Estimating the Near-Ground PM2.5 Concentration over China Based on the CapsNet Model during 2018–2020

Fine particulate matter (PM2.5) threatens human health and the natural environment. Estimating the near-ground PM2.5 concentrations accurately is of great significance in air quality research. Statistical and deep-learning models are widely used for estimating PM2.5 concentration based on remotely s...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 14; H. 3; S. 623
Hauptverfasser:	Zeng, Qiaolin, Xie, Tianshou, Zhu, Songyan, Fan, Meng, Chen, Liangfu, Tian, Yu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.02.2022
Schlagworte:	Accuracy aerosol optical depth Aerosols air pollutants Air pollution Air quality Algorithms Artificial neural networks Atmospheric models CapsNet China Coastal zone Cognition & reasoning Cold season Contaminants data collection Deep learning Depth indicators DNN dynamic routing algorithm Estimation human health Latitude Longitude Machine learning Mathematical models Meteorological parameters Model accuracy Natural environment Neural networks Neurons Normalized difference vegetative index Optical analysis Optical thickness Particulate matter particulates PM2.5 concentration Remote sensing Root-mean-square errors Seasons Statistical analysis China
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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