Opportunities for Reducing Vegetative Ozone Exposure through U.S. - - PowerPoint PPT Presentation

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Opportunities for Reducing Vegetative Ozone Exposure through U.S. - - PowerPoint PPT Presentation

Oct 21, 2022 159 likes •398 views

Opportunities for Reducing Vegetative Ozone Exposure through U.S. Power Plant Carbon Standards Shannon L. Capps 1 , Charles T. Driscoll 2 , Habibollah Fakhraei 2 , Pamela H. Templer 3 , Kathleen F. Lambert 4 , Kenneth J. Craig 5 , Stephen B. Reid

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SLIDE 1

Opportunities for Reducing Vegetative Ozone Exposure through U.S. Power Plant Carbon Standards

Shannon L. Capps1, Charles T. Driscoll2, Habibollah Fakhraei2, Pamela H. Templer3, Kathleen F. Lambert4, Kenneth J. Craig5, Stephen B. Reid5

1Drexel University 2Syracuse University 3Boston University 4Harvard University 5 Sonoma Technology Inc.

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SLIDE 2

Scenarios Relevant to Implementation of EPA Proposed Clean Power Plan (CPP)

2005 Base case 2020 Reference case: NRDC & BPC; EIA demand; existing regs 2020 Scenario 1: BPC; heat-rate improvements for coal-fired EGUs 2020 Scenario 2: NRDC; demand-side efficiency; moderate CO2 standards 2020 Scenario 3: BPC; tax based on social cost of carbon 2020 EPA Clean Power Plan: proposed option 1, partial implementation 2030 EPA Clean Power Plan: proposed option 1, full implementation

Natural Resources Defense Council (NRDC) | Bipartisan Policy Center (BPC) | Energy Information Administration (EIA)

Driscoll et al., Nature Climate Change (2015)

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SLIDE 3
  • Developed using the Integrated Planning Model (IPM)
  • 2,417 fossil-fuel based EGUs in US included

Energy Generation in Each Scenario

Driscoll et al., Nature Climate Change (2015)

4000 3000 2000 1000

Energy Production (TWh)

Total Total fossil generation Combined cycle (gas) Combustion turbine (gas) Coal (no CCS) Coal (CCS) Nuclear Hydro Wind Biomass New energy efficiency

2005 Base case 2020 Reference case 2020 Power plant improvements 2020 Electricity sector improvements 2020 Cost of carbon improvements 2020 EPA Clean Power Plan 2030 EPA Clean Power Plan

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SLIDE 4

2000 1500 1000 500

Emissions (tonnes)

CO2 (million) SO2 (thousand) NOx (thousand)

2020 Reference case 2020 Power plant improvements 2020 Electricity sector improvements 2020 Cost of carbon improvements 2020 EPA Clean Power Plan 2030 EPA Clean Power Plan

Emissions Resulting from Each Scenario

CO2 (million) SO2 (thousand) NOx (thousand)

Driscoll et al., Nature Climate Change (2015)

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SLIDE 5

CMAQ & BenMAP Modeling of Scenarios

  • 2007/2020 modeling platform from

EPA PM2.5 RIA

  • 12-km x 12-km horizontal resolution
  • WRF v3.1 meteorology fixed at 2007
  • CMAQ version 4.7.1
  • CB05 gas chemistry
  • AE5 aerosol chemistry
  • mercury chemistry

Driscoll et al., Nature Climate Change (2015)

  • 1% increase in all-cause mortality rate for adults ≥25 yo per µg m-3

increase in annual average PM2.5 concentration (Roman et al., 2008)

  • respiratory mortality risk for adults ≥30 yo as function of the
  • zone season average of the 1-hour maximum (Jerrett et al., 2009)
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SLIDE 6

difference from 2020 Reference | annual average | Driscoll et al., Nature Climate Change (2015)

Air Quality Co-benefits: PM2.5 | Scenario 1

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SLIDE 7

Air Quality Co-benefits: PM2.5 | Scenario 2

difference from 2020 Reference | annual average | Driscoll et al., Nature Climate Change (2015)

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SLIDE 8

Air Quality Co-benefits: O3 | Scenario 1

difference from 2020 Reference | 6-mn mean 1-hr max | Driscoll et al., Nature Climate Change (2015)

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SLIDE 9

Air Quality Co-benefits: O3 | Scenario 2

difference from 2020 Reference | 6-mn mean 1-hr max | Driscoll et al., Nature Climate Change (2015)

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SLIDE 10

Human Health Co-benefits

Driscoll et al., Nature Climate Change (2015)

  • NOx emissions

contribute to both O3 and PM2.5 formation

  • PM2.5 and O3

contributions to the mortality rate included

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SLIDE 11

Exposure-Response of Vegetation

W12690 day = [O3] 1+ 4403e

−126[O3]

( )

⎛ ⎝ ⎜ ⎜ ⎞ ⎠ ⎟ ⎟

i,8am-8pm (LST) i=1 90

∑

⎡ ⎣ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥

RYL = 1−exp − W126 Ai ⎛ ⎝ ⎜ ⎞ ⎠ ⎟

Bi

⎡ ⎣ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥

100 80 60 40 20 Ozone Concentration (ppb) 20 15 10 5 Hour of Day Hourly W126 Contribution (ppb h)

Relative yield loss (RYL) as a function of the W126 ozone exposure metric has been empirically determined for 5 crops and 11 tree species. Multiplying RYL by the productivity determines the potential productivity loss (PPL) of each species.

