Identification of strength and location of stationary point source of atmospheric pollutant in urban conditions using computational fluid dynamics model

Kovalets, Ivan V.; Andronopoulos, S.; Venetsanos, A.G.; Bartzis, John G.

doi:10.1016/j.matcom.2011.07.002

Cited by 26 publications

(21 citation statements)

References 12 publications

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“…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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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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“…Najafi and Gilbert and Annunzio et al employed the Lagrangian puff models as the forward models [17,18]. A CFD model was used by Chow et al in [19] and Kovalets IV et al in [20]. Each model has its applicable conditions.…”

Section: Introductionmentioning

confidence: 99%

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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“…Cervone and Franzese (2010) studied several error functions to identify suitable measures and cost functions for optimizations and Kovalets et al (2011) used a fluid dynamics model to build up a cost function which could be optimized. These methods can be inconvenient due to problematic convergence and limited information on the uncertainty of the results.…”

Section: Introductionmentioning

confidence: 99%

Bayesian inverse modeling and source location of an unintended <sup>131</sup>I release in Europe in the fall of 2011

et al. 2017

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Abstract. In the fall of 2011, iodine-131 (131I) was detected at several radionuclide monitoring stations in central Europe. After investigation, the International Atomic Energy Agency (IAEA) was informed by Hungarian authorities that 131I was released from the Institute of Isotopes Ltd. in Budapest, Hungary. It was reported that a total activity of 342 GBq of 131I was emitted between 8 September and 16 November 2011. In this study, we use the ambient concentration measurements of 131I to determine the location of the release as well as its magnitude and temporal variation. As the location of the release and an estimate of the source strength became eventually known, this accident represents a realistic test case for inversion models. For our source reconstruction, we use no prior knowledge. Instead, we estimate the source location and emission variation using only the available 131I measurements. Subsequently, we use the partial information about the source term available from the Hungarian authorities for validation of our results. For the source determination, we first perform backward runs of atmospheric transport models and obtain source-receptor sensitivity (SRS) matrices for each grid cell of our study domain. We use two dispersion models, FLEXPART and Hysplit, driven with meteorological analysis data from the global forecast system (GFS) and from European Centre for Medium-range Weather Forecasts (ECMWF) weather forecast models. Second, we use a recently developed inverse method, least-squares with adaptive prior covariance (LS-APC), to determine the 131I emissions and their temporal variation from the measurements and computed SRS matrices. For each grid cell of our simulation domain, we evaluate the probability that the release was generated in that cell using Bayesian model selection. The model selection procedure also provides information about the most suitable dispersion model for the source term reconstruction. Third, we select the most probable location of the release with its associated source term and perform a forward model simulation to study the consequences of the iodine release. Results of these procedures are compared with the known release location and reported information about its time variation. We find that our algorithm could successfully locate the actual release site. The estimated release period is also in agreement with the values reported by IAEA and the reported total released activity of 342 GBq is within the 99 % confidence interval of the posterior distribution of our most likely model.

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Identification of strength and location of stationary point source of atmospheric pollutant in urban conditions using computational fluid dynamics model

Cited by 26 publications

References 12 publications

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

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

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

Bayesian inverse modeling and source location of an unintended <sup>131</sup>I release in Europe in the fall of 2011

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Identification of strength and location of stationary point source of atmospheric pollutant in urban conditions using computational fluid dynamics model

Cited by 26 publications

References 12 publications

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

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

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

Bayesian inverse modeling and source location of an unintended &lt;sup&gt;131&lt;/sup&gt;I release in Europe in the fall of 2011

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Bayesian inverse modeling and source location of an unintended <sup>131</sup>I release in Europe in the fall of 2011