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Use of a pilot-scale fixed-film bioreactor was investigated for remediation of bromate contamination within groundwater. Bromate reduction with stoichiometric production of bromide was observed, providing supporting evidence for complete reduction of bromate with no production of stable intermediates. Reduction of 87-90% bromate from an influent concentration of 1.1 mg L(-1) was observed with retention times of 40-80 h. Lower retention times led to decreases in bromate reduction capability, with 11.5% removal at a 10 h retention time. Nitrate reduction of 76-99% from a 30.7 mg L(-1) as NO(3)(-) influent was observed at retention times of 10-80 h, although an increase in nitrite production to 2.7 mg L(-1) occurred with a 10 h retention time. Backwashing was not required, with the large plastic packing media able to accommodate biomass accumulation without decreases in operational efficiency. This study has provided proof of concept and demonstrated the potential of biological bromate reduction by fixed-film processes for remediation of a bromate contaminated groundwater source.

Suvilahti, a suburb of the city of Vaasa in western Finland, was the first area to use seabed sediment heat as the main source of heating for a high number of houses. Moreover, in the same area, a unique land uplift effect is ongoing. The aim of this paper is to solve the challenges and find opportunities caused by global warming by utilizing seabed sediment energy as a renewable heat source. Measurement data of water and air temperature were analyzed, and correlations were established for the sediment temperature data using Statistical Analysis System (SAS) Enterprise Guide 7.1. software. The analysis and provisional forecast based on the autoregression integrated moving average (ARIMA) model revealed that air and water temperatures show incremental increases through time, and that sediment temperature has positive correlations with water temperature with a 2-month lag. Therefore, sediment heat energy is also expected to increase in the future. Factor analysis validations show that the data have a normal cluster and no particular outliers. This study concludes that sediment heat energy can be considered in prominent renewable production, transforming climate change into a useful solution, at least in summertime.

Abstract The harvesting of energy from ambient environments is an emerging technology with potential for numerous applications, including portable electronic devices for renewable energy. Most of the current research activities refer to classical piezoelectric ceramic materials but more recently the development of electrostrictive polymers has generated novel opportunities for high-strain actuators. At present, the investigation of using electrostrictive polymers for energy harvesting (a conversion of mechanical to electrical energy) is beginning to show potential for this application. Basically, the relative energy gain depends on the current induced by the mechanical strain and frequency. The aim of this work was to determine the optimum operating range for improved electro-mechanical conversion efficiency of electrostrictive polymer composite by the effect of mechanical parameters (mechanical frequency and the amplitude strain) that leads to an increase in the generated current and improve the output power in relation to the injected power. It was also found that the trend of the experimental data is in good accordance with that of the theoretical prediction. Finally, the results indicated that the frequency and the amplitude of mechanical excitation were the critical parameters for the best electromechanical conversion.

All climate change scenarios predict an increase in both global temperature means and the magnitude of seasonal and diel temperature variation. The nonlinear relationship between temperature and biological processes means that fluctuating temperatures lead to physiological, life history, and ecological consequences for ectothermic insects that diverge from those predicted from constant temperatures. Fluctuating temperatures that remain within permissive temperature ranges generally improve performance. By contrast, those which extend to stressful temperatures may have either positive impacts, allowing repair of damage accrued during exposure to thermal extremes, or negative impacts from cumulative damage during successive exposures. We discuss the mechanisms underlying these differing effects. Fluctuating temperatures could be used to enhance or weaken insects in applied rearing programs, and any prediction of insect performance in the field—including models of climate change or population performance—must account for the effect of fluctuating temperatures.

AbstractA better understanding of how climate affects growth in tree species is essential for improved predictions of forest dynamics under climate change. Long-term climate averages (mean climate) and short-term deviations from these averages (anomalies) both influence tree growth, but the rarity of long-term data integrating climatic gradients with tree censuses has so far limited our understanding of their respective role, especially in tropical systems. Here, we combined 49 years of growth data for 509 tree species across 23 tropical rainforest plots along a climatic gradient to examine how tree growth responds to both climate means and anomalies, and how species functional traits mediate these tree growth responses to climate. We showed that short-term, anomalous increases in atmospheric evaporative demand and solar radiation consistently reduced tree growth. Drier forests and fast-growing species were more sensitive to water stress anomalies. In addition, species traits related to water use and photosynthesis partly explained differences in growth sensitivity to both long-term and short-term climate variations. Our study demonstrates that both climate means and anomalies shape tree growth in tropical forests, and that species traits can be leveraged to understand these demographic responses to climate change, offering a promising way forward to forecast tropical forest dynamics under different climate trajectories.

The phosphate (P) fertilizer industry generates a highly hazardous and acidic wastewater. The present study reports the evaluation of an integrated precipitation and Enhanced Biological Phosphorus Removal (EBPR) process for the treatment of fertilizer plant wastewater and effluent detoxification, assessed by microtoxicity and seed germination tests. Effluent samples were collected from a local P fertilizer industry and were characterized by their high fluoride and P content. First, the samples were pre‐treated by precipitation of P and fluoride ions using hydrated lime. The resulting low‐fluoride and phosphorus effluent was then treated with the EBPR process to monitor the simultaneous removal of carbon, nitrogen, and phosphorus. Phosphorus removal included a two‐stage anaerobic/aerobic system operating under continuous flow. Pre‐treated wastewater was added to the activated sludge and operated for 160 days in the reactor. The operating strategy included increasing the organic loading rate from 0.3 to 1.2 g chemical oxygen demand (COD)/L day. The stable and high removal rates of COD, NH4+‐N, and PO43−‐P were then recorded. The mean concentrations of the influent were approximately 3600 mg COD/L, 60 mg N/L and 14 mg P/L, which corresponded to removal efficiencies of approximately 98%, 86%, and 92%, respectively. The microtoxicity of the treated wastewater was then monitored by LUMIStox and its phytotoxicity was investigated on cress, tomato, wheat, maize, ryegrass, and alfalfa seed germination. LUMIStox tests showed that treatment allowed a significant toxicity removal. Moreover, the untreated wastewater inhibited the species germination even when diluted 10 times, whereas a positive effect of treated wastewater was noticed. © 2013 American Institute of Chemical Engineers Environ Prog, 33: 463–471, 2014

Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve.