The text on p21 discusses many sources of uncertainty (including the lifetime issue), but how were these combined to make 30–40% and the error bars (whatever they are) in Fig.
Related to this, the TROPOMI-derived emission uncertainty is stated to be 30–40% (p21, L428), but it isn’t clear where these numbers come from.Would more smoothing produce better SSIMs and more accurate inversion of the emissions? 72 km, but the Gaussian smoothing used to produce Fig. The authors assume an NO2 lifetime of 4 hours when deriving emissions, but shouldn’t this lifetime impact the footprint on the sources when comparing to the satellite data? With 5 m/s winds for example 4 hours of advection transports the NOx ca.Many citations use parentheses incorrectly. Some sentences make no sense by themselves, but do make sense if joined to the next sentence. Many commas are misplaced, making sentences difficult to read and understand. Many statements are unsupported or confusing. The manuscript needs to be thoroughly checked for the logic and for the English grammar.The manuscript deserves to be published after attending to the points raised below. Scientifically the text is well organised, but the text needs to be tightened up and made more readable. This paper presents an interesting comparison of TROPOMI-NO$_2$ satellite data and inventory data for Germany over 2019-2021. Transparency and comparability, two key values for emission reporting, are thus bolstered by this technique.
#Pandora recovery 2.1 1 verification#
The method described in this manuscript also meet the demand for independent verification of the official emission inventories, which will enable inventory compilers to detect potentially problematic reporting issues. This again illustrates the value of having a consistent satellite based methodology for faster projections to guide and check the conventional emission inventory reporting. While emissions from the larger power-plants did rebound to earlier levels, others sectors such as road transport and shipping did not and could be linked to a reduction in the number of heavier transport trucks. The recent projections for the inventory emissions pointed to a recovery of the emissions towards pre-COVID19 levels this increase was not observed. The large reduction of NO x emissions related to the COVID-19 lock-downs were observed in both the inventory and satellite derived emissions.
For the years where reporting is available differences between satellite estimates and inventory totals were within 100 kt. In this study we apply a consistent methodology to derive NO x emissions over Germany for the years of 2019–2021. They give the possibility to check for compliance with emission reduction targets in a timely manner as well as to observe rapid emission reductions such as experienced during the COVID-19 lock-downs. Satellite based atmospheric composition measurements provide a unique opportunity to fill this gap and independently estimate emissions on a large scale in a consistent, transparent and comprehensible way. Next to this temporal lag difficulties in composed inventories can arise from legislative and protocol differences between countries and over time in reporting of emissions. The effort to properly construct an accurate inventory is significant and time consuming which causes a reporting offset between one and five years with respect to the current date. Currently, the construction of emission inventories to keep track of NO x emissions is based on official national reported emissions and proxies such as activity data as well as direct measurements. NO x is directly linked to negative health and environmental impacts. Nitrogen dioxide (NO x) is an important primary air pollutant, dominantly produced by anthropogenic, mostly combustion based, activities from sectors such as industry, traffic and transport.
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