Standards Compliance and Health Implications of Bottled Water in Malawi

Many people around the globe prefer bottled water especially in developing countries, where tap water is not drinkable. This study investigated the quality of bottled drinking water sold in Lilongwe city, Malawi. Compliance with Malawi Standards (MS) 560 (2004) for natural mineral water, MS 699 (2004) for bottled water and the World Health Organisation guidelines for drinking water were examined. Bottled water from different 12 brands was purchased from local stores and analysed for its pH, total dissolved solids (TDS), EC, turbidity, Ca, Mg, Na, K, Fe, NO3−, Cl−, F−, SO42−, hardness, alkalinity, and Escherichia coli. A Hierarchical Cluster Analysis (HCA) resulted in two clusters in which most of the brands (92%, n = 12) belonged to one group. The two clusters and significant differences (ANOVA p < 0.05) in chemical compositions among the brands were attributed to the variations in the water source and the treatment processes. The results showed that 10 brands did not comply with the MS 699 (2004) turbidity standard (1 NTU) and the pH of one of the brands was below the minimum MS 699 (2004) standard of 6.50. This research showed that 12 brands had bottle labelling errors and discrepancies in chemical composition. The article highlighted the need for a strict inspection from the responsible governmental ministry to improve water quality and to adjust water bottles’ labels according to water characteristics

Infilling missing data and outliers for a conventional sewage treatment plant using a self-organizing map: a case study of Kauma Sewage Treatment Plant in Lilongwe, Malawi

Data availability is key for modeling of wastewater treatment processes. However, process data are characterized by missing values and outliers. This study applied a self-organizing map (SOM) to fill in missing values and replace outliers in wastewater treatment data from Kauma Sewage Treatment Plant in Lilongwe, Malawi. We used primary and secondary wastewater data and executed the SOM algorithm to fill missing values and replace outliers in effluent pH, biochemical oxygen demand, and dissolved oxygen. The results suggest that the SOM algorithm is reliable in filling gaps in wastewater time series data with less than 50% missing values with correlation coefficient (R) values of >0.90. The SOM algorithm failed to reliably fill gaps and replace outliers in time series data with >50% missing values. For instance, high mean square error (MSE) values of 3,655.57, 10.62, and 2,153.34 for pH, DO, and BOD, respectively, were registered in datasets with more than 50% missing values, while very small MSE values (MSE ≈ 0) were associated with effluent pH, BOD, and DO data with missing values of >50%. Practitioners can use this approach to improve the planning and management of wastewater treatment facilities where available data records are riddled with missing observations. HIGHLIGHTS Missing data impinge on wastewater treatment plant processes efficiency.; The advancement of information technology and artificial intelligence enables the infilling of missing data.; We proposed to infill missing data and outliers using a Multivariate model called the Self-Organizing Map.; Missing data and outliers are replaced with reasonable estimates.; The approach has provided long series data for modelling the behavior of the wastewater treatment process.