24th Annual National Conference on
Managing Environmental Quality Systems
8:30 - 12:00 TUESDAY, APRIL 12th - AM. Stockholder Meetings
12:00 - 4:30 TUESDAY, APRIL 12th
Opening Plenary (Salons A-H)
• Opening Address
o Reggie Cheatham, Director, OEI Quality Staff, EPA
o Linda Travers, Principal Deputy Assistant Administrator, OEI, EPA
• Invited Speakers
o Tom Huetteman, Deputy Assistant Regional Administrator, EPA Region 9
o John Robertas, Executive Officer of San Diego Regional Water Quality Control Board, Region 9
• Keynote Address
o Thomas Redman, President, Navesink Consulting Group
• Panel Sessions
• Value of the Data Quality Act—Perspectives from OMB, Industry, and EPA (VDQA)
o Nancy Beck, OMB
o Jamie Conrad, American Chemistry Council
o Reggie Cheatham, Director, OEI Quality Staff, EPA
• Wadeable Streams: Assessing the Quality of the Nation's Streams (WS)
o Margo Hunt, Panel Moderator
o Mike Shapiro, Deputy Assistant Administrator, Office of Water
o Steve Paulsen, Research Biologist, ORD
8:30 - 10:00 WEDNESDAY, APRIL 13th
Environmental Measures (EM) (Salons A-C) Chair: L. Bradley, EPA
• Data Error Reduction by Automation throughout the Data Workflow Process (A. Gray, EarthSoft, Inc.)
• Analytical Approaches to Meeting New Notification Levels for Organic Contaminants in Calif. (D.Wijekoon,
Calif. DHS)
• Streamlining Data Management and Communications for the Former Walker AFB Project (R. Amano, Lab
Data Consultants, Inc.)
Quality System Implementation in the Great Lakes Program (QSI-GLP) (Salon D) Chair: M. Cusanelli, EPA
• GLNPO's Quality System Implementation for the New "Great Lakes Legacy Act for Sediment
Remediation"(L. Blume, EPA)
• Black Lagoon Quality Plan Approval by GLNPO, MDEQ, ERRS, and USACE (J. Doan, Environmental
Quality Management, Inc.)
• Remediation of the Black Lagoon Trenton Channel. . . Postdredging Sampling & Residuals Analysis (J.
Schofield, CSC)
Quality Systems Models (QSM) (Salons F-H) Chair: G. Johnson, EPA
• Improving E4 Quality System Effectiveness by Using ISO 9001: 2000 Process Controls (C. Hedin, Shaw
Environmental)
Applications of Novel Techniques to Environmental Problems (ANTEP) (Salon E) Chair: B. Nussbaum, EPA
• On Some Applications of Ranked Set Sampling (B. Sinha, University of Maryland)
• Combining Data from Many Sources to Establish Chromium Emission Standards (N. Neerchal, University of
Maryland)
• Estimating Error Rates in EPA Databases for Auditing Purposes (H. Lacayo, Jr., EPA)
• Spatial Population Partitioning Using Voronoi Diagrams For Environmental Data Analysis (A. Singh,
UNLV)
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Ambient Air Session I (Sierra 5&6) Chair: M.Papp, EPA
• Changes and Improvements in the Ambient Air Quality Monitoring Program Quality System (M. Papp, EPA)
• Guidance for a New Era of Ambient Air Monitoring (A. Kelley, Hamilton County DES)
• Environmental Monitoring QA in Indian Country (M. Ronca-Battista, Northern Arizona University)
• Scalable QAPP IT Solution for Air Monitoring Programs (C. Drouin, Lake Environmental Software)
10:30 - 12:00 WEDNESDAY, APRIL 13th
Environmental Laboratory Quality Systems (ELQS) (Salons A-C) Chair: L. Bradley, EPA
• A Harmonized National Accreditation Standard: The Next Step for INELA Field Activities (D. Thomas,
Professional Service Industries, Inc.)
• Development of a Comprehensive Quality Standard for Environmental Laboratory Accreditation (J. Parr,
INELA)
• Advanced Tracking of Laboratory PT Performance and Certification Status with Integrated Electronic
NELAC-Style Auditing Software (T. Fitzpatrick, Lab Data Consultants, Inc.)
Performance Metrics (PM) (Salon D) Chair: L. Doncet, EPA
• Formulating Quality Management Metrics for a State Program in an Environmental Performance Partnership
Agreement (P. Mundy, EPA)
• How Good Is "How Good Is?" (Measuring QA) (M. Kantz, EPA)
• Performance-Based Management (J. Santillan, US Air Force)
Quality Assurance Plan Guidance Initiatives (QAPGI) (Salons F-H) Chair: A. Battemian, EPA
• A CD-ROM Based QAPP Preparation Tool for Tribes (D. Taylor, EPA)
• Military Munitions Response Program Quality Plans (J. Sikes, U.S. Army)
Ask a Statistician: Panel Discussion (Salon E)Moderator: B. Nussbaum, EPA Panelists:
• Mike Flynn, Director, Office of Information Analysis and Access, OEI, EPA
• Reggie Cheatham, Director, Quality Staff, OEI, EPA
• Tom Curran, Chief Information Officer, OAQPS, EPA
• Diane Harris, Quality Office, Region 7, EPA
• Bill Hunt, Visiting Senior Scientist, North Carolina State University (NCSU)
• Rick Linthurst, OIG, EPA
Ambient Air Session II (Sierra 5&6) Chair: M. Papp, EPA
• National Air Toxics QA System and Results of the QA Assessment (D. Mikel, EPA)
• Technical System Audits (TSAs) and Instrument Performance Audits (IPAs) of the National Air Toxics
Trends Stations (NATTS) and Supporting Laboratories (S. Stetzer Biddle, Battelle)
• Interlaboratory Comparison of Ambient Air Samples (C. Pearson, CARB)
• Developing Criteria for Equivalency Status for Continuous PM2.5 Samplers (B. Coutant, Battelle)
1:00 - 2:30 WEDNESDAY, APRIL 13th
Environmental Laboratory Quality (ELQ) (Salons A-C) Chair: L. Doncet, EPA
• Environmental Laboratory Quality Systems: Data Integrity Model and Systematic Procedures (R. DiRienzo,
DataChem Laboratories, Inc.)
• The Interrelationship of Proficiency Testing, Interlaboratory Statistics and Lab QA Programs (T. Coyner,
Analytical Products Group, Inc.)
• EPA FIFRA Laboratory Challenges and Solutions to Building a Quality System in Compliance with
International Laboratory Quality Standard ISO 17025 (A. Ferdig, Mich. Dept. of Agriculture)
Performance—Quality Systems Implementation (P-QSI) (Salon D) Chair: A. Belle, EPA
• Implementing and Assessing Quality Systems for State, Tribal, and Local Agencies (K. Bolger, D. Johnson,
L. Blume, EPA)
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1:00 - 2:30 WEDNESDAY, APRIL 13th (continued)
Quality Initiatives in the EPA Office of Environmental Information (QI-OEI) (Salons F-H) Chair: J. Worthington,
EPA
• Next Generation Data Quality Automation in EPA Data Marts (P. Magrogan, Lockheed)
• The Design and Implementation of a Quality System for IT Products and Services (J. Scalera, EPA)
• Data Quality is in the Eyes of the Users: EPA's Locational Data Improvement Efforts (P. Garvey, EPA)
A Win-Win-Win Partnership for Solving Environmental Problems (W3PSEP) (Salon E) Co-Chairs: II'. Hunt, Jr.
and K. Weems, NCSU
• Overview of Environmental Statistics Courses at NCSU (B. Hunt, NCSU Statistics Dept.)
• Overview of the Environmental Statistics Program at Spelman College (N. Shah, Spelman)
• Student presentations: H. Ferguson and C. Smith of Spelman College; C. Pitts, B. Stines and J. White of
NCSU
Ambient Air Session III (Sierra 5&6) Chair: M. Papp, EPA
• Trace Gas Monitoring for Support of the National Air Monitoring Strategy (D. Mikel, EPA)
• Comparison of the Proposed Versus Current Approach to Estimate Precision and Bias for Gaseous
Automated Methods for the Ambient Air Monitoring Program (L. Camalier, EPA)
• Introduction to the IMPROVE Program's New Interactive Web-based Data Validation Tools (L. DeBell,
Colorado State University)
• The Role of QA in Determination of Effects of Shipping Procedures for PM2.5 Speciation Filters (D.
Crampler, EPA)
3:00 - 4:30 WEDNESDAY, APRIL 13th
Topics in Environmental Data Operations (TEDO) (Salons A-C) Chair: M. Kantz, EPA
• Ethics in Environmental Operations: It's More Than Just Lab Data (A. Rosecrance, Laboratory Data
Consultants, Inc.)
• QA/QC of a Project Involving Cooperative Agreements, IAGs, Agency Staff and Contracts to Conduct the
Research (A. Batterman, EPA)
• Dealing with Fishy Data: A Look at Quality Management for the Great Lakes Fish Monitoring Program (E.
Murphy, EPA)
Quality System Development (QSD) (Salon D) Chair: A. Belle, EPA
• Development of a QA Program for the State of California (B. van Buuren, Van Buuren Consulting, LLC)
• Integrating EPA Quality System Requirements with Program Office Needs for a Practical Approach to
Assuring Adequate Data Quality to Support Decision Making (K. Boynton, EPA)
• Introducing Quality System Changes in Large Established Organizations (H. Ferguson, EPA)
Auditor Competence (AC) (Salons F-H) Chair: K. On-, EPA
• Determining the Competence of Auditors (G. Johnson, EPA)
To Detect or Not Detect—What Is the Problem? (TDND) (Salon E) Chair: J. Warren, EPA
• A Bayesian Approach to Measurement Detection Limits (B. Venner)
• The Problem of Statistical Analysis with Nondetects Present (D. Helsel, USGS)
• Handling Nondetects Using Survival Anal.(D. Helsel, USGS)
• Assessing the Risk associated with Mercury: Using ReVA's Webtool to Compare Data, Assumptions and
Models (E. Smith, EPA)
Ambient Air Session IV (Sierra 5&6) Chair: M. Papp, EPA
• Status and Changes in EPA Infrastructure for Bias Traceability to NIST (M. Shanis, EPA)
• Using the TTP Laboratory at Sites with Higher Sample Flow Demands (A. Teitz, EPA )
5:00 - 6:00 PM WEDNESDAY, APRIL 13th
EPA SAS Users Group Meeting Contact: Ann Pitchford, EPA
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8:30 - 10:00 THURSDAY, APRIL 14th
Evaluating Environmental Data Quality (EEDQ) (Salons A-C) Chair: M. Kantz, EPA
• QA Documentation to Support the Collection of Secondary Data (J. O'Donnell, Tetra Tech, Inc.)
• Staged Electronic Data Deliverable: Overview and Status (A. Mudambi, EPA)
• Automated Metadata Reports for Geo-Spatial Analyses (R. Booher, INDUS Corporation)
Satellite Imagery QA (SI-QA) (Salon D) Chair: M. Cusanelli, EPA
• Satellite Imagery QA Concerns (G. Brilis and R. Lunetta, EPA)
Information Quality Perspectives (IQP) (Salons F-H) Chair: J. Worthington, EPA
• A Body of Knowledge for Information and Data Quality (J. Worthington, L. Romero Cedeno, EPA)
• Information as an Environmental Technology - Approaching Quality from a Different Angle (K. Hull,
Neptune and Co.)
To Detect or Not Detect—What Is the Answer? (TDND) (Salon E) Chair: A. Pitchford, EPA, Co-Chair: W. Puckett,
EPA
• Using Small Area Analysis Statistics to Estimate Asthma Prevalence in Census Tracts from the National
Health Interview Survey (T. Brody, EPA)
• Logistical Regression and QLIM Using SAS Software (J. Bander, SAS)
• Bayesian Estimation of the Mean in the Presence of Nondetects (A. Khago, University of Nevada)
Ambient Air Workgroup Meeting (Sierra 5&6) Contact: Mike Papp, EPA
NOTE: This is an all-day, closed meeting.
10:30 - 12:00 THURSDAY, APRIL 14™
Environmental Data Quality (EDQ) (Salons A-C) Chair: V. Holloman, EPA
• Assessing Environmental Data Using External Calibration Procedures (Y. Yang, CSC)
• Groundwater Well Design Affects Data Representativeness: A Case Study on Organotins (E. Popek, Weston
Solutions)
Information Quality and Policy Frameworks (IQPF) (Salons F-H) Chair: L. Doncet, EPA
• Modeling Quality Management System Practices to an Organization's Performance Measures (J.
Worthington, L. Romero Cedeno, EPA)
• Development of a QAPP for Agency's Portal (K. Orr, EPA)
• Discussion of Drivers and Emerging Issues, Including IT, That May Result in Revisions to EPA's Quality
Order and Manual (R. Shafer, EPA)
Office of Water; Current Initiatives (OW) (Salon D) Chair: D. Sims, EPA
• Whole Effluent Toxicity—The Role of QA in Litigation (M. Kelly, EPA, H. McCarty, CSC)
• Review of Data from Method Validation Studies: Ensuring Results Are Useful Without Putting the Cart
Before the Horse (W. Telliard, EPA, H. McCarty, CSC)
• Detection and Quantitation Concepts: Where Are We Now? (Telliard, Kelly, and McCarty)
Sampling Inside, Outside, and Under (SIOU) (Salon E) Chair: J. Warren, EPA
• VSP Software: Designs and Data Analyses for Sampling - Contaminated Buildings (B. Pulsipher, J. Wilson,
Pacific Northwest National Laboratory , R. O. Gilbert)
• Incorporating Statistical Analysis for Site Assessment into a Geographic Information System (D. Reichhardt,
MSE Technology Applications, Inc.)
• The OPP's Pesticide Data Program Environmental Indicator Project (P. Villanueva, EPA)
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1:00 - 2:30 THURSDAY, APRIL 14th
Information Management (Salons A-C) Chair: C. Thoma, EPA
• Achieve Information Management Objectives by Building and Implementing a Data Quality
Strategy (F. Dravis, Firstlogic)
UFP Implementation (Salon D) Chair: D. Sims, EPA
• Implementing the Products of the Intergovernmental DQ Task Force: The UFP QAPP (R. Runyon,
M. Carter, EPA)
• Measuring Performance: The UFP QAPP Manual (M. Carter, EPA, C. Rastatter, VERSAR)
Quality Systems Guidance and Training Developments (QSG) (Salons F-H) Chair: M. Kantz, EPA
• A Sampling and Analysis Plan Guidance for Wetlands Projects (D. Taylor, EPA )
• My Top Ten List of Important Tilings I Do as an EPA QA and Records Manager (T. Hughes,
EPA)
• I'm Here—I'm Free—Use Me! Use Me!—Secondary Use of Data in Your Quality System (M.
Kantz, EPA)
Innovative Environmental Analyses (IEA) (Salon E) Chair: M. Conomos, EPA
• Evaluation of Replication Methods between NHANES 1999-2000 and NHANES 2001-2002 (H.
Allender, EPA)
• Assessment of the Relative Importance of the CrEAM Model's Metrics (A. Lubin, L. Lehrman,
and M. White, EPA)
• Statistical Evaluation Plans for Compliance Monitoring Programs (R. Ellgas, Shaw
Enviromnental, Inc.; J. Shaw, EMCON/OWT, Inc.)
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Data Integrity Model
arid Systematic
Procedures
EPA National Conference on Managing
Environmental Quality Systems
San Diego, CA
Robert P. Di Rienzo
Vice President Quality Assurance / Information Technology
DataChem Laboratories, Inc.
Relationship of Leadership to Ethical
Decisions
Importance of Vision and Values to Ethical
Decisions
Systematic Procedures that support Ethical
Decision making
Agenda
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What is Leadership?
The difference between a manager and a
leader is that the manager tells you what
to do while the leader makes you want to
do it.
What is Effective
Leadership?
Effective leadership communicates the
organization's mission, vision, and
values to all employees.
www.datachem.cofn
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'The Leadership Challenge'
James Kouzes
and Barry Posner
There's nothing more demoralizing
than a leader who can't clearly
articulate why we're doing
what we're doing."
www.datachem.com
'On Becoming a Leader"
Warren Bennis, Ph.D.
Managers are people who do things right,
while leaders are people who do the right
thing.
www.datachem.com
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Ethics Policies
> Essential business practices
> Systematic planning
> Vision and Values
www.datachem.com
Conceptual Model
Long-term
Success
The organization's leaders must communicate the vision and values
to create a climate for ethical conduct and then place trust in
employees to carry out the vision and values which leads the
organization to long-term success.
^ www.datacnem.com
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Vision
Vision is defined as the guiding image of
success for the organization
Common vision leads to success
Pt-
unww.rlatanl
www.datachem.com
Vision
> We will be recognized by our
peers and clients as the premier
analytical chemistry laboratory in
the U.S.
> Quality Data on Time
^www.datachem.com
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Values
Organizations need a sound set of
beliefs to serve as a premise for all
policies, procedures, and actions
Values lead to ethical behavior
INTEGRITY - We will be honest and above board in all
dealings with clients, fellow employees, and the company.
QUALITY - We will strive to achieve excellence in all services
Provided and will work continually to show improvement
in all our endeavors.
EMPLOYEE EMPOWERMENT - We will create and maintain an
employment environment that provides employees opportunities
to grow, excel, and achieve upward mobility. Employees will
have authority that is commensurate with their responsibilities.
www.datachem.CQm
Shared Values
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Shared Values
CUSTOMER SATISFACTION - We will meet customer expectations
and needs to the best of our abilities.
PROGRESS/INNOVATION - We will expend the resources necessary
to ensure that we stay on the leading edge of technology. We will
continually work to identify and implement new ideas and ways
of doing things for the betterment of the company.
ORGANIZATIONAL HEALTH - We will provide a safe and secure
workplace for employees, (aka Shareholder's Equity)
www.datachem.CQm
Shared Values
PROTECTION OF THE ENVIRONMENT- We will maintain an
awareness of environmental issues and ensure that the Company
complies with all relevant environmental statutes and regulations.
SHARING COMMON GOALS - We will work together as a team to
achieve established corporate goals. We will communicate these goals
to all employees and do everything in our power to see that those goals
are met.
www.datachem.com
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Shared Values
RECEPTIVENESS - We will create and maintain a corporate culture
that encourages receptivity to employees' ideas and feelings.
Management will listen to suggestions for improvement with
an open mind.
PERSONAL GROWTH - We will work together to ensure individual
and corporate success while maintaining an awareness of
individual challenges and helping employees to resolve difficult
issues.
www.datachem.CQm
"It's not enough that we do our best;
sometimes we have to do what's
required."
Sir Winston Churchill
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NELAC Requirements
Guidance for a quality system for
laboratories that addresses data integrity
and ethics is given in NELAC Chapter 5
section 5.1.7.
The NELAC standard further defines
requirements as Management
Responsibilities, Training, and
Control and Documentation
"Good people do not need laws to tell
them to act responsibly, while bad
people will find a way around the laws."
www.datachem.CQm
Procedures
Plato 348 BC
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Laws are partly formed for the sake of good
men, in order to instruct them how they may
live on friendly terms with one another, and
partly for the sake of those who refuse to be
instructed, whose spirit cannot be subdued,
or softened, or hindered from plunging into
evil.
"Laws" Plato 348 BC
arLj** ™ www.oafachem.com
Weli defined procedures and
responsibilities for laboratory
processes, that support the data
ntegrity and ethics system, are a vital
factor in making ethical choices and
documenting data quality.
www.datachem.cofn
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Systematic
Procedures
Need to be consistent with:
>
Laboratory Data Integrity and
Ethics Policies
>
Required Regulation
>
Common Business Practice
Systematic
Procedures
>
Employee training and documentation
>
Internal auditing and reporting
Documentation of analytical results
including manual integration
K$f-*"4.
