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Combined Aerosol Trajectory Tool, CATT: Status Report on Tools Development

and Use Paper # 97, A&WMA Specialty Conference on Regional and Global

Perspectives on Haze: Causes, Conse...

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Combined Aerosol Trajectory Tool, CATT: Status Report on Tools Development and Use Paper # 97, A&WMA Specialty Conference on Regional and Global Perspectives on Haze: Causes, Consequences and Controversies, Asheville, NC, October 25-29, 2004. R. B. Husar and K. Höijärvi, Washington University, St. Louis, MO 63130-4899, rhusar@me.wustl.edu R.L. Poirot, Vermont Agency of Natural Resources, DEC AP, Waterbury, Vermont, 05671-0402 S. Kayin, Mid-Atlantic Regional Air Management Association, 711 West 40th St. Baltimore, MD 21211, K.A. Gebhart, B.A. Schichtel, W.C. Malm National Park Service/CIRA, Fort Collins, CO 80523-1375

ABSTRACT

An attractive technique for exploring the spatial origin of aerosol species is Paired Aerosol/Trajectory Analysis developed by Poirot and others. The technique combines measured aerosol chemical data with back-trajectories to generate aggregated airmass history pattern for specific chemical conditions, such as high concentrations. This aerosol source analysis method was implemented as a web-based tool, Combined Aerosol Trajectory Tool, CATT. The current CATT implementation uses the IMPROVE database as the chemical filter and a matching ATAD backtrajectory database both prepared as part of the Inter-RPO-supported VIEWS project. The CATT tool is a community-supported development and analysis facility developed at CAPITA. The software incorporates a number of different chemical filtering, trajectory-aggregation and rendering algorithms contributed by different investigators.

The initial use of CATT with the IMPROVE database reveals remarkable spatial coherence for some aerosol source-transport pattern. For example, the fine particle soil composition at Big Bend NP, TX in July is consistent with the composition of Sahara dust at Virgin Island and the CATT-derived airmass back trajectories point exclusively back to Sahara, thus fortifying the case for Sahara dust impact in July. On the other hand in May, at Big Bend, both the composition and airmass history point to local dust as the source. The paper describes the development, initial application and possible future evolution f the CATT tool.

INTRODUCTION

The source-receptor relationship of pollutants can be estimated by a number of alternative techniques, including forward modeling, back trajectories, and chemical source apportionment1,2,3. A particularly attractive source attribution technique, Paired Aerosol/Trajectory Analysis4 combines the chemical, source identification and transport-based techniques.

The combined aerosol trajectory tool (CATT) is a web-based implementation of the Paired Aerosol/Trajectory Method. It is a collaborative project between the Center for Air Pollution

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Impact and Trend Analysis (CAPITA) at Washington University in St. Louis and Cooperative Institute for Research in the Atmosphere (CIRA). Both VIEWS and CATT are inter-RPO projects for supporting all regional planning organizations for haze management (RPOs). For the CATT project, VIEWS provides the chemical data as well as the back trajectories for each IMPROVE site/day, 1988-2002. CAPITA developed the web-based analysis tool, CATT. Members of the RPO data analysis community are providing the analysis algorithms and also conduct tool testing and feedback support.

The Visibility Information Exchange Web System (VIEWS) is an online exchange of visibility data, research, and ideas. The VIEWS web system contains an array of visibility related data for use by the Regional Planning Organizations (RPOs). The evolving VIEWS system also performs extensive data summarization, calculations and delivers the raw and processed data through a simple, user-friendly web interface. The main application of VIEWS is to aid RPOs and the states in the implementation of regional haze regulations over the next years. The VIEWS system is operated at Colorado State University, Cooperative Institute for Research in the Atmosphere (CIRA), under contract from RPOs.

CAPITA has been developing a number of software tools for the analysis, exploration, and presentation for air quality data. Most recently, with support from NSF and NASA, EPA and others, CAPITA has developed the DataFed (Data Federation) infrastructure for web-based sharing, browsing, and processing of distributed air quality data5. One of the applications built on the DataFed infrastructure the Combined Aerosol Trajectory Tool (CATT) for the exploration of airmass transport pattern under specified chemical conditions. The CATT tool received initial funding from the Mid-Atlantic/Northeast Visibility Union/Midwest Regional Planning Organization (MANEVU/MRPO) RPOs6. This project is an extension of the initial CATT residence time analysis tool.

A key feature of the expanded CATT tool is the applicability to all RPOs. The extensions include spatial coverage for the entire US and also the incorporation of all chemical parameters present in the VIEWS aerosol chemistry database. The data used by the tool utilizes both aerosol chemistry as well as meteorological transport data (Figure 1). Both data streams are contributed from different sources and processed through multiple steps. The data processing operations are represented by arrows connecting the data-boxes. These operations include filtering, aggregation, and fusion of data streams. Virtually every step in the multi-stage process is executed by different organizations at different locations and times. Thus, the CATT tool represents a distributed, asynchronous, collaborative effort facilitated by the communication channels of the Internet.

