AIMC Topic: Methane

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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...

Identifying Priority Nutrients for Achieving Water Quality Improvement and Climate Change Mitigation.

Environmental science & technology
Riverine and lake ecosystems are sources of global non-CO greenhouse gases (GHGs) and serve as sinks for pollutants, facing the dual challenges of mitigating GHG emissions and controlling water pollution. However, interactions between pollutant input...

Regression model and artificial neural network model to predict halonitromethane formation from amino acids during UV/monochloramine disinfection in bromide-containing real water.

Environmental pollution (Barking, Essex : 1987)
Halonitromethanes (HNMs) were high-toxicity nitrogenous disinfection byproducts generated by amino acids (AAs) during UV/monochloramine (UV/NHCl) disinfection in bromide-containing water. HNM concentrations fell over time, highlighting disinfection t...

Machine learning analysis of greenhouse gas sources impacting Africa's food security nexus.

Scientific reports
The essential need to identify the most informative sources of greenhouse gas emissions (climate change drivers) impacting the food security nexus in Africa requires a comprehensive and holistic approach. Machine learning method excels in the identif...

Biochar-Augmented Anaerobic Digestion System: Insights from an Interpretable Stacking Ensemble Deep Learning.

Environmental science & technology
This study presents a comprehensive approach for optimizing biochar-augmented anaerobic digestion (AD) system through an interpretable stacking ensemble deep learning model. Extensive experimental data were compiled, incorporating feedstock character...

Application of multimodal machine learning-based analysis for the biomethane yields of NaOH-pretreated biomass.

Scientific reports
This study investigated the impact of alkaline pretreatment on the biomethane yield of Xyris capensis experimentally and computationally using machine-learning (ML)-based techniques. Despite extensive studies on the anaerobic digestion of lignocellul...

Machine learning-based optimization of biogas and methane yields in UASB reactors for treating domestic wastewater.

Biodegradation
This study aimed to optimize biogas and methane production from Up-flow anaerobic sludge blanket reactors for treating domestic wastewater using advanced machine learning models-namely, eXtreme Gradient Boosting (XGBoost) and its hybridized form, XGB...

Impact of microbial profile integration on machine learning predictions of methane production: synergies and trade-offs with physicochemical parameters.

Bioresource technology
Microbial sequencing data were rarely integrated into the prediction of methane production using machine learning (ML) models because of high dimensionality and the lack of a systematic way to evaluate the change of insight gained from modelling with...

An integrated review on the role of different biocatalysts, process parameters, bioreactor technologies and data-driven predictive models for upgrading biogas.

Journal of environmental management
As energy consumption and waste generation from human activities continue to rise, the technology of anaerobic digestion (AD), which converts waste into bioenergy, has gained popularity. Biogas produced from AD commonly contains 60 % CH, 40 % CO and ...

Predicting anaerobic digestion stability in load-flexible operation using gas phase indicators and classification algorithms.

Bioresource technology
This study investigates early warning indicators for process instabilities in anaerobic digestion caused by shock-loadings in biogas plants, focussing on gas-phase parameters to avoid substrate analyses. With the increasing use of renewable energy so...