京津冀地区一次污染过程的星载地基激光雷达联合观测分析

针对2018年3月9—15日京津冀地区的一次空气重污染过程,进行了基于地基颗粒物激光雷达组网的星载-地基联合观测分析。颗粒物激光雷达观测到污染前期为局地污染累积过程,中期有明显的污染物区域传输过程,北京受太行山沿线城市污染输送影响较大。风廓线激光雷达观测结果表明:此次污染过程近地面主要为偏南风且风力较弱,冷空气到来时风向转为较强东北风,导致污染消散。微波辐射计观测到保定在污染过程中出现持续6 d的逆温层,同时在污染过程中近地面相对湿度较高,逆温层被打破后污染开始消散。在污染过程的各个阶段中,污染团的空间分布与变化特征均被很好地反映出来,可见地天联合观测对污染物的累积与输送研究有较大的意义,能对京津冀及周边地区的大气污染联防联控提供有力支持。     

燃煤电厂大气污染物排放考核时间间隔的建议

新的《火电厂大气污染物排放标准》和新《环境保护法》实施后，燃煤火电企业的依法治企自觉性不断提高，但由于不同的环保管理文件对超标处罚依据和处罚手段不同，造成政府对企业处罚的自由裁量权加大，对企业造成一定的压力与困惑。结合企业实际运行情况对燃煤电厂大气污染物排放考核时间间隔提出建议。     

非柔性连接石化储罐配管温差应力分析与对策

储罐配管在温差作用下产生热位移会导致管壁产生热应力，未采用柔性连接的配管由于热应力集中作用可能发生破漏。针对与石油化工储罐非柔性连接的配管的安全问题，应用ANSYS有限元分析软件模拟计算石化企业典型配管的热应力分布；根据不同类型配管的分析结果，提出了工程技术与管理方面的针对性安全措施。研究结果表明:温差效应对石化储罐配管热应力影响显著，随内外温差的增加，配管应力集中的区域增大，最大热应力值增大；补偿措施能够改善配管的柔性，降低配管应力；约束载荷限制配管热膨胀从而增大配管应力；三通管是多根配管接合的区域，这种复杂管道交汇处会形成明显应力集中。研究结果对于提高石油化工非柔性连接配管安全性有一定的指导意义。     

煤与瓦斯共采在错层位巷道布置中的机理研究及应用构想

为了完善现有煤与瓦斯共采技术，创新煤与瓦斯共采方法，对错层位巷道布置下的煤与瓦斯共采系统展开研究，利用相似模拟试验，分析错层位巷道布置覆岩运动情况，预测其开采围岩裂隙发育和瓦斯运移形式，提出了创新煤与瓦斯共采技术构想。研究结果表明:采空区覆岩三带高度随接续工作面的增加而增大，相邻采空区垮落矸石压实区呈现“O-L-O”形变化，多个相邻采空区覆岩出现大“O”形圈裂隙带；相邻采空区内瓦斯可实现相互运移，大“O”形圈裂隙带内赋存大量瓦斯气体；研究提出了地面钻井抽采瓦斯、走向高位瓦斯抽采巷和外错尾巷穿层钻孔3种煤与瓦斯共采技术，比传统巷道布置情况下的煤与瓦斯共采技术在安全、经济等方面更具优势。     

Mitigating Impact of Devils Lake Flooding on the Sheyenne River Sulfate Concentration

Devils Lake is a terminal lake located in northeast North Dakota. Because of its glacial origin and accumulated salts from evaporation, the lake has a high concentration of sulfate compared to the surrounding water bodies. From 1993 to 2011, Devils Lake water levels rose by ~10 m, which flooded surrounding communities and increased the chance of an overspill to the Sheyenne River. To control the flooding, the State of North Dakota constructed two outlets to pump the lake water to the river. However, the pumped water has raised concerns about of water quality degradation and potential flooding risk of the Sheyenne River. To investigate these perceived impacts, a Soil and Water Assessment Tool (SWAT) model was developed for the Sheyenne River and it was linked to a coupled SWAT and CE‐QUAL‐W2 model that was developed for Devils Lake in a previous study. While the current outlet schedule has attempted to maintain the total river discharge within the confines of a two‐year flood (36 m³/s), our simulation from 2012 to 2018 revealed that the diversion increased the Sheyenne River sulfate concentration from an average of 125 to >750 mg/L. Furthermore, a conceptual optimization model was developed with a goal of better preserving the water quality of the Sheyenne River while effectively mitigating the flooding of Devils Lake. The optimal solution provides a “win–win” outlet management that maintains the efficiency of the outlets while reducing the Sheyenne River sulfate concentration to ≤600 mg/L.     

