6.0	Integration of Numerical Models and Ambient Monitoring Data for Effective Air Quality Management

Considerable monitoring and modeling resources have been developed to help manage and improve air quality. Monitoring studies collect extensive information about pollutant levels and meteorological conditions, and these are used to assess air quality. However, ambient data from these monitoring studies do not provide information about the emission sources affecting the pollutant levels. Modeling tools have been developed that can help tie information together about air quality effects from emissions and potential reductions or changes in future emissions. To surmount the limitations encountered when using monitored ambient data and modeling tools individually in air quality research and management, these vital components should be used simultaneously. Together, ambient data and models promote effective air quality management practices through thorough model evaluations and through the development of tools that take advantage of both ambient data and modeling techniques.

Three areas of air quality management that integrate numerical models and ambient monitoring data include: (1) re-evaluating and updating modeling studies with new ambient data, (2) constructing an iterative process to make midcourse corrections, and (3) using ambient data and modeling tools to infer emission rates. These areas are described in this chapter, and examples are provided to illustrate the concepts.

6.A	Re-evaluating and Updating Modeling Studies with New Ambient Data

Modeling tools, a vital component to air quality research and management, are useful for testing the long-term effects of proposed implementation strategies or other changes potentially affecting air quality (e.g., population growth). Because the conclusions from these modeling studies affect policy decisions, models must be evaluated thoroughly to determine the confidence level for the model results. Extensive ambient data is required for complete evaluation of models; however, as discussed in Chapter 5, ambient monitoring data resources are limited. As new ambient data continues to become available, it is beneficial to further evaluate the air quality models that are used extensively for air quality management.

Several criteria should be considered when evaluating these models. The most direct question is whether or not the model is able to predict pollutant levels similar to those observed during the time periods of the simulations. These concentrations should be predicted by the model for the right reasons. For example, the influence of different processes on the final predicted ozone concentrations can be better quantified as more ambient data becomes available for pollutant precursors. As more speciated volatile organic compound (VOC) data become available, the influence of biogenic VOCs on ambient ozone concentrations can be examined by comparing the model's estimated biogenic emissions to the observed concentrations of biogenic VOCs.

The Review of Air Quality Models for Particulate Matter (API, 1997) concluded that past model evaluations lacked thoroughness, and more comparisons between the available models and the most comprehensive ambient air quality data are needed. Data availability has traditionally been a limitation for model evaluations. Therefore, air quality models should be re-evaluated as more speciated particulate matter data from field studies, such as the Interagency Monitoring of Protected Visual Environments (IMPROVE) network and the Northeast Corridor-Oxidant and Particulate Study (NEC-OPS), become available.

More advanced techniques are also being developed with ambient data to study the limiting relationship between pollutant precursors and the pollutants. Photochemical indicators are one way to study limiting relationships that use the ratio of measured chemical species to total reactive nitrogen (e.g., O3 / NOy ) and indicate if ozone production is limited by the emission rates of oxides of nitrogen or VOCs ( Sillman , 1995). This information about limiting behavior is critical when choosing potentially effective emission control strategies for ozone. With an indicator tool, ambient data can be used to evaluate model results and conclusions regarding potentially effective emission control strategies for a particular region.

6.B	Constructing an Iterative Process to Make Midcourse Corrections

Models are a crucial tool for evaluating the potential benefits of air quality management planning efforts. When State and local agencies are required to predict ambient air quality improvements based on future emission controls, the initial model results have significant uncertainties that can be minimized with additional data. Therefore, new ambient air quality data collected after the model's initial run should be used to re-evaluate the model's predicted air quality. A comparison between the model predictions and new ambient data will determine if the implementation strategies produced the predicted reductions in pollutant levels. If significant differences exist between the predicted and observed changes in air quality, the implementation strategies can be adjusted. Because of the uncertainties in the planning process, an iterative process conducted five or six years after the adoption of the plan is recommended ( STSWG , 1997). After evaluating the observed effectiveness of the implemented control measures, midcourse corrections can be made when necessary.

6.C	Using Ambient Data and Modeling Tools to Infer Emission Rates

This section discusses techniques developed to infer emissions from information gained from both ambient monitoring data and modeling tools. Several examples that vary in complexity are discussed: the Chemical Mass Balance ( CMB ) receptor model, multivariate receptor models, and the Kalman filter inverse modeling tool. Each has the ability to estimate emission rates for pollutants and pollutant precursors. Because all approaches except multivariate analysis rely on linear relationships, applications to secondary pollutants and highly reactive pollutant precursors are currently limited. However, case studies are discussed where these tools have been effectively applied to ozone and particulate matter research to determine emission rates.

