Interpolation Tool Research Articles

Conditional simulation of non-stationary spatially variable ground motions for long-span bridges across non-uniform site conditions

Non-stationary spatially variable ground motions (SVGMs) are commonly modelled as multivariate oscillatory processes based on evolutionary power spectral density (EPSD) functions. The existing conditional simulation algorithms require the known EPSD functions. The EPSD functions are usually assumed to be identical for all locations, which is unreasonable for long-span bridges because variable soil conditions are practically observed at different bridge piers. This paper proposes a conditional simulation algorithm for non-stationary SVGMs in consideration of non-uniform site conditions. The spatial interpolation tool, termed inverse-distance-weighted (IDW) interpolation, is introduced to estimate the EPSD functions at sites without ground motion measurement. Subsequently, the covariance matrix of the random Fourier coefficients of the multivariate oscillatory processes can be calculated. The Kriging estimation is adopted to obtain the unknown random Fourier coefficients, from which the time histories of the non-stationary SVGMs can be conditionally simulated. The proposed conditional simulation algorithm is first validated through a numerical example, in which the EPSD functions of non-uniform sites are represented by a non-stationary Kanai-Tajimi spectrum with different soil parameters. Then, the algorithm is applied to the Jiuzhou Channel Bridge, a navigation channel bridge of the Hong Kong-Zhuhai-Macau Bridge (HZMB), with complex soil and water conditions. Based on the limited in-situ seismic measurement data, the site characteristics in the bridge area are analysed, and the ground motion time histories at all piers can be generated.

Groundwater Rise and Associated Flooding in Coastal Settlements Due To Sea‐Level Rise: A Review of Processes and Methods

AbstractCoastal settlements are experiencing the effects of climate change‐induced sea‐level rise, including a significant but often poorly‐characterized impact on groundwater. The shallow water tables present in the built coastal environment contribute to an increased risk from natural hazards such as groundwater flooding and saltwater intrusion. Historical urban development was accompanied by impacts on shallow groundwater, and in the future subsurface environments, including underground infrastructure, will face additional pressure. This article reviews processes of the coastal groundwater zone and simulation tools used to evaluate possible impacts of sea‐level rise. The benefits and limitations of the two main methods to assess coastal groundwater rise and contribution to flooding are discussed using studies and investigations up to 2021. The review addresses challenges associated with the simulation tools to evaluate changes in urban hydrogeology due to sea‐level rise. The models reviewed do not specifically estimate groundwater contribution to land‐surface flooding. We highlight a critical need for methodology comparisons between spatial interpolation and numerical tools that will guide future work. An adequate validation of assessment methods is required and will be supported by improved coastal groundwater monitoring networks focused on water quality, saltwater intrusion, and continuous groundwater levels records. From current monitoring practices, evidence for groundwater rise with rising sea level is not widely observed at present. New monitoring sites are recommended near the coastline and tidally influenced surface water bodies, to better evaluate the rise of the water table and impacts on infrastructure.

Mapping Geotechnical Soil Properties of Ranya City in Kurdistan Region of Iraq Using GIS

Geotechnical map is a vital guidance to visualize the behavior of soils. The objective of this paper is to present the geotechnical maps that can be used for preliminary investigation in Ranya city of northern Iraq. The study area is 13.02 km2 with latitude and longitude of 36°15'14" N 44°52'59" E, respectively. A total number of 116 boreholes with the depth up to 5.0 m were utilized to create allowable bearing capacity, particle size, and Atterberg limit maps. Kriging interpolation tool in the ArcGIS software was used to analyze the soil properties data and to achieve the maps. The appraisal study area was divided into three layers 0.5-1.5, 1.5-3.0, and 3.0-5.0 m and the results show the average bearing capacity of 112.2, 168.5, and 244.2 kN/m2 sequentially. Moreover, Particle size distribution’s results illustrate that gravel percentage increases in the deeper layers, while fines content decreases with no significant change of sand content. In addition, very high bearing capacity areas were mostly found in the southern and northern parts of the studied area. However, the eastern area represents the area with the minimum bearing capacity where it gradually increases toward the west. Furthermore, the liquid limit and plasticity index reduce from the north to south with an increase in depth of the layers from 3.0-5.0 m. The highest liquid limit value is observed in the depth of 1.5-3.0 m.

