MANEVU/MRPO Project: MANEVU/MRPO Project: Paired Aerosol / Trajectory Database Analysis Tool Development Combined Aerosol Trajectory Tool, CATT R. Husar, K. Hoijarvi, S. Falke, CAPITA, Wash. U, St. Louis Project Officer, Serpil Kayin, MARAMA Project Period: September 2002 – July 2003, $50K Presented at MANE-VU Data Analysis Workshop Windsor Locks, CT, June 18-19, 2003 MANE-VU Data Analysis Workshop

Analysis Value Chain: CATT’s Habitat Next Process TrajData Cube CATT TAT CAPITA Aggreg. Traject. AerData Cube CATT CAT CAPITA Aggreg. Aerosol CATT-In CAPITA Aerosol Data Collection IMP. EPA Aerosol Sensors Integration VIEWS Integrated AerData A E R O S O L Weather Data Assimilate NWS Gridded Meteor. Trajectory ARL Traject. Data T R A N S P O R T Why? How? When? Where?

Background Atmospheric aerosol system has three extra dimensions (red), compared to gases (blue): –Spatial dimensions (X, Y, Z) –Temporal Dimensions (T) –Particle size (D) –Particle Composition ( C ) –Particle Shape (S) Bad news: The mere characterization of the 7D aerosol system is a challenge –Spatially dense network -X, Y, Z(??) –Continuous monitoring (T) –Size segregated sampling (D) –Speciated analysis ( C ) –Shape (??) Good news: The aerosol system is self-describing. –Once the aerosol is characterized (Speciated monitoring) and multidimensional aerosol data are organized, (see RPO VIEWS effort), unique opportunities exists for extracting information about the aerosol system (sources, transformations) from the data directly. Analysts challenge: Deciphering the handwriting contained in the data –Chemical fingerprinting/source apportionment –Meteorological back-trajectory analysis –Dynamic modeling

Project Background The source-receptor relationship of particulate matter can be estimated by a number of empirical observation-based techniques. Some techniques are based chemical fingerprinting others on meteorological transport techniques. A particularly attractive source-attribution technique, Paired Aerosol / Trajectory Analysis developed by Poirot and Wishinski. It combines the chemical and transport techniques by: –Establishing the major aerosol types at a specific receptor location and time (PMF and UNMIX) –Estimating the geographic transport regions for each aerosol type (Residence Time Analysis)

Biomass Smoke Avg. Mass:2.4 ug/m3 (32%) Species:OC, EC, S, K Summer Maximum East Coast Residual Oil Avg. Mass:0.38 ug/m3 (5%) Species: OC, EC, S, Si, Ni, V Winter Maximum Secondary Coal Avg. Mass:3.2 ug/m3 (42%) Species: S, OC, EC, Na Summer Maximum Combining Chemical Fingerprints and Transport, Lye Brook, NH Based on Positive Matrix Factorization, PMF results from B. Coutant and ATAD trajectories from K. Gebhart Project Goal: Develop an interactive data query and analysis tool for the paired chemical/trajectory analysis Aerosol Source Type and Transport Origin Analysis ( Wishinski and Poirot (2002)

Project Deliverables 1.Implement a relational database that incorporates both the PMF/UNMIX results and for gridded trajectory data. 2.Develop specific SQL filtering and aggregation queries for aggregated trajectory data based on chemical conditions aggregated chemical data based on geographic conditions 3.Develop a graphic interface for user input (query) and for data output as renderd images or as exportable numeric data. 4.Transfer the resulting database to a designated SQL server and provide instructions for addition of chemical and trajectory data.

Relational Database PMF and UNMIX data transfer to SQL –The PMF and UNMIX results were provided to us as Excel spreadsheet. –The metadata for the PMF and UNMIX were obtained verbally from R. Poirot. –The spreadsheet data were reformatted and imported in the SQL server two tables (ChemFactTable and LocTable) Residence Time data transfer to SQL –The residence time data were provided by P. Wishinski on CDROM including full metadata documentation –The data were imported into two SQL tables (ResTimeFactTable and LocTable) PMF/UNMIX Data CAPITA SQL Database ATAD Residence Time

Data Input: PMF and UNMIX Model Results The results of the Battelle/Sonoma modeling project are source profiles and time series for each source contribution at each location Prepared by Battelle and Sonoma Tech. Inc. Source attribution results (PMF and UNMIX) for 16 receptor sites between Illinois and New England using IMPROVE and CastNet data have been completed by a previous project.previous project

Database Structure The dTrajResTime and dSourceApp tables share the site_code and date keys thereby allowing paired queries to the SQL database.

