Abstract
Abstract. During winter 2013, extremely high concentrations (i.e., 4–20 times higher than the World Health Organization guideline) of PM2.5 (particulate matter with an aerodynamic diameter < 2.5 μm) mass concentrations (24 h samples) were found in four major cities in China including Xi'an, Beijing, Shanghai and Guangzhou. Statistical analysis of a combined data set from elemental carbon (EC), organic carbon (OC), 14C and biomass-burning marker measurements using Latin hypercube sampling allowed a quantitative source apportionment of carbonaceous aerosols. Based on 14C measurements of EC fractions (six samples each city), we found that fossil emissions from coal combustion and vehicle exhaust dominated EC with a mean contribution of 75 ± 8% across all sites. The remaining 25 ± 8% was exclusively attributed to biomass combustion, consistent with the measurements of biomass-burning markers such as anhydrosugars (levoglucosan and mannosan) and water-soluble potassium (K+). With a combination of the levoglucosan-to-mannosan and levoglucosan-to-K+ ratios, the major source of biomass burning in winter in China is suggested to be combustion of crop residues. The contribution of fossil sources to OC was highest in Beijing (58 ± 5%) and decreased from Shanghai (49 ± 2%) to Xi'an (38 ± 3%) and Guangzhou (35 ± 7%). Generally, a larger fraction of fossil OC was from secondary origins than primary sources for all sites. Non-fossil sources accounted on average for 55 ± 10 and 48 ± 9% of OC and total carbon (TC), respectively, which suggests that non-fossil emissions were very important contributors of urban carbonaceous aerosols in China. The primary biomass-burning emissions accounted for 40 ± 8, 48 ± 18, 53 ± 4 and 65 ± 26% of non-fossil OC for Xi'an, Beijing, Shanghai and Guangzhou, respectively. Other non-fossil sources excluding primary biomass burning were mainly attributed to formation of secondary organic carbon (SOC) from non-fossil precursors such as biomass-burning emissions. For each site, we also compared samples from moderately to heavily polluted days according to particulate matter mass. Despite a significant increase of the absolute mass concentrations of primary emissions from both fossil and non-fossil sources during the heavily polluted events, their relative contribution to TC was even decreased, whereas the portion of SOC was consistently increased at all sites. This observation indicates that SOC was an important fraction in the increment of carbonaceous aerosols during the haze episode in China.
Highlights
Driven by continuous urbanization and industrialization and a rapid growth in the number of motor vehicles and energy consumption, large-scale severe air pollution episodes often affect most cities in China
Statistical analysis of a combined data set from elemental carbon (EC), organic carbon (OC), 14C and biomass-burning marker measurements using Latin hypercube sampling allowed a quantitative source apportionment of carbonaceous aerosols
Statistical analysis of concentrations of OC and EC, anhydrosugars and 14C contents of OC and EC using Latin hypercube sampling allowed a quantitative estimation of six different sources
Summary
Driven by continuous urbanization and industrialization and a rapid growth in the number of motor vehicles and energy consumption, large-scale severe air pollution episodes often affect most cities in China. An increase in the number of haze days is expected to have an adverse impact on human health (Chan and Yao, 2008) Atmospheric fine particles such as PM2.5 (particulate matter with an aerodynamic diameter of below 2.5 μm) have been reported as an important air pollutant in China (Donkelaar et al, 2010; Yang et al, 2011; Cao et al, 2012; Huang et al, 2013; Zhao et al, 2013), and its burden is much higher than the h mean of μg m−3 suggested by the Air Quality Guidelines of the of World Health Organization (WHO) (WHO, 2006). POC and precursors of SOC may stem from a vast variety of sources from both anthropogenic (e.g., coal combustion, vehicle emissions and cooking) and natural sources (e.g., biogenic emissions) (Carlton et al, 2009). These sources change over time and space, which makes source apportionment difficult
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