH. When T was higher than 30 °C and RH was in the range of 40–60%, the probability of O₃ pollution events was higher.

The correlation coefficients between WS and MDA8 O₃ and MDA1 O₃were −0.088 and −0.164, respectively, which indicated that the influence of WS on ground O₃ concentrations was insignificant. Table S5 shows that the MDA8 (MDA1) O₃ exceedance probability of WS in the range of 0–1.5 m/s, 1.5–3 m/s, 3–4.5 m/s, and greater than 4.5% were 22.5% (5.8%), 20.36% (3.3%), 9.26% (1.85%), and 0% (0%), respectively. As WS increased, the exceedance probability showed a downward trend. Generally, the increase in WS caused O₃ concentrations to decrease due to enhanced dry deposition, mixing, and dilution (Dawson, Adams & Pandis, 2007). Additionally, WS and WR affected O₃ concentrations by transporting O₃ and its precursors. The MDA8 (MDA1) O₃ exceedance probability of northerly, easterly, southerly, and westerly winds were 11.2% (5.1%), 28.0% (3.7%), 26.4% (5.7%), and 17.2% (5.7%), respectively. The effects of T, RH, and WS on MDA8 O₃ and MDA1 O₃ were similar. In contrast, the easterly winds showed the largest O₃ exceedance probability of MDA8 O₃ and the southerly and westerly winds showed the greatest MDA1 O₃ exceedance probability. The results implied that T and RH can directly affect O₃ formation and loss. However, the influence of wind on O₃ concentrations was complex. In general, high T, low RH, and low WS were shown to cause O₃ pollution, especially when the T exceeded 25 °C, the RH was less than 60%, and the wind speed was less than 3 m/s. O₃ pollution days were predominantly observed when the wind was traveling from the east and south.

Regional transportation

Besides local formation, long-distance regional transportation also plays an important role in increasing O₃ concentration (Hui et al., 2018). Figures 6A–6C show the clustering analysis for May to August 2017 to 2019. In this study, when O₃ concentrations were greater than 75 ppbv, we identified this backward trajectory as a polluted trajectory. In 2017, a total of 487 backward trajectories were categorized into five clusters. Cluster a1 to a5 accounted for 49%, 21%, 3%, 13%. and 10% of the total trajectories, respectively. There was a total of 44 polluted trajectories in 2017. Cluster a1 to a5 contained 27, five, two, one, and eight polluted trajectories, respectively. Cluster a1’ s air masses from Weinan in Shaanxi Province that crossed Yan’an to Yulin had the greatest impact on O₃ concentrations in Yulin in 2017. In 2018, there was a total of 492 backward trajectories across five clusters. Cluster b1 to b5 accounted for 13%, 23%, 27%, 31%, and 5% of the total trajectories, respectively. There was a total of 40 pollution trajectories in 2018. One, 10, 13, 12, and four pollution trajectories were grouped into clusters b1 to b5, respectively. Therefore, when the results of the cluster and the distribution of pollution trajectories were combined, the air masses of clusters b2, b3, and b4 had the greatest impact on the O₃ in Yulin in 2018. Cluster b2 was from Inner Mongolia. Cluster b3 came from Yan’an to Yulin. Cluster b4 came from Kaifeng in Henan Province, passing through Zhengzhou, Luoyang, Yuncheng in Shanxi Province, and Yan’an in Shaanxi Province. In 2019, a total of 492 backward trajectories were divided into three clusters. Cluster c1 to c3 accounted for 16%, 33%, and 51% of total trajectories, respectively. There was a total of 42 pollution trajectories in 2019. Thirty-two and 10 pollution trajectories were grouped into cluster c2 and c3, respectively. Therefore, the air masses of cluster c2, which came from Linfen in Shanxi and passed through Weinan and Yan’an in Shaanxi Province to Yulin, had the greatest impact on the O₃ in Yulin in 2019. Fenwei Plain is to the southeast of Yulin and has 11 cities including Weinan, Luoyang, and Yuncheng, mentioned above. Fenwei Plain has a high O₃ concentration because it has more industry and a larger population (Lu et al., 2019a; Wang et al., 2020a). The airflows from Yan’an and FenWei Plain between May and August may aggravate O₃ pollution in Yulin.

