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A Seasonal Assessment of Opportunistic Premise Plumbing Pathogens in Roof-Harvested Rainwater Tanks Kerry A. Hamilton, Warish Ahmed, Andrew Palmer, Kylie Smith, Simon Toze, and Charles N Haas Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b04814 • Publication Date (Web): 02 Jan 2017 Downloaded from http://pubs.acs.org on January 3, 2017

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Environmental Science & Technology

A Seasonal Assessment of Opportunistic Premise Plumbing Pathogens in Roof-Harvested Rainwater Tanks

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Kerry A. Hamilton†,‡,*, Warish Ahmed†, Andrew Palmer†, Kylie Smith†, Simon Toze†,

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Charles N. Haas‡

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†CSIRO Land and Water, Ecosciences Precinct, 41 Boggo Road, Qld 4102, Australia; ‡Drexel

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University, 3141 Chestnut Street, Philadelphia, PA 19104, USA

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* Corresponding author. Kerry Hamilton. Mailing address: Drexel University Department of Civil, Architectural, and Environmental Engineering, 3141 Chestnut Street, Philadelphia, Pennsylvania, 19104, USA. Tel.: +1 215 895 2000; Fax: +1 215 895 1363. E-mail address: [email protected].

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Running title: Opportunistic pathogens in tank water

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ACS Paragon Plus Environment


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ABSTRACT

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A seasonal study on the occurrence of six opportunistic premise plumbing pathogens

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(OPPPs) in 24 roof-harvested rainwater (RHRW) tanks repeatedly sampled over six monthly

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sampling events (n = 144) from August 2015 to March 2016 was conducted using quantitative

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qPCR. Fecal indicator bacteria (FIB) Escherichia coli (E. coli) and Enterococcus spp. were

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enumerated using culture-based methods. All tank water samples over the six events were

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positive for at least one OPPP (Legionella spp., Legionella pneumophila, Mycobacterium avium,

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Mycobacterium intracellulare, Pseudmonas aeruginosa, or Acanthamoeba spp.) during the

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entire course of the study. FIB were positively but weakly correlated with P. aeruginosa (E. coli

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vs. P. aeruginosa τ = 0.090, p = 0.027; Enterococcus spp. vs. P. aeruginosa τ = 0.126, p =

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0.002), but not the other OPPPs. FIBs were more prevalent during the wet season than the dry

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season, and L. pneumophila was only observed during the wet season. However,

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concentrations of Legionella spp., M. intracellulare, Acanthamoeba spp., and M. avium peaked

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during the dry season. Correlations were assessed between FIB and OPPPs with

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meteorological variables, and it was determined that P. aeruginosa was the only OPPP

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positively associated with an increased antecedent dry period, suggesting stagnation time may

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play a role for the occurrence of this OPPP in tank water. Infection risks may exceed commonly

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cited benchmarks for uses reported in the rainwater usage survey such as pool top-up, and

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warrant further exploration through quantitative microbial risk assessment (QMRA).

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Keywords: Roof-harvested rainwater; opportunistic pathogens; fecal indicator bacteria;

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quantitative PCR; public health risks

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INTRODUCTION Roof-harvested rainwater (RHRW) is currently being used globally to supplement potable

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and non-potable water supplies. As of 2010, 32% of Australian and 36% of Queensland

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households (n = 1,702,200) had a rainwater tank 1. From the year 2007-2010, Australian urban

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capital cities experienced the greatest increase in the number of installed RHRW tanks;

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Brisbane had the largest increase from 18 to 43% (n = 731,200) 1. RHRW tanks are the main

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source of drinking water for 13.6% Queensland households 1.

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RHRW stored in tanks are prone to microbial, chemical, and heavy metal contamination 2.

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Pathogens can enter into the tanks through aerosol deposition, plant litter, and animal fecal

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matter via roof run-off. In addition, the microbial quality of tank water varies with geographical

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location, climatic conditions, roof and tank maintenance practices, tank hydraulics, and

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surrounding environment 3, 4.

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To monitor the microbial quality of tank water, fecal indicator bacteria (FIB) are most

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commonly used. The presence of FIB such as Escherichia coli (E. coli) in 100 mL tank water

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sample indicates fecal contamination and the presence of potential pathogens. Monitoring FIB is

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cost effective and less technical compared to pathogen monitoring, and is the focus of most

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rainwater quality guidance documents

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are a lack of correlations between FIB and pathogens7-9. Therefore, FIB monitoring may not be

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sufficient to provide information on health risks.

