Jiali Li1, Shaocai Yu This email address is being protected from spambots. You need JavaScript enabled to view it.1,2, Xue Chen1, Yibo Zhang1, Mengying Li1, Zhen Li1, Zhe Song1, Weiping Liu1, Pengfei Li This email address is being protected from spambots. You need JavaScript enabled to view it.3 Min Xie4, Jia Xing5 

1 Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education; Research Center for Air Pollution and Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
2 Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
3 College of Science and Technology, Hebei Agricultural University, Baoding, Hebei 071000, China
4 School of Atmospheric Sciences, Jiangsu Collaborative Innovation Center for Climate Change, Joint Center for Atmospheric Radar Research of CMA/NJU, CMA-NJU Joint Laboratory for Climate Prediction Studies, Nanjing University, Nanjing 210023, China
5 School of Environment and State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 00084, China


Received: January 16, 2022
Revised: February 16, 2022
Accepted: March 10, 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.


Download Citation: ||https://doi.org/10.4209/aaqr.220023  


Cite this article:

Li, J., Yu, S., Chen, X., Zhang, Y., Li, M., Li, Z., Song, Z., Liu, W., Li, P., Xie, M., Xing, J. (2022). Evaluation of the WRF-CMAQ Model Performances on Air Quality in China with the Impacts of the Observation Nudging on Meteorology. Aerosol Air Qual. Res. 22, 220023. https://doi.org/10.4209/aaqr.220023


HIGHLIGHTS

  • National evaluations of PM2.5 and O3 simulations were conducted by using observation nudging.
  • Using observation nudging could significantly correct the high wind speed biases.
  • The model failed to simulate high PM2.5 concentrations but nudging improved in central China.
  • The overestimations of O3 during the daytime at the Taizhou site could be eliminated by nudging.
 

ABSTRACT


Accurate meteorological fields are imperative for correct air quality modeling through their influence on the various chemical species in the atmosphere. In this study, the simulations from the Community Multiscale Air Quality (CMAQ) model were conducted with meteorological fields generated by the Weather Research and Forecasting (WRF) using the original and observation nudging approaches to investigate if the better model performances for PM2.5, O3, and their related precursors in China were produced from the latter. Two pollution episodes (one for PM2.5 and another for O3) in 2018 were selected on the basis of the observations at the monitoring supersites in Xianghe and Taizhou cities. The results showed that the Nudging cases had better model performances on all meteorological parameters with higher values of index of agreement (IOA) and lower values of mean bias (MB). It was found that the Nudging case improved model performances for PM2.5 and its chemical components at the Xianghe site with lower values of normalized mean bias (NMB) and higher values of correlation coefficient (R) than the base case. The results for the regional PM2.5 over China indicated that the Nudging case reproduced the spatial patterns of mean PM2.5 concentrations in the 367 cities with the NMB value of –31%, much better than –42.0% in the base case. During the O3 pollution episode, the Nudging case improved the model performances for O3, CO, NO2, and VOCs with lower NMB values and higher R values than the base case. The results of regional O3 over China revealed that the Nudging case reproduced the spatial patterns of observed mean O3 concentrations in the 367 cities very well with the NMB value of 0.97%, much lower than –5.67% in the base case. The results of this study have great implications for better simulations of meteorology and air quality.


Keywords: PM2.5, Ozone, WRF-CMAQ, Nudging, China


1 INTRODUCTION


Since the implementation of the “Air Pollution Prevention and Control Action Plan” in 2013, China has released a series of relatively strict pollutant emission standards and pollution control measures (Zhang et al., 2021). The national ambient air quality has been improved continuously, especially for PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) concentrations which have decreased significantly (Chen et al., 2019; Xue et al., 2019). However, haze events still occur under the adverse weather conditions (Xian et al., 2021; Zhong et al., 2019). At the same time, ozone (O3) pollution is gradually emerging (Wang et al., 2017; Zhao et al., 2020). The complex pollution of PM2.5 and O3 has become a tricky problem restricting the improvement of air quality in China (Li et al., 2019; Chen et al., 2019; Zhao et al., 2020).

Chemical transport models (CTMs), such as Community Multiscale Air Quality (CMAQ), integrate information from emission inventories, meteorological conditions, and chemical mechanisms to estimate the temporal and spatial distributions of pollutant concentrations. CTMs can apportion the sources of pollutants and help the government to formulate effective policies to improve air pollution (Lang et al., 2013; Yu et al., 2014; Li et al., 2015; Wang et al., 2015; Wang et al., 2016; Huang et al., 2018; Wang et al., 2020, 2021), which is important for air quality management. However, the uncertainties of emission inventories and meteorological conditions, as well as the errors of the model itself, cause that the simulation results cannot fully reproduce the actual transformations and reaction processes of air pollutants (Croft et al., 2012; Gilliam et al., 2015; Zheng et al., 2017). In order to achieve better simulation results, it is feasible to improve the accuracies of meteorological conditions.

