An appraisal of data collection, analysis, and reporting adopted for water quality assessment: A case of Nigeria water quality research

• Main Authors: Ugochukwu Ewuzie, Nnaemeka O. Aku, Stephen U. Nwankpa
• Format: Article
• Language: English
• Published: Elsevier 2021-09-01
• Series: Heliyon
• Subjects: Descriptive statistics; Online instrumentation; Artificial intelligence; Inferential statistics; Data visualisation; Nigeria
• Online Access: http://www.sciencedirect.com/science/article/pii/S2405844021020533

The appropriate acquisition and processing of water quality data are crucial for water resource management. As such, published articles on water quality monitoring and assessment are meant to convey essential and reliable information to water quality experts, decision-makers, researchers, students, and the public. The implication is that such information must emanate from data obtained and analysed in an up-to-date, scientifically sound manner. Thus, inappropriate data analysis and reporting techniques could yield misleading results and mar the endeavours of achieving error-free conclusions. This study utilises the findings on water quality assessment in Nigeria over the last 20 years to reveal the likely trends in water quality research regarding data collection, data analysis, and reporting for physicochemical, bacteriological parameters, and trace organics. A total of 123 Web of Science and quartile ranked (Q1–Q4) published articles involving water quality assessment in Nigeria were analysed. Results indicated shortcomings in various aspects of data analysis and reporting. Consequently, we use simulated heatmaps and graphs to illustrate preferred ways of analysing, reporting, and visualising some regularly used descriptive and inferential statistics of water quality variables. Finally, we highlight alternative approaches to the customarily applied water quality assessment methods in Nigeria and emphasise other areas of deficiency that need attention for improved water quality research.