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5, 6

. However, evidence continues to support that there

Although case control studies have indicated some associations between untreated

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rainwater consumption and gastroenteritis 10, 11, epidemiological studies have not supported a

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strong linkage 12, 13. However, these studies mainly focused on enteric pathogens. In recent

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years, improvements in disinfection practices have reduced the health burden of gastroenteritis-

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causing pathogens in many centralized drinking water systems. As a result, the focus for

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waterborne disease burden mitigation has shifted to opportunistic premise plumbing pathogens

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(OPPPs) that live in the biofilms growing on the inner surfaces of the distribution system,

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premise plumbing pipes, and bulk water within a household water system 14. OPPPs are

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frequently measured in drinking water systems, however, rainwater tanks mimic the conditions

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of a high OPPP-risk system due to their high residence times, potentially high nutrient content,

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and suitable temperatures, especially in sub-tropical regions.

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OPPPs infrequently cause illnesses in healthy individuals, and primarily affect those with

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weakened immune systems, children, and/or the elderly 15. OPPPs include Acanthamoeba spp.,

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Legionella pneumophila, Mycobacterium avium complex (MAC, a group of related bacteria that

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includes both Mycobacterium avium and Mycobacterium intracellulare), and Pseudomonas

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aeruginosa, among others 14, 15. Acanthamoeba spp. and other protozoans can form

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relationships with bacteria and contribute to their proliferation and enhanced virulence factors 16.

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L. pneumophila causes Legionellosis (Legionnaires’ Disease) which is the only reportable

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OPPP-associated illness in Australia 17and Pontiac Fever. Legionellosis had an estimated

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incidence of 13 people per million in Australia in 2012 18. Sporadic outbreaks have been linked

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specifically to L. pneumophila and MAC in people who were exposed to RHRW

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19-21

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The presence of multiple OPPPs in tank water samples has been reported by several

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studies 8, 9, 22-25, supporting the need to assess potential health risks. However, studies that

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investigated the occurrence of OPPPs relied upon testing a sample at a single time-point from a

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tank. Therefore, there is limited or no data available on the temporal variations in OPPPs

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occurrence and concentrations. Information regarding occurrence and temporal variations of

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OPPPs is particularly important because water quality management options for individual

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rainwater tank owners are limited. In addition, there are barriers associated with the level of

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technical knowledge necessary, maintenance, and costs.

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In our previous study9, we screened 134 roof-harvested rainwater tank samples for OPPPs.

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Of the 134 tanks, 24 were further selected for this follow-up study. The aims of this study were

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therefore to: (i) provide seasonal data on the concentrations of six potential OPPPs

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(Acanthamoeba spp., Legionella spp., L. pneumophila, M. avium, M. intracellulare, and P.

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aeruginosa) of public health significance in tank water over time (ii) assess the overall

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correlations among OPPPs and FIB; and (iii) compare the presence of OPPPs with

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maintenance and rainwater system characteristics. qPCR methods were chosen for the

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quantification of six OPPPs and culture-based methods were used for the enumeration of two

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FIB. The quantitative data presented in this study would improve the accuracy of quantitative

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microbial risk assessment (QMRA) of RHRW for various domestic uses, and provide information

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for rainwater users regarding potential seasonality of risks.

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MATERIALS AND METHODS

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Tank water sampling. Water samples were collected in two phases. In phase one, a total of

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134 water samples were collected from 134 tanks located in various areas of Brisbane and the

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Currumbin Ecovillage in Southeast Queensland, Australia between March and July 2015 as part

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of a previous study9 (phase one). In phase 1, the concentrations of FIB (E. coli and

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Enterococcus spp.) were determined using culture-based methods, and the gene copies for six

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OPPPs (Acanthamoeba spp., Legionella spp., L. pneumophila, M. avium, M. intracellulare, and

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P. aeruginosa) were quantified using qPCR assays. Based on the high concentrations of FIB

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and OPPPs among the 134 tank water samples (Supplementary Table S1), a subset of tanks

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with high concentrations of OPPPs and FIB (n = 24) was selected for the current seasonal study

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(phase two).

