Chapter 5 Summary and Conclusions




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Chapter 5

Summary and Conclusions


In this project, a general method was developed for the retrieval of emission fields from air pollution monitoring data. This was accomplished by defining the source receptor relationship (SRR) as a linear set of equations that relates source emissions to receptor concentrations by the transfer matrix. The transfer matrix identifies the probable source contribution to a receptor, and it was broken down in the product of the transport contribution, the transit probability Pt, and the kinetic contribution, the kinetic probability, Pk. The transfer matrix and its coefficients were numerically estimated using a Monte Carlo model. Using receptor concentrations and deposition rates along with estimated transfer matrices, the SRR was inverted to retrieve seasonal SO2, NH3, and NO2 emission fields over most of North America. The retrieval of SO2 emission fields provided an opportunity to test the method since all three components of the SRR were available, emission fields, transfer matrices, and receptor data. These initial retrievals showed that it is possible to infer emissions from receptor data. The primary results from the simulation of the SRR using the Monte Carlo dispersion model, and the inversion of the SRR for the retrieval of emission fields, are summarized below.

5.1 Monte Carlo Dispersion Model


A Monte Carlo model for the simulation of regional scale transport, transformation, and dry and wet removal was presented. The simulation of pollutant transport employs a quantized Monte Carlo technique for the representation of atmospheric boundary layer physics. Kinetic processes are employed using rate equations where the rate coefficients are dependent upon meteorological and chemical variables. The rate coefficients are determined through a trial and error tuning process.

The model was tuned for the simulation of SO2 and SO42- concentration and deposition fields over the Eastern US. The best set of rate coefficients had transformation rates, that exhibited substantial seasonal variation and a mild north-south spatial gradient. The transformation rates averaged between 0.2 - 0.3 %/hr in January and ~0.8-1.4 %/hr in July, with the larger transformation rates occurring in the Southeast. Also, the SO2 washout ratio was set to be inversely dependent upon the square root of the SO2 column concentration, simulating the inverse dependence of the solubility of SO2 with solution acidity. This non linearity resulted in washout ratios in Vermont being 35% smaller than those in Ohio, where the simulated SO2 concentrations were among the highest. Using these rate coefficients and the 1985 NAPAP SO2 emission field, the model could reproduce the spatial and temporal trends in the SO42- concentration and deposition rates. This provided some confidence that the transport and kinetics used in the model were valid, but this did not conclusively verify that the derived kinetics were unique.

Transfer matrices for SO42- concentrations and the sum of the SO2 and SO42- wet deposition over the US were generated. Analysis of Q2 receptor oriented transfer matrices revealed distinct variations between those for the surface concentrations and wet depositions. The wet deposition transfer matrices for each receptor exhibited a preferential flow with airmasses coming almost exclusively from south and southwest of the receptor when it rained, while the dry airmasses came from a broad region biased to the northwest of the receptors. Total relative source contributions were relatively constant for the dry SO42- concentrations where they varied about 30% between receptors east of the Rocky Mountains during Q2, but by more than a factor of 3 for the wet deposition transfer matrices due to the spatial variation of precipitation rates and frequencies.
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