• Aerosol transmission is an indispensable route of COVID-19 spread.• Different outbreak sites have different epidemiologic feature.• SRAS-CoV-2 can exist for a long time in aerosol.• SRAS-CoV-2 RNA can be detected in aerosol in diverse places.• Some environmental factors can impact SARS-CoV-2 transportation in aerosol. Patients with COVID-19 have revealed a massive outbreak around the world, leading to widespread concerns in global scope. Figuring out the transmission route of COVID-19 is necessary to control further spread. We analyzed the data of 43 patients in Baodi Department Store (China) to supplement the transmission route and epidemiological characteristics of COVID-19 in a cluster outbreak. Incubation median was estimated to endure 5.95 days (2–13 days). Almost 76.3% of patients sought medical attention immediately upon illness onset. The median period of illness onset to hospitalization and confirmation were 3.96 days (0–14) and 5.58 days (1–21), respectively. Patients with different cluster case could demonstrate unique epidemiological characteristics due to the particularity of outbreak sites. SRAS-CoV-2 can be released into the surrounding air through patient’s respiratory tract activities, and can exist for a long time for long-distance transportation. SRAS-CoV-2 RNA can be detected in aerosol in different sites, including isolation ward, general ward, outdoor, toilet, hallway, and crowded public area. Environmental factors influencing were analyzed and indicated that the SARS-CoV-2 transportation in aerosol was dependent on temperature, air humidity, ventilation rate and inactivating chemicals (ozone) content. As for the infection route of case numbers 2 to 6, 10, 13, 16, 17, 18, 20 and 23, we believe that aerosol transmission played a significant role in analyzing their exposure history and environmental conditions in Baodi Department Store. Aerosol transmission could occur in some cluster cases when the environmental factors are suitable, and it is an indispensable route of COVID-19 spread. 相似文献
Environmental Science and Pollution Research - Trace copper ion (Cu(II)) in water and wastewater can trigger peroxymonosulfate (PMS) activation to oxidize organic compounds, but it only works under... 相似文献
Environmental Science and Pollution Research - This paper presents a quantitative pollutant discharge model for a typical molybdenum roasting plant, which combines the best available technology and... 相似文献
Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (Tmax), and minimum temperature (Tmin), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing. 相似文献
Plastic pollution is a major environmental issue worldwide, calling for advanced methods to recycle waste plastics in the context of the circular economy. Here we review methods and strategies to convert waste plastics into value-added carbon materials, with focus on sources, properties, pretreatment of waste plastics, and on preparation of carbon materials. Pretreatment techniques include mechanical crushing, plastic stabilization and electrospinning. Carbon materials such as carbon nanotubes, graphene, carbon nanosheets, carbon spheres and porous carbon are prepared by oxygen-limited carbonization, catalytic carbonization, the template-based method, and pressure carbonization. We emphasize the conversion of polyethene terephthalate, polyethylene, polypropylene, polystyrene, halogenated plastics, polyurethane and mixed plastics.
Anomalous solute transport in porous media is an important issue in groundwater research. In this paper, we explore the relationship between the anomalous solute transport and the volume fractions of different grains in the porous media. Via simulation, we find that there is a maximum and a minimum in the degree of anomalous transport when changing the volume fractions of different grains. Moreover, the characteristic volume fractions corresponding to the anomalous transport maximum and minimum vary little with the flow field and diffusion coefficient of the solute. We also find that the volume fraction corresponding to the most anomalous dispersion is close to the threshold of the site percolation for simple-cubic networks. 相似文献
The rapid development and increase of antibiotic resistance are global phenomena resulting from the extensive use of antibiotics in human clinics and animal feeding operations. Antibiotics can promote the occurrence of antibiotic resistance genes (ARGs), which can be transferred horizontally to humans and animals through water and the food chain. In this study, the presence and abundance of ARGs in livestock waste was monitored by quantitative PCR. A diverse set of bacteria and tetracycline resistance genes encoding ribosomal protection proteins (RPPs) from three livestock farms and a river were analyzed through denaturing gradient gel electrophoresis (DGGE). The abundance of sul(I) was 103 to 105 orders of magnitude higher than that of sul(II). Among 11 tet-ARGs, the most abundant was tet(O). The results regarding bacterial diversity indicated that the presence of antibiotics might have an evident impact on bacterial diversity at every site, particularly at the investigated swine producer. The effect of livestock waste on the bacterial diversity of soil was stronger than that of water. Furthermore, a sequencing analysis showed that tet(M) exhibited two genotypes, while the other RPPs-encoding genes exhibited at least three genotypes. This study showed that various ARGs and RPPs-encoding genes are particularly widespread among livestock. 相似文献