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Research on Influential Factors of PM2.5 within the Beijing-Tianjin-Hebei Region in China.

1. Introduction

Air pollutant emissions represent a large social and environmental problem in China that is due to rapid urbanization and motorization. In the State of the Environment Report of 2015 [1], China's Ministry of Environmental Protection revealed that only 21.6% among the 338 cities monitored by the central government managed to meet official minimum standards for air quality, and only 22.5% of 338 monitored cities met official minimum standards (less than 35 [micro]g/[m.sup.3]) for fine particle matter (PM2.5). There are 45 cities in which the annual mean concentration ofPM2.5 has exceeded 70 [micro]g/[m.sup.3]. The air pollutant emissions problem is more serious in the Beijing-Tianjin-Hebei region. From November to December in 2015, three extreme conditions of air pollution occurred. During that period, primary and secondary schools and many of the highest polluting industries in the Beijing-Tianjin-Hebei region were temporarily closed, and the use of vehicles was also restricted. However, the mean concentration of PM2.5 in these areas still increased by 9.6% compared to values during 2014 the heating season. To make matters worse, in Beijing, the PM2.5 mean concentration increased by 75.9% in 2015 compared with the same period of 2014. Research has shown that once the concentration of PM2.5 increased by 10 micrograms per cubic meter, the number of emergency patients with hypertension in hospitals increased by 8% [2]. In order to solve the air pollution problems, the Chinese government planned to invest 1840 billion renminbi (RMB) from 2013 to 2017. The direct investment for reducing air pollutant emissions in the Beijing-Tianjin-Hebei region was set at 249.029 billion RMB. An important task for China is to determine the best use of their money in order to achieve the best effect. In 2013, in the State Council Air Pollution Prevention and Control Action Plan [3], the Clean Air Alliance of China pointed out that the industry pollutant emissions in the Beijing-Tianjin-Hebei region should be emphasized for government initiatives. However, this is not specific enough for the huge investment direction in these areas.

As a result of these aforementioned circumstances, teasing out and ranking pollution factors play a vital role in this area of research. This paper aims to solve this problem by exploring the relationships among air pollutant emissions, humans, manufacturing, machines, materials, and energy. We gathered data from the China Statistical Yearbook, Beijing Statistical Yearbook, Tianjin Statistical Yearbook, Hebei Statistical Yearbook, Report on the State of the Environment in China, and relevant government websites. The remainder of the paper is as follows: Section 2 overviews the research literature on analysis of the air pollutant; Section 3 introduces the air quality status of China and the Beijing-Tianjin-Hebei region; Section 4 analyzes and ranks the influence factors of pollution and effects of the air pollutant emissions; Section 5 makes a summary and directs our research for future work.

2. Literature Review

Air pollutant emissions describe the gases and particles that are emitted into the air by various sources. Many studies have shown that air pollutant emissions are related to many diseases [4] (e.g., respiratory system diseases), climate change [5] (e.g., climate warming), and traffic security [6] (e.g., rear-end collisions). Therefore, researching the sources of air pollutant emissions is very important. Considerable researches on air pollution have mentioned some air pollution factors, like human life, industry, energy, transportation, and so on. These published papers are shown in Table 1.

From Table 1, we find that most researchers think air pollutant emissions come from human activities: industry, energy, or traffic. The analysis methods of these papers are mainly from monitoring or calculation. Among the upper fourteen papers, nine articles researched on developed countries' air problem (five articles about Europe, four about USA), and five articles researched on developing countries' air problems (two about China, one about Brazil, one about India, and one about Cuba). Existing researches still need improvement. There is no research on the Beijing-Tianjin-Hebei region, which has exhibited the highest levels of PM2.5 in China.

3. The Air Pollutant Emissions Status of the Beijing-Tianjin-Hebei Region

Located in northeastern China, the Beijing-Tianjin-Hebei (BTH) region consists of the municipalities of Beijing and Tianjin as well as Hebei province. Some information about the BTH region is shown in Table 2 [21].

As shown in Table 2, the areal proportion of the BTH region is only 2.25%, but the GDP, vehicle, and total energy consumption proportions are all over 10%. The data indicates that the BTH region is a major economic region in northern China; besides, Beijing is China's political and cultural center.

Meanwhile, the BTH region also represents three key regions of air pollution control defined by the Twelfth Five-Year Plan on air pollution prevention and control in key regions, which was issued by the Ministry of Environmental Protection, National Development and Reform Commission, and Ministry of Finance in Dec. 2012 [22]. The residents in the BTH region suffer from severe air pollution. Among the cities in the BTH region, there are 11 cities in the top 20 of the worst air pollution in China and 7 cities in the top 10. The average days meeting air quality standards amount to only 37.5% of the whole year. There are 10 cities where more than 50% of days do not meet air standards.

