GLIMPSE An Approach for Determining Optimal Control Strategies for Energy System Emissions of Ozone Precursor Gases Shannon L. Capps, Rob W. Pinder, Dan Loughlin, Sergey Napelenok, Jesse O. Bash, Matthew D. Turner, Daven K. Henze, Peter B. Percell, Shunliu Zhao, Matthew G. Russell, Amir Hakami October 29, 2014 With grateful acknowledgement of funding from EPA through ORISE. Although this presentation has been reviewed by EPA and approved for presentation, it does not necessarily reflect official EPA agency views or policies.
Objective Optimize ozone benefits to human health & ecosystems of potential energy systems emissions reductions which could achieve regulatory endpoints through efficient sensitivity analysis . Health, CMAQ MARKAL Crop, & adjoint Optimization Ecosystem {ozone} Effects
Method emissions, met Calculate Create Execute exposure base case CMAQ effects (2007 12-km (6 mn ozone CONUS, 2007 NEI (health, concentrations) emissions) ecological) concentration (C) cost largest Δ J/ Δ (emissions) function (J) ∂ J/ ∂ C Calculate Optimize change of emissions Execute impact with weighted by CMAQ adjoint concentration effects ( ∂ health/ ∂ O3) ∂ J/ ∂ (emissions)
Method emissions, met Calculate Create Execute exposure base case CMAQ effects (2007 12-km (6 mn ozone CONUS, 2007 NEI (health, concentrations) emissions) ecological) concentration (C) cost largest Δ J/ Δ (emissions) function (J) ∂ J/ ∂ C Calculate Optimize change of emissions Execute impact with weighted by CMAQ adjoint concentration effects ( ∂ health/ ∂ O 3 ) ∂ J/ ∂ (emissions)
Quantifying Ozone Disbenefits Agricultural Ecosystems Estimate reduced productivity Evaluate biomass reduction from of five crops from cumulative exposure of timber to ozone, exposure of crops to ozone, expressed as W126, expressed as W126 for eleven species Approximate mortalities attributable to ozone through population-weighted exposure metric Human Health MARKAL CMAQ adjoint Ozone Effects
Estimating Premature Mortality 0 40 80 120 deaths / yr ppb Baseline mortality of exposed population, ≥ 30 yo 2007 6-month mean of hourly maximum O 3 Δ M = M 0 P (1 − e − β Δ C ) where M 0 is the baseline mortality, P is the exposed population over 30 years old, β is 0.0427% per ppb O 3 , and C is the 6-month mean of hourly maximum O 3 . 0 10 100 1000 (BenMAP , Jerrett et al., 2009) premature deaths
Ecosystem Ozone Exposure Metric 100 Hourly W126 Contribution (ppb h) Ozone Concentration (ppb) ⎡ ⎛ ⎞ ⎢ [ O 3 ] 90 80 ∑ ⎜ ⎟ W126 90 day = ( ) ⎢ ⎜ ⎟ − 126[ O 3 ] 60 1 + 4403 e ⎢ i = 1 ⎝ ⎠ ⎣ i,8am-8pm ( 40 20 ⎡ ⎤ B i 0 ⎛ ⎞ RYL = 1 − exp − W 126 ⎢ ⎥ 5 10 15 20 ⎜ ⎟ Hour of Day ⎢ ⎥ A i ⎝ ⎠ ⎣ ⎦ AOT40 (ppm h) 0 10 20 30 40 50 EPA (2007) 1.0 Cotton W126 W126 EPA (2007) Relative yield loss (RYL) Maize Cotton as a function of the W126 Maize 0.8 Potato Potato ozone exposure metric Soybean Soybean has been empirically Wheat Wang & Mauzerall (2004) Relative Yield Loss determined for 5 crops 0.6 Wheat Maize Soybean and 11 tree species. Wheat Wang & Mauzerall AOT4 AOT40 0.4 Cotton Maize Multiplying RYL by the Potato productivity determines Rice 0.2 Soybean the potential productivity Wheat Mills et al. (2011) loss (PPL) of each species. 0.0 0 20 40 60 80 100 Lehrer, A. et al.,EPA 452/R-07-002, 2007. W126 (ppm h)
Crop Degradation by Ozone Exposure Crop Production USDA National Agricultural Statistics Survey (NASS) 2007 crop production distributed in accordance with the Biogenic Emissions Landuse Database (BELD) v.4 Degradation Rate Time-averaged degradation rate over the 3-month growing period.
