Multivariate Techniques Research Articles

Environmental Health Attitudes, Practices, and Educational Preferences: A National Survey of Reproductive-Aged Women in Canada

Prenatal exposures to environmental toxicants can adversely affect fetal and child development and lead to increased risk of chronic disease. While regulatory action is essential to reduce sources of environmental toxicants, prenatal care presents an opportunity to educate, mobilize, and support prospective parents to reduce exposures to such hazards. As the first phase of an interdisciplinary research collaboration to inform the development of prenatal environmental health education strategy in Canada, we surveyed reproductive-aged female individuals. The online survey (July–September 2021) yielded a nationally representative sample of 1914 reproductive-aged females living in Canada. The questionnaire topics addressed the respondents’ knowledge and perceptions of environmental health risks, preventive actions and related facilitators and barriers, information sources and preferences, reproductive history, and demographics. The analysis included bivariate and multivariate techniques. Our results suggest broad awareness among reproductive-aged females that exposure to toxicants can be harmful, and that reducing prenatal exposures can benefit child health. However, fewer than half of respondents felt that they had enough knowledge to take protective measures. Despite high levels of preference for prenatal care as an ideal context for learning about environmental health risks and protective measures, fewer than one in four respondents had ever discussed environmental health concerns with a healthcare provider. Our findings reveal a knowledge–action gap and a corresponding opportunity to improve environmental health education and advocacy in prenatal care in the Canadian context.

Mathematical modeling for the efficiency function of the Retiro small hydroelectric power plant turbine-generator set

ABSTRACT Many authors use an efficiency constant for the turbine-generator set to calculate energy generation in hydroelectric projects. This often does not reflect reality because the efficiency of this system is relative and is related to the potential of the net head height and the flow of turbine discharge. The present work aims to perform a mathematical modeling for an analysis of the efficiency of the Turbine-Generator Small Hydroelectric Power Plant (SHPP) Retiro. To perform this analysis, a model based on a second-degree polynomial function was employed. A multivariate regression technique was applied by the Least Square Method (LSM) to obtain the coefficients of the function. To verify the behavior of the measured and adjusted data, the Determination Coefficient (R2) of Root Mean Square Error (RMSE) algorithm was applied. The RMSE Mean of 0.022 and the RMSE Std of 0.029 suggest that the second-degree polynomial function is more consistent and stable across different training and validation datasets. The R2 value of 0.993 indicates that 99.3% of the variability in the data is explained by the model. The Omnibus, Durbin-Watson, and Jarque-Bera tests were applied to diagnose potential issues related to normality, autocorrelation, and adequacy, thereby validating the accuracy and significance of the model. The Hessian matrix technique was also used to verify the critical points of the function. The critical point corresponding to a water head of 11.47 meters and a turbine flow of 145.1 m3/s presented the highest operational efficiency. This study allows presenting a turbine efficiency function, its Hill Curve and a generator efficiency function with its corresponding Efficiency Curve. For efficiency analysis in several flow, height and power settings. The technique has proved efficient and can be an applicable tool in energy parameter studies for other hydroelectric projects with the same characteristics as SHPP Retiro.

Spatiotemporal Variation Characteristics and Source Identification of Nitrogen in the Baiyangdian Lake Water, China

To study the characteristics and sources of nitrogen in the Baiyangdian Lake, this research conducted water quality monitoring during three hydrological periods (normal period, flood period, and dry period), and 165 pieces of routine water quality monitoring data were collected from the three national control sections for Baiyangdian Lake and its inflow rivers. By integrating water chemical analysis with multivariate statistical techniques, the study comprehensively investigated the spatiotemporal variation patterns of nitrogen in Baiyangdian Lake and identified the sources of nitrogen pollution. The results showed that the concentration of total nitrogen (TN) was highest during the dry period, reaching an average of 0.924 mg/L, and 31.3% of the sites exceeded the national Grade III surface water quality standard, reflecting a potential risk of nitrogen pollution. Based on the ion ratio method and principal component analysis (PCA), the main sources of nitrogen pollution in Baiyangdian Lake were identified as manure and domestic sewage, with agricultural fertilizers also having a certain impact on water nitrogen pollution. In addition, the study also compared the nitrogen concentration in Baiyangdian Lake with several important lakes in China. The results showed that the concentrations of TN and ammonium nitrogen (NH4+-N) in Baiyangdian Lake are lower than those in lakes in areas with similar human activity intensity, indicating that the water quality of Baiyangdian is relatively good. This study can provide a scientific basis for water quality management and pollution prevention for Baiyangdian Lake.