^^^^wwwidatacnmcofn
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Systematic
Procedures
>
Peer review of analytical
results
>
Non-conformances and corrective
actions
>
Client communication procedures
Employee training and
documentation
Objectives:
To ensure employees receive appropriate
training
To designate and provide new employee
orientation training
www.datachem.cofn
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Employee training and
documentation
Objectives:
To complete initial analytical training and
demonstration of capability
To ensure documentation of analyst's
experience and training
Employee training and
documentation
Responsibilities
Management
All Employees
Quality Assurance
www.datachem.cofn
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Internal auditing and
reporting
Objectives:
To develop procedures for examining
and verifying laboratory operations
To conduct annual audits of all activities to
ensure compliance with written procedures
Objectives:
To ensure applicable regulations (eg. NELAC
or AIHA) for quality systems are met
To facilitate upper management review of
laboratory operations
www.datachem.CQm
Internal auditing and
reporting
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Internal auditing and
reporting
Responsibilities:
Management
All Employees
Quality Assurance
www.datachem.com
Documentation of analytical
results including manual
integration
Objectives:
To implement documentation procedures
applicable to all aspects of environmental
testing activities
To ensure traceabilty requirements are met
www.datachem.com
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Documentation of analytical
results including manual
integration
Objectives:
To design proper narration for all analytical
results
To create manual integration procedures
including appropriateness and techniques for
proper integration
To develop procedures for changes or
amendments to analytical data
'V -,r ;a4S?^E^^^^www!datachem.com
Documentation of analytical
results including manual
integration
Responsibilities:
Management
All Employees
Quality Assurance
¦ www!datachem.com
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Peer review of analytical
results
Objectives:
To develop a peer review process to verify the
accuracy of data generated
To define the experience and training required
for Peer Review
www.datachem.CQm
Peer review of analytical
results
Objectives:
To implement procedures to review client
and project (DQOs) quality requirements,
data deliverable requirements as well as
compliance with applicable regulations
To ensure continuous quality improvement
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Peer review of analytical
results
lAH QttATO<*S j*s.
Responsibilities:
Management
All Employees
Quality Assurance
Non-conformances and
corrective actions
Objectives:
To provide procedures which are easy to
perform and do not carry any weight with
respect to employee performance or
disciplinary actions
To define nonconformance, corrective actions,
and root cause analysis
^^SataiSLcom
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Non-conformances and
corrective actions
LAV.04lAT0HR*v
Objectives:
To implement procedures that evaluate root
cause, assign specific corrective actions, and
initiate follow-up action to verify that corrective
actions are effective and complete
To provide procedures to document and
resolve client concerns
sr- ™ www.dafachem.com
Non-conformances and
corrective actions
Responsibilities:
Management
All Employees
Quality Assurance
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Client communication
procedures
Objectives:
To describe the fundamental components of
communication
To assure problems are identified,
documented and resolved through
communication with the client
To respond to client concerns
Objectives:
To outline procedures to ensure review of
contracts and tenders including a review of
capabilities and resources needed to complete
projects
To define projects in sufficient detail to
ensure client requirements are met that the
data is appropriate for its intended use
Client communication
procedures
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Client communication
procedures
Responsibilities:
Management
All Employees
Quality Assurance
www.datachem.com
Conclusions
> Leaders inspire critical vision and
values to the organization
P Data integrity and ethics play an
immediate role in both the short-term and
long-term success of an organization
www.datachem.com
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Conclusions
> Values influence all ethical decisions
> Systematic procedures are a vital factor
in making ethical choices.
www.datachem.com
Questions and Comments?
Thank You
www.datachem.com
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Sometimes Change happens when we see
the light; sometimes when we feel the heat
Changing World
ISO
17025
^c'°
Defining Data Quality...what does
the number mean?
FIFRA lab
Production lab
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What is FIFRA Work?
Federal Insecticide Fungicide and Rodenticide Act
Mission: Protecting Health and
Environment
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Building a Quality System in A
FIFRA LAB?
Quality Plans
EPA
State laws
MP&QAPP &
Civil Service
Misc Partm
9
riminal Cases
Universities
FDA (FERNJf
USDA
A quality system can calm the waters.
Finding any pesticide in any matrix...
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EPA arid ISO 17025
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P&E Quality Manual
Quality Manual Template
www.e-shoq.com
i
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Hurdles to ISO 17025 Compliance
1. Unplanned work
2. Sample variability
3.Small sample group
4.Results needed fast
Proving Staff Competence?
I know what I'm
doing...I've been here
a long time
We've always done it
this way
I follow written
procedures
I do what I'm told
I follow my notes
• TRUST ME
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Training: documenting skill sets
Cntical Step
Date
Observed
Trainer
Date
Performed
Trainer
How and When
Measured
1
2
3
4
5
6
7
B
9
10
Manifold Setup
Use of Standards
Spiking
Positioning of Speedisk
Use of Speedisk
waste removal
Methanol activation of Speedisk
Drying step
Sodium Sulfate Set-up/Transfer
N-evap Concentration\solvent exchange
Measurement documentation attached yes/no?
Important Concepts
txpiameo-
(date)
Explained
By
Explained
To
Understood
1
Sample Receiving process
2
Sample hold times
3
Sample Storage
4
DCM gas buildup and release
5
vacuum damage to Speedisk
6
Extract Storage
7
Speedisk Activation
Current version of Method distributed to Trainee Tramee initials
Approval to do work Grid
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Complete Picture of staff
competence
Position Description
Employee personnel file
Competence/Approval
Grid
Performance Evaluations
Method Validation
Are you sure about your
result...really sure?
£
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Recovery Chart
Method Used:_^MTP3Q* Qi», ^ r- . a*s<
ICompound Alpha BHC
Percent Recovery
— irafcewn mine* (Twatttorttow » cwrononu Usad beta..
frtMl j
t
|*Z |
; 4% i £
nbni
¦ -
! 3 05
—
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n H
! ]
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¦iiiiiiiiiimiiiiiiimiimiiimimiiimiii
iiiiiiiiiiiiaiiiii
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_____
12: li<
t
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The Level of Quality of Our (data)
Word is now more...
Defined by Us
Legally Defensible
Understood by our Clients
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Any questions?
• contact Information:
• Arina Ferdig
• Michigan Department
of Agriculture
• 1615 South Harrison
Road
^d°pt n
'S°no%P'**A+
">Ode/
• East Lansing, MI
48823
• Goulda@michigan.gov
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ASSESSMENTS OF
DEVELOPING QUALITY
SYSTEMS
Amberina Khan
Kevin Bolger
A Regio
U
r
Goals of today's presentation
~ Provide an overview of R5's
Management Systems Review (MSR)
process for new Quality Systems
~ Typical MSR findings in new systems
~ Using MSR findings to positively
impact Quality Systems (QS)
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Types of Quality System
Assessments
~ Management Systems Review (MSR)
Systematic, independent and documented
assessment of quality system (QS). Broad
in scope and used for initial assessments
of a developing quality system. Can be
Quality Systems Audit (QSA)
»nu rn o
Bind used to
established cju
more rnature or
ity systems,
scope
Types of QS Assessments
[internal MSR or QSA
Conducted within the quality system,
usually led by the organization's QA
Manager or Division/Bureau QA Manager
460 of 1131
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Management Systems Review
~ WHY
-Assessment required per EPA Order
5360.1, 48 CFR Part 46 and 40 CFR
Parts 30, 31, and 35
TWBHMBMHKMHMfflM
decision by or on ben a if of EPA
JviSP_ more appropriate for organiz;
oris
m
Birly stages (he, Lou's stages 1 or
When to assess quality systems?
~ Internal/external MSR of entire
organization at least once during life of
QMP - give organization some time to
implement
organization (Division, Bureau) annua
)r ganization1'
UUS1I
/ Upon rec
management
| Foiiow-up on corrective actions resurnnri
from a previous i7JSP.
461 of 1131
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Managing MSRs:
Roles and Responsibilities
~ Authorizing Entity
Bottom-line individual responsible for
the organization's quality system
HAssessor
TBhIIHBBHmBBBBI
;rrierii
Organization which is being a;
462 of 1131
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Assessment Systems
~ Share responsibilities among a team
staff instead of one assessor
HReview MSR findings to help improve
BHBBiBBIHWBBBBBBlHi
/Apply ci graded approach when
MSR Decision Process
~Organization to be assessed
^BAuthority, criteria & scope for
463 of 1131
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MSR Decision Process
(continued)
^Required assessor qualifications
lAvaWabi I i tv of a u aJjfjgd^assessors
MSR Criteria
~ Use objective, written standards vs
subjective, unwritten expectations of the
assessors
Jllse external policies, procedures and
¦gn|m
sess
ee's JnternejJ policies, procec.
requirements unci quality system planning
documents
AJJ parties concur on ass^-rn^tAa
iteria
464 of 1131
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MSR Scope
~ The authorizing entity approves the
MSR plan and by doing so approves
the scope of the assessment
BlaBBBBBHilBBBBHiBBBBBBBffnWlW
¦-/
n
modify the scope if unrores
relevant quality issues come up
during the JVJSP_
MSR Team
~ Scope determines size and composition of
the MSR team
1 MSR team need to have cumulative
assessment procedures and experience
~ Team members must be free of any real,
potential or perceived conflict of inte1"^^
465 of 1131
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MSR Costs
~ Depends on the MSR scope,
objectives, duration and complexity
¦Affected by the number of assessors
¦jggdg^«ggggjgtgddgton^gg|^Kij^i
MB1H
/Time needed for the generation of
reports and verification of corre^
action
Enti tjf
to conduct
Planning Activities Associatsd with:
it To
Davalopmairt
issessmant Plan
Davalopmairt
Sdact
assassmanl
laam mambans
Review
asMBsmanl
crila iva and
i rfct ma I cii
Raquasl
iiilonii fll»=4~l
ApprcH
asMsunanl
plan
NUta Ini>al
arranga nw rfca-
Idcnliy
L-nTfJela
Kr3£STT>
14.111
FivJira
Bdosdmar
p l.v "i
Ssod finnl
K-s-an<
pLiu
Pr-spar-s
wsassiTiwrl
checklist
466 of 1131
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MSR Preparation:
Initial Steps
~ Obtain information from the assessee to
identify initial quality system issues
HQuality, type and quantity of information
to be collected to determine if quality
HHg|gViH|[£U
~ Develop £j writte.
summarizes wha
Bu
j j j
i m
pJ ej n that
I be clone in
the
MSR Preparation: MSR Team
Review prior to arriving on-site:
^MSR Plan and Agenda
HQuality Management Plan(s)
~ Applicable regulations for
TMbBMHWHT
jronrnentcjJ programs
Previous JvJSr;. reports of organization
OA An n u a J re p ort a rj d vvo rk plan
467 of 1131
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MSR Preparation:
Selecting interviewees
~ Availability of individuals
^Experience &time in position
[Knowledge of the issues/programs
KfigofieeentetMugiieeegafdiifiliiMfiluiejeaiii
similar positior
ross -section of positions:
management, stsiff, field, Jab
information/data specie)JIst^
/
etc
MSR Preparation:
MSR Plan
~ Summarize the MSR
g Cite authority and criteria
1 State the purpose and scope
sues to '
n iqqi.l&q m HR riqqpqqpd
/Team leader transmits JVJSR plan to
the authorizing entity for review
approval
468 of 1131
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MSR Preparation:
Notification of the Assessee
~ Following approval by the
authorizing entity, MSR plan is
transmitted to assessee's
Conducting the MSR
~Opening Meeting
¦Review Documents & Records
Hconduct Interviews
Inrorrn ejci on/PreJ irn in ejry Fi n dine
-Closing Meeting
469 of 1131
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Figure 7. Flow Chart tor Cloud noting the
Assessment
MSR Reporting/Followup:
Reporting Findings
~ Draft report is reviewed by the
MSR team & assessor's internal
management and transmitted to
jce corrective action p
provided in report for
to complete
470 of 1131
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MSR Reporting/Followup:
Reporting Findings
~Assessor transmits final report
via approval authority
wSBBBSBBBl
from the Ejssessee including
proposed corrective action plan
corrects any factual errors Ejnd
resolves any disputes
MSR Reporting
Follow-up: Corrective Action
~ Final report documents required
corrective actions
/JVJSP. cioseout after completion
documentation of corrective
ej ctions
471 of 1131
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Figiiii-e I 0-_ T>-gsie-£ai St-epis. fo«_
A^a.^^a.gi:i:Faat RLgi^oi'tcrt^ ^ " «~|_ Fo^Dow -ibp
R5 MSR Observations & Findings
~ Limited FTEs designated for QA
activities
BFew organizations have dedicated
BBHHBffiBBBBBBBMHI
QA training, if in piac'
introductory in nature
orcen
472 of 1131
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R5 MSR Observations & Findings
~ Quality procedures often in place but
lack documentation, SOPs, etc
BRecords management often
1 Communication problems are often
an issue for quality systems
R5 MSR Observations & Findings
[Few detailed SOPs/documents for
planning, implementing and assessing
environmental data collection activities
during the program or project's life cycle
annual written
work plan
completed
sine!
orgaruzauori:
report of 0;
iictivlti
473 of 1131
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R5 MSR Observations & Findings
~ Assessments, when in place, are field
or lab-oriented and not looking at
overall quality systems
Motivating positive changes in
developing quality systems
~ Work with the organization on
implementing MSR corrective actions
I Assist with QA "train-the-trainer"
nmsenlatjm^
organization :
senior management
-Take a step back if quality system is
overcomplicated or unrealistic
474 of 1131
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r
Goals of today's presentation
~ Provide an Overview of Region 6's Quality
Systems Assessment Program from 1994
thru today
~ Our Plan, Our Goals, the Beginning
~ Mid - Course Corrections
~ Where We Are Now, and What's Next
475 of 1131
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Region 6 Plan for Quality System
Assessments
~ Initial Plan - Perform assessments of Region 6
major State Agency's Quality System.
~ Initial Assessments were to establish a baseline,
and show that Region 6 placed importance on
State Agency Data Quality.
~ Additionally we wanted to meet State QA Staff,
build trust and rapport, show our goal was
improving Data Quality, not "We Gotcha".
Early Beginnings
[First Assessment - Texas Natural
Resources Conservation Commission
[Finding - QAPPs were ineffective
[Finding - QA System was mostly QC
[Finding - QA Staff was nonexistent
[Finding - QA Processes were not robust
[Finding - Staff knew little of QC, No QA
[Finding - No QA Manager
[Finding - No visibility or independence
476 of 1131
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Early Beginnings
~ Louisiana Department of
Environmental Quality - Similar
Findings
~ Oklahoma Department of
Environmental Quality - Similar
Findings
~ A.K.A. - Stage 1
What Region 6 Decided
~ We recognized that each State Agency
needed to have an independent, visible
and viable QA System.
~ These State Agency QA Systems needed
to have processes that would over time
become automatic.
~ Region 6 needed these State QA systems
to become an extension of the Region 6
QA System.
477 of 1131
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Meanwhile, Back at the Ranch
~ There were simultaneous changes
on-going with the Region 6 QA
System.
~ The Regional QMP was approved by
Headquarters, and the assessment
had occurred.
~ We were aware of needed changes in
the Region 6 Quality System.
Our Focus - QA System
Development
~QA Training
~ Requiring Quality Management
Plans prior to grant award
~Thorough reviews of the QMPs
~Verification that QMPs were
followed
478 of 1131
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QA Training
~ Basic QA Training - 4 Days, focus is
on the overall Quality System
~ Includes Data Quality Objectives and
QMP and QAPP development
~ Other QA training:
- QA Conference October
- Data Validation & Data Verification
- QA Assessment Training
15th Annual
Quality Assurance s
Conference
U.S. Environmental Protection
Agency
Region 6, Dallas, TX
October 17 - 21, 2005
Visit our Web site at www.epa.gov/region6/qa
479 of 1131
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Quality Management Plans
~ Since 1994 All Grantees are required to
provide an acceptable QMP prior to grant
award
~ Annual updates/revisions are required as
a Grant Award Condition
~ QMPs require that QAPPs be developed
and submitted for approval prior to start
of work
Reviews of QMPs
~ Each of the 140 plus QMPs are
thoroughly reviewed upon initial
receipt
~ Upon approval grant award can be
made
~ Updates and revisions are reviewed
primarily for changes
480 of 1131
-------
Verification QMPs Are Followed
~ Quality System Assessments are
actually a more thorough onsite
review of both the QMP and how the
QMP is being implemented.
~ Positive findings are common
~ Negative findings are corrected by
QMP revision
Coming to Assess Means
EPA Is Interested
~ We all do well those things that our
boss checks on.
~ Limited resources should prioritize
the important above the trivial.
481 of 1131
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Mid Course Corrections
~ QA Training was thought to be
something that would have a
decreased demand over time.
~ Since 1993 Region 6 has presented
QA training to over 8000 students.
~ The demand continues each year.
Mid Course Corrections
(Continued)
~ Grantee QA capabilities, once
established, would remain.
~ Grantee QA personnel turnover
issues.
482 of 1131
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Credibility
~ Promise "No Gotchas", deliver what
was promised
~ Focus findings on positives and
getting to positives
~ Act as an internal consultant, get
treated as an internal asset
Building Trust and Rapport
~ Offer training on the assessment process
prior to the assessment
~ Use staff of past or future assessed
organizations as Quality System
Assessment team members
~ Conduct the assessment consistently with
the process defined in the training
483 of 1131
-------
Influence on Quality Systems
Quality System Alignment
~ State Agency Mirrors Region 6
Quality System
~ Consistency of Thought on QA Issues
~ Open and Candid Communication
~ Independence of QA
Shifting the Focus
~ Initial Assessments of Major State
Environmental Agencies
~ Currently Assessments are Focused
on Tribal and Municipal Grantees
~ Future Assessments on "Not for
Profits" such as Universities,
Hospitals
484 of 1131
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Climate Cultivation
~ Senior Management Support for
QA is Essential
~ Resources for Assessment
Activities - Proof of Support
~ Senior Management Influence on
Grantees
Climate Cultivation
(Continued)
~Getting to No
~QA Delegation
~QA Disengagement
485 of 1131
-------
Advice
~ Do what they will let you do.
~Always have the next three
moves in mind.
Closing Thoughts
Auditors should keep in mind an
old Slovenian Proverb for
presenting assessment findings:
Speak the truth, then leave
quickly.
486 of 1131
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Comments
If you have comments or further
discussion my contact information is:
Don Johnson
U.S. EPA - Region 6 (6MD)
1445 Ross Avenue
Dallas, TX 75202-2733
Phone: (214) 665-8343
email: iohnson.donald@epa.iiii
487 of 1131
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o»nci<*
INVmOHMlNTAL
MTOBMMKM
Next Generation
Data Quality Automation in EPA Data Marts
Phil Magrogan,
Chief Technology Officer
National Environmental Information
Systems Engineering Center
(NEISEC)
Agenda
>
The State of Data Warehouse Automation
¦ Current State - Data Warehouse
¦ Target State - Data Marts
>
Data Bus Architecture
¦ Star Schema / Conformed Dimensions
¦ Data Mart Franchises
>
Data Quality Automation
¦ Data Cleaning, Profiling, and Correction
>
User View Automation
¦ Meta Data Selection
¦ Dynamic and Reusable Reporting
>
Ready to Reuse
¦ Franchise Methodology, Architecture, and
Reusable Products
488 of 1131
-------
f^T°™
\J * t«v"0
MKMU
Data Warehouse Automation
Current State
Future State
Current State
User View of Air Data
Data from 1982 to 2001...
> Multiple independent systems
Monitor Data Queries
> Data flows into Agency are
Interim Database
uncoordinated
> Users must understand capabilities
Mw Sunurr On Vm 1KB ¦ 3B1
A pjfi* wwt: v/ Ihi '9 wy nwl *05 MHnu -A iipLtcv ttv >MM« Maw Ml Sw rw> AOG Wafen* n inqMi
and limitations of all systems
t »f««m octi m-nm Km im ifiS&i —? 83*>»«"¦*«< ¦*«" i»« ¦««g&f¦«
> Limited data consistency across
systems
Data for 2002 on...
> Data cannot easily be compared on-
Monitor Data Queries
screen
AQS Query
,
> Registries do not enforce active
metadata management
> No coordinated security
rL..r -
> Multiple Sign-Ons
489 of 1131
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Future State
Why Environmental Data Marts?
> Support Environmental Analysts
¦ Science Advisory Board request for timely,
consistent, actionable access for researchers
¦ Increases value of air quality monitoring network
by making existing data more accessible to users
> Get IT out of the expensive query and
reporting business
¦ Provide a more simplistic, higher quality
reporting engine
¦ Provide alternative to extensive customization
> Improve Access Security
¦ Control who is entitled to access and update data
¦ Prevent unauthorized or illegal access to data
Current State
Developer View of Envirofacts DW
SOURCE SYSTEMS
MAINFRAMES
=
0
—
•PCS
—
•GICS
—
•SIDWIS
TEXTFILES
CERCLIS
¦ERAMS
BF/BMS
RIS
DUMP FILES
PROCESS ENGINE
PERL
•Validity Check
•Formatting
•Renaming Source
•Truncate Destination Ta
SQL*Loader
•Load Destination Tables
Oracle Import/Export Utility
•Load Data
•Extract data
Manual Data
Validation
~
~
~
~
~
~
~
~
EnviroFacts
Data Warehouse
I
490 of 1131
-------
The Target State
User View
BS1D8W
mm
u»c
ETC
zt
> One stop for exchanging data
> Integrated access to all data
sources/applications
> Dynamic and static reporting
capabilities
> Intuitive, easy to use
interfaces
> Meets eGov, eSignature, and
other government initiatives
> Secured access for doing
business efficiently
Target State
Bus Architecture Developers View
Admin/Mgmt.
