Spatiotemporal evolution of PM2.5 concentrations in urban agglomerations of China

doi:10.1007/s11442-021-1876-2

Abstract

Abstract:

As the main form of new urbanization in China, urban agglomerations are an important platform to support national economic growth, promote coordinated regional development, and participate in international competition and cooperation. However, they have become core areas for air pollution. This study used PM_2.5 data from NASA atmospheric remote sensing image inversion from 2000 to 2015 and spatial analysis including a spatial Durbin model to reveal the spatio-temporal evolution characteristics and main factors controlling PM_2.5 in China’s urban agglomerations. The main conclusions are as follows: (1) From 2000 to 2015, the PM_2.5 concentrations of China’s urban agglomerations showed a growing trend with some volatility. In 2007, there was an inflection point. The number of low-concentration cities decreased, while the number of high-concentration cities increased. (2) The concentrations of PM_2.5 in urban agglomerations were high in the west and low in the east, with the “Hu Line” as the boundary. The spatial differences were significant and increasing. The concentration of PM _2.5 grew faster in urban agglomerations in the eastern and northeastern regions. (3) The urban agglomeration of PM_2.5 had significant spatial concentrations. The hot spots were concentrated to the east of the Hu Line, and the number of hot-spot cities continued to rise. The cold spots were concentrated to the west of the Hu Line, and the number of cold-spot cities continued to decline. (4) There was a significant spatial spillover effect of PM_2.5 pollution among cities within urban agglomerations. The main factors controlling PM_2.5 pollution in different urban agglomerations had significant differences. Industrialization and energy consumption had a significant positive impact on PM_2.5 pollution. Foreign direct investment had a significant negative impact on PM_2.5 pollution in the southeast coastal and border urban agglomerations. Population density had a significant positive impact on PM_2.5 pollution in a particular region, but this had the opposite effect in neighboring areas. Urbanization rate had a negative impact on PM_2.5 pollution in national-level urban agglomerations, but this had the opposite effect in regional and local urban agglomerations. A high degree of industrial structure had a significant negative impact on PM_2.5 pollution in a region, but this had an opposite effect in neighboring regions. Technical support level had a significant impact on PM_2.5 pollution, but there were lag effects and rebound effects.

Key words: urban agglomeration, PM_2.5, spatiotemporal evolution, influencing factor, spatial Durbin model

WANG Zhenbo, LIANG Longwu, WANG Xujing. Spatiotemporal evolution of PM_2.5 concentrations in urban agglomerations of China[J].Journal of Geographical Sciences, 2021, 31(6): 878-898.

Figures/Tables 10

Figure 1

Figure 2

Table 1

Figure 3

Table 2

Figure 4

Figure 5

Table 3

Spatial autocorrelation indexes of annual average PM2.5 concentrations in China’s urban agglomerations from 2000 to 2015 "

