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Article

Analysis of the Causes of an O3 Pollution Event in Suqian on 18–21 June 2020 Based on the WRF-CMAQ Model

by
Ju Wang
1,2,3,
Wei Zhang
1,2,
Weihao Shi
1,2,
Xinlong Li
1,2 and
Chunsheng Fang
1,2,3,*
1
College of New Energy and Environment, Jilin University, Changchun 130012, China
2
Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130012, China
3
Jilin Province Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130012, China
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(7), 831; https://doi.org/10.3390/atmos15070831
Submission received: 31 May 2024 / Revised: 4 July 2024 / Accepted: 8 July 2024 / Published: 11 July 2024
(This article belongs to the Special Issue Ozone Pollution and Effects in China)
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Abstract

:
In recent years, O3 pollution events have occurred frequently in Chinese cities. Utilizing the WRF-CMAQ model, this study analyzed the causes of an O3 pollution event in Suqian on 18–21 June 2020, considering meteorological conditions, process analysis, and source analysis. It also designed 25 emission reduction scenarios to explore more effective O3 emission reduction strategies. The results show that meteorological conditions such as temperature and wind field play an important role in the formation and accumulation of O3. During the heavy pollution period, the contribution of vertical transport (VTRA) and horizontal transport (HTRA) to O3 concentration is significantly enhanced. The photochemical reactions of precursors, such as NOx and VOCs transported from long distances and O3 directly transported to Suqian from other regions, contribute greatly to O3 pollution in Suqian; local sources contribute very little, between 12.22% and 18.33%. Based on the simulation of 25 emission reduction scenarios, it was found that excessive emission reduction of NOx is not conducive to the reduction of O3 concentration, and it is best to control the emission reduction ratio at about 10%. Without affecting normal production and life, it is recommended to reduce VOCs as much as possible, particularly those generated by traffic sources.

1. Introduction

Ozone (O3) and fine particulate matter (PM2.5) are considered two of the most significant atmospheric pollutants affecting urban air quality in China [1]. Since the Air Pollution Prevention and Control Action Plan has been implemented, many regions in China have achieved remarkable progress in reducing PM2.5 and other pollutants, leading to a significant improvement in air quality. However, ozone pollution has become increasingly serious, with its pollution scope continuously expanding and the over-standard rate significantly increasing [2,3,4]. This trend not only hinders the overall improvement of air quality but also poses a serious threat to human health and crop yields [5,6,7].
O3 is usually generated by volatile organic compounds (VOCs) and nitrogen oxides (NOx) through a series of complex nonlinear photochemical reactions under favorable meteorological conditions, such as strong solar radiation [8,9]. Numerous studies have indicated that this process is closely related to anthropogenic emissions and meteorological conditions [10,11,12,13]. Between 2012 and 2017, anthropogenic emissions and meteorological changes contributed to the increase of O3 in the North China Plain (NCP) by 39% and 49%, and O3 in the Yangtze River Delta (YRD) by 13% and 84%, respectively [14]. There is a significant positive correlation between temperature and ozone concentration, and high temperatures can improve the photochemical reaction rate [15]. Wind field variation contributes significantly to the increase in surface ozone in most areas of China, and the local ozone concentration is affected by the upwind source regions with higher concentrations of O3 and its precursors [16]. The height of the planetary boundary layer, clouds, precipitation, and other meteorological factors can also directly or indirectly affect O3 concentration through chemical and physical processes [17,18].
In addition to meteorological conditions and anthropogenic emissions, stratospheric transport and regional transport also play significant roles in near-surface O3 concentration [19,20,21]. Stratospheric ozone can be transported to the troposphere and even to the ground via the Brewer–Dobson circulation and subtropical jet stream [22,23]. Zhao et al. analyzed the stratospheric invasion in Hong Kong during the spring of 2004 to 2020 and concluded that 24.7% of the O3 enhancement events were related to stratospheric invasion, and 31.7% of the intrusions led to O3 enhancement in the lower troposphere [24]. Recent studies have shown that under the influence of wind, inter-regional transport, and interaction of ozone and its precursors exacerbate ozone pollution in the region [25,26]. Liu et al. found that the long-range transport of ozone and its precursors outside the simulated zone resulted in an increase in O3 in China, especially in the Qinghai–Tibet Plateau, with an increase of 1–4 ppbv [27]. Therefore, to effectively curb O3 pollution, it is essential to comprehensively consider factors such as meteorological conditions and regional transport, and accordingly formulate scientific and reasonable anthropogenic emission control strategies.
Suqian is located in the northwest of Jiangsu Province, in the cross-radiation area of the Longhai Economic Belt, the coastal economic belt, and the river economic belt. It is a typical representative of small and medium-sized cities with rapid economic growth in recent years, but its air quality is not optimistic [28,29]. It is located in the transition zone of China’s northern and southern climates, with high temperature and strong radiation in summer, and often suffers from ozone pollution events. It is also a buffer zone for the Beijing–Tianjin–Hebei (BTH) region and the YRD, two major air pollution areas, with typical regional pollution characteristics and obvious transmission effects. Therefore, we will analyze the contribution of local emissions, physical and chemical processes, and regional transport to O3 pollution in Suqian through model simulation, to enhance our understanding of O3 pollution in Suqian [30,31].
In this study, the Weather Research and Forecasting-Community Multiscale Air Quality Model (WRF-CMAQ) was used to analyze a complicated O3 pollution process in Suqian on 18–21 June, amid serious ozone pollution in June 2020. First, we described the spatio-temporal distribution characteristics of the O3 pollution and the meteorological conditions that contributed to it. Secondly, we quantified the contribution of each physicochemical process to O3 generation using the integrated process rate (IPR) analysis method. The integrated source apportionment method (ISAM) was applied to determine the contribution of regional transport and local industry emissions to the O3 pollution events in Suqian. Finally, 25 emission reduction scenarios were simulated to evaluate the effectiveness of control strategies. In this study, we comprehensively analyzed various factors, including meteorological conditions, anthropogenic emissions, and regional transport, for the O3 pollution events in Suqian and proposed a reasonable basis for emission reduction. These findings will assist Suqian in formulating a more effective O3 pollution prevention and control strategy, providing a scientific basis for continuous improvement of air quality.

