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Assessing the groundwater quality is important for the efficient exploitation of water resources in semi-arid areas. The study area in northeast Algeria mostly depends on groundwater as its main source of water, and the quality of groundwater is becoming important due to the increasing need for freshwater. The hydrochemical characteristics and water quality of groundwater in the Tebessa-Ain Chabro were assessed using water quality indices, geochemical modeling, and multivariate statistical approaches. The study discovered that the groundwater samples could be classified into four distinct water groups using hierarchical cluster analysis in Q mode (HCA) based on their electrical conductivity. We identified three forms of water: mixed (Ca2+–Mg2+–Cl−), Na+–K+–HCO3−, and Ca2+–Cl−. According to the water quality assessment, only 38% of the samples were deemed suitable for human consumption, while 34% were categorized as poor water, 10% as extremely poor, and 17% as unsafe for drinking. The irrigation water quality index identified four classifications: low, moderate, high, and severe restriction, with corresponding percentages of 31%, 31%, 7%, and 31%. The nitrate pollution index (NPI) showed that 48% of samples fell into the moderate pollution class. Human activity, such as sewage infiltration and waste disposal in open areas, was the cause of this nitrate contamination. The saturation index values showed that groundwater was less saturated in halite and sylvite and more saturated in aragonite, calcite, dolomite, anhydrite, gypsum, and hydroxyapatite. The Tebessa region's groundwater's hydrochemical properties and water quality have been assessed using multivariate statistical techniques, geochemical modeling, and water quality indexes. The study's conclusions can provide a foundation for upcoming research evaluating the region's groundwater quality. [ABSTRACT FROM AUTHOR]

Abstract Assessing the groundwater quality is important for the efficient exploitation of water resources in semi-arid areas. The study area in northeast Algeria mostly depends on groundwater as its main source of water, and the quality of groundwater is becoming important due to the increasing need for freshwater. The hydrochemical characteristics and water quality of groundwater in the Tebessa-Ain Chabro were assessed using water quality indices, geochemical modeling, and multivariate statistical approaches. The study discovered that the groundwater samples could be classified into four distinct water groups using hierarchical cluster analysis in Q mode (HCA) based on their electrical conductivity. We identified three forms of water: mixed (Ca2+–Mg2+–Cl−), Na+–K+–HCO3 −, and Ca2+–Cl−. According to the water quality assessment, only 38% of the samples were deemed suitable for human consumption, while 34% were categorized as poor water, 10% as extremely poor, and 17% as unsafe for drinking. The irrigation water quality index identified four classifications: low, moderate, high, and severe restriction, with corresponding percentages of 31%, 31%, 7%, and 31%. The nitrate pollution index (NPI) showed that 48% of samples fell into the moderate pollution class. Human activity, such as sewage infiltration and waste disposal in open areas, was the cause of this nitrate contamination. The saturation index values showed that groundwater was less saturated in halite and sylvite and more saturated in aragonite, calcite, dolomite, anhydrite, gypsum, and hydroxyapatite. The Tebessa region’s groundwater’s hydrochemical properties and water quality have been assessed using multivariate statistical techniques, geochemical modeling, and water quality indexes. The study’s conclusions can provide a foundation for upcoming research evaluating the region’s groundwater quality.

Unpredictable external shocks exacerbate the negative effects usually attributed to cyclical seasonality. Two such recent shocks, the Global Financial Crisis and the COVID-19 pandemic, shifted debates from crisis management toward concerns for greater resilience in visitor economies. Social tourism is an effective stimulus to destination economies and ameliorates some negative effects of seasonality, but there is little evidence on its contributions to greater destination resilience. Furthermore, resilience, a relatively new concept, is poorly defined. We establish a conceptual link between seasonality and resilience through a holistic multivariate analysis of supply, demand and employment patterns. An empirical study examines resilience longitudinally using municipal-level data, applying multivariate analysis and innovative visualization techniques, and assesses how, where and to what degree the Imserso social tourism program has contributed to the greater resilience of Spanish destinations, demonstrating its effectiveness in contributing to resilience in those which are more vulnerable due to high seasonal imbalances. [ABSTRACT FROM AUTHOR]

Evaluating the natural radioactivity in construction materials is significant, particularly considering the prevailing belief that our homes offer the safest living environments. To investigate this, 38 samples of building materials were collected from highly populated residential areas within the Malappuram district in Kerala, India. The levels of naturally occurring radionuclides (226Ra, 232Th, and 40K) were quantified using a NaI (Tl) detector. Radiological indices, such as radium equivalent, absorbed dose rate, effective dose rate, hazard indices, alpha index, gamma index, and cancer risk, were calculated for the samples. The activity concentrations of 226Ra, 232Th, and 40K ranged from 8.84 ± 0.3 (flooring oxide) to 46.84 ± 2.11Bq/kg (rock samples), 11.84 ± 0.2 (pumice sample) to 130.21 ± 8Bq/kg (granite sample), and 58.63 ± 4 (pumice) to 1024.32 ± 22Bq/kg (granite) with an average of 25.80 ± 4.61, 55.05 ± 6.2, and 392.30 ± 16 Bq/kg. (average ± standard deviation), respectively. As a result, all of these computed parameters fell within safe limits. The examined building materials were found to have a negligible impact on radiation exposure, posing no significant radiation risks to residents. Researchers utilized statistical techniques to understand the interrelationships and similarities among radionuclides and radiological characteristics across samples, including Pearson correlation, principal component analysis, and cluster analysis. [ABSTRACT FROM AUTHOR]

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This paper presents a quantitative methodology for rockfall risk analysis focusing on highway crystalline rock slopes. The proposed method resulted in a risk classification given by a matrix, which relates the rockfall likelihood, with its consequence. The likelihood considers the characteristics of the rock masses and the consequence takes into account the chance of the block reaching the highway and causing accidents. The rockfall likelihood and the consequences are obtained by equations, whose values allow classifying the slopes as high, medium (transition zone), or low rockfall risk. These equations and the matrix of the risk analysis system were generated using linear discriminant analysis and confidence ellipses, both multivariate statistical techniques. Principal component analysis was applied to the dataset before the linear discriminant analysis in order to change qualitative variables into quantitative ones. To establish the classes of failure likelihood and its consequences, cluster analysis was applied. Afterwards, discriminant analysis was applied using the scores of the principal components as independent variables and the resulting clusters as dependent variables. The dataset used in this research has 220 rock slopes and the main variables considered are the parameters traditionally used in hazard and risk classifications for highway slopes. [ABSTRACT FROM AUTHOR]

Lentinula edodes is known to show high nutritional and organoleptic properties and can be mixed with different by-products in the production of new foods with important functional characteristics. Cocoa pod husk (CPH) is the main by-product in the cocoa industry and presents important bioactivities. In this research, two mixtures were applied based on Lentinula edodes mushroom flour from fifty different strains (strain 1 to strain 50) and cocoa pod husk flour (CPHF): 60% Lentinula edodes mushroom flour from each strain mixed with 40% CPHF (Mixture 1), and 80% Lentinula edodes mushroom flour from each strain mixed with 20% CPHF (Mixture 2). The parameters evaluated were moisture, proteins, fat, total dietary fiber (TDF), ash, carbohydrates, antioxidant capacity, antimicrobial activity, pH and water activity (aw). The multivariate statistical techniques evaluated those samples that exhibited the highest degree of correlation with the distinct chemical and commercial parameters, showing that sample 13 of both mixture flours (M1 and M2) obtained by the mushrooms of L. edodes (strain 13) with CPH indicated the higher significant of the parameters. In addition, the sensory test of the best flour (sample 13) was applied to 50 panelists, presenting the best sensory characteristics, such as color, aroma, texture and general acceptability. [ABSTRACT FROM AUTHOR]

