Chemometric analysis of groundwater quality data of alluvial aquifer of Gangetic plain, North India

Assessment of surface water quality during different tides and an anthropogenic impact on coastal water at Gulf of Kachchh, West Coast of India

The port-based activity is often associated with industrial growth in the hinterland and similar phenomenon reported from the Gulf of Kachchh, India. Industrialization exerts pressure on coastal water through the release of waste water or effluents which influence the entire marine ecosystem. The present paper tries to evaluate the variation in the water quality during the high tide and low tide in relation to the anthropogenic or natural influence in Gulf of Kachchh. The tidal variation is important as it reflects the influence of the land-based activity on the coastal waters. To prove this logic, a series of stations were taken along the coastal water and statistical analysis, viz., Pearson correlation, Box plot, hierarchical cluster analysis (HCA), and factor analysis (PCA/FA) were conducted. Pearson correlation and Box plot represent visual impact of parameter variations in respected tides. The chemometric analysis, i.e., HCA and PCA/FA, clearly indicates an anthropogenic impact on coastal water. The results of HCA revealed that major anthropogenic and domestic impacts were found at various stations during the low tide. The HCA points out that an anthropogenic and the tidal activity in the Gulf of Kachchh influence the physical water quality parameters like pH, salinity, dissolved solid, oxygen, turbidity, sulfate, and nutrients in the coastal ecosystem. The PCA/FA further ascertains the finding of HCA analysis that the state of the art of the water quality of coastal ecosystem has direct relevance with the land-based activities and sewage outfall points. Tide-based control on the water quality parameters was evident that the high tide nutrients like phosphates and nitrogen were high, while during the low tide, temperature, salinity, total solids, and sulfate showed higher concentrations. The findings of the paper will be useful for developing effective management strategies for policy makers or stakeholders operating in the coastal area.

Water Quality Assessment and Characterization of Rivers in Pasir Gudang, Johor via Multivariate Statistical Techniques

In Pasir Gudang, an accelerated industry-based economy has caused a tremendous increase and diversity of water contamination. The application of multivariate statistical techniques can identify factors that influence water systems and is a valuable tool for managing water resources. Therefore, this study presents spatial evaluation and the elucidation of inordinate complex data for 32 parameters from 25 sampling points spanning 20 rivers across Pasir Gudang, summing up to 1500 observations between 2015-2019. Hierarchical cluster analysis with the K-means method grouped the rivers into two main clusters, i.e., proportionately low polluted rivers for Cluster 1 (C1) and high polluted rivers for Cluster 2 (C2), based on the similitude of water quality profiles. The discriminant analysis applied to the cluster resulted in a data reduction from 32 to 7 parameters (Cl, Cd, S, OG, temperature, BOD, and pH) with a 99.5% correct categorization in spatial analysis. Hence, element complexity was reduced to a few criteria accountable for large water quality differences between C1 and C2. The principal component analysis produced 6 and 7 principal components after rotation for C1 and C2, respectively, where total variance was 62.48% and 66.85%. In addition, several sub-clusters were identified; two from C1 and three from C2, based on the principal contributing components. These results show that the functionality of multivariate techniques can be effectively used to identify spatial water characteristics and pollution sources. The outcomes of this study may benefit legislators in managing rivers within Pasir Gudang.

Assessment of groundwater salinity using principal component analysis (PCA): a case study from Mewat (Nuh), Haryana, India

In the present study, principal component analysis (PCA) is used to investigate the processes controlling groundwater salinity in the Mewat (Nuh) district, Haryana, India. Twenty groundwater samples were collected from salinity-affected areas in the March-April months of years 2018 and 2019 and were analyzed for chemical variables pH, EC, Ca²⁺, Mg²⁺, Na⁺, K⁺, [Formula: see text], Cl^-, SO₄^2-, [Formula: see text], TDS, and total hardness. Three principal components were selected based on the eigen value, which explains 79.58% and 85.08% of the total variation in the years 2018 and 2019, respectively. The first principal component (PC-1) is identified with salinity, the second principal component (PC-2) with alkalinity, and the third principal component (PC-3) described the pollution. When the yearly comparison was made, the samples collected in 2019 were found to have an increased salinity compared to 2018, which shows an increased vulnerability to the aquifer of Mewat on account of the decline in rainfall recharge. It was also evident that declining recharge also triggered the recharge from other sources; thus, the impact of pollution is more pronounced in 2019 compared to 2018.

A comprehensive procedure to develop water quality index: A case study to the Huong river in Thua Thien Hue province, Central Vietnam

This work proposed a novel procedure of Water Quality Index (WQI) development that could be used for practical applications on a local or regional scale, based on available monitoring data. Principal component analysis (PCA) was applied to the monthly data of 11 water quality parameters (pH, conductivity (EC), total suspended solid (TSS), dissolved oxygen (DO), five -day biological oxygen demand (BOD), chemical oxygen demand (COD), ammonia (N-NH₄), nitrate (N-NO₃), phosphate (P-PO₄), total coliform, and total dissolved iron monitored at 11 sites at Huong river in the years 2014–2016. From the PCA, the three extracted principal components explained 67% of the total variance of original variables. From the set of communality values, the weight (w_i) for each parameter was determined. Linear sub-index functions were established based on the permissible limits from the National Technical Regulations on Surface Water Quality set up by the Vietnam Environment Agency (VEA) to derive the sub-index (q_i) for each parameter. The multiplicative formula that is the product of the sub-indices (q_i) raised to the respective weights (w_i), was used for calculation of the final WQI values. The proposed index (WQI) was then applied to the river with quarterly data of the 11 parameters monitored at ten sites in the years 2017–2020. The WQI representatively reflected the actual status of the river overall water quality, of which 97.8% of the WQI values belonged to grades of EXCELLENT and GOOD, and 2.2% of grade MODERATE. Comparison between the river water quality evaluations resulting from the developed WQI with the WQI adopted by National Sanitation Foundation (NSF-WQI) and the index issued by Vietnam Environment Agency (VN-WQI) indicated that the proposed WQI was more suitable for river quality assessment.

Hydrogeochemical Survey along the Northern Coastal Region of Ramanathapuram District, Tamilnadu, India

