Marongiu, A., Collalto, A. G., Distefano, G. G., & Angelino, E. (2024). Application of Machine Learning to Estimate Ammonia Atmospheric Emissions and Concentrations. Air, 2(1), 38-60.
Marongiu, A., Collalto, A. G., Distefano, G. G., & Angelino, E. (2024). Application of Machine Learning to Estimate Ammonia Atmospheric Emissions and Concentrations. Air, 2(1), 38-60.
Marongiu, A., Collalto, A. G., Distefano, G. G., & Angelino, E. (2024). Application of Machine Learning to Estimate Ammonia Atmospheric Emissions and Concentrations. Air, 2(1), 38-60.
Marongiu, A., Collalto, A. G., Distefano, G. G., & Angelino, E. (2024). Application of Machine Learning to Estimate Ammonia Atmospheric Emissions and Concentrations. Air, 2(1), 38-60.
Abstract
Ammonia is an atmospheric pollutant, predominantly emitted from agriculture, leading acidification and eutrophication of soil and water and contributing to secondary PM2.5. The implementation of accurate emission inventories with high spatial and time resolution plays a fundamental role in the development of air modelling simulation and in the impact assessment of actions for air quality improvement. The development and release of new algorithms and the increase of data availability are supporting the implementation of machine learning approaches in environmental and air quality data analysis. In this paper we present a methodology developed by the application of the Random Forest algorithm to bottom-up local emission inventories of ammonia to validate annual time series of ammonia emissions and calculate high resolution temporal profiles. The model has been trained and tested by the hourly measurements of ammonia concentrations and atmospheric turbulence parameters starting from a constant emission scenario. The initial values of emissions are calculated based on a bottom-up emission inventory detailed at the municipal basis and considering a circular area of about 4 km radius centered on measurement sites. By comparing predicted and measured concentrations, the emissions are modified, the model's training and testing are repeated, and the model converges to a very high performance in predicting ammonia concentrations and establishing an hourly time changing emission profile. The site-specific emissions profiles, estimated by the proposed methodology, clearly show a nonlinear relation with measured concentrations and allow to identify the effect of atmospheric turbulence on pollutant accumulation. The estimated time series well confirm the available data of the emission inventories and the monthly emission profiles have been compared with estimated data from satellite.
Keywords
ammonia; emission modelling; emission inventory; random forest
Subject
Environmental and Earth Sciences, Pollution
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
