in Environmental Modeling and Indicators

1. Managing in Environmental Modeling and Indicators

2. Einstein got it right As far as … mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality. Einstein’s remarks to the Prussian Academy of Sciences

3. Why does uncertainty matter? 10.6 million tons of SOx emitted in 2003 in the U.S. 14.4 ug m2 Hg dry deposited in Lake Waccamaw, NC per year U.S. EPA’s Mercury Rule Credible metrics  Credible decisions 0.131 IQ loss per ppm increase in Hg

4. Consideration of Uncertainty inSome Air Quality Examples

5. Current AQI - Ozone

6. Current AQI - Particle Pollution(PM2.5)

8. Current AQI – Particle Pollution(PM2.5)

9. Current AQI - Particle Pollution(PM2.5)

10. Current AQI - Particle Pollution(PM2.5)

12. National Air Quality ForecastingPlanned Capabilities Current: 1-day forecast guidance for ozone • Developed and deployed initially for Northeastern US, September 2004 • Deploy Nationwide by 2009 Intermediate (5-7 years): • Develop and test capability to forecast particulate matter concentration • Particulate size < 2.5 microns Longer range (within 10 years): • Extend air quality forecast range to 48-72 hours • Include broader range of significant pollutants

14. EnviroFlash • EnviroFlash gives individuals instant information that they can customize for their own needs, allowing them to protect their health and their family’s. • Air quality information allows them to adjust your lifestyle when necessary on unhealthy air quality days. • EnviroFlash is especially helpful for people with sensitivities, such as the young, people with asthma, and the elderly.

16. Satellite Data • Emerging source of data(1-10 km grids) • Spatial and Temporal Gaps • Algorithm uncertainties(clouds) • Routinely available data

17. Can Satellite Data help assess influences of large wildfires on surface PM2.5 for public health assessments? Alaskan Fire Complexes June 30, 2004 Data source: NASA MODIS-Aqua

18. 18 July 2004 Smoke from Alaskan/Yukon Fires Over U.S.

19. 19 July 2004 Smoke from Alaskan/Yukon Fires Impact U.S.

20. 12 September 2002 Linear Interpolation Surface PM2.5 Monitors MODIS AOD Satellite measurements capture important spatial gradients and meteorology influences, extremely important for public health side of air quality.

21. “Real-Time” Fire and SmokeInformation Two Different User Perspectives Of Uncertainty

23. CMAQ Uncertainty Forecasting versus Environmental Decision Making (i.e. Developing AQ Implementation Plans

24. CMAQ Modeling System Fifth Generation Mesoscale Model (MM5) (WRF in 2005) NOAA Weather Observations EPA Emissions Inventory Met-Chem Interface Processor (MCIP) Met. data prep SMOKE Anthro and Biogenic Emissions processing CMAQ AQ Model- Chemical-Transport Computations Hourly 3-D Gridded Chemical Concentrations

25. 7/21/04: 8-hour Peak Ozone Forecast Observed 7/22/04: 8-hour Peak Ozone Forecast Observed Forecast and Observed Surface Ozone Distributions

27. June 9th Peak Ozone Forecast

29. CMAQ Sulfate PMJuly 17- Aug. 13 Average Observed Data REMSAD

30. CMAQ’s predictions are much closer to the observations than REMSAD’s

31. Models in Environmental RegulatoryDecision Making • Committee on Models in the Regulatory Decision Process • Board on Environmental Studies and Toxicology • Division on Earth and Life Studies • June 2007

32. Life-Cycle of a Model Problem Formulation Conceptual Model Constructed Model Model Use

33. Life-Cycle Model Evaluation • All models should have a life-cycle evaluation plan of a size and complexity commensurate with its regulatory significance • Plan should address how model evaluation will occur throughout a model’s life cycle • The committee did not make organizational recommendations of how EPA should achieve this • A conceptual commitment to life cycle model evaluation is needed

34. Assessing/Communicating Uncertainty • Two Approaches • Represent uncertainties probabilistically and calculate the probability distribution of any model result • difficult to carry out • obscures the sensitivities of the outcome to individual sources of uncertainty • Scenario assessment and/or sensitivity analysis • often more transparent • may ignore important information corresponding to other scenarios not included in assessment and whatever is known about their relative likelihoods

35. Assessing/Communicating Uncertainty Many instances require probabilistic methods to properly characterize uncertainties, propagate them through the modeling exercise, and clearly communicate the overall uncertainties Recommend the use of case-specific hybrid approaches in which some unknown quantities are treated probabilistically, and others can be manipulated in a scenario-assessment mode by the decision makers Requires communication between modelers and decision-makers

36. Examples of TransAtlantic Activity on Uncertainty Analyses • Harmoni-CA guidance on uncertainty for Water Framework Directive • Dutch National Institute for Public Health and the Environment (RIVM) work on uncertainty in environmental information • TransAtlantic Uncertainty Colloquium (TAUC) in 2006 in Washington, DC • US EPA call for research in uncertainty in integrated models

37. Asked to estimate vulnerability to pollution in a Copenhagen catchment, 5 consultants gave 5 different estimates Refsgaard et al, 2005. Harmoni-CA Guidance to Uncertainty Analysis Harmoni-CA guidance on uncertainty for Water Framework Directive “Estimates of … level of confidence and precision of … results provided by … monitoring programmes shall be given ..” Water Framework Directive

38. c(Hg) = f ( ) • c(MeHg) in fish • fish consumption rate • no. of pregnant women who consume fish US EPA call for research in uncertainty in integrated models How does uncertainty propagate within and across models used to develop metrics? Eulerian air model (36 x 36 km grid) Hg deposition Present value of income loss/child = $8,800 * d(IQ) d(IQ) = 0.131 * c(Hg) in maternal hair

39. Examples of proposed research using Sensitivity Analysis for air quality models, using Bayesian approaches to deal with discount rates to analyze the economic impacts of climate change, and using Monte Carlo to look at probability distributions in mercury regulation. US EPA call for research in uncertainty in integrated models

40. 2006 TransAtlantic Uncertainty Colloquium (TAUC) in Washington, DC To discuss the legal, scientific, and political consequences of uncertainty in environmental models and metrics from the perspective of the EC and the US See http://www.modeling.uga.edu/tauc/

41. EC-US EPA Implementing Arrangement • Cooperation may focus on methods to: • formally analyze and manage uncertainty; and • provide stakeholders with uncertainty information so they may engage fully in environmental policy-making Adapted from 2007 EC-EPA Implementing Arrangement

42. Opportunities for continued collaboration Joint call for research on Uncertainty Analyses Funding institutions across US and EU could collaborate on similar language (encouraging TransAtlantic investigation), jointly review proposals, and co-host workshops Staff Exchange Institutionalize program to allow environmental agency staff to conduct discrete, short-term projects in uncertainty analyses and indicator development