Data collection and processing in research

 

Data collection and processing in research

Data collection and processing are critical steps in the research process. They involve gathering relevant information and transforming it into a usable format for analysis and interpretation. Here's a step-by-step overview of data collection and processing in research:

1. Research Design:

Before data collection begins, researchers need to design a research plan that outlines the research objectives, questions, and hypotheses. They also decide on the type of data needed (quantitative or qualitative) and the methods of data collection.

2. Data Collection:

Data collection involves obtaining information or observations from the target population or sample. There are various methods for data collection, and researchers choose the most appropriate ones based on the nature of the research and the available resources. Some common data collection methods include:

 a. Surveys and Questionnaires: Researchers use surveys and questionnaires to gather data from a large number of participants. They can be conducted in person, over the phone, via email, or through online platforms.

   b. Interviews: Interviews involve one-on-one or group interactions where researchers ask participants specific questions to gather qualitative data.

   c. Observations: Researchers observe and record behaviors, events, or phenomena in their natural setting to collect qualitative or quantitative data.

   d. Experiments: Experimental research involves manipulating variables to observe their effect on the outcome of interest.

   e. Secondary Data: Researchers can use existing data sources, such as databases, government reports, or previous research studies, to collect data for their research.

3. Data Cleaning:

After data collection, researchers need to clean the data to remove errors, inconsistencies, and missing values. Data cleaning ensures that the data is accurate and reliable for analysis. This step may involve identifying and resolving data entry mistakes, dealing with outliers, and handling missing data.

4. Data Entry:

In cases where data is collected manually (e.g., surveys, questionnaires, observations), it needs to be entered into a digital format (e.g., spreadsheet or database) for analysis. Accurate data entry is crucial to maintain the integrity of the data.

5. Data Coding and Categorization:

For qualitative data, researchers often code and categorize the responses or observations into meaningful themes or categories. This process helps in organizing and analyzing the qualitative data efficiently.

6. Data Analysis:

Data analysis involves applying appropriate statistical or qualitative techniques to extract meaningful insights from the collected data. The choice of analysis methods depends on the research questions, data type, and research design. Common data analysis techniques include descriptive statistics, inferential statistics, content analysis, thematic analysis, etc.

7. Interpretation and Conclusion:

Once the data analysis is complete, researchers interpret the results and draw conclusions based on the findings. They relate the results back to the research objectives and discuss the implications of their findings.

8. Reporting and Presentation:

Finally, researchers document their research process, results, and conclusions in a research report or paper. They may also present their findings through presentations, conferences, or other means to share their work with the scientific community or stakeholders.

Data collection and processing are iterative processes, and researchers often go back and forth between these steps to refine their research and ensure the validity and reliability of the results. Thorough and careful data collection and processing are crucial for producing high-quality and credible research outcomes.

Sampling Techniques

 

Sampling Techniques

In the field of Biology science, researchers often use various sampling techniques to collect data from living organisms, ecosystems, or biological processes. Proper sampling is crucial to ensure that the collected data accurately represents the biological phenomena under study. Here are some common sampling techniques used in biology:

1. Random Sampling:

Random sampling is widely used in biology when studying populations of organisms or ecological communities. For example, researchers may use random quadrats or transects to study plant communities in a forest. In random sampling, each individual or location in the study area has an equal chance of being selected for data collection. This technique helps reduce bias and ensures that the sample is representative of the larger population.

2. Stratified Sampling:

Stratified sampling is often employed when the biological population under study consists of distinct subgroups (strata). For instance, when studying fish populations in a lake, the researchers may divide the lake into different depth zones and then take random samples from each depth zone. Stratified sampling ensures that each subgroup is adequately represented in the sample, which can lead to more precise estimates and comparisons within each stratum.

3. Systematic Sampling:

Systematic sampling can be useful when studying biological phenomena that exhibit spatial patterns. For example, when studying plant distribution along a transect, researchers might sample plants at regular intervals along the transect line. Systematic sampling helps cover the entire study area systematically, making it easier to study spatial variations in biological data.

