High-time-resolution source apportionment of PM<sub>2.5</sub> in Beijing with multiple models

Abstract

Abstract. Beijing has suffered from heavy local emissions as well as regional transport of air pollutants, resulting in severe atmospheric fine-particle (PM2.5) pollution. This study developed a combined method to investigate source types of PM2.5 and its source regions during winter 2016 in Beijing, which include the receptor model (positive matrix factorization, PMF), footprint and an air quality model. The PMF model was performed with high-time-resolution measurements of trace elements, water soluble ions, organic carbon and elemental carbon using online instruments during the wintertime campaign of the Air Pollution and Human Health in a Chinese Megacity – Beijing (APHH-Beijing) program in 2016. Source types and their contributions estimated by PMF model using online measurements were linked with source regions identified by the footprint model, and the regional transport contribution was estimated by an air quality model (the Nested Air Quality Prediction Model System, NAQPMS) to analyze the specific sources and source regions during haze episodes. Our results show that secondary and biomass-burning sources were dominated by regional transport, while the coal combustion source increased with local contribution, suggesting that strict control strategies for local coal combustion in Beijing and a reduction of biomass-burning and gaseous precursor emissions in surrounding areas were essential to improve air quality in Beijing. The combination of PMF with footprint results revealed that secondary sources were mainly associated with southern footprints (53 %). The northern footprint was characterized by a high dust source contribution (11 %), while industrial sources increased with the eastern footprint (10 %). The results demonstrated the power of combining receptor model-based source apportionment with other models in understanding the formation of haze episodes and identifying specific sources from different source regions affecting air quality in Beijing.

Significant Contribution of Primary Sources to Water-Soluble Organic Carbon During Spring in Beijing, China

“…The strong correlations of WSOC with EC (p < 0.001, r = 0.95) and K (p < 0.001, r = 0.88) in Table S2 suggested that the WSOC in study period was closely related to combustion related sources especially biomass burning [36]. In addition, WSOC exhibited strong correlations with metal species such as As (correlation coefficient as 0.93), Se (0.84), Pb (0.84), Mn (0.88), Fe (0.87), and Zn (0.90), which were widely used as markers for primary and combustion related sources such as coal combustion [64,65], vehicle emission [12,36], and industrial sources [66]. Contributions of vehicle emissions and industrial sources to WSOC have been reported in Tianjin (41%) [67] and Beijing (20%) [17], respectively.…”

“…In contrast, during pollution episodes, air masses As shown in Figure 4, primary sources were the predominant contributors to WSOC during different periods, accounting for 68% of WSOC on average, including biomass burning (22%), other combustion source (19%), industrial source (17%), coal combustion (7%), and dust (3%). The strong correlations of WSOC with EC (p < 0.001, r = 0.95) and K (p < 0.001, r = 0.88) in Table S2 suggested that the WSOC in study period was closely related to combustion related sources especially biomass burning [36]. In addition, WSOC exhibited strong correlations with metal species such as As (correlation coefficient as 0.93), Se (0.84), Pb (0.84), Mn (0.88), Fe (0.87), and Zn (0.90), which were widely used as markers for primary and combustion related sources such as coal combustion [64,65], vehicle emission [12,36], and industrial sources [66].…”

“…The sampling site was located on the roof of an eight-story building on the campus of Peking University, about 20 m above the ground level [35] (see Figure 1). It is situated in a mixed district of commercial and residential area and can be representative of typical Beijing urban area [36]. There were no obvious emission sources in the vicinity, except two major roads (about 200 m to the east and 500 m to the south).…”

Research Progress of HP Characteristics, Hazards, Control Technologies, and Measures in China after 2013

“…and natural factors comprise sandstorm, volcanic eruptions, forest fire, resuspension of road or soil dust, etc. [40,41]. With regard to meteorological conditions, one consideration is the phenomenon of static wind in the horizontal direction of the atmosphere increases, and there is basically no power source for PM diffusion, which results in more and more suspended PM accumulation.…”

Development and application of a mass closure PM&lt;sub&gt;2.5&lt;/sub&gt; composition online monitoring system

“…However, a major shortcoming in these instruments is the lack or limited species from field measurements. To obtain more comprehensive information on chemical species in PM2.5, some previous studies have employed several online instruments to simultaneously measure the organic carbon, element carbon, important elements, and water-soluble inorganic ions in PM2.5 (Gao et al, 2016;Peng et al, 2016b;Wang et al, 2018;Huang et al, 2019;Liu et al, 2019). Although major species of PM2.5 have been measured in these research studies, the sum concentration of all the determined species was not close to the PM2.5 mass concentrations, which was partly because OM has not been measured, which is required to measure PM2.5 mass closure (Hand et al, 2011;Wang et al, 2018).…”