Project List

1. CLASS PROJECT REPORTSUSTAINABLE AIR QUALITY, EECE 449/549, SPRING 2010WASHINGTON UNIVERSITY, ST. LOUIS, MOINSTRUCTORS: PROFESSOR RUDOLF B. HUSAR, ERIN M. ROBINSONTHE ENERGY ANALYSIS AND CARBON FOOTPRINT OF WASHINGTON UNIVERITY AND BEYOND

2. 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

3. 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

4. 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

5. 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.

6. 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.

7. 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.

8. 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.

9. Regional Causality: South America 975% increase! • 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!

10. Regional Causality: Southeast Asia 1732% increase! 1663% Increase!

11. Regional Causality: United States • Overall US Emissions were driven up by GDP increases, moderated by decreases in Energy/GDP • Southern 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

12. 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

13. 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

14. Approach/Methodology: Danforth Campus • Obtained space breakdown data from the Department of Space Utilization • Eliminated and grouped together specific spaces

15. 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)

16. 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

17. 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

18. 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

19. Natural Gas

20. Electricity

21. Hot Water

22. Chilled Water

23. Natural Gas, Electricity, Hot and Chilled Water

24. 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

25. 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

26. 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

27. 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

28. Hourly Average Consumption

29. Hourly Average Consumption

30. Hourly Average Consumption

31. Results for the DUC (excluding kitchen)

32. Results for DUC Food Service

33. Energy Breakdown: Seigle

34. Seigle Trends

35. 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

36. 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

37. 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?

38. 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

39. Driving Forces for CO2 Emissions

40. Student Aviation Carbon Footprint

41. Ground Transportation Faculty Addresses Student Addresses

42. Comparison of Bounds

43. 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

44. Emissions Due to a Parking Spot

45. 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

46. Project List • Global and Regional Carbon Causality Analysis • Nick Thornburg, Will Hannon, Will Ferriby, Chris Valach • Electricity Use by Application: Danforth Campus • Matt Mitchel, Jacob Cohen • DUC Energy Consumption • Sarah Canniff, Dan Zernickow, Elliot Rosenthal, T.J. Pepping, Brittany Huhmann • Electricity Use by 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

47. 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.

48. 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

49. Overall GHG Emissions Time Comparison Fig. 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.

50. Per Capita Comparison Fig. 2 INCLUDING MED SCHOOL 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.