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NPS/CIRA Group Data Analysis and Receptor Modeling Receptor Modeling Meeting with Carol McCoy, ARD Division Chief CIRA, Fort Collins, CO 16 June 2011.

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Presentation on theme: "NPS/CIRA Group Data Analysis and Receptor Modeling Receptor Modeling Meeting with Carol McCoy, ARD Division Chief CIRA, Fort Collins, CO 16 June 2011."— Presentation transcript:

1 NPS/CIRA Group Data Analysis and Receptor Modeling Receptor Modeling Meeting with Carol McCoy, ARD Division Chief CIRA, Fort Collins, CO 16 June 2011

2 Data Analysis & Interpretation Ongoing (like IMPROVE) & Special Studies Are trends in pollutants increasing or decreasing? Why? Are there geographical differences in the trends? What does that say about sources? Does the monitoring data make sense? Should there be changes in monitoring protocols or has a recent change caused unexpected changes in the data? Do the models make sense? If not, why not? Industry is claiming XXX. Is that a valid claim? Sources have shut down. Was there a noticeable effect? New sources are planned. Will it matter? Is there a better location? For the next special study… What season is best? Will locations on opposite sides of a park see the same trends? What temporal and spatial scales are necessary for monitoring if the data to be useful? What are the expected concentrations?

3 Some Past Topics Source attribution – source types, source locations, international sources Hygroscopicity – water uptake Smoke – natural smoke vs anthropogenic, organic chemistry issues, markers Use of Satellites – fill in spatial patterns, verify models Improving Measurement Techniques – faster, cheaper, better resolution, more accuracy, better documentation Single particle characteristics – size, shape, mixture type Data & information dissemination – web sites, databases, software, books, papers, etc. Tracking trends – emissions, concentrations, visibility Natural Background – what is it and how can we get there? Nitrogen Deposition – why is it increasing, how can we better measure it, what sources are contributing? Human Perception – what do people see, value, remember?

4 What are the Trends in Nitrogen Wet Deposition?

5 April & July Chosen for ROMANS 2006

6 Could Climate Change Be Influencing Ammonia Deposition At Rocky Mountain? Which Meteorological Model Does A Better Job of Predicting Precipitation at Rocky Mountain?

7 2006 Measured Wind Directions By Height Is there more upslope (easterly) transport during the Summer? Do the Meteorological Models Capture this?

8 Model Evaluation

9 Spatial Patterns in Model Statistics More Model Evaluation…

10 Source-Receptor Modeling Photo by Ralph Turcotte Where do air pollutants come from?

11 Some Special Studies Conducted for Source Attribution and/or Optical and Physical Characteristics Grand Canyon, AZ WHITEX (1987) MOHAVE (1991?) Mt. Rainier, WA PANORAMAS (1984) PREVENT (1990) Big Bend, TX Scoping Study (1996) BRAVO (1999) Rocky Mountain, CO ROMANS (2006) ROMANS II (2009) Grand Teton, WY Grand Trends (2011) Yosemite, CA Smoke Characteristics (2002) Eastern U.S. NAPAP SEAVS Shenandoah Assessment Typically intensive monitoring for weeks to months, followed by months to years of data analysis and modeling. Often involving stake holders on all sides of the visibility or deposition issue.

12 “Deterministic” or “Source-based” Models Model(s) Emissions Chemistry Meteorology Predictions of concentrations and source attributions at a receptor of interest Common Problems: Lack of input data Expense Examples -SMOKE (Emissions Model) -CAMx, CMAQ (Air Quality Models) -Boundary conditions – GOCART, GEOSCHEM -MM5 & wrf mesoscale meteorology

13 Receptor Models Simple Statistical Analysis Measured Data at and/or near a receptor Sometimes Meteorology and/or Source Characterization Qualitative and/or Quantitative Source Attribution Problems: Simplifying Assumptions Sometimes long-term averages only. Examples: -Many trajectory analyses – forward & backward -Spatial & temporal patterns (EOF) -Chemical Mass Balance (CMB) -Positive Matrix Factorization (PMF)

14 Hybrid Models Further Analyses, more modeling, a melding of all available information. Results of Deterministic Models Results of Receptor Models Better Predictions of concentrations & source attributions, Insight into problems Examples -Model reconciliation -Tests against tracer data -Tests against synthetic data -Spatial & temporal patterns of error using BRAVO, IMPROVE, & CASTNet Data -Synthesized REMSAD & CMAQ -Scaled receptor techniques More Monitoring Data

15 Saharan Dust and Mineral Ratios

16 Factor Analysis Type 1 (Species by Time, 1 Site) What species vary similarly? Do they suggest source types? Factor 2 - 37% of variance Soil elements, Zn, Pb, SO 2 (Power plant, smelter) Factor 1 - 38% of variance S, Se, Na (TX, marine, industry) Factor 3 - 13% of variance V, Ni & OC, K (Oil & fires) Factor 4 - 12% of variance As, Cu (smelter) Example from Big Bend 1996 Scoping Study

17 BRAVO 1999

18 Factor Analysis Type 2 (Site by Time, 1 Species) What times, sites vary similarly? Do patterns suggest source areas? Example from BRAVO 1999

19 Trajectory Analysis Methods Trajectory Mass Balance Model Residence Time Type Analyses Quadrant Assignment Cluster Analysis Hit - No Hit Emissions Estimation Residence Time Conditional Probability Differential Probability Source Contribution Function Average and Maximum Fields Concentration Weighted Residence Time

20 Qualitative back trajectory analyses… Where does air come from on high and low pollution days ? Several statistical techniques used. This is the simplest. High SulfurLow Sulfur QUALITATIVE RECEPTOR MODELING

21 ROMANS II 2009 Ammonia

22 Winter Spring Summer Fall 2009 Mean Ammonia

23 Five Transport Patterns 1996 Scoping Study, Big Bend National Park Cluster Analysis

24 Concentrations by Transport Patterns Based on Clustered Trajectories 1996 Scoping Study - Big Bend National Park

25 Transport from Northeastern Colorado

26 Regression Techniques of Source Apportionment Assumption: The concentration measured at the receptor is some linear combination of the contributions of several sources Concentration = a 1 Source 1 + a 2 Source 2 + … Variations: CMB (Chemical Mass Balance), PMF (Positive Matrix Factorization), UNMIX Use source profiles and concentrations of several species to predict attributions for 1 measurement period. Source profiles are inferred from the measured concentration data in the latter two. TrMB ( Trajectory Mass Balance) Use many concentrations of 1 species and counts of trajectory endpoints in source regions to predict average attributions over a long period. Others...

27 TrMB Source Regions How do we know TrMB works? Inert Tracers from BRAVO.

28 TrMB modeling of BRAVO tracers at K-Bar, 9/17-10/28 (42 days). Negative concentrations were set to zero before summing. Attributions accurate to within the uncertainty of the measurement and standard error of the regression coefficients shown in bold and a larger font.

29 ROMANS 2006 – Automating thousands of TrMB Runs

30 Sensitivity Analyses … To Source Area Selection… To Height of Trajectories

31 WRF vs MM5 – ROMANS I April 23, 2006 Episode

32 Should be S Westerly Should be Easterly

33 Should be S Westerly Should be Easterly

34 Ammonia 2009 By Season WinterSpring Summer Fall

35 Ammonia 2009 By Season Winter Spring Summer Fall

36 Receptor Models Have Been Used For … Peer-reviewed literature Regulatory decisions (RHR) Education & Outreach Interagency Task Forces Interpretive Exhibits Booklets (Intro To Visibility) Understanding Trends “Reality” checks on models Counterbalance to industry Etc……

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