Understanding and mitigating global change with aquatic sensors: current challenges and future prospects DOI Creative Commons
Dermot Diamond, Rick A. Relyea, Margaret McCaul

и другие.

Frontiers in Sensors, Год журнала: 2023, Номер 4

Опубликована: Дек. 4, 2023

Human activities are causing global change around the world including habitat destruction, invasive species in non-native ecosystems, overexploitation, pollution, and climate change. While traditional monitoring has long been used to quantify aid mitigation of change, in-situ autonomous sensors being increasingly for environmental monitoring. Sensors sensor platforms that can be deployed developed remote areas allow high-frequency data collection, which is critical parameters exhibit important short-term dynamics on scale days, hours, or minutes. In this article, we discuss benefits aquatic ecosystems as well many challenges have experienced over years working with these technologies. These include decisions locations, types, analytical specification, calibration, drift, role conditions, fouling, service intervals, cost ownership, QA/QC. result tradeoffs when making regarding deploy, particularly a network desired cover large area. We also review recent advances designing building chemical-sensor allowing researchers develop next-generation power integrating multiple into provides increased insight water quality space time. coming years, there will an exponential growth related sensing, essential part efforts monitor mitigate its adverse impacts society.

Язык: Английский

A review of nanomaterials for biosensing applications DOI
Lei Li, Tianshu Wang, Yuting Zhong

и другие.

Journal of Materials Chemistry B, Год журнала: 2023, Номер 12(5), С. 1168 - 1193

Опубликована: Дек. 21, 2023

A biosensor is a device that reacts with the analyte to be analyzed, detects its concentration, and generates readable information, which plays an important role in medical diagnosis, detection of physiological indicators, disease prevention. Nanomaterials have received increasing attention fabrication improvement biosensors due their unique physicochemical optical properties. In this paper, properties nanomaterials such as size effect, electrical properties, advantages field biosensing are briefly summarized, application can effectively improve sensitivity reduce limit biosensors. The commonly used gold nanoparticles (AuNPs), carbon nanotubes (CNTs), quantum dots (QDs), graphene, magnetic nanobeads for applications also reviewed. Besides, two main types using involved construction working principles described, toxicity biocompatibility future direction nanomaterial discussed.

Язык: Английский

Процитировано

54
Hydrologic Information Systems: An Introductory Overview DOI Creative Commons
Amber Spackman Jones, Jeffery S. Horsburgh

Environmental Modelling & Software, Год журнала: 2025, Номер unknown, С. 106308 - 106308

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Integrating Wireless Remote Sensing and Sensors for Monitoring Pesticide Pollution in Surface and Groundwater DOI Creative Commons

Titus Mutunga,

Sinan Sinanović,

Colin Harrison

и другие.

Sensors, Год журнала: 2024, Номер 24(10), С. 3191 - 3191

Опубликована: Май 17, 2024

Water constitutes an indispensable resource crucial for the sustenance of humanity, as it plays integral role in various sectors such agriculture, industrial processes, and domestic consumption. Even though water covers 71% global land surface, governments have been grappling with challenge ensuring provision safe use. A contributing factor to this situation is persistent contamination available sources rendering them unfit human common contaminant, pesticides are not frequently tested despite their serious effects on biodiversity. Pesticide determination quality assessment a challenging task because procedures involved extraction detection complex. This reduces popularity many monitoring campaigns harmful effects. If existing methods pesticide analysis adapted by leveraging new technologies, then information concerning presence ecosystems can be exposed. Furthermore, beyond advantages conferred integration wireless sensor networks (WSNs), Internet Things (IoT), Machine Learning (ML), big data analytics, notable outcome attainment heightened degree granularity ecosystems. paper discusses water, emphasizing possible use electrochemical sensors, biosensors, paper-based sensors sensing. It also explores application WSNs IoT, computing models, ML, potential technologies useful water.

Язык: Английский

Процитировано

3
Automated Hydrologic Forecasting Using Open-Source Sensors: Predicting Stream Depths Across 200,000 Km2 DOI
Travis Adrian Dantzer, Branko Kerkez

Опубликована: Янв. 1, 2024

Wireless sensor networks support decision-making in diverse environmental contexts. Adoption of these has increased dramatically due to technological advances that have value while lowering cost. However, real-time information only allows for reactive management. As most interventions take time, predictions across enable better planning and decision making. Prediction engines large water level discharge do exist. they shortcomings their accessibility, automaticity, data requirements. We present an open-source method automatically generating computationally cheap rainfall-runoff models any depth or given its measurements location. characterize reliability a real-world case study 200,000 km2, evaluate long-term accuracy, assess sensitivity measurement noise errors catchment delineation. The method's computational efficiency, automaticity make it valuable asset operational making stakeholders including bridge inspectors utilities.

Язык: Английский

Процитировано

2
Stormwater digital twin with online quality control detects urban flood hazards under uncertainty DOI
Yeji Kim, Jeil Oh, Matthew Bartos

и другие.

Sustainable Cities and Society, Год журнала: 2024, Номер unknown, С. 105982 - 105982

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

2
ADVANCING WATER QUALITY PREDICTION: THE ROLE OF MACHINE LEARNING IN ENVIRONMENTAL SCIENCE DOI
Tymoteusz Miller, Adrianna Łobodzińska,

Polina Kozlovska

и другие.

