Differences in the apparent exposure to mobile source PM2.5 - - PowerPoint PPT Presentation

Differences in the apparent exposure to mobile source PM2.5 emissions: comparing conventional end-point analysis to a novel time-integrated analysis.

• Dr. Gregory Rowangould, Mohammad Tayarani, Amir Poorfakhraei & Razieh Nadafianshahamabadi

Department of Civil Engineering ▪ University of New Mexico

US EPA Workshop: PM and Related Pollutants in a Changing World April 6, 2017 Research Triangle Park, NC

Research Aims

• How do changes in land-use, transportation

systems, and related policies affect:

• Exposure to PM2.5 emissions from vehicle exhaust over

time and space?

• Mobile source GHG emissions over time?
• Which strategies and policies can minimize PM2.5

exposure, PM2.5 exposure inequities, and GHG emissions throughout the entire planning horizon?

Figure from Rowangould, G. (2013). A Census of the United States Near-Roadway Population: Public Health and Environmental Justice Considerations. Transportation Research Part D: Transport and Environment. 25pp. 59-67

Exposure & Environmental Justice

Land Use and Vehicle Emissions

Increasing density & mix of land uses:

• Lower vehicle miles traveled
• Less vehicle mode share
• Lower emission inventories
• Fewer GHGs
• Air quality & Exposure???

Part 1

Modeling Changes Through a Planning Horizon

State of the practice…

Transportation and Land Use Scenarios

Preferred Scenario Encourages

• New development in existing activity centers
• New development near transit corridors (TOD)
• Greater land use mix
• Less development in flood and fire risk areas
• New investments in transit

Trend Scenario Expects

• Continuing development trends
• No new transit infrastructure
• Mostly peripheral housing

development while employment remains more centrally located.

Results: Central New Mexico Climate Change Scenario Planning Study

27%

• 37%
• 2%

36% 33%

• 12%

19%

• 28%
• 7%

31% 23%

• 19%

18%

• 31%
• 8%

34% 23%

• 19%
• 50%
• 40%
• 30%
• 20%
• 10%

0% 10% 20% 30% 40% Land Developed Averge Peak Hour Speed VMT/Capita River Crossings GHG Emissions GHG/Capita Chnage from 2012 Baseline Trend Preferred Constrained

Modeling Approach

Trip Generation Trip Distribution Mode Choice Route Choice

Highway Conditions:

• Link traffic volume
• Link average speed

Land-Use Model (Urbanism) Air quality Model (AERMOD)

iterate at each time period

Emission Model (MOVES)

Population Exposure Levels

Exposure Model

Parcel Location: Households Employment

1 Rowangould, G. M. A new approach for evaluating regional exposure to particulate matter emissions from motor vehicles. Transportation Research Part D:

Transport and Environment, Vol. 34, Jan. 2015, pp. 307–317.

Air Quality Modeling

• Vehicle Emission Rates: US EPA MOVES model
• Air Quality: US EPA AERMOD model
• Apply a unique rastering approach to more quickly model a large urban

area.1

Albuquerque: PM2.5 Concentration

Albuquerque: Change in GHGs 2012-2040

19 21 23 25 27 29 2012 2017 2022 2027 2032 2037

Daily Vehicle Miles Traveled (Millions) Year

Annual VMT End Point VMT 100 200 300 400 500 600 11,000 11,200 11,400 11,600 11,800 12,000 12,200 12,400 2012 2017 2022 2027 2032 2037

GHG Emission Rate (g/mi) GHG Emissions (tonn/day) Year

Annual GHG End Point GHG GHG Emission Factor

Albuquerque: Change in PM2.5 2012-2040

19 21 23 25 27 29 2012 2017 2022 2027 2032 2037

Daily Vehicle Miles Traveled (Millions) Year

Annual VMT End Point VMT 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 100 200 300 400 500 600 2012 2017 2022 2027 2032 2037

PM2.5 Emission Rate (g/mi) PM2.5 Emissions (kg/day) Year

Annual PM2.5 End Point PM2.5 PM2.5 Emission Factor

Land-Use Change 2012-2040

Land-Use:

End-point vs. Annual Modeling Approach

5000 10000 15000 20000 25000 2012 2017 2022 2027 2032 2037

Population Density (persons/mile2) Year

Part 2

Dynamic Exposure

Atlanta: Dynamic Exposure Analysis

Atlanta: Preliminary & Partial Results

0.00 1.00 2.00 3.00 4.00 5.00 6.00 Mean Daily PM2.5 Exposure (µg/m3) Occupation Dynamic Static

Atlanta: Preliminary & Partial Results

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 Mean Daily PM2.5 Exposure (µg/m3) Median Household Income Dynamic Static 0.00 1.00 2.00 3.00 4.00 5.00 6.00 < 20 20 - 40 40 - 60 > 60 Mean Daily PM2.5 Exposure (µg/m3) Age Dynamic Static

Atlanta: Preliminary & Partial Results

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 < 5 5 - 10 10 - 15 15 -20 20 - 25 > 25 Mean PM2.5 Exposure (µg/m3) % of Exposure During Commute

Next Steps

• Albuquerque
• Model exposure over planning horizon
• Develop and model a wide range of land-use and

transportation strategies

• Identify strategies that minimize PM2.5 exposure, GHG

emissions and inequality over the entire planning horizon

• Atlanta
• Model individual level exposure through planning horizon
• Evaluate factors associated with different levels of exposure
• Evaluate environmental justice
• Both Regions
• How do these advanced methods differ from current air

quality analysis requirements and practice?

Acknowledgments

This research has been supported by a grant from the U.S. Environmental Protection Agency's Science to Achieve Results (STAR) program (grant # 83588501 ).