Hybrid Mass Balance/4D-Var Joint Inversion of NO x and SO2 Emissions in East Asia

Development of an integrated machine-learning and data assimilation framework for NOx emission inversion

As major air pollutants, nitrogen oxides (NO_x, mainly comprising NO and NO₂) not only have adverse effects on human health but also contribute to the formation of secondary pollutants, such as ozone and particulate nitrate. To acquire reasonable NO_x simulation results for further analysis, a reasonable emission inventory is needed for three-dimensional chemical transport models (3D-CTMs). In this study, a comprehensive emission adjustment framework for NO_x emission, which integrates the simulation results of the 3D-CTM, surface NO₂ measurements, the three-dimensional variational data assimilation method, and an ensemble back propagation neural network, was proposed and applied to correct NO_x emissions over China for the summers of 2015 and 2020. Compared with the simulation using prior NO_x emissions, the root-mean-square error, normalized mean error, and normalized mean bias decreased by approximately 40 %, 40 %, and 60 % in NO₂ simulation using posterior NO_x emissions corrected by the framework proposed in this work. Compared with the emissions for 2015, the NO_x emission generally decreased by an average of 5 % in the simulation domain for 2020, especially in Henan and Anhui provinces, where the percentage reductions reached 24 % and 19 %, respectively. The proposed framework is sufficiently flexible to correct emissions in other periods and regions. The framework can provide reliable and up-to-date emission information and can thus contribute to both scientific research and policy development relating to NO_x pollution.

Inferring and evaluating satellite-based constraints on NO x emissions estimates in air quality simulations

Satellite observations of tropospheric NO2 columns can provide top-down observational constraints on emissions estimates of nitrogen oxides (NO x ). Mass-balance-based methods are often applied for this purpose but do not isolate near-surface emissions from those aloft, such as lightning emissions. Here, we introduce an inverse modeling framework that couples satellite chemical data assimilation to a chemical transport model. In the framework, satellite-constrained emissions totals are inferred using model simulations with and without data assimilation in the iterative finite-difference mass-balance method. The approach improves the finite-difference mass-balance inversion by isolating the near-surface emissions increment. We apply the framework to separately estimate lightning and anthropogenic NO x emissions over the Northern Hemisphere for 2019. Using overlapping observations from the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI), we compare separate NO x emissions inferences from these satellite instruments, as well as the impacts of emissions changes on modeled NO2 and O3. OMI inferences of anthropogenic emissions consistently lead to larger emissions than TROPOMI inferences, attributed to a low bias in TROPOMI NO2 retrievals. Updated lightning NO x emissions from either satellite improve the chemical transport model's low tropospheric O3 bias. The combined lighting and anthropogenic emissions updates improve the model's ability to reproduce measured ozone by adjusting natural, long-range, and local pollution contributions. Thus, the framework informs and supports the design of domestic and international control strategies.

Sector-Based Top-Down Estimates of NO x , SO2, and CO Emissions in East Asia

Top-down estimates using satellite data provide important information on the sources of air pollutants. We develop a sector-based 4D-Var framework based on the GEOS-Chem adjoint model to address the impacts of co-emissions and chemical interactions on top-down emission estimates. We apply OMI NO₂, OMI SO₂, and MOPITT CO observations to estimate NO _x , SO₂, and CO emissions in East Asia during 2005-2012. Posterior evaluations with surface measurements show reduced normalized mean bias (NMB) by 7% (NO₂)-15% (SO₂) and normalized mean square error (NMSE) by 8% (SO₂)-9% (NO₂) compared to a species-based inversion. This new inversion captures the peak years of Chinese SO₂ (2007) and NO _x (2011) emissions and attributes their drivers to industry and energy activities. The CO peak in 2007 in China is driven by residential and industry emissions. In India, the inversion attributes NO _x and SO₂ trends mostly to energy and CO trend to residential emissions.

Improvement of inorganic aerosol component in PM2.5 by constraining aqueous-phase formation of sulfate in cloud with satellite retrievals: WRF-Chem simulations

High concentrations of PM_2.5 in China have caused severe visibility degradation and health problems. However, it is still challenging to accurately predict PM_2.5 and its chemical components in numerical models. In this study, we compared the inorganic aerosol components of PM_2.5 (sulfate, nitrate, and ammonium (SNA)) simulated by the Weather Research and Forecasting model fully coupled with chemistry (WRF-Chem) model with in-situ data in a heavy haze-fog event during November 2018 in Nanjing, China. Comparisons show that the model underestimates sulfate concentrations by 81% and fails to reproduce the significant increase of sulfate from early morning to noon, which corresponds to the timing of fog dissipation that suggests the model underestimates the aqueous-phase formation of sulfate in clouds. In addition, the model overestimates both nitrate and ammonium concentrations by 184% and 57%, respectively. These overestimates contribute to the simulated SNA being 77.2% higher than observed. However, cloud water content is also underestimated which is a pathway for important aqueous-phase reactions. Therefore, we constrained the simulated cloud water content based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Liquid Water Path observations. Results show that the simulation with MODIS-corrected cloud water content increases the sulfate by a factor of 3, decreases the Normalized Mean Bias (NMB) by 53.5%, and reproduces its diurnal cycle with the peak concentration occurring at noon. The improved sulfate simulation also improves the simulation of nitrate, which decreases the simulated nitrate bias by 134%. Although the simulated ammonium is still higher than the observations, corrected cloud water content leads to a decrease of the modelled bias in SNA from 77.2% to 14.1%. The strong sensitivity of simulated SNA concentration to the cloud water content provides an explanation for the simulated SNA bias. Hence, uncertainties in cloud water content can contribute to model biases in simulating SNA which are less frequently explored from a process-level perspective and can be reduced by constraining the model with satellite observations.

