SUPPORTING INFORMATION

Association between heating seasons and criteria air pollutants in

three provincial capitals in northern China: Spatiotemporal

variation and sources contribution

Lei Wang a, b, Qingyu Guan a*, Feifei Wang a, Liqin Yang a, Zeyu Liu a

a Key Laboratory of Western China's Environmental Systems （ Ministry of

Education) & Gansu Key Laboratory for Environmental Pollution Prediction and

Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou,

730000, China

b Shenzhen Key Laboratory of Organic Pollution Prevention and Control, State Key

Laboratory of Urban Water Resource and Environment, Harbin Institute of

Technology (Shenzhen), China, 518055

Total pages: 27 including cover page

Total tables: 7

Total figures: 7

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Error: Reference source not foundContent

S1 Annual PM2.5 and PM10 concentrations for each continent on earth 3

S2 Detailed information on the studied cities 4

S3 Detailed monitoring site and basic statistics of heating season data 9

S4 Comparison the results of PMF model with those of FA-APCS-MLR 16

S5 The correlation between ambient air temperature and PM2.5, CO and SO2 levels around heating season 19

S6 The influence of meteorological factors on the air pollutant concentrations during heating season 20

S7 The temporal variation of traffic density in Beijing from October 2014 to March 2015 25

S2

S1. Annual PM2.5 and PM10 concentrations for each continent on earth

Fig. S1. Annual PM2.5 and PM10 concentrations for each continent calculated from the 2014 World Health Organization outdoor PM database (WHO, 2014) compared to the number of cities with monitoring capabilities in place to report WHO data. The vertical line within the box represents the median sample value. The ends of the box represent the 25 th and 75th

quantiles. The box has lines that extend from each end, sometimes called whiskers. The whiskers extend from the ends of the box to the outermost data point that falls within the distances, computed as follows: 1st quartile, -1.5× (interquartile range) and 3rd quartile, + 1.5× (interquartile range). If the data points do not reach the computed ranges, then the whiskers are determined by the upper and lower data-point values (not including outliers). Data points not falling within the range of whiskers are considered to be outliers (the solid black dot).

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S2. Detailed information on the studied citiesFig. S2. Distribution of the studied cities (red dots) in the map.

The real time hourly concentrations of PM2.5, PM10, SO2, NO2, CO, and O3 were

recorded by the China National Environmental Monitoring Center, Environmental

Monitoring of China (http://datacenter.mep.gov.cn/). The data obtained around the

heating season from September 15, 2014 to May 15, 2015 were recorded at 24

monitoring sites in the northern China (Figure S2, Table S1).

1. The Northern China

This zone comprises the provinces of Heilongjiang, Liaoning, Jilin, Hebei,

Shanxi, Shaanxi, Gansu and Qinghai, and the autonomous regions of Xinjiang,

Ningxia and Inner Mongolia, as well as the two municipalities of Beijing and Tianjin.

The northern China can be classified three parts: Northeast China, North China and

Northwest China. The Northeast China distributes the largest plain of China and

effects on the policy of reviving industrial bases. The North China marks the dividing

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line between the semiarid and arid regions, the East Asian monsoon region and the

non-monsoon region of the Eurasian continent. It is a crucial geographical and

climatological boundary. Geographically, the Northwest China refers to the vast

inland region to the west of Da Hinggan Mountains and to the north of the Kunlun–

Altun Mountains and Qilian Mountains. The studied cities distribute in these parts.

Table S1 Information of the cities studied during the heating season in the Northern China.

Cities Beijing Lanzhou UrumqiNumber of monitoring

sites 12 5 7Area of the city (km2) 16,800 13,085 14,200Population (million) 21.1 3.6 3.3

Latitude and longitude N 39°54′ E 116°28′

N36°03′ E 103°49′

N 43°48′ E 87°36′

Energy consumption(kt standard coal) 68507 b 17319 c 15716 d

Central heating a

(Fossil flue, %) 82.4 98.9 88.4Individual heating a

(Biomass, %) 0.6 0.3 11.6Individual heating a

(Electricity, %) 16.0 0.1 0.0Non-heatinga (%) 1.0 0.7 0.0

Before heating season

From

Oct. 15th 2014 Oct. 1st 2014 Sep. 10th 2014

To Nov. 14th 2014 Oct. 31st 2014 Oct. 9th 2014

Heating seasonFrom

Nov. 15th 2014 Nov. 1st 2014 Oct. 10th 2014

To Mar. 15th 2015 Mar. 31st 2015 Apr. 10th 2015

After heating season

From

Mar. 16th 2015 Apr. 1st 2015 Apr. 11th 2015

To Apr. 15th 2015 Apr. 30th 2015 May 10th 2015Windstorm season Feb. 22nd 2015 Feb. 14th 2015 Oct. 27th 2014

a. Chinese Environmental Exposure-Related Human Activity Patterns Survey (Ministry of Environmental Protection, 2013)

b. Beijing Statistical Yearbook (Beijing Municipal Bureau of Statistics, 2015)c. Lanzhou Statistical Yearbook (Lanzhou Municipal Bureau of Statistics, 2015)d. Urumqi Statistical Yearbook (Urumqi Municipal Bureau of Statistics, 2015)

2. Beijing

Beijing, the capital of China, is located on the northwest of Beijing-Tianjin-

Hebei region, which is one of the China’s economically well-developed regions

influenced by a series of air pollution problems. In recent decades, with the rapid

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development of industrialization, urbanization and motorization (Han et al., 2014),

Beijing's energy consumption and the resulting multiple pollutant emissions were

increasing year by year, which had adverse impacts on air quality, human health and

eco-environment (Li et al., 2011, 2014). In recent years, particulate pollution,

especially high concentrations of PM2.5 pollution, have been the foremost problem of

Beijing's air pollution (Hu et al., 2014). In order to lower PM2.5 pollution and improve

air quality, Beijing's government had taken a series of strict control actions to prevent

and control air pollution, which, to a large extent, set a good example for other urban

air quality improvement in China and even in other countries around the world. In

addition, Asian dust storms, originating from the deserts of eastern and central Asia,

often inject large amount of dust particles into atmosphere which can be transported to

the eastern China such as Beijing and pose significant impacts on the air quality and

climate radiation. Therefore, it is meaningful to make a deep research about the

impact of short-term anthropogenic and natural activities on Beijing’s air quality.

3. Lanzhou

Lanzhou, the capital and largest city of Gansu Province, is located in northern

China on the narrow floodplain of the Yellow River with a population of

approximately 3.6 million. The valley within which the city located is 1500-1600 m

above sea level, 2-8 km in width and approximately 35 km long. The Tibetan Plateau,

Baita Mountain and Gaolan Mountain are located in the west; north and south of

Lanzhou respectively, which results in comparatively calm conditions for most of the

year (mean annual surface wind speed in Lanzhou of 0.8 m s-1, with calm conditions

reported for 62% of the time). Moreover, a number of industries are based in the city

including an oil refinery, petrochemical industries, machinery and metallurgical

industries, textile mills, food processing centers, cement manufacturing, coal mining,

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rubber processing, electrical power generation, medical industries, lead and zinc

mining and smelting and fertilizer plants (Zhang et al., 2014). In addition to local

pollution, dust transported from adjacent deserts affecting air quality in this area.

Before 2012 (Wang et al., 2009), Lanzhou had some of the highest air pollution levels

in China and in the world but this phenomenon has improved significantly since 2013.

Therefore, it is meaningful to make a deep research about the impact of short-term

anthropogenic and natural activities on Lanzhou’s air quality.

4. Urumqi

Urumqi, the largest city in western interior of China, is the capital of Xinjiang

Uygur Autonomous Region and geographically noteworthy for being the furthest city

in the world from any coastal line with a semi-arid climate prevails. The urban area

lies in the Urumqi valley between Thianshan Mountain and Jungger Basin and the

averaged altitude of this area is 800m above the sea level. The average temperature of

Urumqi in July and January are 25 and -15 ℃, respectively. The annual precipitation

is 271.4 mm. The winter climate can be characterized as cold and wet, with average

maximum and minimum temperatures of -10 and -20 ℃ in January, respectively.

With the rapid growth of economy since the 1990s, most factories such as plants

producing power, petroleum chemicals, cement, etc., located in the northern and

southern suburbs of the city, result in a high concentration of industrial pollutants. In

addition, the number of motor vehicles has rapidly increased in recent years to >

300,000 in October 2010. As Urumqi is located nearby Guerbantonggute Desert and

Jungger Basin, soil dust is a main particulate source in spring (Li et al., 2008).

