# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 2 Introduction to the Study Area and Dataset Sources
### Input Variables  
Air quality is influenced by various factors, including pollution emissions, meteorological conditions, geographical location, and socio-economic factors. Historical concentrations of air pollutants can serve as valuable references for future air quality predictions. Meteorological indicators are reported to be essential input variables for air quality prediction models. Geographical location has a significant influence on climatic factors as well as wind speed and direction. The city's industrialization level and energy structure will also have more or less impact on air quality. Under the combined effect of these factors, the air quality prediction task has spatiotemporal solid characteristics. Therefore, we selected the 6 h air quality, spatiotemporal, and meteorological data as input variables to construct the AQI prediction model (shown in Table 2). The temporal resolution for all data types is set to hourly to capture the dynamic nature of air quality changes accurately.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets Type** & **Variables** & **Unit** & **Min** & **Max** & **Transformed** \\ \hline \multirow{6}{*}{Air quality data} & NO\({}_{2}\) & ug/m\({}^{3}\) & 3 & 221 & \([0,1]\) \\  & CO & mg/m\({}^{3}\) & 0.1 & 9.2 & \([0,1]\) \\  & O\({}_{3}\) & ug/m\({}^{3}\) & 13 & 410 & \([0,1]\) \\  & PM\({}_{2.5}\) & ug/m\({}^{3}\) & 4 & 267 & \([0,1]\) \\  & PM\({}_{10}\) & ug/m\({}^{3}\) & 2 & 190 & \([0,1]\) \\  & SO\({}_{2}\) & ug/m\({}^{3}\) & 2 & 420 & \([0,1]\) \\ \hline \multirow{6}{*}{Meteorological data} & Rainfall & mm & 0.0 & 26.3 & \([0,1]\) \\  & Temperature & °C & \(-14.7\) & 38.4 & \([0,1]\) \\  & Dewpoints & °C & \(-35\) & 27.4 & \([0,1]\) \\  & Air pressure & hPa & 986.4 & 1044.8 & \([0,1]\) \\  & Wind speed & m/s & 0.4 & 10.7 & \([0,1]\) \\  & Wind direction & / & 2.3 & 322.5 & \([0,1]\) \\ \hline \multirow{6}{*}{Spatiotemporal data} & Longitude & / & 108.89 & 108.99 & \([0,1]\) \\  & Latitude & / & 34.18 & 34.31 & \([0,1]\) \\ \cline{1-1}  & Distance & & & & \\ \cline{1-1}  & from & m & 4884.65 & 13,115.34 & \([0,1]\) \\ \cline{1-1}  & Xi’an Station & & & & \\ \cline{1-1}  & Season Index & / & 1 & 4 & \([0,1]\) \\ \cline{1-1}  & Weekday & / & 0 & 1 & \([0,1]\) \\ \cline{1-1}  & Index & & & & \\ \cline{1-1}  & Time & / & 0 & 23 & \([0,1]\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input variables of AQI prediction models.  
Figure 1: Study area.  
Meteorological datasets were sourced from _the China National Meteorological Science Data Center_ ([[http://data.cma.cn/site/index.html](http://data.cma.cn/site/index.html)]([http://data.cma.cn/site/index.html](http://data.cma.cn/site/index.html)), accessed 6 May 2024) [26]. These datasets include rainfall quantity, air pressure, dew points, wind speed, and direction. The air quality datasets were provided by the China Environmental Testing Center (CETC, [[http://www.cnemc.cn/sssj/](http://www.cnemc.cn/sssj/)]([http://www.cnemc.cn/sssj/](http://www.cnemc.cn/sssj/)), accessed 6 May 2024). These datasets encompass hourly concentration data of various pollutants (NO\({}_{2}\), CO, O\({}_{3}\), SO\({}_{2}\), PM\({}_{10}\), and PM\({}_{2.5}\)) collected from four air quality monitoring stations in Xi'an, spanning from 1 January 2019 to 31 December 2020. In addition to these datasets, the spatiotemporal data comprised three types of static spatial data: the longitude and latitude of the ground air quality monitoring stations, the distance from each monitoring station to the Xi'an station, and the structural data of these spatial attributes, which remained constant over time. Furthermore, three types of temporal data were included: seasonal data, weekday indices, and the specific times at each monitoring station. The monitoring season was mapped using 1 to 4, and the weekday index was represented by 0 (Saturday and Sunday) or 1 (Monday to Friday).