[1]

Ministry of Emergency Management of the People’s Republic of China. 2018. Hazardous Chemicals Classification Information Sheet (2015 Edition). www.mem.gov.cn/fw/cxfw/201804/P020190328517303531736.pdf

[2]

Hou J, Gai W, Cheng W, Deng Y. 2021. Hazardous chemical leakage accidents and emergency evacuation response from 2009 to 2018 in China: A review. Safety Science 135:105101

doi: 10.1016/j.ssci.2020.105101
[3]

Cao J, Liang W, Zhang L, Shi S, Feng L, et al. 2013. Statistic analysis and suggestions on pollution accidents of dangerous chemicals. Environmental Science & Technology 36:428−31

[4]

Ministry of Emergency Management of the People’s Republic of China. Warning Information.www.mem.gov.cn/fw/jsxx/

[5]

Sun L, Zhao Y, Cao F, Ye M. 2011. Comparison and analysis on the research situation of release and dispersion models of hazardous chemicals at home and abroad. China Safety Science Journal 21:37−42

doi: 10.16265/j.cnki.issn1003-3033.2011.01.021
[6]

Pan X, Jiang J. 2001. Simulation analysis of diffusion process of hazardous substance leakage. China Occupational Safety and Health Management System Certification 2001:44−46

[7]

Li C. 2019. Study on hazardous liquefied gas release and dispersion in urban environments. Thesis. Tianjin University, China

[8]

Zhu Y, Liu X. 2010. A review of numerical simulation studies on the dispersion of hazardous chemical leaks. Fire Protection Technology and Product Information 12:35−37

[9]

Lushi E, Stockie JM. 2010. An inverse Gaussian plume approach for estimating atmospheric pollutant emissions from multiple point sources. Atmospheric Environment 44:1097−107

doi: 10.1016/j.atmosenv.2009.11.039
[10]

Ma D, Tan W, Wang Q, Zhang Z, Gao J, et al. 2018. Application and improvement of swarm intelligence optimization algorithm in gas emission source identification in atmosphere. Journal of Loss Prevention in the Process Industries 56:262−71

doi: 10.1016/j.jlp.2018.09.008
[11]

Qiu S, Chen B, Wang R, Zhu Z, Wang Y, et al. 2018. Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization. Atmospheric Environment 178:158−63

doi: 10.1016/j.atmosenv.2018.01.056
[12]

Yee E. 2008. Theory for reconstruction of an unknown number of contaminant sources using probabilistic inference. Boundary-Layer Meteorology 127:359−94

doi: 10.1007/s10546-008-9270-5
[13]

Keats A, Yee E, Lien FS. 2007. Bayesian inference for source determination with applications to a complex urban environment. Atmospheric Environment 41:465−79

doi: 10.1016/j.atmosenv.2006.08.044
[14]

Iovino P, Stefano S, Sante C. 2008. Identification of stationary sources of air pollutants by concentration statistical analysis. Chemosphere 73:614−18

doi: 10.1016/j.chemosphere.2008.05.047
[15]

Duijm NJ, Carissimo B, Mercer A, Bartholome C, Giesbrecht H. 1997. Development and test of an evaluation protocol for heavy gas dispersion models. Journal of Hazardous Materials 56:273−85

doi: 10.1016/S0304-3894(97)00069-1
[16]

Yi G, Yang C, Ma L, Wei L, Wu Z. 2008. Research and implementation of treatment system for leakage and diffusion of hazardous material based on GIS. Journal of Safety Science and Technology 4:70−73

[17]

Sun Z, Zheng H, Zhang S. 2011. Classification of heavy gas diffusion models and comparison of industrial application models. China Population, Resources and Environment 21:441−44

[18]

Shi X. 2008. Analysis on the harmfulness of large amounts of toxic heavy gas leakage proliferation. The 10th Annual Meeting of China Association for Science and Technology, Henan, 2008. pp.1391–98

[19]

Huang Q. 2011. Classification of heavy gas diffusion models and comparison of industrial application models. Thesis. Nanjing University of Technology, China

[20]

Ding X, Wang S, Xu G. 1999. A review of studies on the discharging dispersion of flammable and toxic gases. Chemical Industry and Engineering 16:118−22

[21]

Krogstad PA, Pettersen RM. 1967. Windtunnel modelling of a release of a heavy gas near a building 20: 867–78

[22]

Liu G. 2001. Experimental simulation study of heavy gas plume dispersion. Thesis. Beijing University Beijing, China

