Chemical Characterization, Source, and SOA Production of Intermediate Volatile Organic Compounds during Haze Episodes in North China

The growth of secondary organic aerosols (SOA) is a vital cause of the outbreaks of winter haze in North China. Intermediate volatile organic compounds (IVOCs) are important precursors of SOA. Therefore, the chemical characteristics, source, and SOA production of IVOCs during haze episodes have attracted much attention. Hourly time resolution IVOC samples during two haze episodes collected in Hebei Province in North China were analyzed in this study. Results showed that: (1) the concentration of IVOCs measured was within the range of 11.3~85.1 μg·cm−3 during haze episodes, with normal alkanes (n-alkanes), polycyclic aromatic hydrocarbons (PAHs), branched alkanes (b-alkanes), and the residue unresolved complex mixture (R-UCM) accounting for 8.6 ± 2.3%, 6.8 ± 2.2%, 24.1 ± 3.8%, and 60.5 ± 6.5% of IVOCs, respectively. NC12-nC15 in n-alkanes, naphthalene and its alkyl substitutes in PAHs, b-alkanes in B12-B16 bins, and R-UCM in B12-B16 bins are the main components, accounting for 87.0 ± 0.2%, 87.6 ± 2.9%, 85.9 ± 5.4%, 74.0 ± 8.3%, respectively. (2) Based on the component characteristics of IVOCs and the ratios of n-alkanes/b-alkanes in emission sources and the hourly variation of IVOCs during haze episodes, coal combustion (CC), biomass burning (BB), gasoline vehicles (GV), and diesel vehicles (DV)were identified as important emission sources of IVOCs in Hebei Province. (3) During haze episodes, temporal variation of the estimated SOA production based on different methods (such as IVOCs concentration, OC/ECmin tracer, and the PMF model) were similar; however, the absolute values were different. This difference may be due to the transformation of IVOCs to SOA affected by various factors such as SOA production from different IVOC components, meteorological conditions, atmospheric oxidation, etc.

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Screening the emission sources of volatile organic compounds (VOCs) in China by multi-effects evaluation

Frontiers of Environmental Science & Engineering ◽

10.1007/s11783-016-0828-z ◽

2016 ◽

Vol 10 (5) ◽

Cited By ~ 25

Author(s):

He Niu ◽

Ziwei Mo ◽

Min Shao ◽

Sihua Lu ◽

Shaodong Xie

Keyword(s):

Volatile Organic Compounds ◽

Organic Compounds ◽

Emission Sources ◽

Volatile Organic

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Corrigendum to “Compositions, sources, and potential health risks of volatile organic compounds in the heavily polluted rural North China Plain during the heating season” [Sci. Total Environ. 789 (2021) 147956 10.1016/j.scitotenv.2021.147956]

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.149284 ◽

2021 ◽

pp. 149284

Author(s):

Guangzhao Xie ◽

Hui Chen ◽

Fei Zhang ◽

Xiaona Shang ◽

Bixin Zhan ◽

...

Keyword(s):

Volatile Organic Compounds ◽

Organic Compounds ◽

Health Risks ◽

North China Plain ◽

North China ◽

Heating Season ◽

Potential Health ◽

Volatile Organic

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Vertical Evolution of Boundary Layer Volatile Organic Compounds in Summer over the North China Plain and the Differences with Winter

Advances in Atmospheric Sciences ◽

10.1007/s00376-020-0254-9 ◽

2021 ◽

Author(s):

Shuang Wu ◽

Guiqian Tang ◽

Yinghong Wang ◽

Rong Mai ◽

Dan Yao ◽

...

Keyword(s):

Boundary Layer ◽

Volatile Organic Compounds ◽

Organic Compounds ◽

North China Plain ◽

North China ◽

The North ◽

The North China Plain ◽

Volatile Organic ◽

Vertical Evolution

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An Integrated Method for Factor Number Selection of PMF Model: Case Study on Source Apportionment of Ambient Volatile Organic Compounds in Wuhan

Atmosphere ◽

10.3390/atmos9100390 ◽

2018 ◽

Vol 9 (10) ◽

pp. 390 ◽

Cited By ~ 5

Author(s):

Fenjuan Wang ◽

Zhenyi Zhang ◽

Costanza Acciai ◽

Zhangxiong Zhong ◽

Zhaokai Huang ◽

...

Keyword(s):