It can be seen from Table 3 that the air quality in Beijing, Tianjin, and Hebei is relatively poor. However, compared with Beijing, the air quality in Tianjin and Hebei is even worse.

As shown in Figure 1, in 2015, annual average concentration range of PM2.5 is 11~125 [micro]g/[m.sup.3] in China. From Figure 1, we know that the concentration range of PM2.5 in 13.3% of the 338 cities is over 70 [micro]g/[m.sup.3], and the rate of reaching the PM2.5 standard is 22.5%. Differently from PM2.5, the annual average concentration range of PM10 is 24~357 [micro]g/[m.sup.3], the cities whose concentration range is over 150 [micro]g/[m.sup.3] account for 5.0%, and the rate of reaching the PM10 standard is 34.6%. The annual average concentration range of SO2 is 3~87 [micro]g/[m.sup.3], the cities whose concentration range is over 60 [micro]g/[m.sup.3] account for 3.3%, and the rate of reaching the SO2 standard is 96.7%. The annual average concentration range of NO2 is 8~63 [micro]g/[m.sup.3], the cities whose concentration range is over 60 [micro]g/[m.sup.3] account for 0.6%, and the rate of reaching the PM10 standard is 81.7%. The annual average concentration range of O3 is 62~203 [micro]g/[m.sup.3], the cities whose concentration range is over 160 [micro]g/[m.sup.3] account for 16.0%, and the rate of reaching the PM10 standard is 84.0%. The annual average concentration range of CO is 0.4~6.6 mg/[m.sup.3], the cities whose concentration range is over 4.0 mg/[m.sup.3] account for 3.3%, and the rate of reaching the PM10 standard is 96.7%. Compared with PM10, SO2, NO2, O3, and CO, this shows that PM2.5 is one of the most serious air pollutant problems in China now. Meanwhile, as shown in Figure 2, the air pollution problem is very serious in the BTH region.

In the above, PM2.5, PM10, NO2, O3, SO2, and CO are all pollutants that affect air quality and do damage to the human health. Among them, PM2.5 particles which are smaller compared with PM10 contain a lot of hazardous substances and stay a long time in the atmosphere; thus PM2.5 has a far greater impact on the health situation of human and environmental quality. In spite of a downward trend from 2013 to 2015 in China, PM2.5 in Beijing went from 89.5 [micro]g/[m.sup.3] to 80 [micro]g/[m.sup.3], which fell just 9.9 percent. Therefore, the pollution controls of PM2.5 in BTH region have achieved initial results but it is still difficult to achieve the goal of 60 [micro]g/[m.sup.3] in 2017. This shows that there are still a lot of difficulties which need to be overcome in PM2.5. By contrast, the concentrations of SO2, NO2, CO, and PM10 have achieved the goal of 2017 in 2015 in Beijing. The O3 pollution is an emerging pollution problem which is not incorporated into the scope of air monitor before 2012 in China. In recent years, photochemical pollution whose main feature is a high concentration of O3 becomes more visible day by day. However, PM2.5 is still the primary air pollutant in BTH region compared with O3. In this paper, we focus on the causes and effects of PM2.5 within the BTH region.

4. Methodology

4.1. Analysis Process. In this paper, we use the fishbone method [24], the Spearman rank correlation test [25], and a panel data regression model to tease out and rank the influencing factors of PM2.5. The detailed analysis processes are shown in Figure 3.

We first select 15 specialists and gather all aspects of the influencing factors of PM2.5 performed in the form of a fishbone analysis (the result is shown in Figure 4). Based on the fishbone results, we form an initial influencing factors set (the result is shown in Table 4). Next, we collect corresponding data from the BTH region (the result is shown in Table 5) and analyze the relevance of influencing factors and PM2.5 based on the data using the Spearman correlation rank test method. Then, we screen the initial influencing factors set to form the influencing factors set (the result is shown in Table 6). Finally, we rank the influencing factors using a panel data regression model (the result is shown in Table 7).

4.2. Introduction of the Methods

4.2.1. Spearman Rank Correlation Test. Spearman's rank-order correlation is equivalent to Pearson's product-moment correlation coefficient performed on the ranks of the data rather than the raw data, and it is the nonparametric version of the Pearson product-moment correlation. Spearman's correlation coefficient can measure the strength of association between two ranked variables. Its calculation equation is shown in the following formula [26]:

[mathematical expression not reproducible]. (1)

Here, [r.sub.s] is Spearman's correlation and [bar.x], [bar.y] are the average values of the two variables [x.sub.i] and [y.sub.i]. Spearman's correlation rank will yield a value -1 [less than or equal to] [r.sub.s] [less than or equal to] 1. Higher absolute values of [r.sub.s] correspond to stronger correlations between the two variables. A positive value suggests a positive correlation, while a negative value represents a negative correlation.

