Spatial and temporal variation of haze in China from 1961 to 2012

JES-00708; No of Pages 13 J O U RN A L OF E N V I RO N ME N TA L S CI EN CE S X X (2 0 1 6 ) XX X–XXX

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Spatial and temporal variation of haze in China from 1961 to 2012 Rui Han1,2 , Shuxiao Wang2,3,⁎, Wenhai Shen1 , Jiandong Wang2 , Kang Wu4 , Zhihua Ren1 , Mingnong Feng1 1. National Meteorological Information Center, China Meteorological Administration, Beijing100081, China. E-mail: [email protected] 2. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China 3. State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China 4. Department of Precision Instrument, Tsinghua University, Beijing 100084, China

AR TIC LE I N FO

ABS TR ACT

Article history:

The purpose of this study is to analyze the climatic characteristics and long-term spatial

Received 29 May 2015

and temporal variations of haze occurrence in China. The impact factors of haze trends are

Revised 27 November 2015

also discussed. Meteorological data from 1961 to 2012 and daily PM10 concentrations from

Accepted 28 November 2015

2003 to 2012 were employed in this study. The results indicate that the annual-average hazy

Available online xxxx

days at all stations have been increasing rapidly from 4 days in 1961 to 18 days in 2012. The maximum number of haze days occur in winter (41.1%) while the minimum occur in

Keywords:

summer (10.4%). During 1961-2012, the high occurrence areas of haze shifted from central to

China

south and east regions of China. The Beijing-Tianjin-Hebei (Jing-Jin-Ji) region, Shanxi,

Haze

Shaanxi, and Henan Province are the high occurrence areas for haze, while the Yangtze

Spatial distribution

River Delta (YRD) and the Pearl River Delta (PRD) have become regions with high haze

Interannual trend

occurrences in the last 25 years. Temperature and pressure are positively correlated with

Meteorological factors

the number of haze days. However, wind, relative humidity, precipitation, and sunshine

Anthropogenic activities

duration are negatively correlated with the number of haze days. The key meteorological factors affecting the formation and dissipation of haze vary for high and low altitudes, and are closely related to anthropogenic activities. In recent years, anthropogenic activities have played a more important role in haze occurrences compared with meteorological factors. © 2016 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.

Introduction With the rapid expansion of China's economy and urbanization, the emissions of primary gaseous pollutants and particles, as well as the secondary particles formed by photochemical reaction, have gradually increased (Hao et al., 2010). These can scatter and absorb the incident light and therefore lead to atmosphere opacity and horizontal visibility

degradation, and deteriorated air quality (Liu et al., 2013; Oh et al., 2015), which results in haze occurrence. Haze is an atmospheric phenomenon where dust, smoke and other dry particles in the atmosphere obscure the clarity of the sky and reduce the visibility to less than 10 km (CMA, 2010, 2003). The formation of haze is known to be closely related to meteorological factors and environmental pollution (Zhao et al., 2013; Huang et al., 2014), which has caused a number of

⁎ Corresponding author. E-mail: [email protected] (Shuxiao Wang).

http://dx.doi.org/10.1016/j.jes.2015.12.033 1001-0742 © 2016 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.

Please cite this article as: Han, R., et al., Spatial and temporal variation of haze in China from 1961 to 2012, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.12.033

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social and economic issues, such as human health problems, traffic jams from visibility reduction, and deterioration of plant growth (Panyacosit, 2000; Harrison and Yin, 2000; Al-Saadi et al.,2005; Lee et al., 2013; Friedlander 1977). Haze has become a major environmental issue to be resolved in China (Wang et al., 2014; Zhang et al., 2012). Study of the spatial and temporal trends, causes, and human impacts of haze started early in developed countries. Sloane (1983, 1984) examined the effect of meteorology on visibility trends and the extraction of valid air-quality related conclusions from these data. Long term studies of haziness within the United States were also carried out by Husar et al., (1981) and Schichtel et al., (2001). It was shown that haze days increased at first in the 1950s-70s and then declined after the 1980s. Doyle and Dorling (2002) constructed trends from 1950 to 1997 in the United Kingdom using four different statistical methods. These haze occurrences mainly arose due to high incidence of fossil fuel burning for domestic heating purposes. Others tried to illustrate the haze formation and evolution mechanism by using satellite, lidar, upper as well as lower atmosphere composition data and model results (Hand et al., 2012; Tsai et al., 2007; Oanh et al., 2006; Al-Saadi et al., 2005; Malm et al., 2004; Ramanathan and Ramana 2003). In China, haze has drawn wide attention since 2002 (Wu, 2012). During the past two decades, Chinese scientists have carried out many experiments to explain the formation and evolution mechanism of haze. Wu et al. (2010) and Gao (2008) have analyzed and discussed the historical trends of haze. The result showed a marked increase in annual-average haze days took place for years before 2006 in China. Several studies have focused on the trends of important regions, including the Yangtze River Delta (YRD) (Deng et al., 2012; Niu et al., 2010; Tong et al., 2007), the Pearl River Delta (PRD) (Wu et al., 2006, 2007a, 2007b; Fan and Sun, 2009; Qian et al., 2006; Liu et al., 2004), and the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region (Fu et al., 2014; Wang et al., 2013 ; Fan and Li, 2008). Some studies have focused on severe haze events, for which visibility is equal to or less than 5 km (QX/T 113-2010), which have occurred frequently in many Chinese cities over recent decades (Liu et al., 2013; Wu et al., 2009; Fu et al., 2008; Wu, 2005). Meanwhile, some studies using single surface meteorological factors (such as visibility, relative humidity or wind speed) have discussed their relationship with haze (Ding and Liu, 2014; Chen et al., 2012; Wu et al., 2008; Sun, 1985). Other researchers have tended to study the correlation between haze and air pollutant components (including particulate matter (PM) concentrations and gaseous pollutants) accompanying a heavy haze event (Zheng et al., 2014; Zhao et al., 2013; Wang and Hao, 2012; Sun et al., 2006). In fact, most studies have focused on comparatively fragmented regions. Very limited research has analyzed haze climate characteristics and the long-term trend of haze in the whole of China. At the same time, haze phenomena have resulted from the comprehensive effects of natural and anthropogenic factors, such as geographic factors (altitude), meteorological factors, and man-made pollution. In addition, these comprehensive effects of haze formation could be potentially related to the environmental capacity (limiting the size of anthropogenic emissions). However, there is lack of combined-effect analysis on this important issue.

