Lagrangian Modeling Approaches and Measurements to Confirm an Improvement in Environmental Quality due to Reductions in SO2 Emissions

Roland R. Draxler

Overview

Can the effects of reducing pollutant emissions be detected sooner through  "smart" sampling methodologies?

                          Smart = Model Results

Variations in air quality and deposition are primarily due to ...

                meteorological, chemical, and emissions factors

                on diurnal, seasonal, annual, and decadal time scales

If much of the variance can be explained by a model then ...

                remaining trends can be attributed to emissions!

LAGRANGIAN MODELING APPROACHES

           RECEPTOR ORIENTATION

                    Receptor approaches use trajectories, without dispersion, to define the upwind advection path from the measurement point.

                     Dispersion is not reversible. 

          CLUSTER ANALYSIS - Single Station Sectorial Sampling

                   Simple - no restrictions on trajectories

                  Lagrangian - account for upwind variations

          GRIDDED TABULATION - frequencies accumulated on a grid

                   Simple Single Station

                   or from ... Multiple Stations

                   Lagrangian variations can be included

           SOURCE ORIENTATION

                  Calculations including both advection and dispersion are made from a predefined source location to determine the contribution at a receptor.

                    Emissions and chemistry can be included.

          SOURCE-RECEPTOR MATRIX

                   Calculations performed from all source regions to all receptors.

                   Results can be inverted to define a receptor orientation

                   Can include non-linear chemistry

           PREDICTION DIFFERENCING

                    Compute trend in residuals between measurements and calculations.

 SUMMARY

       CLUSTER ANALYSIS

           Advantages -                   Easy to understand

                                                Yields results quickly

           Disadvantages -               Primarily a spatial analysis

                                                Multiple receptors

     GRIDDED TABULATION

           Advantages -                   Lagrangian factors

                                                Multiple stations

           Disadvantages -               Application to time series measurements

     SOURCE-RECEPTOR MATRIX

           Advantages -                   Daily fractional contributions

                                                Lagrangian factors implicit

                                                Chemistry

                                 

          Disadvantages -               Data management of huge files

                                                Chemistry

    PREDICTION DIFFERENCING

 

          Advantages -                   Source integrated version of matrix

                                                Residual distribution

           Disadvantages -               Linking specific receptor to source


    FUTURE DIRECTIONS

    STATISTICAL EVALUATIONS

           Non-parametric methods for computing confidence limits

          Combination of methodologies

          Use cluster methods to identify sites and days appropriate

           for prediction differencing

    MODELING SYSTEM

           Improved precipitation predictions

           Switch from NGM to ETA or RUC with observed data

          Improved emissions inventory

          Nested emissions grids

          Finest resolution near sampling sites

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