Integrating water quality index, GIS and multivariate statistical techniques towards a better understanding of drinking water quality

Groundwater is considered as an imperative component of the accessible water assets across the world. Due to urbanization, industrialization and intensive farming practices, the groundwater resources have been exposed to large-scale depletion and quality degradation. The prime objective of this study was to evaluate the groundwater quality for drinking purposes in Mewat district of Haryana, India. For this purpose, twenty-five groundwater samples were collected from hand pumps and tube wells spread over the entire district. Samples were analyzed for pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), turbidity, total alkalinity (TA), cations and anions in the laboratory using the standard methods. Two different water quality indices (weighted arithmetic water quality index and entropy weighted water quality index) were computed to characterize the groundwater quality of the study area. Ordinary Kriging technique was applied to generate spatial distribution map of the WQIs. Four semivariogram models, i.e. circular, spherical, exponential and Gaussian were used and found to be the best fit for analyzing the spatial variability in terms of weighted arithmetic index (GWQI) and entropy weighted water quality index (EWQI). Hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) were applied to provide additional scientific insights into the information content of the groundwater quality data available for this study. The interpretation of WQI analysis based on GWQI and EWQI reveals that 64% of the samples belong to the “poor” to “very poor” bracket. The result for the semivariogram modeling also shows that Gaussian model obtains the best fit for both EWQI and GWQI dataset. HCA classified 25 sampling locations into three main clusters of similar groundwater characteristics. DA validated these clusters and identified a total of three significant variables (pH, EC and Cl) by adopting stepwise method. The application of PCA resulted in three factors explaining 69.81% of the total variance. These factors reveal how processes like rock water interaction, urban waste discharge and mineral dissolution affect the groundwater quality.

Energy Absorption Characteristics of a CFRP-Al Hybrid Thin-Walled Circular Tube under Axial Crushing

Thin-walled tubes have gained wide applications in aerospace, automobile and other engineering fields due to their excellent energy absorption and lightweight properties. In this study, a novel method of entropy-weighted TOPSIS was adopted to study the energy absorption characteristics of a thin-walled circular tube under axial crushing. Three types of thin-walled circular tubes, namely, aluminum (Al) tubes, carbon-fiber-reinforced plastics (CFRP) tubes and CFRP-Al hybrid thin-walled tubes, were fabricated. Quasi-static axial crushing tests were then carried out for these specimens, and their failure modes and energy absorption performance were analyzed. The CFRP material parameters were obtained through tensile, compression and in-plane shear tests of CFRP laminates. The finite element models for the quasi-static axial crushing of these three types of circular tubes were established. The accuracy of the finite element models was verified by comparing the simulation results with the test results. On this basis, the effects of the geometric dimension and ply parameters of a CFRP-Al hybrid thin-walled circular tube on the axial crushing energy absorption characteristics were studied based on an orthogonal design and entropy-weighted TOPSIS method. The results showed that Al tube thickness, CFRP ply thickness and orientation have great effect on the energy absorption performance of a CFRP-Al hybrid thin-walled circular tube, whereas the tube diameter and length have little effect. The energy absorption capability of a CFRP-Al hybrid tube can be improved by increasing the thickness of the Al tube and the CFRP tube as well as the number of ±45° plies.

Assessment of Surface Water Quality and Monitoring in Southern Vietnam Using Multicriteria Statistical Approaches

The study was conducted to analyze water quality fluctuations in the South of Vietnam using monitoring data at 58 locations, measured 8 times per year, analyzing 16 water quality indicators in 2020. The study has used national technical regulations on surface water quality (QCVN 08-MT:2015/BTNMT, column A1), water quality index (WQI), cluster analysis (CA), principal component analysis (PCA) and Entropy weighted methods to analyze surface water quality. The results showed that the water quality was contaminated with organics (low DO while TSS, BOD, COD was high), nutrients (mainly N-NH4+) and Fe. Pb at some locations exceeded the allowable limit. Cd, Hg and As were within the allowable limits of QCVN 08-MT:2015/BTNMT, column A1. DO, TSS, BOD, COD, N-NH4+, Fe, EC, TDS, Cl- were seasonally fluctuated. WQI classified water quality from bad to very good (WQI=42-100) due to the impact of hydrological conditions, navigation, wastewater and waste from industrial zones, and fishing ports. The findings presented that it is possible to reduce the 11 sampling locations of cluster 1-6, reduce the frequency of monitoring from 8 to 5 times per year, while still ensuring representativeness of water quality over time, reducing the monitoring costs by 56.5%. The PCA identified five major potential sources explaining 87.3% and 8 minor sources explaining only 12.7% of water quality variation. Temperature, pH, EC, DO, BOD, COD, N-NH4+, N-NO2-, Fe, Cl-, Pb need to be monitored, while adding indicators P-PO43-, TP, TN, coliforms into the future monitoring program. The study shows that the medium and bad water quality are concentrated in Dong Nai, Ho Chi Minh City and Long An, so the relevant environmental management agencies needs to find solutions to improve the water quality in those areas. The current results can assist in decision-making related to environmental quality monitoring in the southern region of Vietnam.