The purpose of this study is to analyze the climatic characteristics and long-term spatial and temporal variations of haze occurrence in China from 1961 to 2012. Our manuscript is divided into 4 parts. First, this paper describes the temporal variations of haze. The long-term trend of haze (from 1961 to 2012) in China is analyzed. Monthly trends and seasonal trends using the data of hazy days, precipitation and Air Pollution Index (API) of PM10 are also discussed. Second, in order to describe the phenomenon more clearly, this paper discusses research on the spatial distribution of haze during 6 periods by interannual comparison. Third, to better understand the impact of meteorological parameters on haze occurrence, the correlations between haze and 6 meteorological factors (pressure, temperature, wind speed, relative humidity, precipitation, and sunshine duration) at different altitudes are described. Finally, this paper attempts to explain the causes of the haze trend based on meteorological factors and anthropogenic activities.

1. Data and methods 1.1. Data The location and altitude information for 1701 meteorological stations and 83 PM stations is shown in Fig. 1. In this paper, if a station’s altitude is lower than 1000 m (blue areas in Fig. 1), we defined this station as a ‘low altitude’ station, otherwise the station is categorized as a ‘high altitude’ station (Hu 2009).

1.1.1. Meteorological data Drawing from surface meteorological observations, various types of meteorological data have been recorded since 1951, but the standard surface observation criteria were established and followed starting in 1954 (CMA, 1954). Meanwhile, the number of observation stations has increased from approximately 400 stations in 1954 to about 2400 stations in 2012. Thus, some selection of stations was needed. Selection criteria included the following: (1) a month was considered to have sufficiently complete data if there were seven or fewer missing days within that month; (2) a year was considered to have sufficiently complete data if all months were complete according to (1). While the selected stations in 1961-2012 do not cover all areas equally, their coverage is sufficient for the purposes of this study. Based on surface observations in China, a total of 1701 meteorological stations with successive identifiers and available data were selected during the period 1961-2012 in this work (CMA, 2010; Powell and Keim, 2014). The geographical location of the stations is shown in Fig. 1. In this study, the hourly data used included 1701 meteorological observation stations covering air temperature, air pressure, precipitation, wind speed and direction, relative humidity, visibility, and altitude at surface stations. The daily data also used these stations covering sunshine duration, fog, haze, dust and all other weather phenomena. All data were recorded from 1701 meteorological stations over a 52-year period from January 1st 1961 to December 31th 2012 in China by the National Meteorological Information Center (NMIC). Among these data, wind speed and direction were observed at 10 m off the ground, while air temperature and

Please cite this article as: Han, R., et al., Spatial and temporal variation of haze in China from 1961 to 2012, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.12.033

J O U RN A L OF E N V I RO N ME N TA L S CI EN CE S X X (2 0 1 6 ) XX X–XXX

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PM station Meteorological station

Fig. 1 – Locations and altitudes of 1701 meteorological stations and 83 PM stations in China used in this study (GS(2016)360).

relative humidity were collected at 1.5 m the ground. Before 1980, visibility was recorded using 10 classes based on distance, and has been recorded by distance since 1980 (Wu et al., 2012). In order to reduce the influence of incompatible data formats, this paper applied the method for obtaining continuity for visibility data before and after 1980 used by Qin et al., (2010). The visibility data from the 1701 stations during 1980-2012 were converted to 10 classes using the method in use before 1980 (CMA, 1954). The average distance of each visibility rank was calculated, and then used to replace the visibility rank records before 1980. The quality control for meteorological data was checked at county, provincial and national levels. This study applied the method used by Ren and Xiong (2007). At each level, there were in total eight types of quality control, including format check, station-parameter check, climatic extreme range check, plausible value check, internal consistency check, temporal consistency check, spatial consistency check, and man-machine interaction check.

1.1.2. Air quality data and API In this study, the daily data from were collected from 83 air quality monitoring stations covering PM10 concentrations, SO2 concentrations, and NOx concentrations at these stations. These data were recorded from January 1st 2003 to December 31st 2012 in China by NMIC. The geographical locations of air quality monitoring stations are also shown in Fig. 1. Quality control was applied to air quality data also using the methods described by Ren and Xiong (2007). The quality control for air quality data included format check, plausible value check, temporal consistency check, and man-machine interaction check. Before 2013, the API (air pollution index) was the official data released by the Ministry of Environment Protection of China (MEP) for routine reporting of air quality (Huang et al.,

2014). A value for the API can be converted to a mass concentration of PM10 using the following equation: APIPM10 ¼

 APIPMupper −APIPMlower
CPMupper −CPMlower þ APIPMlower CPMupper −CPMlower

where C is the mass concentration and I is the API value. The upper and lower standard indices, CPMupper and CPMlower respectively, are the PM10 concentrations corresponding to APIPMupper and APIPMlower, respectively. The upper and lower limits of the API and detailed calculation of PM10 concentration are in Zhang et al. (2003) and Qu et al. (2010). The uncertainty in daily PM10 measurements is less than 1% (Zhang et al., 2003; Oh et al., 2015).

1.1.3. Other data This paper used the Chinese data on emissions and consumption to discuss the relationship between anthropogenic factors, meteorological factors, and haze occurrence. Energy consumption (unit: kg oil equivalent per capita) and electricity consumption (unit: kilowatt-hours per capita) during 1971-2011 were collected in this study. These time series data were provided by the World Bank on April 3rd 2015 (http://data.worldbank.org.cn). In order to further interpret the impact of human activity on decadal variations of haze occurrence, this study also collected SO2, NOx, and VOC emission data (unit: Tg) from 1990 to 2012 (Wang et al., 2014; http://www.meicmodel.org).

1.2. Methods 1.2.1. Annual-average and anomaly of haze days During 1961-2012, the annual-average haze days over 1701 meteorological stations in China was calculated for each year. The decadal-average haze days over 1701 meteorological stations in China was calculated for 10 years. The 52-yr-average

Please cite this article as: Han, R., et al., Spatial and temporal variation of haze in China from 1961 to 2012, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.12.033

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haze days over 1701 meteorological stations in China was calculated for all study years (m = (2012-1961+1) years = 52 years). The annual–average of haze days over the entire 52-yr period at each station could be described with this method, as follows: Given that the annual-sum haze days observed at a single station is xi,j, for meteorological station i and year j, the annual-averaged haze days of all meteorological stations in year j was defined as x and was calculated using Eq. (1): n X

xi ¼

xi; j

i−1

n

ðn ¼ 1701; j ¼ 1961; 1962; 1963; …; 2012Þ

ð1Þ

The 52-year-average haze days of 1701 meteorological stations was defined as x, and was calculated using Eq. (2): m X n X

x¼

xi; j

j−1961 i−1

mn

ðm ¼ 2012; n ¼ 1701Þ

ð2Þ

The anomaly of the number of hazy days was calculated as follows. Then the anomaly of hazy days x′ was calculated using Eq. (3): x0 ¼ xi −xði ¼ 1; 2; 3; …; n; j ¼ 1961; 1962; 1963; …; 2012Þ

