The probabilistic linguistic term set (PLTS) is an effective tool for describing linguistic value in the process of decision-making. However, due to its complex structure, there remains numerous defects in utilizing the PLTS information. Therefore, by connecting the PLTS with discrete probability distribution, this study investigates a novel probability distribution-based processing model to process PLTS and further develops a practical multi-criteria decision-making (MCDM) approach for addressing automatic environmental monitoring evaluation problem. First, we propose two types of pre-processing methods to complete probability normalization. Accordingly, the normalized PLTS is represented in the form of probabilistic linguistic probability distribution (PLPD). Then, probabilistic linguistic earth-mover (PLEM) distance is constructed to measure the divergency between PLPDs, and a probabilistic linguistic probability distribution weighted mean (PLPDWM) is also developed to facilitate information fusion. On this basis, an aggregation operator-based MCDM approach is established that integrates the probability distribution-based PLTS processing methods and the proposed worst-priority weight (WPW) method. Finally, the developed approach is demonstrated by solving a practical atmospheric pollutant evaluation problem and its strengths are verified through further sensitivity analysis and comparative analysis.

概率语言术语集（PLTS）是描述决策过程中语言价值的有效工具。然而，由于其复杂的结构，在利用PLTS信息方面仍然存在许多缺陷。因此，通过将PLTS与离散概率分布联系起来，本研究研究了一种基于概率分布的新型处理模型来处理PLTS，并进一步开发了一种实用的多准则决策方法（MCDM），以解决自动环境监测评估问题。首先，我们提出两种预处理方法来完成概率归一化。因此，归一化的PLTS以概率语言概率分布（PLPD）的形式表示。然后，构建概率语言地球移动器（PLEM）距离以测量PLPD之间的差异，并且还开发了概率语言概率分布加权平均数（PLPDWM）以促进信息融合。在此基础上，建立了一种基于聚集算子的MCDM方法，该方法将基于概率分布的PLTS处理方法与建议的最坏优先权重（WPW）方法集成在一起。最后，通过解决实际的大气污染物评估问题证明了该方法的可行性，并通过进一步的敏感性分析和比较分析验证了其优势。建立了基于聚集算子的MCDM方法，该方法将基于概率分布的PLTS处理方法与建议的最坏优先权重（WPW）方法集成在一起。最后，通过解决实际的大气污染物评估问题证明了该方法的可行性，并通过进一步的敏感性分析和比较分析验证了其优势。建立了基于聚集算子的MCDM方法，该方法将基于概率分布的PLTS处理方法与建议的最坏优先权重（WPW）方法集成在一起。最后，通过解决实际的大气污染物评估问题证明了该方法的可行性，并通过进一步的敏感性分析和比较分析验证了其优势。