project list
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CLASS PROJECT REPORT SUSTAINABLE AIR QUALITY, EECE 449/549, SPRING 2010 WASHINGTON UNIVERSITY, ST. LOUIS, MO INSTRUCTORS: PROFESSOR RUDOLF B. HUSAR, ERIN M. ROBINSON THE ENERGY ANALYSIS AND CARBON FOOTPRINT OF WASHINGTON UNIVERITY AND BEYOND. Project List. - PowerPoint PPT PresentationTRANSCRIPT
CLASS PROJECT REPORTSUSTAINABLE AIR QUALITY, EECE 449/549, SPRING 2010
WASHINGTON UNIVERSITY, ST. LOUIS, MO INSTRUCTORS: PROFESSOR RUDOLF B. HUSAR, ERIN M. ROBINSON
THE ENERGY ANALYSIS AND CARBON FOOTPRINT
OF WASHINGTON UNIVERITY AND BEYOND
Project List
Global and Regional Carbon Causality Analysis Nick Thornburg, Will Hannon, Will Ferriby, Chris Valach
Electricity Use by Space and Application: Danforth Campus Matt Mitchel, Jacob Cohen
DUC Energy Consumption Sarah Canniff, Dan Zernickow, Elliot Rosenthal, T.J. Pepping, Brittany
Huhmann
Electricity Use by Space and Application: DUC, Seigle Lindsay Aronson, Alan Pinkert, Will Fischer
WUSTL Transportation Carbon Footprint Update Michal Hyrc, Ryan Henderson, Billy Koury, Eric Tidquist
University Carbon Footprint Comparison Shamus Keohane, Chris Holt, Kristen Schlott, Sonny Ruffino
Project List
Global/Regional Trend Objectives• National causality trend analysis of carbon
emissions of specific world countries• Comparison of the causal commonalities within
and among different world regions and the United States
• Comprehension of global and regional patterns of carbon dioxide emissions over time for insight into the driving forces of climate change
• Quantified causality model of data from 60 world countries and US for future project use
Approach and Methodology
CO2 Emissions =Population x GDP/Person x Energy/GDP x
CO2/Energy
• Population: The total number of people living in a country at a certain point in time.
• GDP/Person: Total GDP in a country divided by its population. Indicates the national economic development and prosperity.
• Energy/GDP: Total kg oil consumed per unit GDP. Indicator of the energy intensity of a country’s economy.
• CO2/Energy: Metric tons of CO2 emitted per kg oil consumed. Measure of the carbon intensity and content of energy consumption.
Causality Factors for Saudi Arabia Increases in Population and
Energy/GDP Decrease in GDP/Person and
CO2/Energy The Population and
Energy/GDP both drive Carbon Emissions up while GDP/Person and CO2/Energy drive it down.
Increase in Population and GDP/Person
Decrease in Energy/GDP and CO2/Energy
Now the forces driving CO2 up are GDP/Person and Population while Energy/GDP and CO2/Energy drove it down.
Causality Factors for South Africa
Transition from population as the driving force to GDP as the driving force
CO2 emissions have decreased because of lowering of population and a lowering of energy per GDP.
Regional Causality: Europe Convergence to two points of CO2 emissions per capita Eastern European Countries: decreasing their emissions to get to these
points. Western European countries: remaining relatively the same in their
Carbon/Capita emissions.
Regional Causality: South America Principal Causality Factor:
GDP/Person: Economy is responsible for footprint.
GDP/Person: skyrocketing trend from 1960-2005. Shift in economic nature.
Energy/GDP: net decrease over 35 year time period.
CO2/Energy: relative stability,near-zero trend evolution changing fuel type is responsible.
Note the uncanny relativity between causal factor magnitudes in countries.
Slight convergence over time: Evolution from 14-fold to only 3-fold difference!
975% increase!
Regional Causality: Southeast Asia
1732% increase!
1663%Increase!
Regional Causality: United States
Overall US Emissions were driven up by GDP increases, moderated by decreases in Energy/GDPSouthern and Western states experienced a significant emissions
Much more than north Due to increase in
Population South also had a larger
drop in Carbon per Energy, less significant than the population change
Summary and Conclusions
• Regional causality frameworks and case studies of countries prove strong socioeconomic and historical dependence of causal factors• No such “master formula” for causality analysis• Intrinsic relationship with economic development• Significance of geographical placement
• Parallel of trends and driving factors in the US• Economic development mostly responsible,
dampened by lowered energy intensity
• Establishment of framework for sustainable future
Project List
Approach/Methodology: Danforth Campus
Obtained space breakdown data from the Department of Space Utilization
Eliminated and grouped together specific spaces
Electricity Breakdown: Danforth Campus
• Electricity consumption= ΣAreai * (cons/sq.ft.)i
• Final Analysis: 23,000,000 kWh/y consumed on Danforth Campus.
