Role of Traffic Emission on Temporal and Spatial Characteristics of Pollutant Concentration on Urban Road Network: A Case of Beijing

被引:5
|
作者
Wang, Zirui [1 ]
Zhou, Huixin [1 ]
Si, Yang [1 ]
Li, Yahui [2 ]
机构
[1] Beihang Univ, Sch Transportat Sci & Engn, Beijing 100191, Peoples R China
[2] Inst Transportat Dev Strategy & Planning Sichuan, Chengdu 610041, Peoples R China
基金
中国国家自然科学基金;
关键词
AIR-POLLUTION; METEOROLOGICAL CONDITIONS; ULTRAFINE PARTICLES; PARTICULATE MATTER; EXPOSURE; DENSITY; COHORT; PM2.5; MODEL;
D O I
10.1155/2020/8883697
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This research aims to calculate PM2.5 concentration on the road network by considering the network-wide traffic status, which can be used to support research about the impact of urban road network pollution concentration on health. The increase in the use and number of vehicles has brought about a large amount of vehicle exhaust emissions and increased urban air pollutants. -is is also one of the important reasons why this issue is worth studying. In this research, traffic emission was an estimate based on networkwide traffic status which was calculated from vehicle trajectories and spatial variance-covariance matrix. An identification method of external input pollutants is proposed to determine the occurrence of external pollutants imported into the urban area. To calculate the impact of multiple influencing factors on the pollution concentration of the entire road network, a multivariate linear model was adopted to calculate a variety of influencing factors and calibrate the model parameters by collecting real data. The results show that traffic emissions, external input pollution, and wind impact are the main factors affecting the PM2.5 concentration on urban road networks. Combined with real-time traffic data, we can obtain the temporal and spatial characteristics of the pollutant concentration of the road network. For policymakers, our research can provide a method for calculating the PM2.5 concentration on the road network, which is useful for establishing a health assessment framework in the future.
引用
收藏
页数:21
相关论文
共 50 条
  • [41] Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction
    Ge, Liang
    Li, Siyu
    Wang, Yaqian
    Chang, Feng
    Wu, Kunyan
    [J]. APPLIED SCIENCES-BASEL, 2020, 10 (04):
  • [42] Traffic State Prediction for Urban Networks: A Spatial-Temporal Transformer Network Model
    Ji, Xinkai
    Mao, Peipei
    Han, Yu
    [J]. JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS, 2023, 149 (11)
  • [43] Estimation of traffic accidents impact on urban road network considering lane queuing characteristics
    Tang, Jinjun
    Liu, Xinyuan
    Ji, Ke
    Ye, Junqing
    [J]. Journal of Railway Science and Engineering, 2022, 19 (09) : 2541 - 2551
  • [44] INVESTIGATION AND PREDICTION OF TRAFFIC TRAVEL TIME FOR THE ROAD NETWORK OF THESSALONIKI THROUGH SPATIAL TEMPORAL MODEL
    Lakakis, Konstantinos
    Kyriakou, Kalliopi
    Savvaidis, Paraskevas
    [J]. FRESENIUS ENVIRONMENTAL BULLETIN, 2014, 23 (11A): : 2832 - 2839
  • [45] Evaluation of Urban Regional Road Network Topological Characteristics Considering Traffic Operating Parameters
    Wei, Ruicong
    Weng, Jiancheng
    Xu, Jinqiang
    He, Hanmei
    [J]. CICTP 2020: TRANSPORTATION EVOLUTION IMPACTING FUTURE MOBILITY, 2020, : 627 - 637
  • [46] The Impacts of COVID-19 and Policies on Spatial and Temporal Distribution Characteristics of Traffic: Two Examples in Beijing
    Guo, Weiwei
    Feng, Yan
    Luo, Wenxiu
    Ren, Yilong
    Tan, Jiyuan
    Jiang, Xiaobei
    Xue, Qingwan
    [J]. SUSTAINABILITY, 2022, 14 (03)
  • [47] Automatic Traffic Anomaly Detection on the Road Network with Spatial-Temporal Graph Neural Network Representation Learning
    Zhang, Hengyuan
    Zhao, Suyao
    Liu, Ruiheng
    Wang, Wenlong
    Hong, Yixin
    Hu, Runjiu
    [J]. WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2022, 2022
  • [48] Deep spatial-temporal fusion network for fine-grained air pollutant concentration prediction
    Ge, Liang
    Wu, Kunyan
    Chang, Feng
    Zhou, Aoli
    Li, Hang
    Liu, Junling
    [J]. INTELLIGENT DATA ANALYSIS, 2021, 25 (02) : 419 - 438
  • [49] Spatio-Temporal Change Characteristics of Spatial-Interaction Networks: Case Study within the Sixth Ring Road of Beijing, China
    Yang, Jing
    Yi, Disheng
    Qiao, Bowen
    Zhang, Jing
    [J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2019, 8 (06)
  • [50] Simulation of temporal and spatial distribution characteristics of air pollutant concentration in residential areas based on random forest
    Fan, Jin-Yu
    Zheng, Bo-Hong
    Zhang, Bo-Yang
    Chen, Bo
    [J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL TECHNOLOGY AND MANAGEMENT, 2023, 26 (1-2) : 105 - 118