Development of AI-based hybrid soft computing models for prediction of critical river water quality indicators.

Prediction of river water quality indicators (RWQIs) using artificial intelligence (AI)-based hybrid soft computing modeling techniques could provide essential predictions required for efficient river health planning and management. The study described the development of a novel AI-based relative weighted ensemble (AIRWE) hybrid model for predicting critical RWQIs, i.e., biochemical oxygen demand (BOD) and total coliform (TC). The study involved comprehensive water quality (WQ) monitoring from 30 locations along the Damodar River to establish the baseline data and delineate the WQ. The representative input features showing a strong association with BOD and TC were identified using Spearman's rank-coupled orthogonal linear transformation (SOT). The relative weighted ensemble (RWE) method was applied to determine the relative weights for base learners in the AIRWE model. The statistical analysis of the developed model revealed that it was most efficient and accurate for predicting BOD (R2, 0.97; RMSE, 0.06; MAE, 0.04) and TC (R2, 0.98; RMSE, 0.06; MAE, 0.05) over the traditional techniques. The tstat (BOD 0.02 and TC 0.47) was lesser than tcrit (1.672), confirming its unbiased predictions. The SOT technique removed the data noise and multicollinearity, whereas RWE curtailed the individual model's limitations and predicted more reliable results. The model resulted 97% accuracy with high precision (96%) in classifying the river water quality for various end uses. The study describes a novel approach for researchers, scientists, and decision-makers for modeling and predicting various environmental attributes.

Application of Monte Carlo simulation for carcinogenic and non-carcinogenic risks assessment through multi-exposure pathways of heavy metals of river water and sediment, India.

Heavy metal contamination has severe detrimental impacts on the entire river ecosystem's quality and causes potential risks to human health. An integrated approach comprising deterministic and probabilistic (Monte Carlo simulation) models with sensitivity analysis was adopted to determine heavy metals' chronic daily intake (CDI) and their associated health risks from the riverine ecosystem. Both carcinogenic and non-carcinogenic risks of water and sediment were estimated through multi-exposure pathways. The analytical results indicated that the concentration patterns of heavy metals in sediment (Fe > Mn > Sr > Zn > Cr > Cu > Cd) were slightly different and higher than in water (Fe > Zn > Cr > Sr > Mn > Cu > Cd). The potential carcinogenic risks of Cr and Cd in sediment (5.06E-02, 5.98E-04) were significantly (p < 0.05) higher than in water (9.08E-04, 8.97E-05). Moreover, 95th percentile values of total cancer risk (TCR) for sediment (1.80E-02, 3.37E-02) were about 22 and 143 times higher than those of water (8.10E-04, 2.36E-04) for adults and children, respectively. The analysis of non-carcinogenic risk revealed a significantly higher overall hazard index (OHI) for both sediment (adults: 1.26E+02, children: 1.11E+03) and water (adults: 3.26E+00, children: 9.85E+00) than the USEPA guidelines (OHI ≤ 1). The sensitivity analysis identified that the concentration of heavy metals was the most influencing input factor in health risk assessment. Based on the reasonable maximum exposure estimate (RME), the study will be advantageous for researchers, scientists, policymakers, and regulatory authorities to predict and manage human health risks.

Development of fuzzy leachate pollution index for treatability-based classification of solid waste landfills.

The fuzzy leachate pollution index (FLPI) was established to classify the landfill sites on the basis of their leachate pollution potential by considering the limitations of traditional methods. The FLPI was developed adopting 9 critical input parameters, i.e., TDS, pH, Cl, Cu, Pb, Cr, Zn, BOD, and COD, from 22 major landfill sites across India. Using these critical parameters, 3 groups, i.e., inorganic leachate strength (INLS), organic leachate strength (ORLS), and heavy metal leachate strength (HMLS), were generated to estimate the FLPI. The regression analysis, ANOVA, and sensitivity analysis were also performed to determine the significance and uncertainty of the index. The results showed that among all MFs, the triangular with overlapping open ends (TOO) MF was best fitted (R = 0.90) for FLPI estimation. Accordingly, 41% of the landfill sites showed less treatment while the others (59%) required moderate degree of treatment. The regression (R2 = 0.92) and ANOVA (F value = 15.003, p = 0.000031) analyses described that the developed tool was significant (p < 0.05). The sensitivity analysis showed that Zn (R = 0.99) was the most influencing factor followed by BOD > COD > pH > Cr > Cu > Cl > Pb > TDS. The study provides an important tool that can also be used by researchers and scientists for investigating and evaluating various environmental problems.

Development and evaluation of an innovative Enhanced River Pollution Index model for holistic monitoring and management of river water quality.

The present study was conceptualized to develop the Enhanced River Pollution Index (ERPI) model. The ERPI model was used to evaluate the river water quality (RWQ) for its beneficial usage, i.e., drinking with (DCD) and without (DD) conventional treatment, outdoor-bathing (OB), wildlife and fisheries (WF), and industrial and irrigation (IIW). The adequacy of multiple linear regression (MLR) and support vector regression (SVR) models was also investigated to predict the ERPI for estimating the RWQ. The accuracy of the MLR and SVR models was tested by using the statistical parameters, i.e., root mean squared error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). The results revealed that the MLR models performed well (RMSE = 0.004 ± 0.0043, R2 = 0.998 ± 0.001, and MAE = 0.002 ± 0.003) for the DD, DCD, and OB. However, the SVR models estimated the RWQ more accurately (RMSE = 0.041 ± 0.001, R2 = 0.962 ± 0.010, and MAE = 0.026 ± 0.002) than the MLR models for WF and IIW. Moreover, this study disclosed that the RWQ was not excellent for DD, OB, and DCD. However, the RWQ was categorized from excellent to poor classes for WF, while it was suitable for IIW.

Demonstration of a Ge/GeSn/Ge quantum-well microdisk resonator on silicon: enabling high-quality Ge(Sn) materials for micro- and nanophotonics.

We theoretically study and experimentally demonstrate a pseudomorphic Ge/Ge0.92Sn0.08/Ge quantum-well microdisk resonator on Ge/Si (001) as a route toward a compact GeSn-based laser on silicon. The structure theoretically exhibits many electronic and optical advantages in laser design, and microdisk resonators using these structures can be precisely fabricated away from highly defective regions in the Ge buffer using a novel etch-stop process. Photoluminescence measurements on 2.7 µm diameter microdisks reveal sharp whispering-gallery-mode resonances (Q > 340) with strong luminescence.

Highly selective dry etching of germanium over germanium-tin (Ge(1-x)Sn(x)): a novel route for Ge(1-x)Sn(x) nanostructure fabrication.

We present a new etch chemistry that enables highly selective dry etching of germanium over its alloy with tin (Ge(1-x)Sn(x)). We address the challenges in synthesis of high-quality, defect-free Ge(1-x)Sn(x) thin films by using Ge virtual substrates as a template for Ge(1-x)Sn(x) epitaxy. The etch process is applied to selectively remove the stress-inducing Ge virtual substrate and achieve strain-free, direct band gap Ge0.92Sn0.08. The semiconductor processing technology presented in this work provides a robust method for fabrication of innovative Ge(1-x)Sn(x) nanostructures whose realization can prove to be challenging, if not impossible, otherwise.