地理探测联合地理加权岭回归的PM2.5驱动因素分析

doi:10.16258/j.cnki.1674-5906.2022.02.011

生态环境学报 ›› 2022, Vol. 31 ›› Issue (2): 307-317.DOI: 10.16258/j.cnki.1674-5906.2022.02.011

地理探测联合地理加权岭回归的PM_2.5驱动因素分析

赵锐¹(), 詹梨苹¹, 周亮², 张军科¹^,^*()

1.西南交通大学地球科学与环境工程学院,四川成都 611756
2.兰州交通大学环境与市政工程学院,甘肃兰州 730070

收稿日期:2021-09-26 出版日期:2022-02-18 发布日期:2022-04-14
通讯作者: *E-mail: zhangjunke@home.swjtu.edu.cn
作者简介:赵锐（1983年生）,男,教授,主要研究方向为环境政策与低碳可持续发展。E-mail: ruizhao@home.swjtu.edu.cn
基金资助:
国家自然科学基金项目(41571520);四川循环经济研究中心课题资助(XHJJ-2002);四川循环经济研究中心课题资助(XHJJ-2005);成都市软科学研究项目(2020-RK00-00240-ZF);成都市软科学研究项目(2020-RK00-00246-ZF);中央高校基本科研业务费专项资金(2682021ZTPY088)

Identification of Driving Factors of PM_2.5 Based on Geographic Detector Combined with Geographically Weighted Ridge Regression

ZHAO Rui¹(), ZHAN Liping¹, ZHOU Liang², ZHANG Junke¹^,^*()

1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, P. R. China
2. School of Environment and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China

Received:2021-09-26 Online:2022-02-18 Published:2022-04-14

摘要/Abstract

摘要：

开展PM_2.5的驱动成因分析,对大气污染防治具有重要意义。利用2015—2018年PM_2.5地面监测数据,结合地理探测器和地理加权岭回归方法,探测了全国282个城市PM_2.5空间分异的关键驱动因素,分析了各关键驱动因素对PM_2.5影响的时空异质性。结果表明,气象参数和社会经济活动可更好地解释PM_2.5呈现的空间分异性;在2015—2018年间,所建地理加权岭回归模型的R²分别为0.698、0.724、0.656和0.712,AICc分别为1317.533、1234.400、1256.107和1110.740,2种指标均优于全局回归模型和地理加权回归模型,说明地理加权岭回归模型可更好地解释PM_2.5产生空间分异的关键影响机制;模型拟合结果进一步显示,气温、比湿度、地区生产总值、年平均人口和工业企业数是引起PM_2.5空间分异的关键驱动因素,各因素的影响既存在正向效应也存在负向效应,其对应的回归系数具有明显的时空异质性。

关键词: PM_2.5, 影响因素, 地理探测器, 地理加权岭回归, 时空异质性

Abstract:

The analysis of the driving causes of PM_2.5 is of great importance to the prevention and control of air pollution. Using the PM_2.5 ground monitoring data from 2015 to 2018, combined with geographic detectors and geographically weighted ridge regression methods, the key driving factors of PM_2.5 and their spatiotemporal heterogeneity in 282 cities in China were investigated. The results indicate that meteorological parameters and socioeconomic activities can better explain the spatiotemporal heterogeneity of PM_2.5distribution. The R² of the proposed model for each year (2015?2018) are 0.698, 0.724, 0.656, and 0.712 and AICc of the model for the four years' data are 1317.533, 1234.400, 1256.107, 1110.740, respectively. Based on these indicators, the proposed model has better fitting results than the global regression model and the geographically weighted regression model. In addition, the model-fitting results show that temperature, specific humidity, GDP, annual average population, and the number of industrial enterprises are the main driving factors of PM_2.5. These factors have either a positive or a negative effect on PM_2.5and their regression coefficients have obvious spatiotemporal heterogeneity.

Key words: PM_2.5, influence factors, geographical detector, geographically weighted ridge regression, spatiotemporal heterogeneity

中图分类号:

赵锐, 詹梨苹, 周亮, 张军科. 地理探测联合地理加权岭回归的PM_2.5驱动因素分析[J]. 生态环境学报, 2022, 31(2): 307-317.

ZHAO Rui, ZHAN Liping, ZHOU Liang, ZHANG Junke. Identification of Driving Factors of PM_2.5 Based on Geographic Detector Combined with Geographically Weighted Ridge Regression[J]. Ecology and Environment, 2022, 31(2): 307-317.

