Rasa Zalakeviciute

TL;DR: A comprehensive literature review of the SARS CoV-2 Virus and the Coronavirus Diseases 2019 (COVID-19) is conducted to offer a clear insight of the extensive literature available.

TL;DR: A machine learning approach based on six years of meteorological and pollution data analyses is proposed to predict the concentrations of PM2.5 from wind (speed and direction) and precipitation levels and demonstrates that the use of statistical models based on machine learning is relevant to predict PM 2.5 concentrations from meteorological data.

TL;DR: The main findings of this literature review show that: (i) machine learning is mainly applied in Eurasian and North American continents and (ii) estimation problems tend to implement Ensemble Learning and Regressions, whereas forecasting make use of Neural Networks and Support Vector Machines.

TL;DR: In this article, the authors investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015-2019, by adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX ǫ) during the COVID-19 pandemic period of exceptionally

TL;DR: A comprehensive literature review of the SARS CoV-2 Virus and the Coronavirus Diseases 2019 (COVID-19) is conducted to offer a clear insight of the extensive literature available.