Articles | Volume 22, issue 24
https://doi.org/10.5194/acp-22-15685-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-15685-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Dec 2022
Research article |
| 15 Dec 2022
| 15 Dec 2022
Development and application of a multi-scale modeling framework for urban high-resolution NO2 pollution mapping
Zhaofeng Lv
State Key Joint Laboratory of ESPC, School of Environment, Tsinghua
University, Beijing 100084, China
Zhenyu Luo
State Key Joint Laboratory of ESPC, School of Environment, Tsinghua
University, Beijing 100084, China
Fanyuan Deng
State Key Joint Laboratory of ESPC, School of Environment, Tsinghua
University, Beijing 100084, China
Xiaotong Wang
State Key Joint Laboratory of ESPC, School of Environment, Tsinghua
University, Beijing 100084, China
Junchao Zhao
State Key Joint Laboratory of ESPC, School of Environment, Tsinghua
University, Beijing 100084, China
Lucheng Xu
State Key Joint Laboratory of ESPC, School of Environment, Tsinghua
University, Beijing 100084, China
Tingkun He
State Key Joint Laboratory of ESPC, School of Environment, Tsinghua
University, Beijing 100084, China
Yingzhi Zhang
College of Ecology and Environment, Chengdu University of Technology,
Chengdu 610059, China
State Key Joint Laboratory of ESPC, School of Environment, Tsinghua
University, Beijing 100084, China
Kebin He
State Key Joint Laboratory of ESPC, School of Environment, Tsinghua
University, Beijing 100084, China
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Short summary
This study developed a hybrid model, CMAQ-RLINE_URBAN, to predict the urban NO2 concentrations at a high spatial resolution. To estimate the influence of various street canyons on the dispersion of air pollutants, a new parameterization scheme was established based on computational fluid dynamics and machine learning methods. This work created a new method to identify the characteristics of vehicle-related air pollution at both city and street scales simultaneously and accurately.
This study developed a hybrid model, CMAQ-RLINE_URBAN, to predict the urban NO2 concentrations...
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