Kai Wu1,2, Ping Kang1, Xin Tie3, Shan Gu4, Xiaoling Zhang1, Xiaohang Wen1, Lingkai Kong5, Sihui Wang1, Yuzi Chen6, Weihao Pan1, Zhanshan Wang 7

Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
Department of Land, Air, and Water Resources, University of California, Davis, CA 95616, USA
Department of Earth and Environmental Sciences, University of Rochester, NY 14620, USA
Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92617, USA
Chunan Meteorological Bureau, Hangzhou 310007, China
Nanjing University of Information Science and Technology, Nanjing 210044, China
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China


Received: April 26, 2019
Revised: September 21, 2019
Accepted: September 22, 2019

Download Citation: ||https://doi.org/10.4209/aaqr.2018.12.0481  


Cite this article:

Wu, K., Kang, P., Tie, X., Gu, S., Zhang, X., Wen, X., Kong, L., Wang, S., Chen, Y., Pan, W. and Wang, Z. (2019). Evolution and Assessment of the Atmospheric Composition in Hangzhou and its Surrounding Areas during the G20 Summit. Aerosol Air Qual. Res. 19: 2757-2769. https://doi.org/10.4209/aaqr.2018.12.0481


Highlights

  • Variations of air pollutants and PM2.5 components were analyzed in the G20 summit.
  • Most of the air pollutants concentrations decreased but O3 concentration increased.
  • Most of the chemical components of PM2.5 decreased markedly during the G20 summit.
 

ABSTRACT


To evaluate the effectiveness of control strategies during the G20 Summit (August 24–September 6, 2016), five sites were selected for investigating the variation in air pollution in the city of Hangzhou and its surrounding areas. The results showed that the air quality in Hangzhou greatly improved after the implementation of strict emission controls. Compared to the same dates from the five preceding years, the G20 period exhibited low relative humidity during the early stage (August 27–31) and no significant differences in other meteorological conditions. The SO2, NO2, PM10, and PM2.5 concentrations averaged across the five sites in Hangzhou decreased by 42.6%, 57.1%, 36%, and 38.5%, respectively, although the average O3 concentration increased by 19%. These changes indicate that the emission reduction measures decreased the concentrations of several pollutants. Compared to the same dates from the previous year (August 24–September 6, 2015), the G20 period exhibited significantly decreased concentrations for most of the chemical components of PM2.5, particularly SO42–, NO3, and NH4+. Additionally, the aerosol optical depth (AOD) and the NO2 column densities were found to exhibit similar trends with that of the pollutants in this city.


Keywords: Air quality; Emission reduction; G20 Summit; Ozone sensitivity.


INTRODUCTION


Characterized by fine particles (PM2.5), nitrogen oxides (NOx), and ozone (O3), regional air pollution has been increasingly prominent in China in recent years due to the rapid development of urbanization and industrialization (Chan and Yao, 2008). Air pollutants such as PM2.5 and ozone have significant impacts on public health and have been estimated to cause millions of premature deaths per year worldwide (Cohen et al., 2017). Extensive studies have been conducted through observational, modeling, and long-term data analyses to investigate the sources, evolution, and formation mechanisms of air pollution in China; however, there are still many uncertainties remaining (Jin et al., 2015; Hu et al., 2016). Therefore, greater insights are required to generate evidence-based emission control policies. Air pollution in the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (SCB) regions have been reported to be the most severe in China (Hu et al., 2017). Strict short-term emission controls have effectively improved the air quality during several special events such as the Beijing Olympics in 2008, the Asia-Pacific Economic Cooperation (APEC) meeting in November 2014, the Nanjing Youth Olympic Games in 2014, and the China Victory Day Parade in 2015. These short-term emission controls provide an ideal opportunity for evaluating our current understanding of the formation mechanisms of air pollution and exploring the impact of alternative control options to aid in the formulation of more effective policies for emission reduction.

Hangzhou, which is located in the southern YRD region, is the capital of Zhejiang Province and a typical megacity that covers an area of about 16,596 km2 and has a population of more than 9.18 million residents. The air pollution in Hangzhou has become more serious in recent years due to substantial local emissions and regional transport of air pollutants (Dong et al., 2013; Liu et al., 2015). Jansen et al. (2014) reported that the contribution of secondary inorganic aerosol to PM2.5 concentration has reached approximately 50% during haze episodes. Li et al. (2018) found that the secondary transformation of organic aerosols are the main sources of particle matter. In addition to local emissions, trans-boundary aerosol and trace gases transport also contributes to the severe air pollution in Hangzhou (Zhang et al., 2018). The G20 Summit, which was held in Hangzhou from September 4 to 5 in 2016, provides an ideal opportunity for evaluating the effectiveness of emission reduction measures in the YRD region. To improve the air quality during the G20 Summit, the Zhejiang Summit Environmental Protection Plan (ZSEPP) was implemented by the Zhejiang provincial government from August 24 to September 6, 2016. In this plan, strict air pollution alleviation measures were adopted to reduce the emissions of air pollutants in Hangzhou and its surrounding cities. Considering the contribution of regional transport, the emission control strategy was extended to the surrounding areas of Hangzhou (e.g., Huzhou, Jiaxing, Jinhua, and Shaoxing).

