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    孙俏, 张彤, 王新阳, 陈志泊. 基于渐进DS证据理论的空气质量评价方法[J]. 北京林业大学学报, 2022, 44(3): 119-128. DOI: 10.12171/j.1000-1522.20210228
    引用本文: 孙俏, 张彤, 王新阳, 陈志泊. 基于渐进DS证据理论的空气质量评价方法[J]. 北京林业大学学报, 2022, 44(3): 119-128. DOI: 10.12171/j.1000-1522.20210228
    Sun Qiao, Zhang Tong, Wang Xinyang, Chen Zhibo. Air quality evaluation method based on progressive DS evidence theory[J]. Journal of Beijing Forestry University, 2022, 44(3): 119-128. DOI: 10.12171/j.1000-1522.20210228
    Citation: Sun Qiao, Zhang Tong, Wang Xinyang, Chen Zhibo. Air quality evaluation method based on progressive DS evidence theory[J]. Journal of Beijing Forestry University, 2022, 44(3): 119-128. DOI: 10.12171/j.1000-1522.20210228

    基于渐进DS证据理论的空气质量评价方法

    Air quality evaluation method based on progressive DS evidence theory

    • 摘要:
        目的  科学地评价环境空气质量对大气污染防治具有重大的现实意义。环境空气质量的好坏是一个不确定性问题,常用的模糊综合评判法综合了多种大气污染物来评价空气质量,但评价结果往往低于我国空气质量指数(AQI)标准等级,弱化了多种污染物同时超标对空气质量的影响。DS证据理论在处理模糊或不确定等问题时具有很大优势,但是目前较少有DS证据理论评价空气质量的研究。DS证据理论的关键问题是基本概率分配(BPA)和证据冲突可能引起反直觉的结果。
        方法  因此本文首先利用Pearson和Spearman相关系数分析了大气污染物浓度数据,确立了高可信度的非线性BPA函数,并提出渐进式DS证据理论策略(Pro-DS)避免了冲突证据的融合问题,建立了环境空气质量综合评价模型。然后将该模型实际应用于2019年天津市空气质量评价,并以国家标准AQI为评价指标,与主因素型与加权型常用的模糊综合评判模型对比。最后本文提出了综合污染相对系数(CPRC)量化了多种污染物对总体空气质量的影响。
        结果  以AQI为评价指标,本文模型F1-score比常用的模糊综合评判模型最少提高了4.58%,最大提高了27.46%,验证了本文模型的优越性。由于AQI标准空气质量评价取决于某个污染物而不是综合多个污染物,几种模糊综合评判模型的F1-score都低于50%。以CPRC为评价指标,AQI评价结果的F1-score超过99.1%,几类模糊综合评判模型F1-score都超过89.0%,验证了CPRC的有效性。而Pro-DS模型F1-score为93.1%,综合评价空气质量的模型最优,具有很强的实际应用价值。
        结论  Pro-DS模型的综合评价结果低于或高于AQI评价级别,更好地体现了多种污染物对空气质量的综合影响。相比AQI评价方式,本文Pro-DS模型得出了综合空气质量级别的概率值,CPRC指标能够对综合空气质量日排名,为相关部门预防治理空气污染和生态环境工程建设提供实质性参考。

       

      Abstract:
        Objective  Evaluating air quality scientifically is crucial for prevention and control of air pollution. Fuzzy synthetic evaluation (FSE) method has been widely used to comprehensively evaluate air quality, considering the influence of multiple pollutants. But the air quality level evaluated is often lower than the national air quality index (AQI) level, which weakens the impact of multiple primary pollutants on air quality. DS evidence theory has great advantages in uncertainty or ambiguity issues, such as the air quality levels. However, there are few studies to comprehensively evaluate air quality using DS evidence theory. Basic probability assignment (BPA) and evidence conflicts are key issues in DS evidence theory, which may lead to counterintuitive results.
        Method  Therefore, in order to establish a highly reliable BPA, Pearson and Spearman correlation coefficients were used to analyze the air pollutant concentration data. And a progressive DS evidence theory strategy (Pro-DS) was proposed to avoid the conflict of evidence fusion. Thereby, a comprehensive evaluation model of ambient air quality based on Pro-DS was established. Then, the Pro-DS model was applied to Tianjin’s air quality evaluation in 2019. Finally, this paper proposes the comprehensive pollution relative coefficient (CPRC) to quantify the air quality under the action of multiple pollutants.
        Result  Taking the AQI level as an indicator, the F1-score of Pro-DS method was 4.58% to 27.46% higher than that of the widely used models based on FSE, which validated the superiority of Pro-DS model. Since the AQI standard was determined by a single pollutant, the F1-score of all FSE models was lower than 50%. However, taking the CPRC as an indicator, the F1-score of all FSE models exceeded 89.0%, which validated the effectiveness of CPRC. Experimental results showed that Pro-DS model had an F1 score of 93.1%, superior to other widely used FSE models.
        Conclusion  The comprehensive air quality evaluated by Pro-DS model is lower or higher than the air quality evaluated by AQI, which better reflects the comprehensive effect of multiple pollutants on air quality. In this paper, the Pro-DS model can comprehensively evaluate the air quality, and the CPRC indicator can rank the degree of daily pollution, which can provide a substantial reference for the construction of ecological environment projects.

       

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