Special Issue "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: 30 June 2023 | Viewed by 2426

Special Issue 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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 2000 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

Published Papers (3 papers)

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Research

Article
A Modified k-ε Turbulence Model for Heavy Gas Dispersion in Built-Up Environment
Atmosphere 2023, 14(1), 161; https://doi.org/10.3390/atmos14010161 - 11 Jan 2023
Viewed by 573
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
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Article
Inversion Method for Multiple Nuclide Source Terms in Nuclear Accidents Based on Deep Learning Fusion Model
Atmosphere 2023, 14(1), 148; https://doi.org/10.3390/atmos14010148 - 09 Jan 2023
Viewed by 395
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
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Article
Estimating PM2.5 Concentrations Using an Improved Land Use Regression Model in Zhejiang, China
Atmosphere 2022, 13(8), 1273; https://doi.org/10.3390/atmos13081273 - 11 Aug 2022
Viewed by 795
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
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A Potential Machine Learning Approach to Update Air Pollutants Emission Inventory Using Observed Surface Concentrations and Meteorology for Air Quality Modeling
Authors: Saloni Vijay1, Xiaoxiao Feng1,2 and Xiaole Zhang1,2 Jing Wang1,2,*
Affiliation: 1. Institute of Environmental Engineering (IfU), ETH Zürich, Zürich, 8093, Switzerland; 2. Swiss Federal Laboratories for Materials Science and Technology, Dübendorf 8600, Switzerland;
Abstract: Emission inventories provide indispensable information in predicting air pollution. However, the emission inventories have large bias from the reality, and the simulated ambient air concen-trations of the pollutants differed from their respective surface observations. A top-down method was thus used to update the baseline inventory. The rule guiding the relationship between the simulated ambient air concentrations and meteorology with the emissions was first learned with the algorithm of random forest. The baseline emission inventory was updated by replacing the simulated ambient air concentration with the observed concentrations. The updated inventory of all 4 pollutants showed more temporal variations for both weekends and weekdays, compared to almost time-static baseline inventory. The performance of predicting pollutants was improved by 10%, 19%, 20%, and 15% for ambient PM10, PM2.5, SO2, and NO2, respectively with the updated emissions. The top-down methodology developed can be extended to update daily emissions in other regions of the world, provided further optimization of the machine learning algorithm.

Title: Choosing the appropriate atmospheric dispersion model, from theoretical to application considerations for accidental situations
Authors: B Truchot, L Joubert and O gentilhomme
Affiliation: Ineris, French National Institute for Industrial Environment and Risks, Parc Technologique Alata BP 2, 60550 Verneuil-en-Halatte, France
Abstract: Toxic or flammable atmospheric releases on industrial facilities can lead to huge consequences for human beings and the environment. Predicting those consequences remains nowadays a challenge for several professionals, from industrial safety engineer during the design process to emergency services during the crisis management. Hopefully, many numerical models are available and can be used for these objectives. However, atmospheric flows are highly specific and such a modelling approach is not so obvious. Many model families exist, from simple Gaussian correlation to CFD (Computational Fluid Dynamics) codes. All approaches got their own advantages and drawbacks and attention should be paid when selecting the most relevant one. As a guideline, this paper presents the different family models in such a way that it will allow the reader to identify the limitations and prerequisite for each of them. Using some examples, this article also provides some validation cases and uncertainties evaluation that can be usefull for all users. The nature of the release and associated target is also discussed since looking for very long distance for dispersion as for the smoke resulting from the Lubrizol/Normandie Logistique fire or looking for flammable zone sizing improvement do not have the same constraints. The discussion is not only focused on the physical and theoretical approaches of each family model but also considers the constraints of each of them keeping in mind that, in emergency situation, the time scale and uncertainty acceptance are strongly different from those at the design stage.

Title: Experimental campaign of CO2 massive atmospheric releases in an urban area
Authors: L. Joubert; G. Leroy; T. Claude
Affiliation: French National Institute for Industrial Environment and Risks, Parc Technologique Alata BP 2, 60550 Verneuil-en-Halatte, France
Abstract: Over the last decades, several campaigns were carried out to collect data regarding releases 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 tools) 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 launched an experimental campaign intended to study atmospheric dispersion of massive CO2 releases on the Cenzub site (action training center in urban area located in Sissonne, France). Three CO2 releases were performed with mass flowrates between 8 and 10 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 a better measurement accuracy. Main experimental campaign features and preliminary data analysis is presented.

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