Search

Der Weinbau ist einer Zunahme von Extremwetterereignissen und dem Klimawandel ausgesetzt und reagiert in besonderen Masse hierauf. Aufgrund der hohen soziookonomischen Bedeutung des Weinbausektors in Europa ist die Entwicklung von geeigneten Anpassungsstrategien zur Abschwachung der Auswirkungen des Klimawandels von groster Bedeutung fur seine kunftige Nachhaltigkeit und Wettbewerbsfahigkeit. In diesem Artikel sollen als Output des Forschungsprojektes Clim4Vitis (https://clim4vitis.eu/) einige mogliche kurzfristigen Anpassungsstrategien (Abbildung 1) zusammengefasst werden. Langzeitanpassungsstrategien werden in einem gesonderten Technical Review beschrieben.

The Earth’s surface is both an important source and sink for atmospheric trace compounds (gases and aerosols). This chapter provides a detailed overview of methods to determine immission, dry deposition fluxes, and deposition velocities of trace substances to the biosphere. In particular, relevant measurement techniques are described, and their requirements and limitations for determining accurate fluxes are analyzed. Furthermore, several quality control procedures are outlined. Typical applications are explained in detail, taking into account current and future challenges and developments.

Meteorological conditions are important factors in the development of fungal diseases in winter wheat and are the main inputs of the decision support systems used to forecast disease and thus determine timing for efficacious fungicide application. This study uses the Fourier transform method (FTM) to characterize temporal patterns of meteorological conditions between two neighbouring experimental sites used in a regional fungal disease monitoring and forecasting experiment in Luxembourg. Three meteorological variables (air temperature, relative humidity, and precipitation) were included, all conducive to infection of wheat by Zymoseptoria tritici cause of Septoria leaf blotch (STB) in winter wheat, from 2006 to 2009. The intraday, diurnal, dekadal and intra-seasonal variations of the meteorological variables were assessed using FTM, and the impact of existing contrasts between sites on the development of STB was analyzed. Although STB severities varied between sites and years (P ≤ 0.0003), the results indicated that the two sites presented the same patterns of meteorological conditions when compared at larger temporal scales (diurnal to intra-seasonal scales, with time periods >11 h). However, the intraday variations of all the variables were well discriminated between the sites and were highly correlated to STB severities. Our findings highlight and confirm the importance of intraday meteorological variation in the development of STB in winter wheat fields. Furthermore, the FTM approach has potential for identifying microclimatic conditions prevailing at given sites and could help in improving the prediction of disease forecast models used in regional warning systems.

Septoria tritici blotch (STB) is among the most important crop diseases causing continuous threats to wheat production worldwide. STB epidemics are the outcome of interactions between susceptible host cultivars, favorable environmental conditions, and sufficient quantities of pathogen inoculum. Thus, to determine whether fungicide sprays should be applied to prevent the risk of epidemics that might otherwise lead to yield loss, weather-based systems as stand-alone or combined with other disease or agronomic variables have been implemented in decision-support systems (DSS). Given the economic importance of wheat in Morocco and increasing concerns caused by fungal plant pathogens in wheat-growing regions, DSS integrating a disease risk model would help to limit potentially harmful side effects of fungicide applications while ensuring economic benefits. Here we describe the use of an artificial intelligence algorithm, i.e. the artificial neural network, within a weather-based modelling approach to predict the progress of STB in wheat in Luxembourg. The reproducibility of area-specific modelling approaches is often a hurdle for their application in operational disease warning system at a regional scale. Hence, we explore the potential of coupling artificial intelligence algorithms with weather-based model for predicting in-season progress of a major economically important fungal disease – wheat stripe rust – in selected wheat-producing regions in Morocco.

Changes in the frequency and intensity of heat waves have shown substantial negative impacts on public health. At the same time, climate change towards increasing air temperatures throughout Europe will foster such extreme events, leading to the population being more exposed to them and societies becoming more vulnerable. Based on two climate change scenarios (Representative Concentration Pathway 4.5 and 8.5) we analysed the frequency and intensity of heat waves for three capital cities in Europe representing a North&ndash, South transect (London, Luxembourg, Rome). We used indices proposed by the Expert Team on Sector-Specific Climate Indices of the World Meteorological Organization to analyze the number of heat waves, the number of days that contribute to heat waves, the length of the longest heat waves, as well as the mean temperature during heat waves. The threshold for the definition of heat waves is calculated based on a reference period of 30 years for each of the three cities, allowing for a direct comparison of the projected changes between the cities. Changes in the projected air temperature between a reference period (1971&ndash, 2000) and three future periods (2001&ndash, 2030 near future, 2031&ndash, 2060 middle future, and 2061&ndash, 2090 far future) are statistically significant for all three cities and both emission scenarios. Considerable similarities could be identified for the different heat wave indices. This directly affects the risk of the exposed population and might also negatively influence food security and water supply.