Emissions Data
Science
EHPAAppl.
0- >
ASA Appl.
Other
Data Sources
Interface Cleansing
Tool
Extraction/Transformation
AQS and TRI
Data Marts
Data Targets End-Users
Copyright © 2004 Pieter R. Mimno
491 of 1131
-------
Target Data Model
Star Schema and Aggregates
Monitor
Fact Table
at the
atomic
level
Date/Time
Permit
Pollutant
Geospatial
Site
Demographic
Facility
Pre-computed Aggregates
Star Schema
Facts and Dimensions
> Potential Data Marts
¦ Air Quality + CAMD
¦ TRI (Toxic Release
Inventory)
¦ Air Quest
¦ Solid Waste
¦ Watershed Initiative
¦ Public Access
¦ Enforcement
¦ Indicators
¦ Financial
> Metrics
¦ Air Monitoring
Measurements
¦ Water Quality
Measurements
¦ Hazardous Waste Data
¦ Solid Waste Data
> Potential Conformed
Dimensions
¦ Date/Time
¦ Parameter (Pollutant,
Chem, Met.)
¦ Monitor
¦ Facility (FRS)
¦ Geospatial (State, City, Zip,
MSA)
¦ Site (Lat/Long, Name,
Street Addr.)
¦ Protocol
¦ Standard
¦ Qualifier
¦ Agency
¦ Permit Type (CAMD)
¦ Enforcement
¦ Industry Sector
¦ Demographics (Census)
492 of 1131
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Future Data Marts - Aggregation
Facility
Registry
System
Registry
Business
Intelligence Engine
Pre-Formatted
Query Tool
Custom
Query Tool
Other
Federal
Data
Census
Tract
Data
Commercial
Data
Dun & Bradstreet
Spatial
Data
Future Data Marts - Franchising
Data Sources
Central Site
Regions, States or Tribes
Copyright © 2004 Pieter R. Mimno
493 of 1131
-------
o»nci<*
INVmOHMlNTAL
MTOBMMKM
Data Quality Automation
Concept of Operations
>Data Cleaning
>Data Profiling
>Data Correction
-Stewards are responsible
for all aspects of the data
within their domain.
-Stewards will ensure that
the data is correct and fit
for use.
-Measures %reduction
In data defects
-Works within IEP
-Uses all EPA sources
EPA DQ
Metric
f-The Lockheed/
Martin Team will
ensure that data
quality and the
processes that create
Ithese data are
continuously improving!
PA Data Sharin;
Solution
Operatic
^sterns
Data assessment
•Process correction
Verification
Defect detection
Data cleansing
-EPA partners will
^access data they can
-trust to satisfy their
information needs!
-The EPA will be
recognized as the
[World's premiere
-provider of quality
.Data!
EFA ]/[
Governance s.
. Process \
\] -Engineering Review
-Technical Investigation
-Approval
DQ Correction,
Process*/
DQ Improvement,
Process s
-Root cause analysis
"Broken process" identification
Data Quality Assurance Con Ops
494 of 1131
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Automation Goal:
Eliminate 3GL Burden
> Reduce CDX Burden
• Replace custom extracts in CDX with ETL extracts
• Integrate Exchange Partner data by extracting and
reconciling native data formats
• Implement CDX standards as business rules in ETL
> Reduce Envirofacts Burden
• Replace custom extracts driving Envirofacts with ETL tool
extracts
• Reconcile multiple "standard" data exchange schemas
• Leverage all available analyses of source data
characteristics and use to specify ETL tool extracts
> Extend the Solution
• Gradually extend the infrastructure to additional Domains
• (Administrative Systems, Program Systems, and OEI)
Data Cleansing Process
>
Measure Data Quality
¦ Identify data with inconsistent, missing,
incomplete, duplicative, or incorrect values
¦ Build profiles to feedback to data stewards
>
Standardize
¦ Use EDR as metadata type definition
¦ Integrate and de-duplicate facility addresses
¦ Identify multiple occurrences of the same
business/facility/site in source systems
>
Clean Data
¦ As part of the ETL process - if not at the source
¦ Load only clean data into the warehouse
¦ Identify and correct the cause of data defects
495 of 1131
-------
Automated Data Profiling
Data Profiling is the assessment of data to
understand its content, structure, quality
and dependencies.
Column Level
Range validation, min/max, average, count
Domain validation: e.g. pre-determined values
Source Level
Row count
Redundancy evaluation
Intersource Level
Outer join analysis
Cardinality analysis
Automated Data Correction
Data Correction is the application of business
rules and logic to fix data problems as an
integral part of the data integration process
T ransform
Convert data into different format, structure,
and content to gain desired output
Parse
Finds and breaks apart patterns within fields
so parts can be processed independently
Name & Address
Standardizes name and address data based
on 3rd party data
496 of 1131
-------
Automated Setup of Profiling or
Correction Rule Data Quality Objects
>ftle Wizard: function Detail*
Add/Edit Pioliling Function
Enter the detaflt aboul the Function
Select one or mors sojice: which be u:ed In the lunciion
XJ
Finctions | Ports | Vanafctes |
A1 Functions
* J Character
* Q Convesaon
» _J Data Cleansing
ffi-Q Date
* _l NumencaJ
it _l Saentihe
* LI Special
w CI Test
ft O Variables
— Functions r> the Al Functions gioup
Fomiia
fldl - "CA1
Humeric keypad
*1
73 £2 I % ® X
3
Operator keypad
AMD | OR | NOT | CancH
[ | I | <| >| ¦ | !- | VaMate
Source: Informatica
Assemble Data Quality Objects into
Automated Workflow
gg Repository £<4l View Tools |_ayocrt Mappings Transformation Window Help
pT& -q y < % t s g & *g 'a1 "v1 *t& & -t -y a ss [ ||» i* ^ ^ ~ -s o |
LsaS^msr
Data Quality Workflow
Source: Informatica
497 of 1131
-------
jb
otnaor
iHvmtmtiMTAL
I WOSMATlQN
Data Marts
Automation Of The Users View
User Meta Data Selection
Dynamic Reporting
Repeatable Results
Source: Business Objects
Data Mart - Concept of Operations
> Shield End Users from Data Complexity
¦ Semantic layer maps database complex metadata
into business terms such as Chemicals,
Totals/Measures, or Facilities
Semantic Layer/ Universe
TRI DATABASE
498 of 1131
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TRI InfoView
Document Orientation
i)
xics Release Inventory (TRI)
€•> ® © © ©
Home Mr InfoVtw* Opt»M H«lp Logout
Agency Documents
Access documents available to you and othe» users
I IE3SI
InfoView Query Results can be:
• Saved on-line and shared among
users
• Scheduled for auto-generation and
publication
• Refreshed and saved as a new
document for comparison
Personal Documents
Sjrfiss
New Documents
Create a new document from a Universe
You can also Add 9 document to InfoView from yout computer.
Add User Defined Graphs of MyTable
12,978
Build My_TRI_Report
Report iS
C'ala Templates Properties Map
- Document
- Query 1 - TRI DEMO
1 Year
•» Total Air Emissions Sum
* Total Releases to Land Sum
¦» TolalAderground Injections Sum
Varial
£> la'View Structure Q> Drill 1
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Drop from
Universe to
MyTable
{ Report F«ers Applied to BlocH
Total Air Emissions
Sum
Total Underground .1
Injections Sum
1998
68.950
0
!
1999
79.000
0
zl
2000
53.008.36
0
J.
Total Releases to Land
Sum
El Report 1
499 of 1131
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Customize My_TRI_Report
> Select Result Objects from Pallet of Business
Objects and then...
To buid a report, add objects from the Universe Ofcjects pane to the Result Objects pane
0 $1 TRI DEMO
TRI DEMO Omerwaons
1 State
1 State Coixity FIRS Code
T1 CAS Number
~l Carty name
"1 TRI 2-dtgA Industry Code
T Chwwcal Listed Starting m 1
1 CliefTtcal Listed 1998 Thai F
1 Chemcal
T Chemical Listed 2000 Thru P
7J Year
1 OSHA Carcinogen
Bii TRI DEMO Measures
j Total Air Emissions Sum
j Total otl-site disposal Sun
Total
FacdttyTtl Waste Dspsd Of Is
Total Releases to Land Sum
Total Underground Injections
-lTRI2-cbg» 1 Chemical 1 Chemical . OSHA Car j Total Air E.. > Total
Scope of Analysis None
Drag and Drop
Group by
Apply Filters
Sorts
Set Alerts
Export
Prompts
Drill Down/ Drill Up
Drag and Drop Attributes to Build a
Report
Data Templates Properties Map
Dtou o
- TRI Chemical Releases
- i) Query 1 - TRI DEMO
"I CAS Number
n Chemical
"1 Chemical Listed 1998 Thru Presef
"1 Chemical Listed 2000 Thru Presei
T Chemical Listed Starting in 1995
~l OSHA Carcinogen
^ State
Year
Chemical
CAS Number
T TRI 2-digit Industry Code °a
1S98
1,1,1-TRICHLOROETHANE
000071556
1 Year
¦* Total Air Emissions Sum
¦* Total off-site disposal Sum
¦* Total Onsite Releases Sum
¦» Total Releases to Land Sum
¦* Total Underground Injections Surry
:il Variables
1998
000071556
1998
000071556
1998
I 1
000071556
1998
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000071556
1998
000071556
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000071556
1998
000071556
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500 of 1131
-------
Group by Attribute
~ IB ~ &
Edit Query [£| Edit Report
Report j[fi - |t?
&
Data Templates Properties Map
-*i TRI Chemical Releases
- ad Query 1 - TRI DEMO
"1 CAS Number
"J Chemical
"I Chemical Listed 1998 Thru Prese
"1 Chemical Listed 2000 Thru Prese
"5 Chemical Listed Starting in 1995
"1 OSHA Carcinogen
"1 State
"I TRI 2-digit Industry Code
I Year
** Total Air Emissions Sum
** Total off-site disposal Sum
Total Onsite Releases Sum
Total Releases to Land Sum
** Total Underground Injections Sum
lifej Variables
Year
Chemical
CAS Numbei
1998 '
1,1,1 -TRICHLOROETHANE
000071556
J398—
—-—
000071556
1993
s
000071556
.1998
J
000071556
1593—
000071556
1998
000071556
1998
000071556
1998
000071556
Apply Filters
xics Release Inventory (TRI)
Edit Save Send View in PDF Format Add to Mv InfoView
E
1.1,1-TRICHLOROETHAf
1,1-DIMETHYL HYDRAZir
2,4-DINITROPHENOL
2-ACE7YLAMINOFLUQRE
4-AM IN OAZO BENZENE
ACETAMIDE
AMITROLE
ANILINE
BENZENE
BIS(TRIBUTYLTIN) OXIDE
CARBARYL
CARBON TETRACHLORI
nwi DRnawp
1998
AL
73
1998
1,1,1-TRICHLOROETHANE
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1998
1,1,1-TRICHLOROETHANE
000071556
CA
73
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THI 2- Chemical Chemical
digit Listed Listed
Industry Starting iri 1998 Thru
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1998
1,1,1- TRICHLOROETHANE (000071556
AL
73
Y
1998
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73
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1998
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371556
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73
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1998
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371556
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73
Y
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371556
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73
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1998
1.1,1-TRICHL
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73
Y
1998
1 ,1 ,1-TRICHL
371556
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73
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1998
1,1,1-TRICHlJ
371556
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73
Y
1998
1,1.1-TRICHL
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1 ,1 ,1-TRICHL
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|^||
73
Y
1998
1,1.1-TRICHL,
X Cadculatioris >
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73
Y
1998
1,1 ,1-TRICHL
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73
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1 ,1 .1-TRICHLOROETHANE
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73
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Alerts / Exception Highlighting
1 D B - & Qj Edit Query | [l1 Edit Report
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Report |[S • • A '
:l Si • ^ •
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- TRI Chemical Releases
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Re
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1 TRI 2-digit Industry Code
?0 ETHANE
000071556
AL
73
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1 Year
JOETHANE
000071556
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73
Y
82.00
>* Total Air Emissions Sum
JOETHANE
000071556
CA
73
Y
1250.70
-> Total off-site disposal Sum
JOETHANE
000071556
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73
Y
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158.00
Total Onsite Releases Sum
JOETHANE
000071556
JL
12
Y
Y
(
•* Total Releases to Land Sum
¦* Total Underground Injections Sum
Variables
JOETHANE
000071556
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V Export to any format
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I Jet- * JL £ 1 S,
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View List of Agency And My
Documents
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TRI Reoortl
f?\ TRI Report2
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Summary
>
EPA is Deploying the Next Generation of
Data Mart Technology
¦ Data Profiling, Data Cleaning, and Data
Correction tools
¦ Business Intelligence tools
>
EPA is implementing the Next Generation of
Data Bus Architecture
« Star Schema / Reusable Conformed Dimensions
¦ Distributed Data Aggregation
« Franchise of Reusable Data Mart Methods and
Data
>
You May Leverage the EPA's investment
¦ Become an Environmental Data Mart Franchisee
Edit Save Sena View in PDF Format Add to My inroView
) as 5
1.1,1-TRICHL0R0ETHAh
1,1 - DI METHYL HYDRAZil"
2,4-DINITROPHENOL
2-AC ETYLAM IN 0 F LU 0 Rf
4-AMIN0AZ0BENZENE
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BENZENE
Year
Chemical
CAS
1998
1,1,1-TRICHLOROETHANE
000071i
1998
1,1,1-TRICHLOROETHANE
000071*
1998
1,1,1-TRICHLOROETHANE
000071i
504 of 1131
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l^fe 1^^ III * Partnership for the Next Generation of ESe""
[J LJ I I I B^k I ¦ "Consistently Exceeding the Customer's Expectation^ j
Phil.Magrogan@lmco.com
(703) 647.5647
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Quality Assurance & Management Made
Easier (QAMME)
The OIAA QA Expert System for
Developing Product Plans
John Scalera
Office of Environmental Information
Office of Information Analysis and Access (OIAA)
Presented at the24th Annual National Conference on
Managing Environmental Quality Systems
San Diego, CA, April 11-14, 2004
What it is—
QAMME is an expert system tool that
guides users in documenting key
management and quality assurance
information related to product
development.
506 of 1131
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What it's not is—
A pain in the #*@%$#
%@*!
JO
QAMME Objectives
• Promote cross-office interaction
• Incorporate marketing design within OIAA
• Identify OIAA Business Lines & End Products
• Help employ a standardized quality management
system
• Reduces future project management burden by
using an electronic data base system
507 of 1131
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QAMME Features
~ QAMME documents what we are doing, why we are doing
it and how we plan to execute it along with the QA
considerations applied to the End Products.
~ It is a living document system.
~ Uses a question and answer format along with tables to
be completed to guide the user in providing the wanted
information.
~ There are no "right" or "wrong" answers.
~ If a question is not applicable—place "NA" and move on.
~ If an answer to a questions is unknown or to be
determined, place "TBD" in the space provided.
~ Allows a graded approach.
Designing and Implementing a Quality Management System:
A Few Basic Pointers
~ Top management support and participation needs to be evident to all
parties.
~ Identify and communicate "why" the change. What needs
precipitated the development and implementation of a new or modified
system?
~ Identify and communicate the potential benefits of the new system.
~ Get the buy-in of all parties. Bring them in at the beginning. Allow
them to contribute to the development of the system so it becomes
relative to their work and they have a vested interest.
~ Build off of existing QA and management systems if possible.
508 of 1131
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Designing and Implementing a Quality Management System:
A Few Basic Pointers (cont.)
~ Keep the system/tool as simple as possible. Take the complex and
make it an assimilation of multiple, simple tasks.
~ Develop documentation as living documents that can be readily
updated.
~ Design the system with flexibility.
~ Consider the option of phasing in the program.
~ Make sure all key terms are clearly defined and relative to the
programs/projects using the system.
Designing and Implementing a Quality Management System:
A Few Basic Pointers (cont.)
~ Design the system with accountability. Measure
performance.
~ Take time for training. Keep it short and to the point.
~ Follow-through.
~ Assess the system and revise as needed.
~ Give it time.
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Key Terms
~Customer: A party that has solicited or expressed an interest in the
acquisition from or development of a product by another party.
~End Product: The item, process or service that is provided to a
Customer outside of OIAA.
~Task: For OIAA, the effort that produces Deliverables (items,
processes and/or services) that support the development of one or more
End Products,
~Deliverables: The items, processes or services produced by a Task
or Tasks in support of the development of an End Product.
~Feature: Desired characteristic of an End Product or a Deliverable.
~Performance Criteria: Specific acceptance limits placed on the
performance of a product feature.
4 OIAA Core Business Areas
1. Analysis and Analytical Tools
2. Information Access and Management
3. Information Infrastructure
4.. Business and Organizational Development
OIAA Planning Process
11 Business Lines
Business Line Desription (QAMME Part 1)
End Products (Items, Processes, Services)
End Product Plan (QAMME Part 2)
Tasks
Task Plans (QAMME Part 3)
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OIAA Planning Framework
Cora Buoines
(Total oM)
End Products
& » » %
Geospatial
ar«1 Analytical
Analytical Services
Tools
£>
¦>
rabies J^>
Indicators
Products and
Services
£ £
THI Chetn
Haz.
Assessment
Regional
Analytical
Services
/ 0 \
Educational
Tools Oev
/ \
Edu
cnlionai roe Student
Strategy Web-Map
Report
OEI Human Health
Strategy Report
End Products & Supporting Tasks
511 of 1131
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OIAA Planning Process
Planning Process
Figure 3
QAMME Part 1: Business Line
Description
Describes types of End Products produced;
Why they are produced—GPRA, Regs;
¦ Who the Customers are;
Specific End Products to be produced.
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OIAA QAMME: Part I- Business Line Description
Time period covered by (tit* docnm
1 I. Bu*inc« Line Dcscrijuin
513
of
1131
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QAMME: Part 2
End Product Plan
IDs responsible End Product Manager;
End Product Description;
Product Features &Customer Input;
Development Milestones;
Supporting Tasks ;
End Product Cost: FTEs & $$$.
Document TlUc OIAA OaMME T P«n 2 Draft
Rnusn Ink Draft" Frf>22. 3*J
~OAA QAMIff. Part H. End Product Phm
fUmlmfl! ft— i
f I. MPwJwlPuirhgM
In 250 w»f*i« kw. 4r.cnbf tlw K«d IWwL V« » p«rt •» flw
wrtlt-vp.»»wHaMc. tadwte «W »cnl. »hJch pmpfcd Ik. drvcH""" ol
tin K..I l-rnduct, .bj««hcv luj Vrni Product lr«l«rr. uid cmlMiwn M
tti< Ki.il I'fwdwcl-
514 of 1131
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•l-rujcci- Title
Contact Stone gc
Number
Dcllwnbln
Dot Ian Pro* Wnl
loOIAAV
(in thuuantk)
I'Ucc received
¦Jullua'aritivipniril
1 /
515 of 1131
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QAMME Part 3:
Task Plan
Who is working on the Task;
End Products Supported and Deliverables;
Deliverable Features , Performance Criteria and
Assessment;
Data/Information Quality;
Training & Certification;
Deliverables Schedule;
Support Task Cost (FTEs & $$$).
Doaraiera Tnk: OlAA QAMME T Part 111 Draft
Revision: Drift Dale Jta. 27.2005
I QAMME Part llf-OIAA Task Plan
Task Plan me:
Time Period Covered by Plan (m/yr): From: To:
Approval/Concurrence
Ipprovah
Tie*
AMWlion
T*"LMa"
inSPrafcdMnifv:
t*l Pisauc Uaujet 2
tattCWfrfTM
Qi«r/Manage
CM
516 of 1131
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| *V»m qnlli) luiinm (Qt| ik _
« «t bri.s dnxjofri mift-|Kn11Q,\ Dinmu «rr i„ Ik |,r|„ on file « .
| |«rl of lb. End Pradltcl'i R
II. Dbtribaffeo
III. TBkUrtmlaiin:
IV. Ohjtethn, MiucijuiiiJ 11
V. Computer Hardware And Sofliiarr Prrfarmsaei
VI. DawlnfornuiUni (Jmlli>
VII. Maintain Operating I'rwnlwm
Vin. Special Iminlucl erlilkatfoo
IX DdMIn
nl Title: OIAA QAMME T Part III Drall
Revision: Draft Dale; Jan. 27,200J
I Task Plan llisturx
I Table I. Tank Plan History
Identify ilir lime period «this T ask Plan »a» rr\Ucd and Ihc sections which were
changed.