4.2.2. Panel Data Regression Model. In short, panel data refers to the combination of time series and cross section data. Its general form is shown below:

[mathematical expression not reproducible]. (2)

In this equation, i = 1, 2, 3, ..., N, N is the unit in the panel data; t = 1, 2, 3, ..., T, T is the maximum length in the time series; [] is the observed value of explained variable for unit i at time point t; [x.sub.kit] is the observed value of the [] nonstochastic explanatory variable for unit i at time point t; [[beta]] is a parameter that is solved for; [] is stochastic error term.

During panel data regression, a user should analyze the stationarity of the data first by carrying out a unit root test in order to avoid spurious regression and ensure the validity of the estimated results. If we found that the relationship between variables is the integration of the same order by the result of unit root test, we can carry out a cointegration test. The cointegration test is a method to investigate long-term equilibrium relationship between variables. The so-called cointegration refers to the fact that the sequence is stationary after the linear combination of two or more nonstationary variable sequences. Passing the cointegration test shows that there is long-term stable equilibrium relationship between variables, and the regression residual is also stable. After passing the test, we can select panel data model and conduct a regression analysis.

5. Influencing Factors Analysis of BTH

5.1. Influential Factors Analysis Result Using Fishbone. The influential factors of PM2.5 are shown in Figure 4. The factors come from human activities, manufacturing, transportation, construction, and energy.

Here, human activities mean the activities which have direct effects on PM2.5. This is signified by the excessive concentration, production without order, illegal incineration, and others of human activities.

Manufacture is the activity of obtaining material resources from nature and processing and reprocessing raw materials. Throughout the history of human development, manufacture occasioned a lot of environmental problems. In this article, we analyzed and calculated its impact on PM2.5 by the production of concrete and steel, the industrial proportion.

In transportation, the problems, such as vehicle emissions and flying dust caused by rolling pavements, lead to the high concentration of PM2.5. Thus, we selected car ownership, highway mileage, and freight volume as specific influencing factors.

Many construction activities can cause PM2.5 pollution with the raising of dust concentration, like forcible entry of construction, pipe laying, road work, and so on. Construction is characterized by construction area, the area of new construction, and road lengths.

Various types of energy have obvious differences on tailpipe emissions in the process of consumption. We use the proportion of coal consumption, electric power consumption, and natural gas consumption to reflect the effect on PM2.5 from coal, electricity, and natural gas.

5.2. Quantitative Analysis of the Influencing Factors. The code table of the fifteen initial factors is shown in Table 4.

The fifteen initial influential factors' descriptive statistics of thirteen cities from 2013 to 2015 are shown in Table 5 for Shijiazhuang, Tangshan, Handan, Qinhuangdao, Baoding, Zhangjiakou, Chengde, Langfang, Cangzhou, Hengshui, and Xingtai.

The data are obtained from statistical reviews and reports of Beijing, Tianjin, Shijiazhuang, Tangshan, Qinhuangdao, Handan, Xingtai, Baoding, Zhangjiakou, Chengde, Cangzhou, Langfang, and Hengshui from 2013 to 2016.

The Spearman correlation rank test method is used to analyze the influencing factors of PM2.5. The analysis results are shown in Table 6.

The results show that population density, energy consumption per unit area, concrete production per unit area, industrial proportion, transportation volume per unit area, new construction areas per unit area, road construction length per unit area, and coal consumption proportion are all positively correlated with PM2.5 because their Spearman's correlation coefficients are positive values. Conversely, electricity consumption proportion is negatively correlated with PM2.5 because its Spearman's correlation coefficient is negative.

5.3. Importance Analysis of the Influencing Factors. Panel data regression model is used to calculate the importance of the influential factors of PM2.5. A panel data regression model is generally needed to conduct a panel unit root test and panel cointegration, but as Table 7 shows (T = 4, N = 13) this table analyzes the data of 13 regions in four years. Because the time span is much less than the number of regions, it makes little sense to conduct the unit root test of panel cointegration, so this article makes panel data regression analysis directly. The analysis results are shown in Table 7.

5.4. Discussion of the Results. From the analysis of the influencing factors, we can see population density, industrial proportion, transportation volume, energy consumption per unit area, and the proportion of electricity consumption have a pivotal influence on PM2.5. From these results, we can provide some discussion of the results from the perspectives of population distribution, industrial structure, transportation system, and energy consumption.

