Characteristics and emission-reduction measures evaluation of PM2.5 during the two major events: APEC and Parade

Science of the Total Environment 595 (2017) 81–92

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Characteristics and emission-reduction measures evaluation of PM2.5 during the two major events: APEC and Parade Gang Wang a, Shuiyuan Cheng a,b,⁎, Wei Wei a, Xiaowen Yang a, Xiaoqi Wang a, Jia Jia a, Jianlei Lang a, Zhe Lv a a b

Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100081, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• PM2.5 and component concentrations significantly reduce during the two events. • Reduction in Beijing contribute more to air quality improvement than other regions. • Meteorological was more beneficial for improvement of air quality during Parade. • Vehicles control in Beijing make PM2.5 reduce by 13.5%–14.7% during the two events. Concentrations of PM2.5 and components in Beijing, Shijiazhuang, and Tangshan during APEC and Parade (the numbers in the top of column refer to the percentage declines of PM2.5 concentrations before and after control compared to control period).

a r t i c l e

i n f o

Article history: Received 27 December 2016 Received in revised form 25 March 2017 Accepted 25 March 2017 Available online xxxx Editor: D. Barcelo Keywords: PM2.5 pollution Water-soluble ions Carbonaceous aerosol Emission control evaluation APEC Parade

a b s t r a c t The measurement of PM2.5 was conducted from 20th October to 25th November 2014, and 15th August to 15th September 2015 at Beijing, Shijiazhuang, and Tangshan, China, covering two important events of Asia-Pacific Economic Cooperation (APEC) and Grand Military Parade (Parade). A series of stringent emission-reduction measures were implemented in Beijing and neighbouring regions to ensure good air quality in Beijing. PM2.5 concentrations and major components (water-soluble ions and carbonaceous aerosol) were analyzed and compared between the two events. PM2.5 concentration during control demonstrated a decreasing trend with 51.6%–65.1% and 34.2%– 64.7% compared to no control during APEC and Parade, respectively. The water-soluble ions and carbonaceous aero− sol concentrations also decreased obviously. The lower secondary inorganic ions (SIA, including SO2− 4 , NO3 , and ) percentage (31.7%–38.3%) during control indicated the relative weak contribution of atmospheric chemical NH+ 4 processes. Due to the unfavorable weather conditions and increased emissions during coal-fired heating, the relative higher organic carbon (OC), secondary organic carbon (SOC), secondary organic aerosol (SOA) concentrations during APEC was found. The WRF-CMAQ modeling system was also used to quantify the effect of emission-reduction and different sources controls on PM2.5. The results indicated PM2.5 decrease by 30.4% and 34.2% under control during APEC and Parade, respectively. We found that the local emission-reduction in Beijing contributes more to “APEC blue” (20.8%) and “Parade blue” (25.8%) in Beijing than those from neighbouring regions of Beijing, and meteorological condition was more beneficial for the improvement of the air quality during Parade than APEC. The emission source apportionment to PM2.5 in Beijing indicated that PM2.5 concentration increased by 13.5% and 14.7% during APEC and Parade, respectively if no vehicles emission-reduction measures were taken in Beijing. © 2017 Published by Elsevier B.V.

⁎ Corresponding author at: Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China. E-mail address: [email protected] (S. Cheng).

http://dx.doi.org/10.1016/j.scitotenv.2017.03.231 0048-9697/© 2017 Published by Elsevier B.V.

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1. Introduction China has experienced rapid economic development, population expansion, industrialization and urbanization in the past few decades. Unfortunately, its growth has also brought severe air compound pollution, especially for the Beijing-Tianjin-Hebei (BTH) region facing urgent need to control high PM2.5 concentration. This circumstances has posed significant challenges to governments to tackle the serious regional PM2.5 pollution effectively (Lee et al., 2006). Therefore, a series of emission-reduction strategies have been carried out by the BTH region to improve the air quality. Under the strict emission controls, PM2.5 concentrations in the BTH region had dropped observably in recent years. The number of days met the national ambient air quality standards (NAAQS) Grade II (75 μg/m3) of PM2.5 mass concentrations accounted for 37.5%, 42.8%, and 52.4% in 2013, 2014, and 2015, respectively. Even so, annual averaged PM2.5 concentrations, such as in Beijing, Shijiazhuang, and Tangshan in the BTH region, reached 80.6, 89.0, and 85.0 μg/m3 in 2015, respectively, far exceeding the NAAQS Grade II (35 μg/m3) for the annual PM2.5 concentration. Severe air pollution will lead to adverse effects on human health and atmospheric visibility (Cheng et al., 2011). In particular, the large-scale air pollution episode occurring in January 2013 over northern and eastern China had arouse worldwide attention on PM2.5. Therefore, great efforts have been devoted to studying PM2.5 from atmospheric scientists and the general public, and previous studies have provided valuable information on air compound pollution. What is more, two major events including the Asia-Pacific Economic Cooperation (APEC) and Grand Military Parade (Parade) were held in November 2014 and September 2015 in Beijing, respectively. Unconventional and stringent emissionreduction measures were implemented by Chinese government to achieve “APEC blue” and “Parade blue”. For instance, Beijing, Tianjin, Hebei, Shandong, Shanxi, and Inner Mongolia implemented measures from 1st to 12th November 2014 during APEC. Henan also joined the reduction team during Parade, and the reduction period ranged from 26th August to 4th September 2015 except Beijing (20th August to 4th September). The large-scale stringent emission-reduction strategies during important events can provide an invaluable opportunity to investigate the relationship between emission controls and PM2.5 concentrations, which are uniquely rare in China and even the world. Several studies had been reported on air quality improvement in Beijing during APEC and Parade. The observations showed that emission reduction measures had effectively improved the air quality in Beijing during APEC (Wang et al., 2015c). And the SO2, NO2, PM10, and PM2.5 concentrations during APEC period in Beijing decreased by 62%, 41%, 36%, and 47%, respectively, whereas O3 concentration increased by 102% compared with the same time period in the previous 5 years (PM2.5 was compared with the last year). The water-soluble ions as well as sulfur and oxygen isotopes of sulfate in PM2.5 collected during Parade were analyzed in Beijing in 2015 (Han et al., 2016). The results exhibited a decrease trend in concentrations of water-soluble ions and aerosol sulfate. However, the concentrations of sulfur and oxygen isotope in PM2.5 during control and no control exhibited no significant differences, which indicated that the decrease in concentration of sulfate mainly resulted from variations in air mass transport. The Artificial Neural Network (ANN) models were used by Li et al. (2016) to predict the pollutant concentrations during APEC without the emission control measures, and the results prove that PM2.5 and PM10 were reduced by 24% and 28%, respectively. Source apportionment of pollutants in Beijing during the two events were also reported. PM2.5 source apportionment and impacts of regional transportation on air quality were analyzed during APEC (Wang et al., 2016b). Data showed that the control on industry source was the most effective measure for reducing pollutant concentrations, while control on the residual oil combustion source was the least effective. The largest reductions in pollutant concentrations occurred when air mass transported from the west-northwest. The average NH3 concentrations were 9.1, 7.3, and 12.7 μg/m3

