We present a protocol for computing core-ionisation energies for molecules, which is essential for reproducing X-ray photoelectron spectroscopy experiments. The electronic structures of both the ground state and the core-ionised states are computed using Multiwavelets and Density-Functional Theory, where the core-ionisation energies are computed by virtue of the ΔSCF method. To avoid the collapse of the core-hole state or its delocalisation, we make use of the Maximum Overlap Method, which provides a constraint on the orbital occupation, while avoiding the use of pseudopotentials. Combining Multiwavelets with the Maximum Overlap Method allows for the first time an all-electron calculation of core-ionisation energies with Multiwavelets, avoiding known issues connected to the use of Atomic Orbitals (slow convergence with respect to the basis set limit, numerical instabilities of core-hole states for large systems). We show that our results are consistent with previous Multiwavelet calculations which made use of pseudopotentials, and are generally more precise than corresponding Atomic Orbital calculations. We analyse the results in terms of precision compared to both Atomic Orbital calculations and Multiwavelets + pseudopotentials calculations. Moreover, we demonstrate how the protocol can be applied to target molecules of relatively large size. Both closed-shell and open-shell methods have been implemented.

This paper presents an extension of the original Local Fraction methodology to allow the tracking of the sensitivity of chemically active air pollutants to emission sources. The generalized Local Fractions are defined as the linear sensitivities of chemical species to source emission changes, as propagated through the full set of non-linear chemical transformations. The method allows us to simultaneously track sensitivities from hundreds of sources (typically countries or emission sectors) in a single simulation. The current work describes how the non-linear chemical transformations are taken into account in a rigorous manner while validating the implementation of the method in the European Monitoring and Evaluation Programme (EMEP) Meteorological Synthesizing Centre - West (MSC-W) chemistry-transport model by examples. While effectively producing the same results as a direct "brute-force" method, where the impact of emission reductions in each source has to be computed in a separate scenario simulation, the generalized Local Fractions are an order of magnitude more computationally efficient when large numbers of scenarios are considered.

We present a methodology and first results for analytical propagation of emissions uncertaintiesthrough the EMEP MSC-W chemical transport model (CTM) and an application of these uncertaintyestimates to policy products provided by the Copernicus Atmosphere Monitoring Service(CAMS) for European cities. CTMs are widely employed in atmospheric modeling to simulatethe transport and transformation of pollutants, but uncertainties in emission estimates cansignificantly impact the accuracy of air quality predictions. Our study systematically analyzesthe propagation of uncertainties arising from emissions. The emissions’ uncertainties are consistentwith the CAMS regional emissions product and are calculated using detailed, countryspecificuncertainty estimates in activity data and generic emission factor uncertainties. Theuncertainties are calculated per source sector and country. The Local Fractions/Sensibilities [1]methodology available in the EMEP MSC-W model is a tool that allows computation of sourcereceptorrelationships more efficiently. In conjunction with analytical methods for uncertaintypropagation, we deliver air quality predictions with uncertainty estimates at a fraction of thecomputational cost and with increased traceability compared to modern surrogate modelingtechniques. In our study we focus on PM2.5 and PM10, and first results will be presented for theimpact of emission uncertainties on forecasted PM concentrations in European cities, as well asuncertainties in contributions from different source sectors and countries. By integrating emissionuncertainty propagation, our study aims to provide decision-makers with a more accurateassessment of the reliability of CAMS policy products under various atmospheric conditionsand in the future provide these estimates as part of their operational delivery. References [1] P. Wind, B. Rolstad Denby, and M. Gauss, “Local fractions – a method for the calculationof local source contributions to air pollution, illustrated by examples using the emepmsc-w model (rv4 33),” Geoscientific Model Development, vol. 13, no. 3, pp. 1623–1634,2020. [Online]. Available: https://gmd.copernicus.org/articles/13/1623/2020/

