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Related Concept Videos

Quality of Water01:19

Quality of Water

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In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
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Testing Water Quality01:14

Testing Water Quality

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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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Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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States of Water01:23

States of Water

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Water exists in any one of the three classical states: solid (ice), liquid (water), and gas (steam or water vapor). The state of water depends on i) the intermolecular forces that draw molecules together and ii) the kinetic energy that leads to movements that pull them apart.
Water freezes when the intermolecular forces are greater than the kinetic energy. Unlike most other substances, water is less dense in its solid state than in its liquid state. This is because each water molecule can form...
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Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges.

Edoardo Borgomeo1, Luke A Holmes2, Camilla G Billari1

  • 1Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom.

Environmental Science & Technology
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Summary
This summary is machine-generated.

Artificial intelligence (AI) offers potential for managing water quality risks. Further development is needed to align AI with user needs and institutional frameworks for effective decision-making.

Keywords:
AIdrinking-water quality regulationwastewater infrastructurewater pollutionwater quality

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Area of Science:

  • Environmental Science
  • Public Health
  • Computer Science

Background:

  • Water pollution poses global risks to public health, environment, and economy.
  • Challenges include emerging pollutants, climate change, and evolving regulations.
  • Current water quality management requires innovative solutions.

Purpose of the Study:

  • To explore artificial intelligence (AI) potential in addressing water quality challenges.
  • To assess AI's role in water quality regulation and decision-making.
  • To evaluate the maturity of current AI applications in this domain.

Main Methods:

  • A system-oriented approach to define an AI-informed water quality decision pipeline.
  • Literature review and a stakeholder workshop in England.
  • Critical assessment of AI maturity against water quality priorities.

Main Results:

  • AI research is mature in operational efficiency, modeling, and prediction.
  • Less attention has been given to aligning AI with user needs, trustworthiness, and explainability.
  • Current AI applications show limited integration with organizational constraints.

Conclusions:

  • Realizing AI's full potential requires clear institutional processes and accountability.
  • Development of AI-ready datasets is crucial for wider adoption.
  • Open-source examples of AI in water quality can support regulators and stakeholders.