Year	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015
All	0.82^***	0.79^***	0.77^***	0.84^***	0.74^***	0.75^***	0.80^***	0.82^***	0.76^***	0.76^***	0.78^***	0.79^***	0.79^***	0.83^***	0.81^***	0.80^***
BTH	0.33^**	0.41^**	0.41^**	0.39^**	0.36^**	0.37^**	0.36^**	0.36^**	0.33^**	0.33^**	0.33^**	0.33^**	0.34^**	0.35^**	0.35^**	0.34^**
YRD	0.68^***	0.66^***	0.68^***	0.67^***	0.68^***	0.65^***	0.66^***	0.64^***	0.69^***	0.66^***	0.66^***	0.68^***	0.67^***	0.67^***	0.67^***	0.68^***
PRD	0.13^*	0.16^*	-0.10	0.20^*	0.18^*	-0.16^*	0.11	-0.14^*	0.13^*	0.15^*	0.17^*	0.12^*	0.14^*	0.17^*	0.25^*	0.23^*
MYR	0.84^***	0.77^***	0.55^***	0.73^***	0.19^***	0.49^***	0.57^***	0.40^***	0.38^***	0.43^***	0.59^***	0.68^***	0.41^***	0.66^***	0.64^***	0.61^***
CC	0.38^**	0.54^***	0.32^**	0.30^*	0.31^**	0.27^*	0.19	0.23	0.15	0.12	0.19	0.20	0.20	0.19	0.12	0.19
CSL	0.11^*	0.11^*	0.12^*	0.28^***	0.15^*	0.15^*	0.14^*	0.21^**	0.15^*	0.21^**	0.10	0.15^*	0.20^**	0.18^**	0.12^*	0.13^*
SP	0.44^***	0.49^***	0.46^***	0.51^***	0.48^***	0.54^***	0.53^***	0.50^***	0.49^***	0.51^***	0.58^***	0.53^***	0.53^***	0.52^***	0.53^***	0.47^***
WTS	0.25^*	-0.16^*	-0.29	0.10^*	-0.14	0.11^*	0.24	0.12^*	0.10	0.24^*	0.11^*	0.10^*	0.17	0.11^*	0.13^*	0.21
HC	0.55^**	0.67^***	0.57^***	0.62^***	0.74^***	0.74^***	0.71^***	0.75^***	0.81^***	0.72^***	0.68^***	0.77^***	0.75^***	0.61^***	0.61^***	0.52^***
CCP	0.55^***	0.52^***	0.28^*	0.41^**	0.42^**	0.38^**	0.41^**	0.47^**	0.47^**	0.49^***	0.48^***	0.42^**	0.41^**	0.45^***	0.53^***	0.49^***
GZ	-0.41^*	-0.31^*	-0.09^*	-0.13	-0.23^*	0.09^*	0.11^*	0.19^**	0.12^*	0.10	0.09^*	0.08	0.10^*	0.11	0.10^*	0.11^*
BG	0.79^***	0.76^***	0.85^***	0.92^***	0.93^***	0.93^***	0.93^***	0.87^***	0.97^***	0.95^***	0.96^***	0.96^***	0.97^***	0.92^***	0.93^***	0.90^***
CS	0.23^*	-0.10^*	-0.44	-0.59^*	-0.52^*	-0.10	-0.34^*	-0.25^*	0.20	0.12^*	0.31^*	0.11^*	0.13^*	0.10^*	0.12	0.15^*
HBEY	-0.18^*	0.15	-0.43	-0.77^*	-0.78^*	-0.56^*	-0.47^*	-0.36	0.58^*	0.79^*	0.67^*	0.50^*	0.91^*	0.87^*	0.88^*	0.93^*
CY	0.42^*	0.46^*	0.57^*	0.85^*	0.87^*	0.65^*	0.13^*	0.80^*	0.61^*	0.73^*	0.81^*	0.45^*	0.82^*	0.78^*	0.67^*	0.73^*
CG	-0.45^*	0.10	-0.42^*	-0.17^*	-0.16^*	-0.16^*	-0.17^*	-0.10	0.11	0.25^*	0.26^*	0.24	0.23^*	0.38^*	0.19^*	0.29^*
LX	0.81^**	0.81^**	0.83^**	0.79^**	0.76^**	0.90^**	0.87^**	0.84^**	0.77^**	0.67^*	0.59^*	0.74^**	0.66^*	0.68^*	0.75^**	0.76^**
NX	0.33^*	0.19^*	0.13	0.18^*	0.23^*	0.23^*	0.15^*	0.25^*	0.30^*	0.22	0.28^*	0.21^*	0.25^*	0.12	0.21^*	0.32^*

Table 3

Figure 6

Table 4

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Index	Index
Index	Per capita GDP	Population density	Urbanization rate	Industrialization rate	High degree of industrial structure	Dependence on foreign trade	Technical support	Energy consumption
Per capita GDP	1
Population density	0.017	1
Urbanization rate	0.002	0.061^**	1
Industrialization rate	0.009	0.045^**	0.000	1
High degree of industrial structure	0.036^*	0.166^**	0.054^**	0.049^**	1
Dependence on foreign trade	0.019	0.226^**	0.049^**	0.036^*	0.183^**	1
Technical support	0.161^**	0.096^**	-0.018	0.006	0.070^**	0.109^**	1
Energy consumption	0.010	0.014	-0.007	0.002	0.031	0.003	0.019	1

WHO Guidelines for Air Quality (2005)			China’s Ambient Air Quality Standard (GB3095-2012) (2016)
Type	Annual average (μg/m³)	Daily average (μg/m³)	Type	Annual average (μg/m³)	Daily average (μg/m³)
Guideline level	10	25	Standard level	35	75
Interim target 1	35	75	—	—	—
Interim target 2	25	50	—	—	—
Interim target 3	15	37.5	—	—	—