Translate this page into:
Artificial Intelligence for the Determination of Factors Affecting Atmospheric Dispersion of Radionuclides, the Potential Concentration of Pollutants Downwind of a Source, to Study the Risk for Any Nuclear Facility
* Corresponding author: Dr. O.S. Ahmed, Nuclear and Radiological Safety Research Center, Atomic Energy Authority, Cairo, Egypt. o.abdeldaaem@qu.edu.sa
-
Received: ,
Accepted: ,
How to cite this article: Ahmed OS, Elbegawy H, Salman KA. Artificial Intelligence for the Determination of Factors Affecting Atmospheric Dispersion of Radionuclides, the Potential Concentration of Pollutants Downwind of a Source, to Study the Risk for Any Nuclear Facility. J Qassim Univ Sci. 2026;2:240-52. doi: 10.25259/JQUS_10_2025
Abstract
Objectives
The study’s primary goal is to use artificial intelligence programs to determine the factors influencing radionuclide atmospheric dispersion and the potential concentration of a pollutant downwind of a source..
Material and Methods
These programs can provide information about atmospheric dispersion and determine the potential concentration of a pollutant downwind of a source, which is typically used to study risk analysis, emergency planning, and comprehension of the pertinent atmospheric dispersion in the study of radiological impact on man and his environment.
Results
The analysis demonstrated that the results of the artificial intelligence program’s atmospheric stability class correspond with the Pasquill-Guifford scheme and that the concentration of pollutants in the atmosphere is directly related to air quality, Our results for this work are shown with application.
Conclusion
As demonstrated by the application of artificial intelligence software tools, the concentration of pollutants decreases as the distance above ground increases at constant parameters like emission rate and height above ground. Finally, from the author’s point of view, recommended this work is used as training.
Keywords
Artificial intelligence
Dispersion parameters
Downwind concentration
Plume rise
Radiological impact
Stability class
Wind speed
INTRODUCTION
When a radioactive gas or aerosol enters the atmosphere, its movement and dispersion are controlled by all physical characteristics and those of the surrounding atmosphere. To picture this motion’s characteristics, it is helpful to consider how effluent generally behaves after being released, as illustrated in Figure 1. The temperature and velocity at which the effluent reaches the environment are typically different from those of the surrounding atmosphere. Because of the impacts of vertical velocity and temperature differential until they are dissipated, the effluent motion has a vertical component. Plume rise is the term for this upward movement of effluents, which modifies the release point’s effective height.[1-3]
When evaluating the environmental impact of radiation leaks from nuclear facilities, the atmosphere is a crucial channel to consider. The Gaussian plume model, as seen in Figure 2, is the fundamental component of air dispersion models used to calculate the effects of air pollution. By establishing several parameters, including the classification of the atmospheric stability class, the Gaussian plume dispersion calculation enables the computation of the possible concentration of a pollutant downwind of a source. The profile of wind speed that is vertical,[4-6] rise and dispersion of plumes downwind and σy and σz parameters. An essential part of risk analysis and emergency preparedness for the regulatory safety assessment is the concentration of radioactivity caused by effluent emissions in the air during regular operating or accident conditions from a nuclear facility. Figure 3 provides an overview of the usage of this data in a computer based air pollution model with its many uses, artificial intelligence (AI) is considered one of the century’s most disruptive technologies.
