Impacts of Chemical Initial Conditions in the WRF-CMAQ Model on the Ozone Forecasts in Eastern China

Received: December 31, 2021
Revised: April 26, 2022
Accepted: May 8, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Cite this article:

Hou, T., Yu, S., Jiang, Y., Chen, X., Zhang, Y., Li, M., Li, Z., Song, Z., Li, P., Chen, J., Zhang, X. (2022). Impacts of Chemical Initial Conditions in the WRF-CMAQ Model on the Ozone Forecasts in Eastern China. Aerosol Air Qual. Res. 22, 210402. https://doi.org/10.4209/aaqr.210402

HIGHLIGHTS

• WRF-FNL showed better model performances on meteorological parameters than WRF-GFS.
• FNL-GFS improved model forecasts for O₃ concentrations compared to GFS-GFS.
• The ozone episodes in Tai’an city were caused by higher temperatures and lower RH.
• Industry emissions were the largest contributor to O₃ formations in Tai’an city.

Keywords: Air quality forecast, Chemical initial condition, FNL, GFS, Ozone

1 INTRODUCTION

With the rapid development of industrialization and urbanization in China over the past decades, the emissions of ozone (O₃) precursors in the troposphere have been increasing, leading to increasingly serious O₃ pollution, especially in eastern China, which is the most economically developed region (Liu et al., 2010; Wang et al., 2017a; Wang et al., 2017b). A report of “Bulletin on China's ecological environment in 2019” (Ministry of ecological environment of the People’s Republic of China, 2020) pointed out that O₃ has become the major factor for exceeding air pollution standard in many cities in China, and also reported that in the Yangtze River Delta, Beijing-Tianjin-Hebei and surrounding areas, which account for about 30% of the total number of cities at the prefectural level and above in China, the days of O₃ pollution exceeding the standard from June to September account for about 70% of the total days of exceeding the standard in China. The study of He et al. (2017) showed that although PM_2.5 showed a downward trend due to the continuous efforts with emission controls and restrictions, O₃ increased. Elevated surface O₃ has become a significant public concern in China because it affects the health of people and has an adverse impact on crops. In terms of the impact of O₃ on human health, O₃ can cause sore throat, headache, chest tightness and shortness of breath, stimulate and damage eyes, and may also lead to bronchitis and other related diseases in some people (Sousa et al., 2013). When exposed to O₃ with high concentrations for a long time, the human body will develop mild symptoms such as dry cough, chest tightness and increases of mucosal secretion (Wang et al., 2017a). In severe cases, symptoms such as impairment of human cardiopulmonary function and destruction of human respiratory system will appear, until the vital signs disappear (Wang et al., 2017b). For corps, O₃ can damage the internal structure and physiological function of vegetation and affect the normal growth of plants, reducing the yield of crops (Bhatia et al., 2012; Feng et al., 2015).

O₃ is a secondary pollutant, which is produced by the complex photochemical reactions involving volatile organic compounds (VOCs), nitrogen oxides (NO_x) and carbon monoxide (CO) emitted by natural and anthropogenic activities under the sunlight. O₃ concentrations are significantly influenced by both emissions and meteorological conditions (Jacob and Winner, 2009; Jhun et al., 2015). It is noteworthy that current emission control measures are all PM related, that is, reducing the primary PM, SO₂, and NO_x emissions, while the control of VOCs is not targeted seriously (Wang et al., 2019). Meteorological conditions also play a crucial role in the change of O₃ concentrations. The increase of O₃ concentrations generally occurs in the days of high temperature, low humidity and low wind, which favor the photochemical reactions of O₃ and the accumulations of O₃ and its precursors (Ding et al., 2013; Gao et al., 2005; Li et al., 2017). The study of Wang et al. (2021b) reported that the model performances of O₃ were similar in two sets of reanalysis data (NCEP Final Operational Global Analysis [FNL] and ECMWF Reanalysis v5.0 [ERA5]) because O₃ was the most sensitive to temperature, and FNL and ERA5 yielded similar temperature simulation results. Ritter et al. (2013) evaluated the effects of different chemical and meteorological boundaries and initial conditions (Global Forecast System [GFS] and Reanalysis II) on the WRF-Chem model, and concluded that the model outputs initialized by GFS model performed slightly better for O₃ because it had higher resolution-GFS with a resolution of 1° and Reanalysis II with a resolution of 2.5°.