Spatial variability and risk assessment of metals in groundwater of district Kamber-Shahdadkot, Sindh, Pakistan

The study examined the groundwater quality of agricultural based area Nasirabad, Warah and Miro Khan talukas of district Kamber-Shahdadkot. Representative 46 samples were collected commonly used for drinking purposes and analyzed for 25 different quality parameters by using standard analytical procedures. The GIS interpolation tools were used to exhibit the spatial variation of groundwater parameters, for drinking as well as irrigation in the study area. The values of EC and TDS varied from 569 to 17790 μS/cm and 364–11385 mg/L respectively. The results of anions varied HCO3− from 140 to 460, Cl− 21 to 4545 and SO42− 28 to 2050 mg/L and results of cations varied from Na+ 20 to 1380, K+ 03 to 143, Ca2+ 32 to 1302 and Mg2+ 15–728 mg/L. Results of heavy metals varied from Pb 0–44 μg/L, Co 3.9 to 63.8, Cu 0 to 3.9 to 63.8, Fe 10.1 to 335, Cr 0 to 134, Ni 0 to 152, Mn 0 to 140 and Cd 0–29.4 μg/L. The fluoride value of 80.44% samples were above the allowable limits for drinking. The water quality index (WQI) results showed that 70% samples were in poor to unfit water categories. The contaminated index showed 47.82% samples with higher Cd values than 3. The chronic daily intake (CDI) and hazards quotient indices (HQ) were also calculated and HQ did not indicate possible hazourds effects on human health. The samples were examined for principal component analysis, Gibb's diagrams, contour diagrams, scatter diagrams, correlation matrix, Piper diagrams and cluster analysis. The Piper plots showed Na+-Cl-, Ca2+-Mg2+-HCO3- and Ca2+-Mg2+-Cl- species and Gibbs diagrams showed rock and evaporation dominancy. The scatter plots were used to know the hydrochemistry of the area like ion exchange and weathering processes. The quality of water was examined for agriculture, based on chloride bicarbonate ratio, sodium percentage, salinity hazard, Kelly's index, chloro alkaline indices I, chloride-sulphate ratio, residual sodium carbonate, sodium adsorption ratio and permeability index. The results showed that 44–80% samples were fit for irrigation purposes depending upon the requirement of agriculture.

Observation‐Based Estimates of Eulerian‐Mean Boundary Downwelling in the Western Subpolar North Atlantic

AbstractA significant fraction of the Eulerian‐mean downwelling feeding the lower limb of the Atlantic Meridional Overturning Circulation (AMOC) occurs along the subpolar North Atlantic continental slopes and is maintained by along‐boundary densification and large‐scale geostrophic balance. We here use Argo and shipboard hydrography data to map the 2002–2015 long‐term mean density field along the boundary via a dedicated optimal interpolation tool. The overall downstream densification implies an Eulerian‐mean downwelling of 2.12 ± 0.43 Sv at 1100 m depth between Denmark Strait and Flemish Cap. A clear regional pattern emerges with downwelling in the Irminger Sea and western Labrador Sea and upwelling along Greenland western continental slope. Comparisons with independent cross‐basin estimates confirm that vertical overturning transport across the marginal seas of the subpolar North Atlantic mainly occurs along the continental slopes, and suggest the usefulness of hydrographic data in providing quantitative information about the sinking branch of the AMOC.

