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Atmosphere
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Atmospheric Dispersion of Pollutants: From Regulatory to Emergency Applications
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Atmospheric Dispersion of Pollutants: From Regulatory to Emergency Applications

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 11177

Special Issue Editor

Dr. Xiaole Zhang

E-Mail Website
Guest Editor
Institute of Environmental Engineering, ETH Zürich, Stefano-Franscini-Platz 3, CH-8093 Zürich, Switzerland
Interests: atmospheric dispersion modelling; aerosol dynamics; data assimilation; inverse modelling; safety science and technology

Special Issue Information

Dear Colleagues,

Atmospheric dispersion is the indispensable physical process for understanding and regulating airborne pollutants. It has become the focus of researchers and governmental agencies regarding the protection of public health and welfare.

Regarding regulatory purposes, people investigate the atmospheric dispersion of pollutants (including SO2, NOx, particulate matter, odor, and bioaerosols) emitted from key sources, e.g., industrial parks, airports, power plants, and farms. The information about the contributions of key sources to ambient pollution is of great importance to implement effective measures to alleviate the associated impacts.

Atmospheric dispersion is also of great concern during emergencies, e.g., the Fukushima Daiichi power plant accident, the volcano eruption of Eyjafjallajökull, and accidental releases of hazardous material. The atmospheric dispersion of hazardous materials, e.g., radioactive pollutants, volcanic ash, and toxic and explosive gases, is essential information for planning accurate countermeasures, e.g., sheltering, evacuation, and iodine-prophylaxis.

This Special Issue is devoted to all theoretical, modeling, and observational aspects of the atmospheric dispersion of pollutants from the key emission sources for regulatory purposes, and applications in accidental releases for emergency management. Both measurements and numerical modeling studies are welcome.

The topics of interest of this Special Issue include but are not limited to in situ and remote sensing measurements of atmospheric dispersion of pollutants, development of emission inventory, parameterization of meteorological processes related to atmospheric dispersion, atmospheric dispersion models at various scales (from local to continental scale), exposure assessment, data assimilation, and inverse modeling.

Dr. Xiaole Zhang
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • atmospheric dispersion of pollutants
  • atmospheric dispersion models
  • in situ and remote sensing
  • emission inventory
  • data assimilation
  • inverse modeling
  • regulatory purposes
  • emergency response

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Published Papers (6 papers)