A real-time air quality forecasting system can provide the public and the local governments with immediate and accurate information about air pollution predictions, help the government to formulate prevention and control strategies, and give the public advices to reduce personal exposures (Song et al., 2019). Numerical forecast models have been widely used in air quality forecasting, such as the Community Multiscale Air Quality (CMAQ) modeling system (Binkowski and Roselle, 2003; Eder and Yu, 2006; Yu et al., 2008, 2007), the Comprehensive Air Quality Model with extensions (CAMx; Xiao et al., 2021), and the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem; Yan, 2012). Many researchers have used CMAQ model to predict air quality in various regions, including Beijing (Gao and Zhang, 2012; Streets et al., 2007), the Yangtze River Delta (Li et al., 2017; Wu et al., 2018), the Pearl River Delta (Deng et al., 2018) and all the provincial capital cities in China (Wang et al., 2019).

Here, we used the real-time air quality forecasting (AQF) system to predict air quality in eastern China, which was composed of the Weather Research and Forecasting (WRF) meteorological model (version 4.0), Community Multiscale Air Quality (CMAQ) model (version 5.0), and Integrated Source Apportionment Method (ISAM) (WRF/CMAQ-ISAM). For 13 provincial capitals in eastern China and Tai'an city in Shandong Province, we evaluated the impacts of initializing CMAQ chemical field with simulated results of different meteorological data (FNL and GFS) as IC and BC in June 2021 on the performance for the O₃ forecasts in eastern China. In addition, in order to deeply understand the relationship between meteorology and O₃ concentrations, we analyzed the dependence of the average O₃ concentrations on different meteorological parameters during different periods in Tai’an city. We discussed characteristics of O₃ and its precursors in three episodes in Tai’an. We applied WRF/CMAQ-ISAM to identify the main sources leading to the increases of O₃ pollution events.

 
2 METHODS

 
2.1 Observation Datasets

Hourly observed O₃ concentrations of monitoring sites in 13 provincial capital cities (Beijing, Fuzhou, Hangzhou, Hefei, Jinan, Nanchang, Nanjing, Shanghai, Shijiazhuang, Taiyuan, Tianjin, Wuhan, Zhengzhou) in eastern China were downloaded from the China National Environmental Monitoring Center (CNEMC, http://www.cnemc.cn/). In order to evaluate the model performance, the model results of each monitoring site in each city were retrieved, and the mean values of observations and simulations of all monitoring sites in each city were compared according to Zhang et al. (2021). The location of each city in eastern China is shown in Fig. 1(a). Hourly measurements of meteorological parameters (temperature [T], relative humidity [RH], wind speed [WS] and wind direction [WD]) used in this study were obtained from the National Climate Data Center (NCDC) (ftp://ftp.ncdc.noaa.gov/pub/data/noaa/).

Fig. 1. (a) Map of WRF-CMAQ simulation domains and locations of 13 eastern cities in China; (b) Locations of Tai’an city, Shandong Province and monitoring stations. (BJ: Beijing, FZ: Fuzhou, HZ: Hangzhou, HF: Heifei, JN: Jinan, NC: Nanchang, NJ: Nanjing, SH: Shanghai, SJZ: Shijiazhuang, TY: Taiyuan, TJ: Tianjin, WH: Wuhan, ZZ: Zhengzhou; JCZ: Jiancezhan, RK: Renkou-xuexiao, DL: Dianli-xuexiao, YK: Shandong-diyiyike-daxue, JT: Jiaotong-jixiao, SUS: super observation station, TA:Tai’an).

