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Lu Q, Zou J, Ye Y, Wang Z. Design and implementation of a Li River water quality monitoring and analysis system based on outlier data analysis. PLoS One 2024; 19:e0299435. [PMID: 38498583 PMCID: PMC10947683 DOI: 10.1371/journal.pone.0299435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 02/11/2024] [Indexed: 03/20/2024] Open
Abstract
The detection of water quality indicators such as Temperature, pH, Turbidity, Conductivity, and TDS involves five national standard methods. Chemically based measurement techniques may generate liquid residue, causing secondary pollution. The water quality monitoring and data analysis system can effectively address the issues that conventional methods require multiple pieces of equipment and repeated measurements. This paper analyzes the distribution characteristics of the historical data from five sensors at a specific time, displays them graphically in real time, and provides an early warning of exceeding the standard; It selects four water samples from different sections of the Li River, based on the national standard method, the average measurement errors of Temperature, PH, TDS, Conductivity and Turbidity are 0.98%, 2.23%, 2.92%, 3.05% and 3.98%.;It further uses the quartile method to analyze the outlier data over 100,000 records and five historical periods are selected. Experiment results show the system is relatively stable in measuring Temperature, PH and TDS, and the proportion of outlier is 0.42%, 0.84% and 1.24%. When Turbidity and Conductivity are measured, the proportion is 3.11% and 2.92%. In the experiment of using 7 methods to fill outlier, K nearest neighbor algorithm is better than others. The analysis of data trends, outliers, means, and extreme values assists in making decisions, such as updating and maintaining equipment, addressing extreme water quality situations, and enhancing regional water quality oversight.
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Affiliation(s)
- Qirong Lu
- College of Information Science and Engineering, Guilin University of Technology, Guilin, China
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, China
| | - Jian Zou
- College of Information Science and Engineering, Guilin University of Technology, Guilin, China
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, China
| | - Yingya Ye
- College of Information Science and Engineering, Guilin University of Technology, Guilin, China
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, China
| | - Zexin Wang
- College of Information Science and Engineering, Guilin University of Technology, Guilin, China
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, China
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2
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McGarrity M, Zhao F. Graphene-Based Chemiresistor Sensors for Drinking Water Quality Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:9828. [PMID: 38139674 PMCID: PMC10747892 DOI: 10.3390/s23249828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/03/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023]
Abstract
Monitoring the quality of drinking water is a crucial responsibility for all water infrastructure networks, as it guarantees access to clean water for the communities they serve. With water infrastructure deteriorating due to age and neglect, drinking water violations are on the rise in the US, underscoring the need for improved monitoring capabilities. Among the different sensor technologies, graphene-based chemiresistors have emerged as a promising technology for water quality monitoring due to advantages such as simple design, sensitivity, and selectivity. This review paper provides an overview of recent advances in the development of graphene-based chemiresistors for water quality monitoring, including principles of chemiresistive sensing, sensor design and functionalization, and performance of devices reported in the literature. The paper also discusses challenges and opportunities in the field and highlights future research directions. The development of graphene-based chemiresistors has the potential to revolutionize water quality monitoring by providing highly sensitive and cost-effective sensors that can be integrated into existing infrastructure for real-time monitoring.
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Affiliation(s)
| | - Feng Zhao
- Micro/Nanoelectronic and Energy Laboratory, School of Engineering and Computer Science, Washington State University, Vancouver, WA 98686, USA;
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3
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Stoll S, Hwang JH, Fox DW, Kim K, Zhai L, Lee WH. Cost-effective screen-printed carbon electrode biosensors for rapid detection of microcystin-LR in surface waters for early warning of harmful algal blooms. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:124854-124865. [PMID: 36194320 DOI: 10.1007/s11356-022-23300-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Abstract
Microcystins (MCs) are toxins produced by cyanobacteria commonly found in harmful algal blooms (HABs). Due to their toxicity to humans and other organisms, the World Health Organization (WHO) sets a guideline of 1 μg L-1 for microcystin-leucine-arginine (MC-LR) in drinking water. However, current analytical techniques for the detection of MC-LR such as liquid chromatography-mass spectrometry (LC-MS) and ELISA are costly, bulky, time-consuming, and mostly conducted in a laboratory, requiring highly trained personnel. An analytical method that can be used in the field for rapid determination is essential. In this study, an anti-MC-LR/MC-LR/cysteamine-coated screen-printed carbon electrode (SPCE) biosensor was newly developed to detect MC-LR, bioelectrochemically, in water. The functionalization of the electrode surface was confirmed with surface characterization methods. The sensor performance was evaluated by electrochemical impedance spectroscopy (EIS), obtaining a linear working range of MC-LR concentrations between 0.1 and 100 μg L-1 with a limit of detection (LOD) of 0.69 ng L-1. Natural water samples experiencing HABs were collected and analyzed using the developed biosensor, demonstrating the excellent performance of the biosensor with a relative standard deviation (RSD) of 0.65%. The interference tests showed minimal error and RSD values against other common MCs and possible coexisting ions found in water. The biosensor showed acceptable functionality with a shelf life of up to 12 weeks. Overall, the anti-MC-LR/MC-LR/cysteamine/SPCE biosensors can be an innovative solution with characteristics that allow for in situ, low-cost, and easy-to-use capabilities which are essential for developing an overarching and integrated "smart" environmental management system.
