ABSTRACT
The novel coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a public health emergency of international concern. Exposure to droplets produced in the coughs and sneezes of infected individuals has been perceived as the dominant transmission mode for COVID-19. However, increasingly more evidence supports claims of COVID-19 having airborne transmission. An in-depth understanding of the transmission pathways and influencing factors of SARS-CoV-2 is of great significance for formulating more effective intervention strategies. A large number of epidemiological investigations into the influence of atmospheric environmental conditions on virus transmission have been conducted. In this paper, we review current understandings of the association between COVID-19 and atmospheric environmental conditions. We first summarize the epidemiological investigations on the impact of atmospheric environmental factors (including solar radiation, temperature and humidity, wind speed, particulate matters, and gaseous pollutants) on the spread of COVID-19, and 164 epidemiological investigations are included, in which air temperature and relative humidity received the greatest attention. However, the impact of these factors on the prevalence of COVID-19 remains largely uncertain. 56% and 41% of investigations of temperature and humidity, respectively, show that cold and dry weather promotes COVID-19 transmission, while some studies come to the opposite conclusion, and still others do not show a significant relationship between them. Investigations of solar radiation are limited, but have come to the consistent conclusion that weak solar radiation is linked to increased severity of COVID-19 infection. Investigation of the impact of air pollution mainly focuses on particulate matters, and more than 70% of investigations indicate that PM2.5 likely contributes to the spread of COVID-19. Similarly, 62%, 51%, and 31% of investigations of NOx, O3, and SO2, respectively, indicate that the exposure to severe pollution can aggravate COVID-19 transmission. Therefore, the available findings reveal the complexity of the impact of atmospheric environmental conditions on the spread of COVID-19. We further discuss their mechanisms from three perspectives: (1) Atmospheric environmental conditions influence the generation of virus-laden aerosols and the occurrence of SARS-CoV-2 in the atmosphere. Relative humidity can affect the evaporation process of water on virus-laden aerosol, and thus affect its atmospheric life and probability of being inhaled by human body. (2) Atmospheric environmental conditions directly affect the stability of infection activity of SARS-CoV-2. Generally, high temperature, medium relative humidity, and intense solar radiation promote the inactivation of SARS-CoV-2. (3) Atmospheric environmental conditions indirectly affect the infection ability of SARS-CoV-2 by changing the defense ability of host cells. Air pollutants, especially PM2.5, can affect human susceptibility to the virus by increasing the expression of the SARS-CoV-2 receptor (angiotensin converting enzyme 2) in host cells. Meanwhile, meteorological conditions and air pollution can lead to respiratory system and other diseases in the human body, thus reducing human immunity and increasing the risk of virus infection, as well as the numbers of severely infected and fatal cases. All three mechanisms may contribute to the prevalence of COVID-19, but the dominant mechanism remains unclear. Finally, future directions of in-depth studies regarding the association between the epidemic and atmospheric conditions are proposed. © 2022 Chinese Academy of Sciences. All rights reserved.
ABSTRACT
The spread of COVID-19 outbreak has promoted truck-drone delivery from trials to commercial applications in end-to-end contactless solutions. To fully integrate truck-drone delivery in contactless solutions, we introduce the robust traveling salesman problem with a drone, in which a drone makes deliveries and returns to the truck that is moving on its route under uncertainty. The challenge is to find, for each customer location in truck-drone routing, an assignment to minimize the expected makespan. Apart from the complexity of this problem, the risk of synchronization failure associated with uncertain travel time should be also considered. The problem is first formulated as a robust model, and a novel efficient frontier heuristic is proposed to solve this model. By coupling the implicit adaptive weighting with epsilon-constraint methods, the heuristic generates a series of scalarized single-objective problems, where the goal is to minimize expected makespan under the constraint of synchronization risk. The experiment results show that the robust (near-)optimal solutions offer a considerable reduction in risk, yet only hint at a small increase in makespan. The heuristic in the present study is effective to construct approximations of Pareto frontier and allows for assignment decisions in a priori or a posteriori manner. © 2022 The Authors. International Transactions in Operational Research © 2022 International Federation of Operational Research Societies.
ABSTRACT
Many places on earth still suffer from a high level of atmospheric fine particulate matter (PM2.5) pollution. Formation of a particulate pollution event or haze episode (HE) involves many factors, including meteorology, emissions, and chemistry. Understanding the direct causes of and key drivers behind the HE is thus essential. Traditionally, this is done via chemical transport models. However, substantial uncertainties are introduced into the model estimation when there are significant changes in the emissions inventory due to interventions (e.g., the COVID-19 lockdown). Here we applied a Random Forest model coupled with a Shapley additive explanation algorithm, a post hoc explanation technique, to investigate the roles of major meteorological factors, primary emissions, and chemistry in five severe HEs that occurred before or during the COVID-19 lockdown in China. We discovered that, in addition to the high level of primary emissions, PM2.5 in these haze episodes was largely driven by meteorological effects (with average contributions of 30-65 mu g m(-3) for the five HEs), followed by chemistry (similar to 15-30 mu g m(-3)). Photochemistry was likely the major pathway of formation of nitrate, while air humidity was the predominant factor in forming sulfate. Our results highlight that the machine learning driven by data has the potential to be a complementary tool in predicting and interpreting air pollution.
ABSTRACT
Objective: To explore the effective control measures for the 2019 novel coronavirus pneumonia (COVID-19) in the blood purification center of the central epidemic area. Methods: We adopted the prevention and control measures and established emergency plans. The infected cases in all the patients and medical staff in our center from December 31, 2019 to February 12, 2020 were recorded and analyzed. Results: A total of 214 patients had received hemodialysis treatment in our blood purification center, with 5 confirmed cases in the patients' group, accounting for 2.53%. And there were 30 medical staff in the center, with 1 confirmed case, accounting for 3.33%. Conclusion: Blood purification center has a moderate crowd gathering risk, and has a high risk of nosocomial infection as the outbreak of COVID-19. Through the implement of the prevention and control measures, we reduced the infection rate and the spread of nosocomial infection effectively. © 2020, Editorial Board of Medical Journal of Wuhan University. All right reserved.