WITHDRAWN: Source apportionment of fine PM by combining high time resolution organic and inorganic chemical composition datasets

Belis, Claudio A.; Pikridas, Michael; Lucarelli, F.; Petralia, Ettore; Cavalli, F.; Calzolai, G.; Berico, M.; Sciare, Jean

doi:10.1016/j.atmosenv.2019.116816

Cited by 1 publication

(1 citation statement)

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“…Receptor modeling by positive matrix factorization (PMF) can identify presumptive sources by apportioning the measured ambient PM 2.5 concentration and composition data by multiple linear regression (MLR) calculation, and this approach is typically applied when the source profiles are unknown [42]. A detailed explanation of the principle of PMF can be found elsewhere [43,44]. In PMF analysis, PMF seeks a solution that minimizes an object function, which is defined as the sum of the squared residuals weighted by the respective uncertainties, and the rotational freedom parameter (Fpeak) is used to control rotational ambiguity [45].…”

Section: Pm 25 Source Apportionmentmentioning

confidence: 99%

Characteristics of PM2.5 at a High-Altitude Remote Site in the Southeastern Margin of the Tibetan Plateau in Premonsoon Season

Zhao

Wang

et al. 2019

Atmosphere

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The Tibetan Plateau (TP) is one of the world's most sensitive areas for climate change. Previous studies have revealed that air pollutants emitted from South and Southeast Asia can be transported to and have a negative impact on the TP. However, the majority of the investigators have focused on the pollutant transport processes from South Asian regions (i.e., India and Bangladesh) and parts of Southeast Asia, while the regions adjacent to the southeast fringe of the TP (i.e., Burma and the Sino-Burmese border) have been neglected. Here, fine particulate matter (PM 2.5 ) samples were collected during the period 11 March to 13 May 2018 at Gaomeigu, a high-altitude remote site in the southeastern margin of the TP. Characteristics, sources of PM 2.5 , and the potential source regions for different chemical components were investigated. During the sampling time, PM 2.5 mass loadings ranged from 3.79 to 54.57 µg m −3 , with an arithmetic mean concentration of 20.99 ± 9.80 µg m −3 . In general, major peaks of organic carbon (OC) and elemental carbon (EC) always coincided with high loadings of K + and NO 3 − , which implies that common combustion sources caused these species' concentrations to covary, while the daily variations of crustal elements showed different trends with the other chemical compositions, suggesting different source regions for crustal materials. Five source factors were identified as possible aerosol sources for PM 2.5 by positive matrix factorization (PMF). They are the mining industry (5.3%), characterized by heavy metal elements; secondary formation (18.8%), described by the high concentrations of NH 4 + and SO 4 2− ; traffic-related emissions (26.7%), dominated by carbonaceous species (especially soot-EC) and some metal elements; fugitive dust (15.2%), represented by crustal elements (Ti, Fe, and Mn), Ca 2+ , and Mg 2+ ; and biomass burning (34.0%), which is typified by high concentrations of K + , NO 3 − , char-EC, primary OC, and secondary OC. The concentration-weighted trajectory (CWT) analysis results showed that the northeast part of Burma is the potential source region for high concentrations of EC and NO 3 − due to biomass burning emissions, while the tourism industry surrounding Gaomeigu gave strong grid cell values of SO 4 2− as well as moderate values of EC and NO 3 − . Moreover, the mining industry in the southwest direction of Gaomeigu has important impacts on the zinc concentrations.

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