Access to groundwater and link to the impact on quality of life: A look at the past, present and future public health needs in Mzimba District, Malawi

Imagine a world where you have to get up at 4am to walk for two hours in the dark to fetch water. This remains true in Malawi, where it is said that the Millennium Development Goals have been met. This research aimed at understanding the impact access to groundwater has on people\u27s ‘Being’, ‘Belonging’ and ‘Becoming,’ as well as on people\u27s capabilities and on their quality of life in Mzimba District, Malawi. Being, Belonging and Becoming define three life domains. Being reveals ‘who one is,’ Belonging reflects ‘connections with one\u27s environments’ and Becoming relates to ‘achieving personal goals, hopes and aspirations.’ The study comprised of 210 households, four treatment groups based on communities consisting of households with access to a hand pump and compared to four control group communities, where households had no access to a hand pump. Results showed current awareness of environmental issues is linked to recognising future (5 years in advance) environmental challenges. There is a need to create awareness of water quality within the communities and point-of-use household water treatment. Both the treatment- and control group had a gap in sanitation facilities, with up to 27 people (5–6 households) sharing a single pit latrine. Polygamous marriages had implications on self-respect and led to neglect on the first wives. Focus group discussions revealed HIV, disabilities and mental health issues, including the use of drugs and alcohol, affect freedom, and created a burden, not only for affected individuals, but also for their extended families. Focus groups highlighted safe and clean drinking water, improved sanitation facilities, better hygiene, and accessible public health services as pressing needs. The implications of this study demonstrate, rural individuals ‘Being’, ‘Belonging’ and ‘Becoming’ need to be considered when addressing pressing public health needs, as Malawi works toward the Sustainable Development Goals for water supply

Comparative study for the performance of pure artificial intelligence software sensor and self-organizing map assisted software sensor in predicting 5-day biochemical oxygen demand for Kauma Sewage Treatment Plant effluent in Malawi

Introduction: Modeling plays a crucial role in understanding wastewater treatment processes, yet conventional deterministic models face challenges due to complexity and uncertainty. Artificial intelligence offers an alternative, requiring no prior system knowledge. This study tested the reliability of the Adaptive Fuzzy Inference System (ANFIS), an artificial intelligence algorithm that integrates both neural networks and fuzzy logic principles, to predict effluent Biochemical Oxygen Demand. An important indicator of organic pollution in wastewater.Materials and Methods: The ANFIS models were developed and validated with historical wastewater quality data for the Kauma Sewage Treatment Plant located in Lilongwe City, Malawi. A Self Organizing Map (SOM) was applied to extract features of the raw data to enhance the performance of ANFIS. Cost-effective, quicker, and easier-to-measure variables were selected as possible predictors while using their respective correlations with effluent. Influents’ temperature, pH, dissolved oxygen, and effluent chemical oxygen demand were among the model predictors.Results and Discussions: The comparative results demonstrated that for the same model structure, the ANFIS model achieved correlation coefficients (R) of 0.92, 0.90, and 0.81 during training, testing, and validation respectively, whereas the SOM-assisted ANFIS Model achieved R Values of 0.99, 0.87 and 0.94. Overall, despite the slight decrease in R-value during the testing stage, the SOM- assisted ANFIS model outperformed the traditional ANFIS model in terms of predictive capability. A graphic user interface was developed to improve user interaction and friendliness of the developed model. Integration of the developed model with supervisory control and data acquisition system is recommended. The study also recommends widening the application of the developed model, by retraining it with data from other wastewater treatment facilities and rivers in Malawi