High-fidelity profiling and modeling of heterogeneity in wastewater systems using milli-electrode array (MEA): Toward high-efficiency and energy-saving operation.

High energy consumption is a critical problem for wastewater treatment systems currently monitored using conventional "single point" probes and operated with manual or automatic open-loop control strategies, exhibiting significant time lag. This challenge is addressed in this study by profiling the variation of three critical water quality parameters (conductivity, temperature and pH) along the depth of a reactor at high spatiotemporal resolution in a real-time mode using flat thin milli-electrode array (MEA) sensors. The profiling accurately captured the heterogeneous status of the reactor under transient shocks (conductivity and pH) and slow lingering shock (temperature), providing an effective dataset to optimize the chemical dosage and energy requirement of wastewater treatment systems. Transient shock models were developed to validate the MEA profiles and calculate mass transfer coefficients. Monte Carlo simulation revealed high-resolution MEA profiling combined with fast closed-loop control strategies can save 59.50% of energy consumption (Temperature and oxygen consumption controls) and 45.29% of chemical dosage, and reach 16.28% performance improvement over the benchmark (defined with ideal conditions), compared with traditional "single-point" sensors that could only monitor the entire system through a single process state. This study demonstrated the capability of MEA sensors to profile reactor heterogeneity, visualize the variation of water quality at high resolution, provide complete datasets for accurate control, and ultimately lead to energy-saving operation with high resilience.

Towards high resolution monitoring of water flow velocity using flat flexible thin mm-sized resistance-typed sensor film (MRSF).

Novel flexible thin mm-sized resistance-typed sensor film (MRSF) fabricated using ink-jet printing technology (IPT) was developed in this study to monitor water flow rate in pipelines in real time in situ mode. The mechanism of MRSF is that the mm-sized interdigitated electrodes made by printing silver nanoparticles on an elastic polyimide film bend under different flow rates, leading to variation of the resistance of the sensor at different degrees of curvature. Continuous flow tests showed that MRSF possessed a high accuracy (0.2 m/s) and excellent sensitivity (0.1447/ms-1). A model of sensor resistance and flow velocity was established to unfold the correlation between the fundamentals of fluid mechanics and the mechanic flexibility of sensor materials. An analytical model yielded a high coefficient of determination (R2 > 0.93) for the relationship between the resistance increment of the MRSF and the square of the flow velocity at the velocity range of 0.25-2 m/s. Furthermore, a temperature-correction model was developed to quantify the effect of water temperature on the sensor resistance readings. MRSF exhibited a low temperature coefficient of resistance (TCR, 0.001) at the water temperature range of 20-60 °C. Computational fluid dynamics (CFD) simulations using the finite element method were conducted and confirmed both the underlying load assumptions and the deformation characteristics of the sensor film under various flow and material conditions. High-resolution monitoring of water flow rate using MRSF technology was expected to save at least 50% energy consumption for a given unit, especially under flow fluctuation. MRSF possesses a great potential to perform real-time in situ monitoring at high accuracy with ultralow cost, thus enabling the feedback control at high spatiotemporal resolution to reduce the overall energy consumption in water and wastewater systems.

Effect of climatic variability on childhood diarrhea and its high risk periods in northwestern parts of Ethiopia.

BACKGROUND: Increasing climate variability as a result of climate change will be one of the public health challenges to control infectious diseases in the future, particularly in sub-Saharan Africa including Ethiopia. OBJECTIVE: To investigate the effect of climate variability on childhood diarrhea (CDD) and identify high risk periods of diarrheal diseases. METHODS: The study was conducted in all districts located in three Zones (Awi, West and East Gojjam) of Amhara Region in northwestern parts of Ethiopia. Monthly CDD cases for 24 months (from July 2013 to June 2015) reported to each district health office from the routine surveillance system were used for the study. Temperature, rainfall and humidity data for each district were extracted from satellite precipitation estimates and global atmospheric reanalysis. The space-time permutation scan statistic was used to identify high risk periods of CDD. A negative binomial regression was used to investigate the relationship between cases of CDD and climate variables. Statistical analyses were conducted using SaTScan program and StataSE v. 12. RESULTS: The monthly average incidence rate of CDD was 11.4 per 1000 (95%CI 10.8-12.0) with significant variation between males [12.5 per 1000 (95%CI 11.9 to 13.2)] and females [10.2 per 1000 (95%CI 9.6 to 10.8)]. The space-time permutation scan statistic identified the most likely high risk period of CDD between March and June 2014 located in Huletej Enese district of East Gojjam Zone. Monthly average temperature and monthly average rainfall were positively associated with the rate of CDD, whereas the relative humidity was negatively associated with the rate of CDD. CONCLUSIONS: This study found that the most likely high risk period is in the beginning of the dry season. Climatic factors have an association with the occurrence of CDD. Therefore, CDD prevention and control strategy should consider local weather variations to improve programs on CDD.

