Protecting Visibility in National Parks and Wilderness Areas (1993) / Chapter Skim
Currently Skimming:
Appendix C: Source Identification and Apportionment Models
Appendix C: Source Identification and Apportionment Models
Pages 359-406
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 359... ...
That assumption be iAs with all models, there is an assumption that the concentrations apply to some given meteorology and given averaging time. In a temporal sense, the rollback model has the requirement that the temporal (e.g., diurnal)
|
From page 360... ...
for airborne particles is an aggregation of several separate rollback models for each individual chemical component of Me atmospheric particle complex. In almost all cases, the anthropogenic materials in the dry particle mass almost entirely consist of five components: sulfates, organics, elemental carbon, nitrates, and crustal material (e.g., soil dust and road dust)
|
From page 361... ...
The latter two modifications are ways of relaxing some of the restrictive assumptions of the rollback technique. Four types of information are needed to implement a speciated rollback model: · Data on airborne particle concentrations disaggregated by components of the particles; · Knowledge or assumptions regarding the controlling precursor for each secondary airborne particle component; · Emission inventories for the important source categories of each airborne particle component and each gaseous precursor substance; · Knowledge or assumptions regarding background concentrations (due to sources other than those that are in the inventory)
|
From page 362... ...
In Equation C-l, the subscripts i, j, and t index ambient aerosol characteristics, emissions sources, and sampling intervals, respectively. The terms c;, Sj, end fj are defined as follows: ci is the ith characteristic of the airborne particles at the receptor site.
|
From page 363... ...
Useful information sometimes can be obtained even when there are more sources than substances (White and Macias, 19911. Given an estimate of source strengths, source contributions can be derived for any conserved substance, whether it is one of the n markers used in the solution or one with additional sources.
|
From page 364... ...
for apportioning conserved characteristics of the ambient airborne particles. Unlike the statistical approaches discussed below (e.g., factor analysis)
|
From page 365... ...
The set of source profiles cannot be recovered uniquely without some such prior knowledge, because the set constitutes only one of an infinite number of possible coordinate systems (Henry, 1987~. In the context of visibility studies, the models of CMB and factor analysis are critically limiteci by their restriction to airborne particle characteristics that are conserved during transport from source to receptor.
|
From page 366... ...
In such studies, the tracer is typically released in discrete puffs. In contrast, tracers used to support receptor modeling should be released over a sustained period to avoid ambient samples that contain an unknown proportion of tagged and untagged effluent.
|
From page 367... ...
Even suspended soil dust varies significantly in composition from site to site (Cahill et al., 1981~. No chemical signature is of value unless it can be identified at ambient concentrations.
|
From page 368... ...
The current state of the art limits the model's regulatory application to particulate matter that is directly emitted to the atmosphere. The ability of the CMB model to apportion airborne particle concentration or light extinction to sources is limited to categories of sources with dissimilar source profiles, because of the assumptions inherent in the mode} and because of its inability to resolve sources of secondary particles.
|
From page 369... ...
The source profile (i.e., the fractional amount of each chemical species in the emissions from each source type) and the ambient concentrations of each species measured at the receptor site with appropriate uncertainty estimates serve as input data to the model.
|
From page 370... ...
If the number of source types that contribute to the airborne particle mass is less than or equal to the number of aerosol chemical features measured, then Equation C-1 can be solved for the unknown source contributions, the Sj'S by a variety of methods. These include tracer, linear programming, ordinary least-squares solutions, ridge regression, weighted least-squares solutions, and effective variance least-squares solutions (Britt and Luecke, 1973; Henry, 19821.
|
From page 371... ...
CMB Model Validation Studies Validation studies (Stevens and Pace, 1984) have shown that the CMB model typically can resolve the separate contributions of five or six major emission sources to the ambient primary airborne particle mass.
|
From page 372... ...
sensitivity studies have shown that (~) underestimation of the number of sources will have little effect on the calculated source contributions if prominent species contributed by unidentified sources are excluded from the calculation procedure; (2)
|
From page 373... ...
only apportions airborne particle mass concentrations among source types, further calculations must be completer! to use those source contribution estimates to apportion contributions to light extinction among source types.
|
From page 374... ...