CMB Receptor Model

Receptor modeling relies on the fundamental principles concerning the conservation of mass. By using this principle, receptor modeling identifies and apportions sources of atmospheric pollutants or their precursors to independent source areas. The receptor models' mass balance equation is generally written as:

where	C	= observed concentration of the pollutant or precursor at the receptor site,

j	= independent source,

N	= total number of independent sources,

fj	= fraction of emissions from source j that constitute the pollutant or precursor,

Sj	= estimated concentration of total emissions from source j, and

= vector of errors (difference between observed and predicted concentrations).

Using the receptor model approach, observed ambient concentrations C are used to find a best-fit solution for Equation 6.1. This approach is limited because it cannot be used for pollutants whose chemistry is best described by non-linear equations. As a physical constraint, non-negative solutions are not allowed. Therefore, a source cannot have a negative fraction of emissions constituting the pollutant or precursor of interest ( fj ) nor can the emission rates ( sj ) be negative (eliminating the consideration of emission sinks). Another limitation is that all significant source categories must be identified to ensure reasonable results.

The EPA-approved CMB receptor model includes another dimension to Equation 6.1, allowing multiple chemical species to be considered in the calculations. CMB methods have been applied in numerous areas, and Table 6.1 lists some examples of CMB applications useful for ozone, particulate matter, and visibility research. The CMB method has been used to investigate source attribution and evaluate emission inventory estimates for some of the less reactive VOC species ( Kenski et al., 1995). Results from a similar VOC receptor modeling study in Detroit, Michigan were used in an Eulerian 3-D ozone model as an alternative to using reported VOC emission inventories (Chung et al., 1996). The results were within the limits of the EPA accuracy guidelines. Results from CMB receptor modeling were used in a photochemical ozone model to test the effectiveness of oxides of nitrogen ( NOx ) reductions in the Michigan area (Chung et al., 1997). The South Coast Air Quality Management Division ( SCAQMD ) also used CMB to apportion sources in the Los Angeles area for PM-10 ( SCAQMD , 1997). Their results suggested that secondary production of PM (e.g., ammonium nitrate, ammonium sulfate, and reactive carbon) was a dominant contributor to the ambient PM-10 concentrations in the Los Angeles area. A study using CMB in Mexico City concluded that the most significant sources of PM-2.5 in Mexico City were cars without catalytic converters and heavy-duty diesel vehicles (Vega et al., 1997).

A peer review of the CMB method concluded that the approach is suitable for sources of primary emissions, because the method solves directly for the emission rates of the pollutant or precursor observed at the receptor (SAW, 1997). Current methods designed to handle secondary pollutants determine sources of secondary pollutants by using CMB ratios that compare the concentrations of secondary pollutant to primary pollutant species. This approach does not account for the removal of the primary pollutant or incomplete production of the secondary pollutant. The review concluded that the CMB receptor model is a useful tool for the quality assurance of emission inventories and the assessment of emission model performance.

Multivariate Analysis Techniques

Multivariate receptor modeling is another approach that uses ambient data in a modeling framework to determine source emissions based on multiple observations (either at different times or geographic locations). This approach relies on physical and statistical analyses of ambient data to perform tasks, such as identifying multiple source locations and apportioning source emissions based on speciated data at the receptor sites. Multivariate analysis techniques can be applied to ambient, speciated measurements of PM and VOCs without requiring speciated emission source profiles as inputs. Multivariate analyses could potentially be used to:

Assure the quality of emission inventories,

Provide support for episode selection in nested grid modeling studies,

Evaluate grid model performance,

Select control strategies to simulate, and

Support weight-of-evidence determinations (SAW, 1997).

Similar to receptor models like CMB , multivariate receptor models rely on the concept that the concentration of each pollutant (or precursor) at a specific time is equal to the product of source contributions and the source composition plus an additional random associated error. The basic equation for the physical model is similar to other receptor models and relies on the constraint of mass balance:

Table 6.1 Sample Applications of the CMB Receptor Model

STUDY	CHEMICAL

SPECIES

LOCATION

SIGNIFICANT FINDINGS
Receptor Modeling Approach to VOC Emission Inventory Evaluation

( Kenski et al., 1995)	VOC	Beaumont, TX; Washington, DC; Atlanta; Detroit; and Chicago	EPA VOC emission inventories were found to be good for vehicles. Refinery emission estimates were too low by a factor of 10 in Detroit and Chicago.
Development of Ozone-
Precursor Relationships

(Chung et al., 1996)	VOC	Detroit	Emission estimates from CMB were used in a photochemical ozone model with results that fell within the EPA accuracy guidelines.
Using Ambient Data to Examine Emission Inventories

