BIS Standards for Drinking Water

The Bureau of Indian Standards (BIS) plays a crucial role in ensuring the quality and safety of drinking water in India. It establishes comprehensive standards that cover various physical, chemical, and microbiological parameters. Let's delve into some key BIS standards, their significance, and relevant concepts:

1. pH Value (6.5 – 8.5)

Definition: pH measures the acidity or alkalinity of water on a scale of 0 to 14, with 7 being neutral.
Significance:
- Influences the solubility and availability of minerals in water.
- Low pH can lead to the leaching of metals like lead and copper from pipes.
- High pH can cause scale buildup in pipes and appliances.
BIS Standard: The pH of drinking water should fall within the range of 6.5 to 8.5 to prevent corrosion and scaling.

2. Turbidity (Maximum 1 NTU)

Definition: Turbidity refers to the cloudiness of water caused by suspended particles like dirt, silt, and microorganisms. It is measured in Nephelometric Turbidity Units (NTU).
Significance:
- High turbidity can indicate contamination and hinder disinfection processes.
- It affects the aesthetic quality of water, making it unpleasant to drink.
BIS Standard: The turbidity should not exceed 1 NTU to ensure clear and aesthetically pleasing water.

3. Total Hardness (Maximum 200 mg/L)

Definition: Hardness is primarily due to the presence of dissolved calcium and magnesium ions. It is usually measured in mg/L as calcium carbonate.
Significance:
- High hardness can affect the taste of water and cause scaling in appliances.
- It can also increase soap and detergent consumption.
BIS Standard: The total hardness should not exceed 200 mg/L to minimize scaling issues and maintain water quality.

4. E. coli (Should Not Be Detectable in 100 mL Sample)

Definition: E. coli is a type of coliform bacteria commonly found in the intestines of humans and animals. Its presence indicates fecal contamination.
Significance: E. coli in drinking water signifies potential contamination with harmful pathogens, posing a risk of diseases like diarrhea and dysentery.
BIS Standard: E. coli should not be detectable in any 100 mL sample to ensure the absence of fecal contamination.

5. Chloride (Maximum 250 mg/L)

Definition: Chlorides are salts of hydrochloric acid, primarily found as chloride ions (Cl⁻).
Significance: High chloride levels can impart a salty taste to water and contribute to corrosion.
BIS Standard: The chloride content should not exceed 250 mg/L to maintain a pleasant taste and prevent corrosion.

6. Arsenic (Maximum 0.01 mg/L)

Definition: Arsenic is a toxic heavy metal that can contaminate water from natural or anthropogenic sources.
Significance: Long-term exposure to arsenic can lead to serious health issues, including cancer and skin lesions.
BIS Standard: The arsenic concentration should not exceed 0.01 mg/L to safeguard public health.

7. Copper (Maximum 0.05 mg/L)

Definition: Copper is an essential trace element, but excessive levels can cause gastrointestinal problems and may affect water taste.
Significance: High copper levels can also stain clothes and utensils.
BIS Standard: The copper concentration should not exceed 0.05 mg/L to ensure safe consumption.

Comments

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification: - You have prior knowledge about the c...

History of GIS

1. 1832 - Early Spatial Analysis in Epidemiology: - Charles Picquet creates a map in Paris detailing cholera deaths per 1,000 inhabitants. - Utilizes halftone color gradients for visual representation. 2. 1854 - John Snow's Cholera Outbreak Analysis: - Epidemiologist John Snow identifies cholera outbreak source in London using spatial analysis. - Maps casualties' residences and nearby water sources to pinpoint the outbreak's origin. 3. Early 20th Century - Photozincography and Layered Mapping: - Photozincography development allows maps to be split into layers for vegetation, water, etc. - Introduction of layers, later a key feature in GIS, for separate printing plates. 4. Mid-20th Century - Computer Facilitation of Cartography: - Waldo Tobler's 1959 publication details using computers for cartography. - Computer hardware development, driven by nuclear weapon research, leads to broader mapping applications by early 1960s. 5. 1960 - Canada Geograph...

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to 1832 , when Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report Rapport sur la marche et les effets du choléra dans Paris et le département de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in 1854 , when John Snow inv...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...

Vineesh V, Geography

Search This Blog