Wavelets and multiwavelets have lately been adopted in quantum chemistry to overcome challenges presented by the two main families of basis sets: Gaussian atomic orbitals and plane waves. In addition to their numerical advantages (high precision, locality, fast algorithms for operator application, linear scaling with respect to system size, to mention a few), they provide a framework that narrows the gap between the theoretical formalism of the fundamental equations and the practical implementation in a working code. This realization led us to the development of the Python library called VAMPyR (Very Accurate Multiresolution Python Routines). VAMPyR encodes the binding to a C++ library for multiwavelet calculations (algebra and integral and differential operator application) and exposes the required functionality to write a simple Python code to solve, among others, the Hartree-Fock equations, the generalized Poisson equation, the Dirac equation, and the time-dependent Schrödinger equation up to any predefined precision. In this study, we will outline the main features of multiresolution analysis using multiwavelets and we will describe the design of the code. A few illustrative examples will show the code capabilities and its interoperability with other software platforms.

Regional scale air quality and integrated assessment models are necessarily limited in their spatial resolution, particularly when applied to larger regions such as Europe or for global applications. The EMEP MSC-W chemical transport model and the GAINS integrated assessment model, which makes use of source receptor information precalculated by the EMEP MSC-W model, use resolutions of 0.1°×0.1° and 0.3°×0.2°. These resolutions cannot account for variability at finer scales. Variability within grids, both concentration and population distributions, can be significant. To improve exposure calculations that take into account the sub-grid variability, the uEMEP model has been applied in this study to provide suitable parameterisations of the sub-grid variability within an EMEP MSC-W grid. The uEMEP model is applied on a European domain at 250 × 250 m2. This provides roughly 7000 sub-grids within each EMEP MSC-W 0.3°×0.2° grid to analyse the sub-grid variability in exposure. The analysis derives an exposure correction factor (ECF) for each source sector in each EMEP MSC-W grid. This factor provides information on the impact of sub-grid resolution on the population weighted concentrations within each grid. The population weighted standard deviation is also calculated, providing information on sub-grid variability to derive frequency distributions and parameterised forms of the sub-grid variability suitable for health risk assessments. The impact of sub-grid variability is assessed for the pollutants PM2.5, NOX and NO2. For NO2, additional chemistry calculations are carried out on the NOX frequency distributions. We show that the parameterised form of the sub-grid variability provides close to equivalent results as the original uEMEP calculations. Exposure to PM2.5 increases by around 13% when the sub-grid variability is taken into account for a 2030 emission scenario. For NO2, this increase is around 31% for the same scenario. A similar increase is found in the health risk indicator’population attributable fraction’ when no lower concentration threshold is used in the concentration–response function. When applying the World Health Organisations recommended threshold value we find that including the sub-grid variability increases the calculated population attributable fraction by 32% and 46% for PM2.5 and NO2 respectively. We conclude that sub-grid variability should be taken into account in future exposure calculations for regional scale modelling and impact assessments.

The importance of relativistic effects in quantum chemistry is widely recognized, not only for heavier elements but throughout the periodic table. At the same time, relativistic effects are strongest in the nuclear region, where the description of electrons through a linear combination of atomic orbitals becomes more challenging. Furthermore, the choice of basis sets for heavier elements is limited compared with lighter elements where precise basis sets are available. Thanks to the framework of multiresolution analysis, multiwavelets provide an appealing alternative to overcoming this challenge: they lead to robust error control and adaptive algorithms that automatically refine the basis set description until the desired precision is reached. This allows one to achieve a proper description of the nuclear region. In this work, we extended the multiwavelet-based code MRChem to the scalar zero-order regular approximation framework. We validated our implementation by comparing the total energies for a small set of elements and molecules. To confirm the validity of our implementation, we compared both against a radial numerical code for atoms and the plane-wave-based code EXCITING.

