A simplified model for reservoir operation considering the water quality issues: Application of the Young conflict resolution theory

In this study, an algorithm combining a multi-objective genetic algorithm (GA)-based optimization model and a water quality simulation model is developed for determining a trade-off curve between objectives related to the allocated water quantity and quality. To reduce the run-time of the GA-based optimization model, the main problem is decomposed to long-term and annual optimization models. The reliability of water supply is considered to be the objective function in the long-term stochastic optimization model, but the objective functions of the annual models are related to both the allocated water quantity and quality. The operating policies obtained using this long-term model provide the time series of the optimum reservoir water storages at the beginning and the end of each water year. In the next step, these optimal reservoir storage values are considered as constraints for water storage in the annual reservoir operation optimization models. The epsilon-constraint method is then used to develop a trade-off curve between the reliability of water supply and the average allocated water quality. The Young conflict resolution theory, which incorporates the existing conflicts among decision-makers and stakeholders, is used for selecting the best solution on the trade-off curve. The monthly reservoir operating rules are then calculated using an Adaptive Neuro-Fuzzy Inference System, which is trained using the optimal operating policies. The proposed model is applied to the 15-Khordad Reservoir in the central part of Iran. The results show that this simplified procedure does not reduce the accuracy of the reservoir operating policies and it can effectively reduce the computational burden of the previously developed models.     

The roles of nuclear energy,renewable energy,and economic growth in the abatement of carbon dioxide emissions in the G7 countries

Environmental Science and Pollution Research - In the current century, the G7 countries have attached more importance to energy security, and have prioritized low-carbon sources which have...     

A review of the global climate change impacts,adaptation, and sustainable mitigation measures

Environmental Science and Pollution Research - Climate change is a long-lasting change in the weather arrays across tropics to polls. It is a global threat that has embarked on to put stress on...     

Twentieth century droughts and agriculture: Examples from impacts on soybean production in Kentucky,USA

Drought is a significant natural hazard that slowly evolves over time. Because of its character, drought is difficult to monitor and impacts are often poorly documented. Agriculture is one of the most sensitive sectors that are prone to drought. The objective of this research is to assess the impacts of drought on soybean production and revenue in Kentucky. Soybeans are one of Kentucky’s most important commodities. In this study, impacts of 1930–1931, 1940–1942, 1952–1955, 1987–1988, 1999–2000, and 2007 droughts were considered. It was found that over the recent years, up to 56 % of the revenue from soybeans was lost due to drought. During the first half of the twentieth century, revenue loss reached up to 77 %. This research is valuable to the general public as well as planners and policy makers. Proper documentation of impacts of past droughts will help identify drought vulnerabilities and results in better risk management and mitigation.     

Effect of DTPA on Cd solubility in soil – Accumulation and subsequent toxicity to lettuce

In a controlled environment experiment, using Cd spiked soil, lettuce plants were grown under a range of DTPA levels and were subsequently harvested to determine levels of phytoaccumulation. Cadmium phytoaccumulation significantly increased with increasing soil Cd level (P < 0.05) but unexpectedly decreased with increasing DTPA levels, despite the fact that solubility of Cd was increased in the soil. Cadmium translocation (from root to shoot) increased after DTPA application. Lettuce growth was inhibited by both Cd and DTPA (at and above 10 and 500 mg kg^?1 respectively), as a result of higher Cd mobility and subsequent toxicity which was caused by DTPA higher dosages. Metal solubility in the soil (ranged between 2.8 and 26.5 mg kg^?1) was found to be significantly higher (P < 0.01) as compared to control with increasing DTPA levels even after 3 months of DTPA application. Cadmium tissue concentration in all DTPA treatments was less than in the corresponding control treatment, indicating a negative effect of DTPA application on Cd uptake. In conclusion, lettuce was an unsuitable plant species for Cd accumulation, at least when associated with a DTPA chelator.     

A novel driver hazard perception sensitivity model based on drivers’ characteristics: A simulator study

Objective: Considering the high annual number of fatal driving accidents in Iran, any approach for reducing the number or severity of driving accidents is a positive step toward decreasing accident-related losses. Accidents can often be avoided by a timely reaction of the driver. One of the steps before reacting to a hazard is perception. Some driver characteristics may affect road hazard perception. In this research, it was assumed that various driver characteristics, including demographic characteristics and cognitive characteristics, have an impact on driver perception.

Methods: The driving simulator used in this research provides various scenarios; for example, passing a pedestrian or animal across the road or placing fixed objects in a 2-lane separated rural road for 2 groups of experienced and inexperienced drivers under day and night lighting conditions. The go/no-go test was carried out in order to assess drivers’ attention to driving tasks and inhibitory control. A structural equation model (SEM) was used to estimate the relation between driver characteristics and sensitivity to road hazard perception. A new hazard perception index was proposed based on the time intervals in the hazard vulnerability.

Results: The results show that the most effective variables in the analysis of sensitivity to hazard perception are driving experience (in kilometers) during the last 3 years and road lighting conditions. Moreover, hazard perception sensitivity was improved by better inhibitory control, selective attention, and decision making, more carefulness, the average amount of daily sleep, and marital status.

Conclusion: The results of this research may be useful in educating and advertising programs. It also could enhance sensitivity to perception of hazards such as pedestrians, animals, and fixed obstacles among young and novice drivers.     

Biosynthesized silver nanoparticles regulate the iron status in the spleen of Plasmodium chabaudi–infected mice

Environmental Science and Pollution Research - Malaria is a dangerous disease affecting millions around the globe. Biosynthesized nanoparticles are used against a variety of diseases including...     

Economic growth,technology, and CO2 emissions in BRICS: Investigating the non-linear impacts of economic complexity

Environmental Science and Pollution Research - Upgrading economic structures and producing less pollution-intensive goods are indispensable for achieving Sustainable Development Goals (SDGs) in...     

The effect of diazinon on blood glucose homeostasis: a systematic and meta-analysis study

Environmental Science and Pollution Research - Though evidence exists on the association between diazinon (DZN), an organophosphate pesticide, with hyperglycemia, contrasting reports also exist....     

Cognitive failures, driving errors and driving accidents

INTRODUCTION: The impact of a driver's cognitive capability on traffic safety has not been adequately studied. This study examined the relationship between cognitive failures, driving errors and accident data. METHOD: Professional drivers from Iran (160 males, ages 18-65) participated in this study. The cognitive failures questionnaire (CFQ) and the driver error questionnaire were administered. The participants were also asked other questions about personal driving information. A principal component analysis with varimax rotation was performed to determine the factor structure of the CFQ. Poisson regression models were developed to predict driving errors and accidents from total CFQ scores and the extracted factors. RESULTS: Total CFQ scores were associated with driving error rates, but not with accidents. However, the 2 extracted factors suggested an increased effect on accidents and were strongly associated with driving errors. DISCUSSION: Although the CFQ was not able to predict driving accidents, it could be used to identify drivers susceptible to driving errors. Further development of a driving-oriented cognitive failure scale is recommended to help identify error prone drivers. Such a scale may be beneficial to licensing authorities or for developing driver selection and training procedures for organizations.