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Article(id=1146098721466450165, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1146500022830469878, articleNumber=null, orderNo=21, doi=10.3981/j.issn.1000-7857.2024.04.00318, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1713628800000, receivedDateStr=2024-04-21, revisedDate=1732809600000, revisedDateStr=2024-11-29, acceptedDate=null, acceptedDateStr=null, onlineDate=1751180828681, onlineDateStr=2025-06-29, pubDate=1743091200000, pubDateStr=2025-03-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1744992000000, onlineIssueDateStr=2025-04-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=1751180828681, onlineFirstDateStr=2025-06-29, sourceXml=null, magXml=null, createTime=1751180828681, creator=18627231156, updateTime=1774079557390, updator=sys-migrate, issue=Issue{id=1146500022830469878, tenantId=1146029695717560320, journalId=1146031591421210625, year='2025', volume='43', issue='6', pageStart='1', pageEnd='100', issueExtLink='null', onlineDate='null', pubDate='1743091200000', pubDateStr='2025-03-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1751276506383, creator='13701087609', updateTime=1774330962150, updator='13041195026', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1243197418926355120, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1146500022830469878, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1243197418926355121, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1146500022830469878, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=57, endPage=64, ext={EN=ArticleExt(id=1146098722821210376, articleId=1146098721466450165, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Study on the relationship between built environment elements and motor vehicle emissions, columnId=1172890533929304542, journalTitle=Science & Technology Review, columnName=Exclusive: Green Building and Urban Governance, runingTitle=null, highlight=null, articleAbstract=

With a developing national economy, the demand for green and low-carbon built environment has also increased significantly. However, the ever-growing number of motor vehicles has made exhaust emissions a major obstacle to urban greening and decarbonization. Taking Beijing for an example, the temporal distribution of motor vehicle exhaust emissions on road segments was estimated by using Computer programme to calculate emissions from road transport（COPERT） model. Based on various elements of built environment, the subjective and objective impact of these elements on motor vehicle emissions were analyzed through the Ordinary Least Squares (OLS) model and the entropy weight method. Finally, the comprehensive integration weighting method was employed to identify the significant factors influencing exhaust emissions. The results showed that the trend of exhaust emissions during weekdays and weekends was similar, both increasing first and then decreasing over time; but the average daily emissions on weekdays were significantly higher than those at weekends. Among the various built environment elements, road segment length was the most significant element affecting vehicle emissions. The study concluded with reasonable suggestions for promoting green and low-carbon built environment.

, authors=null, authorsList=Baozhen YAO, Dongxuan BAI, Rui FENG, Yongjie XUE, Xiaokai ZHANG, authorCompany=null, correspAuthors=Rui FENG, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1146098728730984827, articleId=1146098721466450165, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=建成环境要素与机动车排放影响的关系, columnId=1172890534105465311, journalTitle=科技导报, columnName=特色专题：绿色建筑与城市治理, runingTitle=null, highlight=null, articleAbstract=

随着国民经济不断发展，社会对于绿色低碳建成环境的需求越来越高。但不断增长的机动车保有量，使尾气排放问题成为城市绿色化、低碳化的一大阻碍。以北京市为例，通过道路运输排放计算（computer programme to calculate emissions from road transport，COPERT）模型推算路段中机动车尾气排放量的时间分布变化情况；基于各类建成环境要素，通过普遍最小二乘法（ordinary least squares，OLS）和熵权法分析各类建成环境要素对机动车尾气排放的主、客观影响程度；利用综合集成赋权法，分析尾气排放的显著影响要素。结果显示，工作日与休息日尾气排量变化趋势相似，均随着时间变化呈现先增后减的趋势，但工作日的日均排放量明显高于休息日。在众多建成环境要素中，路段长度为影响机动车排放的最显著要素。

, authors=

姚宝珍, 教授, 研究方向为智慧交通, 电子信箱:

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冯锐(通信作者), 博士后, 研究方向为智慧交通, 电子信箱:
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姚宝珍, 教授, 研究方向为智慧交通, 电子信箱:

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姚宝珍, 教授, 研究方向为智慧交通, 电子信箱:

