Impact of zero-emission vehicles on air quality and human health
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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
![Healthy Cities through Technology: Impact of zero-emission
vehicles on air quality and human health
The George Washington University, School of Engineering and Applied Science (SEAS)
Kaitlin Slimak, KonstantinosOikonomou, ChetanGaonkar
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
DISPERSION CO-EFFICIENT
GROUND PLUME LEVEL CONCENTRATION
Power Law Velocity Equation
Since about 2010 more
people live in urban vs.
rural areas[3].
APPROACH
Downtown Bellevue Network. June 2010. <http://downtown
bellevue.com/2010/06/24/city-bellevue-prepares-electric-vehicles/>.
A survey was distributed to residents of D.C. in order to assess charging habits and build a foundation
for our charging scenarios. EnergyPlus™ is used to model grid capabilities.
WASHINGTON DC SURVEY RESULTS
3
30% Penetration
2.5
50% Penetration
Power [MW]
The Potomac Electric Power Company (PEPCO)
has developed projections for their Maryland
service territory [1], which was used to establish
predictions for Washington, D.C. This data is
correlated with information provided by the DOT
Office of Highway Statistics [2] to calculate the
total number of vehicles present through 2040.
CO2 DISPERSION FOR NIH
COGENERATION FACILITY
The important metrological factors which affect the
dispersion of a pollutant are the average wind speed
at the source level at stack height, cloud cover, and
ambient temperature. Using data from the
Washington Dulles International Airport and the
Ronald Reagan National Airport, the wind speed at
the stack height may be calculated.
2
80% Penetration
Base Load
1.5
1
Nissan Leaf + IEEE 34 Feeder Load, Lv 1
Controlled charging, 15k mi annual driving
0.5
0
0
5
10
15
20
25
30
Example simulation
using the EPA’s
airborne diffusion
simulation software
will be used to model
the air quality
changes in the
metropolitan
Time [Hours]
RR = relevant risk of disease due to
inhalation of pollutant
X = 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.
[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 in
complex 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](https://image.slidesharecdn.com/poster4-140213151611-phpapp01/85/Impact-of-zero-emission-vehicles-on-air-quality-and-human-health-1-320.jpg)
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Impact of zero-emission vehicles on air quality and human health
- 1. Healthy Cities through Technology: Impact of zero-emission vehicles on air quality and human health The George Washington University, School of Engineering and Applied Science (SEAS) Kaitlin Slimak, KonstantinosOikonomou, ChetanGaonkar 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 DISPERSION CO-EFFICIENT GROUND PLUME LEVEL CONCENTRATION Power Law Velocity Equation Since about 2010 more people live in urban vs. rural areas[3]. APPROACH Downtown Bellevue Network. June 2010. <http://downtown bellevue.com/2010/06/24/city-bellevue-prepares-electric-vehicles/>. A survey was distributed to residents of D.C. in order to assess charging habits and build a foundation for our charging scenarios. EnergyPlus™ is used to model grid capabilities. WASHINGTON DC SURVEY RESULTS 3 30% Penetration 2.5 50% Penetration Power [MW] The Potomac Electric Power Company (PEPCO) has developed projections for their Maryland service territory [1], which was used to establish predictions for Washington, D.C. This data is correlated with information provided by the DOT Office of Highway Statistics [2] to calculate the total number of vehicles present through 2040. CO2 DISPERSION FOR NIH COGENERATION FACILITY The important metrological factors which affect the dispersion of a pollutant are the average wind speed at the source level at stack height, cloud cover, and ambient temperature. Using data from the Washington Dulles International Airport and the Ronald Reagan National Airport, the wind speed at the stack height may be calculated. 2 80% Penetration Base Load 1.5 1 Nissan Leaf + IEEE 34 Feeder Load, Lv 1 Controlled charging, 15k mi annual driving 0.5 0 0 5 10 15 20 25 30 Example simulation using the EPA’s airborne diffusion simulation software will be used to model the air quality changes in the metropolitan Time [Hours] RR = relevant risk of disease due to inhalation of pollutant X = 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. [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 in complex 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