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In phase two, for the seasonal study, monthly water samples were collected from these 24

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selected tanks on six separate events from August 2015 to March 2016, giving a total number of

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144 tank water samples. The tap/spigot connected directly to the rainwater tank was wiped with

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70% ethanol, and the stored water was run for 15 s prior to filling a 10 L sterile container.

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Samples were immediately transported to the laboratory, kept at 4°C, and processed within 6-12

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h.

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Tank survey. Tank owners were sent an online survey regarding end uses (drinking, clothes

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laundering, car washing, gardening, swimming pool use), and treatment practices. On site, a

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visual sanitary inspection was undertaken to identify factors that may have been associated with

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sources (the presence of overhanging trees, TV aerials, and wildlife fecal contamination on the

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roof), and to verify the online survey results provided by the residents. Any additional factors

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that might contribute to the presence of fecal contamination in the tank water were noted for

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each property.

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Enumeration of FIB. Colilert® and Enterolert® (IDEXX Laboratories, Westbrook, Maine, USA)

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Test kits were used to determine the concentrations of FIB (total coliforms, E. coli, and

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Enterococcus spp. in 100 mL of each tank water sample. Test kits were incubated at 37 ± 0.5°C

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(E. coli) and 41.5 ± 0.5°C (for Enterococcus spp.) for 24 h as per the manufacturer’s

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recommendation.

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Concentration of rainwater samples and DNA extraction. Approximately 10 L water sample

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from each rainwater tank was concentrated by a hollow-fiber ultrafiltration system (HFUF) using

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Hemoflow FX 80 dialysis filters (Fresenius Medical Care, Bad Homburg, Germany) as

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previously described 9, 26. The concentrated sample was filtered through a 0.45 µm cellulose

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filter paper (Advantec, Tokyo, Japan), and stored at -80°C until DNA extraction. In case of filter

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clogging, multiple filter papers were used for each sample. DNA was extracted using a

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PowerSoil® Max DNA Kit (Mo Bio, Carlsbad, California, USA) according to the manufacturer’s

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instructions. The kit was modified slightly with 2 mL of DNA eluted buffer C6 instead of 5 mL 9.

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DNA concentrations were determined using a NanoDrop spectrophotometer (ND-1000,

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NanoDrop Technology) and stored at -80°C until use.

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PCR inhibition. An experiment was conducted to determine the effect of potential PCR

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inhibitory substances on the quantitative detection of OPPPs in tank water DNA samples using

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a Sketa22 real-time assay 9, 27. Of the 144 samples, 13 (9%) had signs of PCR inhibition. These

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inhibited samples were 10-fold serially diluted, and further tested with the Sketa22 real-time

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PCR assay. The results indicated the relief of PCR inhibition at the 10-fold dilution. Based on

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the results, neat DNA samples (PCR uninhibited samples) and 10-fold diluted (PCR inhibited)

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samples were tested with qPCR methods. Samples with a 2 quantification cycle (Cq) delay were

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considered as having PCR inhibitors.

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qPCR standards. Standard curves for all qPCR assays were constructed using synthesized

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plasmid DNA (pIDTSMART with ampicillin resistance; Integrated DNA Technologies, Coralville,

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IA, USA). The purified plasmid DNA was serially diluted to create a standard ranging from 1 ×

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106 to 1 gene copies per µL of DNA. A 3-uL template from each serial dilution was used to

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prepare a standard curve for each qPCR assay. For each standard, the genomic copies were

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plotted against the cycle number at which the fluorescence signal increased above the

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quantification cycle value (Cq value). The amplification efficiency (E) was determined by analysis

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of the standards and was estimated from the slope of the standard curve as E = 10-1/slope.

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qPCR assays and performance characteristics. qPCR assays were performed using

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previously published primers, probes, and optimized reaction mixtures and cycling parameters

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(Supplementary Table S2). All qPCR amplifications were performed in a 20-µL reaction mixture

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using Sso FastTM Probes Supermix (Bio-Rad Laboratories, CA). The qPCR mixtures contained

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10-µL of Supermixes, optimized concentrations of primers and probe and 3-µL of template DNA.

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Standards (positive controls) and sterile water (negative controls) were included in each qPCR

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run. All qPCR reactions were performed in triplicate using a Bio-Rad® CFX96 thermal cycler.

