Global Modeling and Analysis of Anthropogenic Combustion and Associated Emissions

Anthropogenic combustion and associated emissions have significant impacts on air quality and climate. However, current estimates of emissions from anthropogenic combustion are still subject to large uncertainties, especially in rapidly-developing regions. This hinders accurate assessments of their regional and global impacts on air quality and climate, which presents an urgent need to understand, assess, monitor, and predict anthropogenic combustion and associated emissions particularly at city-to-national scales. Combustion products co-emitted to the atmosphere and their relationships are typically related to characteristics of combustion processes. Thus, in order to understand anthropogenic combustion and associated emissions, my PhD study seeks to answer three major scientific questions: (1) To what extent could current observations of trace gases co-emitted from combustion be used to understand anthropogenic combustion, emissions, and related driving factors? (2) How well do present global climate-chemistry models simulate trace gases from combustion activities and could those models be used to study anthropogenic emissions? (3) To what extent could the current understanding of anthropogenic combustion and emissions be improved by jointly analyzing satellite, ground-based, aircraft measurements, and model simulations of trace gases co-emitted from combustion? To address the first scientific question, I combine air pollution measurements from multiple satellite instruments across 2005-2014 to characterize emergent features of the ratios of carbon monoxide (CO) and sulfate dioxide (SO2) to nitrogen dioxide (NO2) enhancements from anthropogenic emissions over 36 cities in China. The resulting emission pattern is well-correlated with economic development and traces a common emission pathway that resembles the evolution of air pollution in more developed cities. The absence of this progression in the current IPCC Representative Concentration Pathway emission inventory is most likely due to its deficient representation of the shift towards cleaner combustion in more developed cities. The results highlight the usefulness of augmenting observational capabilities by exploiting relationships of combustion tracers in constraining the temporal variation of emissions for gaseous pollutants. In addition, it is also desired to monitor and assess anthropogenic combustion and its impacts through modeling. Thus, to address the second scientific question, I evaluate simulations of two important anthropogenic combustion products (carbon dioxide (CO2) and CO) from a state-of-the-art high-resolution global prediction system, the Copernicus Atmosphere Monitoring Service (CAMS), by comparing with the Korea-United States Air Quality (KORUS-AQ) field measurements (May to June 2016) that aims to understand the factors controlling air quality over East Asia. The results show a slight overestimation for CAMS CO2 and a moderate underestimation for CAMS CO. CAMS also captures the observed more efficient combustion over Seoul compared to China outflows. Furthermore, to address both the second and third scientific questions, I combine observations and model simulations to uncover important combustion sources over East Asia, using the Community Atmosphere Model with chemistry (CAM-chem) with a CO tagging mechanism, where artificial CO tracers (i.e., tags) from specific sources are tracked as standard CO. With 17 CAM-chem tagged CO simulations using various model configurations, I quantify key regional sources of CO during KORUS-AQ. The results show that emissions from middle East Asia dominate continental outflows to Korea, while Korean emissions play an overall more important role for ground sites and plumes within the boundary layer in Korea. The CAM-chem tagging results are generally consistent with other source contribution approaches. Following the CO modeling, together with newly developed CO2 modeling and tagging mechanism in CAM-chem, I demonstrate the use of joint analysis of CO and CO2 towards a multi-species inversion. I simulate atmospheric CO2 as well as CO in CAM-chem using optimized carbon fluxes for CO2. The model results generally agree with observations from satellite, aircraft, and ground-based observations during KORUS-AQ. Then, I implement a CO2 tagging mechanism into the model. The modeled fossil fuel CO2 tags agree well with fossil fuel CO2 derived from radiocarbon samples during the field campaign. I also show that signatures of plume transport and sectoral emissions of CO2 are enhanced in CO analyses. Overall, this work elucidates the use of jointly analyzing CO2 and CO in tracking fossil fuel CO2, quantifying regional sources, and understanding combustion efficiency of sources. In my future work, I will (1) combine observations and model simulations of atmospheric gases to obtain improved estimates of their emissions from anthropogenic combustion based on inverse modeling techniques, and (2) use the improved emission estimates to quantify the impact of trace gases on air quality and climate

