ENVIRONMENTAL MONITORING

ENVIRONMENTAL MONITORING

Sampling:

The sample is a subset of any population and Sampling is a process of collecting samples from the large population to make statistical analysis. A population doesn’t always refer to people, it may be the entire group or media of which we want to conclude. Sampling is an easy way to gather data about a large population by taking only a small proportion of it.

 Environmental sampling:

As sampling is done for monitoring purposes, and research purposes. So in environmental sampling, we collect data from any interested environmental media like air, water, soil, biota, etc, to monitor air and water effluents or to characterize pollutant levels in these environmental media.

For example:

it is difficult to examine all the water in a lake, so bits of the water are gathered and dissected to decide the centralizations of toxins in the lake.

  Objectives of Sampling:

Sampling is done:

• To ease the study or analysis of a very large population because it is tough to study every unit of the whole population.
• To check the nature  of the population
• To investigate the changes that occur in a population over time
• To test the validity
• To estimate the parameters of the population

 The procedure of sampling:

In procedure, we have to follow some steps to collect data for our research purpose.

Identification of issue

First of all, we have to identify an issue that is why we want to take samples, what is the purpose of sampling, also we have to define the population of concern from where we have to collect data.

Specifying a sampling frame

In the sampling frame, we identify the list of items or people forming a population from which we want to take samples for our research.

Specifying a sampling technique

The third step is to identify the sampling technique that we use for collecting samples from a population or environmental media. Generally, probability sampling methods are used because these are non-biased.

Determining the sample size

Sampling size means the number of individuals or things to be taken from the population so that we can make accurate deductions about the general population. The greater the sample size, the more accurate our inference about the population.

Data collection

Data is collected through sampling to gather qualitative and quantitative information to make decisions. There are different methods used for collecting data like

• Direct Observation
• Experiments
• Surveys

Reviewing the sampling process

After analyzing the data inference about population or any environmental parameter is made.

• Sampling Techniques:

There are two techniques used for sampling purposes

1. probability sampling
2. Non-probability sampling

• Probability sampling:

Probability sampling, also known as random sampling is viewed as the highest quality level of testing strategies because it is unbiased and gives more accurate results. It is mainly used in quantitative research. In this technique, each unit in the sampling frame has an equal chance of selection.

Example:

If a researcher wants to collect data from 10 industries, each industry has an equal chance of selection.

Types of probability sampling:

• Simple random sampling

• Systematic sampling

• Stratified sampling

• Cluster sampling

• Simple random sampling:

Simple random sampling is characterized as an examining procedure where each item in the population has an even possibility of being chosen. In this technique, the random number generator tool is used to create a list of random numbers for sampling.

Examples:

• If we have 42 students in our class and we want to know the quality of food in the cafeteria of university then we will assign the number from 1 to 42 to each student then we will make a list of randomly selected students like 1 to 10 out of 42 and will collect the data for our research. In this case, each student has an equal chance of selection.

• Example:(In context of environment)

To check the amount of pollutants in streams of Rawalpindi we will randomly select some streams and we will collect data from selected streams by taking the portion of water from streams to make analysis.

• Systematic sampling

It is also similar to simple random sampling first of all members or items are chosen from the population randomly and then further some members or items are selected but at a regular period.

• Examples:

• A coach in school wanted to select a captain of a football team he would select 5 students randomly and then select only 3 out of 5 at regular intervals for the captain of different teams.

• If a researcher wants to check the amount of heavy metals absorbed by the trees along a road side then he will use a systematic sampling technique and take samples from each fourth tree to collect data.

• Stratified sampling:

In this technique, the population is divided into clusters or groups called strata dependent on common characteristics. Then one or more items are selected randomly from each group.

Examples:

• Suppose a researcher wants to know the educational goals of students of an Islamic international university then he will divide the population of students based on gender (male and female), Then he use random or systematic sampling to select students as a  sample from each subgroup to gather information.

• To check the quality of the air we will divide the population into two strata, rural and urban to gather information.
• Cluster sampling:

It is a technique where researchers divide the population into various gatherings called groups for research. Researchers at that point select gatherings with random examining methods for information assortment and information examination. It is not a more effective technique because it’s difficult to guarantee that the sampled clusters are representative of the whole population.

Examples:

• If a researcher wants to gather data about the academic performance of students in different colleges in the city then it is difficult to visit every college so he will randomly choose only 4 colleges to gather information so these colleges are clusters for sampling.

• If a researcher wants to know the gaseous emissions released by industries of Pakistan he will divide the country into different cities and then choose only 10  large cities (as a cluster)  having more chemical industries with greater amounts of gaseous emissions that affect our environment.

Nonprobability sampling:

It is opposite to probability sampling in this technique individuals are selected based on non-random criteria, and not every individual has a chance of being selected. This technique is usually biased and does not give accurate results to make inferences about the whole population, and it is mostly used for qualitative research purposes.

    Types of non-probability sampling:

• Convenience sampling

• Purposive sampling

• Snowball sampling
• Quota sampling

• Convenience sampling:

This is the technique used by researchers to collect data from the conveniently available population. This is a simple and cheap method of sampling and it is uncomplicated and cost efficient.

The analyst picks individuals only dependent on nearness and doesn’t consider if they represent the whole population. Utilizing this strategy, they can notice propensities, sentiments, and perspectives in the most effortless conceivable way.

Examples:

• If a researcher wants to check the amount of pollutants in any water body then he or she may choose the nearest water body to his house or university to take samples but it is not representative of whole water bodies.

• If we want to check the quality of food provided at our university then we use the convenience sampling method to collect data by interviewing our class fellows. However, they are not representative of all the students of the university.

• Purposive sampling:

This is also called judgmental sampling and is a non-likelihood inspecting method in which the individuals are picked uniquely based on the analyst’s information and judgment.

The main goal of purposive sampling is to focus on particular characteristics of a population that are of interest, which will best enable the researcher to answer their research questions.

Examples:

• If a researcher wants to collect data to check the effect of noise pollution on people then he or she will choose the most crowded places in a city based on his/her judgment and will collect data either through a questionnaire or through direct interview.
• If a researcher requires samples of water to check the amount of lead in water he will choose only those water bodies that are near any industrial area through his knowledge and judgement.

• Snowball sampling:

The sampling technique is a technique in which the researcher recruits a single participant, while the second nominee recruits the third participant. The chain continues to refer linearly up to the end of the sampling. This technique is used when it is hard to access the population. The number of people a researcher has to access is called “snowballs” as through them he will be able to engage with other people to gather data.

Examples:

• This inspecting procedure is frequently utilized in hidden populations, for example, drug users and suppliers, where it is difficult for researchers to access.
• The order of drug clients as indicated by their recurrence (Casual or Addict) may be uncovered if scientists ask somebody who is as of now very acquainted with the medication clients in that area. Additionally, this serves to research various pieces of information on such social affairs as well.
• Meeting a jobless person, interview him, and ask him to introduce you to other jobless persons you might interview.

• Quota sampling:

Quota sampling is nonprobability sampling in which the researcher selects a sample according to some fixed quota. He uses his judgment to select from the population of the study after stratifying the population into groups based on pre-specified characteristics. It is useful when time is limited, a sampling frame is not available, the research budget is very tight, or when detailed accuracy is not important.

Example:

If a cigarette organization needs to discover what age bunch favors what brand of cigarettes in a specific city. He/she applies quotas on the age gatherings of 21-30, 31-40, 41-50, and 51+. From this data, the specialist measures the smoking pattern among the number of inhabitants in the city.

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