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Related Concept Videos

Testing Water Quality01:14

Testing Water Quality

162
When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
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UV–Vis Spectrometers01:14

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The absorbance of UV and visible (UV–visible) radiations is measured using a UV–visible spectrophotometer. Deuterium lamps, which emit UV radiation, and tungsten lamps, which produce radiation in the visible region, are used as light sources in UV–visible spectrophotometers. A monochromator or prism is used for diffraction grating, i.e., to split the incoming radiation into different wavelengths. A system of slits is used to focus the desired wavelength on the sample cell.
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A spectral learning path for simultaneous multi-parameter detection of water quality.

Zhiqiang Guo1, Fenli Liu1, Qiannan Duan2

  • 1Laboratory of Environmental Aquatic Chemistry, Department of Environmental Science, Shaanxi Normal University, Xi'an, 710062, China.

Environmental Research
|November 17, 2022
PubMed
Summary

This study combines spectral imaging and deep learning for rapid water quality detection. The intelligent method accurately measures multiple water quality parameters with high precision, offering a cost-effective solution.

Keywords:
Deep learningIntelligent detectionSpectral imagesWater quality parameters

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Data Science

Background:

  • Water quality parameters (WQP) are crucial indicators of aquatic environmental health.
  • Detecting multiple WQP rapidly and accurately is challenging due to complex water chemistry.

Purpose of the Study:

  • To develop an intelligent method for simultaneous detection of multiple WQP using spectral images (SPIs) and deep learning (DL).
  • To evaluate the accuracy, stability, and cost-effectiveness of the proposed SPIs and DL approach for WQP monitoring.

Main Methods:

  • Acquisition of SPIs using a novel spectroscopic instrument.
  • Conversion of SPIs into feature images representing water chemistry.
  • Development and training of deep convolutional neural networks (CNNs) for WQP prediction.
  • Validation of the model using key WQP including anions, cations, TOC, TP, TN, NO3--N, and NH3-N.

Main Results:

  • The combined SPIs and DL method demonstrated high accuracy and stability in WQP detection.
  • Achieved an average relative error of 1.3% across multiple parameters.
  • Obtained a coefficient of determination (R²) of 0.996 and a residual prediction deviation (RPD) of 16.2.
  • The method offers simple pre-processing and low cost compared to traditional techniques.

Conclusions:

  • The SPIs and DL approach enables rapid, accurate, and high-dimensional detection of multiple WQP.
  • This intelligent method has significant potential for environmental water monitoring.
  • The technique shows promise for broader applications in chemical, biological, and medical fields.