Aircraft-based in situ measurements of CH4 and CO2 downstream of European and Asian urban centres at local to synoptic scales

Die zwei wichtigsten anthropogenen Treibhausgase (THG) sind Kohlenstoffdioxid (CO2) und Methan (CH4), mit globalen Konzentrationen von zurzeit ~414,5 ppm bzw. ~1897 ppb. Zur Begrenzung der globalen Erwärmung ist ein genaues Verständnis ihrer Quellen und Senken erforderlich. Städtische Gebiete sind relevante THG Emittenten, aber aufgrund ihrer vielen individuellen kleinen Quellen sind die gesamten städtischen CO2 und CH4 Emissionen, deren Aufteilung in einzelne Quellsektoren und deren räumliche Verteilung unzureichend bekannt. In dieser Arbeit wird die Hypothese evaluiert, dass flugzeuggestützte in-situ Messungen geeignet sind, um die Auswirkungen urbaner CO2 und CH4 Emissionen auf die lokale bis synoptische THG Verteilung zu identifizieren und zu quantifizieren. Eine hoch empfindliche laser-gestützten Absorptionstechnik (Cavity Ring-Down Spektroskopie) wurde bei drei wissenschaftlichen Messkampagnen eingesetzt: [UC]2 (Urban Climate Under Change), EMeRGe-Europa und EMeRGe-Asien (Effect of Megacities on the transport and transformation of pollutants on the Regional to Global scales). Aufgrund der umfangreichen Charakterisierung des Instruments sowie der Verwendung von Kalibrationsstandards, welche auf die WMO Skala rückführbar sind, beläuft sich die Gesamtunsicherheit der CO2 und CH4 Messungen auf 0,2 ppm bzw. 1,1 ppb (~1 % der atmosphärischen Mischungsverhältnisse). Anhand einer lokalen Fallstudie am 20. Juli während [UC]2 wurde die Berliner THG Fahne vom atmosphärischen Hintergrund abgegrenzt und Emissionsraten für CH4 (5,20 ± 1,70 kg s-1) und CO2 (1,39 ± 0,76 t s-1) mit Hilfe einer Massenbilanz-Methode abgeleitet. Während die extrapolierten jährlichen CO2 Emissionsraten innerhalb der Fehlergrenzen mit aktuellen Emissionskatastern übereinstimmen, liegen sie für CH4 zwei bis siebenmal höher. Der Grund für die Diskrepanz wurde mithilfe von Ergebnissen eines hochauflösenden regionalen Chemie-Klimamodells auf eine Unterschätzung der CH4 Emissionen innerhalb der Stadt, sowie auf fehlende Inventarquellen im Umland zurückgeführt. Für letzteres könnten zahlreiche Mülldeponien und/oder Kläranlagen verantwortlich sein. Diese Arbeit zeigt erfolgreich, dass unabhängige top-down Schätzungen wichtig sind um bottom-up Emissionsraten zu überprüfen. Signaturen von europäischen und asiatischen urbanen CO2 und CH4 Emissionen konnten während EMeRGe im Abwind von London (UK), Barcelona (Spanien) und Manila (Philippinen) detektiert werden. Da die Messentfernung zu den jeweiligen Städten bis zu 250 km betrug, und sich somit die Abluftfahnen bereits mit der Umgebungsluft vermischten, wurde ihre Herkunft mit numerischen Modellsimulationen und zeitgleichen Messungen von kurzlebigen Spurengasen verifiziert. Die Beobachtung von großräumigen CH4 und CO2 Erhöhungen in der freien Troposphäre deuten darauf hin, dass das regionale THG Budget im Frühjahr stark durch den Einfluss vermischter Emissionen von Clustern von Megastädten des chinesischen Festlandes bestimmt wird. Frühere Messkampagnen in Asien (TRACE-P und KORUS-AQ der NASA) weisen ähnliche Muster in der regionalen THG Verteilung auf. Wie erwartet wurden jedoch höhere mittlere Mischungsverhältnisse während EMeRGe-Asien aufgrund der globalen Zunahme atmosphärischer CO2 und CH4 Konzentrationen detektiert. Diese Arbeiten bestätigen, dass in-situ Messungen ebenso erfolgreich eingesetzt werden können, um städtische THG Emissionen auf der meso- bis synoptischen Skala zu untersuchen.The two most important anthropogenic greenhouse gases (GHG) are carbon dioxide (CO2) and methane (CH4) with current global mole fractions of ~414.5 ppm CO2 and ~1897 ppb CH4. In order to develop efficient mitigation strategies limiting global warming, an accurate understanding of their sources and sinks is necessary. Urban areas are recognised as significant GHG emitters but constitute of a large variety of individual smaller sources. Hence, there is a lack of information on the magnitude of total urban CO2 and CH4 emissions, on their division into different source sectors and on their spatial distribution. This thesis evaluates the hypothesis that aircraft-borne in situ measurements are a useful tool to identify and quantify the impact of urban CH4 and CO2 emissions on the local to synoptic scale GHG distribution. A sensitive laser-based absorption technique, cavity ring-down spectroscopy, was deployed within three scientific field campaigns: [UC]2 (Urban Climate Under Change), EMeRGe-Europe and EMeRGe-Asia (Effect of