Monthly blue water footprint caps in a river basin to achieve sustainable water consumption: The role of reservoirs
The blue water footprint (WF) measures the consumption of runoff during a basin. so as to confirm property water consumption, setting a monthly blue WF cap, that’s associate upper-limit to the blue WF during a basin every month, is an appropriate policy instrument. The blue WF cap during a basin depends on the precipitation that becomes runoff and therefore the have to maintain a minimum flow for sustaining ecosystems and livelihoods. Reservoirs on the watercourse usually sleek runoff variability and therefore raise the WF cap and scale back blue water scarceness throughout the season. Previous water scarceness studies, considering the quantitative relation of actual blue WF to the blue WF cap beneath natural background conditions, haven’t studied this result of reservoir storages. Here we tend to assess how water reservoirs influence blue WF caps over time and the way they have an effect on the variability of blue water scarceness during a basin. we tend to take the Huang He Basin over the amount Jan 2002–July 2006 as case study and contemplate information on ascertained storage changes in 5 giant reservoirs on the most stream. Results indicate that reservoirs distribute the blue WF cap and blue water scarceness levels over time. Monthly blue WF caps were usually down by reservoir storage throughout the flood season (July–October) and raised by reservoir releases over the amount of highest crop demand (March–June). However, with water storage prodigious twentieth of natural runoff in most rainy months, reservoirs contribute to “scarcity within the wet months”, which is to be understood as a scenario within which environmental flow needs associated with the incidence of natural peak flows aren’t any longer met. 
Green Smart Technology for Water (GST4Water): Life Cycle Analysis of Urban Water Consumption
The increasing inadequacy of water is encouraging methods in water saving and concrete water management systems dedicated to reducing resource consumption and environmental impact. At family and concrete scales, there’s Associate in Nursing increasing interest in onsite greywater and non-potable water use systems so as to enhance water convenience. during this framework, the project GST4Water funded by the eu Union (EU) beneath the POR-FESR 2014–2020 Program of Italian region Region, has been developed with the aim to implement water consumption observation systems, to outline solutions for greywater use, and to develop tools for environmental property analysis applied to water systems. this study focuses on this last goal, activity a life cycle assessment of the solutions optimized at a vicinity level. specifically, six totally different eventualities are compared, ranging from 2 models considering ancient installation along with or while not energy consumption associated with predicament generation, and 5 extra models connected with totally different assumptions in terms of greywater recovery systems, and energy and predicament production, at variable percentages of renewable and electrical phenomenon energy offer. Finally, Associate in Nursing analysis of the come back time of environmental investment is disbursed, supported the results obtained through the state of affairs analysis. 
Economic development and residential water consumption in Chile
A better understanding of the relative importance of things associated with global climate change and to changes related to economic process would serve to tell water policy and to focus scarce public resources on anticipated issues arising from distinct sources of changes in water demand. this text investigates the determinants of residential water consumption in Chile, a developing country that has seen noteworthy changes in incomes, unit size, financial condition rates and levels of urbanization, and that is projected to expertise vital climatical however varied changes, betting on the region of the country. Panel information for 1998-2010 at the municipal level is employed to research the sensitivity of residential water demand to climate and development-related factors. within the case of Chile, the result on water consumption of those development-related changes is calculable to be many times that of the changes related to climate projections for fifty to eighty years within the future. 
Effect of unboiled water consumption data on sensitivity analysis in quantitative microbial risk assessment
Quantitative microbic risk assessment of drinkable is mostly followed by sensitivity analysis for examining the relative importance of variables of the simulation model on the end result. This study investigated the impact of the applied math strategies applied to unboiled water consumption knowledge on sensitivity analysis. The sensitivity analysis for concentration of E. coli (E. coli) in treated water showed utterly totally different results from the analysis for E. coli dose. This was thanks to the appliance of a Poisson model to the water consumption, that instructed that twenty seven of the folks failed to drink H2O. Our study then applied a unique model—an exponential distribution—to the water consumption knowledge. additionally, incidental water intake was assigned to non-consumers within the Poisson model. The results of sensitivity analyses for these cases were terribly totally different from those obtained from the primary analysis. This study thus incontestible that the applied math strategies wont to analyze water consumption knowledge have nice impacts on sensitivity analysis, though they are doing not have an effect on the yearly risk of infection. Specifically, applied math strategies might devalue sensitivity analysis. To avoid this downside, it’s desirable to use endless model like the exponential model, instead of a separate one like the Poisson model, to explain the variability in water consumption. 
The Nexus between the Sleeping Time, Water Consumption and the Body Mass Index
Aims: A healthy Body Mass Index (BMI) is widely regarded as important for overall health that helps to avert and control many adverse health effects. It is also known that the sleep deprivation and dehydration have a strong impact on healthy life and sleep deprivation is common among university students and has been associated with poor academic performance. We aim to study the relationship between sleep deprivation and dehydration with BMI.
Study Design: Data collection and statistical analysis.
Place and Duration of Study: University of Peradeniya, Sri Lanka, between September 2017 and March 2018.
Methodology: We examined the association of daily sleeping time and daily water consumption with the Body Mass Index among 452 university students of age 24-26 years and 326 females, and 126 males consented to participate in the study. A cross-tabulation analysis was used to identify the relationship between water consumption and daily sleeping time with BMI.
Results: Results of the chi-square test show that there is a significant association between BMI and sleeping time of the students as the calculated chi-square value of 13.771 was significant as p is 0.008 at 4 degrees of freedom. Results of phi and Cramer’s V measures of association show that the correlation coefficient between BMI and sleeping time is 0.175 with a p-value of 0.008. Also between BMI and water consumption of the students, the calculated chi-square value of 11.538 was significant as P is 0.021 (<0.05) at 4 degrees of freedom. Results of phi and Cramer’s V measures of association show that the correlation coefficient between BMI and water consumption as 0.160 with a p-value of 0.021.
Conclusion: The phi measure of symmetric coefficient shows a significant positive association; that is the students who are consuming more water are prone to fall in higher BMI category while students consume less water falls to lower BMI. The phi measure of symmetric coefficient shows a significant positive association; that is the student who gets less sleep are prone to fall in higher BMI category while students take long sleep a day falls to lower BMI. 
 Zhuo, L., Hoekstra, A.Y., Wu, P. and Zhao, X., 2019. Monthly blue water footprint caps in a river basin to achieve sustainable water consumption: The role of reservoirs. Science of the total environment, 650, pp.891-899. (Web Link)
 Bonoli, A., Di Fusco, E., Zanni, S., Lauriola, I., Ciriello, V. and Di Federico, V., 2019. Green Smart Technology for Water (GST4Water): Life Cycle Analysis of Urban Water Consumption. Water, 11(2), p.389. (Web Link)
 Fercovic, J., Foster, W. and Melo, O., 2019. Economic development and residential water consumption in Chile. Environment and Development Economics, 24(1), pp.23-46. (Web Link)
 Effect of unboiled water consumption data on sensitivity analysis in quantitative microbial risk assessment
Sadahiko Itoh & Liang Zhou
npj Clean Watervolume 1, Article number: 18 (2018) (Web Link)
 The Nexus between the Sleeping Time, Water Consumption and the Body Mass Index
M. A. D. Priyadarshani
Department of Mathematics, University of Peradeniya, 20400, Sri Lanka.
J. A. Weliwita
Department of Mathematics, University of Peradeniya, 20400, Sri Lanka.
S. M. M. Lakmali
Department of Mathematics, University of Peradeniya, 20400, Sri Lanka
Higher Colleges of Technology, Abu-Dhabi, United Arab Emirates. (Web Link)