Air pollution characteristics and their relation to meteorological conditions during 2014–2015 in major Chinese cities

Environmental Pollution xxx (2017) 1e13

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Air pollution characteristics and their relation to meteorological conditions during 2014e2015 in major Chinese cities* Jianjun He a, b, *, Sunling Gong a, Ye Yu c, Lijuan Yu d, Lin Wu b, Hongjun Mao b, Congbo Song b, Suping Zhao c, Hongli Liu a, Xiaoyu Li b, Ruipeng Li b a

State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China The College of Environmental Science & Engineering, Nankai University, Tianjin 300071, China c Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China d Jinan Meteorological Bureau, Jinan 250002, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 August 2016 Received in revised form 13 December 2016 Accepted 17 January 2017 Available online xxx

In January 2013, the real-time hourly average concentrations of six pollutants (CO, NO2, O3, PM10, PM2.5 and SO2) based on data from air quality monitoring stations in major Chinese cities were released to the public. That report provided a good opportunity to publicise nationwide temporal and spatial pollution characteristics. Although several studies systematically investigated the temporal and spatial trends of pollutant concentrations, the relation between air pollution and multi-scale meteorological conditions and their spatial variations on a nationwide scale remain unclear. This study analysed the air pollution characteristics and their relation to multi-scale meteorological conditions during 2014e2015 in 31 provincial capital cities in China. The annual average concentrations of six pollutants for 31 provincial capital cities were 1.2 mg m
3, 42.4 mg m
3, 49.0 mg m
3, 109.8 mg m
3, 63.7 mg m
3, and 32.6 mg m
3 in 2014. The annual average concentrations decreased 5.3%, 4.9%, 11.4%, 12.0% and 21.5% for CO, NO2, PM10, PM2.5 and SO2, respectively, but increased 7.4% for O3 in 2015. The highest rate of a major pollutant over China was PM2.5 followed by PM10, O3, NO2, SO2 and CO. Meteorological conditions were the primary factor determining day-to-day variations in pollutant concentrations, explaining more than 70% of the variance of daily average pollutant concentrations over China. Meteorological conditions in 2015 were more adverse for pollutant dispersion than in 2014, indicating that the improvement in air quality was caused by emission controls. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Air pollution Meteorological conditions Emission control Artificial neural network

1. Introduction Rapid economic development and increasing urbanization have rendered air pollution an important and timely issue in Chinese society. Pollution adversely affects health (An et al., 2015) and threatens sustainable development of the economy and society. Unfortunately, the pollutant levels in China are much higher than the values recommended by the World Health Organization (Chai et al., 2014). Although the concentration of particulate matter with an aerodynamic diameter of less than 2.5 mm (PM2.5)

*

This paper has been recommended for acceptance by David Carpenter. * Corresponding author. State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China. E-mail address: [email protected] (J. He).

decreased approximately 23% between 2003 and 2010 in China (Zhou et al., 2016), the Chinese population-weighted mean PM2.5 concentration is the highest value of the world's 10 most populous countries and increased significantly between 1990 and 2010 (Brauer et al., 2016). Chinese air pollution problems attract special attention from government, the public and researchers. Severe atmospheric pollution is closely related to a large number of emission sources and high emission intensity, unfavourable meteorological conditions, special terrain, pollutant transport, and chemical conversion in the atmosphere (Chen et al., 2009; Crippa et al., 2013; Gao et al., 2011; He et al., 2013, 2016a; Liu et al., 2016; Pearce et al., 2011; Wang et al., 2014a; Wu et al., 2011; Zhang et al., 2012, 2015). Meteorological conditions are the primary factor causing the day-to-day variations in pollutant concentrations (He et al., 2016a). Dispersion conditions are determined by large-scale weather phenomena as well as local meteorology.

http://dx.doi.org/10.1016/j.envpol.2017.01.050 0269-7491/© 2017 Elsevier Ltd. All rights reserved.

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J. He et al. / Environmental Pollution xxx (2017) 1e13

Previous research revealed that both emissions and meteorological variations dominated the long-term pollutant concentration trend (Wang et al., 2015). The root cause of atmospheric pollution is that pollutant emissions exceed atmospheric environmental capacity (An et al., 2007). The total emissions of pollutants in China reached 1.97  107, 2.08  107, and 1.74  107 per ton for sulphur dioxide (SO2), nitrogen oxides (NOx) and dust, respectively, in 2014 (National Bureau of Statistics and Ministry of Environmental Protection, 2015). Large amounts of pollutant emissions and the associated uncertainty render controlling air pollution more difficult. The Chinese government enacted the ‘Air Pollution Prevention Action Plan’ on September 10, 2013. That new law of environmental protection, which was called the most stringent law in history, was implemented on January 1, 2015. This law was regarded as an important milestone in Chinese air pollution prevention. According to the bulletin of the Chinese environment (http://www.zhb.gov. cn/gkml/hbb/qt/201606/W020160602413860519309.pdf), the annual emissions of SO2 and NOx decreased 5.8% and 10.9%, respectively, in 2015. Long-term or large-scope observation of pollutant concentrations can develop an understanding of pollution characteristics and formation mechanisms. Because there are limited data available, Chinese researchers focus on local pollution characteristics in the early stages. For example, Chu et al. (2008) introduced the seasonal variation of SO2 concentration and its relation to meteorological conditions in Lanzhou. Seven months of continuous air quality observation at a suburban location was conducted to reveal pollution characteristics, regional transport and correlations to meteorological conditions in the North China Plain (Xu et al., 2011). A large number of observations during the Beijing 2008 Olympic Games revealed pollution trends and the effectiveness of emissions control (Liu et al., 2012; Wang et al., 2009). Local particulate matter (PM) observation from 1988 to 2010 in China was reviewed by Zhou et al. (2016). Although some researchers have attempted to identify temporal and spatial trends of air pollution on a nationwide scale (Qu et al., 2010), such studies are relatively scarce. In January 2013, the real-time hourly average concentrations of six pollutants, i.e., PM2.5, particulate matter with an aerodynamic diameter of less than 10 mm (PM10), carbon monoxide (CO), nitrogen dioxide (NO2), SO2, and ozone (O3), were released to the public based on air quality monitoring (AQM) stations in major Chinese cities (http://106.37.208.233:20035/). This event provided an excellent opportunity to expose nationwide temporal and spatial pollution characteristics. Hu et al. (2014) investigated the concentrations of six pollutants in 33 cities located in the North China Plain and the Yangtze River Delta from June 1 to August 31, 2013 and observed strong temporal correlations for PM among cities within 250 km. Spatial distribution, seasonal variations, and diurnal variations of PM2.5 in 190 cities in China have been reported (Zhang and Cao, 2015). Wang et al. (2014b) systematically examined the spatial and temporal variations of six pollutants in 31 provincial capital cities for the first time. Xie et al. (2015) studied the correlations between six pollutants in 31 provincial capital cities. In previous works, we also examined annual and diurnal variations of six pollutants in 31 provincial capital cities based on cluster analysis (Zhao et al., 2016). Although these studies systematically investigated the temporal and spatial trends of pollutant concentrations, the relation between air pollution and multi-scale meteorological conditions and their spatial variations on a nationwide scale remain unclear. Based on the above concerns, air pollution characteristics in 31 provincial capital cities in China during 2014e2015 are examined in this paper. The correlations between air pollution and meteorological conditions and their spatial variations are investigated. An artificial neural network (ANN) model combined with wavelet

