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Chemical and optical characteristics of atmospheric aerosols in Beijing during the Asia-Pacific Economic Cooperation China 2014
Atmospheric Environment 144 (2016) 8e16
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Chemical and optical characteristics of atmospheric aerosols in Beijing during the Asia-Pacific Economic Cooperation China 2014 Jun Tao a, d, Jian Gao a, b, *, Leiming Zhang c, Han Wang a, Xionghui Qiu a, e, Zhisheng Zhang d, Yunfei Wu f, Fahe Chai a, b, Shulan Wang a, b a
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing, China Air Quality Research Division, Science Technology Branch, Environment and Climate Change Canada, Toronto, Canada d South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou, China e School of Environment, Tsinghua University, Beijing, China f RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China b c
h i g h l i g h t s Secondary inorganic aerosols decreased remarkably during the APEC period. Carbonaceous aerosols became dominant contributor to light extinction during APEC. Reducing secondary inorganic aerosols is most effective for haze prevention.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 18 April 2016 Received in revised form 20 August 2016 Accepted 23 August 2016 Available online 26 August 2016
To evaluate the effectiveness of regional pollution control measures for improving visibility imposed during the Asia-Pacific Economic Cooperation (APEC) period, day- and nighttime PM2.5 and PM10 samples were collected at an urban site in Beijing from October to November, 2014. PM2.5 and PM10 samples were subject to chemical analysis for major water-soluble ions, organic carbon (OC), element carbon (EC), and biomass burning tracers e anhydrosugar levoglucosan (LG). In addition, aerosol scattering coefficient (bsp) and aerosol absorption coefficient (bap) at dry condition were measured. PM2.5 mass concentration was 190 ± 125, 88 ± 60, 199 ± 142 mg m3 during the pre-, during- and post-APEC period, respectively, while the concentration of the sum of (NH4)2SO4 and NH4NO3 was 75 ± 69, 19 ± 22 and 40 ± 46 mg m3, respectively. The sum of (NH4)2SO4 and NH4NO3 accounted for 49 ± 24%, 19 ± 12% and 24 ± 12% of bext (the sum of bsp and bap) at ambient condition during the pre-, during- and post-APEC period, respectively, and the corresponding numbers are 39 ± 18%, 62 ± 8% and 61 ± 10% for the sum of OM and EC. Reduction of secondary inorganic aerosols played a key role in the “APEC blue”, especially under moisture conditions due to their hygroscopic properties. As a result, visibility was improved significantly during the APEC period with five out of the 12 days having a visibility higher than 20 km. Control of biomass burning, especially during the nighttime, was not performed well during the APEC period, which should be paid more attention in making future emission control measures. © 2016 Elsevier Ltd. All rights reserved.
Keywords: PM2.5 Chemical composition Secondary inorganic aerosols Biomass burning
1. Introduction Fine particulate matter with an aerodynamic diameter smaller than 2.5 mm (PM2.5) is the most important air pollutant for adverse
* Corresponding author. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China. E-mail address: [email protected] (J. Gao). http://dx.doi.org/10.1016/j.atmosenv.2016.08.067 1352-2310/© 2016 Elsevier Ltd. All rights reserved.
human health effects and hazy weather occurrence (Pope III and Dockery, 2006; Watson, 2002). Beijing, the capital of China, has been experiencing severe PM2.5 pollution in recent years. Although certain pollution control measures have been imposed in Beijing and surrounding areas as well as in the North China Plain (NCP), air quality in Beijing has not been improved significantly, especially in winter. Meanwhile, heavy hazy weather has also frequently occurred (Liu et al., 2013; Wang et al., 2014). For example, daily average PM2.5 once reached to an extremely high value of
J. Tao et al. / Atmospheric Environment 144 (2016) 8e16
445 mg m3 on 12 January 2013 (Tao et al., 2015a). Monthly average PM2.5 in January 2013 was also evidently higher than that in January 2010 (Zhang et al., 2013). Apparently, more efficient pollution control measures are still needed to improve air quality and alleviate haze in Beijing. To ensure the air quality in Beijing satisfies the NAAQS during the APEC period (5e11 November 2014), six major pollution control measures were exerted from 1 to 12 November, 2014 in Beijing, Tianjin, Hubei province, Shanxi province, Shandong province, and Inner Mongolia autonomous region. Accordingly, the concentrations of many air pollutants, especially PM2.5, decreased significantly compared with those measured during the same dates in 2013 (http://www.bjepb.gov.cn). Thus, air pollution data collected during the APEC 2014 period provided another opportunity to understand the pollution control effects for alleviating PM2.5 and hazy weather since Beijing Olympics Game period in summer 2008. Many previous studies have reported the effect of control measures for PM2.5, other air pollutants and visibility in Beijing, especially during the Beijing Olympics Game period in summer 2008 (Wang et al., 2010a, 2010b; Li et al., 2013; Schleicher et al., 2012). Atmospheric diffusion condition in summer (e.g. stronger solar radiation and higher wind speed) should be better than those in autumn in Beijing (Zhang et al., 2013). Biomass (e.g. rice straw, corn straw or crop waste) burning is popular in autumn in NCP although such activities are strictly forbidden (Cheng et al., 2013; Duan et al., 2004; Wang et al., 2007). Therefore, it needed more effort to assure good air quality or visibility during the APEC period than what was done in summer Olympics period. Accordingly, stricter control measures were implemented in NCP during the APEC period. The impacts of these control measures on aerosol chemical composition and on volatile organic compounds (VOCs) have been investigated (Li et al., 2015; Xu et al., 2015; Yang et al., 2015; Wang et al., 2015; Chen et al., 2015), but the subsequent impacts on aerosol optical properties were still not clear. The present study aims to (1) characterize aerosol optical and chemical properties on day and nighttime scales during the pre-, during- and post-APEC period; (2) identify the relationships between aerosol optical properties and their chemical components; and (3) quantify the contributions of individual chemical components in PM2.5 to the light scattering and absorption. Results generated from this study provide scientific basis for making recommendation in future pollution control measures. 2. Methodology 2.1. Data sampling The instruments used in this study were installed on the roof (20 m above ground) of an office building inside the Chinese Research Academy of Environmental Sciences (CRAES) (116 240 E, 40 020 N) (Fig. 1). Aerosol scattering coefficient (bsp) was measured using a single wavelength integrating nephelometer (Ecotech Pty Ltd, Australia, Model M9003) at the wavelength of 525 nm. Trace gases including sulfur dioxide (SO2) and nitrogen dioxide (NO2) were measured every 1 min using trace gas analyzers (Ecotech Pty Ltd, Australia; Model 9850, Model 9841), respectively. A meteorological parameter, RH, was measured every 1 min. Detailed information can be referred to previous studies (Tao et al., 2015a, 2015b). PM2.5 and PM10 samples were collected with a four-channel sampler (Thermo Scientific Ltd., model RP2300, U.S.A.), which was operated at a flow rate of 10 l min1 for each channel. PM2.5 and PM10 samples were collected on 47 mm Whatman quartz-fibre filter (QM/A, Whatman Inc., UK) and 47 mm Teflon filters (Whatman PTFE). Before sampling, the quartz filters were baked at 500 C for at least 4 h to remove adsorbed organic vapors. 48 day-time
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(from 8:00 a.m. to 7:00 p.m. local time) and 48 night-time (from 8:00 p.m. to 7:00 a.m. the next day) samples were collected for both PM2.5 and PM10. Ten sets of field blank were collected and used to account for any artifacts caused by gas absorption. The aerosolloaded filter samples were stored in a freezer at 20 C before analysis to prevent the volatilization of particles. 2.2. Chemical and optical analysis OC and EC were analyzed using a DRI model 2001 carbon analyzer (Atmoslytic, Inc., Calabasas, CA, USA). The anhydrosugar levoglucosan was measured by a Dionex ICS-3000 system. Detailed information can be found in earlier studies (Cao et al., 2003; Engling et al., 2006; Iinuma et al., 2009). A quarter of Teflon filter samples was used to determine watersoluble inorganic ions. The extraction of water-soluble species from the filters was achieved by placing the cut portion (1/4) of each filter into a separate 4 mL bottles, followed by 4 mL distilleddeionized water (with a resistivity of >18 MU), and then subjected to ultrasonic agitation for 1 h for complete extraction of the ionic compounds. The extract solutions were filtered (0.25 mm, PTFE, Whatman, USA) and stored at 4 C in pre-cleaned tubes until analþ 2þ ysis. Cation (Naþ, NHþ and Ca2þ) concentrations were 4 , K , Mg determined by ion chromatography (Dionex ICS-1600) using a CS12A column with 20 mM Methanesulfonic Acid eluent. Anions (SO2 4 , NO3 , Cl , and F ) were separated on an AS19 column in ion chromatography (Dionex ICS-2100), using 20 mM KOH as the eluent. A calibration was performed for each analytical sequence. Procedural blank values were subtracted from sample concentrations. Aerosol absorption coefficient bap of 532 nm was converted from black carbon concentration measured at 880 nm wavelength by a transmissometer (Magee Scientific Company, Berkeley, CA, U.S.A., Model OT-21). The converted coefficient was 8.28 m2 g1 (Yan et al., 2008). The black carbon concentration in PM2.5 samples (quartzfibre filter) were measured by a transmissometer after 24 h equilibration at relative humidity at 30%. 2.3. Data analysis methods 2.3.1. PM2.5 mass reconstruction To evaluate if the determined chemical components represent the measured PM2.5, the measured PM2.5 mass was reconstructed based on (NH4)2SO4, NH4NO3, OM, EC and fine soil (FS) (Pitchford et al., 2007). The converting factor between OC and OM should vary with the dominant sources when reconstructing PM2.5 mass. Good correlations between OC, EC and LG were found during the whole observation period, which suggested biomass burning was the dominant source of carbonaceous aerosols. Considering the large range of LG concentrations, the impact of biomass burning on carbonaceous aerosols should be different. Thus, the converting factor was chosen to be 2.2, 2.1, 1.9, 1.8, 1.7 and 1.6, respectively, when LG was in the range of >2000, 2000e1500, 1500e1000, 1000e500, 500e200, and <200 ng m3. Due to the lack of soil element measurements, we assumed that Ca2þ is 4.5% of FS mass based on soil source profile in Beijing (Zhang et al., 2013). Thus, [FS] ¼ 22 [Ca2þ]. The reconstructed PM2.5 mass was calculated according to: [PM2.5] ¼ [(NH4)2SO4] þ [NH4NO3] þ [OM] þ [EC] þ [FS]
(1)
2.3.2. bsp and bap reconstruction Generally, (NH4)2SO4, NH4NO3, OM, EC, and FS were the dominant chemical species in PM2.5, which were also the major
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J. Tao et al. / Atmospheric Environment 144 (2016) 8e16
Fig. 1. The sampling location (40 020 N, 116 240 E) in Beijing.
