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Health risk assessment of China’s main air pollutants
© The Author(s). 2017
Received: 26 August 2016
Accepted: 13 February 2017
Published: 20 February 2017
With the rapid development of China’s economy, air pollution has attracted public concern because of its harmful effects on health.
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).
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).
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.
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) , dust, sea salt , heavy metals, and polycyclic aromatic hydrocarbon . Haze incidents are a relatively new threat to human health , air quality , global climate change , 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 , aerosol optical properties , and aerosol chemical components . And the formation and evolution mechanism of haze has been similarly explored : e.g., long-lasting haze occurrences in Nanjing , a winter regional haze in the North China Plain , and the heavy haze pollution episode over central and eastern China . 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 , Harbin , and Fuzhou  was implemented, and the characteristics of atmospheric carbonyls were documented  in Beijing.
We have known for some time that haze boosts air pollution, causing significant harm to human health . 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 , and the effects of dust-haze on mortality were explored . 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 . In China, Sun et al.  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.
Method of health risk assessment
Parameters for health risk assessment through inhalation pathway
30 × 365
30 × 365
0.15 (National third standard)
0.06 (National second standard)
0.04 (National first standard)
30 × 365
30 × 365
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.
The size of main air pollutants
Relationships among principal air pollutants over China from 2014 to 2015
Spatial patterns of air pollutants in China from 2014 to 2015
Risk assessment of population exposure to air pollutants in China from 2014 to 2015
Relationships between air pollution and human health
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 , 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 . The concentration of PM2.5 is closely correlated with SO2 and NO2 in Xi’an . A joint effect of NO2 and PM was found . 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 . This means that SO2 and NO2 play an important role in the enhancement of PM2.5 , 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) ; 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 .
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 . 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) , dust, sea salt , heavy metal, and polycyclic aromatic hydrocarbon , which resulted from vehicle exhaust , coal consumption, secondary production, stagnant meteorological conditions [13, 38–40], biomass open burning [41, 42], and petrol stations . Therefore, replacing coal and fossil fuels with cleaner fuels were the fundamental methods of controlling the concentration of air pollutants . Certainly, we need to encourage new technologies and energy sources for automobiles, still a major contributor to air pollution . 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.  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 , and the PM2.5 contains toxic micro-particles that might increase the risk of respiratory disease . Mortality rates increased due to the high PM pollution in Shenzhen, especially for the elderly and male populations . The cardiovascular, nervous system, respiratory and blood vessels of the brain are damaged by exposure to high concentrations of PM2.5 . Indeed, hemorrhagic stroke was closely associated with PM pollution . Lung and cardiovascular disease are related to PM and NO2 , and NO2 was identified as the principal pollutant for respiratory diseases . Local residents in Shanghai were exposed to high health risks due to NO2 . 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 . Meanwhile, high concentrations of O3 will irritate the eyes, nose, and throat, although long-term effects, if any, need further research . One researcher has revealed that individuals exposed to biomass burning-impacted aerosols over the long term increased carcinogenic risk . 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.
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.
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.
We would like to thank Penglin Zhao for research assistance.
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/.
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.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
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.
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