Interpreting hourly mass concentrations of PM2.5 chemical components with an optimal deep-learning model-Reference-Cited by-同舟云学术

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Interpreting hourly mass concentrations of PM2.5 chemical components with an optimal deep-learning model

Published:2025-05 Issue: Volume:151 Page:125-139
ISSN:1001-0742
Container-title:Journal of Environmental Sciences
language:en
Short-container-title:Journal of Environmental Sciences

Author:

Li Hongyi^ORCID,Yang Ting^ORCID,Du Yiming,Tan Yining,Wang Zifa

Publisher

Elsevier BV

Reference80 articles.

1. A machine learning-based framework for high resolution mapping of PM2.5 in Tehran, Iran, using MAIAC AOD data;Bagheri;Adv. Space Res.,2022

2. Application of the deletion/substitution/addition algorithm to selecting land use regression models for interpolating air pollution measurements in California;Beckerman;Atmospheric Environ.,2013

3. Spatial and temporal variation in PM2.5 chemical composition in the united states for health effects studies;Bell;Environ. Health Persp.,2007

4. Time series analysis and forecasting of air pollution particulate matter (PM2.5): an SARIMA and factor analysis approach;Bhatti;IEEE Access,2021

5. The Role of PM2.5 chemical composition and meteorology during high pollution periods at a suburban background station in Southern Poland;Błaszczak;Aerosol Air Qual. Res.,2020

Cited by 2 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. ConvFormer-KDE: A Long-Term Point–Interval Prediction Framework for PM2.5 Based on Multi-Source Spatial and Temporal Data;Toxics;2024-07-30

2. A Systemic Approach to the Product Life Cycle for the Product Development Process in Agriculture;Sustainability;2024-05-17

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