图/表 8

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影响因素 Influence factors	变量名称 Variables	单位 Unit
气象 Weather	风速	m∙s^-1
	降水率	mm∙h^-1
	气温	℃
	气压	Pa
	比湿度	kg∙kg^-1
社会经济 Social economy	地区生产总值	万元
	年平均人口	万人
	工业企业数	个
	工业烟（粉）尘排放量	t
	公里客运量	万人
	公路货运量	万吨
土地利用类型 Land use types	城市建设用地面积	km²
土地利用类型 Land use types	绿地面积	hm²

影响因素 Influence factors	变量名称 Variables	单位 Unit
气象 Weather	风速	m∙s^-1
	降水率	mm∙h^-1
	气温	℃
	气压	Pa
	比湿度	kg∙kg^-1
社会经济 Social economy	地区生产总值	万元
	年平均人口	万人
	工业企业数	个
	工业烟（粉）尘排放量	t
	公里客运量	万人
	公路货运量	万吨
土地利用类型 Land use types	城市建设用地面积	km²
土地利用类型 Land use types	绿地面积	hm²

变量 Variables	最优分类组合 Best-classified combination	变量 Variables	最优分类组合 Best-classified combination
风速 Wind speed	13(EI)	工业企业数 Number of industrial enterprise	13(GI)
降水率 Precipitation rate	13(Q)	工业烟（粉）尘排放量 Amount of industrial mist and dust discharged	11(GI)
气温 Air temperature	15(EI)	公路客运量 Highway passenger carrying capacity	12(GI)
气压 Atmospheric pressure	11(GI)	公路货运量 Highway freight volume	13(NB)
比湿度 Specific humidity	13(GI)	城市建设用地面积 Area of urban construction land	10(NB)
地区生产总值 Regional GDP	10(GI)	绿地面积 Greenery area	10(Q)
年平均人口 Average annual population	14(Q)	—	—

变量 Variables	最优分类组合 Best-classified combination	变量 Variables	最优分类组合 Best-classified combination
风速 Wind speed	13(EI)	工业企业数 Number of industrial enterprise	13(GI)
降水率 Precipitation rate	13(Q)	工业烟（粉）尘排放量 Amount of industrial mist and dust discharged	11(GI)
气温 Air temperature	15(EI)	公路客运量 Highway passenger carrying capacity	12(GI)
气压 Atmospheric pressure	11(GI)	公路货运量 Highway freight volume	13(NB)
比湿度 Specific humidity	13(GI)	城市建设用地面积 Area of urban construction land	10(NB)
地区生产总值 Regional GDP	10(GI)	绿地面积 Greenery area	10(Q)
年平均人口 Average annual population	14(Q)	—	—

变量名称 Variables	2015		2016		2017		2018
变量名称 Variables	q	P	q	P	q	P	q	P
风速 Wind speed	0.131	0.176	0.113	0.021	0.137	0.000	0.089	0.906
降水率 Precipitation rate	0.078	0.050	0.123	0.093	0.132	0.000	0.139	0.034
气温 Air temperature	0.421	0.000	0.365	0.000	0.287	0.000	0.248	0.000
气压 Atmospheric pressure	0.152	0.000	0.149	0.008	0.165	0.000	0.165	0.000
比湿度 Specific humidity	0.443	0.000	0.440	0.000	0.330	0.000	0.281	0.000
地区生产总值 Regional GDP	0.159	0.000	0.164	0.000	0.175	0.000	0.159	0.000
年平均人口 Average annual population	0.217	0.000	0.225	0.000	0.219	0.000	0.216	0.000
工业企业数Number of industrial enterprise	0.286	0.000	0.257	0.000	0.264	0.000	0.236	0.000
工业烟（粉）尘排放量Amount of industrial mist and dust discharged	0.170	0.240	0.124	0.000	0.070	0.987	0.060	0.134
公路客运量Highway passenger carrying capacity	0.084	0.476	0.115	0.451	0.086	0.593	0.086	0.640
公路货运量 Highway freight volume	0.143	0.054	0.180	0.004	0.178	0.000	0.197	0.000
城市建设用地面积 Area of urban construction land	0.114	0.229	0.134	0.225	0.140	0.046	0.111	0.128
绿地面积 Greenery area	0.133	0.000	0.127	0.113	0.120	0.179	0.090	0.002

地理探测联合地理加权岭回归的PM_2.5驱动因素分析

Identification of Driving Factors of PM_2.5 Based on Geographic Detector Combined with Geographically Weighted Ridge Regression

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年份 Years	变量 Variables	R²			AICc
年份 Years	变量 Variables	OLS	GWR	GWRR	OLS	GWR	GWRR
2015	气温、气压、比湿度、地区生产总值、年平均人口、工业企业数、绿地面积	0.422	0.605	0.698	1498.309	1392.298	1317.533
2016	气温、比湿度、地区生产总值、年平均人口、工业企业数、工业烟（粉）尘排放量	0.405	0.539	0.724	1449.089	1376.570	1234.400
2017	风速、降水率、气温、气压、比湿度、地区生产总值、年平均人口、工业企业数、公路货运量	0.305	0.557	0.656	1451.439	1331.586	1256.107
2018	气温、气压、比湿度、地区生产总值、年平均人口、工业企业数、公路货运量	0.314	0.571	0.712	1354.153	1223.987	1110.740

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地理探测联合地理加权岭回归的PM2.5驱动因素分析

Identification of Driving Factors of PM2.5 Based on Geographic Detector Combined with Geographically Weighted Ridge Regression

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 50

相关文章 15

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Metrics

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地理探测联合地理加权岭回归的PM_2.5驱动因素分析

Identification of Driving Factors of PM_2.5 Based on Geographic Detector Combined with Geographically Weighted Ridge Regression