The reduction of fine particles (PM2.5) and reactive N (N-r) and S (S-r) species is a key objective for air pollution control policies because of their major adverse effects on human health, ecosystem diversity, and climate. The sensitivity of global and regional N-r, S-r, and PM2.5 to 20 % and 40 % individual and collective reductions in anthropogenic emissions of NH3, NOx, and SOx (with respect to a 2015 baseline) is investigated using the EMEP MSC-W (European Monitoring and Evaluation Programme Meteorological Synthesizing Centre - West) atmospheric chemistry transport model with WRF (Weather Research and Forecasting) meteorology. Regional comparisons reveal that the individual emissions reduction has multiple co-benefits and small disbenefits on different species, and those effects are highly geographically variable. A 40 % NH3 emission reduction decreases regional average NH3 concentrations by 47 %-49 % but only decreases NH4+ by 18 % in Euro_Medi, 15 % in East Asia, 12 % in North America, and 4 % in South Asia. This order follows the regional ammonia richness. A disbenefit is the increased SO2 concentrations in these regions (10 %-16 % for 40 % reductions) because reduced NH3 levels decrease SO2 deposition through altering atmospheric acidity. A 40 % NOx emission reduction reduces NOx concentrations in East Asia by 45 %, Euro_Medi and North America by similar to 38 %, and South Asia by 22 %, whilst the regional order is reversed for fine NO3-, which is related to enhanced O-3 levels in East Asia (and also, but by less, in Euro_Medi) and decreased O-3 levels in South Asia (and also, but by less, in North America). Consequently, the oxidation of NOx to NO3- and of SO2 to SO42- is enhanced in East Asia but decreased in South Asia, which causes a less effective decrease in NO3- and even an increase in SO42- in East Asia but quite the opposite in South Asia. For regional policy making, it is thus vital to reduce three precursors together to minimize such adverse effects. A 40 % SOx emission reduction is slightly more effective in reducing SO2 (42 %-45 %) than SO42- (34 %-38 %), whilst the disbenefit is that it yields a similar to 12 % increase in total NH3 deposition in the four regions, which further threatens ecosystem diversity. This work also highlights important messages for policy makers concerning the mitigation of PM2.5. More emissions controls focusing on NH3 and NOx are necessary for regions with better air quality, such as northern Europe and eastern North America. In East Asia, the three individual reductions are equally effective, whilst in South Asia only SOx reduction is currently effective. The geographically varying non-one-to-one proportionality of chemical responses of N-r, S-r, and PM2.5 to emissions reductions revealed by this work show the importance of both prioritizing emissions strategies in different regions and combining several precursor reductions together to maximize the policy effectiveness.

We show that medium-sized Gaussian basis sets lead to significant intramolecular basis set superposition errors at Hartree-Fock and density functional levels of theory, with artificial stabilization of compact over extended conformations for a 186 atom deca-peptide. Errors of ∼80 and ∼10 kJ/mol are observed, with polarized double zeta and polarized triple zeta quality basis sets, respectively. Two different procedures for taking the basis set superposition error into account are tested. While both reduce the error, it appears that polarized quadruple zeta basis sets are required to reduce the error below a few kJ/mol. Alternatively, the basis set superposition error can be eliminated using multiresolution methods based on Multiwavelets.

We have implemented a self-consistent field solver for Hartree–Fock calculations, by making use of Multiwavelets and Multiresolution Analysis. We show how such a solver is inherently a preconditioned steepest descent method and therefore a good starting point for rapid convergence. A distinctive feature of our implementation is the absence of any reference to the kinetic energy operator. This is desirable when Multiwavelets are employed, because differential operators such as the Laplacian in the kinetic energy are challenging to represent correctly. The theoretical framework is described in detail and the implemented algorithm is both presented in the paper and made available as a Python notebook. Two simple examples are presented, highlighting the main features of our implementation: arbitrary predefined precision, rapid and robust convergence, absence of the kinetic energy operator.