ð3Þ

As described earlier, xi is the annual-average haze days for all meteorological stations. If yi is a factor affecting haze, such as air temperature, air pressure, precipitation, wind speed and direction, relative humidity, or sunshine durations, the correlation coefficient r of xi and yi was calculated by Eq. (4):
 ðxi −xÞ yi −y

j¼1961

rx;y ¼ vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u X 2 u X u m
 u m t ðxi −xÞ2  t yi −y j¼1961

ð4Þ

j¼1961

The correlation coefficient testing method was used to test for significance as described below (Wei, 2007). Namely, assuming that the overall correlation coefficient ρ = 0 was established under the condition, the probability density function of the correlation coefficient rx,y was just the t distribution density function; the statistical indicator t can be calculated by Eq. (5): t¼

pffiffiffiffiffiffiffiffiffiffi rx;y m−2 qffiffiffiffiffiffiffiffiffiffiffiffi 1−r2x; j

1.2.4. Other methods The K index was used in distinguishing the effect of air stability (Liao et al., 2012). When the K index is high, it shows that there is a warm atmosphere, abundant water vapor, and stratification instability. K = (t850-t500) + td850-(t700-td700) In the equation, (t850-t500) is equal to the temperature lapse rate, td850 is the low level water vapor conditions, (t700-td700) is the middle degree of saturation. Decadal-average and average-annual haze maps were generated using geostatistical methods by ARCGIS 9.3 software, and the IDW method is the simplest and most widely used spatial interpolator based on the spatial correlation between scattered points (Shepard, 1964; Wackernagel, 1998; Mito et al. 2011).

3. Results and discussion 3.1. Interannual trend analysis and seasonal, monthly variation of haze days

1.2.2. Factors affecting haze days

m X

parametric Mann–Kendall (M-K) test (Mann, 1945; Kendall, 1975). The M-K test can be used to judge whether the annual-average haze trend shows a mutation phenomenon. The principle and method of the M-K test was introduced by these publications (Yao and Ding, 1990; Fu and Wang, 1992; Wei, 2007) and was employed to calculate the trend by MATLAB 7.8.0 software.

ð5Þ

The statistical indicator will comply with the t distribution by degree of freedom (DOF) v = m - 2. Given the significance level α, check the t distribution table; if | t |-tα > 0, then the null hypothesis is rejected and the correlation coefficient rx,y is significant.

1.2.3. Mann–Kendall (M-K) test The trend analysis of the annual-average for haze was based on a non-parametric method. This paper used the non-

The historical trends of annual-sum haze days and the annual-average haze days at 1701 stations during 1961-2012 are shown in Fig. 2. It shows that the number of haze days increased rapidly during the period 1961-2012. The annual-average haze days for all stations has increased from approximately 4 days in 1961 to about 18 days in 2012, with annual average growth rate of 3%. The trough of annualaverage hazy days occurred in 1964 (2 days), and the peak occurred in 2012 (18 days). In addition, the monthly-sum haze days for the 1701 stations during 1961-2012 reached a trough point of 26 days in September 1967, and reached a peak point of 4320 days in December 2011. The anomaly of hazy days and the Mann-Kendall test from 1961 to 2012 are shown in Fig. 3. The intersection (the pink circle) which cuts curves C1 and C2 (the blue line), as shown in Fig. 3, is not within the reliability lines (the red line). According to the Mann-Kendall tests, in this case the trend of haze days did not show a mutation phenomenon during 1961-2012, which means that the annual occurrence of hazy days has been continuously increasing during the period 1961-2012. The increasing trend of haze days can be divided into three periods: 1961-1979, 1980-1989, and 1990-2012, with annualized growth rates of 3.6%, 0.01% and 5.4%, respectively (Fig. 3). The seasonal variation of hazy days is shown in Fig. 4. Over the 52-yr period of record, for most stations, the winter season (December to February) has highest haze occurrence, with an average haze occurrence frequency of 41.1%. Haze days in the autumn season (September to November) exhibit a decadal increasing trend, that is, 12.3% (1960s), 16.2% (1970s), 19.3%

Please cite this article as: Han, R., et al., Spatial and temporal variation of haze in China from 1961 to 2012, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.12.033

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30000

20 Nov

Oct

Sep

Aug

Jul

Jun

Apr

Mar

Feb

Jan

Dec

Average

May

24000

18000 10 12000

Annual-mean hazy days (daya)

Annual-sum days (day)

15

5 6000

0 1961

1966

1971

1976

1981

1986 Year

1991

1996

2001

2006

2011

0

Fig. 2 – Annual-sum hazy days and annual-average hazy days of 1701 stations in China, 1961-2012.

(1980s), 25.5% (1990s), and 30.2% (2000s), with annual-average occurrence frequency of 22.7%. Haze days in the summer season (June to August) have been growing rapidly in recent years, with occurrence frequency of 10.0% in the 1990s and 14.1% in the 2000s, and annual-average occurrence frequency of 10.4%. On the contrary, the occurrence frequency of haze days in spring season (March to May) has decreased from 42.8% in the 1960s to 21.1% after 2000, with an average occurrence frequency of 25.8%. The decrease of haze occurrence in spring might be caused by the change of dust impacts. We have collected the dust data recorded by 1701 meteorological stations from January 1st 1961 to December 31th 2012 in China. Fig. 5a-e show the spatial distribution of dust days over different periods and Fig. 5f shows the seasonal change of dust days. According to Fig. 5a-e, it can be seen that the occurrence of dust weather in northern, northeastern and northwestern China slightly increased

C1 C2 Anomaly of hazy days Reliability line

Spring Summer Autumn

Anomaly of hazy days

Winter

10 8 6 C1 4 2 0 C2 -2 -4 -6 -8 -10

during 1961-1980, and then significantly decreased during 1981-2010. Fig. 5f indicates that dust days mainly occurred in spring, and the number of the dust days also significantly decreased during 1981-2010. Less dust particles lead to fewer haze days. Therefore, the decrease of dust might be the key reason for the decrease of haze occurrence frequency in the spring season. Fig. 6 shows the monthly-average ratio of hazy days, precipitation and Air Pollution Index (API) of PM10 during 2003-2012. As can be seen, hazy days mainly occur in December and January, when PM10 is also highest compared with other months. June has maximum precipitation and the least haze days, as well as the lowest PM10. The correlation test and significance test indicate that the haze days and PM10 are significantly positively correlated (r = 0.52), with a significance of 99%. In contrast, the haze days and precipitation are significantly negatively correlated (r = 0.56), with a

Feb Jan Dec Nov Oct Sep Aug Jul Jun May Apr Mar

1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009

Year Fig. 3 – Anomaly of hazy days by Mann-Kendall test in China, 1961-2012.

