Measurements and Modeling to Support Science/Policy Scenarios for Differential Effects of Particle Components

Transcription

1 Measurements and Modeling to Support Science/Policy Scenarios for Differential Effects of Particle Components Ted Russell* Georgia Tech *And a huge cast of others who have contributed This study was supported by the U.S. EPA under Grants RD , RD , RD , RD and RD , Southern Company, Georgia Power and others. This support should not construe endorsement.

2 Acknowledgements Funding US EPA Southern Company/Georgia Power NASA Gyeongsangnam do Province Government of Korea The Georgia Tech Emory Air Quality and Health Group

3 Linking Health Impacts to Air Quality and Sources Air Quality Measurement and Modeling S j C i Exposure Assessment E j (t) S ij (x,t) Atmospheric Processing Chemistry C i (x,t) X k (t) C i HE E k (t) S ij HE HE(x,t) D k (t) Epidemiologic Analysis

4 Presentation Focus: Concentrations and Source Impacts Air Quality Measurement Model C i (x,t) S ij (x,t) X How well do our current monitoring networks, instrumentation and models support air quality management accounting for differential PM species toxicities? Regulatory frameworks (JB Scenarios) Health studies

5 Initial Thoughts Measurements have spatial, temporal and compositional limitations Can not measure source impacts

6 Questions Measurements of the composition and modeling of the source impacts are central to developing the foundation for policies that account for the potential differential impacts of particle components What can we (currently) do? What might we able to do if asked? What is likely out of the question in the near term? What are the uncertainties? Are they big?

7 Measurements We can measure anything,* But We can t measure everything Monitoring is a different issue *By anything, I mean likely important species component(s) of PM, of various sizes. As always, there a few qualifications we can discuss

8 Monitoring: What you currently get PM 2.5 mass: wealth of data Daily, hourly (could go finer) Filter, TEOM, BAMS Composition (usually every 3 rd or 6 th day) 24 hr (usually) Metals (XRF) Ng m 3 level depending upon species, can go lower (ICP MS) Major ions (IC) Sub μg m 3 level Elemental and organic carbon (EC/OC) (TOR) Sub μg m 3 level What is measured is operationally defined Gases: O 3, NO x,2, CO, SO 2 (useful and important)

9 PM Monitors

10 What more could you want? Similar compositional detail more than every 3 rd /6 th day Composition at shorter time scales More constituents for reasonable cost Can we really use this right now in health studies? We don t seem to be using the hourly mass measurements much. Detailed organic speciation Maybe something between OC and total detail will do? Metal oxidation state Might this be important? More size resolution With corresponding chemical composition???? More spatial coverage Of all of the above

11 You can have it! PM instruments reviewed in Chow et al., (2008) Supersite Review Evaluated both commercial and research instruments: More available now Continuous Element Continuous Mass Sizing and Mass/Comp Integrated Mass/Composition Continuous Carbon Single Particle Continuous Ion Particle # /Size Chow et al., (2008) JAWMA, 58:2

12 First: How well do the current routine measurements support potential regulatory scenarios? Current mass standard: Well supported Speciation measurements also provide support for attainment modeling and management Scenario A (specific species found to have increased toxicity) If it is a currently measured species (specific metals, ionic compounds), well supported. Specific organics: poor support Scenario B (PM from specific sources have increased toxicity) Weak support (no current method to directly measure a source contribution). Provides data that can be used to estimate source impacts Significant uncertainties (discussed under modeling) Scenario C (some component(s) exacerbate toxicity by interactions) Poorly supported (acidity not measured, physico chemical interactions likely complex I have my doubts about this scenario being viable Scenario D (some species have minimal toxicity) Solid support as the likely species involved would be readily identified Exception: some fraction of OC Scenario E (everything) Strong support, except for carbonaceous fraction Measurements readily upgraded for a variety of species if needed.

