Transcript 030618MARAMAHartford

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
Analysis Value Chain: CATT’s Habitat
AEROSOL
Collection
IMP. EPA
Aerosol
Sensors
Integration
VIEWS
Aerosol
Data
Weather
Data
Integrated
AerData
Gridded
Meteor.
Assimilate
NWS
CATT-In
CAPITA
Traject.
Data
Trajectory
ARL
TRANS PORT
CATT CAT
CAPITA
Why? How?
Next
Process
When?Aggreg.
Where?
TrajData
Traject.
Cube
Next
Process
AerData
Cube
CATT-In
CAPITA
Aggreg.
Aerosol
CATT TAT
CAPITA
Background
•
Atmospheric aerosol system has three extra dimensions (red), compared to gases (blue):
–
–
–
–
–
•
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.
–
•
Spatial dimensions (X, Y, Z)
Temporal Dimensions (T)
Particle size (D)
Particle Composition ( C )
Particle Shape (S)
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)
Aerosol Source Type and Transport Origin Analysis (Wishinski and Poirot (2002)
Combining Chemical Fingerprints and Transport, Lye Brook, NH
Based on Positive Matrix Factorization, PMF results from B. Coutant and ATAD trajectories from K. Gebhart
Secondary Coal
Biomass Smoke
Avg. Mass: 3.2 ug/m3 (42%)
Avg. Mass: 2.4 ug/m3 (32%)
Avg. Mass: 0.38 ug/m3 (5%)
Species:
Species:
Species:
S, OC, EC, Na
Summer Maximum
OC, EC, S, K
Summer Maximum
East Coast Residual Oil
OC, EC, S, Si, Ni, V
Winter Maximum
Project Goal:
Develop an interactive data query and analysis tool for the paired chemical/trajectory analysis
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)
ATAD
Residence Time
PMF/UNMIX
Data
CAPITA
SQL
Database
Data Input: PMF and UNMIX Model Results
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.
Prepared by
Battelle and Sonoma Tech. Inc.
The results of the Battelle/Sonoma modeling project are source profiles and time series
for each source contribution at each location
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, 2000.
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: 1988-94; 1995-2000
1988-2000
1994-2000
1988-1994
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:
pijf=rij/SS rij
pija=rij/SS rij
• pijf, is the filtered and pija 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 = pijf - pija
• 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 = pijf / pija
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
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
Current effort to finalize queries
‘Public’ testing and user feedback is in progress now
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 2003
1.
2.
3.
4.
Feb 1, 03: Complete initial queries, user interface and displays
Apr 1, 03: Finalize design/implementation of queries, user interface and displays
Apr-Jun 03: ‘Public’ testing and user feedback
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: