垃圾填埋场微生物气溶胶粒径分布研究

为了了解垃圾填埋场微生物气溶胶粒径分布规律,在北京市某垃圾卫生填埋场填埋区、渗滤液处理区、生活区分别选定监测点,利用安德森六级微生物采样器,对填埋场空气微生物进行了系统的定点取样、测定和分析。研究结果表明,空气细菌粒径分布均为第Ⅰ级(>8.2 μm)最高,填埋区空气细菌粒径呈偏态分布,渗滤液处理区、生活区分别在第Ⅳ级和第Ⅲ级出现第2个峰值。携带细菌的可吸入微粒在渗滤液处理区比例最大。空气真菌与放线菌均在第Ⅳ级分布最高,携带真菌和放线菌的可吸入粒子的比例显著大于细菌(P<0.05)。填埋区不同作业时段空气微生物粒径在各级分布比例基本一致。填埋区细菌气溶胶中值直径为5.7 μm,渗滤液处理区为3.7 μm,生活区为5.3 μm,显著大于真菌气溶胶和放线菌气溶胶的中值直径(P<0.05)。     

An imprecise fuzzy risk approach for water quality management of a river system

Uncertainty plays an important role in water quality management problems. The major sources of uncertainty in a water quality management problem are the random nature of hydrologic variables and imprecision (fuzziness) associated with goals of the dischargers and pollution control agencies (PCA). Many Waste Load Allocation (WLA) problems are solved by considering these two sources of uncertainty. Apart from randomness and fuzziness, missing data in the time series of a hydrologic variable may result in additional uncertainty due to partial ignorance. These uncertainties render the input parameters as imprecise parameters in water quality decision making. In this paper an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system subject to uncertainty arising from partial ignorance. In a WLA problem, both randomness and imprecision can be addressed simultaneously by fuzzy risk of low water quality. A methodology is developed for the computation of imprecise fuzzy risk of low water quality, when the parameters are characterized by uncertainty due to partial ignorance. A Monte-Carlo simulation is performed to evaluate the imprecise fuzzy risk of low water quality by considering the input variables as imprecise. Fuzzy multiobjective optimization is used to formulate the multiobjective model. The model developed is based on a fuzzy multiobjective optimization problem with max–min as the operator. This usually does not result in a unique solution but gives multiple solutions. Two optimization models are developed to capture all the decision alternatives or multiple solutions. The objective of the two optimization models is to obtain a range of fractional removal levels for the dischargers, such that the resultant fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision making. The methodology is demonstrated with a case study of the Tunga–Bhadra river system in India.     

衡水市空气主要污染物体积质量的时间分布特征

利用衡水市环境监测站2005—2007年度大气例行检测的数据,对衡水市大气中主要污染物SO^2、NO^2、PM10体积质量值的逐日数据进行统计分析,得出了衡水市空气污染的现状和时间变化规律：（1）污染物体积质量值的月变化曲线呈槽型分布,非取暖期的空气状况明显好于取暖期,取暖期SO^2平均体积质量是非取暖期的2．45倍;（2）污染物体积质量值的日变化曲线基本为两高两低型,其中取暖期污染指数早晨出现极高值的时间比非取暖期明显偏晚;（3）污染物体积质量值的年变化表明了SO^2的体积质量在降低,而NO^2的体积质量却有了小幅增长。     

移动通信基站电磁辐射时空分布及衰减特征

根据理论计算公式和实测数据,研究基站电磁辐射水平、垂直方向的衰减趋势,分析影响基站电磁辐射时空分布的各种因素,认为基站电磁辐射实际影响范围要远小于安全防护距离。     

基于信息熵评价决策模型的突发公共事件应急处置指挥方案优选

提出基于信息熵评价决策模型对突发公共事件应急处置指挥决策中的作战方案进行优选的方法。首先介绍应急处置指挥决策的概念和过程;其次,分析了信息熵以及基于信息熵评价模型的多属性决策方法;最后,给出基于信息熵的评价决策模型在突发公共事件应急处置指挥方案优选中的应用。实地演练表明,该决策模型在应急指挥决策方案排序与优选是客观合理、切实可行的,能够为应急处置指挥决策提供一定的参考。     