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qPCR standards were analysed in order to determine the amplification efficiencies (E) and the

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correlation coefficient (r2). The qPCR lower limit of quantification (LLOQ) was also determined

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from the Cq values obtained for each standard. The minimum concentration of copies from the

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standard series detected in 3/3 qPCR reactions was considered qPCR LLOQ. The dilution

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below this series was considered the limit of detection (LOD).

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The slope of the standards ranged from -3.567 to -3.399. The amplification efficiencies

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ranged from 90.8 to 95.7%, and the correlation coefficient (r2) ranged from 0.968 to 0.998.

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qPCR performance characteristics for individual assays are shown in Supplementary Table S3.

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The lowest amount of diluted gene copies detected in 3/3 qPCR reactions was considered the

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qPCR LLOQ. The LLOQ of the qPCR was determined to be 30 gene copies for Legionella spp.,

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Acanthamoeba spp., and P. aeruginosa assays and 3 gene copies for L. pneumophila, M.

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avium, and M. intracellulare assays.

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Quality control. Method blank runs were performed to ensure that the disinfection procedure

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was effective in detecting carryover contamination between sampling events. In addition, to

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detect DNA carryover contamination, reagent blanks were included for each batch of DNA

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samples. No carryover contamination was observed. To minimize qPCR contamination, DNA

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extraction and qPCR setup were performed in separate laboratories.

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Meteorological data. Data for daily and monthly rainfall, ambient temperature, and relative

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humidity (RH) at 9am and 3pm on the sampling day were obtained from the Australian Bureau

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of Meteorology (BOM) 28. The closest BOM gauges for daily rainfall data and complete monthly

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meteorological data were determined by mapping gauges and rainwater tanks and determining

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the gauges, which were present at the shortest geodesic distance from each tank

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(Supplemental Table S4). Gauges A-L are closest to the 24 sites, and were used for rainfall

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analysis. Gauges 1-5 were the closest sites that provided full meteorological datasets for

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analysis of temperature and RH.

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Statistical analysis. Statistical analyses were carried out to answer the questions (1) Does

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FIB/OPPP occurrence differ over the six sampling events? and (2) Is each FIB/OPPP correlated

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with another FIB/OPPP or other meteorological factors? Differences in OPPP occurrence over

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six sampling events from August 2015 to March 2016 were assessed using binary

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presence/absence data as well as their concentrations using the SPSS software (IBM, version

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24) and R (www.rproject.org). All samples above the LOD and LOQ were considered positive.

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Differences in binary (presence/absence) occurrence across six sampling events.

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Differences in the number of positive samples for each target over time were determined using

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a Cochran Q test 29. Cochran’s Q test is a method of testing for differences in frequencies or

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proportions between three or more repeated measures groups. Results were considered

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significant for an alpha level of 0.05. Post-hoc McNemar’s tests were performed where

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significant differences were indicated, and compared to an adjusted alpha value using the

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Bonferroni correction (significance level alpha/number of pairwise comparisons), alphaadjusted =

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0.05/15 = 0.003.

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Differences in concentrations of FIB / OPPPs (continuous) occurrence across six

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sampling events. A Shapiro-Wilk test was used to test the normality of the log-transformed

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OPPP data for each sampling event. Non-detect observations were substituted with half of the

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detection limit value for this purpose only. The null hypothesis that the data were normally

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distributed was rejected in all cases (p < 0.05) with the exception of Legionella spp. in February

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2016 (p = 0.145). Quantile-quantile and residual plots were also examined for normality and

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agreed with these findings. As a result, the nonparametric Friedman test for repeated measures

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data was used to determine differences between concentrations of microorganisms obtained

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during the different sampling events. Results were considered significant for an alpha level of

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0.05. Post-hoc Wilcoxon signed-rank tests for differences in median rank scores were

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performed where significant differences were indicated and compared to an adjusted alpha

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value of 0.003 using the Bonferroni correction.

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Correlations among FIB/OPPPs and between FIB/OPPPs and meteorological factors.