Volatile Organic Compounds ◽

Source Apportionment ◽

Correlation Coefficient ◽

Organic Compounds ◽

Industrial Emissions ◽

Integrated Method ◽

Pmf Model ◽

Volatile Organic ◽

Measurement Period

The positive matrix factorization (PMF) model is widely used for source apportionment of volatile organic compounds (VOCs). The question about how to select the proper number of factors, however, is rarely studied. In this study, an integrated method to determine the most appropriate number of sources was developed and its application was demonstrated by case study in Wuhan. The concentrations of 103 ambient volatile organic compounds (VOCs) were measured intensively using online gas chromatography/mass spectrometry (GC/MS) during spring 2014 in an urban residential area of Wuhan, China. During the measurement period, the average temperature was approximately 25 °C with very little domestic heating and cooling. The concentrations of the most abundant VOCs (ethane, ethylene, propane, acetylene, n-butane, benzene, and toluene) in Wuhan were comparable to other studies in urban areas in China and other countries. The newly developed integrated method to determine the most appropriate number of sources is in combination of a fixed minimum threshold value for the correlation coefficient, the average weighted correlation coefficient of each species, and the normalized minimum error. Seven sources were identified by using the integrated method, and they were vehicular emissions (45.4%), industrial emissions (22.5%), combustion of coal (14.7%), liquefied petroleum gas (LPG) (9.7%), industrial solvents (4.4%), and pesticides (3.3%) and refrigerants. The orientations of emission sources have been characterized taking into account the frequency of wind directions and contributions of sources in each wind direction for the measurement period. It has been concluded that the vehicle exhaust contribution is greater than 40% distributed in all directions, whereas industrial emissions are mainly attributed to the west southwest and south southwest.

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Blood concentrations of volatile organic compounds in a nonoccupationally exposed US population and in groups with suspected exposure

Clinical Chemistry ◽

10.1093/clinchem/40.7.1401 ◽

1994 ◽

Vol 40 (7) ◽

pp. 1401-1404 ◽

Cited By ~ 107

Author(s):

D L Ashley ◽

M A Bonin ◽

F L Cardinali ◽

J M McCraw ◽

J V Wooten

Keyword(s):

Volatile Organic Compounds ◽

Organic Compounds ◽

Reference Range ◽

Industrialized Countries ◽

Blood Concentrations ◽

Health And Nutrition ◽

Low Concentrations ◽

Study Results ◽

The Us ◽

Volatile Organic

Abstract Exposure to certain volatile organic compounds (VOCs) commonly occurs in industrialized countries. We developed a method for measuring 32 VOCs in 10 mL of whole blood at low concentration. We used this method to determine the internal dose of these compounds in 600 or more people in the US who participated in the Third National Health and Nutrition Examination Survey. From our study results, we established a reference range for these VOCs in the general population of the US. We found detectable concentrations of 1,1,1-trichloroethane, 1,4-dichlorobenzene, 2-butanone, acetone, benzene, chloroform, ethylbenzene, m,p-xylene, styrene, tetrachloroethane, and toluene in most of the blood samples of nonoccupationally exposed persons. The accuracy of VOC evaluations depends on the ability of investigators to make sensitive and reproducible measurements of low concentrations of VOCs and to eliminate all sources of interference and contamination.

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Source apportionment of volatile organic compounds in the northwest Indo-Gangetic Plain using a positive matrix factorization model

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-15467-2019 ◽

2019 ◽

Vol 19 (24) ◽

pp. 15467-15482 ◽

Cited By ~ 3

Author(s):

◽

Baerbel Sinha ◽

Vinayak Sinha

Keyword(s):

Volatile Organic Compounds ◽

Organic Compounds ◽

Waste Disposal ◽

Isocyanic Acid ◽

Transport Sector ◽

Emission Inventories ◽

Gangetic Plain ◽

Pmf Model ◽

Volatile Organic ◽

Indo Gangetic Plain

Abstract. In this study we undertook quantitative source apportionment for 32 volatile organic compounds (VOCs) measured at a suburban site in the densely populated northwest Indo-Gangetic Plain using the US EPA PMF 5.0 model. Six sources were resolved by the PMF model. In descending order of their contribution to the total VOC burden these are “biofuel use and waste disposal” (23.2 %), “wheat-residue burning”(22.4 %), “cars” (16.2 %), “mixed daytime sources”(15.7 %) “industrial emissions and solvent use”(11.8 %), and “two-wheelers” (8.6 %). Wheat-residue burning is the largest contributor to the total ozone formation potential (32.4 %). For the emerging contaminant isocyanic acid, photochemical formation from precursors (37 %) and wheat-residue burning (25 %) were the largest contributors to human exposure. Wheat-residue burning was also the single largest source of the photochemical precursors of isocyanic acid, namely, formamide, acetamide and propanamide, indicating that this source must be most urgently targeted to reduce human concentration exposure to isocyanic acid in the month of May. Our results highlight that for accurate air quality forecasting and modeling it is essential that emissions are attributed only to the months in which the activity actually occurs. This is important for emissions from crop residue burning, which occur in May and from mid-October to the end of November. The SOA formation potential is dominated by cars (36.9 %) and two-wheelers (21.1 %), which also jointly account for 47% of the human class I carcinogen benzene in the PMF model. This stands in stark contrast to various emission inventories which estimate only a minor contribution of the transport sector to the benzene exposure (∼10 %) and consider residential biofuel use, agricultural residue burning and industry to be more important benzene sources. Overall it appears that none of the emission inventories represent the regional emissions in an ideal manner. Our PMF solution suggests that transport sector emissions may be underestimated by GAINSv5.0 and EDGARv4.3.2 and overestimated by REASv2.1, while the combined effect of residential biofuel use and waste disposal emissions as well as the VOC burden associated with solvent use and industrial sources may be overestimated by all emission inventories. The agricultural waste burning emissions of some of the detected compound groups (ketones, aldehydes and acids) appear to be missing in the EDGARv4.3.2 inventory.

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