Ramanathapuram is a drought-prone southern Indian district that was selected for conducting a hydrogeochemical study. Groundwater samples from 40 locations were collected during January 2020 (pandemic interdiction according to COVID) and January 2021. The hydrogeochemical properties of the groundwater samples were evaluated and compared with drinking water regulations to assess their water quality. The order of cation dominance was as follows: Na+ > Ca2+ > K+ > Mg2+ in January 2020 and Na+ > Ca2+ > Mg2+ > K+ in January 2021 with respect to the mean value. The order of anion dominance was as follows: Cl− > HCO3− > SO42− > NO3− > F− in January 2020 and Cl− > SO42− > HCO3− > NO3− > F− in January 2021 with respect to the mean value. In the study area, the southern coastal region was identified as a groundwater-polluted zone through spatial analysis based on all analysis results. The irrigation water quality was analyzed using various calculated indices, such as Na% (percent sodium), SAR (sodium absorption ratio), PI (permeability index), MgC (magnesium risk), RSC (residual sodium concentration), and KI (Kelly ratio), demonstrating the suitability of the groundwater for irrigation in most parts of the study area. This was also confirmed by the Na% vs. EC Plot, USSL, and Doneen’s Plot for PI. In addition, the WQI results for drinking water and irrigation confirmed the suitability of the groundwater in most parts of the study area, except for the coastal regions. The dominant hydrogeologic facies of Na+-Cl−, Ca2+-Mg2+-SO42−, and Ca2+-Mg2+-Cl− types illustrated by the Piper diagram indicate the mixing process of freshwater with saline water in the coastal aquifers. Rock–water interaction and evaporation were the main controllers of groundwater geochemistry in the study area, as determined using the Gibbs plot. Ion exchange, seawater intrusion, weathering of carbonates, and the dissolution of calcium and gypsum minerals from the aquifer were identified as the major geogenic processes controlling groundwater chemistry using the Chadha plot, scatter plot, and Cl−/HCO3− ratio. Further, multivariate statistical approaches also confirmed the strong mutual relationship among the parameters, several factors controlling hydrogeochemistry, and grouping of water samples based on the parameters. Appropriate artificial recharge techniques must be used in the affected regions to stop seawater intrusion and increase freshwater recharge.

Human Health Risk Assessment of Trace Elements in Tap Water and the Factors Influencing Its Value

(1) Background: The influence of tap water fittings construction and internal pipe-work on the release of heavy metals was investigated. (2) Methods: A statistical approach was applied for the examination of the chemistry of tap water in five different cities in southern Poland. In total, 500 samples were collected (from 100 to 101 samples in each city). The sampling protocol included information on the construction of the water supply network and the physicochemical parameters of measured tap water. (3) Results: The statistical analysis allowed to extract the crucial factors that affect the concentrations of trace elements in tap water. Age of connection, age of tap, age of pipe-work as well as material of connection, material of pipe-work and material of appliance reveal the most significant variability of concentrations observed for As, Al, Cd, Cu, Fe, Mn, Pb, and Zn. Calculated cancer risks (CRs) decrease with the following order of analysed elements Ni > Cd > Cr > As = Pb and can be associated with the factors that affect the appearance of such elements in tap water. The hazard index (HI) was evaluated as negligible in 59.1% of the sampling points and low in 40.1% for adults. For children, a high risk was observed in 0.2%, medium in 9.0%, negligible in 0.4%, and low for the rest of the analysed samples.

Temporal trends and source apportionment of water pollution in Honghu Lake, China

Honghu Lake, the largest shallow lake in Jianghan Plain of China, is essential for maintaining ecosystem functioning in this region. However, water pollution and high disturbance are seriously threatening the ecological security of this lake. To explore the causes of water quality fluctuations in Honghu Lake, the water quality index method (CCME-WQI), multivariate statistical, and source apportionment techniques were adopted to characterize temporal trends in lake water quality (2004-2017), identify the main driving factors of water quality indicators, and quantify the contribution of various pollution sources. Besides, the water periods of the lake have been reclassified due to the seasonal variation of rainfall in the study area. The results of CCME-WQI showed that the water quality in Honghu Lake initially improved over 2004-2011, with better water quality in the wet period than in the dry periods, while the results over 2012-2017 were found to be opposite. Correlation analysis identified untreated industrial wastewater (UIW) as the main pollution source affecting CODMn concentrations in Honghu Lake, while untreated domestic sewage discharge (UDS) was identified as the main pollution source affecting BOD and F. coli concentrations. The main pollution sources affecting nutrient indicators were rainfall and enclosure aquaculture (EA). Principal component analysis (PCA) combined with absolute principal component score-multiple linear regression model (APCS-MLR) further appointed the source contribution of each pollution source to water quality indicators. The results showed that EA in 2012 was reduced by 81% compared with 2004, resulting in the contribution of EA to NH3-N, TP, and TN decreased by 0.2 mg L-1, 0.039 mg L-1, and 0.37 mg L-1, respectively. Compared with 2012, UIW was reduced by 65% in 2016, resulting in the contribution of UIW to CODMn decreased by 1.17 mg L-1. In addition, compared with 2004, UDS decreased by 85% in 2016, and the contribution of UDS to BOD and F. coli decreased by 0.7 mg L-1 and 887 cfu L-1, respectively. Based on the results of APCS-MLR, it was predicted that the concentrations of COD and TP in Honghu Lake would meet the water quality requirements after 2017. However, the rainfall non-point source pollution must be further controlled to achieve the desired level of TN concentration. This study provided an accurate method for analyzing lake water pollution, and the results can provide a valuable reference for optimizing water quality management and pollution control strategies within Honghu Lake.

Assessment of surface water of Gulf of Kachchh, west coast of India: A chemometric approach

The present study is aimed at investigation of surface water quality of Gulf of Kachchh (GoK), Gujarat. The main objective of this study was to convert complex dataset of water quality parameters from GoK into comprehensive, simple and interpretable observations. Hence, physico-chemical parameters and nutrients of surface water from GoK were analyzed. Chemometric results indicated that oxygen, salinity, dissolved solids, nutrient and natural conditions were the factors that affected surface water quality. The water quality index was calculated to identify water quality classes to evaluate the spatio-seasonal trend in the study area. The results revealed that water quality was moderate in summer, worst in pre-monsoon and best in post-monsoon. The study also highlighted that Marine National Park (Central GoK) was observed to be comparatively in good condition with abundant marine biodiversity. Thus, the results of chemometric study of water quality parameters can be a valuable tool for government authorities for sustainable development of GoK.

Evaluation of iron and manganese removal effectiveness by treatment plant modules based on water pollution index; a comprehensive approach

Groundwater is a viable alternative when access to surface water resources is limited. Iron and manganese are known ions in soil and naturally in groundwater sources. However, human activities also are responsible. To identifying the best module for removing manganese and iron in the water treatment plant (WTP) of Mazandaran, 516 samples were taken from raw and treated water. The concentration of manganese, iron, was measured by atomic absorption spectrophotometry, and turbidity was used with the nephelometry method. The water pollution index (WPI) was applied for categorizing the status of pollution in treated water. The effect of seasonal temperature and backwashing (At flow rates of 3.5, 9.2, and 15.3 m h-1) on the sand filter efficiency was also investigated. The highest concentrations of manganese, iron, and turbidity in raw water were 0.744, 6.70 mg L-1, and 41.8 NTU, and in treated water were 0.67, 1.09 mg L-1, and 5.58 NTU, respectively. The mean concentration of manganese and iron in raw and treated water were 0.24 ± 0.1, 0.93 ± 0.91, 0.105 ± 0.06 and 0.18 ± 0.14 mg L-1 respectively. The WPI statuses in drinking water were excellent for manganese and iron in 95.74 and 53.88 % of the samples and very poor in 1.16 and 12.01 % of the samples, respectively, and its classification for drinking water for manganese and iron was excellent ˃ good ˃ extremely polluted ˃ polluted and the concentration of iron was more than manganese in treated water. The study of temperature's effect on sand filters showed that the removal efficiency in warm seasons was higher than in cold seasons. Also, the turbulence in the backwash with the 9.2 m h^- 1 rates, is lesser than other speeds, and in this flow, after 270 s, the turbidity decreases to less than 10 NTU. Spearman correlation comparison showed that the parameters amounts after filtration decreased significantly (p ≤ 0.0001) in comparison to raw water. The results showed that module #1 that used open-aeration and chlorine as oxidations, was most effective in removing iron and manganese. In the end, the WTP couldn't diminish the parameters completely and need subsidiary units.