transformation was constructed to quantify the effect of meteorological conditions by the explained variance. 2. Data and methods 2.1. Air quality data and quality control New ‘Ambient air quality standard’ was published by the Ministry of Environmental Protection (MEP) and the General Administration of Quality Supervision, Inspection and Quarantine of China in 2012. For the first time, PM2.5 was included in the index system. The ambient air quality standard (GB 3095-2012) provides the annual mean concentration limit for SO2, NO2, PM2.5 and PM10. The annual mean concentration limit of the Chinese Ambient Air Quality Standards (CAAQS) is provided in Table S1. In addition, the ‘Technical Regulation of Ambient Air Quality Index’ (HJ 633-2012) has been released. In this regulation, the Air Quality Index (AQI) replaces the Air Pollution Index (API). Air quality is divided into six ranks according to the range of AQI (0e50: excellent air quality, the air quality is satisfactory; 51e100: good air quality, the air quality is acceptable, but some pollutants may have a weak effect on health for a very small number of sensitive groups; 101e150: light pollution, sensitive groups have exacerbated symptoms, healthy people have symptoms of irritation; 151e200: moderate pollution, the symptoms in sensitive groups are further aggravated, health people suffer an adverse effect on heart and respiratory system; 201e300: severe pollution, the number of patients of heart and lung disease significantly increases, exercise tolerance of sensitive groups reduces, healthy people have symptoms commonly; 301e500: very serious pollution, healthy people have significant symptoms and their exercise tolerance reduces). Daily individual Air Quality Index (IAQI) values are calculated with concentrations of individual pollutants. A daily ‘major pollutant’ is identified for every city to determine which pollutant contributes the most to the air quality degradation (the maximum IAQI in six pollutants) when the daily AQI is greater than 50. The daily AQI and major pollutants are posted by the MEP on their website (http://datacenter.mep.gov.cn/ report/air_daily/air_dairy.jsp). The hourly average concentrations of six pollutants are released by the China National Environmental Monitoring Center. Between 4 and 17 AQM sites were established in each city (Zhao et al., 2016). Reflecting a large diversity of potential sources of air pollution variability, the majority of the monitoring sites are in urban areas, with a few in suburban and rural areas which represent the pollution level in the background. The environmental conditions near the sampling sites, such as characteristics of underlying surface and the main emission sources, are relatively stable. SO2, NO2 and O3 are measured by the ultraviolet fluorescence method, the chemiluminescence method, and the UVspectrophotometry method respectively. CO is measured using the non-dispersive infrared absorption method and the gas filter correlation infrared absorption method. The micro oscillating balance method and the b absorption method are used to measure PM2.5 and PM10. The details of AQM sites for each city, the arrangement of sampling sites and the instruments are in Zhao et al. (2016). The daily AQI, major pollutants and concentrations of six pollutants from January 2014 to December 2015 in 31 provincial capital cities were analysed. Although the quality assurance of hourly average concentrations was conducted before releasing data, further quality control is necessary. The method of quality control is similar to the method in the previous document (Barrero et al., 2015). First, the time series of hourly average concentrations were standardized using the z scores method, and then the data were removed from the original time series when meeting the following conditions: an absolute z score larger than 4 (jzt j > 4); the increment of the z score between the current time and a previous time

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larger than 6 (zt
zt
1 > 6); and the ratio of the z score to its centred moving average of order 3 larger than 2 (3zt =ðzt
1 þ zt þ ztþ1 Þ > 2). Determined to be an abnormal value according to the foregoing conditions, if the ratio of data compared with one-hour earlier data was less than twice the average ratio of all urban AQM sites, the rejected value was re-reserved as valid data. The hourly average concentrations in each city were calculated based on the spatial average of multiple AQM site data when valid data exceeded 80%. When valid hourly average concentrations exceeded 80%, daily average concentrations in each city were acquired to further analyse pollution characteristics. The extra 15days' concentrations (December 17 to 31, 2013, January 1 to 15, 2016) were included to calculate monthly moving average concentration.

et al., 2009; Fernando et al., 2012). The high variability of time series renders accurate prediction a difficult task (Feng et al., 2015). The decomposition of high variability time series is an effective method with which to solve this problem. By wavelet decomposition of pollutant concentration, the low frequency signal describes annual variations in pollutant concentration, and a high frequency signal describes abrupt variations in the time series (Chen and Ma, 2006). Previous studies indicated that ANN combined with wavelet transformation could effectively improve air quality prediction (Feng et al., 2015; Siwek and Osowski, 2012). BP-ANN combined with wavelet transformation (hereafter referred as WT-ANN) was used in this study. The original times series x(n) were reconstructed in a simple manner by adding the series together (Siwek and Osowski, 2012):

2.2. Meteorological data

xðnÞ ¼ d1 ðnÞ þ d2 ðnÞ þ $$$ þ dj ðnÞ þ aj ðnÞ

The multi-scale interactions of meteorological conditions affect air quality in complex manners (He et al., 2016a; Jiang et al., 2014). Routine meteorological data in 31 provincial capital cities from January 2014 to December 2015 were obtained from the Meteorological Information Comprehensive Analysis and Process System (MICAPS) of the Chinese Meteorological Administration and used to analyse the relation to air pollution. The observations include 2-m temperature (T2), 2-m relative humidity (RH2), 10-m wind speed (WS10) and wind direction (WD10), sea level pressure (Ps) and 6 h of accumulated precipitation (PREC) and are available every 3 h at 02:00, 05:00, 08:00, 11:00, 14:00, 17:00, 20:00, and 23:00 (Beijing time, hereafter referred as BT). U-component and V-component wind speed (UU and VV) were calculated based on the vector decomposition of the wind field. The planetary boundary layer height (Hpbl) from January 2014 to December 2015, acquired from ERA-Interim reanalysis data of the European Center for MediumRange Weather Forecasts (ECMWF), available every 6 h, was used in this paper. Daily average meteorological parameters in each city were calculated based on the arithmetic average method. Classification of circulation types is effective in air pollution research (He et al., 2016a; Jiang et al., 2014). In this study, the Tmode principal component analysis (PCA) combined with the Kmeans cluster was used because this method is considered the most effective in revealing data structure and can identify circulation types accurately (Huth, 1996). Here, the gridded sea level pressure at 08:00 BT during 2001e2015 covering China and surrounding areas (70 Ee140 E/15 Ne55 N) was extracted from the ERA-Interim reanalysis data and used for circulation classification. First, the components were acquired from the normalized Ps using the PCA method according to the cumulative variance contribution of 85%. Second, the components were clustered using K-means cluster, and circulation classification was determined. The number of clusters depends on the criterion function (Liu and Gao, 2011). In this study, 9 clusters were identified (i.e., CT1eCT9).

where d1-dj represent the detailed coefficients and aj is the coarse approximation of x(n) on j level. Daubechies Db5 wavelets were implemented on the Matlab platform. The 5-level wavelet decomposition was used because its demonstrated good performance in previous studies (Feng et al., 2015; Siwek and Osowski, 2012). Because of the lag of effect of meteorological conditions on air pollution (He et al., 2013), the input layer includes meteorological conditions (eight local meteorological parameters, i.e., T2, RH2, UU, VV, WS10, Ps, Hpbl, and PREC, and circulation type represented by average pollutant concentrations for each circulation type) for the current and previous days. To avoid multicollinearity of local meteorological parameters, six principal components were acquired using the PCA method according to the cumulative variance contribution of 85% and was the input representing the local meteorological conditions. Daily average pollutant concentrations for the previous day were used as the input to represent the cumulative effects of pollution. Apart from the meteorological conditions and cumulative effects, the year and month were considered in the input to represent emission information simply. The output layer was the daily average pollutant concentrations or their wavelet decomposition. The ANN model was constructed individually for individual pollutant. To avoid the appearance of negative value, the daily average pollutant concentrations or their wavelet decomposition was transformed to logarithmic form during the training process and finally returned to normal form. The input and output data were standardized using the z scores method during the training process, which could diminish the effects of units and the magnitude of different variables. A single hidden layer of 8 neurons was used in this study because their performance on air pollution prediction was sufficiently verified in previous studies (Cai et al., 2009; Feng et al., 2015; He et al., 2013, 2016a). To be more precise and avoid overfitting, the data were randomly divided into three subsets: the training set, the test set and the validation set, which account for 80%, 10% and 10% of the total data, respectively. Because of random initial weight, hundred tests were carried out and relative stable result was accepted, which is useful for ANN model to avoid overfitting. The six components of local meteorological parameters were decomposed using 5-level wavelet decomposition on the sensitivity test. Twelve sensitivity tests, including wavelet decomposition of pollution concentration (WDC), wavelet decomposition of meteorological conditions (WDM), the meteorological conditions in the previous day (M-1), pollutant concentrations in the previous day (C-1), and separated training for each coefficient and the coarse approximation from the transformation of pollutants concentrations (ST), were implemented to determine the optimal setting (Table S2). The WT-ANN model was used to quantify the effect of meteorological conditions on day-to-day variations of pollutant

2.3. Wavelet-artificial neural network model A single meteorological parameter could not reflect the overall relation between meteorological conditions and air pollution. A combination of multi-scale meteorological conditions using the ANN model is a useful method with which to study this complex relation (Jiang et al., 2014; He et al., 2016a). There are many types of ANN algorithms, among which the back-propagation (BP) algorithm and its improvement algorithm are among the simplest and most widely applied in air pollution prediction (Cai et al., 2009). The performance of ANN in air pollution prediction has been widely evaluated and is better than the multiple linear regression (MLR) model or air quality numerical model under certain conditions (Cai

(1)

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J. He et al. / Environmental Pollution xxx (2017) 1e13

Table 1 Annual average concentrations of six pollutants in 31 provincial capital cities during 2014e2015 (mg m
3 for CO, mg m
3 for other pollutants).a Cities

CO

Beijing (BJ) Changchun (CC) Changsha (CS) Chengdu (CD) Chongqing (CQ) Fuzhou (FZ) Guangzhou (GZ) Guiyang (GY) Haerbin (HEB) Haikou (HK) Hangzhou (HZ) Hefei (HF) Huhehaote (HT) Jinan (JN) Kunming (KM) Lasa (LS) Lanzhou (LZ) Nanchang (NC) Nanjing (NJ) Nanning (NN) Shanghai (SH) Shenyang (SY) Shijiazhuang (SJZ) Taiyuan (TY) Tianjin (TJ) Wuhan (WH) Wulumuqi (WQ) Xian (XA) Xining (XN) Yinchuan (YC) Zhengzhou (ZZ) 31 urban average

NO2

O3

PM10

PM2.5

SO2

2014

2015

2014

2015

2014

2015

2014

2015

2014

2015

2014

2015

1.3 0.9 1.1 1.1 1.1 0.9 1.0 0.8 0.9 0.7 0.9 1.0 1.9 1.2 1.1 1.0 1.4 1.0 0.9 1.1 0.8 1.1 1.5 1.7 1.7 1.2 1.4 1.8 1.3 1.2 1.8 1.2

1.3 0.9 1.0 1.1 1.1 0.7 1.0 0.7 1.0 0.7 0.9 1.0 1.3 1.4 1.0 0.6 1.4 0.9 1.0 1.0 0.9 1.0 1.4 1.6 1.4 1.1 1.4 1.8 1.4 1.1 1.5 1.1