• Compared to previous observed value of 68,500,000 kWh/y. (33.5% accounted for)
Project List
DUC Energy Consumption Objectives• Find total energy use, CO2 emissions, and cost
for natural gas, electricity, hot water, and chilled water in the DUC for one year
• Identify the portion of the DUC’s total energy use that goes to individual components of the HVAC system and the portion that goes to non-HVAC uses
• Identify daily, weekly, and seasonal trends in the above parameters
• Begin to understand the influence of outdoor temperatures and student use of the DUC on these daily, weekly, and seasonal trends
Approach and Methodology
• Data from Metasys for 5:00 PM April 16, 2009 to 5:00 PM April 16, 2010 electricity, natural gas, hot water, chilled water supply fans, relief fans, and heat recovery fans
for the 3 AHUs pumps for hot and chilled water outdoor air temperature
• All energy data converted to MMBTUs for comparative purposes
Natural Gas
Electricity
Hot Water
Chilled Water
Natural Gas, Electricity, Hot and Chilled Water
Summary and Conclusions
• Annual energy use: 17,300 MMBTU• Annual CO2 emissions: 2,140,000 kg
• Annual Cost: $126,000• Electricity is biggest source of all three metrics
• HVAC electricity is 29% of total electricity consumption• Energy reduction strategies should focus on non-HVAC
electricity
• Two peaks in daily energy consumption corresponding to lunch and dinner rush
• Lower energy consumption on weekends vs. weekdays & during academic-year breaks
• Seasonal patterns based on outdoor temperatures
Project List
Electricity Use Objectives
We aimed to : Examine lighting and appliances for the
Danforth University Center and Seigle Hall Look at energy consumption by appliance
and by space Show trends and suggest improvements to
reduce the carbon footprint of Washington University
Approach and Methodology
Started by identifying how to breakdown spaces within each given area
Researched appliances found in the different kind of spaces identified and determined their wattage
Determined hours of use for appliances/lighting
To confirm, took metered energy data, subtracted HVAC consumption, and compared calculations
Hourly Average Consumption
Hourly Average Consumption
Hourly Average Consumption
Results for the DUC (excluding kitchen)
Results for DUC Food Service
Energy Breakdown: Seigle
Seigle Trends
Summary and Conclusions
Circulation area is the largest energy consumer Recommend installing motion sensor lights
Computers are another major energy drain Stand-by should be used during the day, but at
night computers should be shut down completely Other recommendations:
Install motion sensors in bathrooms and classrooms
Use “Night mode” lighting setting in hallways without motion at night
Schedule night classes and meetings on first and second floors so that other floors’ lights can be turned off
Project List
Transportation Objectives
To better understand the carbon footprint of transportation at Washington University by: Ground Transportation: Improving Past
Estimates Air Travel: Novel Estimates Parking: What happens when we go
underground?
Approach & Methodology
Flying Extracted student locations and
numbers from home zip code data
Found total passenger miles flown by students
Estimated carbon footprint from total number of passenger miles
Parking Used approximate appliance data
to estimate daily carbon emissions
Used approximate size data to estimate initial carbon emission due to pouring concrete
Commuting Used school zip code data
from a similar project conducted in 2009
Calculated commuting distances by mode of transportation Walk/Bike MetroLink MetroBus Drive Alone Carpool
Estimated carbon footprint Upper bound Lower bound Best guess
Driving Forces for CO2 Emissions
Student Aviation Carbon Footprint
Ground Transportation
Faculty Addresses Student Addresses
Comparison of Bounds
Modes of Transportation and Total Carbon
The two leftmost charts represent the number of students (left) and faculty (center) that commute to school in each mode of transportation taken into consideration.
The chart to the right represents the total carbon emissions from students and faculty.
