Characterization and source apportionment of PM2.5-bound polycyclic aromatic hydrocarbons from Shanghai city, China

Wang, Qing; Liu, Min; YiXin, Yu; Li, Ye

doi:10.1016/j.envpol.2016.08.037

Cited by 125 publications

(44 citation statements)

References 68 publications

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“…Positive matrix factorization (PMF) model has been widely employed for VOCs source apportionment (Buzcu-Guven and Fraser, 2008;Leuchner and Rappenglück, 2010;Liu et al, 2016;Lyu et al, 2016). It decomposes a matrix of X (i × j dimension) 15 into factor contributions matrix G (i × k dimensions) and factor profiles matrix F (k × j dimensions) plus a residue matrix E (i × j dimension):…”

Section: Pmf Receptor Model Descriptionmentioning

confidence: 99%

“…The top of the model was set at 500 m above ground level (Zhao et al, 2015;Liu et al, 2016). The FNL global analysis data produced by the National Center for Environmental Prediction's Global Data Assimilation System (GDAS) wind field re-analysis was introduced into the TrajStat model.…”

Section: Backward Trajectory Analysismentioning

confidence: 99%

“…The diurnal, monthly, and seasonal variations were mostly overlooked, which prevented the understanding of the VOCs' temporal 20 behaviors influenced by the real-time emissions, photochemical reactions, and meteorological parameters. Therefore, a long term monitoring with the high-time resolution of VOCs is desired (Baudic et al, 2016;Liu et al, 2016). It should be emphasized that in September of 2013, the VOCs control at petrochemical regions has been listed as one of the main objectives of the Action Plan of Atmospheric Pollution Control released by the central government of China, which proposed new requirements to conduct researches in this type of field.…”

Section: Introductionmentioning

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%

“…It should be emphasized that in September of 2013, the VOCs control at petrochemical regions has been listed as one of the main objectives of the Action Plan of Atmospheric Pollution Control released by the central government of China, which proposed new requirements to conduct researches in this type of field. 25 To identify the sources of VOCs, receptor models including chemical mass balance (CMB), positive matrix factorization (PMF), and principal component analysis/absolute principal component scores (PCA/APCS) have been widely used Rodolfo Sosa et al, 2009;An et al, 2014;Liu et al, 2016). Meanwhile, dispersion models including conditional probability function (CPF), backward trajectory, potential source contribution function (PSCF), and concentration weighted trajectory (CWT) are also employed to locate their potential source origins (Song et al, 2007;Chan et al, 2011;Liu et al, 30 2016).…”

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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Section: Pmf Receptor Model Descriptionmentioning

confidence: 99%

Section: Backward Trajectory Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%