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Non-methane volatile organic compounds (NMVOC), serving as crucial precursors of O3, have a significant impact on atmospheric oxidative capacity and O3 formation. However, both anthropogenic and biogenic NMVOC emissions remain subject to considerable uncertainty. Here, we extended the Regional multi-Air Pollutant Assimilation System (RAPAS) with the EnKF algorithm to optimize NMVOC emissions in China by assimilating TROPOMI HCHO retrievals. We also simultaneously optimize NOx emissions by assimilating in-situ NO2 observations to address the chemical feedback among VOC-NOx-O3. Furthermore, a process-based analysis was employed to quantify the impact of NMVOC emission changes on various chemical reactions related to O3 formation and depletion. NMVOC emissions exhibited a substantial reduction of 50.2%, especially in forest-rich areas of central and southern China, revealing a prior overestimation of biogenic NMVOC emissions. The RAPAS significantly improved HCHO simulations, reducing biases by 75.7%, indicating a notable decrease in posterior emission uncertainties. Moreover, the posterior NMVOC emissions significantly corrected the prior overestimation in O3 simulations, reducing biases by 49.3%. This can be primarily attributed to a significant decrease in the RO2 + NO reaction rate and an increase in the NO2 + OH reaction rate in the afternoon, thus limiting O3 generation. Sensitivity analyses emphasized the necessity of considering both NMVOC and NOx emissions for a comprehensive assessment of O3 chemistry. This study enhances our understanding of the effects of NMVOC emissions on O3 production and can contribute to the development of effective emission reduction policies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The conventional Ensemble Kalman filter (EnKF), which is now widely used to calibrate emission inventories and to improve air quality simulations, is susceptible to simulation errors of meteorological inputs, making accurate updates of high temporal-resolution emission inventories challenging. In this study, we developed a novel meteorologically adjusted inversion method (MAEInv) based on the EnKF to improve daily emission estimations. The new method combines sensitivity analysis and bias correction to alleviate the inversion biases caused by errors of meteorological inputs. For demonstration, we used the MAEInv to inverse daily carbon monoxide (CO) emissions in the Pearl River Delta (PRD) region, China. In the case study, 60% of the total CO simulation biases were associated with sensitive meteorological inputs, which would lead to the overestimation of daily variations of posterior emissions. Using the new inversion method, daily variations of emissions shrank dramatically, with the percentage change decreased by 30%. Also, the total amount of posterior CO emissions estimated by the MAEInv decreased by 14%, indicating that posterior CO emissions might be overestimated using the conventional EnKF. Model evaluations using independent observations revealed that daily CO emissions estimated by MAEInv better reproduce the magnitude and temporal patterns of ambient CO concentration, with a higher correlation coefficient (R , +37.0%) and lower normalized mean bias (NMB, -17.9%). Since errors of meteorological inputs are major sources of simulation biases for both low-reactive and reactive pollutants, the MAEInv is also applicable to improve the daily emission inversions of reactive pollutants. [Display omitted] [ABSTRACT FROM AUTHOR]

In this research, a new time-resolved emission inversion system was developed to investigate variations in SO2 emission in China during the COVID-19 (Corona Virus Disease 2019) lockdown period based on a four-dimensional variational (4DVar) inversion method to dynamically optimize the SO2 inventory by assimilating the ground-based hourly observation data. The inversion results obtained were validated in the North China Plain (NCP). Two sets of experiments were carried out based on the original and optimized inventories during the pre-lockdown and lockdown period to quantify the SO2 emission variations and the corresponding prediction improvement. The SO2 emission changes due to the lockdown in the NCP were quantified by the differences in the averaged optimized inventories between the pre-lockdown and lockdown period. As a response to the lockdown control, the SO2 emissions were reduced by 20.1% on average in the NCP, with ratios of 20.7% in Beijing, 20.2% in Tianjin, 26.1% in Hebei, 18.3% in Shanxi, 19.1% in Shandong, and 25.9% in Henan, respectively. These were mainly attributed to the changes caused by the heavy industry lockdown in these areas. Compared to the model performance based on the original inventory, the optimized daily SO2 emission inventory significantly improved the model SO2 predictions during the lockdown period, with the correlation coefficient (R) value increasing from 0.28 to 0.79 and the root-mean-square error (RMSE) being reduced by more than 30%. Correspondingly, the performance of PM2.5 was slightly improved, with R-value increasing from 0.67 to 0.74 and the RMSE being reduced by 8% in the meantime. These statistics indicate the good optimization ability of the time-resolved emission inversion system. [ABSTRACT FROM AUTHOR]