55.3 44.6 40.7 53.0 37.4 32.8 46.5 28.6 52.2 14.7 47.3 27.6 45.0 54.3 33.8 20.5 43.1 30.7 51.4 36.5 44.2 50.1 51.6 35.7 55.0 53.1 55.2 45.6 33.5 38.2 49.7 42.2

48.3 43.0 36.3 49.4 43.4 30.7 44.7 26.3 49.5 13.1 45.4 32.7 38.8 51.8 27.7 20.3 47.4 29.1 50.3 31.7 45.7 46.0 48.7 36.4 42.1 48.7 50.8 42.6 33.4 34.4 55.6 40.1

56.6 51.0 44.4 46.9 35.1 60.9 49.4 48.0 42.5 40.6 54.6 34.4 41.7 69.5 48.3 80.0 40.1 48.1 56.6 45.8 67.9 57.0 49.4 39.8 49.4 59.2 32.0 36.3 40.2 47.7 45.0 49.0

59.2 61.5 49.6 52.9 36.2 54.9 43.4 52.2 41.9 51.0 56.1 44.8 52.6 64.8 54.0 80.9 47.4 52.4 60.2 47.7 72.1 57.0 48.2 43.7 47.1 57.0 36.3 42.9 56.9 52.4 54.2 52.6

121.6 113.7 99.5 117.4 95.4 63.5 69.3 69.7 110.1 40.7 96.3 115.8 116.4 173.7 64.0 56.6 121.7 81.2 124.9 82.4 74.7 120.9 213.8 130.1 136.1 119.1 154.2 148.1 118.0 105.1 150.8 109.8

108.4 102.9 81.2 103.0 84.8 54.5 59.9 59.0 103.8 38.4 81.7 96.9 103.7 162.7 53.1 57.8 113.9 73.5 97.1 70.9 74.2 111.4 147.6 109.6 120.4 107.0 132.8 127.0 103.3 110.6 166.7 97.3

85.0 65.9 75.3 73.1 63.5 32.4 48.3 45.9 73.0 22.7 62.2 80.8 44.3 90.0 32.8 23.8 58.2 51.8 74.7 48.7 53.0 72.3 123.4 68.5 86.9 81.4 64.2 76.0 61.3 47.1 87.4 63.7

79.3 63.1 60.2 61.4 54.8 28.7 38.6 38.0 68.4 21.4 54.9 65.5 42.7 90.1 28.6 24.8 49.0 41.4 57.1 41.0 54.1 69.5 87.4 59.4 71.2 69.5 67.2 57.6 48.6 47.8 95.4 56.0

21.0 35.8 24.0 17.9 23.8 8.4 17.0 22.6 55.7 5.8 20.4 19.5 46.4 68.4 19.7 10.4 25.7 24.1 23.5 16.4 18.0 72.7 67.0 68.7 47.9 32.2 26.9 31.5 36.8 59.8 41.8 32.6

13.3 33.4 18.3 14.8 16.3 6.3 12.7 16.9 39.0 5.6 15.5 16.7 33.8 46.9 17.6 9.6 20.5 22.0 19.5 12.9 17.1 60.6 49.3 68.7 29.0 19.4 16.2 24.1 26.1 57.6 32.8 25.6

a The ambient air quality standard (GB 3095-2012) provides the annual mean concentration limit for SO2, NO2, PM2.5 and PM10. The bold represents that the annual mean concentration meets the Chinese Ambient Air Quality Standards Grade II.

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N u1 X RMSE ¼ t ðF
Oi Þ2 N i¼1 i

concentrations.

2.4. Definition of statistical index Six statistical indices, i.e. the index of agreement (IOA), the correlation coefficient (R), the standard deviation (STD), the root mean square error (RMSE), the mean bias (MB), and the mean error (ME), were used for WT-ANN model evaluation, as shown in Equations (2)e(7):

IOA ¼ 1


N X

, ðFi
Oi Þ2

i¼1

R¼

N  X   2 Fi
O þ Oi
O

, N    1 X Fi
F Oi
O N i¼1 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 0v u u N  N  2 u1 X 2 u 1 X t Oi
O A @ Fi
F t N i¼1 N i¼1

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N u1 X ðx
xÞ2 STD ¼ t N i¼1 i

(2)

i¼1

(5)

MB ¼

N 1 X ðF
Oi Þ N i¼1 i

(6)

ME ¼

N 1 X jF
Oi j N i¼1 i

(7)

where F and O are the simulated and the observed pollutant concentrations, respectively, F and O are the mean simulated and observed pollutant concentrations, respectively, x represents F or O, x is the F or O, and N is the number of samples. Coefficient of Variation was used to describe the dispersion degree of the data:

CV ¼

STD x

(8)

(3) 3. Results and discussion

(4)

3.1. Air pollution characteristics The 31 average urban concentrations of CO, NO2, O3, PM10, PM2.5 and SO2 were 1.2 (0.7e1.9) mg m
3, 42.4 (14.7e55.3) mg m
3, 49.0

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Fig. 1. The number of days of severe and very serious pollution, and the rate of major pollutant in 31 provincial capital cities in China 2014e2015. The circles and triangles represent the total days of severe and very serious pollution in 2014 and 2015 respectively. The category ‘None’ means the rate of AQI less than 50 in total days.

(32.0e80.0) mg m
3, 109.8 (40.7e213.8) mg m
3, 63.7 (22.7e123.4) mg m
3, and 32.6 (5.8e72.7) mg m
3 respectively in 2014 (Table 1). The range in parentheses represents the maximum and minimum annual average concentrations in 31 cities. The concentrations of PM2.5 and PM10 exceeded 82% and 57% of the CAAQS Grade II standards, and were 6.4 and 5.5 times the guideline values recommended by the World Health Organization (http://www.who. int/mediacentre/factsheets/fs313/zh/). Only four cities (FZ, KM, LS and HK, the abbreviations of cities were provided in Table 1) met the CAAQS Grade II standards of PM2.5, and six cities (FZ, KM, LS, HK, GY and GZ) for PM10. Compared with 2014, the average concentrations decreased 5.3%, 4.9%, 11.4%, 12.0% and 21.5% for CO, NO2, PM10, PM2.5 and SO2, respectively, whereas O3 increased by 7.4% in 2015. The largest decrease of CO, NO2, PM10, PM2.5, and SO2 in 31 provincial capital cities reached 35.3% (LS), 23.5% (TJ), 29.1% (SJZ), 31.0% (SJZ), and 39.7% (WQ), respectively, whereas the largest increase of O3 reached 41.5 (XA). However, in some cities, the primary concentrations increased in 2015. The decrease in SO2 was larger than other primary pollutants, indicating effective control of combustion emissions and perfect application of desulphurization measures in 2015. Traffic emissions are an important source of NOx and CO; previous studies reported that traffic emissions in Beijing contributed to 50% and 20% of total NOx and CO emissions (Jing et al., 2016; He et al., 2016b), respectively. Traffic emission control measures (i.e., oil upgrading, phasing out of vehicles that fail to meet the European No. 1 standard for exhaust emissions, and traffic restrictions) and coal emission control measures resulted in the decrease of CO and NO2 concentrations. The reduction amplitudes

