The COVID-19 epidemic has substantially limited human activities and affected anthropogenic emissions. In this work, daily NO x emissions are inferred using a regional data assimilation system and hourly surface NO 2 measurement over China. The results show that because of the coronavirus outbreak, NO x emissions across the whole mainland China dropped sharply after 31 January, began to rise slightly in certain areas after 10 February, and gradually recover across the country after 20 February. Compared with the emissions before the outbreak, NO x emissions fell by more than 60% and~30% in many large cities and most small to medium cities, respectively. Overall, NO x emissions were reduced by 36% over China, which were mainly contributed by transportation. Evaluations show that the inverted changes over eastern China are credible, whereas those in western China might be underestimated. These findings are of great significance for exploring the reduction potential of NO x emissions in China. Plain Language Summary In this study, we quantitatively estimate the impact of the COVID-19 lockdown on NO x emissions over China based on nationwide surface hourly NO 2 monitoring data. We find that NO x emissions dropped sharply after 31 January and began to gradually recover after 20 February across the country; NO x emissions fell by more than 60% in many large cities and decreased by~30% in most small to medium cities. Across the whole mainland China, NO x emissions were reduced by 36% due to the COVID-19 lockdown. Generally, a "bottom-up" method of emission inventory technology (Zhang et al., 2019) is adopted to quantify the emission changes, which depends on sector-specific emissions factors and activity levels. Due to the temporal resolution and the lag in release of statistical data (i.e., activity level) as well as the large uncertainties in emission factors and statistical data, it is difficult to use the "bottom-up" method to quantify shortterm and nationwide emission changes (Ding et al., 2015). Data assimilation (DA) is a "top-down" method that can improve emissions estimates by combining observations and background fields. 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.

No 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.

No abstract

No abstract