2017 2nd International Conference on Energy, Power and Electrical Engineering (EPEE 2017) ISBN: 978-1-60595-514-8
The Allocation of Responsibilities in Air Pollution: Evidence from Ethnic Minority Urban Agglomerations
* Qiu-ling HU, Wei-qi LIU and Zhe YANG International Business School of Shaanxi Normal University, Xi’an Shaanxi, China, 710119 *Corresponding author
Keywords: High-Frequency AQI Data, VAR, Impulse Response Function, Variance Decomposition.
Abstract. This paper constructed AQI hourly & monthly index to make a comparison based on the high-frequency AQI data and major pollutants statistical data of four ethnic minority urban agglomerations, then established a VAR model to analyze the Variance Decomposition. The results indicated that the air pollution of four urban agglomerations had exhibited noticeable seasonal effect and clustering feature. Once there was a deterioration of air quality in one city, the other neighboring cities would spread it rapidly but decayed slowly. According to the results of the Variance Decomposition, whose orders was determined by the average velocity to the peak of the impulse response function, a scheme was proposed in this paper to solve the issues in the allocation of responsibilities for regional joint prevention in each city.
Introduction With the continuous development of the society, people's pursuit of high-quality life is increasing at the same time. The extensive development of the economy makes China grow fast, followed by the phenomenon of frequent haze as well. Air quality issues are coming into view, and the health hazards and potential economic losses caused by haze are also of great concern. According to the World Health Organization, about 7 million people worldwide died of air pollution (about 1 million in China) in 2012, due to exposure to particulate matter (PM2.5 and PM10), leading to cardiovascular and respiratory diseases and cancer. Comrade Xi Jinping delivered a report to the 19th CPC National Congress, pointed that ‘we will get everyone involved in improving the environment and address environmental issues at the root. We will continue our campaign to prevent and control air pollution to make our skies blue again’, which raises the air pollution issues to a new level. At present, academic studies of haze pollution were focused on elements such as climate terrain, energy structure, industrial structure, transportation patterns and urbanization construction. They pay great attention to the regional socio-economic impact of haze pollution, providing the theoretical basis for the implementation of regional joint defense measures for local governments. Chen-yue Liu [1] analyzed different regions in China, considered that the coal-dominated energy consumption structure is the main factor of haze pollution in eastern districts, and the industrial structure has a significant impact on the western region. Li-mei Ma [2] studied the spatial effect of air pollution, considered that the use of coal, especially low-quality coal in the energy consumption structure could increase air pollution, and the transfer of industry in the long run is invalid. Hai-ya Cai [3] pointed out that the regional haze pollution in China is significant, the main reason is the uneven development within the region. Zhe Wang [4] combined the cross-domain governance experience of United States, Europe, Japan and other countries, provided a useful reference for how to cross the existing institutional barriers and solve the motivations of members at different levels of development. The existing literature for the western regions is relatively small at present due to the economic underdevelopment and incomplete data, especially for the ethnic minority areas. In “the Belt and Road” strategy there are 18 provinces along the route have been delineated, among which there are Xinjiang, Shaanxi, Gansu, Ningxia, Qinghai, Inner Mongolia ranked, in addition Xinjiang was designated as ‘the core zone of the Silk Road Economic Belt’. These provinces that abundant in mineral resources are important industrial basis of energy and raw materials, each of which will gain 276 great opportunities in the “B&R” strategy. The ethnic minority urban agglomerations such as the Hohhot-Baotou-Ordos-Yulin region, the Ningxia urban agglomeration along the Yellow River, the Lan-Xi urban agglomeration and the Tianshan North-slope urban agglomeration are key areas in these provinces, which will be greatly affected by the strategy and play an important role in promoting industrial upgrading and coordinating regional economic development, as well as improving international economic and cultural cooperation and exchanges. In view of this, this paper will conduct a comparative research of the four ethnic minority districts and put forward suggestions on the allocation of responsibilities in atmosphere pollution based on the high-frequency AQI data.
