Pesticides are contaminants of priority concern that continue to present a significant risk to drinking water quality. While pollution mitigation in catchment systems is considered a cost-effective alternative to costly drinking water treatment, the effectiveness of pollution mitigation measures is uncertain and needs to be able to consider local biophysical, agronomic, and social aspects. We developed a probabilistic decision support tool (DST) based on spatial Bayesian belief networks (BBNs) that simulates inherent pesticide leaching risk to ground- and surface water quality to inform field-level pesticide mitigation strategies in a small (3.1 km²) drinking water catchment with limited observational data. The DST accounts for the spatial heterogeneity in soil properties, topographic connectivity, and agronomic practices; the temporal variability of climatic and hydrological processes; and uncertainties related to pesticide properties and the effectiveness of management interventions. The rate of pesticide loss via overland flow and leaching to groundwater and the resulting risk of exceeding a regulatory threshold for drinking water was simulated for five active ingredients. Risk factors included climate and hydrology (e.g. temperature, rainfall, evapotranspiration, and overland and subsurface flow), soil properties (e.g. texture, organic matter content, and hydrological properties), topography (e.g. slope and distance to surface water/depth to groundwater), land cover and agronomic practices, and pesticide properties and usage. The effectiveness of mitigation measures such as the delayed timing of pesticide application; a 10 %, 25 %, or 50 % reduction in the application rate; field buffers; and the presence/absence of soil pan on risk reduction were evaluated. Sensitivity analysis identified the month of application, the land use, the presence of buffers, the field slope, and the distance as the most important risk factors, alongside several additional influential variables. The pesticide pollution risk from surface water runoff showed clear spatial variability across the study catchment, whereas the groundwater leaching risk was uniformly low, with the exception of prosulfocarb. Combined interventions of a 50 % reduced pesticide application rate, management of the plough pan, delayed application timing, and field buffer installation notably reduced the probability of a high risk of overland runoff and groundwater leaching, with individual measures having a smaller impact. The graphical nature of BBNs facilitated interactive model development and evaluation with stakeholders to build model credibility, while the ability to integrate diverse data sources allowed a dynamic field-scale assessment of “critical source areas” of pesticide pollution in time and space in a data-scarce catchment, with explicit representation of uncertainties.

Mass discharge estimates are increasingly being used when assessing risks of groundwater contamination and designing remedial systems at contaminated sites. Such estimates are, however, rather uncertain as they integrate uncertain spatial distributions of both concentration and groundwater flow. Here a geostatistical simulation method for quantifying the uncertainty of the mass discharge across a multilevel control plane is presented. The method accounts for (1) heterogeneity of both the flow field and the concentration distribution through Bayesian geostatistics, (2) measurement uncertainty, and (3) uncertain source zone and transport parameters. The method generates conditional realizations of the spatial flow and concentration distribution. An analytical macrodispersive transport solution is employed to simulate the mean concentration distribution, and a geostatistical model of the Box‐Cox transformed concentration data is used to simulate observed deviations from this mean solution. By combining the flow and concentration realizations, a mass discharge probability distribution is obtained. The method has the advantage of avoiding the heavy computational burden of three‐dimensional numerical flow and transport simulation coupled with geostatistical inversion. It may therefore be of practical relevance to practitioners compared to existing methods that are either too simple or computationally demanding. The method is demonstrated on a field site contaminated with chlorinated ethenes. For this site, we show that including a physically meaningful concentration trend and the cosimulation of hydraulic conductivity and hydraulic gradient across the transect helps constrain the mass discharge uncertainty. The number of sampling points required for accurate mass discharge estimation and the relative influence of different data types on mass discharge uncertainty is discussed.

Ensuring access to adequate and equitable sanitation and ending open defecation by 2030 is the focus of Sustainable Development Goal 6.2 (SDG6.2). We evaluated Malawi’s progress towards SDG 6.2 (specifically the goal to end open defecation), presenting the results of a national survey of over 200,000 sanitary facilities and evaluating their management. Based on non-linear population dynamics, we used a linear model to evaluate the reduction in open defecation between 1992–2018, and to project whether Malawi can meet the SDG target to end open defecation by 2030 under multiple scenarios of population growth. Whilst Malawi has made considerable progress in providing sanitary provision for the population, we estimate that, at the current rate of the provision of sanitary facilities, Malawi will not reach SDG 6.2 by 2030 under any of the modelled socioeconomic scenarios. Furthermore, we compare the estimates of the extent of sanitary provision classed as improved from multiple surveys, including the USAID Demographic and Health (DHS) Surveys and Government of Malawi Census data. We conclude that some of the surveys (particularly the 2015/16 DHS) may be overestimating the level of improved sanitary provision, and we hypothesize that this is due to how pit-latrines with earth/sand slabs are classed. Furthermore, we examine the long-term sustainability of pit-latrine use, investigating the challenge of pit-latrine abandonment and identifying pit-latrine filling as a cause of the abandonment in 30.2% of cases. We estimate that between 2020–2070, 31.8 (range 2.8 to 3320) million pit-latrines will be filled and abandoned, representing a major challenge for the safe management of abandoned latrines, a potential for long-term impacts on the groundwater quality, and a significant loss of investment in sanitary infrastructure. For Malawi to reach SDG 6.2, improvements are needed in both the quantity and quality of its sanitary facilities.

Using wastewater accumulating around rural waterpoints to irrigate community gardens, borehole-garden permaculture (BGP) presents a method of sustainable water management. BGP also presents public health benefits through the removal of stagnant water around boreholes, key Malaria breeding grounds, and through providing year-round food to supplement diets. By analysing a dataset of over 100,000 cases, this research examines the awareness and adoption of BGP across Malawi. Generalised linear models identified significant variables influencing BGP awareness and uptake revealing that socioeconomic, biophysical and waterpoint-specific variables influenced both the awareness and adoption of BGP. BGP had low uptake in Malawi with only 2.4% of communities surveyed practising BGP while 43.0% of communities were aware of BGP. Communities in areas with unreliable rainfall and high malaria susceptibility had low BGP awareness despite BGP being particularly beneficial to these communities. This work suggests that future work in the promotion of BGP should focus their efforts within these areas. Furthermore, this work highlights the value of community networks in knowledge sharing and suggests that such social capital could be further used by NGOs and the Government of Malawi in the promotion of BGP and other sustainable practices.

Pit-latrines are central to achieving UN Sustainable Development Goal 6 (SDG 6) of ensuring "clean water and sanitation for all". Unless safely managed, pit-latrines result in groundwater contamination, which increases morbidity and mortality. Despite this, there have been no long-term spatial projections of future pit-latrine contamination risks. National survey data of over 100,000 water-points and 260,000 pit-latrines in Malawi was used to generate a novel, high-resolution model of pit-latrines from 2020 to 2070 under five population scenarios. The results here are presented as a 'business as usual' scenario of population growth and pit-latrine usage, predicting a three-fold increase in the number of current water-points at risk of short-distance microbial pit-latrine contamination between 2020 and 2070, with a seven-fold increase in number at the highest risk of contamination. Current nitrogen loading into pit-latrines is comparable to national fertiliser application. The model predicts 8.2 mega-tonnes of faecal nitrogen will be disposed of into subsequently abandoned pit-latrines between 2020 and 2070. Change is necessary to prevent SDG6's push for sanitation undermining its goal of clean water.