# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 4 Experiment Analysis
### Analyzing AQI from Spatiotemporal Distribution Characteristics  
The detailed analysis of the AQI trends in Xi'an City from 2019 to 2020, as depicted in Figure 12, highlights the marked seasonal fluctuations and their underlying causes, providing crucial insights for environmental management and policymaking. The \"U\"-shaped pattern observed annually, with AQI peaking in December and reaching its nadir in August, underscores the strong influence of meteorological conditions and human activities on air pollution levels. The summer months benefit from favorable weather conditions for pollutant dispersion--enhanced solar radiation, increased atmospheric instability, and more frequent precipitation. These meteorological phenomena act in concert to cleanse the air, leading to a more consistent and healthier air quality environment. Conversely, the winter season experiences the worst air quality, characterized by a high AQI and substantial variability. The cold temperatures facilitate the formation of temperature inversions, a meteorological phenomenon that traps pollutants near the ground. Figure 11: Model training time. Figure 12: Monthly variation. Additionally, the heightened demand for heating, often reliant on fossil fuels, and occasionalestive fireworks contribute to a surge in pollutant emissions, exacerbating the air pollution problem. Spring and autumn present their unique challenges. Springtime sees an increase in dust storms, introducing additional particulate matter into the atmosphere. While generally more stable regarding weather, Autumn witnesses increased biomass burning from agricultural practices, which releases pollutants such as delicate particulate matter and volatile organic compounds. Understanding these seasonal dynamics is essential for devising targeted interventions to mitigate air pollution. For instance, measures to curb emissions from heating sources and regulate fireworks usage during winter could be prioritized. Similarly, efforts to control dust storms in the spring and manage agricultural burning in autumn would be instrumental in maintaining better air quality throughout the year. The APSO-CNN-Bi-LSTM model, with its demonstrated capability for accurate and efficient prediction, can be a powerful tool in anticipating these seasonal shifts, enabling authorities to proactively implement strategies to mitigate pollution peaks and safeguard public health. By integrating such predictive analytics into policy planning, cities like Xi'an can work toward more sustainable and resilient urban environments. As illustrated in Figure 1 and the subsequent discussion, Xi'an's geographical and meteorological context is crucial in shaping its air quality dynamics. The city's unique topography significantly influences pollutant dispersion and accumulation patterns in the Weihe River Basin. The Qinling Mountains to the south act as a natural barrier, hindering the northwesterly winds that dominate during fall and winter. These winds, weakened upon encountering the mountain range, fail to effectively ventilate and cleanse the air over Xi'an, leading to higher pollutant concentrations. Furthermore, the mountains also obstruct the influx of moist air from southern regions, exacerbating dry conditions that favor pollutant buildup, especially during dry seasons. The basin-like configuration of the Weihe River Valley exacerbates this situation, trapping pollutants and further intensifying their concentration within the city. The region's semi-arid climate, characterized by harsh, cold winters and sweltering summers, creates atmospherically stable conditions during the colder months. Lower wind speeds and atmospheric stability hamper the vertical and horizontal dispersion of pollutants, creating a conducive environment for air pollution episodes. Urbanization and energy consumption patterns also contribute to the spatial variation in air quality within Xi'an. Analysis of the four datasets reveals that central urban areas exhibit higher AQI values than the outskirts, indicating a higher pollution load.