What is the Tap Score™ Algorithm?

The complete guide to how your Tap Score is calculated, with an in-depth look at parameters and the back end algorithm that makes our water testing tick. Tap Score Algorithm Vault

Tap Score is a water quality score designed to give you a straightforward analysis of your water. 

  • A Tap Score ranges from 1 to 99.
  • A score of 1 is the worst outcome,
  • Scoring a 99 is the best possible outcome.

    Developed by SimpleLab, Tap Score is based on an algorithm to evaluate potential health, aesthetic, and plumbing effects of your laboratory-submitted water sample.

    How Is a Tap Score Calculated?

    Tap Score is a weighted average, or composite, of three subscores

    • Health: how your drinking water directly impacts your body over time
    • Aesthetic: noticeable characteristics of your water (taste, smell, color) 
    • Pipe: effects of your water on your plumbing infrastructure 

    In order to calculate a Tap Score and its subscores, SimpleLab uses an algorithm in conjunction with a database full of thousands of parameters (water quality contaminants and properties) and their benchmarks (acceptable thresholds for each parameter). 

    • Parameters and their benchmarks were selected with the express purpose of providing the most comprehensive look at how drinking water impacts health, aesthetics, and plumbing.
    • The Tap Score algorithm was developed to analyze and assess as many parameters as possible, standardizing and organizing the results by relevance to each individual subscore. 
    • Finally, using standard mathematical aggregation methods, the findings of each subscore are averaged—with a priority weight on potential health risks—into the  final Tap Score.  

    This produces easy-to-interpret scores along with their implications:


    How is Tap Score calculated? Algorithm Overview


    Interested in better understanding the ins-and-outs of the Tap Score scoring system?

    Learn more about our methodology here. 

    Tap Score ™Algorithm: An In-Depth Guide

    Below, we present a step-by-step journey through the Tap Score scoring algorithm. 

        • What Is Tap Score?
        • How Is a Tap Score Calculated?
        • Tap Score Basics
          • What Is a Subscore?
          • What Is a Parameter Score?
          • Subscores At A Glance​​
          • What Is a Tap Score?
        • The Lowdown on Parameter Selection and Benchmarks
          • Parameter Selection
          • Understanding Parameter Benchmarks
          • Assigning Values to Parameter Benchmarks
        • The Algorithm Explained
          • Calculating Parameter Scores
          • Calculating Subscores
          • Calculating the Tap Score
        • Your Feedback
        • References
        • Appendix A
          • Essential Parameters
        • Appendix B
          • B1 Health Benchmark Data Sources
          • B2 Aesthetic Benchmark Data Sources
          • B3 Pipe Benchmark Data Sources

    What Is a Subscore?

    Tap Score evaluates three essential features of your drinking water into individual categories, or subscores, that evaluate how drinking water:

    1. might impact you when you drink it, or health;
    2. might look, feel, smell, or taste differently—and why, or aesthetics; and
    3. affects your plumbing, or pipe

    The three subscores are evaluated independently across a range of parameters, or water quality contaminants and characteristics that impact water quality. Parameters include properties of water (like hardness) and chemical (e.g. arsenic), biological (e.g. E. coli), and radiological (e.g. uranium) contaminants. Not every Tap Score includes all of these parameters–it depends on what was tested in the sample.

    Each of the three subscores are an aggregation of underlying parameter scores.

    What Is a Parameter Score?

    Within each subscore, every parameter measured in your water is evaluated against a set of benchmarks, or thresholds of acceptability—thresholds that are determined by the subscore’s functional priorities, e.g. Can it hurt my health? Why does it smell bad? Will my plumbing corrode and leach substances that can hurt me? 

    This evaluation results in a parameter score for each parameter relevant to (and grouped within) a given subscore. 

    • The parameter score is calculated with an algorithm that assigns  a standardized parameter score to each result on your report. 
    • A single parameter can have up to three parameter scores—health, aesthetic, and/or pipe—if it is relevant to more than one subscore category. 
    • A parameter may have no known impact on health, aesthetics, or pipe; in this case it receives no parameter score.

    Once all parameters with available data receive a parameter score, they are aggregated to create the final subscore.

    Subscores At A Glance​​


    The health subscore reveals the extent to which a water sample may have negative health impacts. Each parameter within this subscore has established health-based benchmarks that are used by public health agencies to keep the public safe. 

    • If we detect a contaminant at a level which exceeds the established benchmark(s), it reduces the overall health subscore.


    The aesthetic subscore evaluates parameters that may negatively impact the way you experience your water on a purely sensory level. We’re talking here about odors, colors, off-tastes, or even how it might feel to the touch. 

    • If we detect a parameter at a level that exceeds established benchmark(s) for aesthetic effects, it reduces the overall aesthetic subscore.


    The pipe subscore evaluates parameters believed to impact your plumbing, fixtures, and appliances, either directly or indirectly. Less immediate but no less important than the previous two, this subscore helps you understand the relationship between water quality and your plumbing system’s lifespan.

    • Corrosion and excessive scale build-up play major roles in the pipe subscore. Corrosion can result in the release of harmful metals (like lead), whereas too much scale reduces water flow and shortens the lifespan of household appliances.
    • If we detect a parameter at a level that indicates potential corrosion or potential excessive scale, it reduces the overall pipe subscore.

    Note: For the health subscore, Tap Score uses benchmarks which are publicly available from various health agencies throughout the US and reflect the latest academic and public health findings for drinking water health and safety guidelines. For the aesthetic and pipe subscores, benchmarks reflect the latest findings from both academic and engineering research into aesthetic and pipe impacts on drinking water quality and plumbing infrastructure.

    What Is a Tap Score?

    Once every parameter score is calculated and assigned to its relevant subscore (health, aesthetic, pipe), a sample can be assigned a final Tap Score. 

    • Tap Score is a weighted average of the three subscores—with the health subscore weighted the highest.

    The Lowdown on Parameter Selection and Benchmarks

    We created the SimpleLab Contaminant Database, a database of thousands of parameters and benchmarks found in academic literature and in use by public health agencies, engineering and water treatment industries, to calculate a Tap Score in the most comprehensive way possible.

    • In the health subscore, parameters are all contaminants. In aesthetics, they’re both contaminants and water quality properties. In pipe, parameters are all water quality properties. 

    The benchmarks we use are a combination of those gathered from the expert sources listed in Appendix B, including the SimpleLab Recommendation (SLR). SLR is the minimum available benchmark used by health agencies for a given parameter. This minimum available benchmark reflects the most conservative evaluation of water quality parameters.

    Parameter Selection

    A parameter is a broad term which can include any of the following: chemical concentrations, microbial populations, radiological activities, physical parameters, or observable features. Some parameters are measured in units of concentration, others are measured in standard units, such as the log-scale of pH. 


    The primary parameters included in this subscore represent the various classes of contaminants, and depending on your test can include:

    • microbial (e.g. bacteria and other pathogens)
    • inorganics (e.g. nitrate, arsenic, fluoride, and trace metals) 
    • organics (e.g. disinfection by-products, pesticides, and PFAS)
    • radiological species (e.g. uranium, radon, gross alpha/beta activity)


    Parameters included in this subscore contain reputable sources to indicate both the effect(s) and associated benchmark(s) regarding: 

    • water taste (e.g. chlorine-like, metallic) 
    • color (e.g. reddish-brown, cloudy or milky) 
    • texture (e.g. slicker than usual, slimier than usual) 
    • odor (e.g. rotten eggs, fishy) 

    Note: We also included parameters in the aesthetic subscore that have been linked to benign medical/cosmetic outcomes (i.e., discoloration of the eyes or teeth). 

    In addition, the sensitivity of different sectors of the population to any particular parameter can vary quite drastically (i.e., the most sensitive portion of the population will detect the odor of a compound at a much lower concentration when compared with the least sensitive portion). In our database, we use benchmarks for different sensitivities where the information is available. (More on that to come.)


    In this subscore, there are far more unknown variables to take into account than in either of the previous subscores, such as the age of the plumbing, the exact pipe/fixture materials, the use and dosage of corrosion inhibitors at the treatment plant, the temperature of the water, and the interactions of all the water quality parameters themselves.

    To protect pipes and fixtures, it is very important to prevent both corrosion and excessive scaling (the buildup of calcium carbonate scale inside pipes and appliances). The following parameters are helpful indicators of corrosion and/or excessive scaling: 

    • pH: measures the acidity of water, where:
      • pH <6.5 is corrosive, damaging to both metals and plastics 
      • pH >10 impacts the stability of corrosion by-products
    • Alkalinity: measures water’s ability to neutralize acid to indicate potential corrosivity
    • Hardness: a measure of dissolved calcium and magnesium in water to indicate excessive scaling
    • Langelier Saturation Index (LSI): a calculation using alkalinity, pH, calcium, concentration, temperature, and total dissolved solids to indicate excessive scale
    • Chloride-to-Sulfate Mass Ratio (CSMR): a ratio of chloride to sulfate concentration, to indicate corrosivity, as sulfate is thought to have a protective effect in pipes and chloride a corrosive effect

    Note: We determined the relevant parameters for each subscore by referring to reputable databases of public health, engineering, water treatment, and academic benchmarks. Some parameters have no available health research, and therefore these parameters cannot be included in a score. 

    To receive a Tap Score, a water sample must include a minimum group of parameters which we call the essential parameters (Appendix A). Parameters included in addition to the essential parameters will also be included in the calculation of a sample’s Tap Score if those parameters impact health, aesthetics and/or pipe health. 

    Understanding Parameter Benchmarks


    Health effects from drinking contaminated water depend on variables such as: concentration, exposure length, and an individual’s sensitivity/susceptibility. Because these variables are impossible to identify for every individual person and compound, toxicologists and public-health practitioners rely on lab-based toxicity assessments and epidemiological studies to derive their benchmarks. 

    Health benchmarks help us know the concentration, more or less, at which a parameter likely becomes harmful, as well as what those harmful effects may lead to (i.e. their health endpoints). 

    These health endpoints are grouped as cancer or noncancer causing, with noncancer endpoints including harm to any body system: nervous, cardiovascular, digestive, endocrine, lymphatic, immune, etc.

    • Benchmarks for cancer endpoints are calculated by estimating the concentration of a parameter associated with a specific risk of cancer in the population over the course of a lifetime, usually from a 1 in 10,000 to a 1 in 1,000,000 risk of cancer. 
    • Benchmarks for noncancer endpoints are calculated based on the length of exposure to a compound—e.g. from an acute exposure to a lifetime exposure.


    Because the aesthetic impacts of consuming contaminated water depend on concentration and an individual’s sensitivity (rather than a toxicological effect), benchmarks for the aesthetic subscore are typically derived from observational studies. 

    Using sensory tests (e.g. taste tests, smell tests, etc.) a panel of individuals is asked to assess samples with varying concentrations or dilutions of a parameter (Lin et al., 2019). The resulting benchmarks help suggest the point at which a parameter’s aesthetic impacts can be detected by groups of people with varying sensitivity.


    Benchmarks for pipe-based parameters are based primarily on operational and engineering knowledge (with some support from academic publications) about the acceptable range of values for specific parameters. 

    Assigning Values to Parameter Benchmarks

    Once we have gathered and sorted through the available benchmarks for every parameter, up to two benchmark values are assigned to each parameter for scoring. As most parameters have vastly different units of measurement and ranges of values, benchmarks allow us to incorporate the results of your water quality report into the parameter score algorithm.


    Where data permits, we use two benchmarks to evaluate health parameters:

    • The minimum available benchmark is assigned as the SimpleLab Recommendation (SLR)
    • The maximum available benchmark is assigned as the Maximum Benchmark (Max Benchmark)

    SLR values reflect the most conservative of the health-protective benchmarks for a given parameter. Max Benchmark exposure durations span from subchronic (>30 days) to long-term exposures (e.g. 70 years).

    Note: When selecting minimum and maximum benchmarks, we exclude those benchmarks which reflect health effects from acute or short-term (<30 days) exposures. Acute and short-term health effects typically reflect high doses of exposure, which are unlikely for the majority of drinking water scenarios. 

    Many parameters only have one benchmark available. Often this is due to limited research or lack of consensus on the concentration at which negative effects occur. In these cases, the available benchmark receives our SLR designation, and the Max Benchmark is calculated using the following approach:

    1. The ratio of Max Benchmark/SLR is calculated for all health parameters with 2 benchmarks
    2. The median ratio of Max Benchmark/SLR is selected as the multiplier
    3. The multiplier is then multiplied by the SLR of the parameter with only one threshold to calculate the Max Benchmark


    As in the health subscore, two benchmarks are selected to evaluate aesthetic parameters: the SLR and the Max Benchmark. For each aesthetic parameter, benchmarks are sorted by concentration across endpoints (taste, smell, etc.) and the maximum (Max Benchmark) and minimum (SLR) benchmarks are chosen. 

    Where only one benchmark exists, the same approach as the health subscore (see above) is used.


    Pipe parameters may be impactful across a range of benchmarks, below or above a given benchmark. Acceptable ranges for the five pipe parameters are shown in Table 1 below:

     Acceptable Range Benchmarks For Pipe Parameters

    The Algorithm Explained

    Once every parameter’s benchmarks have a minimum and maximum value assigned in the form of the SLR and Max Benchmark, we can begin scoring individual parameters using the parameter score algorithms.

    Calculating Parameter Scores

    Each parameter has its own equation to relate measured concentrations to potential effects, which in turn yields a result from 1 to 99.

    The functions developed, called parametrization functions, are explained below.


    The equation used to calculate health parameter scores works by comparing the water quality concentration value of the parameter to its assigned benchmarks: the minimum available health benchmark (SLR), the maximum value available (Max Benchmark) for any given parameter, and a floor value (0). The benchmarks and corresponding parameter scores are shown in Table 2.

    • The floor value is always set at zero for mathematical application across parameters with different limits of detection (LOD). Each parameter’s measurement method will have an LOD that is greater than zero, so it’s not technically possible to determine if a parameter is truly absent from the sample. To deal with this ambiguity, we’ve assigned a score of 99 to any parameter that is not detected.

    If a health parameter is detected between the floor value and the SLR, the score is linearly interpolated between 99 and 75. If a health parameter is detected between the SLR and the Max Benchmark, the score is linearly interpolated between 75 and 1 (see Figure 1). A 75 was chosen to reflect a low, but not “failing” grade, because the SLR is typically a point at which no health effect is observed. All concentrations detected above the Maximum Benchmark receive a parameter score of 1. 

    • This linear approach reflects the fact that an increase in concentration correlates directly with an increase in the likelihood of adverse health effects.
    Tap Score calculation
    Tap Score Benchmarks


    For a given concentration of a parameter, the health parameter score is calculated as follows in Eqn 1 for assigning parameter scores:

    Equation 1


    Eqn 1 can be applied generally to any health parameter while being unique to each parameter because every benchmark is parameter-specific.

    • There are cases where a parameter’s SLR is less than its LOD (lead and arsenic, for example). The equation still applies. The difference is that once this parameter is detected, it is already at a score just at or below 75.
    • Various infectious microbial parameters are measured in terms of presence/absence, in which case a score of zero is assigned for presence, and a score of 99 is assigned for absence.


    Essentially, the aesthetic algorithm works the same way as the health algorithm. The major difference here is that detections up to the SLR retain a score of 99. After a parameter reaches the SLR, there is a linear drop to 1 at the Maximum Benchmark (Figure 2). For parameter concentrations greater than the SLR, Eqn 1 can be used to calculate the parameter score.

    Figure 2

    The aesthetic SLR is effectively a “sensory detection limit” for an adverse aesthetic experience. Because aesthetic exposures have no cumulative effects, unlike health exposures, we use a plateau at 99 from the floor value to the SLR; the aesthetic outcome (e.g. the off taste, odor, or color) is not perceived until the SLR is reached.