A systematic review on the role of geographical information systems in monitoring and achieving sustainable development goal 6: Clean water and sanitation

AbstractThe world has changed dramatically in the last two decades due to human intervention at regional and global levels. The fates of humanity and our planet are in our hands. Governments, international agencies, the corporate sector, and individuals must work together to shift away from unsustainable practices. The U.N. General Assembly endorsed the 2030 agenda for Sustainable Development, which comprises 17 Sustainable Development Goals (SDGs). Several published research supports SDG strategies in the last 5 years. Despite initial efforts, the U.N. declared that the world is not really on pace to achieve most SDG targets. Geographic information system (GIS) has been identified as an effective tool in accessing, monitoring, and achieving goals that have 15 years lifespan. This paper presents a systematic review of published articles that combined GIS and SDG in their research. Because these 17 goals include 169 targets, the assessment may be more difficult if all the goals were addressed. This study focused only on “SDG 6: Clean water and sanitation” literature. Google scholar was used to find the most relevant 25 studies. The variable attributes like SDG target addressed, study area, monitoring and improving strategies dealt, the particularity of GIS were analyzed in the literature review through content analysis. The systematic review has found that target 6.1 received enormous attention, and the inverse distance weightage (IDW) interpolation tool from the geostatistical analysis toolset was widely employed in the monitoring stage.

Modelling the geographical distribution of plant species reported as anti-malaria and mosquito repellents in Burundi

The conservation and sustainable management of plant species require knowledge of their potential distribution areas and of the factors driving this distribution. A study modeling the distribution of ten flagship plant species reported as anti-malaria and mosquito repellents in Burundi was carried out, with a view to contributing to the establishment of conservation priorities in Burundi, which could also be a reference for other countries. The study was conducted in the four phytogeographic districts of Burundi. It was based on 98 samples from a field data collection on anti-malaria and mosquito repellent plants from Burundi and plant specimens kept at the University of Burundi Herbarium. Potential distribution areas were determined using the Inverse Distance Weighting (IDW) spatial interpolation tool in ArcGIS 10.5 software. The geographic distribution and the ecological factors likely to influence this distribution were determined. The spatial interpolation show that the ten plant species reported as antimalarial and/or mosquito repellent in Burundi can be predicted in all the phytogeographic districts of Burundi. According to the available literature, seven of the ten species belong to the category of widely distributed species. The study shows that there are some differences in terms of distribution especially in the Mosso-malagarazi district. This could be explained by ecological conditions, typical of the lowlands. The distribution models obtained in this study will guide the sustainable plants management in Burundi and elsewhere. In order to increase the production of essential oils and phytochemicals, we recommend the use of these models to identify potential growth sites of the ten anti-malaria and mosquito repellent plant species.

Geostatistics and Artificial Intelligence Applications for Spatial Evaluation of Bearing Capacity after Dynamic Compaction

This study employs geostatistical and artificial intelligence (AI) methods to estimate the degree of ground improvement after dynamic compaction. We implement artificial neural network (ANN) and random forest (RF) for artificial intelligence spatial interpolation to investigate the efficiency of dynamic compaction considering the spatial distribution of the geotechnical parameters. Data used in this study involve averaged SPT N value before dynamic compaction (Nbefore), averaged SPT N value after dynamic compaction (Nafter), applied energy (AE), X‐ and Y‐coordinates at each borehole location, and degree of ground improvement (DI). This study uses the data obtained from a total of 42 borehole logs with an average depth of 17 m and testing depth intervals of 1.5 m after dynamic compaction and 26 SPT‐N log data before dynamic compaction. An optimal spatial interpolation tool selected in this study develops a bearing capacity map after dynamic compaction. The model performance is examined using the correlations between SPT‐based and predicted bearing capacity in the context of mean absolute error (MAE), coefficient of determination (r2), and root mean square error (RMSE). The model with the least MAE and RMSE and the highest r2 is selected as optimal. The optimal RF (RFVD) model has an RMSE of 15.83 while out of the two geostatistical models considered OK recorded the lower RMSE of 22.62. Results show that RF spatial interpolation techniques outperform traditional geostatistical methods. The artificial neural network model shows good compatibility with physical and intuitive processes pertinent to dynamic compaction. The ANN resulted in a prediction RMSE of 0.11 for DI and an r2 of 0.97. This unique approach for evaluating the efficiency of dynamic compaction will be useful to geotechnical engineers when designing site improvement projects, especially dynamic compaction by employing easily obtainable field data for coarse‐grained soils.

