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Atmosphere
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Data-Driven Methods in Atmospheric Dispersion Modelling
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Data-Driven Methods in Atmospheric Dispersion Modelling

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 12636

Special Issue Editors

Dr. Riccardo Buccolieri

E-Mail Website
Guest Editor
Laboratory of Micrometeorology, Dipartimento di Scienze e Tecnologie Biologiche ed Ambientali (DiSTeBA), University of Salento, 73100 Lecce, Italy
Interests: urban air quality and microclimate; experimental and computational fluid dynamics; turbulence and pollutant dispersion; urban ventilation and vegetation
Special Issues, Collections and Topics in MDPI journals
Dr. Meelis Zidikheri

E-Mail Website
Guest Editor
Bureau of Meteorology, GPO Box 1289, Melbourne, VIC 3001, Australia
Interests: volcanic ash; inverse modelling; dispersion modelling; ensemble modelling; data assimilation; subgrid-scale parameterisations; geophysical turbulence; climate change attribution; nonlinear dynamics; statistical dynamics

Special Issue Information

Dear Colleagues,

Research efforts in modelling the dispersion of atmospheric pollutants have until recently mainly been focused on improving the understanding and modelling of physical and dynamical processes affecting pollutant transport. These include the processes that release the pollutant into the atmosphere (the pollutant source), pollutant removal processes from the atmosphere, physical and chemical properties of the pollutant, and improved atmospheric physics and dynamics by the use of advanced numerical weather prediction (NWP) models at higher resolutions, to better represent physical processes such as rainfall and small-scale motions. The advent of ensemble NWP models has also enabled a quantification of the dispersion model uncertainty related to errors in the NWP model fields.

On the other hand, a new revolution is underway in many areas of science which is fueled by the increased availability of high-quality observational data, ever increasing computer storage and processing capability, and improved data utilization algorithms. Satellite- and ground-based observing systems are becoming more powerful and capable of providing high-quality and nearly continuous observations. New satellite- and ground-based retrieval algorithms are also being developed that can provide important information about the evolution of the pollutants in real time. This information can be assimilated by dispersion models to improve the predictive skill of the models, including the quantification of model uncertainty.

The data assimilation approach has been used in numerical weather prediction for many decades, but the improved observing systems and remote sensing algorithms now make this approach viable in atmospheric pollutant modelling as well. In addition to improving the model predictive skill in real time by direct assimilation, the large volumes of data can also be used to improve the representation of physical, chemical, and dynamical processes that affect pollutant dispersion. This can be done by utilizing a new generation of powerful machine learning techniques to develop more detailed models of how these processes affect the evolution of the pollutant in the atmosphere.

This Special Issue aims to explore how the new generation of data-driven methods may be used to improve the dispersion modelling of atmospheric pollutants. Contributions are sought from the following areas:

  • Dispersion modelling of volcanic ash, dust, smoke, pollen, and other pollutants at different temporal and spatial scales;
  • Remote sensing algorithms for retrieving atmospheric pollutant properties;
  • Use of inverse modelling and data assimilation methods in atmospheric pollutant modelling;
  • Machine learning techniques to improve atmospheric pollutant modelling
  • Comparison of different data-driven methods in atmospheric pollutant modelling;
  • Uncertainty quantification and hazard prediction in atmospheric pollutant modelling.

Kind regards,

Prof. Dr. Riccardo Buccolieri
Dr. Meelis Zidikheri
Guest Editors

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 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

  • Dispersion modelling
  • Data assimilation
  • Inverse modelling
  • Remote sensing
  • Pollutant modelling
  • Uncertainty quantification

Published Papers (5 papers)

