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Industrial point source CO2 emission strength estimation with aircraft measurements and dispersion modelling

Author:
Carotenuto, Federico, Gualtieri, Giovanni, Miglietta, Franco, Riccio, Angelo, Toscano, Piero, Wohlfahrt, Georg, Gioli, Beniamino
Source:
Environmental monitoring and assessment 2018 v.190 no.3 pp. 165
ISSN:
0167-6369
Subject:
aircraft, carbon dioxide, global warming, greenhouse gas emissions, greenhouse gases, guidelines, inventories, models, uncertainty, France
Abstract:
CO₂ remains the greenhouse gas that contributes most to anthropogenic global warming, and the evaluation of its emissions is of major interest to both research and regulatory purposes. Emission inventories generally provide quite reliable estimates of CO₂ emissions. However, because of intrinsic uncertainties associated with these estimates, it is of great importance to validate emission inventories against independent estimates. This paper describes an integrated approach combining aircraft measurements and a puff dispersion modelling framework by considering a CO₂ industrial point source, located in Biganos, France. CO₂ density measurements were obtained by applying the mass balance method, while CO₂ emission estimates were derived by implementing the CALMET/CALPUFF model chain. For the latter, three meteorological initializations were used: (i) WRF-modelled outputs initialized by ECMWF reanalyses; (ii) WRF-modelled outputs initialized by CFSR reanalyses and (iii) local in situ observations. Governmental inventorial data were used as reference for all applications. The strengths and weaknesses of the different approaches and how they affect emission estimation uncertainty were investigated. The mass balance based on aircraft measurements was quite succesful in capturing the point source emission strength (at worst with a 16% bias), while the accuracy of the dispersion modelling, markedly when using ECMWF initialization through the WRF model, was only slightly lower (estimation with an 18% bias). The analysis will help in highlighting some methodological best practices that can be used as guidelines for future experiments.
Agid:
5902105