before, during, and after control, respectively during APEC (Chang et al., 2016). An isotope mixing model was also used to quantify the sources of ambient NH3, and the overall contribution of traffic, waste, livestock, and fertilizer to NH3 concentrations were 20.4%, 25.9%, 24.0%, and 29.7%, respectively. A paradox for air pollution controlling revealed by “APEC Blue” and “Parade Blue” was also reported by Liu et al. (2016). The NO2 columns abruptly decreased both during Parade (43%) and APEC (21%) compared with the periods before these two events. Regional transport from southern peripheral cities played a key role in pollutants observed at Beijing. It was also found that there were not only limited the NO2 pollutant but also suppress the O3 contaminant based on the ratios of HCHO over NO2 during Parade, while O3 increased during the APEC. Even though chemical composition characteristics, source apportionment, and online continuous observation for PM2.5 had been carried out in the BTH region (Gao et al., 2015; Tao et al., 2016), the comparison and overall analysis of the effect of emission-reduction measures and emission source apportionment for PM2.5 in Beijing between the two events is still not clearly explained. What is more, the two events are independent and there is no comparability. The weather conditions, pollution emissions, and emission reduction rates are completely different during APEC and Parade. The discussions between the two events are expected to provide a scientific technique support for regional air pollution control and policy making scientifically during major events in China. From a policy perspective, a comprehensive investigation focusing on PM2.5 components in the BTH region and the effect of emission-reduction strategies were desirable to guide future control strategies. Therefore, PM2.5 samples were collected in Beijing, Shijiazhuang, and Tangshan in the BTH region during the two major events. The objectives of this study mainly include: (1) comparing the characteristics of PM2.5 and main components (water-soluble ions and carbonaceous aerosol) before, during, and after control during the two major events; (2) assessing the effect of emission-reduction measures adopted by Beijing and neighbouring regions to PM2.5 in Beijing; (3) identifying the emission source apportionment to PM2.5 in Beijing during APEC and Parade.

2. Methods 2.1. The monitoring locations and PM2.5 sampling The BTH region is one of the most economically vibrant regions in China, covering 2.25% of the Chinese territory and accounting for 8.08% of the Chinese population in 2014. Beijing, one of the largest megacities in the world with more than 5.61 million vehicles, is the center of politics, economics, culture, and international communication of China. Tianjin, a directly governed municipality of China, is adjacent to the Bohai Sea in North China. Hebei is an important industrial province in northern China, and the industrial products output of steel and coke accounted for 26.5% and 13.3% of Chinese outputs in 2013. More detailed descriptions of the BTH region can be found in our previous works (Lang et al., 2012). Four sites are selected in the BTH region as shown in Fig. 1. The University of Chinese Academy of Sciences (UCAS) located in the Huairou district of Beijing and Beijing Normal University (BNU) located between the North 2nd and 3rd Ring Road are selected for collecting PM2.5 samples in Beijing during APEC and Parade, respectively (Fig. 1 ① and ②). The UCAS and BNU sites are close to the educational activities, residential area, and traffic. The Environmental Monitoring Center (EMC) and Environmental Monitoring Station (EMS) sites located in the urban area of Shijiazhuang and Tangshan, respectively are also selected for collecting PM2.5 (Fig. 1 ③ and ④). The two sites are surrounded by offices, residential area, and traffic. The four sampling sites are all placed on the rooftop of an office building (about 20 m above the ground level) and can be the representative of the urban area. Sampling sites for Beijing, Shijiazhuang, and Tangshan are abbreviated as BJ, SJZ, and TS, respectively in this study.

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Fig. 1. Location of the sampling sites and design of three-level modeling domains.

In order to analyze the characteristics of PM2.5 before, during, and after control, samples were collected from 20th October to 25th November 2014 at BJ, SJZ, and TS sites during APEC. During Parade, samples were collected from 15th August to 15th September 2015. However, two, two, and four PM2.5 samples were not collected due to mechanical failure at BJ, SJZ, and TS sites, respectively. Teflon and quartz filters (90 mm, Whatman Inc. Maidstone, UK) were used to collect PM2.5 samples for analysis of water-soluble ions and OC/EC, respectively. Samples were collected from 9:00 a.m. to 9:00 a.m. the next day at the flow rate of 100 L/min. Filters were pre- and post-weighed using the electronic microbalance with accuracy of 0.01 mg (Sartorius TB-215D, Germany) in a super-clean room (20 ± 5 °C and 40 ± 2% RH) for 48 h. Additionally, the concentrations of four primary atmospheric pollutants, including NO2, SO2, CO, and O3, were obtained from Beijing and Hebei Municipal Environmental Protection Bureau. Meteorological parameters, including ambient temperature, relative humidity, sea level pressure (SLP), visibility, wind speed, and total precipitation (TP) were available from the Weather Underground website (www.wunderground.com). The boundary layer height (BLH) in Beijing were obtained from Aircraft Meteorological Data Relay (AMDAR) at Beijing Capital International Airport. Wind direction were obtained from the Meteorological Information Comprehensive Analysis and Process System of China (MICAPS). 2.2. Chemical analysis and quality control Half of teflon filter was extracted ultrasonically by 10 mL distilleddeionized water and oscillated for 40 min in supersonic cleaner. The + 2+ , and Mg2 +) and concentrations of five cations (Na+, NH+ 4 , K , Ca − 2− − − − five anions (F , Cl , NO3 , SO4 , and NO2 ) were analyzed by Ion Chromatograph (Metrohm 861 Advanced Compact IC, Switzerland). OC and EC concentrations were measured by the thermal/optical carbon analyzer (DRI Model 2001A, Desert Research Institute of United States). Firstly, OC was volatilized from the samples as the temperature gradually rose to 120 °C (OC1), 250 °C (OC2), 450 °C (OC3), and 550 °C (OC4) in a non-oxidizing helium atmosphere. Then the analyzer oven temperature was gradually ramped to 550 °C (EC1), 700 °C (EC2), and 800 °C (EC3). The optical pyrolyzed carbon (OP) is also formed in this process. Carbon in these volatile components was converted into CO2 by oxidant (MnO2), then converted into CH4 through a converter, and was finally quantified with a flame ionization detector (FID). OC was defined as OC1 + OC2 + OC3 + OC4 + OP and EC as EC1 + EC2 + EC3 − OP according to the IMPROVE protocol (Chow et al., 2004). The PM2.5 samplers were cleaned by alcohol before used, and the flow rate was calibrated before sample collection to ensure the deviation of the sampling system was within 5%. A calibration curve of each target component was performed and the correlation coefficient values

