Heavy metals were investigated in groundwater in the central industrial district of Ilorin, Kwara State, Nigeria. Water and sediment samples from 8 wells in the district and 2 away from the district (control) were analysed to assess the suitability of the water for human consumption and domestication purposes. The measured heavy metals concentrations exceeded the World Heaith Organization (WHO) standard guideline for potable water usage. The result reflects probable pollution from the industrial effluent which are often released into storm water runways without further treatment. Analysis of variance (ANOVA), Pearson correlation and principal component (PCA) analysis were used to describe the data.
Key words: Heavy metals, groundwater, pollution, sediments, x-ray fluorescence (XRF), atomic absorption spectrometer (AAS).
Heavy metals were investigated in groundwater in the central industrial district of Ilorin, Kwara State, Nigeria. Water and sediment samples from 8 wells in the district and 2 away from the district (control) were analysed to assess the suitability of the water for human consumption and domestication purposes. The measured heavy metals concentrations exceeded the World Heaith Organization (WHO) standard guideline for potable water usage. The result reflects probable pollution from the industrial effluent which are often released into storm water runways without further treatment. Analysis of variance (ANOVA), Pearson correlation and principal component (PCA) analysis were used to describe the data.
The Rio Santiago basin is one of the most environmentally stressed river systems in Mexico, with persistent organic, nutrient, microbial, surfactant, and metal contamination. This study develops a near-term quantum machine learning workflow for environmental monitoring and water-pollution profiling using multivariate records from 13 stations between 2009 and 2022. QML is evaluated here because quantum feature maps can define nonlinear, interaction-rich kernels that remain executable on present quantum hardware, providing an alternative representation to compare with classical PCA, RBF, UMAP, and HDBSCAN baselines rather than a presumed computational advantage. After quality screening, log transformation, standardization, and domain-guided feature selection, pollution profiles are evaluated across PCA, RBF spectral clustering, UMAP/KMeans, UMAP/HDBSCAN, a simulated ZZ-style quantum feature-map kernel, and Qiskit Runtime hardware evaluations of the same kernel concept. The initial cleaned-data results show that classical PCA clustering identifies broad lower-load, high organic/surfactant, and rain-season solids/microbial profiles. UMAP/HDBSCAN provides the strongest cleaned full-sample nonlinear baseline, with a silhouette score of 0.568 after excluding 177 noise samples. The simulated quantum-kernel representation separates station-linked gradients, while matched n = 650 stability diagnostics show near-identical quantum-kernel clustering across random initializations (mean ARI = 0.994 for cleaned data) but retain the RBF kernel as the strongest nonlinear comparator. Two 24-sample Qiskit hardware runs and two matched 8-record hardware checks provide proof-of-execution evidence. The analysis is framed as a controlled representation study, not as a claim of quantum advantage.
Quantum-Enhanced Water Quality Monitoring: Exploiting Φ Sat-2 Data With Quanvolution
Abstract—Coastal water quality monitoring is crucial for environmental sustainability and public health. This work introduces a very cutting-edge methodology, using ΦSat-2 multispectral data and quanvolutional neural networks to explore quantum-enhanced machine learning for water contaminant assessment. By integrating quantum preprocessing into a classical regression model, it is possible to achieve a significant reduction in model parameters while maintaining high predictive accuracy. Additionally, this work introduces an innovative dataset that integrates simulated ΦSat-2 spectral data with Copernicus Marine Service bio-geochemical products, ensuring a strong alignment between satellite observations and reference turbidity measurements. Our results show that quantum models use up to 98% fewer parameters than their classical counterparts, while achieving a 6.9% improvement in the Pearson correlation coefficient between the ΦSat-2 pre-processed bands and the ground-truth turbidity values, compared to the case without quantum pre-processing. Additionally, the Root Mean Square Error (RMSE) improves by 7.3% over the classical baseline. These findings highlight the potential of quantum-assisted remote sensing to enable more efficient and scalable analysis of large-scale water contaminant data, paving the way for advanced big data approaches in water quality monitoring.