SQL Queries SELECT Lat as lat, Lon as lon, Loc_Code as loc_code, SUM(ResTime) AS [VALUE] FROM dTrajResTime WHERE ([Date] IN (SELECT datetime FROM dSourceApp WHERE (Loc_Code = 'loc_code') sql_filter_clause)) GROUP BY GridCode, Lat, Lon, Loc_Code ORDER BY Lon ASC Settings that are unique to a specific query are designated by red text Query (filter) result: List of dates the satisfy the chemical filter conditions

ATAD Trajectory and Residence Time Grid Residence time and ATAD trajectory data superimposed for June 1, Residence time aggregate (sum) for a range of dates

Airmass Spource Regions by Season e.g. Sum ResTime for Loc=LYBR, Date between June-Sept Lye Brook, DJF Gr Smoky Mtn, JJA Lye Brook, JJA Gr Smoky Mtn, JJA

Source Regions by Concentrations - High and Low ResTime for High C6 (BioSmoke?) Chemical Conditions ResTime for Low C6 (BioSmoke?) Chemical Conditions

Incremental Transport Probalility

Seasonal Incremental Probability

Secular Differences: ;

Transport Probability Metrics The 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 resdence times in all grid cells: p ij f =r ij /  r ij p ij a =r ij /  r ij p ij f, is the filtered and p ij a is the unfiltered residence time probabilitiy that an airmasses passes through a specific grid. There is a choice of transport probaility metrics: The Incremental Residence Time Probability (IRTP) proposed by Poirot et al., 2001 is obtained by subtracting the chemically filtered grid from the unfiltered residence time grid, IRTP = p ij f - p ij a The other metric is the Potential Source Contribution Function (PSCF) proposed by Hopke et al., 19xx which is the ratio of the filtered and unfiltered residence time probabilities, PSCF = p ij f / p ij a

Transport Metric Selection Currently, there is a choice of two different transport probability metrics: Incremental Residence Time Probability (IRTP) proposed by Poirot et al., 2001 is the difference between the chemically filtered and unfiltered residence time probalbilities. Positive values of IRTP in a grid indicates more than average liekihood of transport; (red); negative IRTP values (blue) represent less than average likeihood of transport. Potential Source Contribution Function (PSCF) proposed by Hopke et al., 19?? is computed as the ratio of the filtered and unfiltered residence time probabilities. Higher values of PSCF is indicative of inreased source contribution. The desired metric is selected through a dialog box invoked by clicking on the right-most button in the TRAJ_CHEM layer.

Results

Combined Aerosol Trajectory Tool CATT

CATT Project Status Current effort to finalize queries ‘Public’ testing and user feedback is in progress now Develop relational database of PMF/UNMIX and trajectory data Develop specific SQL filtering and aggregation queries - Chemical filtering/aggregation query - Trajectory filtering/aggregation query - Paired Chemical/Trajectory data query Develop graphical user interface to database Transfer the resulting database to a designated SQL server

CATT Presentation and Workgroup Discussions

Project Status/Summary 1.Relational Database of PMF/UNMIX and trajectory data: Complete 2.Develop specific SQL filtering and aggregation queries Chemical filtering/aggregation: Developed Trajectory filtering/aggregation: Developed Paired Chemical/Trajectory data: Developed, needs user input, testing, feedback 3.Graphic interface for user input (query) and for data output: Developed, needs user input, testing, feedback 4.Transfer the resulting database to a designated SQL server: Not done Project Milestones Jan-June Feb 1, 03: Complete initial queries, user interface and displays 2.Apr 1, 03: Finalize design/implementation of queries, user interface and displays 3.Apr-Jun 03: ‘Public’ testing and user feedback 4.July 03: Tool delivery

Trajectory Tools Project Options VIEWS Database Compatibility Make the chemical-trajectory exploration tool compatible with the evolving VIEWS database at CIRA, Colorado State U.: –insuring consistency of the data base schema –query tools compatibility –data presentation compatibility Dynamic Trajectory Aggregation Online filtering and aggregation of trajectory data –ad hoc gridding, contouring at arbitrary grid resolution –alternative rendering, e.g. trajectory bundles, instead of residence time contours

The CATT tool has two components, usable separately or linked : 1.Chemical filter component. This component is accomplished through queries to chemical data sets. The output of this step is a list of “qualified” dates for a specific receptor location. 2.Trajectory aggregator component. This component receives the list of dates for a specific location and performs the trajectory aggregation, residence time calculation and other spatial operations to yield a transport pattern for specific receptor location and chemical conditions.

Receptor location. Single location; multiple receptors; weighed multi-site Receptor times. Time range for each site Temporal filter/weight conditions. Date range; specific dates; weights for each date Trajectory input files. Pre-computed or on the fly calculated (e.g. HYSPLIT, ATAD etc) Trajectory aggregation metrics. Endpoint counts, residence time, incr. probability TAT Output: ASCII point, XMLGrid, GIS

TAT requires an airmass trajectory dataset for specific locations. The trajectories can be either pre-computed or generated on the fly from meteorological fields. The Trajectory Aggregator Tool, TAT, will performs the residence time and other trajectory aggregations on the fly. For trajectory aggregation, TAT will require user selection of: