ВИКОРИСТАННЯ СПЕЦІАЛІЗОВАНОГО ПРИКЛАДНОГО ІНСТРУМЕНТАРІЮ ДЛЯ АНАЛІЗУ ПРОЦЕСІВ ЕМІСІЇ ПАРНИКОВИХ ГАЗІВ В ЕЛЕКТРОЕНЕРГЕТИЧНІЙ ГАЛУЗІ УКРАЇНИ

This article analyzes the electricity sector of Ukraine in terms of greenhouse gas emissions and the basic algorithms of software tools to automate the process of spatial analysis of greenhouse gas emissions in this branch have been presented. The main advantages of the developed software include the ability of automatic database forming of input data, spatial modeling of greenhouse gas emissions from electricity production at level of point sources and also generate appropriate results in the form of digital thematic maps.Здійснено аналіз електроенергетичної галузі України в сенсі емісії парникових газів та представлено основні алгоритми програмного інструментарію для автоматизації просторового аналізу процесів емісії основних парникових газів в цій галузі. Основні переваги розробленого програмного забезпечення включають можливість автоматично формувати бази вхідних даних, здійснювати просторове моделювання процесів емісії парникових газів при виробництві електроенергії на рівні точкових джерел, а також формувати відповідні результати у вигляді цифрових тематичних карт

Spatial inventory of GHG emissions from fossil fuels extraction and processing: An uncertainty analysis

This article discusses bottom-up inventory analysis for greenhouse gas (GHG) emissions from fossil fuels extraction and processing in Poland. The approaches to modelling geo-referenced cadastres of emissions from fossil fuels extraction and processing are described as well as methods of uncertainty reduction using the knowledge on spatial greenhouse gas emissions distribution. The results of GHG emissions spatial inventory contain the information on geographical coordinates of emission sources. This information is useful for indication the largest emission sources. In this article we present the obtained results on spatial GHG inventory from fossil fuels extraction and processing in Poland, based on IPCC guidelines taking into account locations of emissions sources, official statistics and digital maps of territories investigated. Monte-Carlo method was applied for a detailed estimation of GHG emissions and results uncertainty in the main categories of analyzed sector

ПРОСТОРОВЕ МОДЕЛЮВАННЯ ПРОЦЕСІВ ЕМІСІЇ ПАРНИКОВИХ ГАЗІВ: ГЕНЕРУВАННЯ ТЕПЛО- ТА ЕЛЕКТРОЕНЕРГІЇ У ПІВДЕННІЙ ПОЛЬЩІ

Burning of fossil fuel to produce heat and electricity is one of the key sources of greenhouse gas emissions. This paper presents the developed mathematical models of spatial analysis of greenhouse gas emissions from stationary sources in the sector of production of heat and electricity. These models enable to take into account geographical location of relevant sources and the main factors that affect the magnitude of emissions. Implementation of these models allows us to analyze an amount and structure of emissions at different level. Numerical experiments of spatial inventory of greenhouse gases from production of heat and electricity have been carried out and analyzed for Southern Poland. Results are presented in the form of digital maps.Одним із ключових джерел емісії парникових газів є спалювання палива для генерування тепло- та електроенергії. У роботі представлено розроблені авторами математичні моделі процесів емісії парникових газів від стаціонарних джерел у секторі генерування тепла та електроенергії, які враховують територіальне розташування відповідних джерел емісії та основні регіональні фактори, що впливають на величину емісій, і дають змогу здійснювати їх просторовий аналіз. Реалізація цих моделей уможливлює аналіз величини та структури емісій на рівні елементарних ділянок території заданого розміру. Здійснено числові експерименти з просторової інвентаризації парникових газів у секторі генерування тепла та електроенергії для південної Польщі, проаналізовано отримані значення емісій. Результати представлено у вигляді цифрових карт

Accounting uncertainty for spatial modeling of greenhouse gas emissions in the residential sector: fuel combustion and heat production.