Furthermore, there is a half-year for house heating domestically and industrially from

15 October to 15 April of the next year. Therefore, the air pollution intensity of

Urumqi usually is in the order of winter > spring > fall > summer (Li et al., 2008). It

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was shown clearly that winter is the most polluted season, which is due to the heavy

heating with coal combustion in this season (Li et al., 2008). Feng et al. (2005)

confirmed that the major sources of PM10 were mainly composed of coal combustion

(67%), sulfate (14%), and others (19%) in heating season. Note that dust aerosols are

mostly observed in spring and summer and they are rarely observed in winter due to

snow cover. The above emissions led to an average of 8.7 severe air pollution

episodes within the heating season (Li et al., 2009).

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S3. Detailed monitoring site and basic statistics of heating season data

The air pollutant concentrations recorded around the heating season from

September 2014 to May 2015 in NC are shown in Table S2. As the implementation of

large-scale central heating, the PM2.5, PM10, SO2, NO2 and CO concentrations in the

heating season (10th October, 2014 ~ 10th April, 2015) were 82±57, 140±81, 29±18,

57±26 μg m-3, and 1.86±0.98 mg m-3, respectively, which are notably higher values

than those in the non-heating season (Table S2). However, the highest PM10

concentrations in Beijing was recorded at the non-heating season (131±85μg m-3), and

that in Lanzhou and Urumqi were observed at the heating season (Table S2). Note that

the averaged NO2 concentrations in Beijing, SO2, CO, and O3 concentrations in

Lanzhou, and PM10 concentrations in Urumqi are the highest in these three cities

during the heating season.

The trend of air pollutants in each city monitoring site is consistent with their

averages of the corresponding city. In relation to Beijing, the highest concentration of

PM2.5, SO2 and CO was detected in heating season and NO2 was in non-heating season

at the industrial sites (Tables S2 and S3). While for winter, along with the heating time

during which large amount of coal use in heating plant, air quality of Beijing gets

worse sharply along with the increase in the amount of PM2.5, SO2 and CO. Although

Beijing has already limited the use of coal as fuel, the southern Hebei has not done

that as Beijing does. The air quality, especially in southern Beijing gets worse in

winter. The highest concentration of PM10 was detected in non-heating season at the

urban site (Table S2 and S3). Air qualities of traffic monitoring stations are worse than

the nearby monitoring stations as the normal stations are placed far away from the

main road while traffic monitoring stations are placed just near the main traffic streets

in Beijing (urban site 1 and 3). The heavy traffic motion with large amount of vehicle

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exhaust and road dust would deteriorate local air quality heavily. However, the huge

differences between north and south stations, as well as the associations between wind

and air pollutants' concentrations suggested that the heavy pollution in southern

stations may be related to regional reasons, specifically, air quality in nearby Hebei

and Tianjin area (Chen et al., 2015). At the rural site, the lowest values of particulate

concentrations are observed in heating season and those of SO2, CO and NO2

concentrations are recorded in non-heating season (Table S2). For Lanzhou, the

highest values of PM2.5, PM10, SO2, NO2 and CO occurred in the heating season at the

industry and urban sites (Table S2). The lowest values of the above pollutants

concentrations observed in the non-heating season at the rural site where fewer

pollution sources has (Table S2). Similar to the Lanzhou, the highest values of PM2.5,

PM10, SO2 and CO recorded in the heating season at the industry sites of Urumqi,

where is surrounded by a number of petrochemical companies and power plants,

which produce large amounts of pollutants especially PM2.5.The highest value of NO2

occurred in the heating season at the urban sites of Urumqi (Table S2). The lowest

value of PM2.5, PM10, SO2, NO2 and CO occurred in the non-heating season at the

urban and rural sites of Urumqi.

In order to avoid ignoring the diurnal variation of different kind of measurement

stations in the same city, we have plotted the diurnal variation of PM2.5 in industry,

urban, and rural site in the studied cities for an example. In each city, the PM2.5

concentrations of Lanzhou and Urumqi in industry sites were always the highest of all

and their diurnal variations were similar to the urban site (Fig. S3). In Lanzhou, the

diurnal variations of different functional regions were similar and the PM2.5 in rural

site was not always displayed as a low concentration (Fig. S3). Unlike the variations

mentioned above, the urban sites in Beijing have almost the same diurnal variation of

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PM2.5 concentration with that in rural sites. In the daytime, the PM2.5 concentrations in

industry sites were lower than those in urban and rural sites (Fig. S3).

Fig. S3. Diurnal variations of PM2.5 concentration from different functional regions including industry sites, urban sites, and rural sites in Lanzhou (a), Beijing (b), and Urumqi (c). The dot represents the arithmetic averaged value of PM2.5 and the error bar represents the standard deviation.

