scholarly journals Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation

2022 ◽  
Vol 14 (1) ◽  
pp. 220
Author(s):  
Yiwen Hu ◽  
Zengliang Zang ◽  
Dan Chen ◽  
Xiaoyan Ma ◽  
Yanfei Liang ◽  
...  
Keyword(s):  
Emission Inventory ◽  
Control Experiment ◽  
Southern China ◽  
A Priori ◽  
Time Lag ◽  
Correlation Coefficients ◽  
River Delta ◽  
Forecast Errors ◽  
Emission Inventories ◽  
So2 Emission

Emission inventories are important for modeling studies and policy-making, but the traditional “bottom-up” emission inventories are often outdated with a time lag, mainly due to the lack of accurate and timely statistics. In this study, we developed a “top-down” approach to optimize the emission inventory of sulfur dioxide (SO2) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and a three-dimensional variational (3DVAR) system. The observed hourly surface SO2 concentrations from the China National Environmental Monitoring Center were assimilated and used to estimate the gridded concentration forecast errors of WRF-Chem. The concentration forecast errors were then converted to the emission errors by assuming a linear response from SO2 emission to concentration by grids. To eliminate the effects of modelling errors from aspects other than emissions, a strict data-screening process was conducted. Using the Multi-Resolution Emission Inventory for China (MEIC) 2010 as the a priori emission, the emission inventory for October 2015 over Mainland China was optimized. Two forecast experiments were conducted to evaluate the performance of the SO2 forecast by using the a priori (control experiment) and optimized emissions (optimized emission experiment). The results showed that the forecasts with optimized emissions typically outperformed the forecasts with 2010 a priori emissions in terms of the accuracy of the spatial and temporal distributions. Compared with the control experiment, the bias and root-mean-squared error (RMSE) of the optimized emission experiment decreased by 71.2% and 25.9%, and the correlation coefficients increased by 50.0%. The improvements in Southern China were more significant than those in Northern China. For the Sichuan Basin, Yangtze River Delta, and Pearl River Delta, the bias and RMSEs decreased by 76.4–94.2% and 29.0–45.7%, respectively, and the correlation coefficients increased by 23.5–53.4%. This SO2 emission optimization methodology is computationally cost-effective.

scholarly journals The 2015 and 2016 wintertime air pollution in China: SO<sub>2</sub> emission changes derived from a WRF-Chem/EnKF coupled data assimilation system

2019 ◽  
Vol 19 (13) ◽  
pp. 8619-8650 ◽  
Author(s):  
Dan Chen ◽  
Zhiquan Liu ◽  
Junmei Ban ◽  
Min Chen
Keyword(s):  
Data Assimilation ◽  
Emission Inventory ◽  
Southern China ◽  
Control Strategies ◽  
A Priori ◽  
River Delta ◽  
Western China ◽  
So2 Emissions ◽  
Emission Reductions ◽  
Emission Changes