On the Performance of Pilot-Point Based Hydraulic Tomography with a Geophysical a Priori Model

We present a novel method to estimate the hydraulic and storage properties of a heterogeneous aquifer system using pilot-point-based hydraulic tomography (HT) inversion in conjunction with a geophysical a priori model. The a priori model involved a soil stratification obtained by combining electrical resistivity tomography inversion and field data from hydrogeological experiments. Pilot-point densities were assigned according to the stratification, which also constrained aquifer parameters during HT inversion. The forward groundwater flow model, HydroGeoSphere, was supplied to the parameter-estimation tool, PEST, to perform HT inversion. The performance of our method was evaluated on a hypothetical, two-dimensional, multi-layered, granitic aquifer system representative of those commonly occurring in the Kandi region in Telangana. Inversion results were compared using two commonly adopted methods of modeling parameter-heterogeneity: (1) using piece-wise zones of property values obtained from geostatistical interpolation of local-scale estimates; and (2) HT inversion starting from a homogeneous parameter field with a uniform distribution of pilot-points. Performances of the inverted models were evaluated by conducting independent pumping tests and statistical analyses (using a Taylor diagram) of the model-to-measurement discrepancies in drawdowns. Our results showed that using the aforementioned geophysical a priori model could improve the parameter-estimation process.

Arsenic health risk assessment and the evaluation of groundwater quality using GWQI and multivariate statistical analysis in rural areas, Hashtroud, Iran

Arsenic (As) is a toxic metalloid that can cause significant health issues through drinking water. The present study was aimed to evaluate As distribution and the related health risks from drinking groundwater in rural areas of Hashtroud, Iran. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were also applied to better explain relationship pattern between different resources. The samples were taken from 51 locations in 37 villages. Arsenic concentration was determined by a polarograph device, and the corresponding carcinogenic and non-carcinogenic health risks were calculated based on US Environmental Protection Agency (EPA) guideline. PCA analysis extracted four main components that explained nearly 62% of data variance. Results pointed severe As contamination in the studied area, where As was detected in 78% of the samples ranging from less than 0.001 to 0.250 mg/L. Forty percent of the contaminated places violated guideline value of 10 μg/L suggested by EPA and institute of standards and industrial research of Iran (ISIRI). Based on our findings, 1329 people including 239 children were living in the areas with higher As contamination. Hazard quotient (HQ) in 72%, 59%, and 33% of the samples was higher than one for children, adolescent, and adult age groups, respectively. Excess life time cancer risk (ELCR) in almost 80% of all age groups was significantly higher than EPA recommended guideline (10^-4 or 10^-6). In summary, from the view point of arsenic HQ and ELCR, water resources in the studied areas were not appropriate for drinking and hygienic purposes; necessary and urgent management strategies to guarantee water supply and health safety for local residents should be considered.

Groundwater quality evaluation using Shannon information theory and human health risk assessment in Yazd province, central plateau of Iran

This study aims to evaluate the quality of groundwater in the most arid province of Iran, Yazd. It is highly dependent on groundwater resources to meet the domestic, industrial, and agricultural water demand. Position of water samples on the modified Gibbs diagram demonstrates that the interaction with silicates and the increase in direct cation exchange are responsible for the increased salinity of groundwater. Based on entropy theory, the decreasing order of importance of variables in controlling groundwater chemistry is Fe > As > Ba > Hg > NO₂ > Pb > K > Cl > Na > Mg > SO₄ > NO₃ > HCO₃ > Ca. The results of entropy weighted water quality index (EWWQI) calculation show that about 34 and 32% of 206 samples in the wet and dry seasons, respectively, are classified as extremely poor quality (ranks 4 and 5). Approximately 60 and 55% of 206 samples in wet and dry seasons, respectively, have excellent, good, and medium quality (ranks 1, 2, and 3). The non-carcinogenic human health risk (NHHR) from intake and dermal contact pathways using deterministic approach show that 36 and 17 samples in both seasons are not suitable for drinking by children. Furthermore, 9 and 2 samples are not suitable for drinking by adults. The results show that children are more vulnerable than adults to these health risks. The non-carcinogenic risks through dermal contact were negligible.

'Template-free' hierarchical MoS2 foam as a sustainable 'green' scavenger of heavy metals and bacteria in point of use water purification

Molybdenum disulfide (MoS2), with its unique optical and electrical properties, has been explored for a variety of applications in the recent past. Still, its capabilities in point-of-use heavy metal ion removal remain to be explored. Herein, for the first time using a facile approach, we fabricated three-dimensional (3D) MoS2 foam from exfoliated single to few-layered MoS2 sheets for the selective exclusion of heavy metals and stringent bactericidal response. This foam was able to exclude 99.9% of Pb(ii) and 98.7% of As(iii) instantaneously and reduced more than 98% of bacteria E. coli. Moreover, the foam exhibits selective toxicity towards bacterial cells while showing no observable toxicity towards mammalian cells. The foam can be recycled and reused for at least five cycles under accelerated conditions and thus can be used for a promising non-cytotoxic, facile, and environmentally benign process for inline water remediation to remove heavy metal ions from the feed and as a potential antibacterial agent.

Spatiotemporal Patterns in River Water Quality and Pollution Source Apportionment in the Arid Beichuan River Basin of Northwestern China Using Positive Matrix Factorization Receptor Modeling Techniques

Deteriorating surface water quality has become an important environmental problem in China. In this study, river water quality was monitored in July (wet season) and October (dry season) 2019 at 26 sites, and a water quality index (WQI) and positive matrix factorization (PMF) model were used to assess surface water quality and identify pollution sources in the Beichuan River basin, Qinghai Province, China. The results showed that 53.85% and 76.92% of TN, 11.54% and 34.62% of TP, 65.38% and 76.92% of Fe, and 11.54% and 15.38% of Mn samples in the dry and wet seasons, respectively, exceeded the Chinese Government’s Grade III standards for surface water quality. The spatial variation in water quality showed that it gradually deteriorated from upstream to downstream as a result of human activity. The temporal variation showed that water quality was poorer in the wet season than in the dry season because of the rainfall runoff effect. The PMF model outputs showed that the primary sources of pollution in the wet season were mineral weathering and organic pollution sources, domestic and industrial sewage, and agricultural and urban non-point pollution sources. However, in the dry season, the primary sources were mineral weathering and organic pollution sources, industrial sewage, and domestic sewage. Our results suggest that the point pollution sources (domestic and industrial sewage) should be more strictly controlled, as a priority, in order to prevent the continued deterioration in water quality.

