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Related Concept Videos

Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

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In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...
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Related Experiment Video

Updated: Oct 1, 2025

Laboratory-determined Phosphorus Flux from Lake Sediments as a Measure of Internal Phosphorus Loading
10:49

Laboratory-determined Phosphorus Flux from Lake Sediments as a Measure of Internal Phosphorus Loading

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Development of remote sensing algorithm for total phosphorus concentration in eutrophic lakes: Conventional or

Junfeng Xiong1, Chen Lin2, Zhigang Cao2

  • 1Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resource, Nanjing 210023, China.

Water Research
|March 5, 2022
PubMed
Summary

This study developed a machine learning framework for estimating total phosphorus (TP) in eutrophic lakes. The new model shows potential for large-scale, long-term TP monitoring using remote sensing.

Keywords:
Lake TaihuMachine learningRemote sensingTotal phosphorus

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Area of Science:

  • Environmental Science
  • Remote Sensing
  • Water Quality Monitoring

Background:

  • Phosphorus is a critical limiting nutrient in freshwater ecosystems, making its accurate estimation vital for lake management.
  • Existing total phosphorus (TP) remote sensing models are lake-specific, lacking generalizability for lake groups.
  • Eutrophic lakes, like Lake Taihu, present complex water environments requiring advanced monitoring techniques.

Purpose of the Study:

  • To develop a generalized remote sensing framework for estimating total phosphorus (TP) concentration in eutrophic lakes.
  • To create a robust TP estimation algorithm applicable across multiple lake systems.
  • To evaluate the performance and potential of a machine learning approach for large-scale TP monitoring.

Main Methods:

  • Compared three algorithm development frameworks, selecting Extremely Gradient Boosting (BST) for TP estimation in Lake Taihu.
  • Developed a machine learning algorithm to identify relationships between the Floating Algal Index (FAI) and TP, particularly in phytoplankton-dominated waters.
  • Validated a generalized TP algorithm across seven additional lakes using synchronous remote sensing data.

Main Results:

  • The Taihu TP algorithm achieved R2 = 0.6 (RMSE = 0.07 mg/L, MRE = 43.33%).
  • The generalized TP algorithm demonstrated strong performance with R2 = 0.64 (RMSE = 0.06 mg/L, MRE = 34.13%) across multiple lakes.
  • Algorithm accuracy was influenced by factors such as aquatic vegetation and aquaculture.

Conclusions:

  • The proposed machine learning framework provides a potential solution for lake-group TP estimation.
  • The developed algorithm shows promise for long-term, large-scale spatio-temporal TP monitoring in eutrophic environments.
  • This approach offers a valuable tool for effective lake environmental management and water quality assessment.