Simulation of Arsenic Partitioning in Tributaries to Drinking Water Reservoirs

Arsenic released by bottom sediments was determined by experiments in which the sediments were artificially re-suspended using a particle entrapment simulator (PES) to simulate river conditions. Sediment cores were collected from various tributaries to drinking water reservoirs in Connecticut spiked with arsenic, and run in the PES at simulated bed-flow shear stresses from 0.0 to 0.6 N/m2. Under equilibrium conditions, the dissolved fraction of arsenic was found to range from 8.3 to 22.1 ug/1, which in most cases exceeded EPA Maximum Contaminant Level (MCL) of 10 ug/1. Experimental results from these simulations have shown that bed-flow shear stress causes an increased concentration of dissolved arsenic, most notably at shear stresses of 0.4,0.5, and 0.6 N/m2. For the solid phase under equilibrium, the concentrations of arsenic ranged between 71 and 275 mg/kg. The average concentration of arsenic on the solid phase as well as partitioning coefficient values (Kp) were highest at initial shear stress. This was attributed to the higher fraction of colloidal material and finer organic particles in the suspended solid mixture. Particles of such nature proved to have higher affinity to arsenic. Kp values were determined from PES data and were found to range from 4,687 to 24,090 1/kg. However, on a mass load basis, the amount of arsenic found in suspended sediment increased with the increase of shear stress. Similarly, the amount of arsenic in the solid phase increased significantly for sites with high Volatile Organic Carbon (VOC) content. Because of the influence of Total Suspended Solids (TSS) and VOC concentrations on Kp, the use of the PES is more appropriate in obtaining Kp values that would be found under real stream conditions when compared to the traditional way of measuring Kp using a jar study technique

Parameters Influencing Sediments Resuspension and the Link to Sorption of Inorganic Compounds

In the aquatic environment, the accumulation of chemicalcontaminants by sediments poses a potential threat to endemiclife forms and drinking water resources. Trace metals such asCd, Cu, Cr, Ni, Pb, and toxic organic compounds, are among awide variety of contaminants having an affinity for sediments.In this study, experiments were performed simulating sedimentresuspension in the lower Housatonic River, Connecticut, using aParticle Entrainment Simulator. Analyses of grain sizedistributions, porosities and total organic contents of thesediments suggested that these parameters influence theredistribution and entrainment of settleable solids in the watercolumn. These findings were established by evaluating the impactof one parameter on sediment resuspension as a function ofstream flow with the other two characteristics being heldconstant. Total suspended solids and volatile suspended solidsresuspension concentration ranged from 3.2 to 20648.3 mg L^sup -1^,and 1.5 to 1823.8 mg L^sup -1^, respectively, with subsequentincreases in flow rates from 9 to 6 dynes cm^sup -2^. The resuspension concentrations were augmentedby sediment porosity (22.0 to 57.5%), percent finer grain-size distributions at 0.1 mm, and total organic content (2.7 g kg^sup -1^ to 29.0 g kg^sup -1^). Using K^sub p^ values, and the dissolvedcontaminant levels of various trace metals, the particulatecontaminant levels of the metals were determined under variousoscillation rates. As sediment resuspension increased withincreased stream flow, there was an overall general increasefrom 0.02 to 33.6 μg L^sup -1^ in the particulatecontaminant levels of Cd, Cu, Cr, Ni and Pb

