# EPA Handbook : Optical Remote Sensing for Measurement and Monitoring of Emission s FluxDecember 2011



## EPA Handbook: Optical Remote Sensing for Measurement and Monitoring of Emission s Flux

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Analysis Division Measurement Technology Group Research Triangle, North Carolina, 27711

### Forward

Optical Remote Sensing (ORS) technologies have been available since the late 1980s. In the early days of this technology, there were many who saw the potential of these new instruments for environmental measurements and how this technology co uld be integrated into emission s and ambient air monitoring for the measurement of flux. However, the monitoring community did not embrace ORS as quickly as anticipated. Several factors c ontributing to delayed ORS use

we re:

• Cost: The c ost of these ins truments made it prohibitive to purchase, operate and maintain.

• Utility: Since these instruments were in essence perceived as “black box es. ” M any instrument specialists were wary of how they worked and how the instruments generated the values.

• Ease of use: Many of the early instruments required a well

- trained spectroscopist who would have to spend a large amount of time to setup, operate, collect, validate and verify the data.

• Data Utilization: Results from path integrated units were different from point source data which presented challenges for data use and interpretation.
Over the years, the air monitoring community has come to accept both the challenges and overall utility of ORS technologies and appli cations. The emission s monitoring community and monitored sources have been employing ORS for a number of years and are using these technologies to answer questions that traditional instrumentation could not address

. In addition, ORS technology has been applied to ambient and fenceline monitoring, including near

- roadway monitoring. Therefore, application of ORS technology has an expanding place with other air measurement tools.

The EPA staff and other scientist s and engineers in the monitoring communit y recognized that a compilation of ORS material was needed to encourage wider use and understanding of ORS. Questions on how instruments generate data and how an agency or source validates and verifies data are universal
, whether the instrument is optically remote or an extractive instrument on site or within the stack. With this in mind, the EPA developed this Handbook to assist the “non

spectroscopist” in understanding and using data and information generated by ORS. This Handbook is divided into five sections:

**•** Section 1: D iscusses what ORS means and how this technology can be used. It also has a number of tables that ha ve a “crosswalk” between the different technologies and their use (i.e., techniques).

**•** Section 2: d escribes the different technologies or “hardware” that are currently available that are considered “optically remote.”

**•** Section 3: Explains how t o use the “hardware” with different techniques and how to calculate emission flux.

**•** Section 4: D iscusses the “other” data that needs to be collected to understand and better validate and verify the ORS data.**•** Sect ion 5: Provides a very brief overview of how to validate and verify this data once it is collected.



### Disclaimer of Endorsement

Mention of
, or referral to
, commercial products or services and/or links to non

- EPA internet sites does not imply official EPA endorsement of
, or responsibility for opinions, ideas, data, or products presented at those locations, or guarantee the validity of the information provided

. Mention of commercial products/services and non

- EPA websites is provided solely as a reference to information on topics related to environmental protection that may be useful to EPA staff and the public.

### Acknowledgements

A document such as this requires the work and dedication of many people. This section acknowledges those that have provided their time and effort to create this document.

Team Lead: Dennis K. Mikel, EPA

- OAQPS Team Co

- lead: Raymond Merrill, EPA

- OAQPS
Authors of the Chapters:
Chapter 1: Raymond Merrill, EPA

– OAQPS, Dennis Mikel, EPA

– OAQPS Chapter 2: Jennifer Colby, Tracey Footer, and Phillip Crawford, ERG Chapter 3: Jennifer Colby, Tracey Footer, and Laura Alvarez

- Aviles, ERG Chapter 4: Raymond Merrill, EPA

– OAQPS Chapter 5: Raymond Merrill, EPA

– OAQPS
Special thanks to expert stakeholders for their review and comments.

Comments and questions can be directed to:

Dennis K. Mikel EPA

- OAQPS

- AQAD 109 Alexander Drive Research Triangle Park, NC 27711 Email: mikel.dennisk@epa.gov

Or;
Raymond Merrill EPA

- OAQPS

- AQAD 109 Alexander Drive Research Triangle Park, NC 27711 Email: Merrill.raymond@epa.gov

### Table of Contents

Page Revision Date

Foreword

................................ ................................ ........ ii 1 12 / 01 /11 Disclaimer of Endorsements

................................ ........... ii
Acknowledgments

................................ ......................... iii

Table of Contents

................................ .......................... iv
Table Figures and Tables

................................ .............. ix

1.0 Introduction

................................ ...................... 1 1 12 / 01 /11

1.1 Purpose of the Handbook

........................ 2 1.1.1 Stationary Sources and Emission Points

................................ .......... 3

1.2 Why Remote Measurement?

................... 7 1.2.1 Pollutant Focus: Criteria Pollutants, HAPs, Greenhouse Gases, and Ozone Depleting Substances

................................ ... 9 1.2.2 Increased Knowledge and Advancement of Remote Sensing to Emissions Measurement

............................. 11 1.2.3 Advantages Over Closed Path Techniques

.............................. 14

1.3 Contents and Overview of Han dbook

.... 16 1.3.1 Contents

................................ .... 16 1.3.2 Overview of Handbook Sections

................................ ..... 1 7

1.4 General Discussion of EPA Quality System

................................ .................. 19 1.4.1 Data Quality O bjectives

............. 20 1.4.2 Measurement Quality Objectives

................................ . 22

1.5 Selecting the Right Tool: Technology Cross

- Table R eferences

......................... 24Page Revision Date

1.6 Strengths and Limitations

...................... 25

1.7 Future Evolutions and Updates of this Handbook

................................ ....... 31

1.8 References

................................ ............ 32

2.0 Optical Remote Sensing Technologies

............ 1 1 08/19/11

2.1 Fourier Transform Infrared Spectroscopy

................................ ........... 2 2.1.1 Basic Operation

........................... 2 2.1.2 Pollutants and Relative Levels That Can Be Measured

............... 9 2.1.3 Typical QA/QC

......................... 11 2.1.4 Example Applications and Vendors

................................ ..... 1 5 2.1.5 Strengths and Limitations

.......... 16 2.1.6 References

................................ . 1 8

2.2 Tunable Diode Laser

............................. 2 1 2.2.1 Basic Operation

......................... 2 2 2.2.2 Pollutants and Relative Levels That Can Be Measured

............. 2 6 2.2.3 Typical QA/QC

......................... 2 7 2.2.4 Example Applications and Vendors

................................ ..... 31 2.2.5 Strengths and Limitations

.......... 3 2 2.2.6 References

................................ . 3 3

2.3 Ultraviolet Differential Optical Absorption Spectrometer

....................... 3 5 2.3.1 Basic Operation

......................... 3 5 2.3.2 Pollutants and Relative Levels That Can Be Measured

.............. 40 2.3.3 Typical QA/QC

......................... 41 2.3.4 Example Applications and Vendors

................................ ..... 43 2.3.5 Strengths and Limitations

.......... 44 2.3.6 References

................................ . 45Page Revision Date

2.4 Differential Absorption Light Detection And Ranging Systems

........................... 4 7 2.4.1 Basic Operation

......................... 4 8 2.4.2 Pollutants and Relative Levels That Can Be Measured

.............. 5 4 2.4.3 Typical QA/QC

......................... 5 6 2.4.4 Example Applications and Vendors

................................ ..... 5 7 2.4.5 Strengths and Limitations

.......... 5 9 2.4.6 References

................................ . 61

2.5 Thermal Infrared Cameras

..................... 6 4 2.5.1 Basic Operation

......................... 6 5 2.5.2 Pollutants and Relative Levels That Can Be Detected

................ 6 7 2.53 Typical QA/QC

......................... 6 7 2.5.4 Example Applications and Vendors

................................ ..... 6 8 2.5.5 Strengths/Limitations

................. 70 2.5.6 References

................................ . 71

2.6 Cavity Ring Down Spectroscopy

........... 73 2.6.1 Basic Operation

......................... 73 2.6.2 Pollutants and Relative Levels That Can Be Measured

.............. 80 2.6.3 Typical QA/QC

......................... 8 2 2.6.4 Example Applications and Vendors

................................ ..... 82 2.6.5 Strengths and Limitations

.......... 84 2.6.6 References

................................ . 85

3.0 Measurement s Applica ble to Emissions Flux 1 08/19/11

3.1 Radial Plume Mapping: Other Test Method 10

................................ ............... 3 3.1.1 General Description of Approach

................................ .... 3Page Revision Date 3.1.2 Verification/Validation Studies

................................ ....... 11

3.1.3 Typical QA/QC

......................... 1 3 3.1.4 Siting Concerns

......................... 1 4 3.1.5 Strengths and Limitations

.......... 15 3.1.6 References

................................ . 16

3.2 Range Resolved Measurements using Differential Absorption LIDAR

............ 17 3.2.1 General Description of Approach

................................ .............. 17 3.2.2 Verification/Validation Studies

................................ ....... 20 3.2.3 Typical QA/QC

......................... 22 3.2.4 Siting Concerns

........................ 22 3.2.5 Strengths and Limitations

.......... 2 2 3.2.6 References

................................ . 2 3

3.3 Solar Occultation Flux Measurement

.... 2 5 3.3.1 General Description of Approach

................................ ... 2 6 3.3.2 Verification/Validation Studies

................................ ....... 3 1 3.3.3 Typical QA/QC

......................... 3 3 3.3.4 Siting Concerns

......................... 3 6 3.3.5 Strengths and Limitations

......... 3 7 3.3.6 References

................................ . 3 8

3.4 Tracer Gas Correlation

.......................... 3 9 3.4.1 General Description of Approach

................................ .. 3 9 3.4.2 Verification/Validation Studies

................................ ....... 41 3.4.3 Typical QA/QC

......................... 4 4 3.4.4 Siting Concerns

......................... 44 3.4.5 Strengths and Limitations

.......... 4 5 3.4.6 References

................................ . 4 5

3.5 Backward Lagrangian Stochastic Inverse

Dispersion model

................................ .. 4 7 3.5.1 General Description of Approach

................................ ... 4 7Page Revision Date

3.5.2 Verification/Validation Studies

................................ ...... 51 3.5.3 Typical QA/QC

......................... 5 4 3.5.4 Siting Concerns

......................... 5 5 3.5.5 Strengths and Limitations

.......... 5 6 3.5.6 References

................................ . 5 7

4.0 Ancillary Data Collection

................................ . 1 1 08/19/11

4.1 Meteorological Measurements

................. 2 4.1.1 Meteorological Station Siting

................................ ........... 3 4.1.2 Horizontal Wild Speed and Direction

................................ ..... 5 4.1.3 Relative Humidity

....................... 5 4.1.4 Temperature

................................ 6 4.1.5 Net Solar Radiation

..................... 6 4.1.6 Atmospheric Pressure

.................. 6

4.2 Differential Global Positioning for Tracking Monitoring Locations

............... 8

4.3 Collection of Process Information

........... 9

4.4 Attribution of Emissions to Source of Interest

................................ .............. 1 2

4.5 References

................................ ............ 1 3

5.0 Data Validati on and Verification

..................... 1 1 08/19/11

5.1 General Approach

................................ ... 2 5.1.1 Levels of Data Quality Review

................................ ........ 3

5.2 Data Verification Methods

...................... 5 5.2.1 Visual Data Verification

.............. 5 5.2.2 Data Review and Verification

................................ . 8

5.3 Data Validation Methods

......................... 95.3.1 Validation of Primary and Ancillary Measurements

.............. 9 5.3.2 Final Validation and Evaluation of Measurements for Data Users

................................ . 11

5.4 References

................................ ............ 13Figures

Number Title Section
1

- 1 Types and sources of air pollutants 1.0
1

- 2 Stationary Sources 1.0
1

- 3 Potential Fugitive Sources 1.2
1

- 4 Simplified Diagram for Optical Remote Sensing 1.2
1

- 5 The EPA Quality System 1.4

2

- 1 Diagram Showing the Beam Path and Major Components of an OP

- FTIR 2.1
2

- 2 FTIR Absorption Spectrum Recorded at 1075 o K 2.1
2

- 3 FTIR Closed Cell Unit Used to Monitor Stack Gas 2.1

2

- 4 Basic Setup Used to Make Monostatic Open

- path FTIR Measurements 2.1
2

- 5 The Corner

- cube Reflector 2.1
2

- 6 Typical Telescopic FTIR Transmitting and Detection Unit 2.1

2

- 7 Retro Reflecting Mirror Used to Reflect the IR Beam Back to the Detection Unit 2.1
2

- 8 Calibration Plot of Absorbance vs. Concentration 2.1
2

- 9 TDL Bistatic Configuration 2.2
2

- 10 TDL Monostatic Configuration 2.2
2

- 11 The Corner

- cube Reflector 2.2
2

- 12 Calibration Data for an OP

- TDL System 2.2
2

- 13 Opsis DOAS Unit 2.3
2

- 14 Bi static Configuration of UV

- DOAS 2.3
2

- 15 The Corner

- cube Reflector 2.3

2

- 16 Basic Setup Used to Make Mono

- static Open

- path UV

DOAS Measurements 2.3
2

- 17 Beam Path and Major Components of DIAL Unit 2.4
2

- 18 Monostatic Coaxial and Biaxial Configuration for DIAL 2.4
2

- 19 Bistatic Configuration for DIAL 2.4
2

- 20 Illustration of DIAL unit mapping an emission plume 2.4
2

- 21 Mobile DIAL Unit 2.4

2

- 22 Detection of Gaseous VOC Emissions from a Chemical Storage Tank 2.5Figures

Number Title Section
2

- 23 Flare Detection by Thermal IR Camera 2.5

2

- 24 The essential components of any CRDS experimental set

up 2.6
2

- 25 Schematic representation of the expected rate of decay 2.6
2

- 26 Comparison of Pulsed and Continuous Wave Laser Light 2.6

2

- 27 Optical components schematic of a cavity ring

- down spectrometer 2.6

2

- 28 Differences between photomultiplier tube (PMT) and charge

- coupled device (CCD) detector outputs 2.6

3

- 1 Horizontal RPM setup, taken from Hashmonay et al, 2008. 3.1
3

- 2 Vertical RPM setup, taken from Hashmonay et al, 2008. 3.1

3

- 3 One

- dimensional RPM setup, taken from Hashmonay et al, 2008. 3.1
3

- 4 Examples of RPM algorithm outputs 3.1
3

- 5 Conceptual picture on the operation of DIAL 3.2

3

- 6 Contour profile of SO 2 concentration measured 2.1 km downwind of source, at Cement Works, by Environmental Measurements Group National Physical Laboratory, UK 3.2
3

- 7 Solar tracker configurations 3.3
3

- 8 Rough overview of a mobile SOF system 3.3
3

- 9 SOF cartoon 3.3

3

- 10 An example of path

- integrated calculations determined from SOF measurements 3.3

3

- 11 Tracer gas/SOF experiment measuring SF emissions in an open field over time of day 3.3
3

- 12 Wind velocity profiles by height 3.3
3

- 13 Tracer Gas release setup cartoon 3.4

3

- 14 Methane concentration verses acetylene concentration Tracer gas characterization using FTIR 3.4

3

- 15 Illustration of an inverse

- dispersion model for estimating emission rate Q 3.5

3

- 16 Illustration of the WindTrax bLS Modeling Software Graphical Interface 3.5Figures

Number Title Section

3

- 17 Map of the laser paths used in the 2004 Flesch et al experiment 3.5
5

- 1 Generalized Data Verification and Validation Process Flow 5.1

5

- 2 Level 0 Verification Checks the Most Fundamental Quality Requirements 5.2

5

- 3 Example of Optical Remote Measurement Visual Check List 5.2

5

- 4 Level 1 Verification Ensures Quality Requirements are Met in the Field 5.2
5

- 5 Level 2 Quality Checks Start the Data Validation Process 5.3

5

- 6 Level 3 Quality Checks Ensure the Data is Usable for the Purpose Intended 5.3Tables

Number Title Section
1

- 1 Technology vs. Application Cross

- Table 1.5
1

- 2 Technology Strengths and Limitations 1.6
1

- 3 Application Strengths and Limitations 1.6
1

- 4 Quantitative Summary of ORS Technologies 1.6

2

- 1 Example List of Compounds Measured by FTIR Open

- path Systems 2.1
2

- 2 Typical Applications for OP

- FTIR. 2.1
2

- 3 FTIR Supply Vendors 2.1
2

- 4 Summary Table of the OP

- FTIR’s Strengths 2.1
2

- 5 Summary Table of the OP

- FTIR’s Limitations 2.1

2

- 6 Example List of Gaseous Compounds Measured by Near IR OP

- TDL Systems 2.2
2

- 7 Near

- IR Laser Types Available for OP

- TDL Systems 2.2
2

- 8 Potentially Usable Mid

- IR Lasers 2.2
2

- 9 Typical Applications for OP

- TDL 2.2
2

- 10 Near

- IR OP

- TDL Vendors 2.2
2

- 11 Summary Table of the TDL's Strengths 2.2
2

- 12 Summary Table of the TDL’s Limitations 2.2
2

- 13 Species Measured with UV

- DOAS Systems 2.3
2

- 14 Approximate Detection Limits for UV

- DOAS 2.3
2

- 15 Typical Applications for UV

- DOAS 2.3
2

- 16 UV

- DOAS Vendors 2.3
2

- 17 Summary Table of the UV

- DOAS’s Strengths 2.3
2

- 18 Summary Table of the UV

- DOAS’s Limitations 2.3
2

- 19 Reported Species Measured with DIAL Systems 2.4
2

- 20 Approximate Detection Limits for DIAL 2.4
2

- 21 Typical Applications for LIDAR 2.4
2

- 22 DIAL Vendors 2.4
2

- 23 Summary Table of the DIAL’s Strengths 2.4
2

- 24 Summary Table of the DIAL’s Limitations 2.4
2

- 25 Example List of Gaseous Compounds that can be Detected 2.5Tables

Number Title Section by Thermal IR Cameras
2

- 26 Typical Applications for Thermal IR Camera 2.5
2

- 27 Thermal IR Camera Vendors 2.5
2

- 28 Summary Table of the IR Camera’s Strengths 2.5
2

- 29 Summary Table of the IR Camera’s Limitations 2.5
2

- 30 Example list of detectable pollutants by CRDS 2.6
2

- 31 Typical Applications for OP

- TDL 2.6
2

- 32 CRDS Vendors 2.6
2

- 33 Summary Table of CRDS Strengths 2.6
2

- 34 Summary Table of CRDS Limitations 2.6
3

- 1 Data quality indicators for the QA/QC process 3.1
3

- 2 Summary Table of the VPRM’s Strengths 3.1
3

- 3 Summary Table of the VPRM’s Limitations 3.1

3

- 4 Results from the comparison of DIAL and plant measurements of SO 2 mass emissions 3.2
3

- 5 DIAL Strengths 3.2
3

- 6 DIAL Limitations 3.2
3

- 7 SOF technique VOC emissions compared to DIAL 3.3
3

- 8 Summary for measurements on the Aby field, 2002 3.3
3

- 9 SOF traverse done on day 24

- June 2002 3.3
3

- 10 Estimation of statistical errors for the SOF measurements 3.3
3

- 11 Feature strengths of using the SOF method 3.3
3

- 12 Feature limitations of using the SOF method 3.3
3

- 13 Tracer Gas Correlation Strengths 3.4
3

- 14 Tracer Gas Correlation Limitations 3.4
3

- 15 bLS Model Strengths 3.5
3

- 16 bLS Model Limitations 3.5List of Acronyms
Acronym Description μg/m 3 micrograms per cubic meter Μ m micrometer 2

- D two dimensional 3

- D three dimensional ANSI American National Standards Institute AOM acousto

- optic modulator ASQC American Society for Quality bLS Backward Lagrangian Stochastic BTX benzene, toluene, elemental mercury and p

- xylene C2H2 acetylene CCD charge

- coupled device CEA cavity

- enhanced absorption CEMs continuous emissions monitors CFCs chlorofluorocarbons CH4 methane CO carbon monoxide CO2 carbon dioxide COSPEC correlation spectrometer CRDS cavity ring

- down spectroscopy CRLAS cavity ring

- down laser absorption spectroscopy CW continuous wave DGPS differential global positioning system DIAL Differential absorption LIDAR DIAL/LIDAR differential absorption light detection and ranging DOAS differential optical absorption DQI data quality indicators DQO data quality objective EPA Environmental Protection Agency ETV environmental technology verification FID flame ionization detectors FTIR Fourier transform infrared g/s grams per second GPS global positioning system HAP hazardous air pollutants

HITRAN high

- resolution transmission molecular absorption database HRPM horizontal RPM ICOS integrated cavity output spectroscopy IR infrared LASER light amplification by stimulated emission of radiationAcronym List of Acronyms
Description LDAR leak detection and repair LOD levels of detection MAX

- DOAS Multi

- axis Differential Absorption Spectroscopy MCT mercury

- cadmium

- telluride MOST Monin

– Obukhov similarity theory MQOs measurement quality objectives N2O n itrous oxide NO n itric o xide NO2 nitrogen dioxide NOx nitrogen oxides NUC non

- uniformity correction NWIR Northwest Infrared NWS National Weather Service O3 ozone OA

- CEA off

- axis cavity enhanced absorption OAICOS off

- axis integrated cavity output spectroscopy OP open

- path OP

- TDL open

- path tunable diode laser ORS optical remote sensing OTM10 Other Test Method 10 PDCs path

- determining components PIC path integrated concentration PID photo

- ionization detectors PMT photo multiplier tube ppb parts per billion ppm parts per million ppmm ppm for one meter of path length PRF pulse repetition frequency PZT piezoelectric transducer QA quality assurance QAPP quality assurance project plan QC quality control QC

- Laser Quantum Cascade Laser QMP quality management plan QP quality program QS quality system RADAR radio detection and ranging RDT ring

- down time RH relative humidity RPM radial plume mappingList of Acronyms
Acronym Description

SBFM smooth basis function minimization SF6 sulfur hexafluoride SO2 sulfur dioxide SOF Solar Occultation Flux SOx sulfur oxides SSE sum of squared errors TDC tracer dilution correlation TDL tunable diode laser TDLAS tunable diode laser absorption spectroscopy UV ultraviolet VOCs volatile organic compounds VRPM vertical radial plume mapping μs microsecondsORS Handbook, Section 1.0 Revision No: 1 Date: 12 / 01 /11 Page 1 of 33 

### 1.0 Introduction

This document is intended as a Handbook for those planning to use or review remote emissions

measurement and monitoring approaches for stationary sources or for data users building their expertise about current information concerning the te chnologies and application in these types
of measurements. For the purposes of this Handbook , “remote measurement” is defined as any
measurement of air emissions conducted away from the point or area where the pollutant is
released. This definition includes optical remote sensing (ORS)
, as well as other approaches such as those coupling point measurements with a mobile measurement platform. As our air

qu ality management programs evolve, we need more measurements of non

- point or unvented sources, often referred to as fugitive sources or fugitive emissions. Remote measurement technologies offer approaches that have been otherwise unavailable to measure emi ssions from these challenging sources.

