Rollback Modeling: Basic and Modified

Nevers, Noel de; Morris, Jon R.

doi:10.1080/00022470.1975.10468116

Cited by 21 publications

(10 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Emissions inventory models are often used to develop control strategies by linear rollback modeling (Barth, 1970;deNevers and Morris, 1975;Cass, 1981;Cass andMcRae, 1981, 1983). The linear rollback model assumes that atmospheric concentrations in excess of background are proportional to aggregate emission rates.…”

Section: Step 2: Compile Emissionsmentioning

confidence: 99%

Receptor modeling application framework for particle source apportionment

Watson¹,

Zhu²,

Chow³

et al. 2002

Chemosphere

269

144

View full text Add to dashboard Cite

Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties. Receptor models complement source models that estimate concentrations from emissions inventories and transport meteorology. Enrichment factor, chemical mass balance, multiple linear regression, eigenvector. edge detection, neural network, aerosol evolution, and aerosol equilibrium models have all been used to solve particulate air quality problems, and more than 500 citations of their theory and application document these uses. While elements, ions, and carbons were often used to apportion TSP, PM10, and PM2.5 among many source types, many of these components have been reduced in source emissions such that more complex measurements of carbon fractions, specific organic compounds, single particle characteristics, and isotopic abundances now need to be measured in source and receptor samples. Compliance monitoring networks are not usually designed to obtain data for the observables, locations, and time periods that allow receptor models to be applied. Measurements from existing networks can be used to form conceptual models that allow the needed monitoring network to be optimized. The framework for using receptor models to solve air quality problems consists of: (1) formulating a conceptual model; (2) identifying potential sources; (3) characterizing source emissions; (4) obtaining and analyzing ambient PM samples for major components and source markers; (5) confirming source types with multivariate receptor models; (6) quantifying source contributions with the chemical mass balance; (7) estimating profile changes and the limiting precursor gases for secondary aerosols; and (8) reconciling receptor modeling results with source models, emissions inventories, and receptor data analyses.

show abstract

Section: Step 2: Compile Emissionsmentioning

confidence: 99%

Receptor modeling application framework for particle source apportionment

Watson¹,

Zhu²,

Chow³

et al. 2002

Chemosphere

269

144

View full text Add to dashboard Cite

show abstract

“…In this category of models, spatial diffusion is either neglected or highly simplified. Early such models have been developed by Gifford and Hanna (1973), and generalized by Hone (1974) and de Nevers and Morris (1975). They treat urban areas as uniform pollution sources, with no account for specific sources nor for the directionality of the diffusion process.…”

Section: Aggregate Modelsmentioning

confidence: 99%

Modeling Air Quality in Urban Areas: A Cell‐Based Statistical Approach

Guldmann¹,

Kim

2001

Geographical Analysis

View full text Add to dashboard Cite

Statistical regression models are presented that explain the observed variations, across urban areas, in the concentrations of two major pollutants, ozone and carbon monoxide. Model specijkatwn and estimation are based on an explicit and new spatial framework derived from the theoretical concept of well-mixed cells, whereby the basic Fickian system of difusion equations is integrated over the regional space partitioned into a grid of large cells. The Concentration in each cell results from the balance of pollutant flows into and out of this cell and of pollutant emissions and removal within that cell, and i s expressed as the sum of two concentration contributions: (1) the local efect, dependent upon pollution-related factors around the measuring station, and (2) the regional g e d , dependent upon pollutant flows originating outside the local area. A large database is developed, making extensive use of CIS technology, to spatially relate such data as pollution measurements, meteorological factors, landuse characteristics, census socioeconomic data, and major highway network characteristics. The results con$rm the appropriateness of the well-mixed cell framework, are in line with general knowledge regarding the determinants of ozone and carbon monoxide concentrations, and clarify the role of transportation, residential fuel use, economic activities, natural environments, and meteorological factors such as temperature and solar radiation. About 50 percent of the variations in concentrations are explained by these models. Several areas of further research are outlined. a 31 percent decrease in aggregate pollution emissions, including decreases of 31 percent for carbon monoxide (CO), 37 percent for sulfur dioxide (SO,), 71 percent for particulate matter (PM,,), 98 percent Financial support from the Urban Affairs and Urban Assistance Program, Ohio Board of Regents, is gratefully acknowled ed. Many thanks are due to Phil Viton, Steve Gordon, and three anonymous referees of this journal for hekful comments and suggestions on earlier drafts. Jean-Michel Guldmann is a professor in the Jean-Michel Guldmann and Hag-Ye01 Kim / 157for lead (Pb), and 42 percent for volatile organic compounds (VOCs), and an increase of 17 percent for nitrogen oxides (NO,). More striking, these improvements have occurred while the population grew by 33 percent, the vehicle miles traveled (VMT) by 140 percent, and the gross domestic product (GDP) by 147 percent. However, this aggregate assessment masks the fact that, in 1999, 150 million tons of air pollution were released into the air, and approximately 62 million people lived in counties with monitored concentrations above the primary standards (those designed to protect public health) for one or more of the six principal pollutants, particularly ozone in the Northeast, California, some rural areas, and some national parks. Also, despite a 60-80 percent decline in per-car emissions since the 1960s, total emissions from mobile sources have not decreased proportionately, because more frequent an...

show abstract

“…Empirical models are those in which a historical data base is used to develop a functional dependence between source emissions and air quality. These models, termed statistical and rollback type models, [2] assume some proportional relationship between the rate of emissions and atmospheric pollution levels. In Its simplest form, the proportionality Is taken to be strictly linear.…”

Section: B Empirical and Deterministic Modelsmentioning

confidence: 99%

“…Data on topography are generally not employed so that these models are also valid only for use in regions of fairly level terrain, Some of the most sophisticated species -conservation-of-mass models utilize topography and inversion base height data. Since the mixing depth is in general not known for all times and points within a computational grid, an interpolation scheme is employed to give the required spatial and (2) where R is the fractional reduction in emissions required, GF is the growth factor, PAQ is the present air quality, STD is the air qual ity standard and b is again the background concentration. The assumption of linearity in this form of rollback has validity only when applied to stable pollutants such as carbon monoxide.…”

Section: B Empirical and Deterministic Modelsmentioning

confidence: 99%

Review of air quality modeling techniques. Volume 8. [Assessment of environmental effects of nuclear, geothermal, and fossil-fuel power plants]

Rosén¹

1977

View full text Add to dashboard Cite

Rollback Modeling: Basic and Modified

Cited by 21 publications

References 4 publications

Receptor modeling application framework for particle source apportionment

Receptor modeling application framework for particle source apportionment

Modeling Air Quality in Urban Areas: A Cell‐Based Statistical Approach

Review of air quality modeling techniques. Volume 8. [Assessment of environmental effects of nuclear, geothermal, and fossil-fuel power plants]

Contact Info

Product

Resources

About