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Vocs Regulated by Emission Control During APEC China 2014

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Vocs Regulated by Emission Control During APEC China 2014 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Atmos. Chem. Phys. Discuss., 15, 12453–12490, 2015 www.atmos-chem-phys-discuss.net/15/12453/2015/ doi:10.5194/acpd-15-12453-2015 ACPD © Author(s) 2015. CC Attribution 3.0 License. 15, 12453–12490, 2015 This discussion paper is/has been under review for the journal Atmospheric Chemistry VOCs regulated by and Physics (ACP). Please refer to the corresponding final paper in ACP if available. emission control during APEC China Characterization of ambient volatile 2014 organic compounds and their sources J. Li et al. in Beijing, before, during, and Title Page after Asia-Pacific Economic Abstract Introduction Cooperation China 2014 Conclusions References Tables Figures J. Li, S. D. Xie, L. M. Zeng, L. Y. Li, Y. Q. Li, and R. R. Wu College of Environmental Science and Engineering, State Key Joint Laboratory of J I Environmental Simulation and Pollution Control, Peking University, Beijing, China J I Received: 4 March 2015 – Accepted: 5 April 2015 – Published: 29 April 2015 Back Close Correspondence to: S. D. Xie ([email protected]) Full Screen / Esc Published by Copernicus Publications on behalf of the European Geosciences Union. Printer-friendly Version Interactive Discussion 12453 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Abstract ACPD Ambient volatile organic compounds (VOCs) were measured using an online system, gas chromatography–mass spectrometry/flame ionization detector (GC-MS/FID), in 15, 12453–12490, 2015 Beijing, China, before, during and after Asia-Pacific Economic Cooperation (APEC) 5 China 2014, when stringent air quality control measures were implemented. Positive VOCs regulated by matrix factorization (PMF) was applied to identify the major VOC contributing sources emission control and their temporal variations. The secondary organic aerosols potential (SOAP) ap- during APEC China proach was used to estimate variations of precursor source contributions to SOA for- 2014 mation. The average VOC mixing ratios during the three periods were 86.17, 48.28, 10 and 72.97 ppbv, respectively. The mixing ratios of total VOC during the control pe- J. Li et al. riod were reduced by 44 %, and the mixing ratios of acetonitrile, halocarbons, oxy- genated VOCs (OVOCs), aromatics, acetylene, alkanes, and alkenes decreased by approximately 65, 62, 54, 53, 37, 36, and 23 %, respectively. The mixing ratios of all Title Page measured VOC species decreased during control, and the most affected species were Abstract Introduction 15 chlorinated VOCs (chloroethane, 1,1-dichloroethylene, chlorobenzene). PMF analysis indicated eight major sources of ambient VOCs, and emissions from target control Conclusions References sources were clearly reduced during the control period. Contributions of vehicular ex- Tables Figures haust were most reduced (19.65 ppbv, the contributions before the control period minus the values after the control period), followed by industrial manufacturing (10.29 ppbv) J I 20 and solvent utilization (6.20 ppbv). Contributions of evaporated or liquid gasoline and industrial chemical feedstock were slightly reduced, with values of 2.85 and 0.35 ppbv, J I respectively. Contributions of secondary and long-lived species were relatively stable. Back Close Due to central heating, emissions from fuel combustion kept on increasing during the whole campaign; because of weak control of liquid petroleum gas (LPG), the high- Full Screen / Esc 25 est emissions of LPG occurred in the control period. Vehicle-related sources were the most important precursor sources likely responsible for the reduction in SOA formation Printer-friendly Version during this campaign. Interactive Discussion 12454 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1 Introduction ACPD Beijing, the capital of China, is one of the megacities in the world, with a population of more than 20 million and a vehicle fleet of more than 5 million (Beijing Statistical 15, 12453–12490, 2015 Yearbook, 2014). High levels of coal consumption, thousands of active construction 5 sites, and rapid increases in vehicles have resulted in high emissions of fine particles VOCs regulated by (PM2.5), sulfur dioxides (SO2), nitrogen oxides (NOx), and volatile organic compounds emission control (VOCs) in Beijing (Tang et al., 2009; Han et al., 2013; Y. S. Wang et al., 2014). during APEC China In November 2014, China hosted the Asia-Pacific Economic Cooperation (APEC) 2014 Meeting in Beijing, including the Concluding Senior Officials’ Meeting on 5–6 Novem- 10 ber, the 26th APEC Ministerial Meeting on 7–8 November, and the 22nd APEC Eco- J. Li et al. nomic Leaders’ Meeting on 10–11 November. As the host city, Beijing has set rig- orous plans to reduce emissions of air pollutants in Beijing and neighboring regions from 1 to 12 November 2014, resulting in a period of air quality control. The target Title Page sources included vehicles, paint and solvent