Jeroen Kuenen

Exiopol - Development And Illustrative Analyses Of A Detailed Global Mr Ee Sut/Iot

EXIOPOL (A New Environmental Accounting Framework Using Externality Data and Input–Output Tools for Policy Analysis) was a European Union (EU)-funded project creating a detailed, global, multiregional environmentally extended Supply and Use table (MR EE SUT) of 43 countries, 129 sectors, 80 resources, and 40 emissions. We sourced primary SUT and input–output tables from Eurostat and non-EU statistical offices. We harmonized and detailed them using auxiliary national accounts data and co-efficient matrices. Imports were allocated to countries of exports using United Nations Commodity Trade Statistics Database trade shares. Optimization procedures removed imbalances in these detailing and trade linking steps. Environmental extensions were added from various sources. We calculated the EU footprint of final consumption with resulting MR EE SUT. EU policies focus mainly on energy and carbon footprints. We show that the EU land, water, and material footprint abroad is much more relevant, and should be prioritized in the EUs environmental product and trade policies.

EXIOBASE 3: Developing a Time Series of Detailed Environmentally Extended Multi‐Regional Input‐Output Tables

Environmentally extended multiregional input-output (EE MRIO) tables have emerged as a key framework to provide a comprehensive description of the global economy and analyze its effects on the environment. Of the available EE MRIO databases, EXIOBASE stands out as a database compatible with the System of Environmental-Economic Accounting (SEEA) with a high sectorial detail matched with multiple social and environmental satellite accounts. In this paper, we present the latest developments realized with EXIOBASE 3-a time series of EE MRIO tables ranging from 1995 to 2011 for 44 countries (28 EU member plus 16 major economies) and five rest of the world regions. EXIOBASE 3 builds upon the previous versions of EXIOBASE by using rectangular supply-use tables (SUTs) in a 163 industry by 200 products classification as the main building locks. In order to capture structural changes, economic developments, as reported by national statistical agencies, were imposed on the available, disaggregated SUTs from EXIOBASE 2. These initial estimates were further refined by incorporating detailed data on energy, agricultural production, resource extraction, and bilateral trade. EXIOBASE 3 inherits the high level of environmental stressor detail from its precursor, with further improvement in the level of detail for resource xtraction. To account for the expansion of the European Union (EU), EXIOBASE 3 was developed with the full EU28 country set (including the new member state Croatia). EXIOBASE 3 provides a unique tool for analyzing the dynamics of environmental pressures of economic activities over time.

Discrepancies Between Top-Down and Bottom-Up Emission Inventories of Megacities: The Causes and Relevance for Modeling Concentrations and Exposure

A state-of-the-art regional European emission data base is combined and cross-checked with bottom-up emission inventories of Paris, London, Rhine-Ruhr area (Germany) and the Po-valley (Italy). It is shown that the allocation of the emission in the regional top-down inventory can deviate substantially from the megacity bottom-up inventories. For example, the PM10 and NOx in local inventories are respectively 26% and 62% (London), 33% and 95% (Paris), 55% and 108% (Rhine-Ruhr) and 110% and 107% (Po valley) of the emission allocated to the same area in the regional inventory. The match for the Po Valley is reasonable but if we zoom in on a city level (Milan) similar problems as seen in Paris and London surface. We conclude that the European scale inventory is consistent with official reported national emissions but local-national-regional scale inventories are not consistent. Since the discrepancies are large, predicted concentrations and population exposure estimates may be significantly different. Our work shows the importance of regionalization of emissions for model input and argues that consistency between emission inventories at different scales deserves more attention.

Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model

The development and application of chemistry transport models has a long tradition. Within the Netherlands the LOTOS–EUROS model has been developed by a consortium of institutes, after combining its independently developed predecessors in 2005. Recently, version 2.0 of the model was released as an open-source version. This paper presents the curriculum vitae of the model system, describing the model’s history, model philosophy, basic features and a validation with EMEP stations for the new benchmark year 2012, and presents cases with the model’s most recent and key developments. By setting the model developments in context and providing an outlook for directions for further development, the paper goes beyond the common model description. With an origin in ozone and sulfur modelling for the models LOTOS and EUROS, the application areas were gradually extended with persistent organic pollutants, reactive nitrogen, and primary and secondary particulate matter. After the combination of the models to LOTOS–EUROS in 2005, the model was further developed to include new source parametrizations (e.g. road resuspension, desert dust, wildfires), applied for operational smog forecasts in the Netherlands and Europe, and has been used for emission scenarios, source apportionment, and long-term hindcast and climate change scenarios. LOTOS–EUROS has been a front-runner in data assimilation of ground-based and satellite observations and has participated in many model intercomparison studies. The model is no longer confined to applications over Europe but is also applied to other regions of the world, e.g. China. The increasing interaction with emission experts has also contributed to the improvement of the model’s performance. The philosophy for model development has always been to use knowledge that is state of the art and proven, to keep a good balance in the level of detail of process description and accuracy of input and output, and to keep a good record on the effect of model changes using benchmarking and validation. The performance of v2.0 with respect to EMEP observations is good, with spatial correlations around 0.8 or higher for concentrations and wet deposition. Temporal correlations are around 0.5 or higher. Recent innovative applications include source apportionment and data assimilation, particle number modelling, and energy transition scenarios including corresponding land use changes as well as Saharan dust forecasting. Future developments would enable more flexibility with respect to model horizontal and vertical resolution and further detailing of model input data. Published by Copernicus Publications on behalf of the European Geosciences Union. 4146 A. M. M. Manders et al.: Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model This includes the use of different sources of land use characterization (roughness length and vegetation), detailing of emissions in space and time, and efficient coupling to meteorology from different meteorological models.

European Emission Inventories and Projections for Road Transport Non-Exhaust Emissions: Analysis of Consistency and Gaps in Emission Inventories From EU Member States

The relative contribution of wear emissions to urban road transport PM10 emissions has steadily grown and reaches more than 50% in many countries. Based on reported emissions by European countries for brake and tire wear and road abrasion, an overview of the emissions for Europe is presented and further analyzed for gaps and consistency. Implied emission factors per vehicle kilometer are calculated to facilitate country intercomparison, showing that differences up to a factor of 2–4 persist even if the extremes are excluded. If the current trends continue, wear emissions are predicted to grow to about 80%–90% of the direct PM10 emissions from road transport after 2020. Resuspension of road dust PM10 is currently excluded from official emission reporting. A model-based estimate of resuspension emissions is used to assess its importance in comparison with wear emissions, suggesting that resuspension of road dust may be the dominant source of road transport particulate matter (PM) emissions in many cities. It is recommended to include road resuspension in the emission reporting to obtain a more transparent and complete overview of all road transport contributions to urban PM concentrations

Modeling emissions for three-dimensional atmospheric chemistry transport models

ABSTRACT Poor air quality is still a threat for human health in many parts of the world. In order to assess measures for emission reductions and improved air quality, three-dimensional atmospheric chemistry transport modeling systems are used in numerous research institutions and public authorities. These models need accurate emission data in appropriate spatial and temporal resolution as input. This paper reviews the most widely used emission inventories on global and regional scales and looks into the methods used to make the inventory data model ready. Shortcomings of using standard temporal profiles for each emission sector are discussed, and new methods to improve the spatiotemporal distribution of the emissions are presented. These methods are often neither top-down nor bottom-up approaches but can be seen as hybrid methods that use detailed information about the emission process to derive spatially varying temporal emission profiles. These profiles are subsequently used to distribute bulk emissions such as national totals on appropriate grids. The wide area of natural emissions is also summarized, and the calculation methods are described. Almost all types of natural emissions depend on meteorological information, which is why they are highly variable in time and space and frequently calculated within the chemistry transport models themselves. The paper closes with an outlook for new ways to improve model ready emission data, for example, by using external databases about road traffic flow or satellite data to determine actual land use or leaf area. In a world where emission patterns change rapidly, it seems appropriate to use new types of statistical and observational data to create detailed emission data sets and keep emission inventories up-to-date. Implications: Emission data are probably the most important input for chemistry transport model (CTM) systems. They need to be provided in high spatial and temporal resolution and on a grid that is in agreement with the CTM grid. Simple methods to distribute the emissions in time and space need to be replaced by sophisticated emission models in order to improve the CTM results. New methods, e.g., for ammonia emissions, provide grid cell–dependent temporal profiles. In the future, large data fields from traffic observations or satellite observations could be used for more detailed emission data.