In the terminology of regression analysis, c is the response, or dependent, variable; the Si are the regressor, or independent, variables; and the are the regression coefficients. In practice, the regressors are often taken to be variables that are proportional to source strengths, rather than the source strengths Sj themselves.
|
From page 375... ...
as the response variable, directly apportioning effects rather than Me gravimetrically or chemically determined airborne particle mass. (Regression analysis is used more often to relate light extinction to the optically important airborne particle components, rather than to source tracers; that application was cliscussec]
|
From page 376... ...
The simulated data did not incorporate certain aspects of real atmospheric problems, most notably the variability of source effluent chemical composition from one observation to the next. In the real atmosphere, regression-derived apportionments of predominantly primary airborne particle fractions have survived various crosschecks against emissions data.
|
From page 377... ...
377 et ed ' L 1 " 1 1 "1 L .
|
From page 378... ...
Statistical Assumptions and Consequences of Violation Regression analysis determines coefficients ~ for the regression mode] described by Equation C-4 that optimize its fit to the data for ambient concentrations car and source strengths Sit.
|
From page 379... ...
The scatter in Equation C-5 arises most fundamentally from the fact that the true coefficients (~ generally fluctuate from observation to observation, as indicated in the original Equation C-1. Such fluctuations can be pronounced particularly in applications to visibility, whereby typically represents a ratio of secondary airborne particles to primary tracer.
|
From page 380... ...
of the unknown source characteristics hi. Those estimates are multiplied by the observed mean source strengths Sj = mosey to derive the estimated mean source contributions fSj = m(~Sj~.
|
From page 381... ...
One such guide, for ambient concentrations of pollutants with moderate atmospheric lifetimes, is that the standard deviation and mean are generally of comparable size (e.g., HammerIe and Pierson, 1975; Tunce!
|
From page 382... ...
. The ambient correlation of unrelated emissions can be seen in the observed correlations of distinct source tracers or calculated source strengths where these are available.
|
From page 383... ...
Consider the actual and ideal regressions c = f'O + f'~S'~ and c = fo + fish of ambient concentration on estimated and true source strengths. The regression coefficient for SO is fit = cov~c, S~/s2(S~)
|
From page 384... ...
Prominent among the types are fluctuations in the true coefficients that correlate with one or more source strengths. In regression analysis of secondary pollutants, (~ and Sit may be correlated because of their dependence on common environmental influences.
|
From page 385... ...
Practical Guidelines From the foregoing discussion, it is possible to identity some critical elements in measurement programs designed to support source apportionment studies that are based on regression analysis. The foremost objective of such a program must be to provide accurate estimates for the source strengths of all major contributors to the ambient mix.
|
From page 386... ...
Such measurements are relevant, however, only if the background concentrations under these conditions are similar to those in the presence of tagged emissions. Since meteorological factors tend to impose a common temporal pattern on all ambient concentrations, as noted earlier, it is generally risky to estimate the background concentrations in one period from a measurement in another period.
|
From page 387... ...
for computing pollutant concentrations in the plume and schemes for calculating radiative transfer processes that describe the visual aspects of the resulting plume. The pollutant reaction and transport codes that can be embedded in such a mode]
|
From page 388... ...
The mode} represents the particle size distribution in terms of three dynamic lognormal modes that evolve in response to homogeneous nucleation, coagulation, condensation, and gravitational settling. LightscaKering calculations are based on this computed particle size distribution.
|
From page 389... ...
The evolution of the particle size distribution, through homogeneous nucleation, coagulation, diffiusion-limited condensation and evaporation, aqueous reaction, and sedimentation, is based on the sectional techniques introduced by Gelbard (1984~. MODELS FOR TRANSPORT ONLY AND FOR TRANSPORT WITH LINEAR CHEMISTRY An analysis of wind flow during sampling periods is a necessary but insufficient test of source apportionment.
|
From page 390... ...