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Affiliation(s)
- Stephanie Stoll
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, 32816, USA
| | - Jae-Hoon Hwang
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, 32816, USA
| | - David W Fox
- Nanoscience Technology Center and Department of Chemistry, University of Central Florida, Orlando, FL, 32816, USA
| | - Keugtae Kim
- Department of Environmental and Energy Engineering, The University of Suwon, 17 Wauan-gil, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 18323, South Korea
| | - Lei Zhai
- Nanoscience Technology Center and Department of Chemistry, University of Central Florida, Orlando, FL, 32816, USA
| | - Woo Hyoung Lee
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, 32816, USA.
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4
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Cojbasic S, Dmitrasinovic S, Kostic M, Turk Sekulic M, Radonic J, Dodig A, Stojkovic M. Application of machine learning in river water quality management: a review. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2023; 88:2297-2308. [PMID: 37966184 PMCID: wst_2023_331 DOI: 10.2166/wst.2023.331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Machine learning (ML), a branch of artificial intelligence (AI), has been increasingly used in environmental engineering due to the ability to analyze complex nonlinear problems (such as ones connected with water quality management) through a data-driven approach. This study provides an overview of different ML algorithms applied for monitoring and predicting river water quality. Different parameters could be monitored or predicted, such as dissolved oxygen (DO), biological and chemical oxygen demand (BOD and COD), turbidity levels, the concentration of different ions (such as Mg2+ and Ca2+), heavy metal or other pollutant's concentration, pH, temperature, and many more. Although many algorithms have been investigated for the prediction of river water quality, there are several which are most commonly used in engineering practice. These models mostly include so-called supervised learning algorithms, such as artificial neural network (ANN), support vector machine (SVM), random forest (RF), decision tree (DT), and deep learning (DL). To further enhance prediction power, novel hybrid algorithms, could be used. However, the quality of prediction is not only dependent on the applied algorithm but also on the availability of previously mentioned water quality parameters, their selection, and the combination of input data used to train the ML model.
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Affiliation(s)
- Sanja Cojbasic
- Faculty of Technical Sciences, Department of Environmental Engineering and Occupational Safety and Health, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia E-mail:
| | - Sonja Dmitrasinovic
- Faculty of Technical Sciences, Department of Environmental Engineering and Occupational Safety and Health, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia
| | - Marija Kostic
- Faculty of Technical Sciences, Department of Environmental Engineering and Occupational Safety and Health, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia
| | - Maja Turk Sekulic
- Faculty of Technical Sciences, Department of Environmental Engineering and Occupational Safety and Health, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia
| | - Jelena Radonic
- Faculty of Technical Sciences, Department of Environmental Engineering and Occupational Safety and Health, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia
| | - Ana Dodig
- Institute for Artificial Intelligence R&D of Serbia, Fruskogorska 1, Novi Sad, Serbia
| | - Milan Stojkovic
- Institute for Artificial Intelligence R&D of Serbia, Fruskogorska 1, Novi Sad, Serbia
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5
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Kermorvant C, Liquet B, Litt G, Mengersen K, Peterson EE, Hyndman RJ, Jones JB, Leigh C. Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data. PLoS One 2023; 18:e0287640. [PMID: 37390064 PMCID: PMC10313027 DOI: 10.1371/journal.pone.0287640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 06/12/2023] [Indexed: 07/02/2023] Open