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Figure 1. CATT as a distributed, asynchronous, community tool.

With the CATT tool, the aerosol data analysis community can:

a. Explore the airmass histories associated with specific chemical conditions. b. Render the associated airmass histories as contours or trajectories.

c. Superimpose the transport data on other spatial data sets (emissions, satellites).

Based on experience in developing the prototype, the current software consists of two loosely coupled components:

1. Chemical Filter Tool (CFT). This component is accomplished through queries to chemical data sets. The output of this step is a list of “qualified” set of date-location pairs.

2. Trajectory Aggregator Tool (TAT). This component receives the list of date-location pairs and performs the trajectory aggregation, residence time calculation and other spatial operations to yield a transport pattern for specific receptor location and chemical conditions.

The two software tools have substantial utility individually as well as linked as the combined CATT tool. CFT Inputs and outputes:

• Input: Chemical database (e.g. VIEWS). • Input: Filter conditions (threshold or percentile). • Output: Table of site ID, receptor date, concentration value.

TAT Input/Output: • Input: Receptor location/time. • Input: Temporal filter/weight conditions. Date range, specific dates, weights for each

date. • Input: Trajectory input files. Pre-computed or on the fly calculated (e.g. HYSPLIT,

ATAD etc). • Input: Trajectory aggregation metrics. Endpoint counts, residence time, incremental

probability.

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• Outputs: XMLGrid, GIS layers, ASCII point. • Outputs: Rendered contour images of transport metric.

The CATT tool is built on top of the DataFed infrastructure which provides the general data access, rendering and navigational facilities. The DataFed distributed environmental data browser facilitates access, overlay and rendering of spatial data from a multiplicity of web sources. Dynamic rendering allows spatial and temporal data layers to be navigated (zoom, pan, point) by the user.

THE CATT FAMILY

As of June 2004, the CATT project resulted in the development of a family of transport analysis tools stated below. It is conceivable that the further evolution of CATT will result in consolidation or expansion of the tool set. Trajectory Browser Tool. This tool is used to show back trajectories for IMPROVE sites. Trajectory Aggregator Tool. Aggregates trajectories for predefined locations. Kitty – Simple Combined Aerosol Trajectory Tool. Limited filtering and aggregation tool for IMPROVE-ATAD data. CATT - Combined Aerosol Trajectory Tool Full featured chemical-transport exploration tool.

IMPROVE-ATAD Trajectory Browser Tool

This tool is used to find and display the backtrajectory for any IMPROVE location and sampling time (Figure 2). The browser selects the receptor location by clicking on the map of site locations and date selector. Backtrajectory browser for IMPROVE sampling sites. Click on location and date. For a given sampling date, the backtrajectories for multiple receptor sites can be retrieved.

Figure 2. Trajectory browser tool.

The sites are specified in the URL, e.g.: http://webapps.datafed.net/dvoy_services/datafed.aspx?view=atad_map&loc_code=SALM+SAMA+SHEN+UPBU. The resulting trajectories (4x day) are plotted on a map layer, which can be overlaid on any of the spatial data layers available through the DataFed data sharing system.

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TrajAgg- Trajectory Aggregator

The Trajectory Aggregator tool performs weighed trajectory aggregation using user-supplied filter (Figure 3a). The filter consists of a table containing date-receptor location pairs. The filter table can be submitted as a comma separated value, CSV table. The table submission is accomplished by the CSV File Editor and Submitter.The aggregator returns either a superimposed trajectory plot or a residence time contour for the specified filter condition.

Figure 3a. Trajectory aggregator tool.

The origin of aerosol nitrate is a particularly nagging measurement and analysis problem. Using the TrajAgg tool, the nitrate origin is identified to be much of the West Coast, the Midwest and some of the urban areas in the East. (Fig.3b). Similar application of TrajAgg yield the origins of Arsenic, Selenium and Nickel (Fig. 3c).

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Figure 3b. Application of the TrajAgg tool to evaluate the origin of aerosol Nitrate.

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Figure 3c. Evaluation of the origin of aerosol Arsenic, Selenium and Nickel.

Kitty Tool

The ‘Kitty’ software, a simple version of the CATT software is shown below (Figure 4a). The chemical filter conditions are applied to the VIEWS database. The user can select the chemical parameter, e.g. soil from a drop-down listbox. The receptor location is selected by clicking on the site location on the map. The time domain for the chemical filter is defined by the start and end dates. The Kitty software also facilitates filtering by specific month of the year. The Kitty software returns either a superimposed trajectory plot or a normalized residence time contour , KittyGrid, for the specified filter condition.

