Assessment of groundwater quality in Ranchi district, Jharkhand, India, using water evaluation indices and multivariate statistics

Groundwater is the most abundant liquid freshwater on earth. Rapid urbanization in developing nations (like India) has led to increased groundwater withdrawal, adversely affecting the physicochemical characteristics. Ranchi district, Jharkhand, is a part of the smart city mission development plan of the government of India. Hence, to ensure safe and clean drinking water, it is necessary to assess groundwater quality and devise development plans. Seventeen physicochemical properties and metal(loid)s contents were analyzed to determine the groundwater quality. Various pollution indices such as water quality index (WQI), metal evaluation index (MI), heavy metal pollution index (HPI), and modified degree of contamination (mC_d) are evaluated using arithmetic weighted value index and presented in a map using Arc GIS inverse distance weighting interpolation method. Chemometric analyses such as correlation, principal component, and cluster analysis were done to identify the source and determine the pollution state. A multiple linear regression model is employed to predict the impact of heavy metal and metalloid concentration on the WQI of the region. WQI shows that groundwater quality in Khelari (100.95) and Bundu (92.52) regions are highly degraded, whereas MI and HPI suggest that Ormanjhi (MI = 53.98) and Rahe (HPI = 109.20) are highly affected by metal contamination. The mC_d suggests that Ormanjhi (97.15) has the highest degree of contamination. The contaminant sources were natural (geogenic processes) and anthropogenic (mining and industrial emissions). The high metal(loid)s concentration may soon result in groundwater quality degradation in the metal-affected regions.

Prediction of the oxidation potential of PM2.5 exposures from pollutant composition and sources

The inherent oxidation potential (OP) of atmospheric particulate matter has been shown to be an important metric in assessing the biological activity of inhaled particulate matter and is associated with the composition of PM_2.5. The current study examined the chemical composition of 388 personal PM_2.5 samples collected from students and guards living in urban and suburban areas of Beijing, and assessed the ability to predict OP from the calculated metrics of carcinogenic risk, represented by ELCR (excess lifetime cancer risk), non-carcinogenic risk represented by HI (hazard index), and the composition and sources of the particulate matter using multiple linear regression methods. The correlations between calculated ELCR and HI and the measured OP were 0.37 and 0.7, respectively. HI was a better predictor of OP than ELCR. The prediction models based on pollutants (Model_1) and pollution sources (Model_2) were constructed by multiple linear regression method, and Pearson correlation coefficients between the predicted results of Model_1 and Model_2 with the measured volume normalized OP are 0.81 and 0.80, showing good prediction ability. Previous investigations in Europe and North America have developed location-specific relationships between the chemical composition of particulate matter and OP using regression methods. We also examined the ability of relationships between OP and composition, sources, developed in Europe and North America, to predict the OP of particulate matter in Beijing from the composition and sources determined in Beijing. The relationships developed in Europe and North America provided good predictive ability in Beijing and it suggests that these relationships can be used to predict OP from the chemical composition measured in other regions of the world.

Application of Bioavailability Models to Derive Chronic Guideline Values for Nickel in Freshwaters of Australia and New Zealand

There has been an increased emphasis on incorporating bioavailability-based approaches into freshwater guideline value derivations for metals in the Australian and New Zealand water quality guidelines. Four bioavailability models were compared: the existing European biotic ligand model (European Union BLM) and a softwater BLM, together with 2 newly developed multiple linear regressions (MLRs)-a trophic level-specific MLR and a pooled MLR. Each of the 4 models was used to normalize a nickel ecotoxicity dataset (combined tropical and temperate data) to an index condition of pH 7.5, 6 mg Ca/L, 4 mg Mg/L, (i.e., approximately 30 mg CaCO₃ /L hardness), and 0.5 mg DOC/L. The trophic level-specific MLR outperformed the other 3 models, with 79% of the predicted 10% effect concentration (EC10) values within a factor of 2 of the observed EC10 values. All 4 models gave similar normalized species sensitivity distributions and similar estimates of protective concentrations (PCs). Based on the index condition water chemistry proposed as the basis of the national guideline value, a protective concentration for 95% of species (PC95) of 3 µg Ni/L was derived. This guideline value can be adjusted up and down to account for site-specific water chemistries. Predictions of PC95 values for 20 different typical water chemistries for Australia and New Zealand varied by >40-fold, which confirmed that correction for nickel bioavailability is critical for the derivation of site-specific guideline values. Environ Toxicol Chem 2021;40:100-112. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Molecular Docking and QSAR Study of 2-Benzoxazolinone, Quinazoline and Diazocoumarin Derivatives as Anti-HIV-1 Agents

A series of 2-benzoxazolinone, diazocoumarin and quinazoline derivatives have been shown to inhibit HIV replication in cell culture. To understand the pharmacophore properties of selected molecules and design new anti-HIV agents, quantitative structure–activity relationship (QSAR) study was developed using a descriptor selection approach based on the stepwise method. Multiple linear regression method was applied to relate the anti-HIV activities of dataset molecules to the selected descriptors. Obtained QSAR model was statistically significant with correlation coefficient R² of 0.84 and leave one out coefficient Q² of 0.73. The model was validated by test set molecules giving satisfactory prediction value (R²_test) of 0.79. Molecules also were docked on HIV integrase enzyme and showed important interactions with the key residues in enzyme active site. These data might be helpful for design and discovery of novel anti-HIV compounds.

