Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 3 de 3
Add filters

Document Type
Year range
Building and Environment ; : 109527, 2022.
Article in English | ScienceDirect | ID: covidwho-2003903


The dispersion of the coronavirus pandemic has caused immense damage worldwide, and people have begun to ruminate epidemic prevention strategies for public places. Airport terminals with a high number of occupied passengers have become potentially high-risk regions for aerosol transmission of coronavirus disease 2019 (COVID-19). In this study, the Eulerian–Lagrangian approach and realizable k-ε turbulence model were used to numerically simulate airflow organization and aerosol transmission when passengers are moving slowly in a line. During the aerosol transmission period, evaporation was considered a key factor influencing the particle size distribution at the beginning of aerosol transmission from humans. Moreover, passenger movement at the airport terminal was attained by employing dynamic mesh algorithms. Based on the relative direction of passengers and air vents when queuing in the terminal building, we studied three conditions: windward walking, leeward walking, and crosswind walking. The results of this study showed that the walking has an important influence on droplet distribution. Droplet distribution indicates that individuals standing behind patients during queuing movements have a higher risk of infection than those standing in front of them. A significant aerosol accumulation was discovered at 0.5 m behind the patient when passengers moved simultaneously. An aerosol transmission distance of 15 s aligned with the passenger's walking direction could reach up to 9.32 m. Furthermore, although the evaporation time of the large droplets was longer than that of the small droplets, both large and small droplets evaporated rapidly after exhalation. The crosswind influence caused the droplets to travel farther away in a direction perpendicular to human movement, which increased the distance by approximately 1.26 m compared to the absence of the crosswind influence.

Energy ; : 119952, 2021.
Article in English | ScienceDirect | ID: covidwho-1046466


The aim of this research is to forecast seasonal fluctuations in electricity consumption, and electricity usage efficiency of industrial sectors and identify the impacts of the novel coronavirus disease 2019 (COVID-19). For this purpose, a new seasonal grey prediction model (AWBO-DGGM(1,1)) is proposed: it combines buffer operators and the DGGM(1,1) model. Based on the quarterly data of the industrial enterprises in Zhejiang Province of China from the first quarter of 2013 to the first quarter of 2020, the GM(1,1), DGGM(1,1), SVM, and AWBO-DGGM(1,1) models are employed, respectively, to simulate and forecast seasonal variations in electricity consumption, the added value, and electricity usage efficiency. The results indicate that the AWBO-DGGM(1,1) models can identify seasonal fluctuations and variations in time series data, and predict the impact of COVID-19 on industrial systems. The minimum mean absolute percentage errors (MAPEs) of the electricity consumption, added value, and electricity usage efficiency of industrial enterprises separately are 0.12%, 0.10%, and 3.01% in the training stage, while those in the test stage are 6.79%, 4.09%, and 2.25%, respectively. The electricity consumption, added value, and electricity usage efficiency of industrial enterprises in Zhejiang Province will still present a tendency to grow with seasonal fluctuations from 2020 to 2022. Of them, the added value is predicted to increase the fastest, followed by electricity consumption.

Int J Environ Res Public Health ; 17(12)2020 06 25.
Article in English | MEDLINE | ID: covidwho-614073


The outbreak of a novel coronavirus (SARS-CoV-2) has caused a large number of residents in China to be infected with a highly contagious pneumonia recently. Despite active control measures taken by the Chinese government, the number of infected patients is still increasing day by day. At present, the changing trend of the epidemic is attracting the attention of everyone. Based on data from 21 January to 20 February 2020, six rolling grey Verhulst models were built using 7-, 8- and 9-day data sequences to predict the daily growth trend of the number of patients confirmed with COVID-19 infection in China. The results show that these six models consistently predict the S-shaped change characteristics of the cumulative number of confirmed patients, and the daily growth decreased day by day after 4 February. The predicted results obtained by different models are very approximate, with very high prediction accuracy. In the training stage, the maximum and minimum mean absolute percentage errors (MAPEs) are 4.74% and 1.80%, respectively; in the testing stage, the maximum and minimum MAPEs are 4.72% and 1.65%, respectively. This indicates that the predicted results show high robustness. If the number of clinically diagnosed cases in Wuhan City, Hubei Province, China, where COVID-19 was first detected, is not counted from 12 February, the cumulative number of confirmed COVID-19 cases in China will reach a maximum of 60,364-61,327 during 17-22 March; otherwise, the cumulative number of confirmed cases in China will be 78,817-79,780.

Coronavirus Infections/epidemiology , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Betacoronavirus/isolation & purification , COVID-19 , China/epidemiology , Coronavirus Infections/virology , Humans , Models, Statistical , Pneumonia, Viral/virology , SARS-CoV-2