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The reduction of fine particles (PM 2.5) 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 PM 2.5 to 20 % and 40 % individual and collective reductions in anthropogenic emissions of NH 3 , NO x , and SO x (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 % NH 3 emission reduction decreases regional average NH 3 concentrations by 47 %–49 % but only decreases NH 4+ 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 SO 2 concentrations in these regions (10 %–16 % for 40 % reductions) because reduced NH 3 levels decrease SO 2 deposition through altering atmospheric acidity. A 40 % NO x emission reduction reduces NO x concentrations in East Asia by 45 %, Euro_Medi and North America by ∼ 38 %, and South Asia by 22 %, whilst the regional order is reversed for fine NO 3- , 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 NO x to NO 3- and of SO 2 to SO 42- is enhanced in East Asia but decreased in South Asia, which causes a less effective decrease in NO 3- and even an increase in SO 42- 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 % SO x emission reduction is slightly more effective in reducing SO 2 (42 %–45 %) than SO 42- (34 %–38 %), whilst the disbenefit is that it yields a ∼ 12 % increase in total NH 3 deposition in the four regions, which further threatens ecosystem diversity. This work also highlights important messages for policy makers concerning the mitigation of PM 2.5. More emissions controls focusing on NH 3 and NO x 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 SO x reduction is currently effective. The geographically varying non-one-to-one proportionality of chemical responses of N r , S r , and PM 2.5 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. [ABSTRACT FROM AUTHOR]

The reduction of fine particles (PM2.5) and reactive N (Nr) and S (Sr) 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 Nr, Sr, 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 atmospheric chemistry transport model with WRF 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. Reductions in NH3 emissions are effective at decreasing NH3 concentrations and deposition but much less so for NH4 +. A 40% NH3 emissions reduction decreases regional average NH3 concentrations by 47-49%, while sensitivities of NH4 + concentrations decrease in the order Euro_Medi (Europe and Mediterranean, 18%), East Asia (15%), North America (12%), and South Asia (4%), reflecting the increasing regional ammonia-richness. A disbenefit is the increased SO2 concentrations in these regions (10-16% for 40% NH3 emissions reductions) because reduced NH3 levels decrease SO2 deposition by altering atmospheric acidity. The 40% NOx emissions reductions decrease NOx concentrations in East Asia by 45%, Euro_Medi and North America by ~38%, and South Asia by 22%, whilst decreases in fine ... are regionally reversed, which is related to enhanced O3 levels in East Asia (and also, but by less, in Euro_Medi), and decreased O3 levels in South Asia (and also, but by less, in North America). Consequently, the oxidation of NOx to ... and of SO2 to ... is enhanced in East Asia but decreased in South Asia, which in East Asia causes a more effective decrease in NOx and SO2 but a less effective decrease in ... and even an increase in ...; in South Asia it causes a less effective decrease in NOx and an increase in SO2 but a more effective decrease in ... and ... . For regional policy making, it is thus important to reduce NH3, NOx and SOx emissions together and/or go for stronger reductions to minimise such adverse effects in East Asia and Euro_Medi. Reductions in SOx emissions are slightly more effective for SO2 than ... . A disbenefit is that SOx emissions reductions increase NH3 total deposition and ecosystem eutrophication (~12% increase for 40% emissions reduction). PM2.5 mitigation in South Asia is most sensitive to 40% SOx reduction (3.10 µg m-3, 10%) and least sensitive to NH3 reduction (0.29 µg m-3, 1%), which is because South Asia is so ammonia-rich that reducing NH3 has little impact. The most effective measure for North America is reducing NOx emissions with an 8% (0.63 µg m-3) decrease in PM2.5 in response to a 40% reduction. In Euro_Medi, the sensitivities of PM2.5 to 40% individual emissions reductions range 5-8% (0.55-0.82 µg m-3). In the UK and Scandinavia PM2.5 is more sensitive to NH3, in central Europe it is more sensitive to NOx, while in the Mediterranean it is more sensitive to SOx. In East Asia, reductions in SOx, NOx and NH3 emissions are almost equally effective with PM2.5 sensitivities to 40% reductions of 7-8% (1.89-2.33 µg m-3). Due to the varying contributions of SIA, PM2.5 sensitivities to 40% collective reductions in all 3 precursors decrease in the order East Asia (20%), Euro_Medi and North America (17%), South Asia (13%). The geographically-varying non-linear chemical responses of Nr, Sr, and PM2.5 to emissions reductions revealed by this work show the importance of both prioritising emissions strategies in different regions and combining several precursor reductions together to maximise the policy effectiveness. [ABSTRACT FROM AUTHOR]

