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Investigating the Changes in Air Pollutant Emissions over the Beijing-Tianjin-Hebei Region in February from 2014 to 2019 through an Inverse Emission Method


doi: 10.1007/s00376-022-2039-9

  • In recent years, China has implemented several measures to improve air quality. The Beijing-Tianjin-Hebei (BTH) region is one area that has suffered from the most serious air pollution in China and has undergone huge changes in air quality in the past few years. How to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the future. To evaluate the changes in major air pollutant emissions over this region, this paper employs ensemble Kalman filtering (EnKF) for integrating the national ground monitoring pollutant observation data and the Nested Air Quality Prediction Modeling System (NAQPMS) simulation data to inversely estimate the emission rates of SO2, NOX, CO, and primary PM2.5 over BTH region in February from 2014 to 2019. The results show that SO2, NOX, CO, and primary PM2.5 emissions in the BTH region decreased in February from 2014 to 2019 by 83%, 37%, 41%, and 42%, while decreases in Beijing during this period were 86%, 67%, 59%, and 65%, respectively. Compared with the prior emission inventory, the inversion emission inventory reduces the uncertainty of multi-pollutant simulation in the BTH region, with simulated root mean square errors of the monthly average concentrations of SO2, NOX, PM2.5, and CO reduced by 41%, 30%, 31%, and 22%, respectively. The average uncertainties of SO2, NOX, PM2.5, and CO inversion emissions in 2014–19 are ±14.03% yr–1, ±28.91% yr–1, ±126.15% yr–1, and ±43.58% yr–1. Compared with the uncertainty of MEIC emission, the uncertainties of all species changed by +2% yr–1, –2% yr–1, –26% yr–1, and –4% yr–1, respectively. The spatial distribution results illustrate that air pollutant emissions are mainly distributed over the eastern and southern BTH regions. The spatial gap between the inversion emissions and MEIC emissions was further closed in 2019 compared to 2014. The results of this paper can provide a new reference for assessing changes in air pollution emissions over the BTH region in recent years and validating a bottom-up emission inventory.
    摘要: 近年来,中国已经实施了一系列改善空气质量的措施。京津冀地区(BTH)是中国空气污染最严重的地区之一,在过去几年里空气质量发生了显著变化。如何科学地评价这些变化过程是未来进一步改善该地区空气质量的关键问题。为评估该区域主要大气污染物排放的变化,本文采用集合卡尔曼滤波(EnKF)方法,结合全国地面污染物观测数据和嵌套网格空气质量预报系统(NAQPMS),反演了2014-19年2月BTH区域SO2、NOX、CO和一次PM2.5的排放速率。结果表明,2014-19年2月BTH区域SO2、NOX、CO和一次PM2.5排放量分别下降了83%、37%、41%和42%,同期北京下降了86%、67%、59%和65%。与先验的排放清单相比,反演排放清单降低了BTH区域多污染物模拟的不确定性,SO2、NOX、PM2.5和CO月平均浓度的模拟均方根误差分别降低了41%、30%、31%和22%。2014-19年SO2、NOX、PM2.5和CO反演排放的平均不确定度分别为±14.03%、±28.91%、±126.15%、±43.58%。与MEIC排放的不确定度相比,所有物种的不确定度分别变化了+2% yr−1、-2% yr−1、−26% yr−1和−4% yr−1。空间分布结果表明,BTH区域大气污染物排放主要分布在东部和南部。与2014年相比,2019年反演排放与MEIC排放的空间差距进一步缩小。本文的研究结果可为评价近年来京津冀地区大气污染排放变化及自下而上排放清单的验证提供新的参考。
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  • Figure 1.  The model domain of the WRF in this study is defined in a Lambert conform projection.

    Figure 2.  Comparison of observed and simulated meteorological parameters. Time series of simulated (red) and observed (black) daily averaged meteorological parameters at the Beijing site in February 2014–19.

    Figure 3.  The comparison between SO2, NOX, CO, and PM2.5 concentrations in the BTH region based on the priori and inversion inventory simulations. The scattered points represent the pollutant monthly concentrations for each site in the BTH region. The red points depend on the simulation concentrations based on the inversion emission inventory and the observed concentrations. The blue points represent the concentrations simulated based on the priori emission inventory and the observed concentrations. The black dotted line represents the 1:1 value.

    Figure 4.  Validation of the simulated average concentrations of SO2, NOX, CO, and PM2.5 based on the inversion inventory in February from 2014 to 2019. The colored areas represent the simulated averages in February from 2014 to 2019, and the colored dots represent the observed averages.

    Figure 5.  The emissions and change trends of SO2, NOX, CO, and PM2.5 in the BTH region in February from 2014 to 2019 for both the MEIC* emissions and inversion emissions. The histogram represents the inverted emissions, and the black dotted line represents the MEIC* emissions.

    Figure 6.  Monthly mean observations and simulated values based on inversion inventory in February from 2014 to 2019.

    Figure 7.  The wind rose in February from 2014 to 2019 in the Beijing site.

    Figure 8.  Spatial distributions of inversion estimated emissions of different air pollutants over the BTH region in 2014 and 2019 and their associated changes. The left column shows the SO2, NOX, CO, and PM2.5 inversion emissions in February 2014, and the middle column shows the SO2, NOX, CO, and PM2.5 inversion emissions in February 2019. The right column shows spatial differences in SO2, NOX, CO, and PM2.5 emissions between 2014 and 2019.

    Figure 9.  The spatial distribution difference between the MEIC* and inversion emission in February 2014 and February 2019 (inversion emissions – emissions from MEIC*). The red shading represents areas where inversion emissions are higher than the MEIC* emissions, and the blue shading represents areas where the MEIC* emissions are higher.

    Table 1.  Uncertainty of the prior emission of SO2, NOX, NH3, NMVOC, CO, PM10, PM2.5, BC, and OC.

