Predicting greenhouse gases emissions from decentralized composting by applying explainable machine learning method
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.Predicting greenhouse gas (GHG) emissions from composting is challenging. Machine learning models, particularly Adapt Boosting and Gradient Boosting, improve prediction accuracy, identifying pile temperature and C/N ratio as key drivers.
Area Of Science
- Environmental Science
- Waste Management
- Climate Change Research
Background
- Greenhouse gas (GHG) emissions from composting are a significant environmental concern.
- Predicting these emissions is difficult due to the complex nature of organic waste and varying composting conditions.
Purpose Of The Study
- To enhance the predictability of methane (CH4) and nitrous oxide (N2O) emissions from composting.
- To identify key factors influencing GHG emissions during the composting process.
Main Methods
- Collected 501 filed-monitoring datasets on methane and nitrous oxide effluxes from seven decentralized composting sites in China.
- Applied explainable machine learning methods, including Adapt Boosting and Gradient Boosting, to predict GHG emissions.
- Analyzed the influence of factors such as pile temperature and C/N ratio on emissions.
Main Results
- Methane effluxes ranged from 2.57 × 10⁻⁵ to 32.41 mg·m⁻²·min⁻¹, and nitrous oxide effluxes ranged from 1.98 × 10⁻⁴ to 2.27 mg·m⁻²·min⁻¹.
- Adapt Boosting and Gradient Boosting models achieved the highest prediction accuracy.
- Pile temperature and C/N ratio were identified as the primary drivers for methane and nitrous oxide emissions.
- Lifecycle GHG emission factors were found to be 4.5%–15.0% of IPCC average defaults.
Conclusions
- Machine learning offers a powerful approach for predicting and understanding GHG emissions from composting.
- Controlling pile temperature and C/N ratio can help mitigate GHG emissions.
- The findings provide lower, site-specific emission factors compared to IPCC defaults, aiding in more accurate environmental assessments.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
Microorganisms play a pivotal role in maintaining ecosystem balance by recycling essential elements such as carbon, nitrogen, and phosphorus, as well as supporting processes like bioremediation, wastewater treatment, and biofuel production.Microbes in Elemental CyclesIn the carbon cycle, microorganisms decompose organic matter, releasing carbon dioxide via aerobic respiration. This carbon dioxide is subsequently used by photosynthetic organisms to synthesize organic compounds, closing the...
Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.
Past Periods of Warming and Cooling
In...