2. Data and Methods

2.1. Model Configuration

In this study, WRF v4.3.3 and CMAQ v5.4 were used to simulate the meteorological conditions and O3 concentration in Suqian. To minimize the impact of initial conditions, the simulation commenced at 00:00 on 27 May 2020, and terminated at 00:00 on 1 July 2020. The initial five days were designated as the start-up period to ensure the stability and accuracy of the simulation results. For the WRF simulation, the meteorological input was generated using final analysis (FNL) data from the National Center for Environmental Prediction (NCEP), with a spatial resolution of 1.0° × 1.0°, a temporal resolution of 6 h, a vertical layer number of 44 layers, and a central latitude and longitude of 112.78° E and 31.48° N, respectively. CMAQ is equipped with the AERO7 aerosol module and CB06 vapor chemistry mechanism, with vertical layers and central latitude and longitude consistent with WRF. The model uses three layers of nested domains for simulation, in which domain 01 (d01) encompasses a grid size of 18 km × 18 km, covering most of East China; domain 02 (d02) encompasses a grid size of 6 km × 6 km, covering Jiangsu Province; and the grid size of domain 03 (d03) is 2 km × 2 km, covering Suqian (Figure 1).

2.2. Data Source

The meteorological data used in this study, including 2 m temperature (T2), 10 m wind speed (WS10), and 10 m wind direction (WD10), were obtained from the China Meteorological Data Service Center (http://data.cma.cn (accessed on 4 January 2024)), with a time resolution of 3 h. The air quality data (O3) comes from the China Environmental Monitoring Center (http://www.cnemc.cn/ (accessed on 4 January 2024)), with the time resolution of 1 h. The weather map is from the National Meteorological Centre (http://www.nmc.cn/ (accessed on 25 March 2024)). The China Multi-Scale Emission Inventory (MEIC, http://meicmodel.org/ (accessed on 16 January 2024)), with spatial resolution of 0.25° × 0.25°, released by Tsinghua University was used for the emission inventory, which covers the emission data of five sectors: power, industry, residential, transportation, and agriculture. It also includes SO2, NOx, CO, VOCs, NH3, PM2.5, BC, OC, CO2, and other pollutants [32,33]. The Inventory Spatial Allocation Tool (ISAT) was used to allocate the time and space of the emission inventory, and the processed emission data were input into the CMAQ model for simulation [34].