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北京市机动车分类 COPERT模型中机动车分类
详细类型 对应类型
微型客车 汽油驱动，微型载客汽车 微型客车
小型客车出租车 汽油/柴油驱动，小型载客汽车 小型客车
中型客车公交车 城市公共汽车质量≤15 t 中大型货车
大型客车 城市公共汽车质量为15~18 t
低速货车 柴油驱动，轻型货运车辆N1-I 轻型货车
轻型货车 柴油/汽油驱动，轻型货运车辆N1-Ⅱ
中型货车 柴油/汽油驱动，重型货车质量为3.5~12 t 中大型货车
重型货车 柴油/汽油驱动，重型货车质量>12 t
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机动车类型对照表

, figureFileSmall=null, figureFileBig=null, tableContent=
北京市机动车分类 COPERT模型中机动车分类
详细类型 对应类型
微型客车 汽油驱动，微型载客汽车 微型客车
小型客车出租车 汽油/柴油驱动，小型载客汽车 小型客车
中型客车公交车 城市公共汽车质量≤15 t 中大型货车
大型客车 城市公共汽车质量为15~18 t
低速货车 柴油驱动，轻型货运车辆N1-I 轻型货车
轻型货车 柴油/汽油驱动，轻型货运车辆N1-Ⅱ
中型货车 柴油/汽油驱动，重型货车质量为3.5~12 t 中大型货车
重型货车 柴油/汽油驱动，重型货车质量>12 t
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月份 平均低温/℃ 平均高温/℃ 极端低温/℃ 极端高温/℃ 湿度/%
10 7 20 3 24 63
11 1 11 −5 20 56
12 −6 3 −13 8 47
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北京市月平均气象参数

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月份 平均低温/℃ 平均高温/℃ 极端低温/℃ 极端高温/℃ 湿度/%
10 7 20 3 24 63
11 1 11 −5 20 56
12 −6 3 −13 8 47
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排放类型 βj1 βj2 βj3 βj4 βj5 βj6 βj7
CO 4.43e-04 −0.112 6.353 9.991 0.002 −0.424 19.877
PM 5.58e-07 −1.22e-04 0.006 0.065 3.15e-04 −0.078 5.981
VOC 3.53e-06 6.82-04 0.043 0.166 7.02e-05 −0.044 6.091
NOx 2.99e-04 0.993 0.205 −0.033 0.037 2.102 1.304
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COPERT模型排放因子

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排放类型 βj1 βj2 βj3 βj4 βj5 βj6 βj7
CO 4.43e-04 −0.112 6.353 9.991 0.002 −0.424 19.877
PM 5.58e-07 −1.22e-04 0.006 0.065 3.15e-04 −0.078 5.981
VOC 3.53e-06 6.82-04 0.043 0.166 7.02e-05 −0.044 6.091
NOx 2.99e-04 0.993 0.205 −0.033 0.037 2.102 1.304
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变量 回归系数 标准差 t－值 VIF
常数项 －26.88 89.77 －1.002 /
商业密度 51.11 43.62 1.121* 3.437
娱乐密度 21.54 30.31 0.221 2.357
住宅密度 －16.09 65.82 －0.244* 4.439
教育密度 166.38 75.38 2.242** 1.402
医疗密度 －49.36 67.23 －0.767 3.433
土地利用度 145.27 120.06 0.958** 3.927
路段长度 697.71 80.02 9.016*** 1.247
交叉口数目 11.99 34.43 0.778* 1.264
公交站数目、地铁站距离 48.87 57.83 1.249* 1.102
目的地可达性 －50.82 62.93 1.208* 3.249
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OLS回归模型拟合结果

