Water Security最新文献

While there is significant awareness of the importance of addressing water, sanitation and hygiene (WASH) inequalities, measurement continues to present a challenge. Addressing how inequalities are measured, tracked and communicated is fundamental to accelerating progress in ensuring universal WASH coverage and associated benefits. We review how WASH inequalities have been measured and monitored to date on a global level, particularly in relation to SDG 6. We describe gaps in several areas, including how inequalities are measured in relation to gender and social differences, and limitations due to a focus on measuring access to infrastructure that overlooks other contributions of WASH services to wellbeing. Approaches for improved measurement and monitoring of inequalities are discussed, including making better use of existing datasets, as well as developing a broader range of indicators for the WASH sector. Finally, we emphasize the importance of improving visualization and communication of inequalities to policy audiences.

Deep cyanobacteria layers are an emerging concern in harmful algal bloom research, posing a “known unknown” risk to human health. A known unknown risk is one of which society is aware but cannot accurately assess the potential impacts due to insufficient research. Deep cyanobacteria layers develop below the surface. At this depth, the presence of cyanobacteria is not casually recognized and therefore seldom evokes public health concerns or advisories. However, the potential risk of deep cyanobacteria layers to public health places heightened importance on learning more about depth-differentiation among phytoplankton. We identify four scientific gaps about deep cyanobacteria layers. Advancing our understanding by filling these scientific gaps is crucial to reducing the risks associated with deep cyanobacteria layers to human health and safeguarding water security.

Management of groundwater contaminants, that are primarily of geogenic origin, such as fluoride, is a major public health concern. Worldwide, around 200 million people are dependent on drinking water resources that contain elevated levels of fluoride that exceeds WHO’s drinking water threshold limit of 1.5 mg/L. According to the Ministry of Drinking Water and Sanitation of India, about 11.7 million people, mostly in the Rajasthan state, are exposed to high fluoride risk. It is important to understand the soil–water interaction mechanisms to properly assess the fluoride contamination that are primrily due to geogenic origins prevalent in the region. In this study, batch desorption experiments were performed with soils obtained from varied depths at two sites in Rajasthan that has high fluoride levels in groundwater. The fluoride release kinetics followed a pseudo first-order kinetic model. The results of the batch experiments indicate higher release of fluoride from lower soil layers when compared to the upper layers. Further, the release of fluoride was dependent on pH wherein higher release was noticed under basic pH. Since the natural pH of the soils from this region is ca. pH 8 it is expected to play a vital role in the continued release of fluoride to the groundwater system. Furthermore, a simplified geochemical model, incorporating a general composite approach, has been used to simulate the experimental results that include dissolved Al and Al-F surface complexes. The model was able to capture the observed experimental results for various soils within a reasonable RMSE of 11.74%. The results of this study not only further the current understanding of the fate and transport mechanisms of fluoride in the contaminated subsurface but also would aid in designing remedial strategies to ensure future water security in this region.

The present study investigates the interaction of the soil in Garhbaga village, located in the Rupnagar district of Punjab, India for As(V) adsorption under the influence of pH, contact time and varying arsenic concentrations. To understand the geochemical controls of arsenic mobilization in the region, batch sorption experiments were performed using soil obtained from arsenic contaminated district of Punjab. This study presents a novel approach by employing surface complexation models (SCMs) to investigate arsenic adsorption onto natural soils in the Punjab region, which has not been explored in previous literature. Furthermore, a comparison between Fe-based models, assuming ferrihydrite binding, and general composite (GC) approach, assuming adsorption on soil component surfaces, has not been conducted before, adding to the originality of this research. The adsorption kinetic experiment indicates about 70% adsorption of As(V) in about 4 h. The results of batch isotherm experiment shows that As(V) adsorption saturation onto the soil is reached at an aqueous concentration of about 0.89 mgL⁻¹. The results of the pH edges study shows a maximum As(V) adsorption of 93.88% at a pH of 4. The Langmuir’s isotherm was the best fitted because the value of linear regression coefficient (R² = 0.997) which verifies the monolayer adsorption of As(V). It was observed that the pseudo first order model best fitted for explaining the kinetic of As(V) adsorption onto the soil because it showed higher value of linear regression coefficient (R² = 0.995). Further, three different diffused layer models under varied assumptions were used to capture the batch experimental results. The surface complexation model with general-composite (GC) approach fairly predicted the experimental results when compared to Fe-oxide based models. The GC model was able to capture the observed experimental results for adsorption isotherm and pH edges for the soil within reasonable RMSE of 6.22 % and 7.97 %, respectively.

Anthropogenic water stress, especially in mountain habitats worldwide, affects water supply and threatens water security. Evaluating past trends, assessing current conditions, and anticipating future change is paramount for the sustainable use of increasingly scarce freshwater resources. This study simulates water yield using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to generate reliable information for decision-making related to watershed management programs in data-scarce Himalayan regions. The results are translated into watershed indices easily communicable to watershed managers, stakeholders and administrative agencies. The analysis demonstrates the utility of hydrological modeling using limited data within a scoping protocol for the pre-implementation phase of any watershed management program.

Conventional Flood Frequency Analysis (FFA) may underestimate flood quantiles and increase hydraulic infrastructure vulnerability in changing climates. This study uses annual maximum streamflow data from 17 hydrologic stations along west-flowing rivers in Kerala, India, for Non-Stationary (NS) FFA. The Generalized Extreme Value model with a linear temporal location parameter worked effectively for five stations. Kidangoor and Pattazhy stations must account for non-stationarity for longer return periods (RPs) (>50 years), whereas Neeleeswaram and Perumannu stations must for shorter RPs (<50 years). An extensive study was conducted for Neeleswaram station (Periyar basin) by simulating NS models incorporating four large-scale climate oscillations as covariates. The stationary assumption underestimated flood return levels of 2-year RP by about 61% which increases the flood risk leading to failure of hydraulic infrastructures. It was observed that the best fitted climate-based NS model achieves stationary return level of 150-years RP at 25-years RP itself. The study proved that the climate-based NS models captured the 2018 August Floods in Periyar basin better than stationary and time-based models. The regional variability in FF curve behaviour concludes that NSFFA for Kerala cannot be generalised and must be done at a local-scale.

Due to a changing climate and increased urbanization, an escalation of urban flooding occurrences and its aftereffects are ever more dire. Notably, the frequency of extreme storms is expected to increase, and as built environments impede the absorption of water, the threat of loss of human life and property damages exceeding billions of dollars are heightened. Hence, agencies and organizations are implementing novel modeling methods to combat the consequences. This review details the concepts, impacts, and causes of urban flooding, along with the associated modeling endeavors. Moreover, this review describes contemporary directions towards urban flood resolutions, including the more recent hydraulic-hydrologic models that use modern computing architecture and the trending applications of artificial intelligence/machine learning techniques and crowdsourced data. Ultimately, a reference of utility is provided, as scientists and engineers are given an outline of the recent advances in urban flooding research.