Regional and Descriptive Statistical Analysis Research Regions. Considering the availability of data, this paper concerns the following cities in the four urban agglomerations. The Hohhot-Baotou-Ordos-Yulin region mainly includes Hohhot, Baotou, Ordos and Yulin, the Ningxia urban agglomeration along the Yellow River includes Yinchuan, Shizuishan, Wuzhong and Zhongwei, the Lan-Xi urban agglomeration mainly contains Lanzhou, Baiyin, Dingxi and Xining, and the Tianshan North-slope urban agglomeration consists of Urumqi, Karamay, Shihezi and Changji. These regions are generally rich in mineral resources such as coal, oil and natural gas, and the industrial structure is much similar as well. As resource constraints have been tightened in recent years, Baiyin and Shizuishan had been even listed as resource-exhausted cities, making it urgent to accelerate the transformation and promotion of industrial structure. Each of the cities has a very high proportion of the secondary industry, which is dominated by heavy industry especially by energy industry, and the differences of AQI in each regions will be the concentration. In this paper, we will select the high-frequency AQI data from January 1, 2015 to December 31, 2015, and use the three-time interpolation method to complete the missing data. Comparison in Statistics of Major Pollutants. Through the comparison of the main pollutant data in the past two years, it is found that there are some similar characteristics in each urban agglomeration. The most frequent major pollutants in each region are PM2.5, PM10 and ozone, showing a strong seasonal effect and cluster characteristics. The air quality is best in summer, then deteriorates from autumn gradually, and gets worst in winter and spring, in which the major pollutants are PM2.5 and PM10. Ozone mainly occurs in summer, which is consistent with the previous research that ozone density is inversely proportional to the concentration of suspended particulate matters [6]. Table 1. The average annual statistics1 of major pollutants
Urban Agglomerations Years PM2.5 PM10 O3 NO2 SO2 CO Hohhot-Baotou-Ordos-Yuli 2015 58.25 137 107.75 8.5 2.5 14.25 n region 2016 46.5 123 119.5 22.5 0 1.5 Ningxia urban 2015 69.75 177.75 72.25 2.25 26 0 agglomeration along the 2016 74.25 149.5 104 0.75 22.25 0 Yellow River 2015 68.25 189.5 66.75 12.5 5 1.75 Lan-Xi urban agglomeration 2016 59.5 181 73.75 18.25 7.25 0.5 Tianshan North-slope urban 2015 82.25 152.25 59.75 12.5 0 1 agglomeration 2016 109.25 75.00 72.00 23.75 0 1.75 1 The average annual statistics is the arithmetic average of the major pollutants in each regions each year. The differences become more evident when the internal cities of each regions were compared. In the Hohhot-Baotou-Ordos-Yulin region, the air quality of ordos is the best, the frequency of NO2 in Hohhot and Yulin shows higher in both autumn and winter, and CO mainly appears in winter and spring in Yulin. NO2 appears less frequently in the Ningxia urban agglomeration along the Yellow River, while SO2 shows up from Nov. to Feb. The frequency of NO2 in Lanzhou is the highest of the Lan-Xi urban agglomeration, while SO2 appears mainly in Baiyin. The air quality of Karamay is the
277 best of the Tianshan North-slope urban agglomeration, and NO2 appears more in Urumqi and Changji. Analysis of AQI Hourly & Monthly Index. Furthermore, we build AQI hourly index and monthly index to show the difference of time distribution of AQI, referring to the method of Qiu-ling Hu [7]. Firstly, we calculate the hourly and monthly average of AQI in separate urban agglomerations:
. (1)
. (2)
In Eq.1 AQIi is the AQI at time i, m is the sample numbers at time i, and is the hourly average of AQI at time i. In Eq.2 AQIj is the AQI at month j, n is the sample numbers at month j, and is the monthly average of AQI at month j. Secondly, we calculate the arithmetic mean of AQI in all cities and mark it as , and then we will gain Hi—the AQI hourly index, and Mj—the AQI monthly index.
. (3)
. (4)
It indicates that the hourly or monthly average is higher than the population mean if Hi>0 or Mj>0, which means that the air quality is worse than average, and the absolute value of Hi or Mj shows the degree of deviation from the mean of the population. Hi was approximately a w-shaped curve whose peak was at 11 a.m. and 22 p.m., while the trough was at 6 a.m. and 15 p.m. In addition, the Hi was the lowest at 6 a.m. and the highest at 22 p.m., which shows that the air quality was well in the morning and bad at night. The location of the Hi curve of the Hohhot-Baotou-Ordos-Yulin region was the lowest among all the regions, most of which below the zero line, meaning that its air quality was better than the average of the groups. The Hi of Tianshan North-slope urban agglomeration is slightly different from the others that was 2 hours later than other regions in the peaks and valleys, which maybe caused by the difference of Time Zones. Mj was roughly a U-shaped curve that was high in winter and low in summer, peaked in December to next February and reduced to a minimum in September after then, supplying a further evidence for the seasonal effects.