    SpeciesSO2NOXNH3NMVOCCOPM10PM2.5BCOC
    Uncertainty±12%±31%±53%±68%±70%±132%±130%±208%±258%
    DownLoad: CSV

    Table 2.  The uncertainty of inversion emissions inventory in BTH region

    BTHSO2NOXCOPM2.5BCOC
    2014±10.50%±27.21%±45.54%±133.35%±223.73%±251.30%
    2015±23.37%±28.91%±45.29%±126.00%±214.39%±220.63%
    2016±18.65%±28.26%±45.54%±120.86%±230.38%±238.24%
    2017±12.79%±32.60%±41.89%±123.19%±217.89%±234.56%
    2018±10.50%±27.21%±41.12%±128.32%±223.73%±251.30%
    2019±8.33%±29.28%±42.07%±125.20%±219.95%±268.09%
    mean±14.03%±28.91%±43.58%±126.15%±221.68%±244.02%
    DownLoad: CSV

    Table 3.  Monthly emissions rates of the BTH region in February from 2014 to 2019, in units of Gg month–1, including Beijing (BJ), Tianjin (TJ), and cities of Hebei [Zhangjiakou (ZJK), Chengde (CD), Qinhuangdao (QHD), Tangshan (TS), Baoding (BD), Langfang (LF), Cangzhou (CZ), Shijiazhuang (SJZ), Hengshui (HS), Xintai (XT), Handan (HD)].

    BJTJZJKCDQHDTSBDLFCZSJZHSXTHDBTH
    SO2
    2014819147630134121781013160
    201571175312174121571016126
    201688753141138155918114
    2017364431172613481486
    2018233329413935854
    2019123215202312428
    NOX
    2014302513942717692261217199
    20152218995251249157915159
    2016161511852215491761119159
    201723301010631166132281218204
    20181114884211038156914130
    20191016575201039145811125
    CO
    20142632633202151386534241132872341551773383579
    20152752112802571124055081293412621832844703715
    20162643102492321295594191253901851632966313951
    20172362742142551184412941022703211312544743382
    2018139165246238118449212531862791152303462775
    201910915214121912830318654154166851472652109
    PM2.5
    201413710951817382171413145
    2015137610411224111481416139
    201697793111139857998
    2017786731018411156911116
    201866683101138115101298
    2019464637102610491285
    DownLoad: CSV
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Manuscript received: 10 March 2022
Manuscript revised: 26 August 2022
Manuscript accepted: 09 September 2022
通讯作者: 陈斌, bchen63@163.com
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Investigating the Changes in Air Pollutant Emissions over the Beijing-Tianjin-Hebei Region in February from 2014 to 2019 through an Inverse Emission Method

    Corresponding author: Xiao TANG, tangxiao@mail.iap.ac.cn
    Corresponding author: Haoyue WANG, wanghaoyue22@ynu.edu.cn
  • 1. School of Earth Sciences, Yunnan University, Kunming 650500, China
  • 2. LAPC & ICCES, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 3. University of Chinese Academy of Sciences, Beijing 100049, China
  • 4. Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China

Abstract: In recent years, China has implemented several measures to improve air quality. The Beijing-Tianjin-Hebei (BTH) region is one area that has suffered from the most serious air pollution in China and has undergone huge changes in air quality in the past few years. How to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the future. To evaluate the changes in major air pollutant emissions over this region, this paper employs ensemble Kalman filtering (EnKF) for integrating the national ground monitoring pollutant observation data and the Nested Air Quality Prediction Modeling System (NAQPMS) simulation data to inversely estimate the emission rates of SO2, NOX, CO, and primary PM2.5 over BTH region in February from 2014 to 2019. The results show that SO2, NOX, CO, and primary PM2.5 emissions in the BTH region decreased in February from 2014 to 2019 by 83%, 37%, 41%, and 42%, while decreases in Beijing during this period were 86%, 67%, 59%, and 65%, respectively. Compared with the prior emission inventory, the inversion emission inventory reduces the uncertainty of multi-pollutant simulation in the BTH region, with simulated root mean square errors of the monthly average concentrations of SO2, NOX, PM2.5, and CO reduced by 41%, 30%, 31%, and 22%, respectively. The average uncertainties of SO2, NOX, PM2.5, and CO inversion emissions in 2014–19 are ±14.03% yr–1, ±28.91% yr–1, ±126.15% yr–1, and ±43.58% yr–1. Compared with the uncertainty of MEIC emission, the uncertainties of all species changed by +2% yr–1, –2% yr–1, –26% yr–1, and –4% yr–1, respectively. The spatial distribution results illustrate that air pollutant emissions are mainly distributed over the eastern and southern BTH regions. The spatial gap between the inversion emissions and MEIC emissions was further closed in 2019 compared to 2014. The results of this paper can provide a new reference for assessing changes in air pollution emissions over the BTH region in recent years and validating a bottom-up emission inventory.

摘要: 近年来,中国已经实施了一系列改善空气质量的措施。京津冀地区(BTH)是中国空气污染最严重的地区之一,在过去几年里空气质量发生了显著变化。如何科学地评价这些变化过程是未来进一步改善该地区空气质量的关键问题。为评估该区域主要大气污染物排放的变化,本文采用集合卡尔曼滤波(EnKF)方法,结合全国地面污染物观测数据和嵌套网格空气质量预报系统(NAQPMS),反演了2014-19年2月BTH区域SO2、NOX、CO和一次PM2.5的排放速率。结果表明,2014-19年2月BTH区域SO2、NOX、CO和一次PM2.5排放量分别下降了83%、37%、41%和42%,同期北京下降了86%、67%、59%和65%。与先验的排放清单相比,反演排放清单降低了BTH区域多污染物模拟的不确定性,SO2、NOX、PM2.5和CO月平均浓度的模拟均方根误差分别降低了41%、30%、31%和22%。2014-19年SO2、NOX、PM2.5和CO反演排放的平均不确定度分别为±14.03%、±28.91%、±126.15%、±43.58%。与MEIC排放的不确定度相比,所有物种的不确定度分别变化了+2% yr−1、-2% yr−1、−26% yr−1和−4% yr−1。空间分布结果表明,BTH区域大气污染物排放主要分布在东部和南部。与2014年相比,2019年反演排放与MEIC排放的空间差距进一步缩小。本文的研究结果可为评价近年来京津冀地区大气污染排放变化及自下而上排放清单的验证提供新的参考。

    • The years 2013–19 marked a milestone in China’s air pollution control and ecological civilization construction. In 2013, China released the Action Plan on the Prevention and Control of Air Pollution for the first time with PM2.5 concentration as a binding indicator, representing the transition of China's pollution control route from total emission control to air quality management (Lu et al., 2020). With the continuous deepening of China's air pollution control process, China has done much work in air pollution control and air quality management and has attained remarkable achievements. As a result, air quality has been markedly improved (Lu et al., 2019; Li et al., 2020; Qu et al., 2020). In 2017, the annual average concentration of PM2.5 in Chinese cities dropped by 35% compared with 2013, and the annual average PM2.5 concentration in Beijing decreased from 90 μg m–3 to 58 μg m–3 (Xue et al., 2021). In 2018, the Three-Year Action Plan for Winning the Blue Sky Defense War (Lu et al., 2020) was released. Consequently, the air quality of key regions in China has improved after three years of effort. In the BTH region, the annual average PM2.5 concentration decreased by 21% (Xue et al., 2021). The Chinese government has undertaken strict air pollution emission control measures throughout this period, including restructuring the energy and industrial sectors, revamping the structure of the transportation sector, and promoting technological progress.

