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Development and validation of a drinking water temperature model in domestic drinking water supply systems
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... The analyses in this study were based on a plumbing configuration from a previous study on a typical Dutch terraced house. 34 The supply pipe from the distribution system was much shorter than typically found for homes in the United States, and the home had a tankless water heater. The house consisted of three stories, with a washing machine on the third floor, a bathroom with a toilet, shower, and sink on the second floor, and a kitchen with a tap and dishwater, and a bathroom with a toilet and tap on the first floor (Fig. 1). ...
... The house consisted of three stories, with a washing machine on the third floor, a bathroom with a toilet, shower, and sink on the second floor, and a kitchen with a tap and dishwater, and a bathroom with a toilet and tap on the first floor (Fig. 1). 34 Information about node location and elevation (Table S1 †), reservoir ( 35 are good for analyzing measured flows, SIMDEUM is a predictive, Monte Carlo model, accommodating seamlessly a statistical, stochastic analysis. By producing demands for each household occupant at each fixture, SIMDEUM allows for study of water use changes when the occupants or fixture types change at a household level. ...
... There is no mixing of mass between adjacent water parcels in the model, though mixing at junctions is complete and instantaneous. 42 To track the parcels of water 34 The lines correspond to the pipes of the system and the dots correspond to pipes junctions or fixtures. The water enters from the distribution system, pictured as a reservoir, with a water age of 0 hours. ...
Water age is often used as a proxy for water quality in drinking water systems, but the highly stochastic nature of water demands in premise plumbing systems, and nonlinear relationship...
... C) (Table S17). Elevated indoor air temperature may have influenced water quality at the basement fixture (Zlatanovic et al., 2017;Zlatanovi et al., 2017), particularly due to longer stagnation periods. Although the bacteria population from another green building (Rhoads et al., 2016) were greater than the present study and included PEX piping (no pipe type or age description was found), plumbing and water quality characteristics differed significantly. ...
Residential plumbing is critical for the health and safety of populations worldwide. A case study was conducted to understand fixture water use, drinking water quality and their possible link, in a newly plumbed residential green building. Water use and water quality were monitored at four in-building locations from September 2015 through December 2015. Once the home was fully inhabited average water stagnation periods were shortest at the 2nd floor hot fixture (90 percentile of 0.6-1.2 h). The maximum water stagnation time was 72.0 h. Bacteria and organic carbon levels increased inside the plumbing system compared to the municipal tap water entering the building. A greater amount of bacteria was detected in hot water samples (6-74,002 gene copy number/mL) compared to cold water (2-597 gene copy number/mL). This suggested that hot water plumbing promoted greater microbial growth. The basement fixture brass needle valve may have caused maximum Zn (5.9 mg/L), Fe (4.1 mg/L), and Pb (23 μg/L) levels compared to other fixture water samples (Zn ≤ 2.1 mg/L, Fe ≤ 0.5 mg/L and Pb ≤ 8 μg/L). At the basement fixture, where the least amount of water use events occurred (cold: 60-105, hot: 21-69 event/month) compared to the other fixtures in the building (cold: 145-856, hot: 326-2230 event/month), greater organic carbon, bacteria, and heavy metal levels were detected. Different fixture use patterns resulted in disparate water quality within a single-family home. The greatest drinking water quality changes were detected at the least frequently used fixture.
According to the Dutch Drinking Water Directive, the maximum temperature of drinking water should be 25 degrees C. Occasionally, samples at the tap exceed this limit. With climate change, this limit may be exceeded more often. This article describes a model that predicts water temperatures in drinking water distribution systems (DWDSs). Soil temperature is influenced by weather conditions including atmospheric temperature and radiation and environmental conditions such as the soil's thermal conductivity and heat capacity. DWDS water approaches soil temperature at a rate that depends on flow velocity and the main's heat conductivity. In practice, the heating time required for drinking water to reach the soil temperature is shorter than the residence time in the DWDS. Two practical examples confirm the hypothesis that soil temperature predicts water temperature in the DWDS.
Procedures that may be used to evaluate the operational performance of a wide spectrum of geophysical models are introduced. Primarily using a complementary set of difference measures, both model acccuracy and precision can be meaningfully estimated, regardless of whether the model predicitons are manifested as scalars, directions, or vectors. It is additionally suggested that the reliability of the accuracy and precision measures can be determined from bootstrap estimates of confidence and significance. Recommended procedures are illustrated with a comparative evaluation of two models that estimate wind velocity over the South Atlantic Bight.
The end-use model SIMDEUM for residential water demand has been extended to incorporate nonresidential water demand. The model was developed to predict water-demand patterns with a small timescale (1 s) and small spatial scale (at the water meter connection). The end-use model is based on statistical information about users and end uses: data on occupancy; the frequency of use; duration and flow per water-use event; and the occurrence over the day of different end uses, such as flushing the toilet, doing the laundry, and washing hands. The model follows a modular approach, in that each type of building is composed of functional rooms, such as lodgings, restaurants, and conference rooms. A functional room is characterized by its typical users and water-using appliances. With this approach, nonresidential buildings' water-demand patterns over the day can be simulated. The simulation results for an office building, a hotel, and a nursing home were compared to measured water-demand patterns with regard to attributes such as peak flow and daily total water use, as well as the shape of the pattern. The simulation results show a good correspondence to measured water demands. The end-use model is based on independent statistical information and not on flow measurements. The input parameters are available before any information on annual or daily water use is available; the parameters are not fitted on flow measurements. Therefore, the model is transferable to a diverse range of nonresidential water-demand types. The model can be applied in the design stage (prebuild), in scenario studies, and in distribution network models. DOI: 10.1061/(ASCE)WR.1943-5452.0000146. (C) 2011 American Society of Civil Engineers.
An 18-month survey of 31 water systems in North America was conducted to determine the factors that contribute to the occurrence of coliform bacteria in drinking water. The survey included analysis of assimilable organic carbon (AOC), coliforms, disinfectant residuals, and operational parameters. Coliform bacteria were detected in 27.8% of the 2-week sampling periods and were associated with the following factors: filtration, temperature, disinfectant type and disinfectant level, AOC level, corrosion control, and operational characteristics. Four systems in the study that used unfiltered surface water accounted for 26.6% of the total number of bacterial samples collected but 64.3% (1,013 of 1,576) of the positive coliform samples. The occurrence of coliform bacteria was significantly higher when water temperatures were > 15 degrees C. For filtered systems that used free chlorine, 0.97% of 33,196 samples contained coliform bacteria, while 0.51% of 35,159 samples from chloraminated systems contained coliform bacteria. The average density of coliform bacteria was 35 times higher in free-chlorinated systems than in chloraminated water (0.60 CFU/100 ml for free-chlorinated water compared with 0.017 CFU/100 ml for chloraminated water). Systems that maintained dead-end free chlorine levels of < 0.2 mg/liter or monochloramine levels of < 0.5 mg/liter had substantially more coliform occurrences than systems that maintained higher disinfectant residuals. Free-chlorinated systems with AOC levels greater than 100 micrograms/liter had 82% more coliform-positive samples and 19 times higher coliform levels than free-chlorinated systems with average AOC levels less than 99 micrograms/liter. Systems that maintained a phosphate-based corrosion inhibitor and limited the amount of unlined cast iron pipe had fewer coliform bacteria. Several operational characteristics of the treatment process or the distribution system were also associated with increased rates of coliform occurrence. The study concludes that the occurrence of coliform bacteria within a distribution system is dependent upon a complex interaction of chemical, physical, operational, and engineering parameters. No one factor could account for all of the coliform occurrences, and one must consider all of the parameters described above in devising a solution to the regrowth problem.
Modeling of water quality in water distribution systems can be improved significantly when using stochastic demands. The stochastic demand pattern generator SIMDEUM is based on stochastic information on end users as retrieved from surveys. Stochastic demand patterns from SIMDEUM were applied in a small network model of 550 demand nodes in a residential area in the Netherlands. An EPANET based network solver was used to assess velocities and travel times. The actual measurements agree well with the simulations.
In Europe, the Energy Performance of Buildings Directive (EPBD) provides for compulsory energy performance certification (labelling) for all existing dwellings. In the Netherlands, a labelling scheme was introduced in 2008. Certificates contain the energy label of the dwelling and corresponding theoretical gas and electricity consumption, calculated based on the dwellings physical characteristics, its heating, ventilation and cooling systems and standard use characteristics. This paper reports on a large-scale study of around 200,000 dwellings comparing labels and theoretical energy use with data on actual energy use. The study shows that dwellings with a low energy label actually consume much less energy than predicted by the label, but on the other hand, energy-efficient dwellings consume more than predicted. In practice, policy targets are set according to the theoretical rather than the actual consumptions of the building stock. In line with identified discrepancies, the study shows that whereas most energy reduction targets can be met according to the theoretical energy consumption of the dwelling stock, the future actual energy reduction potential is much lower and fails to meet most of the current energy reduction targets.
Effects of plumbing-specific installation factors on brass corrosion were investigated in a series of pipe-loop experiments. Increased flow velocity increased corrosion rates, but did not affect corrosion type. The presence of copper tubing in the plumbing system increased selectivity of brass corrosion for zinc, unless a galvanic connection was made between copper and brass, in which case corrosion became more uniform and was accelerated. Plastic tubing allowed oxidant (i.e., free chlorine) to persist in water, increasing brass exposure. Additionally, hot water significantly increased lead leaching from brass. These findings may inform future investigations into brass corrosion issues and plumbing designs.
Samples were taken from 72 high-rise apartment suites (6 suites in 12 individual high-rise towers) and 60 single-family houses located within the Greater Vancouver Regional District. The influence of the following factors on trace metal concentrations in 1-L first-flush drinking water samples and 'running' hot water samples was investigated: building height, location, plumbing age, type of plumbing, and type of building. Results of this survey show that with the exception of building height, all factors had a correlation with one or more of the trace metals investigated. The trace metals examined were lead, copper, iron, and zinc. Lead was influenced primarily by building type, copper by plumbing age and type of plumbing, and iron by location. Elevated lead levels were associated with high-rise samples. New copper plumbing systems resulted in high copper levels. Highest iron levels in the drinking water were measured in the East Vancouver location. Zinc did not show a distinct correlation with any of the factors investigated. Brass faucets were the primary source of zinc in tap water. They also contributed substantially to the lead detected in the 1-L first-flush sample. Metal concentrations measured in the high-rise and house samples were compared with the U.S. Environmental Protection Agency's (USEPA) maximum contaminant levels (MCLs) and the proposed 'no-action' level for lead. In high-rise samples, the 0.01 mg/L 'no-action' level proposed for lead was exceeded in 43% of the samples, and 62% of the samples exceeded the current 1.0 mg/L MCL standard for copper. In single-family house samples, these values were 47% and 73%, respectively. The average lead concentrations were 0.020 mg/L for all high-rise samples and 0.013 mg/L for house samples.
