The greatest air quality experiment ever: Policy suggestions from the COVID-19 lockdown in twelve European cities.

COVID-19 (Coronavirus disease 2019) hit Europe in January 2020. By March, Europe was the active centre of the pandemic. As a result, widespread "lockdown" measures were enforced across the various European countries, even if to a different extent. Such actions caused a dramatic reduction, especially in road traffic. This event can be considered the most significant experiment ever conducted in Europe to assess the impact of a massive switch-off of atmospheric pollutant sources. In this study, we focus on in situ concentration data of the main atmospheric pollutants measured in twelve European cities, characterized by different climatology, emission sources, and strengths. We propose a methodology for the fair comparison of the impact of lockdown measures considering the non-stationarity of meteorological conditions and emissions, which are progressively declining due to the adoption of stricter air quality measures. The analysis of these unmatched circumstances allowed us to estimate the impact of a nearly zero-emission urban transport scenario on air quality in 12 European cities. The clearest result, common to all the cities, is that a dramatic traffic reduction effectively reduces NO2 concentrations. In contrast, each city's PM and ozone concentrations can respond differently to the same type of emission reduction measure. From the policy point of view, these findings suggest that measures targeting urban traffic alone may not be the only effective option for improving air quality in cities.

Using Machine Learning Methods to Forecast Air Quality: A Case Study in Macao

Despite the levels of air pollution in Macao continuing to improve over recent years, there are still days with high-pollution episodes that cause great health concerns to the local community. Therefore, it is very important to accurately forecast air quality in Macao. Machine learning methods such as random forest (RF), gradient boosting (GB), support vector regression (SVR), and multiple linear regression (MLR) were applied to predict the levels of particulate matter (PM10 and PM2.5) concentrations in Macao. The forecast models were built and trained using the meteorological and air quality data from 2013 to 2018, and the air quality data from 2019 to 2021 were used for validation. Our results show that there is no significant difference between the performance of the four methods in predicting the air quality data for 2019 (before the COVID-19 pandemic) and 2021 (the new normal period). However, RF performed significantly better than the other methods for 2020 (amid the pandemic) with a higher coefficient of determination (R2) and lower RMSE, MAE, and BIAS. The reduced performance of the statistical MLR and other ML models was presumably due to the unprecedented low levels of PM10 and PM2.5 concentrations in 2020. Therefore, this study suggests that RF is the most reliable prediction method for pollutant concentrations, especially in the event of drastic air quality changes due to unexpected circumstances, such as a lockdown caused by a widespread infectious disease.

Lives and livelihoods: Estimates of the global mortality and poverty effects of the Covid-19 pandemic.

We evaluate the global welfare consequences of increases in mortality and poverty generated by the Covid-19 pandemic. Increases in mortality are measured in terms of the number of years of life lost (LY) to the pandemic. Additional years spent in poverty (PY) are conservatively estimated using growth estimates for 2020 and two different scenarios for its distributional characteristics. Using years of life as a welfare metric yields a single parameter that captures the underlying trade-off between lives and livelihoods: how many PYs have the same welfare cost as one LY. Taking an agnostic view of this parameter, we compare estimates of LYs and PYs across countries for different scenarios. Three main findings arise. First, we estimate that, as of early June 2020, the pandemic (and the observed private and policy responses) had generated at least 68 million additional poverty years and 4.3 million years of life lost across 150 countries. The ratio of PYs to LYs is very large in most countries, suggesting that the poverty consequences of the crisis are of paramount importance. Second, this ratio declines systematically with GDP per capita: poverty accounts for a much greater share of the welfare costs in poorer countries. Finally, a comparison of these baseline results with mortality estimates in a counterfactual "herd immunity" scenario suggests that welfare losses would be greater in the latter in most countries.

Equipe Técnica Temporária Especializada em hospitais de pequeno porte: estratégia de controle do Covid-19

