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.
5.2 Retrieval of Emission Fields
The retrieval of emission rates required the inversion of the SRR. The inversion process exploited the differences between the source contributions on the receptor concentrations to infer the source emission rates. Differences in source impacts were due to variations in the relative source impacts, i.e. elements of the transfer matrix, and the unknown emission field. Difficulties in the inversion of the system occurred when there was not enough variation between the transfer matrix elements and/or source emission rates, resulting in instabilities in the solution. The lack of variability in the transfer matrix is an indication of an ill-conditioned problem. The inversion of an ill-conditioned transfer matrix can result in a highly unstable reconstructed emission field due to amplification of errors in the transfer matrix and receptor data. It was demonstrated that the transfer matrix was indeed ill-conditioned, and that it was dependent upon dispersion, advection, and kinetic processes defining the SRR. The ill-conditioning was also dependent on the spatial and temporal resolution of the input data.
An inversion process was developed based on a robust version of singular value decomposition (SVD). SVD is a least square inversion that allows for the damping of instabilities in the solution. By making it robust, large errors in the observation data and transfer matrix could not dominate the solution. This technique was tested on fabricated data, and was shown to be able to reproduce emission fields, and not to be highly influenced by large errors in the system.
The retrieval of emission fields is dependent upon a linear relationship between the receptor concentrations and source emission rates. If the relationship is highly nonlinear, resulting from nonlinear kinetic rate processes, and the actual transfer matrices are unknown, then the system can not be used to retrieve the emission field. However, for certain circumstances kinetic processes can be represented by pseudo first order rate process. For example, it has been found that the kinetic processes for long range transport of SO2 and NOX can be approximated by first order rate processes (National Research Council, 1983; Spicer 1983; EPA, 1984). A set of transfer matrices will then be valid in a range of source emission rates. This creates the problem of generating a set of transfer matrices that will be valid for the retrieved emissions in the inversion process. In order to accomplish this, it may be necessary to use best estimates of the emission fields and measure ambient concentrations of the species in interest. In this study, this problem was avoided by using a set of transfer matrices that were created from a known emission field.
Retrieval of SO2 Emission Fields. The retrieval of North American seasonal SO2 emission fields was conducted using the tuned SO2 - SO42- transfer matrices, and the IMPROVE/NESCAUM sulfate aerosol and NADP SO42- wet deposition data. The retrieved emission fields were compared to the 1985 NAPAP SO2 emission inventory. The NAPAP emissions are able to identify the major source regions and their emission rates, so the comparison provided an opportunity to test the capabilities and the limitations of the inversion scheme.
Retrievals using each input data set separately produced similar emission fields. However, by combining the two data sets the retrieved emission field had higher resolution, lower standard error, and compared better to the 1985 NAPAP emissions. Also, the wet deposition data alone could not identify any Canadian sources. This is a result of the fact that precipitating airmasses infrequently came from the north, and that no receptors were located in Canada.
The reconstructed Q2 and Q3 SO2 emission fields compared favorably with the NAPAP inventory. The reconstructed emission had a total emission rate over the spatial domain of 77 kTons/day compared to 70 kTons/day for NAPAP. The regions of high emissions in the NAPAP emission fields, such as in the Ohio River Valley, were generally seen in the reconstructions. However, in these regions, the retrieved high emission rates were spread out over a larger domain. This was a result of the damping in the retrieval process that reduced the resolution of the reconstructed emission fields. The reconstructed Q1 and Q4 emission fields displayed a much more uniform emission pattern over the Eastern US than Q2 and Q3, but reproduced the NAPAP total emission rate of 72 kTons/day. The causes of this low resolution are not known.
Using the reconstructed SO2 emission fields, the input observation data were simulated. The simulated data compared well with the measured input observation data for all quarters. The simulation reproduced the general spatial and temporal patterns of the observation data, and correlations between the data were as high as r = 0.9. However, they tended to underestimate the observation data by 10 - 15%. The simulated observations using the reconstructed emissions compared to the observation data about as well as the simulated observations using the 1985 NAPAP emission field.
Retrieval of NH3 Emission Fields. The seasonal NH3 emission fields were reconstructed using the NH4+ wet deposition data and the SO2 - SO42- transfer matrices. The reconstructed NH3 emission fields were seasonal, with the peak emissions occurring during Q2 (22 kTons/day), and the largest emission rates occurring over the Industrial Midwest. The reconstructed total emission rates were between 250% and 25% greater than other available estimates. Also, the reconstructed emissions had the largest emission rates from Illinois to Ohio while estimates from the NAPAP emission inventory showed the highest emission rates over Iowa. The correlation between the simulated input observations and the measured input observations was the lowest for the NH3 reconstructed emissions of the three reconstructed emission fields. It is believed that part of the cause of the discrepancy between the reconstructed and other emission inventories was due to deficiencies in using SO2 - SO42- transfer matrices to simulate NH3 wet deposition rates.