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变量 回归系数 标准差 t－值 VIF
常数项 －26.88 89.77 －1.002 /
商业密度 51.11 43.62 1.121* 3.437
娱乐密度 21.54 30.31 0.221 2.357
住宅密度 －16.09 65.82 －0.244* 4.439
教育密度 166.38 75.38 2.242** 1.402
医疗密度 －49.36 67.23 －0.767 3.433
土地利用度 145.27 120.06 0.958** 3.927
路段长度 697.71 80.02 9.016*** 1.247
交叉口数目 11.99 34.43 0.778* 1.264
公交站数目、地铁站距离 48.87 57.83 1.249* 1.102
目的地可达性 －50.82 62.93 1.208* 3.249
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要素类型 OLS权重 摘权法权重 综合权重
商业密度 0.042 0.078 0.057
娱乐密度 0.022 0.116 0.062
住宅密度 0.021 0.070 0.042
教育密度 0.121 0.203 0.156
医疗密度 0.045 0.068 0.055
土地利用度 0.101 0.051 0.079
路段长度 0.482 0.108 0.321
交叉口数目 0.017 0.071 0.040
公交站数目、地铁站距离 0.097 0.133 0.112
目的地可达性 0.052 0.105 0.075
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各类建成环境要素的权重

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要素类型 OLS权重 摘权法权重 综合权重
商业密度 0.042 0.078 0.057
娱乐密度 0.022 0.116 0.062
住宅密度 0.021 0.070 0.042
教育密度 0.121 0.203 0.156
医疗密度 0.045 0.068 0.055
土地利用度 0.101 0.051 0.079
路段长度 0.482 0.108 0.321
交叉口数目 0.017 0.071 0.040
公交站数目、地铁站距离 0.097 0.133 0.112
目的地可达性 0.052 0.105 0.075
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建成环境要素与机动车排放影响的关系
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姚宝珍 1 , 白东轩 1 , 冯锐 2, * , 薛勇杰 3 , 张宵恺 1
科技导报 | 特色专题：绿色建筑与城市治理 2025,43(6): 57-64
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科技导报 |特色专题：绿色建筑与城市治理 2025 , 43 (6) : 57 -64
建成环境要素与机动车排放影响的关系
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姚宝珍1 , 白东轩1, 冯锐2, * , 薛勇杰3, 张宵恺1
作者信息
  • 1. 大连理工大学机械工程学院, 大连 116024
  • 2. 大连理工大学建设工程学院, 大连 116024
  • 3. 北京航空航天大学交通科学与工程学院, 北京 100191
通讯作者:
冯锐(通信作者), 博士后, 研究方向为智慧交通, 电子信箱:
Study on the relationship between built environment elements and motor vehicle emissions
Baozhen YAO1 , Dongxuan BAI1, Rui FENG2, * , Yongjie XUE3, Xiaokai ZHANG1
Affiliations
  • 1. School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
  • 2. School of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China
  • 3. School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
出版时间: 2025-03-28 doi: 10.3981/j.issn.1000-7857.2024.04.00318
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摘要
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随着国民经济不断发展，社会对于绿色低碳建成环境的需求越来越高。但不断增长的机动车保有量，使尾气排放问题成为城市绿色化、低碳化的一大阻碍。以北京市为例，通过道路运输排放计算（computer programme to calculate emissions from road transport，COPERT）模型推算路段中机动车尾气排放量的时间分布变化情况；基于各类建成环境要素，通过普遍最小二乘法（ordinary least squares，OLS）和熵权法分析各类建成环境要素对机动车尾气排放的主、客观影响程度；利用综合集成赋权法，分析尾气排放的显著影响要素。结果显示，工作日与休息日尾气排量变化趋势相似，均随着时间变化呈现先增后减的趋势，但工作日的日均排放量明显高于休息日。在众多建成环境要素中，路段长度为影响机动车排放的最显著要素。

绿色低碳  /  建成环境  /  尾气排放  /  综合集成赋权法  /  排放影响因素

With a developing national economy, the demand for green and low-carbon built environment has also increased significantly. However, the ever-growing number of motor vehicles has made exhaust emissions a major obstacle to urban greening and decarbonization. Taking Beijing for an example, the temporal distribution of motor vehicle exhaust emissions on road segments was estimated by using Computer programme to calculate emissions from road transport（COPERT） model. Based on various elements of built environment, the subjective and objective impact of these elements on motor vehicle emissions were analyzed through the Ordinary Least Squares (OLS) model and the entropy weight method. Finally, the comprehensive integration weighting method was employed to identify the significant factors influencing exhaust emissions. The results showed that the trend of exhaust emissions during weekdays and weekends was similar, both increasing first and then decreasing over time; but the average daily emissions on weekdays were significantly higher than those at weekends. Among the various built environment elements, road segment length was the most significant element affecting vehicle emissions. The study concluded with reasonable suggestions for promoting green and low-carbon built environment.