for CO and NO2 are less than for other primary pollutant concentrations, which may be related to the rapid increase of vehicles in Chinese cities. O3 is a secondary pollutant related to solar radiation, NOx, volatile organic compounds (VOC), vertical transport in the boundary layer, etc. A previous study revealed the O3 is significantly negatively correlated with its precursors, such as CO and NO2 (Wang et al., 2014c). The formation of O3 weakened with the decrease in ultraviolet radiation relating to aerosols (Deng et al., 2011). Thus, the increase of O3 concentration may be related to the decrease in primary pollutants. Similar variation characteristics of O3 were observed during Beijing APEC 2014 (Wang et al., 2016). Coefficient of Variation (CV) describes the dispersion degree of data. The definition of CV was provided in Equation (8). Based on average concentrations of six pollutants during two years for each city, the CV of six pollutant concentrations was calculated. The spatial variation is largest for SO2 (CV ¼ 0.60), followed by PM2.5 (CV ¼ 0.33), PM10 (CV ¼ 0.32), CO (CV ¼ 0.26), NO2 (CV ¼ 0.25), and O3 (CV ¼ 0.19). A similar phenomenon was observed in previous studies (Chai et al., 2014; Wang et al., 2014b). Fig. 1 shows the number of days of severe and extremely serious pollution (AQI>200) and the rate of major pollutant in 31 provincial capital cities. Air pollution is most severe in northern China, and relatively severe in central China and northeastern China. The days of severe and extremely serious pollution in SJZ reached 102 and 50 in 2014 and 2015, respectively, and 45 and 54 days for BJ. Compared with 2014, the days of severe and extremely serious pollution increased in BJ, ZZ, JN, SY, CC, WQ, HT, and YC in 2015, and decreased in other cities. The Gobi Desert and other deserts, located

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Fig. 2. The ratio of annual average concentrations between PM2.5 and CO (a), PM2.5 and PM10 (b) in 31 provincial capital cities.

in northwestern China, are the source regions of sandstorms and dust (Zhao et al., 2016). The rate of major pollutants for PM10 is high in northwestern cities. Because of excessive pollutant emissions from coal-fired heating in winter, biomass burning and industrial combustion (Chai et al., 2014; Wang et al., 2014b), air pollution is serious in northern China and northeastern China, in addition to a high rate of major pollutants for PM2.5. In some cities in the north of China, such as YC, TY and SY, SO2 pollution is serious, which indicates the importance of strengthening coal combustion control and desulphurization for further improvement of air quality. The rate of major pollutants for PM2.5 is high in most cities in southern China. O3 and NO2 pollution are serious in the Yangtze River Delta and the Pearl River Delta. In KM and FZ, located in the south of China, the rate of major pollutants for PM10 is high, which may be related to local dust because of urban construction. With a high altitude and strong solar radiation, O3 pollution is serious in LS. Compared with 2014, the rate of major pollutants for O3 increased significantly in most cities in 2015. As mentioned above, the reduction of primary pollutant concentrations is responsible for the increase of O3 concentration. With the strengthening of emission control efforts, O3 pollution will be increasingly prominent, challenging the continuous improvement in air quality. Overall, the highest rate of major pollutants during 2014e2015 over China was PM2.5, followed by PM10, O3, NO2, SO2 and CO. With the further emissions control, the contribution to aerosol from direct emissions may decrease, and secondary aerosol contribution should attract more attention. The variations and formation of secondary aerosols are affected by meteorological

conditions, which result in significant regional haze in China (Zhang et al., 2013). Based on source apportionment results, the concentration of secondary aerosol is large in northeastern China, North China, central China, and eastern China (Sheng, 2015). Huang et al. (2014) investigated the chemical components during severe pollution periods in four typical cities in China (BJ, SH, GZ and XA) and observed that secondary aerosol contributed 50%e75% of PM2.5 in eastern cities (BJ, SH and GZ) and 30% of PM2.5 in a western city (XA). CO can be used to normalize PM2.5 to exclude the effects of primary combustion emissions and meteorological conditions (Zhang and Cao, 2015). The rate of fine particulate matter indirectly reflects emissions characteristics, new particle formation, and removal processes (Zhao et al., 2016). To further discuss the secondary formation of PM2.5, the ratios of PM2.5 to CO and PM2.5 to PM10 were investigated (Fig. 2). Large values of the ratio of PM2.5 to CO appear in northeastern China, North China, the Yangtze River Delta and the Pearl River Delta, which demonstrates the large contribution of secondary aerosols to PM2.5 concentrations in these areas. The ratio of PM2.5 to CO was low in northwestern China, indicating the small contribution of secondary aerosols to PM2.5 concentrations. Similar characteristics were reported in previous studies (Huang et al., 2014; Sheng, 2015). Compared with 2014, the ratio of PM2.5 to CO decreased in most cities in 2015, which may have several possible explanations. First, with the decrease of SO2 and NO2 concentrations, the formations of sulphate and nitrate were reduced in 2015. Second, based on meteorological data analysis, the increase in clouds and precipitation resulted in a decrease in solar radiation and temperature, which reduced the

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Fig. 3. The mean sea level pressure and frequency of 9 circulation types from January 2001 to December 2015.