Marker Peptides for Indicating the Spoilage of Milk-Sample Preparation and Chemometric Approaches for Yielding Potential Peptides in a Raw Milk Model.

The diminishing of food waste is gaining increasing importance, especially in context with a growing population and a need for the sustainable use of food resources. A more precise determination of the best-before date can contribute to this general aim. As proteoforms can be regarded as indicators for ecophysiological influences, their suitability for determining the spoilage and, consequently, the shelf-life of food is suggested. Proteoforms reflect the spoilage of food more accurately. The aim of the present study was to develop an efficient proteomics workflow to determine the shelf-life of milk as a prominent target. In this case, raw milk was chosen as model, as it degrades much faster. The integration of different multivariate analysis techniques was used to analyze the spoilage of raw milk with regard to aspects of its proteome. As the feasibility of such an approach has already been demonstrated in previous studies, it is further necessary to enable a robust and reproducible workflow, primarily gaining appropriate numbers and amounts of peptides when the research question differs and other dairy products are evaluated. In the present study, two approaches for gaining peptides were considered: In addition to a direct hydrolysis of a protein-rich sample solution, in-gel hydrolysis is another common approach in proteomics. By separating the proteins in a traditional gel electrophoresis before hydrolysis, the change in the individual proteins and, consequently, potential peptides can be monitored more specifically during storage. However, the traditional approach offers not only possibilities but also limitations that must be considered. The study showed that it is beneficial to apply a combination of different application strategies, as they complement each other and can thus increase the information content of a sample or confirm a theory. Mass spectrometric features, which represent a chemical-structural change of all kinds of compounds during storage, were selected, and three of them were identified as peptides, originating from α-s1-casein.

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Attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) analysis of human nails: Implications for age determination in forensics.

A person's age estimation from biological evidence is a crucial aspect of forensic investigations, aiding in victim identification and criminal profiling. In this study, we present a novel approach of utilizing Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) spectroscopy to predict the age of donors based on nail samples. A diverse dataset comprising nails from donors spanning different age groups was analyzed using ATR FT-IR, with subsequent multivariate analysis techniques used for age prediction. The developed partial least squares regression (PLS-R) model demonstrated promising accuracy in age estimation, with a root mean square error of prediction (RMSEP) equal to 11.1 during external validation. Additionally, a partial least squares discriminant analysis (PLS-DA) classification model achieved high accuracy of 88% in classifying donors into younger and older age groups during external validation. This proof-of-concept study highlights the potential of ATR FT-IR spectroscopy as a non-destructive and efficient tool for age estimation in forensic investigations, offering a new approach to forensic analysis with practical implications.

Influence of Individualism or Communitarianism of Actors on Opportunism in a Coopetition

This paper attempts to show that opportunism in a coopetitive market can be influenced not only by the individualism of the actors but also by communitarianism. Our theoretical anchor is the contract theory to explain the opportunism of the actors by communitarianism or individualism. We adopted a mixed methodology (quali-quanti). The data used are primary, from the administration of a questionnaire to 207 clandos in a coopetitive market in the alternative transport sector in Gabon. The multivariate qualitative comparative analysis technique (mvQCA) was used. The results indicate that opportunistic behavior in a coopetitive market is explained mainly by national communitarianism (Gabonese), then transnational communitarianism (natives of Central Africa except nationals and natives of West Africa), under conditions specific to each community; Opportunism is subsidiarily explained by the individualism of the actors who paradoxically trust each other in a competitive coopetition but these dishonest actors are paradoxically not sanctioned.

Deep Information Retention Network‐Enabled Data Modeling for Key Quality Indicator Prediction in the Chemical Industry

ABSTRACTDeep learning has attracted widespread attention in data modeling and key quality indicator prediction in the chemical industry. However, traditional deep learning networks usually distort the original data distribution due to the superposition effect of multiple layers of nonlinear activation functions. In this case, multivariate statistical learning techniques present an avenue to reveal the intrinsic relationship of the data by combining the linear trends between input and predictor variables. To comprehensively capture data features from multiple perspectives, this study proposes a deep learning‐based data modeling network called the information retention unit (IRU). This network combines intrinsic attributes to partial least squares (PLS) and autoencoder (AE) modalities, thus engendering an adaptive response to the complex linear and nonlinear data features. Furthermore, multiple IRUs can be stacked to construct a deep information retention network (DIRN), which enhances the robust extraction of deep data features. Finally, the effectiveness of the proposed network is validated through its prediction application on a dataset obtained from a real‐world chemical industrial process. This method combines multivariate statistical learning techniques based on deep learning, providing an innovative and practical solution for data analysis and prediction in the chemical industry.

Determination of Factors Affecting Farmers' Preference for Corn Seed by Conjoint Analysis

Modeling consumer preferences among multi-attribute alternatives has been one of the main activities in consumer research. Conjoint analysis is a multivariate statistical analysis technique that reveals the acceptance of criteria such as yield, price and adaptability that are important in consumers' usage habits and preferences. In this study, it was aimed to determine the degree of importance given by grain corn growers to seed traits in corn seed purchase. For this purpose, conjoint analysis was applied and preferences in seed purchase were determined according to the data obtained from the survey conducted among farmers. The survey questions used in the research were applied to 123 farmers. Kendall's Tau value, which is the representativeness of the model, was 0.935 (p

Study on the concentration, distribution, and persistence of health spending for the contributory scheme in Colombia

Colombia is among the countries with the most robust financial protection against personal health spending in the world, with out-of-pocket spending ranking lowest across OECD countries. We investigate the evolution, distribution, and persistence of health spending by age group, sex, health care setting, health condition and geographic region for over 19 million users of Colombia’s health system between 2013 and 2021 (contributory scheme). We use average patient-level expenditure data from the Health-Promoting Entities of the Ministry of Health and Social Protection. We applied multivariate statistical techniques such as multiple correspondence analysis, factor maps and correlations. For both sexes, average health expenditure increases gradually with age until 60 years, accelerating thereafter abruptly. Health conditions with the highest percentage of expenditure were those related to neoplasms, blood diseases, circulatory system, pregnancy, puerperium and perinatal period. We found that home-based care in Amazonía-Orinoquía is almost non-existent, and that outpatient care represents a high proportion in all age groups (over 65%) compared to the other regions. There is a strong persistence of expenditure from one year to the next (i.e. they can provide relevant information for prediction), especially in areas with a larger supply of health services such as Bogotá-Cundinamarca. To the authors’ knowledge, this is the most comprehensive and detailed micro-analysis of health spending that has been developed for a Latin American country to date.

Effects of oral contraceptive pills on brain networks: A replication and extension.

Neuroimaging research has identified significant effects of oral contraceptive pills (OCPs) on brain networks. A wide variety of approaches have been employed, largely in observational samples, with few converging results. This study therefore was designed to test for replication and extend this previous work using a randomized, double-blind, placebo-controlled crossover trial of the effects of OCPs on brain networks. Using functional MRI, we focused on brain regions identified in prior studies. Our analyses did not strictly replicate previously reported effects of OCPs on functional connectivity. Exploratory analyses suggested that traditional seed-based approaches may miss broader, network-level effects of OCPs on brain circuits. We applied data-driven, multivariate techniques to assess these network-level changes, A deeper understanding of neural effects of OCPs can be important in helping patients make informed decisions regarding contraception, mitigating unwanted side effects. Such information can also identify potentially confounding effects of OCPs in other neuroimaging investigations.

