- Email: [email protected]
Spatial and temporal variability of PM2.5 and PM10 over the North China Plain and the Yangtze River Delta, China
Atmospheric Environment 95 (2014) 598e609
Contents lists available at ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Spatial and temporal variability of PM2.5 and PM10 over the North China Plain and the Yangtze River Delta, China Jianlin Hu a, Yungang Wang b, Qi Ying c, Hongliang Zhang a, * a
Department of Civil and Environmental Engineering, University of California, Davis, CA 95616, USA Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA c Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA b
h i g h l i g h t s Summertime PM2.5 and PM10 in the NCP and YRD regions of China were analyzed. Average PM2.5 and PM10 concentrations are 77.0 and 136.2 mg/m3 in the NCP region. Average PM2.5 and PM10 concentrations are 42.8 and 74.9 mg/m3 in the YRD region. Strong temporal correlation between cities within 250 km is found. PM2.5 concentrations on episode days are 2e4 times greater than non-episode days.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 27 January 2014 Received in revised form 4 June 2014 Accepted 7 July 2014 Available online 8 July 2014
The North China Plain (NCP) and the Yangtze River Delta (YRD) in China have been experiencing severe particulate matter (PM) pollution problems associated with the rapid economic growth and the accelerated urbanization. In this study, hourly mass concentrations of PM2.5 and PM10 during June 1steAugust 31st, 2013 were collected in 13 cities located in or adjacent to the NCP region and 20 cities located in the YRD region. The overall average PM2.5 and PM10 concentrations were 77.0 mg/m3 and 136.2 mg/m3 in the NCP region, respectively, and 42.8 mg/m3 and 74.9 mg/m3 in the YRD region, respectively. The frequencies of occurrence of concentrations exceeding the China's Ambient Air Quality Standard (AAQS) (BG3095-12) Grade I standards were 83% for PM2.5 and 93% for PM10 in the NCP region, and 51% for PM2.5 and 66% for PM10 in the YRD region. Strong temporal correlation for both PM2.5 and PM10 between cities within 250 km was frequently observed. PM2.5 was found to be negatively associated with wind speed. On the PM2.5 episode days (when the 24 h PM2.5 concentration is greater than 75 mg/m3), average PM2.5 concentrations were 2e4 times greater compared to the non-episode days. The PM2.5 to PM10 ratio increased from 0.50 (0.57) on the non-episode days to 0.64 (0.64) on the episode days in the NCP (YRD) region. No distinct weekday/weekend difference was observed for PM2.5, PM10, and other gaseous pollutants (CO, SO2, NO2, and O3) in all cities. The results presented in this paper will serve as an important basis for future regional air quality modeling and source apportionment studies. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Particulate matter PM2.5 Spatial variation Temporal variation China
1. Introduction Along with the rapid economic growth and urbanization, China has been experiencing severe particulate matter (PM) pollution problem, especially in the most developed regions, such as the North China Plain (NCP), the Yangtze River Delta region (YRD), and
* Corresponding author. E-mail addresses: [email protected], [email protected], hlzhang@lsu. edu (H. Zhang). http://dx.doi.org/10.1016/j.atmosenv.2014.07.019 1352-2310/© 2014 Elsevier Ltd. All rights reserved.
the Pearl River Delta region (PRD) (van Donkelaar et al., 2010). Annual average concentrations of particles with aerodynamic diameter equal to or less than 2.5 mm (PM2.5) were observed over 100 mg/m3 in Beijing (He et al., 2001) and 60 mg/m3 in Shanghai (Ye et al., 2003), greatly exceeding the World Health Organization (WHO) guideline value of 10 mg/m3 (WHO, 2005). An increasing trend in the severity and frequency of PM pollution in more recent years has been observed (Deng et al., 2008; Li et al., 2013; Zhang et al., 2008; Zhao et al., 2013). Sustained exposure to high PM air pollution level has significant health effects (Correia et al., 2013; Dockery, 2001; Dockery et al.,
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
599
Fig. 1. Locations of the 33 cities analyzed in this study. The red triangles represent the cities in the NCP region. The green squares represent the two cities not in the administrative NCP region, but are included in the analysis. The blue dots represent the cities in the YRD region. This satellite image taken on June 15, 2013 illustrates the large smog layer cover the entire NCP region and partial YRD region.
1993; Fann et al., 2012; Franklin et al., 2007; Ito et al., 2011; Ostro et al., 2006). According to the Global Burden of Disease Study, 3.2 million people died from air pollution in 2010 among which 1.2 million were from China (Lim et al., 2012). Half-a-billion people alive in northern China in the 1990s will live an average of 5.5 years less than their southern counterparts because of inhaling more polluted air generated from coal burning activities (Chen et al., 2013). Retrospective regression analysis of 80,515 deaths recorded at eight urban districts in Beijing between 2004 and 2008 showed an association between increased air pollution and increased years of life lost (Guo et al., 2013). In the past decade, a remarkable effort has been made to investigate the characteristics, sources, mechanism and adverse health effects of PM pollution in China, mostly focusing on a few mega-cities, such as Beijing, Shanghai, and Guangzhou (Li et al., 2008, 2011; Liu et al., 2008a; Liu et al., 2008b; Wang et al., 2014a, 2012, 2010; Zheng et al., 2009). Recent studies indicate that the spatial extent of PM pollution in these areas has been expanding to broader regional scales (Liu et al., 2013; Zhang et al., 2008). This trend urges needs for large-scale ambient PM monitoring for the purpose of designing effective control strategies.
Starting from June 2000, the government of China started publishing daily air pollution index (API) for a number of key cities (increased from 47 cities in 2000 to 120 cities in 2011), which was calculated based on ground monitoring of 24 h average concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter with aerodynamic diameter equal to or less than 10 mm (PM10) (http://datacenter.mep.gov.cn/). PM2.5 was not included in this routine measurement and no PM2.5 standards were available. On February 29th, 2012, the third revision of the “Ambient Air Quality Standard” (AAQS) (BG3095-12) was released, and PM2.5 was adopted into the AAQS for the first time. The AAQS Grade I and Grade II standards are 35 and 75 mg/m3, respectively, for 24 h average PM2.5, and 50 and 150 mg/m3 for 24 h average PM10 (MEP, 2012a). In March 2012, the Chinese Ministry of Environmental Protection (MEP) released the official revisions of the ambient air quality index (AQI), which was calculated based on seven pollutants including SO2, NO2, PM2.5 PM10, carbon monoxide (CO), 1 h peak ozone (O3), and 8 h peak O3 (MEP, 2012b). In January 2013, more than 100 of Chinese major cities started releasing concentrations of these seven pollutants and calculated AQI values to the public based on all monitoring stations throughout each city (MEP, 2012a). These
600
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
Fig. 2. Summertime average concentrations of (a) PM2.5, (b) PM10, (c) CO, (d) SO2, (e) NO2, and (f) 8 h peak O3. The unit is mg/m3 for PM2.5 and PM10, and ppb for the gaseous pollutants.
extensive air quality observation data are publicly accessible. By studying this rich dataset, we could improve our understanding of PM emission sources, its formation mechanisms, transport pathways, and potential causes of air quality degradation. In this study, we analyzed the hourly and daily concentrations of six air pollutants (SO2, NO2, PM2.5, PM10, CO, 1 h O3 and 8 h O3) measured at 13 cities in the NCP region and 20 cities in the YRD region in summer 2013. The spatial and temporal variations of these pollutants were investigated. This study was motivated by a few reasons. First, high PM pollution have been reported in summer in the two regions. Second, many studies have been conducted in the literature to investigate the high PM pollution in winter and biomass burning seasons (for examples, Chen and Xie, 2014; Ding et al., 2013; Sun et al., 2014), but only limited studies which examined the annual variability of PM included some summer studies. Third, the previous studies on summer episodes are mostly limited to a small urban/regional scale (for examples, He et al., 2001; Peng et al., 2011; Sun et al., 2004b; Ye et al., 2003). The
information of spatial and temporal variations of summer PM pollution in a broader regional scale is sparse in literature. This is the first attempt of charactering regional PM2.5 pollution using ground-measurement data with hourly time resolution across 33 cities in China. 2. Methods 2.1. Study areas The NCP represents a geographically flat region in the northern part of Eastern China, including the municipalities of Beijing (the national capital) and Tianjin (an important industrial city and commercial port), most part of Hebei, Heinan, Shangdong provinces, and the northern part of Anhui and Jiangsu provinces, with an approximately 1/5 of the China total population (China, 2012). PM2.5 has been studied in Beijing and Tianjin for more than 10 years (Gao et al., 2011; Guo et al., 2012; He et al., 2001; Ianniello et al.,
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
601
Fig. 3. Frequency distribution of observed PM2.5 and PM10 in the NCP and YRD regions. Red and blue lines represent the Grade II standard of PM2.5 and PM10, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
2011; Shen et al., 2011; Song et al., 2008; Xing et al., 2011; Zhang et al., 2012, 2004). Ammonium sulfate, ammonium nitrate, and organic material are found to be the main PM2.5 components, total accounting for over 60% of the PM2.5 mass concentrations in Beijing (He et al., 2001). Coal combustion for domestic heating is identified as the major source of PM2.5 in the winter (Hao et al., 2005; Wang et al., 2005b). Strong northwestern winds in the spring transport dust from the Gobi desert leading to high PM10 concentrations (Sun et al., 2004a; Wang et al., 2005a). In the summer, stabilized weather conditions along with high temperature and humidity result in high PM concentrations characterized by the large formation of secondary pollutants (Gao et al., 2011; Song et al., 2002; Yao et al., 2003). The YRD region represents the eastern part of the Yangtze Plain adjacent to the NCP. The traditional definition of the YRD region includes the mega-city Shanghai and the well-industrialized and urbanized areas of the south part of Jiangsu province and the north part of Zhejiang province. An expanded definition (Fang et al., 2011) includes Shanghai, Jiangsu, Zhejiang, and Anhui provinces, with a total population of 160 million (China, 2012). High ozone and fine PM concentrations monitored in this region demonstrated air quality deterioration resulted from the rapid growth of transportation, industries, and urbanization (Gao et al., 2009; Huang et al., 2011; Tie and Cao, 2009; Tie et al., 2009). The NCP and YRD regions are not geographically separated by mountain ranges. Regional transport of pollutants between these two regions can occur with northerly or southerly winds. 13 cities in the NCP region (Beijing, Langfang, Tianjin, Baoding, Tangshan, Cangzhou, Hengshui, Shijiazhuang, Qinhuangdao, Jinan, Handan, Qingdao, and Zhengzhou) are included in the current study. 20 cities in the YRD region including Shanghai, Suzhou,
Jiaxing, Wuxi, Huzhou, Shaoxing, Hangzhou, Yangzhou, Jinhua, Nantong, Huaian, Quzhou, Nanjing, Taizhoushi, Lianyungang, Suqian, Xuzhou, Wenzhou, Taizhou, and Lishui are chosen for the analysis. Locations of the NCP and YRD regions and the 33 cities are shown in Fig. 1.
2.2. Data source and description In January 2013, MEP started publishing real-time hourly concentrations of SO2, NO2, PM2.5 PM10, CO, and O3, which are used to calculate AQI (http://113.108.142.147:20035/emcpublish/). The data presented in this study were obtained from the website for the period of June 1, 2013 to August 31, 2013. All the measurements were conducted at the national air quality monitoring sites located in each city. Automated monitoring systems were installed and used to measure the ambient concentration of SO2, NO2, O3 and CO according to China Environmental Protection Standards HJ 1932013 (http://www.es.org.cn/download/2013/7-12/2627-1.pdf), and of PM2.5 and PM10 according to China Environmental Protection Standards HJ 655-2013 (http://www.es.org.cn/download/2013/712/2626-1.pdf). Meteorological observations were obtained from the National Climate Data Center (NCDC) (ftp://ftp.ncdc.noaa.gov/ pub/data/noaa/). For each city, the hourly and daily concentrations of all the air pollutants were calculated by averaging the hourly data from all monitoring sites in the city. 1 h and 8 h peak ozone concentrations were calculated based on the average hourly ozone concentrations. The daily average concentrations of each pollutant were calculated only when there were more than 16-h valid data. The days with 24 h average concentration greater than the Grade II standard are
602
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
Table 1 Pearson correlation coefficients between cities in the NCP of PM2.5 (cells above the diagonal) and of PM10 (cells below the diagonal).
Beijing Langfang Tianjin Baoding Tangshan Cangzhou Hengshui Shijiazhuang Qinhuangdao Jinan Handan Qingdao Zhengzhou
Beijing
Langfang
Tianjin
Baoding
Tangshan
Cangzhou
Hengshui
Shijiazhuang
Qinhuangdao
Jinan
Handan
Qingdao
Zhengzhou
e 0.67 0.50 0.72 0.41 0.69 0.60 0.58 0.40 0.40 0.45 0.23 0.46
0.79 e 0.73 0.75 0.59 0.59 0.59 0.63 0.28 0.33 0.35 0.21 0.30
0.52 0.82 e 0.57 0.72 0.52 0.57 0.48 0.43 0.33 0.36 0.13 0.30
0.78 0.78 0.69 e 0.43 0.69 0.70 0.78 0.13 0.27 0.37 0.24 0.27
0.55 0.70 0.82 0.49 e 0.36 0.29 0.35 0.47 0.22 0.06 0.24 0.06
0.62 0.55 0.73 0.72 0.42 e 0.67 0.64 0.27 0.30 0.52 0.07 0.33
0.49 0.49 0.66 0.60 0.39 0.83 e 0.73 0.24 0.42 0.62 0.03 0.39
0.77 0.55 0.56 0.82 0.36 0.75 0.70 e 0.09 0.29 0.55 0.30 0.36
0.46 0.37 0.56 0.26 0.59 0.39 0.35 0.27 e 0.12 0.03 0.12 0.08
0.34 0.33 0.41 0.28 0.35 0.37 0.54 0.37 0.11 e 0.39 0.10 0.63
0.38 0.28 0.45 0.38 0.19 0.57 0.70 0.57 0.09 0.49 e 0.11 0.62
0.17 0.12 0.04 0.21 0.07 0.16 0.05 0.21 0.01 0.11 0.22 e 0.14
0.08 0.19 0.25 0.13 0.09 0.24 0.49 0.26 0.02 0.65 0.58 0.27 e
defined as PM episode days. Pearson correlation coefficients are calculated for correlation analyses.