This study analyzed fire‐pollutant‐meteorological variables and their impact on cardio‐respiratory mortality in Portugal during wildfire season. Data of burned area, particulate matter with a diameter of 10 or 2.5 μm (μm) or less (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, relative humidity, wind speed, aerosol optical depth and mortality rates of Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease, and Asthma (ASMA), were used. Only the months of 2011–2020 wildfire season (June–July–August–September‐October) with a burned area greater than 1,000 ha were considered. Principal component analysis was used on fire‐pollutant‐meteorological variables to create two indices called Pollutant‐Burning Interaction (PBI) and Atmospheric‐Pollutant Interaction (API). PBI was strongly correlated with the air pollutants and burned area while API was strongly correlated with temperature and relative humidity, and O3. Cluster analysis applied to PBI‐API divided the data into two Clusters. Cluster 1 included colder and wetter months and higher NO2 concentration. Cluster 2 included warmer and dried months, and higher PM10, PM2.5, CO, and O3 concentrations. The clusters were subjected to Principal Component Linear Regression to better understand the relationship between mortality and PBI‐API indices. Cluster 1 showed statistically significant (p‐value < 0.05) correlation (r) between RSDxPBI (rRSD = 0.58) and PNEUxPBI (rPNEU = 0.67). Cluster 2 showed statistically significant correlations between RSDxPBI (rRSD = 0.48), PNEUxPBI (rPNEU = 0.47), COPDxPBI (rCOPD = 0.45), CSDxAPI (rCSD = 0.70), RSDxAPI (rCSD = 0.71), PNEUxAPI (rPNEU = 0.49), and COPDxAPI (rPNEU = 0.62). Cluster 2 analysis indicates that the warmest, driest, and most polluted months of the wildfire season were associated with cardio‐respiratory mortality. Plain Language Summary: Aiming to enhance the knowledge of the effects of fire‐pollutants‐meteorological variables on health outcomes, this study evaluates how the combination of multiple hazards impact on the Portugal Continental's population mortality during the fire seasons of 2011–2020. The association between five cause‐specific cardio‐respiratory mortality and Pollutant‐Atmospheric variables during wildfire seasons in Portugal were investigated through multivariate statistical analysis. To this end, data of ambient atmospheric pollutants, meteorological variables, burned area, and mortality were used for exposure assessment. Through multivariate statistical methods it was found that in months with low‐relative humidity, high‐temperature, high‐pollution concentrations and high‐wildfire activities, the incidence of cardio‐respiratory mortality was higher. Key Points: Combining fire‐pollutant‐meteorological data can be an effective way to understand how these combined hazards can affect health outcomesThe hottest, driest, and most polluted months of the fire season were associated with higher cardio‐respiratory mortalityColder and wetter months within the wildfire season were not associated with increased or decreased cardio‐respiratory mortality [ABSTRACT FROM AUTHOR]

Abstract Groundwater is the only freshwater source for agriculture and domestic use in the Khulais region of Saudi Arabia (SA). Anthropogenic activities, particularly agricultural runoff and lithogenic sources cause groundwater contamination, posing health risks to all generations (infant, child, and adult) via ingestion, dermal, and inhalation exposure routes. The configuration of non-carcinogenic and carcinogenic health concerns posed by contaminated water is, thus, a time-sensitive requirement. This study uses multivariate statistical techniques to assess health risks and to identify health impacts and pollution sources. Sampling of groundwater at nineteen sampling sites was carried out in two seasons (winter and summer) of 2021. The samples were analyzed for major ions and toxic metals. Results show that the average hazard quotient (HQ) and health hazard index (HHI) in infants was higher than 1 for most of toxic metal in both the seasons. The health risks associated with ingestion were significantly higher than those associated with the dermal and inhalation pathways. The aquifer’s carcinogenic risk ranged from high to extremely high in terms of chromium (Cr), nickel (Ni), and lead (Pb) concentrations. The estimated health risk values may have a negative impact on people’s health, which suggests that active aquifer management should be implemented. Multivariate statistical analyses were accomplished to determine the primary contamination sources in the aquifer system. The total dissolved solid (TDS) correlated strongly among chloride (Cl−), sulphate (SO4 2−), sodium (Na+), potassium (K+), magnesium (Mg2+), calcium (Ca2+), iron (Fe), strontium (Sr), fluoride (F−) and bromide (Br−), which caused by seawater intrusion within winter and summer seasons. The hierarchical cluster analysis (HCA) identifies two clusters and one independent case within the summer and winter seasons. One-way analysis of variance (ANOVA) showed significant variations in the concentrations of Mg2+, Aluminum (Al), Cobalt (Co), Cr, Ni, and Pb in winter, while TDS, electrical conductivity (EC), Cl−, SO4 2−, Na+, Mg2+, Ca2+, Sr, F− and Br− in summer. This research demonstrates that aquifer cleanup and management protocols should be implemented, encouraged, and maintained. Applying the best hygienic practices and pre-clean-up of polluted groundwater is recommended before application for different purposes.

Abstract This study analyzed fire‐pollutant‐meteorological variables and their impact on cardio‐respiratory mortality in Portugal during wildfire season. Data of burned area, particulate matter with a diameter of 10 or 2.5 μm (μm) or less (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, relative humidity, wind speed, aerosol optical depth and mortality rates of Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease, and Asthma (ASMA), were used. Only the months of 2011–2020 wildfire season (June–July–August–September‐October) with a burned area greater than 1,000 ha were considered. Principal component analysis was used on fire‐pollutant‐meteorological variables to create two indices called Pollutant‐Burning Interaction (PBI) and Atmospheric‐Pollutant Interaction (API). PBI was strongly correlated with the air pollutants and burned area while API was strongly correlated with temperature and relative humidity, and O3. Cluster analysis applied to PBI‐API divided the data into two Clusters. Cluster 1 included colder and wetter months and higher NO2 concentration. Cluster 2 included warmer and dried months, and higher PM10, PM2.5, CO, and O3 concentrations. The clusters were subjected to Principal Component Linear Regression to better understand the relationship between mortality and PBI‐API indices. Cluster 1 showed statistically significant (p‐value

From a public health perspective, the monitoring of water quality intended for human consumption belongs to the operational and audit management of the supply zones. Our study explores the spatial and temporal patterns of the parameters of drinking water in Sibiu County, Romania. We related the relevant physical-chemical parameters (ammonia, chlorine, nitrates, Al, Fe, Pb, Cd, Mn, pH, conductivity, turbidity, and oxidizability) and radioactivity (gross alpha activity, gross beta activity, and radon-222 content) from a 5-year survey to the water source (surface water and groundwater, which may be of subsurface or deep origin), space (sampling locality) and time (sampling month and year). We conducted a combined evaluation using the generalized linear mixed models (GLMMs), Pearson correlation analysis of the physical-chemical parameter, multivariate linear redundancy analysis (RDA), t-value biplots construction, and co-inertia analysis. The obtained regional model shows that the source, locality, and month of sampling are significant factors in physical-chemical parameters' variation. Fe and turbidity have significantly higher values in surface water, and nitrates and conductivity in groundwater. The highest values are recorded in January (nitrates), March (Cl, ammonia, pH) and August (Fe, turbidity). The RDA ordination diagram illustrates the localities with particular or similar characteristics of drinking water, two of which (rural sources) being of concern. The water source is the best predictor for radioactivity, which increases from surface to ground. The gross alpha and beta activities are significantly and positively correlated, and are both correlated with conductivity. In addition, the gross alpha activity is positively correlated with nitrates and negatively with pH, while the gross beta activity is positively correlated with Mn and negatively with Fe; these relationships are also revealed by the co-inertia analysis. In conclusion, our model using multilevel statistical techniques illustrates a potential approach to short-term dynamics of water quality which will be useful to local authorities. [ABSTRACT FROM AUTHOR]

