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"Çalışmada 29 yazar bulunmaktadır. Bu yazarlardan sadece Bursa Uludağ Üniversitesi mensuplarının girişleri yapılmıştır” The paper presents the first modelling experiment of the European-scale olive pollen dispersion, analyses the quality of the predictions, and outlines the research needs. A 6-model strong ensemble of Copernicus Atmospheric Monitoring Service (CAMS) was run throughout the olive season of 2014, computing the olive pollen distribution. The simulations have been compared with observations in eight countries, which are members of the European Aeroallergen Network (EAN). Analysis was performed for individual models, the ensemble mean and median, and for a dynamically optimised combination of the ensemble members obtained via fusion of the model predictions with observations. The models, generally reproducing the olive season of 2014, showed noticeable deviations from both observations and each other. In particular, the season was reported to start too early by 8 days, but for some models the error mounted to almost 2 weeks. For the end of the season, the disagreement between the models and the observations varied from a nearly perfect match up to 2 weeks too late. A series of sensitivity studies carried out to understand the origin of the disagreements revealed the crucial role of ambient temperature and consistency of its representation by the meteorological models and heat-sum-based phenological model. In particular, a simple correction to the heat-sum threshold eliminated the shift of the start of the season but its validity in other years remains to be checked. The short-term features of the concentration time series were reproduced better, suggesting that the precipitation events and cold/warm spells, as well as the large-scale transport, were represented rather well. Ensemble averaging led to more robust results. The best skill scores were obtained with data fusion, which used the previous days' observations to identify the optimal weighting coefficients of the individual model forecasts. Such combinations were tested for the forecasting period up to 4 days and shown to remain nearly optimal throughout the whole period. European Union's Copernicus Programme University of Latvia

Through the comparison of several regional-scale chemistry transport modeling systems that simulate meteorology and air quality over the European and North American continents, this study aims at (i) apportioning error to the responsible processes using timescale analysis, (ii) helping to detect causes of model error, and (iii) identifying the processes and temporal scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition, and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2. 5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance, and covariance) can help assess the nature and quality of the error. Each of the error components is analyzed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intraday) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emission and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high interdependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. This will require evaluation methods that are able to frame the impact on error of processes, conditions, and fluxes at the surface. For example, error due to emission and boundary conditions is dominant for primary species (CO, particulate matter (PM)), while errors due to meteorology and chemistry are most relevant to secondary species, such as ozone. Some further aspects emerged whose interpretation requires additional consideration, such as the uniformity of the synoptic error being region- and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.

Four regional chemistry transport models were applied to simulate the concentration and composition of particulate matter (PM) in Europe for 2005 with horizontal resolution ∼ 20 km. The modelled concentrations were compared with the measurements of PM chemical composition by the European Monitoring and Evaluation Programme (EMEP) monitoring network. All models systematically underestimated PM10 and PM2.5 by 10–60 %, depending on the model and the season of the year, when the calculated dry PM mass was compared with the measurements. The average water content at laboratory conditions was estimated between 5 and 20 % for PM2.5 and between 10 and 25 % for PM10. For majority of the PM chemical components, the relative underestimation was smaller than it was for total PM, exceptions being the carbonaceous particles and mineral dust. Some species, such as sea salt and NO3−, were overpredicted by the models. There were notable differences between the models' predictions of the seasonal variations of PM, mainly attributable to different treatments or omission of some source categories and aerosol processes. Benzo(a)pyrene concentrations were overestimated by all the models over the whole year. The study stresses the importance of improving the models' skill in simulating mineral dust and carbonaceous compounds, necessity for high-quality emissions from wildland fires, as well as the need for an explicit consideration of aerosol water content in model–measurement comparison.

Accurate modelling of mitigation measures for nitrogen deposition and secondary inorganic aerosol (SIA) episodes requires a detailed representation of emission patterns from agriculture. In this study the meteorological influence on the temporal variability of ammonia emissions from livestock housing and application of manure and fertilizer are included in the chemistry transport model LOTOS-EUROS. For manure application, manure transport data from Flanders (Belgium) were used as a proxy to derive the emission variability. Using improved ammonia emission variability strongly improves model performance for ammonia, mainly by a better representation of the spring maximum. The impact on model performance for SIA was negligible as explained by the limited, ammonia rich region in which the emission variability was updated. The contribution of Flemish agriculture to modelled annual mean ammonia and SIA concentrations in Flanders were quantified at respectively 7-8 and 1-2 μg/m3. A scenario study was performed to investigate the effects of reducing ammonia emissions from manure application during PM episodes by 75%, yielding a maximum reduction in modelled SIA levels of 1-3 μg/m3 during episodes. Year-to-year emission variability and a soil module to explicitly model the emission process from manure and fertilizer application are needed to further improve the modelling of the ammonia budget. © 2016 Elsevier Ltd.

We present an overview of the modelling of particle number concentrations (PNCs) in five major European cities, namely Helsinki, Oslo, London, Rotterdam, and Athens, in 2008. Novel emission inventories of particle numbers have been compiled both on urban and European scales. We used atmospheric dispersion modelling for PNCs in the five target cities and on a European scale, and evaluated the predicted results against available measured concentrations. In all the target cities, the concentrations of particle numbers (PNs) were mostly influenced by the emissions originating from local vehicular traffic. The influence of shipping and harbours was also significant for Helsinki, Oslo, Rotterdam, and Athens, but not for London. The influence of the aviation emissions in Athens was also notable. The regional background concentrations were clearly lower than the contributions originating from urban sources in Helsinki, Oslo, and Athens. The regional background was also lower than urban contributions in traffic environments in London, but higher or approximately equal to urban contributions in Rotterdam. It was numerically evaluated that the influence of coagulation and dry deposition on the predicted PNCs was substantial for the urban background in Oslo. The predicted and measured annual average PNCs in four cities agreed within approximately ≤ 26 % (measured as fractional biases), except for one traffic station in London. This study indicates that it is feasible to model PNCs in major cities within a reasonable accuracy, although major challenges remain in the evaluation of both the emissions and atmospheric transformation of PNCs.

Anaerobic ammonium oxidation (anammox) is a novel way to reduce nitrogen in ammonium rich wastewater. Although aquatic eutrophication will certainly be reduced, it is unknown how other environmental impacts may change by including anammox in the treatment of wastewater. Here, life cycle assessment (LCA) was used to assess the environmental profile of a full scale wastewater treatment plant over its complete life cycle. Changes in the environmental profile by introducing a two-step anammox system in the side stream were assessed based on monitoring data from the full scale Dokhaven wastewater treatment plant (Rotterdam, The Netherlands). Our results confirmed that the two-step anammox technique further reduced life cycle nitrogen emissions compared to the regular treatment of nitrogen in wastewater. This led to a decrease in marine eutrophication potential of 16% for the total wastewater treatment plant. However, our LCA results showed that these ammonium reductions came at the cost of increasing climate change and other environmental impacts. Climate change impacts increased with 9% going from a traditional wastewater plant to the one including two-step anammox, due to increased direct emissions next to electricity use. Our LCA highlights trade-offs when adding two-step anammox for nitrogen removal in wastewater treatment systems. This has significant implications for other WWTPs as these trade-offs should not neglected when implementation of anammox is considered. © 2015 Elsevier B.V. All rights reserved. Chemicals/CAS: ammonia, 14798-03-9, 51847-23-5, 7664-41-7; nitrogen, 7727-37-9