Buy Book For Preparation

4. Cluster Sampling:

Cluster sampling is often used in biology when it is challenging to access individual members of a population scattered across a large area. For instance, when studying bird populations, researchers might select specific geographical areas (clusters) where they can easily access and observe multiple birds. They collect data from all birds within the selected clusters. Cluster sampling can save time and resources when studying dispersed populations.

5. Line Transects:

Line transects are commonly used in ecological studies to estimate population densities or the distribution of organisms along a straight line. Researchers walk along the transect line and record observations at specified intervals or distances. This technique is useful for studying plant populations, animal tracks, and certain types of marine life, such as coral reefs.

6. Capture-Recapture Sampling:

Capture-recapture sampling is employed when studying animal populations where individuals can be captured, marked, and released without harm. After some time, a second sample is taken, and the number of marked and unmarked individuals is recorded. This technique is particularly useful for estimating population sizes and migration patterns of mobile species.

7. Quadrat Sampling:

Quadrat sampling involves laying out square or rectangular frames (quadrats) in the study area and recording the presence or abundance of organisms within each quadrat. It is commonly used in vegetation studies to estimate plant abundance and species composition.

The choice of sampling technique in biology depends on the research objectives, characteristics of the organisms or ecosystems being studied, and logistical constraints. Careful consideration of the sampling method is essential to ensure that the data collected is representative, reliable, and suitable for drawing meaningful biological conclusions.

 

Samples and Population

In statistics, "samples" and "population" are fundamental concepts used to describe the data that researchers or analysts work with. They are used in various statistical analyses and inference procedures. Let's define each term:

1. Population:

The population refers to the entire group or set of individuals, items, or elements that share a common characteristic of interest. It is the complete collection of all the elements about which you want to make inferences or draw conclusions. The population is often large and may not be practically feasible to observe or collect data from every member of the population. For example, if you are studying the average height of all people in a country, the population would include every person living in that country.

2. Sample:

A sample is a subset of the population that is selected for observation or data collection. It represents a smaller group of individuals or items taken from the larger population. The sample is used as a representative or a smaller version of the population for analysis. Researchers use samples because they are more practical to obtain, less time-consuming, and less costly than trying to study the entire population. However, the goal of sampling is to ensure that the selected sample is representative of the entire population, so the results can be generalized back to the larger group.

The key distinction between a population and a sample is that a population includes all the elements of interest, while a sample is just a part of the population used for analysis. Statisticians use various sampling techniques to ensure that the sample is chosen randomly or systematically to minimize bias and improve the generalizability of the results to the population.

Statistical inference involves using the information obtained from the sample to make inferences or draw conclusions about the entire population. Common statistical techniques, such as hypothesis testing and confidence intervals, rely on the relationship between samples and populations to make valid and reliable conclusions based on the observed data.

Study Design in Statistical Methods for Biological Research

 

Study Design in Statistical Methods for Biological Research

Study design is a critical aspect of statistical analysis in biological research. It involves planning and organizing the research project in a way that enables scientists to collect relevant data and draw reliable conclusions. The study design must address key questions such as what data to collect, how to collect it, and how to control for potential biases and confounding factors.

Details:

1. Objective and Hypothesis: Clearly define the research objective and formulate testable hypotheses. The hypothesis serves as a basis for statistical analysis, as it allows researchers to assess the validity of their findings.

2. Sampling Method: Decide on an appropriate sampling method to select study participants or biological samples. Random sampling is often preferred, as it minimizes selection bias and allows for generalization of results to the larger population.

3. Experimental Design: If the study involves experiments, choose an appropriate experimental design (e.g., randomized controlled trial, factorial design, crossover design). Randomization helps ensure that the groups being compared are comparable and minimizes the influence of confounding variables.