Aim: Grape (Vitis vinifera L.) phenology is mainly temperature-driven. Consequently, the shift in thermal conditions due to climate change is supposed to have a distinct influence on grape phenology, grape maturity and wine typicity. This study aims to investigate (i) the future phenological development, as well as (ii) the consequences on the temperature conditions in specific phenophases under the conditions of the Luxembourg grapegrowing region.Methods and Results: A budburst model and a phenological model were combined with an ensemble of ten regional climate change projections for Luxembourg. Analyses comparing four 30-year time spans (reference period: 1971-2000; present: 2001-2030; near future: 2031-2060; far future: 2061-2090) demonstrated that each of the 27 phenological stages according to BBCH code is projected to be reached significantly earlier than in the reference period. According to these projections, the length of phenophases at the early stages is not affected, whereas the ripening period length is significantly shortened. The air temperature increase in the ripening period (far future compared to reference period: + 4.6 °C to + 5.3 °C) is projected to be markedly higher than in the annual averages (+ 2.6 °C).Conclusions: Since (i) air temperatures are generally projected to increase in the future and (ii) the ripening period will take place earlier (usually in the warmer parts of the season), climate change is implicating a two-fold impact on air temperature increase in the ripening period.Significance and impact of the study: This two-fold impact potentially threatens the wine typicity of the traditional grapegrowing regions and therefore calls for specific adaptation strategies.

The aim of the present investigations was to simulate the annual risk of bunch rot (Botrytis cinerea) on Vitis vinifera L. cv. Riesling grapes based on three long-term (n = 3 × 7 = 21 cases) assessment data sets originating from three Central European grape-growing regions. Periods when meteorological parameters were significantly (p

Air quality and thermal stress lead to increased morbidity and mortality. Studies on morbidity and the combined impact of air pollution and thermal stress are still rare. To analyse the correlations between air quality, thermal stress and morbidity, we used a two-stage meta-analysis approach, consisting of a Poisson regression model combined with distributed lag non-linear models (DLNMs) and a meta-analysis investigating whether latitude or the number of inhabitants significantly influence the correlations. We used air pollution, meteorological and hospital admission data from 28 administrative districts along a north-south gradient in western Germany from 2001 to 2011. We compared the performance of the single measure particulate matter (PM10) and air temperature to air quality indices (MPI and CAQI) and the biometeorological index UTCI. Based on the Akaike information criterion (AIC), it can be shown that using air quality indices instead of single measures increases the model strength. However, using the UTCI in the model does not give additional information compared to mean air temperature. Interaction between the 3-day average of air quality (max PM10, max CAQI and max MPI) and meteorology (mean air temperature and mean UTCI) did not improve the models. Using the mean air temperature, we found immediate effects of heat stress (RR 1.0013, 95% CI: 0.9983-1.0043) and by 3 days delayed effects of cold stress (RR: 1.0184, 95% CI: 1.0117-1.0252). The results for air quality differ between both air quality indices and PM10. CAQI and MPI show a delayed impact on morbidity with a maximum RR after 2 days (MPI 1.0058, 95% CI: 1.0013-1.0102; CAQI 1.0068, 95% CI: 1.0030-1.0107). Latitude was identified as a significant meta-variable, whereas the number of inhabitants was not significant in the model.