By specifying several parameters, including the identification of the atmospheric stability class, this study sought to use AI in air dispersion models (the Gaussian plume dispersion) for estimating the possible concentration of a pollutant downwind of a source. The profile of wind speed that is vertical, plume rise, downwind radioactivity concentration, and dispersion parameters σy and σz.[7-10]
MATERIAL AND METHODS
The Gaussian plume model is one of the fundamental air dispersion models used to estimate the impacts of air pollution.[11-14] The Gaussian plume dispersion calculation helps to calculate the potential concentration of a pollutant downwind of a source by defining a number of parameters. Data were collected from the National Ocean Atmospheric Administration (NOAA), such as wind speed for 2020 in the northern part of Egypt, and hypothetical data used for stack diameter, stack gas exit velocity, stack gas exit temperature, and ambient temperature, distance of the receptor downwind of the source, emission rate, and height above ground for any nuclear facility.[15-20]
The determination of the atmospheric stability class
The tendency of the atmosphere to either promote or inhibit the buoyant upward flow of air is known as atmospheric stability. Since heat flow and thermal turbulence are correlated, neutral circumstances are observed with high cloud cover, since the amount of cloud cover reduces heating or cooling depending on the time of day. Neutral circumstances are also often produced by high wind speeds. Although low wind speeds and clear skies at night typically result in little dispersion (stable circumstances), the same good dispersion occurs during daylight [unstable conditions as Table 1]. According to the Pasquill-Guifford scheme, choose the atmospheric stability class using the selection tool.[21-24]
| Wind speed U (m.s-1) at 10 m |
Stability class, day, with solar radiation RD (langleys.h-1) |
Stability class, night, with net radiation RN (langleys.h-1) |
|---|---|---|
| RD ≥ 50 50 > RD ≥ 25 25 > RD ≥ 12.5 12.5 > RD | RN > -1.8 -1.8 ≥ RN > -3.6 -3.6≥ RN | |
|
U < 2 2 ≤ U < 3 3 ≤ U < 4 4 ≤ U < 6 6 ≤ U |
A A-B B D A-B B C D B B-C C D C C-D D D C D D D |
D - - D E F D D E D D D D D D |
Where: - A: - Extremely unstable, D: - Neutral, B: - Moderately unstable E: - Slightly stable, C: - Slightly unstable, F: Moderately stable
The vertical wind speed at stack height due to surface/terrain friction
How to determine wind speed at stack height the vertical wind speed profile can be defined as the tendency for wind speeds at higher elevations to be higher than wind speeds at lower elevations due to surface/terrain friction.[25-27] us = ua(hs/ha)p Where: us = wind speed at stack height (m s-1) ua = wind speed at anemometer height (m s-1) ha = anemometer height (m) hs = stack height (m) p = exponent dependent on stability class and environment classification, which can be determined in accordance of the ISC3 Dispersion Models user guide.
Calculate dispersion parameters σy and σz
The dispersion parameters σy and σz are the standard deviations in the y and z directions, respectively, and the Gaussian dispersion model basically implies that gas dispersion and the concentration that results from it are normally distributed in the lateral directions (perpendicular to the wind direction).The atmospheric stability and the distance downwind of the source determine the dispersion parameters in the Gaussian dispersion model. In order to calculate the dispersion parameters, this stage automatically selects the proper constants and equations from the Pasquill-Guifford scheme. Just enter the receptor’s distance from the source, downwind. Based on a combination of theory and experimental data, the dispersion parameters σy and σz are provided as functions of stability and downwind distance (x). Pasquill (1961) created the most popular plan, which Turner (1967) somewhat altered. Using constants based on x (m) distances and Pasquill-Gifford atmospheric stability conditions, the horizontal (σy) and vertical (σz) dispersion coefficients are computed.[28-31]
Calculation plume rise:
Thermal buoyancy and momentum are the two main factors that cause plume ascent. The temperature and density differential between the exhaust gas and the surrounding air determine plume buoyancy. The mass and velocity of the exhaust gas leaving the stack determine momentum. Based on these two mechanisms, a variety of techniques have been devised to estimate the plume rise.[32-39] The Davidson-Bryant formula has been utilized for this calculator since it combines momentum and thermal buoyancy into a single formula:
Where:
ΔH = plume rise above stack (m)
d = diameter of the stack (m)
Vs = stack gas exit velocity (m s-1)
us = wind speed (m s-1)
ΔT = stack gas temperature - ambient temperature (K)
Ts = stack gas temperature (K)
Calculate the downwind concentration
The Gaussian dispersion equation can be used to calculate the concentration of pollutant downwind of a source as follows:
Where:
C = concentration (µg m-3)
Q = emission rate (g s-1)
π = 3.141593
Us = stack height wind speed (m s-1)
σy = lateral dispersion parameter (m)
σz = vertical dispersion parameter (m)
y = crosswind distance (m)
z = elevation of receptor (m)
Hs = effective stack height (hs + ΔH)
RESULTS AND DISCUSSION
The atmospheric stability class
