Project 5: Projecting and Quantifying Future Changes in Socioeconomic Drivers of Air Pollution and its Health-Related Impacts

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1 Project 5: Projecting and Quantifying Future Changes in Socioeconomic Drivers of Air Pollution and its Health-Related Impacts Noelle E. Selin Associate Professor Institute for Data, Systems and Society and Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology Associate Director, Technology and Policy Program Co-Is: Susan Solomon (MIT EAPS), Steven R. H. Barrett (MIT Aero/Astro), John Reilly (MIT Sloan) Science Advisory Board 16 May 2017

2 Objectives Objective 1. Improving methods and tools Further develop and enhance methods and tools for understanding and assessing the relative importance of global change, technologies, and policies to air quality, relative to other uncertainties. Objectives 2 and 3: Air pollution and health implications of policies and technologies Quantify the future implications of modifiable factors such as technologies and efficiency improvements in the energy and transportation sectors on regional differences in air pollution impacts. Characterize state- and regional-level carbon policy implementation measures with respect to their air pollution health co-benefits. Objective 4. Air toxics Assess how human exposure and impacts from different pollutants and mixtures may shift over time, and identify potential strategies for regions to shift to less toxic mixtures. Objective 5. Influence of Climate Identify the importance of climate (e.g., temperature, meteorological) change to the formulation of robust strategies for mitigating health and environmental impacts.

3 Outline Introduce Objectives Initial Results: Climate variability, change, and air quality (Objective 1, Objective 5) Cross-state pollutant transport (Objective 1, Objective 2-3)

4 Objective 1: Improving methods and tools MIT Integrated Economy-Air Quality-Health Assessment Framework 1 activity linked to emissions Economic SMOKE preprocessor; emission scaling Atmospheric chemistry and transport CAMx, GEOS-Chem USREP, ReEDS Economic and sector modeling Policies, strategies, technologies Health outcomes and economic estimates BenMAP

5 Objective 2/3: Air pollution and health implications of policies and technologies Potential for co-benefits from CO 2 policy at national scale Co-benefits exceed costs for cap-and-trade, clean energy policies at national scale Each line: a different economic assumption Vertical error bars: 95% CI for benefits For more information: Thompson, T.M., S. Rausch, R. K. Saari, and N. E. Selin "A Systems Approach to Evaluating the Air Quality Co-Benefits of U.S. Carbon Policies." Nature Climate Change 4,

6 Objective 2/3: Air pollution and health implications of policies and technologies Regional policies can have nation-wide impacts Co-benefits for Northeast clean energy, cap-andtrade policies Regional benefits exceed costs Some areas of potential disbenefit For more information: Thompson, T.M., S. Rausch, R. K. Saari, and N. E. Selin Air Quality Co-Benefits of Subnational Climate Policies. Journal of the Air and Waste Management Association

7 Objective 2/3: Air pollution and health implications of policies and technologies New work: What are the costs and (air pollution health co-)benefits of state-level Renewable Portfolio Standards? Policies -RPS repealed -RPS unchanged -RPS expanded US Regional Energy Policy CGE Model SMOKE Reduced-form air pollution cost model (EASIUR, InMAP ) Future welfare cost Future VSL-based benefits For more information: see Emil Dimantchev, Mei Yuan

8 Objective 4: Air toxics CO 2 controls can also have benefits for mercury emissions Projected emissions in 2050 More stringent mercury (end of pipe) regulations Energy transformation (e.g. climate policy) For more information: A. Giang, L. C. Stokes, D. G. Streets, E. S. Corbitt, and N. E. Selin "Impacts of the Minamata Convention on mercury emissions and global deposition from coal-fired power generation in Asia." Environmental Science and Technology 49,

9 Objective 4: Air toxics Small sources (Pb from general aviation) can have large impacts Lead emissions from general aviation aircraft over the U.S. can lead to $1 billion in damages from lifetime earnings reductions (due to IQ loss), plus an additional $0.5 billion due to lost productivity For more information: P. J. Wolfe, A. Giang, A. Ashok, N. E. Selin and S. R. H. Barrett Costs of IQ Loss from Leaded Aviation Gasoline Emissions. Environmental Science and Technology, 50 (17):