Applying multivariate analysis for optimising the electrodialytic removal of Cu and Pb from shooting range soils

Multivariate analysis was applied to simultaneously evaluate the influence of soil properties and experimental variables on electrodialytic removal of Cu and Pb from three shooting range soils. Both stationary and stirred set-ups in laboratory scale were tested, representing in-situ and ex-situ remediation conditions, respectively. Within the same experimental space, higher removal of the targeted metals, Cu and Pb, were observed in the stirred set-up (9-81%) compared to the stationary set-up (0-41%). Multivariate analysis (projections onto latent structures) revealed that the influence of soil type on the remediation efficiency was dependent on the metal and varied in the stationary and stirred set-ups. Optimising the removal of Cu by adjusting the experimental settings was easier to achieve in the stirred set-up and could be done by increasing the current density. Optimising the removal of Pb could be done by prolonging the treatment and in the stirred set-up also by increasing the current density.

Applying multivariate statistics for identification of groundwater resources and qualities in NW Turkey

This study, performed in Çanakkale-Ezine in NW of Turkey, analyzes the physicochemical properties of 37 groundwater wells. These 37 wells were chosen to represent each geological unit in the study area. The main purpose of the study and its contribution to the literature is to produce information about the resources and availability of groundwater by using multivariate statistical methods and lithology. For determination hydrochemical facies of groundwater, Piper trilinear diagram was used. Gibbs diagram was applied for determining the mechanism of groundwater chemistry and diagram showed that the interaction of rock-water is more dominant in the study area. Multivariate statistics were applied to physicochemical properties for identification origins of waters. According to the Piper diagram, 16 of the wells were identified as Ca-HCO₃ type, 13 of them as Ca-Cl type, 5 of them as mixed Ca-Mg-Cl type, 2 of them as Na-Cl type, and 1 as Ca-Na-HCO₃ type. In the study with the purpose of determining the resources of groundwater, the physicochemical properties of the wells are analyzed with hierarchical cluster (HCA) and non-hierarchical cluster (K-means) methods, and the resources are associated with the lithology based on these methods. A total of 37 wells are divided into five different clusters through the HCA method. Further, for the interpretation of the resources of the groundwater, the facies of the waters on the Piper diagram are evaluated based on the five clusters generated through the HCA method and on the lithology. In the study, the results obtained from the K-means method are not significant and in line with the lithology for the interpretation of the resources of the groundwater. In conclusion, this study with limited dataset reveals that using HCA method is very effective to identify the origins of groundwater and present the association with lithology.

Modelling the water-plant cuticular polymer matrix membrane partitioning of diverse chemicals in multiple plant species using the support vector machine-based QSAR approach

In this study, a support vector machine (SVM) based multi-species QSAR (quantitative structure-activity relationship) model was developed for predicting the water-plant cuticular polymer matrix membrane (MX) partition coefficient, K_MXw of diverse chemicals using two simple molecular descriptors derived from the chemical structures and following the OECD guidelines. Accordingly, the Lycopersicon esculentum Mill. data were used to construct the QSAR model that was externally validated using three other plant species data. The diversity in chemical structures and end-points were verified using the Tanimoto similarity index and Kruskal-Wallis statistics. The predictive power of the developed QSAR model was tested through rigorous validation, deriving a wide series of statistical checks. The MLOGP was the most influential descriptor identified by the model. The model yielded a correlation (r²) of 0.966 and 0.965 in the training and test data arrays. The developed QSAR model also performed well in another three plant species (r² > 0.955). The results suggest the appropriateness of the developed model to reliably predict the plant chemical interactions in multiple plant species and it can be a useful tool in screening the new chemical for environmental risk assessment.

Modeling the pH and temperature dependence of aqueousphase hydroxyl radical reaction rate constants of organic micropollutants using QSPR approach

Designing of advanced oxidation process (AOP) requires knowledge of the aqueous phase hydroxyl radical (^●OH) reactions rate constants (k _OH), which are strictly dependent upon the pH and temperature of the medium. In this study, pH- and temperature-dependent quantitative structure-property relationship (QSPR) models based on the decision tree boost (DTB) approach were developed for the prediction of k _OH of diverse organic contaminants following the OECD guidelines. Experimental datasets (n = 958) pertaining to the k _OH values of aqueous phase reactions at different pH (n = 470; 1.4 × 10⁶ to 3.8 × 10¹⁰ M^-1 s^-1) and temperature (n = 171; 1.0 × 10⁷ to 2.6 × 10¹⁰ M^-1 s^-1) were considered and molecular descriptors of the compounds were derived. The Sanderson scale electronegativity, topological polar surface area, number of double bonds, and halogen atoms in the molecule, in addition to the pH and temperature, were found to be the relevant predictors. The models were validated and their external predictivity was evaluated in terms of most stringent criteria parameters derived on the test data. High values of the coefficient of determination (R ²) and small root mean squared error (RMSE) in respective training (> 0.972, ≤ 0.12) and test (≥ 0.936, ≤ 0.16) sets indicated high generalization and predictivity of the developed QSPR model. Other statistical parameters derived from the training and test data also supported the robustness of the models and their suitability for screening new chemicals within the defined chemical space. The developed QSPR models provide a valuable tool for predicting the ^●OH reaction rate constants of emerging new water contaminants for their susceptibility to AOPs.

Factors controlling groundwater quality in the Yeonjegu District of Busan City, Korea, using the hydrogeochemical processes and fuzzy GIS

The hydrogeochemical processes and fuzzy GIS techniques were used to evaluate the groundwater quality in the Yeonjegu district of Busan Metropolitan City, Korea. The highest concentrations of major ions were mainly related to the local geology. The seawater intrusion into the river water and municipal contaminants were secondary contamination sources of groundwater in the study area. Factor analysis represented the contamination sources of the mineral dissolution of the host rocks and domestic influences. The Gibbs plot exhibited that the major ions were derived from the rock weathering condition. Piper's trilinear diagram showed that the groundwater quality was classified into five types of CaHCO₃, NaHCO₃, NaCl, CaCl₂, and CaSO₄ types in that order. The ionic relationship and the saturation mineral index of the ions indicated that the evaporation, dissolution, and precipitation processes controlled the groundwater chemistry. The fuzzy GIS map showed that highly contaminated groundwater occurred in the northeastern and the central parts and that the groundwater of medium quality appeared in most parts of the study area. It suggested that the groundwater quality of the study area was influenced by local geology, seawater intrusion, and municipal contaminants. This research clearly demonstrated that the geochemical analyses and fuzzy GIS method were very useful to identify the contaminant sources and the location of good groundwater quality.

Modeling the aqueous phase reactivity of hydroxyl radical towards diverse organic micropollutants: An aid to water decontamination processes

The rate constants of the hydroxyl radical reactions (k_OH) with organic micropollutants (OMPs) in aqueous medium are important in designing the advanced oxidation processes (AOPs) for their removal. In this study, a quantitative structure-property relationship (QSPR) model for the prediction of k_OH of diverse and emerging OMPs was developed in accordance with the OECD guidelines. A large experimental data set (n = 995) comprised of compounds with k_OH values ranging from 7.9 × 10⁵ to 6.8 × 10¹⁰ M^-1 s^-1 was considered and several molecular descriptors were calculated. As a result, five descriptors were found to be important in predicting the k_OH values which related to the electronegativity, topological polar surface area, double bonds, average molecular weight, and halogen atoms in the molecule. The optimal model was validated internally and externally and several statistical stringent parameters were derived. High values of the coefficient of determination (R²) and small root mean squared error (RMSE) in the training (0.954; 0.17) and validation (0.925; 0.14) sets indicated high generalization and predictivity of the developed model. Other statistical parameters derived from the training and validation data also supported the robustness of the model. The proposed model outperformed the earlier QSARs reported for k_OH prediction. Overall, the developed QSPR model provides a valuable tool for an initial assessment of the susceptibility of organic micropollutants to AOPs.

