Michael Stichaner

Bio: Michael Stichaner is an academic researcher from University of Innsbruck. The author has contributed to research in topics: Air quality index & Air pollution. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

Decoupling of urban CO 2 and air pollutant emission reductions during the European SARS-CoV-2 lockdown

Abstract: . Lockdown and the associated massive reduction in people's mobility imposed by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) mitigation measures across the globe provide a unique sensitivity experiment to investigate impacts on carbon and air pollution emissions. We present an integrated observational analysis based on long-term in situ multispecies eddy flux measurements, allowing for quantifying near-real-time changes of urban surface emissions for key air quality and climate tracers. During the first European SARS-CoV-2 wave we find that the emission reduction of classic air pollutants decoupled from CO2 and was significantly larger. These differences can only be rationalized by the different nature of urban combustion sources and point towards a systematic bias of extrapolated urban NOx emissions in state-of-the-art emission models. The analysis suggests that European policies, shifting residential, public, and commercial energy demand towards cleaner combustion, have helped to improve air quality more than expected and that the urban NOx flux remains to be dominated (e.g., >90 % ) by traffic.

Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks between atmospheric chemistry and climate change.

Abstract: The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. Furthermore, the abrupt reduction in emissions during the lockdown periods led to clearly observable changes in atmospheric composition, which provide direct insight into feedbacks between the Earth system and human activity. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes, due in large part to their different lifetimes. Here, we discuss several key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed, in part due to decreased ocean uptake of CO2 and a likely increase in CH4 lifetime from reduced NO x emissions. Second, the response of O3 to decreased NO x emissions showed significant spatial and temporal variability, due to differing chemical regimes around the world. Finally, the overall response of atmospheric composition to emissions changes is heavily modulated by factors including carbon-cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality, such as wildfires and the ozone climate penalty.

A Simple Two-dimensional Parameterisation for Flux Footprint Predictions

Abstract: Abstract. Flux footprint models are often used for interpretation of flux tower measurements, to estimate position and size of surface source areas, and the relative contribution of passive scalar sources to measured fluxes. Accurate knowledge of footprints is of crucial importance for any upscaling exercises from single site flux measurements to local or regional scale. Hence, footprint models are ultimately also of considerable importance for improved greenhouse gas budgeting. With increasing numbers of flux towers within large monitoring networks such as FluxNet, ICOS (Integrated Carbon Observation System), NEON (National Ecological Observatory Network), or AmeriFlux, and with increasing temporal range of observations from such towers (of the order of decades) and availability of airborne flux measurements, there has been an increasing demand for reliable footprint estimation. Even though several sophisticated footprint models have been developed in recent years, most are still not suitable for application to long time series, due to their high computational demands. Existing fast footprint models, on the other hand, are based on surface layer theory and hence are of restricted validity for real-case applications. To remedy such shortcomings, we present the two-dimensional parameterisation for Flux Footprint Prediction (FFP), based on a novel scaling approach for the crosswind distribution of the flux footprint and on an improved version of the footprint parameterisation of Kljun et al. (2004b). Compared to the latter, FFP now provides not only the extent but also the width and shape of footprint estimates, and explicit consideration of the effects of the surface roughness length. The footprint parameterisation has been developed and evaluated using simulations of the backward Lagrangian stochastic particle dispersion model LPDM-B (Kljun et al., 2002). Like LPDM-B, the parameterisation is valid for a broad range of boundary layer conditions and measurement heights over the entire planetary boundary layer. Thus, it can provide footprint estimates for a wide range of real-case applications. The new footprint parameterisation requires input that can be easily determined from, for example, flux tower measurements or airborne flux data. FFP can be applied to data of long-term monitoring programmes as well as be used for quick footprint estimates in the field, or for designing new sites.

Impact of Singapore's COVID-19 confinement on atmospheric CO2 fluxes at neighborhood scale

Abstract: Singapore entered a two-month partial lockdown in Apr. 2020 to curb the spread of COVID-19. The imposed measures in addition to contain the virus spread, cut the emissions of greenhouse gases as many economic activities stopped across the city. The advice of stay-at-home changed the pattern of carbon dioxide (CO2) emissions within the community. To examine how CO2 emissions responded to the COVID-19 measures at neighborhood scale, anonymized mobility data released by Google and Apple, and traffic congestion information from TomTom were used to track daily and diurnal changes in emissions related to driving, cooking and metabolic breathing in a residential neighborhood of Singapore, in which the anthropogenic and biogenic fluxes of CO2 have been widely characterized. During the lockdown, traffic emissions dropped 41%, but emissions from cooking and metabolic breathing increased 21% and 20%, respectively. The uptake of CO2 by vegetation was not able to offset these emissions, and after adding the biogenic contribution from soil and plants, a net reduction of 24% was found. During the following six months the city got its pace back, with the rate of CO2 emissions reaching similar or slightly higher levels than those predicted before the pandemic crisis. Unfortunately, the stark drop in emissions was just a temporary relief, which reduced only 3.5% the annual CO2 flux over the studied neighborhood.