Recent Published Articles

Development of an automatic measurement system using atmospheric pressure photoionization ultrahigh-resolution mass spectrometry and application for on-line analysis of particulate matter

Zifa Wang , Yayuan Dong , Ranran Liu , Ling Xie , Xiaole Pan , Yele Sun , Lin Wu


Received November 02, 2022,Revised , Accepted March 22, 2023, Available online April 03, 2023

Volume 36,2024,Pages 516-530

On-line chemical characterization of atmospheric particulate matter (PM) with soft ionization technique and ultrahigh-resolution Mass Spectrometry (UHRMS) provides molecular information of organic constituents in real time. Here we describe the development and application of an automatic measurement system that incorporates PM2.5 sampling, thermal desorption, atmospheric pressure photoionization, and UHRMS analysis. Molecular formulas of detected organic compounds were deducted from the accurate (±10 ppm) molecular weights obtained at a mass resolution of 100,000, allowing the identification of small organic compounds in PM2.5. Detection efficiencies of 28 standard compounds were determined and we found a high sensitivity and selectivity towards organic amines with limits of detection below 10 pg. As a proof of principle, PM2.5 samples collected off-line in winter in the urban area of Beijing were analyzed using the Ionization Module and HRMS of the system. The automatic system was then applied to conduct on-line measurements during the summer time at a time resolution of 2 hr. The detected organic compounds comprised mainly CHON and CHN compounds below 350 m/z. Pronounced seasonal variations in elemental composition were observed with shorter carbon backbones and higher O/C ratios in summer than that in winter. This result is consistent with stronger photochemical reactions and thus a higher oxidation state of organics in summer. Diurnal variation in signal intensity of each formula provides crucial information to reveal its source and formation pathway. In summary, the automatic measurement system serves as an important tool for the on-line characterization and identification of organic species in PM2.5.

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