13 Temporally Extended Composition Measurements*(similar compositional detail) Insight can be gained from SEARCH** Great data set being used for Epi studies Limited geographically Same methods (filter based), just everyday Cost goes up and better ways for EC/OC and ions Metals? Shorter time scales (plus everyday) Semi continuous EC/OC: Sunset, optical Semi continuous ionic: PILS, vaporization, Metals are tougher (but oh so important) Can be done for limited sets of metals (SEAS, PILS) *Great reference: JAWMA, (2008) 58:2 Supersite Issue ** Hansen, Edgerton et al., JAWMA articles

14 Organic Aerosol (OA) Composition Currently get limited information on carbonaceous composition from routine monitors EC/OC operationally defined (e.g., TOR vs. TOT) GC MS analysis of filters can provide concentrations of a range of individual OC s Need to have targets Expensive More limited OA composition possible and informative Aerosol mass spectrometry, FTIR to give more limited info. Structural information, HOA, OOA Water soluble OA and some components (PILS+sensors) Provides information on source impacts

15 Size, but no composition Important if ultrafine particle number is linked to health effects Size distributions readily measured Additional separation to get mass of finer PM size range Size distribution Mix of optical and mobility analyzers can go down to nm levels. Molecular clusters Level of detail achievable beyond what is likely usable in most health studies or health based NAAQS analyses

16 Size and Composition Impactors can provide size and composition for a limited set of compounds Metals Ions Some OC components Man power intensive Aerosol mass spectrometry Some significant limitations and expensive* Lower size range cut off Not species composition *but getting cheaper

17 Satellite Retrievals Satellite data can provide broad spatial coverage Growing availability of satellite retrievals, e.g., Aerosol Optical Depth (AOD) AOD related to light extinction, which is related to particulate loading Dimensionless measure of light extinction over the entire vertical column of air through the atmosphere Satellites MODIS (Moderate resolution Imaging Spectroradiometer 550 nm) Polar orbiting satellite (Terra), Frequency: 1 2 days, Resolution: 10 km MISR (Multi angle Imaging Spectroradiometer 558 nm) Also aboard Terra, 4 7 days, 17.6 km GOES (Geostationary Operational Environmental Satellite 550 nm) Geostationary orbit, ~15min, 4 km Linked to mass, but little compositional information. Can fill in spatial gaps (Koutrakis and coworkers, Paciorek &cw) Utilization of retrievals (Liu et al., 2004, 2009) Add AOD in Land Use regression modeling

18 Annual average predicted PM2.5 on days when AOD is retrieved (sample size = 2,570). High correlation with observations r = Annual average predicted PM2.5 on days when AOD is not retrieved (same model form but without AOD, N = 7,009 ). Lower correlation with observations r = 0.70 GOES AOD is not the strongest predictor of PM2.5 in this case, but it stratifies PM2.5 concentrations into two different spatial patterns. 18 Liu, Y; Paciorek, CJ; Koutrakis, P (2009) Env. Helth Persp., 117:

19 Measurement Information: Identifying Key Needs What additional measurements would be most valuable to conducting health studies?

20 Effect of Errors on Health Associations (Goldman et al., subm.) Objectives To characterize air pollutant exposure measurement error due to instrument precision and spatial variability To assess the impact of these measurement errors on a timeseries epidemiologic study Approach Use observations from monitors in Atlanta to estimate types of errors involved and simulate their effects on health association (RR) results Develop modified semi variograms describing variations between observed concentrations at one location and concentrations a distance away Atlanta Monitoring Network (Routine, SEARCH, ASACA)

21 Change in Risk Ratio per IQR due to Error Introduction (CVD and CO, 99 04) CO risk ratio per IQR Risk Ratio % bias to null 34% bias to null hr max. CO, central monitor Instrument error added Instrument + spatial error added Goldman et al., show the results for multiple pollutants, type of error added

22 Reducing Error Increased spatial information reduces error more than improved instrumental accuracy But it would take a lot of additional monitors Lamp post problem Does not address: What species might be more valuable Importance of frequency Models can help fill in spatial and temporal gaps But introduce an additional uncertainty

23 Measurements: Summary Routine networks Mass Significant compositional information (Scenarios A, D, E) Individual OC s missing (Poor support for some of A, B, E) Non routine measurements can provide great wealth of information Need to think what is most usable, biggest bang for buck Measurement technology can and will respond to health results Chicken and egg problem Key weaknesses OC composition Spatial coverage (satellites can help fill in gaps, but limted) Models can assist Possibly: UFP characterization May not be measuring the primary actor(s) (Weakens Scenarios A support) Need to relate PM to sources and potential enabling (Scenarios B,C) Models provide key