(1) Optimization of Population Distribution. The results show that population density has a significant relationship with PM2.5 in the BTH region. PM2.5 emissions are serious hazards in the high population density areas. This is mainly because the increase in population density has caused an increased demand for housing and motor vehicles; the increased demand for housing can lead to an increase of construction of buildings. These engineering constructions can produce environmental pollution; the increases of motor vehicle ownership result in a large number of motor vehicle exhaust emissions that produce large amounts of nitrogen oxide, carbon oxide, sulfur oxides, and volatile organic compounds. Meanwhile, the generation and incineration of household refuse as well as the burning of heating energy have also increased; they can release a large amount of volatile organic compounds, which aggravate PM2.5 emissions. Especially in the BTH region, the days containing haze tend to increase during the winter heating period. In 2014, the number of fog days in the winter accounts for about 27/45 of all haze days. In 2015, they account for 35/42 of all haze days. Human production activities have indirect effects on PM2.5 emissions, so the population density is closely related to the density of PM2.5.

Aiming at the problem of PM2.5 emissions caused by human activities, the government can craft effective human migration policies to mitigate the problem of large population density. This is because there are significant differences in the distribution and concentration variation of PM2.5 between China regions. The differences are caused by the unbalanced economic development. For instance, BTH region is the capital-circle of China and the political and cultural center, but the orient of urban functions of BTH region becomes unclear gradually with the common development of economy and politics. So in order to solve the problem, we can establish new urban zones near BTH region to weaken noncapital functions of Beijing and manage population migration.

(2) Optimization of Industrial Structure. At present, the heavy industry makes up a large proportion in BTH region, especially in Hebei province. The heavy industry, which focuses on raw materials, increased the demand of energy and greatly influenced the environment. In these industries, energy-intensive industries are obvious. As a main district of production of iron and steel, by the end of 2014, crude steel production accounted for 23 percent nationwide (185 million tons) in Hebei. Besides steel, the output values of many industries like electricity, heating power, oil processing, coking, and chemical raw materials make up about a half of the total output value of all industries in Hebei province. These industries commonly have high pollution as well as high energy and low efficiency. It is one reason why PM2.5 is serious in Hebei.

For this problem, if we close high energy-consuming and high-polluting enterprises by some restrictions, it can cause severe economic downturn and unemployment. Thus we can improve investment structure by developing green finance to change high-polluting industrial structure. In the past, much of investments flow into high-polluting industries, energy sources, and transportation project because of the bad profit ability of green industries. In order to improve the development of green finance, we can build green finance system to expand financial input for green industries and promote the transitions of investment structure and industrial structure. Detailed measures can be setting up the interest deduction system of green credit, developing guarantee mechanisms for green projects, establishing green industry funds with government involvement, issuing green bonds, and so on.

(3) Optimization of Transportation Systems. For a long time, traditional energy vehicles are still the main part of all vehicles, among traditional energy vehicles; statistics show that the nitrous oxides and PM2.5 from trucks are significantly higher than passenger cars and the largest polluters are heavy trucks [27]. The insufficiency of fuel combustion which caused the pollution of PM2.5 can be solved by improving the technology and adding units for the treatment of tail gas. Next, we can encourage and support the popularity of new energy vehicles. For more and more vehicles in the cities, it is useful to change the behaviour of companies and consumers by a combination of market methods and administration methods, such as levying emissions taxes according to the emission level or collecting congestion charges. Meanwhile, the separation phenomenon between dwelling districts and working areas and the parallel development of economy and politics are common in BTH region especially in Beijing, which caused the high trip rate of motor vehicles. Thus it needs us to plan city structure reasonably and use advanced public transportation systems, in order to reduce the utilization rate of motor vehicles by reasonable traffic diverging.

(4) Optimization of Energy Consumption. From the results, we see that coal consumption proportion affects PM2.5 emissions positively, but natural gas and electricity consumption proportion are negatively correlated with PM2.5. This shows that the reason of serious PM2.5 pollution on the energy side is that coal still occupies a large proportion of energy consumption in China, thus causing the high-carbon energy system which is dominated by coal. The high-carbon energy structure and rapidly rising total energy consumption have led to a rapid growth of greenhouse gas emissions. Although the energy industry scale in China is large, for a long time it has formed a dependence on high-carbon energy and has hindered the optimization of the energy industry; the energy consumption structure in our country must make adjustments. From the results of our analysis, we can change our problem of energy consumption by "electric power replacement." Implementing electric power replacement is an effective measure to control PM2.5. As a secondhand energy, electrical energy in a terminal utilization link does not create pollution, although thermal power generation still needs to burn coal in the production process to exhaust pollutants. By taking many technical measures to make large-scale handling, the emission intensity of pollutants and governance costs are well below traditional combustion modes.

6. Conclusions

In this paper, we analyze the influential factors of PM2.5 in the BTH region of China using the fishbone method, the Spearman rank correlation test, and a panel data regression model. The results show that population density, energy consumption per unit area, concrete production per unit area, industrial proportion, transportation volume per unit area, new construction areas per unit area, road construction length per unit area, and coal consumption proportion are all positively correlated with PM2.5 emissions. Conversely, electricity consumption proportion is negatively correlated with PM2.5. The results also showed that population density, industrial proportion, and transportation volume per unit area are the top three influential factors. Finally, we provided suggestions for optimization of population distribution, industrial structure, transportation system, and energy consumption.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Authors' Contributions

Jinchao Li contributed to the conception, design, and computation. Lin Chen and Yuwei Xiang collected and interpreted the data. Ming Xu performed manuscript review. Ming Xu and Jinchao Li contributed to the article revision. All of the authors drafted and revised the manuscript together and approved its final publication.


