Machine learning-enabled river water quality monitoring using lithography-free 3D-printed sensors

• URL: http://arxiv.org/abs/2507.14152v1
• Date: Thu, 03 Jul 2025 22:58:25 GMT
• Title: Machine learning-enabled river water quality monitoring using lithography-free 3D-printed sensors
• Authors: Frank Efe Erukainure, Feidra Gjata, Matin Ataei Kachouei, Henry Cox, Md. Azahar Ali,
• Abstract summary: Excessive contaminants, including phosphate, deplete dissolved oxygen and trigger eutrophication.<n>In this work we present a lithography-free phosphate sensor (P-sensor) that detects phosphate in river water at parts-per-billion levels.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: River water quality monitoring is important for aquatic life, livestock, and humans because clean water is critical to meeting food demand during the global food crisis. Excessive contaminants, including phosphate, deplete dissolved oxygen and trigger eutrophication, leading to serious health and ecological problems. Continuous sensors that track phosphate levels can therefore help prevent eutrophication. In this work we present a lithography-free phosphate sensor (P-sensor) that detects phosphate in river water at parts-per-billion levels. The device uses a solid-state indicator electrode formed by 3D-printed periodic polymer patterns (8 um feature size) coated with a thin phosphate ion-selective membrane. The P-sensor detects as little as 1 ppb phosphate across 0 - 475 ppm with a response time under 30 seconds. We validated the sensor on Rappahannock River water, Virginia (less than 0.8 ppm phosphate) at sites upstream and downstream of a sewage treatment plant and benchmarked the results against a commercial phosphate meter. A feed-forward neural network was trained to predict phosphate levels, achieving a mean-squared error below 1e-3, zero standard deviation, and a Pearson correlation coefficient of 0.997 for river samples. These results demonstrate a practical tool for continuous water-quality monitoring that can inform stakeholders and policymakers and ultimately improve public health.

