The Great Lake’s future at a cross road

Some argue that a collective vision for the future of the Laurentian Great Lakes is embodied in the␣Great Lakes Water Quality Agreement (GLWQA). The GLWQA is a binational agreement, first signed in 1972 by Prime Minister Pierre Trudeau and President Richard Nixon, wherein the two countries (the Parties) commit to “restore and maintain the chemical, physical and biological integrity of the waters of the Great Lakes Basin Ecosystem.” Article X of the Agreement states that the Parties shall conduct a comprehensive review of the operation and effectiveness of this Agreement following every third biennial report of the [International Joint] Commission (IJC). The IJC’s 12th Biennial Report, released in 2004, triggered this important science, program, and policy review which commenced May 2006. This essay makes the case for a rigorous review, that explores deliberately the future scope of the Agreement to protect the world’s largest surface freshwater resource, and calls for innovation in the governance regime of this binational ecosystem.     

Levels of perfluorochemicals in water samples from Catalonia,Spain: is drinking water a significant contribution to human exposure?

Background, aim, and scope  In recent years, due to a high persistence, biomagnification in food webs, presence in remote regions, and potential toxicity, perfluorochemicals (PFCs) have generated a considerable interest. The present study was aimed to determine the levels of perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), and other PFCs in drinking water (tap and bottled) and river water samples from Tarragona Province (Catalonia, Spain). Materials and methods  Municipal drinking (tap) water samples were collected from the four most populated towns in the Tarragona Province, whereas samples of bottled waters were purchased from supermarkets. River water samples were collected from the Ebro (two samples), Cortiella, and Francolí Rivers. After pretreatment, PFC analyses were performed by HPLC-MS. Quantification was done using the internal standard method, with recoveries between 68% and 118%. Results  In tap water, PFOS and PFOA levels ranged between 0.39 and 0.87 ng/L (0.78 and 1.74 pmol/L) and between 0.32 and 6.28 ng/L (0.77 and 15.2 pmol/L), respectively. PFHpA, PFHxS, and PFNA were also other detected PFCs. PFC levels were notably lower in bottled water, where PFOS could not be detected in any sample. Moreover, PFHpA, PFHxS, PFOA, PFNA, PFOS, PFOSA, and PFDA could be detected in the river water samples. PFOS and PFOA concentrations were between <0.24 and 5.88 ng/L (<0.48 and 11.8 pmol/L) and between <0.22 and 24.9 ng/L (<0.53 and 60.1 pmol/L), respectively. Discussion  Assuming a human water consumption of 2 L per day, the daily intake of PFOS and PFOA by the population of the area under evaluation was calculated (0.78–1.74 and 12.6 ng, respectively). It was found that drinking water might be a source of exposure to PFCs as important as the dietary intake of these pollutants. Conclusions  The contribution of drinking water (tap and bottled) to the human daily intake of various PFCs has been compared for the first time with data from dietary intake of these PFCs. It was noted that in certain cases, drinking water can be a source of exposure to PFCs as important as the dietary intake of these pollutants although the current concentrations were similar or lower than those reported in the literature for surface water samples from a number of regions and countries. Recommendations and perspectives  Further studies should be carried out in order to increase the knowledge of the role of drinking water in human exposure to PFCs.     

Nutrient-impregnated charcoal: an environmentally friendly slow-release fertilizer

The widespread contamination of surface and ground water quality from the heavy use of fertilizer in modern agriculture is the current concern. Therefore, this study was carried out to develop a slow-release fertilizer using charcoal. The morphology of the charcoal impregnated fertilizer was investigated by scanning electron microscopy (SEM). This study also evaluated the release patterns of N, P, and K from impregnated charcoal using a simulated soil solution and distilled water as leaching solutions. The patterns of N, P, and K releases were examined in both static and continuous-flow conditions for 360 h. Releases of N, P, and K from impregnated charcoal were found to be slow and steady. However, the release trends of N, P, and K were higher in soil solution than distilled water under both the above conditions. Dissolution occurred when N, P, and K were released in the above leached solutions. As a result, the fertilizer impregnated charcoal could be developed as slow-release type fertilizer to minimize the contamination.     

Social media and disasters: a functional framework for social media use in disaster planning,response, and research

A comprehensive review of online, official, and scientific literature was carried out in 2012–13 to develop a framework of disaster social media. This framework can be used to facilitate the creation of disaster social media tools, the formulation of disaster social media implementation processes, and the scientific study of disaster social media effects. Disaster social media users in the framework include communities, government, individuals, organisations, and media outlets. Fifteen distinct disaster social media uses were identified, ranging from preparing and receiving disaster preparedness information and warnings and signalling and detecting disasters prior to an event to (re)connecting community members following a disaster. The framework illustrates that a variety of entities may utilise and produce disaster social media content. Consequently, disaster social media use can be conceptualised as occurring at a number of levels, even within the same disaster. Suggestions are provided on how the proposed framework can inform future disaster social media development and research.     

Prevalence of Rotaviruses Groups A and C in Egyptian Children and Aquatic Environment

The objective of this study is to compare the prevalence of rotaviruses groups A and C in Egyptian children and aquatic environment. From 110 stool specimens of children with acute diarrhea and using RT-PCR, 35 samples (31.8 %) were positive for human rotavirus group A and 15 samples (13.6 %) were positive for human rotavirus group C. From 96 samples collected from Zenin wastewater treatment plant over a 2-year period (November 2009–October 2011) and using RT-PCR, rotavirus group A was detected in (4/24) 16.7 %, (5/24) 20.8 %, (4/24) 16.7 %, and (4/24) 16.7 %, while rotavirus group C was detected in (2/24) 8.3 %, (3/24) 12.5 %, (3/24) 12.5 %, and (0/24) 0 % in raw sewage, after primary sedimentation, after secondary sedimentation, and after final chlorination, respectively. Moreover, from 96 samples collected from El-Giza water treatment plant over a 2-year period (November 2009–October 2011), rotavirus group A was detected in (7/24) 29.2 %, (6/24) 25 %, (5/24) 20.8 %, and (3/24) 12.5 %, while rotavirus group C was detected in (3/24) 12.5 %, (1/24) 4.2 %, (1/24) 4.2 %, and (0/24) 0 % in raw Nile water, after sedimentation, after sand filtration, and after final chlorination, respectively. Using SYBR Green real-time RT-PCR, the number of human rotavirus group A genome or infectious units was higher than rotavirus group C. VP6 sequence analysis of the RT-PCR positive rotavirus group C samples revealed that four clinical specimens and three environmental samples showed similar sequences clustered with Moduganari/Human Nigerian strain AF 325806 with 98 % homology, and two clinical specimens and one environmental sample showed similar sequences clustered with Dhaka CB/Human Bangladesh strain AY 754826 with 97 % homology.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.