Ozone, nitrogen dioxide, and PM2.5 estimation from observation-model machine learning fusion over S. Korea: Influence of observation density, chemical transport model resolution, and geostationary remotely sensed AOD-Reference-Cited by-同舟云学术

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Ozone, nitrogen dioxide, and PM2.5 estimation from observation-model machine learning fusion over S. Korea: Influence of observation density, chemical transport model resolution, and geostationary remotely sensed AOD

Published:2024-08 Issue: Volume:331 Page:120603
ISSN:1352-2310
Container-title:Atmospheric Environment
language:en
Short-container-title:Atmospheric Environment

Author:

Tang Beiming^ORCID,Stanier Charles O.,Carmichael Gregory R.,Gao Meng

Publisher

Elsevier BV

Reference51 articles.

1. Global crop yield reductions due to surface ozone exposure: 1. Year 2000 crop production losses and economic damage;Avnery;Atmos. Environ.,2011

2. Spatio-temporal Modeling of Air Pollutant Concentrations in Germany Using Machine Learning;Balamurugan,2023

3. Random forests;Breiman;Mach. Learn.,2001

4. Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data;Chen;Sci. Total Environ.,2023

5. Spatiotemporal dynamics and exposure analysis of daily PM2.5 using a remote sensing-based machine learning model and multi-time meteorological parameters;Chen;Atmos. Pollut. Res.,2021

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1. The Reconstruction of FY-4A and FY-4B Cloudless Top-of-Atmosphere Radiation and Full-Coverage Particulate Matter Products Reveals the Influence of Meteorological Factors in Pollution Events;Remote Sensing;2024-09-10

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