Back trajectory analysis provides an estimate of the mean path followed by air en route to a sampling location. It is understood, but seldom articulated, that the estimate represents only the path with the highest probability that transport occurred along that line.
|
From page 391... ...
can be expected to identify source areas that consistently influence observed concentrations. ETA uses simple stratification of trajectories by concentration or through weighting of estimates of the probabilities that transport to a particular receptor site will occur from all of the possible surrounding source areas.
|
From page 392... ...
trajectory can be assumed to represent the highest probability at any time that a particular upwind path is contributing to the trace substance composition at the monitor location. The spatial distribution of the transition-probability density function away from the axis of the trajectory can be adjusted to depend on meteorological conditions.
|
From page 393... ...
The spatial distribution of the field represents the "natural" potential for contribution to atmospheric pollutant concentrations if the source of that pollutant is spatially homogeneous. The measured concentrations of trace substances are used to derive an implied transport bias.
|
From page 394... ...
Pollutant concentrations can be estimated through bookkeeping of the number of particles that fall within the air volume represented by each grid cell in the model. Over regional scales, several authors (e.g.
|
From page 395... ...
Such models focused exclusively on gas-phase chemical transformations, neglecting particles of central importance to visibility modeling. More recently, mechanistic modeling has been applied to problems involving secondary airborne particle formation and acid deposition (Chang et al., 1987; Russell et al., 1988a)
|
From page 396... ...
for visibility impairment, direct calculations of chemically resolved airborne particle size distributions are needed in conjunction with a theoretical treatment of scattering and absorption of light by particles to calculate the optical properties of aerosols. The current understanding of atmospheric aerosol processes requires considerable refinement before such models can be used with confidence.
|
From page 398... ...
398 ,, .ca:.,, 1 , ., ~ .
|
From page 399... ...
The models that use this information to calculate the time-dependent three-dimensional distributions of gases and particle size distributions. Mechanistic models generally solve the following chemical conservation equation for each of the transported gas phase chemicals: = -V VC + V (KCVC)
|
From page 400... ...
400 T ;-,c ·~ E ,= ~ >~e I o =,o C -~C Cal · m Cat o :~ C G ,' O ~ At ~ C ~ dell E-'8 t~2C = -~'a Yes _ r _ Be .~ D D _ _ ~ ~ C ~e} ~3 3~l l]
|
From page 401... ...
Mechanistic visibility models should involve two separate components. First, chemically resolved airborne particle size distributions need to be calculated at specified grid points.
|
From page 402... ...
Therefore, models that are used to determine airborne particle size distributions must be linked with meteorology and gas-phase chemistry models. Lorentz-Mie theory is used to calculate the optical characteristics of airborne particles by integrating over calculated particle size distributions.
|
From page 403... ...
The total extent of particle scattering and absorption is determined by integrating over calculated particle size distributions that are chemically resolved. Although the assumption is often reasonable for submicron particles, little experimental work has been done to examine its validity.
|
From page 404... ...
is complete and that the secondary substances have not been preferentially deposited en route to the receptor, secondary particles can be apportioned among contributing source types. In real-worId applications where conversion is not complete or the secondary particles are deposited during transport, CMB has been used only in research settings to quantitatively estimate secondary aerosol source contributions.
|
From page 405... ...
particle fractionation effects during transport due to the differing size distributions of the chemical species must not occur; and (3) the estimate of plume age required to calculate T becomes less certain as the distance from the source increas es.
|
From page 406... ...
has been developed that employs CMB receptor modeling for attribution of primary airborne particles to their sources, accompanied by a separate deterministic mode! for sulfate formation and transport that is driven by atmospheric transport, reaction, and dilution calculations rather than by tracer concentrator data (Harley et al., 19891.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.