Best guess total: 5627 metric tons of CO2
Emissions Due to a Parking Spot
Summary & Conclusions
Our best estimates for annual transportation footprints are ~23,000 metric tons of CO2 from student air commute ~5,500 metric tons of CO2 from faculty and student regional
ground commute ~527 metric tons of CO2 from lighting and ventilation of parking
on campus
This is an underestimation of the actual total footprint
The transportation footprint has been and will continue to increase
To reduce the transportation footprint, we recommend the University Merge fall and thanksgiving break to reduce flight emissions Try to reduce the number of people that drive to work by themselves
Project List
University Carbon Footprint Objectives• The primary objective of this project was
to compile GHG data from other Universities to make comparative analysis with respect to Washington University’s place among other schools when it comes to sustainability.
• An additional goal of the data analysis is a qualitative subject investigation to see which areas of a GHG inventory Wash U can improve upon or is already succeeding in.
Approach and Methodology
• This project began with a review of the previous class’ report, where size data was only available for 12 schools, and transportation data was only available for 19. Their analysis only really compared these two subjects. We expanded to include net GHG emissions, total campus area, purchased electricity and student population.
• Tufts, Smith, Lewis and Clark, Wellesley, College of Charleston, Cal St. Polytech, College of William & Mary, and Occidental College were removed due to lack of data.
• Arizona State University, Cornell, and Bates were added as they are known to be sustainable schools
• Data for most of the schools was available either on their sustainability websites or through the ACUPCC website. The latter providing a nice and unified way of reporting and measuring GHG emissions
• The data was tabulated into a Google Doc. work space along with general statistics for each school (area, pop., etc). From this common source of data, we began to analyze the information for trends
Overall GHG Emissions Time ComparisonFig. 1
Fig.1 This is a time comparison of total GHG emissions, from the 2008 group data to current data. Note that Wash U ranks 3rd amongst the analyzed schools in terms of gross emissions of CO2, despite Wash U’s size compared to other schools. Also noteworthy is the fact that schools are generally trending to emit more GHG than previously evaluated, this is most likely due to many schools expanding their GHG inventories to account for transportation effects. The large disparity between transportation reporting from the 2008 report to this report is likely the cause of the overall increase in emissions seen in this time period. More information on transportation data reporting can be seen in figures 4a and 5b.
• Immediately attention grabbing in this figure is Harvard’s dramatic decline since the time of the previous inventory. More information on this is included in figure 5a.
INCLUDING MED SCHOOL
Fig. 2
Without Med School
Fig. 2 Per Capita Emissions: Gross emissions per number of students. This graph includes results from the most recent GHG Index results from Wash U, including the medical school. Also, there is no 2008 data for Wash U, but rather there is data for Wash U including only the Danforth Campus (not med school). We included both values to show the dramatic impact medical schools can have on overall emissions. For Gross GHG Emissions, all other indices studied included medical schools. Additionally, the student population counts are a total count, including graduate and medical students. We think this graph (including Wash U + med school) is the most accurate indication of per capita emissions, because of the all inclusiveness of using graduate school campuses + graduate and medical school students, where applicable.
Per Capita Comparison
(W/ Med School)
Gross Emissions & Population Trends Time ComparisonFig. 3
Fig. 3 This is a time comparison of the gross emissions normalized by population.
Student Populations
2010 Transportation Data Reported
Total 2010 Transportation Emissions per Capita
***for schools that report all categoriesFig. 4b
Fig. 4a
4a) This chart shows the breakdown of transportation data that was available in each school’s GHG Emissions Index. Most schools had a good log of transportations emissions data, but not all. As mentioned above, transportation can have a huge impact on overall emissions, when included in emissions reports. For example, as seen from the report by the transportation group, international student travel can have a major impact on Transportation GHG emissions. Yale currently has 8% international students while Duke has 13%. The 2008 group mentioned great inconsistency and difficulty tracking data, so we are doing an isolated study of 2010 data only.
4b) Not all schools had the same information available, so we felt that a comparison of the 2010 transportation emissions by school should be normalized. This graph compares only schools that reported data in all three transportation categories: university fleets, student and faculty commuting, and air travel. This graph represents the total combined emissions for those three categories, controlled by university population. It is the only graph that is not also a time comparison to the 2008 group data. This is because we could not be sure which transportation data the 2008 group included in their graphs, though they did include mention of their raw data’s inconsistencies.