were all above 0.999. Replicate analysis was also conducted using blank filters, and the average value of blank filters was used as the background concentration. More detailed analytical procedures about water-soluble ions and carbonaceous aerosol, and quality control could be found in our previous works (Lang et al., 2013; Wang et al., 2015a; Wang et al., 2015b). 2.3. WRF-CMAQ modeling system 2.3.1. Emission reduction in different control strategies Quantifying the effect of emission controls on PM2.5 during the two major events will be of great value for future policy making. To ensure the air quality satisfies the NAAQS, the control measures mainly included restricting traffic based on the odd and even number plate rule, suspension of production by factories, construction site restrictions to mitigate road dust, and vacation days off for the public. Based on the emission-reduction plan, the emission coefficients method was applied to calculate the reductions of pollutants. Compared to the original emission inventory, the emission reduction rates for SO2, NOx, VOCs, and PM2.5 in Beijing, Shijiazhuang, and Tangshan during APEC and Parade are displayed in Fig. 2. The original emission inventory was mainly obtained from local environmental protection bureaus or administrations. The data was calculated based on the categories of activities and their emission coefficients. More detailed descriptions of the complete emission inventory can be found in previous works published by our colleagues (Cheng et al., 2012; Lang et al., 2013; Wang et al., 2016a). The sampling periods were divided based on the emission-reduction measures, into before, during, and after control during the two events (Table 1). 2.3.2. Modeling description and application To facilitate the study of the contribution of local and regional emission measures and different sources on PM2.5 in Beijing, the Weather Research and Forecast model coupled with Community Multiscale Air Quality (WRF-CMAQ) was built. The initial and boundary conditions for the WRF simulation are prepared using the 1° × 1° resolution final global tropospheric analyses data (FNL) which is produced by National Centers for Environmental Prediction's (NCEP) Global Forecast System (GFS). In this study, a three-level nested-grid architecture was employed for the implementation of the WRF-CMAQ modeling system (Fig. 1). The simulated domain 1 covers the most areas of northeastern China with a spatial resolution of 27 km × 27 km. Modeling domain 2 coveres Beijing and its neighbouring regions with a 9 km × 9 km spatial resolution. Modeling domain 3 coveres Beijing with a spatial resolution of 3 km × 3 km (Fig. 1). In the vertical dimension, 28 sigma levels are designed in the WRF simulation. The WRF outputs from 28 vertical levels are transformed into 14 levels with the format required by the CMAQ model using the Meteorology Chemistry Interface Processor (MCIP). The

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G. Wang et al. / Science of the Total Environment 595 (2017) 81–92 Table 2 Description of emission control scenarios. Type of scenarios

Description

ZERS ERS BJERS

Without emission reduction Emission reduction in Beijing and neighbouring regions Emission reduction in Beijing, no emission reduction in neighbouring regions Emission reduction except industrial sources in Beijing Emission reduction except vehicles in Beijing Emission reduction except fugitive dust in Beijing Emission reduction except other sources in Beijing

IZERS VZERS FDZERS OZERS

Fig. 2. Emission reduction rates for SO2, NOx, VOCs, and PM2.5 in Beijing, Shijiazhuang, and Tangshan during APEC and Parade.

Carbon Bond mechanism (CB05) is chosen as the gas-phase chemistry mechanism. In order to assess the impact of emission-reduction measures, as well as the local and regional contribution on PM2.5 concentrations in Beijing, three scenarios simulated using a zero-out method, namely, Zero Emission Reduction Scenario (ZERS), Emission Reduction Scenario (ERS), and Beijing Emission Reduction Scenario (BJERS), as defined in Table 2, were run separately with the same meteorological conditions. The ZERS was refer to a scenario “without emission reduction”, while ERS and BJERS correspond to the situation “emission reduction in Beijing and neighbouring regions” and “emission reduction in Beijing”, respectively. To evaluate the overall effect of control measures from different sources, four scenarios namely, Industrial Zero Emission Reduction Scenario (IZERS), Vehicle Zero Emission Reduction Scenario (VZERS), Fugitive Dust Zero Emission Reduction Scenario (FDZERS), and Other Zero Emission Reduction Scenario (OZERS), were also simulated to calculate the contribution of different sources on PM2.5 concentrations in Beijing. The IZERS, VZERS, FDZERS, and OZERS were corresponding to the scenarios where the emissions from Beijing and neighbouring regions were set to reduction inventories except industrial sources, vehicles, fugitive dust, and other sources in Beijing, respectively. The simulation results ranged from 1st to 12th November 2014 during APEC and 26th August to 4th September 2015 during Parade were used for analyses in the following sections. 3. Results and discussion 3.1. PM2.5 characteristics during sampling period Fig. 3 shows the time series of PM2.5, gaseous pollutants, and main components at BJ, SJZ, and TS during different sampling periods. Table 1 Periods of emission-reduction measures conducted before, during, and after control during the two events. Events

Period

APEC

Before During After Parade Before During After

BJ

SJZ

TS

20th to 31th October 2014 1st to 12th November 2014 13th to 25th November 2014 15th to 19th August 2015 15th to 25th August 2015 20th August to 4th September 2015 26th August to 4th September 2015 5th to 15th September 2015

Measured PM2.5 concentrations averaged 77.5–131.5 and 20.7– 46.2 μg/m3 at each site during APEC and Parade, respectively. During the period before APEC, the average daily PM2.5 concentrations were 109.7, 164.8, and 111.2 μg/m3 at BJ, SJZ, and TS sites, respectively. During control, those were reduced to 39.9, 58.7, and 52.8 μg/m3. After control, PM2.5 concentrations were 82.4, 168.1, and 127.7 μg/m3. Thus, PM2.5 concentration during control demonstrated a decreasing trend obviously with 63.6%, 64.4%, and 52.5% compared to before control at BJ, SJZ, and TS sites, respectively. Additionally, the average concentrations of NO2, SO2, and CO were also obviously reduced by 26.0%–45.7%, 35.3%–55.4%, and 41.2%–56.9%, respectively at each site, as compared with those before APEC. Compared with after control, PM2.5 concentrations were reduced by 51.6%, 65.1%, and 58.6% during control. Similar reductions of PM2.5 were also shown during Parade. The PM2.5 concentrations were 50.3, 65.3, and 29.7 μg/m3 at BJ, SJZ, and TS sites, respectively before control. During control, those were reduced to 26.1, 27.7, and 10.5 μg/m3. After control, PM2.5 concentrations were 39.7, 43.6, and 21.1 μg/m3. Thus, PM2.5 concentration during control also demonstrated a decreasing trend with 48.1%, 57.6%, and 64.7% compared to before control at BJ, SJZ, and TS, sites respectively. Compared with after control, PM2.5 concentration were 34.2%, 36.5%, and 50.4% lower during control. The notable improvements of PM2.5 concentrations in the BTH region reflected the combined effect of various emission reduction measures during APEC and Parade. It was found that the average PM2.5 concentrations during Parade all met the standard (75 μg/m3) before, during, and after control at BJ, SJZ, and TS sites (Fig. 3). However, PM2.5 concentrations during APEC were 1.1 to 2.2 times higher than this standard before and after control. That was mainly due to that the atmosphere was often influenced by the upper-level westerly air flow during Parade in the BTH region (Fig. 4a), while uniform pressure field and low pressure trough in North China Plain during APEC (Fig. 4b). For instance, the BLH in Beijing during Parade was higher (1290.0–1567.2 m), which was favorable for pollutant dispersion (Table 3). What is more, the abundant precipitation (67.3 mm) in Beijing along with winds from the Bohai Sea can alleviate air pollution to a great extent (Jiang et al., 2009). In addition, coal and biomass combustion for heating increased after control, which also aggravated air pollution during APEC. Higher PM2.5 concentration at SJZ was also observed during the two major events owing to a great deal of coal combustion by industrials and increased vehicle population. The wind speed was also lowest at SJZ. All of these conditions made a severe PM2.5 pollution in Shijiazhuang, which was consistent with the observed results conducted by Zhao et al. (2013a). 3.2. Water-soluble ions characteristics during sampling period Time series and the average of water-soluble ions concentrations in PM2.5 during APEC and Parade are presented in Fig. 3, Tables 4 and 5. As for aspect of APEC, the average daily concentrations of total water-soluble ions (TWSI) at each site were 54.7–81.7, 15.9–25.4, and 38.7– 81.1 μg/m3 before, during, and after the control period, respectively. As for aspect of Parade, the concentrations were 17.4–34.9, 5.3–16.2, and 12.6–22.8 μg/m3. As a whole, the TWSI concentration during control