Fig 1. The AQI Hourly & Monthly Index In addition, the meteorological factors such as wind speed, air pressure and relative humidity can explain the fluctuation of the above indexes to a certain extent. Studies [8,9,10] have showed that the concentration of PM2.5 is negatively correlated with wind speed and air pressure, and is positively correlated with relative humidity. The wind speed can affect the transmission and distribution of
278 pollutants, and it is easy to form static wind weather in low pressure, which is unfavorable to the diffusion of pollutants. The air can be saturated and condensed into fog, and then combined with pollutants into haze once the relative humidity is high and the wind speed is small, but it is easy to rain and has a scour effect on the pollutants when the relative humidity is higher than 80%. Besides, the atmospheric mixed layer is low and there is an inversion layer [11] at night, which makes haze pollution worse. In the whole year, the change of atmosphere structure is also an important factor in winter except relative humidity and low precipitation. In winter, the change of atmosphere structure is also an important factor other than relative humidity and low precipitation. Hou-feng Liu [12] promoted that under the influence of atmospheric circulation and inversion layer, the stability of the atmosphere increases and the pollutant is more easily accumulated in the lower atmosphere, the wind speed of the whole layer decreases and the pollutant diffusion ability weakens and the pollutant concentration increases rapidly because of the confrontation of cold air and southerly air flow. As the cold front transits, the wind speed and precipitation increased, the air quality gradually became improved when the structure of the inversion layer destroyed and the pollutants diffused.
Empirical Results Model. A VAR model is used to fit the pollution status of the four urban agglomerations: . (5) where yt denotes the AQI of city t in a certain region, Φt is the coefficient matrix to be estimated, p is the lag order, T represents the number of cities in the city group and εt is a white noise disturbance term. Data Analysis. VAR models are set up for each urban agglomeration respectively, and the lag order of each model is determined according to the SC criterion. The structure of each model is stable after testing, and the corresponding impulse response function is analyzed as shown in Table 2. Table 2. Analyses to impulse response function of urban agglomerations Emit an impulse Peak conditions2 for each city after the impulse VAR(8) Hohhot Baotou Ordos Yulin Hohhot - 4 [6.9] 5 [2.3] 3 [2.3] Baotou 4 [7.6] - 7 [3.5] 4 [2.2] Ordos 6 [2.6] 6 [4.6] - 6 [1.3] Yulin 14 [1.6] 13 [4.5] 5 [7] - VAR(6) Yinchuan Shizuishan Wuzhong Zhongwei Yinchuan - 5 [8.4] 2 [1.9] 6 [2.9] Shizuishan 3 [5.4] - 4 [2.5] 4 [4.8] Wuzhong 3 [7.6] 4 [8] - 5 [8.7] Zhongwei 4 [3.3] 6 [5.5] 3 [4.7] - VAR(7) Lanzhou Xining Baiyin Dingxi Lanzhou - 15 [2] 5 [9.7] 17 [0.6] Xining 7 [4.2] - 12 [3.5] 5 [1.4] Baiyin 4 [3.5] 13 [1.9] - 14 [1.1] Dingxi 4 [4.4] 14 [2.2] 12 [6.1] - VAR(9) Urumqi Karamay Shihezi Changji Urumqi - 8 [6.4] 7 [6.3] 4 [1.6] Karamay 27 [1.5] - 21 [1.6] 6 [-] Shihezi 19 [3.9] 7 [4.8] - 3 [0.9] Changji 3 [6.7] 9 [5.4] 4 [6.3] - 2 The number in [] is the peak value after the impulse, and the number out of the [] indicates the time period required to reach the peak after impact.
