Effects of Land Use and Urban Landscape Pattern on PM2.5 Concentration: A Shenzhen Case Study

doi:10.13209/j.0479-8023.2016.120

Abstract

Abstract:

This study took Shenzhen as study area. PM_2.5 concentration in air quality monitoring stations was used and five kinds of landscape metrics including PLAND (percentage of landscape), ED (edge density) at class-level, and CONTAG (contagion), NP (number of patches), AREA_MN (mean patch area) at landscape-level were applied. Further, other data, such as street length, catering number, elevation and land use types considered as factors influencing PM_2.5, were also obtained. By means of correlation analysis and stepwise multiple regression, the effects of land use and landscape pattern on PM_2.5 concentration were explored. The results showed that among land use as sink landscape for PM_2.5, vegetation had the most obvious influence on PM_2.5 concentration; at class-level metrics, both composition metric (PLAND) and configuration metric (ED) were significantly related with PM_2.5 concentration; at landscape-level, fragment (CONTAG and AREA_MN) of the whole landscape had a significant relationship with PM_2.5 pollution. This study could widen the understanding on relationship between landscape and process in landscape ecology and offer advice for air pollution control and landscape planning. Furthermore, it would also provide an effective method to estimate PM_2.5 concentration in case of no measurement.

Key words: land use, landscape patterns, PM_2.5, stepwise multiple regression, Shenzhen

摘要：

以深圳市为研究区, 利用空气质量监测站点PM_2.5浓度数据, 选取类型水平的景观类型所占比例(PLAND), 边缘密度(ED), 以及景观水平的蔓延度(CONTAG), 斑块数量(NP)和斑块平均面积(AREA_MN)共5 个景观指数, 并结合道路长度、餐饮点分布数量、海拔和土地利用类型等影响PM_2.5浓度的因子, 运用相关分析和多元逐步回归分析方法, 探究深圳市土地利用和城市景观格局对PM_2.5浓度的影响。结果表明: 1) 土地利用中, 植被对 PM_2.5浓度的削减起着至关重要的作用; 2) 城市各类型景观格局特征中, 组成特征(PLAND)和结构特征(ED)对PM_2.5浓度的影响显著; 3) 城市整体景观中, 景观水平的破碎度与PM_2.5浓度关系密切。研究结果可以加深对景观生态学中过程-格局相互作用的认识, 为大气污染防治和城市景观格局的规划管理提供参考和借鉴, 同时在监测数据缺失的情况下, 提供一种借助景观指数估算PM_2.5浓度的方法。

关键词: 土地利用, 景观格局, PM_2.5, 多元逐步回归, 深圳

CLC Number:

Wudan XIE, Jiansheng WU. Effects of Land Use and Urban Landscape Pattern on PM_2.5 Concentration: A Shenzhen Case Study[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2017, 53(1): 160-170.

谢舞丹, 吴健生. 土地利用与景观格局对 PM_2.5 浓度的影响—— 以深圳市为例[J]. 北京大学学报自然科学版, 2017, 53(1): 160-170.

Figures/Tables 5

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变量类别	变量描述	变量子类别		缓冲区	变量名称
道路长度	每种类型道路在不同缓冲区内的总长度(m)	mr (主要干道) cr (一般道路)		100, 200, 300, 500, 750, 1000	mr_xx cr_xx
餐饮数量	不同缓冲区内的餐饮业的店铺数量(个)	cat (餐饮业)		100, 200, 300, 500, 750, 1000, 2000, 3000	cat_xx
海拔	监测站点所在的海拔高度(m)	DEM (海拔)		-	DEM
土地利用类型	每种土地利用类型在不同缓冲区内的总面积(m²)	crop (耕地), vege (林地); wat (水体), cons (建设用地), bare (裸地);		100, 300, 500, 1000, 2000, 3000	crop_xx vege_xx wat_xx cons_xx bare_xx
景观指数(按景观功能分)	不同缓冲区内的景观指数, 包括类型水平和景观水平	crop (耕地) vewa (林地和水体) coba (建设用地和裸地)	PLAND, ED, CONTAG, NP, AREA_MN	100, 300, 500, 1000, 2000, 3000, 5000	crop_yy_xx vewa_yy_xx coba_yy_xx
景观指数(按土地类型分)	不同缓冲区内的景观指数, 包括类型水平和景观水平	crop (耕地) vege (林地) wat (水体) cons (建设用地) bare (裸地)	PLAND, ED, CONTAG, NP, AREA_MN	100, 300, 500, 1000, 2000, 3000, 5000	crop_yy_xx vege_yy_xx wat_yy_xx cons_yy_xx bare_yy_xx