2.3. The Integrated Process Rate Analysis

Process analysis (PA) is an important analysis tool of the CMAQ model, which includes two modules: the integrated process rate (IPR) analysis and the integrated reaction rate (IRR) analysis. IPR can quantify the contribution of each atmospheric physicochemical process to the concentration of certain pollutants, reflecting the importance of different atmospheric processes in the formation of pollutants. Previous studies have extensively used IPR to investigate the formation mechanism of O3 pollution events and explore the significance of various atmospheric physicochemical processes in pollutant generation [35,36,37,38]. This study primarily leverages the process analysis module of CMAQ model to analyze the contribution of various physicochemical processes to the O3 pollution event in Suqian. The processes include 9 major categories: horizontal advection (HADV), horizontal diffusion (HDIF), vertical advection (ZADV), vertical diffusion (VDIF), chemical production or consumption (CHEM), dry deposition (DDEP), cloud processes (CLDS), aerosol processes (AERO), and emissions (EMIS). However, the contributions of CLDS, AERO, and EMIS to O3 are comparatively minor. Therefore, in the study, four processes are mainly analyzed, namely, HTRA (HADV + HDIF) and VTRA (ZADV + VDIF), to represent the net contribution of horizontal and vertical transport to O3 generation; CHEM represents the net O3 generation of all chemical reactions; and DDEP represents the removal of O3 by dry deposition.

2.4. Scenario Setting

To assess the effectiveness of different pollution control strategies, pursuant to the Action Plan for Continuous Improvement of Air Quality, which aims to reduce the total emission of NOx and VOCs in the country by more than 10% by 2025 compared with 2020, as well as the response measures outlined in the Suqian Heavy Pollution Weather Emergency Plan, which requires pollutant reduction rates of not less than 30%, 40%, and 50% under yellow, orange, and red warning levels, respectively. One basic scenario and 25 different emission reduction scenarios are designed, as shown in Table 1. Among them, NOx emissions are reduced by 10%, 20%, 30%, 40%, and 50%, and VOCs emissions are reduced by 10%, 20%, 30%, 40%, and 50%, respectively, and they are combined to form 25 emission reduction scenarios. These scenarios are then compared with a basic scenario so as to accurately evaluate the actual effects of various emission reduction measures and provide strong support for the scientific formulation of regional emission reduction policies.

3. Results and Discussion

3.1. Evaluation of the Model Performances

The simulation performance of WRF-CMAQ was evaluated by comparing the simulation data (SIM) with the monitoring data (OBS). To quantify the effectiveness of the evaluation, a number of statistical measures were used, including Pearson correlation coefficient (R), normalized mean bias (NMB), the normalized mean error (NME), the mean fractional bias (MFB), and the mean fractional error (MFE). The statistical indicators are listed in Table S1, and the definitions and calculation methods of these indicators are detailed in the Supplementary Materials.
The comparison between simulation results and monitoring results is shown in Figure 2. The WRF model reproduces the change in T2, with an R value of 0.90, and an MFB and MFE of 3.67% and 5.73%, respectively. Compared to T2, the simulation accuracy of WS10 and WD10 is relatively poor, and the simulation results are slightly higher than the observation results. This is mainly caused by the underlying surface, weather process, and time resolution. However, the overall trend of WS10 and WD10 is generally acceptable [39]. For WS10, the R value is 0.59, with an MFB and MFE of 22.32% and 32.18%, respectively, and the simulation results are satisfactory. For WD10, the R value is 0.70, with an MFB and MFE of 31.29% and 40.01%, respectively. Overall, the WRF model can simulate meteorological conditions very well and can be used as input data for CMAQ simulation for follow-up research.
In evaluating the performance of the CMAQ model, observations from environmental monitoring stations were compared with simulated hourly O3 concentrations. The results indicate that the model has accurately captured the change trend and peak times of O3. With an R value of 0.63, the MFB and MFE stand at −24.80% and 32.64%, respectively. Most of the time, the model successfully simulates hourly changes in observed O3 concentrations, including peak and valley values. The R value is in line with the recommended standard, and the results of statistical indicators are close to or better than previous studies [40,41,42]. Therefore, we think the simulation results are acceptable. The slightly lower simulation results may be attributed to uncertainties in emission inventories and meteorological data [43].
Overall, despite some simulation biases, the model excels in simulating the major atmospheric chemical and physical processes associated with weather conditions, as well as the temporal variation of O3 concentrations. Consequently, the simulation data serves as a valuable resource for further analysis and discussion.

