Complex Circular Intuitionistic Fuzzy Heronian Mean Aggregation for Dynamic Air Quality Monitoring and Public Health Risk Prediction

In recent years, air pollution has become a global concern due to its adverse impact on human health, leading to increased chronic conditions and early deaths. The effects of poor-quality air extend beyond individual health to communities and economies. Therefore, improving the prediction of air pollution levels and identifying related health hazards is crucial. Traditional models struggle with the complexities and uncertainties of dynamic air contaminants, limiting their effectiveness. To overcome these challenges, we introduce a novel Complex Circular Intuitionistic Fuzzy Heronian Mean (CCIFHM) approach for monitoring dynamic air quality and assessing public health risks. This approach defines the proposed fuzzy models’ properties, theorems, and axioms. Extensive experimental validations using air quality data from various operators compared the CCIFHM with existing models, demonstrating its superior ability to estimate air pollution levels and assess public health risks. Our findings suggest that the CCIFHM model can significantly enhance air quality monitoring and risk prediction by providing more accurate and reliable data, thus supporting better policy formulation and decision-making. By addressing the limitations of current models, the CCIFHM approach can potentially improve public health protection and foster a healthier environment.