A number of previous studies investigated the effect of emission controls during the G20 Summit by using surface observations and numerical models (Feng et al., 2019; Ni et al., 2019). Ji et al. (2018) analyzed the effects of regional transport and emission control on the formation of fine particles during the G20 Summit and found that the suppression of daytime peak secondary organic carbon (SOC) formation was the major reason for the decreased particle matter concentration. Su et al. (2017) utilized ozone lidar and vertical column densities (VCDs) of ozone for exploring ozone variation during the G20 Summit and reported that meteorological conditions dominated the variations of ozone concentration. However, these studies mainly focused on single pollutants such as PM2.5 or ozone while the overall variation of air pollutants during the G20 Summit remains unclear and should be explored further.

In this study, the meteorological conditions, concentrations of six criteria air pollutants (PM2.5, PM10, O3, NO2, SO2, and CO), chemical composition of PM2.5, aerosol optical depth (AOD), and NO2 column data retrieved by satellite in Hangzhou before, during, and after the 2016 G20 Summit were analyzed for accurately assessing the contributions of the safeguard measures implemented during the G20 Summit to improve air quality. This paper is organized as follows. We first analyzed the daily average pollutant concentrations during the G20 period. Then we investigated the effect of meteorological conditions, diurnal variations, and the components on reducing air pollution by comparing the pollutant concentrations during days with stable meteorological conditions. Finally, we compared the emission reduction measures implemented for the G20 Summit with that of previous events to comprehensively evaluate the pollution control strategies implemented for the G20 Summit.


METHODS



Study Domain

Through the collaboration with surrounding cities, regional control levels were divided into three types (i.e., the core area, strictly controlled area, and controlled area), which were classified by their respective distances to the central main court (50 km, 100 km, and 300 km, as shown in Fig. 1). Of these three areas, the core area included Hangzhou (HangZ), Huzhou (HuZ), Jiaxing (JX), and Shaoxing (SX); the strictly controlled area included Ningbo (NB), Jinhua (JH), and Quzhou (QZ); and the control area included Wenzhou (WZ), Zhoushan (ZS), Taizhou (TZ), and Lishui (LS). 


Fig. 1. Location of the study area and air quality monitoring sites in Hangzhou. (a) Location of Zhejiang Province. (b) Locations of cities in Zhejiang Province. (c) Central urban area of Hangzhou.Fig. 1. Location of the study area and air quality monitoring sites in Hangzhou. (a) Location of Zhejiang Province. (b) Locations of cities in Zhejiang Province. (c) Central urban area of Hangzhou.


Measurements of Air Pollutants

In this study, the concentrations and variation characteristics of six air pollutants as well as the corresponding meteorological conditions during the G20 period were compared with those observed during the same period from 2011 to 2015 to explore the change in air quality in different situations (i.e., with vs. without the air quality improvement contributions of the safeguard measures). In addition, monitoring data of six air pollutants were collected from five sites in Hangzhou (Table 1 and Fig. 1), including Xiasha (XS; a regional site), Linping (LP; a suburban site), Xixi (XX; a background site), Zhaohui (ZH; a traffic pollution site), and Hemu (HM; an urban site). The method of observing air pollutants from 2011 to 2015 is the same as that monitored in the G20 Summit.