3.2. Spatio-Temporal Characterization of O3 Concentration Change and Meteorological Conditions

According to the Ambient Air Quality Standards, in June 2020, the number of days in Suqian when the O3 hourly concentration exceeded the first-class standard (160 μg/m3) was 16, accounting for 53.33% of the total number of days; and the number of days with O3 hourly concentration exceeding the secondary standard (200 μg/m3) was 12, accounting for 40.00% of the total number of days. O3 pollution events occurred frequently in Suqian in June 2020. Figure 3 describes in detail the whole process of an O3 pollution event in Suqian on 18–21 June 2020. The ozone pollution event went through three distinct phases: formation, persistence, and removal. In the formation period (from 04:00 on the 18th to 12:00 on the 19th), the O3 concentration increased from 50 μg/m3 to 126 μg/m3. During the persistence period (from 12:00 on the 19th to 19:00 on the 20th), the daytime O3 concentration ranged between 140 μg/m3 and 184 μg/m3. Finally, in the removal period (from 19:00 on the 20th to 23:00 on the 21st), the O3 concentration decreased from 151 μg/m3 to 28 μg/m3.
There is a good consistency between O3 concentration and temperature, and the correlation coefficient is 0.80. The rise in temperature is conducive to the generation of O3. There is a negative correlation between O3 concentration and wind speed, with a correlation coefficient of −0.45. Low wind speed is not conducive to the diffusion of pollutants. Figure 4 and Figure 5 reveal the changes in T2 and WS10 during this ozone pollution process. The spatial and temporal changes in ozone pollution are consistent with the change in temperature, and T2 remains above 25 °C in most areas when ozone pollution is severe. High temperatures and strong solar radiation are conducive to the secondary photochemical reaction of O3 precursors in the atmosphere to produce O3 [44,45]. The influence of wind fields on pollutant concentration is complex. On the one hand, high wind speed is conducive to the dilution, diffusion, and transportation of pollutants, thus effectively reducing the concentration of atmospheric pollutants. On the other hand, when the wind speed is low, the diffusion of pollutants is hindered, resulting in an increase in the concentration of pollutants in the atmosphere [46]. When ozone pollution is severe, the wind speed is generally below 3 m/s. Since the 18th, the temperature has been increasing and the wind speed has gradually decreased, providing favorable conditions for the accumulation of ozone precursors and photochemical reactions, thus promoting the formation of ozone. On the morning of the 19th, the convergence area of the wind field was located in the southeast, where the O3 pollution began, and it was also the area with the most severe O3 pollution. In the afternoon, the wind speed gradually increased, and the O3 pollution spread to the entire territory of Suqian under the influence of the southeast wind. After the 20th day, the temperature decreased and the wind speed increased, and the ozone pollution gradually eased until the pollution process ended on the 21st. It is worth noting that the change in O3 pollution has a certain lag compared with the change in wind.
Figure 6 shows the surface weather map at 14:00 on 18–21 June 2020, and the process of weather change is also consistent with the process of this pollution event. At 14:00 on June 18, Suqian was located between the high pressure in the Yunnan–Guizhou Plateau and the low pressure in the Yellow Sea. Under the balanced pressure field, O3 precursors, brought by weak southeasterly winds, are easily accumulated here. At 14:00 on June 19, Suqian was in the center of high pressure, the weather was sunny, and the surface wind speed was low. Sunny and high temperature weather is conducive to photochemical reaction and promotes the formation of O3. The subsidence motion caused by high pressure also facilitates the transport of O3 from the upper layer to the ground. At the same time, under high pressure weather conditions, the diffusion capacity of air is weak, which limits the diffusion of O3, making it easy for O3 to accumulate in the near-ground area. The strengthened southeasterly winds at 14:00 on the 20th and 21st are conducive to the diffusion and removal of O3 in Suqian. In conclusion, in this pollution event, adverse meteorological conditions, such as high temperature, high pressure, and low wind speed, played a crucial role in the formation and accumulation of O3. In short, ozone pollution has a good consistency with the changes in temperature and wind field in space and time, and meteorological conditions play a crucial role in the formation and accumulation of O3.