      As one of the areas with the most serious air pollution in China, Beijing and its surrounding areas show a marked improvement in air quality in recent years under a series of strict air pollution control measures. The reasons for this change include the adjustment for the long-term energy structure, such as the Road to Railway and Coal to Gas projects, as well as measures for specific areas such as "one city, one policy" and temporary emission reduction measures under a heavy pollution forecast or an early warning situation. Under the superposition of various pollutant emission reduction measures and urbanization processes, pollutant emission shows complex temporal and spatial variation characteristics. It is urgent to analyze the trends and causes of emissions changes to acquire a better understanding of the recent air quality changes in China and the impact of mitigation measures on air quality. Zheng et al. (2018b) found that emission control measures are the primary driving force behind the downward trend in the concentration of major air pollutants such as PM2.5, SO2, NOX, and CO from 2013 to 2017.

      Traditional bottom-up inventories are still limited in their ability to capture the dynamic short-term changes of pollutant emissions in urban and regional areas, as there are still challenges and limitations. The main challenges are: (1) Emissions are estimated differently by different inventories. Due to uncertainty in activity rates and emission factors, global and regional emissions vary widely among different emission inventories (Cao et al., 2011; Granier et al., 2011). (2) In general, inventory updates are slow and lag behind the current emissions situation for several years (Wang et al., 2018). As a result, inventories tend to become quickly outdated (Zheng et al., 2018b). (3) The process of making inventories is time-consuming and often costly. (4) The impact of some short-term pollution control measures or policies is difficult to extract using the existing bottom-up inventory. The current demand for emission inventories is still challenged by significant uncertainties, such as estimating regional sources and estimating short-term changes in emissions (Streets et al., 2013).

      As atmospheric environment monitoring capabilities evolve and the development of data assimilation methods advances, the use of pollutant concentration observations to invert and optimize emissions offers a new way to reduce the uncertainty of emissions. Through an atmospheric chemical model, the pollutant emission inversion method integrates various observation data into the inversion system to optimize and rapidly update the existing emission inventory (Carmichael et al., 2008a; Wu et al., 2021). This method has the advantages of low cost and good forecasting capability, which has helped to attain some achievements in the inversion and optimization of SO2, NOX, CO, CH4, NH3, and other emissions (Xu et al., 2008; Houweling et al., 2017; Zhang et al., 2018; Kong et al., 2019). In air pollution emission research, the emission inversion method gradually shows its unique value and has become a significant development and application of air pollution data assimilation.

      Zheng et al. (2018a) used the CO observation data of the Measurement of Pollution in the Troposphere (MOPITT) to invert the CO emission trend in East Asia from 2005 to 2016. Miyazaki et al. (2020) used an ensemble Kalman filter to assimilate multiple sets of satellite data to invert the changes of global NOX emissions from 2005 to 2018, and Qu et al. (2019) used the GEOS-Chem adjoint model and NO2 concentration data of OMI (Ozone Monitoring Instrument) to obtain the variation trend of NOX emissions in China from 2005 to 2012. The existing long-term emission inversion research is heavily dependent on satellite observation data, but the emission inversion results are easily affected by the uncertainty of satellite observation. For example, using two different sets of OMI SO2 satellite observation data for SO2 emissions in the same month, the difference between SO2 inversion emissions obtained by Wang et al. (2016) and Koukouli et al. (2018) can exceed 200%. Compared with satellite observation data, ground concentration data has a higher temporal and spatial resolution which can better identify changes of air pollutant emissions. There are many practical studies on inversion that use ground pollutant concentration observations. For example, Tang et al. (2013) estimated the summer CO emissions in Beijing in 2010 based on the ensemble Kalman filtering method combined with site observation data and found that CO emissions were underestimated in the bottom-up inventory in Beijing and its surrounding areas. Furthermore, the inverted emission inventory can reduce the CO simulation bias. Dai et al. (2021) dynamically updated the SO2 grid emission across China by assimilating the hourly ground observations data with a four-dimensional local ensemble Kalman filter method. Feng et al. (2021) developed a regional assimilation system based on ground observation data with a three-dimensional variational algorithm to estimate CO, SO2, NOX, PM2.5, and PMC emissions in China in December 2016. Based on the priori emission inventory and the EnKF method to quickly update and optimize the emissions, this paper inverts and estimates the SO2, NOX, CO, and PM2.5 emissions in the Beijing-Tianjin-Hebei (BTH) region in February from 2014 to 2019 through ground concentration observation-based data. February was selected as the typical month of winter as the inversion period. Based on the inversion results, the long-term change characteristics were analyzed, and the results were compared with the bottom-up inventory for evaluation and validation, providing a reference for revealing the changes in major pollutant emissions in the BTH region in February from 2014 to 2019.

      The remainder of this paper is organized as follows. Section 2 describes the data and methods, section 3 presents the results. Section 4 discusses the results and finally section 5 provides a conclusion and summary.

    2.   Data and methods
    • The meteorological simulation was evaluated with the hourly observations from China Meteorological Administration (http://data.cma.cn/). In the assimilation process, the observation data of the China National Environmental Monitoring Network (http://www.cnemc.cn) was used. In 2013, the monitoring network had 612 observation sites. By 2015, the monitoring network had covered 369 cities, including 1436 observation sites. The data used in this study included hourly ground observation concentrations of PM2.5, SO2, NO2, and CO. To ensure data quality, this study uses the adaptive quality control method developed by Wu et al. (2018) to eliminate outliers in the original observation data, including temporal and spatial inconsistencies, small abnormal changes over a long period, periodic calibration exceptions, and less PM10 than PM2.5 in concentration observations.

