基于PMF模型的土壤重金属源解析中变量敏感性研究

为探究应用受体模型对土壤污染物进行源解析，输入变量对模型运行及其结果的影响，以乐安河中上游地区土壤重金属调查数据作为典型受体模型（PMF模型）的输入数据集，并在PMF模型基础方案运行结果的基础上，采用局部敏感性分析法来探讨输入变量变化对模型诊断及源识别结果的影响.结果表明：6因子数情景是研究区土壤重金属源解析PMF模型最佳运行结果；土壤中Cu、Mo、Na₂O、As、Mn和Cd等参数属于敏感变量，这些变量均为每个因子中的主要载荷元素，即每个源的特征污染物；不同变量的敏感性有较大差异，Cu、Mo的总敏感性最大，分别为12.1，8.2，大于其他输入变量的敏感性.因此，在应用PMF模型进行源解析时，特征污染物是敏感性较强的变量，其数据质量的优劣是影响源解析结果可靠性的重要因素.

Sensitivity of input variables in source apportionment of soil heavy metal base on PMF model

To explore the impacts of variables on receptor model results in source apportionment for soil pollutants, the sampling data set of soil heavy metals in the middle and upper reaches of Le'an River was used as the input data set for the typical receptor model (PMF model). After obtaining the results of basic scenarios by PMF model, local sensitivity analysis method was introduced to study the sensitivity of variables on PMF diagnosis and source identification. The six-factor scenario was the best result for the simulation of PMF base model, Cu、Mo、Na₂O、As、Mn and Cd in the soil were the sensitive variables and also the main loading elements in each factor profile (i.e. the typical pollutants of each source). There was a significant difference on the sensitivity for these variables:the total sensitivity of Cu and Mo are much higher than that of the other variables, reaching 12.1 and 8.2 respectively. Therefore, it revealed that the sensitive variables should be the specific pollutants when applying the receptor model for source apportionment, and the data quality was an important factors affecting of the reliability of source apportionment.