Quan%fying Impacts of Emission Reduc%ons on Environmental Jus%ce - PowerPoint PPT Presentation

Quan%fying Impacts of Emission Reduc%ons on Environmental Jus%ce and Human Health in a Metropolitan Area Robyn Chatwin-Davies, Amir Hakami & Adjoint Development Team

Introduc%on • Globally, ambient par?culate maBer (PM) pollu?on accounts for approximately 3.2 million premature deaths every year, and is considered one of the largest environmental health risks • Environmental jus?ce inves?gates how environmental risk factors are associated with socioeconomic status (SES; e.g. income, race, etc.) o Previous studies have found that lower income households are more oPen located in areas with higher air pollu?on

Objec%ves For PM 2.5 exposure in New York City and surrounding areas: 1. Iden?fy emission control measures to improve: a) human health b) environmental equity across income groups 2. Contrast the sensi?vi?es of health and equity measures to emission reduc?ons, to beBer coordinate air quality management strategies

Forward Sensi%vity Analysis SOURCES RECEPTORS Forward: where impacts go to … 4

Backward/Adjoint Sensi%vity Analysis SOURCES RECEPTORS Adjoint/backward: where influences come from 5

Mone%zed Health Impacts: Marginal Benefits Δ $ Δ $ Δ Mortality × Δ Concentrations = × Δ Emissions Δ Mortality Δ Concentrations Δ Emissions Economics Epidemiology Air quality modeling

Adjoint cost func%on • We can use the adjoint method so long as • our “policy” metric can be condensed into a single number, called the adjoint cost function , • The functionality between the metric and concentrations is known. • Health outcomes, precipitation to a lake, average concentrations, crop damage, etc. • Example: nationwide mortality due to long- term exposure.

Area of Study • 1km grid focused on New York City and surrounding area • Focused on PM 2.5 concentra?ons • CMAQ 5.0 and its adjoint • July 1 st – 14 th , 2008 • Income data was taken from the U.S. Census: 12-month household income, divided into 16 income bins

Health Benefits vs. Health Inequity • Health Benefits: Mone?zed domain-wide reduc?on in mortality per ton of emissions (primary PM 2.5 ) • Chronic exposure mortality • Local baseline mortality • Health Inequity: Change in domain-wide inequity metric (or its mone?zed form) due to one tonne reduc?on in emissions • Disparity in share of PM 2.5 mortality risk • Results only shown for primary PM emissions

Es%ma%ng Environmental Inequity from PM 2.5 Hypothe?cal Concentra?on Curve • Concentra)on Curve plots the 100% Cumula?ve Frac?on of PM 2.5 Health Burden frac?on of PM 2.5 health burden 90% 80% earned by the cumula?ve frac?on of 70% the popula?on, sorted by income 60% • Concentra)on Index is double the 50% 40% area between the Concentra?on 30% Curve and the Line of Equity 20% o Index ranges from 0 – 1 10% 0% o 0 – Indicates equity 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% o 1 – Indicates inequity Cumula?ve Frac?on of Popula?on, sorted by income Line of Equity Concentra?on Curve

Results

Marginal Benefits of Reduced Mortality • Annual health benefits experienced across the region • For a reduc?on of primary PM emissions by 1 tonne/year at that loca?on • Highly sensi?ve to popula?on

Current State of Environmental Equity Concentra?on Curve for PM 2.5 Health Burden Inequity, CMAQ 1 0.9 Concentra)on Index: Cumula?ve Frac?on of PM 2.5 Health Burden CMAQ = 0.0140 0.8 LUR = 0.0122 – 0.0152 0.7 Concentra?on Index = 0.0140 0.6 Typical values: 0.5 Los Angeles = 0.020 – 0.031 0.4 (Su et al., 2009) 0.3 Detroit = 0.010 – 0.067 0.2 (Martenies et al., 2017) 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Cumula?ve Frac?on of Households, sorted by Income Concentra?on Curve Equity

Sensi%vity of Health Burden Inequity • Posi?ve sensi?vity = a reduc?on in emissions reduces inequity • Biggest posi?ve sensi?vi?es occur in areas with a high propor?on of low-income people • Nega?ve sensi?vity = a reduc?on in emissions aggravates inequity • Biggest nega?ve sensi?vi?es occur in areas with a high propor?on of high-income people

Mone%zed Health Burden Inequity • Represents the amount of money that would need to be added to the system to create an equivalent reduc?on in inequity • Equivalent to reducing 1 tonne/year of Primary PM at that loca?on.

Synergis%c Emission Reduc%ons on Equity and Health Impact of 1 tonne/year Reduc?on in Primary PM Emissions at Each Loca?on $6M Monetary Value ($ millions) of Reduced PM 2.5 Inequity $4M $2M $0 $2M $4M $6M $8M $10M -$2M -$4M -$6M Marginal Health Benefit ($ millions) from Reduced PM 2.5 Exposure

Synergis%c Emission Reduc%ons on Equity and Health

Emission Reduc%on Case Study #2 #3 #4 #1 Health Benefits Equity Benefits Equity Benefits Scenario ($ billion USD) ($ billion USD) (% Reduc)on in Inequity) #1: Priori)ze Health $ 4.01 $ 0.15 13.9 % #2: Priori)ze Equity $ 3.48 $ 1.02 95.1 % #3: Percen)le Scores $ 3.65 $ 0.98 91.4 % #4: Combined Mone)za)on $ 3.71 $ 0.95 88.3 %

Conclusion • Considering synergis?c emission reduc?ons can lead to substan?al benefits for both health and equity • This can provide policy-relevant informa?on to beBer coordinate air quality policies that target various endpoints

Adjoint vs. Reduced Form Models • Development of an adjoint model is difficult • It’s now done • Adjoint simula?ons are computa?onally expensive • Quite affordable for medium size domains • May necessitate episodic simula?on • Preparing high resolu?on inputs is a demanding task • Also true for reduced form models • Adjoint is as accurate as the underlying model • All the results in a single run

• Carleton Atmospheric Modelling Group • Burak Oztaner, Shunliu Zhao, Melanie Fillingham, Marjan Soltanzadeh, Angele Genereux, Sina Voshtani, Rabab Mashayekhi, Pedram Falsafi, Sahar Saeednooran, MaBhew Russell, Amanda Pappin • New York City Department of Health and Mental Hygiene • Iyad Kheirbek, Kazuhiko Ito • ICF Interna?onal Acknowledgements • Jay Haney, Sharon Douglas • CMAQ-Adjoint Development Team • MaB Turner, Daven Henze (University of Colorado); Shannon Capps (Drexel University); Peter Percell (University of Houston); Jaroslav Resler (ICS Prague); Jesse Bash, Sergey Napelenok, Kathleen Fahey, Rob Pinder (USEPA); Armistead Russell, Athanasios Nenes (Georgia Tech); Jaemeen Baek, Greg Carmichael, Charlie Stanier (University of Iowa); Adrian Sandu (Virginia Tech); Tianfeng Chai (University of Maryland)