Particulate matter (PM) air quality in Europe has improved substantially over the past decades, but it still poses a significant threat to human health. Accurate regional scale maps of PM10 concentrations are needed for monitoring progress in mitigation strategies and monitoring compliance with statutory limit values. Chemistry transport models (CTM) use emission databases and simulate the transport and deposition of pollutants. They deliver such maps but are known to be inaccurate. A promising approach is to use geostatistics to model the relationship between the in situ observations and the CTM. This has been shown to be more accurate than using either observations or CTM's alone. This paper presents a spatially varying coefficients (SVC) geostatistical model as an extension of the standard spatially varying intercept (SVI) geostatistical model. SVC allowed the regression coefficient to vary spatially according to a covariance function, the parameters of which were estimated from the data. It was built as a Bayesian hierarchical model and implemented using Markov chain Monte Carlo. The procedure was applied to Airbase PM10 observations and LOTOS-EUROS simulated PM10 for central, southern and eastern Europe. Model-fit diagnostics showed that SVC delivered a better fit to the data than SVI. Mapping the spatially varying coefficients allowed identification of the locations where the CTM performed well or poorly. This could be used for objective CTM evaluation purposes. The posterior predictive simulations were also used to map median PM10 concentrations as well as the probability of exceeding the 50 μg m−3 EU daily PM10 concentration threshold. Although posterior median prediction accuracy was similar for SVI and SVC, SVC better modelled the process and yielded narrower credible intervals. As such, SVC was more appropriate for quantifying uncertainty and for mapping threshold exceedances. The resulting maps may be used to guide air quality assessment and mitigation strategies, including those related to health impacts

A large shortcoming of current chemistry transport models (CTM) for simulating the fate of ammonia in the atmosphere is the lack of a description of the bi-directional surface–atmosphere exchange. In this paper, results of an update of the surface–atmosphere exchange module DEPAC, i.e. DEPosition of Acidifying Compounds, in the chemistry transport model LOTOS-EUROS are discussed. It is shown that with the new description, which includes bi-directional surface–atmosphere exchange, the modeled ammonia concentrations increase almost everywhere, in particular in agricultural source areas. The reason is that by using a compensation point the ammonia lifetime and transport distance is increased. As a consequence, deposition of ammonia and ammonium decreases in agricultural source areas, while it increases in large nature areas and remote regions especially in southern Scandinavia. The inclusion of a compensation point for water reduces the dry deposition over sea and allows reproducing the observed marine background concentrations at coastal locations to a better extent. A comparison with measurements shows that the model results better represent the measured ammonia concentrations. The concentrations in nature areas are slightly overestimated, while the concentrations in agricultural source areas are still underestimated. Although the introduction of the compensation point improves the model performance, the modeling of ammonia remains challenging. Important aspects are emission patterns in space and time as well as a proper approach to deal with the high concentration gradients in relation to model resolution. In short, the inclusion of a bi-directional surface–atmosphere exchange is a significant step forward for modeling ammonia.

We have made a number of developments to the Weather, Research and Forecasting model coupled with Chemistry (WRF-Chem), with the aim of improving model prediction of trace atmospheric gas-phase chemical and aerosol composition, and of interactions between air quality and weather. A reduced form of the Common Reactive Intermediates gas-phase chemical mechanism (CRIv2-R5) has been added, using the Kinetic Pre-Processor (KPP) interface, to enable more explicit simulation of VOC degradation. N2O5 heterogeneous chemistry has been added to the existing sectional MOSAIC aerosol module, and coupled to both the CRIv2-R5 and existing CBM-Z gas-phase schemes. Modifications have also been made to the sea-spray aerosol emission representation, allowing the inclusion of primary organic material in sea-spray aerosol. We have worked on the European domain, with a particular focus on making the model suitable for the study of nighttime chemistry and oxidation by the nitrate radical in the UK atmosphere. Driven by appropriate emissions, wind fields and chemical boundary conditions, implementation of the different developments are illustrated, using a modified version of WRF-Chem 3.4.1, in order to demonstrate the impact that these changes have in the Northwest European domain. These developments are publicly available in WRF-Chem from version 3.5.1 onwards.

Aerosol-cloud interactions (ACI) constitute the single largest uncertainty in anthropogenic radiative forcing. To reduce the uncertainties and gain more confidence in the simulation of ACI, models need to be evaluated against observations, in particular against measurements of cloud condensation nuclei (CCN). Here we present a data set - ready to be used for model validation - of long-term observations of CCN number concentrations, particle number size distributions and chemical composition from 12 sites on 3 continents. Studied environments include coastal background, rural background, alpine sites, remote forests and an urban surrounding. Expectedly, CCN characteristics are highly variable across site categories. However, they also vary within them, most strongly in the coastal background group, where CCN number concentrations can vary by up to a factor of 30 within one season. In terms of particle activation behaviour, most continental stations exhibit very similar activation ratios (relative to particles 20nm) across the range of 0.1 to 1.0% supersaturation. At the coastal sites the transition from particles being CCN inactive to becoming CCN active occurs over a wider range of the supersaturation spectrum. Several stations show strong seasonal cycles of CCN number concentrations and particle number size distributions, e.g. at Barrow (Arctic haze in spring), at the alpine stations (stronger influence of polluted boundary layer air masses in summer), the rain forest (wet and dry season) or Finokalia (wildfire influence in autumn). The rural background and urban sites exhibit relatively little variability throughout the year, while short-term variability can be high especially at the urban site. The average hygroscopicity parameter, calculated from the chemical composition of submicron particles was highest at the coastal site of Mace Head (0.6) and lowest at the rain forest station ATTO (0.2-0.3). We performed closure studies based on -Kohler theory to predict CCN number concentrations. The ratio of predicted to measured CCN concentrations is between 0.87 and 1.4 for five different types of . The temporal variability is also well captured, with Pearson correlation coefficients exceeding 0.87. Information on CCN number concentrations at many locations is important to better characterise ACI and their radiative forcing. But long-term comprehensive aerosol particle characterisations are labour intensive and costly. Hence, we recommend operating migrating-CCNCs to conduct collocated CCN number concentration and particle number size distribution measurements at individual locations throughout one year at least to derive a seasonally resolved hygroscopicity parameter. This way, CCN number concentrations can only be calculated based on continued particle number size distribution information and greater spatial coverage of long-term measurements can be achieved. © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License.

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 blocks. 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 extraction. 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.

Long-term monitoring stations for atmospheric pollutants are often equipped with low-resolution concentration samplers. In this study, we analyse the errors associated with using monthly average ammonia concentrations as input variables for bidirectional biosphere-atmosphere exchange models, which are commonly used to estimate dry deposition fluxes. Previous studies often failed to account for a potential correlation between ammonia exchange velocities and ambient concentrations. We formally derive the exact magnitude of these errors from statistical considerations and propose a correction scheme based on parallel measurements using high-frequency analysers. In case studies using both modelled and measured ammonia concentrations and micrometeorological drivers from sites with varying pollution levels, we were able to substantially reduce bias in the predicted ammonia fluxes. Neglecting to account for these errors can, in some cases, lead to significantly biased deposition estimates compared to using high-frequency instrumentation or corrected averaging strategies. Our study presents a first step towards a unified correction scheme for data from nation-wide air pollutant monitoring networks to be used in chemical transport and air quality models.