Parameters Affecting Partitioning of 6 PCB Congeners in Natural Sediments

Polychlorinated biphenyls (PCBs) released by bottom sediments were determined by experiments in which the sediments were artificially resuspended using sediment contaminated with PCBs in a particle entrainment simulator (PES). Sediment cores, spiked with PCBs, were collected from the Housatonic River in Connecticut and run in the PES at simulated shear stresses from 0 to 0.6 N m(-2). Experimental results from these simulations have shown that mean concentration of PCBs in the solid phase for sites with high volatile organic carbon (VOC) were significantly greater than samples with low VOC; the reverse was true for the water phase. In addition, on a mass load basis, the amount of PCBs found in sediment increased when shear stresses were increased from 0 to 0.6 N m(-2), beyond which shear stress did not affect mass loads in the water column. Partition coefficients (Kp) were determined from PES sediment and water data for the following congeners: PCB 28, PCB 52, PCB 101, PCB 138, PCB 153, PCB 180. Kp was determined to be inversely proportional to total suspended solids (TSS), but directly proportional to chlorine content of the congener. Because of the strong influence of TSS and VOC concentrations on Kp values, agitation of samples using a PES better simulated real environmental conditions when compared to jar studies where no agitation was employed. Therefore, a device like the PES is more appropriate in obtaining Kp that would be found under real stream flow conditions when compared to the traditional way of measuring Kp using the jar study technique

Wet Weather Impact on Trihalomethane Formation Potential in tributaries to Drinking Water Reservoirs

During rain storm events, land surface runoff and resuspension of bottom sediments cause an increase in Trihalomethane (THM) precursors in rivers. These precursors, when chlorinated at water treatment facilities will lead to the formation of THMs and hence impact drinking water resources. In order to evaluate the wet weather impact on the potential formation of THMs, river samples were collected before, during and after three rain storms ranging from 15.2 to 24.9 mm precipitation. The samples were tested for THM formation potential and other indicators including UV254 absorbance, turbidity and volatile suspended solid (VSS). Average levels of THMs increased from 61 microg/l during dry weather to 131 microg/l during wet weather, and then went back to 81 microg/l after rain ended. Wet weather values of THM are well above the maximum contaminant level (MCL) 80 microg/l, set by EPA for drinking water. THM indicators also exhibited similar trends. Average levels increased from 0.6 to 1.8 abs; 2.6 to 6 ntu; and 7.5 to 15 mg/l respectively for UV254, turbidity and VSS. A positive correlation was observed between THM formation and THM indicators. The t-test of significance (p-value) was less than 0.05 for all indicators, and R values ranged from 0.85 to 0.92 between THMs and the indicators, and 0.72 to 0.9 among indicators themselves

Informal Executive Actions and Agency Guidance: Legal and Political Implications for Immigration and Other Policy Areas

This paper is a study of the use of various forms of informal executive actions used by presidents and cabinet officials to guide the policy implementation of federal agencies. Among these are policy manuals, guidance statements, statements of administration policy, and announcements of executive priorities. The recent executive action regarding immigration announced by President Obama is also an example of this kind of behavior. The paper will rely upon the analysis of legal scholars who have examined the causes and consequences of informal executive actions. We will examine the implications that these actions have for public participation, transparency, consistency in decision-making, and inter-branch comity. The analysis will be applied to President Obama’s recent action regarding immigration enforcement

Multi-Regression Prediction of Metal Partition Coefficients under Various Physical/Chemical Conditions Design of Experiments As, Cr, Cu, Ni and Zn

The behavior of metals in surface water is complex and their partition coefficients can be impacted by many factors. Organic matter (OM) content in sediments, pH and salinity, are factors that may influence speciation and partitioning of metals. The difficulty in describing the impacts and relationships are that these processes are interconnected with no dominant associations among all. In this study, the partitioning of five metals (As, Cr, Cu, Ni and Zn) under different levels of salinity, pH, and OM content were investigated. A series of factorial design experiments are evaluated in which three levels of OM are tested each time against five levels each of salinity and pH; the design of experiments was generated by the statistical software program MiniTab16®. All metals tested showed a trend of increasing Kd with the increase of OM 0.36% to 4.32%. Higher Kd were the result of the increase in pH from 3-10.5 and lower Kd values resulted after an increase in salinity 0-3%. However, within that lower range of salinity, a positive linear correlation between Kd and salinity was observed which is attributed to potential formation of insoluble metal species with the increase of salinity. Multiple regression equations with the variables pH, OM and salinity were generated to predict Kd of each metal. The study showed no interaction between salinity/OM and pH/OM for all five metals