The Weather Research and Forecasting Model (WRF) is a widely used mesoscale meteorological model, which can provide the required meteorological fields for CTMs. The commonly used methods to improve the performance of WRF simulations include using updated and high-resolution terrain data, optimizing physical parameterization schemes, and using four-dimensional data assimilation (FDDA) (Borge et al., 2008; Tao et al., 2018). FDDA can be categorized into nudging (Stauffer and Seaman, 1990, 1994) and variational methods (Le Dimet and Talagrand, 1986). Nudging is a continuous dynamic assimilation method, which makes the model state approach the observed state gradually by adding an artificial tendency term to the model control equations (Reen, 2016). Compared with the variational methods, nudging needs less computational costs and is mainly used for long-term meteorological field simulations. There are several nudging methods including observation and analysis nudgings. In each grid point, observation nudging relaxes the model’s state to observations at individual locations both on the surface and aloft, while analysis nudging relaxes the model’s state to values in a gridded analysis field (Stauffer and Seaman, 1990, 1994). Tran et al. (2018) conducted four WRF-CMAQ sensitivity tests with observation and analysis nudgings to study the impact of different nudging methods on the simulated inversion layer structure within the Uintah Basin, in winter 2013. Their results indicated that the analysis nudging increased the errors in the modeled vertical structures of inversion layer compared to the observation nudging due to the poor analysis of vertical temperature profiles. Li et al. (2016) showed that the CMAQ with WRF using the observation nudging performed better for simulating meteorology and O3 concentrations, with the index of agreement (IOA) improved by about 9% for surface temperature, 6–11% for surface zonal and meridional winds and 6% for surface O3 during the 2013 Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) Texas campaign. Jia et al. (2021) indicated that in winter 2017 over eastern China, by assimilating meteorological data from surface and radiosonde observations, the WRF model could well represent the planetary boundary layer (PBL) dynamics and wind fields, especially near the ground surface, which then substantially improved particle tracing in the Lagrangian particle dispersion model. The benefits of applying the nudging to improve meteorological and air quality simulations have been demonstrated by several previous studies (Barna and Lamb, 2000; Otte, 2008; Reen and Stauffer, 2010; Choi et al., 2014; Jeon et al., 2015). However, there are relatively few studies which comprehensively investigate the impacts of the observation nudging on both PM2.5 and O3 pollution simulations in China.

In this study, we utilized the WRF-CMAQ model to simulate PM2.5 and O3 pollution episodes. The observation nudging was used in the WRF model, and the differences of meteorological fields and pollutant simulations between the original (base) and observation nudging cases (Nudging) were compared. This study can provide ideas for better simulations of the model in the current air quality management.

 
2 METHODS


 
2.1 Model Description and Configuration

The offline WRF (v3.9.1)-CMAQ (v5.3.2) model (Eder and Yu, 2006; Yu et al., 2014; U.S. EPA, 2020) was applied to understand the impacts of the observation nudging on meteorology and air quality. The output from WRF was processed by MCIP (v5.1) (Meteorology-Chemistry Interface Processor) to the format required by the CMAQ. The CMAQ model version 5.3.2 was released in October 2020 by the U.S. Environmental Protection Agency (U.S. EPA). Several updates and revisions, such as advances in the science of modeling particulate matter composition, size distributions and optical properties (U.S. EPA, 2020), have been added in this new version.

Fig. 1 shows the model domain with a horizontal resolution of 12 km × 12 km covering most of China and a portion of East Asia. In order to ensure the accuracy of the boundary meteorological fields, the WRF’s horizontal domain was slightly bigger than CMAQ’s. The number of grid cells is 432 × 372 for the WRF simulations and 395 × 345 for the CMAQ simulations. Thirty-one vertical layers for all the grid cells were used from ground level to the top pressure of 100 hPa in the WRF and twelve vertical layers in the CMAQ to enhance the modeling proficiency. The projection type was Lambert Conformal Conic (LCC). The true latitudes of the projection cone were 30°N and 60°N, and the central meridian was 102.8°E.

Fig. 1. The mean results of the model simulations: (a), (d), and (g) show scatterplots and linear regressions of observed (OBS) and simulated (SIM) for mean temperature (T), relative humidity (RH), and wind speed (WS) in 367 cities for the Nudging and Base simulations during the pollution episode from 11 to 19 January 2018, respectively. The model results for the Nudging case ((b), (e), and (h)); ((c), (f), and (i) are for the Base case) with observations overlaid (circle) for T, RH, and WS over China during the pollution episode from 11 to 19 January 2018. Colored circles denote locations of ground measurement sites. The black triangles in (b) denote the locations of the Xianghe and Taizhou air quality monitoring supersites. (b) shows the model domain.
Fig. 1. The mean results of the model simulations: (a), (d), and (g) show scatterplots and linear regressions of observed (OBS) and simulated (SIM) for mean temperature (T), relative humidity (RH), and wind speed (WS) in 367 cities for the Nudging and Base simulations during the pollution episode from 11 to 19 January 2018, respectively. The model results for the Nudging case ((b), (e), and (h)); ((c), (f), and (i) are for the Base case) with observations overlaid (circle) for T, RH, and WS over China during the pollution episode from 11 to 19 January 2018. Colored circles denote locations of ground measurement sites. The black triangles in (b) denote the locations of the Xianghe and Taizhou air quality monitoring supersites. (b) shows the model domain.

The main selected physical schemes used in the WRF included Asymmetric Convective Model (ACM2) for the PBL scheme (Pleim, 2007), the Kain-Fritsch (KF2) cumulus parameterization scheme, the Morrison double-moment microphysics scheme, the RRTMG longwave and shortwave radiation scheme, the Noah land-surface scheme and the revised Monin-Obukhov surface layer scheme. The updated and expanded version of the AERO7 aerosol module (Pye, 2019) and CB6 gas-phase chemical mechanism (Luecken et al., 2019) were configured in the CMAQ model. Anthropogenic emissions were obtained from Emission Inventory of Air Benefit and Cost and Attainment Assessment System (EI-ABaCAS) established by Tsinghua University (Zheng et al., 2019; Dong et al., 2020) for 2017. The Biogenic Emission Inventory System version 3.14 (BEISv3.14) was used to calculate inline the natural sources for biogenic emissions. The clean initial and boundary chemical conditions provided with CMAQ were used and a spin-up period of 4 days was used to minimize the influence of initial chemical conditions (Yu et al., 2014; Wang et al., 2021)

 
2.2 Four-Dimensional Data Assimilation (FDDA) Options in WRF

The Fifth Generation Atmospheric Reanalysis of the Global Climate (ERA5) dataset with a spatial resolution of 31 km, a temporal resolution of 1 h and 38 barometric layers in the vertical direction produced by the European Center for Medium-Range Weather Forecasts (ECMWF) was used as the background field to drive the WRF. The ECMWF regularly uses its prediction model and data assimilation system to reanalyze the archived observations to create a global dataset describing the atmosphere, land and oceans. The ERA5 dataset is the latest reanalysis product of the ECMWF, which has assimilated a large number of global observation data and is commonly used in numerical modeling (Hoffmann et al., 2019; Jia et al., 2021). In order to ensure more accurate simulation results, the grid nudging (also called analysis nudging) or observation nudging in the WRF were used in this study by setting "grid_fdda = 1" or "obs_nudge_opt = 1" in the fdda option in the namelist.input file, respectively. The collected observational data for observation nudging include routine hourly surface observations such as atmospheric pressure, air temperature, dew point temperature, RH, wind direction, and wind speed from 36 surface meteorological stations (http://weather.uwyo.edu/surface/meteorogram/seasia.shtml), and the twice-daily (0:00 and 12:00 UTC) regular observations such as potential height, temperature, dew point temperature, RH, wind direction, wind speed of each specified isobaric surface and pressure, temperature and humidity characteristic layer from 85 radiosonde observation stations in China (http://weather.uwyo.edu/upperair/sounding.html). These collected data were nudged into the model for the sounding data level and up to the top pressure of 100 hpa, while no grid nudging was applied in the planetary boundary layer (PBL). The calculation frequency is consistent with the density of collected observation data for observation nudging and is hourly for grid nudging. The variables nudged included temperature, water vapor mixing ratio, and horizontal wind components. The nudging coefficients, which determine the strength of the assimilation tendency term were set to be 6 × 104 for observation nudging and 3 × 104 for grid nudging. The values of coefficients were recommended by the WRF user guide and tested to be appropriate in the previous studies (Borge et al., 2008).

In order to evaluate the effects of the observation nudging, two experiments were conducted and ran in parallel, one experiment with observation nudging (Nudging case) and the other without observation nudging (base case) while grid nudging for both cases and all the other settings were kept the same.

 
2.3 Episode Selections and Observational Data

Two pollution episodes (one for PM2.5, the other for O3) in the year 2018 were selected in this study to understand the impact of the observation nudging on air quality. The period of the PM2.5 episode was from 00:00:00 Local Time (LT) on 11 January to 23:00:00 LT on 19 January, 2018 in Xianghe. The period of the O3 episode was from 00:00:00 LT on 28 May to 23:00:00 LT on 15 June, 2018 in Taizhou. As shown in Fig. 1(b), Xianghe is subordinate to the city of Langfang (Hebei Province), which is located in the north of North China Plain, bordering Beijing and Tianjin on all sides. Taizhou is a prefecture level city in Jiangsu Province and an important industrial and trade port city in the Yangtze River Delta. Both Xianghe and Taizhou are located in the regions with severe haze and ozone pollutions in recent years, which are the focus of intensive research (Gao et al., 2020; Gong et al., 2021; Wang et al., 2021; Zhao et al., 2021).

The hourly measurements of meteorological parameters (temperature, RH and wind speed) used in this study were obtained from the surface stations of Chinese National Meteorological Centre (http://data.cma.cn). Using the Bulk Richardson number method, the planetary boundary layer height (PBLH) observations at 8:00 LT (0:00 UTC + 8 h) and 20:00 LT were retrieved from the high-resolution (1-second) radiosonde measurements from China radiosonde network operated by China Meteorological Administration (CMA), which comprised 120 operational radiosonde stations (Guo et al., 2016, 2019; Lou et al., 2019). The hourly concentrations of PM2.5 and its major components (OC, EC, NH4+, NO3, SO42) were collected at Xianghe air quality monitoring supersite (116.95°E, 39.79°N) (Zhao et al., 2021). EC and OC were measured using the online EC/OC analyzer, NH4+, NO3, and SO42 were acquired using an online collection and analysis system of water-soluble ions of gas/aerosol, with a time resolution of 1 h. The hourly concentrations of O3 and its precursors (NO2, VOCs, CO) were collected at Taizhou air quality monitoring supersite (119.99°E, 32.55°N) (Wang et al., 2021). Total VOC (TVOC) were analyzed hourly using a custom-built gas chromatography-mass spectrometry/flame ionization detector (GC-MS/FID), which including 98 species of alkanes, alkenes, aromatics, alkynes and others. The specific information of the instrument can be found in Yang et al. (2019). Hourly PM2.5 and O3 concentrations of 1597 monitoring sites in 367 Chinese prefecture-level cities during the two pollution episodes were obtained from China National Environmental Monitoring Center (CNEMC, http://www.cnemc.cn/), which were used to evaluate model performance for the spatial scale.

 
2.4 Model Evaluation

Hourly observations of meteorological parameters and air pollutants were used to evaluate the model performance. The WRF model performances of temperature, RH and wind speed were evaluated by using the following statistical metrics: mean bias (MB), gross error (GE), root mean square error (RMSE), the Pearson’s correlation coefficient (R) and the index of agreement (IOA). In addition, normalized mean bias (NMB) and normalized mean error (NME) were added to assess the CMAQ model simulated concentrations of PM2.5, O3 and other pollutants (Yu et al., 2006). The MB, GE, RMSE, NMB, NME are measures of the deviation between simulations and observations. The IOA and R values represent the degrees of coincidence of the variations between simulations and observations. See the supplementary material for the specific calculation formula of these statistical metrics (Emery et al., 2001; Yu et al., 2006).

 
3 RESULTS AND DISCUSSION


 
3.1 Impacts of Observation Nudging on Meteorology


3.1.1 Meteorology at the Xianghe and Taizhou sites

Table 1 shows statistical comparisons between the simulated and observed results for meteorological variables during the two pollution episodes. With higher R and IOA and lower MB, GE, and RMSE values, the Nudging cases showed better model performance on all meteorological parameters than the base case. For example, the model could basically simulate the actual trends of meteorological parameters with time as shown in Fig. 2. Obviously, the simulated values of the Nudging cases were more consistent with the observed values for meteorological parameters (temperature, RH, and wind speed) than the base cases as shown by the IOA values. The temperatures of the base cases were consistently lower than the in-situ measurements at both sites (see Fig. 2) with the MB values from –0.98°C to –4.16°C and exceeded the benchmark (±0.5°C, see Table 1). The high biases in the base cases for the temperatures were significantly reduced in the Nudging cases with the MB values of 0.40°C at the Xianhe site and –0.46°C at the Taizhou site which met the benchmark as shown in Table 1. The GE values satisfied the benchmark (2.0°C) only at the Taizhou site of the Nudging case. The IOA of the Nudging case was about 20% higher than the base case for the temperature at the Taizhou site.

Table 1. Model performances for meteorological parameters at two sites during the two study periods.

Fig. 2. Time series of hourly observed and simulated wind speed (WS), relative humidity (RH) and temperature (T) during the studying periods at (a) Xianghe and (b) Taizhou sites.Fig. 2. Time series of hourly observed and simulated wind speed (WS), relative humidity (RH) and temperature (T) during the studying periods at (a) Xianghe and (b) Taizhou sites.

For the wind speeds, the MB values of the base cases at two sites exceeded the benchmark (±0.5 m s1) and the base cases had consistently stronger winds than the observations as reflected by the MB values (2.10 and 2.71 m s1 at the Xianghe and Taizhou sites, respectively), whereas the winds were reduced significantly after observation nudging was performed as indicated by the corresponding MB values (0.92 and 0.89 m s1 at the Xianghe and Taizhou sites, respectively). The GE and RMSE values were also greater than the benchmark (2.0 m s1) in the base cases, while they stayed within the benchmark in the Nudging cases. Although the overestimation of wind speeds is a common phenomenon in the model simulations for meteorology (Sekiguchi et al., 2018; Zhang et al., 2021), the Nudging cases corrected the biases in the wind speeds with increase of the IOA values by about 28% at the Xianghe and 63% at the Taizhou sites relative to the base cases. For the RH, there are no recommended benchmarks for the MB and GE values. The base cases overestimated the observations significantly, especially for the low values at the Taizhou site (see Fig. 2), whereas the Nudging case successfully corrected these low biases of RH values with the substantially lower MB value of 1.04% relative to that in the base case (i.e., 17.44%).

 
3.1.2 Regional meteorology over China

The impacts of observation nudging on meteorological simulations were further studied nationwide. Figs. 1 and S1 show the evaluation results of temperature, RH, and wind speed over the whole domain during the PM2.5 and O3 pollution episodes, respectively. The scatterplots in Figs. 1(a) and S1(a) indicated that the Nudging cases corrected the relatively cold biases for temperatures in the base cases by the improvement of MB values from –1.17 to –0.53°C for the PM2.5 pollution episode from 11 to 19 January 2018 and from –0.55 to –0.15°C for the O3 pollution episode from 28 May to 15 June 2018. Compared to the base cases, the Nudging cases improved the moist biases for RH by increasing the R values from 0.66 to 0.78 (the PM2.5 pollution episode) and from 0.85 to 0.93 (the O3 pollution episode) (see Figs. 1 and S1). The wind speeds were over-predicted by the WRF in most parts of China, which might be caused by the inconsistency between the surface roughness and the reality due to the rapid processes of urbanization (Tao et al., 2018). With assimilation of observational data, the Nudging cases effectively reduced the high biases for the wind speeds in the base cases as reflected in lower MB and higher R values (see Table S1).

Since the PBLH plays a critical role in mixing and spreading atmospheric pollutants, the modeled PBLH values were evaluated at 8:00 and 20:00 LT over China during two pollution episodes (Fig. 3). The results in Fig. 3 indicated that the Nudging case tended to overpredict while the base case tended to underpredict the observed PBLH values, especially at 20:00 LT. Observation nudging helped to reduce the large underestimations in the base case with the NMB value changes from –30.8% to –25.7% at 8:00 and from –45.2% to 31.7% at 20:00 for the PM2.5 pollution episode from 11 to 19 January 2018, while they were from –17.2% to 15.2% at 8:00 and from –37.3% to 40.6% at 20:00 for the O3 pollution episode from 28 May to 15 June 2018. Fig. S2 shows the spatial distributions of simulated mean PBLH during two pollution episodes. As can be seen, the simulated PBLH values for the Nudging case were higher than those for the base case except the junction of Qinghai-Tibet-Sichuan during the period from January 11 to 19. Tao et al. (2018) pointed out that the changes of PBLH showed an obvious diurnal and seasonal pattern similar as temperature after the update of land use and land cover. Since the PBLH is highly affected by the factors including sensible heat flux, evaporation, and flow modification (Stull, 1988), the increases of the simulated PBLH in the Nudging case were likely the results of improved simulations of temperature, humidity, and wind (Li et al., 2016).

Fig. 3. Scatterplots and spatial distributions of the model simulations with observations overlaid for mean planetary boundary layer height (PBLH) at 8:00 and 20:00 LT over China during the pollution episodes from ((a)–(f)) 11 to 19 January and from ((g)–(l)) 28 May to 15 June 2018. The middle column refers to Nudging case, and the right column refers to Base case.Fig. 3. Scatterplots and spatial distributions of the model simulations with observations overlaid for mean planetary boundary layer height (PBLH) at 8:00 and 20:00 LT over China during the pollution episodes from ((a)–(f)) 11 to 19 January and from ((g)–(l)) 28 May to 15 June 2018. The middle column refers to Nudging case, and the right column refers to Base case.

The differences of meteorological parameters between the Nudging and base simulations likely resulted from initial conditions and model physics errors (Tran et al., 2018). The results of the Nudging case presented a significant improvement in both pollution episode periods, indicating the reduction in errors as a result by including the observation nudging in the WRF model which are consistent with the other studies (Li et al., 2016; Tran et al., 2018; Jia et al., 2021). Therefore, the WRF model with the observation nudging could provide better reasonable meteorological field for the CMAQ simulations.

 
3.2 Impacts of Observation Nudging on PM2.5


3.2.1 PM2.5 and its chemical components at the Xianghe site

Observation nudging affected not only the WRF performance in simulating meteorological conditions, but also formations and distributions of air pollutants in the subsequent CMAQ calculations. During the PM2.5 pollution episode (January 11–19, 2018), there were two peaks of the PM2.5 concentrations occurring at 10:00 of January 14 and 20:00 of January 18 with the maximum hourly value nearly 200 μg m–3 at the Xianghe site as shown in Fig. 4. The low wind speed at the Xianghe site (Fig. 2(a)) and the regional transport from the central of China with severe haze pollution (Fig. 5(b)) leaded to such high PM2.5 concentrations. The concentrations of PM2.5 and its major components during this period were investigated to evaluate the impacts of observation nudging on PM2.5 simulations. Fig. 4 shows the time series of hourly simulated and observed concentrations of PM2.5 and its major components, and the model performances are summarized in Table 2. The Nudging case captured the two peaks of PM2.5 around January 14 and 19 very well with a high R value of 0.81, while the base case overestimated observed PM2.5 concentrations significantly, especially for the peak values with the R value of 0.54.

Fig. 4. Time series of hourly observed and simulated PM2.5 and its major components (SO42–, NO3–, NH4+, EC and OC) concentrations at the Xianghe site.Fig. 4Time series of hourly observed and simulated PM2.5 and its major components (SO42–, NO3, NH4+, EC and OC) concentrations at the Xianghe site.

Fig. 5. Scatterplots and linear regressions of observed (OBS) and simulated (SIM) mean (a) PM2.5 and (d) O3 concentrations in 367 cities for the Nudging and base simulations during pollution episodes (PM2.5 from 11 to 19 January; O3 from 28 May to 15 June 2018). The mean results of the model simulations with observations overlaid (circle) for PM2.5 ((b), (c)) and O3 ((e), (f)) over China during the studying periods are also shown.Fig. 5. Scatterplots and linear regressions of observed (OBS) and simulated (SIM) mean (a) PM2.5 and (d) O3 concentrations in 367 cities for the Nudging and base simulations during pollution episodes (PM2.5 from 11 to 19 January; O3 from 28 May to 15 June 2018). The mean results of the model simulations with observations overlaid (circle) for PM2.5 ((b), (c)) and O3 ((e), (f)) over China during the studying periods are also shown.

The improvement in model performance for PM2.5 and its chemical components was also reflected in lower values of GE, NMB, and NME, and higher values of R in the Nudging case than the base case as shown in Table 2. For example, the Nudging case improved the predictions of SO42– by decreasing the NMB values from –21.5% (the base case) to –10.0% with slight increase of the R value (from 0.80 to 0.82). The Nudging case improved the trend predictions of NH4+ significantly, especially for the peak values on January 14, as indicated by the large R value of 0.84 although NH4+ were still underestimated (see Table 2 and Fig. 4). The model performance for OC in the Nudging case was also improved by increasing the R values from 0.63 to 0.75 although the OC values were still underestimated sometime. Note that the NMB values for SO42–, NH4+, and OC in both cases were within ±30%, meeting the criteria for the model performance (≤ ± 30%) (U.S. EPA, 2007). The evaluation standards mainly refer to the suggestions of the U.S. EPA (U.S. EPA, 2007) which established two-level reference standards: the first level is the "Performance Goal", which stipulates that the conditions for meeting the standard are that the absolute value of NMB is ≤ ±15% and NME is ≤ 35%. Reaching the "Performance Goal" means that the model result has been in the optimal range; The second level is "Performance Criteria", which stipulates that the conditions for meeting the standard are that the absolute value of NMB is ≤ ±30% and NME is ≤ 75%. Meeting the "Performance Criteria" means that the model result is acceptable. NMB ±30% is refers to the second level here. However, both NO3 and EC were overestimated with the NMB values more than 70%, especially for the peak values in both cases although the Nudging case had some improvements as reflected in lower values of GE, NMB, and NME, and higher value of R than the base case. Underestimations of SO42– and overestimations of NO3 are general issues in the PM simulations (Park and Kim, 2014; Ghim et al., 2017; Yu et al., 2005), which likely result from the uncertainty of heterogeneous reactions in the chemical model.

Table 2. Model performances for PM2.5, SO42–, NO3–, NH4+, EC, and OC concentrations (μg m–3) at the Xianghe site.

