Research progress and hot spots of hydrothermal liquefaction for bio-oil production based on bibliometric analysis

Environmental Science and Pollution Research - Hydrothermal liquefaction (HTL) of biomass used HTL reaction under high temperature and pressure to produce bio-oil. This technology is considered as...     

武汉地区夏季和秋冬季大气污染的天气概念模型

利用2013～2019年武汉市生态环境局监测数据、L波段雷达探空资料、NCEP/NCAR逐日再分析资料,对夏季和秋冬季武汉地区污染日的大气污染特征、边界层结构、环流形势、物理量场进行研究,建立了武汉地区大气污染的天气概念模型.主要结论如下:(1)武汉市空气质量具有季节性变化特征,大气污染程度四季分布表现为冬＞秋＞春＞夏.夏季首要污染物是臭氧,冬季首要污染物是PM2.5.(2)比较挑选出的夏季清洁日和污染日的气象要素特征,污染日逆温的平均强度约为清洁日的一倍,逆温底高一般在600 m以下,空气质量一般为轻度-中度污染;静风频率(37.1％)明显高于清洁日的静风频率(2.9％);污染日平均风速小(0.8 m/s),边界层内相对湿度较低.同样比较秋冬季两类天气的气象要素特征,污染日逆温底高低、厚度小,不及清洁日的一半,不利于污染物的扩散,易出现重度污染天气.静风频率(20％)高于清洁日的静风频率(7.5％),风速小(1.6 m/s),污染日边界层内呈明显上千下湿的格局.(3)建立了夏季大气污染的天气概念模型,污染日副高偏弱位置偏东,长江流域易少雨干旱;地面我国东部大范围地区处于均压场中,武汉地区为偏东北异常小风,不利于大气污染物的扩散.(4)建立了秋冬季大气污染的天气概念模型,长江流域环流平直少波动,配合地面弱低压的天气形势和较强的逆温使得大气污染物聚集在近地面.蒙古冷高压强度偏弱,使得入侵我国的冷空气强度偏弱;武汉地区为偏北小风,对雾霾的移除和稀释扩散作用差.该研究结论可供大气污染预测预警研究和环境管理部门大气污染的联防联控参考.     

Protective effects of dietary betaine on intestinal barrier function and cecal microbial community in indigenous broiler chickens exposed to high temperature environment

Environmental Science and Pollution Research - High temperature environment causes reduction in productivity in broilers by disrupting the intestinal barrier function. This study aimed to...     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

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 (T_max), and minimum temperature (T_min), 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.     

Uptake and concentration of heavy metals in dominant mangrove species from Hainan Island,South China

Environmental Geochemistry and Health - By investigating three dominant mangrove species, namely Aegiceras corniculatum, Kandelia candel, Ceriops tagal and their rhizosediment in Mangrove wetlands...     

Probabilistic health risk assessment of nitrosamines in drinking water of Shaoxing,Zhejiang, China

Environmental Science and Pollution Research - Nitrosamines (NAms) are potent genotoxic and carcinogenic but widely detected in drinking water. This study aimed to investigate the occurrence of...     

Control of NOx emissions by air staging in small- and medium-scale biomass pellet boilers

Environmental Science and Pollution Research - The effect of air staging strategies on NOx control was investigated on a 210-kW small-scale biomass boiler (SBB) and a 1.4-MW medium-scale biomass...     

Synthesis of branched allylic sulfones by regioselective boron-catalysed hydrosulfonylation

Environmental Chemistry Letters - Branched allylic sulfones are scaffolds widely distributed in bioactive molecules and organic functional materials. The synthesis of allylic sulfones has been...     

Structural and technological determinants of carbon intensity reduction of China’s electricity generation

Environmental Science and Pollution Research - Electricity generation is the largest sector with decarbonization potential for China and the world. Based on the new emission factors, this paper...     

Zinc recovery from hazardous construction wastes via an integrated process of the air classification,alkaline leaching,and water washing

Zinc- and lead-containing wastes are often mixed with construction and demolition wastes in many factories, generating abundant of heavy metal-enriched hazardous waste. In the present study, a novel integrated process of air classification, alkaline leaching, and water washing dechlorination was proposed for the efficient recycling of Zinc (Zn) resources. The first air classification process was realized via venturi tube, wherein the content of Zn could increase by 20 wt.%. After that, the product underwent an alkaline leaching process. Results showed that Zn recovery rate increased with fine particle sizes, and a 65% recovery rate was obtained under the following conditions of 5 mol/L NaOH, liquid/solid 10:1, and leaching time 1 h. Finally, water washing associated with microwave and ultrasonic treatments could remove over 85% of Cl and other water-soluble salts. All the results indicated that the integrated method had an excellent recovery rate for Zn resources from construction and demolition wastes.