Rajesh M, Babu R Ganesh, Moorthy Usha, Easwaramoorthy Sathishkumar Veerappampalayam
Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission's Research Foundation (DU), Paiyanur, Tamilnadu, India.
Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, 522 302, India.
Sci Rep. 2025 Aug 6;15(1):28801. doi: 10.1038/s41598-025-14214-6.
This research introduces a practical and innovative approach for real-time air quality assessment and health risk prediction, focusing on urban, industrial, suburban, rural, and traffic-heavy environments. The framework integrates data from multiple sources, including fixed and mobile air quality sensors, meteorological inputs, satellite data, and localised demographic information. Using a combination of machine learning techniques such as Random Forest, Gradient Boosting, XGBoost, and Long Short-Term Memory (LSTM) networks the system predicts pollutant concentrations and classifies air quality levels with high temporal accuracy. Interpretability is achieved through SHAP analysis, which provides insight into the most influential environmental and demographic variables behind each prediction. A cloud-based architecture enables continuous data flow and live updates through a web dashboard and mobile alert system. Visual risk maps and health advisories are generated every five minutes to support timely decision-making. The framework not only forecasts pollution trends but also identifies vulnerable populations through spatial overlays. Future validation will include real-world sensor deployment and comparison with health impact records to ensure both scientific accuracy and community relevance.
本研究引入了一种实用且创新的方法，用于实时空气质量评估和健康风险预测，重点关注城市、工业、郊区、农村和交通繁忙的环境。该框架整合了来自多个来源的数据，包括固定和移动空气质量传感器、气象输入数据、卫星数据以及本地化的人口统计信息。通过结合随机森林、梯度提升、XGBoost和长短期记忆（LSTM）网络等机器学习技术，该系统能够以高时间精度预测污染物浓度并对空气质量水平进行分类。通过SHAP分析实现了可解释性，该分析深入了解了每个预测背后最具影响力的环境和人口变量。基于云的架构通过网络仪表板和移动警报系统实现了连续的数据流和实时更新。每五分钟生成一次视觉风险地图和健康建议，以支持及时决策。该框架不仅预测污染趋势，还通过空间叠加识别脆弱人群。未来的验证将包括实际传感器部署以及与健康影响记录的比较，以确保科学准确性和与社区的相关性。
