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Developing the engineering design standard of wind speed is a key aspect of the climate research in the Yangtze River Inland Waterway (YRIW), which is highly sensitive to extreme weather and climate processes. An engineering design wind speed projection model was established to evaluate the distribution of extreme wind speeds in the YRIW region at spatiotemporal scales from 1979 to 2100, integrating the Weibull distribution and generalized extreme value (GEV) distribution characteristics. We also used high-precision climate model products and integrated analysis methods to predict the evolution of engineering design wind speeds in the study area in the future. The results show that: (1) The maximum wind speed in the study area shows a decline—recovery trend in the historical period in general and a weak increase in Wuhan and Shanghai. (2) The maximum wind speed does not follow the Weibull distribution, and the extracted extreme wind speed types include type I, II, and III GEV distributions. (3) The updated inland port project design wind speed can meet the climatic and topographic characteristics of the YRIW. (4) The model of CNRM-CM6-1-HR product accurately captures the spatial and temporal characteristics of the maximum wind speed. (5) In the future, the design wind speed shows a slight decrease in Shanghai, Jiujiang and Yueyang. These findings provide a scientific theoretical reference and engineering reference for the development of design wind speeds for inland port projects at various cross-sections in the YRIW. [ABSTRACT FROM AUTHOR]

To date, research regarding the changes of the sulfur and nitrogen rates in Wuhan during the summer is limited. In this study, we analyzed the air quality in Wuhan, China, using water-soluble ion, gaseous precursor, and weather data. A Spearman correlation analysis was then performed to investigate the temporal changes in air quality characteristics and their driving factors to provide a reference for air pollution control in Wuhan. The results indicate that SO2 in the atmosphere at Wuhan undergoes secondary conversion and photo-oxidation, and the conversion degree of SO2 is higher than that of NO2. During the summers of 2016 and 2017, secondary inorganic atmospheric pollution was more severe than during other years. The fewest oxidation days occurred in summer 2020 (11 days), followed by the summers of 2017 and 2014 (25 and 27 days, respectively). During the study period, ion neutralization was the strongest in summer 2015 and the weakest in August 2020. The aerosols in Wuhan were mostly acidic and NH4+ was an important neutralizing component. The neutralization factors of all cations showed little change in 2015. K+, Mg2+, and Ca2+ level changes were the highest in 2017 and 2020. At low temperature, high humidity, and low wind speed conditions, SO2 and NO2 were more easily converted into SO42− and NO3−. [ABSTRACT FROM AUTHOR]

This study uses online monitoring data from the Hubei Environmental Monitoring Center's Atmospheric Compound Pollution Automatic Monitoring Station from 2016 to 2019 to analyze the diurnal changes in the concentration of water-soluble ions in particulate matter in Wuhan. During the study period, the concentrations of SO2, NO3−, and SO42− changed significantly, while those of NH4+, NH3, and Ca2+ exhibited minimal differences. SO2 and NO3− showed an annually increasing trend, while NH4+ and SO42− exhibited an annually decreasing trend. The ion concentration was generally higher in the winter and spring and lower in the summer and autumn. The concentration of water-soluble ions was generally higher during the day than at night. However, the "weekend effect" on the change in ion concentrations was substantial and higher during the day than at night. This effect was the strongest for NO3− and the weakest for NH3. These changes in the weekend effect of water-soluble ions in particulate matter clearly revealed the impact of periodic human activities on atmospheric pollution. Taken together, the results of this novel study reveal the diurnal pollution characteristics and "weekend effect" of water-soluble ions with high concentrations in atmospheric aerosols in Wuhan over a four-year period, thus providing relevant insights for Wuhan's atmospheric mitigation plan. [ABSTRACT FROM AUTHOR]

Recent research has revealed that COVID-19 pneumonia is often accompanied by pulmonary edema. Pulmonary edema is a manifestation of acute lung injury (ALI), and may progress to hypoxemia and potentially acute respiratory distress syndrome (ARDS), which have higher mortality. Precise classification of the degree of pulmonary edema in patients is of great significance in choosing a treatment plan and improving the chance of survival. Here we propose a deep learning neural network named Non-local Channel Attention ResNet to analyze the lung ultrasound images and automatically score the degree of pulmonary edema of patients with COVID-19 pneumonia. The proposed method was designed by combining the ResNet with the non-local module and the channel attention mechanism. The non-local module was used to extract the information on characteristics of A-lines and B-lines, on the basis of which the degree of pulmonary edema could be defined. The channel attention mechanism was used to assign weights to decisive channels. The data set contains 2220 lung ultrasound images provided by Huoshenshan Hospital, Wuhan, China, of which 2062 effective images with accurate scores assigned by two experienced clinicians were used in the experiment. The experimental results indicated that our method achieved high accuracy in classifying the degree of pulmonary edema in patients with COVID-19 pneumonia by comparison with previous deep learning methods, indicating its potential to monitor patients with COVID-19 pneumonia. [ABSTRACT FROM AUTHOR]

• The biomass of phytoplankton was highest in summer and lowest in winter. • Water quality, followed by land use, had the strongest influences on phytoplankton. • Land use and lake morphology affecting phytoplankton biomass were indirect. • Lake area was negatively correlated with phytoplankton biomass. The community composition and biomass of phytoplankton in shallow lakes are impacted by many environmental factors including water quality physicochemical parameters, land use in the watershed, and lake morphology. However, few studies have simultaneously evaluated the relative importance of these factors on the effect of community composition and biomass of phytoplankton. The relative importance of the water quality physicochemical parameters (water temperature [WT], total nitrogen [TN], total phosphorus [TP], pH, dissolved oxygen [DO], electrical conductivity [EC], turbidity and Secchi depth [SD]), land use (built-up land, farmland, waters, forest, grassland, and unused land) in the watershed, and lake morphology (area and depth) on the composition and biomass of phytoplankton communities were assessed in 29 subtropical shallow lakes in Wuhan, China, during different seasons from December 2017 to November 2018. The results showed that phytoplankton in all 29 lakes was mainly composed of Cyanophyta, Chlorophyta, and Bacillariophyta. Phytoplankton abundance was highest in summer and lowest in winter. We analyzed the relative importance of the three groups of variables to the community composition of the phytoplankton by variance decomposition. The results showed that the three groups of environmental variables had the highest explanation rate (> 80%) for the composition of the phytoplankton community in summer and autumn, and the explanation rates in spring and winter were 42.1% and 39.8%, respectively. The water quality physicochemical parameters were the most important variables affecting the composition of phytoplankton communities, followed by land use in the watershed. Through generalized additive model and structural equation model analysis, we found that the land use and lake morphology had minimal direct impact on the Chl-a and cell density of phytoplankton, mainly by altering the TN, TP, turbidity, SD, DO, and EC, which indirectly affected phytoplankton. WT and nutrients were still the main predictors of phytoplankton abundance. Built-up land was the main source of nitrogen and phosphorus in lakes. Correlation analysis found that forest and grassland had positive impacts on reducing lake nitrogen and phosphorus contents. This showed that increasing grassland and forest in the watershed could reduce the pollutants entering the lake. Our findings will contribute to water quality management and pollution control for subtropical shallow lakes. [Display omitted] [ABSTRACT FROM AUTHOR]