Kffectiye Dale
Table 2- Distribution
i l ist wlin needs to Rcl copies of the approved Task Plan and future revisions. These
| parlies should be a pari of an electronic nulling list for docuuienl distribution.
III. Task Organisation: Ol V\ Personnel. Responsibilities & Kesiiurces
1 Table l \. Personnel and KesjmntU
517 of 1131
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Document Tiik OIAA QAM WET Pan II] Draft
Rcvfatoa: Dealt Due: ho, 27,2005
IV. T«*k Ohjrrtitcv IMi«rr»b>«. t'ratarn uid IVrfonnaacr
W fc»i .re (tic... rail »h|rctnn «f ilu. Talk?
Include Ikf Dmli » hlcli prompted ihr Ink.
518 of 1131
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fXx-amsnlTcIc OIAA QAMME T Pan III Ofji
Rcviwm Draft DmcjBi27.JOOj
Docummi Title: OlAA QAM ME T Pan III Draft
Revuion: DctR Date: Jan. 27.2005
! tiiven Ihrdiiln'.iifomialion quaint ia|uitnnrni< jtatcd mi Table 5. In™ vmIIu
ilflcmunaliuii be itmic on lite itata/mfomiatian so. uxd lo deiotmoc the quality of the
I scrondaty dalit'mformalum'.' Include (or rclefciKcl the planned fbimuiat to be applied
and tuilMu-iil jiul>sc\ of the retrieved data to altom the dam Duality.
| VI C. Data Analvtlt, Reduction and Validation of Final Rctulu
LI.»'*•- IW«I« <« mttem) tltc |W
oo lanbCrfU o! .liTi MduCtfcu lol uulytu
519 of 1131
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IXxumrni Titic: OlAA QAMME T Pan III Draft
Revision: Drafl Date: J311.27.2005
I (km
I Ihcn
"•"•I mullv inrnrnuitoa !><>», rn~nrt '>'
f .r ibu fToteMint wn . Uirfi tn M Sr cuvrml
urKkr ike CMC propmn. ju* type *1 Xcntmi
under ifac £PA CMC
V1L SttnOard Operating Procedures
h r« UN of font* prcoKfem t>r un a** a t
—I*—i. tank!
Mil Special Icuiiiii&C rrlilkaiion
520 of 1131
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Getting Started
1. Business Line Managers are identified by the Office Director.
2. Business Line Managers come up with a team of potential
End Product Managers along with proposed End Products to
be worked on during the next 12 month period. As a team,
they complete QAMME Part 1, Business Line Description.
3. Once the Business Line Description is signed off on by the
Office Director, identified End Product Managers initiate
development of an End Product Plan, (QAMME Part 2). Tasks
which are critical to support the development of the End
Product are identified as a part of the End Product Plan.
4. Task Leaders identified by management, along with their Task
Teams, are responsible for developing Task Plans (QAMME
Part 3). Task Plans are based upon the needs communicated
by the End Product Managers to the Task Leaders.
521 of 1131
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Locational Data Improvement:
an Overview
Mainstreaming Locational Improvement
In Our Work
2005 Data Quality National Conference
EPA's Vision for Locational Data
© EPA's architecture enables the Agency bv location to
describe the quality of the environment and the
activities occurring at that location
© Three action steps:
Communicate importance of location
a Help EPA get locations right!
a Leverage enterprise data and geo-processing
services
522 of 1131
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Why is Locational Data Important?
© EPA business decisions revolve around specific locations
(facility, superfund site, etc.)
• EPA spends $$ on locational data:
h > 50 EPA data systems collect "locations" thru >200 separate
reporting requirements
« Key Agency products depend on locational data:
h Report on the Environment
* Emergency response and remediation plans
h Indicators
x Risk assessments
a Homeland security
ss Effective enforcement
What are the Challenges?
• Quality
a Of EPA's 1,600,000+ "known" places, over half of these
have no or poor (zip code only) locations
• Cost
a Duplication of collection/management is costing EPA and its
partners $$'s!
• Integration
a Integration of data by place is difficult due to incomplete or
non-standard values
• Bottom line
a Inaccurate locations impede our work to complete the
mission
523 of 1131
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Locational Challenges:
All Effect Quality
© Policy Areas
¦ Interoperability
b Access
# Enterprise Architecture
ss Centrally Managed Services
S3 Distributed Geospatial Network
# Governance
¦ QA/QC
ss Developer Guidance
ss Measure Success
The Locational Data Improvement Plan
©Align Policies and Procedures
© Improve FRS Data
© Improve Data Flow
©Develop Locational Tools for Collection
524 of 1131
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Multi-Pronged Approach
For Governance, Quality and Use
© Partnerships
Leverage Exchange Network with States
© Tools and Technologies
¦ Develop reusable tools and authoritative systems
© Governance
¦ Implement policies with EPA and Partners
© Data
¦ Target critical data for consolidated acquisition/access
Align Policies and Procedures
©Currently, locational policy focuses only
on data standards and 25 meter
accuracy - It needs to do more:
¦ Polygons & Lines
¦ Metadata & MAD codes
e Access & Web Services
¦ Interoperability
525 of 1131
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Improve FRS Data
©FRS gets data from many sources-
We need to:
h Upgrade geo-coding tools to better
address-match in an automated process
h Prioritize FRS data from Program, Region
and States to focus attention on
• Re-designing locational database tables to handle Agency
needs
• Cleaning up current data values we already store
• Develop quality checks in XML schema file structure
Progress to Date
• 1.6million+ unique records linking to program
interests such as:
¦ TRI
a AIRS/AQS
a RCRAInfo
a SDWIS
¦ RMP
a CAMDBS
a N EI-1999
¦ RADInfo
a States
a ICIS (Docket)
a PCS-Majors &
S3 NCDB
Minors
¦ CERCLIS
a Nat'l
Performance
a BRS
Partnership
a and others...
526 of 1131
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What Partnerships Can Buy:
Comprehensive Access to Locational Data
North Dakota
IWagh'ingfor
I Montana'
innesota
kvvi scons lir
Dakota
KTermont^H
ffiSsachusefts
wersey
Michigi
Vtyoming
liNebraskai
Indiana
Nevada
^stViminia1 M ™l|nd xDel i
Ki'iorad'
IKansasi
Kentucky
[£alifo7nia|
Oklahoma
|North|iaroli"
New Mexico
'MississlppPAIabamaj
Georgii
Louisiana
Florida;
States represented in green are currently sharing Facility Registry
Data through the Exchange Network
Develop Locational Tools
• CERCLIS and TRI have locational tools to help
locate latitude & longitude values. We are:
53 Building a site locator tool (building upon the
above) which delivers data and metadata to FRS
~ Deploying to Regions, States and CDX registration
~ Piloting wireless integration to populate locational
database
527 of 1131
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What Is The Locational Message?
© Value and Quality
a Locational data is more important than ever and directly supports
our Homeland Security efforts, program initiatives, ROE reports and
many public access tools
© Approach
h Locational data is a shared responsibility, and strong partnering is
required with our Programs, Region and States. Common
infrastructure, acquisition strategies, standards and policies are
critical to our success.
<> Goal
h Our federal, state, tribal and public partners will look to EPA's data
as a critical national and authoritative resource of high quality
locational data for environmental places of interest.
What is the Pay-Off?
• Integrate disparate data for strategic planning,
homeland security, and our main business areas
© Reduce burden on regulated community, states, and
EPA IT infrastructure
© Support high demand for reliable, high quality
locational data
528 of 1131
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OEI3-5 Year Goal
95% or all our places (facilities) are
accessible to all locational oriented
applications with high degree of
accuracy and documentation
529 of 1131
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A Win-Win-Win Partnership for
Solving Environmental Problems
William F. Hunt, Jr., Dr. Kimberly Weems & NCSU
Dr. Nagambal Shah & Dr. Monica Stephens, Spelman College
Clients: Dr. Barry Nussbaum & Margaret Conomos
Students: Helen Ferguson, Spelman College
Bryan Stines, NCSU
Che Smith, Spelman College
Cathy Pitts, NCSU
John White, NCSU
EPA Nat. Conf. on Managing Environmental Quality Meeting
San Diego, CA
April 13, 2005 y\
on
Spelman College
Training Environmental
Statisticians-Tomorrow's
Problem-solvers
—NSF NCSU/Spelman College
Collaborative Effort:
Environmental Statistics
Practicum
Wiiliam F. Hunt, Jr., Kimberly S. Weems, and
Michael T. Crotty,
North Carolina State University
and
Nagambal Shah and Monica Stephens,
Spelman College
530 of 1131
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Ralph Waldo Emerson
¦ His poem, Voluntaries
¦ "When duty whispers low, Thou must,
The youth relies I can."
Academic Review Board Findings
Dr. Ellis Cowling
University Distinguished Professor at Large
¦ While much money and professional time and energy are currently
being spent by many different federal, state, and private sector
organizations in the US and other countries collecting environmental
data, much too little professional time, energy, and money is
invested in analysis, interpretation, and dissemination of
environmental data and information.
¦ We believe, together with Bill Hunt, Nagambal Shah, and other
participants in this program at NCSU and Spelman College, that
wider implementation of the Environmental Statistics Practicum idea
could lead to a renaissance in what can be learned from
environmental data that has already been collected and will be
collected in the future.
¦ The Environmental Statistics Practicum idea developed under this
NSF grant provides a win-win-win opportunity for all parties
involved: undergraduate and graduate students, university faculty,
and various federal and state government agencies and private-
sector clients.
531 of 1131
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Dr. Thomas Gerig - Student
Exit Interviews
¦ Before the Practicum:
¦ Typical response from students: "I have taken all
of these statistical courses but now what do I do
with them?"
¦ After the Practicum:
¦ The students focused on the importance of their
projects
¦ How much they enjoyed the projects
¦ They had confidence in themselves
¦ Knew what they were going to do when they
graduated.
Work Addresses Critical Areas
¦ The need to train undergraduates in
analyzing important complicated and messy
data sets.
¦ The National, State and international need to
analyze environmental data to make better
environmental policy decisions.
¦ The need to encourage students to pursue
graduate degrees in statistics, keeping people
in the pipeline to pursue PhDs.
¦ The need to analyze real data for real clients
in the workplace and make the student a
desirable candidate for employment upon
graduation.
532 of 1131
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NCSU Course Objectives
¦ Provide consulting opportunity with USEPA,
NCDENR, Forsyth County, Environment
Canada, US State Dept., TCEQ, Univ. of TX,
etc.
¦ Focus on application of student's technical
skills to real problem
¦ Student's gain consulting experience
¦ Develop communication skills: written and
oral
¦ Brief faculty and outside scientists
NCSU Prerequisites
¦ Introduction to Experimental Design
¦ Introduction to Regression Analysis
¦ Knowledge of SAS
NCSU Course Guide
¦Handouts are provided
¦National Air Quality and Emissions
Trends Report
¦No textbook
533 of 1131
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NCSU Environmental Statistics Practicum -
Major Topics
¦ Course Overview
¦ Short History of Air Pollution
¦ Environmental Monitoring
¦ Assign First Homework Assignment - Lead Monitors
¦ NCDENR Air Monitoring Site Visit
¦ How Are Environmental Data Used?
¦ Teams
¦ Student Presentations of First Assignment
¦ Clients Come
¦ Students Form Teams & Work with Client's Data
¦ Student's Conduct Analysis
¦ Student Discussion of Interim Results with Clients
¦ Students Prepare Final Report
¦ Student's Prepare Briefing
¦ Student Pre-Briefing
¦ Student Briefings for Clients
NCSU Clients
¦ Southern Oxidant Study at NCSU
¦ USEPA, Office of Research and Development
¦ USEPA's Office of Air Quality Planning and Standards
¦ North Carolina Department of Environment and
Natural Resources (NCDENR) Air Division
¦ Air Monitoring Division of the Forsyth County
Environmental Affairs Department
¦ U. S. Department of State
¦ Environment Canada
¦ University of Texas
¦ Texas Commission on Environmental Quality
¦ State Climate Office of NC, NC State University
¦ North Carolina Department of Environment and
Natural Resources (NCDENR) Water Division
¦ Mid-Atlantic Regional Air Management Association
534 of 1131
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Professional & EPA Technical Meetings
& Undergraduate Research Symposia
¦ Southern Oxidant Study Data Analysis Workshop, Research Triangle Park, NC,
March 9, 2000;
¦ NCSU Undergraduate Research Symposium, McKimmon Center, Raleigh, NC,
April 27, 2000;
¦ USEPA Technical Workshop on PM 2.5 Monitoring, Quality Assurance, and Data
Analysis, Cary, NC, May 22-25, 2000;
¦ Future Directions in Air Quality Research, Ecological, Atmospheric,
Regulatory/Policy and Educational Issues, Research Triangle Park, NC February
12, 2001;
¦ NCSU Undergraduate Research Symposium, McKimmon Center, Raleigh, NC,
April 19, 2001;
¦ NC Department of Environment and Natural Resources Data Analysis
Colloquium, Raleigh, NC, May 23, 2001.
¦ Second Annual NC State University Minority Graduate Education (MGE) Summer
Research Program Poster Session, July 23, 2001.
¦ Mathfest 2001, sponsored by Mathematical Association of America and Pi Mu
Epsilon, Madison, Wisconsin, August 2-3, 2001.
¦ 2001 Sigma Xi Student Research Symposium, Raleigh, North Carolina on
November 10, 2001.
Professional & EPA Technical Meetings &
Undergraduate Research Symposia
¦ NCSU Undergraduate Research Symposium, Raleigh, NC, April
18, 2002.
¦ North Carolina Department of Environment and Natural
Resources Data Analysis Colloquium, Raleigh, NC, May 23, 2002.
¦ First Annual NC State Undergraduate Summer Research
Program Symposium, August 9, 2002.
¦ Joint Statistical Meetings, New York City, New York, August 11 -
15, 2002.
¦ Air & Waste Management Association's Annual South Atlantic
States Section Meeting, Research Triangle Park, NC, December
4, 2002.
¦ NCSU Undergraduate Research Symposium, McKimmon Center,
Raleigh, NC, April 10, 2003.
¦ NC Depart, of Environment & Natural Resources Data Analysis
Colloquium, Raleigh, NC, May 23, 2003.
¦ 96th Annual Air & Waste Management Association Meeting, San
Diego from June 22-26, 2003.
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Professional & EPA Technical Meetings &
Undergraduate Research Symposia
¦ Second Annual NC State Undergraduate Summer Research
Symposium, Raleigh, NC. August 9, 2003.
¦ Triangle University Undergraduate Research Symposium, Duke
University, Durham, NC. November 1, 2003.
¦ Water Resources Research Institute 2004 Annual Conference,
Raleigh, NC. March 31, 2004.
¦ NCSU Undergraduate Research Symposium, McKimmon Center,
Raleigh, NC, April 22, 2004.
¦ 97th Annual Air & Waste Management Association Meeting,
Indianapolis, IN, June 22-25, 2004.
¦ Third Annual NC State Undergraduate Summer Research
Symposium, Raleigh, NC. August 5, 2004.
¦ OPT-ED Alliance Day, Raleigh, NC, Sept. 24, 2004
¦ South Atlantic States Section (SASS) of the AWMA Annual
Meeting, Virginia Beach, VA Nov. 4-5, 2004
Professional & EPA Technical Meetings
& Undergraduate Research Symposia
¦ Triangle Undergraduate Research Symposium, North Carolina
State University, Raleigh, NC. November 6, 2004.
¦ Statistics/Biomathematics/ Bioengineering Undergraduate
Poster Session, North Carolina State University, Raleigh, NC,
February 4, 2005.
¦ Meredith College: Mathematical Association of America,
Southeastern Section, 84th Annual Meeting, Raleigh, NC,
March 11-12, 2005.
¦ 24th Annual National Conference on Managing Environmental
Quality Systems, San Diego, California, April 11 - 14, 2005.
¦ USEPA Earth Day Celebration, Research Triangle Park, NC,
April 21, 2005.
¦ NCSU Undergraduate Research Symposium, McKimmon
Center, Raleigh, NC, April 28, 2005.
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Student Awards ($24,475)
¦ D. R. Harrington, "Protecting the Public Health - Forecasting
Photochemical Air Pollution in Charlotte, INC." NCSU Undergraduate
Res. Symposium, April, 27, 2000. $200 CASH AWARD
¦ Jason Grissom, Comparison of Particulate Matter Levels in
Worldwide Megacities, report prepared for, US State Dept., August
17, 2000. (USA Today Award)
¦ Kathy Woodside, "Protecting the Public Health: Forecasting Fine
Particular Matter in Forsyth County." Mathfest 2001, Mathematical
Association of America & Pi Mu Epsilon, Madison, WI, August 2-3,
2001. $125 CASH AWARD for Best Talk
¦ Darious Brooker, Ho Ling Cheng and Jeffrey Thomas,
Undergraduate Research Award for $2000 to pursue their research
on the USEPA's Toxic Research Inventory.
¦ Tracy Robinson, "Saving the Earth by Reducing Ground Level
Ozone: What Can We Learn by Examining the Atlanta Ozone
Precursor Data?" NCSU Undergraduate Research Symposium,
Raleigh, NC, Apr. 18, 2002. $200 cash award.
Student Awards
¦ Karen Donaghy and Courtney Sorrell, "Designing Models to Predict
Tomorrow's Air Pollution." 1st Ann. NC State Undergraduate
Summer Research Symposium, August 9, 2002. Award
¦ Karen Donaghy and Courtney Sorrell won the Undergraduate
Research Award for $2000 each to pursue their research on
Predicting Tomorrow's Air Pollution, November 27, 2002 .
¦ Karen Donaghy and Courtney Sorrell, "Designing Models to Predict
Tomorrow's Air Pollution." Air & Waste Management Association's
Annual South Atlantic States Section Meeting, December 4, 2002.
Won 3rd prize.
¦ Karen Donaghy and Courtney Sorrell, "Improving the Forecast for
Tomorrow's Air Pollution." NCSU Undergraduate Research
Symposium, McKimmon Center, Raleigh, NC, April 10, 2003. Both
students won the $200 cash prize for poster.
¦ Caleb Rowe and Valerie Harris, "A Tale of Three Cities - How Does
Urban Growth Impact Air Pollution? Second Annual NC State
Undergraduate Summer Research Symposium, Raleigh, NC. August
9, 2003. Received an Award.
537 of 1131
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Student Awards
¦ Louise Camalier, Brendan Yoshimoto and Brian Stines won the
Undergraduate Research Award for $500 each for their project, "Solving the
Houston Air Quality Emission Inventory Discrepancy - Expanded Statistical
Methodology Applications to Atlanta, GA," on November 18, 2003.
¦ Ornella Darlington-Turner and Brian Currier won the Undergraduate Research
Award for $500 each for their project, "Water Quality Trends in the Raleigh-
Durham Metropolitan Area" on November 18, 2003.
¦ Jamie Ridenhour and Jennifer Lawhorn won the Undergraduate Research
Award for $500 each for their project, A Statistical Model to Forecast Fine
Particulate Matter Air in Charlotte, NC" on November 18, 2003.
¦ Jera Mendenhall and Audria Humes, "Is there a better way to define
swamplands in the Coastal Plain and Sandhills?" Won 3rd Prize in Poster
Competition at the WRRI Meeting and each student received a $25 cash
award on March 31, 2004.
¦ Louise Camalier, Brendan Yoshimoto and Brian Stines, "A Statistical Method
to Corroborate VOC Emission Inventories Using Air Quality Data - Applied to
Houston and Atlanta." Won the $200 cash prize for poster at the NCSU
Undergraduate Research Symposium on June 22, 2004.
¦ John T. White, "Is the Fine Particulate Matter Air Pollution "Non-attainment"
Problem in Hickory and Lexington, NC Regional or Local?" Received an Award
at the Third Annual NC State Undergraduate Summer Research Symposium,
Raleigh, NC. August 5, 2004.
Student Awards
¦ John T. White, "Is the Fine Particulate Matter Air
Pollution "Non-attainment" Problem in Hickory and
Lexington, NC Regional or Local?" Received an Award
at the Third Annual NC State Undergraduate Summer
Research Symposium, Raleigh, NC. August 5, 2004.
¦ NCSU Undergraduate Research Awards, November
19, 2004. Eight students - Ken Hayden, Audria
Humes, Kimberly Madsen, Jera Mendenhall, Cathy
Pitts, Bryan Stines, Paul Tillman and John White -
each received an Undergraduate Research Award for
$500 each to pursue their research.
538 of 1131
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Training Undergraduates to
Analyze Environmental Data to
Help Make More Informed
Environmental Decisions
¦ Nagambal Shah and Monica Stephens
¦ Department of Mathematics
¦ Spelman College
¦ Atlanta, GA
w
Spelman College
Introduction
¦ Collaborative Project
¦ Involves work with Real World Problems
¦ Interdisciplinary Approach
¦ Academic and Summer Components
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$$ National Science Foundation $$
North Carolina State
University
Other collaborators
US Environmental
Protection Agency
Region 4
Ga. Department of
Natural Resources
Our I earn
540 of 1131
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Components of Spelman
Project
Academic Year
Environmental Stats Practicum (taught in Spring
Semester 04, 05, and 06)
¦ Enrollment^ students): Majors: Math (5), Psychology
(1), Economics (1), Environmental Science (1); Class
contains First Year through Senior Year students
¦ Course includes presentations from professionals from the
EPA, Ga. DNR, Industry, some of which are alumni
¦ Students have been exposed to SAS and GIS software
Environmental Statistics Practicum Course Outline
January 14:
Introduction to the Course
Shah/Stephens
January 21:
Overview of Air Quality
Van Shrieves
January 26:
Overview co nt.
Van Shrieves
January 28:
Introduction to Statistics w/ SAS
Shah/Stephens
February 2:
Introduction to Stats cont.
Shah/Stephens
February 4:
Introduction to Stats cont.
Shah/Stephens
Assignment #1
February 9
SAS T utorial
Doug Jager
February 11:
Health Effects of Air Pollution and Fine Particles
Ofia Hodoh
February 16:
Environmental Justice
Debra Carter
February 18
Air Pollution Monitoring (National Perspective)
Van Shrieves
February 23:
Meteorology and Other Aspects of Air Pollutants
Brenda Johnson
February 25
Air Pollution Monitoring (State Perspective)
Susan Zimmer-Dauphinee
March 1:
Work on Assignment #2
March 3:
March 8:
Spring Break
March 10:
Spring Break
March 15:
NCSU Student Projects and Perspectives
Hunt/Weems
March 17:
How are Environmental Data Used
Hunt/Weems
March 22:
Mapping Environmental Data and Interpretation
Darren Palmer
March 24:
Team Building Skills
Marquette Brown
(Assign Final Projects)
March 29:
Industry Perspective on Air Quality
John Jansen
March 31:
How to Write a Technical Paper
Stephens
April 5:
High School Visitation
April 7:
Work on Group Project
April 19:
Work on Group Project (Problem Statement Due)
April 21:
Work on Group Project (Discussion of Results Due)
April 26:
Work on Group Project or Field Trip State of Georgia Monitoring Site
May 4:
Final Presentations (8:00am-10:00am)
Van Shrieves
541 of 1131
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Components of Spelman Program
Summer Program
Spelman Environmental Science Summer Institute (SESSI)
• 6 student participants: Majors: Math (2), Economics (1),
Political Science (2), Psychology (1)
•Duration: 6 weeks, from June 1 - July 9
•Students will collaborate with professionals from the EPA,
Region 4 and GA. DNR to analyze data
Spelman Clients
¦ Mr. Van Shrieves, USEPA Region 4,
Atlanta, GA
¦ Ms. Susan Zimmer-Dauphinee, Georgia
Department of Natural Resources
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Other Student Projects
•Analysis of Louisville Air Toxic Data in "Rubber
Town."
•Investigation of Photochemical Assessment
Monitoring Stations (PAMS) Data in Metropolitan
Atlanta.
•Analysis of Ozone data and its health effects (ie.
reported cases of Asthma)
• 1285/175 Connector - What is the impact of Truck and
Bus Traffic?
• How have the new federal regulations of mercury
impacted air quality (fine particulate matter)?
• How well can ozone precursor data be used to predict
ozone levels?
Spelman Students Professional
Meetings & Symposia
¦ Georgia Collegiate Honors Council, Savannah, GA, February 20,
2004
¦ Spelman College Research Day, Spelman College, Atlanta, GA, April
5, 2004.
¦ Associated Colleges of the South (ACS) Environmental Conference,
April 24, 2004.
¦ Briefing for Georgia Department of Natural Resources, May 20,
2004.
¦ Briefing for Deputy Regional Administrator, USEPA, June 10, 2004.
¦ Second Briefing for Deputy Regional Administrator, USEPA, June 17,
2004.
¦ South Atlantic States Section (SASS) of the AWMA Annual
Meeting, Virginia Beach, VA Nov. 4-5, 2004
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Spelman Student Awards
¦ Che' Smith, Investigating Effects of
Airplane Emissions in Metropolitan
Atlanta Before and After 9/11/01,
Spelman College Science Day, Atlanta,
GA. Won First Prize.
¦ Iesha Brown & Che' Smith, Math Poster
Competition, Spelman College Research
Day, Won Second Prize.
NCSU Web Page
¦ Used to introduce course (syllabus)
¦ Contains course lectures
¦ Lists student accomplishments
¦ Provides student access to client data
¦ Also contains:
¦ Photo gallery of the program
¦ Previous project data & results
544 of 1131
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Course/Conference Web Pages
I Objectives
Us to work on a consulting
¦ project with a tesearchet/dient uang real envaaonienul data
¦T5i- students wffl work wah faculty as NCSU ar scientists from
¦ the U S Environmental Protection Agency or sctenasts 6om
I the North Carolina Department of Environment and Natural
es (NCDENR) Hie students will meet informally every
| week The course anS focus on the appbahon of the student's
:echrK ji skills, die stodenss' interaction with cients. the analysts of i
I of their writing and oral ikils. effective report wntaig, making presentations
| scientists The students will make a field trip to see how environmental data
le ar.-i will present the re raits of their work to the sc
I Prerequisites
it should have It
Undergraduate Statistical Training of Environmental Problem Solvers
:t3shVy completed the foBowng i
• lotroduehon to Experimental Design (ST 431)
• Introduction to Recession Analysis (ST 430)
S you're sttersted in getting more information regarding fins summer's workshop,
ptease contact Bill Hunt or Di NagambtU M
Data Management
¦ Incoming data formats
¦ Text (comma-separated, tab-delimited)
¦ Excel format
¦ SAS dataset
¦ Data manipulation ("Clean up")
¦ Remove missing values and extraneous
information
¦ Import into SAS datasets
¦ Provide data to students via web page
545 of 1131
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NCSU/Spelman College Students
¦ Helen Ferguson, "Rubbertown: What are they
breathing?"
¦ Bryan Stines, "Alternative Methods of Graphically
Representing Ambient Air Quality Data, Wind Speed and
Wind Direction to Identify the Locations of Point
Sources of Emissions"
¦ Che Smith, "Investigating Effects of Airport Emissions in
Metropolitan Atlanta Before and After 9/11/01"
¦ Cathy Pitts, "Trends in the Toxic Release Inventory"
¦ John White, "Is the Fine Particulate Matter
"Nonattainment" Problem in Hickory and Lexington, NC
Local or Regional?"
Earlier Projects
¦ Karen Donaghy & Courtney Sorrel I
¦ Environment Canada, Hull, Quebec
¦ Michael Crotty
. USEPA, OAQPS, RTP, NC
¦ Jeffrey Thomas, Ho-Ling Cheng & D. J. Brooker
¦ USEPA, OEI, Washington, DC
¦ Alan Shoulders,
. NCDENR, Raleigh, NC
¦ Jason Grissom
¦ US State Dept., Washington, DC
546 of 1131
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Karen Donaghy and Courtney Sorrell
¦ "Designing Models to Predict
Tomorrow's Air Pollution." 1st Ann. NC
State Undergraduate Summer
Research Symposium, August 9,
2QQ2. AWARD
¦ Both won the Undergraduate
Research Award for $2000 each to
pursue their research on Predicting
Tomorrow's Air Pollution, November 27,
2002 ,
¦ "Designing Models to Predict
Tomorrow's Air Pollution." Air & Waste
Management Association's Annual
South Atlantic States Section
Meeting, December 4, 2002. Won
3rd prize.
¦ "Improving the Forecast for Tomorrow's
Air Pollution." NCSU Undergraduate
Research Symposium, McKimmon
Center, Raleigh, NC, April 10, 2003.
Both students won the $200 cash
prize for poster.
PM, Ozone, and CO
Karen Donaghy & Courtney Sorrell
Summer 1-hour maxhour barchart
..¦III
ll.-illllllhllilll
Summer Diurnal Pattern of Ozone
boxplot fa 1 hour max ozone
A
liia. „sllllll!:=SB=B
Diurnal Pattern of winter daily one
hour maximum PM fine
Winter 1—hour maxhour barchart
1
¦i—iliiii—ii
mull
Diurnai Pattern or mean winter
hourly CO concentrations
547 of 1131
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Spatial and Temporal Analysis of Ammonium in
the Eastern United States. 1990-99
Michael Crotty
Can Toxic Release Trends in the
Petroleum Industry be Explained? Jeffrey
A. Thomas, Darious J. Brooker & Ho-Ling
Cheng
$2000 Each Undergraduate Research
Award
548 of 1131
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Alan Shoulders, Hampton University
Summer 2002 - Data we have so far...
(Morning Averages)
Atlanta
and
Charlotte
Benzene
Toluene
M&P
Xylene
O-Xylene
Acetylene
South
DeKalb
3.09315
11.8396
6.08995
2.36634
2.29447
Conyers
1.31618
4.2379
2.01683
0.77217
0.90676
Tucker
3.28853
11.8426
7.00279
2.46414
1.93565
Yorkville
0.46961
1.5019
0.58188
0.14572
0.35302
Plaza
6.36945
17.1967
10.0580
3.78818
10.0343
Enochville
2.48195
7.0644
3.1062
1.27516
4.9708
Jason Grissom, Comparison of Particulate Matter Levels
in Worldwide Megacities, report prepared for, US State Dept.,
August 17, 2000. (USA Today Award)
Table 3. CJcnpariscn of TSP, estimated E^.5 annual mean statistics in
worldwide cities.
City
TSP Mean Cone.
Est. PM2.5 Mean
Ratio of EM2.5
Mean to annual
NAAQS
Barcelona
117
36.9
2.46
Bogota
120
37.8
2.52
Rio de Janeiro
139
43.8
2.92
Quito
175
55.2
3.68
Athens
178
56.1
3.74
Sofia
195
61.4
4.09
Manila
200
63.1
4.20
Bangkok
223
70.3
4.69
Bombay
240
75.7
5.04
Shanghai
246
77.5
5.17
Jakarta
271
85.4
5.69
Msxico City
279
87.9
5.86
Chengdu
366
115.4
7.69
Shenyang
374
117.9
7.86
Calcutta
375
118.2
7.88
Beij ing
377
118.9
7.93
New Delhi
415
130.8
8.72
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Other Student Accomplishments
¦ Nine students graduated with a master's degree in
statistics
¦ Four are continuing on for a Ph.D.
¦ Seventeen students have gone onto graduate school
programs in statistics
¦ Seven are employed at the Research Triangle
Institute as environmental statisticians
¦ Louise Camalier is employed as an Environmental
Statistician at EPA, RTP, NC.
¦ Ten students have worked part-time at the U. S.
Environmental Protection Agency as statisticians
Conclusion
¦ Win-win-win situation for everyone.
¦ Develop statistical partners in academia, government,
and industry
¦ Collaboration between majority and minority institutions
¦ Contribute to diversity in environmental decision making.
¦ Make more informed environmental policy decisions
¦ Students win
¦ gain experience in doing research/consulting
¦ writing reports
¦ giving briefings
¦ presenting papers
¦ go on to graduate programs in statistics
¦ go to work as environmental statisticians
¦ students are placed in rewarding careers
550 of 1131
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Conclusion Cont'd
¦ University wins
¦ more students are pursuing graduate study
¦ the faculty develops new contacts with environmental
agencies
¦ Clients win
¦ because their data are analyzed
¦ they can make more informed environmental policy
decisions
¦ they can hire the students for permanent or part time
work
. SUMMARY
¦ students have given over 100 professional presentations
and have written almost as many papers and reports.
¦ Students have won $24,975 in awards.
The Future
¦ Find other interested partners.
¦ Similar Programs can be implemented in each of the
USEPA's Regional Offices
¦ Boston
¦ New York
¦ Philadelphia
¦ Atlanta - Spelman College
¦ Chicago
¦ Kansas City
¦ Dallas
¦ Denver
¦ Seattle
¦ San Francisco
¦ Similar Programs can be implemented with each State
Agency, etc.
¦ Are you interested?
¦ See Bill Hunt, Dr. Nagambal Shah, Dr. Kimberly Weems and/or
Dr. Monica Stephens.
551 of 1131
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www.stat.ncsu.edu/USTEPS
NC STATE UNIVERSITY
Spelman
A Winning Approach for Training
Environmental Statisticians: j, „
^A Summer Workshop ftlj
Provide a worVbook which Includes:
• 2 Approaches to Gaining
• Client Srleflngs to students
• Samples of student posters
and presentations
information on training students
A chance to present student p<
NCSU/Soelman Experience
• 34 Environmental Clients at 14
agencies and departments,
Including: USEPA, NCDENR, Georgia
ONH. Environment Canada, Texas
Commission on Environmental
Quality, etc.
• 89 Potters and Briefings presented
at 30 technical and professional
meetings and research symposia
Why Should You Attend?
This workshop wl
-------
Using the EPA Toxic Release Inventory to
Compare Styrene Releases by Industrial
Facilities located in North Carolina Counties
Ken Hayden, Paul Tillman & Cathy Pitts
Undergraduate Program in Statistics
North Carolina State University
Faculty Mentor: William F. Hunt, Jr.
Clients:
Dr. Barry Nussbaum & Ms. Margaret Conomos
USEPA Office of Environmental Information
Washington, DC
April 13, 2005
Toxic Release Inventory (TRI)
The EPA TRI Database
» Currently spans 1988 through 2002
> Facilities report annual routine releases to EPA
29 industry categories
Over 650 chemicals
> Releases are not directly measured
TRI Explorer
> Tool for extracting information from database
> http://www.epa.gov/triexplorer
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Information from the TRI Could...
Enable EPA to
> Identify releases to be examined
> Set realistic goals
> Evaluate progress
Facilitate communication
> Government
> Industry
> Public
Purpose
Assist EPA in its effort to provide the
public with information on routine toxic
chemical releases by industry in an easy
to understand format.
¦ We hope to...
> Stimulate ideas
> Point out areas of confusion
554 of 1131
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Objectives
Compare styrene release trends
> North Carolina counties
Counties with high releases
Wake County releases
> Wake County facilities
2. Visit a Wake County facility that releases
styrene.
> How are releases quantified?
Why We Chose Styrene
Changes in Top Ten Chemical in the
Year 2002 from Their 1988 Values
Searching by 1988 "Core Year Chemicals"
oo
00
o>
E
2
01
0
c
01
60
40
20
0
-20
-40
-60
-80
-100
38 % increase
C 73
Z. CD
f!
Chemical
555 of 1131
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Top Three USA industry Categories that
Release Styrene into the Atmosphere
Plastics
Transportation
Equipment
Chemicals
1988 1990 1992
1994
1996
Year
1998 2000 2002 2004
Scope
Total air releases of
styrene
> Stack
> Fugitive
www.epa.gov/.../programs/
caa/caaenfstatreq.html
North Carolina counties
" ¦ ¦" *|
> There are 100 counties
> Approximately 1/3 of the
counties had facilities
that reported styrene
releases.
* ^
556 of 1131
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Methods
¦ Downloaded data for North Carolina counties
from the TRI database
¦ Used SigmaPlot to make a time series of Box
Plots
¦ Used EXCEL to plot county trends
Results
Comparison of Styrene Releases in North Carolina Counties
lissiiisitliiSi
Styrene Releases by Facilities Located in
Wake County, North Carolina
Majestic Marble and Glass Co
Land-O-Sheen, Inc
Land-O-Sh
:LL
BeautimarMFG
1988 1991 1994 1997 2000 2003
Year
557 of 1131
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Comparison of Styrene Releases in North Carolina Counties
600
(/)
¦O
h()()
c
03
(/)
-J
O
4UU
-C
**->
c
300
200
CO
4—
o
(/>
100
¦O
c
3
o
0
Q_
Maximum
Observation
• •
t •
es£Q
—i—i—i—
1988 1990
1992
1994 1996
Year
1998
2000
2002
Magnification of the Box Plot Area
Comparison of Styrene Releases in North Carolina Counties
Comparison of Styrene Releases in North Carolina Counties
1988 1990 1992 1994 1996 1998 2000 2002
Year
558 of 1131
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How do Counties that Have High Releases
of Styrene Compare with Other North
Carolina Counties?
Comparison of Styrene Releases in North Carolina Counties
North Carolina Counties that Have Released More
than 300,000 Pounds of Styrene in One Year
•
• •
t •
•
w 500
TJ
C
« 400
3
O
-C
Wilson
• • •
Pound (in j
o g g jj
\ ' Pitt
88 1990 1992 1994 1996 1998 2000 2002
How Do Wake County Releases Compare
to the Other North Carolina Counties?
Comparison of Styrene Releases in North Carolina Counties
Styrene Release in Wake County, NC
1988 1990 1992 1994 1996 1998 2000 2002
Year
|70
i60
o50
i40
„30
-=2&-
-1^6-
0
1988 1990 1992 1994 1996 1998 2000 2002 2004
Year
559 Of 1131
7
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Wake County Facility Releases
20
8 15
Styrene Releases by Facilities Located in
Wake County, North Carolina
Majestic Marble and Glass Co
10
5 -
Land-O-Sheen, Inc
Beautimar MFG
1988
1991
1994
1997
2000
2003
Year
2. Visit to a Wake County Facility that
Releases Styrene.
¦ Majestic Marble and
Glass Co.
> Marquee Division
¦ Currently located in
Wake County, NC
¦ Manufactures cultured
marble bathroom
fixtures
560 of 1131
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Cultured Marble
¦ Reinforced plastic composite
> Ground up fillers such as marble dust
> Mixed with a liquid polyester resin
¦ Resin contains styrene
¦ Styrene is released
> Gel coat is sprayed onto molds
> Resin mixture is poured into molds and cured
Molds for Sinks (left) and Finished
Products (right)
561 of 1131
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Majestic Marble's Styrene Emissions
¦ Stack
> Spraying molds with gel coat
> Responsible for most of the styrene emissions
¦ Fugitive
> Polyester resin pouring
> Curing process
Stack Emissions
Duct to vent fumes from
spray ge! into the
atmosphere.
Spray booth
Estimated pounds of stack
emissions are based on
volume of gel coat sprayed.
Regulation;
Volume of gel coat sprayed
cannot exceed 11.4 gallons
per hour.
562 of 1131
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"Tick-on-Counter " Estimate of Volume
of Gel Coat Used
¦ The pump has a
counter
¦ 1 tick = 0.05 gallons of
gel coat
Gel Coat
¦ The counter and pump
are iocated behind the
wall
¦ Drums containing gel -
coat.
563 of 1131
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Fugitive Emissions
¦ Auto pouring machine
for the polyester resin
¦ Estimations of fugitive
styrene emissions are
based on volume of
resin mixture poured.
¦ Regulation:
Volume resin mixture
poured cannot exceed
74.7 gallons per hour.
Estimation of Pounds of Styrene Released
¦ Industry wide studies have been conducted
to develop emission factors for estimating
pounds of styrene released per gallon of gel
coat sprayed and per gallon of resin poured.
564 of 1131
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Majestic Marble Will Be Relocating this
Summer to Franklin County, NC
Styrene Releases by Facilities Located in
Wake County, North Carolina
Majestic Marble
0 1 4 1 1 1
1988 1991 1994 1997 2000 2003
Year
Conclusions
Most of the median values for annual styrene
releases for 1988 through 2002 were between
10,000 to 20,000 pounds.
Wilson and Pitt counties have released more that
300,000 pounds of styrene in a single year.
Wake County's annual styrene releases increased
from below 10,000 to almost 20,000 pounds.
> Majestic Marble's releases were the reason for this
increase.
A site visit to Majestic Marble gave us a better
understanding of how styrene emissions are
quantified.
565 of 1131
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Limitations
The number of North Carolina counties
reporting styrene releases varied from year to
year.
Some of the TRI Explorer reports had 0
emissions entries for a county in some years
but not in other years.
> We considered these 0 values to be artifacts;
Therefore, we did not include them in our
analysis.
