Inversion of CO emissions over Beijing and its surrounding areas with ensemble Kalman filter

Tang, Xiao; Zhu, Jiang; Wang, Z.F.; Wang, M.; Gbaguidi, Alex; Li, J.; Shao, Min; Tang, Guiqian; Ji, Dongsheng

doi:10.1016/j.atmosenv.2013.08.051

Cited by 59 publications

(61 citation statements)

References 31 publications

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“…Figure 6 shows the monthly mean spatial distributions of the prior and posterior CO emissions and their differences. This analysis with monthly posterior emissions can reduce the influences from random model errors and temporal variations in emissions (X. Tang et al, 2013). Although the posterior emissions are much higher than the prior emissions in both months of December, the spatial distributions are similar in general, with higher emissions mainly in the NCP, YRD, and PRD regions and lower emissions across Northeast, Northwest, and Southwest China.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, due to the complexity of the hourly emissions, it is very difficult to simulate hourly CO concentrations that can match the observations, so daily mean simulations and observations are used in the EnSRF algorithm, and daily emissions are optimized in this study. Following X. Tang et al (2013), the posterior CO emissions used for analysis in this study are temporally averaged using the optimized emissions of each DA window.…”

Section: Methods and Datamentioning

confidence: 99%

“…In China, which has special weather conditions and much higher CO emissions levels, DA studies for CO emission estimates in China are still insufficient. As a pioneering study, X. Tang et al (2013) assimilated surface CO observations from 25 sites to estimate local CO emissions across BTH with the ensemble Kalman filter (EnKF) method to improve short‐term simulations. China has implemented unprecedented measures to reduce emissions.…”

Section: Introductionmentioning

confidence: 99%

“…As a pioneering study, X. Tang et al (2013) assimilated surface CO observations from 25 sites to estimate local CO emissions across BTH with the ensemble Kalman filter (EnKF) method to improve short-term simulations. China has implemented unprecedented measures to reduce emissions.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

CO Emissions Inferred From Surface CO Observations Over China in December 2013 and 2017

Feng

Jiang

Wu³

et al. 2020

JGR Atmospheres

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China has implemented active clean air policies in recent years, and the spatiotemporal patterns of major pollutant emissions have changed substantially. In this study, we construct a regional air pollution data assimilation system based on the WRF/CMAQ model and ensemble Kalman filter algorithm to quantitatively optimize gridded CO emissions using hourly surface CO measurements over China. The Multi‐resolution Emission Inventory of China CO emission inventories in December 2012 and 2016 are treated as prior emissions, and the CO emissions in December 2013 and 2017 are optimized using the CO observations of December of 2013 and 2017, respectively. The results show that in both periods, assimilation of CO observations significantly improves the CO simulations and emission estimates. Assimilation increases the CO emissions in most areas of mainland China, especially in northern China, and the spatial patterns of the increases in the two periods are similar. Overall, the posterior CO emissions in December 2017 are 17% lower than those in December 2013. Large emission decreases are mainly found in most key urban areas and developed regions, and emission increases are mainly located in their surrounding areas and certain central and western regions, which might reflect the emission migration from developed regions or urban areas to developing regions or surrounding areas. These changes are not found in the prior emissions but are basically consistent with the emission control strategies and industrial transformation and upgrade phenomena in recent years, indicating that our CO assimilation system could successfully capture the temporal and spatial variations.

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Section: Resultsmentioning

confidence: 99%

Section: Methods and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

CO Emissions Inferred From Surface CO Observations Over China in December 2013 and 2017

Feng

Jiang

Wu³

et al. 2020

JGR Atmospheres

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“…The ensemble Kalman filter (EnKF), introduced by Evensen (1994), a technology based on ensemble forecasting and Kalman filter theory, has been successfully employed in atmospheric chemistry analyses, such as dust storm and aerosol data assimilation (Lin et al, 2008;Sekiyama et al, 2010;Tang et al, 2013). EnKF has some advantages over 4DVar insofar as it does not require the reconstruction of an adjoint model, which is technically difficult and cumbersome for the complex chemical transport model.…”

Section: Introductionmentioning

confidence: 99%

Inverse modeling of black carbon emissions over China using ensemble data assimilation

Wang

et al. 2016

Atmos. Chem. Phys.

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Abstract. Emissions inventories of black carbon (BC), which are traditionally constructed using a bottom-up approach based on activity data and emissions factors, are considered to contain a large level of uncertainty. In this paper, an ensemble optimal interpolation (EnOI) data assimilation technique is used to investigate the possibility of optimally recovering the spatially resolved emissions bias of BC. An inverse modeling system for emissions is established for an atmospheric chemistry aerosol model and two key problems related to ensemble data assimilation in the top-down emissions estimation are discussed: (1) how to obtain reasonable ensembles of prior emissions and (2) establishing a scheme to localize the background-error matrix. An experiment involving 1-year-long simulation cycle with EnOI inversion of BC emissions is performed for 2008. The bias of the BC emissions intensity in China at each grid point is corrected by this inverse system. The inverse emission over China in January is 240.1 Gg, and annual emission is about 2539.3 Gg, which is about 1.8 times of bottom-up emission inventory. The results show that, even though only monthly mean BC measurements are employed to inverse the emissions, the accuracy of the daily model simulation improves. Using topdown emissions, the average root mean square error of simulated daily BC is decreased by nearly 30 %. These results are valuable and promising for a better understanding of aerosol emissions and distributions, as well as aerosol forecasting.

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Investigating the Transport Mechanism of PM2.5 Pollution during January 2014 in Wuhan, Central China

Tang

Wang

et al. 2019

Adv. Atmos. Sci.

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Severe haze pollution that occurred in January 2014 in Wuhan was investigated. The factors leading to Wuhan's PM 2.5 pollution and the characteristics and formation mechanism were found to be significantly different from other megacities, like Beijing. Both the growth rates and decline rates of PM 2.5 concentrations in Wuhan were lower than those in Beijing, but the monthly PM 2.5 value was approximately twice that in Beijing. Furthermore, the sharp increases of PM 2.5 concentrations were often accompanied by strong winds. A high-precision modeling system with an online source-tagged method was established to explore the formation mechanism of five haze episodes. The long-range transport of the polluted air masses from the North China Plain (NCP) was the main factor leading to the sharp increases of PM 2.5 concentrations in Wuhan, which contributed 53.4% of the monthly PM 2.5 concentrations and 38.5% of polluted days. Furthermore, the change in meteorological conditions such as weakened winds and stable weather conditions led to the accumulation of air pollutants in Wuhan after the long-range transport. The contribution from Wuhan and surrounding cities to the PM 2.5 concentrations was determined to be 67.4% during this period. Under the complex regional transport of pollutants from surrounding cities, the NCP, East China, and South China, the five episodes resulted in 30 haze days in Wuhan. The findings reveal important roles played by transregional and intercity transport in haze formation in Wuhan.

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Inversion of CO emissions over Beijing and its surrounding areas with ensemble Kalman filter

Cited by 59 publications

References 31 publications

CO Emissions Inferred From Surface CO Observations Over China in December 2013 and 2017

CO Emissions Inferred From Surface CO Observations Over China in December 2013 and 2017

Inverse modeling of black carbon emissions over China using ensemble data assimilation

Investigating the Transport Mechanism of PM2.5 Pollution during January 2014 in Wuhan, Central China

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