Modelling spatio-temporal heterogeneities in groundwater quality in Ghana: a multivariate chemometric approach

Chemometric techniques were applied to evaluate the spatial and temporal heterogeneities in groundwater quality data for approximately 740 goldmining and agriculture-intensive locations in Ghana. The strongest linear and monotonic relationships occurred between Mn and Fe. Sixty-nine per cent of total variance in the dataset was explained by four variance factors: physicochemical properties, bacteriological quality, natural geologic attributes and anthropogenic factors (artisanal goldmining). There was evidence of significant differences in means of all trace metals and physicochemical parameters (p < 0.001) between goldmining and non-goldmining locations. Arsenic and turbidity produced very high value F's demonstrating that 'physical properties and chalcophilic elements' was the function that most discriminated between non-goldmining and goldmining locations. Variations in Escherichia coli and total coliforms were observed between the dry and wet seasons. The overall predictive accuracy of the discriminant function showed that non-goldmining locations were classified with slightly better accuracy (89%) than goldmining areas (69.6%). There were significant differences between the underlying distributions of Cd, Mn and Pb in the wet and dry seasons. This study emphasizes the practicality of chemometrics in the assessment and elucidation of complex water quality datasets to promote effective management of groundwater resources for sustaining human health.

QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes

The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R ²) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

Impact of Kishnica and Badovci Flotation Tailing Dams on Levels of Heavy Metals in Water of Graçanica River (Kosovo)

The main objective of this study was to perform assessment of water quality of Graçanica River (Kosovo), impacted by Kishnica and Badovci flotation tailing dams, using ICP-OES method. The obtained results show that the mean values of all heavy metals in studied river water samples were significantly high, with following maximal concentrations: As (0.033 mgL−1), Cd (0.002 mgL−1), Cr (0.225 mgL−1), Cu (0.015 mgL−1), Hg (0.004 mgL−1), Mn (15.66 mgL−1), Ni (0.255 mgL−1), Pb (0.013 mgL−1), and Zn (0.612 mgL−1), but only two samples from locations influenced by Kishnica and Badovci flotation tailing dams showed statistically anomalous values of Cr3+, Cu2+, Mn2+, Zn2+, and Hg2+. According to assessment based on Croatian standards, locations near both flotation tailing dams are significantly polluted with majority of studied metals, while downstream sampling stations are almost unpolluted or slightly polluted. Mercury is found to be the most significant contaminant. According to WHO recommended values for drinking water, on all locations values were within the limits for Al, Cd, Cu, and Zn, while for As, Cr, Hg, Mn, Ni, and Pb values exceed recommended values on some sampling stations. Further monitoring of water and possibly sediments of Graçanica River is advised, as well as performing of remediation of Kishnica and Badovci mine tailing dams.

Integrated Application of Multivariate Statistical Methods to Source Apportionment of Watercourses in the Liao River Basin, Northeast China

Source apportionment of river water pollution is critical in water resource management and aquatic conservation. Comprehensive application of various GIS-based multivariate statistical methods was performed to analyze datasets (2009–2011) on water quality in the Liao River system (China). Cluster analysis (CA) classified the 12 months of the year into three groups (May–October, February–April and November–January) and the 66 sampling sites into three groups (groups A, B and C) based on similarities in water quality characteristics. Discriminant analysis (DA) determined that temperature, dissolved oxygen (DO), pH, chemical oxygen demand (COD_Mn)_, 5-day biochemical oxygen demand (BOD₅), NH₄⁺–N, total phosphorus (TP) and volatile phenols were significant variables affecting temporal variations, with 81.2% correct assignments. Principal component analysis (PCA) and positive matrix factorization (PMF) identified eight potential pollution factors for each part of the data structure, explaining more than 61% of the total variance. Oxygen-consuming organics from cropland and woodland runoff were the main latent pollution factor for group A. For group B, the main pollutants were oxygen-consuming organics, oil, nutrients and fecal matter. For group C, the evaluated pollutants primarily included oxygen-consuming organics, oil and toxic organics.

QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes

The experimental determination of multi-generation reproductive toxicity of chemicals involves high costs and a large number of animal studies over a long period of time. Computational toxicology offers possibilities to overcome such difficulties. In this study, we have established ensemble machine learning (EML) based quantitative structure-activity relationship models for predicting the reproductive toxicity potential (LOAEL) of structurally diverse chemicals in accordance with the OECD guidelines. Accordingly, decision tree forest (DTF) and decision tree boost (DTB) QSAR models were developed using a novel dataset composed of the toxicity endpoints for 334 chemicals. Relevant structural features of chemicals responsible for toxicity potential were identified and used in QSAR modeling. The generalization and prediction abilities of the constructed QSAR models were evaluated by internal and external validation procedures and by deriving several stringent statistical criteria parameters. In the test set, the two models (DTF and DTB) yielded R2 of 0.856 and 0.945, between the experimental and predicted endpoint toxicity values. The models were also evaluated for predictive use through the most recent criteria based on root mean squared error (RMSE) and mean absolute error (MAE). The values of various statistical validation coefficients derived for the test data were above their respective threshold limits and thus put a high confidence in this analysis. The applicability domains of the constructed QSAR models were defined using the leverage and standardization approaches. The results suggest that the proposed QSAR models can reliably predict the reproductive toxicity potential of diverse chemicals and can be useful tools for screening new chemicals for safety assessment.

Room-temperature and temperature-dependent QSRR modelling for predicting the nitrate radical reaction rate constants of organic chemicals using ensemble learning methods

Experimental determinations of the rate constants of the reaction of NO3 with a large number of organic chemicals are tedious, and time and resource intensive; and the development of computational methods has widely been advocated. In this study, we have developed room-temperature (298 K) and temperature-dependent quantitative structure-reactivity relationship (QSRR) models based on the ensemble learning approaches (decision tree forest (DTF) and decision treeboost (DTB)) for predicting the rate constant of the reaction of NO3 radicals with diverse organic chemicals, under OECD guidelines. Predictive powers of the developed models were established in terms of statistical coefficients. In the test phase, the QSRR models yielded a correlation (r(2)) of >0.94 between experimental and predicted rate constants. The applicability domains of the constructed models were determined. An attempt has been made to provide the mechanistic interpretation of the selected features for QSRR development. The proposed QSRR models outperformed the previous reports, and the temperature-dependent models offered a much wider applicability domain. This is the first report presenting a temperature-dependent QSRR model for predicting the nitrate radical reaction rate constant at different temperatures. The proposed models can be useful tools in predicting the reactivities of chemicals towards NO3 radicals in the atmosphere, hence, their persistence and exposure risk assessment.

