@article{WangWangWangetal.2016, author = {Wang, Hao and Wang, Xue-jiang and Wang, Wei-shi and Yan, Xiang-bo and Xia, Peng and Chen, Jie and Zhao, Jian-fu}, title = {Modeling and optimization of struvite recovery from wastewater and reusing for heavy metals immobilization in contaminated soil}, series = {Journal of chemical technology \& biotechnology}, volume = {91}, journal = {Journal of chemical technology \& biotechnology}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0268-2575}, doi = {10.1002/jctb.4931}, pages = {3045 -- 3052}, year = {2016}, abstract = {BACKROUND: Few studies have been carried out to connect nutrients recovery from wastewater and heavy metals immobilization in contaminated soil. To achieve the goal, ammonia nitrogen (AN) and phosphorus (P) were recovered from rare-earth wastewater by using the formation of struvite, which was used as the amendment with plant ash for copper, lead and chromium immobilization. RESULTS: AN removal efficiency and residual P reached 95.32 +/- 0.73\% and 6.14 +/- 1.72mgL(-1) under optimal conditions: pH= 9.0, n(Mg): n(N): n(P)= 1.2: 1: 1.1, which were obtained using response surface methodology (RSM). The minimum available concentrations of Cu, Pb and Cr (CPC) separately reduced to 320.82 mg kg(-1), 190.77 mg kg(-1) and 121.46 mg kg(-1) with increasing immobilization time at the mass ratio of phosphate precipitate (PP)/plant ash (PA) of 1: 3. Humic acid (HA) and fulvic acid (FA) were beneficial to immobilize Cu, both of which showed no effect or even a negative effect on Pb and Cr immobilization.}, language = {en} } @misc{MurawskiBuergerVorogushynetal.2016, author = {Murawski, Aline and B{\"u}rger, Gerd and Vorogushyn, Sergiy and Merz, Bruno}, title = {Can local climate variability be explained by weather patterns?}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {525}, issn = {1866-8372}, doi = {10.25932/publishup-41015}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-410155}, pages = {24}, year = {2016}, abstract = {To understand past flood changes in the Rhine catchment and in particular the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. This approach assumes a strong link between weather patterns and local climate, and sufficient GCM skill in reproducing weather pattern climatology. These presuppositions are unprecedentedly evaluated here using 111 years of daily climate data from 490 stations in the Rhine basin and comprehensively testing the number of classification parameters and GCM weather pattern characteristics. A classification based on a combination of mean sea level pressure, temperature, and humidity from the ERA20C reanalysis of atmospheric fields over central Europe with 40 weather types was found to be the most appropriate for stratifying six local climate variables. The corresponding skill is quite diverse though, ranging from good for radiation to poor for precipitation. Especially for the latter it was apparent that pressure fields alone cannot sufficiently stratify local variability. To test the skill of the latest generation of GCMs from the CMIP5 ensemble in reproducing the frequency, seasonality, and persistence of the derived weather patterns, output from 15 GCMs is evaluated. Most GCMs are able to capture these characteristics well, but some models showed consistent deviations in all three evaluation criteria and should be excluded from further attribution analysis.}, language = {en} }