 
3.2.2 Regional PM2.5 over China

Figs. 5(b) and 5(c) show comparisons of spatial patterns of the mean results of the model simulations for the Nudging and base cases with observations overlaid for PM2.5 concentrations over China during the pollution episode from 11 to 19 January 2018. The statistics are listed in Table S2. Scatterplots and linear regressions of observed and simulated mean PM2.5 concentrations in 367 cities for the Nudging and base simulations during the studying period were shown in Fig. 5(a). As shown in Figs. 5(b) and 5(c), there were a severe haze episode observed in the central of China, including Henan, southern Hebei and central Shaanxi provinces with mean PM2.5 concentrations near 200 μg m–3. These provinces are included in the North China Plain (NCP), which is one of the regions with intensive pollutant emissions and serious air pollution in China (Li et al., 2019). The base case failed to simulate such high PM2.5 concentrations, while the Nudging case improved because the simulated temperatures of the Nudging case in this area were closer to the reality (Figs. 1(b) and 1(c)). The simulations of the Nudging case reproduced the spatial patterns of mean PM2.5 concentrations with the NMB value of –31%, much better than –42.0% in the base case. Based on linear fitting, the correction coefficient (slope) of fits to the data in Fig. 5(a) was 0.74 (0.66) for the Nudging case, better than the corresponding values for the base case. A close inspection of Figs. 5(b) and 5(c) indicates that both cases failed to reproduce the hotspots for the high PM2.5 concentrations in Xi’an (Shaanxi province) and Linfen (Shanxi province) cities. One of the reasons for these underestimations is because of uncertainties in the emission inventory for these regions used in this study. The underestimations of PM2.5 were also observed over China (Wu et al., 2021), especially in Northwest China (Hu et al., 2016), which attributed to the lack of dust emissions in the inventory and the underprediction of anthropogenic emissions for CO, NO2, and primary PM2.5. Contrary to the positive bias for PM2.5 at the Xianghe site (see Table 2), there was the negative bias (i.e., NMB = –31%) for the Nudging case over the China mainly because of the systematical underestimations of portion of low PM2.5 concentrations, especially over the western part of China, as reflected in Figs. 5(b) and 5(c).


3.3 Impacts of Observation Nudging on O3 and its Precursors


3.3.1 O3 and its precursors at the Taizhou site

There was a heavy O3 pollution event at the Taizhou site in June 2018, in which the observed hourly O3 concentrations exceeded the secondary standard for ambient air quality (200 μg m–3, GB 3095-2012) largely from 5 June to 7 June as shown in Fig. 6(a). The elevated precursors concentrations and favorable meteorological conditions promoted the chemical formation of the O3, thus resulting in this heavy O3 pollution. We focused on this episode (28 May–15 June 2018) to analyze the impacts of the observation nudging on O3 by comparing the differences of O3 and its precursors concentrations between observations and simulations. Fig. 6(a) shows time series of observed and simulated O3, CO, NO2, and VOCs hourly concentrations at the Taizhou site, and the performance statistics are summarized in Table 3. The simulated concentrations of O3, CO, NO2, and VOCs in the base case were higher than those in the Nudging case. One of the reasons might be that the underestimation of the PBLH in the base case decreased the vertical diffusion, thus causing the overestimations by increasing the accumulation of air pollutants to a lower height. The improvements in the model performance for O3 were reflected in the lower values of MB, GE, NMB, and NME in the Nudging case than the base case as shown in Table 3. For instance, the Nudging case significantly improved the predictions of O3 by decreasing the NMB values from 31.2% (the base case) to 4.4% with the high R value of 0.78, especially for overestimations of peak values of O3 in each day for the base case as indicated in Fig. 6.

Fig. 6. (a) Time series of hourly observed and simulated O3, CO, NO2 and VOCs concentrations at the Taizhou site. The green dash line refers to the secondary standard for ambient air quality (200 μg m–3). (b) Diurnal variations of observed and simulated O3, CO, NO2 and VOCs concentrations averaged from 28 May to 15 June 2018 at the Taizhou site. The error bars represent standard deviation from the mean.Fig. 6(a) Time series of hourly observed and simulated O3, CO, NO2 and VOCs concentrations at the Taizhou site. The green dash line refers to the secondary standard for ambient air quality (200 μg m–3). (b) Diurnal variations of observed and simulated O3, CO, NO2 and VOCs concentrations averaged from 28 May to 15 June 2018 at the Taizhou site. The error bars represent standard deviation from the mean.

The Nudging case significantly corrected the overestimation bias for VOC simulations in the base case by decreasing the NMB (NME) value from 114.3% (120.6%) (the base case) to 35.0% (49.8%) and increasing the R value from 0.58 to 0.72. This is due to the improvement of model performance for VOC during the nighttime in the Nudging case as indicated in the averaged diurnal variations in Fig. 6(b). Both model cases simulated the observed CO concentrations and their diurnal variations very well with the NMB values < 20% as shown in Fig. 6 and Table 3. For NO2, the Nudging case successfully corrected the overestimations of NO2 during the nighttime from 20:00 to 5:00 in the base case but underestimate observed NO2 in the early morning from 6:00 to 8:00 and late afternoon from 16:00 to 19:00 as shown in Fig. 6(b). The formation of O3 is mainly affected by the photochemistry of its precursors and the solar radiation. The O3 concentrations began to increase at 6:00 a.m., and gradually reached a peak at 15:00 p.m. as the photochemistry due to the increase of solar radiation enhanced. The overestimations of O3 for the base case were concentrated in the daytime, which might result from the overestimations of NO2 and VOCs during the nighttime, while the Nudging case could well capture the diurnal variation of O3.