4. Control Groups: In experimental studies, include control groups that receive either a placebo or an existing standard treatment. This allows researchers to compare the effects of different interventions accurately.

5. Blinding: Implement blinding techniques (single-blind or double-blind) to prevent biases in data collection or interpretation. Blinding ensures that both researchers and participants are unaware of the treatment assignments during the study.

6. Sample Size Calculation: Conduct a power analysis to determine the required sample size. An adequately powered study increases the chances of detecting significant effects if they exist, while minimizing the risk of Type II errors (false negatives).

7. Data Collection Methods: Choose appropriate data collection methods, such as surveys, observations, or laboratory assays. Ensure that the measurements are reliable, valid, and consistent throughout the study.

8. Data Management and Quality Control: Establish protocols for data entry, storage, and validation to maintain data integrity. Regularly check for errors and outliers during data cleaning.

Example:

Let's consider an example of a biological research study examining the effects of a new drug on blood pressure in hypertensive patients.

Objective: To assess whether Drug X lowers blood pressure in patients with hypertension.

Hypothesis: The administration of Drug X to hypertensive patients will result in a significant reduction in blood pressure compared to a placebo.

Study Design:

1. Sampling Method: Randomly select hypertensive patients from a larger pool of eligible participants attending a clinic.

2. Experimental Design: Conduct a randomized controlled trial (RCT) with two groups: the treatment group receiving Drug X and the control group receiving a placebo.

3. Control Groups: The control group receives a placebo, ensuring that any observed effects are specific to Drug X and not due to placebo effects.

4. Blinding: Implement double-blind blinding, where both the researchers and the participants are unaware of the treatment assignments.

5. Sample Size Calculation: Perform a power analysis to determine the required sample size to detect a clinically significant reduction in blood pressure with a specified level of confidence.

6. Data Collection Methods: Measure blood pressure using standardized and validated instruments before and after the treatment period for both groups.

7. Data Management and Quality Control: Regularly check the accuracy and completeness of data during the study. Address any data entry errors or outliers.

By following this study design, researchers can obtain reliable and interpretable results, allowing them to draw conclusions about the effectiveness of Drug X in lowering blood pressure in hypertensive patients.

Principles and practices of statistical methods in biological research (Introduction)

 

Principles and practices of statistical methods in biological research (Introduction)

 

Statistical methods are essential tools in biological research, helping scientists analyze and interpret data to draw meaningful conclusions about biological processes. Here are some key principles and practices of statistical methods in biological research:

1. Study Design: The foundation of statistical analysis lies in the study design. Researchers must carefully plan their experiments, including the choice of sampling methods, control groups, randomization, and replication, to ensure the validity and reliability of their findings.

2. Descriptive Statistics: Descriptive statistics provide a summary of the data collected, giving researchers an overview of the central tendency (mean, median, mode) and the variability (standard deviation, range) within the dataset.

3. Inferential Statistics: Inferential statistics help researchers make inferences and generalizations about a population based on a sample of data. Techniques such as hypothesis testing, confidence intervals, and p-values are commonly used in biological research to assess the significance of observed effects.

4. Null Hypothesis Testing: Null hypothesis testing is a fundamental concept in statistical analysis. Researchers form a null hypothesis that there is no effect or difference between groups, and then they try to gather evidence to either reject or fail to reject this hypothesis.

5. p-values: The p-value is a measure of the evidence against the null hypothesis. It represents the probability of obtaining results as extreme or more extreme than the observed data, assuming the null hypothesis is true. A small p-value (typically below 0.05) suggests evidence against the null hypothesis.

6. Effect Size: In addition to p-values, effect size measures quantify the magnitude of a treatment or difference between groups. It provides a more meaningful understanding of the practical significance of the observed effect.

7. Experimental Control: Proper control of confounding variables is crucial in biological research. Researchers must ensure that any observed effects are due to the manipulated factor and not other variables that could influence the outcome.

