Improvements in source apportionment of multiple time-resolved PM2.5 inorganic and organic speciation measurements using constrained Positive Matrix Factorization
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引用次数: 0
Abstract
The equation of Positive Matrix Factorization (PMF) has been modified to resolve multiple time resolution inputs and applied in numerous field studies. The refined modeling results provide a solution with an increased number of factors and enriched profile features. However, the incorporation of low time-resolved data may retrieve unfavorable mixed factor profiles, introducing high uncertainties into the PMF solution computations. To address this issue, a dual-stage PMF modeling procedure with predefined constraints was proposed. Multiple time-resolved PM2.5 inorganic and organic speciation measurements were collected from autumn of 2022 to summer of 2023 in Taipei, Taiwan. Without using the proposed approach, a mixed factor of vehicle/biomass burning and an unphysically meaningful factor of sodium ion- and ammonium ion-rich were identified. After implementing the proposed approach, a refined number of eight factors with separated and reasonable profiles were retrieved. Over the sampling period, the largest contributor to PM2.5 and organic carbon was vehicle (contribution = 26% and 47%, respectively), while those for secondary inorganic aerosols of SO42−, NO3−, and NH4+ were industry (27%, 25%, and 31%, respectively), highlighting the importance of regulating these two sources. The low vehicle contribution to NO3− may be due to time-lag effects from gas-to-particle conversion, which led to different temporal patterns between NO3− and primary species. Addressing this issue is crucial in future studies for better apportionment of secondary aerosols.
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