Heat transfer optimization using genetic algorithm and artificial neural network in a heat exchanger with partially filled different high porosity metal foam

The metal foam is well known for its high surface area to volume ratio and thus used to transfer heat from the exhaust gas leaving the heat exchanger system. The present work deals with the numerical simulations of a heat exchanger partially filled with three different metal foams made up of Aluminum (Al), Copper (Cu) and Nickel (Ni) having two pore densities namely 20 PPI and 40 PPI, respectively. The hot gas is made to flow through the 8 mm channel in which metal foams are inserted and different heights of foams such as 2 mm, 4 mm, 6 mm and 8 mm are considered for the analysis. The purpose of this study is to optimize thermal performance by increasing heat transfer and decreasing pressure drop which is calculated from the simulations using a commercial software ANSYS FLUENT. In order to achieve this, a optimization technique called Non-dominated Sorting Genetic Algorithm (NSGA-II) is coded in MATLAB by making use of artificial neural network (ANN tool) as an interpolation tool to generate more data based on the already existing data. Finally, Pareto front is obtained for the optimized functional values of heat transfer and drop in pressure after running the code for NSGA-II. From the numerical simulations it is observed that there is 5.68 times enhancement in heat transfer rate when copper metal foam is used for higher inlet velocities, when compared with non-porous channel. From the optimization study, it is found that 50% filled metal foam porous channel is showing enhanced heat transfer rate with decreased pressure drop as depicted in the pareto optimal plot for copper and aluminium.

CityGML model generation using parametric interpolations

With an upsurge in energy demand throughout the globe, efficient energy simulations and smart energy-management solutions are imperative. Often a lack of substantial three-dimensional geometrical data restricts simulation scientists and urban planners to analyse energy demand patterns on a city scale. Therefore, in this paper, the authors describe the open-source City Geographical Markup Language (CityGML) Building Interpolation Tool (CityBIT) for the creation of user-defined and interpolated CityGML LoD2 building models for planned and/or existing buildings. CityBIT can be used to generate CityGML models with different roof types, orientations and elevations. A brief explanation of the functionalities of the tool and the methodologies adapted to develop the tool is provided in this paper. CityBIT aims to facilitate CityGML geometric model development focusing on urban energy performance simulations.

Multivariate geostatistical modeling of seismic data: Case study of the Late Pleistocene paleodelta architecture (SW off-shore Hainan Island, south China sea)

This study investigates a Late Pleistocene paleodelta located on the northwestern shelf of South China Sea, in Beibu Gulf southwest offshore Hainan Island, and details data acquisition to generate reliable 3D geometric models. High-resolution seismic profiles and geostatistical interpolation tools, combined with assumptions about paleo-sea-level changes, allow examination of paleodelta geometrical features. This paper presents methods and procedures that generate morphological models of seismic reflector surfaces and sediment thicknesses. Reliable model-based maps require several trials to optimize various kriging parameters, in particular semivariogram interpretation. Two semivariogram models, the widely-used spherical and nested Gaussian-power methods, are described and applied. Uncertainty among directionally undersampled seismic data is here improved by reduced kriging variance and validation by “goodness-of-fit”. As the investigated paleodelta extends beyond the profile area, an innovative method for data extrapolation is applied based on paleogeographic and paleoeustatic data. Obtained results allow estimation of paleodelta volume and mass and riverine annual sediment discharge to reconstruct Gulf paleogeographic evolution and pathways of sediment distribution.

