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 5 1 Drexel University 2 Syracuse University 3 Boston University 4 Harvard University 5 Sonoma Technology Inc.
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 CO 2 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)
Energy Generation in Each Scenario 2005 Base case 4000 2020 Reference case Energy Production (TWh) 2020 Power plant improvements 2020 Electricity sector improvements 3000 2020 Cost of carbon improvements 2020 EPA Clean Power Plan 2000 2030 EPA Clean Power Plan 1000 0 Total fossil generation Total Combined cycle (gas) Combustion turbine (gas) Coal (no CCS) Coal (CCS) Nuclear Hydro Wind Biomass New energy efficiency • Developed using the Integrated Planning Model (IPM) • 2,417 fossil-fuel based EGUs in US included Driscoll et al., Nature Climate Change (2015)
Emissions Resulting from Each Scenario 2000 2020 Reference case 2020 Power plant improvements 2020 Electricity sector improvements 2020 Cost of carbon improvements 2020 EPA Clean Power Plan 1500 2030 EPA Clean Power Plan Emissions (tonnes) 1000 500 0 CO 2 (million) SO 2 (thousand) NO x (thousand) CO2 (million) SO2 (thousand) NOx (thousand) Driscoll et al., Nature Climate Change (2015)
CMAQ & BenMAP Modeling of Scenarios • CMAQ version 4.7.1 • 2007/2020 modeling platform from EPA PM 2.5 RIA CB05 gas chemistry • • 12-km x 12-km horizontal resolution AE5 aerosol chemistry • • WRF v3.1 meteorology fixed at 2007 mercury chemistry • • 1% increase in all-cause mortality rate for adults ≥ 25 yo per µ g m -3 increase in annual average PM 2.5 concentration (Roman et al., 2008) • respiratory mortality risk for adults ≥ 30 yo as function of the ozone season average of the 1-hour maximum (Jerrett et al., 2009) Driscoll et al., Nature Climate Change (2015)
Air Quality Co-benefits: PM 2.5 | Scenario 1 difference from 2020 Reference | annual average | Driscoll et al., Nature Climate Change (2015)
Air Quality Co-benefits: PM 2.5 | Scenario 2 difference from 2020 Reference | annual average | Driscoll et al., Nature Climate Change (2015)
Air Quality Co-benefits: O 3 | Scenario 1 difference from 2020 Reference | 6-mn mean 1-hr max | Driscoll et al., Nature Climate Change (2015)
Air Quality Co-benefits: O 3 | Scenario 2 difference from 2020 Reference | 6-mn mean 1-hr max | Driscoll et al., Nature Climate Change (2015)
Human Health Co-benefits • NO x emissions contribute to both O 3 and PM 2.5 formation • PM 2.5 and O 3 contributions to the mortality rate included Driscoll et al., Nature Climate Change (2015)
Exposure-Response of Vegetation 100 Hourly W126 Contribution (ppb h) Ozone Concentration (ppb) ⎡ ⎤ ⎛ ⎞ ⎢ [ O 3 ] ⎥ 90 ∑ 80 ⎜ ⎟ W126 90 day = ( ) ⎢ ⎥ ⎜ ⎟ − 126[ O 3 ] 1 + 4403 e ⎢ ⎥ 60 i = 1 ⎝ ⎠ ⎣ ⎦ i,8am-8pm (LST) 40 ⎡ ⎤ B i 20 ⎛ ⎞ RYL = 1 − exp − W 126 ⎢ ⎥ 0 ⎜ ⎟ ⎢ ⎥ 5 10 15 20 A i ⎝ ⎠ Hour of Day EPA (2007) ⎢ ⎥ ⎣ ⎦ Cotton 0.12 Cotton Maize Corn Relative yield loss (RYL) as a Potato Potential Productivity Loss (fractional) Potato Soybean 0.10 function of the W126 ozone Wheat Soybean exposure metric has been Wheat 0.08 empirically determined for 5 Wang & Mauzerall 0.06 crops and 11 tree species. 0.04 Multiplying RYL by the productivity determines the 0.02 potential productivity loss 0.00 (PPL) of each species. 0 5 10 15 20 W126 (ppm h) Lehrer, A. et al., EPA 452/R-07-002, (2007)
Reference Case: W126 0 5 10 15 20 25 30 Reference W126 (ppm h ) Capps et al., in review
Air Quality Co-benefits: W126 | Scenario 1 -3 -2 -1 0 1 2 3 Scenario W126 - Reference W126 (ppm h) Capps et al., in review
Air Quality Co-benefits: W126 | Scenario 2 -3 -2 -1 0 1 2 3 Scenario W126 - Reference W126 (ppm h) Capps et al., in review
Crop Distribution 0.0 0.2 0.4 0.6 0.8 6 1.0x10 Soybean Production (bu) 0 10 20 3 30x10 Cotton Production (bales) 0.0 0.5 1.0 1.5 2.0 6 2.5x10 Potato Production (cwt) 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) Capps et al., in review
Crop Exposure-Response Functions 0.12 Cotton Corn Potato Potential Productivity Loss (fractional) Soybean 0.10 Wheat 0.08 0.06 0.04 0.02 0.00 0 5 10 15 20 W126 (ppm h) Lehrer, A. et al., EPA 452/R-07-002, (2007)
Crop Potential Productivity Co-benefits Relative Change in PPL from Reference Scenario (% 0 -2 -4 -6 -8 -10 -12 -14 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
Tree Distributions 10 20 30 40 50 60 2 4 6 8 10 10 20 30 40 50 60 -1 ) -1 ) -1 ) Quaking Aspen ( tons ha Eastern Cottonwood ( tons ha Black cherry ( tons ha 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
Tree Exposure-Response Functions 1.0 Hardwood oods Eastern Cottonwood ( Populus deltoides ) Quaking Aspen ( Populus tremuloides ) Red Alder ( Alnus rubra ) Red Maple ( Acer rubrum ) 0.8 Sugar Maple ( Acer saccharum ) Potential Productivity Loss (fractional) Tulip Poplar ( Liriodendron tulipifera ) Black Cherry ( Prunus serotina ) Sof Softwood oods 0.6 Douglas Fir ( Pseudotsuga menzeiesii ) Eastern White Pine ( Pinus strobus ) Ponderosa Pine ( Pinus ponderosa ) Virginia Pine ( Pinus virginiana ) 0.4 0.2 0.0 0 5 10 15 20 W126 (ppm h)
Tree Potential Productivity Co-benefits 0 Relative Change in PPL from Reference Scenario (%) -2 -4 -6 -8 -10 -12 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) • Consistent with the exposure-response • Tulip poplar and black cherry respond functions, eastern cottonwood is most most significantly to change in W126. impacted by ozone exposure.
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 NO x emission reductions with CO 2 emissions mitigations, more substantial gains for crops and trees are possible with moderately stringent CO 2 emissions standards or a CO 2 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
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.
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