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。     

井下光谱多参数分析系统开发

本系统基于气体浓度光学分析方法理论朗伯-比尔(Lambert-Beer)定律、光谱气体检测技术开发,实现了对煤矿火灾与瓦斯灾害超前预警、灾害产生的有毒有害气体实时监测和煤矿环境气体爆炸危险性辨识,对于煤矿灾害防治、救灾过程中杜绝次生灾害,保障煤矿工人及救护队员的生命安全,促进煤矿安全生产具有重要意义。     

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.     

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.     

Depth perception,dark adaptation,vigilance and accident proneness of Chinese coal mine workers

To explore the relationships between human factors and accident proneness of coal mine workers, the depth perception, dark adaptation and vigilance abilities of 239 Chinese coal mine workers were tested and their accident proneness was surveyed with an accident proneness questionnaire. The results indicated that dark adaptation and vigilance abilities of the mine workers declined with increasing age. Vigilance had a significant negative relationship with accident proneness. There were significant differences in vigilance between coal mine workers doing different types of work. Individual difference in vigilance was relevant to the type of work that an individual did in a coal mine. The dark adaptation index had a significant positive relationship with accident proneness. Coal mine workers with weaker dark adaptation ability were also more accident prone. Some ergonomics recommendations concerning coal mine safety management in China are proposed.     

长江三峡区间数字流域水系的构建

基于全球陆地一公里基础高程GLOBE数据 ,采用Martz和Garbrecht研制的数字高程流域水系模型自动提取长江三峡万县～宜昌区间的河网水系、各子流域分水线、河网与子流域编码及河网结构拓扑关系 ,从而构成该区域的数字流域和数字水系。结果表明该模型生成的水系是可以接受的 ,与 1:10万地形图上长江三峡万县～宜昌区间的水系一致 ,这为空间分布式模型的建立提供了必备的空间信息数据。最后 ,探讨了数字流域水系在流域生态学中的应用前景 ,构建的数字水系可为中尺度以上流域水体和水生生物的资源保护与合理利用提供决策平台 ,服务于社会经济的可持续发展。     

人工神经网络在预测深基坑周边地表沉降变形中的应用研究

深基坑开挖引起的周边地表变形预测是一个复杂非线性问题,引起地表沉降的影响因素很多,各因素之间呈高度的非线性关系。传统的基坑用边地表沉降变形预测方法存在着一定的局限性,其预测精度有待提高,而人工神经网络是一种多元非线性动力学系统,可以灵活方便地对多成因的复杂未知系统进行高度建模,实现全面考虑各种主要影响因素的深基坑周边地表沉降变形预测。本文介绍了误差反向传播(BP)网络模型的结构、学习过程及其算法的改进,径向基函数(RBF)网络模型的结构及其学习过程;分析了影响深基坑开挖周边土体沉降变形的主要影响因素;以25个基坑工程的地表沉降实测资料为训练样本,建立了11个输入影响因素的BP神经网络模型和RBF神经网络模型,通过对样本的学习训练过程及对5个检验样本的预测精度,说明了人工神经网络用于预测基坑周边地表沉降的可行性和准确性。     

Source characterization of ambient fine particles at multiple sites in the Seattle area

To identify major PM_2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) sources with a particular emphasis on the ship engine emissions from a major port, integrated 24 h PM_2.5 speciation data collected between 2000 and 2005 at five United State Environmental Protection Agency's Speciation Trends Network monitoring sites in Seattle, WA were analyzed. Seven to ten PM_2.5 sources were identified through the application of positive matrix factorization (PMF). Secondary particles (12–26% for secondary nitrate; 17–20% for secondary sulfate) and gasoline vehicle emissions (13–31%) made the largest contributions to the PM_2.5 mass concentrations at all of the monitoring sites except for the residential Lake Forest site, where wood smoke contributed the most PM_2.5 mass (31%). Other identified sources include diesel vehicle emissions, airborne soil, residual oil combustion, sea salt, aged sea salt, metal processing, and cement kiln. Residual oil combustion sources identified at multiple monitoring sites point clearly to the Port of Seattle suggesting ship emissions as the source of oil combustion particles. In addition, the relationship between sulfate concentrations and the oil combustion emissions indicated contributions of ship emissions to the local sulfate concentrations. The analysis of spatial variability of PM_2.5 sources shows that the spatial distributions of several PM_2.5 sources were heterogeneous within a given air shed.     

羽序灯心草作为酸性矿山废弃地先锋植物潜力

生态复垦是当前世界各国酸性矿山废弃地修复的主要方法.通过研究云南来利山锡矿区废弃地土壤pH值、肥力特征及重金属含量,以及矿区生长的羽序灯心草形态特征和植株体内重金属的分布特征,分析植株耐酸性、对废弃地土壤肥力的适应性及对Zn和Cu等重金属污染的耐性,探讨其作为酸性矿山废弃地先锋植物的潜力.结果表明,研究区域根际土壤pH值均值范围为3.46～4.01,呈酸性;土壤中有机质、全钾、全磷和速效磷含量分别为10.28～25.75g·kg~(-1)、 8.84～9.32g·kg~(-1)、 0.56～0.63g·kg~(-1)和1.82～5.72mg·kg~(-1),处于较低水平;土壤中Zn、Cu和Fe含量均值范围分别为54.93～114.49、 92.53～127.59和47 133.60～112 259.63 mg·kg~(-1),其中重金属Cu含量超出风险筛选值1.85～2.55倍;研究区域羽序灯心草株高均值范围为43.77～55.42 cm,对照组植株高度为51.38～57.66 cm,无显著差异,表明羽序灯心草具有耐酸性及对重金属污染具有耐性.进一步分析对重金属Cu和Zn的富集能力和转移特征,发现对Cu和Zn都具有富集能力,且对Zn具有运转能力,具有一定的富集吸收潜力.株高与根际土壤中速效磷的含量显著相关,后期羽序灯心草作为先锋植物在矿山废弃地进行种植时,可针对性地补充含速效磷的肥料改善土壤肥力条件.综合分析结果表明,羽序灯心草作为先锋植物修复酸性矿山废弃地具有巨大潜力.     

色氨酸对生物成因铁硫酸盐次生矿物形成的影响

采用氧化亚铁硫杆菌催化合成铁硫酸盐次生矿物,研究不同L-色氨酸添加浓度对矿物合成体系pH、氧化还原电位（ORP）、Fe²⁺氧化率、总Fe沉淀率,以及次生矿物产量、化学组成及矿物相的影响.结果表明,随着体系色氨酸浓度的增加,pH降低幅度越小,ORP上升越不明显.色氨酸对铁硫酸盐次生矿物合成的影响依赖于其浓度,当色氨酸浓度低于1.67 g·L^-1时,色氨酸对铁硫酸盐次生矿物的形成起促进作用,表现为总Fe沉淀率及矿物产量随着色氨酸浓度升高而增加.而当色氨酸浓度升高至6.67 g·L^-1时,Fe²⁺氧化率、总Fe沉淀率和矿物产量远低于对照组,表明高浓度色氨酸会抑制铁硫酸盐次生矿物的形成.次生矿物内Fe/S比介于施氏矿物和黄钾铁矾的理论值之间,表明不同合成体系所得次生矿物均为黄钾铁矾和施氏矿物的混合物.矿物学特征分析表明,随着色氨酸浓度的升高,矿物的合成表现为黄钾铁矾向施氏矿物转移.