中国城市群地区PM2.5时空演变格局及其影响因素

doi:10.11821/dlxb201912014

Abstract

Abstract:

As the main form of China new urbanization, urban agglomerations are the important platform to support national economic growth, promote regional coordinated development and participate in international competition and cooperation, but they are also the core area of air pollution. This paper selects PM_2.5 data from NASA atmospheric remote sensing image inversion from 2000 to 2015, and uses GIS spatial analysis and Spatial Durbin Model to reveal the temporal and spatial evolution pattern characteristics and main controlling factors of PM_2.5 in China's urban agglomerations. The main conclusions are as follows: (1) From 2000 to 2015, the PM_2.5 concentration of China urban agglomerations showed a volatility growth trend. In 2007, there was an inflection point. The number of low-concentration cities declined, and the number of high-concentration cities increased. (2) The concentration of PM_2.5 in urban agglomerations was in the pattern of high in the east and and low in the west, with the "Hu Huanyong Line" as the boundary. The spatial difference between urban agglomerations is significant, and the difference is increasing. The concentration of PM_2.5 is growing faster in urban agglomerations in the eastern and northeastern regions. (3) The urban agglomeration of PM_2.5 has a significant spatial concentration. The hot spots are concentrated to the east of the "Hu Huanyong Line", and the number of cities continues to rise. The cold spots are concentrated to the west of the "Hu Huanyong Line", and the number of cities continues to decline. (4) There is a significant spatial spillover effect of PM_2.5 pollution among cities within urban agglomerations. The main controlling factors of PM_2.5 pollution in different urban agglomerations have significant differences. Industrialization and energy consumption have a significant positive impact on PM_2.5 pollution. Foreign direct investment has a significant negative impact on PM_2.5 pollution in the southeast coastal and border urban agglomerations. Population density has the significant positive impact on PM_2.5 pollution in the region, and has the opposite result in the neighbouring areas. Urbanization level has a negative impact on PM_2.5 pollution in national-level urban agglomerations, and has the opposite result in regional and local urban agglomerations. The high degree of industrial structure has a significant negative impact on PM_2.5 pollution in the region, and has the opposite result in the neighboring regions. Technical support has a significant impact on PM_2.5 pollution, but there are also lag effects and rebound effects.

Key words: urban agglomeration, PM_2.5, spatial-temporal evolution, influencing factor, spatial Durbin Model

WANG Zhenbo, LIANG Longwu, WANG Xujing. Spatio-temporal evolution patterns and influencing factors of PM_2.5 in Chinese urban agglomerations[J].Acta Geographica Sinica, 2019, 74(12): 2614-2630.

Figures/Tables 10

Fig. 1

Fig. 2

Tab. 1

Fig. 3

Tab. 2

Fig. 4

Fig. 5

Tab. 3

Spatial autocorrelation index of PM2.5 annual average concentration in China's urban agglomerations from 2000 to 2015

年份	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015
所有城市群	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^***
京津冀	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^**
长江三角洲	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^***
珠江三角洲	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^*
长江中游	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^***
成渝	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
辽中南	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^*
山东半岛	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^***
海峡西岸	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
哈长	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^***
中原	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^***
关中	-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^*
北部湾	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^***
晋中	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^*
呼包鄂榆	-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^*
滇中	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^*
黔中	-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^*
兰西	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^**
宁夏沿黄	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^*

Tab. 3

Fig. 6

Tab. 4

Results of factors affecting PM2.5 pollution in China's urban agglomerations from 2000 to 2015