Water is an essential element in nature. It is used in drinking, irrigation, and industry mainly. Human health is directly linked to groundwater quality and is affected by poor groundwater quality caused by excessive fertilizer application and unhygienic circumstances. Because of increased pollution, investigating the water quality became a point of research for many researchers. There are numerous approaches to assessing water quality, and statistical methods are essential among them. This review paper discusses Multivariate Statistical Techniques, including Cluster Analysis, Principal Component Analysis, Factor Analysis, Geographical Information System, and Analysis of Variance, to name a few. We have presented the significance of each method concisely and how it is being used. In addition, an extensive table is prepared to demonstrate the individual technique along with the computational tool, the type of water bodies, and their respective regions. The advantages and disadvantages of the statistical techniques are also discussed therein. It is found that Principal Component Analysis and Factor Analysis are widely explored techniques. [ABSTRACT FROM AUTHOR]

Groundwater is the only freshwater source for agriculture and domestic use in the Khulais region of Saudi Arabia (SA). Anthropogenic activities, particularly agricultural runoff and lithogenic sources cause groundwater contamination, posing health risks to all generations (infant, child, and adult) via ingestion, dermal, and inhalation exposure routes. The configuration of non-carcinogenic and carcinogenic health concerns posed by contaminated water is, thus, a time-sensitive requirement. This study uses multivariate statistical techniques to assess health risks and to identify health impacts and pollution sources. Sampling of groundwater at nineteen sampling sites was carried out in two seasons (winter and summer) of 2021. The samples were analyzed for major ions and toxic metals. Results show that the average hazard quotient (HQ) and health hazard index (HHI) in infants was higher than 1 for most of toxic metal in both the seasons. The health risks associated with ingestion were significantly higher than those associated with the dermal and inhalation pathways. The aquifer's carcinogenic risk ranged from high to extremely high in terms of chromium (Cr), nickel (Ni), and lead (Pb) concentrations. The estimated health risk values may have a negative impact on people's health, which suggests that active aquifer management should be implemented. Multivariate statistical analyses were accomplished to determine the primary contamination sources in the aquifer system. The total dissolved solid (TDS) correlated strongly among chloride (Cl−), sulphate (SO42−), sodium (Na+), potassium (K+), magnesium (Mg2+), calcium (Ca2+), iron (Fe), strontium (Sr), fluoride (F−) and bromide (Br−), which caused by seawater intrusion within winter and summer seasons. The hierarchical cluster analysis (HCA) identifies two clusters and one independent case within the summer and winter seasons. One-way analysis of variance (ANOVA) showed significant variations in the concentrations of Mg2+, Aluminum (Al), Cobalt (Co), Cr, Ni, and Pb in winter, while TDS, electrical conductivity (EC), Cl−, SO42−, Na+, Mg2+, Ca2+, Sr, F− and Br− in summer. This research demonstrates that aquifer cleanup and management protocols should be implemented, encouraged, and maintained. Applying the best hygienic practices and pre-clean-up of polluted groundwater is recommended before application for different purposes. [ABSTRACT FROM AUTHOR]

Identifying spatiotemporal variation patterns and predicting future water quality are critical for rational and effective surface water management. In this study, an exploratory analysis and forecast workflow for water quality in Pearl River, Guangzhou, China, was established based on the 4-h interval dataset selected from 10 stations for water quality monitoring from 2019 to 2021. The multiple statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), correlation analysis (CoA), and redundancy analysis (RDA), as well as data-driven model (i.e., gated recurrent unit (GRU)), were applied for assessing and predicting the water quality in the basin. The investigated sampling stations were classified into 3 categories based on differences in water quality, i.e., low, moderate, and high pollution regions. The average water quality indexes (WQI) values ranged from 38.43 to 92.63. Nitrogen was the most dominant pollutant, with high TN concentrations of 0.81–7.67 mg/L. Surface runoff, atmospheric deposition, and anthropogenic activities were the major contributors affecting the spatiotemporal variations in water quality. The decline in river water quality during the wet season was mainly attributed to increased surface runoff and extensive human activities. Furthermore, the short-term prediction of river water quality was achieved using the GRU model. The result indicated that for both DLCK and DTJ stations, the WQI for the 5-day lead time were predicted with accuracies of 0.82; for the LXH station, the WQI for the 3-day lead time was forecasted with an accuracy of 0.83. The finding of this study will shed a light on an effective reference and systematic support for spatio-seasonal variation and prediction patterns of water quality. [ABSTRACT FROM AUTHOR]

The impact of the operation of inter-basin water diversion projects on the integrity and stability of regional ecosystems cannot be ignored. In this study, water quality samplings were conducted monthly at 16 national monitoring sites in the mid-downstream of the Hanjiang River (HJR, the downstream of the water source of the South-to-North Water Diversion Project of China) over 3 years, covering seven physiochemical water quality indicators and six heavy metal elements. The water quality index (WQI) and multivariate statistical techniques were introduced to comprehensively evaluate water quality status and understand the corresponding driving factors of water quality variations. The heavy metal risks were evaluated using the Nemerow Pollution Index (Pn), the Heavy Metal Pollution Index (HPI), and the human health risk assessment model. The results showed that after the operation of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC), water quality in the mid-downstream of the HJR was generally at a “good” status, with the average WQI of 86.37, showing no water quality deterioration trends. The operation of the MRSNWDPC did significantly decrease the monthly flow in the HJR by about 4.05–74.27%, and the flow variation processes also became more stable than before. Most water quality indicators and WQIs have no correlations with the flow and water level changes. The human health risks of all heavy metal elements caused by dermal exposure and ingestion pathways increased over time. The average individual health risk caused by carcinogenic heavy metal Cr was the highest. Chromium is the major carcinogenic factor and should be a critical indicator to pay special attention to for water risk management in the HJR. This study provides a scientific reference for the water quality safety management of HJR under the influence of a water diversion project.

Identifying potential sources of pollution in tributaries and determining their contribution rates are critical to the treatment of water pollution in main streams. In this paper, we conducted a multivariate statistical analysis on the water quality data of 12 parameters for 3 years (2018–2020) at six sampling sites in the Laixi River to qualitatively identify potential pollution sources and quantitatively calculate the contribution rates to reveal the tributaries' pollution status. Spatio-temporal cluster analysis (CA) divided 12 months into two parts, corresponding to the lightly polluted season (LPS) and highly polluted season (HPS), and six sampling sites were divided into two regions, corresponding to the lightly polluted region (LPR) and highly polluted region (HPR). Principal component analysis (PCA) was used to determine the potential sources of contamination, identifying four and three potential factors in the LPS and HPS, respectively. The absolute principal component score-multiple linear regression (APCS-MLR) receptor model quantitatively analyzed the contribution rates of identified pollution sources, and the importance of the different pollution sources in LPS can be ranked as domestic sewage and industrial wastewater and breeding pollution (33.80%) > soil weathering (29.02%) > agricultural activities (20.95%) > natural influence (13.03%). HPS can be classified as agricultural cultivation (41.23%), domestic sewage and industrial wastewater and animal waste (33.19%), and natural variations (21.43%). Four potential sources were identified in LPR ranked as rural domestic sewage (31.01%) > agricultural pollution (26.82%) > industrial effluents and free-range livestock and poultry pollution (25.13%) > natural influence (14.82%). Three identified latent pollution sources in HPR were municipal sewage and industrial effluents (37.96%) > agricultural nonpoint sources and livestock and poultry wastewater (33.55%) > natural sources (25.23%). Using multivariate statistical tools to identify and quantify potential pollution sources, managers may be able to enhance water quality in tributary watersheds and develop future management plans. [ABSTRACT FROM AUTHOR]

