Nitrogen dioxide decline and rebound observed by GOME-2 and TROPOMI during COVID-19 pandemic.

Unlabelled: Since its first confirmed case in December 2019, coronavirus disease 2019 (COVID-19) has become a worldwide pandemic with more than 90 million confirmed cases by January 2021. Countries around the world have enforced lockdown measures to prevent the spread of the virus, introducing a temporal change of air pollutants such as nitrogen dioxide (NO) that are strongly related to transportation, industry, and energy. In this study, NO variations over regions with strong responses to COVID-19 are analysed using datasets from the Global Ozone Monitoring Experiment-2 (GOME-2) sensor aboard the EUMETSAT Metop satellites and TROPOspheric Monitoring Instrument (TROPOMI) aboard the EU/ESA Sentinel-5 Precursor satellite.

Pinpointing nitrogen oxide emissions from space.

Satellite observations of nitrogen dioxide (NO) provide valuable information on the location and strength of NO emissions, but spatial resolution is limited by horizontal transport and smearing of temporal averages due to changing wind fields. In this study, we map NO emissions on high spatial resolution from TROPOMI observations of NO combined with wind fields based on the continuity equation. The divergence of horizontal fluxes proves to be highly sensitive for point sources like exhaust stacks.

Top-Down NO Emissions of European Cities Based on the Downwind Plume of Modelled and Space-Borne Tropospheric NO₂ Columns.

Top-down estimates of surface NO emissions were derived for 23 European cities based on the downwind plume decay of tropospheric nitrogen dioxide (NO₂) columns from the LOTOS-EUROS (Long Term Ozone Simulation-European Ozone Simulation) chemistry transport model (CTM) and from Ozone Monitoring Instrument (OMI) satellite retrievals, averaged for the summertime period (April⁻September) during 2013. Here we show that the top-down NO emissions derived from LOTOS-EUROS for European urban areas agree well with the bottom-up NO emissions from the MACC-III inventory data (R² = 0.88) driving the CTM demonstrating the potential of this method.

NO emission trends over Chinese cities estimated from OMI observations during 2005 to 2015.

Satellite NO observations have been widely used to evaluate emission changes. To determine trends in NO emission over China, we used a method independent of chemical transport models to quantify the NO emissions from 48 cities and 7 power plants over China, on the basis of Ozone Monitoring Instrument (OMI) NO observations during 2005 to 2015. We found that NO emissions over 48 Chinese cities increased by 52% from 2005 to 2011 and decreased by 21% from 2011 to 2015.

Abrupt recent trend changes in atmospheric nitrogen dioxide over the Middle East.

Nitrogen oxides, released from fossil fuel use and other combustion processes, affect air quality and climate. From the mid-1990s onward, nitrogen dioxide (NO2) has been monitored from space, and since 2004 with relatively high spatial resolution by the Ozone Monitoring Instrument. Strong upward NO2 trends have been observed over South and East Asia and the Middle East, in particular over major cities.

Megacity emissions and lifetimes of nitrogen oxides probed from space.

Megacities are immense sources of air pollutants, with large impacts on air quality and climate. However, emission inventories in many of them still are highly uncertain, particularly in developing countries. Satellite observations allow top-down estimates of emissions to be made for nitrogen oxides (NO(x) = NO + NO(2)), but require poorly quantified a priori information on the NO(x) lifetime.

Threading neural feedforward into a mechanical spring: how biology exploits physics in limb control.

A solution is proposed of the hitherto unsolved problem as to how neural feedforward through inverse modelling and negative feedback realised by a mechanical spring can be combined to achieve a highly effective control of limb movement. The revised spring approach that we suggest does not require forward modelling and produces simulated data which are as close as possible to experimental human data. Control models based on peripheral sensing with forward modelling, which are favoured in the current literature, fail to create such data.