# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 2 Introduction to the Study Area and Dataset Sources  
### Air Quality in Xi'an  
Xi'an was chosen as the study area because it is an essential city in northwest China and belongs to the temperate semi-arid climate zone. It borders the Loess Plateau to the east and the Qinling Mountains to the west, with high elevations on all sides and low in the center. Therefore, this geographical location makes Xi'an susceptible to cold air masses in winter, creating meteorological conditions unfavorable to the dispersion of pollutants. At the same time, the Qinling Mountains, while blocking some of the intrusion of sandstorms to a certain extent, may also act as a barrier to the accumulation of pollutants under certain meteorological conditions. In summer, humid airflow from the south can bring better purification and improve air quality. According to the annual statistical report of Xi'an City, in 2019 and 2020, there were 297 days with significant air pollution levels, and the primary pollutants were PM\({}_{2.5}\), O\({}_{3}\), and PM\({}_{10}\). These pollutants pose health risks to residents and impact the city's ecological balance and economic sustainability. To quickly and accurately assess air quality, it is necessary to adopt scientific evaluation methods and reasonable air pollution evaluation indicators to qualitatively or comprehensively characterize the current air quality situation [25]. Adopting the Air Quality Index (AQI) as a primary assessment tool underscores the necessity for a comprehensive and standardized approach to understanding air quality conditions. The AQI offers a holistic picture of air pollution levels by consolidating data on six key pollutants (NO\({}_{2}\), CO, O\({}_{3}\), SO\({}_{2}\), PM\({}_{10}\), and PM\({}_{2.5}\)). The AQI quantifies the individual pollutant concentrations and translates these concentrations into a single, easily understandable index, categorizing air quality into several levels ranging from good to hazardous. This simplification facilitates communication with the public, policymakers, and researchers, allowing for quick assessments and appropriate responses. The acquisition of hourly AQI data from four monitoring stations in Xi'an City over two years (2019-2020) from _the China Environmental Testing Center_ (CETC, [[http://www.cnemc.cn/sssi/](http://www.cnemc.cn/sssi/)]([http://www.cnemc.cn/sssi/](http://www.cnemc.cn/sssi/)), accessed 6 May 2024) highlights a rigorous and detailed data collection process. Such granular data are vital for precise modeling and prediction exercises, as they capture the dynamic nature of air pollution and its variation across different locations and times. Table 1 and Figure 1 show the information on the four air quality monitoring stations and the study area.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Monitoring Station** & **Dataset** & **Latitude** & **Longitude** & **District** \\ \hline Xing Qing Community & 1036A & 34.26 \({}^{\circ}\)N & 108.99 \({}^{\circ}\)E & Bei Lin \\ Yan Liang Aviation Base & 1037A & 34.64 \({}^{\circ}\)N & 109.20 \({}^{\circ}\)E & Yan Liang \\ CPCCC Building & 1038A & 34.36 \({}^{\circ}\)N & 108.62 \({}^{\circ}\)E & Zhou Zhi \\ Tin Ka Ping Secondary School & 1039A & 34.16 \({}^{\circ}\)N & 109.52 \({}^{\circ}\)E & Lan Tian \\ Chong Wen Tower Station & 1040A & 34.24 \({}^{\circ}\)N & 108.92 \({}^{\circ}\)[PERSON] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Investigated Air Quality Monitoring Sites in Xian.