Megacities on the transport and transformation of pollutants on the Regional to Global scales). The extensive characterisation and calibration with gas standards traceable to the WMO scales allows for measuring CO2 and CH4 mole fractions with an overall uncertainty of 0.2 ppm and 1.1 ppb, respectively, representing less than 1 % of the current atmospheric background. Based on a local case study on July 20th during [UC]2 it was possible to clearly distinguish Berlin’s urban GHG plume from the atmospheric background and to derive emission rates for CH4 (5.20 ± 1.70 kg s-1) and CO2 (1.39 ± 0.76 t s-1) using a mass balance method. While extrapolated annual CO2 emission rates agree within error bars with current inventories, they are two to seven times higher for CH4. Combining aircraft measurements with results from a high-resolution regional chemistry climate model, it was shown that the discrepancy is due to an underestimation of urban CH4 emissions within the city, as well as due to missing inventorial sources in the surroundings, which may include numerous waste dumps and/or wastewater treatment plants. This study successfully demonstrates that such independent airborne top-down estimates are important to evaluate bottom-up emission inventories. Signatures of European and Asian urban CO2 and CH4 emissions were detected in the regional GHG budget during EMeRGe for London (United Kingdom), Barcelona (Spain) and Manila (the Philippines) even at downwind distances up to 250 km. Due to the large distances from the respective sources, emissions were already mixed with cleaner background air or other pollution plumes. Their identification therefore was verified by numerical model simulations and co-measured short-lived species. The frequent observation of large-scale GHG plumes in the free troposphere downstream of China, indicate that the regional GHG budget during springtime is heavily impacted by the outflow from mixed emissions from megacity clusters from mainland China. A comparison with previous aircraft campaigns conducted in Asia (TRACE-P and KORUS-AQ of NASA) shows that similar patterns were observed in the regional GHG distributions. However, as expected, larger mean mole fractions were detected during EMeRGe-Asia due to the increase in global atmospheric CO2 and CH4 concentrations. These studies show that in situ instruments can also be successfully used to study the impact of urban emissions on the meso- to synoptic scale GHG budget

The analysis of the spatial patterns and controls governing the global occurrence of fatal landslides

In the research presented here, a global inventory of fatal landslides has been generated allowing the investigation of the spatial distribution and temporal occurrence of mass movement events. There are important regional differences within these data with Asian fatalities being characterised by high frequency, low magnitude landslide events. In comparison, high magnitude events were found to be responsible for the high fatality totals in the Americas. This research has demonstrated that the spatial distribution of fatal landslides is best explained by a combination of physical and social factors and has yielded some interesting results.87% of the fatal landslide events recorded within the database were triggered by high intensity of prolonged rainfall events associated with tropical cyclones or monsoon rainfall that are compounded in areas of high relief associated with tectonically active mountain belts. Increasing landslide impacts are often associated with less developed countries, where there is rising population density, rural to urban migration, growth of megacities, and severe land degradation. However, the results indicate that the occurrence of landslide fatalities are not simply a function of level of development of a country or population density but that fatalities predominantly occur within middle income countries and rural areas which are increasingly vulnerable to landslide disasters. This can be attributed to changes in physical systems, most notably climate variation

Doctor of Philosophy

dissertationThis dissertation is to theoretically and empirically explore how development impacts the environment in the largest developing country in the world from the perspective of sociology of development, and environmental and urban sociology. This dissertation focuses on the context of China, reviews the development trajectories adopted during the past more than six decades, and presents the spatial and temporal pattern of environmental degradation across regions and over time. Then, this dissertation empirically examines the relationship between development and environmental degradation answering the following questions: (1) whether economic development level is positively or negatively associated with air and water pollution; (2) whether industrialization, urbanization