Timber PPL by Ozone Exposure Tree Biomass Distribution 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. Potential Productivity Loss (PPL) Rate Time-averaged degradation rate over the 3-month growing period. Blackard et al., 2008, Remote Sensing of the Environment
Connecting Ozone Effects to Emissions with CMAQ adjoint ∂ (O 3 effect) ∂ ( emissions ) ∂(emission ¡parameters) ∂(modeled ¡concentrations) ∂ (Ethane Emissions) ∂ (Health Disbenefit) ∂ (NOx Emissions) or ∂ (Crop Yield Losses) ∂ (Isoprene Emissions) or ∂ (Toluene Emissions) ∂ (Ecosystem Service Losses) ∂ (Chlorine Emissions) ∂ ! ¡ ¡ ¡ ¡ ¡=(F’) T (x, ¡ ¡ ¡ ¡ ¡) x ∂ y MARKAL CMAQ adjoint Ozone Effects
Emissions Proof of Concept Scenario Ozone Concentration NOx June 11-24, 2007 CMAQ 4.7.1 adjoint WRF meteorology Isoprene 2007 National Emissions Inventory
Sensitivity of Mortality to Emissions NOx time-averaged σ ⎡ ⎤ ∂ (mortality) Δ M = M 0 P (1 − e − β Δ C ) ∂ J = ⎢ ⎥ 180 ⎣ ⎦ ⎡ ⎤ max 1-hr O 3 ⎣ ⎦
Emissions Influences on Mortality Urban nature of the cost function leads to negative influence of NOx and positive influence of VOCs on mortality. Ethane NOx Isoprene σ σ
NOx Emissions Influences on Corn PPL Small VOC-limited regime near Chicago σ leads to negative influence of NOx Isoprene emissions from this location. Otherwise, NOx contributes to the ozone that reducing biomass yield of corn. Isoprene & ethane have similar levels of influence on corn degradation. σ Ethane σ
Emissions Influences on Tulip Poplar PPL More rural nature of cost Isoprene & ethane function leads to positive differ by orders of contributions for NOx & VOCs . magnitude in influence. Isoprene NOx Ethane σ σ σ
Mortality Health & Ecosystem Responses to NOx Differ Significantly Tulip Poplar σ Corn σ σ
Capabilities & Next Steps •Assessed the rate of degradation of •Complete the modeling human mortality, crop productivity, of May-August 2007 and timber biomass with O 3 exposure •Connect the NOx •Determined relative influence of emissions influences to NOx and various VOC emissions on the MARKAL framework these end points for a brief episode for propagating the in June 2007 influence of energy sector emissions changes •Confirmed hypothesis that emissions on ozone benefits controls can benefit human health differently than ecosystems MARKAL CMAQ adjoint Ozone Effects
Emissions Influences on Red Maple Biomass PPL Based on W126 calculated from summer 2007, potential productivity loss rates can be applied for each specific day of the early June episode. Through the adjoint, these are related to the influence of emissions of each species.
Emissions Influences on Red Maple Biomass PPL
Timber Ozone Exposure Effects AOT40 (ppm h) 0 20 40 60 80 100 120 140 1.0 Hardwood oods W126 W126 EPA (2007) Black Cherry Eastern Cottonwood Quaking Aspen 0.8 Red Alder Red Maple Sugar Maple Relative Biomass Loss Tulip Poplar 0.6 AOT40 AOT4 Mills et al. (2011) Birch or Beech Oak 0.4 Sof Softwood oods W126 W126 EPA (2007) Douglas Fir Eastern White Pine Ponderosa Pine 0.2 Virginia Pine AOT4 AOT40 Mills et al. (2011) Spruce or Pine 0.0 0 20 40 60 80 100 W126 (ppm h)
Connecting Ozone Effects to Emissions with CMAQ adjoint Spatial ¡Distribution ¡ δ E NO x δ E NO x δ E NO x δ E NO x δ E NO x δ E NO x of ¡Relative ¡ Contributions ¡ δ E NO x δ E NO x δ E NO x δ E NO x δ E NO x δ E NO x ∂ ({O 3,exposure }) δ E NO x δ E NO x δ E NO x δ E NO x δ E NO x δ O 3 ∂ ( emissions ) δ E NO x δ E NO x δ E NO x δ E NO x δ E NO x δ E NO x Modeling domain: Continental US δ E NO x δ E NO x δ E NO x δ E NO x δ E NO x δ E NO x 2007 δ E NO x δ E NO x δ E NO x δ E NO x δ E NO x δ E NO x CMAQ adjoint image ¡credit: ¡Google ¡Earth; ¡adapted ¡from ¡Daven ¡Henze’s ¡representation ¡of ¡sensitivity ¡methods
2007 Crop Production USDA National Agricultural Statistics Survey (NASS) 2007 crop production distributed in accordance with the Biogenic Emissions Landuse Database (BELD) v.4
Effects of Ozone Exposure on Crops ∂ J = ∂ W 126 ∂ RYL ∂ YL ∂ C O 3 ∂ W 126 ∂ RYL
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