Sources of elemental carbon (EC) and organic carbon (OC) in atmospheric aerosols (carbonaceous aerosols) were investigated by collection of weekly aerosol filter samples at six background sites in Northern Europe (Birkenes, Norway; Vavihill, Sweden; Risoe, Denmark; Cabauw and Rotterdam in The Netherlands; Melpitz, Germany) during winter 2013. Analysis of 14C and a set of molecular tracers were used to constrain the sources of EC and OC. During the four-week campaign, most sites (in particular those in Germany and The Netherlands) were affected by an episode during the first two weeks with high concentrations of aerosol, as continental air masses were transported westward. The analysis results showed a clear, increasing north to south gradient for most molecular tracers. Total carbon (TC = OC + EC) at Birkenes showed an average concentration of 0.5 ± 0.3 μg C m−3, whereas the average concentration at Melpitz was 6.0 ± 4.3 μg C m−3. One weekly mean TC concentration as high as 11 μg C m−3 was observed at Melpitz. Average levoglucosan concentrations varied by an order of magnitude from 25 ± 13 ng m−3 (Birkenes) to 249 ± 13 ng m−3 (Melpitz), while concentrations of tracers of fungal spores (arabitol and mannitol) and vegetative debris (cellulose) were very low, showing a minor influence of primary biological aerosol particles during the North European winter. The fraction of modern carbon generally varied from 0.57 (Melpitz) to 0.91 (Birkenes), showing an opposite trend compared to the molecular tracers and TC. Total concentrations of 10 biogenic and anthropogenic carboxylic acids, mainly of secondary origin, were 4–53 ng m−3, with the lowest concentrations observed at Birkenes and the highest at Melpitz. However, the highest relative concentrations of carboxylic acids (normalized to TC) were observed at the most northern sites. Levels of organosulphates and nitrooxy organosulphates varied more than two orders of magnitude, from 2 to 414 ng m−3, between individual sites and samples. The three sites Melpitz, Rotterdam and Cabauw, located closest to source regions in continental Europe, showed very high levels of organosulphates and nitrooxy organosulphates (up to 414 ng m−3) during the first two weeks of the study, while low levels (40% of TC), while use and combustion of fossil fuels was the second most important source. Furthermore, EC from biomass burning accounted for 7–16% of TC, whereas EC from fossil sources contributed to

The visibility on New Year's nights in the Netherlands is low during stagnant weather. This is due to the scattering and absorption of light by the aerosol-smoke from the fireworks. We made an assessment of the responsible aerosol-species. The investigation took place during the New Year's night of 2009. Measurements were made at a regional site in the centre of the country away from specific local sources. An Integrating Nephelometer measured the light-scattering by the inherent compounds after removal of water from the aerosol by drying the air. The actual light-scattering was determined in an open-air scatterometer; it was a factor of five higher than the “dry” value. The difference in actual and “dry” light-scattering can only be explained by water–uptake of the salty hygroscopic components of the aerosol. This hypothesis is substantiated by measurements of the composition of the aerosol. The size-dependent concentrations of the salty ionic species were determined on-line with a MARGA-“sizer”. These components were for a large part in particles in the size range that most effectively scatter light. The “dry” light-scattering was exerted by the inorganic salt components and the sooty carbonaceous material alike. However, the salty products from the fireworks are hygroscopic and take up water at the high relative humidities occurring that night. This explains the fivefold larger light-scattering by the wet ambient aerosol as compared to that by the dry aerosol in the integrating nephelometer. The visibility, which is the inverse of the open-air scattering, is thus indirectly governed by the salty products of the fireworks due to their uptake of water. Under stagnant weather conditions during New Year's nights in the Netherlands both the aerosol concentrations and the relative humidity are high; this implies that the ionic species govern the low visibilities in general, be it via their uptake of water. © 2017 Elsevier Ltd Chemicals/CAS: water, 7732-18-5

This article presents a study of aerosol optical and microphysical properties under different relative humidity (RH) but well mixed layer conditions using optical and microphysical aerosol properties from multi-wavelength (MW) Raman lidar and in-situ aerosol observations collected at the Cabauw Experimental Site for Atmospheric Research (CESAR). Two hygroscopic events are described through 3 backscatter (β) and 2 extinction (α) coefficients which in turn provide intensive parameters such as the backscatter-related Ångström exponent (åβ) and the lidar ratio (LR). Along with it, profiles of RH were inferred from Raman lidar observations and therefore, as a result of varying humidity conditions, a shift on the aerosol optical properties can be described. Thus, it is observed that as RH increases, aerosols uptake water vapour, augment their size and consequently the åβ diminishes whereas the LR increases. The enhancement factor based on the backscatter coefficient at 532 nm, which characterizes the aerosol from hygroscopic standpoint, is also estimated. Finally, microphysical properties that are necessary for aerosol radiative forcing estimates - such as volume, effective radii, refractive index and size distribution, all vertically resolved - are retrieved using the inversion with regularization. Using this method, two hygroscopic events are described in detail. © 2015 Elsevier Ltd.

The mandate of the Task Force Hemispheric Transport of Air Pollution (TF HTAP) under the Convention on Long-Range Transboundary Air Pollution (CLRTAP) is to improve the scientific understanding of the intercontinental air pollution transport, to quantify impacts on human health, vegetation and climate, to identify emission mitigation options across the regions of the Northern Hemisphere, and to guide future policies on these aspects. The harmonization and improvement of regional emission inventories is imperative to obtain consolidated estimates on the formation of global-scale air pollution. An emissions data set has been constructed using regional emission grid maps (annual and monthly) for SO2, NOx, CO, NMVOC, NH3, PM10, PM2.5, BC and OC for the years 2008 and 2010, with the purpose of providing consistent information to global and regional scale modelling efforts. This compilation of different regional gridded inventories – including that of the Environmental Protection Agency (EPA) for USA, the EPA and Environment Canada (for Canada), the European Monitoring and Evaluation Programme (EMEP) and Netherlands Organisation for Applied Scientific Research (TNO) for Europe, and the Model Inter-comparison Study for Asia (MICS-Asia III) for China, India and other Asian countries – was gap-filled with the emission grid maps of the Emissions Database for Global Atmospheric Research (EDGARv4.3) for the rest of the world (mainly South America, Africa, Russia and Oceania). Emissions from seven main categories of human activities (power, industry, residential, agriculture, ground transport, aviation and shipping) were estimated and spatially distributed on a common grid of 0.1° × 0.1° longitude-latitude, to yield monthly, global, sector-specific grid maps for each substance and year. The HTAP_v2.2 air pollutant grid maps are considered to combine latest available regional information within a complete global data set. The disaggregation by sectors, high spatial and temporal resolution and detailed information on the data sources and references used will provide the user the required transparency. Because HTAP_v2.2 contains primarily official and/or widely used regional emission grid maps, it can be recommended as a global baseline emission inventory, which is regionally accepted as a reference and from which different scenarios assessing emission reduction policies at a global scale could start. An analysis of country-specific implied emission factors shows a large difference between industrialised countries and developing countries for acidifying gaseous air pollutant emissions (SO2 and NOx) from the energy and industry sectors. This is not observed for the particulate matter emissions (PM10, PM2.5), which show large differences between countries in the residential sector instead. The per capita emissions of all world countries, classified from low to high income, reveal an increase in level and in variation for gaseous acidifying pollutants, but not for aerosols. For aerosols, an opposite trend is apparent with higher per capita emissions of particulate matter for low income countries.

The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together sixteen modeling groups from Europe and North America, running eight operational online-coupled air quality models over Europe and North America on common emissions and boundary conditions. With the advent of online-coupled models providing new capability to quantify the effects of feedback processes, the main aim of this study is to compare the response of coupled air quality models to simulate levels of O3 over the two continental regions. The simulated annual, seasonal, continental and sub-regional ozone surface concentrations and vertical profiles for the year 2010 have been evaluated against a large observational database from different measurement networks operating in Europe and North America. Results show a general model underestimation of the annual surface ozone levels over both continents reaching up to 18% over Europe and 22% over North America. The observed temporal variations are successfully reproduced with correlation coefficients larger than 0.8. Results clearly show that the simulated levels highly depend on the meteorological and chemical configurations used in the models, even within the same modeling system. The seasonal and sub-regional analyses show the models' tendency to overestimate surface ozone in all regions during autumn and underestimate in winter. Boundary conditions strongly influence ozone predictions especially during winter and autumn, whereas during summer local production dominates over regional transport. Daily maximum 8-h averaged surface ozone levels below 50-60 μg m-3 are overestimated by all models over both continents while levels over 120-140 μg m-3 are underestimated, suggesting that models have a tendency to severely under-predict high O3 values that are of concern for air quality forecast and control policy applications. © 2014 Elsevier Ltd. Chemicals/CAS: isoprene, 78-79-5; ozone, 10028-15-6