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Table S2. Mean concentrations with standard deviation for ambient air pollutants in each studied auto-monitoring site (from September 2014 to May 2015 in NC).

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Table S3. Maximum, medium, minimum and mean concentrations with standard deviation for ambient air pollutants during the heating season in each studied auto-monitoring site.

PM2.5 (μg m-3) PM10 (μg m-3) SO2 (μg m-3)

Max. Med. Min. Ave. S.D. Number Max. Med. Min. Ave. S.D.Numbe

rMax. Med. Min. Ave. S.D. Number

Beijing 371 72 4 89 70 2889 640 103 7 124 85 2039 84 20 2 25 17 2844industry-1 652 63 3 91 88 2961 817 112 5 135 109 2430 246 20 2 27 23 2981industry-2 483 66 3 91 90 2945 700 118 5 143 117 2571 131 20 2 29 24 2861industry-3 497 62 3 92 91 2920 862 103 5 134 113 2527 143 17 2 26 24 2941urban-1 481 63 3 90 88 2934 572 90 5 116 98 2095 157 18 2 29 28 2844urban-2 462 58 3 92 96 2942 794 90 5 125 114 2259 149 19 2 27 24 2955urban-3 518 66 3 93 91 2954 642 89 5 120 103 2331 273 12 2 21 22 2959urban-4 443 41 3 78 86 2964 1040 47 5 75 87 2039 151 15 2 20 19 2974urban-5 525 70 3 97 96 2921 783 106 5 135 115 2523 273 16 2 27 27 2860rural-1 762 48 3 74 76 2950 1000 75 5 105 110 2226 314 9 2 16 20 2951rural-2 641 56 3 86 89 2927 1000 89 5 119 111 2069 161 10 2 19 23 2935rural-3 434 51 3 82 83 2926 992 82 5 113 102 2448 153 18 2 26 25 2928rural-4 602 68 3 101 103 2889 1000 98 5 132 117 2385 188 20 2 31 29 2905Lanzhou 212 57 10 63 30 2985 1132 121 30 130 61 2970 137 26 5 31 18 2977industry site 301 47 1 54 31 3350 1289 122 3 133 74 3344 236 26 2 35 28 3359urban site-1 342 59 1 66 34 3351 1034 112 2 121 63 3331 298 30 3 40 31 3355urban site-2 323 64 2 75 43 3362 1326 132 4 144 76 3363 245 31 1 36 23 3371urban site-3 201 50 1 59 33 3323 1379 109 4 122 76 3242 254 11 1 18 20 3327rural site 239 54 1 61 34 2985 1438 122 6 134 79 2970 90 18 1 22 14 2977Urumqi 352 74 7 92 59 3912 567 141 8 161 88 3245 137 25 2 30 19 3973industry site-1 474 61 1 78 64 4017 879 138 2 162 117 3555 312 19 1 29 31 4046industry site-2 641 43 1 61 55 3944 909 106 3 142 118 3768 464 13 1 20 26 3993urban site-1 746 81 1 111 94 3988 894 124 2 161 127 3273 370 25 1 35 33 4002urban site-2 677 64 1 91 87 3912 934 118 1 158 133 3655 477 25 1 35 34 4029urban site-3 926 53 1 93 104 3977 951 130 1 169 133 3245 375 20 1 28 29 3982

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urban site-4 634 72 1 101 91 4072 949 117 3 155 123 3265 431 22 1 29 26 3973rural site 586 97 1 114 80 4001 961 168 1 203 145 3544 232 26 1 33 26 4049

O3 (μg m-3) NO2 (μg m-3) CO (mg m-3)Max. Med. Min. Ave. S.D. Number Max. Med. Min. Ave. S.D. Number Max. Med. Min. Ave. S.D. Number