    • The priori emission inventories used in this study include the HTAP_v2.2 global anthropogenic emission inventory for the base year of 2010 that was overwritten by regional anthropogenic emissions from the Multi-resolution Emission Inventory for China (MEIC) (Li et al., 2017b; Zheng et al., 2018b)(http://meicmodel.org.cn) for the base year of 2016 in China, the biomass burning emissions from the Global Fire Emissions Database (GFED_v4) (Van der Werf et al., 2010; Randerson et al., 2017), biogenic volatile organic compounds (BVOC) emissions from MEGAN-MACC (Sindelarova et al., 2014), marine VOCs emissions from the POET database (Granier et al., 2005), soil NOX emissions from Regional Emission inventory in Asia (REAS) (Yan et al., 2003), and the lightning NOX emissions obtained by averaging the lightning NOX emissions from the year 1983 to 1990 (Price et al., 1997).

      The emission inventory used in China is MEIC. Therefore, the priori emission uncertainty in this study is set according to the uncertainty estimates by Zhang et al. (2009), as shown in Table 1.

      SpeciesSO2NOXNH3NMVOCCOPM10PM2.5BCOC
      Uncertainty±12%±31%±53%±68%±70%±132%±130%±208%±258%

      Table 1.  Uncertainty of the prior emission of SO2, NOX, NH3, NMVOC, CO, PM10, PM2.5, BC, and OC.

    • This study adopts NAQPMS developed by Wang et al. (2006) to simulate air quality. The physical and chemical processes in the model mainly include advection, diffusion, dry and wet deposition, gas phase, liquid phase, and heterogeneous chemical processes. Its spatial structure adopts a three-dimensional regional Eulerian model, with terrain-following coordinates as vertical coordinates. It can simulate dust, PM2.5, PM10, SO2, NOX, CO, O3, NH3, and other pollutants at regional and urban scales.

      The gas-phase chemistry module adopts Carbon-Bond Mechanism Z (CBM-Z) (Zaveri and Peters, 1999), which includes 71 species that participate in chemical reactions with 133 chemical reactions, while the liquid-phase chemistry module uses the RADM reaction mechanism, including 22 kinds of gases and aerosols (Chang et al., 1987). Aerosols in NAQPMS include sulfate, nitrate, ammonium, and secondary organic aerosols.

      In this study, the hourly meteorological inputs to NAQPMS are provided by the Weather Research and Forecasting model (WRF). The National Center for Atmospheric Research/National Center for Environmental Prediction (NCAR/NCEP) provided the Final (FNL) Operational Global Analysis data (http://rda.ucar.edu/datasets) for the WRF with a 1° × 1° spatial resolution, which were utilized for lateral boundary conditions and initial conditions of the meteorological fields. Figure 1 shows the domain of the WRF in this study. Domain 1 includes grid points with a horizontal resolution of 45 km × 45 km, covering most of Asia; Domain 2 includes 432 × 339 grids with a horizontal resolution of 15 km × 15 km. The central longitude and latitude of the area are 105°E and 34°N, respectively. The domain of NAQPMS is the same as Domain 2 of WRF. Vertically, the model calculates pollutant concentrations between 20 layers, with the top layer at a height of 20 km. The chemical boundary conditions of NAQPMS were provided by MOZART, the global atmospheric chemical transport model (Brasseur et al., 1998; Hauglustaine et al., 1998).

      Figure 1.  The model domain of the WRF in this study is defined in a Lambert conform projection.

      The simulation period is from January 24 to February 28 in 2014–19. The first seven days are used as the "spin-up" time for NAQPMS. The WRF runs have been integrated over an individual 36-hour period in the daily meteorological simulation. For each NAQPMS run, there was a meteorological "spin-up" period at the beginning of the first 12 hours of meteorological input.

    • This study establishes the link between concentrations and emissions through historical ensemble datasets simulated by the NAQPMS and then uses observed concentrations to invert emissions. The inversion method adopts the extended state vector method based on EnKF, which combines the concentration and the emission as the state variable to invert and estimate the multi-pollutant emission. The augmented state variable used is shown in Eqs. (1–3):

      The x represents the augmented state, $ \boldsymbol{c} $ and $ \,\boldsymbol{\beta } $ are the vectors of concentration and emission, respectively:

      The pollutants in this study include SO2, NO2, CO, and PM2.5 concentrations, and $ \boldsymbol{\beta } $ represents the perturbation factor of the pollutant emission. This study includes the following perturbation factors: $\,{\boldsymbol{\beta }}_{{\mathrm{P}\mathrm{M}}_{2.5}} $ for primary fine particulate matter emissions; $ \,{\boldsymbol{\beta }}_{\mathrm{B}\mathrm{C}} $ for black carbon emissions; $ \,{\boldsymbol{\beta }}_{\mathrm{O}\mathrm{C}} $ for primary organic carbon emissions; $\,{\boldsymbol{\beta }}_{{\mathrm{N}\mathrm{O}}_{X}}$ for nitrogen oxide emissions; $ \,{\boldsymbol{\beta }}_{\mathrm{C}\mathrm{O}} $ for carbon monoxide emissions; $ \,{\boldsymbol{\beta }}_{{\mathrm{S}\mathrm{O}}_{2}} $ for sulfur dioxide emissions. According to the error distribution of the priori inventory, $ \,\boldsymbol{\beta } $ is assumed to follow a Gaussian distribution with a mean equal to 1 and a standard deviation equal to the uncertainty of the priori emissions for each species (Zhang et al., 2009).

      We use the DEnKF (Deterministic EnKF) analysis scheme, developed by Sakov and Oke (2008), to perform inversion estimates for the emission of multiple pollutants:

      The augmented state variable $\overline{{\boldsymbol{x}}_{\rm{b}}}$ represents the background state and $\overline{{\boldsymbol{x}}_{\rm{a}}}$ represents the analysis state. The $ {\boldsymbol{y}}_{\rm{o}} $ represents the vector corresponding to the observations of each pollutant. $\boldsymbol{H}$ represents the observation operator, which is used to map the dimension of state vector x to the dimension of observational vector $\boldsymbol{H} \overline{{\boldsymbol{x}}_{\rm{b}}}$. $ \boldsymbol{R} $ is the observational error covariance matrix, noting that the representative error of observation in this study is given according to the study of representative error of observation in the BTH region by Li et al. (2019). The calculated gain matrix, $ \boldsymbol{K} $ is as follows:

      where the ${\boldsymbol{P}}_{\rm{e}}^{\rm{b}}$ represents the ensemble-estimated background error covariance matrix, which is calculated by the following:

      where the $ \lambda $ is the inflation factor. To prevent the underestimation of the error caused by the limited set of ensemble size, we use the dynamic error estimation method to inflate the background error according to the local analysis of Wang and Bishop (2003). The $ \boldsymbol{d} $ is the corresponding localized innovation vector, and $ p $ is the number of observations as shown in Eqs. 7 and 8.

