Enhancing the Evaluation and Interpretability of Data-Driven Air Quality Models-Reference-Cited by-同舟云学术

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Enhancing the Evaluation and Interpretability of Data-Driven Air Quality Models

Published:2021-02 Issue: Volume:246 Page:118125
ISSN:1352-2310
Container-title:Atmospheric Environment
language:en
Short-container-title:Atmospheric Environment

Author:

Gu Jiajun,Yang Bo,Brauer Michael,Zhang K. Max

Funder

New York State Energy Research and Development Authority

Publisher

Elsevier BV

Subject

Atmospheric Science,General Environmental Science

Reference35 articles.

1. Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models;Adams;J. Environ. Manag.,2016

2. Spatial estimation of urban air pollution with the use of artificial neural network models;Alimissis;Atmos. Environ.,2018

3. Spatiotemporal land use random forest model for estimating metropolitan NO2 exposure in Japan;Araki;Sci. Total Environ.,2018

4. Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe–the ESCAPE project;Beelen;Atmos. Environ.,2013

5. Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches;Brokamp;Atmos. Environ.,2017

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4. Do Machine Learning Methods Improve Prediction of Ambient Air Pollutants with High Spatial Contrast? A Systematic Review;Environmental Research;2024-08

5. Understanding the Driving Forces of Summer PM₁ Composition in Seoul, Korea, with Explainable Machine Learning;ACS ES&T Air;2024-07-10

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