Groundwater Quality Evaluation of the Dawu Water Source Area Based on Water Quality Index (WQI): Comparison between Delphi Method and Multivariate Statistical Analysis Method

Surface water assessments are of critical importance for balancing economic development with the ecological environment in rapidly developing regions. In this research, Shengzhou City, a typical town in the Yangtze River Delta region of China, was chosen to conduct a surface water quality study. As a region with a well-developed water system, monthly water quality monitoring data from eight sampling sites on the major tributaries and the mainstream were selected for six consecutive years from 2013 to 2018, containing seven important water quality indicators (pH, DO, CODMn, CODCr, BOD, NH4+-N, and TP). The comprehensive evaluation method based on the water quality index (WQI) and multivariate statistical analysis methods of cluster analysis (CA) and principal component analysis (PCA) were applied to explore the spatial and temporal changes of water quality in Shengzhou City. The main findings are as follows: (1) spatially, for three main tributaries, Xinchang River had the worst water quality, followed by Changle River, while Huangze River had the best. The water quality of the tributaries had higher volatility than the mainstream. (2) The sampling sites with similar locations had similar water quality characteristics. (3) Seasonally, for the four indicators of DO, CODMn, CODCr, and BOD, the water quality was better in the dry season while, for NH4+-N and TP, water quality was better in the wet season. The low WQI points were more likely to appear in the wet season. (4) The results of WQI assessment showed an improving trend in water quality. (5) Nitrogenous substances and organic matter were the key pollutants in this area. The research results prove that water quality evaluation methods and multivariate statistical methods are effective for the study of regional surface water quality.

Insights into the long-term pollution trends and sources contributions in Lake Taihu, China using multi-statistic analyses models.

In order to understand the sources of pollutants and the temporal and spatial distribution characteristics of the water quality in Cihu Lake, China, the monitoring data of seven water quality indicators from 12 sampling sites from 2015 to 2019 were selected, and the temporal and spatial variation laws of the water quality and pollution sources were analyzed by the use of the multivariate statistical analysis method. The results show that nitrogen and phosphorus pollution in the lake is dominant. The average concentrations of total nitrogen (TN) and total phosphorus (TP) exceed the surface water quality Class III standards by 1.6 and 2.2 times, respectively. Spatially, the results of the cluster analysis showed that the water quality in Cihu Lake can be categorized into three regions: the northern half of the lake, the southern half of the lake, and the canal entering the lake. Temporally, the water quality in these three regions can be classified into three categories: March to May (the northern half of Cihu Lake), September to November (the southern half of Cihu Lake), and September (the canal entering Cihu Lake). The discriminant analysis results showed that NH3-N, TN, CODCr, and BOD5 are the main factors that affect the uneven spatial distribution of the water quality of Cihu Lake, while TN, DO, and CODMn are the main factors that affect the temporal difference in the northern half of Cihu Lake, and NH3-N, TP, CODCr, DO, CODMn, TN, and TP are the main factors affecting the temporal difference in the southern half of Cihu Lake and the canal entering Cihu Lake. It was found that the water pollution in the study area can be mainly attributed to the incoming water and urban domestic pollution. The main pollution sources for the canal entering Cihu Lake and the southern half of Cihu Lake are the water from the sewage treatment plant and the domestic sewage that has not been intercepted, while the northern half of Cihu Lake is mainly affected by surface runoff, mixed rainwater and sewage, and internal pollution.

