Hydrological modeling and field validation of a bioretention basin.

An emerging green infrastructure, the bioretention basin, has been deployed world-wide to reduce peak flows, encourage infiltration, and treat pollutants. However, inadequate design of a basin impairs its treatment potential and necessitates the development and validation of a suitable hydrological model for design and analysis of bioretention basins. In this study, an existing numerical model, RECHARGE, has been adopted to simulate hydrological performance of a basin in the tropical climate of Singapore over a half year that included 80 storm events. Comparison of the model predictions with field observations shows that RECHARGE successfully simulates the basin hydrology of 80 events of varying rainfall characteristics with mass balance error of 5.1 ±â€¯7.5% per event and 0.3% overall. Using the verified model, we develop new design curves that predict bioretention basin performance as a function of three basin design parameters: detention depth; ratio of drainage basin area to bioretention area; and saturated hydraulic conductivity of the basin soil media. We evaluate basin performance in terms of the percentage of water that infiltrates and is treated in the subsurface portion of the basin and define an infiltration index to measure the change in infiltrated percentage caused by unit change in the basin design parameters. The marginal improvement in basin performance drops significantly when the basin depth (hd) is increased above 40 cm, when the ratio of drainage area to bioretention area (R) is decreased below 20, or when the saturated hydraulic conductivity (Ks) is increased above 10 cm/h.

Variation of Bacterial Communities with Water Quality in an Urban Tropical Catchment.

A major challenge for assessment of water quality in tropical environments is the natural occurrence and potential growth of Fecal Indicator Bacteria (FIB). To gain a better understanding of the relationship between measured levels of FIB and the distribution of sewage-associated bacteria, including potential pathogens, in the tropics this study compared the abundance of FIB (Total coliforms and E. coli) and the Bacteroidales (HF183 marker) with bacterial community structure determined by next-generation amplicon sequencing. Water was sampled twice over 6 months from 18 sites within a tropical urban catchment and reservoir, followed by extraction of DNA from microorganisms, and sequencing targeting the V3-V4 region of the 16S rRNA gene. Multivariate statistical analyses indicated that bacterial community composition (BCC) varied between reservoir and catchment, within catchment land-uses, and with E. coli concentration. Beta-regression indicated that the proportion of sequences from sewage-associated taxa (SAT) or pathogen-like sequences (PLS) were predicted most significantly by measured levels of E. coli(log MPN/100 mL) (χ2 > 8.7; p < 0.003). In addition, SAT were significantly predicted by log HF183 levels (χ2=13.1; p = 0.0003) while PLS were not. Our study suggests that measurements of E. coli concentration could be useful in predicting samples enriched in sewage-associated and pathogen-like bacteria in tropical environments despite the potential for nonconservative behavior.

SIMPLE is a good idea (and better with context learning).

Selective and iterative method for performance level estimation (SIMPLE) is a multi-atlas segmentation technique that integrates atlas selection and label fusion that has proven effective for radiotherapy planning. Herein, we revisit atlas selection and fusion techniques in the context of segmenting the spleen in metastatic liver cancer patients with possible splenomegaly using clinically acquired computed tomography (CT). We re-derive the SIMPLE algorithm in the context of the statistical literature, and show that the atlas selection criteria rest on newly presented principled likelihood models. We show that SIMPLE performance can be improved by accounting for exogenous information through Bayesian priors (so called context learning). These innovations are integrated with the joint label fusion approach to reduce the impact of correlated errors among selected atlases. In a study of 65 subjects, the spleen was segmented with median Dice similarity coefficient of 0.93 and a mean surface distance error of 2.2 mm.

Shape-Constrained Multi-Atlas Segmentation of Spleen in CT.

