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As part of the Eskom rebate programme, energy-efficient hot water systems such as solar water heaters (low pressure), heat pumps and energy-efficient showerheads were rolled out to the public as a measure to conserve and save energy. There has been a concern that these systems may not reach the required high temperatures, especially during winter, and, as a result of this, Acanthamoeba and its associated bacteria such as Legionella pneumophila, Pseudomonas aeruginosa and nontuberculous mycobacteria could flourish within these systems causing a potential health risk to consumers. This study examined the relationship between Acanthamoeba and its associated bacteria at different temperature ranges. A total of 156 water (69) and biofilm samples (87) were collected from a solar water heater, heat pump, geyser and showerheads and examined for these organisms using amoebal enrichment and molecular techniques. Amoeba could be cultivated from 45 (65.2%) of the water samples and 56 (64.4%) of the biofilm samples. The study confirmed the presence of Legionella pneumophila, Pseudomonas aeruginosa and nontuberculous mycobacteria in the hot water systems at both of the simulated winter (20°C to 30°C) and higher summer (40°C to 55°C) temperatures as well as the control system. There was a significant positive correlation between the presence of Acanthamoeba and the presence of Pseudomonas. Based on this association it is suggested that Pseudomonas aeruginosa could be investigated as an indicator organism for the presence of Acanthamoeba and opportunistic pathogens. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology, various intelligent algorithms are applied in building energy system optimization. Deep reinforcement learning (DRL) algorithms have garnered substantial attention from researchers. However, all current DRL-based control methods suffer from two problems. The first problem is that current DRL-based methods require an offline training process and therefore cannot be directly applied to houses. The offline training not only increases the waiting process for occupants but also creates the risk of degrading the occupant experience. The second problem is that current DRL-based methods do not continuously learn online. As a result of the second problem, the control methods are unable to consistently execute the optimal policy in the face of changing hot water demand habits. In this study, a fast-apply deep autoregressive recurrent proximal policy optimization (FDPPO) for controlling hot water systems is proposed. In practical systems, the FDPPO can be applied directly to houses without the need for occupants to wait. The proposed FDPPO can adapt to hot water demands that change over time through continuous online learning. In addition, the proposed FDPPO that applies the model-free reinforcement learning approach does not require modeling complex water heater models. The proposed FDPPO is evaluated by actual hot water demand data of over 55 weeks collected from two typical houses. The results show that the proposed FDPPO can save between 41.24% and 59.01% of energy consumption, all the while ensuring occupant comfort and safeguarding water hygiene. • Two-point control, deep autoregressive and deep reinforcement learning are combined. • Proposed method enables direct application to house without offline training process. • Continuously changing hot water usage behaviours and water heater system are adapted. • Feature pre-extraction improves prediction accuracy and reduces training requirement. • Achieves energy savings of 41.24% to 59.01% while ensuring water comfort and hygiene. [ABSTRACT FROM AUTHOR]

The presence of Waddlia chondrophila has been related to respiratory tract infections and human and animal fetal death. Although several sources of infection have been suggested, the actual source remains unknown and limited information exists on the prevalence of W. chondrophila in the environment. This pathogen has been previously detected in well water but its presence has not been confirmed in water networks. Since these bacteria have been detected in water reservoirs, it has been hypothesized that they can access artificial water systems and survive until they find appropriate conditions to proliferate. In this work, their presence in water samples from 19 nondomestic water networks was tested by quantitative polymerase chain reaction (qPCR). Approximately half of the networks (47%) were positive for W. chondrophila and the overall results revealed 20% positive samples (12/59). Furthermore, most of the samples showed low concentrations of the pathogen (<200 genomic units/L). This finding demonstrates that W. chondrophila can colonize some water networks. Therefore, they must be considered as potential infection sources in future epidemiological studies. [ABSTRACT FROM AUTHOR]

Aims: To investigate the prevalence of culturable and nonculturable Legionella species in hot water systems of public buildings in Japan and assess the risk factors associated with Legionella contamination in hot water systems. Methods and Results: Legionella species were detected by conventional culture and molecular methods in 130 water samples collected from 40 buildings. A total of 26 (20·0%) water samples from 17 (42·5%) buildings were positive by culture, qualitative PCR or both methods: Legionella pneumophila and Leg. anisa were detected in four samples by a culture method, whereas 23 samples were positive by qualitative PCR, with the presence of various Legionella species confirmed by sequencing. Of these 23 samples, bacterial counts were quantifiable in 21 by real-time PCR (from 1·7 × 105 to 2·6 × 1011 cells per litre). Phylogenetic analysis of amplified partial 16S rRNA gene showed close relations to various species of Legionella, including Leg. anisa and Leg. micdadei, all of which have been associated with respiratory diseases or increased antibody titres in human sera. Assessment of risk factors showed that turbidity, free chlorine concentration, iron concentration and heterotrophic plate count (HPC) were significantly associated with Legionella contamination ( P < 0·05). Conclusions: Contamination of hot water systems of public buildings with culturable and nonculturable Legionella species may be a potential risk factor for Legionella infection in Japan. Adequate levels of chlorine, low levels of iron and HPC are important maintenance measures in the reduction of Legionella contamination in hot water systems. Significance and Impact of the Study: More than 40% of hot water systems in the Japanese public buildings examined were contaminated by not only culturable Leg. pneumophila and Leg. anisa but also by nonculturable pathogenic species. To our knowledge, this is the first report of both culturable and nonculturable Legionella contamination in hot water systems of public buildings in Japan. [ABSTRACT FROM AUTHOR]

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