A new approach for monthly updates of anthropogenic sulfur dioxide emissions from space: Application to China and implications for air quality forecasts

Wang, Yi; Wang, Jun; Xu, Xiaoguang; Henze, D. K.; Wang, Yuxuan; Qu, Zhen

doi:10.1002/2016gl070204

Cited by 32 publications

(36 citation statements)

References 37 publications

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“…Finally, we note that quantitative impact of BB transport from Southeast Asia to YGP largely depends on the inventories of smoke emissions that can vary year by year. Furthermore, the underestimation of simulated AOD in the months without biomass burning is unclear, although it is likely that the seasonal variation of industrial emissions may not be well represented in the model [ Wang et al , ]. Therefore, further studies of smoke transport in other years as well as long‐term observation of aerosol properties (especially from accurate ground‐based measurements) are required to quantify both the impacts of biomass burning aerosols and anthropometric aerosols on the air quality and climate in this region.…”

Section: Discussionmentioning

confidence: 99%

Impact of Southeast Asian smoke on aerosol properties in Southwest China: First comparison of model simulations with satellite and ground observations

et al. 2017

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Smoke aerosols have been observed in Southwest China as a result of long‐range transport from surrounding areas in March and April. The processes driving this transport and the resultant impact on regional aerosol optical properties are studied here through a combined use of the Goddard Earth Observing System (GEOS)‐Chem chemistry transport model in conjunction with satellite and the first‐ever ground‐based observations in the Southwest China. The potential biomass burning source regions as well as their respective contributions to aerosol loading in Southwest China are quantified. Compared to Sun photometer observations of aerosol optical depth (AOD) at 550 nm at eight stations in the study region (10–28°N, 90–115°E, comprising Northeast India, Indo‐China Peninsula, and Southwest and South China), the AOD simulated by GEOS‐Chem (nested grid with 0.5° × 0.667° resolution) by using the Fire Inventory from National Center for Atmospheric Research shows an average bias of −0.17 during January 2012 to May 2013. However, during the biomass burning months (March–April), the simulated AOD is much improved with a bias of −0.04. Model sensitivity experiments show that biomass burning in Burma and Northeast India is the largest contributor to smoke AOD (~88%) and total AOD (~57%) over Kunming, an urban site in Southwest China. Case studies on 21–23 March 2013 show that the smoke layer in Northeast India and North Burma can extend from the surface to 4 km and then be transported to Southwest China by prevailing westerly airflow. Model‐simulated AOD and vertical distribution of aerosols are respectively in good agreement with satellite measurements from Moderate Resolution Imaging Spectroradiometer and Cloud‐Aerosol Lidar with Orthogonal Polarization.

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Section: Discussionmentioning

confidence: 99%

Impact of Southeast Asian smoke on aerosol properties in Southwest China: First comparison of model simulations with satellite and ground observations

et al. 2017

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“…However, the magnitude of posterior SO 2 emissions constrained by the BIRA product is closer to prior emissions from INTEX-B Xu et al, 2013). Differences in the emission estimate method (4D-Var in Wang et al (2016), mass balance in Koukouli et al (2018)), the atmospheric models employed, or other factors such as meteorological inputs may also contribute to these ranges of estimates for SO 2 emissions.…”

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confidence: 89%

SO₂ Emission Estimates Using OMI SO₂ Retrievals for 2005–2017

Henze

et al. 2019

JGR Atmospheres

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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.