Quantum Machine Learning is a growing field with the potential to unlock several opportunities in groundwater management. A novel approach integrating advanced quantum machine learning (QML) models with classical machine learning (ML) algorithms for robust groundwater quality assessment, using the SABHQ (Systematically Averaged Battery Hybrid Quantum) method, is proposed. We attempted to reduce noise and enhance generalization by stimulating composite sampling across both ML and QML pipelines. Accuracy, precision, recall, and F 1 scores were calculated across all models, using normalization and aggregation strategies to achieve relatively unbiased benchmarking. We observed that the dominant predictors, such as EC, Cl, HCO 3 , SO 4 , Na, TH, Latitude, and Longitude, reflect both spatial variability and anthropogenic influences on groundwater quality. The systematic averaging method yielded noticeable improvements for QML models, with QCNN achieving accuracy and F 1 scores of up to 0.95 and 0.98, respectively, outperforming classical machine learning models. We report that QCNN metrics improved due to aggregation, noise reduction, and possibly quantum entanglement effects, leading to better generalization, whereas VQC showed gradual but minor improvements. We believe our work can be extended to other environmental domains, not only to refine the method we proposed but also to enhance its adaptability.
Quantum Machine Learning (QML) has emerged as an active area of research, yet its practical readiness for real-world geospatial applications remains uncertain due to constraints related to data availability, preprocessing requirements, and current quantum hardware limitations. This study evaluates the feasibility and behaviour of quantum kernel-based learning models within a realistic GIS workflow, using land suitability analysis for reforestation in the Mumbai metropolitan region as a representative case study although demonstrated using the Mumbai metropolitan region as a case study, the proposed GIS-driven preprocessing pipeline and quantum kernel evaluation framework are transferable to other regions with comparable remote-sensing data availability and environmental indicators. A geospatial dataset is constructed from satellite-derived environmental variables, including Normalized Difference Vegetation Index (NDVI), annual rainfall, terrain slope, soil pH, and soil organic carbon, with suitability labels assigned using ecologically motivated rule-based thresholds that serve as heuristic ground truth. The dataset is processed through a standardized pipeline involving feature scaling, Principal Component Analysis (PCA), stratified train–test splitting, and Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Quantum Support Vector Machines (QSVMs) employing Pauli and ZZ feature maps are implemented using Qiskit and evaluated under noiseless simulation conditions, with performance compared against a classical Support Vector Machine baseline using identical preprocessing and evaluation metrics. While classical models achieve superior computational efficiency and higher predictive performance, QSVMs exhibit non-trivial classification behaviour on small, structured datasets when appropriate preprocessing and feature-to-qubit alignment are applied. Rather than claiming quantum advantage, this work provides an empirical assessment of the current capabilities and limitations of QML in applied geospatial analysis, positioning quantum kernel methods as complementary tools for exploratory environmental modelling under present technological constraints.
Multiple sources of pollution, nonlinear interactions, and competing environmental and economic goals are characteristics of complicated optimisation problems in urban air pollution control. When dealing with such high-dimensional decision spaces, conventional optimisation strategies frequently encounter scaling issues. This study suggests an optimisation structure for urban air pollution management methods based on quantum computing. In order to minimise pollutant concentrations while meeting operational and regulatory restrictions, the suggested method formulates air pollution reduction as a constrained optimisation problem in which industry rules, traffic management policies, and emission reduction strategies are optimised. To effectively explore the solution space, a hybrid quantum–classical optimisation model is created employing quantum-inspired algorithms. The framework's efficiency is compared with traditional optimisation techniques with respect to of convergence effectiveness and solution quality using simulated municipal air quality & emission data. The results show that for complicated pollution management scenarios, the quantum-based optimisation strategy achieves better optimisation performance and faster convergence. The results show how quantum computing paradigms can be a useful tool for solving large-scale environmental optimisation issues and assisting with data-driven the air quality in cities management.