Abstract. Ambient pollutants and emissions in China have changed significantly in recent years due to strict control strategies implemented by the government. It is of great interest to evaluate the reduction of emissions and the air quality response using a data assimilation (DA) approach. In this study, we updated the WRF-Chem/EnKF (Weather Research and Forecasting – WRF, model coupled with the chemistry/ensemble Kalman filter – Chem/EnKF) system to directly analyze SO2 emissions instead of using emission scaling factors, as in our previous study. Our purpose is to investigate whether the WRF-Chem/EnKF system is capable of detecting the emission deficiencies in the bottom-up emission inventory (2010-MEIC, Multi-resolution Emission Inventory for China), dynamically updating the spatial–temporal emission changes (2010 to 2015/2016) and, most importantly, locating the “new” (emerging) emission sources that are not considered in the a priori emission inventory. The 2010 January MEIC emission inventory was used as the a priori inventory (to generate background emission fields). The 2015 and 2016 January emissions were obtained by assimilating the hourly surface SO2 concentration observations for January 2015 and 2016. The SO2 emission changes for northern, western, and southern China from 2010 to 2015 and from 2015 to 2016 (for the month of January) from the EnSRF (ensemble square root filter) approach were investigated, and the emission control strategies during the corresponding period were discussed. The January 2010–2015 differences showed inhomogeneous change patterns in different regions, including (1) significant emission reductions in southern China; (2) significant emission reductions in larger cities with a wide increase in the surrounding suburban and rural regions in northern China, which may indicate missing raw coal combustion for winter heating that was not taken into account in the a priori emission inventory; and (3) significantly large emission increases in western China due to the energy expansion strategy. The January 2015–2016 differences showed wide emission reductions from 2015 to 2016, indicating stricter control strategies having been fully executed nationwide. These derived emission changes coincided with the period of the energy development national strategy in northwestern China and the regulations for the reduction of SO2 emissions, indicating that the updated DA system was possibly capable of detecting emission deficiencies, dynamically updating the spatial–temporal emission changes (2010 to 2015/2016), and locating newly added sources. Forecast experiments using the a priori and updated emissions were conducted. Comparisons showed improvements from using updated emissions. The improvements in southern China were much larger than those in northern and western China. For the Sichuan Basin, central China, the Yangtze River Delta, and the Pearl River Delta, the BIAS (bias, equal to the difference between the modeled value and the observational value, representing the overall model tendency) decreased by 61.8 %–78.2 % (for different regions), the RMSE decreased by 27.9 %–52.2 %, and CORR values (correlation coefficient, equal to the linear relationship between the modeled values and the observational values) increased by 12.5 %–47.1 %. The limitation of the study is that the analyzed emissions are still model-dependent, as the ensembles are conducted using the WRF-Chem model; therefore, the performances of the ensembles are model-dependent. Our study indicated that the WRF-Chem/EnSRF system is not only capable of improving the emissions and forecasts in the model but can also evaluate realistic emission changes. Thus, it is possible to apply the system for the evaluation of emission changes in the future.

scholarly journals Hotspot of Glyoxal Over the Pearl River Delta Seen from the OMI Satellite Instrument: Implications for Emissions of Aromatic Hydrocarbons

2016 ◽  
Author(s):  
Christopher Chan Miller ◽  
Daniel J. Jacob ◽  
Gonzalo González Abad ◽  
Kelly Chance
Keyword(s):  
Aromatic Hydrocarbons ◽  
Pearl River Delta ◽  
Transport Model ◽  
Southern China ◽  
River Delta ◽  
Pearl River ◽  
Emission Inventories ◽  
The Pearl River Delta ◽  
Volatile Organic ◽  
The Pearl River

Abstract. The Pearl River Delta (PRD) is a densely populated hub of industrial activity located in southern China. OMI satellite observations reveal a large hotspot of glyoxal (CHOCHO) over the PRD that is almost twice as large as any other in Asia. Formaldehyde (HCHO) and NO2 observed by OMI are also high in the PRD but no more than in other urban/industrial areas of China. The CHOCHO hotspot in the PRD can be explained by industrial paint and solvent emissions of aromatic volatile organic compounds (VOCs), with toluene being a dominant contributor. By contrast, HCHO in the PRD originates mostly from VOCs emitted by combustion (principally vehicles). By applying a plume transport model to wind-segregated OMI data, we show that the CHOCHO and HCHO enhancements over the PRD observed by OMI are consistent with current VOC emission inventories. Prior work using CHOCHO retrievals from the SCIAMACHY satellite instrument suggested that aromatic VOC emissions in the PRD were too low by a factor of 10-20; we attribute this result in part to bias in the SCIAMACHY data and in part to underestimated CHOCHO yields from oxidation of aromatics. Our work points to the importance of better understanding CHOCHO yields from the oxidation of aromatics in order to interpret CHOCHO observations from space.

scholarly journals Hotspot of glyoxal over the Pearl River delta seen from the OMI satellite instrument: implications for emissions of aromatic hydrocarbons

