Identifying Missing Sources and Reducing NO_x Emissions Uncertainty over China using Daily Satellite Data and a Mass-Conserving Method

Abstract. This study applies a mass-conserving model-free analytical approach to daily observations on a grid-by-grid basis of NO₂ from TROPOMI, to rapidly and flexibly quantify changing and emerging sources of NO_x emissions at high spatial and daily temporal resolution. The inverted NO_x emissions and optimized underlying ranges include quantification of the underlying atmospheric in-situ processing, transport and physics. The results are presented over three changing regions in China, including Shandong and Hubei which are rapidly urbanizing and not frequently addressed in the global literature. The day-to-day and grid-by-grid emissions are found to be 1.96±0.27 µg/m²/s on pixels with available priori values (1.94 µg/m²/s), while 1.22±0.63 µg/m²/s extra emissions are found on pixels in which the a priori inventory is lower than 0.3 µg/m²/s. Source attribution based on thermodynamics of combustion temperature, atmospheric transport, and in-situ atmospheric processing successfully identify 5 different industrial source types. Emissions from these industrial sites adjacent to the Yangtze River are found to be 160.5±68.9 Kton/yr (163 % higher than the a priori) consistent with missing light and medium industry located along the river, contradicting previous studies attributing the water as the source of NO_x emissions. Finally, the results demonstrate those pixels with an uncertainty larger than day-to-day variability, providing quantitative information for placement of future monitoring stations. It is hoped that these findings will drive a new approach to top-down emissions estimates, in which emissions are quantified and updated continuously based consistently on remotely sensed measurements and associated uncertainties that actively reflect land-use changes and quantify misidentified emissions, while quantifying new datasets to inform the bottom-up emissions community.