The information presented in this document is written to be accurate and informative, as well as more “user friendly” than technical papers or review articles found in the open literature (i.e., peer reviewed literat ure and articles in periodicals that are available on the Internet) . Practical information is provided for those who need to understand the principles behind the use of spectroscopy or other remote measurement technologies, but who may not be trained in th ese technologies and their applications. This document is intended to aid readers in understand ing the uses and limitations of data generated by remote measurement approaches. In this
document
, you will find discussion of the practical uses and operation of remote sensing equipment and applications of these and other technologies to produce emissions data. Some
of the complex technical information has been provided in summary form with illustrations.ORS Handbook, Section 1.1 Revision No: 1 Date: 12 / 01 /11 Page 2 of 33 

### 1.1 Purpose of the Handbook

The purpose of this Handbook is to describe the primary remote measurement technologies and current approaches to use these technologies. This Handbook also describes how potential users can assess the applicability of remote measurements and the resulting data to their
emissions measurement needs. We designed this Handbook for EPA, state, local, and tribal

measurem ent project leads; measurement contractors; industry managers planning measurements to create emission factors; and those reviewing test plans and test reports. When
the term “ measurement ” is used in this Handbook, it is referring to short

- term studies ( e

. g., emission fluxes assessment) ; the term monitoring is used for long

- term studies ( i

. e . spatial and temporal trend assessment).

Optical remote measurement techniques are most typically designed and used to measure concentrations and, when paired with meteorological data, allow for calculation of mass fluxes of pollutants downwind of fugitive and non

- point emission sources. Optical remote techniques provide opportunities to measure sources that are not conducive to measurement using more traditional stack testing or single point ambient techniques. Actual application, however, needs
to be determined on a case

- by

- case basis.

This H andbook describes t he more prevalent and technologically demonstrated open

- path, cell

based, and point measurement technologies used to make remote measurements This
Handbook provide s a background for the application of remote measurement techniques for
emissions measurements. Viable applications for qualitative and quantitative measurements of constituents in air are also described as examples of different ways remote measurement technologies can be applied to meet measurement and monito ring requirements. Quantitative emissions data from remote measurements may then be used for multiple purposes including development of emission factors, evaluation of exposure levels, compliance with ambient regulatory limits, and identification of sourc es of air pollution. Examples of several pollutant detection and quantification methods are provided to show the focus of current monitoring applications.ORS Handbook, Section 1.1 Revision No: 1 Date: 12 / 01 /11 Page 3 of 33

Applications of ORS are relativ ely new, but maturing rapidly. For example, Differential Optical Absorption (DOAS) and Fourier Transform Infrared (FTIR) systems have been commercially available since the early 1990s. These earlier instruments, although designed for both background ambient and higher stationary source emission

- related monitoring applications, have mostly been employed to measure stationary and fugitive source emissions.

This document is intended to be “user friendly”

— it will have as much practical information
for those not trained in these technol ogies and approaches as those who are. This Handbook will discuss the practical uses and operation of the instruments us ed to make remote

measurements a n d the data generated. Some of the more technical information ha ve been simplified, and illust rations have been updated and clarified to make them more understandable

. Internet links and references have been added throughout the document to
allow the reader to research quickly more detailed information.

1.1.1 Stationary Sources and Emissions Po ints

Stationary sources are one of the major contributors of pollution to the atmosphere. They are

non

- moving, fixed

- site producers of air pollution such as power plants, chemical plants, oil refineries, manufacturing facilities, and other industrial facilities. Air pollution from stationary sources is produced by two primary activities: (1) stationary combustion of fuel such as coal, oil, wood, or natural gas, and (2) pollutant losses from industrial processes. Industrial processes include petroleum wells, refineries, chemical manufacturing facilities, and smelters.ORS Handbook, Section 1.1 Revision No: 1 Date: 12 / 01 /11 Page 4 of 33

Figure 1

- 1. Types and sources of air pollutants

EPA defines e mission points from stationary sources vary between EPA programs. An emission point is the specific place or piece of equipment from which a pollutant is emitted. Stationary sources
, such as a facility or factory may have many possible emission points

. Air pollutants can be emitted from smokestacks, storage tanks, equipment leaks, process wastewater handling/treatment area s , loading and unloading facilities, and process vents. These sources emit various types of pollutants which are discussed in Section 1.2.1.

Ducted or Vented Emissions

A process vent is basically an opening where substances (mostly in gaseous form) are “vented” into the atmosphere. Common process vent s in a chemical plant are distillation columns and
oxidation vents , for example.ORS Handbook, Section 1.1 Revision No: 1 Date: 12 / 01 /11 Page 5 of 33

Figure 1

- 2. Stationary sources

Historically, ducted or vented stationary source emissions have been measured in the ducts or stacks before release into the atmosphere. These sources were often referred to as point sources because the final release of emissions can be traced to a single or multiple defined duct
or stack ex h aust. Ducted sources permit emission stream parameters
, such as flow rates, temperature, pressure, and other physical characteristics
, to be recorded within the accuracy requirements for end data use because they are confined under relatively steady conditions.

Area or Fugitive Emissions Sources

Those stationary facilities or activities whose individual air emissions do not qualify them as point sources are called area sources. Area and fugitive sources are often collections of a multitude of minor sources with individually small emissions that are impractical to consider as separate point sources. Area source s , including fugitive emissions, are those emissions that
could not re asonably pass through a stack, chimney, vent, or other ducted line that could easily be characterized with conventional point source or stack sampling methods

. Measur e ment ofORS Handbook, Section 1.1 Revision No: 1 Date: 12 / 01 /11 Page 6 of 33 emissions from these sources traditionally require s total enclosure techniques, or a combination of point measurements and modeling using upwind

- downwind or exposure

- profiling methods.

Area sources represent numerous facilities and activities
, including various unintended or irregular emissions. F ugitiv e and area sources may release small amounts of a given pollutant individually , but collectively can release significant amounts of a pollutant. For example, dry cleaners, vehicle refinishing, animal feeding operations, gasoline dispensing facilities, and residential heating do not typically qualify as point sources, but collectively, the various
emissions from these sources are classified as area sources. 2

Fugitive emissions from storage tanks are due to pollutants that can leak through the roofs and
thr ough tank openings when liquids expand or cool because of outdoor temperature changes.
In addition , air pollutants can escape during the filling and emptying of a storage tank. Air pollution is also produced when wastewater containing volatile chemicals comes in contact
with the air.

Both stationary point source and area source emissions m easurements have traditionally been performed using single point sampling that accumulates and integrates sampled gas for a set period of time followed by analysis for target components. Continuous point source instrumental methods have also been applied to stationary source emissions and area source

measurements. Instrumental methods collect sample s from a single point and provide
information on the concentration of a target component of interest over relatively small
increments of time. A critical issue with traditional air measurements is collection and reporting of data from a single point that is assumed to be representative of the air or emission being monitored. This assumption is verifiable when ducts or stacks are sampled but much less
certain for area source and ambient measurements.ORS Handbook, Section 1.3 Revision No: 1.0

Date: 12 / 01 /11 Page 7 of 33 

### 1.2 Why Remote Measurement?

Fugitive emissions are emissions not contained or caught by a capture system and are often caused by equipment leaks, evaporative processes, and windblown disturbances. These emissions may occur from breaks or small cracks in seals, tubing, valves, or pip elines, as well

as when lids or caps on equipment or tanks have not been properly closed or tightened. For example, when natural gas escapes via fugitive emissions, methane
, volatile organic compounds (VOCs)
, and any other contaminants in the gas (e.g., h ydrogen sulfide) are released to the atmosphere. Other examples of area sources with significant fugitive emissions include landfills or waste lagoons. 3

Figure 1

- 3

. Potential Fugitive Sources Kantamaneni, R., MUSCAT 10

- 11 May 2010 Regional forum, Methane to Mar kets Partnership: An Overview of OpportunitesORS Handbook, Section 1.3 Revision No: 1.0

Date: 12 / 01 /11 Page 8 of 33

Figure 1

- 3. (cont). Potential Fugitive Sources

Area and fugitive emissions sources are especially challenging to monitor because the pollutants of interest are not contained withi n a duct or stack before release. The development of emission factors for area sources is equally difficult due to the measurement challenges. In contrast, stationary stack measurements and their related emission factor s’ determination are
well

- establish ed and documented. Over the past 20 years, remote measurement approaches
, including ORS methods
, have been progressing technologically and gaining greater use as an emissions estimation tool, especially for stationary area sources and some on

- road/near

- road
mobile sources. A significant number of remote measurement activities have been performed for open area sources such as landfills, wastewater treatment plant ponds, agricultural waste, wastewater lagoons, oil and gas field production sites, waste pon ds for mining operations, and ambient fenceline concentrations surrounding large chemical and refinery facilities. These types of sources are prime candidates for the application of remote measurement techniquesORS Handbook, Section 1.3 Revision No: 1.0

Date: 12 / 01 /11 Page 9 of 33
because the ORS technology and techniques are small, mobile, do not take lengthy setup time and can return data quickly to the staff collecting the data and to the operators of the facilities.

1.2.1 Pollutant F ocus : Criteria Pollutants, HAPs, Greenhouse Gases, and Ozone Depleting Substances

The remote measurement technologies and approaches addressed in this Handbook are focused on four groups of pollutants currently regulated or on the regu latory horizon under the Clean
Air Act

. These four groups are:

**•** criteria pollutants,

**•** hazardous air pollutants or HAP,

**•** greenhouse gases, and

**•** o zone

- depleting substances.

Gaseous Criteria pollutants are those inorganic pollutants (e.g., carbon monoxide, sulfur oxides, nitrogen oxides and o zone) that are common and found all over the United States. The EPA uses these “criteria pollutants” as indicators of air quality. Each of the criteria pollutants

are discussed in detail below. 4

Carbon monoxide (CO) is a colorless, odorless gas formed when carbon in fuel is not burned completely. Motor vehicle exhaust contributes about 60 percent of all CO
emissions nationwide. 4 Other sources of CO emissions inc lude industrial processes (such as metals processing and chemical manufacturing), residential wood burning, and natural
sources such as forest fires.

Sulfur oxides (SO x ) are colorless gases formed when fuel containing sulfur, such as coal and oil, is bur ned and when gasoline is extracted from oil or metals are extracted from ore. Sulfur dioxide (SO 2 ) is the criteria pollutant that is the indicator of SO x concentrations in
the ambient air. Other sources of SO 2 are industrial facilities that derive their p roducts from raw materials like metallic ore, coal, and crude oil, or that burn coal or oil to produce process heat. Examples are petroleum refineries, cement manufacturing, sulfuric acidORS Handbook, Section 1.3 Revision No: 1.0

Date: 12 / 01 /11 Page 10 of 33 plants and metal processing facilities. Also, locomotives, large ships, and some non

- road diesel equipment currently burn high sulfur fuel and release SO 2 emissions in to the air in large quantities.

Nitrogen oxides (NO x ) , is the generic term used to describe the sum of nitric oxide ( NO )
, nitrogen dioxide ( NO 2 ) , which is a criteria pollutant, and other oxides of nitrogen. NO x is a group of highly reactive gases that play a major role in the formation of ozone. N O x form when fuel is burned at high temperatures, as in a combustion process. The primary sources

o f NO x are motor vehicles, electric utilities, and other industrial, commercial, and
residential sources that burn fuels.

Ozone (O 3 ) is a gas composed of three oxygen atoms. It is a unique criteria pollutant in that it is exclusively a secondary pollutant

. It is not usually emitted directly into the air, but at ground level is created by a chemical reaction between NO x and VOCs in the presence of heat and sunlight. O 3 has the same chemical structure whether it occurs miles above the earth or at ground le vel and can be “ useful ” or “ damaging ” to the environment depending on its location in the atmosphere. Useful O 3 occurs naturally in the stratosphere and forms a layer that protects life on earth from the sun’s harmful rays or ultraviolet radiation. In the earth’s lower atmosphere, or troposphere, ground

- level O 3 is considered damaging or
destructive

. O 3 is the most prevalent chemical found in photochemical air pollution, or

smog. 4

HAPs or air toxics are those pollutants that are known or suspected to cause cancer, respiratory problems or other serious health effects, or are thought to have adverse
environmental or ecological effects. The presence of HAPs in the air can be more localized than criteria pollutants and the highest levels are usually foun d close to the emission sources. Examples of air toxic s include benzene, found in gasoline; mercury from coal combustion; perchloroethylene from some dry cleaning facilities; and methylene chloride used as a solvent by a number of industries. Most air tox ics originate from man

- made sources including mobile sources (e.g., cars, trucks, construction equipment), stationary sources (e.g., factories,ORS Handbook, Section 1.3 Revision No: 1.0

Date: 12 / 01 /11 Page 11 of 33 refineries, power plants), and indoor sources (e.g., some buildings materials and cleaning solvents). 5

Greenhou se gases are those compounds that enhance the retention of the sun’s heating of the
earth. Clouds and a natural layer of atmospheric gases absorb a portion of earth’s heat and prevent it from escaping into space. This keeps our planet warm enough for lif e and is known as the natural “greenhouse effect.” Scientific evidence shows that the greenhouse warming effect is being increased by the release of certain gases into the atmosphere that cause the earth’s temperature to rise. This rise in temperature ca used by greenhouse gases is called “global warming” and contributes to climate change on the planet. Carbon dioxide (CO 2 )
, methane, particulate matter (especially black carbon or soot), NO x , fluorinated compounds, and
O 3 are some of the compounds contributing to global warming. C O 2 emissions account for about 81 percent of greenhouse gases released in the United States and are largely due to the combustion of fossil fuels in electric power generation, motor vehicles, and industries. Methane emissions, which result from agricultural activities, landfills, and other sources, are the next largest contributors to greenhouse gas emissions in the United States and worldwide. 6

Ozone

- depleting substances are compounds such as chlorofluorocarbons (CFCs), halons, carbon tetrachloride, methyl bromide, and methyl chloroform. The stratosphere contains a layer of O 3 gas that protects living organisms from harmful ultraviolet

- B radiation from the s un which has been linked to many harmful effects
, including various types of skin cancer, cataracts, and harm to crops, materials, and marine life. 7

1.2.2 Increased Knowledge and Advancement of Remote Sensing to Emissions Measurement

There are four major optical sensing approaches that will be described in more detail in later
sections of this Handbook

. They include the following:

**•** Active

. Open

- path ORS techniques typically use optical telescopes to transmit and receive energy beams, such as ultraviolet (UV) , infrared (IR) , or visible wavelength range.ORS Handbook, Section 1.3 Revision No: 1.0

Date: 12 / 01 /11 Page 12 of 33

**•** Passive

. O pen

- path ORS techniques receive light energy from pollutants activated by an external uncontrolled source such as combustion gases (e.g., Passive FTIR radiation) or t he sun (e.g., Solar Occultation and mobile DOAS ).

**•** Backscatter. ORS techniques use energy reflected from pollutants after activation from a controlled source of light energy (e.g., Differential Absorption Light Detection and Ranging ( DIAL/ LIDAR) systems).

**•** Mobile

. M easurement methods do not have to be optically based. However, o ptical techn olog ies have been engineered to be rugged enough to allow stable operation from a moving vehicle. Typically these optical techniques sample the gas into a confined cell while moving along a path to be measured (e.g., cavity ringdown, white cell and FTIR tracer release systems).

Active

. ORS techniques use the light generated under controlled conditions from one of many sources including heated glow bars for IR light, quartz lamps filled with deuterium or xenon gas, or laser light. The light energy is broadcast over relatively long distances (up to 1,000 meters) in an open

- air setting. A simplified schematic of an open

- path ORS technique to measure emissions from an open source is provided in Figure 1

- 4. In general, open

- path ORS test methods involve the transmission of an energy beam across a path (straight line or two

dimensional plan e) located downwind of the emission source to be measured (e.g., wastewater lagoon). The pollutant concentration along the line or plane is determined by evaluating certain qualities of the energy beam (e.g., the amount of light absorbed) after it has pas sed through the sample path and is captured by a receiver. Chemical compound reference spectra and computational algorithms are used to translate the instrument signal into a pollutant path

integrated concentration (e.g., parts per billion (ppb) benzene p er meter). Additionally, a mathematical calculation routine, combined with meteorological data (wind speed, wind direction) collected during the sampling event, is needed to convert the ORS instrument output (e.g., a path

- integrated concentration or a flu x measurement) to an emission flux rate (e.g., milligrams per second). Open

- path ORS methods can be designed and applied in several different ways to capture area source emissions in both vertical and horizontal planes. The predominant measurement applic ations that use ORS technologies in the open

- path mode include line of sight monitoring, Radial Plume Mapping 8 (RPM), and Backward Lagrangian Stochastic (bLS) Modeling.ORS Handbook, Section 1.3 Revision No: 1.0

Date: 12 / 01 /11 Page 13 of 33

Figure 1

- 4

. Diagram for Monostatic Optical Remote Sensing

Passive

. O pen

- path techniques use the same technology as active without the need for a controlled source of energy. The PFTIR technique is an example of this technology. PFTIR can be used to measure infrared spectra in air at elevated temperatures because hot gases em it radiation with the same infrared signature as their absorption spectrum. Hot gases above the flame zone in an industrial flare contain combustion products such as CO 2 , CO, and vapor phase organic material resulting from products of incomplete combustio n. For example, hot gases emitted by the flare can be identified and quantified using the radiant FTIR absorption

measurements.

The primary difference that must be taken into account between optical remote infrared absorption (e.g., FTIR) and hot gas r adiance measurements using PFTIR is the temperatureORS Handbook, Section 1.3 Revision No: 1.0

Date: 12 / 01 /11 Page 14 of 33 dependence of the FTIR spectral measurements. The results of PFTIR are both temperature and concentration dependent. Knowing the source temperature at the location where the gas
concentrations are measu red is necessary to quantify the compounds of interest.

Backscatter

. O pen

- path optical measurement approaches used in this Handbook refer to the use of Light LIDAR technology. DIAL is an application of LIDAR using powerful lasers
directed into the atmosphere to measure reflected light energy from aerosols, dust, and gases. The DIAL measurement is achieved by the direct impingement of the laser beam on these materials and its subsequent reflection and scattering. Because the target substances v ary in concentration along the axis (optical path) of the transmitted beam, the receiving telescope equipment analyzes the strength of the returning (reflected) beam continually during its reception. 9 The reflected beam strength is reduced from the original transmission strength by a measureable amount that is proportional to the concentration of the target matter.