This work has been supported by "Ministry of Education, Humanities and Social Science Fund, no. 15YJC630058," "China Scholarship Council Project," "the Fundamental Research Funds for the Central Universities, no. 2017MS083," and "the Science and Technology Project of SGCC."


[1] China's Ministry of Environmental Protection, State of the China Environment Report, 2015.

[2] J. Peng, S. Chen, H. Lu, Y. Liu, and J. Wu, "Spatiotemporal patterns of remotely sensed PM2.5 concentration in China from 1999 to 2011," Remote Sensing of Environment, vol. 174, pp. 109-121, 2016.

[3] The State Concil of China, State Council Air Pollution Prevention and Control Action Plan, The State Concil of China, 2013.

[4] Y. Yang, Y. Cao, W. Li et al., "Multi-site time series analysis of acute effects of multiple air pollutants on respiratory mortality: A population-based study in Beijing, China," Science of the Total Environment, vol. 508, pp. 178-187, 2015.

[5] H. Zhang, Y. Wang, J. Hu, Q. Ying, and X. Hu, "Relationships between meteorological parameters and criteria air pollutants in three megacities in China," Environmental Research, vol. 140, pp. 242-254, 2015.

[6] A. G. Progiou and I. C. Ziomas, "Road traffic emissions impact on air quality of the greater Athens area based on a 20 year emissions inventory," Science of the Total Environment, vol. 410-411, pp. 1-7, 2011.

[7] S. Dewangan, S. Pervez, R. Chakrabarty, and L. Joyce Rai, "Study of carbonaceous fractions associated with indoor PM2.5/PM10 during Asian cultural and ritual burning practices," Building Environment, vol. 106, pp. 229-236, 2016.

[8] N. Gilbraith and S. E. Powers, "Residential demand response reduces air pollutant emissions on peak electricity demand days in New York City," Energy Policy, vol. 59, pp. 459-469, 2013.

[9] M. Amodio, E. Andriani, P. R. Dambruoso et al., "A monitoring strategy to assess the fugitive emission from a steel plant," Atmospheric Environment, vol. 79, pp. 455-461, 2013.

[10] S. Wang, J. Hao, M. S. Ho, J. Li, and Y. Lu, "Intake fractions of industrial air pollutants in China: Estimation and application," Science of the Total Environment, vol. 354, no. 2-3, pp. 127-141, 2006.

[11] P. Jaramillo and N. Z. Muller, "Air pollution emissions and damages from energy production in the U.S.: 2002-2011," Energy Policy, vol. 90, pp. 202-211, 2016.

[12] I. Herrera, J. De Ruyck, V S. Ocana, M. Rubio, R. M. Martinez, and V. Nunez, "Environmental impact of decentralized power generation in Santa Clara City, Cuba: an integrated assessment based on technological and human health risk indicators," Applied Energy, vol. 109, pp. 24-35, 2013.

[13] M. Carreras-Sospedra, S. Vutukuru, J. Brouwer, and D. Dabdub, "Central power generation versus distributed generation An air quality assessment in the South Coast Air Basin of California," Atmospheric Environment, vol. 44, no. 26, pp. 3215-3223, 2010.

[14] Z. Ma, B. Xue, Y. Geng et al., "Co-benefits analysis on climate change and environmental effects of wind-power: a case study from Xinjiang, China," Journal of Renewable Energy, vol. 57, pp. 35-42, 2013.

[15] G. Genon, M. F. Torchio, A. Poggio, and M. Poggio, "Energy and environmental assessment of small district heating systems: Global and local effects in two case-studies," Energy Conversion Management, vol. 50, pp. 522-529, 2009.

[16] A. B. Lobscheid, W. W. Nazaroff, M. Spears, A. Horvath, and T. E. McKone, "Intake fractions of primary conserved air pollutants emitted from on-road vehicles in the United States," Atmospheric Environment, vol. 63, pp. 298-305, 2012.

[17] A. C. Targino, M. D. Gibson, P. Krecl, M. V. C. Rodrigues, M. M. dos Santos, and M. de Paula Correa, "Hotspots of black carbon and PM2.5 in an urban area and relationships to traffic characteristics," Environmental Pollution, vol. 218, pp. 475-486, 2016.

[18] B. Muresan, A. Capony, M. Goriaux et al., "Key factors controlling the real exhaust emissions from earthwork machines," Transportation Research Part D: Transport and Environment, vol. 41, pp. 271-287, 2015.