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Research

20 pages, 3408 KiB  
Article
Harmonisation in Atmospheric Dispersion Modelling Approaches to Assess Toxic Consequences in the Neighbourhood of Industrial Facilities
by Jean-Marc Lacome, Guillaume Leroy, Lauris Joubert and Benjamin Truchot
Atmosphere 2023, 14(11), 1605; https://doi.org/10.3390/atmos14111605 - 26 Oct 2023
Viewed by 1201
Abstract
In the land use planning framework in the neighbourhood of industrial facilities, the current approach to predicting the consequences of massive toxic gas releases is generally based on Gaussian or integral models. For many years, CFD models have been more and more used [...] Read more.
In the land use planning framework in the neighbourhood of industrial facilities, the current approach to predicting the consequences of massive toxic gas releases is generally based on Gaussian or integral models. For many years, CFD models have been more and more used in this context, in accordance with the development of high-performance computing (HPC). The present paper focuses on harmonising input data for atmospheric transport and dispersion (AT&D) modelling between the widely used approaches. First, a synthesis of the practice’s harmonisation for atmospheric dispersion modelling within the framework of risk assessment is presented. Then, these practices are applied to a large-scale INERIS ammonia experimental release. For illustration purposes, the impact of the proposed harmonisation will be evaluated using different approaches: the SLAB model, the FDS model, and the Code_Saturne model. The two main focuses of this paper are the adaptation of the source term dealing with a massive release and the wind flow modelling performance using an experimental signal for CFD model inflow. Finally, comparisons between the modelling and experimental results enable checking the consistency of these approaches and reinforce the importance of the input data harmonisation for each AT&D modelling approach. Full article
(This article belongs to the Special Issue Atmospheric Dispersion of Pollutants: From Regulatory to Emergency Applications)
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18 pages, 10264 KiB  
Article
Experimental Campaign of Massive CO2 Atmospheric Releases in an Urban Area
by Lauris Joubert, Guillaume Leroy, Théo Claude and Omar Riahi
Atmosphere 2023, 14(9), 1428; https://doi.org/10.3390/atmos14091428 - 12 Sep 2023
Cited by 2 | Viewed by 1314
Abstract
Over recent decades, several campaigns have been carried out to collect data regarding the release and atmospheric dispersion of dense chemical products in an open field. All these experimental data are valuable information to challenge the predictions of numerical tools (Gaussian, integral-type, and [...] Read more.
Over recent decades, several campaigns have been carried out to collect data regarding the release and atmospheric dispersion of dense chemical products in an open field. All these experimental data are valuable information to challenge the predictions of numerical tools (Gaussian, integral-type, and CFD) and, if needed, to improve the code itself and the way we are using it. On the other hand, little attention has been paid to atmospheric dispersion releases with massive flow rates in a complex urban environment. To fill this gap, Ineris carried out an experimental campaign intended to study the atmospheric dispersion of massive CO2 releases on the CENZUB site (an action training center in an urban area located in Sissonne, France). Three CO2 releases were performed with mass flow rates of about 7 kg/s in three different configurations: one axial street release and two impacting releases (against a small and high-rise building). Several technologies of CO2 sensors were used to ensure better measurement accuracy. The main experimental campaign features and preliminary data analysis are presented. The results demonstrated the influence of the built environment on dispersion patterns. Full article
(This article belongs to the Special Issue Atmospheric Dispersion of Pollutants: From Regulatory to Emergency Applications)
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22 pages, 14633 KiB  
Article
Identification Method of Source Term Parameters of Nuclear Explosion Based on GA and PSO for Lagrange-Gaussian Puff Model
by Yang Zheng, Yuyang Wang, Longteng Wang, Xiaolei Chen, Lingzhong Huang, Wei Liu, Xiaoqiang Li, Ming Yang, Peng Li, Shanyi Jiang, Hao Yin, Xinliang Pang and Yunhui Wu
Atmosphere 2023, 14(5), 877; https://doi.org/10.3390/atmos14050877 - 17 May 2023
Cited by 1 | Viewed by 1555
Abstract
Many well-established models exist for predicting the dispersion of radioactive particles that will be generated in the surrounding environment after a nuclear weapon explosion. However, without exception, almost all models rely on accurate source term parameters, such as DELFIC, DNAF-1, and so on. [...] Read more.
Many well-established models exist for predicting the dispersion of radioactive particles that will be generated in the surrounding environment after a nuclear weapon explosion. However, without exception, almost all models rely on accurate source term parameters, such as DELFIC, DNAF-1, and so on. Unlike nuclear experiments, accurate source term parameters are often not available once a nuclear weapon is used in a real nuclear strike. To address the problems of unclear source term parameters and meteorological conditions during nuclear weapon explosions and the complexity of the identification process, this article proposes a nuclear weapon source term parameter identification method based on a genetic algorithm (GA) and a particle swarm optimization algorithm (PSO) by combining real-time monitoring data. The results show that both the PSO and the GA are able to identify the source term parameters satisfactorily after optimization, and the prediction accuracy of their main source term parameters is above 98%. When the maximum number of iterations and population size of the PSO and GA were the same, the running time and optimization accuracy of the PSO were better than those of the GA. This study enriches the theory and method of radioactive particle dispersion prediction after a nuclear weapon explosion and is of great significance to the study of environmental radioactive particles. Full article
(This article belongs to the Special Issue Atmospheric Dispersion of Pollutants: From Regulatory to Emergency Applications)