The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together seventeen modeling groups from Europe and North America, running eight operational online-coupled air quality models over Europe and North America using common emissions and boundary conditions. The simulated annual, seasonal, continental and sub-regional particulate matter (PM) surface concentrations for the year 2010 have been evaluated against a large observational database from different measurement networks operating in Europe and North America. The results show a systematic underestimation for all models in almost all seasons and sub-regions, with the largest underestimations for the Mediterranean region. The rural PM10 concentrations over Europe are underestimated by all models by up to 66% while the underestimations are much larger for the urban PM10 concentrations (up to 75%). On the other hand, there are overestimations in PM2.5 levels suggesting that the large underestimations in the PM10 levels can be attributed to the natural dust emissions. Over North America, there is a general underestimation in PM10 in all seasons and sub-regions by up to ~90% due mainly to the under predictions in soil dust. SO2/4 levels over EU are underestimated by majority of the models while NO-3 levels are largely overestimated, particulary in east and south Europe. NH4 levels are also under- underestimated largely in south Europe. SO4 levels over North America are particularly overestimated over the western US that is characterized by large anthropogenic emissions while the eastern USA is characterized by underestimated SO4 levels by the majority of the models. Daytime AOD levels at 555 nm is simulated within the 50% error range over both continents with differences attributed to differences in concentrations of the relevant species as well as in approaches in estimating the AOD. Results show that the simulated dry deposition can lead to substantial differences among the models. Overall, the results show that representation of dust and sea-salt emissions can largely impact the simulated PM concentrations and that there are still major challenges and uncertainties in simulating the PM levels.

Chemistry-transport models are increasingly used in Europe for estimating air quality or forecasting changes in pollution levels. But with this increased use of modeling arises the need of harmonizing the methodologies to determine the quality of air quality model applications. This is complex for planning applications, i.e. when models are used to assess the impact of realistic or virtual emission scenarios. In this work, the methodology based on the calculation of potencies proposed by Thunis and Clappier (2014) to analyze the model responses to emission reductions is applied on three different domains in Europe (Po valley, Southern Poland and Flanders). This methodology is further elaborated to facilitate the inter-comparison process and bring in a single diagram the possibility of differentiating long-term from short-term effects. This methodology is designed for model users to interpret their model results but also for policy-makers to help them defining intervention priorities. The methodology is applied to both daily PM10 and 8h daily maximum ozone. © 2015 The Authors.

In this study we present a dynamic model evaluation of chemistry transport model LOTOS-EUROS (LOng Term Ozone Simulation – EURopean Operational Smog) to analyse the ability of the model to reproduce observed non-linear responses to emission changes and interannual variability of secondary inorganic aerosol (SIA) and its precursors over Europe from 1990 to 2009. The 20 year simulation was performed using a consistent set of meteorological data provided by RACMO2 (Regional Atmospheric Climate MOdel). Observations at European rural background sites have been used as a reference for the model evaluation. To ensure the consistency of the used observational data, stringent selection criteria were applied, including a comprehensive visual screening to remove suspicious data from the analysis. The LOTOS-EUROS model was able to capture a large part of the seasonal and interannual variability of SIA and its precursors' concentrations. The dynamic evaluation has shown that the model is able to simulate the declining trends observed for all considered sulfur and nitrogen components following the implementation of emission abatement strategies for SIA precursors over Europe. Both the observations and the model show the largest part of the decline in the 1990s, while smaller concentration changes and an increasing number of non-significant trends are observed and modelled between 2000 and 2009. Furthermore, the results confirm former studies showing that the observed trends in sulfate and total nitrate concentrations from 1990 to 2009 are lower than the trends in precursor emissions and precursor concentrations. The model captured well these non-linear responses to the emission changes. Using the LOTOS-EUROS source apportionment module, trends in the formation efficiency of SIA have been quantified for four European regions. The exercise has revealed a 20–50% more efficient sulfate formation in 2009 compared to 1990 and an up to 20% more efficient nitrate formation per unit nitrogen oxide emission, which added to the explanation of the non-linear responses. However, we have also identified some weaknesses in the model and the input data. LOTOS-EUROS underestimates the observed nitrogen dioxide concentrations throughout the whole time period, while it overestimates the observed nitrogen dioxide concentration trends. Moreover, model results suggest that the emission information of the early 1990s used in this study needs to be improved concerning magnitude and spatial distribution.

A 14-month data set of MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) tropospheric NO2 column observations in De Bilt, the Netherlands, has been compared with the regional air quality model Lotos-Euros. The model was run on a 7×7 km2 grid, the same resolution as the emission inventory used. A study was performed to assess the effect of clouds on the retrieval accuracy of the MAX-DOAS observations. Good agreement was found between modeled and measured tropospheric NO2 columns, with an average difference of less than 1% of the average tropospheric column (14.5 · 1015 molec cm−2). The comparisons show little cloud cover dependence after cloud corrections for which ceilometer data were used. Hourly differences between observations and model show a Gaussian behavior with a standard deviation (σ) of 5.5 · 1015 molec cm−2. For daily averages of tropospheric NO2 columns, a correlation of 0.72 was found for all observations, and 0.79 for cloud free conditions. The measured and modeled tropospheric NO2 columns have an almost identical distribution over the wind direction. A significant difference between model and measurements was found for the average weekly cycle, which shows a much stronger decrease during the weekend for the observations; for the diurnal cycle, the observed range is about twice as large as the modeled range. The results of the comparison demonstrate that averaged over a long time period, the tropospheric NO2 column observations are representative for a large spatial area despite the fact that they were obtained in an urban region. This makes the MAX-DOAS technique especially suitable for validation of satellite observations and air quality models in urban regions.

The fact that polarisation lidars measure a depolarisation signal in liquid clouds due to the occurrence of multiple scattering is well known. The degree of measured depolarisation depends on the lidar characteristics (e.g. wavelength and receiver field of view) as well as the cloud macrophysical (e.g. cloud-base altitude) and microphysical (e.g. effective radius, liquid water content) properties. Efforts seeking to use depolarisation information in a quantitative manner to retrieve cloud properties have been undertaken with, arguably, limited practical success. In this work we present a retrieval procedure applicable to clouds with (quasi-)linear liquid water content (LWC) profiles and (quasi-)constant cloud-droplet number density in the cloud-base region. Thus limiting the applicability of the procedure allows us to reduce the cloud variables to two parameters (namely the derivative of the liquid water content with height and the extinction at a fixed distance above cloud base). This simplification, in turn, allows us to employ a fast and robust optimal-estimation inversion using pre-computed look-up tables produced using extensive lidar Monte Carlo (MC) multiple-scattering simulations. In this paper, we describe the theory behind the inversion procedure and successfully apply it to simulated observations based on large-eddy simulation (LES) model output. The inversion procedure is then applied to actual depolarisation lidar data corresponding to a range of cases taken from the Cabauw measurement site in the central Netherlands. The lidar results were then used to predict the corresponding cloud-base region radar reflectivities. In non-drizzling condition, it was found that the lidar inversion results can be used to predict the observed radar reflectivities with an accuracy within the radar calibration uncertainty (2–3 dBZ). This result strongly supports the accuracy of the lidar inversion results. Results of a comparison between ground-based aerosol number concentration and lidar-derived cloud-droplet number densities are also presented and discussed. The observed relationship between the two quantities is seen to be consistent with the results of previous studies based on aircraft-based in situ measurements.

In this study, we investigate a strategy to accelerate the data assimilation (DA) algorithm. Based on evaluations of the computational time, the analysis step of the assimilation turns out to be the most expensive part. After a study of the characteristics of the ensemble ash state, we propose a mask-state algorithm which records the sparsity information of the full ensemble state matrix and transforms the full matrix into a relatively small one. This will reduce the computational cost in the analysis step. Experimental results show the mask-state algorithm significantly speeds up the analysis step. Subsequently, the total amount of computing time for volcanic ash DA is reduced to an acceptable level. The mask-state algorithm is generic and thus can be embedded in any ensemble-based DA framework. Moreover, ensemble-based DA with the mask-state algorithm is promising and flexible, because it implements exactly the standard DA without any approximation and it realizes the satisfying performance without any change in the full model.

Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrieved data are often two-dimensional (2-D) and the ash plume is usually vertically located in a narrow band, directly assimilating the 2-D ash mass loadings in a three-dimensional (3-D) volcanic ash model (with an integral observational operator) can usually introduce large artificial/spurious vertical correlations.In this study, we look at an approach to avoid the artificial vertical correlations by not involving the integral operator. By integrating available data of ash mass loadings and cloud top heights, as well as data-based assumptions on thickness, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The 3-D SOO makes the analysis step of assimilation comparable in the 3-D model space.Ensemble-based DA is used to assimilate the extracted measurements of ash concentrations. The results show that satellite DA with SOO can improve the estimate of volcanic ash state and the forecast. Comparison with both satellite-retrieved data and aircraft in situ measurements shows that the effective duration of the improved volcanic ash forecasts for the distal part of the Eyjafjallajökull volcano is about 6 h.

This paper presents a holistic performance measurement framework for harmonized and transparent monitoring and comparability of the European cities activities during the implementation of Smart City solutions. The work methodology was based on extensive collaboration and communication with European cities in order to develop a set of Key Performance Indicators (KPIs) specific for Smart Cities initiatives evaluation and comparability. The baseline was established by analysis and integration of existing results from previous initiatives. The research developed and validated a performance evaluation framework, including KPIs definition, guidelines for data collections, a performance system prototype and testing in case-cities. In addition recommendations for the implementation of the performance system into the cities decision-making process and recommendations for the development of new business opportunities are discussed.

Ammonia (NH3) is difficult to monitor at atmospheric concentrations due its high solubility and reac-tivity and the strong spatial and temporal variations of its concentrations. Monitoring is mostly per-formed using passive samplers or filter packs with daily coverage at best. Only at a few sites ammonia is measured with more expensive wet chemical or spectroscopic measurement techniques. lnstruments using an open path show the most potential as these avoid the use of inlets and thus the interactions of NH3 with tubing, filters, and inlets. Measurements on the vertical distribution of NH3 are even scarcer, with only a few available airborne and tower measurements. Satellite observations of NH3 show potential to be used for real-time monitoring as these have global coverage often with daily overpasses. Unfor-tunately, validation of satellite NH3 products representing the total atmospheric column with ground based instruments measuring in situ NH3 has been troublesome due to a lack of knowledge about the vertical distribution. Validation with FTIR instruments has shown potential but has been performed for only a limited number of stations. In this study we report on measurements performed during the Measuring atmospheric Ammonia with Remote Sensing (MARS) field campaign at Cabauw, the Netherlands. The aim of the campaign was to improve the general understanding of the vertical distribution of NH3. An approach was taken using four mini-DOAS instruments installed in the meteorological tower at Cabauw, supplemented by measurements with a MARGA and a mobile FTIR instrument. The measure-ments between May and October 2014 showed large variations in the concentrations and maximum concentrations reached up to 240 μg m-3. The lower three mini-DOAS and MARGA measurements showed large differences on an hourly basis, which were shown to originate from multiple measurement artefacts of the MARGA. The mini-DOAS concentrations varied sharply between the different levels with the lower three instruments showing maxima at night while the mini-DOAS at 160 m in the tower showed maxima during the day. The study shows that the daytime boundary layer is welt-mixed with only small gradients between concentrations measured at various heights. Differences were found in the origin of the NH3 with the instrument at 20 m showing higher concentrations with transport from the north, where the largest nearby sources are located, while the instrument at 160 m showed the highest concentrations coming from the east which shows a more regional influence. The measurement campaign data is freely available for model and satellite validation studies

The most important climate forcer over the period 1750-present is CO2 from fossil fuel combustion (IPCC, 2013). European countries that are Parties to the United Nations Framework Convention on Climate Change (UNFCCC) submit national greenhouse gas (GHG) inventories to the Climate Change secretariat. However, these reported emissions are annual totals and do not provide spatial or temporal patterns within the country. Recently the interest in high resolution CO2 emission data is growing, both from the side of the research community as well as cities that want to make tailored climate action plans. Here we present a 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 (CO2_ff) and CO2 from biofuels (CO2_bf) to support carbon cycle 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 growing importance of biofuel over the time 2000-2014 time period is clearly visible. This changes the isotopic signature of anthropogenic emissions which is important for quantifying fossil fuel emissions. The inventory is compatible with the TNO-MACC emission inventory for air pollutants (Kuenen et al., 2014) which can provide information on co-emitted species like CO and NOx. The dataset is complemented by two projections based on the CIRCE project scenarios and using the latest historic year (2014) as the starting point for projection. The scenarios include a business-as-usual and a climate change scenario. The projections provide a range of possible future emissions that can be used for sensitivity tests, for example when designing a possible future observational system. The annual grid-maps are available for the historical years 2000-2014 at https://doi.org/10.5281/zenodo.112889, and for the future years 2018-2050 at https://doi.org/10.5281/zenodo.1009519.

Presented here is the validation of the CrIS (Cross-track Infrared Sounder) fast physical NH3 retrieval (CFPR) column and profile measurements using ground-based Fourier transform infrared (FTIR) observations. We use the total columns and profiles from seven FTIR sites in the Network for the Detection of Atmospheric Composition Change (NDACC) to validate the satellite data products. The overall FTIR and CrIS total columns have a positive correlation of r = 0.77 (N = 218) with very little bias (a slope of 1.02). Binning the comparisons by total column amounts, for concentrations larger than 1.0 × 1016 molecules cm−2, i.e. ranging from moderate to polluted conditions, the relative difference is on average ∼ 0–5 % with a standard deviation of 25–50 %, which is comparable to the estimated retrieval uncertainties in both CrIS and the FTIR. For the smallest total column range (16 molecules cm−2) where there are a large number of observations at or near the CrIS noise level (detection limit) the absolute differences between CrIS and the FTIR total columns show a slight positive column bias. The CrIS and FTIR profile comparison differences are mostly within the range of the single-level retrieved profile values from estimated retrieval uncertainties, showing average differences in the range of ∼ 20 to 40 %. The CrIS retrievals typically show good vertical sensitivity down into the boundary layer which typically peaks at ∼ 850 hPa (∼ 1.5 km). At this level the median absolute difference is 0.87 (std = ±0.08) ppb, corresponding to a median relative difference of 39 % (std = ±2 %). Most of the absolute and relative profile comparison differences are in the range of the estimated retrieval uncertainties. At the surface, where CrIS typically has lower sensitivity, it tends to overestimate in low-concentration conditions and underestimate in higher atmospheric concentration conditions.

Radiocarbon analysis is a widely-used tool for source apportionment of aerosol particles. One of the big challenges of this method, addressed in this work, is to isolate elemental carbon (EC) for 14C analysis. In the first part of the study, we validate a two-step method (2stepCIO) to separate total carbon (TC) into organic carbon (OC) and EC against the EUSAAR_2 thermal-optical method regarding the recovered carbon concentrations. The 2stepCIO method is based on the combustion of OC in pure oxygen at two different temperature steps to isolate EC. It is normally used with a custom-built aerosol combustion system (ACS), but in this project, it was also implemented as a thermal protocol on a Sunset OC-EC analyzer. Results for the recovered EC mass concentration showed poor agreement between the 2stepCIO method on the ACS system and on the Sunset analyzer. This indicates that the EC recovery is sensitive not only to the temperature steps, but also to instrument-specific parameters, such as heating rates. We also found that the EUSAAR_2 protocol itself can underestimate the EC concentration on untreated samples compared to water-extracted samples. This is especially so for highly loaded filters, which are typical for 14C analysis. For untreated samples, the EC concentration on long-term filter samples (two to five days sampling time) was 20-45% lower than the sum of EC found on the corresponding 24-h filter samples. For water-extracted filter samples, there was no significant difference between long-term and the sum of daily filter samples. In the second part of this study, the 14C was measured on EC isolated by the 2stepCIO method and compared to methods from two other laboratories. The different methods agree well within their uncertainty estimates. © 2017 by the authors.