Mobile

. O ptical monitoring approaches use optical techniques to measure gas samples pu mped into measurement cells where pressure and temperature are controlled. Unlike stationary monitoring techniques, mobile optical techniques allow the user to move along and between the emission plumes generated by area or fugitive sources. A tracer rat io application of mobile monitoring can be used to simulate emissions from a source through the release of a tracer gas at or near the center of the area source with subsequent measurement of the tracer and emission compound(s) concentrations downwind of t he source. Measurements must be conducted at a distance from the source that is sufficient for the plume (e.g., from a landfill or wastewater lagoon) and tracer gas to be well mixed and close enough that emission plume is measurable well above background concentrations. These distances can range from 1 to 5 km to achieve proper mixing. 10

1.2.3 Advantages Over Closed Path Techniques

Although open

- path ORS techniques have been used for 20 years and are well

- established, they are constantly improving and gaining use to characterize and quantify pollutant emissions fromORS Handbook, Section 1.3 Revision No: 1.0

Date: 12 / 01 /11 Page 15 of 33 sources that are not conducive to traditional point source testing methods, such as large area

sources. Improvements often include changes to technologies that improve detection limits or the types of compounds detected. For large area sources, ORS methods have distinct advantages when compared with traditional single point measurement techniques, such as

p hoto

- ionization detectors (PID), PID/flame ionization detectors (FID), Summa canisters, various sorbent methods, and flux boxes. Specific advantages and disadvantages of the ORS measurement technologies and applications are addressed in Chapters 2 and 3 o f this
Handbook . The advantages of ORS applications should be determine d on a case

- by case

- basis tailored to specific measurement goals and objectives. Some of the general ORS advantages
are as follows:

**•** More likely to identify emissions “hot spots” because measurements are collected over a large area,

**•** Achieve better spatial and temporal emissions resolution,

**•** No sample shipping costs,

**•** Perform direct, measurement

- based emission calculations
, and

**•** Represent personal exposure better than fixed poi nt monitoring

.

Some general issues that require consideration when ORS methods are used include the following:

**•** More costly initial sampling instrumentation investment,

**•** Experienced manpower and higher site preparation cost more to deploy,

**•** Dependent on we ather conditions (e.g., heavy rain, fog, dust), and

**•** Dependent on chemical interferences (e.g., water, oxygen, O 3 and CO 2 ).

As the use of open

- path ORS technologies to quantify emissions from area sources has advanced, the desire to use ORS data in the development of atmospheric models and to support air quality standards has increased. However, use of remote sensing presents some challenging
issues. Classical point measurement technologies and their associated results are typically based on the size of the stack or leak, flow data, moisture, bulk gas molecular weight and stack pollutants to be measured. Performance tests for emission masses are usually snapshots of
short duration and not continuous. Using ORS data, unlike point sources wherein emissionsORS Handbook, Section 1.3 Revision No: 1.0

Date: 12 / 01 /11 Page 16 of 33 measurements are typically straightforward, a more critical evaluation of the ORS method application, the emission mechanis m of the source, and the source activity is needed of the emissions developer to ensure that the resulting data provides an accurate representation of average emissions from the source. While developing emission factors from optical remote technology appl ications is beyond the scope of this document, it is the aim of the Handbook to provide the technology background, application examples, and quality information for optical remote measurements that can assist all data users to develop emission resu lts comparable to those routinely generated by traditional point source testing methods.



### 1.3 Contents and Overview of the Handbook

1.3.1 Contents

Remote sensing emissions data are often collected through an un

- ducted or uncontained air parcel or emission plume. Results are reported in terms of “path

- integrated” values. One issue in using these data is how comparable the remote sensing techniques are to short term stack or point source testing. Other potential issues include how representative remote sensing measurement techniques are for fugitive and area sources and if they adequately characterize temporal and spatial variability. It is possible that properly pla n ned ORS measurement
activities conducted over multiple weeks provide cost

- effective
, accurate , temporal
, and spatial
data from an emissions source enabling annualized emissions mass flux to be determined. This
Handbook provides information that can be used by remote sensing da ta generators and users to answer questions regarding ORS monitoring data use. The Handbook is organized with the following sections:

**•** Section 2.0 focuses on ORS Technologies that form the backbone of the applications that generate emissions data.

**•** Section 3.0 describes the approaches and applications of the technologies described in Section 2.0 as they relate to fenceline and open

- path measurements.

**•** Section 4.0 addresses ancillary data that needs to be collected in conjunction with the ORS measure ments.ORS Handbook, Section 1.3 Revision No: 1.0

Date: 12 / 01 /11 Page 17 of 33

**•** Section 5.0 discusses data validation and verification.

1.3.2 Overview of Handbook Sections

This Handbook discusses remote measurement technologies, applications of those technologies, ancillary data necessary to use the remote measurement data, potential issues with using remote measurement data for emission factors development, models, and other atmospheric process needs. The discussions of remote measurements used to make the emissions measurement are in two separate sections. The first focuses only on the technologies including the specific hardware, scientific principles involved, how the pollutant conc entrations
are measured , pollutant and performance capabilities, and general strengths and limitations. The second section addresses how the technologies can and are being used to measure and monitor stationary source emissions including measuring mass emissions flux, monitoring emissions concentrations, and detecting fugitive emissions leaks. The H andbook also includes examples of remote measurement projects and readily available test reports.

Section 1.0 provides an introduction to the Handbook including background information that is necessary to understand the more detailed sections to follow. In this section you will also find a description of the EPA Quality System (QS) and how it can be used to create a data collection system that gathers data of sufficient quality for its intended use. The Measurement Quality Objectives (MQOs) in S ection 1. 4.2 will be useful to organizations planning remote measurement programs. The tables will help users to quickly review the requirements of a particular program. If an agency is required to perform a particular type of monitoring, such as
those l isted in Tables 1.1 through 1. 5 , this Handbook clearly lists all of the MQOs and calibration and accuracy criteria so that the agency can make the right choices when purchasing or upgrading its equipment for a specific program.

Section 2.0 provide s an overview of the remote measurement detection technologies that are currently available for remotely measuring pollutant emissions concentrations. Included in this
overview are discussion s of FTIR, Tunable Diode Laser (TDL), UV

- DOAS, and LIDARORS Handbook, Section 1.3 Revision No: 1.0

Date: 12 / 01 /11 Page 18 of 33
spectro scopy technologies. In addition, qualitative ORS technologies including Thermal Infrared Imaging are described. Each of these technology descriptions includes information on the basic principles of operation, the pollutants that can be monitored, typical quality control (QC) and quality assurance (QA) for the technology, strengths, limitations, example vendors, and applications.

Section 3.0 describes the predominant remote measurement applications used to deploy the detection technologies addressed on Se ction 2.0 and to quantify emissions concentrations and
flux measurements. This section also describes how the different technologies are applied to measurement methods, which is an extremely important section in this Handbook . The application desc riptions briefly summarize the activity and ex plains how the application is
verified or validated in field tests
, and details typical Q A / Q C associated with the application. Section 3.0 also describes siting considerations or information

. Each application in Section 3.0 includes a table of strengths and limitations that must be taken into account during the planning, implementation, and interpretation of field study results. Applications covered in
Section 3.0 include RPM usin g EPA Other Test Method 10 (OTM

- 10), Differential Absorption LIDAR (DIAL), Tracer Dilution Correlation (TDC), Solar Occultation Flux (SOF), and bLS emissions modeling. Section 3.0 provides examples of how these applications are used in fugitive emissions and area source emissions flux and concentration measurement, site remediation, plant fenceline monitoring, fugitive leak detection, and ambient air measurement.

Section 4.0 presents the ancillary measurements and data that may be needed for each ORS appl ication. Ancillary data may include meteorological measurements, industrial process information and source activity necessary to translate ORS results generated from the detection technique and measurement application combinations described in Sections 2. 0 and 3.0, respectively, into emission data that meet project specific data quality objectives.

Section 5.0 addresses various methods to validate and verify remote measurement data.ORS Handbook, Sectio n 1.4 Revision No: 1.0

Date: 12 / 01 /11 Page 19 of 33 

### 1.4 General Discussion of the EPA Quality System

The EPA recently issued new guidance on its Quality Program (QP) policy. The document, “EPA Quality Program Policy” 1 1 states that this policy:

**•** Recognizes existing policies and procedures as the foundation of an Agency

- wide Quality Program ,

**•** Establishes an approach for identifying and addressing Agency quality issues,

**•** Provides a structure and procedures to ensure and enhance the effectiveness of the Quality Program and its application to Agency products and services.

Systematic planning is a key project

- level component of the EPA QPs and is a component of the QS as sh own in Figure 1.5.

The EPA policy is based on the national consensus standard, ANSI/ASQC E4

- 1994, Specifications and Guidelines for Environmental Data Collection and Environmental Technology Programs, developed by the American National Standards Institute and the American Society for Quality (ANSI/ASQC)

. 1 2 The ANSI/ASQC E4

- 1994 specification is
consistent with the international standard ISO 17025. The ANSI document describes the necessary management and technical elements for developing and implementing a QS by using a tiered approach. The standard recommends documenting : (1) each organization

- wide QS in a Quality Management Plan (QMP) or Quality Manual (to address requirements of Part A: Management Systems of the standard) and (2) the applicability o f the QS to technical activity

specific efforts in a Quality Assurance Project Plan (QAPP) or similar document (to address the requirements of Part B: Collection and Evaluation of Environmental Data of the standard).
The EPA has adopted this tiered approach for its mandatory agency

- wide QS. This document addresses Part B requirements of the standard for systematic planning for environmental data operations.

In accordance with EPA Order 2106.0 1 1

, the EPA requires that env ironmental programs performed for or by the Agency must be supported by data of the type and quality appropriate to their expected use. The EPA defines environmental data as information collected directlyORS Handbook, Sectio n 1.4 Revision No: 1.0

Date: 12 / 01 /11 Page 20 of 33 from measurements, produced from models, or compil ed from other sources such as databases
or literature.

1.4.1 Data Quality Objectives

EPA Order 2106.0 requires that all EPA organizations (and organizations with extramural agreements with EPA) follow a systematic planning process to develop acceptance or perf ormance criteria for the collection, evaluation, or use of environmental data. A systematic planning process is the first component in the planning phase of the project tier (see the bottom tier of Figure 1.5), while the actual data collection activities take place in the implementation phase

.

Internal EPA Policies EPA Order CIO 2106.0

EPA Manual CIO

2106-P-01-0 External Policies Contracts - 48 CFR 46 Assistance Agreements - 40 CFR 30, 31, and 35

Project Tier Consensus Standards ANSI/ASQC E4 ISO 9000 Series ISO 1700 Series

Defensible Products and Decisions EPA Program Policy 40 CFR

Quality System Documentation (e.g., QMP)

Supporting Systems Elements (e.g., Procurements, Computer Hardware/Software Training/Communication (e.g., Training Plan,

Conferences)

System Assessments (e.g., Quality System Audit) Policy Tier

EPA QA & Program Policies

Annual Review and Planning (e.g., QA Annual Report and Work Plan)

Data Verifications and Validation

Standard Operating Procedures Technical Assessments Data Quality Assessment

ASSESSMENT PLANNING

Reporting IMPLEMENTATION Systematic Planning (e.g., DQO Process) Conduct Study / Sampling

QA Project Plan Program Tier

Monitoring Org. Overall Quality System

Monitoring Org. Project Specific Quality System

Figure 1

- 5

. The EPA Quality SystemORS Handbook, Sectio n 1.4 Revision No: 1.0

Date: 12 / 01 /11 Page 21 of 33 Systematic planning is a planning process based on the scientific method and includes concepts such as objectivity of approach and acceptability of results. Systematic planning is a common

sense, graded approach to ensure that the level of detail in plann ing is commensurate with the importance and intended use of the work and available resources. This framework promotes communication among all organizations and individuals involved in an environmental

program. Through a systematic planning process, a team can develop acceptance or performance criteria for the quality of the data collected and for the quality of the decision. When these data are being used in decision

- making by selecting between two clear alterna tive conditions (e.g., compliance/non

- compliance with a standard), the EPA’s recommended systematic planning tool is called the Data Quality Objective (DQO) Process.

The DQO Process is a seven

- step planning approach to develop sampling designs for data

c ollection activities that support decision

- making. This process uses systematic planning and statistical hypothesis testing to differentiate between two or more clearly defined alternatives.

Step 1. Define the problem.
Step 2. Identify the problem and the associated decision(s)

. Step 3. Identify information needed for the decision.
Step 4. Define the boundaries of the study.
Step 5. Develop a decision rule.
Step 6. Specify limits on decision errors.
Step 7. Optimize the design for obtaining data.

The DQO Process is iterative and allows the planning team to incorporate new information and modify outputs from previous steps as inputs for a subsequent step. Although the principles of systematic planning and the DQO Process are applicable to all scie ntific studies, the DQO Process is particularly designed to address problems that require making a decision betweenORS Handbook, Sectio n 1.4 Revision No: 1.0

Date: 12 / 01 /11 Page 22 of 33
two clear alternatives. The final outcome of the DQO Process is a design for collecting data (e.g., the number of samples to collect and wh en, where, and how to collect samples).

The development and implementation of a QS should be based on a “graded approach,” that is, the components and tools of a QS (Figure 1.5) apply according to the scope and nature of an organization, program, or proj ect and the intended use of its products or services. This approach recognizes that a “one size fits all” approach to quality management is not appropriate and that the QS of different organizations and programs should (and will) vary according to the spe cific needs of the organization. For example, the quality expectations of a research program are different from those of a regulatory compliance program because the intended use of the products differs. The same applies to remote sensing data. Monitorin g agencies that use this Handbook are strongly encouraged to understand their data objectives, perform the DQO Process if needed, and use the MQOs described in Section 1. 4 .2 if they are applicable to an agency’s program. Additional explanation and details on the DQO Process
can be found in EPA’s Guidance on Systematic Planning Using the Data Quality Objectives
Process

. 12

When an agency or entity is monitoring for non

- regulatory purposes (e.g., background concentrations, modeling applications, or exposure), these MQOs are recommended
information

. Meeting MQOs for non

- regulatory meteorological monitoring is strongly advised

.

1.4.2 Measurement Quality Objectives

Once DQOs are designated for a program or project, measurement indicators must be determined to understand if the DQOs are being met. Most s tate/local/tribal agencies that collect data do so to support programs that are federally mandated or that need to meet federal requirements. However, other non

- regulatory applications exist, such as modeling applications, state implementation plan development, and forecasting. These programs require different MQOs because the appli cation is different (i.e., different DQOs). The following prescribed objectives should be decided and discussed within the QS

.ORS Handbook, Sectio n 1.4 Revision No: 1.0

Date: 12 / 01 /11 Page 23 of 33

**•** Measurement

. Type of measurements and/or the parameter needed to be collected.

**•** Method

. The method is different from the mea surement in that a particular instrument can be utilized in different methods. The method will dictate the precision, bias, and representativeness of the sampling data.

**•** Reporting Units

. Reporting units must be decided before the program begins. If it is a regulatory program, then ppb or micrograms per cubic meter ( μ g/m 3 ) would be the appropriate units. However, if it is a modeling exercise, then grams per second (g/s) may be the appropriate unit.

**•** Detection Limits

. It is very critical to state the levels of detection (LOD) for a particular program. The LOD can be very difficult to quantify until the ORS is actually in the field of operation. It should also be noted that LODs can be defined in different ways. It is best to define and state LOD in t he quality documents developed for a particular program.

**•** Minimum Sample Frequency

. This objective is required to define how often data must be collected to meet the end user’s requirements for precision and representativeness. Measurements must be taken often enough to meet model or modeling input criteria.

**•** Completeness

. For most programs/projects, there is a minimum amount of data required to allow the data users to make decisions concerning the environment. A rule of thumb is 75 to 85 percent data completeness.

**•** Precision

. Precision is the measure of agreement among repeated measurements of the same property under identical conditions. This can be very difficult to measure using ORS.

**•** Bias

. Bias is the systematic or persistent distortion of a measurement process that causes errors in one direction. Bias, like precision, can be very difficult to determine with ORS. The project or program must be able identify and determine the magnitude of its measureme nt bias.

**•** Representativeness

. Representativeness is collection of the measurement location, frequency, duration, and other factors that demonstrate the results correspond to the emission characterization required by the data users.ORS Handbook, Section 1.5 Revision No: 1.0

Date: 12/01 /11

Page 24 of 33 

### 1.5 Selecting the Right Tool: Technology/Application Cross

- Table References

This section outlines the possible “cross

- over” of different technologies (i.e., instruments) with the applications (i.e., methods) described in this Handbook

. Tables 1

- 1 and 1

- 2 are not meant to be the final word on how these technologies and app lications can be utilized, but serve as a reference for those seeking a match between the technologies and applications. As applications and technologies expand and new technologies and applications are developed, it is foreseen that these tables will exp and as well.

Table 1

- 1

. Technology vs. Application Cross

- Table

Emission

- Rate Estimation Methods

Active ORS Technology Point Source Area Source Stationary and Mobile

Tracer Correlation Vertical Radial Plume (VRPM) Solar Occultation Flux (SOF) Plane Concentration Flux* Back Lagrangian Stochastic

(bLS)

Fourier Transform Infrared (FTIR)

Ultraviolet (UV) and UV

- DOAS

Tunable Diode Laser (TDL )

Differential Lidar (DIAL)

Cavity Ring down Spectroscopy (CRDS)ORS Handbook, Section 1.6 Revision No: 1.0

Date: 08/19/11

Page 25 of 33 

### 1.6 Strengths and Limitations

Tables 1

- 2 and 1

- 3 provide summaries of each technology and each application’s strengths and limitations, respectively. More detail is provided for each in the sections specific to the technology or application being investigated.

Table 1

- 2

. Technology Strengths and Limit ations Strengths Limitations Fourier Transform Infrared Spectroscopy

– 2.1 Relatively low instrument cost

(about $80,000

- $125,000) Gas

- phase water spectral interference as well as CO and CO 2 interference 5,1 4 ,1 5 FTIR equipment is fairly rugged and portable Typical set

- up time can range from several minutes to several hours and a minimum of two people There are a large number of compounds that are infrared active (absorb IR light) Weak IR absorption features for many inorganic molecules such as sulfur dioxide and nitrogen oxides 6

Large number of compounds can be analyzed simultaneously Infrared beam has a limited range and may not be sensitive enough to meet ambient data quality objectives. Maximum path length is on the order of 400

– 500 meters

. No gas calibration standards necessary (uses standard reference spectral library) Multiple vertical or horizontal path measurements necessary to calculate plume flux, can require significant time and cost to set up and implement
FTIR can be used to locate discrete emissions hotspots at a facility/area source . Some instruments are electrically cooled and sensitive Typical infrared detectors require cryogenic cooling to operate. Liquid nitrogen used for detector cooling must be refilled and maintained regularly (weekly). Multi

- compound coverage makes FTIR ideal for leak detection or source location where the facility being monitored has multiple compounds present Not applicable to homonuclear diatomic gases such as chlorine, oxygen, and nitrogen 1,2,3

Equipment can be allowed to run unattended for

extended periods with remote access to check instrument operation and recover data Single beam open

- path method measures concentration along a path. The path must capture

most if not all of an analyte plume to provide accurate measure of emissions. No sample collection, handling, or preparation is necessary Field implementation and data collection requires highly experienced personnel
Tunable Diode Laser

– 2.2

Field u nits are light weight
, typically under 75 Kg, and relatively easy to transport and setup Detects o nly o ne c ompound per l aser

.
Less expensive than alternative technologies F ewer measurable compounds and limited sensitivity. Wide linear response resulting in measurements from 0.1 to 1
, 000 ppm for many target compounds. Quantitation l imited to c ompounds with o vertone
a bsorbencies in the n ear

- and m id

- IR r angeORS Handbook, Section 1.6 Revision No: 1.0

Date: 08/19/11

Page 26 of 33
Table 1

- 2

. Technology Strengths and Limitations (continued) Strengths Limitations
Tunable Diode Laser

– 2.2 (continued) The ability to measure longer path lengths (1 km compared to other ORS technologies) With all open path optical measurements, blocked beams result in no measurements Robust f ield use with low maint e nance, minimal consumables to operate Rapid response

– typically 1 second

Unattended measurements collection
Minimizes interference from other gases resulting in high compound specificity Lower equipment cost per measurement, ability to multiplex signals
UV

- DOAS

– 2.3
24/7 remote monitoring Set up of remote receiving optics or reflectors can be difficult Low cost long term deployment Fixed observation area. Long term deployment depends on constant wind direction. Monitoring of three criteria pollutants and trace species simultaneously as per 40 CFR 58. A number of species do not have appropriate UV

visible absorption structures making them undetectable by UV

- DOAS Absolute measurement system

– off line or laboratory gas calibra tions can be performed Accurate reference spectra needed

– reference spectra can be a source of error Typical path length is 500 m, Long er measurement path length possible

– up to 10 km. Long path lengths can cause complications when analyzing results Non

- contact measurements can detect unstable atmospheric species. Affected by poor visibility conditions
Can provide near real time data. Retro reflector or remote optics do not allow collection of vertical plume evolution data
Portable . Can be used in mobile app l ications