3. Results and discussion 3.1. Summertime air quality overview Fig. 2 shows the concentrations of PM2.5 (a), PM10 (b), CO (c), SO2 (d), NO2 (e), and 8 h peak O3 (f) averaged over the entire study time period (June 1steAugust 31st, 2013) for the 36 cities in the study area. Average PM2.5 concentrations in the NCP were high with an average concentration of over 70 mg/m3 in all cities except Qingdao. Excluding Qingdao, small difference in the average PM2.5 concentration was observed among cities, indicating small spatial gradients in PM2.5 in the NCP. The PM2.5 concentrations in the YRD were relatively low (~40 mg/m3 for most cities) with a decreasing spatial pattern from north (~60 mg/m3) to south (~30 mg/m3). The summertime concentrations were lower compared to winter, spring and annual average concentrations (He et al., 2001; Ye et al., 2003). PM10 displayed similar spatial pattern. PM10 concentrations ranged from 120 to 200 mg/m3 in most NCP cities, and from 40 to 90 mg/m3 in most YRD cities. The gaseous pollutants including CO, SO2, NO2, and O3, however, showed different spatial distributions. Greater inter-city difference was observed, indicating that local emissions are more dominant in the gases than PM2.5 and PM10. CO, NO2, and SO2 concentrations were much higher in a few industrial cities (i.e., Tangshan and Handan in NCP) than other cities due to high emissions from fossil fuel power plants and industrial sources (Chen et al., 2006; Hao et al., 2005)). NO2 concentrations in more developed cities located around Shanghai were greater than other cities in the YRD due to the higher vehicular emissions (Chen et al., 2006). Summer average concentrations in all the cities are available in Table S1 in the Supplementary Materials. Fig. 3 shows the frequency distributions of PM2.5 and PM10 in the NCP and YRD regions. During summer 2013, the average PM2.5 and PM10 mass concentration were 77.0 ± 41.9 and 136.2 ± 62.0 mg/m3 in the NCP region (the error denotes one standard deviation), respectively, and were 42.8 ± 25.9 and 74.9 ± 39.7 mg/m3 in the YRD region, respectively. In the NCP region, 83% and 47% of the PM2.5 mass concentration exceeded the NAAQS Grade I and Grade II for PM2.5, respectively, 93% and 33% of the PM10 mass concentration exceeded the NAAQS Grade I and Grade II standards for PM10, respectively. In the YRD region, these percentage values were 51% and 8% for PM2.5, respectively, and 66% and 4% for PM10, respectively.
3.2. Spatial variability The Pearson correlation analysis between different cities was performed to investigate the regional correlation for PM air pollution in the two regions. Table 1 and Table 2 show the correlation coefficients (r) of daily average values of PM10 and PM2.5 between all cities in the NCP and YRD regions, respectively. Most city-pairs showed a positive correlation, indicating that a significant fraction of the PM2.5 in the cities was either secondary PM such as ammonium sulfate and secondary organic aerosol (SOA) or fugitive dust, which typically has broader regional distributions than anthropogenic primary pollutants. The correlation coefficients for PM2.5 were generally higher than PM10, suggesting that the secondary PM formation is likely a more important PM source than fugitive dust in summertime. The city-pairs with shorter distances showed higher correlations, and were given in bold in the tables. Qingdao and Zhengzhou in the NCP region, and Lishui, Huaian, Quzhou, and Wenzhou in the YRD region were not as well correlated as the other cities. These cities were mostly exposed to local sources. The correlation coefficients were further compared with the consideration of the distance between cities. The results are shown in Fig. 4. Strong and similar dependence was found between the r values and the distance in both regions. Pearson correlation coefficient over 0.6 was rarely found for both PM2.5 and PM10 when the distance between cities was over 250 km in the NCP region. The correlation coefficients between cities in the YRD became mostly less than 0.6 when cities were 250 km away for PM2.5 and 180 km for PM10. The radius of influence for PM10 in the NCP region was larger than that in the YRD region, suggesting dust had likely greater impact in the NCP region. This radius of influence suggests that for any urban areas, coordinated regional emission controls are needed in addition to controlling the local emissions. Back-trajectory analysis was used to examine the histories of air masses that led to high PM concentrations during high PM pollution episodes: June 15, 2013 for the NCP region and June 12, 2013 for the YRD region. The trajectories were calculated using the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) developed by Air Resources Laboratory (ARL) in the National Oceanic and Atmospheric Administration (NOAA) (Draxler, 2013; Rolph, 2013). 24 h and 72 h back-trajectories of air parcels arriving in cities at the noon time of selected episodes in the NCP and the YRD region were calculated and are shown in Fig. 5. The trajectories demonstrate clearly regional transport of PM2.5 and PM10 during the high PM pollution episodes. In 24 h, the average trajectory distances among the NCP and the YRD cities are 249 ± 43 km and 215 ± 86 km, respectively. Within 72 h, most the
Table 2 Pearson correlation coefficients between cities in the YRD of PM2.5 (cells above the diagonal) and of PM10 (cells below the diagonal). Huzhou Shaoxing Hangzhou Taizhoushi Yangzhou Nanjing Taizhou Jinhua Lishui Huaian Quzhou Wenzhou Suqian Lianyungang Xuzhou
e 0.80 0.84 0.78 0.53 0.49 0.42 0.39 0.23 0.18 0.24 0.70 0.52 0.22 0.12 0.01 0.51 0.18 0.09 0.16
0.52 0.83 0.73 0.62 0.87 e 0.49 0.81 0.59 0.61 0.74 0.23 0.39 0.33 0.08 0.36 0.03 0.50 0.44 0.46
0.79 e 0.81 0.74 0.78 0.72 0.42 0.59 0.43 0.41 0.48 0.47 0.45 0.32 0.13 0.20 0.32 0.34 0.26 0.29
0.89 0.88 e 0.60 0.66 0.66 0.56 0.57 0.28 0.30 0.40 0.62 0.54 0.27 0.18 0.24 0.43 0.36 0.23 0.34
0.84 0.80 0.73 e 0.62 0.59 0.38 0.49 0.63 0.41 0.39 0.41 0.37 0.46 0.19 0.12 0.22 0.17 0.34 0.21
0.66 0.93 0.77 0.71 e 0.84 0.47 0.68 0.57 0.61 0.74 0.33 0.42 0.23 0.08 0.39 0.01 0.45 0.45 0.38
0.65 0.76 0.81 0.60 0.68 0.77 e 0.65 0.26 0.28 0.34 0.47 0.57 0.41 0.04 0.39 0.32 0.26 0.20 0.32
0.46 0.72 0.69 0.59 0.74 0.87 0.86 e 0.50 0.49 0.66 0.30 0.52 0.52 0.12 0.55 0.21 0.53 0.40 0.48
0.41 0.67 0.47 0.72 0.65 0.60 0.49 0.60 e 0.69 0.62 0.11 0.26 0.39 0.18 0.14 0.05 0.32 0.47 0.31
0.21 0.55 0.36 0.49 0.63 0.59 0.43 0.57 0.85 e 0.77 0.16 0.25 0.08 0.36 0.27 0.08 0.66 0.50 0.58
0.33 0.66 0.48 0.45 0.79 0.79 0.56 0.70 0.70 0.80 e 0.17 0.34 0.18 0.11 0.35 0.11 0.59 0.52 0.53
0.70 0.61 0.73 0.59 0.61 0.52 0.74 0.51 0.41 0. 0.44 e 0.60 0.18 0.15 0.20 0.68 0.07 0.20 0.05
0.51 0.60 0.60 0.52 0.58 0.63 0.80 0.73 0.46 0.35 0.47 0.60 e 0.51 0.16 0.40 0.41 0.14 0.19 0.09
0.28 0.35 0.45 0.35 0.29 0.43 0.72 0.56 0.26 0.10 0.20 0.48 0.73 e 0.13 0.29 0.31 0.01 0.14 0.07
0.10 0.39 0.24 0.32 0.46 0.39 0.39 0.43 0.74 0.86 0.62 0.23 0.34 0.18 e 0.08 0.11 0.33 0.23 0.29
0.19 0.42 0.45 0.33 0.48 0.61 0.66 0.74 0.37 0.32 0.41 0.39 0.75 0.71 0.34 e 0.01 0.58 0.40 0.55
0.48 0.26 0.44 0.41 0.16 0.14 0.46 0.25 0.15 0.03 0.07 0.66 0.41 0.50 0.00 0.24 e 0.08 0.07 0.10
0.21 0.45 0.33 0.22 0.50 0.46 0.43 0.52 0.57 0.71 0.64 0.26 0.25 0.09 0.79 0.53 0.01 e 0.50 0.92
0.15 0.35 0.24 0.30 0.39 0.30 0.28 0.28 0.54 0.48 0.44 0.35 0.13 0.12 0.52 0.30 0.05 0.53 e 0.57
0.24 0.39 0.36 0.23 0.39 0.38 0.42 0.44 0.53 0.63 0.54 0.23 0.15 0.01 0.70 0.42 0.00 0.89 0.53 e
603
trajectories for the NCP cities are still in the NCP region, with an average distance of 510 ± 120 km. The 72 h trajectories for the YRD cities indicate that pollutants from the NCP region are transported south and lead to high pollution episode in the YRD regions. Effects of transport on YRD cities were also found by studies using chemical transport models (Wang et al., 2014b; Ying et al., 2014). The results confirm that summer PM pollution in the NCP and the YRD are regional, and regional control strategies are needed to solve the PM pollution problems.
3.3. Temporal variability
3.3.1. PM2.5 and meteorology variation Fig. 6 illustrates the temporal variation of daily average PM2.5 concentrations, wind speed, and temperature in Beijing and Shanghai, the two largest megacities in NCP and YRD, respectively. A negative correlation between PM2.5 and wind speed was observed, i.e., high PM2.5 concentrations occur generally with weak winds and low PM2.5 concentrations occur with strong winds. Overall no apparent correlation between PM2.5 and temperature was observed, even though temperature was found to be positively correlated with PM2.5 in certain short periods, for example July 11the16th in Beijing and June 24theJuly 1st in Shanghai, but to be negatively correlated with PM2.5 in other certain episodes, for example July 25the30th in Beijing and June 4the9th in Shanghai. The associations between PM2.5 concentrations and wind speed, relative humidity and temperature were further investigated using multiple linear regression analysis for the entire summer days. In Beijing, the multiple regression coefficient was 0.51. PM2.5 was negatively associated to wind speed with a coefficient of 0.53, but
Fig. 4. Pearson Correlation Coefficients of PM2.5 and PM10 between cities in the NCP and YRD regions.
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
Shanghai Suzhou Jiaxing Nantong Wuxi Huzhou Shaoxing Hangzhou Taizhoushi Yangzhou Nanjing Taizhou Jinhua Lishui Huaian Quzhou Wenzhou Suqian Lianyungang Xuzhou
Shanghai Suzhou Jiaxing Nantong Wuxi
604
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
The PM2.5 concentrations on the episode days in the NCP region showed no substantial difference compared to the concentrations in the YRD region, but the number of episode days in the NCP was about three times greater (Fig. 3). The average PM2.5/PM10 ratios increased from 0.50 on non-episode days to 0.64 on episode days in the NCP, and increased from 0.57 on non-episode days to 0.64 on episode days in the YRD. This increase in the PM2.5/PM10 values suggests more secondary PM2.5 formation during high PM2.5 pollution events (Song et al., 2002; Yao et al., 2003). However, the difference in the PM2.5/PM10 ratios on episode and non-episode days was not statistically significant (as indicated by the overlapped standard deviations in Fig. 6). On episode days, the PM2.5/ PM10 ratios in the NCP region showed large variations (0.52e0.78) while in the YRD region the PM2.5/PM10 ratios showed small variations among cities (0.62e0.66). The larger variation in the PM2.5/ PM10 ratios in the NCP indicates more heterogeneity in the PM2.5 and PM10 pollution in this region. The cities with relatively lower PM2.5/PM10 ratios, i.e. Shijiazhuang, Zhengzhou, and Qingdao, were likely having more primary PM sources. Comprehensive control strategies that include both primary and secondary PM2.5 will be more effective to reduce the high PM2.5 concentrations. The PM2.5/PM10 ratios in this study were compared to ratios measured in other areas/countries (shown in Fig. 7 and the data and references are listed in Table 3) (Brook et al., 1997; Chen et al., 1999; Chow et al., 1994; Das et al., 2006; Gomiscek et al., 2004; Ho et al., 2003; Zakey et al., 2008). The PM2.5/PM10 ratios in this study on non-episode days were generally lower than ratios found in other areas/countries with similar average PM2.5 concentrations, indicating high fraction of coarse particles (PM2.5e10) in the NCP and YRD regions. Average PM2.5 concentrations over 60 mg/m3 were rarely reported in other areas/countries, and the PM2.5/PM10 ratios on episode days in this study were generally higher than ratios found in literature, suggesting significant production of secondary PM on episode days. The substantial fraction of PM10 mass was in the PM2.5 size range suggests that PM2.5 control strategies will also effectively reduce the PM10 pollution.
positively associated to temperature and relative humidity with a coefficient of 0.71 and 1.23, respectively. In Shanghai, the association between PM2.5 and the three meteorological parameters was insignificant, with a multiple regression coefficient of 0.18. Both NCP and YRD region were significantly influenced by the subtropical high, which usually lead to low wind speeds that are not preferable for pollutant dispersion. However, Shanghai was also influence by sea breeze which helps transport pollutant away. Generally, stagnant conditions with wind speed less than 2 m/s, which trap PM and other gaseous pollutants at the ground level, were frequently observed in Beijing but were rarely observed in Shanghai and thus led to higher PM pollution.
3.3.3. Weekday-weekend difference Weekday-weekend differences in ambient air pollutant concentrations have been observed in many locations for many years, due to the weekly circle of human activities (Altshuler et al., 1995; Blanchard and Tanenbaum, 2006, 2003; Clevelan.Ws et al., 1974; Graedel et al., 1977; Khoder and Hassan, 2008; Motallebi et al., 2003; Qin et al., 2004). Fig. 8 shows the weekday/weekend ratios of PM2.5 and PM10 at all cities in this study. The ratios of CO, SO2, NO2, 1 h peak O3 and 8 h peak O3 are shown in Fig. S1. In the NCP cities (except Qingdao), PM2.5 and PM10 concentrations on weekdays were slightly lower than concentrations on weekends, with the weekday/weekend ratios of PM2.5 and PM10 in the range of 0.8e1.0. The PM2.5 and PM10 concentrations in the YRD cities showed small variations between weekdays and weekends, with the weekday/weekend ratios varying mostly ±0.1 around 1.0. Small weekday-weekend variations were also found for the gas pollutants, with ratios mostly within the range of 0.9e1.1, and only a few cases exceeded 1.2. The ratios were smaller than what were observed in the literature listed above, indicating that the weekday-weekend differences were not typical in the summer of 2013.
3.3.2. PM2.5/PM10 ratio on episode and non-episode days Fig. 7 shows the PM2.5 to PM10 ratios (PM2.5/PM10) during the episode (daily average PM2.5 > 75 mg/m3) and non-episodes days in all cities. Average PM2.5 concentrations during the episode days were between 100 and 120 mg/m3, much higher than PM2.5 concentrations during the non-episode days which were 30e50 mg/m3.
3.3.4. Diurnal variations Diurnal variations of PM2.5, PM10, O3, NO2, SO2, and CO during the episode and non-episode days were investigated in Beijing and Shanghai, and shown in Figs. 9 and 10, respectively. In Beijing, PM2.5 and PM10 showed dramatically higher concentrations and stronger diurnal variations in the episode days than in the non-episode days.
Fig. 5. Back-trajectories in (a) 24 h and (b) 72 h for all cities in the NCP (in red) at noon time of a PM2.5 episode day (June 15, 2013) and for all cities in the YRD (in blue) at noon time of a PM2.5 episode day (June 12, 2013). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
605
Fig. 6. Daily PM2.5 concentrations, wind speed and temperature in summer 2013 in Beijing and Shanghai.
Fig. 7. PM2.5/PM10 ratios in the PM2.5 episode days and non-episode days at all cities in the NCP and YRD regions.
PM2.5 and PM10 concentrations on episode days were very similar, suggesting low contribution of coarse particles on high PM episodes, which can be explained by slow wind speed in the summer and more stringent controls on construction emissions in Beijing (BEIJING, Accessed May, 2014). On episode days, both PM2.5 and PM10 started to increase rapidly around 6e7 am local time with an hourly rate of 6 mg/m3 for 5e6 h and reached highest concentration of ~140 mg/m3 at noon. Concentrations remained high in the afternoon and late evening. PM2.5 concentration started to decrease after 8 pm while PM10 remained high until midnight. Dust and primary PM emissions from increased cross-city truck traffic from
other provinces, which were regulated during the day, may explain this persistent high PM10. Interestingly, O3 generally showed only slight difference in terms of both concentrations and diurnal variability in the episode and non-episode days, and the difference in NO2 was not significant at night time either. However, SO2 and CO showed similar diurnal variations with much higher concentrations in the episode days. In Shanghai, PM2.5 and PM10 in episode days also showed higher concentrations and stronger diurnal variations with multiple peaks throughout the day. However, the rate of increase was slower than that in Beijing. PM10 concentrations were similar to PM2.5 only in the afternoon hours from noon to 8 pm,
Table 3 PM2.5 concentrations and PM2.5/PM10 ratios in other cities. The sampling frequency is 24 h, except 30 min for Gomiscek et al. (2004) and 4e7 h for Chow et al. (1994). City/Area/Country
PM2.5
PM2.5/PM10
References
Episodes
Hongkong, China S. Taiwan, China Kolkata, India Austria Hamilton, Canada Los Angeles, USA Rubidox, USA Cairo, Egypt
32.50 48.50 178.60 18.50 19.00 41.10 63.90 85.00
0.64 0.63 0.59 0.67 0.61 0.61 0.53 0.50
Ho et al., 2003 Chen et al., 1999 Das et al., 2006 Gomiscek et al., 2004 Brook et al., 1997 Chow et al., 1994 Chow et al., 1994 Zakey et al., 2008
Nov. 2000eFeb. 2001 Oct. 1996eJun. 1997 Nov. 2002eDec. 2002 Jun. 1999eMay 2000 Sep. 1992eDec. 1994 Jun.eSep., Nov.eDec., 1987 Jun.eSep., Nov.eDec., 1988 Jan. 2001eDec. 2002
606
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
Fig. 8. Weekday/weekend ratios of PM2.5 and PM10, at each city in the NCP and YRD regions.
with a similar increase at night after that. NO2, SO2, and CO also showed higher concentrations but similar diurnal variations in episode days. NO2 showed clearly double-peak distribution during the early morning and late afternoon rush hours. O3 in Shanghai showed significantly higher concentrations of 80 ppb than the nonepisode days of 60 ppb peak concentration. Photochemical processes in the YRD were likely stronger during the episode days in summer due to higher temperature and relative humidity in this region (Chan and Yao, 2008) From the above analysis, it appears that although both megacities experienced high PM2.5 concentrations and showed many similar features in concentration and diurnal variations, and both cities showed significant contributions
of secondary contributions to PM2.5, the difference in ozone and NO2 and relative importance of coarse particles need to be further studied. 4. Conclusion PM concentration data in the NCP and the YRD regions in China between June 1st, 2013 and August 31st, 2013 were recorded at the air quality monitoring network of the Chinese Ministry of Environmental Protection. Our analysis shows that averaged PM2.5 concentrations at both regions exceeded the World Health Organization guideline values, indicating severe human exposure to
Fig. 9. Diurnal variations of PM2.5, PM10, O3, NO2, SO2 and CO in the PM2.5 episode and non-episode days in Beijing.
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
607
Fig. 10. Diurnal variations of PM2.5, PM10, O3, NO2, SO2 and CO in the PM2.5 episode and non-episode days in Shanghai.
ambient PM in the most developed and populated areas in China. The frequencies of occurrence of PM2.5 and PM10 concentrations exceeding the AAQS Grade I and II standards were 81% and 82%, respectively, in the NCP, and 70% and 54%, respectively, in the YRD. In both regions, PM2.5 concentrations were similar for all studied cities, indicating that PM pollution is a regional problem. Higher fraction of coarse particles (PM2.5-10) was found in the NCP and YRD regions compared to other areas and countries. No distinct difference between weekday and weekend concentrations for all seven monitored pollutants was observed, suggesting weak impact of the weekly circle of human activities. Ambient PM concentrations in other seasons and regions in China should be further investigated in order to obtain a deep and comprehensive understanding of the entire nationwide air quality problem. This study provides an important scientific basis for the design of nationwide PM pollution control strategies in China. Joint actions among provincial administrations need to be considered.
Acknowledgement The authors gratefully acknowledge Dr. Gong Zhang of National Aeronautics and Space Administration (NASA) Ames Research Center (ARC) for his support to this project. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or READY website (http://www.ready.noaa.gov) used in this publication.
Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2014.07.019. References Altshuler, S.L., Arcado, T.D., Lawson, D.R., 1995. Weekday vs weekend ambient ozone concentrations - discussion and hypotheses with focus on northern California. J. Air Waste Manag. Assoc. 45, 967e972. BEIJING, Accessed May, 2014. Emergency Plan for Beijing heavy air pollution (Trial). http://govfile.beijing.gov.cn/Govfile/ShowNewPageServlet?id¼6161. Blanchard, C.L., Tanenbaum, S., 2006. Weekday/weekend differences in ambient air pollutant concentrations in Atlanta and the Southeastern United States. J. Air Waste Manag. Assoc. 56, 271e284. Blanchard, C.L., Tanenbaum, S.J., 2003. Differences between weekday and weekend air pollutant levels in southern California. J. Air Waste Manag. Assoc. 53, 816e828. Brook, J.R., Wiebe, A.H., Woodhouse, S.A., Audette, C.V., Dann, T.F., Callaghan, S., Piechowski, M., Dabek-Zlotorzynska, E., Dloughy, J.F., 1997. Temporal and spatial relationships in fine particle strong acidity, sulphate, PM10, and PM2.5 across multiple Canadian locations. Atmos. Environ. 31, 4223e4236. Chan, C.K., Yao, X., 2008. Air pollution in mega cities in China. Atmos. Environ. 42,1e42. Chen, C.H., Wang, B.Y., Fu, Q.Y., Green, C., Streets, D.G., 2006. Reductions in emissions of local air pollutants and co-benefits of Chinese energy policy: a Shanghai case study. Energ. Policy 34, 754e762. Chen, M.L., Mao, I.F., Lin, I.K., 1999. The PM2.5 and PM10 particles in urban areas of Taiwan. Sci. Total Environ. 226, 227e235. Chen, Y., Ebenstein, A., Greenstone, M., Li, H., 2013. Evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River policy. Proc. Natl. Acad. Sci. 110 (32), 12936e12941. http://dx.doi.org/10.1073/ pnas.1300018110. Chen, Y., Xie, S.D., 2014. Characteristics and formation mechanism of a heavy air pollution episode caused by biomass burning in Chengdu, Southwest China. Sci. Total Environ. 473, 507e517. China, N.B.o.S.o., 2012. China Statistical Yearbook. China Statistics Press, Beijing.
