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The next decade is likely to produce any number of global challenges that will affect health and health care, including pan-national infections such as the new coronavirus COVID-19 and others that will be related to global warming. Nurses will be required to react to these events, even though they will also be affected as ordinary citizens. The future resilience of healthcare services will depend on having sufficient numbers of nurses who are adequately resourced to face the coming challenges.

Agricultural water requirements differ between foods. Population-level dietary preferences are therefore a major determinant of agricultural water use. The “water footprint” (WF) represents the volume of water consumed in the production of food items, separated by water source; blue WF represents ground and surface water use, and green WF represents rain water use. We systematically searched for published studies using the WF to assess the water use of diets. We used the available evidence to quantify the WF of diets in different countries, and grouped diets in patterns according to study definition. “Average” patterns equated to those currently consumed, whereas “healthy” patterns included those recommended in national dietary guidelines. We searched 7 online databases and identified 41 eligible studies that reported the dietary green WF, blue WF, or total WF (green plus blue) (1964 estimates for 176 countries). The available evidence suggests that, on average, European (170 estimates) and Oceanian (18 estimates) dietary patterns have the highest green WFs (median per capita: 2999 L/d and 2924 L/d, respectively), whereas Asian dietary patterns (98 estimates) have the highest blue WFs (median: 382 L/d per capita). Foods of animal origin are major contributors to the green WFs of diets, whereas cereals, fruits, nuts, and oils are major contributors to the blue WF of diets. “Healthy” dietary patterns (425 estimates) had green WFs that were 5.9% (95% CI: −7.7, −4.0) lower than those of “average” dietary patterns, but they did not differ in their blue WFs. Our review suggests that changes toward healthier diets could reduce total water use of agriculture, but would not affect blue water use. Rapid dietary change and increasing water security concerns underscore the need for a better understanding of the amount and type of water used in food production to make informed policy decisions.

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Sand-storage dams have proven to be a successful water harvesting method and potential solution to water and food security issues in semi-arid regions such as south east Kenya. This paper examines the microbiological quality of water both contained in the sand dam via test holes and abstracted from it through covered wells and scoop holes. In total, the values of thermotolerant coliform (TTC) concentration, turbidity, and pH are presented for 47 covered wells, 36 scoop holes, and 29 test holes, as well as the conductivity values in conductivity in 39 covered wells and 11 scoop holes. The water from test holes and covered wells was microbiologically of better quality than the scoop holes with median TTC levels of 0/100 mL and 159/100 mL respectively. However, the median values of turbidity for both scoop holes (20-30 NTU) and covered wells (5-10 NTU) exceed the World Health Organisation (WHO) guideline values. In addition the conductivity of water from 23% of scoop holes and 26% of covered wells is above the recommended WHO limit. This study also found that sanitary surveys are not a useful indicator of water quality in sand dams; however, they can identify areas in which sanitation and improvement of water sources are needed.

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Rainwater harvesting systems are often used as both an alternative water source and a stormwater management tool. Many studies have focused on the water-saving potential of these systems, but research into aspects that impact stormwater retention-such as demand patterns and climate change-is lacking. This paper investigates the short-term impact of demand on both water supply and stormwater management and examines future and potential performance over a longer time scale using climate change projections. To achieve this, data was collected from domestic rainwater harvesting systems in Broadhempston, UK, and used to create a yield-after-spillage model. The validation process showed that using constant demand as opposed to monitored data had little impact on accuracy. With regards to stormwater management, it was found that monitored households did not use all the non-potable available water, and that increasing their demand for this was the most effective way of increasing retention capacity based on the modelling study completed. Installing passive or active runoff control did not markedly improve performance. Passive systems reduced the outflow to greenfield runoff for the longest time, whereas active systems increased the outflow to a level substantially above roof runoff in the 30 largest events.Research measuring the performance of these systems in the UK is limited to monitored commercial buildings [7]; few household-scale empirical studies have been performed and are limited to single homes [8,9]. Studies in the USA have examined the stormwater performance of specifically designed active release systems which were emptied automatically before storm events [10]. However, these systems were large and installed on high-demand industrial facilities and not intended for domestic use. The long-term stormwater management of domestic systems designed for water supply is unclear.Other studies have conceptualised the systems' performance through modelling either at an allotment, neighbourhood or catchment scale. Xu et al. [11] modelled the ability of three types of allotment-scale RWH systems to simultaneously deliver the dual benefits discussed above in addition to river baseflow restoration. Using a historic 11 year rainfall dataset, they defined six metrics (the efficiency and frequency of water supply, baseflow and retention) to quantify system performance. These indicators were average values and did not indicate behaviour during storm events with specific return periods, which are of interest to drainage designers.More detailed models, such as the study of a sewer catchment in Palermo by Freni and Liuzzo [12] and the catchment response framework developed by Jamali et al. [13] capture the stormwater management of RWH systems on a larger scale. Due to the size of their spatial grid, the temporal resolution of these models was often low, in the order of daily [12] or hourly [13] time steps. Campisano and Modica [14] illustrated that their mass-balance approach proved unreliable for the evaluation of water supply ...