IoT in Water Quality Monitoring—Are We Really Here?

Miller, Małgorzata; Kisiel, Anna; Cembrowska-Lech, Danuta; Durlik, Irmina; Miller, Tymoteusz

doi:10.3390/s23020960

Cited by 37 publications

(29 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Many excellent resources exists which provide guidelines for analytical method development and determination of validation parameters. ,,,− If sensors are to be implemented at large scale, the generation of large volumes of data necessitates the utilization of advanced data analysis techniques, such as machine learning and geographic information systems (GIS), to integrate diverse data sets and uncover valuable insights into the occurrence, fate, and impacts of emerging contaminants. Integration of low-cost sensors with smartphones and the IoT infrastructure shows promise for the remote water quality monitoring, particularly for monitoring physical parameters, , and analytes like nitrite and metal ions , but several challenges need to be addressed for large scale adoption of IoT-enabled chem/bio sensors. These include the need for sample treatment, multistep analysis, and a lack of capabilities to provide continuous real-time monitoring with high spatiotemporal resolution when deployed.…”

Section: Strategic Objectives and Best Practices To Overcome Roadblocksmentioning

confidence: 99%

Sensors for Emerging Water Contaminants: Overcoming Roadblocks to Innovation

Ateia,

Wei,

Andreescu

2024

Environ. Sci. Technol.

View full text Add to dashboard Cite

Ensuring water quality and safety requires the effective detection of emerging contaminants, which present significant risks to both human health and the environment. Field deployable low-cost sensors provide solutions to detect contaminants at their source and enable large-scale water quality monitoring and management. Unfortunately, the availability and utilization of such sensors remain limited. This Perspective examines current sensing technologies for detecting emerging contaminants and analyzes critical barriers, such as high costs, lack of reliability, difficulties in implementation in real-world settings, and lack of stakeholder involvement in sensor design. These technical and nontechnical barriers severely hinder progression from proof-of-concepts and negatively impact user experience factors such as ease-of-use and actionability using sensing data, ultimately affecting successful translation and widespread adoption of these technologies. We provide examples of specific sensing systems and explore key strategies to address the remaining scientific challenges that must be overcome to translate these technologies into the field such as improving sensitivity, selectivity, robustness, and performance in real-world water environments. Other critical aspects such as tailoring research to meet end-users' requirements, integrating cost considerations and consumer needs into the early prototype design, establishing standardized evaluation and validation protocols, fostering academia-industry collaborations, maximizing data value by establishing data sharing initiatives, and promoting workforce development are also discussed. The Perspective describes a set of guidelines for the development, translation, and implementation of water quality sensors to swiftly and accurately detect, analyze, track, and manage contamination.

show abstract

Section: Strategic Objectives and Best Practices To Overcome Roadblocksmentioning

confidence: 99%

Sensors for Emerging Water Contaminants: Overcoming Roadblocks to Innovation

Ateia,

Wei,

Andreescu

2024

Environ. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…This allows for the early detection of pollution events and the assessment of water health over large areas. Remote sensing can identify sources of pollution, such as industrial discharges or agricultural runoff, by analyzing changes in water color and patterns over time (Miller et al, 2023). IoT sensors placed in water bodies and infrastructure provide real-time data on water levels, flow rates, and quality.…”

Section: Jurisdiction Key Provisionsmentioning

confidence: 99%

Problems of prevention of criminal offenses related to the use of water resources in Kazakhstan