Sensitivity analysis

The relationships between O₃ and VOCs and NO_x are nonlinear. In order to establish effective O₃ control measures, these relationships must be explored. In this study, we applied two methods to evaluate O₃-VOC-NO_x sensitivity in Yulin: the RIR, which was mentioned in sec2.3, and the EKMA (Carter, Winer & PittsJr, 1982). The average hourly VOC concentrations during the sampling period were put in the OBM model as a base scenario. By increasing or decreasing the ratio of precursors, we were able to simulate the changes in MDA8 O₃. The relationship between MDA8 O₃ and the relative changes of anthropogenic VOCs (AVOCs) and NO_x can be expressed using a contour plot for MDA8 O₃, which we named EKMA (Fig. 7A). Isoprene was mainly emitted by the biogenic source and was difficult to control. Therefore, the AVOCs excluded isoprene. The EKMA plot was split into two parts by a ridgeline that denoted the local maxima of the rate of O₃ formation (Li et al., 2020b; Hui et al., 2018). The upper-left and lower-right areas represent the O₃ formation under VOC-limited and NO_x-limited conditions, respectively. In Fig. 7A, the circles and pentagram represent pollution days and the base scenario, respectively. These points were all located in the VOC-limited regime and showed the negative effects of NO_x reduction in response to O₃ production control. Previous studies reported that in China, unban areas, rural regions, and urban agglomerations were under VOC-limited, NO_x-limited, and VOC-NO_x-limited conditions (Liu et al., 2019; Wang et al., 2017a), respectively.

Figure 7B shows the RIR values for AVOCs, isoprene, CO, and NO_x during pollution days. The RIR values for AVOCs were in the range of 0.73–2.83%/%, which was significantly higher than those for isoprene (0.22–0.42%/%) and CO (under 0.01%/%). This suggested that AVOCs played a more significant role in O₃ formation during these EPs, while the influence of isoprene, especially CO, was negligible. Excluding 26 July, the RIR values for NO_x were between −0.26 and −0.10%/%, indicating that the O₃ formation during these days was in the VOC-limited regime. This result was consistent with that of EKMA. However, on 26 July, the RIR values of NO_x and VOCs were 0.60 and 0.73, respectively, and these two positive values with a small difference implied that the O₃ formation on 26 July was controlled by both VOCs and NO_x. This result was contrary to that of the EKMA plot, which may be because the EKMA simulation was based on the average of the sampling period. The average RIR values for AVOCs from EP1 to EP6 were 1.79%/%, 1.92%/%, 1.11%/%, 1.13%/%, 1.34%/%, and 2.83%/%, respectively, and for NO_x were -0.99%/%, 1.26%/%, -0.14%/%, -0.10%/%, -0.68%/%, and -2.36%/%, respectively. This demonstrated that these EPs were all in the VOC-limited regime. The absolute RIR values of the AVOCs were larger than those of the NO_x, suggesting that O₃ formation was most sensitive to AVOC reduction.