At these five sites, a TEOM 1405-D double-channel air particulate monitor by Rupprecht & Patashnick Co. was used to monitor PM2.5. The aerosol passed through a PM2.5 cyclone; then, the PM2.5 particles in the ambient air were constantly inhaled to the internal region by the sampling cutter. Finally, a filter dynamic measurement system (FDMS) and a tapered element oscillating microbalance (TEOM) were utilized to measure the PM2.5 concentration. A Thermo Fisher 1400 monitor was used to monitor PM10 using the TEOM method. A 49i ultraviolet spectrophotometry O3 analyzer produced by Thermo Electron Corp. in the United States was used for O3 observations, and it has a minimum detection limit of 2.0 µg m–3, a zero drift of less than 2.0 µg m–3 day–1, and a span drift of less than ±1% day–1. A Model 300E gas filter correlation analyzer was used as the CO analyzer, with a minimum detection limit of 1.0 µg m–3, a zero drift of less than 2.0 µg m–3 day–1, and a cross drift of ±1% day–1. The sampling and observations of NOx were performed using a Model 400E NO-NO2-NOx analyzer. This instrument measures the fluorescence of the excited-state NO2 produced from the chemical reaction of NO and O3 to obtain the ambient NO concentration. There is a linear positive correlation between the intensity of the emitted light and the volume fraction of NO. While detecting NO2, the NO2 is converted to NO through a molybdenum converter, and then the quantitative analysis is conducted using chemiluminescence reactions. This converter has a minimum detection limit of 5 × 103 µg m–3, a zero drift of less than 2.5 × 103 µg m–3 day–1, and a full drift of ± 1% day–1. All of the instruments collected data 24 h per day, and the data were read every 5 minutes. The accuracy of the observation data was ensured by the regular inspection of the zero point, full range, and precision of the instruments.


Table 1. Information about air pollutant monitoring sites and meteorological station.


Meteorological Data

The meteorological data were obtained from the China Meteorological Station Data Sharing Service System (http://cdc.cma.gov.cn/home.do). The meteorological parameters were monitored at the Hangzhou meteorological station, which is located near the Xixi monitoring site, and these parameters consisted of temperature (T), relative humidity (RH), sunshine duration (SD), solar radiation (SR), wind direction (WD), wind speed (WS), sea-level pressure (P), and precipitation (PREC).


Analysis of the PM2.5 Chemical Composition

The chemical composition of PM2.5 was measured at the Hangzhou meteorological station (as shown in Fig. 1), which is located on the roof of a four-story building, approximately 20 m above the ground and approximately 800 m south of the Xixi monitoring site. We used an electronic balance to weigh the Teflon filters before and after sampling to obtain the daily average PM2.5 mass concentrations. The detection limit is 10 µg (AX105DR) in a superclean lab (T: 20 ± 1°C; RH: 40 ± 3%). Then, an online analyzer for the monitoring of aerosols (ADI 2080 MARGA; Applikon-ECN, Netherlands) was used to measure the water-soluble ions and trace elements in PM2.5. A wet rotating denuder was used in the MARGA samples to allow gases to absorb on the wall of a liquid-coated annulus through which particles pass. A supersaturation treatment method was used in the steam-jet aerosol collector where the particles ultimately arrive. Droplets form due to the condensation of steam and can then be collected. The detection limits of major ions, Cl, NO3, SO42–, NH4+, Na+, K+, Mg2+, and Ca2+, were 0.008, 0.005, 0.007, 0.040, 0.070, 0.050, 0.013, and 0.030 µg m–3, respectively. We used a syringe system to collect both gases and aerosols and analyzed them via ion chromatography, which provided hourly results. The R2 values of the calibration curves for the concentrations of ions and elements exceeded 0.999.


MODIS Aerosol and OMI NO2 Column Data Set

The Moderate Resolution Imaging Spectroradiometer (MODIS) is carried on the Terra and Aqua satellites (Jin and Holloway, 2015). MODIS scans Hangzhou twice per day (10:30 and 13:30 BJT), and the recently released MODIS AOD product (Level 3 version 5) was applied to this study (http://modis-atmos.gsfc.nasa.gov/). The AOD is obtained by the 0.47 µm and 0.66 µm spectral channels and interpolated to the 0.55 µm channel. These MODIS AOD results include multiple AOD pixels covering the spatial scale of 0.5° × 0.5° and centered on Hangzhou (i.e., 30°N, 120°E). The total density of the NO2 column in Hangzhou was derived from the Ozone Monitoring Instrument (OMI; which passes Hangzhou once per day, i.e., near 13:30 BJT) at a 0.25° × 0.25° resolution. The uncertainty in the NO2 column density per pixel was estimated to be ± 0.5–1.5 × 1015 molecules cm–2 with a relative error between 10% and 40%.


RESULTS AND DISCUSSION



Analysis of Daily Average Pollutant Concentrations during the G20 Period

Fig. 2 shows a comparison of the daily average concentrations of six air pollutants during the G20 period (2016) and those during the same period in the past three years (2013–2015). It can be seen that the concentrations of SO2, NO2, CO, PM10, and PM2.5 decreased significantly during the G20 period. The NO2 concentrations of the five sites decreased by 43.9–70.5%, with an average of 57.1%, which was the largest of the five air pollutants. And the SO2 concentrations of the five sites declined by 17.5–56.6% (with an average of 42.6%). The PM2.5 and PM10 concentration decreased by 34.5–42% (with an average of 38.5%) and 30.1–46.4% (with an average of 36.0%), respectively.


Fig. 2. Daily average concentrations of six air pollutants at different sites during the G20 period of 2016 and during the same period in the previous three years (2013–2015).Fig. 2. Daily average concentrations of six air pollutants at different sites during the G20 period of 2016 and during the same period in the previous three years (2013–2015).

Notably, the O3 concentration in Hangzhou increased significantly during the G20 period. The average increase of O3 at the five sites was 19%, especially the XX site reached 48.7%. This phenomenon may correspond to the O3-NOx-VOCs sensitivity. Yan et al. (2016) utilized the WRF-CMAQ model to analyze the O3 formation in the YRD region and found that the sensitivity pattern of O3 formation in Hangzhou was VOCs-limited. In these areas, if NOx emissions are reduced, the O3 concentration will first increase and then decrease. Therefore, the possible reason for the increase in O3 concentration during the G20 period in 2016 is that the NOx emission control measures were stronger than the VOCs emission control measures, which resulted in the weak inhibition of NOx on O3 production and significant increase of O3 concentrations (Xing et al., 2011; Yarwood et al., 2013). In addition, biogenic VOC (BVOC) emissions also play an essential role in O3 formation because they are highly reactive hydrocarbons that cause the rapid increase of O3 when oxidized by hydroxyl radicals (Geng et al., 2011; Situ et al., 2014). Using the MEGAN model, Liu et al. (2018) estimated biogenic emissions at 18.86 × 105 ton yr–1 over the YRD region with higher emissions observed in Hangzhou and surrounding areas. Wu et al. (2019) employed the WRF-CMAQ model to investigate the effects of BVOC emissions on ozone formation over China and pointed out that the high BVOC emissions across eastern China increased the surface ozone levels, particularly in the YRD regions. During the G20 Summit, the T reached 31°C, and such a high T is beneficial to the emission of large amounts of BVOC. Therefore, BVOC emissions may also have significant contributions to the increased O3 concentrations observed during the G20 period.


Meteorological Conditions

Fig. S1 presents the daily averages of the meteorological parameters (e.g., T, RH, P, and WS) during the G20 period and the same time period in the previous five years. In general, the RH during the G20 period was low in the early stage compared with the previous five years, and the T, P and WS did not show significant differences. In addition, we compared the meteorological parameters during the G20 period with those before and after the period (as shown in Table 2). The average T was highest before the G20 period, reaching up to 33.2 ± 1.8°C. The average RH and P during the G20 period were similar to those before the G20 period. The WS, SD, and SR were slightly higher during the G20 period than those before and after the G20 period.

To further investigate the impacts of meteorological conditions on changes in pollutant concentrations during the G20 period, the variations in surface meteorological parameters and their relationships with air pollutants were analyzed. According to the intensity of the emission reductions and joint control strategies over Hangzhou and surrounding areas, we divided the whole period into three stages. S1 (August 24–27) was the starting stage with industrial and construction emission control. During S2 (August 28–31), there was traffic restrictions added to the emission control strategy. And S3 (September 1–6) was the G20 Summit period, which was the strictest emission control stage. In the S1 stage, the concentrations of all pollutants (except O3)


Table 2. Comparison of average values of meteorological parameters before, during, and after the G20 period from Aug. to Sep. 2016.

decreased. Compared with the NO2 and PM2.5 concentrations on August 24, the average concentration of NO2 and PM2.5 during S1 decreased by 19.9% and 6.8%, respectively. This occurred because construction sites temporarily stopped production and the meteorological conditions (i.e., easterly wind) were conducive to the dilution and removal of various pollutants. As shown in Fig. 3, with the effect of high pressure (see Fig. S2 in Supporting Information), the weather in Hangzhou was fair, with an average maximum T of 33.8°C over the four days. The maximum T on August 25 was 35.5°C, and the SD reached up to 11.6 h, which was the maximum for the entire study period. Under the influence of strong radiation, the maximum 8-h daily average of O3 on August 25 was 208 µg m–3 (i.e., the peak value during the study period). The average WS at 10 m was 2.9 m s–1, and the dominant wind direction was easterly (the frequency of east-northeasterly, easterly, and east-southeasterly winds was 39.6%). With the effect of cold air on the night of August 26 (see Fig. S3), there were significant wind (the average WS at 10 m increased to 3.7 m s–1), cooling (T decreased by approximately 3°C), and PREC (14.6 mm) processes in Hangzhou. 