Wheat leaf rust caused by Puccinia triticina Eriks. was identified for the first time in 2000 in the Grand Duchy of Luxembourg on the basis of orange-to-brown, round-to-ovoid, erumpent uredinia (1 to 1.5 mm in diameter) scattered on the upper and lower leaf surfaces and producing orange-brown urediniospores that are subgloboid, approximately 20 μm in diameter, and with up to eight germ pore scattered in thick, echinulate walls. In a second phase, wheat was monitored weekly (starting from Zadoks growth stage 30, pseudo stem erection) during the 2003–2008 cropping seasons for wheat leaf rust. Disease severity (percentage of leaf area with symptoms) was recorded in four, replicated field experiments located in three villages (Diekirch District: Reuler; and Grevenmacher District: Burmerange and Christnach), which are representative of the different agroclimatological zones of Luxembourg. A significant difference in severity was observed between the sites (P < 0.01) and the years (P < 0.05). Over the 6-year period, Burmerange and Reuler consistently showed the highest and lowest disease severity, respectively. In 2003 and 2007, Burmerange (a southern site with the highest average spring temperatures of 13.6 and 14.0°C, respectively) showed the highest disease severity with 66 and 57%, respectively, whereas the lowest severity (

Wheat stripe rust (caused by Puccinia striiformis f. sp. tritici) is a major threat in most wheat growing regions worldwide, which potentially causes substantial yield losses when environmental conditions are favorable. Data from 1999 to 2015 for three representative wheat-growing sites in Luxembourg were used to develop a threshold-based weather model for predicting wheat stripe rust. First, the range of favorable weather conditions using a Monte Carlo simulation method based on the Dennis model were characterized. Then, the optimum combined favorable weather variables (air temperature, relative humidity, and rainfall) during the most critical infection period (May-June) was identified and was used to develop the model. Uninterrupted hours with such favorable weather conditions over each dekad (i.e., 10-day period) during May-June were also considered when building the model. Results showed that a combination of relative humidity >92% and 4°C < temperature < 16°C for a minimum of 4 continuous hours, associated with rainfall ≤0.1 mm (with the dekad having these conditions for 5 to 20% of the time), were optimum to the development of a wheat stripe rust epidemic. The model accurately predicted infection events: probabilities of detection were ≥0.90 and false alarm ratios were ≤0.38 on average, and critical success indexes ranged from 0.63 to 1. The method is potentially applicable to studies of other economically important fungal diseases of other crops or in different geographical locations. If weather forecasts are available, the threshold-based weather model can be integrated into an operational warning system to guide fungicide applications.

The first step in the formulation of disease management strategy for any cropping system is to identify the most important risk factors. This is facilitated by basic epidemiological studies of pathogen life cycles, and an understanding of the way in which weather and cropping factors affect the quantity of initial inoculum and the rate at which the epidemic develops. Weather conditions are important factors in the development of fungal diseases in winter wheat, and constitute the main inputs of the decision support systems used to forecast disease and thus determine the timing for efficacious fungicide application. Crop protection often relies on preventive fungicide applications. Considering the slim cost−revenue ratio for winter wheat and the negative environmental impacts of fungicide overuse, necessity for applying only sprays that are critical for disease control becomes paramount for a sustainable and environmentally friendly crop production. Thus, fungicides should only be applied at critical stages for disease development, and only after the pathogen has been correctly identified. This chapter provides an overview of different weather-based disease models developed for assessing the real-time risk of epidemic development of the major fungal diseases (Septoria leaf blotch, leaf rusts and Fusarium head blight) of winter wheat in Luxembourg.

Foliar fungicides in wheat are typically used to safeguard against economic losses from diseases. In this study, we assessed the effects of three fungicide spray regimes [single, double, and triple treatments] on four different grain quality parameters [thousand grain weight (TGW), test weight (TW), grain protein content (GPC), and Zeleny sedimentation volume (ZSV)] during the 2006–2009 period at two sites in Luxembourg. The fungicides used were generally a mix of chlorothalonil and triazoles. At Burmerange,(cultivar Cubus), the values of TGW, TW, GPC and ZSV ranged from 38 to 62 g, 67 to 83 kg hl−1, 12.0%to 14.7% dry matter (DM), and 27 to 54 ml, respectively. Whereas, at Everlange (cultivar Achat), the ranges of TGW, TW, GPC and ZSV were 42 to 65 g, 65 to 81 kg hl−1, 11.0% to 15.0% DM, and 21 to 66 ml,respectively. In more than 75% cases, the results indicate that fungicides did not significantly affect TW or ZSV at either sites (P > 0.05). However, there was a significant and positive fungicide effect on GPC in 2006 and 2009 at Burmerange, and only in 2006 at Everlange (P < 0.05). On the contrary, TGW was significantly affected at Burmerange in all years, except 2008 when a positive increase was observed compared to control plots; and in 2006 and 2007 at Everlange. Interestingly, when there was an effect of fungicides on a quality parameter, there was no difference among different fungicide treatments. Thus under conditions prevailing in Luxembourg, a single fungicide treatment applied with judicious timing generally resulted in statistically similar grain quality parameters when compared with a double or triple fungicide treatment.