Related papers

• Neural Tucker Convolutional Network for Water Quality Analysis [24.74498274699181]
  This paper proposes a Neural Tucker Convolutional Network (NTCN) model for water quality data imputation.<n> Experiments on three real-world water quality datasets show that the proposed NTCN model outperforms several state-of-the-art imputation models in terms of accuracy.
  arXiv  Detail & Related papers  (2025-12-01T09:49:18Z)
• Data-driven strategic sensor placement for detecting disinfection by-products in water distribution networks [0.0]
  Disinfection byproducts are contaminants that can cause long-term effects on human health.<n>We present DBPFinder, a simulation software that assists in the strategic sensor placement for detecting disinfection byproducts.
  arXiv  Detail & Related papers  (2025-11-14T10:37:21Z)
• Hyperspectral in situ remote sensing of water surface nitrate in the Fitzroy River estuary, Queensland, Australia, using deep learning [0.2094057281590807]
  The recent increase in river-discharged nitrate poses a major risk for coral bleaching in the Great Barrier Reef lagoon.<n>Previous studies have demonstrated there is an indirect, non-causal relationship between water surface nitrate and water-leaving reflectance.<n>Time-series observations revealed periodic cycles in nitrate loads due to the tidal influence in the estuarine study site.<n>The accuracy assessment of model-predicted nitrate against in-situ measured nitrate values showed that the predicted nitrate values correlated well with the ground-truth data.
  arXiv  Detail & Related papers  (2025-05-23T05:22:29Z)
• Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network [32.50971951245164]
  This study develops and validates a deep learning-based diagnostic model for pulmonary hypertension (PH)<n>It is designed to classify patients as non-PH, pre-capillary PH, or post-capillary PH.<n>It has the potential to support clinical decision-making by effectively integrating multimodal data.
  arXiv  Detail & Related papers  (2025-03-28T01:14:17Z)
• Water Quality Data Imputation via A Fast Latent Factorization of Tensors with PID-based Optimizer [21.261626027956737]
  There are numerous missing values in water quality data due to sensor failure.<n>A Latent Factorization of PIDs (LFT) with Gradient Descent (SGD) proves to be an efficient imputation method.<n>This paper proposes a Fast Latent Factorization of PIDs (FLFT) model to tackle this issue.
  arXiv  Detail & Related papers  (2025-03-10T07:22:54Z)
• Integrating Boosted learning with Differential Evolution (DE) Optimizer: A Prediction of Groundwater Quality Risk Assessment in Odisha [0.0]
  This study developed a machine learning-based predictive model to evaluate the Groundwater Quality Index (GWQI)<n>It has been achieved with the help of a hybrid machine learning model i.e. LCBoost Fusion.
  arXiv  Detail & Related papers  (2025-02-25T07:47:41Z)
• Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation [53.2981100111204]
  Photoplethysmography and electrocardiography can potentially enable continuous blood pressure (BP) monitoring.<n>Yet accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors.<n>In this work, we investigate whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type.<n>Our approach achieves near state-of-the-art accuracy for diastolic BP and surpasses by 1.5x the accuracy of prior works for systolic BP.
  arXiv  Detail & Related papers  (2025-02-10T13:33:12Z)
• Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes [22.23531900474421]
  Video photoplethysmography is an emerging method for non-invasive and convenient measurement of physiological signals. This dataset is designed to validate video vitals estimation algorithms and fusing videos from different positions. Our findings suggest that simultaneous training on multiple indicators, such as PPG and blood oxygen, can reduce MAE in SpO2 estimation by 17.8%.
  arXiv  Detail & Related papers  (2024-09-28T03:36:16Z)
• An insertable glucose sensor using a compact and cost-effective phosphorescence lifetime imager and machine learning [7.47339309134629]
  We report a computational continuous glucose monitoring (CGM) system, which integrates a biocompatible phosphorescence-based insertable biosensor and a custom-designed phosphorescence lifetime imager (PLI) The PLI is designed to capture phosphorescence lifetime images of an insertable sensor through the skin, where the lifetime of the emitted phosphorescence signal is modulated by the local glucose concentration. The lifetime images are processed by neural network-based models for misalignment-tolerant inference of glucose levels.
  arXiv  Detail & Related papers  (2024-06-12T00:18:47Z)
• Investigating Data Contamination for Pre-training Language Models [46.335755305642564]
  We explore the impact of data contamination at the pre-training stage by pre-training a series of GPT-2 models. We highlight the effect of both text contamination (textiti.e. input text of the evaluation samples) and ground-truth contamination (textiti.e. the prompts asked on the input and the desired outputs) from evaluation data.
  arXiv  Detail & Related papers  (2024-01-11T17:24:49Z)
• Using Multi-Temporal Sentinel-1 and Sentinel-2 data for water bodies mapping [40.996860106131244]
  Climate change is intensifying extreme weather events, causing both water scarcity and severe rainfall unpredictability. This paper aims to provide valuable insights for comprehensive water resource monitoring under diverse meteorological conditions.
  arXiv  Detail & Related papers  (2024-01-05T18:11:08Z)
• Towards Generating Large Synthetic Phytoplankton Datasets for Efficient Monitoring of Harmful Algal Blooms [77.25251419910205]
  Harmful algal blooms (HABs) cause significant fish deaths in aquaculture farms. Currently, the standard method to enumerate harmful algae and other phytoplankton is to manually observe and count them under a microscope. We employ Generative Adversarial Networks (GANs) to generate synthetic images.
  arXiv  Detail & Related papers  (2022-08-03T20:15:55Z)
• Early Detection of Fish Diseases by Analyzing Water Quality Using Machine Learning Algorithm [0.0]
  A state-of-art machine learning algorithm has been adopted in this paper to detect and predict the degradation of water quality timely and accurately. The experimental results show a high accuracy in detecting fish diseases particular to specific water quality based on the algorithm with real datasets.
  arXiv  Detail & Related papers  (2021-02-15T18:52:58Z)
• Real-time detection of uncalibrated sensors using Neural Networks [62.997667081978825]
  An online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. The solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively.
  arXiv  Detail & Related papers  (2021-02-02T15:44:39Z)
• Water Quality Prediction on a Sigfox-compliant IoT Device: The Road Ahead of WaterS [0.27998963147546135]
  We focus on an Internet of Things water quality prediction system, namely WaterS, that can remotely communicate the gathered measurements. The solution addresses the water pollution problem while taking into account the peculiar Internet of Things constraints such as energy efficiency and autonomy. The source code of WaterS ecosystem has been released as open-source, to encourage and promote research activities from both Industry and Academia.
  arXiv  Detail & Related papers  (2020-07-27T11:21:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.