green and low-carbon  /  built environment  /  exhaust gas emission  /  comprehensive ensemble weighting method  /  emission influencing factors
姚宝珍, 白东轩, 冯锐, 薛勇杰, 张宵恺. 建成环境要素与机动车排放影响的关系. 科技导报, 2025 , 43 (6) : 57 -64 . DOI: 10.3981/j.issn.1000-7857.2024.04.00318
Baozhen YAO, Dongxuan BAI, Rui FENG, Yongjie XUE, Xiaokai ZHANG. Study on the relationship between built environment elements and motor vehicle emissions[J]. Science & Technology Review, 2025 , 43 (6) : 57 -64 . DOI: 10.3981/j.issn.1000-7857.2024.04.00318
正文
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自2021年国务院印发《2030年前碳达峰行动方案》以来，构建绿色低碳友好的建成环境已成为各个地区发展的主旋律[1−3]。但随着经济的快速发展，人民物质生活水平不断提高，持续增高的机动车保有量及其带来的污染物排放问题对绿色低碳环境的建设造成了不小的挑战。据统计，2022年，全国机动车4项污染物排放总量为1466.2万t。其中，一氧化碳（CO）、碳氢化合物（HC）、氮氧化物（NOx）、颗粒物（PM）排放量分别为743.0万t、191.2万t、526.7万t、5.3万t。汽车是污染物排放总量的主要贡献者，其排放的CO、HC、NOx和PM占比超过90%[4]。
近年来，新能源汽车的兴起和城市公共交通设施的完善一定程度上缓解了燃油机动车的污染问题。但新能源汽车尚未完全普及，燃油机动车依然是人们出行和公共交通的主要方式之一。将交通出行模式从高污染、高耗能向低耗、低碳、绿色方向的转型工作已迫在眉睫。
城市建成环境因素丰富，机动车在不同的城市环境会有不同的排放特性。因此明确城市建成环境与机动车排放的关联因素是绿色低碳转型工作的重点之一。已经有研究人员针对建成环境与交通出行的关系做了研究[5−7]。也有研究人员分析了建成环境对交通排放的影响，大多数研究都着手于交通的碳排放或PM污染物排放[8−10]。Zhi等[11]基于机器学习建立了建成环境的异质元素与二氧化碳排放的时空相关性分析模型。Song等[12]从建成区的规模、紧凑度和隔离度的角度，研究了相邻建成环境下跨域交通的二氧化碳排放关系。Wu等[13]建立了一种结合高斯过程和随机效应模型的树增强算法来预测建成环境中的非线性关联的交通排放，旨在通过动态调整城市环境政策来降低碳排放。Zhao等[14]通过一种单侧加权回归模型估计了建成环境对城市出行排放的时空异质影响并有效提高了测算精度。Feng等[15]以霍林郭勒为研究区域，通过建立地理加权回归模型分析多源融合数据，阐述了交通排放在建成环境中具有空间聚集性的特点，并分析了建成环境对机动车排放的空间异质性影响。张嵌玮等[16]通过构建市郊多因素建成环境与交通排放的影响关系模型，确定并量化了市郊居民交通行为与交通碳排放的相关程度。但大部分建成环境与交通排放的相关性研究针对城市的碳排放问题，少有针对机动车排放的污染物问题进行研究。
基于此背景，本文以北京市为研究区域，对城市建成环境要素与机动车污染物排放之间的关联性进行研究。通过道路运输排放计算（COPERT）模型测算路网中隔断机动车尾气排放量，并基于测算结果，通过普遍最小二乘法（OLS）模型建立全局回归模型，分析建成环境因素对于机动车排放的主观影响程度，通过熵权法分析客观影响程度，最后通过综合集成赋权法获得建成环境要素对于机动车排放的影响情况，并根据研究成果从低碳绿色的角度为城市发展规划提出建议。
1 考虑路段交通量的机动车排放测算
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本研究基础数据为北京市2020年11月中某周的出租车GPS轨迹数据，没有囊括所有类型的机动车。因此需要通过出租车数据来对路网中机动车出行交通量进行估算。以Aslam等[17]提出的城市路网交通量预测模型为基础，代入北京市出租车GPS数据与机动车组成比例，对路段交通量进行推算。交通量推算公式为