Objetivo: traçar uma estratégia por meio da criação das Equipes Técnicas Temporárias Especializadas, a ser implementadas em hospitais de pequeno porte no atendimento aos casos com suspeita de infecção por Covid-19. Método: trata-se de um estudo de inovação tecnológica desenvolvido na relação entre a situação de calamidade pública mundial e as possibilidades pautadas na literatura. Realizou-se um levantamento bibliográfico na Biblioteca Virtual em saúde, ao final da pesquisa, elaborou-se como instrumento norteador um fluxograma em que consta o direcionamento para implementação dessa estratégia, e uma tabela dinâmica adaptável construída em eixos. Resultados: busca-se com esse projeto, desenvolver tecnologias rápidas e instantâneas no atendimento de enfermagem dentro de hospitais de pequeno porte, como também, orientar profissionais na conduta de pacientes suspeitos de Corona Vírus. Na mesma ótica, desperta o interesse da classe para aquisição de uma visão mais crítica no setor de trabalho, através da adaptação fortalece os vínculos da administração com os membros assistencialistas. Considerações finais: A Equipe Técnica Temporária Especializada foi formulada com base em uma necessidade emergente em período de pandemia, em razão do grande número de casos suspeitos atendidos em hospitais interioranos, garantindo a segurança profissional. (AU) Objective: outline a strategy through the creation of Temporary Specialized Technical Teams, to be implemented in small hospitals to assist cases with suspected Covid-19 infection. Method: it is a study of technological innovation developed in the relationship between the situation of global public calamity and the possibilities based on the literature. A bibliographic survey was carried out at the Virtual Health Library, at the end of the research, a flowchart was drawn up as a guiding instrument containing the direction for implementing this strategy, and an adaptable dynamic table built on axes. Results: this project seeks to develop rapid and instantaneous technologies in nursing care within small hospitals, as well as to guide professionals in the management of suspected Corona virus patients. In the same perspective, it arouses the interest of the class to acquire a more critical view in the work sector, through adaptation it strengthens the bonds of the administration with the assistentialist members. Final Considerations: the Temporary Specialized Technical Team was formulated based on an emerging need in a pandemic period, due to the large number of suspected cases treated in rural hospitals, guaranteeing professional safety. (AU) Objetivo: esbozar una estrategia a través de la creación de equipos técnicos especializados temporales, que se implementarán en pequeños hospitales para ayudar a casos con sospecha de infección por Covid-19. Método: es un estudio de innovación tecnológica desarrollado en la relación entre la situación de calamidad pública global y las posibilidades basadas en la literatura. Se realizó una encuesta bibliográfica en la Biblioteca Virtual de Salud, al final de la investigación, se elaboró un diagrama de flujo como instrumento guía que contiene la dirección para implementar esta estrategia, y una tabla dinámica adaptable construida sobre ejes. Resultados: este proyecto busca desarrollar tecnologías rápidas e instantáneas en la atención de enfermería en pequeños hospitales, así como orientar a los profesionales en el manejo de pacientes sospechosos del virus Corona. En la misma perspectiva, despierta el interés de la clase de adquirir una visión más crítica en el sector laboral, a través de la adaptación fortalece los lazos de la administración con los miembros asistenciales. Consideraciones Finales: el Equipo Técnico Especializado Temporal fue formulado en base a una necesidad emergente en un período de pandemia, debido a la gran cantidad de casos sospechosos tratados en hospitales rurales, garantizando la seguridad profesional. (AU)

Statistical Forecast of Pollution Episodes in Macao during National Holiday and COVID-19

Statistical methods such as multiple linear regression (MLR) and classification and regression tree (CART) analysis were used to build prediction models for the levels of pollutant concentrations in Macao using meteorological and air quality historical data to three periods: (i) from 2013 to 2016, (ii) from 2015 to 2018, and (iii) from 2013 to 2018. The variables retained by the models were identical for nitrogen dioxide (NO2), particulate matter (PM10), PM2.5, but not for ozone (O3) Air pollution data from 2019 was used for validation purposes. The model for the 2013 to 2018 period was the one that performed best in prediction of the next-day concentrations levels in 2019, with high coefficient of determination (R2), between predicted and observed daily average concentrations (between 0.78 and 0.89 for all pollutants), and low root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). To understand if the prediction model was robust to extreme variations in pollutants concentration, a test was performed under the circumstances of a high pollution episode for PM2.5 and O3 during 2019, and the low pollution episode during the period of implementation of the preventive measures for COVID-19 pandemic. Regarding the high pollution episode, the period of the Chinese National Holiday of 2019 was selected, in which high concentration levels were identified for PM2.5 and O3, with peaks of daily concentration exceeding 55 μg/m3 and 400 μg/m3, respectively. The 2013 to 2018 model successfully predicted this high pollution episode with high coefficients of determination (of 0.92 for PM2.5 and 0.82 for O3). The low pollution episode for PM2.5 and O3 was identified during the 2020 COVID-19 pandemic period, with a low record of daily concentration for PM2.5 levels at 2 μg/m3 and O3 levels at 50 μg/m3, respectively. The 2013 to 2018 model successfully predicted the low pollution episode for PM2.5 and O3 with a high coefficient of determination (0.86 and 0.84, respectively). Overall, the results demonstrate that the statistical forecast model is robust and able to correctly reproduce extreme air pollution events of both high and low concentration levels.