Location of contaminant emission source in atmosphere based on optimal correlated matching of concentration distribution

Abstract Source location is crucial to manage contaminant emissions in atmosphere, In order to determine the source location without dependence on the absolute measurement data, a method based on optimal correlated matching of concentration distribution (OCMCD) was proposed. First, the estimation efficiency, accuracy and dependence on source strength of OCMCD were compared with the common method which estimates multiple parameters of the source term simultaneously. The results show that the method of OCMCD performs better than the common multiple parameters estimation method based on the mean errors between prediction and measurement in both estimation accuracy and efficiency. The test results with different sets of source strength manifest that OCMCD relies minimally on the source strength Then, a wind direction correction parameter and a weighted term of normalization concentration error were introduced into the model to compensate some missed information and improve the location results. The influence of data noises on the estimation accuracy of OCMCD method was also verified by adding extra manual noises on the measurement data. Then, the dependence of estimation performance with OCMCD method on atmosphere conditions were investigated statistically with experiment release cases. The results showed that source location was identified well in most of cases. Finally, OCMCD method was extended to determine the source location during the source trace process with a mobile sensor. The test results with a simulation scenario based on Zigzag search strategy demonstrate that the source location determined by OCMCD source criterion is much closer to the real source position than that determined by the criterion of the maximum concentration. Therefore, the results have proven the feasibility and superiority of OCMCD proposed in this paper to estimate source location in cases of both static sensor distribution and mobile sensors. OCMCD will be a potentially useful method to identify emission source location in atmosphere.

[1]  Yu Li,et al.  Sequential multi-nuclide emission rate estimation method based on gamma dose rate measurement for nuclear emergency management. , 2017, Journal of hazardous materials.

[2]  Charles Audet,et al.  Analysis of Generalized Pattern Searches , 2000, SIAM J. Optim..

[3]  Sue Ellen Haupt,et al.  Assessing sensitivity of source term estimation , 2010 .

[4]  Denglong Ma,et al.  Parameter identification for continuous point emission source based on Tikhonov regularization method coupled with particle swarm optimization algorithm. , 2017, Journal of hazardous materials.

[5]  Achim J. Lilienthal,et al.  Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms , 2013, Adv. Robotics.

[6]  Mimi Haryani Hassim,et al.  A hybrid approach for estimating fugitive emission rates in process development and design under incomplete knowledge , 2017 .

[7]  Giuseppe Ricci,et al.  Realtime Gas Emission Monitoring at Hazardous Sites Using a Distributed Point-Source Sensing Infrastructure , 2016, Sensors.

[8]  Lowry A. Harper,et al.  Deducing Ground-to-Air Emissions from Observed Trace Gas Concentrations: A Field Trial with Wind Disturbance , 2005 .

[9]  Sarvesh Kumar Singh,et al.  Inverse modelling methods for identifying unknown releases in emergency scenarios: an overview , 2015 .

[10]  Fue-Sang Lien,et al.  Bayesian inference for source determination with applications to a complex urban environment , 2007 .

[11]  Pramod Kumar,et al.  CFD simulation of short-range plume dispersion from a point release in an urban like environment , 2015 .

[12]  J. Hodgkinson,et al.  Detection of a simulated gas leak in a wind tunnel , 2006 .

[13]  Marc J. Weissburg,et al.  Role of olfactory appendages in chemically mediated orientation of blue crabs , 2003 .

[14]  Tov Elperin,et al.  Effect of raindrop size distribution on scavenging of aerosol particles from Gaussian air pollution plumes and puffs in turbulent atmosphere , 2016 .

[15]  R. Andrew Russell,et al.  Robot Odor Localization: A Taxonomy and Survey , 2008, Int. J. Robotics Res..

[16]  L. Marques,et al.  Olfactory sensory system for odour-plume tracking and localization , 2003, Proceedings of IEEE Sensors 2003 (IEEE Cat. No.03CH37498).

[17]  Branko Ristic,et al.  Evaluation of Bayesian source estimation methods with Prairie Grass observations and Gaussian plume model: A comparison of likelihood functions and distance measures , 2017 .

[18]  Sue Ellen Haupt,et al.  A demonstration of coupled receptor/dispersion modeling with a genetic algorithm , 2004 .

[19]  Anna Wawrzynczak,et al.  Application of the Approximate Bayesian Computation methods in the stochastic estimation of atmospheric contamination parameters for mobile sources , 2016 .

[20]  Denglong Ma,et al.  Gas emission source term estimation with 1-step nonlinear partial swarm optimization-Tikhonov regularization hybrid method , 2017 .

[21]  Marc Bocquet.  Reconstruction of an atmospheric tracer source using the principle of maximum entropy. II: Applications , 2005 .

[22]  Zaoxiao Zhang,et al.  CO2 Leakage Identification in Geosequestration Based on Real Time Correlation Analysis Between Atmospheric O2 and CO2 , 2014 .

[23]  R. Andrew Russell,et al.  Tracking Chemical Plumes in 3-Dimensions , 2006, 2006 IEEE International Conference on Robotics and Biomimetics.

[24]  Fei Xue,et al.  Bayesian identification of a single tracer source in an urban-like environment using a deterministic approach , 2017 .

[25]  Denglong Ma,et al.  Hybrid algorithm of minimum relative entropy-particle swarm optimization with adjustment parameters for gas source term identification in atmosphere , 2014 .

[26]  Ting Yao,et al.  A risk-based methodology for the optimal placement of hazardous gas detectors , 2017 .

[27]  Maithili Sharan,et al.  An inversion technique for the retrieval of single-point emissions from atmospheric concentration measurements , 2009, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[28]  Chang-Fu Wu,et al.  Large-scale search method for locating and identifying fugitive emission sources in petrochemical processing areas , 2016 .

[29]  F. Heymes,et al.  Forecasting powder dispersion in a complex environment using Artificial Neural Networks , 2017 .

[30]  R. Andrew Russell,et al.  A comparison of reactive robot chemotaxis algorithms , 2003, Robotics Auton. Syst..

[31]  Pierre Ngae,et al.  An urban scale inverse modelling for retrieving unknown elevated emissions with building-resolving simulations , 2016 .

[32]  Hyondong Oh,et al.  A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors , 2017, Inf. Fusion.

[33]  S. Kabanikhin.  Inverse and Ill-posed Problems: Theory and Applications , 2011 .

[34]  Yang Wang,et al.  Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm , 2011, Auton. Robots.

[35]  Zaoxiao Zhang,et al.  Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere. , 2016, Journal of hazardous materials.

[36]  Nathan Platt,et al.  Comparative investigation of Source Term Estimation algorithms using FUSION field trial 2007 data: linear regression analysis , 2012 .

[37]  Ji Wang,et al.  Locating hazardous gas leaks in the atmosphere via modified genetic, MCMC and particle swarm optimization algorithms , 2017 .

[38]  Zhihua Qu,et al.  A control-design-based solution to robotic ecology: Autonomy of achieving cooperative behavior from a high-level astronaut command , 2006, Auton. Robots.

[39]  Steven R. Hanna,et al.  Handbook on atmospheric diffusion , 1982 .

[40]  Janusz A. Pudykiewicz,et al.  APPLICATION OF ADJOINT TRACER TRANSPORT EQUATIONS FOR EVALUATING SOURCE PARAMETERS , 1998 .

[41]  Qingsheng Wang,et al.  Consequence analysis of accidental release of supercritical carbon dioxide from high pressure pipelines , 2016 .

[42]  X. L. Zhang,et al.  Iterative ensemble Kalman filter for atmospheric dispersion in nuclear accidents: An application to Kincaid tracer experiment. , 2015, Journal of hazardous materials.

[43]  Zhang Yu,et al.  Approach to identifying pollutant source and matching flow field , 2013 .

[44]  B. Kosović,et al.  Source Inversion for Contaminant Plume Dispersion in Urban Environments Using Building-Resolving Simulations , 2005 .

[45]  Maithili Sharan,et al.  A point-source reconstruction from concentration measurements in low-wind stable conditions , 2012 .

[46]  Zaoxiao Zhang,et al.  Comparison and improvements of optimization methods for gas emission source identification , 2013 .

[47]  Jing Gu,et al.  An artificial moth: Chemical source localization using a robot based neuronal model of moth optomotor anemotactic search , 2006, Auton. Robots.