formation of secondary aerosols by photochemical reactions. The ratio of PM2.5 to CO in ZZ, HT and LS increased significantly in 2015, which may be related to the increase in non-combustion emissions such as urban construction. The ratio of PM2.5 to PM10 shows significant regional and seasonal variations (Speranza et al., 2016). The average ratio of PM2.5 to PM10 during 2014e2015 in 31 provincial capital cities was 0.58. Compared with 2014, the ratio decreased slightly in 2015. Overall, the ratio of PM2.5 to PM10 was large in eastern China and small in western China. One of the reasons for the small ratio of PM2.5 to PM10 in western China is that sparse vegetation, an arid climate, and desert generate dust storms and stir up local dust. Similar results were reported in previous documents (Zhang and Cao, 2015). The spatial trend of the ratio of PM2.5 to PM10 is similar to the ratio of PM2.5 to CO, which further indicates the spatial variation of the contribution of secondary aerosols to PM2.5, as mentioned above. 3.2. Relation between air pollution and meteorological conditions As mentioned above, pollutant concentration has significant seasonal variations, which are disadvantageous to analysing the relation between air pollutants and meteorological conditions. Seasonal variation trends can effectively be excluded based on the difference between daily average concentrations (Cd ) and monthly moving average concentrations (Cd ) (He et al., 2016c):

C ¼ Cd
Cd

(9)

where C is the daily average concentrations excluding seasonal trends. To avoid the effect of spatial variation of concentrations, the relative variation (RVC) is used to investigate the effect of meteorological conditions:

RVC ¼

C Cd

 100%

(10)

The mean Ps and frequency of 9 circulation types during 2001e2015 are shown in Fig. 3. The weather characteristics of 9 circulation types are shown in Figs. S1eS3. The RVC of 9 circulation types for six pollutants in 31 provincial capital cities are listed in Fig. 4. The positive (negative) value of RVC represents adverse (favourable) dispersion situations. The effect of circulation types on primary pollutants is similar; however, significant differences are observed for O3. For CT1, weak high pressure covers eastern China with a small pressure gradient, relatively high RH2, and relatively small T2 and Hpbl. The northwest wind dominates in northeastern China whereas the northeast wind and southwest wind dominate southeastern China and western China, respectively. The CT1 primarily appears in winter, then spring and autumn. These circulation and meteorological conditions are favourable to pollutant dispersion and removal in China except for North China and northeastern China. For CT2, relatively strong high pressure covers most regions of China, and cold fronts reach the southeast Chinese coastal areas. The near-surface wind field is similar to the field for CT1. Low RH2 appears in North China and western China. The Hpbl is lower than for other circulations. This circulation primarily appears in winter and may cause pollutant accumulation in most regions of China. The meteorological conditions are adverse to pollutant dispersion in China except for central China. For CT3, the pressure gradient is weak, and RH2 is high in most regions of China. The easterly wind occurs in southeastern China whereas a southerly wind occurs in North China and a westerly wind in northeastern China. Occurring primarily in the autumn, this circulation is generally favourable to pollutant dispersion except for North China. The flow field of CT4, which occurs primarily in the spring, is similar

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Fig. 4. The relative variation of concentrations of 9 circulation types for six pollutants in 31 provincial capital cities.

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Fig. 5. The correlation between pollutant concentrations and T2 (a), RH2 (b), WS10 (c), Hpbl (d). The trend of concentrations and meteorological parameters were excluded based on daily average values subtracting monthly moving average values. The dashed lines represent the critical value of the correlation coefficient that passes the T test of 0.05 significance level.

to CT3. Relatively large Hpbl and small RH2 appear in most regions of China except for South China. This circulation is not conducive to pollutant dispersion in the majority of provincial capital cities. For CT5, low pressure with a small pressure gradient covers China except for the Qinghai-Tibet Plateau. Southerly winds occur in most regions of China, and westerly winds are prevalent in northeastern China. CT5 primarily appears in the summer, followed by spring and autumn. This circulation is not conducive to pollutant

dispersion except for North China and South China. With low Ps and high T2, CT6 occurs primarily in the summer. Easterly winds from the western Pacific cover most regions of China. Air pollution appears in southwestern China although other regions enjoy good air quality. For CT7, a strong cold anticyclone is located over Outer Mongolia. The cold front reaches southern China. With a large pressure gradient, strong northerly winds occur in most regions of China. This circulation occurs primarily in the winter followed by

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Fig. 6. The wind dependency map of average PM2.5 concentration excluding the trend in 31 provincial capital cities.

autumn and spring and is appropriate for rapid pollutant dispersion. The Ps is smallest for CT8, which occurs primarily in the summer. Compared with other circulations, T2 and Hpbl are the largest. The southerly winds occur in most regions of China whereas northerly winds occur in northwestern China with a convergence zone in Tibet. Apart from South China, this circulation results in adverse dispersion conditions in China. For CT9, relatively weak high pressure appears in northern China whereas a uniform pressure field covers the south of China. The westerly winds occur in most regions of China, and northeast winds control southeastern China. An obvious convergence zone occurs in the central portion of China. CT9 occurs in the autumn, followed by winter and spring. This circulation is favourable to pollutant dispersion in northern China but not conducive to dispersion in southern China. In summary, atmospheric circulation has different effects on urban air quality in different regions. The weather conditions under CT2 are adverse for pollutant dispersion in the majority of regions of China whereas the opposite is true for CT7. Wind fields affect the dispersion and the local and regional transport of pollutants in the atmosphere. The seasonal trends of pollutant concentrations and local meteorological parameters were excluded based on daily average values subtracting monthly average moving values, and the correlation (Pearson productmoment correlation coefficient) between the values is shown in Fig. 5. Overall, pollutant concentrations decrease with the increase in wind speed except for O3 (Fig. 5c). Low primary pollutant concentrations result in high O3 concentrations (Deng et al., 2011; Wang et al., 2014c) and cause a positive correlation between wind speed and O3 concentration. The correlation is basically significant at a 95% confidence interval. Because the strong wind easily stirs the dust, the concentration of PM10 is positively correlated with WS10 in XA, WQ and LS. Compared with other pollutants, the correlation coefficient to wind speed is highest for NO2, which is closely related for two reasons. Distributed widely in urban areas, the emission of NO2 incudes combustion sources and traffic emissions, which are affected more uniformly by the wind. In addition, NO2 is easily involved in chemical reactions and has a short lifetime in the atmosphere; the concentration of NO2 is less affected by regional transport. The spatial variation of the correlation coefficient with wind speed is observed with a relatively low correlation in some cities in the west of China. Fig. 6 shows the wind dependency map of average PM2.5 concentration, excluding the trend. The wind dependency map for other pollutants is provided in

