Investigation of PM2.5 pollution during COVID-19 pandemic in Guangzhou, China

The COVID-19 pandemic has raised awareness about various environmental issues,including PM_2.5 pollution.Here,PM_2.5 pollution during the COVID-19 lockdown was traced and analyzed to clarify the sources and factors influencing PM_2.5 in Guangzhou,with an emphasis on heavy pollution.The lockdown led to large reductions in industrial and traffic emissions,which significantly reduced PM_2.5 concentrations in Guangzhou.Interestingly,the trend of PM_2.5     

高铁扣件弹条疲劳断裂原因分析

通过对WJ-7型扣件中60Si2MnA弹条在拉伸-扭转试验过程发生断裂的原因进行分析和数值模拟研究,得出弹条断裂的原因.对60Si2MnA弹条承受的荷载进行分析可知,WJ-7型扣件在铁垫板与弹条后端接触区域内弯处应力集中最大,是造成弹条断裂破坏的主要原因之一,而弹条断裂的根本原因是弹条长期处于极限强度下工作,使弹条最终...     

多元回归在大气环境污染事故预测中的应用

运用多元回归法,通过预测模型的选择、数学模型的建立、基础数据的整理和回归效果的检验,建立环境污染范围与诸条件的关系,达到快速估算的目的,从而形成一种有效的大气环境污染事故范围预测的方法.     

桥式起重机典型事故分析及安全管理

分析了桥式起重机典型事故发生机理,运用事故树分析法对桥式起重机事故发生进行了分析,从而为能够找出安全隐患,制定出最优的控制措施提供了理论上的依据.最后根据分析结果,系统地提出了控制危害,降低事故发生率的安全管理措施,提出一些具有可行性的建议.     

影响脱硫塔性能的因素分析与研究

湿法烟气脱硫是成熟且具有较好发展前景的工艺,其中关键是对脱硫塔的选择及通过各项参数的控制达到良好的脱硫效率.通过对脱硫塔酸碱度(PH)、气液比(L/G)、流场特性、结垢等各种性能进行分析研究,对脱硫塔的选择和实际运行具有一定的指导意义.     

Ni/Fe二元金属在氯代有机物污染治理中的应用

对Ni/Fe二元金属表面特性、在氯代有机物还原脱氯中的应用以及还原机理的研究概况进行了综述,并讨论了实际应用中存在的问题和发展前景.     

偏电压对Ti/TiO2光电催化氧化富里酸的影响

采用光电催化反应器对水中天然有机物富里酸(FA)进行降解试验,考察了外加阳极偏电压对光电催化反应器降解富里酸的影响.结果表明,当外加电压为1.2V时,具有最佳的UV254和有机碳TOC去除率,当反应时间为2h时,富里酸的UV254和总有机碳TOC的去除率分别为77.4%和45.4%.另外,在反应初期(前1h内)富里酸光电催化反应动力学常数主要受外加偏电压的影响.     

运用QSPR预测有机磷农药的色谱保留值

通过计算43种有机磷农药的各种结构参数,运用多元线性回归分析方法比较了适用于有机磷农药色谱保留值的定量关系表达式,建立了有机磷农药结构参数对色谱保留值的QSPR模型.模型分析表明:磷酸酯与硫逐磷酸酯两类有机磷农药的模型非交叉验证相关系数R2分别为0.991和0.998,标准误差SE分别为0.0539和0.2874,交叉验证相关系数Q2分别为0.976和0.990,标准偏差Scv分别为0.086和0.610.在已知磷酸酯与硫逐磷酸酯两类有机磷农药结构参数的情况下,此模型可有助于有机磷农药的色谱分析.     

Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.     

TUGAI: An Integrated Simulation Tool for Ecological Assessment of Alternative Water Management Strategies in a Degraded River Delta

The development of ecologically sound water allocation strategies that account for the needs of riverine ecosystems is a pressing issue, especially in semiarid river basins. In the Aral Sea Basin, a search for strategies to mitigate ecological and socioeconomic deterioration has been in process since the early 1990s. The Geographic Information System–based simulation tool TUGAI has been developed to support the policy determination process by providing a simple, problem-oriented method to assess ecological effects of alternative water management strategies for the Amudarya River. It combines a multiobjective water allocation model with simple, spatially explicit statistical and rule-based models of landscape dynamics. Changes in environmental conditions are evaluated by a fuzzy habitat suitability index for Populus euphratica, which is the dominant species of the characteristic riverine Tugai forests. Water management scenarios can be developed by altering spatiotemporal water distribution in the delta area or the amount of water inflow into the delta. Outcomes of scenario analysis are qualitative comparisons of the ecological effects of different options for a time period of up to 28 years. The given approach utilizes different types of knowledge, from quantitative hydrological data to qualitative local expert knowledge. The main purpose of the tool is to integrate the knowledge in a comprehensive way to make it available for discussions on alternative policies in moderated workshops with stakeholders. In this article, the modules of the tool, their integration, and three hypothetical scenarios are presented. Based on the experience gained when developing the TUGAI tool, we propose that the general framework can be transferred to other areas where tradeoffs in water allocation between the environment and other water users are of major concern. The potential for a simulation tool to structure and inform a complex resource management situation by involving local experts and stakeholders in the development of possible future scenarios will become increasingly valuable for transparent and participatory resource management.