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“…Bottom-up NO x and SO 2 emission inventories from Streets et al (2003), Zhang et al (2009), Regional Emission inventory in ASia (REAS) (Kurokawa et al, 2013;Ohara et al, 2007), and MIX (Li, Zhang, et al, 2017) have been extensively used for air quality and human health studies in Asia (e.g., Gao et al, 2018;Kanaya et al, 2017;Uno et al, 2003;Wang et al, 2016). However, uncertainties in anthropogenic NO x and SO 2 emissions from these inventories are up to 49% and 35% (Kurokawa et al, 2013;Lu et al, 2011;Zhang et al, 2009;Zhao et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Extended Kalman filter (e.g., Mijling et al, 2013) and ensemble Kalman filter (e.g., Miyazaki et al, 2017) are employed to estimate multiyear NO x emissions in China. 4D-Var (e.g., Qu et al, 2019;Wang et al, 2016;Xu et al, 2013), mass balance (e.g., Koukouli et al, 2018;Lee et al, 2011), and plume methods (e.g., Fioletov et al, 2013;McLinden et al, 2016) have also been applied to derive SO 2 emissions from OMI SO 2 and Moderate Resolution Imaging Spectroradiometer aerosol optical depth (AOD) observations. Though posterior emissions estimated based on single-species observations provide insights into the trend of air pollutants, chemical interactions among atmospheric species are often overlooked in the assimilation system, which can lead to errors in the derived emissions.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Mass Balance/4D‐Var Joint Inversion of NO_x and SO₂ Emissions in East Asia

Henze

Theys

et al. 2019

JGR Atmospheres

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Accurate estimates of NOx and SO2 emissions are important for air quality modeling and management. To incorporate chemical interactions of the two species in emission estimates, we develop a joint hybrid inversion framework to estimate their emissions in China and India (2005–2012). Pseudo observation tests and posterior evaluation with surface measurements demonstrate that joint assimilation of SO2 and NO2 can provide more accurate constraints on emissions than single‐species inversions. This occurs through synergistic change of O3 and OH concentrations, particularly in conditions where satellite retrievals of the species being optimized have large uncertainties. The percentage changes of joint posterior emissions from the single‐species posterior emissions go up to 242% at grid scales, although the national average of monthly emissions, seasonality, and interannual variations are similar. In China and India, the annual budget of joint posterior SO2 emissions is lower, but joint NOx posterior emissions are higher, because NOx emissions increase to increase SO2 concentration and better match Ozone Monitoring Instrument SO2 observations in high‐NOx regions. Joint SO2 posterior emissions decrease by 16.5% from 2008 to 2012, while NOx posterior emissions increase by 24.9% from 2005 to 2011 in China—trends which are consistent with the MEIC inventory. Joint NOx and SO2 posterior emissions in India increase by 15.9% and 19.2% from 2005 to 2012, smaller than the 59.9% and 76.2% growth rate using anthropogenic emissions from EDGARv4.3.2. This work shows the benefit and limitation of joint assimilation in emission estimates and provides an efficient framework to perform the inversion.

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A new approach for monthly updates of anthropogenic sulfur dioxide emissions from space: Application to China and implications for air quality forecasts

Cited by 32 publications

References 37 publications

Impact of Southeast Asian smoke on aerosol properties in Southwest China: First comparison of model simulations with satellite and ground observations

Impact of Southeast Asian smoke on aerosol properties in Southwest China: First comparison of model simulations with satellite and ground observations

SO₂ Emission Estimates Using OMI SO₂ Retrievals for 2005–2017

Hybrid Mass Balance/4D‐Var Joint Inversion of NO_x and SO₂ Emissions in East Asia

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A new approach for monthly updates of anthropogenic sulfur dioxide emissions from space: Application to China and implications for air quality forecasts

Cited by 32 publications

References 37 publications

Impact of Southeast Asian smoke on aerosol properties in Southwest China: First comparison of model simulations with satellite and ground observations

Impact of Southeast Asian smoke on aerosol properties in Southwest China: First comparison of model simulations with satellite and ground observations

SO2 Emission Estimates Using OMI SO2 Retrievals for 2005–2017

Hybrid Mass Balance/4D‐Var Joint Inversion of NOx and SO2 Emissions in East Asia

Contact Info

Product

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About

SO₂ Emission Estimates Using OMI SO₂ Retrievals for 2005–2017

Hybrid Mass Balance/4D‐Var Joint Inversion of NO_x and SO₂ Emissions in East Asia