Abstract
Real-time monitoring using in-situ sensors is becoming a common approach for measuring water-quality within watersheds. High-frequency measurements produce big datasets that present opportunities to conduct new analyses for improved understanding of water-quality dynamics and more effective management of rivers and streams. Of primary importance is enhancing knowledge of the relationships between nitrate, one of the most reactive forms of inorganic nitrogen in the aquatic environment, and other water-quality variables. We analysed high-frequency water-quality data from in-situ sensors deployed in three sites from different watersheds and climate zones within the National Ecological Observatory Network, USA. We used generalised additive mixed models to explain the nonlinear relationships at each site between nitrate concentration and conductivity, turbidity, dissolved oxygen, water temperature, and elevation. Temporal auto-correlation was modelled with an auto-regressive-moving-average (ARIMA) model and we examined the relative importance of the explanatory variables. Total deviance explained by the models was high for all sites (99%). Although variable importance and the smooth regression parameters differed among sites, the models explaining the most variation in nitrate contained the same explanatory variables. This study demonstrates that building a model for nitrate using the same set of explanatory water-quality variables is achievable, even for sites with vastly different environmental and climatic characteristics. Applying such models will assist managers to select cost-effective water-quality variables to monitor when the goals are to gain a spatial and temporal in-depth understanding of nitrate dynamics and adapt management plans accordingly.
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Affiliation(s)
- Claire Kermorvant
- Le CNRS et l’Université de Pau et des Pays de l’Adour, Laboratoire de Mathématiques et de leurs Applications de Pau, Anglet, France
| | - Benoit Liquet
- Le CNRS et l’Université de Pau et des Pays de l’Adour, Laboratoire de Mathématiques et de leurs Applications de Pau, Anglet, France
- School of Mathematical and Physical Sciences, Macquarie University, Sydney, New South Wales, Australia
- ARC Centre of Excellence for Mathematics and Statistical Frontiers, Brisbane, Queensland, Australia
| | - Guy Litt
- Battelle, National Ecological Observatory Network, Boulder, Colorado, United States of America
| | - Kerrie Mengersen
- ARC Centre of Excellence for Mathematics and Statistical Frontiers, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Erin E. Peterson
- ARC Centre of Excellence for Mathematics and Statistical Frontiers, Brisbane, Queensland, Australia
- Peterson Consulting, Brisbane, Queensland, Australia
| | - Rob J. Hyndman
- ARC Centre of Excellence for Mathematics and Statistical Frontiers, Brisbane, Queensland, Australia
- Department of Econometrics and Business Statistics, Monash University, Clayton, Victoria, Australia
| | - Jeremy B. Jones
- Institute of Arctic Biology and Department of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, Alaska, United States of America
| | - Catherine Leigh
- ARC Centre of Excellence for Mathematics and Statistical Frontiers, Brisbane, Queensland, Australia
- Biosciences and Food Technology Discipline and School of Science, RMIT University, Bundoora, Victoria, Australia
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Tselemponis A, Stefanis C, Giorgi E, Kalmpourtzi A, Olmpasalis I, Tselemponis A, Adam M, Kontogiorgis C, Dokas IM, Bezirtzoglou E, Constantinidis TC. Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6216. [PMID: 37444064 PMCID: PMC10341787 DOI: 10.3390/ijerph20136216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/19/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023]
Abstract
In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009-2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.