[23]

Li C. 2019. Study on hazardous liquefied gas release and dispersion in urban environments. Thesis.Tianjin University, China

[24]

Xin B, Dang W, Yan X, Yu J, Bai Y. 2021. Dispersion characteristics and hazard area prediction of mixed natural gas based on wind tunnel tests and risk theory. Process Safety and Environmental Protection 152:278−90

doi: 10.1016/j.psep.2021.06.012
[25]

Jiang C, Ding H, Liu G, Du K, Xuan J, et al. 2003. Comparison and analysis of wind tunnel tests data and dispersion model prediction for accidental continuous release of dense gases. China Safety Science Journal 2003:9−14

doi: 10.16265/j.cnki.issn1003-3033.2003.02.003
[26]

Yuan D, Li S, Huang Y, Wu M. 2013. Research progress on diffusion model of petrochemical industry gas leakage. Journal of Chemical Industry & Engineering 34:21−26

[27]

Crower D, Louvar J, Jiang J, Pan X. (Eds. ) 2006. Chemical process safety theory and application. Beijing: Chemical Industry Press

[28]

He H, Peng Y, Ling M. 2015. Exploring the influence of terrain relief on toxic gas dispersion based on Gaussian model. Geospatial Information 13:152−54

[29]

Liu C, Zhou R, Su T, Jiang J. 2022. Gas diffusion model based on an improved Gaussian plume model for inverse calculations of the source strength. Journal of Loss Prevention in the Process Industries 75:104677

doi: 10.1016/j.jlp.2021.104677
[30]

Lee B, Cho S, Lee SK, Woo C, Park J. 2019. Development of a Smoke Dispersion Forecast System for Korean Forest Fires. Forests 10:219

doi: 10.3390/f10030219
[31]

He N. 2009. Application of poisonous gas diffusion models to rescue of chemical accidents. Journal of Natural Disasters 18:197−200

[32]

Pasquill F. 1961. The Estimation of the Dispersion of Windborne Material. Australian Meteorological Magazine 90:33−49

[33]

Luo J. 2012. The research method on industrial park and pollutants scenario simulation implementation — Taking Huaqiao industrial park of Qingyuan city in Guangzhou province as an example. Thesis. Guangzhou University, China

[34]

Hanna S, Chang J, Strimaitis D. 1993. Hazardous gas model evaluation with field observations. Atmospheric Environment. Part A. General Topics 27:2265−85

doi: 10.1016/0960-1686(93)90397-H
[35]

Ulden A. 1974. On the spreading of a heavy gas released near the ground. Proc. 1st International Loss Prevention Symposium, Hague, 1974. pp. 221−26

[36]

Jiang J, Pan X. 2002. New model for heavy gas releasing dispersion analysis. Journal of Nanjing Tech University (Natural Science Edition) 24:41−46

[37]

Mannan D. (Eds.). 2012. Lee' loss prevention in the process industries. Oxford: Elsevier. pp. 924−31

[38]

Xia Y. 2016. Simulation study on leakage dispersion in the parking lot under the CNG bus overpass. Thesis. Capital University of Economics and Business, China

[39]

Hu S, Zhang Z, Wei L, Wu Z. 2000. Mathematical simulation of heavy gas dispersion for accidental release of hazardous substances. Labor protection science and technology 20:34−38

[40]

Ermak D. 1997. User's manual for SLAB: An atmospheric dispersion model for denser-than-air releases. Lawrence Livermore National Laboratory

[41]

Zhu H, Chen H, Wang Q, Sun J. 2014. Simulation research on leakage and dispersion of muli-source heavy gas. Journal of the University of Science and Technology of China 44:697−703

doi: 10.3969/j.issn.0253-2778.2014.08.010
[42]

Chen H, Dong Y, Liu Y, Zhao Y, Gu Z. 2014. Preliminarily study on applicability of UF6 leakage accident assessment by Shallow model. Radiation Protection 34:343−48

[43]

Stohl A, Forster C, Frank A, Seibert P, Wotawa G. 2005. Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2. Atmospheric Chemistry and Physics 5:2461−74

doi: 10.5194/acp-5-2461-2005
[44]

An X, Yao B, Li Y, Zhou L, Liu Z, et al. 2014. Estimating emission of SF6 in China by atmospheric observation data and inverse modeling. Journal of Environmental Science 34:1133−40

doi: 10.13671/j.hjkxxb.2014.0178
[45]

