Assessment and Optimization of Laboratory Air Quality Based on Multi-Indicator Fusion Algorithm

The assessment of laboratory air quality is critical for ensuring experimental safety and personnel health. However, existing evaluation methods often exhibit significant limitations in terms of accuracy, real-time performance, and comprehensive analysis, particularly when dealing with the complex pollutants unique to laboratory environments. To address these limitations, this paper proposes an air quality assessment method based on multi-indicator integrated analysis. Specifically, the traditional Air Quality Index (AQI) algorithm is enhanced by incorporating two key indicators: benzene compound concentration and biological contaminant concentration. Through advanced multi-indicator data processing and integrated analysis, the proposed method significantly improves the accuracy and comprehensiveness of the assessment. The weight distribution has been adjusted according to the degree of harm that pollutants pose to human health, leading to the development of a specialized air quality evaluation grading standard. To validate the effectiveness of the improved algorithm, air quality assessments were conducted using random data in three different types of laboratory environments. The experimental results show that the proposed method performs well across various datasets, effectively monitoring and alerting potential air quality issues. The improved algorithm provides a new method and tool for laboratory air quality assessment, significantly enhancing the precision and reliability of the evaluation, and holds important practical application value.