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 (N r) and S (S r) species. These are quantities that cannot be derived from measurements alone. In areas with high levels of reduced N r (RDN = NH 3+ NH 4+), oxidized N r (OXN = NO x+ HNO 3+ HONO + N 2 O 5 + NO 3-+ "Other OXN" species), and oxidized S r (OXS = SO 2+ SO 42-), RDN is predominantly in the form of NH 3 (NH 4+ typically <20 %), OXN has majority gaseous species composition, and OXS predominantly comprises SO 42- except near major SO 2 sources. Most continental regions are now "ammonia rich", more so than previously, which indicates that, although reducing NH 3 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 NH 4 NO 3 formation is limited by NH 3. These locations experience transport of OXN from the adjacent continent and/or direct shipping emissions of NO x , but NH 3 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 NH 3 is the largest contributor to RDN deposition in most regions except for the Rest of Asia area and marine sectors where NH 3 emissions are small and RDN deposition is mainly determined by the transport and rainout of NH 4+ (rather than rainout of gaseous NH 3). Thus, reductions in NH 3 would efficiently reduce the deposition of RDN in most continental regions. The two largest contributors to OXN deposition in all regions are HNO 3 and coarse NO 3- (via both wet and dry deposition). The deposition of fine NO 3- is only important over East Asia. The tropospheric burden of RDN is 0.75 TgN, of which NH 3 and NH 4+ 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 NO 3- only constituting 10 % of this total, although fine NO 3- 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 N r and S r pollution is revealed by the large net import of RDN, OXN, and OXS to these areas. Our work demonstrates the substantial regional variation in N r and S r budgets and the need for modelling to simulate the chemical and meteorological linkages underpinning atmospheric responses to precursor emissions. [ABSTRACT FROM AUTHOR]

The Eurodelta-Trends (EDT) multi-model experiment, aimed at assessing 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 particulate matter (PM) pollution. An in-depth trend analysis has been performed for PM 10 and PM 2.5 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 model-simulated trends could be regarded as an indicator for modelling uncertainty. The model ensemble simulations indicate overall decreasing trends in PM 10 and PM 2.5 from 2000 to 2010, with the total reductions of annual mean concentrations by between 2 and 5 (7 for PM 10) µ g m -3 (or between 10 % and 30 %) across most of Europe (by 0.5–2 µ g m -3 in Fennoscandia, the north-west of Russia and eastern Europe) during the studied period. Compared to PM 2.5 , relative PM 10 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 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 the northern and eastern parts of the EDT domain. Averaged over measurement sites (26 for PM 10 and 13 for PM 2.5), the mean ensemble-simulated trends are -0.24 and -0.22 µ g m -3 yr -1 for PM 10 and PM 2.5 , which are somewhat weaker than the observed trends of -0.35 and -0.40 µ g m -3 yr -1 respectively, partly due to model underestimation of PM concentrations. The correspondence is better in relative PM 10 and PM 2.5 trends, which are -1.7 % yr -1 and -2.0 % yr -1 from the model ensemble and -2.1 % yr -1 and -2.9 % yr -1 from the observations respectively. The observations identify significant trends (at the 95 % confidence level) for PM 10 at 56 % of the sites and for PM 2.5 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 are found for the summer season, according to both the model ensemble and observations. The winter PM trends are very weak and mostly insignificant. Important reasons for that are 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 modelled versus observed PM trends are limited to the regions where the sites are located. The analysis reveals considerable variability of the role of the individual aerosols in PM 10 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 PM 10 decreasing trends in Germany, Denmark, Poland and the Po Valley, while the reductions of primary PM emissions appear to be a dominant factor in bringing down PM 10 in France, Norway, Portugal, Greece and parts of the UK and Russia. Further discussions are given with respect to emission uncertainties (including the implications of not accounting for forest fires and natural mineral dust by some of the models) and the effect of inter-annual meteorological variability on the trend analysis. [ABSTRACT FROM AUTHOR]