Measurements of O₃ were taken during the study period at five observation stations and one super observation stations in Tai’an city, and the data were provided by Tai'an Ecological Environment Bureau. Fig. 1(b) shows the locations of the observation sites selected for this study, including Jiancezhan (JCZ), Renkou-xuexiao (RK), Dianli-xuexiao (DL), Shandong-diyiyike-daxue (YK), Jiaotong-jixiao (JT), and super observation stations (SUS). The site location information is shown in Table S1. The hourly meteorological data at the JCZ site were used to do model evaluation and the concentrations of O₃ precursors (VOC and NO_x) were from SUS in this study.

 
2.2 Description of AQF System

The real-time AQF system contains three major components. The first component is the WRF meteorological model (version 4.0) which simulates meteorological fields using the meteorological initial conditions (IC) and boundary conditions (BC) derived from the FNL (WRF-FNL) and GFS (WRF-GFS) results and provides hourly meteorological fields to drive the CMAQ model. Both FNL data (with spatial resolution of 1° × 1° and temporal resolution of 6 h) and GFS data (with spatial resolution of 0.5° × 0.5° and temporal resolution of 6 h) were obtained from the National Centers for Environmental Prediction (NCEP). FNL are retrospective data which assimilate a large number of ground and satellite observation data, and have high temporal and spatial resolutions. It includes 26 ground standard isobaric layers (10–1000 hpa), surface boundary layer and many physical quantities of tropopause (Yan, 2012). On the other hand, GFS are forecast data which releases 384 hours (16 days) of forecast data four times a day, and its temporal and spatial accuracies will decrease with the forecast time. It is generally believed that the accuracy of the forecast data from the GFS model after 7 days is not high, and most official agencies rarely use the GFS forecast data after 10 days (Yan, 2012). The second component is the US EPA’s CMAQ model, version 5.0.2, which provides spatial and temporal prediction of O₃ by simulating physical and chemical processes. The WRF-CMAQ system adopted an offline paradigm. Fig. 1(a) shows the simulation domain with a horizontal resolution of 36 km × 36 km covering central and eastern China, and a portion of East Asia, with a grid distribution of 82 × 118. The location of Tai’an city is shown in Fig. 1(b), in which RK, DL and JT were located in the same grid. The third component is an emission processing system which processes anthropogenic emissions from the Multiresolution Emission Inventory for China (MEIC) (http://www.meicmodel.org) developed by Tsinghua University for 2016, with a horizontal resolution of 0.25° × 0.25°. This MEIC includes monthly anthropogenic emissions of SO₂, NO_x, CO, ammonia (NH₃), PM_2.5, PM_coarse, black carbon (BC), organic carbon (OC) and non-methane volatile organic compounds (NMVOCs). The Biogenic Emission Inventory System version 3.14 (BEISv3.14) was used to calculate inline the natural sources for biogenic emissions (Wang et al., 2021a; Yu et al., 2014).

ISAM (Integrated Source Apportionment Method) was applied to provide information about O₃ source apportionments, mainly to track the contributions of industrial and regional sources to atmospheric O₃ concentrations (Kwok et al., 2013). Based on geographical distributions, 6 regions including Beijing-Tianjin-Hebei (BTH), Henan (HN), Jiangsu (JS), Tai’an (TA), Shandong except Tai’an (SD), and other region except the marked areas in the domain (OTH) inside eastern China were set as tracked source regions of O₃ concentrations using the ISAM source apportionment method (supplementary materials Fig. S1) in this study.

