Legionella is a genus of pathogenic bacterial mesophiles that cause a range of diseases collectively referred to as Legionellosis, with immunocompromised individuals being particularly susceptible. Water heaters, a potential domestic niche for these pathogens, are heavy energy consumers, causing cost-sensitive users to employ energy-saving initiatives, such as scheduling and lower temperature set points. However, lower heated water temperatures allow Legionella to ourish. This paper uses computational uid dynamics modelling to show that the pipes downstream of a horizontal electric water heater provide an environment that is conducive to Legionella growth, not the heater itself. The presence of Legionella in water heaters is established through water sampled from ve in- eld water heaters, of which the temperatures and heating schedules are known. Microbiological techniques (PCR and weight-based qRT-PCR) are used to assess Legionella and L. pneumophila presence at point-of-use taps. A model is used to determine the potential infection rate from these concentrations, demonstrating that undiagnosed Legionellosis infection is likely. In low- and middle- income countries, like South Africa, misdiagnosis of Legionellosis may be common due to the shadow cast by HIV and TB prevalence. Keywords: Legionellosis, Legionella pneumophila, Electric water heaters. 1. Introduction 1 The occurance of waterborne Legionella and the Legionellosis-causing pathogenic bacterium Legionella 2 pneumophila in domestic water heaters in South Africa (SA) is not known. In SA, Legionellosis is a noti able 3 disease, yet rarely reported. 4 Several studies have related waterborne disease outbreaks to the growth of Legionella in large plumbing 5 systems (Schoen and Ashbolt, 2011; Borella et al., 2004; Zacheus and Martikainen, 1994). A small number of 6 Corresponding author Email addresses: wstone@sun.ac.za (W. Stone), tmlouw@sun.ac.za (T.M. Louw), mnieuwoudt@sun.ac.za (M.J. Nieuwoudt), mjbooysen@sun.ac.za (M.J. Booysen) Preprint submitted to Energy for Sustainable Development December 2, 2018Preprint of article accepted for publication in Energy for Sustainable Development recent studies indicate that a strong possibility exists for Legionella growth and infection in single-households 7 (Schoen and Ashbolt, 2011; Armstrong et al., 2014). Legionnaires' disease is usually only diagnosed when 8 an outbreak occurs at public institutions, leading to the possibility of Legionella growth in single-households 9 being largely overlooked in SA. 10 Tuberculosis (TB) remains a global health concern. Of the 10.4 million new cases in 2015, one third 11 were never diagnosed and of those diagnosed, only a minority were bacteriologically con rmed (WHO, 2016). 12 Furthermore, of the 600,000 cases with rifampicin resistance, only 120,000 were diagnosed. 13 South Africa is faced with the dual epidemics of HIV (a prevalence of 12 :7 % in 2016) and TB (781 cases 14 per 100,000 population in 2016), as well as resource and medical care limitations, making it a potential 15 incubator for drug-resistant M. tuberculosis . This is evidenced by the country having one of the highest 16 burdens of multi drug resistant TB in the world (WHO, 2016). In South Africa, approximately 5 % of all 17 TB cases are believed to be multi drug resistant TB of which one-tenth are extremely drug resistant TB 18 (NIfCD, 2016). Highest rates of multi drug resistant TB and extremely drug resistant TB were noti ed for 19 the Western Cape, Eastern Cape and KwaZulu-Natal provinces (NIfCD, 2016). This heavy burden creates 20 both a diagnosis bias hiding many other diseases, as well as an immuno-compromised population susceptible 21 to many other diseases. This might explain the imbalance in reported Legionellosis cases in comparison to 22 developed countries. 23 The Legionella genus comprises of more than 50 gram-negative bacterial species that are ubiquitous in soil 24 and water, at least 20 of which are pathogenic (EPA, 2001; WHO, 2007; Fields et al., 2002; Diederen, 2008; 25 Burstein et al., 2016). L. pneumophila is the most notorious species, responsible for respiratory diseases such 26 as the milder Pontiac's fever and the more severe Legionnaires' disease. These organisms are thermophyllic, 27 with optimal growth temperatures ranging from 37 to 42C (Piao et al., 2006), while temperatures around 28 45C stimulate bio lm growth (Rogers et al., 1994). 29 Infections are often reported in immunocompromised patients due to ubiquitous environmental exposure. 30 L. pneumophila infects patients via droplet inhalation, rather than the typical ingestion or patient-to-patient 31 routes. 32 A prior study of the prevalence of Legionella spp. infections in SA demonstrated that 21 of 1805 (1 :2 %) 33 patients tested were polymerase chain reaction (PCR) positive for Legionella spp. (Wolter et al., 2016). 34 Within this group, 9 of the 21 (43 %) tested positive for TB, while 75 % were HIV positive. HIV or TB or both 35 were detected in 18 of 20 (90 %) of these patients. Symptomatic Legionellosis presents as community-acquired 36 pneumonia in common with several other potential opportunistic bacterial infections and is often associated 37 with immunosuppression. Thus, evidence such as the above would suggest that under normal circumstances, 38 i.e. those in which it is not actively tested for, diagnoses of Legionellosis might be missed due to the shadow 39 2 cast by HIV and TB. This seems particularly likely in resource-constrained settings in SA, where the burdens 40 of HIV and/or TB infections are high, and clinicians lack access to appropriate diagnostic testing1. This 41 picture is complicated by the fact that antimicrobial treatment for community-acquired pneumonia and TB, 42 for example rifampicin, has demonstrated ecacy against Legionella (Klein and Cunha, 1998; Vesely et al., 43 1998). However, e ective treatment normally requires the addition of macrolides or uoroquinolone (Phin 44 et al., 2014). As a result, morbidity due to Legionellosis may remain underestimated in SA. 45 Despite recent advances, South Africa still has high incidence of poverty (Burger et al., 2017), resulting 46 in nancially-constrained consumers resorting to various means to limit the cost of water heating. Water 47 heating is responsible for 32 % of household energy consumption in South Africa, where water is predominantly 48 heated with horizontally-oriented cylindrical electric water heaters. Water heaters nominally heat water to 49 65C, although temperatures of as low as 40C are considered suciently warm for user satisfaction (Belov 50 et al., 2015; Nel et al., 2018a). The energy consumed by a domestic water heater can be reduced by 29 % 51 through schedule control and lowering the thermostat's target temperature (Booysen and Cloete, 2016; Nel 52 et al., 2018b). Despite the nancial bene t to the user of operating at these lower temperatures, the heater 53 and its hot water distribution system could be creating ideal temperature niches for the growth of the L. 54 pneumophila pathogen. Legionella is often proposed to be a threat only to immuno-compromised individuals, 55 and yet is repeatedly reported in association with widespread outbreaks related to water cooling systems, 56 water distribution systems, spas and whirlpools, largely in developed countries (EPA, 2001; WHO, 2007; 57 European Agency for Safety and Health at Work, 2011). Con rming the presence of Legionella in general 58 and L. pneumophila in particular is key to validating the temperature results and understanding the risk to 59 immunucompromised individuals, at this interface between immunity, load of exposure and nancial heating 60 considerations. 61 This paper evaluates the presence and survival of Legionella , and the pathogenic bacterium L. pneumophila 62 in horizontal domestic water heaters, which are ubiquitous in South Africa. 63 A computational uid dynamics (CFD) approach is used to evaluate whether the horizontal electric water 64 heater provides an environment that is conducive to the growth of Legionella in bio lms inside the heater 65 even under thermostat control. The analysis is also used to determine the streamlines for particles that exit 66 the heater during a shower. Linking the potential risk of proliferation to the potential risk of infection, an 67 existing infection model is also improved to determine the probability of an immunocompromised individual 68 contracting Legionellosis. This aims to add to the international epidemiological work feeding into these 69 1A counterfactual to this hypothesis is the determination of the cause of the unexpected passing of the prominent Minister of Environmental A airs, Edna Molewa. The cause of death was determined to be a Legionella infection (The South African, 2018) 3 questions, in terms of the balance between energy consumption and sanitation of water heaters (Armstrong 70 et al., 2014), heating and cooling systems (Zhao et al., 2015), the bio lm proliferation of Legionella (Murga 71 et al., 2001), the impact of materials on the control of these bio lms (Rogers et al., 1994; Buse et al., 2014), 72 as well as water quality (Bargellini et al., 2011) and temperature and hydraulics (Boppe et al., 2016). 73 Grappling with real-world challenges to inform the models demanded environment-driven culturing and 74 molecular techniques. Samples and scrapings are taken from decommissioned water heaters to determine 75 the presence of Legionella , and the microbial loads at point-of-use in tap water from ve active heaters are 76 evaluated in comparison to cold water from the same source. Culturing and a PCR-based technique are 77 used to demonstrate the presence of Legionella , while Quantitative Real Time PCR (qRT-PCR), quanti ed 78 against a weight-based standard curve, is used to quantify the L. pneumophila present. These results are 79 used to calculate an infection probability, and relevance related to other disease in South Africa, and low- to 80 medium-income countries in general. 81 82 2. Materials and Methods 83 2.1. Computational uid dynamics model 84 A domestic cylindrical water heater in SA has a heating element (typically 2 to 4kW), controlled by 85 a thermostat that is mounted near the element. Importantly, electric water heaters in South Africa are 86 mounted horizontally, with the inlet on the lower end near the element, and the outlet at the upper end on 87 the opposite horizontal side. 88 A CFD model of a horizontal heater is developed in this paper to simulate temperature strati cation and 89 determine the velocity elds which in uence the motion and growth of resident microbes. The CFD model 90 represented a horizontal heater operating at 600 kPa using a 2 kW element. The simulated heater had a 91 length and diameter of 1 m and 0 :4 m, respectively. Particular attention was paid to the detailed geometry 92 of the heating coil as it has a direct in uence on natural convection. The resulting mesh consisted of more 93 than 280 000 elements and passed all typical mesh quality metrics. 94 Natural convection was simulated using the Boussinesq approximation for buoyancy driven ow (Tritton, 95 2012). This involves solving the incompressible Navier-Stokes equations in conjunction with a linear approx- 96 imation for thermal expansion to model the buoyancy force. Heat ux boundary conditions were applied 97 throughout, with temperature dependent heat loss at the tank walls and a xed heat ux at the heating 98 element to ensure an overall heat supply of 2 kW. Two ow conditions were simulated: (1) no ow occurred 99 into the heater, and (2) 5 L =min ow into (and out of) the heater and a pressure speci ed outlet. 100 4 The CFD implementation described above was extremely computationally intensive and it was not pos- 101 sible to simulate ow patterns over the duration of an entire day. To this end, a second CFD model was 102 developed using a coarse mesh. The heat ux as well as the inlet ow boundary conditions were set based 103 on eld measurements from heater controllers. The coarse CFD model was able to simulate entire days of 104 usage. While it is unlikely that the ow patterns simulated using the coarse model is accurate, the dynamic 105 temperature pro les presented a fair approximation. 106 All simulations were implemented using ANSYS ®CFD software. 107 2.2. Sterilization model 108 The CFD model (from the previous section) produced a vector-valued velocity eld and a scalar temper- 109 ature eld which was used to predict the thermal exposure experienced by planktonic- and bio lm associated 110 microbes. Multiple seeding points were selected and the velocity eld was used to track the movement of 111 the microbe through the heater. Interpolating the temperature with respect to the microbes' position in the 112 heater tank yielded a temperature pro le which was subsequently used to predict the viability of microbes 113 leaving the heater outlet. Microbial viability was estimated using eqns. 1 and 2: 114 dfX dt=kd (1) kd=k0exp(EA=RT) (2) WherefXrepresents the fraction of microbes remaining viable at time t,andkdrepresent microbial 115 growth and decay, respectively. The rate of decay is estimated using an Arrhenius-type equation (eq. 2) 116 with pre-exponential coecient k0= exp(95:7) s1and activation energy EA= 276 kJ/(mol.K) . A speci c 117 growth rate of = 1:04 hr1was used . These parameters were chosen to ensure a decimal reduction rate 118 of 80 min at 50C and 2 min at 60C (Bartram, 2007), while maintaining a speci c hourly growth rate of 119 = 0:86 at 45C, corresponding to an estimated maximum growth rate (Sharaby et al., 2017). 120 2.3. Remote controller and failed water heaters 121 The ve in- eld water heaters used in the study are part of a larger eld trial of water heaters, in which 122 users were shown water and energy consumption information and given heating schedule control through an 123 online platform. The temperature is measured at the outlet, using a temperature sensor that is strapped 124 onto the pipe with self-fusing silicon tape, and reported as an average temperature every 1 min (Fig. A.1). 125 The electricity supply to the heating element is controlled by a cloud-based Set Point Controller (SPC). The 126 5 SPC controls the heating element based on a control schedule and a target temperature, both set by the user 127 on the online interface. More information on the control system can be found in (Roux and Booysen, 2017). 128 All the water heaters are pressurised, horizontally mounted, had a volume of 150 L, and are manufactured 129 from mild steel with a thermo-fused porcelain enamel. This is the most common set-up found in South Africa. 130 2.4. Legionella quanti cation: relative and absolute 131 Two approaches were taken to empirically evaluate Legionella in water heaters. The rst approach 132 was to cut open random water heaters that failed mechanically (\burst"), and to take water samples and 133 bio lm scrapings from these, shortly after the failure. In these samples, culturing was employed for relative 134 quanti cation of Legionella in comparison to general heterotrophic plate counts. The method is described in 135 Section 2.4.1. 136 The second approach, described from Section 2.4.2, was to at take three water samples at the point- 137 of-use in the water distribution systems of ve active household water heaters, of which the user-chosen 138 heating control schedule was known through the controllers. In these point-of-use samples, real time PCR 139 was employed for absolute quanti cation of Legionella exposed to water users. A cold water sample was 140 taken as a control; a rst hot water sample was taken to establish the presence and quantity of Legionella 141 in the piping downstream from the heater; and a second hot sample was taken to establish the presence and 142 quantity of Legionella in the heater tank itself. 143 2.4.1. Direct sampling and relative quanti cation (culture-based) 144 Four domestic water heaterss that recently failed mechanically, were cut open on site approximately 12 145 to 24h post-decommissioning, and (a) grab samples and (b) bio lm scrapings were taken from inside the 146 heaters. 147 Grab samples were collected in sterile 50 mL bottles, and scrapings were collected in sterile Petridishes. 148 Bio lm scrapings were taken near the outlet and the inlet, focusing on regions likely to see the least ow 149 disturbance, as well as directly from the base of the elements which had notable precipitate deposition (Fig. 150 A.2). 151 Coupons were cut from the various heater tanks (copper, steel and plastic; inlet and outlet regions), for 152 direct incubation on agar. Samples were transported on ice and processed within 6 hours. 153 All liquid (four, one per heater) and bio lm (eight, two per heater) samples were diluted in physiological 154 saline solution (0 :9 % w=v NaCl; Sigma Aldrich, Modderfontein, South Africa) and dilution ranges (undiluted 155 - 107) plated on (a) Legionella CYE agar base, with Legionella BCYE growth supplement (Chat eld and 156 Cianciotto, 2013), and (b) Tryptic Soy agar. Cultures were grown and isolated at 35C. Single colonies grown 157 6 onLegionella -speci c medium were isolated on the same medium (every 3 days over a month-long interval), 158 and subsequently cultured on Legionella CYE agar base with BCYE growth supplement without L-cysteine. 