An optimized inverse modelling method for determining the location and strength of a point source releasing airborne material in urban environment

Efthimiou, George C.; Kovalets, Ivan V.; Venetsanos, A.G.; Andronopoulos, S.; Argyropoulos, Christos D.; Kakosimos, Konstantinos

doi:10.1016/j.atmosenv.2017.09.034

Cited by 39 publications

(20 citation statements)

References 33 publications

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“…The model is interchangeable without any other changes to the algorithm and should be chosen to reflect the current scenario. For example, the numerical atmospheric‐dispersion modelling environment (NAME) dispersion model is used by the UK Met Office to forecast long range ash dispersion from a volcanic eruption (Jones, Thomson, Hort, & Devenish, ), whereas CFD‐based methods have been developed for complex geometries, such as urban environments (Efthimiou et al, ; Keats, Yee, & Lien, ). In this study, two models are compared, both derived from analytical solutions to the advection–diffusion equation with various assumptions: The standard GP model (Wang et al, ) and a more simplified model assuming isotropic diffusion (Vergassola et al, ), which shall be referred to as the IP model.…”

Section: Source Term Estimationmentioning

confidence: 99%

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

Hutchinson

Liu

Chen

2018

Journal of Field Robotics

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Gaining information about an unknown gas source is a task of great importance with applications in several areas, including responding to gas leaks or suspicious smells, quantifying sources of emissions, or in an emergency response to an industrial accident or act of terrorism. In this paper, a method to estimate the source term of a gaseous release using measurements of concentration obtained from an unmanned aerial vehicle (UAV) is described. The source term parameters estimated include the three‐dimensional location of the release, its emission rate and other important variables needed to forecast the spread of the gas using an atmospheric transport and dispersion model. The parameters of the source are estimated by fusing concentration observations from a gas detector on‐board the aircraft, with meteorological data and an appropriate model of dispersion. Two models are compared in this paper, both derived from analytical solutions to the advection–diffusion equation. Bayes’ theorem, implemented using a sequential Monte Carlo algorithm, is used to estimate the source parameters to take into account the large uncertainties in the observations and formulated models. The system is verified with novel, outdoor, fully automated experiments, where observations from the UAV are used to estimate the parameters of a diffusive source. The estimation performance of the algorithm is assessed subject to various flight path configurations and wind speeds. Observations and lessons learned during these unique experiments are discussed, and areas for future research are identified.

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Section: Source Term Estimationmentioning

confidence: 99%

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

Hutchinson

Liu

Chen

2018

Journal of Field Robotics

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“…The probabilistic category treats source parameters as random variables associated with the probability distribution. This includes the Bayesian estimation theory (Bocquet, 2005;Monache et al, 2008;Yee et al, 2014), Monte Carlo algorithms using Markov chains (MCMC) (Gamerman and Lopes, 2006;Keats, 2009), and various stochastic sampling algorithms (Zhang et al, 2014(Zhang et al, , 2015. Deterministic methods use cost functions to assess the difference between observed and modeled concentrations and are based on an iterative process to minimize this difference (Seibert, 2001;Penenko et al, 2002;Sharan et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

An optimization for reducing the size of an existing urban-like monitoring network for retrieving an unknown point source emission

et al. 2019

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Abstract. This study presents an optimization methodology for reducing the size of an existing monitoring network of the sensors measuring polluting substances in an urban-like environment in order to estimate an unknown emission source. The methodology is presented by coupling the simulated annealing (SA) algorithm with the renormalization inversion technique and the computational fluid dynamics (CFD) modeling approach. This study presents an application of the renormalization data-assimilation theory for optimally reducing the size of an existing monitoring network in an urban-like environment. The performance of the obtained reduced optimal sensor networks is analyzed by reconstructing the unknown continuous point emission using the concentration measurements from the sensors in that optimized network. This approach is successfully applied and validated with 20 trials of the Mock Urban Setting Test (MUST) tracer field experiment in an urban-like environment. The main results consist of reducing the size of a fixed network of 40 sensors deployed in the MUST experiment. The optimal networks in the MUST urban region are determined, which makes it possible to reduce the size of the original network (40 sensors) to ∼1/3 (13 sensors) and 1∕4 (10 sensors). Using measurements from the reduced optimal networks of 10 and 13 sensors, the averaged location errors are obtained as 19.20 and 17.42 m, respectively, which are comparable to the 14.62 m obtained from the original 40-sensor network. In 80 % of the trials with networks of 10 and 13 sensors, the emission rates are estimated within a factor of 2 of the actual release rates. These are also comparable to the performance of the original network, whereby in 75 % of the trials the releases were estimated within a factor of 2 of the actual emission rates.

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“…However, few have focused on the source reconstruction performance of AQMN in optimal design. Recently, many studies in the literature have explored how to reconstruct source characteristics based on the measurements from a dense AQMN and have analyzed the influence of the AQMN distribution on the back-calculation [17][18][19][20], while only single emission episodes were considered as the concerned objectives in these studies.…”

Section: Introductionmentioning

confidence: 99%

“…Ambient concentration data were replaced by simulated concentrations generated by the dispersion model. Different types of dispersion models such as Gaussian plume model, Gaussian puff model, Lagrangian stochastic model, and computational fluid dynamics [20,[22][23][24][25][26][27][28][29][30][31] can be used for simulating dispersion in pollution detection and source reconstruction. The Gaussian puff model was employed here instead of a refined method to reproduce the spatial and temporal variations of H 2 S concentrations for the studied industrial park because emission sources arranged densely and accurate environmental conditions are always difficult to obtain for a refined simulation using a model.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Air Quality Monitoring Network for Pollution Detection and Source Identification in Industrial Parks

Huang

Liu

et al. 2019

Atmosphere

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Dense air quality monitoring network (AQMN) is one of main ways to surveil industrial air pollution. This paper is concerned with the design of a dense AQMN for H 2 S for a chemical industrial park in Shanghai, China. An indicator (Surveillance Efficiency, SE) for the long-term performance of AQMN was constructed by averaging pollution detection efficiency (r d ) and source identification efficiency (r b ). A ranking method was developed by combing Gaussian puff model and Source area analysis for improving calculation efficiency. Candidate combinations with highest score were given priority in the selection of next site. Two existing monitors were suggested to relocate to the west and southwest of this park. SE of optimized AQMN increased quickly with monitor number, and then the growth trend started to flatten when the number reached about 60. The highest SE occurred when the number reached 110. Optimal schemes of AQMNs were suggested which can achieve about 98% of the highest SE, while using only about 60 monitors. Finally, the reason why the highest SE is less than 1 and the variation characteristics of r d and r b were discussed. Overall, the proposed method is an effective tool for designing AQMN with optimal SE in industrial parks.

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An optimized inverse modelling method for determining the location and strength of a point source releasing airborne material in urban environment

Cited by 39 publications

References 33 publications

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

An optimization for reducing the size of an existing urban-like monitoring network for retrieving an unknown point source emission

Optimal Design of Air Quality Monitoring Network for Pollution Detection and Source Identification in Industrial Parks

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