长三角城市群城市空间形态对PM2.5与O3污染空间异质性特征的影响研究

doi:10.16258/j.cnki.1674-5906.2023.10.006

生态环境学报 ›› 2023, Vol. 32 ›› Issue (10): 1771-1784.DOI: 10.16258/j.cnki.1674-5906.2023.10.006

长三角城市群城市空间形态对PM_2.5与O₃污染空间异质性特征的影响研究

叶深¹(), 王鹏¹^,²^,^*(), 黄祎¹, 折远洋¹, 丁明军¹^,²

1.江西师范大学地理与环境学院，江西南昌 330022
2.江西师范大学鄱阳湖湿地与流域研究教育部重点实验室，江西南昌 330022

收稿日期:2023-09-06 出版日期:2023-10-18 发布日期:2024-01-16
通讯作者: *王鹏。E-mail: wangpengjlu@jxnu.edu.cn
作者简介:叶深（1996年生），男，硕士，主要研究方向为城市生态学。E-mail: ys1996120@outlook.com
基金资助:
中国科学院战略性先导科技专项(XDA20040201)

Urban Morphology and the Influence of the Spatial Heterogeneity of PM_2.5 and O₃ Pollution: The Case of the Yangtze River Delta

YE Shen¹(), WANG Peng¹^,²^,^*(), HUANG Yi¹, SHE Yuanyang¹, DING Mingjun¹^,²

1. School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, P. R. China
2. Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, P. R. China

Received:2023-09-06 Online:2023-10-18 Published:2024-01-16

摘要/Abstract

摘要：

城市空间形态作为城市建设用地扩张及社会经济空间结构聚合体反映了城市化进程，探究城市空间形态及PM_2.5与O₃污染对区域大气环境治理具有重要意义。基于“十三五”规划期间长三角城市群空气质量监测站及气象站观测数据、中国土地覆盖数据（China Land Cover dataset）、人口密度及夜间灯光遥感影像从城市空间格局指数（城市建设用地紧凑度、城市建设用地边缘密度、城市建设用地斑块密度等）及城市空间结构指数（城市夜间平均夜光遥感指数、城市人口密度、城市通勤度等）角度计算城市空间形态指数，并运用地理探测器解析PM_2.5与O₃污染空间异质性特征。结果表明，1）2020年长三角城市群城市PM_2.5年均质量浓度值较2016年下降15.9%，而MDA8 O₃年均质量浓度值增长9.94%；PM_2.5与O₃季节质量浓度时空分异特征显著，其相关性系数体现出“自东南沿海向西北内陆递减”的特征。2）长三角城市群的城市空间形态指数时空分异特征强于气象要素。除2019－2020年交通通勤度和城市建设用地紧凑度分别出现短暂44.8%和5.86%下降外，其余指数均逐年上升；长三角城市群城市空间形态指数受空间异质性影响整体呈“北高、中部次之、南低”的特征。3）城市建设用地紧凑度是长三角城市群城市PM_2.5与O₃污染的最主要城市空间格局影响因子，对PM_2.5质量浓度值、MDA8 O₃质量浓度值及PM_2.5与O₃浓度相关性解释率分别为0.259、0.419和0.258。研究结果揭示长三角城市群城市的主要空间形态指数将增强城市PM_2.5与O₃污染空间异质性特征，为探明城市化扩张背景下大气污染物的空间演化规律提供了新思路。

关键词: 长三角城市群, 城市空间形态, 多源数据, PM_2.5与O₃污染物, 地理探测器, 空间异质性

Abstract:

Urban spatial morphology, defined as the aggregation of urban construction land expansion and socio-economic spatial structure, is critical for exploring urban spatial patterns and their relationships with PM_2.5 and O₃ pollution; its assessment is essential for socio-economic development, urban planning, and regional air quality management. Using datasets from the Yangtze River Delta in China during the 13th Five-Year Plan period, including air quality monitoring data, meteorological stations data, land cover data, population data, night-time lights data, and self-defined urban spatial morphology indices (e.g., compactness index, complexity, and density), we analyzed driving mechanisms of the spatial heterogeneity of urban morphology and PM_2.5 and O₃ pollution by employing Geo-detector models. The key findings were as follows: 1) The annual PM_2.5 concentration in 2020 decreased by 15.9% compared to 2016, while the annual average MDA8 O₃ increased by 9.94%. Both PM_2.5 and O₃ showed significant seasonal and spatial patterns, with higher PM_2.5 concentrations in the southeast region and during winter, and higher O₃ levels in summer. 2) Urban morphology indices demonstrated stronger spatial-temporal differentiation compared to meteorological factors in the study area. These indices generally showed an increasing trend except for short-term fluctuations in commuting and compactness. The spatial patterns showed high morphology indices in northern and central cities, and low morphology indices in the south. 3) The compactness of urban construction land was the most critical influencing factor, explaining 0.259 and 0.419 of PM_2.5 and O₃ concentrations, respectively, as well as having a correlation of 0.258 with these pollutants. The interactions between morphology indices enhanced the spatial heterogeneity of PM_2.5 and O₃ pollution, providing insights into the spatial evolution of pollutants under urbanization, and empirical support for regional air quality management.