159 Legionella have a unique absolute metabolic requirement for L-cysteine, thus all isolates that did not survive 160 the transfer to Legionella media sans L-cysteine were tentatively positively identi ed as Legionella species. 161 All media was purchased from Thermo-Scienti c, Johannesburg, South Africa. 162 All species tentatively identi ed as Legionella via the culture-based technique were con rmed by DNA 163 sequencing, employing standard primers to amplify a 386-bp fragment of the V3{V5 region of the 16S rRNA 164 gene, speci c to Legionella spp. (Parthuisot et al., 2010). Primers include JRP (5'-AGG GTT GAT AGG 165 TTA AGA GC-3') and JFP (5'-CCA ACA GCT AGT TGA CAT CG-3'). Microbial DNA was extracted 166 from individual isolates, scraped directly from the agar plates, with the Zymo Quick DNA Fungal/Bacterial 167 Kit according to manufacturer's instructions (Inqaba Biotechnical Industries, Pretoria, South Africa). Each 168 25 µL Polymerase Chain Reaction (PCR) contained 3 to 5 ng DNA, 1 µM primers, 0.8 mM deoxynucleoside 169 triphosphates (dNTPs), and 1 to 1.5 U of Taq DNA polymerase. Reagents were purchased from Inqaba 170 Biotechnical Industries (Pretoria, South Africa). The PCR protocol included an initial denaturation of 5 min 171 at 95C, followed by 30 cycles of 1 min at 95C, 1 min at 57C, and 1 min at 72C, followed by a nal ex- 172 tension of 10 min at 72C. PCR products were ampli ed in a BioRad T100 Thermal Cycler, con rmed with 173 gel electrophoresis and sequenced on an Applied Biosystems 3500XL Genetic Analyzer (Thermo Fischer). 174 Sequences were positively or negatively identi ed as Legionella by cleaning up the sequences on 4Peaks Soft- 175 ware (Nucleobytes, 2004), and subsequent comparison against the international BLAST database (BLAST, 176 nd). 177 Attempts were made to harness molecular techniques for direct identi cation and quanti cation of Le- 178 gionella in the heaters' planktonic and bio lm biomass, using the above-mentioned kit for DNA extraction, 179 as well as manual protocols, including adding bovine serum albumin to PCR reactions to minimize inhibition. 180 However, the bio lm samples and liquid samples were red with precipitate, likely containing heavy metals 181 such as iron (Fig. A.3), and thus the lack of molecular success due to PCR inhibitors was not surprising. 182 2.4.2. Distribution system sampling and molecular quanti cation 183 Since direct molecular quanti cation is a more robust and reliable technique for measuring microbial loads 184 in water, and point-of-use bacterial concentrations are of greater infectious relevance than concentrations in 185 the heater tank, samples from household taps were analysed for Legionella presence and L. pneumophila 186 concentrations using PCR and quantitative Real-Time PCR (qRT-PCR), respectively. 187 Samples (2 L) were taken aseptically from each of the ve heaters in sterile screw-top glass bottles from 188 cold water taps 3 min after opening (CT), hot water taps directly after opening whilst water is still cold (HT1) 189 7 and hot water taps after running at maximum heat for 1 min (HT2). Water was transported immediately to 190 the laboratory and processed within 1 h. Microbial cells in the samples were concentrated by ltration (2 L) 191 and released from the lters into suspension by incubation in an acidic bu er according to Dobrowsky et al. 192 (2015). The samples were occulated by the addition of 2 mL =L CaCl 2(1M) and 2 mL =L Na 2HPO 4(1M) 193 and subsequent stirring (5 min). Flocculated tap water samples were ltered ( ±50 mL=min=cm) through 194 non-charged, mixed-ester membrane lters (47 mm diameter, 0 :45 m pore size; Whatman GmbH, Germany). 195 Filters were incubated for 3 min in 4 mL citrate bu er (0.3 M, pH 3.5; in 9 cm Petridishes), with occasional 196 shaking. The membrane was rubbed gently with a pipette tip, the citrate bu er solution containing the 197 bacterial cells and DNA transferred to 2 mL centrifuge tubes, centrifuged, combined and re-suspended in 198 200 µL phosphate bu ered saline (1X PBS). 199 Microbial DNA was extracted from the concentrated tap water samples with the Zymo Quick DNA 200 Fungal/Bacterial Kit, according to manufacturer's instructions (Inqaba Biotechnical Industries, Pretoria, 201 South Africa). Quality of DNA was assessed by comparison to a standard DNA ladder (1 kb Plus O'Gene 202 Ruler, ThermoFischer Scienti c, Johannesburg, South Africa) via agarose gel electrophoresis, as well as 203 quanti cation and quality assessment with A260/A280 ratios on an ND1000 NanoDrop spectrophotometer 204 (Inqaba Biotec). 205 A standard PCR, using the JFP and JRP primers as described above, was employed to determine presence 206 or absence of Legionella spp. in cold (CT), cold hot (HT1) and hot hot (HT2) tap water. A 98 % homology 207 was used to classify organisms as Legionella . Whereas these primers ampli ed a genomic region common 208 to most Legionella species Parthuisot et al. (2010), quanti cation was narrowed down to include only L. 209 pneumophila , the species most often responsible for pneumonia outbreaks (Welti et al., 2003; EPA, 2001; Yu 210 et al., 2002). 211 For qRT-PCR quanti cation of L. pneumophila , primers were selected that ampli ed a 73 bp region 212 of the gene encoding the macrophage infectivity potentiator (MIP, GenBank accession number AF022336), 213 according to Welti et al. (2003), directly correlated to colony forming units in L. pneumophila serotypes 214 (Welti et al., 2003; Behets et al., 2007). Primers LPTM1 (5'-AAA GGC ATG CAA GAC GCT ATG-3'), 215 LPTM2 (5'-TGT TAA GAA CGT CTT TCA TTT GCT G-3') and an LP probe (5'-FAM-TGG CGC TCA 216 ATT GGC TTT AAC CGATAMRA-3') were purchased from Inqaba Biotechnical Industries (Pretoria, South 217 Africa). Reactions of 25 µL were set up, with 3 to 5ng DNA, 0.1 µM primers, and Taqman Universal qPCR 218 Mastermix according to the manufacturer's protocol (Roche Industries, Sandton, South Africa). Thermal 219 cycling ran for 2 min at 50C, 10 min at 95C, followed by 50 cycles of 15 sec at 95C and 1 min at 60C, 220 and was detected in real time on a Roche LightCycler 96 System. 