Childhood diarrhea in high and low hotspot districts of Amhara Region, northwest Ethiopia: a multilevel modeling.

BACKGROUND: Childhood diarrhea is one of the major public health problems in Ethiopia. Multiple factors at different levels contribute to the occurrence of childhood diarrhea. The objective of the study was to identify the factors affecting childhood diarrhea at individual and community level. METHODS: A cross-sectional study design was employed from February to March 2015 in high and low hotspot districts of Awi and West and East Gojjam zones in Amhara Region, northwest Ethiopia. Districts with high and low hotspots with childhood diarrhea were identified using SaTScan spatial statistical analysis. A total of 2495 households from ten (five high and five low hotspot) randomly selected districts were included in the study. A semi-structured questionnaire was used to collect data. Data were entered and cleaned in Epi Info 3.5.2 version and analyzed using Stata version 12. A multilevel logistic regression was used to identify factors associated with childhood diarrhea. RESULTS: The prevalence of childhood diarrhea was 13.5 % and did not show significant variation between high [14.3 % (95 % CI 12.3-16.2 %)] and low [12.7 % (95 % CI 10.9-14.6 %)] hotspot districts. Individual- and community-level factors accounted for 35 % of childhood diarrhea variation across the communities in the full model. Age of children (6-35 months), complementary feeding initiation below 6 months, inadequate hand washing practices, limited knowledge of mothers on diarrhea, lowest wealth status of households, and longer time interval to visit households by health extension workers were factors for increasing the odds of childhood diarrhea at the individual level. At the community level, lack of improved water supply and sanitation and unvaccinated children with measles and rotavirus vaccine were the factors associated with childhood diarrhea. CONCLUSIONS: In this study, childhood diarrhea occurrences remained high. Both individual- and community-level factors determined the occurrence of diarrhea. Interventions should consider both individual- and community-level factors to reduce the occurrence of childhood diarrhea.

Towards high power output of scaled-up benthic microbial fuel cells (BMFCs) using multiple electron collectors.

This study aimed at achieving high power output of benthic microbial fuel cells (BMFCs) with novel geometric anode setups (inverted tube granular activated charcoal (IT-GAC) and carbon cloth roll (CCR)) and multiple anodes/electron collectors. The lab-scale tests showed the power density of IT-GAC and CCR anodes achieved at 2.92 and 2.55 W m(-2), the highest value ever reported in BMFCs. The power density of BMFCs substantially increased with electron collector number (titanium rods) in anodes. The connection of multiple electron collectors with multiple cathodes had much higher total voltage/current output than that with single cathode. The possibility of maintaining high power density at scaled-up BMFCs was explored by arranging multiple anodes in sediment. The compact configuration of multiple CCR anodes contacting each other did not deteriorate the performance of individual anodes, showing the feasibility of maximizing anode numbers per sediment footprint and achieving high power output. Multiple IT-GAC and CCR anodes with multiple collectors effectively utilized sediment at both horizontal and vertical directions and enhanced electron collection efficiency. This study demonstrated that bacterial adhesion and electron collection should be optimized on small anodes in order to maintain high power density and achieve high power output in the scaled-up BMFCs.

Childhood Diarrhea Exhibits Spatiotemporal Variation in Northwest Ethiopia: A SaTScan Spatial Statistical Analysis.