10 Objective 5: Climate Carbon policy can have direct benefit to U.S. air pollution US-average population-weighted annual concentrations: Daily max. 8hr O 3 PM ± ± ± ± ± ± 0.1 For more information: F. Garcia-Menendez, R. K. Saari, E. Monier, and N. E. Selin U.S. air quality and health benefits from avoided climate change under greenhouse gas mitigation. Environmental Science and Technology, 49,

11 Objective 5: Climate Climate policy health benefits and costs Climate policy relative to Reference scenario: Policy cost & mortality benefits (VSL-based) as fraction of REF scenario U.S. GDP: Modeled reductions: (U.S. population-weighted) > 1 µg m -3 and 2.5 ppb by 2100 Avoided U.S. deaths: 2050: > 10,000 (4,000-22,000) 2100: > 50,000 (19,000-95,000) Garcia-Menendez et al., ES&T, 2015

12 Objective 5: Climate and air quality Multi-model Framework Climate Chemistry GEOS-Chem CESM/CAM

13 Objective 5: Climate and air quality Dimensions of Uncertainty Change in US-averaged temperature simulated by CAM-Chem ensemble for reference climate sensitivity (3.0 deg C) and policy case

14 Objective 5: Climate and air quality Dimensions of Uncertainty Change in US-averaged temperature simulated by CAM-Chem ensemble for reference climate sensitivity (3.0 deg C) and policy case + alternative climate sensitivities

15 Objective 5: Climate Dimensions of Uncertainty Change in US-averaged temperature simulated by CAM-Chem ensemble for reference climate sensitivity (3.0 deg C) and policy case + alternative emissions pathways

16 Objectiv 5 : Climate and air quality Dimensions of Uncertainty Change in US-averaged temperature simulated by CAM-Chem ensemble for reference climate sensitivity (3.0 deg C) and policy case with overlapping periods simulated by GEOS-Chem

17 Results 1: Climate and air quality Natural variability can affect estimates of the climate penalty 2100 Reference scenario U.S.-average O 3 climate penalty estimated using 5 model initializations: Averaging period (years) F. Garcia-Menendez et al., GRL, 2017

18 Results 1: Climate and air quality Large changes in summer O3/PM2.5 detectable by 2100 in reference case O3 CESM/CAM-Chem GEOS-Chem PM2.5 See poster: D. Rothenberg Signal/Noise Ratio

19 Results 1: Climate and air quality It may be difficult to detect climate-forced changes in O 3 /PM 2.5 under policy scenarios No Policy Moderate Policy Ambitious Policy Signal/Noise Ratio Summertime Sfc Ozone

20 Results 1: Climate and air quality It may be difficult to detect climate-forced changes in O 3 /PM 2.5 under policy scenarios No Policy Moderate Policy Ambitious Policy Signal/Noise Ratio Summertime Sfc PM2.5

21 Pollution transport Results 2: Pollution Transport Air pollution does not consider political boundaries 8 states (46 counties) do not meet the 24-hour PM 2.5 standards * 4 states (20 counties ) do not meet the annual PM 2.5 standards * Clean Air Act (CAA) section 110(a)(2)(D)(i)(I) [prohibits] any source or other type of emissions activity within the State from emitting any air pollutant in amounts which will [ ] contribute significantly to nonattainment in, or interfere with maintenance by, any other State with respect to any such national primary or secondary ambient air quality standard * Source:

22 Transport Pollution transport rule I Results 2: Pollution Transport Transport Rule I May 2005: Clean Air Interstate Rule (CAIR) 28 states and the District of Columbia to reduce SO 2 and/or NO x emissions July 2008: US Court of Appeals for the DC Circuit remanded CAIR to the Agency July 2011: Cross State Air Pollution Rule (CSAPR) 23 states to reduce annual SO 2 and NO X emissions to help downwind areas attain the 24-Hour and/or Annual PM 2.5 NAAQS CSAPR implementation in 2015 and 2017

23 Results 2: Pollution Transport This work Objective: Assess the cross-state impacts in the US For every sector For every species For 2005 and 2011 (and 2018) Other work: Specific sectors [Greco et al.(2007), Bastien et al. (2015)] Specific locations/regions [Menut et al. (2000)] Specific species [Turner et al. (2015), Zhu et al. (2015)] Older inventories Simplified models