221 8 2.4.3. Quanti cation of L. pneumophila against a standard curve 222 Isolating and growing individual L. pneumophila colonies to set up a standard curve is expensive, tedious 223 and undesirable due to the infection potential of these Biosafety Level 2 organisms. Thus, a standard curve 224 was set up using conventional PCR from environmental samples, based on the fact that the primers are highly 225 speci c for a single-copy gene in L. pneumophila . Welti and colleagues (2003) rigorously demonstrated single- 226 copy genes for quanti cation, using multiple controls in monoplex and multiplex identi cation experiments 227 for quality control. These included (1) negative (no template), (2) inhibition (IPC block control) and positive 228 (plasmid) for each pathogen in each experiment, as well as verifying quanti cation in pure culture experiments. 229 The melting temperature (Tm) of the probes was also chosen at 10 degrees C higher than the primer Tm, 230 for optimal extension hybridization, as described by authors. Thus, this quanti cation is based on the 231 assumption of a single MIP gene per cell, but this assumption was demosntrated during method development 232 (Welti et al., 2003). 233 A conventional PCR was set up with the above-mentioned qRT-PCR primers, LPTM1 and LPTM2, using 234 DNA extracted from river water according to the sampling and ltering protocol described above. Also as 235 described above, each reaction contained 3 to 5 ng DNA, 1 µM primers, 0.8 µM deoxynucleoside triphosphates 236 (dNTPs), and 1 to 1 :5 U of Taq DNA polymerase. Thermocycling was as described for qRT-PCR, but in a 237 conventional BioRad T100 Thermal Cycler. 238 The ampli ed fragments were separated from PCR reagents via agarose gel electrophoresis (Fig. A.4), 239 extracted from the gel using the QIAquick Gel Extraction kit according to manufacturer's instructions (White- 240 head Scienti c, Cape Town, South Africa), and quanti ed using an ND1000 NanoDrop spectrophotometer 241 (Inqaba Biotec). The fragment concentration in the ampli ed and isolated solution was calculated using the 242 known molecular weight of each fragment. 243 As described by Dr. John Hildyard (Royal Veterinary College, London, UK, ResearchGate communica- 244 tion), the solution was used to make a dilution range (0 108fragments/mL), and qRT-PCR was performed 245 on each sample to set up a standard curve (in duplicate) for quanti cation. The cuto (Ct) value for each 246 dilution was determined with the LightCycler 96 System Application and Instrument Software, and plotted 247 against the known fragment concentration, calculated based on weight. As there is one MIP gene per cell, as 248 demonstrated by the authors described above, the number of cells is directly equal to the number of molecules 249 in solution. Typically, this concentration is halved in quantifying cDNA for gene expression, but since these 250 PCR products are double-stranded DNA, the Ct values were used directly to plot the standard curve (Fig. 251 A.5). The Ct values in a biological duplication of the standard curve did not vary more than 5 % per dilution. 252 Ct values of the unknown samples were quanti ed, in terms of concentration, against this standard curve. 253 9 2.5. Infection model 254 Legionella infection requires the deposition of pathogenic microbes in the alveolar region of the lungs 255 (Schoen and Ashbolt, 2011). The risk of infection is greatest during shower events where water is aerosolized. 256 Schoen and Ashbolt (2011) outlined a method to determine critical Legionella concentrations in the water 257 supply which would lead to microbial infection during a shower event, as shown in eqns. 3 and 4: 258 DD="X iF(1) iF(2) i# nl (3) nl=VairPCcw (4) The total deposited dose ( DD as colony-forming-units, CFUs) depends on the number of Legionella 259 microbes inhaled ( nlin CFUs) as well as the size distribution of the inhaled aerosol: speci cally, the product 260 of the fraction F(1) iofLegionella cells that partition to aerosols in size range iand the fraction F(2) iof aerosols 261 in size range ithat are deposited in the alveolar region of the lungs. The number of Legionella microbes 262 inhaled is a product of the volume of air inhaled during a typical shower event ( Vair, 1/m3), the partition 263 coecient ( PCas CFU/m3in air / CFU/L in water) describing the likelihood of Legionella partitioning into 264 the aerosol phase, and the concentration of Legionella in the water ( cw;CFU/L). 265 Using parameters obtained from an extensive literature review, the authors predicted a critical density of 266 Legionella in the water supply based on a required DDof 1 CFU (low estimate) or 10 CFU (best estimate) 267 for infection. However, the estimated concentrations are higher than reported concentrations associated with 268 cases of Legionellosis. A more appropriate approach is to predict a probability of infection dependent on the 269 microbial density cw. Speci cally, given the probability pof a single aerosol droplet leading to deposition of 270 Legionella on the alveoli, and given that naerosol droplets are inhaled during a shower event, the probability 271 PsthatkLegionella microbes will be deposited on the alveoli during a single shower event is described by a 272 Poisson distribution (eq. 5, (Beers, 2006)): 273 Ps(k;p;n) =(pn)k k!epn(5) The probability of deposition pcan be estimated as the product of the fraction of inhaled aerosols being 274 deposited on the lungsP