8. Multiple Comparisons: When conducting multiple statistical tests, the risk of obtaining false positives increases. Researchers should apply appropriate corrections, such as the Bonferroni correction, to adjust the significance level and control the overall Type I error rate.

9. Power Analysis: Before conducting an experiment, researchers can perform a power analysis to determine the sample size required to detect a meaningful effect with sufficient statistical power. A larger sample size increases the chances of detecting true effects.

10. Data Visualization: Visualizing data using graphs and plots can help researchers understand the patterns and relationships within the data. Visualizations can also aid in conveying results effectively to others.

11. Non-parametric Methods: In cases where data do not meet the assumptions of parametric tests, non-parametric methods can be used to analyze the data. These methods do not require assumptions about the underlying distribution and are more robust in such situations.

12. Ethical Considerations: Researchers must adhere to ethical principles in statistical analysis, including ensuring data privacy, avoiding data manipulation, and reporting results transparently.

13. Reproducibility: To strengthen the scientific process, researchers should provide detailed documentation of their statistical methods and data analysis, enabling others to replicate the study and validate the findings.

By adhering to these fundamental principles and employing sound statistical practices, scientists can successfully extract valuable insights from biological data, enrich the pool of scientific knowledge, and make well-informed conclusions in the realm of biology.

ANOVA Use in Air Pollution Level

 

ANOVA- & It use in Air Polluition level-

ANOVA (Analysis of Variance) is a statistical method used to test the difference between two or more group means. It helps to determine whether there is a statistically significant difference between the means of different groups or samples. In air pollution level studies, ANOVA can be used to compare the means of different pollutant concentrations in different areas or at different times. For example, ANOVA can be used to compare the mean concentrations of particulate matter (PM) in different cities or to compare the mean concentrations of PM at different times of the day. ANOVA helps to determine whether the differences between the means of the groups are statistically significant or whether they could have occurred by chance. The null hypothesis in ANOVA is that there is no significant difference between the means of the groups, and the alternative hypothesis is that at least one of the group means is different from the others. The results of ANOVA can be presented in the form of an F-test, which provides a ratio of the between-group variance to the within-group variance. If the F-value is greater than the critical value, then the null hypothesis is rejected, and it can be concluded that at least one group mean is different from the others. Overall, ANOVA is a useful statistical tool in air pollution level studies for comparing the means of different pollutant concentrations in different areas or at different times, and it can help to identify areas or times with significantly higher or lower levels of pollution.

Example-

Sure, let's consider an example of air pollution level comparison at different sites using ANOVA analysis.

Suppose we want to compare the mean concentrations of particulate matter (PM) at three different sites - Site A, Site B, and Site C. We have collected data on PM concentrations over a period of one week at each site and have calculated the mean and standard deviation for each site. The data is presented in the table below:

Site	Mean PM Concentration (µg/m3)	Standard Deviation (µg/m3)
A	25	5
B	32	6
C	28	4

 

To test whether there is a significant difference in the mean PM concentrations at these sites, we can use ANOVA analysis. The null hypothesis is that there is no significant difference in the mean PM concentrations at the three sites, and the alternative hypothesis is that at least one site has a different mean PM concentration than the others. To perform ANOVA analysis, we first calculate the total sum of squares (SST), which represents the total variation in the PM concentrations across all three sites. We then calculate the between-group sum of squares (SSB), which represents the variation in the PM concentrations between the three sites, and the within-group sum of squares (SSW), which represents the variation in the PM concentrations within each site. Using these values, we can calculate the F-value, which represents the ratio of the between-group variance to the within-group variance.  If the F-value is greater than the critical value at the desired level of significance (e.g., 0.05), then we reject the null hypothesis and conclude that there is a significant difference in the mean PM concentrations at the three sites.In this example, the calculations for SST, SSB, SSW, and the F-value are as follows:

SST = 276.67

SSB = 60.67

SSW = 216

F-value = 3.18

Assuming a desired level of significance of 0.05 and 2 degrees of freedom for both the numerator and denominator, the critical F-value is 3.89. Since the calculated F-value (3.18) is less than the critical value (3.89), we fail to reject the null hypothesis and conclude that there is no significant difference in the mean PM concentrations at the three sites. Therefore, based on this ANOVA analysis, we can conclude that there is no significant difference in the mean PM concentrations at Site A, Site B, and Site C.

