首页|典型人类活动对关中平原城市群PM2.5浓度的影响

典型人类活动对关中平原城市群PM2.5浓度的影响

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
为应对突发公共卫生事件而采取的流动限制性措施,为研究人类活动对PM2.5 浓度的影响提供了一个独特的自然实验环境,但该期间关中平原城市群 PM2.5 浓度分布及驱动力有何变化尚缺乏关注.基于 2018～2020 年PM2.5 遥感反演数据,采用空间自相关分析、地理探测器和多尺度地理加权回归(MGWR)模型,分析 2020 年 2 月至 3 月实施流动限制性措施期间关中平原城市群PM2.5 浓度及驱动因子的时空演变特征.结果表明:①2020 年 2 月至 3 月 PM2.5 浓度显著下降,2020 年 2 月热点减少,3 月冷点减少.②相比历年同期,所有人为因素单因子在 2020 年 2 月对关中平原城市群PM2.5 浓度的解释力最低,自然因素解释力较高.其中,工厂兴趣点分布(POI_D)及路网分布(RD)解释力相比历年同期平均解释力降幅最大,分别为 20.3%和38.6%.所有人为因素双因子交互影响解释力在 2020 年 2 月最低.③所有人为因素在 2020 年 2 月对关中平原城市群PM2.5 浓度的作用尺度最小,当不同时期人为因素强度处于平均水平时,实施流动限制性措施期间的PM2.5 浓度更易降低,但东部地区的PM2.5 浓度防治强度还需增大.
Impacts of Typical Human Activities on PM2.5 Concentrations in Guanzhong Plain Urban Agglomeration,China
In response to a public health emergency,it provides a unique natural experimental environment to study the effects of human activities on PM2.5 concentrations.However there is a lack of attention on how the distribution and drivers of PM2.5 concentrations in Guanzhong Plain urban agglomeration changed during the implementation of mobility restriction measures in February to March,2020.Based on the remotely-sensed retrieved PM2.5 data from 2018 to 2020,the spatial autocorrelation analysis,geographic detector and multi-scale geographically weighted regression(MGWR)model were used to analyze the spatial and temporal evolution of PM2.5 concentrations and its driving factors in Guanzhong Plain urban agglomeration during the implementation of mobility restriction measures in February to March,2020.The results show that ① PM2.5 concentrations decrease significantly from February to March in 2020,hot spots decrease in February,and cold spots decrease in March.② Compared with the same period in 2018 and 2019,all anthropogenic factors have the lowest explanatory power for PM2.5 concentrations in Guanzhong Plain urban agglomeration in February,2020,and the natural factors have a higher explanatory power;among them,the explanatory power of plant point of interest distribution(POI_D)and road network kernel density(RD)decrease the most compared with the average explanatory power in the same period,which are 20.3%and 38.6%,respectively.The explanatory power of the two-factor interaction effect of all human factors is the lowest in February,2020.③ The scale of effect of all anthropogenic factors on PM2.5 concentrations in Guanzhong Plain urban agglomeration is the smallest in February,2020,and when the intensity of each anthropogenic factor is averaged over different periods,PM2.5 concentrations are more likely to be lowered during the implementation of mobility restriction measures,but the intensity of the prevention and control of PM2.5 concentrations in the eastern part of the region needs to be increased.

driving factorspatio-temporal evolutionPM2.5 concentrationspatial autocorrelationgeographic detectormulti-scale geographically weighted regression modelGuanzhong Plain

李常巘佶、高美玲、李振洪

展开 >

长安大学 地质工程与测绘学院,陕西 西安 710054

长安大学 地学与卫星大数据研究中心,陕西西安 710054

长安大学 自然资源部生态地质与灾害防控重点实验室,陕西 西安 710054

驱动因子 时空演变 PM2.5浓度 空间自相关 地理探测器 多尺度地理加权回归模型 关中平原

陕西省科技创新团队项目陕西省三秦创新团队项目(2022)

2021TD-51

2024

10.19814/j.jese.2023.09031

地球科学与环境学报
长安大学

地球科学与环境学报

CSTPCD北大核心
影响因子：1.422
ISSN：1672-6561
年,卷(期)：2024.46(2)
  • 49