Significance
A time series of Box Plots and virtual tours of
facilities could assist EPA in communicating
information to the public on routine releases
of toxic chemicals by industry in an easy to
understand format.
566 of 1131
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Recommendations
Use a Box Plot time series to compare county
trends.
> Easy to understand and widely used
> Uses the median as the measure of central
tendency
Median is usually a better indicator of central tendency
than mean when data is skewed
> Complements TRI Explorer's dynamic map
Addition of a virtual tour of representative
facilities of different industries would be
educational.
Future Research
Compare the styrene release trends of
Wilson and Pitt counties to the highest
releases in the USA.
> Compare the styrene release trends of facilities in
these counties
Correlate TRI styrene trends with
> Industry growth / decline
> Implementation of MACT rules (compliance date)
Boat Manufacturing (08/22/2004)
Reinforced Plastic Composites Manufacturing
(04/21/2006)
567 of 1131
15
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Acknowledgements
We want to thank our North Carolina State
University faculty mentor, William F. Hunt, Jr.
Our EPA clients, Dr. Barry Nussbaum & Ms.
Margaret Conomos.
Acknowledgements
Tour of Majestic Marble
> Mike Spence, Marquee Division Manager of
Majestic Marble and Glass
Setting up the tour
> Eric Peterson, Health and Safety Director of
Majestic Marble and Glass
> Jeff Twisdale, Environmental Engineer II, NC
DENR-DAQ
568 of 1131
16
-------
Sources
Spence, Mike, Marquee Division Manager of
Majestic Marble and Glass. Personal
Interview. 18 March 2005.
"Part II Environmental Protection Agency 40
CFR Part 63 National Emission Standards for
Hazardous Air Pollutants: Reinforced Plastic
Composites Production" Proposed Rule
Thursday, August 2, 2001, Federal Register.
Vol.66, No. 149, p 40326
569 of 1131
17
-------
Is the Fine Particulate Matter Air
Pollution "Nonattainment" Problem in
Hickory and Lexington, NC Regional
or Local?
BBBmBBT
Faculty
A
-\d visor
William F, Hunt
Jn
Clients
~ North Carolina Department of
Environment and Natural Resources
-Division of Air Quality
MBBBaHB
L
/ George BricJ
~ Dn Wayne Corn
Ml SeJJo
570 of 1131
-------
Background
~ We were given four sets of data
- Federal Reference Method data (24 Hr)
- Continuous data (TEOM) (1/2 Hr)
- Meteorological data
- Speciated data
~ The FRM data was the largest with over five years of data.
were many more observations because of the frequency of
~ We were able to match most of the meteorological data to
B|Q|
1 Most of our data was raw data a nd has not been examined
Purpose
~ Determine whether the non-
attainment in Hickory and Lexington
for PM fine is regional or local.
HcnmDar^h^m±jnuoM
BWBBHWBBBMbH
/Compare the different sites inside
each town
/
rind possible source location;
571 of 1131
-------
Site Locations
~ Hickory
-Lexington-
2-sites
3-sites
321 Ellendale ALEXANDER
Lenoir T .
MapPoint'
t NW, Hickory,
lMC 28601
C A), TAW 0 A
^Maiden
CLEVELAND
Belwoodp, jJStfftaojJ- I N CIJo/C N
iolnton
"untlolr
•&2004 Microsoft Corf ©2003 NavTech.and/orGDT.l
572 of 1131
-------
Hickory water tower: Correlations usigg
tfEBMJ2ata
Lexington Water Tower; Correlations using
rtffRM Data
573 of 1131
-------
Normalizing the data to see possible
soiree locations
~ Doubled observations of the hourly
Meteorological data to match our thirty
minute PM data
~ Multiplied Wind Speed by Concentration
because of inverse relationship the data
should smooth out
~ High concentrations being am piffled by
eed should point to possible souri
lOUT I
Hickory; Wind Speed * Continuous
Concentration vs. Wind Direction
-------
Lexington Airport: Wind Speed *
Continuous Concentration vs. Wind
Direction
- •£
? £
i i i i i ¦ II i i > i i i i i J i i i i » ; i i—i i ! ! !—i—I—: I I : ¦ I : I—¦ i L ¦ t j I I . I II I I 1 I 8 1—I i I—: I I V t : i ; T i—r*
80 100 120 140 160 ISO 200 220 240 260 280 300 320 340 36<
wind dtrectIon
Hickory Day of the Week ERM
.Sunday Monday
Tuesday
Wednesday Thursday
weekday
Friday Saturday
575 of 1131
-------
Lexington Water Tower Bay of
WeekfiRM
so -
40:
30:
20-
10 -
0
i ? ?
i § *
f 1
jf
•
+ * 1 3
-+ -t
-t
I I I
Sunday Monday Tuesday
1 1 » I
Wednesday Thursday Friday Saturday
weekday
|jexiaatortAirD0itlla!ly Diurnal n I 39
J Sunday
Monday j
it
v-v'—
' .......
_ • ...
»««,« o'„«
0?0
i„. T^TT ", .,
,00
1 Tuesday
Wednesday ||
«—«V-
" ......... -
|-™ v*"'" "¦¦¦
| Thursday
Friday J
«
,D»
o°,i
I 00
1 Saturday
!-»¦ ...i.« »«¦« —» „,J
576 of 1131
-------
Lexington Water Tower Daily Diurnal
n=22
| Sunday
Monday ||
•
-
_
D
„„ ZT rrr—rr^ZTl.... ....
,0.
o?
^rrrir^v......
.00
Tuesday
Wednesday |
" - "
"«¦ ""•» »»»- "•«'«
.... ....... ....... ....... ........ ,.».... ........ ........ ........
j Thursday
Friday
•
«
Saturday
""
9».D» t>«.U B.N.U J.20.DP I1.IH44_M.NIU 1lc44.D» 1 f. f 144 11, 13. CD »>¦<
"""
Hickory : Daily Diurnal n = 79
577 of 1131
-------
2004 FRM Correlations
R
P-value
Obs.
Lexington
Airport
Lexington
Water
Tower
Lexington
Fairground
s
Hickory
Water
Tower
Hickory
Fire
station
Lexington
1.0000
0.7246
0.9715
0.7813
0.9187
Airport
<.0001
<.0001
<.0001
<.0001
45
45
43
40
45
Lexington
0.7246
1.0000
0.7138
0.6392
0.7410
Water
<.0001
<.0001
<.0001
<.0001
Tower
45
48
46
42
48
Lexington
0.9715
0.7138
1.0000
0.7658
0.8891
Fairground
<.0001
<.0001
<.0001
<.0001
s
43
46
46
40
46
Hickory
0.7813
0.6392
0.7658
1.0000
0.8139
Water
<.0001
<.0001
<.0001
<.0001
Tower
40
42
40
42
42
Hickory
0.9187
0.7410
0.8891
0.8139
1.0000
Fire
<.0001
<.0001
<.0001
<.0001
station
45
48
46
42
48
2004 TEOM Correlations
R
P-value
Number of
Matching
Observations
Lexington
Water
Tower
Lexington
Airport
Hickory
Lexington
Water
1.00000
0.89179
<.0001
6870
0.66267
<.0001
6392
Lexington
Airport
0.89179
HHH
6870
16718
<.0001
15015
Hickory
<,0001
0392
<.0
15
1
27727
578 of 1131
-------
Conclusions
Diurnal Patterns were examined by Day of the Week.
The 95th and 5th percentile were displayed with an
average. The Lexington airport and water tower sites
differ in their daily diurnal patterns. This shows
variation within the city, especially on the weekends.
There is a clear difference in the diurnal pattern for
Iickory and the airport and fairgrounds site in
exington. Thev are even stronaer than the
with it:
id that all si
"ongi / corrslatscL
Particulars lvlattsr con
that mads Davidson county a
was locatsd at ths wat sr towsr sits.
ey are even stronger than the
mBBKEnBaSKSBSenBBKBBKBInBBB
579 of 1131
-------
Investigating the Effects of Airplane Emissions on
Ambient Air Quality in Metropolitan Atlanta:
The Impact of September 11, 2001
and the 1996 Olympic Games
Che Smith, Spelman College
EPA Environmental Quality Management Conference
San Diego, CA, April 13, 2005
Overview
• Air Quality & Ozone
• Ozone Precursors
• Significance of this Study
• Initial Exploratory Analysis
• Research Objectives
• Methodology
• Results
• Conclusions
• Recommendations
580 of 1131
-------
Air Quality & Ozone
• The Ozone Problem
- Atmospheric (good) vs. Ground-level (bad)
• EPA Ozone Standards
- 0.12 ppm
• Nonattainment Areas
- Atlanta's severe status
• How can ozone be monitored?
- Regulation of ozone precursors
Ozone Precursors
Volatile Organic
Compounds (VOCs)
+
Nitrogen
Oxides (NOx)
Ozone (03)
Regulation of ozone precursors can help to assuage
"the ozone problem."
Our study focuses on several volatile organic
compounds found in kerosene, a major component
of airplane/jet fuels.
The BPTEX Compound Group
- Benzene, Propylene, Toluene, Ethylbenzene, and o-Xylene
581 of 1131
-------
Significance of this Study
• How can one quantify the impact of airplane
emissions on air quality?
• Transportation changes in Atlanta as a result of
- September 11, 2001 terrorist attacks
- 1996 Summer Olympic Games
• Analysis of Atlanta ozone precursor data can
provide a basis for other cities in similar situations.
Initial Exploration
• September 2001 Data
- Diurnal pattern
- Air traffic patterns
• Day of the week trends
Hourly Average of Benzene,
August 2001
-
IT
¦ n n
-
"
..
582 of 1131
-------
Research Objectives
• Do these time periods reveal significant
changes in air quality?
- If so, how much of an impact is observ ed?
• What is the primary emission source of
benzene?
- H artsfield-Jackson International Airport?
• Are there similar trends for the other BPTEX
compounds?
• Public health implications
- Asthma
Methodology
PAMS South Dekalb Site, Decatur, GA
• Collection of data
•PAMS Site Choice
•Comparing the two time periods
583 of 1131
-------
September 11, 2001
Benzene Emission
Before, During, and After the week of Sept. 11,2001
South Dekalb Site
Sept. 1-10 Sept. 11 Sept. 12 Sept. 13 Sept. 14 Sept. 15-20
Date
• How much of an impact did the 9/11 flight delays have on benzene
emission in Atlanta (South Dekalb Site)?
September 11, 2001
Benzene
Week of September 11, 2001
South Dekalb
11-Sep
12-Sep
13- Sep
14-Sep
^ v jy
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
584 of 1131
-------
September 11, 2001
From which direction is benzene being emitted?
Primary Source
Source Direction of Benzene Emission
Just After Sept. 11, 2001
Secondary Source
~
¦ ~
i; 1 1 . ¦—
" " ¦
. »
I 13-Sep
14-Sep
50 100 150
200 250
Direction (degrees)
300 350 400
Does this implicate Atlanta's airport as a primary source of emission?
1996 Summer Olympic Games
585 of 1131
-------
1996 Olympic Games
Diurnal Pattern During the 1996 Olympic Games
~
~ 19-Jul
¦ 20-Jul
21-Jul
X 22-Jul
* 23-Jul
• 24-Jul
n
a.
a.
~
~ ~ n
- 26-Jul
0)
c
0)
~ ¦
~
+
28-Jul
~ + ¦ ¦
X ~ . te
- _ - " X
30-Jul
x 31-Jul
€ ° i + ~ xM
+ • . + " : • ~
• i- * + a 0
x 1-Aug
2-Aug
.. * + *
I- ± * * + ' +"ri,+ +
+ 3-Aug
- 4-Aug
-" i ? 5 ¦ ¦ ¦~ ¥ £ § s i K e * M * ~T»
5 10 15 20 25 30
Hour
1996 Olympic Games
Hourly Wind Direction
586 of 1131
-------
1996 Olympic Games
Benzene Emission Source Direction
During the 1996 Olympic Games
(Late July - Early August 1996)
2.5
~
~
~
2
¦0
01
~~
w
TJ
C
P 15
~~ ~ ~ ~
W ~
c
01
c
01
~ ~~ ~
a 1
~
~
~
0.5
~
~
50 100 150 200 250 300
Wind Direction
1996 Olympic Games
Did the Centennial Park Bombing (July 27) affect
transportation and event attendance?
Effects of Centennial Olympic Park Bombing
587 of 1131
-------
Public Health Implications
Friedman, et.al. Study
Impact of transportation changes during 1996 Olympics on
ozone levels and childhood asthma prevalence in
Atlanta.
Baseline period = four weeks before and after the games
During the Olympic Games:
Asthma acute care (daily) events decreased ~42%
Max. daily ozone levels decreased ~28%
Morning traffic counts dropped ~23%
Meteorological conditions did not differ significantly
Conclusion: results support efforts to reduce air pollution
and improve public health by reducing motor vehicle
traffic
Conclusions
• It is clear that the transportation changes that took place
during both time periods impacted the emission of ozone
precursors (benzene).
- Similar results obtained for toluene
• Is there a better method for quantifying this impact?
• The assumption that the airport is a primary source of
benzene emission is supported by the 1996 data, but not
as strongly with the Sept. 2001 data.
588 of 1131
-------
Current Progress
• Obtain results for the remaining BPTEX
compounds
- P, E, and X
• Additional SAS analysis
Recommendations
• Determine other possible sources of emission
• Identify other VOCs found in airplane fuels
• Examine ozone directly
• Appropriate statistical methodology to account for
missing data
• Fuse September 2001 data with asthma statistics
• Examine other public health implications
• Investigate additional non-attainment areas
• Create more opportunities for undergraduates to
work with real data
589 of 1131
-------
Acknowledgements
• Dr. Nagambal Shah, Dr. Monica Stephens,
Spelman College Mathematics Department
• Van Shrieves, US EPA, Region 4
• Susan Zimmer-Dauphinee, GA DNR
• Bill Hunt & Dr. Kimberly Weems, North Carolina State
University (NCSU) Department of Statistics
• Spelman College MIE Research Internship
- NSF Grant Due-0229344
References
Camalier, L., Stines, B., Yoshimoto, B, & Hunt, W. (2003). "A Statistical Methodology for
Further Solving the Houston Emission Inventory / Air Quality Discrepancy." 3-8.
Cardelino, C., & Chameides, W.L. (2000). "The application of data from photochemical
assessment monitoring stations to the observation-based model." Atmos. Environ., 34,
2325-2332.
Friedman, M., et.al. (2001) "Impact of Changes in Transportation and Commuting Behaviors
during the 1996 Summer Olympic Games in Atlanta on Air Quality and Childhood
Asthma." Journal of the American Medical Association
(2004) Accessed March 2005
Goldan, P.D., et.al. (2001). Airborne measurements of isoprene, CO and anthropogenic
hydrocarbons and their implications. J. Geophys. Res.
PAMS: Enhanced Ozone and Precursor Monitoring (1995). NationalAir Quality and
Emissions Trends Report, 39-55.
Paulson, S. (1999). Total Non-Methane Organic Carbon Development and Validation of a New
Instrument and Measurements of Total Non-Methane Organic Carbon and C2-C10
Hydrocarbons in the South Coast Air Basin. ARB Contract No. 95-335. 4-6.
590 of 1131
-------
The Air Up There: A look at
Rubbertown and Its Air Toxins
By: Helen Ferguson, Spelman College
Dr. Nagambal Shah & Dr. Monica Stephens,
Spelman College
Environmental Protection Agency
National Conference on Managing
Environmental Quality Systems
San Diego, CA
April 13, 2005
Outline
~ Background Information
~ Problem Statement
~ Initial Thoughts and Findings
~ Hypothesis
~ Methodology
~ Results
~ Conclusion
<#> Acknowledgements
591 of 1131
-------
Background Information
# Louisville, Kentucky houses
"Rubbertown," an area that is heavily
industrialized.
~It is an area that encompasses a two
mile radius.
Many of the industries are rubber and
oil companies.
Wonitcring: Locations
592 of 1131
-------
The Data
#The ambient air was collected in
canisters
^Air was collected every 12 days, to
create random sampling, for 24 hours
^Samples were collected from April 2000
to April 2001.
Problem Statement
^Compare the air quality in Rubbertown,
the community nearest certain
industrial areas, to communities farther
away from the industries. Identify
pollutants of interest and compare
concentration levels in these
communities.
593 of 1131
-------
Hypotheses
#The sites furthest away from
Rubbertown would have lower
concentrations of the chemicals of
interest.
#The site closest to industry would have
more chemicals over the exposure
limits.
Initial Findings
# However, initial findings show that selected
sites did not vary significantly in
concentration levels.
#In fact, exposure limits were not a primary
concern.
#Concerns were also raised about the quality
assurance of sites 2 and 3, which were
designated as Quality Assurance sites.
594 of 1131
-------
Quality Assurance
#The quality assurance of sites 2 and 3
became an issue as the data were
evaluated.
¦# TTests were conducted to evaluate the
differences between sites 2 and 3.
Rest Results
Compound
N
t
Significance
Acrylonitrile
18
-.535
.599
Arsenic
14
.671
.242
Bromoform
21
2.476
.022
Cadmium
12
-1.413
.185
Chromium
14
-.659
.521
1,4 Dichlorobenzene
20
1.690
.107
Bonferroni Correction for level of significance is p = .008.
595 of 1131
-------
Cadmium Graph of Quality Assurance
Date vs. Concentration
.002
. , '<5^ ^ ^ ^ ^ o>
/"S //"S /*S /*N /"S //"S /*N /*N /*N
^ ^ ^ ^
Date
1, 4 Dichlorobenzene Bromoform
It is also called pararnoth,
because it is mainly found in
mothballs
It is used as a deodorizer to
block odors in bathrooms,
animal holding cells, and
garbage cans
At room temperature it is a
white solid but changes into a
vapor
Humans are exposed through
breathing vapors, which enter
the bloodstream and cause
difficulty breathing, and an
upset stomach
A colorless, heavy, nonburnable,
liquid with a sweet odor
Humans are mostly exposed
through drinking water that has
been treated with chlorine
which produces bromoform as a
byproduct
Breathing or drinking large
amounts of this chemical can
cause a slowing down of normal
brain activity
596 of 1131
-------
Acrylonitrile
~ At room temperature it is a <$>
clear colorless, or slightly yellow
liquid. It is very volatile,
producing toxic flammable and
toxic air concentrations
~ It is absorbed through the lungs
and fatigue develops rapidly
~
Chromium
A naturally occurring element
found in rocks, animals, plants,
and volcanic dust and gases. In
air chromium compounds are
mostly found as fine dust
particles that settle over land
and water.
Breathing high levels can cause
nose bleeds, ulcers, and holes
in nasal septum.
Cadmium
Arsenic
An element that occurs
naturally in the earths crust.
Pure cadmium is a soft,
silver-white metal
Food and cigarette smoke is
the biggest source of
exposure
Air levels are greater in
urban areas with higher
levels of air pollution from
burning fossil fuels
~ Most inorganic and organic
compounds are white or
colorless powders that do
not evaporate
~ Most arsenic goes up the
stacks from coal-fired power
plants and enters the air as
fine dust particles
597 of 1131
-------
Site Selection
#From the 7 sites provided, they were
narrowed down to 3 based on their
specific location
<§>Since our goal was to determine
whether the pollution was a local or
regional problem we chose sites directly
inside the industrial district and two
further away for comparison
Site Map
598 of 1131
-------
Data Analysis
^Correlations were calculated to test the
difference between chemicals using
SPSS.
#Two chemicals differed greatly between
sites: chromium and arsenic.
^Overall the sites did not differ much
between chemicals.
Correlations of Chemicals
Arsenic Site 2 Site 6 Site 7 Bromoform Site 2 Site 6 Site 7
Site 2 1.0 0.400 0.579 Site 2 1.0 0.825 0.977
Site 6 0.400 1.0 0.490 Site 6 0.825 1.0 0.822
Site 7 0.579 0.490 1.0 Site 7 0.977 0.822 1.0
Chromium
Site 2
Site 6
Site7
1,4 Dichlorobenzene
Site 2
Site 6
Site 7
Site 2
1.0
0.071
-0.001
Site 2
1.0
0.920
0.976
Site 6
0.071
1.0
0.489
Site 6
0.920
1.0
0.964
Site 7
-0.001
0.489
1.0
Site 7
0.976
0.964
1.0
599 of 1131
-------
Chromium
Date vs. Concentration
Arsenic
Site 6
Site 7
Bromoform
1,4 Dichlorobenzene
Date vs. Concentration
Site 6
Site 7
, o, o7 o7 o, cl, cl, cl,
600 of 1131
-------
Cadmium Acrylonitrile
Date vs. Concentration
Seasonal Information
^Seasonal graphs were constructed to
see if there were seasonal patterns
between locations and certain
chemicals.