Improved Anthropogenic SO2 Retrieval from High-Spatial-Resolution Satellite and its Application during the COVID-19 Pandemic

Sulfur dioxide (SO₂) measured by satellites is widely used to estimate anthropogenic emissions. The Sentinel-5 Precursor (S-5P) operational SO₂ product is overestimated compared to the ground-based multiaxis differential optical absorption spectroscopy (MAX-DOAS) measurements in China and shows an opposite variation to the surface measurements, which limits the application of TROPOspheric monitoring instrument (TROPOMI) products in emissions research. Radiometric calibration, a priori profiles, and fitting windows might cause the overestimation of S-5P operational SO₂ product. Here, we improve the optimal-estimation-based algorithm through several calibration methods. The improved retrieval agrees reasonably well with the ground-based measurements (R > 0.70, bias <13.7%) and has smaller biases (-28.9%) with surface measurements over China and India. It revealed that the SO₂ column in March 2020 decreased by 51.6% compared to March 2019 due to the lockdown for curbing the spread of the COVID-19 pandemic, and there was a decrease of 50% during the lockdown than those after the lockdown, similar to the surface measurement trend, while S-5P operational SO₂ product showed an unrealistic increase of 19%. In India, the improved retrieval identified obvious "hot spots" and observed a 30% decrease of SO₂ columns during the lockdown.

Air Quality Response in China Linked to the 2019 Novel Coronavirus (COVID-19) Lockdown

Efforts to stem the spread of COVID-19 in China hinged on severe restrictions to human movement starting 23 January 2020 in Wuhan and subsequently to other provinces. Here, we quantify the ancillary impacts on air pollution and human health using inverse emissions estimates based on multiple satellite observations. We find that Chinese NOx emissions were reduced by 36% from early January to mid-February, with more than 80% of reductions occurring after their respective lockdown in most provinces. The reduced precursor emissions increased surface ozone by up to 16 ppb over northern China but decreased PM2.5 by up to 23 μg m-3 nationwide. Changes in human exposure are associated with about 2,100 more ozone-related and at least 60,000 fewer PM2.5-related morbidity incidences, primarily from asthma cases, thereby augmenting efforts to reduce hospital admissions and alleviate negative impacts from potential delayed treatments.

An Estimation of Top-Down NOx Emissions from OMI Sensor Over East Asia

This study focuses on the estimation of top-down NOx emissions over East Asia, integrating information on the levels of NO2 and NO, wind vector, and geolocation from Ozone Monitoring Instrument (OMI) observations and Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model simulations. An algorithm was developed based on mass conservation to estimate the 30 km × 30 km resolved top-down NOx emissions over East Asia. In particular, the algorithm developed in this study considered two main atmospheric factors—(i) NOx transport from/to adjacent cells and (ii) calculations of the lifetimes of column NOx (τ). In the sensitivity test, the analysis showed the improvements in the top-down NOx estimation via filtering the data (τ ≤ 2 h). The best top-down NOx emissions were inferred after the sixth iterations. Those emissions were 11.76 Tg N yr−1 over China, 0.13 Tg N yr−1 over North Korea, 0.46 Tg N yr−1 over South Korea, and 0.68 Tg N yr−1 over Japan. These values are 34%, 62%, 60%, and 47% larger than the current bottom-up NOx emissions over these countries, respectively. A comparison between the CMAQ-estimated and OMI-retrieved NO2 columns was made to confirm the accuracy of the newly estimated NOx emission. The comparison confirmed that the estimated top-down NOx emissions showed better agreements with observations (R2 = 0.88 for January and 0.81 for July).

SO2 Emission Estimates Using OMI SO2 Retrievals for 2005-2017

SO2 column densities from Ozone Monitoring Instrument provide important information on emission trends and missing sources, but there are discrepancies between different retrieval products. We employ three Ozone Monitoring Instrument SO2 retrieval products (National Aeronautics and Space Administration (NASA) standard (SP), NASA prototype, and BIRA) to study the magnitude and trend of SO2 emissions. SO2 column densities from these retrievals are most consistent when viewing angles and solar zenith angles are small, suggesting more robust emission estimates in summer and at low latitudes. We then apply a hybrid 4D-Var/mass balance emission inversion to derive monthly SO2 emissions from the NASA SP and BIRA products. Compared to HTAPv2 emissions in 2010, both posterior emission estimates are lower in United States, India, and Southeast China, but show different changes of emissions in North China Plain. The discrepancies between monthly NASA and BIRA posterior emissions in 2010 are less than or equal to 17% in China and 34% in India. SO2 emissions increase from 2005 to 2016 by 35% (NASA)-48% (BIRA) in India, but decrease in China by 23% (NASA)-33% (BIRA) since 2008. Compared to in situ measurements, the posterior GEOS-Chem surface SO2 concentrations have reduced NMB in China, the United States, and India but not in South Korea in 2010. BIRA posteriors have better consistency with the annual growth rate of surface SO2 measurement in China and spatial variability of SO2 concentration in China, South Korea, and India, whereas NASA SP posteriors have better seasonality. These evaluations demonstrate the capability to recover SO2 emissions using Ozone Monitoring Instrument observations.