contributors to bsp (Pitchford et al., 2007). Moreover, coarse particulate matter (CM, PM2.5e-10) was also a non-negligible contributor to bsp in Beijing due to high PM10 concentrations. EC was the dominant absorption chemical component of aerosols and mainly distributed in PM2.5. Multiple linear regressions of measured bsp against the dominant species (NH4)2SO4, NH4NO3, OM, EC and FS) and other faction (measured PM2.5 minus reconstructed PM2.5) mass concentrations in PM2.5 and CM were conducted to obtain their mass scattering efficiencies (MSEs) (Tao et al., 2015a). Although EC in PM2.5 was the dominant species absorbing light, absorption of OM was not negligible when OM concentrations were high. Biomass burning produced a significant amount of carbonaceous aerosols (Cheng et al., 2011). Thus, mass absorption efficiencies (MAEs) of carbonaceous aerosols can be obtained by regression of bap against EC and OM. An external mixing of individual species was assumed in the above analysis. The amount of scattering and absorption associated with individual species can be estimated statistically using (Tao et al., 2014): bsp ¼ a1 fL(RH) [(NH4)2SO4] þ a2 fL (RH) [NH4NO3] þ a3 [OM] þ a4 [EC] þ a5 [FS] þ a6 [Other] þ a7 [CM] (2) bap ¼ b1 [EC] þ b2 [OM]
(3)
Here, bsp, bap and mass concentrations of chemical species are given in units of Mm1 and mg m3, respectively. RH growth curves of fL(RH) of sulfate and nitrate can be referred to IMPROVE net results (Pitchford et al., 2007). Considering that most of the sulfate and nitrate mass distributed in droplet mode in Beijing (Tao et al., 2015a,b), we used fL(RH) in this study. 3. Results and discussion For evaluating the impact of pollution control measures on PM chemical and optical properties, the whole study period was divided into three intervals e pre-, during- and post-APEC period. The pre-APEC period covered from day-time 8 October to nighttime 31 October, the during-APEC period covered from day-time 1 November to night-time 12 November, and the post-APEC
period covered from day-time 13 November to night-time 24 November.
3.1. Chemical composition of PM2.5 and PM10 The average (±standard deviation) PM2.5 and PM10 mass concentrations during the whole observation period were 167 ± 125 mg m3 and 256 ± 188 mg m3, respectively, which exceeded 2.2 and 1.7 times of the NAAQS (Table 1 and Fig. 2). NHþ 4, SO2 4 , NO3 , OC, and EC were the dominant components in PM2.5 and PM10. Most of them (>76%) were distributed in PM2.5 rather than in CM. In contrast, 91% of Ca2þ was distributed in CM. PM2.5 mass concentration was higher in this study, which was mainly caused by the much higher NO 3 mass concentration, than those observed in previous studies during the same season in Beijing (He et al., 2001; Song et al., 2006; Duan et al., 2006; Wang et al., 2005; Zhao et al., 2013b). The higher NO 3 mass concentrations in the most recent Table 1 Summary of PM2.5 and its chemical components, aerosol optical properties and meteorological parameters during the pre-, during- and post-APEC period in Beijing. Components
Average
Pre-APEC
During-APEC
Post-APEC
PM2.5 (mg m3) LG (ng m3) OC (mg m3) EC (mg m3) Naþ (mg m3) 3 NHþ 4 ( mg m ) Kþ (mg m3) Mg2þ (mg m3) Ca2þ (mg m3) Cl (mg m3) 3 SO2 4 (mg m ) 3 NO 3 (mg m ) CM (mg m3) bsp (Mm1) bap (Mm1) RH (%) SO2 (mg m3) NO2 (mg m3)
167 ± 125 629 ± 643 27.7 ± 19.8 8.6 ± 5.9 1.2 ± 0.7 13.0 ± 14.2 1.6 ± 1.4 0.1 ± 0.1 0.5 ± 0.3 4.7 ± 5.5 13.9 ± 16.6 26.0 ± 29.1 89 ± 72 590 ± 551 83 ± 48 41 ± 21 10 ± 9 90 ± 53
190 ± 125 657 ± 628 27.0 ± 14.8 8.9 ± 4.8 1.2 ± 0.7 18.0 ± 15.9 1.9 ± 1.2 0.1 ± 0.0 0.5 ± 0.2 4.2 ± 3.0 19.9 ± 19.3 38.2 ± 33.3 115 ± 78 750 ± 598 92 ± 46 50 ± 22 6±4 100 ± 52
88 ± 60 291 ± 314 16.5 ± 10.7 5.1 ± 3.6 1.0 ± 0.7 4.7 ± 5.9 0.7 ± 0.8 0.0 ± 0.0 0.3 ± 0.2 2.1 ± 3.0 4.2 ± 4.1 10.0 ± 12.8 37 ± 23 253 ± 202 53 ± 33 28 ± 12 7±5 57 ± 41
199 ± 142 911 ± 774 40.1 ± 27.5 11.7 ± 7.6 1.4 ± 0.7 11.4 ± 12.6 2.0 ± 1.8 0.1 ± 0.1 0.6 ± 0.4 8.4 ± 8.5 11.7 ± 13.7 18.3 ± 20.9 92 ± 67 615 ± 559 96 ± 54 36 ± 17 21 ± 11 102 ± 52
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several years was believed to be caused by more efficient conversion from NOx to HNO3 due to the increased emissions of nonmethane hydrocarbons, despite the decreasing NOx emissions (Zhao et al., 2013a; Tang et al., 2009). PM2.5 and PM10 mass concentrations during the APEC period were 88 ± 60 mg m3 and 125 ± 79 mg m3, respectively, which were 54% and 67%, respectively, lower than the pre-APEC period without control measures. The percentage decrease of CM was higher than PM2.5 due to the pause of all the constructive activities in Beijing and the other five provinces. PM2.5 and PM10 mass concentrations during the post-APEC period recovered to the similar levels of the pre-APEC period once the control measures ceased. 2 The concentrations of NHþ 4 , NO3 and SO4 in PM2.5 decreased by 74%e79% during the APEC period compared with the pre-APEC period. The emissions of SO2 and NOx from industries and vehicles were decreased by 40%e65% during the APEC period in the whole region, which was equivalent to a total reduction of 80 kiloton SO2 and 600 kiloton NOx according to their emission inventories (Fig. 3, a grid ¼ 16 km2). As a result, the total sulphur 3 (SO2 þ SO2 4 ) decreased by 53% (to the level of 0.14 mol m ) during the APEC period than the pre-APEC period, although SO2 concentration did not differ much during the two periods. This is because gas/particle partitioning between SO2 and SO2 4 also depends on other factors such as meteorology and NH3 level. The sulfur oxidation ratio (SOR) and RH were both higher during the pre-APEC period than during-APEC period. Higher RH favors the formation of SO2 4 (Sun et al., 2013). As expected, sulfur level rapidly recovered (to a level of 0.45 mol m3) during the post-APEC period. Similarly,
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NO2 concentration decreased by 43% during the APEC period and then recovered during the post-APEC period. OC and EC in PM2.5 decreased by 39%e43% during the APEC period compared with the pre-APEC period. These percentage decreases were much lower than those of the secondary products of SO2 and NOx. Carbonaceous aerosols emissions from industries and vehicles were expected to be decreased at similar magnitudes to those of SO2 and NOx. The differences were likely caused by additional sources of carbonaceous aerosols that were not well regulated in the pollution control measures, as further discussed below. Good correlations (R2 ¼ 0.85, 0.96 and 0.95) were found between OC and EC with slopes of 2.8, 2.9 and 3.5, respectively, during the pre-, during- and post-APEC period (Fig. 4a). Thus, OC and EC had some common sources, especially during the during- and postAPEC period. OC/EC ratio has been identified in literature for coal combustion (0.3e7.6), vehicle emission (0.7e2.4), and biomass burning (4.1e14.5) (Watson et al., 2001). Comparison of the regression slopes with OC/EC ratios from known sources cannot tell exactly which sources were dominant ones. The medium to high LG concentrations (657 ± 628 ng m3, 291 ± 314 ng m3, and 911 ± 774 ng m3 during the pre-, during- and post-APEC period, respectively) suggested that biomass burning should be one of the sources despite the control polity for forbidding open space biomass burning activities. The medium to good correlations between LG and OC or EC (R2 ¼ 0.81 and 0.58 during the pre-APEC period and R2 ¼ 0.88 and 0.86 during the APEC period) supported this hypothesis (Fig. 4b and c). Higher LG concentrations during nighttime than daytime during the APEC period were likely caused by a combination of various reasons, such as lower boundary height, more burning activities. Even better correlations (R2 ¼ 0.94 and 0.94) and much higher LG concentrations were