A study of the emission and concentration distribution of vehicular pollutants in the urban area of Beijing

Hao, Jiming; He, Daiping; Wu, Ye; Fu, Lixn; He, Kebin

doi:10.1016/s1352-2310(99)00324-6

Cited by 143 publications

(71 citation statements)

References 3 publications

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“…The early morning peak is consistent with the rush hour emissions in Beijing urban area (Hao et al, 2000). The large nighttime-afternoon differences should be mainly due to larger emissions from heating in combination with stronger temperature inversion during the nighttime.…”

Section: Concentration Levels and Variabilitysupporting

confidence: 77%

Gaseous pollutants in Beijing urban area during the heating period 2007–2008: variability, sources, meteorological, and chemical impacts

Lin

et al. 2011

Atmos. Chem. Phys.

122

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Abstract. Gaseous pollutants, NO y /NO x , SO 2 , CO, and O 3 , were measured at an urban site in Beijing from 17 November 2007 to 15 March 2008. The average concentrations (with ± 1σ ) of NO, NO 2 , NO x , NO y , CO, SO 2 , and O 3 were 29.0 ± 2.7 ppb, 33.7 ± 1.4 ppb, 62.7 ± 4.0 ppb, 72.8 ± 4.5 ppb, 1.99 ± 0.13 ppm, 31.9 ± 2.0 ppb, and 11.9 ± 0.8 ppb, respectively, with hourly maxima of 200.7 ppb, 113.5 ppb, 303.9 ppb, 323.2 ppb, 15.06 ppm, 147.3 ppb, and 69.7 ppb, respectively. The concentrations of the pollutants show "saw-toothed" patterns, which are attributable mainly to changes in wind direction and speed. The frequency distributions of the hourly mean concentrations of NO y , SO 2 , CO, and O 3 can all be decomposed in the two Lorentz curves, with their peak concentrations representing background levels under different conditions. During the observation period, the average ratio NO x /NO y was 0.86 ± 0.10, suggesting that the gaseous pollutants in Beijing in winter are mainly from local emissions. Data of O 3 , NO z , and NO x /NO y indicate that photochemistry can take place in Beijing even in the cold winter period. Based on the measurements of O 3 , NO x , and NO y , ozone production efficiency (OPE) is estimated to be in the range of 0-8.9 (ppb ppb −1 ) with the mean(±1σ ) and median values being 1.1(±1.6) and 0.5 (ppb ppb −1 ), respectively, for the winter 2007-2008 in Beijing. This low OPE would cause a photochemical O 3 source of 5 ppb day −1 , which is small but significant for surface O 3 in winter in Beijing.Correspondence to: X. Xu (xuxb@cams.cma.gov.cn) Downward transport of O 3 -rich air from the free troposphere is the more important factor for the enhancement of the O 3 level in the surface layer, while high NO level for the destruction of O 3 . The concentrations of SO 2 , CO, and NO x are strongly correlated among each other, indicating that they are emitted by some common sources. Multiple linear regression analysis is applied to the concentrations of NO y , SO 2 , and CO and empirical equations are obtained for the NO y concentration. Based the equations, the relative contributions from mobile and point sources to NO y is estimated to be 66 ± 30 % and 40 ± 16 %, respectively, suggesting that even in the heating period, mobile sources in Beijing contribute more to NO y than point sources.

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Section: Concentration Levels and Variabilitysupporting

confidence: 77%

Gaseous pollutants in Beijing urban area during the heating period 2007–2008: variability, sources, meteorological, and chemical impacts

Lin

et al. 2011

Atmos. Chem. Phys.

122

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“…They found that 74% of the ground NO x was due to vehicular emissions while power plants and industrial sources only contributed 2% and 13%, respectively. In addition, in Beijing and Guangzhou, automobile pollution contribution in terms of CO was estimated to be more than 80% with two peak vehicle pollution levels occurring during each day, one from about 08:00-10:00 and the other from 15:00-17:00 during the rush hours (Hao et al, 2000). NO x peaks were observed between 08:00 and 12:00 on 9 February (Fig.…”

Section: Wintermentioning

confidence: 99%

Occurrence of gas phase ammonia in the area of Beijing (China)

et al. 2010

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Abstract. The atmospheric concentrations of gaseous ammonia have been measured during two field campaigns in the winter and in the summer of 2007 at Beijing (China). These measurements were carried out by means of diffusion annular denuders coated with phosphorous acid. The results were discussed from the standpoint of temporal and diurnal variations and meteorological effects. The daily average NH 3 concentrations were in the range of 0.20-44.38 µg/m 3 and showed regular temporal variations with higher concentrations during summer and with lower during winter. The temporal trends seemed to be largely affected by air temperature because of agricultural sources. No diurnal variability was observed for gaseous NH 3 levels in both winter and summer seasons. The highest ammonia value of 105.67 µg/m 3 was measured in the early morning during the summer period when stable atmospheric conditions occurred. The diurnal winter and summer trends of ammonia showed a weak dependence on the air temperature and they were affected nearly by wind direction suggesting regional and local source influences. Ammonia was also correlated with the atmospheric mixing in the boundary layer, and, with NO x , CO and PM 2.5 air concentrations supporting the hypothesis that the traffic may be also an important source of ammonia in Beijing.

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“…Until recently, most of the emission inventories in Chinese cities have been developed by utilizing the MOBILE model from the US Environmental Protection Agency (EPA) or similar macro-scale models (Hao et al, 2000;Fu et al, 2001;Cai and Xie, 2007;Guo et al, 2007), in which the inventory approach is defined as a top-down method. For this method, the emission factors are uniform for the same vehicle category in the entire study region, combined with the number of kilometres travelled (VKT) for each vehicle fleet, to estimate the average emissions on a large geographic scale.…”

Section: Introductionmentioning

confidence: 99%

Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 1: Development and evaluation of vehicle emission inventory

Jing¹,

Wu²,

Mao³

et al. 2016

Atmos. Chem. Phys.

122

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Abstract. This paper presents a bottom-up methodology based on the local emission factors, complemented with the widely used emission factors of Computer Programme to Calculate Emissions from Road Transport (COPERT) model and near-real-time traffic data on road segments to develop a vehicle emission inventory with high temporal-spatial resolution (HTSVE) for the Beijing urban area. To simulate real-world vehicle emissions accurately, the road has been divided into segments according to the driving cycle (traffic speed) on this road segment. The results show that the vehicle emissions of NO x , CO, HC and PM were 10.54 × 10 4 , 42.51 × 10 4 and 2.13 × 10 4 and 0.41 × 10 4 Mg respectively. The vehicle emissions and fuel consumption estimated by the model were compared with the China Vehicle Emission Control Annual Report and fuel sales thereafter. The gridbased emissions were also compared with the vehicular emission inventory developed by the macro-scale approach. This method indicates that the bottom-up approach better estimates the levels and spatial distribution of vehicle emissions than the macro-scale method, which relies on more information. Based on the results of this study, improved air quality simulation and the contribution of vehicle emissions to ambient pollutant concentration in Beijing have been investigated in a companion paper (He et al., 2016).

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A study of the emission and concentration distribution of vehicular pollutants in the urban area of Beijing

Cited by 143 publications

References 3 publications

Gaseous pollutants in Beijing urban area during the heating period 2007–2008: variability, sources, meteorological, and chemical impacts

Gaseous pollutants in Beijing urban area during the heating period 2007–2008: variability, sources, meteorological, and chemical impacts

Occurrence of gas phase ammonia in the area of Beijing (China)

Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 1: Development and evaluation of vehicle emission inventory

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