# The Power of Machine Learning Methods and PSO in Air Quality Prediction
## 1 Introduction  
Air pollution is an environmental problem caused by increased harmful gases, particles, and biological molecules in the atmosphere. Natural and artificial causes cause this pollution, leading to severe ecological, economic, and health problems [1]. Climate change, acid rain, and ozone depletion are ecological factors caused by air pollution. It causes respiratory diseases, cardiovascular diseases, cognitive development in children, neurological diseases in adults, and premature deaths [2]. Air pollution also has economic and social impacts. The treatment costs of air pollution-related diseases significantly burden countries' health systems. The absence of employees from the labor force due to diseases reduces economic productivity. Regions with poor air quality may lose their tourist attraction [3]. Industrial activities, use of fossil fuels, agriculture, animal husbandry, domestic use of fuels such as coal and wood for heating, forest fires, volcanic activities, and dust storms are the primary sources of air pollution [4]. Maintaining ambient air quality requires routine air pollution forecasts and monitoring. There are many studies in the literature for this purpose. [PERSON] et al. [5] used machine learning to predict the air quality index. They applied various machine learningtechniques for a city in India. With a root mean square error (RMSE) of 0.76, the CatBoost algorithm fared better than the other models. [PERSON] et al. [6] aimed to predict air quality using machine learning methods. The random forest algorithm provided 100% accuracy in the dataset they applied, while multivariate naive Bayes provided 52% accuracy with the lowest performance. Using two months of air quality data, [PERSON] and [PERSON] [7] presented a machine learning approach for precise air quality estimates. To forecast the air quality for the following day, they used meteorological, air pollutant, and air quality index (AQI) data from 197 capital cities in the World Weather Depository. Random forest classifiers produced reliable predictions. [PERSON] and [PERSON] [8] estimated and forecasted CO2 emissions throughout China's city level using open access data and machine learning models. To create CO2 emission estimation models, they chose 18 of the 31 predictor variables. Compared with other approaches, XGBoost models had the lowest relative error and the best prediction accuracy (0.98 R2). To forecast the daily AQI and evaluate the air quality grade (AQG), [PERSON] et al. [9] examined various machine learning methods. The study analyzed publicly accessible data for six contaminants during five years. AQI was estimated using lasso regression, random forest, stacked regressor, and gradient boosting. AQG was calculated using several methods, including support vector machine (SVM) and K-nearest neighbors. In the study, for AQG, the stacked model outperformed the other machine learning models in all metric scores. [PERSON] and [PERSON] [10] combined a comparative analysis of two metropolitan settings to provide machine learning modeling techniques for air pollution mitigation. For AQI prediction, the DNN-Markov hybrid model produced the best results. In another study, [PERSON] [11] comparatively considered the traditional ARIMA model and deep learning-based LSTM networks to predict the future CO2 emission levels of Turkey. LSTM and ARIMA achieved an R2 score of 0.904 and 0.943, respectively. The studies' findings related to air quality prediction in the literature show that machine learning techniques are an effective tool for predicting air quality. However, when a single machine learning model is traditionally used to predict air quality, it is difficult to obtain a good prediction result under various AQI fluctuation trends. To overcome this problem, studies have been combining machine learning methods with different optimization algorithms for air quality prediction. [PERSON] et al.