A Study of Characteristics and Origins of Haze Pollution in Zhengzhou, China, Based on Observations and Hybrid Receptor Models

Home , Puyang, Xuchang, Zhoukou

Si Wang1,2, Shaocai Yu1,2*, Pengfei Li1,2, Liqiang Wang1,2, Khalid Mehmood1,2, Weiping Liu1,2, Renchang Yan3, Xianjue Zheng3

1 Research Center for Air Pollution and Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China 2 Key Laboratory of Environmental Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China 3 Hangzhou Environmental Monitoring Center, Hangzhou, Zhejiang 310058, China

ABSTRACT

To obtain a comprehensive picture of characteristics and sources of haze pollution in Zhengzhou, we analyzed annual air pollutant (fine particulate matter (PM2.5), inhalable particulate matter (PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2) and ozone (O3)) observations at nine monitoring stations from March 1, 2013 to February 28, 2014. A case study on haze pollution was carried out using observations, metrological data, aerosol optical depth (AOD) values and Hybrid receptor models. Results of annual variations of air pollutants indicated that PM2.5 pollution in Zhengzhou was the most severe. Monthly variations revealed that all air pollutants except O3 showed peak values in December because of the increased local emissions during heating, while the lowest value found in August was probably because of the favorable dispersion conditions. The monthly variation patterns of O3 concentrations show the peak values in August due to higher temperature and stronger solar radiation. The diurnal variations showed that PM2.5 concentration variations were consistent with the traffic flow. The high values of PM2.5/PM10 and PM2.5/CO occurred in the afternoon probably due to the strong photochemical reactions. Results of the case study showed that relative humidity and wind speed were the main meteorological factors influencing PM2.5 concentrations. Back trajectories show that regional transport from the northeast and southeast of Zhengzhou (such as Puyang, Kaifeng, Zhoukou, and Xuchang in Henan province) also made a big contribution to the PM2.5 pollution in Zhengzhou. Our results demonstrated that the spatial- temporal distributions of PM2.5 in Zhengzhou were determined by complex factors such as primary emissions, secondary production, meteorological conditions and local/regional-transport.

Keywords: Haze; Primary emissions; Meteorological factors; Potential sources.

INTRODUCTION is associated with morbidity and mortality (Dockery et al., 1993; Brunekreef and Forsberg, 2005; Tong et al., 2009; China has made phenomenal progress in economic Pope and Dockery, 2012). The increased concentrations of growth in the past few decades because of unprecedented PM2.5 can lead to climate changes due to their direct and urbanization and industrialization. This rapid growth has indirect effects (Shao et al., 2006; Yu et al., 2006a; IPCC, led to dramatic and widespread decline in the quality of the 2007; Hyslop, 2009; Wang and Hao, 2012; Yu et al., 2014b, environment, especially in air quality (Hao and Wang, 2005; c). PM2.5 can also make a significant impact on atmospheric Chan and Yao, 2008; Chang et al., 2009; Yu et al., 2016). properties during their global transport processes (Yang et Studies have proved that fine particulate matter (PM2.5) is al., 2005; Wang et al., 2015a, b, c). China has frequently one of the major air pollutants which cause the severe haze suffered from haze pollution, which was characterized by (McKeen et al., 2007; Tan et al., 2009; Bao et al., 2010; more episodes, longer duration and higher pollutant Eder et al., 2010; Zhang et al., 2010; Yang et al., 2012; Yu concentration levels, especially in the densely-populated et al., 2014a). There have been plenty of evidences that PM2.5 and highly-industrialized eastern China (Tao et al., 2014; Liu et al., 2016). Zhengzhou is the capital of Henan province with total population of 11 million and land area of 7446 km2. * Corresponding author. Zhengzhou has a typical temperate continental monsoon E-mail address: [email protected] climate, characterized by prevailing northerly wind in