Table 3. Model performances for O3, CO, NO2, and VOCs concentrations at the Taizhou site.

 
3.3.2 Regional O3 over China

Figs. 5(e) and 5(f) show comparisons of the spatial patterns of the mean results of the model simulations for the Nudging and base cases with observations overlaid for O3 during the pollution episode over China from 28 May to 15 June 2018. There was a severe O3 pollution observed in the central of China with the highest O3 concentration in Shandong province and its surrounding areas. The simulations of the Nudging case reproduced the spatial patterns of observed mean O3 concentrations very well with the NMB value of 0.97%, much lower than –5.67% in the base case as reflected by the scatter plots and regression equations in 367 cities in Fig. 5(d). A close inspection of Figs. 5(e) and 5(f) shows that the Nudging case improved the model predictions for the low O3 concentrations in the Chengdu–Chongqing areas and southern Shaanxi province where the base had significant overestimations. This is because the Nudging case improved the simulations for the meteorological conditions such as RH in this region, while the base had apparent underestimations (Figs. S1(e) and S1(f)). The overestimations of the high O3 concentrations near the Bohai Sea in the base case were also reduced in the Nudging case as reflected in Figs. 5(e) and 5(f). Tran et al. (2018) found that over-predictions of cloud cover by using observation nudging directly led to underestimation of the shortwave radiation budget which could inhibit O3 production in the CMAQ model. Therefore, a future study to compare the meteorological conditions such as cloud cover and radiation should be helpful in explaining the differences of O3 simulations between the Nudging and base cases.

 
4 CONCLUSIONS


In this study, the CMAQ simulations were conducted with meteorological fields generated by WRF using the original and observation nudging approaches to investigate if more accurate meteorological conditions were created and the better model performances for PM2.5, O3, and their related precursors in China were produced from the latter. Two pollution episodes (one for PM2.5 and another for O3) in 2018 were selected on the basis of the observations at the air quality monitoring supersites in Xianghe and Taizhou cities. In evaluating meteorological simulations, the Nudging cases showed better model performances on temperature, RH, and wind speed with higher IOA and lower MB, GE, and RMSE values. The Nudging case substantially corrected the high biases for wind speed with increase of the IOA values by about 28% in Xianghe and 63% in Taizhou relative to the base case.

During the PM2.5 pollution episode, the Nudging case improved model performances for PM2.5 and its chemical components (SO42–, NH4+, OC, NO3, and EC) at the Xianghe site with lower values of GE, NMB, and NME, and higher values of R than the base case. The simulated PM2.5 concentrations in the Nudging case agreed well with the observations, while they were overestimated at the peaks in the base case. For the regional PM2.5 over China, the simulations of the Nudging case reproduced the spatial patterns of mean PM2.5 concentrations in the 367 cities with the NMB value of –31%, much better than –42.0% in the base case. The negative bias (i.e., NMB = –31%) for PM2.5 in the Nudging case over the China was mainly because of the systematical underestimations of portion of low concentrations, especially over the western part of China.

During the O3 pollution episode, the improvements in the model performances in the Nudging case for O3, CO, NO2, and VOCs with lower values of GE, NMB, and NME, and higher values of R than the base case. The Nudging case significantly improved the predictions of O3 by decreasing the NMB values from 31.2% to 4.4% with a high R value of 0.78, and significantly corrected the overestimation bias for VOC simulations in the base case by decreasing the NMB value from 114.3% to 35.0% and increasing the R value from 0.58 to 0.72. For regional O3 over China, the simulations of the Nudging case reproduced the spatial patterns of observed mean O3 concentrations in the 367 cities very well with the NMB value of 0.97%, much lower than –5.67% in the base case. The Nudging case improved the model predictions for the low O3 concentrations in the Chengdu-Chongqing areas and southern Shaanxi province where the base had significant overestimations. The results of this study can provide ideas for better simulations of meteorology and air quality. It is noted that modeled PM2.5 and O3 biases are also impacted by the uncertainties in emissions, lateral boundary conditions and model biases, which may minimize the influence of the observation nudging on chemistry.

 
ACKNOWLEDGMENTS


This study is supported by the National Natural Science Foundation of China (No. 42175084, 21577126, 41561144004), Department of Science and Technology of China (No. 2018YFC0213506 and 2018YFC0213503), and National Research Program for Key Issues in Air Pollution Control in China (No. DQGG0107). Pengfei Li is supported by National Natural Science Foundation of China (No. 22006030), Initiation Fund for Introducing Talents of Hebei Agricultural University (412201904), and Hebei Youth Top Fund (BJ2020032)


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