Beijing 147 26 2 28 17 2838 175 59 7 62 29 2844 5.32 1.43 0.17 1.60 0.96 2842industry-1 213 14 2 28 27 2979 224 65 2 64 40 2977 7.10 1.50 0.10 1.78 1.39 2981industry-2 112 2 2 14 17 2859 219 77 6 77 38 2918 9.20 1.40 0.10 1.90 1.58 2926industry-3 163 16 2 27 28 2909 285 67 2 69 45 2924 7.90 1.20 0.20 1.61 1.33 2943urban-1 183 15 2 23 22 2838 230 70 5 73 43 2844 6.90 1.20 0.10 1.68 1.43 2842urban-2 160 18 2 26 24 2949 237 59 2 62 38 2953 7.50 1.30 0.10 1.72 1.43 2955urban-3 194 17 2 30 27 2958 194 59 3 63 36 2960 7.40 1.20 0.10 1.63 1.39 2961urban-4 168 40 2 40 29 2962 174 26 2 36 33 2974 7.40 0.80 0.10 1.14 1.02 2956urban-5 182 16 2 28 26 2892 219 71 2 72 44 2889 9.60 1.30 0.10 1.80 1.52 2892rural-1 198 26 2 32 28 2953 173 41 2 43 30 2946 6.20 1.10 0.10 1.27 0.98 2952rural-2 184 19 2 28 24 2932 204 52 2 54 34 2922 8.90 1.20 0.10 1.48 1.31 2874rural-3 162 23 2 29 24 2929 196 51 2 55 35 2929 10.40 1.10 0.10 1.58 1.40 2930rural-4 167 20 2 29 28 2903 234 71 4 72 42 2906 9.40 1.10 0.10 1.67 1.54 2904Lanzhou 119 36 5 38 23 2979 149 47 10 49 21 2986 6.39 1.87 0.39 2.00 0.93 2979industry site 160 47 2 51 39 3359 208 40 1 46 29 3358 9.38 1.94 0.02 2.29 1.43 3358urban site-1 187 13 1 27 28 3341 181 43 1 50 30 3354 9.67 1.67 0.20 2.11 1.46 3354urban site-2 254 33 1 41 39 3333 268 58 3 65 35 3369 5.60 1.37 0.00 1.53 0.92 3368urban site-3 345 34 1 41 35 3277 119 27 5 30 15 3327 14.62 1.74 0.00 2.05 1.24 3290rural site 186 22 4 29 21 2979 227 50 9 53 27 2986 6.84 1.70 0.04 1.90 1.09 2979Urumqi 73 15 2 18 13 3757 142 59 11 60 25 3965 5.24 1.71 0.35 1.93 0.96 3959industry site-1 89 10 1 12 10 3739 125 39 2 42 26 3974 10.06 1.50 0.00 1.87 1.35 4028industry site-2 62 6 1 12 13 3906 240 74 1 74 38 3980 9.72 1.63 0.02 2.07 1.46 3996urban site-1 114 9 1 19 21 3986 225 67 2 67 38 4011 12.70 1.65 0.01 2.10 1.55 3959urban site-2 134 15 1 26 28 3757 231 67 2 70 36 3984 7.68 1.66 0.07 1.92 1.14 4028urban site-3 132 14 1 25 27 3980 176 48 2 54 29 3965 8.79 1.31 0.04 1.60 1.10 3998urban site-4 96 10 1 15 15 3774 241 60 4 66 36 4069 10.94 1.63 0.30 1.93 1.21 4086

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rural site 79 14 1 18 14 3924 173 44 1 51 33 4072 9.90 1.78 0.12 2.10 1.49 4077

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S4. Comparison the results of PMF model with those of FA-APCS-MLR

PCA and FA are dimension-reduction technique that provides information by the

most significant factors with a simpler representation of the data. These two

multivariate statistical methods generally used for data structure determination, and to

provide qualitative information about potential pollution sources. However, PCA

alone cannot determine quantitative contributions of the identified pollution sources to

each variable (Hopke, 2015). Receptor-based models, such as APCS-MLR, can be

used for this purpose (Hopke, 2015). The PMF model is a multivariate factor analysis

tool that decomposes a matrix of sample data into two matrices: factor contributions

and factor profiles. PMF is a new approach to FA in which the problem of non-

optimal scaling has been explicitly addressed. In order to properly scale the data, it is

necessary to look explicitly at the problem as a least-squares problem. The users could

specify uncertainty values for each variable in each sample to reduce the impact of

measurements with high uncertainties on the final results (Hopke, 2016). However, in

order to derive profiles, PMF requires a large number of air pollutants, which are

often unavailable. Therefore, both of APCS-MLR and PMF methods have their own

advantages and disadvantages, and it is beneficial to conduct source apportionment of

air pollutants in these study using both PMF and APCS-MLR.