Since the FNL and GFS data are retrospective and forecast data, respectively, and the official website updates them regularly every day, we download them immediately after the updated data become available. We downloaded GFS data of today and the next three days at 1 p.m. and downloaded FNL data for the time from 0:00 yesterday to 0:00 today at 3 p.m. (Fig. S2). In this study, the spin-up period of 7 days was used for model forecasts of the first day on June 1, while the chemical IC derived from the CMAQ simulation results for the previous day will be used for the future model forecasts on the continuous basis. The following two forecasts cases were carried out on the basis of available times for the FNL and GFS data to study the impacts of chemical IC on the O₃ forecast: (1) Case 1: The chemical IC derived from the CMAQ simulation results by using the GFS data as initial and boundary conditions for the WRF model, and the forecasts of the next few days with GFS data as initial and boundary conditions for the WRF model (GFS-GFS). This means that the WRF-CMAQ simulations were carried out with the GFS data all times as initial and boundary conditions. (2) Case 2: The chemical IC derived from the CMAQ simulation results by using the FNL data as initial and boundary conditions for the WRF model, and the forecasts of the next few days with GFS data as initial and boundary conditions for the WRF model (FNL-GFS). This means that the WRF-CMAQ forecasts had to start at 0:00 of today because of availability of FNL data. (3) In addition, in order to analyze whether Case 2 can improve the forecast results relative to Case 1, we conducted retrospective simulations as a standard case, that is, Case 3: The WRF-CMAQ simulations were carried out with the FNL data all times as initial and boundary conditions (FNL-FNL) (Fig. 2).

Fig. 2. Design diagram for model simulation times in this study.

 
2.3 Model Evaluation Protocol

We used statistical metrics to evaluate the performance of WRF/CMAQ models by calculating the relationship between observed and predicted values (Jiang et al., 2010; Wang et al., 2013, 2010; Yu et al., 2006). The statistical metrics including the average of observations and predictions (MEAN), mean bias (MB), mean error (ME), root mean square error (RMSE), normalized mean deviation (NMB), normalized mean error (NME) and Pearson correlation coefficient (R) were calculated for each city. The definitions for these metrics were found in Yu et al. (2006).

 
3 RESULTS AND DISCUSSION

 
3.1 Evaluation of Model Performances of Meteorology and Air Quality in Eastern China

3.1.1 Meteorological prediction evaluations

Table 1 compares the simulation performances of WRF model using both FNL (WRF-FNL) and GFS (WRF-GFS) data as IC and BC for T and RH. The statistical results of meteorological predictions indicated that WRF-FNL was slightly better than WRF-GFS in the simulations of T and RH. The MB values of T predicated by WRF-GFS in Hefei, Nanchang, Shanghai, Tianjin, Wuhan, Zhengzhou were 1.30, –0.91, –0.56, 0.57, 0.96 and –0.90°C, respectively, which exceeded the benchmark (± 0.5°C, Emery et al., 2017; Wang et al., 2021b), whereas these for WRF-FNL were –0.06, –0.36, –0.22, 0.39, –0.18, and –0.17°C, respectively, and were within the benchmark. The ME values of T simulated by WRF-FNL in four cities (1.91°C [Jinan], 1.98°C [Nanchang], 1.67°C [Shanghai], 1.95°C [Wuhan]) satisfied the benchmark (2.0°C, Emery et al., 2017), while the predictions by WRF-GFS did not meet the benchmark in all cities except in Shanghai (1.51°C). No benchmarks were suggested for the MB and ME values for RH. The deviations of WRF-FNL for RH were significantly lower than those of WRF-GFS in nine cities. For example, as shown in Table 1, the MB values for RH in Beijing, Hangzhou, Hefei, Jinan, Nanjing, Shanghai, Taiyuan, Wuhan and Zhengzhou for WRF-GFS and WRF-FNL decreased from –4.15% to –2.26%, 7.99% to 5.61%, –6.98% to –1.65%, 3.44% to 1.19%, –3.22% to –0.90%, –4.31% to –0.07%, –4.34% to –2.45%, –10.18% to –5.91%, and –5.22% to 0.45%, respectively. The ME and RMSE values of RH in Fuzhou, Hangzhou, Hefei, Nanchang, Nanjing, Shanghai, Taiyuan and Wuhan predicted by WRF-FNL were all lower than WRF-GFS. The higher R values in WRF-FNL also indicate that the simulated values of T and RH by WRF-FNL were more consistent with the observed values than WRF-GFS.

