Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model
View abstract on PubMed
Summary
This summary is machine-generated.This study developed a novel fuzzy intelligent computing model to assess urban health risks using environmental data. The model accurately estimates risks, showing PM2.5
Area Of Science
- Environmental Health
- Intelligent Computing
- Urban Planning
Background
- Urban environments pose complex health risks.
- Evaluating these risks requires sophisticated assessment tools.
- Existing methods may lack precision in diverse conditions.
Purpose Of The Study
- Introduce a novel fuzzy intelligent computing model.
- Enhance the precision of urban health risk evaluation.
- Utilize multi-media environmental monitoring data.
Main Methods
- Employed a Fuzzy Inference System (FIS) model.
- Synthesized multi-media environmental data (PM2.5, noise, IAQI, TCI, NPI).
- Validated the model across three cities with varying pollution levels (Beijing, Kunming, Wuxi).
Main Results
- The model accurately estimated health risks (high R² values: 0.89-0.92).
- Positive correlation found between PM2.5 and health risks; noise impact varied by city.
- Model demonstrated high accuracy (low RMSE) and credibility (p < 0.05 for key indices).
Conclusions
- The fuzzy intelligent model effectively assesses urban health risks in diverse environments.
- Findings support enhanced environmental protection and health management strategies.
- Provides scientific basis for sustainable urban development and planning.
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