Surrogate parametric metamodel based on Optimal Transport

The description of a physical problem through a model necessarily involves the introduction of parameters. Hence, one wishes to have a solution of the problem that is a function of all these parameters: a parametric solution. However, the construction of such parametric solutions exhibiting localization in space is only ensured by costly and time-consuming tests, which can be both numerical or experimental. Numerical methodologies used classically imply enormous computational efforts for exploring the design space. Therefore, parametric solutions obtained using advanced nonlinear regressions are an essential tool to address this challenge. However, classical regression techniques, even the most advanced ones, can lead to non physical interpolation in some fields such as fluid dynamics, where the solution localizes in different regions depending on the problem parameters choice. In this context, Optimal Transport (OT) offers a mathematical approach to measure distances and interpolate between general objects in a, sometimes, more physical way than the classical interpolation approach. Thus, OT has become fundamental in some fields such as statistics or computer vision, and it is being increasingly used in fields such as computational mechanics. However, the OT problem is usually computationally costly to solve and not adapted to be accessed in an online manner. Therefore, the aim of this paper is combining advanced nonlinear regressions with Optimal Transport in order to implement a parametric real-time model based on OT. To this purpose, a parametric model is built offline relying on Model Order Reduction and OT, leading to a real-time interpolation tool following Optimal Transport theory. Such a tool is of major interest in design processes, but also within the digital twin rationale.

The Groundwater Geochemistry and the Human Health Risk Assessment of Drinking Water in an Area with a High Prevalence of Chronic Kidney Disease of Unknown Etiology (CKDu), Sri Lanka

Chronic kidney disease of unknown etiology (CKDu) has become an alarming health issue in Sri Lanka. The disease is more notable among farming communities and people who consume groundwater as their main source of drinking water. To assess the possible links between drinking water chemistry and expansion of CKDu, the study was compared with hydrogeochemical data of drinking water sources in a CKDu prevalent area (Girandurukotte GND, Badulla District) and a reference area (Dambethalawa GND, Ampara District) in Sri Lanka. Based on the results, nephrotoxic heavy metal (Cd, Cr, Pb, and As) concentrations were significantly higher in the CKDu prevalent site than the reference area, compromised the harmful consequences to the people in the CKDu hotspot. Results of the inverse distance weighted (IDW) interpolation tool indicated the nephrotoxic heavy metals contents including Cd, Pb, As, and Cr in CKDu hotspot were changed in the ranges of 9.78–187.25 μg/L, 0.08–0.66 μg/L, 20.76–103.30 μg/L, and 0.03–0.34 μg/L. The random distribution patterns were shown by the result in Moran’s index values. Noteworthy, the results have emphasized a strong association between fluoride and water hardness. The frequency of occurrence above the threshold limit of fluoride was 28% in non-CKDu water samples, while 81% in CKDu prevalent sites. The hardness values in the CKDu prevalent site indicated “moderately hard water,” while the non-CKDu area indicated the “soft water.” Furthermore, this paper quantified overall water quality and heavy metal contamination and assessed the human health risks associated with drinking water. According to the results of the water quality index, 90% of the samples in the CKDu prevalent area were classified as “poor water” and “very poor water” for drinking purposes, while 73.33% of the samples in the non-CKDu area were “good” and “excellent” for drinking usage. Calculated chronic daily intake (CDIoral) and hazard quotient (HQoral) of nephrotoxicants were higher in CKDu hotspot than the non-CKDu site. Besides, the hazard index (HI) values obtained for the CKDu prevalent area exceeding the acceptable limit (HI = 1) indicated potential health risks to the people in those areas. This study suggests that long-term exposure to nephrotoxic heavy metals, water hardness, and fluoride present in drinking water may threaten human health and affect kidney functions. Therefore, regular monitoring and better management of water supplies in CKDu prevalent areas are essential to determine the contamination load and reduce the health impacts due to excessive and long-term exposure to the nephrotoxicants.

Spatial-temporal variability and extreme climate indices of precipitation in a coastal watershed of southeastern Brazil.

The analysis of multi-temporal and spatial trends of rainfall in a river basin is an essential approach for water resource planning and management approach. In this study, a combination of trend analysis and spatial-temporal variability of the rainfall for 1970-2017 was applied to examine rainfall distribution patterns in a coastal watershed, Santa Maria da Vitória River Basin (southeastern Brazil). Data from 42 meteorological stations were analyzed using kriging as a geostatistical tool for point data interpolation. Trends in rainfall were computed using the RClimDex package with eleven extreme climate indices. The results have shown spatial and temporal rainfall variability, with drought events becoming more persistent in recent years in the upper sector of the basin, where agricultural land use prevails. Water shortage may impact crops and threatening the water supply and hydropower production. Trend analysis suggests that the annual total wet-day precipitation (PRCPTOT) increases in the coastal section and decreases in the upper basin sector. Consecutive dry days (CDD) and consecutive wet days (CWD) show a strong positive tendency in the lower basin section, where the metropolitan area is located, flooding risks increase in response to positive trends of intensive short-term rainfall events. These results support managers developing and planning sustainability strategies to assure water security and subsidize adaptative responses to extreme hydrological events.

