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Comparison of Fuzzy Synthetic Evaluation Techniques for Evaluation of Air Quality: A Case Study

机译:模糊综合评价技术评价空气质量的比较 - 以案例研究

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Urban air quality has degraded at an alarming rate due to rapid urbanisation and industrialization in megacities. Therefore, there is an urgent need to assess air quality and suggest risk mitigation measures. In this paper, air quality of Chennai city was evaluated using different Fuzzy Synthetic Evaluation (FSE) techniques i.e. Fuzzy similarity method (FSM) and Simple fuzzy classification (SFC) and the results are compared with the National air quality index (NAQI). In the case of SFC weights for different pollutants were computed using Shannon's information entropy. Seasonal analysis of the criteria pollutants shows highest concentration during the winter season followed by pre-monsoon and summer season. The lowest concentration was observed during Monsoon in most cases. The FSE results are optimistic as compared to the NAQI due to aggregation of pollutant concentration as opposed to maximising function in NAQI which reconfirms the findings of earlier researchers. FSE can be used as a decision making tool to communicate the overall air quality to policy makers/end users (Public) in a simplified qualitative form.
机译:由于市场的快速城市化和工业化,城市空气质量以惊人的速度降低。因此,迫切需要评估空气质量并建议风险缓解措施。本文采用不同的模糊综合评价(FSE)技术评估了钦奈城的空气质量。模糊相似性方法(FSM)和简单的模糊分类(SFC),与国家空气质量指数(Naqi)进行比较。在使用Shannon的信息熵计算不同污染物的SFC重量的情况下。标准污染物的季节性分析显示冬季最高的浓度,然后是季季季季和夏季。在大多数情况下,季风期​​间观察到最低浓度。由于污染物浓度的聚集,FSE结果与Naqi的聚集相反,与Naqi的最大化功能相比,与Naqi的最大化功能相比,重新认实了早期研究人员的结果。 FSE可以用作决策工具,以简化的定性形式将整体空气质量传达给政策制造商/最终用户(公共)。

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