      Due to the limited ensemble size, the EnKF always suffers from spurious long-distance correlations. This limitation requires the use of localization. Consistent with the reanalysis data construction of Kong et al. (2021), a local assimilation scheme is used for inversion emission estimation.

      Here, the $ \rho (i,j) $ represents the localization coefficient for correlations between grids $ i $ and $ j $. The $ h(i,j) $ represents the distance between these two grids and $ L $ is the decorrelation length, which is used to control the intensity of localization. The smaller $ L $ is, the greater the degree of localization is. The converse is also true; a larger L indicates a smaller degree of localization.

      Considering China's strict emission reduction measures, emissions are rapidly changing, so there may be a gap between the priori emissions and the actual emissions, which does not conform to the unbiased assumption in EnKF and may easily lead to the problem of insufficient adjustment. Therefore, we use an iterative scheme for the inversion emission of PM2.5, SO2, NOX, and CO. According to the actual inversion results, three iterations are enough to correct the gap of the priori emission.

      In addition, to avoid the problem of spurious correlation between different pollutants, we set the cross-species correlation coefficient to zero in the inversion. The primary fine particulate matter components, PM2.5, BC, and OC, are inverted using PM2.5 observation data. Because of the lack of detailed observation data of BC and OC, this study uses the PM2.5 observation concentration to constrain the emissions of BC and OC. In the following, we define the PM2.5 emission as the sum of PM2.5, BC, and OC emissions, and the emission of the remaining pollutants are inverted using the observation data of the corresponding pollutants.

    3.   Results
    • The WRF was employed to provide the hourly meteorological inputs to NAQPMS. The meteorological simulation was evaluated with the hourly observations from China Meteorological Administration (http://data.cma.cn/). Figure 2 shows a time series comparison of observed and simulated temperature, relative humidity, wind direction, and wind speed at the Beijing site. For the diurnal variation simulation, especially temperature and relative humidity, the correlation coefficients ranged from 0.75–0.91. The simulated temperature values of the model fit the observed values well. For the wind field simulation, the correlation coefficients range from 0.57–0.68. As a whole, the WRF can reproduce the temporal distribution characteristics of major meteorological factors during the simulation period, which can provide reliable input data for NAQPMS.

      Figure 2.  Comparison of observed and simulated meteorological parameters. Time series of simulated (red) and observed (black) daily averaged meteorological parameters at the Beijing site in February 2014–19.

      To validate the inversion emission inventories, we indirectly validate the inversion emissions by comparing the simulated corresponding pollutant concentrations with their observations (e.g., Tang et al., 2013; Feng et al., 2020). This paper uses the priori and inversion emissions to simulate SO2, NO2, CO, and PM2.5 concentrations at each observation site in the BTH region during February from 2014 to 2019. The statistical parameters for comparison include the mean absolute error (MAE), mean deviation (MBE), root mean square error (RMSE), and correlation coefficient (r).

      Figure 3 shows that the inversion emissions can improve the SO2 simulation performance compared with the priori emissions. For all the stations in the BTH region, compared with the simulation results based on the priori emission, the MAE decreased from 48.5 μg m–3 to 10.2 μg m–3, the RMSE reduced by 41% and the r increased by 0.48 based on the inversion emission. The NO2 inversion emissions reduced the overestimation compared with the simulation results based on the priori emission, and the MBE in the BTH region decreased from 9.8 μg m–3 to 6.5 μg m–3. The RMSE was reduced by 30%. For CO, based on the inversion emissions, the simulated value is closer to the observed value, which reduced the MAE from 0.42 mg m–3 to 0.17 mg m–3 and decreased the RMSE by 22% in the BTH region. For PM2.5, based on the inversion emissions, the simulated value is closer to the observed value. In Hebei, the MBE increased from –1.44 μg m–3 to 14.45 μg m–3, and the other statistical parameters show an improvement in model performance. In the BTH region, base on the inversion emission inventory, the MAE decreased from 25.1 μg m–3 to 16.5 μg m–3, the RMSE can be reduced by 31%, and the r can be increased by 0.12.

      Figure 3.  The comparison between SO2, NOX, CO, and PM2.5 concentrations in the BTH region based on the priori and inversion inventory simulations. The scattered points represent the pollutant monthly concentrations for each site in the BTH region. The red points depend on the simulation concentrations based on the inversion emission inventory and the observed concentrations. The blue points represent the concentrations simulated based on the priori emission inventory and the observed concentrations. The black dotted line represents the 1:1 value.

      Figure 4 shows that based on the inversion emission inventory, the spatial distribution characteristics of SO2, NO2, CO, and PM2.5 simulation concentrations agree with the observation concentrations in the BTH region. The MBE of SO2 in the BTH region is –13.4 μg m–3. The spatial variation is consistent in most areas, and the RMSE is 16.0 μg m–3. The MBE of NO2 is –0.3 μg m–3, which was slightly underestimated at the sites in Beijing and Tianjin. The r is 0.74, and the spatial variation of the simulated and observed concentrations are generally consistent. The MBE of CO is –0.05 mg m–3. The model slightly overestimates the concentrations at the sites in southern Hebei and slightly underestimates them at the sites of Beijing and Tianjin. The model agrees well with the center of the high CO concentration area. For PM2.5, the MBE is 14.0 μg m–3, indicating a general overestimation by the model; however, the simulated values of the sites are in good agreement with the concentration values of the observation sites. The simulated concentrations were mostly consistent with the observed concentrations, as evidenced by an r of 0.95 and the RMSE of 18.83 μg m–3. Overall, based on the inversion emission inventory, the model performs well in simulating the concentrations of the above-mentioned pollutants over the BTH region.

      Figure 4.  Validation of the simulated average concentrations of SO2, NOX, CO, and PM2.5 based on the inversion inventory in February from 2014 to 2019. The colored areas represent the simulated averages in February from 2014 to 2019, and the colored dots represent the observed averages.

