When waste management infrastructure is built, there can be resistance from the local affected populations, often termed the Not in My Backyard (NIMBY) phenomenon. This study aims to understand the forms of resistance that may develop in such contexts, focusing on 2 solid waste and 1 liquid waste management site within Mzuzu City, Malawi. At the newest solid waste site, community resistance had grown to the extent that the site was reportedly destroyed by the local community. Interviews and observations of the sites are complemented by examining historic and recent satellite images. It was found that, at the new solid waste site, community engagement had not been conducted effectively prior to construction and as part of ongoing site operations. This was compounded by poor site management and the non-delivery of the promised benefits to the community. In contrast, at the liquid waste site, the community could access untreated sludge for use as fertilizer and were happier to live within its vicinity. While NIMBYism is a frustrating phenomenon for city planners, it is understandable that communities want to protect their health and well-being when there is a history of mismanagement of waste sites which is sadly common in low-income settings. It is difficult for government agencies to deliver these services and broader waste management. In this study, an unsuccessful attempt to do something better with a legitimate goal is not necessarily a failure, but part of a natural learning process for getting things right.

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 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.

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