514 Wang et al., Aerosol and Air Quality Research, 17: 513–528, 2017 winter and prevailing southerly wind in summer. In 2013, the DATA AND METHODOLOGY Gross Domestic Product in Zhengzhou was ¥ 620.2 billion (Henan Statistical Yearbook, 2014). As one of major hubs Air Monitoring Stations of transportation and communication in China, Zhengzhou Fig. 1 indicates the distribution of nine monitoring stations is in the boom of industrial development with the worsening in Zhengzhou: Yanchang (34.75°N, 113.68°E) and Heyida air pollution in future. Wang et al. (2015a) showed that the (34.75°N, 113.64°E) located in the transportation hub and air in winter is heavily polluted in Zhengzhou, mainly due business district, Zhengfangji (34.77°N, 113.64°E) and to the emissions from heating. Geng et al. (2013) studied Shijiancezhan (34.75°N, 113.60°E) located in the industrial chemical composition and source apportionment of PM2.5 and residential area, Yinhangxuexiao (34.80°N, 113.67°E), in Zhengzhou in 2010. They found that there were three Gongshuigongsi (34.80°N, 113.56°E), Jingkaiquguanwei main sources (soil dust, secondary aerosols and coal (34.72°N, 113.73°E) and Sishiqizhong (34.72°N, 113.73°E) combustion) which contributed 26%, 24% and 23% of the located in the cultural and educational area, while PM2.5 concentration respectively. The Department of Ganglishuiku (34.92°N, 113.61°E) is the background station. Environmental Protection of Henan province published the This study used hourly air pollutants (PM2.5, PM10, NO2, results of PM2.5 source apportionments in 2015, indicating CO, SO2 and O3) data at nine urban monitoring stations that dust was the largest source of PM2.5 in Zhengzhou from March 1, 2013 to February 28, 2014 obtained from urban area with contribution of 24.5%, while industry, coal China National Environmental Monitoring Center (CNEMC) and vehicle emissions contributed 26%, 24% and 23% of (http://106.37.208.233:20035/) and meteorological data PM2.5 concentrations, respectively. (http://www.wunderground.com/cn/zhengzhou/zmw:00000 Zhengzhou has suffered from air pollution for years, and .1.WZHCC). the air pollution in Zhengzhou often ranked among top ten worst cities in China. However, the regional haze episodes Satellite Observations in Zhengzhou have not yet been studied systematically. Moderate Resolution Imaging Spectroradiometer (MODIS) The objectives of this study are to identify the characteristics is a main sensor aboard the satellites EOS/Terra and and sources of main air pollution and examine the EOS/Aqua. The aerosol loading in the atmosphere can be underlying formation mechanisms which would help to described by the Aerosol Optical Depth (AOD) retrieved guide the development of the emission control strategies to from satellite sensors. AOD has been widely considered to be reduce regional hazes in Zhengzhou in future. In this study, a useful tool for mapping air pollution distribution over large we analyzed air pollutant (PM2.5, PM10, NO2, CO, SO2 and spatial domains (Chu et al., 2003; Wang and Christopher, O3) observations from March 1, 2013 to February 28, 2014. 2003; Guo et al., 2009; Zhang and Reid, 2010; Wu et al., Meteorology and satellite observational data, and hybrid 2012). To study spatial characteristics of haze, this work receptor models were also employed to analyze the sources used the Deep Blue daily AOD data at 550 nm obtained and formation mechanisms of haze pollution in Zhengzhou. from MOD08_D3 data (Level–3 data) on NASA’s Terra

Fig. 1. Distributions of air quality monitoring stations in Zhengzhou City.