"十三五"国家自然科学基金对能源环境管理领域的资助状况和趋势

能源环境管理是各界关注的热点话题,也是科学研究的重要方向.以国家自然科学基金资助能源环境管理领域的科研项目为基础数据信息,本文重点分析了"十三五"期间国家自然科学基金在该领域资助项目的总体特征、研究主题和热点变化,并结合新时代科学基金资助导向提出了可能的发展趋势.主要结论包括:①能源环境管理领域面上、青年、地区等自由探索类项目的立项绝对数和学科占比均呈上升趋势,并更多向青年学者倾斜,资助强度保持稳定;②碳、能源和环境是自由探索项目中出现频率最高的热词,与之相关的研究主题随着政策等调整具有动态变化特征;③能源环境管理领域重点、重大等引导类项目和优青、杰青、创新研究群体等人才类项目不断实现突破,增速明显,学科占比优势突出;④问题导向与本土情景、理论体系和一般规律、学科交叉融合,以及市场和微观主体作用是研究选题确立与项目申请时可能需要重点关注的方向.     

风电场环境影响评价原则及评价重点研究

针对风电场环境影响评价的评价原则及评价重点进行了探讨性的研究。提出了风电场环境影响评价应以早期介入和避让措施优先为基本原则；对风电场环境影响评价重点进行了识别，并提出风电场选址的环境可行性分析是风电场环评的重中之重；风电项目建设对生态环境的影响及风机噪声、光影影响也是风电场环境影响评价的重点，并创造性的提出风机噪声光影联合防护区的概念，以期对风电场环境影响评价提供理论及技术参考依据。     

A hybrid model for human factor analysis in process accidents: FBN-HFACS

Human factors are the largest contributing factors to unsafe operation of the chemical process systems. Conventional methods of human factor assessment are often static, unable to deal with data and model uncertainty, and to consider independencies among failure modes. To overcome the above limitations, this paper presents a hybrid dynamic human factor model considering Human Factor Analysis and Classification System (HFACS), intuitionistic fuzzy set theory, and Bayesian network. The model is tested on accident scenarios which have occurred in a hot tapping operation of a natural gas pipeline. The results demonstrate that poor occupational safety training, failure to implement risk management principles, and ignoring reporting unsafe conditions were the factors that contributed most failures causing accident. The potential risk-based safety measures for preventing similar accidents are discussed. The application of the model confirms its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.     

情感诉求对交通安全广告效果的影响

为检验交通公益广告中情感诉求变化对广告效果的影响,采用试验法操作广告情感诉求类型,使用眼动技术和Go/no-go联想测验(GNAT),通过比较不同效价和唤醒度的情感诉求广告对受众的注意力和内隐态度的影响,评估交通安全广告情感诉求类型的说服效果。研究发现:被试对低唤醒广告文字区的眼动注视时间和注视点个数显著长于/多于对高唤醒广告文字区的注视时间和注视点个数;被试观看高唤醒消极的广告后,正确驾驶行为的积极态度显著下降,而低唤醒消极/高唤醒积极/低唤醒积极的公益广告对于被试的内隐态度的改变没有显著差异。结果表明,高唤醒度积极广告能够提升受众的注意力,而高唤醒度消极广告则会损害受众对正确驾驶行为的内隐态度。     

Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks

A sequencing batch reactor was modeled using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). Then, the effects of influent concentration (IC), filling time (FT), reaction time (RT), aeration intensity (AI), SRT and MLVSS concentration were examined on the effluent concentrations of TSS, TP, COD and NH₄⁺-N. The results showed that the optimal removal efficiencies would be obtained at FT of 1 h, RT of 6 h, aeration intensity of 0.88 m³/min and SRT of 30 days. In addition, COD and TSS removal efficiencies decreased and TP and NH₄⁺-N removal efficiencies did not change significantly with increases of influent concentration. The TSS, TP, COD and NH₄⁺-N removal efficiencies were 86%, 79%, 94% and 93%, respectively. The training procedures of all contaminants were highly collaborated for both RBFANN and MLPANN models. The results of training and testing data sets showed an almost perfect match between the experimental and the simulated effluent of TSS, TP, COD and NH₄⁺-N. The results indicated that with low experimental values of input data to train ANNs the MLPANN models compared to RBFANN models are more precise due to their higher coefficient of determination (R²) and lower root mean squared errors (RMSE) values.