Transcript 100512 AWMA LepaFrog Xian Databese.ppt

Professional Development Course:
Web-Based Decision Support Systems
in Support of
Air Quality Science and Regulation
Part II:
Leapfrog to Networked Decision Support Systems
Rudolf B. Husar
Washington University, St. Louis, MO, USA
A&WMA International Specialty Conference, May 10-14, 2010, Xi’an, Shaanxi Province, China
Leapfrogging Opportunities for Air Quality Improvement
Air Quality Management Process:
Human activities e.g burning fossil fuels, degrades air quality.
The degraded air quality elicits regulations and actions
Monitoring collects multi-sensory data from surface
and satellite platforms and
Monitoring
(Sensing)
Set Goals
Decision Support
Compare to Goals, Plan Reductions
Track Progress
Controls
(Actions)
Decision Support System turns observations into
knowledge for decision making & actions through
analysis (science & eng.)
Air quality control measures bring air quality to the desired level
This feedback loop of sensing, evaluation and control actions are
‘Sustainable Development’
Decision Support:
From Observations, Models to Societal Benefit
Generic Decision Support for Air Quality Decisions
Reports:
Model Forecasts,
Obs. Evidence
Technical
Analysts
Decision Support
System
Decisions
Decision- making
managers
Regulatory
Analysts
Data Acquisition and Usage Activities
(Select View Show, click to step through PPT)
Data Acquisition
Usage Activities
Data
Repository
• The
Decisions
data life cycle consists of the acquisition and the usage parts
• The acquisition part processes the sensory data by firmly linked procedures
• The collected and cleaned data are stored in the repository
• The usage activities are more iterative, dynamic procedures
• The usage cycle transform data into knowledge for decision making
The focus is on data usage activities
Information Providers:
Geography, Content, Agency, Form
Satellite
Internet
EPA
EPA
AIRNow
Ambient
Ambient
NASA
NASA
IDEA
FS
Emission
FS
IDEA
FireInv
FireInv
AIRNow
NOAA
NOAA
EPA-AQS
Forecast
Model
Forecast
EPA
AIRS
NOAA
EPA R&D
NOAA
WeaMod
Model
ModelWeaMod
NOAA
NASA GASP
DAACs Satellite
NOAA
GASP
EPA
AQModel
EPA
Model
AQModel
NASA
Model
GloModel
NASA
Satellite
DAACs
NASA
DataMart
EPA-AQS
Ambient
AIRNow
others
EPA
EPA
NEISGEI
NEISGEI
Emission
DAACs
EPA
EPA NEI
Ambient
RPO
Emission
NEI
RPO
VIEWS
VIEWS
NOAA
NOAA
Ambient
Ambient
ASOS
ASOS
NASA
Satellite
NASA
Missions
Mission
State/Local
State/Local
Emission
Emission
Emission
Content
Content ||Agency
Agency || Form
Form
EPA
Emission
Emission
EPA
Ambient
Ambient
Satellite
Satellite
Model
Model
NOAA
NOAA
NASA
NASA
Other
Other
• Data are distributed geographically by autonomous providers
• Data includes emissions,
emissions ambient data, satellite data and model output
• Data are provided by multiple agencies: EPA, NOAA, NASA and others
• Furthermore, data are provided in varied formats and access protocols
• Data on Internet are geography-independent and can be ‘linearized’
Users:
By Types, Agency, Info Needs
Policy
Policy
Internet
Scientist
Scientist
Manager
Manager
Public
Public
Scientist
Scientist
Policy
Policy
Scientist
Scientist
Public
Public
Manager
Manager
Public
Manager
Stakeholder | Agency |
Policy
Policy
Policy
Public
Manager
Scientist
Form
Scientist
EPA
NOAA
other
NASA
Other
• Users are distributed geographically
• Users includes policy makers,
makers the public,
public AQ managers and scientist
• Users are affiliated with multiple agencies: EPA, NOAA, NASA, as well as others
• Furthermore, users need various types of information provided in multiple formats
• Since the users are also on the Internet, their geographic location is irrelevant
Agile Information System:
Data Access, Processing and Products
Providers
Uniform
Access
Data Processing
Service Chain
Web
Info Products
Users
Reports, Websites
SciFlo
EPA
AIRNow
EPA R&D
Model
NASA
DAACs
others
DataFed
Forecasting
Public
Compliance
Manager
Sci. Reports
Custom Processing
Scientist
Other
• The info system transforms the data into info products for each user
• In the first stage the heterogeneous data are prepared for uniform access
• The second stage performs filtering, aggregation, fusion and other operations
• The third stage prepares and delivers the needed info products
other
Stages of AQ Data Flow and Value-Adding Processes
Data
Value-Adding
Processes
Data Sharing
Organizing
Document
Structure/Format
Interfacing
Decision Support System
Std. Interface
Std. Interface
Obs. & Models
Gen. Processing
Characterizing
Display/Browse
Compare/Fuse
Characterize
Control
Reports
Domain Processing
Analyzing
Filter/Integrate
Aggregate/Fuse
Custom Analysis
Reporting
Reporting
Inclusiveness
Iterative/Agile
Dynamic Report
The Federated Data System
DataFed
-Non-intrusive data integration infrastructure
-Based on standards-based web services
-Processing tools created from reusable components
‘Stovepipe’ and Federated Usage Architectures Landscape
Data Providers
AIRNow
Info System
AIRNow
Model
Info Users
Public
Manager
Compliance
DAACs
Science
Scientist
• Current info systems are project/program oriented and provide end-to-end solutions
• Part of the data resources of any project can be shared for re-use through DataFed
• Through the Federation, the data are homogenized into multi-dimensional cubes
• Data processing and rendering can then be performed through web services
• Each project/program can be augmented by Federation data and services
Web Services:
Building Blocks of
DataFed Programming
Access, Process, Render Data
by Service Chaining
LAYERS
Data
Access
Data
Processing
Layer Overlay
NASA SeaWiFS Satellite
Web Service Composition
RPO VIEWS Chemistry
NOAA ATAD Trajectory
OGC Map Boundary
Combined Aerosol Trajectory Tool, CATT (RPO – R. Poirot)
Trajectory
Browser
CATT Transport
Analyzer
Kitty: Simple
CATT
AEROSOL
Collection
IMP. EPA
Aerosol
Sensors
Integration
VIEWS
Aerosol
Data
CATT-In
CAPITA
Integrated
AerData
AerData
Cube
Aggreg.