Arabic Educational Neural Network Chatbot

Chatbots (machine-based conversational systems) have grown in popularity in recent years. Chatbots powered by artificial intelligence (AI) are sophisticated technologies that replicate human communication in a range of natural languages. A chatbot’s primary purpose is to interpret user inquiries and give relevant, contextual responses. Chatbot success has been extensively reported in a number of widely spoken languages; nonetheless, chatbots have not yet reached the predicted degree of success in Arabic. In recent years, several academics have worked to solve the challenges of creating Arabic chatbots. Furthermore, the development of Arabic chatbots is critical to our attempts to increase the use of the language in academic contexts. Our objective is to install and create an Arabic chatbot that will help the Arabic language in the area of education. To begin implementing the chabot, we collected datasets from Arabic educational websites and had to prepare these data using the NLP methods. We then used this data to train the system using a neural network model to create an Arabic neural network chabot. Furthermore, we found relevant research, conducted earlier investigations, and compared their findings by searching Google scholar and looking through the linked references. Data was gathered and saved in a json file. Finally, we programmed the chabot and the models in Python. As a consequence, an Arabic chatbot answers all questions about educational regulations in the United Arab Emirates

Prediction of Metal Remobilization from Sediments under Various Physical/ Chemical Conditions “Design of Experiments Cd, Co and Pb”

The metal partition coefficient Kd (L/kg) is the ratio of sorbed metal concentration on the solid phase m (mg/kg) to the dissolved metal concentration at equilibrium. The behavior of metals in surface water is complex and their partition coefficients can be impacted by many factors. Organic matter (OM) content in sediments, pH and salinity, are factors that may influence speciation and partitioning of metals. In this study, the partitioning coefficient of three metals (Cd, Co and Pb) under different levels of salinity, pH, and OM content were examined. A series of factorial experiments were evaluated in which three levels of OM are tested each time against five levels each of salinity and pH; the design of experiments was generated by the statistical software program, MiniTab16®. All metals tested showed a trend of increasing Kd with an increase of OM from 0.36% to 4.36%. Salinity experiments showed that the lower values of Kd were all recorded in freshwater and the highest Kd values were recorded in saltwater. The metal Pb showed the highest Kd values. The average Kd values under acidic conditions for Cd, Co and Pb are 234, 83 and 5,618 L/kg respectively. The relatively higher value of Kd for Pb compared to that of Cd and Co can be attributed to its lower precipitating pH. Multiple regression equations were generated to predict Kd of each metal when comparing multiple factors at the same time (salinity/OM and pH/OM). The study showed no significant interactions between salinity/OM and pH/ OM for all three metals. This supports that tested factors are all affected Kd but act independently

Artificial Intelligence Chatbots: A Survey of Classical versus Deep Machine Learning Techniques

Artificial Intelligence (AI) enables machines to be intelligent, most importantly using Machine Learning (ML) in which machines are trained to be able to make better decisions and predictions. In particular, ML-based chatbot systems have been developed to simulate chats with people using Natural Language Processing (NLP) techniques. The adoption of chatbots has increased rapidly in many sectors, including, Education, Health Care, Cultural Heritage, Supporting Systems and Marketing, and Entertainment. Chatbots have the potential to improve human interaction with machines, and NLP helps them understand human language more clearly and thus create proper and intelligent responses. In addition to classical ML techniques, Deep Learning (DL) has attracted many researchers to develop chatbots using more sophisticated and accurate techniques. However, research has paid chatbots have widely been developed for English, there is relatively less research on Arabic, which is mainly due to its complexity and lack of proper corpora compared to English. Though there have been several survey studies that reviewed the state-of-the-art of chatbot systems, these studies (a) did not give a comprehensive overview of how different the techniques used for Arabic chatbots in comparison with English chatbots; and (b) paid little attention to the application of ANN for developing chatbots. Therefore, in this paper, we conduct a literature survey of chatbot studies to highlight differences between (1) classical and deep ML techniques for chatbots; and (2) techniques employed for Arabic chatbots versus those for other languages. To this end, we propose various comparison criteria of the techniques, extract data from collected studies accordingly, and provide insights on the progress of chatbot development for Arabic and what still needs to be done in the future