危险化学品泄漏源的定位研究
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摘要
泄漏源的定位是危险化学品事故应急救援的基础与关键。论文基于监测模式、扩散模式以及源强反算算法研究进行设计，以―定位模型建立——模型优化——定位模型验证‖为研究主线，开展危险化学品泄漏源定位的相关科学研究。主要完成了以下工作：
（1）以优化方法为模型框架建立泄漏源定位的优化反算模型，率先提出利用模式搜索法进行毒气泄漏源的定位。利用事故现场数据和扩散模式将泄漏源定位转化为优化问题求解，利用模式搜索法逐步更新寻找计算浓度与监测浓度的最优匹配。另一方面，模式搜索法提供了领域空间的搜索思想，为嵌入其他全局搜索法提供理论基础，提高定位准确性和有效性。进而，通过设计混合算法结构以及混合时机的选取等角度分析，利用基于镶嵌型的混合优化算法进行源强反算试算，结果表明混合算法显著提高了反算精度。
（2）建立基于贝叶斯推理和优化算法相结合的泄漏源参数识别方法，将模型参数的先验信息、最终反算结果都通过概率分布来描述。在贝叶斯推理的基础上，利用MCMC抽样方法对后验概率分布进行抽样，得到参数的估计值。为了改善MCMC抽样过程的计算效率，提出基于优化算法的初始化过程，在抽样之前利用优化算法进行全局最佳化采样，使得混合的算法既能保持贝叶斯方法对不确定性问题的求解性能，又提高计算效率。
（3）建立基于元胞自动机方法的气体扩散模式，实现事故物质浓度在空间中的实时动态分布预测。通过优化模型方法或贝叶斯推理方法，将元胞自动机模型与实际观测数据相结合进行泄漏源的反演。仿真结果表明该模型方法能够提高反演结果精度，降低错误识别的概率。
（4）通过仿真模拟与外场试验验证相结合，验证泄漏源定位方法的有效性。在理论模型研究的基础上，通过仿真数据在简单环境中进行验证；再利用外场试验进行实证检验。建立外场试验平台，通过固定监测网络与移动监测的结合，有效获取事故物质的浓度信息。结果表明优化方法在复杂环境中若扩散模型选取不当将造成较大误差，而贝叶斯推理由于考虑到观测误差以及模型误差，不论是仿真验证还是实证检验都能够获取相对较好的结果。
创新之处主要体现在：1）引入了源强反算思想，率先提出利用模式搜索法进行源强反算研究。在优化模型框架下，建立了不同监测模式下的泄漏源定位方法。2）将贝叶斯推理与优化方法相结合，利用优化算法获取MCMC抽样的初始抽样点，既保证贝叶斯方法对不确定性问题的求解性能，又提高了计算的效率和精度。3）将元胞自动机方法引入用于毒气扩散的建模与求解，更准确的描述物质在空间中的动态分布。
Accurate and timely evaluation of the strength and location of pollutantsources plays an important role in emergency responses involving hazardouschemicals, particularly when toxic gases are released. An effective way is touse concentration observations, in and around the accident scenes, combinedwith a dispersion model to construct an inversion model.
Basing on observations of the concentration and atmospheric environmentcondition, dispersion modeling and inversion methods, the research wasconducted with following steps:(1) Source identification modeling;(2) Modelmodification using practical dispersion model;(3) Validation with syntheticdata and practical data. Therefore, the main tasks and findings are as follows:
Firstly, source inversion model is constructed by an optimizationframework. The pattern search method is applied to source identification forthe first time. Using the observations of concentration and atmosphericenvironment condition, the source identification problem was transferred intoan optimization problem. Then the pattern search method was employed toadjust parameters to find the optimal matching of calculated and observed concentrations. The idea of neighborhood search of the pattern search methodmade it convenient to combine with other global methods for a more accuratesolution. Then the structure of hybrid optimization method and the timing forthe combination were studied. As a result, a inlaid structure was employed,and the accuracy of the inversion results was improved significantly as well.
Secondly, an approach based on Bayesian inference and optimizationmethod is proposed to identify the release source. The priori information ofthe model parameters, as well as the final inversion results are presented byprobability distribution. Based on the Bayesian inference, a posteriordistribution is obtained. The Markov Chain Monte Carlo (MCMC) samplingis employed to sample the posterior distribution so as to get the estimatedvalue of the parameters and to evaluate the error. In order to improve theefficiency of the MCMC sampling, the optimization method is combined withthe Bayesian inference. The optimization method is employed in theinitialization process before the sampling to get a comparatively bettersolution for the MCMC sampling. With the combination of the Bayesianinference and optimization method, it can not only maintain the performanceof the Bayesian inference in solving the uncertainty problem, but also canimprove the computational efficiency.
Thirdly, a dispersion model based on cellular automata(CA) is constructed.The model can effectively predict the concentration in the environment at anytime. According to the optimization modeling method and/or Bayesian inference, cellular automata model and the actual observations of theconcentration are used to get a better solution. The validation results show thatthis approach can improve the accuracy of the results, and can reduce theprobability of the error identification.
Finally, simulation and field testing was combined in the research forsource inversion validation. The source inversion methods were first validatedwith synthetic data, and then the practical data from field testing were used toverify the validation of the inversion method. A field testing platform isestablished. The concentrations were obtained effectively though thecombination of fixed and mobile monitoring network. The results show thatthe optimization method based approach may cause large error in complexenvironment when the dispersion model is selected improperly. As theobservation error and model error are considered in Bayesian inference, it isable to obtain comparatively better results.
Innovation is mainly reflected as follows:
First of all, the inversion method is introduced to the source identification,using the concentration observations, the dispersion model, as well as theoptimization algorithms and/or Bayesian inference. The pattern search methodwas used in the source identification for the first time. Based on a frameworkof optimization, different models were established to study the sourceidentification with the concentration observations from different observationnetwork. Secondly, the Bayesian inference is combined with optimization algorithms. The optimization algorithms are used in the initialization processto obtain a better sample for MCMC sampling. The combination of themethods can ensure the performance of the Bayesian inference in solving theuncertainty problem, and the calculation accuracy and efficiency are improvedas well. Thirdly, we introduce the cellular automata to the gas dispersionmodeling. The CA-based dispersion model can predict the variation of theconcentration in different place and time; therefore, the injury scope can beestimated and thus the accident can be effectively controlled.
（1）以优化方法为模型框架建立泄漏源定位的优化反算模型，率先提出利用模式搜索法进行毒气泄漏源的定位。利用事故现场数据和扩散模式将泄漏源定位转化为优化问题求解，利用模式搜索法逐步更新寻找计算浓度与监测浓度的最优匹配。另一方面，模式搜索法提供了领域空间的搜索思想，为嵌入其他全局搜索法提供理论基础，提高定位准确性和有效性。进而，通过设计混合算法结构以及混合时机的选取等角度分析，利用基于镶嵌型的混合优化算法进行源强反算试算，结果表明混合算法显著提高了反算精度。
（2）建立基于贝叶斯推理和优化算法相结合的泄漏源参数识别方法，将模型参数的先验信息、最终反算结果都通过概率分布来描述。在贝叶斯推理的基础上，利用MCMC抽样方法对后验概率分布进行抽样，得到参数的估计值。为了改善MCMC抽样过程的计算效率，提出基于优化算法的初始化过程，在抽样之前利用优化算法进行全局最佳化采样，使得混合的算法既能保持贝叶斯方法对不确定性问题的求解性能，又提高计算效率。
（3）建立基于元胞自动机方法的气体扩散模式，实现事故物质浓度在空间中的实时动态分布预测。通过优化模型方法或贝叶斯推理方法，将元胞自动机模型与实际观测数据相结合进行泄漏源的反演。仿真结果表明该模型方法能够提高反演结果精度，降低错误识别的概率。
（4）通过仿真模拟与外场试验验证相结合，验证泄漏源定位方法的有效性。在理论模型研究的基础上，通过仿真数据在简单环境中进行验证；再利用外场试验进行实证检验。建立外场试验平台，通过固定监测网络与移动监测的结合，有效获取事故物质的浓度信息。结果表明优化方法在复杂环境中若扩散模型选取不当将造成较大误差，而贝叶斯推理由于考虑到观测误差以及模型误差，不论是仿真验证还是实证检验都能够获取相对较好的结果。
创新之处主要体现在：1）引入了源强反算思想，率先提出利用模式搜索法进行源强反算研究。在优化模型框架下，建立了不同监测模式下的泄漏源定位方法。2）将贝叶斯推理与优化方法相结合，利用优化算法获取MCMC抽样的初始抽样点，既保证贝叶斯方法对不确定性问题的求解性能，又提高了计算的效率和精度。3）将元胞自动机方法引入用于毒气扩散的建模与求解，更准确的描述物质在空间中的动态分布。
Accurate and timely evaluation of the strength and location of pollutantsources plays an important role in emergency responses involving hazardouschemicals, particularly when toxic gases are released. An effective way is touse concentration observations, in and around the accident scenes, combinedwith a dispersion model to construct an inversion model.