608
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
Chow, J.C., Watson, J.G., Fujita, E.M., Lu, Z., Lawson, D.R., Ashbaugh, L.L., 1994. Temporal and spatial variations of PM2.5 and PM10 aerosol in the Southern California air quality study. Atmos. Environ. 28, 2061e2080. Clevelan, Ws, Graedel, T.E., Kleiner, B., Warner, J.L., 1974. Sunday and workday variations in photochemical air-pollutants in New-Jersey and New-York. Science 186, 1037e1038. Correia, A.W., Pope, C.A., Dockery, D.W., Wang, Y., Ezzati, M., Dominici, F., 2013. Effect of air pollution control on life expectancy in the United States an analysis of 545 US counties for the period from 2000 to 2007. Epidemiology 24, 23e31. Das, M., Maiti, S.K., Mukhopadhyay, U., 2006. Distribution of PM2.5 and PM10e2.5 in PM10 fraction in ambient air due to vehicular pollution in Kolkata megacity. Environ. Monit. Assess. 122, 111e123. Deng, X.J., Tie, X.X., Wu, D., Zhou, X.J., Bi, X.Y., Tan, H.B., Li, F., Hang, C.L., 2008. Longterm trend of visibility and its characterizations in the Pearl River Delta (PRD) region, China. Atmos. Environ. 42, 1424e1435. Ding, A.J., Fu, C.B., Yang, X.Q., Sun, J.N., Pet€ aj€ a, T., Kerminen, V.M., Wang, T., Xie, Y., Herrmann, E., Zheng, L.F., Nie, W., Liu, Q., Wei, X.L., Kulmala, M., 2013. Intense atmospheric pollution modifies weather: a case of mixed biomass burning with fossil fuel combustion pollution in eastern China. Atmos. Chem. Phys. 13, 10545e10554. Dockery, D.W., 2001. Epidemiologic evidence of cardiovascular effects of particulate air pollution. Environ. Health Perspect. 109, 483e486. Dockery, D.W., Pope, C.A., Xu, X.P., Spengler, J.D., Ware, J.H., Fay, M.E., Ferris, B.G., Speizer, F.E., 1993. An association between air-pollution and mortality in 6 United-States cities. N. Engl. J. Med. 329, 1753e1759. Draxler, R.R., 2013. HYSPLIT (HYbrid Single-particle Lagrangian Integrated Trajectory) Model Access via NOAA ARL READY. NOAA Air Resources Laboratory, College Park, MD. http://www.arl.noaa.gov/HYSPLIT.php. Fang, C., Yao, S., Liu, S., et al., 2011. Report on Development of City Clusters in China. Science Press, ISBN 978-7-03-030643-2, p. 2010. Fann, N., Lamson, A.D., Anenberg, S.C., Wesson, K., Risley, D., Hubbell, B.J., 2012. Estimating the national public health Burden associated with exposure to ambient PM2.5 and ozone. Risk Anal. 32, 81e95. Franklin, M., Zeka, A., Schwartz, J., 2007. Association between PM2.5 and all-cause and specific-cause mortality in 27 US communities. J. Expo. Sci. Environ. Epidemiol. 17, 279e287. Gao, J., Wang, T., Zhou, X., Wu, W., Wang, W., 2009. Measurement of aerosol number size distributions in the Yangtze River delta in China: formation and growth of particles under polluted conditions. Atmos. Environ. 43, 829e836. Gao, Y., Liu, X., Zhao, C., Zhang, M., 2011. Emission controls versus meteorological conditions in determining aerosol concentrations in Beijing during the 2008 Olympic Games. Atmos. Chem. Phys. 11, 12437e12451. Gomiscek, B., Hauck, H., Stopper, S., Preining, O., 2004. Spatial and temporal variations of PM1, PM2.5, PM10 and particle number concentration during the AUPHEP-project. Atmos. Environ. 38, 3917e3934. Graedel, T.E., Farrow, L.A., Weber, T.A., 1977. Photochemistry of Sunday effect. Environ. Sci. Technol. 11, 690e694. Guo, S., Hu, M., Guo, Q., Zhang, X., Zheng, M., Zheng, J., Chang, C.-C., Schauer, J.J., Zhang, R., 2012. Primary sources and secondary formation of organic aerosols in Beijing, China. Environ. Sci. Technol. 46 (18), 9846e9853. http://dx.doi.org/ 10.1021/es2042564. Guo, Y.M., Li, S.S., Tian, Z.X., Pan, X.C., Zhang, J.L., Williams, G., 2013. The burden of air pollution on years of life lost in Beijing, China, 2004e08: retrospective regression analysis of daily deaths. BMJ Brit Med. J., 347. Hao, J.M., Wang, L.T., Li, L., Hu, J.N., Yu, X.C., 2005. Air pollutants contribution and control strategies of energy-use related sources in Beijing. Sci. China Ser. D. 48, 138e146. He, K.B., Yang, F.M., Ma, Y.L., Zhang, Q., Yao, X.H., Chan, C.K., Cadle, S., Chan, T., Mulawa, P., 2001. The characteristics of PM2.5 in Beijing, China. Atmos. Environ. 35, 4959e4970. Ho, K.F., Lee, S.C., Chan, C.K., Yu, J.C., Chow, J.C., Yao, X.H., 2003. Characterization of chemical species in PM2.5 and PM10 aerosols in Hong kong. Atmos. Environ. 37, 31e39. Huang, C., Chen, C.H., Li, L., Cheng, Z., Wang, H.L., Huang, H.Y., Streets, D.G., Wang, Y.J., Zhang, G.F., Chen, Y.R., 2011. Emission inventory of anthropogenic air pollutants and VOC species in the Yangtze River Delta region, China. Atmos. Chem. Phys. 11, 4105e4120. Ianniello, A., Spataro, F., Esposito, G., Allegrini, I., Hu, M., Zhu, T., 2011. Chemical characteristics of inorganic ammonium salts in PM2.5 in the atmosphere of Beijing (China). Atmos. Chem. Phys. 11, 10803e10822. Ito, K., Mathes, R., Ross, Z., Nadas, A., Thurston, G., Matte, T., 2011. Fine particulate matter constituents associated with cardiovascular hospitalizations and mortality in New York city. Environ. Health Perspect. 119, 467e473. Khoder, M.I., Hassan, S.K., 2008. Weekday/weekend differences in ambient aerosol level and chemical characteristics of water-soluble components in the city centre. Atmos. Environ. 42, 7483e7493. Li, L., Chen, C.C., Cheng, H., et al., 2008. Regional air pollution characteristics simulation of O3 and PM10 over Yangtze River Delta Region. Environ. Sci. 29, 237e245. Li, L., Chen, C.H., Fu, J.S., Huang, C., Streets, D.G., Huang, H.Y., Zhang, G.F., Wang, Y.J., Jang, C.J., Wang, H.L., Chen, Y.R., Fu, J.M., 2011. Air quality and emissions in the Yangtze River Delta, China. Atmos. Chem. Phys. 11, 1621e1639. Li, Z.Q., Gu, X., Wang, L., Li, D., Li, K., Dubovik, O., Schuster, G., Goloub, P., Zhang, Y., Li, L., Xie, Y., Ma, Y., Xu, H., 2013. Aerosol physical and chemical properties retrieved from ground-based remote sensing measurements during heavy haze days in Beijing winter. Atmos. Chem. Phys. Discuss. 13, 5091e5122.
Lim, S.S., Vos, T., Flaxman, A.D., Danaei, G., Shibuya, K., Adair-Rohani, H., et al., 2012. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990e2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 2224e2260. Liu, S., Hu, M., Slanina, S., He, L.-Y., Niu, Y.-W., Bruegemann, E., Gnauk, T., Herrmann, H., 2008a. Size distribution and source analysis of ionic compositions of aerosols in polluted periods at Xinken in Pearl River Delta (PRD) of China. Atmos. Environ. 42, 6284e6295. Liu, S., Hu, M., Wu, Z., Wehner, B., Wiedensohler, A., Cheng, Y., 2008b. Aerosol number size distribution and new particle formation at a rural/coastal site in Pearl River Delta (PRD) of China. Atmos. Environ. 42, 6275e6283. Liu, X.G., Li, J., Qu, Y., Han, T., Hou, L., Gu, J., Chen, C., Yang, Y., Liu, X., Yang, T., Zhang, Y., Tian, H., Hu, M., 2013. Formation and evolution mechanism of regional haze: a case study in the megacity Beijing, China. Atmos. Chem. Phys. 13, 4501e4514. MEP, 2012a. China National Ambient Air Quality Standards. MEP, Beijing, China. MEP, 2012b. In: Protection, M.o.E. (Ed.), Technical Regulation on Ambient Air Quality Index (On Trial). MEP, Beijing, China. Motallebi, N., Tran, H., Croes, B.E., Larsen, L.C., 2003. Day-of-week patterns of particulate matter and its chemical components at selected sites in California. J. Air Waste Manag. Assoc. 53, 876e888. Ostro, B., Broadwin, R., Green, S., Feng, W.Y., Lipsett, M., 2006. Fine particulate air pollution and mortality in nine California counties: results from CALFINE. Environ. Health Persp. 114, 29e33. Peng, G.L., Wang, X.M., Wu, Z.Y., Wang, Z.M., Yang, L.L., Zhong, L.J., Chen, D.O.H., 2011. Characteristics of particulate matter pollution in the Pearl River Delta region, China: an observational-based analysis of two monitoring sites. J. Environ. Monit. 13, 1927e1934. Qin, Y., Tonnesen, G.S., Wang, Z., 2004. Weekend/weekday differences of ozone, NOx, Co, VOCs, PM10 and the light scatter during ozone season in southern California. Atmos. Environ. 38, 3069e3087. Rolph, G.D., 2013. Real-time Environmental Applications and Display SYstem (READY). NOAA Air Resources Laboratory, College Park, MD. Website. http:// www.ready.noaa.gov. Shen, J., Tang, A., Liu, X., Kopsch, J., Fangmeier, A., Goulding, K., Zhang, F., 2011. Impacts of pollution controls on air quality in Beijing during the 2008 Olympic Games. J. Environ. Qual. 40, 37e45. Song, Y., Dai, W., Shao, M., Liu, Y., Lu, S.H., Kuster, W., Goldan, P., 2008. Comparison of receptor models for source apportionment of volatile organic compounds in Beijing, China. Environ. Pollut. 156, 174e183. Song, Y., Tang, X., Zhang, Y., Hu, M., Fang, C., Zen, L., Wang, W., 2002. Effects on fine particles by the continued high temperature weather in Beijing. Environ. Sci. 23, 33e36 (in Chinese with abstract in English). Sun, Y., Jiang, Q., Wang, Z., Fu, P., Li, J., Yang, T., Yin, Y., 2014. Investigation of the sources and evolution processes of severe haze pollution in Beijing in January 2013. J. Geophys. Res. Atmos. 119, 2014JD021641. Sun, Y., Zhuang, G.S., Yuan, H., Zhang, X.Y., Guo, J.H., 2004a. Characteristics and sources of 2002 super dust storm in Beijing. Chin. Sci. Bull. 49, 698e705. Sun, Y.L., Zhuang, G.S., Ying, W., Han, L.H., Guo, J.H., Mo, D., Zhang, W.J., Wang, Z.F., Hao, Z.P., 2004b. The air-borne particulate pollution in Beijing e concentration, composition, distribution and sources. Atmos. Environ. 38, 5991e6004. Tie, X., Cao, J., 2009. Aerosol pollution in China: present and future impact on environment. Particuology 7, 426e431. Tie, X., Geng, F., Peng, L., Gao, W., Zhao, C., 2009. Measurement and modeling of O3 variability in Shanghai, China: application of the WRF-Chem model. Atmos. Environ. 43, 4289e4302. van Donkelaar, A., Martin, R.V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., Villeneuve, P.J., 2010. Global estimates of ambient Fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ. Health Perspect. 118, 847e855. Wang, D., Hu, J., Xu, Y., Lv, D., Xie, X., Kleeman, M.J., Xing, J., Zhang, H., Ying, Q., 2014a. Source contributions to primary and secondary inorganic particulate matter during a severe wintertime PM2.5 pollution episode in Xi’an, China. Atmos. Environ. (still under review). Wang, T., Jiang, F., Deng, J., Shen, Y., Fu, Q., Wang, Q., Fu, Y., Xu, J., Zhang, D., October 2012. Urban air quality and regional haze weather forecast for Yangtze River Delta region. Atmos. Environ. 58, 70e83. http://dx.doi.org/10.1016/ j.atmosenv.2012.01.014. Wang, X., Zhang, Y., Hu, Y., Zhou, W., Lu, K., Zhong, L., Zeng, L., Shao, M., Hu, M., Russell, A.G., 2010. Process analysis and sensitivity study of regional ozone formation over the Pearl River Delta, China, during the PRIDE-PRD2004 campaign using the Community Multiscale Air Quality modeling system. Atmos. Chem. Phys. 10, 4423e4437. Wang, Y., Li, L., Chen, C., Huang, C., Huang, H., Feng, J., Wang, S., Wang, H., Zhang, G., Zhou, M., Cheng, P., Wu, M., Sheng, G., Fu, J., Hu, Y., Russell, A.G., Wumaer, A., 2014b. Source apportionment of fine particulate matter during autumn haze episodes in Shanghai, China. J. Geophys. Res. Atmos. 119, 2013JD019630. Wang, Y., Zhuang, G.S., Sun, Y., An, Z.S., 2005a. Water-soluble part of the aerosol in the dust storm season e evidence of the mixing between mineral and pollution aerosols. Atmos. Environ. 39, 7020e7029. Wang, Y., Zhuang, G.S., Tang, A.H., Yuan, H., Sun, Y.L., Chen, S.A., Zheng, A.H., 2005b. The ion chemistry and the source of PM2.5 aerosol in Beijing. Atmos. Environ. 39, 3771e3784. WHO, 2005. World Health Organization Air Quality Guidelines Global Update. E87950.
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609 Xing, J., Zhang, Y., Wang, S.X., Liu, X.H., Cheng, S.H., Zhang, Q., Chen, Y.S., Streets, D.G., Jang, C., Hao, J.M., Wang, W.X., 2011. Modeling study on the air quality impacts from emission reductions and atypical meteorological conditions during the 2008 Beijing Olympics. Atmos. Environ. 45, 1786e1798. Yao, X.H., Lau, A.P.S., Fang, M., Chan, C.K., Hu, M., 2003. Size distributions and formation of ionic species in atmospheric particulate pollutants in Beijing, China: 1 e inorganic ions. Atmos. Environ. 37, 2991e3000. Ye, B.M., Ji, X.L., Yang, H.Z., Yao, X.H., Chan, C.K., Cadle, S.H., Chan, T., Mulawa, P.A., 2003. Concentration and chemical composition of PM2.5 in Shanghai for a 1year period. Atmos. Environ. 37, 499e510. Ying, Q., Wu, L., Zhang, H., September 2014. Local and inter-regional contributions to PM2.5 nitrate and sulfate in China. Atmos. Environ. 94, 582e592. http:// dx.doi.org/10.1016/j.atmosenv.2014.05.078. Zakey, A.S., Abdel-Wahab, M.M., Pettersson, J.B.C., Gatari, M.J., Hallquist, M., 2008. Seasonal and spatial variation of atmospheric particulate matter in a developing megacity, the Greater Cairo, Egypt. Atmosfera 21, 171e189.
609
Zhang, H., Li, J., Ying, Q., Yu, J.Z., Wu, D., Cheng, Y., He, K., Jiang, J., 2012. Source apportionment of PM2.5 nitrate and sulfate in China using a source-oriented chemical transport model. Atmos. Environ. 62, 228e242. Zhang, Y., Zhu, X., Zeng, L., Wang, W., 2004. Source apportionment of fine-particle pollution in Beijing. In: Urbanization, Energy, and Air Pollution in China: the Challenges Ahead e Proceedings of a Symposium. Zhang, Y.H., Hu, M., Zhong, L.J., Wiedensohler, A., Liu, S.C., Andreae, M.O., Wang, W., Fan, S.J., 2008. Regional integrated Experiments on air quality over Pearl River Delta 2004 (PRIDE-PRD2004): Overview. Atmos. Environ. 42, 6157e6173. Zhao, X.J., Zhao, P.S., Xu, J., Meng, W., Pu, W.W., Dong, F., He, D., Shi, Q.F., 2013. Analysis of a winter regional haze event and its formation mechanism in the North China Plain. Atmos. Chem. Phys. 13, 5685e5696. Zheng, J., Shao, M., Che, W., Zhang, L., Zhong, L., Zhang, Y., Streets, D., 2009. Speciated VOC emission inventory and spatial patterns of ozone formation potential in the Pearl River Delta, China. Environ. Sci. Technol. 43, 8580e8586.