#The comparisons show that there is not
much difference between the locations
for concentration of chemicals and
season.
601 of 1131
-------
Bromoform 1,4 Dichlorobenzene
Season vs. Average Concentration
Cadmium
Chromium
Season vs. Average Concentration
0.0000,
spring
Site 2
Site 6
Site 7
Site 2
Site 6
Site 7
602 of 1131
-------
Conclusions
#Quality Assurance of sites 2 and 3 is lacking
consistency.
# Early graphs suggest that chromium and
arsenic concentration levels might be local
<#> There are seasonal trends that apply to all
sites of interest.
# However, all other chemicals point to a
regional problem not local.
Acknowledgements
~
Environmental Protection Agency Region 4
¦ Danny France
¦ Doug Jager
¦ Van Shrieves
¦ Paul Wagner
<$>
North Carolina State University
¦ Michael Crotty, SAS expert
¦ William Hunt
¦ Dr. Kimberly Weems
<$>
Metro 4/SESARM
¦ John Hornback
~
Spelman College Mathematics Department
¦ Dr. Nagambal Shah
¦ Dr. Monica Stephens
~
National Science Foundation
- Grant #0229344
603 of 1131
-------
Training Environmental Statisticians -
Tomorrow's Problem Solvers
Nagambal Shah and Monica Stephens
Department of Mathematics
Spelman College
EPA 24th Annual National Conference on Managing
Environmental Quality Systems
San Diego, CA
April 13,2005
rm
Spelman College
Protection Agency Natural Resources
Region 4
604 of 1131
-------
Introduction
Spelman College/NC State Collaborative
Project
Involves work with Real World Problems
Multidisciplinary Approach
Academic Year and Summer Components
Our Team
605 of 1131
-------
Components of Spelman
Project
Academic Year
Environmental Stats Practicum (taught in Spring Semester
04, 05, and 06)
- Spring 04 Enrollment (8 students): Math, Psychology,
Economics, Environmental Science; Class contains First Year
through Senior Year students
- Spring 05 Enrollment (5 students): Math, Psychology,
Environmental Science, Environmental Engineering, Continuing
Education
- Course includes presentations from professionals from the EPA,
Ga. DNR, and Industry
- Students have been exposed to SAS, SPSS, and GIS software
Environmental Statistics Practicum Course Outline
January 14
Introduction to the Course
Shah/Stephens
January 21
Overview of Air Quality
Van Shrieves
January 26
Overview cont.
Van Shrieves
January 28
Introduction to Statistics w/ SAS
Shah/Stephens
February 2
Introduction to Stats cont.
Shah/Stephens
February 4
Introduction to Stats cont.
Shah/Stephens
Assignment #1
February 9
SAS Tutorial
Doug Jager
February 11:
Health Effects of Air Pollution and Fine Particles
Ofia Hodoh
February 16:
Environmental Justice
Debra Carter
February 18:
Air Pollution Monitoring (National Perspective)
Van Shrieves
February 23:
Meteorology and Other Aspects of Air Pollutants
Brenda Johnson
February 25:
Air Pollution Monitoring (State Perspective)
Susan Zimmer-Dauphinee
March 1:
Work on Assignment #2
March 3:
March 8:
Spring Break
March 10:
Spring Break
March 15:
NCSU Student Projects and Perspectives
Hunt/Weems
March 17:
How are Environmental Data Used
Hunt/Weems
March 22:
Mapping Environmental Data and Interpretation
Darren Palmer
March 24:
Team Building Skills
Marquette Brown
(Assign Final Projects)
March 29:
Industry Perspective on Air Quality
John Jansen
March 31:
How to Write a T echnical Paper
Stephens
April 5:
High School Visitation
April 7:
Work on Group Project
April 19:
Work on Group Project (Problem Statement Due)
April 21:
Work on Group Project (Discussion of Results Due)
April 26:
Work on Group Project or Field Trip State of Georgia Monitoring Site
May 4:
Final Presentations (8:00 am-10:00 am)
Van Shrieves
606 of 1131
-------
Components of Spelman Program
Summer Program
Spelman Environmental Statistics Summer Institute
(SESSI)
•SESSI 2004, 6 student participants: Majors: Math,
Economics, Political Science, Psychology
•Duration: 6 weeks, from June 1 - July 9
•Students collaborated with professionals from the
EPA, Region 4 and GA. DNR to analyze data
•SESSI 2005: Planning underway
Selected Student Presentations
Project Kentucky: Rubbertown and its Air Toxins, Helen Ferguson, AWMA Southeast
Atlantic States Section, Virginia Beach, VA, Thursday, November 4, 2004
Investigating the Effects of Airplane Emissions on Atlanta's Air Quality Before and After
September 11, 2001: An Analysis of Selected Ozone Precursors, Che Smith, AWMA
Southeast Atlantic States Section, Virginia Beach, VA, Thursday, November 4,2004
An Analysis of Air Toxins at Rubbertown: Project Kentucky, Helen Ferguson, National
Association of Mathematicians (NAM) MathFest, Morehouse College, October 9, 2004
Investigating the Effects of Airplane Emissions on Atlanta's Air Quality Before and After
September 11, 2001: An Analysis of Selected Ozone Precursors, Che Smith, National
Association of Mathematicians (NAM) MathFest, Morehouse College, October 9, 2004
Further Investigating the Effects of Airplane Emissions on Atlanta's Air Quality Before
and After September 11, 2001, Che Smith, Second Briefing for Deputy Regional
Administrator, USEPA, Atlanta, GA, June 17, 2004
607 of 1131
-------
Student Projects
Investigating Air Quality in Atlanta Before and After Sept. 11
Che' Smith
Week of September 11, 2001
Diurnal Pattern of Benzene
South Dekalb
/~\
N/ —/—
•Briefings for EPA Region 4 Deputy Administrator
•Student currently completing honors thesis
Student Projects - SESSI
608 of 1131
-------
Project Kentucky
Chromium
.006 1
.005 '
.004 -
.003 '
Wo/^A
site 2
Site 6
.002 ,
\/
Site 7
-<2- ^ ^ ^ ^#V Tfe ^3
% % % % % % % % %7 %7 %7 \ %, %,
Date
Students analyzed air quality data at 3 main sites in Rubbertown, KY
609 of 1131
-------
Student Awards
Che Smith, Investigating Effects of Airplane Emissions in
Metropolitan Atlanta Before and After 9/11/01, Math
Oral Competition, Spelman College MIE Research
Day, Atlanta, GA„ April 2, 2004, Won First Prize for
oral presentation in Mathematics.
Iesha B rown & Che Smith, Investigating Effects of
Airplane Emissions in Metropolitan Atlanta Before and
After 9/11/07, Math Poster Competition, Spelman
College MIE Research Day, April 2,2004, Won
Second Prize for poster presentation in Mathematics.
Benefits of the Program
A Win-Win-Win Strategy for Academia,
Government, and Industry
•Develop statistical partners in academia, government, and
industry
•Collaboration between majority and minority institutions
•Provide consulting opportunity for undergraduates with Federal,
State, and Local Environmental Agencies
•Focus on the application of students technical skills to a real
problem through a multi-disciplinary approach
•Training in problem solving through team approach
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Benefits of the Program
A Win-Win-Win Strategy for Academia,
Government, and Industry
•Develop students oral and written communication skills
•Opportunities to make technical presentations (local, regional, and
national conferences
•Encourages students to pursue advanced degrees and careers in
environmental science and statistics
•Contribute to diversity in environmental decision making.
•Make more informed environmental policy decisions
•Paid summer internship
USTEPS Workshop
Undergraduate Statistical Training of
Environmental Problem Solvers
• July 6-7, 2005
North Carolina State University
• Contact:
- Dr. Nagambal Shah, n
- William Hunt, Jr.,
• http://www.stat.ncsu.edu/USTEPS
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Undergraduate Statistical Training of Environmental Problem Solvers
HOME LINKS LODGING NCSU-STATS NCSU-HOME
Spelman
College
USTEPS Workshop
July 6-7, 2005
North Carolina State University
Raleigh, NC
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Precursor Gas
Monitoring Overview
Dennis K. Mikel
U.S. EPA, Office of Air Quality
Planning and Standards
$ A \
% PR0^
24th Annual National Conference
San Diego, CA
April 13, 2005
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Outline
¦ Why we need it ?
. What is it?
¦ Products and Deliverables
- "TTT"
. QA
¦ Next Steps
$ A \
% PR0^
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Why Precursor Gas
Monitoring is Needed
National Ambient Air
Monitoring Strategy
(NAAMS)
¦ Roll out Starts 2005
¦ Rethinking Monitoring
Network
¦ Designation of National
Core "NCORE" sites —
NCore Measurement:
Level 2- «• 75 Multi-
polhitant (MP)
Sites,"Cere Species"
Plus Leveraging From
PAWS,
Speciat'cn Program.
A\r Toxics
Leve 1. 3-10 MastC;n
Sites Comprehensive
Measurements,
Advance Methods
Serving Science ard
Technology Transfer
tweeds
WininuT 'Core"' Level 2 Mem.n&T.cnts
Continuous H,S02.CO, PW2.5, PM10, 0S; PM2.5
Fm, Meteorology (T,RH,WS,Wb)
Level 3- Single
Pollutant Sites
{e.§.> 500 sites
each fcr 03 one
PM2.5
Mapping Support
v.o
i
\ ,
*1 PRO-&°
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Why Precursor Gas
Monitoring is Needed
Develop emission control strategies
¦ Air quality model evaluation
¦ Rural monitoring - background transport
¦ Source apportionment
¦ Observation-based models
Support long-term health and epidemiology
studies
£
ISSKj
\ PR0^
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Precursor Gases - What are
they?
Carbon monoxide (CO)
Sulfur dioxide (S02)
Reactive oxides of nitrogen (NOy)
Ammonia (NH3)
Nitric Acid (HN03)
617 of 1131
i A \
isas i
% PR0^
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Precursor Gas Monitoring
What makes a Precursor Gas instrument?
¦ Higher sensitivity
¦ Digital and analog interface
¦ Ethernet or Internet interface (optional)
¦ Dual range - low range -> high range
¦ Increased interferent rejection
¦ Auto referencing/adjustment
618 of 1131
i A \
i as j
% PR0^
-------
Products and Deliverables
Products and deliverables
¦ Short method summaries (1 pagers)
¦ Instrument testing and evaluation - lab & field
¦ Standard Operating Procedures (SOPs)
¦ Technical Assistance Document (TAD)
¦ Technology Transfer and Training
619 of 1131
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J Precursor Gas Team
> Joann Rice: Methods Team Lead
> Tim Hanley: Monitoring Program Lead
> Kevin Cavender: N0y Method
> Dennis Mikel: CO Method
¦ Michael Papp: Quality Assurance
> Solomon Ricks: S02 Method
> Nealson Watkins: Data Management
> Lewis Weinstock: Technology Transfer
> Keith Kronmiller: Contractor Support
¦ Louise Camalier: Statistical Support
620 of 1131
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Technology Transfer
and Training
Technology Transfer Topics
¦ Background - why monitoring is needed
¦ Individual methods and principles of operation
¦ Statistical evaluations (LDL, precision, bias, etc.)
¦ Hardware considerations and technical issues
¦ Quality Assurance
¦ Information Technology
¦ Data management and digital data transfer
considerations
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Quality Assurance
¦ Data Quality Objectives (DQOs)
¦ Planned for completion Fall 2005
¦ Data Quality Indicators (DQIs)
¦ Precision, bias, sensitivity, completeness, comparability
¦ Measurement Quality Objectives (MQOs)
¦ Includes DQIs and method specific Quality Control
¦ Redbook revisions (in progress)
¦ Data validation templates, QC and QA
¦ National Performance Evaluation Program (NPEP)
i A \
i as j
% PR0^
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Next Steps
¦ Products and deliverables -> SLTs
¦ Pilot monitoring network of ~22 sites
¦ Sites to begin implementation during 2005
¦ Technology transfer
¦ Workshop under development for
spring/summer 2005
623 of 1131
i A \
isas i
% PR0^
-------
Precision and Bias of Precursor
Gas Instruments
Using Proposed vs. Current CFR
Precision and Bias Estimators
Louise H. Camalier
USEPA, Office of Air Quality Planning
and Standards
24th Annual National QA Conference
San Diego, CA
April 13, 2005
as
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c
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Precursor Gas Monitoring
Development of the NCore network :
Higher sensitivity in instruments is needed to
monitor precursor gases that lead to particle
and ozone formation
Precursor gases tested:
Carbon Monoxide (CO)
Sulfur Dioxide (S02)
Reactive oxides of nitrogen (NOy)
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Precursor Gas Instrumentation
OAQPS' testing facility
(RTP, NC)
CO and S02:
• 2 samplers
API and Thermo
NOy:
• 1 sampler, 2 channels
Thermo
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Testing Procedures
Precision check:
• Run zero air (pre-zero)
• Collect readings using
a target concentration
• Average concentration
from collected readings
• Run zero air (post-zero)
Precision Check for S02
Trace S02 Analyzers Precision Tests 12/21 - 12/28
A-SD2
T-3D2
1
Jl
—
A—
Precision Response for S02
27.5
27
26.5
e 26
•2 25.5
2 £- 25
E a.
-------
After Data Collection
Yt\ Sampler
X '¦ Target concentration
d : Relative Percent Difference (individual bias)
. Y.-X
d. — — 100
X
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Dealing with Real-World" Data
Target and ambient
concentrations serve
as...
TRUE VALUES
Combinations of bias
and precision within
measurements result
in...
OUR DATA
TRUTH
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Precision
"A measure of mutual
agreement among
individual measurements
of the same property,
usually under prescribed
similar conditions"
precision _ = sa =
n — 1
n
1
r
2X— Ld
i.i «V f-i
n
\
Equation 3 in CFR, precision equation is at the site ievei
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New Precision
Precision is more conservative when evaluated at
the 90% one-sided upper confidence level
new _ precision _ est — pr_ est • —
V x 0.1,(»|i)
2
* Where xo.io,(»-i) is the 10th percentile
of a Chi-Squared Distribution
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Bias
"A systematic or persistent
distortion of a
measurement process
which causes errors in one
direction"
bias* = —-JVj
n 7=1
* Bias estimator is not estab
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New Bias
Absolute Bias Point Estimate
1
n
mabs
= -'ZK
n m
Absolute Bias Upper Bound (NEW BIAS):
new_bias = mabs +?0.95,,„_l)
Vn
Where t 0.95,(o-v 'squant
with n-1 df and sdabs is the standa
value of the relative
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Associating a Sign to the Absolute Bias
A sign (+/-) is associated with the absolute bias
only if the 25th and 75th percentiles of the relative
differences have the sam
Sampler A
Sampler B
BIAS (%)
P75 = +8.1
CO (Sampler A):
OLD BIAS
NEW BIAS
Ze
tro
-0.7
14.6
P25 = -12.9
P75 = -13.9
CO (Sampler B):
P25 = -28.1
OLD BIAS
-22.9
NEW BIAS
-27.7
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Preliminary Results for S02
Sampler A
(f
i)
Prec. (%)
Bias (%)
OLD
NEW
OLD
NEW
0.9
1.3
5.8
+6.3
Sampler B
(f
i)
Prec. (%)
Bias (%)
OLD
NEW
OLD
NEW
1.0
1.4
10.8
+11.3
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Preliminary Results for N0V
Channel A
d
|)
Free. (%)
Bias (%)
OLD
NEW
OLD
NEW
1.5
2.2
9.5
+10.3
Channel B
(f
|)
Prec. (%)
Bias (%)
OLD
NEW
OLD
NEW
1.6
2.3
10.3
+11.1
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Preliminary Results for CO
Sampler A
NO S
Prec. (%)
Bias (%)
OLD
NEW
OLD
NEW
15.2
18.1
-0.7
14.6
Sampler B
(1
v ~
i)
~ /
Prec. (%)
Bias (%)
OLD
NEW
OLD
NEW
16.5
20.1
22.9
-27.7
W
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Advantages of Conservative Methods
Provides a more stringent
to improved data quality
• Minimizes probability of making harmful
decisions due to unknown risk
• Early detection of calibration problems
• Performance-based system allows state/locals
flexibility in QC check frequency
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Behind the Theory
(A Simple Simulation)
Current CFR technique
risks underestimating
measurement
uncertainty 50% of the
time
New, conservative
estimators reduce risk
of underestimating to
5-10% of the
100%
80%
60%
40%
20%
0%
Old Precision
New Precision
~ Safe ~ Underestimate
100%
80%
60%
40%
20%
0%
Old Bias
New Bias
\
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Introduction to the IMPROVE
program's new interactive
web-based data validation
tools
Linsey DeBell
Interagency Monitoring of Protected Visual Environments
(IMPROVE)
What is IMPROVE?
IMPROVE is a cooperative measurement effort governed by a steering committee composed of
representatives from Federal and regional-state organizations. The IMPROVE monitoring
program was established in 1985 to aid the creation of Federal and State implementation
plans for the protection of visibility in Class I areas (156 national parks and wilderness areas)
as stipulated in the 1977 amendments to the Clean Air Act.
The objectives of IMPROVE are:
(1) to establish current visibility and aerosol conditions in mandatory class I areas;
(2) to identify chemical species and emission sources responsible for existing man-made
visibility impairment;
(3) to document long-term trends for assessing progress towards the national visibility goal;
(4) and with the enactment of the Regional Haze Rule, to provided regional haze monitoring
representing all visibility-protected federal class I areas where practical.
IMPROVE has also been a key participant in visibility-related research, including the
advancement of monitoring instrumentation, analysis techniques, visibility modeling, policy
formulation and source attribution field studies.
http://vista.cira.colostate.edu/improve
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Interplay between Data Collection, Quality Control,
Management, and Validation
Updated Data
Collection and
QC
Procedures
Updated Data
Validation
Procedures
Validated
Data
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Current Data Validation Theory and Process
~ Process
> Qualitative visual inspection of the data
> Scatter plots and time series charts
> Metadata review
> Simple pairwise statistics for identifying swapped samples
> Data integrity checks
> Some tests applied routinely others periodically
~ Theory: Beyond confirming that the dataset has no obvious errors
in content or form, the validation process is designed to check that:
> internal consistency between redundant measurements exists
> spatial and temporal comparability are being maintained
> external consistency between the aerosol chemical and physical
measurements and the optical measurements exists
New Data Validation Tools Currently Only Address a Limited Number of
Data Validation Steps
Level 0 (Performed by field and lab
staff)
•Sample Identification
•Operator Observations
•Sampler Flags
•Shipping & Disassembly
•Laboratory Checks (per SOPs)
•Range Checking
•Flow Rate audits
•Exposure Duration checks
•Elapsed time before retrieval
•Holding times
Level 1 (Performed by QA
personnel at CNL)
•Mass balance
•Field Operations Database
Review
•Lab Operations Database
Review
•Flow Rate Analysis
•Flagged Samples Review
•QC samples and analytical
accuracy and precision review
Level 2 (Performed by QA
personnel at CNL)
•Internal Consistency Analysis
•Outlier Analysis
•Data Completeness
•Collocated Bias and Precision
•Data Integrity
Level 3 (Performed by QA
personnel at CIRA)
•Time Series Analysis
•Correlations between sites
•Mass Reconstruction Analysis
•Species Distribution Analysis
•Optical Reconstruction Analysis
•Others
•Modeling
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Measurement-to-Measurement Comparisons
Interna! Consistency
Between Redundant
Measurements
rnk
wfcrf
Consistency Through
Time
Measurement-to-Model Comparisons
Species Distribution as a
Fraction of Reconstructed
Fine Mass
, ,» I A f/|
'• dh
**+++ V Mr V
If
~. /* * * : .
V «*•-' <
•
*
V *v"
^hh/L «,.*>¦
3
*
*
«x
Mass Reconstruction Analysis
>e &ux< m
Is
i,o?s ¦
i;" —
S o» t «
1
° vi „ • *«"—«*
?0«
A-NV« .
I S"V.>VA^S%
fc1"5
tn ?
|oH
<«-l2 Tm (T
8
I
S
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Measurement-to-Model Comparisons
Internal Consistency
Between Redundant
Measurements Taking into
Account Measurement
Uncertainty
Consistency Through
Space
New validation tools built in context of the VIEWS data management
system
O Maior Components of the VIEWS Svstem Utilized in the New Tools
;>Fully relational SQL Server Databases
>Visual Basic ASP .NET web applications
>Third party charting package (Chart FX)
Data Holdinqs Include
VRoutine IMPROVE data
>AQS STN and FRM
VNADP
>CASTNet
>1 MP ROVE special studies
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What is VIEWS?
