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.

As global urbanization, industrialization, and motorization keep worsening air quality, a continuous rise in health problems is projected. Limited spatial resolution of the information on air quality inhibits full comprehension of urban population exposure. Therefore, we propose a method to predict urban air pollution from traffic by extracting data from Web-based applications (Google Traffic). We apply a machine learning approach by training a decision tree algorithm (C4.8) to predict the concentration of PM2.5 during the morning pollution peak from: (i) an interpolation (inverse distance weighting) of the value registered at the monitoring stations, (ii) traffic flow, and (iii) traffic flow + time of the day. The results show that the prediction from traffic outperforms the one provided by the monitoring network (average of 65.5% for the former vs. 57% for the latter). Adding the time of day increases the accuracy by an average of 6.5%. Considering the good accuracy on different days, the proposed method seems to be robust enough to create general models able to predict air pollution from traffic conditions. This affordable method, although beneficial for any city, is particularly relevant for low-income countries, because it offers an economically sustainable technique to address air quality issues faced by the developing world.

Air pollution represents one of the greatest risks to human health, with most of the world's cities exceeding World Health Organization's recommendations for air quality. In developing countries, a major share of air pollution comes from traffic, consequently, creating air pollution hot spots inside urban street networks. While the world needs to switch to more active and sustainable ways of commuting in order to reduce traffic emissions and help improve degrading cardiopulmonary health due to increasingly sedentary habits, studies point to the negative effects of physical activity near traffic emissions. Common approaches of urban cycling infrastructure planning rely on space availability and route needs, omitting the most vital aspect-air quality. This study, therefore, combines the worldwide need for active commute and health benefits of the cyclists. Our goal was to produce urban pollution map through the geoprocessing of Google Traffic data, validated through the correlation of street level PM 2.5 (particulate matter <2.5 μm) concentrations and traffic intensity in a selected district of Quito, Ecuador. The multidisciplinary approach presented in this study can be used by city planners all over the world to help identify the cycling network based on air quality conditions and, consequently, promoting active travel.