English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The recent important developments in satellite measurements of thecomposition of the lower atmosphere open the challengingperspective to use such measurements as independent information onsources and sinks of atmospheric pollutants. This study exploresthe possibility to improve estimates of gridded NOxemissions used in a continental scale chemistry transport model(CTM), CHIMERE, by employing measurements performed by the GOMEand SCIAMACHY instruments. We set-up an original inverse modellingscheme that not only enables a computationally efficientoptimisation of the spatial distribution of seasonally averagedNOx emissions (during summertime), but also allowsestimating uncertainties in input data and a priori emissions. Thekey features of our method are (i) replacement of the CTM by a setof empirical models describing the relationships betweentropospheric NO2 columns and NOx emissions withsufficient accuracy, (ii) combination of satellite data fortropospheric NO2 columns with ground based measurements ofnear surface NO2 concentrations, and (iii) evaluation ofuncertainties in a posteriori emissions by means of a specialBayesian Monte-Carlo experiment which is based on random samplingof errors of both NO2 columns and emission rates. We haveestimated the uncertainty in a priori emissions based on the EMEPemission inventory to be about 1.9 (in terms of geometric standarddeviation) and found the uncertainty in a posteriori emissionsobtained from our inverse modelling scheme to be significantlylower (about 1.4). It is found also that a priori NOxemission estimates are probable to be persistently biased in manyregions of Western Europe, and that the use of a posterioriemissions in the CTM improves the agreement between the modelledand measured data

Inverse modelling of the spatial distribution of NO&lt;sub&gt;x&lt;/sub&gt; emissions on a continental scale using satellite data

Inverse modelling of the spatial distribution of NO<sub>x</sub> emissions on a continental scale using satellite data