Component One
The Data Quality Objectives (DQO Process) is a planning process that has been developed by the United States Environmental Protection Agency. This particular planning process is used when environmental information or data is used to make a choice between two specific alternatives or to make a derivation of contamination estimate. The process is also used in the development of acceptance and performance criteria that find use in the clarification of study objectives, defining of appropriate data type and the specification of potential errors of decisions that are then used as the basis for the establishment of the quantity and the quality of data required to support decisions.
The EPA has actually established a quality system that the agency uses to manage the general quality of environmental data collection, recording, generation and dissemination. This system’s major goal is ensure that all environmental data collected daily for a variety of uses reaches appropriate quantity and quality standards. Environmental data that is sufficient quantity and quality is able to serve its intended use. As will be seen below, this quality system is comprised of several components in its lifecycle.
Systematic planning is a process that is to a huge part based on widely accepted scientific principle or method. It includes concepts like objectivity approach and results acceptability. Common sense values are used to make sure that documentation level and the planning effort is consistent or in line with the available resources and also with information’s intended use. The systematic planning process uses well established scientific and management elements that ultimately lead to the logical development of a project, good use of minimal resources, transparency of direction and intent, good formulation of project conclusions and proper documentation.
The performance criteria generally represents a complete set of written specifications that are required to come with an information or data collection plan that when implemented will ultimately lead to the generation of sufficient quality and quantity data that can fully address the goals of the project.
On the other hand, acceptance criteria is the complete set of specifications that are used to evaluate a given information source’s adequacy and whether it I can be accepted as a credible source that will fulfill the project goals.
The planning process is composed of several elements which are coordinated to ensure that the entire process is indeed effective. These elements are: organization, project goals, schedule, data needs, criteria, data collection, quality assurance and analysis.
As seen, the collection and subsequent dissemination and use of environmental data of appropriate and known quality are one of the most vital constituents of the EPA mission. The Information Quality Guidelines (IQG’S) describe EPA’s polices about the amount of quality of information disseminated by the EPA. These guidelines applies to several types of information, for example, information that is generated for or by the EPA, Information endorsed by the EPA and information used by the EPA to make decisions or regulations.
There are several types or variations of the systematic planning process that are tailored for specific applications. The Observational method type of systematic planning is most often used in engineering. The Triad Approach that incorporates a lot of modern technological aspects is another variation of systematic planning. However, the Data Quality Objectives (DQO) process is the most common and widely used variation of the systematic planning process. This process is used mainly to establish both the performance and the acceptance criteria. The criteria are then used to provide a basis for establishing a design or plan for data collection. The plan or design that is chosen is the one that gives rise to data of appropriate and required quality and quantity and that satisfies the project goals.
The DQO process is comprised of seven steps and these are:
- Statement of the problem-This is the first step where the main problem is defined. This is followed by the identification of planning team and examination of the schedule and the budget.
- Identification of study goal(s)-In this second step, a statement is made of how the environmental data will actually be used to meet the project’s objectives or for problem solving. Study questions are identified and this is followed by the definition of alternative outcomes.
- Information Inputs Identification- This is the third step where the information and data that is needed to solve study questions is identified.
- Definition of study boundaries-Here, the study interest characteristics and the target population are specified. This is followed by the definition inference scale and the temporal and spatial limits.
- Development of the analytic approach- Here, inference types and parameter interests are defined and specified and logics for conclusion drawing are developed.
- The Specification of Performance and Acceptance Criteria-this is the step where probability limits for the false acceptance and false rejection decision errors are made. Performance criteria for fresh data and acceptable criteria for data that already exists are made.
- Developing the general plan for data obtainment- This is the final step where the most effective and efficient analysis and sampling plan that also meets performance criteria is selected.
Component 2
1.