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Affiliation(s)
- Athanasios Tselemponis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Christos Stefanis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Elpida Giorgi
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Aikaterini Kalmpourtzi
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Ioannis Olmpasalis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Antonios Tselemponis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Maria Adam
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Christos Kontogiorgis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Ioannis M. Dokas
- Department of Civil Engineering, Democritus University of Thrace, 69100 Komotini, Greece;
| | - Eugenia Bezirtzoglou
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Theodoros C. Constantinidis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
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7
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O'Rourke K, Virgiliou C, Theodoridis G, Gika H, Grintzalis K. The impact of pharmaceutical pollutants on daphnids - A metabolomic approach. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2023:104157. [PMID: 37225008 DOI: 10.1016/j.etap.2023.104157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/06/2023] [Accepted: 05/20/2023] [Indexed: 05/26/2023]
Abstract
Pharmaceuticals have been classified as emerging contaminants in the aquatic ecosystem, mainly due to their increased use and improper disposal. A significant range of pharmaceutical compounds and their metabolites have been globally detected in surface waters and pose detrimental effects to non-target organisms. Monitoring pharmaceutical water pollution relies on the analytical approaches for their detection, however, such approaches are limited by their sensitivity limit and coverage of the wide range pharmaceutical compounds. This lack of realism in risk assessment is bypassed with effect-based methods, which are complemented by chemical screening and impact modelling, and are able to provide mechanistic insight for pollution. Focusing on the freshwater ecosystem, in this study we evaluated the acute effects on daphnids for three distinct groups of pharmaceuticals; antibiotics, estrogens, and a range of commonly encountered environmentally relevant pharmaceutical pollutants. Combining several endpoints such as mortality, biochemical (enzyme activities) and holistic (metabolomics) we discovered distinct patterns in biological responses. In this study, changes in enzymes of metabolism e.g. phosphatases and lipase, as well as the detoxification enzyme, glutathione-S-transferase, were recorded following acute exposure to the selected pharmaceuticals. A targeted analysis of the hydrophilic profile of daphnids revealed mainly the up-regulation of metabolites following metformin, gabapentin, amoxicillin, trimethoprim and β-estradiol. Whereas gemfibrozil, sulfamethoxazole and oestrone exposure resulted in the down-regulation of majority of metabolites.
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Affiliation(s)
- Katie O'Rourke
- School of Biotechnology, Dublin City University, Republic of Ireland.
| | - Christina Virgiliou
- Department of Chemical Engineering, Laboratory of Analytical Chemistry, and Center for Interdisciplinary Research and Innovation (CIRI-AUTH) Biomic_AUTh, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - Georgios Theodoridis
- Department of Chemistry, Aristotle University, Thessaloniki 54124, Greece; Biomic_AUTh, Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Balkan Center, B1.4, Thessaloniki, 10th km Thessaloniki-Thermi Rd, P.O. Box 8318, GR 57001, Greece; FoodOmicsGR, Research Infrastructure, Aristotle University Node, Thessaloniki, 10th km Thessaloniki-Thermi Rd, P.O. Box 8318, 57001,Greece.
| | - Helen Gika
- Laboratory of Forensic Medicine & Toxicology, School of Medicine, Aristotle University of Thessaloniki, 54124, Greece; Biomic AUTH, Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Balkan Center B1.4, Thessaloniki GR 57001, Greece.
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8
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River ecosystem metabolism and carbon biogeochemistry in a changing world. Nature 2023; 613:449-459. [PMID: 36653564 DOI: 10.1038/s41586-022-05500-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 10/31/2022] [Indexed: 01/20/2023]
Abstract
River networks represent the largest biogeochemical nexus between the continents, ocean and atmosphere. Our current understanding of the role of rivers in the global carbon cycle remains limited, which makes it difficult to predict how global change may alter the timing and spatial distribution of riverine carbon sequestration and greenhouse gas emissions. Here we review the state of river ecosystem metabolism research and synthesize the current best available estimates of river ecosystem metabolism. We quantify the organic and inorganic carbon flux from land to global rivers and show that their net ecosystem production and carbon dioxide emissions shift the organic to inorganic carbon balance en route from land to the coastal ocean. Furthermore, we discuss how global change may affect river ecosystem metabolism and related carbon fluxes and identify research directions that can help to develop better predictions of the effects of global change on riverine ecosystem processes. We argue that a global river observing system will play a key role in understanding river networks and their future evolution in the context of the global carbon budget.
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Huynh TMT, Ni CF, Su YS, Nguyen VCN, Lee IH, Lin CP, Nguyen HH. Predicting Heavy Metal Concentrations in Shallow Aquifer Systems Based on Low-Cost Physiochemical Parameters Using Machine Learning Techniques. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191912180. [PMID: 36231480 PMCID: PMC9566676 DOI: 10.3390/ijerph191912180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 09/20/2022] [Accepted: 09/20/2022] [Indexed: 05/07/2023]
Abstract
Monitoring ex-situ water parameters, namely heavy metals, needs time and laboratory work for water sampling and analytical processes, which can retard the response to ongoing pollution events. Previous studies have successfully applied fast modeling techniques such as artificial intelligence algorithms to predict heavy metals. However, neither low-cost feature predictability nor explainability assessments have been considered in the modeling process. This study proposes a reliable and explainable framework to find an effective model and feature set to predict heavy metals in groundwater. The integrated assessment framework has four steps: model selection uncertainty, feature selection uncertainty, predictive uncertainty, and model interpretability. The results show that Random Forest is the most suitable model, and quick-measure parameters can be used as predictors for arsenic (As), iron (Fe), and manganese (Mn). Although the model performance is auspicious, it likely produces significant uncertainties. The findings also demonstrate that arsenic is related to nutrients and spatial distribution, while Fe and Mn are affected by spatial distribution and salinity. Some limitations and suggestions are also discussed to improve the prediction accuracy and interpretability.
