Machine learningdriven framework for realtime air quality assessment and predictive environmental health risk mapping

Area of Science:

• Environmental Science
• Public Health
• Data Science

Background:

• Urban and industrial environments face significant air pollution challenges impacting public health.
• Accurate, real-time air quality assessment is crucial for effective health risk prediction and mitigation strategies.

Purpose of the Study:

• To develop and validate a practical, innovative framework for real-time air quality assessment and health risk prediction.
• To integrate multi-source data and advanced machine learning for accurate pollutant forecasting across diverse environments.

Main Methods:

• Utilized a combination of machine learning algorithms including Random Forest, Gradient Boosting, XGBoost, and Long Short-Term Memory (LSTM) networks.
• Integrated data from fixed/mobile sensors, meteorological, satellite, and demographic sources.
• Employed SHAP analysis for model interpretability and identified key predictive variables.

Main Results:

• Achieved high temporal accuracy in predicting pollutant concentrations and classifying air quality levels.
• Developed a cloud-based system providing live updates via web dashboard and mobile alerts.
• Generated visual risk maps and health advisories every five minutes, identifying vulnerable populations.

Conclusions:

• The framework offers a robust solution for real-time air quality monitoring and health risk assessment.
• It enhances decision-making capabilities for public health officials and communities.
• Future validation will focus on real-world sensor deployment and correlation with health impact records.