Basing on observations of the concentration and atmospheric environmentcondition, dispersion modeling and inversion methods, the research wasconducted with following steps:(1) Source identification modeling;(2) Modelmodification using practical dispersion model;(3) Validation with syntheticdata and practical data. Therefore, the main tasks and findings are as follows:
Firstly, source inversion model is constructed by an optimizationframework. The pattern search method is applied to source identification forthe first time. Using the observations of concentration and atmosphericenvironment condition, the source identification problem was transferred intoan optimization problem. Then the pattern search method was employed toadjust parameters to find the optimal matching of calculated and observed concentrations. The idea of neighborhood search of the pattern search methodmade it convenient to combine with other global methods for a more accuratesolution. Then the structure of hybrid optimization method and the timing forthe combination were studied. As a result, a inlaid structure was employed,and the accuracy of the inversion results was improved significantly as well.
Secondly, an approach based on Bayesian inference and optimizationmethod is proposed to identify the release source. The priori information ofthe model parameters, as well as the final inversion results are presented byprobability distribution. Based on the Bayesian inference, a posteriordistribution is obtained. The Markov Chain Monte Carlo (MCMC) samplingis employed to sample the posterior distribution so as to get the estimatedvalue of the parameters and to evaluate the error. In order to improve theefficiency of the MCMC sampling, the optimization method is combined withthe Bayesian inference. The optimization method is employed in theinitialization process before the sampling to get a comparatively bettersolution for the MCMC sampling. With the combination of the Bayesianinference and optimization method, it can not only maintain the performanceof the Bayesian inference in solving the uncertainty problem, but also canimprove the computational efficiency.
Thirdly, a dispersion model based on cellular automata(CA) is constructed.The model can effectively predict the concentration in the environment at anytime. According to the optimization modeling method and/or Bayesian inference, cellular automata model and the actual observations of theconcentration are used to get a better solution. The validation results show thatthis approach can improve the accuracy of the results, and can reduce theprobability of the error identification.
Finally, simulation and field testing was combined in the research forsource inversion validation. The source inversion methods were first validatedwith synthetic data, and then the practical data from field testing were used toverify the validation of the inversion method. A field testing platform isestablished. The concentrations were obtained effectively though thecombination of fixed and mobile monitoring network. The results show thatthe optimization method based approach may cause large error in complexenvironment when the dispersion model is selected improperly. As theobservation error and model error are considered in Bayesian inference, it isable to obtain comparatively better results.
Innovation is mainly reflected as follows:
First of all, the inversion method is introduced to the source identification,using the concentration observations, the dispersion model, as well as theoptimization algorithms and/or Bayesian inference. The pattern search methodwas used in the source identification for the first time. Based on a frameworkof optimization, different models were established to study the sourceidentification with the concentration observations from different observationnetwork. Secondly, the Bayesian inference is combined with optimization algorithms. The optimization algorithms are used in the initialization processto obtain a better sample for MCMC sampling. The combination of themethods can ensure the performance of the Bayesian inference in solving theuncertainty problem, and the calculation accuracy and efficiency are improvedas well. Thirdly, we introduce the cellular automata to the gas dispersionmodeling. The CA-based dispersion model can predict the variation of theconcentration in different place and time; therefore, the injury scope can beestimated and thus the accident can be effectively controlled.
引文
[1] Varma R, Varma D R. The Bhopal disaster of1984[J]. Bulletin of Science, Technology&Society,2005,25(1):37-45
[2] Broughton E. The Bhopal disaster and its aftermath: a review[J]. Environmental Health: AGlobal Access Science Source,2005,4(1):6
[3]《危险化学品重特大事故案例精选》编委会.危险化学品重特大事故案例精选[M].中国劳动社会保障出版社,2007
[4]国家安全监管总局监管三司,中国化学品安全协会.国内外危险化学品典型事故案例分析[M].中国劳动社会保障出版社,2009
[5]吴宗之,张圣柱,张悦,等.2006-2010年我国危险化学品事故统计分析研究[J].中国安全生产科学技术,2011,7(7):5-9
[6]钟晶晶.石油天然气管道保护法10月1日实施油气管道泄漏致污须治理[N].新京报.2010.9.28
[7] Ucinski D, Patan M. Sensor Network Design for the Estimation of Spatially DistributedProcesses[J]. International Journal of Applied Mathematics and Computer Science,2010,20(3):459-481
[8] Rao K S. Source estimation methods for atmospheric dispersion[J]. AtmosphericEnvironment,2007,41(33):6964-6973
[9] Zhai Z, Liu X. Inverse modeling methods for indoor airborne pollutant tracking: literaturereview and fundamentals[J]. Indoor Air,2007,17(6):419-438
[10]郑小平,陈增强.模式搜索算法在毒气泄漏中的源强反算[J].中国安全科学学报,2010,20(5):29-34
[11] Zheng X P, Chen Z Q. Inverse calculation approaches for source determination inhazardous chemical releases[J]. Journal of Loss Prevention in the Process Industries,2011,24(4):293-301
[12] Capasso S, Iovino P, Salvestrini S. Identification of stationary sources of air pollutants byconcentration statistical analysis[J]. Chemosphere,2008,73(4):614-618
[13] Woodbury A D, Ulrych T J. Minimum relative entropy inversion: Theory and applicationto recovering the release history of a groundwater contaminant[J]. Water ResourcesResearch,1996,32(9):2671-2681
[14] Woodbury A, Sudicky E, Ulrych T J, et al. Three-dimensional plume source reconstructionusing minimum relative entropy inversion[J]. Journal of contaminant hydrology,1998,32(1-2):131-158
[15] Newman M, Hatfield K, Hayworth J, et al. A hybrid method for inverse characterization ofsubsurface contaminant flux[J]. Journal of contaminant hydrology,2005,81(1-4):34-62
[16] Cervone G, Franzese P. Monte Carlo source detection of atmospheric emissions and errorfunctions analysis[J]. Computers&Geosciences,2010,36(7):902-909
[17] Patwardhan A, Small M J. Bayesian methods for model uncertainty analysis withapplication to future sea level rise[J]. Risk analysis,1992,12(4):513-523
[18] Bergin M S, Milford J B. Application of Bayesian Monte Carlo analysis to a Lagrangianphotochemical air quality model[J]. Atmospheric Environment,2000,34(5):781-792
[19] Sreedharan P, Sohn M D, Gadgil A J, et al. Systems approach to evaluating sensorcharacteristics for real-time monitoring of high-risk indoor contaminant releases[J].Atmospheric Environment,2006,40(19):3490-3502
[20] Sohn M D, Small M J, Pantazidou M. Reducing uncertainty in site characterization usingBayes Monte Carlo methods[J]. Journal of environmental engineering,2000,126(10):893-902
[21] Sohn M D, Reynolds P, Gadgil A J, et al. Rapidly locating sources and predictingcontaminant dispersion in buildings[C]. Proceedings of the Indoor Air2002Conference,Santa Cruz, CA,2002,4:211-216