0.12 0.10 0.08 0.06 0.04 0.02 0.00 Potential Productivity Loss (fractional) 20 15 10 5 W126 (ppm h) Cotton Corn Potato Soybean Wheat

EPA (2007) Cotton Maize Potato Soybean Wheat Wang & Mauzerall

Lehrer, A. et al., EPA 452/R-07-002, (2007)

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SLIDE 12

30 25 20 15 10 5 Reference W126 (ppm h )

Capps et al., in review

Reference Case: W126

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SLIDE 13

Air Quality Co-benefits: W126 | Scenario 1

  • 3
  • 2
  • 1

1 2 3 Scenario W126 - Reference W126 (ppm h)

Capps et al., in review

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SLIDE 14

Air Quality Co-benefits: W126 | Scenario 2

  • 3
  • 2
  • 1

1 2 3 Scenario W126 - Reference W126 (ppm h)

Capps et al., in review

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SLIDE 15

Crop Distribution

USDA National Agricultural Statistics Survey (NASS) 2007 crop production distributed in accordance with the Biogenic Emissions Landuse Database (BELD) v.4. (R. Pinder, E. Cooter)

1.0x10

6

0.8 0.6 0.4 0.2 0.0 Soybean Production (bu) 30x10

3

20 10 Cotton Production (bales) 2.5x10

6

2.0 1.5 1.0 0.5 0.0 Potato Production (cwt)

Capps et al., in review

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SLIDE 16

0.12 0.10 0.08 0.06 0.04 0.02 0.00 Potential Productivity Loss (fractional) 20 15 10 5 W126 (ppm h) Cotton Corn Potato Soybean Wheat

Lehrer, A. et al., EPA 452/R-07-002, (2007)

Crop Exposure-Response Functions

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SLIDE 17

Crop Potential Productivity Co-benefits

  • 14
  • 12
  • 10
  • 8
  • 6
  • 4
  • 2

Relative Change in PPL from Reference Scenario (% Potato Soybean Cotton Corn Relative Change in Productivity Loss from Reference: Scenario 1 Scenario 2 Scenario 3 Marker scaled by PPL in Reference Scenario (% of NASS-estimated biomass)

Capps et al., in review

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SLIDE 18

60 50 40 30 20 10 Black cherry ( tons ha

  • 1 )

60 50 40 30 20 10 Quaking Aspen ( tons ha

  • 1 )

10 8 6 4 2 Eastern Cottonwood ( tons ha

  • 1 )

Tree Distributions

USDA Forest Inventory Analysis tree biomass distributed in accordance with the National Land Cover Database; MODIS-derived image composites and percent tree cover; and other geographic and climatological parameters. (Bash et al., 2016)

Capps et al., in review

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SLIDE 19

Tree Exposure-Response Functions

1.0 0.8 0.6 0.4 0.2 0.0 Potential Productivity Loss (fractional) 20 15 10 5 W126 (ppm h) Hardwood

  • ods

Eastern Cottonwood (Populus deltoides) Quaking Aspen (Populus tremuloides) Red Alder (Alnus rubra) Red Maple (Acer rubrum) Sugar Maple (Acer saccharum) Tulip Poplar (Liriodendron tulipifera) Black Cherry (Prunus serotina) Sof Softwood

  • ods

Douglas Fir (Pseudotsuga menzeiesii) Eastern White Pine (Pinus strobus) Ponderosa Pine (Pinus ponderosa) Virginia Pine (Pinus virginiana)

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SLIDE 20
  • 12
  • 10
  • 8
  • 6
  • 4
  • 2

Relative Change in PPL from Reference Scenario (%)

  • East. Cottonwood Black Cherry

Quaking Aspen

  • Pon. Pine

Tulip Poplar

  • East. White Pine

Virginia Pine Red Maple Red Alder

Relative Change in PPL from Reference: Scenario 1 Scenario 2 Scenario 3 Marker scaled by Potential Productivity Loss (PPL) in Reference Scenario (% of FIA-estimated biomass)

Tree Potential Productivity Co-benefits

  • Consistent with the exposure-response

functions, eastern cottonwood is most impacted by ozone exposure.

  • Tulip poplar and black cherry respond

most significantly to change in W126.

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SLIDE 21

Conclusions

  • The reduction of co-pollutant emissions with the potential

implementations of the Clean Power Plan could improve both human health and public welfare.

  • Due to coincident NOx emission reductions with CO2

emissions mitigations, more substantial gains for crops and trees are possible with moderately stringent CO2 emissions standards or a CO2 tax than with power plant improvements.

  • reductions in potential productivity losses (PPL) up to

15.6% for corn and up to 8.4% for soybean crops

  • reductions in PPL up to 7.6% for black cherry and up to

8.4% for eastern cottonwood trees

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SLIDE 22

Additional Resources

  • Project website
  • Shannon Capps, Drexel University
  • shannon.capps@drexel.edu
  • Charles Driscoll, Syracuse University
  • ctdrisco@syr.edu

Acknowledgements: Jesse Bash, Ellen Cooter, and Rob Pinder for spatial allocation of crops and trees.