3.3. Process Analysis of O3

Figure 7 shows the hourly change in the process analysis of the near-surface O3 concentration at the air quality monitoring station in Suqian on 18–21 June 2020. The contribution of DDEP to O3 throughout the pollution process is negative, indicating its depleting effect on surface ozone. As O3 is a product of photochemical reactions, CHEM contributes positively during the day when sunlight radiation is strong, but negatively at night when the photolysis reaction of precursors ceases. Transport processes (HTRA and VTRA) are significant contributors affecting ozone, closely related to meteorological conditions and exhibiting clear temporal and regional differences. During the period of gradual increase of O3 concentration on the 19th and 20th, HTRA makes the greatest contribution. As shown in Figure 5, during this period, the wind speed is relatively high, and O3 and its precursors in the upwind area are easy to accumulate in Suqian and its surrounding areas under the action of regional transport. However, in the stage of continuous high O3 pollution in the afternoon, HTRA’s contribution is weakened or even negative. Meanwhile, O3 precursors are not easy to diffuse after forming O3, thereby causing ozone pollution at the aggregation site. The positive contribution of VTRA to the near-surface O3 concentration remains at a consistently high level. As shown in Figure 8, the VTRA effect near the ground is significantly enhanced in the sustained stage, and O3 in the upper layer transmits downward and accumulates near the ground. The decrease in O3 concentration at night is mainly due to the transport process and certain O3-consuming chemical reactions, such as NO titration. In the clearing stage, the wind speed gradually increases to about 5 m/s, which is conducive to the decomposition and diffusion of O3 and reduces the concentration of O3.
Figure 8 shows the process analysis of O3 concentration at different heights at the air quality monitoring station in Suqian at 16:00 on 18–21 June 2020. DDEP only contributes negatively to ozone near the surface (layer 1), while CHEM mainly contributes positively to ozone near the surface and at high altitudes (layer 2–12). The photochemical reaction producing ozone in the YRD region is stronger at 300–1500 m height than at the ground level, which leads to the accumulation of a large amount of O3 at high altitudes. It is then transported to the surface through vertical transport, significantly increasing the positive contribution of surface VTRA to O3 concentration [47,48]. On the days of high O3 pollution (19th and 20th), various physicochemical effects were significantly enhanced, and the combined action of various reactions resulted in a significant increase in O3 concentration on the surface. Among them, vertical transport has an obvious positive contribution to ozone and is an important source of near-surface ozone.
In general, the ozone pollution process is based on the positive contribution of daytime CHEM to the stability of O3, and the positive contribution of regional transport to O3 is the main driving force. Therefore, regional transport is a key factor that cannot be ignored. The contribution of pollutants from different regions and industries to O3 concentration will be further discussed below.

3.4. Source Analysis of O3

3.4.1. Regional Source Analysis of O3

Shen et al. believe that sunny weather with high temperature, low humidity, and light winds is significantly correlated with persistent O3 pollution events [49]. When the wind speed increases, the transport of upstream precursors can also cause local O3 pollution to exceed the specified limit. ISAM in CMAQ can be used to identify the contribution of pollutants emitted by different regions and industries to O3 concentration. In this study, ISAM module was used to track the contributions of five source regions: Shuyang County (SHY), Sucheng District (SC), Siyang County (SY), Sihong County (SH), and Suyu District (SYU), and five source industries: industrial source, power source, transportation source, residential source, and agricultural source to O3 generation in Suqian.
In order to further analyze the characteristics of O3 source areas in Suqian, this study quantitatively simulated the regional transport of O3 pollution in June 2020 in Suqian. The emission concentration of five districts in Suqian (SHY, SC, SH, SY, and SYU) is defined as the contribution of local sources, and the emission concentration of other unlabeled regions (OTH) is defined as the contribution of peripheral sources. Additionally, the boundary condition (BCON) contribution is designated as the long-distance transmission contribution. Figure 9 illustrates the contribution of different regions to O3 concentration in different regions of Suqian in June 2020. Among the contribution sources of O3 concentration in Suqian, long-distance transmission (BCON) is the most significant contributor, with values ranging from 44.45 μg/m3 to 50.01 μg/m3 and contribution rates between 64.56% and 73.93%. Long-distance transported O3 includes both O3 generated by photochemical reactions of NOx, VOCs, and other precursors transported from outside the simulated region in Suqian, as well as O3 transported directly to Suqian from outside the simulated region. These contributions greatly impact ozone pollution in Suqian [21]. The second largest contributor is OTH, which primarily refers to several adjacent urban areas of Suqian, with contributions ranging from 13.25% to 18.15%. In Suqian, O3 is weakly affected by local emissions, ranging from 12.22% to 18.33% of the total contribution. Among these districts, MY district contributes the largest proportion of local emissions, accounting for between 2.35% and 7.58%, followed by SC district with a contribution of 0.74% to 5.24%, SH district with 1.73% to 5.60%, SY district with 1.69% to 4.45%, and SYU district with 0.64% to 2.30%. In general, the local contribution to O3 pollution in Suqian is small, and it is more susceptible to the influence of external transport, which is consistent with the diffusion process of O3 pollution shown in Figure 3. Therefore, in addition to formulating local emission reduction policies, future efforts to control O3 concentration in Suqian should pay more attention to inter-regional joint prevention and control.

