Assessment of insecticide contamination in runoff and stream water of small agricultural streams in the main soybean area of Argentina

The first- and second-order streams, Brown and Horqueta, respectively, which are located in the main area of soybean production in Argentina were examined for insecticide contamination caused by runoff from nearby soybean fields. The insecticides most widely used in Argentina (chlorpyrifos, cypermethrin and endosulfan) were detected in sediments, suspended particles and water. Highest concentrations in suspended particles were 318 microg/kg for endosulfan in the stream Horqueta, while 226 microg/kg chlorpyrifos and 13.2 microg/kg cypermethrin were measured in the stream Brown. In the Horqueta stream 150 and 53 microg/kg chlorpyrifos and cypermethrin were detected in runoff sediments, respectively. Whereas cypermethrin concentrations in the suspended particles were relatively low, levels in the floodwater of Brown reached 0.7 microg/l. The highest chlorpyrifos concentration in floodwater was 0.45 microg/l in Brown. However, endosulfan was not detected in the water phase. In runoff water the highest concentrations measured were 0.3 microg/l for chlorpyrifos in Horqueta and 0.49 microg/l for cypermethrin in the Brown stream. On five sampling dates during the pesticide application period in Brown stream (2002/2003) the concentration of chlorpyrifos and cypermethrin in runoff and/or floodwater exceeded the water quality criteria for freshwater mentioned in this study. In three cases this insecticide concentration was measured in stream water, indicating an acute risk to aquatic life. The acute toxicity-exposure-ratio (TER) for chlorpyrifos and cypermethrin also shows an acute risk for aquatic invertebrates in the Brown stream. In the Horqueta chlorpyrifos concentrations in the runoff exceeded the safety levels three times during the application period (2001/2002), potentially endangering the aquatic fauna. Effects on aquatic macroinvertebrates after insecticide contamination were reported in earlier studies in Horqueta stream.     

Fertility signals in the bumblebee Bombus terrestris (Hymenoptera: Apidae)

In eusocial Hymenoptera, queen control over workers is probably inseparable from the mechanism of queen recognition. In primitively eusocial bumblebees (Bombus), worker reproduction is controlled not only by the presence or absence of a dominant queen but also by other dominant workers. Furthermore, it was shown that the queen dominance is maintained by pheromonal cues. We investigated whether there is a similar odor signal released by egg-laying queens and workers that may have a function as a fertility signal. We collected cuticular surface extracts from nest-searching and breeding Bombus terrestris queens and workers that were characterized by their ovarian stages. In chemical analyses, we identified 61 compounds consisting of aldehydes, alkanes, alkenes, and fatty acid esters. Nest-searching queens and all groups of breeding females differed significantly in their odor bouquets. Furthermore, workers before the competition point (time point of colony development where workers start to develop ovaries and lay eggs) differed largely from queens and all other groups of workers. Breeding queens showed a unique bouquet of chemical compounds and certain queen-specific compounds, and the differences toward workers decrease with an increasing development of the workers' ovaries, hinting the presence of a reliable fertility signal. Among the worker groups, the smallest differences were found after the competition point. Egg-laying females contained higher total amounts of chemical compounds and of relative proportions of wax-type esters and aldehydes than nest-searching queens and workers before the competition point. Therefore, these compounds may have a function as a fertility signal present in queens and workers.     

A new approach to data evaluation in the non-target screening of organic trace substances in water analysis

Non-target screening via high performance liquid chromatography-mass spectrometry (HPLC-MS) has gained increasingly in importance for monitoring organic trace substances in water resources targeted for the production of drinking water. In this article a new approach for evaluating the data from non-target HPLC-MS screening in water is introduced and its advantages are demonstrated using the supply of drinking water as an example. The crucial difference between this and other approaches is the comparison of samples based on compounds (features) determined by their full scan data. In so doing, we take advantage of the temporal, spatial, or process-based relationships among the samples by applying the set operators, UNION, INTERSECT, and COMPLEMENT to the features of each sample. This approach regards all compounds, detectable by the used analytical method. That is the fundamental meaning of non-target screening, which includes all analytical information from the applied technique for further data evaluation. In the given example, in just one step, all detected features (1729) of a landfill leachate sample could be examined for their relevant influences on water purification respectively drinking water. This study shows that 1721 out of 1729 features were not relevant for the water purification. Only eight features could be determined in the untreated water and three of them were found in the final drinking water after ozonation. In so doing, it was possible to identify 1-adamantylamine as contamination of the landfill in the drinking water at a concentration in the range of 20 ng L⁻¹. To support the identification of relevant compounds and their transformation products, the DAIOS database (Database-Assisted Identification of Organic Substances) was used. This database concept includes some functions such as product ion search to increase the efficiency of the database query after the screening. To identify related transformation products the database function “transformation tree” was used.     

Monitoring and assessing of landscape heterogeneity at different scales

In numerous studies, spatial and spectral aggregations of pixel information using average values from imaging spectrometer data are suggested to derive spectral indices and the subsequent vegetation parameters that are derived from these. Currently, there are very few empirical studies that use hyperspectral data, to support the hypothesis for deriving land surface variables from different spectral and spatial scales. In the study at hand, for the first time ever, investigations were carried out on fundamental scaling issues using specific experimental test flights with a hyperspectral sensor to investigate how vegetation patterns change as an effect of (1) different spatial resolutions, (2) different spectral resolutions, (3) different spatial and spectral resolutions as well as (4) different spatial and spectral resolutions of originally recorded hyperspectral image data compared to spatial and spectral up- and downscaled image data. For these experiments, the hyperspectral sensor AISA-EAGLE/HAWK (DUAL) was mounted on an aircraft to collect spectral signatures over a very short time sequence of a particular day. In the first experiment, reflectance measurements were collected at three different spatial resolutions ranging from 1 to 3 m over a 2-h period in 1 day. In the second experiment, different spectral image data and different additional spatial data were collected over a 1-h period on a particular day from the same test area. The differently recorded hyperspectral data were then spatially and spectrally rescaled to synthesize different up- and down-rescaled images. The normalised difference vegetation index (NDVI) was determined from all image data. The NDVI heterogeneity of all images was compared based on methods of variography. The results showed that (a) the spatial NDVI patterns of up- and downscaled data do not correspond with the un-scaled image data, (b) only small differences were found between NDVI patterns determined from data recorded and resampled at different spectral resolutions and (c) the overall conclusion from the tests carried out is that the spatial resolution is more important in determining heterogeneity by means of NDVI than the depth of the spectral data. The implications behind these findings are that we need to exercise caution when interpreting and combining spatial structures and spectral indices derived from satellite images with differently recorded geometric resolutions.