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–3 satellite with a 1° spatial resolution during December 11– corresponding hourly CNAQSS of 500 µg m . The SO2 29, 2013 (http://giovanni.sci.gsfc.nasa.gov/giovanni/). Xin concentrations in urban atmospheric environment are directly et al. (2014) showed that there were high correlations affected by various pollution sources, such as thermal between the observed AOD and MODIS AOD when the power plants, sulphur production and other high-sulfur coal Angstrom exponent was less than 1.5 and that the slopes combustion. The economy in Zhengzhou is still highly decreased from 0.97 to 0.83 with the Angstrom exponent dependent on industry, especially heavy industry, with more increasing (the dominant aerosol size decreasing). Therefore, than 70% of the primary energy consumption from coal the MODIS AOD data are good for this study. (ZMBS, 1999–2009). The annual average concentrations of –3 –3 PM2.5, PM10, and NO2 were 97.17 µg m , 157.22 µg m , and Back Trajectory and Cluster Analysis 50.01 µg m–3, respectively, and exceeded their corresponding –3 –3 Air mass back trajectories were calculated using the CNAQSS (35 µg m for PM2.5, 70 µg m for PM10, and –3 National Ocean and Atmospheric Administration (NOAA) 40 µg m for NO2) by 277.63%, 224.60%, and 125.03%, Hybrid Single-Particle Lagrangian Integrated Trajectory respectively. The primary sources of NO2 emissions are motor (HYSPLIT) model (http://ready.arl.noaa.gov/HYSPLIT.php) vehicles, power plants and residential energy consumption. and meteorological data supplied by the NOAA/Air The heavy traffic flow led to the high NO2 emissions in Resources Lab (NOAA/ARL, http://ready.arl.noaa.gov/gdas Zhengzhou because of its role as an important transportation 1.php) and Global Data Assimilation System (GDAS). In hub in China. The PM2.5 is major major pollutant in 2013, this study, 48 hr back trajectories starting at the arrival level with 65% of days exceeded AQI standard and is responsible of 100 m from nine urban monitoring sites in Zhengzhou for 77% of the polluted days in Zhengzhou (ZZEPB, 2013). were calculated to explain when and where the pollutants These results suggested that PM2.5 pollution in Zhengzhou were potentially transported (Brankov et al., 1998; Wang et was very severe and should remain a concern. al., 2009; Yu et al., 2014a). In this study, 48 hrs was chosen to calculate the back trajectories because it was sufficient Monthly Variations of Air Pollutants to locate possible regional transport pathways (Yu et al., In this study, we grouped 12 months into four seasons: 2014a; Yan et al., 2015). According to meteorological fields, spring (March, April and May), summer (June, July and the backward trajectory model was run eight times per day August), autumn (September, October and November) and at starting times of 00:00, 03:00, 06:00, 09:00, 12:00, winter (December, January and February). As shown in 15:00, 18:00 and 21:00 LT (local time). The trajectory Fig. 2, all of these air pollutants except O3 showed high cluster analysis was performed with the clustering option values in winter months due to the heating period and low of Euclidean distance (Wang et al., 2009; Yu et al., 2014a; values in summer months because of the favorable diffusion Yan et al., 2015). and dilution conditions. The O3 concentrations have opposite monthly variation pattern with peak value occurring in August Receptor Model Description due to the higher temperature and stronger solar radiation. Identifying the origins of the pollutants and distinguishing PM2.5 and PM10 concentrations showed similar variation between long range and local contributions could give patterns: winter > autumn > spring > summer. In winter, government crucial information for the implementation of the increased emissions from heating and low planetary effective control strategies. Concentration Weighted Trajectory boundary layer (PBL) may enhance PM concentrations and (CWT) method was introduced to confirm the influence of induce slower pollutant diffusion (Liu et al., 2013). In air masses transported from heavily-polluted area at the spring and autumn, local particles are re-suspended in the receptor site (Ashbaugh et al., 1985; Hsu et al., 2003; Yu et atmosphere due to strong winds, which could also increase al., 2014a; Li et al., 2015). The CWT method computes the the PM concentrations (Deng and Li, 2015). In summer, concentration at the receptor site weighted by the residence the favorable diffusion and dilution conditions could decrease time of the trajectory for each grid cell. The high CWT the concentrations of PM (Yoo et al., 2014). Fig. 2 showed values reveal that air parcels arriving at the grid cell can two peaks of PM2.5 and PM10 concentrations in October contribute to high pollutant values at the receptor site. and December, which were mainly caused by the biomass burning and increased coal consumption during the heating DISSCUSION AND RESULTS period, respectively (Zheng et al., 2014). As shown in the white paper of Zhengzhou "blue sky" project (2013–2015) Temporal Variations of Air Pollutants (http://www.henan.gov.cn/jrhn/system/2013/04/22/010386 Annual Variations of Air Pollutants 360.shtml), Zhengzhou city increased the total GDP from The annual average concentrations of SO2, O3, and CO 166.1 billion to 554.7 billion Yuans, expanded the built-up were 53.94 µg m–3, 45.21 µg m–3, 2.43 mg m–3, respectively, area from 262 to 373 km2, increased the total coal and did not exceed the corresponding China National Air consumption from 21.12 million tons to 35 million tons, –3 Quality Secondary Standard (CNAQSS) (60 µg m for SO2, and increased the number of vehicle from 0.985 million to –3 –3 200 µg m for O3, 4.0 mg m for CO) (because there are 2.105 million for the period of 2005 to 2012, On the other no annual CNAQSS for O3 and CO, we used 1 h and 24 h hand, on the basis of the national remote sensing monitoring CNAQSS to evaluate O3 and CO pollution). For the whole of straw burning points released by the Ministry of study period, the hourly concentrations of SO2 ranged from Environmental Protection from October 6 to October 12, 5 to 486 µg m–3, with the highest concentration close to the 2014, the number of straw burning points in Henan was