O₃ control strategies

It is necessary to discuss how much AVOCs should be controlled for the most efficient O₃ reduction. Using the sampling period as an example, we will discuss O₃ control measures. Figure 8A shows the increments of the MDA8 O₃ response (positive and negative values represented the increase and decrease in O₃ compared to the base case with no VOC or NO_x reductions, respectively) to different emission reductions in AVOCs and NO_x (AVOC reduction/NO_x reduction=1:2, 1:1, 2:1, 3:1, or 4:1, and only reducing AVOCs or NO_x). The horizontal axis represents the combined reduction percentage of AVOCs and NO_x. For instance, a total reduction percentage of 120% indicates that there were both 60% reductions in AVOCs and NO_x emissions for AVOC/NO_x=1:1, or 80% and 40% reductions in the AVOC and NO_x emissions, respectively, for AVOC/NO_x=2:1. When the AVOC/NO_x ratio was more than 1:1, namely if the AVOC emission was reduced more than NO_x, the O₃ concentration would decline. When a certain O₃ reduction target was achieved, the VOC and NO_x ratio was higher and the total emission reduction was lower. For example, to reduce O₃ concentration by 5%, the total reduction emissions of AVOC and NO_x were 12%, 20%, and 174% for an abatement AVOC/NO_x ratio of 4:1, 2:1, and 1:1, respectively, or by only cutting AVOC emissions by 7%. Only cutting AVOC emissions seemed to be the most efficient way to control O₃. However, NO₂ is one of the most significant ambient air pollutants that can influence ambient air quality. NO_x needs to be reduced since it is an important precursor of PM_2.5. It is important to cooperatively control the emission of AVOCs and NO_x. During sampling periods, the mean and highest MDA8 O₃ concentrations were 79 and 102 ppbv, respectively, which exceeded the Nation Grade by 5% and 36%. According to this, we set four O₃ control targets, 5%, 10%, 30%, and 40%, to explore the abatement percentages of AVOCs and NO_x (Fig. 8B). To achieve the O₃ control target of 5%, AVOC emissions needed to be reduced 10%–90%, with NO_x also in the range of 10%–90%. To achieve the O₃ control target of 10%, AVOC emissions needed to be reduced 20% to 90% with NO_x in the range of 5% to 80%. However, to achieve the targets of 30% and 40%, the AVOC emissions needed to be reduced by at least 60% and 80%, respectively. Even though more stringent VOC and NO_x control measures have been implemented, it is still challenging to achieve the 30% and 40% O₃ control objectives.

Q_true/Q_expected was an appropriate index to decide how many factors were optimal in the PMF model. Here, we evaluated four to 10 factor solutions and found that the Qtrue/Qexpected values of different resolved numbers and that eight to nine factors were the lowest (Fig. S2). Therefore, we identified eight types of major pollution sources using PMF source analysis (Fig. 9 and Fig. S3). Factor 1 had a high percentage of aromatics and a relatively high ratio of toluene. In previous studies, toluene was the most abundant VOC species in painting and factor 1 was considered to be the source of the solvent (Wang et al., 2014). Coking plants and the petrochemical industry are pillars in Yulin and emit a lot of ethene (Zhang et al., 2020c; Han et al., 2018), so we considered factor 2 to be industrial sources. The main source of isoprene in the city is plant emissions (Zhang et al., 2020a; Loivamaki et al., 2007) and factor 3 is considered a biological source. Factor 4 was characterized by a high percentage of ethane and high loadings of OVOCs. Previous studies in Shenzhen and Wangdu found that background pollution largely contributed to OVOCs (Han et al., 2019). Factor 4 was considered background pollution. High percentages of propane, acetylene, n-butane, cyclohexane, ethylbenzene, o-xylene, isobutane, m/p-xylene, and n-hexane are found in vehicle exhaust (Fan et al., 2020), and these characteristics are consistent with factors 5 and 7. The proportion of isopentane in gasoline exhaust is more than that found in diesel exhaust (Ly et al., 2020). The proportion of aromatic hydrocarbons is higher in gasoline vehicles than in diesel vehicles because aromatic hydrocarbons are the main contributor to octane in gasoline, and gasoline is more volatile than diesel (Huang et al., 2020). Factors 5 and 7 were considered gasoline exhaust and diesel exhaust, respectively. Isopentane is mainly distributed in factor 6 and is a typical marker of fuel evaporation (Liu et al., 2008), so factor 6 was considered fuel evaporation. Factor 8 was characterized by a high percentage of ethane, ethene, and 1-butene. Previous studies reported that VOCs emitted from coal combustion (Zhang et al., 2020a) are mainly C2-C3 alkenes and C2-C3 alkanes. Factor 7 was considered coal combustion. Vehicle emissions count for 51.1% of emissions (Fig. 9) and diesel exhaust is most dominant. Industrial sources account for 17.0%. Previous emission reduction strategies focused on reducing vehicle and industry emissions, especially diesel exhaust.