The WS values were over-predicted by both WRF-FNL and WRF-GFS, while the over-prediction of WRF-GFS was higher than WRF-FNL, as indicted by the MB, ME and RMSE values (see Table 2). Some studies have reported that the WRF model over-predicted low WS values, especially when the WS value was less than 3 m s^–1 (Angevine et al., 2012; Fast et al., 2014; Hu et al., 2015; Wang et al., 2021b). The MB values of WS in all cities did not meet the benchmark (± 0.5 m s^–1, Emery et al., 2017), while the ME values satisfied the benchmark (2 m s^–1, Emery et al., 2017). The MB and ME values of WRF-FNL in Beijing, Hangzhou, Jinan, Shijiazhuang, Taiyuan, Tianjin and Zhengzhou were all lower than WRF-GFS. The observations showed that the prevailing WD values during the study period was from the southeast. As shown in Table 2, the WNMB values of both WRF-FNL and WRF-GFS in Hefei, Jinan, Nanchang, Shanghai, Shijiazhuang, and Tianjin met the benchmark (± 10%) (Emery et al., 2017), while their WNMB values in Fuzhou, Taiyuan, Wuhan and Zhengzhou did not meet the benchmark. The WNME values in all cities except Shanghai exceeded the benchmark (WNME ≤ 30%). In conclusion, the simulated T and RH values of WRF-FNL were closer to the actual observations, and WRF-FNL captured T, RH and WS in most cities better than WRF-GFS.

Therefore, the WRF-FNL can provide a more reasonable meteorological field for the CMAQ simulations.

 
3.1.2 Ozone prediction evaluations

Different meteorological IC and BC data not only affected the simulation performance of the WRF model on weather conditions, but also strongly affected CMAQ simulations of O₃ formations. Table 3 summarizes the CMAQ model performances for O₃ simulations in thirteen cities for Case 1 (GFS-GFS), Case 2 (FNL-GFS) and Case 3 (FNL-FNL). The O₃ concentrations predicted by FNL-GFS were similar to those in FNL-FNL in all cities. The NMB values of both FNL-FNL and FNL-GFS met the benchmark of NMB (± 15%, Emery et al., 2017) in eight cities, including Hangzhou, Hefei, Jinan, Nanjing, Shijiazhuang, Taiyuan, Wuhan, Zhengzhou, while the NMB values of GFS-GFS in Hangzhou and Shijiazhuang did not meet the benchmark. The deviations of simulation values in cities with low O₃ observation concentrations were generally high. For example, when the observed mean concentrations in Beijing, Fuzhou, Nanchang, Shanghai and Tianjin were 94.8, 53.3, 66.0, 70.1 and 98.9 µg m^–3, respectively, their corresponding NMB and NME values in both FNL-GFS and GFS-GFS were between 18%–41% and between 36%–48%, respectively. However, in cities with the high observed O₃ concentrations (Jinan, Nanjing, Shijiazhuang, Taiyuan, Zhengzhou), the models yielded lower NMB and NME values.

Fig. 3 shows the time series of daily predicted and observed maximum 8-h O₃ (MDA8 O₃) concentrations in thirteen cities and the results are summarized in Table 4. Both FNL-FNL and FNL-GFS captured the daily variations of observed MDA8 O₃ concentrations at all cities except Fuzhou where the R values were less than 0.5 because of very low O₃ concentrations (observed mean O₃ concentration was 84.2 µg m^–3). The NMB and NME values of both FNL-FNL and FNL-GFS in all cities except Beijing, Fuzhou and Wuhan met the benchmark (± 15% for NMB and 30% for NME), whereas the corresponding values of GFS-GFS were higher. The comparisons of the results for three cases in Table 4 reveal that the simulations of FNL-GFS were closer to observations and more similar to FNL-FNL than GFS-GFS for all thirteen cities as expected. For example, for Shanghai city, the results of model performance in FNL-GFS (NMB [13.9%], NME [20.1%], and R [0.89]) were closer to FNL-FNL (NMB [13.1%], NME [19.4%], and R [0.90]) than those in GFS-GFS (NMB [22.8%], NME [28.8%], and R [0.81]).

