Comparative reductions of norovirus, echovirus, adenovirus, Campylobacter jejuni and process indicator organisms during water filtration in alluvial sand.

Sand filtration is a cost-effective means of reducing microbial pathogens in drinking-water treatment. Our understanding of pathogen removal by sand filtration relies largely on studies of process microbial indicators, and comparative data from pathogens are sparse. In this study, we examined the reductions of norovirus, echovirus, adenovirus, bacteriophage MS2 and PRD1, Campylobacter jejuni, and Escherichia coli during water filtration through alluvial sand. Duplicate experiments were conducted using 2 sand columns (50 cm long, 10 cm diameter) and municipal tap water sourced from chlorine-free untreated groundwater (pH 8.0, 1.47 mM) at filtration rates of 1.1-1.3 m/day. The results were analysed using colloid filtration theory and the HYDRUS-1D 2-site attachment-detachment model. The average log10 reduction values (LRVs) of the normalised dimensionless peak concentrations (Cmax/C0) over 0.5 m were: MS2: 0.28; E. coli: 0.76; C. jejuni: 0.78; PRD1: 2.00; echovirus: 2.20; norovirus: 2.35; and adenovirus: 2.79. The relative reductions largely corresponded to the organisms' isoelectric points rather than their particle sizes or hydrophobicities. MS2 underestimated virus reductions by 1.7-2.5 log, and the LRVs, mass recoveries relative to bromide, collision efficiencies, and attachment and detachment rates differed mostly by ∼1 order of magnitude. Conversely, PRD1 reductions were comparable with those of all 3 viruses tested, and its parameter values were mostly within the same orders of magnitude. E. coli seemed an adequate process indicator for C. jejuni with similar reductions. Comparative data describing pathogen and indicator reductions in alluvial sand have important implications for sand filter design, risk assessments of drinking-water supplies from riverbank filtration and the determination of safe setback distances for drinking-water supply wells.

Achieving unbiased predictions of national-scale groundwater redox conditions via data oversampling and statistical learning.

An important policy consideration for integrated land and water management is to understand the spatial distribution of nitrate attenuation in the groundwater system, for which redox condition is the key indicator. This paper proposes a methodology to accommodate the computational demands of large datasets, and presents national-scale predictions of groundwater redox class for New Zealand. Our approach applies statistical learning methods to relate the redox class determined on groundwater samples to spatially varying attributes. The trained model uses these spatial variables to predict redox status in areas without sample data. We assembled the groundwater sample data from regional authority databases, and assigned each sample a redox class. A key achievement was to overcome the influence of sample selection bias on model training via oversampling. We removed additional bias imposed by imbalances in the predictor variables by applying a conditional inference random forest classifier. The unbiased trained model uses eight predictors, and achieves a high validation performance (accuracy 0.81, kappa 0.71), providing good confidence in model predictions. National maps are provided for redox class and probability at specified depths. Feature importance rankings indicate that reducing conditions are associated with poorly-drained soils, and to a lesser extent, high hydrological variability, low elevation, and low-permeability lithology. These conditions are common in New Zealand's coastal and lowland plains, where artificial drainage is required to make land suitable for production. The spatial extent of reduced groundwater increases with depth, suggesting a shallow influence of soil infiltration or mobile organic carbon, and a deeper influence of lithological electron donors. Our model provides unbiased predictions at a scale relevant for environmental policy development and legislation. Identifying where the ecosystem service provided by denitrification can be utilised will enable spatially targeted interventions that can achieve the desired environmental outcome in a more cost-effective manner than non-targeted interventions.