    • pH works a little differently. The ideal pH for drinking water is a range that determines what’s acceptable, rather than a series of benchmarks indicating a worse score for increasing concentrations. If a parameter is out of the acceptable range (6.5-8.5), the parameter score is a 1 (Figure 3).
    Figure 3


    If you recall from Section 2, the pipe subscore is calculated by evaluating the following five parameters: pH, alkalinity, hardness, Langelier Saturation Index (LSI), and Chloride-to-Sulfate Mass Ratio (CSMR).

    pH is parameterized in the same way as in the aesthetic subscore (above), although the upper and lower limits reflect a pipe-relevant pH range.

    Hardness and LSI are parameterized in the same way as parameters in the aesthetic subscore (above).

      Alkalinity also functions similarly to the parameters in the aesthetic subscore. In this case, however, alkalinity values greater than the SLR are acceptable and receive a score of 99 (Figure 4). Scores for values below the SLR drop linearly from 99 to 1 as calculated using Eqn 1.

      Figure 4 function to assign parameter score for alkalinity
      CSMR parameterization is unique because its parameter score depends on the concentration of another parameter (Figure 5). Using a simple set of heuristics in addition to the sample’s alkalinity level, we are able to determine the severity of impact.
      Function to assign parameter score to CSMR


      CSMR’s parameter score stays at a 99 until the ratio of Chloride to Sulfate concentration is 1:5, or 0.2—the threshold below which CSMR is of no concern. Between a CSMR of 0.2 and 0.5, the parameter score decreases linearly to a score of 62.25. If the CSMR exceeds 0.5, there are two possibilities for its parameter score. First, if alkalinity measured is above 50 mg/L, the parameter score stays the same, at 62.25, to reflect that the water is likely buffered against corrosion indicated by a high CSMR. Second, if alkalinity measured is below 50 mg/L, the parameter score decreases linearly to 1 between a CSMR of 0.5 and 1. A CSMR greater than 1 has a score of 1, reflecting a threshold of greater concern.

      Calculating Subscores

      Once all parameters have been scored by the algorithm, they are aggregated into subscores. We used two different mathematical methods, the binning method for both the health and aesthetic subscores, and an unweighted arithmetic mean for the pipe subscore. 


      We wanted the health subscore’s aggregation method to be:

      • Simple: A method that can be understood by almost anyone. 
      • Comprehensive: The method must involve all relevant parameter scores and yet remain—
      • Conservative: Parameters with higher concentrations and lower parameter scores must play a larger role in the final subscore than parameters with lower concentrations and higher parameter scores. Prioritizing parameter scores with the highest risk is key.
      • Resilient: In mathematical aggregation methods, something called ambiguity is common (a case where many small detections combine to result in a low final score). For our purposes, the method we chose had to be unambiguous, able to withstand many small deductions in individual parameter scores without unduly skewing the subscore. 

      After extensive testing, we opted for a binning method, similar to what is used in the Air Quality Index—wherein parameters with low scores have a more substantial impact on the overall subscore. 

      • In the binning method, parameter scores are put into “bins” that reflect their relative severity, and the number of parameters in the lowest bin (i.e. most severe impact) for a given sample determines the final subscore. Refer to Appendix C for a visual representation of the binning method structure.

      The bins and final scores underwent a rigorous tuning process via our database of past reports.


      For the aesthetic subscore, we prioritized the same features as above. If someone experiences an off-taste or odor in their water, this needs to be reflected in the subscore. After testing with a number of prior reports, the binning method emerged once again as the aggregation method of choice.


      The pipe subscore is more theoretical compared to the other two subscores for the following reasons:

      • While it’s safe to say lead solder is likely to be found in the majority of pipes and fixtures, it is still impossible to determine precise plumbing materials with certainty—which is key to the accuracy of interpreting CSMR.
      • Pipe-related benchmarks are founded on an aggregate of operational knowledge and, as such, are less definitive.
      • The very nature of the relationship between water quality characteristics and pipe conditions is volatile.
      • The subscore reflects scenarios that act in opposition to one another: corrosion or excessive scaling.

