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Since February 2022, the full-scale war in Ukraine has been strongly affecting society and economy in Ukraine and beyond. Satellite observations are crucial tools to objectively monitor and assess the impacts of the war. We combine satellite-based tropospheric nitrogen dioxide (NO 2 ) and carbon dioxide (CO 2 ) observations to detect and characterize changes in human activities, as both are linked to fossil fuel combustion processes. We show significantly reduced NO 2 levels over the major Ukrainian cities, power plants and industrial areas: the NO 2 concentrations in the second quarter of 2022 were 15–46% lower than the same quarter during the reference period 2018–2021, which is well below the typical year-to-year variability (5–15%). In the Ukrainian capital Kyiv, the NO 2 tropospheric column monthly average in April 2022 was almost 60% smaller than 2019 and 2021, and about 40% smaller than 2020 (the period mostly affected by the COVID-19 restrictions). Such a decrease is consistent with the essential reduction in population and corresponding emissions from the transport and commercial/residential sectors over the major Ukrainian cities. The NO 2 reductions observed in the industrial regions of eastern Ukraine reflect the decline in the Ukrainian industrial production during the war (40–50% lower than in 2021), especially from the metallurgic and chemical industry, which also led to a decrease in power demand and corresponding electricity production by thermal power plants (which was 35% lower in 2022 compared to 2021). Satellite observations of land properties and thermal anomalies indicate an anomalous distribution of fire detections along the front line, which are attributable to shelling or other intentional fires, rather than the typical homogeneously distributed fires related to crop harvesting. The results provide timely insights into the impacts of the ongoing war on the Ukrainian society and illustrate how the synergic use of satellite observations from multiple platforms can be useful in monitoring significant societal changes. Satellite-based observations can mitigate the lack of monitoring capability during war and conflicts and enable the fast assessment of sudden changes in air pollutants and other relevant parameters.

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.

CO2 emissions from fossil fuel combustion (FFCO2) are conventionally estimated from fuel used (as activity data (AD)) and CO2 emissions factor. Recent traffic emission changes under the impact of the COVID-19 pandemic have been estimated using emerging non-fuel consumption data, such as human mobility data that tech companies reported as AD, due to the unavailability of timely fuel statistics. The use of such unconventional activity data (UAD) might allow us to provide emission estimates in near-real time; however, the errors and uncertainties associated with such estimates are expected to be larger than those of common FFCO2 inventory estimates, and thus should be provided along with a thorough evaluation/validation of the methodology and the resulting estimates. Here, we show the impact of COVID-19 on traffic CO2 emissions over the first six months of 2020 in Japan. We calculated CO2 monthly emissions using fuel consumption data and assessed the emission changes relative to 2019. Regardless of Japan’s soft approach to COVID-19, traffic emissions significantly declined by 23.8% during the state of emergency in Japan (April–May). We also compared relative emission changes among different estimates available. Our analysis suggests that UAD-based emission estimates during April and May could be biased by −19.6% to 12.6%. We also used traffic count data for examining the performance of UAD as a proxy for traffic and/or CO2 emissions. We found the assumed proportional relationship between traffic changes and CO2 emissions was not enough for estimating emissions with accuracy, and moreover, the traffic-based approach failed to capture emission seasonality. Our study highlighted the challenges and difficulties in repurposing data, especially ones with limited traceability/reproducibility, for modeling human activities and assessing the impact on the environment, and the importance of a thorough error and uncertainty assessment before using these data in policy applications.

An improvement of methods for the inventory of greenhouse gas (GHG) emissions is necessary to ensure effective control of commitments to emission reduction. The national inventory reports play an important role, but do not reflect specifics of regional processes of GHG emission and absorption for large-area countries. In this article, a GIS approach for the spatial inventory of GHG emissions in the energy sector, based on IPCC guidelines, official statistics on fuel consumption, and digital maps of the region under investigation, is presented. We include mathematical background for the spatial emission inventory of point, line and area sources, caused by fossil-fuel use for power and heat production, the residential sector, industrial and agricultural sectors, and transport. Methods for the spatial estimation of emissions from stationary and mobile sources, taking into account the specifics of fuel used and technological processes, are described. Using the developed GIS technology, the territorial distribution of GHG emissions, at the level of elementary grid cells 2 km × 2 km for the territory of Western Ukraine, is obtained. Results of the spatial analysis are presented in the form of a geo-referenced database of emissions, and visualized as layers of digital maps. Uncertainty of inventory results is calculated using the Monte Carlo approach, and the sensitivity analysis results are described. The results achieved demonstrated that the relative uncertainties of emission estimates, for CO₂ and for total emissions (in CO₂ equivalent), depend largely on uncertainty in the statistical data and on uncertainty in fuels’ calorific values. The uncertainty of total emissions stays almost constant with the change of uncertainty of N₂O emission coefficients, and correlates strongly with an improvement in knowledge about CH₄ emission processes. The presented approach provides an opportunity to create a spatial cadastre of emissions, and to use this additional knowledge for the analysis and reduction of uncertainty. It enables us to identify territories with the highest emissions, and estimate an influence of uncertainty of the large emission sources on the uncertainty of total emissions. Ascribing emissions to the places where they actually occur helps to improve the inventory process and to reduce the overall uncertainty.