Contents lists available at ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Spatial and temporal variability of PM2.5 and PM10 over the North China Plain and the Yangtze River Delta, China Jianlin Hu a, Yungang Wang b, Qi Ying c, Hongliang Zhang a, * a
Department of Civil and Environmental Engineering, University of California, Davis, CA 95616, USA Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA c Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA b
h i g h l i g h t s Summertime PM2.5 and PM10 in the NCP and YRD regions of China were analyzed. Average PM2.5 and PM10 concentrations are 77.0 and 136.2 mg/m3 in the NCP region. Average PM2.5 and PM10 concentrations are 42.8 and 74.9 mg/m3 in the YRD region. Strong temporal correlation between cities within 250 km is found. PM2.5 concentrations on episode days are 2e4 times greater than non-episode days.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 27 January 2014 Received in revised form 4 June 2014 Accepted 7 July 2014 Available online 8 July 2014
The North China Plain (NCP) and the Yangtze River Delta (YRD) in China have been experiencing severe particulate matter (PM) pollution problems associated with the rapid economic growth and the accelerated urbanization. In this study, hourly mass concentrations of PM2.5 and PM10 during June 1steAugust 31st, 2013 were collected in 13 cities located in or adjacent to the NCP region and 20 cities located in the YRD region. The overall average PM2.5 and PM10 concentrations were 77.0 mg/m3 and 136.2 mg/m3 in the NCP region, respectively, and 42.8 mg/m3 and 74.9 mg/m3 in the YRD region, respectively. The frequencies of occurrence of concentrations exceeding the China's Ambient Air Quality Standard (AAQS) (BG3095-12) Grade I standards were 83% for PM2.5 and 93% for PM10 in the NCP region, and 51% for PM2.5 and 66% for PM10 in the YRD region. Strong temporal correlation for both PM2.5 and PM10 between cities within 250 km was frequently observed. PM2.5 was found to be negatively associated with wind speed. On the PM2.5 episode days (when the 24 h PM2.5 concentration is greater than 75 mg/m3), average PM2.5 concentrations were 2e4 times greater compared to the non-episode days. The PM2.5 to PM10 ratio increased from 0.50 (0.57) on the non-episode days to 0.64 (0.64) on the episode days in the NCP (YRD) region. No distinct weekday/weekend difference was observed for PM2.5, PM10, and other gaseous pollutants (CO, SO2, NO2, and O3) in all cities. The results presented in this paper will serve as an important basis for future regional air quality modeling and source apportionment studies. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Particulate matter PM2.5 Spatial variation Temporal variation China
1. Introduction Along with the rapid economic growth and urbanization, China has been experiencing severe particulate matter (PM) pollution problem, especially in the most developed regions, such as the North China Plain (NCP), the Yangtze River Delta region (YRD), and
* Corresponding author. E-mail addresses: [email protected], [email protected], hlzhang@lsu. edu (H. Zhang). http://dx.doi.org/10.1016/j.atmosenv.2014.07.019 1352-2310/© 2014 Elsevier Ltd. All rights reserved.
the Pearl River Delta region (PRD) (van Donkelaar et al., 2010). Annual average concentrations of particles with aerodynamic diameter equal to or less than 2.5 mm (PM2.5) were observed over 100 mg/m3 in Beijing (He et al., 2001) and 60 mg/m3 in Shanghai (Ye et al., 2003), greatly exceeding the World Health Organization (WHO) guideline value of 10 mg/m3 (WHO, 2005). An increasing trend in the severity and frequency of PM pollution in more recent years has been observed (Deng et al., 2008; Li et al., 2013; Zhang et al., 2008; Zhao et al., 2013). Sustained exposure to high PM air pollution level has significant health effects (Correia et al., 2013; Dockery, 2001; Dockery et al.,
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
599
Fig. 1. Locations of the 33 cities analyzed in this study. The red triangles represent the cities in the NCP region. The green squares represent the two cities not in the administrative NCP region, but are included in the analysis. The blue dots represent the cities in the YRD region. This satellite image taken on June 15, 2013 illustrates the large smog layer cover the entire NCP region and partial YRD region.
1993; Fann et al., 2012; Franklin et al., 2007; Ito et al., 2011; Ostro et al., 2006). According to the Global Burden of Disease Study, 3.2 million people died from air pollution in 2010 among which 1.2 million were from China (Lim et al., 2012). Half-a-billion people alive in northern China in the 1990s will live an average of 5.5 years less than their southern counterparts because of inhaling more polluted air generated from coal burning activities (Chen et al., 2013). Retrospective regression analysis of 80,515 deaths recorded at eight urban districts in Beijing between 2004 and 2008 showed an association between increased air pollution and increased years of life lost (Guo et al., 2013). In the past decade, a remarkable effort has been made to investigate the characteristics, sources, mechanism and adverse health effects of PM pollution in China, mostly focusing on a few mega-cities, such as Beijing, Shanghai, and Guangzhou (Li et al., 2008, 2011; Liu et al., 2008a; Liu et al., 2008b; Wang et al., 2014a, 2012, 2010; Zheng et al., 2009). Recent studies indicate that the spatial extent of PM pollution in these areas has been expanding to broader regional scales (Liu et al., 2013; Zhang et al., 2008). This trend urges needs for large-scale ambient PM monitoring for the purpose of designing effective control strategies.
Starting from June 2000, the government of China started publishing daily air pollution index (API) for a number of key cities (increased from 47 cities in 2000 to 120 cities in 2011), which was calculated based on ground monitoring of 24 h average concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter with aerodynamic diameter equal to or less than 10 mm (PM10) (http://datacenter.mep.gov.cn/). PM2.5 was not included in this routine measurement and no PM2.5 standards were available. On February 29th, 2012, the third revision of the “Ambient Air Quality Standard” (AAQS) (BG3095-12) was released, and PM2.5 was adopted into the AAQS for the first time. The AAQS Grade I and Grade II standards are 35 and 75 mg/m3, respectively, for 24 h average PM2.5, and 50 and 150 mg/m3 for 24 h average PM10 (MEP, 2012a). In March 2012, the Chinese Ministry of Environmental Protection (MEP) released the official revisions of the ambient air quality index (AQI), which was calculated based on seven pollutants including SO2, NO2, PM2.5 PM10, carbon monoxide (CO), 1 h peak ozone (O3), and 8 h peak O3 (MEP, 2012b). In January 2013, more than 100 of Chinese major cities started releasing concentrations of these seven pollutants and calculated AQI values to the public based on all monitoring stations throughout each city (MEP, 2012a). These
600
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
Fig. 2. Summertime average concentrations of (a) PM2.5, (b) PM10, (c) CO, (d) SO2, (e) NO2, and (f) 8 h peak O3. The unit is mg/m3 for PM2.5 and PM10, and ppb for the gaseous pollutants.
extensive air quality observation data are publicly accessible. By studying this rich dataset, we could improve our understanding of PM emission sources, its formation mechanisms, transport pathways, and potential causes of air quality degradation. In this study, we analyzed the hourly and daily concentrations of six air pollutants (SO2, NO2, PM2.5, PM10, CO, 1 h O3 and 8 h O3) measured at 13 cities in the NCP region and 20 cities in the YRD region in summer 2013. The spatial and temporal variations of these pollutants were investigated. This study was motivated by a few reasons. First, high PM pollution have been reported in summer in the two regions. Second, many studies have been conducted in the literature to investigate the high PM pollution in winter and biomass burning seasons (for examples, Chen and Xie, 2014; Ding et al., 2013; Sun et al., 2014), but only limited studies which examined the annual variability of PM included some summer studies. Third, the previous studies on summer episodes are mostly limited to a small urban/regional scale (for examples, He et al., 2001; Peng et al., 2011; Sun et al., 2004b; Ye et al., 2003). The
information of spatial and temporal variations of summer PM pollution in a broader regional scale is sparse in literature. This is the first attempt of charactering regional PM2.5 pollution using ground-measurement data with hourly time resolution across 33 cities in China. 2. Methods 2.1. Study areas The NCP represents a geographically flat region in the northern part of Eastern China, including the municipalities of Beijing (the national capital) and Tianjin (an important industrial city and commercial port), most part of Hebei, Heinan, Shangdong provinces, and the northern part of Anhui and Jiangsu provinces, with an approximately 1/5 of the China total population (China, 2012). PM2.5 has been studied in Beijing and Tianjin for more than 10 years (Gao et al., 2011; Guo et al., 2012; He et al., 2001; Ianniello et al.,
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
601
Fig. 3. Frequency distribution of observed PM2.5 and PM10 in the NCP and YRD regions. Red and blue lines represent the Grade II standard of PM2.5 and PM10, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
2011; Shen et al., 2011; Song et al., 2008; Xing et al., 2011; Zhang et al., 2012, 2004). Ammonium sulfate, ammonium nitrate, and organic material are found to be the main PM2.5 components, total accounting for over 60% of the PM2.5 mass concentrations in Beijing (He et al., 2001). Coal combustion for domestic heating is identified as the major source of PM2.5 in the winter (Hao et al., 2005; Wang et al., 2005b). Strong northwestern winds in the spring transport dust from the Gobi desert leading to high PM10 concentrations (Sun et al., 2004a; Wang et al., 2005a). In the summer, stabilized weather conditions along with high temperature and humidity result in high PM concentrations characterized by the large formation of secondary pollutants (Gao et al., 2011; Song et al., 2002; Yao et al., 2003). The YRD region represents the eastern part of the Yangtze Plain adjacent to the NCP. The traditional definition of the YRD region includes the mega-city Shanghai and the well-industrialized and urbanized areas of the south part of Jiangsu province and the north part of Zhejiang province. An expanded definition (Fang et al., 2011) includes Shanghai, Jiangsu, Zhejiang, and Anhui provinces, with a total population of 160 million (China, 2012). High ozone and fine PM concentrations monitored in this region demonstrated air quality deterioration resulted from the rapid growth of transportation, industries, and urbanization (Gao et al., 2009; Huang et al., 2011; Tie and Cao, 2009; Tie et al., 2009). The NCP and YRD regions are not geographically separated by mountain ranges. Regional transport of pollutants between these two regions can occur with northerly or southerly winds. 13 cities in the NCP region (Beijing, Langfang, Tianjin, Baoding, Tangshan, Cangzhou, Hengshui, Shijiazhuang, Qinhuangdao, Jinan, Handan, Qingdao, and Zhengzhou) are included in the current study. 20 cities in the YRD region including Shanghai, Suzhou,
Jiaxing, Wuxi, Huzhou, Shaoxing, Hangzhou, Yangzhou, Jinhua, Nantong, Huaian, Quzhou, Nanjing, Taizhoushi, Lianyungang, Suqian, Xuzhou, Wenzhou, Taizhou, and Lishui are chosen for the analysis. Locations of the NCP and YRD regions and the 33 cities are shown in Fig. 1.
2.2. Data source and description In January 2013, MEP started publishing real-time hourly concentrations of SO2, NO2, PM2.5 PM10, CO, and O3, which are used to calculate AQI (http://113.108.142.147:20035/emcpublish/). The data presented in this study were obtained from the website for the period of June 1, 2013 to August 31, 2013. All the measurements were conducted at the national air quality monitoring sites located in each city. Automated monitoring systems were installed and used to measure the ambient concentration of SO2, NO2, O3 and CO according to China Environmental Protection Standards HJ 1932013 (http://www.es.org.cn/download/2013/7-12/2627-1.pdf), and of PM2.5 and PM10 according to China Environmental Protection Standards HJ 655-2013 (http://www.es.org.cn/download/2013/712/2626-1.pdf). Meteorological observations were obtained from the National Climate Data Center (NCDC) (ftp://ftp.ncdc.noaa.gov/ pub/data/noaa/). For each city, the hourly and daily concentrations of all the air pollutants were calculated by averaging the hourly data from all monitoring sites in the city. 1 h and 8 h peak ozone concentrations were calculated based on the average hourly ozone concentrations. The daily average concentrations of each pollutant were calculated only when there were more than 16-h valid data. The days with 24 h average concentration greater than the Grade II standard are
602
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
Table 1 Pearson correlation coefficients between cities in the NCP of PM2.5 (cells above the diagonal) and of PM10 (cells below the diagonal).
Beijing Langfang Tianjin Baoding Tangshan Cangzhou Hengshui Shijiazhuang Qinhuangdao Jinan Handan Qingdao Zhengzhou
Beijing
Langfang
Tianjin
Baoding
Tangshan
Cangzhou
Hengshui
Shijiazhuang
Qinhuangdao
Jinan
Handan
Qingdao
Zhengzhou
e 0.67 0.50 0.72 0.41 0.69 0.60 0.58 0.40 0.40 0.45 0.23 0.46
0.79 e 0.73 0.75 0.59 0.59 0.59 0.63 0.28 0.33 0.35 0.21 0.30
0.52 0.82 e 0.57 0.72 0.52 0.57 0.48 0.43 0.33 0.36 0.13 0.30
0.78 0.78 0.69 e 0.43 0.69 0.70 0.78 0.13 0.27 0.37 0.24 0.27
0.55 0.70 0.82 0.49 e 0.36 0.29 0.35 0.47 0.22 0.06 0.24 0.06
0.62 0.55 0.73 0.72 0.42 e 0.67 0.64 0.27 0.30 0.52 0.07 0.33
0.49 0.49 0.66 0.60 0.39 0.83 e 0.73 0.24 0.42 0.62 0.03 0.39
0.77 0.55 0.56 0.82 0.36 0.75 0.70 e 0.09 0.29 0.55 0.30 0.36
0.46 0.37 0.56 0.26 0.59 0.39 0.35 0.27 e 0.12 0.03 0.12 0.08
0.34 0.33 0.41 0.28 0.35 0.37 0.54 0.37 0.11 e 0.39 0.10 0.63
0.38 0.28 0.45 0.38 0.19 0.57 0.70 0.57 0.09 0.49 e 0.11 0.62
0.17 0.12 0.04 0.21 0.07 0.16 0.05 0.21 0.01 0.11 0.22 e 0.14
0.08 0.19 0.25 0.13 0.09 0.24 0.49 0.26 0.02 0.65 0.58 0.27 e
defined as PM episode days. Pearson correlation coefficients are calculated for correlation analyses.