LiFE

 

LiFE 

The mission of LiFE (Lesser Florican and its Ecosystem) program by MOEFCC (Ministry of Environment, Forest and Climate Change) is to conserve the critically endangered Lesser Florican bird and its habitat in India. The program aims to achieve this through a range of activities that include habitat conservation, promotion of sustainable agricultural practices, community engagement, and scientific research and monitoring.

 

Specifically, the mission of LiFE includes the following objectives:

Habitat conservation: The program aims to conserve and restore the grassland habitat of the Lesser Florican through measures such as controlled grazing, reforestation, and protection of nesting sites.

Promotion of sustainable agricultural practices: LiFE seeks to promote agricultural practices that are compatible with the conservation of the Lesser Florican, such as organic farming, crop rotation, and use of agroforestry systems.

Community engagement: The program aims to engage local communities in conservation efforts, through activities such as awareness-raising campaigns, capacity building, and establishment of community-managed conservation areas.

Scientific research and monitoring: LiFE seeks to increase scientific knowledge and understanding of the Lesser Florican and its habitat through research and monitoring activities, such as population surveys, habitat assessments, and satellite tracking of the birds.

Overall, the mission of LiFE is to conserve the critically endangered Lesser Florican and its ecosystem, and to promote sustainable development practices that are compatible with conservation.

Food Preservation By Chemical Methods

 

Food Preservation Method by Chemicals

Food preservation by chemical methods involves the use of chemicals to prevent or slow down the growth of microorganisms, which can spoil food and make it unsafe to eat. Here are some common chemical methods of food preservation:

Antimicrobial agents: These are chemical compounds that inhibit or kill microorganisms that cause food spoilage or disease. Examples include sodium benzoate, sorbic acid, and potassium sorbate.

 Antioxidants: These are compounds that prevent oxidation, a process that can lead to rancidity and spoilage of fats and oils in foods. Examples include butylated hydroxyanisole (BHA) and butylated hydroxytoluene (BHT).

 Acids: Acids can be used to preserve food by creating an acidic environment that inhibits the growth of bacteria, yeasts, and molds. Examples include vinegar, citric acid, and lactic acid.

  Sulfites: These are chemicals that inhibit the growth of bacteria and yeasts by releasing sulfur dioxide gas. They are commonly used to preserve dried fruits, wine, and beer.

    Nitrites and nitrates: These chemicals are used to preserve meats by inhibiting the growth of bacteria and preventing the development of botulism. They are commonly used in cured meats such as bacon and ham.

 Sugar: Sugar can be used to preserve fruits by creating a high osmotic pressure that inhibits the growth of microorganisms. It can also be used to preserve jams and jellies by preventing the growth of bacteria and mold.

It's important to note that while these chemicals can be effective at preserving food, they can also have potential health risks if used in excess or if an individual has a sensitivity or allergy to them. Therefore, it's important to use these chemicals in moderation and follow safety guidelines when using them in food preservation.

 

· S alt (Sodium chloride) - lowers the water activity in food, inhibiting the growth of bacteria and other microorganisms.

·  Sugar (Sucrose) - inhibits bacterial growth by decreasing the water activity in food.

·  Vinegar (Acetic acid) - creates an acidic environment in which bacteria cannot grow.

·  Citric acid - used to preserve flavor, prevent discoloration, and inhibit bacterial growth.

·  Nitrites - used in cured meats to prevent the growth of Clostridium botulinum, which can cause botulism.

·  Sulfites - used to prevent the oxidation of fruits and vegetables, and to preserve the color of dried fruits.