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Affiliation(s)
- Thi-Minh-Trang Huynh
- Graduate Institute of Applied Geology, National Central University, Taoyuan 32001, Taiwan
| | - Chuen-Fa Ni
- Graduate Institute of Applied Geology, National Central University, Taoyuan 32001, Taiwan
- Center for Environmental Studies, National Central University, Taoyuan 32001, Taiwan
- Correspondence: (C.-F.N.); (Y.-S.S.)
| | - Yu-Sheng Su
- Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung 202301, Taiwan
- Correspondence: (C.-F.N.); (Y.-S.S.)
| | - Vo-Chau-Ngan Nguyen
- College of Environment and Natural Resources, Can Tho University, Can Tho 94000, Vietnam
| | - I-Hsien Lee
- Graduate Institute of Applied Geology, National Central University, Taoyuan 32001, Taiwan
- Center for Environmental Studies, National Central University, Taoyuan 32001, Taiwan
| | - Chi-Ping Lin
- Graduate Institute of Applied Geology, National Central University, Taoyuan 32001, Taiwan
- Center for Environmental Studies, National Central University, Taoyuan 32001, Taiwan
| | - Hoang-Hiep Nguyen
- Graduate Institute of Applied Geology, National Central University, Taoyuan 32001, Taiwan
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10
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Park J, Lee WH, Kim KT, Park CY, Lee S, Heo TY. Interpretation of ensemble learning to predict water quality using explainable artificial intelligence. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 832:155070. [PMID: 35398119 DOI: 10.1016/j.scitotenv.2022.155070] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/31/2022] [Accepted: 04/02/2022] [Indexed: 06/14/2023]
Abstract
Algal bloom is a significant issue when managing water quality in freshwater; specifically, predicting the concentration of algae is essential to maintaining the safety of the drinking water supply system. The chlorophyll-a (Chl-a) concentration is a commonly used indicator to obtain an estimation of algal concentration. In this study, an XGBoost ensemble machine learning (ML) model was developed from eighteen input variables to predict Chl-a concentration. The composition and pretreatment of input variables to the model are important factors for improving model performance. Explainable artificial intelligence (XAI) is an emerging area of ML modeling that provides a reasonable interpretation of model performance. The effect of input variable selection on model performance was estimated, where the priority of input variable selection was determined using three indices: Shapley value (SHAP), feature importance (FI), and variance inflation factor (VIF). SHAP analysis is an XAI algorithm designed to compute the relative importance of input variables with consistency, providing an interpretable analysis for model prediction. The XGB models simulated with independent variables selected using three indices were evaluated with root mean square error (RMSE), RMSE-observation standard deviation ratio, and Nash-Sutcliffe efficiency. This study shows that the model exhibited the most stable performance when the priority of input variables was determined by SHAP. This implies that on-site monitoring can be designed to collect the selected input variables from the SHAP analysis to reduce the cost of overall water quality analysis. The independent variables were further analyzed using SHAP summary plot, force plot, target plot, and partial dependency plot to provide understandable interpretation on the performance of the XGB model. While XAI is still in the early stages of development, this study successfully demonstrated a good example of XAI application to improve the interpretation of machine learning model performance in predicting water quality.
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Affiliation(s)
- Jungsu Park
- Department of Civil and Environmental Engineering, Hanbat National University,125, Dongseo-daero, Yuseong-gu, Daejeon 34158, Republic of Korea.
| | - Woo Hyoung Lee
- Department of Civil, Environmental and Construction Engineering, University of Central Florida, 12800 Pegasus Dr., Orlando, FL 32816, USA.
| | - Keug Tae Kim
- Department of Environmental & Energy Engineering, The University of Suwon, 17 Wauan-gil, Bongdam-eup, Hwaseong-si, Gyeonggi-do 18323, Republic of Korea.
| | | | - Sanghun Lee
- Department of Information & Statistics, Chungbuk National University, Chungdae-Ro 1, SeoWon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Tae-Young Heo
- Department of Information & Statistics, Chungbuk National University, Chungdae-Ro 1, SeoWon-Gu, Cheongju, Chungbuk 28644, Republic of Korea.
