Modeling the air pollution process using a novel multi-site and multi-scale method with adaptive utilization of spatio-temporal information-Reference-Cited by-同舟云学术

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Modeling the air pollution process using a novel multi-site and multi-scale method with adaptive utilization of spatio-temporal information

Published:2024-02 Issue: Volume:349 Page:140799
ISSN:0045-6535
Container-title:Chemosphere
language:en
Short-container-title:Chemosphere

Author:

Shi Guang,Leung Yee,Zhang Jiangshe,Zhou Yu

Publisher

Elsevier BV

Subject

General Medicine,General Chemistry,Environmental Chemistry,Environmental Engineering,Pollution,Health, Toxicology and Mutagenesis,Public Health, Environmental and Occupational Health

Reference77 articles.

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

2. Neural Machine Translation by Jointly Learning to Align and Translate;Bahdanau,2014

3. Time Series Analysis: Forecasting and Control;Box,2015

4. Ambient air pollution exposure estimation for the global burden of disease 2013;Brauer;Environ. Sci. Technol.,2016

5. Review of the governing equations, computational algorithms, and other components of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system;Byun;Appl. Mech. Rev.,2006

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Air quality forecasting using a spatiotemporal hybrid deep learning model based on VMD–GAT–BiLSTM;Scientific Reports;2024-08-01

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