Quantitative Bestimmung des lokalen Flusses mit elektrochemisch erzeugtem Wasserstoff

The Science of Nature -     

High frequency monitoring of the coastal marine environment using the MAREL buoy

The MAREL Iroise data buoy provides physico-chemical measurements acquired in surface marine water in continuous and autonomous mode. The water is pumped 1.5 m from below the surface through a sampling pipe and flows through the measuring cell located in the floating structure. Technological innovations implemented inside the measuring cell atop the buoy allow a continuous cleaning of the sensor, while injection of chloride ions into the circuit prevents biological fouling. Specific sensors for temperature, salinity, oxygen and fluorescence investigated in this paper have been evaluated to guarantee measurement precision over a 3 month period. A bi-directional link under Internet TCP-IP protocols is used for data, alarms and remote-control transmissions with the land-based data centre. Herein, we present a 29 month record for 4 parameters measured using a MAREL buoy moored in a coastal environment (Iroise Sea, Brest, France). The accuracy of the data provided by the buoy is assessed by comparison with measurements of sea water weekly sampled at the same site as part of SOMLIT (Service d'Observation du Milieu LIToral), the French network for monitoring of the coastal environment. Some particular events (impact of intensive fresh water discharges, dynamics of a fast phytoplankton bloom) are also presented, demonstrating the worth of monitoring a highly variable environment with a high frequency continuous reliable system.     

A Metamodeling Approach for Integrated Assessment of Air Quality Policies

A method is proposed to build integrated models (also called Metamodels) aimed at quantifying the economic efficiency of air quality policies. This Metamodeling approach is based on the coupling of two complementary models, that operate at different scales in space and time, and which represent the economic and the physical and chemical processes, respectively. The joint consideration of the physico-chemical and techno-economic structure of the pollution control problems permits a comprehensive evaluation of air pollution abatement strategies. The motivating pollution control problems include urban-regional air quality management through efficient energy and traffic control policies. A pilot study, exploiting data collected in the Geneva canton (Switzerland), is used to demonstrate the potential of the approach.     

Trace Element Patterns and Seasonal Variability of Dust Precipitation in a Low Polluted City – The Example of Karlsruhe/Germany

Urban areas of different land uses can be distinguished by their specific patterns of atmospheric dust and trace element precipitation. Dusts emitted from industrial areas with fossil fuel processing, for instance, are enriched in V, Ni and Co. Cluster analysis groups sampling sites based on their specific element patterns. The resulting groups correspond to the surrounding land use: Urban sampling sites were identified showing similar patterns of dust and trace element precipitation as a reference site; dusts of other urban sites were influenced by diffuse pollution (caused by non-point and dispersed pollution sources), or by specific industrial emissions. Cluster analysis was also used to characterize the chemical dust composition. Three clusters of typical element associations were distinguished. These clusters represent the dust matrix, diffuse urban pollution and pollution due to fossil fuel processing. Dust precipitation and chemical dust composition show seasonal variations. Dust precipitation and concentrations of trace elements in the precipitated dust are negatively correlated during the annual courses. The highest concentrations of trace elements occur during winter, whereas the highest precipitations of dust were found during summer. This finding stresses that both, precipitation and concentration have to be addressed for the environmental assessment of urban dusts.     

Selecting optimal hyperspectral bands to discriminate nitrogen status in durum wheat: a comparison of statistical approaches

Hyperspectral data can provide prediction of physical and chemical vegetation properties, but data handling, analysis, and interpretation still limit their use. In this study, different methods for selecting variables were compared for the analysis of on-the-ground hyperspectral signatures of wheat grown under a wide range of nitrogen supplies. Spectral signatures were recorded at the end of stem elongation, booting, and heading stages in 100 georeferenced locations, using a 512-channel portable spectroradiometer operating in the 325–1075-nm range. The following procedures were compared: (i) a heuristic combined approach including lambda-lambda R² (LL R²) model, principal component analysis (PCA), and stepwise discriminant analysis (SDA); (ii) variable importance for projection (VIP) statistics derived from partial least square (PLS) regression (PLS-VIP); and (iii) multiple linear regression (MLR) analysis through maximum R-square improvement (MAXR) and stepwise algorithms. The discriminating capability of selected wavelengths was evaluated by canonical discriminant analysis. Leaf-nitrogen concentration was quantified on samples collected at the same locations and dates and used as response variable in regressive methods. The different methods resulted in differences in the number and position of the selected wavebands. Bands extracted through regressive methods were mostly related to response variable, as shown by the importance of the visible region for PLS and stepwise. Band selection techniques can be extremely useful not only to improve the power of predictive models but also for data interpretation or sensor design.