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Fig. 2. Monthly mean variations of air pollutant concentrations in Zhengzhou from March 1, 2013 to February 28, 2014.

245, the highest in the country. According to Zhengzhou (0.041) were higher than spring (0.037) and autumn (0.036), Environmental Protection Bureau, the increased concentrations suggesting a profound impact of secondary particle formation of PM2.5 and PM10 in May and June were due to stable processes in these two seasons. The intense summer sunlight metrological conditions. However, the highest hourly PM10 made emission precursors more readily form secondary concentration occurred in March 9, 2013, probably due to particles through a series of photochemical reactions the dust storm in the day (Zheng et al., 2014). (Wang et al., 2016), while the subsequent secondary PM Fig. 3(a) shows monthly variations of PM2.5/PM10 and production because of the increased emissions for heating PM2.5/CO ratios in Zhengzhou from March 1, 2013 to in winter can cause the enhanced PM2.5 concentrations in February 28, 2014. The mean value of PM2.5/PM10 was the atmosphere. higher in winter (0.690) because of the increased local emissions from heating and the peak value in October (0.711) Diurnal Variations of Air Pollutants was the result of biomass burning (Zheng et al., 2014). Fig. 4 shows diurnal mean variations of hourly PM2.5, PM2.5/PM10 ratios were lower in March (0.526) and April O3, PM10, SO2, NO2 and CO concentrations in Zhengzhou (0.545) because of the frequent dust storm and the strong for different seasons. In general, PM2.5, PM10, SO2 and NO2 winds which increased the concentrations of coarse particles showed significant seasonal variations with the highest in in the air (Zheng et al., 2014). Cheng et al. (2008) reported the winter and the lowest in the summer. The maximum that aerosols consisted of many coarse particles in spring, level in winter is due to a growing number of anthropogenic probably due to the dust particles transported from northern activities such as fossil fuel combustion. Primary emissions China. For the whole study period, PM2.5 contributed to and secondary production of aerosols with adverse 61% of PM10. The higher abundance of coarse particles in metrological conditions could also further augment air PM10 indicated that local dust emissions and regional dust pollutant concentrations. As can be seen, SO2, NO2 and CO transport may play an important role in the PM10 formation concentrations reached peaks between 9 am to 12 pm, a little (Yan, et al., 2015). The main sources of PM10 are from the earlier than the peak of the PM2.5 concentrations (between traffic exhaust, power plants, industrial dust, and energy 10 am to 2 pm), reflecting a significant contribution of combustion in Zhengzhou. In addition, sand beach of Yellow primary emissions from automobiles and coal combustion and River in northern Zhengzhou is the significant source of the subsequent secondary PM production to the enhanced urban dusts (Du et al, 2012). As shown in de Gouw et al., PM2.5 concentrations in a day time. The PM2.5 concentrations (2009), one can use the enhancement ratios of the decreased from late afternoon partly due to a combination observations to the mixing ratio of an inert trace gas such of the increased PBL and reduced anthropogenic emissions. as CO to account for the effect of emissions and boundary- After 7pm vehicular emission increased and resultantly layer height. In this study, the enhancement of PM2.5 to CO NO2 concentration can raise the PM2.5 level in atmosphere. ratios was used to further discuss the secondary production The stagnant atmospheric conditions could also further of PM2.5 (de Gouw et al., 2009; Zhang and Cao, 2015). The contribute to the accumulation of PM2.5 in the evening. average ratios of PM2.5/CO in winter (0.047) and summer The lowest O3 level observed around 7 to 9 am is probably

