Research articleMonitoring and source apportionment of trace elements in PM2.5: Implications for local air quality management
Graphical abstract
Introduction
Beijing is the capital city of China with a population of over 21.7 million in 2015 (BJSTATS, 2016). With rapid economic development and urbanization, Beijing has experienced serious air quality problems characterized by high concentrations of fine particulate matter (PM2.5) (Cheng et al., 2013). The annual average PM2.5 concentration was 85.9 μg m−3 (BJEPB, 2016), which was considerably higher than the National Ambient Air Quality Standard (NAAQS) of 35 μg m−3 (Class Ⅱ Annual Average Level). High PM2.5 masses significantly influence air quality and urban visibility and adversely affect human health (Pui et al., 2014). In response to the severity of particulate air pollution, a series of air quality control measures have been proposed, including the reduction of emission sources, regulations on vehicular emission standards, improvements to industrial and energy structures, and reductions in coal burning. The design and assessment of air quality control strategies require a thorough understanding of major sources of PM2.5.
Numerous source apportionment (SA) methods, such as positive matrix factorization (PMF) (Gao et al., 2014, Zhang et al., 2013), enrichment factor (Lü et al., 2012), principal component analysis (Li et al., 2013), and chemical mass balance (Wang et al., 2009b), have been implemented to identify the main sources that emit PM2.5 or its precursors. Over the last decade, extensive studies of particulate matter (PM) in Beijing have revealed that major sources include coal burning, vehicular emissions, biomass burning, secondary aerosols, soil dust, and industrial emissions (Song et al., 2006, Song et al., 2012, Zhang et al., 2013). Minor sources, including Chinese cooking emissions (Wang et al., 2009b), cigarette smoke, and vegetative detritus (Zheng et al., 2005), also contribute to total PM concentrations. Previous PM2.5 chemical profiles have been typically based on offline filter collections of 12–24 h periods, which involves labor-intensive and time-consuming sampling collection and chemical analysis processes. Small-scale fluctuations in emissions or incursions of polluted air masses (e.g., plume events) cannot be identified as well, and thus additional information on source profiles cannot be acquired. These limitations restrict the application of SA to air quality strategies.
To overcome these limitations, automatic observation instruments have provided alternative methods to obtain high time-resolution measurements of air samples by collecting a large volume of data with quick and easy processes and providing data at sampling intervals similar to meteorological variations. Data from these instruments can provide temporal variations of contributing sources and meteorology (Lioy et al., 1989). A large number of samples is vital for multivariate models to produce reliable receptor model results (Kim Oanh et al., 2009). Among various online measurements of PM chemical profiles, measurements of trace elements (TEs) cover a large proportion. Although TEs generally account for a few percent of the total mass of PM2.5, they are characteristic chemical species associated with PM emissions from both anthropogenic and natural sources. Given their high source specificity and atmospheric stability, TEs have been employed as effective tracers and used in numerous SA calculations for identifying and apportioning the source contributions to ambient aerosol (Visser et al., 2015, Yu et al., 2013). Despite their practicability, simultaneous online measurements and SA analyses of TEs at multiple sites in Beijing have been seldom reported.
This work primarily aimed to develop a procedure for capturing spatiotemporal variations across a range of environments to gather precise information on PM2.5 source profiles and provide appropriate data-based policy recommendations for local air quality measures. In this study, the concentrations of PM2.5 mass and its chemical species of 13 TEs were detected at five sites in Beijing between April 2014 and April 2015. Online X-ray fluorescence (XRF) analysis was utilized for elemental analysis. Information on spatial and temporal (seasonal, weekly, and diurnal) variations of PM2.5 and TEs is reported together with an evaluation of the likely SA of PM2.5 using PMF modeling. The results are important in assessing long- and short-term air quality, applying PMF analysis of online XRF data to capture different PM2.5 sources, and establishing effective air quality measures.
Section snippets
Sampling sites
Beijing is located at 39°56′N and 116°20′E on the northwestern edge of the Great North China Plain and surrounded by the Taihang Mountains in the west and Yanshan Mountains in the north and northeast, approximately 100 and 50 km far from the urban region (Guinot et al., 2007). The city is 160 km away from the Bohai Sea via Tianjin in the southeast. This region is affected by predominant westerly wind and Asian monsoon climate featuring warm and humid southern winds in summer and cold and dry
TE concentrations in PM2.5
The highest annual average PM2.5 concentrations were observed at Site 4, followed in descending order by Sites 5, 3, and 2; the lowest concentrations were recorded at Site 1 (background site). All sites showed annual average PM2.5 concentrations that were 2.1–2.9 times higher than 35 μg m−3. The values reported in this study were comparable to the PM2.5 values of 83 μg m−3 measured at several sites in Beijing (Gao et al., 2014). The number of days exceeding 75 μg m−3 (Class II 24 h average
Conclusions
PM2.5 samples were collected continuously every hour between April 2014 and April 2015 at five sites in Beijing. The characterization of PM2.5 elemental composition was achieved via online XRF, and sources contributing to PM2.5 were identified via PMF. The study provided a practical methodology using environmental monitoring data for local air quality management.
During the sampling period, the annual average PM2.5 concentration ranged from 76.8 ± 78.1 to 102.7 ± 99.9 μg m−3 and exceeded NAAQS
Acknowledgements
This work was supported by the National Science & Technology Pillar Program [grant number 2015BAK12B02] and the National Natural Science Foundation of China [grant number 71573149]. We thank Lei Jiang for his technical support in the drawing of sampling sites.
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