Abstract
Abstract. Source contributions to ambient PM10 (particles with an aerodynamic diameter of 10 μm or less) in Beijing, China were determined with positive matrix factorization (PMF) based on ambient PM10 composition data including concentrations of organic carbon (OC), elemental carbon (EC), ions and metal elements, which were simultaneously obtained at six sites through January, April, July and October in 2004. Results from PMF indicated that seven major sources of ambient PM10 were urban fugitive dust, crustal soil, coal combustion, secondary sulfate, secondary nitrate, biomass burning with municipal incineration, and vehicle emission, respectively. In paticular, urban fugitive dust and crustal soil as two types of dust sources with similar chemical characteristics were differentiated by PMF. Urban fugitive dust contributed the most, accounting for 34.4% of total PM10 mass on an annual basis, with relatively high contributions in all four months, and even covered 50% in April. It also showed higher contributions in southwestern and southeastern areas than in central urban areas. Coal combustion was found to be the primary contributor in January, showing higher contributions in urban areas than in suburban areas with seasonal variation peaking in winter, which accounted for 15.5% of the annual average PM10 concentration. Secondary sulfate and secondary nitrate combined as the largest contributor to PM10 in July and October, with strong seasonal variation peaking in summer, accounting for 38.8% and 31.5% of the total PM10 mass in July and October, respectively. Biomass burning with municipal incineration contributions were found in all four months and accounted for 9.8% of the annual average PM10 mass concentration, with obviously higher contribution in October than in other months. Incineration sources were probably located in southwestern Beijing. Contribution from vehicle emission accounted for 5.0% and exhibited no significant seasonal variation. In sum, PM10 source contributions in Beijing showed not only significant seasonal variations but also spatial differences.
Highlights
According to the Report on the State of the Environment in China issued by State Environmental Protection Administration (SEPA), from 2003 through 2006, there were 54.4%, 53.2%, 48.1% and 43.4% of Chinese cities where annual average daily concentration of PM10 exceeded the level II of National Ambient Air Quality Standard 100 μg m−3 (State Environmental Protection Administration (SEPA), 2005, 2007)
The highest PM10 concentration 482 μg m−3 appeared on 15 April at FS and the lowest concentration 33.8 μg m−3 was on October 21 at Ming Tombs (MT)
Secondary nitrate, biomass burning with municipal incineration and vehicle emission mainly emitted PM2.5, urban fugitive dust and crustal soil were mainly www.atmos-chem-phys.net/8/2701/2008/
Summary
According to the Report on the State of the Environment in China issued by State Environmental Protection Administration (SEPA), from 2003 through 2006, there were 54.4%, 53.2%, 48.1% and 43.4% of Chinese cities where annual average daily concentration of PM10 (inhaled particulate matter, particles with an aerodynamic diameter of 10 μm or less) exceeded the level II of National Ambient Air Quality Standard 100 μg m−3 (State Environmental Protection Administration (SEPA), 2005, 2007). Source apportionment studies are of great significance for controlling ambient PM10 pollution in China. Research on sources of ambient particulate matter began with analyzing source emission inventories and using dispersion models based on them. Receptor models identify and apportion sources by analyzing aerosol chemical compositions and physical parameters at a sampling site (or receptor) without information about source strengths, do not rely on meteorological data, and can identify fugitive emission sources. With such advantages, receptor models have been developing fast from its birth. Based on whether source profiles should be known at first, receptor models can be divided into two categories: chemical mass balance model (CMB) and various forms of multivariate statistical models
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
