Iterative integration of deep learning in hybrid Earth surface system modelling
Earth system modelling (ESM) is essential for understanding past, present and future Earth
processes. Deep learning (DL), with the data-driven strength of neural networks, has …
processes. Deep learning (DL), with the data-driven strength of neural networks, has …
A comprehensive investigation of surface ozone pollution in China, 2015–2019: Separating the contributions from meteorology and precursor emissions
Despite the considerable reductions in primary and secondary air pollutants in China,
surface ozone levels have increased in recent years. We report a trend of 3.3±4.7 μg. m− 3 …
surface ozone levels have increased in recent years. We report a trend of 3.3±4.7 μg. m− 3 …
NPP accident prevention: Integrated neural network for coupled multivariate time series prediction based on PSO and its application under uncertainty analysis for …
X Xiao, X Zhang, M Song, X Liu, Q Huang - Energy, 2024 - Elsevier
Due to the requirement of a cleaner, more sustainable form of energy production and the
rapid development of DT, nuclear energy needs to be digital transformed. This paper aims at …
rapid development of DT, nuclear energy needs to be digital transformed. This paper aims at …
Deep learning based emulator for simulating CMAQ surface NO2 levels over the CONUS
This study details the development and evaluation of an emulator model of the Community
Multiscale Air Quality (CMAQ) model, utilizing a U-Net deep learning architecture to …
Multiscale Air Quality (CMAQ) model, utilizing a U-Net deep learning architecture to …
A comprehensive approach combining positive matrix factorization modeling, meteorology, and machine learning for source apportionment of surface ozone …
Abstract Ozone concentrations in Houston, Texas, are among the highest in the United
States, posing significant risks to human health. This study aimed to evaluate the impact of …
States, posing significant risks to human health. This study aimed to evaluate the impact of …
A deep convolutional neural network model for improving WRF simulations
Advancements in numerical weather prediction (NWP) models have accelerated, fostering a
more comprehensive understanding of physical phenomena pertaining to the dynamics of …
more comprehensive understanding of physical phenomena pertaining to the dynamics of …
Optimized neural network for daily-scale ozone prediction based on transfer learning
Tropospheric ozone (O 3) pollution is worsening in China, and an accurate forecast is a
prerequisite to lower the O 3 peak level. In recent years, machine learning techniques have …
prerequisite to lower the O 3 peak level. In recent years, machine learning techniques have …
Surface ozone trends and related mortality across the climate regions of the contiguous United States during the most recent climate period, 1991–2020
In this study, we leverage multiple linear regression and quantile regression combined with
a novel deep learning tool (SHapley Additive exPlanations) to isolate the impact of …
a novel deep learning tool (SHapley Additive exPlanations) to isolate the impact of …
Decomposition of meteorological and anthropogenic contributions to near-surface ozone trends in Northeast China (2013–2021)
N Shang, K Gui, H Zhao, W Yao, H Zhao… - Atmospheric Pollution …, 2023 - Elsevier
Recent years have seen an increase in regional ozone (O 3) pollution in China. This study
explored the interannual variability in daily maximum 8-h average O 3 concentration (MDA8 …
explored the interannual variability in daily maximum 8-h average O 3 concentration (MDA8 …
Deep learning solver for solving advection–diffusion equation in comparison to finite difference methods
In numerical modeling, the advection–diffusion equation describes the long-range transport
of atmospheric pollutants. Most numerical models in the atmospheric science community are …
of atmospheric pollutants. Most numerical models in the atmospheric science community are …