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)

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]

Figure 1. The main steps in operational sampling of water for human consumption.

Due to the increasing importance attached to the representativeness of sampling, greater emphasis has been placed on the adequate collection, conditioning, preservation, and transport of samples. Thus, it is advisable that laboratories help to plan a sampling program in partnership with their customers. This partnership is essential to ensure that the selection of samples is adequate in order to minimize errors and to correspond to the needs of the consumer. [Nollet & Gelder 2014] At this point, it is important to reiterate that the main goal of water analysis is to aid the decision-making process.

Thus, the reliability of the decisions made depends on the variability of the experimental results. Despite variability being inherent to natural systems, if the variability due to errors in sampling, transport, handling, and/or analysis is underestimated, wrong decisions can be made, with negative consequences for health, environment, and financial matters, just to name a few. Indeed, the errors that occur during the water analysis process are difficult to quantify. Inadequate training, over-reliance on automated systems, and complicated software resources are some of the relevant issues behind the errors that may occur. [Reason 2000] More broadly, knowledge is crucial to solve the problems of the economy and society. In everyday situations, however, the information that flows from various sources is often incomplete, inaccurate, uncertain, contradictory, and even unknown. On the other hand, existing models are built based on some idealizations that remove these real-world properties.

The result is a system that, due to its inability to model the world or universe of discourse, never delivers the expected answers. [Parsons 1996] In the water sector, particularly with regard to sampling errors, there are many situations where the information available on errors is insufficient or ambiguous. The system proposed in this work is based on the Eindhoven Classification Model (ECM) [van der Schaaf 1995] and focuses on avoiding the causes of errors by applying a model specially developed for the operational sampling phase.

The ECM was created to address human error in the chemical process industry and has since been applied in different areas. [Henneman et al. 2006; Raab et al. 2006; Simmons & Graves 2008; Rodrigues et al. 2011; Vicente et al. 2015; Fernandes et al. 2019] The ECM classifies two types of errors, namely, human or active errors and latent ones. With regard to human error, the ECM incorporates Rasmussen's SRK thematic, which includes three levels of behavior, namely, competence-based, rule-based, and knowledge-based behavior levels. [Rasmussen 1976] In the case of latent errors, the ECM differentiates between technical and organizational errors [van der Schaaf 1995] The former occurs when there is an obstacle associated with physical components (e.g., devices and physical installations), while the latter is associated with protocols, procedures, or knowledge transfers.

The main objective of this work is to present an adverse event reporting and learning system, developed specifically for the sampling phase. Based on the internal audits carried out in the first half of 2019 in water laboratories, the adverse event "Failure in Sampling" was chosen to demonstrate how the proposed system can be applied to real-world problems. This system, known as Adverse Event Reporting and Learning System related to Water Sampling (AERLS-WS), intends to record the nonconformities, errors, and adverse events that occur during sampling. It also intends to be a learning system that enables data analysis to ensure continuous improvement in drinking water quality and the promotion of public health.

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.