Non-methane volatile organic compound emission inventories in Beijing during Olympic Games 2008
Highlights
► The variations of NMVOCs emissions during summer 2008 in Beijing city were estimated. ► The yellow-label vehicles were the most important fleet in vehicular emissions. ► The biogenic emissions should not be ignored in the summer. ► The emissions were consistent with ambient concentration based results.
Introduction
The air quality in Beijing has been of concern especially for the 2008 Summer Olympic Games. The Beijing Municipal Government implemented a series of control measures to improve air quality. Prior to the Olympic Games, SO2 and CO concentrations were at safe levels, NO2 concentrations remained constant, while O3 increased, becoming a major pollutant (Chan and Yao, 2008, Shao et al., 2009). Non-methane volatile organic compounds (NMVOCs) play a key role in the photochemical reactions that form O3 in the atmosphere (Altshuller, 1991; Song et al., 2007). Literature values indicate that elevated O3 may be due to increased NMVOC emissions (Tang et al., 2009). As a result of control measures for the Beijing Olympics in 2008, concentrations of major gaseous pollutants (SO2, CO, NOx, O3) were lower in August than June (Wang and Xie, 2009, Wang et al., 2009a, Wang et al., 2009b), and NMVOC concentrations showed the same trend (Wang et al., 2010). The vehicular VOC emissions were reduced by 56% due to transportation control measures (Zhou et al., 2009).
Other sources, such as solvent use, petroleum storage and transport, and industrial processing, were also controlled. From July 1 to September 19 2008, yellow-labeled vehicles (YLV) were banned throughout Beijing. YLV are highly polluting vehicles, which do not meet the National I emission standards (Equivalent to Euro 1). From July 20 to September 20, odd–even plate restrictions were implemented and 30–70% of government cars were not allowed to drive. Solvent use, open painting, spraying, and indoor decorating were forbidden during the Olympics and Paralympics. Additionally, some chemical plants halted production. Detailed information was reported by Wang et al. (2010).
NMVOC emissions come from both biogenic and anthropogenic sources. Recently, anthropogenic NMVOC emissions (Wei et al., 2008, Bo et al., 2008, Liu et al., 2008, Zhang et al., 2009) have been studied for Beijing. There are discrepancies among these studies; Bo et al. (2008) estimated anthropogenic NMVOC emissions at 301 Gg in Beijing in 2005, while Zhang et al. (2009) reported 497 Gg in 2006. These differences may be due to the different emission factors used in these studies when domestic values were not available for China (Bo et al., 2008). Lu et al. (2006) performed source apportionment using a chemical mass balance (CMB) model and found that vehicle exhaust, painting, and gasoline vapors contributed 58, 12, and 11%, respectively, from 2002 to 2003. These results differ from the emission inventories for 2005–2006 (Bo et al., 2008, Liu et al., 2008, Wei et al., 2008, Zhang et al., 2009). Differences may be due to limitations inherent to emission inventory estimations and source apportionment methods. Additionally, Wang and Bai (2003) estimated the annual biogenic NMVOC emissions at 16 Gg in Beijing. Compared with anthropogenic sources, biogenic emissions were low. However, biogenic emissions may play an important role in ozone formation (Wang et al., 2010).
Recently, the Beijing economy has developed rapidly (Beijing Statistical Yearbook, 2005–2009). Bo et al. (2008) reported that Chinese NMVOC emissions increased with economic growth from 1985 to 2005. Meanwhile, strict control measures were also implemented. For example, the number of vehicles increased by 15% per year in Beijing (Wang and Xie, 2009), while dozens of new standards, including recent emission standards equivalent to Euro IV, were implemented in March 2008. Thus, current variations in NMVOC emissions may differ from those reported in previous studies. Additionally, emission inventories with high temporal resolution are urgently needed in Beijing to represent the dynamic activity data due to control measures implemented for the 2008 Olympic Games.
In this study, total NMVOC emissions in Beijing in June, July, August, and September of 2008 were estimated based on short-term air quality control measures. NMVOC emission variations, source contributions, and reduction percentages were analyzed and used to evaluate the effectiveness of 2008 air quality control measures. These inventories were also compared with non-methane hydrocarbon (NMHC) concentrations and source apportionment results.
Section snippets
NMVOC emission estimations
According to actual measurements in Beijing, NMVOC emissions were from seven sectors: vehicles, solvent use, petroleum storage and transport, fossil fuel combustion, industrial processing, miscellaneous sources, and biogenic sources.
Vehicular emission factors were taken from a COPERT III model, because the predominant Chinese vehicle technologies are rooted in Europe, and emission regulations implemented in China are almost the same as those in Europe (Cai and Xie, 2007). Solvent use and
Variations in total NMVOC emissions
NMVOC emissions at the sector level in the summer of 2008 in Beijing were 22.6, 20.2, 14.9, and 14.6 Gg in June, July, August, and September, respectively (Fig. 1). Compared with June, emissions were reduced by 11 and 34% in July and August, respectively, similar to the variations of 18 and 39% in July and August for NMHC concentrations at an urban site (Wang et al., 2010). Anthropogenic NMVOC emissions were reduced by 17, 45, and 32% in July, August, and September, respectively, which
Conclusions
To reduce ground-level ozone and secondary organic aerosols in Beijing, short-term control measures for 2008 air quality were implemented during the Olympics and Paralympics. This work demonstrated the effectiveness of control measures using a “bottom-up” method. Compared with June levels, anthropogenic emissions were reduced by 45%. Among sectors, vehicle emission control was the most effective, reducing emissions by 66%, followed by solvent use and industrial processing, with 48% and 15%
Acknowledgments
This work was supported by the National 863 Program of China (2006AA06A309). We thank H. Cai for assistance and suggestions related to the COPERT III model.
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