Use the selection tool for AI accordance with the Pasquill - Guifford scheme as shown in Scheme 1i when wind speed less than 2 m s-1, the time of day is day time and solar radiation is strong, the resultant atmospheric stability class is A, [Scheme 1ii] when wind speed less than 2 m s-1, the time of day is day time and solar radiation is moderate, the resultant atmospheric stability class is A-B, [Scheme 1iii] when wind speed less than 2 m s-1, the time of day is day time and solar radiation is slight, the resultant atmospheric stability class is B, [Scheme 1iv] when wind speed from 3 to 4 m s-1, the time of day is day time and solar radiation is strong, the resultant atmospheric stability class is B, [Scheme 1v]when wind speed from 3 to 4 m s-1, the time of day is day time and solar radiation is moderate, the resultant atmospheric stability class is B -C, [Scheme 1vi] when wind speed from 3 to 4 m s-1, the time of day is day time and solar radiation is slight, the resultant atmospheric stability class is C, [Scheme 1vii] when wind speed from 3 to 4 m s-1, the time of day is nighttime and cloud Cover is higher than(4/8)low cloud, the resultant atmospheric stability class is D, [Scheme 1viii] when wind speed from 3 to 4 m s-1, the time of day is nighttime and cloud cover is less than or equal (4/8), the resultant atmospheric stability class is E, [Scheme 1ix] when wind speed higher than 6 m s-1, the time of day is day time and solar radiation is moderate the resultant atmospheric stability class is D, [Scheme 1x] when wind speed higher than 6 m s-1, the time of day is day time and solar radiation is slight the resultant atmospheric stability class is D, [Scheme 1xi] when wind speed higher than 6 m s-1, the time of day is nighttime and cloud cover is higher than(4/8)low cloud, the resultant atmospheric stability class is D, [Scheme 1xii] when wind speed higher than 6 m s-1, the time of day is nighttime and cloud cover is less than or equal (4/8), the resultant atmospheric stability class is D, observation from calculating the stability class using the effect of solar radiation and wind speed as follows increased wind speeds increase turbulence and dispersion, which in turn promotes neutral to unstable circumstances (D to A), strong solar radiation causes vertical air motions (convection), which exacerbates instability (A, B, and C), At night, calm conditions encourage stability (E, F), which results in poor dispersion of pollutants, and this classification important in environmental impact evaluations. It is evident from these results that using AI is in agreement with Table 1.
The calculated wind speed at stack height due to surface/terrain friction
Use the selection tool as shown the wind speed of Scheme 2A is higher than Scheme 2B Although the same stability class, anemometer height, wind speed at anemometer height, stack height but different in the type environmental because the urban free from obstacles other than rural, else for Scheme 2C and Scheme 2D as shown below, where surface/terrain friction’s contribution to wind speed calculation at stack elevation wind speed near the ground and at various elevations, including stack height, is greatly influenced by surface and terrain friction. The amount that the wind slows down because of obstructions like hills, trees, and buildings depends on how rugged the terrain, and the effects of surface and terrain friction near to the ground (high friction) surface impediments cause a large reduction in wind speed, turbulence, which is produced by friction, causes changes in wind direction and speed, else at greater altitude (reduced friction), the impact of surface friction reduces with height and because there are fewer barriers to impede the wind, its speed rises.
Calculate plume rise
Use the selection tool of AI its notice when stack diameter is large at constant all parameter such as stack gas exit velocity, stack gas exit temperature and ambient temperature as shown in Scheme 3A and B, the plume rise above stack tip is high and the effective stack height is high, this happens due to the following reasons increased mass flow rate of hot gases, greater buoyancy force (thermal effect) and increased momentum flux. Then a larger stack diameter leads to a higher mass flow rate, which leads to stronger buoyancy, greater plume rise, and higher effective stack height.
It is noticed when stack gas exit velocity is high at constant all parameter such as, stack diameter, stack gas exit temperature and ambient temperature as shown in Scheme 3C and D, the plume rise above stack tip is high and the effective stack height is high, this occurs because higher stack gas exit velocity leads to higher momentum flux leads to higher starting lift leads to greater plume rise and higher effective stack height
It is noticed when stack gas exit temperature is high at constant all parameter such as, stack diameter, stack gas exit velocity and ambient temperature, as shown in Scheme 3E and F, the plume rise above stack tip is high therefore the effective stack height is high, This happens due to increased buoyancy force (thermal effect), this means higher stack gas exit temperature leads to stronger buoyancy leads to greater plume rise and higher effective stack height
Finally, notice that the plume rise above stack tip is very higher when stack diameter is large than the stack gas exit velocity is high or stack gas exit temperature is high because it greatly increases both mass flow rate and buoyancy
Calculate dispersion parameters σy and σz
This step automatically determines the appropriate constants and formulae from the Pasquill-Guifford scheme to determine the dispersion parameters. Simply enter the distance of the receptor downwind of the source and use the selection tool for AI. Notice that the σy and σz increase as the distance downwind increases. Due to air dispersion, turbulent mixing spreads them out more as the distance downwind increases, increasing both σy and σz, as shown in Scheme 4 (A-D), respectively.
