GLIMPSE An Approach for Determining Optimal Control Strategies for - - PowerPoint PPT Presentation

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

GLIMPSE

Although this presentation has been reviewed by EPA and approved for presentation, it does not necessarily reflect official EPA agency views or policies. With grateful acknowledgement of funding from EPA through ORISE.

Objective

Optimize ozone benefits to human health & ecosystems of potential energy systems emissions reductions which could achieve regulatory endpoints through efficient sensitivity analysis.

Health, Crop, & Ecosystem Effects CMAQ adjoint

{ozone}

MARKAL Optimization

Method

Calculate exposure effects

(health, ecological)

Execute CMAQ

(6 mn ozone

concentrations)

Create base case

(2007 12-km CONUS, 2007 NEI emissions)

Calculate change of impact with concentration

(∂health/∂O3)

Execute CMAQ adjoint Optimize emissions weighted by effects emissions, met concentration (C) cost function (J) ∂J/∂C ∂J/∂(emissions)

largest Δ J/Δ(emissions)

Method

Calculate exposure effects

(health, ecological)

Execute CMAQ

(6 mn ozone

concentrations)

Create base case

(2007 12-km CONUS, 2007 NEI emissions)

Calculate change of impact with concentration

(∂health/∂O3)

Execute CMAQ adjoint Optimize emissions weighted by effects emissions, met concentration (C) cost function (J) ∂J/∂C ∂J/∂(emissions)

largest Δ J/Δ(emissions)

Quantifying Ozone Disbenefits

CMAQ adjoint MARKAL

Human Health Agricultural Ecosystems

Ozone Effects Estimate reduced productivity

• f five crops from cumulative

exposure of crops to ozone, expressed as W126 Approximate mortalities attributable to ozone through population-weighted exposure metric Evaluate biomass reduction from exposure of timber to ozone, expressed as W126, for eleven species

Estimating Premature Mortality

2007 6-month mean of hourly maximum O3 Baseline mortality of exposed population, ≥30 yo deaths / yr

0 40 80 120 ppb

ΔM = M0P(1− e−βΔC)

where M0 is the baseline mortality, P is the exposed population over 30 years old, β is 0.0427% per ppb O3, and C is the 6-month mean of hourly maximum O3.

(BenMAP , Jerrett et al., 2009)

0 10 100 1000 premature deaths

Ecosystem Ozone Exposure Metric

W12690 day = [O3] 1+ 4403e

−126[O3]

( )

⎛ ⎝ ⎜ ⎜ ⎞ ⎠ ⎟ ⎟

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

∑

⎡ ⎣ ⎢ ⎢ ⎢

1.0 0.8 0.6 0.4 0.2 0.0 Relative Yield Loss 50 40 30 20 10 AOT40 (ppm h) 100 80 60 40 20 W126 (ppm h) W126 W126 EPA (2007) Cotton Maize Potato Soybean Wheat Wang & Mauzerall (2004) Maize Soybean Wheat AOT4 AOT40 Cotton Maize Potato Rice Soybean Wheat

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

Mills et al. (2011)

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)

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

Relative yield loss (RYL) as a function of the W126

• zone 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.

Crop Degradation by Ozone Exposure

Crop Production Degradation Rate

USDA National Agricultural Statistics Survey (NASS) 2007 crop production distributed in accordance with the Biogenic Emissions Landuse Database (BELD) v.4 Time-averaged degradation rate

• ver the 3-month

growing period.

Timber PPL by Ozone Exposure

Tree Biomass Distribution Potential Productivity Loss (PPL) Rate

Time-averaged degradation rate

• ver the 3-month

growing period.

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.

Blackard et al., 2008, Remote Sensing of the Environment

Ozone Effects

Connecting Ozone Effects to Emissions with CMAQ adjoint

MARKAL CMAQ adjoint

∂(emission ¡parameters) ∂(modeled ¡concentrations)

∂(Ethane Emissions)

∂(NOx Emissions) ∂(Isoprene Emissions)

¡ ¡ ¡ ¡ ¡=(F’)T(x, ¡ ¡ ¡ ¡ ¡)

∂! x ∂y

∂(O3 effect) ∂(emissions)

∂(Toluene Emissions) ∂(Chlorine Emissions) ∂(Health Disbenefit)

• r

∂(Crop Yield Losses)

• r

∂(Ecosystem Service Losses)

Proof of Concept Scenario

June 11-24, 2007 CMAQ 4.7.1 adjoint WRF meteorology 2007 National Emissions Inventory

Ozone Concentration Emissions

Isoprene NOx

σ

NOx

Sensitivity of Mortality to Emissions

∂J = ∂(mortality) 180 ⎡ ⎣ ⎢ ⎤ ⎦ ⎥

max 1-hr O3 ⎡ ⎣ ⎤ ⎦

time-averaged

ΔM = M0P(1− e−βΔC)

σ

NOx

Emissions Influences

• n Mortality

Ethane Isoprene

σ

Urban nature of the cost function leads to negative influence of NOx and positive influence of VOCs on mortality.

σ σ

σ

Isoprene

Emissions Influences on Corn PPL

NOx

Small VOC-limited regime near Chicago leads to negative influence of NOx 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.

Ethane

σ σ σ

Emissions Influences

• n Tulip Poplar PPL

Isoprene NOx Ethane

More rural nature of cost function leads to positive contributions for NOx & VOCs. Isoprene & ethane differ by orders of magnitude in influence.

Mortality

σ

Corn

σ

Tulip Poplar

σ

Health & Ecosystem Responses to NOx Differ Significantly

Ozone Effects

Capabilities & Next Steps

MARKAL CMAQ adjoint

• Assessed the rate of degradation of

human mortality, crop productivity, and timber biomass with O3 exposure

• Determined relative influence of

NOx and various VOC emissions on these end points for a brief episode in June 2007

• Confirmed hypothesis that emissions

controls can benefit human health differently than ecosystems

• Complete the modeling
• f May-August 2007
• Connect the NOx

emissions influences to the MARKAL framework for propagating the influence of energy sector emissions changes

• n ozone benefits

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

1.0 0.8 0.6 0.4 0.2 0.0 Relative Biomass Loss 100 80 60 40 20 W126 (ppm h) 140 120 100 80 60 40 20 AOT40 (ppm h) Hardwood

• ods

W126 W126 Black Cherry Eastern Cottonwood Quaking Aspen Red Alder Red Maple Sugar Maple Tulip Poplar AOT4 AOT40 Birch or Beech Oak Sof Softwood

• ods

W126 W126 Douglas Fir Eastern White Pine Ponderosa Pine Virginia Pine AOT4 AOT40 Spruce or Pine

Mills et al. (2011) Mills et al. (2011) EPA (2007) EPA (2007)

δO3 δENOx

Spatial ¡Distribution ¡

• f ¡Relative ¡

Contributions ¡

image ¡credit: ¡Google ¡Earth; ¡adapted ¡from ¡Daven ¡Henze’s ¡representation ¡of ¡sensitivity ¡methods

Connecting Ozone Effects to Emissions with CMAQ adjoint

CMAQ adjoint

∂({O3,exposure}) ∂(emissions)

δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx δENOx

Modeling domain: Continental US

2007

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 = ∂W126 ∂CO3 ∂RYL ∂W126 ∂YL ∂RYL