Use of Multiple Low Cost Carbon Dioxide Sensors to Measure Exhaled Breath Distribution with Face Mask Type and Wearing Behaviour.

The use of cloth face coverings and face masks has become widespread in light of the COVID-19 pandemic. This paper presents a method of using low cost wirelessly connected carbon dioxide (CO2) sensors to measure the effects of properly and improperly worn face masks on the concentration distribution of exhaled breath around the face. Four types of face masks are used in two indoor environment scenarios. CO2 as a proxy for exhaled breath is being measured with the Sensirion SCD30 CO2 sensor, and data are being transferred wirelessly to a base station. The exhaled CO2 is measured in four directions at various distances from the head of the subject, and interpolated to create spatial heat maps of CO2 concentration. Statistical analysis using the Friedman’s analysis of variance (ANOVA) test is carried out to determine the validity of the null hypotheses (i.e., distribution of the CO2 is same) between different experiment conditions. Results suggest CO2 concentrations vary little with the type of mask used; however, improper use of the face mask results in statistically different CO2 spatial distribution of concentration. The use of low cost sensors with a visual interpolation tool could provide an effective method of demonstrating the importance of proper mask wearing to the public.

Radial Basis Functions Vector Fields Interpolation for Complex Fluid Structure Interaction Problems

Fluid structure interaction (FSI) is a complex phenomenon that in several applications cannot be neglected. Given its complexity and multi-disciplinarity the solution of FSI problems is difficult and time consuming, requiring not only the solution of the structural and fluid domains, but also the use of expensive numerical methods to couple the two physics and to properly update the numerical grid. Advanced mesh morphing can be used to embed into the fluid grid the vector fields resulting from structural calculations. The main advantage is that such embedding and the related computational costs occur only at initialization of the computation. A proper combination of embedded vector fields can be used to tackle steady and transient FSI problems by structural modes superposition, for the case of linear structures, or to impose a full non-linear displacement time history. Radial basis functions interpolation, a powerful and precise meshless tool, is used in this work to combine the vector fields and propagate their effect to the full fluid domain of interest. A review of industrial high fidelity FSI problems tackled by means of the proposed method and RBF is given for steady, transient, and non-linear transient FSI problems.

Relation between soundscape and spatial configuration in different urban contexts

During the last decade, the problem of noise pollution has continued to increase in Europe as well as in under-developed countries. This issue is stressed in city centers, owing to the abundance of residential activities, vehicle traffic and multiple services. This study investigates the relationship between urban spatial configuration and environment soundscape in two different areas: Pisa historic center, Italy and Biskra downtown, Algeria, using the potential of Space Syntax theory in predicting noise levels distribution. For this analysis, thirty stations of measurements were held in each area during day time using a Sound Level Meter. A Noise map was modeled using the interpolation tool provided by a Geographic Information System program, while the collected data were correlated with the Angular Segment Analysis variables. The findings reveal a close relationship between the sound levels obtained and Space Syntax theory global and local indexes such as Normalized Choice and Integration, which signifies the ability of the approach in describing the sound phenomenon in different urban contexts.