Calculate the downwind concentration
To clarify the calculation of downwind concentration using the selection tool of AI [Scheme 5], it is noted that pollutant concentration increases with an increase in height above ground to a value, then decreases with an increase in height above ground at a constant parameter, such as emission rate and distance crosswind. As we move away from the plume’s main mass due to dispersion, the concentration of pollutants decreases. Where vertical dispersion is large, concentration drops more gradually above effective stack height, and where vertical dispersion is small, concentration drops sharply above effective stack height. The highest concentration is found near the plume centerline, or closer to the core of the plume. The highest concentration happens at the effective stack height because the plume has a bell-shaped distribution in the vertical direction. Pollutant content rises with height below this point as we get closer to the plume core. As contaminants spread out and combine with cleaner air above this altitude, the concentration of pollutants falls, as shown in Table 2 and Figure 4.
| Height above ground (z) (m) | Pollutant concentration (µg m-3) |
|---|---|
| 2 | 0.01 |
| 4 | 0.29 |
| 6 | 7.89 |
| 8 | 103.71 |
| 10 | 659.92 |
| 12 | 2032.34 |
| 14 | 3029.27 |
| 16 | 2185.31 |
| 18 | 763 |
| 20 | 128.93 |
| 22 | 10.55 |
| 24 | 0.42 |
| 26 | 0.01 |
| 28 | 0 |
Scheme 5 from AI :-
To clarify the calculation of downwind concentration using the selection tool [Scheme 6], it is noted that pollutant concentration decreases with increasing crosswind distance at constant parameters such as emission rate and height above ground. As the crosswind distance increases, the concentration of pollutants decreases due to dilution caused by the Gaussian dispersion of pollutants, as shown in Figure 5, Table 3.
| Distance crowswind (m) | Pollutant concentration (μg m-3) |
|---|---|
| 2 | 243802.29 |
| 4 | 139600.09 |
| 6 | 55118.69 |
| 8 | 15006.43 |
| 10 | 2817.22 |
| 12 | 364.7 |
| 14 | 32.55 |
| 16 | 2 |
| 18 | 0.09 |
| 20 | 0 |
Scheme 6 from AI:
CONCLUSION
Applying AI to identify the variables influencing radioactive atmospheric dispersion offers important way to enhance environmental safety and radiological risk assessment in the vicinity of nuclear sites. In order to anticipate the possible concentration of pollutants downwind of a release site, AI models examine important meteorological factors, including temperature gradients, wind direction, wind speed, and air stability. This improves population exposure assessment, contamination zone forecasting, and disaster planning. AI-based modeling combined with conventional dispersion techniques ultimately results in more accurate, real-time decision-making, reducing the possible radioactive hazards related to nuclear and non - nuclear site operations, As demonstrated by the application of AI software tools, the concentration of pollutants decreases as the distance above ground increases at constant parameters like emission rate and height above ground. Finally, from the author’s point of view, recommended this work is used as training.
Author’s contribution
All authors contributed equally to this work and approved the final manuscript.
Ethical approval
Institutional Review Board approval is not required.
Declaration of Patient Consent
Patient consent is not required as no patients are involved in the study.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
References
- Atmospheric dispersion and modeling. AERB Safety Guide No AERB/NF/SG/S-1. India: Atomic Energy Regulatory Board; 2008.
- Dispersion of radioactive material in air and water and consideration of population distribution in site evaluation for nuclear power plants. Safety Standards Series No NS-G-3.2. Vienna: IAEA; 2002.
- Harmonization within atmospheric dispersion modelling for regulatory purposes (8th Workshop) Int J Environ Pollut. 2004;24:1-6.
- [Google Scholar]
- International technical meeting on air pollution modeling and its application (26th). Dordrecht: Kluwer Academic/Plenum Press; 2004.
- Tracking and predicting the atmospheric dispersion of hazardous material releases. Washington DC: National Academies Press; 2003.
- Atmospheric dispersion of hazardous material releases (Workshop). Washington DC: National Academies Press; 2003.
