# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 1 Introduction  
Air quality is intrinsically linked to human well-being and health. Air is a fundamental constituent of life and is pivotal for sustaining ecosystems [1]. The state of air quality transcends ecological health, directly impacting human life and public well-being. Severe air pollution inflicts substantial harm upon economies and livelihoods [2]. Recently, with the rapid development of urbanization and industrialization, air pollution has become increasingly severe. The World Health Organization (WHO) estimates that air quality issues account for approximately 7 million yearly fatalities globally [3; 4; 5]. Consequently, air quality has become a focal point of global concern. Investigating the determinants of air quality and devising accurate air quality prediction methods hold considerable relevance for regulating and remedying atmospheric pollution [6]. Presently, numerous urban centers in our country have deployed environmental monitoring systems capable of yielding extensive data on particulate matter concentrations [7]. Nevertheless, air quality prediction remains a formidable challenge, given the substantial temporal fluctuations and many influencing factors [8]. In the current era of pollution prevention and control, alongside efforts toward carbon neutrality, achieving high-precision prediction of the Air Quality Index (AQI) has become a critical research focus. Accurate AQI prediction holds significant value for urban development and public health, providing essential insights for policy making and environmental management. This research contributes to enhancing urban air quality and plays a pivotal role in safeguarding national health by enabling timely and effective pollution mitigation strategies. Air quality prediction models can be categorized as numerical or statistical. Numerical models are often used to predict air quality based on atmospheric physical and chemicalprocesses using meteorological and mathematical principles related to them [9]. Since the 1950s, there has been a rapid development of such models, including Community Multiscale Air Quality Modeling Systems [10] (CMAQ) and Weather Research and Forecasting Model with Chemistry [11] (WRF-Chem). These models are overly dependent on specific values of airborne pollutant concentrations and require complex calculations. Even so, the prediction results of these models have high uncertainty. The development of air quality prediction models from a data-driven perspective is a common approach to statistical modeling. There are a number of methods based on statistical modeling that have been widely used, such as autoregressive integrated moving average [12] (ARIMA) and multiple linear regression [13] (MLR). In 1993, [PERSON] et al. [14] developed a Multi-Layer Perceptron (MLP)-based model to predict SO2 concentration in air. Since then, machine learning models have been rapidly developed and widely used in the field of air quality prediction, such as the artificial neural networks [15] (ANN), the random forest [16] (RF), and the support vector regression [17] (SVR). Machine learning methods and other methods are effectively combined to establish the corresponding hybrid model, which can improve the accuracy of the model. Through the continuous development of machine learning, two improved methods have achieved greater success. One of them is LSSVRGSA, proposed by [PERSON] et al. [18] in 2020, which uses the Gravity Search Algorithm (GSA) to optimize the unknown parameters of the Least Squares Support Vector Regression (LSSVR) to reach the best performance of the model. Another improvement method was proposed by [PERSON] et al. [19] in 2017 to obtain the input series after empirical modal decomposition (EMD) of the raw air quality data and input them into SVR to obtain more accurate prediction performance. It is well-known that air quality data have obvious spatial and temporal characteristics; however, these models have defects in extracting the spatial and temporal characteristics contained in the series data.