1. Eye piece 5. Frame/Arm
2. Objective lens 6.Coarse adjustment knob
3. Stage 7.Fine adjustment knob
4. Base
2. What is the procedure (step by step) which you followed to do gram staining? (20 points)
1. Acquiring a gram stain tissue sample.
2. Place one or two drops of the sample on the slide (glass).
3. Heat the smear by passing it over a flame quickly. This can also be done using the electric slide warmer.
4. Flooding the smear using crystal violet.
5. Rinse the crystal violet with water.(tap water).
6. Add iodine to the smear
7. Rinse the iodine using water.
8. Decolourize the smear using acetone or alcohol.
9. Rinse the excess ethanol/acetone using tap water.
10. Add a counter stain (safranin) to stain the bleached negative bacteria.
11. Rinse off the unused safranin using water.
12. Drain and air dry the stain.
13. The final step is examination of the slide using a light microscope.
3. What is the step by step process for looking at your prepared gram stain slide?
i. Place the slide on the stage and hold it intact by the use of the metal clips.
ii. using the coarse adjustment knob, bring the stage as close as possible to the objective lens to start viewing.
iii. Look at the eye piece to view the stain. Adjust the position of the stage and move it further from the objective lens to make the image clearer.
iv. The final step is seeing the specimen. At times dust particles may appear like the micro organism. To ascertain that what you are viewing is the real image; it is advisable to use the coarse adjustment knob to move the stage away from the objective lens. If the image moves with the stage, then, it is confirmed that what you are viewing is the intended image.
4. What does Gram-positive tell you about the microorganism?
When a bacteria tests gram positive it means that it has a lot of peptidoglycan layers within their cell walls. This means that the bacteria are able to retain the crystal violet dye. This is why it tests gram positive. Gram positive bacteria exhibit a blue or violet color.
5 What does Gram-negative tell you about the microorganism?
On the other hand, the gram negative test confirms that the bacteria have less peptidoglycan layers .This explains why the cells are unable to retain the crystal violet dye.
6. What color indicates positive?
Blue or violet color indicates a positive gram test.
7 .What colors indicates negative.
Pink or a red color indicates a negative gram test.
8. What is the name of the other staining technique we used?
Acid fast stain is the other technique of staining that was used. This method classifies bacteria into acid fast bacteria and non acid fast acid bacteria.
9. What are the 5 Kingdoms of microorganisms? Give an example of each one.
1) Viruses e.g. Bovine polyomavirus
2) Bacteria e.g. Lactobacillus
3) Protozoan e.g. paramecium
4) Algae e.g. anabaena
5) Fungi e.g. Ascomycota
Component 3
1. The minimal sample size that should have been used is one liter. This is because this is a sample size that is sufficient to carry out all the tests that are needed. It is also sufficient to carry out a rerun of the entire experiment.
2. A box and whisker plot is a statistical method of representing numerical data by the use of their quartiles. This plot usually has lines that extend from boxes (whiskers) vertically. This indicates the variability that is outside the lower and the upper quartiles.
0.035 0.038 0.042 0.046
3.
Mean= 0.0429
Median= 0.042
Mode= 0.049
4.
Standard deviation= 0.004706
Variance= 2.215 x 10-5
5. A null hypothesis is usually a statement in research that is used to assert a status quo. It implies that any kind of change or deviation from what is thought to be the true or accurate result in an experiment is a result of random errors during sampling.
An alternate hypothesis is actually quite the opposite of null hypothesis. It implies that a difference observed between the expected value and the observed value is existent in reality and does not result from random errors.
6.
Null hypothesis: The pond has a phosphorus concentration of 0.039mg/L
Null Hypothesis (H0)
H0: u= 0.039
Alternate hypothesis: The pond does not have a phosphorus concentration of 0.039mg/L
Alternate hypothesis (Ha)
Ha: u≠20
References
Hounslow, Arthur. Water Quality Data: Analysis and Interpretation. Boca Raton: Lewis Publishers, 1995. Print.
United States & United States. (2000). EPA: 600 9 / Office of Research and Development, US Environmental Protection Agency. Washington, DC: US Gov.Print.Off.
Environmental Protection Agency (n.d.). EPA Quality System: Systematic Planning FAQs. Retrieved from http://www.epa.gov/QUALITY/faq10.html
Fifield, F W, and P J. Haines. Environmental Analytical Chemistry. Oxford: Blackwell Science, 2000. Print