·  Benzoates - used to inhibit the growth of yeasts and molds in acidic foods such as pickles, salad dressings, and carbonated drinks.

·  Sorbates - used to inhibit the growth of yeasts and molds in acidic foods such as cheese, wine, and dried fruits.

·  Propionates - used to inhibit the growth of molds in bread and other baked goods.

·  Lactic acid - used to preserve and enhance the flavor of pickles, sauerkraut, and other fermented foods.

·  Potassium sorbate - used as a preservative in foods such as cheese, dried fruit, and wine.

·  Sodium erythorbate - used as an antioxidant in processed meats to prevent discoloration and spoilage.

·  Calcium propionate - used to inhibit the growth of molds in baked goods.

·  Sodium benzoate - used to prevent the growth of yeasts and molds in acidic foods such as pickles, salad dressings, and carbonated drinks.

·  Sodium nitrate - used in cured meats to prevent the growth of Clostridium botulinum and to enhance flavor.

·  EDTA (Ethylenediaminetetraacetic acid) - used as a preservative in canned fruits and vegetables to prevent discoloration and flavor loss.

·  Ascorbic acid (Vitamin C) - used as an antioxidant in food products to prevent discoloration and spoilage.

·  Butylated hydroxyanisole (BHA) - used as an antioxidant to prevent rancidity in fats and oils.

·  Butylated hydroxytoluene (BHT) - used as an antioxidant to prevent rancidity in fats and oils.

·  Propyl gallate - used as an antioxidant in fats and oils to prevent rancidity.

Rajasthan Pollution Control board JSO Exam Paper

 Rajasthan pollution control board JSO Old Exam Paper in Image Format 

Environment Laws in India

 Environment Laws List in Image format - 

वायु (प्रदूषण निवारण और नियंत्रण ) अधिनियम 1981

 1972 में स्टॉकहोम में प्रयोग सम्मेलन में लिए वीडियो के फल स्वरुप भारत ने भी पर्यावरण को बचाने के लिए विभिन्न अधिनियम बनाएं, इसमें से एक वायु अधिनियम 1981 भी है। अधिनियम से केंद्रीय प्रदूषण नियंत्रण बोर्ड को विभिन्न शक्तियां प्रदान की गई और उसके कार्यों के पालन के लिए बाध्य किया गया।

इस अधिनियम के अनुसार केंद्रीय बोर्ड के मुख्य कार्य देश में वायु क्वालिटी में सुधार लाना, वायु प्रदूषण का निवारण और उन पर नियंत्रण करना है। इसके अलावा अन्य कार्यों के निष्पादन की जिम्मेदारी केंद्रीय प्रदूषण नियंत्रण बोर्ड को भी गई।  कुछ कार्य निम्नलिखित है- 

- वायु क्वालिटी में सुधार लाने और उसके प्रदूषण के निवारण नियंत्रण या उपशमन से संबंध किसी विषय पर केंद्रीय सरकार को सलाह देना।

-राष्ट्रव्यापी कार्यक्रम की योजना बनाना।

- राज्य बोर्डों के क्रियाकलापों में समन्वय में स्थापित करना वह उनके विवादों को समझाना।

- राज्य बोर्डों को तकनीकी सहायता देना और मार्गदर्शन करना। 

-वायु प्रदूषण तथा वायु प्रदूषण के निवारण नियंत्रण या समन की समस्या से संबंधित अन्वेषण और अनुसंधान क्रियान्वित और प्रायोजित करना।

-विभिन्न संस्थाएं मार्ग निर्देशिका को तैयार करना।

-वायु क्वालिटी के लिए मानक अधिकथित करना।

- वही प्रदूषण से संबंधित विषयों के बारे में जानकारी एकत्रित करना और उनका प्रसार कराने की शक्ति।

-वाहनों द्वारा उत्सर्जित होने वाले प्रदूषण की मात्रा के मानकों का निर्धारण करना।

Air Pollution

 

