Computing Daily NOx Inversion over Energy Consuming and Urbanized Regions Using TROPOMI Data – A Novel Model Free Approach

Current approaches to estimate NO_x emissions fail to account for new and small sources, sources with large spatial and temporal variability, and sources which have significantly changed. Furthermore, the current generation of NO_x emissions estimates don’t provide a sufficiently robust uncertainty analysis. They tend to use a fixed combination of localized models (in space and time), and do not adequately consider the variability in observed chemistry, dynamics, and thermodynamics. This work introduces a new, model-free analytical approach that assimilates daily-scale remotely sensed tropospheric columns of NO₂ from TROPOMI in a mass-conserving manner, to invert daily NO_x emissions. This approach is flexibly applied over a rapidly developing and energy-consuming region of Northwest China which is chosen due to substantial economic and population changes, new environmental policies, large use of coal, and access to independent emissions measurements for validation [EGT]. It is also applied over two densely urbanized regions, as well as their surrounding rural and rapidly developing outer suburban regions, including the Pearl River Delta and the Yangtze River Delta, both of which are chosen due to the amount and variability of sources, rapid economic development, and strong changes in environmental emissions policy and regulation. Over the EGT area, this technique computes a net NO_x emissions gain of 70% distributed in a see-saw manner: emissions are more than doubled in cleaner regions, at chemical plants, and in regions thought to be emissions-free, while at the same time, emissions are more than halved in city centers and at other well-regulated and large commercial locations such as steel smelters and powerplants. There is a considerable amount of NO_x emissions observed in suburban areas and rapidly developing rural areas, while a priori datasets do not account for these sources. A few interesting scientific points are explained in detail. First, the error over land surfaces which are not changing is smaller than the day-to-day variability, supporting the idea that daily variability is essential. The errors over areas undergoing land-use change and water are similar to or larger than the day-to-day variability. Second, source attribution is quantified with respect to the local thermodynamics of the combustion temperature, with measured atmospheric transport, and with in-situ chemical processing. Third, there are a significant number of sources identified which do not exist in the a priori datasets, but which are consistent with surface observations. Fourth, sensitivity runs are performed which account for the wide-range of uncertainty estimates of TROPOMI and the self-consistency of the estimated emissions is analyzed on a grid-by-grid and day-by-day basis, showing that the physically realistic constrains on the first order differential equation terms and bootstrapping approach are robust. It is hoped that these findings will drive a new approach to emissions estimation, one in which emissions are based consistently on remotely sensed measurements and associated uncertainties. Such approaches are essential in rapidly developing regions and in the Global South, where local measurements do not otherwise exist.