A year-long study from May 2018 to April 2019 investigated the spatio-temporal changes in water quality of seven major rivers in the Giresun Province in northeastern Turkey. The results of the analysis were classified according to WHO limit values for drinking water and water quality classes were determined with respect to the Turkish Surface Water Quality Regulation (TSWQR). In addition, sodium percentage (% Na), sodium absorption ratio (SAR), magnesium hazard (MH), and residual sodium carbonate (RSC) values were calculated to evaluate the eligibility of the streams for irrigation purposes. Multivariate statistical analysis methods such as one-way ANOVA, Pearson correlation index (PCI), cluster analysis (CA), principal components analysis/factor analysis (PCA/FA) were used to determine the differences and similarities of streams, pollution sources and relations between parameters. As a result of the PCA, four factors accounted for 71.52% of all factors. These factors show that the major changes in water quality parameters were associated with point-based pollution such as wastewater from household and industrial resources, non-point sources such as agricultural activities and natural phenomena such as flooding, rock and soil erosion. The pH values in the range of 7.77 to 9.8 were slightly alkaline compared to the TSWQR range of pH 6–9. In certain months, the average NO2-N values (> 0.06 mg/L) in Batlama Stream, Aksu Stream, Yağlıdere Stream and Gelevera Stream was class III (medium) water quality and mean fluoride values in streams (1 and 1.19 mg/L) were class II water quality according to TSWQR. The levels of anionic surfactants in all streams were class III (polluted) water quality, except for Pazarsuyu Stream with class II (less polluted) water quality. The annual mean water quality index (WQI) values of the streams ranged from 25.69 (excellent) to 32.39 (good). When the water quality was evaluated for irrigation, the SAR, Na%, RSC and MH did not exceed the limit values. Based on these indices it was assessed that settlements and related anthropogenic activities along the riverbanks did not affect the water quality currently. This does not mean that it may not have an effect in the near future.

The severe water quality pollution of the Bohai Sea impacts both the ecosystem and the economy of the region. This study assesses the water quality of the Bohai Sea using a two-year (2020–2021) dataset to investigate the spatial distribution and sources of contamination. Multivariate statistical analysis methods, including principal component analysis (PCA), cluster analysis (CA), and discriminant analysis, are employed. Thirteen chemical indicators are analyzed through PCA, resulting in the extraction of three principal components that reflect different pollution sources related to domestic, industrial, and agricultural activities. Additionally, the corresponding water quality index (WQI) is calculated to categorize the water quality into three levels using CA. The PCA-based WQI method is feasible and shows similarities to the traditional WQI method. Higher pollution levels are observed in Panjin and Tianjin, while Huludao, Yantai, and Dalian exhibit relatively good water quality. The results indicate complex, multifactorial pollution causes in the Bohai Sea, including eutrophication, heavy metal contamination, and ammonia pollution. These findings can guide region-specific water quality management: Panjin should control heavy metal discharges from industry and transportation, while Tianjin requires improvements in runoff management of ammonia-based fertilizers. Together, these strategies support the ecological and sustainable development of the Bohai Sea.

Statistics is an important component of data science, and multivariate statistical analysis methods play a crucial role in analyzing high-dimensional data and extracting effective information from it in the era of big data. Under the wave of domestic curriculum ideological and political education, this paper explores the organic integrations of multivariate statistical analysis methods such as regression analysis, variance analysis, factor analysis, principal component analysis, canonical correlation analysis, k-nearest neighbor algorithm, and hierarchical clustering with ideological and political education. The study makes analogies between the principles, ideas, advantages, and applications of these methods and some ideological and political contents, making abstract multivariate statistical analysis methods more vivid and helping students establish correct worldviews, outlooks on life, and values.

In this study, it was aimed to investigate the water quality data and heavy metal analysis results in Coruh basin by using multivariate statistical analysis methods. Measurement of 21 water quality parameters was evaluated by collecting monthly samples between June 2014 and May 2015 from 14 monitoring sites in Coruh basin. These data were interpreted by using statistical methods such as analysis of variance (ANOVA), cluster analysis, factor analysis/principal component analysis. According to ANOVA analysis, there are statistically significant differences between samples obtained from these 14 monitoring sites in terms of electrical conductivity, Zn, Cd, Cr, Cu, Fe, Mn, NO2, SO4, alkalinity, Na, K, Ca parameters (p < 0.05). Two groups were obtained by cluster analysis. The first group consists of the areas with wastewater of mining fields with high heavy metal pollution, and the second group includes areas with domestic wastewater and wastewater coming from agricultural areas causing less pollution. As a result of factor analysis/principal component analysis, four factors were determined. These four factors explain 62.7 % of the data of water quality. It is concluded that water quality is affected by mining activities, domestic wastewater, geological structure and wastewater of agricultural areas. In this study, environmental hazards affecting the water quality of Coruh river basin at different points are identified. Thus, it is expected to guide decision makers and administrators in environmental management works conducted to improve water quality in Coruh basin.

