Causes of Haze Assessment (COHA) Update

Current and near-future Major Tasks Visibility trends analysis Assess meteorological representativeness of 2002 (modeling base year) PMF modeling and case study Evaluate winds used in back-trajectory analysis

Trends Analysis Pages - Done Are there any statistically significant multi year trends in the haze levels and causes of haze? National maps and tables Individual site analysis

Trends Analysis for Aerosol Light Extinction Coefficients (1/Mm) in 20% Worst Days

Meteorological Representativeness of Backtrajectory Analysis Generate 8-day back-trajectories of all WRAP IMPROVE aerosol monitoring sites (every 3 hrs, from 3 starting heights) for 2003 and 2004 to give 5 years of trajectories - 80% Done, will be done by October. Produce residence time maps for 2002 and the 5- year period (2000 – 2004), plus maps of ratios and of differences of 2002 and the 5-year period for each site. Interpret the maps for each monitoring site and document on the COHA web site – Will be done by November

GRCA2 difference and ratio in residence time between 2002 and the 5-yr period 2000 to 2004 Difference Map Ratio Map

Receptor Modeling - Positive Matrix Factorization (PMF) and Chemical Mass Balance (CMB) Mathematical technique for determining the contributions of various sources to a given sample of air SP ij – Source Profile: Emissions of compound i from source j (100%). I j – Contribution of source j (  g/m 3 ). C i – Concentration of compound i (  g/m 3 ). CMBPMF InputBoth C and SPOnly C OutputOnly IBoth SP and I

Receptor Modeling - Positive Matrix Factorization (PMF) and Chemical Mass Balance (CMB) (Cont.) CMBPMF AssumptionsComposition of source emissions is relatively constant Emissions do not react or selectively deposit between source and receptor (mass is conserved) Source profiles are linearly independent For CMB, all major sources should be included in the model LimitationsReactive compounds Only identifies categories of sources, not individual sources Identifies only relative contributions, not mass emission rates LimitationsMust know source profiles High sensitivity to uncertainty / error in source profiles Omission of a source can lead to large errors Pure statistical model large number of samples (100+) are needed Need to make arbitrary decision of the number of sources (factors)

Positive Matrix Factorization for Groups of Sites Using 2000 – 2004 Aerosol Data Grouping of Class I areas by TSSA source region attribution of sulfate and nitrate – 22 groups including Hawaii and Alaska Ready to go. Waiting for 2004 aerosol data, will be finished in ~1 month once data are available

PMF Running Parameters Running Mode: Robust Mode, the value of outlier threshold distance = 4.0 (i.e. if the residue exceeds 4 times of the standard deviation, a measured value is considered outlier). Error Mode (decides the standard deviation of the data): EM = -12 (based on observed value) FPEAK and FKEY Matrix (controls the rotation) – default: 0 (central), may try different numbers

PMF Input Data – Data Value and Uncertainty aerosol PM10 and PM2.5 mass and chemical speciation data from the VIEWS web site (Al data are excluded due to the large uncertainties in measurements). Data are screened to remove the days when either PM10 or PM2.5 mass concentration is missing. Data value and associated uncertainty If data is missing Then data value = geometric mean of the measured values uncertainty = 4 * geometric mean of the measured values Else if data bellows detection limit data value = 1/2 * detection limit uncertainty = 5/6 * detection limit Else data value = measured data uncertainty = analytical uncertainty + 1/3 * detection limit

PMF Output Source profiles

PMF Output (Cont.) Contributions of each source to aerosol mass and light extinction for each sampling day  g/m 3

Other Planned Work (FY06) 1.Case study for selected sites: PMF modeling for individual sites 2.Compare PMF results for the selected sites based on group modeling and individual modeling 3.Compare PMF smoke factor contribution with 2002 fire emissions inventory and DRI fire database 4.Combine PMF modeling results with the backtrajectories and emission inventories to investigate the major source regions of certain aerosol sources (e.g. smoke) for each site 5.Episode analysis based on PMF results

Other Planned Work (FY06) cont. 6.Redo aerosol composition statistics using baseline period? 7.Evaluate winds used in back-trajectory calculations- Measurement data for evaluation collected- Evaluation done by December or so 8.Prepare overview page for each site: list of products available for the site

Comparison of Source Factors Based on PMF Modeling for AGTI1 and Group 6 (AGTI1, JOSH1, PINN1, PORE1, RAFA1, SAGA1, SAGO1)

Comparison of Factor Contributions to AGTI1 PM 2.5 Based on PMF Individual and Group Modeling  g/m 3

Backtrajectory Analysis for PMF Factor - Example Backtrajectory analysis for PMF modeled factor 5 (BWS5) (Weighted – Unweighted). This serves to confirm that the factor 5 is in actual fact a “vegetative burn” factor from wildfires to the northwest of Boundary Waters Canoe Area IMPROVE site (Engelbrecht et al., 2004).

Causes of haze questions- 1. What are the aerosol components responsible for haze? – Aerosol summary for 5 baseline period 2. What is the role of meteorology in the causes of haze? – Baktrajectory analysis of transport, difference of 2002 from average, episode analysis 3. What are the emissions sources responsible for haze? – PMF analysis, off-shore shipping analysis, dust analysis, fire analysis, EI data comparison 4. Are there any detectable and/or statistically significant multi-year trends in the causes of haze? – Trend analysis already completed