新时期广东韶关的区域旅游开发战略

在我国已正式加入世界贸易组织、各地区旅游产业迅速发展、旅游市场竞争日趋激烈的新形势下,作为广东旅游资源最富集地区之一的韶关市的区域旅游开发必须进行战略性调整.对该区旅游开发的战略背景、战略思想、战略定位、战略重点和战略措施等进行了探讨.     

水源热泵空调系统的水质处理技术

针对影响水源热泵系统运行效能的地下水水质和回灌过程中井堵塞的问题,提出相应的水质处理方法和水源热泵用地下水水质参考标准.     

页岩气加砂压裂高压管汇失效爆裂风险 控制措施研究与实践

随着页岩气平台井工厂化压裂施工启动,在大排量、高压力、长时间施工条件下,高压管汇失效爆裂大幅增加。为减少高压管汇在压裂过程中失效爆裂风险,运用故障假设、因果分析法从人的因素、物的因素、技术工艺、生产管理、工作环境5个方面分析事故的原因,提出了提升采购产品质量、修订完善高压管汇使用技术规范和管理制度、实施专业化管理、应用大数据管理高压管汇、优化现场地面高压管汇流程分配布局设计与联接工艺等高压管汇失效爆裂风险预防控制措施和失效后应急安全防护措施,建立了生命周期全过程、专业化的高压管汇失效爆裂防控与应急防护体系,实现风险可防受控。为页岩气压裂作业的安全提供了参考。     

Improve Compliance with Limited Resources by a Three-Group Inspection Regime

Frequent monitoring and relatively high fines are usually necessary to bring about improvements in environmental quality, but more challenging for many countries with limited human, material, and financial resources is to put them into practice. This paper developed a three-group model of a state-dependent enforcement in a repeated game to improve the policy implementation under limited inspection capacities. A certain number of firms are grouped (group 1, group 2, group 3) for different supervision intensity (e.g., the order of inspection probability corresponding to each group is p ₁?<?p ₂?<?p ₃) based on their environmental performance. The optimal policy parameters, such as inspection probability of each group and the probability that a firm found in compliance is moved to a better reputation group, were obtained as the basis for regulator’s policy making. Numerical simulations indicated that the three-group inspection regime can significantly increase compliance rate as compared with static enforcement with the same monitoring probability. Among the number of firms in each group under steady state conditions, group 2 had the most, group 1 was the second, and group 3 had the smallest. Analysis and prediction of a three-group reputation example provided a good experiment for the model. The results give a practical reference for the policy makers with inspection capacity constraints to achieve higher compliance rate.     

Removal of arsenic(III,V) by a granular Mn-oxide-doped Al oxide adsorbent: surface characterization and performance

In order to remove arsenic (As) from contaminated water, granular Mn-oxide-doped Al oxide (GMAO) was fabricated using the compression method with the addition of organic binder. The analysis results of XRD, SEM, and BET indicated that GMAO was microporous with a large specific surface area of 54.26 m^2/g, and it was formed through the aggregation of massive Al/Mn oxide nanoparticles with an amorphous pattern. EDX, mapping, FTIR, and XPS results showed the uniform distribution of Al/Mn elements and numerous hydroxyl groups on the adsorbent surface. Compression tests indicated a satisfactory mechanical strength of GMAO. Batch adsorption results showed that As(V) adsorption achieved equilibrium faster than As(III), whereas the maximum adsorption capacity of As(III) estimated from the Langmuir isotherm at 25 °C (48.52 mg/g) was greater than that of As(V) (37.94 mg/g). The As removal efficiency could be maintained in a wide pH range of 3~8. The presence of phosphate posed a significant adverse effect on As adsorption due to the competition mechanisms. In contrast, Ca²⁺ and Mg²⁺ could favor As adsorption via cation-bridge involvement. A regeneration method was developed by using sodium hydroxide solution for As elution from saturated adsorbents, which permitted GMAO to keep over 75% of its As adsorption capacity even after five adsorption–regeneration cycles. Column experiments showed that the breakthrough volumes for the treatment of As(III)-spiked and As(V)-spiked water (As concentration = 100 μg/L) were 2224 and 1952, respectively. Overall, GMAO is a potential adsorbent for effectively removing As from As-contaminated groundwater in filter application.

     

基于机器视觉的鱼类行为特征提取与分析

近年来,水污染问题备受关注。生物式水质监测成为目前国家环境保护工作的重要任务之一。为准确监测水质污染情况,本文以青鳉鱼(Oryzias latipes)为研究对象,采用非接触式的机器视觉监测技术,提取青鳉鱼的生理特征(呼吸频率)和运动特征(胸鳍和尾鳍的摆动频率),并分析这些特征与水质之间的关系。本文采用支持向量机(Support Vector Machine,SVM)准确提取鱼鳃,并根据鱼鳃呼吸面积大小变化计算出鱼的呼吸频率。基于形态学细化算法提取青鳉鱼骨架,求出胸鳍和尾鳍的摆动频率。结果显示:不同浓度铜离子暴露实验测得的青鳉鱼生理特征和运动特征与实际情况一致;通过对不同铜离子浓度下的毒性实验数据对比,发现了青鳉鱼的生理特征和运动特征会随不同的铜离子浓度发生相应变化,可以作为水质监测的评价标准。     

Effect of Soybean Cultivation on Soil Collembola Community in Marshland of Sanjiang Plain,China

The Sanjiang Plain, the largest inland freshwater marshland in China, was extensive reclaimed into agricultural land. To assess the effects of marshland reclamation on Collembola, we investigated collembolan communities in a chronosequence of soybean plantations (2, 15, and 25 years) in Sanjiang marshland, Northeastern China. We found that: 1) the densities and species richness of Collembola were promoted after short-term (2 years) cultivation of soybean, but significantly decreased after medium-term cultivation (15 years); 2) the densities of epi-edaphic Collembola increased while the densities of hemi-edaphic Collembola decreased as the elongation of soybean cultivation; 3) compared with S0, two species of Collembola appeared while five species disappeared in S25. The changes of plant communities and the soil traits were supposed to be the key factors affecting the composition of soil Collembola. We thus suggest that original marshland should be saved for preserving high diversity and densities of Collembola in the Sanjiang Plain.

     

生态福利绩效视角下的上海市可持续发展评价研究

资源与环境约束加剧的大背景下,在"生态门槛"内实现社会福利最大化的可持续发展模式是未来城市发展的必然选择。从生态福利绩效视角出发,选取人均生态足迹和人类发展指数构建城市可持续发展评价指标体系,并采用DEA方法计算二者的投入产出效率,对上海市1999~2012年可持续发展水平进行多维度、动态化的实证分析。研究发现:1上海市人类发展指数、人均生态足迹与经济发展同步增长,但人均生态足迹水平大于生态承载能力,处于严重"生态赤字"状态,并不断恶化;2上海市多数年份的生态福利绩效处于DEA无效状态,且2005年以来进入规模收益递减通道。最后提出政策建议。     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.