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# Open Geosciences

### formerly Central European Journal of Geosciences

Editor-in-Chief: Jankowski, Piotr

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Volume 9, Issue 1

# Influence of Social-economic Activities on Air Pollutants in Beijing, China

Xiaolu Li
• Chongqing Key Laboratory of Karst Environment, School of Geographical Sciences, Southwest University, Beibei, Chongqing, P. R. China
• Other articles by this author:
/ Wenfeng Zheng
• Corresponding author
• School of Automation, University of Electronic Science and Technology of China, Chengdu; P. R. China
• Email
• Other articles by this author:
/ Lirong Yin
• Geographical & Sustainability Sciences Department; University of Iowa, Iowa City, IA, United States of America
• Other articles by this author:
/ Zhengtong Yin
/ Lihong Song
• School of Automation, University of Electronic Science and Technology of China, Chengdu; P. R. China
• Other articles by this author:
/ Xia Tian
• School of Automation, University of Electronic Science and Technology of China, Chengdu; P. R. China
• Other articles by this author:
Published Online: 2017-08-10 | DOI: https://doi.org/10.1515/geo-2017-0026

## Abstract

With the rapid economic development, the serious air pollution in Beijing attracts increasing attention in the last decade. Seen as one whole complex and grey system, the causal relationship between the social development and the air pollution in Beijing has been quantitatively analyzed in this paper. By using the grey relational model, the aim of this study is to explore how the socio-economic and human activities affect on the air pollution in the city of Beijing, China. Four air pollutants, as the particulate matter with size 2.5 micrometers or less (PM2.5), particulate matter with size 10 micrometers or less (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NOx), are selected as the indicators of air pollution. Additionally, fifteen socio-economic indicators are selected to account for the regional socio-economic characteristics (economy variables, energy consumption variables, pollution emissions variables, environment and construction activity variables). The results highlight that all variables are associated with the concentrations of the four selected air pollutants, but with notable differences between the air pollutants. Most of the socio-economic indicators, such as industrial output, total energy consumption are highly correlated with PM2.5, while PM10, SO2, and NOx present in general moderate correlations with most of the socio-economic variables. Contrary to other studies and reports this study reveals that vehicles and life energy do not have the strongest effect on air pollution in Beijing. This study provides useful information to reduce air pollution and support decision-making for sustainable development.

## 1 Introduction

The healthy air quality is an important conditional factor for human surviving. With the rapid development of industrialization in the last two decades in China, especially in Beijing, the air pollution has progressed to an astonishing level. The poor air condition would be responsible for 350-500 thousand of premature death each year in China [1], which exerts extreme influence and damage on human and the living condition [2]. Following the 2008 Olympic Games, air pollution problems in Beijing became a public focus around the world. Since Beijing PM2.5 real - time data was published by the U.S. Consulate in 2008, it has attracted wide attention on air pollution in Beijing, the capital of China [3].

Air pollution in Chinese cities is of the worst status around the world after decades of rapid economic growth with loose environmental regulation (Rohde RA, Muller RA 2015). In 2012, Jun Wang’s study found that, in Beijing, the air pollution mainly consists of inhalable particulate (PM2.5 & PM10), sulfur dioxide (SO2) and nitrogen dioxide(NOx), of which the inhalable particles are recognized as the major problem [4]. In the later decade (2004-2013), the air pollutants concentrations (PM2.5, PM10, SO2, and NOx) stay at a high level. The yearly air pollutants concentrations were the highest in 2005-2006, and had a clear decline in 2008. In 2013, the percentage of high-pollution days in Beijing was 52%, the average yearly concentration of PM2.5 was 101 ug/m3, the average yearly concentration of PM10 was 108 ug/m3, and the average yearly concentration of SO2 and NOx were 26 ug/m3 and 56 ug/m3 respectively. The air quality guidelines from World Health Organization (WHO) shows that the yearly average concentration of PM2.5 < 10 ug/m3, PM10 < 20 ug/m3, SO2 < 20 ug/m3 and NOx < 40 ug/m3 are the thresholds of unhealthy atmospheric pollutants. The air pollution level in Beijing is already significantly exceeding the upper limit of the air quality guidelines from WHO. Air pollution in Beijing is stuck in the worst status after decades of rapid economic growth with loose environmental regulation [4, 5]. Faced with potential future extreme influence and damage on human and living condition within Beijing, we need to address some questions such as: a) would the socio-economic activities have a significant effect on the air pollution in Beijing; b) which socio-economic activities would contribute most to the air quality in Beijing.