Energy consumption in households has a great potential for energy savings as well as for greenhouse gas emission reduction. As national inventory reports provide estimates at only a country or regional level, we have developed a new GIS approach that increases the resolution of emission inventories. We consider stationary emission sources, such as fossil fuel combustion and heat production for household energy needs that cover energy demand for cooking, water and space heating. We estimate the spatial emissions of greenhouse gases based on IPCC guidelines using official statistics on fuel consumption and spatial data about population density. The heating degree-day method was then used to determine the climatic conditions and spatial variability in energy demand. The results of the spatial inventory are obtained for settlements that are presented as area-type emission sources in a geospatial database. The uncertainties in the inventory results are estimated using a Monte Carlo method. The results show that uncertainties in greenhouse gas emissions at the regional level are significantly higher than at the country level although the uncertainty of emissions in CO2-equivalent does not exceed 17.0%

Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources

Greenhouse gas (GHG) inventories at national or provincial levels include the total emissions as well as the emissions for many categories of human activity, but there is a need for spatially explicit GHG emission inventories. Hence, the aim of this research was to outline a methodology for producing a high-resolution spatially explicit emission inventory, demonstrated for Poland. GHG emission sources were classified into point, line, and area types and then combined to calculate the total emissions. We created vector maps of all sources for all categories of economic activity covered by the IPCC guidelines, using official information about companies, the administrative maps, Corine Land Cover, and other available data. We created the algorithms for the disaggregation of these data to the level of elementary objects such as emission sources. The algorithms used depend on the categories of economic activity under investigation. We calculated the emissions of carbon, nitrogen sulfure and other GHG compounds (e.g., CO2, CH4, N2O, SO2, NMVOC) as well as total emissions in the 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 municipalities, which is not possible using a traditional gridded emission approach. We demonstrate that any grid size can be chosen to match the aim of the spatial inventory, but not less than 100 m in this example, which corresponds to the coarsest resolution of the input datasets. We then considered the uncertainties in the statistical 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. Such an approach is flexible, provided the data are available, and can be applied to other countries

A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling

Industrial processes cause significant emissions of greenhouse gases (GHGs) to the atmosphere and, therefore, have high mitigation and adaptation potential for global change. Spatially explicit (gridded) emission inventories (EIs) should allow us to analyse sectoral emission patterns to estimate the potential impacts of emission policies and support decisions on reducing emissions. However, such EIs are often based on simple downscaling of national level emission estimates and the changes in subnational emission distributions do not necessarily reflect the actual changes driven by the local emission drivers. This article presents a high-definition, 100-m resolution bottom-up inventory of GHG emissions from industrial processes (fuel combustion activities in energy and manufacturing industries, fugitive emissions, mineral products, chemical industries, metal production and food and drink industries), which is exemplified for data for Poland. The study objectives include elaboration of the universal approach for mapping emission sources, algorithms for emission disaggregation, estimation of emissions at the source level and uncertainty analysis. We start with IPCC-compliant national sectoral GHG estimates made using Polish official statistics and, then, propose an improved emission disaggregation algorithm that fully utilises a collection of activity data available at the national/provincial level to the level of individual point and diffused (area) emission sources. To ensure the accuracy of the resulting 100-m resolution emission fields, the geospatial data used for mapping emission sources (point source geolocation and land cover classification) were subject to thorough human visual inspection. The resulting 100-m emission field even holds cadastres of emissions separately for each industrial emission category. We also compiled cadastres in regular grids and, then, compared them with the Emission Database for Global Atmospheric Research (EDGAR). A quantitative analysis of discrepancies between both results reveals quite frequent misallocations of point sources used in the EDGAR compilation that considerably deteriorate high-resolution inventories. We also use a Monte-Carlo method-based uncertainty assessment that yields a detailed estimation of the GHG emission uncertainty in the main categories of the analysed processes. We found that the above-mentioned geographical coordinates and patterns used for emission disaggregation have the greatest impact on the overall uncertainty of GHG inventories from the industrial processes. We evaluate the mitigation potential of industrial emissions and the impact of separate emission categories. This study proposes a method to accurately quantify industrial emissions at a policy relevant spatial scale in order to contribute to the local climate mitigation via emission quantification (local to national) and scientific assessment of the mitigation effort (national to global). Apart from the above, the results are also of importance for studies that confront bottom-up and top-down approaches and represent much more accurate data for global high-resolution inventories to compare with