Atmospheric ammonia has been hazardous to the environment and human health for decades. Current inventories are usually constructed in a bottom-up manner and subject to uncertainties and incapable of reproducing the spatiotemporal characteristics of ammonia emission. Satellite measurements, for example, Infrared Atmospheric Sounder Interferometer (IASI) and Cross-Track Infrared Sounder, which provide global coverage of ammonia distribution, have gained popularity in ammonia emission estimation through data assimilation methods. However, satellite-based emission inversion studies on China are limited. In this study, we propose a four-dimensional ensemble variational-based ammonia emission inversion system to optimize ammonia emissions in China. It was developed by assimilating the IASI ammonia retrievals onboard Meteorological Operational satellite A and B into a chemical transport model Goddard Earth Observing System Chemical model (GEOS-Chem). Monthly inversion experiments were conducted in April, July, and October 2016 to test the performance. The inversion result indicated that the prior inventory from the MEIC model captured ammonia spreads in general; however, it heterogeneously underrated the emission intensity. The increments obtained in the assimilation were as high as 50% in North, East, and Northwest China. The posterior emission inventory presented a regional emission flux consistent with relevant studies. Driven by the optimized source estimate, GEOS-Chem provides superior results than using the prior in the evaluation of the assimilated IASI retrievals and the surface ammonia concentration measured by the ground-based Ammonia Monitoring Network in China.

Abstract We present the results from a global 0.56°‐resolution chemical data assimilation that integrates satellite observations of ozone, NO2, CO, HNO3, and SO2 from OMI, GOME‐2, SCIAMACHY, TES, MOPITT, and MLS. The assimilation is based on an ensemble Kalman filter technique and simultaneously optimizes ozone precursor emissions and concentrations of various species. The data assimilation at 0.56° resolution reduced model errors against independent surface, aircraft, and ozonesonde observations, which was larger than at coarser resolutions for many cases. By the data assimilation, surface model errors over major polluted regions were reduced by 33%–75% for NO2 and by 15%–18% for ozone. Agreements against assimilated observations for NO2 were improved using the data assimilation at 0.56° resolution by a factor of 1.5–3 compared to 2.8° resolution over major polluted regions. The estimated global total NOx emission over medium and strong source areas were smaller by 15% at 0.56° resolution than at 2.8° resolution associated with resolving small‐scale transport and chemistry processes, while 2%–26% smaller emissions were found for regional total emissions over Europe, the United States, China, India, and South Africa, with larger differences over megacities such as Los Angeles (−41%). The estimated ship emissions were 5%–7% smaller at 0.56° resolution over the Pacific and Atlantic. The 0.56°‐resolution data assimilation provides globally consistent analyses of the emissions and concentrations on a megacity scale, which benefit studies on air quality and its impact on human health at various spatial scales over different regions of the world.

We present the results from a global 0.56°‐resolution chemical data assimilation that integrates satellite observations of ozone, NO2, CO, HNO3, and SO2 from OMI, GOME‐2, SCIAMACHY, TES, MOPITT, and MLS. The assimilation is based on an ensemble Kalman filter technique and simultaneously optimizes ozone precursor emissions and concentrations of various species. The data assimilation at 0.56° resolution reduced model errors against independent surface, aircraft, and ozonesonde observations, which was larger than at coarser resolutions for many cases. By the data assimilation, surface model errors over major polluted regions were reduced by 33%–75% for NO2 and by 15%–18% for ozone. Agreements against assimilated observations for NO2 were improved using the data assimilation at 0.56° resolution by a factor of 1.5–3 compared to 2.8° resolution over major polluted regions. The estimated global total NOx emission over medium and strong source areas were smaller by 15% at 0.56° resolution than at 2.8° resolution associated with resolving small‐scale transport and chemistry processes, while 2%–26% smaller emissions were found for regional total emissions over Europe, the United States, China, India, and South Africa, with larger differences over megacities such as Los Angeles (−41%). The estimated ship emissions were 5%–7% smaller at 0.56° resolution over the Pacific and Atlantic. The 0.56°‐resolution data assimilation provides globally consistent analyses of the emissions and concentrations on a megacity scale, which benefit studies on air quality and its impact on human health at various spatial scales over different regions of the world. Plain Language Summary: Air pollutants such as nitrogen oxides (NOx) and ozone are important for air quality and climate change. This study demonstrates that global 0.56°‐resolution data assimilation of multiple chemical species from multiple satellites can improve chemical concentrations of numerous key species including NO2 and ozone, which was better than that at coarser resolutions. With the 0.56°‐resolution data assimilation, improved agreements with independent surface, ozonesonde, and aircraft observations were obtained for many cases than at coarser resolutions. Over polluted areas, increasing horizontal resolution substantially impacts upon top‐down NOx emission estimates associated with nonlinear atmospheric transport and chemical processes. The globally consistent analyses of emissions and concentrations on a megacity scale obtained from the 0.56°‐resolution data assimilation would benefit studies on air quality and its impact on human health at various spatial scales over different regions of the world. Key Points: Global 0.56°‐resolution data assimilation system was developed to integrate satellite observations of ozone, NO2, CO, HNO3, and SO2The developed framework provides globally consistent analyses of emissions and concentrations on a megacity scaleThe data assimilation at 0.56°‐resolution reduced errors against independent observations, which was larger than at lower resolutions [ABSTRACT FROM AUTHOR]

Accurate estimates of fossil fuel CO 2 (FFCO 2 ) emissions are of great importance for climate prediction and mitigation regulations but remain a significant challenge for accounting methods relying on economic statistics and emission factors. In this study, we employed a regional data assimilation framework to assimilate in situ NO 2 observations, allowing us to combine observation-constrained NO x emissions coemitted with FFCO 2 and grid-specific CO 2 -to-NO x emission ratios to infer the daily FFCO 2 emissions over China. The estimated national total for 2016 was 11.4 PgCO 2 ·yr -1 , with an uncertainty (1σ) of 1.5 PgCO 2 ·yr -1 that accounted for errors associated with atmospheric transport, inversion framework parameters, and CO 2 -to-NO x emission ratios. Our findings indicated that widely used "bottom-up" emission inventories generally ignore numerous activity level statistics of FFCO 2 related to energy industries and power plants in western China, whereas the inventories are significantly overestimated in developed regions and key urban areas owing to exaggerated emission factors and inexact spatial disaggregation. The optimized FFCO 2 estimate exhibited more distinct seasonality with a significant increase in emissions in winter. These findings advance our understanding of the spatiotemporal regime of FFCO 2 emissions in China.

Emissions of reactive nitrogen as ammonia (NH _3 ) and nitrogen oxides (NO _x ), together with sulfur dioxide (SO _2 ), contribute to formation of secondary PM _2.5 in the atmosphere. Satellite observations of atmospheric NH _3 , NO _2 , and SO _2 levels since the 2000s provide valuable information to constrain the spatial and temporal variability of their emissions. Here we present a bottom-up Chinese NH _3 emission inventory combined with top-down estimates of Chinese NO _x and SO _2 emissions using ozone monitoring instrument satellite observations, aiming to quantify the interannual variations of reactive nitrogen emissions in China and their contributions to PM _2.5 air pollution over 2005–2015. We find small interannual changes in the total Chinese anthropogenic NH _3 emissions during 2005–2016 (12.0–13.3 Tg with over 85% from agricultural sources), but large interannual change in top-down Chinese NO _x and SO _2 emissions. Chinese NO _x emissions peaked around 2011 and declined by 22% during 2011–2015, and Chinese SO _2 emissions declined by 55% in 2015 relative to that in 2007. Using the GEOS-Chem chemical transport model simulations, we find that rising atmospheric NH _3 levels in eastern China since 2011 as observed by infrared atmospheric sounding interferometer and atmospheric infrared sounder satellites are mainly driven by rapid reductions in SO _2 emissions. The 2011–2015 Chinese NO _x emission reductions have decreased regional annual mean PM _2.5 by 2.3–3.8 μ g m ^−3 . Interannual PM _2.5 changes due to NH _3 emission changes are relatively small, but further control of agricultural NH _3 emissions can be effective for PM _2.5 pollution mitigation in eastern China.

Data assimilation for multiple air pollutant concentrations has become an important need for modeling air quality attainment, human exposure, and related health impacts, especially in China, which experiences both PM2.5 and O3 pollution. Traditional data assimilation or fusion methods are mainly focused on individual pollutants and thus cannot support simultaneous assimilation for both PM2.5 and O3. To fill the gap, this study proposed a novel multipollutant assimilation method by using an emission-concentration response model (noted as RSM-assimilation). The new method was successfully applied to assimilate precursors for PM2.5 and O3 in the 28 cities of the North China Plain (NCP). By adjusting emissions of five pollutants (i.e., NOx, sulfur dioxide = SO2, ammonia = NH3, VOC, and primary PM2.5) in the 28 cities through RSM-assimilation, the RMSEs (root mean square errors) of O3 and PM2.5 were reduced by about 35% and 58% from the original simulations. The RSM-assimilation results in small sensitivity to the number of observation sites due to the use of prior knowledge of the spatial distribution of emissions; however, the ability to assimilate concentrations at the edge of the control region is limited. The emission ratios of five pollutants were simultaneously adjusted during the RSM-assimilation, indicating that the emission inventory may underestimate NO2 in January, April, and October, and SO2 in April, but overestimate NH3 in April, and VOC in January and October. Primary PM2.5 emissions were also significantly underestimated, particularly in April (dust season in NCP). Future work should focus on expanding the control area and including NH3 observations to improve the RSM-assimilation performance and emission inventories.