Wu B, Wang K, Jia L, Guo R. 2017. Influences of source term on long-range transport of radionuclides from the fukushima daiichi nuclear accident with FLEXPART model. Climatic and Environmental Research 22:10−22

[46]

Ermak D, Rodean H, Lange R, Chan S. 1988. A survey of denser-than-air atmospheric dispersion models. Lawrence Livermore National Laboratory

[47]

Liu T, 2020. Research on spatio-temporal data visualization of light gas diffusion model. Thesis. North China University of Science and Technology, China

[48]

Li X, Zhou R, Konovessis D. 2016. CFD analysis of natural gas dispersion in engine room space based on multi-factor coupling. Ocean Engineering 111:524−32

doi: 10.1016/j.oceaneng.2015.11.018
[49]

Luo T, Yu C, Liu R, Li M, Zhang J, et al. 2018. Numerical simulation of LNG release and dispersion using a multiphase CFD model. Journal of Loss Prevention in the Process Industries 56:316−27

doi: 10.1016/j.jlp.2018.08.001
[50]

Dasgotra A, Varun Teja GVV, Sharma A, Mishra KB. 2018. CFD modeling of large-scale flammable cloud dispersion using FLACS. Journal of Loss Prevention in the Process Industries 56:531−36

doi: 10.1016/j.jlp.2018.01.001
[51]

Cen K, Yao T, Wang Q, Xiong S. 2018. A risk-based methodology for the optimal placement of hazardous gas detectors. Chinese Journal of Chemical Engineering 26:1078−86

doi: 10.1016/j.cjche.2017.10.031
[52]

Both AL, Atanga G, Hisken H. 2019. CFD modelling of gas explosions: Optimising sub-grid model parameters. Journal of Loss Prevention in the Process Industries 60:159−73

doi: 10.1016/j.jlp.2019.04.008
[53]

Gant S, Weil J, Monache LD, McKenna B, Garcia MM, et al. 2018. Dense gas dispersion model development and testing for the Jack Rabbit II phase 1 chlorine release experiments. Atmospheric Environment 192:218−40

doi: 10.1016/j.atmosenv.2018.08.009
[54]

Min DS, Choi S, Oh YE, Lee J, Lee C, et al. 2020. Numerical modelling for effect of water curtain in mitigating toxic gas release. Journal of Loss Prevention in the Process Industries 63:103972

doi: 10.1016/j.jlp.2019.103972
[55]

Lim H, Um K, Jung S. 2017. A study on effective mitigation system for accidental toxic gas releases. Journal of Loss Prevention in the Process Industries 49:636−44

doi: 10.1016/j.jlp.2017.05.017
[56]

Yang S, Jeon K, Kang D, Han C. 2017. Accident analysis of the Gumi hydrogen fluoride gas leak using CFD and comparison with post-accidental environmental impacts. Journal of Loss Prevention in the Process Industries 48:207−15

doi: 10.1016/j.jlp.2017.05.001
[57]

Chan ST, Ermak DT, Morris LK. 1987. FEM3 model simulations of selected thorney island phase I trials. Journal of Hazardous Materials 16:267−92

doi: 10.1016/0304-3894(87)80038-9
[58]

Ermak DL, Chan ST, Morgan DL, Morris LK. 1982. A comparison of dense gas dispersion model simulations with burro series LNG spill test results. Journal of Hazardous Materials 6:129−60

doi: 10.1016/0304-3894(82)80037-X
[59]

Bellegoni M, Chicchiero C, Landucci G, Galletti C, Salvetti MV. 2022. A UQ based calibration for the CFD modeling of the gas dispersion from an LNG pool. Process Safety and Environmental Protection 162:1043−56

doi: 10.1016/j.psep.2022.04.073
[60]

Liu W, Zhang J, Lv M, Lv S, Yao R. 2022. Numerical wind tunnel simulation for atmospheric dispersion of pollutants in urban areas with Urban2003. Environmental Science and Management 47:78−83

[61]

Dong L, Yu Y, Zuo H, Fang M, Zhao S, et al. 2019. Elaborated simulation of urban atmospheric dispersion using an established WRF-Fluent coupling model. China Environmental Science 39:2311−19

[62]

Sklavounos S, Rigas F. 2012. Advanced multi-perspective computer simulation as a tool for reliable consequence analysis. Process Safety and Environmental Protection 90:129−40

doi: 10.1016/j.psep.2011.06.008
[63]

Vendel F. 2011. Modélisation de la dispersion atmosphérique en présence d’obstacles complexes: application à l’étude de sites industriels. Thesis. Ecole Centrale de Lyon, France

[64]

Shi J. 2019. Research on prediction technology of radioactive pollution atmospheric diffusion based on global 3D grid. Thesis. Academy of Military Sciences, China.

