Abstract
In recent years, smog has frequently hit many parts of China, arousing widespread concerns. Our conjecture is that urban spatial structures have an impact on the generation of smog. In this chapter, we have calculated the sprawl index of cities in China using ground-level PM2.5 concentration data, global nighttime light data, LandScan population distribution data, and economic statistics and analyzed the influence of urban sprawl and size on smog in prefecture-level cities. The results suggest that urban sprawl increases local PM2.5 concentrations, and the size of population has a similar effect. Moreover, the correlation between urban sprawl and smog concentrations weakens as the size of cities increases, and the spatial spread of small cities leads to more serious air pollution. In addition, industrial cities often have higher smog concentrations. The conclusions of this chapter have the following practical significances: the spatial plan of a city, especially a small-sized one, should control the urban sprawl and make the city structurally compact. In the optimization of an urban system, we should steadily develop small or medium-sized cities and properly control the size of a large city, in order to facilitate coordinated development of large, medium, and small-sized cities.
Projects sponsored by foundations: The general project sponsored by the National Social Science Fund: “A Study on the Mechanism of Urban Sprawl Formation and Its Impact on Economic Efficiency in China” (15BJL107). The project sponsored by Jiangsu Provincial Research Base of Philosophy and Social Science: “On the Difficulties, Restrictive Factors of and Countermeasures for New Urbanization in Jiangsu” (13JDB024). And the project sponsored by the Research and Innovation Program for the Graduates of General Higher Education in Jiangsu: “A Study on the Causes of Urban Sprawl under New Urbanization in China” (KYLX15_0224). Special thanks go to the editors and anonymous reviewers who offer comments on this chapter. And the ideas and opinions expressed in this chapter are of course our own responsibility.
CLC No.: F062.2 Document Code: A Paper No.: 1002-8102(2016)11-0146-15
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Notes
- 1.
Smog pollution is a mixture of respirable coarse particulate matter, PM10, and fine particulate matter, PM2.5, which can penetrate deeply into the lung. PM10 refers to particles in the ambient air that are equal to or less than 10 micrometer in aerodynamic diameter. PM2.5 refers to particles that are equal to or less than 2.5 micrometer in diameter, also known as fine particulate matter, or fine particles.
- 2.
The eastern part of China includes Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. The central region includes Shanxi, Jilin, Heilongjiang, Henan, Anhui, Hubei, Hunan, and Jiangxi. The rest is the western region.
- 3.
The samples of this study are prefecture-level cities or municipalities (hereinafter referred to as prefecture-level cities), excluding cities in Hong Kong, Macao Special Administrative Region, and Taiwan Province. However, some cities are also excluded due to data loss or significant change in areas of jurisdiction (more than 10% change) during the sample period.
- 4.
The PM2.5 concentration variable in this chapter was based on the data obtained from and processed according to the global PM2.5 surface annual average concentrations provided by Socioeconomic Data and Applications Center, Columbia University. Battelle Memorial Institute and Columbia University used the satellite-mounted equipment to determine the aerosol optical depth (AOD) concentrations based on the idea of Donkelaar et al. (2010). So far we have obtained global PM2.5 annual average data from 1998 to 2013. To download this data: http://sedac.ciesin.columbia.edu/
- 5.
Global nighttime light data was derived from a series of satellite observations collected by the U.S. Defense Meteorological Satellite Program (DMSP). DMSP satellite carries sensors that accurately detect low-intensity lights emitted by the city lights, flares, and even traffic, among others. Brief lights were removed from the data. The background noise was also identified and replaced with 0. And ultimately the data contains relatively stable lights from cities and towns. Its spatial resolution is 30 seconds (this resolution corresponds to the LandScm population data), and the gray values range of light is in the range of 0 to 63 (saturation value is 63). Night light can act as a proxy variable for human activity. In recent years, some scholars have begun to use the night light data to study the economic growth and urban space problems in China (Xu Kangning et al., 2015). To download the data: http://ngdc.nodc.gov/eog/dmsp/downloadV4composites.html
- 6.
LandScan population distribution data is a global 30-second resolution population dataset. It combines geographic information systems, remote sensing images, and multivariate zonal density models, and it comprehensively utilizes census data and administrative division data, as well as high resolution satellite images from Landsat TM, including land cover data; roads, elevations, slopes, coastline data; and QuickBird, IKONOS, etc. To download LandScan data: http://web.ornl.gov/sci/landscan/
- 7.
If a weather station happens to be located in a city, we directly used the data of the site as the city’s climatic data. If there are multiple meteorological stations in the city, we averaged the values. For a city that does not have any weather station, the city’s climatic data was measured by the observations of its nearest weather stations. National Climate Center website: http://www.ncccma.net/cn/
- 8.
The variable struc in the table represents the urban space structure. SP is listed in Column 2 and 3, SA in Column 4 and 5, and lndensity in Column 6 and 7. The results of the Ilausman test show that the regression tends to select a fixed effect. Due to limitation of this chapter, the specific Ilausman test results and random effects are not listed here.
- 9.
For example, the geographical locations of the cities. The central heating line was delineated in China in the 1950s. But some of the cities without providing central heating are on the south side of the line and have climates very close to those of the Northern city. Another example is that the quality of coal varieties used in urban thermal stations also has an impact on the pollution effects of central heating.
- 10.
Part of the prefecture-level cities in some of these provinces are not located within the limits of areas for providing centralized heating, such as Xinyang in Henan, Ankang in Shaanxi, and so on. And for the simplicity of data processing, we also took into account that the air quality of these cities may be affected by other central-heating-provided cities in the province.
- 11.
The results of the test with the two quadratic terms included are not listed, due to page limitation of this chapter. Of course, we do not rule out the possibility of “optimal sprawl” in some areas at some point. For cities that are overly compact or overcrowded, a moderate urban sprawl may reduce energy consumption and pollution emissions. Only is this kind of city very few in the sample. It is difficult to find this kind of non-linear effect by testing the whole sample. Here, we are grateful to all anonymous reviewers who took trouble to offer their advice.
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Qin, M., Liu, X., Tong, Y. (2019). Does Urban Sprawl Aggravate Smog Pollution?. In: He, D., Wang, C. (eds) A New Era. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-10-8357-0_8
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