Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Health risk assessment of China’s main air pollutants

BMC Public HealthBMC series – open, inclusive and trusted201717:212

https://doi.org/10.1186/s12889-017-4130-1

Received: 26 August 2016

Accepted: 13 February 2017

Published: 20 February 2017

Abstract

Background

With the rapid development of China’s economy, air pollution has attracted public concern because of its harmful effects on health.

Methods

The source apportioning of air pollution, the spatial distribution characteristics, and the relationship between atmospheric contamination, and the risk of exposure were explored. The in situ daily concentrations of the principal air pollutants (PM2.5, PM10, SO2, NO2, CO and O3) were obtained from 188 main cities with many continuous air-monitoring stations across China (2014 and 2015).

Results

The results indicate positive correlations between PM2.5 and SO2 (R 2 = 0.395/0.404, P < 0.0001), CO (R 2 = 0.187/0.365, P < 0.0001), and NO2 (R 2 = 0.447/0.533, P < 0.0001), but weak correlations with O3 (P > 0.05) for both 2014 and 2015. Additionally, a significant relationship between SO2, NO2, and CO was discovered using regression analysis (P < 0.0001), indicating that the origin of air pollutants is likely to be vehicle exhaust, coal consumption, and biomass open-burning. For the spatial pattern of air pollutants, we found that the highest concentration of SO2, NO2, and CO were mainly distributed in north China (Beijing-Tianjin-Hebei regions), Shandong, Shanxi and Henan provinces, part of Xinjiang and central Inner Mongolia (2014 and 2015).

Conclusions

The highest concentration and risk of PM2.5 was observed in the Beijing–Tianjin–Hebei economic belts, and Shandong, Henan, Shanxi, Hubei and Anhui provinces. Nevertheless, the highest concentration of O3 was irregularly distributed in most areas of China. A high-risk distribution of PM10, SO2 and NO2 was also observed in these regions, with the high risk of PM10 and NO2 observed in the Hebei and Shandong province, and high-risk of PM10 in Urumchi. The high-risk of NO2 distributed in Beijing-Yangtze River Delta region-Pearl River Delta region-central. Although atmospheric contamination slightly improved in 2015 compared to 2014, humanity faces the challenge of reducing the environmental and public health effects of air pollution by altering the present mode of growth to achieve sustainable social and economic development.

Keywords

Air pollutants Haze Spatial patterns Health risk China

Background

Haze is principally formed by an increase in particle size in the atmospheric medium, which affects atmospheric absorption, emission, and scattering of light. PM2.5: fine inhalable particles, with diameters that are generally 2.5 micrometers and smaller, and originates from construction sites, unpaved roads, fields, smokestacks or fires, including congregated aerosols (e.g. sulfur dioxide, nitrogen dioxide, carbon monoxide, and so on), black carbon (the incomplete combustion of carbonaceous combustibles) [1], dust, sea salt [2], heavy metals, and polycyclic aromatic hydrocarbon [3]. Haze incidents are a relatively new threat to human health [4], air quality [5], global climate change [6], ecological suitability for human settlement, and regional sustainable development.

Recently, haze has become a principal environmental issue in China. Consequently, the causes of particulate pollution have been discussed widely: e.g., secondary aerosol [5], aerosol optical properties [7], and aerosol chemical components [8]. And the formation and evolution mechanism of haze has been similarly explored [9]: e.g., long-lasting haze occurrences in Nanjing [10], a winter regional haze in the North China Plain [11], and the heavy haze pollution episode over central and eastern China [12]. In addition, we know that understanding the origin of fine particulate matter is essential to finding appropriate strategies to combat haze and the harm it causes. Thus, the source apportioning of fine particulate during the haze events in Shanghai [13], Harbin [14], and Fuzhou [15] was implemented, and the characteristics of atmospheric carbonyls were documented [16] in Beijing.

We have known for some time that haze boosts air pollution, causing significant harm to human health [17]. Previous studies have reported extensively on cardiovascular disease, lung disease, exposure time, mortality, and the mechanisms of biochemistry for haze. Short-term exposure was investigated in metropolitan areas [18], and the effects of dust-haze on mortality were explored [19]. In fact, the relationship between haze and respiratory diseases in Brunei Darussalam were analyzed, and it was found that PM10 and CO levels have a significant bearing on the incidence of respiratory diseases [20]. In China, Sun et al. [4] explored the relationship between economic development and air pollution, and they found that the variation explained by both total SO2 emissions and total smoke and dust emissions were 33 and 24% of pertussis (whooping cough), respectively.

However, source apportioning of fine particulate requires considerable investment in time and money. Thus, in this study, we attempt to analyze source apportioning using data mining. Because the risk of exposure to haze across China has been insufficiently discussed, the object of the present study is to address the relationship between atmospheric contamination and human health in China. Specifically, we seek to accomplish the following: (1) to analyze the spatial-temporal distribution of atmospheric contamination over China; (2) to explore the source apportioning of fine particulate in China; and (3) to analyze the relationship between atmospheric contamination and the risk of human exposure in China.

Methods

Data collection

All air measurements (SO2, NO2, CO, O3, PM10, and PM2.5) were obtained from 188 main cities with continuous air-monitoring stations (Fig. 1), the stations were set up accord with the standard “Technical regulation for selection of ambient air quality monitoring stations (HJ 664–2013)”, and were obtained from the Ministry of Environmental Protection of the People’s Republic of China (http://datacenter.mep.gov.cn/). The disease data of pertussis was collected from China Statistical Yearbook (www.stats.gov.cn, 2004–2015) and China Statistical Yearbook of Health and Family Planning (www.moh.gov.cn, 2004–2015). In addition, the atmospheric contamination was compiled annual means to analyze the spatial-temporal distribution of atmospheric contamination over China, and monthly in every city to analyze the relationships among air pollutions for 2014 and 2015.
Fig. 1

Study areas and the continuous air monitoring stations (CAMS) in China

Method of health risk assessment

This study used the risk assessment method of the U.S. Environmental Protection Agency (EPA) [21], which focused on the health risk assessment through inhalation pathway for three kinds of people (adult males, adult females, and children), thus, avoided the effects of population density. The assessment study focuses on the risk of exposure to air pollutants (PM10, SO2, and NO2) in China; R i was the individual health risk for exposure pollution, calculated as Eq. 1 [22]:
$$ {R}_i = AD{D}_{opt}\times {10}^{-6}/\ \left( Rf{d}_{i j}\times 70\right) $$
(1)
ADD opt was the average daily dose, calculated as Eq. 2 [23]:
$$ AD{D}_{opt} = \left( CA \times IR \times ED\right)\ /\ \left( BW \times AT\right) $$
(2)
where CA was the concentration (mg m−3) of air pollutants, the average values of inhalation rate (IR), ED (exposure duration in days) and AT (averaging exposure time in days) were showed in Table 1 [22], and the average weight (BW) was obtained from national physical fitness test communiqués (http://www.gov.cn/test). The Rfdij (reference dose) values for PM10, SO2, and NO2 referred to the U.S EPA (https://www3.epa.gov/).
Table 1

Parameters for health risk assessment through inhalation pathway

Crowds

IR(m3/d)

BW(kg)

ED(d)

AT(d)

CA-PM10(mg m−3)

CA-SO2(mg m−3)

CA-NO2(mg m−3)

Adult male

15.2

60

30 × 365

30 × 365

0.15 (National third standard)

0.06 (National second standard)

0.04 (National first standard)

Adult female

11.3

57

30 × 365

30 × 365

Children

8.7

44

18 × 365

18 × 365

Based on the IDW (inverse distance weighted) interpolation method to model the spatial distribution of health risk in China, then, calculated the Rfdij values and reclassified by the national air quality standard to get the expose risk level of air pollutants.

Tools of analysis

In the present study, the ArcGIS 10.2 (ESRI, Inc., Redlands, CA, USA) was used to draw spatial graphs, and SigmaPlot for Windows 10.0 (Systat Software, Inc., Chicago, IL, USA) was used to conduct correlation and regression analysis. Correlations between different variables were determined using two-tailed Pearson’s Correlation at 0.05 levels.

Results

The size of main air pollutants

As shown in Fig. 2, the frequency distribution of air pollutant (PM2.5, SO2, CO, NO2, and O3) concentrations in 2014 and 2015 was observed. The values of the PM2.5, SO2, CO, NO2 and O3 range from 18.58 μg m−3 to 130.46 μg m−3, from 2.17 μg m−3 to 117.82 μg m−3, from 0.47 μg m−3 to 2.42 mg m−3, from 12.56 μg m−3 to 66.09 μg m−3, from 48.78 μg m−3 to 198.61 μg m−3, with median values of 61.44 μg m−3, 30.11 μg m−3, 1.12 μg m−3, 36.05 μg m−3, 103.97 μg m−3, respectively. The high occurrence frequency of the PM2.5, SO2, CO, NO2, and O3 was around 40–75 μg m−3, around 15–40 μg m−3, around 0.75–1.3 mg m−3, around 25–50 μg m−3, and around 75–130 μg m−3 (upper panel). In 2015, there was not only a similar shift in the trend of air pollutants (PM2.5, SO2, CO, NO2, and O3) observed but also the median concentration of air pollutants reduced and centered generally in 52.84 μg m−3, 23.52 μg m−3, 1.02 mg m−3, 34.18 μg m−3, 102.67 μg m−3, respectively. As an example, the values of the PM2.5, SO2, CO, NO2 and O3 range from 17.05 to 106.32 μg m−3, from 2.89 to 82.05 μg m−3, from 0.44 to 2.36 mg m−3, from 12.84 to 61.24 μg m−3, from 58.56 to 136.38 μg m−3. In addition, the high occurrence frequency of the PM2.5, SO2, CO, NO2 and O3 narrowed and became more centralized at the range of 25–55 μg m–3, around 10–30 μg m−3, around 0.75–1.25 mg m−3, around 25–50 μg m−3, and around 90–125 μg m−3 (below panel), respectively.
Fig. 2

The frequency distribution of the principal air pollutants. The upper and blow panels represent the documents were collected in 2014 and 2015

Relationships among principal air pollutants over China from 2014 to 2015

We postulated that the entire concentration of PM2.5 depends on air pollutants (SO2, CO, NO2, and O3), and, per regression analysis, there are close positive correlations between PM2.5 and SO2, CO, NO2 in 2014. In Fig. 3 the appropriate functions of SO2, CO and NO2 with PM2.5 are Y = 0.579X-1.485 (R 2  = 0.395, P < 0.0001) (Fig. 3a), Y = 0.008X + 0.678 (R 2  = 0.187, P < 0.0001) (Fig. 3b), and Y = 0.357X + 14.32 (R 2  = 0.447, P < 0.0001) (Fig. 3c), respectively. However, there was a weak correlation trend between PM2.5 and the concentration of O3 (P > 0.05) (Fig. 3d). Compared to other air pollutants, NO2 had the greatest influence on PM2.5. Thus, the contribution rate of SO2 and NO2 is as high as 44.7% for PM2.5.
Fig. 3

The PM2.5 was explained by the principal air pollutants over China (2014), and graph a, b, c, and d represent SO2, CO, NO2, O3, respectively

