基于LUR模型的城市带状绿地冬季降减空气PM10和PM2.5效应研究
作者:
作者单位:

华中农业大学园艺林学学院,武汉 430070

作者简介:

孙伊湄,E-mail:2567226928@qq.com

通讯作者:

朱春阳,E-mail:zhuchunyang@mail.hzau.edu.cn

中图分类号:

X513

基金项目:

国家自然科学基金项目(32371950);中央高校自主创新基金项目(2662022YLYJ005);新疆生产建设兵团财政科技计划项目(2023CB008-24)


Effects of urban greenbelt on mitigating PM10 and PM2.5 in winter based on land use regression (LUR) model
Author:
Affiliation:

College of Horticulture & Forestry Sciences,Huazhong Agricultural University,Wuhan 430070,China

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    摘要:

    为探究城市带状绿地降减空气颗粒物的作用机制和效应,选取武汉市罗家港带状公园绿地及其周边建成环境作为研究对象,选择冬季天气晴朗无风且气象条件相似的3天进行重复观测,采用土地利用回归(land use regression,LUR)模型和主成分分析相结合的方法,分析冬季城市带状绿地对空气PM10和PM2.5质量浓度的降减效应,识别其关键影响因素。结果显示,基于LUR模型得出城市带状绿地对空气PM10和PM2.5的降减作用存在宽度效应,宽度30~40 m的绿地在冬季对空气PM10的降减效率最显著。同时,研究发现冬季城市带状绿地内部的空气PM2.5和PM10质量浓度会出现高于邻近道路位置的现象,空气PM10和PM2.5在城市带状绿地内存在明显的积聚效应。结果表明,城市带状绿地对空气PM10和PM2.5的降减作用会受到周边交通污染排放的干扰,不同宽度带状绿地创造的微气象条件也会对空气PM10和PM2.5的质量浓度产生一定影响。

    Abstract:

    The green space of Luojiagang Greenbelt Park in Wuhan and its surrounding built environment were used to study the mechanism and effect of urban greenbelts on reducing particulate matter in the air. Three days with clear and windless weather and similar meteorological conditions in winter were repeatedly observed. A combination of land use regression (LUR) model and principal component analysis (PCA) was used to analyze the effect of urban greenbelt on mitigating the concentration of PM10 and PM2.5 in the air in winter and identify the key influencing factors. The results showed that there was a width effect of urban greenbelt on mitigating the PM10 and PM2.5 in the air in winter based on the LUR model. The green space with a width of 30-40 meters had the most significant mitigation efficiency of PM10 in the air in winter. In winter, the concentration of PM10 and PM2.5 in the air inside urban greenbelt was higher than that in adjacent road locations. There was a significant accumulation effect of PM10 and PM2.5 in the air in urban greenbelt. It is indicated that the effect of urban greenbelt on mitigating the PM10 and PM2.5 in the air was disturbed by emissions of surrounding traffic pollution. The micro-meteorological conditions created by green spaces of different widths can have a certain impact on the concentration of PM10 and PM2.5 in the air.

    图1 研究区域的位置及实测样点Fig.1 Location of the study area and measured sample points
    图2 罗家港带状绿地周边主要干道单个时间段内的平均车流量Fig.2 Average traffic volume of major roads in the vicinity of Luojiagang Greenbelt Park for a single period of time
    图3 LUR模型流程构建图Fig.3 LUR model process construction
    图4 PCA成分一和成分二的方差解释和3类变量的载荷Fig.4 Explanation of variance for the first two components of the PCA and the loadings of the three types of variables
    图5 空气PM10和PM2.5的土地利用回归(LUR)模型Fig.5 Land use regression (LUR) models for PM10 and PM2.5
    图6 不同宽度带状绿地的空气PM2.5、PM10和关键影响变量相关性分析Fig.6 Correlation analysis of PM2.5,PM10 and key impact variables for different widths of greenbelts
    图7 带状绿地降减空气PM2.5(A)和PM10(B)的强度和范围Fig.7 Intensity and extent of PM2.5 (A) and PM10 (B) abatement by greenbelt
    图8 距道路污染源不同距离处绿地内空气PM2.5(A)和PM10(B)质量浓度变化Fig.8 Changes in air PM2.5 (A) and PM10 (B) concentrations in green spaces at different distances from road pollution sources
    图9 不同绿地宽度内的PM比值Fig.9 PM ratios within different green space widths
    图10 空气PM2.5(A)和PM10(B)的日变化规律及差异性分析Fig.10 Analysis of daily variation patterns and variability of PM2.5 (A) and PM10 (B)
    图11 冬季PM2.5(A)和PM10(B)的LUR模型中各自变量的标准化系数和95%置信区间Fig.11 Standardised coefficients and 95% confidence intervals for the respective variables in the LUR models for PM2.5 (A) and PM10 (B) in winter
    表 1 冬季提取与旋转后成分的变量加载Table 1 Winter extraction and variable loading of post-rotation components
    表 2 冬季空气PM2.5和PM10的LUR模型验证结果Table 2 LUR model validation results for PM2.5 and PM10 in winter
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引用本文

孙伊湄,朱春阳.基于LUR模型的城市带状绿地冬季降减空气PM10和PM2.5效应研究[J].华中农业大学学报,2024,43(6):150-160

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  • 收稿日期:2024-04-29
  • 在线发布日期: 2025-01-07
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