CEE 400 ESEM/Waste Audit (WARM) Assignment:

CEE 400 ESEM/Waste Audit (WARM) Assignment:Evaluate the CO2 equivalent emissions and energy use/production from your personal waste generation. You will learn about the scope, capabilty, and uncertainty of models. You will learn about the different impacts from various waste handling scenarios. And you will practice good graphing skills.

Your final homework should be turned in as an excel spreadsheet. You should have two separate worksheets. The first will be your ‘Raw’ data from your week of collecting all of your trash, recycling, etc. The second worksheet should have the WARM output data (which you will copy and paste from WARM), one CO2 graph, one energy graph, and one text box describing your findings.

Week prior to due date: Record your waste (trash, recycle, etc) for one week & enter data into excel
You should have kept a log of your personal waste generation for the past week. An excel spreadsheet with example weights of organic waste is available on Blackboard to aid in estimating your food and organic waste. If the exact trash is not available, you should estimate the weight and type of your trash using the example materials provided in class.

You should use WARM to model your current waste scenario (collect both CO2 emissions and energy). WARM is typically used by cities or large organizations; you should assum that you are the average citizen in Phoenix and scale your data up to an annual waste generation for the population of Phoenix. You should also evaluate ONE way to improve your waste footprint and create that scenario in WARM. Your final graphs will be bar graphs comparing the your current versus your idea for improvement.

You should use a text box in excel to write two paragraphs summarizing your findings from the WARM model. Your writeup should describe (paragraph 1) why emissions or energy is negative, and comment on the accuracy of the tools and/or your estimates. (e.g. what might be your largest source of error?) Paragraph two should discuss the findings from your improvement scenario. Were the emissions and energy actually better than your current waste? Don’t forget to refer to the principles of ESEM in either of these paragraphs and bold and italicize the sentence that reflects an ESEM principle and identify the ESEM principle number.

Download & Use WARM for this assignment:
• WARM Calculates CO2 equivalent emissions and BTUs for municipal waste scenarios
• Inputs are in tons and output is in MTCO2E and million BTU (you will have to do unit conversions!
• WARM models large scale local waste system (i.e. for a city)
• Download the xls: http://epa.gov/epawaste/conserve/tools/warm/index.html

Use the following inputs to the WARM.xls:
#3 Arizona
#4 Current Mix
#5 a. LFG Recovery b. Flare
#6 a. Dry b. Typical operation
#7 a. Provide Information
#8b. 45, 20, 41, 35

Because of the scale of the WARM model, you will need to increase your weekly waste consumption to the population of Phoenix for one year.

Rubric:
100% of your grade will be allocated to the WARM rubric, below.

WARM Rubric

Points Possible Points Earned
Data & GRAPHs:
Raw data reflects weekly trash collection for a person. Weights of trash estimated in xls. Amounts of waste scaled up to one year and Phx population. (1 pt ea) 3
Two graphs comparing two different waste management scenarios: current scenario and one example improvement. (2 pts each) 4
Graph aesthetics: Graph is properly labeled on both axes, has a title, proper units. (1 pt each) 4
Graph aesthetics: Graph is easy to read, maximizes real estate. 1
2-Paragraph Summary:
Paragraph 1 Describes any negative emissions/energy numbers. 1
Paragraph 1 Discusses major sources of error that might influence model results. 1
Bold & italicized at least one relevant ESEM principle 1
Paragraph 2 Describes improvement scenario shown in the graphs. Addresses whether the CO2 & energy results actually improved. Describes rationale for why CO2 & energy changed from ‘current’ scenario. (1 pt ea) 3
Writing is professional, well written, free of grammatical mistakes and typos. 3

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