Benefits of protected areas for nonbreeding waterbirds adjusting their distributions under climate warming

Climate warming is driving changes in species distributions and community composition. Many species have a so-called climatic debt, that is, shifts in range lag behind shifts in temperature isoclines. Inside protected areas (PAs), community changes in response to climate warming can be facilitated by greater colonization rates by warm-dwelling species, but also mitigated by lowering extirpation rates of cold-dwelling species. An evaluation of the relative importance of colonization-extirpation processes is important to inform conservation strategies that aim for both climate debt reduction and species conservation. We assessed the colonization-extirpation dynamics involved in community changes in response to climate inside and outside PAs. To do so, we used 25 years of occurrence data of nonbreeding waterbirds in the western Palearctic (97 species, 7071 sites, 39 countries, 1993–2017). We used a community temperature index (CTI) framework based on species thermal affinities to investigate species turnover induced by temperature increase. We determined whether thermal community adjustment was associated with colonization by warm-dwelling species or extirpation of cold-dwelling species by modeling change in standard deviation of the CTI (CTI_SD). Using linear mixed-effects models, we investigated whether communities in PAs had lower climatic debt and different patterns of community change than communities outside PAs. For CTI and CTI_SD combined, communities inside PAs had more species, higher colonization, lower extirpation, and lower climatic debt (16%) than communities outside PAs. Thus, our results suggest that PAs facilitate 2 independent processes that shape community dynamics and maintain biodiversity. The community adjustment was, however, not sufficiently fast to keep pace with the large temperature increases in the central and northeastern western Palearctic. Our results underline the potential of combining CTI and CTI_SD metrics to improve understanding of the colonization-extirpation patterns driven by climate warming.     

Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic

The average summer temperatures as well as the frequency and intensity of hot days and heat waves are expected to increase due to climate change. Motivated by this consequence, we propose a methodology to evaluate the monthly heat wave hazard and risk and its spatial distribution within large cities. A simple urban climate model with assimilated satellite-derived land surface temperature images was used to generate a historic database of urban air temperature fields. Heat wave hazard was then estimated from the analysis of these hourly air temperatures distributed at a 1-km grid over Athens, Greece, by identifying the areas that are more likely to suffer higher temperatures in the case of a heat wave event. Innovation lies in the artificial intelligence fuzzy logic model that was used to classify the heat waves from mild to extreme by taking into consideration their duration, intensity and time of occurrence. The monthly hazard was subsequently estimated as the cumulative effect from the individual heat waves that occurred at each grid cell during a month. Finally, monthly heat wave risk maps were produced integrating geospatial information on the population vulnerability to heat waves calculated from socio-economic variables.     

Fixed bed adsorption for the removal of pollutants from water

The adsorption of phenol, p-chlorophenol and mercuric ions from aqueous solution onto activated carbon has been studied in fixed bed columns. The influence of varying parameters such as bed depth, solution flowrate and pollutant concentration has been studied. The Bed Depth Service Time has been used to analyse the experimental data and identify design correlations. Furthermore, an optimization procedure based on the Empty Bed Residence Time has been applied to the data.