2016 ◽  
Vol 16 (7) ◽  
pp. 4631-4639 ◽  
Author(s):  
Christopher Chan Miller ◽  
Daniel J. Jacob ◽  
Gonzalo González Abad ◽  
Kelly Chance
Keyword(s):  
Pearl River Delta ◽  
Transport Model ◽  
Southern China ◽  
River Delta ◽  
Pearl River ◽  
Emission Inventories ◽  
Ozone Monitoring Instrument ◽  
The Pearl River Delta ◽  
Volatile Organic ◽  
The Pearl River

Abstract. The Pearl River delta (PRD) is a densely populated hub of industrial activity located in southern China. OMI (Ozone Monitoring Instrument) satellite observations reveal a large hotspot of glyoxal (CHOCHO) over the PRD that is almost twice as large as any other in Asia. Formaldehyde (HCHO) and NO2 observed by OMI are also high in the PRD but no more than in other urban/industrial areas of China. The CHOCHO hotspot over the PRD can be explained by industrial paint and solvent emissions of aromatic volatile organic compounds (VOCs), with toluene being a dominant contributor. By contrast, HCHO in the PRD originates mostly from VOCs emitted by combustion (principally vehicles). By applying a plume transport model to wind-segregated OMI data, we show that the CHOCHO and HCHO enhancements over the PRD observed by OMI are consistent with current VOC emission inventories. Prior work using CHOCHO retrievals from the SCIAMACHY satellite instrument suggested that emission inventories for aromatic VOCs in the PRD were too low by a factor of 10–20; we attribute this result in part to bias in the SCIAMACHY data and in part to underestimated CHOCHO yields from oxidation of aromatics. Our work points to the importance of better understanding CHOCHO yields from the oxidation of aromatics in order to interpret space-based CHOCHO observations in polluted environments.

scholarly journals 2015 and 2016 winter-time air pollution in China: SO<sub>2</sub> emission changes derived from a WRF/Chem-EnKF coupled data assimilation system

2019 ◽  
Author(s):  
Dan Chen ◽  
Zhiquan Liu ◽  
Junmei Ban ◽  
Min Chen
Keyword(s):  
Data Assimilation ◽  
Emission Reduction ◽  
Emission Inventory ◽  
Southern China ◽  
Control Strategies ◽  
River Delta ◽  
Central China ◽  
Western China ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. Ambient pollutants in China changes significantly in recent years due to strict control strategies implemented by the government. The control strategies also bring uncertainties to both the bottom-up emission inventory and the model-ready gridded emission inputs especially in winter season. In this study, we updated the WRF/Chem-EnKF Data Assimilation system to quantitatively estimate the gridded hourly SO2 emissions using hourly surface observations as constraints. Different from our previous study, in which meteorology and emission were both perturbed to obtain larger spread aiming to improve forecast skills; in this study, only emission was perturbed to ensure analyzed emission purely reflect necessary adjustments due to the emission uncertainties. In addition, direct emissions instead of emission scaling factors were used as analysis variable, which allowed for the detection of new emission sources. 2010 MEIC emission inventory (for January) was used as priori to generate 2015 and 2016 January analyzed emissions. The SO2 emission changing trends for northern, western and southern China from 2010 to 2015 and that from 2015 to 2016 (for the month of January) were investigated. The January 2010–2015 differences showed inhomogeneous change patterns in different regions: (1) significant emission reduction in southern China, (2) significant emission reduction in larger cities but widely increase in surrounding suburban and rural regions for northern China which may indicate the missing raw coal combustion for winter heating that not taken into account in the priori emission inventory; (3) significantly large emission increase in western China due to the energy expansion strategy. This not only reflected the changes during the five years, but also combined the uncertainties in the priori emissions. The January 2015–2016 differences showed widely emission reduction from 2015 to 2016, indicating the stricter control strategy fully executed nationwide. These changes were corresponded to facts in reality, indicating that the updated DA system was capable to detect the emission deficiencies and optimize the emission. By generating the hourly analyzed emissions, the diurnal pattern of emissions (in terms of hourly factors) were also obtained. Forecast experiments showed the improvements by using analyzed emissions were much larger in southern China than that in northern and western China. For Sichuan Basin, Central China, Yangzi River Delta, and Pearl River Delta, BIAS and RMSE decreased by 61.8 %–78.2 % and 27.9 %–52.2 %, respectively, and correlation coefficients increased by 12.5 %–47.1 %. However, the improvement in northern and western China were limited due to small spread. Another limitation of the study is that the analyzed emissions are still model dependent, as the ensembles are conducted through WRF/Chem model and thus the performances of ensembles are model dependent.

