Application of genetic algorithm for the simultaneous identification of atmospheric pollution sources

Cantelli, Antonio; D'Orta, F.; Cattini, A.; Sebastianelli, F.; Cedola, Luca

doi:10.1016/j.atmosenv.2015.05.030

Cited by 40 publications

(15 citation statements)

References 29 publications

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“…Singh et al (2013) modified the two-step inversion algorithm by introducing weights given by Issartel et al (2007) representing information from the monitoring network. Cantelli et al (2015) developed a genetic algorithm based computational model to modify the search procedure in Sharan, Singh, et al (2012) for the simultaneous estimation of locations and emission rates in steady state conditions. In present study, a criteria-based methodology is proposed to estimate number of point releases in a multiple sources dispersion event.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Unknown Number of Source Terms in Dispersion Events Involving Multiple Point Sources

Singh

Sharan

2019

Earth and Space Science

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In dispersion events involving multiple sources, estimation of correct number of sources is imperative prior to their localization or source term estimation. The study pertains to a dispersion scenario of multiple point releases simultaneously emitting a nonreactive tracer that results into a mixture of concentrations, as measurements, sampled at the receptors. The objective is to estimate correct number of sources along with their release parameters (mainly locations and strengths) using a limited merged set of concentration measurements. A new methodology, based on five criteria coupled with a multiple release identification algorithm, is proposed here. Criterion helps in discriminating the releases and accounts for detection and false‐alarm probability associated with each retrieved source. The number of source terms are estimated based on sum of scores achieved by each predicted source under each criterion. The methodology is evaluated with synthetic, noisy synthetic, and pseudo‐real data prepared from India Institute of Technology Delhi diffusion experiment conducted in 1991 at Delhi, India. With synthetic data, the number of point sources and their parameters are retrieved exactly as their true values. With pseudo‐real data, true number of sources are retrieved. The source locations and strengths are retrieved within 30 m and a factor of 3, respectively. Synthetic simulations with controlled measurement noise shows that the proposed methodology is robust in presence of model uncertainties and can be applicable in real scenarios.

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Section: Introductionmentioning

confidence: 99%

Retrieval of Unknown Number of Source Terms in Dispersion Events Involving Multiple Point Sources

Singh

Sharan

2019

Earth and Space Science

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“…Locating emission sources of an ambient pollution episode is a critical step for air pollution control. Back-calculation of source parameters based on ambient concentration monitoring is one of the popular methods for locating emission sources, and has attracted increasing attention from researchers and environmental protection authorities [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Increased monitoring sites might improve the obtained result. Penenko V et al addressed the data from 10 monitors to obtain the probability distribution density function of radionuclide emission sources [5]; Rude et al indicated that at least 4 monitors on the ground were necessary to evaluate source location and source strength of a constant ground source by means of the inverse framework method [6]; Cantelli et al applied 25 monitors to retrieve 3 different pollution sources using the genetic algorithm inverse model (GAIM) successfully [1].…”

Section: Introductionmentioning

confidence: 99%

“…Back-calculation methods in the literature were investigated to find a proper method for our case study. Many inverse methods have been reported in the literature for the determination of pollution source parameters, such as genetic algorithms [1,8,9], simulated annealing algorithms [2,10], pattern search method [11], least square method [4,12], and probability modeling methods, including Markov chain Monte Carlo [3,7], and Sequential Monte Carlo [13]. These algorithms were used to find an optimal result of unknown parameters by looping over their solution domain, although they were with heavy computational loads.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Key Potential Source Areas for Ambient Methyl Mercaptan Pollution Based on Long-Term Environmental Monitoring Data in an Industrial Park