[22] Sohn M D, Sextro R G, Gadgil A J, et al. Responding to sudden pollutant releases in officebuildings:1. Framework and analysis tools[J]. Indoor Air,2003,13(3):267-276
[23] Qian S S, Stow C A, Borsuk M E. On Monte Carlo methods for Bayesian inference[J].Ecological Modelling,2003,159(2-3):269-277
[24] Senocak I, Henuartner N W, Short M B, et al. Stochastic event reconstruction ofatmospheric contaminant dispersion using Bayesian inference[J]. AtmosphericEnvironment,2008,42(33):7718-7727
[25] Chow F K, Kosovic B, Chan S. Source inversion for contaminant plume dispersion inurban environments using building-resolving simulations[J]. Journal of AppliedMeteorology and Climatology,2008,47(6):1553-1572
[26] Kosovic B, Sugiyama G, Chan S, et al. Stochastic source inversion methodology andoptimal sensor network design[C].9th Annual George Mason University Conference onAtmospheric Transport and Dispersion Modeling, July,2005
[27] Keats A, Yee E, Lien F S. Bayesian inference for source determination with applications toa complex urban environment[J]. Atmospheric Environment,2007,41(3):465-479
[28] Keats A, Yee E, Lien F S. Efficiently characterizing the origin and decay rate of anonconservative scalar using probability theory[J]. Ecological Modelling,2007,205(3-4):437-452
[29] Keats A, Cheng M T, Yee E, et al. Bayesian treatment of a chemical mass balance receptormodel with multiplicative error structure[J]. Atmospheric Environment,2009,43(3):510-519
[30] Yee E, Lien F S, Keats A, et al. Bayesian inversion of concentration data: Sourcereconstruction in the adjoint representation of atmospheric diffusion[J]. Journal of WindEngineering and Industrial Aerodynamics,2008,96(10-11):1805-1816
[31] Yee E. Theory for reconstruction of an unknown number of contaminant sources usingprobabilistic inference[J]. Boundary-Layer Meteorology,2008,127(3):359-394
[32] Guo S D, Yang R, Zhang H, et al. Source identification for unsteady atmosphericdispersion of hazardous materials using Markov Chain Monte Carlo method[J].International Journal of Heat and Mass Transfer,2009,52(17-18):3955-3962
[33] Johannesson G, Hanley B, Nitao J. Dynamic Bayesian Models via Monte Carlo-AnIntroduction with Examples [R]. US: Lawrence Livermore National Laboratory,2004
[34] Marzouk Y M, Najm H N, Rahn L A. Stochastic spectral methods for efficient Bayesiansolution of inverse problems[J]. Journal of Computational Physics,2007,224(2):560-586
[35] Keats A, Yee E, Lien F S. Information-driven receptor placement for contaminant sourcedetermination[J]. Environmental Modelling&Software,2010,25(9):1000-1013
[36]朱江,汪萍.集合卡尔曼平滑和集合卡尔曼滤波在污染源反演中的应用[J].大气科学,2006,30(05):871-882
[37]郭少冬,杨锐,翁文国.基于MCMC方法的城区有毒气体扩散源反演[J].清华大学学报(自然科学版),2009,49(05):629-634
[38] Delle Monache L, Lundquist J K, Kosovic B, et al. Bayesian inference and Markov ChainMonte Carlo sampling to reconstruct a contaminant source on a continental scale[J].Journal of Applied Meteorology and Climatology,2008,47(10):2600-2613
[39] Stockie J M, Lushi E. An inverse Gaussian plume approach for estimating atmosphericpollutant emissions from multiple point sources[J]. Atmospheric Environment,2010,44(8):1097-1107
[40] Li F, Niu J L. An inverse approach for estimating the initial distribution of volatile organiccompounds in dry building material[J]. Atmospheric Environment,2005,39(8):1447-1455
[41] Elbern H, Schmidt H, Talagrand O, et al.4D-variational data assimilation with an adjointair quality model for emission analysis[J]. Environmental Modelling&Software,2000,15(6-7):539-548
[42] Yumimoto K, Uno I. Adjoint inverse modeling of CO emissions over Eastern Asia usingfour-dimensional variational data assimilation[J]. Atmospheric Environment,2006,40(35):6836-6845
[43] Zheng X P, Chen Z Q. Back-calculation of the strength and location of hazardous materialsreleases using the pattern search method[J]. Journal of Hazardous Materials,2010,183(1-3):474-481
[44] Rege M A, Tock R W. A simple neural network for estimating emission rates of hydrogensulfide and ammonia from single point sources[J]. Journal of the Air&Waste ManagementAssociation,1996,46:953-962
[45] Reich S L, Gomez D, Dawidowski L. Artificial neural network for the identification ofunknown air pollution sources[J]. Atmospheric Environment,1999,33(18):3045-3052
[46] Thomson L C, Hirst B, Gibson G, et al. An improved algorithm for locating a gas sourceusing inverse methods[J]. Atmospheric Environment,2007,41(6):1128-1134
[47] Haupt S E. A demonstration of coupled receptor/dispersion modeling with a geneticalgorithm[J]. Atmospheric Environment,2005,39(37):7181-7189
[48] Haupt S E, Young G S, Allen C T. Validation of a receptor-dispersion model coupled witha genetic algorithm using synthetic data[J]. Journal of Applied Meteorology andClimatology,2006,45(3):476-490
[49] Haupt S E, Beyer-Lout A, Long K J, et al. Assimilating concentration observations fortransport and dispersion modeling in a meandering wind field[J]. AtmosphericEnvironment,2009,43(6):1329-1338
[50] Haupt S E, Allen C T, Young G S. Source characterization with a geneticalgorithm-coupled dispersion-backward model incorporating SCIPUFF[J]. Journal ofApplied Meteorology and Climatology,2007,46(3):273-287
[51] Allen C T, Young G S, Haupt S E. Improving pollutant source characterization by betterestimating wind direction with a genetic algorithm[J]. Atmospheric Environment,2007,41(11):2283-2289
[52] Yang X, Yang Z, Yin X, et al. Chaos gray-coded genetic algorithm and its application forpollution source identifications in convection-diffusion equation[J]. Communications inNonlinear Science and Numerical Simulation,2008,13(8):1676-1688
[53] Khlaifi A, Ionescu A, Candau Y. Pollution source identification using a coupled diffusionmodel with a genetic algorithm[J]. Mathematics and Computers in Simulation,2009,79(12):3500-3510
[54] Jaroslav R, Krystof E, Pavel J, et al. Inverse modeling of emissions and their timeprofiles[J]. Atmospheric Pollution Research,2010,1(4):288-295
[55] Vira J, Sofiev M. On variational data assimilation for estimating the model initialconditions and emission fluxes for short-term forecasting of SOx concentrations[J].Atmospheric Environment,2012,46:318-328
[56] Miller S L, Anderson M J, Daly E P, et al. Source apportionment of exposures to volatileorganic compounds: II. Application of receptor models to TEAM study data[J].Atmospheric Environment,2002,36(22):3643-3658
[57] Miller S L, Anderson M J, Daly E P, et al. Source apportionment of exposures to volatileorganic compounds. I. Evaluation of receptor models using simulated exposure data[J].Atmospheric Environment,2002,36(22):3629-3641
[58] Long K J, Haupt S E, Young G S. Assessing sensitivity of source term estimation[J].Atmospheric Environment,2010,44(12):1558-1567
[59] Haupt S E, Haupt R L, Young G S. A mixed integer genetic algorithm used in biologicaland chemical defense applications[J]. Soft Computing-A Fusion of Foundations,Methodologies and Applications,2011,15(1):51-59
[60] Cervone G, Franzese P, Keesee A P K. Algorithm quasi‐optimal (AQ) learning[J]. WileyInterdisciplinary Reviews: Computational Statistics,2010,2(2):218-236
[61] Cervone G, Franzese P, Grajdeanu A. Characterization of atmospheric contaminant sourcesusing adaptive evolutionary algorithms[J]. Atmospheric Environment,2010,44(31):3787-3796
[62]吕武轩,施小丁,赵宝元,等.危险气体扩散时仿真的关键技术——―反求源强‖方法初探[J].中国安全科学学报,1997,7(S1):35-39
[63]吴维平.港口油气污染扩散及源强计算方法的探讨[J].交通环保,1999,20(06):11-15
[64]苏芳,邵敏,蔡旭辉,等.利用逆向轨迹反演模式估算北京地区甲烷源强[J].环境科学学报,2002,22(05):586-591
[65]陈军明,徐大海,朱蓉.遗传算法在点源扩散浓度反演排放源强中的应用[J].气象,2002,28(9):12-16