We use the technique of Observing System Simulation Experiments (OSSEs) to quantify the impact of spaceborne carbon monoxide (CO) total column observations from the Sentinel-5 Precursor (S-5P) platform on tropospheric analyses and forecasts. We focus on Europe for the period of northern summer 2003, when there was a severe heat wave episode associated with extremely hot and dry weather conditions. We describe different elements of the OSSE: (i) the nature run (NR), i.e., the truth; (ii) the CO synthetic observations; (iii) the assimilation run (AR), where we assimilate the observations of interest; (iv) the control run (CR), in this study a free model run without assimilation; and (v) efforts to establish the fidelity of the OSSE results. Comparison of the results from AR and the CR, against the NR, shows that CO total column observations from S-5P provide a significant benefit (at the 99 % confidence level) at the surface, with the largest benefit occurring over land in regions far away from emission sources. Furthermore, the S-5P CO total column observations are able to capture phenomena such as the forest fires that occurred in Portugal during northern summer 2003. These results provide evidence of the benefit of S-5P observations for monitoring processes contributing to atmospheric pollution.

Sisal fibre can potentially replace glass fibre in natural fibre composites. This study focuses on the environmental performance of sisal fibre production by quantifying the greenhouse gas (GHG) emissions and energy use of producing sisal fibre in Tanzania and Brazil using life cycle assessment (LCA), based on region-specific inventory data. The results show that sisal fibre production has much lower GHG emissions (75–95%) and non-renewable energy use (85–95%) compared to glass fibre on a kg-basis, which is in line with published LCAs on natural fibres. Sisal fibre's GHG emissions are strongly influenced by potential methane emissions arising from the wet disposal of sisal leaf residues. Furthermore, because the direct energy and material requirements of sisal fibre production are low, its environmental performance is shown to vary strongly based on local practices such as residue disposal and fertiliser use, and is also sensitive to transportation distances. Several improvement options are explored to understand potential improvements in environmental sustainability. The most attractive option is limiting inadvertent methane emissions occurring at residue disposal sites, for instance by using them for the production of biogas. © 2017 Elsevier Ltd

Monitoring urban-industrial emissions is often challenging because observations are scarce and regional atmospheric transport models are too coarse to represent the high spatiotemporal variability in the resulting concentrations. In this paper we apply a new combination of an Eulerian model (Weather Research and Forecast, WRF, with chemistry) and a Gaussian plume model (Operational Priority Substances-OPS). The modelled mixing ratios are compared to observed CO2 and CO mole fractions at four sites along a transect from an urban-industrial complex (Rotterdam, the Netherlands) towards rural conditions for October-December 2014. Urban plumes are well-mixed at our semi-urban location, making this location suited for an integrated emission estimate over the whole study area. The signals at our urban measurement site (with average enhancements of 11 ppm CO2 and 40 ppb CO over the baseline) are highly variable due to the presence of distinct source areas dominated by road traffic/residential heating emissions or industrial activities. This causes different emission signatures that are translated into a large variability in observed δCO: δCO2 ratios, which can be used to identify dominant source types. We find that WRF-Chem is able to represent synoptic variability in CO2 and CO (e.g. the median CO2 mixing ratio is 9.7 ppm, observed, against 8.8 ppm, modelled), but it fails to reproduce the hourly variability of daytime urban plumes at the urban site (R2 up to 0.05). For the urban site, adding a plume model to the model framework is beneficial to adequately represent plume transport especially from stack emissions. The explained variance in hourly, daytime CO2 enhancements from point source emissions increases from 30 % with WRF-Chem to 52 % with WRF-Chem in combination with the most detailed OPS simulation. The simulated variability in δCO: δCO2 ratios decreases drastically from 1.5 to 0.6 ppb ppm-1, which agrees better with the observed standard deviation of 0.4 ppb ppm-1. This is partly due to improved wind fields (increase in R2 of 0.10) but also due to improved point source representation (increase in R2of 0.05) and dilution (increase in R2 of 0.07). Based on our analysis we conclude that a plume model with detailed and accurate dispersion parameters adds substantially to top-down monitoring of greenhouse gas emissions in urban environments with large point source contributions within a ∼ 10 km radius from the observation sites. © 2018 by ASME.

We determined NOx emissions from Paris in summer 2009 and winter 2009/2010 by applying the closed integral method (CIM) to a large set of car multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements performed within the framework of the MEGAPOLI project (http://megapoli.dmi.dk/). MAX-DOAS measurements of the tropospheric NO2 vertical column density (VCD) were performed in large circles around Paris. From the combination of the observed NO2 VCDs with wind fields, the NO2 influx into and the outflux from the encircled area was determined. The difference between the influx and outflux represents the total emission. Compared to previous applications of the CIM, the large number of measurements during the MEGAPOLI campaign allowed the investigation of important aspects of the CIM. In particular, the applicability of the CIM under various atmospheric conditions could be tested. Another important advantage of the measurements during MEGAPOLI is that simultaneous atmospheric model simulations with a high spatial resolution (3×3 km2) are available for all days. Based on these model data, it was possible to test the consistency of the CIM and to derive information about favourable or non-favourable conditions for the application of the CIM. We found that in most situations the uncertainties and the variability in the wind data dominate the total error budget, which typically ranges between 30 and 50 %. Also, measurement gaps and uncertainties in the partitioning ratio between NO and NO2 are important error sources. Based on a consistency check, we deduced a set of criteria on whether measurement conditions are suitable or not for the application of the CIM. We also developed a method for the calculation of the total error budget of the derived NOx emissions. Typical errors are between ± 30 and ±50% for individual days (with one full circle around Paris). From the application of the CIM to car MAX-DOAS observations we derive daily average NOx emissions for Paris of 4.0×1025 molec s-1 for summer and of 6.9×1025 molec s-1 in winter. These values are a factor of about 1.4 and 2.0 larger than the corresponding emissions derived from the application of the CIM to the model data, using the Toegepast Natuurwetenschappelijk Onderzoek (TNO) MEGAPOLI emission inventory, in summer and winter, respectively. Similar ratios (1.5 and 2.3 for summer and winter, respectively) were found for the comparison with the Monitoring Atmospheric composition and climate III (MACC-III) emission inventory. The highest NOx emissions were found during some cold days in February. Enhanced domestic heating and a reduced conversion efficiency of catalytic converters might contribute to these enhanced NOx emissions.

Significance Nationally recommended diets are a prominent method for informing the public on dietary choices. Although dietary choices drive both health and environmental outcomes, these diets make almost no reference to environmental impacts. Our study provides a comparison between the environmental impacts of average dietary intakes and a nation-specific recommended diet across 37 middle- and high-income nations. We find that following a nationally recommended diet in high-income nations results in a reduction in greenhouse gases, eutrophication, and land use. In upper-middle–income nations, we find a smaller reduction in impacts, and in lower-middle–income nations we find a substantial increase. The net result from large-scale adoption of nationally recommended diets for countries studied here results in a reduction in environmental impacts., Dietary choices drive both health and environmental outcomes. Information on diets come from many sources, with nationally recommended diets (NRDs) by governmental or similar advisory bodies the most authoritative. Little or no attention is placed on the environmental impacts within NRDs. Here we quantify the impact of nation-specific NRDs, compared with an average diet in 37 nations, representing 64% of global population. We focus on greenhouse gases (GHGs), eutrophication, and land use because these have impacts reaching or exceeding planetary boundaries. We show that compared with average diets, NRDs in high-income nations are associated with reductions in GHG, eutrophication, and land use from 13.0 to 24.8%, 9.8 to 21.3%, and 5.7 to 17.6%, respectively. In upper-middle–income nations, NRDs are associated with slight decrease in impacts of 0.8–12.2%, 7.7–19.4%, and 7.2–18.6%. In poorer middle-income nations, impacts increase by 12.4–17.0%, 24.5–31.9%, and 8.8–14.8%. The reduced environmental impact in high-income countries is driven by reductions in calories (∼54% of effect) and a change in composition (∼46%). The increased environmental impacts of NRDs in low- and middle-income nations are associated with increased intake in animal products. Uniform adoption of NRDs across these nations would result in reductions of 0.19–0.53 Gt CO2 eq⋅a−1, 4.32–10.6 Gt PO43− eq⋅a−1, and 1.5–2.8 million km2, while providing the health cobenefits of adopting an NRD. As a small number of dietary guidelines are beginning to incorporate more general environmental concerns, we anticipate that this work will provide a standardized baseline for future work to optimize recommended diets further.

