ZrO2负载过渡金属氧化物催化剂对CO＋NO（O2）反应的影响

本文运用固定床微反技术考察了Ｃｕ,Ｆｅ,Ｍｎ,Ｃｒ,Ｃｏ和Ｎｉ负载（ＺｒＯ２载体）氧化物对ＣＯ＋ＮＯ（Ｏ２）反应的催化活性。研究了ＮＯ和ＣＯ在不同比例时,催化剂对Ｎ２Ｏ和Ｎ２生成的影响。结果表明,在ＮＯ＋ＣＯ反应中,ＮＯ和ＣＯ的比例对催化剂活性和Ｎ２Ｏ,Ｎ２生成均有明显的影响,ＣｕＯｘ／ＺｒＯ２催化剂的活性最高;Ｎ２Ｏ是ＮＯ＋ＣＯ反应的中间产物,低温或ＮＯ过量时有利于生成Ｎ２Ｏ,高温或ＮＯ不足时有     

固硫型煤渣烧制水泥熟料的研究初探

发展固硫型煤工业是治理煤烟型大气污染及减少二氧化硫对大气污染的有效途径。燃烧固硫型煤将产生大量固硫煤渣,对环境构成新的威胁。本文从实验探索的角度,研究开发利用固硫渣的途径。实验中对固硫渣做了化学全分析,用Ｘ－射线粉末衍射法（ＸＲＰＤ）对固硫渣及其加入一定量的添加剂后的煅烧产物进行物相鉴定,证实在一定的条件下固硫渣可以烧制成硫铝酸盐水泥熟料     

中国蔬菜硝酸盐和亚硝酸盐污染因素及控制研究

本文综述了近十几年来我国城市蔬菜中硝酸盐和亚硝酸盐污染的研究结果,表明随着氮肥使用量的增大,蔬菜中硝酸盐污染较为严重而亚硝酸盐污染也不容忽视。探讨了蔬菜受硝酸盐和亚硝酸盐污染的影响因素及控制对策     

从委托代理理论谈中国的环境影响评价制度

根据委托代理理论,分析了中国环境影响评价制度中存在的问题,立足于绿色招商的理念,理顺建设项目环评委托代理关系,明确了建设单位、环评单位、招商部门、环保部门等相互之间的关系和职责,以使中国的环境影响评价制度更适应新型工业化道路发展的要求。     

城市规划的环境影响评价研究初探

环境影响评价法的颁布是中国环境影响评价的一个新阶段。中国至今尚无明确的、系统的规划环境影响评价的技术导则和规范,其评价理论仍在探索之中。分析了国内外规划环境影响评价的历史和现状。从区域环境影响评价的实践经验总结出了城市规划环境影响评价应强调城市发展的可持续性。探讨了该层次评价的介入时间、评价原则、环境影响因素识别及评价内容,提出从生态合理性、绿色化程度等方面考虑城市规划的生态环境影响。     

SF6组合电器典型缺陷模型电场仿真优化设计

SF₆组合电器设备逐年增多,以及部分设备运行年限较长,SF6组合电器设备发生故障也越来越多。组合电器局部放电故障极其复杂,缺陷的特性、电场分布情况无法被直观观察到,造成检测人员不能准确地分析放电缺陷。通过电场仿真分析悬浮电位放电、绝缘缺陷、电晕放电、自由颗粒缺陷4种典型缺陷,2种结构模型的电场分布情况,进一步阐释局部放电缺陷形成机理,有助于检测人员分析局部放电缺陷形成过程。     

智能型防碰天车装置的安全可靠性分析

针对目前油田钻井现场使用的智能型防碰天车装置存在一些安全问题，分析了问题的实质是设计测量精度和安全可靠性不高。提出了具体改进设计的方案与意见。     

Mechanism and characterization of microplastic aging process: A review

● Methods for estimating the aging of environmental micro-plastics were highlighted. ● Aging pathways & characterization methods of microplastics were related and reviewed. ● Possible approaches to reduce the contamination of microplastics were proposed. ● The prospect and deficiency of degradable plastics were analyzed. With the increasing production of petroleum-based plastics, the problem of environmental pollution caused by plastics has aroused widespread concern. Microplastics, which are formed by the fragmentation of macro plastics, are bio-accumulate easily due to their small size and slow degradation under natural conditions. The aging of plastics is an inevitable process for their degradation and enhancement of adsorption performance toward pollutants due to a series of changes in their physiochemical properties, which significantly increase the toxicity and harm of plastics. Therefore, studies should focus on the aging process of microplastics through reasonable characterization methods to promote the aging process and prevent white pollution. This review summarizes the latest progress in natural aging process and characterization methods to determine the natural aging mechanism of microplastics. In addition, recent advances in the artificial aging of microplastic pollutants are reviewed. The degradation status and by-products of biodegradable plastics in the natural environment and whether they can truly solve the plastic pollution problem have been discussed. Findings from the literature pointed out that the aging process of microplastics lacks professional and exclusive characterization methods, which include qualitative and quantitative analyses. To lessen the toxicity of microplastics in the environment, future research directions have been suggested based on existing problems in the current research. This review could provide a systematic reference for in-depth exploration of the aging mechanism and behavior of microplastics in natural and artificial systems.     

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

● MSWNet was proposed to classify municipal solid waste. ● Transfer learning could promote the performance of MSWNet. ● Cyclical learning rate was adopted to quickly tune hyperparameters. An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.