Machine Learning Predicts Emissions of Brake Wear PM<sub>2.5</sub>: Model Construction and Interpretation-Reference-Cited by-同舟云学术

Machine Learning Predicts Emissions of Brake Wear PM_2.5: Model Construction and Interpretation

Published:2022-03-29 Issue:5 Volume:9 Page:352-358
ISSN:2328-8930
Container-title:Environmental Science & Technology Letters
language:en
Short-container-title:Environ. Sci. Technol. Lett.

Author:

Wei Ning¹,Jia Zhenyu¹,Men Zhengyu¹,Ren Chunzhe¹,Zhang Yanjie¹,Peng Jianfei¹,Wu Lin¹,Wang Ting¹,Zhang Qijun¹^ORCID,Mao Hongjun¹

Affiliation:

1. Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China

Funder

Central University Basic Research Fund of China

National Natural Science Foundation of China

Tianjin Science and Technology Plan Project

Publisher

American Chemical Society (ACS)

Subject

Health, Toxicology and Mutagenesis,Pollution,Waste Management and Disposal,Water Science and Technology,Ecology,Environmental Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.estlett.2c00117

Reference60 articles.

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2. Physical and chemical properties of non-exhaust particles generated from wear between pavements and tyres

3. PM10 and PM2.5 emission factors for non-exhaust particles from road vehicles: Dependence upon vehicle mass and implications for battery electric vehicles

4. A model for vehicle-induced non-tailpipe emissions of particles along Swedish roads

5. Estimation of the Contributions of Brake Dust, Tire Wear, and Resuspension to Nonexhaust Traffic Particles Derived from Atmospheric Measurements

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