Uncertainty associated with fossil fuel carbon dioxide (CO2) gridded emission datasets

CO2 emissions from fossil fuel combustion (FFCO2) serves as a reference in carbon budget analysis and thus needs to be accurately quantified. FFCO2 estimates from different emission inventories often agree well at global and national level, however their subnational emission spatial distributions are unique and subject to uncertainty in the proxy data used for disaggregation of country emissions. In this study, we attempt to assess the uncertainty associated with emission spatial distributions in gridded FFCO2 emission inventories. We compared emission distributions from four gridded inventories at a 1 W 1 degree resolution and used the differences as a proxy for uncertainty. The calculated uncertainties typically range from 30% to 200% and inversely correlated with the emission magnitude. We also discuss limitations of our approach and possible difficulties when implemented at a higher spatial resolution

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

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

Errors and uncertainties in a gridded carbon dioxide emissions inventory

Emission inventories (EIs) are the fundamental tool to monitor compliance with greenhouse gas (GHG) emissions and emission reduction commitments. Inventory accounting guidelines provide the best practices to help EI compilers across different countries and regions make comparable, national emission estimates regardless of differences in data availability. However, there are a variety of sources of error and uncertainty that originate beyond what the inventory guidelines can define. Spatially explicit EIs, which are a key product for atmospheric modeling applications, are often developed for research purposes and there are no specific guidelines to achieve spatial emission estimates. The errors and uncertainties associated with the spatial estimates are unique to the approaches employed and are often difficult to assess. This study compares the global, high-resolution (1 km), fossil fuel, carbon dioxide (CO2), gridded EI Open-source Data Inventory for Anthropogenic CO2 (ODIAC) with the multi-resolution, spatially explicit bottom-up EI geoinformation technologies, spatio-temporal approaches, and full carbon account for improving the accuracy of GHG inventories (GESAPU) over the domain of Poland. By taking full advantage of the data granularity that bottom-up EI offers, this study characterized the potential biases in spatial disaggregation by emission sector (point and non-point emissions) across different scales (national, subnational/regional, and urban policy-relevant scales) and identified the root causes. While two EIs are in agreement in total and sectoral emissions (2.2% for the total emissions), the emission spatial patterns showed large differences (10~100% relative differences at 1 km) especially at the urban-rural transitioning areas (90–100%). We however found that the agreement of emissions over urban areas is surprisingly good compared with the estimates previously reported for US cities. This paper also discusses the use of spatially explicit EIs for climate mitigation applications beyond the common use in atmospheric modeling. We conclude with a discussion of current and future challenges of EIs in support of successful implementation of GHG emission monitoring and mitigation activity under the Paris Climate Agreement from the United Nations Framework Convention on Climate Change (UNFCCC) 21st Conference of the Parties (COP21). We highlight the importance of capacity building for EI development and coordinated research efforts of EI, atmospheric observations, and modeling to overcome the challenges

Usage Of Specialised Applied Toolkit For Ghg Emission Analysis In Electricity Production Sector Of Ukraine

This article analyzes the electricity sector of Ukraine in terms of greenhouse gas emissions and the basic algorithms of software tools to automate the process of spatial analysis of greenhouse gas emissions in this branch have been presented. The main advantages of the developed software include the ability of automatic database forming of input data, spatial modeling of greenhouse gas emissions from electricity production at level of point sources and also generate appropriate results in the form of digital thematic maps