We evaluated the cost effectiveness of a decision–support system (DSS) developed for assessing in real time the risk of progression of the main fungal diseases (i.e., Septoria leaf blotch, powdery mildew, leaf rusts and Fusarium head blight) of winter wheat in the Grand-Duchy of Luxembourg (GDL). The study was conducted in replicated field experiments located in four agricultural locations (representative of the main agro-ecological regions of the country) over a 10-year period (2003–2012). Three fungicide spray strategies were compared: a single DSS-based system and two commonly used spray practices in the GDL, a double- (2T) and a triple-spray (3T) treatment; there was also a non-treated control. In years with a high disease pressure, the DSS-based recommendation resulted in protection of the three upper leaves comparable to that achieved with the 2T and 3T treatments, with significant grain yield increases (P > 0.05) compared to the control (a 4 to 42% increase, depending on the site and year). Overall, the financial gain in treated plots compared with the control ranged from 3 to 16% at the study sites. Furthermore, in seasons when dry weather conditions precluded epidemic development, the DSS recommended no fungicide spray, reducing use of fungicide, and thus saving the cost of the product. The gain in yield for the 2T and 3T plots (compared with control) did not necessarily result in a financial gain during the duration of the experiment. This study demonstrates the potential advantages and profitability of using a DSS-based approach for disease management.

Sets of treatments that were applied against varroa mites in the Luxembourgish beekeeper community were surveyed annually with a questionnaire between the winters 2010/11 and 2014/15. The average temperature and the precipitation sum of the month, when the respective varroa control method was applied were considered as co-variables when evaluating the efficacy of varroa control regimes. Success or failure of control regimes was evaluated based on the percentage of colonies lost per apiary in the winter following the treatment(s). Neither a positive nor a negative effect of formic acid (concentration 60%, w/v) on the colony losses could be found, irrespective of the weather conditions around the time of application. The higher concentration of 85% formic acid was linked with reduced colony losses when applications were done in August. Colony losses were reduced when Thymovar was applied in July or August, but applications in September were associated with increased losses compared with apiaries not treated with Thymovar during the same period. Apilife application in July as well as Apivar applications between July and September were associated with reduced colony losses. The removal of the drone brood and trickled oxalic acid application had beneficial effects when being done in April and December, respectively. Relatively warm (3.0 ± 1.3 °C) and wet (507.0 ± 38.6 mm/2 months) conditions during the winter months December and January and relatively cool (17.2 ± 1.4 °C average monthly temperature) and wet (110.8 ± 55.5 mm/month) conditions in July were associated with elevated honey bee colony losses.

The cabbage stem weevil Ceutorhynchus pallidactylus (Mrsh.) (Col.: Curculionidae) is a common pest in oilseed rape Brassica napus L. throughout Europe. The abundance of the cabbage stem weevil was monitored in field surveys by using yellow water traps between 2007 and 2012 at five locations in Luxembourg. We forecast the abundance of the cabbage stem weevil at levels above the control threshold in oilseed rape in springtime. If mean winter temperatures between 5 and 13 February are closer to 4.0 °C than to −2.4 °C, no breaching of the control threshold is to be expected in the subsequent March. The monitoring effort might be saved in those years. Leave-one-out cross-validation revealed that whether the control threshold was exceeded could be correctly predicted based on the observed temperature differences identified in 22 out of 27 cases (81.5%) in the present study. The present approach can be easily transferred to regions where air temperature data are available, although it should not be used without prior local validation.

To address the incomparability of the large number of existing air quality indices, we propose a new normalization method that is suited to directly compare air quality indices based on the common European World Health Organization (WHO) air quality guidelines for NO2, O3, and PM10. Using this method, we compared three air quality indices based on the European guidelines, related them to another air quality index based on the relative risk concept, and used them to assess the air quality and its trends in northwest central Europe. The average air quality in the area of investigation is below the recommended European guidelines. The majority of index values exceeding this threshold are caused by PM10, which is also, in most cases, responsible for the degrading trends in air quality. Eleven out of 29 stations tested showed significant trends, of which eight indicated trends towards better air quality.