In silico prediction of the developmental toxicity of diverse organic chemicals in rodents for regulatory purposes

The experimental determination of the developmental toxicity potential (LEL) of chemicals is not only tedious, time and resource intensive, but it also involves unethical tests on animals. In this study, we have established quantitative structure activity relationship (QSAR) models for predicting the developmental toxicity potential of chemicals in rodents following the OECD guidelines. Accordingly, decision tree forest (DTF) and decision tree boost (DTB) based local (L-QSAR), global (G-QSAR) and interspecies quantitative structure activity-activity relationship (ISC QSAAR) models were developed for estimating the LEL (lowest effective level) dose of chemicals for developmental toxicity in rats and rabbits. The structural features of chemicals responsible for developmental toxicity in rodents were extracted and used in QSAR/QSAAR analysis. The external predictive power of the developed models was evaluated through the internal and external validation procedures. In test data, the L-QSAR models (DTF, DTB) yielded R2 values of >0.846 (rat) and >0.906 (rabbit), whereas in G-QSAR, the correlation value was >0.870 between the measured and predicted endpoint values. In ISC QSAAR models, the R2 values in test data were 0.830 (DTF) and 0.927 (DTB), respectively. Values of various statistical validation coefficients derived from the test data (except rm2 in DTF based rat L-QSAR and ISC QSAAR models) were above their respective threshold limits, thus putting a high confidence in this analysis. The prediction quality of the developed QSAR/QSAAR models was also assessed using the mean absolute error (MAE) criteria and found good. The applicability domains of the constructed models were defined using the descriptor range, leverage, and standardization approaches. The results suggest that the developed QSAR/QSAAR models can reliably predict the developmental toxicity potential of structurally diverse chemicals in rodents, generating useful toxicity data for risk assessment in humans.

Inter-moieties reactivity correlations: an approach to estimate the reactivity endpoints of major atmospheric reactants towards organic chemicals

The figure shows the DTB based IMRC QRRR modelling and predicted values of the rate constants (log k_OH, log k_O3).

Assessment of metal species in river Ganga sediment at Varanasi, India using sequential extraction procedure and SEM-EDS

Aim of the present study was to assess impact of urban drains over river water and sediments by physico-chemical and metal analysis. Metal speciation (Sequential Extraction Procedure) and elemental composition analysis (SEM-EDS) was used to quantify metal pollution load in river sediments. Metal speciation analysis showed dominance of available and labile fractions of all heavy metals (Cr, Ni, Cu, Zn, Cd and Pb) except Mn and Fe which were dominant in residual forms. Cluster analysis (CA), Principal Components Analysis (PCA) and Partial Least Square Regression (PLSR) were applied as source receptor modeling for pollutants. Results classified river stretch into three zones i.e. moderately, severely and extremely polluted, on the basis of pollutant concentration released from anthropogenic sources. SEM-EDS study revealed the elemental composition percentage in river sediments. Pollution Load Index (PLI) varied from 1.8 (S1)-3.9 (S15). The Geo accumulation index (GAI) was found highest for Cd (6.88-8.97) and Pb (2.41-3.24).

Application of chemometric analysis and self Organizing Map-Artificial Neural Network as source receptor modeling for metal speciation in river sediment

Present study deals with the river Ganga water quality and its impact on metal speciation in its sediments. Concentration of physico-chemical parameters was highest in summer season followed by winter and lowest in rainy season. Metal speciation study in river sediments revealed that exchangeable, reducible and oxidizable fractions were dominant in all the studied metals (Cr, Ni, Cu, Zn, Cd, Pb) except Mn and Fe. High pollution load index (1.64-3.89) recommends urgent need of mitigation measures. Self-organizing Map-Artificial Neural Network (SOM-ANN) was applied to the data set for the prediction of major point sources of pollution in the river Ganga.

Assessment of ground and surface water quality along the river Varuna, Varanasi, India

Multivariate statistical techniques were employed for monitoring of ground-surface water interactions in rivers. The river Varuna is situated in the Indo-Gangetic plain and is a small tributary of river Ganga. The study area was monitored at seven sampling sites for 3 years (2010-12), and eight physio-chemical parameters were taken into account for this study. The data obtained were analysed by multivariate statistical techniques so as to reveal the underlying implicit information regarding proposed interactions for the relevant area. The principal component analysis (PCA) and cluster analysis (CA), and the results of correlations were also studied for all parameters monitored at every site. Methods used in this study are essentially multivariate statistical in nature and facilitate the interpretation of data so as to extract meaningful information from the datasets. The PCA technique was able to compress the data from eight to three parameters and captured about 78.5% of the total variance by performing varimax rotation over the principal components. The varifactors, as yielded from PCA, were treated by CA which grouped them convincingly into three groups having similar characteristics and source of contamination. Moreover, the loading of variables on significant PCs showed correlations between various ground water and surface water (GW-SW) parameters. The correlation coefficients calculated for various physiochemical parameters for ground and surface water established the correlations between them. Thus, this study presents the utility of multivariate statistical techniques for evaluation of the proposed interactions and effective future monitoring of potential sites.

Tropical soils with high aluminum concentrations cause oxidative stress in two tomato genotypes

Tropical and subtropical soils are usually acidic and have high concentrations of aluminum (Al). Aluminum toxicity in plants is caused by the high affinity of the Al cation for cell walls, membranes, and metabolites. In this study, the response of the antioxidant-enzymatic system to Al was examined in two tomato genotypes: Solanum lycopersicum var. esculentum (Calabash Rouge) and Solanum lycopersicum var. cerasiforme (CNPH 0082) grown in tropical soils with varying levels of Al. Plant growth; activities of superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), guaiacol peroxidase (GPOX), and glutathione reductase (GR) enzymes; stress-indicating compounds (malondialdehyde (MDA) and hydrogen peroxide); and morphology (root length and surface area) were analyzed. Increased levels of Al in soils were correlated with reduced shoot and root biomass and with reduced root length and surface area. Calabash Rouge exhibited low Al concentrations and increased growth in soils with the highest levels of Al. Plants grown in soils with high availability of Al exhibited higher levels of stress indicators (MDA and hydrogen peroxide) and higher enzyme activity (CAT, APX, GPOX, and GR). Calabash Rouge absorbed less Al from soils than CNPH 0082, which suggests that the genotype may possess mechanisms for Al tolerance.

Multivariate methods for evaluating the efficiency of electrodialytic removal of heavy metals from polluted harbour sediments

Chemometrics was used to develop a multivariate model based on 46 previously reported electrodialytic remediation experiments (EDR) of five different harbour sediments. The model predicted final concentrations of Cd, Cu, Pb and Zn as a function of current density, remediation time, stirring rate, dry/wet sediment, cell set-up as well as sediment properties. Evaluation of the model showed that remediation time and current density had the highest comparative influence on the clean-up levels. Individual models for each heavy metal showed variance in the variable importance, indicating that the targeted heavy metals were bound to different sediment fractions. Based on the results, a PLS model was used to design five new EDR experiments of a sixth sediment to achieve specified clean-up levels of Cu and Pb. The removal efficiencies were up to 82% for Cu and 87% for Pb and the targeted clean-up levels were met in four out of five experiments. The clean-up levels were better than predicted by the model, which could hence be used for predicting an approximate remediation strategy; the modelling power will however improve with more data included.