Wild edible plants have played a crucial role since ages, in sustaining the local communities across globe, particularly during food scarcity, by supplementing the household diets. Despite their significance, the nutritional composition of wild edible plants has been inadequately researched and generally poorly understood. Moreover the studies of nutritional contents usually overlook the presence of potentially toxic elements(PTEs) which may be detrimental to the human health. This study was aimed at assessing the levels of PTEs (Al, As, Ba, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Se, Sr and Zn), in selected wild edible plants traditionally consumed by rural people of District Doda. For this purpose the leaves of three wild edible plants, namely Taraxacum officinale, Urtica dioica and Phytolacca acinosa were collected, air-dried and processed into powder. The plant samples were analyzed using Inductively Coupled Plasma Mass Spectrometer (ICP-MS). The findings of this study exhibited that PTEs were detectable in all the three tested wild edible plants. The levels of PTEs (mg/1000gdw) were found to be Cu (18.1 to 36.1), Fe (646.6 to 1457.8), Mn (80.4 to 207.6), Se (0.1 to 0.3) and Zn (58.2 to 86.8), PTEs Al, As, Ba, Cd, Co, Cr, Hg, Ni, Pb, and Sr were also detectable in all the three analyzed plant samples. The Al content was found to be highest among these PTEs (427.7 to 1224.4 mg/ 1000 g dw) which can pose health risks. • Heavy metals were assessed in wild edible plants using ICP-MS. • Among the analyzed elements aluminum content was found maximum. • The contents of Zn, Ni, Al and Hg were found higher in contrast with their recommended limits. [ABSTRACT FROM AUTHOR]

Lentinula edodes is known to show high nutritional and organoleptic properties and can be mixed with different by-products in the production of new foods with important functional characteristics. Cocoa pod husk (CPH) is the main by-product in the cocoa industry and presents important bioactivities. In this research, two mixtures were applied based on Lentinula edodes mushroom flour from fifty different strains (strain 1 to strain 50) and cocoa pod husk flour (CPHF): 60% Lentinula edodes mushroom flour from each strain mixed with 40% CPHF (Mixture 1), and 80% Lentinula edodes mushroom flour from each strain mixed with 20% CPHF (Mixture 2). The parameters evaluated were moisture, proteins, fat, total dietary fiber (TDF), ash, carbohydrates, antioxidant capacity, antimicrobial activity, pH and water activity (aw). The multivariate statistical techniques evaluated those samples that exhibited the highest degree of correlation with the distinct chemical and commercial parameters, showing that sample 13 of both mixture flours (M1 and M2) obtained by the mushrooms of L. edodes (strain 13) with CPH indicated the higher significant of the parameters. In addition, the sensory test of the best flour (sample 13) was applied to 50 panelists, presenting the best sensory characteristics, such as color, aroma, texture and general acceptability.

This study assessed groundwater quality in Alaçam, where irrigations are performed solely with groundwaters and samples were taken from 35 groundwater wells at pre and post irrigation seasons in 2014. Samples were analyzed for 18 water quality parameters. SAR, RSC and %Na values were calculated to examine the suitability of groundwater for irrigation. Hierarchical cluster analysis and principal component analysis were used to assess the groundwater quality parameters. The average EC value of groundwater in the pre-irrigation period was 1.21 dS/m and 1.30 dS/m after irrigation in the study area. It was determined that there were problems in two wells pre-irrigation and one well post-irrigation in terms of RSC, while there was no problem in the wells in terms of SAR. Piper diagram and cluster analysis showed that most groundwaters had CaHCO3 type water characteristics and only 3% had NaCl- as the predominant type. Seawater intrusion was identified as the primary factor influencing groundwater quality. Multivariate statistical analyses to evaluate polluting sources revealed that groundwater quality is affected by seawater intrusion, ion exchange, mineral dissolution and anthropogenic factors. The use of multivariate statistical methods and geographic information systems to manage water resources will be beneficial for both planners and decision-makers. HIGHLIGHTS Multivariate statistical analysis was used to evaluate the groundwater quality.; According to the Piper diagram, 83% of the groundwater was of the CaHCO3 type in both periods.; The effect of seawater was observed in some wells located 4 km inland from the sea in the post-irrigation period.; Seawater intrusion has been detected in coastal areas.; Well number 28 should not be used for irrigation purposes.;

The present study explores the water quality status, hydrogeochemical characteristics and factors affecting the water quality of Tawi River in Jammu. More accurate information of water quality was obtained by applying the hydrochemical plot like Piper's diagram, Water Quality Index and Multivariate Statistical Techniques such as Principal Component Analysis (PCA) on various physicochemical parameters. All the analyzed parameters were well within the permissible limits of Bureau of Indian Standards (BIS 2012) for drinking and domestic purposes for pre and post monsoon season. Based on the inferences obtained from Piper's diagram, Ca–Mg–HCO3 type predominated in the study area during both seasons. Most dominant cations were Ca2+ and Mg2+ whereas and Cl– were the dominant anions in the analyzed water samples. The computed WQI values of the surface water ranged from 60.05 to 107.60 and from 75.25 to 123.38 during pre and post monsoon season respectively and fall under good to poor category. Varifactors obtained from PCA indicated that water quality variation was primarily due to the dissolution of minerals from rock water interactions, secondary effect of anthropogenic activities and ion exchange processes in water. These results provide fundamental and baseline information for developing more effective water pollution abatement strategies for River Tawi. [ABSTRACT FROM AUTHOR]

In intermittent drinking water distribution systems, large volumes of the water are wasted due to leaks in the distribution networks. Similarly, user service is not always satisfied in the time required to fill the storage, nor with sufficient pressure. Hence the importance of this study. Measuring the variability of pressure in the distribution network and determining the factors that influence the definition of a sufficient minimum hours of service, is a first step to change to a continuous service 24/7, in order to minimize the volumes of lost water and meet demand. In total, 347 pressure sensors were placed in a network to detect changes in pressure and obtain data for 3 years. This study presents a new approach to determine the operating policy of the operating agency that provides the service intermittently. Two objectives are pursued: pressure variability – to minimize leaks – and define the minimum hours of service. The analysis was performed using multivariate statistical techniques, including principal component analysis, correlation matrix and ANOVAs, to explore the association between objectives. The results obtained show that the pressure distribution has a Gaussian behavior and that the hours of service have a Poisson distribution. HIGHLIGHTS A pressure range between 7 and 30 mwc is essential for user satisfaction.; A minimum of 6 hours of service is recommended in IWS systems.; The behavior of service hours is not linear but exponential in its effect on the satisfaction of demand.; Multivariate analysis is an adequate technique to facilitate the decision making in a water supply system.; Pressure variation has normal behavior regardless of the number of hours supplied.;

From a public health perspective, the monitoring of water quality intended for human consumption belongs to the operational and audit management of the supply zones. Our study explores the spatial and temporal patterns of the parameters of drinking water in Sibiu County, Romania. We related the relevant physical-chemical parameters (ammonia, chlorine, nitrates, Al, Fe, Pb, Cd, Mn, pH, conductivity, turbidity, and oxidizability) and radioactivity (gross alpha activity, gross beta activity, and radon-222 content) from a 5-year survey to the water source (surface water and groundwater, which may be of subsurface or deep origin), space (sampling locality) and time (sampling month and year). We conducted a combined evaluation using the generalized linear mixed models (GLMMs), Pearson correlation analysis of the physical-chemical parameter, multivariate linear redundancy analysis (RDA), t-value biplots construction, and co-inertia analysis. The obtained regional model shows that the source, locality, and month of sampling are significant factors in physical-chemical parameters’ variation. Fe and turbidity have significantly higher values in surface water, and nitrates and conductivity in groundwater. The highest values are recorded in January (nitrates), March (Cl, ammonia, pH) and August (Fe, turbidity). The RDA ordination diagram illustrates the localities with particular or similar characteristics of drinking water, two of which (rural sources) being of concern. The water source is the best predictor for radioactivity, which increases from surface to ground. The gross alpha and beta activities are significantly and positively correlated, and are both correlated with conductivity. In addition, the gross alpha activity is positively correlated with nitrates and negatively with pH, while the gross beta activity is positively correlated with Mn and negatively with Fe; these relationships are also revealed by the co-inertia analysis. In conclusion, our model using multilevel statistical techniques illustrates a potential approach to short-term dynamics of water quality which will be useful to local authorities.