found during the post-APEC period, suggesting much more biomass burning activities when the control measures expired and heating season started. PM2.5 mass was reconstructed using the method described in Section 2.3.1. Good correlations (R2 > 0.97) between the reconstructed and measured mass concentrations were found with the slopes of 0.78, 0.71 and 0.76, respectively, during the pre-, duringand post-APEC period (Fig. 5a), indicating that the above chemical components could closely represent the measured PM2.5. On average, (NH4)2SO4, NH4NO3, OM, EC and FS accounted for 6 ± 2%, 10 ± 8%, 32 ± 6%, 6 ± 2% and 8 ± 4%, respectively, of PM2.5 mass during the APEC period and up to 7% absolute percentage differences were found during the pre- and post- APEC period (Fig. 5b). OM was absolutely the top dominant component, accounting for more than 30% of PM2.5 mass in any of the three periods and as high as 38% during the post-APEC period. PM2.5 and LG both evidently increased during the post-APEC period when heating season started. These results suggested that biomass burning was an important source due to the use of biomass fuel for heating purpose in rural areas of NCP. Replacing biomass fuel with clearer energy is needed for reducing PM pollution in the NCP. 3.2. Aerosol optical properties and their relationship with PM2.5 and CM
Fig. 2. Temporal variations of daily PM2.5 mass concentrations, its chemical components, CM, bsp, and bap during the pre-, during- and post-APEC period.
On average, dry bsp and bap, with temporal coherence, were 586 ± 550 Mm1 and 83 ± 48 Mm1, respectively, during the whole study period (representative of autumn) of 2014 in Beijing (Fig. 2). Dry bsp evidently increased and bap slightly increased compared with earlier year's data in the same season (Zhao et al., 2011; He et al., 2009; Jing et al., 2015). Apparently, visibility in Beijing has not been improved in recent years although many control measures or polices have been implemented. Dry bsp and bap decreased by 66% and 42%, respectively, during
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J. Tao et al. / Atmospheric Environment 144 (2016) 8e16
Fig. 3. Spatial distributions of SO2 and NOx emissions in the North China Plain under the base (left column) and the APEC scenario (middle column) and their differences (right column).
Fig. 4. Scatter plots of OC versus EC (a), LG versus OC (b), and LG versus EC (c).
Fig. 5. Reconstructed PM2.5 mass concentrations during the pre-, during- and post- APEC period.
the APEC period than before the APEC period, and then recovered quickly during the post-APEC period. bext was lower than
180 Mm1 on five of the 12 days during the APEC period, resulting a visibility of 20 km of larger (the so-called “APEC blue”). These
J. Tao et al. / Atmospheric Environment 144 (2016) 8e16
results demonstrated that the strict control measures were effective in improving visibility in NCP during the APEC period. However, three polluted events still occurred during the APEC period, although much weaker than the other seven polluted events in other days (Fig. 2). The peak value of bext was 832, 502 and 462 Mm1, respectively, on 4, 8 and 10 November, all exceeding 391 Mm1 (corresponding to a visibility of 10 km). LG concentrations on these three days (with an average of 528 ng m3) were much higher than on the other days during the APEC period (with an overall average of 291 ng m3). Biomass burning should have played a role on the three hazy days. “APEC blue” or haze weather during the APEC period should be mainly attributed to the decrease or increase of PM2.5 concentrations (Fig. 6a). Good correlations (R2 > 0.90) between PM2.5 or CM and measured bsp were found during the pre-, during- and postAPEC period. The mass scattering efficiency (MSE) of PM2.5 e the slope of the linear regression equation, varied widely from 2.9 to 4.0 m2 g1; in contrast, CM MSE changed little from 0.4 to 0.5 m2 g1. Although CM accounted for 35% of PM10, it only contributed 7% to bsp on average. PM2.5 MSE was 2.9 m2 g1 during the APEC period, lower than those during the pre- and post-APEC period, due to the lower PM2.5 mass. The lower PM2.5 mass suggested that most PM mass was distributed in the condensation mode with lower MSE according to the Mie theory (Tao et al., 2015a). However, PM2.5 MSE during the pre- APEC was much higher than that during the post-APEC although PM2.5 mass concentrations were similar. The differences in PM2.5 MSE between the two periods were caused by the different fractions of dominant chemical components in PM2.5, instead of by the total PM2.5 concentrations. During the pre-APEC period, SO2 and NO 4 3 were nearly two times while OC and EC were only 65e80% of those in the post- PEC period. As discussed in Section 3.3 (Table 2), the MSE of OM during the pre-APEC period was close to that during the postAPEC period while the MSEs of (NH4)2SO4 and NH4NO3 were much higher. The higher concentrations of SO2 4 and NO3 were the main causes of the higher PM2.5 MSE during the pre-APEC period. EC is generally the dominant chemical component for light absorption, although OM can also absorb light at short wavelengths. On average 87% EC and 83% OC in PM10 were distributed in PM2.5 and the rest fractions were in CM. However, the MAEs of EC and OM in CM should be less than 0.5 m2 g1 according to the Mie theory. Moreover, the fractions of EC and OC in CM only accounted for 1.4% and 6.7% of the total CM mass. Thus, light absorption by CM can be ignored. Medium correlations (R2 > 0.50) were found between PM2.5 and measured bap during the pre-, during- and post-APEC
13
period (Fig. 6b). The MAEs of PM2.5 varied within a narrow range from 0.43 to 0.57 m2 g1. These values were slightly lower than those obtained in urban Beijing (0.6 m2 g1) (Liu et al., 2013) and Guangzhou (0.8e1.0 m2 g1) (Andreae et al., 2008; Tao et al., 2015b) in the same season. In conclusion, MSEs of PM2.5 were much higher than their MAEs, which suggested bsp by PM2.5 should be the key control factor for haze weather. 3.3. Estimate of the MSEs and MAEs of individual chemical components To obtain the contributions of individual chemical components in PM2.5 to bsp and bap, the MSEs and MAEs of each chemical component should be estimated first. Considering the differences in PM2.5 MSE and MAE between the pre-, during- and post-APEC period, the MSEs of (NH4)2SO4, NH4NO3, OM, EC, FS, Other and CM and the MAE of EC were estimated separately for the three periods. The estimated MSEs of (NH4)2SO4, NH4NO3, OM, EC, FS, Other and CM and MAEs of EC and OM using the method described in Section 2.3.2 are summarized in Table 2. MSEs of (NH4)2SO4 and NH4NO3 were 3.7 and 3.8 m2 g1, respectively, lower during the APEC period than the pre-APEC (5.6 and 5.8 m2 g1, respectively) or post-APEC (4.4 and 4.7 m2 g1, respectively) period. In contrast, MSEs of the other individual chemical components were similar between the three time periods. In a previous study, SO2 and NO 4 3 were found to peak at 0.18e0.56 mm during the clean days in Beijing (Tao et al., 2015a). According to the Mie theory, MSEs of (NH4)2SO4 and NH4NO3 ranged from 1.4 to 4.6 m2 g1 and 1.6e5.3 m2 g1, respectively. Thus, the lower MSEs of (NH4)2SO4 and NH4NO3 during the APEC Table 2 Estimated MSEs and MAE of individual species by stepwise multiple linear regression. Components 2
1
(NH4)2SO4 (m g ) NH4NO3 (m2 g1) a OM (m2 g1) a EC (m2 g1) FS (m2 g1) Other (m2 g1) CM (m2 g1) b EC (m2 g1) b OM (m2 g1) a b
Pre-APEC
During-APEC
Post-APEC
5.6 ± 1.4 5.8 ± 1.2 4.5 ± 0.9 1.9 ± 0.4 0.9 ± 0.3 0.9 ± 0.7 0.5 ± 0.3 9.2 ± 0.7 0.16 ± 0.11
3.7 ± 1.2 3.8 ± 1.5 4.6 ± 1.6 1.8 ± 0.6 0.9 ± 0.5 0.8 ± 0.6 0.5 ± 0.3 8.9 ± 0.3 0.14 ± 0.11
4.4 ± 2.6 4.7 ± 2.2 4.4 ± 1.4 2.0 ± 0.6 0.8 ± 0.7 0.7 ± 1.0 0.5 ± 0.5 6.9 ± 0.5 0.13 ± 0.11
MSE. MAE.