The form of air pollution is created by pollution sources, which in addition to offsite source, is more closely related to human activities and social development in urban areas [6]. The Air pollution is complex and separating the haze components has proved complex [7]. Pai et al. [8] confirmed the effectiveness of adopting the grey system method to study air pollution problems and their main determinants. However, they utilized grey analysis method on forecasting air pollution, but failed to explore the causes of pollution [8]. Thus, in this study, we focus on the most serious air polluted region, the capital of China, and explore the relationship between the socio-economic development and the air quality in Beijing.

In additional, because of the linear hypothesis, there is bias on the influence factor analysis of air pollution using some method, such as the Principal Component Analysis and the Multivariate Statistical Methods [9], while the influence of factors on air pollution is nonlinear. For this reason, it is complex to explore the impact of human activities on the formation of urban haze components in Beijing [7]. However, the grey relational analysis is a multifactor method to evaluates the effects between factors [1012], which usually used in the grey condition which lack of function mechanism and physical prototype based on the geometric similarity of behavior factor sequences [1315]. The air pollution exerted by the social economic activities is a multifactor system in conformity with the grey characteristics of air pollution system [16, 17]. Hence, in this study, using the grey relational analysis on the interaction correlation between social economic and air pollution is feasible [18]. In this study, we introduce the grey relational analysis to build grey relational model to estimate the anthropogenic impacts on the formation of air pollution by discussing the major causes from social development in Beijing. To avoid the major challenges in function mechanism and physical prototype of air pollution, the grey relational model could help to explore the association of air pollution with socioeconomic characteristics, and derive the specific management plans for the air pollution in Beijing.

## 2 Data source and method

In this study, using the grey relational analysis described in Section 3, it aims to study the air pollutants contributed by human activities in the socio-economic development process. The yearly average concentration of PM10, PM2.5, SO2, and NOx are selected as indicators of air pollutants. The PM2.5data (from 2008 to 2013) is collected from the Mission China air quality monitoring program by US Embassy, while the PM10, SO2, and NOx data (from 2004 to 2013) is collected from Beijing Statistical Yearbook published by the Provincial Bureau of Statistics [1928]. The evolution of the air pollutants concentration in the city of Beijing (from 2004 to 2013) is presented in Figure 1.

Figure 1

The average air pollutants concentration in Beijing (from 2004-2013).

Considering that dynamic process of air pollution affected by human activities is complicated, and the selection of the socio-economic variables need to be considered comprehensively. Many indicators for the measurement of socio-economic development are found in the air pollution risk literature. Chan, C.K. and Yao, X. [29] found that the economic characteristics of the mega city, including regional economy, energy consumption, emissions of air pollutants, were related to air quality. Han, L., Zhou, W., et al. [30] assessed the urban air quality according to urbanization level. Yang, J. and McBride, J. [31] concluded that air pollution reduction was influenced by environment characteristics, including urban green coverage rate, the rate of woody plant cover, and others. But there has not been a consensus on the best socio-economic indicators with which to assess the influence on the air quality. In light of previous vulnerability and resilience research [2931] and considering the limited data availability in China, we selected a total of 15 indicators of the socio-economic development activities associated with air pollution formation for the grey relational analysis, which were collected from the standards in the annual economic and social development data of Beijing Statistical Yearbook in 2004-2013 (Beijing Statistical Yearbook 2005-2014). The 15 socio-economic indicators are listed in Table 1.

Table 1

The indicators list of the socio-economic development activities selected in this study.

Among the 15 socio-economic indicators in this paper, the regional economy in Beijing is represented by gross regional domestic product (GDP), the primary industrial output (PriIndO), the secondary industrial output (SecIndO) and the tertiary industrial output (TerIndO). The total energy consumption (TEC) and the energy consumption per GDP unit (ECPG) are used to stand for the energy consumption in Beijing. We selected the sales of liquefied petroleum gas (SaleLGP) & natural gas sales (SaleLNG) and possession of civil vehicles (Vehicle) as the measurement indicators of pollution emissions. The regional environment status is characterized by two indicators, which are the rate of urban green coverage (GreenCov) and rate of woody plant coverage (WoodyCov). The construction activity was accounted for using total investment in infrastructure (InfInv), kilometers of the highway (Highway), kilometers of the road (Road) and the building under construction (ConstBldg).