This study assesses the concentrations of the 54 ozone precursors (all being volatile organic compounds (VOCs)) detected at the four photochemical assessment monitoring stations that are part of the air quality monitoring network in the Kaohsiung-Pingtung area in Taiwan. Factor and cluster analyses of the multivariate statistical analysis are performed to explore the interrelationship among the 10 VOCs of relatively higher concentrations selected from the 54 ozone precursors to identify significant factors affecting ozone pollution levels in the study area. Moreover, the multivariate statistical analysis can faithfully reflect why the study area has been affected by photochemical pollution. First, results of the factor analysis suggest that the factors affecting how photochemical reactions occur in the study area can be divided into the following: "pollution from mobile sources", "pollution from stationary sources", and "pollution from energy sources". Among them, mobile sources have the greatest impact on photochemical pollution levels. Second, the impacts of photochemical pollution on air quality in the study area can be classified into four clusters via cluster analysis. Each cluster represents how the 10 VOCs affect air quality, with different characteristics, and how they contribute to photochemical pollution in the study area. If there are more types and samples of photochemical pollutants when performing a multivariate statistical analysis, the analysis results will be more stable. This study adopts data on VOC monitoring over a period of nearly two years, which can effectively improve the validity and reliability of the factor analysis results, while helping environmental agencies review the effectiveness of air quality management in the future and serving as reference for the effectiveness of reducing photochemical pollution in the atmosphere. [ABSTRACT FROM AUTHOR]

In this study, spatiotemporal fluctuations in surface water quality in Vinh Long province, Vietnam, were conducted using entropy weighting, water quality index (WQI), and multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), and discriminant analysis (DA). The samples collected at 63 monitoring locations in March, June, and September were measured for 15 parameters. Compared to the Vietnamese standard, surface water was contaminated with organic matters, nutrients, microorganisms, and salinity. DA identified the most typical parameters (pH, turbidity, TSS, EC, DO, Cl−, E. coli, coliform) in distinguishing temporal variations in water quality with greater than 75% of the correction. CA group 63 sampling sites into 22 clusters representing different land use patterns. WQI determined the worst water quality was found in the agricultural areas. Based on the results of entropy weighting, EC, coliform, N-NH4+, BOD, N-NO3−, and Fe had significantly controlled surface water quality. Four principal components obtained from PCA explained 66.45% of the variance, suggesting the influences of geohydrological factors and anthropogenic activities, such as domestic, market area, agriculture, and industry. The findings of this study can provide useful information for authorities to evaluate the effectiveness of monitoring systems and plan for water quality management strategies. [ABSTRACT FROM AUTHOR]

Abstract In the present study, evaluation of spatial variations and interpretation of Zohrehh River water quality data were made by using multivariate analytical techniques including factor analysis and cluster analysis also the Arc GIS® software was used. The research method was formulated to achieve objectives herein, including field observation, numerical modeling, and laboratory analyses. The results showed that dataset consisted of 11,250 observations of seven-year monitoring program (measurement of 15 variables at 3 main stations from April 2010 to March 2017). Factor analysis with principal component analysis extraction of the dataset yielded seven varactors contributing to 82% of total variance and evaluated the incidence of each varactor on the total variance. The results of cluster analysis became complete with t-test and made water quality comparison between two clusters possible. Results of factor analysis were employed to facilitate t-test analysis. The t-test revealed the significant difference in a confidence interval of 95% between the mean of calculated varactors 1, 2, 6 and 7 between two clusters, but there was no significant difference in the mean of other varactors 3, 4 and 5 between two groups. The result shows the effect of agricultural fertilizers on stations located at downstream of the ASK dam.

This study conducted a comprehensive analysis of trace element concentrations in the Upper Indus River Basin (UIRB), a glacier-fed region in the Western Himalayas (WH), aiming to discern their environmental and anthropogenic sources and implications. Despite limited prior data, 69 samples were collected in 2019 from diverse sources within the UIRB, including mainstream, tributaries, and groundwater, to assess trace element concentrations. Enrichment factor (EF) results and comparisons with regional and global averages suggest that rising levels of Zn, Cd, and As may pose safety concerns for drinking water quality. Advanced multivariate statistical techniques such as principal component analysis (PCA), absolute principal component scores (APCS-MLR), Monte Carlo simulation (MCS), etc were applied to estimate the associated human health hazards and also identified key sources of trace elements. The 95th percentile of the MCS results indicates that the estimated total cancer risk for children is significantly greater than (>1000 times) the USEPA's acceptable risk threshold of 1.0 × 10−6. The results classified most of the trace elements into two distinct groups: Group A (Li, Rb, Sr, U, Cs, V, Ni, TI, Sb, Mo, Ge), linked to geogenic sources, showed lower concentrations in the lower-middle river reaches, including tributaries and downstream regions. Group B (Pb, Nb, Cr, Zn, Be, Al, Th, Ga, Cu, Co), influenced by both geogenic and anthropogenic activities, exhibited higher concentrations near urban centers and midstream areas, aligning with increased municipal waste and agricultural activities. Furthermore, APCS-MLR source apportionment indicated that trace elements originated from natural geogenic processes, including rock-water interactions and mineral dissolution, as well as anthropogenic activities. These findings underscore the need for targeted measures to mitigate anthropogenic impacts and safeguard water resources for communities along the IRB and WH. [Display omitted] • Elements (Li, Rb, Sr, U, Cs, V, Ni, TI, Sb, Mo, and Ge) reveals geogenic sources. • High levels of Al and Cd in groundwater may pose threats to human health. • Statistical analysis revealed anthropogenic signatures in the middle segments. • Elevated levels of Zn and As in surface water could pose health risks. • Distinct trace element characteristics are observed in surface and groundwater. [ABSTRACT FROM AUTHOR]

Metal concentrations (Cu, Zn, Cr, Fe, As, Pb, Ni, V, and Co) in surface sediments were determined in the southern Caspian Sea. Sediment pollution was investigated using geochemical index such as Enrichment Factor (EF) and ecological risk indices like modified Hazard Quotient (mHQ) and Toxic Risk Index (TRI). The highest calculated geochemical index was observed in sediments near the cities of Chalus and Ramsar, which are highly polluted due to human activities. The ecological potential indices indicated that Ni contents in all the studied stations would impose harmful effects on aquatic organisms, while other metals had a low risk. Principal component and cluster analyses suggested that As and Pb emanated from anthropogenic sources, and other metals probably originated from lithogenic sources. • Metal pollution was evident in the sediments near the cities of Chalus and Ramsar. • Cu, Zn, Cr, Ni, V, and Co originated from lithogenic sources. • As and Pb emanated from anthropogenic sources. • Ni contents in the studied stations imposed harmful effects on aquatic organisms. [ABSTRACT FROM AUTHOR]

