15 February 2019
High resolution spatial inventory of GHG emissions from stationary and mobile sources: Uncertainty analysis
Greenhouse gas (GHG) inventories at national scale and corresponding submissions to the UNFCCC include the total emissions as well as the emissions for many categories of human activity. But for many practical implementations there is a need for spatially explicit emission inventories which are traditionally presented as gridded data. On the basis of our previous collaboration we proposed a completely different methodology for producing a high resolution spatially explicit emission inventory at the level of individual facilities (emission sources), but this approach is not gridded. GHG emission sources were classified into point-, line- and area-types, and then combined to calculate the total emissions. The approach is implemented for Poland. We created the vector maps of all sources for all categories of economic activity covered by the IPCC guidelines, as well as the algorithms for the disaggregation of activity data to the level of emission sources. We calculated the emissions of CO2, CH4, N2O, SO2, NMVOC, and other GHGs as well as total emissions in CO2-equivalent. Gridded data were only created in the final stage to present the summarized emissions of very diverse sources from all categories. In our approach, information on the administrative assignment of corresponding emission sources is retained, which makes it possible to aggregate the final results to different administrative levels including settlements or municipalities, which is not possible using a traditional gridded emission approach. We demonstrate that any gridded emissions can be build and any grid size can be chosen to match the aim of the spatial inventory, but not less than 100 m, which corresponds to the coarsest resolution of the input datasets. We also considered the uncertainties caused by geolocation errors, statistical and proxy data, the calorific values, and the emission factors, with symmetric and asymmetric (lognormal) distributions. Using the Monte-Carlo method, uncertainties, expressed using 95% confidence intervals, were estimated for high point-type emission sources, the provinces, and the subsectors. Results were compared with EDGAR and ODIAC data based on satellite monitoring night-time lights. On this basis the bias of night-time data was estimated. Such an approach is flexible, provided the data are available, and can be applied to other countries. The results showed that satellite remote sensing data will be widely used to build gridded and non-gridded emission models in the near future. A lot of proxy data will be used, and we need to evaluate their uncertainty, as well as how this uncertainty changes over time.
Last edited: 18 February 2019
Jonas, M. , Bun, R., Nahorski, Z., Marland, G., Gusti, M., & Danylo, O. (2019). Quantifying greenhouse gas emissions. Mitigation and Adaptation Strategies for Global Change 24 (6), 839-852. 10.1007/s11027-019-09867-4.
Oda, T., Bun, R., Kinakh, V., Topylko, P., Halushchak, M., Marland, G., Lauvaux, T., Jonas, M. , et al. (2019). Errors and uncertainties in a gridded carbon dioxide emissions inventory. Mitigation and Adaptation Strategies for Global Change 24, 1007-1050. 10.1007/s11027-019-09877-2.
Charkovska, N., Halushchak, M., Bun, R., Nahorski, Z., Oda, T., Jonas, M. , & Topylko, P. (2019). A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling. Mitigation and Adaptation Strategies for Global Change 24, 907-939. 10.1007/s11027-018-9836-6.
Charkovska, N., Horabik-Pyzel, J., Bun, R., Danylo, O., Nahorski, Z., Jonas, M. , & Xiangyang, X. (2018). High resolution spatial distribution and associated uncertainties of greenhouse gas emissions from the agricultural sector. Mitigation and Adaptation Strategies for Global Change 23, 1-25. 10.1007/s11027-017-9779-3.
Haluschak, M., Jonas, M. , Zebrowski, P. , Jarnicka, J., Bun, R., & Nahorski, Z. (2016). Taking advantage of the UNFCCC Kyoto Policy Process: What can we learn about learning? In: Public Scientific Conference of Institute of Applied Mathematics and Fundamental Science. pp. 112-113 Lviv, Ukraine: Lviv Polytechnic National University. ISBN 978-617-607-398-7
Charkovska, N., Halushchak, M., Bun, R., & Jonas, M. (2015). Uncertainty analysis of GHG spatial inventory from the industrial activity. A case study for Poland. In: Proceedings, 4th International Workshop on Uncertainty in Atmospheric Emissions, 7-9 October 2015, Krakow, Poland. pp. 57-63 Warsaw, Poland: Systems Research Institute, Polish Academy of Sciences. ISBN 83-894-7557-X
Charkovska, N.R., Bun, R., Danylo, O., Horabik-Pyzel, J., & Jonas, M. (2015). Spatial GHG inventory in the agriculture sector and uncertainty analysis: A case study for Poland. In: Proceedings, 4th International Workshop on Uncertainty in Atmospheric Emissions, 7-9 October 2015, Krakow, Poland. pp. 16-24 Warsaw, Poland: Systems Research Institute, Polish Academy of Sciences. ISBN 83-894-7557-X
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