Fig. 6. Scatter plots of bsp versus PM2.5 and CM (a), and bap versus PM2.5 (b).
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period should be related with their lower mass concentrations and size distributions. This is because other components, although accounted for more than 22% of PM2.5 mass, only contributed 0.7e0.9 m2 g1 to MSE due to their distribution in larger size ranges than the dominant chemical components (e.g. (NH4)2SO4, NH4NO3 and OM). Other chemical components in PM2.5 mainly distributed in PM1.1e2.1, especially in heavily polluted days in Beijing (Tian et al., 2014) and their MSE ranged from 0.6 to 1.3 m2 g1 according to the Mie theory (Tao et al., 2015a). The MAEs of EC were 9.2, 8.9 and 6.9 m2 g1, respectively, during pre-, during- and post-APEC periods. MAE of EC was determined by its size distribution and coating. EC and black carbon (BC) were mainly produced from urban fossil combustion and biomass burning. The mass size distributions of BC in volume equivalent diameter, or the geometric mean diameter peaked at 147e210 nm if produced from fossil combustion or urban plume, and at >210 nm if produced from biomass burning plume (Huang et al., 2012; Schwarz et al., 2008). Thus, MAE of EC from urban plume should be about 1.28 times of that from biomass burning plume according to the Mie theory assuming an external mixing state. The average MAE of EC during the APEC period was 1.29 times of that during the post-APEC period. Note that MAE of EC from diesel plume was higher than those from various biomass burning types in Beijing (Cheng et al., 2011). According to discussions in 3.1, biomass burning was an important source for carbonaceous aerosols during the pre- and postAPEC period. Thus, MAE of EC during the pre- and post-APEC period should be lower than that during the APEC period. It is noticed that MAE of EC was much higher during the pre-APEC period than the post-APEC period, which was believed to be caused by the much higher RH during the pre-APEC period. High RH enhances coating of EC which in turn amplifies MAE of EC (Wu et al., 2016). Thus, the large variations of the EC MAE during the three periods should be related to different sources of EC and RH levels. Water-soluble organic carbon (WSOC) (e.g. humic-like substance), a fraction of OM, can absorb light at short wavelengths. Biomass burning was an important source of WSOC (Tao et al., 2016; Du et al., 2014b). Light absorption of WSOC during the biomass burning periods was ignored in several previous studies in Beijing, results from which should be reexamined (Du et al., 2014a; Cheng et al., 2011). In this study, MAE of OM was 0.16, 0.15 and 0.13 m2 g1, respectively, during the pre-, during- and post-APEC period. Although the MAEs of OM were much lower than those of EC, OM mass concentrations were 5.8 times of EC. On average, absorption from OM accounted for 9% of bap. The estimated MAEs of EC and OM using the regression equations were reasonable estimations.
3.4. Source apportionment of aerosol light extinction The above discussions showed good correlations (R2 > 0.92) between the reconstructed and measured bsp and bap under dry condition (Fig. 7a and b), with the same slope of 1.0. bsp under ambient condition was further estimated using hygroscopic curves of sulfate and nitrate and MSEs in Table 2 due to the lack of synchronously measured aerosol hygroscopic curves. According to the estimated MSEs and MAEs, (NH4)2SO4, NH4NO3, OM, EC, FS, Other and CM accounted for 7 ± 2%, 11 ± 8%, 45 ± 5%, 18 ± 3%, 2 ± 1%, 10 ± 5%, and 7 ± 4%, respectively, of the estimated bext under dry condition during the APEC period (Fig. 8), and similar magnitudes were also found under ambient condition due to the low RH (28 ± 12%). However, their contributions were up to 8%, 15%, 13%, 5%, 0%, 5% and 0% absolute percentage differences during the pre- and post-APEC periods due to the different fractions of individual chemical compounds in PM2.5. Evidently, carbonaceous aerosols were the main contributors for dry bext during the pre-, during- and post-APEC periods. As discussed above, biomass burning was still an important source of carbonaceous aerosols even during the APEC period. The contributions of (NH4)2SO4 and NH4NO3 to the estimated bext under dry condition were 17 ± 10% lower during the APEC period than during the pre- (40 ± 18%) or post-APEC (20 ± 8%) period. These results suggested that secondary inorganic aerosols were under control during the APEC period. In contrast, the contributions of (NH4)2SO4 and NH4NO3 to the estimated bext under ambient condition were much higher those by carbonaceous aerosols due to the higher RH (50 ± 22%) before the APEC period. However, carbonaceous aerosols were dominant contributors for bext under ambient condition and were much higher than (NH4)2SO4 and NH4NO3 during the APEC and postAPEC period. Moreover, although ~30% mass fraction of PM2.5 was not identified, this fraction only accounted for 7% of bext under dry or ambient condition due its low MSE. To compare the effectiveness of mass reduction of individual chemical components in reducing their corresponding bext, the reduction ratio was defined here as the ratio of reduction in bext to that in mass concentration. On average, the reduction ratio of (NH4)2SO4, NH4NO3, OM and EC were 5.4, 6.1, 4.5 and 9.6 m2 g1, respectively, under dry condition, and of other chemical components, less than 1.0 m2 g1. The ratio increased to 10.4 m2 g1 for (NH4)2SO4 and 11.7 m2 g1 for NH4NO3, but changed little for OM and EC under ambient condition. These result suggested that the decreasing secondary inorganic aerosols was more efficient than decreasing carbonaceous aerosols under moisture condition due to the hygroscopic properties of sulfate and nitrate. On average, (NH4)2SO4, NH4NO3, OM, EC, FS, Other and CM accounted for 6 ± 3%,
Fig. 7. Correlations between the reconstructed and measured bsp and bap under dry condition.
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15
Fig. 8. Daily bext load percentages during the pre-, during- and post-APEC period.
4 ± 3%, 47 ± 4%, 18 ± 4%, 3 ± 1%, 13 ± 5%, and 9 ± 4%, respectively, of the estimated bext under dry condition during the “APEC blue” period. This result further indicated the control of secondary inorganic aerosols or their precursor gases were the key reasons for the “APEC blue”. However, decreasing biomass burning sources should also be taken into account since they are important sources of OM, a dominant chemical component contributing to bext.
this approach are generally smaller than 11% under dry condition. Empirical relationships between aerosol hygroscopic properties and chemical components mass concentrations need to be researched further to reduce the uncertainties under ambient condition.
4. Conclusions
This study was supported by the National Natural Science Foundation of China (41375132, 91544226 and 41475119) and Beijing Municipal Science and Technology Plan (Z131100006113013).
