scholarly journals A three-dimensional atmospheric dispersion model for Mars

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
D. Viúdez-Moreiras
Keyword(s):  
Dispersion Model ◽  
Regional Scale ◽  
Atmospheric Dispersion ◽  
Three Dimensional ◽  
Chemical Species ◽  
Dispersion Models ◽  
Uniform Grids ◽  
Atmospheric Dispersion Model ◽  
Meteorological Models

Abstract Atmospheric local-to-regional dispersion models are widely used on Earth to predict and study the effects of chemical species emitted into the atmosphere and to contextualize sparse data acquired at particular locations and/or times. However, to date, no local-to-regional dispersion models for Mars have been developed; only mesoscale/microscale meteorological models have some dispersion and chemical capabilities, but they do not offer the versatility of a dedicated atmospheric dispersion model when studying the dispersion of chemical species in the atmosphere, as it is performed on Earth. Here, a new three-dimensional local-to-regional-scale Eulerian atmospheric dispersion model for Mars (DISVERMAR) that can simulate emissions to the Martian atmosphere from particular locations or regions including chemical loss and predefined deposition rates, is presented. The model can deal with topography and non-uniform grids. As a case study, the model is applied to the simulation of methane spikes as detected by NASA’s Mars Science Laboratory (MSL); this choice is made given the strong interest in and controversy regarding the detection and variability of this chemical species on Mars.

Using emissions derived from atmospheric observations to inform the reported UK inventory

2021 ◽  
Author(s):  
Alistair Manning ◽  
Alison Redington ◽  
Simon O'Doherty ◽  
Dickon Young ◽  
Dan Say ◽  
...  
Keyword(s):  
Best Practice ◽  
Dispersion Model ◽  
Regional Scale ◽  
Atmospheric Dispersion ◽  
Three Dimensional ◽  
Ghg Emissions ◽  
Atmospheric Observations ◽  
Emission Modelling ◽  
National Inventory Report ◽  
The Uk

<p align="justify">Verification of the nationally reported greenhouse gas (GHG) inventories using inverse modelling and atmospheric observations is considered to be best practice by the United Nations Framework Convention on Climate Change (UNFCCC). It allows for an independent assessment of the nationally reported GHG emissions using a comprehensively different approach to the inventory methods. Significant differences in the emissions estimated using the two approaches are a means of identifying areas worthy of further investigation.</p><p align="justify"> </p><p align="justify"><span>An inversion methodology called Inversion Technique for Emission Modelling (InTEM) has been developed that uses a non-negative least squares minimisation technique to determine the emission magnitude and distribution that most accurately reproduces the observations. By estimating the underlying </span><span><em>baseline</em></span><span> time series, atmospheric concentrations where the short-term impact of regional pollution has been removed, and by modelling where the air has passed over on route to the observation stations on a regional scale, estimates of UK emissions are made. </span>In this study we use an extensive network of observations with six stations across the UK and six more in neighbouring countries<span>. InTEM uses information from a</span> Lagrangian dispersion model NAME (Numerical Atmospheric dispersion Modelling Environment), driven by three-dimensional, modelled meteorology, to understand how the air mixes during transport from the emission sources to observation points. <span>The InTEM inversion results are submitted annually by the UK as part of their National Inventory Report to the UNFCCC. They are used within the UK inventory team to highlight areas for investigation and have led to significant improvements to the submitted UK inventory. The latest UK comparisons will be shown along with examples of how the inversion results have informed the inventory.</span></p>

scholarly journals Estimation of the emission factors of PAHs by traffic with the model of atmospheric dispersion and deposition from heavy traffic road

2007 ◽  
Vol 56 (1) ◽  
pp. 233-242 ◽  
Author(s):  
N. Ozaki ◽  
H. Tokumitsu ◽  
K. Kojima ◽  
T. Kindaichi
Keyword(s):  
Mass Balance ◽  
Dispersion Model ◽  
Heavy Traffic ◽  
Regional Scale ◽  
Atmospheric Dispersion ◽  
Emission Factors ◽  
Chemical Mass Balance ◽  
Atmospheric Concentration ◽  
Polycyclic Aromatic ◽  
Atmospheric Dispersion Model