      We opted to implement an unweighted arithmetic mean to aggregate relevant pipe parameters (Eqn 2a). This approach gives equal weight to each parameter, and provides us with information on whether or not the combined parameters point toward corrosion or excessive scaling.

      In addition, if any of the pipe score parameters receive a score of < 99 and lead, nickel, or cadmium are present in the water, 10 points are deducted from the subscore (Eqn 2b). The presence of these metals may be an indicator that corrosion is occuring.

      Calculating the Tap Score

      With our subscores now calculated, the only thing left to calculate is the overall Tap Score. The final Tap Score is a weighted arithmetic mean of the health, aesthetic, and pipe subscores (Eqn 3). The weights reflect the relative importance of each of the three subscores.


      • The health subscore makes up 75% of the final score because of its direct relationship with human well-being. 
      • The aesthetic subscore makes up 15% of the final score because tastes and odors are very noticeable and often drive consumers to get their water tested in the first place. 
      • Finally, the pipe subscore makes up 10% of the final subscore. While pipe effects are important for the functioning of plumbing infrastructure, the pipe subscore is more of an indication of possible issues than a categorical statement of actual risk.

      And there you have it: a Tap Score. The result of years of research into water quality and its impacts on people, infrastructure, and the environment. We’ve tried to take the mess out of water testing, the headache out of data analysis, and let one number give you a clear indication of the state of your water. 

      Your Feedback

      test, treat, and monitor your drinking water and local environmental health

      Contact Us! (hello@gosimplelab.com)


      Lin, T.-F., Watson, S., Dietrich, A.M., Suffet, M. (Eds.), 2019. Taste and Odour in Source and Drinking Water: Causes, Controls, and Consequences. IWA Publishing, U.K. ISBN13: 9781780406657; eISBN: 9781780406664.

      Nguyen, C., Stone, K., Clark, B., Edwards, M. Gagnon, G., and Knowles, A., 2010. Impact of Chloride:Sulfate Mass Ratio (CSMR) Changes on Lead Leaching in Potable Water. WRF Project #4088. Water Research Foundation. Denver, CO. 

      Appendix A

      Essential Parameters:

      The parameters in the essential list were chosen through analysis of 20,000+ customer reports; the compounds most frequently detected above their individual SLRs were determined to be of concern and therefore essential in terms of calculating a Tap Score. The cutoff employed to define “most frequently detected” is detection of the parameter above its SLR in at least one percent of instances in which it is measured. The additional parameters (denoted with asterisks) are quantified in the same analyses as some of the essential parameters and are included as they are available at no extra cost. Water quality parameters that are essential for the aesthetic and/or pipe subscores (e.g., pH, alkalinity, etc.) were also included. 

      The following parameters were detected above their SLRs in at least one percent of tests in which they were measured, but they are not included as required parameters to calculate a Tap Score. These parameters are excluded due to the cost of the lab methods used to measure them. SimpleLab wants to keep costs down to maximize the customer base able to afford the essential test and thus receive a Tap Score. One of the compounds below, dichloromethane aka DCM, is a solvent. The remaining four are trihalomethanes, a group of common disinfection byproducts.

      Appendix B

      B1 Health Benchmark Data Sources:

      Table B1 summarizes the various sources of health benchmark data used in calculating Tap Scores. The health endpoints used to calculate the benchmarks in each data source are listed (the specific cancer risk and/or noncancer exposure duration), and a brief description of the benchmark is included. Note that most of the benchmarks are not enforceable regulations, however, enforceable drinking water standards from six states are included for PFAS chemicals. 

      SimpleLab uses health-based benchmarks in Tap Score, and does not use US EPA enforceable standards (MCLs), because these benchmarks are set using health protective concentrations adjusted for technical and economic feasibility. Due to a lack of benchmarks for PFAS chemicals in the non-enforceable benchmark databases, various state MCLs are used but these values differ from US EPA MCLs in that they are entirely health based (no technical and economic feasibility considerations went into the calculations).

      B2 Aesthetic Benchmark Data Sources:

      The aesthetic benchmarks used in the SimpleLab Contaminant Database are compiled from the sources listed below. Unlike the health-based benchmarks, aesthetic benchmarks are not organized into large databases by state and federal agencies due to the fact that aesthetic aspects of drinking water are not generally regulated. As regulations are not needed for most of these compounds, most aesthetic benchmarks are not readily available and must be gathered from various institutional, governmental and academic sources. 

      Agency for Toxic Substances and Disease Registry (ATSDR), 1996. Toxicological Profile for Carbon Disulfide. Public Health Service, U.S. Department of Health and Human Services, Atlanta, GA. https://www.atsdr.cdc.gov/toxprofiles/tp82.pdf

      Agency for Toxic Substances and Disease Registry (ATSDR), 1998. Public Health Statement: Chloroethane. Public Health Service, U.S. Department of Health and Human Services, Atlanta, GA. https://www.atsdr.cdc.gov/ToxProfiles/tp105-c1-b.pdf

      Agency for Toxic Substances and Disease Registry (ATSDR), 1999. Public Health Statement: Hexachlorocyclopentadiene (HCCPD). Public Health Service, U.S. Department of Health and Human Services, Atlanta, GA. https://www.atsdr.cdc.gov/ToxProfiles/tp112-c1-b.pdf

      Agency for Toxic Substances and Disease Registry (ATSDR), 2005. Toxicological Profile for Naphthalene, 1-Methylnaphthalene, and 2-Methylnaphthalene. Public Health Service, U.S. Department of Health and Human Services, Atlanta, GA. https://www.atsdr.cdc.gov/toxprofiles/tp67.pdf

      Agency for Toxic Substances and Disease Registry (ATSDR), 2020. Toxicological Profile for 2-Hexanone. Public Health Service, U.S. Department of Health and Human Services, Atlanta, GA. https://www.atsdr.cdc.gov/ToxProfiles/tp44.pdf

      Amoore, J.; Hautala, E., 1983. Odor as an aid to chemical safety: odor thresholds compared with threshold limit values and volatilities for 214 industrial chemicals in air and water dilution. J. Appl. Toxicol. 3 (6), 272−90.

      Dietrich, A.M., Burlingame, G.A., 2015. Critical review and rethinking of USEPA secondary standards for maintaining consumer acceptability of organoleptic quality of drinking water. Environ. Sci. Technol. 49 (2), 708–720. https://doi.org/10.1021/es504403t.

      Lin, T.-F., Watson, S., Dietrich, A.M., Suffet, M. (Eds.), 2019. Taste and Odour in Source and Drinking Water: Causes, Controls, and Consequences. IWA Publishing, U.K. ISBN13: 9781780406657; eISBN: 9781780406664.

      NIOSH, 2016. Immediately dangerous to life or health (IDLH) value profile: acrylonitrile. By Dotson GS, Maier A, Parker A, Haber L. Cincinnati, OH: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, DHHS (NIOSH) Publication 2016-167.

      United States Environmental Protection Agency (USEPA), 1980. Ambient Water Quality Criteria for 2-chlorophenol. Office of Water Regulations and Standards. EPA 440/5-80-034. October 1980. https://www.epa.gov/sites/default/files/2018-12/documents/ambient-wqc-2chlorophenol.pdf

      United States Environmental Protection Agency (USEPA), 1980. Ambient Water Quality Criteria for Nitrophenols. Office of Water Regulations and Standards. EPA 440/5-80-063. October 1980. https://www.epa.gov/sites/default/files/2019-03/documents/ambient-wqc-nitrophenols-1980.pdf

      United States Environmental Protection Agency (USEPA), 1980. Ambient Water Quality Criteria for Nitrobenzene. Office of Water Regulations and Standards. EPA 440/5-80-061. October 1980. https://www.epa.gov/sites/default/files/2019-03/documents/ambient-wqc-nitrobenzene-1980.pdf