[66]关磊,魏利军,吴宗之,等.基于人工神经网络的储罐区泄漏源实时定位技术初步研究[J].中国安全生产科学技术,2006,2(4):79-83
[67]张建文,刘茜,魏利军.危险化学品泄漏事故泄漏源强反算方法比较研究[J].中国安全科学学报,2009,19(002):165-171
[68] Gilbert E J, Khajehnajafi S. Estimation of toxic substance release[P]. US Patent,US6772071B2.2004-8-3
[69]张久凤,姜春明,王正,等.粒子群优化算法在源强反算问题中的应用研究[J].中国安全科学学报,2010,20(10):123-128
[70]王煜薇.基于混合遗传--Nelder Mead单纯形算法的源强及位置反算[D].北京:北京化工大学,2011
[71] Tian N, Sun J, Xu W B, et al. An improved quantum-behaved particle swarm optimizationwith perturbation operator and its application in estimating groundwater contaminantsource[J]. Inverse Problems in Science and Engineering,2011,19(2):181-202
[72] Mandal P, Dutta U. Estimation of Earthquake Source Parameters in the Kachchh SeismicZone, Gujarat, India, from Strong-Motion Network Data Using a Generalized InversionTechnique[J]. Bulletin of the Seismological Society of America,2011,101(4):1719-1731
[73] Levy A, Gannot S, Habets E A P. Multiple-Hypothesis Extended Particle Filter forAcoustic Source Localization in Reverberant Environments[J]. Audio, Speech, andLanguage Processing, IEEE Transactions on,2011,19(6):1540-1555
[74] Tian N, Sun J, Xu W B, et al. Estimation of unknown heat source function in inverse heatconduction problems using quantum-behaved particle swarm optimization[J]. InternationalJournal of Heat and Mass Transfer,2011,54(17-18):4110-4116
[75] Havens J. Review of dense gas dispersion field experiments[J]. Journal of Loss Preventionin the Process Industries,1992,5(1):28-41
[76]丁信伟,王淑兰.可燃及毒性气体泄漏扩散研究综述[J].化学工业与工程,1999,16(2):118-122
[77] Mazzola C, Addis R. Atmospheric dispersion modeling resources [M]. DOE SCAPA andORISE Emergency Management Laboratory, Oak Ridge, TN,1995
[78] Holmes N S, Morawska L. A review of dispersion modelling and its application to thedispersion of particles: An overview of different dispersion models available [J].Atmospheric Environment,2006,40(30):5902-5928
[79] Levy J I, Spengler J D, Hlinka D, et al. Using CALPUFF to evaluate the impacts of powerplant emissions in Illinois: model sensitivity and implications [J]. AtmosphericEnvironment,2002,36(6):1063-1075
[80] Carslaw D C, Beevers S D. Dispersion modelling considerations for transient emissionsfrom elevated point sources [J]. Atmospheric Environment,2002,36(18):3021-3029
[81] Fan L, Yi S, Li Z, et al. Research on toxic Gas Diffusion Simulation in Urban areas basedon GIS[C]. New Trends in Information and Service Science, NISS'09. InternationalConference on. IEEE,2009:166-169
[82] Stockie J M. The mathematics of atmospheric dispersion modeling [J]. Siam Review,2011,53(2):349-372
[83] Walklate P. A random-walk model for dispersion of heavy particles in turbulent air flow[J].Boundary-Layer Meteorology,1987,39(1):175-190
[84] Cogan J L. Monte Carlo simulation of buoyant dispersion[J]. Atmospheric Environment(1967),1985,19(6):867-878
[85] Castelli S T, Anfossi D, Tinarelli G, et al. A new Lagrangian particle model for thesimulation of dense gas dispersion[J]. Atmospheric Environment,2010,44(6):753-762
[86] Anfossi D, Tinarelli G, Castelli S T, et al. Proposal of a new Lagrangian particle model forthe simulation of dense gas dispersion[J]. International Journal of Environment andPollution,2011,44(1-4):200-207
[87] Avila R, Raza S, Cervantes J.3D Lagrangian particle model for the atmospheric dispersionof toxic pollutants[J]. International Journal of Energy Research,2002,26(2):93-104
[88] Laurence D, Addad Y, Talotte C, et al. Large eddy simulation of a forward-backwardfacing step for acoustic source identification[J]. International Journal of Heat and FluidFlow,2003,24(4):562-571
[89] Park H J, Holland D. The effect of location of a convective heat source on displacementventilation: CFD study[J]. Building and Environment,2001,36(7):883-889
[90] Zajaczkowski F J, Haupt S E, Schmehl K J. A preliminary study of assimilating numericalweather prediction data into computational fluid dynamics models for wind prediction[J].Journal of Wind Engineering and Industrial Aerodynamics,2011,99(4):320-329
[91] Crowl D A, Louvar J F. Chemical process safety: fundamentals with applications [M].NewJersy: Prentice Hall PTR,2002
[92] Gifford F A. Turbulent diffusion-typing schemes: A review[J]. Nuclear Safety,1976,17(1):68-86
[93] Gifford Jr F A. Use of routine meteorological observations for estimating atmosphericdispersion[J]. Nuclear Safety,1961,2:47-51
[94] Hanna S, Chang J, Strimaitis D. Hazardous gas model evaluation with field observations[J].Atmospheric Environment. Part A. General Topics,1993,27(15):2265-2285
[95] Chang J C, Franzese P, Chayantrakom K, et al. Evaluations of CALPUFF, HPAC, andVLSTRACK with two mesoscale field datasets[J]. Journal of Applied Meteorology,2003,42(4):453-466
[96] Holland J H. Adaptation in natural and artificial systems[M]. US: University of MichiganPress,1975
[97] Goldberg D E. Genetic algorithms in search, optimization, and machine learning[M].NewYork: Addison-Wesley Professional,1989
[98] Allen C T, Haupt S E, Young G S. Source characterization with a geneticalgorithm-coupled dispersion-backward model incorporating SCIPUFF[J]. Journal ofApplied Meteorology and Climatology,2007,46(3):273-287
[99] Jimenez A, Garcia J M, Romacho M D. Simultaneous inversion of source parameters andattenuation factor using genetic algorithms[J]. Bulletin of the Seismological Society ofAmerica,2005,95(4):1401-1411
[100] Kuroki Y, Young G S, Haupt S E. UAV navigation by an expert system for contaminantmapping with a genetic algorithm[J]. Expert Systems with Applications,2010,37(6):4687-4697
[101] Liu F B. A modified genetic algorithm for solving the inverse heat transfer problem ofestimating plan heat source[J]. International Journal of Heat and Mass Transfer,2008,51(15-16):3745-3752
[102] Marseguerra M, Giacobbo F, Zio E. Solving the inverse problem of parameter estimationby genetic algorithms: the case of a groundwater contaminant transport model[J]. Annals ofNuclear Energy,2002,29(8):967-981
[103] Kirkpatrick S, Gelatt C D, Vecchi M P. Optimization by simulated annealing[J]. Science,1983,220(4598):671-680
[104] Lundy M, Mees A. Convergence of an annealing algorithm[J]. Mathematical programming,1986,34(1):111-124
[105] Guo J, Zheng L. A modified simulated annealing algorithm for estimating solute transportparameters in streams from tracer experiment data[J]. Environmental Modelling&Software,2005,20(6):811-815
[106] Metropolis N, Rosenbluth A W, Rosenbluth M N, et al. Equation of state calculations byfast computing machines[J]. The Journal of Chemical Physics,1953,21(6):1087-1092
[107] Kennedy J, Eberhart R. Particle swarm optimization[C] Neural Networks, Proceedings.,IEEE International Conference on. IEEE,1995:1942-1948
[108] Ai J, Hu S H, Qin S, et al. Determination of trace rare earth elements, Sc, and Y in groundwater by inductively coupled plasma mass spectrometry with preconcerntration method[J].Chinese Journal of Analytical Chemistry,2002,30(10):1226-1230
[109] Nashed-Samuel Y, Chen G X, Fujimori K, et al. Detection of adulteration in acetonitrile[J].Spectrochimica Acta Part a-Molecular and Biomolecular Spectroscopy,2011,78(5):1646-1650
[110] Leahy-Hoppa M R, Fitch M J, Osiander R. Terahertz spectroscopy techniques forexplosives detection[J]. Analytical and Bioanalytical Chemistry,2009,395(2):247-257
[111] Hooke R, Jeeves T.―Direct Search‖Solution of Numerical and Statistical Problems[J].Journal of the ACM (JACM),1961,8(2):212-229
[112] Lewis R M, Torczon V, Trosset M W. Why pattern search works[J]. Optima,1998,59:1-7