Evaluations of Groundwater Quality using Principal Component Analysis and Associated Multivariate Techniques: A Case History in Ewekoro Communities, South-West Nigeria

The quest for quality water consumption is comprised of integration of numerous techniques from different professionals whose contributions go a long way in determining overall health status and wellness of any given society. The water quality status of boreholes in Ewekoro communities of Ewekoro Local Government Area, South-West Nigeria was investigated in this work. Water samples were collected from 25 boreholes at various sampling stations across the study area. Standard analytical water quality methods using Inductively Coupled Mass Spectrometry (ICP-MS)/Optical Emission Spectrometry (ICP-OES) and Pour Plate Techniques were respectively employed in the laboratory for the samples’ geochemical and bacteriological analyses. The raw data were processed and analyzed using Principal Component Analysis and other multivariate techniques with Excel and SPSS 20.0 statistical software packages with the corresponding outcomes systematically compared with water quality standards. For the PCA, the varimax rotation converged in 23 iterations with 6 components extracted which accounted for 92.76% of the total cumulative variance respectively observed for geochemical parameters. Na2+, K+, Ca2+, Cl-, SO42-, NH4+, PO43-, and HCO3-, Mg3+, Cu2+, Cd2+, Zn2+, Ni, S , Si, Fe3+, NO3− and Al3+ played a significant contribution with higher factor loading supported by the dominance of HCO3− among the water quality determinants. The rotation converged in 8 iterations with 3 extracted components that accounted for a total cumulative variance of 90.99% % with pH, TEMP, EC, BOD, TS, TURB, ALK, TH, TDS, THC, and DO in the Physico-chemical Parameters series. Most quality determinants in the sampled water are within allowable limits of the approved water quality standards including WHO except Alkalinity, HCO3−, MgCO3−, Cl−, BOD, Cd2+, Fe3+ and Zn2+. The overall ionic dominance pattern follows the same trend K+ >Na2+ > Fe3+ >Mn2+ and Cl- >NO3- >NO2-. The results of the microbial analyses revealed the highest Total Coliform Count of 17 x 102 MPN/100 ml while that of THBC and FCC were reported as 7.8 ×102 cfu/ml and 1.2×102 cfu/ml respectively. The hygienic status of the water sources was found to be poor. High BOD, Coliform Count and BOD5:NO3- ratios of 248.23 recorded for the groundwater samples were suggestive of possible organic pollution due to faecal invasion with the lowest measured depth of 35m. Multivariate statistical approaches namely Correlation, Principal Component Analysis and Descriptive statistics simplified and identified the interrelationship among the analyzed physicochemical parameters and the pollution sources. This study has therefore revealed that boreholes in Ewekoro were polluted and posed potential risk to biomedical safety and overall human health. Intervention measures are therefore necessary to safeguard the inhabitants from water-related diseases and their consequences.

Spatial variation in physicochemical water quality parameters associated with Rice farming in the Dakalt Branch Canal, Kafrelsheikh governorate, Egypt

AbstractThe inappropriate use of chemical fertilizers and pesticides in rice farming has been identified as a significant source of pollution in the aquatic environments. The declining water quality has been a cause of concern for the riparian communities owing to ill health associated with the usage of contaminated water from lotic systems draining such rice‐growing areas and its potential to cause lethal and sublethal effects on aquatic organisms. The spatial variation of selected water quality parameters was assessed using multivariate techniques. Data from three sampling stations, collected in triplicates, were analyzed to understand the effects of rice farming practices on the Dakalt Branch Canal's water quality. Eleven physicochemical parameters were measured in situ with a portable multimeter, while nutrients and other chemical parameters were determined following the standard procedures outlined in the American Public Health Association using the UV/Visible spectrophotometer. One‐way ANOVA was used to establish whether or not there were significant differences among means of various parameters. The findings revealed considerable spatial variability in the means for nearly all water quality parameters (p < 0.05). The Water Quality Index revealed that all the sampling stations were polluted with indices of more than 100. We conclude that poor rice management practices are responsible for the observed pollution of the River Nile and potentially lethal and sublethal effects on aquatic organisms and human health. We therefore recommend regular monitoring of the water quality of the Dakalt Branch Canal and that the relevant stakeholders, including farmers, embrace Best Management Practices, especially when applying pesticides and fertilizers.