As illustrated in Fig. 4, the concentration of PM2.5 had been increasing with the rise of SO2, CO, NO2, and there are significant relationships between them. High correlation coefficients were noted between SO2 (R 2  = 0.404, P < 0.0001, Fig. 4a), CO (R 2  = 0.365, P < 0.0001, Fig. 4b), NO2 (R 2  = 0.533, P < 0.0001, Fig. 4c) and PM2.5, which illustrates that the PM2.5 are significantly associated with SO2, and NO2, with CO following, but there were no observable relationships between PM2.5 and O3 (Fig. 4d).
Fig. 4

The PM2.5 was explained by the principal air pollutants over China (2015), and graph a, b, c, and d represent SO2, CO, NO2, O3, respectively

Based on regression analysis, we found close relationships among SO2, CO and NO2 in 2014 and 2015. The appropriate functions were Y = 0.011X + 0.805 (R 2  = 0.289, P < 0.0001) (Fig. 5a), Y = 0.320X + 25.28 (R 2  = 0.306, P < 0.0001) (Fig. 5c), and Y = 10.41X + 23.84 (R 2  = 0.139, P < 0.0001) (Fig. 5e) in 2014. Similar positive correlations were also observed in 2015 with R 2  = 0.386 (P < 0.0001, Fig. 5b), R 2  = 0.230 (P < 0.0001, Fig. 5d), and R 2  = 0.271 (P < 0.0001, Fig. 5f), respectively.
Fig. 5

The coupled analysis of the principal air pollutants in China. The graph a, c, and e represent relationships among SO2, CO, and NO2 in 2014, and the graph b, d, and f represent relationships among SO2, CO, and NO2 in 2015

Spatial patterns of air pollutants in China from 2014 to 2015

According to the spatial distribution of air pollutants in China in 2014 (Fig. 6), we discovered that the concentration of CO ranges from 0.08 mg m−3 to 2.42 mg m−3, with the maximum distribution around Hebei and Shanxi province, and the minimum distribution in the southeast, northwest, and northeast (Fig. 6a). In contrast, the concentration range of NO2 was between 2 μg m−3 and 64 μg m−3, with the maximum distribution occurring in Beijing, Tianjin, Hebei, Shandong, Henan province, and northeastern Xinjiang (Fig. 6b). However, O3 concentrations ranged from 3 μg m−3 to 198 μg m−3, with the maximum distribution in eastern China, southern China, and Hubei province; and the minimum distribution regions including Shanxi, Sichuan and Chongqing (Fig. 6c). The concentration of SO2 ranges from 1 μg m−3 to 113 μg m−3, with the maximum distribution in northern China and in Shandong province (Fig. 6).
Fig. 6

Spatial patterns of air pollutants in China (2014), and the graph a, b, c, and d represent CO, O3, NO2, and SO2, respectively

According to Fig. 7a, the concentration of CO ranges from 0.01 mg m−3 to 2.35 mg m−3 in Shanxi, Shandong, Hebei, Henan, Beijing and Tianjin had the maximum values; we also found that the minimum was chiefly located in Heilongjiang, Gansu and Tibet, and the southeast of China (the coastal urban belt). The concentration ranges of NO2 was between 0.6 μg m−3 and 60 μg m−3, with maximum values distributed primarily in northeastern China (the Beijing-Tianjin-Hebei-Shanxi-Henan-Shandong region, Fig. 7b). In addition, we found that the concentration of O3 ranges from 1 μg m−3 to133μg m−3 in Fig. 7c. The higher concentration values were observed in most areas of China, including eastern China, northern and central China (except Hunan province), and the regions of Gansu, Qinghai, Tibet (around Lhasa) and the Pearl River Delta region. The concentration of NO2 ranged from 0.3 μg m−3 and 80 μg m−3, with maximum values primarily distributed in Shanxi, Shandong and Hebei provinces (Fig. 7d).
Fig. 7

Spatial patterns of air pollutants in China (2014), and the graph a, b, c, and d represent CO, O3, NO2, and SO2, respectively

The PM2.5 concentration ranges from 3 μg m−3 to 103 μg m−3 in 2014 (Fig. 8a), with the maximum values distributed mainly around Hebei province (Beijing-Tianjin and a part of Shandong-Henan-Hubei. The concentration of PM2.5 ranges from 1 μg m−3 to 106 μg m−3 in 2015 (Fig. 8b), with maximum distribution in Hebei, Shandong, Henan and Hubei provinces, and the region of Central Bohai, while minimum values were observed in areas that include Tibet, Yunnan, Hainan, Fujian, the Pearl River Delta region and the northwest of Gansu province.
Fig. 8

Spatial patterns of air pollutant in China (graph a, b represent PM2.5 in 2014 and 2015, respectively)

Risk assessment of population exposure to air pollutants in China from 2014 to 2015

For adult males, according to Fig. 9a, the high-risk values of PM10 main distributed in central of Xinjiang province, in Hebei province, the southwest of Shandong and eastern of Shanxi, and a part of Beijing and Tianjin. The high-risk values of SO2 occurring mainly in the central of Shandong province, the border of Hebei and Shanxi, a small part of Inner Mongolia (Erdos) and Liaoning provinces (Shenyang) (Fig. 9b). Surprisingly, the high-risk values of NO2 were mainly distributed in northeast China (Fig. 9c), regions of Hebei-Shandong-Henan-Beijing-Tianjin, a part of Inner Mongolia, the provincial capital cities of Guangzhou, Chengdu, Lanzhou, Xian, Shenyang, Changchun and Harbin, and the central of Jiangsu province. For adult females and children, a similar distribution pattern of the high-risk values for PM10/SO2/NO2 was observed. The high-risk values for PM10 distributed primarily in the central of Inner Mongolia, the south of Hebei province (Fig. 9d and g). The high-risk values for SO2 distributed in the central of Shandong province, the border of Shanxi and Hebei province, and a part of Erdos (Fig. 9e and h). As shown in Fig. 9f and i, the high-risk values of distributed primarily in Beijing-Tianjin-Hebei-Shandong regions, a part of Xinjiang province, and the cities of Chengdu, Shanghai, Wuhan, Wenzhou and Harbin.
Fig. 9

