Inverse calculation approaches for source determination in hazardous chemical releases

Source determination is vital in decision making and emergency planning involving hazardous chemical releases. This work was concentrated on inverse calculation approaches for source determination as well as current trends and future perspectives. In this paper, these different approaches are reviewed by dividing them into two categories: probability modeling methods and optimization modeling methods. The traits of these approaches are comparatively analyzed. Then it is shown how these approaches behave when applied to practical cases, and their feasibility, applicability, stability, and limitation in determining the location and strength are presented. It is argued that when experimenting with potential terrorist attacks involving hazardous chemical releases, observation points should be around the main line of the downwind direction when the source is known; while the uniform distribution of observation points is an efficient solution for unknown incidents. Probability modeling methods are demonstrated to be insufficient during emergency responses due to their lacking of enough prior information of unknown parameters, while optimization modeling methods are efficient and become a new trend in source determination. Findings reflect an urgent need for the development of high-accuracy detectors and further research of data transmission techniques in order to ensure the validity of these approaches.     

New approach to calculate the probability of ignition

The probabilistic approach to the evaluation of fire hazard and the effectiveness of fire-precaution measures enables a rational response to the randomness of fire outbreaks. This article employs the statistical analysis methods to elucidate the causes for the ignition of fire on a random sample of industrial buildings in the Republic of Slovenia. The analyses are based on the Structural Equation Modeling (SEM), which is a well established and important statistical analysis technique in the fields of social science, biology, psychology and medicine, but hitherto rarely applied in safety research. The results of the study demonstrate that for the analyzed random sample of industrial buildings the frequency of fire outbreaks statistically significantly depends only on the presence and the probability of exposure to the heat sources (flames, sparks, and hot surfaces), but does not significantly depend on the available quantity of flammable materials within the industrial structure.     

Progress in quantitative research on the relationship between atmospheric oxidation and air quality

Atmospheric oxidizing capacity (AOC) is an essential driving force of troposphere chemistry and self-cleaning, but the definition of AOC and its quantitative representation remain uncertain. Driven by national demand for air pollution control in recent years, Chinese scholars have carried out studies on theories of atmospheric chemistry and have made considerable progress in AOC research. This paper will give a brief review of these developments. First, AOC indexes were established that represent apparent atmospheric oxidizing ability (AOIe) and potential atmospheric oxidizing ability (AOIp) based on aspects of macrothermodynamics and microdynamics, respectively. A closed study refined the quantitative contributions of heterogeneous chemistry to AOC in Beijing, and these AOC methods were further applied in Beijing-Tianjin-Hebei and key areas across the country. In addition, the detection of ground or vertical profiles for atmospheric OH·, HO₂·, NO₃· radicals and reservoir molecules can now be obtained with domestic instruments in diverse environments. Moreover, laboratory smoke chamber simulations revealed heterogeneous processes involving reactions of O₃ and NO₂, which are typical oxidants in the surface/interface atmosphere, and the evolutionary and budgetary implications of atmospheric oxidants reacting under multispecies, multiphase and multi-interface conditions were obtained. Finally, based on the GRAPES-CUACE adjoint model improved by Chinese scholars, simulations of key substances affecting atmospheric oxidation and secondary organic and inorganic aerosol formation have been optimized. Normalized numerical simulations of AOIe and AOIp were performed, and regional coordination of AOC was adjusted. An optimized plan for controlling O₃ and PM_2.5 was analyzed by scenario simulation.     

城市化影响杭州城市热环境的数值模拟研究

通过对城市区域的气候要素进行精细化数值模拟,可以研究城市化对城市热环境的重要影响.利用WRF/UCM模式,以杭州为例,通过采用不同的下垫面土地利用分类数据,分析量化历史城市化进程对月尺度城市热环境的影响,并进一步设计了敏感性模拟试验,研究杭州地区在理想化条件下城区面积增加约2倍时,城市热环境的变化情况.模拟结果表明,杭州地区在2010~2017年的城市化进程中,城区面积约增加了1倍,导致8月热岛强度等级高于”无”（Ht>0.5℃）的区域扩大了91%,且主城区的2m平均气温增加了0.4℃,但中心城区热岛强度等级没有明显提升.杭州城区面积在2017年基础上约增加2倍时,8月热岛强度等级高于”无”的区域扩大157%,1846.4km²的中心城区热岛强度等级由”弱”或”无”（Ht≤1.5℃）提升为”中等”（1.5相似文献   

Review of Urban Stormwater Quality Models: Deterministic,Stochastic, and Hybrid Approaches1

Abstract: The growing impact of urban stormwater on surface‐water quality has illuminated the need for more accurate modeling of stormwater pollution. Water quality based regulation and the movement towards integrated urban water management place a similar demand for improved stormwater quality model predictions. The physical, chemical, and biological processes that affect stormwater quality need to be better understood and simulated, while acknowledging the costs and benefits that such complex modeling entails. This paper reviews three approaches to stormwater quality modeling: deterministic, stochastic, and hybrid. Six deterministic, three stochastic, and three hybrid models are reviewed in detail. Hybrid approaches show strong potential for reducing stormwater quality model prediction error and uncertainty. Improved stormwater quality models will have wide ranging benefits for combined sewer overflow management, total maximum daily load development, best management practice design, land use change impact assessment, water quality trading, and integrated modeling.     

