“十三五”时期黄河流域PM2.5时空分布规律及多尺度社会经济影响机制分析

doi:10.12082/dqxxkx.2022.210534

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (6): 1163-1175.doi: 10.12082/dqxxkx.2022.210534

• 地理空间分析综合应用 • 上一篇下一篇

“十三五”时期黄河流域PM_2.5时空分布规律及多尺度社会经济影响机制分析

耿佳辰¹^,²(), 沈石¹^,², 程昌秀¹^,²^,^*()

1.北京师范大学地表过程与资源生态国家重点实验,北京 100875
2.北京师范大学地理科学学部,北京 100875

收稿日期:2021-09-06 修回日期:2021-10-09 出版日期:2022-06-25 发布日期:2022-08-25
通讯作者: *程昌秀（1973— ）,女,新疆乌鲁木齐人,教授,博导,主要从事地理数据挖掘与分析研究。E-mail: chengcx@bnu.edu.cn
作者简介:耿佳辰（1998— ）,男,北京人,硕士生,主要从事地理数据挖掘与分析研究。E-mail: gengjc@mail.bnu.edu.cn
基金资助:
国家自然科学基金项目(42041007)

Spatio-temporal Evolution and the Multi-scale Socio-economic Influencing Mechanism of PM_2.5 in the Yellow River Basin during the China's 13^th Five-Year Plan

GENG Jiachen¹^,²(), SHEN Shi¹^,², CHENG Changxiu¹^,²^,^*()

1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
2. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Received:2021-09-06 Revised:2021-10-09 Online:2022-06-25 Published:2022-08-25
Contact: CHENG Changxiu
Supported by:
National Natural Science Foundation of China(42041007)

摘要/Abstract

摘要：

黄河流域生态保护与高质量发展是我国新时代的重大战略任务。探究黄河流域PM_2.5浓度的时空变化趋势,并分析其社会经济影响因素,对该地区的大气污染防治和实现高质量发展具有重要意义。本文采用时间序列趋势估计、空间自相关分析、地理探测器和地理加权回归方法,揭示了“十三五”时期年黄河流域不同尺度（全流域以及61个地级市）PM_2.5浓度值和变化趋势的时空分异特征,定量探究了社会经济因素对PM_2.5浓度的影响机制及其时空过程。结果表明：① 在“十三五”时期,黄河流域的PM_2.5污染情况整体好转,其浓度呈波动下降的趋势,平均每年下降约4.2 μg/m³,彰显了我国大气污染防治攻坚战取得的重要成效;② 山东、河南、山西和陕西的PM_2.5浓度值较高,但山东、河南改善速度较快,山西和陕西改善速度较慢;③ 以目前的改善速率推算,在黄河流域61个地级市中,54个市将能够完成“十四五”规划中PM_2.5浓度下降10%的目标,而咸阳、西安、榆林、太原、临汾、运城、晋城7市将难以完成;④ 人口密度、工业企业数、土地利用强度是导致黄河流域PM_2.5浓度升高的主要社会经济因素,其解释力均达到50%以上;⑤ 从2015—2019年,土地利用程度指数和人口密度的主要影响区域均转移至黄河流域中部地区,建议山西、陕西、内蒙古应作为下一阶段治理工作关注的重点区域;⑥ 促进与环境承载力相协调的城乡发展、控制工业规模并提高其环保水平,是进一步治理黄河流域大气污染的重要策略。

关键词: 大气污染, 污染防治, 时间序列估计, 空间自相关, 地理探测器, 地理加权回归, 高质量发展, “十四五”规划

Abstract:

The ecological protection and high-quality development of the Yellow River Basin is a major mission in the new era of China, and the air pollution abatement is among the necessities of the high-quality development. It is of great significance to explore the spatiotemporal variation of PM_2.5 and its socio-economic influencing mechanisms for the air pollution abatement and high-quality development of the Yellow River Basin. By conducting time series estimation, spatial autocorrelation analysis, geodetector, and geographically weighted regression, this paper reveals the spatiotemporal variations and trends of PM_2.5 concentration from 2015 to 2020 in the Yellow River Basin, and quantitatively explores the driving mechanism of socio-economic factors on both watershed scale and city scale. The main findings are as follows: (1) During the study period, the Yellow River Basin has an overall reduction in PM_2.5 pollution. The PM_2.5 concentration decreases with a fluctuating downward trend of 4.2 μg/m³ per year on average; (2) The PM_2.5 concentrations in Shandong and Henan are high but decrease fast, whereas the concentrations in Shanxi and Shaanxi are also high but decrease slowly; (3) Future prediction based on the current trend shows that among the 61 cities in the Yellow River Basin, 54 cities will achieve the goal asserted in the 14^th Five Year Plan of China, which proposes to reduce PM_2.5 concentration by 10% in 2025. However, it would be a struggle for other 7 cities, i.e., Jincheng, Xi'an, Yulin, Taiyuan, Linfen, and Yuncheng, to meet the policy requirement due to their heavier pollution and slower improvements; (4) Population density, industrial enterprises amount, and land use intensity are the main socio-economic factors leading to the increasement of PM_2.5 concentration in the Yellow River Basin, of which the explanatory power reached more than 50%; (5) From 2015 to 2019, the main regions influenced by high land use intensity and population density are transferred to the central part of the Yellow River Basin, which indicates that Shanxi, Shaanxi, and Inner Mongolia should be regarded as the key area to carry out the PM_2.5 control programs; (6) Promoting the urban-rural development coordinated with environmental carrying capacity, as well as controlling the industrial scale and improving the environmental protection level of industrial enterprises are practical strategies to control the PM_2.5 pollution in the Yellow River basin. Findings of this study are expected to provide theoretical reference for relevant decision makers in order to boost the high-quality development of the Yellow River Basin.

