Multi-resolution Emission Inventory For China Research Articles

Characterization of winter PM2.5 source contributions and impacts of meteorological conditions and anthropogenic emission changes in the Sichuan Basin, 2002–2020

In this study, the Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality–Integrated Source Apportionment Method (CMAQ–ISAM) were utilized, which were integrated with the Multiresolution Emission Inventory for China (MEIC) emission inventory, to simulate winter PM2.5 concentrations, regional transport, and changes in emission source contributions in the Sichuan basin (SCB) from 2002 to 2020, considering variations in meteorological conditions and anthropogenic emissions. The results indicated a gradual decrease in the basin's winter average PM2.5 concentration from 300 μg/m3 to 120 μg/m3, with the most significant decrease occurring after 2014, reflecting the actual impact of China's air pollution control measures. Spatially, the main pollution area shifted from Chongqing to Chengdu and the western basin. The sources of PM2.5 at the eastern and western margins of the basin have remained stable and have been dominated by local emissions for many years, while the sources of PM2.5 in the central part of the basin have evolved from a multiregional co-influenced source during the early period to a high proportion of local emissions; except for boundary condition sources, residential sources were the main PM2.5 sources in the basin (approximately 29.70 %), followed by industrial sources (approximately 14.11 %). Industrial sources exhibited higher contributions in Chengdu and Chongqing and gradually stabilized with residential sources over the years, while residential sources dominated in the eastern and western parts of the basin and exhibited a declining trend. Meteorological conditions exacerbated pollution in the whole basin from 2008 to 2014, especially in the west (21–40 μg/m3). The eastern basin and Chongqing exhibited more years with alleviated meteorological pollution, including a 40+ μg/m3 decrease in Chongqing from 2002 to 2005. Reduced anthropogenic emissions alleviated annual pollution levels, with a greater reduction (> −20 μg/m3) after 2011 due to pollution control measures.

An Evaluation of the Influence of Meteorological Factors and a Pollutant Emission Inventory on PM2.5 Prediction in the Beijing–Tianjin–Hebei Region Based on a Deep Learning Method

In this study, a Long Short-Term Memory (LSTM) network approach is employed to evaluate the prediction performance of PM2.5 in the Beijing–Tianjin–Hebei region (BTH). The proposed method is evaluated using the hourly air quality datasets from the China National Environmental Monitoring Center, European Center for Medium-range Weather Forecasts ERA5 (ECMWF-ERA5), and Multi-resolution Emission Inventory for China (MEIC) for the years 2016 and 2017. The predicted PM2.5 concentrations demonstrate a strong correlation with the observed values (R2 = 0.871–0.940) in the air quality dataset. Furthermore, the model exhibited the best performance in situations of heavy pollution (PM2.5 > 150 μg/m3) and during the winter season, with respective R2 values of 0.689 and 0.915. In addition, the influence of ECMWF-ERA5’s hourly meteorological factors was assessed, and the results revealed regional heterogeneity on a large scale. Further evaluation was conducted by analyzing the chemical components of the MEIC inventory on the prediction performance. We concluded that the same temporal profile may not be suitable for addressing emission inventories in a large area with a deep learning method.

Sensitivities of ozone to its precursors during heavy ozone pollution events in the Yangtze River Delta using the adjoint method

Although the concentrations of five basic ambient air pollutants in the Yangtze River Delta (YRD) have been reduced since the implementation of the “Air Pollution Prevention and Control Action Plan” in 2013, the ozone concentrations still increase. In order to explore the causes of ozone pollution in YRD, we use the GEOS–Chem and its adjoint model to study the sensitivities of ozone to its precursor emissions from different source regions and emission sectors during heavy ozone pollution events under typical circulation patterns. The Multi-resolution Emission Inventory for China (MEIC) of Tsinghua University and 0.25° × 0.3125° nested grids are adopted in the model. By using the T–mode principal component analysis (T–PCA), the circulation patterns of heavy ozone pollution days (observed MDA8 O3 concentrations ≥160 μg m−3) in Nanjing located in the center area of YRD from 2013 to 2019 are divided into four types, with the main features of Siberian Low, Lake Balkhash High, Northeast China Low, Yellow Sea High, and southeast wind at the surface. The adjoint results show that the contributions of emissions emitted from Jiangsu and Zhejiang are the largest to heavy ozone pollution in Nanjing. The 10 % reduction of anthropogenic NOx and NMVOCs emissions in Jiangsu, Zhejiang and Shanghai could reduce the ozone concentrations in Nanjing by up to 3.40 μg m−3 and 0.96 μg m−3, respectively. However, the reduction of local NMVOCs emissions has little effect on ozone concentrations in Nanjing, and the reduction of local NOx emissions would even increase ozone pollution. For different emissions sectors, industry emissions account for 31 %–74 % of ozone pollution in Nanjing, followed by transportation emissions (18 %–49 %). This study could provide the scientific basis for forecasting ozone pollution events and formulating accurate strategies of emission reduction.

Retrieval anthropogenic CO2 emissions from OCO-2 and comparison with gridded emission inventories

Estimating anthropogenic CO2 emissions from satellite observations contributes to transparency in CO2 emissions reporting. In this study, we proposed a method for calculating CO2 emissions using Orbiting Carbon Observatory-2 (OCO-2) XCO2 (the column-averaged CO2 dry-air mole fraction) observations. We identified local XCO2 plume enhancements on the OCO-2 track and retrieved CO2 emissions through minimizing the difference between the XCO2 enhancement identified from OCO-2 and the XCO2 enhancement simulated by the Gaussian plume model based on emission rates provided by Open-Data Inventory for Anthropogenic Carbon Dioxide (ODIAC). Among 473 cases successfully retrieved from the OCO-2 from September 2014 to June 2023, the average hourly estimated CO2 emissions (OCO-2 emissions) are 6166.52 tCO2/h with the uncertainty of 1604.51 tCO2/h. Comparison with ODIAC, EDGAR (Emissions Database for Global Atmospheric Research) and MEIC (Multi-resolution Emission Inventory for China) emission inventories shows that the OCO-2 emissions are slightly overestimated. Globally, OCO-2 emissions are approximately 1.25 times that of ODIAC and 1.22 times that of EDGAR, while in China, they are 1.39 times that of MEIC. By conducting comprehensive assessments encompassing case studies in California, Riyadh, and Xinjiang, and by comparing the results with relevant investigations, this discrepancy can be attributed to missing statistics on emission inventories, actual emissions varying with the time of day, or complex terrain and other factors. This study effectively quantifies CO2 emissions using OCO-2 satellite data and provides valuable insights for monitoring significant regional CO2 emissions.

First estimation of hourly full-coverage ground-level ozone from Fengyun-4A satellite using machine learning

Ground-level ozone (O3), renowned for its adverse impacts on human health and crop production, has garnered significant attention from governmental and public sectors. To address the limitations posed by sparse and uneven ground-level O3 observations, this study proposes an innovative method for hourly full-coverage ground-level O3 estimation using machine learning. Meteorological data from National Centers for Environmental Prediction global forecasting system, satellite data from Fengyun-4 A(FY-4 A) and Ozone Monitoring Instrument, emission inventory from Multi-resolution Emission Inventory for China, and other auxiliary data are utilized as input variables, while ground-based O3 observations serve as the response variable. The method is applied on a monthly basis across China for the year 2022, resulting in the generation of an hourly full-coverage high-resolution (4 km) ground-level O3 estimation, termed ML-derived-O3. Cross-validation results demonstrate the robustness of ML-derived-O3 yielding a coefficient of determination (R 2) of 0.96 (0.91) for sample-based (site-based) evaluations and a root-mean-square error (RMSE) of 9.22 (13.65) µg m−3. However, the date-based evaluation is less satisfactory due to the imbalanced training data, resulting from the pronounced daily variations in ground-level O3 concentrations. Nevertheless, the seasonal and hourly ML-derived-O3 exhibits high prediction accuracy, with R 2 values surpassing 0.95 and RMSE remaining below 7.5 µg m−3. This study marks a significant milestone as the first successful attempt to obtain hourly full-coverage ground-level O3 data across China. The diurnal variation of ML-derived-O3 demonstrates high consistency with ground-based observations, irrespective of clear or cloudy days, effectively capturing ground-level O3 pollution exposure events. This novel estimation method will be employed to establish a long-term high spatial-temporal resolution ground-level O3 dataset, which holds valuable applications for air pollution monitoring and environmental health research in future endeavors.