    • Previous studies have shown that the uncertainty of emissions is an important source of uncertainty in an atmospheric chemical simulation (Hanna et al., 1998; Carmichael et al., 2008b; Li et al., 2017b). Uncertainty in the quantification of emission inventories helps decision-makers determine the accessibility of pollutant emission reduction targets and scientifically formulate air quality control strategies (Frey et al., 1999). The core of EnKF is to use a group of disturbed set samples to characterize the uncertainty of emissions. Therefore, this study constructs collective samples by disturbing the emissions. We analyzed the uncertainty of SO2, NOX, CO, and PM2.5 in February form 2014 to 2019, and the variation coefficient (CV; units: %) was employed to calculate the uncertainty of pollutant emission. The standard deviation STD of ensemble simulation is given by Eq. (10) where $ \overline{x} $ represents the mean value of ensemble simulation. The sample here refers to the disturbance coefficient after inversion.

      The uncertainty obtained by the emission disturbance coefficient after inversion is shown in Table 2. For fine particles, the uncertainties of PM2.5, BC, and OC are still relatively large, especially BC and OC, as the uncertainty exceeds 200%. In the inversion process, there is no direct observation of BC and OC, and the observed concentration of PM2.5 is used to adjust their emissions, so there is still a large uncertainty. Except for SO2, the uncertainty of other species decreased, especially CO, which decreased by more than 20%. Overall, although some uncertainty remains, this approach does provide a reference for regional emissions.

      BTHSO2NOXCOPM2.5BCOC
      2014±10.50%±27.21%±45.54%±133.35%±223.73%±251.30%
      2015±23.37%±28.91%±45.29%±126.00%±214.39%±220.63%
      2016±18.65%±28.26%±45.54%±120.86%±230.38%±238.24%
      2017±12.79%±32.60%±41.89%±123.19%±217.89%±234.56%
      2018±10.50%±27.21%±41.12%±128.32%±223.73%±251.30%
      2019±8.33%±29.28%±42.07%±125.20%±219.95%±268.09%
      mean±14.03%±28.91%±43.58%±126.15%±221.68%±244.02%

      Table 2.  The uncertainty of inversion emissions inventory in BTH region

    • Figure 5 shows the changes in the inversion emissions of SO2, NOX, CO, and PM2.5 in the BTH region in February from 2014 to 2019, and Table 3 shows the values of the inversion emissions. To better compare the inversion emissions, we add the natural emission based on the MEIC (Li et al., 2017b; Zheng et al., 2018b; Zheng et al., 2021) emission, and the MEIC emissions with added natural emissions are denoted as MEIC*. However, in the BTH region, the monthly natural emissions are more than two orders of magnitude smaller than the anthropogenic emissions, which explains why the natural emissions are difficult to detect in the figure. For example, in the BTH region, the SO2 emissions from natural emissions are about 0.03 Gg, the emissions of NOX from natural emissions are about 0.03 Gg, CO emissions are about 0.000019 Gg, and the emissions of PM2.5 are about 0.01 Gg.

      Figure 5.  The emissions and change trends of SO2, NOX, CO, and PM2.5 in the BTH region in February from 2014 to 2019 for both the MEIC* emissions and inversion emissions. The histogram represents the inverted emissions, and the black dotted line represents the MEIC* emissions.

      BJTJZJKCDQHDTSBDLFCZSJZHSXTHDBTH
      SO2
      2014819147630134121781013160
      201571175312174121571016126
      201688753141138155918114
      2017364431172613481486
      2018233329413935854
      2019123215202312428
      NOX
      2014302513942717692261217199
      20152218995251249157915159
      2016161511852215491761119159
      201723301010631166132281218204
      20181114884211038156914130
      20191016575201039145811125
      CO
      20142632633202151386534241132872341551773383579
      20152752112802571124055081293412621832844703715
      20162643102492321295594191253901851632966313951
      20172362742142551184412941022703211312544743382
      2018139165246238118449212531862791152303462775
      201910915214121912830318654154166851472652109
      PM2.5
      201413710951817382171413145
      2015137610411224111481416139
      201697793111139857998
      2017786731018411156911116
      201866683101138115101298
      2019464637102610491285

      Table 3.  Monthly emissions rates of the BTH region in February from 2014 to 2019, in units of Gg month–1, including Beijing (BJ), Tianjin (TJ), and cities of Hebei [Zhangjiakou (ZJK), Chengde (CD), Qinhuangdao (QHD), Tangshan (TS), Baoding (BD), Langfang (LF), Cangzhou (CZ), Shijiazhuang (SJZ), Hengshui (HS), Xintai (XT), Handan (HD)].

      The results show that pollution emissions in BTH have declined. Compared with February 2014, the inversion emissions of SO2, NOX, CO, and PM2.5 in February 2019 decreased by 86%, 67%, 59%, and 65% in Beijing, decreased by 81%, 32%, 39%, and 41% in Hebei, and decreased by 90%, 34%, 42%, and 18% in Tianjin, respectively. In general, SO2, NOX, CO, and PM2.5 inversion emissions in the BTH region have decreased by 83%, 37%, 41%, and 42%. Similarly, the MEIC* emissions of SO2, NOX, CO, and PM2.5 in the BTH region decreased by 58%, 15%, 38%, and 44%, respectively. The SO2, NOX, and CO inversion emission reduction rates are higher than the reduction rates of the MEIC* emissions, except for PM2.5. For emissions of both inventories, the monthly average deviation of emissions among SO2, NOX, CO, and PM2.5 in February from 2014 to February 2019 were –3.5 Gg, 29.7 Gg, 1598 Gg, and 2.7 Gg in the BTH region, respectively.

    • The SO2 inversion emissions show a downward trend in the BTH region (Fig. 5a), which corresponds with the downward trend of the MEIC*. Some studies show that the emission of SO2 has decreased in recent years for the following reasons: (1) The majority of SO2 emissions come from power plants and industrial sectors. Since power plants and industrial sectors have gradually installed more desulfurization facilities, SO2 emissions have decreased significantly (Liu et al., 2018; Zheng et al., 2020); (2) The SO2 emission rapid drawdown after 2017 reflects the fact that coal heating had been replaced by natural gas and was successful in reducing SO2 emissions (Meng et al., 2018).