26

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 Year 500

1000 1500 2000 2500 3000 3500 40004320

Fig. 4 – The seasonal change of hazy days in China, 1961-2012.

Please cite this article as: Han, R., et al., Spatial and temporal variation of haze in China from 1961 to 2012, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.12.033

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Fig. 5 – The spatial distribution and seasonal change of dust days in China, 1961-2012 (GS(2016)360).

significance of 99%. This is affected by both meteorological conditions and emissions of air pollutants. According to the statistics, the K index was equal to -30.8°C in December 2012, while in July 2012, the K index was equal to 31.3°C. The value of the K index directly illustrated the atmospheric stability. In winter (December to February), the K index is low, and it is shown that the surface atmosphere is relatively stable. Thus the anthropogenic emissions are higher due to coal combustion used for heating, which results in the accumulation of air pollutants and eventually the formation of haze (Wu et al., 2009). In summer (June to August), however,

the K index is high, and it is shown that the surface atmosphere is not relatively stable and easily produces precipitation. Precipitation can help remove some of the particles in the air, and thus reduce the formation of haze. Although it was shown that the monthly-average of haze days strongly correlated with PM10 and precipitation, the gap between the monthly-average haze days of all stations and the upper and lower limits of haze days was greater. That could indicate that the strength of the correlation was also limited. This limitation may come from other meteorological factors; see Section 3.3 for details.

Please cite this article as: Han, R., et al., Spatial and temporal variation of haze in China from 1961 to 2012, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.12.033

Percentage of haze days (%)

10%

100

Percentage of haze Precipitation API of PM10

8%

80

6%

60

4%

40

2%

20

0%

1

2

3

4

5

6

7 8 Month

9

10

11 12

0

API of PM10, and precipitation (mm)

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Fig. 6 – Monthly-average of ratio of hazy days, precipitation, and API of PM10.

3.2. Spatial distribution of haze Fig. 7 shows the spatial distribution of haze days in China over different time periods. The number of stations with more than 50 haze days increased from 14 in the 1960s (Fig. 7a) to 42 in the 1970s (Fig. 7b). During the same period, the number of stations with 10-50 haze days increased by 28. These stations were located in Central and North China, including the Provinces of Shanxi, Hubei, Hunan and Jiangxi extending southeastward from the Provinces of Shaanxi and Henan. Fig. 7a and b also show that the stations in Beijing, the north end of Liaoning, in the middle edge of Heilongjiang, south end of Yunnan, and east end of Qinghai are high incidence areas for haze days. According to the statistics, the maximum decadal-average hazy days of a single station increased from 129 days in the 60s (Fig. 7a) to 228 days in the 70s (Fig. 7b). This station with the highest number of haze days was located in Shanxi. The number of stations with equal to or greater than 10 and less than 50 (namely, 10-50) haze days suddenly increased. These stations were located in Central and Southwest China, surrounding high incidence areas and points where more than 50 haze days occurred. The number of stations with haze days less than 10 also appeared to experience a rapid boom; these were located in the South, Northeast and Northwest China, mainly the Provinces of Yunnan, Heilongjiang, Jilin, Ningxia, Inner Mongolia, Xinjiang, Gansu and Qinghai. Overall, the regions with more than 10 haze days increased significantly. On the whole, in the 1960s-70s, there was a gradually increasing trend of haze days and a statistically significant regional disparity. Fig. 7c and d depicts the changes in the number of haze days over the periods spanning from 1981 to 2000. During 1981-1990, the average number of haze days was over 6 days; while during 1991-2000, the average number was less than 8 days. Compared with the number in the 1960s (4 days) and 1970s (8 days), there was a slight increase in haze days during 1981-2000. As can be seen, there was an obvious change in the spatial distribution of haze occurrence. The area with high occurrence of haze has been continuously shifting from central to south and east China. The haze days in the west and north of China, like the Provinces of Xinjiang, Tibet,

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Qinghai, Gansu, and Inner Mongolia, dramatically decreased. The number of stations with more than 50 haze days has markedly grown from 35 in the 1980s (Fig. 7c) to 66 in the 1990s (Fig. 7d). These stations were located in the Provinces of Shanxi, Hebei, Henan, Hubei, Hunan and Jiangxi. The high incidence areas of haze spread southeast along the coastline, and gradually formed frequent haze regions such as Jing-Jin-Ji, the YRD, and the PRD. In 1981-1990 (Fig. 7c), the station with the highest number of haze days was located in Beijing, accounting for 148 days, and there were 7 stations where haze days surpassed 100 days. Meanwhile, the station in Shanxi Province reached the peak in the 1990s, with 261 days, the only station with more than 200 days. Between 1991 and 2000 (Fig. 7d), there were 12 stations with over 100 haze days. The number of stations with 10-50 haze days increased from 255 in the 1980s (Fig. 7c) to 285 in the 1990s (Fig. 7d). These stations mostly are located around stations having more than 50 haze days. As can be seen from Fig. 7c and d, The number of stations with less than 10 haze days has rapidly dropped from 991 stations (Fig. 7c) to 751 stations (Fig. 7d). These stations were located in North, Southwest and Northeast China, mainly in the Provinces of Gansu, Inner Mongolia, Yunnan, Sichuan, Heilongjiang, Liaoning and Jilin. As can be seen from the two graphs, the number of haze days increased slightly, but the geographical differences in haze days has become more and more significant. It can be seen from Fig. 7e that a sharp increase in haze days took place after the year 2000, and haze occurred much more often in Southeast China. During 2001-2012, the annual-average haze days was 13 days. Compared with other periods, there was a rapid increase in the number of haze days. The number of stations with more than 50 haze days increased to 133, and over 55.6% of which were located in the regions of Jing-Jin-Ji, YRD, and PRD, the key haze regions in China (Tong et al., 2007). One station in Shanxi Province had the highest haze occurrence, 285 days. Between 2001 and 2012, there were 32 stations with over 100 haze days. The stations experiencing more than 10 haze days were mainly located in Southeastern China. Stations with haze days less than 1 day were mainly focused in the Provinces of Heilongjiang, Jilin, Liaoning, Ningxia and Guizhou. At the same time, regarding stations without haze, the number of stations increased sharply to 628 stations (Fig. 7e), which were located in the Provinces of Xinjiang, Tibet, Qinghai, Hainan, Gansu, and Inner Mongolia. It is apparent from the five graphs (Fig. 7a-e) that the high occurrence areas of haze shifted from central to south and east China, and the haze day intensities became heavier. Low occurrence areas of haze were mainly concentrated in northern and western China, where the haze day intensity was 0. Fig. 7f shows the spatial distribution of average haze days in China during 1961-2012. It can be seen that the high incidence areas of haze include the Province of Shanxi, Shaanxi, and Henan, regions of Jing-Jin-Ji, YRD and PRD. The top 10 stations all have annual haze days more than 70 days. The YRD and the PRD are the regions having high haze occurrence since 1990. For example, the number of haze days in Shenzhen city increased from 0.4 days in the 1960s to 242 days in 2008; the number of haze days in Nanjing city increased from 0 days in 1961 to 226 days in 2012. The