Background and Aims Late frosts are a significant risk to grape production in frost-prone viticultural regions. Increasing air temperature because of climate change is likely to advance grape budburst and last frost events in spring. So far, it is unclear whether one trend will be more pronounced than the other, and hence, whether the risk of late frost damage will increase or decrease. The aim of this work was to investigate the future frost risk in the Luxembourgish winegrowing region by assessing the effect of simulated future climate conditions on the timing of budburst and last frost date. Methods and Results Late frost risk was assessed by combining: (i) a phenological model for budburst of the grapevine (DORMPHOT); and (ii) ensemble-based projections of future air temperature. Analyses indicated that increasing spring temperature will advance the timing of budburst and the date of the last frost. This advancement, however, will be more pronounced for last frost events than for budburst. Conclusions Modelled projections showed that the frequency of spring frost damage in the Luxembourgish winegrowing region will decrease, without completely excluding them for the near (2021–2050) or the far future (2069–2098). Significance of the Study The application of a combination of a phenological model for grape budburst and ensemble-based projections of future air temperature enables the assessment of the future late frost risk in a frost-prone viticulture region.

Common cumulative degree day models used to forecast grape growth stages often are only of local validity, restricted to a limited number of phenological stages, or do not take into consideration that the forcing effect of temperature is limited at higher temperatures. A new model was developed to simulate all 26 phenological stages (according to the BBCH scheme; Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie) of Vitis vinifera L. Muller-Thurgau between budburst and harvest. Sixty time series of grape phenology from four European countries were used to set up and validate the model. Three cumulative degree day models (starting with budburst: BBCH 09) with one, two, or three optimized temperature threshold values were compared. The incorporation of an upper threshold temperature, above which a further increase of the temperature will not accelerate plant development, and of a heat threshold, above which a further increase of the temperature leads to a development deceleration, significantly improved the accuracy of the model compared to previous cumulative degree day approaches. The threshold triplet 5oC, 20oC, and 22oC for lower (base), upper, and heat threshold temperature, respectively, allowed the most precise forecast. In 70.5 or 95.8% of the cases, phenological stages were correctly predicted in 3 or 7 days (assuming daily mean temperatures of 20oC), respectively, around the predicted cumulative degree day. The model can be used for a range of applications in viticultural research and practical viticulture and could further be parameterized for other varieties.

Projected future regional climate changes in Luxembourg are assessed based on a six-member ensemble of regional climate models (RCM) from the ENSEMBLES project. The key aspects are projected changes in air temperature and their impacts on vegetation. Up to now, there have been only few assessments of future climate conditions for Luxembourg. As agriculture is the dominant land use in Luxembourg, possible effects on crops and vegetation in general are highly relevant. Different RCMs at 25 km spatial and a daily temporal resolution, ranging from 1961 to 2100 based on the SRES A1B emission scenario are used. To reduce systematic biases in the RCM-derived time series, a bias correction is applied. Multi-model annual mean temperatures are projected to increase by 3.1 °C between the reference time span (1961 to 1990) and the far future (2069 to 2098). Clear change signals are found in seasonal bivariate frequency distributions of air temperature and precipitation. Derived impacts are an elongation of the thermal vegetation period by 6.2 days per decade due to an earlier onset in spring; growing degree day sums show a substantial increase leading to potentially better growth conditions; the earlier onset of the vegetation period causes an increase in late frost risk, especially in the near future (2021 to 2050) projections compared to the reference period.

Tourism destinations often require information about climate to assess their climate potential. This can be performed in terms of mean conditions of relevant climatological parameters. For a user-friendly analysis and visualization of climate data relevant for tourism application in Luxembourg, information is prepared based on the facets of climate in tourism. Information on thermal comfort/stress conditions as well as aesthetical and physical parameters is considered. In the present study, relevant and sensible factors were identified and presented. Therefore, physiologically equivalent temperature, precipitation patterns and the Climate-Tourism/Transfer-Information-Scheme are applied. In addition, extreme events relevant for heat stress are analysed based on existing data sets (i.e. heat waves of 2010). Expected climatic conditions for the future are investigated using the projections of two different regional climate models. The results concerning climate change conditions reveal increasing heat stress and sultriness but decreasing cold stress. This information is the basis for an adequate assessment to provide relevant information for different environmental planning issues as well as for the growing tourism sector of Luxembourg.