A Hybrid Multi-Objective Optimization Method Based on NSGA-II Algorithm and Entropy Weighted TOPSIS for Lightweight Design of Dump Truck Carriage

The lightweight design of vehicle components is regarded as a complex optimization problem, which usually needs to achieve two or more optimization objectives. It can be firstly solved by a multi-objective optimization algorithm for generating Pareto solutions, before then seeking the optimal design. However, it is difficult to determine the optimal design for lack of engineering knowledge about ideal and nadir values. Therefore, this paper proposes a multi-objective optimization procedure combined with the NSGA-II algorithm with entropy weighted TOPSIS for the lightweight design of the dump truck carriage. The finite element model of the dump truck carriage was firstly developed for modal analysis under unconstrained free state and strength analysis under the full load and lifting conditions. On this basis, the multi-objective lightweight optimization of the dump truck carriage was carried out based on the Kriging surrogate model and the NSGA-II algorithm. Then, the entropy weight TOPSIS method was employed to select the optimal design of the dump truck from Pareto solutions. The results show that the optimized dump truck carriage achieves a remarkable mass reduction of 81 kg, as much as 3.7%, while its first-order natural frequency and strength performance are slightly improved compared with the original model. Accordingly, the proposed procedure provides an effective way for vehicle lightweight design.

Multi-Objective Reliability-Based Optimization of Control Arm Using MCS and NSGA-II Coupled with Entropy Weighted GRA

Lightweight design is one of the important ways to reduce automobile fuel consumption and exhaust emissions. At the same time, the fatigue life of automobile parts also greatly affects vehicle safety. This paper proposes a multi-objective reliability optimization method by integrating Monte Carlo simulation (MCS) with the NSGA-II algorithm coupled with entropy weighted grey relational analysis (GRA) for lightweight design of the lower control arm of automobile Macpherson suspension. The dynamic load histories of the control arm were extracted through dynamic simulations of a rigid-flexible coupling vehicle model on virtual proving ground. Then, the nominal stress method was used to predict its fatigue life. Six design variables were defined to describe the geometric dimension of the control arm, while mass and fatigue life were taken as optimization objectives. The multi-objective optimization design of the control arm was carried out based on the Kriging surrogate model and NSGA-II algorithm. Aiming at the uncertainty of design variables, the reliability constraint was added to the multi-objective optimization to improve the reliability of the fatigue life of the control arm. The optimal design of the control arm was determined from Pareto solutions by entropy weighted grey relational analysis (GRA). The optimization results show that the mass of the control arm was reduced by 4.1% and the fatigue life was increased by 215.8% while its reliability increased by 7.8%. The proposed multi-objective reliability optimization method proved to be feasible and effective for lightweight design of a suspension control arm.

Hydrochemistry and Entropy-Based Groundwater Quality Assessment in the Suining Area, Southwestern China

Groundwater is an essential resource for sustainable development, whose quality is significant for human health. In the present study, twenty-eight groundwater samples were collected from domestic tube wells and public water supply wells in the Suining area, southwestern China. The integration of statistical analysis, correlations of ions, geomodelling, and entropy-weighted water quality index (EWQI) was carried out to clarify the hydrochemistry and groundwater quality in the study area. By the statistical analysis, the cations followed the concentration order as Ca2+> Na+> Mg2+> K+, while anions’ concentrations were HCO3− > SO42− > Cl− > NO3− > F−. Piper trilinear diagram showed the hydrochemical type was characterized as Ca-HCO3. Correlations of ions and geomodelling revealed the concentrations of major ions were mainly determined by carbonate dissolution and ion exchange process, and NO3− concentrations were controlled by agriculture activities. EWQI computation demonstrated that most of the groundwater samples possessed EWQI values higher than 100. Therefore, groundwater quality is lower than the permissible limit of the World Health Organization (WHO), suitable for drinking purposes in the Suining area. Our study provides vital knowledge for groundwater management in the Suining and other similar areas.

The Status of Surface Water in West Tripura District, India: An Approach by Using Water Quality Index and Multivariate Statistical Technique

The present investigation provides a better interpretation of surface water (rivers, ponds, bills, lakes, etc.) quality utilising entropy weighted water quality index (EWWQI) and different multivariate statistical techniques. Eleven physicochemical parameters including alkalinity, dissolved oxygen (DO), pH, total dissolved solids (TDS), electrical conductivity (EC), calcium (Ca), turbidity, magnesium (Mg), total hardness (TH), chloride (Cl-), and iron (Fe) were analysed and monitored at 23 sampling sites (in December 2018) of West Tripura district. Experimental outcomes of turbidity followed by Fe contamination exceeded recommended WHO standard limit. The maximum values of Fe and turbidity were estimated as 8.745 mg/L and 797.7 NTU, respectively. WQI values confirmed that most of the monitoring locations had poor water quality except three reported areas (S7, S14, and S15) but without Fe and turbidity, estimated WQI confirmed drinkable water condition for entire samples. Multivariate statistical approaches like correlation analysis, principal component analysis (PCA) and cluster analysis (CA) were applied to explore water quality. PCA outcomes recognised three principal factors explaining almost 85% of the total variance. CA investigated three major clusters of 23 sampling sites namely less polluted, highly polluted and moderately polluted zone. Confirming all above, the surface water at the monitoring locations is a major concern which may lead to serious health issues in local people.