    • In the BTH region, NOX emissions have decreased since 2014 (Fig. 5b). In 2017, NOX emissions were still high. It is evident from both inventories that NOX emissions have been reduced less than SO2 emissions. Some studies offer the following explanation: (1) SO2 emissions mainly come from coal-burning, and NOX emissions mainly come from coal-burning and vehicle emissions (Meng et al., 2018). Emission control measures for coal burning have met the SO2 emission reduction requirements but do not meet the NOX emission reduction requirements (Li et al., 2020). In addition, Zhang et al. (2019) estimated that measures to phase out small coal-fired boilers and "small and polluting" enterprises greatly contributed to reducing SO2 emissions yet did not significantly reduce NOX emissions; (2) The increase in the number of motor vehicles has led to an increase in NOX emissions, and relatively weak motor vehicle management measures have offset emissions reductions from controls on coal-fired power plants (Li et al., 2020); (3) The control of NOX emissions from industrial sectors is less than ideal: for example, Zheng et al. (2020) established a point-source emission inventory of the steel and coking industry from 2005 to 2017, which shows that the control over the iron and steel and coking industries is relatively weak so that the emissions in this sector have been showing an upward trend.

      It is worth noting that both the inversion and MEIC* emissions showed that NOX emissions in Beijing rebounded in February 2017. The reason may be that the Coal to Gas project was widely promoted in 2017 throughout the BTH region. The Coal to Gas project refers to the use of natural gas instead of coal. While natural gas is also a fossil fuel, much NOX will be produced during its combustion (Gao, 2014). As a result of comparing the NOX emissions of gas-fired cogeneration and coal-fired cogeneration in Beijing, Jiang et al. (2014) concluded that coal-to-gas cogeneration did not reduce the emissions significantly; on the contrary, natural gas consumption will increase substantially, which will result in more NOX emissions. To obtain the same heat, the gas-fired cogeneration needs to burn more fuel, resulting in more NOX emissions than coal-fired cogeneration (Guan et al., 2015). Zhao et al. (2020) pointed out that Coal to Gas increased the concentration of NOX by 51.8% in the southeastern area of Beijing during the heating period, which indicated that the Coal to Gas policy significantly reduced the SO2 concentration but increased the NOX concentration. It can be seen that the rebound of NOX emissions in Beijing and surrounding areas in February 2017 may be closely related to the large-scale implementation of the Coal to Gas project.

    • There is an obvious gap between the two inventories (Fig. 5c). Compared with the emissions of MEIC*, the CO inversion emissions are higher on average. Previous studies show significant uncertainties in the CO emissions inventory (Ma and van Aardenne, 2004; Zhang et al., 2009). Saikawa et al. (2017) found that CO emissions have the largest differences among different inventories in China by comparing five different anthropogenic emission inventories. Most previous studies have found that bottom-up inventories underestimated anthropogenic CO emissions (Kopacz et al., 2010; Tang et al., 2013). The residential sector is a major source of uncertainty in current inventories of anthropogenic emissions in China (Li et al., 2017a).

      After 2017, the BTH and Surrounding Areas Air Pollution Prevention and Control Work Plan and the BTH and Surrounding Areas 2018−19 Autumn and Winter Comprehensive Air Pollution Control Action Plan were issued by the Ministry of Ecology and Environment. It was aimed at the “2+26 city” to formulate air pollution prevention and control measures in autumn and winter, including implementing intensive measures such as establishing the means to replace residential scattered coal, comprehensively treating coal-fired boilers, adjusting the transportation structure, and managing “small and polluting” enterprises. The residential sector emissions are the primary source of CO, PM2.5, BC, and OC in China (Zheng et al., 2018b). The control of residential sources and dust sources was also strengthened in recent years, especially through the implementation of Coal to Gas and Coal to Electricity projects. These measures have greatly reduced the burning of solid fuels such as scattered coal in residential sources.

    • For PM2.5, the uncertainty of the two inventories is still large (Fig. 5d). Based on field survey results, Cheng et al. (2017), found that official statistics may underestimate the actual amount of residential coal in North China by three times. This will have the effect of causing greater uncertainty in PM2.5 emissions.

      PM2.5 emissions from residential sources have become increasingly important in recent years. In winter, emissions from residential sources in Beijing and its surrounding areas exceed those from the transportation and power sectors combined (Liu et al., 2017; Zhang et al., 2021). Zhang et al. (2017) used the brute force approach to find that residential coal combustion significantly contributed to the high PM2.5 concentrations, representing nearly half of the regional monthly average. Ji et al. (2022) found the coal ban policy had worked effectively in winter after 2017 in the BTH region. The implemented measures to control the residential sources have greatly reduced the emissions of incomplete combustion products. The control of residential emissions can have a significant effect on reducing PM2.5 pollution.

    • Figure 6 shows the time series of observed and simulated concentrations based on the inversion inventory from February 2014 to 2019. The results show that in 2017, the concentrations of all pollutants tended to rebound compared to the previous year. At the same time, the inversion emission in 2017 shows that except for the increase of NOX in the BTH region and the increase of PM2.5 in Tianjin and Hebei, the rest of the pollutants emissions are in decline. In terms of meteorological conditions, in Beijing and its surrounding areas, in most cases, the upstream area relative to the northwest gale that appears in winter is a clean source (Lu et al., 2017), while southerly winds will lead to an unfavorable diffusion of pollutants. The wind field (Fig. 7) in February 2017 shows that compared with other years it had a high frequency of southwesterly winds, and such weather conditions are not conducive to the diffusion of pollutants. Shen et al. (2021) found that meteorological conditions caused NO2 changes of more than 5 mg m–3 in the winters of 2017 (including December 2016) in BTH, and the increase of NO2 in winter was mainly attributed to unfavorable weather conditions. Consequently, meteorological conditions cannot be ignored in February 2017.

      Figure 6.  Monthly mean observations and simulated values based on inversion inventory in February from 2014 to 2019.

      Figure 7.  The wind rose in February from 2014 to 2019 in the Beijing site.

      In February 2019, except for NOX inversion emissions, which rebounded in Tianjin, the emissions of other pollutants showed a downward trend in the BTH region. The concentration of NO2 has increased in the BTH region, and the concentration of PM2.5 has increased in Tianjin and Hebei. In 2019, there was also a high frequency of southwesterly winds, which offset the emission reduction effect.

    • Air pollution in the heating season in the BTH region has always been particularly serious. Adverse weather conditions combined with high emissions in winter make the BTH region extremely prone to heavy-pollution weather. Since implementing the Action Plan and the Three-Year Action Plan, the pollution situation in the BTH region has improved.