Key words: Yangtze River Delta, urban morphology, multi-source data, PM_2.5and O₃ pollution, Geo-detector, spatial heterogeneity

中图分类号:

叶深, 王鹏, 黄祎, 折远洋, 丁明军. 长三角城市群城市空间形态对PM_2.5与O₃污染空间异质性特征的影响研究[J]. 生态环境学报, 2023, 32(10): 1771-1784.

YE Shen, WANG Peng, HUANG Yi, SHE Yuanyang, DING Mingjun. Urban Morphology and the Influence of the Spatial Heterogeneity of PM_2.5 and O₃ Pollution: The Case of the Yangtze River Delta[J]. Ecology and Environment, 2023, 32(10): 1771-1784.

图/表 14

图1 研究区地形、监测站点、土地利用及平均夜光遥感指数的空间分布

Figure 1 Spatial distribution of topography, monitoring stations, land use and mean nighttime light index values in the study area

表1 各类城市指标汇总

Table 1 Urban spatial morphology index and significance

数据类型	数据名称	数据年份	单位	数据来源
大气污染物监测数据	PM_2.5年均质量浓度值	2016‒2020	μg∙m⁻³	全国城市空气质量实时发布平台 (https://air.cnemc.cn:18007/)
大气污染物监测数据	MDA8 O₃年均质量浓度值		μg∙m⁻³	全国城市空气质量实时发布平台 (https://air.cnemc.cn:18007/)
土地利用数据	中国土地覆盖数据 (CLCD)		m	https://doi.org/10.5281/zenodo.4417810
人口密度数据	LandScan人口密度数据		person∙km⁻²	https://hub.worldpop.org/
夜光遥感数据	DMSP夜间灯光数据集		‒	https://doi.org/10.11888/Socioeco.tpdc.271202
社会经济数据	公共汽车保有量	2017‒2021	辆	2017‒2021年《中国城市统计年鉴》
社会经济数据	第三产业结构	2017‒2021	%	2017‒2021年《中国城市统计年鉴》
气象数据	温度	2016‒2020	℃	国家气象科学数据中心 (http://data.cma.cn/)
	降水		mm
	相对湿度		%
	风速		m∙s⁻¹

表2 城市空间形态指数及意义

Table 2 Urban spatial morphology index and significance

类型	数据名称	缩写	意义	计算公式
城市空间格局	城市建设用地紧凑度	C_I	表示城市空间形态形状、破碎度	C_I=2 $π A /$ C
	城市建设用地边缘密度	E_D	表示城市空间形态复杂性	E_D=E/A×10000
	城市建设用地斑块密度	P_D	表示城市空间形态内部连通性	P_D=N/A
	城市建设用地最大斑块占比	L_PI	表示城市空间形态中心性	L_PI=a_max/A×100
城市空间结构	城市人口密度	P	表示城市空间规模	P=P_OP/A
	城市平均夜光遥感指数	T_NLI	表示城市空间规模	$T NLI =1/ n ∑ i = 1 n D N i$
	第三产业结构占比	I_nd	表示城市功能结构	I_nd=I_ND/G_DP
	交通通勤度	T_C	表示城市交通可达性	T_C=B_us/A

表2 城市空间形态指数及意义

Table 2 Urban spatial morphology index and significance

类型	数据名称	缩写	意义	计算公式
城市空间格局	城市建设用地紧凑度	C_I	表示城市空间形态形状、破碎度	C_I=2 $π A /$ C
	城市建设用地边缘密度	E_D	表示城市空间形态复杂性	E_D=E/A×10000
	城市建设用地斑块密度	P_D	表示城市空间形态内部连通性	P_D=N/A
	城市建设用地最大斑块占比	L_PI	表示城市空间形态中心性	L_PI=a_max/A×100
城市空间结构	城市人口密度	P	表示城市空间规模	P=P_OP/A
	城市平均夜光遥感指数	T_NLI	表示城市空间规模	$T NLI =1/ n ∑ i = 1 n D N i$
	第三产业结构占比	I_nd	表示城市功能结构	I_nd=I_ND/G_DP
	交通通勤度	T_C	表示城市交通可达性	T_C=B_us/A