LIDAR

– 2.4
In contrast to PIC measurements taken by other instrumentation system, DIAL relay concentration data as a function of distance along the beam path Measureable species are limited to those with unique absorption bands. Chemical species with common absorption characteristics can only be measured as classes of compounds

Reported path lengths up to 3000 meters The absorption wavelengths of species are temperature and pressure dependent. It is

necessary to check the applic ability of wavelengths selected for measurement based on

temperature and pressure variation in target absorption. Collects backscattered light without the use retro

reflectors Sufficient aerosol or molecular material must be in the atmosphere to create sufficient backscatterORS Handbook, Section 1.6 Revision No: 1.0

Date: 08/19/11

Page 27 of 33
Table 1

- 2

. Technology Strengths and Limitations (continued) Strengths Limitations
LIDAR

– 2. 4 (continued)

Scan along measurement plane requires 10 to 15 minutes Rapidly changing wind speed or direction may cause measurements to change rapidly and may affect mixing ratios of measured surrogates to compounds of interest. Instrumentation is moved around measurement site obtaining multiple plume scans from various locations increasing accuracy of plume characterization Small numbe r of vendors providing DIAL systems and services

Instrumentation can also be mounted on airborne

platform for increased mobility and expanded applications High cost has limited t he number of commercial DIAL studies in the United States
Real time data allows for leak identification and inputs to process change decisions Beam path is limited by the geographical constraints of the measurement location Allows for simultaneous concentration measurements of multiple species and classes of species Only two wavelengths are used operationally, this makes it difficult to post process data and investigate spectral artifacts IR Camera

– 2.5 Fast screening speed compared to conventional leak detection methods Cannot quantify the concentration of a leak without additional technology. Leak assessment can be done without interruption to plant operations. Camera use requires individuals with specific training. Some models are easier to use than others. Cost

- effective compared to traditional leak detection methods. Cannot be used during rain or fog and is not as effective during overcast skies. Accurately assess the size of each leak. The camera has a specified nominal operating range for ambient temperature. Better able to isolate the exact source of a leak despite close proximity to other leaking sources in real time and record in a video format. Operation is not intrinsically safe and use is limited in hazar dous areas.
Wide field of view: More likely to identify leaking components in unconventional places. Exposure risk minor because leaking components can be viewed at a distance. Cavity Ring Down Spectroscopy

– 2.6 Minimal maintenance required and no consumables are needed. Turnkey operation with the potential for remote access and control. “User friendly”. May need to apply sample filtering components to avoid interferences.
Ability to measure very small changes in short time frames. Can rapidly scan spectra continuously for high temporal resolution and real

time results. Limits the method to the laser spectral ranges available.
Greatly increases sensitivity with much longer effective pathlengths. Insensitive to vibrations during measurements. Are only able to reflect over a small wavelength range (about ± 15%) relative to the center wavelength. Multiple species detection difficult.ORS Handbook, Section 1.6 Revision No: 1.0

Date: 08/19/11

Page 28 of 33
Table 1

- 2

. Technology Strengths and Limitations (continued) Strengths Limitations Cavity Ring Down Spectroscopy

– 2. 6 (continued) Allows for extended wavelength range scanning, increasing sensitivity by probing multiple absorption lines while also eliminating other interferences. Key components that typically drive up the cost of the instrumentation, d epending on application.

Minimal

- to

- no drift making frequent calibration unnecessary. Enhanced accuracy and system stability. Renders the method immune to ambient changes (such as relative humidity and temperature) and laser intensity fluctuations. Also produces a large linear dynamic range. Little

- to

- no sample pre

- conditioning or treatment required before analysis. Easy field deployment and installation. Quick sample exchange in a smaller volume cavity with moderate flow rates. Advances in com ponents allow for a fairly rugged portable system. Logistically simpler for field use to eliminate the need for a large power source.

Table 1

- 3

. Application Strengths and Limitations
Strengths Limitations

OTM 10 Radial Plume Mapping

- 3.1
Measures high temporal and spatial emission data. Reliant upon ideal meteorological conditions.
Directly calculates emission rates. Difficulties in characterizing emissions from side

sloping areas. Captures the distribution of all major emissions in an area and isolates emissions from specific measurement areas. Larger uncertainty associated with capturing emissions from sources far away from the VRPM configuration. Can provide real

- time PIC data for multiple compounds simultaneously. Compounds can create interference depending on the technology used. This can be avoided by using to systems, but this is very expensive. DIAL

– 3. 2
See strengths and limitations listed in the technology table

.ORS Handbook, Section 1.6 Revision No: 1.0

Date: 08/19/11

Page 29 of 33
Table 1

- 3

. Application Strengths and Limitations (continued)
Strengths Limitations

Solar Occultation Flux

– 3. 3 Increases measurement accuracy by reducing uncertainty. System requires vibration reduction platform and a smooth mobile path.
Decreases instrumental complexity for field operations and reduces amount of scattering errors in the UV. Calculations based on wind speed measurements inherently add uncertainty due to the stochastic, uncontrollable, and highly variable nature of wind speed. Multiple species detection over a wide range of wavelengths. Uncertainty of plume height increases measurement error from wind speed term. More suitable for frequent field application. Inappropriate to make measurements in the presence of clouds.
Decreas ed cost and easier field application. Difficulty in separating emissions sources that are close together. Higher specificity and better signal

- to

- noise (relative to DIAL). Corresponds to unstable meteorological conditions where wind gradients due to con vection are smoothed out.
Tracer Correlation

– 3. 4 Provides the ability to determine if varying meteorological conditions affect the calculation of emission rates Logistical challenges such as the availability of roads and location. Ability to calculat e emission rates within 15

- 30% precision. Tracer gas cylinders are expensive and more difficult to transport. Changing weather conditions affect the calculation of emission rates. Backward Lagrangian Stochastic

– 3. 5 Requires only a single concentration measurement

–

as opposed to many concentration measurements

made in the vertical or horizontal plane of the plume Assumes ideal atmospheric conditions . Uncertainty of emission height can cause errors in predictions

Substantial freedom to cho ose convenient measurement locations Placement of atmospheric sensors relative to source can have a major effect on the quality of the predictions generated by the model
An economical alternative for determining emissions When multiple unknown sources are present the calculations are very sensitive to sensor placement

Handles complex source shapes and sizes with relative ease Rapid atmospheric changes or extreme stability invalidate MOST and cause Q bLS estimates to be inaccurate
Free downloadable software available online Very experienced personnel required to generate high

- quality data from the bLS modelORS Handbook, Section 1.6 Revision No: 1.0

Date: 08/19/11

Page 30 of 33
Table 1

- 3

. Application Strengths and Limitations (continued)
Strengths Limitations
Backward Lagrangian Stochastic

– 3. 5 (continued) Meteorological data can be directly entered into the

model based on field measurements or the model can estimate them using data variables developed from standard empirical relationships Where flow obstacles such as buildings or fences

are present, measurements are often better made further downwind of the source, well away from the obstacles

. Can be used in locations with wind disturbances Short to l ong computational time Cavity Ring Downs Spectroscopy

– 3.6

See strengths and limitations listed in the technology table.

Table 1

- 4 summarizes the quantitation range for each of the optical technologies, as well as the expected sensitivity, accuracy, and precision. Where possible, quantitative values verified by the EPA’s Environmental Technology Verification (ETV) Program were used. This table is a general comparison between the different types of optical technologies. The parameters listed

are dependent upon fact o r s such as the chemical properties of the analyte measured, path length and signal strength
, and may be better or worse for each specific site specific application depending on these conditions. It is also valuable to note that quantitative limits of eac h technology establish the limit for application in which they are used (i.e., the sensitivity of vertical radial plume mapping ( VRPM ) is limited by the FTIR or TDL technology used to collect the measurements).
Table 1

- 4

. Quantitative Summary of ORS Tech nologies

TECHNOLOGY TYPICAL RANGE OF APPLICATION SENSITIVITY (DETECTION LIMITS) ACCURACY PRECISION (RSD)

FTIR 1 4 0

- 50 ppm < 1.0 ppmm 0

- 30% < 1.0%

TDL 1 5 ,1 6 0

- 800 ppm < 15.0 ppmm 0

- 40% < 5.0%

UV

- DOAS 1 7 , 1 8 0

- 1 00 0 ppb < 0

. 1 0 pp b m 0

- 20% < 5.0%

LIDAR 0

- 120 ppb low

- ppb Range 0

- 15% < 1.5%

IR Camera N/A N/A Qualitative Only Qualitative OnlyORS Handbook, Section 1.7 Revision No: 1.0

Date: 08/19/11

Page 31 of 33 

### 1.7 Future Evolution and Updates of this Handbook

The EPA will periodically update and correct this Handbook

. Updates will include the addition
of new information as well as feedback from stakeholder s. T his document will be updated , at the discretion of the EPA, depending on the availability of resources.

This document doe s not contain EPA policy information; it is strictly a n information document.
I t is envisioned that in later editions, new ORS technologies and techniques will be described in
this document.ORS Handbook, Section 1.8 Revision No: 1.0

Date: 08/19/11

Page 32 of 33 

### 1.8 References

1. U

. S

. DOE/ORNL. 1999. NARSTO Measurement Met hods Compendium.
< http://narsto.ornl.gov/Compendium/methods/DOAS.htm >

. 20 May

.
2. U

. S

. EPA. 2010 Sources of Pollutants in the A mbient Air

– Stationary Sources.
< http://www.epa.gov/apti/course422/ap3b.html >

. 15 July

.
3. Earthworks. Sourc es of Oil and Gas Air Pollution.
< http://www.earthworksaction.org/airpollutionsources.cfm >

.
4. U

. S

. EPA. Criteria Pollutants. 2010. < http://www.epa.gov/apti/course422/ ap5.html >

. 19 January.

5. U

. S

. EPA. Hazardou s Air Pollutants. 2010.
< http://www.epa.gov/apti/course422/ap6.html >

. 29 January.

6. U

. S

. EPA. Welfar e Effects

– Greenhouse Effect. 2010.
< http://www.epa.gov/apti/course422/ap7b2.html >. 15 July.

7. U

. S

. EPA. Welfare Effects

– Stratospheric Ozone Depletion. 2010.
< http://www.epa.gov/apti/course422/ap7b3.html >

. 15 July. 8. Other Test Method 10 (OTM 10)

- Optical Remote Sensing for Emission Characterization from Non

- Point Sources, FINAL ORS Protocol, June 14, 2006, http://www.epa.gov/ttnemc01/prelim.html

9. Ednar, H., P. Ragnarson, and E. Wallinder. 1995. Industrial Emission Control Using Lidar Techniques. Environ. Sci. Technol

. 29:330

- 337.

10. Mosher, B. W.; Czepiel, P. M.; Harriss, R. C.; Shorter, J. H.; Kolb, C. E.; McManus, J. B.; Allwine, E.; Lamb, B. K. 1999. Methane em issions at nine landfill sites in the northeastern United States. Environmental Science & Technology

. 33 , (12): 2088

- 2094.

11. US EPA. 2008. US EPA Quality Policy
, CIO 2106.0, October 2008

. http://www.epa.gov/quality/qa_docs.html

12. Guidance on Systematic Planning Using the Data Quality Objectives Process EPA QA/G

- 4, EPA/240/B

- 06/001 February 2006, http://www.epa.gov/quality/qa_docs.html

.
13. U S EPA. 2010. Implementation of Quality Assurance Requirements for Organizations Recei ving EPA Financial Assistance. http://www.epa.gov/ogd/grants/assurance.htm . 19 January.ORS Handbook, Section 1.8 Revision No: 1.0

Date: 08/19/11

Page 33 of 33
14. US EPA. 2000. Environmental Technology Verification Report: RAM 2000 Fourier Transform Open

- Path Monitor. Report prepared by AIL Systems, Inc. Battelle.

15. US EPA. 2000. Environmental Technology Verification Report: GasFinder 2.0 Tunable Diode Laser Open

- Pat h Monitor. Report prepared by Boreal Laser, Inc. Battelle

.
16. US EPA. 2000. Environmental Technology Verification Report: LasIR Tunable Diode Laser Open

- Path Monitor. Report prepared by UNISEARCH Associates. Battelle.

17. US EPA. 2000. Environmental Technology Verification Report: AR

- 500 Ultra

- violet Open

- Path Monitor. Report prepared by Opsis, Inc. Battelle.

18. US EPA. 200 1

. Environmental Technology Verification Report: SafEye 420 Ultra

violet Open

- Path Monitor. Report prepared by Spectrex, Inc. Battelle.ORS Handbook, Section 2. 0 Revision No.: 1.0

Date: 08/19/11

Pag e 1 of 86


### 2 .0 Optical Remote Sensing Technologies

ORS technologies measure the concentration of chemicals in an open air path or in contained air samples collected from discreet sampling points. They do this by measuring the interaction of electromagnetic energy (i.e., different wavelengths of light) wit h the air’s components. Open

path technologies measure the concentrations of chemicals or particulates across an open path of
air. They do this by emitting a concentrated beam of electromagnetic energy into the air and measuring its interactions with the air’s components

. Open

- path technologies provide an average concentration over a line of sight. Point

- source applications of these technologies measure the concentration of a confined sample of air drawn into the apparatus from a point or points in air

.

Some technologies (e.g., T DL ) are capable of simultaneously measuring one or two compounds. Other technologies (e.g., UV

- DOAS ) are capable of measuring several compounds simultaneously, while others (e.g., F TIR ) can measure a large number of compounds simultaneously. ORS technologies are used to measure the average chemical concentrations over a set distance or at a stationary point. The path average over a set distance has an advantage over point

- source measurements that may miss high

- concentration p lumes running between sampling devices. Both open

- path and point

- source applications of these technologies have been used to detect hotspots in area sources and to obtain path integrated averages. Each of the technologies has advantages and disadvantages for these applications. The technologies described in Chapter 2 can be used alone or in combination to provide three major types of data : plume characterization, short term flux measurements and long term monitoring studies. In Chapter 2
, we discuss ho w each of several prominent ORS technologies operates.ORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 2 of 86


### 2.1 Fourier Transform Infrared Spectroscopy

FTIR spectroscopy is an optical spectroscopy technology adapted to perform real

- time monitoring of gaseous and volatile organic compounds in air. The technique is capable of detecting and quantifying multiple compounds simultaneously, even in harsh indust rial environments, using the characteristic spectral features of the individual compounds. 1

FTIR spectrometers are well

- suited to remote sensing applications because they are durable, portable, and do not require daily routine calibration. The technology , however, is not simple to operate and requires experienced staff to ensure correct operation and valid results. Both extractive and open

- path (OP

- FTIR) environmental applications of the technology have been
demonstrated. The EPA test methods have been written for both open

- path (OTM10, TO

- 16r) and extractive (Method 318, Method 320 and ASTM D6328

- 03) measurement by FTIR. In

open

- path mode, the IR radiation beam can be directed over distances of up to 400 to 500 meters to measure selected compounds in e mission plumes or dispersed air parcels. Alternatively, in extractive mode, gas can be drawn into a closed cell with a folded path length of 10 to 100

meters. Optical remote spectroscopy applications focus on open

- path mode. Mobile tracer applications f ocus on extractive mode.

Compound

- specific concentration is determined using standard IR spectra of known
concentration. The onboard computer software and spectra library allow real time determination of concentration for preset compounds. Post

- test pro cessing of IR spectra allow other compounds in air samples to be determined. Every measurement uses calibrated reference spectra taken at conditions similar to the unknown field samples to determine compound
concentrations therein.

2.1.1 Basic Operation

The FTIR instrument sends an IR beam of light through a region (closed

- cell or open

- path) containing the compounds of interest and captures the resulting IR spectra from the sample. Figure 2

- 1 illustrates the basic components of an open

- path FTIR spectro meter. Infrared lightORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 3 of 86 generated by an IR emitting source is guided through an interferometer. The interferometer consists of an IR source, beam

- splitter, mirrors, a laser, and a detector. The IR energy goes from
the source to the beam

- splitter, which splits the beam into two parts. One part is transmitted to a moving mirror and one part is reflected to a fixed mirror. The moving mirror oscillates back and forth at a constant velocity. Th is velocity is timed according to the very precise laser wavelength in the system, which also acts as an internal wavelength calibration. The beam reflected from the moving mirror and the beam reflected from the fixed mirror have traveled different distan ces since being generated by the source and are recombined at the beam

- splitter. 1,2

Figure 2

- 1. Diagram Showing the Beam Path and Major Components of an OP

- FTIR

When the beams are recombined, some of the wavelengths recombine constructively and some destructively, which creates an interference pattern. This interference pattern is called an interferogram. The recombined IR beam then passes from the beam

- splitter into the open

- path where a portion of the IR energy is absorbed by the gaseous compounds to be measured. The resulting IR beam reaches the IR detector where the interference pattern is detected, digitized, and transformed mathematically into a standard s ingle beam infrared frequency spectrum using IR Detector Moving Mirror Fixed Mirror

Beam

splitter

IR Source

Laser Interferometer

Reflecting Mirror Open

- pathORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 4 of 86 an algorithm known as a Fourier transform. A reference or background single beam spectrum is also collected without a sample and the ratio of the two single beam spectra is computed to produce a background corr ected transmittance spectrum. This transmittance spectrum can be converted to absorbance by taking the negative log 10 of the data points. 2

The vibrational frequencies of all the infrared absorbing molecules in the IR beam path are captured in the IR spec trum. When a molecule absorbs light, the energy of the molecule is increased and the molecule is promoted from its lowest energy state (ground state) to an excited

state. Light energy in the infrared region of the electromagnetic spectrum stimulates mole cular
vibrations. Molecular species display their own characteristic vibrational structure when stimulated by IR radiation. 3 Figure 2

- 2 shows the IR absorption spectra for nitrous oxide, CO 2 , CO
, NO
, NO 2 , and ammonia. The units of vibrational frequency are wave number. Wave number and vibrational structure are used to identify a particular molecule.

Figure 2

- 2. FTIR Absorption Spectrum Recorded at 1075 o K

Once a compound has been identified, its spectrum can also be used to measure the compound’s
concentration because the amount of IR radiation absorbed from the IR beam is proportional toORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 5 of 86 the concentration of the compound in the sample or open path. Accord ing to the Beer

- Lambert law, there is a linear relationship between absorbance and concentration as shown in Equation
1. 1,2,3



# cl A **ε =**  Eq. 1
Where: A = absorbance intensity

ε = absorption coefficient c = sample concentration
l = sample path length

FTIR systems typically operate in two modes: extractive cell or open

- path. Extractive cell measurements can be conducted either from a single location or from a mobile measurement platform. 4 I n the field, OP

- FTIR systems can operate with telescopes transmitting and receiving the IR beam so monitoring of long outdoor paths is possible. The pollutants normally measured in this process are at ambient temperature and usually in the low ppb concentr ation range. 5 Typical applications of open

- path monitoring include fence

- line monitoring of industrial sites, landfill sites, waste lagoons, urban air monitoring in metropolitan areas, accidental release detection/identification, and detection of agents or surrogates important to homeland security monitoring applications. 6,7 It should be noted that moisture due to fog or high humidity will cause spectral interferences, which can limit the use of this technique to pollutants that do not have overlapping ab sorption features with gas

- phase water. 5

Extractive (Closed) Cell Measurement Applications

For extractive or closed

- cell FTIR measurements, the beam is sent through a cell that is mounted
in the instrument itself. As shown in Figure 2

- 1, the IR beam pas ses through the cell and is
focused onto the detector. Gas

- phase samples are pumped into a sealed, constant temperature cell and analyzed. Sample cell path lengths can vary from 10 cm to 150 m folded

- path cells. The longer the path length the more sensi tive the measurement becomes because the IR beam has a greater chance of interacting with the absorbing compounds. The pollutants measured inORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 6 of 86 this type of arrangement are usually at higher concentrations than those found in open

- path FTIR

measurements. T ypical applications of extractive FTIR monitoring include stack testing of flue gases and vehicle exhaust. 6,7 Figure 2

- 3 shows a photo of an FTIR unit used for extractive monitoring of stack gas. This unit is equipped with a 32 m folded path length cell that extends
from the end of the instrument.

Figure 2

- 3. FTIR Closed Cell Unit Used to Monitor Stack Gas

Open

- Path Measurement Applications

There are a number of instrumental configurations for open

- path ( OP ) instruments. The simplest
OP systems are bistatic configurations. This configuration derives its name from the fact that both the transmitter and receiver must be fixed in a static position and precisely aimed at each
other. The OP

- FTIR equipment projects the IR light beam directly along a path to a
detector/receiver. Bistatic configurations in general have the requirement of supplying power at both the receiver and transmitter, which can be a disadvantage in some locations. Additionally
, there is a requirement for alignment at bot h receiver and transmitter, which can be time

consuming for mobile systems. 8

Monostatic configurations were developed to address issues raised with bistatic designs. In a monostatic configuration, all of the optical components of the transmitter and rece iver are in the same location, and a retro

- reflector is used to return the light from the transmitter to the receiver. This configuration derives its name from the fact that only the transceiver portion of the instrument needs to be precisely pointed as t he retro

- reflector returns light to its sourceORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 7 of 86 regardless of orientation. A diagram of a typical monostatic configuration is shown in Figure 2

- 4.