The main objective of the present study is to perform risk assessment of groundwater contaminated by nitrate (NO3−) and evaluate the suitability of groundwater for domestic purposes in the Palani region of South India. Thirty groundwater samples were collected in the study area. Various groundwater quality analysis parameters such as the pH, electrical conductivity, total dissolved solids, total hardness, major cations (Ca2+, Mg2+, Na+, and K+), and major anions (Cl−, SO42−, F−, CO32−, and HCO3−) were adopted in this study to evaluate the drinking water suitability according to 2011 World Health Organization (WHO) standards. Piper and Gibbs’s diagrams for the tested groundwater indicated that, due to the influence of rock–water interactions, evaporation, and reverse ion exchange, the chemical composition of groundwater varied. According to water quality index (WQI) mapping results, 46.67% of the sample locations was identified as contaminated zones via GIS spatial analysis. Multivariate statistical analysis methods, such as principal component analysis, cluster analysis, and the Pearson correlation matrix, were applied to better understand the relationship between water quality parameters. The results demonstrated that 40% of the samples could be identified as highly affected zones in the study region due to a high nitrate concentration. The noncarcinogenic health risks among men, women, and children reached 40, 50, and 53%, respectively. The results illustrated that children and women occurred at a higher risk than did men in the study region. The major sources of contamination included discharge from households, uncovered septic tanks, leachate from waste dump sites, and excess utilization of fertilizers in the agricultural sector. Furthermore, using the nitrate health hazard integrated method with the conventional indexing approach ensures that groundwater reliability can be guaranteed, contamination can be explored, and appropriate remedial measures can be implemented.

Extensive agricultural regions commonly face issues of poor groundwater management, non-standard agricultural production practices, and unordered discharge of domestic pollution, leading to a continuous decline in groundwater quality and a sharp increase in risks. A comprehensive understanding of groundwater conditions and pollution is a crucial step in effectively addressing the water quality crisis. This study employs the Comprehensive Water Quality Index, Irrigation parameter, and Pollution Index to comprehensively investigate the groundwater quality in a typical agricultural area in Shandong, China, and assesses the suitability of groundwater for irrigation and the risks to human health. Furthermore, multivariate statistical analysis methods are utilized to analyze the relationship between groundwater quality and agricultural production and human activities. The results of the comprehensive quality evaluation indicate that the groundwater in the study area is primarily characterized as weakly alkaline hard freshwater and slightly brackish water, with a hydrochemical type of HCO3-Ca. 42% of the groundwater is unsuitable for drinking, with the main pollutants being TDS, TH, F-, and NO3-. The shallow groundwater level and high soil permeability provide favorable conditions for pollutant migration. Residual Sodium Carbonate (RSC) and Potential Salinity (PS) indicate that 37% of the water samples have excessive bicarbonate levels and 5% have excessive salinity, making them unsuitable for irrigation. Nitrate poses non-carcinogenic risks to all three age groups. Multivariate analysis results show that agricultural pollution dominates in the groundwater, with major pollutants including SO42-, NO3-, COD, NH4-N, F-, etc. Domestic pollution mainly increases the concentrations of ions such as Ca2+, Na+, Mg2+, and also contributes to Cl- and NO3-. The findings of this study contribute to enhancing the rational utilization of groundwater quality in agricultural areas, standardizing agricultural production activities, and promoting the sustainable development of green agriculture.

This paper investigates the interindividual variability of underlying glucose dynamics using multivariate statistical analysis methods for subjects with type 1 diabetes mellitus. Here two types of glucose dynamics are defined, the general dynamics and the output-relevant predictive dynamics. The concerned important issues are whether the underlying glucose dynamics change from subject to subject? Can a global (or universal) empirical model be developed from glucose data for a single subject and then used to explain the glucose dynamics for other subjects? These and related issues are investigated using multivariate statistical analysis methods based on clinical data for two groups of subjects. Together, these findings provide insights into more efficient development of data-driven models to understand and capture the glucose information in diabetes subjects.