Liu

Huang

et al. 2018

Atmosphere

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Precise source identification for ambient pollution incidents in industrial parks were often difficult due to limited measurements. Source area analysis method was one of the applicable source identification methods, which could provide potential source areas under these circumstances. However, a source area usually covered several sources and the method was unable to identify the real one. This article introduces a case study on the statistical source identification of methyl mercaptan based on the long-term measurements, in 2014, in an industrial park. A procedure for statistical source area analysis was established, which contains independent pollution episode extraction, source area calculation scenario definition, meteorological data selection, and source area statistical analysis. A total of 414 violation records were detected by five monitors inside the park. Three kinds of calculation scenarios were found and, finally, three key source areas were revealed. The typical scenarios of source area calculations were described in detail. The characteristics of the statistical source areas for all pollution episodes were examined. Finally, the applicability of the method, as well as the source of uncertainties, was discussed. This study shows that more concentrated source areas can be identified through the statistical source area method if several excessive emission sources exist in an industrial park.

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“…Due to problem complexity, studies looking at multiple sources are notable (e.g. Peneko et al, 2002;Allen et al, 2007;Yee, 2008;Lane et al, 2009;Huang et al, 2010;Konda et al, 2010;Annuzio et al, 2012;Sharan et al, 2012;Wade and Senocak, 2013;Cantelli et al, 2015;Singh and Rani, 2015).…”

Section: Multi-source Estimation Problemsmentioning

confidence: 99%

Adjoint-Based Optimization Scheme for Characterizing Sources of Fugitive Emissions

Brereton¹

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Fugitive emissions, such as leaking components, are important sources of methane and other pollutants within the oil and gas sector. These gas emission sources are difficult to mitigate because their existence, location, and magnitude are unknown, especially within facilities which may have thousands of potential source components. Improving the speed at which these sources are characterized (located and quantified) can improve these mitigation efforts. Using sparse concentration information (from finite sensor locations) and estimated wind fields, a scalar transport adjoint-based optimization approach was developed to characterize both single and multiple simultaneous gas releases representative of the fugitive emissions problem. This approach was tested using data from an open-field gas release to determine a single source location within 5 m and source magnitude within 13%. Simulated simultaneous releases over a complex 3D geometry based on an Alberta gas plant were also characterized using both detailed transient wind and wind fields approximated with a series of steady-state wind simulations more readily implemented in a field application. Magnitudes were predicted within 10% and major regions located. By extending the method with a database of pre-computed retro-tracers generated on simplified steady-state wind fields, the required computational time for the solution optimization was reduced by a factor of 200-600, making computations feasible on a desktop machine and raising the possibility of near-continuous fugitive emissions quantification in the future. Major sources were successfully located, and magnitudes estimated within-75 to-32%, even with limited wind coverage (60° direction variation). Turbulent Schmidt number selection (which scales diffusivity) had little effect on estimated source locations, but strongly influenced estimated magnitudes. For a tested turbulent Schmidt number range of 0.33 (diffuse) to 2.0 (highly advective), the predicted emission rates for the open field release varied between-35 to +128% of actual. Buildings dampened this effect, suggesting that open-field estimates can act as an error bound. iii Preface This thesis contains chapters which have already been published or have been prepared for publication jointly as journal articles. Chapter 4 has been peer-reviewed and was published in Atmospheric Environment and reproduced according to the journal's copyright policy. Only minor clarifications and formatting edits have been made following feedback from the thesis examination committee and for formatting consistency. This paper was co-authored by the thesis author, Carol Brereton, her thesis supervisors Prof. Matthew Johnson and Prof. Lucy Campbell, as well as former student Ian Joynes who was involved in initial discussions and background development work. Ms. Brereton setup , developed, and tested the relevant procedures and code; generated synthetic test data; performed the test simulations and

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Application of genetic algorithm for the simultaneous identification of atmospheric pollution sources

Cited by 40 publications

References 29 publications

Retrieval of Unknown Number of Source Terms in Dispersion Events Involving Multiple Point Sources

Retrieval of Unknown Number of Source Terms in Dispersion Events Involving Multiple Point Sources

Identifying Key Potential Source Areas for Ambient Methyl Mercaptan Pollution Based on Long-Term Environmental Monitoring Data in an Industrial Park

Adjoint-Based Optimization Scheme for Characterizing Sources of Fugitive Emissions

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