Fig. 3. Solar radiation (SR), sunshine duration (SD), wind speed (WS), temperature (T), relative humidity (RH), pressure (P), and hourly concentrations of six air pollutants at the XX site from August 24 to September 6, 2016. Blue arrows indicate precipitation.Fig. 3. Solar radiation (SR), sunshine duration (SD), wind speed (WS), temperature (T), relative humidity (RH), pressure (P), and hourly concentrations of six air pollutants at the XX site from August 24 to September 6, 2016. Blue arrows indicate precipitation.

In S2, under the influence of static weather and regional transport of air pollutants, the concentrations of various pollutants in Hangzhou reached peak values before and after August 29 (Ji et al., 2018). During this stage, the WD in Hangzhou transformed from northwesterly to westerly winds and then to southwesterly winds. On August 28, Hangzhou experienced strong northwesterly winds, with the daily average WS reaching 3.9 m s–1 (the peak value during the study period). Influenced by the cold northwesterly winds, the pollutant concentrations increased compared with those observed on August 27 because of upstream pollutant transport, while the RH decreased to 45.6% (the minimum value during the study period). From August 29 to 31, the weather condition was constant, with a low planet boundary layer at night accompanied by low WS (with an average value of 2.3 m s–1), which were unfavorable to the diffusion of pollutants. In addition, PREC was not observed in Hangzhou during this period, which had an average maximum T of 32°C and daily average SD of 10.75 h. These findings revealed that the radiation condition was good and the diffusion capacity was weak, which was beneficial for the formation and accumulation of O3 from photochemical reactions. Compared with the S1 stage, the SO2 concentration had the largest increase of 30.5% and reached 12.0 µg m–3 on August 28 and 29. Because the coal combustion control measures in this stage were maintained or more strictly controlled, the 30.5% increase should be considered a reflection of the contribution of regional transported pollutants to the air pollution level in Hangzhou (see Table S2).

During S3, a dramatic weather change occurred with many cloud systems. The daily average SD was 5.6 h, a decrease of 47.9% compared with that in the S2 which meant that photochemical consumption of O3 precursors might be weakened on these days. And the average WS was 3.3 m s–1 on September 1 which was beneficial to the transmission of pollutants. The warm southwest flow that entered Hangzhou had higher aerosol loading, which could have originated an aged air mass with secondary particle growth (Ji et al., 2018). Then the PM2.5 and PM10 concentrations reached their peak values due to the unexpected increases of secondary aerosol components during this period. On September 2–3, northwesterly flow following the passage of a trough dominated, with a low pressure field (see Fig. S4) with an average WS of 2.1 m s–1. There was no PREC over 3 continuous days, which was not conducive to the diffusion and removal of pollutants. On September 4–6, persistent easterly winds with weak PREC occurred in Hangzhou, and these conditions had a wet scavenging effect on pollutants. The concentrations of PM2.5, PM10, NO2, and CO were lowest on September 5. In this stage, the total PREC, average RH, and average T were 2.3 mm, 65%, and 31°C, respectively. With high cloud coverage and low radiation at this time, the formation and accumulation of O3 from photochemical reactions were suppressed. The stable weather was beneficial for the accumulation of particles, and the humidity (65%) was greatly increased compared with that during S2 (50%). High-humidity conditions may have promoted the formation of sulfate and further increased the concentration of PM2.5 (by 33.2%) and PM10 (by 21.5%). However, the pollutant concentrations on September 5, when the G20 Summit was held in Hangzhou, were significantly lower than those during the entire period due to the strict control measures.


Characteristics of Diurnal Variations in Pollutants during G20

The diurnal variations of SO2, CO, NO2, O3, PM10, and PM2.5 during the G20 period were compared with the same time period in 2015 (following the named non-G20 period, August 24–September 6, 2015), as shown in Fig. 4. The diurnal variations of PM2.5, PM10, SO2, NO2, and CO during the G20 period were significantly lower than the non-G20 period. The O3 showed a single peak maximum at 16:00 local time which related to atmospheric photochemical reactions near the surface. Diurnal variation of PM2.5 and PM10 were similar in these two periods with high concentrations in the morning due to the traffic source emissions. And the concentration of SO2 exhibited a single-peak curve, and the peak appeared at 11:00. The strong vertical diffusion may have essential contribution on high SO2 during daytime while the low concentration at night may correspond to the wet deposition of SO2 from PREC events and dry deposition (Quan and Zhang, 2008). Diurnal variations of NO2 and CO showed a less obvious bimodal diurnal change. During the rush hours in the late afternoon, the concentration of NO2 began to increase until the following second peak appeared at 01:00 the next day. The second peak may be related to the decreased boundary layer height at night then impact the diffusion of pollutants. 


