Temporal Dynamics and Influencing Factors of Urban PM2.5 Pollution Risk Based on Ecosystem Service Supply and Demand

doi:10.16258/j.cnki.1674-5906.2026.02.009

Ecology and Environmental Sciences ›› 2026, Vol. 35 ›› Issue (2): 256-266.DOI: 10.16258/j.cnki.1674-5906.2026.02.009

• Environmental Science • Previous Articles Next Articles

Temporal Dynamics and Influencing Factors of Urban PM_2.5 Pollution Risk Based on Ecosystem Service Supply and Demand

MU Haofeng¹^,²(), SONG Zhelu²^,³, GAO Zhen²^,³^,⁴, HOU Ying²^,³^,^*(), CHEN Weiping²^,³

1. Henan Institutes of Advanced Technology, Zhengzhou University, Zhengzhou 450003, P. R. China
2. State Key Laboratory of Regional and Urban Ecology/Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China
3. University of Chinese Academy of Sciences, Beijing 100049, P. R. China
4. School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, P. R. China

Received:2025-07-12 Revised:2025-11-26 Accepted:2025-12-24 Online:2026-02-18 Published:2026-02-09
Contact: HOU Ying

基于生态系统服务供需的城市PM_2.5污染风险时间变化特征与影响因素分析

慕浩枫¹^,²(), 宋喆禄²^,³, 高镇²^,³^,⁴, 侯鹰²^,³^,^*(), 陈卫平²^,³

1.郑州大学河南先进技术研究院，河南郑州 450003
2.中国科学院生态环境研究中心/区域与城市生态安全全国重点实验室，北京 100085
3.中国科学院大学，北京 100049
4.上海交通大学环境科学与工程学院，上海 200240

通讯作者: 侯鹰
作者简介:慕浩枫（2001年生），男，硕士研究生，研究方向为城市生态风险评价。E-mail: 2448185300@qq.com
基金资助:
国家自然科学基金项目(42471313)

Abstract

Abstract:

With the accelerated urbanization and industrialization in China, the risk of PM_2.5 pollution in cities has become increasingly prominent. The spatiotemporal pattern characteristics and influencing mechanisms of PM_2.5 pollution risk have become a focus of academic research. Taking the area within the Fifth Ring Road of Beijing (AWFRRB) as a case study, this study constructed a PM_2.5 pollution risk characterization method based on ecosystem service supply and demand, quantitatively assessed the PM_2.5 pollution risk in this area from 2008 to 2021. Moreover, this study analyzed the interannual and intra-annual temporal variation characteristics of PM_2.5 pollution risk using the temporal clustering method based on k-means, and applied the XGBoost machine learning method to analyze the meteorological factors influencing the supply and demand of PM_2.5 removal services and the pollution risk. The results show that the PM_2.5 pollution risk in the AWFRRB exhibited a changing pattern of first increasing and then decreasing from 2008 to 2021. The risk levels within a year in the four evaluated years were generally stable. The period exhibiting an intersection of high-risk and low-risk clusters generally prevailed from autumn through winter to spring, with a consistent period of low-risk cluster and non-risk occurring during the summer. The primary meteorological influencing factors of PM_2.5 removal service supply were specific humidity and wind speed, while the primary influencing factor of service demand was surface shortwave radiation. Precipitation, specific humidity, and surface shortwave radiation were the main influencing factors of PM_2.5pollution risk in 2012 and 2016. Long-wave radiation was the main influencing factor of pollution risk in summer and 2021. Although the influence of wind speed and temperature was relatively weak on a season scale, they had a significant negative correlation with the pollution risk. This study provides a new perspective for understanding the temporal pattern characteristics of urban PM_2.5 pollution risk and offers a new method for quantitatively analyzing the meteorological influencing factors of pollution risk.

Key words: ecosystem service demand and supply, PM_2.5 pollution risk, influencing factors, temporal sequence analysis, XGBoost model

摘要：

随着中国城市化和工业化进程的加快，城市PM_2.5污染风险日益突出，PM_2.5污染风险的时空格局变化特征和影响机制成为学界关注的热点。以北京市五环内区域为例，构建了基于生态系统服务供需的PM_2.5污染风险表征方法，定量评估了2008－2021年该区域的PM_2.5污染风险，使用基于k-means的时间聚类方法分析了PM_2.5污染风险的年际和年内时间变化特征，应用XGBoost机器学习方法解析了影响PM_2.5去除服务供需和污染风险的气象因素。结果表明，北京市五环内区域的PM_2.5污染风险在2008－2021年呈现出先加重后减轻的变化特征；不同年份的高风险和低风险聚类在年内的时间分布基本稳定，高、低风险类交叉的时期大致为每年的春季、秋季和冬季，低风险类和无风险为主的时期大致为夏季。PM_2.5去除服务供给的最主要气象影响因素为比湿度和风速，服务需求的最主要气象影响因素为地表短波辐射。降水、比湿度、地表短波辐射在2012年和2016年均为PM_2.5污染风险的主要影响因素；地表长波辐射在夏季和2021年为污染风险的主要影响因素；风速和温度在季节尺度的影响虽较弱，但与污染风险有显著的负相关性。该研究可为理解城市PM_2.5污染风险时间格局特征提供新视角，并为定量分析污染风险的气象影响因素提供新方法。

关键词: 生态系统服务供需, PM_2.5污染风险, 影响因素, 时间序列分析, XGBoost模型

CLC Number:

X513
X16

MU Haofeng, SONG Zhelu, GAO Zhen, HOU Ying, CHEN Weiping. Temporal Dynamics and Influencing Factors of Urban PM_2.5 Pollution Risk Based on Ecosystem Service Supply and Demand[J]. Ecology and Environmental Sciences, 2026, 35(2): 256-266.

慕浩枫, 宋喆禄, 高镇, 侯鹰, 陈卫平. 基于生态系统服务供需的城市PM_2.5污染风险时间变化特征与影响因素分析[J]. 生态环境学报, 2026, 35(2): 256-266.

Figures/Tables 8

Figure 1 Land use classification of the AWFRRB in 2008, 2012, 2016, and 2021

Figure 2 Changes in the supply of PM2.5 removal service in the AWFRRB in 2008, 2012, 2016, and 2021

Figure 3 Changes in the demand of PM2.5 removal service in the AWFRRB in 2008, 2012, 2016, and 2021

Figure 4 Temporal clustering results of PM2.5 pollution risk in the AWFRRB in 2008, 2012, 2016, and 2021

Table 1 PM2.5 pollution risk in the AWFRRB in different temporal clustering in 2008, 2012, 2016, and 2021

年份	高风险类频率¹⁾	低风险类频率²⁾	高风险类风险总量占比/%	低风险类风险总量占比/%
2008	0.213	0.787	76.93	23.07
2012	0.210	0.790	74.18	25.82
2016	0.161	0.839	82.11	17.89
2021	0.153	0.847	97.79	2.21

Figure 5 Explanatory power of influencing factors for PM2.5 removal service supply in the AWFRRB

Figure 6 Explanatory power of influencing factors for PM2.5 removal service demand in the AWFRRB

Figure 7 Explanatory power of influencing factors for PM2.5 pollution risk in the AWFRRB

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