Objective This study explores the statistical relations between the concentration of nine heavy metals (HM) (arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), vanadium (V), zinc (Zn)), and nitrogen (N) in moss and potential explanatory variables (predictors) which were then used for mapping spatial patterns across Europe. Based on moss specimens collected in 2010 throughout Europe, the statistical relation between a set of potential predictors (such as the atmospheric deposition calculated by use of two chemical transport models (CTM), distance from emission sources, density of different land uses, population density, elevation, precipitation, clay content of soils) and concentrations of HMs and nitrogen (N) in moss (response variables) were evaluated by the use of Random Forests (RF) and Classification and Regression Trees (CART). Four spatial scales were regarded: Europe as a whole, ecological land classes covering Europe, single countries participating in the European Moss Survey (EMS), and moss species at sampling sites. Spatial patterns were estimated by applying a series of RF models on data on potential predictors covering Europe. Statistical values and resulting maps were used to investigate to what extent the models are specific for countries, units of the Ecological Land Classification of Europe (ELCE), and moss species. Results Land use, atmospheric deposition and distance to technical emission sources mainly influence the element concentration in moss. The explanatory power of calculated RF models varies according to elements measured in moss specimens, country, ecological land class, and moss species. Measured and predicted medians of element concentrations agree fairly well while minima and maxima show considerable differences. The European maps derived from the RF models provide smoothed surfaces of element concentrations (As, Cd, Cr, Cu, N, Ni, Pb, Hg, V, Zn), each explained by a multivariate RF model and verified by CART, and thereby more information than the dot maps depicting the spatial patterns of measured values. Conclusions RF is an eligible method identifying and ranking boundary conditions of element concentrations in moss and related mapping including the influence of the environmental factors.

Environmental footprints are increasingly used to quantify and compare environmental impacts of for example products, technologies, households, or nations. This has resulted in a multitude of footprint indicators, ranging from relatively simple measures of resource use (water, energy, materials) to integrated measures of eventual damage (for example, extinction of species). Yet, the possible redundancies among these different footprints have not yet been quantified. This paper analyzes the relationships between two comprehensive damage footprints and four resource footprints associated with 976 products. The resource footprints accounted for >90% of the variation in the damage footprints. Human health damage was primarily associated with the energy footprint, via emissions resulting from fossil fuel combustion. Biodiversity damage was mainly related to the energy and land footprints, the latter being mainly determined by agriculture and forestry. Our results indicate that relatively simple resource footprints are highly representative of damage to human health and biodiversity. © 2017 American Chemical Society.

Remote sensing, as a powerful tool for monitoring atmospheric phenomena, has been playing an increasingly important role in inverse modeling. Remote sensing instruments measure quantities that often combine several state variables as one. This creates very strong correlations between the state variables that share the same observation variable. This may cause numerical problems resulting in a low convergence rate or inaccurate estimates in gradient-based variational assimilation if improper error statistics are used. In this paper, two criteria or scoring rules are proposed to quantify the numerical robustness of assimilating a specific set of remote sensing observations and to quantify the reliability of the estimates of the parameters. The criteria are derived by analyzing how the correlations are created via shared observation data and how they may influence the process of variational data assimilation. Experimental tests are conducted and show a good level of agreement with theory. The results illustrate the capability of the criteria to indicate the reliability of the assimilation process. Both criteria can be used with observing system simulation experiments (OSSEs) and in combination with other verification scores.

The origin of particulate matter (PM) concentrations in the Netherlands is established using the LOTOS-EUROS model with a source attribution module. Emissions from the ten main economic sectors (SNAP1) were tracked, separating Dutch and foreign sources. Of the modelled PM10 in the Netherlands, about 25% comes from natural sources and 75% is of anthropogenic origin. Dutch sources contribute one third to the anthropogenic part, with the highest contributions from transport and agriculture. During episodes, the contribution of foreign sources becomes more important. This source attribution is based on model results only, and LOTOS-EUROS captures only about 60% of the measured concentrations. This is mainly caused by a strong underestimation of the concentration of organic carbon particles. Reducing the missing mass is an important step to improve the source attribution of PM. Source attribution of PM has also been performed for Flanders, with a focus on agriculture. Flanders has a large agricultural sector and therefore considerable ammonia emissions, which contribute to secondary inorganic aerosol formation. Manure transport data was used to estimate the temporal variability of ammonia emission from manure application in the LOTOS-EUROS model. This approach strongly improves the model performance for ammonia, but does not affect secondary inorganic aerosol. A scenario study showed that restricting manure spreading shortly before and during a period with high particulate matter concentrations is not an effective way to reduce particulate matter concentrations during these episodes. Two papers in this thesis investigate possible consequences of energy transitions for air quality. During the energy transition from fossil fuels to renewable alternatives there is likely a phase in which fossil fuel energy production will be mainly used to provide back-up capacity. This causes a change in the temporal emission variability from power plants. The effect of this projected shift in emission timing on air pollutant concentrations is assessed. For all PM components considered, air concentrations in a model experiment accounting for the change in emission timing were higher than expected based on the emission change. As back-up capacity, power plants are more likely to operate during stagnant weather conditions and during nighttime in this transition phase, conditions in which air pollution is transport and dilution is ineffective. This work illustrates that emission timing is important to look into when assessing the impacts of system changes on air quality. The second scenario study focuses on ozone. When bioenergy use in increased, more production area is needed causing a change in land use. In this work the effects of land use change as well as changes in anthropogenic emissions are taken into account using realistic energy and air quality scenarios for Europe. The increase in ozone damage caused by increased production of biomass is only marginal compared to the reduction in ozone damage because of reduced ozone precursor emissions in other sectors. The total effect is a significant reduction in health damage due to ozone, especially in southern Europe. However, taking into account the effects of projected climate change, ozone damage towards 2050 might actually increase.

Reliable estimates of atmospheric deposition of nitrogen and sulphur are essential for assessing the risks of eutrophication and acidification to sensitive ecosystems. Atmospheric chemistry and transport models are frequently used to provide these estimates. Several models could be used to estimate deposition rates and, therefore, it is desirable that the models’ abilities to estimate atmospheric deposition are evaluated and compared. Since these models are used to study future scenarios (e.g. emission abatement), it is also desirable that the model estimates respond realistically to changing conditions (e.g. emissions, boundary conditions, meteorology, etc.), something that can only be tested by evaluating the models’ abilities to estimate past changes in deposition. As part of the EURODELTA/TFMM trend modelling exercise, wet deposition of sulphur and nitrogen in Europe was simulated by seven models for the period 1990-2010. The model estimates of annual deposition rates and their temporal trends were compared and evaluated for two ten year periods (1990-2000 and 2000-2010) using data from EMEP background measurement stations. Model estimates of wet deposition differ greatly for all species and all models tended to underestimate the observed values, apart from one model that tended to overestimate sulphur deposition. Most of the observed and modelled trends of reduced nitrogen are not significant, reflecting the smaller emission changes for ammonia compared with those of nitrogen oxides and sulphur dioxide. Observed and modelled trends of wet deposition of oxidised nitrogen were more significant, with decreasing trends at most of the measurement stations. The most significant observed and modelled trends were found for sulphur wet deposition, the majority of which were decreasing trends, reflecting the reported European sulphur dioxide emission reductions of approximately 70% for the modelling period. Most of the models underestimated the trends in wet deposition of sulphur and oxidised nitrogen over the two ten year periods, although model performance differed greatly. Model performance was better and less variable for the relative trends (trends with respect to the values at the beginning of the period) than for the absolute trends. This suggests that relative trends could be used to produce more reliable deposition estimates for future scenarios.

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.

Building on theories of cue utilization, this paper investigates whether and how packaging sustainability influences consumer perceptions, inferences and attitudes towards packaged products. A framework is tested in an empirical study among 249 students using soup products varying in packaging material and graphics. The findings show that (packaging) sustainability is a highly salient association but is only moderately important for consumer attitudes. A comparison between consumer judgments and life-cycle assessment indicates that consumers rely on misleading, inaccurate lay beliefs to judge packaging sustainability and are therefore susceptible to making ineffective environmental decisions. The research also demonstrates the power of packaging in shaping perceptions of food products. Particularly, it shows that changes in actual environmental impacts (by altering packaging materials) affect not only sustainability perceptions but also several other benefits, such as perceived taste and quality. At the same time, consumers' sustainability assessments are also highly influenced by mere graphical packaging cues that have no obvious actual sustainability consequences. © 2017 Elsevier Ltd

Cloud condensation nuclei (CCN) number concentrations alongside with submicrometer particle number size distributions and particle chemical composition have been measured at atmospheric observatories of the Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS) as well as other international sites over multiple years. Here, harmonized data records from 11 observatories are summarized, spanning 98,677 instrument hours for CCN data, 157,880 for particle number size distributions, and 70,817 for chemical composition data. The observatories represent nine different environments, e.g., Arctic, Atlantic, Pacific and Mediterranean maritime, boreal forest, or high alpine atmospheric conditions. This is a unique collection of aerosol particle properties most relevant for studying aerosol-cloud interactions which constitute the largest uncertainty in anthropogenic radiative forcing of the climate. The dataset is appropriate for comprehensive aerosol characterization (e.g., closure studies of CCN), model-measurement intercomparison and satellite retrieval method evaluation, among others. Data have been acquired and processed following international recommendations for quality assurance and have undergone multiple stages of quality assessment.

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.

In the framework of the UNECE Task Force on Measurement and Modelling (TFMM) under the Convention on Long-range Transboundary Air Pollution (LRTAP), the EURODELTAIII project is evaluating how well air quality models are able to reproduce observed pollutant air concentrations and deposition fluxes in Europe. In this paper the sulphur and nitrogen deposition estimates of six state-of-the-art regional models (CAMx, CHIMERE, EMEP MSC-W, LOTOS-EUROS, MINNI and CMAQ) are evaluated and compared for four intensive EMEP measurement periods (25 Feb–26 Mar 2009; 17 Sep–15 Oct 2008; 8 Jan–4 Feb 2007 and 1–30 Jun 2006). For sulphur, this study shows the importance of including sea salt sulphate emissions for obtaining better model results; CMAQ, the only model considering these emissions in its formulation, was the only model able to reproduce the high measured values of wet deposition of sulphur at coastal sites. MINNI and LOTOS-EUROS underestimate sulphate wet deposition for all periods and have low wet deposition efficiency for sulphur. For reduced nitrogen, all the models underestimate both wet deposition and total air concentrations (ammonia plus ammonium) in the summer campaign, highlighting a potential lack of emissions (or incoming fluxes) in this period. In the rest of campaigns there is a general underestimation of wet deposition by all models (MINNI and CMAQ with the highest negative bias), with the exception of EMEP, which underestimates the least and even overestimates deposition in two campaigns. This model has higher scavenging deposition efficiency for the aerosol component, which seems to partly explain the different behaviour of the models. For oxidized nitrogen, CMAQ, CAMx and MINNI predict the lowest wet deposition and the highest total air concentrations (nitric acid plus nitrates). Comparison with observations indicates a general underestimation of wet oxidized nitrogen deposition by these models, as well as an overestimation of total air concentration for all the campaigns, except for the 2006 campaign. This points to a low efficiency in the wet deposition of oxidized nitrogen for these models, especially with regards to the scavenging of nitric acid, which is the main driver of oxidized N deposition for all the models. CHIMERE, LOTOS-EUROS and EMEP agree better with the observations for both wet deposition and air concentration of oxidized nitrogen, although CHIMERE seems to overestimate wet deposition in the summer period. This requires further investigation, as the gas-particle equilibrium seems to be biased towards the gas phase (nitric acid) for this model. In the case of MINNI, the frequent underestimation of wet deposition combined with an overestimation of atmospheric concentrations for the three pollutants indicates a low efficiency of the wet deposition processes. This can be due to several reasons, such as an underestimation of scavenging ratios, large vertical concentration gradients (resulting in small concentrations at cloud height) or a poor parameterization of clouds. Large differences between models were also found for the estimates of dry deposition. However, the lack of suitable measurements makes it impossible to assess model performance for this process. These uncertainties should be addressed in future research, since dry deposition contributes significantly to the total deposition for the three deposited species, with values in the same range as wet deposition for most of the models, and with even higher values for some of them, especially for reduced nitrogen. © 2016 Elsevier Ltd

International audience Emissions from oil/gas extraction activities in the Arctic are already important in certain regions and may increase as global warming opens up new opportunities for industrial development. Emissions from oil/gas extraction are sources of air pollutants, but large uncertainties exist with regard to their amounts and composition. In this study, we focus on detailed investigation of emissions from oil/gas extraction in the Norwegian Sea combining measurements from the EU ACCESS aircraft campaign in July 2012 and regional chemical transport modeling. The goal is to (1) evaluate emissions from petroleum extraction activities and (2) investigate their impact on atmospheric composition over the Norwegian Sea. Numerical simulations include emissions for permanently operating offshore facilities from two datasets: the TNO-MACC inventory and emissions reported by Norwegian Environment Agency (NEA). It was necessary to additionally estimate primary aerosol emissions using reported emission factors since these emissions are not included in the inventories for our sites. Model runs with the TNO-MACC emissions are unable to reproduce observations close to the facilities. Runs using the NEA emissions more closely reproduce the observations although emissions from mobile facilities are missing from this inventory. Measured plumes suggest they are a significant source of pollutants, in particular NOx and aerosols. Sensitivities to NOx and NMVOC emissions show that, close to the platforms, O3 is sensitive to NOx emissions and is much less sensitive to NMVOC emissions. O3 destruction, via reaction with NO, dominates very close to the platforms. Far from the platforms, oil/gas facility emissions result in an average daytime O3 enhancement of +2% at the surface. Larger enhancements are predicted at noon ranging from +7% at the surface to +15% at 600 m. Black carbon is the aerosol species most strongly influenced by petroleum extraction emissions. The results highlight significant uncertainties in emissions related to petroleum extraction emissions in the Arctic.

In spite of the still increasing number of engineered nanomaterial (ENM) applications, large knowledge gaps exist with respect to their environmental fate, especially after release into air. This review aims to summarize the current knowledge of emissions and behavior of airborne engineered nanomaterials. The whole ENM lifecycle is considered from the perspective of possible releases into the atmosphere. Although in general, emissions during use phase and end-of-life seem to play a minor role compared to entry into soil and water, accidental and continuous emissions into air can occur especially during production and some use cases such as spray application. Implications of ENMs on the atmosphere as e.g.; photo-catalytic properties or the production of reactive oxygen species are reviewed as well as the influence of physical processes and chemical reactions on the ENMs. Experimental studies and different modeling approaches regarding atmospheric transformation and removal are summarized. Some information exists especially for ENMs, but many issues can only be addressed by using data from ultrafine particles as a substitute and research on the specific implications of ENMs in the atmosphere is still needed. © 2017 by the authors.