3.4.2. Industry Source Analysis of O3

In addition to the contribution of regional transportation, quantifying the contribution of local emissions from different industries to O3 in Suqian also plays an important role in policy formulation. In this paper, we analyze the emission contributions of the transportation sector (TS), industrial sector (IS), power sector (PS), and residential sector (RS) in Suqian. Figure 10 shows the contribution of different industries to O3 concentration in various regions of Suqian in June 2020. The transportation sector is the main contributor to O3 in Suqian, and its contribution to each district ranges from 55.99% to 59.66%. This is followed by the industrial sector (19.73–20.82%) and the residential sector (11.86–13.41%). In addition, emissions from the power sector cannot be ignored, with its contribution ranging from 7.88% to 10.24%. From the perspective of the sectors, the transportation sector contributes the most to O3 concentration, and it is necessary to focus on control in the future; for example, strengthen the supervision of vehicle exhaust emissions, promote effective measures such as limiting license plate numbers, and limit off-peak travel so as to effectively reduce its contribution to O3 pollution. Compared with the research results in other regions, the contribution of the industrial sector to O3 in Suqian is relatively small [50]. This is mainly due to the active implementation of emission reduction measures by the industrial sector in Suqian, and remarkable results have been achieved in promoting the replacement policy of VOCs for clean, raw materials.

3.5. Emission Reduction Strategies for O3

Targeting the ozone pollution event period (18–21 June), 25 emission reduction scenarios were designed to explore the effect of reducing ozone precursor emissions on ozone concentration in Suqian. Figure 11 shows the difference between the average O3 concentration of the 25 emission reduction scenarios and that of the basic scenario during the period of severe pollution during the ozone pollution event (16:00 on 18–21 June) in Suqian. On the basis of this, the emission reduction effect can be evaluated.
After the implementation of emission reduction policies, compared with the baseline scenario, ozone concentrations in most regions have significantly decreased, with a maximum of 50 μg/m3. However, ozone concentrations in urban centers have slightly increased, with a maximum of 20 μg/m3, which is consistent with the findings of Santiago et al. [51]. The difference in average O3 concentration between the 25 emission reduction scenarios of this ozone pollution event and the base scenario is shown in Table S2. An increase in O3 concentration in a small part of the region and a decrease in the rest will lead to a small difference in the mean O3 concentration between the emission reduction scenario and the base scenario. The small contribution of local emissions is also an important reason for the difference. According to the research results in Section 3.3 and Section 3.4, external regional transport has a great impact on O3 concentration in Suqian, which means that when O3 pollution events occur, it is necessary to formulate a scientific and reasonable O3 pollution joint prevention and control policy.
By comparing 25 emission reduction scenarios, it is concluded that the effect of controlling NOx emissions is completely different from that of controlling VOC emissions in terms of reducing O3 concentration. When the NOx reduction ratio remains unchanged and the VOC reduction ratio continues to increase, the O3 concentration continues to decrease. When the reduction ratio of VOCs remains the same and the reduction ratio of NOx continues to increase, the O3 concentration continues to increase. Among the 25 scenarios, when NOx is reduced by 10% and VOCs are reduced by 50%, the reduction of O3 concentration has the best effect. Excessive NOx emission reduction will cause an O3 concentration rebound phenomenon, but such a rebound phenomenon is not observed in VOC reduction. Therefore, we suggest that short-term emission reduction targets can be set at a 10% reduction of NOx and VOCs, respectively. The long-term emission reduction target for NOx emissions remains unchanged at 10%, while VOC emissions should be targeted for maximum possible reduction. The experience of other countries in significantly reducing volatile organic compound emissions can be learned. For example, Japan plans to reduce VOC emissions by 30%. The reduction target is divided between reductions achieved through policy (10%) and reductions achieved through voluntary action (20%). According to the results in Section 3.4, more attention should be given to the emission reduction of VOCs produced by traffic sources, especially chemicals such as alcohols, aldehydes, and ketones, due to their high potential for generating O3.
There are some uncertainties and limitations in this study. Factors such as emission inventories, resolution, and model mechanisms can lead to some differences between the simulated O3 concentration and the monitored value, which may slightly affect the content of subsequent analyses. Different mechanisms of the model may affect the contribution of various physicochemical processes to O3 concentration during the process analysis. The size of the labeled regions and the boundary conditions used in the source analysis can further influence the contribution of each region to the overall results [41,52]. In the future, these can be further refined to obtain more accurate results. In addition, we applied a uniform reduction ratio to all species of ozone precursor VOCs. However, the emission potential of different types of VOCs varies. In the future, we plan to evaluate the maximum incremental reactivity (MIR) and prioritize the emission reduction of VOC species with a high ozone formation potential. Furthermore, we aim to refine the reduction strategies for specific VOCs in certain industries or factories in order to achieve the best ozone emission reduction with the lowest cost.