24 Roles Health Air Quality Modeling Source impacts (getting to Scenario B) Fill in spatial/temporal gaps in measured concentrations Estimate unmeasured concentrations Management/Regulation Source impacts (Scenario B) Control strategy determination (all Scenarios) Science

25 Traditional Approach in Health Assessment Health endpoints Sulfate Sulfate SDK FTM TUC JST μ g / m 3 YG Statistical Analysis Association 01/01/04 01/08/04 01/15/04 01/22/0 4 01/29/0 4 02/05/04 02/12 /0 4 02/19 /0 4 02/26 /0 4 03/04 /0 4 03/11 /0 4 03/18/04 03/25/04 04/01/0 4 04/08/0 4 04/15/0 4 04/22/0 4 04/29/0 4

26 Model Enhanced Analyses Data Health Endpoints Air Quality Model(s) Concentration Fields: C(x,t) and Source Impacts: S(x,t) Statistical Analysis Association between Concentrations or Source Impact and Health Endpoints

27 Types of Air Quality Models Empirical: Driven by observed concentration data Regression (e.g., LUR) Receptor (e.g., CMB, PMF, UNMIX) (Chemical) Transport: First principles, driven by estimated emissions, meteorology, chemistry Gaussian plume (AERMOD) Grid (CMAQ, CAMx)

28 Source Apportionment (SA) Modeling: Emprical vs. Emissions Based Models Source-compositions Emissions Inventory Chemistry Meteorology c i fijs j + e = i Source Impacts Empirical or Receptor (monitor) Air Quality Receptor Model: CMB, PMF, UNMIX Emissions-based Model (3D Air-quality Model): CMAQ, CAMX, AERMOD

29 Fractional Source Impacts Fractional Source Impacts SOC decreases in winter SOC AMMONIUM NITRATE SULFATE Coal Combustion Biomass burning Road dust Diesel vehicle Gasoline vehicle EBSP Summer MBSP Summer EBSP Winter MBSP Winter

30 Regression Models Improved estimates of concentrations in space/time Land use regression (LUR) C*(x,t)=f(C obs (x,t),additional information) Use multiple observations to develop spatially varying fields (C(x,t)) C(x i,t) = β 0 + Σβ 1ij *C(x j mon,t) + Σβ 2j *VMT ij + Σβ 3ij *LU j + β 4j *E j (t) + e ij Often based on saturation monitoring to provide additional detail Can integrate satellite retrievals Source apportionment Blanchard et al. SOA/POA estimation OC = a + b*ec + c*co + d*o 3 + e*lag(o 3 ) + f*so 4 + g*no 3 POC = b*ec + c*co SOC = d*o 3 + e*lag(o 3 ) + f*so 4 + g*no 3

31 Issues: Empirical Models Uncertainties (in rough order of importance) Observational network limitations Scarcity (spatially limited, temporal limitations on species) How well monitor(s) represents population exposure Capturing microenvironments (e.g., near source) Regression (LUR or other) can help fill Species captured/identified Limited quantification of components» Little organic speciation, metal oxidation state May not be observing the most important components Source apportionment of observed concentrations PM (or OC) from which sources have health effect(s)? For Regression Techniques Extrapolating model to other locations and times Within area extrapolation included Collecting added observations Monitor accuracy

32 Source Based AQMs (e.g., CMAQ) Air Quality Goals Air Quality/Health Impacts Controls Chemistry Pollutant Distributions Air Quality Model Emissions Meteorology

33 CMAQ Results Found little variation in daily fractional impact of sources to PM Fractional Source Contributions to Primary PM2.5 Epidemiologic analyses depends on variability Less impact on regulatory application 100% 80% 60% 40% 20% 0% Fraction of PM by source JULY JANUARY LDGV HDDV SDUST BURN Coal Lack of fractional impact variation tied to lack of variation in input emissions and meteorology