Retrieval of NO2 Emission Fields. The seasonal NO2 emission fields were reconstructed using the 1992 NADP NO3- wet deposition rates, and SO2 - SO42- wet removal kinetics. The reconstructed emissions show some seasonal variations in the total emission rates, with the highest emissions during Q1 (69 kTons/day), and the lowest during Q3 (54 kTons/day). The largest reconstructed emission rates were in the Ohio River Valley and the Industrial Midwest (0.4 - 1 kTons/day). The spatial pattern of the reconstructed emission fields were similar to the NAPAP inventory with both emission fields showing high emissions in approximately the same regions. However, the reconstructed emissions were spread out over a larger domain, and were approximately 60% greater than NAPAP's. Simulated NO3- wet deposition data using the reconstructed emissions compared well with the input NO3- wet deposition data.
The results from the three pollutant emission retrievals show that it is possible to infer emissions from receptor data. However, the retrievals are dependent upon the quality of the input observation data and the transfer matrices. As improved transfer matrices become available it is suspected that the retrievals for species such as NH3 will improve.
The emission retrieval technique has been implemented as a tool on the IBM-PC platform. This tool can be used for the retrieval of emissions on the local, regional, and global scale. As long as transfer matrices and corresponding receptor data exist, and the relationship between the receptor concentrations and pollutant emissions can be approximated by first order rate processes, the emission fields can be retrieved. This tool has a wide array of uses. It can be used for estimating unknown emission fields, provide independent checks on known emission fields, and identify the sources contributing to individual receptors. The tool can be used by regulatory agencies for enforcement of emission regulations. Also, by incorporating fabricated data with monitoring data, the best locations for new monitoring sites to aid in the monitoring of pollutant emissions can be accomplished.
The retrieval process and tool can also be used for scientific exploration of the SRR. In cases where the transfer matrices are unknown or have large uncertainties, the retrieval process can help to identifying these deficiencies. Thus, aid in the understanding of the roles of transport and kinetic processes in relating the source emissions to receptor concentrations.
Agee, W. S. and Turner, R. H. 1979. Application of Robust Regression to Trajectory Data Reduction. From Launer, R. L. And Wilkinson, G. N. 1979. Robustness in Statistics, Academic Press, New York.
Allison, H. 1979. Inverse unstable problems and some of their applications. Math and Scientist 4, 9-30.
Alsmiller, F.S., Alsmiller R.G., H.W. Bertini and C.L. Begovich. 1979. Monte Carlo simulation of turbulent atmospheric transport and comparisons with experimental data. J. Appl. Meteor. 18, 17-26.
Ashbaugh, L.L. 1983. A statistical trajectory technique for determining air pollution source regions. J. Air Pollut. Control. Assn. 33, 1069-1098.
Ashbaugh L.L, Malm, W.C., Sadeh, W.Z. 1985. A residence time probability analysis of sulfur concentrations at Grand Canyon National park. Atmos. Environ. 19, 1263 - 1270.
Backus, G. and Gilbert, F. 1967. Numerical application of a formalism for geophysical inverse problems. Geophys. J. R. astr. Soc. 13, 247-276.
Backus, G. and Gilbert, F. 1968. The resolving power of gross earth data. Geophys. J. R. astr. Soc. 16, 169-205.
Backus, G. and Gilbert, F. 1970. Uniqueness in the inversion of inaccurate gross earth data. Phil. Trans. R. Soc. Lond. 266A, 123-192.
Barrie, L.A. 1978. An improved model of reversible SO2 washout by rain. Atmos. Environ. 12, 407-412.
Barrie, L.A. 1981. The prediction of rain acidity and SO2 scavenging in Eastern North America. Atmos. Environ. 15, 31-41.
Belsley D. A., E. Kuh, and R.E. Welsch, 1980. Regression Diagnostics. John Wiley and Sons, New York.
Berthouex, Paul Mac and Brown, Linfield C. 1994. Statistics for Environmental Engineers, Lewis Publishers, Boca Raton.
Bolin, B. And Keeling, C.D. 1963. Large-scale atmospheric mixing as deduced from seasonal and meridional variations of carbon dioxide, J. Geophys. Res. 68, 3889-3920.
Boughton, B.A., J.M. Delaurentis, and W.E. Dunn. 1987. A stochastic model of particle dispersion in the atmosphere. Boundary-Layer Meteorology 40, 147-163.