$V_{i, T}^{\text {car }}=V_{i, T}^{\text {taxi }}+r_{\text {car:taxi }} \times r_{T \text { :day }} \times \sum\limits_T V_{i, T}^{\text {taxi }}$
式中，Vi, Tcar表示T时间段内路段i的机动车总量，Vi, Ttaxi表示T时间段内路段i的出租车总量，rcar: taxi表示路段中非出租车类机动车与出租车的流量比例，rT: day表示T时间段内路段交通量站全天中路段交通量的比例。
考虑到中国排放标准与欧洲类似，因此由欧洲环境署研究开发的COPERT模型在中国也具有良好的适用性，被广泛用于中国城市的尾气排放测算研究中[18−20]。本研究选取COPERT模型作为测算各路段机动车尾气排放情况的模型。
本文将机动车尾气排放分为冷启动排放和热排放2类。则机动车尾气总排放可具体表现为
$E E^{\text {total }}=E E^{\text {cold }}+E E^{\text {hot }}$
式中，EEtotal指机动车尾气总排放量，单位g；EEcold表示机动车冷启动尾气排放量，单位g；EEhot表示机动车热启动排放量，单位g。
COPERT模型设定了私人载客车辆、轻型商用车以及包括货车、公交车、摩托车等不同机动车类型的排放因子，本文基于中国机动车分类标准与其他学者成果，将北京市道路常见机动车类型与COPERT模型对车辆类型的设定进行对照，如表 1所示。本研究将COPERT模型用于计算机动车在单位道路长度内行驶过程中排放的污染物总量，单位为g/km。基于排放因子，机动车的冷启动排放计算方式为
$E E_{j, k}^{\text {cold }}=a_{j, k} \times M_k \times S T_k \times E f_{j, k}^{\text {hot }} \times\left(\frac{E E_{j, k}^{\text {cold }}}{E E_{j, k}^{\text {hot }}}-1\right)$
式中，EEj, kcold表示类型为k的机动车尾气排放物j的冷启动排放量，单位g；Mk表示k类机动车的数量；STk表示k类机动车的单车行驶总里程，单位km；Efj, khot表示k类机动车排放污染物j的排放因子；aj, k表示k类机动车产生j类排放物时，冷发动机工况下行驶里程的比例，主要取决于机动车行驶时的环境温度Te和机动车平均里程Ltrip，具体计算方式为
$\begin{aligned}a_{j, k}= & 0.6474-0.02545 \times L_{\text {trip }}- \\& \left(0.00974-0.000385 \times L_{\text {trip }}\right) \times T_{\text {e }}\end{aligned}$
机动车的热排放因子影响因素主要是机动车行驶速度，本研究以平均速度为输入特征，可以获得热排放因子估算方法
$E f_{i, j, k}^{\mathrm{hot}}=\frac{\beta_j^1 v_{i, k}^2+\beta_j^2 v_{i, k}+\beta_j^3+\frac{\beta_j^4}{v_{i, k}}}{\beta_j^5 v_{i, k}^2+\beta_j^6 v_{i, k}+\beta_j^7}$
式中，Ef hoti,j,k表示k类机动车在路段i中的j类排放物的热排放因子，单位g/km；vi,k表示k类机动车在路段i中的行驶速度，单位km/h；β1j ~ β7j是j类排放物的热排放参数。由此可以推算机动车在某路段的热排放量为
$E E_{i, j, k}^{\mathrm{hot}}=M_k \times E f_{i, j, k}^{\mathrm{hot}} \times S T_{i, k}$
式中，EE hoti,j,k为k类机动车在路段i中产生的j类排放物的热排放量，单位g；STi,k表示k类机动车在路段i中的行驶里程，单位km/veh。
基于上述公式，可获取路段中机动车各类排放物的排放量分布。
2 建成环境要素对机动车排放影响分析
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2.1 建成环境要素选取
