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

Abstract

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.

NOx Emission Changes Over China During the COVID‐19 Epidemic Inferred From Surface NO2 Observations

“…A regional CO assimilation system has been constructed and successfully applied in our previous study (Feng et al, 2020) (Feng2020), which optimized the gridded CO emissions using hourly surface CO measurements across China. In this study, the DA system was further extended to infer the gridded NO x emissions using hourly surface NO 2 observations over China.…”

“…As mentioned previously, the optimized emissions of the current DA window are transferred to the next DA window as the prior emissions. To avoid filter divergence, and considering compensation of the model error, prior inventory, and daily emission uncertainties, we perturb the emissions at each DA window with the same uncertainty, which is similar to the CO inversion (Feng et al, 2020). The uncertainty is set to be 25%, a value much smaller than that of CO (40%) in Feng2020 because studies (Li, Zhang, et al, 2017; Zhang et al, 2009) have shown that the uncertainty of NO x is much smaller than that of CO in the MEIC inventory.…”

“…For example, Zhang et al (2016) applied 4D‐VAR DA to optimize daily aerosol primary and precursor emissions over North China during the APEC 2014. Feng et al (2020) inferred the CO emissions changes over China during the “Action Plan” using surface CO observations. Chu et al (2018) and Ding et al (2015) estimated PM 2.5 and NO x emission changes during the 2015 China Victory Day parade and the 2014 Youth Olympic Games by assimilating surface PM 2.5 and OMI retrievals, respectively.…”

Regional CO<sub>2</sub> fluxes from 2010 to 2015 inferred from GOSAT XCO<sub>2</sub> retrievals using a new version of the Global Carbon Assimilation System

“…In this way, more observation stations will sense the flux change in one area; therefore, more observations can be used to optimize the flux of that place. For this reason, many previous ensemble-based assimilation systems have used a longer DA window (e.g., Peters et al, 2005;Feng et al, 2009;Jacobson et al, 2020). However, the farther that the observation station is from the source, the weaker signal that the stations can sense.…”

Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ

“…Emissions from East Asia are known to impact regional air quality (AQ) and significantly contribute to surface O 3 pollution at regional, continental, and even intercontinental scales through trans-Pacific transport, in particular in spring when meteorological conditions favor rapid transport (Akimoto et al, 1996;Jacob et al, 1999;Wilkening et al, 2000;Heald et al, 2006). Frontal lifting in warm conveyor belts (WCBs) efficiently contributes to the transport of pollution (Cooper et al, 2004;Zhang et al, 2008;Lin et al, 2012), which can be observed by satellite retrievals of tropospheric O 3 (Foret et al, 2014) and aircraft in situ measurements (Ding et al, 2015). However, the mechanisms that cause the uplifted pollution to effectively descend to the downwind surface layers at regional, continental, and intercontinental scales are complex.…”