and globalization (international trade and Foreign Direct Investment inflows) are positively or negatively associated with air and water pollution; (3) how the impact has changed across regions and over time; and (4) how the sources of foreign capital have differentially affected environmental pollution across cities and over time. The dissertation presents how economic development level (GDP per capita as the indicator), globalization, industrialization and urbanization have impact on air and water pollution respectively across regions and over time, and examines whether globalization, industrialization and urbanization serves as the pathways in the association between development and environmental pollution in such a rapid economy with the most population in the world. Multilevel modeling is used to analyze the longitudinal data at the city level from 2004 to2013. The findings confirm that there is inverted-U shape only between economic development and SO2 emission (not for dust emission or water pollution), indicating whether Environmental Kuznets Curve (EKC) holds depending on the specific indicators of environmental degradation analyzed. More importantly, the results show that industrialization and urbanization are more likely to positively impact air pollution, while there is no strong evidence supporting that globalization has impact on air pollution. Meanwhile, industrialization and globalization are more likely to positively impact water pollution, while population density is negatively associated with water pollution

Study of vegetation cover change and its driving forces

The dynamic change of vegetation cover exerts significant influences on the energetic and chemical circulation worldwide. Systematically monitoring the global vegetation cover change is critical to promote a better understanding of the basic biogeochemical processes, and their possible feedbacks to the global climate system. It is of great practical value to study dynamic vegetation variation related to climate change, human activities, and natural factors to explore the underlying relationships between vegetation cover change and its driving forces and the responding mechanisms of vegetation cover to the variability of the driving forces. Vegetation degradation is continually proceeding worldwide, but the degradation situation is more serious in developing countries than in developed countries. China is the largest developing country, and it has been experiencing significant socio-economic development, rapid urban expansion, and sharp population growth in eastern China in particular after launching the program of reform and opening-up termed "Socialism with Chinese Characteristics" in China in 1978. The unprecedented socio-economic development, urban expansion, and population growth have led to land use and land cover change, soil fertility decline, vegetation degradation, water contamination, and biodiversity loss in eastern China. Eastern China, a place with a highly developed socioeconomic status than other regions of China, covers seven provinces (e.g., Liaoning, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, and Guangdong) and three municipalities (e.g., Beijing, Tianjin, and Shanghai) with an area of about 1.0277 million km2. It is of critical importance for monitoring the dynamic vegetation variation on multi-spatiotemporal scales, exploring the underlying relationship between vegetation cover change and its driving forces (e.g., climate forces, topographic forces, and socio-economic forces), and investigating the time lag effects of vegetation variation in response to climate variables (e.g., precipitation and temperature) in eastern China from 2001 to 2016. To achieve the objectives of this study, the Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index (NDVI) time series with a 250 m spatial resolution and a 16-day temporal resolution, monthly meteorological data from meteorological (automatic) base station , Digital Elevation Model data with a 30 m spatial resolution, socio-economic statistical data, and the map of land use types, gross domestic product, and population density in 2000 and 2015 with an 1 km spatial resolution, and the vector map of eastern China at city level were used. A set of mathematical methods such as the maximum value composite method, linear regression analysis, rescaled range analysis, coefficient of variation, Person’s correlation coefficient, t-test, and spatial analysis methods (e.g., surface analysis and overlap analysis) were applied in this study. This study aims at monitoring the dynamic change of vegetation cover and investigating the relationship between vegetation cover and its driving forces on multiple spatiotemporal scales in eastern China from 2001 to 2016. The objectives of this study are fulfilled and the main findings and new results of this study are summarized