A simple semi-empirical model for predicting missing carbon monoxide concentrations

Carbon monoxide monitoring using continuous samplers is carried out in most major urban centres in the world and generally forms the basis for air quality assessments. Such assessments become less reliable as the proportion of data missing due to equipment failure and periods of calibration increases. This paper presents a semi-empirical model for the prediction of atmospheric carbon monoxide concentrations near roads for the purpose of interpolating missing data without the need for any traffic or emissions information. The model produces reliable predictions while remaining computationally simple by being site-specifically optimized. The model was developed for, and evaluated at, both a suburban site and an inner city site in Hamilton, New Zealand. Model performance statistics were found to be significantly better than other simple methods of interpolation with little additional computational complexity.     

基于改进社会力模型的地铁大客流疏散能力研究*

为有效应对大客流情景下人群拥挤对地铁站正常运行带来的影响,采用社会力模型和Anylogic仿真软件,对地铁站大客流疏散能力进行建模和分析,从客观、主观、管理视角分析影响客流疏散能力的因素,基于改进社会力模型刻画行人行为,分析影响地铁车站疏散能力的3个关键因素,并以南宁地铁1号线朝阳广场站为背景进行研究。研究结果表明:行人密度是影响地铁站疏散能力的关键因素,出入口选择策略和行人亲属关系比例对疏散能力影响显著,研究结果可为地铁客流疏散提出针对性建议。     

The Reynolds transport theorem: Application to ecological compartment modeling and case study of ecosystem energetics

The Reynolds transport theorem (RTT) from mathematics and engineering has a rich history of success in mass transport dynamics and traditional thermodynamics. This paper introduces RTT as a complementary approach to traditional compartmental methods used in ecological modeling and network analysis. A universal system equation for a generic flow quantity is developed into a generic open-system differential expression for conservation of energy. Nonadiabatic systems are defined and incorporated into control volume (CV) and control surface (CS) perspectives of RTT where reductive assumptions in empirical data are then formally introduced, reviewed, and appropriately implemented. Compartment models are abstract, time-dependent systems of simultaneous differential equations describing storage and flow of conservative quantities between interconnected entities (the compartments). As such, they represent a set of flexible and somewhat informal, assumptions, definitions, algebraic manipulations, and graphical depictions subject to influence and selectively parsed expression by the modeler. In comparison, RTT compartment models are more rigorous and formal integro-differential equations and graphics initiated by the RTT universal system equation, forcing an ordered identification of simplifying assumptions, ending with clearly identified depictions of the transfer and transport of conservative substances in physical space and time. They are less abstract in the rigor of their equation development leaving less ambiguity to modeler discretion. They achieve greater consistency with other RTT compartment style models while possibly generating greater conformity with physical reality. Characteristics of the RTT approach are compared with those of a traditional compartment model of energy flow in an intertidal oyster-reef community.     

A Markov model for assessing ecological stability properties

Ecological systems are frequently modeled as dynamic systems. It is natural then to use the techniques of dynamic systems theory to analyze such models. However, the methods and results that are produced by dynamic systems theory do not always capture the aspects of ecological systems that are of greatest interest to managers and decision-makers. We identify some of the challenges of using dynamic systems theory to explore ecological systems and propose an alternative approach that emphasizes the understanding of transient effects in light of uncertainty and variability. We illustrate this method by an examination of a model for phosphorus levels in fresh water lakes.     

Multiple-pattern parameter identification and uncertainty analysis approach for water quality modeling

This paper presents a multiple-pattern parameter identification and uncertainty analysis approach for robust water quality modeling using a neural network (NN) embedded genetic algorithm (GA). The modeling approach uses an adaptive NN–GA framework to inversely solve the governing equations in a water quality model for multiple parameter patterns, along with an alternating fitness method to maintain solution diversity. The procedure was demonstrated through a coupled 2D hydrodynamic and eutrophication model for Loch Raven Reservoir in Maryland. The inverse problem was formulated as a nonlinear optimization problem minimizing the degree of misfit (DOM) between model results and observed data. A set of NN models was developed to approximate the input-output response relationship of the Loch Raven Reservoir model and was incorporated into a GA framework in an adaptive fashion to search for near-optimal solutions minimizing the DOM. The numerical example showed that the adaptive NN–GA approach is capable of identifying multiple parameter patterns that reproduce the observed data equally well. The resulting parameter patterns were incorporated into the numerical model, and a multiple-pattern robust water quality modeling analysis, along with a compound margin of safety (CMOS) method, was proposed and applied to analyze the parameter pattern uncertainty.