[Display omitted] • Water quality was analyzed in the eastern route of South-to-North Diversion Project. • Water quality is satisfying and meets Class III standards except for total nitrogen. • The reverse flow between diversion and non-diversion periods impacts water quality. • Impounding lakes play an important role in restoring the water environment. • Mining, shipping, and land use are the potential factors affecting water quality. The eastern route of the South-to-North Water Diversion Project (SNWDP-ER) in China, an open-channel cross-basin project, is exposed to multiple potential pollution sources and has changing hydrodynamic conditions between diversion and non-diversion periods. After a decade of pollution control efforts, it is imperative to perceive the water quality conditions and identify potential pollution factors for further maintenance. Combining the water quality identification index (WQII) and multivariate statistical techniques (MSTs), this study conducted an exploratory analysis in SNWDP-ER, based on the monitoring data in 2020–2021. The monitoring and WQII results show that the overall water quality in SNWDP-ER is satisfactory and meets the Class III requirements in China Environmental Quality Standard for Surface Water , except for total nitrogen (TN) which falls into Class V or even worse. The periodic alternations of flow direction between diversion and non-diversion periods tend to deteriorate water quality, particularly in the initial stage of alternations. Water quality appears more stable during the diversion period, and impounding lakes along the project are crucial to water environment restoration in this period. Due to the positive influence brought by the self-purification and environmental carrying in the Luoma Lake, Nansi Lake, and Dongping Lake, water quality exhibits an enhancing trend from upstream to downstream in the Lianghu and Jiaodong reaches. Besides, the monitoring stations in Shandong reach exhibit significant divisional heterogeneity under hierarchical cluster analysis (HCA) with the Nansi Lake and Dongping Lake as the demarcation points, and the results of factor analysis/principal component analysis (FA/PCA) further identified the contributions of mining, shipping, and land use to each section. Based on the findings, the study recommends implementing wastewater discharge control, abandoned mines remediation, and continued water quality monitoring for further maintenance and improvement in SNWDP-ER. [ABSTRACT FROM AUTHOR]

With rapid urbanization and industrial development, waste discharges in west central Tunisia have multiplied. The focus here is on the waste manifestation from the National Company of Cellulose and Alfa Paper (SNCPA), which has been considered as a serious pollutant affecting natural resources as surface and groundwaters, human health and hygiene in the Kasserine land of semi‐arid climate. In order to preserve the quality assurance of available groundwater and determine its suitability for domestic an agricultural needs, a hydrogeochemical analysis was carried out to 30 samples for water samples taken from various source points of the study area (2018). This study attempt to assess the hydrogeochemical characteristics and ionic concentration with respect to WHO (2017) and Tunisian (NT.09.14) standards in the El Arich aquifer, in association with multivariate‐statistical‐analysis. Also DRASTIC model index was integrated with geographical information system (GIS) tools for the evaluation of the aquifer vulnerability to pollution. Results highlighted that only 17.65% of the groundwater samples are suitable for consumption purposes based on TDS, EC, Na+, K+, SO42–, and F–. The modeling of equilibrium geochemical processes elucidated that the groundwater's along flow‐paths are mostly in the oversaturated condition with respect to carbonate minerals, but they are undersaturated with evaporatic minerals. Based on some pre‐specified criteria and the validation by the water quality risk indices for soil salinization, the final vulnerability map shows four classes of vulnerability: very high, high, moderate, and low, depending on the intrinsic properties. HCA and FA suggest that high mineralization were related to rock‐water interactions and anthropogenic impact from the effluents discharged by the SNCPA. This multidisciplinary analysis would provide an overview for decision making authorities to take necessary action for the sustainable management and future planning in groundwater resource. [ABSTRACT FROM AUTHOR]

Abstract Multivariate statistical techniques including principal component and factor analyses were applied in this study to assess the quality of surface water from Tano basin in Ghana. The water samples were obtained from three monitoring stations from January to October 2016. The obtained data set was analysed using multivariate statistical methods. The results obtained from Rho Spearman's correlation revealed that at P

Increasing the soil productivity is challenged by the increasing biotic threat to plants and microorganisms, by the resistance to agrochemicals, and by the declining soil health. Soil management strategy is, therefore, aimed at erosion prevention and the minimisation of soil organic matter losses. A key factor in an agroecosystem is the appropriate biological stability. It is essential not only at present, but also for further sustainable agriculture. This study was based on the hypothesis that afforestation and conversion from arable land to permanent grassland improves the organic matter status and biological stability in the agroecosystem. The experiment was conducted from 2014 to 2018 in the Uhřice bio-corridor (Kroměříž region, the Czech Republic). Haplic Luvisol has been investigated for its basic biological and chemical properties after the arable land was converted to a natural vegetation system. The afforested segment (F), permanent grassland segment (G), and arable land segment (A) have been sampled in the upper soil horizon (0-0.30 m). Standard analytical methods were applied for the determination of the basic soil properties. A principal component analysis and factor analysis were used for interpreting the connection between the parameters of the soil organic carbon, the humic substances, the humic acids, and the fulvic acids, the agrochemical properties of the soil (the pH, the content of the nitrogen, phosphorus and potassium, etc.), and the soil biological properties (basal soil respiration (BSR), the ratios of the N/BSR, NG/BSR, etc.). After five years of investigation, the differences in the studied parameters were evident. The factor analysis and multivariate exploratory techniques showed that the soil properties were grouped based on the management into three different categories - F, G and A. The different land use directly influenced the quality and stability of the humic substances, basal soil respiration, and carbon and nitrogen utilisation. In comparison to the arable land, the forest and grassland were considered to have a higher accumulation potential of carbon and nitrogen. A negative correlation between the soil basal respiration (r = -0.95); total nitrogen (r = -0.93); total organic carbon (Cox) content (r = -0.82); and partial Ca (r = -0.82) was found. A positive correlation (r = 0.80) between the humic substances (C-HS) and soil reaction (pH) was determined.

This study assessed the surface water monitoring system in Hau Giang Province in 2019. The monitoring data for pH, temperature, total suspended solids (TSS), dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammonium (NH4+_N), nitrite (NO2-_N), nitrate (NO3-_N), orthophosphate (PO43-_P), coliforms, and iron (Fe) were collected from the Department of Natural Resources and Environment, Hau Giang Province, Vietnam. The results were compared with the national technical regulation on surface water quality (QCVN 08-MT: 2015/BTNMT). Then, these parameters were used to determine the locations and parameters for water quality monitoring using multivariate analyses including cluster analysis (CA) and principal component analysis (PCA). The results indicated that the main concerns for the quality of water in the canal of Hau Giang Province were organic matter (high BOD and COD), nutrients (NH4+_N, NO2-_N, PO43-_P), coliforms and iron. The CA results showed that 42 monitoring locations could be decreased to 26 locations, reducing monitoring costs by up to 32%. The PCA identified 12 sources of pollution, of which three main sources were PC1, PC2 and PC3 accounting for 75.6% of the variation in water quality. PCA findings showed that all the current water variables in the 2019 monitoring program were significant. The present study could help local environmental managers to reconsider the selected locations and parameters in the environmental monitoring program.

The combined effects of ultrasound and the antioxidants of bamboo leaves (AOB) on the quality maintenance of the adductor muscle of scallops (AMSs) during cold storage was investigated. Ultrasound power at 350 W coupled with AOB solution (2% w/v) (UAOB-350) was applied to treat the AMSs according to Taylor diagram analysis. The microstructure, oxidative changes (lipid and protein oxidation), total numbers of colonies, total volatile basic nitrogen, and texture of the AMSs during 6 days of cold storage were analysed. The results indicated that UAOB-350 treatment could effectively retard protein and lipid oxidation and bacterial growth and maintain better microstructure and texture characteristics than AOB solution treatment alone, prolonging the shelf life of the AMSs by 2 days during storage at 4 °C. These results indicate that the UAOB-350 combination method has promising potential to maintain the quality and extend the shelf life of AMSs during cold storage.