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Fig. 3. Time series of PM2.5, gaseous pollutants, and main components during APEC and Parade in Beijing (a), Shijiazhuang (b), and Tangshan (c).

demonstrated a decreasing trend with 52.2%–71.9% lower than before control. Compared with after control, the TWSI concentration were 25.9%–68.5% lower during control. This might be due to the emissionreduction measures as well as the better diffusion conditions during control. − + The secondary inorganic ions (SIA, including SO2− 4 , NO3 , and NH4 ) concentrations basically formed by heterogeneous or homogeneous reactions as a result of precursor gases during the two major events accounted for 38.1%–42.9%, 31.7%–38.3%, and 36.3%–44.7% of the total mass of PM2.5 before, during and after control, respectively. The lower value during control mainly due to that the atmospheric chemical

processes were relative weak. What is more, the strong correlations between SIA and PM2.5 ranged from 0.96 to 0.99 were observed during the two major events at each site, indicating that the SIA variations might be related to the formation and removal of PM2.5. It also could be found that 2− 2− N NH+ the SIA concentrations were ranked as NO− 3 N SO4 4 and SO4 + N NO− N NH at each site during APEC and Parade, respectively. The 3 4 higher SO2− 4 concentrations during Parade were mainly owing to the increase of ambient temperature (22.6–23.6 °C) and concentration of the most important photochemical oxidant (O3), with 2.4–3.1 and 1.9–3.3 times higher than APEC, leading to the strong photochemical oxidation and higher conversion rate from SO2 to SO2− 4 . NO2 is easier to produce

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Fig. 3 (continued).

HNO3 by photochemical reactions, and then convert into secondary particle. However, the higher temperature does not favor the formation of NO− 3 , and make NH4NO3 exist in a form of gaseousness (Zhao et al., 2013a). Ca2+ and Mg2+ are also the important water-soluble ions and cannot be ignored. The higher correlation coefficient (CC) between Ca2+ and Mg2 + was found with mostly higher than 0.81 at each site, reflecting the common crustal sources. In our study, the sum concentrations of Mg2+ and Ca2+ in PM2.5 were 0.7–2.0 and 0.4–1.5 μg/m3 during control, which were 54.8%–71.7% and 20.2%–76.4% lower than no control during APEC and Parade, respectively. That was reasonably attributed to the emission-reduction measures. For instance, the construction sites taken measures to restrain dust rising such as sprinkler and cover, increase the frequency of street sweepings and road washing, strengthen management and supervision of road dust. Additionally, traffic restriction could decrease road dust, leading to a relatively lower Mg2+ and Ca2+ concentration. Research indicated that Na+ and Cl− are the characteristic components of sea salt. The decrease of Na+ was 37.3%–56.9% and 46.6%–53.6% during APEC and Parade, respectively. Sea salt aerosols can scatter short wave solar radiation directly, and affect the concentration of particulates in coastal areas through the atmospheric transmission. The calculation of sea salt aerosols concentrations in atmosphere can be described as 3.246 times of Na+ concentration in PM2.5 (Terzi et al., 2010). The concentrations of sea salt of coastal city at TS site were 1.1–1.2 and 1.2–1.3 times higher than BJ and SJZ during the two major events due to typical inland cities of Beijing and Shijiazhuang. Cl− will also be released in the process of coal combustion. The emission-reduction of coal combustion made the obvious concentration differences of Cl−, with the decrease rate of 49.2%–71.1% and 33.5%–75.1% during APEC and Parade, respectively. K+ is enriched in the aerosol as a result of biomass burning. The proportion of K+ also showed obvious variation, and the concentration were decreased by 39.0%–68.0% and 39.8%–66.6% during control compared to no control during APEC and Parade respectively, which might be attributed to the burning of wheat straw and maize stalks (Wang et al., 3 2016a). The concentrations of F− and NO− 2 were 0.02–0.12 μg/m

during sampling period, only accounting for 0.04%–0.11% of PM2.5 at each site. 3.3. Carbonaceous aerosol characteristics during sampling period The OC and EC concentrations in PM2.5 at each site are presented in Fig. 5. From the event variation perspective, the average concentrations were 13.7 and 3.7 μg/m3 at BJ, 28.4 and 8.6 μg/m3 at SJZ, 18.8 and 5.9 μg/ m3 at TS, respectively during APEC. Heating supply was implied from 15th November in Beijing, Shijiazhuang, and Tangshan. As Beijing has changed from residential coal combustion to fuel natural gas, lower concentrations of OC and EC were observed compared to SJZ and TS sites. Accordingly, the concentrations were 6.0 and 1.8 μg/m3 at BJ, 9.3 and 2.0 μg/m3 at SJZ, 4.1 and 0.9 μg/m3 at TS, respectively during Parade. The carbonaceous concentrations during Parade were lower at TS site, which were in consistence with the observed PM2.5 levels. The total carbon (TC = OC + EC) accounted for 22.6%–24.2%, 24.2%–25.8%, and 24.7%–25.5% of PM2.5 at BJ, SJZ, and TS sites, respectively during the two major events, indicating that carbonaceous materials represent a significant component in PM2.5. The average daily TC concentrations during control demonstrated a decreasing trend obviously with 62.8%, 68.1%, and 55.7% compared to before control at BJ, SJZ, and TS, respectively during APEC, while 49.5%, 57.5%, and 64.4% during Parade. Compared with the period after control, the TC concentrations were 30.2%–56.8%, 39.2%–70.0%, and 53.9%–67.2% lower during control. Carbonaceous aerosols mainly come from biomass burning, coal combustion, and vehicle exhaust. The emission-reductions measures, including stopping biomass burning, suspension of production by factories, and cutting the number of on-road vehicles during control, lead to a lower TC concentration. As indicated in Fig. 5, the OC concentrations at each site during APEC were 2.3 to 4.6 times higher than during Parade. The reasons might be the unfavorable weather conditions and increased emissions during coal-fired heating. A relatively low temperature (7.6–9.7 °C) and a stable atmosphere (1023.0–1023.2 HPa) at each site (Table 3) could facilitate the accumulation of contaminants and create conditions for the