Spleen segmentation on clinically acquired CT data is a challenging problem given the complicity and variability of abdominal anatomy. Multi-atlas segmentation is a potential method for robust estimation of spleen segmentations, but can be negatively impacted by registration errors. Although labeled atlases explicitly capture information related to feasible organ shapes, multi-atlas methods have largely used this information implicitly through registration. We propose to integrate a level set shape model into the traditional label fusion framework to create a shape-constrained multi-atlas segmentation framework. Briefly, we (1) adapt two alternative atlas-to-target registrations to obtain the loose bounds on the inner and outer boundaries of the spleen shape, (2) project the fusion estimate to registered shape models, and (3) convert the projected shape into shape priors. With the constraint of the shape prior, our proposed method offers a statistically significant improvement in spleen labeling accuracy with an increase in DSC by 0.06, a decrease in symmetric mean surface distance by 4.01 mm, and a decrease in symmetric Hausdorff surface distance by 23.21 mm when compared to a locally weighted vote (LWV) method.

Screening analysis of human pharmaceutical compounds in U.S. surface waters.

The PhATE (Pharmaceutical Assessment and Transport Evaluation) model presented in this paper was developed as a tool to estimate concentrations of active pharmaceutical ingredients (APIs) in U.S. surface waters that result from patient use (or consumption) of medicines. PhATE uses a mass balance approach to model predicted environmental concentrations (PECs) in 11 watersheds selected to be representative of most hydrologic regions of the United States. The model divides rivers into discrete segments. It estimates the mass of API that enters a segment from upstream or from publicly owned treatment works (POTW) and is subsequently lost from the segment via in-stream loss mechanisms or flow diversions (i.e., man-made withdrawals). POTW discharge loads are estimated based on the population served, the API use per capita, the potential loss of the compound associated with human use (e.g., metabolism), and the portion of the API mass removed in the POTW. Simulations using three surrogate compounds showthat PECs generated by PhATE are generally within an order of magnitude of measured concentrations and that the cumulative probability distribution of PECs for all watersheds included in PhATE is consistent with the nationwide distribution of measured concentrations of the surrogate compounds. Model simulations for 11 APIs yielded four categories of results. (1) PECs fit measured data for two compounds. (2) PECs are below analytical method detection limits and thus are consistent with measured data for three compounds. (3) PECs are higher than (i.e., not consistent with) measured data for three compounds. However, this may be the consequence of as yet unidentified depletion mechanisms. (4) PECs are several orders of magnitude below some measured data but consistentwith most measured data forthree compounds. For the fourth category, closer examination of sampling locations suggests that the field-measured concentrations for these compounds do not accurately reflect human use. Overall, these results demonstrate that PhATE may be used to predict screening-level concentrations of APIs and related compounds in the environment as well as to evaluate the suitability of existing fate information for an API.

Solar disinfection (SODIS): simulation of solar radiation for global assessment and application for point-of-use water treatment in Haiti.

Haiti and other developing countries do not have sufficient meteorological data to evaluate if they meet the solar disinfection (SODIS) threshold of 3-5 h of solar radiation above 500 W/m2, which is required for adequate microbial inactivation in drinking water. We have developed a mathematical model based on satellite-derived daily total energies to simulate monthly mean, minimum, and maximum 5-h averaged peak solar radiation intensities. This model can be used to assess if SODIS technology would be applicable anywhere in the world. Field measurements were made in Haiti during January 2001 to evaluate the model and test SODIS efficacy as a point-of-use treatment option. Using the total energy from a measured solar radiation intensity profile, the model recreated the intensity profile with 99% agreement. NASA satellite data were then used to simulate the mean, minimum, and maximum 5-h averaged peak intensities for Haiti in January, which were within 98.5%, 62.5%, and 86.0% agreement with the measured values, respectively. Most of the discrepancy was attributed to the heterogeneous nature of Haiti's terrain and the spatial resolution of the NASA data. Additional model simulations suggest that SODIS should be effective year-round in Haiti. Actual SODIS efficacy in January was tested by the inactivation of total coliform, E. coli, and H2S-producing bacteria. Exposure period proved critical. One-day exposure achieved complete bacterial inactivation 52% of the time, while a 2-day exposure period achieved complete microbial inactivation 100% of the time. A practical way of providing people with cold water every morning that has undergone a 2-day exposure would be to rotate three groups of bottles every morning, so two groups are out in the sun and one is being used for consumption.