自动喷水灭火系统喷水强度概率分布特性及其控火性能研究

喷水强度是体现自动喷水灭火系统控火能力的重要参数,具有不均匀性和不确定性.传统的喷水强度设计主要考虑喷头的流量和保护面积,没有考虑其分布特性,对系统控火性能的影响.因此本文将喷水强度分布特性引入自动喷水灭火系统的控火性能研究.以公称口径为15 mm的传统下垂型玻璃球洒水喷头的喷水强度分布实验为基础,建立了包含径向距离和喷头工作压力等参数的喷水强度分布模型,采用蒙特卡罗方法获得了喷水强度的概率分布特性.参照不同危险等级场控火所需的喷水强度参数和不同喷头开启个数下的有效控火百分比,分析了喷水强度概率分布特性对控火性能的影响.研究结果表明,喷水强度概率分布特性对控火性能有重要影响,随径向距离的增加,喷水强度出现较小值的概率增大,控火性能下降,在某些危险较大的场所不能达到控火作用.对于同一危险等级场所,控火性能随着喷头安装间距减小而增强.研究方法可为合理评估喷头适用范围、优化喷头布置提供参考.     

道路交通紧急救援服务点的优化选址

针对道路交通紧急救援对公平性、效率性、事故并发性的多目标决策需求,提出用于道路交通救援点选址的多目标决策模型。该模型通过最小化最大救援时间满足救援公平性;通过最大化一次覆盖的需求量和最小化救援点到需求点总加权时间满足救援效率;通过最大化备份覆盖的需求量满足事故并发与重大事故的救援。针对多目标决策模型,开发基于模拟退火法的优化解法,结合算例分析,能够获得减少最大救援时间、最大化一次覆盖和备份覆盖需求量的多个目标方向上最优解,实现pareto最优化的成功率均在95%以上。实例计算比较表明,笔者提出的模型与算法可以为决策者提供多目标选址方案。     

浅析总量监测中"生产周期"的确定及频次优化

根据排污周期确定加密监测采样频次,用实例说明有规律排污过程的生产特点,提出固定频次法进行监测频次优化的应用建议。     

Application of statistical modeling to optimize a coastal water quality monitoring program

The long-term water quality monitoring program implemented by the Massachusetts Water Resources Authority in 1992 is extensive and has provide substantial understanding of the seasonality of the waters in both Boston Harbor and Massachusetts Bay and the response to improvements in effluent quality and offshore transfer of the effluent in September 2000. The monitoring program was designed with limited knowledge of spatial and temporal variability and long-term trends within the system. This led to an extensive spatial and temporal sampling program. The data through 2003 showed high correlation within physical parameters measured (e.g., salinity, dissolved oxygen) and in biological measures such as chlorophyll fluorescence. To address the potential sampling redundancies in the measurement program, an assessment of the impact of reduced levels of monitoring on the ability to make water quality decisions was completed. The optimization was conducted by applying statistical models that took into account whether there was evidence of a seasonal pattern in the data. The optimization used model survey average readings to identify temporal fixed effects, model survey-average-corrected individual station readings to identify spatial fixed effects, corrected the individual station readings for temporal and spatial fixed effects and derived a correlation model for the corrected data, and applied the correlation model to characterize the correlation of annual average readings from reduced monitoring programs with true parameter levels. Reductions in the number of sampling stations were found less detrimental to the quality of the data for annual decision-making than reductions in the number of surveys per year, although there is less of a difference in this regard for dissolved oxygen than there is for chlorophyll. The analysis led to recommendations for a substantially lower monitoring effort with minimal loss of information. The recommendation supported an annual budget savings of approximately $183,000. Most of the savings was from fewer surveys as approximately $21,000 came from the reduction in the number of stations monitored from 21 to 7 and associated laboratory analytical costs.