CO 2 emissions from fossil fuel combustion (FFCO2) are conventionally estimated from fuel used (as activity data (AD)) and CO 2 emissions factor. Recent traffic emission changes under the impact of the COVID-19 pandemic have been estimated using emerging non-fuel consumption data, such as human mobility data that tech companies reported as AD, due to the unavailability of timely fuel statistics. The use of such unconventional activity data (UAD) might allow us to provide emission estimates in near-real time; however, the errors and uncertainties associated with such estimates are expected to be larger than those of common FFCO2 inventory estimates, and thus should be provided along with a thorough evaluation/validation of the methodology and the resulting estimates. Here, we show the impact of COVID-19 on traffic CO 2 emissions over the first six months of 2020 in Japan. We calculated CO 2 monthly emissions using fuel consumption data and assessed the emission changes relative to 2019. Regardless of Japan’s soft approach to COVID-19, traffic emissions significantly declined by 23.8% during the state of emergency in Japan (April–May). We also compared relative emission changes among different estimates available. Our analysis suggests that UAD-based emission estimates during April and May could be biased by −19.6% to 12.6%. We also used traffic count data for examining the performance of UAD as a proxy for traffic and/or CO 2 emissions. We found the assumed proportional relationship between traffic changes and CO 2 emissions was not enough for estimating emissions with accuracy, and moreover, the traffic-based approach failed to capture emission seasonality. Our study highlighted the challenges and difficulties in repurposing data, especially ones with limited traceability/reproducibility, for modeling human activities and assessing the impact on the environment, and the importance of a thorough error and uncertainty assessment before using these data in policy applications.

The development of high-resolution greenhouse gas (GHG) inventories is an important step towards emission reduction in different sectors. However, most of the spatially explicit approaches that have been developed to date produce outputs at a coarse resolution or do not disaggregate the data by sector. In this study, we present a methodology for assessing GHG emissions from the residential sector by settlements at a fine spatial resolution. In many countries, statistical data about fossil fuel consumption is only available at the regional or country levels. For this reason, we assess energy demand for cooking and water and space heating for each settlement, which we use as a proxy to disaggregate regional fossil fuel consumption data. As energy demand for space heating depends heavily on climatic conditions, we use the heating degree day method to account for this phenomenon. We also take the availability of energy sources and differences in consumption patterns between urban and rural areas into account. Based on the disaggregated data, we assess GHG emissions at the settlement level using country and regional specific coefficients for Poland and Ukraine, two neighboring countries with different energy usage patterns. In addition, we estimate uncertainties in the results using a Monte Carlo method, which takes uncertainties in the statistical data, calorific values, and emission factors into account. We use detailed data on natural gas consumption in Poland and biomass consumption for several regions in Ukraine to validate our approach. We also compare our results to data from the EDGAR (Emissions Database for Global Atmospheric Research), which shows high agreement in places but also demonstrates the advantage of a higher resolution GHG inventory. Overall, the results show that the approach developed here is universal and can be applied to other countries using their statistical information.

The assessment of greenhouse gases (GHGs) and air pollutants emitted to and removed from the atmosphere ranks high on international political and scientific agendas. Growing international concern and cooperation regarding the climate change problem have increased the need to consider the uncertainty in inventories of GHG emissions. The approaches to address uncertainty discussed in this special issue reflect attempts to improve national inventories, not only for their own sake but also from a wider, system analytic perspective. They seek to strengthen the usefulness of national emission inventories under a compliance and/or global monitoring and reporting framework. The papers in this special issue demonstrate the benefits of including inventory uncertainty in policy analyses. The issues raised by the authors and featured in their papers, along with the role that uncertainty analysis plays in many of their arguments, highlight the challenges and the importance of dealing with uncertainty. While the Intergovernmental Panel on Climate Change (IPCC) clearly stresses the value of conducting uncertainty analyses and offers guidance on executing them, the arguments made here in favor of performing these studies go well beyond any suggestions made by the IPCC to date. Improving and conducting uncertainty analyses are needed to develop a clear understanding and informed policy. Uncertainty matters and is key to many issues related to inventorying and reducing emissions. Considering uncertainty helps to avoid situations that can create a false sense of certainty or lead to invalid views of subsystems. Dealing proactively with uncertainty allows for the generation of useful knowledge that the international community should have to hand while strengthening the 2015 Paris Agreement, which had been agreed at the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC). However, considering uncertainty does not come free. Proper treatment of uncertainty is demanding because it forces us to take the step from “simple to complex” and to grasp a holistic system view. Only, thereafter, can we consider potential simplifications. That is, comprehensive treatment of uncertainty does not necessarily offer quick or easy solutions for policymakers. This special issue brings together 13 papers that resulted from the 2015 (4th) International Workshop on Uncertainty in Atmospheric Emissions, in Cracow, Poland. While they deal with many different aspects of the uncertainty in emission estimates, they are guided by the same principal question: “What GHGs shall be verified at what spatio-temporal scale to support conducive legislation at local and national scales, while ensuring effective governance at the global scale?” This question is at the heart of mitigation and adaptation. It requires an understanding of the entire system of GHG sources and sinks, their spatial characteristics and the temporal scales at which they react and interact, the uncertainty (accuracy and/or precision) with which fluxes can be measured, and last but not least, the consequences that follow from all of the aforementioned aspects, for policy actors to frame compliance and/or global monitoring and reporting agreements. This bigger system context serves as a reference for the papers in the special issue, irrespective of their spatio-temporal focus, and is used as a guide for the reader.