In the study region of Peddapanjani revenue area, twenty-five groundwater samples have been gathered in dissimilar panchayats to inspect the groundwater quality for suitability of irrigation and drinking. It was evaluated by pH, EC, TDS, hardness, and alkalinity other than considerable cations (Na+, K+ Ca2+Mg2+) and anions (HCO3−, Cl−, SO42−, F−). Based on the results obtained, sodium absorption ratio (SAR), sodium percent (Na%), permeability index (PI), residual sodium carbonate (RSC), Kelley's ratio (KR), magnesium ratio (MR), and non-carbonate hardness (NCH) were calculated. The chemical elements generated rock-water interaction, i.e., indicate Gibbs plot diagram. The sulfate concentration was observed high due to extensive usage of fertilizers and pesticide in agriculture cultivation. Sixty percent of area is magnesium elevated due to the presence of ferro magnesium minerals (silicate group of minerals, olivine, amphiboles, and pyroxenes). The USSL diagram indicates no alkalinity hazard in focused region, and Wilcox suggests that groundwater of that particular province is good to permissible category. Factor analysis and cluster analysis were performed of water chemistry. [ABSTRACT FROM AUTHOR]

The chemical characterization and assessment of the water quality in the headwater areas of the Himalaya are necessary for securing the water in the future. This study aims to assess the hydrochemistry and water quality concerning drinking and irrigational uses in the Seti River Basin (SRB), Nepal. A total of 45 water samples were collected in 2016 from the SRB during pre-monsoon, monsoon, and post-monsoon seasons, and pH, EC, TDS, and DO were measured on-site, whereas Ca2+, Mg2+, K+, Na+, Cl˗, SO42˗, NO3˗, and dissolved Si were analyzed in the laboratory. The results revealed mildly alkaline pH (8.40 ± 0.43) with the pattern of average ionic dominancy: Ca2+ > Mg2+ > Na+ > K+ and HCO3− > SO42− > Cl˗ > NO3˗ for cations and anions, respectively. Gibbs diagram implied that the lithogenic weathering mainly controlled the solute acquisition process, which was further confirmed by the Piper diagram, exhibiting Ca–HCO3 as the governing hydrochemical facies (91%). The average molar ratios were 0.88, 8.33, and 6.86 of (Ca2+ + Mg2+)/TZ+, (Ca2+ + Mg2+)/(Na+ + K+), and HCO3−/(Na+ + K+), respectively, which specified that the carbonate weathering largely controlled the solute acquisition processes with a minor contribution of silicates. The mass budget calculation also confirmed the dominance of carbonate weathering (72.0%, 78.9%, and 62.0% in Pre-Monsoon, Monsoon, and Post-Monsoon, respectively) and the high monsoon rainfall's dilution effect to anthropogenic input of cations. Principal component analysis and correlation matrix exhibited that the major sources of ions in the basin were geogenic with minor anthropic signatures. Furthermore, water quality in connection to drinking and irrigation uses revealed that the basin has mostly retained its natural water quality. This investigation suggests that regular monitoring and assessment are essential for maintaining the water quality and ecological integrity in the Himalayan river basins. [ABSTRACT FROM AUTHOR]

The present study uses the multivariate statistical techniques by applying the Factor Analysis (Principle component method) to explain the observed water quality data of Tigris river within Baghdad city. The water quality was analyzed at eleven different sites, along the river, over a period of one year (2017) using 20 water quality parameters. Five factors were identified by factor analysis which was responsible from the 72.291% of the total variance of the water quality in the Tigris river. The first factor called the pollution factor explained 34.387% of the total variance and the second factor called the surface runoff and erosion factor explained 11.875% of the total variance. While, the third, fourth, and fifth factors explained 10.213%, 8.861% and 6.956% of the total variance and called pH, Silica and nutrient factors, respectively. Multivariate statistical techniques can be effective methods to aid water resources managers understand complex nature of water quality issues and determine the priorities to sustain water quality.

Soil is a substantive environmental medium that is subjected to various physiochemical challenges derived by natural, as well as human activities. The present review attempts to summarize the pollution status of soil from the catchments areas of Beas and Ravi Rivers in Punjab, India, as reported by different workers. Principal component analysis (PCA) showed that anthropogenic activities and lithogenic factors are the major sources of metals. The average values of heavy metals of Beas River for heavy metals, Cr, Cu, Co, and Cd, were lower than the values suggested by Awasthi, European Union, and Ewers. The average values of C (0.29%), P (0.05 mg/g), and N (0.13 mg/g) were found for the Beas River, whereas for Ravi River the average values recorded were C (0.32%), P (0.01 mg/g), and N (0.16 mg/g). The results of contamination factor (CF) indicate that soil of Beas River is less contaminated by the metals. The results of ecological risk index indicate that metals showed low ecological risk in the soils of Beas River.

This study assesses the concentrations of the 54 ozone precursors (all being volatile organic compounds (VOCs)) detected at the four photochemical assessment monitoring stations that are part of the air quality monitoring network in the Kaohsiung-Pingtung area in Taiwan. Factor and cluster analyses of the multivariate statistical analysis are performed to explore the interrelationship among the 10 VOCs of relatively higher concentrations selected from the 54 ozone precursors to identify significant factors affecting ozone pollution levels in the study area. Moreover, the multivariate statistical analysis can faithfully reflect why the study area has been affected by photochemical pollution. First, results of the factor analysis suggest that the factors affecting how photochemical reactions occur in the study area can be divided into the following: “pollution from mobile sources”, “pollution from stationary sources”, and “pollution from energy sources”. Among them, mobile sources have the greatest impact on photochemical pollution levels. Second, the impacts of photochemical pollution on air quality in the study area can be classified into four clusters via cluster analysis. Each cluster represents how the 10 VOCs affect air quality, with different characteristics, and how they contribute to photochemical pollution in the study area. If there are more types and samples of photochemical pollutants when performing a multivariate statistical analysis, the analysis results will be more stable. This study adopts data on VOC monitoring over a period of nearly two years, which can effectively improve the validity and reliability of the factor analysis results, while helping environmental agencies review the effectiveness of air quality management in the future and serving as reference for the effectiveness of reducing photochemical pollution in the atmosphere.

In the present study, evaluation of spatial variations and interpretation of Zohrehh River water quality data were made by using multivariate analytical techniques including factor analysis and cluster analysis also the Arc GIS® software was used. The research method was formulated to achieve objectives herein, including field observation, numerical modeling, and laboratory analyses. The results showed that dataset consisted of 11,250 observations of seven-year monitoring program (measurement of 15 variables at 3 main stations from April 2010 to March 2017). Factor analysis with principal component analysis extraction of the dataset yielded seven varactors contributing to 82% of total variance and evaluated the incidence of each varactor on the total variance. The results of cluster analysis became complete with t-test and made water quality comparison between two clusters possible. Results of factor analysis were employed to facilitate t-test analysis. The t-test revealed the significant difference in a confidence interval of 95% between the mean of calculated varactors 1, 2, 6 and 7 between two clusters, but there was no significant difference in the mean of other varactors 3, 4 and 5 between two groups. The result shows the effect of agricultural fertilizers on stations located at downstream of the ASK dam. [ABSTRACT FROM AUTHOR]