[113] Torczon V. On the convergence of pattern search algorithms[J]. Siam Journal onOptimization,1997,7(1):1-25
[114] Polak E. Computational methods in optimization: A unified approach[M]. New York:Academic Press,1971
[115] Torczon V. On the convergence of the multidirectional search algorithm[J]. Siam Journalon Optimization,1991,1(1):123-145
[116] Rudd A, Robins A, Lepley J, et al. An inverse method for determining sourcecharacteristics for emergency response applications [J]. Boundary-Layer Meteorology,2012:1-20
[117] Bastani A, Haghighat F, Kozinski J A. Contaminant source identification within a building:Toward design of immune buildings [J]. Building and Environment,2012,51:320-329
[118] Sharan M, Singh S K, Issartel J. Least square data assimilation for identification of thepoint source emissions [J]. Pure and Applied Geophysics,2012,169(3):483-497
[119] Sharan M, Yadav A K, Singh M, et al. A mathematical model for the dispersion of airpollutants in low wind conditions [J]. Atmospheric Environment,1996,30(8):1209-1220
[120] Annunzio A J, Young G S, Haupt S E. Utilizing state estimation to determine the sourcelocation for a contaminant [J]. Atmospheric Environment,2012,46:580-589
[121] Zhang T T, Li H, Wang S. Inversely tracking indoor airborne particles to locate theirrelease sources [J]. Atmospheric Environment,2012,55:328-338
[122] Addepalli B, Sikorski K, Pardyjak E, et al. Source characterization of atmospheric releasesusing stochastic search and regularized gradient optimization [J]. Inverse Problems inScience and Engineering,2011,19(8):1097-1124
[123] Cervone G, Franzese P. Non-Darwinian evolution for the source detection of atmosphericreleases [J]. Atmospheric Environment,2011,45(26):4497-4506
[124] Rodriguez L M, Ellen Haupt S, Young G S. Impact of sensor characteristics on sourcecharacterization for dispersion modeling [J]. Measurement,2011,44(5):802-814
[125] Yee E, Flesch T K. Inference of emission rates from multiple sources using Bayesianprobability theory [J]. Journal of Environmental Monitoring,2010,12(3):622-634
[126] Liu X, Zhai Z J. Protecting a whole building from critical indoor contamination withoptimal sensor network design and source identification methods [J]. Building andEnvironment,2009,44(11):2276-2283
[127] Annunzio A J, Haupt S E, Young G S (2008) Source Characterization and MeteorologyRetrieval Including Atmospheric Boundary Layer Depth Using a Genetic Algorithm[C]15th Joint Conference on the Applications of Air Pollution Meteorology with theA&WMA, New Orleans, LA,2008
[128] Haupt S E, Haupt R L, Young G S. Using genetic algorithms in chem-bio defenseapplications[C] Bio-inspired, Learning, and Intelligent Systems for Security,2007. BLISS2007. ECSIS Symposium on. IEEE,2007:151-154
[129] Haupt S E, Young G S, Allen C T. A genetic algorithm method to assimilate sensor datafor a toxic contaminant release [J]. Journal of Computers,2007,2(6):85-93
[130] Long K J, Haupt S E, Young G S, et al. Characterizing contaminant source andmeteorological forcing using data assimilation with a genetic algorithm[C]86th AmericanMeteorological Society Annual Meeting,2006
[131] Haupt R L, Haupt S E. Practical genetic algorithms[M].Second edition. New Jersey: JohnWiley&Sons, Inc.,2004
[132]张建文,王煜薇,郑小平,等.基于混合遗传-Nelder Mead单纯形算法的源强及位置反算[J].系统工程理论与实践,2011,31(8):1581-1587
[133] Nelder J A, Mead R. A simplex method for function minimization[J]. The ComputerJournal,1965,7(4):308-313
[134]陆璇.数理统计基础[M].北京:清华大学出版社,1998
[135] Brooks S. Markov chain Monte Carlo method and its application[J]. Journal of the RoyalStatistical Society: Series D (The Statistician),1998,47(1):69-100
[136]李贤平.概率论基础[M].北京:高等教育出版社,1997
[137] Goyal A, Mitchell J, von Stackelberg K, et al. Estimation of fugitive lead emission ratesfrom secondary lead facilities using hierarchical Bayesian models[J]. EnvironmentalScience&Technology,2005,39(13):4929-4937
[138] Gilks W R, Richardson S, Spiegelhalter D J. Markov chain Monte Carlo in practice[M].UK: Chapman&Hall,1996
[139] Hastings W K. Monte Carlo sampling methods using Markov chains and theirapplications[J]. Biometrika,1970,57(1):97-109
[140] Smith A F M, Roberts G O. Bayesian computation via the Gibbs sampler and relatedMarkov chain Monte Carlo methods[J]. Journal of the Royal Statistical Society. Series B(Methodological),1993,55(1):3-23
[141] German S, German D. Stochastic relaxation, Gibbs distribution, and the Bayesianrestoration of images[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,1984, PAMI-6(6):721-741
[142] Hall D, Waters R. Wind tunnel model comparisons with the Thorney Island dense gasrelease field trials[J]. Journal of Hazardous Materials,1985,11:209-235
[143] Knudsen S, Krogstad P. A wind tunnel simulation of the Thorney Island Phase II Trial20[J]. Journal of Hazardous Materials,1987,16:173-183
[144] van Heugten W, Duijm N. Some findings based on wind tunnel simulation and modelcalculations of Thorney Island trial No.008[J]. Journal of Hazardous Materials,1985,11:409-416
[145] Sklavounos S, Rigas F. Validation of turbulence models in heavy gas dispersion overobstacles[J]. Journal of Hazardous Materials,2004,108(1-2):9-20
[146] Tauseef S M, Rashtchian D, Abbasi S. CFD-based simulation of dense gas dispersion inpresence of obstacles[J]. Journal of Loss Prevention in the Process Industries,2011,24(4):371-376
[147] Chang J, Hanna S. Air quality model performance evaluation[J]. Meteorology andAtmospheric Physics,2004,87(1):167-196
[148] Puttock J, Colenbrander G. Thorney Island data and dispersion modelling[J]. Journal ofHazardous Materials,1985,11:381-397
[149] McQuaid J. Design of the Thorney Island continuous release trials[J]. Journal of HazardousMaterials,1987,16:1-8
[150] Pfenning D, Cornwell J. Computerized processing of Thorney Island trial data forcomparison with model predictions[J]. Journal of Hazardous Materials,1985,11:347-368
[151] Brighton P, Prince A. Overall properties of the heavy gas clouds in the Thorney IslandPhase II Trials[J]. Journal of Hazardous Materials,1987,16:103-138
[2] Broughton E. The Bhopal disaster and its aftermath: a review[J]. Environmental Health: AGlobal Access Science Source,2005,4(1):6
[3]《危险化学品重特大事故案例精选》编委会.危险化学品重特大事故案例精选[M].中国劳动社会保障出版社,2007
[4]国家安全监管总局监管三司,中国化学品安全协会.国内外危险化学品典型事故案例分析[M].中国劳动社会保障出版社,2009
[5]吴宗之,张圣柱,张悦,等.2006-2010年我国危险化学品事故统计分析研究[J].中国安全生产科学技术,2011,7(7):5-9
[6]钟晶晶.石油天然气管道保护法10月1日实施油气管道泄漏致污须治理[N].新京报.2010.9.28
[7] Ucinski D, Patan M. Sensor Network Design for the Estimation of Spatially DistributedProcesses[J]. International Journal of Applied Mathematics and Computer Science,2010,20(3):459-481
[8] Rao K S. Source estimation methods for atmospheric dispersion[J]. AtmosphericEnvironment,2007,41(33):6964-6973
[9] Zhai Z, Liu X. Inverse modeling methods for indoor airborne pollutant tracking: literaturereview and fundamentals[J]. Indoor Air,2007,17(6):419-438
[10]郑小平,陈增强.模式搜索算法在毒气泄漏中的源强反算[J].中国安全科学学报,2010,20(5):29-34
[11] Zheng X P, Chen Z Q. Inverse calculation approaches for source determination inhazardous chemical releases[J]. Journal of Loss Prevention in the Process Industries,2011,24(4):293-301
[12] Capasso S, Iovino P, Salvestrini S. Identification of stationary sources of air pollutants byconcentration statistical analysis[J]. Chemosphere,2008,73(4):614-618
[13] Woodbury A D, Ulrych T J. Minimum relative entropy inversion: Theory and applicationto recovering the release history of a groundwater contaminant[J]. Water ResourcesResearch,1996,32(9):2671-2681