We used the EMEP MSC-W (European Monitoring and Evaluation Programme Meteorological Synthesizing Centre – West) model version 4.34 coupled with WRF (Weather Research and Forecasting) model version 4.2.2 meteorology to undertake a present-day (2015) global and regional quantification of the concentrations, deposition, budgets, and lifetimes of atmospheric reactive N (Nr) and S (Sr) species. These are quantities that cannot be derived from measurements alone. In areas with high levels of reduced Nr (RDN = NH3+ NH4+), oxidized Nr (OXN = NOx+ HNO3+ HONO + N2O5 + NO3-+ “Other OXN” species), and oxidized Sr (OXS = SO2+ SO42-), RDN is predominantly in the form of NH3 (NH4+ typically <20 %), OXN has majority gaseous species composition, and OXS predominantly comprises SO42- except near major SO2 sources. Most continental regions are now “ammonia rich”, more so than previously, which indicates that, although reducing NH3 emissions will decrease the RDN concentration, decreasing these emissions will have little effect on mitigating secondary inorganic aerosol (SIA). South Asia is the most ammonia-rich region. Coastal areas around East Asia, northern Europe, and the north-eastern United States are “nitrate rich” where NH4NO3 formation is limited by NH3. These locations experience transport of OXN from the adjacent continent and/or direct shipping emissions of NOx, but NH3 concentrations are lower. The least populated continental areas and most marine areas are “sulfate rich”. Deposition of OXN (57.9 TgN yr−1, 51 %) and RDN (55.5 TgN yr−1, 49 %) contribute almost equally to total nitrogen deposition. OXS deposition is 50.5 TgS yr−1. Globally, wet and dry deposition contribute similarly to RDN deposition; for OXN and OXS, wet deposition contributes slightly more. Dry deposition of NH3 is the largest contributor to RDN deposition in most regions except for the Rest of Asia area and marine sectors where NH3 emissions are small and RDN deposition is mainly determined by the transport and rainout of NH4+ (rather than rainout of gaseous NH3). Thus, reductions in NH3 would efficiently reduce the deposition of RDN in most continental regions. The two largest contributors to OXN deposition in all regions are HNO3 and coarse NO3- (via both wet and dry deposition). The deposition of fine NO3- is only important over East Asia. The tropospheric burden of RDN is 0.75 TgN, of which NH3 and NH4+ comprise 32 % (0.24 TgN; lifetime of 1.6 d) and 68 % (0.51 TgN; lifetime of 8.9 d) respectively. The lifetime of RDN (4.9–5.2 d) is shorter than that of OXN (7.6–7.7 d), which is consistent with a total OXN burden (1.20 TgN) almost double that of RDN. The tropospheric burden of OXS is 0.78 TgS with a lifetime of 5.6–5.9 d. Total nitrate burden is 0.58 TgN with fine NO3- only constituting 10 % of this total, although fine NO3- dominates in eastern China, Europe, and eastern North America. It is important to account for contributions of coarse nitrate to global nitrate budgets. Lifetimes of RDN, OXN, and OXS species vary by a factor of 4 across different continental regions. In East Asia, lifetimes for RDN (2.9–3.0 d), OXN (3.9–4.5 d), and OXS (3.4–3.7 d) are short, whereas lifetimes in the Rest of Asia and Africa regions are about twice as long. South Asia is the largest net exporter of RDN (2.21 TgN yr−1, 29 % of its annual emission), followed by the Euro_Medi region. Despite having the largest RDN emissions and deposition, East Asia has only small net export and is therefore largely responsible for its own RDN pollution. Africa is the largest net exporter of OXN (1.92 TgN yr−1, 22 %), followed by Euro_Medi (1.61 TgN yr−1, 26 %). Considerable marine anthropogenic Nr and Sr pollution is revealed by the large net import of RDN, OXN, and OXS to these areas. Our work demonstrates the substantial regional variation in Nr and Sr budgets and the need for modelling to simulate the chemical and meteorological linkages underpinning atmospheric responses to precursor emissions.