[65]

Cao H, Fan T, Li S. 2016. CA-based dynamic risk assessment of toxic gas leakage accidents. Systems Engineering - Theory & Practice 36:253−62

[66]

Yang Q, Li X, Shi X. 2008. Cellular automata for simulating land use changes based on support vector machines. Computers & Geosciences 34:592−602

doi: 10.1016/j.cageo.2007.08.003
[67]

Li X, Lao C, Liu Y, Liu X, Chen Y, et al. 2013. Early warning of illegal development for protected areas by integrating cellular automata with neural networks. Journal of Environmental Management 130:106−16

doi: 10.1016/j.jenvman.2013.08.055
[68]

Li X, Lao C, Liu Y, Liu X, Chen Y, et al. 2002. Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Systems 16:323−43

doi: 10.1080/13658810210137004
[69]

Ghosh S, Bhattacharya S. 2020. A data-driven understanding of COVID-19 dynamics using sequential genetic algorithm based probabilistic cellular automata. Applied Soft Computing 96:106692

doi: 10.1016/j.asoc.2020.106692
[70]

Biswas K, Vasant PM, Gamez Vintaned JA, Watada J. 2021. Cellular automata-based multi-objective hybrid grey wolf optimization and particle swarm optimization algorithm for wellbore trajectory optimization. Journal of Natural gas Science and Engineering 85:103695

doi: 10.1016/j.jngse.2020.103695
[71]

Ke W. 2017. The simulation system of hazardous chemical gas diffusion in plant based on cellular automata. Chemical Engineering Transactions 59:661−66

[72]

Almeida CM, Gleriani JM, Castejon EF, Soares-Filho BS. 2008. Using neural networks and cellular automata for modelling intra-urban land-use dynamics. International Journal of Geographical Information Science 22:943−63

doi: 10.1080/13658810701731168
[73]

Lauret P, Heymes F, Aprin L, Johannet A. 2016. Atmospheric dispersion modeling using artificial neural network based cellular automata. Environmental Modelling & Software 85:56−69

doi: 10.1016/j.envsoft.2016.08.001
[74]

Yu HL, Chang TJ. 2022. Modeling particulate matter concentration in indoor environment with cellular automata framework. Building and Environment 214:108898

doi: 10.1016/j.buildenv.2022.108898
[75]

Wolfram S. 1984. Cellular automata as models of complexity. Nature 311:419−24

doi: 10.1038/311419a0
[76]

So W, Koo J, Shin D, Yoon ES. 2010. The estimation of hazardous gas release rate using optical sensor and neural network. Computer Aided Chemical Engineering 28:199−204

doi: 10.1016/S1570-7946(10)28034-3
[77]

Wang B, Chen B, Zhao J. 2015. The real-time estimation of hazardous gas dispersion by the integration of gas detectors, neural network and gas dispersion models. Journal of Hazardous Materials 300:433−42

doi: 10.1016/j.jhazmat.2015.07.028
[78]

Jiao Z, Ji C, Sun Y, Hong Y, Wang Q. 2021. Deep learning based quantitative property-consequence relationship (QPCR) models for toxic dispersion prediction. Process Safety and Environmental Protection 152:352−60

doi: 10.1016/j.psep.2021.06.019
[79]

Ma D, Zhang Z. 2016. Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere. Journal of Hazardous Materials 311:237−45

doi: 10.1016/j.jhazmat.2016.03.022
[80]

Wang R, Chen B, Qiu S, Zhu Z, Wang Y, et al. 2018. Comparison of Machine Learning Models for Hazardous gas Dispersion Prediction in Field Cases. International Journal of Environmental Research and Public Health 15:1450

doi: 10.3390/ijerph15071450
[81]

Qian F, Chen L, Li J, Ding C, Chen X, et al. 2019. Direct prediction of the toxic gas diffusion rule in a real environment based on LSTM. International Journal of Environmental Research and Public Health 16:2133

doi: 10.3390/ijerph16122133
[82]

Ni J, Yang H, Yao J, Li Z, Qin P. 2020. Toxic gas dispersion prediction for point source emission using deep learning method. Human and Ecological Risk Assessment:An International Journal 26:557−70

doi: 10.1080/10807039.2018.1526632
[83]

