Spatial and temporal properties of environmental exposure assessments related to ground water contamination

This paper describes a new methodology for modeling contaminant transport in ground water for (1) better quantifying the magnitude of exposure in a contaminated aquifer, (2) characterizing the spatial and temporal variation of exposure in a heterogeneous aquifer, and (3) providing more tools for characterizing population exposure. This methodology is also applied to a semi-hypothetical case study.     

Endosulfan sulfate sorption on natural organic substances

This work proposes a viable remediation method based on the use of natural organic substances (NOSs) that characterize the Mediterranean region to improve the ecological system. A series of experiments, including variable conditions, such as temperature, pH, contact time, and pesticide concentration, were performed to demonstrate the efficiency of endosulfan sulfate removal from water by NOSs. Experimental results showed that the pH and temperature of pesticide solutions negatively affect the adsorption process. The maximum adsorption capacity for a specific initial concentration of endosulfan sulfate (0.5 microg/L) was achieved with Origanum compactum (75%), followed by Cistus ladaniferus and Raphanus raphanistrum (72 and 68%, respectively). The adsorption tests gave very satisfying results and point to the possible application of these supports as a remediation technique to prevent pesticide contamination of aquatic ecosystems.     

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator

● Hybrid deep-learning model is proposed for water quality prediction. ● Tree-structured Parzen Estimator is employed to optimize the neural network. ● Developed model performs well in accuracy and uncertainty. ● Usage of the proposed model can reduce carbon emission and energy consumption. Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants (WWTPs). However, some water quality metrics are not measurable in real time, thus influencing the judgment of the operators and may increase energy consumption and carbon emission. One of the solutions is using a soft-sensor prediction technique. This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit (BiGRU) combined with Gaussian Progress Regression (GPR) optimized by Tree-structured Parzen Estimator (TPE). TPE automatically optimizes the hyperparameters of BiGRU, and BiGRU is trained to obtain the point prediction with GPR for the interval prediction. Then, a case study applying this prediction method for an actual anaerobic process (2500 m³/d) is carried out. Results show that TPE effectively optimizes the hyperparameters of BiGRU. For point prediction of COD_eff and biogas yield, R² values of BiGRU, which are 0.973 and 0.939, respectively, are increased by 1.03%–7.61% and 1.28%–10.33%, compared with those of other models, and the valid prediction interval can be obtained. Besides, the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation. It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.     

Characterization of municipal and dairy wastewaters with 30 quality parameters and potential wastewater treatment by biological trickling filters

This study aimed to characterize raw municipal and dairy wastewaters. The quality of the collected wastewaters was determined by measuring 30 quality parameters, such as phenols, oxidation-reduction potential, oil and grease, total organic carbon (TOC), manganese, total viable count of bacteria (TVC), salinity, and biodegradability. Results were compared with wastewater disposal standards. This study also aimed to describe the potential application and performance of biological trickling filters (BTFs) in municipal and dairy wastewater treatment. Detailed reviews of trickling filter models were explained. Results revealed that two wastewater samples exceeded standard limits. The raw municipal and dairy wastewaters need treatment prior to disposal to the natural environment. Additionally, BTFs are efficient municipal and dairy wastewater treatments.     

The impact of Local Authorities’ interventions on household waste collection: A case study approach using time series modelling

At a local Government level there have been many interventions and changes made to household waste collection services to meet new regulatory requirements. These changes include separate collection of recyclable and organic materials. This paper has used a time series model to quantify the success of interventions introduced by a LA.The case study was a medium sized UK LA, Charnwood Borough Council (CBC), the research analyses monthly data of quantities of recyclates, garden waste for composting and residual waste for landfill disposal. The time series model was validated with a five year data set and used to measure the impacts of the various changes to identify which intervention was the most successful, while controlling for season and number of working days. The results show the interventions analysed both had abrupt and permanent positive impacts on the yield of recyclable materials, and a corresponding negative impact on the residual waste.The model could be added to the National data base to help LAs to compare interventions and to understand which schemes encourage householder participation and improve recycling performance.     

Technical‐administrative task performance,leadership task performance,and contextual performance: considering the influence of team‐ and task‐related composition variables

The purpose of the present study was to examine how the mix of ability, experience, and personality impacts three types of team performance: technical‐administrative task performance, leadership task performance, and contextual performance. Relationships were tested using data collected from student management teams, who were required to plan and supervise the preparation and service of meals in a cafeteria‐style dining room patronized by university students, staff, and faculty. Results revealed that both team‐ and task‐related composition variables predicted leadership and contextual performance. Specifically, grade point average was significantly related to technical‐administrative task performance, and extraversion, neuroticism, and grade point average were related to leadership task performance. Agreeableness and restaurant experience predicted contextual performance. Surprisingly, conscientiousness did not account for significant variance in any of the three types of performance measured. The implications of these findings for the study of team performance in organizations are discussed. Copyright © 2002 John Wiley & Sons, Ltd.