Source apportionment of VOCs and the contribution to photochemical ozone formation during summer in the typical industrial area in the Yangtze River Delta, China

Shao, Ping; An, Junling; Xin, Jinyuan; Wu, Fuzhong; Wang, Junxiu; Ji, Dongsheng; Wang, Yuesi

doi:10.1016/j.atmosres.2016.02.015

Cited by 210 publications

(94 citation statements)

References 44 publications

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“…PMF is a multivariate factor analysis tool that decomposes a matrix of speciated sample data into two matrices-factor contributions and factor profiles that can be interpreted by an analyst as to what sources are represented based on observations at the receptor site (Guo et al, 2010;Ou et al, 2015;Shao et al, 2016;Shi et al, 2009;Xie et al, 2008;Lanz et al, 2007;Zhang et al, 2013). The object function Q, based on the uncertainties inherent in each observation, can allow the analyst to review the distribution for each species to evaluate the stability of the solution:…”

Section: Pmf Model Analysismentioning

confidence: 99%

The levels, variation characteristics, and sources of atmospheric non-methane hydrocarbon compounds during wintertime in Beijing, China

Liu

et al. 2017

Atmos. Chem. Phys.

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Abstract. Atmospheric non-methane hydrocarbon compounds (NMHCs) were measured at a sampling site in Beijing city from 15 December 2015 to 14 January 2016 to recognize their pollution levels, variation characteristics, and sources. We quantified 53 NMHCs, and the proportions of alkanes, alkenes, acetylene, and aromatics to the total NMHCs were 49. 8-55.8, 21.5-24.7, 13.5-15.9, and 9.3-10.7 %, respectively. The variation trends in the NMHC concentrations were basically identical and exhibited remarkable fluctuation, which was mainly ascribed to the variation in meteorological conditions, especially wind speed. The diurnal variations in NMHCs on clear days exhibited two peaks during the morning and evening rush hours, whereas the rush hours' peaks diminished or even disappeared on the haze days, implying that the relative contribution of the vehicular emissions to atmospheric NMHCs depended on the pollution status. Two evident peaks of the propane / propene ratios appeared in the early morning before sun rise and at noontime on clear days, whereas only one peak occurred in the afternoon during the haze days, which were attributed to the relatively fast reactions of propene with OH, NO 3 , and O 3 . Based on the chemical kinetic equations, the daytime OH concentrations were calculated to be in the range of 3.47 × 10 5 -1.04 × 10 6 molecules cm −3 on clear days and 6.42×10 5 -2.35×10 6 molecules cm −3 on haze days. The nighttime NO 3 concentrations were calculated to be in the range of 2.82 × 10 9 -4.86 × 10 9 molecules cm −3 on clear days. The correlation coefficients of typical hydrocarbon pairs (benzene / toluene, o-xylene / m,p-xylene, isopentane / n-pentane, etc.) revealed that vehicular emissions and coal combustion were important sources for atmospheric NMHCs in Beijing during the wintertime. Five major emission sources for atmospheric NMHCs in Beijing during the wintertime were further identified by positive matrix factorization (PMF), including gasoline-related emissions (gasoline exhaust and evaporation), coal combustion, diesel exhaust, acetylene-related emissions, and consumer and household products. Coal combustion (probably domestic coal combustion) was found to make the greatest contribution (29.6-33.4 %) to atmospheric NMHCs during haze days.

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Section: Pmf Model Analysismentioning

confidence: 99%

The levels, variation characteristics, and sources of atmospheric non-methane hydrocarbon compounds during wintertime in Beijing, China

Liu

et al. 2017

Atmos. Chem. Phys.

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“…Wuhan (Lyu et al, 2016) or industrial areas Wei et al, 2015;Shao et al, 2016). These studies found that vehicle emission and solvent usage contributed most to the ambient VOCs.…”

Section: Introductionmentioning

confidence: 94%

“…In theory, if the number of sources is estimated properly, the Qtrue value should be approximately to Qexcept. If the number of sources is not well determined, the Q value may deviate from the theoretical value (Guo et al, 2011;Bressi et al, 2014;Baudic et al, 2016 However, the Qtrue always deviate from the Qexcept in many cases especially in large dataset (Liu et al, 2016(Liu et al, , 2017Shao et al, 2016). The variation of the Q values to the number of factors is shown in Fig.…”