iF(1) iF(2) i and the fraction of aerosol droplets containing Legionella (Fl= 275 nl=n). However, calculating pnyieldsDDas calculated in eq. 3. Equation 5 can therefore be simpli ed as 276 10 Figure 1: CFD results showing the temperature distribution superimposed on ow streamlines in the tank in the absence of ow. Notice the high temperatures >60C directly adjacent to the heating element. shown in eq. 6: 277 Ps(k;DD) =DDk k!eDD(6) Infection is dose-dependent. For a healthy individual, a minimum number of deposited Legionella microbes 278 kmin>10 may be required for infection. However, it is possible that immunocompromised individuals can 279 be infected by kmin>1. The probability of infection Pican be de ned in terms of kmin(eq. 7): 280 Pi(kmin;DD) =Pi(k>k min;DD) =X kkminPs(k;DD) (7) Finally,Piis the probability of infection during a single shower event. Assuming a person showers every 281 day, the probability of being infected over a period of one year Pyris given by eq. 8: 282 Pyr(kmin;DD) = 1 (1Pi(kmin;DD))365(8) 11 Figure 2: Sterilization model results showing a minimum microbial reduction of 80 % after 60 min. 3. Results and discussion 283 3.1. CFD model results 284 Planktonic cells may also grow in the tank, but would be subjected to varying temperatures as the 285 microbes circulate through the tank. The velocity eld generated by natural convection in the tank is shown 286 in Fig. 1. The streamlines shown in Fig. 1 are coloured according to the local temperature. Assuming 287 Legionella cells will follow these streamlines, a temporal temperature pro le can be generated for individual 288 cells and used to predict cellular growth or sterilization as per eq. 1. These growth pro les (2) show that 289 planktonic cells are exposed to high temperatures at an adequate frequency to ensure a decimal reduction 290 time of approximately 85 min. The likelihood of planktonic Legionella surviving in a heater with the element 291 turned on is low. 292 A coarse mesh CFD simulation was used to determine the e ect of controlled heater scheduling on 293 planktonic Legionella survival. The temperature distribution in an tank was approximated over the course 294 of 24 hours, based on usage data obtained from controller eld units. The average temperature as a function 295 of time, in conjunction with eq. 1, was used to estimate the growth of planktonic Legionella in a controlled 296 heater. The results from the coarse mesh simulation con rm that the survival of planktonic Legionella is 297 unlikely during the course of an average day (data not shown). However, both the detailed- and the coarse- 298 mesh CFD results clearly show that the lower surfaces of the heater remain at temperatures below 45C, 299 creating an ideal environment for Legionella growth (Video provided in the Supplementary material). Thus, 300 it is likely that only bio lm-associated Legionella survive within a heater tank. 301 12 Figure 3: CFD results showing the temperature distribution superimposed on ow streamlines in the tank given a ow rate of 5 L/min. (A) All streamlines, (B) only streamlines associated with surface seed points that result in particles exiting the heater within 300 s. (A) -0.2 -0.1 0 0.1 0.2-0.2-0.100.10.2 40 45 50 55 60(B) -0.2 -0.1 0 0.1 0.2-0.2-0.100.10.2 40 45 50 55 60 Figure 4: Relates to section 3.1. Starting positions of particles exiting the heater within a typical shower event with surface temperatures generated during no ow conditions: (A) cross-section at inlet and heating element, (B) cross-section at outlet. The starting positions near the heater outlet correspond to a no ow temperature of 49C 13 The ow rate through a heater during a typical shower event is approximately 5 L =min. The cold water 302 entering the system causes the temperature to drop signi cantly. The uid dynamics are subject to both 303 forced- and natural-convection. 304 Given the low probability of survival for planktonic cells, special attention was given to surface adherent 305 cells which may detach and exit the tank within the timespan of a typical shower event. Figure 3 (A) shows the 306 streamlines generated by particles seeded on the tank inner wall (corresponding to cells present in bio lms), 307 while (B) is limited to particles which report to the outlet pipe within 5 min, taken as a representative time 308 for a shower event. While a large surface area of the heater remains at a temperature conducive to Legionella 309 growth, the surface area that allows cells to detach and exit the heater within the timespan of a shower 310 event is quite limited: cells detaching from other regions of the heater are typically entrapped in eddies 311 created by natural convection. Furthermore, heater surfaces corresponding to regions which could lead to 312 cells exiting the tank within a shower event are subjected to temperatures exceeding the optimal temperature 313 forLegionella growth under no ow conditions. These positions are shown in Fig. 4, superimposed on the 314 temperature distribution of the pertinent surfaces.. 315 It is improbable for surface adherent cells exposed to temperatures leading to optimal Legionella growth to 316 exit the heater within the timespan of a typical shower event. In light of these results, it can be concluded that 317 Legionella detected in plumbing systems are unlikely to originate in the heater, but rather in downstream 318 piping. The decreasing temperature in the pipes leading away from the heater will ensure the existence 319 of a thermally optimal region for Legionella growth. These bio lms will periodically be exposed to high 320 temperatures during usage events, which may lead to sterilization, if the outlet temperatures are high enough. 