Current status of model predictions of volatile organic compounds and impacts on surface ozone predictions during summer in China

Abstract. Volatile organic compounds (VOCs) play a crucial role in the formation of tropospheric ozone (O3) and secondary organic aerosols. VOC emissions are generally considered to have larger uncertainties compared to other pollutants, such as sulfur dioxide and fine particulate matter (PM2.5). Although predictions of O3 and PM2.5 have been extensively evaluated in air quality modeling studies, there has been limited reporting on the evaluation of VOCs, mainly due to a lack of routine VOC measurements at multiple sites. In this study, we utilized VOC measurements from the “Towards an Air Toxic Management System in China” (ATMSYC) project at 28 sites across China and assessed the predicted VOC concentrations using the Community Multiscale Air Quality (CMAQ) model with the widely used Multi-resolution Emission Inventory for China (MEIC). The ratio of predicted to observed total VOCs was found to be 0.74 ± 0.40, with underpredictions ranging from 2.05 to 50.61 ppbv (5.77 % to 85.40 %) at 24 sites. A greater bias in VOC predictions was observed in industrial cities in the north and southwest, such as Jinan, Shijiazhuang, Lanzhou, Chengdu, and Guiyang. In terms of different VOC components, alkanes, alkenes, non-naphthalene aromatics (ARO2MN), alkynes, and formaldehyde (HCHO) had prediction-to-observation ratios of 0.53 ± 0.38, 0.51 ± 0.48, 0.31 ± 0.38, 0.41 ± 0.47, and 1.21 ± 1.61, respectively. Sensitivity experiments were conducted to assess the impact of the VOC prediction bias on O3 predictions. While emission adjustments improved the model performance for VOCs, resulting in a change in the ratio of total VOCs to 0.86 ± 0.47, they also exacerbated O3 overprediction relative to the base case by 0.62 % to 6.27 % across the sites. This study demonstrates that current modeling setups and emission inventories are likely to underpredict VOC concentrations, and this underprediction of VOCs contributes to lower O3 predictions in China.

A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application

Abstract. Top-down atmospheric inversion infers surface–atmosphere fluxes from spatially distributed observations of atmospheric composition in order to quantify anthropogenic and natural emissions. In this study, we developed a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) based on the Weather Research and Forecasting–Community Multiscale Air Quality (WRF–CMAQ) modeling system model, the three-dimensional variational (3D-Var) algorithm, and the ensemble square root filter (EnSRF) algorithm. This system can simultaneously assimilate hourly in situ CO, SO2, NO2, PM2.5, and PM10 observations to infer gridded emissions of CO, SO2, NOx, primary PM2.5 (PPM2.5), and coarse PM10 (PMC) on a regional scale. In each data assimilation window, we use a “two-step” scheme, in which the emissions are inferred first and then input into the CMAQ model to simulate initial conditions (ICs) of the next window. The posterior emissions are then transferred to the next window as prior emissions, and the original emission inventory is only used in the first window. Additionally, a “super-observation” approach is implemented to decrease the computational costs, observation error correlations, and influence of representative errors. Using this system, we estimated the emissions of CO, SO2, NOx, PPM2.5, and PMC in December and July 2016 over China using nationwide surface observations. The results show that compared to the prior emissions (2016 Multi-resolution Emission Inventory for China – MEIC 2016)), the posterior emissions of CO, SO2, NOx, PPM2.5, and PMC in December 2016 increased by 129 %, 20 %, 5 %, 95 %, and 1045 %, respectively, and the emission uncertainties decreased by 44 %, 45 %, 34 %, 52 %, and 56 %, respectively. With the inverted emissions, the RMSE of simulated concentrations decreased by 40 %–56 %. Sensitivity tests were conducted with different prior emissions, prior uncertainties, and observation errors. The results showed that the two-step scheme employed in RAPAS is robust in estimating emissions using nationwide surface observations over China. This study offers a useful tool for accurately quantifying multi-species anthropogenic emissions at large scales and in near-real time.

A novel method for spatial allocation of volatile chemical products emissions: A case study of the Pearl River Delta

The spatial allocation of volatile chemical products (VCPs) emissions is often inaccurate, which hampers the evaluation and improvement of models and the study of urban secondary pollution and exposure risk. This work developed a 1 km grid spatial allocation factors dataset based on the actual emission characteristics of each VCPs sector. For the industrial VCPs sector, we used industrial output per unit firm as the spatial allocation factors. For the non-industrial VCPs sector, we used specific datasets (e.g., GDP (Gross Domestic Product) for personal care products use, etc.) as the spatial allocation factors. Our work accounted for the high emission characteristics of industrial point sources and the dependence conditions of non-industrial VCPs use. Using this methodology, we developed a 1 km gridded emission inventory (denoted by Mo-E) of VCPs in the Pearl River Delta (PRD, Excluding Hong Kong Special Administrative Region (SAR) and Macau SAR) region in 2017. The results show that the non-methane volatile organic compounds (NMVOCs) emissions of VCPs in the PRD region in 2017 were 1046.7 Kt, with coatings (567.3 Kt) and personal care products (225.5 Kt) being the key sectors for emission control. Different cities in the PRD region have different VCPs control strategies. The industrial VCPs sector was the dominant VCPs emitter in high-emitting cities (64.9%–72.2%), while the non-industrial VCPs sector was the main contributor in low-emitting cities (43.3%–58.4%). More VCPs emissions were allocated to medium-population-density areas (37.6% higher than before) and within 3 km of the urban-rural fringe (41.4% higher than before), compared with the spatial characteristics of VCPs emissions from Multi-resolution Emission Inventory for China (MEIC), due to the relocation of industrial VCPs enterprises and the architectural activities during urban expansion. Meanwhile, the uniform distribution pattern caused by population density allocation in these areas was corrected, and the point-source high-emission feature was tracked. The VCPs emissions in high-population-density areas were overestimated (31.4%), with personal care products (57.9%) as the main emission sources in these areas. We compared the Mo-E inventory with an inventory (Mo-M) derived using MEIC's spatial allocation factors, and found a maximum difference in emissions per unit grid of +14,602 t and a minimum of −880 t. Finally, we discuss the uncertainties in the emissions from each VCP sector (±30–80%) and the spatial emission patterns, which resulted from the spatial allocation dataset (±10.4–20%) and the spatial analysis methods (−6.9-+7.8%).

An improved hourly-resolved atmospheric NOx emission inventory of industrial sources based on Continuous Emission Monitoring System data: Case of Jiangsu Province, China

Current emission inventories with low spatial and temporal resolutions, slow updates, and great uncertainty can no longer meet the new demands for the precise prevention and control of air pollution. This study considered industrial sources of Jiangsu province in 2018 as the research object and divided key industrial sources into 16 processes. Based on the continuous emission monitoring system (CEMS) data of key enterprises, millions of hourly scale monitoring data from 17842-point source enterprises at 169289 emission outlets were collected. An hourly scale high-resolution industrial source NOx emission inventory was constructed and compared with existing inventories. The total NOx emissions of power plants, industrial boilers, ferrous metal manufacturing, non-metallic mineral manufacturing, and chemical manufacturing industries in Jiangsu Province in 2018 were 55, 27, 64, 28, and 3 Gg, respectively. The total emissions from industrial sources were high in summer, reaching a peak of 17 Gg in July, and low in winter, reaching 11 Gg in February. The emission factors of the power plants, coking, and cement industries decreased by 15.95%, 29.03%, and 51.61%, respectively. Hourly scales showed that the power plants had the most considerable fluctuation in 24 h emissions at 5.94%, with high emissions occurring in the afternoon and at night. However, the 24 h emissions of ferrous metal manufacturing fluctuated slightly, at only 3.22%, and the high emissions mostly occurred at night. The WRF-Chem model was used for simulation validation. The NO2 simulation results based on this study's inventory were significantly better than those based on the Multi-resolution Emission Inventory for China (MEIC), with normalized mean biases (NMBs) of −7.1% and −10.7% for January and July, respectively, as opposed to 30.8% and 14.4% in the MEIC. The hourly scale high-precision emission inventory established in this study is significant for formulating real-time differentiated precise prevention and control policies and improving the accuracy of air quality models.

Contribution of marine biological emissions to gaseous methylamines in the atmosphere: An emission inventory based on multi-source data sets

Methylamines are a class of highly reactive organic alkaline gases in the atmosphere. At present, the gridded emission inventories of amines used in the atmospheric numerical model is mostly based on the amine/ammonia ratio method and do not consider the air-sea exchange of methylamines, which oversimplifies the emission scenario. Marine biological emissions (MBE), an important source of methylamines, has been insufficiently investigated. These shortcomings in the inventories can limit the simulation of amines by numerical models in the context of compound pollution in China. To acquire a more complete gridded inventory of amines (monomethylamine (MMA), dimethylamines (DMA), and trimethylamines (TMA)), we established a more reasonable MBE inventory of amines by using multi-source data sets (Sea Surface Temperature (SST), Chlorophyll-a (Chla), Sea Surface Salinity (SSS), NH3 column concentration (NH3), and Wind Speed (WS)), and merged it with the anthropogenic emissions (AE) inventory (by adopting the amine/ammonia ratio method and the Multi-resolution Emission Inventory for China (MEIC)). The new methodology can reveal the air-sea exchange fluxes and direction of different amines. Oceans can act as a sink for DMA and source for TMA while it can be either a source or sink for MMA. The concentration of amines above the coastal area increased significantly when the MBE was merged to the AE inventory. TMA and MMA showed significant increases, TMA increased by 43,917.0 %, and 804.0 %, in July 2015 and December 2019, respectively; while MMA increased by 2635.4 % and 0.37 % during the same periods; however, only slight changes were observed in the DMA concentration (−3.9 % in July 2015, and 1.1 % in December 2019). WS, Chla, and the total dissolved concentration of amines ([C+(s)tot]) were found to be the dominant factors affecting MBE fluxes. In addition, the emission fluxes and spatial distribution of AE, and wet deposition also affect the simulation of amines concentration.