Risk assessment of population exposure to air pollutants in China (2014), and the graph a, b, c/d, e, f/g, h, i represent PM10, SO2, and NO2 for adult male/adult female/children, respectively

In 2015, for adult males, the high-risk values of PM10were mainly distributed around the border of Hebei-Shandong-Henan province, thus, the cities of Baoding, Hengshui, Xingtai, Handan, Shijiazhuang in Hebei; Liaocheng, Dezhou and Heze in Shandong; and Zhengzhou in Henan (Fig. 10a). As shown in Fig. 10b, the high-risk values of SO2 occurring in the central of Shanxi (Taiyuan and Linfen) and Shandong province, and a small part of Inner Mongolia (Erdos). The high-risk values of NO2 mainly distributed in the central and northeastern of China (Fig. 10c), regions of Beijing–Tianjin–Hebei-Shandong-Henan, the central of Jiangsu province, and the cities of Urumchi, Lanzhou, Yanan, Chengdu, Shenyang, Changchun and Harbin, and the main city area of Chongqing. As for adult females and children, the regions of high-risk values for PM10/SO2/NO2 were alike. As for PM10, the high-risk values distribution primarily in the main city area of Baoding, Hengshui, and Handan (Fig. 10d and g). The high-risk values for SO2 occurring in the central of Shandong, the main city area of Taiyuan and Shizuishan (Fig. 10e and h). As shown in Fig. 10f and i, the high-risk values of NO2 were chiefly distributed in Beijing-Tianjin-Hebei regions, the central of Shandong and the north of Henan province; and the province capital cities of Urumchi, Lanzhou, Chengdu, Wuhan and Harbin.
Fig. 10

Risk assessment of population exposure to air pollutants in China (2015), and the graph a, b, c/d, e, f/g, h, i represent PM10, SO2, and NO2 for adult male/adult female/children, respectively

Relationships between air pollution and human health

The concentrations of air pollutions were the average values during 2014–2015, the mean rate of total pertussis was calculated from 30 provinces from 2004 to 2014. General linear models analysis illustrated that the mean rate of total pertussis was significantly associated with the average concentrations of PM2.5, PM10 and CO (Fig. 11a, b, and d), and the variation explained by them were 61% (P < 0.06) for the rate of total pertussis. Meanwhile, the rate of total pertussis was related to SO2 and NO2 to some extent (Fig. 11c and e). However, there was no significant relationship between O3 and total pertussis (Fig. 11f).
Fig. 11

The relationships of pertussis mean rate and the average concentration of air pollutions from 2014 to 2015 in 30 provinces. The graph a, b, c, d, e and f represent the relationship between pertussis mean rate and PM2.5, PM10, SO2, CO, NO2, and O3

Discussion

Relationships among air pollutants

For this study, the most extensive data for analyzing the concentration and relationship of air pollutants and time-series datasets in China were used, with the aim of understanding pollution and to mitigate the heavy haze on the Chinese mainland. The Min, Median and Max concentration of air pollutants in 2014 and 2015 were presented, after finding that the occurrence frequency narrowed and became more centralized from 2014 to 2015 (Fig. 2). Our study also identified that the main components were NO2 and SO2 in PM2.5 (R 2  = 0.395, R 2  = 0.447) in 2014 (Fig. 3) and (R 2  = 0.404, R 2  = 0.533) in 2015 (Fig. 4) based on regression analysis. The same phenomenon was found in Beijing [9], central and eastern China [12, 24, 25]. Previous research has observed that the concentration of SO2 and NO2 is lowest in the autumn and highest in the winter [26]. The concentration of PM2.5 is closely correlated with SO2 and NO2 in Xi’an [23]. A joint effect of NO2 and PM was found [27]. Wang not only analyzed the relationships of NO2, SO2, and PM2.5 but also found that with the increase of sulfate and nitrate, their particle hygroscopicity enhances and drives the formation and evolution of haze pollution [12]. This means that SO2 and NO2 play an important role in the enhancement of PM2.5 [28], and reminds us to explore the relationships between SO2, NO2, and CO.

As shown in Fig. 5, the most significant correlation coefficient was NO2 and SO2 (R 2  = 0.306), followed by CO and SO2 (R 2  = 0.289); NO2 and CO (R 2  = 0.139) were the least pronounced in 2014. Nevertheless, a different significant association order was observed in 2015. Thus, the correlation coefficient of CO and SO2 (R 2  = 0.386) was greater than NO2 and CO (R 2  = 0.271) in 2015. This result suggests a strong relationship between CO and SO2, and NO2 and CO; one study reported that the critical factor for formation droplet-mode particles was the availability of the water-vapor contents and precursor gases (SO2 and NO2) [29]; in other words, under iron and manganese catalysis, the heterogeneous oxidation of SO2 and NO2 change into the secondary sulfates (SO4 2− and NO3 ) in the droplet mode [30, 31], namely, the complex interaction of SO4 2−, NO3 , congregated aerosols (e.g. sulfur dioxide, nitrogen dioxide, carbon monoxide, and so on), black carbon (the incomplete combustion of carbonaceous combustibles) determined the formation of haze and its particulate size [1, 29, 32]. NH3, however, should not be neglected, which may result in the particulate sulfate and nitrate increase [28].