[19] M. Gonzalez-de-Soto, L. Emmi, C. Benavides, I. Garcia, and P. Gonzalez-de-Santos, "Reducing air pollution with hybrid-powered robotic tractors for precision agriculture," Biosystems Engineering, vol. 143, pp. 79-94, 2016.

[20] A. G. Megaritis, C. Fountoukis, P. E. Charalampidis, H. A. C. Denier Van Der Gon, C. Pilinis, and S. N. Pandis, "Linking climate and air quality over Europe: effects of meteorology on PM2.5 concentrations," Atmospheric Chemistry and Physics, vol. 14, no. 18, pp. 10283-10298, 2014.

[21] X. Li and Y. Wei, "Collaborated development of the Beijing-Tianjin-Hebei region to improve regional environmental quality," in WIT Transactions on the Built Environment 168, vol. 1 of WIT Transactions on The Built Environment, pp. 337-338, WIT Press, 2015.

[22] MEP (Ministry of Environmental Protection of China), Twelfth Five year Plan on Air Pollution Prevention and Control in Key Region, MEP (Ministry of Environmental Protection of China), 2012.

[23] China's Ministry of Environmental Protection, State of the China Environment Report, China's Ministry of Environmental Protection, 2015.

[24] S. Celik, M. T. Taner, G. Kagan, M. Simcek, M. K. Kagan, and I. Oztek, "A retrospective study of six sigma methodology to reduce inoperability among lung cancer patients," Procedia--Social and Behavioral Sciences, vol. 229, pp. 22-32, 2016.

[25] Q. Yin, J. Wang, M. Hu, and H. Wong, "Estimation of daily PM2.5 concentration and its relationship with meteorological conditions in Beijing," Journal of Environmental Sciences, vol. 48, pp. 161-168, 2016.

[26] M.-T. Puth, M. Neuhauser, and G. D. Ruxton, "Effective use of spearman's and kendall's correlation coefficients for association between two measured traits," Animal Behaviour, vol. 105, pp. 77-84, 2015.

[27] China's Ministry of Environmental Protection, China Vehicle Emission Control Annual Report, 2013.

Jinchao Li (iD), (1,2,3) Lin Chen, (1) Yuwei Xiang, (1) and Ming Xu (2)

(1) School of Economics and Management, North China Electric Power University, No. 2 Beinong Road, Huilongguan, Changping District, Beijing 102206, China

(2) School of Natural Resources and Environment, University of Michigan, 440 Church St., Ann Arbor, MI 48109-1041, USA

(3) Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, No. 2 Beinong Road, Huilongguan, Changping District, Beijing 102206, China

Correspondence should be addressed to Jinchao Li;

Received 30 August 2017; Revised 29 December 2017; Accepted 22 January 2018; Published 5 March 2018

Academic Editor: Allan C. Peterson

Caption: Figure 2: Smog maps of the BTH region in 2014 (sources: NASA).

Caption: Figure 3: The process of the PM2.5 influential factors analysis.

Caption: Figure 4: Influencing factors of PM2.5.
Table 1: Information on influencing factors and analyzing methods in
related papers.

Authors                   Research subjects      Influencing factors

Dewangan et al.            PM2.5 and PM10          Cultural ritual
(2016) [7]

Gilbraith and Powers        Air pollutant        Residential demand
(2013) [8]                    emissions               response

Amodio et al. (2013)       PM2.5 and PM10            Steel plant

Wang et al. (2006)      S[O.sub.2] and total          Industry
[10]                     suspended particles

Jaramillo and Muller        Air pollution         Energy production
(2016) [11]                   emissions

Herrera et al. (2013)       Air pollution        Decentralized power
[12]                                                 generation

Carreras-Sospedra et         Air quality            Central power
al. (2010) [13]                                      generation

Ma et al. (2013) [14]       Air pollutant       Wind power generation

Genon et al. (2009)          C[O.sub.2],           Small district
[15]                         N[O.sub.x],           heating systems
                           S[O.sub.x], PM

Lobscheid et al. (2012)    Air pollutants         On-road vehicles
[16]                           emitted

Targino et al. (2016)     Black carbon and             Traffic
[17]                            PM2.5

Muresan et al. (2015)     Exhaust emissions      Earthwork machines

Gonzalez-de-Soto et         Air pollution          Hybrid-powered
al. (2016) [19]                                   robotic tractors

Megaritis et al.                PM2.5             Temperature, wind
(2014) [20]                                        speed, absolute
                                                  precipitation and
                                                    mixing height

Authors                 Correlation analysis      Geographical area

Dewangan et al.               Empirical             Raipur, India
(2016) [7]

Gilbraith and Powers     Graphical analysis         New York, USA
(2013) [8]

Amodio et al. (2013)     Principal component           Europe
[9]                           analysis

