Physically constrained source apportionment (PCSA) for polycyclic aromatic hydrocarbon using the Multilinear Engine 2-species ratios (ME2-SR) method

Liu, Guirong; Shi, Guoliang; Tian, Yingze; Wang, Yinan; Zhang, Caiyan; Feng, Yinchang

doi:10.1016/j.scitotenv.2014.09.011

Cited by 22 publications

(6 citation statements)

References 43 publications

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“…Atmospheric aerosol has an important role in atmospheric physics and chemistry (Lohmann and Feichter, 2005;Pöschl, 2005) and significant impacts on climate (Buseck and Pósfai, 1999;Ramanatham et al, 2001) and human health (Pope, 2000;Brunekreef and Fosberg, 2005;Tobias et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Source apportionment of atmospheric aerosol in a marine dusty environment by ionic/composition mass balance (IMB)

Cardoso

Almeida²,

Nunes

et al. 2018

Atmos. Chem. Phys.

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PM 10 aerosol was sampled in Santiago, the largest island of Cabo Verde, for 1 year, and analysed for elements, ions and carbonaceous material. Very high levels of dust were measured during the winter months, as a result of the direct transport of dust plumes from the African continent. Ionic and mass balances (IMBs) were applied to the analysed compounds, permitting the determination of six to seven different processes and source contributions to the aerosol loading: insoluble and soluble dust, sea salt, carbonaceous material and secondary inorganic compounds resulting from the reaction of acidic precursors with ammonia, sea salt and dust. The mass balance could be closed by the consideration and estimation of sorbed water that constituted 20 %-30 % of the aerosol mass. The balance methodology was compared with positive matrix factorisation (PMF), showing similar qualitative source composition. In quantitative terms, while for soil dust and secondary inorganic compound source classes, the results are similar, for other sources such as sea-salt spray there are significant differences in periods of dust episodes. The discrepancies between both approaches are interpreted based on calculated source profiles. The joint utilisation of the two methodologies, which are complementary, gives confidence in our capability for the correct source apportionment of aerosol particles.Published by Copernicus Publications on behalf of the European Geosciences Union.

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Section: Introductionmentioning

confidence: 99%

Source apportionment of atmospheric aerosol in a marine dusty environment by ionic/composition mass balance (IMB)

Cardoso

Almeida²,

Nunes

et al. 2018

Atmos. Chem. Phys.

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“…It is essential to identify and apportion PM 2.5 sources to provide targeted control policies. To date, receptor models have been applied in a number of source apportionment studies of PM 2.5 , including factor analysis models (such as PCA-MLR, PMF, UN-MIX, and ME2) and chemical mass balance (CMB) techniques (Shi et al, 2009;Choi et al, 2015;Hasheminassab et al, 2014;Liu et al, 2015). These receptor models have proven to be effective methods for identifying and apportioning sources.…”

mentioning

confidence: 99%

Measurement report: Spatiotemporal and policy-related variations of PM<sub>2.5</sub> composition and sources during 2015–2019 at multiple sites in a Chinese megacity

et al. 2021

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Abstract. A thorough understanding of the relationship between urbanization and PM2.5 (fine particulate matter with aerodynamic diameter less than 2.5 µm) variation is crucial for researchers and policymakers to study health effects and improve air quality. In this study, we selected a rapidly developing Chinese megacity, Chengdu, as the study area to investigate the spatiotemporal and policy-related variations of PM2.5 composition and sources based on long-term observation at multiple sites. A total of 836 samples were collected from 19 sites in winter 2015–2019. According to the specific characteristics, 19 sampling sites were assigned to three layers. Layer 1 was the most urbanized area and referred to the core zone of Chengdu, layer 2 was located in the outer circle of layer 1, and layer 3 belonged to the outermost zone with the lowest urbanization level. The average PM2.5 concentrations for 5 years were in the order of layer 2 (133 µg m−3) > layer 1 (126 µg m−3) > layer 3 (121 µg m−3). Spatial clustering of the chemical composition at the sampling sites was conducted for each year. The PM2.5 composition of layer 3 in 2019 was found to be similar to that of the other layers 2 or 3 years ago, implying that urbanization levels had a strong effect on air quality. During the sampling period, a decreasing trend was observed for the annual average concentration of PM2.5, especially at sampling sites in layer 1, where the stricter control policies were implemented. The SO42-/NO3- mass ratio at most sites exceeded 1 in 2015 but dropped to less than 1 since 2016, reflecting decreasing coal combustion and increasing traffic impacts in Chengdu, and these values can be further supported by temporal variations of the SO42- and NO3- concentrations. The positive matrix factorization (PMF) model was applied to quantify PM2.5 sources. A total of five sources were identified, with average contributions of 15.5 % (traffic emissions), 19.7 % (coal and biomass combustion), 8.8 % (industrial emissions), 39.7 % (secondary particles), and 16.2 % (resuspended dust). From 2015 to 2019, a dramatic decline was observed in the average percentage contributions of coal and biomass combustion, but the traffic emission source showed an increasing trend. For spatial variations, the high coefficient of variation (CV) values of coal and biomass combustion and industrial emissions indicated their higher spatial difference in Chengdu. High contributions of resuspended dust occurred at sites with intensive construction activities, such as subway and airport construction. Combining the PMF results, we developed the source-weighted potential source contribution function (SWPSCF) method for source localization. This new method highlighted the influences of spatial distribution for source contributions, and the effectiveness of the SWPSCF method was evaluated.

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“…在PMF模型的基础上Paatero [25] 建立了Multilinear [25] . 2014年, Liu等人 [26] 利用ME2模型的因子拉伸功能, 结合源谱组分的比值, 构建了ME2-元素比值源解析技术, 通过对因子提取过程中源谱组分比值的限制, 提高了源谱的物理意义.…”

Section: 在传统受体模型的基础上国内外很多研究者unclassified

Overview of the development and application of multi-time resolution source apportionment for particulate matters

Gao

Shi

et al. 2016

Chin. Sci. Bull.

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Physically constrained source apportionment (PCSA) for polycyclic aromatic hydrocarbon using the Multilinear Engine 2-species ratios (ME2-SR) method

Cited by 22 publications

References 43 publications

Source apportionment of atmospheric aerosol in a marine dusty environment by ionic/composition mass balance (IMB)

Source apportionment of atmospheric aerosol in a marine dusty environment by ionic/composition mass balance (IMB)

Measurement report: Spatiotemporal and policy-related variations of PM<sub>2.5</sub> composition and sources during 2015–2019 at multiple sites in a Chinese megacity

Overview of the development and application of multi-time resolution source apportionment for particulate matters

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Physically constrained source apportionment (PCSA) for polycyclic aromatic hydrocarbon using the Multilinear Engine 2-species ratios (ME2-SR) method

Cited by 22 publications

References 43 publications

Source apportionment of atmospheric aerosol in a marine dusty environment by ionic/composition mass balance (IMB)

Source apportionment of atmospheric aerosol in a marine dusty environment by ionic/composition mass balance (IMB)

Measurement report: Spatiotemporal and policy-related variations of PM&lt;sub&gt;2.5&lt;/sub&gt; composition and sources during 2015–2019 at multiple sites in a Chinese megacity

Overview of the development and application of multi-time resolution source apportionment for particulate matters

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Product

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Measurement report: Spatiotemporal and policy-related variations of PM<sub>2.5</sub> composition and sources during 2015–2019 at multiple sites in a Chinese megacity