A review of atmospheric fine particulate matters: chemical composition, source identification and their variations in Beijing

ABSTRACT

Fine particulate matter (PM_2.5) is a major air pollutant worldwide. Characterizing its chemical compositions and source contributions is a critical prerequisite for effective control of PM_2.5 pollution. This paper systematically reviews the sampling methods, chemical compositions, and source apportionments of PM_2.5. Sampling methods have significant influences on the identification of chemical compositions and source contributions, with Quartz and Teflon filters being the most widely used. Receptor models are commonly adopted for identifying the sources of PM_2.5, such as positive matrix factorization, chemical mass balance, principal component analysis, and UNMIX models, which have their respective advantages and limitations that determine their applications. The variations of PM_2.5 compositions and sources in the past two decades in Beijing are also reviewed, which is the political, economic, and cultural center of China and is experiencing severe haze pollution events frequently. It was found that organic matters were the largest component (28.2%) in PM_2.5, followed by sulfate (15.1%) during 2004–2013, which was overtaken by nitrate (14.9%) after 2013. Each PM_2.5 source demonstrated significant seasonal and annual variations due to changes in climatic conditions and anthropogenic activities. Future research on the impacts of these external factors is urgently needed. This review is expected to provide valuable advice and evidence for those fast-growing megacities like Beijing to identify and control their PM_2.5-related air pollution problems.

KEYWORDS:

• Fine particulate matter (PM_2.5)
• chemical compositions
• source identification
• seasonal and annual variations
• air pollution control

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Acknowledgments

This work was jointly supported by the National Natural Science Foundation of China (Grant No. 52006058), the Natural Science Foundation of Hunan Province, China (Grant No. 2020JJ5071), and the Fundamental Research Funds for the Central Universities (Grant No. 531118010164). The corresponding author Y.H. is a recipient of the ARC Discovery Early Career Research Award (DE220100552).

Disclosure statement

No potential conflict of interest was reported by the author(s).