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Research

14 pages, 2653 KiB  
Article
High Spatial Resolution Assessment of the Effect of the Spanish National Air Pollution Control Programme on Street-Level NO2 Concentrations in Three Neighborhoods of Madrid (Spain) Using Mesoscale and CFD Modelling
by Jose-Luis Santiago, Beatriz Sanchez, Esther Rivas, Marta G. Vivanco, Mark Richard Theobald, Juan Luis Garrido, Victoria Gil, Alberto Martilli, Alejandro Rodríguez-Sánchez, Riccardo Buccolieri and Fernando Martín
Atmosphere 2022, 13(2), 248; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13020248 - 31 Jan 2022
Cited by 7 | Viewed by 2460
Abstract
Current European legislation aims to reduce the air pollutants emitted by European countries in the coming years. In this context, this article studies the effects on air quality of the measures considered for 2030 in the Spanish National Air Pollution Control Programme (NAPCP). [...] Read more.
Current European legislation aims to reduce the air pollutants emitted by European countries in the coming years. In this context, this article studies the effects on air quality of the measures considered for 2030 in the Spanish National Air Pollution Control Programme (NAPCP). Three different emission scenarios are investigated: a scenario with the emissions in 2016 and two other scenarios, one with existing measures in the current legislation (WEM2030) and another one considering the additional measures of NAPCP (WAM2030). Previous studies have addressed this issue at a national level, but this study assesses the impact at the street scale in three neighborhoods in Madrid, Spain. NO2 concentrations are modelled at high spatial resolution by means of a methodology based on computational fluid dynamic (CFD) simulations driven by mesoscale meteorological and air quality modelling. Spatial averages of annual mean NO2 concentrations are only estimated to be below 40 µg/m3 in all three neighborhoods for the WAM2030 emission scenarios. However, for two of the three neighborhoods, there are still zones (4–12% of the study areas) where the annual concentration is higher than 40 µg/m3. This highlights the importance of considering microscale simulations to assess the impacts of emission reduction measures on urban air quality. Full article
(This article belongs to the Special Issue Data-Driven Methods in Atmospheric Dispersion Modelling)
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27 pages, 8010 KiB  
Article
Impact of Indoor-Outdoor Temperature Difference on Building Ventilation and Pollutant Dispersion within Urban Communities
by Yun Hu, Yihui Wu, Qun Wang, Jian Hang, Qingman Li, Jie Liang, Hong Ling and Xuelin Zhang
Atmosphere 2022, 13(1), 28; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13010028 - 25 Dec 2021
Cited by 9 | Viewed by 3985
Abstract
Mechanical ventilation consumes a huge amount of global energy. Natural ventilation is a crucial solution for reducing energy consumption and enhancing the capacity of atmospheric self-purification. This paper evaluates the impacts of indoor-outdoor temperature differences on building ventilation and indoor-outdoor air pollutant dispersion [...] Read more.
Mechanical ventilation consumes a huge amount of global energy. Natural ventilation is a crucial solution for reducing energy consumption and enhancing the capacity of atmospheric self-purification. This paper evaluates the impacts of indoor-outdoor temperature differences on building ventilation and indoor-outdoor air pollutant dispersion in urban areas. The Computational Fluid Dynamics (CFD) method is employed to simulate the flow fields in the street canyon and indoor environment. Ventilation conditions of single-side ventilation mode and cross-ventilation mode are investigated. Air change rate, normalized concentration of traffic-related air pollutant (CO), intake fraction and exposure concentration are calculated to for ventilation efficiency investigation and exposure assessment. The results show that cross ventilation increases the air change rate for residential buildings under isothermal conditions. With the indoor-outdoor temperature difference, heating could increase the air change rate of the single-side ventilation mode but restrain the capability of the cross-ventilation mode in part of the floors. Heavier polluted areas appear in the upstream areas of single-side ventilation modes, and the pollutant can diffuse to middle-upper floors in cross-ventilation modes. Cross ventilation mitigates the environmental health stress for the indoor environment when indoor-outdoor temperature difference exits and the personal intake fraction is decreased by about 66% compared to the single-side ventilation. Moreover, the existence of indoor-outdoor temperature differences can clearly decrease the risk of indoor personal exposure under both two natural ventilation modes. The study numerically investigates the building ventilation and pollutant dispersion in the urban community with natural ventilation. The method and the results are helpful references for optimizing the building ventilation plan and improving indoor air quality. Full article
(This article belongs to the Special Issue Data-Driven Methods in Atmospheric Dispersion Modelling)
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26 pages, 33873 KiB  
Article
A Near-Real-Time Method for Estimating Volcanic Ash Emissions Using Satellite Retrievals
by Rachel E. Pelley, David J. Thomson, Helen N. Webster, Michael C. Cooke, Alistair J. Manning, Claire S. Witham and Matthew C. Hort
Atmosphere 2021, 12(12), 1573; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12121573 - 27 Nov 2021
Cited by 4 | Viewed by 1704
Abstract
We present a Bayesian inversion method for estimating volcanic ash emissions using satellite retrievals of ash column load and an atmospheric dispersion model. An a priori description of the emissions is used based on observations of the rise height of the volcanic plume [...] Read more.
We present a Bayesian inversion method for estimating volcanic ash emissions using satellite retrievals of ash column load and an atmospheric dispersion model. An a priori description of the emissions is used based on observations of the rise height of the volcanic plume and a stochastic model of the possible emissions. Satellite data are processed to give column loads where ash is detected and to give information on where we have high confidence that there is negligible ash. An atmospheric dispersion model is used to relate emissions and column loads. Gaussian distributions are assumed for the a priori emissions and for the errors in the satellite retrievals. The optimal emissions estimate is obtained by finding the peak of the a posteriori probability density under the constraint that the emissions are non-negative. We apply this inversion method within a framework designed for use during an eruption with the emission estimates (for any given emission time) being revised over time as more information becomes available. We demonstrate the approach for the 2010 Eyjafjallajökull and 2011 Grímsvötn eruptions. We apply the approach in two ways, using only the ash retrievals and using both the ash and clear sky retrievals. For Eyjafjallajökull we have compared with an independent dataset not used in the inversion and have found that the inversion-derived emissions lead to improved predictions. Full article
(This article belongs to the Special Issue Data-Driven Methods in Atmospheric Dispersion Modelling)
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24 pages, 4659 KiB  
Article
Improving Ensemble Volcanic Ash Forecasts by Direct Insertion of Satellite Data and Ensemble Filtering
by Meelis J. Zidikheri and Chris Lucas
Atmosphere 2021, 12(9), 1215; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12091215 - 17 Sep 2021
Cited by 6 | Viewed by 1631
Abstract
Improved quantitative forecasts of volcanic ash are in great demand by the aviation industry to enable better risk management during disruptive volcanic eruption events. However, poor knowledge of volcanic source parameters and other dispersion and transport modelling uncertainties, such as those due to [...] Read more.
Improved quantitative forecasts of volcanic ash are in great demand by the aviation industry to enable better risk management during disruptive volcanic eruption events. However, poor knowledge of volcanic source parameters and other dispersion and transport modelling uncertainties, such as those due to errors in numerical weather prediction fields, make this problem very challenging. Nonetheless, satellite-based algorithms that retrieve ash properties, such as mass load, effective radius, and cloud top height, combined with inverse modelling techniques, such as ensemble filtering, can significantly ameliorate these problems. The satellite-retrieved data can be used to better constrain the volcanic source parameters, but they can also be used to avoid the description of the volcanic source altogether by direct insertion into the forecasting model. In this study we investigate the utility of the direct insertion approach when employed within an ensemble filtering framework. Ensemble members are formed by initializing dispersion models with data from different timesteps, different values of cloud top height, thickness, and NWP ensemble members. This large ensemble is then filtered with respect to observations to produce a refined forecast. We apply this approach to 14 different eruption case studies in the tropical atmosphere. We demonstrate that the direct insertion of data improves model forecast skill, particularly when it is used in a hybrid ensemble in which some ensemble members are initialized from the volcanic source. Moreover, good forecast skill can be obtained even when detailed satellite retrievals are not available, which is frequently the case for volcanic eruptions in the tropics. Full article
(This article belongs to the Special Issue Data-Driven Methods in Atmospheric Dispersion Modelling)
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13 pages, 504 KiB  
Article
Concentration Fluctuations of Single Particle Stochastic Lagrangian Model Assessment with Experimental Field Data
by Enrico Ferrero and Filippo Maccarini
Atmosphere 2021, 12(5), 589; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12050589 - 01 May 2021
Cited by 3 | Viewed by 1518
Abstract
A single particle Lagrangian Stochastic model has been developed and applied with the purpose of simulating the concentration fluctuations dispersion. This model treats concentration variance as a quantity whose motion is driven by an advection-diffusion process so that it can be studied by [...] Read more.
A single particle Lagrangian Stochastic model has been developed and applied with the purpose of simulating the concentration fluctuations dispersion. This model treats concentration variance as a quantity whose motion is driven by an advection-diffusion process so that it can be studied by a single particle model. A parameterization for both velocity standard deviations and Lagrangian time-scales is required as input to the model. The paper is focused on the estimation of the best parameterization needed to simulate both mean and standard deviation concentrations in a case study. We consider the FFT-07 field experiment. The trials took place at Dugway Proving Ground, UTAH (USA) and consist of a dispersion analysis of a gas emitted from a point-like source in different atmospheric conditions with a continuous emission technique. The very small spatial scales (a few hundred meters) and short duration (about 10 min) that characterize the trials make the comparison with model results very challenging, since traditional boundary layer parameterizations fail in correctly reproducing the turbulent field and, as a consequence, the dispersion simulation yields unsatisfactorily results. We vary the coefficients of the turbulence parameterization to match the small-scale turbulence. Furthermore, we show that the parameterization for the variance dissipation time-scale, already tested in neutral conditions, can be used also in stable and unstable conditions and in low-wind speed conditions. The model gives good results as far as mean concentration is concerned and rather satisfactory results for the concentration standard deviations. Comparison between model results and observation is shown through both statistical and graphical analyses. Full article
(This article belongs to the Special Issue Data-Driven Methods in Atmospheric Dispersion Modelling)
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