3. Results and discussion 3.1. Summertime air quality overview Fig. 2 shows the concentrations of PM2.5 (a), PM10 (b), CO (c), SO2 (d), NO2 (e), and 8 h peak O3 (f) averaged over the entire study time period (June 1steAugust 31st, 2013) for the 36 cities in the study area. Average PM2.5 concentrations in the NCP were high with an average concentration of over 70 mg/m3 in all cities except Qingdao. Excluding Qingdao, small difference in the average PM2.5 concentration was observed among cities, indicating small spatial gradients in PM2.5 in the NCP. The PM2.5 concentrations in the YRD were relatively low (~40 mg/m3 for most cities) with a decreasing spatial pattern from north (~60 mg/m3) to south (~30 mg/m3). The summertime concentrations were lower compared to winter, spring and annual average concentrations (He et al., 2001; Ye et al., 2003). PM10 displayed similar spatial pattern. PM10 concentrations ranged from 120 to 200 mg/m3 in most NCP cities, and from 40 to 90 mg/m3 in most YRD cities. The gaseous pollutants including CO, SO2, NO2, and O3, however, showed different spatial distributions. Greater inter-city difference was observed, indicating that local emissions are more dominant in the gases than PM2.5 and PM10. CO, NO2, and SO2 concentrations were much higher in a few industrial cities (i.e., Tangshan and Handan in NCP) than other cities due to high emissions from fossil fuel power plants and industrial sources (Chen et al., 2006; Hao et al., 2005)). NO2 concentrations in more developed cities located around Shanghai were greater than other cities in the YRD due to the higher vehicular emissions (Chen et al., 2006). Summer average concentrations in all the cities are available in Table S1 in the Supplementary Materials. Fig. 3 shows the frequency distributions of PM2.5 and PM10 in the NCP and YRD regions. During summer 2013, the average PM2.5 and PM10 mass concentration were 77.0 ± 41.9 and 136.2 ± 62.0 mg/m3 in the NCP region (the error denotes one standard deviation), respectively, and were 42.8 ± 25.9 and 74.9 ± 39.7 mg/m3 in the YRD region, respectively. In the NCP region, 83% and 47% of the PM2.5 mass concentration exceeded the NAAQS Grade I and Grade II for PM2.5, respectively, 93% and 33% of the PM10 mass concentration exceeded the NAAQS Grade I and Grade II standards for PM10, respectively. In the YRD region, these percentage values were 51% and 8% for PM2.5, respectively, and 66% and 4% for PM10, respectively.
3.2. Spatial variability The Pearson correlation analysis between different cities was performed to investigate the regional correlation for PM air pollution in the two regions. Table 1 and Table 2 show the correlation coefficients (r) of daily average values of PM10 and PM2.5 between all cities in the NCP and YRD regions, respectively. Most city-pairs showed a positive correlation, indicating that a significant fraction of the PM2.5 in the cities was either secondary PM such as ammonium sulfate and secondary organic aerosol (SOA) or fugitive dust, which typically has broader regional distributions than anthropogenic primary pollutants. The correlation coefficients for PM2.5 were generally higher than PM10, suggesting that the secondary PM formation is likely a more important PM source than fugitive dust in summertime. The city-pairs with shorter distances showed higher correlations, and were given in bold in the tables. Qingdao and Zhengzhou in the NCP region, and Lishui, Huaian, Quzhou, and Wenzhou in the YRD region were not as well correlated as the other cities. These cities were mostly exposed to local sources. The correlation coefficients were further compared with the consideration of the distance between cities. The results are shown in Fig. 4. Strong and similar dependence was found between the r values and the distance in both regions. Pearson correlation coefficient over 0.6 was rarely found for both PM2.5 and PM10 when the distance between cities was over 250 km in the NCP region. The correlation coefficients between cities in the YRD became mostly less than 0.6 when cities were 250 km away for PM2.5 and 180 km for PM10. The radius of influence for PM10 in the NCP region was larger than that in the YRD region, suggesting dust had likely greater impact in the NCP region. This radius of influence suggests that for any urban areas, coordinated regional emission controls are needed in addition to controlling the local emissions. Back-trajectory analysis was used to examine the histories of air masses that led to high PM concentrations during high PM pollution episodes: June 15, 2013 for the NCP region and June 12, 2013 for the YRD region. The trajectories were calculated using the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) developed by Air Resources Laboratory (ARL) in the National Oceanic and Atmospheric Administration (NOAA) (Draxler, 2013; Rolph, 2013). 24 h and 72 h back-trajectories of air parcels arriving in cities at the noon time of selected episodes in the NCP and the YRD region were calculated and are shown in Fig. 5. The trajectories demonstrate clearly regional transport of PM2.5 and PM10 during the high PM pollution episodes. In 24 h, the average trajectory distances among the NCP and the YRD cities are 249 ± 43 km and 215 ± 86 km, respectively. Within 72 h, most the
Table 2 Pearson correlation coefficients between cities in the YRD of PM2.5 (cells above the diagonal) and of PM10 (cells below the diagonal). Huzhou Shaoxing Hangzhou Taizhoushi Yangzhou Nanjing Taizhou Jinhua Lishui Huaian Quzhou Wenzhou Suqian Lianyungang Xuzhou
e 0.80 0.84 0.78 0.53 0.49 0.42 0.39 0.23 0.18 0.24 0.70 0.52 0.22 0.12 0.01 0.51 0.18 0.09 0.16
0.52 0.83 0.73 0.62 0.87 e 0.49 0.81 0.59 0.61 0.74 0.23 0.39 0.33 0.08 0.36 0.03 0.50 0.44 0.46
0.79 e 0.81 0.74 0.78 0.72 0.42 0.59 0.43 0.41 0.48 0.47 0.45 0.32 0.13 0.20 0.32 0.34 0.26 0.29
0.89 0.88 e 0.60 0.66 0.66 0.56 0.57 0.28 0.30 0.40 0.62 0.54 0.27 0.18 0.24 0.43 0.36 0.23 0.34
0.84 0.80 0.73 e 0.62 0.59 0.38 0.49 0.63 0.41 0.39 0.41 0.37 0.46 0.19 0.12 0.22 0.17 0.34 0.21
0.66 0.93 0.77 0.71 e 0.84 0.47 0.68 0.57 0.61 0.74 0.33 0.42 0.23 0.08 0.39 0.01 0.45 0.45 0.38
0.65 0.76 0.81 0.60 0.68 0.77 e 0.65 0.26 0.28 0.34 0.47 0.57 0.41 0.04 0.39 0.32 0.26 0.20 0.32
0.46 0.72 0.69 0.59 0.74 0.87 0.86 e 0.50 0.49 0.66 0.30 0.52 0.52 0.12 0.55 0.21 0.53 0.40 0.48
0.41 0.67 0.47 0.72 0.65 0.60 0.49 0.60 e 0.69 0.62 0.11 0.26 0.39 0.18 0.14 0.05 0.32 0.47 0.31
0.21 0.55 0.36 0.49 0.63 0.59 0.43 0.57 0.85 e 0.77 0.16 0.25 0.08 0.36 0.27 0.08 0.66 0.50 0.58
0.33 0.66 0.48 0.45 0.79 0.79 0.56 0.70 0.70 0.80 e 0.17 0.34 0.18 0.11 0.35 0.11 0.59 0.52 0.53
0.70 0.61 0.73 0.59 0.61 0.52 0.74 0.51 0.41 0. 0.44 e 0.60 0.18 0.15 0.20 0.68 0.07 0.20 0.05
0.51 0.60 0.60 0.52 0.58 0.63 0.80 0.73 0.46 0.35 0.47 0.60 e 0.51 0.16 0.40 0.41 0.14 0.19 0.09
0.28 0.35 0.45 0.35 0.29 0.43 0.72 0.56 0.26 0.10 0.20 0.48 0.73 e 0.13 0.29 0.31 0.01 0.14 0.07
0.10 0.39 0.24 0.32 0.46 0.39 0.39 0.43 0.74 0.86 0.62 0.23 0.34 0.18 e 0.08 0.11 0.33 0.23 0.29
0.19 0.42 0.45 0.33 0.48 0.61 0.66 0.74 0.37 0.32 0.41 0.39 0.75 0.71 0.34 e 0.01 0.58 0.40 0.55
0.48 0.26 0.44 0.41 0.16 0.14 0.46 0.25 0.15 0.03 0.07 0.66 0.41 0.50 0.00 0.24 e 0.08 0.07 0.10
0.21 0.45 0.33 0.22 0.50 0.46 0.43 0.52 0.57 0.71 0.64 0.26 0.25 0.09 0.79 0.53 0.01 e 0.50 0.92
0.15 0.35 0.24 0.30 0.39 0.30 0.28 0.28 0.54 0.48 0.44 0.35 0.13 0.12 0.52 0.30 0.05 0.53 e 0.57
0.24 0.39 0.36 0.23 0.39 0.38 0.42 0.44 0.53 0.63 0.54 0.23 0.15 0.01 0.70 0.42 0.00 0.89 0.53 e
603
trajectories for the NCP cities are still in the NCP region, with an average distance of 510 ± 120 km. The 72 h trajectories for the YRD cities indicate that pollutants from the NCP region are transported south and lead to high pollution episode in the YRD regions. Effects of transport on YRD cities were also found by studies using chemical transport models (Wang et al., 2014b; Ying et al., 2014). The results confirm that summer PM pollution in the NCP and the YRD are regional, and regional control strategies are needed to solve the PM pollution problems.
3.3. Temporal variability
3.3.1. PM2.5 and meteorology variation Fig. 6 illustrates the temporal variation of daily average PM2.5 concentrations, wind speed, and temperature in Beijing and Shanghai, the two largest megacities in NCP and YRD, respectively. A negative correlation between PM2.5 and wind speed was observed, i.e., high PM2.5 concentrations occur generally with weak winds and low PM2.5 concentrations occur with strong winds. Overall no apparent correlation between PM2.5 and temperature was observed, even though temperature was found to be positively correlated with PM2.5 in certain short periods, for example July 11the16th in Beijing and June 24theJuly 1st in Shanghai, but to be negatively correlated with PM2.5 in other certain episodes, for example July 25the30th in Beijing and June 4the9th in Shanghai. The associations between PM2.5 concentrations and wind speed, relative humidity and temperature were further investigated using multiple linear regression analysis for the entire summer days. In Beijing, the multiple regression coefficient was 0.51. PM2.5 was negatively associated to wind speed with a coefficient of 0.53, but
Fig. 4. Pearson Correlation Coefficients of PM2.5 and PM10 between cities in the NCP and YRD regions.
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
Shanghai Suzhou Jiaxing Nantong Wuxi Huzhou Shaoxing Hangzhou Taizhoushi Yangzhou Nanjing Taizhou Jinhua Lishui Huaian Quzhou Wenzhou Suqian Lianyungang Xuzhou
Shanghai Suzhou Jiaxing Nantong Wuxi
604
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
The PM2.5 concentrations on the episode days in the NCP region showed no substantial difference compared to the concentrations in the YRD region, but the number of episode days in the NCP was about three times greater (Fig. 3). The average PM2.5/PM10 ratios increased from 0.50 on non-episode days to 0.64 on episode days in the NCP, and increased from 0.57 on non-episode days to 0.64 on episode days in the YRD. This increase in the PM2.5/PM10 values suggests more secondary PM2.5 formation during high PM2.5 pollution events (Song et al., 2002; Yao et al., 2003). However, the difference in the PM2.5/PM10 ratios on episode and non-episode days was not statistically significant (as indicated by the overlapped standard deviations in Fig. 6). On episode days, the PM2.5/ PM10 ratios in the NCP region showed large variations (0.52e0.78) while in the YRD region the PM2.5/PM10 ratios showed small variations among cities (0.62e0.66). The larger variation in the PM2.5/ PM10 ratios in the NCP indicates more heterogeneity in the PM2.5 and PM10 pollution in this region. The cities with relatively lower PM2.5/PM10 ratios, i.e. Shijiazhuang, Zhengzhou, and Qingdao, were likely having more primary PM sources. Comprehensive control strategies that include both primary and secondary PM2.5 will be more effective to reduce the high PM2.5 concentrations. The PM2.5/PM10 ratios in this study were compared to ratios measured in other areas/countries (shown in Fig. 7 and the data and references are listed in Table 3) (Brook et al., 1997; Chen et al., 1999; Chow et al., 1994; Das et al., 2006; Gomiscek et al., 2004; Ho et al., 2003; Zakey et al., 2008). The PM2.5/PM10 ratios in this study on non-episode days were generally lower than ratios found in other areas/countries with similar average PM2.5 concentrations, indicating high fraction of coarse particles (PM2.5e10) in the NCP and YRD regions. Average PM2.5 concentrations over 60 mg/m3 were rarely reported in other areas/countries, and the PM2.5/PM10 ratios on episode days in this study were generally higher than ratios found in literature, suggesting significant production of secondary PM on episode days. The substantial fraction of PM10 mass was in the PM2.5 size range suggests that PM2.5 control strategies will also effectively reduce the PM10 pollution.
positively associated to temperature and relative humidity with a coefficient of 0.71 and 1.23, respectively. In Shanghai, the association between PM2.5 and the three meteorological parameters was insignificant, with a multiple regression coefficient of 0.18. Both NCP and YRD region were significantly influenced by the subtropical high, which usually lead to low wind speeds that are not preferable for pollutant dispersion. However, Shanghai was also influence by sea breeze which helps transport pollutant away. Generally, stagnant conditions with wind speed less than 2 m/s, which trap PM and other gaseous pollutants at the ground level, were frequently observed in Beijing but were rarely observed in Shanghai and thus led to higher PM pollution.