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many researchers (Feng et al., 2009). The calculation of SOC and POA can be described as follows (Castro et al., 1999). SOA can be further calculated from SOC by multiplying a coefficient of 1.6. SOC ¼ OC−EC  ðOC=ECÞmin

ð1Þ

POA ¼ 1:6  ðOC−SOCÞ

ð2Þ

where OC and EC are the measured ambient total OC and EC concentrations, and (OC/EC)min is the lowest ratio during different seasons. The variations of calculated SOC, SOA, POA concentrations and SOC/ PM2.5 ratio at each site are depicted in Fig. 6. The average SOC concentrations were 4.9, 11.9, and 8.2 μg/m3 at BJ, SJZ, and TS sites, accounting for 35.5%, 42.9%, and 40.7% of the OC, respectively during APEC, while 2.7, 5.6, and 2.4 μg/m3 at BJ, SJZ, and TS sites, accounting for 44.3%, 57.2%, and 49.3% of the OC, respectively during Parade. This indicated that SOC was an important OC component in PM2.5. Similar to OC and EC, the SOC concentration was distinctly higher during APEC than Parade, with a value of 1.8, 2.0, and 3.4 times higher for the BJ, SJZ, and TS sites, respectively. The SOC and SOA in the atmosphere is mainly controlled by meteorological condition, with higher temperature facilitating its formation. When the temperature is lower than 15 °C, it is difficult to form SOC (Strader et al., 1999). The average temperature was 7.6–9.7 °C and 22.6–23.6 °C at each site during APEC and Parade, respectively. Obviously, the photochemical reactions leading to secondary pollution were not active during APEC. Therefore, the higher SOC and SOA concentrations during APEC could be due to the secondary pollution by stable atmosphere and increased concentration of precursor gases, which could facilitate the condensation of VOCs on aerosol. POA mainly comes from the direct emission of combustion process. The POA concentrations were higher during APEC, which were in consistence with the observed PM2.5 and TC levels. Higher emission reduction rates and abundant precipitation made a lower value during Parade. 3.5. The effect of emission control assessment during APEC and Parade

Fig. 4. Surface pressure at 05:00 on September 01st, 2015 (a) and 08:00 on November 19th, 2014 (b) in the BTH region.

adsorption or condensation of volatile organic compounds (VOCs). What is more, the increased combustion sources during heating period from 15th November could also increase the emissions of PM2.5 and its precursor gases. In fact, NO2, SO2, and CO concentrations during APEC were 1.7–3.7, 1.3–2.2, and 1.3–3.6 times higher than that of Parade, respectively (Fig. 3). The time of cold start of motor vehicle prolonged owning to the low temperature (Chow et al., 1993) which was favorable for converting semi volatile substances to particle state. Finally, the precipitation was higher during Parade (67.3 mm) and this could also reduce OC concentrations. As compared to the OC, EC showed little change during the two major events because it mainly came from primary pollutants.

3.4. Secondary organic carbon (SOC) and secondary organic aerosol (SOA) OC not only exists in the primary organic aerosol (POA), but also in SOC which can be easily affected by weather conditions and external emission sources. The OC/EC ratio method was used to calculate the SOC in aerosol by assuming that a relatively stable ratio exists for primary emission sources (Pachauri et al., 2013; Zhao et al., 2013b; Zhou et al., 2012). Chow et al. (1996) suggested that secondary pollution exists if OC/EC value is higher than 2.0 and this method has been adopted by

The WRF/CMAQ model was used to analyze the air quality during control in Beijing. To assess the modeling performance, the average simulated PM2.5 concentrations within the grid cells containing twelve, six, and six monitoring stations were calculated to compare with the observation results during APEC and Parade in Beijing, Shijiazhuang, and Tangshan, respectively. Fig. 7 shows the observed and simulated 2− + PM2.5, NO− 3 , SO4 , and NH4 concentrations under ERS. The CC and normalized mean error (NME) of PM2.5 between the simulated and observed concentrations were 0.79–0.84 and 0.32–0.36, respectively. 2− + Meanwhile, the simulated concentrations of NO− 3 , SO4 , and NH4 were also compared with the observation results from BJ, SJZ, and TS 2− sites. The CCs were 0.71–0.81, 0.65–0.79, and 0.64–0.78 for NO− 3 , SO4 + , and NH4 , respectively, while the NMEs were 0.36–0.58, 0.29–0.49, and 0.41–0.57. Considering the uncertainty and complexity of higher resolution emission inventories, and the defects in chemical mechanism of air quality and meteorological simulation, the modeling performance of the WRF-CMAQ for simulating PM2.5 concentrations was acceptable (Chen et al., 2007; Wang et al., 2011). Based on the three scenarios of ZERS, ERS, and BJERS (Table 2), three simulation experiments were separately run, and grid cells in Beijing within modeling domain 3 were selected to investigate the effect of emission-reduction adopted by Beijing and neighbouring regions on air quality during APEC and Parade. The model results simulated 66.5 and 34.9 μg/m3 under ZERS and 46.3 and 23.1 μg/m3 under ERS during APEC and Parade, respectively. The simulated lower values under ERS explained 30.4% and 34.2% of the reduction for PM2.5, which indicated that the stringent air quality restrictions implemented during the events were successful for “APEC blue” and “Parade blue”. Meteorology also played a non-negligible role in the improved air quality and should be

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Table 3 Meteorological conditions during APEC and Parade. Events

Sites

Period

Temperature (°C)

Humidity (%RH)

SLP (HPa)

Visibility (km)

Wind speed (m/s)

TP (mm)

BLH (m)

APEC

BJ

Entire Before During After Entire Before During After Entire Before During After Entire Before During After Entire Before During After Entire Before During After

7.6 11.5 7.5 4.2 8.9 12.8 9.3 5.0 9.7 13.1 9.7 6.6 23.5 26.8 24.7 20.4 22.6 25.8 23.7 19.5 23.6 27.8 24.4 20.6

54.6 66.0 45.0 52.8 67.6 81.2 56.9 64.9 57.1 69.7 43.9 57.5 67.7 61.6 69.2 68.2 78.8 79.6 79.1 78.2 63.6 56.8 67.9 60.4

1023.2 1021.0 1023.0 1025.3 1023.1 1020.7 1023.3 1025.1 1023.0 1020.8 1022.8 1025.1 1011.7 1007.4 1010.0 1016.2 1011.8 1007.6 1009.9 1016.5 1011.3 1007.2 1009.5 1015.8