Fig. 5a
Emissions Resulting from Purchased Electricity Time Comparison
Fig. 5b Abbrev. Data Category
PE Purchased Electricity
RE Renewable Energy
ST Stationary Sources
Tr-UF Transp: University Fleet
Tr-CST Transp: Commuting, Students
Tr-CSF Transp: Commuting, Faculty
Tr-A Transp: Air
Ag Agricultural Waste
SW Solid Waste
Index Data Reporting Time Comparison
54
Figure 5 Analysis
5a)This graph shows a comparison over time of the total emissions resulting from electricity purchased. As mentioned above, Harvard in particular shows a dramatic decrease in their EP emissions. This is because of the installation of a new on-campus power plant since the previous inventory, drastically reducing their GHG emissions from purchased power.
5b) This graph is a time comparison of available data in each school’s GHG index. The 2008 group included this bar graph in their data to demonstrate the inconsistencies in reporting, as well as the dramatic differences in reporting methods from school to school. We decided this was a pertinent graph for comparison. Considering that a) we studied fewer schools b) that emissions from student vs. teacher commuting have been combined and in 2010 is simply referred to as overall "commuting," and c) considering that agricultural waste no longer seems to be included in most GHG inventories, a general trend shows increased reporting for all data categories. Air travel and renewable energy reporting has increased the most. It should also be noted that data reporting seems to be much more standardized (most schools were included in the comprehensive ACUPCC GHG Emissions Index) in 2010 than in 2008. We didn't have to resort to any "alternative methods" for GHG inventories, and another recent trend is that significantly more inventories were available as a university sponsored report (including Harvard and Wash U), indicating increased interest and university involvement in GHG inventories.
Summary and Conclusions
• It is clear from the previous data that Wash U has reported drastically more CO2 emissions from the last group’s report in 2008. Wash U currently still does not include transportation, so the current estimates for Wash U emissions are lower than they are in reality.
• Wash U’s poor rank among other Universities in GHG emissions can primarily be attributed to the amount of electricity Wash U purchases and the source of that Electricity. If Wash U were to contract with utility companies to purchase electricity produced from renewable resources, Wash U could greatly improve its standing in the academic community.
• In conclusion, while Wash U may take an open and active stance toward it’s sustainability goals, the University need to look to new areas that can have greater impacts in reducing the University’s Carbon Footprint.
Questions?
References (Global)1. http://www.google.com/publicdata/overview?ds=d5bncppjof8f9_2. https://www.cia.gov/library/publications/the-world-factbook/geos/ve.html3. http://inflationdata.com/inflation/inflation_Rate/Historical_Oil_Prices_Table.asp4. http://web.archive.org/web/20080226202420/http://www.jica.go.jp/english/global/pov/profiles/pdf/s
au_eng.pdf5. http://www.state.gov/r/pa/ei/bgn/35639.htm6. http://www2.census.gov/prod2/statcomp/documents/1980-02.pdf7. https://www.cia.gov/library/publications/the-world-factbook/geos/br.html8. https://www.cia.gov/library/publications/the-world-factbook/geos/ar.html9. http://en.wikipedia.org/wiki/France#Economy10. http://www.bea.gov/regional/index.htm#gsp11. http://www.census.gov/compendia/statab/12. http://en.wikipedia.org/wiki/Economy_of_Thailand13. http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/LACEXT/HONDURASEXTN/0,,contentMDK:2
1035522~pagePK:141137~piPK:141127~theSitePK:295071,00.html14. http://www40.statcan.gc.ca/l01/cst01/econ40-eng.htm15. http://www.statcan.gc.ca/pub/88-221-x/2008002/part-partie1-eng.htm16. http://capitawiki.wustl.edu/ME449-07/index.php/Image:All_State_Energy_BTU_EmissionR.xls17. http://www.eia.doe.gov/emeu/states/_seds.html18. http://www.epa.gov/climatechange/emissions/state_energyco2inv.html19. http://www.eia.doe.gov/oiaf/1605/state/state_emissions.html20. http://www.eia.doe.gov/oiaf/1605/ggrpt/carbon.html21. http://open.worldbank.org/countries/AFG/indicators/EN.ATM.CO2E.KT?per_page=100&api_key=4kzb