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Fig. 3. (a) Monthly mean variations of PM2.5/PM10 and PM2.5/CO ratios, diurnal variations of (b) PM2.5/PM10 and (c) PM2.5/CO ratios for different seasons in Zhengzhou from March 1, 2013 to February 28, 2014. because of relatively weak solar radiation in the morning. with depletion reactions, dilution and dispersion processes. Another reason for the lowest O3 is that it takes some time Figs. 3(b) and 3(c) show diurnal variations of PM2.5/ for NO2 emitted during morning rush hours to form O3. The PM10 and PM2.5/CO ratios in Zhengzhou from March 1, high negative correlation coefficient (R = –0.604) between 2013 to February 28, 2014 for different seasons. The variation O3 and NO2 indicates that O3 was remarkably influenced patterns of PM2.5/PM10 ratios showed winter > autumn > by the NO2 concentration (Fig. 5(a)) because of the titration summer > spring. The increased emissions from heating of NO from the car emissions (Yu et al., 2006b; Ran et al., sources in winter created more precursors, which further 2009; Shan et al., 2009). The highest O3 concentration in enhanced the PM2.5 concentrations during wintertime. The late afternoon during 3 pm to 5 pm was mainly caused by lower PM2.5/PM10 ratios in spring might be attributed to high temperature and stronger photochemical effect (Chen dust storm in this season. Fig. 3(b) revealed that the peak et al., 2015; Zhou et al., 2015). Fig. 5(b) shows the positive values of PM2.5/PM10 occurred in the afternoon (2 pm–4 pm) correlation between O3 and temperature (R = 0.615). The in all seasons. This was because of the stronger solar radiation concentrations of O3 kept low at night which is associated and higher temperature accelerated the photochemical

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Fig. 4. Diurnal mean variations of air pollutant concentrations in Zhengzhou from March 1, 2013 to February 28, 2014.

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Fig. 5. Correlations (a) between daily average O3 and NO2 concentrations, and (b) between daily average O3 concentrations and temperatures in Zhengzhou.

