Validation and uncertainty analysis of a stormwater biofilter treatment model for faecal microorganisms.

Stormwater biofilters, also known as rain gardens or bioretention systems, are effective stormwater treatment systems. This paper presents the validation, sensitivity and uncertainty analyses of a model for microbial removal in stormwater biofilters. The model, previously developed based on a rather limited laboratory study, was fully validated using the data collected in extensive laboratory experiments and field tests. The lab-scale and field-scale systems used for validation were of various designs (e.g., system size, plant type, media type), and have been operated under a wide range of operational conditions (e.g., length of antecedent dry period, and the inflow volume and concentration). For each tested biofilter design, the predicted E. coli concentrations in biofilters' outflow showed relatively good agreement with the measured ones: e.g., Nash-Sutcliffe Efficiency (Ec) ranged from 0.50 to 0.60 for the laboratory tests, and Ec = 0.55 for the field system. The results from sensitivity analysis confirmed the significance of adsorption and desorption processes, and also revealed the impact of temperature on microbial die-off (which was not fully represented in the model development stage). Finally, parameter transferability from one system to another with similar design was examined, achieving generally promising Ec values (0.04-0.56 with the best-fit parameter set for the other system; maximum value: 0.46-0.63) and acceptable uncertainties (intersection between prediction uncertainty band and observation: 50%-97%). Most importantly, the prediction of E. coli outflow concentrations from the field system was reasonably good when laboratory-determined parameter values were adopted: with the best-fit parameter set for the lab-scale system, Ec = 0.39; maximum Ec = 0.55; intersection between prediction and observation = 83%. These results suggested that the very rare biofilter model for microbial removal could provide reliable prediction for large scale field systems, by simply calibrating parameters with limited laboratory-scale experiments.

Stormwater biofilter treatment model for faecal microorganisms.

This paper presents a new model to simulate long-term microbial removal in stormwater biofilters. The water flow module uses a 'three-bucket' approach to describe the flow processes in biofilters, while the microbial quality module employs the one-dimensional advection-dispersion equation to represent microbial transport and fate under different design and operational conditions. Three governing processes for microbial removal, adsorption, desorption and die-off, are included; temperature is also incorporated as a key factor for die-off. The model was tested using long term monitoring data collected from laboratory columns in which five different biofilter configurations were studied over a period of 44weeks. A multi-objective calibration with the balance of instantaneous ponding levels and event outflow volumes was implemented on the water flow module, and the Nash-Sutcliffe Efficiency (E) values ranged from 0.82 to 0.95. The microbial quality module was tested using the effluent Escherichia coli concentration data, and the E values obtained for different configurations were between 0.46 and 0.68. The optimized parameter values agreed with those presented in literature. However, sensitivity analyses suggested that the model's prediction was not sensitive to all parameters, the explanation for which was hypothesized to be data paucity rather than model structural uncertainties. Model validation was also conducted by splitting the data into calibration and validation datasets. The results further reinforced the needed for more data for model calibration.

Environmental monitoring of waterborne Campylobacter: evaluation of the Australian standard and a hybrid extraction-free MPN-PCR method.

Campylobacter is the leading agent of diarrheal disease worldwide. This study evaluates a novel culture-PCR hybrid (MPN-PCR) assay for the rapid enumeration of Campylobacter spp. from estuarine and wastewater systems. To first evaluate the current, culture-based, Australian standard, an inter-laboratory study was conducted on 69 subsampled water samples. The proposed Most-Probable Number (MPN)-PCR method was then evaluated, by analysing 147 estuarine samples collected over a 2 year period. Data for 14 different biological, hydrological and climatic parameters were also collated to identify pathogen-environment relationships and assess the potential for method specific bias. The results demonstrated that the intra-laboratory performance of the MPN-PCR was superior to that of AS/NZS (σ = 0.7912, P < 0.001; κ = 0.701, P < 0.001) with an overall diagnostic accuracy of ~94%. Furthermore, the analysis of both MPN-PCR and AS/NZS identified the potential for the introduction of method specific bias during assessment of the effects of environmental parameters on Campylobacter spp. numbers.