BACKGROUND: Childhood diarrhea continues to be a public health problem in developing countries, including Ethiopia. Detecting clusters and trends of childhood diarrhea is important to designing effective interventions. Therefore, this study aimed to investigate spatiotemporal clustering and seasonal variability of childhood diarrhea in northwest Ethiopia. METHODS: Retrospective record review of childhood diarrhea was conducted using quarterly reported data to the district health office for the seven years period beginning July 1, 2007. Thirty three districts were included and geo-coded in this study. Spatial, temporal and space-time scan spatial statistics were employed to identify clusters of childhood diarrhea. Smoothing using a moving average was applied to visualize the trends and seasonal pattern of childhood diarrhea. Statistical analyses were performed using Excel® and SaTScan programs. The maps were plotted using ArcGIS 10.0. RESULTS: Childhood diarrhea in northwest Ethiopia exhibits statistical evidence of spatial, temporal, and spatiotemporal clustering, with seasonal patterns and decreasing temporal trends observed in the study area. A most likely purely spatial cluster was found in the East Gojjam administrative zone of Gozamin district (LLR = 7123.89, p <0.001). The most likely spatiotemporal cluster was detected in all districts of East Gojjam zone and a few districts of the West Gojjam zone (LLR = 24929.90, p<0.001), appearing from July 1, 2009 to June 30, 2011. One high risk period from July 1, 2008 to June 30, 2010 (LLR = 9655.86, p = 0.001) was observed in all districts. Peak childhood diarrhea cases showed a seasonal trend, occurring more frequently from January to March and April to June. CONCLUSION: Childhood diarrhea did not occur at random. It has spatiotemporal variation and seasonal patterns with a decreasing temporal trend. Accounting for the spatiotemporal variation identified in the study areas is advised for the prevention and control of diarrhea.

Simulating water-quality trends in public-supply wells in transient flow systems.

Models need not be complex to be useful. An existing groundwater-flow model of Salt Lake Valley, Utah, was adapted for use with convolution-based advective particle tracking to explain broad spatial trends in dissolved solids. This model supports the hypothesis that water produced from wells is increasingly younger with higher proportions of surface sources as pumping changes in the basin over time. At individual wells, however, predicting specific water-quality changes remains challenging. The influence of pumping-induced transient groundwater flow on changes in mean age and source areas is significant. Mean age and source areas were mapped across the model domain to extend the results from observation wells to the entire aquifer to see where changes in concentrations of dissolved solids are expected to occur. The timing of these changes depends on accurate estimates of groundwater velocity. Calibration to tritium concentrations was used to estimate effective porosity and improve correlation between source area changes, age changes, and measured dissolved solids trends. Uncertainty in the model is due in part to spatial and temporal variations in tracer inputs, estimated tracer transport parameters, and in pumping stresses at sampling points. For tracers such as tritium, the presence of two-limbed input curves can be problematic because a single concentration can be associated with multiple disparate travel times. These shortcomings can be ameliorated by adding hydrologic and geologic detail to the model and by adding additional calibration data. However, the Salt Lake Valley model is useful even without such small-scale detail.

Programs for calibration-based Monte Carlo simulation of recharge areas.

One use of groundwater flow models is to simulate contributing recharge areas to wells or springs. Particle tracking can be used to simulate these recharge areas, but in many cases the modeler is not sure how accurate these recharge areas are because parameters such as hydraulic conductivity and recharge have errors associated with them. The scripts described in this article (GEN_LHS and MCDRIVER_LHS) use the Python scripting language to run a Monte Carlo simulation with Latin hypercube sampling where model parameters such as hydraulic conductivity and recharge are randomly varied for a large number of model simulations, and the probability of a particle being in the contributing area of a well is calculated based on the results of multiple simulations. Monte Carlo simulation provides one useful measure of the variability in modeled particles. The Monte Carlo method described here is unique in that it uses parameter sets derived from the optimal parameters, their standard deviations, and their correlation matrix, all of which are calculated during nonlinear regression model calibration. In addition, this method uses a set of acceptance criteria to eliminate unrealistic parameter sets.

Impact of temperature and precipitation on propagation of intestinal schistosomiasis in an irrigated region in Ethiopia: suitability of satellite datasets.