Britt H. I. and Luecke R. H. 1973. The estimation of parameters in non-linear, implicit models. Technometrics 15, 233-247.
Brown, Margaret. 1993. Deduction of emissions of source gases using an objective inversion algorithm and a chemical transport model. J. of Geophysical Research 98, 12,639-12,660.
Calvert, J.G., F. Su, J.W. Bottenhein, and O.P. Strausz. 1978. Mechanism of the homogeneous oxidation of sulfur dioxide in the troposphere. Atmos. Environ. 12:197-226.
Calvert, J.G., and Mohnen, V., 1983. Chemistry of acid formation. In: Acid Deposition: Atmospheric Processes in Eastern North America, pp.155-201, National Academy of Sciences, Washington, DC.
Calvert, J. G., A. Lazrus, G. L. Kok, B. Heikes, J. Walega, J. Lind, and C. Cantrell, 1985. Chemical mechanisms of acid generation in the troposphere. Nature 317, pp.27-35.
Cass G.R.. 1981. Sulfate air Quality control strategy design. Atmos. Environ. 15:1227-1249.
Carras J.N. and Williams D.J. 1980. The long-range dispersion of a plume from an isolated point source. Atmos. Environ. 15, 2205-2217.
Chang, J.S., R. A. Brost, I.S.A. Isaksen, S. Madronich, P. Middleton, W. R. Stockwell, and C.J. Walcek. 1987. A three-dimensional Eulerian acid deposition model: physical concepts and formulation. J. Geophys. Res. 92.14681-14700.
Cheung, Ivan, Brian Lamb, and Hal Westberg. 1991. Uncertainties in a biogenic emissions model: Use of satellite data to derive land use and biomass density data. Emission Inventory Issues in the 1990’s: Proceedings of a U.S. EPA/A&WMA International Specialty Conference held in Durham, North Carolina 9-12 September 1991, 723-735. Pittsburgh, PA: Air and Waste Management Association
Clarke, J.F., T.L. Clark, J.S. Ching, P.L.Haagenson, R.B. Husar, and D.E. Patterson, 1983. Assessment of model simulation of long-distance transport. Atmos. Environ. 17, 2449 - 2462.
Constable, C.G., 1988. Parameter estimation in non Gaussian noise, Geophysical Journal 94, 131-142.
Cunnold, D.M., R.G. Prinn, R.A. Rasmussen, P.G. Simmonds, F.N. Alyea, C.A. Cardelino, A.J. Crawford, P.J. Fraser, and R.D. Rosen. 1983. The atmospheric lifetime experiment 3. Lifetime methodology and application to three years of CFCl3 data. J. Geophys. Res. 88. 8379 - 8400.
DeMarrais, Gerard A. 1979. A history of air pollution meteorology through 1969. NOAA Technical Memorandum ERL, ARL-74.
Dilts, Stephen B., Eugine Allwine, Hal Westberg, and Brian Lamb. 1991. Measurement and modeling of diurnal and seasonal emissions of biogenic hydrocarbons. Emission Inventory Issues in the 1990’s: Proceedings of a U.S. EPA/A&WMA International Specialty Conference held in Durham, North Carolina 9-12 September 1991, 712-722. Pittsburgh, PA: Air and Waste Management Association.
Dimri, V. 1992. Deconvolution and Inverse Theory: Application to Geophysical Problems, Elsevier, Amsterdam.
Draxler R.R. and Elliott W.P. 1977. Long-range travel of airborne material subjected to dry deposition. Atmos. Environ. 11, 35-40.
Draxler, R.R., 1987. Sensitivity of a trajectory model to the spatial and temporal resolution of the meteorological data during CAPTEX. J. Of Climate and Applied Meteorology 26, 1577-1588.
Dzubay, T.G. Ann N.Y. Acad. Sci. 1980, 338, 126-44.
Efron, B. and Tibshirani. 1986. Bootstrap methods for standard errors, confidence intervals and other measures of statistical accuracy. Stat. Science 1, 54-77.
Eliassen A. 1978. The OECD Study of long-range transport of air pollutants: Long range transport modeling. Atmos. Environ. 12, 479-487.
Eliassen A. and Saltbones J. 1983. Modeling of long-range transport of sulphur over Europe: a two-year model run and some model experiments.
Atmos. Environ. 17, 1457-1473.Enting, I.G., 1985. A classification of some inverse problems in geochemical modeling, Tellus 37B, 216-229.
Enting, I.G. and Newsam G.N. 1990. Atmospheric constituent inversion problems: Implications for baseline monitoring. J. of Atmospheric Chemistry 11, 69-87.
