A highly resolved temporal and spatial air pollutant emission inventory for the Pearl River Delta region, China and its uncertainty assessment

Zheng, Junyu; Zhang, Lijun; Che, Wenwei; Zheng, Zhaohui; Yin, Shasha

doi:10.1016/j.atmosenv.2009.04.060

Cited by 416 publications

(253 citation statements)

References 10 publications

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“…Large uncertainties for emission inventories would exist by using those estimated emission factors. Zheng et al (2009) claimed that uncertainties were from −60% to +70% for particle pollutants in vehicle emission inventory. Lacking of the specific characterization of PM 2.5 from vehicle emission is the major reason for the difference and uncertainties, in China.…”

Section: Introductionmentioning

confidence: 99%

Chemical composition of PM 2.5 from two tunnels with different vehicular fleet characteristics

Cui

Chen

Tian

et al. 2016

Science of The Total Environment

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• Composition profiles of PM 2.5 were obtained in smaller city, Yantai.• Characteristics of PM 2.5 were changed by emission standards and fuel policy.• Source profiles of PM 2.5 for gasoline vehicles and diesel vehicles were given.• The diesel vehicles play a major role in PM 2.5 emissions in Yantai. The chemical compositions of PM 2.5 including OC, EC, water soluble ions, elements, and organic components such as polycyclic aromatic hydrocarbons (PAHs), hopanes, and steranes, emitted in Wuzushan (WZS) and Kuixinglou (KXL) tunnels were determined. WZS tunnel is a major route for diesel vehicles traveling, while KXL tunnel has limited to diesel vehicles. The results showed that the proportions of the different constituents of PM 2.5 in the Wuzushan (WZS) tunnel were OC (27.7%), EC (32.1%), elements (13.9%), and water soluble ions (9.2%). Whereas the chemical profile of PM 2.5 in the Kuixinglou (KXL) tunnel was OC (17.7%), EC (10.4%), elements (8.90%), and water soluble ions (8.87%). The emission factors (EFs) of PM 2.5 and proportions of SO 4 2− and Pb were decreased by vehicle emission standards and fuel quality policy in China, and the higher molecular weight PAHs (4 + 5 + 6 rings) were more abundant than the lower molecular weight PAHs (2 + 3 rings) in the two tunnels. The proportions of 17A(H)-21B(H)-30-Norhopane and 17A(H)-21B(H)-Hopane in the hopane and sterane were not dependent on the vehicles types. In addition, specific composition profiles for PM 2.5 from gasoline-fueled vehicles (GV) G R A P H I C A L A B S T R A C T a b s t r a c t a r t i c l e i n f o Contents lists available at ScienceDirectScience of the Total Environment j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / s c i t o t e n v and diesel-fueled vehicles (DV) emissions were drafted, which indicated that OC (0.974 mg·veh −1 ·km −1 ) was the most abundant component in PM 2.5 , followed by Fe, Cl − , and Mg for GV. The relative proportions of the different constituents in the PM 2.5 for DV were EC (35.9%), OC (27.2%), elements (12.8%), and water soluble ions (11.7%). Both the PM 2.5 EFs and EC proportions in DV were higher than those in GV, and the HMW PAHs were the dominant PAHs for both GV and DV. The PM 2.5 emissions from the vehicles in Yantai were 581 ± 513 tons to 1353 ± 1197 tons for GV, and 19,627 ± 2477 tons to 23,042 ± 2887 tons for DV, respectively.

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Section: Introductionmentioning

confidence: 99%

Chemical composition of PM 2.5 from two tunnels with different vehicular fleet characteristics

Cui

Chen

Tian

et al. 2016

Science of The Total Environment

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“…Bottom-up emission inventories of air pollutants in the PRD region were developed with 2006 as the base year [30], and VOCs emission inventory with 10 speciated species including four aromatics (toluene, m,p-xylene, o-xylene and 1,2,4-trimethylbenzene) was further established [31]. Nevertheless, −75% to 150% uncertainties still existed for the anthropogenic VOC emission sources [30].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, −75% to 150% uncertainties still existed for the anthropogenic VOC emission sources [30]. Based on observed data, receptor models including principal component analysis/absolute principal component scores (PCA/APCS) [32,33] and positive matrix factorization (PMF) have been previously applied to explore VOC emission sources based on VOCs measured at four stations with different environments in Hong Kong (HK) during two sampling campaigns [34], at a Hong Kong urban site and a rural site in inland PRD during October-December 2007 [35], and at 84 sites in a grid study with four campaigns during 2008-2009 over the PRD region [36]; and in the inner PRD region sources of VOCs was apportioned by chemical mass balance (CMB) with ambient VOC data observed at fall-winter 2004 [37].…”

Section: Introductionmentioning

confidence: 99%

Source attributions of hazardous aromatic hydrocarbons in urban, suburban and rural areas in the Pearl River Delta (PRD) region

Zhang

Wang

Barletta

et al. 2013

Journal of Hazardous Materials

134

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h i g h l i g h t sWe measured aromatic hydrocarbons at four contrasting sites in the PRD region. Diagnostic ratios were used to imply sources of aromatic hydrocarbons. Sources of aromatic hydrocarbons were apportioned by PMF receptor model. Solvent use, vehicle exhaust and biomass burning contributed over 89% AHs. Biomass burning contributed to AHs, particularly for Benzene in the rural. t r a c tAromatic hydrocarbons (AHs) are both hazardous air pollutants and important precursors to ozone and secondary organic aerosols. Here we investigated 14 C 6 -C 9 AHs at one urban, one suburban and two rural sites in the Pearl River Delta region during November-December 2009. The ratios of individual aromatics to acetylene were compared among these contrasting sites to indicate their difference in source contributions from solvent use and vehicle emissions. Ratios of toluene to benzene (T/B) in urban (1.8) and suburban (1.6) were near that of vehicle emissions. Higher T/B of 2.5 at the rural site downwind the industry zones reflected substantial contribution of solvent use while T/B of 0.8 at the upwind rural site reflected the impact of biomass burning. Source apportionment by positive matrix factorization (PMF) revealed that solvent use, vehicle exhaust and biomass burning altogether accounted for 89-94% of observed AHs. Vehicle exhaust was the major source for benzene with a share of 43-70% and biomass burning in particular contributed 30% to benzene in the upwind rural site; toluene, C 8 -aromatics and C 9 -aromatics, however, were mainly from solvent use, with contribution percentages of 47-59%, 52-59% and 41-64%, respectively.

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“…To better understand the uncertainties in our inventory, Monte Carlo simulation is used to quantify the uncertainty of Cd, Cr, and Pb emissions depending on available activity data and emission factors distribution (Zheng et al, 2009;Zhao et al, 2011). Emissions of Cd, Cr, and Pb from coal burning with uncertainty are shown in Table 4.…”

Section: Uncertainty Analysismentioning

confidence: 99%

Temporal and spatial variation characteristics of atmospheric emissions of Cd, Cr, and Pb from coal in China

Tian

Cheng

et al. 2012

Atmospheric Environment

215

107

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A highly resolved temporal and spatial air pollutant emission inventory for the Pearl River Delta region, China and its uncertainty assessment

Cited by 416 publications

References 10 publications

Chemical composition of PM 2.5 from two tunnels with different vehicular fleet characteristics

Chemical composition of PM 2.5 from two tunnels with different vehicular fleet characteristics

Source attributions of hazardous aromatic hydrocarbons in urban, suburban and rural areas in the Pearl River Delta (PRD) region

Temporal and spatial variation characteristics of atmospheric emissions of Cd, Cr, and Pb from coal in China

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