Analyzing the change in the concentration of pollutants during the covid-19 epidemic and presenting a model based on machine learning to predict air pollution

جغرافیا و مخاطرات محیطی

Article 12, Volume 13, Issue 3 - Serial Number 51, November 2024, Pages 310-338   

Document Type: Research Article

DOI: 10.22067/geoeh.2023.83939.1405

Authors

Abbas Maleki¹; Sadegh Abedi^{* 2}; Alireza Airajpour²

¹PH.D. Student, Faculty of Management and Accounting, Islamic Azad University, Qazvin Branch, Qazvin, Iran.

²Assistant Professor, Faculty of Management and Accounting, Islamic Azad University, Qazvin Branch, Qazvin, Iran.

Abstract

In response to the Covid-19 pandemic, governments worldwide implemented crisis management strategies to reduce emissions from traffic sources. This study examines changes in air pollutant concentrations and traffic volume considered subsets of the environmental index of sustainable urban development—during the Covid-19 epidemic, comparing them with the pre-epidemic period from January 21, 2018, to March 20, 2022. The primary objective of this research is to compare pollutant concentrations during the epidemic with those of the pre-pandemic period and to develop a model for predicting the Air Quality Index (AQI) in Iran's metropolitan cities.
First, collected pollutant data from Iran’s metropolises were processed and cleaned. Following feature selection using the Particle Swarm Optimization (PSO) algorithm, machine learning methods were applied to analyze the data. The results reveal that no consistent pattern of increase or decrease in pollutant concentrations was observed across all metropolitan cities during the Covid-19 pandemic compared to the pre-pandemic period. The effects of restrictions on pollutant concentrations varied significantly across different cities.
To manage both the pandemic crisis and the associated air pollution crisis, which may exacerbate the spread of disease, it is essential to design traffic restriction models tailored to the specific conditions of each urban location. Additionally, the findings indicate that the Air Quality Index in most of Iran’s major cities did not decrease during the pandemic; in fact, it increased. Therefore, targeted and precise measures must be adopted to manage similar crises in the future. Such measures should aim to reduce pollutant concentrations and improve the air quality index, taking into account the geographical characteristics of each city.

Keywords

Air quality index(AQI); Traffic Emission; covid-19 Pandemic; machine learning Models; Urban Air Pollution; Environmental Impact Assessment

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