in following. The overall annual NDVI displays a distinctive spatial heterogeneity across eastern China, presenting a gradient decrease from the south to the north of eastern China. The spatial distribution of NDVI in spring, summer, and autumn follows a similar pattern, but the overall NDVI value is higher in summer than in spring and autumn. Our calculation indicated that, during the past 16 years, the vegetation cover had gradually increased in eastern China with a magnitude of 0.0003 year-1. Areas with a greening trend and areas with a browning trend account for 49% and 33% of the study area, respectively. Spatially, we found that the browning areas are mainly distributed in city centers and the three economic zones and its surrounding areas. Considering the vegetation variation on seasonal scale, NDVI performs an increasing trend in spring and autumn but a decreasing trend in summer. In this study, we detected that areas expected to show consistency accounting for a larger proportion when compared with the areas expected to show anti-consistency on annual scale, while an opposite phenomenon was found on seasonal scale. In terms of the future changing trend of vegetation cover, areas with certain vegetation degradation will be larger than areas with certain vegetation improvement for eastern China both on annual and seasonal scales in the future. Estimating the vegetation stability on the basis of variation of coefficient, we found that the vegetation cover is relatively stable in the south of the study area, but it fluctuated wildly in the north of the study area. Our calculation suggested that temperature can be considered as the dominant climate factor controlling the vegetation growth in eastern China. The relationship is more pronounced between NDVI and temperature than between NDVI and precipitation both on annual and seasonal scales in eastern China for the study period. Moreover, the relationship between NDVI and precipitation is higher in autumn than in spring and summer, while the response of NDVI to temperature is stronger in spring than in autumn, followed by in summer. In this study, we observed, spatially, the overall maximum correlation coefficients between NDVI and precipitation as well as NDVI and temperature are basically higher in the north and lower in the south of the study area both on annual and seasonal scales. Temporally, on annual scale, the NDVI shows no lag time to changes in temperature but a 1-month lag time to precipitation variation. On seasonal scale, the maximum responses of NDVI to changes in precipitation and temperature establish 1-month longer in summer than in spring and autumn. Spatially, the lag time for maximum NDVI response to precipitation and temperature gradually increase from the north to the south of the study area. Elevation is regarded to be a dominant factor affecting the vertical distribution of vegetation cover. Our findings indicated that both the vegetation cover and vegetation stability increase with the elevation increase and reach its peak at an elevation of about 500 m. The vegetation degradation is more serious at the elevation range of 0 to 100 m than at higher elevation ranges. It is worth noticing that, in this study, our result is against our initial assumptions that the vegetation growth on the north-facing slope is better than the vegetation growth on the south-facing slope. However, we found that the vegetation cover, vegetation cover change, and vegetation stability show no statistical difference on the south-facing slope and north-facing slope. Similar to the responding mechanisms between the elevation-vegetation cover and elevation-vegetation stability, the vegetation cover and vegetation stability show a gradient upward trend with slope range increase. Furthermore, the proportion of the areas with a greening trend shows a “humped” pattern with the slope range increase, and it reaches the peak at the slope range of 6° to 15°. Our findings indicated that vegetation degradation is generally attributed to socio-economic development, urban expansion, and population growth, particularly in Tianjin, Shanghai, Jiangsu, Zhejiang, Fujian, and Guangdong. However, implementing large-scale reforestation and afforestation programs such as the Natural Forest Conservation Program, Three-North Shelter Forest Program, Beijing and Tianjin Sandstorm Source Controlling Program, and Grain for Green Program contribute to the vegetation greening phenomenon since 1978, in Liaoning, Beijing, Shandong, and Hebei in particular. We further observed that, spatially, the dynamic change of vegetation cover is negatively coupled with socio-economic development, urban expansion, and population growth. Areas with a high-speed socio-economic development, rapid urban expansion, and sharp population growth are along with severe vegetation degradation and strong vegetation oscillation spatially