Modeling of cigarette smoke constituents - From intense to less intense smoking regime

Since it was first required to measure and to report NFDPM and nicotine yields in a limited number of countries, there has been an increasing trend for more testing and reporting requirements. Historically, the ISO 3308 smoking regime has been used to determine NFDPM and nicotine yields. However recommendations from the World Health Organization, now include the use of two smoking regimes such as the ISO 3308 and the WHO TobLabNet Official Method SOP01, the latter being considered as an intense smoking regime. Considering the increase in data produced and similarities between some smoke constituents formed during combustion, we explored possible correlations between emissions under intense and less intense smoking conditions. A set of 22 commercial cigarettes was tested. Eighty five smoke constituents were determined under both intense and less intense regimes. In addition 36 tobacco constituents, 14 cigarette design parameters and eight cigarette burning features were determined. A computational process was designed to implement multiple linear regression analyses enabling the identification of the best subsets of explanatory variables among emissions under intense conditions, cigarette design parameters, tobacco constituents and burning parameters. We succeeded in building simple linear models, involving four to six variables, while reaching satisfactory goodness of fit and R-squared values ranging from 0.87 to 1.00. Our findings suggest, in the range of products tested, that the additional data gained by using a second smoking regime does not necessarily increase the volume of information and consequently does not necessarily improve knowledge. This study supports the premise that the application of two smoking regimes does not produce a more comprehensive product characterisation compared to using one.

QSAR Study of 17β-HSD3 Inhibitors by Genetic Algorithm-Support Vector Machine as a Target Receptor for the Treatment of Prostate Cancer

The 17β-HSD3 enzyme plays a key role in treatment of prostate cancer and small inhibitors can be used to efficiently target it. In the present study, the multiple linear regression (MLR), and support vector machine (SVM) methods were used to interpret the chemical structural functionality against the inhibition activity of some 17β-HSD3inhibitors. Chemical structural information were described through various types of molecular descriptors and genetic algorithm (GA) was applied to decrease the complexity of inhibition pathway to a few relevant molecular descriptors. Non-linear method (GA-SVM) showed to be better than the linear (GA-MLR) method in terms of the internal and the external prediction accuracy. The SVM model, with high statistical significance (R2train = 0.938; R2test = 0.870), was found to be useful for estimating the inhibition activity of 17β-HSD3 inhibitors. The models were validated rigorously through leave-one-out cross-validation and several compounds as external test set. Furthermore, the external predictive power of the proposed model was examined by considering modified R2 and concordance correlation coefficient values, Golbraikh and Tropsha acceptable model criteria's, and an extra evaluation set from an external data set. Applicability domain of the linear model was carefully defined using Williams plot. Moreover, Euclidean based applicability domain was applied to define the chemical structural diversity of the evaluation set and training set.

A quantitative structure-activity relationship (QSAR) study of some diaryl urea derivatives of B-RAF inhibitors

In the current study, both ligand-based molecular docking and receptor-based quantitative structure activity relationships (QSAR) modeling were performed on 35 diaryl urea derivative inhibitors of ^V600EB-RAF. In this QSAR study, a linear (multiple linear regressions) and a nonlinear (partial least squares least squares support vector machine (PLS-LS-SVM)) were used and compared. The predictive quality of the QSAR models was tested for an external set of 31 compounds, randomly chosen out of 35 compounds. The results revealed the more predictive ability of PLS-LS-SVM in analysis of compounds with urea structure. The selected descriptors indicated that size, degree of branching, aromaticity, and polarizability affected the inhibition activity of these inhibitors. Furthermore, molecular docking was carried out to study the binding mode of the compounds. Docking analysis indicated some essential H-bonding and orientations of the molecules in the active site.

Quantities, sources and adsorption of polybrominated diphenyl ethers in components of surficial sediments collected in Songhua River (Jilin City), China

Quantities of polybrominated diphenyl ethers (PBDEs, BDE-28, 47, 99, 100, 153, 154, 183 and 209) in surficial sediments (SSs) of the Songhua River, China were extracted and detected by Soxhlet extractor and gas chromatography/mass spectrometry (GC/MS). Sources of the PBDEs were investigated by factor analysis. Contributions of Fe oxides, Mn oxides and organic matters (OMs), and their interactions of SSs to the adsorption of PBDEs were described based on multiple linear regressions. The analysis results from GC/MS indicated that the concentrations of PBDEs ranged from 2.90 to 9871 ng g(-)(1) (dry weight) with a mean value of 397 ng g(-)(1). The congener profiles of the SSs were dominated by BDE-209 (⩾71.8%). Relatively high contents of PBDEs were observed in SSs from the upstream section. Deca-BDE commercial formulations constituted the largest contribution (33.6%) to PBDEs in the SSs, followed by Penta-BDE commercial formulations (21.7%) and Octa-BDE commercial formulations (13.2%). Each of the components in the SSs contributes positively to PBDEs' adsorption. Synergism of Fe oxides and OMs was observed in the PBDEs' adsorption. The interactions of Mn oxides and other components inhibited the PBDEs' adsorption onto SSs, and the antagonism in the BDE-209 adsorption was stronger than other Σ7PBDEs (BDE-28, 47, 99, 100, 153, 154 and 183). However, the synergism observed in the Σ7PBDEs adsorption was stronger than BDE-209. The BDE-209 in SSs mainly came from Deca-BDE commercial formulations. The adsorption of PBDEs onto SSs was affected by the octanol-water coefficient (Kow) of the PBDEs' congeners and the components of the SSs.