Energy Efficiency and GHG Emissions: Prospective Scenarios for the Aluminium Industry

This study examines the possibilities for energy efficiency and GHG emission improvements in the European aluminium industry. The first part of the study presents the status quo of the industry in the EU28 and Iceland by compiling a database of existing plants with their production characteristics and the best available and innovative technologies (BATs/ITs). A model EU is then developed to simulate the trend in each plant towards 2050. The use of the model in different scenarios allows the analysis of the cost-effectiveness of investments in BATs/ITs. The results show that in absolute terms, for the whole industry the energy consumption and direct GHG emissions can decrease from 2010 to 2050 by 21% and 66%, respectively. And, in almost all scenarios, for the primary aluminium production there is a convergence in the reduction of specific energy consumption and direct GHG emissions of 23% and 72%, respectively. Since most of the savings come from technologies that are in early stages of research, there is a clear need of a decided push and of creating the right conditions to make these potential savings happen.JRC.F.6-Energy Technology Policy Outloo

Assessment of the MACC reanalysis and its influence as chemical boundary conditions for regional air quality modeling in AQMEII-2

The Air Quality Model Evaluation International Initiative (AQMEII) has now reached its second phase which is dedicated to the evaluation of online coupled chemistry-meteorology models. Sixteen modeling groups from Europe and five from North America have run regional air quality models to simulate the year 2010 over one European and one North American domain. The MACC re-analysis has been used as chemical initial (IC) and boundary conditions (BC) by all participating regional models in AQMEII-2. The aim of the present work is to evaluate the MACC re-analysis along with the participating regional models against a set of ground-based measurements (O3, CO, NO, NO2, SO2, SO42−) and vertical profiles (O3 and CO). Results indicate different degrees of agreement between the measurements and the MACC re-analysis, with an overall better performance over the North American domain. The influence of BC on regional air quality simulations is analyzed in a qualitative way by contrasting model performance for the MACC re-analysis with that for the regional models. This approach complements more quantitative approaches documented in the literature that often have involved sensitivity simulations but typically were limited to only one or only a few regional scale models. Results suggest an important influence of the BC on ozone for which the underestimation in winter in the MACC re-analysis is mimicked by the regional models. For CO, it is found that background concentrations near the domain boundaries are rather close to observations while those over the interior of the two continents are underpredicted by both MACC and the regional models over Europe but only by MACC over North America. This indicates that emission differences between the MACC re-analysis and the regional models can have a profound impact on model performance and points to the need for harmonization of inputs in future linked global/regional modeling studies

Results of the first European Source Apportionment intercomparison for Receptor and Chemical Transport Models

In this study, the performance of the source apportionment model applications were evaluated by comparing the model results provided by 44 participants adopting a methodology based on performance indicators: z-scores and RMSEu, with pre-established acceptability criteria. Involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), provided a unique opportunity to cross-validate them. In addition, comparing the modelled source chemical profiles, with those measured directly at the source contributed to corroborate the chemical profile of the tested model results. The most used RM was EPA- PMF5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) and more difficulties are observed with SCE time series (72% of RMSEu accepted). Industry resulted the most problematic source for RMs due to the high variability among participants. Also the results obtained with CTMs were quite comparable to their ensemble reference using all models for the overall average (>92% of successful z-scores) while the comparability of the time series is more problematic (between 58% and 77% of the candidates’ RMSEu are accepted). In the CTM models a gap was observed between the sum of source contributions and the gravimetric PM10 mass likely due to PM underestimation in the base case. Interestingly, when only the tagged species CTM results were used in the reference, the differences between the two CTM approaches (brute force and tagged species) were evident. In this case the percentage of candidates passing the z-score and RMSEu tests were only 50% and 86%, respectively. CTMs showed good comparability with RMs for the overall dataset (83% of the z-scores accepted), more differences were observed when dealing with the time series of the single source categories. In this case the share of successful RMSEu was in the range 25% - 34%.JRC.C.5-Air and Climat

De stabiliteit van Nationale emissierapportages. Over hoe emissies uit voorgaande jaren regelmatig toch anders blijken te zijn

Emissieschattingen voor historische jaren veranderen met grote regelmaat. Voor de emissie-experts wellicht vanzelfsprekend, voor een buitenstaander niet: De gemeten temperatuur of NO2-concentratie in bijvoorbeeld 2010 wordt toch ook niet elk jaar herzien? Als gebruiker van deze emissieschattingen is het belangrijk dit te beseffen, vooral wanneer men ook emissiedata van buurlanden of heel Europa wenst te gebruiken voor bijvoorbeeld modelering van grensoverschrijdende luchtkwalitei

The revised EMEP/EEA Guidebook compared to the country specific inventory system in the Netherlands

Parties to the LRTAP convention have agreed to annually report atmospheric emissions and are required to set up an emission inventory. As a minimum, parties shall use the latest version of the EMEP/EEA Air Pollutant Inventory Guidebook, but most countries - including the Netherlands - have set up their own inventory, which uses country specific information to supplement the information from the Guidebook. In this study, emissions estimated within the Dutch Emission Inventory are compared to emissions estimated using Guidebook emission factors and Dutch statistics for the year 2005. The objective is to explore the quality of both methods and to find major differences and similarities. The comparison shows that for most sources, emission estimates are within uncertainty ranges for both methodologies, especially for sources where a higher Tier (more detailed) methodology is used to estimate the emissions. This is in line with the Guidelines which indicate that for key categories a more detailed methodology should be used. The comparison also shows some surprising differences, such as large differences in emission factors (especially Tier 1) and missing sources (fireworks and abrasion of railway overhead wires, causing 16% of total copper emissions in the Netherlands) which have not been included in the Guidebook. This comparison is shown to be a useful tool to identify areas where improvements and further research are necessary. © 2010 Elsevier Ltd