Aerosol
Next
Process
CATT
Weather
Data
Gridded
Meteor.
Assimilate
NWS
TRANSPORT
Traject.
Data
Trajectory
ARL
TrajData
Cube
CATT-In
CAPITA
Aggreg.
Traject.
Next
Process
DataFed Tools - Subset
Consoles: Data from diverse sources
are displayed to create a rich context
for exploration and analysis
Viewer: General purpose spatio-temporal
data browser and view editor applicable
for all DataFed datasets
CATT: Combined Aerosol Trajectory
Tool for the browsing backtrajectories
for specified chemical conditions
Origin of Fine Dust
Events over the US
Gobi dust transport in spring
Sahara dust import in summer
Fine dust spikes over the
entire US are mainly
from intercontinental
transport
Sulfate is local, no
major spikes
Data Fusion: AIRNOW PM25 - ASOS Bext
July 21, 2004
July 22, 2004
ARINOW
PM25
ASOS
RHBext
2004 July 20 14:00
ARINOW
PM25
ASOS
RHBext
July 23, 2004
ARINOW
PM25
ASOS
RHBext
FASTNET Report: 0409FebMystHaze (RPO – R. Poirot)
Mystery Winter Haze:
Natural? Nitrate/Sulfate? Stagnation?
AIRNOW PM25 - February
Feb-Mar
peak, of
unknown
origin
Sulfatedriven JulAug peak
Contributed by the FASNET Community, Sep. 2004
Correspondence to R Husar , R Poirot
Coordination Support by
Inter-RPO WG Fast Aerosol Sensing Tools for Natural Event Tracking, FASTNET
NSF Collaboration Support for Aerosol Event Analysis
NASA REASON Coop
EPA -OAQPS
Regulatory Application of DataFed:
Exceptional Event Rule
Show that the cause is in category of uncontrollable/preventable
Transported Pollution
Natural Events
Transported African, Asian
Dust; Smoke from Mexican
fires & Mining dust, Ag.
Emissions
Nat. Disasters.; High Wind
Events; Wildland Fires;
Stratospheric Ozone;
Prescribed Fires
Human Activities
Chemical Spills; Industrial
Accidents; July 4th; Structural
Fires; Terrorist Attack
2. No
exceedance/violation but for the event.
Show that the exceedance is explicitly caused by the exceptional event
Exceptional Event
NOT Exceptional Event
NOT Exceptional Event
The 'exceptional'
concentration raises the
level above the standard.
A valid EE to be flagged.
Controllable sources are
sufficient to cause
exceedance. Not a 'but
for‘, not an EE.
No exceedance, hence,
there is no justification for
an EE flag.
.
4. Clear
support of event causality with data.
EE causality may come from multiple lines of observational evidence
Chemical Signature
Source & Transport
Spatial Pattern
Temporal Pattern
The EE sample shows
the fingerprints of
'exceptional‘ source.
Clear evidence of
transport from known
source region.
Unusual spatial pattern
as evidence of
Exceptional source.
Unusual concentration
spike as indication of
an Exceptional Event.
Near-Real-Time Data for May 11, 07 GA Smoke
Displayed on DataFed Analysts Console
1
2
3
4
5
6
7
8
9
10
11
12
Pane 1,2: MODIS visible satellite images – smoke pattern
Pane 3,4: AirNOW PM2.5, Surf. Visibility – PM surface conc.
Pane 5,6: AirNOW Ozone, Surf. Wind – Ozone, transport pattern
Pane 7,8: OMI satellite Total, Tropospheric NO2 – NO2 column conc.