reactions, thus favoring the secondary production of PM2.5. marker of photochemical reactions (Statheropoulos et al., Fig. 3(c) reported that the peak value of PM2.5/CO ratio was 1998; Vardoulakis and Kassomenos, 2008). Correlations in the afternoon (1 pm–3 pm) in all seasons, which could between gaseous pollutants and particulate matter are also be attributed to the stronger photochemical reactions commonly used as an indicator for the identification of the and secondary PM production. The times of the lower values PM sources (Juda-Rezler et al., 2011; Dimitriou and were consistent with the traffic flow, highlighting the Kassomenos, 2013). There was a very strong correlation profound influence of traffic emissions. between PM2.5 and NO2 with correlation coefficient of 0.783, followed by CO (R = 0.610), while the correlation Case study of a Typical Haze Episode coefficient between PM2.5 and SO2 was only 0.109. Nitrate Characteristics of Air Pollution during the Case Study and nitrite are typical signs of traffic sources, which are Period produced by combustion processes and high temperatures Above analyses revealed that the PM2.5 pollution in in engines. As a result, their concentrations in the air can Zhengzhou was very severe, especially in winter. To identify be largely associated with vehicular emissions (Gilio et al., the characteristics and origins of haze pollution in Zhengzhou, 2015). Considering that the mobile sources emit high level we conducted a case study covering a heavy haze period of CO and NOx but relatively low level of SO2 (Yu et al., during December 11–29, 2013. The average concentration 2006b), this suggests that the formation of PM2.5 were –3 of PM2.5 during this period was 160.78 µg m and the partly contributed by increasing automotive emissions in proportion of days with fairly good air quality was only Zhengzhou. However, a more detailed explanation about 26.3%. To analyze the characteristics of haze pollution PM2.5 source apportionment in Zhengzhou is subject to during this period, we divided the period into 6 events as further studies including more comprehensive observations shown in Fig. 6. Events 1 and 6 were the clean cases with of chemical components of PM2.5 and its precursors as well average concentrations of 41.68 µg m–3 and 32.81 µg m–3, as modeling effort at multiple levels. respectively. Events 2, 3, 4, 5 were associated with the haze cases with average concentrations of 177.97 µg m–3, 167.01 Influence of Meteorological Conditions µg m–3, 242.05 µg m–3, and 187.39 µg m–3, respectively. Fig. 6 showed consistent changes in relative humidity During the case study period, PM2.5 had the highest (RH), visibility and PM2.5 concentration: the high PM2.5 correlation with PM10 (R = 0.918), suggesting that the sources concentration corresponds to the high RH and low visibility. of PM2.5 and PM10 were somewhat similar. Generally, NO2 During December, the decrease of the temperature in China is an indicator of vehicular emissions and SO2 is attributed was accompanied by strong winds. This condition may to industrial and household emissions, whereas O3 is a explain high visibility and lower PM2.5 concentration in the

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Fig. 6. Time series of (a) daily mean concentrations of PM2.5, (b) mean temperature, (c) mean RH, (d) mean pressure, (e) mean visibility (f) mean wind speed during December 11–29, 2013 in Zhengzhou.

Event 1 and Event 6 (Fig. 6). However, the visibility During this period, the average visibility in Zhengzhou was decreased while RH increased in the Event 2. From Events less than 2 km. This is consistent with the finding of Wang 2 to 5, high pressure and low wind speed led to stable et al. (2014a, b) who reported that equal pressure field with meteorological conditions which favor the air pollutant high RH was the cause of low visibility and pollutant accumulation and severe haze formation in Zhengzhou. accumulation during the haze period.