## 3 Grey relational analysis

Grey relational analysis is proposed by Deng(1990) from grey systems methodology [11]. It is an approach for quantitative analysis of dynamic process using similarity of trend and pattern between the reference sequence and comparative sequences. In the grey relational model, it hypothesizes that the closer relationship between reference sequence and comparative sequences, the more similar of trend and pattern of the dynamic processes. The comparative sequence, of which trend and rate is closer to the reference sequence, has stronger associations with the reference sequence [10]. The comparative sequences are referred to the causes of the grey system, while the effect of dynamic process in the system is expressed as the reference sequence. A dynamic system often consists of many factors. The integrated influence of many factors is crucial to the subsequent development of the dynamic system. Under the condition of incomplete information, small sample data, and lack of experience, grey relational analysis could be used among the multiple factors to parse some question, like which ones are major factors, which ones are minor, which ones may make or break progress, and which ones have a prominent influent on the dynamic system. It overcomes the drawbacks of multivariable regression analysis and stochastic process approach.

First, it is needed to determine the data sequence as the reference sequence Y = {y (1), y (2),......, y (n)} to reflect the behavior characteristics of the dynamic system. Then, the comparative sequences Xi = {xi (1), xi (2),......, xi (n)} are constructed to represent the multiple factors in the dynamic system. By analyze the distance between the reference sequence and each comparative sequence, it determines the differences of the influence extents, and the differences are used to tell the major and minor factors which affect behavior characteristics. For each comparative sequence, the grey relevancy coefficient ξi (k) represents the association extent between reference sequence Y and comparative sequences Xi at a certain point in time k: $ξi(k)=miniminkΔi(k)+ρmaximaxkΔi(k)Δi(k)+ρmaximaxkΔi(k)$(1) Where Δi(k) = |y(k) -xi(k)|, and k = 1,2, ...,n represent the value at the point in time k. The m is the total number of the comparative sequences Xi={xi (1), xi (2),......, xi (n)}. and i = 1, 2,..., m. 0 < ρ < 1 is the discrimination coefficient. The smaller ρ value is, the better discriminant resolution. Generally, ρ = 0.5463 [10]. The value of ρ has no effect on the grey relational degree orders of comparative sequences on the reference sequence. The grey relational degree γ (Y, Xi) between the reference sequence Y and the comparative sequence Xi, was the average of the grey relevancy coefficient values, shown as followed. Because of any two sequence cannot be strictly independent in the grey system, the range is 0 < γ(Y, Xi) ≤ 1.

$γ(Y,Xi)=1n∑k=1nξi(k)$(2)

In this study, the yearly concentrations of air pollutants (PM2.5, PM10, SO2, and NOx), as the reference sequence in this study respectively, is caused by a variety of factors. As the comparative sequences in this study, the 15 socio-economic indicators represent the multiple social and economic factors which the air quality is affected by. According to grey relational analysis, the major factors, which play significant roles in air quality and pollution, can be confirmed from the 15 socio-economic indicators. Because of the disparity of the grey relational degree between different indicators and each air pollutant, the influence of each socio-economic indicator on different air pollutants could be evaluated based on the comparison of the grey relational degree. Based on the gray relational model, it shows that, despite considering delay time issue, the stronger the causal relationship, the similar the change tendency and the distribution between the economic activity and the air pollution. Hence, among the 15 social economic indicators, those indicators which show high similarity with the change and tendency of the pollutant concentrations (PM2.5, PM10, SO2, and NOx), have stronger correlations and influences with the pollutant concentrations. The raw data of the socio-economic activities were converted into z-score before the grey relational analysis. In this study, we utilized the function tool in Excel for the grey relational model.

## 4 Results

As a result of grey relational analysis, the influence of each socio-economic indicators on the air pollutants are shown in Table 2. The higher the grey relational degree, the more influence of the socio-economic indicators on the air pollution, the more significant contribution to the air quality and vice versa. Typically, when 0 < γ (Y, Xi) ≤ 0.30, there was a weak association between the reference sequence and the comparative sequence. If 0.30 < γ (Y, Xi ) ≤ 0.60, the correlation is usually regarded as moderate correlation. It is considered a strong association when 0.60 < γ (Y, Xi) ≤ 1.0 [10]. The extent of grey correlation of the socio-economic indicator on the air pollutant is shown as Table 3.

Table 2

Grey relational degree on the selected air pollutant in Beijing.

Table 3

The extent of grey correlation on the selected air pollutant in Beijing.

By the grey relevancy on the air pollutants, all the relational degree is equal or above 0.5. It shows the social economic activities are complex [32], multi-variable, mutual interaction [33], and directly or indirectly have great roles on air pollution [3436]. Another unexpected discovery is that the indicators with less effect on PM2.5, have high influence in PM10, SO2 and, NOx, and vice versa.

