Comparison of Measurement-Based Methodologies to Apportion Secondary Organic Carbon (SOC) in PM2.5: A Review of Recent Studies

Srivastava, Deepchandra; Favez, Olivier; Perraudin, Émilie; Villenave, Éric; Albinet, Alexandre

doi:10.3390/atmos9110452

Cited by 56 publications

(62 citation statements)

References 379 publications

(621 reference statements)

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“…A MRS approach for calculating the secondary BrC absorption at different wavelengths (Abs BrC,sec (λ)) was developed from the widely used BC‐tracer method for calculating SOC (Srivastava et al, ). Aerosol light absorption is due to carbonaceous particles from both primary and secondary sources; thus, Abs BrC,sec (λ) can be calculated as

{Abs}_{BrC, sec} ((), λ) = Abs ((), λ) - {Abs}_{pri} ((), λ),

where Abs pri (λ) is the light absorption at a given wavelength (e.g., λ = 370, 470, 520, 590, or 660) caused by primary light‐absorbing materials from combustion and noncombustion sources.…”

Section: Methodsmentioning

confidence: 99%

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High Contribution of Secondary Brown Carbon to Aerosol Light Absorption in the Southeastern Margin of Tibetan Plateau

Wang

Han

et al. 2019

Geophysical Research Letters

100

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The optical properties of atmospheric secondary brown carbon (BrC) aerosol are poorly understood because of its chemical complexity, and this has hampered quantitative assessments of the impacts of this light‐absorbing material on glaciers on the Tibetan Plateau. For this study, a statistical approach was developed to investigate BrC light absorption over the southeastern margin of the Tibetan Plateau. Secondary sources for BrC were more important for absorption than primary ones. A diurnal cycle in secondary BrC absorption was explained by the formation of light‐absorbing chromophores by photochemical oxidation after sunrise followed by photobleaching of the chromophores under the more oxidizing conditions as the day progressed. Multimethod analyses showed that biomass burning in northern Burma and along the Sino‐Burmese border was the most important source for the secondary BrC. The mean integrated simple forcing efficiency was 79 W/g, indicating that secondary BrC can cause substantial radiative effects.

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{Abs}_{BrC, sec} ((), λ) = Abs ((), λ) - {Abs}_{pri} ((), λ),

where Abs pri (λ) is the light absorption at a given wavelength (e.g., λ = 370, 470, 520, 590, or 660) caused by primary light‐absorbing materials from combustion and noncombustion sources.…”

Section: Methodsmentioning

confidence: 99%

“…A MRS approach for calculating the secondary BrC absorption at different wavelengths (Abs BrC,sec (λ)) was developed from the widely used BC-tracer method for calculating SOC (Srivastava et al, 2018). Aerosol light 10.1029/2019GL082731…”

Section: Separation Of Secondary Brc Absorptionmentioning

confidence: 99%

High Contribution of Secondary Brown Carbon to Aerosol Light Absorption in the Southeastern Margin of Tibetan Plateau

Wang

Han

et al. 2019

Geophysical Research Letters

100

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“…The set of cooking (PCP) is not presented in CAMx-VBS due to missing cooking emission input in this study. Fountoukis et al, 2014;Murphy and Pandis, 2009;Theodoritsi and Pandis, 2019). For other basis sets, the default parameters of CAMx v6.3 were used.…”

Section: Modified Parameterization: Newmentioning

confidence: 99%

Sources of organic aerosols in Europe: a modeling study using CAMx with modified volatility basis set scheme