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Figure 4a. ‘Kitty’-simple CATT tool user interface.

An interesting ‘systems-check’ on the tool can be performed using KittyGrid: Selecting all VIEWS sites, setting the threshold Sodium concentration to 1.5 ug/m3, KittyGrid shows the origin of sea salt in the aerosol chemical samples. Figure 4b shows clearly, that the

origin of sea salt at both east and west coast sites is indeed the adjacent ocean.

Figure 4a. ‘KittyGrid’-showing the origin of air masses with high sodium concentration.

CATT – Combined Aerosol Trajectory ToolThe CATT tool has the same chemical filter facilities as the Kitty tool. However, the trajectory aggregator includes a number of different algorithms for representing the transport pattern.

Trajectory weight factors. The trajectory rendering includes weight factors representing:

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1. Chemical concentration associated with the trajectory. This factor allows weighing the trajectory significance by the species concentration at the receptor site. It is reasonable to apply higher weights to those trajectories that correspond to higher concentrations.

2. Distance (age) of the trajectory point from the receptor. Trajectory points can be aggregated onto a fixed size grid, which represent a trajectory, residence time map. The grids in the vicinity of a receptor site will always have more trajectory points (residence time) since all trajectories have to pass through those grids. This residence time gradient can be compensated by a distance (age) close-dependant factor, which enhances the weight with distance from the receptor.

In the display below (Figure 5), the trajectory color is linked to the concentration weight. Blue color trajectories are associated with low concentrations, while red trajectories are linked to high concentrations. The distance weight is represented by the point size along the trajectory. Trajectory points further from the receptor are larger than points near the receptor. The trajectory/residence time rendering is now implemented and is ready for user feedback.

The two transport display modes are either individual trajectory plots or as residence time grids. Depending on user needs either display mode can be used to delineate the nature of the transport pattern. For qualitative pattern analysis the individual trajectory view is helpful. More quantitative pattern analysis can be derived from gridded residence time maps (Figure 6).

For residence time maps, the trajectory end points are summed for each grid and the total number of endpoints in a grid represents the residence time map. For sake of uniformity, the residence time grid is normalized by the total number of endpoints in all grids. Note, that trajectory endpoints can be further weighed by chemical concentrations and distance.

Figure 5. User input screen for the CATT chemical filter.

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Figure 6. Contour plot of the CATT residence time distribution for specific chemical conditions.

In the CATT tool, all these factors and rendering parameters are user- specified with reasonable default values (Figure 7).

Figure 7. Rendering of ensemble trajectories. Weight is represented by color and point circle size.

The impact of age weighing on the resulting residence time pattern is illustrated below for fine soil at the Shenandoah receptor site in July (Figure 8). In the left most Figure 8, the residence

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time is calculated with the distance-independent weight factor, i.e. all trajectory points are weighed equally. In the middle map, the trajectory points are weighed in proportion to the square root of age. In the right most map, the trajectory points are weighed in proportion to the age. It is evident, that the unweighed trajectory points emphasize the region adjacent to the receptor, while the fully age-weighed approach highlights the most distant grids.

A clear theoretical justification for selecting distance weight factors is not yet in hand. It is hoped that further experimentation with the CATT tool along with theoretical considerations a recommended age factor setting will be developed.

Figure 8. Illustration of the age weighing.

Transport Probability MetricsThe transport metric is calculated from two residence time grids, one for all trajectories and another for trajectories on selected (filtered days). Both residence time grids are normalized by the sum of all residence times in all grid cells:

pijf=rij/ΣΣ rij pija=rij/ΣΣ rij

pijf, is the filtered and pija is the unfiltered residence time probability that an air masses passes through a specific grid. There is a choice of transport probability metrics:

The Incremental Residence Time Probability (IRTP) proposed by Poirot7 is obtained by subtracting the chemically filtered grid from the unfiltered residence time grid, IRTP = pijf - pijaThe other metric is the Potential Source Contribution Function (PSCF) proposed by Hopke et al., which is the ratio of the filtered and unfiltered residence time probabilities, PSCF = pijf / pijaExample: Incremental Transport Probability Analysis: First, the trajectories for top 10 percent fine soil are calculated as shown in Figure 9a. Next, the trajectories for all days are calculated Figure 9b.

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Figure 9. a. Chemical-filtered trajectories; b. All trajectories.

Thirdly, the trajectory endpoints are gridded. Each grid is normalized to probability. The all-data grid is subtracted from filtered grid, Figure 10. In the Incremental Probability Map, red areas represent high probability transport path. Blue zones represent the regions where a species are NOT coming from.

Figure 10. Incremental probability map.