## (1) PM2.5

The highest grey relational degree is the influence of TerIndO (Tertiary Industrial Output) on PM2.5, which is 0.96. Most of the socio-economic indicators (11 of 15) have high relational degree on PM2.5, near or above 0.8, except GDP (Gross Regional Domestic Product), ECPG (Energy consumption per GDP unit), SaleLGP (Sales of Liquefied Petroleum Gas), and Road (Kilometers of Urban Road). In Table 2, this shows three economic output indicators, PriIndO, SecIndO & TerIndO, have more than 0.9 grey relational degrees on PM2.5, the same as TEC. It indicates that a) enhancement of each industrial output may lead to further serious concentration of PM2.5 in this area, b) the promotion of energy utilization level does not have greatly help to reduce the concentration of PM2.5, and c) the concentration level of PM2.5 is directly related to the total urban energy consumption.

The low grey relational degree of SaleLGP (0.50) shows the correlation between PM2.5 and urban LGP consumption is small in Beijing. When comparing LGP with LNG, it is shown that LNG has a greater influence on PM2.5 (0.83). It can be interpreted that the clean energy project has been proceeding in most cities in China, which replace coal with natural gas, including renovation of power plant and coal - burning boiler. However, in the process it could increase energy consumption and resource investment. Therefore, this could lead to a temporary increase in the concentration of PM2.5.

The grey relational degree of Vehicles on PM2.5 is 0.80. It indicates that vehicles exhaust emissions indeed have some impact on PM2.5 concentration. Two indicators related to vehicles, kilometers of highway and road, shows that highway plays a far greater role than kilometers of road on PM2.5. The exhaust emissions of diesel truck or freight vehicles powered by diesel fuel have much bigger influence on the concentration of PM2.5.

The grey relational degrees of the rest three indicators, GreenCov, WoodyCov & ConstBldg, respectively are 0.93, 0.86 and 0.83 on PM2.5. With China’s economic rise a large number of industrial activities such as railways, highways and building construction have resulted in an increase in atmospheric particles and dust haze components of PM2.5. It has been suggested that these sources, together with certain weather conditions would result in a concentration of local pollutants [37, 38].

## (2) PM10, SO2, and NOx

The grey relational degrees of PM10, SO2, and NOx with the socio-economic indicators have obvious similarity features. The thirteen of fifteen socio-economic indicators have the same correlation extent on PM10, SO2, and NOx in Beijing (shown as Table 3). ECPG, Energy consumption per thousand yuan of GDP, has the highest grey relational degree on PM10, SO2, and NOx, which is near 0.85. ECPG can precisely show the energy consumption level and energy saving status. Low ECPG shows the high efficiency utilization of energy. ECPG has high correlation with PM10, SO2 and NOx, and low with PM2.5. At the same time, we notice that the grey relational degree of total energy consumption is not high (0.55). It implies that energy utilization efficiency is closely related with PM10, SO2 and NOx. SaleLGP has the biggest impact on NOx (0.73) than on PM10 and SO2 (0.67). This shows that LGP combustion emissions have a greater contribution to air pollution in this region, especially NOx. The Highway has a greater influence on SO2 (0.70) than on PM10 (0.66) and NOx (0.56). The kilometers of urban road has higher grey relational degree than the most of indicator, 0.69 on PM10 and 0.66 on SO2 &NOx.The kilometers of Highway and city road affect the formation of PM10 and SO2 in Beijing [39, 40]. Kilometers of urban highway and road have no direct connection with SO2 and PM10. But the large yield of cement caused by expansion of urban highway and road network, and high-emissions truck running is the direct factor to increase the concentration of SO2 and PM10, especially in the cement production process. It is more intense to the relatively backward desulfurization technology. The grey relational degrees of the rest of indicators are between 0.55 and 0.6 on PM10, SO2, and NOx.

## 5 Discussion

According to the grey relational analysis, the result shows that the influence of human activities on PM2.5 was independent of those on any other air pollutant in Beijing. All the socio-economic indicators are closely related to different air pollutant to some extent.

## (1) The Regional Economy and Total Energy Consumption

Each industrial output and total energy consumption have high relevance with PM2.5 (above 0.90), but general relevance with PM10, SO2 and NOx (below 0.60). It indicates that the power of the fast-growing economy in Beijing depends on energy consumption, like coal and oil. Soaring Energy consumption result from industrial development and economic growth may give rise to severe PM2.5 air pollution. Therefore, it is necessary to reduce the energy dependence on coal and oil.