Significant contribution of inland ships to the total NOx emissions along the Yangtze River

Abstract. Despite the large number of domestic inland river vessels in China, information on inland ship emissions is very limited, since legislation for shipping emission control is limited and there is no monitoring infrastructure. Taking the Yangtze River in the region of Nanjing as a research area, we compiled a ship emission inventory based on real-time information received from automatic identification system (AIS) signals combined with ship-related data provided by the China Classification Society (CCS) database. The total ship emissions we derived for the Jiangsu section of the Yangtze River from September 2018 to August 2019 for NOx, SO2, PM10 and PM2.5 were 83.5, 0.04, 0.006 and 0.005 kt yr−1, respectively. This ship emission inventory we constructed was compared with the Multi-resolution Emission Inventory for China (MEIC), the Shipping Emission Inventory Model (SEIM) and the satellite-derived emissions using the Daily Emissions Constrained by Satellite Observations (DECSO) algorithm. The results show a consistent spatial distribution, with riverine cities having high NOx pollution. With this comparison we analyzed the relative impact of ship emissions on densely populated regions along the river. Inland ship emissions of NOx are shown to contribute significantly, accounting for at least 40 % of air pollution close to the river.

High-resolution regional emission inventory contributes to the evaluation of policy effectiveness: a case study in Jiangsu Province, China

Abstract. China has been conducting a series of actions on air quality improvement for the past decades, and air pollutant emissions have been changing swiftly across the country. Provinces are an important administrative unit for air quality management in China; thus a reliable provincial-level emission inventory for multiple years is essential for detecting the varying sources of pollution and evaluating the effectiveness of emission controls. In this study, we selected Jiangsu, one of the most developed provinces in China, and developed a high-resolution emission inventory of nine species for 2015–2019, with improved methodologies for different emission sectors, best available facility-level information on individual sources, and real-world emission measurements. Resulting from implementation of strict emission control measures, the anthropogenic emissions were estimated to have declined 53 %, 20 %, 7 %, 2 %, 10 %, 21 %, 16 %, 6 %, and 18 % for sulfur dioxide (SO2), nitrogen oxides (NOx), carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), ammonia (NH3), inhalable particulate matter (PM10), fine particulate matter (PM2.5), black carbon (BC), and organic carbon (OC) from 2015 to 2019, respectively. Larger abatement of SO2, NOx, and PM2.5 emissions was detected for the more developed region of southern Jiangsu. During the period from 2016 to 2019, the ratio of biogenic volatile organic compounds (BVOCs) to anthropogenic volatile organic compounds (AVOCs) exceeded 50 % in the month of July, indicating the importance of biogenic sources for summer O3 formation. Our estimates in annual emissions of NOx, NMVOCs, and NH3 were generally smaller than the national emission inventory, MEIC (the Multi-resolution Emission Inventory for China), but larger for primary particles. The discrepancies between studies resulted mainly from different methods of emission estimation (e.g., the procedure-based approach for AVOC emissions from key industries used in this work) and inconsistent information of emission source operation (e.g., the penetration and removal efficiencies of air pollution control devices). Regarding the different periods, more reduction of SO2 emissions was found between 2015 and 2017 and of NOx, AVOCs, and PM2.5 between 2017 and 2019. Among the selected 13 major measures, the ultra-low-emission retrofit in the power sector was the most important contributor to the reduced SO2 and NOx emissions (accounting for 38 % and 43 % of the emission abatement, respectively) for 2015–2017, but its effect became very limited afterwards as the retrofit had been commonly completed by 2017. Instead, extensive management of coal-fired boilers and the upgrade and renovation of non-electrical industry were the most important measures for 2017–2019, accounting collectively for 61 %, 49 %, and 57 % reduction of SO2, NOx, and PM2.5, respectively. Controls on key industrial sectors were the most effective for AVOC reduction in the two periods, while measures relating to other sources (transportation and solvent replacement) have become more important in recent years. Our provincial emission inventory was demonstrated to support high-resolution air quality modeling for multiple years. Through scenario setting and modeling, worsened meteorological conditions were found from 2015 to 2019 for PM2.5 and O3 pollution alleviation. However, the efforts on emission controls were identified to largely overcome the negative influence of meteorological variation. The changed anthropogenic emissions were estimated to contribute 4.3 and 5.5 µg m−3 of PM2.5 concentration reduction for 2015–2017 and 2017–2019, respectively. While O3 was elevated by 4.9 µg m−3 for 2015–2017, the changing emissions led to 3.1 µg m−3 of reduction for 2017–2019, partly (not fully though) offsetting the meteorology-driven growth. The analysis justified the validity of local emission control efforts on air quality improvement and provided a scientific basis to formulate air pollution prevention and control policies for other developed regions in China and worldwide.

