Relative impact of environmental variables on the lake trophic state highlights the complexity of eutrophication controls.

For the effective management of lakes apart from defining and monitoring their current state it is crucial to identify environmental variables that are mostly responsible for the nutrient input. We used interpretative machine learning to investigate the environmental parameters that influence the lake's trophic state and recognize their patterns. We analysed the influence of the 25 environmental variables on the commonly used trophic state indicators values: total phosphorus (TP), Chlorophyll-a (Chl-a) and Secchi depth (SD) of 60 lakes located in the Central European Lowlands. We attempted to delineate the lakes into groups due to the influence of common prevailing environment variable/variables on the water trophic state reflected by each indicator. The results indicated that the relative impact of environmental variables on the lake trophic state has an individual hierarchy unique for each indicator. The most important are variables related to catchment impact on the lake, Ohle ratio (L. catchment area/L. area) for TP and Schindler ratio (L. area + L. catchment area)/L. volume for Chl-a and SD. There are also few variables strongly influential only for small sub-groups of lakes that stand out: lake maximum depth, catchment slope steepness expressed by the height standard deviation. The methods used in the study enabled the assessment of the character of the influence of the environmental variables on the indicator value and revealed that most essential variables (Ohle ratio for TP and Schindler ratio for Chl-a and SD) have bimodal distribution with a clear threshold value. These findings contribute to a better understanding of the drivers shaping the lake trophic status and have implication for planning effective management strategies.

Origin and fate of nanoparticles in marine water - Preliminary results.

The number, morphology and elemental composition of nanoparticles (<100 nm) in marine water was investigated using Variable Pressure Scanning Electron Microscopy (VP-SEM) and Energy-dispersive X-ray spectroscopy (EDS). Preliminary research conducted in the Baltic Sea showed that the number of nanoparticles in seawater varied from undetectable to 380 (x102) cm-3. Wind mixing and density barriers (thermocline) had a significant impact on the abundance and distribution of nanoparticles in water. Many more nanoparticles (mainly nanofibers) were detected in periods of intensive primary production and thermal stratification of water than at the end of the growing season and during periods of strong wind mixing. Temporal and spatial variability of nanoparticles as well as air mass trajectories indicated that the analysed nanofibers were both autochthonous and allochthonous (atmospheric), while the nanospheres were mainly autochthonous. Chemical composition of most of analysed nanoparticles indicates their autochthonous, natural (biogenic/geogenic) origin. Silica nanofibers (probably the remains of flagellates), nanofibers composed of manganese and iron oxides (probably of microbial origin), and pyrite nanospheres (probable formed in anoxic sediments), were all identified in the samples. Only asbestos nanofibers, which were also detected, are probably allochthonous and anthropogenic.