Septoria tritici is the causal agent of leaf blotch in wheat and among the most damaging fungal cereal pathogens in the humid regions of central Europe. The percentage of the leaf area colonized by S. tritici was recorded weekly between April and July every season between 2004 and 2010. A total of 11 cultivars with moderate susceptibility [ratings of 4–6 on a 1 (resistant) to 9 (susceptible) scale] were included. The disease level was assessed on the upper three leaf layers at 2 locations between 2004 and 2006 and at 3 locations between 2007 and 2010. The period between sowing and the point of time, when 50% of the leaf area was necrotized due to colonization by S. tritici ( T 50 ) was estimated for each year, site, cultivar and leaf layer by non-linear regression. T 50 values followed a bimodal distribution with one maximum at 245 days after sowing (DAS; early epidemics) and one maximum at 270 DAS (late epidemics). Early epidemics were preceded by almost constant daily average temperatures of 13.2 ± 0.8 °C between 181 and 210 DAS. Late epidemics were preceded by an approximately linear increase in temperature from 8.7 ± 0.9 to 12.1 ± 0.9 °C during the same period of time. Based on these differences, it seems possible to predict whether an early or a late epidemic can be expected at least 35 days before the epidemic outbreak. Temperature sums calculated with a base temperature of 6.6 °C starting at sowing and ending when T 50 was reached were not significantly different between early and late epidemics ( P = 0.73) and averaged 1721 ± 49°days. Fungicide applications, which resulted into a delay of the epidemic development similar to the difference between early and late epidemics, resulted in a yield increase between 11.7 and 12.6%.

Fungi of the genus Fusarium are prominent plant pathogens producing various toxic secondary metabolites. Digital microscopic images were taken from the asexual spores of Fusarium avenaceum, F. cerealis, F. culmorum, F. graminearum, F. poae, F. oxysporum, F. sambucinum, and F. verticillioides. The asexual spore type formed first on Potato Dextrose Broth (PDB) medium was used. Fusarium avenaceum, F. cerealis, F. culmorum, F. graminearum and F. sambucinumformed macroconidia first, whereas F. poae, F. oxysporum and F. verticillioides formed microconidia first. Different shape parameters of the spore images were calculated using a freely available software package (FRAGSTATS 3.3), which was originally designed for spatial pattern analysis of maps. Fusarium cerealis could neither be distinguished unambiguously from F. sambucinum nor from F. graminearum based on the spore shape parameters analysed here. All other species could be distinguished from each other based on significant differences of at least one of the calculated spore shape parameters.

An impact related daily air quality index (DAQx), calculated for 15 air quality monitoring stations (traffic, background, and industry) in Belgium, France, Germany and Luxembourg, was compared to mesoscale atmospheric patterns between 2001 and 2007. Meteorological conditions were described by the Hess and Brezowsky synoptic weather classification system and gridded data of the EU FP6 ENSEMBLES project of total precipitation and mean surface temperature. DAQx values indicate sufficient to poor air quality in the urban area of Brussels and at urban traffic stations, as well as satisfactory air quality at the background stations. The air quality index refers to more than 90% to the presence of high PM10, O3 and NO2 concentrations. SO2 and CO play only a minor role. The investigation of weather regimes indicates that zonal and mixed cyclonic circulation regimes are associated with better air quality than meridional and anticyclonic weather regimes. In general, weather regimes with high daily precipitation lead to better air quality than dryer air masses because of lower contribution of PM10 to the air quality index. A trend analysis of weather regimes from 1978 to 2007 shows significant (α = 0.05) positive trends for weather classes associated with lower PM10 concentrations. The results of a case study at a German station examining the relationship between PM10 concentrations and local meteorological quantities (wind speed and precipitation) confirm the results of the regional analysis.

The model PROCULTURE has been developed by the Université Catholique de Louvain – UCL (Belgium) to simulate the progress of the septoria leaf blotch disease on winter wheat during the cropping season. The model has been validated in Luxembourg for four years at four distinct representative sites. It is able to identify infection periods due to the causal agent Mycosphaerella graminicola on the last five leaf layers by combining meteorological data with phenological data from PROCULTURE's crop growth model component. The meteorological forcing consists of hourly time-series of air temperature, relative humidity and cumulative rainfall since the time of sowing, retrieved from automatic weather stations for hindcast and numerical weather prediction model outputs for the forecast periods. In order to improve the model, leaf wetness – which is one of the most important drivers for the spread of the disease – shall be added as an additional predictor. Therefore leaf wetness sensors were set up at four test sites during the 2007 growing season. To get a continuous spatial coverage of the country, it is planned to couple the PROCULTURE model offline to 12-hourly operational weather forecasts from an implementation of the Weather Research and Forecasting (WRF) model for Luxembourg at 1 km resolution. Because the WRF model does not provide leaf wetness directly, an artificial neural network (ANN) is used to model this parameter.