Agricultural activity plays a significant role in the atmospheric carbon balance as a source and sink of greenhouse gases (GHGs) and has high mitigation potential. The agricultural emissions display evident geographical differences in the regional, national, and even local levels, not only due to spatially differentiated activity, but also due to very geographically different emission coefficients. Thus, spatially resolved inventories are important for obtaining better estimates of emission content and design of GHG mitigation processes to adapt to global carbon rise in the atmosphere. This study develops a geoinformation approach to a high-resolution spatial inventory of GHG emissions from the agricultural sector, following the categories of the United Nations Intergovernmental Panel on Climate Change guidelines. Using the Corine Land Cover data, a digital map of emission sources is built, with elementary areal objects that are split up by administrative boundaries. Various procedures are developed for disaggregation of available emission activity data down to a level of elementary emission objects, conditional on covariate information, such as land use, observable in the elementary object scale. Among them, a statistical scaling method suitable for spatially correlated areal emission sources is applied. As an example of implementation of this approach, the spatial distribution of methane (CH4) and Nitrogen Oxide (N2O) emissions was obtained for areal emission sources in the agriculture sector in Poland with a spatial resolution of 100 m. We calculated the specific total emissions for different types of animal and manure systems as well as the total emissions in CO2-equivalent. We demonstrated that the emission sources are located highly nonuniformly and the emissions from them vary substantially, so that average data may provide insufficient approximation. In our case, over 11% smaller emission was estimated using spatial approach as compared with the national inventory report where average data were used. In addition, we quantified uncertainties associated with the developed spatial inventory and analysed the dominant components in total emission uncertainties in the agriculture sector. We used the activity data from the lowest possible (municipal) level. The depth of disaggregation of these data to the level of arable lands is minimal, and hence, the relative uncertainty of spatial inventory is smaller when comparing with traditional gridded emissions. The proposed technique allows us to discuss factors driving the geographical distribution of GHG emissions for different categories of the agricultural sector. This may be particularly useful in high-resolution modelling of GHG dispersion in the atmosphere.

A severe reduction of greenhouse gas emissions is necessary to reach the objectives of the Paris Agreement. The implementation and continuous evaluation of mitigation measures requires regular independent information on emissions of the two main anthropogenic greenhouse gases, carbon dioxide (CO2) and methane (CH4). Our aim is to employ an observation-based method to determine regional-scale greenhouse gas emission estimates with high accuracy. We use aircraft- and ground-based in situ observations of CH4, CO2, carbon monoxide (CO), and wind speed from two research flights over the Upper Silesian Coal Basin (USCB), Poland, in summer 2018. The flights were performed as a part of the Carbon Dioxide and Methane (CoMet) mission above this EuropeanCH4 emission hot-spot region. A kriging algorithm interpolates the observed concentrations between the downwind transects of the trace gas plume, and then the mass flux through this plane is calculated. Finally, statistic and systematic uncertainties are calculated from measurement uncertainties and through several sensitivity tests, respectively.For the two selected flights, the in-situ-derived annual CH4 emission estimates are 13.8±4.3 and 15.1±4.0 kg s-1, which are well within the range of emission inventories. The regional emission estimates of CO2, which were determined to be 1.21±0.75 and1.12±0.38 t s-1, are in the lower range of emission inventories. CO mass balance emissions of 10.1±3.6 and 10.7±4.4 kg s-1 for the USCB are slightly higher than the emission inventory values. TheCH4 emission estimate has a relative error of 26 %–31 %, theCO2 estimate of 37 %–62 %, and the CO estimate of 36 %–41 %. These errors mainly result from the uncertainty of atmospheric background mole fractions and the changing planetary boundary layer height during the morning flight. In the case of CO2, biospheric fluxes also add to the uncertainty and hamper the assessment of emission inventories. These emission estimates characterize the USCB and help to verify emission inventories and develop climate mitigation strategies.

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.