Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak.

Loktak Lake, one of the largest freshwater lakes in Manipur, India, is critical for the eco-hydrology and economy of the region, but faces deteriorating water quality due to urbanisation, anthropogenic activities, and domestic sewage. Addressing the urgent need for effective pollution management, this study aims to assess the lake's water quality status using the water quality index (WQI) and develop advanced machine learning (ML) tools for WQI assessment and ML model interpretation to improve pollution management decision making. The WQI was assessed using entropy-based weighting arithmetic and three ML models - Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) - were optimised using a grid search algorithm in the H2O Application Programming Interface (API). These models were validated by various metrics and interpreted globally and locally via Partial Dependency Plot (PDP), Accumulated Local Effect (ALE) and SHapley Additive exPlanations (SHAP). The results show a WQI range of 72.38-100, with 52.7% of samples categorised as very poor. The RF model outperformed GBM and DNN and showed the highest accuracy and generalisation ability, which is reflected in the superior R2 values (0.97 in training, 0.9 in test) and the lower root mean square error (RMSE). RF's minimal margin of error and reliable feature interpretation contrasted with DNN's larger margin of error and inconsistency, which affected its usefulness for decision making. Turbidity was found to be a critical predictive feature in all models, significantly influencing WQI, with other variables such as pH and temperature also playing an important role. SHAP dependency plots illustrated the direct relationship between key water quality parameters such as turbidity and WQI predictions. The novelty of this study lies in its comprehensive approach to the evaluation and interpretation of ML models for WQI estimation, which provides a nuanced understanding of water quality dynamics in Loktak Lake. By identifying the most effective ML models and key predictive functions, this study provides invaluable insights for water quality management and paves the way for targeted strategies to monitor and improve water quality in this vital freshwater ecosystem.

Fixed-bed column study for hexavalent chromium removal and recovery by short-chain polyaniline synthesized on jute fiber.

Fixed-bed column studies were conducted to evaluate performance of a short-chain polymer, polyaniline, synthesized on the surface of jute fiber (PANI-jute) for the removal of hexavalent chromium [Cr(VI)] in aqueous environment. Influent pH, column bed depth, influent Cr(VI) concentrations and influent flow rate were variable parameters for the present study. Optimum pH for total chromium removal was observed as 3 by electrostatic attraction of acid chromate ion (HCrO(4)(-)) with protonated amine group (NH(3)(+)) of PANI-jute. With increase in column bed depth from 40 to 60 cm, total chromium uptake by PANI-jute increased from 4.14 to 4.66 mg/g with subsequent increase in throughput volume from 9.84 to 12.6L at exhaustion point. The data obtained for total chromium removal were well described by BDST equation till 10% breakthrough. Adsorption rate constant and dynamic bed capacity at 10% breakthrough were observed as 0.01 L/mgh and 1069.46 mg/L, respectively. Adsorbed total chromium was recovered back from PANI-jute as non-toxic Cr(III) after ignition with more than 97% reduction in weight, minimizing the problem of solid waste disposal.