Figs. S4eS8. Regional transport is closely related to the distribution of emission sources and wind fields. The concentrations of pollutants in North China are significantly affected by regional transport under the domination of the south wind. Previous studies also identified regional transport from the southern portion of North China (Zhang et al., 2012). The regional transport from a southwesterly direction is obvious in northeastern China because of a pollution banding in a southwest-northeast direction (Ma et al., 2016). With a great deal of industry and vehicle emissions in the south of Jiangsu province and the north of Zhejiang province, air pollution is severe in SH under the domination of the west wind. Although there is clear air in Hainan province (Table 1), significant transport from the Pearl River Delta is observed in HK. Apart from O3 destruction mechanisms, the transport of pollutant precursors could obviously cause a high O3 level in northeastern China and Hainan province. A similar chemical mechanism was identified on the eastern Mediterranean coast (Asaf et al., 2011). Temperature is closely related to pollutant concentrations by affecting atmospheric turbulence and chemical reactions. The T2 is positively correlated with pollutant concentrations in the majority of cities except for NN and HK because the clear air mass reaches NN and HK from the South China Sea with high temperatures (Fig. 5a). A high correlation was identified in the cities along the lower-middle reaches of the Yangtze River whereas a low correlation was identified in the cities over northwestern China and North China. The positive correlation with temperature was also detected in other areas, such as the United States (Tai et al., 2010). The correlation with RH2 has a significant regional difference (Fig. 5b). O3 concentration is negatively correlated with RH2 in 31 provincial capital cities, and the correlation is significant at a 95% confidence interval except for LS. The primary pollutant concentrations are positively correlated with RH2 mostly in North China and northeastern China and negatively correlated in other regions. Significant regional differences correlated with RH2 were also identified in the United States (Tai et al., 2010). The Hpbl reflects the atmospheric turbulent mixing and vertical dispersion capability. The primary pollutant concentrations are negatively correlated with Hpbl (Fig. 5d). The development of a boundary layer in HK may enhance the transport of primary pollutants and cause a positive correlation except with NO2, which has a short lifetime. With the development of a boundary layer, O3 concentration may increase because of low primary pollutant concentrations. Meanwhile O3 is transported from the upper atmosphere to near the surface because of high

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Fig. 7. Explained variance of meteorological conditions for CO (a), NO2 (b), O3 (c), PM10 (d), PM2.5 (e) and SO2 (f) in 31 provincial capital cities during 2014e2015.

Table 2 The occurrence frequency of 9 circulation types in 2014 and 2015 (Unit: %). Year

CT1

CT2

CT3

CT4

CT5

CT6

CT7

CT8

CT9

2014 2015

13.2 15.1

11.8 15.6

11.2 9.0

12.3 11.8

12.6 16.7

8.8 8.5

9.6 5.8

9.9 6.6

10.7 11.0

(low) O3 concentration in the upper (lower) atmosphere (Sun et al., 2010). This is a possible reason for a positive correlation between O3 concentration and Hpbl. In a previous work, a numerical-model-based ANN model was established, and the contribution of four factors, i.e., local meteorological, large-scale circulation, emission variation and wet removal process, was quantified to day-to-day variations of pollutant concentrations in Lanzhou (He et al., 2016a). This concept was adopted in this paper to quantify the effect of meteorological conditions by explained variance in 31 provincial capital cities. The ratio of RMSE from twelve sensitivity tests (Table S2) to the lowest RMSE in all sensitivity tests for six pollutants is listed in Table S3. The values close to 1 represent the good performance for the WTANN model. The results indicate that the 7th sensitivity test

(SEN7) generally has the best performance. The SEN7 includes the input of multi-scale meteorological conditions for the day and the previous day with wavelet decomposition of meteorological conditions and the output of wavelet decomposition of pollutant concentrations. SEN7 was thus used for further analysis. Table S4 shows the performance statistics of WT-ANN validation. WT-ANN performs well on pollutant concentrations prediction, with the IOA of 0.96e0.98 and the R of 0.92e0.96. The simulated STD is close to the observed STD, indicating that WT-ANN effectively reproduces the fluctuation of pollutant concentrations. Overall, the performance of the WT-ANN model in this study is comparable to previous studies (Feng et al., 2015; Siwek and Osowski, 2012). Explained variance, which is the ratio of the simulated variance by the WT-ANN model to the observed variance, is used to describe the contribution of multi-scale meteorological conditions and dayto-day variations of pollutant concentrations in 31 provincial capital cities (Fig. 7). The year and month, which represent emission information simply, are averaged (i.e., constant) for input of WTANN model to exclude the impact of emission information. Relative low explained variance for PM reveals that meteorological conditions have more significant effects on gas pollutants than PM.

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Fine particles (PM2.5) are affected by meteorological conditions more easily than coarse particles (PM10). The regional heterogeneity of the effect of meteorological conditions was identified. For CO and SO2, the contribution of meteorological conditions to concentration variation in southeastern China was lower than in other areas. For PM2.5 and PM10, the contribution in western China is larger than in eastern China. The rate of secondary aerosol in eastern China is larger than in western China (Huang et al., 2014), which implies that the chemical process of aerosol in eastern China is more complex than in western China. And this may explain the regional difference of explained variance for PM. In northwestern cities, such as XN, LZ, and YC, relative low explained variance for PM relates to natural dust emission. For O3, the contribution in northern China is larger than in southern China, and its' spatial difference is smallest in six pollutants. Active chemical process for O3 due to strong solar radiation can explain the relative low explained variance in southern China. Because of strong chemical activity and the short lifetime of NO2, regional characteristics of meteorological condition contributions are unclear. Meteorological conditions are the most important factor for determining the dayto-day variation of pollutant concentrations in major Chinese cities and contribute more than 70% of the basic day-to-day variations. The Chinese government makes a great effort to control air pollution and decrease pollutant emissions. As reported by the Bulletin of the Chinese Environment from 2005 to 2015 (http://jcs. mep.gov.cn/hjzl/zkgb/), the annual emission of SO2 and the annual mean concentration of SO2 decreased gradually with a high correlation coefficient of 0.97 (Fig. S9), which indicates that effective emissions control improves air quality in China. However, pollutant concentrations are not only closely related to pollutant emissions but also related to meteorological conditions. Table 2 shows the occurrence frequency of nine circulation types in 2014 and 2015. Overall, the circulation in 2015 was more unfavourable for pollutant dispersion than in 2014 because of the increase in the frequency of occurrence of CT2 and the decrease in the frequency of occurrence of CT7. Considering the adverse meteorological conditions in 2015, the variations in pollutant concentrations between 2014 and 2015 (Table 1) imply the benefits of the emission controls. 4. Conclusions Air pollution is a serious social and environmental problem in China. This study analysed air pollution characteristics and their relation to multi-scale meteorological conditions during 2014e2015. The effect of the variations in meteorological conditions on ambient pollutant concentrations in 2015 was also investigated. The most serious air pollution was observed in North China, followed by central China and northeastern China. The average concentrations of CO, NO2, O3, PM10, PM2.5 and SO2 for 31 provincial capital cities were 1.2 mg m
3, 42.4 mg m
3, 49.0 mg m
3, 109.8 mg m
3, 63.7 mg m
3, and 32.6 mg m
3 in 2014. The average concentrations decreased 5.3%, 4.9%, 11.4%, 12.0% and 21.5% for CO, NO2, PM10, PM2.5 and SO2, respectively, and increased 7.4% for O3 in 2015. The highest rate of major pollutants over China was for PM2.5, followed by PM10, O3, NO2, SO2 and CO. The rate of major pollutants for O3 increased significantly in most cities in 2015. The ratio of PM2.5 to PM10 was large in eastern China and small in western China, and the ratio decreased slightly in 2015. Nine circulation types were determined based on ECMWF sea-level pressure data and the objective weather types methods of the T-mode PCA combined with the K-means cluster. The circulation types may effectively determine the dispersion, transport and accumulation processes. Pollutant concentrations were positively correlated with temperature and negatively correlated with wind speed and boundary layer height (opposite for O3) in China. The correlation