Average contributions of different potential sources to industry (S1), traffic (S2),

and dust (S3) during heating season in NC calculated based on their percentage in

Table S4, are presented as a bar chart in Fig. S4. Fig. S4a shows the average

contributions of different pollution sources to air quality of the NC in aerosol and

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windstorm season obtained from the EPA PMF model. Average contributions

indicated that coal-based industry discharges and traffic-associated sources for

different variables concentrations in the windstorm season were 42.5 and 57.5 %,

respectively. Also, average contributions of dust storm, coal-based industry

discharges, and traffic-associated sources for different variables concentrations in the

aerosol season were 29.9, 47.5 and 22.6 %, respectively. The variation trend of the

contributions calculated by PMF model was the same as those by FA-APCS-MLR.

Table S4. Source contributions (%) using the different receptor models to different variables

concentrations in North China. R2 is the squared correlation coefficient obtained with the

calculations using the receptor modeling and actual observations.

Windstorm Season Aerosol Season

APCS-MLR PMF APCS-MLR PMF

S1 S2 S3 R2 S1 S2 S3 R2 S1 S2 R2 S1 S2 R2

CO68.

517.5 14 0.852

80.

86.8

12.

40.811

48.

151.9 0.79 32.4

67.

60.934

NO255.

935.7 8.4 0.897

88.

90 11.1 0.896

23.

776.3 0.73 41.5

58.

50.778

O3 4.3 88.6 7.1 0.99 0 93.7 6.3 0.963 0 1000.84

919 81 0.801

SO273.

314.5

12.

20.758

81.

318.7 0 0.528 0 100

0.57

528.4

71.

60.772

PM2.520.

517.5 62 1

33.

84.3

61.

90.53

87.

612.4

0.70

271.2

28.

80.999

PM1012.

120.2

67.

70.854 0 12.2

87.

80.735

95.

64.4

0.63

766.5

33.

50.78

Ave.28.

639.1

32.

3-

29.

947.5

22.

6-

43.

256.8 - 42.5

57.

5-

The p value of Mann-Whitney U test between the obtained results of PMF and

APCS-MLR models was 0.501 (> 0.05), indicating that there were non-significant

differences in estimated contribution for potential pollution source between the results

of these two models. The relatively large difference between the apportioned

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contributions using two models was observed for the coal-based industry discharges

during the windstorm season. To facilitate the comparison, predicted vs. observed

ratios from the results of two PMF and FA/APCS-MLR analyses were used for six air

pollutants (Table S4). Analysis of the results and the R-squared values of the

predicted/observed data for most of the air quality variables showed better goodness-

of-fit with the FA-APCS-MLR receptor modeling approach to the pollution source

apportionment in the studied air.

Fig. S4. Source contribution variations (a) and the predicted and observed ratios for air

pollutants (b) in North China based on the results of PMF and APCS-MLR models.

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S5. The correlation between ambient air temperature and PM2.5, CO and SO2 levels around heating season

Figure S5. The correlation between ambient temperature with CO, SO2 and PM2.5 concentrations from September 15, 2014, to May 15, 2015 in northern China.

Figure S5 has shown the correlation between ambient air temperatures with PM2.5, CO and SO2 concentrations around heating season. The

results showed that ambient temperatures have significant correlation with PM2.5, CO and SO2 concentrations and the correlation coefficients are

-0.816, -0.954 and -0.911 respectively. Hence, we have a justification for the importance of the heating season and the dataset afterwards can be

divided into the non-heating and heating season.

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S6. The influence of meteorological factors on the air pollutant concentrations during heating season

Table S5 presents the corresponding meteorology taken at Beijing, Lanzhou and

Urumqi station. Ambient air temperature, (T in ); relative humidity (RH in %);℃

wind speed, (WS in m s-1) and wind direction, (WD in degree) are listed here as the

parameters during the heating season in northern China. T is measured using dry bulb,

wet bulb and maximum-minimum thermometers installed in a Stevenson screen. RH

is derived from the dry and wet bulb temperatures from a hygrometric chart. WS is

measured by an automatic anemometer and WD by a standard wind vane. Table S4

shows surface average temperatures between -21 ℃ and 30 and relative humidity℃

0-100% with heating seasonal average wind velocity of 2.41 m s-1.

Table S5 The corresponding meteorological factors recorded during the heating season in the northern China (Beijing,, Lanzhou, and Urumqi stations).