Long-term records of solar UV radiation reaching the Earth's surface are scarce. Radiative transfer calculations and statistical models are two options used to reconstruct decadal changes in solar UV radiation from long-term records of measured atmospheric parameters that contain information on the effect of clouds, atmospheric aerosols and ground albedo on UV radiation. Based on earlier studies, where the long-term variation of daily solar UV irradiation was derived from measured global and diffuse irradiation as well as atmospheric ozone by a non-linear regression method [Feister et al. (2002) Photochem Photobiol 76:281-293], we present another approach for the reconstruction of time series of solar UV radiation. An artificial neural network (ANN) was trained with measurements of solar UV irradiation taken at the Meteorological Observatory in Potsdam, Germany, as well as measured parameters with long-term records such as global and diffuse radiation, sunshine duration, horizontal visibility and column ozone. This study is focussed on the reconstruction of daily broad-band UV-B (280-315 nm), UV-A (315-400 nm) and erythemal UV irradiation (ER). Due to the rapid changes in cloudiness at mid-latitude sites, solar UV irradiance exhibits appreciable short-term variability. One of the main advantages of the statistical method is that it uses doses of highly variable input parameters calculated from individual spot measurements taken at short time intervals, which thus do represent the short-term variability of solar irradiance.

Die stetige Zunahme des Verkehrs und der steigende Energie- und Stoffverbrauch sowohl in Industrie und Gewerbe als auch in privaten Haushalten bedeuter eine standige Herausforderung an den Umweltschutz und an die Luftteinhaltung. Mit der Einfuhrung des Bundes-Immissionsschutzgesetzes am 1. April 1974 [1] wurde die gesetzliche Grundlage zur Uberwachung der Schadstoffbelastung der Luft geschaffen. Nach zahlreichen Anderungsvorschriften liegt es derzeit in der Fassung vom 26. September 2002 vor. Zukunftig haben die Grenzwerte der Europaischen Union fur die Immissionskonzentrationen der Schadstoffe Gultigkeit, die in den Tochterrichtlinien 1999/30/EG (Grenzwerte fur Schwefeldioxid, Stickstoffdioxid, Stickstoffoxide, Partikel und Blei), und 2000/69/EG (Grenzwerte fur Benzol und Kohlenmonoxid) der Rahmenrichtlinie 96/62/EG uber die Beurteilung und Kontrolle der Luftqualitat definiert sind. Es besteht sowohl seitens der Stadt- und Regionalplanung als auch seitens der Bevolkerung der Bedarf nach einer zusammenfassenden Bewertung der Luftqualitat mittels eines Indexes, moglichst wirkungsbezogen. Aus den bisher verfugbaren Daten (PM10-Messugen liegen ab Januar 2001 bis Dezember 2002 vor) sollen Trends der Luftqualitat abgeleitet werden. fur die Bewertung der Luftqualitat und ihrer zeitlichen Entwicklung wurden die von Mayer et al. 2002 beschriebenen Indices LQI und TLQ fur ausgewahlte Stationen in Rheinland-Pfalz auf Basis der stundlichen Messwerte des staarlichen Messnetzes ZIMEN berechnet. Zur Interpretation werden die Mittelwerte und Hausigkeitsverteilungen der Indices fur charakteristische grosraumige Werterlagen bestimmt und zeitliche Anderungen abgeleitet. Wahrend die Indices der Stationen Bad Kreuznach bis Neuwied geringe Betrage aufweisen, fallen die Werte von Speyer, Trier, Worms und Worth deutlich hoher aus. Da die Messtandorte Speyer, Worms und Worth alle relativ nahe beieinander im sudostlichen Teil von Rheinland-Pflaz liegen, konnte von einer starkeren Belastung dieses Raumes ausgegangen werden. Die Untersuchungen haben gezeigt, dass bei allen Stationen, unabhangig von der Art des Indexes, die atmospharischen Ausbreitungsbedingungen, verbunden mit zyklonalen und antizyklonalen Wetterlagen sowie der Lufttemperatur, eine grose Rolle spielen. Die zeitlichen Muster der Belastungssituationen wiesen an allen Stationen grose Ahnlichkeiten auf, ein Jahresgang ist nicht vorhanden. Hobe Belastungsindices treten bei extrem niedrigen und extrem hohen Tagestmittelwerten der Lufttemperatur auf. Der absolute Betrag des Luftqualitats-Indexwertes hangt von den lokalen Einflussen des jeweiligen Standortes ab. Beide Indices eignen sich gut zur Bewertung der Luftqualitat und zur Erfolgskontrolle von Masnahmen zur Luftreinhaltung. Daher konnten dise Indices taglich als Zusatzinformation zu den aktuell im Internet veroffentlichten Messwerten des ZIMEN dienen.

