AUTHOR=Wang Weijia , Guan Xin , Peng Xiaoyan , Wang Zeyu , Liang Xinyi , Zhu Junfan 

TITLE=Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model

JOURNAL=Frontiers in Public Health

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2024.1357715

DOI=10.3389/fpubh.2024.1357715

ISSN=2296-2565

ABSTRACT=To enhance the precision of evaluating the impact of urban environments on resident health, this study introduces a novel fuzzy intelligent computing model designed to address health risk concerns using multi-media environmental monitoring data. Three cities were selected for the study: Beijing (B City), Kunming (K City), and Wuxi (W City), representing high, low, and moderate pollution levels, respectively. The study employs a Fuzzy Inference System (FIS) as the chosen fuzzy intelligent computing model, synthesizing multi-media environmental monitoring data for the purpose of urban health risk assessment. Key findings include: (1) The model reliably estimates health risks across diverse cities and environmental conditions. (2) There is a positive correlation between PM2.5 concentrations and health risks, though the impact of noise levels varies by city. In cities B, K, and W, the respective correlation coefficients are 0.65, 0.55, and 0.7. (3) The Root Mean Square Error (RMSE) values for cities B, K, and W are 0.0132, 0.0125, and 0.0118, respectively, indicate that the model has high accuracy. The R² values for the three cities are 0.8963, 0.9127, and 0.9254, respectively, demonstrating the model's high explanatory power. The residual values for the three cities are 0.0087, 0.0075, and 0.0069 respectively, indicating small residuals, demonstrating robustness and adaptability, and enabling effective assessment of health risks in different cities.Additionally, the model's p-values for the Indoor Air Quality Index (IAQI), Thermal Comfort Index (TCI), and Noise Pollution Index (NPI) all satisfy p < 0.05 for the three cities. These results affirm the model's credibility in estimating health risks under varied urban environments, showcasing its ability to adapt to diverse geographical conditions and aiding in the accurate assessment of existing risks in urban settings. This study significantly advances environmental health risk assessment by integrating multidimensional data, enhancing the formulation of comprehensive environmental protection and health management strategies, and providing scientific support for sustainable urban planning.