Enting, I.G. and J.V. Mansbridge. 1989. Seasonal sources and sinks in atmospheric CO2: results of an inversion study. Tellus 41B, 111-126.
Enting, I.G. and J.V. Mansbridge. 1991. Latitudinal distribution of sources and sinks of CO2 Tellus 43B, 156-170.
EPA. 1984.
The Acidic Deposition Phenomenon and its Effects: Critical Assessment Review Papers, Vol 1 Atmospheric Sciences. EPA-600/8-83-016AF.Fieber, Julie, Barbara Austin, and Jeremy Heiken. 1991. Assessment of Computer Models for Estimating Vehicle Emission Factors.
Emission Inventory Issues in the 1990’s: Proceedings of a U.S. EPA/A&WMA International Specialty Conference held in Durham, North Carolina 9-12 September 1991, 572-584. Pittsburgh, PA: Air and Waste Management Association.Fisher, B.E.A. 1978. The calculation of long term sulphur deposition in Europe. Atmos. Environ. 12, 479-487.
Fraser, P.J., Devek, N., O’Brien, R., Shepard, R., Rasmussen, R.A., Crawford, A.J., and Steele, L.P. 1985. Intercomparison of halocarbon and nitrous oxide measurements at Cape Grim, 1976-1984. In Baseline 1983-84, Editors: R.J. Fraucey and B. Morgan. Dept. Of Science and Technology. CSIRO, Canberra.
Friedlander, S.K. 1977. Smoke, Dust, and Haze. John Wiley and Sons. New York.
Fung, I., J. John, J. Lerner, E. Matthews, M. Prather, L.P. Steele, and P.J. Fraser. 1991. Three dimensional model synthesis of the global methane cycle. J. Geophys. Res. 96. 13033-13066.
Garland, J.A. 1978. Dry and wet removal of sulphur from the atmosphere. Atmos. Environ. 12, 349 - 362.
Gebhart, K.A. and W.C. Malm. 1991. Examination of source regions and transport pathways of organic and light absorbing carbon into remote areas of the United States, Paper 91-82.4 presented at the 84th Annual Meeting of the Air and Waste Management Association, Vancouver.
Gillani, N.V. 1978. Project MISTT: mesoscale plume modeling of the dispersion, transformation and ground deposition of SO2. Atmos. Environ. 12, 569-588.
Gillani, N.V. 1981. Gas to particle conversion of sulfur in power plant plumes. Atmos. Environ. 15, 2293-2313.
Gryning S.E., A.A.M. Holtslag, J.S. Irwin, and B. Sivertsen. 1987. Applied dispersion modeling based on meteorological scaling parameters. Atmos. Environ. 21, 79-89.
Hall, C.D. 1975. The simulation of particle motion in the atmosphere by a numerical random walk model. Quart. J. Roy. Meteor. Soc. 101, 235-244.
Hales, J. 1978. Wet removal of sulphur compounds from the atmosphere. Atmos. Environ. 12, 389-400.
Harriss, R.C. and Michaels, J.T. 1982. Sources of atmospheric ammonia. Proceedings Second Symposium, Composition of the Nonurban Troposphere, pp. 33-35, American Meteorological Society, Boston MA.
Hartley D. and R. Prinn. 1993. On the feasibility of determining surface emissions of trace gases using and inverse method in a three dimensional chemical transport model. J. Geophys. Res. 98, 5183-5198.
Hegg, D.A. and Hobbs P.V., 1981. Cloud water chemistry and the production of sulfates in clouds. Atmos. Environ. 15:1597-1604.
Hegg, D.A. and Hobbs, P.V. 1984. Solution reactions, Section 4.3 in The Acidic Deposition Phenomenon and its Effects: Critical Assessment Review Papers, Vol 1 Atmospheric Sciences. EPA-600/8-83-016AF.
Hegg, D.A., 1989. The relative importance of major aqueous sulfate formation reactions in the atmosphere. Atmospheric Research 22, 323-333.
Henry R.C. and Hidy G.M. 1979. Multivariate analysis of particulate sulfate and other air quality variables by principal components - 1. Atmos. Environ. 13, 1581-1596.
Henry R.C., C.W. Lewis, P.K. Hopke, and H.J. Williamson. 1984. Review of receptor model fundamentals. Atmos. Environ. 18, 1507-1515.
Hidy, G.M. and Friedlander S.K. 1972. The nature of the Los Angeles aerosol. Second IUAPPA Clean Air Congress, Washington D.C.
Hidy, G.M., E.Y. Tong, and P.K. Mueller. 1976. Design of the Sulfate Regional Experiment (SURE). Electric Power Research Institute, EC-125.