      World Health Organization (WHO), 2017. Chapter 10: acceptability aspects: taste, odour and appearance. In: Guidelines for Drinking Water Quality, WHO, Geneva, Switzerland, pp. 219–230. https://www.ncbi.nlm.nih.gov/books/NBK442378/

      World Health Organization (WHO), 2003. 1,2-Dichloropropane (1,2-DCP) in Drinking-water: Background document for development of WHO Guidelines for Drinking-water Quality. WHO/SDE/WSH/03.04/61. Geneva, Switzerland. https://www.who.int/water_sanitation_health/dwq/1,2-Dichloropropane.pdf

      World Health Organization (WHO), 2003. Dichlorobenzenes in Drinking-water: Background document for development of WHO Guidelines for Drinking-water Quality. WHO/SDE/WSH/03.04/28. Geneva, Switzerland. https://www.who.int/water_sanitation_health/dwq/chemicals/dichlorobenzenes.pdf

      World Health Organization (WHO), 2003. Tetrachloroethene in Drinking-water: Background document for development of WHO Guidelines for Drinking-water Quality. WHO/SDE/WSH/03.04/23. Geneva, Switzerland. https://www.who.int/water_sanitation_health/dwq/chemicals/tetrachloroethene.pdf?ua=1

      World Health Organization (WHO), 2004. Trihalomethanes in Drinking-water: Background document for development of WHO Guidelines for Drinking-water Quality. WHO/SDE/WSH/03.04/64. Geneva, Switzerland. https://www.who.int/water_sanitation_health/water-quality/guidelines/chemicals/trihalomethanes.pdf

      World Health Organization (WHO), 2005. Methyl tertiary-Butyl Ether (MTBE) in Drinking-water: Background document for development of WHO Guidelines for Drinking-water Quality. WHO/SDE/WSH/05.08/122. Geneva, Switzerland. https://www.who.int/water_sanitation_health/water-quality/guidelines/chemicals/MTBE200605.pdf

      Young, W.F., Horth, H., Crane, R., Ogden, T., Arnott, M., 1996. Taste and odour threshold concentrations of potential potable water parameters. Water Res. 30 (2), 331–340.

      B3 Pipe Benchmark Data Sources:

      The pipe benchmarks used in the SimpleLab Contaminant Database are compiled from the sources listed below. Most water quality characteristics that impact pipe health are not regulated with pipe effects in mind, therefore databases of benchmarks are not maintained by state and federal agencies. Much of the information regarding pipe health and drinking water is derived from engineering and operational knowledge, in addition to academic literature.

      Edwards, M. & Triantafyllidou, S., 2007. Chloride-to-sulfate mass ratio and lead leaching to water. Jour. AWWA, 99 (7), 96-109.

      Edwards, M., Meyer, T.E. & Schock, M.R., 1996. Alkalinity, pH and Copper Corrosion By-Product Release. Jour. AWWA, 88 (3), 81-94.

      Health Canada, 2009. Guidance on Controlling Corrosion in Drinking Water Distribution Systems. Water, Air and Climate Change Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario. Catalogue No. H128-1/09-595E. https://www.canada.ca/en/health-canada/services/publications/healthy-living/guidance-controlling-corrosion-drinking-water-distribution-systems.html

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      Schock, M.R. & Lytle, D.A., 1993. Corrosion control principles and strategies for reducing lead and copper in drinking water systems. Water Quality Association Conference, 1992.

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      Appendix C

      The binning methodology follows several steps:

      1) Each parameter measured in a sample is assigned a parameter score
      2) Parameter scores are tallied into the score ranges shown in the “Parameter Scores” column; e.g. if there are 2 parameter scores between 2-25, a “2” is placed in the 2-25 column for a given report
      3) The tally of scores in the lowest bin is used to determine the corresponding subscore; e.g. if the lowest parameter score bin is 2-25 and there are 2 parameter scores in that range, the overall subscore is assigned according to the corresponding row in the “Subscore” column, or a 40 in this case

      Table C1. Structure of the binning method used to aggregate parameters into a single subscore.