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Correlations were assessed among FIB and OPPPs, and between FIB/OPPPs and

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meteorological parameters using binary presence/absence data as well as FIB and OPPP

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concentrations. For binary FIB / OPPP data, Odds ratio (OR) estimates and 95% confidence

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intervals (CI) for the estimates were calculated between FIB and OPPPs for the pooled dataset

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using a chi-square test. For binary pathogen data vs. meteorological data, binary logistic

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regression was used to calculate ORs. Fisher's exact test was used to assess the significance

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of the ORs, where an odds ratio greater than one (with a 95% CI that does not overlap 1)

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indicates a positive association between two factors while an odds ratio less than one (with a

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95% CI that does not overlap 1) indicates a negative association.

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To assess correlations among continuous FIB/OPPP concentrations and meteorological

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information for all sampling events, nonparametric Kendall’s Tau correlations were computed

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using the NADA package in the R software environment (www.r-project.org). Although a

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Spearman Rho is typically used for this purpose, pathogen datasets involving censored data

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with non-detect (below the method detection limit) and non-quantifiable (below the limit of

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quantification) values require the use of Kendall’s Tau as the Spearman rho does not

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accommodate multiple reporting limits for censored datasets 30. Correlations were considered

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significant at an alpha level of 0.05.

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We performed multiple comparisons for both the OR and Kendall’s Tau analysis and used

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the false discovery rate (FDR) approach to correct our decisions on significance based on the

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number of comparisons and the p-values of the tests that we performed 31. For tests between

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the 8 microorganisms (6 OPPP + 2 FIB), 28 comparisons were performed and the modified p-

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value for significance (assuming a 10% FDR and noting that FDR is not equivalent to α32) was

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0.017 for odds ratios and 0.031 for Kendall’s Tau analysis. For tests between the 8

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microorganisms and 17 meteorological variables, 136 comparisons were performed and the

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modified p-value for significance (10% FDR) was 0.016 for odds ratios and 0.021 for Kendall’s

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Tau analysis.

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RESULTS

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Survey data. Ninety-six percent (23 of 24 tanks) of tank owners responded to the survey. The

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sizes of the tanks ranged from 3,000 to >40,000 L, and the tank ages ranged from 2 to 20 years

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(Table 1). Most of the tanks were made of galvanized steel (50%) or polyethylene (37.5%),

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whereas, only three tanks (12.5%) were made with concrete. Most of the houses had metal roof

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(87.5%) and only 3 houses (12.5%) had tile roofs. Of the 24 tanks surveyed, 21% had trees

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overhanging the roof, 46% had TV aerials, and 33% had visible signs of debris on the roof. Fifty-

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percent of the tanks were never cleaned and desludged in their lifetime. Of the 24 tanks, 46%

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had a first-flush diverter installed and only 38% treated the water (all used filtration) before

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drinking. 54% tanks were used for both potable and non-potable purposes and the remaining

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tanks were used only for non-potable purposes.

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Meteorological data. Queensland is located in the sub-tropical climate zone of Australia, and

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accordingly exhibits wet (November to March) and dry (April to October) seasons 33. Consistent

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with this pattern, total monthly rainfall generally increased over the sampling period from August

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to March, with the exception of February which was unusually dry for the region (Supplementary

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Fig. S1). Rainfall was generally higher for coastal areas (Gauge L, Gold Coast) compared to

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inland samples (Gauge G, urban Brisbane) and was highly localized (Supplementary Fig. S2).

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Most sampling events were taken within 48 h of a rainfall event and all were taken within 7 days

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of a rain event with the exception of nine samples in February. The February sampling time

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point was maintained in order to maintain the seasonal sampling scheme every 3-4 weeks.

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Maximum daily rainfall in Brisbane (Gauge G) ranged from 7.8 mm (February) to 26.4 mm

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(March) during the sampling period. Maximum daily rainfall on the Gold Coast (Gauge L) ranged

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from 9.2 mm (February) to 98.2 mm (November).

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The Queensland wet season is typically accompanied by higher temperatures and

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humidity. Temperatures increased steadily over the sampling period (Supplementary Fig. S3)

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with the greatest variations in daily temperature occurring at Gauge 2. Large daily variations in

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RH over the sampling period are shown in Supplementary Fig. S4. For 100/144 sampling times,

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RH increased between 9am and 3pm. Average daily RH change calculated as (|RH9am – RH3pm|/

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(No. samples in group) was 9.34% increase (maximum increase 55%) for increasing samples,

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and 8.02% decrease (max decrease 16%) for decreasing samples.

