Influence of solar radiation on chlorophyll a concentration assessment using fluorescence measured by the submersible sensor in Lake Baikal

Moiseeva, Nataliia; Ефимова, Т. В.; Churilova, T. Ya.; Макаров, М. М.; Gnatovsky, R.Yu.

doi:10.31951/2658-3518-2019-a-4-281

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Several efforts have been made to correct fluorometer data for NPQ in both inland waters (Serra et al 2009;Moiseeva et al 2019) and, more commonly, in marine systems (Xing et al 2012;Thomalla et al 2018;Xing et al 2018). Serra et al (2009) compared data collected in a freshwater reservoir from two in vivo fluorometers with data from HPLC laboratory analyses.…”

Section: Correcting For Nonphotochemical Quenchingmentioning

confidence: 99%

“…While this method is ideal for low-frequency data, it is impractical for high-frequency data. Moiseeva et al (2019) determined the relationship between Photosynthetically Active Radiation (PAR) and the percentage of open reaction centers in PSII in samples from Lake Baikal with the use of PAM fluorometry. From this relationship, they corrected in vivo fluorescence profiles with an exponential model.…”

Section: Correcting For Nonphotochemical Quenchingmentioning

confidence: 99%

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Using machine learning to correct for nonphotochemical quenching in high‐frequency, in vivo fluorometer data

Lucius

Johnston

Eichler

et al. 2020

Limnology & Ocean Methods

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In vivo fluorometers use chlorophyll a fluorescence (Fchl) as a proxy to monitor phytoplankton biomass. However, the fluorescence yield of Fchl is affected by photoprotection processes triggered by increased irradiance (nonphotochemical quenching; NPQ), creating diurnal reductions in Fchl that may be mistaken for phytoplankton biomass reductions. Published correction methods are mostly designed for pelagic oceans and are ill suited for inland waters or for high‐frequency data collection. A machine learning‐based method was developed to correct vertical profiler data from an oligotrophic lake. NPQ was estimated as a percent reduction in Fchl by comparing daytime values to mean, unquenched values from the previous night. A random forest regression was trained on sensor data collected coincident with Fchl; including solar radiation, water temperature, depth, and dissolved oxygen saturation. The accuracy of the model was assessed using a grouped 10‐fold cross validation (mean absolute error [MAE]: 7.6%; root mean square error [RMSE]: 10.2%), which was then used to correct Fchl profiles. The model also predicted NPQ and corrected unseen Fchl profiles from a future period with excellent results (MAE: 9.0%; RMSE: 14.4%). Fchl profiles were then correlated to laboratory results, allowing corrected profiles to be compared directly to collected samples. The correction reduced error (RMSE) due to NPQ from 0.67 μg L−1 to 0.33 μg L−1 when compared to uncorrected Fchl data. These results suggest that the use of machine learning models may be an effective way to correct for NPQ and may have universal applicability.

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Section: Correcting For Nonphotochemical Quenchingmentioning

confidence: 99%

Section: Correcting For Nonphotochemical Quenchingmentioning

confidence: 99%

Using machine learning to correct for nonphotochemical quenching in high‐frequency, in vivo fluorometer data

Lucius

Johnston

Eichler

et al. 2020

Limnology & Ocean Methods

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“…Water flowing through the pipeline quickly enters the measuring site, and phytoplankton remains adapted to the current in situ illumination regime; hence, some recreation centres of photosystem 2 are inactived, underestimating the measurement results [48], which explains the daytime minimum chlorophyll-a values. To obtain the true values, it is necessary to recalculate them, considering the PAR intensity at the water sampling site according to the method developed for Lake Baikal [49]. In the evening on 2 May 2021, the wind began to intensify from the NW direction and, consequently, the ice cover was carried away from the shore for several kilometres.…”

Section: Discussionmentioning

confidence: 99%

Environmental Monitoring of the Littoral Zone of Lake Baikal Using a Network of Automatic Hydro-Meteorological Stations: Development and Trial Run

Макаров

Aslamov

Gnatovsky

2021

Sensors

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An automatic hydro-meteorological station (AHMS) was designed to monitor the littoral zone of Lake Baikal in areas with high anthropogenic pressure. The developed AHMS was installed near the Bolshiye Koty settlement (southern basin). This AHMS is the first experience focused on obtaining the necessary competencies for the development of a monitoring network of the Baikal natural territory. To increase the flexibility of adjustment and repeatability, we developed AHMS as a low-cost modular system. AHMS is equipped with a weather station and sensors measuring water temperature, pH, dissolved oxygen, redox potential, conductivity, chlorophyll-a, and turbidity. This article describes the main AHMS functions (hardware and software) and measures taken to ensure data quality control. We present the results of the first two periods of its operation. The data acquired during this periods have demonstrated that, to obtain accurate measurements and to detect and correct errors that were mainly due to biofouling of the sensors and calibration bias, a correlation between AHMS and laboratory studies is necessary for parameters such as pH and chlorophyll-a. The gained experience should become the basis for the further development of the monitoring network of the Baikal natural territory.

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Creating and Managing Data From High-Frequency Environmental Sensors

Rose¹,

McBride²,

Moriarty³

2022

Encyclopedia of Inland Waters

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Influence of solar radiation on chlorophyll a concentration assessment using fluorescence measured by the submersible sensor in Lake Baikal

Cited by 4 publications

References 7 publications

Using machine learning to correct for nonphotochemical quenching in high‐frequency, in vivo fluorometer data

Using machine learning to correct for nonphotochemical quenching in high‐frequency, in vivo fluorometer data

Environmental Monitoring of the Littoral Zone of Lake Baikal Using a Network of Automatic Hydro-Meteorological Stations: Development and Trial Run

Creating and Managing Data From High-Frequency Environmental Sensors

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