Unveiling Spatiotemporal Differences and Responsive Mechanisms of Seamless Hourly Ozone in China Using Machine Learning

Unveiling Spatiotemporal Differences and Responsive Mechanisms of Seamless Hourly Ozone in China Using Machine Learning

Surface ozone (O<sub>3</sub>) is a multifaceted threat that not only deteriorates the environment but also poses risks to human health. Here, we estimated the seamless hourly surface O<sub>3</sub> in China using Extreme Gradient Boosting (XGBoost) with multisource data fusion...

Full description

Saved in:
Bibliographic Details
Main Authors: Jiachen Fan, Tijian Wang, Qingeng Wang, Mengmeng Li, Min Xie, Shu Li, Bingliang Zhuang, Ume Kalsoom
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Remote Sensing
Subjects:
surface ozone
machine learning
spatiotemporal differences
responsive mechanisms
Online Access:https://www.mdpi.com/2072-4292/17/13/2318
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2072-4292/17/13/2318

Similar Items