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FIB and OPPPs in tank water samples collected in phase 1. In all, 134 tank water samples

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were screened for the FIB and OPPPs in phase 1. Twenty-four tanks were further selected for

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longitudinal study based on the high occurrence of FIB and OPPPs. The data obtained for only

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the selected 24 tank water samples is presented. The 24 samples tested were positive for E.

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coli (63%), Enterococcus spp. (50%), Acanthamoeba spp. (17%), Legionella spp. (100%), L.

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pneumophila (17%), M. avium (88%), M. intracellulare (92%), and P. aeruginosa (21%). The

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concentrations of FIB and OPPPs in tank water samples are shown in Supplementary Table S1.

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FIB and OPPPs in tank water samples collected in phase 2. Among the 144 tank water

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samples tested in phase 2 (24 tanks × six events), 44, 42, 40, 97, 5, 57, 60, and 31% were

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positive for E. coli, Enterococcus spp., Acanthamoeba spp., Legionella spp., L. pneumophila, M.

296

avium, M. intracellulare, and P. aeruginosa, respectively. Notably, all tank water samples

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(denoted T1 through T24 for tank 1 through tank 24) over six events were positive for at least

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one OPPP during the entire course of the study (Table 2). All except three tanks for E. coli (T6,

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T9, T15) and Enterococcus spp. (T5, T6, T23) were positive at least once during the course of

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the study. Among the OPPPs tested, Legionella spp. were detected most frequently in all tank

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water samples during the course of the study. Acanthamoeba spp. were present at least once

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in all tanks over the course of the study, with the exception of T14. M. avium (T2, T5, T9, T10,

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T16, T21) and M. intracellulare (T2, T3, T5, T9, T12, T13, T16, T19, T20) were frequently

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detected in several tanks during the course of the study. P. aeruginosa also intermittently

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detected in small numbers of tank water samples collected in August, September, October and

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November and then all tank water samples collected in February were PCR positive. In March,

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P. aeruginosa was detected in over half of the tank water samples. L. pneumophila was only

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found in three tanks (T8, T22 and T24) and only during the second half of the study (November

309

2015 through March 2016).

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Concentrations of FIB in positive samples are shown in Fig. 1. Concentrations of E. coli and

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Enterococcus spp. in positive samples ranged from 1 to 687 and 1 to > 2419 MPN per 100 mL

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of tank water, respectively. The concentrations of FIB in water samples collected in August,

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September, October and November were lower than February and March. Concentrations of

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OPPPs in positive samples are shown in Fig. 2. In order of highest to lowest maximum

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concentration, the concentrations of P. aeruginosa in positive samples were 3.6 × 102 to 4.7 ×

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108 gene copies per 100 mL, followed by Legionella spp. (3.2 × 102 to 2.3 × 106 gene copies per

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100 mL), Acanthamoeba spp. (2.2 × 102 to 9.8 × 105 gene copies per 100 mL), M. intracellulare

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(2.2 × 101 to 6.8 × 105 gene copies per 100 mL), M. avium (2.4 × 101 to 3.6 × 105 gene copies

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per 100 mL), and L. pneumophila (2.3 × 101 to 1.5 × 102 gene copies per 100 mL).

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Differences in FIB and OPPPs occurrence across six sampling events. Regarding variation

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in the proportion of samples positive for FIB and OPPPs over the six sampling events, a

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Cochran’s Q test determined that there was a significant difference for E. coli (Q = 14.1, p
0.05), M. intracellulare (Q = 11.1,

326

p = 0.05), or L. pneumophila (Q = 10.6, p > 0.05). Significant post-hoc differences are

