ABSTRACT
The COVID-19 outbreak and the lockdown situation have generated a significant negative impact on the world economy but have provided a unique opportunity to understand the impact of human activity on environmental pollution and how it affects the urban climate. This study takes the city of Granada (Spain) in order to carry out an evaluation of the environmental parameters (So2, No2, Co and O3) obtained through Sentinel 5P images and how they affect the Terrestrial Surface Temperature (TST) and the Surface Urban Heat Island (ICUS) obtained through Sentinel 3 images. Knowing the environmental impact on the TST and ICUS of the different Local Climate Zones (ZCL) of the city will have an impact on future urban resilience studies. As a result, and during the confinement period, the following variations have been obtained with respect to environmental pollutants: So2 (-24.0%), No2 (-6.7%), Co (-13.2%) and O3 (+4.0%). The TST has experienced an average reduction of-8.7 degrees C (-38.0%) while the ICUS has decreased by-1.6 degrees C (-66.0%).
ABSTRACT
Covid-19 has brought with it an onslaught of information for the public, some true and some false, across virtually every platform. For an individual, the task of sifting through the deluge for reliable, accurate facts is significant and potentially off-putting. This matters since fundamentally, containment of the pandemic relies on individuals' compliance with public health measures and their understanding of the need for them, and any barrier to this, including misinformation, can have profoundly negative effects. In this paper we present a conversational AI system which tackles misinformation using a two-pronged approach: firstly, by giving users easy, Natural Language access via speech or text to concise, reliable information synthesised from multiple authoritative sources;and secondly, by directly rebutting commonly circulated myths surrounding coronavirus. The initial system is targeted at staff and students of a University, but has the potential for wide applicability. In tests of the system's Natural Language Understanding (NLU) we achieve an F1-score of 0.906. We also discuss current research challenges in the area of conversational Natural Language interfaces for health information. © 2021 ACM.