Correlation Analysis of Fine Particulate Pollutants and Land Cover Landscape Pattern in Beijing

doi:10.13466/j.cnki.lyzygl.2021.04.013

Abstract

Abstract:

Air pollution is a key environmental issue nowadays.It is of great significance to understand the interaction between different land cover landscape patterns and air fine particulate pollutants for improving urban ecological environment.Aerosol optical depth(AOD) is the premise of the generation of fine particulate pollutants in the air.The AOD data measured by the AERONET ground monitoring points in Beijing and the surface fine particulate pollutants data were used for fitting analysis,and then the AOD of four seasons in Beijing was retrieved by using MODIS data.The Landsat8 image was processed to obtain the landscape type index of Beijing in 2018,and the correlation analysis was conducted with four seasons AOD data.The results showed that:1) fine particulate pollutants were negatively correlated with LPI(maximum patch index),ED(boundary density),COHESION(patch connectivity) and AI(patch aggregation),but positively correlated with PD(patch fragmentation) and LSI(landscape shape index);2) The annual forest and grassland were the core types with significant negative correlation with fine particulate pollutants,while the correlation between farmland and water body was significantly related to seasonal changes;3) Multiple linear regression analysis was used to get the regular model of forest,grassland,farmland,water and fine particulate pollutants in four seasons,which further proved that landscape index could be used to estimate the mass concentration of regional fine particulate pollutants.

Key words: remote sensing, aerosol, fine particulate pollutants, landscape pattern index

CLC Number:

X513

MA Bolun, WANG Lei, HUA Yongchun. Correlation Analysis of Fine Particulate Pollutants and Land Cover Landscape Pattern in Beijing[J]. FOREST RESOURCES WANAGEMENT, 2021, (4): 94-103.

Figures/Tables 10

Fig.1

Tab.1

Landscape index and description

景观指数	公式	描述
PLAND	PLAND= $∑ j = 1 n a ij A$ ×100	PLAND为某一斑块类型的总面积占整个景观面积的百分比;a_ij为斑块ij的面积;A是所有景观的总面积,其值越小则景观斑块类型越少
PD	PD= $N ij A$	PD为斑块破碎度,其值越大,则破碎化程度越高;N_ij为斑块数目,A为斑块面积之和
LPI	LPI= $Max (a 1 × a n) A$ ×100	LPI为最大斑块占景观面积的比例,其值的变化可以改变干扰的强度和频率,反映人类活动的方向和强弱;Max(a₁×a_n)为景观中最大斑块面积;A为景观总面积
ED	ED= $∑ k = 1 m E ik A$	ED为边界密度;E_ik为优势斑块的边缘总长度;A为景观总面积,其反映斑块中相互影响的强度与能量物质交换的潜力
LSI	LSI= $0.25 E A$	LSI为景观形状指数,可衡量整体几何景观的复杂性,其值越大,斑块形状越接近正方形;E为景观边缘的总长度;A为景观总面积
IJI	IJI= $- ∑ k = 1 m [e ik ∑ k = 1 m e ik In (e ik ∑ k = 1 m e ik)] In (m - 1)$ ×100	IJI为斑块类型i在其他斑块类型中的平均平均分散占最大可能分散的比例,取值范围为0~100,值越小聚集性越强,e_ik为斑块类型i与k相邻的总边缘长度
COHESION	COHESION=(1- $∑ i = 1 n p ∑ i = 1 n p a$ )(1- $1 A$ )	COHESION为斑块连接度;反映相应斑块之间的物理连通性P为斑块在该类型中的周长;a为斑块i在该类型中的面积;A为斑块总面积
AI	AI=MAX×G_ii×100	AI反映景观中不同斑块类型的非随机性或聚集程度;G_ii为景观类型的相似邻接斑块数量

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季节	拟合方程	R²	皮尔逊相关系数	残差平方和
春季	y=0.0024x+0.0245	0.682	0.787^**	8.7071
夏季	y=-0.0026x-0.0188	0.796	0.853^**	4.4395
秋季	y=0.0017x+0.0418	0.732	0.817^**	5.8095
冬季	y=0.0012x+0.0919	0.634	0.728^**	7.6319

季节	景观破碎度 (PD)	最大斑块指数 (LPI)	边界密度 (ED)	形状指数 (LSI)	散布并列指数 (IJI)	斑块连接度 (COHESION)	聚集程度 (AI)
春季	0.063	-0.070	-0.271^*	-0.052	-0.084	-0.047	-0.056
夏季	-0.163	-0.222^*	-0.176	0.438^**	0.137	-0.343^**	-0.500^**
秋季	0.301^**	-0.171	-0.347^**	0.453^**	0.074	-0.173	-0.381^**
冬季	0.400^**	-0.138	-0.295^**	0.329^**	0.006	-0.261^**	-0.422^**
全年	0.552^**	-0.241^**	-0.234^*	0.420^**	0.101	-0.400^**	-0.556^**

类型	景观类型比 (PLAND)	景观破碎度 (PD)	最大斑块指数 (LPI)	边界密度 (ED)	形状指数 (LSI)	散布并列指数 (IJI)	斑块连接度 (COHESION)	聚集程度 (AI)
农田	0.666^**	0.224^*	0.571^**	-0.134	0.400^**	-0.231^*	0.610^**	0.647^**
森林	-0.567^**	0.569^**	-0.544^**	-0.369^**	-0.126	-0.091	-0.545^**	-0.580^**
草地	-0.054	0.220	-0.002	-0.340^**	-0.359^**	0.117	-0.285^*	-0.142
水体	0.332^*	0.615^**	0.099	0.259	0.343^**	-0.134	0.039	-0.191

类型	回归方程	调整后R²	F	系数VIF
农田	Y=-0.082+0.337PD-0.246ED+0.253LSI-0.108IJI+0.670AI	0.549	60.682	<2
森林	Y=0.121+0.580PD-0.778ED+0.475LSI+0.007IJI	0.534	94.809	<3
草地	Y=0.189+0.517PD-0.683ED+0.163IJI+0.68COHESION+0.008AI	0.398	37.366	<4
水体	Y=0.187+0.946PD-0.487ED-0.056LSI-0.153IJI+0.486COHESION	0.423	49.063	<4