Figure 2

- 4. Basic Setup Used to Make Monostatic Open

- path FTIR Measurements

Retro

- reflecting mirrors, as they are called, are configured with three perpendicular reflective surfaces in the shape of a corner. A combination of three mutually perpendicular mirrors reflects light incident from any direction through 180° as shown in Figure 2

- 5. Such a combination of
mirrors is called a corner

- cube reflector. Corner

- cube reflectors beam FTIR light back to its exact point of origin. This property reduces the divergence of the beam on its return path back to the detector compared to divergen ce that would result from a flat mirror. Also
, the retro

reflector array can be very large to capture and return essentially the entire divergent signal from the telescope.

( http://farside.ph.utexas.edu/teaching/316/lectures/node133.html ) Figure 2

- 5. The Corner

- cube ReflectorORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 8 of 86

In the monostatic mode, the IR laser beam is split twice, once leaving the OP

- FTIR and once on
its return. This design requires a beam splitter in th e optical path that removes 50 percent of the light from the outgoing beam and 50 percent of the light from the return beam for an overall loss of 75 percent of the total light intensity.

The dual

- telescope monostatic configuration has lower detection lim its because it does not utilize a beam splitter in the optical path. A translating retro

- reflector, which is essentially a portion of a very large cube, is used to return the light beam offset to align with the receiving telescope. This single, large ret ro

- reflector does not have the divergence reversal properties of the corner

cube array. The second telescope adds cost and complexity to the system. 8 However, when compared with monostatic mode, bistatic systems are harder to align and maintain because any shift in the transmitter or detector can result in system misalignment. 7,9 B oth operating modes measure only the compounds that are in the beam path. Emissions outside the beam path are not
measured. In these situations, measurements have been condu cted along multiple beam paths to more accurately characterize the emission plume.

Figure 2

- 6 shows a telescopic FTIR transmitting and detection unit, which would be used for

open

- path field monitoring applications. Figure 2

- 7 shows a typical retro

- refle cting mirror. The retro mirrors are surface coated with a reflective material to reduce extraneous glare from outside stray light. Retro

- reflecting mirrors are often contained in a protective housing, which is closed when the unit is not in use to protec t the sensitive reflecting surfaces from exposure to inclement
weather conditions.ORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 9 of 86

Figur2

- 6. Typical Telescopic Figure 2

- 7. Retro Reflecting Mirror Used to FTIR Transmitting and Detection Unit R eflect the IR Beam Back to Det ection Unit

2.1.2 Pollutants and Relative Levels That Can Be Measured

Table 2

- 1 provides an example list of compounds that have been measured using OP

- FTIR spectroscopy. 6,7 T his list is not all

- inclusive, but shows that many compounds can be measured
via OP

- FTIR. Another feature of OP

- FTIR is that many compounds can be monitored simultaneously as opposed to other beam technologies that can monitor only single compounds. As with other optical sensing systems, OP

- FTIR produces a path integrated concentration (PIC)
in units of parts per million ( ppm ) or ppb times length, i.e., ppb (meters). 6 D ividing the final ppb (meters) result by the total optical path length gives the p ath integrated gas concentration in ppb.ORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 10 of 86
Table 2

- 1. Example List of Compounds Measured by FTIR Open

- path Systems Species acetaldehyde 1,4

- dimethyl piperazine methyl mercaptan

acetic acid 1,4

- dioxane methyl methacrylate

acetone ethane 2

- methyl propene acetonitrile ethanol morphaline

acetylene ethyl acetate nitric acid acrolein ethylamine nitric oxide acrylic acid ethylbenzene nitrogen dioxide acrylonitrile ethylene nitrous acid

ammonia ethylene oxide ozone benzene ethyl mercaptan pentane 1,3

- butadiene formaldehyde phosgene butane formic acid phosphine butanol furan propane 1

- butene halocarb

- 11 (CCl3F) propanol cis

- 2

- butene halocarb

- 12 (CCl2F2) propionaldehyde
trans

- 2

- butene halocarb

- 22 (CHClF2) propylene butyl acetate halocarb

- 113 (CFCl2CF2Cl) propylene dichloride carbon disulfide hexafluoropropene propylene oxide carbon monoxide hydrocarbon continuum pyridine carbon tetrachloride hydrogen chloride silane

carbonyl sulfide hydrogen cyanide styrene chlorobenzene hydrogen sulfide sulfur dioxide chloroethane isobutene sulfur hexafluoride chloroform isobutanol 1,1,1,2

- tetrachloroethane

m - cresol isobutyl acetate 1,1,2,2

- tetrachloroethane

o - cresol isobutylene tetrachloroethylene p - cresol isoprene toluene cyclohexane isopropanol 1,1,1

- trichloroethane 1,2

- dibromoethane isopropyl ether 1,1,2

- trichloroethane

m

- dichlorobenzene methanol trichloroethylene

o

- dichlorobenzene methylamine trimethylamine p - dichlorobenzene methyl benzoate 1,2,4

- trimethylbenzene 1,1

- dichloroethane methyl chloride vinyl chloride 1,2

- dichloroethane methylene chloride m - xylene 1,1

- dichloroethylene methyl ether o - xylene dimethylamine methyl ethyl ketone p - xylene dimethyl disulfide methyl isobutyl ketone Compounds in bold are EPA Hazardous Air Pollutants (HAPs) CAA

- 112Title 42, Chapter 85, Subchapter I, Part a U.S. Code 7412 (b)ORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 11 of 86 Detection limits can vary widely from compound to compound depending on a number of factors such as instrument configuration, the condition of retro

- reflecting mirrors, humidity, beam path length and the absorbance strength of the target compound(s) at the wavelength chosen for analysis. Detection limits are typically reported in ppm for one meter of path length (ppmm). They can be determined empirically using cell based measurements or estimated by solving equation 1 for an absorbance that is three times the mean signal noise if the absorbance coefficient and noise are known at the wavelength used to measure the compound(s) of interest.
Detec tion limits for specific sampling episodes are calculated by dividing ppmm by the actual meters of path length during field sampling. Typically, FTIR manufacturers report detection limits for commonly monitored pollutants as part of the literature for the ir instrumentation. In general, open

- path FTIR detection limits in the single digit ppb levels can be achieved for a number of strong IR absorbing compounds. 7,10 Extractive FTIR detection limits for a 10 meter folded path length are typically on the orde r of 1 to 10 ppm. Some compounds such as benzene
have detection limits in the 30

- 50 ppb range because gas

- phase water interferes with this

measurement. 7 Other compounds
, such as hydrogen sulfide
, are weakly IR absorbing molecules
and have detection limit s in the 300

- 800 ppb range. 7

2.1.3 Typical QA/QC

To ensure measurement accuracy and data verification, instrumentation response should be verified annually (detector and IR source) using a known concentration of a standard gas
mixture. Certificates of calibration should be kept on file and available for review. Maintenance records should be kept in bound notebooks for any equipment adjustments or repairs that could affect measurement performance. Maintenance notebooks should include th e date and description of maintenance performed. Calibration checks should be performed after major service and regularly during analysis. 11,12 Q A and QC procedures for the measurement of gaseous compounds by extractive FTIR are discussed in great detail in EPA Test Method 320 or
ASTM D6328

- 03. These procedures for OP

- FTIR are discussed in more detail below.ORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 12 of 86 Calibration Spectra

A gas

- phase FTIR reference spectrum is collected at a known temperature and pressure in a fixed path length enclosed cell for the compound of interest from a sample of known concentration. A
series of measurements can be made at different concentrations a nd a calibration curve that relates the measured absorbance and the gas concentration can be developed to confirm a linear response of signal with concentration. These calibration spectra are stored in a spectral reference library used by the computer dur ing real

- time sample processing. Several national databases exist of IR spectra collected under different conditions that can be used for sample analysis. Because Beer’s law is theoretically linear, often only a single point calibration is necessary to
d etermine sample concentration. Spectra collected over a range of concentrations allow instrumental nonlinearity to be evaluated. Use of spectra at or near the concentration measured in the field reduces bias caused by nonlinearity. Unknown sample concen trations can be determined by comparing sample absorption intensities to absorption intensities in the standard reference spectra. The higher the concentration of compound being measured, the more IR radiation characteristic of that compound is absorbed. Complex mixtures of IR sensitive compounds can be determined for a single spectrum by solving a multiple linear regression matrix using characteristic wavelengths of compounds and the relative intensities of sample IR spectral features compared to calibra tion spectral features.

Tables of absorbance coefficients are available
, and standard reference spectra for numerous compounds can be purchased. S uppliers of reference spectra include Pacific Northwest National Laboratory, which continues to develop the Northwest Infrared (NWIR) spectral library of quantitative infrared absorption spectra 13 and the high

- resolution transmission molecular absorption database (HITRAN) compiled by Harvard University. 14

Figure 2

- 8 shows an example of a typical single point ca libration curve where the sample absorbance is plotted against concentration. Interpolation of the curve at a given absorbance measurement gives the concentration of the molecular species being analyzed.ORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 13 of 86

Figure 2

- 8. Calibration Plot of Absorbance vs. C oncentration

QA/QC for OP

- FTIR Instrumentation

Several quality checks should be performed on FTIR instrumentation prior to deployment to the field and for the duration of the field campaign. 15 P rior to field deployment, the spectral baseline
is checked to determine the amount of signal intensity, instrument noise, and baseline drift. Baseline drift is due to detector signal fluctuations that cause the signal to increase gradually
over time. Typically, instruments are powered on and allowed to warm up for at least one hour prior to data collection to minimize baseline drift effects. Baseline noise should be checked prior to initial data collection and on each subsequent day of a field campaign to determine the amount of baseline signal due to the instr ument’s electronics and detector noise. All checks must be in accordance with the method or test protocol being performed.

On the first day of a field study, a stray light instrument check should be performed. This involves collecting, measuring, and id entifying stray light as either background or instrument

related. All QC checks must be conducted prior to actual data collection and the results must Absorbance vs. Concentration

0 0.1 0.2 0.3 0.4 0.5 0.6

0 10 20 30 40 50 60 Concentration/ppbAbsorbanceORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 14 of 86 indicate that the instrument is operating within the acceptable criteria range as specified in the
metho d or protocol appropriate for the field testing campaign. 15 Typical quality control for this technology includes method quality objectives of 10

- 25 percent accuracy depending on path length and a precision target of 10 percent . Spectral quality is verified through the procedures and guidelines set by the manufacturers and specific EPA method in use. 16

In addition to the QC checks performed on the FTIR, the quality of the instrument signal (interferogram) should be checked regularly during the fiel d campaign. This is done by ensuring that the intensity of the signal is at least five times the intensity of the stray light signal and
instrument noise. In addition to checking the strength of the signal, checks should be done regularly in the field to ensure that the data are being collected and stored to the data collection computer. 15

Data Quality Indicators for Precision and Accuracy for OP

- FTIR

Instrument baseline noise and signal intensity are key data quality indicators for OP

- FTIR

measurements. Some investigators evaluate the precision and accuracy of the PIC measurements collected with an FTIR instrument by analyzing nitrous oxide concentrations in the atmosphere. A typical background atmospheric marker concentration for nitr ous oxide is about 315 ppb. 17 However, this value may fluctuate due to seasonal variations in nitrous oxide concentrations or the topographical elevation of the site. 17

The precision of the OP

- FTIR measurements should be evaluated by calculating the rela tive standard deviation of ubiquitous IR active compounds (e.g., nitrous oxide) in each data subset. A subset is defined as the data collected along one particular path length during one particular survey or sampling episode. 15 T he number of data points in a data subset depends on the number of sample events conducted in a particular survey. For a stable air parcel, the standard data quality indicator ( DQI ) criterion set forth for precision is ±10 percent. 15ORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 15 of 86 The accuracy of the analyte PIC measurements can be evaluated by comparing the calculated nitrous oxide concentrations from the data subsets to the typical background concentration of 315 ppb. 11 The standard DQI criterion for accuracy is ±25 percent. 15

2.1.4 Example Applications and Vendors

Details on the OP

- FTIR application of open path technologies are provided in Section 3 of this
Handbook. The OP

- FTIR has been used for a wide variety of source emission measurements in the field including applications such as line of sight optical remote, b LS modeli ng and RPM

. Table 2

- 2 summarizes optical technologies and the typical applications of each of the technologies.

Table 2

- 2. Typical Applications for OP

- FTIR. Technology Applications

OP

- FTIR bLS, RPM , SOF, Tracer Gas Correlation, TO

- 16

We are aware of multiple vendors that currently manufacture OP

- FTIR units; two of these vendors have verified their instrumentation through the EPA’s ETV program. The cost of an
OP

- FTIR field ready system can range from $75,000 to $120,000 in 2010 U.S. do llars
, depending on configuration and application. Gas standards used in fixed path length enclosed cells to confirm instrument calibration can range between $300 and $500. Table 2

- 3 lists several
of these vendors and indicates which have verified their OP

- FTIRs. The table also lists potential vendors for FTIR gas standards.

Table 2

- 3. FTIR Supply Vendors

OP

- FTIR Instruments KASSAY FSI

– *Ail Systems Inc. 18 www.kassay.com

*Spectrex, Inc. 19 http://www.spectrex

- inc.com IMACC Instruments http://www.ftirs.com/ MIDAC Corporation http://www.midac.com/ORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 16 of 86 ruker Optics http://www.brukeroptics.com/opag.html ABB/Bomem http://www.abb.com/analytical Gas Standard Suppliers** Air Gas http://www.airgas.com/

Linde http://www.linde.com/ Matheson Gas http://www.mathesongas.com/index.aspx

Spectra Gas http://www.spectragases.com Praxair http://www.praxair.com/ *ETV Verified Technologies ** Requires gas regulator in addition to gas cylinder
In addition to instrumentation and gas standards, tables of absorbance coefficients are available and standard reference spectra for numerous compounds can be purchased. S uppliers of reference spectra include Pacific Northwest National Laboratory, which c ontinues to develop the NWIR spectral library of quantitative infrared absorption spectra 13 and Harvard University, which compiled the HITRAN database. 14 These spectra have been measured under tightly controlled conditions using state

- of

- the

- art instrumentation.

2.1.5 Strengths and Limitations
FTIR can be used as a qualitative tool to provide specific information about volatile IR energy absorbing molecules. It can also be used as a quantitative tool to provide the concentration of

many gas

- phase mo lecules. A summary of strengths and limitations is shown in Table 2

- 4 and
Table 2

- 5. One of its limitations is that gas

- phase water and CO 2 are a very strong IR absorbing species. Water has strong absorption features in the 3200

– 4000 wave number range. 5 ,17,20 Molecules that have coincident vibrational frequencies with water cannot be reliably analyzed using frequencies in this range. FTIR is also limited to measuring gaseous compounds that
absorb IR radiation. Homonuclear diatomic gases such as nitrog en, oxygen, and halogen gases cannot be measured by FTIR.

FTIR’s major strength is that it can provide real

- time, simultaneous analysis of multiple gaseous
contaminants. 6 Additionally, the FTIR is a robust field instrument that allows for unattended sampling for as long as a week period. Not only can the FTIR be used for open pathORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 17 of 86 concentration measurement of a variety of contaminants, but it can also be used for leak and hotspot detection.

Table 2

- 4. Summary Table of the OP

- FTIR’s Strengths

Feature Strength

Economical Relatively low instrument cost (about $80,000

- $125,000) Low

- cost long term deployment Compact Instrumentation FTIR equipment is fairly rugged and easily portable

Multiple Wavelength Operation There are a large number of compounds that are infrared active (absorb IR light) Large number of compounds can be analyzed simultaneously . Spectra can be saved and post analyzed.

Ease of Calibration No gas calibration standards necessary for field testing (uses standard reference spectral library) . Gas standards are only needed for laboratory confirmation of instrument performance and calibration.

Multiple Applications FTIR can be used to locate disc rete emissions hotspots at a facility/area source Multi

- compound coverage makes FTIR ideal for leak detection or source location where the facility being

monitored has multiple compounds present (e.g., chemical plants)

Automated Real

- time Measurements Equipment can be allowed to run with minimal attention for months at a time with remote access to check instrument operation , schedule cryogen replenishment and recover data

. No sample collection, handling, or preparation is necessary

Table 2

- 5. Summary Table of the OP

- FTIR’s Limitations

Feature Limitation Spectral Interferences Gas

- phase water spectral interference as well as CO and CO 2 interference 5
, 16
, 17

Diatomoic Molecules Not applicable to homonuclear diatomic gases such as chlorine, oxygen, and nitrogen 1,2,3

IR Wavelength Range Weak IR absorption features for many inorganic molecules such as sulfur dioxide and nitrogen oxides 6

Infrared beam has a limited range and may not be

sensitive enough to meet ambient data quality objectives.ORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 18 of 86

Path Length Range Maximum path length is on the order of 400

– 500 meters

Field Implementation Requirements Typical infrared detectors require cryogenic cooling to operate. Liquid nitrogen used for detector cooling must be refilled and maintained regularly (weekly). Field implementation and data collection requires highly experienced personnel

Setup Time Consuming and Costly Typical set

- up time usually requires about 5 to 8 hours and a minimum of two people Multiple vertical or horizontal path measurements necessary to calculate plume flux, can require significant time and cost to set up and implement

Measurement Limitations Single beam open

- path method measures concentration along a path. The path must capture most if not all of an analyte pl ume to provide accurate measure of emissions.

2.1.6 References

1. Bell, R. J. 1972. Introductory Fourier Transform Spectroscopy. New York, NY: Academic Press.

2. Hollas, M. J. 1992. Modern Spectroscopy. 2 nd ed. New York, NY: John Wiley & Sons.

3. Laidler, J. K. 1995. Physical Chemistry. 2 nd ed. Toronto, Canada: Houghton Mifflin Company.

4. Galle, B., J. Samuelsson, B. Svensson, and G. Borjesson. 2000. Measurements of Methane Emissions from Landfills Using a Time Correlation Tracer Method Based on FTIR Absorption Spectroscopy. Environmental Science and Technology. 201, 35 (1): 21

- 25.

5. U.S. EPA. 2009. Measurement and Monitoring Technologies for the 21 st Century, CLU

IN. http://www.clu

- in.org/program s/21m2/openpath/op

- ftir/

6. Liptak, B. 1995. Instrument Engineers’ Handbook: Process Measurement and Analysis. Boca Raton, FL: CRC Press.

7. Spellicy, R. 2003. IMACC Industrial Monitoring Corporation, Round Rock Texas. http ://www.ftirs.com/

8. Liptak, B. 2003. Instrument Engineer’s Handbook: Process Measurement and Analysis. Radnor, PA: Chilton Book Company.ORS Handbook, Section 2.1 Revision No: 1.0

Date: 08/19/11

Page 19 of 86
9. Liptak, B. 1995. Instrument Engineers’ Handbook: Process Measurement and Analysis. Boca Raton, FL: CRC Press.

10. Minnich, T. and R. Scotto. 1999. Use of Open

- Path FTIR Spectroscopy to Address Air Monitoring Needs During Site Remediations. Invited Article Published in Remediation. Summer 1999.

11. U.S. EPA. 2007. Evaluation of Fugitive Emissions Using Ground

- Based Optical Remote Sensing Technology. EPA/600/R

- 07/032.

12. U.S. EPA. 2005. Evaluation of Fugitive Emissions at a Former Landfill Site in Colorado Springs, Colorado Using Ground

- Based Optical Remote Sensing Technology. EPA

600/R

- 05/041.

13. U.S. DOE Pacific Northwest National Laboratory. 2009. Optics and Infrared Sensing. Richland WA. http://infrared.pnl.gov/

14. Harvard University. 2009. The HITRAN Database. http://www.cfa.harvard.edu/HITRAN/

15. U.S. EPA. 2004. Optical Remote Sensing Facility Manual, U.S. EPA National Risk Management Research Laboratory, Air Pollution Prevention and Control Division, Emissions Characterization and Prevention Branch. Contract No . EP

- C

- 04

- 023, Work Assignment 0

- 33.

16. U.S. EPA. 2006. Optical Remote Sensing for Emission Characterization from Non

- point Sources. http://www.epa.gov/ttn/emc/prelim/otm10.pdf

17. Godish, T. 2004. Air Q uality, Fourth Edition. Boca Raton, FL: Lewis Publishers

- CRC Press.

18. U.S. EPA and Battelle. 2000. Environmental Technology Verification Report: RAM 2000 Fourier Transform Open

- Path Monitor. Report prepared by AIL Systems, Inc. http://www.epa.gov/etv/pubs /01_vr_ail.pdf.

