Background: Air pollution has been a serious health issue in Beijing for years. Airborne antibiotic-resistant bacteria could be a potential health crisis as reserve of antibiotic resistance transmission in environment. The composition and antibiotic resistance pattern of culturable bacterial community and how these are affected by air pollution remain unclear.Objectives: This study aimed to compare the compositions and antibiotic resistance patterns of culturable bacteria in polluted and non-polluted weather conditions in Beijing. Methods: Air samples were collected indoors and outdoors during polluted and non-polluted weather using sixstage Andersen Samplers. For each isolated bacterium, the 16S ribosomal RNA gene was amplified, sequenced, and blasted against the National Center for Biotechnology Information database Antibiotic resistance was conducted by antimicrobial susceptibility testing. Results: Bacterial concentration in polluted weather was significantly higher than in non-polluted weather, both indoors and outdoors (P < 0.05). Gram-positive bacteria (GPB) were dominant in both weathers but gramnegative bacteria (GNB) were more abundant in polluted weather than non-polluted weather both indoors and outdoors. Multidrug-resistant (MDR) bacteria occupied 23.7% of all bacterial isolates, 22.4% of isolates from polluted weather and 27.8% of isolates from non-polluted weather. Penicillins were resisted by 72.4% and 83.3% of isolates from polluted and non-polluted weather, respectively. Conclusions: The bacterial concentration was significantly higher in polluted weather, compared to non-polluted weather. Polluted weather is correlated with changes in the bacterial composition in the air, with a greater abundance of GNB. Penicillins was resisted by over 70% of bacterial isolates. The abundance of MDR bacteria suggested potential risks for human health.

From June 11, 2020, a surge in new cases of coronavirus disease 2019 (COVID-19) in the largest wholesale market of Beijing, the Xinfadi Market, leading to a second wave of COVID-19 in Beijing, China. Understanding the transmission modes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the personal behaviors and environmental factors contributing to viral transmission is of utmost important to curb COVID-19 rise. However, currently these are largely unknown in food markets. To this end, we completed field investigations and on-site simulations in areas with relatively high infection rates of COVID-19 at Xinfadi Market. We found that if goods were tainted or personnel in market was infected, normal transaction behaviors between sellers and customers, daily physiological activities, and marketing activities could lead to viral contamination and spread to the surroundings via fomite, droplet or aerosol routes. Environmental factors such as low temperature and high humidity, poor ventilation, and insufficient hygiene facilities and disinfection practices may contribute to viral transmission in Xinfadi Market. In addition, precautionary control strategies were also proposed to effectively reduce the clustering cases of COVID-19 in large-scale wholesale markets.

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Face masks are critical in preventing the spread of respiratory infections including coronavirus disease 2019 (COVID-19). Different types of masks have distinct filtration efficiencies (FEs) with differential costs and supplies. Here we reported the impact of breathing volume and wearing time on the inward and outward FEs of four different mask types (N95, surgical, single-use, and cloth masks) against various sizes of aerosols. Specifically 1) Mask type was an important factor affecting the FEs. The FEs of N95 and surgical mask were better than those of single-use mask and cloth mask; 2) As particle size decreased, the FEs tended to reduce. The trend was significantly observed in FEs of aerosols with particle size < ; 3) After wearing N95 and surgical masks for 0, 2, 4, and 8 h, their FEs (%) maintained from 95.75 ± 0.09 to 100 ± 0 range. While a significant decrease in FEs were noticed for single-use masks worn for 8 h and cloth masks worn >2 h under deep breathing (30 L/min); 4) Both inward and outward FEs of N95 and surgical masks were similar, while the outward FEs of single-use and cloth masks were higher than their inward FEs; 5) The FEs under deep breathing was significantly lower than normal breathing with aerosol particle size ¡1 m. In conclusion, our results revealed that masks have a critical role in preventing the spread of aerosol particles by filtering inhalation, and FEs significantly decreased with the increasing of respiratory volume and wearing time. Deep breathing may cause increasing humidity and hence decrease FEs by increasing the airflow pressure. With the increase of wearing time, the adsorption capacity of the filter material tends to be saturated, which may reduce FEs Findings may be used to provide information for policies regarding the proper use of masks for general public in current and future pandemics.

Different sources of factors in environment can affect the spread of COVID-19 by influencing the diffusion of the virus transmission, but the collective influence of which has hardly been considered. This study aimed to utilize a machine learning algorithm to assess the joint effects of meteorological variables, demographic factors, and government response measures on COVID-19 daily cases globally at city level. Random forest regression models showed that population density was the most crucial determinant for COVID-19 transmission, followed by meteorological variables and response measures. Ultraviolet radiation and temperature dominated meteorological factors, but the associations with daily cases varied across different climate zones. Policy response measures have lag effect in containing the epidemic development, and the pandemic was more effectively contained with stricter response measures implemented, but the generalized measures might not be applicable to all climate conditions. This study explored the roles of demographic factors, meteorological variables, and policy response measures in the transmission of COVID-19, and provided evidence for policymakers that the design of appropriate policies for prevention and preparedness of future pandemics should be based on local climate conditions, population characteristics, and social activity characteristics. Future work should focus on discerning the interactions between numerous factors affecting COVID-19 transmission. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-023-27320-7.