Possibilities of Real Time Monitoring of Micropollutants in Wastewater Using Laser-Induced Raman &amp; Fluorescence Spectroscopy (LIRFS) and Artificial Intelligence (AI)

Post, Claudia; Heyden, Niklas; Reinartz, André; Foerderer, Aaron; Bruelisauer, Simon; Linnemann, Volker; Hug, W.; Amann, Florian

doi:10.3390/s22134668

Cited by 9 publications

(4 citation statements)

References 62 publications

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“…This is far more frequent than e.g., in general environmental monitoring water chemistry characteristics are measured few times a year. Still, only a few reliable web-enabled instruments for measuring MP concentrations exist (Post et al 2022). Indirect detection of MPs with commercial online sensors can be performed by e.g., monitoring tryptophan concentrations online with an automatic sensor.…”

Section: Automatic Water Quality Sensorsmentioning

confidence: 99%

Uncertainty in Environmental Micropollutant Modeling

Ahkola,

Kotamäki,

Siivola

et al. 2024

Environmental Management

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Water pollution policies have been enacted across the globe to minimize the environmental risks posed by micropollutants (MPs). For regulative institutions to be able to ensure the realization of environmental objectives, they need information on the environmental fate of MPs. Furthermore, there is an urgent need to further improve environmental decision-making, which heavily relies on scientific data. Use of mathematical and computational modeling in environmental permit processes for water construction activities has increased. Uncertainty of input data considers several steps from sampling and analysis to physico-chemical characteristics of MP. Machine learning (ML) methods are an emerging technique in this field. ML techniques might become more crucial for MP modeling as the amount of data is constantly increasing and the emerging new ML approaches and applications are developed. It seems that both modeling strategies, traditional and ML, use quite similar methods to obtain uncertainties. Process based models cannot consider all known and relevant processes, making the comprehensive estimation of uncertainty challenging. Problems in a comprehensive uncertainty analysis within ML approach are even greater. For both approaches generic and common method seems to be more useful in a practice than those emerging from ab initio. The implementation of the modeling results, including uncertainty and the precautionary principle, should be researched more deeply to achieve a reliable estimation of the effect of an action on the chemical and ecological status of an environment without underestimating or overestimating the risk. The prevailing uncertainties need to be identified and acknowledged and if possible, reduced. This paper provides an overview of different aspects that concern the topic of uncertainty in MP modeling.

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Section: Automatic Water Quality Sensorsmentioning

confidence: 99%

Uncertainty in Environmental Micropollutant Modeling

Ahkola,

Kotamäki,

Siivola

et al. 2024

Environmental Management

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“…An increasing number of measured data and AI models, as well as multivariate statistical methods, have made data-driven modeling and real-time prediction attractive. Post et al [61] combined a CNN model with laser-induced Raman and fluorescence spectroscopy (LIRFS) to achieve real-time monitoring of the micropollutants of WWTP with a correlation coefficient of R 2 = 0.74 for all samples. The results show that this method can lead to high-precision measurement results, reach detection limits and detect micropollutants that cannot be monitored using the monitoring methods of WWTPs.…”

Section: Water Quality Monitoring For Data Acquisitionmentioning

confidence: 99%

A Review on Applications of Artificial Intelligence in Wastewater Treatment

Wang,

Cheng,

Liu

et al. 2023

Sustainability

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In recent years, artificial intelligence (AI), as a rapidly developing and powerful tool to solve practical problems, has attracted much attention and has been widely used in various areas. Owing to their strong learning and accurate prediction abilities, all sorts of AI models have also been applied in wastewater treatment (WWT) to optimize the process, predict the efficiency and evaluate the performance, so as to explore more cost-effective solutions to WWT. In this review, we summarize and analyze various AI models and their applications in WWT. Specifically, we briefly introduce the commonly used AI models and their purposes, advantages and disadvantages, and comprehensively review the inputs, outputs, objectives and major findings of particular AI applications in water quality monitoring, laboratory-scale research and process design. Although AI models have gained great success in WWT-related fields, there are some challenges and limitations that hinder the widespread applications of AI models in real WWT, such as low interpretability, poor model reproducibility and big data demand, as well as a lack of physical significance, mechanism explanation, academic transparency and fair comparison. To overcome these hurdles and successfully apply AI models in WWT, we make recommendations and discuss the future directions of AI applications.

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“…However, the detection capabilities of fluorescence spectroscopy are improving, especially as this technique is validated alongside other analytical techniques (e.g., LC-MS, GC-MS, NMR). Furthermore, artificial intelligence is even being used to improve data analysis by increasing the accuracy of constituent identification and expanding feature interpretation of the fluorescence spectra. , …”

Section: Enabling Reuse Through Mwrc Regulationsmentioning

confidence: 99%

“…Furthermore, artificial intelligence is even being used to improve data analysis by increasing the accuracy of constituent identification and expanding feature interpretation of the fluorescence spectra. 76,92 Bioassays to determine the adverse biological effects on living organisms may prove to be useful tools for the direct monitoring of MWRC. Deviating from the methods described thus far, this effect-based approach to monitoring focuses on the outcome of MWRC exposure as opposed to the chemical composition.…”

Section: ■ Introductionmentioning

confidence: 99%

The Future of Municipal Wastewater Reuse Concentrate Management: Drivers, Challenges, and Opportunities

Finnerty,

Childress,

Hardy

et al. 2023

Environ. Sci. Technol.

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Water reuse is rapidly becoming an integral feature of resilient water systems, where municipal wastewater undergoes advanced treatment, typically involving a sequence of ultrafiltration (UF), reverse osmosis (RO), and an advanced oxidation process (AOP). When RO is used, a concentrated waste stream is produced that is elevated in not only total dissolved solids but also metals, nutrients, and micropollutants that have passed through conventional wastewater treatment. Management of this RO concentrate�dubbed municipal wastewater reuse concentrate (MWRC)�will be critical to address, especially as water reuse practices become more widespread. Building on existing brine management practices, this review explores MWRC management options by identifying infrastructural needs and opportunities for multi-beneficial disposal. To safeguard environmental systems from the potential hazards of MWRC, disposal, monitoring, and regulatory techniques are discussed to promote the safety and affordability of implementing MWRC management. Furthermore, opportunities for resource recovery and valorization are differentiated, while economic techniques to revamp cost-benefit analysis for MWRC management are examined. The goal of this critical review is to create a common foundation for researchers, practitioners, and regulators by providing an interdisciplinary set of tools and frameworks to address the impending challenges and emerging opportunities of MWRC management.

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Possibilities of Real Time Monitoring of Micropollutants in Wastewater Using Laser-Induced Raman & Fluorescence Spectroscopy (LIRFS) and Artificial Intelligence (AI)

Cited by 9 publications

References 62 publications

Uncertainty in Environmental Micropollutant Modeling

Uncertainty in Environmental Micropollutant Modeling

A Review on Applications of Artificial Intelligence in Wastewater Treatment

The Future of Municipal Wastewater Reuse Concentrate Management: Drivers, Challenges, and Opportunities

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