Evaluation of a Mixed Receptor Model Which Combined the Constrained PMF Model with the Multiple-Time-Resolution PMF Model: a Simulation Study

• Main Authors: Nathan Chen, 陳博文
• Other Authors: Chang-Fu Wu
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/jxt7n2

碩士 === 國立臺灣大學 === 職業醫學與工業衛生研究所 === 103 === Fine particulate matter (PM2.5) and volatile organic compounds (VOCs) have been well known to relate to adverse health effects. Thus, to precisely identify and to evaluate the sources of both PM2.5 and VOCs are important. Chemical mass balance (CMB), absolute principal component scores (APCS), and positive matrix factorization (PMF), have been used to attend the above purposes. These three models can be defined as receptor models. CMB, APCS and PMF have been used in different situations. However, they usually do not work for the situation that the data has multiple time resolution and the factor profile or the source contribution is incomplete. Thus, an evaluation of the performance of a combination of the constrained PMF model and a multiple-time-resolution PMF model was conducted in this study. A synthetic data was used in this study to achieve the above purposes. This study was conducted through the method of data simulation, model implementation, evaluation of the performance of models and application to field study. The synthetic data was created as an hourly measurement contribution matrix, where the hourly fluctuation was added and was assumed to be random and log-normal distribution. Six source profiles, which included petroleum refinery, vehicle exhaust, industrial coating, coal combustion, natural gas and fugitive dust were created from US EPA SPECIATE dataset. Two models were run in this section so that two models could compare with each other. One was a multiple-time-resolution model, and the other was a mixed model which combined a multiple-time-resolution model and a constrained model. These two models were compared to each other by the average absolute error. The data of field study was collected from the Wanhua monitoring site in Taipei. The AEEf of the multiple-time-resolution-only receptor model and of the combined receptor model are 0.18 and 0.13, respectively; The AAEg of the multiple-time-resolution-only receptor model and of the combined receptor model are 0.11 and 0.10, respectively; The R square are 0.68 and 0.78, respectively. For a conclusion, the performances of the combined model in the prediction of the factor profile and in the source contribution were better than the performance of the multiple-time-resolution-only model.