Fine Soil transport at decreasing concentration limit

The role of the chemical filtering is illustrated below. At the highest fine soil concentration of 30 µg/m3, only one occurrence is registered for which the curved backtrajectory points to the Caribbean Figure 11a). Setting the fine soil concentration limit to 15 µg/m3 yields multiple sites with the majority of the backtrajectories pointing to the Caribbean. Similar transport pattern is also evident for the transport maps corresponding to lower concentrations. Since the 5-day backtrajectories do not reach the Sahara Desert, this analysis only indicates that the fine particle soil in the southeastern US in July originates from sources in the Caribbean or beyond.

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Figure 11. Illustration of chemical filter set at 30; 15; 7.5; 3.7, and 1.8 µg/m3; Residence time map.

Incremental Probability Maps: Upper 10 percentile, Big Bend, Jun, Jul Aug

The final illustration of the CATT tool is through maps of incremental residence time probability of aerosol species at Big Bend, TX and Brigantine, NJ receptor sites. The IRTP maps are derived as the difference of the upper (90th percentile probability) minus the all data probabilities. The aerosol species are total fine mass, ammonium sulfate, soil, organic carbon, elemental carbon, and nitrate in the fine mode. The chemical data are further filtered for June, July, and August. At Big Bend (Figure 12) high sulfate values are associated with transport from the Gulf States, TX, LA, MS, etc. The preferential transport pattern of summer-time fine soil is through a very narrow transport corridor passing through the Caribbean. This is clearly the Sahara dust transport signature. The summer-time organic and elemental carbon organic transport points southward into Mexico. The fine particle nitrate at Big Bend is associated with transport from the coastal zone of the Gulf of Mexico.

Upper 10 percentile, Brigantine, Jun, Jul Aug

The incremental probability analysis for Brigantine, NJ shows distinctly different species transport pattern (Figure 13). High sulfate, organic carbon, and elemental carbon levels are associated with high incremental probabilities in the industrial Ohio River region. Fine particle dust transport to Brigantine is linked to curved trajectories from the Atlantic passing through the southeastern US. Elevated nitrate values are associated with the narrow transport band that is pointing to the Midwestern states and beyond.

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Figure 12. Incremental probability maps for Big Bend, TX: Fine Mass, Ammonium sulfate, Soil, Organic carbon, Elemental carbon and Ammonium nitrate.

Figure 13. Incremental probability maps for Brigantine, NJ: Fine Mass, Ammonium sulfate, Soil, Organic carbon, Elemental carbon and ammonium nitrate.

CONCLUSION

The Combined Aerosol Trajectory Tool (CATT) uses the entire VIEWS chemical database as a chemical filter as an input for a Trajectory Aggregator. This filter/aggregator combination

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provides a flexible tool to explore the transport pattern associated with specified chemical conditions. The CATT tool was implemented and is ready or testing and feedback by the broader community. The initial applications of CATT clearly demonstrate that the trajectory aggregation can considerably expand the benefits of the VIEWS aerosol chemistry database. These benefits are manifested in the delineation of “source” areas. However, considerable work needs to be conducted in assessing the specific settings and limitations associated with the tool. Specific further development include: • Additional ensemble trajectory metrics • “Automatic” update of new aerosol and trajectory data (VIEWS, ATAD) • Additional aerosol data (e.g. EPA Speciated Trend data) and associated ATAD In this project, both, the chemical-transport data as well as the key processing algorithms are supplied by the community. It is hoped that the further evolution of this source/receptor analysis tool will also be supported and guided by the analysis community.

ACKNOWLEDGMENTS

This work is for the Inter-RPO Monitoring and Analysis Workgroup, supported through RPO, MARAMA, NSF, NASA.

REFERENCES

1. Paatero, P., “User's Guide for Positive Matrix Factorization Programs PMF2 and PMF3” (1998).

2. Henry, R.C., “UNMIX Version 2 Manual” Prepared for the U.S. Environmental Protection Agency; (2000).

3. Battelle and Sonoma Technology, “Source Apportionment Analysis of IMPROVE and Castnet Data - Phase I” (2002). http://www.marama.org/visibility/SA_report/

4. Poirot, R.L.; Wishinski, P.R., Atmospheric Environment 1986, 20, 1457-1469.

5. CAPITA, “Combined Aerosol & Trajectory Tool Development: Trajectory Tool Instructions and Illustrations”. Prepared for the Mid-Atlantic/Northeast Visibility Union and Midwest Regional Planning Organization, (2003).

6. CAPITA, DataFed, http://datafed.net/, (2004).

7. Poirot, R.L.; Wishinski, P.R.; Hopke, P.K.; Polissar, A.V., Environmental Science and Technology 2001, 35: 4622-4636.

KEY WORDS

Back trajectory Source-receptor Databases Analysis tool

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