Ammonia (NH 3 ) is critical to the nitrogen cycle and PM 2.5 formation, yet a great deal of uncertainty exists in its urban emission quantifications. Model-underestimated NH 3 concentrations have been reported for cities, yet few studies have provided an explanation. Here, we explore reasons for severe WRF-Chem model underestimations of NH 3 concentrations in Beijing in August 2018, including simulated gas-particle partitioning, meteorology, regional transport, and emissions, using spatially refined (3 km resolution) NH 3 emission estimates in the agricultural sector for Beijing-Tianjin-Hebei and in the traffic sector for Beijing. We find that simulated NH 3 concentrations are significantly lower than ground-based and satellite observations during August in Beijing, while wintertime underestimations are much more moderate. Further analyses and sensitivity experiments show that such discrepancies cannot be attributed to factors other than biases in NH 3 emissions. Using site measurements as constraints, we estimate that both agricultural and non-agricultural NH 3 emission totals in Beijing shall increase by ∼5 times to match the observations. Future research should be performed to allocate underestimations to urban fertilizer, power, traffic, or residential sources. Dense and regular urban NH 3 observations are necessary to constrain and validate bottom-up inventories and NH x simulation.

Abstract: In this paper, the NOx emission scaling factors applied over the 2001 National Emissions Inventory (NEI) are estimated through a four-dimensional variational (4D-Var) approach using SCIAMACHY (Scanning Imaging Absorption spectroMeter for Atmospheric CHartographY) tropospheric NO2 columns measured during summer 2004. In the “top-down” approach, two-month average NO2 columns are assimilated into a regional chemical transport model (CTM), STEM, using different assimilation setups. In a basic setup, NOx emissions are adjusted by assimilating the NO2 columns. A more general setup of emission inversion allows the initial O3 concentrations be adjusted along with the NOx emissions. A final case is set up to assimilate both the NO2 columns and O3 measurement from various platforms while allowing adjustments of both the NOx emissions and the initial O3 concentrations. It is found that the addition of O3 measurements did not improve the NOx emission inversion. With the NOx emission at surface and upper levels being adjusted separately, results from four cases show that the elevated NOx emission reduction ranges from 8.9% to 11.4%, and the surface NOx emission reduction is up to 6.6%. All the cases show NOx emission reduction in Ohio valley and Washington, District of Columbia areas. [Copyright &y& Elsevier]

Data assimilation for multiple air pollutant concentrations has become an important need for modeling air quality attainment, human exposure, and related health impacts, especially in China, which experiences both PM2.5 and O3 pollution. Traditional data assimilation or fusion methods are mainly focused on individual pollutants and thus cannot support simultaneous assimilation for both PM2.5 and O3. To fill the gap, this study proposed a novel multipollutant assimilation method by using an emission-concentration response model (noted as RSM-assimilation). The new method was successfully applied to assimilate precursors for PM2.5 and O3 in the 28 cities of the North China Plain (NCP). By adjusting emissions of five pollutants (i.e., NOx, sulfur dioxide = SO2, ammonia = NH3, VOC, and primary PM2.5) in the 28 cities through RSM-assimilation, the RMSEs (root mean square errors) of O3 and PM2.5 were reduced by about 35% and 58% from the original simulations. The RSM-assimilation results in small sensitivity to the number of observation sites due to the use of prior knowledge of the spatial distribution of emissions; however, the ability to assimilate concentrations at the edge of the control region is limited. The emission ratios of five pollutants were simultaneously adjusted during the RSM-assimilation, indicating that the emission inventory may underestimate NO2 in January, April, and October, and SO2 in April, but overestimate NH3 in April, and VOC in January and October. Primary PM2.5 emissions were also significantly underestimated, particularly in April (dust season in NCP). Future work should focus on expanding the control area and including NH3 observations to improve the RSM-assimilation performance and emission inventories. [ABSTRACT FROM AUTHOR]