Improving indoor air quality through an air purifier able to reduce aerosol particulate matter (PM) and volatile organic compounds (VOCs): Experimental results.

The adverse effects of fine particulate matter (PM) and many volatile organic compounds (VOCs) on human health are well known. Fine particles are, in fact, those most capable of penetrating in depth into the respiratory system. People spend most of their time indoors where concentrations of some pollutants are sometimes higher than outdoors. Therefore, there is the need to ensure a healthy indoor environment and for this purpose the use of an air purifier can be a valuable aid especially now since it was demonstrated that indoor air quality has a high impact on spreading of viral infections such as that due to SARS-COVID19. In this study, we tested a commercial system that can be used as an air purifier. In particular it was verified its efficiency in reducing concentrations of PM10 (particles with aerodynamic diameter less than 10 µm), PM2.5 (particles with aerodynamic diameter less than 2.5 µm), PM1 (particles with aerodynamic diameter less than 1 µm), and particles number in the range 0.3 µm-10 µm. Furthermore, its capacity in reducing VOCs concentration was also checked. PM measurements were carried out by means of a portable optical particle counter (OPC) instrument simulating the working conditions typical of a household environment. In particular we showed that the tested air purifier significantly reduced both PM10 and PM2.5 by 16.8 and 7.25 times respectively that corresponds to a reduction of about 90% and 80%. A clear reduction of VOCs concentrations was also observed since a decrease of over 50% of these gaseous substances was achieved.

Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach

The contribution of this paper is twofold. First, a new data driven approach for predicting the Covid-19 pandemic dynamics is introduced. The second contribution consists in reporting and discussing the results that were obtained with this approach for the Brazilian states, with predictions starting as of 4 May 2020. As a preliminary study, we first used an Long Short Term Memory for Data Training-SAE (LSTM-SAE) network model. Although this first approach led to somewhat disappointing results, it served as a good baseline for testing other ANN types. Subsequently, in order to identify relevant countries and regions to be used for training ANN models, we conduct a clustering of the world"s regions where the pandemic is at an advanced stage. This clustering is based on manually engineered features representing a country"s response to the early spread of the pandemic, and the different clusters obtained are used to select the relevant countries for training the models. The final models retained are Modified Auto-Encoder networks, that are trained on these clusters and learn to predict future data for Brazilian states. These predictions are used to estimate important statistics about the disease, such as peaks and number of confirmed cases. Finally, curve fitting is carried out to find the distribution that best fits the outputs of the MAE, and to refine the estimates of the peaks of the pandemic. Predicted numbers reach a total of more than one million infected Brazilians, distributed among the different states, with São Paulo leading with about 150 thousand confirmed cases predicted. The results indicate that the pandemic is still growing in Brazil, with most states peaks of infection estimated in the second half of May 2020. The estimated end of the pandemics (97% of cases reaching an outcome) spread between June and the end of August 2020, depending on the states.