with relative humidity showed a significant regional difference. The concentrations of primary pollutants were positively correlated with relative humidity primarily in North China and northeastern China and negatively correlated in other regions. The O3 concentration was negatively correlated with relative humidity over China. The wind dependency map of pollutant concentrations revealed that significant regional transport occurred in North China, northeastern China, the Yangtze River Delta and the Pearl River Delta. A WT-ANN model was established to investigate the effects of meteorological conditions on ambient pollutant concentrations. The meteorological conditions explained more than 70% of the variance of pollutant concentrations over China. Meteorological conditions affected gas pollutants more significantly than PM. Fine particle (PM2.5) was affected by meteorological conditions more easily than coarse particle (PM10). Compared with 2014, the meteorological conditions in 2015 were adverse for pollutant dispersion, indicating that the improvement in air quality in 2015 was caused by strict emissions controls. Acknowledgments This work was supported by the National Science and Technology Infrastructure Program (2014BAC16B03), and the Opening Research Foundation of the Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Chinese Academy of Sciences (LPCC201405). Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.envpol.2017.01.050. References An, X.Q., Zuo, H.C., Chen, L.J., 2007. Atmospheric environmental capacity of SO2 in winter over Lanzhou in China: a case study. Adv. Atmos. Sci. 24, 688e699. An, X.Q., Tao, Y., Mi, S.Q., Sun, Z.B., Hou, Q., 2015. Association between PM10 and respiratory hospital admissions in different seasons in Lanzhou. J. Environ. Health 77, 64e71. Asaf, D., Peleg, M., Alsawair, J., Soleiman, A., Matveev, V., Tas, E., Gertler, A., Luria, M., 2011. Trans-boundary transport of ozone from the Eastern Mediterranean coast. Atmos. Environ. 45, 5595e5601. http://dx.doi.org/10.1016/ j.atmosenv.2011.04.045. Barrero, M.A., Orza, J.A.G., Cabello, M., Canton, L., 2015. Categorisation of air quality monitoring stations by evaluation of PM10 variability. Sci. Total Environ. 524e525, 225e236. http://dx.doi.org/10.1016/j.scitotenv.2015.03.138. Brauer, M., Freedman, G., Forstad, J., Donkelaar, A., Martin, R.V., Dentener, F., Dingenen, R., Estep, K., Amini, H., Apte, J.S., Balakrishnan, K., Barregard, L., Broday, D., Feigin, V., Ghosh, S., Hopke, P.K., Knibbs, L.D., Kokubo, Y., Liu, Y., Ma, S., Morawska, L., Sangrador, J.L.T., Shaddick, G., Anderson, H.R., Vos, T., Forouzanfar, M.H., Burnett, R.T., Cohen, A., 2016. Ambient air pollution exposure estimation for the global burden of disease 2013. Environ. Sci. Technol. 50, 79e88. http://dx.doi.org/10.1021/acs.est.5b03709. Cai, M., Yin, Y.F., Xie, M., 2009. Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach. Transp. Res. D-Tr. E. 14, 32e41. http://dx.doi.org/10.1016/j.trd.2008.10.004. Chai, F., Gao, J., Chen, Z., Wang, S., Zhang, Y., Zhang, J., Zhang, F., Yun, Y., Ren, C., 2014. Spatial and temporal variation of particulate matter and gaseous pollutants in 26 cities in China. J. Environ. Sci. 26, 75e82. http://dx.doi.org/10.1016/S10010742(13)60383-6. Chen, L., Ma, G.D., 2006. The application of wavelet analysis in PM10 time series of concentration. Environ. Eng. 24, 61e63 (in Chinese). Chen, Y., Zhao, C.S., Zhang, Q., Deng, Z.Z., Huang, M.Y., Ma, X.C., 2009. Aircraft study of mountain chimney effect of Beijing. China. J. Geophys. Res. 114, D08306. http://dx.doi.org/10.1029/2008JD010610. Chu, P.C., Chen, Y.C., Lu, S.H., 2008. Atmospheric effects on winter SO2 pollution in Lanzhou, China. Atmos. Res. 89, 356e373. http://dx.doi.org/10.1016/ j.atmosres.2008.03.008. Crippa, M., Canonaco, F., Slowik, J.G., El Hadda, I., DeCarlo, P.F., Mohr, C., Heringa, M.F., Chirico, R., Marchand, N., Temime-Roussel, B., Abidi, E., Poulain, L., Wiedensohler, A., Baltensperger, U., Prevot, A.S.H., 2013. Primary and secondary organic aerosol origin by combined gas-particle phase source apportionment. Atmos. Chem. Phys. 13, 8411e8426. http://dx.doi.org/10.5194/acp-13-84112013.

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