hfty3mz6v293vrq5uphw&date=1960:200522. http://datafedwiki.wustl.edu/index.php/2010-02-15:_World_Bank_Coutry_Data23. http://capitawiki.wustl.edu/EECE449/index.php/Global-Regional_Trends_of_Carbon_Emissions24. http://capita.wustl.edu/me449%2D00/
References (University) Duke University (2007) http://acupcc.aashe.org/ghg-report.php?id=225 Penn State University Park (2009) http://www.ghg.psu.edu/campus_inv/default.asp Washington University in St. Louis (2009) http://www.wustl.edu/sustain/GHGEmissions.pdf U of Pennsylvania (2008) http://acupcc.aashe.org/ghg-report.php?id=258 Cornell (2008) http://acupcc.aashe.org/ghg-report.php?id=237 Yale (2008) http://sustainability.yale.edu/sites/default/files/GHG2008.pdf Arizona State University (2008) 2008: http://acupcc.aashe.org/ghg-report.php?id=628
2007: http://acupcc.aashe.org/ghg-report.php?id=386 U of Illinois at Chicago (2008) http://acupcc.aashe.org/ghg-report.php?id=102 UT Knoxville (2009) http://acupcc.aashe.org/ghg-report.php?id=1018 Colorado State University (2009) http://acupcc.aashe.org/ghg-report.php?id=932 UC Berkeley(2008) http://acupcc.aashe.org/ghg-report.php?id=142 U of Connecticut (2007) http://acupcc.aashe.org/ghg-report.php?id=587 Harvard(2007) http://www.provost.harvard.edu/institutional_research/FACTBOOK_2007-08_FULL.pdf Tulane University (2008) http://green.tulane.edu/PDFs/Inventory_Complete_2008_FINAL.pdf University of Central Florida (2008) http://acupcc.aashe.org/ghg-report.php?id=1108 Utah State University (2008) http://acupcc.aashe.org/ghg-report.php?id=971 Rice (2009) http://acupcc.aashe.org/ghg-report.php?id=843 UC Santa Barbara (2009) http://acupcc.aashe.org/ghg-report.php?id=963 University of New Hampshire (2007) http://www.sustainableunh.unh.edu/climate_ed/greenhouse_gas_inventory.html Oberlin College(2007) http://acupcc.aashe.org/ghg-report.php?id=367 Middlebury College (2007) http://acupcc.aashe.org/ghg-report.php?id=441 Carleton College (2007) http://acupcc.aashe.org/ghg-report.php?id=236 Colby College (2008) http://acupcc.aashe.org/ghg-report.php?id=801 Bates College (2008) 2008: http://www.bates.edu/Prebuilt/GHGInventory.pdf
2007: http://acupcc.aashe.org/ghg-report.php?id=329 Connecticut College (2009)
http://www.conncoll.edu/green/greenliving/GreenlivingDocs/CC_greenhouse_gas_emissions_inventory_0809.pdf
References (Application)
Tom Dixon, DUC General Manager DUC Electrical Binder:
http://capita.wustl.edu/me449-09/Elect%20Binder.pdf Leslie Heusted, Director, Danforth University Center Kellie Briggs, Assistant Director, Facilities, Danforth
University Center Jessica Stanko, Career Center Assistant; Lauren Botteron,
Hatchet Yearbook; Alan Liu, StudLife staff member Frank Freeman Larry Downey and Kevin Watkins in Facilities Seigle Construction Plans
http://capitawiki.wustl.edu/EECE449/images/0/0c/Seigle_Hall_Construction_Plans.pdf
Excel files with the data for graphs shown in this presentation can be found on our wiki report page.
References (Transportation)
1. http://hypertextbook.com/facts/1999/KatrinaJones.shtml
2. http://apps.olin.wustl.edu/mba/casecompetition/PDF/oscc_case2.pdf
3. http://www.engineeringtoolbox.com/garage-ventilation-d_1017.html
4. http://www.docstoc.com/docs/2392070/Overview-of-Existing-Regulations-for-Ventilation-Requirements-of/
5. http://www.epa.gov/ttnchie1/conference/ei13/ghg/hanle.pdf
6. http://capitawiki.wustl.edu/EECE449/index.php/Commuting
7. http://capitawiki.wustl.edu/EECE449/index.php/Shuttles
8. http://capitawiki.wustl.edu/EECE449/index.php/Transportation
9. http://www.bts.gov/xml/air_traffic/src/index.xml#CustomizeTable
10. http://www.ghgprotocol.org/
11. http://www.eia.doe.gov/oiaf/1605/coefficients.html
12. http://www.whatsmycarbonfootprint.com/faq.htm
13. http://www.carbonfund.org/site/pages/carbon_calculators/category/Assumptions
14. http://www.epa.gov/oms/climate/420f05001.htm
15. http://capitawiki.wustl.edu/EECE449/index.php/Transportation