Enhancing sustainable human and environmental health through nexus planning

A combination of climate change and accelerated population growth is instigating some of the worst challenges that humankind faces today that include resource depletion and degradation. Both built environment and ecological infrastructure have been modified and are creating novel socioecological interactions posing the risk of novel infectious diseases transmission to humans. The experiences of the COVID-19 exposed the vulnerability of human health from wildlife and the risk of novel socioecological interactions on livelihoods. This chapter enhances the preparedness and improves the resilience against novel pathogens by assessing vulnerability and the available options to reduce risk through the water-health-ecosystem-nutrition nexus. As a transformative, nexus planning provides integrated pathways toward resilience and preparedness to reduce health risks on humans posed by novel pathogens. A systematic review of literature facilitated an understanding of the trends of novel infectious diseases and the available options to improve sanitation, nutrition, and adaptative capacity in the advent of novel socioecological interactions. The aim is to guide policy formulations to achieve Sustainable Development Goals such as 3 (good health and wellbeing), 6 (clean water and sanitation), and 13 (climate action). Risk reduction framing in the health sector through nexus planning provides pathways toward healthy environments and mutual socioecological interactions. © 2022 Elsevier Inc. All rights reserved.

Resilience and Sustainability of the Water Sector during the COVID-19 Pandemic

The COVID-19 pandemic brought unprecedented socio-economic changes, ushering in a “new (ab)normal” way of living and human interaction. The water sector was not spared from the effects of the pandemic, a period in which the sector had to adapt rapidly and continue providing innovative water and sanitation solutions. This study unpacks and interrogates approaches, products, and services adopted by the water sector in response to the unprecedented lockdowns, heralding novel terrains, and fundamental paradigm shifts, both at the community and the workplace. The study highlights the wider societal perspective regarding the water and sanitation challenges that grappled society before, during, after, and beyond the pandemic. The premise is to provide plausible transitional pathways towards a new (ab)normal in adopting new models, as evidenced by the dismantling of the normal way of conducting business at the workplace and human interaction in an era inundated with social media, virtual communication, and disruptive technologies, which have transitioned absolutely everything into a virtual way of life. As such, the novel approaches have fast-tracked a transition into the 4th Industrial Revolution (4IR), with significant trade-offs to traditional business models and human interactions. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Application of the random forest classifier to map irrigated areas using google earth engine

Improvements in irrigated areas’ classification accuracy are critical to enhance agricultural water management and inform policy and decision-making on irrigation expansion and land use planning. This is particularly relevant in water-scarce regions where there are plans to increase the land under irrigation to enhance food security, yet the actual spatial extent of current irrigation areas is unknown. This study applied a non-parametric machine learning algorithm, the random forest, to process and classify irrigated areas using images acquired by the Landsat and Sentinel satellites, for Mpumalanga Province in Africa. The classification process was automated on a big-data management platform, the Google Earth Engine (GEE), and the R-programming was used for post-processing. The normalised difference vegetation index (NDVI) was subsequently used to distinguish between irrigated and rainfed areas during 2018/19 and 2019/20 winter growing seasons. High NDVI values on cultivated land during the dry season are an indication of irrigation. The classification of cultivated areas was for 2020, but 2019 irrigated areas were also classified to assess the impact of the Covid-19 pandemic on agriculture. The comparison in irrigated areas between 2019 and 2020 facilitated an assessment of changes in irrigated areas in smallholder farming areas. The approach enhanced the classification accuracy of irrigated areas using ground-based training samples and very high-resolution images (VHRI) and fusion with existing datasets and the use of expert and local knowledge of the study area. The overall classification accuracy was 88%. © 2021 by the authors.