A new database for time-series monitoring data: the NitroEurope approach

The NitroEurope Integrated Project (2006-2011) aimed to gain a better understanding of the nitrogen cycle and its impacts on the European greenhouse gas balance. Over sixty project partners collected large volumes of data from sites all over Europe. There are currently more than 15 forest sites in 11 countries, including beech, oak, spruce and mixed forests. The collected data are at different temporal resolutions, from one-off measurements to 30-minute time-series data. Around 500 variables (plus attributes) are measured, including soil and plant data, details of cultivation management for natural and manipulated sites, long-term datasets of flux and concentrations of greenhouse gas and pollutants and their precursors, and micrometeorology data. The NitroEurope research community therefore needed a database to provide easy upload and extraction of data for analysis, interpretation and modelling. A bespoke database with a web-based graphical user interface (GUI) in a user-friendly and attractive online environment was produced by the NitroEurope (NitroEurope 2011) database development team. The software provides a flexible interface between Microsoft Excel workbooks and an Oracle database. The database “Form Manager” enables construction of database forms which reflect the project’s Excel data workbook templates. The software locates data according to Excel worksheet and cell references. The database “Uploader” extracts the specified data from completed Excel templates and uploads data directly to the Oracle relational database tables through the web front-end. Data are run through a series of automatic checks on upload which can be verified using an online graphing tool. Data are then validated and made available to the NitroEurope community for downloading. The database “Reporting” tools enable data from different sites and activities to be brought together and datasets can be previewed and graphed before they are downloaded. The database has almost 300 users and currently contains over eight million rows of data. The data will be made available to the wider scientific community two years after the project ends (Spring 2013). The database structure is extremely flexible and has been used for the storage and reporting of other time-series project data at our institute

Erfassung der Anreicherung von Metallen und Stickstoff in baden-württembergischen Moosen

Background, aim, and scope Since 1990 the UN ECE Heavy Metals in Mosses Surveys provide data inventories of the atmospheric heavy metal bioaccumulation in mosses across Europe. In the 2005 survey the nitrogen accumulation was measured for the first time in most of the participating countries. In Germany, the surveys were conducted in close cooperation with the relevant authorities of both the Federal Republic and the sixteen states. Therefore, statistical evaluations of the moss survey data with regard to the whole German territory and single federal states are of interest. This article concentrates on the federal state Baden-Württemberg, dealing with the mapping of the spatiotemporal trends of metal accumulation from 1990 to 2005, the spatial patterns of nitrogen accumulation in 2005, and the spatial variability of bioaccumulation due to characteristics of the sampling sites and their surroundings. Furthermore, for the first time the relationship between the element concentrations in the mosses and Europe-wide modelled data on total depositions from the European Monitoring and Evaluation Programme (EMEP) on total depositions and were evaluated by means of correlation analysis using lead as an example. EMEP is a scientifically based programme under the Convention on Long-range Transboundary Air Pollution (CLRTAP) for international co-operation to model transboundary air pollution problems. Materials and methods In Baden-Württemberg the bioaccumulation of metal elements was determined mainly in Hypnum cupressiforme moss samples since 1990 and in 2005 also nitrogen was investigated according to a Europe-wide harmonised methodology. The according experimental protocol describes the selection of sampling sites and moss species, the chemical analysis and quality control and the dissemiation of the measured values for mapping spatial patterns. In Baden-Württemberg all sampling sites were described with regard to topographical and ecological characteristics and several criteria to be fulfilled according to the guideline. Together with the measurements this metadata was combined with other information regarding land use in the surroundings of the sampling sites in the WebGIS MossMet. The spatial structure of the metal bioaccumulation was analysed and modelled by variogram analyses and then mapped by applying different kriging techniques. Furthermore, multi metal indices (MMI) were derived for both the sampling sites and raster maps with help of percentile statistics: The MMI1990–2005 was calculated for As, Cd, Cr, Cu, Fe, Ni, Pb, Ti, V and Zn. The statistical association of the metal bioaccumulation, site specific characteristics as well as information on land use and emissions were analysed by bivariate nonparametric correlation analysis, contingency tables and Chisquare Automatic Interaction Detection (CHAID). Results The moss analyses show a statistically significant decrease of the bioaccumulation of most metal elements from 1990 till 2005. Only the Cr and Sb concentrations in the mosses increased from 2000 till 2005, however not statistically significant. Also the decline of the MMI from 2000 till 2005 is not statistically significant. The nitrogen concentrations in the mosses sampled in Baden-Württemberg range from 1.15 to 1.74 % and are negatively correlated with the tree height (r s = 0.43, p < 0.01). The rank correlation coefficients which reflect the statistical association between the metal concentrations in the mosses and the land uses in the surroundings of the sampling sites range from r s = 0.3 to r s = 0.7 (p < 0.05). Among the site descriptors mainly the variables proportion of forested land uses (especially Cd, Pb, Zn), proportion of agricultural land uses (Cd, Hg, Pb, Zn) precipitation sum (Cd, Pb, Zn), altitude (Cr, Fe, V), tree height (As, Hg, Pb, V) and the distance of the sampling site to the nearest road (Hg) as well as to tree crowns and bushes (Cu, Hg, Pb, V) feature significant correlations with the metal concentrations in the mosses. Without consideration of the EMEP deposition data the multivariate statistical CHAID analysis identifies the proportion of forested land uses in a radius of 5 km around the sampling site as well as the slope and altitude as the statistically most significant factors for the Cd concentrations in the mosses sampled in 2005. The total deposition of Pb (EMEP) and the Pb concentrations in mosses in Baden-Württemberg are correlated (2005: r = 0.52, p < 0.01). Discussion For the first time it could be shown that element concentrations measured in mosses of Baden-Württemberg are associated with modelled total depositions of lead (EMEP). The strength of the statistical correlations was found to vary with time. The comparison of atmospheric depositions and element concentration in mosses should be repeated with measured deposition data. In a pilot study this was carried out with data coming from e. g. the ICP Forest Level 2 data on throughfall and open field bulk deposition measurements. By relating data on atmospheric depositions with those on element concentration in mosses modelled deposition maps from the ICP Mapping and Modelling Programme could be validated. Furthermore the rather low resolution of the EMEP maps could be enhanced. Highly resolved deposition data is needed for the calculation of practice-oriented regionally differentiated exceedances of critical loads. In comparison with the deposition measurements, which feature a higher temporal resolution, the moss monitoring spaciously encompasses a wide spectrum of elements containing also elements with a human-toxicological relevance (e. g. As, Al, Hg, Sb, V) which are rarely measured in other monitoring networks. Hence the standardised biomonitoring of air pollutions with ectohydric mosses forms an important link between the technical acquisition of element depositions and their accumulation in biological material. Conclusions The moss surveys contribute to the heavy metal and the multi-component-model of the Convention on Long-range Transboundary Air Pollution (CLRTAP) because they show on different spatial scales how air pollution control influences the accumulation of emitted substances in environmental subjects of protection like vegetation. If environmental monitoring is seen as a continuous task and the applied methodology works well as an early warning system then environmental policy is enabled to act in preventative way and to pursue unexpected developments. No other environmental monitoring programme provides such a wide range of ecotoxicologically relevant elements measured as spatially dense as the case for the moss surveys. The spatial distribution of environmental information is an essential criterion for their usability in terms of political measures for the federal states and the federation. Recommendations and perspectives The Heavy Metals in Mosses Surveys are a good example for environmental monitoring activities reaching across three spatial and administrative levels: regional (e. g. federal state or natural landscape), nation wide (e. g. Germany) and continental (e. g. Europe). In Germany the harmonised and quality controlled moss data are made available via a WebGIS portal. Therefore, the moss data may easily be accessed for environmental monitoring purposes and the control of environmental political actions. Hence, the continuous task of environmental monitoring can be met and carried on in the future. Further, the moss monitoring is the only monitoring network in Europe which provides sufficiently differentiated area-wide data on the nitrogen exposure of semi-natural and agriculturally influenced ecosystems, which are also spatially meaningful for single countries and their administrative subdivisions (e. g. federal states). The Europe-wide correlations between element concentrations in mosses and modelled EMEP deposition data proved in other recent studies will be used to improve the spatial resolution of metal and nitrogen deposition mapping in order to comply with the requirements of science and praxis better than so far

Tools for landscape science: theory, models and data

We review the different roles of theory, models and data in landscape science. The need for science at the landscape scale is argued. Landscape theory is considered as a repository of probabilistic patterns rather than as a collection of laws of nature. We present a typology of such patterns for five distinct landscape features: land cover, land use, patch properties, patch interactions, exogenous influences. We show how theory for these features can support landscape modelling, and we provide a checklist of questions for model developers. The limited availability of data on landscapes is discussed, and how that leads to uncertainties in theoretical patterns as well as models. We analyse how probability theory can be used to account for these uncertainties, strengthening the links between theory, models and data, and facilitating decision-support

Modelling Exchanges: From the Process Scale to the Regional Scale

International audienceThis chapter shows how the knowledge on the processes of surface exchange and atmospheric fate of different pollutants from agriculture or with an impact on agroecosystems is factored into mathematical simulation tools. It also considers the complexity of the interactions involved, the quantities of matter exchanged between agroecosystems and the atmosphere, and the measurement methods used to quantify them. The resulting models, which range from highly local (plant, leaf …) to global scales, ultimately enable to assess the impacts of changes in agricultural practices or climate change on pollutant exchanges between the atmosphere and agroecosystems. We describe different modelling approaches at the process, field, landscape and regional scales with different integrative levels. Model results are useful to understand how different processes interact and to predict how different environmental conditions, future climate or agricultural practices affect air quality. Models can also help identify levers for emission mitigation and estimate their efficiency