Exploring barriers to Quality 4.0 implementation: a multivariate analysis

PurposeIndustry 4.0 (I4.0) technologies and practices are changing the way quality is managed in organizations, giving rise to the movement known as “Quality 4.0” (Q4.0). In this context, this study aims to analyze the barriers organizations face when trying to implement Q4.0 and to examine whether there are significant differences according to size, sector, level of excellence and degree of readiness for Q4.0 implementation.Design/methodology/approachThe sample comprises 107 Spanish organizations with some form of European Foundation for Quality Management (EFQM) excellence recognition system. The methodology includes multivariate techniques (factor analysis and mean difference analysis).FindingsThe study has managed to summarize the ten main barriers to the implementation of Q4.0 identified in the literature in three groups: organizational (five barriers), knowledge (two barriers) and technological (three barriers). The main barriers are the high investment in technology required to implement Q4.0 and the need for more training and competence of workers. In addition, SMEs encounter more significant technological barriers when implementing Q4.0 than large companies. However, organizational and knowledge barriers are the same. Organizations in the service sector come across more organizational and knowledge barriers to implementing Q4.0 than industrial organizations; no differences were identified for technological barriers. The higher the EFQM seal of excellence achieved, the lower the knowledge barriers faced, with no differences in organizational and technological barriers. Finally, it is observed that the more prepared organizations are to implement Q4.0, the lower the barriers they face.Research limitations/implicationsThe study helps managers identify the barriers they may encounter when implementing Q4.0, which helps design short- and long-term plans to mitigate or break down these barriers.Originality/valueThe study provides empirical evidence on an organization’s barriers to implementing Q4.0. On the other hand, the barriers are related to some of the relevant characteristics that define the organizations, such as their size, the sector of activity, their level of excellence and their degree of preparation for implementing Q4.0.

PET Imaging of the Serotonin 1A Receptor in Major Depressive Disorder: Hierarchical Multivariate Analysis of [11C]WAY100635 Overcomes Outcome Measure Discrepancies

Abstract The serotonin 1A receptor has been linked to both the pathophysiology of major depressive disorder (MDD) and the antidepressant action of serotonin reuptake inhibitors. Most PET studies of the serotonin 1A receptor in MDD used the receptor antagonist radioligand, [carbonyl-11C]WAY100635; however the interpretation of the combined results has been contentious owing to reports of higher or lower binding in MDD with different outcome measures. The reasons for these divergent results originate from several sources, including properties of the radiotracer itself, which complicate its quantification and interpretation; as well as from previously reported differences between MDD and healthy volunteers in both reference tissue binding and plasma free fraction, which are typically assumed not to differ. Recently, we have developed two novel hierarchical multivariate methods which we validated for the quantification and analysis of [11C]WAY100635, which show better accuracy and inferential efficiency compared to standard analysis approaches. Importantly, these new methods should theoretically be more resilient to many of the factors thought to have caused the discrepancies observed in previous studies. We sought to apply these methods in the largest [11C]WAY100635 sample to date, consisting of 160 individuals, including 103 MDD patients, of whom 50 were not-recently-medicated and 53 were antidepressant-exposed, as well as 57 healthy volunteers. While the outcome measure discrepancies were substantial using conventional univariate analysis, our multivariate analysis techniques instead yielded highly consistent results across PET outcome measures and across pharmacokinetic models, with all approaches showing higher serotonin 1A autoreceptor binding potential in the raphe nuclei of not-recently-medicated MDD patients relative to both healthy volunteers and antidepressant-exposed MDD patients. Moreover, with the additional precision of estimates afforded by this approach, we can show that while binding is also higher in projection areas in this group, these group differences are approximately half of those in the raphe nuclei, which are statistically distinguishable from one another. These results are consistent with the biological role of the serotonin 1A autoreceptor in the raphe nuclei in regulating serotonin neuron firing and release, and with preclinical and clinical evidence of deficient serotonin activity in MDD due to over expression of autoreceptors resulting from genetic and/or epigenetic effects. These results are also consistent with downregulation of autoreceptors as a mechanism of action of selective serotonin reuptake inhibitors. In summary, the results using multivariate analysis approaches therefore demonstrate both face and convergent validity, and may serve to provide a resolution and consensus interpretation for the disparate results of previous studies examining the serotonin 1A receptor in MDD.