Wang et al. (2006)      Sensitivity analyses      Beijing, Dalian,
[10]                     Graphical analysis       Jinan, Chongqing,
                                                   Liuzhou, China

Jaramillo and Muller    Pearson's correlation            USA
(2016) [11]                 coefficients

Herrera et al. (2013)     Scenario analysis       Santa Clara City,
[12]                                                    Cuba

Carreras-Sospedra et      Scenario analysis        California, USA
al. (2010) [13]

Ma et al. (2013) [14]         Empirical            Xinjiang, China

Genon et al. (2009)       Dispersion model              Italy

Lobscheid et al. (2012)  Graphical analysis              USA

Targino et al. (2016)    Graphical analysis     Londrina city, Brazil

Muresan et al. (2015)    Graphical analysis            France

Gonzalez-de-Soto et      Graphical analysis             Spain
al. (2016) [19]

Megaritis et al.          Three-dimensional            Europe
(2014) [20]              chemical transport

Table 2: Some Information about the Beijing-Tianjin-Hebei region in

                Area          Permanent      GDP (billion
             ([km.sup.2])     population         RMB)
                            (thousand men)

Beijing        16410.00        21520.00        2133.10
Tianjin        11946.00        15170.00        1572.70
Hebei         188800.00        73840.00        2942.10
BTH           217156.00       110530.00        6647.90
China         9634057.00      1368000.00       63591.00
Proportion      2.25%           8.08%           10.45%

                Vehicle        Total energy
               ownership       consumption
             (10000 units)   ([10.sup.4] tce)

Beijing         559.10           6831.23
Tianjin         280.00           8145.06
Hebei           997.00           29320.21
BTH             1836.10          44296.50
China          14000.00         439945.90
Proportion      13.12%            10.07%

GDP: gross domestic product; tce: ton coal equivalent.

Table 3: Environmental status of the BTH region in 2015.

Item                                               Beijing   Tianjin

[PM.sub.10] annual average ([micro]g/[m.sup.3])     101.5      116
[PM.sub.2.5] annual average ([micro]g/[m.sup.3])    80.6       70
N[O.sub.2] annual average ([micro]g/[m.sup.3])       50        42
S[O.sub.2] annual average ([micro]g/[m.sup.3])       22        29
[O.sub.3] annual average ([micro]g/[m.sup.3])       202.6      142
CO annual average (mg/[m.sup.3])                     3.6       3.1

Item                                               Hebei   average

[PM.sub.10] annual average ([micro]g/[m.sup.3])     136      87
[PM.sub.2.5] annual average ([micro]g/[m.sup.3])    77       50
N[O.sub.2] annual average ([micro]g/[m.sup.3])      46       30
S[O.sub.2] annual average ([micro]g/[m.sup.3])      41       25
[O.sub.3] annual average ([micro]g/[m.sup.3])       160      134
CO annual average (mg/[m.sup.3])                    3.7      2.1

Table 4: The codes of the fifteen initial influential factors.

Name                                              Code

Population density                             [IF.sub.1]
Straw burning amount                           [IF.sub.2]
Energy consumption per unit area               [IF.sub.3]
Concrete production per unit area              [IF.sub.4]
Steel production per unit area                 [IF.sub.5]
Industrial proportion                          [IF.sub.6]
Retained number of automobiles per unit area   [IF.sub.7]
Total length of road per unit area             [IF.sub.8]
Transportation volume per unit area            [IF.sub.9]
Total construction areas per unit area         [IF.sub.10]
New construction areas per unit area           [IF.sub.11]
Road construction length per unit area         [IF.sub.12]
Coal consumption proportion                    [IF.sub.13]
Natural gas consumption proportion             [IF.sub.14]
Electricity consumption proportion             [IF.sub.15]

Table 5: Descriptive statistics of the influencing factors.

Variable              Unit           N      Mean

PM2.5          [micro]g/[m.sup.3]    52    98.04
[IF.sub.1]     Person/[km.sup.2]     52    615.57
[IF.sub.2]       Ton/[km.sup.2]      52     0.05
[IF.sub.3]        kgce/person        52   2875.24
[IF.sub.4]       Ton/[km.sup.2]      52    749.88
[IF.sub.5]       Ton/[km.sup.2]      52   1515.26
[IF.sub.6]             %             52    38.50
[IF.sub.7]      Unit/[km.sup.2]      52    114.49
[IF.sub.8]       km/[km.sup.2]       52     1.14
[IF.sub.9]       Ton/[km.sup.2]      52   16020.78
[IF.sub.10]   [m.sup.2]/[km.sup.2]   52   3666.67
[IF.sub.11]   [m.sup.2]/[km.sup.2]   52   1108.49
[IF.sub.12]      km/[km.sup.2]       52     0.05
[IF.sub.13]            %             52    50.09
[IF.sub.14]            %             52     4.15
[IF.sub.15]            %             52    11.17

Variable       StDev       Min       Max

PM2.5          36.73      34.00     186.42
[IF.sub.1]     347.56     88.72    1348.14
[IF.sub.2]      0.03      0.00       0.11
[IF.sub.3]    2199.52    345.85    7532.65
[IF.sub.4]     737.21     91.89    2855.55
[IF.sub.5]    2112.26     18.21    8297.95
[IF.sub.6]      8.90      16.12     55.34
[IF.sub.7]     87.43      12.65     349.01
[IF.sub.8]      0.37      0.49       1.84
[IF.sub.9]    11903.79   1243.95   44516.16
[IF.sub.10]   3206.75    224.90    13096.12
[IF.sub.11]   1425.79     71.61    7733.04
[IF.sub.12]     0.04      0.01       0.19
[IF.sub.13]    14.52      13.70     73.00
[IF.sub.14]     6.47      0.02      25.80
[IF.sub.15]     5.26      3.93      29.00

In this table, N is the number of the analysis examples.
StDev is the standard deviation.

Table 6: Influencing factors quantitative analysis results.

              Correlation   Sig. (two-side)   N

[IF.sub.1]      0.326 *          0.018        52
[IF.sub.2]       0.143           0.311        52
[IF.sub.3]     0.375 **          0.006        52
[IF.sub.4]     0.490 **          0.000        52
[IF.sub.5]       0.172           0.224        52
[IF.sub.6]     0.471 **          0.000        52
[IF.sub.7]       0.220           0.117        52
[IF.sub.8]       0.164           0.247        52
[IF.sub.9]     0.396 **          0.004        52
[IF.sub.10]      0.227           0.106        52
[IF.sub.11]     0.275 *          0.048        52
[IF.sub.12]    0.653 **          0.000        52
[IF.sub.13]     0.301 *          0.030        52
[IF.sub.14]     -0.233           0.097        52
[IF.sub.15]    -0.279 *          0.045        52

* means the confidence level of regression coefficient is above 99%;
** means the confidence level of regression coefficient is above 95%.

Table 7: The importance analysis results.

Variable                Coefficient       Std. error

log(IF1?)                0.558207          0.148701
log(IF3?)                0.164398          0.100609
log(IF4?)                0.042059          0.051307
log(IF6?)                0.463459          0.219537
log(IF9?)                -0.357751         0.103945
log(IF11?)               -0.052365         0.059223
log(IF12?)               0.122102          0.047022
log(IF13?)               0.045020          0.120676
log(IF15?)               -0.158461         0.101994
C                        2.142050          0.955355

Weighted statistics

R-squared                0.867354
Adjusted R-squared       0.838930
SE of regression         0.235279
F-statistic              30.51465
Prob. (F-statistic)      0.000000

Unweighted statistics

R-squared                0.672834
Sum squared resid        2.865887

Variable                   t-Statistic        Prob.

log(IF1?)                    3.753885         0.0005
log(IF3?)                    1.634033         0.1097
log(IF4?)                    1.819736         0.4170
log(IF6?)                    2.111081         0.0408
log(IF9?)                    3.441728         0.0013
log(IF11?)                   1.884206         0.3816
log(IF12?)                   2.596724         0.0129
log(IF13?)                   1.373067         0.7110
log(IF15?)                  -1.553635         0.1278
C                            2.242151         0.0303

Weighted statistics

R-squared               Mean dependent var   5.937831
Adjusted R-squared      SD dependent var     3.341899
SE of regression        Sum squared resid    2.324971
F-statistic             Durbin-Watson stat   0.962111
Prob. (F-statistic)

Unweighted statistics

R-squared               Mean dependent var   4.508054
Sum squared resid       Durbin-Watson stat   0.767658

Figure 1: Air quality (or air pollution) variation among 338
monitoring cities in China (source: 2015 State of the China
Environment Report [23]).



[less than or equal to]15     0.6%
15~35                        21.9%
35~70                        64.2%
>70                          13.3%



[less than or equal to]40     4.7%
40~70                        29.9%
70~150                       60.4%
>70                           5.0%



[less than or equal to]20    46.1%
20~60                        50.6%
>60                           3.3%



[less than or equal to]20    21.0%
20~40                        60.7%
40~60                        17.8%
>60                           0.6%



[less than or equal to]100     8.6%
100~160                       75.4%
>160                          16.0%



[less than or equal to]2.0    60.6%
2.0~4.0                       36.1%
>4.0                           3.3%

Note: Table made from pie chart.
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Article Details
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Title Annotation:Research Article
Author:Li, Jinchao; Chen, Lin; Xiang, Yuwei; Xu, Ming
Publication:Discrete Dynamics in Nature and Society
Geographic Code:9CHIN
Date:Jan 1, 2018
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