Sabyr,

Ramazanova,

Koishybaiuly

et al. 2024

World Water Policy

View full text Add to dashboard Cite

The protection of the environment and its elements is the responsibility of modern society for the possibility of its development in the future. That is why the issues of protection of this category of relations are relevant today in the legal plane. It is important to establish not only an effective mechanism for punishing the guilty but also for preventing the commission of criminal offenses against natural objects. The purpose of the study is to identify effective approaches for the prevention of crimes related to the use of water bodies. In the study, the methods of analysis and synthesis, comparison, deduction, generalization, formal legal, and abstraction were applied. As a result, it was possible to determine that the current state of water bodies in Kazakhstan is deteriorating, so they may be at a crisis level in the future. In view of this, the paper investigated the composition of a criminal offense related to the illegal use of water bodies. This made it possible to determine the current position of the legislator on the regulation of this issue in the legal dimension. In addition, the methods and tools that are used in Kazakhstan to prevent the spread of this type of criminal offense were established. Based on this, their shortcomings were analyzed, and proposals were developed to improve the process of preventing such crimes. Therefore, the place of environmental crimes in the modern criminal law doctrine was determined, and international experience in preventing such offenses was considered. The practical value of the findings is the possibility of using them to develop new national strategies and programs aimed at countering crimes in the field of water use.

show abstract

“…The field of satellite-based water quality monitoring, driven by advancements in machine and deep learning technologies, relies on a sophisticated and comprehensive technology stack. This stack encompasses crucial hardware and software devices, including efficient data collection mechanisms, robust data storage solutions, cutting-edge machine and deep learning algorithms, powerful frameworks and libraries, scalable cloud platforms, and intuitive visualization tools [307][308][309][310][311][312]. These elements collectively form the backbone of the technology infrastructure that enables the effective analysis and interpretation of satellite data for accurate water quality monitoring.…”

Section: Technology Stack and Cyber Infrastructure For Machine Learni...mentioning

confidence: 99%

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Mukonza,

Chiang

2023

Environments

View full text Add to dashboard Cite

This review paper adopts bibliometric and meta-analysis approaches to explore the application of supervised machine learning regression models in satellite-based water quality monitoring. The consistent pattern observed across peer-reviewed research papers shows an increasing interest in the use of satellites as an innovative approach for monitoring water quality, a critical step towards addressing the challenges posed by rising anthropogenic water pollution. Traditional methods of monitoring water quality have limitations, but satellite sensors provide a potential solution to that by lowering costs and expanding temporal and spatial coverage. However, conventional statistical methods are limited when faced with the formidable challenge of conducting pattern recognition analysis for satellite geospatial big data because they are characterized by high volume and complexity. As a compelling alternative, the application of machine and deep learning techniques has emerged as an indispensable tool, with the remarkable capability to discern intricate patterns in the data that might otherwise remain elusive to traditional statistics. The study employed a targeted search strategy, utilizing specific criteria and the titles of 332 peer-reviewed journal articles indexed in Scopus, resulting in the inclusion of 165 articles for the meta-analysis. Our comprehensive bibliometric analysis provides insights into the trends, research productivity, and impact of satellite-based water quality monitoring. It highlights key journals and publishers in this domain while examining the relationship between the first author’s presentation, publication year, citation count, and journal impact factor. The major review findings highlight the widespread use of satellite sensors in water quality monitoring including the MultiSpectral Instrument (MSI), Ocean and Land Color Instrument (OLCI), Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and the practice of multi-sensor data fusion. Deep neural networks are identified as popular and high-performing algorithms, with significant competition from extreme gradient boosting (XGBoost), even though XGBoost is relatively newer in the field of machine learning. Chlorophyll-a and water clarity indicators receive special attention, and geo-location had a relationship with optical water classes. This paper contributes significantly by providing extensive examples and in-depth discussions of papers with code, as well as highlighting the critical cyber infrastructure used in this research. Advances in high-performance computing, large-scale data processing capabilities, and the availability of open-source software are facilitating the growing prominence of machine and deep learning applications in geospatial artificial intelligence for water quality monitoring, and this is positively contributing towards monitoring water pollution.

show abstract

IoT in Water Quality Monitoring—Are We Really Here?

Cited by 37 publications

References 33 publications

Sensors for Emerging Water Contaminants: Overcoming Roadblocks to Innovation

Sensors for Emerging Water Contaminants: Overcoming Roadblocks to Innovation

Problems of prevention of criminal offenses related to the use of water resources in Kazakhstan

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Contact Info

Product

Resources

About