Effect of agricultural soil wind erosion on urban PM2.5 concentrations simulated by WRF-Chem and WEPS: A case study in Kaifeng, China

Dust emission induced by agricultural soil wind erosion is one of the main sources of atmospheric particulate matter (PM) in dryland areas. However, most current air quality models do not consider this emission source, resulting in large uncertainties in PM simulations. Here we estimated the agricultural PM2.5 (particulate matter with an aerodynamic diameter <2.5 μm) emission around Kaifeng, a prefecture-level city in central China, using the Wind Erosion Prediction System (WEPS), with the MEIC (Multi-resolution Emission Inventory for China) as an anthropogenic emission source. We then plugged these estimates into the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) to simulate an air pollution episode in Kaifeng, China. Results showed that the addition of agricultural soil PM2.5 emissions significantly improved the ability of WRF-Chem to accurately simulate PM2.5 concentrations. The PM2.5 concentration mean bias and correlation coefficient of not considered and considered agricultural dust emission were −72.35 μg m−‍3 and 3.31 μg m−‍3 and 0.3 and 0.58, respectively. The PM2.5 emitted by the agricultural soil wind erosion contributed around 37.79% of the PM2.5 in the Kaifeng municipal district during this pollution episode. This study confirmed that the dust emission caused by agricultural soil wind erosion can significantly impact urban PM2.5 concentrations which surrounded by large areas of farmland, and also indicated that coupling dust emissions from farmland with anthropogenic air pollutant emissions can improve the accuracy of air quality models.

Anthropogenic NOx Emission Estimations over East China for 2015 and 2019 Using OMI Satellite Observations and the New Inverse Modeling System CIF-CHIMERE

The Chinese government introduced regulations to control emissions and reduce the level of NOx pollutants for the first time with the 12th Five-Year Plan in 2011. Since then, the changes in NOx emissions have been assessed using various approaches to evaluate the impact of the regulations. Complementary to the previous studies, this study estimates anthropogenic NOx emissions in 2015 and 2019 over Eastern China using as a reference the Hemispheric Transport of Air Pollution (HTAP) v2.2 emission inventory for 2010 and the new variational inversion system the Community Inversion Framework (CIF) interfaced with the CHIMERE regional chemistry transport model and OMI satellite observations. We also compared the estimated NOx emissions with the independent Multi-resolution Emission Inventory for China (MEIC) v1.3, from 2015. The inversions show a slight global decrease in NOx emissions (in 2015 and 2019 compared to 2010), mainly limited to the most urbanized and industrialized locations. In the locations such as Baotou, Pearl River Delta, and Wuhan, the estimations in 2015 compared to 2010 are consistent with the target reduction (10%) of the 12th Five-Year Plan. Comparisons between our emission estimates and MEIC emissions in 2015 suggest that our estimates likely underestimate the emission reductions between 2010 and 2015 in the most polluted locations of Eastern China. However, our estimates suggest that the MEIC inventory overestimates emissions in regions where MEIC indicates an increase of the emissions compared to 2010.

Downward trend of NO2 in the urban areas of Beijing-Tianjin-Hebei region from 2014 to 2020: Comparison of satellite retrievals, ground observations, and emission inventories

