AIMC Topic: Atmosphere

Clear Filters Showing 1 to 10 of 39 articles

Spatial-temporal distribution and variation of atmospheric NO dry deposition in the Yellow River Basin from 2015 to 2023.

Environmental monitoring and assessment
Nitrogen dioxide (NO) is a major atmospheric pollutant that threatens human health and environmental quality amid rapid urbanization and industrialization. The Yellow River Basin is a heavily populated and economically significant area that is essent...

Quantifying key drivers of atmospheric methane across Pakistan using a machine learning approach.

Environmental monitoring and assessment
Atmospheric methane (CH), a potent greenhouse gas, has shown a consistent rise since the Industrial Revolution, contributing significantly to global warming and climate change. Understanding the temporal and spatial variability of methane concentrati...

Oxygenated Organic Molecules over the Boundary Layer Aloft in Beijing.

Environmental science & technology
Oxygenated organic molecules (OOMs) originate from both direct emissions and secondary formation via the oxidation of volatile organic compounds (VOCs) emitted from biogenic and anthropogenic sources. OOMs are suggested to play a crucial role in the ...

Exploring Aerosol Vertical Distributions and Their Influencing Factors: Insight from MAX-DOAS and Machine Learning.

Environmental science & technology
Understanding aerosol vertical distribution is crucial for aerosol pollution mitigation but is hindered by limited observational data. This study employed multiaxis differential optical absorption spectroscopy (MAX-DOAS) technology with a coupled rad...

Source apportionment of PM particles in the urban atmosphere using PMF and LPO-XGBoost.

Environmental research
Atmospheric particulate matter (PM), as a leading part of air pollution, affects health in many ways. Thus, identifying and quantifying the contribution of atmospheric particulate matter sources of PM is vital for developing effective air quality man...

Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO Emissions.

Environmental science & technology
Satellite data provides essential insights into the spatiotemporal distribution of CO concentrations. However, many atmospheric inverse models fail to adequately incorporate the spatial and temporal correlations inherent in satellite observations and...

Advancing Source Apportionment of Atmospheric Particles: Integrating Morphology, Size, and Chemistry Using Electron Microscopy Technology and Machine Learning.

Environmental science & technology
To further reduce atmospheric particulate matter concentrations, there is a need for a more precise identification of their sources. The SEM-EDS technology (scanning electron microscopy and energy-dispersive X-ray spectroscopy) can provide high-resol...

Neural network emulator for atmospheric chemical ODE.

Neural networks : the official journal of the International Neural Network Society
Modelling atmospheric chemistry is complex and computationally intense. Given the recent success of Deep neural networks in digital signal processing, we propose a Neural Network Emulator for fast chemical concentration modelling. We consider atmosph...

Integrating Chemical Mechanisms and Feature Engineering in Machine Learning Models: A Novel Approach to Analyzing HONO Budget.

Environmental science & technology
Nitrous acid (HONO) serves as the primary source of OH radicals in the atmosphere, exerting significant impacts on atmospheric secondary pollution. The heterogeneous reactions of NO on surfaces and photolysis of particulate nitrate or adsorbed nitric...

Accurate and efficient prediction of atmospheric PM, PM, PM, and O concentrations using a customized software package based on a machine-learning algorithm.

Chemosphere
Particulate matter (PM) and ozone (O) pollution have been attracting increasing attention recently due to their severe harm to human health. PM and O are secondary pollutants, and there remain significant challenges in accurately and efficiently pred...