A new insight into analysis of linear elastic fracture mechanics with spherical Hankel boundary elements

In this research, the solution of linear elastic fracture mechanics problems when using spherical Hankel shape functions in the boundary element method is investigated. These shape functions benefit from the advantages of Bessel function fields of the first and second kind in addition to the polynomial ones, which makes them a robust and efficient tool for interpolation. Among the most important features of Hankel shape functions, the infinite piecewise continuity and the utilization of complex space with no influence on the imaginary part of the partition of unity property can be pointed out. The calculation of the stress intensity factors (SIFs) is carried out in five numerical examples so that the accuracy of the present study can be illustrated. Comparisons are made between the results of using spherical Hankel shape functions, the experimental ones, and those obtained by classical BEM with Lagrange shape functions. According to the results of these comparisons, the proposed spherical Hankel shape functions present more accurate and reliable results in comparison with the classic Lagrange ones.

Live Road Condition Assessment with Internal Vehicle Sensors

Modern cars are equipped with many sensors that measure information about the vehicle and its surroundings. These measurements are therefore related to the ride-surface conditions over which the vehicle is passing. The paper commences by outlining a four-component vision for performing road condition evaluation based on in-vehicle sensor readings and subsequent feeding of pavement management systems (PMSs) with live condition information. This is expected to enrich the functionalities of PMSs, and ultimately lead to improved maintenance and repair decisions. Next the LiRA (Live Road Assessment) project—an ongoing proof-of-concept attempt to realize the vision components—is presented. The project elements and software architecture are described in detail, listing any assumptions, compromises, and challenges. LiRA involves a fleet of 400 electric cars operating in Copenhagen, both within the city streets and nearby highways. Sensor data collection is performed with a customized Internet of Things (IoT) device installed in the cars. Data processing and structuring involve new software tools for quality control, spatio-temporal interpolation, and map matching. A hybrid approach, combining machine learning models with physical (mechanics-based) models, is currently being applied to convert data into relevant information for PMSs. Based on the experience thus far with LiRA, the vision actualization is deemed achievable, workable, and up-scalable.

Groundwater quality of an hard rock aquifer in the Subledu Basin of Khammam district, India

Hard rock aquifers of Indian peninsula are loaded with excess nitrate due to heavy use of fertilizers during irrigation and excess fluoride due to the geogenic contamination. This study is focused on the groundwater quality in Subledu Basin in view of the large-scale use of groundwater for both irrigation and drinking purposes as no such study was carried out earlier in the basin. The study area is located at Khammam district, Telangana state, India, which is a hard rock terrain mostly covered with granites and gneisses. Twenty-two groundwater samples were collected covering the entire basin in the month of May 2019 from running hand pumps for analyzing the major anions and cations in the groundwater. The samples were analyzed by using standard gravimetric method for evaluation of total dissolved solids; titrimetric methods to analyze carbonates, bicarbonates and chloride; UV spectrometric method for estimation of nitrate; and ion-selective electrode method for fluoride and spectrophotometer for sulfate and phosphate. These chemical constituents are used to calculate parameters, namely total hardness, sodium adsorption ratio, residual sodium carbonate, sodium percentage, Kelley’s ratio and magnesium hazard. The spatial distribution maps of important chemical constituents are prepared by using the contour maps created by utilizing the inverse distance weighted interpolation tool in the Geographical Information System. The excess fluoride values of 2.84 mg/l, 2.76 mg/l and 1.87 mg/l are observed in the villages of Pocharam, Kistapuram and Turakagudem, respectively, as against the maximum permissible concentration of 1.5 mg/l prescribed by World Health Organization. Excess use of fertilizers for agriculture is causing the nitrate pollution of groundwater in more than 50% of the samples with concentrations ranging from a minimum of 2 mg/l to a maximum of 460 mg/l in the villages of Medidapalle and Bachodu. It is identified that the total hardness is ranging between 200 and 820 mg/l which is very high when compared with the Bureau of Indian Standards. Based on sodium adsorption ratio, residual sodium carbonate, Kelley’s ratio, and sodium percentage analyses, two samples were not suitable for irrigation. Similarly, 13 samples are not suitable for drinking water purposes based on the excess presence of fluoride and nitrate. Groundwater quality maps of Subledu Basin depicting the areas suitable or not for the irrigation as well as for drinking purposes were prepared. From these maps, it is found that groundwater from large parts of the basin is not suitable for drinking purposes while for irrigation purposes it is suitable.