3.3.3. Weekday-weekend difference Weekday-weekend differences in ambient air pollutant concentrations have been observed in many locations for many years, due to the weekly circle of human activities (Altshuler et al., 1995; Blanchard and Tanenbaum, 2006, 2003; Clevelan.Ws et al., 1974; Graedel et al., 1977; Khoder and Hassan, 2008; Motallebi et al., 2003; Qin et al., 2004). Fig. 8 shows the weekday/weekend ratios of PM2.5 and PM10 at all cities in this study. The ratios of CO, SO2, NO2, 1 h peak O3 and 8 h peak O3 are shown in Fig. S1. In the NCP cities (except Qingdao), PM2.5 and PM10 concentrations on weekdays were slightly lower than concentrations on weekends, with the weekday/weekend ratios of PM2.5 and PM10 in the range of 0.8e1.0. The PM2.5 and PM10 concentrations in the YRD cities showed small variations between weekdays and weekends, with the weekday/weekend ratios varying mostly ±0.1 around 1.0. Small weekday-weekend variations were also found for the gas pollutants, with ratios mostly within the range of 0.9e1.1, and only a few cases exceeded 1.2. The ratios were smaller than what were observed in the literature listed above, indicating that the weekday-weekend differences were not typical in the summer of 2013.
3.3.2. PM2.5/PM10 ratio on episode and non-episode days Fig. 7 shows the PM2.5 to PM10 ratios (PM2.5/PM10) during the episode (daily average PM2.5 > 75 mg/m3) and non-episodes days in all cities. Average PM2.5 concentrations during the episode days were between 100 and 120 mg/m3, much higher than PM2.5 concentrations during the non-episode days which were 30e50 mg/m3.
3.3.4. Diurnal variations Diurnal variations of PM2.5, PM10, O3, NO2, SO2, and CO during the episode and non-episode days were investigated in Beijing and Shanghai, and shown in Figs. 9 and 10, respectively. In Beijing, PM2.5 and PM10 showed dramatically higher concentrations and stronger diurnal variations in the episode days than in the non-episode days.
Fig. 5. Back-trajectories in (a) 24 h and (b) 72 h for all cities in the NCP (in red) at noon time of a PM2.5 episode day (June 15, 2013) and for all cities in the YRD (in blue) at noon time of a PM2.5 episode day (June 12, 2013). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
605
Fig. 6. Daily PM2.5 concentrations, wind speed and temperature in summer 2013 in Beijing and Shanghai.
Fig. 7. PM2.5/PM10 ratios in the PM2.5 episode days and non-episode days at all cities in the NCP and YRD regions.
PM2.5 and PM10 concentrations on episode days were very similar, suggesting low contribution of coarse particles on high PM episodes, which can be explained by slow wind speed in the summer and more stringent controls on construction emissions in Beijing (BEIJING, Accessed May, 2014). On episode days, both PM2.5 and PM10 started to increase rapidly around 6e7 am local time with an hourly rate of 6 mg/m3 for 5e6 h and reached highest concentration of ~140 mg/m3 at noon. Concentrations remained high in the afternoon and late evening. PM2.5 concentration started to decrease after 8 pm while PM10 remained high until midnight. Dust and primary PM emissions from increased cross-city truck traffic from
other provinces, which were regulated during the day, may explain this persistent high PM10. Interestingly, O3 generally showed only slight difference in terms of both concentrations and diurnal variability in the episode and non-episode days, and the difference in NO2 was not significant at night time either. However, SO2 and CO showed similar diurnal variations with much higher concentrations in the episode days. In Shanghai, PM2.5 and PM10 in episode days also showed higher concentrations and stronger diurnal variations with multiple peaks throughout the day. However, the rate of increase was slower than that in Beijing. PM10 concentrations were similar to PM2.5 only in the afternoon hours from noon to 8 pm,
Table 3 PM2.5 concentrations and PM2.5/PM10 ratios in other cities. The sampling frequency is 24 h, except 30 min for Gomiscek et al. (2004) and 4e7 h for Chow et al. (1994). City/Area/Country
PM2.5
PM2.5/PM10
References
Episodes
Hongkong, China S. Taiwan, China Kolkata, India Austria Hamilton, Canada Los Angeles, USA Rubidox, USA Cairo, Egypt
32.50 48.50 178.60 18.50 19.00 41.10 63.90 85.00
0.64 0.63 0.59 0.67 0.61 0.61 0.53 0.50
Ho et al., 2003 Chen et al., 1999 Das et al., 2006 Gomiscek et al., 2004 Brook et al., 1997 Chow et al., 1994 Chow et al., 1994 Zakey et al., 2008
Nov. 2000eFeb. 2001 Oct. 1996eJun. 1997 Nov. 2002eDec. 2002 Jun. 1999eMay 2000 Sep. 1992eDec. 1994 Jun.eSep., Nov.eDec., 1987 Jun.eSep., Nov.eDec., 1988 Jan. 2001eDec. 2002
606
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
Fig. 8. Weekday/weekend ratios of PM2.5 and PM10, at each city in the NCP and YRD regions.
with a similar increase at night after that. NO2, SO2, and CO also showed higher concentrations but similar diurnal variations in episode days. NO2 showed clearly double-peak distribution during the early morning and late afternoon rush hours. O3 in Shanghai showed significantly higher concentrations of 80 ppb than the nonepisode days of 60 ppb peak concentration. Photochemical processes in the YRD were likely stronger during the episode days in summer due to higher temperature and relative humidity in this region (Chan and Yao, 2008) From the above analysis, it appears that although both megacities experienced high PM2.5 concentrations and showed many similar features in concentration and diurnal variations, and both cities showed significant contributions
of secondary contributions to PM2.5, the difference in ozone and NO2 and relative importance of coarse particles need to be further studied. 4. Conclusion PM concentration data in the NCP and the YRD regions in China between June 1st, 2013 and August 31st, 2013 were recorded at the air quality monitoring network of the Chinese Ministry of Environmental Protection. Our analysis shows that averaged PM2.5 concentrations at both regions exceeded the World Health Organization guideline values, indicating severe human exposure to
Fig. 9. Diurnal variations of PM2.5, PM10, O3, NO2, SO2 and CO in the PM2.5 episode and non-episode days in Beijing.
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
607
Fig. 10. Diurnal variations of PM2.5, PM10, O3, NO2, SO2 and CO in the PM2.5 episode and non-episode days in Shanghai.
ambient PM in the most developed and populated areas in China. The frequencies of occurrence of PM2.5 and PM10 concentrations exceeding the AAQS Grade I and II standards were 81% and 82%, respectively, in the NCP, and 70% and 54%, respectively, in the YRD. In both regions, PM2.5 concentrations were similar for all studied cities, indicating that PM pollution is a regional problem. Higher fraction of coarse particles (PM2.5-10) was found in the NCP and YRD regions compared to other areas and countries. No distinct difference between weekday and weekend concentrations for all seven monitored pollutants was observed, suggesting weak impact of the weekly circle of human activities. Ambient PM concentrations in other seasons and regions in China should be further investigated in order to obtain a deep and comprehensive understanding of the entire nationwide air quality problem. This study provides an important scientific basis for the design of nationwide PM pollution control strategies in China. Joint actions among provincial administrations need to be considered.
Acknowledgement The authors gratefully acknowledge Dr. Gong Zhang of National Aeronautics and Space Administration (NASA) Ames Research Center (ARC) for his support to this project. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or READY website (http://www.ready.noaa.gov) used in this publication.
Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2014.07.019. References Altshuler, S.L., Arcado, T.D., Lawson, D.R., 1995. Weekday vs weekend ambient ozone concentrations - discussion and hypotheses with focus on northern California. J. Air Waste Manag. Assoc. 45, 967e972. BEIJING, Accessed May, 2014. Emergency Plan for Beijing heavy air pollution (Trial). http://govfile.beijing.gov.cn/Govfile/ShowNewPageServlet?id¼6161. Blanchard, C.L., Tanenbaum, S., 2006. Weekday/weekend differences in ambient air pollutant concentrations in Atlanta and the Southeastern United States. J. Air Waste Manag. Assoc. 56, 271e284. Blanchard, C.L., Tanenbaum, S.J., 2003. Differences between weekday and weekend air pollutant levels in southern California. J. Air Waste Manag. Assoc. 53, 816e828. Brook, J.R., Wiebe, A.H., Woodhouse, S.A., Audette, C.V., Dann, T.F., Callaghan, S., Piechowski, M., Dabek-Zlotorzynska, E., Dloughy, J.F., 1997. Temporal and spatial relationships in fine particle strong acidity, sulphate, PM10, and PM2.5 across multiple Canadian locations. Atmos. Environ. 31, 4223e4236. Chan, C.K., Yao, X., 2008. Air pollution in mega cities in China. Atmos. Environ. 42,1e42. Chen, C.H., Wang, B.Y., Fu, Q.Y., Green, C., Streets, D.G., 2006. Reductions in emissions of local air pollutants and co-benefits of Chinese energy policy: a Shanghai case study. Energ. Policy 34, 754e762. Chen, M.L., Mao, I.F., Lin, I.K., 1999. The PM2.5 and PM10 particles in urban areas of Taiwan. Sci. Total Environ. 226, 227e235. Chen, Y., Ebenstein, A., Greenstone, M., Li, H., 2013. Evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River policy. Proc. Natl. Acad. Sci. 110 (32), 12936e12941. http://dx.doi.org/10.1073/ pnas.1300018110. Chen, Y., Xie, S.D., 2014. Characteristics and formation mechanism of a heavy air pollution episode caused by biomass burning in Chengdu, Southwest China. Sci. Total Environ. 473, 507e517. China, N.B.o.S.o., 2012. China Statistical Yearbook. China Statistics Press, Beijing.
