ALBERT

Description: Reported sulfur dioxide (SO2) emissions from US and Canadian sources have declined dramatically since the 1990s as a result of emission control measures. Observations from the Ozone Monitoring Instrument (OMI) on NASA's Aura satellite and ground-based in situ measurements are examined to verify whether the observed changes from SO2 abundance measurements are quantitatively consistent with the reported changes in emissions. To make this connection, a new method to link SO2 emissions and satellite SO2 measurements was developed. The method is based on fitting satellite SO2 vertical column densities (VCDs) to a set of functions of OMI pixel coordinates and wind speeds, where each function represents a statistical model of a plume from a single point source. The concept is first demonstrated using sources in North America and then applied to Europe. The correlation coefficient between OMI-measured VCDs (with a local bias removed) and SO2 VCDs derived here using reported emissions for 1° by 1° gridded data is 0.91 and the best-fit line has a slope near unity, confirming a very good agreement between observed SO2 VCDs and reported emissions. Having demonstrated their consistency, seasonal and annual mean SO2 VCD distributions are calculated, based on reported point-source emissions for the period 1980–2015, as would have been seen by OMI. This consistency is further substantiated as the emission-derived VCDs also show a high correlation with annual mean SO2 surface concentrations at 50 regional monitoring stations.

Description: A main concern surrounding (shale) gas production and exploitation is the leakage of methane, a potent greenhouse gas. High leakage rates have been observed outside of Europe but the representativeness of these observations for Europe is unknown. To facilitate the monitoring of methane leakage from a future shale gas industry in Europe we developed potential production scenarios for ten major shale gas plays and identified a suitable tracer in (shale) gas to distinguish oil and gas related emissions from other methane sources. To distinguish gas leakage from other methane sources we propose ethane, a known tracer for leakage from oil and gas production but absent in emissions from other important methane sources in Europe. Ethane contents for the ten plays are estimated from a European gas composition database and shale gas composition and reservoir data from the US, resulting in three different classes of ethane to methane ratios in the raw gas (0.015, 0.04 and 0.1). The ethane content classes have a relation with the average thermal maturity, a basic shale gas reservoir characteristic, which is known for all ten European shale gas plays. By assuming different production scenarios in addition to a range of possible gas leakage rates, we estimate potential ethane tracer release by shale gas play. Ethane emissions are estimated by play following a low, medium or high gas production scenario in combination with leakage rates ranging from 0.2 %–10 % based on observed leakage rates in the US.

Description: 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. 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.

Description: Reported sulfur dioxide (SO2) emissions from U.S. and Canadian sources have declined dramatically since the 1990s as a result of emissions control measures. Observations from the Ozone Monitoring Instrument (OMI) on NASA's Aura satellite and ground-based in-situ measurements are examined to verify whether the observed changes from SO2 abundance measurements are quantitatively consistent with the reported changes in emissions. To make this connection, a new method to link SO2 emissions and satellite SO2 measurements was developed. The method is based on fitting satellite SO2 vertical column densities (VCDs) to a set of functions of OMI pixel coordinates and wind speeds, where each function represents a statistical model of a plume from a single point source. The concept is first demonstrated using sources in North America, and then applied to Europe. The correlation coefficient between OMI-measured VCDs (with a local bias removed) and SO2 VCDs derived here using reported emissions for 1° by 1° gridded data is 0.91 and the best-fit line has a slope near unity, confirming a very good agreement between observed SO2 VCDs and reported emissions. Having demonstrated their consistency, seasonal and annual mean SO2 VCD distributions are calculated, based on reported point-source emissions for the period 1980–2015, as would have been seen by OMI. This consistency is further substantiated as the emissions-derived VCDs also show a high correlation with annual mean SO2 surface concentrations at 50 regional monitoring stations.

Description: Quantification of greenhouse gas emissions is receiving a lot of attention because of its relevance for climate mitigation. Complementary to official reported bottom-up emission inventories, quantification can be done with an inverse modelling framework, combining atmospheric transport models, prior gridded emission inventories and a network of atmospheric observations to optimize the emission inventories. An important aspect of such a method is a correct quantification of the uncertainties in all aspects of the modelling framework. The uncertainties in gridded emission inventories are, however, not systematically analysed. In this work, a statistically coherent method is used to quantify the uncertainties in a high-resolution gridded emission inventory of CO2 and CO for Europe. We perform a range of Monte Carlo simulations to determine the effect of uncertainties in different inventory components, including the spatial and temporal distribution, on the uncertainty in total emissions and the resulting atmospheric mixing ratios. We find that the uncertainties in the total emissions for the selected domain are 1 % for CO2 and 6 % for CO. Introducing spatial disaggregation causes a significant increase in the uncertainty of up to 40 % for CO2 and 70 % for CO for specific grid cells. Using gridded uncertainties, specific regions can be defined that have the largest uncertainty in emissions and are thus an interesting target for inverse modellers. However, the largest sectors are usually the best-constrained ones (low relative uncertainty), so the absolute uncertainty is the best indicator for this. With this knowledge, areas can be identified that are most sensitive to the largest emission uncertainties, which supports network design.