      To illustrate more clearly the effectiveness of the emission reduction measures during the heating period in recent years, Figure 8 shows the spatial distribution of the inversion emission of different pollutants and their emission reduction percentage in the BTH region in February from 2014 to 2019.

      Figure 8.  Spatial distributions of inversion estimated emissions of different air pollutants over the BTH region in 2014 and 2019 and their associated changes. The left column shows the SO2, NOX, CO, and PM2.5 inversion emissions in February 2014, and the middle column shows the SO2, NOX, CO, and PM2.5 inversion emissions in February 2019. The right column shows spatial differences in SO2, NOX, CO, and PM2.5 emissions between 2014 and 2019.

      The spatial distribution of SO2 emissions is similar to the results of Wang et al. (2018). The spatial analysis indicates an imbalanced spatial distribution pattern, with higher SO2 emissions in the southern BTH and lower emissions in the north in recent years. In 2019, the emissions of SO2 in the BTH region were obviously reduced, and the emission reduction ratio was generally above 80%. The NOX emission reduction ratios were more than 60% in Beijing. It is worth noting that the coastal ports in the Qinhuangdao area are high-value areas of NOX emissions. The CO emission reduction ratio in Beijing is above 30%, but the surrounding areas in northern Henan and Shijiazhuang show an increasing trend in emissions. The emission reduction of PM2.5 is high in Beijing, with an emission reduction ratio is above 60%. The emission reduction ratios in Zhangjiakou and Hebei are also high, mostly above 60%. It is worth noting that PM2.5 emissions in Tianjin, Cangzhou, and Handan have increased compared to 2014.

      To compare the differences in emissions assessments for the BTH region between the two inventories, Figure 9 displays the difference in the spatial distribution between the MEIC* and inversion emissions in February 2014 and February 2019 and generally illustrates that the SO2, NOX, CO, and PM2.5 inversion emissions in the southeastern part of the BTH region are lower than those in the MEIC*. In the northwestern BTH region (i.e., near Zhangjiakou), the inversion emissions are higher than the MEIC* in some areas. Especially for CO, in the northern region of the BTH region, inversion emissions are mostly higher than MEIC* emissions. We found that the larger differences between the two emissions were concentrated in places with high emissions. Overall, the difference between the two emission inventories in 2019 was smaller than in 2014.

      Figure 9.  The spatial distribution difference between the MEIC* and inversion emission in February 2014 and February 2019 (inversion emissions – emissions from MEIC*). The red shading represents areas where inversion emissions are higher than the MEIC* emissions, and the blue shading represents areas where the MEIC* emissions are higher.

    4.   Summary and discussion
    • The method based on the improved EnKF can help to quickly generate high spatiotemporal resolution pollutant emissions in the BTH region. Localization is applied to avoid false adjustments between distant grids. Using higher-resolution data from ground observations may offer better corrections and thereby provide more reliable constrained emissions. Based on the high spatiotemporal resolution of emissions, the effectiveness of the measures undertaken to reduce emissions in the BTH region in recent years can be further confirmed. This approach allows for a better understanding of the distribution of emissions in the Beijing-Tianjin-Hebei region. It can refine the emission source locations and provide an effective technical means to control seasonal air pollution.

      The changing trend of pollutant inversion emissions showed a fluctuation trend. There are a few things worth noting, NOX inversion emissions rebounded in BTH in 2017, and NOX emissions of MEIC* show the same trend in Beijing, which is likely related to the large-scale implementation of the Coal to Gas project. The inversion emission estimates provide a partial validation for bottom-up inventory. According to the inversion emissions and emission of MEIC*, the monthly average deviations between SO2, NOX, CO, and PM2.5 in February from 2014 to February 2019 are –3.5 Gg, 29.7 Gg, 1598 Gg, and 2.7 Gg in the BTH region. There is still a big gap between the two lists of CO emissions. The large gaps between the two emission inventories were concentrated in places with high emissions. Overall, the difference between the two emissions inventories in 2019 was smaller than in 2014. We quantified the uncertainty of the inversion emissions and found that there are still large uncertainties in the emissions of fine particulate matter.

      The simulation results, based on inversion inventories, show that the RMSE of the monthly average simulation concentrations of SO2, NOX, CO, and PM2.5 in the BTH decreased by 41%, 30%, 31%, and 22%, respectively, compared with the simulation based on the priori inventory. However, there are some limitations, as this method for estimating emissions is affected by observational data, model errors, and inversion methods. The limitations include: (1) In this study, only the uncertainty of emissions determined simulation uncertainty. Other error sources, such as meteorological simulation uncertainty and parameterization process uncertainty of physical or chemical processes, were ignored, which may lead to underestimating simulation uncertainty. (2) Limited ensemble samples may also lead to underestimating simulation errors, especially for high-resolution assimilation applications. (3) When estimating pollutant emissions, the model does not distinguish between anthropogenic and natural sources of pollution. It estimates all sources of pollutants, which may not fully describe anthropogenic emissions. (4) Emission uncertainty in the assimilation experiment is set according to the uncertainty of the priori emission inventory, which follows a Gaussian distribution where the standard deviation is equal to the uncertainty of the a priori emissions of each species. Due to the limited number of ensemble samples used during the use of EnKF, a filtering divergence would occur in the process of assimilation, so an inflation algorithm is used to increase the uncertainty. In addition, the emission inversion scheme with EnKF assumes that the assimilated variables are unbiased, which is not entirely true for the emissions. The inverse estimation, with one step of assimilation, can easily lead to the problem of insufficient adjustment in the case of large emission deviations. In this study, an iterative inversion method was adopted. The emission uncertainty is inflated, and the assimilated step is repeated at one time step. The advantage of this approach is that the difference between observation and model simulation can be minimized to achieve optimal estimation. The disadvantage is that this method cannot effectively distinguish the fast-change errors from the effects of meteorological modeling or the physical and chemical parameterization during the inflation process. This approach may result in uncertainty after error inflation and hinder the ability to characterize the real uncertainty of emission. Since the difference between top-down emissions and bottom-up inventories leads to different trends, what is causing the difference and how to close the gap in the future remain questions worth exploring.

      Acknowledgements. We acknowledge the MEIC group for providing the emission data. This work was supported by National Natural Science Foundation (Grant Nos. 41875164 and 92044303).

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