Characterization and Source Apportionment of PM2.5 in an Urban Environment in Beijing

Abstract

Daily 24-hour PM 2.5 samples were collected continuously from January 1 to December 31, 2010. Elemental concentrations from Al to Pb were obtained using particle induced X-ray emission (PIXE) method. This was the first full year continuous daily PM 2.5 elemental composition dataset in Beijing. Source apportionment analysis was conducted on this dataset using the positive matrix factorization method. Seven sources and their contributions to the total PM 2.5 mass were identified and quantified. These include secondary sulphur-13.8 μg/m 3 ) compared to those in the spring and summer (9.6 and 8.0 μg/m 3 , respectively). Secondary sulphur contributed the most in the summer while vehicle exhaust and metal processing sources did not show any clear seasonal pattern. The different seasonal highs and lows from different sources compensated each other. This explains the very small seasonal variations (< 20%) in the total PM 2.5 .

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

“…The heavy air pollution in China is likely to have more diverse sources than that in European and North American cities: not only traditional sources such as traffic and industry emission but also coal burning and biomass combustion for cooking and winter heating (Carter et al 2014;Yu et al 2013;Zhao et al 2013b). Moreover, other specific activities like setting off fireworks during the Spring Festival (Wang et al 2007;Zhang et al 2010) and open straw burning over the harvest season (Cheng et al 2013b;Li et al 2014;Qu et al 2012) may also aggravate pollution concentrations in some periods, especially in winter.…”

“…The winter model with only two variables produced a lower R 2 , which was mainly on account of a larger spatial variation in the measured PM 2.5 concentrations. This might be explained by specific PM 2.5 sources that are distributed in a small scale, such as fossil fuel combustion, biomass burning for cooking and winter heating (Carter et al 2014;Cheng et al 2013b;Yu et al 2013;Zhao et al 2013b), and setting off fireworks in the winter (Wang et al 2007;Zhang et al 2010). Road length variables were no longer significant in winter compared with these in other three seasons, as the p values were 0.54 and 0.59 for the most predictive road-length variables in categories of major and common roads in the winter LUR model.…”

Characteristics and Source Analysis of Trace Elements in PM2.5 in the Urban Atmosphere of Wuhan in Spring

“…The characteristics of PM sources have been widely investigated in previous studies all around world, including China, as reviewed by (Karagulian et al, 2015), and several methods of receptor modeling have been used for identification and apportionment of PM (Viana et al, 2008). Enrichment Factor (EF) and Positive Matrix Factorization (PMF) are widely used because they only require observation data at the receptor site other than detailed prior knowledge of the sources and source profiles, and tools to perform these analyses are widely available (Schauer, et al, 2006;Yu et al, 2013;Gao et al, 2014;Banerjee et al, 2015).…”

Review on the Recent PM_2.5 Studies in China

“…중국에서 PM 2.5 질량농도 측정은 주로 tapered element oscillating microbalance (TEOM) 방법 또는 beta- Many monitoring sites are overlaid in this map (Ma et al, 2014 mass balance) 모델과 PMF (positive matrix factorization) 모델이 활용되고 있다 Gao et al, 2013;Wang et al, 2013b;Yu et al, 2013;Zhang et al, 2013b). Chen et al, 2011), log-linear 모델 (Huang et al, 2012b;Huang et al, 2009), lag non-linear 모델 (Geng et al, 2013), generalized additive model (GAM) (Dai et al, 2004)을 이용하며 시간-층화된 (time-stratified) 환자교 차 (case-crossover) 연구방법도 제안되었다 (Yang et al, 2012;.…”

“…관측과 모델링 연구와 더불어 베이징, 상하이 (Shanghai) 및 광저우 (Guangzhou) 등지에서 PM 2.5 의 배출원 기여도를 산정하는 연구 Gao et al, 2013;Wang et al, 2013b;Yu et al, 2013;Zhang et al, 2013b)를 통해 그 원인을 규명하려고 노력하고 있다.…”