PM2.5 mass was evidently decreased and visibility was significantly improved during the APEC period when the six major pollution control measures were exerted in Beijing and the surrounding five provinces from 1 to 12 November, 2014. (NH4)2SO4, NH4NO3, OM and EC were the dominant chemical components in PM2.5 and were also the dominant contributors to bext under dry and ambient conditions during the observed period. Reductions of secondary inorganic aerosols or their gaseous precursors were the key reasons for the “APEC blue”. Carbonaceous aerosols were the dominant contributors to bext under dry and ambient conditions during the APEC period due to the less effective control of biomass burning than industrial and traffic emissions. New control policies or technologies for reducing biomass burning emissions are needed, as have also been recommended in many previous studies. Multiple linear regressions can be an alternative method for source apportionment of bext when information of size distributions of major chemical components is lacking. Uncertainties using
Acknowledgments
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Chemical and optical characteristics of atmospheric aerosols in Beijing during the Asia-Pacific Economic Cooperation China 2014 Jun Tao a, d, Jian Gao a, b, *, Leiming Zhang c, Han Wang a, Xionghui Qiu a, e, Zhisheng Zhang d, Yunfei Wu f, Fahe Chai a, b, Shulan Wang a, b a
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing, China Air Quality Research Division, Science Technology Branch, Environment and Climate Change Canada, Toronto, Canada d South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou, China e School of Environment, Tsinghua University, Beijing, China f RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China b c
h i g h l i g h t s Secondary inorganic aerosols decreased remarkably during the APEC period. Carbonaceous aerosols became dominant contributor to light extinction during APEC. Reducing secondary inorganic aerosols is most effective for haze prevention.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 18 April 2016 Received in revised form 20 August 2016 Accepted 23 August 2016 Available online 26 August 2016
To evaluate the effectiveness of regional pollution control measures for improving visibility imposed during the Asia-Pacific Economic Cooperation (APEC) period, day- and nighttime PM2.5 and PM10 samples were collected at an urban site in Beijing from October to November, 2014. PM2.5 and PM10 samples were subject to chemical analysis for major water-soluble ions, organic carbon (OC), element carbon (EC), and biomass burning tracers e anhydrosugar levoglucosan (LG). In addition, aerosol scattering coefficient (bsp) and aerosol absorption coefficient (bap) at dry condition were measured. PM2.5 mass concentration was 190 ± 125, 88 ± 60, 199 ± 142 mg m3 during the pre-, during- and post-APEC period, respectively, while the concentration of the sum of (NH4)2SO4 and NH4NO3 was 75 ± 69, 19 ± 22 and 40 ± 46 mg m3, respectively. The sum of (NH4)2SO4 and NH4NO3 accounted for 49 ± 24%, 19 ± 12% and 24 ± 12% of bext (the sum of bsp and bap) at ambient condition during the pre-, during- and post-APEC period, respectively, and the corresponding numbers are 39 ± 18%, 62 ± 8% and 61 ± 10% for the sum of OM and EC. Reduction of secondary inorganic aerosols played a key role in the “APEC blue”, especially under moisture conditions due to their hygroscopic properties. As a result, visibility was improved significantly during the APEC period with five out of the 12 days having a visibility higher than 20 km. Control of biomass burning, especially during the nighttime, was not performed well during the APEC period, which should be paid more attention in making future emission control measures. © 2016 Elsevier Ltd. All rights reserved.
Keywords: PM2.5 Chemical composition Secondary inorganic aerosols Biomass burning
1. Introduction Fine particulate matter with an aerodynamic diameter smaller than 2.5 mm (PM2.5) is the most important air pollutant for adverse
* Corresponding author. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China. E-mail address: [email protected] (J. Gao). http://dx.doi.org/10.1016/j.atmosenv.2016.08.067 1352-2310/© 2016 Elsevier Ltd. All rights reserved.
human health effects and hazy weather occurrence (Pope III and Dockery, 2006; Watson, 2002). Beijing, the capital of China, has been experiencing severe PM2.5 pollution in recent years. Although certain pollution control measures have been imposed in Beijing and surrounding areas as well as in the North China Plain (NCP), air quality in Beijing has not been improved significantly, especially in winter. Meanwhile, heavy hazy weather has also frequently occurred (Liu et al., 2013; Wang et al., 2014). For example, daily average PM2.5 once reached to an extremely high value of
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445 mg m3 on 12 January 2013 (Tao et al., 2015a). Monthly average PM2.5 in January 2013 was also evidently higher than that in January 2010 (Zhang et al., 2013). Apparently, more efficient pollution control measures are still needed to improve air quality and alleviate haze in Beijing. To ensure the air quality in Beijing satisfies the NAAQS during the APEC period (5e11 November 2014), six major pollution control measures were exerted from 1 to 12 November, 2014 in Beijing, Tianjin, Hubei province, Shanxi province, Shandong province, and Inner Mongolia autonomous region. Accordingly, the concentrations of many air pollutants, especially PM2.5, decreased significantly compared with those measured during the same dates in 2013 (http://www.bjepb.gov.cn). Thus, air pollution data collected during the APEC 2014 period provided another opportunity to understand the pollution control effects for alleviating PM2.5 and hazy weather since Beijing Olympics Game period in summer 2008. Many previous studies have reported the effect of control measures for PM2.5, other air pollutants and visibility in Beijing, especially during the Beijing Olympics Game period in summer 2008 (Wang et al., 2010a, 2010b; Li et al., 2013; Schleicher et al., 2012). Atmospheric diffusion condition in summer (e.g. stronger solar radiation and higher wind speed) should be better than those in autumn in Beijing (Zhang et al., 2013). Biomass (e.g. rice straw, corn straw or crop waste) burning is popular in autumn in NCP although such activities are strictly forbidden (Cheng et al., 2013; Duan et al., 2004; Wang et al., 2007). Therefore, it needed more effort to assure good air quality or visibility during the APEC period than what was done in summer Olympics period. Accordingly, stricter control measures were implemented in NCP during the APEC period. The impacts of these control measures on aerosol chemical composition and on volatile organic compounds (VOCs) have been investigated (Li et al., 2015; Xu et al., 2015; Yang et al., 2015; Wang et al., 2015; Chen et al., 2015), but the subsequent impacts on aerosol optical properties were still not clear. The present study aims to (1) characterize aerosol optical and chemical properties on day and nighttime scales during the pre-, during- and post-APEC period; (2) identify the relationships between aerosol optical properties and their chemical components; and (3) quantify the contributions of individual chemical components in PM2.5 to the light scattering and absorption. Results generated from this study provide scientific basis for making recommendation in future pollution control measures. 2. Methodology 2.1. Data sampling The instruments used in this study were installed on the roof (20 m above ground) of an office building inside the Chinese Research Academy of Environmental Sciences (CRAES) (116 240 E, 40 020 N) (Fig. 1). Aerosol scattering coefficient (bsp) was measured using a single wavelength integrating nephelometer (Ecotech Pty Ltd, Australia, Model M9003) at the wavelength of 525 nm. Trace gases including sulfur dioxide (SO2) and nitrogen dioxide (NO2) were measured every 1 min using trace gas analyzers (Ecotech Pty Ltd, Australia; Model 9850, Model 9841), respectively. A meteorological parameter, RH, was measured every 1 min. Detailed information can be referred to previous studies (Tao et al., 2015a, 2015b). PM2.5 and PM10 samples were collected with a four-channel sampler (Thermo Scientific Ltd., model RP2300, U.S.A.), which was operated at a flow rate of 10 l min1 for each channel. PM2.5 and PM10 samples were collected on 47 mm Whatman quartz-fibre filter (QM/A, Whatman Inc., UK) and 47 mm Teflon filters (Whatman PTFE). Before sampling, the quartz filters were baked at 500 C for at least 4 h to remove adsorbed organic vapors. 48 day-time
9
(from 8:00 a.m. to 7:00 p.m. local time) and 48 night-time (from 8:00 p.m. to 7:00 a.m. the next day) samples were collected for both PM2.5 and PM10. Ten sets of field blank were collected and used to account for any artifacts caused by gas absorption. The aerosolloaded filter samples were stored in a freezer at 20 C before analysis to prevent the volatilization of particles. 2.2. Chemical and optical analysis OC and EC were analyzed using a DRI model 2001 carbon analyzer (Atmoslytic, Inc., Calabasas, CA, USA). The anhydrosugar levoglucosan was measured by a Dionex ICS-3000 system. Detailed information can be found in earlier studies (Cao et al., 2003; Engling et al., 2006; Iinuma et al., 2009). A quarter of Teflon filter samples was used to determine watersoluble inorganic ions. The extraction of water-soluble species from the filters was achieved by placing the cut portion (1/4) of each filter into a separate 4 mL bottles, followed by 4 mL distilleddeionized water (with a resistivity of >18 MU), and then subjected to ultrasonic agitation for 1 h for complete extraction of the ionic compounds. The extract solutions were filtered (0.25 mm, PTFE, Whatman, USA) and stored at 4 C in pre-cleaned tubes until analþ 2þ ysis. Cation (Naþ, NHþ and Ca2þ) concentrations were 4 , K , Mg determined by ion chromatography (Dionex ICS-1600) using a CS12A column with 20 mM Methanesulfonic Acid eluent. Anions (SO2 4 , NO3 , Cl , and F ) were separated on an AS19 column in ion chromatography (Dionex ICS-2100), using 20 mM KOH as the eluent. A calibration was performed for each analytical sequence. Procedural blank values were subtracted from sample concentrations. Aerosol absorption coefficient bap of 532 nm was converted from black carbon concentration measured at 880 nm wavelength by a transmissometer (Magee Scientific Company, Berkeley, CA, U.S.A., Model OT-21). The converted coefficient was 8.28 m2 g1 (Yan et al., 2008). The black carbon concentration in PM2.5 samples (quartzfibre filter) were measured by a transmissometer after 24 h equilibration at relative humidity at 30%. 2.3. Data analysis methods 2.3.1. PM2.5 mass reconstruction To evaluate if the determined chemical components represent the measured PM2.5, the measured PM2.5 mass was reconstructed based on (NH4)2SO4, NH4NO3, OM, EC and fine soil (FS) (Pitchford et al., 2007). The converting factor between OC and OM should vary with the dominant sources when reconstructing PM2.5 mass. Good correlations between OC, EC and LG were found during the whole observation period, which suggested biomass burning was the dominant source of carbonaceous aerosols. Considering the large range of LG concentrations, the impact of biomass burning on carbonaceous aerosols should be different. Thus, the converting factor was chosen to be 2.2, 2.1, 1.9, 1.8, 1.7 and 1.6, respectively, when LG was in the range of >2000, 2000e1500, 1500e1000, 1000e500, 500e200, and <200 ng m3. Due to the lack of soil element measurements, we assumed that Ca2þ is 4.5% of FS mass based on soil source profile in Beijing (Zhang et al., 2013). Thus, [FS] ¼ 22 [Ca2þ]. The reconstructed PM2.5 mass was calculated according to: [PM2.5] ¼ [(NH4)2SO4] þ [NH4NO3] þ [OM] þ [EC] þ [FS]
(1)
2.3.2. bsp and bap reconstruction Generally, (NH4)2SO4, NH4NO3, OM, EC, and FS were the dominant chemical species in PM2.5, which were also the major
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Fig. 1. The sampling location (40 020 N, 116 240 E) in Beijing.