评价建成环境具有多种维度，最早由Cervero和Kockelman[21]提出了基于密度（density）、多样性（diversity）和设计（design）的“三维”评价体系。2010年Ewing等[22]在其基础上，增加了到公共交通的距离（distance to transit）和目的地可达性（destination accessibility）2个维度，形成了更全面完善的“五维”评价体系。本研究考虑北京市复杂多样的城市环境和数据资源的丰富程度，从上述“五维”角度入手，分析影响机动车尾气排放的建成环境要素。
1）密度。本研究将研究路段的周边500 m范围作为路段缓冲区域，区域内各类信息点（points of interest，POI）设施密度作为环境要素，POI类型主要包括商业、娱乐、住宅、教育、医疗共5类。密度的计算式为
$\rho=\frac{M P_k}{S_{area }}$
式中，ρ表示密度，MPk表示各类POI的值，Sarea表示路段缓冲区域面积。
2）多样性。建成环境的多样性一般用土地利用混合度来表征，取值范围大多在[0, 1]，越靠近1则说明该建成环境内土地用类越丰富。本研究使用POI数据来计算土地混合利用度，计算公式为
$M U_{\mathrm{land}}=-\sum\limits_{k=1}^n \frac{p_k \times \operatorname{In} p_k}{\operatorname{In} n}$
式中，MUland指土地利用混合度；pk表示为各类POI在所有POI中的占比；n表示POI类型总数。
3）设计。本研究选取现有路网数据作为设计要素，包括路段长度和路段两侧交叉口数量2个变量。
4）公共交通距离。考虑数据获取便利性，本研究将路段中公交站数量与路段到达最近地铁站的距离作为公共交通距离要素。
5）目的地可达性。目的地可达性一般通过建立空间阻隔模型来评价，评价越高则可达性越差。本研究沿用这一研究习惯，以空间阻隔模型的评价结果作为目的地可达性要素。空间阻隔模型计算方式为
$P_{\text {access }, i}=\frac{1}{N_{\mathrm{OD}}} \sum\limits_{j=1, j \neq i}^N d_{i j}$
式中，Paccess, i表示起始点i的目的地可达性；NOD表示起点为i时的起点−终点对（origin-destination pair，OD对）总数；j表示不同于起始点i的目标点；N表示可选目标点总数；dij表示起点到终点间的空间阻隔。
2.2 环境要素影响模型
本研究利用OLS模型来评估建成环境要素与机动车排放之间的主观关系，并寻找影响机动车排放最显著的变量要素。OLS模型具体表达形式为
$V_{\text {emission }}=\sum\limits_{i=1}^{N_{E F}} \varphi_i E F_i+\mu_0+\varepsilon$
式中，Vemission是机动车尾气排放；EFi表示建成环境要素i；φi表示环境要素i的回归系数；μ0表示回归常数项；ε表示要素无法被模型解释时产生的随机误差；NEF表示建成环境要素的数量。
在构建拟合模型之前，需要先对建成环境要素进行多重共线性检验，防止存在高相关性的环境要素造成拟合结果不准确。本研究采用方差膨胀因子（variance inflation factor，VIF）来检验环境要素的多重共线性，具体计算方式为
$V I F_i=\frac{1}{1-R_i^2}$
式中，VIFi表示计算结果，其数值越大，表明自变量之间的线性关系越强；R2i表示某个自变量与其他所有自变量进行线性回归得到的决定系数。
3 基于综合集成赋权法的综合影响研究
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3.1 基于熵权法的环境影响评估
熵权法是一种客观赋权的方法，本研究通过量化建成环境要素的信息，来确定各类要素指标的权重。熵权法需要利用信息熵的原理，本研究首先通过Max-Min方法对建成环境要素数据进行归一化处理，将其转换为无量纲的相对值。然后根据归一化数据，计算对应要素信息熵，具体为
$e_j=-\frac{\sum\limits_{i=1}^m p e_{i j} \operatorname{In} p e_{i j}}{\operatorname{In} m}$
$P e_{i j}=\frac{E e_{i j}}{\sum\limits_{i=1} E e_{i j}}$
式中，ej为建成环境要素j的熵值；peij表示路段i中要素j所占的比重；Eeij表示归一化后路段i的环境要素值。获得各要素信息熵后，建成环境要素的熵权计算公式为