Uncertainties in emission inventories

Emission inventories provide information about the amount of a pollutant that is emitted to the atmosphere as a result of a specific anthropogenic or natural process at a given time or place. Emission inventories can be used for either policy or scientific purposes. For policy purposes, emission inventories can be used to monitor the progress of environmental policy or to check compliance with conventions and protocols. For scientific purposes, emission inventories can be used as input into atmospheric dispersion models that are aimed at understanding the chemical and physical processes and the behaviour of air pollutants in the atmosphere. A strict separation between policy and scientific oriented emission inventories is not always possible. The usefulness of emission inventories for policy or science depends on the accuracy and the reliability of the inventories. There is uncertainty about an emission inventory when the accuracy and reliability of the emission estimates are not known. Proper use of emissions inventories requires an assessment of the uncertainties, including identification, qualification and quantification of the uncertainty. Although different methods for the assessment of uncertainty in emission inventories have been proposed, a systematic approach for identification, qualification and quantification of uncertainty does not exist. The objective of this thesis is to develop such a systematic approach for large-scale inventories. In order to meet this objective three research questions have been formulated:(i) What are the potential sources of uncertainty in emission inventories(ii) Which methods can be followed for the assessment of uncertainty(111) To what extent can uncertainty in emission inventories be identified, qualified or quantified.The methodology of emission inventory compilation typical for large-scale emission inventories has been illustrated by two emission inventories. In chapter 2, time series of past worldwide emission of anthropogenic trace gases for the period 1890 - 1990 are described. Chapter 3 presents projections for NOx emissions in Asia for the period 1990 -2020. The construction of these emission inventories was hampered by the lack of experimental data on the different sources of emission. As a result, the emissions were calculated on another scale than on which the emission processes occur in reality. The activity data and emission factors were based on extrapolation of existing information. Due to these aggregations and extrapolations, the emission inventories are inaccurate representations of the actual emissions.Chapter 4 describes the theoretical basis for our definitions of uncertainties, followed by a categorisation of uncertainties in emission inventories. It is argued that two types of uncertainty in emission inventories exist. Uncertainty about accuracy is the lack of knowledge about the sources and size of the inaccuracy. Uncertainty about reliability is the lack of knowledge about the degree to which the emission inventory is meeting user-specified quality criteria. These user-specified criteria depend on the purpose of the emission inventory. For scientific purposes the reliability is defined by the accuracy of the inventory. For policy purposes, quality criteria can be related to transparency, application of agreed upon methodologies or sometimes also to the assessment of accuracy. Uncertainty about reliability exists when either the accuracy of the emission inventory is not known or when the documentation of the inventory is inadequate and incomplete. Uncertainty about accuracy exists when the different sources of inaccuracy or the extent to which the inventory is inaccurate is not known. A categorisation of uncertainty about different sources of inaccuracy has been presented. Uncertainty about structural inaccuracy is the lack knowledge about the extent to which the structure of an emission inventory allows for an accurate calculation of the 'real' emission. Three causes for structural inaccuracy have been defined. These are aggregation error, incompleteness and mathematical formulation error. Uncertainty about input value inaccuracy is the lack of knowledge about the values of activity data and emission factors. Four causes for input value inaccuracy have been identified. These are extrapolation error, measurement error, unknown developments and reporting error.Uncertainty about reliability can be assessed through peer review. For the assessment of inaccuracy, a distinction is made between internal and external assessment of uncertainty. In an internal assessment, the methodology and information to construct an emission inventory form the basis for the assessment of inaccuracy. Based on review of available methodologies six methods for internal assessment are proposed: (i) qualitative