Section: A2 the Optimal Number Of Factorsmentioning

confidence: 99%

“…A1); (2) species indicating VOC sources (i.e., acetylene is marker of combustion sources) are retained; (3) species that are highly reactive are excluded (i.e., i-pentene), since they are rapidly reacted away in the ambient atmosphere (Guo et al, 2011;Shao et al, 2016). Prior to the PMF model run, the retained species are firstly classified into strong, weak, and bad based on their signal-noise-ratios (S/N).…”

Section: A1 Data Preparationmentioning

confidence: 99%

“…Previous studies mainly focused on measurements in urban agglomerations, such as the Pearl River Delta (PRD) region (Tang et al, 2007;Liu et al, 2008b;Cheng et al, 2010;Ling et al, 2011), Yangtze River Delta (YRD) region Li et al, 2016;Shao et al, 2016) and Beijing-Tianjin-Hebei (BTH) region and megacities including Beijing (Song et al, 2007;Yuan et al, 2010), Shanghai (Cai et al, 2010;, Guangzhou (Zou et al, 2015) and 10…”

Section: Introductionmentioning

confidence: 99%

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One year monitoring of volatile organic compounds (VOCs) from an oil-gas station in northwest China

Zheng

Kong

Xing

et al. 2017

Preprint

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Abstract. Oil and natural gas are important energy supply around the world. The exploring, drilling, transportation, and processing in oil-gas regions can release abundant volatile organic compounds (VOCs). To understand the atmospheric behaviors of VOCs in such region, the fifty-six VOCs designed as the photochemical precursors by the United State 15Environmental Protection Agency were continuously measured for an entire year (September 2014-August 2015) by a set of on-line monitor system at an oil-gas station in northwest China. The VOC concentrations in this study were 1-50 times higher than those measured in many other urban and industrial regions. The VOC compositions were also different from other studies with alkanes contributing up to 87.5% of the total VOCs in this study. According to the propylene-equivalent concentration and maximum incremental reactivity method, alkanes were identified as the most important VOC groups to the ozone 20 formation potential. The photochemical reaction, meteorological parameters (temperature, relative humidity, pressure, and wind speed) and boundary layer height were found to influence the temporal variations of VOCs at different time scales. The positive matrix factorization analysis showed that the natural gas, fuel evaporation, combustion sources, oil refining process, and asphalt contributed 62.6%, 21.5%, 10.9%, 3.8%, and 1.3%, respectively to the total VOCs on the annual average. Clear temporal variations differed from one source to another was observed, due to their differences in source emission strength and 25 the influence of meteorological parameters. Potential source contribution function and contribution weighted trajectory models based on backward trajectories indicated that five identified sources had similar geographic origins. Raster analysis based on CWT analysis indicated that the local emissions contributed 48.4%-74.6% to the VOCs. This research filled the gaps in understanding the VOCs in the oil-gas field region, where exhibited different source emission behaviors compared with the urban/industrial regions. 30

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Analysis on temporal variation law of ozone pollution and meteorological driving factors in Pudong New Area of Shanghai

Guan

Geng

Yang³

et al. 2023

Env Prog and Sustain Energy

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In order to deeply understand the influence of meteorological drivers on ozone (O3) pollution during the high season and explore O3 pollution control options, we analyzed the temporal variation characteristics of O3 pollution based on the ambient air observation data of Pudong New Area, Shanghai from 2020 to 2021, studied the influence of meteorological drivers on O3 pollution, and explored seasonal emission reduction options based on the results of EKMA (Empirical Kinetics Modeling Approach) curves. The results show that the seasonal O3 concentration variation is spring > summer > autumn > winter; relative humidity, temperature and wind speed are the main meteorological drivers of O3 pollution; the cross‐regional transport of high O3 concentrations and precursors from the south and southwest has a strong influence on the local O3 concentration; Pudong New Area is in the “VOCs control zone” during the O3 pollution period in May. Therefore, it is important to control the production and emission of local VOCs and to strengthen the synergistic control of O3 and its precursors between regions for the control of spring O3 in Pudong New Area.

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Source apportionment of VOCs and the contribution to photochemical ozone formation during summer in the typical industrial area in the Yangtze River Delta, China

Cited by 210 publications

References 44 publications

The levels, variation characteristics, and sources of atmospheric non-methane hydrocarbon compounds during wintertime in Beijing, China

The levels, variation characteristics, and sources of atmospheric non-methane hydrocarbon compounds during wintertime in Beijing, China

One year monitoring of volatile organic compounds (VOCs) from an oil-gas station in northwest China

Analysis on temporal variation law of ozone pollution and meteorological driving factors in Pudong New Area of Shanghai

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