Fig. 3. Comparisons of daily MDA8 O₃ concentrations of observations and simulations (FNL-FNL, FNL-GFS, GFS-GFS) in 13 cities in the eastern China region during the study period.

 
3.2 Evaluation of Meteorology and Air Quality in Tai’an

3.2.1 Meteorological prediction evaluation

Fig. 4 depicts the comparisons between hourly simulated and observed T, RH, WS and WD in Tai’an city from June 1 to 30, 2021. Compared to WRF-GFS (–1.70°C), WRF-FNL yielded a smaller negative MB value (–1.32°C) for T. For RH, WRF-FNL also yielded slightly better simulated values with MB and ME values of 8.12% and 12.32%, respectively, while their corresponding values for WRF-GFS were 10.40% and 14.58%, respectively (see Table S4). Generally, the simulated values of T and RH in Tai'an city were in good agreement with the observations, but with over-predictions of WS in WRF-FNL. The correlation coefficients between simulations and observations for T and RH for WRF-FNL were 0.78 and 0.85, respectively, better than WRF-GFS which were 0.72 and 0.80, respectively. The model captured the synoptic features and daily deviations of WD with slight deviation in both WRF-FNL and WRF-GFS.

Fig. 4. Comparison of hourly temperature (T), relative humidity (RH), wind speed (WS) and wind direction (WD) simulated by WRF-FNL and WRF-FNL and observation values for Tai’an during the study period.

 
3.2.2 Effects of meteorology on ozone formation in Tai’an

Temperature is related to the chemical kinetic rates and the formation mechanism of O₃. According to variation analyses for the entire day, daytime (06:00–19:00) and nighttime (20:00–05:00) in Fig. 5(a), it is found that the change patterns of O₃ were consistent with those of T in Tai’an city. High temperature is often accompanied by strong radiation, being conducive to the formation of photochemical O₃ (Li et al., 2017). As shown in Fig. 5(a), the ground-level O₃ concentrations present a directly positive correlation with T. The trends of FNL-GFS were more consistent with the observations compared to GFS-GFS (see Fig. 5(a)).

Fig. 5. (a) Dependence of O₃ mean concentration on T, RH and WS; (b) Wind frequency plots of O₃ concentrations for entire day, daytime (6:00–19:00) and nighttime (20:00–05:00). The frequency of wind direction was divided into six O₃ concentration ranges: 0–79, 80–119, 120–159, 160–199, 200–239, and ≥ 240 µg m^–3. All results are based on the JCZ site.

As shown by the dependences of the average concentrations of O₃ on RH during entire day, daytime and nighttime in Fig. 5(a), the O₃ concentrations decreased with the increases of humidity, and these trends were more obvious in the daytime than the nighttime. As pointed by Yu (2019), moisture can suppress O₃ formation by lowering the air temperature, decreasing chain length of peroxy radical chemical amplifiers, decreasing chain length of NO₂, and destroying the existing O₃ photochemically through water vapor by catalytic O₃ destruction cycle. The model results of the correlational analyses between RH and O₃ in FNL-GFS were more consistent with the observations than GFS-GFS. The simulated values of FNL-GFS were higher than GFS-GFS when the RH was low, while the simulated values of FNL-GFS were lower than GFS-GFS when the RH was high. The results of FNL-GFS were closer to the observations than GFS-GFS as indicated in Fig. 5(a).