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The correlations among PM2.5, visibility and meteorological heavy PM2.5 pollution in Zhengzhou. conditions were summarized in Table 1. The results showed To locate the sources contributing to the PM2.5 pollution that visibility has higher correlation with RH (R = –0.917) in Zhengzhou, we calculated CWT values in Figs. 8(a)–8(c) and wind speed (R = 0.643), while PM2.5 also has higher for the whole period, event 2 and event 4. Fig. 8(a) shows correlation with RH (R = 0.815) and wind speed (R = –0.699). that the potential pollution sources were located in northeast Thus, RH and wind speed were the main meteorological and southeast of Zhengzhou. Fig. 8(b) indicates that northeast factors influencing both visibility and PM2.5 concentrations. of Zhengzhou such as Puyang and Kaifeng in Henan Lower wind speeds can reduce the dispersion processes province led to the ascending stage of PM2.5 for the event 2 and the higher relative humidity could increase aqueous- in Zhengzhou. Fig. 8(c) reveals that southeast of Zhengzhou phase production of PM, both of which can increase the PM2.5 such as Zhoukou and Xuchang in Henan province were the concentration and decrease visibility. potential source regions resulting in the PM2.5 ascending stage for the event 4 in Zhengzhou. In addition, the local Cluster Trajectory and CWT Analyses emissions were also an important factor to the haze pollution To characterize the general behavior of air masses during in Zhengzhou. According to the environment bulletin of the study period and to evaluate the relative contributions Henan province in 2013, SO2 emission was 1.254 million by long range transport of the air pollution, 48 h backward tons, of which industry contributed 1.1027 million tons. trajectories starting at the arrival level of 100 m for the 9 Meanwhile, NO2 emission was 1.5656 million tons, including urban monitoring sites in Zhengzhou were calculated industrial source of 1.0288 million tons, and vehicle during December 11–29, 2013. Fig. 7(a) represents the 48 h emission of 0.5125 million tons in Henan. This means that back trajectories for the whole period, and Figs. 7(c)–7(h) industry emissions in Henan province may make a significant show the maps of the 48 h back trajectories from events 1 impact on PM2.5 pollution formation in Zhengzhou. to 6. In Figs. 7(a) and 7(b), five clusters for all data during the whole period were determined by the cluster analysis Ground Observations and AOD Analyses over the Region algorithm, three short distance transport pathways: SE Fig. 9 gives spatial distributions of the average PM2.5 (Southeast) (16.1%), NE (Northeast) (15%), and N (North) concentrations in China for the periods of December 11– (20%), two long distance transport pathways: N–NW 12, December 13–16, December 17–18, December 19–24, (North–Northwest) (43%) and NW (Northwest) (5.9%). December 25–26 and December 27–29, 2013, corresponding Figs. 7(c) and 7(h) indicate that the 48 h back trajectories with events 1–6, respectively. Fig. 9(a) reveals that higher for the relatively clean air periods (event 1 and event 6) which PM2.5 concentrations occurred in the southern China during mainly belongs to N–NW cluster (as shown in Fig. 7(b), the the event 1. Figs. 9(b)–9(c) show that haze pollution moved dominant N–NW cluster (43%) originating from low to the northern China first and then extended to cover the barometric altitude (623.109 hPa) with the height of 3511.269 most cities in China (Figs. 9(d)–9(e)). Zhengzhou city has m) were coming from the regions far away from the receptor suffered from severe haze pollution during these four site (like Mongolia, and Inner Mongolia) brought the clean periods until December 27–29 as shown in Fig. 9(f). air masses to Zhengzhou. Figs. 7(e) and 7(g) indicate that Fig. 10 gives the spatial distributions of the daily most of the back trajectories were coming from north average AOD data for the region of 100–125 E and 25–45 during the declining period of the PM2.5 concentrations. As N from events 1 to 6, respectively. The areas with red color shown in Fig. 7(d), for the first PM2.5 concentration ascending showed the areas with severe haze pollution. Fig. 10 reveals stage (event 2), most of the 48 h back trajectories were that the haze pollutions were formed and located in the coming from northwest of Zhengzhou, such as Yanan and northeast and south of Zhengzhou for the haze events 2 and Xiaoyi in Shanxi province, and passed through Xinxiang 4, respectively. The results were consistent with spatial and Kaifeng in Henan province before they finally reached distributions of the average PM2.5 concentrations. Zhengzhou. Compared to back trajectories in other periods, most of back trajectories in event 4 (the second PM2.5 CONCLUSION concentration ascending stage with the highest PM2.5 concentration of 242.05 µg m–3) were from the short As one of the most-polluted city in China, Zhengzhou pathways (NE (999.312 hPa) and SE (1025.116 hPa) clusters) will face major challenges and has a long way to go to meet which brought the heavily-polluted air masses, resulting in the proposed daily PM2.5 standard. Although Zhengzhou city

Table 1. Pearson Correlation Coefficients among PM2.5, visibility and meteorological conditions for the case study period.