327

summarized in Supplementary Table S5. Notably, Acanthamoeba spp. were detected

328

significantly more frequently in September than all other months (p 3 yr

N

Nonpotable

N

N

Yearly

>3 yr

-

Potable, nonpotable

N

Y

Yearly

Never

N

Nonpotable

N

Never

N

Nonpotable

N

>3 yr

Y

Nonpotable

N

Never

N

Nonpotable

N

T6

Urban

5

Galvanized steel

Metal

Y

Y

-

T7

Periurban

>5,00010,000

4

Polyethylene

Metal

N

Y

N

T8

Urban

3,0005,000

5.5

Concrete

N

Y

N

9

Polyethylene

Clay/ concrete tile Metal

Gutter guards Never, has gutter guards Yearly

N

N

N

Yearly

Never

N

Nonpotable

N

5

Polyethylene

Metal

Y

N

N

Yearly

>3 yr

Y

Nonpotable

N

10

Polyethylene

Metal

N

N

Y

Never

>3 yr

Y

Nonpotable

N Y

T9

Urban

T10

Urban

T11

Urban

T12

Rural

>5,00010,000 3,0005,000 >10,00015,000 >40,000

2

Galvanized steel

Metal

N

Y

N

Never

Never

Y

Potable, nonpotable

T13

Rural

-

5

Galvanized steel

Metal

N

N

N

-

>2-3 yr

N

Potable, nonpotable

Y

T14

Periurban

>40,000

3

Galvanized steel

Metal

N

N

N

Never

Never

Y

Potable, nonpotable

Y

T15

Rural

Concrete

Metal

Y

N

N

Yearly

>3 yr

Y

Potable, nonpotable

Y

Rural

4

Galvanized steel

Metal

N

N

Y

Never

Never

N

Potable, nonpotable

Y

T17

Periurban

>20,00040,000 >15,00020,000 >40,000

7

T16

4

Galvanized steel

Metal

N

Y

N

Never

Y

Potable, nonpotable

N

T18

Periurban

>40,000

3

Colourbond Steel

Metal

N

Y

Y

Gutter guards Every 1.5 yr

Never

Y

Potable, nonpotable

Y

T19

Periurban

>40,000

7

Galvanized steel

Metal

N

N

Y

Yearly

Never

Y

Potable, nonpotable

Y

T20

Periurban

>40,000

8

Galvanized steel

Metal

N

N

N

Never

>3 yr

Y

Potable, nonpotable

Y

T21

Periurban

-

-

Galvanized steel

Metal

Y

-

Y

-

-

-

Potable, nonpotable

Y

T22

Rural

20

Galvanized steel

Metal

N

Y

Y

Yearly

>3 yr

N

Potable, nonpotable

N

T23

Urban

>15,00020,000 3,0005,000

4

Polyethylene

N

Y

N

Never

Never

N

Nonpotable

N

T24

Periurban

>15,00020,000

8

Concrete

Clay/ concrete tile Clay/ concrete tile

Y

Y

N

Every 6 months

>3 yr

Y

Nonpotable

N

22 ACS Paragon Plus Environment

Page 23 of 31

TABLE 2. Occurrence of fecal indicator bacteria and opportunistic pathogens (OPPPs) in tank water samples (n = 24) over six events

F

M

A

S

O

N

F

M

A

S

O

N

F

M

3

2

3

1

24

11

N

13

O

10

S

17

A

13

M

17

F

16

N

P. aeruginosa

9

O

M. intracellulare

15

S

11

A

24

M

12

F

3

N

M. avium

11

O

2

S

2

A

0

M

0

13

F

0

15

N

23

14

O

L. pneumophila

23

10

S

24

4

A

23

M

23

F

6

N

Legionella spp.

23

O

3

S

9

A

9

M

9

F

5

7

N

17

7

O

13

S

9

A

Acanthamoeba spp.

22

Enterococcus spp.

E. coli

Tanks

11

536


T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20 T21 T22 T23 T24 TOTAL POS

537 538

A: August; S: September; O: October; N: November; F: February: M: March; ■ represents samples that were above quantification limit; ■ represents samples that are positive but not quantifiable; ■ represents samples that were below detection limit

23 ACS Paragon Plus Environment


539 540 541 542 543

TABLE 3. Correlations between fecal indicator bacteria (FIB) and opportunistic premise plumbing pathogens (OPPPs) in roof-harvested rainwater (RHRW) stored in tanks. Significant values (p < 0.031) are bold-faced; see methods for discussion of correcting for multiple comparisons using a FDR approach.

Pathogen (Kendall’s a tau, p) Enterococcus spp. Acanthamoeba spp. Legionella spp. L. pneumophila M. avium M. intracellulare P. aeruginosa

544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562

Page 24 of 31

E. coli 0.208 (

Seasonal Assessment of Opportunistic Premise ... - ACS Publications

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