discussion, (ii) data quality rating, (iii) calculation cheek and evaluation of mathematical formulation, (iv) expert judgement, (v) error propagation and (vi) importance analysis. In an external assessment, the difference between the emission inventory and external sources of information is used to identify, qualify or quantify inaccuracy in the emission inventory. Four methods can be used:(1)comparison with other emission inventories, (ii) comparison with (in)direct measurements, (iii) forward air quality modelling and (iv) inverse air quality modelling.Against this background we developed a systematic approach for the assessment of uncertainty in emission inventories. This framework, FRAULEIN (FRamework for the Assessment of Uncertainty in Large-scale Emission INventories) can be used to assess uncertainty about reliability and uncertainty about accuracy. It provides guidance for selection of the methods that can be used to identify, qualify or quantify different sources of uncertainty.Several methods included in the framework have been analysed in more detail to identify the advantages and disadvantages of these methods in practice. Chapter 5 presents the results of assessment of uncertainties in estimates of 1990 N20 emissions from agriculture in The Netherlands using the methods of error propagation and importance analysis. The results indicate that only a small number (three out of 23) of uncertain inventory parameters have large share in the inaccuracy of the emission inventory. These parameters include emission factors for indirect N20 emissions (EF5), the fraction of N leaching from agricultural soils (Fracleach) and the emission factor for direct soil emissions (EF1). Reducing the inaccuracy in the inventory should therefore focus on improved quantification of indirect emissions (based on EF5 and Fracleach) and direct soil emissions (EF1). From a methodological point of view, the results of the N20 case study show that quantification of input value inaccuracy through error propagation is influenced by the statisticalquantification interpretation of the available information in the IPCC Guidelines (default values, and uncertainty ranges of emission factors in particular). This result provides an indication that the extent to which inaccuracies can be assessed depends not only on the characteristics of the method used for the assessment but also on the available information on inventory parameters. Identification of inventory parameters having the largest share in the inaccuracy, on the other hand, was not influenced by the statistical interpretation of IPCC information.Chapter 6 describes the results of assessment of uncertainty in a European emission inventory of S02 in 1994 using forward air quality modelling and atmospheric measurements. The problem with this type of assessment is that it is not easy to pinpoint emission inventory inaccuracy as single cause of the deviation between measurements and model results. Inaccuracies exist in both the inventory, model and measurements. In the case study it has been analysed whether wind-direction-dependent differences between calculated and measured concentrations can be used to assess inaccuracies in emission inventories. The results indicate that in three regions within the study domain inaccuracy in the emission inventory is the most likely cause for the discrepancy between modelled and observed S02 concentrations. These regions are Sachsen/Brandenburg (Germany), Central England and the western part of the Russian Federation. In Sachsen/Brandenburg and Central England the spatial distribution of the emissions seems to be inaccurate while in the western part of the Russian Federation the total emission estimate seems to be inaccurate. We developed a relatively simple method to identify inventory inaccuracies based on differences between the air quality model and atmospheric measurements. However, it was also shown that the method is primarily a tool for identifying relatively inaccurate parts of the inventory. The method cannot be used to analyse causes of the inaccuracies, such as inaccurate structure or input values. Furthermore, it was concluded that the method is more a qualitative than a quantitative approach.There are three ways to use FRAULEIN in practice. First, in situations where the method for uncertainty assessment is prescribed, FRAULEIN clarifies the sources of uncertainty that can be identified, qualified or quantified. Second, if the objective of a study is to assess a specific source of uncertainty, FRAULEIN may serve as a guide for selection of the appropriate methods. Third, if the aim is to perform a full assessment of inaccuracy, FRAULEIN forms the basis of a four-step approach: (1) identification, qualification (2) and quantification (3) of the sources of inaccuracy, followed by evaluation to prioritise further research (4)