Hidy G.M. Atmospheric Sulfur and Nitrogen Oxides: Eastern North Qmerican Source-Receptor Relationships. Academic Press, San Diego, 1994.
Hobbs, P.V. 1994. Aerosol-Cloud-Climate Interactions. Academic Press, San Diego.
Hogg, R. V. 1979. An Introduction to Robust Estimation. From Launer, R. L. And Wilkinson, G. N. 1979. Robustness in Statistics. Academic Press, New York.
Hoke J.E., N.A. Phillips, G.J. DiMego, D.G. Deaven. 1985. NMC’s regional analysis and forecast system--results from the first year of daily, real-time forecasting. Preprints, Seventh Conference of Numerical Weather Prediction, Montreal, American Meteorological Society, 444-451.
Hoke J.E., N.A. Phillips, G.J. DiMego, J.J. Tuccillo, J.G. Sela. 1989. The regional analysis and forecast system of the National Meteorological Center. Wea. and Forecasting 4, 323-334.
Hopke, P.K. 1985. Receptor Modeling in Environmental Chemistry. Wiley-Interscience, New York.
Huber, Peter J. 1964. Robust estimation of a location parameter, Ann Math. Stat. 35, 73-101.
Huber, Peter J. 1972. Robust statistics: A Review, Ann Math. Stat. 43, 1041-1067.
Huber, Peter J. 1973. Robust regression: asymptotics, conjectures, and Monte Carlo, Ann. Statist. 1, 799-821.
Huber, Peter J. 1981. Robust Statistics. Wiley, New York.
Humphreys, W. J. 1964. Physics of the Air. Dover Publications. New York.
Husar, R.B. Lodge Jr., J.P. and D.J. Moore. 1978. Sulfur in the atmosphere. Proceedings of the International symposium held in Dubrovnik Yugoslavia, 7-14 September 1977. Pergamon Press, Oxford.
Husar, R.B., D.E. Patterson, J.D. Husar, N.V. Gillani. 1978. Sulfur budget of a power plant plume. Atmos. Environ. 12, 549-568.
Husar, R.B. and D.E. Patterson. 1981. Monte Carlo simulation of daily regional sulfur distribution: Comparison with SURE sulfate data and visual range observations during August 1977. J. of Applied Meteorology 20, 404-420.
Husar, R.B., D.E. Patterson, W.E. Wilson. 1986. A semi-empirical approach for selecting rate parameters for A Monte Carlo regional model. CAPITA Publication 86r2 ,Washington University St. Louis MO, 63130.
Husar R.B., Elkins J.B. and Wilson W.E. 1994. US visibility trends, 1960-1992, regional and national. Presented at the 87th Annual Air & Waste Management Meeting, Cincinnati, OH.
Husar. R.B. 1995. Ecosystem and the Biosphere: Metaphors for Human-Induced Material Flows. Chapter for Industrial Metabolism: Restructuring for Sustainable Development. (Edited by Ayres, R.U. and Simonis U.E.)
Husar, R.B. and Husar, J.D. 1995. Fine particle maps derived from regional PM2.5 and visibility data. Final Report to Radian Corporation, 3200 Progress Center/Nelson Hwy, P.O. Box 13000 Research Triangle Park, NC 27709. EPA Contract Number 68-D2-0160, Work Assignment Number 2 032.
Hwang, C.S., K.G. Severin, and P.K. Hopke. 1984. A comparison of R- and Q-modes in target transformation factor analysis for resolving environmental data. Atmos. Environ. 18, 345-352.
Irving, Patricia M. 1991. Acidic Deposition: State of Science and Technology, Vol. I, Report 1, (Emissions). Available from Superintendent of Documents, Government Printing Office, Washington, DC 20402-9325.
Jackson, D.D. 1972. Interpretation of inaccurate, insufficient and inconsistent data. Geophys. J. R. astr. Soc. 28, 97-109.
Junker, N. W., J. E. Hoke and R.H. Grumm. 1989. Performance of NMC’s operational model, Wea. and Forecasting 4, 335-342.
Kanamitsu M. 1989. Description of the NMC global data assimilation and forecast systems. Wea. and Forecasting 4, 335-342.
Kleinman, M.T., B.S. Pasternack, M. Eisenbud, and T.J. Kneip. 1980. Identifying and estimating the relative importance of sources of airborne particulates. Environ. Sci Technol. 14, 62-65.
Kondratyev, K. YA. 1969. Radiation in the Atmosphere. Academic Press, New York.
Lamb, Robert G. 1971. The application of a generalized Lagrangian diffusion model to air pollution simulation studies. In Proceedings of the Symposium on Air Pollution, Turbulence and Diffusion, (Edited by H.W. Church and R.R. Luna) New Mexico State University, Dec. 7-10, 1971, Las Cruces, New Mexico.
Lamb, Robert G. and Neiburger M. 1971. An interim version of a generalized urban air pollution model. Atmos. Environ. 5, 239-264.
Lamb, Robert G. and Seinfeld, John H. 1973. Mathematical modeling of urban air pollution: General theory., Environmental Science & Technology 7, 253 - 261.
Lanczos, C. 1956. Applied Analysis. Prentice-Hall, Englewood Cliffs, N.J.
Launer, R. L. And Wilkinson, G. N. 1979. Robustness in Statistics, Academic Press, New York.
Lawson C. L. and Hanson, R.J. 1974. Solving Least Squares Problems. Prentice-Hall, Englewood Cliffs, New Jersey.
Lenschow, D.H. and Stephens, P.L. 1980. The role of thermals in the convective boundary layer. Boundary layer Meteor. 19, 509 - 532.
Levine, J.S., T.R. Augustsson, and J.M. Hoell. 1980. The vertical distribution of tropospheric ammonia. Geophys. Res. Lett. 17, 317-320.
Luhar, A.K. and Rao, K.S. 1994. Lagrangian stochastic dispersion model simulations of tracer data in nocturnal flows over complex terrain. Atmos. Environ. 28, 3417-3431.
Manahan, S.E. 1994. Environmental Chemistry, Lewis Publishers, Boca Raton.
McClenny, M.C. and Bennett, Jr. C.A. 1980. Integrative technique for detection of atmospheric ammonia. Atmos. Environ. 14, 641 - 645.
Middleton P., J. S. Chang, J.C. Del Conal, H. Geiss and J. M. Rosinkski. 1988. Comparison of RADM and Oscar precipitation chemistry data. Atmos. Environ. 22, 1195-1208.
Miller, J.M. 1981. A five-year climatology of back trajectories from the Mauna Loa observatory, Hawaii. Atmos. Environ. 20, 1457-1469.
Moody, J.L. and Samson P.J. 1989. The influence of atmospheric transport on precipitation chemistry at two sites in the mid-western United States. Atmos. Environ. 23, 2117-2132.
Mulholland, Michael. 1989. An auto regressive atmospheric dispersion model for fitting combined source and receptor data sets. Atmos. Environ. 23, 1443-1458.
Mulholland, Michael and John Seinfeld. 1995. Inverse air pollution modeling of urban-scale carbon monoxide emissions. Atmos. Environ. 29, 497-516.
Munn, R.E.; Bolin B. 1971. Global air pollution-meteorological aspects: A survey. Atmos. Environ. 5, 363-402.
(NADP/NTN) National Atmospheric Deposition Program (NRSP-3)/National Trends Network. 1993. NADP/NTN Coordination Office, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523
NCAR (National Center for Atmospheric Research). 1995. DS505.0 NCDC TD3240, US Controlled Hourly precipitation, Daily 1948-Cont.
National Renewable Energy Laboratory (NREL). September 1992. User’s Manual: National Solar Radiation Database (1961-1990), Volume 1.0. Distributed by National Climatic Data Center, Federal Building, Asheville, NC 28801.
National Research Council. 1976. Nitrogen Oxides. Subcommitte on Nitrogen Oxides, Committee on medical and biological effects of environmental pollutants. National Academy of Sciences, Washington, D.C.
National Research Council. 1983. Acid Deposition: Atmospheric Processes in Eastern North America, pp. 155-201, National Academy of Sciences, Washington, D.C.
National Research Council, 1993. Protecting Visibility in National Parks and Wilderness Area. National Academy Press, Washington, D.C.
National Research Council. 1994. Science and Judgment in Risk Assessment. Washington DC: National Academy Press.
Newsam, G.N. and Enting, I.G., 1988, Inverse problems in atmospheric constituent studies: I. Determination of surface sources under a diffusive transport approximation. Inverse Problems 4, 1037-1054.
Omatu, S. J.H. Seinfeld, T. Soeda, and Y. Sawaragi. 1988. Estimation of nitrogen dioxide concentrations in the vicinity of a roadway by Optimal Filtering Theory. Automatica 24, 19-29.
Ottar, B. 1976. Monitoring long-range transport of air pollutants: the OECD study. Ambio 6, 203-206.
Pack, D.H., G.J. Ferber, J.L. Heffter, K. Telegadas, J.K. Angell, W.H. Hoecker, and L. Machta. 1978. meteorology of long range transport. Atmos. Environ. 12, 425-444.
