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      CONSUMPTION OF UNREGULATED DRINKING WATER AND HUMAN HEALTH RISK IN RURAL COMMUNTIES

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      FORD-THESIS-2017.pdf (1.304Mb)
      Date
      2017-12-20
      Author
      Ford, Lorelei Lynne 1978-
      ORCID
      0000-0003-3411-0710
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      Abstract
      Establishing safe drinking water for rural populations dependent on unregulated water is a global challenge. Despite initiatives to improve access to drinking water, hazards associated with unregulated sources pose a potential risk to human health for rural populations. In the absence of accurate information and monitoring of water quality, consumers form heuristic perceptions of risk associated with their drinking water. Risk perception affects water consumption contributing to uncertainty in risk exposure. Quantifying risk through human health risk assessments (HHRA) has been implemented since the 1940s and advances in risk assessment modeling have created an opportunity to improve HHRA by applying probabilistic Bayesian risk assessment methods. A holistic HHRA integrating risk perception, as it relates to exposure, can quantify uncertainty and provide feedback to improve risk communication and management. The literature lacks a review or summary that characterizes the type and frequency of HHRAs applied to rural populations dependent on unregulated drinking water. The purpose of this thesis is to: (1) summarize studies with HHRA methods applied to unregulated drinking water and rural communities, and describe the characteristics of methods, publications, and current literature gaps; and, (2) characterize and quantify risk perception as it relates to unregulated groundwater wells, and determine the impact of risk perception on human health risk using a holistic HHRA. A systematic scoping review of peer-reviewed literature (Jan 2000 to May 2014) was used to identify studies with HHRAs applied to unregulated or unspecified drinking water. At least one drinking water source was identified as unregulated (21%) or unspecified (79%) in 100 studies, and 7% identified rural communities dependent on unregulated drinking water. No studies integrated non-traditional factors (e.g. risk perception) into a holistic HHRA. HHRAs applied to rural populations dependent on unregulated water are poorly represented in the literature even though almost half of the global population is rural. The scoping review confirmed a lack of HHRA studies addressing unregulated drinking water risks, and the absence of applied methods that facilitated the quantification and integration of non-traditional factors. Based on the review findings, a community-based participatory observational case study and holistic HHRA was applied using arsenic concentrations and survey responses from two communities dependent on unregulated groundwater wells. Risk perception and human health risk was determined using probabilistic (Bayesian) risk assessment methods. Community tap water quality exceeded at least one health standard at a rate of 56% and 65%. Integration of risk perception did not change the overall risk status but lowered the cancer risk for arsenic by 3% for both communities. The probability of exposure to arsenic concentrations over 1:100,000 negligible risk for the two communities was 23% and 22%. There was no correlation between risk perception and drinking water safety in either community. This study achieved a holistic Bayesian risk assessment through the integration of risk perception and provided a probability of risk that can be used to inform risk communication and management specific to the participating communities.
      Degree
      Master of Environment and Sustainability (M.E.S.)
      Department
      School of Environment and Sustainability
      Program
      Environment and Sustainability
      Supervisor
      Bharadwaj, Lalita
      Committee
      Waldner, Cheryl; Feng, Cindy; Lawson, Joshua
      Copyright Date
      November 2017
      URI
      http://hdl.handle.net/10388/8309
      Subject
      drinking water, human health risk, Bayesian, probabilistic, arsenic
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