Approaches for identifying PM2.5 source types and source areas at a remote background site of South China in spring.

Abstract

The receptor model is an effectively and widely used tool for analyzing the source of PM2.5, and its development and improvement have always been focused and challenged. In this study, approaches of source analysis is applied and compared. The PM2.5 samples were collected in spring of 2015 at a remote background site of Weizhou, South China and were analyzed for water-soluble ions, trace metals, and sugars. The 28 measurement species were introduced into the positive matrix factorization (PMF) and a non-negative matrix factorization (NMF) model for inter-comparison of PM2.5 prediction. Results showed that the NMF model is a more robust tool to identify source types and source apportionment in the case of a small sample size (n = 31). In NMF, four source variants were obtained as dust (15.6%), biomass combustion (11.8%), secondary formation (17.6%), and coal combustion (54.9%), corresponding to four main source areas. These were Southeast Asia, South China Sea, Taiwan Strait, as well as Pearl River Delta, respectively. The areas were distinguished based on hybrid receptor models, potential source contribution function (PSCF) and concentration weighted trajectory (CWT), by introducing the daily loadings of each source factor from NMF method. These model results were highly consistent with categorized chemical characteristics of PM2.5, suggesting that NMF linking with hybrid receptor models provides valuable implications for exploring source types and source areas of PM2.5. Meanwhile, biomass combustion and coal combustion comparably contributed to the high PM2.5 concentrations indicating control strategy in South China in spring.