Using non-negative matrix factorization for the identification of daily patterns of particulate air pollution in Beijing during 2004–2008

Abstract

Abstract. Increasing traffic density and a changing car fleet on the one hand as well as various reduction measures on the other hand may influence the composition of the particle population and, hence, the health risks for residents of megacities like Beijing. A suitable tool for identification and quantification of source group-related particle exposure compositions is desirable in order to derive optimal adaptation and reduction strategies and therefore, is presented in this paper. Particle number concentrations have been measured in high time- and space-resolution at an urban background monitoring site in Beijing, China, during 2004–2008. In this study a new pattern recognition procedure based on non-negative matrix factorization (NMF) was introduced to extract characteristic diurnal air pollution patterns of particle number and volume size distributions for the study period. Initialization and weighting strategies for NMF applications were carefully considered and a scaling procedure for ranking of obtained patterns was implemented. In order to account for varying particle sizes in the full diameter range [3 nm; 10 μm] two separate NMF applications (a) for diurnal particle number concentration data (NMF-N) and (b) volume concentration data (NMF-V) have been performed. Five particle number concentration-related NMF-N factors were assigned to patterns mainly describing the development of ultrafine (particle diameter Dp DP) as well as fine particles (Dp Dp In order to gain insight in the day-by-day varying source-associated composition of the particle burden non-negative linear combinations of individual source-associated pollution patterns were used to approximate the original particle data. Consequently, this NMF-based procedure provides a reasonable numerical-statistical tool for the description of daily patterns of particle pollution, source identification and reconstruction of daily patterns by summarizing weighted factors.