T (℃) RH (%) WS (m s-1)Number 11647 11664 11647Minimum -21 0 0Medium -1 60 2Maximum 30 100 20Mean -0.96 57.56 2.41Standard deviation 8.34 23.87 1.91

The temperature variation experienced during the heating season is sufficient to

examine the relationship of T with the air pollutants observed. The temperature

changes did not significantly influence PM2.5-10, PM10 and NO2 concentrations (Tables

S5). The concentration of O3 shows a clear trend of increasing with temperature

(Tables S5). The concentrations of PM2.5, SO2 and CO, which are the indicators of

central heating process, show the opposite trend of O3 increasing with temperature

decrease. The highest average concentrations of PM2.5, PM10, SO2, NO2 and CO in

heating season occurred at T below -15 ℃. These observables except PM10 showed

the lowest average concentrations at T over 20 ℃.

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Table S6. The average hourly concentration with standard deviation of air pollutants during the heating season (from 10 th October, 2014 to 10th April, 2015) according to the meteorological parameters in northern China. The units of PM2.5, PM2.5-10, PM10, SO2, NO2 and O3 are μg m-3 and that of CO is mg m-3.

Class Ranges PM2.5 PM2.5-10 PM10 SO2 CO NO2 O3

Tempreture (℃) 1 T ≥ 20 61±43 103±64 164±76 18±17 0.84±0.43 43±30 63±27

2 10 ≤ T＜ 20 74±63 69±58 144±88 20±16 1.18±0.63 58±31 39±29

3 0 ≤ T ＜ 10 70±60 50±55 121±83 26±22 1.53±1.01 53±30 33±25

4 -10 ≤ T ＜ 0 80±64 50±58 130±96 28±20 2.04±1.13 56±29 24±19

5 T ＜ -10 122±77 74±45 196±108 41±21 2.68±0.97 68±25 11±10

Relative humidity (%) 1 0 ≤ RH ＜ 20 76±64 48±61 86±77 20±24 0.95±1.02 36±27 60±21

2 20 ≤ RH ＜ 40 56±46 55±64 110±86 28±23 1.32±0.99 49±29 44±24

3 40 ≤ RH ＜ 60 74±50 63±60 137±81 32±20 1.85±0.98 57±27 28±21

4 60 ≤ RH ＜ 80 90±67 56±45 146±88 30±20 2.07±0.99 61±28 17±16

5 80 ≤ RH ＜ 100 113±80 53±54 166±113 26±20 2.30±1.15 64±30 14±14

Wind speed (m s-1) 1 0 ≤ WS ＜ 3 96±71 55±50 151±96 31±22 2.07±1.08 63±29 20±19

2 3 ≤ WS ＜ 6 62±46 56±57 118±81 26±19 1.64±1.05 49±26 35±25

3 6 ≤ WS ＜ 9 29±29 49±67 77±82 13±12 0.68±0.53 26±17 57±19

4 9 ≤ WS ＜ 12 20±22 90±134 111±150 6±5 0.42±0.25 16±10 59±13

5 WS ≥ 12 15±16 115±174 130±181 5±6 0.35±0.22 12±8 59±12

Wind direction 1 Calm 97±72 62±44 159±98 31±21 2.28±1.09 64±29 17±17

2 NNE/NE/ENE/E 72±62 58±70 129±100 25±18 1.55±0.97 49±31 37±26

3 ESE/ES/SSE/S 87±60 57±53 143±81 31±23 1.93±1.01 63±28 30±27

4 SSW/SW/WSW/W 76±59 53±46 129±88 27±20 1.73±1.02 57±27 24±23

5 WNW/NW/NNW/N 70±63 54±58 125±95 26±19 1.74±1.17 50±27 27±21

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The statistical analysis of hourly air pollutant concentrations and hourly values

of the RH for the heating season in northern China was summarized in Table S6.

Inspecting the results we found that, the highest average concentration for O3, 60±21

μg m-3, occurred at humidity less than 20%. This can be mainly attributed to the

enhanced oxidation of hydrocarbons in the afternoon, which support the ozone

production (Xu et al., 2008). On the contrary, the NO2 concentration is the lowest

value at the same humidity (36±27 μg m-3). The highest average concentrations for

PM2.5, PM10, NO2 and CO occurred at humidity above 80%. An increase in humidity

reflects an updraft of boundary layer air masses to the 3 km level, leading to a higher

level of air pollution (Hu et al., 2014). In order to study the relationship between the

air pollutants concentrations and RH a correlation analysis was performed. Table S7,

shows the correlation matrix between RH and the corresponding data for air pollutants

concentrations. As for our expectation, the significant negative trend occurred

between O3 with RH. It can be noticed the barely relation between SO2 and PM2.5-10

concentrations with RH, and significant positive trend for PM2.5, PM10, NO2 and CO.

