Skip to main content

WHO Water Quality Standards


The World Health Organization (WHO) sets global standards for drinking water quality to safeguard public health. These scientifically-based guidelines aim to prevent waterborne diseases, protect human health, and promote sustainable water resource management.

Key Concepts and Terminologies

  • Potable Water: Water that is safe for human consumption without posing any significant health risks.
  • Contaminants: Substances that pollute or degrade water quality, including microorganisms, chemicals, and physical agents.
  • Threshold Limit: The maximum permissible concentration of a substance in drinking water deemed safe for human health.
  • Guideline Values: WHO's recommended maximum concentrations of various substances in drinking water considered safe.

WHO's Approach

WHO's guidelines are based on two primary factors:

  1. Health-based targets: Prioritize protecting public health by ensuring water is free from disease-causing contaminants.
  2. Operational guidelines: Provide practical recommendations for monitoring, managing, and treating water to maintain quality.

Key WHO Water Quality Standards

Here are some key WHO-recommended guidelines for drinking water parameters:

ParameterWHO Standard
pH6.5 - 8.5
TurbidityBelow 5 NTU (preferably <1 NTU)
Total Coliforms/E. coliAbsent in 100 mL sample
Nitrate (NO₃⁻)≤ 50 mg/L
Arsenic (As)≤ 0.01 mg/L
Lead (Pb)≤ 0.01 mg/L
Chlorine (Residual)0.2 - 0.5 mg/L
Fluoride (F⁻)≤ 1.5 mg/L
Copper (Cu)≤ 2 mg/L

Parameter Explanations and Health Impacts

  • pH:
    • Explanation: Measures water acidity/alkalinity.
    • Health Impact: Extremes can corrode pipes, release metals, or cause scaling.
  • Turbidity:
    • Explanation: Measures water cloudiness due to suspended particles.
    • Health Impact: High turbidity can hinder disinfection and increase the risk of pathogen contamination.
  • Microbiological Quality:
    • Explanation: Focuses on the absence of disease-causing microorganisms.
    • Health Impact: Coliform bacteria, especially E. coli, indicate fecal contamination and potential for serious illnesses.
  • Total Dissolved Solids (TDS):
    • Explanation: Measures the total amount of dissolved inorganic and organic substances.
    • Health Impact: High TDS can affect taste, indicate other contaminants, and cause scaling.
  • Nitrate:
    • Explanation: Primarily from agricultural runoff.
    • Health Impact: High levels can cause methemoglobinemia ("blue baby syndrome") in infants.
  • Arsenic:
    • Explanation: A toxic heavy metal that can occur naturally or be introduced through industrial activities.
    • Health Impact: Long-term exposure increases the risk of cancer, skin lesions, and other health issues.
  • Lead:
    • Explanation: Can leach from lead pipes or plumbing fixtures.
    • Health Impact: A potent neurotoxin, especially harmful to children.
  • Chlorine (Residual):
    • Explanation: Essential for disinfection, ensures water remains free from microbial contamination.
    • Health Impact: High levels can cause irritation and affect taste.
  • Fluoride:
    • Explanation: Added to water to prevent tooth decay.
    • Health Impact: Excessive levels can cause dental fluorosis and, in severe cases, skeletal fluorosis.
  • Copper:
    • Explanation: Can leach from copper pipes.
    • Health Impact: High levels can cause gastrointestinal distress and long-term health issues.

WHO's Approach: A Risk Management Framework

  • Health-Based Focus: Prioritizes protecting human health by establishing safe contaminant limits.
  • Risk Management: Emphasizes a comprehensive approach, including water testing, source protection, and appropriate treatment methods.
  • Local Adaptation: Acknowledges that countries may need to adapt guidelines based on their specific water quality challenges and public health needs.

Comments

Popular posts from this blog

Applications of Remote Sensing

Remote sensing is the science of obtaining information about the Earth's surface without direct contact, using sensors mounted on satellites, aircraft, or drones. It is widely applied in environmental monitoring, agriculture, urban studies, and disaster management due to its synoptic coverage, temporal continuity, and objective data acquisition.  Vegetation, Soil, and Water Studies 1.1 Vegetation Studies Vegetation analysis using remote sensing is based on the spectral reflectance behavior of plants . Healthy vegetation absorbs most of the red light for photosynthesis and reflects strongly in the near-infrared (NIR) region due to internal leaf structure. Key Concepts and Terminologies Spectral Signature : Unique reflectance pattern of vegetation across wavelengths. Chlorophyll Absorption : Strong absorption in blue and red wavelengths. Canopy Reflectance : Combined reflectance of leaves, branches, and background soil. Important Vegetation Indices NDVI (Normalized Difference Vegetat...

Quantitative Revolution in Geography

The Quantitative Revolution was a major change in Geography during the 1950s and 1960s. It introduced mathematical, statistical and scientific methods to understand geographical phenomena. Before this revolution, geographers mainly described places, regions, landscapes and differences between areas. After the revolution, they increasingly used numerical data, statistical analysis, mathematical models and theories to explain why geographical patterns occur and how they may change. For example: Traditional approach: Describe the distribution of population in different regions. Quantitative approach: Use statistical methods to identify population patterns, explain the factors influencing population distribution and predict future population growth. The revolution helped establish Geography as a spatial science, focusing on the location, distribution, relati...

Classification of Mixed Pixels

In remote sensing, a mixed pixel (also called a mixed cell or mixel ) is a pixel that contains more than one land-cover type inside its area. This is very common when: spatial resolution is coarse (e.g., 30 m, 250 m) land cover is patchy or complex boundaries between features exist (forest–agriculture, land–water edges) Traditional hard classification assigns each pixel to one single class only → either forest or soil or water. But mixed pixels contain fractions of several classes simultaneously, so hard classification produces errors. Two major solutions are: Spectral Mixture Analysis (SMA) Fuzzy Classification 1. Spectral Mixture Analysis (SMA) (Also called Linear Spectral Unmixing ) ✔ Concept SMA assumes that the reflectance recorded by a pixel is a linear combination of the reflectance of pure materials within that pixel. These pure materials are called endmembers . ✔ Endmembers Endmembers are spectrally pure classes found...

Satellite Remote Sensing: Concepts and Imaging Systems

  Satellite remote sensing relies on detectors (sensors) that measure reflected/emitted electromagnetic radiation from the Earth. How sensors collect data depends on: Spectral coverage → Multispectral (few bands), Hyperspectral (hundreds), Thermal, Microwave. Detector type → Discrete detectors, linear arrays, or area arrays. Scanning mechanism → Scanning mirrors (whiskbroom) vs. linear arrays (pushbroom). Multispectral Imaging Using Discrete Detectors and Scanning Mirrors (Whiskbroom scanners) Principle : A single detector (or a few detectors) measures radiation one pixel at a time. A scanning mirror sweeps across-track (perpendicular to the satellite path) to build up the image line by line. The forward motion of the satellite provides the along-track dimension. Known as a whiskbroom scanner . Characteristics : Good calibration stability. Narrow instantaneous field of view (IFOV). Susceptible to mechanical wear (moving ...

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature  A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...