An Integrated Method for Factor Number Selection of PMF Model: Case Study on Source Apportionment of Ambient Volatile Organic Compounds in Wuhan

Abstract

The positive matrix factorization (PMF) model is widely used for source apportionment of volatile organic compounds (VOCs). The question about how to select the proper number of factors, however, is rarely studied. In this study, an integrated method to determine the most appropriate number of sources was developed and its application was demonstrated by case study in Wuhan. The concentrations of 103 ambient volatile organic compounds (VOCs) were measured intensively using online gas chromatography/mass spectrometry (GC/MS) during spring 2014 in an urban residential area of Wuhan, China. During the measurement period, the average temperature was approximately 25 • C with very little domestic heating and cooling. The concentrations of the most abundant VOCs (ethane, ethylene, propane, acetylene, n-butane, benzene, and toluene) in Wuhan were comparable to other studies in urban areas in China and other countries. The newly developed integrated method to determine the most appropriate number of sources is in combination of a fixed minimum threshold value for the correlation coefficient, the average weighted correlation coefficient of each species, and the normalized minimum error. Seven sources were identified by using the integrated method, and they were vehicular emissions (45.4%), industrial emissions (22.5%), combustion of coal (14.7%), liquefied petroleum gas (LPG) (9.7%), industrial solvents (4.4%), and pesticides (3.3%) and refrigerants. The orientations of emission sources have been characterized taking into account the frequency of wind directions and contributions of sources in each wind direction for the measurement period. It has been concluded that the vehicle exhaust contribution is greater than 40% distributed in all directions, whereas industrial emissions are mainly attributed to the west southwest and south southwest.Atmosphere 2018, 9, 390 2 of 18 and urbanization, megacities and city clusters in China are suffering from severe photochemical pollution [7]. Concern about VOC concentrations in China caused the central government to start controlling VOCs, during the 12th five-year period (2011)(2012)(2013)(2014)(2015), in key regions as well as the launch of VOCs monitoring programs and pilot projects [8]. There are extensive offline and online observations performed to investigate the current ambient status of VOCs in main populated cluster areas of China like the North China Plain [9-11], Yangtze Delta [12], and Pearl River Delta [13,14]. Reliable source identification is the premise of effective VOCs control measures. Bottom-up emission inventory summarizing the products of activity data and emission factors for all known individual sources and the up-bottom receptor model (RM) are widely used for source identification [15][16][17]. Studies have been conducted to obtain more accurate and reliable VOCs source profiles for on-road vehicle emissions [18,19] and industries [20,21]. However, large and inherent uncertainties exist in current emission inventories owing to inaccurate and incomplete infor...

Formation potential and source contribution of secondary organic aerosol from volatile organic compounds

“…Wang et al (2016) reported that vehicle exhaust emission was the main VOC contributor in the suburban area of Beijing, China, with a contribution of 38.5-44.2%. Wang, Zhang, et al (2018) repoted that vehicle emissions were the major VOC sources in Wuhan, China, contributing 45.4% of the measured VOC concentrations. Moreover, Yu et al (2014) reported that the dominant VOC source to ambient air in New Jersey, USA, was vehicle exhaust (20.3%).…”

Identification of Metal Pollutants in Ambient Air (Case Study: West Surabaya, East Java, Indonesia)

“…PMF is conducted assuming some factors to obtain optimal and interpretable results. The number of factors to assume was determined by looking at the surrounding conditions at the research location or the emission inventory data [17].…”

“…The contribution factor and factor pro le come from the PMF model, minimizing the Q function. The equation is as follows: PMF requires two input data sets, which here are species concentration data and uncertainty data, to estimate the contribution factor (G) and factor pro le (F) [16,17]. The selection of the number of factors can be made from the emission inventory data to determine sources of pollutants.…”

“…The Q value in Eq 3 is obtained by experimentation using the assumption of several possible numbers of factors (4-8 factors) to obtain an interpretable result. The Q value obtained from the model is the goodness of t parameter and an estimate of how well the model ts the input data [17][18][19].…”

Source Apportionment of Volatile Organic Compounds (VOCs) during Ozone Polluted Days in Hangzhou, China

“…However, the name of factors mostly depends on the experience of analyzers to explain the intrinsic characteristics of different sources, which causes the explanations to vary from study to study. Even different tracers were chosen in the same factor name, like the description of LPG profiles in several papers [16][17][18].…”