Occurrence of pharmaceuticals in urban wastewater of north Indian cities and risk assessment

Six pharmaceuticals of different categories, such as nonsteroidal anti-inflammatory drugs (ibuprofen, ketoprofen, naproxen, diclofenac), anti-epileptic (carbamazepine), and anti-microbial (trimethoprim), were investigated in wastewater of the urban areas of Ghaziabad and Lucknow, India. Samples were concentrated by solid phase extraction (SPE) and determined by high-performance liquid chromatography (HPLC) methods. The SPE-HPLC method was validated according to the International Conference on Harmonization guidelines. All the six drugs were detected in wastewater of Ghaziabad, whereas naproxen was not detected in Lucknow wastewater. Results suggest that levels of these detected drugs were relatively higher in Ghaziabad as compared to those in Lucknow, and diclofenac was the most frequently detected drug in both the study areas. Detection of these drugs in wastewater reflects the importance of wastewater inputs as a source of pharmaceuticals. In terms of the regional distribution of compounds in wastewater of two cities, higher spatial variations (coefficient of variation 112.90-459.44%) were found in the Lucknow wastewater due to poor water exchange ability. In contrast, lower spatial variation (162.38-303.77%) was observed in Ghaziabad. Statistical analysis results suggest that both data were highly skewed, and populations in two study areas were significantly different (p < 0.05). A risk assessment based on the calculated risk quotient (RQ) in six different bioassays (bacteria, duckweed, algae, daphnia, rotifers, and fish) showed that the nonsteroidal anti-inflammatory drugs (NSAIDs) posed high (RQ >1) risk to all the test species. The present study would contribute to the formulation of guidelines for regulation of such emerging pharmaceutical contaminants in the environment.

Groundwater quality in Imphal West district, Manipur, India, with multivariate statistical analysis of data

The aim of this paper was to analyze the groundwater quality of Imphal West district, Manipur, India, and assess its suitability for drinking, domestic, and agricultural use. Eighteen physico-chemical variables were analyzed in groundwater from 30 different hand-operated tube wells in urban, suburban, and rural areas in two seasons. The data were subjected to uni-, bi-, and multivariate statistical analysis, the latter comprising cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA). Arsenic concentrations exceed the Indian standard in 23.3% and the WHO limit in 73.3% of the groundwater sources with only 26.7% in the acceptable range. Several variables like iron, chloride, sodium, sulfate, total dissolved solids, and turbidity are also beyond their desirable limits for drinking water in a number of sites. Sodium concentrations and sodium absorption ratio (SAR) are both high to render the water from the majority of the sources unsuitable for agricultural use. Multivariate statistical techniques, especially varimax rotation of PCA data helped to bring to focus the hidden yet important variables and understand their roles in influencing groundwater quality. Widespread arsenic contamination and high sodium concentration of groundwater pose formidable constraints towards its exploitation for drinking and other domestic and agricultural use in the study area, although urban anthropogenic impacts are not yet pronounced.

Application of multivariate statistical techniques in the assessment of groundwater quality in seawater intrusion area in Bafra Plain, Turkey

Multivariate statistical techniques such as cluster analysis and principal component analysis were performed on 28 groundwater wells in Bafra Plain. Cluster analysis results show that the groundwater in the study area is classified into three groups (A, B, and C), and factor analysis indicates that groundwater is composed of 89.64 % of total variance of 12 variables and is mainly affected by three factors. Factor 1 (seawater salinization) includes concentrations of electrical conductivity, TDS, Cl(-), Na(+), and sodium adsorption ratio, factor 2 (mixing water) includes δ(18)O, δD, and T, and factor 3 (fresh) includes Ca(2+). For determination of the source of water, Ca/Cl, Cl/HCO(3), Mg/Cl, and Ca/Na as initials and Mg/Ca and SO(4)/Cl as molar rates which were identified, the rates had been found to be very useful. Cluster analysis was made by using these rates and the waters were classified in two groups (group 1 and group 2). First group waters were affected by seawater, and the second group were very less affected by freshwater or seawater. According to the comparison of two different parameters, group 1 comprised group A and group B-2, -3, and -4 from the same wells, and group 2 comprised group B-1 and group C from the same well. As a result of this study, it could be said that multivariate statistical methods gave very useful results for the determination of the source.

Application of chemometrics to identifying solid fuels and their origin

The aim of this work was to implement a chemometric analysis to detect the relationships between the analysed variables in samples of solid fuels. Efforts are being made to apply chemometrics methods in environmental issues by developing methods for the rapid assessment of solid fuels and their compliance with the required emission characteristics regulations. In the present investigation, two clustering techniques—hierarchical clustering analysis (HCA) and principal components analysis (PCA)—are used to obtain the linkage between solid fuel properties and the type of sample. These analyses allowed us to detect the relationships between the studied parameters of the investigated solid fuels. Furthermore, the usefulness of chemometrics methods for identification of the origin of biofuels is shown. These methods will enable control of the degree of contamination.

Application of receptor models on water quality data in source apportionment in Kuantan River Basin

Recent techniques in the management of surface river water have been expanding the demand on the method that can provide more representative of multivariate data set. A proper technique of the architecture of artificial neural network (ANN) model and multiple linear regression (MLR) provides an advance tool for surface water modeling and forecasting. The development of receptor model was applied in order to determine the major sources of pollutants at Kuantan River Basin, Malaysia. Thirteen water quality parameters were used in principal component analysis (PCA) and new variables of fertilizer waste, surface runoff, anthropogenic input, chemical and mineral changes and erosion are successfully developed for modeling purposes. Two models were compared in terms of efficiency and goodness-of-fit for water quality index (WQI) prediction. The results show that APCS-ANN model gives better performance with high R2 value (0.9680) and small root mean square error (RMSE) value (2.6409) compared to APCS-MLR model. Meanwhile from the sensitivity analysis, fertilizer waste acts as the dominant pollutant contributor (59.82%) to the basin studied followed by anthropogenic input (22.48%), surface runoff (13.42%), erosion (2.33%) and lastly chemical and mineral changes (1.95%). Thus, this study concluded that receptor modeling of APCS-ANN can be used to solve various constraints in environmental problem that exist between water distribution variables toward appropriate water quality management.

A comparative chemometric study for water quality expertise of the Athenian water reservoirs

The aim of the present study is to compare the application of unsupervised and supervised pattern recognition techniques for the quality assessment and classification of the reservoirs used as the source for the domestic and industrial water supply of the city of Athens, Greece. A new optimization strategy for sampling, monitoring, and water management is proposed. During the period of October 2006 to April 2007, 89 samples were collected from the three water reservoirs (Iliki, Mornos, and Marathon), and 13 parameters (metals and metalloids) were analytically determined. Generally, all the elements were found to fluctuate at very low levels, especially for Mornos that comprises the main water reservoir of Athens. Iliki and Marathon showed relatively elevated values, compared to Mornos, but below the legislative limits. Multivariate unsupervised statistical techniques, such as factor analysis/principal components analysis, and cluster analysis and supervised ones, like discriminant analysis and classification trees, were applied to the data set, and their classification abilities were compared. All the chemometric techniques successfully revealed the critical variables and described the similarities and dissimilarities among the sampling points, emphasizing the individual characteristics in every sample and revealing the sources of elements in the region. New data from posterior samplings (November and December 2007) were used for the validation of the supervised techniques. Finally, water management strategies were proposed concerning the sampling points and representative parameters.