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21 pages, 4682 KiB  
Article
A Modified k-ε Turbulence Model for Heavy Gas Dispersion in Built-Up Environment
by Sebastian Schalau, Abdelkarim Habib and Simon Michel
Atmosphere 2023, 14(1), 161; https://doi.org/10.3390/atmos14010161 - 11 Jan 2023
Cited by 4 | Viewed by 2132
Abstract
For hazard assessment purposes, the dispersion of gases in complex urban areas is often a scenario to be considered. However, predicting the dispersion of heavy gases is still a challenge. In Germany, the VDI Guideline 3783, Part 1 and 2 is widely used [...] Read more.
For hazard assessment purposes, the dispersion of gases in complex urban areas is often a scenario to be considered. However, predicting the dispersion of heavy gases is still a challenge. In Germany, the VDI Guideline 3783, Part 1 and 2 is widely used for gas dispersion modelling. Whilst Part 1 uses a gauss model for calculating the dispersion of light or neutrally buoyant gases, Part 2 uses wind tunnel experiments to evaluate the heavier-than-air gas dispersion in generic built up areas. In practice, with this guideline, it is often not possible to adequately represent the existing obstacle configuration. To overcome this limitation, computational fluid dynamics (CFD) methods could be used. Whilst CFD models can represent obstacles in the dispersion area correctly, actual publications show that there is still further research needed to simulate the atmospheric flow and the heavy gas dispersion. This paper presents a modified k-ε-turbulence model that was developed in OpenFOAM v5.0 (England, London, The OpenFOAM Foundation Ltd Incorporated) to enhance the simulation of the atmospheric wind field and the heavy gas dispersion in built-up areas. Wind tunnel measurements for the dispersion of neutrally buoyant and heavy gases in built-up environments were used to evaluate the model. As a result, requirements for the simulation of the gas dispersion under atmospheric conditions have been identified and the model showed an overall good performance in predicting the experimental values. Full article
(This article belongs to the Special Issue Atmospheric Dispersion of Pollutants: From Regulatory to Emergency Applications)
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19 pages, 6325 KiB  
Article
Inversion Method for Multiple Nuclide Source Terms in Nuclear Accidents Based on Deep Learning Fusion Model
by Yongsheng Ling, Chengfeng Liu, Qing Shan, Daqian Hei, Xiaojun Zhang, Chao Shi, Wenbao Jia and Jing Wang
Atmosphere 2023, 14(1), 148; https://doi.org/10.3390/atmos14010148 - 9 Jan 2023
Cited by 6 | Viewed by 1696
Abstract
During severe nuclear accidents, radioactive materials are expected to be released into the atmosphere. Estimating the source term plays a significant role in assessing the consequences of an accident to assist in actioning a proper emergency response. However, it is difficult to obtain [...] Read more.
During severe nuclear accidents, radioactive materials are expected to be released into the atmosphere. Estimating the source term plays a significant role in assessing the consequences of an accident to assist in actioning a proper emergency response. However, it is difficult to obtain information on the source term directly through the instruments in the reactor because of the unpredictable conditions induced by the accident. In this study, a deep learning-based method to estimate the source term with field environmental monitoring data, which utilizes the bagging method to fuse models based on the temporal convolutional network (TCN) and two-dimensional convolutional neural network (2D-CNN), was developed. To reduce the complexity of the model, the particle swarm optimization algorithm was used to optimize the parameters in the fusion model. Seven typical radionuclides (Kr-88, I-131, Te-132, Xe-133, Cs-137, Ba-140, and Ce-144) were set as mixed source terms, and the International Radiological Assessment System was used to generate model training data. The results indicated that the average prediction error of the fusion model for the seven nuclides in the test set was less than 10%, which significantly improved the estimation accuracy compared with the results obtained by TCN or 2D-CNN. Noise analysis revealed the fusion model to be robust, having potential applicability toward more complex nuclear accident scenarios. Full article
(This article belongs to the Special Issue Atmospheric Dispersion of Pollutants: From Regulatory to Emergency Applications)
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15 pages, 4368 KiB  
Article
Estimating PM2.5 Concentrations Using an Improved Land Use Regression Model in Zhejiang, China
by Sheng Zheng, Chengjie Zhang and Xue Wu
Atmosphere 2022, 13(8), 1273; https://doi.org/10.3390/atmos13081273 - 11 Aug 2022
Cited by 4 | Viewed by 1929
Abstract
Fine particulate matter (PM2.5) pollution affects the environment and poses threat to human health. The study of the influence of land use and other factors on PM2.5 is crucial for the rational development and utilization of territorial space. To explore [...] Read more.
Fine particulate matter (PM2.5) pollution affects the environment and poses threat to human health. The study of the influence of land use and other factors on PM2.5 is crucial for the rational development and utilization of territorial space. To explore the intrinsic mechanism between PM2.5 pollution and related factors, this study used the land use regression (LUR) model, and introduced geographically weighted regression (GWR), and random forest (RF) to optimize the basic LUR model. The basic LUR model was constructed to predict the annual average PM2.5 concentrations using three elements: artificial surfaces, forest land, and wind speed as explanatory variables, with adjusted R2 of 0.645. The improved LUR models based on GWR and RF, with an adjusted R2 of 0.767 and 0.821, respectively, show better fitting effects. The LUR simulation results show that the PM2.5 pollution in the northern Zhejiang is more serious and concentrated. The concentrations are also higher in regions such as the river valley plains in central Zhejiang and the coastal plains in southeastern Zhejiang. These findings show that pollution emissions should be further reduced and environmental protection should be strengthened. Full article
(This article belongs to the Special Issue Atmospheric Dispersion of Pollutants: From Regulatory to Emergency Applications)
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