8.9 6.2 13.7 7.0 4.0 3.0 5.7 3.5 3.8 3.3 4.7 3.8 13.0 10.6 14.3 12.3 6.0 4.6 6.8 5.5 6.3 5.2 6.2 7.1

2.3 1.8 3.0 2.1 2.0 1.7 2.5 1.9 2.7 2.1 3.4 2.5 2.3 2.0 2.2 2.6 2.0 1.9 1.9 2.1 2.6 2.5 2.5 2.9

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 67.3 1.0 50.3 16.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

1141.0 1099.0 1284.0 1048.0 – – – – – – – – 1382.8 1567.2 1389.0 1290.0 – – – – – – – –

SJZ

TS

Parade

BJ

SJZ

TS

taken into account to determine the actual influence of emission-reduction. The results of wind direction showed that the air flow in Beijing was mainly transported from northern area of Beijing, with the frequency (WNW, NW, NNW, N, NNE, NE, and ENE) of 66% and 56%, respectively during APEC and Parade, respectively (Fig. 8). Those areas correspond to the mountain (e.g., Yanshan) and cities with rare emission sources (e.g., Zhangjiakou, Qinhuangdao, and Inner Mongolia). Additionally, PM2.5 concentrations greatly decreased under BJERS scenario during APEC and Parade, with the values of 13.8 and 9.0 μg/ m3, respectively. Therefore, the emission-reduction by neighbouring regions made the PM2.5 concentrations decrease by 6.4 and 3.0 μg/m3. This finding obviously indicated that the reduction percentage in PM2.5 concentrations under BJERS were 20.8% and 25.8% during APEC and Parade, respectively compared to no reduction measures. Accordingly, the reduction declined to 9.6% and 8.4% for PM2.5 based on emission control only implemented from neighbouring regions of Beijing. Thus it could be seen that the local emission reduction in Beijing contributes more to “APEC blue” and “Parade blue” in Beijing than those from neighbouring regions of Beijing. It was also observed that the effect of emission-reduction during Parade was better than APEC. That was mainly due to that the reduction

rates for PM2.5 (42.0%) during Parade were higher than APEC (37.0%) (Fig. 2). As the main component in PM2.5 in Beijing atmosphere, the higher reduction rate for the precursor of NO− 3 during Parade was also decrease the PM2.5 concentrations to a degree. From the meteorological perspective, although the average wind speed during APEC (3.0 m/s) was higher than Parade (2.2 m/s) in Beijing, the average pressure and BLH (i.e. 1010.0 HPa and 1389.0 m) during Parade were conducive to the diffusion of pollutants (Table 3). The atmosphere was often controlled by obviously pressure gradient according to MICAPS data discussed in section “PM2.5 characteristics during sampling period” (Fig. 3). What is more, abundant precipitation (50.3 mm) along with winds from the Bohai Sea could alleviate air pollution to a great extent during Parade in Beijing. The WRF/CMAQ model was further applied to quantitatively investigate the influence of meteorology. The same emission reduction inventories during APEC and meteorological conditions during Parade was simulated with the period of APEC. The simulation result indicated that PM2.5 concentration decreased by 32.5% under this scenario, which higher than the actual situation during APEC (30.4%). Therefore, we concluded that meteorology was beneficial for the improvement of the air quality to a certain extent during Parade than APEC.

Table 4 PM2.5 and main components concentrations at BJ, SJZ, and TS sites during APEC (μg/m3). Components

PM2.5 NO− 3 SO2− 4 NH+ 4 Na+ Cl− K+ Mg2+ Ca2+ F− NO− 2 OC EC

BJ

SJZ

TS

Entire

Before

During

After

Entire

Before

During

After

Entire

Before

During

After

79.3 14.9 9.8 7.9 0.2 1.3 1.0 0.2 1.1 0.0 0.0 13.7 3.7

115.5 23.3 14.2 12.8 0.2 1.4 1.2 0.2 1.3 0.0 0.0 18.2 5.2

39.9 6.6 4.3 2.8 0.2 0.7 0.6 0.1 0.5 0.0 0.0 6.9 1.7

82.3 14.7 10.8 8.2 0.3 1.6 1.2 0.3 1.6 0.1 0.0 15.7 4.2

131.5 21.9 18.2 11.7 0.7 4.2 2.3 0.8 3.5 0.0 0.0 28.4 8.6

164.8 29.3 24.4 14.6 0.8 4.3 3.1 0.8 4.2 0.0 0.0 36.2 10.1

58.7 8.9 7.0 4.2 0.5 2.1 1.0 0.4 1.6 0.0 0.0 11.3 3.4

168.1 27.0 22.7 15.8 1.0 6.1 2.7 1.0 4.6 0.1 0.0 37.0 12.1

97.5 16.6 14.6 9.1 0.6 4.7 1.6 1.0 3.7 0.1 0.0 18.8 5.9

109.5 18.5 16.9 11.3 0.6 4.6 1.6 0.8 3.6 0.0 0.0 21.5 6.0

52.8 8.3 7.1 4.4 0.3 2.1 1.0 0.3 1.7 0.1 0.0 8.7 2.6

127.7 22.4 19.5 11.3 0.8 7.3 2.3 1.6 5.5 0.1 0.0 25.7 8.9

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Table 5 PM2.5 and main components concentrations at BJ, SJZ, and TS sites during Parade (μg/m3). Components