Chemical characteristics of groundwater and assessment of groundwater quality in Varaha River Basin, Visakhapatnam District, Andhra Pradesh, India

Study on chemical characteristics of groundwater and impacts of groundwater quality on human health, plant growth, and industrial sector is essential to control and improve the water quality in every part of the country. The area of the Varaha River Basin is chosen for the present study, where the Precambrian Eastern Ghats underlain the Recent sediments. Groundwater quality is of mostly brackish and very hard, caused by the sources of geogenic, anthropogenic, and marine origin. The resulting groundwater is characterized by Na(+) > Mg(2+) > Ca(2+) : [Formula: see text] > Cl(-) > [Formula: see text], Na(+) > Mg(2+) > Ca(2+) : [Formula: see text] > Cl(-) > [Formula: see text] > [Formula: see text], Na(+) > Mg(2+) > Ca(2+) : [Formula: see text] > Cl(-), and Na(+) > Mg(2+) > Ca(2+) : Cl(-) > [Formula: see text] > [Formula: see text] facies, following the topographical and water flow-path conditions. The genetic geochemical evolution of groundwater ([Formula: see text] and Cl(-)-[Formula: see text] types under major group of [Formula: see text]) and the hydrogeochemical signatures (Na(+)/Cl(-), >1 and [Formula: see text]/Cl(-), <1) indicate that the groundwater is of originally fresh quality, but is subsequently modified to brackish by the influences of anthropogenic and marine sources, which also supported by the statistical analysis. The concentrations of total dissolved solids (TDS), TH, Mg(2+), Na(+), K(+), [Formula: see text], Cl(-), [Formula: see text], and F(-) are above the recommended limits prescribed for drinking water in many locations. The quality of groundwater is of mostly moderate in comparison with the salinity hazard versus sodium hazard, the total salt concentration versus percent sodium, the residual sodium carbonate, and the magnesium hazard, but is of mostly suitable with respect to the permeability index for irrigation. The higher concentrations of TDS, TH, [Formula: see text], Cl(-), and [Formula: see text] in the groundwater cause the undesirable effects of incrustation and corrosion in many locations. Appropriate management measures are, therefore, suggested to improve the groundwater quality.

Linear and nonlinear modeling approaches for urban air quality prediction

In this study, linear and nonlinear modeling was performed to predict the urban air quality of the Lucknow city (India). Partial least squares regression (PLSR), multivariate polynomial regression (MPR), and artificial neural network (ANN) approach-based models were constructed to predict the respirable suspended particulate matter (RSPM), SO(2), and NO(2) in the air using the meteorological (air temperature, relative humidity, wind speed) and air quality monitoring data (SPM, NO(2), SO(2)) of five years (2005-2009). Three different ANN models, viz. multilayer perceptron network (MLPN), radial-basis function network (RBFN), and generalized regression neural network (GRNN) were developed. All the five different models were compared for their generalization and prediction abilities using statistical criteria parameters, viz. correlation coefficient (R), standard error of prediction (SEP), mean absolute error (MAE), root mean squared error (RMSE), bias, accuracy factor (A(f)), and Nash-Sutcliffe coefficient of efficiency (E(f)). Nonlinear models (MPR, ANNs) performed relatively better than the linear PLSR models, whereas, performance of the ANN models was better than the low-order nonlinear MPR models. Although, performance of all the three ANN models were comparable, the GRNN over performed the other two variants. The optimal GRNN models for RSPM, NO(2), and SO(2) yielded high correlation (between measured and model predicted values) of 0.933, 0.893, and 0.885; 0.833, 0.602, and 0.596; and 0.932, 0.768 and 0.729, respectively for the training, validation and test sets. The sensitivity analysis performed to evaluate the importance of the input variables in optimal GRNN revealed that SO(2) was the most influencing parameter in RSPM model, whereas, SPM was the most important input variable in other two models. The ANN models may be useful tools in the air quality predictions.

Inorganic nitrogen, sterols and bacterial source tracking as tools to characterize water quality and possible contamination sources in surface water

The effects of agricultural activities on stream water quality were assessed by nitrogen analysis, further investigated by gas chromatography mass spectrometry (GC-MS) sterol analysis (including chemometric analysis), and characterized by bacterial source tracking (BST). Surface water samples were collected from five sites, throughout the agriculturally-influenced Nathan Creek watershed, British Columbia, Canada and a nearby control site between October 2005 and March 2006. From a total of 48 samples, Canadian Water Quality Guidelines were exceeded nineteen times for nitrate (NO3-; guideline value: 2.94 mg/L N) and four times for un-ionized ammonia (NH3; guideline value 0.019 mg/L N). Gas chromatography mass spectrometry single ion monitoring (GC-MS SIM) analysis of 18 sterols showed that five fecal sterols (coprostanol, episoprostanol, cholesterol, cholestanol, desmosterol) were detected at all sites except the control site (where only cholesterol, cholestanol and desmosterol were detected). Three phytosterols (campesterol, stigmasterol and β-sitosterol) were also detected at all sites while the hormone estrone was present at one site on two occasions at concentrations of 0.01 and 0.04 μg/L. Chemometric analysis (principal component analysis and cluster analysis) grouped sites based on their similarities in sterol composition. Analysis of ten sterol ratios (seven for identifying human fecal contamination and four for differentiating sources of fecal contamination) showed multiple instances of human and animal contamination for every site but the control site. Application of a Bacteroides-BST method confirmed contamination from ruminant animals, pigs and dogs in varying combinations at all impact sites. Together, these results confirmed the impact of agricultural activities on the Nathan Creek watershed and support a need for better land management practices to protect water quality and aquatic life.

Chemometric application in identifying sources of organic contaminants in Langat river basin

Increasing urbanization and changes in land use in Langat river basin lead to adverse impacts on the environment compartment. One of the major challenges is in identifying sources of organic contaminants. This study presented the application of selected chemometric techniques: cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) to classify the pollution sources in Langat river basin based on the analysis of water and sediment samples collected from 24 stations, monitored for 14 organic contaminants from polycyclic aromatic hydrocarbons (PAHs), sterols, and pesticides groups. The CA and DA enabled to group 24 monitoring sites into three groups of pollution source (industry and urban socioeconomic, agricultural activity, and urban/domestic sewage) with five major discriminating variables: naphthalene, pyrene, benzo[a]pyrene, coprostanol, and cholesterol. PCA analysis, applied to water data sets, resulted in four latent factors explaining 79.0% of the total variance while sediment samples gave five latent factors with 77.6% explained variance. The varifactors (VFs) obtained from PCA indicated that sterols (coprostanol, cholesterol, stigmasterol, β-sitosterol, and stigmastanol) are strongly correlated to domestic and urban sewage, PAHs (naphthalene, acenaphthene, pyrene, benzo[a]anthracene, and benzo[a]pyrene) from industrial and urban activities and chlorpyrifos correlated to samples nearby agricultural sites. The results demonstrated that chemometric techniques can be used for rapid assessment of water and sediment contaminations.