TNO-MACC_II emission inventory; a multi-year (2003-2009) consistent high-resolution European emission inventory for air quality modelling

Abstract. Emissions to air are reported by countries to EMEP. The emissions data are used for country compliance checking with EU emission ceilings and associated emission reductions. The emissions data are also necessary as input for air quality modelling. The quality of these “official” emissions varies across Europe. As alternative to these official emissions, a spatially explicit high-resolution emission inventory (7×7 km) for UNECE-Europe for all years between 2003 and 2009 for the main air pollutants was made. The primary goal was to supply air quality modellers with the input they need. The inventory was constructed by using the reported emission national totals by sector where the quality is sufficient. The reported data were analysed by sector in detail, and completed with alternative emission estimates as needed. This resulted in a complete emission inventory for all countries. For particulate matter, for each source emissions have been split in coarse and fine particulate matter, and further disaggregated to EC, OC, SO4, Na and other minerals using fractions based on the literature. Doing this at the most detailed sectoral level in the database implies that a consistent set was obtained across Europe. This allows better comparisons with observational data which can, through feedback, help to further identify uncertain sources and/or support emission inventory improvements for this highly uncertain pollutant. The resulting emission data set was spatially distributed consistently across all countries by using proxy parameters. Point sources were spatially distributed using the specific location of the point source. The spatial distribution for the point sources was made year-specific. The TNO-MACC_II is an update of the TNO-MACC emission data set. Major updates included the time extension towards 2009, use of the latest available reported data (including updates and corrections made until early 2012) and updates in distribution map

TNO-CAMS European CO2 emissions 2000-2014 v1

<p><strong>Introduction</strong></p> <p>This TNO_CAMS_CO2 emission dataset was prepared by TNO as a contribution to the H2020 project MACC-III and the subsequent Copernicus Atmospheric Monitoring Service. This model-ready historic emission inventory at high spatial resolution (~7x7 km) for UNECE-Europe for 15 consecutive years (2000–2014) providing CO₂ from fossil fuels and CO₂ from biofuels is intended to support modelling and sub-national scale identification of emissions. Where available and considered fit for purpose, we have used CO₂ estimates as reported by the Parties to UNFCCC. The data have been supplemented by other estimates, most notable from the IIASA GAINS model and the JRC EDGAR database to create a complete coverage.  The approach to the spatial distribution of the dataset is similar to the TNO-MACC emission dataset for air pollutants ( see Kuenen et al., ACP, 2014).</p> <p>The emission grid consists of UNECE-Europe in WGS84 projection (lon-lat) with a spatial resolution of 1/8 x 1/16 degrees (lon x lat). The lower left of the grid is at lon = -60, lat = 30 and the upper right is at lon = 60, lat = 72.</p> <p>The grid files TXT (.csv)  & netcdf (.nc) both contain annual total emissions per grid cell for the year 2000-2014. A separate file has been prepared for each year. </p> <p>The unit in the .csv files is Mg/gridcell/yr</p> <p>The unit in the .nc files is kg/gridcell/yr</p> <p>Sectoral breakdown  uses the SNAP classification. Compared to the default SNAP1 sectors (1 to 10), a couple of refinements have been made to the sectors:</p> <ul> <li> <p>SNAP 3 and SNAP 4 are grouped as SNAP 34</p> </li> <li> <p>SNAP 7 is split in SNAP 71 to 75</p> </li> </ul> <p>The dataset is described in </p> <p>Denier van der Gon, H.A.C., J.J.P. Kuenen, G. Janssens-Maenhout, U. Döring, S. Jonkers, A.J.H. Visschedijk., TNO_CAMS high resolution European emission inventory for anthropogenic CO₂ for 2000-2014 and future years following two different pathways, ESSD, in preparation, 2017.</p> <p> </p

Tropospheric NO2 over China

The results are presented of a study to tropospheric NO2 over China, based on measurements from the satellite instruments GOME and SCIAMACHY. A data set of 10 year tropospheric NO2 has been processed from GOME and SCIAMACHY observations using a combined retrieval/assimilation approach. This approach allows the retrieval of global, accurate tropospheric concentrations and detailed error estimates. The resulting dataset has been analysed with statistical methods to derive trends in NO2 and the seasonal variability on a grid of 1×1 degree for all regions of China. The variance and the autocorrelation of the noise are used to calculate the significance of the trend. The results show a large growth of tropospheric NO2 over eastern China, especially above the industrial areas with a fast economical growth. The seasonal pattern of the NO2 concentration shows a clear difference between East and West China. This spatial difference correlates with the dominating source of emission

Tropospheric NO2 over China

The results are presented of a study to tropospheric NO2 over China, based on measurements from the satellite instruments GOME and SCIAMACHY. A data set of 10 year tropospheric NO2 has been processed from GOME and SCIAMACHY observations using a combined retrieval/assimilation approach. This approach allows the retrieval of global, accurate tropospheric concentrations and detailed error estimates. The resulting dataset has been analysed with statistical methods to derive trends in NO2 and the seasonal variability on a grid of 1×1 degree for all regions of China. The variance and the autocorrelation of the noise are used to calculate the significance of the trend. The results show a large growth of tropospheric NO2 over eastern China, especially above the industrial areas with a fast economical growth. The seasonal pattern of the NO2 concentration shows a clear difference between East and West China. This spatial difference correlates with the dominating source of emission