The spatial patterns of air pollutants

Apart from the regression analysis of air pollutants, spatial patterns of yearly average simulation values clearly present different air pollutants concentration distributions in different regions in 2014 and 2015 (Figs. 6 and 7). A few studies found that the Beijing–Tianjin–Hebei-Shandong-Shanxi-Henan regions had the highest concentration of air pollutants [24, 25], including the local characteristics of high populations, city traffic, exhaust emissions, and rapid urban expansion [23, 33]. In this study, the same spatial patterns of air pollutants were observed (Figs. 6, 7 and 8). Furthermore, we found the concentration of SO2, NO2, and CO in Inner Mongolia cannot be negligible (Fig. 6a, b and d); meanwhile, the Tibet Plateau and coastal areas from Tianjin to Guangxi were affected by O3 (Fig. 6c) in 2014. In addition, similar spatial patterns of the maximum values were observed in China. However, from 2014 to 2015, the spatial variation of O3 concentration displayed a rapid increasing trend in China, especially on the Tibet Plateau (Fig. 7c), which may be influenced by its origin and long-distance transport. The PM2.5 is distributed mainly in the region of Beijing-Tianjin-Shandong-Henan-Hubei in 2014 and 2015. Fortunately, its distribution narrowed and became more centralized in2015, and showed that the extent and area of PM2.5 were lower than in 2014.

Emissions of PM2.5 (97%), SO2 (90%), NO2 (70%) and CO (32%) were mainly due to the combustion of coal [34]. A number of previous studies have explored the origin and transportation [32, 35] of air pollutants [36, 37], including the congregated aerosols (e.g. sulfur dioxide, nitrogen dioxide, carbon monoxide, and so on), black carbon (the incomplete combustion of carbonaceous combustibles) [17], dust, sea salt [2], heavy metal, and polycyclic aromatic hydrocarbon [3], which resulted from vehicle exhaust [36], coal consumption, secondary production, stagnant meteorological conditions [13, 3840], biomass open burning [41, 42], and petrol stations [43]. Therefore, replacing coal and fossil fuels with cleaner fuels were the fundamental methods of controlling the concentration of air pollutants [39]. Certainly, we need to encourage new technologies and energy sources for automobiles, still a major contributor to air pollution [44]. But uncertainties exist from various sources, particularly the air pollutants in Xinjiang Uygur Autonomous Region, which might cause sand-dust storms. Therefore, ecological conservation projects should be developed and implemented; for instance, building key forest shelterbelts to shield against sandstorms in Xinjiang.

Health risk assessment and human health

R i for adult males, adult females, and children, obtained for different pollutants (PM10, SO2, and NO2) in 2014 and 2015. It was observed that the high-risk of PM10was mainly distributed in the cities of Baoding, Hengshui, Xingtai, Handan, Shijiazhuang, Liaocheng, Dezhou, Heze and Zhengzhou (Figs. 9a, d, g and 10a, d, g), and Urumchi (Fig. 9a, d and g). The high-risk values of SO2 were chiefly distributed in the cities of Taiyuan and Linfen, a small part of Erdos, and the central of Shandong province (Figs. 9b, e, h and 10b, e, h). The high-risk values of NO2 were mainly occurring around in coastal areas from Beijing-Yangtze River Delta region-Pearl River Delta region-central, especially the cities of Urumchi, Lanzhou, Chengdu, Wuhan and Harbin (Figs. 9c, f, i, and 10c, f, i). In comparison, the coverage area of the highest risk level was smaller in 2015 (Figs. 9 and 10).

A large portion of China’s population has been significantly exposed to high-risk areas. Feng et al. [45] evaluated the ILI risk significantly associated with the concentrations of PM in Beijing during the flu season. In Guangzhou, the dust haze clearly increased mortality [19], and the PM2.5 contains toxic micro-particles that might increase the risk of respiratory disease [46]. Mortality rates increased due to the high PM pollution in Shenzhen, especially for the elderly and male populations [47]. The cardiovascular, nervous system, respiratory and blood vessels of the brain are damaged by exposure to high concentrations of PM2.5 [48]. Indeed, hemorrhagic stroke was closely associated with PM pollution [49]. Lung and cardiovascular disease are related to PM and NO2 [50], and NO2 was identified as the principal pollutant for respiratory diseases [18]. Local residents in Shanghai were exposed to high health risks due to NO2 [26]. Vulnerable people particularly (asthmatics, children, and the elderly), but all people generally, should not be exposed to high concentrations of SO2 for any length of time [51]. Meanwhile, high concentrations of O3 will irritate the eyes, nose, and throat, although long-term effects, if any, need further research [50]. One researcher has revealed that individuals exposed to biomass burning-impacted aerosols over the long term increased carcinogenic risk [6]. For these reasons and more, it is a matter of considerable urgency that policies be developed and implemented to mitigate the heavy haze in China.

Limitations of the current study

In this study, although 188 main cities across China used to get the spatial distribution patterns of air pollutions, uncertainties exist for limited data, especially in the sparsely distributed area of Xinjiang, Tibet, and Qinghai. Though significant relationships among main air pollutions were observed, inorganic substance and organic matter also correlated with each other in haze. In addition, PM2.5 has other sources of crustal materials, such as from Asian dust storms. We analyzed the relationships among gaseous pollutant emissions. Meanwhile, the average parameter values for crowds in Eq. 2 referred from articles rather than actual measurements, led to the above conclusions about health risk in China. In addition, the data of pertussis was collected from the China Statistical Yearbook on Environment, although exposure to pollutions related to the increases in morbidity, accurate and concrete data for long-term effects is urgently needed. Thus, detailed data need collect to define air pollutions and risk assessment of human health in future.

