砷尾矿污染土壤的细菌群落结构多样性及其相关环境影响因子分析

以湖南石门雄黄尾矿污染土壤为对象,研究纵向不同深度、横向不同距离土样中的重金属污染程度以及细菌群落结构变化规律,查明砷污染土壤的核心微生物组成并将其与土壤理化指标进行共存网络图分析。结果表明:该尾矿区的土壤各项重金属指标严重超标,尤以铅(626.54 mg·kg^?1,Ei=105.48)、砷(1804.75 mg·kg^?1,Ei=565.75)、镉(31.46 mg·kg^?1,Ei=7491.5)的生态危害性最强;土壤采样深度与重金属综合潜在生态风险指数(RI)呈显著正相关(r=0.79,P=0.000),而横向样品中RI与采样距离显著负相关(r=?0.85,P=0.000)。在污染土壤中,变形杆菌门(Proteobacteria,54.35%±17.16%)和放线杆菌门(Actinobacteria,22.39%±10.64%)占主导地位,属层级中假单孢杆菌属(Pseudomonas,16.47%±11.84%)、不动杆菌属(Acinetobacter,8.07%±7.11%)以及硫酸状杆菌属(Acidithiobacillus,7.53%±14.68%)相对丰度较高;而26个共享类群占据了该尾矿污染土壤中微生物群落总平均相对丰度90%以上,尽管不同属的具体相对丰度在不同样品间的分布趋势差异较大。纵剖采集的污染土样中,铁原体属(Ferroplasma)、硫酸状杆菌属(Acidithiobacillus)、硫化杆菌属(Sulfobacillus)、乳杆菌属(Lactobacillus)和硝化螺旋菌属(Nitrospira)占优势,与理化的共存网络图分析(相关系数|r|≥0.6,P<0.05)显示部分类群与亚铁、游离态砷和镉成显著正相关,而与pH成显著负相关;横向采集的土样中,以嗜酸菌属(Acidiphilium)、假单孢杆菌属(Pseudomonas)、棒状杆菌属(Corynebacterium)、硫杆菌属(Thiobacillus)等为主,部分类群与总砷、铅成显著正相关,而与结合态或包蔽型砷成显著负相关。综上,该研究不仅对目标区域尾矿不同重金属污染程度进行了分析,同时探讨了污染土壤中核心响应类群的组成多样性,为筛选潜在重金属抗性菌群或工程菌群提供理论基础。     

大数据解析技术在大气环境监测中的应用研究

针对近年来新兴的大数据及挖掘、分析技术,对大数据解析技术在环境科学研究中的应用进行了分析和探析。以城市局部地区大气细颗粒污染物PM2.5浓度计算为例,筛选了PM2.5浓度历史数据、气象条件、交通状况、人群活动情况、网格道路状况等数据为影响特征量,分析了用神经网络法进行大数据解析和推演的原理和数学过程,是用数学、计算机、统计等方法研究解决多元、非线性复杂环境问题的一种新的探索。     

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。     

走滑断层作用下X80管道设计应变规律和神经网络预测

走滑断层是埋地管道常见的地质灾害威胁，断层作用下管道会发生较大的拉压应变而失效。为得到X80管道的设计应变，基于有限元方法建立了走滑断层作用下管道的应变响应数值计算模型，模型使用壳单元模拟管道，非线性弹簧单元模拟土壤约束，采用西二线实际工程的管道应变影响参数范围，计算了管道的设计应变；为预测管道的设计应变值，基于以上参数化分析得到的4 817组设计应变结果，采用人工神经网络建立了管道设计应变预测模型。结果表明:该神经网络模型预测结果的最大相对误差小于10%，预测准确性良好，且该方法具有较高的计算效率，可以为断层作用下埋地管道的应变设计与评估提供参考。     

供水管网震后流量监测点的动态分级优化布局研究

为了提高管网地震监测点布局的准确性和合理性，基于管网微观水力计算模型和动态分级法，提出供水管网震后流量监测点的动态分级优化布局模型。首先，利用管网微观水力计算模型计算管段流量的影响系数，构建管段的影响系数矩阵，并利用信息熵确定管段权重；其次，标准化处理影响系数矩阵，通过聚类迭代提出供水管网地震流量监测点优化布局的动态分级方法，对供水管网震后流量监测点进行优化布置分级评定；最后，根据工程实例进行方法实践，结果表明:供水管网中的管线分类较为科学合理，地震监测点在供水管网上分布也比较均匀，而且该模型在一定程度上消除了人为因素的影响，保障了震时管网的监控效果和日常建设的合理性。     

基于AGA-BP神经网络的采空区危险性评价

针对采空区危险性评价的影响因素众多且关系复杂的特点,提出了基于AGA-BP神经网络算法评价采空区危险性。将岩体结构、地质构造、岩石抗压强度等13个影响因子作为神经网络输入,采空区危险性等级作为输出,建立一个采空区危险性评价的BP神经网络模型;采用自适应遗传算法(AGA)对BP网络的初始权值和阈值进行全局寻优,将寻优结果回代入网络中进行训练并预测得出采空区危险性等级;利用其它智能算法与该预测结果做出比较,以验证AGA-BP算法的有效性及优越性。结果表明:该算法的优化效果明显,同时在训练时间与预测精度上较其它智能算法有突出的优势,是一种在采空区危险性评价方面值得推广的新方法。     

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.     

Assessing the current state of ecological connectivity in a large marine protected area system

The establishment of marine protected areas (MPAs) is a critical step in ensuring the continued persistence of marine biodiversity. Although the area protected in MPAs is growing, the movement of individuals (or larvae) among MPAs, termed connectivity, has only recently been included as an objective of many MPAs. As such, assessing connectivity is often neglected or oversimplified in the planning process. For promoting population persistence, it is important to ensure that protected areas in a system are functionally connected through dispersal or adult movement. We devised a multi-species model of larval dispersal for the Australian marine environment to evaluate how much local scale connectivity is protected in MPAs and determine whether the extensive system of MPAs truly functions as a network. We focused on non-migratory species with simplified larval behaviors (i.e., passive larval dispersal) (e.g., no explicit vertical migration) as an illustration. Of all the MPAs analyzed (approximately 2.7 million km²), outside the Great Barrier Reef and Ningaloo Reef, <50% of MPAs (46-80% of total MPA area depending on the species considered) were functionally connected. Our results suggest that Australia's MPA system cannot be referred to as a single network, but rather a collection of numerous smaller networks delineated by natural breaks in the connectivity of reef habitat. Depending on the dispersal capacity of the taxa of interest, there may be between 25 and 47 individual ecological networks distributed across the Australian marine environment. The need to first assess the underlying natural connectivity of a study system prior to implementing new MPAs represents a key research priority for strategically enlarging MPA networks. Our findings highlight the benefits of integrating multi-species connectivity into conservation planning to identify opportunities to better incorporate connectivity into the design of MPA systems and thus to increase their capacity to support long-term, sustainable biodiversity outcomes.     

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.     

基于BP神经网络的受限空间火灾联合探测方法

针对航空受限空间火灾探测高误报的问题，在现有技术成果基础上对多种火灾探测方式进行研讨，并提出1种基于BP神经网络技术的飞机机身内部受限空间火灾联合探测报警方法。该方法结合现有烟雾感应、气体传感器探测等常用火灾探测技术，以红外热成像探测为辅助手段，采用神经网络实现数据融合，对模拟实验舱火灾烟雾进行联合探测，在单一火灾探测方式基础上提高了探测准确率。