Composition profiles of organic aerosols from Chinese residential cooking: case study in urban Guangzhou, south China

Zhao, Xiaofei; Hu, Qihou; Wang, Xinming; Ding, Xiang; He, Quanfu; Zhang, Zhou; Shen, Ruinan; Lu, Shaoping; Liu, Tengyu; Fu, Xiaogang; Chen, Laiguo

doi:10.1007/s10874-015-9298-0

Cited by 71 publications

(63 citation statements)

References 49 publications

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“…We note that if the contribution of cooking emitted POC to OC is not negligible (Zhao et al, 2015), the calculation of SOC b can be subjected to a bias since high (OC/EC) pri from cooking has been observed (Hayes et al, 2013). Because cooking originated POC does not correlate with EC, it can be miscounted as SOC b , leading to the overestimation of SOC b .…”

Section: Journal Of Geophysical Research: Atmospheresmentioning

confidence: 98%

Estimation and Uncertainty Analysis of Secondary Organic Carbon Using 1 Year of Hourly Organic and Elemental Carbon Data

Wang

2019

JGR Atmospheres

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Hourly particulate matter 2.5 organic carbon (OC) and elemental carbon (EC) ambient concentrations were measured from February 2012 to January 2013 at a suburban site in the city of Guangzhou, China. The major primary OC and EC sources at this site are vehicular exhaust and regional combustion sources. The annual average OC and EC concentrations (±1SD) were 7.30 ± 5.68 and 2.67 ± 2.28 μgC/m3, respectively. Taking advantage of this large data set of more than 7,000 measurements, we examined the diurnal variation dynamics of the primary OC‐to‐EC ratio ([OC/EC]pri), derived using the minimum R squared (MRS) method, which deploys an algorithm that calculates (OC/EC)pri by exploring the assumed independency between EC and secondary OC (SOC). The MRS‐derived hour‐by‐hour (OC/EC)pri was the lowest (1.8–2.0) for the morning hours while higher (2.4–3.0) for the afternoon and evening hours. Cumulative evidence from other pollutant measurements suggests that the increase in MRS‐derived (OC/EC)pri in the afternoon and evening was a result of SOC formation from photooxidation of precursors that covary with EC (e.g., vehicular exhaust) and thereby inflate (OC/EC)pri. We subsequently separate SOC into two categories based on their correlations with EC, that is, SOCa that covary with EC and SOCb that is unassociated with EC variations. SOCa and SOCb make comparable contributions to SOC, demonstrating that their separation is essential for a more accurate SOC estimation. Otherwise, SOCa would be mistakenly accounted as primary OC. This approach in estimating SOC may underestimate SOCa and subject to bias stemming from OC emissions from cooking and biomass burning.

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Section: Journal Of Geophysical Research: Atmospheresmentioning

confidence: 98%

Estimation and Uncertainty Analysis of Secondary Organic Carbon Using 1 Year of Hourly Organic and Elemental Carbon Data

Wang

2019

JGR Atmospheres

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“…The chemical nature of PM 2.5 emitted from commercial cooking has been investigated in many different studies, with the source profiles varying greatly due to factors such as cooking style, the food cooked, seed oils, and the fuel used (He et al, 2004;Zhao et al, 2007bZhao et al, , 2015bHou et al, 2008b;Pei et al, 2016). Robinson et al (2006) found that the contribution of cooking emissions to OC in PM 2.5 , calculated using a chemical mass-balance model with different source profiles, yielded a difference of a factor of more than 9.…”

Section: Cooking Emissionsmentioning

confidence: 99%

“…Previous studies have found that OM accounts for 66.9 % of the total suspended particle (TSP) mass emitted from cooking activities (Zhao et al, 2015b). OC is the major constituent and accounts for 36.2 %-42.9 % of the total mass, whereas the fraction of EC is much lower.…”

Section: Cooking Emissionsmentioning

confidence: 99%

“…Source profile studies were initially implemented in China in the 1980s (Dai et al, 1987a, b). Over the past 3 decades, hundreds of source profiles have been compiled across the country (Zhao et al, 2006(Zhao et al, , 2007a(Zhao et al, , b, 2015aBi et al, 2007;Kong et al, 2011Kong et al, , 2014Qi et al, 2015;Wang et al, 2015;Zhang et al, 2015;Pei et al, 2016;Tian et al, 2017;Guo et al, 2017). These profiles cover more than 40 cities and several source types.…”

Section: Introductionmentioning

confidence: 99%

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Characteristics of the main primary source profiles of particulate matter across China from 1987 to 2017

Dai

et al. 2019

Atmos. Chem. Phys.