contributors to bsp (Pitchford et al., 2007). Moreover, coarse particulate matter (CM, PM2.5e-10) was also a non-negligible contributor to bsp in Beijing due to high PM10 concentrations. EC was the dominant absorption chemical component of aerosols and mainly distributed in PM2.5. Multiple linear regressions of measured bsp against the dominant species (NH4)2SO4, NH4NO3, OM, EC and FS) and other faction (measured PM2.5 minus reconstructed PM2.5) mass concentrations in PM2.5 and CM were conducted to obtain their mass scattering efficiencies (MSEs) (Tao et al., 2015a). Although EC in PM2.5 was the dominant species absorbing light, absorption of OM was not negligible when OM concentrations were high. Biomass burning produced a significant amount of carbonaceous aerosols (Cheng et al., 2011). Thus, mass absorption efficiencies (MAEs) of carbonaceous aerosols can be obtained by regression of bap against EC and OM. An external mixing of individual species was assumed in the above analysis. The amount of scattering and absorption associated with individual species can be estimated statistically using (Tao et al., 2014): bsp ¼ a1 fL(RH) [(NH4)2SO4] þ a2 fL (RH) [NH4NO3] þ a3 [OM] þ a4 [EC] þ a5 [FS] þ a6 [Other] þ a7 [CM] (2) bap ¼ b1 [EC] þ b2 [OM]
(3)
Here, bsp, bap and mass concentrations of chemical species are given in units of Mm1 and mg m3, respectively. RH growth curves of fL(RH) of sulfate and nitrate can be referred to IMPROVE net results (Pitchford et al., 2007). Considering that most of the sulfate and nitrate mass distributed in droplet mode in Beijing (Tao et al., 2015a,b), we used fL(RH) in this study. 3. Results and discussion For evaluating the impact of pollution control measures on PM chemical and optical properties, the whole study period was divided into three intervals e pre-, during- and post-APEC period. The pre-APEC period covered from day-time 8 October to nighttime 31 October, the during-APEC period covered from day-time 1 November to night-time 12 November, and the post-APEC
period covered from day-time 13 November to night-time 24 November.
3.1. Chemical composition of PM2.5 and PM10 The average (±standard deviation) PM2.5 and PM10 mass concentrations during the whole observation period were 167 ± 125 mg m3 and 256 ± 188 mg m3, respectively, which exceeded 2.2 and 1.7 times of the NAAQS (Table 1 and Fig. 2). NHþ 4, SO2 4 , NO3 , OC, and EC were the dominant components in PM2.5 and PM10. Most of them (>76%) were distributed in PM2.5 rather than in CM. In contrast, 91% of Ca2þ was distributed in CM. PM2.5 mass concentration was higher in this study, which was mainly caused by the much higher NO 3 mass concentration, than those observed in previous studies during the same season in Beijing (He et al., 2001; Song et al., 2006; Duan et al., 2006; Wang et al., 2005; Zhao et al., 2013b). The higher NO 3 mass concentrations in the most recent Table 1 Summary of PM2.5 and its chemical components, aerosol optical properties and meteorological parameters during the pre-, during- and post-APEC period in Beijing. Components
Average
Pre-APEC
During-APEC
Post-APEC
PM2.5 (mg m3) LG (ng m3) OC (mg m3) EC (mg m3) Naþ (mg m3) 3 NHþ 4 ( mg m ) Kþ (mg m3) Mg2þ (mg m3) Ca2þ (mg m3) Cl (mg m3) 3 SO2 4 (mg m ) 3 NO 3 (mg m ) CM (mg m3) bsp (Mm1) bap (Mm1) RH (%) SO2 (mg m3) NO2 (mg m3)
167 ± 125 629 ± 643 27.7 ± 19.8 8.6 ± 5.9 1.2 ± 0.7 13.0 ± 14.2 1.6 ± 1.4 0.1 ± 0.1 0.5 ± 0.3 4.7 ± 5.5 13.9 ± 16.6 26.0 ± 29.1 89 ± 72 590 ± 551 83 ± 48 41 ± 21 10 ± 9 90 ± 53
190 ± 125 657 ± 628 27.0 ± 14.8 8.9 ± 4.8 1.2 ± 0.7 18.0 ± 15.9 1.9 ± 1.2 0.1 ± 0.0 0.5 ± 0.2 4.2 ± 3.0 19.9 ± 19.3 38.2 ± 33.3 115 ± 78 750 ± 598 92 ± 46 50 ± 22 6±4 100 ± 52
88 ± 60 291 ± 314 16.5 ± 10.7 5.1 ± 3.6 1.0 ± 0.7 4.7 ± 5.9 0.7 ± 0.8 0.0 ± 0.0 0.3 ± 0.2 2.1 ± 3.0 4.2 ± 4.1 10.0 ± 12.8 37 ± 23 253 ± 202 53 ± 33 28 ± 12 7±5 57 ± 41
199 ± 142 911 ± 774 40.1 ± 27.5 11.7 ± 7.6 1.4 ± 0.7 11.4 ± 12.6 2.0 ± 1.8 0.1 ± 0.1 0.6 ± 0.4 8.4 ± 8.5 11.7 ± 13.7 18.3 ± 20.9 92 ± 67 615 ± 559 96 ± 54 36 ± 17 21 ± 11 102 ± 52
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several years was believed to be caused by more efficient conversion from NOx to HNO3 due to the increased emissions of nonmethane hydrocarbons, despite the decreasing NOx emissions (Zhao et al., 2013a; Tang et al., 2009). PM2.5 and PM10 mass concentrations during the APEC period were 88 ± 60 mg m3 and 125 ± 79 mg m3, respectively, which were 54% and 67%, respectively, lower than the pre-APEC period without control measures. The percentage decrease of CM was higher than PM2.5 due to the pause of all the constructive activities in Beijing and the other five provinces. PM2.5 and PM10 mass concentrations during the post-APEC period recovered to the similar levels of the pre-APEC period once the control measures ceased. 2 The concentrations of NHþ 4 , NO3 and SO4 in PM2.5 decreased by 74%e79% during the APEC period compared with the pre-APEC period. The emissions of SO2 and NOx from industries and vehicles were decreased by 40%e65% during the APEC period in the whole region, which was equivalent to a total reduction of 80 kiloton SO2 and 600 kiloton NOx according to their emission inventories (Fig. 3, a grid ¼ 16 km2). As a result, the total sulphur 3 (SO2 þ SO2 4 ) decreased by 53% (to the level of 0.14 mol m ) during the APEC period than the pre-APEC period, although SO2 concentration did not differ much during the two periods. This is because gas/particle partitioning between SO2 and SO2 4 also depends on other factors such as meteorology and NH3 level. The sulfur oxidation ratio (SOR) and RH were both higher during the pre-APEC period than during-APEC period. Higher RH favors the formation of SO2 4 (Sun et al., 2013). As expected, sulfur level rapidly recovered (to a level of 0.45 mol m3) during the post-APEC period. Similarly,
11
NO2 concentration decreased by 43% during the APEC period and then recovered during the post-APEC period. OC and EC in PM2.5 decreased by 39%e43% during the APEC period compared with the pre-APEC period. These percentage decreases were much lower than those of the secondary products of SO2 and NOx. Carbonaceous aerosols emissions from industries and vehicles were expected to be decreased at similar magnitudes to those of SO2 and NOx. The differences were likely caused by additional sources of carbonaceous aerosols that were not well regulated in the pollution control measures, as further discussed below. Good correlations (R2 ¼ 0.85, 0.96 and 0.95) were found between OC and EC with slopes of 2.8, 2.9 and 3.5, respectively, during the pre-, during- and post-APEC period (Fig. 4a). Thus, OC and EC had some common sources, especially during the during- and postAPEC period. OC/EC ratio has been identified in literature for coal combustion (0.3e7.6), vehicle emission (0.7e2.4), and biomass burning (4.1e14.5) (Watson et al., 2001). Comparison of the regression slopes with OC/EC ratios from known sources cannot tell exactly which sources were dominant ones. The medium to high LG concentrations (657 ± 628 ng m3, 291 ± 314 ng m3, and 911 ± 774 ng m3 during the pre-, during- and post-APEC period, respectively) suggested that biomass burning should be one of the sources despite the control polity for forbidding open space biomass burning activities. The medium to good correlations between LG and OC or EC (R2 ¼ 0.81 and 0.58 during the pre-APEC period and R2 ¼ 0.88 and 0.86 during the APEC period) supported this hypothesis (Fig. 4b and c). Higher LG concentrations during nighttime than daytime during the APEC period were likely caused by a combination of various reasons, such as lower boundary height, more burning activities. Even better correlations (R2 ¼ 0.94 and 0.94) and much higher LG concentrations were found during the post-APEC