The performance of two popular watershed scale simulation models — HSPF and SWAT — were evaluated for simulating the hydrology of the 5,568 km2 Iroquois River watershed in Illinois and Indiana. This large, tile drained agricultural watershed provides distinctly different conditions for model comparison in contrast to previous studies. Both models were calibrated for a nine-year period (1987 through 1995) and verified using an independent 15-year period (1972 through 1986) by comparing simulated and observed daily, monthly, and annual streamflow. The characteristics of simulated flows from both models are mostly similar to each other and to observed flows, particularly for the calibration results. SWAT predicts flows slightly better than HSPF for the verification period, with the primary advantage being better simulation of low flows. A noticeable difference in the models' hydrologic simulation relates to the estimation of potential evapotranspiration (PET). Comparatively low PET values provided as input to HSPF from the BASINS 3.0 database may be a factor in HSPF's overestimation of low flows. Another factor affecting baseflow simulation is the presence of tile drains in the watershed. HSPF parameters can be adjusted to indirectly account for the faster subsurface flow associated with tile drains, but there is no specific tile drainage component in HSPF as there is in SWAT. Continued comparative studies such as this, under a variety of hydrologic conditions and watershed scales, provide needed guidance to potential users in model selection and application.
Soft, low alkalinity drinking waters tend to cause relatively high copper corrosion by-product release in plumbing systems. Long-term tests (6-8 months) in a synthetic, microbially stable soft tap water confirmed that lower pHs and higher temperatures increased copper release to water. Soluble copper release increased at lower temperature and lower pH. Low levels of free chlorine (0.7 mg/L) slightly increased copper release at pH 9.5, in marked contrast to the dramatic reductions in copper release that have been observed in soft waters in which Type III pitting corrosion is occurring. Gum xanthan and sodium alginate produced a microbially unstable water that reduced the pH and DO during stagnation in pipes--these indirect effects far outweighed their possible role in chelation or other modes of direct attack on copper surfaces.
This study was aimed at investigating the further formation of trihalomethanes (THM) in drinking water when it is being heated at home. Experimental results confirmed that there could be a further significant formation during heating drinking water. Overall variation of THM concentration in water during the heating depended on its formation rate and volatilisation rate. The rate of THM formation was mainly affected by not only both the concentrations of THM precursors, such as humic substances and residual chlorine, but also temperature and reaction time. The rate of THM volatilisation depended on water temperature and the extent of water agitation in the water heaters. It was concluded that the THM residuals in drinking water could be effectively eliminated to a very low level, if the boiling of water was kept in the open for few minutes after water boiling.
Water quality within water distribution system may vary with both location and time. Water quality models are used to predict the spatial and temporal variation of water quality throughout water system. A model of residual chlorine decay in water pipe has been developed, given the consumption of chlorine in reactions with chemicals in bulk water, bio-films on pipe wall, in corrosion process, and the mass transport of chlorine from bulk water to pipe wall. Analytical methods of the flow path from water sources to the observed point and the water age of every observed node were proposed. Model is used to predict the decay of residual chlorine in an actual distribution system. Good agreement between calculated and measured values was obtained.
The first generation of mechanistic models of bacterial regrowth in distribution systems (DS) provides insight into cause-and-effect relationships. However, the state of knowledge about the processes included in these models is insufficient to warrant deterministic predictions. Even if the process descriptions are reasonable, the uncertainty in values of key system constants limits predictions of bacterial growth. A new mechanistic model was developed to incorporate the accepted knowledge of physical, chemical, and biological processes with the hydraulic features in order to capture the unsteady state behavior of the DS. Sensitivitytesting showed that the extent of bacterial regrowth was affected mainly by the rate constants for chlorine decay reactions in bulk water and on the pipe wall and by the maximum growth rate constant of attached bacteria. A simple hypothetical network was used to evaluate the effects of uncertainty in these three system constants by running 100 Monte Carlo simulations. Cumulative probability plots showed a wide range of predictions for concentrations of bacteria and chlorine in bulk water at various nodes in the DS. The magnitude of these concentrations and the range of values were greatly affected by water residence time to each node. Once the chlorine residual is depleted, bacterial growth is mainly influenced by the amount of substrate available. However, high values of the coefficients for the maximum growth rate of attached bacteria, the chlorine decay in bulk water, and the chlorine decay by wall reaction did not necessarily lead to the maximum bacterial growth at a given location.
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