[14] Woodbury A, Sudicky E, Ulrych T J, et al. Three-dimensional plume source reconstructionusing minimum relative entropy inversion[J]. Journal of contaminant hydrology,1998,32(1-2):131-158
[15] Newman M, Hatfield K, Hayworth J, et al. A hybrid method for inverse characterization ofsubsurface contaminant flux[J]. Journal of contaminant hydrology,2005,81(1-4):34-62
[16] Cervone G, Franzese P. Monte Carlo source detection of atmospheric emissions and errorfunctions analysis[J]. Computers&Geosciences,2010,36(7):902-909
[17] Patwardhan A, Small M J. Bayesian methods for model uncertainty analysis withapplication to future sea level rise[J]. Risk analysis,1992,12(4):513-523
[18] Bergin M S, Milford J B. Application of Bayesian Monte Carlo analysis to a Lagrangianphotochemical air quality model[J]. Atmospheric Environment,2000,34(5):781-792
[19] Sreedharan P, Sohn M D, Gadgil A J, et al. Systems approach to evaluating sensorcharacteristics for real-time monitoring of high-risk indoor contaminant releases[J].Atmospheric Environment,2006,40(19):3490-3502
[20] Sohn M D, Small M J, Pantazidou M. Reducing uncertainty in site characterization usingBayes Monte Carlo methods[J]. Journal of environmental engineering,2000,126(10):893-902
[21] Sohn M D, Reynolds P, Gadgil A J, et al. Rapidly locating sources and predictingcontaminant dispersion in buildings[C]. Proceedings of the Indoor Air2002Conference,Santa Cruz, CA,2002,4:211-216
[22] Sohn M D, Sextro R G, Gadgil A J, et al. Responding to sudden pollutant releases in officebuildings:1. Framework and analysis tools[J]. Indoor Air,2003,13(3):267-276
[23] Qian S S, Stow C A, Borsuk M E. On Monte Carlo methods for Bayesian inference[J].Ecological Modelling,2003,159(2-3):269-277
[24] Senocak I, Henuartner N W, Short M B, et al. Stochastic event reconstruction ofatmospheric contaminant dispersion using Bayesian inference[J]. AtmosphericEnvironment,2008,42(33):7718-7727
[25] Chow F K, Kosovic B, Chan S. Source inversion for contaminant plume dispersion inurban environments using building-resolving simulations[J]. Journal of AppliedMeteorology and Climatology,2008,47(6):1553-1572
[26] Kosovic B, Sugiyama G, Chan S, et al. Stochastic source inversion methodology andoptimal sensor network design[C].9th Annual George Mason University Conference onAtmospheric Transport and Dispersion Modeling, July,2005
[27] Keats A, Yee E, Lien F S. Bayesian inference for source determination with applications toa complex urban environment[J]. Atmospheric Environment,2007,41(3):465-479
[28] Keats A, Yee E, Lien F S. Efficiently characterizing the origin and decay rate of anonconservative scalar using probability theory[J]. Ecological Modelling,2007,205(3-4):437-452
[29] Keats A, Cheng M T, Yee E, et al. Bayesian treatment of a chemical mass balance receptormodel with multiplicative error structure[J]. Atmospheric Environment,2009,43(3):510-519
[30] Yee E, Lien F S, Keats A, et al. Bayesian inversion of concentration data: Sourcereconstruction in the adjoint representation of atmospheric diffusion[J]. Journal of WindEngineering and Industrial Aerodynamics,2008,96(10-11):1805-1816
[31] Yee E. Theory for reconstruction of an unknown number of contaminant sources usingprobabilistic inference[J]. Boundary-Layer Meteorology,2008,127(3):359-394
[32] Guo S D, Yang R, Zhang H, et al. Source identification for unsteady atmosphericdispersion of hazardous materials using Markov Chain Monte Carlo method[J].International Journal of Heat and Mass Transfer,2009,52(17-18):3955-3962
[33] Johannesson G, Hanley B, Nitao J. Dynamic Bayesian Models via Monte Carlo-AnIntroduction with Examples [R]. US: Lawrence Livermore National Laboratory,2004
[34] Marzouk Y M, Najm H N, Rahn L A. Stochastic spectral methods for efficient Bayesiansolution of inverse problems[J]. Journal of Computational Physics,2007,224(2):560-586
[35] Keats A, Yee E, Lien F S. Information-driven receptor placement for contaminant sourcedetermination[J]. Environmental Modelling&Software,2010,25(9):1000-1013
[36]朱江,汪萍.集合卡尔曼平滑和集合卡尔曼滤波在污染源反演中的应用[J].大气科学,2006,30(05):871-882
[37]郭少冬,杨锐,翁文国.基于MCMC方法的城区有毒气体扩散源反演[J].清华大学学报(自然科学版),2009,49(05):629-634
[38] Delle Monache L, Lundquist J K, Kosovic B, et al. Bayesian inference and Markov ChainMonte Carlo sampling to reconstruct a contaminant source on a continental scale[J].Journal of Applied Meteorology and Climatology,2008,47(10):2600-2613
[39] Stockie J M, Lushi E. An inverse Gaussian plume approach for estimating atmosphericpollutant emissions from multiple point sources[J]. Atmospheric Environment,2010,44(8):1097-1107
[40] Li F, Niu J L. An inverse approach for estimating the initial distribution of volatile organiccompounds in dry building material[J]. Atmospheric Environment,2005,39(8):1447-1455
[41] Elbern H, Schmidt H, Talagrand O, et al.4D-variational data assimilation with an adjointair quality model for emission analysis[J]. Environmental Modelling&Software,2000,15(6-7):539-548
[42] Yumimoto K, Uno I. Adjoint inverse modeling of CO emissions over Eastern Asia usingfour-dimensional variational data assimilation[J]. Atmospheric Environment,2006,40(35):6836-6845
[43] Zheng X P, Chen Z Q. Back-calculation of the strength and location of hazardous materialsreleases using the pattern search method[J]. Journal of Hazardous Materials,2010,183(1-3):474-481
[44] Rege M A, Tock R W. A simple neural network for estimating emission rates of hydrogensulfide and ammonia from single point sources[J]. Journal of the Air&Waste ManagementAssociation,1996,46:953-962
[45] Reich S L, Gomez D, Dawidowski L. Artificial neural network for the identification ofunknown air pollution sources[J]. Atmospheric Environment,1999,33(18):3045-3052
[46] Thomson L C, Hirst B, Gibson G, et al. An improved algorithm for locating a gas sourceusing inverse methods[J]. Atmospheric Environment,2007,41(6):1128-1134
[47] Haupt S E. A demonstration of coupled receptor/dispersion modeling with a geneticalgorithm[J]. Atmospheric Environment,2005,39(37):7181-7189
[48] Haupt S E, Young G S, Allen C T. Validation of a receptor-dispersion model coupled witha genetic algorithm using synthetic data[J]. Journal of Applied Meteorology andClimatology,2006,45(3):476-490
[49] Haupt S E, Beyer-Lout A, Long K J, et al. Assimilating concentration observations fortransport and dispersion modeling in a meandering wind field[J]. AtmosphericEnvironment,2009,43(6):1329-1338
[50] Haupt S E, Allen C T, Young G S. Source characterization with a geneticalgorithm-coupled dispersion-backward model incorporating SCIPUFF[J]. Journal ofApplied Meteorology and Climatology,2007,46(3):273-287
[51] Allen C T, Young G S, Haupt S E. Improving pollutant source characterization by betterestimating wind direction with a genetic algorithm[J]. Atmospheric Environment,2007,41(11):2283-2289
[52] Yang X, Yang Z, Yin X, et al. Chaos gray-coded genetic algorithm and its application forpollution source identifications in convection-diffusion equation[J]. Communications inNonlinear Science and Numerical Simulation,2008,13(8):1676-1688
[53] Khlaifi A, Ionescu A, Candau Y. Pollution source identification using a coupled diffusionmodel with a genetic algorithm[J]. Mathematics and Computers in Simulation,2009,79(12):3500-3510
[54] Jaroslav R, Krystof E, Pavel J, et al. Inverse modeling of emissions and their timeprofiles[J]. Atmospheric Pollution Research,2010,1(4):288-295
[55] Vira J, Sofiev M. On variational data assimilation for estimating the model initialconditions and emission fluxes for short-term forecasting of SOx concentrations[J].Atmospheric Environment,2012,46:318-328