et al. 2019

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Abstract. Source apportionment of organic aerosols (OAs) is of great importance to better understand the health impact and climate effects of particulate matter air pollution. Air quality models are used as potential tools to identify OA components and sources at high spatial and temporal resolution; however, they generally underestimate OA concentrations, and comparisons of their outputs with an extended set of measurements are still rare due to the lack of long-term experimental data. In this study, we addressed such challenges at the European level. Using the regional Comprehensive Air Quality Model with Extensions (CAMx) and a volatility basis set (VBS) scheme which was optimized based on recent chamber experiments with wood burning and diesel vehicle emissions, and which contains more source-specific sets compared to previous studies, we calculated the contribution of OA components and defined their sources over a whole-year period (2011). We modeled separately the primary and secondary OA contributions from old and new diesel and gasoline vehicles, biomass burning (mostly residential wood burning and agricultural waste burning excluding wildfires), other anthropogenic sources (mainly shipping, industry and energy production) and biogenic sources. An important feature of this study is that we evaluated the model results with measurements over a longer period than in previous studies, which strengthens our confidence in our modeled source apportionment results. Comparison against positive matrix factorization (PMF) analyses of aerosol mass spectrometric measurements at nine European sites suggested that the modified VBS scheme improved the model performance for total OA as well as the OA components, including hydrocarbon-like (HOA), biomass burning (BBOA) and oxygenated components (OOA). By using the modified VBS scheme, the mean bias of OOA was reduced from −1.3 to −0.4 µg m−3 corresponding to a reduction of mean fractional bias from −45 % to −20 %. The winter OOA simulation, which was largely underestimated in previous studies, was improved by 29 % to 42 % among the evaluated sites compared to the default parameterization. Wood burning was the dominant OA source in winter (61 %), while biogenic emissions contributed ∼ 55 % to OA during summer in Europe on average. In both seasons, other anthropogenic sources comprised the second largest component (9 % in winter and 19 % in summer as domain average), while the average contributions of diesel and gasoline vehicles were rather small (∼ 5 %) except for the metropolitan areas where the highest contribution reached 31 %. The results indicate the need to improve the emission inventory to include currently missing and highly uncertain local emissions, as well as further improvement of VBS parameterization for winter biomass burning. Although this study focused on Europe, it can be applied in any other part of the globe. This study highlights the ability of long-term measurements and source apportionment modeling to validate and improve emission inventories, and identify sources not yet properly included in existing inventories.

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“…It is composed of a large variety of inorganic and organic compounds, in which the organic composition is much more complicated because of a very large chemical space with respect to molecular weight, functional groups and polarity (Zhang et al, 2007;Jimenez et al, 2009;Huang et al, 2014;Wang et al, 2018). Molecular-level characterization of the organic compounds in PM 2.5 is therefore essential for better understanding of the chemical nature, sources, atmospheric processes and impacts of PM 2.5 (Surratt et al, 2008;Chan et al, 2011;Fu et al, 2016). Various efforts have been committed to studying the composition of organic aerosol in different environments, including urban areas (Chan et al, 2013) and forested areas, such as the Amazon (Hu et al, 2015) and the southeastern United States (Zhang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Determination of n-alkanes, polycyclic aromatic hydrocarbons and hopanes in atmospheric aerosol: evaluation and comparison of thermal desorption GC-MS and solvent extraction GC-MS approaches

et al. 2019

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Abstract. Organic aerosol (OA) constitutes a large fraction of fine particulate matter (PM) in the urban air. However, the chemical nature and sources of OA are not well constrained. Quantitative analysis of OA is essential for understanding the sources and atmospheric evolution of fine PM, which requires accurate quantification of some organic compounds (e.g., markers). In this study, two analytical approaches, i.e., thermal desorption (TD) gas chromatography mass spectrometry (GC-MS) and solvent extract (SE) GC-MS, were evaluated for the determination of n-alkanes, polycyclic aromatic hydrocarbons (PAHs) and hopanes in ambient aerosol. For the SE approach, the recovery obtained is 89.3 %–101.5 %, the limits of detection (LODs) are 0.05–1.1 ng (1.5–33.9 ng m−3), repeatability is 3.5 %–14.5 % and reproducibility is 1.2 %–10.9 %. For the TD approach, the recovery is 57.2 %–109.8 %, the LODs are 0.1–1.9 ng (0.04–0.9 ng m−3), repeatability is 2.1 %–19.4 % and reproducibility is 1.1 %–12.9 %. Ambient aerosol samples were collected from Beijing, Chengdu, Shanghai and Guangzhou during the winter of 2013 and were analyzed by the two methods. After considering the recoveries, the two methods show a good agreement with a high correlation coefficient (R2 > 0.98) and a slope close to unity. The concentrations of n-alkanes, PAHs and hopanes are found to be much higher in Beijing than those in Chengdu, Shanghai and Guangzhou, most likely due to emissions from traffic and/or coal combustion for wintertime heating in Beijing.

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Comparison of Measurement-Based Methodologies to Apportion Secondary Organic Carbon (SOC) in PM2.5: A Review of Recent Studies

Cited by 56 publications

References 379 publications

High Contribution of Secondary Brown Carbon to Aerosol Light Absorption in the Southeastern Margin of Tibetan Plateau

High Contribution of Secondary Brown Carbon to Aerosol Light Absorption in the Southeastern Margin of Tibetan Plateau

Sources of organic aerosols in Europe: a modeling study using CAMx with modified volatility basis set scheme

Determination of n-alkanes, polycyclic aromatic hydrocarbons and hopanes in atmospheric aerosol: evaluation and comparison of thermal desorption GC-MS and solvent extraction GC-MS approaches

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