period, suggesting much more biomass burning activities when the control measures expired and heating season started. PM2.5 mass was reconstructed using the method described in Section 2.3.1. Good correlations (R2 > 0.97) between the reconstructed and measured mass concentrations were found with the slopes of 0.78, 0.71 and 0.76, respectively, during the pre-, duringand post-APEC period (Fig. 5a), indicating that the above chemical components could closely represent the measured PM2.5. On average, (NH4)2SO4, NH4NO3, OM, EC and FS accounted for 6 ± 2%, 10 ± 8%, 32 ± 6%, 6 ± 2% and 8 ± 4%, respectively, of PM2.5 mass during the APEC period and up to 7% absolute percentage differences were found during the pre- and post- APEC period (Fig. 5b). OM was absolutely the top dominant component, accounting for more than 30% of PM2.5 mass in any of the three periods and as high as 38% during the post-APEC period. PM2.5 and LG both evidently increased during the post-APEC period when heating season started. These results suggested that biomass burning was an important source due to the use of biomass fuel for heating purpose in rural areas of NCP. Replacing biomass fuel with clearer energy is needed for reducing PM pollution in the NCP. 3.2. Aerosol optical properties and their relationship with PM2.5 and CM
Fig. 2. Temporal variations of daily PM2.5 mass concentrations, its chemical components, CM, bsp, and bap during the pre-, during- and post-APEC period.
On average, dry bsp and bap, with temporal coherence, were 586 ± 550 Mm1 and 83 ± 48 Mm1, respectively, during the whole study period (representative of autumn) of 2014 in Beijing (Fig. 2). Dry bsp evidently increased and bap slightly increased compared with earlier year's data in the same season (Zhao et al., 2011; He et al., 2009; Jing et al., 2015). Apparently, visibility in Beijing has not been improved in recent years although many control measures or polices have been implemented. Dry bsp and bap decreased by 66% and 42%, respectively, during
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J. Tao et al. / Atmospheric Environment 144 (2016) 8e16
Fig. 3. Spatial distributions of SO2 and NOx emissions in the North China Plain under the base (left column) and the APEC scenario (middle column) and their differences (right column).
Fig. 4. Scatter plots of OC versus EC (a), LG versus OC (b), and LG versus EC (c).
Fig. 5. Reconstructed PM2.5 mass concentrations during the pre-, during- and post- APEC period.
the APEC period than before the APEC period, and then recovered quickly during the post-APEC period. bext was lower than
180 Mm1 on five of the 12 days during the APEC period, resulting a visibility of 20 km of larger (the so-called “APEC blue”). These
J. Tao et al. / Atmospheric Environment 144 (2016) 8e16
results demonstrated that the strict control measures were effective in improving visibility in NCP during the APEC period. However, three polluted events still occurred during the APEC period, although much weaker than the other seven polluted events in other days (Fig. 2). The peak value of bext was 832, 502 and 462 Mm1, respectively, on 4, 8 and 10 November, all exceeding 391 Mm1 (corresponding to a visibility of 10 km). LG concentrations on these three days (with an average of 528 ng m3) were much higher than on the other days during the APEC period (with an overall average of 291 ng m3). Biomass burning should have played a role on the three hazy days. “APEC blue” or haze weather during the APEC period should be mainly attributed to the decrease or increase of PM2.5 concentrations (Fig. 6a). Good correlations (R2 > 0.90) between PM2.5 or CM and measured bsp were found during the pre-, during- and postAPEC period. The mass scattering efficiency (MSE) of PM2.5 e the slope of the linear regression equation, varied widely from 2.9 to 4.0 m2 g1; in contrast, CM MSE changed little from 0.4 to 0.5 m2 g1. Although CM accounted for 35% of PM10, it only contributed 7% to bsp on average. PM2.5 MSE was 2.9 m2 g1 during the APEC period, lower than those during the pre- and post-APEC period, due to the lower PM2.5 mass. The lower PM2.5 mass suggested that most PM mass was distributed in the condensation mode with lower MSE according to the Mie theory (Tao et al., 2015a). However, PM2.5 MSE during the pre- APEC was much higher than that during the post-APEC although PM2.5 mass concentrations were similar. The differences in PM2.5 MSE between the two periods were caused by the different fractions of dominant chemical components in PM2.5, instead of by the total PM2.5 concentrations. During the pre-APEC period, SO2 and NO 4 3 were nearly two times while OC and EC were only 65e80% of those in the post- PEC period. As discussed in Section 3.3 (Table 2), the MSE of OM during the pre-APEC period was close to that during the postAPEC period while the MSEs of (NH4)2SO4 and NH4NO3 were much higher. The higher concentrations of SO2 4 and NO3 were the main causes of the higher PM2.5 MSE during the pre-APEC period. EC is generally the dominant chemical component for light absorption, although OM can also absorb light at short wavelengths. On average 87% EC and 83% OC in PM10 were distributed in PM2.5 and the rest fractions were in CM. However, the MAEs of EC and OM in CM should be less than 0.5 m2 g1 according to the Mie theory. Moreover, the fractions of EC and OC in CM only accounted for 1.4% and 6.7% of the total CM mass. Thus, light absorption by CM can be ignored. Medium correlations (R2 > 0.50) were found between PM2.5 and measured bap during the pre-, during- and post-APEC
13
period (Fig. 6b). The MAEs of PM2.5 varied within a narrow range from 0.43 to 0.57 m2 g1. These values were slightly lower than those obtained in urban Beijing (0.6 m2 g1) (Liu et al., 2013) and Guangzhou (0.8e1.0 m2 g1) (Andreae et al., 2008; Tao et al., 2015b) in the same season. In conclusion, MSEs of PM2.5 were much higher than their MAEs, which suggested bsp by PM2.5 should be the key control factor for haze weather. 3.3. Estimate of the MSEs and MAEs of individual chemical components To obtain the contributions of individual chemical components in PM2.5 to bsp and bap, the MSEs and MAEs of each chemical component should be estimated first. Considering the differences in PM2.5 MSE and MAE between the pre-, during- and post-APEC period, the MSEs of (NH4)2SO4, NH4NO3, OM, EC, FS, Other and CM and the MAE of EC were estimated separately for the three periods. The estimated MSEs of (NH4)2SO4, NH4NO3, OM, EC, FS, Other and CM and MAEs of EC and OM using the method described in Section 2.3.2 are summarized in Table 2. MSEs of (NH4)2SO4 and NH4NO3 were 3.7 and 3.8 m2 g1, respectively, lower during the APEC period than the pre-APEC (5.6 and 5.8 m2 g1, respectively) or post-APEC (4.4 and 4.7 m2 g1, respectively) period. In contrast, MSEs of the other individual chemical components were similar between the three time periods. In a previous study, SO2 and NO 4 3 were found to peak at 0.18e0.56 mm during the clean days in Beijing (Tao et al., 2015a). According to the Mie theory, MSEs of (NH4)2SO4 and NH4NO3 ranged from 1.4 to 4.6 m2 g1 and 1.6e5.3 m2 g1, respectively. Thus, the lower MSEs of (NH4)2SO4 and NH4NO3 during the APEC Table 2 Estimated MSEs and MAE of individual species by stepwise multiple linear regression. Components 2
1
(NH4)2SO4 (m g ) NH4NO3 (m2 g1) a OM (m2 g1) a EC (m2 g1) FS (m2 g1) Other (m2 g1) CM (m2 g1) b EC (m2 g1) b OM (m2 g1) a b
Pre-APEC
During-APEC
Post-APEC
5.6 ± 1.4 5.8 ± 1.2 4.5 ± 0.9 1.9 ± 0.4 0.9 ± 0.3 0.9 ± 0.7 0.5 ± 0.3 9.2 ± 0.7 0.16 ± 0.11
3.7 ± 1.2 3.8 ± 1.5 4.6 ± 1.6 1.8 ± 0.6 0.9 ± 0.5 0.8 ± 0.6 0.5 ± 0.3 8.9 ± 0.3 0.14 ± 0.11
4.4 ± 2.6 4.7 ± 2.2 4.4 ± 1.4 2.0 ± 0.6 0.8 ± 0.7 0.7 ± 1.0 0.5 ± 0.5 6.9 ± 0.5 0.13 ± 0.11
MSE. MAE.
