Predicting ammonia emissions and global warming potential in composting by machine learning

The amounts of gases emitted from composting are key to evaluating global warming potential (GWP). However, few methods can accurately predict the quantities of relevant gas emissions. In this study, three developed machine-learning models were used to predict NH3 emissions and GWP. The extreme gradient boosting model provided the best predictions (R-2 > 90 %) compared to random forest, making it a suitable method for calculating NH3 emissions and GWP. The k-nearest neighbor classification model was utilized to determined compost maturity achieving 92 % accuracy. Shapley Additive ExPlanation analysis was applied to identify key factors influencing gas emissions and maturity. Aeration rate, carbon-to-nitrogen ratio and moisture content showed high importance in decreasing order for predicting NH3 emissions, while NO3- was the most significant factor for predicting GWP. Practical applications of predictive models suggested that prediction of GWP was 792614 Mg CO2e year(-1) close to annual calculation of 789000 Mg CO2e year(-1) in California.