Water, sanitation and hygiene (WASH) index for primary healthcare facilities: Towards achieving WASH security.

This study focused on designing a WASH Index for assessing the status of WASH in Primary Healthcare Facilities (PHCs) especially for low- and middle-income countries. To assess the effectiveness of the WASH Index in evaluating the WASH in PHCs, PHCs were selected from 70 Local Government Areas (LGA) across 3 Southwestern States in Nigeria. The WASH index comprises of the five Joint Monitoring Programme service ladders as outlined in the World Health Organization Global Baseline Report for monitoring basic WASH services in health care facilities: water, sanitation, hygiene, waste management and environmental cleaning.

Machine learning framework for modeling flocculation kinetics using non-intrusive dynamic image analysis.

The implementation of a machine learning (ML) model to improve both the effectiveness and sustainability of the water treatment system is a significant challenge in the water sector, with the optimization of flocculation processes being a major setback. The objective of this study was to develop a ML model for predicting flocs evolution of the flocculation process in water treatment. Furthermore, we have devised a framework for its potential adoption in large-scale water treatment.

Human health risks of metal contamination in Shallow Wells around waste dumpsites in Abeokuta Metropolis, Southwestern, Nigeria.

Metal contamination in shallow wells through solid waste leaching is a serious environmental problem with contribution to global cancer cases. This paper evaluated the health risks of metals in shallow wells around dumpsites in the Abeokuta metropolis, Nigeria. Five dumpsites were purposively selected to sample twenty-five shallow wells.