Abstract. The Eurodelta-Trends multi-model experiment, aimed to assess the efficiency of emission mitigation measures in improving air quality in Europe during 1990–2010, was designed to answer a series of questions regarding European pollution trends. i.e. were there significant trends detected by observations? do the models manage to reproduce observed trends? how close is the agreement between the models and how large are the deviations from observations? In this paper, we address these issues with respect to PM pollution. An in-depth trend analysis has been performed for PM10 and PM2.5 for the period of 2000–2010, based on results from six chemical transport models and observational data from the EMEP (Cooperative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe) monitoring network. Given harmonization of set up and main input data, the differences in model results should mainly result from differences in the process formulations within the models themselves, and the spread in the models simulated trends could be regarded as an indicator for modelling uncertainty. The model ensemble simulations indicate overall decreasing trends in PM10 and PM2.5, with reduction by between 2 and 6 μg m−3 m−3 (or between 10 and 30 %) from 2000 to 2010. Compared to PM2.5, relative PM10 trends are weaker due to large inter-annual variability of natural coarse PM within the former. The changes in the concentrations of PM individual components are in general consistent with emission reductions. There is a reasonable agreement in PM trends estimated by the individual models, with the inter-model variability below 30–40 % over most of Europe, increasing to 50–60 % in northern and eastern parts of EDT domain. Averaged over measurement sites (26 for PM10 and 13 for PM2.5), the mean ensemble simulated trends are −0.24 and −0.22 μg m−3 year−1 for PM10 and PM2.5, which are somewhat weaker than the observed trends of −0.35 and −0.40 μg m−3 year−1, respectively, partly due to models underestimation of PM concentrations. The correspondence is better in relative PM10 and PM2.5 trends, which are −1.7 and −2.0 % year−1 from the model ensemble and −2.1 and −2.9 % year−1 from the observations, respectively. The observations identify significant trends for PM10 at 56 % of the sites and for PM2.5 at 36 % of the sites, which is somewhat less that the fractions of significant modelled trends. Further, we find somewhat smaller spatial variability of modelled PM trends with respect to the observed ones across Europe and also within individual countries. The strongest decreasing PM trends and the largest number of sites with significant trends is found for the summer season, according to both the model ensemble and observations. The winter PM trends are very weak and mostly insignificant. One important reason for that is the very modest reductions and even increases in the emissions of primary PM from residential heating in winter. It should be kept in mind that all findings regarding modeled versus observed PM trends are limited the regions where the sites are located. The analysis reveals a considerable variability of the role of the individual aerosols in PM10 trends across European countries. The multi-model simulations, supported by available observations, point to decreases in SO4−2 concentrations playing an overall dominant role. Also, we see relatively large contributions of the trends of NH4+ and NO3− to PM10 decreasing trends in Germany, Denmark, Poland and the Po Valley, while the reductions of primary PM emissions appears to be a dominant factor in bringing down PM10 in France, Norway, Portugal, Greece and parts of the UK and Russia. Further discussions are given with respect to emission uncertainties and the effect of inter-annual meteorological variability on the trend analysis.

MRChem is a code for molecular electronic structure calculations, based on a multiwavelet adaptive basis representation. We provide a description of our implementation strategy and several benchmark calculations. Systems comprising more than a thousand orbitals are investigated at the HF level of theory, with an emphasis on scaling properties. With our design, terms which formally scale quadratically with the system size, in effect have a better scaling because of the implicit screening introduced by the inherent adaptivity of the method: all operations are performed to the requested precision, which serves the dual purpose of minimizing the computational cost and controlling the final error precisely. Comparisons with traditional Gaussian-type orbitals based software, show that MRChem can be competitive with respect to performance.

The following article will be submitted to the Journal of Chemical Physics. It is thus not a peer-reviewed manuscript. After it is hopefully accepted for publication, it will be found (in revised form) at https://aip.scitation.org/journal/jcpTransition metal-catalyzed reactions invariably include steps, where ligands associate ordissociate. In order to obtain reliable energies for such reactions, sufficiently large basis sets needto be employed. In this paper, we have used high-precision Multiwavelet calculations to computethe metal-ligand association energies for 27 transition metal complexes with common ligands suchas H2, CO, olefins and solvent molecules. By comparing our Multiwavelet results to a variety offrequently used Gaussian-type basis sets, we show that counterpoise corrections, which are widelyemployed to correct for basis set superposition errors, often lead to underbinding. Additionally,counterpoise corrections are difficult to employ, when the association step also involves achemical transformation. Multiwavelets, which can be conveniently applied to all types ofreactions, provide a promising alternative for computing electronic interaction energies free fromany basis set errors.

