ABSTRACT
The aim of this study is to quantify the BIAS in air pollution (PM2.5, NO2) exposure estimates that arise from neglecting population activity under COVID-19 lockdown conditions. We applied mobility data as derived from different sources (Google, Eurostat, Automatic Identification System, etc.) to model the impact of (1) changing emissions and (2) the change in population activity patterns in a European multi-city (Hamburg, Liège, Marseille) exposure study. Our results show significant underestimations of exposure estimates when activity profiles are either neglected or not adjusted for lockdown conditions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Corona lockdown measures caused unprecedented emission reductions in many parts of the world. However, this does not linearly translate into improved air quality, since meteorological conditions also have a significant impact on air pollutant concentration patterns. This study disentangles effects of emission reduction and the meteorological situation on the concentrations of air pollutants with a focus on elemental carbon (EC) in Central Europe during the first major lockdown in spring 2020. European emission data for 2016 was updated for 2020 by extrapolating previous emission trends for each country and sector for 2020. Lockdown emission reductions were approximated with daily adjustment factors for the sectors traffic (road, air, and ship), public power and industry and for almost all countries based on mobility data, energy consumption and industrial productivity data. Chemistry transport model results focus on traffic related emissions and show significant reductions between 20% and 55% for NO2 concentrations during strongest lockdown measures between mid of March and mid of April. PM2.5 concentration reductions were less strong compared to NO2, only up to 15% in large parts of Europe. Elemental carbon (EC) was reduced by up to 20% in southern Germany and France. Exceptionally low EC concentrations, as observed in April in Northern Central Europe, were not an effect of lockdown measures. They were caused by advection of clean air from Scandinavia. To investigate this further, EC concentrations as well as lockdown emission reductions of EC were also calculated with meteorological data for the same spring period in 2016 and 2018. It could be seen that meteorological effects resulted in modified effects of the lockdown on EC concentration changes, particularly in Denmark and northern Germany in April 2020. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Based on the assumption that the success of an organization is largely determined by the knowledge and skills of its employees, human resource (HR) departments invest considerable resources in the employee recruitment process with the aim of selecting the best, most suitable employees. Due to the high cost of the recruitment process along with its high rate of uncertainty, HR recruiters utilize a variety of methods and instruments to improve the efficiency and effectiveness of this process. Thus far, however, neurological methods, in which neurobiological signals from an examined person are analyzed, have not been utilized for this purpose. This study is the first to propose a neuro-based decision support system to classify cognitive functions into levels, whose target is to enrich the information and indications regarding the candidate along the employee recruitment processes. We first measured relevant functional and cognitive abilities of 142 adult participants using traditional computer-based assessment, which included a battery of four tests regarding executive functions and intelligence score, consistent with actual recruitment processes. Second, using electroencephalogram (EEG) technology, which is one of the dominant measurement tools in NeuroIS research, we collected the participants' brain signals by administering a resting state EEG (rsEEG) on each participant. Finally, using advanced machine and deep learning algorithms, we leveraged the collected rsEEG to classify participants' levels of executive functions and intelligence score. Our empirical analyses show encouraging results of up to 72.6% accuracy for the executive functions and up to 71.2% accuracy for the intelligence score. Therefore, this study lays the groundwork for a novel, generic (non-stimuli based) system that supports the current employee recruitment processes, that is based on psychological theories of assessing executive functions. The proposed decision support system could contribute to the development of additional medium of assessing employees remotely which is especially relevant in the current Covid-19 pandemic. While our method aims at classification rather than at explanation, our intriguing findings have the potential to push forward NeuroIS research and practice. © 2023 Elsevier B.V.