Parker, Robert L. 1977. Understanding Inverse Theory. Ann Rev. Earth Planet Sci. 5, 35-64.
Patterson, D.E., R.B. Husar, W.E. Wilson and L.F. Smith. 1981. Monte Carlo simulation of daily regional sulfur distribution: comparison with SURE data and visibility observations during August 1977. J. Appl. Meteor. 20, 404 - 420.
Penrose, R. 1955. A generalized inverse for matrices. Proc. Camb. Phil. Soc. 51, 406-413.
Phillips, D.L. 1962. Journal of the Association for Computing Machinery 9, 84-97.
Pielke, R.A., W.R. Cotton, R.L. Walko, C.J. Tremback, W.A. Lyons, L.D. Grasso, M.E. Nicholls, M.D. Moran, D.A. Ewlsey, T.J. Lee, and J.H. Copeland. 1992. A comprehensive meteorological modeling system--RAMS. Meteorol. Atmos. Phys. 49, 69-91.
Pierson, William R. and Branchaczek, Wanda W. 1983. Particulate matter associated with vehicles on the road II. Aerosol Science and Technology 2, 1-39.
Poirot, Richard L and Paul R. Wishinski. 1986. Visibility, sulfate and air mass history associated with the smmertime aerosol in Northern Vermont. Atmos. Environ. 20, 1457-1469.
Poirot, R. L., P. J. Galvin, N. Gordon, S. Quan, A.V. Arsdale, and R.G. Flocchini. 1991. Annual and seasonal fine particle composition in the Northeast: second year results from the NESCAUM monitoring network. Presented at the A&WMA conference in Vancouver, Canada. Paper No. 91-49.1.
Pope, Anne A. and Susan K. Lynch. 1991. Data sources and availability of tools for estimating air toxics emissions. Emission Inventory Issues in the 1990’s: Proceedings of a U.S. EPA/A&WMA International Specialty Conference held in Durham, North Carolina 9-12 September 1991, 379-390.. Pittsburgh, PA: Air and Waste Management Association
Prahm, L.P., K. Conradsen, and L.B. Nielsen. 1980. Regional source quantification model for sulphur oxides in Europe. Atmos. Environ. 14, 1027-1054.
Press, W.H., S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery. 1984. Numerical Recipes in C. Cambridge Press. Cambridge.
Reiguam, H.E. 1970. An atmospheric transport and accumulation model for airsheds. Atmos. Environ. 4, 233-7.
Rodhe, Henning. 1995. Acid reign `95. Summary Statement from the 5th International Conference on Acidic Depostion Science and Policy held in Goteborg, Sweedon 26-30 June 1995.
Rolph, G.D. and Draxler, R.R. 1990. Sensitivity of three-dimensional trajectories to the spatial and temporal densities of the wind field. J. of Appl. Met. 29, 1043-1054.
Rolph G.D. 1992. NGM Archive. NCDC Report TD-6140, July, National Climatic Data Center.
Schichtel, B.A. et al 1992 (aerosol composition)
Schichtel, B.A. and Husar, R.B. 1994. Wind comparisons
Schichtel, B.A. and Husar, R.B. 1995. Regional simulation of atmospheric pollutants with the CAPITA Monte Carlo model. J. Air & Waste Manage. Assoc. Accepted for publication.
Seibert P., Kromp-Kolb H., Baltensperger U., Jost D.T., Schwikowski M., Kasper A. And Puxbaum H. 1994. Trajectory analysis of aerosol measurements at high alpine sites. In Transport and Transformation of Pollutants in the Troposphere. Edited by Den Haag. Academic Publishing, 689-693.
Seinfeld, John H. 1986. Atmospheric Chemistry and Physics of Air Pollution. John Wiley and Sons. New York.
Seinfeld, John H. 1988. Ozone air quality models: critical review. Journal of Air Pollution Control Association 38, 666.
Sisler J.F., D. Huffman, D.A. Latimer, W.C. Malm, and M. Pitchford. 1993. Spatial and temporal patterns and the chemical composition of the haze in the Unites States: An analysis of data from the IMPROVE network, 1988-1991. Report #ISSn No. 0737-5352-26 CIRA, CSU, Fort Collins, CO.
Smith, F.B. and Hunt, R.D., 1978. Meteorological aspects of the transport of pollution over long distances. Atmos. Environ. 12, 461-477.
Stedman, D.H. and Shetter, R.E. 1983. The global budget of atmospheric nitrogen species, in Trace Atmospheric Constituents, S.E. Schwartz (Ed.), Wiley, New York.