PM2.5 NO− 3 SO2− 4 NH+ 4 Na+ − Cl K+ Mg2+ Ca2+ F− NO− 2 OC EC

BJ

SJZ

TS

Entire

Before

During

After

Entire

Before

During

After

Entire

Before

During

After

34.2 5.0 5.7 4.1 0.2 1.1 0.4 0.1 0.4 0.0 0.0 6.0 1.8

50.3 7.1 8.3 6.0 0.3 1.3 0.6 0.2 0.8 0.0 0.0 9.4 2.7

26.1 3.4 3.8 2.7 0.1 0.7 0.3 0.1 0.3 0.0 0.0 4.7 1.4

39.7 6.6 7.8 5.5 0.3 1.6 0.5 0.2 0.5 0.0 0.0 6.6 2.1

46.2 6.0 7.2 4.9 0.5 2.3 1.2 0.6 1.2 0.1 0.0 9.3 2.0

65.3 8.9 10.4 7.2 0.7 3.4 1.7 0.8 1.6 0.1 0.0 13.2 2.8

29.9 3.7 4.3 3.1 0.3 1.4 0.9 0.4 0.9 0.1 0.0 5.9 1.0

43.6 5.5 6.7 4.4 0.6 2.1 1.2 0.5 1.1 0.1 0.0 8.9 2.3

20.7 2.9 3.9 2.1 0.3 1.0 0.5 0.2 1.1 0.0 0.0 4.1 0.9

29.7 4.2 5.7 3.0 0.4 1.4 0.7 0.2 1.9 0.0 0.0 5.9 1.1

10.5 1.4 1.9 0.9 0.2 0.4 0.2 0.1 0.4 0.0 0.0 2.0 0.5

21.1 3.2 4.2 2.2 0.4 1.1 0.5 0.2 0.8 0.0 0.0 4.3 1.1

3.6. The PM2.5 source apportionment in Beijing during APEC and Parade

4. Conclusions

The sources contribution to the PM2.5 concentrations during APEC and Parade were also quantified. The sources were divided into industrial sources, vehicles (including road dust), fugitive dust, and other sources. The emission-reduction contribution of industrial sources, vehicles, fugitive dust, and other sources in Beijing to PM2.5 pollution calculated by the concentration difference between ZERS and IZERS, VZERS, FDZERS, or OZERS are displayed in Fig. 9. It could be found that the emission-reduction contributions of industrial sources, fugitive dust, and other sources were 10.7%–11.2%, 4.5%– 5.6%, and 1.7%–2.7%, respectively which was lower than that of vehicles. PM2.5 concentration increased by 13.5% and 14.7% during APEC and Parade, respectively if no vehicles emission-reduction measures were taken. In other words, emission-reduction contribution of vehicles to PM2.5 concentration accounted for 44.4% and 43.1%, respectively. That was consistent with the results released by Beijing Municipal Environmental Protection Bureau (39.5%) (http://www.bjepb.gov.cn), which might be associated with the growth of the rapid increase of vehicle population with 5.61 million in Beijing in 2015. And the strength of the vehicles reduction was relatively larger, including odd-even plate number rule, and non-local vehicles forbidden to drive throughout Beijing. Therefore, the emission-reduction of vehicles had significant effects on air quality improvements.

PM2.5 samples were collected from 20th October to 25th November 2014, and 15th August to 15th September 2015 at BJ, SJZ, and TS sites during APEC and Parade, respectively. PM2.5 concentrations and major components were analyzed and compared between the two events. The average daily PM2.5 concentration during control demonstrated a decreasing trend obviously with 63.6% (48.1%), 64.4% (57.6%), and 52.5% (64.7%) compared to before control at BJ, SJZ, and TS sites, respectively during APEC (Parade). Compared with after control, PM2.5 concentrations were reduced by 51.6% (34.2%), 65.1% (36.5%), and 58.6% (50.4%) during control. The notable improvements of PM2.5 concentrations in the BTH region reflected the combined effect of various emission reduction measures during APEC and Parade. The SIA concentrations during the two major events accounted for 38.1%– 42.9%, 31.7%–38.3%, and 36.3%–44.7% of PM2.5 before, during and after control, respectively. The lower percentage indicated the relative weak contribution of atmospheric chemical processes during control. TC concentrations during control demonstrated a decreasing trend obviously with 49.5%–62.8%, 57.5%–68.1%, and 55.7%–64.4% compared to the period before control at BJ, SJZ, and TS, respectively during the two major events. The OC concentrations at the three sites during APEC were 2.3 to 4.6 times higher than during Parade due to the unfavorable weather conditions and increased emissions during coal-fired heating. SOC

Fig. 5. Concentrations of carbonaceous components and TC/PM2.5 ratio during APEC and Parade.

Fig. 6. Variations of the calculated SOC, SOA, POA concentrations and SOC/PM2.5 ratio at each site during APEC and Parade.

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− + Fig. 7. Scatter plots of the simulated PM2.5, SO2− 4 , NO3 , and NH4 concentrations versus the observation results during APEC (a) and Parade (b) at BJ, SJZ, and TS sties.

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Fig. 8. Wind direction rose map in Beijing during APEC and Parade.

concentration was distinctly higher during APEC than Parade, with a value of 1.8, 2.0, and 3.4 times higher for the BJ, SJZ, and TS sites, respectively due to the secondary pollution by increased concentration of precursor gases. The WRF-CMAQ modeling system was also used to assess the effect of emission reduction measures. The results indicated PM2.5 decrease by 30.4% and 34.2% under ERS. The reduction percentage in PM2.5 concentrations under BJERS were 20.8% and 25.8% during APEC and Parade, respectively, compared to when no reduction measures were implemented. Thus it could be seen that the local emission reduction in Beijing contributes more to “APEC blue” and “Parade blue” in Beijing than those from neighbouring regions of Beijing. We also found that the effect of emission-reduction during Parade was better than APEC due to the higher reduction rate for PM2.5 and NO2 and the favorable meteorological condition. The sources contributing to the PM2.5 concentrations during APEC and Parade were also quantified. The emission-reduction contribution of vehicles to PM2.5 concentration accounted for 44.4% and 43.1%, respectively, which has significant effects on air quality improvements. Acknowledgements This work was supported by the National Natural Science Foundation of China (Nos. 51638001 & 51478017) and the National Key Research and Development Plan (No. 2016YFC0202705). In addition, we greatly appreciated the project supported by Beijing Municipal Commission of Science and Technology (Nos. D161100004416001 &

Fig. 9. Source apportionment to PM2.5 in Beijing during APEC and Parade.

