Spatial-temporal Characteristics of PM2.5 and Its Influencing Factors in the Yellow River Jiziwan Metropolitan Area

doi:10.16258/j.cnki.1674-5906.2023.04.007

Ecology and Environment ›› 2023, Vol. 32 ›› Issue (4): 697-705.DOI: 10.16258/j.cnki.1674-5906.2023.04.007

• Research Articles • Previous Articles Next Articles

Spatial-temporal Characteristics of PM_2.5 and Its Influencing Factors in the Yellow River Jiziwan Metropolitan Area

LI Jianhui(), DANG Zheng, CHEN Lin()

School of Surveying and Mapping Engineering, Yellow River Conservancy Technical Institute, Kaifeng 475004, P. R. China

Received:2023-02-02 Online:2023-04-18 Published:2023-07-12
Contact: CHEN Lin

黄河几字弯都市圈PM_2.5时空特征及影响因素分析

李建辉(), 党争, 陈琳()

黄河水利职业技术学院测绘工程学院，河南开封 475004

通讯作者: 陈琳
作者简介:李建辉（1980年生），男，副教授，硕士，主要从事资源环境与GIS应用研究。E-mail: 280439690@qq.com
基金资助:
河南省科技攻关项目(222102210092)

Abstract

Abstract:

The Jiziwan metropolitan area is an important pole of the development pattern named "one axis, two regions and five poles" in the Yellow River basin. Revealing the spatial-temporal characteristics and driving forces of regional PM_2.5 is of great significance for implementing regional joint prevention and control, and promoting healthy environmental development. Based on the pollutant dataset from 2015 to 2021, the spatial-temporal evolution characteristics of PM_2.5 in the Jiziwan metropolitan area were analyzed using geographical spatial analysis method, and their influencing factors were explored with the help of GeoDetector. The results showed that (1) in terms of time, the annual average concentration of PM_2.5 in the Jiziwan metropolitan area showed an overall downward trend during 2015 and 2021, from 48 μg?m^?3 to 27 μg?m^?3, a 44% decrease; the monthly average concentration showed a “U” shape variation characteristic. The highest value was in January (61 μg?m^?3) and the lowest was in August (25 μg?m^?3); the seasonal average concentration showed that winter (55 μg?m^?3)>Autumn (38 μg?m^?3)>Spring (34 μg?m^?3)>Summer (27 μg?m^?3). (2) In terms of space, from 2015 to 2021, the annual average concentration of PM_2.5 showed an overall spatial distribution pattern of decreasing in high concentration areas, which shrank from 14 to 2 cities; and there were significant differences in the spatial distribution of monthly average concentrations, with the high concentration areas widely distributed in November, December, January and February, and large low concentration areas during April-August in spring and summer. (3) In terms of correlations, from 2015 to 2021, the annual average concentration of PM_2.5 showed significant spatial clustering distribution characteristics, with the hot spot area gradually contracted, with a reduction rate of more than 50%; the cold spot area gradually expanded, with an expansion of 1.6 times; and the scope of good air quality increased significantly. (4) The q-value of social factors showed that the proportion of secondary industry (0.790) > urbanization rate (0.699)>population density (0.590)>GDP (0.566), which had a great influence on PM_2.5 concentrations; for the natural factors, vegetation index (0.199) and average annual precipitation (0.127) had small influences; and the interaction of the factors had a two-factor enhanced and non-linear enhanced synergistic effect, and the proportion of secondary industry interacted with other factors with a force of more than 90%. The results can provide a reference for the formulation of a targeted PM_2.5 management policy in the Yellow River Jiziwan metropolitan area.

Key words: PM_2.5, spatial-temporal characteristics, influencing factors, spatial autocorrelation, GeoDetector

摘要：

黄河几字弯都市圈是黄河流域“一轴两区五极”发展动力格局的重要一极，揭示区域PM_2.5的时空特征和驱动力，对实施区域联防联控和促进环境的健康发展具有重要意义。基于2015－2021年PM_2.5污染物数据，运用地理空间分析方法分析黄河几字弯都市圈PM_2.5的时空演变特征，并借助地理探测器工具探究其时空特征的影响因素。结果表明，（1）在时间上，2015－2021年黄河几字弯都市圈PM_2.5年均质量浓度整体呈下降趋势，由48 μg?m^?3降至27 μg?m^?3，降幅达44%；月均质量浓度呈“U”型变化特征，1月（61 μg?m^?3）最高，8月（25 μg?m^?3）最低；季均质量浓度表现为冬季 (55 μg?m^?3)>秋季 (38 μg?m^?3)>春季 (34 μg?m^?3)>夏季 (27 μg?m^?3)。（2）在空间上，2015－2021年PM_2.5年均浓度整体呈高浓度区减少的空间格局，由14个城市缩减至2个城市；月均浓度空间分布差异显著，秋冬季中11月、12月、1月和2月的高浓度区域分布范围广，春夏季中4－8月的低浓度区域分布范围大。（3）在关联上，2015－2021年PM_2.5年均浓度呈显著的空间集聚分布特征，热点区逐渐收缩，缩减率超过50%，冷点区逐渐扩散，扩大1.6倍，空气质量优良范围增加显著。（4）社会因素的q值大小为第二产业占比 (0.790)>城镇化率 (0.699)>人口密度 (0.590)>地区生产总值 (0.566)，对PM_2.5浓度的影响程度较大，自然因素中植被指数（0.199）和年均降水量（0.127）的影响程度较小；各因子交互作用后具有双因子增强和非线性增强的协同效应，第二产业占比与其他因子交互作用力达到90%以上。研究结果可为黄河几字弯都市圈制定针对性的PM_2.5综合治理政策提供参考。

关键词: PM_2.5, 时空特征, 影响因素, 空间自相关, 地理探测器

CLC Number:

LI Jianhui, DANG Zheng, CHEN Lin. Spatial-temporal Characteristics of PM_2.5 and Its Influencing Factors in the Yellow River Jiziwan Metropolitan Area[J]. Ecology and Environment, 2023, 32(4): 697-705.

李建辉, 党争, 陈琳. 黄河几字弯都市圈PM_2.5时空特征及影响因素分析[J]. 生态环境学报, 2023, 32(4): 697-705.

Figures/Tables 9

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年份	Global Moran’s I	z得分
2015	0.605	11.138
2016	0.665	12.251
2017	0.679	12.467
2018	0.690	12.704
2019	0.646	11.911
2020	0.606	11.165
2021	0.713	13.182

年份	Global Moran’s I	z得分
2015	0.605	11.138
2016	0.665	12.251
2017	0.679	12.467
2018	0.690	12.704
2019	0.646	11.911
2020	0.606	11.165
2021	0.713	13.182

因子	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈
q值	0.461	0.463	0.127	0.199	0.590	0.699	0.566	0.790
P值	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

因子	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈
q值	0.461	0.463	0.127	0.199	0.590	0.699	0.566	0.790
P值	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

因子	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈
X₁	0.461
X₂	0.883*	0.463
X₃	0.890^	0.861^	0.127
X₄	0.773^	0.993^	0.391^	0.199
X₅	0.821*	0.989*	0.878^	0.774*	0.590
X₆	0.954*	0.989*	0.882^	0.822*	0.890*	0.699
X₇	0.936*	0.896*	0.789^	0.859^	0.736*	0.858*	0.566
X₈	0.993*	0.988*	0.977^	0.903*	0.921*	0.900*	0.910*	0.790