Fig. 6. Scatter plots of bsp versus PM2.5 and CM (a), and bap versus PM2.5 (b).
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J. Tao et al. / Atmospheric Environment 144 (2016) 8e16
period should be related with their lower mass concentrations and size distributions. This is because other components, although accounted for more than 22% of PM2.5 mass, only contributed 0.7e0.9 m2 g1 to MSE due to their distribution in larger size ranges than the dominant chemical components (e.g. (NH4)2SO4, NH4NO3 and OM). Other chemical components in PM2.5 mainly distributed in PM1.1e2.1, especially in heavily polluted days in Beijing (Tian et al., 2014) and their MSE ranged from 0.6 to 1.3 m2 g1 according to the Mie theory (Tao et al., 2015a). The MAEs of EC were 9.2, 8.9 and 6.9 m2 g1, respectively, during pre-, during- and post-APEC periods. MAE of EC was determined by its size distribution and coating. EC and black carbon (BC) were mainly produced from urban fossil combustion and biomass burning. The mass size distributions of BC in volume equivalent diameter, or the geometric mean diameter peaked at 147e210 nm if produced from fossil combustion or urban plume, and at >210 nm if produced from biomass burning plume (Huang et al., 2012; Schwarz et al., 2008). Thus, MAE of EC from urban plume should be about 1.28 times of that from biomass burning plume according to the Mie theory assuming an external mixing state. The average MAE of EC during the APEC period was 1.29 times of that during the post-APEC period. Note that MAE of EC from diesel plume was higher than those from various biomass burning types in Beijing (Cheng et al., 2011). According to discussions in 3.1, biomass burning was an important source for carbonaceous aerosols during the pre- and postAPEC period. Thus, MAE of EC during the pre- and post-APEC period should be lower than that during the APEC period. It is noticed that MAE of EC was much higher during the pre-APEC period than the post-APEC period, which was believed to be caused by the much higher RH during the pre-APEC period. High RH enhances coating of EC which in turn amplifies MAE of EC (Wu et al., 2016). Thus, the large variations of the EC MAE during the three periods should be related to different sources of EC and RH levels. Water-soluble organic carbon (WSOC) (e.g. humic-like substance), a fraction of OM, can absorb light at short wavelengths. Biomass burning was an important source of WSOC (Tao et al., 2016; Du et al., 2014b). Light absorption of WSOC during the biomass burning periods was ignored in several previous studies in Beijing, results from which should be reexamined (Du et al., 2014a; Cheng et al., 2011). In this study, MAE of OM was 0.16, 0.15 and 0.13 m2 g1, respectively, during the pre-, during- and post-APEC period. Although the MAEs of OM were much lower than those of EC, OM mass concentrations were 5.8 times of EC. On average, absorption from OM accounted for 9% of bap. The estimated MAEs of EC and OM using the regression equations were reasonable estimations.
3.4. Source apportionment of aerosol light extinction The above discussions showed good correlations (R2 > 0.92) between the reconstructed and measured bsp and bap under dry condition (Fig. 7a and b), with the same slope of 1.0. bsp under ambient condition was further estimated using hygroscopic curves of sulfate and nitrate and MSEs in Table 2 due to the lack of synchronously measured aerosol hygroscopic curves. According to the estimated MSEs and MAEs, (NH4)2SO4, NH4NO3, OM, EC, FS, Other and CM accounted for 7 ± 2%, 11 ± 8%, 45 ± 5%, 18 ± 3%, 2 ± 1%, 10 ± 5%, and 7 ± 4%, respectively, of the estimated bext under dry condition during the APEC period (Fig. 8), and similar magnitudes were also found under ambient condition due to the low RH (28 ± 12%). However, their contributions were up to 8%, 15%, 13%, 5%, 0%, 5% and 0% absolute percentage differences during the pre- and post-APEC periods due to the different fractions of individual chemical compounds in PM2.5. Evidently, carbonaceous aerosols were the main contributors for dry bext during the pre-, during- and post-APEC periods. As discussed above, biomass burning was still an important source of carbonaceous aerosols even during the APEC period. The contributions of (NH4)2SO4 and NH4NO3 to the estimated bext under dry condition were 17 ± 10% lower during the APEC period than during the pre- (40 ± 18%) or post-APEC (20 ± 8%) period. These results suggested that secondary inorganic aerosols were under control during the APEC period. In contrast, the contributions of (NH4)2SO4 and NH4NO3 to the estimated bext under ambient condition were much higher those by carbonaceous aerosols due to the higher RH (50 ± 22%) before the APEC period. However, carbonaceous aerosols were dominant contributors for bext under ambient condition and were much higher than (NH4)2SO4 and NH4NO3 during the APEC and postAPEC period. Moreover, although ~30% mass fraction of PM2.5 was not identified, this fraction only accounted for 7% of bext under dry or ambient condition due its low MSE. To compare the effectiveness of mass reduction of individual chemical components in reducing their corresponding bext, the reduction ratio was defined here as the ratio of reduction in bext to that in mass concentration. On average, the reduction ratio of (NH4)2SO4, NH4NO3, OM and EC were 5.4, 6.1, 4.5 and 9.6 m2 g1, respectively, under dry condition, and of other chemical components, less than 1.0 m2 g1. The ratio increased to 10.4 m2 g1 for (NH4)2SO4 and 11.7 m2 g1 for NH4NO3, but changed little for OM and EC under ambient condition. These result suggested that the decreasing secondary inorganic aerosols was more efficient than decreasing carbonaceous aerosols under moisture condition due to the hygroscopic properties of sulfate and nitrate. On average, (NH4)2SO4, NH4NO3, OM, EC, FS, Other and CM accounted for 6 ± 3%,
Fig. 7. Correlations between the reconstructed and measured bsp and bap under dry condition.
J. Tao et al. / Atmospheric Environment 144 (2016) 8e16
15
Fig. 8. Daily bext load percentages during the pre-, during- and post-APEC period.
4 ± 3%, 47 ± 4%, 18 ± 4%, 3 ± 1%, 13 ± 5%, and 9 ± 4%, respectively, of the estimated bext under dry condition during the “APEC blue” period. This result further indicated the control of secondary inorganic aerosols or their precursor gases were the key reasons for the “APEC blue”. However, decreasing biomass burning sources should also be taken into account since they are important sources of OM, a dominant chemical component contributing to bext.
this approach are generally smaller than 11% under dry condition. Empirical relationships between aerosol hygroscopic properties and chemical components mass concentrations need to be researched further to reduce the uncertainties under ambient condition.
4. Conclusions
This study was supported by the National Natural Science Foundation of China (41375132, 91544226 and 41475119) and Beijing Municipal Science and Technology Plan (Z131100006113013).
PM2.5 mass was evidently decreased and visibility was significantly improved during the APEC period when the six major pollution control measures were exerted in Beijing and the surrounding five provinces from 1 to 12 November, 2014. (NH4)2SO4, NH4NO3, OM and EC were the dominant chemical components in PM2.5 and were also the dominant contributors to bext under dry and ambient conditions during the observed period. Reductions of secondary inorganic aerosols or their gaseous precursors were the key reasons for the “APEC blue”. Carbonaceous aerosols were the dominant contributors to bext under dry and ambient conditions during the APEC period due to the less effective control of biomass burning than industrial and traffic emissions. New control policies or technologies for reducing biomass burning emissions are needed, as have also been recommended in many previous studies. Multiple linear regressions can be an alternative method for source apportionment of bext when information of size distributions of major chemical components is lacking. Uncertainties using
Acknowledgments
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