Please cite this article as: Han, R., et al., Spatial and temporal variation of haze in China from 1961 to 2012, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.12.033

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Fig. 7 – Spatial distribution of average haze days in China (GS(2016)360).

annual-average hazy days of 11 stations in Shanghai increased from 0.4 days in 1969 to 102 days in 2003. In contrast, there was no haze at all in some parts of Xinjiang, Qinghai, Gansu, Tibet, and Hainan during 1961-2012.

3.3. Impact of meteorological conditions on haze occurrence We selected 6 meteorological factors, including diurnal-average data for pressure, temperature, wind speed, relative humidity, precipitation, and sunshine duration, and analyzed their correlations with haze occurrence in China. The correlation

coefficients between diurnal-average haze days and the 6 meteorological factors are shown in Table 1. Correlation is indicated by -, as shown in Table 1. We can see that temperature and pressure are positively correlated with the number of haze days, with correlation coefficients of 0.27 and 0.34, respectively. On the contrary, wind speed, relative humidity, precipitation, and sunshine duration are negatively correlated with the number of haze days, with correlation coefficients of -0.57, -0.47, -0.54 and -0.52, respectively. Moreover, based on the classification method for correlation (Li, 2010), relative humidity, wind speed, precipitation, and

Please cite this article as: Han, R., et al., Spatial and temporal variation of haze in China from 1961 to 2012, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.12.033

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Correlation coefficient

Pressure Temperature Wind speed Relative humidity Precipitation Sunshine durations

0.27 0.34 -0.57 -0.47 -0.54 -0.52

hazy

| t | - tα α= 0.05

α= 0.01

0.05 0.53 3.03 1.85 2.66 2.33

-0.63 -0.15 2.35 1.17 1.98 1.65

days

and

Correlation

weakly weakly significantly significantly significantly significantly

sunshine duration are significantly correlated with the number of haze days; temperature and pressure are weakly correlated. Although haze is correlated with 6 meteorological factors, under the same meteorological factors at different altitudes, the numbers of haze days were different. Fig. 7e shows the spatial distribution of haze days in China during 2001-2012. All the stations with hazy days above 30 days are located in the eastern part of China and the areas with altitude less than 1000 m. 1000 m is the demarcation line between high and low altitudes (Hu, 2009). During this period, the 12-year-average hazy days in areas with low altitude stations was more than 17 days, 29 times more than that in high altitude stations. The visibility in areas with low altitudes was 15.7 km, only half that at high altitudes. In other words, the frequency of haze was higher in the low altitude areas than in the high altitude areas. Meteorological factors are the main factors affecting the self-purification capacity of the atmospheric environment, and indirectly influencing the formation of haze. In China, meteorological factors play different roles in the formation of haze at different altitudes (Table 2). The formation of haze in low altitude areas is negatively correlated with the relative humidity and precipitation, with correlation coefficients of -0.69 and -0.58, respectively. In contrast, the haze formation at the high altitude areas is mainly affected by temperature, wind speed and sunshine duration. In these areas, the number of haze days is found to be positively related to the temperature, with correlation coefficient of 0.82, and negatively related to the wind speed and sunshine duration, with correlation coefficients of -0.58 and -0.79, respectively.

3.4. Impact of human activity on interannual variations of haze occurrence Fig. 8 provides an overview of the historical changes of annual-average hazy days, as well as energy consumption

Table 2 – Role of meteorological factors at different altitudes. Meteorological factors

Low altitudes

High altitudes

Pressure Temperature Relative humidity Precipitation Wind Sunshine durations

uncorrelated uncorrelated -0.69 -0.58 uncorrelated uncorrelated

uncorrelated 0.82 uncorrelated uncorrelated -0.58 -0.79

3500

Electricity consumption (unit: kilowatt-hours per capita) Energy consumption (unit: kg oil equivalent per capita) Haze day (unit: day) 20

3000 15

2500 2000

10

Hazy

Meteorological factors

between

Energy consumption and electricity consumption

Table 1 – Correlation meteorological factors.

1500 1000

5

500 0

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

0

2015

Year

Fig. 8 – The variation of energy consumption (unit: kg oil equivalent per capita) and electricity consumption (unit: kilowatt-hours per capita) during 1961-2011; annual-average hazy days, between 1961 and 2012; all of these data in China come from the World Bank.

and electricity consumption. Fig. 8 shows how the haze occurrence varied along with the change of meteorological factors in 1961-2012. Fig. 10 depicts the variations in anthropogenic emissions of SO2, NOx, and VOC in China during 1990-2012. As discussed in Section 3.1, the growth trends of haze days can be classified into three periods: 1961-1979 (moderate increase), 1980-1989 (stable) and 1990-2012 (rapid increase). As shown in Fig. 8, the average-annual haze days of 1701 stations increased from 4 days in 1961 to 9 days in 1979, with annual average growth rate of 3.6%. During this period, the energy consumption per capita and the power consumption per capita gradually increased from 465.5 kg oil equivalent in 1971 to 619.5 kg oil equivalent in 1979, and from 151.2 kilowatt hours (kWh) in 1971 to 267.4 kWh in 1979, respectively. The correlation coefficients between haze and anthropogenic factors, including energy consumption and electricity consumption, were 0.04 and 0.05, respectively. This implied that anthropogenic activities might not be the cause of the increase in haze days. During the same period, the atmospheric pressure and wind speed increased, while other meteorological factors such as sunshine duration, RH and precipitation did not show substantial changes. The number of haze days was positively correlated with the atmospheric pressure and wind speed, with correlation coefficients of 0.48 and 0.73, respectively. As we discussed in 3.1, the occurrence of dust weather in northern, northeastern and northwestern China slightly increased during 1961-1980. The increase in wind speed might cause an increase in dust particles, which may be the key reason for the increase of haze occurrence frequency. Therefore, we conclude that meteorological factors were the major reason for the increase of haze days during 1961-1979. During 1980-1989 (Fig. 8), the annual average growth rate of hazy days in 1701 stations was only 0.01%. During this period, the energy consumption per capita increased from 609.8 kg oil