Fig. 4. Characteristics of diurnal variations in six air pollutants at the XX site during the G20 period (August 24–September 6, 2016) and the non-G20 period (August 24–September 6, 2015). The bars at the bottom of each panel represent the concentrations of pollutants in the G20 period minus the concentrations of pollutants in the non-G20 period. (Note: The arrows denote the max value).Fig. 4.
 Characteristics of diurnal variations in six air pollutants at the XX site during the G20 period (August 24–September 6, 2016) and the non-G20 period (August 24–September 6, 2015). The bars at the bottom of each panel represent the concentrations of pollutants in the G20 period minus the concentrations of pollutants in the non-G20 period. (Note: The arrows denote the max value).

In addition, it can be found that there were some similar patterns of air pollutants. The diurnal variation of PM2.5 was highly correlated with PM10, which is possibly because the ratios of PM2.5 to PM10 were high (0.64–0.92) (as shown in Fig. 4). And this phenomenon may also suggest that they may have the same potential sources (Xu et al., 2018). As NO2 and SO2 are both precursors for particle matter through secondary inorganic aerosol (SIA) formation, NO2 and SO2 had positive correlations with PM2.5 and PM10. In addition, the SO2 correlation with PM2.5 and PM10 was better than that with NO2. This difference can be explained by the relative contribution of sulfate exceeding the nitrate in SIA which will be discussed later.


Analysis of Chemical Components of PM2.5 during the G20 Period

Previous studies have shown that within PM2.5, NH4+ is mainly formed through NH3 gas-particle conversion; SO42– is mainly related to H2SO4, which is the result by the gas-phase oxidation of SO2; and NO3 is related not only to the heterogeneous reaction of HNO3 but also to the heterogeneous hydrolysis of highly humid N2O5 (Riemer et al., 2003; Baek and Aneja, 2004; Chang et al., 2016). Fig. 5 compares the average concentrations of several PM2.5 components during the G20 period and the non-G20 period. The concentrations of all components (except for Mg2+) exhibited obvious decreases. Among them, SO42– showed the largest decline of 62.7%, reflecting the effect of the “coal-reducing” measures during the G20 period. The decrease in NO3 was nearly 50%, which reflected the combined effects of motor vehicle restrictions and the reduction of biofuel combustion from human sources and point sources. Cl also exhibited a significant reduction, indicating the effect of the reduction of combustion emissions (Tao et al., 2017). The decreased concentrations of Na+ and Ca2+ reflected the effect of dust control (Louie et al., 2005). K+ is considered a tracer ion for biomass combustion (Wang et al., 2015). Notably, the decrease in the NH4+ concentration was 61.89% during the G20 period, which exceeded the decreases in NO3. It is reported that NH4+, NO3, and SO42– form a buffer system, existing as (NH4)2SO4 and NH4NO3 (Tao et al., 2012). And the NH4+ concentration will not exceed those of nitrate and sulfate because the chemical activity of NH4+ needs an acidic environment. Therefore, the possible reason for this may be that decreases in SO42– and NO3 lead to decreased NH4+ concentrations. 


Fig. 5. Average concentration of PM2.5 chemical components during the G20 period (August 24–September 6, 2016) and the non-G20 period (August 24–September 6, 2015).Fig. 5. Average concentration of PM2.5 chemical components during the G20 period (August 24–September 6, 2016) and the non-G20 period (August 24–September 6, 2015).

Fig. 6 shows the proportions of sulfate, nitrate, and ammonia in PM2.5 (ρ(SNA/PM2.5)), the sulfur oxidation ratio (SOR = [SO42–]/[SO2 + SO42–], the nitrogen oxidation ratio (NOR = [NO3]/[NOx + NO3]), and the PM2.5 concentrations during the G20 period (Fig. 6(a)) and the non-G20 period (Fig. 6(b)). Higher SOR and NOR indicate increased formation of secondary aerosols in the atmosphere. During the G20 period, the average ρ(SNA/PM2.5) was 15%, which was significantly lower than that during the non-G20 period (34%). However, the SOR and NOR exhibited similar patterns in which they reached their minimum values in S2 and their maximum values in S3 during the G20 period. Sun et al. (2013) reported that RH has the most significant effect on NO3 and SO42– because higher RH can promote the formation of secondary inorganic ions in particles. The average RH during S3 was 65%, which was higher than that during S2 (only 48%). Therefore, although the concentration of SO2 during S2 was similar to that during S3, the SOR and NOR were lowest in S2 and highest in S3.


