中国城市NO2浓度的时空分布及社会经济驱动力

doi:10.18402/resci.2017.07.15

摘要/Abstract

摘要：

城市空气质量改善是当前中国社会经济转型发展过程中的重要目标。基于中国274个地级及以上城市2004-2013年以来的监测数据对NO₂浓度的时空特征进行分析,并通过空间计量经济模型构建了社会经济要素对城市NO₂污染影响的动力机制。结果表明：①10年间,城市NO₂浓度年均值整体呈现先波动下降再增加的“U”形曲线特征,NO₂浓度年达标率由2004年78.1%降至2013年最低值73.0%,NO₂污染的控制总体不稳定。②城市NO₂浓度分布格局总体发生一定变化,并在局部区域有显著改变。京津冀、山东半岛、长三角等东部经济发达城市是主要NO₂高污染区,并存在显著的空间正相关关系（集聚状态）。③解析了城市NO₂浓度分布变化的社会经济驱动力。10年间,城市经济增长依然是影响NO₂浓度和变化的主要原因,并呈“U”型曲线特征;人口城市化水平、二产比重和机动车数量激增是恶化城市NO₂浓度的重要因素。空间自回归系数值为0.236 659,表明城市的NO₂浓度除受自身影响外,还受相邻城市的溢出扩散影响。未来,需继续加强氮氧化物减排控制,并在NO₂治理过程中注重城市间的联合防控。

关键词: NO₂浓度, 时空演化, 社会经济, 驱动力, 空间计量模型, 城市, 中国

Abstract:

With China's socioeconomic development and transition,a significant goal of improving urban air quality remains. Using monitoring data for cities at the prefecture level and above from 2004 to 2013,we analyzed temporal and spatial characteristics of NO₂ quality concentration. A dynamical mechanism of socioeconomic influencers on urban NO₂ pollution was constructed based on a Spatial Econometric Model. We found that annual average values in NO₂ concentration varied along a U curve during 10 years in urban areas. P-values changed from 78.1%（214 cities）in 2004 to the lowest at 73.0%（200 cities）in 2013. The control of NO₂ pollution was unstable. The total spatial distribution of NO₂ pollution presented certain change during study period, and there were significant changes in some local zones. The economically developed eastern cities such as Beijing-Tianjin-Hebei,Shandong Peninsula and Yangtze River Delta were the main pollutant regions for NO₂ in China,and there was a significant spatial positive correlation. The socioeconomic driving forces of NO₂ concentration distribution change were analyzed. In the past 10 years,urban economic growth was the main factor affecting NO₂ concentration and variation,and it was characterized by a U curve. The population urbanization level,proportion of secondary production and number of motor vehicles were important factors that increased urban NO₂ concentrations. The spatial autoregressive coefficient is 0.236 659,indicating that the NO₂ concentration in a city depends not only on its own concentration but also on the pollution of neighboring cities. In the future,we need to strengthen the control of NO_X emission reduction and pay attention to prevention and control between cities during the NO₂ governance process.

Key words: NO₂ concentration, spatiotemporal variations, social economy, driving forces, spatial economic model, city, China

姚尧, 李江风, 胡涛, 杨媛媛, 丁镭. 中国城市NO₂浓度的时空分布及社会经济驱动力[J]. 资源科学, 2017, 39(7): 1383-1393.

Yao YAO, Jiangfeng LI, Tao HU, Yuanyuan YANG, Lei DING. Spatiotemporal variation in NO₂ concentrations and socioeconomic driving forces in Chinese cities[J]. Resources Science, 2017, 39(7): 1383-1393.

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	变量	数量	均值	标准差	最小值	最大值
NO₂	NO₂/（µg/m³）	2 740	30.230	11.202	3.000	73.000
建成区面积	Area/km²	2 740	108.297	146.280	5.000	1 350.000
民用汽车总量	Car/万辆	2 740	23.600	8 164.540	0.840	518.900
绿化覆盖率	Green/%	2 740	36.258	8.525	2.410	91.030
第二产业比重	Industry/%	2 740	52.671	78.474	12.400	90.970
人均GDP	PGDP/万元	2 740	3.974	3.028	0.185	26.775
人口城市化率	Urban/%	2 740	47.940	16.244	9.210	100.000

模型估计	OLS		SLM-FE		SLM-RE		SEM-FE		SEM-RE
模型估计	系数值	t值	系数值	t值	系数值	t值	系数值	t值	系数值	t值
常数项	4.910***	5.9	-	-	6.273***	7.1	-	-	5.284***	5.9
lnPGDP	-0.695***	-4.0	-0.954***	-5.4	-0.751***	-4.4	-0.828***	-4.7	-0.815***	-4.5
ln²PGDP	0.035***	4.3	0.048***	5.5	0.038***	4.3	0.039***	4.6	0.038***	4.3
lnUrban	0.218***	4.4	0.375***	5.5	0.252***	4.6	0.337***	5.1	0.172***	3.1
lnArea	0.052**	2.5	0.040*	1.8	0.029	1.4	-0.027	-1.3	0.065**	3.2
lnIndustry	0.118***	2.9	0.108***	3.0	0.142***	4.2	0.088***	2.6	0.267***	8.9
lnCar	0.081***	4.5	0.089***	4.9	0.099***	5.3	0.092***	5.1	0.088***	4.5
lnGreen	-0.066**	-2.5	-0.108***	-4.2	-0.090***	-3.4	-0.061***	-2.5	-0.086***	-3.4
T	0.012	-0.6	0.016	-0.9	0.015	-0.8	0.013	-0.6	0.012	-0.6
δ	-	-	0.236***	8.6	0.236***	8.7	-	-	-	-
λ	-	-	-	-	-	-	0.234***	9.5	0.222***	8.5
R²	0.608		0.788		0.744		0.707		0.679
Hausman	5.87		-		41.25***		-		217.81***
log-likelihood		-	-		-		336.367		-269.810

中国城市NO₂浓度的时空分布及社会经济驱动力

Spatiotemporal variation in NO₂ concentrations and socioeconomic driving forces in Chinese cities

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