图2 基于地理探测器分析城市空间变量对PM2.5与O3污染空间异质性特征的影响

Figure 2 Geo-detector analysis of the spatial variable on the spatial heterogeneity characteristics of PM2.5 and O3 pollution

表3 地理探测器交互探测

Table 3 Geo-detector interaction detection

判断依据	交互作用
q(x₁∩x₂)<Min(q(x₁), q(x₂))	非线性减弱
Min(q(x₁), q(x₂))<q(x₁∩x₂)<Max(q(x₁), q(x₂))	单因子非线性减弱
q(x₁∩x₂)>Max(q(x₁), q(x₂))	双因子增强
q(x₁∩x₂)=q(x₁)+q(x₂)	独立
q(x₁∩x₂)>q(x₁)+q(x₂)	非线性增强

图3 长三角城市群城市PM2.5与MDA8 O3质量浓度时间变化特征

Figure 3 Contrast between temporal changes of PM2.5 and MDA8 O3 in the Yangtze River Delta

图4 长三角城市群PM2.5与O3季节质量浓度相关性时间变化特征

Figure 4 Seasonal temporal changes of PM2.5 and O3 correlation in Yangtze River Delta

图5 长三角城市群PM2.5与O3季节质量浓度相关性时空分布特征

Figure 5 Spatial temporal changes of PM2.5 and O3 correlation in Yangtze River Delta

图6 长三角城市群城市空间形态及气象要素时间变化特征

Figure 6 Temporal changes in the urban morphology and meteorological of the Yangtze River Delta

图7 长三角城市群城市空间形态及气象要素时空演变特征

Figure 7 Spatial and temporal changes of the urban morphology and meteorological of the Yangtze River Delta

图8 长三角城市群城市PM2.5与O3空间异质性单因子驱动分析

Figure 8 Factor analysis of PM2.5 and O3 spatial heterogeneity in the Yangtze River Delta

图9 长三角城市群PM2.5与O3交互分析

Figure 9 Analysis of the interaction between PM2.5 and O3 in the Yangtze River Delta

表4 长三角城市群城市空PM2.5与O3交互驱动机制

Table 4 Interactive driving mechanism between PM2.5 and O3 in the Yangtze River Delta

PM_2.5交互	交互值	驱动机制	O₃交互	交互值	驱动机制	相关性交互	交互值	驱动机制
C_I∩E_D	0.512	非线性增强	C_I∩E_D	0.436	非线性增强	C_I∩E_D	0.574	非线性增强
C_I ∩P_D	0.606	相互增强	C_I∩P_D	0.476	相互增强	C_I ∩P_D	0.476	非线性增强
C_I∩L_PI	0.598	相互增强	C_I∩L_PI	0.347	相互增强	C_I∩L_PI	0.393	相互增强
P∩T_NLI	0.336	相互增强	P∩T_NLI	0.307	非线性增强	P∩T_NLI	0.718	相互增强
P∩I_nd	0.307	相互增强	P∩I_nd	0.449	非线性增强	P∩I_nd	0.657	相互增强
P∩TC	0.304	非线性增强	P∩TC	0.419	非线性增强	P∩TC	0.635	相互增强
P_CP∩t	0.425	非线性增强	P_CP∩t	0.282	相互增强	P_CP∩t	0.217	非线性增强
P_CP∩H_r	0.501	非线性增强	P_CP∩H_r	0.333	非线性增强	P_CP∩H_r	0.205	相互增强
P_CP∩V_w	0.358	非线性增强	P_CP∩V_w	0.235	相互增强	P_CP∩V_w	0.178	非线性增强

图10 长三角城市群城市PM2.5与O3空间分层异质性分析

Figure 10 Significance analysis of the spatial interaction between PM2.5 and O3 in the Yangtze River Delta

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长三角城市群城市空间形态对PM_2.5与O₃污染空间异质性特征的影响研究

Urban Morphology and the Influence of the Spatial Heterogeneity of PM_2.5 and O₃ Pollution: The Case of the Yangtze River Delta

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