4. Conclusions

In this study, the WRF-CMAQ model was used to analyze the causes of an O3 pollution event in Suqian on 18–21 June 2020. The results show that vertical and horizontal transport are the main physicochemical processes of the pollution event. Part of O3 comes from long-distance and stratospheric transport, while the other part is generated by photochemical reactions of precursors (NOx and VOCs, etc.) transported from local emissions and upwind regions. The source analysis results indicate that long-distance transmission contributes the most, ranging from 64.56% to 73.93%, followed by the contribution of Suqian’s neighboring urban areas, ranging from 13.25% to 18.15%. Local emissions contribute very little, between 12.22% and 18.33%. Among local emission sources, transportation sources make the largest contribution, accounting for 55.99% to 59.66% of all industry sources. Based on the effects of 25 emission reduction scenarios, it is found that reducing NOx by 10% and VOCs by 50% can most effectively reduce O3 pollution in Suqian. In the future, Suqian should prioritize reducing VOC emissions as much as possible, particularly those emitted by transportation sources. Furthermore, attention should be given to inter-regional joint prevention and control policies as well as O3 transmission in heavily polluted areas.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos15070831/s1, Text S1: Statistical Indicators; Table S1: Statistical indicators of T2, WS10, WD10 and O3 simulation results in June 2020 (among them, T2 and WS10, WD10 are calculated by WRF, O3 is calculated by CMAQ); Table S2: Difference in average O3 concentration between 25 emission reduction scenarios and the base scenario of ozone pollution event (18–21 June) in Suqian.

Author Contributions

Conceptualization, W.Z. and J.W.; data curation, C.F. and W.Z.; formal analysis, W.Z. and W.S.; investigation, W.Z.; methodology, W.Z., W.S. and X.L.; supervision, C.F., X.L. and J.W.; visualization, W.Z., W.S. and X.L.; writing—original draft, W.Z.; writing—review and editing, C.F. and J.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s. The data are not publicly available due to policy.

Acknowledgments

The authors would like to thank the group members of Laboratory 537 and 142 of Jilin University for their help and guidance in this study. Additionally, the authors would like to thank the MEIC team from Tsinghua University for providing the Multiscale Emission Inventory of China (MEIC).

