Importance of the Colmation Layer in the Transport and Removal of Cyanobacteria, Viruses, and Dissolved Organic Carbon during Natural Lake-Bank Filtration.

This study focused on the importance of the colmation layer in the removal of cyanobacteria, viruses, and dissolved organic carbon (DOC) during natural bank filtration. Injection-and-recovery studies were performed at two shallow (0.5 m deep), sandy, near-shore sites at the southern end of Ashumet Pond, a waste-impacted, kettle pond on Cape Cod, MA, that is subject to periodic blooms of cyanobacteria and continuously recharges a sole-source drinking-water aquifer. The experiment involved assessing the transport behaviors of bromide (conservative tracer), sp. IU625 (cyanobacterium, 2.6 ± 0.2 µm), AS-1 (tailed cyanophage, 110 nm long), MS2 (coliphage, 26 nm diameter), and carboxylate-modified microspheres (1.7 µm diameter) introduced to the colmation layer using a bag-and-barrel (Lee-type) seepage meter. The injectate constituents were tracked as they were advected across the pond water-groundwater interface and through the underlying aquifer sediments under natural-gradient conditions past push-point samplers placed at ∼30-cm intervals along a 1.2-m-long, diagonally downward flow path. More than 99% of the microspheres, IU625, MS2, AS-1, and ∼44% of the pond DOC were removed in the colmation layer (upper 25 cm of poorly sorted bottom sediments) at two test locations characterized by dissimilar seepage rates (1.7 vs. 0.26 m d). Retention profiles in recovered core material indicated that >82% of the attached IU625 were in the top 3 cm of bottom sediments. The colmation layer was also responsible for rapid changes in the character of the DOC and was more effective (by three orders of magnitude) at removing microspheres than was the underlying 20-cm-thick segment of sediment.

Effects of sediment-associated extractable metals, degree of sediment grain sorting, and dissolved organic carbon upon Cryptosporidium parvum removal and transport within riverbank filtration sediments, Sonoma County, California.

Oocysts of the protozoan pathogen Cryptosporidium parvum are of particular concern for riverbank filtration (RBF) operations because of their persistence, ubiquity, and resistance to chlorine disinfection. At the Russian River RBF site (Sonoma County, CA), transport of C. parvum oocysts and oocyst-sized (3 µm) carboxylate-modified microspheres through poorly sorted (sorting indices, σ(1), up to 3.0) and geochemically heterogeneous sediments collected between 2 and 25 m below land surface (bls) were assessed. Removal was highly sensitive to variations in both the quantity of extractable metals (mainly Fe and Al) and degree of grain sorting. In flow-through columns, there was a log-linear relationship (r(2) = 0.82 at p < 0.002) between collision efficiency (α, the probability that colloidal collisions with grain surfaces would result in attachment) and extractable metals, and a linear relationship (r(2) = 0.99 at p < 0.002) between α and σ(1). Collectively, variability in extractable metals and grain sorting accounted for ∼83% of the variability in α (at p < 0.0002) along the depth profiles. Amendments of 2.2 mg L(-1) of Russian River dissolved organic carbon (DOC) reduced α for oocysts by 4-5 fold. The highly reactive hydrophobic organic acid (HPOA) fraction was particularly effective in re-entraining sediment-attached microspheres. However, the transport-enhancing effects of the riverine DOC did not appear to penetrate very deeply into the underlying sediments, judging from high α values (∼1.0) observed for oocysts being advected through unamended sediments collected at ∼2 m bls. This study suggests that in evaluating the efficacy of RBF operations to remove oocysts, it may be necessary to consider not only the geochemical nature and size distribution of the sediment grains, but also the degrees of sediment sorting and the concentration, reactivity, and penetration of the source water DOC.

Use of artificial neural networks to evaluate the effectiveness of riverbank filtration.

Riverbank filtration (RBF) is a low-cost water treatment technology in which surface water contaminants are removed or degraded as the infiltrating water moves from the river/lake to the pumping wells. The removal or degradation of contaminants is a combination of physicochemical and biological processes. This paper illustrates the development and application of three types of artificial neural networks (ANNs) to estimate the effectiveness of two RBF facilities in the US. The feed-forward back-propagation network (BPN) and radial basis function network (RBFN) model prediction results produced excellent agreement with measured data at a correlation coefficient above 0.99 for filtrate water quality parameters, including temperature as well as turbidity, heterotrophic bacteria, and coliform removal. In comparison, the fuzzy inference system network (FISN) predicted only temperature and bacteria removal with reasonable accuracy. It is shown that the predictive performances of the ANNs depend on the model structure and model inputs.