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 forPM2.5 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 SO-2 4 concentrations playing an overall dominant role. Also, we see relatively large contributions of the trends of NH+4 and NO-3 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. [ABSTRACT FROM AUTHOR]

Atmospheric pollution has many profound effects on human health, ecosystems, and the climate. Of concern are high concentrations and deposition of reactive nitrogen (N r) species, especially of reduced N (gaseous NH3 , particulate NH4+). Atmospheric chemistry and transport models (ACTMs) are crucial to understanding sources and impacts of N r 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 N r 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 ECLIPSE E (ECLIPSE annual total with EDGAR monthly profile) emissions inventories; those for 2015 used ECLIPSE E 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 µgm-3 ; ECLIPSE E : 0.31 µgm-3) but is only 3.5 % for NH4+ (HTAP: 0.316 µgm-3 ; ECLIPSE E : 0.305 µgm-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 N r and SIA. [ABSTRACT FROM AUTHOR]

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 NO 2 , which has the best known emissions, and moderately good for PM 10 and PM 2.5. 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 NO x exhaust emission from traffic, which is the major contributor to NO 2 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Eleven of the world's 20 most polluted cities are located in India and poor air quality is already a major public health issue. However, anthropogenic emissions are predicted to increase substantially in the short-term (2030) and medium-term (2050) futures in India, especially if no further policy efforts are made. In this study, the EMEP/MSC-W chemical transport model has been used to predict changes in surface ozone (O3/ and fine particulate matter (PM2:5/ for India in a world of changing emissions and climate. The reference scenario (for present-day) is evaluated against surfacebased measurements, mainly at urban stations. The evaluation has also been extended to other data sets which are publicly available on the web but without quality assurance. The evaluation shows high temporal correlation for O3 (r D0.9) and high spatial correlation for PM2:5 (r D0.5 and r D0.8 depending on the data set) between the model results and observations. While the overall bias in PM2:5 is small (lower than 6 %), the model overestimates O3 by 35 %. The underestimation in NOx titration is probably the main reason for the O3 overestimation in the model. However, the level of agreement can be considered satisfactory in this case of a regional model being evaluated against mainly urban measurements, and given the inevitable uncertainties in much of the input data. For the 2050s, the model predicts that climate change will have distinct effects in India in terms of O3 pollution, with a region in the north characterized by a statistically significant increase by up to 4% (2 ppb) and one in the south by a decrease up to -3% (-1.4 ppb). This variation in O3 is assumed to be partly related to changes in O3 deposition velocity caused by changes in soil moisture and, over a few areas, partly also by changes in biogenic non-methane volatile organic compounds. Our calculations suggest that PM2:5 will increase by up to 6.5% over the Indo-Gangetic Plain by the 2050s. The increase over India is driven by increases in dust, particulate organic matter (OM) and secondary inorganic aerosols (SIAs), which are mainly affected by the change in precipitation, biogenic emissions and wind speed. The large increase in anthropogenic emissions has a larger impact than climate change, causing O3 and PM2:5 levels to increase by 13 and 67% on average in the 2050s over the main part of India, respectively. By the 2030s, secondary inorganic aerosol is predicted to become the second largest contributor to PM2:5 in India, and the largest in the 2050s, exceeding OM and dust. [ABSTRACT FROM AUTHOR]

