Effectiveness of the tropical plants Rhynchospora corymbosa and Coix lacryma-jobi in vertical flow constructed wetlands for municipal primary sewage effluent treatment

The performance of two tropical plants, Rhynchospora corymbosa L. (RC) and Coix lacryma-jobi, L (CL) in treatment of primary sewage effluent in lab-scale vertical-flow constructed wetlands (VFCW) along with no plant control wetland was investigated. A batch-flow VFCWs were operated under batch fill and drain hydraulic loading system with hydraulic retention times (HRT) of 0.5, 1, and 2 days and fill rate of 8 L/day. Removal of solids, organics, nutrients, and pathogens were monitored. The volumetric contaminant removal rates were best described by 1st order kinetics except for ammonia and phosphate, which was best described by Stover-Kincannon kinetics. Influent TSS, PO43-, COD, BOD5, and total coliform concentration were low but high in NH4+ concentration. CL was better in nutrient removal as HRT increases compared to RC. RC was more efficient at TSS, turbidity, and organics removal. Pathogen removal was independent of plant type but HRT. Solids and organic removal were lower in CL planted CWs due to preferential flow paths created by their bulky root. CL planted CWs removed more nutrients followed by RC planted CWs and then no-plant control CWs. The results of these tests demonstrate that both CL and RC are suitable for the treatment of municipal wastewater in VFCW system.

Parsimonious Mechanistic Modeling of Bacterial Runoff into Irrigation Ponds To Inform Food Safety Management of Agricultural Water Quality

Pond irrigation water comprises a major pathway of pathogenic bacteria to fresh produce. Current regulatory methods have been shown to be ineffective in assessing this risk when variability of bacterial concentrations is large. This paper proposes using mechanistic modeling of bacterial transport as a way to identify improved strategies for mitigating this risk pathway. If the mechanistic model is successfully tested against observed data, global sensitivity analysis (GSA) can identify important mechanisms to inform alternative, preventive bacterial control practices. Model development favored parsimony and prediction of peak bacterial concentration events. Data from two highly variable surface water irrigation ponds showed that the model performance was similar or superior to that of existing pathogen transport models, with a Nash-Sutcliffe efficiency of 0.48 and 0.18 for the two ponds. GSA quantified bacterial sourcing and hydrology as the most important processes driving pond bacterial contamination events. Model analysis has two main implications for improved regulatory methods: that peak concentration events are associated with runoff-producing rainfall events and that intercepting bacterial runoff transport may be the best option to prevent bacterial contamination of surface water irrigation ponds and thus fresh produce. This research suggests the need for temporal management strategies. IMPORTANCE Preventive management of agricultural waters requires understanding of the drivers of bacterial contamination events. We propose mechanistic modeling as a way forward to understand and predict such events and have developed and tested a parsimonious model for rain-driven surface runoff contributing to generic Escherichia coli contamination of irrigation ponds in Central Florida. While the model was able to predict the timing of peak events reasonably well, the highly variable magnitude of the peaks was less well predicted. This indicates the need to collect more data on the fecal contamination inputs of these ponds and the use of mechanistic modeling and global sensitivity analysis to identify the most important data needs.

Climate and Land Use Influences on Bacteria Levels in Stormwater

The influence of climatic variables and land use on fecal coliform (FC) levels in stormwater collected from outfalls throughout southern Vancouver Island between 1995 and 2011 are examined through statistical analyses, Fourier analysis, Multiple Linear Regression (LR) and Multivariate Logistic Regression (MLR). Kendall’s τ-b demonstrated that FC levels were significantly and positively correlated with the amount of residential area within a drainage catchment generating the runoff, and that FC levels were location dependent. Climatic variables of temperature and antecedent dry period length were significantly and positively correlated with FC levels at both the sampling location level and across the region overall. Precipitation and flowrates were negatively correlated with FC levels. Fourier analysis showed that monthly FC levels shared the same 12 month cycle (peaking in July) as precipitation and temperature. MLR modelling was applied by aggregating the LogFC data by order of magnitude. The MLR model shows that the data are subject to different influences depending on the season and as well, the month of the year. The land use and climate analyses suggest that future climate change impact studies attempted on nearshore bacterial water quality should be conducted at the urban catchment scale.