24 Modeling challenge Results 2: Pollution Transport Modeling Challenge Computationally expensive chemistry transport models simulate chemistry, transport and deposition Emissions Population exposure to PM 2.5

25 Modeling challenge Results 2: Pollution Transport Modeling Challenge Computationally expensive chemistry transport models simulate chemistry, transport and deposition Forward modeling: resolution in the output (impacts) Emissions ~X T E PM2.5 Population exposure to PM 2.5

26 Modeling challenge Results 2: Pollution Transport Modeling Challenge Computationally expensive chemistry transport models simulate chemistry, transport and deposition Forward modeling: resolution in the output Adjoint modeling: resolution in the input (sources) - allows us to quantify contribution to a quantity of interest (objective function) from control parameters Emissions Control parameters ~x t E w Population exposure to PM 2.5 Objective function

27 Results 2: Pollution Transport Adjoint Motivation The adjoint method is a computationally efficient way of calculating sensitivities of a (scalar) quantity of interest to a variety of parameters (e.g. emissions) It provides high resolution in the inputs (temporal, spatial and in terms of species), and it thus allows to quantify what parameters drive the aggregated quantity of interest (objective function) objective function (scalar) 3D w (i, j, k, t) Emissions species time

28 Results 2: Pollution Transport Adjoint applications so far Mostly scientific (focusing on specific processes and/or species and/or optimizing the model) In the past few years applications have also started moving toward policy: - Koo et al. (2013) on intercontinental aircraft pollution transport - Dedoussi and Barrett (2014) on origins of PM 2.5 mortalities in the US - Turner et al. (2015) on BC source apportionment (CMAQ adjoint) - Lee at al. (2015) on global PM 2.5 mortalities origins -

29 Results 2: Pollution Transport Cross-state pollution in the U.S. High number of inputs that drive impacts PM 2.5 exposure in every state origin of w (i, j, k, t) for each emissions species time of emission Can quantify exchange of air pollution impacts (PM 2.5 exposure) between the states

30 Results 2: Pollution Transport Cross-state: approach summary Using: Adjoint of GEOS-Chem CTM [Henze et al. (2007)] EPA NEI 2005 and 2011[US EPA (2008, 2013)] EPA derived Concentration Response Function [US EPA (2011)] Sectors defined: [Caiazzo et al. (2013); Dedoussi and Barrett (2014)] Electric power generation Commercial/residential Industry Road transportation Marine transportation Rail transportation w : = E w Change in population exposure

31 Results 2: Pollution Transport Regional Trans-boundary results Source-receptor matrix produced for all states, sectors and species Impacts expressed in premature mortalities Preliminary results Please do not cite or quote Bureau of Economic Analysis regions

32 Results 2: Pollution Transport Cross-state results Preliminary results Please do not cite or quote

33 Results 2: Pollution Transport Cross-state results Preliminary results Please do not cite or quote

34 Results 2: Pollution Transport Cross-state results Preliminary results Please do not cite or quote

35 Results 2: Pollution Transport Cross-state results Preliminary results Please do not cite or quote

36 Results 2: Pollution Transport Net Mortalities Net exchange = Total deaths exported total deaths imported +ve: exporter states ~ 0: neutral states --ve: importer states IN 4,690 KY 2,750 WV 2,530 PA 2,030 AL 1,520 MO 60 NV 50 VT 20 AR -2 CA -80 CT -1,000 GA -1,420 MA -2,110 NC -2,490 NY -10,400

37 Results 2: Pollution Transport Top 10 net fluxes Net flux of impacts between states, shown for the top 10 net fluxes Expressed in premature deaths

38 Acknowledgments Funding: EPA ACE Center Other work shown here: U.S. EPA Science to Achieve Results (STAR) Program, U.S. EPA Climate Change Division; MIT's Leading Technology and Policy Initiative; U.S. National Science Foundation Atmospheric Chemistry, Coupled Natural and Human Systems, and Arctic Natural Sciences programs; MIT Joint Program on Science and Policy of Global Change (and its industrial and foundation sponsors); MIT Research Support Committee Wade Fund; MIT Environmental Solutions Initiative; MIT Center for Environmental Health Sciences/National Institutes of Health (NIEHS)