( Kenski et al., 1996)	VOC	Wisconsin, Illinois, Indiana, and Michigan	Emission rate estimates from CMB and the Lake Michigan Ozone Study ( LMOS ) compared similarly except for the rates from oil refineries.
Receptor Model Evaluation of Ambient NMOC Measurements

( Scheff et al., 1996)	VOC	Michigan	NMOC emission estimates from the Southeast Michigan Ozone Study were realistic for all categories except for underestimations from petroleum refineries and graphic art industries.
Application of CMB Receptor Model (Vega et al., 1997)	PM-2.5	Mexico City	The predominant sources of PM-2.5 emissions in Mexico City were identified as automotive vehicles without catalytic converters and then heavy-duty diesel vehicles.
1997 Air Quality Management Plan

( SCAQMD , 1997)	PM-10	Los Angeles	Results suggested that secondary pollutants (i.e., ammonium nitrate, ammonium sulfate, and secondary carbons) comprised a significant fraction of the ambient PM-10 concentrations. Geologic and vehicle emission sources were also significant.

where	Cik	= concentration of chemical species i at observation point k,

i	= chemical species,

j	= independent source,

N	= total number of sources,

k	= observation,

fij	= source composition or profile for source j and species i ,

Sij	= contributions of species i from source j, and

ik	= random and lack of fit errors.

Three different multivariate models have been developed using the statistical approach of empirical orthogonal functions ( EOF ). These models could potentially be used in regulatory development (Henry, 1997a ; SAW, 1997):

UNMIX model to identify source contributions to a single receptor based on the composition of ambient data,

Receptor Model Applied to Patterns in Space ( RMAPS ) model to study spatial influence patterns on a receptor from multiple sources, and

Source Identification by Empirical Orthogonal Functions ( SITEOF ) to evaluate the spatial and temporal concentration variations as a function of the precursor sources. The SITEOF method also includes meteorological information to identify these sources.

Each of these multivariate receptor models might be applicable for addressing air quality management of ozone, PM, and regional haze. The Source Attribution Workshop (SAW, 1997) concluded that this technique was a solid alternative to the CMB receptor model for primary pollutants, and the ability to handle non- linearities of secondary pollutants should be further tested. Sample applications include source apportionment, evaluation of primary versus secondary pollutants, and determination of transported and local area sources. Multivariate receptor model applications have been used in previous studies of VOCs and PM. The UNMIX model was used for VOC source apportionment using Houston Ship Channel Coast data (Henry et al., 1997), and the apportionment of airborne particulate sulfur from MOHAVE (Measurement of Haze and Visual Effects) and PREVENT (Pacific Northwest Regional Visibility Experiment Using Natural Tracers) studies used the RMAPS model (Henry, 1997b , c, a).

Another multivariate receptor analysis tool is the Principal Component Analysis ( PCA ) model. The PCA approach is mathematically similar in form to the physical model presented for CMB in Equation 6.1 ( Thurston and Lioy , 1987):

where Wij	= scoring coefficient matrix (analogous to source profiles [ fj ] in equation 6.1),

i	= chemical species,

j	= independent source,

N	= total number of j sources,

k	= observation,

Pjk	= source impact variations, and

Zik	= standardized value of the concentration of the chemical species ( Cik ):

where	Cik	= the concentration of chemical element i from the kth source,

= average concentration of the ith chemical element over all sources, and

i	= the standard deviation of the element i over all of the observations.

The source impact variations are calculated as part of the PCA analysis from the correlation matrix and are uncorrelated. Eder et al. (1994) used PCA analysis to clarify the impact of meteorology on ozone in Birmingham, AL, and found that it offered an improved parameterization of ozone concentrations as a function of meteorological variables.

When more than one chemical species are considered in a receptor model such as CMB , the only difference between Equations 6.1 and 6.2 is that multiple observations are not used in CMB . Rather, CMB is applied to an individual receptor, and the source categories that affect the receptor and their emission profiles are specified. For this reason, receptor models like CMB are only appropriate when the emission source categories are well understood. Multivariate modeling approaches, on the other hand, are useful when significant uncertainties are associated with the source categories. Multivariate methods can help to identify potential sources and their impacts ( Thurston and Lioy , 1987). The most significant limitation of multivariate receptor models is that they rely on many more observations than simpler receptor models (SAW, 1997; Thurston and Lioy , 1987). Therefore, multivariate analyses are most useful in intensive studies where many monitoring sites are available for the area.