19. U.S. EPA and Battelle. 2001. Environmental Technology Verification Report: SafEye 227 Infrared Open

- Path Monitor. Report prepared by Spectrex, Inc. http://www.epa.gov/etv/pubs/01_vr_safeyetwo.pdf.

20. Jaakkola, P., T. Vahlman, A. Roos, P. Saa rinen, J. Kauppinen. 1998. On

- line Analysis of Stack Gas Composition by a Low Resolution FT

- IR Gas Analyzer. Water, Air, & Soil Pollution. 101 (1

- 4): 79

- 92.ORS Handbook, Section 2.2 Revision No: 1.0

Date: 08/19/11

Page 21 of 86



### 2.2 Tunable Diode Laser

Light amplification by stimulated emission of radiation (LASER) is a technique to generate a narrow wavelength of light with a small cross sectional area. Diode lasers generate this beam of light using a semiconductor material that em its light when electrical current is “injected” into the semiconductor junction. When the TDL was first introduced, its measurement applications were limited to laboratory functions because of the instrument functionality and cost. The rise of the
fiber

- optics communication industry in the 1980s led to the development of open

- path TDL (OP

TDL) instrumentation that is compact and affordable

. 1 Since that time, the OP

- TDL has become recognized as a reliable technology for use in the field for in situ measur ement of a variety of gaseous pollutants. New laser development demonstrated in 1994 using a repeated stack of thin semiconductor layers (Quantum cascade lasers) offers the possibility to produce LASER beams at additional wavelengths and add to the list o f compounds that can be measured. 2

Laser

- based gas detectors are now being used in a wide variety of applications for process and quality monitoring, and safety and environmental compliance. Laser detectors combine semiconductor TDLs and optical fibers d eveloped by the telecommunications industry with detection techniques based on frequency or wavelength modulation (similar to radio). Laser detectors measure gas concentrations by shining a laser beam through a sample of gas and measuring the amount of la ser light absorbed. Lasers emit light at a single wavelength. In
TDLs , the wavelength can be “ tuned ” over a small range to match the exact absorption wavelength of a target compound by adjusting temperature and bias current. The wavelength of
the laser is tuned over a selected absorption feature of the target species. The measured absorption spectra is recorded and, combined with measured gas temperature and pressure, effective path length, and known line strength, used to determine a quantitative measu rement of
concentration. These properties give laser detectors a combination of selectivity, sensitivity, dynamic range and rapid response time. The OP

- TDL is able to make quantitative measurements of select gases based on the principals of Beer

- Lambert law. Gas molecules absorb energy at specific wavelengths based on rotational and vibrational motion within the
molecule. By measuring the energy absorbed for a compound

- specific wavelength over a laser’sORS Handbook, Section 2.2 Revision No: 1.0

Date: 08/19/11

Page 22 of 86 path, the OP

- TDL can determine the concentration p resent of a specific gaseous compound. This technology can be used for several open

- path and point monitoring applications.

The OP

- TDL is a relatively inexpensive technology that emits very narrow wavelengths in the

near IR ranges. While mid

- IR wavelen gth lasers are available
, they are much more difficult to operate or are currently cost prohibitive for general use. Because the wavelength emitted is very narrow and can be chosen specific to a vibration or rotation of a specific compound, the OP

- TDL
eli minates most interference. Lack of interference and high intensity of the laser beam allows longer open path lengths, up to 1 to 2 km and therefore
, higher sensitivity for the compounds
TDL can measure. The near

- IR OP

- TDL units currently in use are limited by the small number of compound specific wavelengths available from commonly available TDLs and the necessity to use a different TDL for each compound of interest.

2.2.1 Basic Operation

TDL Absorption Spectrosc opy instruments rely on spectroscopic principles and sensitive detection techniques, coupled with advanced diode lasers and optical fibers developed by the telecommunications industry. Gas molecules absorb energy at specific wavelengths in the electromagn etic spectrum. At wavelengths slightly different than these absorption lines, there is essentially no absorption. Measurement of the relative strengths of off

- line to on

- line transmission yields a precise and highly sensitive measure of the target gas co ncentration along the path transited by the laser beam. Measurements are made by (1) transmitting a beam of light through a gas mixture sample containing a quantity of the target gas, (2) tuning the beam’s wavelength to one of the target gas’s absorption lines, and (3) accurately measuring the absorption of that beam. The concentration of target gas molecules can then be integrated over the beam’s path length.

While results generated by traditional optical instrumentation are generally in concentration units such as ppb, the output generated by the OP

- TDL, similar to all open

- path technologies, represents units of concentration over distance, such as ppb(m). This is also known as a PIC.ORS Handbook, Section 2.2 Revision No: 1.0

Date: 08/19/11

Page 23 of 86 Each gaseous compound absorbs energy at different wavelengths, usually more than one, depending on vibrational and rotational excitement within the molecule. Therefore, each compound has its own “signature” of bands from which energy may be absorbed. Eac h band is highly selective, with virtually no absorption occurring outside of a specific wavelength.
Because the OP

- TDL emits a laser at a very narrowly tuned wavelength range, it is an ideal instrument for single compound measurement. The OP

- TDL’s laser is selected for an overtone band specific to the compound of interest. The absorption of energy over the laser’s path length is measured by the instrument’s detector. The absorption is used to determine the concentration of the target gaseous compound u sing the principals of Beer

- Lambert Law as described in Equation 1.



# cl A **ε =**  Eq. 1

Where: A = absorbance intensity

ε = absorption coefficient c = sample concentration l = sample path length

There are a number of instrumental configurations for OP instruments. The simplest OP

- systems are bistatic configurations. The arrangement of the components of this design for OP

- TDL is shown in Figure 2

- 9. This configuration derives its name from the f act that both the transmitter and receiver must be fixed in a static position and precisely aimed at each other. The OP

- TDL equipment projects the laser beam directly along a path to a detector/receiver. Bistatic configurations
, in general
, have the requ irement of supplying power at both the receiver and transmitter, which can be a disadvantage in some locations. Additionally
, there is a requirement for alignment at both receiver and transmitter, which can be time

- consuming for mobile systems. 11ORS Handbook, Section 2.2 Revision No: 1.0

Date: 08/19/11

Page 24 of 86

Figur e 2

- 9. TDL Bistatic Configuration

Monostatic configurations were developed to address issues raised with bistatic designs. In a monostatic configuration, all of the optical components of the transmitter and receiver are in the same location, and a retro

- reflector is used to return the light from the transmitter to the receiver. This configuration derives its name from the fact that only the transceiver portion of the instrument needs to be precisely pointed as the retro

- reflector returns light to its sou rce regardless of orientation. A diagram of two monostatic configurations is shown in Figure 2

- 10.

Figure 2

- 10. TDL Monostatic ConfigurationORS Handbook, Section 2.2 Revision No: 1.0

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Page 25 of 86
Retro

- reflecting mirrors, as they are called, are configured with three perpendicular reflective surfaces in the shape of a corner. A combination of three mutually perpendicular mirrors reflects light incident from any direction through 180° as shown in Figure 2

- 11. Such a combination of
mirrors is called a corner

- cube reflector. Corner

- cube reflectors beam TDL light back to its exact point of origin. This property reduces the divergence of the beam on its return path back to the detector compared to divergen ce that would result from a flat mirror. Also the retro

- reflector array can be very large to capture and return essentially the entire divergent signal from the telescope.

( http://farside.ph.utexas.edu/teaching/316/lectures/node133.html ) Figure 2

- 1 1. The Corner

- cube Reflector

In the mono

- static mode, the IR laser beam is split twice, once leaving the OP

- TDL and once on
its return. This design requir es a beam splitter in the optical path that removes 50 percent of the light from the outgoing beam and 50 percent of the light from the return beam for an overall loss of 75 percent of the total light intensity.

The dual

- telescope monostatic configuration has lower detection limits because it does not utilize a beam splitter in the optical path. A translating retro

- reflector, which is essentially a portion of a very large cube, is used to return the light beam offset to align with the receiving telescope.ORS Handbook, Section 2.2 Revision No: 1.0

Date: 08/19/11

Page 26 of 86 This single, large retro

- reflector does not have the divergence reversal properties of the corner

cube array. The second telescope adds cost and complexity to the system. 3 However, when compared with mono

- static mode, bi

- static systems are harder to al ign and maintain because any shift in the transmitter or detector can result in system misalignment. 4,5 Both operating modes measure only the compounds that are in the beam path. Emissions outside the beam path are not
measured. In these situations, measurements have been conducted along multiple beam paths to more accurately characterize the emission plume.

OP

- TDL units are designed to operate under computer control, where the interfacing software
controls the function of the OP

- TDL, controls the tu ning of the laser, and collects resulting data
from the detector. Commercially available OP

- TDL units can be equipped with multiple lasers, allowing the measurement of several compounds at one time. Field units typically include a hardware controller, a laptop, a telescope receiver, and a reflector. Instruments can run unattended via computer control for extended periods of time. 6

2.2.2 Pollutants and Relative Levels That Can Be Measured

Near

- IR TDLs have been used to measure approximately 20 compounds tha t have
absorbencies in the 1.4

– 1.8 micrometer (μm) wavelength range. Using an open

- path setup, concentrations into the low ppm range can be detected over a path length of approximately
1000 m to 2000 m. Table 2

- 6 lists airborne compounds that can be me asured by OP

- TDL systems and their approximate wavelengths. The compounds measured by TDLs are limited by the wavelength range commonly available using electrical current driven semiconductor
lasers. Quantum Cascade Laser (QC

- Laser) development offers th e possibility of expanding the list by extending available laser wavelengths into the mid

- infrared range, where many compounds of interest strongly absorb these wavelengths. However, one issue is that the measurements are also limited by the ability of fib er optic cables to transmit the raw LASER energy in those instruments using remote modules. Current TDL light sources cost $2,000 to $3,000. Experimental Q C

- Lasers are available at a cost up to $100,000.ORS Handbook, Section 2.2 Revision No: 1.0

Date: 08/19/11

Page 27 of 86
Table 2

- 6. Example List of Gaseous Compounds Meas ured by Near IR OP

- TDL Systems

Species Approximate
near - IR λ (nm) Reported Detection Limit (ppm

- m) ammonia 760, 1500 0.5

- 5.0 carbon monoxide 1570 40

- 1,000

carbon dioxide 1570 40

- 1,000 hydrogen chloride 1790 0.15

- 1 hydrogen cyanide 1540 1.0 hydrogen fluoride 1310 0.1

- 0.2

hydrogen sulfide 1570 20 methane 1650 0.5

- 1 nitric oxide 1800 30 nitrogen dioxide 680 0.2
oxygen 760 50
water 970, 1200, 1450 0.2

- 1.0 Compounds in bold are EPA Hazardous Air Pollutants (HAPs)
CAA

– 112 Title 42, Chapter 85, Subchapter I, Part A U.S. Code 7412 (b)

Table 2

- 6. Example List of Gaseous Compounds Measured by Near IR OP

- TDL Systems (continued)

Species Approximate
near - IR λ (nm) Reported Detection Limit (ppmm) acetylene 1520

These compounds are not commonly measured, therefore detection limits are not readily available. ethylene 1693 formaldehyde 1930 hydrogen bromide 1960

hydrogen iodide 1540

nitrous oxide 2260

phosphine 2150

propane 1400, 1500, 1700 Compounds in bold are EPA Hazardous Air Pollutants (HAPs)
CAA

- 112 Title 42, Chapter 85, Subchapter I, Part A U.S. Code 7412 (b)

2.2.3 Typical QA/QC

Three major QA requirements are necessary when using a TDL system: (1) selection of the appropriate laser and absorption line for the compound of interest, (2) establishment and use of an appropriate calibration procedure, and (3) establishment of QC procedures that ensure the instrument’s performance as measurements are made. 6ORS Handbook, Section 2.2 Revision No: 1.0

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Page 28 of 86

Selection of the Laser and Absorption Line

A TDL optical system is typically built to generate one wavelength at a time. The range of wavelengths from each type of laser limits measurement to one compound at a time. Therefore, laser and instrument selection must be carefully considered. A few la sers can be configured for one of a limited range of wavelengths, while others provide a wider selection of wavelengths. It is also important to note that many compounds have multiple absorption bands in both the near

and mid

- IR regions. However, the av ailability of mid

- IR lasers is limited and may not be available for open

- path monitoring or measurement programs. Table 2

- 7 lists commercially available lasers producing wavelengths in the near

- IR range. Table 2

- 8 lists other laser types that
have been developed for mid

- IR applications. While this list covers most of the lasers available, TDLs represent a limited set from a larger array of laser types.

Table 2

- 7. Near

- IR Laser Types Available for OP

- TDL Systems

Laser Type Tunable λ Range (nm) Target Com pounds

InGaAsP 1200

- 2000 CO, CO 2 , NO, CH 4 , C 2 H 2 , HF
, HCl , HBr, HI, HCN, NH 3 , H 2 CO , PH 3 , H 2 O

Antimonide* 2000

- 4000 CO, CO 2 , NO, N 2 O, CH 4 , HCl , HBr, H 2 CO

* Laser emits wavelengths in both the near

- IR and mid

- IR spectrums.

Table 2

- 8. Potentially Usable Mid

- IR Lasers

Laser Type Tunable λ Range (nm) Target Compounds

AlGaInP 630

- 690 NO 2

AlGaAs 750

- 1000 O 2, NH 3
Vertical Cavity 650

- 1680 H 2 O, C 2 H 2 , HF , H 2 S, O 2 , H 2 O, NH 3

Antimonide* 2000

- 4000 CO, CO 2 , NO, N 2 O, CH 4 , HCl , HBr, H 2 COORS Handbook, Section 2.2 Revision No: 1.0

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Page 29 of 86

Quantum Cascade** 4000

- 12000 H 2 O, CO, CO 2 , NO, NO 2 , N 2 O, SO 2 , C 2 H 2 , HCN, NH 3 , PH 3 , 0 3

Lead

- salt** 3000

- 30000 H 2 O, CO, CO 2 , NO, NO 2 , N 2 O, SO 2 , CH 4 , C 2 H 2 , HCl , HBr, HCN, NH 3 , H 2 CO , PH 3 , O 3

* Laser emits wavelengths in both the near

- IR and mid

- IR spectrums. ** Laser emits wavelengths in the mid

- IR spectrum. http://www.swsciences.com/technology/sensors.html Compounds in bold are EPA Hazardous Air Pollutants (HAPs) CAA

- 112 Title 42, Chapter 85, Subchapter I, Part A U.S. Code 7412 (b)

Because compounds often have multiple absorption bands that can be detected by a TDL system, it is also important to consider which band is best for quantitative purposes. 7 Higher intensity absorption bands provide the best sensitivity. However, interfer ence from other compounds may eliminate the use of the most sensitive wavelengths. It also may be worthwhile to measure the concentrations from a second absorption band to verify the nonexistence of interferences. It is highly unlikely that the same inte rference would exist for both absorption bands. 6

Calibration

In a closed

- gas cell TDL instrument, known concentrations of the compound of interest are introduced into the white cell used for sample analysis. Calibration gas is added through the same line used to collect the sample. Varying concentrations of one compound can be introduced by adjusting the inlet flow of the calibration gas relative to the dilution gas. For each concentration step in the calibr ation curve, the absorption trend should be recorded and the mean
and standard variation calculated. 8

The calibration factors are typically determined in the laboratory with short path length gas cells. One instrument vendor provides an insertion slot th at can contain a gas cell of known concentration into the path of the optical beam during measurement. Other OP

- FTIR instruments can also be calibrated with gas cells of known concentration by introducing the cell into the laser’s path for measurement. F ield calibration checks can be accomplished using the absorption signal provided by the calibration gas cell added to the open path field absorption signal. The signal increases above the open path signal, proportionally to the gas concentration and path length of the gas cell. The instrument response is checked using the difference of theORS Handbook, Section 2.2 Revision No: 1.0

Date: 08/19/11

Page 30 of 86 measurements with and without the gas cell. An example of a calibration curve for methane is provided as Figure 2

- 12.

Figure 2

- 12. Calibra tion Data for an OP

- TDL System. 9

Calibration frequency depends on the duration of the measurement period as well as the concentrations of compounds that are measured. Shorter term measurements projects need calibration verification at the beginning of a measurement episode. Also note that regulatory requirements may also dictate calibration frequencies. Low concentrations in ambient conditions may require background and calibration determinations on a weekly or monthly basis 6 because a small drift in instrument response is more significant at lower measured concentrations.ORS Handbook, Section 2.2 Revision No: 1.0

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Page 31 of 86 Quality Control Procedures

Each OP

- TDL manufacturer recommends its own QC procedures; however, it is necessary to verify the accuracy of the calibration throughout a set of field measurements. This can be done by reinserting a calibration standard cell periodically during a measurement episode to ensure

correct measurement. Recalibration during field measurements may be necessary due to
instrument drift and is typically performed using the instrument’s system software.

2.2.4 Example Applications and Vendors

Applications

Details on the near

- IR TDL application of open path technologies are provided in Section 3 of
this Handbook. The OP

- TDL has been used for a wide variety of source emission measurements in the field including applications such as line of sight optical remote, bLS modeling, RPM , and
mobile tracer release correlation. Table 2

- 9 summarizes optical technologies and t he typical applications of each of the technologies.

Table 2

- 9. Typical Applications for OP

- TDL. Technology Applications

OP

- TDL , bLS , RPM, Tracer Gas Correlation, TO

- 16

Vendors

While there are many sources for TDL instrumentation that is suitable for laboratory applications, there are only a few vendors currently offering field ready OP

- TDL
instrumentation. Vendors offering instrumentation exclusive to fire detection and monitor ing
have not been included. The cost of a TDL field ready system can range from $35,000 to $75,000 in 2010 U.S. dollars depending on the configuration and application. Table 2

- 10 lists example vendors and their internet contact address.ORS Handbook, Section 2.2 Revision No: 1.0

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Page 32 of 86

Table 2

- 10. Nea r - IR OP

- TDL Vendors

Vendors Boreal Laser www.boreal

- laser.com

OPSIS AB www.opsis.se Leister Process Technologies, Axetris Division www.ir

- microsystems.com

Norsk Elektro Optikk (NEO, Norway) www.neo.no
PKL Technologies, Inc. www.pktechnologies.com PSI Physical Sciences, Inc. www.tdlas.com www.psicorp.com Senscient www.senscient.com Simtronics group www.simtronics.eu Unisearch Associates, Inc. (Concord, Canada) www.unisearch

associates.com

2.2.5 Strengths and Limitations

The TDL has an array of strengths and limitations that must be considered for each OP

- TDL application. A summary of strengths and limitations is shown in Table 2

- 11 and Table 2

- 12, respectively. Perhaps the most striking limitation is the fact that each TDL laser can detect only one compound at a time and each laser can scan only a limited range of wavelengths. It is also true that only compounds with overtone absorbencies in the near

- and mid

- IR ranges can be detected and quantified, of which there are approximately twenty. 9 The instrument’s sensitivity is limited because of noise created by the laser 10 , though this can be improved by either of the
modulations described above. However, because the laser emits such a narrow bandwidth, interferences from other gaseous compounds are unlikely.

The TDL’s strengths in field application are numerous. Technological developments originating
from the fiber optics communication field have allowed the TDL to become compact, robust, and economical compared to other technologies. The TDL can be used for several applications, including open

- path, RPM, and cavity ring

- down spectroscopy (CRDS) measurements. The high

- po wered laser source also promotes fast instrument response times (as low as one measurement per second) and longer path lengths up to 1,000 meters. Commercially

- availableORS Handbook, Section 2.2 Revision No: 1.0

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Page 33 of 86 TDL instrumentation allows for optical beam path flexibility, as well as automated re al

- time

measurements. Calibration is also simplified by the ability to insert a gas cell standard into the
instrument. 9

Table 2

- 11. Summary Table of the TDL's Strengths

Feature Strength

Automated Real

- time Measurements 24/7 remote monitoring Can provide near real time data Unattended measurements collection Compact instrumentation Field units are lightweight, typically under 75 Kg, and relatively easy to transport and setup Economical 0.5 to .01 the cost of alternative technologies

High intensity light source Wide linear response over a wide dynamic range resulting in measurements from 0.1 to 1000 ppm The ability to measure longer path lengths (1 km compared to other ORS technologies) Solid state technology Robust field use with low maintenance, minimal consumables to operate Low response times Rapid response

– typically 1 second High spectral resolution Minimizes interference from other gases resulting in high compound specificity Uses fiber optics for signal processing Lower equi pment cost per measurement, ability to multiplex signals Vendor

- specific calibration cells Self calibration, zero and span drift correction

Table 2

- 12. Summary Table of the TDL’s Limitations

Feature Limitation Single wavelength operation Detects only one compound per laser, fewer measureable compounds, and limited sensitivity

Mid

- IR wavelength range Quantitation limited to compounds with overtone absorbencies in the near

- and mid

- IR range Dust and objects block the laser beam With all open path optical measurements, blocked beams result in no measurements

2.2.6 References

1. Harris, Shores and Thoma. 2007. Using Tunable Diode Lasers to Measure Emissions from Animal Housing and Waste Lagoons, 16 th Annual International Emission Inventory Conference Emission Inventories: “Integration, Analysis, and Communications” Raleigh.
< http://www.epa.gov/ttn/chief/conference/ei16/ses sion12/harris.pdf >. May 14

- 17.ORS Handbook, Section 2.2 Revision No: 1.0

Date: 08/19/11

Page 34 of 86
2. Faist, Jerome, Federico Capasso, Deborah L. Sivco, Carlo Sirtori, Albert L. Hutchinson, and Alfred Y. Cho. 1994. "Quantum Cascade Laser” Science 264 (5158): 553

– 556.

