Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Review Clinical, molecular, and epidemiological characterization of the SARS-CoV-2 virus and the Coronavirus Disease 2019 (COVID-19), a comprehensive literature review

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Current studies show that traditional deterministic models tend to struggle to capture the non-linear relationship between the concentration of air pollutants and their sources of emission and dispersion. To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques. Nevertheless, it is puzzling why a certain algorithm is chosen over another for a given task. This systematic review intends to clarify this question by providing the reader with a comprehensive description of the principles underlying these algorithms and how they are applied to enhance prediction accuracy. A rigorous search that conforms to the PRISMA guideline is performed and results in the selection of the 46 most relevant journal papers in the area. Through a factorial analysis method these studies are synthetized and linked to each other. 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. The next challenges of this approach are to improve the prediction of pollution peaks and contaminants recently put in the spotlights (e.g., nanoparticles).

Levels of urban pollution can be influenced largely by meteorological conditions and the topography of the area. The impact of the relative humidity (RH) on the daily average PM 2.5 concentrations was studied at several sites in a mid-size South American city at a high elevation over the period of nine years. In this work, we show that there is a positive correlation between daily average urban PM 2.5 concentrations and the RH in traffic-busy central areas, and a negative correlation in the outskirts of the city in more industrial areas. While in the traffic sites strong events of precipitation (≥9 mm) played a major role in PM 2.5 pollution removal, in the city outskirts, the PM 2.5 concentrations decreased with increasing RH independently of rain accumulation. Increasing PM 2.5 concentrations are to be expected in any highly motorized city where there is high RH and a lack of strong precipitation, especially in rapidly growing and developing countries with high motorization due to poor fuel quality. Finally, two models, based on a logistic regression algorithm, are proposed to describe the effect of rain and RH on PM 2.5 , when the source of pollution is traffic-based vs. industry-based.

In this article, a robust statistical analysis of particulate matter (PM2.5) concentration measurements is carried out. Here, the region chosen for the study was the urban park La Carolina, which is one of the most important in Quito, Ecuador, and is located in the financial center of the city. This park is surrounded by avenues with high traffic, in which shopping centers, businesses, entertainment venues, and homes, among other things, can be found. Therefore, it is important to study air pollution in the region where this urban park is located, in order to contribute to the improvement of the quality of life in the area. The preliminary study presented in this article was focused on the robust estimation of both the central tendency and the dispersion of the PM2.5 concentration measurements carried out in the park and some surrounding streets. To this end, the following estimators were used: (i) for robust location estimation: α-trimmed mean, trimean, and median estimators; and (ii) for robust scale estimation: median absolute deviation, semi interquartile range, biweight midvariance, and estimators based on a subrange. In addition, nonparametric confidence intervals were established, and air pollution levels due to PM2.5 concentrations were classified according to categories established by the Quito Air Quality Index. According to these categories, the results of the analysis showed that neither the streets that border the park nor the park itself are at the Alert level. Finally, it can be said that La Carolina Park is fulfilling its function as an air pollution filter.

Coronaviruses are an extensive family of viruses that can cause disease in both animals and humans. The current classification of coronaviruses recognizes 39 species in 27 subgenera that belong to the family Coronaviridae. From those, at least seven coronaviruses are known to cause respiratory infections in humans. Four of these viruses can cause common cold-like symptoms, while others that infect animals can evolve and become infectious to humans. Three recent examples of this viral jumps include SARS CoV, MERS-CoV and SARS CoV-2 virus. They are responsible for causing severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) and the most recently discovered coronavirus disease during 2019 (COVID-19).COVID-19, a respiratory disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organization (WHO) on 11 March 2020. The rapid spread of the disease has taken the scientific and medical community by surprise. Latest figures from 14 April 2020 show more than 2 million people had been infected with the virus, causing more than 120,000 deaths in over 210 countries worldwide. The large amount of information we receive every day concerning this new disease is so abundant and dynamic that medical staff, health authorities, academics and the media are not able to keep up with this new pandemic. In order to offer a clear insight of the extensive literature available, we have conducted a comprehensive literature review of the SARS CoV-2 Virus and the Coronavirus Diseases 2019 (COVID-19).

In the beginning of 2020, the global human population encountered the pandemic of novel coronavirus disease 2019 . Despite social and economic concerns, this epidemiologic emergency has brought unexpected positive consequences for environmental quality as human activities were reduced. In this paper, the impact of restricted human activities on urban air quality in Ecuador is investigated. This country implemented a particularly strict set of quarantine measures at the very dawn of the exponential growth of infections on March 17, 2020. As a result, significant reductions in the concentrations of NO 2 (-68%), SO 2 (-48%), CO (-38%) and PM 2.5 (-29%) were measured in the capital city of Quito during the first month of quarantine. This large drop in air pollution concentrations occurred at all the monitoring sites in Quito, serving as a valuable proof of the anthropogenic impact on urban air quality. The spatial evolution of atmospheric pollution using observed surface and satellite data, showed different results for the two major cities: Quito and Guayaquil. While the population in Quito adhered to the quarantine measures immediately, in the port city of Guayaquil, quarantine measures were slow to be adopted and, thus, the effect on air quality in Guayaquil occurred more slowly. This lag could have a considerable cost to the mortality rate in the port city, not only due to the spread of the disease but also due to the poor air quality. Overall, the air quality data demonstrate how quickly air quality can improve when emissions are reduced.

Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM 2.5 ). Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM 2.5 from wind (speed and direction) and precipitation levels. The results of the classification model show a high reliability in the classification of low (<10 g/m 3 ) versus high (>25 g/m 3 ) and low (<10 g/m 3 ) versus moderate (10-25 g/m 3 ) concentrations of PM 2.5 . A regression analysis suggests a better prediction of PM 2.5 when the climatic conditions are getting more extreme (strong winds or high levels of precipitation). The high correlation between estimated and real data for a time series analysis during the wet season confirms this finding. The study demonstrates that the use of statistical models based on machine learning is relevant to predict PM 2.5 concentrations from meteorological data.