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Triple-nested map of the WRF-CMAQ model (green triangles represent air quality monitoring stations, green circles represent a meteorological station).
Figure 1. Triple-nested map of the WRF-CMAQ model (green triangles represent air quality monitoring stations, green circles represent a meteorological station).
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Figure 2. Time series of monitored (black line) and simulated (red line) T2, WS10, WD10, and O3 in June 2020.
Figure 2. Time series of monitored (black line) and simulated (red line) T2, WS10, WD10, and O3 in June 2020.
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Figure 3. Spatial distribution of O3 (μg/m3) at 00:00, 04:00, 08:00, 12:00, 16:00, and 20:00 on 18–21 June 2020 in Suqian.
Figure 3. Spatial distribution of O3 (μg/m3) at 00:00, 04:00, 08:00, 12:00, 16:00, and 20:00 on 18–21 June 2020 in Suqian.
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Figure 4. Spatial distribution of T2 (°C) at 00:00, 04:00, 08:00, 12:00, 16:00, and 20:00 on 18–21 June 2020 in Suqian.
Figure 4. Spatial distribution of T2 (°C) at 00:00, 04:00, 08:00, 12:00, 16:00, and 20:00 on 18–21 June 2020 in Suqian.
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Figure 5. Spatial distribution of WS10 (m/s) at 00:00, 04:00, 08:00, 12:00, 16:00, and 20:00 on 18–21 June 2020 in Suqian.
Figure 5. Spatial distribution of WS10 (m/s) at 00:00, 04:00, 08:00, 12:00, 16:00, and 20:00 on 18–21 June 2020 in Suqian.
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Figure 6. Surface weather chart in Suqian at 14:00 on 18–21 June 2020 (The blue H represents high pressure and the red L represents low pressure).
Figure 6. Surface weather chart in Suqian at 14:00 on 18–21 June 2020 (The blue H represents high pressure and the red L represents low pressure).
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Figure 7. Daily variation chart of the contribution of HTRA, VTRA, CHEM, and DDEP to surface O3, including the daily variation of O3 concentration and net O3 production in Suqian on (a) 18 June 2020, (b) 19 June 2020, (c) 20 June 2020, and (d) 21 June 2020.
Figure 7. Daily variation chart of the contribution of HTRA, VTRA, CHEM, and DDEP to surface O3, including the daily variation of O3 concentration and net O3 production in Suqian on (a) 18 June 2020, (b) 19 June 2020, (c) 20 June 2020, and (d) 21 June 2020.
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Figure 8. Daily average variation chart of the contribution of HTRA, VTRA, CHEM, and DDEP to O3 at different heights, including net O3 production in Suqian on (a) 18 June 2020, (b) 19 June 2020, (c) 20 June 2020, and (d) 21 June 2020.
Figure 8. Daily average variation chart of the contribution of HTRA, VTRA, CHEM, and DDEP to O3 at different heights, including net O3 production in Suqian on (a) 18 June 2020, (b) 19 June 2020, (c) 20 June 2020, and (d) 21 June 2020.
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Figure 9. Regional source analysis diagram of O3 in Suqian in June 2020, including seven parts: the boundary condition (BCON), other unlabeled regions (OTH), Shuyang County (SHY), Sucheng District (SC), Siyang County (SY), Sihong County (SH), and Suyu District (SYU).
Figure 9. Regional source analysis diagram of O3 in Suqian in June 2020, including seven parts: the boundary condition (BCON), other unlabeled regions (OTH), Shuyang County (SHY), Sucheng District (SC), Siyang County (SY), Sihong County (SH), and Suyu District (SYU).
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Figure 10. Industry source analysis diagram of O3 in Suqian in June 2020, including four sectors: the transportation sector (TS), industrial sector (IS), power sector (PS), and residential sector (RS).
Figure 10. Industry source analysis diagram of O3 in Suqian in June 2020, including four sectors: the transportation sector (TS), industrial sector (IS), power sector (PS), and residential sector (RS).
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Figure 11. Spatial distribution of the difference in average O3 concentration (μg/m3) between the 25 emission reduction scenarios and the basic scenario at 16:00 on 18–21 June 2020 in Suqian.
Figure 11. Spatial distribution of the difference in average O3 concentration (μg/m3) between the 25 emission reduction scenarios and the basic scenario at 16:00 on 18–21 June 2020 in Suqian.
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Table 1. Simulation scenario designs.
Table 1. Simulation scenario designs.
ScenarioNOx ReductionVOCs Reduction
B10%0%
S110%10%
S210%20%
S310%30%
S410%40%
S510%50%
S620%10%
S720%20%
S820%30%
S920%40%
S1020%50%
S1130%10%
S1230%20%
S1330%30%
S1430%40%
S1530%50%
S1640%10%
S1740%20%
S1840%30%
S1940%40%
S2040%50%
S2150%10%
S2250%20%
S2350%30%
S2450%40%
S2550%50%
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Wang, J.; Zhang, W.; Shi, W.; Li, X.; Fang, C. Analysis of the Causes of an O3 Pollution Event in Suqian on 18–21 June 2020 Based on the WRF-CMAQ Model. Atmosphere 2024, 15, 831. https://doi.org/10.3390/atmos15070831

AMA Style

Wang J, Zhang W, Shi W, Li X, Fang C. Analysis of the Causes of an O3 Pollution Event in Suqian on 18–21 June 2020 Based on the WRF-CMAQ Model. Atmosphere. 2024; 15(7):831. https://doi.org/10.3390/atmos15070831

Chicago/Turabian Style

Wang, Ju, Wei Zhang, Weihao Shi, Xinlong Li, and Chunsheng Fang. 2024. "Analysis of the Causes of an O3 Pollution Event in Suqian on 18–21 June 2020 Based on the WRF-CMAQ Model" Atmosphere 15, no. 7: 831. https://doi.org/10.3390/atmos15070831

APA Style

Wang, J., Zhang, W., Shi, W., Li, X., & Fang, C. (2024). Analysis of the Causes of an O3 Pollution Event in Suqian on 18–21 June 2020 Based on the WRF-CMAQ Model. Atmosphere, 15(7), 831. https://doi.org/10.3390/atmos15070831

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