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Shi Z, Chow CWK, Fabris R, Liu J, Jin B. Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:2987. [PMID: 35458971 PMCID: PMC9024714 DOI: 10.3390/s22082987] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 03/22/2022] [Accepted: 04/07/2022] [Indexed: 01/27/2023]
Abstract
Water quality monitoring is an essential component of water quality management for water utilities for managing the drinking water supply. Online UV-Vis spectrophotometers are becoming popular choices for online water quality monitoring and process control, as they are reagent free, do not require sample pre-treatments and can provide continuous measurements. The advantages of the online UV-Vis sensors are that they can capture events and allow quicker responses to water quality changes compared to conventional water quality monitoring. This review summarizes the applications of online UV-Vis spectrophotometers for drinking water quality management in the last two decades. Water quality measurements can be performed directly using the built-in generic algorithms of the online UV-Vis instruments, including absorbance at 254 nm (UV254), colour, dissolved organic carbon (DOC), total organic carbon (TOC), turbidity and nitrate. To enhance the usability of this technique by providing a higher level of operations intelligence, the UV-Vis spectra combined with chemometrics approach offers simplicity, flexibility and applicability. The use of anomaly detection and an early warning was also discussed for drinking water quality monitoring at the source or in the distribution system. As most of the online UV-Vis instruments studies in the drinking water field were conducted at the laboratory- and pilot-scale, future work is needed for industrial-scale evaluation with ab appropriate validation methodology. Issues and potential solutions associated with online instruments for water quality monitoring have been provided. Current technique development outcomes indicate that future research and development work is needed for the integration of early warnings and real-time water treatment process control systems using the online UV-Vis spectrophotometers as part of the water quality management system.
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Affiliation(s)
- Zhining Shi
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia; (Z.S.); (B.J.)
| | - Christopher W. K. Chow
- Sustainable Infrastructure and Resource Management, UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
- Future Industries Institute, University of South Australia, Mawson Lakes, SA 5095, Australia
| | - Rolando Fabris
- South Australia Water Corporation, Adelaide, SA 5000, Australia;
| | - Jixue Liu
- UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia;
| | - Bo Jin
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia; (Z.S.); (B.J.)
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12
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SmartWater: A Service-Oriented and Sensor Cloud-Based Framework for Smart Monitoring of Water Environments. REMOTE SENSING 2022. [DOI: 10.3390/rs14040922] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Due to the sharp increase in global industrial production, as well as the over-exploitation of land and sea resources, the quality of drinking water has deteriorated considerably. Furthermore, nowadays, many water supply systems serving growing human populations suffer from shortages since many rivers, lakes, and aquifers are drying up because of global climate change. To cope with these serious threats, smart water management systems are in great demand to ensure vigorous control of the quality and quantity of drinking water. Indeed, water monitoring is essential today since it allows to ensure the real-time control of water quality indicators and the appropriate management of resources in cities to provide an adequate water supply to citizens. In this context, a novel IoT-based framework is proposed to support smart water monitoring and management. The proposed framework, named SmartWater, combines cutting-edge technologies in the field of sensor clouds, deep learning, knowledge reasoning, and data processing and analytics. First, knowledge graphs are exploited to model the water network in a semantic and multi-relational manner. Then, incremental network embedding is performed to learn rich representations of water entities, in particular the affected water zones. Finally, a decision mechanism is defined to generate a water management plan depending on the water zones’ current states. A real-world dataset has been used in this study to experimentally validate the major features of the proposed smart water monitoring framework.
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13
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Kermorvant C, Liquet B, Litt G, Jones JB, Mengersen K, Peterson EE, Hyndman RJ, Leigh C. Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:12803. [PMID: 34886529 PMCID: PMC8657025 DOI: 10.3390/ijerph182312803] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/26/2021] [Accepted: 12/02/2021] [Indexed: 11/16/2022]
Abstract
In situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day- and week-long sequences of data from a two-year time series of nitrate concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation, and dissolved oxygen data were available. In 72% of cases with missing point data, predicted values were within the sensor precision interval of the original value, although predictive ability declined when sequences of missing data occurred. Precision also depended on the availability of other water quality covariates. When covariates were available, even a sudden, event-based peak in nitrate concentration was reconstructed well. By providing a promising method for accurate prediction of missing data, the utility and confidence in summary statistics and statistical trends will increase, thereby assisting the effective monitoring and management of fresh waters and other at-risk ecosystems.
