Temporal Analysis and Prediction of Air Quality Based on Environmental Data

Air pollution is one of the most pressing environmental challenges of the modern era, significantly impacting public health, ecosystems, and economic stability. Pollutants such as PM2.5, PM10, and NO₂—largely driven by urbanization, industrialization, and vehicular emissions—have exacerbated global health issues, including respiratory illnesses, cardiovascular diseases, and premature mortality. According to the World Health Organization, air pollution contributes to over 7 million deaths annually, with economic losses amounting to billions of dollars. Beyond its health and financial toll, air pollution worsens climate change, underscoring the urgent need for accurate air quality index (AQI) prediction to enable timely interventions and evidence-based policymaking. Traditional statistical models like ARIMA and linear regression have long been used for air quality forecasting. However, their inability to capture non-linear relationships, temporal patterns, and meteorological influences limits their effectiveness. Recent advancements in machine learning and deep learning techniques have revolutionized air quality prediction. Models such as Random Forest and Gradient Boosting are adept at handling complex data relationships and hybrid approaches like CNN-LSTM and ARIMA-LSTM offer superior capabilities in modeling spatial and temporal dependencies. These approaches, when combined with real-time meteorological and geospatial data, have significantly improved the accuracy and reliability of air quality predictions. This study aims to develop a robust and adaptive AQI prediction framework using the OpenAQ dataset, a comprehensive repository of real-time air quality data from diverse global locations. The research will focus on preprocessing, exploratory data analysis (EDA), and the application of advanced modeling techniques to address existing limitations. By integrating variables such as temperature, humidity, and wind speed, the models will enhance prediction accuracy and regional adaptability. Metrics including RMSE, MAE, and R² will be used to evaluate model performance. The outcomes of this research are expected to provide actionable insights for policymakers, urban planners, and environmental agencies, fostering proactive pollution mitigation strategies.