Enhancing urban real-time PM2.5 monitoring in street canyons by machine learning and computer vision technology-Reference-Cited by-同舟云学术

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Enhancing urban real-time PM2.5 monitoring in street canyons by machine learning and computer vision technology

Published:2024-01 Issue: Volume:100 Page:105009
ISSN:2210-6707
Container-title:Sustainable Cities and Society
language:en
Short-container-title:Sustainable Cities and Society

Author:

Fan Zhiguang,Zhao Yuan,Hu Baicheng,Wang Li,Guo Yuxuan,Tang Zhiyuan,Tang Junwen,Ma Jianmin,Gao Hong,Huang Tao,Mao Xiaoxuan

Publisher

Elsevier BV

Subject

Transportation,Renewable Energy, Sustainability and the Environment,Civil and Structural Engineering,Geography, Planning and Development

Reference50 articles.

1. High-resolution air pollution mapping with google street view cars: Exploiting big data;Apte;Environmental Science & Technology,2017

2. Near-road traffic-related air pollution: Resuspended PM2.5 from highways and arterials;Askariyeh;IJERPH,2020

3. Influential parameters on particle exposure of pedestrians in urban microenvironments;Buonanno;Atmospheric Environment,2011

4. XGBoost: A scalable tree boosting system;Chen,2016

5. The nature and frequency of low wind speed conditions;Deaves;Journal of Wind Engineering and Industrial Aerodynamics,1998

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1. Analyzing the impact of planning schemes on the neighborhood-scale PM2.5 distribution through Pix2PixGAN and crowd-source data;Building and Environment;2024-11

2. Characterizing leaf-deposited particles: Single-particle mass spectral analysis and comparison with naturally fallen particles;Environmental Science and Ecotechnology;2024-09

3. Spatially resolved air quality index prediction in megacities with a CNN-Bi-LSTM hybrid framework;Sustainable Cities and Society;2024-08

4. ADVANCEMENTS AND INNOVATIONS IN PM2.5 MONITORING: A COMPREHENSIVE REVIEW OF EMERGING TECHNOLOGIES;FUDMA JOURNAL OF SCIENCES;2024-06-30

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