ГРААЛЬ НАУКИ, Год журнала: 2024, Номер 36, С. 519 - 525

Опубликована: Фев. 26, 2024

This article delves into the burgeoning domain of machine learning (ML) applications within environmental science, with a specific focus on water quality prediction. Amidst escalating challenges, precision and efficiency ML models have emerged as pivotal tools for analyzing complex datasets, offering nuanced insights forecasts about trends. We explore integration in monitoring, highlighting its comparative advantage over traditional statistical methods handling vast, multifaceted data streams. exploration encompasses critical evaluation various algorithms tailored predictive accuracy assessment, including supervised unsupervised models. The also addresses challenges inherent applications, such model interpretability, anticipates future trajectories this rapidly evolving field. potential to revolutionize policy-making resource management through enhanced capabilities is central theme, underscoring transformative impact these technologies science.

Язык: Английский

Процитировано

2
Automated hydrologic forecasting using open-source sensors: Predicting stream depths across 200,000 km2 DOI
Travis Adrian Dantzer, Branko Kerkez

Environmental Modelling & Software, Год журнала: 2024, Номер 180, С. 106137 - 106137

Опубликована: Июль 8, 2024

Язык: Английский

Процитировано

1
SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters DOI Creative Commons
Sofie G. M. van’t Veen, Brian Kronvang, Joachim Audet

и другие.

Open Research Europe, Год журнала: 2024, Номер 4, С. 244 - 244

Опубликована: Ноя. 7, 2024

The growing use of sensors in fresh waters for water quality measurements generates an increasingly large amount data that requires assurance (QA)/quality control (QC) before the results can be exploited. Such a process is often resource-intensive and may not consistent across users sensors. SentemQC (QA-QC high temporal resolution sensor data) cost-efficient, open-source Python approach developed to ensure by performing QA QC on volumes high-frequency (HF) data. method computationally efficient features six-step user-friendly setup anomaly detection. marks anomalies using five moving windows. These windows connect each point neighboring points, including those further away window. As result, mark only individual outliers but also clusters anomalies. Our analysis shows robust detecting HF from multiple measuring nitrate, turbidity, oxygen, pH. were installed three different freshwater ecosystems (two streams one lake) experimental lake mesocosms. Sensor stream stations yielded percentages 0.1%, 0.2%, which lower than 0.5%, 0.6%, 0.8% Lake mesocosms, respectively. While this study contained relatively few (<2%), they represent best-case scenario terms maintenance. allows user include uncertainty/accuracy when QA-QC. However, cannot function independently. Additional QA-QC steps are crucial, calibration correct zero offsets implementation gap-filling methods prior determination final real-time concentrations load calculations.

Язык: Английский

Процитировано

1
Calibration and Performance Evaluation of Cost-Effective Capacitive Moisture Sensor in Slope Model Experiments DOI Creative Commons
Muhammad Nurjati Hidayat,

Hemanta Hazarika,

Haruichi Kanaya

и другие.

Sensors, Год журнала: 2024, Номер 24(24), С. 8156 - 8156

Опубликована: Дек. 20, 2024

Understanding the factors that contribute to slope failures, such as soil saturation, is essential for mitigating rainfall-induced landslides. Cost-effective capacitive moisture sensors have potential be widely implemented across multiple sites landslide early warning systems. However, these need calibrated specific applications ensure high accuracy in readings. In this study, a soil-specific calibration was performed laboratory setting integrate sensor with an automatic monitoring system using Internet of Things (IoT). This research aims evaluate low-cost (SKU:SEN0193) and develop equations purpose model experiment under artificial rainfall condition silica sand. The results indicate polynomial function best fit, coefficient determination (R2) ranging from 0.918 0.983 root mean square error (RMSE) 1.171 2.488. equation validated through experiments, samples taken models after finished. Overall, content readings showed approximately 12% deviation actual content. findings suggest cost-effective has used development system.

Язык: Английский

Процитировано

0
Understanding and mitigating global change with aquatic sensors: current challenges and future prospects DOI Creative Commons
Dermot Diamond, Rick A. Relyea, Margaret McCaul

и другие.

Frontiers in Sensors, Год журнала: 2023, Номер 4

Опубликована: Дек. 4, 2023

Human activities are causing global change around the world including habitat destruction, invasive species in non-native ecosystems, overexploitation, pollution, and climate change. While traditional monitoring has long been used to quantify aid mitigation of change, in-situ autonomous sensors being increasingly for environmental monitoring. Sensors sensor platforms that can be deployed developed remote areas allow high-frequency data collection, which is critical parameters exhibit important short-term dynamics on scale days, hours, or minutes. In this article, we discuss benefits aquatic ecosystems as well many challenges have experienced over years working with these technologies. These include decisions locations, types, analytical specification, calibration, drift, role conditions, fouling, service intervals, cost ownership, QA/QC. result tradeoffs when making regarding deploy, particularly a network desired cover large area. We also review recent advances designing building chemical-sensor allowing researchers develop next-generation power integrating multiple into provides increased insight water quality space time. coming years, there will an exponential growth related sensing, essential part efforts monitor mitigate its adverse impacts society.

Язык: Английский

Процитировано

0