$\omega_j^e=\frac{1-e_j}{\sum\limits_{j=1}^{N_{E F}}\left(1-e_j\right)}$
式中，ωej表示建成要素j的熵权。
3.2 基于综合集成赋权法的综合影响评估模型
通过前文OLS模型和熵权法2种模型得到建成环境要素对路段机动车尾气排放影响程度的不同评价，在此基础上，本研究通过综合集成赋权法来构建建成环境要素对尾气排放综合影响的评估模型，具体表示为
$\omega_j=\eta_1 \omega_j^{\mathrm{o}}+\eta_2 \omega_j^e$
式中，ωOj表示由OLS模型获得的建成环境要素权重，且OLS权重和熵权重满足累加和为1；η1和η2为待定系数。
由此构建不同路段建成环境要素综合影响模型，可表示为
$Q_i=\sum\limits_{j=1}^{N_{E F}} E F_{i j} \omega_j$
式中，Qi表示路段i的建成环境对机动车排放的综合影响值。该值越大，意味着影响程度越高，各要素评价区分度越大。
因此，需要使综合评价值越高越好，则可构建基于待定系数η1和η2的最优化模型为
$\begin{aligned}& \max Q=\left(Q_1, Q_2, \ldots, Q_m\right) \\& \text { s.t. } \eta_1+\eta_2=1 \\& \eta_1, \eta_2 \geqslant 0\end{aligned}$
通过求解优化模型获得最佳待定系数η1和η2后，可构建机动车排放的综合评估模型
$C A_i=Q_i+E E_i^{\text {total }}$
式中，CAi表示路段i的机动车排放综合评估值，包括路段i的建成环境要素综合影响值和路段i的尾气排放总量。
4 实验结果与分析
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4.1 机动车测算排放结果
本研究将北京2020年11月内某周的出租车GPS轨迹数据作为基础数据进行机动车尾气排放量测算。测算过程中本研究基于所得数据将机动车平均行驶里程设为15.3 km，并用月平均温度作为驾驶环境温度。经中国气象网查询，北京市2020年10~12月的气象参数如表 2所示。基于上述参数，本研究通过COPERT模型计算获得的机动车各类尾气排放因子如表 3所示。
基于上述参数，通过COPERT模型可以计算出各路段的尾气排放，并利用COPERT模型分析机动车尾气排放的时间分布特点。通过模型获得机动车日平均污染物排放情况，如图 1所示，尾气主要污染物如CO、PM、VOC和NOx的排放变化趋势相同，都呈现出夜晚少、白天多的特点，具有明显的波峰、波谷特征，且都在午后15:00左右达到峰值，凌晨4:00左右达到低谷。但各类排放物量度差别较大，具体来说，CO的排放占据尾气总排放的绝大部分，其次是NOx、VOC，PM类污染物的排放是最少的。
本研究以污染物排放量占比最高的CO为例，进一步分析机动车尾气排放在工作日与休息日的不同特征。测算结果如图 2所示，工作日和休息日的尾气排放变化趋势大体一致，符合前述特征。比较明显的不同点在于从6:00开始，工作日的尾气排放开始有明显大幅上升，且随着时间推演波动较大；休息日整体上升幅度较小，变化波动也较为平缓。整体特征符合城市居民日常出行情况。
4.2 建成环境要素对机动车尾气排放的影响
本研究以路段缓冲区内各类POI数据为基础，结合COPERT模型测算所得路段机动车排放数据，根据OLS模型进行建成环境要素与机动车排放的线性拟合关系，获取建成环境主观影响要素。模型拟合结果与各因素VIF值如表 4所示。
由拟合结果能够看出，在本研究设立的环境要素中，商业密度、教育密度、土地利用度、路段长度、公交站点数目与地铁站距离和目的地可达性等变量对尾气排放的影响是显著的，而其他变量如娱乐密度、住宅密度、医疗密度、交叉口数目等对尾气排放影响程度较小。在显著影响的建成环境变量中，商业密度、娱乐密度、教育密度、土地利用、路段长度、交叉口数目、公交站点数目和地铁站距离等变量要素与尾气排放呈正相关，其中又以路段长度影响最甚；而住宅密度、医疗密度、目的地可达性等变量则与尾气排放的变化呈负相关，其中以目的地可达性的负相关性最高。按照建成环境变量对尾气排放的影响程度排序，发现各建成环境的影响程度依次为：路段长度 > 教育密度 > 土地利用混合度 > 目的地可达性 > 医疗密度 > 商业密度 > 公交站数目、地铁站距离 > 娱乐密度 > 住宅密度 > 交叉口数目。
4.3 机动车尾气排放的综合影响