Significant downward trend of NO2 over BTH region has been detected by both satellite and ground surface observations in recent years due to the continuous regulatory policies in China. Previous studies mostly focused on the scientific issue of analyzing the distribution characteristics of NO2 and the drivers of interannual NO2 variations. Whether the declines derived from independent data sets with different spatiotemporal representations are comparable? If they are, what is the significance of these observed quantitative interannual trends for evaluating the estimated NOx emission changes is poorly characterized. This issue is still need to be brought to more attention.This study first investigates the variations of the long-term trends derived from satellite and ground observations, and then uses these data to evaluate the updates of NOx emissions from MEIC (Multi-resolution Emission Inventory for China) inventory during 2014–2020 over the BTH (Beijing-Tianjin-Hebei) region. In the 12 cities examined here, both the OMI (Ozone Monitoring Instrument) and the AQS (Air Quality System) detected comparable and substantial downward trends, with the overall reduction ranging from 28.2% to 49.5% and from 27.4% to 54.8% in urban areas according to OMI and AQS, respectively. The NOx emission adopted from MEIC showed a similar downward trend, but with a much lower reduction ranging from 4.3% to 32.5% (from 2014 to 2019). Large discrepancies were also found in the annual trends between NOx emissions (MEIC) and NO2 observations (OMI and AQS) especially after 2016, suggesting that the magnitude of recent emission reductions over the BTH region doesn't seem to be well captured by the MEIC emission projection, and in urban areas, in particular, there are significant overestimations. Assuming a linear relationship between NOx emissions and NO2 columns, 45.2% overestimation errors of simulations using priori NOx emissions can be explained by using posteriori inventory optimized by OMI observation. Our findings demonstrate that the combination of satellite and ground observations is likely to provide more reliable estimates of NOx emission and its trend over the BTH region under continuous emission reductions.

Update of SO2 emission inventory in the Megacity of Chongqing, China by inverse modeling

Chongqing, a metropolitan with over 32 million residents in southwest China, has suffered from SO2 pollution since 1980s. The emission inventory is an important tool to evaluate the SO2 pollution and to design the effective emission reduction policies. The present work developed a scheme to update the obsolescent SO2 emission inventory in Chongqing obtained from Multi-resolution Emission Inventory for China in 2008 (MEIC2008). The updated emission inventory was estimated by integrating the a priori knowledge of the baseline emissions and the current observations based on Bayesian inference, in which the source-receptor sensitivities were calculated by the Decoupled Direct Method in Three Dimensions in the Community Multiscale Air Quality Modeling System (CMAQ DDM-3D). An analytical solution of the Bayesian theorem was derived based on the linear response assumption and applied to estimate the actual SO2 emissions. The updated emission inventory was comparable with the most recent MEIC emission inventory in 2016 and 2017, and was in line with the decline trend of SO2 emissions in Chongqing in the last decade. The adjustment of the emissions improved the accuracy in predicting SO2 concentrations with the developed method.

Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis

Abstract. Nitrogen dioxide (NO2) column density measurements from satellites have been widely used in constraining emissions of nitrogen oxides (NOx = NO + NO2). However, the utility of these measurements is impacted by reduced observational coverage due to cloud cover and their reduced sensitivity toward the surface. Combining the information from satellites with surface observations of NO2 will provide greater constraints on emission estimates of NOx. We have developed a deep-learning (DL) model to integrate satellite data and in situ observations of surface NO2 to estimate NOx emissions in China. A priori information for the DL model was obtained from satellite-derived emissions from the Tropospheric Chemistry Reanalysis (TCR-2). A two-stage training strategy was used to integrate in situ measurements from the China Ministry of Ecology and Environment (MEE) observation network with the TCR-2 data. The DL model is trained from 2005 to 2018 and evaluated for 2019 and 2020. The DL model estimated a source of 19.4 Tg NO for total Chinese NOx emissions in 2019, which is consistent with the TCR-2 estimate of 18.5 ± 3.9 Tg NO and the 20.9 Tg NO suggested by the Multi-resolution Emission Inventory for China (MEIC). Combining the MEE data with TCR-2, the DL model suggested higher NOx emissions in some of the less-densely populated provinces, such as Shaanxi and Sichuan, where the MEE data indicated higher surface NO2 concentrations than TCR-2. The DL model also suggested a faster recovery of NOx emissions than TCR-2 after the Chinese New Year (CNY) holiday in 2019, with a recovery time scale that is consistent with Baidu “Qianxi” mobility data. In 2020, the DL-based analysis estimated about a 30 % reduction in NOx emissions in eastern China during the COVID-19 lockdown period, relative to pre-lockdown levels. In particular, the maximum emission reductions were 42 % and 30 % for the Jing-Jin-Ji (JJJ) and the Yangtze River Delta (YRD) mega-regions, respectively. Our results illustrate the potential utility of the DL model as a complementary tool for conventional data-assimilation approaches for air quality applications.