Alternative Tree Species for Sustainable Forest Management in the Brazilian Amazon

The scarcity of hardwoods from tropical forests makes the search for alternative species necessary for commercialization. This study aimed to establish groups of timber species from the Amazon Forest with potential for logging purposes through the assessment of their physical-mechanical properties, aiming to identify alternative species that can meet the market demands. We utilized data from the Forest Products Laboratory (LPF) (containing information on basic density and other wood mechanical properties) and the Timberflow platform, as well. We applied a multivariate cluster analysis technique with the aim of grouping species based on the technological characteristics of their wood and evaluating similarity among them to obtain homogeneous groups in terms of economic potential and utilization. The results indicated four homogeneous groups: Cluster 1 (40.72% of species, basic density-db: 690 kg m−3), Cluster 2 (13.92%, db: 260 and 520 kg m−3), Cluster 3 (27.32%, db: 550 and 830 kg m−3), and Cluster 4 (18.04%, db: 830 kg m−3). Most of the 20 listed species are classified as more commercially viable (70%), with high wood density. Species identified as alternatives include Dialium guianense and Zollernia paraensis for Dipteryx odorata, Terminalia argentea for Dinizia excelsa, Terminalia amazonia and Buchenavia grandis for Goupia glabra, and Protium altissimum and Maclura tinctoria for Hymenaea courbaril. The analysis highlighted the overexploitation of a restricted group of species and the need to find alternatives to ensure the sustainability of forest management. This study contributed to identifying species that can serve as alternatives to commercial ones, promoting a more balanced and sustainable forest management.

Characterization of Pasture Production Systems in Makueni County of Kenya

Livestock feed is a key factor influencing animal production and productivity as evidenced by the increased demand for animal source foods (ASFs) to feed a growing human population in Kenya. However, there exists untapped potential of pasture commercialization and hence the need to harness the social, economic and environmental benefits in the ASALs for overall rural development. A study was conducted to characterize the existing pasture production systems in Makueni County. A purposive random sampling of 300 respondents drawn from 3 Sub-counties and 12 wards was conducted in Makueni County in January, 2021. The study aimed to characterize existing pasture production systems using a structured questionnaire. Data were analyzed using descriptive and inferential statistics. Multivariate statistical techniques; principal Component Analysis (PCA) and Cluster Analysis (CA) were used to determine whether or not there were significant differences in the pasture production systems in Makueni County. Results of the study showed that majority (97%) of the farmers were small scale farmers (SSFs) who dedicated less than 5 acres of their land to pasture production. The mean age of small- scale farmers was 52 years compared to 55 years for the large-scale farmers (LSFs). Most of the households were male headed (83%). Most of the SSFs household heads had primary level of education level or lower while most of the large-scale household heads had secondary education and above. Majority (35%) of SSFs owned the land under pasture without a title while most of the LSFs had a title. The land under pasture for SSFs was about 2 acres with an average of 102 bales per season while LSFs had about 23 acres under pasture and produced about 1,762 bales per season. Majority (92%) grew local grass varieties and sold their pasture in form of a bale. 58% of SSFs sited NGOs as their main source of grass seeds while LSFs mainly sourced from agrovets Results of PCA revealed that 6 of the 17 components had eigen values greater than 1 and accounted for 58% of the total variance. Based on Euclidian distance, six clusters were determined using the agglomeration schedule. ANOVA analysis of the six profiles were estimated to have p-values of 0.000, suggesting the existence of significance difference between cluster 1 to 6 in relation to the 6 profiles and hence concluding the existence of variations in pasture production systems in Makueni County. Development strategies should focus on knowledge of and improved access to grass seeds to farmers as well as development of standards of the mode of sale of pasture.