Fig. 6. Variations in sulfate, nitrate, and ammonia (SNA) in PM2.5; sulfur oxidation ratio (SOR); and nitrogen oxidation ratio (NOR) during (a) the G20 period (August 24–September 6, 2016) and (b) the non-G20 period (August 24–September 6, 2015).Fig. 6.
 Variations in sulfate, nitrate, and ammonia (SNA) in PM2.5; sulfur oxidation ratio (SOR); and nitrogen oxidation ratio (NOR) during (a) the G20 period (August 24–September 6, 2016) and (b) the non-G20 period (August 24–September 6, 2015).

Aw and Kleeman (2003) found that high T favors the volatilization of nitric acid and ammonia from the particulate phase of nitrate. As shown in Fig. 3, the T was similar during the G20 and non-G20 periods; thus, the NOR did not obviously change during the G20 and non-G20 periods. As for the variation of SOR, Liang et al. (2017) reported that the active photochemical oxidation could result in high SO2-to-sulfate conversion rates. And the higher RH in summer can also favor the heterogeneous reaction of sulfate formation (as shown in Fig. 3). It is recognized that the SOR was significantly positively correlated with the RH but only slightly correlated with the T in Hangzhou (Wu et al., 2017). Hence, the variation of RH is the main reason for the change of SOR during different stages.


Changes in Satellite AOD and NO2 Column during the G20 Period

In Fig. 7, the variation of AOD and NO2 column over Hangzhou shows similar patterns to the air pollutants. In S3, the average AOD was 1.01, which decreased by 41% relative to S1 and decreased by 38% relative to S2. In S3, the average columnar NO2 was 10.1 molecules cm–2, which decreased by 30.3% relative to S1 and S2. This trend was consistent with the results of surface observations, which were discussed above. 


Fig. 7. Aerosol optical depth (AOD) derived from MODIS satellite data and columnar NO2 derived from OMI satellite data in the region of 29.5–30.5°N, 119.4–120.4°E in the G20 period. Green lines represent the average values of AOD and columnar NO2 during S1, S2, and S3.Fig. 7. Aerosol optical depth (AOD) derived from MODIS satellite data and columnar NO2 derived from OMI satellite data in the region of 29.5–30.5°N, 119.4–120.4°E in the G20 period. Green lines represent the average values of AOD and columnar NO2 during S1, S2, and S3.


Analysis of the Spatial Evolution of Air Pollutants between Hangzhou and the Surrounding Cities

Fig. 8 shows the pollutant concentrations of Hangzhou and the surrounding cities during the G20 period and non-G20 period. The decreases in PM2.5, PM10, and CO in the core area were the most prominent, followed by the strictly controlled area, with the controlled area being the least affected. The percentages by which the PM2.5 concentrations decreased in the three controlled areas from the non-G20 period to the G20 period were 42.64%, 30.25%, and 6.92%. However, the concentrations of PM10 and CO in the controlled area increased. The SO2 concentrations decreased by 38.07% and 40.79% in the core area and the strictly controlled area, respectively, while SO2 only decreased by 4.33% in the controlled area. The NO2 concentrations decreased by 43.30% in the core area and only decreased by 16.53% and 18.80% in the strictly controlled area and controlled area, respectively. Despite the increase in the O3 concentration in Hangzhou, the O3 concentration in the core area was essentially the same as that observed during the same period in 2015. In addition, the O3 concentration in the controlled area increased by 5.34% compared to that observed during the same period in 2015. 


Fig. 8. Comparison of pollutant concentrations in Hangzhou and the surrounding cities during the G20 period (August 24–September 6, 2016) and the non-G20 period (August 24–September 6, 2015).Fig. 8. Comparison of pollutant concentrations in Hangzhou and the surrounding cities during the G20 period (August 24–September 6, 2016) and the non-G20 period (August 24–September 6, 2015).


Comparison with Previous Studies

By comparing the pollutant concentration during the G20 Summit with that during the Beijing APEC conference in 2014, the Beijing military parade in 2015, and the Nanjing Youth Olympic Games in 2014, we found that the concentrations of NO2, SO2, CO, PM2.5, and PM10 were reduced under the influence of emission reduction measures during these special events (as shown in Table S3). The O3 concentration decreased significantly during the military parade and the Nanjing Youth Olympic Games while the O3 concentration increased by 19% and 102% during the G20 Summit and APEC conference, respectively. This difference may be due to the large differences in meteorological conditions during the four important events and their different effects on O3 generation. Huang et al. (2017) analyzed the meteorological conditions during the 2014 Nanjing Youth Olympic Games. The results showed that the T during the Youth Olympic Games was lower than that in the same period in 2013, and there were more cloudy and rainy days. The low T and cloudy weather were not conducive to the formation of O3, and the O3 concentration clearly decreased. Wang et al. (2017b) analyzed the Beijing military parade in 2015 and maintained that the continuous low T during the parade had an important contribution to the reduction in the O3 concentration. We found that the average T during the G20 Summit reached 31°C, which is conducive to the large BVOC emissions and enhanced photochemical reactions.

The aforementioned differences during the four events may be due to a lack of sufficient consideration of the O3 sensitivity pattern during the implementation of emission reduction measures. Previous studies have shown that the O3 sensitivity patterns in Beijing, Nanjing, and Hangzhou are VOCs-limited (Tang et al., 2012; An et al., 2015; Li et al., 2017). For these areas, if NOx emission control measures are stronger than the VOC emission control measures, the inhibitory effect of NOx on O3 production will be weak, and the O3 concentration will increase significantly. Wang et al. (2015) found that the regional VOC reduction during the APEC conference was significantly less than that of NOx, while the regional VOC reduction during the parade was significantly higher than that of NOx, which increased the O3 concentration during the APEC conference and decreased the O3 concentration during the parade. Ding et al. (2015) further verified this conclusion by estimating the NOx emission reduction during the 2014 Nanjing Youth Olympic Games. Because this research did not synchronize VOCs and the Zhejiang provincial government did not publish the specific emission reduction plan and implementation strength during the G20 period, the reason for the increase in O3 concentration during the G20 Summit requires further investigation.


CONCLUSIONS



In this study, the variations in the air pollutant characteristics and their corresponding meteorological conditions in Hangzhou and its surrounding areas during the G20 period (August 24–September 6, 2016) were analyzed and compared to those observed during the same dates for the five previous years. The RH was lower during the G20 period than the preceding years, whereas the other meteorological conditions were similar to their average levels. Furthermore, compared to the three previous years, the majority of the studied air pollutants were significantly reduced, with the average daily concentrations for SO2, NO2, PM2.5, and PM10 decreasing by 42.6%, 57.1%, 38.5%, and 36%, respectively; the O3 concentration, however, significantly increased (by 19%). In addition, the concentrations of most of the chemical components of PM2.5 decreased, with sulfate and nitrate exhibiting the largest reductions. The AOD and the columnar NO2 in the region of 29.5–30.5°N and 119.4–120.4°E during the G20 period also displayed a trend similar to that of the observed surface pollutant concentrations.

Based on our comprehensive results and observational analysis, emission reduction measures implemented on the regional scale effectively improved the air quality during the G20 period, suggesting that regional jointly emission reduction are ideal for controlling air pollution in megacities such as Hangzhou, and even the entire YRD region. Moreover, the effects of O3-VOCs-NOx sensitivity must be fully considered, particularly for VOCs-limited cities, e.g., Hangzhou; specifically, to prevent a significant increase in O3, emission control measures should ensure that the reduction in VOCs exceeds that of NOx. Finally, collaborative efforts in pollutant emission and control between adjacent regions should be intensified a few days in advance—before the occurrence of adverse weather conditions—to maintain low pollutant concentrations during major events. The strategies implemented during the G20 Summit can be used as a reference when devising air quality improvement measures for similar activities in the future.


ACKNOWLEDGMENTS


This study was supported by the National Key Research and Development Program of China (No. 2018YFC0214001), the Major Research Plan of the National Natural Science Foundation of China (No. 91644226), the Major Science And Technology Project of Sichuan (No. 2018SZDZX0023), the Key Science and Technology Plan Project of Sichuan (No. 2018JY0011), the Key Project of Science Foundation of Education Department of Sichuan (18ZA0086), and the Scientific Research Foundation of Chengdu University of Information Technology (KYTZ201725). We thank the editor and reviewers for their valuable comments. The meteorological data set was provided by the China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn). Additionally, the air pollutant data were downloaded from the National Urban Air Quality Real-time Publishing Platform (http://106.37.208.233:20035/). The data of MODIS AOD Level 3 products in 2016 were obtained from https://modis.gsfc.nasa.gov/, which is maintained by NASA. The NCEP Global Forecast System (GFS) analysis data were obtained from https://www.ncdc.noaa.gov/data-access, which is maintained by the National Centers for Environmental Information. All data are available from the corresponding author upon request.


DISCLAIMER


The authors declare that they have no conflicts of interest.


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