城市群变量	京津冀	长江三角洲	珠江三角洲	长江中游	成渝	辽中南	山东半岛	海峡西岸	哈长	中原	关中	北部湾	晋中	呼包鄂榆	滇中	黔中	兰西	宁夏沿黄
Intercept	-2.128^***	3.366^***	-0.031	-0.073	-0.295	0.852^***	0.697^***	0.599^***	-1.010^***	-0.694	0.225	3.413^***	0.578	2.067	1.797^*	-1.699^*	0.583	0.655
lnPGDP	0.022	0.056^**	-0.006	0.108^***	0.066^*	0.083^***	0.074^***	0.064^***	0.116^***	-0.093	0.009	-0.048^***	0.046	-0.018	0.032	0.114	-0.002	-0.094
lnPD	0.495^***	-0.020	0.099^***	0.137^***	0.317^***	0.269^***	0.096^***	0.103^***	0.597^***	0.454^***	0.306^***	0.231^**	0.166^**	0.091^**	-0.058	-0.118	-0.041	0.123
UR	-0.218^**	-0.002^**	-0.075^***	0.024	-0.104	0.304^***	0.115^***	0.001	0.265^***	-0.024	0.206^***	0.143	-0.006	0.858^***	0.231^***	-0.091	0.232	0.693^**
IR	-0.818	0.605^**	0.742^***	-0.164	-0.237	-0.077	0.410^**	-0.001	0.083	0.139^*	0.351	0.063	-0.059	9.764^***	0.108^**	0.331	-0.105	1.577
ADIS	-1.300^**	0.114	-0.605^***	-0.371	-0.170	-0.583^***	-0.527^***	-0.205^*	-0.742^**	-0.042	0.040	0.042	-0.247	-8.500^***	0.233^**	-1.242^***	0.095	1.796
FDI	-0.232^***	-0.013^**	-0.002^**	-0.263^***	0.043	-0.011	-0.034	-0.382^***	-0.542^***	0.741	0.044	-0.104^**	0.722	0.171	-1.694^*	1.464	-0.422	0.325
TS	4.844	-6.201	-4.615^**	-7.039^**	-3.623	-0.973	0.704	3.952^*	-1.468	-5.727	-6.114	13.827	-18.973^**	0.389	-4.938^*	5.407^**	-0.611	-20.180
lnEC	-0.003	0.013^*	0.009^**	0.001	-0.001	0.032^***	0.016^***	0.004	0.046^***	0.047^***	0.026^***	-0.030	0.021^*	0.015	0.065^**	0.052^***	-0.008	0.005
W×lnPM_2.5	0.676^***	0.236^***	0.786^***	0.652^***	0.903^***	0.872^***	0.948^***	0.788^***	0.734^***	0.236^*	0.662^***	0.236^*	0.787^***	0.690^***	0.770^***	0.721^***	0.817	0.487^***
W×lnPGDP	0.066	0.037^***	0.070	0.002^***	0.034^**	0.060^**	0.133^***	0.056^***	0.178^***	0.222	-0.042	-0.230^***	0.083	0.185	-0.019	-0.225	0.188	0.214
W×lnPD	-0.203^***	-0.041	0.121	0.088	-0.247^***	-0.303^***	-0.066^*	-0.106^***	-0.329^***	-0.254^**	-0.280^***	-0.330^**	-0.353^**	-0.026	-0.298	-0.797^***	-0.168	-0.183
W×UR	0.147	-0.013^***	-0.003	-0.210	-0.611*	0.385^***	0.229^***	0.007	2.088^***	0.049	0.208^**	-0.197	-0.005	0.780^**	0.346^**	1.067^*	-0.507	-0.550
W×IR	2.615^*	-0.612	2.187^***	1.152^***	-0.377	0.660^***	0.169	0.001	0.003	0.282^***	1.206^***	0.383	0.071	6.792^**	0.182	-0.253	0.100	-0.744
W×ADIS	2.024	-0.566	2.296^***	-0.605	-0.077	-0.004	0.071	0.216*	0.455^**	-0.058	0.910^**	-0.885	0.161	5.740	0.266^*	1.842^***	-0.466	-1.28
W×FDI	0.204	-0.367^***	-0.034^***	-0.169	0.234	0.174	0.008	-0.058	-0.644^***	1.215	0.060	0.621	-1.640^**	-1.735^*	2.639	-2.607^*	0.991	0.610
W×TS	-8.491^*	5.481	2.973	7.609	3.475	0.298	-2.713^***	-3.932	-0.688	-15.23^**	10.358^*	-2.935	-21.698^*	20.184	0.717^***	-3.575	-0.402	3.257
W×lnEC	0.101^***	0.099^***	0.020^**	-0.016	0.024	0.058^***	0.034^***	-0.010	0.140^***	0.066^**	0.025^**	-0.141	0.043^*	0.152^***	-0.023	0.168^***	0.120^***	-0.035
R²	0.942	0.816	0.952	0.768	0.940	0.947	0.972	0.871	0.945	0.705	0.888	0.685	0.891	0.952	0.960	0.946	0.953	0.796
loglikehood	114.39	135.64	198.26	115.54	201.96	195.92	324.19	167.49	121.30	194.61	159.78	168.39	101.84	50.59	69.35	76.22	74.34	48.64

Tab. 4

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	人均 GDP	人口密度	城市化水平	工业化水平	产业结构高级度	外贸依存度	技术扶持水平	能源消耗
人均GDP	1
人口密度	0.017	1
城市化水平	0.002	0.061^**	1
工业化水平	0.009	0.045^**	0.000	1
产业结构高级度	0.036^*	0.166^**	0.054^**	0.049^**	1
外贸依存度	0.019	0.226^**	0.049^**	0.036^*	0.183^**	1
技术扶持水平	0.161^**	0.096^**	-0.018	0.006	0.070^**	0.109^**	1
能源消耗	0.010	0.014	-0.007	0.002	0.031	0.003	0.019	1

世界卫生组织(WHO)2005年发布的《空气质量准则》			中国2016年实施的《环境空气质量标准》
类别	年均值(ug/m³)	日均值(ug/m³)	类别	年均值(ug/m³)	日均值(ug/m³)
准则值	10	25	准则值	35	75
过渡期目标1	35	75	-	-	-
过渡期目标2	25	50	-	-	-
过渡期目标3	15	37.5	-	-	-

Spatio-temporal evolution patterns and influencing factors of PM_2.5 in Chinese urban agglomerations

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Spatio-temporal evolution patterns and influencing factors of PM2.5 in Chinese urban agglomerations

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Spatio-temporal evolution patterns and influencing factors of PM_2.5 in Chinese urban agglomerations