Conclusions

Air pollution is harmful to the environment and to public health. This study focused on the source apportioning and the spatial-temporal characteristics of air pollutants and analyzed the relationship between atmospheric contamination and human exposure risk in China from 2014 to 2015. The main conclusions are as follows:
  1. (1)

    Regression analysis illustrates that there are close positive correlations between PM2.5 and SO2, CO and NO2, but weak correlations with O3 in 2014 and 2015. Additionally, the relationships between SO2, NO2 and CO were significant, suggesting that vehicle exhaust, coal consumption secondary production, stagnant meteorological conditions, and biomass open-burning are the main factors driving the formation and evolution of air pollution.

     
  2. (2)

    In general, air pollutants in China have stabilized, showing a slight decline from 2014 to 2015. Accompanying the highest concentrations are high-risk areas distributed in provinces of Hebei, Shanxi, and Henan, and along the coast from Beijing-Yangtze River Delta to the Pearl River Delta region. The high-risk of NO2 occurred in the Beijing–Tianjin–Hebei economic belts.

     

Measuring air pollutants, tracking contaminant paths and assessing pollutants in different areas with volatile weather conditions are complex challenges and need further research. The objective of this study is to help provide healthy, sustainable development not only for the people of China but for developing and developed nations alike. In particular, this study aims to initiate a constructive forum on the Beijing-Tianjin-Hebei collaborative development.

Declarations

Acknowledgements

We would like to thank Penglin Zhao for research assistance.

Funding

This research was funded by the Science and Technology Service Network Initiative (No. KFJ-SW-STS-175-01).

Availability of data and materials

The datasets generated during and/or analysed during the current study are available in the Ministry of Environmental Protection repository, http://datacenter.mep.gov.cn/.

Authors’ contributions

JS contributed to the study design, JS and TCZ were involved in drafting the manuscript, approving the final draft, and agree to be accountable for the work. Both authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
(2)
Chengdu University of Technology