scholarly journals Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

2017 ◽  
Author(s):  
Jianlin Hu ◽  
Xun Li ◽  
Lin Huang ◽  
Qi Ying ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Health Effects ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Model Performance ◽  
Observation Data ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1 h. The study demonstrates that ensemble predictions by combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories and the results are publicly available for future health effects studies.

scholarly journals Variability and changes in Pearl River Delta water level: oceanic and atmospheric forcing perspectives

2021 ◽  
Author(s):  
Xiangbo Feng ◽  
Wei Zhang ◽  
Zhenglei Zhu ◽  
Amulya Chevuturi ◽  
Wenlong Chen
Keyword(s):  
Water Level ◽  
Pearl River Delta ◽  
Southern China ◽  
Northern South China Sea ◽  
River Delta ◽  
Pearl River ◽  
Decadal Variations ◽  
Oceanic Forcing ◽  
The Pearl River

AbstractUnderstanding water level (WL) fluctuations in river deltas is of importance for managing water resources and minimizing the impacts of floods and droughts. Here, we demonstrate the competing effects of atmospheric and oceanic forcing on multi-timescale variability and changes in the Pearl River Delta (PRD) WLs in southern China, using 52 years (1961–2012) of in-situ observations at 13 hydrological stations. PRD WL presents significant seasonal to decadal variations, with large amplitudes upstream related to strong variability of southern China rainfall, and with relatively small amplitudes at the coastal stations determined by sea level (SL) fluctuations of the northern South China Sea. We find that the strengths of atmospheric and oceanic forcing in PRD are not mutually independent, leading to a distinct contrast of WL–forcing relationships at upstream and coastal stations. In the transition zone, because of counteracts of atmospheric and oceanic forcing, no robust relationships are identified between WL and either of the forcing. We further show that in the drought season of the warm ENSO and PDO epochs, the effect of atmospheric (oceanic) forcing on PRD WL is largely enhanced (weakened), due to increased southern China rainfall and negative SL anomalies. Over the observation period, WL significantly decreased at upstream stations, by up to 28–42 mm/year for flood season, contrasting with the upward trends of <4.3 mm/year at coastal stations across all seasons. Southern China rainfall explains little of the observed WL trends, whilst SL rise is mostly responsible for the WL trends at coastal stations.

scholarly journals Quantifying the relationship between lockdowns, mobility, and effective reproduction number (Rt) during the COVID-19 pandemic in the Greater Toronto Area

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Christopher Dainton ◽  
Alexander Hay
Keyword(s):  
Public Health ◽  
Secular Trend ◽  
Reproduction Number ◽  
Time Lag ◽  
Correlation Coefficients ◽  
Greater Toronto Area ◽  
Global Mobility ◽  
The Relationship ◽  
Second Wave ◽  
Over Time