[56] Miller S L, Anderson M J, Daly E P, et al. Source apportionment of exposures to volatileorganic compounds: II. Application of receptor models to TEAM study data[J].Atmospheric Environment,2002,36(22):3643-3658
[57] Miller S L, Anderson M J, Daly E P, et al. Source apportionment of exposures to volatileorganic compounds. I. Evaluation of receptor models using simulated exposure data[J].Atmospheric Environment,2002,36(22):3629-3641
[58] Long K J, Haupt S E, Young G S. Assessing sensitivity of source term estimation[J].Atmospheric Environment,2010,44(12):1558-1567
[59] Haupt S E, Haupt R L, Young G S. A mixed integer genetic algorithm used in biologicaland chemical defense applications[J]. Soft Computing-A Fusion of Foundations,Methodologies and Applications,2011,15(1):51-59
[60] Cervone G, Franzese P, Keesee A P K. Algorithm quasi‐optimal (AQ) learning[J]. WileyInterdisciplinary Reviews: Computational Statistics,2010,2(2):218-236
[61] Cervone G, Franzese P, Grajdeanu A. Characterization of atmospheric contaminant sourcesusing adaptive evolutionary algorithms[J]. Atmospheric Environment,2010,44(31):3787-3796
[62]吕武轩,施小丁,赵宝元,等.危险气体扩散时仿真的关键技术——―反求源强‖方法初探[J].中国安全科学学报,1997,7(S1):35-39
[63]吴维平.港口油气污染扩散及源强计算方法的探讨[J].交通环保,1999,20(06):11-15
[64]苏芳,邵敏,蔡旭辉,等.利用逆向轨迹反演模式估算北京地区甲烷源强[J].环境科学学报,2002,22(05):586-591
[65]陈军明,徐大海,朱蓉.遗传算法在点源扩散浓度反演排放源强中的应用[J].气象,2002,28(9):12-16
[66]关磊,魏利军,吴宗之,等.基于人工神经网络的储罐区泄漏源实时定位技术初步研究[J].中国安全生产科学技术,2006,2(4):79-83
[67]张建文,刘茜,魏利军.危险化学品泄漏事故泄漏源强反算方法比较研究[J].中国安全科学学报,2009,19(002):165-171
[68] Gilbert E J, Khajehnajafi S. Estimation of toxic substance release[P]. US Patent,US6772071B2.2004-8-3
[69]张久凤,姜春明,王正,等.粒子群优化算法在源强反算问题中的应用研究[J].中国安全科学学报,2010,20(10):123-128
[70]王煜薇.基于混合遗传--Nelder Mead单纯形算法的源强及位置反算[D].北京:北京化工大学,2011
[71] Tian N, Sun J, Xu W B, et al. An improved quantum-behaved particle swarm optimizationwith perturbation operator and its application in estimating groundwater contaminantsource[J]. Inverse Problems in Science and Engineering,2011,19(2):181-202
[72] Mandal P, Dutta U. Estimation of Earthquake Source Parameters in the Kachchh SeismicZone, Gujarat, India, from Strong-Motion Network Data Using a Generalized InversionTechnique[J]. Bulletin of the Seismological Society of America,2011,101(4):1719-1731
[73] Levy A, Gannot S, Habets E A P. Multiple-Hypothesis Extended Particle Filter forAcoustic Source Localization in Reverberant Environments[J]. Audio, Speech, andLanguage Processing, IEEE Transactions on,2011,19(6):1540-1555
[74] Tian N, Sun J, Xu W B, et al. Estimation of unknown heat source function in inverse heatconduction problems using quantum-behaved particle swarm optimization[J]. InternationalJournal of Heat and Mass Transfer,2011,54(17-18):4110-4116
[75] Havens J. Review of dense gas dispersion field experiments[J]. Journal of Loss Preventionin the Process Industries,1992,5(1):28-41
[76]丁信伟,王淑兰.可燃及毒性气体泄漏扩散研究综述[J].化学工业与工程,1999,16(2):118-122
[77] Mazzola C, Addis R. Atmospheric dispersion modeling resources [M]. DOE SCAPA andORISE Emergency Management Laboratory, Oak Ridge, TN,1995
[78] Holmes N S, Morawska L. A review of dispersion modelling and its application to thedispersion of particles: An overview of different dispersion models available [J].Atmospheric Environment,2006,40(30):5902-5928
[79] Levy J I, Spengler J D, Hlinka D, et al. Using CALPUFF to evaluate the impacts of powerplant emissions in Illinois: model sensitivity and implications [J]. AtmosphericEnvironment,2002,36(6):1063-1075
[80] Carslaw D C, Beevers S D. Dispersion modelling considerations for transient emissionsfrom elevated point sources [J]. Atmospheric Environment,2002,36(18):3021-3029
[81] Fan L, Yi S, Li Z, et al. Research on toxic Gas Diffusion Simulation in Urban areas basedon GIS[C]. New Trends in Information and Service Science, NISS'09. InternationalConference on. IEEE,2009:166-169
[82] Stockie J M. The mathematics of atmospheric dispersion modeling [J]. Siam Review,2011,53(2):349-372
[83] Walklate P. A random-walk model for dispersion of heavy particles in turbulent air flow[J].Boundary-Layer Meteorology,1987,39(1):175-190
[84] Cogan J L. Monte Carlo simulation of buoyant dispersion[J]. Atmospheric Environment(1967),1985,19(6):867-878
[85] Castelli S T, Anfossi D, Tinarelli G, et al. A new Lagrangian particle model for thesimulation of dense gas dispersion[J]. Atmospheric Environment,2010,44(6):753-762
[86] Anfossi D, Tinarelli G, Castelli S T, et al. Proposal of a new Lagrangian particle model forthe simulation of dense gas dispersion[J]. International Journal of Environment andPollution,2011,44(1-4):200-207
[87] Avila R, Raza S, Cervantes J.3D Lagrangian particle model for the atmospheric dispersionof toxic pollutants[J]. International Journal of Energy Research,2002,26(2):93-104
[88] Laurence D, Addad Y, Talotte C, et al. Large eddy simulation of a forward-backwardfacing step for acoustic source identification[J]. International Journal of Heat and FluidFlow,2003,24(4):562-571
[89] Park H J, Holland D. The effect of location of a convective heat source on displacementventilation: CFD study[J]. Building and Environment,2001,36(7):883-889
[90] Zajaczkowski F J, Haupt S E, Schmehl K J. A preliminary study of assimilating numericalweather prediction data into computational fluid dynamics models for wind prediction[J].Journal of Wind Engineering and Industrial Aerodynamics,2011,99(4):320-329
[91] Crowl D A, Louvar J F. Chemical process safety: fundamentals with applications [M].NewJersy: Prentice Hall PTR,2002
[92] Gifford F A. Turbulent diffusion-typing schemes: A review[J]. Nuclear Safety,1976,17(1):68-86
[93] Gifford Jr F A. Use of routine meteorological observations for estimating atmosphericdispersion[J]. Nuclear Safety,1961,2:47-51
[94] Hanna S, Chang J, Strimaitis D. Hazardous gas model evaluation with field observations[J].Atmospheric Environment. Part A. General Topics,1993,27(15):2265-2285
[95] Chang J C, Franzese P, Chayantrakom K, et al. Evaluations of CALPUFF, HPAC, andVLSTRACK with two mesoscale field datasets[J]. Journal of Applied Meteorology,2003,42(4):453-466
[96] Holland J H. Adaptation in natural and artificial systems[M]. US: University of MichiganPress,1975
[97] Goldberg D E. Genetic algorithms in search, optimization, and machine learning[M].NewYork: Addison-Wesley Professional,1989
[98] Allen C T, Haupt S E, Young G S. Source characterization with a geneticalgorithm-coupled dispersion-backward model incorporating SCIPUFF[J]. Journal ofApplied Meteorology and Climatology,2007,46(3):273-287
[99] Jimenez A, Garcia J M, Romacho M D. Simultaneous inversion of source parameters andattenuation factor using genetic algorithms[J]. Bulletin of the Seismological Society ofAmerica,2005,95(4):1401-1411
[100] Kuroki Y, Young G S, Haupt S E. UAV navigation by an expert system for contaminantmapping with a genetic algorithm[J]. Expert Systems with Applications,2010,37(6):4687-4697
[101] Liu F B. A modified genetic algorithm for solving the inverse heat transfer problem ofestimating plan heat source[J]. International Journal of Heat and Mass Transfer,2008,51(15-16):3745-3752
[102] Marseguerra M, Giacobbo F, Zio E. Solving the inverse problem of parameter estimationby genetic algorithms: the case of a groundwater contaminant transport model[J]. Annals ofNuclear Energy,2002,29(8):967-981
[103] Kirkpatrick S, Gelatt C D, Vecchi M P. Optimization by simulated annealing[J]. Science,1983,220(4598):671-680