321 However, the average outlet temperature in schedule-controlled heaters are typically lower in comparison to 322 those on thermostat control only. The short exposure times to lower temperatures during usage events may 323 not be enough to sterilize bio lms in the piping system. These results are in line with a study of 452 hot 324 water systems in two cities in Germany, which showed that the relationship between Legionella proliferation 325 and piping systems as well as heater temperatures is statistically signi cant (Mathys et al., 2008), as well asa 326 more recent study showing preferential bio lm growth on copper piping (Buse et al., 2014). This is further 327 corroborated by eld measurements, as described below. 328 3.2. Biological results 329 The heating schedules that were in e ect at the time of taking the samples and the average temperature 330 of the hot samples are shown in Table 1 on page 16. 331 A recent review compared culturing and molecular quanti cation (Whiley and Taylor, 2016), indicating 332 that culturing techniques detect less than half of the Legionella quanti ed with genetic techniques but did 333 14 not mention the limitations of molecular techniques in environmental niches rich in PCR inhibitors such as 334 metal ions, which were a signi cant challenge in the particular water heater environment (Fig. A.3). Because 335 of these challenges in quanti cation standardization, there are few studies exploring the full arc of transfer 336 and infection, from thermal simulation of the environment, to quanti cation of pathogen loads, to infection 337 and public health. 338 Semi-quantitative assessment of Legionella prevalence was carried out by culturing direct heater samples, 339 and molecular techniques were employed for detection and quanti cation at point-of-use in the water distri- 340 bution system (household taps). The standard curve set up as described in the methods section produced a 341 strong linear correlation (Fig. A.5, R2= 0.97), and proved an accessible, robust (less than 5 percent variation 342 with a biological duplicate) technique for quanti cation, relying on the authors' thoroughly demonstrated 343 claim that the gene is a single copy gene highly speci c to L. pneumophila (Whiley and Taylor, 2016). The 344 clean tap water as an environmental source thus permitted the use of PCR-based techniques to easily assess 345 both the presence and concentrations of Legionella and L. pneumophila , respectively, in tap water sourced 346 from controlled and well-characterised heaters. 347 Qualitative culturing of the scrapings demonstrated the presence of Legionella within the heater. However, 348 even the selective media (BCYE) enriched for a plethora of organisms that were morphologically distinct from 349 one another, and the relative percentage of Legionella within these samples was low (3 :1 % of 62 isolates; 350 Figures A.6 and A.7), based on the semi-quantitative selective media. Legionella species were identi ed by 351 metabolic limitation on enriched BCYE media sans L-Cysteine, as Legionella are unique in their inability to 352 synthesize this amino acid, needing it to survive. Isolates were subsequently sequenced, using the 16S rDNA 353 to con rm genus identi cation. A heterogeneous community is critical for Legionella growth (EPA, 2001; 354 Surman et al., 1994; Winn, 1998; Ensminger, 2016; Kwaik et al., 1998). 355 Whilst the models and qualitative data from within heaters provide information about this niche, as well 356 as relative Legionella presence within the heaters, the true epidemiological impact lies in the infectious agents 357 that reach point-of-use in the water distribution system, that is, household taps. The analysis of cold water 358 (CT), hot water in the pipes prior to heating (HT1) and hot water running at maximum temperature from 359 the heater (HT2), showed that L. pneumophila predominated in the hot taps prior to taking the water to 360 maximum temperature (Table 1, columns 5-7). There were signi cant di erences between the means of the 361 HT1 group (water in hot taps, prior to heating) and both other groups ( p<0:05), as assessed with a 2-tailed 362 Student's t-Test with independent variances (CT and HT1, p = 0.022; CT and HT2, p = 0.226; HT1 and 363 HT2, p = 0.049). 364 The genus-speci c Legionella primers showed relatively ubiquitous presence in most of the samples (Table 365 1, columns 2-4), however the primers unique to L. pneumophila were more source-speci c (Table 1, columns 366 15 Table 1: PCR (Qualitative) results and qRT-PCR (Quantitative) cell count results from the water heaters. The presence of Legionella spp. was assessed qualitatively in cold taps (Cold: CT), hot taps prior to heating (Hot-Cold: HT1) and hot taps run at maximum temperature (Hot-Hot: HT2). The quanti cation of L. pneumophila was also carried out for each of these environments (columns 5-7). Heater Heating schedule Sample PCR (Qualitative) qRT-PC (Quantitative) no. temp (C) Legionella spp. Leg. pneum. (cells/ml) CT HT1 HT2 CT HT1 HT2 1 03:00 - 05:00;15:00 - 17:00 47 - + + 0 6 5 2 On (Thermostat) 42 + + + 0 7 0 3 04:00 - 07:00;16:00 - 19:00 45 + + + 0 7 0 4 02:00 - 06:00;15:00 - 20:00 46 - - + 0 0 2 5 18:00 - 21:00 44 + + + 0 10 0 5-7). This may be related to temperature (the less ubiquitous growth of L. pneumophila , or the growth of L. 367 pneumophila in the pipes at the lower temperatures between heating events) or to ow dynamics (an initial 368 sloughing event due to turbulence patterns as ow is initiated). The fact that the water stored in the pipes 369 between uses has a higher L. pneumophila presence may indicate the pipes, rather than the heater, as a niche 370 forL. pneumophila bio lm growth. 