Atmospheric pollution has many profound effects on human health, ecosystems, and the climate. Of concern are high concentrations and deposition of reactive nitrogen (Nr) species, especially of reduced N (gaseous NH3, particulate NH4+). Atmospheric chemistry and transport models (ACTMs) are crucial to understanding sources and impacts of Nr chemistry and its potential mitigation. Here we undertake the first evaluation of the global version of the EMEP MSC-W ACTM driven by WRF meteorology (1∘×1∘ resolution), with a focus on surface concentrations and wet deposition of N and S species relevant to investigation of atmospheric Nr and secondary inorganic aerosol (SIA). The model–measurement comparison is conducted both spatially and temporally, covering 10 monitoring networks worldwide. Model simulations for 2010 compared use of both HTAP and ECLIPSEE (ECLIPSE annual total with EDGAR monthly profile) emissions inventories; those for 2015 used ECLIPSEE only. Simulations of primary pollutants are somewhat sensitive to the choice of inventory in places where regional differences in primary emissions between the two inventories are apparent (e.g. China) but are much less sensitive for secondary components. For example, the difference in modelled global annual mean surface NH3 concentration using the two 2010 inventories is 18 % (HTAP: 0.26 µg m−3; ECLIPSEE: 0.31 µg m−3) but is only 3.5 % for NH4+ (HTAP: 0.316 µg m−3; ECLIPSEE: 0.305 µg m−3). Comparisons of 2010 and 2015 surface concentrations between the model and measurements demonstrate that the model captures the overall spatial and seasonal variations well for the major inorganic pollutants NH3, NO2, SO2, HNO3, NH4+, NO3-, and SO42- and their wet deposition in East Asia, Southeast Asia, Europe, and North America. The model shows better correlations with annual average measurements for networks in Southeast Asia (mean R for seven species: R7‾=0.73), Europe (R7‾=0.67), and North America (R7‾=0.63) than in East Asia (R5‾=0.35) (data for 2015), which suggests potential issues with the measurements in the latter network. Temporally, both model and measurements agree on higher NH3 concentrations in spring and summer and lower concentrations in winter. The model slightly underestimates annual total precipitation measurements (by 13 %–45 %) but agrees well with the spatial variations in precipitation in all four world regions (0.65–0.94 R range). High correlations between measured and modelled NH4+ precipitation concentrations are also observed in all regions except East Asia. For annual total wet deposition of reduced N, the greatest consistency is in North America (0.75–0.82 R range), followed by Southeast Asia (R=0.68) and Europe (R=0.61). Model–measurement bias varies between species in different networks; for example, bias for NH4+ and NO3- is largest in Europe and North America and smallest in East Asia and Southeast Asia. The greater uniformity in spatial correlations than in biases suggests that the major driver of model–measurement discrepancies (aside from differing spatial representativeness and uncertainties and biases in measurements) are shortcomings in absolute emissions rather than in modelling the atmospheric processes. The comprehensive evaluations presented in this study support the application of this model framework for global analysis of current and potential future budgets and deposition of Nr and SIA.

Benchmarking molecular properties with Gaussian-type orbital (GTO) basis sets can be challenging, because one has to assume that the computed property is at the complete basis set (CBS) limit, without a robust measure of the error. Multiwavelet (MW) bases can be systematically improved with a controllable error, which eliminates the need for such assumptions. In this work, we have used MWs within Kohn-Sham density functional theory to compute static polarizabilities for a set of 92 closed-shell and 32 open-shell species. The results are compared to recent benchmark calculations employing the GTO-type aug-pc4 basis set. We observe discrepancies between GTO and MW results for several species, with open-shell systems showing the largest deviations. Based on linear response calculations, we show that these discrepancies originate from artifacts caused by the field strength and that several polarizabilies from a previous study were contaminated by higher order responses (hyperpolarizabilities). Based on our MW benchmark results, we can affirm that aug-pc4 is able to provide results close to the CBS limit, as long as finite difference effects can be controlled. However, we suggest that a better approach is to use MWs, which are able to yield precise finite difference polarizabilities even with small field strengths.

We present a computationally inexpensive method for individually quantifying the contributions from different sources to local air pollution. It can explicitly distinguish between regional–background and local–urban air pollution, allowing for fully consistent downscaling schemes. The method can be implemented in existing Eulerian chemical transport models and can be used to distinguish the contribution of a large number of emission sources to air pollution in every receptor grid cell within one single model simulation and thus to provide detailed maps of the origin of the pollutants. Hence, it can be used for time-critical operational services by providing scientific information as input for local policy decisions on air pollution abatement. The main limitation in its current version is that nonlinear chemical processes are not accounted for and only primary pollutants can be addressed. In this paper we provide a technical description of the method and discuss various applications for scientific and policy purposes.

