Evaluating deep learning time series models for PM2.5 forecasting across diverse horizons
Summary: Air pollution, particularly PM2.5, poses a major health challenge in urban areas such as Chengdu, China, where basin topography and intense emission sources exacerbate pollutant concentrations. This study evaluates four deep-learning time series
Ling Zeng +4 more
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Editorial: Machine learning in studies of atmospheric environment and climate change
Ying Chen +3 more
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Depressed chemical weathering inhibited climate stabilization after the end-Permian biocrisis. [PDF]
Wei GY +10 more
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Spatiotemporal Variability in and Factors Associated with Wet Deposition of Atmospheric Mercury in China from 2000 to 2022. [PDF]
Cheng X +5 more
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An NB-IoT-Based Architecture with Spatial-Statistical Analytics for Cross-Domain Air and Water Quality Monitoring in Aquaculture and Aquatic Environments. [PDF]
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Nitrogen deposition does not alter the temperate forest soil carbon pool: A 14-y experiment on varied nitrogen fertilization. [PDF]
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Instantaneous heating rate forcing differentiates tropical high-cloud adjustments across greenhouse gases. [PDF]
Chen YT, Merlis TM, Huang Y.
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Patagonian Ice Sheet discharge enhanced by AMOC slowdown through thermal bipolar seesaw. [PDF]
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