279 In the Hohhot-Baotou-Ordos-Yulin region, Hohhot, Baotou and Ordos are distributed in triangle direction, and the peak will be reached in 4-7 hours when the impulse emits, Yulin located in the south of the region and far away from Hohhot and Baotou. According to the historical wind statistics from January 1, 2011 to October 1, 2017, the wind of Baotou mostly comes from north, west and northwest, and most days of the Hohhot is northwest wind. There are no sustained wind in most of the time in Yulin, leading to a slow transmit speed when an impulse has emitted from Yulin to Hohhot and Baotou. On the contrary, the impulse from Hohhot and Baotou will impact on Yulin soon because of the wind. The four cities in the Ningxia urban agglomeration are distributed from north to south along the Yellow River, and they are closely related to each other. Once an impulse emitted, it will reach the peak in 6 hours and fade in almost 60 hours. The asymmetry phenomenon of the impulse also exists in the Lan-Xi urban agglomeration. Lanzhou is located in the middle of the region and mainly affected by the northeast & east wind, Baiyin is located in the northeast of Lanzhou and affected mainly by the north wind, Dingxi in the southeast of Lanzhou and affected by southeast wind principally. Xining is located in the west of the urban region and far away from the other three cities, mainly affected by the westerly wind. Once an impulse generated at Lanzhou, it will transmit to Dingxi and Xining at a slow speed against the wind, as the same as the impulse propagate from Dingxi to Xining. Differing from the other city groups the impulse decays slow in the Tianshan North-slope urban agglomeration. Karamay, Shihezi, Changji and Urumqi are distributed successively from northwest to southeast, as the elevation varies from low to high, among which most of Karamay is Gobi Desert while the terrain of Shihezi and Changji is high in south and low in north. Urumqi is surrounded by mountains on three sides with plains in north, making a great influence on the transmission of pollutants and wind. Regional Joint Prevention. Atmosphere pollution is not only the responsibility of a particular city, but also the collective effect of all the neighboring cities. Combining with the asymmetry phenomenon in the impulse response function we will be able to provide suggestions for allocation of responsibilities in the prevention of air pollution. It is concluded that the air pollution of a particular city is caused by not only itself but also other neighboring cities according to the results of impulse response function. We define ra,b as the peak value of city a impacted by city b, set ea,b as the period before city a reach its peak after the impact of b, define va,b as the average velocity of a till its peak since a impacted by b: . (6)
In this way we could figure out the average velocity of all the cities, based on which we could choose the orders for Cholesky, and furthermore we can perform Variance Decomposition on each urban regions. Taking the Lan-Xi urban agglomeration as an example, the values in the diagonal of the matrix are the largest of the columns and most of which higher than 50, indicating that most cities need to bear primary responsibilities for their own air pollution. Besides, the two sides of the diagonal values are asymmetry, indicating that the cities will not be offset by the influence of other neighboring ones, which means that it could provide the basis for settling the air pollution among the regions. Table 3. Variance Decomposition of the Lan-Xi urban agglomeration Factors leading to change The percentage in the contribution by the first column [%] Lanzhou Xining Baiyin Dingxi
Lanzhou 50 7 3 8 Xining 2 75 1 3 Baiyin 46 15 95 20 Dingxi 2 3 1 69 According to the results of the Variance Decomposition we could propose a solution for the allocation of responsibilities in regional joint prevention, based on the different contribution of each
280 city to the air pollution in other districts. It is necessary to establish a mechanism for reward and punishment, which can afford incentives for governments to ameliorate the environment.
Concluding Remarks In this paper we investigate the air pollution status of the four urban regions based on the high-frequency AQI data from 2015 to 2016, obtain a few notable laws of the atmosphere pollution. The AQI was low in summer and high in winter, with the major pollutants of PM2.5 and PM10 in winter and ozone in summer, showing an obvious seasonal effect. It also has a periodic distribution in the day, with the worst air quality at night and the best in the morning. When the air quality deteriorates in a city, the influence will be transmitted to other neighboring cities within one day or even half a day, while it will take about three days for the impact decays, which will be a great challenge for the joint regional prevention of haze pollution. Dealing with this matter we could allocate the responsibilities of each city according to the results of the Variance Decomposition, and furthermore we could make strategies to punish or reward the cities based on their accomplishment for haze pollution, forcing administrations to improve the environmental governance, and in this way our skies may be blue again.
Acknowledgement This research was financially supported by the Western Project of the Humanities and Social Sciences Research of Ministry of Education of PRC, NO15XJA910001.
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