Some facts:
The Visibility Information Exchange Web System is a database system
and set of online tools originally designed to support the Regional Haze
Rule enacted by the EPA to reduce regional haze in national parks and
wilderness areas.
What are some of its other goals?
> Provide easy online access to a wide variety of air quality data.
> Provide online tools for exploring and analyzing this data.
> Maintain a catalog of relevant air quality-related resources.
> Facilitate the research and understanding of global air quality issues.
Web Address: http://vista.cira.colostate.edu/views
Sponsor: Five EPA Regional Planning Organizations (RPOs)
Guiding Body: VIEWS Steering Committee
Location: Cooperative Institute for Research in the Atmosphere (CIRA),
Colorado State University, Fort Collins, CO
Staff: Scientists, researchers, and IT professionals
Affiliations: Interagency Monitoring of Protected Visual Environments
(IMPROVE)
> Over 600 registered users
> Over 200 organizations represented
> Almost 100 countries represented
> 300+ unique hits a day
> Linked to by over four dozen sites
> Over 40 million records of air data
> Dozens of monitoring networks
> CSU Research Initiative Award
> Uses the new Manifold GIS
> Monitoring site photographs
> Class I Area webcams
> Visibility photographs
> Visibility Grey Literature
> Periodic Newsletter
> Contour Maps
> Trends Analysis
> Air Mass Composition Analysis
O Maior Components
> Website and associated online tools
> Integrated Database and Data Ingest procedures
> Raw data files and support documents
> Code libraries
€> Software Environment
> Database Technology: Microsoft SQL Server, ADO .Net, ODBC
> Website Technology: Microsoft Internet Information Server (IIS), FrontPage Server Extensions
> Development Technologies:
MS SQL Server Tools, MS .Net Framework, MS Visual Studio .Net, MS FrontPage, C#, Visual Basic, ASP .Net, HTML,
DHTML, Javascript, VBScript
& Hardware Environment
> Web server and Database server
> Source Code Control server
> Backup and Build server
> Development machines
> T3 Internet Connection
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IMPROVE „^3E5IESE3&
The new web tools currently allow the user to
produce IMPROVE's data validation charts and
calculated parameters through:
~ Interactive real-time data selection from the VIEWS database
>Site(s)
>Start and end dates
>Pre-designed data validation chart types
~ On-the-fly calculation of diagnostic statistics and/or composite
parameters
~ On-line charting capabilities
^•Download charts asjpeg images
^•Display interactive charts
^•Scalable axes
>Color selection
>Edit chart and axes titles
>Zoom features enable scrollable axes
>Mouse over or datagrid identification of chart point values
>Series display
~ On-line data table display
Advantages of Direct Database Connectivity
~ Unification of the Data Management and Data Validation Systems
in terms of design environment and shared code base increases
efficiency and reduces the risks inherent in data transformation
processes typically necessary for importing data into data analysis
software packages
~ Selection list boxes pull content from metadata tables based on
SQL query and therefore instantly reflect database updates upon
web page refresh
~ Validation charts reflect current database content and can easily
be recreated after database updates
~ The option of direct database edits through the data validation
tools exists. This option would further increase efficiency and
create a system more robust to unintended or forgotten data edits.
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VIEWS Architecture Detail: Data Acquisition & Import
St
The Data Validation
process at CIRA starts
with the Data and
Metadata Import Systems
which test for data integrity
Data
Acquisition
System
Pr^t
AIRDATA SOURCE
Data Acquisition System:
• Accepts submission of data in a variety of
schemas and formats
• Can automatically extract data from known
online sources
• Uses database replication where possible
• Initially imports data and metadata "as-is"
into the source database
Metadata Import System: Data Import System:
Facilitates the entry of new
metadata
Validates new metadata
entries
Detects overlap with existing
metadata
' Extracts data from the source database
• Scrubs data and performs conversions
• Maps source metadata to integrated metadata
• Transforms the data into an integrated schema
• Verifies and validates imported data
¦ Loads data into the back-end OLTP system
VIEWS Architecture Detail (cont'd): Data Management
OLTP:
• Functions as the "back-end" database
• Fully relational and in 3rd normal form
• Used for data import, validation, and
management
• Technologies: Microsoft SQL Server
Data Warehouse Generation System: Data Warehouse:
• Extracts data from the OLTP
¦ De-normalizes and transforms data
• Loads data into the Data Warehouse
• Builds table indexes
¦ Archives "snapshots" of the database
¦ Technologies: VB, stored procedures
¦ Functions as the "front-end" database
• Uses a de-normalized "star schema"
• Used for querying and archiving data
¦ Automatically generated from the OLTP
• Technologies: Microsoft SQL Server
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IMPROVE Aerosol QA Utility
Users select data
based on site(s) or
regions, dates and
pre-designed chart
type
X and Y axes are
scalable
irt without rawing
h€,Jlc.daf».«WI N
The Chart Fx Data
Editor offers
additional display
options
Mouse over displays
chart point X and Y
values
* - J ¦' ! S»»ch
IMPROVE Aerosol QA Utility
ApB.lKhl.^
"1
si
001 |
<• View Chart
f Download chart without viewing
Data can be
displayed in a read
only or editable VB
.Net Data Grid
^^^W/30/2003
\A 02/02/2003
Ac »d-a
Acadia NP ME TOWD03
WOato SCMIVAl
03*2003 01968
06/2003 1 6282
t8/2GQ3 07682
'12/2003 0 8331
'15/2003 06133
'18/2003 07497
<21/2003 07505
¦24/3X13 0 7634
<27/2003 1 8171
<30/2003 4 4204
0 325i
*15/2003 0 7751
CMS/JOGS 1 4043
Northeast
Northeast
Northeast
ACA01 Acad
ACAD1 Acaifta
ACAD1 Acatfrt
ACA01 Acadia NP ME 02/11/2003 2 0614
ACA01 Acadia NP ME 02/14/2003 t 1608
ACAOI Acadia NP ME 02/17/2003 t ®94
ACAOI Acadia NP ME 02/20/2003 4 9059
ACAD1 Acadia NP ME 02/23/2003 0 6195
ACAOI Acadca NP ME 0226/2003 16379
ACAOI Acadia NP ME 03431/2CD3 4 06
ACAOI Acafca NP ME 03434/2003 1 5241
ACA01 Acadia NP ME 03437/2003 1 4379
ACA01 Acadia NP ME 03/10/2003 1 5418
Users select data
based on site(s) or
regions, dates and
pre-designed chart
type
00234
00279
00279
0 0516
0 0751
0.043
S04(MDl
00128
0.0137
00133
0013
00134
aoisi
00132
00132
00138
00133
00183
0 0189
00192
0 0205
0 019
00163
00166
00168
SfVAl
0 16746
052567
0 3003
033617
021856
028287
0 27925
026594
064943
140452
010299
0248
0 45001
070177
044755
032493
1 67412
021673
063123
152296
053065
055067
SIUNC
0 00659
0 02654
0 01523
001717
00112
001439
0 01423
001355
0 03273
007049
0 00544
001268
0 02279
003538
0.02267
001653
0 08401
001111
003184
007659
00X96
0 02799
SIWDI
0.00062
00CO72
000104
000057
0 00C82
0 00103
648 of 1131
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VIEWS Database Query Wizard - Query Construction interface
Select Nelwotks
CjEj Gkj
Seted Sites
I " 1 f3g~| t afc mfannjBow ;
CAjTOM
EPftrRM
EFASPEC
MOHAVE
HESCAUM
PREVENT
REVEAl
SEAVS
SfU
IMPROVE AK- Soneaflof
IMPROVE AV Trawer Creek
IMPROVE AK Tuxedm
IMPROVE At: Sipsi Wilderness
IMPROVE AR: Cane» Creel
IMPROVE AR: Upper Buffalo Wilderness
IMPROVE AZi Chlncahua National MonvmeM
IMPROVE AZ: hinee Camp ft Grand C anion NP
IMPROVE AZ: Hfltide
IMPROVE AZ' Hopt Point #1
IMPROVE At: Hop* Pom! #2 (High Sensitivity)
IMPROVE AZ Ike's Baekfcorie
IMPROVE AZ. Indian Gardens
IMPROVE AZ: Indian Gardens 2 {High Sensitive)
s»l»ilt. RfQ
r mup
r gswwp
r U>M»RPO
r iwne-vu
r VBTAS
& M* Coda
& ObHrvilon OaWTim
P ObiaivatKin Value
r OiietvMton l}M«ta*rty fUHO
P Minimum Oeta«Owi Urn* fMOl)
r 8t»tu» »1»j (Ft»j)
r «e Ham* r Patametat Coda
l~ elajaow P Paramatai 6aio>etion
I- Utttvda
P Lwigitusl*
" By ttm rod Months
« * I «<*«* i t * 11 «*» i
Report Foimat
Row Fwmat
Column Fwroat:
OaieFatma!
Additional Oata
15 Smart Otitf r HTML Text r Texl File
®Wiita ^ Skmnj
*• Fixed \Mdih r Delimited f
|m«3H ^
r By Date Ranges
P Show Metadata F Sho* Headers
Substitutions Missing Values fi® htappfccabJe Values |
Teitl File Warns. f~
January
'fbnjSr>
Martii
Aerd
nr
Network:
Site:
P&awr.ec:
The VIEWS Query
Wizard Tool enables
easy access to
additional data fields
Air Temperature
Alumnus; Fine
Aiasoriiuu ion: fine
eiu» nitrate extinction: fine (Calculated)
tuus Nitrate: Fine (Calculated!
lmtmiun sulfate extinction: Fine |Calculated|
iuua suilate: Fine (Calculated)
Arsenic: Fine (Particle)
Bromine: Fine (Particle)
rsppovr
lltPROTt IK: tenali National Park
Aerosol extinction (Calculated)
January 1, 1968 - tecentoer 31, 19S8
Netaork Code
Site Code
Cfcservation Hate/Tuse
Observation Value
Option: Table Format • Start Grid
anat • Bide
Option: Column Format • Fixed Width
Options fceliffliter • ,
Option: C'lap lav Retadate * true
Option: Column Headers ¦ true
Option: Hissing Values * -999 Jtj
Field
Field
Field
Field
Tabular Data Summary (? Aggregate Statistics <* Method Information
Site Code Year Parametet Average Value Units N Samples N Substitutions Aggregation
ACAD1 2001 Aerosol extinction 40 2 Mm-1 12? 1 Annual
VIEWS Annual Summary - Spatial and Seasonal Patterns
Data Views:
Map View
This view displays contours of the
selected parameter for a selected year
and aggregation. Data aggregation
options include the average of the
annual, quarter, and best or worst 20%
of sampled days. The best and worst
20% days in a year can be chosen by
using chemical extinction
(aerosol_bext) or the selected
parameter as a sort variable. Selecting
a site icon from the map populates the
timeline view with data for that site.
Timeline View
This view shows daily values for
the selected site and
parameter. Sampling days in
the chosen data aggregation
are highlighted. Either chemical
extinction or the selected parameter
can be used as a sort variable for the
best or worst 20% days. Grayed-out
selections are not currently available.
Tabular Data Summary
This view shows method information or
data statistics for the selected
monitoring site. When viewing data
statistics, 'N samples' is the number of
samples in the data aggregation and 'N
substitutions' is the number of values
substituted using guidelines outlined in
the RHR tracking progress document.
• Selected i
a Contour S
12001 2J
Parameter
| aerosol.bext 3
Oolo Aggregation
1st Quartet
ZrtdQuarfet
' 3rd Quartet
r 4th Quartet
ACAD1 200
The VIEWS Annual
Summary analysis
enables easy access to
additional data analysis
for contextualizing data
validation results
Timeline View
649 of 1131
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VIEWS Site Browser Tool - Site Specific Metadata Display
< <¦ • . >j. i:
Acadia NP IstBaustsj&iafi
ACAD I
8. Vnllonal Park Sorvico (NPS)
The VIEWS Site Browser
Tool enables easy access
to site specific metadata
including site location, site
history of sampling
changes or events and
network history of
analytical changes
8/18/1994 Moved M
12/1/2001 Analysis of with dome wwghtt from Na
W2001 Ion MtnplH e*ti acted uimg M water it aH Mt*l
10/11/3000 Ion samples extracted using anion «*jKit at an sit
1/28/1999 Ion samples extracted wing 01 water at all utw
Future Directions: Validation Theory
~ Deeper into IMPROVE data
> Incorporate auto-validation checks
> Integrity checks (currently done through SQL queries)
p Range checks for every parameter (currently not done)
> Flow rate analysis based on continuous flow data (currently not
done)
~ Broader into External Validation
> Incorporate inter-comparison of collocated sites from other networks
(currently not done routinely)
650 of 1131
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Dt i - i j , v' i
ill J.t'l 1 -
Future Directions: Validation Too! Functionality
~Access to additional data
> Incorporate QC data into the data management system
> Develop algorithms for cross-network comparisons
~ Design, develop and incorporate auto-validation checks
> Develop and test algorithms
> Translate existing validation code base to be compatible with the
VIEWS design environment
> Develop and test output display options
VIEWS Simple Comparison Between IMPROVE Aerosol and NADP NTN Data
Programs:
Parameters:
AIRMONl AIRMoN /
AFRD: AQS Fine Mass FRM - Daily ¦¦
AFRHi AQS Fine Mass FRM - Hourly
ASPDi AQS Fine Speciation - Daily
AlODi AQS PM 10 - Daily
AlOHi AQS PM 10 - Hourly
CDC: CASTNet Dry Chemistry
CVC: CASTNet Visibility Chemistry
QAVIM: GAViM
INN: IMPROVE Nephelometer
MOH: MOHAVE Special Study
INA: BRMA1
INA: CABA1
NADP_NTN: MEOO
NADP_NTN: ME02
NADP_NTN: ME04
NADP_NTN: ME08
NADP_NTN: ME09
NADP_NTN: ME95
NADP_NTN: ME96
NADP NTN: ME97
NADPJfTN: ME93
The VIEWS Data Browser
Tool (still in development)
will enable easy access to
additional data networks
and provide a code basis
for broadening the data
validation process
Display As:
©Chart(s)
OSpreadsheet
Group Charts By:
Osite
O Parameter
©Site & Parameter
Averaging:
OlMone
O Daily
O Monthly
©Annual
O All
Chart Type:
©Timeline
OBar Chart
Multiple Site & Parameter
2.40
2.00
0.40
0.00
.
This simple comparison was
performed by "normalizing" the
aerosol units (ug/m3) and the
deposition units (mg/L) to a
common scale and plotting on a
single graph.
H INA: AGAD1: S04f
NADP.NTN: ME98: S04
651 Of 1131
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IMPROVE
People Involved
IMPROVE
Linsey DeBell
Nicole Hyslop
Lowell Ashbaugh
Bret Schichtel
Chuck McDade
Bill Malm
Marc Pitchford
Doug Fox
VIEWS
Shawn McClure
Rodger Ames
Contact Information
Linsey DeBell
debell@cira.colostate.ed u
Colorado State University
1375 Campus Delivery
Fort Collins, CO 80523
652 of 1131
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Dennis Crumpler
US EPA OAQPS EMAD
Before the
24th Annual National Conference on Managing Quality
Systems
April 13, 2005
-------
Overview
• The Evolution of the shipping study
• The Data Quality Objectives
• This Measurement Quality Objectives
• The Data Starts to Speak
• The Lessons Learned
-------
Important?
The current PM2.5 speciation tmnds and
supplemental network annual shipping bill
$1,600,000
EPA Budget-shrinking
National Monitoring Strategy-evolving
Speciation QA program-beerlng up
-------
Wh oa!! 13 Speciati on?
What are We Talking About?
• PM2.5 Chemical composition
• 24 - hr. integrated filter samples-3 media
• Multiple analyses
~Gravimetric
~Chromatograph ic
~X-ray fluorescence and Thermo optical
• Results used for pollutant source
attribution in SIP development
-------
• -250 sites collecting filter samples
~1 in 3 day or 1 in 8 day sampling -50/50
• Cold shipping requirement
~Coolers with ice packs-35 lbs (16 kg)
~Overnight delivery
~Both Ways
~Average $40 per cooler one way
-------
Why ship cold?
-------
How to Attack the Questi
Devise a study where wa can limit
variables to just the procedure by
which the sample filters are shipped
Seems simple enough...
Doesn't it ???
-------
lit
• Three different filter media-Teflon, Nylon, Quark
• Which Sites do we pick
~ Lab vs reality?
~ Dominant Semi-volatiles: nitrates and organics
• Time!! - limited windows for optimum effect
• Money!! - adequate number of events $$$
• Quality!! - Instrument variability; operators'
experience and expertise
-------
The Study
Sites: dominant
pollutants
Atlanta: sulfates, organic carbon
Riverside, CA: nitrates
Tacoma, WA: woodsmoke carbon
Instruments
2 Collocated Metone™ SASS
Channels per filter
media
1-2, 2-1 Teflon alternating days
2-1, 1-2 Nylon alternating days
2- Quartz
Target no. sampling
events
30 24-hr periods
-------
rai're
r-- |C
First approximation - reliance on network data
for collocated instruments
Collocated
Average
(Abs Rel Diff)
Lab
Average
(Abs Rel Diff)
Mass
9.3%
4.6%
Organic C
14.2%
5.5%
Sulfate (IC)
8.2%
3.9%
Courtesy of James Flanagan, et.al., Ref 1
-------
cision
ints
Differences in measured pollutant concentrations
would constitute a discardable and significant
impact by ambient shipping if the values were at
the 95% confidence limit:
~ >10% for mass,
~ >15% for nitrate and ammonium
~ >20% organic carbon, and
~ >7% for sulfate.
Ref 121,131, Ml
-------
The Lynch-pin of the Study:
Measurement Quality Objectives
• rtawrates 6.7 l/min
• Paired Channel Concentrations within
network collocated! values
-------
m
• Careful Instrument installation and
calibration
• Operator Training
• Weekly Flowcbecks and recalibration
• Trip and Fieid Blanks
• Skipped rainy days
-------
-------
Comparison of Channels 2 & 3 Collecting
~
Compares
Channels 2 &
3oneachinstr
umentwhen Ic
jaded with Tea
on Rlters
Cold-Shipped
Slope = 0.926
Intercept = 0.417
r2 = 0.977
RSD = 0.044
>1
Arbient-Shipped
Slope = 1.044
Intercept = -1.51
„ 2 __ r\ mnrt
Rl
r -U.OOC
3D = 0.048u
>
g/rrf
Channel 3 (ug/mB)
-------
Comparison of Sulfates on Channels 1 & 2
Nylon Ft Iters
Cold shipped
y=0.9977x+ 0.2183
R2 = 0.9971
Channel 1 (ug/,m3)
-------
-------
Lessons Learned
• The DQO process helps design the study
• Setting and diligently pursuing MQO's is crucial
to getting believable results
• Make sure the instrumentation is completely serviced
• The Data Quality Assessment can reveal things about
the network
• Weather can be a huge determinant factor
• Scope of this kind of study is a challenge
logistically
• Labor, materials and hardware (boxes), scheduling
-------
Conclusions
• Appears Instruments sampled consistently
on Nylon and Teflon Channels (#1-3)
• Some loss of mass does seem noticeable,
but the difference appears to be within
network variability DQOs.
• Sulfates do not appear to affect loss of
mass
• More analysis of the Nitrate and carbon
losses and variability should be conducted
-------
References
• Ml James B. Flanagan, Edward E. Rickman, Max R. Peterson, Eva
D. Hardison, Lisa Greene, Andrea McWilliams, William F.
Gutknecht, and R.K.M. Jayanty, Speciation Trends Network:
Evaluation Of Whole-System Uncertainties Using Collocated Data.
2005 AAAR PM Supersites Program and Related Studies
International Specialty Conference, Atlanta, GA, February 7-11,
2005.
f21 Evaluation of PM2.5 Chemical Speciation Samplers for Use in
the EPA National PM2.5 Chemical Speciation Network, Paul A.
Solomon .William Mitchell, Michael Tolocka, Gary Norris, David
Gemmill Russell Wiener, EPA-454/R-01-005, May 2001.
f31 Final Report: Evaluation of PM2.5 Speciation Sampler
Performance and Related Sample Collection and Stability Issues
[41 Recommendations Of The Expert Panel On The EPA Speciation
Network Final Summary-8/3/99, By Petros Koutrakis, Chair,
Speciation Expert Panel.
httD://www.eDa.aov/ttn/amtic/files/ambient/Dm25/sDec/lvpanel.Ddf.
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