Please cite this article as: Han, R., et al., Spatial and temporal variation of haze in China from 1961 to 2012, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.12.033

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equivalent in 1980 to 724.4 kg oil equivalent in 1989. The electricity consumption per capita significantly increased from 281.6 kWh in 1980 to 487.4 kWh in 1989. The correlation coefficients between haze and energy consumption and electricity consumption were 0.16 and 0.21, respectively. This indicates that the anthropogenic activities mildly affected the haze occurrence frequency. During the same period, there was little change in meteorological conditions, as shown in Fig. 9. Therefore, the haze occurrences were stable during 1980-1989. In the third period, the average-annual hazy days of 1701 stations increased from 5 days in 1990 to about 18 days in 2012, with an annual average growth rate of 5.4%. During this period, the energy consumption per capita markedly increased from 767.0 kg oil equivalent in 1990 to 2029.4 kg oil equivalent in 2011. The electricity consumption per capita

significantly increased from 510.6 kWh in 1990 to 3298.0 kWh in 2011. The rapid increase of economic development and energy consumption caused a significant increase in air pollutant emissions. As shown in Fig. 10, the emissions of SO2, NOx, and VOCs in 2012 were 2.34 times, 4.21 times, 2.65 times that in 1990. These pollutants are the main precursors of fine particles, which are recognized as the key reason for haze. This implies that anthropogenic emissions played an important role in haze occurrences during the period 1990-2012. At the same time, sunshine duration, RH and precipitation rapidly decreased (Fig. 9), while atmospheric pressure and wind speed did not change. The decrease of precipitation also contributed to the increase of haze days. Therefore, during the period 1990-2012, both the increase in air pollutant emissions and meteorological factors caused increasing haze occurrence.

3

25

8

1100

7

1050

2 10

6

1000

5

950

4

900

Pressure

Hazy

15

Sunshine duration

20

Wind speed

Pressure (unit: hpa) Sunshine duration (unit: hr) Wind speed (unit: m/sec) Haze day (unit: day)

5

0 1960

1965

1970

1975

1980

1985

1990 Year

1995

2000

2005

1 2015

2010

75

2000

25

Precipitation (unit: mm) RH (unit: %) Haze day (unit: day) 20

70

1600

Hazy

RH

Precipitatipn

15

10 65

1200

5

800 1960

1965

1970

1975

1980

1985

1990 Year

1995

2000

2005

2010

60 2015

0

Fig. 9 – Variation in haze occurrence with changing meteorological conditions in 1961-2012. Please cite this article as: Han, R., et al., Spatial and temporal variation of haze in China from 1961 to 2012, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.12.033

J O U RN A L OF E N V I RO N ME N TA L S CI EN CE S X X (2 0 1 6 ) XX X–XXX

Traffic

Civil

Industry

Power

35

SO2 emissions (Tg)

30

a

25 20 15 10 5 0

1990

1995

2000 Year

2005

2010

1995

2000 Year

2005

2010

35

VOC Emissions (Tg)

30

b

25 20 15 10 5 0

1990

35

NOx emissions (Tg)

30

11

Haze has become a regional issue in China. During the period 1961-2012, the high occurrence areas of haze have shifted from central to south and east China, like Jing-Jin-Ji, YRD, and PRD. There is no haze at all in some parts of Xinjiang, Qinghai, Gansu, Tibet, and Hainan Provinces. Ambient temperature and pressure are weakly positively correlated with the number of haze days. In contrast, wind, precipitation, relative humidity, and sunshine duration are significantly negatively correlated with the number of haze days. Meteorological factors play different roles in the formation of haze at different altitudes. The formation of haze in low altitude areas is correlated with the relative humidity and precipitation. In contrast, the haze formation in high altitude areas is mainly affected by temperature, wind speed and sunshine duration. The growth trends of haze occurrence can be classified into three periods: 1961-1979, 1980-1989 and 1990-2012. During 1961-1979, meteorological factors played an important role in the increase in haze occurrence. In the contrast, during 1990-2012, the rapid increase of air pollutant emissions as well as meteorological factors caused increasing haze occurrence. These results could be potentially helpful to further investigations of the causes of haze, as well as being useful for haze forecasting and air pollution control policy-making.

Acknowledgments

c

This work was supported by the CMA’s special Funds for climate change (No. CCSF201439), the CMA’s special Funds for key technology (No. CMAGJ2015M79) and the MEP’s Special Funds for Research on Public Welfares (No. 201409002).

25 20 15 10

REFERENCES

5 0

1990

1995

2000 Year

2005

2010

Fig. 10 – Anthropogenic emissions of SO2 (a), NOx (b), VOCs (c) in China, 1990-2012.

4. Conclusions This paper summarized the spatial and temporal variation of haze in China from 1961 to 2012, discussed the relationship of meteorological factors to haze occurrence, and preliminarily analyzed the impact of anthropogenic activities and meteorological factors on haze occurrence. The occurrence of haze continuously and rapidly increased in China during the period 1961-2012. The annual-average hazy days for all stations increased from approximately 4 days in 1961 to about 18 days in 2012, with an annual average growth rate of 3%. For most stations, the winter season had the highest haze occurrence frequency.

Al-Saadi, J., Szykman, J., Pierce, R.B., Kittaka, C., Neil, D., Chu, D.A., et al., 2005. Improving national air quality forecasts with satellite aerosol observations. Bull. Amer. Meteor. Soc. 86 (9), 1249–1261. CMA (China Meteorological Administration), 1954. Standard of the Surface Observation about Meteorology. China Meteorological Press, Beijing, China (in Chinese). CMA (China Meteorological Administration), 2003. Standard of the Surface Observation about Meteorology. China Meteorological Press, Beijing, China. CMA (China Meteorological Administration), 2010. National meteorological standard of the People's Republic of China: Observation and forecast levels of haze (QX/T113-2010). China Meteorological Press, Beijing, China (in Chinese). Chen, J., Zhao, C.S., Ma, N., Liu, P.F., Göbel, T., Hallbauer, E., et al., 2012. A parameterization of low visibilities for hazy days in the North China Plain. Atmos. Chem. Phys. 12 (11), 4935–4950. http://dx.doi.org/10.5194/acp-12-4935-2012. Deng, J.J., Du, K., Wang, K., Yuan, C.-S., Zhao, J.J., 2012. Long-term atmospheric visibility trend in Southeast China, 1973-2010. Atmos. Environ. 59, 11–21. Ding, Y.H., Liu, Y.J., 2014. Analysis of long-term variations of fog and haze in China in recent 50 years and their relations with atmospheric humidity. Sci. China: Earth Sci. 57 (1), 36–46. http://dx.doi.org/10.1007/s11430-013-4792-1.