Z161100004516013) and National Science Technology Support Plan (No. 2014BAC23B02). The authors are grateful to the anonymous reviewers for their insightful comments. References Castro, L.M., Pio, C.A., Harrison, R.M., Smith, D.J.T., 1999. Carbonaceous aerosol in urban and rural European atmospheres: estimation of secondary organic carbon concentrations. Atmos. Environ. 33, 2771–2781. Chang, Y.H., Liu, X.J., Deng, C.R., Dore, A.J., Zhuang, G.S., 2016. Source apportionment of atmospheric ammonia before, during, and after the 2014 APEC summit in Beijing using stable nitrogen isotope signatures. Atmos. Chem. Phys. 16, 11635–11647. Chen, D.S., Cheng, S.Y., Liu, L., Chen, T., Guo, X.R., 2007. An integrated MM5-CMAQ modeling approach for assessing trans-boundary PM10 contribution to the host city of 2008 Olympic summer games — Beijing, China. Atmos. Environ. 41, 1237–1250. Cheng, S.-h, Yang, L.-x., Zhou, X.-h., Xue, L.-k., Gao, X.-m., Zhou, Y., Wang, W.-x., 2011. Size-fractionated water-soluble ions, situ pH and water content in aerosol on hazy days and the influences on visibility impairment in Jinan, China. Atmos. Environ. 45, 4631–4640. Cheng, S., Zhou, Y., Li, J., Lang, J., Wang, H., 2012. A new statistical modeling and optimization framework for establishing high-resolution PM10 emission inventory — I. Stepwise regression model development and application. Atmos. Environ. 60, 613–622. Chow, J.C., Watson, J.G., Lowenthal, D.H., Solomon, P.A., Magliano, K.L., Ziman, S.D., Richards, L.W., 1993. PM10 and PM2.5 compositions in california san joaquin valley. Aerosol Sci. Technol. 18, 105–128. Chow, J.C., Watson, J.G., Lu, Z.Q., Lowenthal, D.H., Frazier, C.A., Solomon, P.A., Thuillier, R.H., Magliano, K., 1996. Descriptive analysis of PM2.5 and PM10 at regionally representative locations during SJVAQS/AUSPEX. Atmos. Environ. 30, 2079–2112. Chow, J.C., Watson, J.G., Chen, L.W.A., Arnott, W.P., Moosmuller, H., Fung, K., 2004. Equivalence of elemental carbon by thermal/optical reflectance and transmittance with different temperature protocols. Environ. Sci. Technol. 38, 4414–4422. Feng, Y.L., Chen, Y.J., Guo, H., Zhi, G.R., Xiong, S.C., Li, J., Sheng, G.Y., Fu, J.M., 2009. Characteristics of organic and elemental carbon in PM2.5 samples in Shanghai, China. Atmos. Res. 92, 434–442. Gao, Q.-x., Liu, J.-r., Wang, N., Li, W.-t., Gao, W.-k., Su, B.-d., 2015. Analysis on Regional characteristics of Air Quality Index and weather situation in Beijing and its surrounding cities during the APEC. Environ. Sci. 36, 3952–3960 (in Chinese). Han, X., Guo, Q., Liu, C., Strauss, H., Yang, J., Hu, J., Wei, R., Tian, L., Kong, J., Peters, M., 2016. Effect of the pollution control measures on PM2.5 during the 2015 China Victory Day Parade: implication from water-soluble ions and sulfur isotope. Environ. Pollut. 218, 230–241. Jiang, Y., Hou, X., Zhuang, G., Li, J., Wang, Q., Zhang, R., Lin, Y., 2009. The sources and seasonal variations of organic compounds in PM2.5 in Beijing and Shanghai. J. Atmos. Chem. 62, 175–192. Lang, J., Cheng, S., Wei, W., Zhou, Y., Wei, X., Chen, D., 2012. A study on the trends of vehicular emissions in the Beijing-Tianjin-Hebei (BTH) region, China. Atmos. Environ. 62, 605–614. Lang, J., Cheng, S., Li, J., Chen, D., Zhou, Y., Wei, X., Han, L., Wang, H., 2013. A monitoring and modeling study to investigate regional transport and characteristics of PM2.5 pollution. Aerosol Air Qual. Res. 13, 943–956. Lee, C.W., Dai, Y.T., Chien, C.H., Hsu, D.J., 2006. Characteristics and health impacts of volatile organic compounds in photocopy centers. Environ. Res. 100, 139–149. Li, R., Mao, H., Wu, L., He, J., Ren, P., Li, X., 2016. The evaluation of emission control to PM concentration during Beijing APEC in 2014. Atmos. Pollut. Res. 7, 363–369. Liu, H., Liu, C., Xie, Z., Li, Y., Huang, X., Wang, S., Xu, J., Xie, P., 2016. A paradox for air pollution controlling in China revealed by “APEC Blue” and “Parade Blue”. Sci. Rep. UK. 6, 34408. Pachauri, T., Satsangi, A., Singla, V., Lakhani, A., Kumari, K.M., 2013. Characteristics and sources of carbonaceous aerosols in PM2.5 during wintertime in Agra, India. Aerosol Air Qual. Res. 13, 977–991.

92

G. Wang et al. / Science of the Total Environment 595 (2017) 81–92

Strader, R., Lurmann, F., Pandis, S.N., 1999. Evaluation of secondary organic aerosol formation in winter. Atmos. Environ. 33, 4849–4863. Tao, J., Gao, J., Zhang, L.M., Wang, H., Qiu, X.H., Zhang, Z.S., Wu, Y.F., Chai, F.H., Wang, S.L., 2016. Chemical and optical characteristics of atmospheric aerosols in Beijing during the Asia-Pacific Economic Cooperation China 2014. Atmos. Environ. 144, 8–16. Terzi, E., Argyropoulos, G., Bougatioti, A., Mihalopoulos, N., Nikolaou, K., Samara, C., 2010. Chemical composition and mass closure of ambient PM10 at urban sites. Atmos. Environ. 44, 2231–2239. Wang, S., Xing, J., Jang, C., Zhu, Y., Fu, J.S., Hao, J., 2011. Impact assessment of ammonia emissions on inorganic aerosols in East China using response surface modeling technique. Environ. Sci. Technol. 45, 9293–9300. Wang, G., Cheng, S., Li, J., Lang, J., Wen, W., Yang, X., Tian, L., 2015a. Source apportionment and seasonal variation of PM2.5 carbonaceous aerosol in the Beijing-Tianjin-Hebei Region of China. Environ. Monit. Assess. 187. Wang, G., Cheng, S., Wei, W., Wen, W., Wang, X., Yao, S., 2015b. Chemical characteristics of fine particles emitted from different Chinese cooking Styles. Aerosol Air Qual. Res. 15, 2357–2366. Wang, Z., Li, Y., Chen, T., Li, L., Liu, B., Zhang, D., Sun, F., Wei, Q., Jiang, L., Pan, L., 2015c. Changes in atmospheric composition during the 2014 APEC conference in Beijing. J. Geophys. Res. Atmos. 120, 12695–12707.

Wang, G., Cheng, S., Lang, J., Yang, X., Wang, X., Chen, G., Liu, X., Zhang, H., 2016a. Characteristics of PM2.5 and assessing effects of emission-reduction measures in the heavy polluted city of Shijiazhuang, before, during, and after the ceremonial parade 2015. Aerosol Air Qual. Res. http://dx.doi.org/10.4209/aaqr.2016.05.0181. Wang, Y., Zhang, Y., Schauer, J.J., de Foy, B., Guo, B., Zhang, Y., 2016b. Relative impact of emissions controls and meteorology on air pollution mitigation associated with the Asia-Pacific Economic Cooperation (APEC) conference in Beijing, China. Sci. Total Environ. 571, 1467–1476. Zhao, P.S., Dong, F., He, D., Zhao, X.J., Zhang, X.L., Zhang, W.Z., Yao, Q., Liu, H.Y., 2013a. Characteristics of concentrations and chemical compositions for PM2.5 in the region of Beijing, Tianjin, and Hebei, China. Atmos. Chem. Phys. 13, 4631–4644. Zhao, P., Dong, F., Yang, Y., He, D., Zhao, X., Zhang, W., Yao, Q., Liu, H., 2013b. Characteristics of carbonaceous aerosol in the region of Beijing, Tianjin, and Hebei, China. Atmos. Environ. 71, 389–398. Zhou, S.Z., Wang, Z., Gao, R., Xue, L.K., Yuan, C., Wang, T., Gao, X.M., Wang, X.F., Nie, W., Xu, Z., Zhang, Q.Z., Wang, W.X., 2012. Formation of secondary organic carbon and longrange transport of carbonaceous aerosols at Mount Heng in South China. Atmos. Environ. 63, 203–212.