Abstract Background The effectiveness of lockdowns in mitigating the spread of COVID-19 has been the subject of intense debate. Data on the relationship between public health restrictions, mobility, and pandemic growth has so far been conflicting. Objective We assessed the relationship between public health restriction tiers, mobility, and COVID-19 spread in five contiguous public health units (PHUs) in the Greater Toronto Area (GTA) in Ontario, Canada. Methods Weekly effective reproduction number (Rt) was calculated based on daily cases in each of the five GTA public health units between March 1, 2020, and March 19, 2021. A global mobility index (GMI) for each PHU was calculated using Google Mobility data. Segmented regressions were used to assess changes in the behaviour of Rt over time. We calculated Pearson correlation coefficients between GMI and Rt for each PHU and mobility regression coefficients for each mobility variable, accounting for time lag of 0, 7, and 14 days. Results In all PHUs except Toronto, the most rapid decline in Rt occurred in the first 2 weeks of the first province-wide lockdown, and this was followed by a slight trend to increased Rt as restrictions decreased. This trend reversed in all PHUs between September 6th and October 10th after which Rt decreased slightly over time without respect to public health restriction tier. GMI began to increase in the first wave even before restrictions were decreased. This secular trend to increased mobility continued into the summer, driven by increased mobility to recreational spaces. The decline in GMI as restrictions were reintroduced coincides with decreasing mobility to parks after September. During the first wave, the correlation coefficients between global mobility and Rt were significant (p < 0.01) in all PHUs 14 days after lockdown, indicating moderate to high correlation between decreased mobility and decreased viral reproduction rates, and reflecting that the incubation period brings in a time-lag effect of human mobility on Rt. In the second wave, this relationship was attenuated, and was only significant in Toronto and Durham at 14 days after lockdown. Conclusions The association between mobility and COVID-19 spread was stronger in the first wave than the second wave. Public health restriction tiers did not alter the existing secular trend toward decreasing Rt over time.

scholarly journals Updated national emission inventory and comparison with the Emissions Database for Global Atmospheric Research (EDGAR): case of Lebanon

2022 ◽  
Author(s):  
Anwar Al Shami ◽  
Elissar Al Aawar ◽  
Abdelkader Baayoun ◽  
Najat A. Saliba ◽  
Jonilda Kushta ◽  
...  
Keyword(s):  
Emission Inventory ◽  
Model Performance ◽  
Atmospheric Modeling ◽  
Atmospheric Pollutants ◽  
Pollutant Emissions ◽  
Emission Inventories ◽  
Gaseous Species ◽  
Emission Standard ◽  
Activity Data ◽  
Atmospheric Research

AbstractPhysically based computational modeling is an effective tool for estimating and predicting the spatial distribution of pollutant concentrations in complex environments. A detailed and up-to-date emission inventory is one of the most important components of atmospheric modeling and a prerequisite for achieving high model performance. Lebanon lacks an accurate inventory of anthropogenic emission fluxes. In the absence of a clear emission standard and standardized activity datasets in Lebanon, this work serves to fill this gap by presenting the first national effort to develop a national emission inventory by exhaustively quantifying detailed multisector, multi-species pollutant emissions in Lebanon for atmospheric pollutants that are internationally monitored and regulated as relevant to air quality. Following the classification of the Emissions Database for Global Atmospheric Research (EDGAR), we present the methodology followed for each subsector based on its characteristics and types of fuels consumed. The estimated emissions encompass gaseous species (CO, NOx, SO2), and particulate matter (PM2.5 and PM10). We compare totals per sector obtained from the newly developed national inventory with the international EDGAR inventory and previously published emission inventories for the country for base year 2010 presenting current discrepancies and analyzing their causes. The observed discrepancies highlight the fact that emission inventories, especially for data-scarce settings, are highly sensitive to the activity data and their underlying assumptions, and to the methodology used to estimate the emissions.

scholarly journals Evaluating the performance of pyrogenic and biogenic emission inventories against one decade of space-based formaldehyde columns

2008 ◽  
Vol 8 (5) ◽  
pp. 16981-17036 ◽  
Author(s):  
T. Stavrakou ◽  
J.-F. Müller ◽  
I. De Smedt ◽  
M. Van Roozendael ◽  
G. R. van der Werf ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Amazon Basin ◽  
Transport Model ◽  
Correlation Coefficients ◽  
Global Scale ◽  
Chemical Mechanism ◽  
Emission Inventories ◽  
Chemical Transport Model ◽  
Emissions Inventory ◽  
Fire Emissions