Please cite this article as: Han, R., et al., Spatial and temporal variation of haze in China from 1961 to 2012, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.12.033

12

J O U RN A L OF E N V I RO N ME N TA L S CIE N CE S X X (2 0 1 6 ) XXX –XXX

Doyle, M., Dorling, S., 2002. Visibility trends in the UK 1950-1997. Atmos. Environ. 36 (19), 3161–3172. Fan, Y.Q., Li, C.Q., 2008. Visibility trends in Beijing, Tianjin and Hebei Province during 1980-2003. Plateau Meteor. 27 (6), 1392–1400. Fan, X.Q., Sun, Z.B., 2009. Analysis on features of haze weather in Xiamen City during 1953-2008. Trans. Atmos. Sci. 32 (5), 604–609. Friedlander, S.K., 1977. Smoke, Dust and Haze. Wiley-Interscience, New York, USA. Fu, C.B., Wang, Q., 1992. The definition and detection of the abrupt climatic change. Chinese J. Atmos. Sci. 16 (4), 482–493. Fu, Q.Y., Zhuang, G.S., Wang, J., Xu, C., Huang, K., Li, J., et al., 2008. Mechanism of formation of the heaviest pollution episode ever recorded in the Yangtze River Delta. China. Atmos. Environ. 42 (9), 2023–2036. Fu, G.Q., Xu, W.Y., Yang, R.F., Li, J.B., Zhao, C.S., 2014. The distribution and trends of fog and haze in the North China Plain over the past 30 years. Atmos. Chem. Phys. 14 (21), 11949–11958. Gao, G., 2008. The climatic characteristics and change of haze days over China during 1961–2005 [J]. Acta Geophys. Sin. 63 (7), 761–768. Hand, J.L., Gebhart, K.A., Schichtel, B.A., Malm, W.C., 2012. Increasing trends in wintertime particulate sulfate and nitrate ion concentrations in the Great Plains of the United States (2000-2010). Atmos. Environ. 55, 107–110. Hao, J.M., Ma, G.D., Wang, S.X., 2010. Air Pollution Control Engineering (3rd ed.). Higher Education Press, Beijing, China. Harrison, R.M., Yin, J.X., 2000. Particulate matter in the atmosphere: which particle properties are important for its effects on health? Sci. Total Environ. 249 (1-3), 85–101. Hu, Q.M., 2009. Encyclopedia of China: Chinese Geography. China Encyclopedia Press, Beijing, China. Huang, K., Zhuang, G., Wang, Q., Fu, J.S., Lin, Y., Liu, T., et al., 2014. Extreme haze pollution in Beijing during January 2013: chemical characteristics, formation mechanism and role of fog processing. Atmos. Chem. Phys. Discuss. 14 (6), 7517–7556. http://dx.doi.org/10.5194/acpd-14-7517-2014. Husar, R.B., Holloway, J.M., Patterson, D.E., Wilson, W.E., 1981. Spatial and temporal pattern of eastern U.S. haziness: a summary. Atmos. Environ. 15 (10-11), 1919–1928. Kendall, M.G., 1975. Rank Correlation Methods. Charles Griffin, London, UK. Lee, S., Ho, C.-H., Lee, Y.G., Choi, H.-J., Song, C.-K., 2013. Influence of transboundary air pollutants from China on the high-PM10 episode in Seoul, Korea for the period October 16-20, 2008. Atmos. Environ. 77, 430–439. Li, J.M., 2010. General Theory of Statistics (5th ed.). Fudan University Press, Shanghai, China. Liao, B.T., Wu, D., Chen, J., Li, F., Chen, H.H., Liu, Y.C., et al., 2012. A Study of the variation trends of haze and application of the vertical exchange coefficient. J. Trop. Meteor. 28 (3), 417–424. Liu, A.J., Du, Y.D., Wang, H.Y., 2004. Climatic characteristics of haze in Guangzhou. Meteor. Mon. 30 (12), 68–71. Liu, X.G., Li, J., Qu, Y., Han, T., Hou, L., Gu, J., et al., 2013. Formation and evolution mechanism of regional haze: a case study in the megacity Beijing. China. Atmos. Chem. Phys. 13 (9), 4501–4514. Malm, W.C., Schichtel, B.A., Pitchford, M.L., Ashbaugh, L.L., Eldred, R.A., 2004. Spatial and monthly trends in speciated fine particle concentration in the United States. J. Geophys. Res.: Atmos. 109(D3), D03306. Mann, H.B., 1945. Nonparametric test against trend. Econometrica 13 (3), 245–259. Mito, Y., Ismail, M.A.M., Yamamoto, T., 2011. Multidimensional scaling and inverse distance weighting transform for image processing of hydrogeological structure in rock mass. J. Hydrol. 411 (1-2), 25–36. Niu, Y.W., Gu, J.Q., Pu, J.J., Li, W.J., Wu, J.S., 2010. The long-term variation of haze weather in urban areas of Zhejiang. J. Trop. Meteor. 26 (6), 807–812.