In order to consider the total atmospheric loadings of the PAHs (polycyclic aromatic hydrocarbons) from traffic activities, the emission factors of PAHs were estimated and from the obtained emission factors and vehicle transportation statistics, total atmospheric loadings were integrated and the loadings into the water body were estimated on a regional scale. The atmospheric concentration of PAHs was measured at the roadside of a road with heavy traffic in the Hiroshima area in Japan. The samplings were conducted in summer and winter. Atmospheric particulate matters (fine particle, 0.6–7 μm; coarse particle, over 7 μm) and their PAH concentration were measured. Also, four major emission sources (gasoline and diesel vehicle emissions, tire and asphalt debris) were assumed for vehicle transportation activities, the chemical mass balance method was applied and the source partitioning at the roadside was estimated. Furthermore, the dispersion of atmospheric particles from the vehicles was modelled and the emission factors of the sources were determined by the comparison to the chemical mass balance results. Based on emission factors derived from the modelling, an atmospheric dispersion model of nationwide scale (National Institute of Advanced Industrial Science and Technology - Atmospheric Dispersion Model for Exposure and Risk assessment) was applied, and the atmospheric concentration and loading to the ground were calculated for the Hiroshima Bay watershed area.

scholarly journals Simulation of Diurnal Variation of Atmosheric 222Rn Concentrations with Three-Dimensional Atmospheric Dispersion Model.

1996 ◽  
Vol 31 (2) ◽  
pp. 161-168 ◽  
Author(s):  
Tetsuya SAKASHITA ◽  
Toshiyuki MURAKAMI ◽  
Takao IIDA ◽  
Yukimasa IKEBE ◽  
Masamichi CHINO ◽  
...  
Keyword(s):  
Diurnal Variation ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Three Dimensional ◽  
Atmospheric Dispersion Model

scholarly journals THE STUDY OF ATMOSPHERIC DISPERSION MODEL ON ACCIDENT SCENARIO OF RESEARCH REACTOR G. A. SIWABESSY USING HOTSPOT CODES AS A NUCLEAR EMERGENCY DECISION SUPPORT SYSTEM

2019 ◽  
Vol 21 (1) ◽  
pp. 1
Author(s):  
Arif Yuniarto ◽  
Moh. Cecep Cepi Hikmat
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Research Reactor ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Google Earth ◽  
Radioactive Materials ◽  
Atmospheric Dispersion Model ◽  
Nuclear Emergency

G.A. Siwabessy Multipurpose Reactor (RSG-GAS) is a research reactor with thermal power of 30 MW located in the Serpong Nuclear Area (KNS), South Tangerang, Banten, Indonesia. Nuclear emergency preparedness of RSG-GAS needs to be improved by developing a decision support system for emergency response. This system covers three important aspects: accident source terms estimation, radioactive materials dispersion model into the atmosphere and radiological impact visualization. In this paper, radioactive materials dispersion during design basis accident (DBA) is modeled using HotSpot, by utilizing site-specific meteorological data. Based on the modelling, maximum effective dose and thyroid equivalent dose of 1.030 mSv and 26 mSv for the first 7 days of exposure are reached at distance of 1 km from the release point. These values are below IAEA generic criteria related to risk reduction of stochastic effects. The results of radioactive dispersion modeling and radiation dose calculations are integrated with Google Earth Pro to visualize radiological impact caused by a nuclear accident. Digital maps of demographic and land use data are overlayed on Google Earth Pro for more accurate impact estimation to take optimal emergency responses.Keywords: G.A. Siwabessy research reactor, Nuclear emergency, Atmospheric dispersion model, Decision support system, HotSpot codes

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