Abstract
Abstract. This study presents the source apportionment of PM2.5 performed by positive matrix factorization (PMF) on data presented here which were collected at urban (Institute of Atmospheric Physics – IAP) and rural (Pinggu – PG) sites in Beijing as part of the Atmospheric Pollution and Human Health in a Chinese megacity (APHH-Beijing) field campaigns. The campaigns were carried out from 9 November to 11 December 2016 and from 22 May to 24 June 2017. The PMF analysis included both organic and inorganic species, and a seven-factor output provided the most reasonable solution for the PM2.5 source apportionment. These factors are interpreted as traffic emissions, biomass burning, road dust, soil dust, coal combustion, oil combustion, and secondary inorganics. Major contributors to PM2.5 mass were secondary inorganics (IAP: 22 %; PG: 24 %), biomass burning (IAP: 36 %; PG: 30 %), and coal combustion (IAP: 20 %; PG: 21 %) sources during the winter period at both sites. Secondary inorganics (48 %), road dust (20 %), and coal combustion (17 %) showed the highest contribution during summer at PG, while PM2.5 particles were mainly composed of soil dust (35 %) and secondary inorganics (40 %) at IAP. Despite this, factors that were resolved based on metal signatures were not fully resolved and indicate a mixing of two or more sources. PMF results were also compared with sources resolved from another receptor model (i.e. chemical mass balance – CMB) and PMF performed on other measurements (i.e. online and offline aerosol mass spectrometry, AMS) and showed good agreement for some but not all sources. The biomass burning factor in PMF may contain aged aerosols as a good correlation was observed between biomass burning and oxygenated fractions (r2= 0.6–0.7) from AMS. The PMF failed to resolve some sources identified by the CMB and AMS and appears to overestimate the dust sources. A comparison with earlier PMF source apportionment studies from the Beijing area highlights the very divergent findings from application of this method.
Highlights
Atmospheric particulate matter (PM) is composed of various chemical components and can affect air quality, visibility, and ecosystems (Boucher et al, 2013; Heal et al, 2012)
positive matrix factorization (PMF) results were compared with sources resolved from another receptor model and PMF performed on other measurements and showed good agreement for some but not all sources
This study presents the outcomes of PMF performed on the combined dataset collected at two sites (IAP and Pinggu site (PG)) in the Beijing metropolitan area, including their comparison with source apportionment results from other approaches or based on different measurements
Summary
Atmospheric particulate matter (PM) is composed of various chemical components and can affect air quality (and human health), visibility, and ecosystems (Boucher et al, 2013; Heal et al, 2012). A study compared the number of cases of acute cardiovascular, cerebrovascular, and respiratory diseases in the Beijing Emergency Center and haze data from Beijing Observatory between 2006 and 2013 Their results showed a rising trend, highlighting that the average number of cases per day for all three diseases was higher on hazy days than on non-hazy days (Zhang et al, 2015). The lockdown led to an improvement in air quality and managed to bring down the levels of PM2.5 Despite these improvements, PM2.5 concentrations during the lockdown periods remained higher than the World Health Organization recommendations, suggesting much more effort is needed (He et al, 2020; Le et al, 2020; Shi et al, 2021). A quantitative source apportionment provides key information to support such efforts
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