A statistical model to estimate long-term concentrations of pollutants associated with long-range transport

Venkatram, Akula; Ley, Barbara; Wong, S.

doi:10.1016/0004-6981(82)90440-1

Cited by 54 publications

(16 citation statements)

References 16 publications

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“…The semiempirical, climatological model of Venkatram et al 52 has yielded results for wet deposition of sulfate and nitrate that agree suprisingly well with 1980 annual average observations at 34 sites in the U.S. and Canada. 62 The model calculation of wet deposition for 1980 conditions and 500. sources correlated with observations with a coefficient (R) of 0.87 for SO 4 = and 0.89 for nitrate.…”

supporting

confidence: 52%

“…63 This evaluation focused on long-term averages. It showed that the Eulerian model of the evaluators 62 (RTM-LT) was superior to others examined: ENAMAP-2, a Lagrangian expanding puff calculation, and the MIT Model, 68 a climatologically based calculation similar to the OME model 52 when compared with ambient and sulfate data taken in 1977-1978. However, none of the models performed well for wet deposition of sulfate.…”

mentioning

confidence: 99%

“…With the exception of the Canadian Ontario Ministry of the Environment (OME) statistically based model, 52 the computations use some form of wind field calculation as input which relies on existing surface wind and/or rawinsonde observations. Surface temperature or temperature profiles provide data to estimate mixing height or stability.…”

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confidence: 99%

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Source-Receptor Relationships for Acid Deposition: Pure and Simple

Hidy

1984

Journal of the Air Pollution Control Association

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confidence: 52%

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confidence: 99%

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Source-Receptor Relationships for Acid Deposition: Pure and Simple

Hidy

1984

Journal of the Air Pollution Control Association

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“…22 It provides for the longterm transport, dispersion, chemical transformation, deposition, and precipitation scavenging of SO 2 and sulfate. In this Appendix, the technical features of the model are reviewed, the model domain for this application is described, the choices of parameter values are explained, and a brief discussion of the relationship of sulfur chemistry to nitrogen and hydrocarbon chemistry is provided.…”

Section: Appendix: Description Of Model Tmentioning

confidence: 99%

Estimates of Sulfate Deposition in the Middle Eastern United States: 1975, 1990, and 2010

Norris

Mueller

Langstaff³

1999

Journal of the Air & Waste Management Association

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Deposition of airborne sulfates and nitrates is considered to be an important factor in the decline of certain fish populations and of forest health in the eastern United States. A solution has been sought through legislation that mandates significant reductions in anthropogenic emissions of oxides of sulfur and nitrogen, precursors of sulfates and nitrates. This paper looks at the changes in sulfate deposition in the middle eastern United States from 1975 to 1990 and 2010 due to SO2 emission reductions brought about by 1970, 1977, and 1990 clean-air legislation. Recently developed emission inventories, together with a statistical, long-range transport model, were used to construct the estimates. The results show that sulfate deposition decreased over the middle eastern United States by about 25% between 1975 and 1990 and will decrease another 45% between 1990 and 2010-reductions commensurate with the overall decreases in SO2 emissions. The results further indicate that about half of the sulfur deposited in the environmentally sensitive southern Appalachian Mountains comes from states not a part of the southern Appalachian region. These estimates contain a measure of uncertainty due to uncertainty in emission estimates, the interannual variability of meteorology, and uncertainty arising from model assumptions and choices of oxidation and deposition rates. The model was able to reproduce about 10% of the variability in observations of wet sulfate deposition at over 100 sites over a 12-year period.

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“…The initial models tended to be simple, using aggregated and parameterized meteorology and rate coefficients to simulate the physical/chemical processes responsible for regional transport. These included trajectory models (3,4), statistical models (5,6), as well simple diffusion and box models (7,8). As the understanding of the atmospheric processes increased, these types of models were generalized to include processes such as non-linear chemistry and atmospheric diffusion (9,10).…”

Section: Introductionmentioning

confidence: 99%

Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model

Schichtel

Husar

1997

Journal of the Air & Waste Management Association

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Presented is a Monte Carlo model for the simulation of regional scale transport, transformation, and dry and wet removal. The model was newly re-designed in a modular framework, separating the emissions, transport, and kinetics calculations. The transport module employs a quantized Monte Carlo technique for the simulation of atmospheric boundary-layer physics. Kinetic processes are simulated using rate equations where the rate coefficients are dependent upon meteorological variables, and consequently fluctuate in space and time. The rate coefficient equations are determined via a tuning process comparing simulated to observed measurements. Results from simulations of SO 2 and SO 4 2-over the eastern United States during 1992 are presented. Comparisons of simulated daily SO 4 2-concentrations to observations had a rate of r 2 = 0.35-0.83 depending on season and location. The rate for weekly SO 4 2-wet deposition rates was r 2 = 0.5-0.95. The utility of the model to investigate the source receptor relationship is demonstrated by explicitly examining the role of emission rates, transport, and kinetic processes in the attribution of sulfur dioxide and sulfate at a receptor in Massachusetts during the summer of 1992. INTRODUCTIONDuring the 1970's, air quality measurements had shown that pollutant emissions could be transported over large distances, significantly influencing distant receptor concentrations. The realization of regional scale transport of pollutants, led to numerous air quality networks, models, and large scale field studies to relate the contributions from near as well as distant sources to receptor concentrations, i.e. to quantify the source receptor relationship. Many of the initial studies focused on sulfur oxides, and were design to attain better understandings of the physical/chemical processes governing regional scale transport. For example, in Europe, the OECD established the Long Range Transport of Air Pollutants (LRTAP) program in 1972 (1), and in the US, the Sulfate Regional Experiment (SURE) was conducted (2), along with numerous plume studies.Associated with these studies, were the development of models. The initial models tended to be simple, using aggregated and parameterized meteorology and rate coefficients to simulate the physical/chemical processes responsible for regional transport. These included trajectory models (3, 4), statistical models (5, 6), as well simple diffusion and box models (7,8). As the understanding of the atmospheric processes increased, these types of models were generalized to include processes such as non-linear chemistry and atmospheric diffusion (9, 10).During the 1980's, with the concern over acid rain, substantial effort was devoted to the development of elaborate Eulerian models, such as the Regional Acid Deposition Model (RADM) (11) and the Acid Deposition and Oxidant Model (ADOM) (12). These models attempt to simulate atmospheric processes, such as in cloud oxidation, with great detail. However, the data and computer resource needs have restricted ...

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A statistical model to estimate long-term concentrations of pollutants associated with long-range transport

Cited by 54 publications

References 16 publications

Source-Receptor Relationships for Acid Deposition: Pure and Simple

Source-Receptor Relationships for Acid Deposition: Pure and Simple

Estimates of Sulfate Deposition in the Middle Eastern United States: 1975, 1990, and 2010

Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model

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