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Affiliation(s)
- Claire Kermorvant
- Laboratoire de Mathématiques et de Leurs Applications de Pau Fédération MIRA, UMR CNRS 5142, Université de Pau et des Pays de l’Adour, 64600 Anglet, France;
| | - Benoit Liquet
- Laboratoire de Mathématiques et de Leurs Applications de Pau Fédération MIRA, UMR CNRS 5142, Université de Pau et des Pays de l’Adour, 64600 Anglet, France;
- Department of Mathematics and Statistics, Macquarie University, Sydney, NSW 2109, Australia
| | - Guy Litt
- National Ecological Observatory Network, Battelle Boulder, Boulder, CO 80301, USA;
| | - Jeremy B. Jones
- Institute of Arctic Biology and Department of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, AK 99775, USA;
| | - Kerrie Mengersen
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4000, Australia;
- ARC Centre of Excellence for Mathematics and Statistical Frontiers, Melbourne, VIC 3000, Australia; (E.E.P.); (R.J.H.); (C.L.)
| | - Erin E. Peterson
- ARC Centre of Excellence for Mathematics and Statistical Frontiers, Melbourne, VIC 3000, Australia; (E.E.P.); (R.J.H.); (C.L.)
- Peterson Consulting, Brisbane, QLD 4000, Australia
| | - Rob J. Hyndman
- ARC Centre of Excellence for Mathematics and Statistical Frontiers, Melbourne, VIC 3000, Australia; (E.E.P.); (R.J.H.); (C.L.)
- Department of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, Australia
| | - Catherine Leigh
- ARC Centre of Excellence for Mathematics and Statistical Frontiers, Melbourne, VIC 3000, Australia; (E.E.P.); (R.J.H.); (C.L.)
- Biosciences and Food Technology Discipline, School of Science, RMIT University, Bundoora, VIC 3083, Australia
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14
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Bousquin J. Discrete Global Grid Systems as scalable geospatial frameworks for characterizing coastal environments. ENVIRONMENTAL MODELLING & SOFTWARE : WITH ENVIRONMENT DATA NEWS 2021; 146:1-14. [PMID: 35355513 PMCID: PMC8958999 DOI: 10.1016/j.envsoft.2021.105210] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Data portals and services have increased coastal water quality data availability and accessibility. However, tools to process this data are limited - geospatial frameworks at the land-sea interface are either adapted from open- water frameworks or extended from watershed frameworks. This study explores use of a geospatial framework based on hexagons from a Discrete Global Grid System (DGGS) in a coastal area. Two DGGS implementations are explored, dggridR and H3. The geospatial frameworks are compared based on their ability to aggregate data to scales from existing frameworks, integrate data across frameworks, and connect flows across the land-sea interface. dggridR was simpler with more flexibility to match scales and use smaller units. H3 was more performant, identifying neighbors and moving between scales more efficiently. Point, line and grid data were aggregated to H3 units to test the implementation's ability to model and visualize coastal data. H3 performed these additional tasks well.
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Affiliation(s)
- Justin Bousquin
- Gulf Ecosystem Measurement and Modeling Division, U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Gulf Breeze, FL, USA
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15
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A Low-Cost Multi-Parameter Water Quality Monitoring System. SENSORS 2021; 21:s21113775. [PMID: 34072361 PMCID: PMC8198326 DOI: 10.3390/s21113775] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 05/14/2021] [Accepted: 05/26/2021] [Indexed: 11/17/2022]
Abstract
Multi-parameter water quality monitoring is crucial in resource-limited areas to provide persistent water safety. Conventional water monitoring techniques are time-consuming, require skilled personnel, are not user-friendly and are incompatible with operating on-site. Here, we develop a multi-parameter water quality monitoring system (MWQMS) that includes an array of low-cost, easy-to-use, high-sensitivity electrochemical sensors, as well as custom-designed sensor readout circuitry and smartphone application with wireless connectivity. The system overcomes the need of costly laboratory-based testing methods and the requirement of skilled workers. The proposed MWQMS system can simultaneously monitor pH, free chlorine, and temperature with sensitivities of 57.5 mV/pH, 186 nA/ppm and 16.9 mV/°C, respectively, as well as sensing of BPA with <10 nM limit of detection. The system also provides seamless interconnection between transduction of the sensors' signal, signal processing, wireless data transfer and smartphone app-based operation. This interconnection was accomplished by fabricating nanomaterial and carbon nanotube-based sensors on a common substrate, integrating these sensors to a readout circuit and transmitting the sensor data to an Android application. The MWQMS system provides a general platform technology where an array of other water monitoring sensors can also be easily integrated and programmed. Such a system can offer tremendous opportunity for a broad range of environmental monitoring applications.
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Lee HK, Choo J, Kim J. Multiplexed Passive Optical Fiber Sensor Networks for Water Level Monitoring: A Review. SENSORS 2020; 20:s20236813. [PMID: 33260659 PMCID: PMC7731013 DOI: 10.3390/s20236813] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 11/20/2020] [Accepted: 11/24/2020] [Indexed: 11/25/2022]
Abstract
Water management is a critical mission required to protect the water resources that is essential in diverse industrial applications. Amongst a variety of parameters such as level (or depth), temperature, conductivity, turbidity, and pH, the water level is the most fundamental one that needs to be monitored on a real-time basis for securing the water management system. This paper presents an overview of water level monitoring technologies based on optical fiber sensor (OFS) networks. Firstly, we introduce and compare the passive distributed and quasi-distributed (discrete) sensor networks with the recent achievements summarized. The performance (i.e., sensing range and resolution) of the OFS networks can be enhanced through diverse multiplexing techniques based on wavelength, time, coherence, space, etc. Especially, the dense wavelength division multiplexing (DWDM)-based sensor network provides remote sensing (where its reach can be extended to >40 km) with high scalability in terms of the channel number that determines the spatial resolution. We review the operation principle and characteristics of the DWDM-based OFS network with full theoretical and experimental analysis being provided. Furthermore, the key system functions and considerations (such as the link protection from physical damages, self-referencing, management of sensing units, and so on) are discussed that could be a guideline on the design process of the passive OFS network.
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Affiliation(s)
- Hoon-Keun Lee
- Department of Safety Research, Korea Institute of Nuclear Safety, 62 Gwahak-ro, Yuseong-gu, Daejeon 34142, Korea;
| | - Jaeyul Choo
- Department of Electronics Engineering, Andong National University, 1375 Gyengdong-ro, Andong-si, Gyeongsangbuk-do 36729, Korea;
| | - Joonyoung Kim
- Department of Smart Information Communication Engineering, Sangmyung University, 31 Sangmyungdae-gil, Dongnam-gu, Cheonan-si 31066, Korea
- Correspondence:
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17
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Chlorophyll-a Variability during Upwelling Events in the South-Eastern Baltic Sea and in the Curonian Lagoon from Satellite Observations. REMOTE SENSING 2020. [DOI: 10.3390/rs12213661] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Based on the analysis of multispectral satellite data, this work demonstrates the influence of coastal upwelling on the variability of chlorophyll-a (Chl-a) concentration in the south-eastern Baltic (SEB) Sea and in the Curonian Lagoon. The analysis of sea surface temperature (SST) data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua/Terra satellites, together with Chl-a maps from Medium Resolution Imaging Spectrometer (MERIS) onboard Envisat, shows a significant decrease of up to 40–50% in Chl-a concentration in the upwelling zone. This results from the offshore Ekman transport of more productive surface waters, which are replaced by cold and less-productive waters from deeper layers. Due to an active interaction between the Baltic Sea and the Curonian Lagoon which are connected through the Klaipeda Strait, coastal upwelling in the SEB also influences the hydrobiological conditions of the adjacent lagoon. During upwelling inflows, SST drops by approximately 2–8 °C, while Chl-a concentration becomes 2–4 times lower than in pre-upwelling conditions. The joint analysis of remotely sensed Chl-a and SST data reveals that the upwelling-driven reduction in Chl-a concentration leads to the temporary improvement of water quality in terms of Chl-a in the coastal zone and in the hyper-eutrophic Curonian Lagoon. This study demonstrates the benefits of multi-spectral satellite data for upscaling coastal processes and monitoring the environmental status of the Baltic Sea and its largest estuarine lagoon.
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