3. Liptak, B. G. 2003. Instrument Engineer’s Handbook: Proce ss Measurement and Analysis. Radnor, PA: Chilton Book Company

4. Liptak, B. G. 1995. Instrument Engineers’ Handbook: Process Measurement and Analysis. CRC Press. 5. Spellicy, R. 2003. IMACC Industrial Monitoring Corporation, Round Rock Texas.

6. Schiff, H. I. 198 7. Measurement of Atmospheric Gases by Tunable Diode Laser Absorption Spectrometry. Monitoring of Gaseous Pollutants by Tunable Diode Lasers: Proceedings of the International Symposium held in Freiburg, F.R.G, 13

- 14 November 1986. D. Reidel Publishing Company.

7. Ku, R. T., E. D. Hinkley, and J.O. Sample. 1975. Long

- Path Monitoring of Atmospheric Carbon Monoxide with a Tunable Diode Laser System. Applied Optics. 14 (4).

8. Mock, A., C. Roller, J. Jeffers, N. Khosrow. P. McCann, and J. Grego. 2001. Real

- time ground level atmospheric nitric oxide measured by calibrated TDLAS system. Optical Society of America.

9. Thoma, E. D., Shores, R. C.; Thompson Jr, E. L.; Harris, D. B.; Thorneloe, S. A.; Varma,R. M.; Hashmonay, R. A.; Modrak, M. T.; Natschke, D. F. and Gam ble, H. A. 2005. Open

- Path Tunable Diode Laser Absorption Spectroscopy for Acquisition of Fugitive Emission Flux Data.. J. Air & Waste Manage. Assoc. 55 : 658

- 668.

10. Cappellani, F., G. Melandrone, and G. Restelli. 1987. Post Detection Data Handling Technique s for Application in Derivative Monitoring. Monitoring of Gaseous Pollutants by Tunable Diode Lasers: Proceedings of the International Symposium held in Freiburg, F.R.G, 13

- 14 November 1986. D. Reidel Publishing Company.ORS Handbook, Section 2.3 Revision No: 1.0

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Page 35 of 86 

### 2.3 Ultraviolet Differential Optical Absorption Spectro scopy

The UV

- DOAS is an optical remote sensing technology that quantifies concentrations of gaseous compounds by measuring the absorption of UV light by chemical compounds in the air and applying the Beer

- Lambert law. 1

A significant strength of the UV

- DOAS is its extremely long path

- length capability

– typically 500 meters with some research applications up to 10 kilometers. 2 UV

- DOAS has been deployed in a wide variety of environmental measurement applications. It is most frequently used to measure or monitor criteria and smog

- related air pollutants. It is also able to accurately monitor several pollutants that do not produce i deal IR absorption bands. However, because the absorption bands for UV

- DOAS are very wide, there are many compounds that cannot be accurately quantified by UV

- DOAS. Nitrogen and oxygen molecules in the air cause broad spectral scattering and interfere wi th many of the compounds that can be measured. The UV

DOAS is reported 2 to have detection limits in the low (ppb) range and can reach parts per trillion in some research applications when used with optimum measurement path lengths.

2.3.1 Basic Operati on

In general, UV, visible, and near

- IR light is that radiation within the 180

- 780 nanometer wavelength range that causes changes in energy between the bonding electrons in molecules that absorb the light. While wavelength range s produced by UV

- DOAS inst rumentation include the rotational and vibrational transitions caused by near

- IR light, the typical application of UV

DOAS restricts the UV light to a wavelength range of 245 to 380 nanometers. Due to the range of excitations measured, molecular absorptio n bands tend to be far broader than that of IR
instrumentation. Compounds that can be accurately detected and measured with the UV

- DOAS possess specific chemical structure characteristics that allow for unique absorption bands, which
limits the number of compounds that can be monitored. 3ORS Handbook, Section 2.3 Revision No: 1.0

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Page 36 of 86 DOAS is based on the principal that the Beer

- Lambert Law (Equation 1) and cannot be used to directly measure the concentration of the compounds of interest because the initial intensity of the signal cannot be measured.



# cl A **ε =**  Eq. 1
Where: A = absorbance intensity

ε = absorption coefficient c = sample concentration l = sample path length

Interferences in the atmosphere cause absorption to occur at all points in the measurement spectrum. In the atmosphere
, the light of the beam undergoes extinction processes by air molecules and aerosols, turbulence, and absorption by many trace gases. DOAS overcomes the effects of the beam extinction by mathematically separating and removing the nonspecific beam extinction from the target gas absorption

. 4 To address this issue, DOAS measures the difference between the absorption peak caused by the compound of interest and absorption peaks at wavelengths on either side of that targeted peak

. 3 The concentrat ion is determined by the light intensity in the absence of a structured absorption band, rather than the light intensity in the absence of all absorption.

A typical UV

- DOAS system consists of a light source, optics, a spectrometer, and depending on
the system configuration, a retro

- reflector. Most systems employ a tungsten halogen or xenon arc lamp, though some use deuterium lamps. 2 From the source, the light is focused and directed into the atmosphere by means of a transmitting telescope. A receiving telescope retrieves and focuses the attenuated light beam and the spectrometer measures the change in absorbance caused of the UV light. Data collected by the UV

- DOAS can be stored in the analyzer and can
be transferred off

- site via external storage or I nternet connection. 5 The digital signal from the spectrometer is collected by a computer system and compared to laboratory

- developed reference spectra to ensure a match between all absorption bands associated with a targeted compound are present to confir m its identification and quantification. 7 Some technologies use specific gasORS Handbook, Section 2.3 Revision No: 1.0

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Page 37 of 86 calibrations to fine tune the library reference spectra and improve instrument performance. Figure 2

- 13 shows an Opsis DOAS source unit.

Figure 2

- 13. Opsis DOAS Unit

UV

- DOA S instruments can practically measure path lengths up to 500 m eter s . Optimum light path length depends on the compound of interest, the desired detection limit, the clear line of sight available, and the expected interferences (e.g., dust and fog). Measurement noise increases and beam intensity decreases as path length increases. 1

Certain chemical species can also pose interference issues at particular wavelengths. For example, when trying to measure nitrous oxide (N 2 O) in the presence of other nit rogen oxides (NO, NO 2 ), absorption from NO and NO 2 can cause interference. 8 Special considerations must also be made when measuring concentrations of aliphatic hydrocarbons in ambient air since oxygen is a major interferent for these compounds. 5

Addition ally, there are several operational concerns that must be considered when operating a
UV

- DOAS in the field. These instruments are approved for use in temperatures of 5

- 30  ̊C with humidity ranging from 0

- 80 percent. High humidity can cause fog to build up on the receiver mirrors and windows, which can substantially decrease the detected light intensity and deteriorate the condition of the instrument optics and mirrors. This can be corrected by installing heaters on the mirrors and windows or by changing the site where the UV

- DOAS i s installed. 5ORS Handbook, Section 2.3 Revision No: 1.0

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Page 38 of 86
UV

- DOAS Field Implementation

There are a number of instrumental configurations for o pen

- p ath UV

- DOAS instruments. UV

DOAS instrumentation can be deployed in both bistatic and monostatic open

- path configurations.

The simplest OP

- systems are bistatic configurations. The arrangement of the components of this design for UV

- DOAS is shown in Figure 2

- 14. This configurat ion derives its name from the fact that both the transmitter and receiver must be fixed in a static position and precisely aimed at
each other. The UV

- DOAS equipment projects the light beam directly along a path to a
detector/receiver. Bistatic configura tions
, in general
, have the requirement of supplying power at both the receiver and transmitter, which can be a disadvantage in some locations. The receiver and transmitter must be accurately aligned to optimize signal intensity. 9

Figure 2

- 14. Bi static Configuration of UV

- DOAS

Monostatic configurations were developed to address issues raised with bistatic designs. In a monostatic configuration, all of the optical components of the transmitter and receiver are in the
same location and a retro

- reflector is used to return the light from the transmitter to the receiver. A noted disadvantage of a monostatic system is that the physical path is only half the distance of a bistatic system.

“Retro

- reflecting” mirrors are configured with three perpendicular re flective surfaces in the shape of a corner. A combination of three mutually perpendicular mirrors reflects light incident from any direction through 180° as shown in Figure 2

- 15. Such a combination of mirrors is Light Source TARGET POLLUTANT Attenuated Light Receiving Optics

Spectrometer Detector Fiber-Optic Cable

ComputerORS Handbook, Section 2.3 Revision No: 1.0

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Page 39 of 86
called a “corner

- cube reflector.” Corner

- cube reflectors beam light back to its exact point of origin. This property reduces the divergence of the beam on its return path back to the detector compared to divergence that would result from a flat mirror. Also the retro

- reflector array can be

very large to capture and return essentially the entire divergent signal from the telescope.

( http://farside.ph.utexas.edu/teaching/316/lectures/node133.html )

Figure 2

- 15. The Corner

- cube Reflector

The monostatic configuration derives its name from the fact that only the transceiver portion of the instrument needs to be precisely aimed because the retro

- reflector returns light to its source

.

Figure 2

- 16. Bas ic Setup Used to Make Monostatic Open

- path UV

- DOAS MeasurementsORS Handbook, Section 2.3 Revision No: 1.0

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Page 40 of 86 When compared with monostatic mode, bi

- static systems are harder to align and maintain because any shift in the transmitter or detector can result in system misalignment. 10,11 Both operating modes measure only the compounds that are in the beam path. Emissions outside the beam path are not measured. Siting and additional QA procedures for ambient measurements
found in 40 CFR P art 58 provide basic guidance for criteria pollutants using open

- path

measurements. In addition, EPA QA Handbook Volume II 12 has siting requirements and other useful information on using UVDOAS in ambient/background monitoring situations.

Passive UV

- DOAS

A third configuration is known as passi ve UV

- DOAS. Passive UV

- DOAS uses ambient lighting, such as sunlight, as its light source and does not require a transmitting telescope. Passive UV

DOAS instruments can be fitted into balloons and used to measure concentrations of pollutant gases at diffe ring heights in the atmosphere. 1,2

2.3.2 Pollutants and Relative Levels That Can Be Measured

Table 2

- 13 lists compounds that have been measured with UV

- DOAS systems. The list is not exhaustive and includes only compounds reported in recent literature. 2,12 UV

- DOAS systems have the most widespread environmental use in the detection and measurement of inorganic gases and vapors, monoaromatics (i.e., benzene), and aldehydes.

Table 2

- 13. Species Measured with UV

- DOAS Systems * Species Species Species 1,3

- Butadiene Formaldehyde Ozone Acrolein Hydrogen Fluoride Sulfur Dioxide Ammonia Isoprene Styrene Benzene Mercury Toluene Carbon Disulfide Nitric Oxide m,p - Xylene Chlorine Nitrogen Dioxide o - Xylene Ethyl Benzene Nitrous Acid

* Compounds in bold are EPA Hazardous Air Pollutants (HAPs).

Detection limits have been reported in the ppb with at least one research application reporting cases of detection down to parts per trillion ranges. 2 Detection limits vary based on factors suchORS Handbook, Section 2.3 Revision No: 1.0

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Page 41 of 86 as the deploymen t configuration, light path length, measurement noise, and meteorological
conditions. 1,2 Table 2

- 14 gives example detection limits found in the literature.

Table 2

- 14. Approximate Detection Limits for UV

- DOAS

Pollutant Lower Detection Limit (pp b ) Path Le ngth (m) Ammonia 800 200 Benzene single digit ppb 500 Carbon Disulfide 500 5000

Formaldehyde single digit ppb 500 Nitrous Acid single digit ppb 500 Nitrogen Dioxide single digit ppb 1000

Nitrogen Oxide 240 200 Ozone single digit ppb 1000 Sulfur Dioxide single digit ppb 1000 Toluene single digit ppb 200 m,p

- Xylene 10 500

o

- Xylene single digit ppb 500

2.3.3 Typical QA/QC

QA/QC ensure the validity of data and calculations performed by UV

- DOAS systems. Each instrument manufacturer establishes its own quality assurance procedures based on the specifications of the individual instrumentation, but there are several procedures that should be followed universally.

Record Keeping

As with all environmental measurements, it is necessary to keep accurate records during measurement periods to ensure accurate data collection. For UV

- DOAS
, information such as meteorological conditions, path lengths, UV filter numbers, lamp type, light intensities and

measuremen t times should be recorded. 5 Light intensities must be recorded anytime the UV emitter or receiver is adjusted and compared to the intensities measured when the UV

- DOAS
was installed. The measured recoveries of standard gas cells with known concentration s should
also be documented.ORS Handbook, Section 2.3 Revision No: 1.0

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Instrument Performance

Before measurements are begun and throughout the measurement process, several instrument performance checks are required to make sure the instrument is accurately collecting data.
Individual vendors recommend specific instrument performance checks such as correcting for slight variances in the reference spectrum (i.e., the lamp spectrum with no concentration bands) caused by changes in the spectrometer and instrument electronics. This is performed b y periodically re

- recording the lamp spectrum and comparing it to the initial reference spectrum for agreement. The reference spectrum is critical to the analysis of collected data and performing regular reference checks also minimizes noise collected by the instrument. 5

Calibration checks are also very important for the collection of accurate data. The analyzer is checked by measuring gas standards of known concentrations for accuracy. Calibration cells are filled with the gas standard, allowed to st abilize, and the absorption is measured. A valid calibration curve should contain six equally

- spaced calibration points, including zero, and cover at least 80 percent of the perceived measurement range. Because UV

- DOAS measurements are based on absorptio n and
, therefore
, the number of target compound molecules in a specific path length, the calibration points can be obtained either by decreasing the measurement path or diluting the gas standard. 5

A function check is also required to periodically validate the instrument’s performance. During a function test, a cell with a known concentration of gas is placed in front of the receiver. The instrument measures the concentration of the compound in the cell plus the concentration of the compound in ambient air. This check serves two purposes: (1) to ensure that the analyzer is producing accurate measurements and (2) to ensure that no cross

- sensitivities occur between the test gas in the cell and other gases. Function tests must be performed in stable ambient pollution conditions because spikes in pollutants may cause the results of the function test to be difficult to interpret. 5

Accuracy and precision tests are defined by the EPA regulations at 40 CFR p art 58 (Ambient Air Quality Surveillance). Often these values are determined by performing calibration checksORS Handbook, Section 2.3 Revision No: 1.0

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Page 43 of 86 against known gas standards and verifying the MDL provided by the instrument manufacturer. Instrument manufacturers provide their own inst ructions on how to perform accuracy and precision tests in accordance with EPA regulations.

2.3.4 Example Applications and Vendors

Example Applications

UV

- DOAS has been deployed in a wide variety of environmental measurement applications which are discussed for specific applications in Chapter 3 of this Handbook. UV

- DOAS is most frequently used for monitoring smog

- related air pollutants, where its long r ange is used to verify Eulerian models that are used in air quality management. Multiple pathways have been used to

create 2

- D and 3 dimensional ( 3

- D ) tomographic depictions of pollutants around a large area
source or urban area. UV

- DOAS is used for fenc eline monitoring of air pollutant emissions. Benzene has been measured in residential areas downwind of chemical manufacturing plants and ammonia has been monitored in areas around large

- capacity swine feeding operations. UV

DOAS was also used to measure mercury emissions from a chlor

- alkali plant. 6 These types of ambient and fugitive or area source measurements have been useful to government agencies to identify places where harmful levels of pollution exist and determine the level of injunctive relief

necessary. 2 Table 2

- 15 summarizes those applications that utilize UV

- DOAS technology.

Table 2

- 1 5. Typical Applications for UV

- DOAS.

TECHNOLOGY APPLICATIONS UV

- DOAS OTM 10, Tracer Gas, bLS

One developing application for UV

- DOAS is known as Multi

- Axis Differential Optical Absorption Spectroscopy (MAX

- DOAS). The application provides the ability to derive a vertical profile of pollutants by completing multiple scans simultaneously using either passive or active techniques. 2ORS Handbook, Section 2.3 Revision No: 1.0

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Page 44 of 86
Ve ndors There are currently five vendors of field

- ready UV

- DOAS instruments, as summarized by Table
2

- 16. Opsis, Inc., has had two separate instruments verified through the EPA’s ETV program. 14 The Opsis System has also been designated as an “Equivalent Me thod” for the measurement of
SO 2 , NO 2 and O 3 in ambient air.

Table 2

- 16. UV

- DOAS Vendors

Vendors Websites Argos Scientific www.argos

- sci.com Environnement S.A. Sanoa UV/Visable DOAS www.environnement

- sa.com
ETG Risorse e Tecnologia www.etgrisorse.com IMACC www.ftirs.com Opsis, Inc. www.opsis.se

Spectrex www.spectrex

- inc.com Cerex Monitoring Solutions www.cerexms.com

2.3.5 Strengths and Limitations

Tables 2

- 17 and 2

- 18 summarize the strengths and limitations associated with the use of the UV

DOAS. Some UV

- DOAS can provide concentration data for up to three compounds simultaneously. In the field, the instrument is portable 1 5 and can be deployed long

- term for
continuous remote in situ monitoring. 1 4 UV

- DOAS quanti fies compounds more successfully that have strong UV light and weak infrared absorption characteristics. Since NO 2 measurement by
UV

- DOAS does not require conversion to NO and measurement by difference, as conventional
chemiluminescent monitors operate , this ORS technology provides a direct rather than indirect measure of the criteria pollutants.ORS Handbook, Section 2.3 Revision No: 1.0

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Page 45 of 86

Table 2

- 1 7 . Summary of UV

- DOAS Strengths

Feature Strengths

Automated Real

- time Measurements 24/7 remote monitoring Can provide near real time data Unattended measurements collection

Economical Relatively low instrument cost (about $ 6 0,000

- $ 200 ,000) Low

- cost long term deployment

Multiple Wavelength Operation Broad spectrum instruments allow monitoring of three criteria pollutants and trace species simultaneously . Spectra can be saved and post analyzed Range of Measurement Long measurement path length

– up to 500 m. Detectability Many compounds are detectable in the low ppb range Compact Instrumentation Portable

However, the number of species that can be analyzed with UV

- DOAS is limited due to the lack of appropriate absorption characteristics in the UV

- visible wavelength range of many compounds.
Table 2

- 1 8 . Summary of UV

- DOAS Limitations

Feature Limitations Difficulty in Deployment Alignment of remote receiving optics or reflectors can be difficult at long path length.
Meteorological Limitations Fixed observation area. Long term deployment depends on constant wind direction. Affected by poor visibility conditions
Limited Compounds A number of species do not have appropriate UV

- visible absorption structures making them undetectable by UV

- DOAS
Application Limitations Some bistatic systems are more difficult to use for radial plume mapping due to difficulty aligning optics from multiple paths

2.3.6 References

1. Sigrist, M. W. (Eds.). 1994. Air Monitoring by Spectroscopic Techniques. New York, NY: John Wiley & Sons, Inc. 2. U.S. EPA. 2009. Measurement and Monitoring Technologies for the 21 st Century (21M 2 ). Open Path Technologies: Measurement at a Distance UV

- DOAS. http://www.clu

- in.org/download/misc/21M2flier.pdf. Washington, DC.ORS Handbook, Section 2.3 Revision No: 1.0

Date: 08/19/11

Page 46 of 86
3. Finlaysson

- Pitts, B.J., J.N. Pitts. 1986. Differential Optical Absorption Spectroscopy, from Atmospheric Chemis try; Fundamentals and Experimental Techniques. John Wiley & Sons.

4. Stutz, J., The Stutz Research Group, http://www.atmos.ucla.edu/~jochen/research/doas/DOAS.html

5. Opsis AB. 1996. Quality Assurance and Quality Control using Opsis Analyzers for Air Quality Monitoring. Version 1.1. Furulund.