Eleven of the world's 20 most polluted cities are located in India and poor air quality is already a major public health issue. However, anthropogenic emissions are predicted to increase substantially in the short-term (2030) and medium-term (2050) futures in India, especially if no more policy efforts are made. In this study, the EMEP/MSC-W chemical transport model has been used to calculate changes in surface ozone (O3) and fine particulate matter (PM2.5) for India in a world of changing emissions and climate. The reference scenario (for present-day) is evaluated against surface-based measurements, mainly at urban stations. The evaluation has also been extended to other data sets which are publicly available on the web but without quality assurance. The evaluation shows high temporal correlation for O3 (r=0.9) and high spatial correlations for PM2.5 (r=0.5 and r=0.8 depending on the data set) between the model results and observations. While the overall bias in PM2.5 is small (lower than 6%), the model overestimates O3 by 35%. The underestimation in NOx titration is probably the main reason for the O3 overestimation in the model. However, the level of agreement can be considered satisfactory in this case of a regional model being evaluated against mainly urban measurements, and given inevitable uncertainties in much of the input data For the 2050s, the model predicts that climate change will have distinct effects in India in terms of O3 pollution, with a region in the North characterized by a statistically significant increase by up to 4% (2 ppb) and one in the South by a decrease up to -3% (-1.4 ppb). This variation in O3 is found to be partly related to changes in O3 deposition velocity caused by changes in soil moisture and, over a few areas, partly also by changes in biogenic NMVOCs. Our calculations suggest that PM2.5 will increase by up to 6.5% in the 2050s, driven by increases in dust, particulate organic matter (OM) and secondary inorganic aerosols (SIA), which are mainly affected by the change in precipitation, biogenic emissions and wind speed. The large increase in anthropogenic emissions has a larger impact than climate change, causing O3 and PM2.5 levels to increase by 13% and 67% in average in 2050s, respectively. By the 2030s, secondary inorganic aerosol is predicted to become the second largest contributor to PM2.5 in India, and the largest in 2050s, exceeding OM and dust. [ABSTRACT FROM AUTHOR]

This paper presents a new version of the EMEP MSC-W model called eEMEP developed for transportation and dispersion of volcanic emissions, both gases and ash. EMEP MSC-W is usually applied to study problems with air pollution and aerosol transport and requires some adaptation to treat volcanic eruption sources and effluent dispersion. The operational set-up of model simulations in case of a volcanic eruption is described. Important choices have to be made to achieve CPU efficiency so that emergency situations can be tackled in time, answering relevant questions of ash advisory authorities. An efficient model needs to balance the complexity of the model and resolution. We have investigated here a meteorological uncertainty component of the volcanic cloud forecast by using a consistent ensemble meteorological dataset (GLAMEPS forecast) at three resolutions for the case of SO2 emissions from the 2014 Barðarbunga eruption. The low resolution (40×40 km) ensemble members show larger agreement in plume position and intensity, suggesting that the ensemble here does not give much added value. To compare the dispersion at different resolutions, we compute the area where the column load of the volcanic tracer, here SO2, is above a certain threshold, varied for testing purposes between 0.25 and 50 Dobson units. The increased numerical diffusion causes a larger area (C34 %) to be covered by the volcanic tracer in the low resolution simulations than in the high resolution ones. The higher resolution (10×10 km) ensemble members show higher column loads farther away from the volcanic eruption site in narrower clouds. Cloud positions are more varied between the high resolution members, and the cloud forms resemble the observed clouds more than the low resolution ones. For a volcanic emergency case this means that to obtain quickly results of the transport of volcanic emissions, an individual simulation with our low resolution is sufficient; however, to forecast peak concentrations with more certainty for forecast or scientific analysis purposes, a finer resolution is needed. The model is further developed to simulate ash from highly explosive eruptions. A possibility of increasing the number of vertical layers, achieving finer vertical resolution, as well as a higher model top, is included in the eEMEP version. Ash size distributions may be altered for different volcanic eruptions and assumptions. Since ash particles are larger than typical particles in the standard model, gravitational settling across all vertical layers is included.We attempt finally a specific validation of the simulation of ash and its vertical distribution. Model simulations with and without gravitational settling for the 2010 Eyjafjallajökull eruption are compared to lidar observations over central Europe. The results show that with gravitation the centre of the ash mass can be 1 km lower over central Europe than without gravitation. However, the height variations in the ash layer caused by real weather situations are not captured perfectly well by either of the two simulations, playing down the role of gravitation parameterization imperfections. Both model simulations have on average an ash centre of mass below the observed values. Correlations between the observed and corresponding model centres of mass are higher for the model simulation with gravitational settling for four of the six stations studied here. The inclusion of gravitational settling is suggested to be required for a volcanic ash model. [ABSTRACT FROM AUTHOR]