- Offsite consequence analysis. In: General guidance on risk management programs for chemical accident prevention (40 CFR Part 68). Washington DC: Office of Solid Waste and Emergency Response; 2009.
- [Google Scholar]
- Computation of gamma dose due to atmospheric releases with sea breeze fumigation. In: Jesan T, Anand S, Rajaram S, Ravi PM, Tripathi RM, eds. Proc 20th Natl Symp Radiat Meas (NSRP-20). Mangalore: India; 2015.
- [Google Scholar]
- A plume rise model compared with observations. J Air Pollut Control Assoc. 1965;15:433-8.
- [CrossRef] [Google Scholar]
- Discussion of the comparative consequences of different plume rise formulas. Atmos Environ. 1968;2:228-32.
- [CrossRef] [Google Scholar]
- Plume rise. USAEC Critical Review Series. Washington DC: US Atomic Energy Commission; 1969.
- Fundamentals of stack gas dispersion. 4th ed. USA: Author-published; 2005.
- Advances in air pollution modeling for environmental security. Dordrecht: Springer; 2005.
- Some recent analyses of plume rise observation. In: Proc 2nd Int Clean Air Congr. New York: Academic Press; 1971.
- [Google Scholar]
- Chimney plumes in neutral and stable surroundings. Atmos Environ. 1972;6:507-10.
- [CrossRef] [Google Scholar]
- Dispersion characteristics of radioactive materials estimated by wind patterns. Sci Rep. 2018;8:9926.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Atmospheric dispersion in nuclear power plant siting. Safety Series No 50-SG-S3. Vienna: IAEA; 1980.
- Short and long term dispersion patterns of radionuclides in the atmosphere around the Fukushima nuclear power plant. J Environ Radioact. 2011;102:1117-21.
- [CrossRef] [PubMed] [Google Scholar]
- Statistical analysis of rainfall events at Kalpakkam. In: Proc 3rd Natl Conf Reliab Saf Eng (NCRS-2016). Chennai: India; 2016.
- [Google Scholar]
- Empirical investigations of the diffusion of waste air plumes in the atmosphere. Nucl Technol. 1977;34:43-57.
- [CrossRef] [Google Scholar]
- National Oceanic and Atmospheric Administration. Air Resources Laboratory READY system. Available from: https://www.arl.noaa.gov/ready [Last accessed on 2025 Aug 18].
- WKC Group. Online air dispersion model. Available from: https://www.wkcgroup.com/tools-room/online-air-dispersion-model [Last accessed on 2025 Aug 18].
- Atmospheric dispersion patterns of radionuclides originating from nuclear power plant accidents under various release types. Int J EQ. 2019;4:75-8.
- [CrossRef] [Google Scholar]
- Dispersion of radionuclides released by nuclear accident and dose assessment in the Greater Bay Area of China. Ann Nucl Energy. 2019;132:593-602.
- [CrossRef] [Google Scholar]
- Atmospheric chemistry and physics: from air pollution to climate change. Hoboken: Wiley; 2006.
- Handbook on atmospheric diffusion. Washington DC: US Department of Energy; 1982.
- Tracer experiments on dispersion of plumes over terrain of major surface roughness. Julich: Kernforschungsanlage Julich; 1974.
- Workbook of atmospheric dispersion estimates. Cincinnati: US Public Health Service; 1967.
- Air dispersion modelling. New Jersey: John Wiley & Sons; 2014.
- Mathematical analysis of air pollution from stacks using Briggs method. Ann Fac Eng Hunedoara Int J Eng. 2020;18:145-48.
- [Google Scholar]
- Impacts of estimated plume rise on PM2.5 exceedance prediction during extreme wildfire events: A comparison of three schemes (Briggs, Freitas, and Sofiev) Atmos Chem Phys. 2023;23:3083-101.
- [Google Scholar]
- Predicting emission source terms in a reduced-order fire spread model—part 1: Particulate emissions. Fire. 2020;3:4.
- [Google Scholar]
- Modelling of the plume rise phenomenon due to warehouse fires considering penetration of the mixing layer. In: Proc 22nd Annu Int Symp. Texas: USA; 2019.
- [Google Scholar]
- Review of low emission zones in Europe: Case of London and German cities. Teh Glas. 2017;11:55-62.
- [Google Scholar]
- Prediction of plume rise dispersion patterns under variations of chimney exhaust emission velocity using computer simulation. J Rekayasa Mesin. 2022;13:939-45.
- [Google Scholar]