Key words: Air pollution, pollution treatment, time series estimation, spatial autocorrelation analysis, geodetector, geographically weighted regression, high-quality development, the 14^th Five Year Plan of China

耿佳辰, 沈石, 程昌秀. “十三五”时期黄河流域PM_2.5时空分布规律及多尺度社会经济影响机制分析[J]. 地球信息科学学报, 2022, 24(6): 1163-1175.DOI:10.12082/dqxxkx.2022.210534

GENG Jiachen, SHEN Shi, CHENG Changxiu. Spatio-temporal Evolution and the Multi-scale Socio-economic Influencing Mechanism of PM_2.5 in the Yellow River Basin during the China's 13^th Five-Year Plan[J]. Journal of Geo-information Science, 2022, 24(6): 1163-1175.DOI:10.12082/dqxxkx.2022.210534

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数据	描述	来源
行政区划	2015年中国地市行政边界矢量数据	https://www.resdc.cn/
PM_2.5浓度值	2015—2020年逐月空气质量数据,基于全国367个城市的站点实测	https://www.aqistudy.cn/
土地利用	2015年和2020年基于Landsat TM影像的1 km分辨率栅格数据	https://www.resdc.cn/
社会经济数据	中国国家统计局,2016—2020年《中国城市年鉴》	http://www.stats.gov.cn/

社会经济因素	变量名	描述
产业结构	GDP	当年价格/亿元
	GDP_P1	第一产GDP占比/%
	GDP_P2	第二产GDP占比/%
	GDP_P3	第三产GDP占比/%
	IndEA	工业企业数量/个
市政交通	UrbRA	城市道路面积/万m²
市政交通	Bus	公共汽电车拥有量/辆
开发强度	LI	土地利用程度指数,LI∈[1, 4]
开发强度	Pop	人口密度/（万人/km²）
能源消耗	Elc	全社会用电量/（万kW/h）

	未利用土地级	林、草、水用地级	农业用地级	城镇聚落用地级
土地利用类型	未利用地或难利用地	林地、草地、水域	耕地、园地、人工草地	城镇、居民点、工矿用地、交通用地
分级指数	1	2	3	4

社会经济因子	2015年		2016年		2017年		2018年		2019年
社会经济因子	q	p	q	p	q	p	q	p	q	p
GDP	0.43	0.0229*	0.41	0.0373*	0.19	0.9520	0.45	0.0088**	0.46	0.0149*
GDP_P1	0.27	0.0071**	0.26	0.0092**	0.07	0.6521	0.19	0.1292	0.15	0.3571
GDP_P2	0.16	0.2463	0.17	0.0858	0.12	0.4193	0.21	0.0424*	0.29	0.0045**
GDP_P3	0.09	0.6530	0.07	0.6956	0.08	0.6823	0.09	0.3958	0.30	0.0028**
IndEA	0.62	0.0001***	0.62	0.0001***	0.56	0.0442*	0.48	0.0861	0.55	0.0154*
UrbRA	0.20	0.9243	0.21	0.8495	0.16	0.9362	0.14	0.9106	0.13	0.9720
Bus	0.17	0.9940	0.22	0.9822	0.25	0.9642	0.16	0.9745	0.19	0.9926
LI	0.50	0.0001***	-	-	-	-	-	-	0.58	0.0001***
Elc	0.11	0.9855	0.11	0.9905	0.19	0.8483	0.10	0.9617	0.25	0.7489
Pop	0.74	0.0001***	0.77	0.0001***	0.77	0.0001***	0.74	0.0001***	0.73	0.0001***

“十三五”时期黄河流域PM_2.5时空分布规律及多尺度社会经济影响机制分析

Spatio-temporal Evolution and the Multi-scale Socio-economic Influencing Mechanism of PM_2.5 in the Yellow River Basin during the China's 13^th Five-Year Plan

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“十三五”时期黄河流域PM2.5时空分布规律及多尺度社会经济影响机制分析

Spatio-temporal Evolution and the Multi-scale Socio-economic Influencing Mechanism of PM2.5 in the Yellow River Basin during the China's 13th Five-Year Plan

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“十三五”时期黄河流域PM_2.5时空分布规律及多尺度社会经济影响机制分析

Spatio-temporal Evolution and the Multi-scale Socio-economic Influencing Mechanism of PM_2.5 in the Yellow River Basin during the China's 13^th Five-Year Plan