爆炸超压场景下危化品社会风险对允许规划人口密度的影响分析*

为研究危化品重大危险源基于社会风险基准的规划管控影响,采用我国标准规定的定量风险评价方法,TNT当量炸药简化方法,针对最大TNT当量炸药、事故发生总累计频率、人口密度分布控制参数等不同工况条件,对比分析国土开发强度的允许人口密度受社会风险约束影响的变化规律。研究结果表明:人口密度指数分布控制参数Nk与Nb的允许取值随最大TNT当量炸药和事故发生总累计频率降低而提高;确定工况下,允许总人口规模受最大TNT当量炸药影响很小;在最大TNT当量炸药大于100 t或事故发生总累计频率小于1×10-8次/a时可按最大事故场景进行规划控制分析。     

基于Logistic回归的燃气管网风险因素重要度分析方法研究*

为确定燃气管网风险评估的关键风险因素,以A省各地市燃气管网为研究对象,基于燃气专家经验确定燃气管线风险等级,提出基于Logistic回归的燃气管网风险因素重要度分析方法。采用样本增强及随机抽样的方式,选取400个均衡样本作为管网评估数据输入,通过因子分析方法对其进行降维,得到3个公共因子并作为一级指标反向构建燃气管网风险评估指标体系;利用有序多分类Logistic回归方法,根据回归系数绝对值大小对风险因素进行重要度排序。研究结果表明:外界环境对燃气管网风险的贡献程度相对较高,管道自身因素和巡检养护次之。研究结果可为城市燃气风险防控提供理论依据和方法参考。     

北京市PM5.0可吸入颗粒物反演及其时空分布分析

分析城市可吸入颗粒物的空间特征及影响因素对于制定更为有效的可吸入颗粒物污染控制政策具有重要的现实意义.本研究在实际采样和遥感数据处理基础上,构造差值植被指数(DVI),通过建立TM影像与对应PM5.0颗粒物实测值之间的相关关系模型,对北京市2008~2010年3 a的PM5.0的分布进行了反演计算和精度分析;研究了NDBI和NDMI对PM5.0浓度的影响,进一步揭示了北京五环区域范围内可吸入颗粒物的时空分布特征.结果表明,采用DVI反演PM5.0分布的方法可行,反演精度在可接受范围内;整体上研究区2008年PM5.0颗粒物污染最轻,西南三环和四环以及东南三四环之间为颗粒物污染的高值区,西北五环附近颗粒物污染较轻;NDBI(归一化建筑指数)、NDMI(归一化水汽指数)与PM5.0可吸入颗粒物的分布分别呈现显著的负相关和正相关关系,且二者对PM5.0的影响相近.     

克山县空气质量及变化趋势

通过对克山县2003—2012年的空气监测数据分析,二氧化硫和总悬浮物颗粒物呈下降趋势,氮氧化物和降尘浓度变化不大;尘类是影响环境质量的主要污染物,县区环境空气质量呈轻度污染,根据各年度的监测数据来看,环境空气质量逐年得到改善。     

造纸过程白水封闭循环回用对抄造的影响

研究了网下白水不同循环次数的性能指标。白水中干扰物质DCS对聚乙烯亚胺(PEI)与阳离子聚丙烯酰胺(CPAM)、膨润土组成的三元微粒助留助滤效果的影响,结果表明,随着白水循环次数的增加,白水性能指标变差;DCS的累积降低了阳离子聚丙烯酰胺的助留效果;白水循环次数的增加使纸页裂断长降低;助留助滤效果随着白水循环次数的增加而降低。     

“十一五”期间肇源境内地表水环境质量状况评价及分析

针对我省入境流域肇源,全面开展了十一五期间枯、平、丰水期的河流水环境现状调查,采用秩相关系数法对水环境现状进行评价。研究表明:肇源境内"十一五"期间主要污染因子为高锰酸盐指数、石油类、氨氮和生化需     

昆明市建设项目环境影响评价公众参与现状研究

以昆明市为研究对象,深入分析现阶段环境影响评价公众参与中存在的主要问题,并提出改进的对策建议。     

模糊综合评价法及主成分分析法在额尔齐斯河水质评价中的应用

利用额尔齐斯河2010年监测数据进行水质评价。用模糊综合评级法对水质状况进行评价,判断水质级别;用主成分分析法判断主要污染物类型,确定各污染物的主要贡献率。并将两种方法的评价结果与单项指标评价结果相比较,结果表明：在水质评价中水质级别判断采用模糊综合评价法,水质主要污染物判断采用主成分分析法,用两种评价方法结合起来对水环境质量进行评价,结果较准确。     

Multi-objective transportation route optimization for hazardous materials based on GIS

Safety and security are of paramount importance, it is important to optimize and improve the routes of trucks that carry hazardous materials. In this study, we not only ensure the risk in the network, but also consider the transportation cost and the factors such as buildings and emergency facilities around the routes. The Geographic Information System (GIS) is used to quantify the factors on each section in the network. We present an epsilon constrained multi-objective mixed-integer linear programming optimization model to find the robust and stable transportation optimization solutions. At the end, we complete a case analysis of the proposed methodology to determine the motorway segments in Jiangsu province, China and test the above algorithm on the network, which has 144 nodes and 388 sections. The results we get show that the factors of buildings play a very important role in the model, and the multi-objective mixed-integer linear optimization model is reasonable and performs good quality.     

A new pre-assessment model for failure-probability-based-planning by neural network

At present, the prediction of failure probability is based on the operation period for laid pipelines, and the method is complicated and time-consuming. If the failure probability can be predicted in the planning stage, the risk assessment system of gas pipeline will be greatly improved. In this paper, the pre-laying assessment model is established to minimize risk of leakage due to piping layout. Firstly, Fault Tree Analysis (FTA) modeling is carried out for urban natural gas pipeline network. According to expert evaluation, 84 failure factors, which can be determined in the planning stage, are selected as the input variables of the training network. Then the FTA model is used to calculate the theoretical failure probability value, and the failure probability prediction model is determined through repeated trial calculation based on BP (Back Propagation Neural Network) and RBF (Radial Basis Function), for obtaining the optimal network parameter combination. Finally, two prediction models are used to calculate the same example. By comparing our pre-assessment model with the theoretical prediction consequences of the fault tree, the results show that the error of RBF prediction model can be close to 3%, which proves the validity and correctness of the method.