[104] Lundy M, Mees A. Convergence of an annealing algorithm[J]. Mathematical programming,1986,34(1):111-124
[105] Guo J, Zheng L. A modified simulated annealing algorithm for estimating solute transportparameters in streams from tracer experiment data[J]. Environmental Modelling&Software,2005,20(6):811-815
[106] Metropolis N, Rosenbluth A W, Rosenbluth M N, et al. Equation of state calculations byfast computing machines[J]. The Journal of Chemical Physics,1953,21(6):1087-1092
[107] Kennedy J, Eberhart R. Particle swarm optimization[C] Neural Networks, Proceedings.,IEEE International Conference on. IEEE,1995:1942-1948
[108] Ai J, Hu S H, Qin S, et al. Determination of trace rare earth elements, Sc, and Y in groundwater by inductively coupled plasma mass spectrometry with preconcerntration method[J].Chinese Journal of Analytical Chemistry,2002,30(10):1226-1230
[109] Nashed-Samuel Y, Chen G X, Fujimori K, et al. Detection of adulteration in acetonitrile[J].Spectrochimica Acta Part a-Molecular and Biomolecular Spectroscopy,2011,78(5):1646-1650
[110] Leahy-Hoppa M R, Fitch M J, Osiander R. Terahertz spectroscopy techniques forexplosives detection[J]. Analytical and Bioanalytical Chemistry,2009,395(2):247-257
[111] Hooke R, Jeeves T.―Direct Search‖Solution of Numerical and Statistical Problems[J].Journal of the ACM (JACM),1961,8(2):212-229
[112] Lewis R M, Torczon V, Trosset M W. Why pattern search works[J]. Optima,1998,59:1-7
[113] Torczon V. On the convergence of pattern search algorithms[J]. Siam Journal onOptimization,1997,7(1):1-25
[114] Polak E. Computational methods in optimization: A unified approach[M]. New York:Academic Press,1971
[115] Torczon V. On the convergence of the multidirectional search algorithm[J]. Siam Journalon Optimization,1991,1(1):123-145
[116] Rudd A, Robins A, Lepley J, et al. An inverse method for determining sourcecharacteristics for emergency response applications [J]. Boundary-Layer Meteorology,2012:1-20
[117] Bastani A, Haghighat F, Kozinski J A. Contaminant source identification within a building:Toward design of immune buildings [J]. Building and Environment,2012,51:320-329
[118] Sharan M, Singh S K, Issartel J. Least square data assimilation for identification of thepoint source emissions [J]. Pure and Applied Geophysics,2012,169(3):483-497
[119] Sharan M, Yadav A K, Singh M, et al. A mathematical model for the dispersion of airpollutants in low wind conditions [J]. Atmospheric Environment,1996,30(8):1209-1220
[120] Annunzio A J, Young G S, Haupt S E. Utilizing state estimation to determine the sourcelocation for a contaminant [J]. Atmospheric Environment,2012,46:580-589
[121] Zhang T T, Li H, Wang S. Inversely tracking indoor airborne particles to locate theirrelease sources [J]. Atmospheric Environment,2012,55:328-338
[122] Addepalli B, Sikorski K, Pardyjak E, et al. Source characterization of atmospheric releasesusing stochastic search and regularized gradient optimization [J]. Inverse Problems inScience and Engineering,2011,19(8):1097-1124
[123] Cervone G, Franzese P. Non-Darwinian evolution for the source detection of atmosphericreleases [J]. Atmospheric Environment,2011,45(26):4497-4506
[124] Rodriguez L M, Ellen Haupt S, Young G S. Impact of sensor characteristics on sourcecharacterization for dispersion modeling [J]. Measurement,2011,44(5):802-814
[125] Yee E, Flesch T K. Inference of emission rates from multiple sources using Bayesianprobability theory [J]. Journal of Environmental Monitoring,2010,12(3):622-634
[126] Liu X, Zhai Z J. Protecting a whole building from critical indoor contamination withoptimal sensor network design and source identification methods [J]. Building andEnvironment,2009,44(11):2276-2283
[127] Annunzio A J, Haupt S E, Young G S (2008) Source Characterization and MeteorologyRetrieval Including Atmospheric Boundary Layer Depth Using a Genetic Algorithm[C]15th Joint Conference on the Applications of Air Pollution Meteorology with theA&WMA, New Orleans, LA,2008
[128] Haupt S E, Haupt R L, Young G S. Using genetic algorithms in chem-bio defenseapplications[C] Bio-inspired, Learning, and Intelligent Systems for Security,2007. BLISS2007. ECSIS Symposium on. IEEE,2007:151-154
[129] Haupt S E, Young G S, Allen C T. A genetic algorithm method to assimilate sensor datafor a toxic contaminant release [J]. Journal of Computers,2007,2(6):85-93
[130] Long K J, Haupt S E, Young G S, et al. Characterizing contaminant source andmeteorological forcing using data assimilation with a genetic algorithm[C]86th AmericanMeteorological Society Annual Meeting,2006
[131] Haupt R L, Haupt S E. Practical genetic algorithms[M].Second edition. New Jersey: JohnWiley&Sons, Inc.,2004
[132]张建文,王煜薇,郑小平,等.基于混合遗传-Nelder Mead单纯形算法的源强及位置反算[J].系统工程理论与实践,2011,31(8):1581-1587
[133] Nelder J A, Mead R. A simplex method for function minimization[J]. The ComputerJournal,1965,7(4):308-313
[134]陆璇.数理统计基础[M].北京:清华大学出版社,1998
[135] Brooks S. Markov chain Monte Carlo method and its application[J]. Journal of the RoyalStatistical Society: Series D (The Statistician),1998,47(1):69-100
[136]李贤平.概率论基础[M].北京:高等教育出版社,1997
[137] Goyal A, Mitchell J, von Stackelberg K, et al. Estimation of fugitive lead emission ratesfrom secondary lead facilities using hierarchical Bayesian models[J]. EnvironmentalScience&Technology,2005,39(13):4929-4937
[138] Gilks W R, Richardson S, Spiegelhalter D J. Markov chain Monte Carlo in practice[M].UK: Chapman&Hall,1996
[139] Hastings W K. Monte Carlo sampling methods using Markov chains and theirapplications[J]. Biometrika,1970,57(1):97-109
[140] Smith A F M, Roberts G O. Bayesian computation via the Gibbs sampler and relatedMarkov chain Monte Carlo methods[J]. Journal of the Royal Statistical Society. Series B(Methodological),1993,55(1):3-23
[141] German S, German D. Stochastic relaxation, Gibbs distribution, and the Bayesianrestoration of images[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,1984, PAMI-6(6):721-741
[142] Hall D, Waters R. Wind tunnel model comparisons with the Thorney Island dense gasrelease field trials[J]. Journal of Hazardous Materials,1985,11:209-235
[143] Knudsen S, Krogstad P. A wind tunnel simulation of the Thorney Island Phase II Trial20[J]. Journal of Hazardous Materials,1987,16:173-183
[144] van Heugten W, Duijm N. Some findings based on wind tunnel simulation and modelcalculations of Thorney Island trial No.008[J]. Journal of Hazardous Materials,1985,11:409-416
[145] Sklavounos S, Rigas F. Validation of turbulence models in heavy gas dispersion overobstacles[J]. Journal of Hazardous Materials,2004,108(1-2):9-20
[146] Tauseef S M, Rashtchian D, Abbasi S. CFD-based simulation of dense gas dispersion inpresence of obstacles[J]. Journal of Loss Prevention in the Process Industries,2011,24(4):371-376
[147] Chang J, Hanna S. Air quality model performance evaluation[J]. Meteorology andAtmospheric Physics,2004,87(1):167-196
[148] Puttock J, Colenbrander G. Thorney Island data and dispersion modelling[J]. Journal ofHazardous Materials,1985,11:381-397
[149] McQuaid J. Design of the Thorney Island continuous release trials[J]. Journal of HazardousMaterials,1987,16:1-8
[150] Pfenning D, Cornwell J. Computerized processing of Thorney Island trial data forcomparison with model predictions[J]. Journal of Hazardous Materials,1985,11:347-368
[151] Brighton P, Prince A. Overall properties of the heavy gas clouds in the Thorney IslandPhase II Trials[J]. Journal of Hazardous Materials,1987,16:103-138