Lagrangian inversion of anthropogenic CO2 emissions from Beijing using differential column measurements

We estimated CO2 emissions from the Beijing region of China using differential ground-based observations of the column-averaged dry air mole fractions of CO2 (; urban minus upwind background column observations) in winter (1 November 2020–1 March 2021). Beijing is one of the most world’s populated cities and the CO2 emissions from this region contain large uncertainties in different bottom-up anthropogenic inventories. Differential measurements are potentially able to capture urban signals over Beijing (3.46 ± 2.35 ppm). The simulated XCO2 enhancements were calculated () based on three emission inventories (the Open-source Data Inventory for Anthropogenic CO2 (ODIAC), Multiresolution Emission Inventory for China (MEIC) and Emissions Database for Global Atmospheric Research (EDGAR) datasets) for Beijing. The values based on the ODIAC dataset were much higher than the observations, whereas the values from the EDGAR dataset were much lower and the MEIC dataset was more consistent. We performed a Lagrangian inversion framework based on Bayesian theory. The average and uncertainty of a priori estimates (12.18 ± 8.0, 7.09 ± 7.5 and 3.53 ± 11.4 μmol (m2 s)−1) were optimized to the posterior emissions (9.44 ± 5.7, 7.13 ± 4.9 and 7.15 ± 5.7 μmol (m2 s)−1), suggesting that the three posterior estimates tended to converge to become more consistent, transport errors (especially the horizontal transport errors) and the spatially uneven corrections in Beijing were the main reason for the differences between the posterior estimates. Sensitivity tests suggested that the prescribed spatial and temporal structures affected up to about 12.9%, 4.9% and 20.8%, respectively, of the three inventories.

Characterization of Regional Combustion Efficiency using ΔXCO: ΔXCO2 Observed by a Portable Fourier-Transform Spectrometer at an Urban Site in Beijing.

Measurements of column-averaged dry-air mole fractions of carbon dioxide and carbon monoxide, CO2 (XCO2) and CO (XCO), were performed throughout 2019 at an urban site in Beijing using a compact Fourier Transform Spectrometer (FTS) EM27/SUN. This data set is used to assess the characteristics of combustion-related CO2 emissions of urban Beijing by analyzing the correlated daily anomalies of XCO and XCO2 (e.g., ΔXCO and ΔXCO2). The EM27/SUN measurements were calibrated to a 125HR-FTS at the Xianghe station by an extra EM27/SUN instrument transferred between two sites. The ratio of ΔXCO over ΔXCO2 (ΔXCO:ΔXCO2) is used to estimate the combustion efficiency in the Beijing region. A high correlation coefficient (0.86) between ΔXCO and ΔXCO2 is observed. The CO:CO2 emission ratio estimated from inventories is higher than the observed ΔXCO:ΔXCO2 (10.46 ± 0.11 ppb ppm−1) by 42.54%–101.15%, indicating an underestimation in combustion efficiency in the inventories. Daily ΔXCO:ΔXCO2 are influenced by transportation governed by weather conditions, except for days in summer when the correlation is low due to the terrestrial biotic activity. By convolving the column footprint [ppm (µmol m−2 s−1)−1] generated by the Weather Research and Forecasting-X-Stochastic Time-Inverted Lagrangian Transport models (WRF-X-STILT) with two fossil-fuel emission inventories (the Multi-resolution Emission Inventory for China (MEIC) and the Peking University (PKU) inventory), the observed enhancements of CO2 and CO were used to evaluate the regional emissions. The CO2 emissions appear to be underestimated by 11% and 49% for the MEIC and PKU inventories, respectively, while CO emissions were overestimated by MEIC (30%) and PKU (35%) in the Beijing area.

Estimating city NOX emissions from TROPOMI high spatial resolution observations – A case study on Yangtze River Delta, China

In this study, TROPOspheric Monitoring Instrument (TROPOMI) observations were resampled to obtain 0.01° × 0.01° NO2 VCD (vertical column density) over Yangtze River Delta (YRD), China. Based on this high spatial resolution satellite observations, NO2 VCDs in megacities cluster of YRD region were examined with a reduction of ~35% during COVID-19 lockdown. The adjusted Exponentially-Modified Gaussian (EMG) model was used to estimate the NOX emission in typical cities under regionally polluted YRD region. Taking 100 km of mass integration interval as an example, during 2018–2019, the averaged NOX emission of Shanghai, Hangzhou, Nanjing, and Ningbo is 139.65 mol/s, 84.49 mol/s, 79.87 mol/s and 88.73 mol/s, respectively. This estimation has a good correlation with Multi-resolution Emission Inventory for China (MEIC) emission with R more than 0.9 but lower results mainly due to the underestimation of NO2 VCD by TROPOMI in polluted areas. It was also found that the NOX emissions of Ningbo are higher than expected, which is closely related to massive ship emissions. This study indicates that this approach based on adjusted EMG model can enhance the ability to quantify NOX emissions at city level by utilizing the high spatial resolution observations of TROPOMI.