Rivers are a vital component of both urban and rural aquatic ecosystems, and the rapidly increasing pollution in rivers severely threatens the security of these ecosystems. In this study, the surface water quality in the Maozhou River basin, Guangdong Province, China, was assessed from 2018 to 2020 using multivariate statistical techniques and the Integrated Water Quality Index (IWQI). In addition, spatial trends in surface water quality were studied using a geographic information system. The results indicated that the water quality in 82.17% of the studied section met the Class V standard for surface water quality, with IWQI values ranging from 12.157 to 3.650. The surface water quality was clustered into eight groups and further divided into unsuitable (low quality) and suitable (acceptable quality) according to standard surface water quality thresholds (China in Environmental quality standards for surface water, 2002). Four groups (G1, G2, G3, and G4) were classified as unsuitable, because they fell short of the Class V standard for surface water quality. In these groups, industrial sewage, endogenous pollution, domestic sewage, and rainfall runoff, were the primary sources of pollution, and the main background pollutants for the water quality target of the functional zones were COD, NH3-N, TP, and LAS. The other four groups were classified as suitable. In these groups, endogenous pollution and rainfall runoff were the primary sources of pollution, and the main background pollutants for the water quality target of the functional zones were NH3-N and TP. Among them, NH3-N and LAS were recognized as responsive and sensitive to the surface water quality and spatio-temporal variability. Owing to pollution treatment and management measures undertaken by the Chinese government, the black-odorous water in the Maozhou River basin has disappeared, and the water quality in the Maozhou River basin has been maintained at the "medium and good" level. However, the surface water quality in the estuary region and the southwest tributary in the basin requires further improvement. This calls for further efforts to improve surface water quality and to properly deal with various sources of pollution in the watershed. This combined method has proved to be effective for surface water quality evaluation and management at river or basin scales. The results of this work are expected to provide a scientific foundation for aquatic ecosystem management and planning. [ABSTRACT FROM AUTHOR]

Temporal/spatial variations of surface water quality were examined for the Nile River in the Damietta region where it serves as the major source of water for the inhabitants of Damietta Governorate. A total of 32 water quality parameters were monitored at six sampling sites for 12 months from February 2016 to January 2017. Higher values of chemical oxygen demand (COD), biological oxygen demand (BOD), heavy metals, and nutrients were observed upstream. About ~ 70% of the total variance in observations was explained by five main influences using factor analysis. The first factor (24.6% of the variance) was indicative of the mixed sources of natural and anthropogenic inputs. The second (nutritional) and the third (organic) factors were mainly controlled by the discharges from agricultural and domestic sources, respectively. Human activities and natural processes controlled the fourth and fifth factors. Only 11 parameters (K, temperature, COD, HPC, total hardness, DO, NO2, Na, TDS, Cl, and EC) were necessary for distinguishing temporal variations according to Discriminant analysis (DA). Seven parameters (BOD, PO4, SiO3, Al, Turbidity, Fe, and Chlorophyll-a) were the most important variables responsible for spatial variations. Using the results we developed a water quality index (WQI) using only those parameters identified as important. All water quality parameters were below the permissible limits except for turbidity according to the World Health Organization standards, BOD and COD according to the Egyptian regulations. The calculated WQI values ranged between 12.73 and 33.73. According to these values, the Nile River Damietta branch represents a good to an excellent source of drinking water for entering secondary treatment. [ABSTRACT FROM AUTHOR]

Monitoring and forecasting of chemical and physicochemical parameters of groundwater is an important factor in quality control and water management. In order to optimize these processes, the initial purpose of this paper was to identify sources of pollution and predict spatial changes in groundwater quality. Patterns of spatial changes in groundwater quality in the Danube river basin (Serbia) have been identified using multivariate statistical techniques. The results of the applied cluster analysis are the indicators of the existance of two spatial clusters. The principal component/factor analysis (PCA/FA) has shown that beside natural, anthropogenic factor has an influence in spatial grouping. Discriminant analysis (DA) was applied in order to identify discriminant groundwater quality parameters. DA reduced the number of data by extracting two parameters (iron and arsenic). The spatial distribution of identified dominant factors and discriminant parameters were graphically represented using GIS. Finally, the artificial neural network technique was used to test the ability to predict spatial changes in the values of discriminant parameters, and the reliability of this technique to predict the spatial variations of the two extracted variables has been proven. [ABSTRACT FROM AUTHOR]

Multivariate statistical techniques including principal component and factor analyses were applied in this study to assess the quality of surface water from Tano basin in Ghana. The water samples were obtained from three monitoring stations from January to October 2016. The obtained data set was analysed using multivariate statistical methods. The results obtained from Rho Spearman's correlation revealed that at P < 0.05 two-tailed, a positive correlation between pH and total dissolved solids, pH and alkalinity, pH and electrical conductivity, pH and major anions and cations such as SO4, F, Ca, K, Na and Mg was established. However, negative correlation existed between pH-colour, pH-turbidity and total suspended solids. The results of the principal component analysis show that the five principal components explain more than 91.57% of the total variance and hence can be relied upon for identification of the main sources of variation in the physicochemical properties of the water samples. Principal component 1 embodies about 54.26% of the variance and possesses a high loading for electrical conductivity, Na, Ca, K, Mg. Principal component 2, which also explains 33.94% of the total variance, holds high loadings for pH, SO4, HCO3, and total alkalinity. Component 3 also shows high loadings for TDS, TSS and conductivity, which account for 3.378% of the variation in the hydrochemistry. Components 4 and 5 show a joint influence of anthropogenic activities and partial ecological recovery system of the river and its basin which influence the overall water quality within the basin. [ABSTRACT FROM AUTHOR]

The rapid development of Rayong Province has resulted in increased demands on groundwater usage. This has potentially induced the release of contaminants such as arsenic (As), among others (i.e., NO3−, PO43−) from various land use types—especially in intensive agricultural areas and heavy industrial areas, including landfill sites. The objectives of this research are to investigate the As speciation and groundwater chemistry occurring due to different hydrogeological settings and the influence of human activities and to explain the mechanism of As release in the coastal alluvial aquifers in Rayong Province using multivariate statistical techniques and hydrogeochemical modeling (PHREEQC). Six major water facies, mainly consisting of Ca–Na–HCO3–Cl and Ca–Na–Cl, were included in the hydrochemical analysis. Arsenic levels were inversely correlated with NO3−, SO42−, DO, and ORP, confirming the reducing environment in the groundwater system. The results from the PHREEQC model show that most wells were strongly under-supersaturated with respect to arsenorite, scorodite, and arsenic pentoxide. Arsenic (As) is probably derived from the dissolution of Fe oxide and hydroxide (i.e., Fe(OH)3, goethite, maghemite, and magnetite). The multivariate statistical techniques revealed that the As species mainly consisted of As(III), governed by the reducing environment, while As(V) may be desorbed from Fe oxide and hydroxide as the pH increases. Anthropogenic inputs and intensive pumping may enhance the reducing environment, facilitating the release of As(III) into the groundwater. The knowledge gained from this study helps to better understand the mechanisms of As contamination in coastal groundwater aquifers, which is useful for groundwater management, including the optimum pumping rate and long-term monitoring of groundwater quality. [ABSTRACT FROM AUTHOR]

The wine market is very heterogeneous and complex, being the knowledge of the behaviour and attitudes of consumers a key tool to design efficient marketing plans, namely in countries that are traditionally wine producers and consumers, such as Portugal. In this country, Port wine is an economic and cultural icon, but, in the last decade, total sales have been decreasing. Despite this trend the domestic consumption has been gaining relevance and therefore a focus to reverse the negative cycle of total sales. The main goal of this paper is to analyse the profile and behaviour of domestic Port wine consumers, identifying homogeneous market segments. For this purpose, an online survey was applied and random sample of 678 Portuguese Port wine consumers was collected. Using multivariate statistical techniques, three profiles of purchasers/consumers emerged: experienced, less experienced and inexperienced consumers. This segmentation shows that Port wine consumers can be grouped according to their involvement with the product, consumption occasion and price they are willing to pay. In addition to the academic contribution of wine market segmentation, the paper provides useful insights to be used in designing marketing plans, both by institutional stakeholders and wineries.