Abstract. A new one-decade dataset of formaldehyde (HCHO) columns retrieved from GOME and SCIAMACHY is compared with HCHO columns simulated by an updated version of the IMAGES global chemical transport model. This model version includes an optimized chemical scheme with respect to HCHO production, where the short-term and final HCHO yields from pyrogenically emitted non-methane volatile organic compounds (NMVOCs) are estimated from the Master Chemical Mechanism (MCM) and an explicit speciation profile of pyrogenic emissions. The model is driven by the Global Fire Emissions Database (GFED) version 1 or 2 for biomass burning, whereas biogenic emissions are provided either by the Global Emissions Inventory Activity (GEIA), or by a newly developed inventory based on the Model of Emissions of Gases and Aerosols from Nature (MEGAN) algorithms driven by meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF). The comparisons focus on tropical ecosystems, North America and China, which experience strong biogenic and biomass burning NMVOC emissions reflected in the enhanced measured HCHO columns. These comparisons aim at testing the ability of the model to reproduce the observed features of the HCHO distribution on the global scale and at providing a first assessment of the performance of the current emission inventories. The high correlation coefficients (r>0.8) between the observed and simulated columns over most regions indicate a very good consistency between the model, the implemented inventories and the HCHO dataset. The use of the MEGAN-ECMWF inventory improves the model/data agreement in almost all regions, but biases persist over parts of Africa and the Northern Australia. Although neither GFED version is consistent with the data over all regions, a better match is achieved over Indonesia and Southern Africa when GFEDv2 is used, but GFEDv1 succeeds better in getting the correct seasonal patterns and intensities of the fire episodes over the Amazon basin, as reflected by the higher correlations calculated in this region.

scholarly journals Updated SO<sub>2</sub> emission estimates over China using OMI/Aura observations

2018 ◽  
Vol 11 (3) ◽  
pp. 1817-1832 ◽  
Author(s):  
Maria Elissavet Koukouli ◽  
Nicolas Theys ◽  
Jieying Ding ◽  
Irene Zyrichidou ◽  
Bas Mijling ◽  
...  
Keyword(s):  
Emission Inventory ◽  
Satellite Observations ◽  
Ozone Monitoring Instrument ◽  
So2 Emissions ◽  
Transport Modelling ◽  
So2 Emission ◽  
Bottom Up ◽  
Beijing Area ◽  
Entire Domain ◽  
Particulate Matter Emissions

Abstract. The main aim of this paper is to update existing sulfur dioxide (SO2) emission inventories over China using modern inversion techniques, state-of-the-art chemistry transport modelling (CTM) and satellite observations of SO2. Within the framework of the EU Seventh Framework Programme (FP7) MarcoPolo (Monitoring and Assessment of Regional air quality in China using space Observations) project, a new SO2 emission inventory over China was calculated using the CHIMERE v2013b CTM simulations, 10 years of Ozone Monitoring Instrument (OMI)/Aura total SO2 columns and the pre-existing Multi-resolution Emission Inventory for China (MEIC v1.2). It is shown that including satellite observations in the calculations increases the current bottom-up MEIC inventory emissions for the entire domain studied (15–55° N, 102–132° E) from 26.30 to 32.60 Tg annum−1, with positive updates which are stronger in winter ( ∼  36 % increase). New source areas were identified in the southwest (25–35° N, 100–110° E) as well as in the northeast (40–50° N, 120–130° E) of the domain studied as high SO2 levels were observed by OMI, resulting in increased emissions in the a posteriori inventory that do not appear in the original MEIC v1.2 dataset. Comparisons with the independent Emissions Database for Global Atmospheric Research, EDGAR v4.3.1, show a satisfying agreement since the EDGAR 2010 bottom-up database provides 33.30 Tg annum−1 of SO2 emissions. When studying the entire OMI/Aura time period (2005 to 2015), it was shown that the SO2 emissions remain nearly constant before the year 2010, with a drift of −0.51 ± 0.38 Tg annum−1, and show a statistically significant decline after the year 2010 of −1.64 ± 0.37 Tg annum−1 for the entire domain. Similar findings were obtained when focusing on the greater Beijing area (30–40° N, 110–120° E) with pre-2010 drifts of −0.17 ± 0.14 and post-2010 drifts of −0.47 ± 0.12 Tg annum−1. The new SO2 emission inventory is publicly available and forms part of the official EU MarcoPolo emission inventory over China, which also includes updated NOx, volatile organic compounds and particulate matter emissions.

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