Pane 9,10: OMI satellite Aerosol Index, Fire P-xels – Smoke, Fire
Pane 11,12: GOCART, NAAPS Models of smoke – Smoke forecast
Console Links
May 07, 2007,
May 08, 2007
May 09, 2007
May 10, 2007
May 11, 2007
May 12, 2007
May 13, 2007
May 14, 2007
May 15, 2007
May 2007 Georgia Fires
The fires in S. Georgia emitted intense smoke throughout May 07.
May 5, 2007
May 12, 2007
Google Earth Video (small 50MB, large 170mb)
Leapfrog, Leapfrog Effect
Advancing by leaping over obstacles or competitors;
Progress by large jumps instead of small increments
Leapfrog Effect (Wikipedia):
Sustainable development for developing countries as a theory of
development which may accelerate development by skipping
inferior, less efficient, more expensive or more polluting
technologies and industries and move directly to more advanced
ones.
The Network Effect:
Less Cost, More Benefits through Data Multi-Use
Data
Data
Data
Data
Programs
ask/get Data
Data Re-Use
Network Effect
Program
Orgs Develop
Programs
Organization
Program
Public sets
up Orgs
Public
Program
Organization
Program
Data
Data
Pay only once
Richer content
Less Prog. Cost
More Knowledge
Less Soc. Cost
More Soc. Benefit
Data Reuse
Data are costly resource – should be reused (recycled) for multiple applications
Data reuse saves $$ to programs and allows richer knowledge creation
Data reuse, like recycling takes some effort: labeling, organizing, distributing
Networking Multiplies Value Creation
Enclosed Value-Creating Process - ‘Stovepipe’
Data
1 User Stovepipe Value = 1
Application
1 Data x 1 Program = 1
Networking Multiplies Value Creation
Application
Application
Data
Stovepipe
Application
Application
Application
1 User Stovepipe Value = 1
5 Uses of Data
Value = 5
1 Data x 1 Program = 1
1 Data x 5 Program = 5
Networking Multiplies Value Creation
Data
Application
Data
Application
Data
Stovepipe
Application
Data
Application
Data
Application
1 User Stovepipe Value = 1
5 Uses of Data
Value = 5
Open Network
Value = 25
1 Data x 1 Program = 1
1 Data x 5 Program = 5
5 Data x 5 Program = 25
Merging data may creates new, unexpected opportunities
Not all data are equally valuable to all programs
New International Program (China is key formal participant):
Global Observing System of Systems (GEOSS)
Pooling of
observations
Building New
Things
Observing Systems
Surf. Obs.
Satellite
Population
Model
Emission
Informing the
Public
Enforcing
Standards
Real-time
Service
Regulatory
Analysis
Hemispheric
Transport
Atmospheric
Composition
Policy
Assessment
Air Quality & Health Applications
Science &
Education
Service Oriented Architecture:
Publish – Find - Bind
Actions:
Register– Discover -Access
GEOSS
Clearinghouse
Discover
Get Access
Register
Metadata
Provider
Access
Data
User
Finding and Accessing
GEOSS
Clearinghouse
Discover, Get
Access Key
Provider
User
Metadata for Finding and Accessing Data
Data
Binding
OGC CSW OGC CSW ISO 19115 Metadata Air Quality
Queryable Returnable CSW Profile Description Specific
Sensing
Revolution
Web 2.0
GEOSS
Imagine…More Shared Obs & Models….
On Your Fingertips..
2010
2015..
More surface observations..
More satellite observations..
More air quality models
Summary
System of Systems architecture is suitable for integrating data
– Standard data access is a key interoperability protocol
– Heterogeneous data can be non-intrusively standardized by mediators
– Service-based software architecture delivers tailored products to diverse uses
Federated data and shared web-based tools are in use
– GEOSS and DataFed already includes over 100 datasets (emissions, ground, satellite)
– The system has been applied to EPA policy, regulatory and science development
However,
– To be effective, much more data would need to be federated
GEOSS DataSets for China:
See Satellite Session tomorrow
Q & A and Discussion
– More Questions?
– Decision Support Systems applications in China ( i.e in AQ management)?
– Leapfrogging Opportunities (e.g. GEOSS principles)?
Asian Dust Cloud over N. America
Asian Dust
100 mg/m3
Hourly PM10
On April 27, 1998 the dust cloud
arrived in North America.
Regional average PM10
concentrations increased to 65
mg/m3
In Washington State, PM10
concentrations exceeded 100 mg/m3
Local, Regional, Global Pollution
Before 1950s:
Local
Smoke, Fly ash
•
•
•
1970s-1990s:
Regional impacts
Post- 2000s:
Global Impacts
Acid Rain, Haze
O3, PM, Climate
The LRTP/HTAP flow of air pollutants is likely to increase as overseas economies grow.
Pollutant influx leads to significant exceedances of O3 PM NAAQS in some regions
Even after domestic controls, some US areas will be no-compliant because of LRTP
Monitoring:
New Global Measurements - Satellites
TOPEX/Poseidon
Landsat 7
Aqua
SORCE
Sage
QuikScat
EO-1
SeaWiFS
IceSat
TRMM
SeaWinds
ACRIMSAT
Toms-EP
ERBS
Grace
Terra
UARS
Jason