Sun W. 2021. Research on smoke detection technology based on video image. Thessis. Beijing University of Posts and Telecommunications, China

[84]

Shi J, Wang W, Gao Y, Yu N. 2020. Optimal placement and intelligent smoke detection algorithm for wildfire-monitoring cameras. IEEE Access 8:72326−39

doi: 10.1109/ACCESS.2020.2987991
[85]

Li C, Yang B, Ding H, Shi H, Jiang X, Sun Ji. 2020. Real-time video-based smoke detection with high accuracy and efficiency. Fire Safety Journal 117:103184

doi: 10.1016/j.firesaf.2020.103184
[86]

Emmy Prema C, Vinslsy SS, Suresh S. 2016. Multi feature analysis of smoke in YUV color space for early forest fire detection. Fire Technology 52:1319−42

doi: 10.1007/s10694-016-0580-8
[87]

Yu C, Mei Z, Zhang X. 2013. A real-time video fire flame and smoke detection algorithm. Procedia Engineering 62:891−98

doi: 10.1016/j.proeng.2013.08.140
[88]

Töreyin BU, Dedeoğlu Y, Çetin AE. 2005. Wavelet based real-time smoke detection in video. 13th European Signal Processing Conference, Antalya, Türkiye, 2005, 1–4. USA: IEEE https://ieeexplore.ieee.org/document/7077943

[89]

Alamgir N, Nguyen K, Chandran V, Boles W. 2018. Combining multi-channel color space with local binary co-occurrence feature descriptors for accurate smoke detection from surveillance videos. Fire Safety Journal 102:1−10

doi: 10.1016/j.firesaf.2018.09.003
[90]

Peng Y, Wang Y. 2019. Real-time forest smoke detection using hand-designed features and deep learning. Computers and Electronics in Agriculture 167:105029

doi: 10.1016/j.compag.2019.105029
[91]

Yuan F. 2008. A fast accumulative motion orientation model based on integral image for video smoke detection. Pattern Recognition Letters 29:925−32

doi: 10.1016/j.patrec.2008.01.013
[92]

Lee CY, Lin CT, Hong CT, Su MT. 2012. Smoke detection using spatial and temporal analyses. International Journal of Innovative Computing, Information and Control 66:4749−70

[93]

Tian H, Li W, Ogunbona P, Nguyen DT, Zhan C. 2011. Smoke detection in videos using Non-Redundant Local Binary Pattern-based features. IEEE 13th International Workshop on Multimedia Signal Processing, Hangzhou, China, 2011, pp. 1–4. USA: IEEE http://doi.org/10.1109/MMSP.2011.6093844

[94]

Yuan F. 2012. A double mapping framework for extraction of shape-invariant features based on multi-scale partitions with AdaBoost for video smoke detection. Pattern Recognition 45:4326−36

doi: 10.1016/j.patcog.2012.06.008
[95]

Hu Y, Lu X. 2018. Real-time video fire smoke detection by utilizing spatial-temporal ConvNet features. Multimedia Tools and Applications 77:29283−301

doi: 10.1007/s11042-018-5978-5
[96]

Wang L, Li A. 2017. Early Fire Recognition Based on Multi-Feature Fusion of Video Smoke. IEEE 2017 36th Chinese Control Conference, Dalian, China, 2017, pp. 5318–23. USA: IEEE http://doi.org/10.23919/ChiCC.2017.8028197

[97]

Feng L, Wang H, Wang K, Lu Y, Wang J. 2020. Convolutional neural network fire smoke detection based on target region. Laser & Optoelectronics Progress 57:83−91

doi: 10.3788/lop57.161004
[98]

Yu C, Fang J, Wang J, Zhang Y. 2010. Video Fire Smoke Detection Using Motion and Color Features. Fire Technology 46:651−63

doi: 10.1007/s10694-009-0110-z
[99]

Wang W, Xu Q, Han Z. 2017. Fire and smoke detection based on the optimal mass transmission optical flow method and neural network. Journal of Harbin University of Science and Technology 22:86−90

doi: 10.15938/j.jhust.2017.01.015
[100]

Yang L, Yuan F, Yang S, Lei B, Zhang X. 2019. Continuous graph convolutional model for video smoke detection. Journal of Image and Graphics 2019:1658−69

doi: 10.11834/jig.190232
[101]

Ko B, Park J, Nam JY. 2013. Spatiotemporal bag-of-features for early wildfire smoke detection. Image and Vision Computing 31:786−95

doi: 10.1016/j.imavis.2013.08.001
[102]