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Abstract. Based on published literature and typical profiles from the Nankai University source library, a total of 3326 chemical profiles of the main primary sources of ambient particulate matter (PM) across China from 1987 to 2017 are investigated and reviewed to trace the evolution of their main components and identify the main influencing factors concerning their evolution. In general, the source chemical profiles are varied with respect to their sources and are influenced by different sampling methods. The most complicated profiles are likely attributed to coal combustion (CC) and industrial emissions (IE). The profiles of vehicle emissions (VE) are dominated by organic carbon (OC) and elemental carbon (EC), and vary due to the changing standards of sulfur and additives in gasoline and diesel as well as the sampling methods used. In addition to the sampling methods used, the profiles of biomass burning (BB) and cooking emissions (CE) are also impacted by the different biofuel categories and cooking types, respectively. The variations of the chemical profiles of different sources, and the homogeneity of the subtype source profiles within the same source category are examined using uncertainty analysis and cluster analysis. As a result, a relatively large variation is found in the source profiles of CC, VE, IE, and BB, indicating that these sources urgently require the establishment of local profiles due to their high uncertainties. The results presented highlight the need for further investigation of more specific markers (e.g., isotopes, organic compounds, and gaseous precursors), in addition to routinely measured components, in order to properly discriminate sources. Although the chemical profiles of the main sources have been previously reported in the literature, it should be noted that some of these chemical profiles are currently out of date and need to be updated immediately. Additionally, in the future, specific focus should be placed on the source profile subtypes, especially with respect to local IE in China.

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“…Methods used to measure particulate organic compounds are described in detail elsewhere (Ding et al, 2008;Zhao et al, 2015). In brief, one-half of each filter was first spiked with 100 μL internal standards, including n-tetracosane-D 50 , lauricacid-D 23 , palmiticacid-D 31 , phthalic acid-D 4 , and levoglucosan-13 C 6 , and was ultrasonically extracted twice with 30 mL hexane/dichloride methane (DCM) (1:1, vol/vol) each time, and then twice with 30 mL DCM/methanol (1:1, vol/vol) each time.…”

Section: Experiments Description 231 Filter Extraction and Measuremmentioning

confidence: 99%

Water Soluble Organic Nitrogen (WSON) in Ambient Fine Particles Over a Megacity in South China: Spatiotemporal Variations and Source Apportionment

Zhu

et al. 2017

JGR Atmospheres

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Organic nitrogen aerosols are complex mixtures and important compositions in ambient fine particulate matters (PM2.5), yet their sources and spatiotemporal patterns are not well understood particularly in regions influenced by intensive human activities. In this study, filter‐based ambient PM2.5 samples at four stations (one urban, two rural, plus one urban roadside) and PM samples from combustion sources (vehicle exhaust, ship emission, and biomass burning) were collected in the coastal megacity Guangzhou, south China, for determining water soluble organic nitrogen (WSON) along with other organic and inorganic species. The annual average WSON concentrations, as well as the ratios of WSON to water soluble total nitrogen, were all significantly higher at rural sites than urban sites. Average WSON concentrations at the four sites during the wet season were quite near each other, ranging from 0.41 to 0.49 μg/m3; however, they became 2 times higher at the rural sites than at the urban sites during the dry season. Five major sources for WSON were identified through positive matrix factorization analysis. Vehicle emission (29.3%), biomass burning (22.8%), and secondary formation (20.2%) were three dominant sources of WSON at the urban station, while vehicle emission (45.4%) and dust (28.6%) were two dominant sources at the urban roadside station. At the two rural sites biomass burning (51.1% and 34.1%, respectively) and secondary formation (17.8% and 30.5%, respectively) were dominant sources of WSON. Ship emission contributed 8–12% of WSON at the four sites. Natural vegetation seemed to have very minor contribution to WSON.

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Composition profiles of organic aerosols from Chinese residential cooking: case study in urban Guangzhou, south China

Cited by 71 publications

References 49 publications

Estimation and Uncertainty Analysis of Secondary Organic Carbon Using 1 Year of Hourly Organic and Elemental Carbon Data

Estimation and Uncertainty Analysis of Secondary Organic Carbon Using 1 Year of Hourly Organic and Elemental Carbon Data

Characteristics of the main primary source profiles of particulate matter across China from 1987 to 2017

Water Soluble Organic Nitrogen (WSON) in Ambient Fine Particles Over a Megacity in South China: Spatiotemporal Variations and Source Apportionment

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