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<p>This study critically reviews the tools and methodologies used to assess water quality in lentic ecosystems, highlighting their effectiveness in tracking temporal variations and supporting evidence-based environmental policymaking. Emphasizing applications across tropical, arid/semi-arid, and alpine water bodies, the study integrates insights from over 120 peer-reviewed articles spanning two decades. Traditional techniques, such as in-situ sampling and laboratory-based physicochemical analysis, are compared with advanced technologies (including statistical tools, multivariate models, and machine learning) which have improved predictive accuracy by up to 25–40% in various case studies. Innovative methods like satellite observation and real-time sensing technologies, and LiDAR (Light Detection and Ranging), demonstrate the ability to reduce monitoring costs by 30–50% while increasing spatial coverage and frequency of measurements. Predictive models, including ANN (Artificial Neural Networks) and PCA (Principal Component Analysis), show high performance with R² values exceeding 0.90 in forecasting water quality indicators such as dissolved Oxygen (DO), (chemical oxygen demand) COD, and chlorophyll-a. The study also addresses challenges such as data scarcity, sensor calibration issues, and the limitations of remote sensing in turbid or cloud-covered regions. Effects of climate change especially increased temperatures and shifts in rainfall patterns are linked to a 30–60% increase in eutrophication events and harmful algal blooms in several regions. Adaptive strategies employing satellite imagery and LiDAR are proposed to enhance monitoring and mitigation responses. A case study of a large reservoir in a semi-arid region demonstrates the integration of remote sensing with in-situ validation, achieving contamination reduction of up to 45% through targeted interventions. These findings reinforce the importance of combining traditional and modern assessment tools for robust, cost-effective, and adaptive water quality management in lentic ecosystems facing growing environmental pressures.</p> Graphical Abstract <p></p>

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Advancing Water Quality Monitoring in Lentic Ecosystems: Innovations for Freshwater Protection

  • 期刊论文

  • 发表日期：

    2025年10月11日

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  • Yaneth A. Bustos-Terrones,
  • Alberto Quevedo-Castro,
  • Erick R. Bandala,
  • Tonni Agustiono Kurniawan,
  • Juan G. Loaiza

摘要

This study critically reviews the tools and methodologies used to assess water quality in lentic ecosystems, highlighting their effectiveness in tracking temporal variations and supporting evidence-based environmental policymaking. Emphasizing applications across tropical, arid/semi-arid, and alpine water bodies, the study integrates insights from over 120 peer-reviewed articles spanning two decades. Traditional techniques, such as in-situ sampling and laboratory-based physicochemical analysis, are compared with advanced technologies (including statistical tools, multivariate models, and machine learning) which have improved predictive accuracy by up to 25–40% in various case studies. Innovative methods like satellite observation and real-time sensing technologies, and LiDAR (Light Detection and Ranging), demonstrate the ability to reduce monitoring costs by 30–50% while increasing spatial coverage and frequency of measurements. Predictive models, including ANN (Artificial Neural Networks) and PCA (Principal Component Analysis), show high performance with R² values exceeding 0.90 in forecasting water quality indicators such as dissolved Oxygen (DO), (chemical oxygen demand) COD, and chlorophyll-a. The study also addresses challenges such as data scarcity, sensor calibration issues, and the limitations of remote sensing in turbid or cloud-covered regions. Effects of climate change especially increased temperatures and shifts in rainfall patterns are linked to a 30–60% increase in eutrophication events and harmful algal blooms in several regions. Adaptive strategies employing satellite imagery and LiDAR are proposed to enhance monitoring and mitigation responses. A case study of a large reservoir in a semi-arid region demonstrates the integration of remote sensing with in-situ validation, achieving contamination reduction of up to 45% through targeted interventions. These findings reinforce the importance of combining traditional and modern assessment tools for robust, cost-effective, and adaptive water quality management in lentic ecosystems facing growing environmental pressures.

Graphical Abstract