变量类别	变量描述	变量子类别		缓冲区	变量名称
道路长度	每种类型道路在不同缓冲区内的总长度(m)	mr (主要干道) cr (一般道路)		100, 200, 300, 500, 750, 1000	mr_xx cr_xx
餐饮数量	不同缓冲区内的餐饮业的店铺数量(个)	cat (餐饮业)		100, 200, 300, 500, 750, 1000, 2000, 3000	cat_xx
海拔	监测站点所在的海拔高度(m)	DEM (海拔)		-	DEM
土地利用类型	每种土地利用类型在不同缓冲区内的总面积(m²)	crop (耕地), vege (林地); wat (水体), cons (建设用地), bare (裸地);		100, 300, 500, 1000, 2000, 3000	crop_xx vege_xx wat_xx cons_xx bare_xx
景观指数(按景观功能分)	不同缓冲区内的景观指数, 包括类型水平和景观水平	crop (耕地) vewa (林地和水体) coba (建设用地和裸地)	PLAND, ED, CONTAG, NP, AREA_MN	100, 300, 500, 1000, 2000, 3000, 5000	crop_yy_xx vewa_yy_xx coba_yy_xx
景观指数(按土地类型分)	不同缓冲区内的景观指数, 包括类型水平和景观水平	crop (耕地) vege (林地) wat (水体) cons (建设用地) bare (裸地)	PLAND, ED, CONTAG, NP, AREA_MN	100, 300, 500, 1000, 2000, 3000, 5000	crop_yy_xx vege_yy_xx wat_yy_xx cons_yy_xx bare_yy_xx

类别	变量	类型水平_组成指数(r)	类型水平_结构指数(r)	景观水平指数(r)
景观指数 (按景观功能分)	汇景观	vewa_PLAND_5000 (-0.689)	vewa_ED_5000 (0.613) vewa_ED_300 (-0.655)	CONTAG_5000 (-0.729) AREA_MN_5000 (-0.734)
	源景观	coba_PLAND_5000 (0.677)	coba_ED_5000 (0.634) coba_ED_300 (-0.732)
	不确定景观	crop_PLAND_5000 (0.638)	-
景观指数 (按土地类型分)	林地	vege_PLAND_5000 (-0.724)		CONTAG_5000 (-0.875) AREA_MN_5000 (-0.778)
	水体	-	wat_ED_5000 (0.617)
	建设用地	cons_PLAND_5000 (0.651)	cons_ED_300 (-0.682) cons_ED_5000 (0.688)
	裸地	-	bare_ED_5000 (0.711)
	耕地	crop_PLAND_5000 (0.638)	-

类别	变量	类型水平_组成指数(r)	类型水平_结构指数(r)	景观水平指数(r)
景观指数 (按景观功能分)	汇景观	vewa_PLAND_5000 (-0.689)	vewa_ED_5000 (0.613) vewa_ED_300 (-0.655)	CONTAG_5000 (-0.729) AREA_MN_5000 (-0.734)
	源景观	coba_PLAND_5000 (0.677)	coba_ED_5000 (0.634) coba_ED_300 (-0.732)
	不确定景观	crop_PLAND_5000 (0.638)	-
景观指数 (按土地类型分)	林地	vege_PLAND_5000 (-0.724)		CONTAG_5000 (-0.875) AREA_MN_5000 (-0.778)
	水体	-	wat_ED_5000 (0.617)
	建设用地	cons_PLAND_5000 (0.651)	cons_ED_300 (-0.682) cons_ED_5000 (0.688)
	裸地	-	bare_ED_5000 (0.711)
	耕地	crop_PLAND_5000 (0.638)	-

回归模型	变量	方程参数
回归模型	变量	B	Beta	t	Sig.	其他参数
模型a	常数项	34.399	-	20.514	0.000	修正R²=0.627 D-W值= 1.976 RMSE=2.741 μg/m³ F=9.420 (Sig.=0.008)
	vege_5000	-1.472×10^-7	-0.575	-2.914	0.019
	crop_5000	8.826×10^-6	0.501	2.540	0.035
模型b	常数项	30.569	-	15.832	0.000	修正R²=0.710 D-W值=1.542 RMSE=4.642 μg/m³ F=13.233 (Sig.=0.003)
	coba_ED_300	-0.091	-0.621	-3.552	0.007
	coba_ED_5000	0.150	0.495	2.832	0.022
模型c	常数项	43.498	-	35.441	0.000	修正R²=0.912 D-W值=2.184 RMSE=1.350 μg/m³ F=35.477 (Sig.=0.000)
	AREA_MN_5000	-0.626	-0.513	-5.143	0.001
	con_ED_300	-0.084	-0.623	-6.287	0.000
	AREA_MN_100	-1.333	-0.369	-3.747	0.007