The correlation of low humidity with low concentrations can be attributed to the

influence of clean free tropospheric air masses (Hu et al., 2016). From the previous

discussion one concludes that, the correlation coefficient between air pollutant

concentrations and relative humidity is not very high (Table S7). The lower

correlation coefficient for all pollutants illustrates the competition of two

mechanisms: atmospheric dispersion (particles are removed from contaminated

surface air by dry deposition and by wet deposition in precipitation), and

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aerosolization from surfaces (emissions of aerosol particles by vehicles traveling on

the city’s narrow roads, industry and resuspended soil dust).

Table S7 Pearson correlation coefficients between air pollutants and meteorological

parameters during the heating season in northern China (from 10 th October, 2014 to 10th

April, 2015; T: Temperature; RH: Relative Humidity; WS: Wind Speed). * Significant at

1%; ** Significant at 5% or better

T RH WS

PM2.5 -0.203** 0.364** -0.325**

PM2.5-10 0.000 -0.015 0.039

PM10 -0.142** 0.246** -0.203 **

SO2 -0.235** 0.015 -0.253**

CO -0.420** 0.375** -0.370**

NO2 -0.114* 0.254** -0.395**

O3 0.401** -0.587** 0.471**

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Previous studies have shown that the high WS was beneficial for dilution of air

pollutants, and could decrease the concentrations (Wang et al., 2005; Wang et al.,

2014), which is the reason that WS and concentrations of PM2.5, SO2, NO2 and CO

show a significant negative correlation during the heating season in northern China

(Table S7). Thus, the highest average concentration with standard deviation of these

observables occurred in the WS below 3 m s-1 was 96±71, 31±22, 63±29 μg m-3 and

2.07±1.08 mg m-3 respectively (Table S6). The coarse aerosol (PM2.5-10) shows a weak

negative correlation with WS (-0.039; Table S7), however, the highest average

concentration with standard deviation of this parameter occurred in the WS exceed 12

m s-1 which may affect by the dust events in heating season. According to statistical

results of the analysis of meteorological factors measured in three stations, the major

prevailing WD is northwesterly (N, NNW, NW, WNW) and least frequent from the

southwest and calm. The heating seasonally average frequency for northwesterly wind

was 42% and that for southwesterly and calm wind were only 11% and 6%

respectively. Table S6, demonstrates the significance of WD for the resulting air

pollution. This behavior closely reflects the geological structure of the region. The

concentration levels of PM2.5, PM2.5-10, PM10, SO2, NO2 and CO in heating season were

highest under calm condition, with average concentrations of 97, 62, 159, 31, 64 μg

m-3 and 2.28 mg m-3 respectively. Lanzhou and Urumqi are typical valley-situated

cities in northwestern China. This geological structure may weaken the dispersion and

dilution processes of air pollutants. These observables, except PM2.5-10, also have high

values for the southeast where industry areas are.

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S7. The temporal variation of traffic density in Beijing from October 2014 to March 2015

Figure S6. The temporal variation of congestion delay index during the heating season in Beijing

Congestion delay index (CDI) is utilized to reflect the traffic density in the urban

area. The CDI value equals to the ratio between the passing travel time in traffic

congestion with the passing travel time in free flow. The higher CDI reflects a more

crowed traffic. Taken Beijing as an example, the CDI values measured as weekly

cycle that the traffic was more densely in workdays than that in weekends. Two

specific periods including the APEC period and spring festival in 2015 showed lower

traffic density than the ordinary time (Fig. S6). This phenomenon consisted with the

monthly variation of PM2.5, NO2 and CO concentrations during the heating season

(Fig. 2). Moreover, before Chinese New Year in 2015, increasing traffic density

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occurring as numerous migrant residents returned to their hometowns to reunite with

their families and celebrate Chinese New Year (Figure S6), which is the likely reason

why the peak monthly averaged concentrations of PM2.5, PM10, SO2, NO2 and CO in

Beijing occurred in January 2015 (Fig. 2). In addition, the diurnal variation of CDI

during the heating season measured as two peaks (Fig. S7), which was similar to the

variance of NO2 in Beijing (Fig. 2). The CDI data was collected from “the Traffic

Analysis Report of China Major Cities in 2014 and 2015”, which lacked the traffic

density data of Lanzhou and Urumqi.

Figure S7. The diurnal variation of congestion delay index during the heating season in Beijing

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