With the use of different multivariate statistical analysis methods, spatio-temporal fluctuations in the water parameters of Tiru reservoir located at the Marathwada drought-prone area of Maharashtra, India, have been analysed and reported in this case study. Tiru reservoir, situated on the tributary of the Godavari River, was regularly monitored at five different sites from August 2017 to January 2019 for the estimation of 20 water quality parameters. Various multivariate methods such as pattern reorganisation using cluster analysis (CA), factor analysis/principal component analysis (FA/PCA), and discriminant analysis (DA) were used for handling complex datasets. CA extracted three different clusters from five sampling sites with similar water quality characteristics. FA/PCA extracted thirteen factors (65% of 20 measured) required to explain 74% of the data variability and identified the factors accountable for variation in water quality and also evaluated the prevalence of each cluster on the overall dissimilarity at five different sampling sites. Discriminant analysis extracted a total of 16 parameters with 97.7% right assignations. Varifactors (VFs) acquired by factor analysis recommended that the water quality parameters accounted for variation were linked to two groups. The first group included water quality parameters like T, DO, SDD, turbidity, TDS, PA, and MA, whereas the second group covered most of the nutrients Cl-, silicates, PP, TP, NO3-N, NO2-N, and NH3-N; hardness; and CHL-a and mainly entered the reservoir during surface runoff from agriculture fields and the surrounding area containing domestic as well as animal waste. Thus, the present work showed the efficiency of multivariate methods for the assessment of spatial as well as a temporal variation in the water quality of a small reservoir.

Analyses on the spatial evolution and distribution of surface water quality are important to the treatment and protection of water environment in a lake. In Balihe Lake, an inland freshwater lake in east China, 7 water environmental factors at 45 sampling sites were monitored and served as the basis of this study. Cluster analysis (CA), a multivariate statistical analysis method, was utilized to study the spatial variation and grouping of these sampling sites based on the monitored water quality data. The results of this study showed that the water quality characteristics at these 45 sampling sites, which was grouped into the clusters of upstream, midstream and downstream, highly depended on the spatial location of the lake. Some nutrients content of the upstream area was much higher, while the water quality of the downstream area was much better although some of water quality indicators at the outlet still didn’t match the standards of local government. The CA results of the study may provide some guidance to the priority areas of water environment protection or treatment for the government.

Multivariate statistics book hits target – mostly

Rivers are critical to agriculture, industry, and the needs of humans and wildlife. This study evaluates the water quality of the Beheshtabad River in Iran’s Chaharmahal and Bakhtiari Province, using water quality index and multivariate statistical methods. Nitrate, temperature, phosphate, turbidity, dissolved oxygen, biological oxygen demand, electrical conductivity, total solids, and pH were measured at five selected stations along the river over 6 months using standard methods. Water quality index results demonstrated that water quality varied in the selected stations between average and good and that pollution in this section of the Beheshtabad River increases from upstream to downstream. Clustering and principal component analysis were also utilized. Multivariate statistical methods were used to analyze water conditions for efficient management of surface water quality. Agricultural fertilizers, upstream wastewater discharge, and fish farms constitute the main elements that decrease the water quality of the Beheshtabad River. To preserve this water resource against pollution, the implementation of stringent rules and guidelines are needed to enhance health and preserve water resources for future generations.

Dynamic quality changes of jujube wine during 10 days (240 hr) fermentation were evaluated by electronic nose and electronic tongue combined with multivariate statistical analysis methods. Fermentation significantly reduced the relative content of aromatic compounds, but promoted the generation of alcohol, sour compounds, umami compounds, sulfur-containing organics, and broad range sensitivity compounds. The results of principal component analysis and cluster analysis indicate that, according to the overall quality similarity, the whole fermentation process of jujube wine can be categorized into three stages: early stage (0–24 hr), middle stage (36–132 hr), and late stage (144–240 hr). By Multivariate Analysis of Variance, significant differences (p < .001) were observed among jujube wine samples in the three fermentation stages. Further analysis using Redundancy Analysis found that the major changes of jujube wine quality occurred in the middle stage of fermentation, particularly 36–132 hr. Practical applications In the case of the wine production, employing of the appropriate fermentation time throughout the process can be problematic. Hence, in this paper, electronic nose and electronic tongue technology combined with multivariate statistical analysis methods were applied to reveal the dynamic changes in quality of jujube wine, which provided a promising instruction for quality control of jujube wine.