The correlational analyses show that there were nonlinear relationships between WS and O₃ (Fig. 5(a)). The maximum O₃ level was found when the WS values were in the range of 2.6 to 3 m s^–1, and then decreased with the increases of WS at daytime (Fig. 5(a)). The reasonable explanation was that with the increase of WS, the stability of boundary layer started to decrease, and more O₃ was transported from the upper layer to the surface layer. On the other hand, exogenous transport was strong at relatively low WS, yet very high WS promoted atmospheric mixing, dispersion, and transport, which favored O₃ dilution (Li et al., 2017).

Fig. 5(b) provides the wind frequency rose diagrams of O₃ concentrations at different time periods of the day. The observation results show that the prevailing surface WDs during the daytime were NE, ENE and ESE with the wind frequencies of 18.3%, 13% and 10.5%, respectively. For the daytime, both FNL-GFS and GFS-GFS captured WDs of NE and ENE, while missed the low wind frequencies of SW, WSW and W, while GFS-GFS over-predicted the O3 concentration from the E directions. The observed prevailing WDs during the nighttime turned to NE, ENE and W with the wind frequencies of 29.8%, 19.1% and 15%, respectively. Both FNL-GFS and GFS-GFS captured WD of ENE, while under-predicted the wind frequency of NE and missed the WD of W, while GFS-GFS over-predicted the O₃ concentration from the ENE direction. In general, FNL-GFS had a better performance than GFS-GFS in capturing the O₃ concentrations for different WD values.

 
3.2.3 Ozone prediction evaluation

Fig. 6 shows the time variations of hourly predicted and observed averaged O₃ concentrations at monitoring sites in Tai’an. Compared to GFS-GFS, FNL-GFS improved O₃ prediction mainly because it captured peak values more effectively. For example, FNL-GFS captured the O₃ pollution events in Tai’an on June 11–12 and 22–24, 2021, better than GFS-GFS. The peak values of O₃ observations at RK/DL/JT sites on June 11 and 22 were 242 and 281 µg m^–3, respectively. The corresponding peak concentrations in the FNL-GFS predictions were 207 and 270 µg m^–3, respectively, while they were 171 and 185 µg m^–3 in the GFS-GFS predictions, respectively. The NMB values in JCZ, RK/DL/JT and YK sites for FNL-GFS and GFS-GFS improved from –5.1% to –2.4%, –2.2% to –0.8%, and –3.4% to -1.4%, respectively, while the corresponding NME values improved from 28.8% to 26.2%, 28.3% to 22.8%, and 31.0% to 26.8%, respectively. Meanwhile, the spatiotemporal distributions of these two days in eastern China in Fig. S3 also showed that the simulations of FNL-GFS were more similar to FNL-FNL than GFS-GFS, and the case of GFS-GFS under-predicted the peak values of O₃ concentrations in most of eastern cities (see Fig. S3). Fig. 7 shows time-series of comparisons of the observations with the results of different chemical IC conditions in the FNL-GFS and GFS-GFS simulations for O₃ and its precursors (NO_x and VOC) at the super station in Tai’an city for each day in June 2021. As can be seen, the results of the chemical IC conditions in the FNL-GFS simulations were closer to the observations for O₃, NO_x and VOC than those in the GFS-GFS simulations for both values of NMB and NME. The NMB (NME) values for O₃, NO_x and VOC in the FNL-GFS simulations were –6.1% (13.3%), –12.7% (20.3%) and 93.9% (44.3%), respectively, while they were –14.2% (31.9%), –44.3% (28.8%) and 125% (100.5%) in the GFS-GFS simulations, respectively. The similar results can be found for both O₃ and NO₂ in 13 cities in eastern China with the better performances of chemical IC conditions in the FNL-GFS simulations than the GFS-GFS simulations (see Tables S2 and S3).

Fig. 6. Comparison of time series of O₃ concentrations of observations and simulations (FNL-FNL, FNL-GFS, GFS-GFS) for five monitoring stations in Tai’an city in June 2021 (JCZ: Jiancezhan; RK: Renkou-xuexiao; DL: Dianli-xuexiao; YK: Shandong-diyiyike-daxue; JT: Jiaotong-jixiao; NMB: normalized mean bias; NME: normalized mean error). Among these air quality monitoring stations, RK, DL and JT in the same grid.