608
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609
Chow, J.C., Watson, J.G., Fujita, E.M., Lu, Z., Lawson, D.R., Ashbaugh, L.L., 1994. Temporal and spatial variations of PM2.5 and PM10 aerosol in the Southern California air quality study. Atmos. Environ. 28, 2061e2080. Clevelan, Ws, Graedel, T.E., Kleiner, B., Warner, J.L., 1974. Sunday and workday variations in photochemical air-pollutants in New-Jersey and New-York. Science 186, 1037e1038. Correia, A.W., Pope, C.A., Dockery, D.W., Wang, Y., Ezzati, M., Dominici, F., 2013. Effect of air pollution control on life expectancy in the United States an analysis of 545 US counties for the period from 2000 to 2007. Epidemiology 24, 23e31. Das, M., Maiti, S.K., Mukhopadhyay, U., 2006. Distribution of PM2.5 and PM10e2.5 in PM10 fraction in ambient air due to vehicular pollution in Kolkata megacity. Environ. Monit. Assess. 122, 111e123. Deng, X.J., Tie, X.X., Wu, D., Zhou, X.J., Bi, X.Y., Tan, H.B., Li, F., Hang, C.L., 2008. Longterm trend of visibility and its characterizations in the Pearl River Delta (PRD) region, China. Atmos. Environ. 42, 1424e1435. Ding, A.J., Fu, C.B., Yang, X.Q., Sun, J.N., Pet€ aj€ a, T., Kerminen, V.M., Wang, T., Xie, Y., Herrmann, E., Zheng, L.F., Nie, W., Liu, Q., Wei, X.L., Kulmala, M., 2013. Intense atmospheric pollution modifies weather: a case of mixed biomass burning with fossil fuel combustion pollution in eastern China. Atmos. Chem. Phys. 13, 10545e10554. Dockery, D.W., 2001. Epidemiologic evidence of cardiovascular effects of particulate air pollution. Environ. Health Perspect. 109, 483e486. Dockery, D.W., Pope, C.A., Xu, X.P., Spengler, J.D., Ware, J.H., Fay, M.E., Ferris, B.G., Speizer, F.E., 1993. An association between air-pollution and mortality in 6 United-States cities. N. Engl. J. Med. 329, 1753e1759. Draxler, R.R., 2013. HYSPLIT (HYbrid Single-particle Lagrangian Integrated Trajectory) Model Access via NOAA ARL READY. NOAA Air Resources Laboratory, College Park, MD. http://www.arl.noaa.gov/HYSPLIT.php. Fang, C., Yao, S., Liu, S., et al., 2011. Report on Development of City Clusters in China. Science Press, ISBN 978-7-03-030643-2, p. 2010. Fann, N., Lamson, A.D., Anenberg, S.C., Wesson, K., Risley, D., Hubbell, B.J., 2012. Estimating the national public health Burden associated with exposure to ambient PM2.5 and ozone. Risk Anal. 32, 81e95. Franklin, M., Zeka, A., Schwartz, J., 2007. Association between PM2.5 and all-cause and specific-cause mortality in 27 US communities. J. Expo. Sci. Environ. Epidemiol. 17, 279e287. Gao, J., Wang, T., Zhou, X., Wu, W., Wang, W., 2009. Measurement of aerosol number size distributions in the Yangtze River delta in China: formation and growth of particles under polluted conditions. Atmos. Environ. 43, 829e836. Gao, Y., Liu, X., Zhao, C., Zhang, M., 2011. Emission controls versus meteorological conditions in determining aerosol concentrations in Beijing during the 2008 Olympic Games. Atmos. Chem. Phys. 11, 12437e12451. Gomiscek, B., Hauck, H., Stopper, S., Preining, O., 2004. Spatial and temporal variations of PM1, PM2.5, PM10 and particle number concentration during the AUPHEP-project. Atmos. Environ. 38, 3917e3934. Graedel, T.E., Farrow, L.A., Weber, T.A., 1977. Photochemistry of Sunday effect. Environ. Sci. Technol. 11, 690e694. Guo, S., Hu, M., Guo, Q., Zhang, X., Zheng, M., Zheng, J., Chang, C.-C., Schauer, J.J., Zhang, R., 2012. Primary sources and secondary formation of organic aerosols in Beijing, China. Environ. Sci. Technol. 46 (18), 9846e9853. http://dx.doi.org/ 10.1021/es2042564. Guo, Y.M., Li, S.S., Tian, Z.X., Pan, X.C., Zhang, J.L., Williams, G., 2013. The burden of air pollution on years of life lost in Beijing, China, 2004e08: retrospective regression analysis of daily deaths. BMJ Brit Med. J., 347. Hao, J.M., Wang, L.T., Li, L., Hu, J.N., Yu, X.C., 2005. Air pollutants contribution and control strategies of energy-use related sources in Beijing. Sci. China Ser. D. 48, 138e146. He, K.B., Yang, F.M., Ma, Y.L., Zhang, Q., Yao, X.H., Chan, C.K., Cadle, S., Chan, T., Mulawa, P., 2001. The characteristics of PM2.5 in Beijing, China. Atmos. Environ. 35, 4959e4970. Ho, K.F., Lee, S.C., Chan, C.K., Yu, J.C., Chow, J.C., Yao, X.H., 2003. Characterization of chemical species in PM2.5 and PM10 aerosols in Hong kong. Atmos. Environ. 37, 31e39. Huang, C., Chen, C.H., Li, L., Cheng, Z., Wang, H.L., Huang, H.Y., Streets, D.G., Wang, Y.J., Zhang, G.F., Chen, Y.R., 2011. Emission inventory of anthropogenic air pollutants and VOC species in the Yangtze River Delta region, China. Atmos. Chem. Phys. 11, 4105e4120. Ianniello, A., Spataro, F., Esposito, G., Allegrini, I., Hu, M., Zhu, T., 2011. Chemical characteristics of inorganic ammonium salts in PM2.5 in the atmosphere of Beijing (China). Atmos. Chem. Phys. 11, 10803e10822. Ito, K., Mathes, R., Ross, Z., Nadas, A., Thurston, G., Matte, T., 2011. Fine particulate matter constituents associated with cardiovascular hospitalizations and mortality in New York city. Environ. Health Perspect. 119, 467e473. Khoder, M.I., Hassan, S.K., 2008. Weekday/weekend differences in ambient aerosol level and chemical characteristics of water-soluble components in the city centre. Atmos. Environ. 42, 7483e7493. Li, L., Chen, C.C., Cheng, H., et al., 2008. Regional air pollution characteristics simulation of O3 and PM10 over Yangtze River Delta Region. Environ. Sci. 29, 237e245. Li, L., Chen, C.H., Fu, J.S., Huang, C., Streets, D.G., Huang, H.Y., Zhang, G.F., Wang, Y.J., Jang, C.J., Wang, H.L., Chen, Y.R., Fu, J.M., 2011. Air quality and emissions in the Yangtze River Delta, China. Atmos. Chem. Phys. 11, 1621e1639. Li, Z.Q., Gu, X., Wang, L., Li, D., Li, K., Dubovik, O., Schuster, G., Goloub, P., Zhang, Y., Li, L., Xie, Y., Ma, Y., Xu, H., 2013. Aerosol physical and chemical properties retrieved from ground-based remote sensing measurements during heavy haze days in Beijing winter. Atmos. Chem. Phys. Discuss. 13, 5091e5122.
Lim, S.S., Vos, T., Flaxman, A.D., Danaei, G., Shibuya, K., Adair-Rohani, H., et al., 2012. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990e2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 2224e2260. Liu, S., Hu, M., Slanina, S., He, L.-Y., Niu, Y.-W., Bruegemann, E., Gnauk, T., Herrmann, H., 2008a. Size distribution and source analysis of ionic compositions of aerosols in polluted periods at Xinken in Pearl River Delta (PRD) of China. Atmos. Environ. 42, 6284e6295. Liu, S., Hu, M., Wu, Z., Wehner, B., Wiedensohler, A., Cheng, Y., 2008b. Aerosol number size distribution and new particle formation at a rural/coastal site in Pearl River Delta (PRD) of China. Atmos. Environ. 42, 6275e6283. Liu, X.G., Li, J., Qu, Y., Han, T., Hou, L., Gu, J., Chen, C., Yang, Y., Liu, X., Yang, T., Zhang, Y., Tian, H., Hu, M., 2013. Formation and evolution mechanism of regional haze: a case study in the megacity Beijing, China. Atmos. Chem. Phys. 13, 4501e4514. MEP, 2012a. China National Ambient Air Quality Standards. MEP, Beijing, China. MEP, 2012b. In: Protection, M.o.E. (Ed.), Technical Regulation on Ambient Air Quality Index (On Trial). MEP, Beijing, China. Motallebi, N., Tran, H., Croes, B.E., Larsen, L.C., 2003. Day-of-week patterns of particulate matter and its chemical components at selected sites in California. J. Air Waste Manag. Assoc. 53, 876e888. Ostro, B., Broadwin, R., Green, S., Feng, W.Y., Lipsett, M., 2006. Fine particulate air pollution and mortality in nine California counties: results from CALFINE. Environ. Health Persp. 114, 29e33. Peng, G.L., Wang, X.M., Wu, Z.Y., Wang, Z.M., Yang, L.L., Zhong, L.J., Chen, D.O.H., 2011. Characteristics of particulate matter pollution in the Pearl River Delta region, China: an observational-based analysis of two monitoring sites. J. Environ. Monit. 13, 1927e1934. Qin, Y., Tonnesen, G.S., Wang, Z., 2004. Weekend/weekday differences of ozone, NOx, Co, VOCs, PM10 and the light scatter during ozone season in southern California. Atmos. Environ. 38, 3069e3087. Rolph, G.D., 2013. Real-time Environmental Applications and Display SYstem (READY). NOAA Air Resources Laboratory, College Park, MD. Website. http:// www.ready.noaa.gov. Shen, J., Tang, A., Liu, X., Kopsch, J., Fangmeier, A., Goulding, K., Zhang, F., 2011. Impacts of pollution controls on air quality in Beijing during the 2008 Olympic Games. J. Environ. Qual. 40, 37e45. Song, Y., Dai, W., Shao, M., Liu, Y., Lu, S.H., Kuster, W., Goldan, P., 2008. Comparison of receptor models for source apportionment of volatile organic compounds in Beijing, China. Environ. Pollut. 156, 174e183. Song, Y., Tang, X., Zhang, Y., Hu, M., Fang, C., Zen, L., Wang, W., 2002. Effects on fine particles by the continued high temperature weather in Beijing. Environ. Sci. 23, 33e36 (in Chinese with abstract in English). Sun, Y., Jiang, Q., Wang, Z., Fu, P., Li, J., Yang, T., Yin, Y., 2014. Investigation of the sources and evolution processes of severe haze pollution in Beijing in January 2013. J. Geophys. Res. Atmos. 119, 2014JD021641. Sun, Y., Zhuang, G.S., Yuan, H., Zhang, X.Y., Guo, J.H., 2004a. Characteristics and sources of 2002 super dust storm in Beijing. Chin. Sci. Bull. 49, 698e705. Sun, Y.L., Zhuang, G.S., Ying, W., Han, L.H., Guo, J.H., Mo, D., Zhang, W.J., Wang, Z.F., Hao, Z.P., 2004b. The air-borne particulate pollution in Beijing e concentration, composition, distribution and sources. Atmos. Environ. 38, 5991e6004. Tie, X., Cao, J., 2009. Aerosol pollution in China: present and future impact on environment. Particuology 7, 426e431. Tie, X., Geng, F., Peng, L., Gao, W., Zhao, C., 2009. Measurement and modeling of O3 variability in Shanghai, China: application of the WRF-Chem model. Atmos. Environ. 43, 4289e4302. van Donkelaar, A., Martin, R.V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., Villeneuve, P.J., 2010. Global estimates of ambient Fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ. Health Perspect. 118, 847e855. Wang, D., Hu, J., Xu, Y., Lv, D., Xie, X., Kleeman, M.J., Xing, J., Zhang, H., Ying, Q., 2014a. Source contributions to primary and secondary inorganic particulate matter during a severe wintertime PM2.5 pollution episode in Xi’an, China. Atmos. Environ. (still under review). Wang, T., Jiang, F., Deng, J., Shen, Y., Fu, Q., Wang, Q., Fu, Y., Xu, J., Zhang, D., October 2012. Urban air quality and regional haze weather forecast for Yangtze River Delta region. Atmos. Environ. 58, 70e83. http://dx.doi.org/10.1016/ j.atmosenv.2012.01.014. Wang, X., Zhang, Y., Hu, Y., Zhou, W., Lu, K., Zhong, L., Zeng, L., Shao, M., Hu, M., Russell, A.G., 2010. Process analysis and sensitivity study of regional ozone formation over the Pearl River Delta, China, during the PRIDE-PRD2004 campaign using the Community Multiscale Air Quality modeling system. Atmos. Chem. Phys. 10, 4423e4437. Wang, Y., Li, L., Chen, C., Huang, C., Huang, H., Feng, J., Wang, S., Wang, H., Zhang, G., Zhou, M., Cheng, P., Wu, M., Sheng, G., Fu, J., Hu, Y., Russell, A.G., Wumaer, A., 2014b. Source apportionment of fine particulate matter during autumn haze episodes in Shanghai, China. J. Geophys. Res. Atmos. 119, 2013JD019630. Wang, Y., Zhuang, G.S., Sun, Y., An, Z.S., 2005a. Water-soluble part of the aerosol in the dust storm season e evidence of the mixing between mineral and pollution aerosols. Atmos. Environ. 39, 7020e7029. Wang, Y., Zhuang, G.S., Tang, A.H., Yuan, H., Sun, Y.L., Chen, S.A., Zheng, A.H., 2005b. The ion chemistry and the source of PM2.5 aerosol in Beijing. Atmos. Environ. 39, 3771e3784. WHO, 2005. World Health Organization Air Quality Guidelines Global Update. E87950.
J. Hu et al. / Atmospheric Environment 95 (2014) 598e609 Xing, J., Zhang, Y., Wang, S.X., Liu, X.H., Cheng, S.H., Zhang, Q., Chen, Y.S., Streets, D.G., Jang, C., Hao, J.M., Wang, W.X., 2011. Modeling study on the air quality impacts from emission reductions and atypical meteorological conditions during the 2008 Beijing Olympics. Atmos. Environ. 45, 1786e1798. Yao, X.H., Lau, A.P.S., Fang, M., Chan, C.K., Hu, M., 2003. Size distributions and formation of ionic species in atmospheric particulate pollutants in Beijing, China: 1 e inorganic ions. Atmos. Environ. 37, 2991e3000. Ye, B.M., Ji, X.L., Yang, H.Z., Yao, X.H., Chan, C.K., Cadle, S.H., Chan, T., Mulawa, P.A., 2003. Concentration and chemical composition of PM2.5 in Shanghai for a 1year period. Atmos. Environ. 37, 499e510. Ying, Q., Wu, L., Zhang, H., September 2014. Local and inter-regional contributions to PM2.5 nitrate and sulfate in China. Atmos. Environ. 94, 582e592. http:// dx.doi.org/10.1016/j.atmosenv.2014.05.078. Zakey, A.S., Abdel-Wahab, M.M., Pettersson, J.B.C., Gatari, M.J., Hallquist, M., 2008. Seasonal and spatial variation of atmospheric particulate matter in a developing megacity, the Greater Cairo, Egypt. Atmosfera 21, 171e189.
609
Zhang, H., Li, J., Ying, Q., Yu, J.Z., Wu, D., Cheng, Y., He, K., Jiang, J., 2012. Source apportionment of PM2.5 nitrate and sulfate in China using a source-oriented chemical transport model. Atmos. Environ. 62, 228e242. Zhang, Y., Zhu, X., Zeng, L., Wang, W., 2004. Source apportionment of fine-particle pollution in Beijing. In: Urbanization, Energy, and Air Pollution in China: the Challenges Ahead e Proceedings of a Symposium. Zhang, Y.H., Hu, M., Zhong, L.J., Wiedensohler, A., Liu, S.C., Andreae, M.O., Wang, W., Fan, S.J., 2008. Regional integrated Experiments on air quality over Pearl River Delta 2004 (PRIDE-PRD2004): Overview. Atmos. Environ. 42, 6157e6173. Zhao, X.J., Zhao, P.S., Xu, J., Meng, W., Pu, W.W., Dong, F., He, D., Shi, Q.F., 2013. Analysis of a winter regional haze event and its formation mechanism in the North China Plain. Atmos. Chem. Phys. 13, 5685e5696. Zheng, J., Shao, M., Che, W., Zhang, L., Zhong, L., Zhang, Y., Streets, D., 2009. Speciated VOC emission inventory and spatial patterns of ozone formation potential in the Pearl River Delta, China. Environ. Sci. Technol. 43, 8580e8586.