371 The cold tap showed no L. pneumophila during quanti cation, suggesting that the heater provides the 372 temperatures necessary to stimulate growth, either within the heater or in the distribution system directly 373 downstream of the heater. This supports reports of Legionella 's thermal preference (growth between 25C 374 and 42C (EPA, 2001; Fields et al., 2002), and is con rmation of elegant research by Piao et al. (2006), 375 which demonstrated that of 42 Legionella strains, L. pneumophila was most likely to form bio lms, and 376 bio lm formation was temperature dependent, promoted at temperatures between 35C and 47C. This 377 con rms the idea that the widely-reported ubiquity of Legionella might actually be species-dependent and 378 temperature-dependent. 379 3.3. Infection model results 380 The infection model in section 2.5 was used to assess whether the detected concentrations of Legionella 381 in the water supply may indeed lead to infections. The probability of infection occurring per year (calculated 382 using eq. 8) is dependent on the minimum deposited dose kminrequired for infection as well as the average 383 deposited dose DD per shower event. The original infection model estimated a minimum of 10 CFUs 384 for infection of otherwise healthy individuals (Schoen and Ashbolt, 2011). However, immunocompromised 385 individuals may su er infection if even a single CFU were to reach the lower alveolar region. The average 386 deposited dose DDcan be estimated based on previously determined parameters as well as the concentration 387 ofLegionella cells in the water supply (eqns. 3 and 4; Table A.2). 388 If only a single CFU would result in infection, there is a 19 % probability per year of Legionellosis occurring. 389 This probability decreases dramatically as the required dosage increases, with the probability of infection 390 16 becoming negligible even if kmin= 2. 391 The results of the probabilistic form of the previously developed infection model combined with biological 392 sampling results indicate that the probability of Legionellosis occurring in healthy individuals is negligible, 393 which explains the fact that the disease commonly appears as a pandemic associated with public spaces 394 which may be compromised. However, the probability of infection of immunocompromised individuals is 395 much higher. If a single Legionella CFU could lead to Legionellosis in an immunocompromised individual, 396 the probability of infection over a timespan of 10 years is approximately 88 %. These issues are of partic- 397 ular concern in low- to medium-income countries in light of the HIV/AIDS epidemic, and bear a striking 398 resemblance to the transition of latent to active tuberculosis: the relative risk of latent tuberculosis infections 399 progressing to the active stage is 10 to 110 times higher in patients with compromised immune systems (Ai 400 et al., 2016). 401 4. Conclusions 402 The combination of the CFD model- and biological-results presents a strong case for the growth of Le- 403 gionella in piping systems downstream of the water heater, although the infection model seems to indicate 404 that Legionellosis from single household plumbing systems is unlikely except in the case of immunocompro- 405 mised individuals. This work ts directly into the US EPA's identi ed research areas (EPA, 2001), trying to 406 understand the reservoirs for this pathogen, as well as the transfer of the pathogen to the user. 407 Within economically-challenged communities, the regulation of water heating cycles is necessary for nan- 408 cial reasons. The balance between the regulation of the heaters and the energy cost has also been explored, 409 however, the consideration of the post-heater distribution system has not been included in models. This 410 work highlights the connection between heating regimes and Legionella proliferation. A further suggestion 411 might be to explore distribution system materials that might prevent the spread of bio lms, if models can 412 demonstrate that sloughing events play a role in Legionella distribution (Piao et al., 2006; Murga et al., 2001; 413 Buse et al., 2014). 414 It must be added that, as with any pathogenic outbreak, the rst and most e ective point of resistance is 415 the human immune system. Where economically and practically possible, the health of the individual is more 416 e ective in preventing outbreaks than design or habits. However, in the low- to medium-income countries 417 context, there is already an extensive national nosocomial burden, in terms of economy, morbidity, mortality 418 and resources (Klevens et al., 2007; Pooran et al., 2013). Water distribution system design, water 419 heater regulation habits and e ective diagnosis all play a critical role in minimizing the burden of Le- 420 gionella outbreaks, as part of managing the AIDS/TB crisis. Thus, building and testing models to understand 421 17 and regulate these pathogenic niches can assist with the management of these nosocomial burdens, through 422 simple shifts in engineering and habits. 423 In summary, the Baas Becking phrase \Alles is overals; maar het milieu selekteert" (Everything is ev- 424 erywhere, but the environment selects) has been harnessed extensively in microbial ecology (De Wit and 425 Bouvier, 2006), and this robust principle is particularly applicable at the intersection of microbiology and 426 engineering. If we understand how the environment selects, we increase the possibility of manipulating it 427 through engineering and management to protect the most vulnerable and prevent the selection of pathogens 428 such as this genus. For instance, risk assessments based on temperature diagnostics for Legionella growth have 429 been developed by B edard et al. (2015), as well as elegant thermal regulation systems inspired by biomimicry 430 according to Altorkmany et al. (2017). Models such as the one developed in this study can further inform 431 such e orts. 432