Ye W, Zhao J, Wang S, Wang Y, Zhang D, et al. 2015. Dynamic texture based smoke detection using Surfacelet transform and HMT model. Fire Safety Journal 73:91−101

doi: 10.1016/j.firesaf.2015.03.001
[103]

Yuan F, Zhang Y, Liu S, Yu C, Shen S. 2008. Video smoke detection based on accumulation and main motion orientation. Journal of Image and Graphics 13:808−13

[104]

Kim H, Ryu D, Park J. 2014. Smoke Detection Using GMM and Adaboost. International Journal of Computer and Communication Engineering 3:123−26

doi: 10.7763/IJCCE.2014.V3.305
[105]

Yuan F, Fang Z, Wu S, Yang Y, Fang Y. 2015. Real-time image smoke detection using staircase searching-based dual threshold AdaBoost and dynamic analysis. IET Image Processing Iet 9:849−56

doi: 10.1049/iet-ipr.2014.1032
[106]

Chen R, Luo L, Cai Z, Ma W. 2021. Algorithm for real-time smoking detection based on deep learning. Journal of Frontiers of Computer Science and Technology 15:327−37

doi: 10.3778/j.issn.1673-9418.2009069
[107]

Yuan F, Zhao X, Wang Y, Zhao Z. 2020. Smoke recognition algorithm based on lightweight convolutional neural network. Journal of Southwest Jiaotong University 55:1111−16

[108]

Lin G. 2018. Studies on dynamic texture and convolutional neural networks based smoke detection in video sequences. Thesis. University of Science and Technology of China, China.

[109]

Yang C. 2020. Research and development of smoke video detection system based on deep learning. Thesis. Nanjing University of Posts and Telecommunications, China.

[110]

Chang RH, Peng YT, Choi S, Cai C. 2022. Applying Artificial Intelligence (AI) to improve fire response activities. Emergency Management Science and Technology 2:7

doi: 10.48130/emst-2022-0007
[111]

Ji C, Jiao Z, Yuan S, El-Halwagi MM, Wang Q. 2021. Development of novel combustion risk index for flammable liquids based on unsupervised clustering algorithms. Journal of Loss Prevention in the Process Industries70.104422

doi: 10.1016/j.jlp.2021.104422
[112]

I Y, Fu J. 2021. Risk analysis of a cross-regional toxic chemical disaster by using the integrated mesoscale and microscale consequence analysis model. Journal of Loss Prevention in the Process Industries 71:104424

doi: 10.1016/j.jlp.2021.104424
[113]

Liang T, Chen G, Zhang R, Yan W, Chen Q. 2006. Application Research of SAFETI on the Fatal Accident Consequence Assessment in LPG Storage Tanks. Oil & gas Storage and Transportation 25:53−58

[114]

Gerbec M, Pontiggia M, Antonioni G, Tugnoli A, Cozzani V, et al. 2017. Comparison of UDM and CFD simulations of a time varying release of LPG in geometrical complex environment. Journal of Loss Prevention in the Process Industries 45:56−68

doi: 10.1016/j.jlp.2016.11.020
[115]

Witlox HWM, Fernandez M, Harper M, Oke A, Stene J, et al. 2018. Verification and validation of Phast consequence models for accidental releases of toxic or flammable chemicals to the atmosphere. Journal of Loss Prevention in the Process Industries 55:457−70

doi: 10.1016/j.jlp.2018.07.014
[116]

Pandya N, Gabas N, Marsden E. 2012. Sensitivity analysis of Phast’s atmospheric dispersion model for three toxic materials (nitric oxide, ammonia, chlorine). Journal of Loss Prevention in the Process Industries 25:20−32

doi: 10.1016/j.jlp.2011.06.015
[117]

Shi T. 2010. Evaluation flame gas dispersion and its heat radiation using PHAST software. Safety Health & Environment 10:35−38

[118]

Holborn PG, Benson CM, Ingram JM. 2020. Modelling hazardous distances for large-scale liquid hydrogen pool releases. International Journal of Hydrogen Energy 45:23851−71

doi: 10.1016/j.ijhydene.2020.06.131
[119]

Hansen OR, Gavelli F, Ichard M, Davis SG. 2010. Validation of FLACS against experimental data sets from the model evaluation database for LNG vapor dispersion. Journal of Loss Prevention in the Process Industries 23:857−77

doi: 10.1016/j.jlp.2010.08.005