A description of the new air quality downscaling model – the urban EMEP (uEMEP) and its combination with the EMEP MSC-W model (European Monitoring and Evaluation Programme Meteorological Synthesising Centre West) – is presented. uEMEP is based on well-known Gaussian modelling principles. The uniqueness of the system is in its combination with the EMEP MSC-W model and the “local fraction” calculation contained within it. This allows the uEMEP model to be imbedded in the EMEP MSC-W model and downscaling can be carried out anywhere within the EMEP model domain, without any double counting of emissions, if appropriate proxy data are available that describe the spatial distribution of the emissions. This makes the model suitable for high-resolution calculations, down to 50 m, over entire countries. An example application, the Norwegian air quality forecasting and assessment system, is described where the entire country is modelled at a resolution of between 250 and 50 m. The model is validated against all available monitoring data, including traffic sites, in Norway. The results of the validation show good results for NO2, which has the best known emissions, and moderately good for PM10 and PM2.5. In Norway, the largest contributor to PM, even in cities, is long-range transport followed by road dust and domestic heating emissions. These contributors to PM are more difficult to quantify than NOx exhaust emission from traffic, which is the major contributor to NO2 concentrations. In addition to the validation results, a number of verification and sensitivity results are summarised. One verification showed that single annual mean calculations with a rotationally symmetric dispersion kernel give very similar results to the average of an entire year of hourly calculations, reducing the runtime for annual means by 4 orders of magnitude. The uEMEP model, in combination with EMEP MSC-W model, provides a new tool for assessing local-scale concentrations and exposure over large regions in a consistent and homogenous way and is suitable for large-scale policy applications.

The wet deposition of nitrogen and sulfur in Europe for the period 1990–2010 was estimated by six atmospheric chemistry transport models (CHIMERE, CMAQ, EMEP MSC-W, LOTOS-EUROS, MATCH and MINNI) within the framework of the EURODELTA-Trends model intercomparison. The simulated wet deposition and its trends for two 11-year periods (1990–2000 and 2000–2010) were evaluated using data from observations from the EMEP European monitoring network. For annual wet deposition of oxidised nitrogen (WNOx), model bias was within 30 % of the average of the observations for most models. There was a tendency for most models to underestimate annual wet deposition of reduced nitrogen (WNHx), although the model bias was within 40 % of the average of the observations. Model bias for WNHx was inversely correlated with model bias for atmospheric concentrations of NH3+NH4+, suggesting that an underestimation of wet deposition partially contributed to an overestimation of atmospheric concentrations. Model bias was also within about 40 % of the average of the observations for the annual wet deposition of sulfur (WSOx) for most models. Decreasing trends in WNOx were observed at most sites for both 11-year periods, with larger trends, on average, for the second period. The models also estimated predominantly decreasing trends at the monitoring sites and all but one of the models estimated larger trends, on average, for the second period. Decreasing trends were also observed at most sites for WNHx, although larger trends, on average, were observed for the first period. This pattern was not reproduced by the models, which estimated smaller decreasing trends, on average, than those observed or even small increasing trends. The largest observed trends were for WSOx, with decreasing trends at more than 80 % of the sites. On average, the observed trends were larger for the first period. All models were able to reproduce this pattern, although some models underestimated the trends (by up to a factor of 4) and others overestimated them (by up to 40 %), on average. These biases in modelled trends were directly related to the tendency of the models to under- or overestimate annual wet deposition and were smaller for the relative trends (expressed as % yr−1 relative to the deposition at the start of the period). The fact that model biases were fairly constant throughout the time series makes it possible to improve the predictions of wet deposition for future scenarios by adjusting the model estimates using a bias correction calculated from past observations. An analysis of the contributions of various factors to the modelled trends suggests that the predominantly decreasing trends in wet deposition are mostly due to reductions in emissions of the precursors NOx, NH3 and SOx. However, changes in meteorology (e.g. precipitation) and other (non-linear) interactions partially offset the decreasing trends due to emission reductions during the first period but not the second. This suggests that the emission reduction measures had a relatively larger effect on wet deposition during the second period, at least for the sites with observations.

