Journal:Avoidance of operational sampling errors in drinking water analysis
| Full article title | Avoidance of operational sampling errors in drinking water analysis |
|---|---|
| Journal | AQUA - Water infrastructure, Ecosystems and Society |
| Author(s) | Fernandes, Ana; Figueiredo, Margarida; Ribeiro, Jorge; Neves, José; Vicente, Henrique |
| Author affiliation(s) | Universidade Lusófona, Universidade de Évora, Instituto Politécnico de Viana do Castelo, Instituto Universitário de Ciências da Saúde, Universidade do Minho |
| Primary contact | Email: hvicente at uevora dot pt |
| Year published | 2022 |
| Volume and issue | 71(3) |
| Page(s) | 373–386 |
| DOI | 10.2166/aqua.2022.074 |
| ISSN | 2709-8036 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://iwaponline.com/aqua/article/71/3/373/87050 |
| Download | https://iwaponline.com/aqua/article-pdf/71/3/373/1026718/jws0710373.pdf (PDF) |
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Abstract
The internal audits carried out in the first half of 2019 in water laboratories as part of quality accreditation in accordance with ISO/IEC 17025:2017 showed a high frequency of adverse events in connection with sampling. These faults can be a consequence of a wide range of causes, and in some cases, the information about them can be insufficient or unclear. Considering that sampling has a major influence on the quality of the analytical results provided by water laboratories, this work presents a system for reporting and learning from adverse events. Its aim is to record nonconformities, errors, and adverse events, making possible automatic data analysis to better ensure continuous improvement in operational sampling. The system is based on the Eindhoven Classification Model and enables automatic data analysis and reporting to identify the main causes of failure. Logic programming is used to represent knowledge and support the reasoning mechanisms to model the universe of discourse in scenarios of incomplete, contradicting, or even unknown information. In addition to suggesting solutions to the problem, the system provides formal evidence of the solutions presented, which will help to continuously improve drinking water quality and promote public health.
Keywords: drinking water, Eindhoven Classification Model, Knowledge Representation and Reasoning, logic programming, sampling errors, water quality
Highlights:
- An adverse event reporting and learning system for water sampling is described.
- The Eindhoven Classification Model is extended and adapted for water sampling.
- Logic programming is used for knowledge representation.
- The proposed system can deal with insufficient or ambiguous information.
- The system allows users to identify the relevant issues behind the errors that may occur.
Introduction
Securing water quality for human consumption through a public supply system is an essential element of health policy. By the end of the nineteenth century, an assessment and control of risks to human health from transmission of diseases caused by water consumption had been carried out empirically on the basis of the physical appearance of water. [Bagchi 2013] Epidemiological studies conducted by John Snow showed a close association between the consumption of water with fecal contamination and a cholera outbreak in London. [Snow 1855] The discovery of the existence of microorganisms by Louis Pasteur in 1863 and the isolation of Vibrio cholera bacillus in 1883 by Robert Koch formed the crucial scientific basis for the association of water use with public health and served as a starting point for the establishment of practices and protocols to control water quality. [Geison 1995; Brock 1999]
Until the middle of the twentieth century, the quality of water for human consumption had been largely assessed based on its organoleptic characteristics, i.e., its colorless, tasteless, and odorless features. [Eaton et al. 2017] However, this type of assessment does not guarantee the protection of public health from pathogenic microorganisms and hazardous chemicals. Therefore, it is imperative to establish standards based on quantifiable parameters that define the properties of water for human consumption in terms of microbiological, physical, chemical, and radiological factors. [ISO/IEC 17025 2017] Current methods of controlling water quality are time-consuming and complex, and they involve the use of significant technical and financial resources (e.g., calibrated equipment, infrastructure, skilled technicians, and reagents). With all this complexity, a large number of errors and failures can occur. [Byleveld et al. 2008] A failure can be defined as a planned action not meetings its required end result, at times because of use of an incorrect plan. These failures can be in connection with products, processes, and systems. [Hommerson et al. 2008]
The best way to prevent similar errors and failures from happening again is to report them, i.e., create experiential learning systems to identify their causes. Although this approach is widely used in the water sector, it is important to note that classification systems work better when they are limited to a specific field/stage/phase (e.g., pre-analytical, analytical, and post-analytical phases). Sampling is an important stage in a water quality control program as it can affect results if a sample is not representative of the water being controlled. On the one hand, sampling errors can arise if the sampling is not designed to answer specific questions (usually regulators specify the sampling approach and the sampling design to be adopted). On the other hand, sampling errors can also be related to operational issues.
In this work, a system for reporting and learning from errors is presented, with the aim of describing and preventing those that occur in operational sampling of water for human consumption. The operational sampling of water for human consumption involves four main steps: collection, conditioning, preservation, and transportation. These steps are depicted in Figure 1. Water samples are delivered to the laboratory for analysis, and the sampling technician is responsible for taking a valid and representative sample. [Li & Migliaccio 2011]
References
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. Grammar was tweaked significantly to improve readability. The PMCID and DOI were also added when they were missing from the original reference. The original article lists references alphabetically, but this version—by design—lists them in order of appearance.
