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Healthy Cities through Technology: Impact of zero-emission
vehicles on air quality and human healthThe George Washington University, School of Engineering and Applied Science (SEAS)
Kaitlin Slimak, KonstantinosOikonomou, ChetanGaonkar
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Nissan Leaf + IEEE 34 Feeder Load, Lv 1 Controlled charging, 15k mi annual driving
30% Penetration
50% Penetration
80% Penetration
Base Load
[1] Stewart, Rob. “A Discussion on Electric Vehicle Charging.” U.S. Department of Energy Solar Decathlon. 2011.[2] Highway Statistics Series. Office Highway Policy Information.<http://www.fhwa.dot.gov/policyinformation/statistics.cfm>.[3] United Nations, Dpt. of Economic and Social Affairs, Population division. 2011.[4] ‘Gaussian Plume Model’ by Prof. Allen and Durrenberger[5] Bruno Sportisse “Air Pollution Modelling and Simulation” University Pierre and Marie Curie, 2007[6] DespinaDeligiorgi, Kostas Philippopoulos, George Karvounis and MagdaliniTzanakou. “Identification of pollution dispersion patterns incomplex terrain using AERMOD modeling system”, International Journal of Energy and Environment, 2009[7] Power Plant Information. http://www.epa.gov/cleanenergy/documents/egridzips/eGRID2012V1_0_year09_SummaryTables.pdf[8] ArvindBalaji J and Muralidharan M , “Gaussian Plume Air Dispersion Model for Pointe Source Emission”, Anna University , 2005
Downtown Bellevue Network. June 2010. <http://downtown
bellevue.com/2010/06/24/city-bellevue-prepares-electric-vehicles/>.
The important metrological factors which affect thedispersion of a pollutant are the average wind speedat the source level at stack height, cloud cover, andambient temperature. Using data from theWashington Dulles International Airport and theRonald Reagan National Airport, the wind speed atthe stack height may be calculated.
GROUND PLUME LEVEL CONCENTRATION
RR = relevant risk of disease due toinhalation of pollutantX = pollutant concentration, (μg/m3)X0 = background concentration in D.C.β = lung cancer coefficient,ex. [PM2.5] = 0.2322
Develop load simulations for different charging scenarios. This allows us to determine if electric vehicle projections are feasible. Create dispersion models for all PEPCO power plants and for each pollutant, including effects of changing fuel mixes through 2040 Correlate health impacts (risk of illness, disease, cancer) with pollutant inhalation Consideration of resident versus commuter driving patterns
Erdal, Serap. “Chapter 7: Risk Assessment Methodology for Conventional and Alternative Sustainability Options.” Sustainability: A Comprehensive Foundation, June 2011, Version 1.43, pp 294-299.
Since about 2010 more
people live in urban vs.
rural areas[3].
The Potomac Electric Power Company (PEPCO)has developed projections for their Marylandservice territory [1], which was used to establishpredictions for Washington, D.C. This data iscorrelated with information provided by the DOTOffice of Highway Statistics [2] to calculate thetotal number of vehicles present through 2040.
A survey was distributed to residents of D.C. in order to assess charging habits and build a foundationfor our charging scenarios. EnergyPlus™ is used to model grid capabilities.
WASHINGTON DC SURVEY RESULTS
CO2 DISPERSION FOR NIH
COGENERATION FACILITY
We seek to test the hypothesis that the
adoption and usage of low emission
vehicles positively influences both the air
quality and hence human health in urban
environments. This correlation will impact:
urban planning
transportation and environmental policy
electrification of the transportation sector
Example simulation
using the EPA’s
airborne diffusion
simulation software
will be used to model
the air quality
changes in the
metropolitan
DISPERSION CO-EFFICIENT
Power Law Velocity Equation
APPROACH
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