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<p>Air pollution remains a major public health concern, with fine particulate matter (PM<sub>2.5</sub>) and its associated toxic pollutants contributing substantially to cancer risk. This study introduces a novel machine learning framework to predict Incremental Lifetime Cancer Risk (ILCR) using routine meteorological and air quality data, offering a cost-effective alternative to direct Polycyclic Aromatic Hydrocarbons (PAHs) measurements. In the present study, two modelling strategies were evaluated. The first is the Pollution Source Method (PSM), which incorporates wind parameters transformed according to local pollution source directions, and the second is the Conventional Method (CM), which uses unprocessed meteorological inputs. Artificial Neural Network (ANN) and Extreme Gradient Boosting (XGBoost) models were applied under both strategies and assessed using R², MAE, MSE, RMSE, and MAPE. The PSM-ANN model showed the strongest performance (R² = 0.944; MAE = 0.037), while the CM-XGB model performed the weakest (R² = 0.799; MAE = 0.061). Error analyses confirmed that PSM-based models produced more stable predictions with reduced uncertainty. By enabling real-time ILCR prediction from low-cost sensors, this framework can support early public health interventions and risk communication. Future work will expand this approach to diverse regions and explore deep learning techniques to further enhance predictive accuracy.</p>

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Innovative integration of wind transformation in AI models for real-time carcinogenic risk assessment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月25日

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  • Shivam Singh,
  • Pratibha Vishwakarma,
  • Tarun Gupta

摘要

Air pollution remains a major public health concern, with fine particulate matter (PM2.5) and its associated toxic pollutants contributing substantially to cancer risk. This study introduces a novel machine learning framework to predict Incremental Lifetime Cancer Risk (ILCR) using routine meteorological and air quality data, offering a cost-effective alternative to direct Polycyclic Aromatic Hydrocarbons (PAHs) measurements. In the present study, two modelling strategies were evaluated. The first is the Pollution Source Method (PSM), which incorporates wind parameters transformed according to local pollution source directions, and the second is the Conventional Method (CM), which uses unprocessed meteorological inputs. Artificial Neural Network (ANN) and Extreme Gradient Boosting (XGBoost) models were applied under both strategies and assessed using R², MAE, MSE, RMSE, and MAPE. The PSM-ANN model showed the strongest performance (R² = 0.944; MAE = 0.037), while the CM-XGB model performed the weakest (R² = 0.799; MAE = 0.061). Error analyses confirmed that PSM-based models produced more stable predictions with reduced uncertainty. By enabling real-time ILCR prediction from low-cost sensors, this framework can support early public health interventions and risk communication. Future work will expand this approach to diverse regions and explore deep learning techniques to further enhance predictive accuracy.