Stephens, Robert D. and Peter Groblicki. 1991. Remote sensing measurements of in-use vehicle carbon monoxide and hydrocarbon emissions. Emission Inventory Issues in the 1990’s: Proceedings of a U.S. EPA/A&WMA International Specialty Conference held in Durham, North Carolina 9-12 September 1991, 483-492. Pittsburgh, PA: Air and Waste Management Association.
Stocker, R.A., M. Uliasz, and R.A. Pielke. 1994. The validation of the RAMS/NGM meteorological fields used in the MOHAVE field study. In Aerosol and Atmospheric Optics: Radiative Balance and Visual Air Quality, Volume A. Proceedings of the International Specialty Conference, Snowbird, Utah, USA, September 26-30, 1994.
Stockwell, W.R. and Calvert, J.G. 1983. The mechanism of the HO-SO2 reaction. Atmos. Environ. 17, 2231 - 2236.
Stohl, Andreas. 1995. Trajectory statistics - - A new method to establish source-receptor relationships of air pollutants and its application to the transport of particulate sulfate in Europe. Atmos. Environ. 30, 579-587.
Tans, P.P., T.J. Conway, and T. Nakazawa. 1989. Latitudinal distribution of the sources and sinks of atmospheric carbon dioxide derived from surface observations and an atmospheric transport model. J. Geophys. Res. 94, 5151-5172.
Tarantola, Albert. 1987. Inverse Problem Theory, Elsevier, Amsterdam.
Taylor, Gordon W.R. 1991. Probabilistic emission models: The Case for changing our modeling approach. Emission Inventory Issues in the 1990’s: Proceedings of a U.S. EPA/A&WMA International Specialty Conference held in Durham, North Carolina 9-12 September 1991, 128-139. Pittsburgh, PA: Air and Waste Management Association
Tuovinen, J., K. Barrett, and H. Styve. July 1994.
Transboundary Acidifying Pollution in Europe: Calculated Fields and Budgets 1985 - 93. Meteorological Synthesizing Centre - West the Norwegian Meteorological Institute, P.O. Box 43-Blindern, N-0313 Oslo 3, Norway. EMEP/MSC-W Report 1/94.Twomey, S. 1977. Introduction to the Mathematics of Inversion in Remote Sensing and Indirect Measurements, Elsevier, Amsterdam.
US Environmental Protection Agency. February 1994. AIRS User’s Guide, Volume AQ1: AQS Data Dictionary. EPA-454/B-94-005.
Vasconcelos L.A.P. 1995. Aerosol and Transport Climatology at the Grand Canyon. Doctoral Dissertation presented at Washington University, St. Louis, MO.
Venkatram, A., B. Ley, and S. Wong. 1982. A statistical model to estimate long-term concentrations of pollutants associated with long-range transport. Atmos. Environ. 16, 249-257.
Venkatram, A., P.K. Karamchandani, and P.K. Misra. 1988. Testing a comprehensive acid deposition model. Atmos. Environ. 22, 737 - 747.
Wiel, J.C. 1994. A hybrid Lagrangian dispersion model for elevated sources in the convective boundary layer. Atmos. Environ. 28, 3433-3448.
Whelpdale, D.M. 1981. Sulfate scavenging ratios at Norwegian EMEP stations. EMEP/CCC Report 5/81, Norwegian Institute for Air Research.
White, W.H. 1986. On the theoretical and empirical basis for apportioning extinction by aerosols: A critical review. Atmos. Environ. 20, 1659-1672.
White, W.H., Macias E.S., Kahl J.D., Samson P.J., Molenar J.V. and Malm W.C. 1994. On the potential of regional-scale emissions zoning as an air quality management tool for the Grand Canyon. Atmos. Environ. 28, 1035-1045.
Wiggins, Ralph. 1972. The general linear inverse problem: Implications of surface waves and free oscillations on earth structure. Review Geophys. Space Phys. 10.
Wilkinson, Graham N. 1979. Robust inference - the Fisherian approach. In Robustness in Statistics. Academic Press, New York.
Willis, G. E. and Deardorff, J. W. 1974. A laboratory model of the unstable planetary boundary layer. J. Atmos. Sci. 31, 1297-1307.
Wilson, W.E. 1981. Sulfate formation in point source plumes: a review of recent field studies. . Atmos. Environ. 15, 2573 - 2581.
Winchester J.W. and Nifong G.D. 1971. Water pollution in Lake Michigan by trace elements from aerosol fallout. Water, Air, Pollution, 1, 50-64.
Yamartino, R.J. and Lamich D.J. 1979. The formulation and application of a source finding algorithm. Preprint volume, 4th Symp. On Turbulence, Diffusion and Air Pollution. 15-18 January 1979, Reno. American Meteorological Society, Boston.