Oanh, N.T.K., Upadhyay, N., Zhuang, Y.H., Hao, Z.P., Murthy, D.V.S., Lestari, P., et al., 2006. Particulate air pollution in six Asian cities: Spatial and temporal distributions, and associated sources. Atmos. Environ. 40 (18), 3367–3380. Oh, H.-R., Ho, C.-H., Kim, J., Chen, D.L., Lee, S., Choi, Y.-S., et al., 2015. Long-range transport of air pollutants originating in China: A possible major cause of multi-day high-PM10 episodes during cold season in Seoul. Korea. Atmos. Environ. 109, 23–30. Panyacosit, L., 2000. A review of particulate matter and health: focus on developing countries. IIASA, Laxengurg, Austria. Powell, E.J., Keim, B.D., 2014. Trends in daily temperature and precipitation extremes for the southeastern United States: 1948-2012. J. Climate 28 (4), 1592–1612. http://dx.doi.org/10. 1175/JCLI-D-14-00410.1. Qian, J.P., Huang, F., Du, J., Wang, G.F., Jin, A.F., Peng, L.J., 2006. Spatial-temporal characteristic of meteorological visibility under fog and haze condition in Guangdong province. Ecol. Environ. Sci. 15 (6), 1324–1330. Qin, S.G., Shi, G.Y., Chen, L., Wang, B., Zhao, J.Q., Yu, C.W., et al., 2010. Long-term variation of aerosol optical depth in China based on meteorological horizontal visibility observations. Chinese J. Atmos. Sci. 34 (2), 449–456. Qu, W.J., Arimoto, R., Zhang, X.Y., Zhao, C.H., Wang, Y.Q., Sheng, L.F., 2010. Spatial distribution and interannual variation of surface PM10 concentrations over eighty-six Chinese cities. Atmospheric Chemistry and Physics 10, 5641–5662. Ramanathan, V., Ramana, M.V., 2003. Atmospheric brown clouds: Long-range transport and climate impacts. EM: Air and Waste Management Association's Magazine for Environmental Managers December 2003 Issue, 28-33. Schichtel, B.A., Husar, R.B., Falke, S.R., Wilson, W.E., 2001. Haze trends over the United States 1980-1995. Atmos. Environ. 35 (30), 5205–5210. Shepard, D., 1964. A two-dimensional interpolation function. Proceedings of the 1968 23rd ACM national conference. ACM New York, NY, USA, pp. 517–524. Sloane, C.S., 1983. Summertime visibility declines: Meteorological influences. Atmos. Environ. 17 (4), 763–774. Sloane, C.S., 1984. Meteorologically adjusted air quality trends: Visibility. Atmos. Environ. 18 (6), 1217–1229. Sun, J.Q., 1985. The relationship between visibility and relative humidity. Acta Meteor. Sinica 43 (2), 230–234. Sun, Y.L., Zhuang, G.S., Tang, A.H., Wang, Y., An, Z.S., 2006. Chemical characteristics of PM2.5 and PM10 in haze-fog episodes in Beijing. Environ. Sci. Technol. 40 (10), 3148–3155. Tong, Y.Q., Yin, Y., Qian, L., An, J.L., 2007. Analysis of the characteristics of hazy phenomena in Nanjing area. China Environ. Sci. 27 (5), 584–588. Tsai, Y.I., Kuo, S.C., Lee, W.J., Chen, C.L., Chen, P.T., 2007. Long-term visibility trends in one highly urbanized, one highly industrialized, and two rural areas of Taiwan. Sci. Total Environ. 382 (2-3), 324–341. Wackernagel, H., 1998. Multivariate Geostatistics: An Introduction with Applications. Springer-Verlag, Berlin, Germany. Wang, S.X., Hao, J.M., 2012. Air quality management in China: issues, challenges, and options. J. Environ. Sci. 24 (1), 2–13. Wang, X.Q., Sun, M.S., Yang, T., Wang, Z.F., 2013. Interdecadal change in frequency of dust-haze episodes in North China Plain. Climatic Environ. Res. 18 (2), 165–170. Wang, S.X., Zhao, B., Cai, S.Y., Klimont, Z., Nielsen, C.P., Morikawa, T., et al., 2014. Emission trends and mitigation options for air pollutants in East Asia. Atmos. Chem. Phys. Discuss. 14 (2), 2601–2674. Wei, F.Y., 2007. Modern Climatic Statistical Diagnosis and Prediction (2nd ed.). China Meteorological Press, Beijing, China. Wu, D., 2005. A discussion on difference between haze and fog and warning of ash haze weather. Meteor. Mon. 31 (4), 3–7. Wu, D., 2012. Hazy weather research in China in the last decade: A review. Acta Scient. Circumst. 32(2), 257-269.

Please cite this article as: Han, R., et al., Spatial and temporal variation of haze in China from 1961 to 2012, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.12.033

J O U RN A L OF E N V I RO N ME N TA L S CI EN CE S X X (2 0 1 6 ) XX X–XXX

Wu, D., Bi, X.Y., Deng, X.J., Li, F., Tan, H.B., Liao, G.L., et al., 2006. Effect of atmospheric haze on the deterioration of visibility over the Pearl River Delta. Acta Meteor. Sinica 64 (4), 510–518. Wu, D., Deng, X.J., Bi, X.Y., Li, F., Tan, H.B., Liao, G.L., 2007a. Study on the visibility reduction caused by atmospheric haze in Guangzhou area. J. Trop. Meteor. 13 (1), 77–80. Wu, D., Deng, X.J., Bi, X.Y., Li, F., Tan, H.B., 2007b. Distinguishing of fog or haze and the operational criteria for observation, forecasting and early warning of haze in urban areas of Guangdong, Hong Kong and Macau. Guangdong Meteor. 29 (2), 5–10. Wu, D., Liao, G.L., Deng, X.J., Bi, X.Y., Tan, H.B., Li, F., et al., 2008. Transport condition of surface layer under haze weather over the Pearl River Delta. J. Appl. Meteor. Sci. 19 (1), 1–9. Wu, D., Wu, X.J., Zhu, X.X., 2009. Fog and Haze. China Meteorological Press, Beijing, China. Wu, D., Wu, X.J., Li, F., Tan, H.B., Chen, J., Cao, Z.Q., et al., 2010. Temporal and spatial variation of haze during 1951-2005 in Chinese mainland. Acta Meteor. Sinica 68 (5), 680–688. Wu, J., Fu, C.B., Zhang, L.Y., Tang, J.P., 2012. Trends of visibility on sunny days in China in the recent 50 years. Atmos. Environ. 55, 339–346. Ren, Z.H, Xiong, A.Y., 2007. Operational system development on three-step quality control of observations from AWS. Meteor. Mon. 33(1), 19-24.

13

Yao, Z.S., Ding, Y.G., 1990. Climatic Statistical. China Meteorological Press, Beijing, China. Zhang, X.Y., Gong, S.L., Shen, Z.X., Mei, F.M., Xi, X.X., Liu, L.C., Zhou, Z.J., Wang, D., Wang, Y.Q., Cheng, Y., 2003. Characterization of soil dust aerosol in China and its transport and distribution during 2001 ACE-Asia: 1. Network observations. J. Geophys. Res. 108, 1984–2012. Zhang, X.Y., Wang, Y.Q., Niu, T., Zhang, X.C., Gong, S.L., Zhang, Y.M., et al., 2012. Atmospheric aerosol compositions in China: spatial-temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols. Atmos. Chem. Phys. 12 (2), 779–799. Zhao, X.J., Zhao, P.S., Xu, J., Meng, W., Pu, W.W., Dong, F., et al., 2013. Analysis of a winter regional haze event and its formation mechanism in the North China Plain. Atmos. Chem. Phys. 13 (11), 5685–5696. http://dx.doi.org/10.5194/acp-13-5685-2013. Zheng, G.J., Duan, F.K., Su, H., Ma, Y.L., Cheng, Y., Zheng, B., et al., 2014. Exploring the severe winter haze in Beijing: the impact of synoptic weather, regional transport and heterogeneous reactions. Atmos. Chem. Phys. Discuss. 14 (12), 17907–17942.

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