                                              POLLUTION

Human activities directly or indirectly affect the environment adversely. A stone crusher adds a lot of suspended particulate matter and noise into the atmosphere. Automobiles emit from their tail pipes oxides of nitrogen, sulphur dioxide, carbon dioxide, carbon monoxide and a complex mixture of unburnt hydrocarbons and black soot which pollute the atmosphere. Domestic sewage and run off from agricultural fields, laden with pesticides and fertilizers, pollute water bodies. Effluents from tanneries contain many harmful chemicals and emit foul smell. These are only a few examples which show how human activities pollute the environment. Pollution may be defined as addition of undesirable material into the environment as a result of human activities. The agents which cause environmental pollution are called pollutants. A pollutants may be defined as a physical, chemical or biological substance unintentionally released into the environment which is directly or indirectly harmful to humans and other living organisms.

Pollution may be of the following types:
• Air pollution
• Noise pollution
• Water pollution
• Soil pollution
• Thermal pollution
• Radiation pollution

AIR POLLUTION

Air pollution is a result of industrial and certain domestic activity. An ever increasing use of fossil fuels in power plants, industries, transportation, mining, construction of buildings, stone quarries had led to air pollution. Air pollution may be defined as the presence of any solid, liquid or gaseous substance including noise and radioactive radiation in the atmosphere in such concentration that may be directly and indirectly injurious to humans or other living organisms, plants, property or interferes with the normal environmental processes. Air pollutants are of two types (1) suspended particulate matter, and (2) gaseous pollutants like carbon dioxide (CO2), NOx etc.

Particulate pollutants
Particulate matter suspended in air are dust and soot released from the industrial chimneys. Their size ranges from 0.001 to 500 μm in diameter. Particles less than 10μm float and move freely with the air current. Particles which are more than 10μm in diameter settle down. Particles less than 0.02 μm form persisent aerosols. Major source of SPM (suspended particulate matter) are vehicles, power plants, construction activities, oil refinery, railway yard, market place, industries, etc.

• Fly ash
Fly ash is ejected mostly by thermal power plants as by products of coal burning operations. Fly ash pollutes air and water and may cause heavy metal pollution in water bodies. Fly ash affects vegetation as a result of its direct deposition on leaf surfaces or indirectly through its deposition on soil. Fly ash is now being used for making bricks and as a land fill material.

• Lead and other metals particles
Tetraethyl lead (TEL) is used as an anti-knock agent in petrol for smooth and easy running of vehicles. The lead particles coming out from the exhaust pipes of vehicles is mixed with air. If inhaled it produces injurious effects on kidney and liver and interferes with development of red blood cells. Lead mixed with water and food can create cumulative poisoning. It has long term effects on children as it lowers intelligence. Oxides of iron, aluminum, manganese, magnesium, zinc and other metals have adverse effect due to deposition of dust on plants during mining operations and metallurgical processes. They create physiological, biochemical and developmental disorders in plants and also contribute towards reproductive failure in plants.

Gaseous pollutants
Power plants, industries, different types of vehicles – both private and commercial use petrol, diesel as fuel and release gaseous pollutants such as carbon dioxide, oxides of nitrogen and sulphur dioxide along with particulate matter in the form of smoke. All of these have harmful effects on plants and humans.

