Causes of Haze Assessment Update for the Haze Attribution Forum Meeting By Marc Pitchford 9/24/04

Evaluation of the Transport Regression Attribution Approach Sensitivity to back trajectory start heights Relative merits of EDAS and FNL wind fields for back trajectories Utility of a Pacific coastal source region Annual versus seasonal (cool season and warm season) regression assessments Appropriate air quality parameters Regressions forced through zero or not

Transport Regression Model The model finds regression coefficients (A i ) for each source region (i) that produces a best fit to the following relationship between the measured air quality (e.g. sulfate concentration) and residence time in each source region (T i ) for several years of data Air Quality = Σ (A i x T i ) = A 1 T 1 + A 2 T A n T n Residence time is the amount of time air is calculated to be in a source region using a back trajectory model.

Source Regions of Grand Canyon (GRCA2) PAC CAN ATL Gulf MEX NW NE SE CA NVUTCO NM SWAZ SEAZ NWAZ NEAZ EDAS Domain

Comparison of Regression Modeling Results at Grand Canyon Using Residence Time (Based on EDAS) at 10m, 500m, 1500m and All Three Heights Sulfur Concentration (ng/m 3 ) Results are similar, so we will use a start height of 500 meters.

Comparison Between Measured and Calculated Sulfur Concentrations (ng/m 3 ) at Grand Canyon Based on Trajectory Regression at 500m Using EDAS The regression model did a relatively good job. But it missed some peak values. Sulfur Concentration (ng/m 3 )

Normalized Total Residence Time Maps at Grand Canyon

Comparison of Trajectory Regression Results for Sulfur at GRCA2 Using FNL and EDAS Similar results are found in GRCA2 using FNL and EDAS as HYSPLIT trajectory modeling inputs. Sulfur Concentration (ng/m 3 )

Comparison of Trajectory Regression Results at BADL1 Using FNL and EDAS Similar regression results at BADL1 using FNL and EDAS Smaller contribution from the Pacific Ocean based on FNL

Comparison of Trajectory Regression Results at MORA1 Using FNL and EDAS Similar regression results at MORA1 using FNL and EDAS

300Km 500Km GRCA - Create a coastal area (300 Km or 500 Km off of the coast line) in the Pacific Ocean Trajectory regression modeling suggests that a large amount of particulate sulfate is from the Pacific Ocean. Is it from the coastal area or from longer distance?

Comparison of trajectory regression results (Using EDAS data) at GRCA with / without the dividing up of the Pacific Ocean 1. Similar regression coefficients are found at GRCA2 with / without the dividing up of the Pacific Ocean. 2. About half of the sulfur from the Pacific Ocean is from within 300Km off of the coast – Shipping emissions, long range transported aerosols, or trajectory uncertainties?

Seasonal Variation of the Regression Results for sulfur at GRCA (EDAS with 300Km Coastal Area) Sulfur Concentration (ng/m 3 ) There are some seasonal variations in the regression coefficients. But the source areas with big variations in regression coefficients are those with relatively less end points (i.e. with relatively lower contribution to total sulfur concentration) Overall, trajectory regressions give similar results in GRCA2 with and without the consideration of the seasonal variations.

Comparison of Trajectory Regression Results for Nitrate With/Without Seasonal Division (EDAS with 300Km Coastal Area) Contribution to Nitrate Overall trajectory regression results for nitrate are similar in GRCA2 with and without the consideration of the seasonal variation. The regression is better in the warm season (multiple regression with no intercept R 2 ~ 0.75) than the cool season (multiple regression with no intercept R 2 ~ 0.4).

Contributions to Sulfur, Extinction Coefficient, OC and Nitrate at GRCA2 (EDAS with 300Km Coastal Area) The contributions to sulfur and B ep are similar, while the contributions of southwestern Arizona to Nitrate and California to OC are higher. The regressions for sulfur and B ep (multiple regression with no intercept R 2 ~ 0.8) are better than for OC and Nitrate (multiple regression with no intercept R 2 ~ 0.5)

Comparison of Percentage Contributions to Bep, Sulfur and Nitrate Concentrations at Joshua Tree Wilderness Area Regression for Sulfur (multiple regression with no intercept R 2 ~ 0.8) and B ep (multiple regression with no intercept R 2 ~ 0.7) are better than Nitrate (multiple regression with no intercept R 2 ~ 0.4)

Comparison of Trajectory Regression Results at JOSH1 Using FNL and EDAS Contribution to Nitarte Similar regression results using FNL and EDAS

Comparison of Trajectory Regression Results at JOSH1 Using FNL and EDAS (300 Km Coastal Area) Most of the Pacific Ocean sulfur is from the coastal area. More of the Pacific Ocean sulfur is attributed to the coastal area based on EDAS than FNL.

Comparison Between Regression Results With / Without Intercept at GRCA Similar results whether intercept is forced to zero or not. Intercept could be some combination of global background influence and statistical noise. We’ll do both since it doesn’t require much extra work and may be useful

Summary of Evaluations Results Back trajectory regression is not sensitive to start heights so we’ll use 500 meters EDAS and FNL wind fields give similar results so we’ll use EDAS except for Alaska & Hawaii sites Having a Pacific coastal source region is useful so we’ve defined one at 300km from the coast Seasonal regressions don’t seem to provide sufficient value to justify their use Sulfate is most appropriate and light extinction is useful, so will do them in the first effort. We will calculate regressions both forced through zero or not forced through zero to maintain options to choose on an individual site basis

Next Steps & Schedule Transport Regression –Source regions are being defined & residence times determined ~End of November –Regressions will be calculated & displays generated ~Mid-December –All will be uploaded onto the COHA web site ~End of Year Conceptual Models w/o transport inclusion of transport regression ~End of November