城市交通空气污染暴露评价及行人最优路径规划  

Assessment of urban traffic air pollution exposure and optimal path planning

作　　者：李旭 刘济洲 刘吉营 周倩 LI Xu;LIU Jizhou;LIU Jiying;ZHOU Qian(School of Thermal Engineering,Shandong Jianzhu University,Jinan 250101,China)

机构地区：[1]山东建筑大学热能工程学院,山东济南250101

出　　处：《山东建筑大学学报》2025年第2期77-85,共9页Journal of Shandong Jianzhu University

基　　金：山东省自然科学基金项目(ZR2021ME199,ZR2021QD150);山东省高等学校青创人才引育计划创新团队项目(鲁教科函[2021]51号)。

摘　　要：道路空气污染物对行人的健康有重要影响,数值模拟作为可以快速准确模拟室外风环境的方法,能有效地评价街道的空气污染物分布情况。利用数值模拟软件与反向传播(Back Propagation, BP)神经网络,预测道路污染物浓度分布,并将空气污染物浓度转换为道路上的障碍物,使用快速扩展随机树(Rapidly Exploring Random Tree, RRT)算法为行人规划低风险路径。结果表明:汽车排放污染物、背景浓度以及植被对空气污染物浓度有显著影响,汽车污染物排放每增加30.25μg/m^(3),街道间空气污染物浓度就会增加20~30μg/m^(3);结合模拟仿真、BP神经网络和RRT算法,提供了一条行人最优路径规划的合理建议。Road air pollutants have important impacts on people's health.Numerical simulation technology,a method to simulate outdoor wind quickly and accurately,can be used to effectively evaluate the air pollutants distribution in the street.This study utilizes simulation software and Back Propagation(BP)neural network to calculate and predict the distribution of air pollutants concentration on the road,and converts the pollutants concentration into obstacles on the road,planning low-risk paths for pedestrians by using Rapidly Exploring Random Tree(RRT)algorithm.The results reveal that vehicle emission,background concentration and vegetation have significant effects on air pollutant concentration,with the increase of vehicle emission by 30.25μg/m^(3) and the increase of interstreet air pollutant concentration by 20~30μg/m^(3).Combining simulation,BP neural network and RRT algorithm,this paper provides a reasonable suggestion for planning the optimal path for pedestrians.

关 键 词：路径规划 污染物分布 数值模拟 

分 类 号：X51[环境科学与工程—环境工程]