use, steel factories, chemical factories, Abstract Introduction 15 power plants, etc. A detailed description of the control measures is provided in Ta- ble S1. As a result, air quality was greatly improved, and the phrase “APEC blue” Conclusions References was coined on social media to describe the clear sky. The city’s daily PM2.5 concen- Tables Figures tration during the control period fell to 43 µgm−3, a 55 % reduction compared with the same dates the prior year, and daily average levels of SO2, nitrogen dioxide (NO2), and J I 20 PM10 (aerosol particles with an aerodynamic diameter of less than 10 µm) decreased by 57, 31 and 44 %, respectively (Beijing Municipal Environmental Protection Bureau, J I http://www.bjepb.gov.cn/). However, sufficiently detailed information of ambient VOC Back Close mixing ratios and chemical compositions, as well as variations in their sources before, during, and after the control period has not been reported. Full Screen / Esc 25 Many VOCs adversely affect public health (The Clean Air Act Amendments of 1990, http://www.epa.gov/oar/caa/caaa_overview.html), and high levels of ambient VOCs Printer-friendly Version have been detected in Beijing, likely associated with rapid economic development. For Interactive Discussion example, during 1980–2005, VOC emissions increased at an annual average rate of 12455 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 10.6 % in Beijing (Bo et al., 2008). Ambient VOC measurements during 2002–2003 at six sites in Beijing revealed an average total VOC concentration of 132.6±52.2 µgm−3, ACPD with contributions from alkanes (35 %), alkenes (17 %), and aromatics (22 %; Liu et al., 15, 12453–12490, 2015 2005). A recent study has shown that non-methane hydrocarbon (NMHC) concentra- 5 tions in Beijing are more than twice as high as in other cities (M. Wang et al., 2014). Therefore, it is very necessary to formulate a cost-effective policy for reducing VOC VOCs regulated by emissions in Beijing. emission control VOCs play an important role in the formation of secondary organic aerosol (SOA) during APEC China (Johnson et al., 2006; Ran et al., 2011; Zhang et al., 2014). PM2.5 is a key air pollutant 2014 10 in terms of adverse human health effects and visibility degradation (Tao et al., 2014). The severe haze pollution in Beijing was driven to a large extent by secondary aerosol J. Li et al. formation, which contributed 30–77 and 44–71 % of PM2.5 and of organic aerosol con- centrations, respectively (Huang et al., 2014). Obtaining detailed information on VOC Title Page characteristics before, during, and after the control period will help future study on 15 SOA formation mechanisms. Assessing VOC source variations will be essential to un- Abstract Introduction derstanding the effect of abatement measures for VOCs and SOA formation. Conclusions References To quantitatively assess the contributions of different sources to ambient VOC lev- els, we can use a combination of direct VOC measurements and receptor models. Tables Figures Receptor models are statistical tools used to identify and quantify sources of ambient 20 air pollution at a given location by analyzing concentration data obtained at a receptor J I site without emission inventories. Source apportionment tools such as principal compo- nent analysis, Unmix, chemical mass balance, and positive matrix factorization (PMF) J I have been previously developed (Paatero et al., 1994; Watson et al., 2001). The latter Back Close is widely used to study VOC source contributions in urban areas because only time Full Screen / Esc 25 series of observed concentrations are used for the input parameters of the PMF cal- culation, which means that PMF results are not affected by uncertainties in emission Printer-friendly Version profiles (Bon et al., 2011; McCarthy et al., 2013). With PMF, it is also possible to calcu- late contributions from unknown emission sources. The concept of secondary organic Interactive Discussion aerosol potential (SOAP) has been developed to reflect the propensity of each organic 12456 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | compound to form SOA on the basis of an equal mass emitted relative to toluene (Der- went et al., 1998, 2010). By combining the SOAP scale with contributions from different ACPD sources to ambient VOC levels, it has been possible to evaluate the effect of abatement 15, 12453–12490, 2015 measures for SOA formation. 5 In this study, we measured 102 VOC species using online instruments at an ob- servatory at Peking University in 2014, from 18 October to 22 November. The hourly VOCs regulated by mixing ratios and chemical compositions of ambient VOCs before, during and after the emission control control period were investigated. A PMF model was used to extract the VOC sources during APEC China for this campaign, and comparison of the source contributions before, during and after 2014 10 the control period help to evaluate the effect of the control measures on VOCs. SOAP- weighted mass contributions of each VOC source were used to estimate variations of J.
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