Predicting the Percentage of Air Pollution Gases Using the Particle Swarm Optimization Algorithm
Article Sidebar
Main Article Content
Abstract
Caring for the environment is a major priority in many countries, as environmental pollution poses a serious threat to natural resources, including water, air, and land. Pollution levels have reached alarming rates, prompting researchers across various scientific disciplines to focus on studies aimed at reducing and controlling these pollutants within permissible limits.
Air and atmospheric pollution are among the most dangerous forms of pollution affecting human health and the environment. They contribute to global warming and ozone layer depletion by emitting harmful gases, especially nitrogen dioxide (NO₂). When the concentration of NO₂ in the air reaches 0.07%, it transforms into nitric acid, a lethal gas that can cause death within half an hour. These oxides react with hemoglobin in the blood, hindering oxygen transport to cells, making children particularly vulnerable. Symptoms such as blue lips are common signs of this type of poisoning. In industrial regions like the United States, nitrogen oxides are major contributors to acid rain.
With significant advances in digital data recording technologies, environmental data is now captured as a time series. This allows the application of mathematical models to analyze pollutant behavior for control and prediction. In this research, the Particle Swarm Optimization (PSO) algorithm was applied to analyze nitrogen dioxide (NO₂) levels in Baghdad from 2015 to 2017, using weekly averages across 157 observations. The model achieved a prediction accuracy rate of approximately 94%.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Tikrit Journal of Pure Science is licensed under the Creative Commons Attribution 4.0 International License, which allows users to copy, create extracts, abstracts, and new works from the article, alter and revise the article, and make commercial use of the article (including reuse and/or resale of the article by commercial entities), provided the user gives appropriate credit (with a link to the formal publication through the relevant DOI), provides a link to the license, indicates if changes were made, and the licensor is not represented as endorsing the use made of the work. The authors hold the copyright for their published work on the Tikrit J. Pure Sci. website, while Tikrit J. Pure Sci. is responsible for appreciate citation of their work, which is released under CC-BY-4.0, enabling the unrestricted use, distribution, and reproduction of an article in any medium, provided that the original work is properly cited.
References
Caring for the environment is a major priority in many countries, as environmental pollution poses a serious threat to natural resources, including water, air, and land. Pollution levels have reached alarming rates, prompting researchers across various scientific disciplines to focus on studies aimed at reducing and controlling these pollutants within permissible limits.
Air and atmospheric pollution are among the most dangerous forms of pollution affecting human health and the environment. They contribute to global warming and ozone layer depletion by emitting harmful gases, especially nitrogen dioxide (NO₂). When the concentration of NO₂ in the air reaches 0.07%, it transforms into nitric acid, a lethal gas that can cause death within half an hour. These oxides react with hemoglobin in the blood, hindering oxygen transport to cells, making children particularly vulnerable. Symptoms such as blue lips are common signs of this type of poisoning. In industrial regions like the United States, nitrogen oxides are major contributors to acid rain.
With significant advances in digital data recording technologies, environmental data is now captured as a time series. This allows the application of mathematical models to analyze pollutant behavior for control and prediction. In this research, the Particle Swarm Optimization (PSO) algorithm was applied to analyze nitrogen dioxide (NO₂) levels in Baghdad from 2015 to 2017, using weekly averages across 157 observations. The model achieved a prediction accuracy rate of approximately 94%.