Inverse modeling

Inverse modeling is a relatively new approach for air quality studies; however, it has been used for global greenhouse gas research since the 1980s . The technique uses both ambient observations and modeling together to develop and evaluate emission inventories. Inverse modeling uses a statistical approach in a chemical-transport model to determine the emissions profile that would optimally predict pollutant (or precursor) concentrations similar to the observed ambient concentrations. This approach makes use of all available model information (initial emission estimates, observed ambient pollutant concentrations, advective and diffusive transport, and chemistry) to approximate the rates and distribution of emissions. In this modeling application, an inverse technique is used to compare ambient pollutant concentrations and model the predicted concentrations. The inverse technique then adjusts the model's emission inventory until a solution is found where the predicted concentrations agree with the observed ambient concentrations at the monitoring sites.

Inverse methods were first used in atmospheric modeling to address pollutants that affect global issues, such as greenhouse gases and chlorofluorocarbons. Various inverse method techniques were used on the global scale to quantify emissions, including singular value decomposition and the Kalman filter. Tans et al. (1989) and Enting and Mansbridge (1989) both used inverse methods in two-dimensional global chemical tracer models to evaluate carbon dioxide ( CO2 ) emissions. The findings of Tans et al. (1989) suggested that there was an unknown CO2 sink in the Northern Hemisphere, which led to much interest in locating the "missing sink." Hartley and Prinn (1993) applied the Kalman filter inverse method to deduce the global chlorofluorocarbon ( CFCl3 ) emissions. The study by Hartley and Prinn demonstrated the feasibility of developing an inverse method for a three-dimension model.

The Kalman filter compares the difference between observed and predicted concentrations ( ) to calculate the emissions adjustment necessary to optimally reduce , using the following formula:

where	C	= estimated emission error (covariance matrix),

P	= sensitivity of the chemical species to emission changes as derived by the model
( Jacobian matrix),

N	= error of the observed concentrations (noise matrix),

E	= change in emissions, and

PT	= the transpose of the matrix.

This calculation is performed until the change in emissions ( E ) goes to zero. The model error can also be included in the noise matrix (N) as shown by Haas- Laursen et al. (1996).

With the Kalman filter's 3-dimensional capability, the Kalman filter inverse method became a useful tool for regional or urban scale emission studies. Several examples of inverse modeling studies (including global and smaller scale) are listed in Table 6-2. On the urban scale, Chang et al. (1997) used the Kalman filter to perform an inverse on the biogenic isoprene emissions predicted by the Biogenic Emission Inventory System ( BEIS ) and by the newest version BEIS2 (Pierce et al., 1990). Using the Urban Airshed Model ( UAM ) and observed concentrations of isoprene that were collected during the intensive Southern Oxidant Study (SOS) 1992 Atlanta Intensive, biogenic emission estimates were calculated on an hourly basis. The adjusted isoprene emissions produced by the inverse directly improved the modeled isoprene concentrations and the ozone concentrations predicted by UAM .

Study	Chemical Species	Model Name	Scale (dimensions)	Inverse Method
Surface Observations and Atmospheric Transport Model (Tans et al., 1989)	CO2	Tans et al. [1989]	Global (2D)	singular value decomposition
Atmospheric CO2 : Direct Inversion of Filtered Data

( Enting and Mansbridge , 1989)	CO2	Enting and Mansbridge [1989]	Global (2D)	inverted filtered data directly
Determining Surface Emissions (Hartley and Prinn , 1993)	CFCl3	CCM2	Global (2D)	Kalman filter
Inverse Modeling of Emissions ( Mulholland and Seinfeld , 1995)	CO	CIT	Urban (3D)	Kalman filter
Inverse Modeling of Emissions (Chang et al., 1996)	isoprene	UAM -IV	Urban (3D)	Kalman filter

Inverse modeling applications using the Kalman filter can be tested for uniqueness, and it can be determined if the solution is optimal. The Kalman filter inverse method was also modified to account for model error when inverting the emissions (Haas- Laursen et al., 1996). With this approach, inverse modeling provides a unique tool for estimating emissions while avoiding compensating errors.

The Review of Air Quality Models for Particulate Matter (API, 1997) suggested that inverse modeling could be useful for calculating limits for emission estimate errors and identifying missing data gaps in the emission inventories. A specific example suggested improvements to the ammonia emission inventories since ammonia inventories have large uncertainties associated with them, and ammonium sulfates constitute a large fraction of the measured PM-2.5 in eastern States.

Limitations to inverse modeling include both model and monitoring uncertainties and the extent of ambient data. The Kalman filter is affected by the air quality model's performance. Thus, an inaccuracy in the model will result in erroneous emissions estimates. This weakness emphasizes the need for thorough model evaluations and indicates that the air quality model for an inverse modeling application should be chosen carefully. Inverse methods are also limited by the available ambient data. The number of source areas for inverse modeling applications is limited by the available monitoring sites. This limitation is of particular importance when large spatial inhomogeneities exist in the emissions (Chang et al., 1997).

References

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