PM2.5 Pressure RH Visibility Temperature Wind speed (µg m–3) (hPa) (%) (km) (°C) (km h–1) PM2.5 1.000 Pressure 0.421 1.000 RH 0.815 0.579 1.000 Visibility –0.788 –0.516 –0.917 1.000 Temperature –0.309 –0.835 –0.511 0.330 1.000 Wind speed –0.699 0.002 –0.743 –0.643 0.273 1.000

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event3, (f) event(g) 4, trajectories in Zhengzhou for (a) the whole period, (b) the pressure profile of five clusters, (c) event 1, (d) event 2, (e) (e) 2, event (d) 1, event (c) clusters, five of profile pressure the (b) period, whole the (a) for Zhengzhou in trajectories

48 h air mass back mass air h 48

. 7

Fig. 6. event (h) 5, event

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Fig. 8. The CWT maps for PM2.5 for (a) the whole period, (b) event 2 and (c) event 4. has made significant efforts in alleviating air pollution, the photochemical reactions. air quality in Zhengzhou is still not much improved so far. To The results of the case study indicated that meteorological determine the characteristics of air pollution in Zhengzhou, conditions were the important factors for the PM2.5 pollution we analyzed surface air pollution observations (PM2.5, PM10, formation in Zhengzhou. In addition to the local emissions, O3, NO2, CO and SO2) from March 1, 2013 to February 28, northeast and southeast of Zhengzhou such as Puyang, 2014 from the nine monitoring stations. The annual average Kaifeng, Zhoukou, and Xuchang in Henan province were concentrations of PM2.5, PM10, NO2, SO2, O3 and CO were the most potential source regions for the haze formation in 97.17 µg m–3, 157.22 µg m–3, 50.01 µg m–3, 53.94 µg m–3, Zhengzhou. The characteristics analyses of air pollution in 45.21 µg m–3 and 2.431 mg m–3, respectively. Monthly Zhengzhou showed that the air pollution was associated variations revealed that all pollutants except O3 showed with primary emissions (such as emissions from automobiles high values in winter months and low values in summer and coal combustions), secondary production of aerosols, months. The two peaks of PM2.5 and PM10 concentrations stagnant weather and regional transport. Therefore, it is in October and December were mainly attributed to biomass essential to reduce primary emissions from cars and coal burning and increased coal consumption, respectively. The combustion and implement air pollution control not only at O3 concentrations revealed the opposite monthly variation a local level, but also for all surrounding areas, especially pattern with peak value in August due to higher temperature for the regions in the northeast and southeast of Zhengzhou. and stronger solar radiation. The diurnal variations showed Since the results of this work can only determine the potential that PM2.5 concentration variations were consistent with the sources responsible for the haze formation in Zhengzhou in traffic flow. The lowest values of PM2.5/PM10 and PM2.5/CO terms of transport, there is a need to use 3-D air quality occurred in the afternoon probably because of the stronger models to do further study.

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Fig. 9. Average PM2.5 concentrations for the periods of (a) event 1, (b) event 2, (c) event 3, (d) event 4, (e) event 5, (f) event 6.

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Fig. 10. Observations of AOD at 550 nm from the MODIS for the periods of (a) event 1, (b) event 2, (c) event 3, (d) event 4, (e) event 5, (f) event 6.

ACKNOELEDGMENTS of China and the Israel Science Foundation.

The part of this work is supported by the “Zhejiang 1000 REFERENCES Talent Plan” and Research Center for Air Pollution and Health in Zhejiang University. The part of this work is also Ashbaugh, L.L., Malm, W.C. and Sadeh, W.Z. (1985). A partially supported by the National Natural Science residence time probability analysis of sulfur concentrations Foundation of China (No. 21577126) and Department of at grand Canyon National Park. Atmos. Environ. 19: Science and Technology of China (No. 2014BAC22B06; 1263–1270. No. 2016YFC0202702). This work was supported by the Bao, H., Yu, S.C. and Tong, D. (2010). Massive volcanic Joint NSFC–ISF Research Program (No. 41561144004), SO2 oxidation and sulphate aerosol deposition in jointly funded by the National Natural Science Foundation Cenozoic North America. Nature 465: 909–912.

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