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 0 0.5 1 1.5 2 2.5 3 0 5 10 15 20 25 30 Power [MW] Time [Hours] 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 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 Downtown Bellevue Network. June 2010. <http://downtown bellevue.com/2010/06/24/city-bellevue-prepares-electric-vehicles/>. 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. GROUND PLUME LEVEL CONCENTRATION RR = relevant risk of disease due to inhalation of pollutant X = pollutant concentration, (μg/m 3 ) X 0 = 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 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. 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. W ASHINGTON DC SURVEY RESULTS CO 2 DISPERSION FOR NIH COGENERATION F ACILITY 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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Page 1: Impact of zero-emission vehicles on air quality and human health

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

0.5

1.5

2.5

0 5 10 15 20 25 30

r [M

Time [Hours]

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