In the framework of the EURODELTA-Trends (EDT) modeling experiment, several chemical transport models (CTMs) were applied for the 1990–2010 period to investigate air quality changes in Europe as well as the capability of the models to reproduce observed long-term air quality trends. Five CTMs have provided modeled air quality data for 21 continuous years in Europe using emission scenarios prepared by the International Institute for Applied Systems Analysis/Greenhouse Gas – Air Pollution Interactions and Synergies (IIASA/GAINS) and corresponding year-by-year meteorology derived from ERA-Interim global reanalysis. For this study, long-term observations of particle sulfate (SO42-), total nitrate (TNO3), total ammonium (TNHx) as well as sulfur dioxide (SO2) and nitrogen dioxide (NO2) for multiple sites in Europe were used to evaluate the model results. The trend analysis was performed for the full 21 years (referred to as PT) but also for two 11-year subperiods: 1990–2000 (referred to as P1) and 2000–2010 (referred to as P2). The experiment revealed that the models were able to reproduce the faster decline in observed SO2 concentrations during the first decade, i.e., 1990–2000, with a 64 %–76 % mean relative reduction in SO2 concentrations indicated by the EDT experiment (range of all the models) versus an 82 % mean relative reduction in observed concentrations. During the second decade (P2), the models estimated a mean relative reduction in SO2 concentrations of about 34 %–54 %, which was also in line with that observed (47 %). Comparisons of observed and modeled NO2 trends revealed a mean relative decrease of 25 % and between 19 % and 23 % (range of all the models) during the P1 period, and 12 % and between 22 % and 26 % (range of all the models) during the P2 period, respectively. Comparisons of observed and modeled trends in SO42- concentrations during the P1 period indicated that the models were able to reproduce the observed trends at most of the sites, with a 42 %–54 % mean relative reduction indicated by the EDT experiment (range of all models) versus a 57 % mean relative reduction in observed concentrations and with good performance also during the P2 and PT periods, even though all the models overpredicted the number of statistically significant decreasing trends during the P2 period. Moreover, especially during the P1 period, both modeled and observational data indicated smaller reductions in SO42- concentrations compared with their gas-phase precursor (i.e., SO2), which could be mainly attributed to increased oxidant levels and pH-dependent cloud chemistry. An analysis of the trends in TNO3 concentrations indicated a 28 %–39 % and 29 % mean relative reduction in TNO3 concentrations for the full period for model data (range of all the models) and observations, respectively. Further analysis of the trends in modeled HNO3 and particle nitrate (NO3-) concentrations revealed that the relative reduction in HNO3 was larger than that for NO3- during the P1 period, which was mainly attributed to an increased availability of “free ammonia”. By contrast, trends in modeled HNO3 and NO3- concentrations were more comparable during the P2 period. Also, trends of TNHx concentrations were, in general, underpredicted by all models, with worse performance for the P1 period than for P2. Trends in modeled anthropogenic and biogenic secondary organic aerosol (ASOA and BSOA) concentrations together with the trends in available emissions of biogenic volatile organic compounds (BVOCs) were also investigated. A strong decrease in ASOA was indicated by all the models, following the reduction in anthropogenic non-methane VOC (NMVOC) precursors. Biogenic emission data provided by the modeling teams indicated a few areas with statistically significant increase in isoprene emissions and monoterpene emissions during the 1990–2010 period over Fennoscandia and eastern European regions (i.e., around 14 %–27 %), which was mainly attributed to the increase of surface temperature. However, the modeled BSOA concentrations did not linearly follow the increase in biogenic emissions. Finally, a comprehensive evaluation against positive matrix factorization (PMF) data, available during the second period (P2) at various European sites, revealed a systematic underestimation of the modeled SOA fractions of a factor of 3 to 11, on average, most likely because of missing SOA precursors and formation pathways, with reduced biases for the models that accounted for chemical aging of semi-volatile SOA components in the atmosphere.