基于上文，本研究采用熵权法获取建成环境各要素的信息熵并确定其对尾气排放的客观权重，并利用综合集成赋权法评价建成环境对尾气排放的综合影响程度。计算结果如表 5所示，其中求解优化问题式（17）后，系数η1和η2分别为0.57和0.43。
从表 5中熵权法分析结果看，教育密度对机动车尾气排放影响最大，影响权重为0.203，土地混合利用度的影响最小，权重为0.051。按照分析结果将影响程度从大到小排列为：教育密度 > 娱乐密度 > 公交站数目、地铁站距离 > 路段长度 > 目的地可达性 > 商业密度 > 交叉口数目 > 住宅密度 > 医疗密度 > 土地利用混合度。
能够看出，OLS模型与熵权法对于各类环境要素的影响程度评价并不一致，2种方法下影响权重最大和最小的环境要素均存在差异。为了获得更科学合理的影响程度评价，通过综合集成赋权法来对OLS模型和熵权法所得结果进行综合。
由表 5第4列可以看出，在综合法的分析结果下，对机动车尾气排放的影响最显著的建成环境要素为路段长度，综合权重达到0.321，交叉口影响程度最弱，权重为0.040。按照综合权重从高到低的排序分别为：路段长度 > 教育密度 > 公交站数目、地铁站距离 > 土地利用混合度 > 目的地可达性 > 娱乐密度 > 商业密度 > 医疗密度 > 住宅密度 > 交叉口数目。
5 绿色低碳建成环境建设建议
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在明确城市中机动车尾气排放与建成环境要素的影响关系后，本文基于研究成果，为构建绿色低碳建成环境提出3个方面的建议。（1）实行道路规划动态管理，根据居民出行时间与出行目的，及时对道路规划进行调整和优化，合理拓展道路网络，补全交通设施，优化交叉口配时方案。（2）合理规划城市土地利用类型，降低利用混合度，减少不同功能区的相互干扰，分散人群聚集型场所，根据学校、商场等场所的覆盖范围合理规划居民住宅区。（3）持续推进和完善公共交通事业发展，如增加公交线路、优化公交网络、提升公交服务质量等。加强公共交通基础设施建设，鼓励更多居民选择公共交通工具出行。
6 结论
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本研究以北京市出租车GPS数据为基础，通过COPERT模型测算路段工作日和休息日的机动车污染物排放情况；选取路段缓冲区域内POI数据和其他建成环境要素为变量，通过OLS模型获悉建成环境要素与机动车排放之间的主观影响关系，通过熵权法明确客观影响关系，并通过综合集成赋权法获得更为科学合理的建成环境要素与机动车排放的影响关系，明确路段长度为机动车排放影响最显著的环境要素，交叉口数目为影响最微弱的环境要素。最后根据研究成果，为建立健全城市绿色低碳建成环境提出了合理建议。
基金
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  • 国家自然科学基金面上项目(52372313)
  • 中国博士后科学基金面上项目(2024M750324)
  • 中国博士后创新人才支持计划(BX20240050)
文献
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doi: 10.3981/j.issn.1000-7857.2024.04.00318
  • 接收时间：2024-04-21
  • 首发时间：2025-06-29
  • 出版时间：2025-03-28
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  • 收稿日期：2024-04-21
  • 修回日期：2024-11-29
基金
国家自然科学基金面上项目(52372313)
中国博士后科学基金面上项目(2024M750324)
中国博士后创新人才支持计划(BX20240050)
作者信息
    1. 大连理工大学机械工程学院, 大连 116024
    2. 大连理工大学建设工程学院, 大连 116024
    3. 北京航空航天大学交通科学与工程学院, 北京 100191

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冯锐(通信作者), 博士后, 研究方向为智慧交通, 电子信箱:
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
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