34 CMAQ Biases CMAQ (and other AQMs) have known biases OC: Low Incomplete knowledge of secondary organic aerosol (SOA) formation Crustals: High Effective emission estimates Corrected using MATS for regulatory applications Species specific adjustments RCFM: Reconstructed fine mass MATS: Model Attainment Test Software

35 Issues with Source based Air Quality for Identifying/Regulating PM Differential Toxicity Concerns (decreasing order of importance) Significant biases in OC Related to lack of understanding of secondary organic aerosol formation Hot area, research models show marked improvements For regulatory application, bias corrected using MATS adjustment Estimating uncertainties in source impacts and simulated concentrations Emissions uncertainties Can deal with these Spatial variation of inputs Temporal variation of inputs Emissions Same temporal distribution (almost) every day in every location Meteorology Scale (e.g., CMAQ at 4 km resolution) Apply fine scale model

36 Improving Source Apportionment Results and Uncertainty Estimation Various source apportionment techniques (receptor and source based) quantify source impacts Results can differ significantly Even between similar receptor modeling approaches Uncertainties poorly characterized Some methods provide a measure of uncertainty (untested) Address using ensembling of model results Combine the results of 5 different source apportionment results to develop an ensemble average for each day of a two one month periods (winter and summer) Receptor based: PMF, and three flavors of CMB (Regular: RG, Molecular marker: MM and LGO (integrates gaseous species info)) Source based: CMAQ

37 Re estimation of Impact and Uncertainty Initial and revised estimates of uncertainty and ensemble source impact for diesel vehicles on PM2.5 Fractional Diesel Source impact on PM 2.5 Fractional Source Impact Ensemble Results for DV on 7/12/01 Initial Ensemble Updated Ensemble CMB-RG CMB-LGO PMF CMB-MM CMAQ Ensemble Revised uncertainties in each method about 25-40% of fractional estimate Ensemble about 15%

38 Integrated Indicators for Health Association Analysis and Regulatory App. Develop integrated indicator for Epi analysis Integrated Mobile Source Indicator: IMSI=f(NO 2, CO, EC) Scenario B Stronger correlation than other measures Potential for regulatory application (Scenario B)? Indicators & Circulatory Outcomes RR IMSI_2 PMF MOBILE PMF DIESEL EC

39 Evolving Approach: Statistical Data Model Blending Given the issues with using different types of SA models and data directly, greater data model blending has been suggested Observed Concentrations Emissions Air Quality Model(s) Simulated Concentrations C i (x,t), S j (x,t) Feedback to modify emissions Likely best approach Can use C i (x,t) or S j (x,t) in health analyses Still need to quantify uncertainties

40 Where I think we can/will be in 5 10 years? Knowledge of pollutant health effects will support: Probably: Mix of A (e.g., specific metals, maybe a class of organics) and B (e.g., primary EC and OC from IC engines) Primary EC/OC from a source not directly measurable Potentially: D (e.g., sea salt PM being relatively non toxic) Doubts about: C (role of acids in the environment as enablers not evident, being able to show enabling gases) E, but rather unlikely to be there Measurement and modeling capabilities will support: Current approach Scenarios A, D B and C problematic

41 Problem with Scenario B What if we determine that PM from a source (e.g., diesels) has increased toxicity? We can not directly measure source impacts so: Measurement would be operationally defined (if definable) Possibly identify one or more surrogates that are measurable California links DPM to NOx, others to EC, could use combination (IMSI) Model estimation would introduce non trivial uncertainties And require significant attention to what modeling approach is viable NAAQS on source PM presents multiple complexities Have to identify indicator(s)

42 Summary PM Measurements Growing wealth of information on mass and some composition from routine networks Spatial limitations OC speciation Temporal limitations can readily be addressed using semi continuous monitors Instruments available to provide added detail OC Structural information Indication of primary versus secondary Specific species more resource intensive Temporal limitations readily addressed using semicontinuous monitors for EC/OC, ionics, metals more limited Size Measurement capabilities can readily support most aspects of JB scenarios A, D, E B and C largely excluded Air quality modeling Identify source impacts Partial support of JB Scenario B Central to air quality management Provide additional temporal, spatial and compositional information Introduce uncertainties Source apportionment adds % to initial uncertainty at receptor Is it better to just use observed concentrations directly?» Use multipollutant indicators