References

  1. Zhi GR, Chen YJ, Xue ZG, Meng F, Cai J, Sheng GY, Fu JM. Comparison of elemental and black carbon measurements during normal and heavy haze periods: Implications for research. Environ Monit Assess. 2014;186:6097–106.View ArticlePubMedGoogle Scholar
  2. Wang YX, Zhang QQ, Jiang JK, Zhou W, Wang BY, He KB, Duan FK, Zhang Q, Philip Sajeev, Xie YY. Enhanced sulfate formation during china’s severe winter haze episode in January 2013 missing from current models. J Geophys Res-Atmos. 2014;119(10):425-40.Google Scholar
  3. Yoon YH, Horst SM, Hicks RK, Li R, de Gouw JA, Tolbert MA. The role of benzene photolysis in titan haze formation. Icarus. 2014;233:233–41.View ArticleGoogle Scholar
  4. Sun J, Wang J, Wei Y, Li Y, Liu M. The Haze Nightmare Following the Economic Boom in China: Dilemma and Tradeoffs. Int J Environ Res Public Health. 2016;13(4):402.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Huang RJ, Zhang YL, Bozzetti C, Ho KF, Cao JJ, Han YM, Daellenbach KR, Slowik JG, Platt SM, Canonaco F, Zotter P, Wolf R, Pieber SM, Bruns EA, Crippa M, Ciarelli G, Piazzalunga A, Schwikowski M, Abbaszade G, Schnelle-Kreis J, Zimmermann R, An ZS, Szidat S, Baltensperger U, El Haddad I, Prévôt AS. High secondary aerosol contribution to particulate pollution during haze events in China. Nature. 2014;514:218–22.PubMedGoogle Scholar
  6. Betha R, Behera SN, Balasubramanian R. 2013 southeast asian smoke haze: Fractionation of particulate-bound elements and associated health risk. Environ Sci Technol. 2014;48:4327–35.View ArticlePubMedGoogle Scholar
  7. Che H, Xia X, Zhu J, Li Z, Dubovik O, Holben B, Goloub P, Chen H, Estelles V, Cuevas-Agulló E, Blarel L, Wang H, Zhao H, Zhang X, Wang Y, Sun J, Tao R, Zhang X, Shi G. Column aerosol optical properties and aerosol radiative forcing during a serious haze-fog month over north china plain in 2013 based on ground-based sunphotometer measurements. Atmos Chem Phys. 2014;14:2125–38.View ArticleGoogle Scholar
  8. Tian SL, Pan YP, Liu ZR, Wen TX, Wang YS. Size-resolved aerosol chemical analysis of extreme haze pollution events during early 2013 in urban Beijing, China. J Hazard Mater. 2014;279:452–60.View ArticlePubMedGoogle Scholar
  9. Liu XG, Li J, Qu Y, Han T, Hou L, Gu J, Chen C, Yang Y, Liu X, Yang T, Zhang Y, Tian H, Hu M. Formation and evolution mechanism of regional haze: A case study in the megacity Beijing, China. Atmos Chem Phys. 2013;13:4501–14.View ArticleGoogle Scholar
  10. Kang HQ, Zhu B, Su JF, Wang HL, Zhang QC, Wang F. Analysis of a long-lasting haze episode in Nanjing, China. Atmos Res. 2013;120:78–87.View ArticleGoogle Scholar
  11. Zhao XJ, Zhao PS, Xu J, Meng W, Pu WW, Dong F, He D, Shi QF. Analysis of a winter regional haze event and its formation mechanism in the North China plain. Atmos Chem Phys. 2013;13:5685–96.View ArticleGoogle Scholar
  12. Wang YS, Yao L, Wang LL, Liu ZR, Ji DS, Tang GQ, ZHANG JK, SUN Y, HU B, XIN JY. Mechanism for the formation of the January 2013 heavy haze pollution episode over central and Eastern China. Sci China Earth Sci. 2014;57:14–25.View ArticleGoogle Scholar
  13. Wang YJ, Li L, Chen CH, Huang C, Huang HY, Feng JL, Wang SX, Wang HL, Zhang GF, Zhou M, Cheng P, Wu MH, Sheng GY, Fu JM, Hu YT, Russell AG, Wumaer A. Source apportionment of fine particulate matter during autumn haze episodes in Shanghai, China. J Geophys Res-Atmos. 2014;119:1903–14.View ArticleGoogle Scholar
  14. Huang LK, Yuan CS, Wang GZ, Wang K. Chemical characteristics and source apportionment of pm10 during a brown haze episode in Harbin, China. Particuology. 2011;9:32–8.View ArticleGoogle Scholar
  15. Zhang FW, Xu LL, Chen JS, Chen XQ, Niu ZC, Lei T, Li CM, Zhao JP. Chemical characteristics of pm2.5 during haze episodes in the urban of Fuzhou, China. Particuology. 2013;11:264–72.View ArticleGoogle Scholar
  16. Duan JC, Guo SJ, Tan JH, Wang SL, Chai FH. Characteristics of atmospheric carbonyls during haze days in Beijing, China. Atmos Res. 2012;114:17–27.View ArticleGoogle Scholar
  17. Zhang RH, Li Q, Zhang RN. Meteorological conditions for the persistent severe fog and haze event over Eastern China in January 2013. Sci China Earth Sci. 2014;57:26–35.View ArticleGoogle Scholar
  18. Zhang ZL, Wang J, Chen LH, Chen XY, Sun GY, Zhong NS, et al. Impact of haze and air pollution-related hazards on hospital admissions in Guangzhou, China. Environ Sci Pollut R. 2014;21:4236–44.View ArticleGoogle Scholar
  19. Liu T, Zhang YH, Xu YJ, Lin HL, Xu XJ, Luo Y, Xiao JP, Zeng WL, Zhang WF, Chu C, Keogh K, Rutherford S, Qian ZM, Du YD, Hu MJ, Ma WJ. The effects of dust-haze on mortality are modified by seasons and individual characteristics in Guangzhou, China. Environ Pollut. 2014;187:116–23.View ArticlePubMedGoogle Scholar
  20. Yadav AK, Kumar K, Kasim AMBHA, Singh MP, Parida SK, Sharan M. Visibility and incidence of respiratory diseases during the 1998 haze episode in Brunei Darussalam. Pure Appl Geophys. 2003;160:265–77.View ArticleGoogle Scholar
  21. U.S. Environmental Protection Agency (EPA). Epa releases probabilistic risk assessment guidance for superfund. Hazardous Waste Cons. 2002;20:B17-B21.Google Scholar
  22. Dong J, Zhang P, L N, Tian ZH. Health risk assessment of main air pollutants in yulin city. Ground water. 2014;36:209-210.Google Scholar
  23. Li YA, Miao QG, Song JF, Quan YN, Li WS. Single image haze removal based on haze physical characteristics and adaptive sky region detection. Neurocomputing. 2016;182:221–34.View ArticleGoogle Scholar
  24. Wang H, Tan SC, Wang Y, Jiang C, Shi GY, Zhang MX, Che HZ. A multisource observation study of the severe prolonged regional haze episode over Eastern China in January 2013. Atmos Environ. 2014;89:807–15.View ArticleGoogle Scholar
  25. Wang XF, Chen JM, Sun JF, Li WJ, Yang LX, Wen L, Yang LX, Wen LA, Wang WX, Wang XM, Collett Jr JL, Shi Y, Zhang QZ, Hu JT, Yao L, Zhu YH, Sui X, Sun XM, Mellouki A. Severe haze episodes and seriously polluted fog water in Ji’nan, China. Sci Total Environ. 2014;493:133–7.View ArticlePubMedGoogle Scholar
  26. Zhao WC, Cheng JP, Li DL, Duan YS, Wei HP, Ji RX, Wang WH. Urban ambient air quality investigation and health risk assessment during haze and non-haze periods in Shanghai, China. Atmos Pollut Res. 2013;4:275–81.View ArticleGoogle Scholar
  27. Kirrane E, Svendsgaard D, Ross M, Buckley B, Davis A, Johns D, et al. A comparison of risk estimates for the effect of short-term exposure to pm, no2 and co on cardiovascular hospitalizations and emergency department visits: Effect size modeling of study findings. Atmosphere-Basel. 2011;2:688–701.View ArticleGoogle Scholar
  28. Ye XN, Ma Z, Zhang JC, Du HH, Chen JM, Chen H, et al. Important role of ammonia on haze formation in shanghai. Environ Res Lett. 2011;6:024019.Google Scholar
  29. Kang E, Han J, Lee M, Lee G, Kim JC. Chemical characteristics of size-resolved aerosols from asian dust and haze episode in Seoul Metropolitan City. Atmos Res. 2013;127:34–46.View ArticleGoogle Scholar
  30. Frossard AA, Shaw PM, Russell LM, Kroll JH, Canagaratna MR, Worsnop DR, Quinn PK, Bates TS. Springtime arctic haze contributions of submicron organic particles from european and asian combustion sources. J Geophys Res-Atmos. 2011;116:D05205.Google Scholar
  31. Wang XF, Wang WX, Yang LX, Gao XM, Nie W, Yu YC, Xu PJ, Zhou Y, Wang Z. The secondary formation of inorganic aerosols in the droplet mode through heterogeneous aqueous reactions under haze conditions. Atmos Environ. 2012;63:68–76.View ArticleGoogle Scholar
  32. Zhang XL, Huang YB, Zhu WY, Rao RZ. Aerosol characteristics during summer haze episodes from different source regions over the coast city of North China plain. J Quant Spectrosc Ra. 2013;122:180–93.View ArticleGoogle Scholar
  33. Lin G, Fu JY, Jiang D, Hu WS, Dong DL, Huang YH, Zhao MD. Spatio-temporal variation of pm2.5 concentrations and their relationship with geographic and socioeconomic factors in China. Int J Env Res Pub He. 2014;11:173–86.View ArticleGoogle Scholar
  34. Xie YY, Zhao B, Zhang L, Luo R. Spatiotemporal variations of pm2.5 and pm10 concentrations between 31 chinese cities and their relationships with so2, no2, co and o-3. Particuology. 2015;20:141–9.View ArticleGoogle Scholar
  35. Jung J, Kim YJ. Tracking sources of severe haze episodes and their physicochemical and hygroscopic properties under asian continental outflow: Long-range transport pollution, postharvest biomass burning, and asian dust. J Geophys Res-Atmos. 2011;116:D02206.Google Scholar
  36. Liu JW, Li J, Zhang YL, Liu D, Ding P, Shen CD, Shen KJ, He QF, Ding X, Wang XM, Chen DH, Szidat S, Zhang G. Source apportionment using radiocarbon and organic tracers for pm2.5 carbonaceous aerosols in guangzhou, south china: Contrasting local- and regional-scale haze events. Environ Sci Technol. 2014;48:12002–11.View ArticlePubMedGoogle Scholar
  37. Tang LL, Yu HX, Ding AJ, Zhang YJ, Qin W, Wang Z, Chen WT, Hua Y, Yang XX. Regional contribution to PM1 pollution during winter haze in Yangtze River Delta, China. Sci Total Environ. 2016;541:161–6.View ArticlePubMedGoogle Scholar
  38. Sun YL, Jiang Q, Wang ZF, Fu PQ, Li J, Yang T, Yin Y. Investigation of the sources and evolution processes of severe haze pollution in Beijing in January 2013. J Geophys Res-Atmos. 2014;119:4380–98.View ArticleGoogle Scholar
  39. Wang K, Liu YN. Can beijing fight with haze? Lessons can be learned from London and Los angeles. Nat Hazards. 2014;72:1265–74.View ArticleGoogle Scholar
  40. Wang LT, Wei Z, Yang J, Zhang Y, Zhang FF, Su J, Meng CC, Zhang Q. The 2013 severe haze over southern hebei, china: Model evaluation, source apportionment, and policy implications. Atmos Chem Phys. 2014;14:3151–73.View ArticleGoogle Scholar
  41. Cheng Z, Wang S, Fu X, Watson JG, Jiang J, Fu Q, Chen C, Xu B, Yu J, Chow JC, Hao J. Impact of biomass burning on haze pollution in the yangtze river delta, China: A case study in summer 2011. Atmos Chem Phys. 2014;14:4573–85.View ArticleGoogle Scholar
  42. Phoothiwut S, Junyapoon S. Size distribution of atmospheric particulates and particulate-bound polycyclic aromatic hydrocarbons and characteristics of pahs during haze period in Lampang province, Northern Thailand. Air Qual Atmos Hlth. 2013;6:397–405.View ArticleGoogle Scholar
  43. Demirel G, Ozden O, Dogeroglu T, Gaga EO. Personal exposure of primary school children to btex, no2 and ozone in Eskisehir, Turkey: Relationship with indoor/outdoor concentrations and risk assessment. Sci Total Environ. 2014;473:537–48.View ArticlePubMedGoogle Scholar
  44. Fang YP. Economic welfare impacts from renewable energy consumption: The China experience. Renew Sust Energ Rev. 2011;15:5120–8.View ArticleGoogle Scholar
  45. Feng C, Li J, Sun W, Zhang Y, Wang Q. Impact of ambient fine particulate matter (PM2.5) exposure on the risk of influenza-like-illness: a time-series analysis in Beijing, China. Environ Health. 2016;15:17.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Pan QC, Yu YS, Tang ZH, Xi M, Zang GQ. Haze, a hotbed of respiratory-associated infectious diseases, and a new challenge for disease control and prevention in China. Am J Infect Control. 2014;42:688–688.Google Scholar
  47. Zhang FY, Liu XJ, Zhou L, Yu Y, Wang L, Lu JM, Wang WY. Spatiotemporal patterns of particulate matter (pm) and associations between pm and mortality in Shenzhen, China. BMC Public Health. 2016;16.Google Scholar
  48. Li MN, Zhang LL. Haze in China: Current and future challenges. Environ Pollut. 2014;189:85–6.View ArticlePubMedGoogle Scholar
  49. Lin HL, Tao J, Du YD, Liu T, Qian ZM, Tian LW, et al. Differentiating the effects of characteristics of pm pollution on mortality from ischemic and hemorrhagic strokes. Int J Hyg Envir Heal. 2016;219:204–11.View ArticleGoogle Scholar
  50. Liu SK, Cai S, Chen Y, Xiao B, Chen P, Xiang XD. The effect of pollutional haze on pulmonary function. J Thorac Dis. 2016;8:E41–56.PubMedPubMed CentralGoogle Scholar
  51. Klose CD. Health risk analysis of volcanic so2 hazard on vulcano island (Italy). Nat Hazards. 2007;43:303–17.View ArticleGoogle Scholar

Copyright

© The Author(s). 2017

Advertisement