Fig. 7. Time-series of comparisons of the observations with the results of different chemical IC conditions in the FNL-GFS and GFS-GFS simulations for O₃ and its precursors (NO_x and VOC) at the super station in Tai’an city for each day in June 2021.

 
3.2.4 Ozone source apportionment analyses for the three episodes

As shown in Fig. 6, two O₃ episodes with high concentrations (episodes I: June 4–7 and episodes II: June 20–23, 2021) and a non-episode without precipitation with very low concentrations (June 14–17, 2021) were selected for comparative analyses. The average values of O₃ concentrations and meteorological parameters for these episodes are listed in Table S5. The observed mean concentration of O₃ in the non-episode period was 78.5 µg m^–3, while their values in the episodes I and II were 148.8 and 172.5 µg m^–3, respectively. Table S5 shows that higher T and lower RH were observed for both pollution episodes compared to the non-pollution episode. The predicted results also captured these features well. As mentioned in Section 3.2.2, high T and low RH values were accompanied by strong UV radiation, which favored photochemical oxidation reactions for the O₃ formation. Therefore, meteorological conditions can be the good explanation for the O₃ episodes. The results of the FNL-GFS predictions for both high and low O₃ concentrations were closer to the observations than those of GFS-GFS as indicated in Table S4.

Table 5 summarizes average contributions of source regions to O₃ concentrations in the receptor Tai’an city for three episodes estimated by the three cases. Table 5 indicates that different episodes in Tai’an city had different relative contributions of source regions although BCON was the largest contributor for all three episodes. For example, for the non-episode case on the basis of the FNL-FNL simulations, BCON was the largest contributor (46.9%), followed by SD (18.0%), JS (14.7%), OTH (11.4%), TA (4.0%), BTH (3.5%), and HN (1.6%), while for the episode II, BCON was the largest contributor (33.8%), followed by SD (25.5%), HN (12.9%), OTH (8.1%), JS (8.0%), BTH (7.4%), and TA (4.2%). The comparisons of the results for three cases in Table 5 reveal that the simulations of FNL-GFS were more similar to FNL-FNL than GFS-GFS as expected. For example, for the episode II, the results of contributions of source regions in FNL-GFS (BCON [32.9%], SD [26.2%], HN [12.9%], OTH [8.6%], JS [8.5%], BTH [7.0%], and TA [4.0%]) were closer to FNL-FNL (BCON [33.8%], SD [25.5%], HN [12.9%], OTH [8.1%], JS [8.0%], BTH [7.4%], and TA [4.2%]) than those in GFS-GFS (BCON [37.4%], SD [22.8%], HN [12.4%], OTH [8.2%], JS [8.3%], BTH [6.9%], and TA [4.0%]).

According to the results of ISAM source apportionments, the mean contribution values of different source sectors to the O₃ concentrations in Tai’an city for three episodes were estimated by the three cases and are summarized in Table 6. Table 6 indicates that the industry emissions the largest contributor, followed by transportation, power plants and residential emissions for all three episodes without exception for all three simulation cases. For example, for the episode II on the basis of the FNL-FNL simulations, the industry emissions were the largest contributor (33.4%), followed by transportation (26.9%), power plants (22.7%) and residential (17.0%) emissions. The comparisons of the results for three simulation cases in Table 6 reveal that the simulations of FNL-GFS were more similar to FNL-FNL than GFS-GFS as expected. For example, for the episode I, the results of contributions of source sectors in FNL-GFS (industry [30.9%], transportation [26.1%], power plants [22.9%] and residential [20.1%]) were closer to FNL-FNL (industry [30.9%], transportation [26.1%], power plants [22.9%] and residential [20.1%]) than those in GFS-GFS (industry [30.6%], transportation [26.1%], power plants [23.1%] and residential [20.2%]).