OBJECTIVE: To assess the suitability of satellite temperature and precipitation datasets for investigating the dependence of Schistosoma mansoni disease transmission on meteorological conditions in an irrigated agricultural region in Ethiopia. METHODS: Data used were monthly number of patients infected with S. mansoni and seeking treatment at the local hospital, monthly maximum air temperature from a local weather station, monthly average land surface temperature from MODIS satellite data, monthly total precipitation from a local rain gauge and precipitation estimates from four widely used satellite products, namely, TMPA 3B42RT, TMPA 3B42, CMORPH and PERSIANN. The number of patients was used as proxy for vector abundance. RESULTS: Temperature and precipitation play a role in the transmission of S. mansoni disease. There is a weak but significant positive correlation between monthly maximum air temperature derived from a meteorological station (or average land surface temperature derived from MODIS satellite product) and the number of patients in the same month. There is a significant negative correlation between monthly precipitation volume (derived from rain gauge or satellite data) and number of patients at lags of 1 and 2 months. CONCLUSION: Satellite temperature and precipitation products provide useful information to understand and infer the relationship between meteorological conditions and S. mansoni prevalence.

Using atmospheric tracers to reduce uncertainty in groundwater recharge areas.

A Monte Carlo-based approach to assess uncertainty in recharge areas shows that incorporation of atmospheric tracer observations (in this case, tritium concentration) and prior information on model parameters leads to more precise predictions of recharge areas. Variance-covariance matrices, from model calibration and calculation of sensitivities, were used to generate parameter sets that account for parameter correlation and uncertainty. Constraining parameter sets to those that met acceptance criteria, which included a standard error criterion, did not appear to bias model results. Although the addition of atmospheric tracer observations and prior information produced similar changes in the extent of predicted recharge areas, prior information had the effect of increasing probabilities within the recharge area to a greater extent than atmospheric tracer observations. Uncertainty in the recharge area propagates into predictions that directly affect water quality, such as land cover in the recharge area associated with a well and the residence time associated with the well. Assessments of well vulnerability that depend on these factors should include an assessment of model parameter uncertainty. A formal simulation of parameter uncertainty can be used to delineate probabilistic recharge areas, and the results can be expressed in ways that can be useful to water-resource managers. Although no one model is the correct model, the results of multiple models can be evaluated in terms of the decision being made and the probability of a given outcome from each model.

Enhanced mixing and plume containment in porous media under time-dependent oscillatory flow.

Solute transport experiments were conducted in a decimeter scale flow cell packed with sand to study the potential for enhanced mixing of solutes in porous media and improved containment of injected plumes under multiple pumping-well driven, time-dependent oscillatory flow with respect to constant flow. Real-time imaging of the colorimetric reaction of Tiron (1,2-dihydroxybenzene-3,5-disulfonic acid) and molybdate was used to quantify mixing, whereas fluorescein was used to better examine plume size. Results from the small scale experiments clearly demonstrated the enhanced mixing of solutes under low Reynolds number oscillatory flow (a factor of 2 with respect to constant flow in homogeneous sand and a factor of 3 in layered sand), as the result of increased contact interface for solute diffusion. Further, the injected solute plume was better contained under oscillatory flow (25% less area with respect to constant flow in homogeneous sand) due to the cancellation of advective transport at each well over time. Enhanced mixing under oscillatory flow may enhance the processes of chemical and biological remediation. Furthermore, improved plume containment under oscillatory flow may require smaller amounts of chemicals to be injected during aquifer remediation.

Zonal management of arsenic contaminated ground water in Northwestern Bangladesh.

This paper used ordinary kriging to spatially map arsenic contamination in shallow aquifers of Northwestern Bangladesh (total area approximately 35,000 km(2)). The Northwestern region was selected because it represents a relatively safer source of large-scale and affordable water supply for the rest of Bangladesh currently faced with extensive arsenic contamination in drinking water (such as the Southern regions). Hence, the work appropriately explored sustainability issues by building upon a previously published study (Hossain et al., 2007; Water Resources Management, vol. 21: 1245-1261) where a more general nation-wide assessment afforded by kriging was identified. The arsenic database for reference comprised the nation-wide survey (of 3534 drinking wells) completed in 1999 by the British Geological Survey (BGS) in collaboration with the Department of Public Health Engineering (DPHE) of Bangladesh. Randomly sampled networks of zones from this reference database were used to develop an empirical variogram and develop maps of zonal arsenic concentration for the Northwestern region. The remaining non-sampled zones from the reference database were used to assess the accuracy of the kriged maps. Two additional criteria were explored: (1) the ability of geostatistical interpolators such as kriging to extrapolate information on spatial structure of arsenic contamination beyond small-scale exploratory domains; (2) the impact of a priori knowledge of anisotropic variability on the effectiveness of geostatistically based management. On the average, the kriging method was found to have a 90% probability of successful prediction of safe zones according to the WHO safe limit of 10ppb while for the Bangladesh safe limit of 50ppb, the safe zone prediction probability was 97%. Compared to the previous study by Hossain et al. (2007) over the rest of the contaminated country side, the probability of successful detection of safe zones in the Northwest is observed to be about 25% higher. An a priori knowledge of anisotropy was found to have inconclusive impact on the effectiveness of kriging. It was, however, hypothesized that a preferential sampling strategy that honored anisotropy could be necessary to reach a more definitive conclusion in regards to this issue.

Management of adverse effects of a public water supply well field on the aquatic habitat of a stratified drift stream in eastern Connecticut.

A study was conducted to determine the effect of water withdrawals from the University of Connecticut's (Storrs) water supply wells on the fisheries habitat of the Fenton River adjacent to the well field. The study was designed to investigate the relationships between in-stream flow and selected fish habitat in the section of the Fenton River situated in the main zone of influence of the pumping field. With the aid of historical data, new data collection, and mathematical simulation modeling, the relation between the magnitude and timing of groundwater withdrawals on the stage and flow of water in the stream was derived. Fish sampling and habitat modeling were used to assess the effects of human influence on certain reaches of the Fenton River. Among the various water management scenarios studied, several are presented that would optimize water withdrawals, while minimizing adverse effects on the stream flow and in-stream habitat.

Satellite-based Flood Modeling Using TRMM-based Rainfall Products.

Increasingly available and a virtually uninterrupted supply of satellite-estimatedrainfall data is gradually becoming a cost-effective source of input for flood predictionunder a variety of circumstances. However, most real-time and quasi-global satelliterainfall products are currently available at spatial scales ranging from 0.25o to 0.50o andhence, are considered somewhat coarse for dynamic hydrologic modeling of basin-scaleflood events. This study assesses the question: what are the hydrologic implications ofuncertainty of satellite rainfall data at the coarse scale? We investigated this question onthe 970 km² Upper Cumberland river basin of Kentucky. The satellite rainfall productassessed was NASA's Tropical Rainfall Measuring Mission (TRMM) Multi-satellitePrecipitation Analysis (TMPA) product called 3B41RT that is available in pseudo real timewith a latency of 6-10 hours. We observed that bias adjustment of satellite rainfall data canimprove application in flood prediction to some extent with the trade-off of more falsealarms in peak flow. However, a more rational and regime-based adjustment procedureneeds to be identified before the use of satellite data can be institutionalized among floodmodelers.

Effect of chaotic mixing on enhanced biological growth and implications for wastewater treatment: a test case with Saccharomyces cerevisiae.

Mixing patterns and modes have a great influence on the efficiency of biological treatment systems. A series of laboratory experiments was conducted with a controlled, small-scale analog of a pilot wastewater aeration tank, consisting of two eccentrically placed cylinders. By controlling the rotation direction and speed of the two cylinders, it has been possible to develop chaotic flow fields in the space between the walls of the cylinders. Our experiments utilized Saccharomyces cerevisiae as the biological oxidation organism and air bubbles as the mixing agent supplied by a large fine pore diffuser to the cells in their exponential growth phase. The effect of various mixing patterns on cell growth was studied at different cylinder eccentricities, rotation directions and speeds. It was found that chaotic advection flow patterns: (a) enhanced growth, and (b) sped up the onset of maximal growth of the organism by 15-18% and 14-20%, respectively.