## (2) Energy Consumption

TEC and ECPG have different characters. TEC has a high correlation with PM2.5, low correlation with other air pollutants. On the contrary, ECPG has low correlation with PM2.5, and high correlation with PM10, SO2 and NOx. In economic activities, given that the utilization rate of energy is low, the gas emitted at the process could directly exacerbate the density of PM10, SO2, and NOx in the air. ECPG is the factor that has the highest grey relational degree with PM10, SO2, and NOx. The increase of PM10, SO2, and NOx are mainly depended on the utilization level of energy. For example, as to whether the coal and oil were combusted totally, or whether clean energy was used. However, this has little relation to the total energy capacity.

## (3) The Regional Environment Status

GreenCov and WoodyCov show relatively high correlation with PM2.5. Though urban afforestation and vegetation have purification effect on air pollutants, the high correlation can be explained by the rapid economic development and exploitation of real estate, the purification effect thanks to the effort on afforestation construction was far inferior to the pollution effect of industrial production. In fact, since 2008 Beijing started various urban afforestation projects, which largely increased green coverage rate of Beijing. However, during the Beijing Olympic Games, the 60th anniversity of China, and other large activities, the government temporarily shut down the industrial workshops in Beijing and its surrounding areas to control air pollution and guarantee the air quality.

## (4) The Construction Activity

Holding capacity of private car does not show the highest grey relational degree (0.80) with PM2.5, and presents normal grey relational degrees (about 0.56) with PM10, SO2, and NOx, which disagreed with the results from documents. On the other hand, the grey relational degrees of the kilometers of urban road are about 0.65 on PM2.5, PM10, SO2, and NOx. The kilometers of urban road partly reflects the economic activity and the living patterns of regional tenants. It can be explained that the urban transportation could affect the regional air pollution, and the car emission is not the largest contributor to the air pollution. The high grey relational degree between the kilometers of highway and PM2.5 indicates that large-displacement vehicles have even more serious influence on air pollution. Moreover, the construction activity like infrastructure and building under construction, also could exacerbate existing concentration level of the regional PM2.5.

## 6 Conclusion

This study brings up a simple and helpful new statistical method for quantitative assessment of socio-economic activities’ influence on air pollution. The air quality in Beijing is extremely serious polluted. Previous researches on the air pollution causation were mainly analyzed on the relationship between the activity of a specific economic aspect and the air pollution. Furthermore, the research on the mechanisms of air pollution is still in a discovery phase. In 2003, Dr. Pai and his colleges adopted grey system method on air pollution study, and confirmed it effectiveness [8]. Thus, we used the grey relational analysis to study the nonlinear multiple-dimensional model of the social economic activities and the impacts on air pollution.

In this study, the grey relational degree between human activity and air pollution in Beijing was analyzed using grey relational analysis. PM2.5, PM10, SO2, and NOx were the four selected indicators representing the air contamination condition. Fifteen indicators were selected to represent five dimensions of socio-economic activities in Beijing. As a result, it shows that, the total energy consumption is the most major factor on PM2.5, and energy consumption per GDP unit is the most major factor on PM10, SO2, and NOx.

In grey relational analysis, the whole city, Beijing, was analyzed as a grey system in this study. This model solves many difficulties existing in current air pollution analysis. Former studies adopted factorial analysis, which neglect the relevance between the major factor and air pollution indicators. Those studies using regression analysis only use dependent variables to avoid multicollinearity among dependent variables. This study adopted the collected real data for analysis, which more directly reflect the real social state. However, the grey relational analysis method is sensitive to index data. In the case of the social and economic state in the study area was at a transition stage, instead of a steady developing stage, the grey relational degree is allowed to have an error to some extent. Thus, samples as many as possible are required to increase the noninterference of system analysis result.

## Acknowledgement

This study was supported by the China Postdoctoral Science Foundation (no. 2016M592647); The National Natural Science Foundation of China (Grant NO.61305022); Fund for International S&T Cooperation and Exchange R&D Project of Sichuan Province (Grant No. 2017HH0054); and Opening fund of State Key Laboratory of Virtual Reality Technology and Systems (Beihang University) (Grant No.BUAA-VR-16KF-11). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.

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All authors have contributed equally to this work and They are both co-first authors

Accepted: 2017-05-30

Published Online: 2017-08-10

Conflict of Interests: The authors declare that there is no conflict of interests regarding the publication of this paper.

Citation Information: Open Geosciences, Volume 9, Issue 1, Pages 314–321, ISSN (Online) 2391-5447,

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