环境科学
航程(航空)
物种均匀度
线性回归
城市形态
还原(数学)
回归分析
污染
大气科学
自然地理学
统计
地理
数学
城市规划
生态学
材料科学
几何学
地质学
生物
物种丰富度
复合材料
作者
Ming Chen,Jincheng Bai,Shengwei Zhu,Bo Yang,Fei Dai
标识
DOI:10.1016/j.apr.2021.101147
摘要
To improve the atmospheric environment by optimizing urban morphology, this study develops a random forest (RF) model to investigate the influence of urban morphology on PM2.5 variations via the relative importance of urban morphology and the nonlinear response relationship between urban morphology and PM2.5. Two indices—reduction range (C↓) and rate (C˅) of PM2.5 concentrations—are defined to evaluate the temporal variations of PM2.5. Results show that RF models are more accurate and perform better than multiple linear regression models, with R2 ranging from 0.861 to 0.936. Five out of nine urban morphological indicators have the most significant contribution to PM2.5 reduction. For each indicator, the nonlinear response relationship shows similar trends in general, despite of the difference at the higher pollution level. Building evenness index and water body area ratio have a similar response such that C↓ and C˅ sharply increase and tend to be stable when they reach at 0.05 and 8 %, respectively. With the increase in vegetated area ratio, the change of C↓ presents an inverted V-shape trend with the turning point of about 20 %; however, the change of C˅ greatly differs from the pollution level. A higher density of the low-rising buildings with one to three floors will lead to a small reduction rate but a greater reduction range of PM2.5. Floor area ratio values generally show a negative and nonlinear influence on C↓ and C˅. This study provides useful implications for planners and managers for PM2.5 reduction through neighborhood morphology optimization.
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