Prevalence and abundance of traditional and host-associated fecal indicators in urban estuarine sediments: Potential implications for estuarine water quality monitoring.

This study aimed to determine the prevalence and abundance of sewage and animal fecal contamination of sediment at seven estuarine locations in Sydney, NSW, Australia. Sediment samples were tested for the occurrence of microbial targets including molecular marker genes of enterococci (ENT), Bacteroides HF183 (HF183), Methanobrevibacter smithii (nifH), human adenovirus (HAdV) and emerging sewage-associated marker genes crAssphage (CPQ_056) and Lachnospiraceae (Lachno3) and animal feces-associated marker genes, including avian feces-associated Helicobacter spp. (GFD), canine-feces associated Bacteroides (DogBact), cattle-feces associated (cowM2) and horse feces-associated Bacteroides (HoF597). Results from this study showed that urban estuarine sediment can act as a reservoir of fecal indicator bacteria (FIB) and several microbial source tracking (MST) marker genes, including previously unreported Lachno3. The sewage-associated marker gene CPQ_056 was most prevalent, in 63.8% of sediment samples, while the avian associated marker gene GFD had the highest mean abundance. The GFD marker gene was highly abundant and widely detected in sediment samples from all seven locations compared to the other animal feces-associated marker genes. In all, 31 (44.9%) sediment samples were positive for at least two sewage-associated marker genes. However, the non-quantifiable detection of the HAdV marker gene did not always align with the detection of two or more sewage-associated marker genes. In addition, the most frequent wet weather overflow exposure occurred at locations that did not have a consistent pattern of detection of the sewage-associated marker genes, suggesting sediments may not be a suitable measure of recent sewage contamination. To assist water quality and public health managers better understand past microbial contamination of estuarine sediment, further studies seem justified to explore the role of decay of MST marker genes in sediment. Further work is also needed on the role of resuspension of MST marker genes from sediment during storm events to the water column as a source of contamination for both the GFD and sewage-associated marker genes.

Marker genes of fecal indicator bacteria and potential pathogens in animal feces in subtropical catchments.

We investigated the abundance of marker genes of two fecal indicator bacteria (FIB) and eight potential pathogens in fecal samples of humans (n = 14) and 10 domestic and native wild animals (n = 134). For each target animal, between 10 and 14 individual fecal samples were collected (n = 148 individual fecal samples in total). The abundance of FIB and potential pathogens within each sample was determined using quantitative PCR (qPCR) assays. All animals tested were positive for Escherichia coli (EC) and the concentrations ranged from 6.13 (flying fox) to 8.87 (chicken) log10 GC/g of feces. These values for Enterococcus spp. (ENT) were 5.25 log10 GC/g for flying fox and 8.12 log10 GC/g of feces for chicken. Moderate correlations were observed between EC with P. aeruginosa, EC O157 and Cryptosporidium parvum, whereas weak correlations were observed between EC and Salmonella spp. and Giardia lamblia, Mycobacterium avium complex (MAC) and Campylobacter spp. The prevalence of MAC and P. aeruginosa were low in dog (14.3% each) and moderate (57.2%, MAC; 42.9% P. aeruginosa) in Eastern grey kangaroo fecal samples. Cryptosporidium parvum was detected in one cattle and one human fecal sample, while G. lamblia was detected in one dog, one flying fox, and one pig fecal samples. Among the eight potential pathogens tested, five pathogens were detected in chicken and dog fecal samples. The remaining animal species contained up to three potential pathogens in their feces. The data generated in this study may aid in the calculation of pathogen loads in the environment, and hence to assess the risks from human and animal fecal contamination of source waters.

Precipitation influences pathogenic bacteria and antibiotic resistance gene abundance in storm drain outfalls in coastal sub-tropical waters.

Stormwater contamination can threaten the health of aquatic ecosystems and human exposed to runoff via nutrient and pathogen influxes. In this study, the concentrations of 11 bacterial pathogens and 47 antibiotic resistance genes (ARGs) were determined by using high-throughput microfluidic qPCR (MFQPCR) in several storm drain outfalls (SDOs) during dry and wet weather in Tampa Bay, Florida, USA. Data generated in this study were also compared with the levels of fecal indicator bacteria (FIB) and sewage-associated molecular markers (i.e., Bacteroides HF183 and crAssphage markers) in same SDOs collected in a recent study (Ahmed et al., 2018). Concentration of FIB, sewage-associated markers, bacterial pathogens and many ARGs in water samples were relatively high and SDOs may be potentially hotspots for microbial contamination in Tampa Bay. Mean concentrations of culturable E. coli and Enterococcus spp. were tenfold higher in wet compared to dry weather. The majority of microbiological contaminants followed this trend. E. coli eaeA, encoding the virulence factor intimin, was correlated with levels of 20 ARGs, and was more frequently detected in wet weather than dry weather samples. The blaKPC gene associated with carbapenem resistant Enterobacteriaceae and the beta-lactam resistant gene (blaNPS) were only detected in wet weather samples. Frequency of integron genes Intl2 and Intl3 detection increased by 42% in wet weather samples. Culturable E. coli and Enterococcus spp. significantly correlated with 19 of 47 (40%) ARG tested. Sewage-associated markers crAssphage and HF183 significantly correlated (p < 0.05) with the following ARGs: intl1, sul1, tet(M), ampC, mexB, and tet(W). The presence of sewage-associated marker genes along with ARGs associated with sewage suggested that aging sewage infrastructure contributed to contaminant loading in the Bay. Further research should focus on collecting spatial and temporal data on the microbiological contaminants especially viruses in SDOs.

Evaluating water quality investments using cost utility analysis.

This study borrows concepts from healthcare economics and uses cost utility analysis (CUA) to select an optimum portfolio of water quality enhancement projects in Perth, Western Australia. In CUA, costs are handled via standard discounted cash flow analysis, but the benefits, being intangible, are measured with a utility score. Our novel methodology combines CUA with a binary combinatorial optimisation solver, known as a 'knapsack algorithm', to identify the optimum portfolio of projects. We show how water quality projects can be selected to maximise an aggregate utility score while not exceeding a budget constraint. Our CUA model applies compromise programming (CP) to measure utility over multiple attributes in different units. CUA is shown to provide a transparent and analytically robust method to maximise benefits from water quality remediation investments under a constrained budget.