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This is the first study that quantifies explicitly the impact of present vegetation on concentrations and depositions, considering simultaneously its effects on meteorology, biogenic emissions, dispersion, and dry deposition in three European cities: Bologna, Milan, and Madrid. The behaviour of three pollutants (O3, NO2, and PM10) was investigated considering two different scenarios, with the actual vegetation (VEG) and without it (NOVEG) for two months, representative of summer and winter seasons: July and January. The evaluation is based on simulations performed with two state-of-the-art atmospheric modelling systems (AMS) that use similar but not identical descriptions of physical and chemical atmospheric processes: AMS-MINNI for the two Italian cities and WRF-CMAQ for the Spanish city. The choice of using two AMS and applying one of them in two cities has been made to ensure the robustness of the results needed for their further generalization. The analysis of the spatial distribution of the vegetation effects on air concentrations and depositions shows that they are highly variable from one grid cell to another in the city area, with positive/negative effects or high/low effects in adjacent cells being observed for the three pollutants investigated in all cities. According to the pollutant, on a monthly basis, the highest differences in concentrations (VEG-NOVEG) produced by vegetation were estimated in July for O3 (−7.40 μg/m3 in Madrid and +2.67 μg/m3 in Milan) and NO2 (−3.01 μg/m3 in Milan and +7.17 μg/m3 in Madrid) and in January for PM10 (−3.14 μg/m3 in Milan +2.01 μg/m3 in Madrid). Thus, in some parts of the cities, the presence of vegetation had produced an increase in pollutant concentrations despite its efficient removal action that ranges from ca. 17% for O3 in Bologna (January) to ca. 77% for NO2 in Madrid (July). [ABSTRACT FROM AUTHOR]

This study quantifies the vegetation impact on urban meteorology by means of the numerical model WRF (Weather Research and Forecasting model). The assessment was made for two months: July and January. These were considered as representative for the summer and winter seasons, for the reference year 2015 in three European cities: Bologna, Milano, and Madrid. Two simulations at 1 km resolution were conducted over the cities with and without the actual urban vegetation, called VEG and NOVEG, respectively, in the model input. Then, the impact of vegetation was evaluated as the difference between the two simulations (VEG-NOVEG) for temperature, relative humidity, and wind speed fields. In general, we found that, as can be expected, urban vegetation tends to cool the atmosphere, enhance the humidity, and reduce the wind speed. However, in some cases, areas with the opposite behaviour exist, so that no a priori results can be attributed to the presence of urban vegetation. Moreover, even when major impact is confined around grid cells where urban vegetation is present, changes in meteorological quantities can be observed elsewhere in the city's area. The magnitude of urban vegetation impact is higher in summer than in winter and it depends on the city's morphological peculiarities, such as urban texture and vegetation types and distribution: average July temperature variations due to the presence of urban vegetation reach peaks of −0.8 °C in Milano, −0.6 °C Madrid, and −0.4 °C in Bologna, while in January, the values range between −0.3 and −0.1 °C. An average heating effect of ca. +0.2 °C is found in some parts of Madrid in January. For relative humidity, we found increments of 2%–3% in July and 0.5%–0.8% in January, while a decrease in wind speed was found between 0.1 and 0.5 m/s, with the highest occurring in Madrid during July. [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]

An application of the Weather Research and Forecasting model aiming to estimate wind and photovoltaic energy resources over Lesotho is presented. To this scope, the whole year 2015 was simulated in a two-way nesting mode reaching 1 km horizontal resolution. Different model parameterizations were tested and compared to available observations, then the best performing simulation was used to elaborate wind and photovoltaic potential maps over Lesotho. Concerning the photovoltaic potential, Lesotho presents a good potential countrywide, having values ranging from around 1600 kWh/kWp to 1750 kWh/kWp, with maxima in the highlands. The results also show that there are many promising areas for wind power exploitation. Zones with higher wind potential are mainly found all along the eastern border with South Africa, from north to south and in general in the highlands. The wind potential exhibits much more variability respect to the photovoltaic one, with estimated energy production ranging from 1000 kWh/kW up to 4000 kWh/kW. Finally, an evaluation of complementarity of wind and solar electricity generation was done at different time scales, showing that some areas with negative correlation coefficient exist, where it is possible to minimise intermittent production and allowing an optimal integration in the electricity grid. • Assessment of photovoltaic and wind energy resource over Lesotho is presented. • A numerical atmospheric model is tuned and used for the evaluation. • For the first time 1 km resolution energy potential maps are produced over Lesotho. • Complementarity of wind and photovoltaic electricity generation in time is explored. [ABSTRACT FROM AUTHOR]

Air pollution represents a global threat leading to large impacts on health and ecosystems and many European areas still show a poor air quality. Many measures and policies have been adopted in the past decades at European, national, regional and even local level and many tools have been developed to tackle this issue. Among these tools, the European Air Quality Directive places more emphasis on the use of models for air quality assessment and management. Within this context, air quality forecasting systems play an important role in supporting decision makers when short-term actions are required to reduce human health risks by limiting population exposure. In this framework, at European level within the Copernicus Atmosphere Monitoring Service (CAMS), the regional air quality models participating to the service provide 4-day daily forecasts of the main atmospheric pollutants concentrations, in the lowest layers of the atmosphere. This work presents the development and the performances evaluation of FORAIR_IT, an high-resolution air quality forecasting system operating at both European (20 km) and Italian (4 km) scales. Its skill results are compared with CAMS_50 interim ensemble reanalysis (IRA) ones and a long lasting PM10 exceeding event, occurred in Emilia Romagna region in October 2017, is studied in more detail. Results show similar skill scores between FORAIT_IT and CAMS_50. Comparing the annual average of the monthly Root Mean Square Error Difference (RMSED) between FORAIT_IT first forecast day and CAMS_50, over the European domain the RMSED is 0.6, 1.7, 1.4 and 7.4 µg/m³ for daily mean PM10 and PM2.5 and daily maximum for NO2 and O3, respectively, while over the Italian domain it is 1.2, 0.3, 4.3 and 3.8 µg/m³. The importance of increasing model resolution in the region of interest is highlighted by the lower values of RMSED over Italy with respect to Europe. The results obtained by the detailed analysis of the PM10 exceeding event suggests the crucial role of the meteorological forcing in capturing both the timing and the intensity of the exceedances. As far as we know FORAIR_IT is the first forecasting system at high spatial resolution at Italian National level. [ABSTRACT FROM AUTHOR]