High resolution spatial inventory of GHG emissions emissions from stationary and mobile sources in Poland: summarized results and uncertainty analysis

Bun, R., Nahorski, Z., Horabik-Pyzel, J., Danylo, O., Charkovska, N., Topylko, P., Halushchak, M., Lesiv, M. ORCID: https://orcid.org/0000-0001-9846-3342, & Striamets, O. (2015). High resolution spatial inventory of GHG emissions emissions from stationary and mobile sources in Poland: summarized results and uncertainty analysis. In: Proceedings, 4th International Workshop on Uncertainty in Atmospheric Emissions, 7-9 October 2015, Krakow, Poland. pp. 41-48 Warsaw, Poland: Systems Research Institute, Polish Academy of Sciences. ISBN 83-894-7557-X

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Abstract

Greenhouse gases (GHG) inventories at national or regional levels include the total emissions and emissions for many categories of economic activity. The aim of our research is to analyze the high resolution spatial distributions of emissions for all categories of economic activity in Poland. GHG emission sources are classified into point-, line- and area-type sources. We created maps of such sources for all categories of economic activities covered by IPCC Guidelines, using official information of companies, administrative maps, Corine Land Cover maps, and other available data. The worst resolution is for area-type sources (100 m). We used statistical data at the lowest level as possible (regions, districts, and municipalities). We created the algorithms for these data disaggregation to the level of elementary objects for GHG spatial inventory. These algorithms depend on category of economic activity and cover all categories under investigation. We analyzed emissions of CO2, CH4, N2O, SO2, NMVOC, and others, and we calculated the total emissions in CO2-equivalent. We used a grid to calculate the summarizing emissions from the all categories. The grid size depends on the aim of spatial inventory, but it can't be less than 100 m. For uncertainty analysis we used uncertainty of statistical data, uncertainty of calorific values, and uncertainty of emission factors, with symmetric and asymmetric (lognormal) distributions. On this basis and using Monte-Carlo method the 95% confidence intervals of results' uncertainties were estimated for big point-type emission source, the regions, and the subsectors.

Item Type:	Book Section
Uncontrolled Keywords:	4th International Workshop on Uncertainty in Atmospheric Emissions
Research Programs:	Ecosystems Services and Management (ESM) Advanced Systems Analysis (ASA)
Depositing User:	Luke Kirwan
Date Deposited:	10 Feb 2016 11:54
Last Modified:	27 Aug 2021 17:25
URI:	https://pure.iiasa.ac.at/11886

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