In the course of the European Council Directive on permissible air pollutant limit values, valid starting from 2005 there is an urgent call for action, particularly for fine dust (PM10). Current investigations (Junk & Helbig 2003, Reuter & Baumuller 2003) show that the limit values in certain places in congested areas are exceeded. Only if it is possible to locate these Hot Spots purposeful measures to reduce the ambient air pollution can be conducted. For an efficient identification of these Hot Spots numerical computer models or establishing special measurements networks are too expensive. Using the statistical model STREET 5.0 (KTT 2003) a cost-effective screening of the air pollution situation caused by the traffic can be done. STREET is based on the 3-dimensional micro-scale non-hydrostatic flow- and dispersion model MISCAM (Eichhorn 1989). The results of over 100.000 different calculations with MISCAM are stored in a Database and used to calculate the emissions with STREET. In collaboration with the city council of Trier more than 150 streets were investigated, mapped, and calculated. A special urban climate measuring network supplies the necessary meteorological input data about the wind field and precipitation events in the valley of the Moselle. Information about road width and road orientation as well as building density was derived from aerial photographs. Traffic censuses and mobile air pollutants measurements supplied the remaining input data. We calculated the mean annual air pollutant concentrations for NO2, CO, SO2, O3, benzene as well as PM10-. A comparison of the model results with the values obtained from the stations of the central emission measuring network of Rhineland-Palatinate (ZIMEN, annual report 2002) shows very good agreements. The model was not only used to calculate the annual air pollutant but also for urban planning and management. The absolute level of the air pollutant is mainly dependent on the amount of traffic in the street canyons. Therefore four different case-scenarios with varying quantity of traffic were calculated and interpreted for each street. The results of the calculation show that on the basis of the mean values for both NO2 and benzene, it is not to be expected that the limits will be exceeded significantly. Furthermore the model can be used to find the maximum tolerable numbers of cars for a street without exceeding the air pollutant thresholds.

This study examines the dependencies between emission sources, meteorological conditions, and particle mass concentration on different temporal and spatial scales. Particulate matter (PM)10, PM2.5, and PM1 were measured in an urban area in Southwest Luxembourg during two field campaigns carried out between 2008 and 2010. Data sampling at various suburban and urban sites accounts for different emission sources and human exposure to particle pollution. Long-range transport and regional source characteristics dominate PM1 mass concentrations resulting in high Pearson correlation coefficients (0.77–0.96) and low coefficients of divergence (0.12–0.18) between measurement stations. In comparison, the coarse particle fractions PM10−PM2.5 and PM2.5−PM1 show higher spatial gradients between stations, which are mainly governed by local sources with lower Pearson correlation coefficients (0.39–0.70) and higher coefficients of divergence (0.21–0.61). The PM10−PM2.5 particle fraction is largely influenced by road transport characteristics reproducing weekday and daily cycles. PM10 mass concentration during winter is dominated by the finer particle fractions, due to domestic heating, whereas concentrations during spring and summer are mostly coarse particles, originating from pollen and windblown dust. Particle mass concentrations of different size fractions were sensitive to dispersion. Unstable atmospheric boundary conditions during the day caused a shift of the PM2.5 /PM10 and PM1/PM2.5 ratios to smaller values in comparison to neutral boundary conditions. A comparison between large-scale clean air and pollution episodes showed differences in the daily cycle of PM10−PM2.5 resulting from local emission sources, transport, and removal.