Prevention and control of air pollution
(i) Indoor air pollution
Poor ventilation due to faulty design of buildings leads to pollution of the confined space. Paints, carpets, furniture, etc. in rooms may give out volatile organic compounds (VOCs). Use of disinfectants, fumigants, etc. may release hazardous gases. In hospitals, pathogens present in waste remain in the air in the form of spores. This can result in hospital acquired infections and is an occupational health hazard. In congested areas, slums and rural areas burning of firewood and biomass results in lot of smoke. Children and ladies exposed to smoke may suffer from acute respiratory problems which include running nose, cough, sore throat, lung infection, asthama, difficulty in breathing, noisy respiration and wheezing.
(ii) Prevention and control of indoor air pollution
Use of wood and dung cakes should be replaced by cleaner fuels such as biogas, kerosene or electricity. But supply of electricity is limited. Similarly kerosene is also limited. Improved stoves for looking like smokeless chullahs have high thermal efficiency and reduced emission of pollutants including smoke. The house designs should incorporate a well ventilated kitchen. Use of biogas and CNG (Compressed Natural Gas) need to be encouraged. Those species of trees such as baval (Acacia nilotica) which are least smoky should be planted and used. Charcoal is a comparatively cleaner fuel. Indoor pollution due to decay of exposed kitchen waste can be reduced by covering the waste properly. Segregation of waste, pretreatment at source, sterilization of rooms will help in checking indoor air pollution.

 (iii) Prevention and control of industrial pollutio

Industrial pollution can be greatly reduced by:

(a) use of cleaner fuels such as liquefied natural gas (LNG) in power plants, fertilizer plants etc. which is cheaper in addition to being environmentally friendly

(b) employing environment friendly industrial processes so that emission of pollutants and hazardous waste is minimized.

(c) installing devices which reduce release of pollutants. Devices like filters, electrostatic precipitators, inertial collectors, scrubbers, gravel bed filters or dry scrubbers are described below:
  (i) Filters – Filters remove particulate matter from the gas stream. The medium of a filter may be made of fibrous materials like cloth, granular material like sand, a rigid material like screen, or any mat like felt pad. Baghouse filtration system is the most common one and is made of cotton or synthetic fibres ( for low temperatures) or glass cloth fabrics (for higher temperature up to 290oC).
 (ii) Electrostatic precipitators (ESP)- The emanating dust is charged with ions and the ionized particulate matter is collected on an oppositely charged surface. The particles are removed from the collection surface by occasional shaking or by rapping the surface. ESPs are used in boilers, furnaces, and many other units of thermal power plants, cement factories, steel plants, etc.
(iii) Inertial collectors – It works on the principle that inertia of SPM in a gas is higher
than its solvent and as inertia is a function of the mass of the particulate matter this
device collects heavier particles more efficiently. ‘Cyclone’ is a common inertial collector used in gas cleaning plants.
(iv) Scrubbers – Scrubbers are wet collectors. They remove aerosols from a stream of gas either by collecting wet particles on a surface followed by their removal, or else the particles are wetted by a scrubbing liquid. The particles get trapped as they travel from supporting gaseous medium across the interface to the liquid scrubbing medium.

Gaseous pollutants can be removed by absorption in a liquid using a wet scrubber and depends on the type of the gas to be removed e.g. for removal of sulphur dioxide alkaline solution is needed as it dissolves sulphur dioxide. Gaseous pollutants may be absorbed on an activated solid surface like silica gel, alumina, carbon, etc. Silica gel can remove water vapour. Condensation allows the recovery of many by products in coal and petroleum processing industries from their liquid effluents.
Apart from the use of above mentioned devices, other control measures are-
• increasing the height of chimneys.
• closing industries which pollute the environment.
• shifting of polluting industries away from cities and heavily populated areas.
• development and maintenance of green belt of adequate width.

(iv) Control of vehicular pollution
• The emission standards for automobiles have been set which if followed will reduce the pollution. Standards have been set for the durability of catalytic converters which reduce vehicular emission.
• In cities like Delhi, motor vehicles need to obtain Pollution Under Control (PUC)
certificate at regular intervals. This ensures that levels of pollutants emitted from vehicle exhaust are not beyond the prescribed legal limits.
• The price of diesel is much cheaper than petrol which promotes use of diesel. To
reduce emission of sulphurdioxide, sulphur content in diesel has been reduced to 0.05%.
• Earlier lead in the form of tetraethyl lead was added in the petrol to raise octane level for smooth running of engines. Addition of lead in petrol has been banned to prevent emission of lead particles with the vehicular emission.