Multivariate Calibration Techniques Using UV Spectrophotometry for Quantifying Ticagrelor in Pharmaceutical Formulations

Multivariate Calibration Techniques Using UV Spectrophotometry for Quantifying Ticagrelor in Pharmaceutical Formulations

Impacts on the quality of surface water in a urban perimeter of the Rio Grande watershed, Brazilian Cerrado.

The aim of this study was to assess the spatiotemporal variation in water quality in the Grande River and the Ondas River, in the city of Barreiras, Bahia, Brazil. Water samples were collected at 11 points along the rivers, and eight physical-chemical parameters (electrical conductivity, pH, alkalinity, apparent and true color, turbidity, dissolved oxygen, and biochemical oxygen demand) and three microbiological indicators (heterotrophic bacteria, total and thermotolerant coliforms) were analyzed. Spatiotemporal variation was assessed using the multivariate techniques of principal component analysis/factorial analysis (PCA/FA) and hierarchical cluster analysis (HCA). The results of the PCA/FA highlighted eight of the eleven parameters as the main ones responsible for the variations in water quality, with the greatest increase in these parameters being observed in the rainy season, especially among the points influenced by sewage discharges and by the influence of the urban area. The CA grouped the results from 11 points into three main groups: group 1 corresponded to points influenced by sewage discharges; group 2 grouped points with mainly urban influences; and group 3 grouped points in rural areas. These groupings showed the negative influence of urbanization and also statistically significant variations between the groups and periods. The most degraded conditions were in group 1, and the least degraded conditions were in group 3. Assessment of the variations between the monitoring periods showed that rainfall had a significant impact on the increase or decrease in the parameters assessed, as a result of surface runoff linked to urbanization and increased river flow.

Assessing drinking water quality in Eloued, south-East Algeria, using the groundwater pollution index (GPI) and the synthetic pollution index (SPI) model.

Groundwater quality degradation is a significant environmental issue worldwide, with potentially severe economic consequences and harm to ecosystems and biodiversity. This can directly affect human health, particularly in developing countries where rapid and uncontrolled urbanization is on the rise. Groundwater is the primary resource for meeting the water needs of the Eloued region, located in southeastern Algeria. Water is considered unfit for human consumption if its physico-chemical elements exceed national or international standards or guidelines. We used the GPI and SPI indices to evaluate the quality of groundwater suited for drinking. Groundwater samples were obtained from 22 wells at depths of more than 250m. Standard analytical procedures were used to determine the physicochemical characteristics of the collected samples, which included pH, EC, TDS, Na+, Ca+2, Mg+2, K+, Cl-, HCO3-, SO4-2, NO3-, NO2-, NH4+ and PO4-3. Multivariate statistical analysis and GIS techniques were used to process the results. The results of the selected physicochemical parameters were compared with World Health Organization (WHO) guidelines to determine the quality of drinking water. The findings indicate that the waters of the terminal complex aquifer are salty and contain medium to high quantities of main ions that surpass the established drinking water limits. The primary ions' relative abundance is Cl- > SO4-2 > HCO3- > NO3 for anions and Na+ > Ca+2 > Mg+2 > K+ for cations. Groundwater chemical types were dominated by Na+, Ca+2, Cl-, and SO4-2. Principal Component Analysis (PCA) showed that alteration and dissolution of carbonates, evaporates, salts, partly silicates, and evaporation, are the main reasons affecting the chemical composition of water in Eloued. The GPI results show that 18.18%, 54.54%, and 27.27% of the water samples were classed as lightly polluted, moderately polluted, or substantially polluted for drinking purposes, respectively. According to the SPI study, 9.09%, 36.36%, 36.36%, and 18.18% were considered drinkable, mildly contaminated, moderately polluted, and seriously polluted for drinking purposes, respectively. According to the GPI and SPI models' geographical distribution maps, potable water is generally scarce and concentrated in the northeastern section of the research area, near the town of Ourmes.