6. Thoma, Eben, Measurement of Total Site Mercury Emissions from a Chlor

- alkali Plant Using Open

- Path UV

- DOAS, EPA/R

- 07/077, July 2007, http://www.epa.gov/ord/NRMRL/pubs/600r07077/600r07077.pdf

7. Pundt, I. 2 006. DOAS tomography for the localization and quantification of anthropogenic air pollution. Analytical and Bioanalytical Chemistry, 385 (1) 18

- 21. doi:10.1007/s00216

- 005

- 0205

- 4

8. US DOE/ORNL. 1999. NARSTO Measurement Methods Compendium.
< http://narsto.ornl.gov/Compendium/methods/DOAS.htm >. 20 May.

9. Liptak, B.. 2003. Instrument Engineer’s Handbook: Process Measurement and Analysis. Chilton Book Company. Radnor, PA.

10. Liptak, B. 1995. Instrument Engineers’ Handbook: Process Measurement and Analysis. Boca Raton, FL: CRC Press.

11. Spellicy, R. 2003. IMACC Industrial Monitoring Corporation. Round Rock, TX http://www.ftirs.com.

12. ETG Resorse e Tecnologia. 2007. The UV DOAS SENTINEL System Represents the Most Cost Effective. < http://www.etgrisorse.com/prodotti_en.php?id=13 >.

13. U.S. EPA
, QA Handbook for Air Po llution Measurement Systems: Volume II: Ambient Air Quality Monitoring Program" EPA

- 454/B

- 08

- 003, December 2008

. http://www.epa.gov/ttn/amtic/qalist.html

14. U.S. EPA. 2000. Environmental Technology Verification Report: AR

- 500 Ultraviolet Open

- Path Monitor. Report prepared by Opsis Inc. Battelle, Columbus, OH.
< http://www.epa.gov/nrmrl/std/etv/pubs/01_vr_opsis.pdf >.

15. U.S. EPA. 2008. 2 nd International Workshop on Remote Sensing of Emissions: New Technologies and Recent Work.ORS Handbook, Section 2.4 Revision No: 1.0

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Page 47 of 86 

### 2.4 Differential Absorption Light Detection And Ranging Systems

LIDAR is a technology used to measure area source, fugitive or ambient air pollutants without the requirement for line of sight or retro

- reflector measurement paths. LIDAR operates on the same principles as radio detection and ranging ( RADAR ) except light is used rather than radio

waves. In early applications of LIDAR, investigators used search lights, a telescope, and a photoelectric light detector to collect information about Earth’s atmosphere. The technology was used to determine atmosph eric density by studying the backscattered light intensity along the path of the searchlight beam. Since the 1930s, the use of LIDAR has expanded to applications in aerial surveying, three

- dimensional imaging, chemical warfare agent detection, and forestr y. LIDAR is also used to study atmospheric parameters such as aerosol and cloud properties, temperature, wind velocity, and species concentration. 1,2

There are three generic LIDAR applications:

**•** range finders

**•** Differential absorption LIDAR (DIAL)

**•** Dopple r LIDARs

In 1964, a new LIDAR application called DIAL was proposed to locate and measure trace chemical concentrations in the atmosphere. 1 The goal of LIDAR

– based DIAL technique was to precisely measure constituents of ambient air using a simple remote sensing technique that lacked the complexity of traditional optical techniques such as FTIR. Since then, DIAL has developed into a commercially available technology capable of mapping concentrations of multiple atmospheric pollutants.

DIAL uses lasers di rected into the atmosphere to measure species concentrations of target aerosols, dust, and gases in the lower few kilometers of the atmosphere. Using the DIAL approach, spatial concentrations are obtained from the reflected or backscattered light from two wavelengths of light: one that is strongly absorbed by the species of interest and the other just outside of the absorption range of the target compound, which is used to measure background light scattering. As these wavelengths of light are emitted fro m the laser source, the ratio of theORS Handbook, Section 2.4 Revision No: 1.0

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Page 48 of 86 backscattered light intensity between the two wavelengths is measured and coupled with the time delay of the return signal. The target compounds absorb or reflect and scatter the light back to a telescope or receiving optics, where intensity of the backscattered light is detected and evaluated. Concentration is determined based on the amount of light absorbed and the location of the observed compounds is based on the time delay of the backscattered light at the detecto r. 3 By measuring the backscatter at different angles from the source, the data can be processed to show
the two

- dimensional plume shape of the target compound emission profile.

The main advantage of DIAL over other ORS technologies is the ability to spatially resolve the concentration of a single compound, or class of compounds, based on the radiation absorption characteristics of the pollutants being measured. 3, 4 The ability to spatially resolve concentration data is a unique advantage when compare d to alternate remote sensing methods, which yield average concentration data over a predetermined path length. The main limitation in using DIAL is the limited number of wavelengths that can be generated by current laser technology at the precise wavelen gth for compounds of environmental interest to be monitored. Additionally, the use of DIAL in the United States has been limited due to limited availability and the high cost of the associated equipment. 5

2.4.1 Basic Operation

LIDAR technology operates by transmitting a laser into a medium containing gaseous compounds. The laser light can be transmitted in the UV, visible, or IR wave range. 6 For the DIAL application of LIDAR, an appropriate wavelength is chosen for the species to be measured along with a nearby wavelength that will not be absorbed by the target compound. 7

During operation, light generated by a laser is directed through a wavelength switching unit. As the laser pulses, the switching unit alternates the laser between the two different wavelengths designated as “on” (the wavelength that is absorbed by the target compound) and “off” (the wavelength just outside of the target compound’s absorption). Some DIAL systems use separate lasers to produce both wavelengt hs. A continuous laser is used when the measurement range is short or for long measurement time periods. A pulsed laser is used when higher energy is needed for long measurement ranges or short time intervals. 6ORS Handbook, Section 2.4 Revision No: 1.0

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A small portion of the laser output is di rected to a calibration cell. The cell is filled with a known concentration calibration gas and the absorption for a given path length is measured. The remaining narrow beam of laser light is expanded or widened to make it “eye safe” and then
directed in to the atmosphere by a series of mirrors and optics. 7 The expanded laser beam interacts with molecules and particles, and is scattered by them. As the light travels through the atmosphere, the “off” wavelength is scattered elastically by atmospheric part icles. The “on” wavelength is absorbed by the target gaseous compound(s) and scattered at a reduced intensity. 3 Light is scattered in various directions and a small portion is reflected back towards its source. This backscattered light is collected, focused, and a detector converts the light information into a digital signal for use in determination of pollutant location and concentration. 7

DIAL laser systems are chosen based on the absorption characteristics of the compounds under study. Requirem ents for lasers include low beam divergence, adequately low pulse repetition frequency ( PRF ) , and appropriate wavelength specificity. 1 Capture of the entire beam by the receiving optics is preferable to reduce background noise. A low beam divergence (narr ow beam) is necessary to retain the beam in the receiving optics field of view. If a pulsed laser is used, a low PRF ensures measurement cycles are separated by a sufficient length of time to avoid
inference from one measurement to the next.

Wavelength specificity defines which compounds can be measured by DIAL systems. Although other applications of LIDAR, such as measuring aerosols, are operated across a range of wavelengths, DIAL wavelengths are specific to the absorption characteristics of the pollu tants being measured. 1 Advances in tunable laser technology have allowed simultaneous multi

wavelength, hence multi

- component, measurements to be made. 8ORS Handbook, Section 2.4 Revision No: 1.0

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Figure 2

- 17. Beam Path and Major Components of DIAL Unit.

The difference in the backscattered inten sity between the “on” and “off” wavelengths allows for a concentration of the compound to be calculated. The “off” wavelength is reflected predictably and decreases in intensity (P) by the inverse square of the distance the light has traveled (R)
shown in Equation 2. 9

2 1 R P **=** Eq. 2

The “on” wavelength is absorbed by the target pollutant and backscattered at a reduced intensity to that of the “off” wavelength. The two intensities are divided by one another and transformed
into concentration data using the Beer

- Lambert law. 9 When the time delay of backscattering is added into the calculation, the distance from the laser to the compound can be determined. 3,10,11

DIAL measurements collect pollutant concentration data over a relat ively long path length. Beam path lengths range from a few hundred meters to 3,000 meters. 5 Since DIAL systems do not require a remote detector or reflector, a 2

- D scan can be completed in approximately 10
minutes. 4, 5, 8 Pollution emission flux is calc ulated by collecting wind speed data and plume concentration during DIAL testing. Wind speed is multiplied by the pollutant concentration across the emission plane to obtain a flux value. 3 LASER WAVELENGTH SWITCHING UNIT

CALIBRATION UNIT DETECTOR TARGET POLLUTANT

BACKSCATTERED LIGHT

DIAL UNITORS Handbook, Section 2.4 Revision No: 1.0

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Page 51 of 86 The majority of atmospheric sensing DIAL systems operate in a m onostatic mode where light is
emitted and received at the same location. A monostatic mode may be deployed in two sub

configurations; monostatic coaxial mode, where light is received by the same optics through which it was emitted, and monostatic biaxial mode, where light is received by optics located adjacent to the originating optics. The monostatic system allows multiple measurements to be completed quickly without the need of retro

- reflectors or line of site detection systems. Figure 2

- 18 illustrates the monostatic coaxial and biaxial modes.

Adapted from “Measurement and Monitoring Technologies for the 21 st Century [21M 2 ] Open Path Technologies: Measurement at a Distance LIDAR.” Avalable : http://www.clu

- in.org/programs/21m2/openpath/lidar/ Figure 2

- 18. Monostatic Coaxial and Biaxial Configuration for DIAL The use of a bistatic DIAL configuration is less common. In bistatic mode light is produced and detected at separate locations. The bistatic configuration requires frequent repositioning of theORS Handbook, Section 2.4 Revision No: 1.0

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Page 52 of 86
d etector to obtain an emissions concentration profile. 1, 5 Figure 2

- 19 illustrates a bistatic DIAL system.

Adapted from “Measurement and Monitoring Technologies for the 21 st Century [21M 2 ] Open Path Technologies: Measurement at a Distance LIDAR.” Avalable : http://www.clu

in.org/programs/21m2/openpath/lidar/ Figure 2

- 19. Bistatic Configuration for DIAL

The signal receiving and detection system consists of primary optics, a spectral filteri ng unit, a detector, and a photon counter. Receiving optics collect the backscattered light for analysis. Primary optics vary by application and can include Cassegrain or Newtonian telescopes, multiple small mirrors, and liquid mirror telescopes among oth ers. 1, 6 Spectral filtering systems remove background light and reduce signal noise. Spectral filtering can be accomplished by using simple systems such as narrow bandwidth interference filters or more complicated systems suchORS Handbook, Section 2.4 Revision No: 1.0

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Page 53 of 86 as depolarization technique s. 1 A detector converts the incoming spectral signal into a digital signal for photon counting. Typical detectors include traditional Photomultiplier Tubes (PMTs), Charge Coupled Devices (CCDs), mercury

- cadmium

- telluride (MCT) detectors, and avalanche ph otodiodes. 1, 6 The photon counter performs two steps. The first step removes dark counts, which are a type of signal noise created by the detector. The second step counts the number of photons based on the time they were received. By counting photons o n a sequential basis, range
resolved measurements are realized. 1

The DIAL system yields a spatially resolved concentration measurement along the specified path length. Multiple closely spaced scans are often performed over a cross section of an emissio n plume to produce concentration maps as illustrated in Figure 2

- 20. This type of output is unique compared to that of measurements such as OP

- FTIR spectroscopy, for which the output is a PIC without spatial resolution along the path length.

Used with permission from Spectrasyne Ltd (J. Moncrieff, personal communication, April 1, 2010). Obtained from http://www.spectrasyne.ltd.uk/html/technique.html Figure 2

- 20. Illustration of DIAL unit mapping an emission plumeORS Handbook, Section 2.4 Revision No: 1.0

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Page 54 of 86 Wind characteristics play a major role in how the DIAL system should be positioned to take measurements, as well as the validity of data obtained from those measurements. Equipment set up is recommended to be at least 50 meters from the plume cross section and measurements
should be taken perpendicular to the wind direction. During the measurement period, wind direction may change, which effectively skews the measurement plane. 12 Therefore, wind direction is typically analyzed throughout the measurement process to accurately adjust the measurement plane for skew. 12,13 Changes in wind direction and speed may cause variation in the emission plume over time and affect the results of a scan along a measurement plane, which takes about 10 to 15 minutes to complete. Averaging multiple s cans of the same cross section helps to suppress the error associated with a dynamic emission plume. 12 Wind speed analyzed using dual wind monitors typically do not vary more than 20 percent between the independent
wind measurements. Wind data from thre e elevations ranging from 15 to 25 meters, provide sufficient information to determine plume flux through the plane. 14

2.4.2 Pollutants and Relative Levels That Can Be Measured

A variety of atmospheric parameters can be measured with DIAL techniques including: temperature, pressure, water

- vapor concentration, and selected atmospheric gases. Additionally, back scatter and light absorption of cloud particles and aerosols can be investigated.

DIAL has historically been used to measure criteria pollutants in t he upper atmosphere. Approximately 15 species in the spectral range of ultraviolet to infrared can be detected by DIAL systems. Table 2

- 19 lists compounds that can be measured with DIAL systems. The list is not
all

- inclusive but displays compounds reporte d in literature.

Table 2

- 19: Reported Species Measured with DIAL Systems

COMPOUND COMPOUND COMPOUND

Acetylene Hydrogen Chloride Nitrous Oxide Alkanes Mercury Ozone Benzene Methane Sulfur Dioxide Ethane Methanol Toluene Ethyl Benzene Nitric Oxide Xylenes Ethylene Nitrogen Dioxide Compounds in bold are EPA Hazardous Air Pollutants (HAPs).ORS Handbook, Section 2.4 Revision No: 1.0

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Detection limits in the ppb range have been reported at distances of 500 to 3000

meters. 6,15 Detection limits vary based on a number of factors. Atmospheric effects, such as laser beam wander from atmospheric turbulence, influence the accuracy of DIAL measurements. Laser type and internal instrument noise can also have a negative influence on detection limits. 16,17 The path length also affects th e resolution of the system. Reported minimum detection limits range from 0.001 ppb for mercury to 90 ppb for hydrogen chloride gas at a 200 meter absorption path length. 9 Detection limits are given as estimates in Table 2

- 20 and are based on
various abso rption path lengths.

Table 2

- 20. Approximate Detection Limits for DIAL 6

Compound Reported Minimum Detection Limit (ppb) Detection Range (m)
Acetylene 26 800 Alkanes 10 800 Benzene 3 800 Ethane 16 800 Ethylene 9 800 Hydrogen chloride 13 1,000 Mercury 0.06 3,000 Methane 76 1,000 Methanol 153 500 Nitric Oxide 4 500 Nitrogen Dioxide 5 500

Nitrous Oxide 56 800 Ozone 3 2,000 Sulfur Dioxide 4 3,000 Toluene 3 800 Xylenes 5 500

The specific wavelength used for detection depends upon the absorption characteristics of the target compounds. The number of identifiable pollutants is further limited by the number of absorbing wavelengths that are unique to a specific compound without additional interferences, as well as the laser technology that is currently available. This technology is improving with expectation that the range of detectable pollutants will expand. 6ORS Handbook, Section 2.4 Revision No: 1.0

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Page 56 of 86 DIAL systems can also be used in a mode like a fugitive source mon itor. In this mode, an entire class of chemicals can be measured using a single laser wavelength that the entire chemical class
absorbs. DIAL results from such a study must be interpreted as an average.

2.4.3 Typical QA/QC

While the EPA provides information by general reference in Other Test Method 10 (OTM10) 13

, the verification of DIAL measurements is challenging due to its unique ability to produce spatially resolved concentration data. Limited QA/QC guidelines exist that verify such data in
the literatur e. Information specific to LIDAR technology has been published by the Association of German Engineers (VDI) in VDI 4210 Part 1 (1999) Remote sensing, Atmospheric measurements with LIDAR, Measuring gaseous air pollution with DAS LIDAR. 18

Record Keeping

As with all environmental measurements, it is necessary to keep accurate records during measurement periods to ensure accurate data collection including records of calibrations, meteorological conditions, path lengths, and measurement times.

Instrumen t Performance

Initial measurements should be conducted over a time period so that emission source fluctuations may be considered during data analysis; one half to an entire day is recommended for an initial

measurement. 14 Scanning the same plume over dif ferent days and varying conditions is also recommended to assess the impact of varying conditions on measurement results. 12 An initial site assessment should also be conducted to determine any interference that may disrupt data acquisition. Interferences include geographic constraints and off

- site upwind emissions

sources. 13

Some DIAL systems are programmed with verification systems to ensure that quality
measurements are taken. One reported DIAL system employs an internal “wavelength” verification sys tem, known as a wavemeter, to identify and dispose of any data produced fromORS Handbook, Section 2.4 Revision No: 1.0

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Page 57 of 86 the emission of an inconsistent wavelength. 8 This prevents erroneous data from being produced if the light source emits radiation outside of the specified wavelength.

2.4.4 Example Ap plications and Vendors

Applications

With the ability to develop spatial concentration information of particular air pollutants, DIAL systems have been implemented in a variety of applications including fenceline monitoring, fugitive emissions measuremen t, and plume fate analysis. DIAL may also be used to measure flare efficiency from industrial processes. 5,11 For each application, the strengths and limitations of a DIAL system must be considered to produce results that meet users ' DQOs. For example,
the use of CO 2 laser assumes sufficient aerosol concentration in the atmosphere to provide
sufficient backscatter. High wavelength visible and UV light sources rely on molecular
backscatter.

Table 2

- 21. Typical Applications for LIDAR.

TECHNOLOGY APPLICATIONS LIDAR DIAL

DIAL systems are either in a fixed or mobile arrangement. Fixed DIAL units used in laboratories are typically less complicated than mobile systems used for field operation. 9 A number of mobile systems have been constructed based on an enclosed truck platform. The truck is driven to the testing site and positioned accordingly to obtain emissions data. 3, 7, 19 DIAL systems have also been implemented onboard ocean vessels and in airborne systems, collecting emissions information while flying over a target area. 11, 20 Figure 2

- 21 illustrates a
truck

- based mobile DIAL platform.ORS Handbook, Section 2.4 Revision No: 1.0

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Specific field implementation examples include studies complete d on gas processing plants and
oil refineries. These studies used DIAL systems and plume mapping techniques in Canada and European nations. Measured fugitive emissions were four to 20 times greater than factor estimated fugitive emissions. 5,6,14,21 Whi le these studies have concluded emissions factors may underestimate actual fugitive emissions, objection to using annual emissions figures calculated by DIAL measurements is apparent. Industry objects to using DIAL

- based calculated emissions
due to the sh ort time

- period of measurement relative to the long time

- period of annual operation. 6

In other studies, DIAL systems have been implemented to measure emissions from mobile sources such as air and highway traffic. Additional DIAL systems have been develop ed to detect chemical warfare agents as well as natural gas pipeline leaks from airplane mounted platforms. 6 Figure 2

- 21. Mobile DIAL UnitORS Handbook, Section 2.4 Revision No: 1.0

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Vendors

A number of vendors manufacture laboratory

- scale DIAL applications; however, field

- ready measurement instruments are only offered by a small number of vendors. Table 2

- 22 lists vendors that manufacture or provide field

- ready DIAL instruments.

Table 2

- 22: DIAL Vendors

Vendors Spectrasyne http://www.spectrasyne.ltd.uk/

LASEN http://www.lasen.com/ National Physical Laboratory http://www.npl.co.uk/ ITT http://www.itt.com/

2.4.5 Strengths and Limitations

A significant limitation of DIAL technology is the cost and limited availability of the
measurement service. Multiple measurements in North America have relied on importing the instrumentation from the United Kingdom. 12,15,21 Additionally, the number of chemical species measurable by DIAL is restricted to the unique absorption characteristics of those species and the availability of laser technologies able to produce the absorption wavelengths.

The most notable strength of a DIAL system is the ability to spatially resolve pollutant concentration information in three dimensions in a short period of time. Concentration gradient data obtained in short periods of time enables DIAL to be deployed in a number of applications and a number of configurations. DI AL has been used in scenarios from ground

- based emissions plume monitoring to helicopter and fixed

- wing aircraft aerial surveys. Table 2

- 23 and Table 2

- 24 summarize the strengths and limitations of DIAL systems.ORS Handbook, Section 2.4 Revision No: 1.0

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Table 2

- 2 3 : Summary of the DIAL Strengths

Feature Strength
Concentration data is spatially resolved In contrast to PIC measurements taken by other instrumentation system, DIAL relay concentration data as a function of distance along the beam path Beam Path Reported path lengths up to 3000 meters Receiving Optics Collects backscattered light without the use retro

reflectors Measurement Time Scan along measurement plane requires 10 to 15 minutes

Mobile Platform Instrumentation is moved around measurement site obtaining multiple plume scans from various locations increasing accuracy of plume characterization
Instrumentation can also be mounted on airborne

platform for increased mobility and expanded applications

Near Real Time Data Real time data allows for leak identification and inputs to process change decisions

Multi

- Wavelength Operation Allows for simultaneous concentration measurements of multiple species and classes of speciesORS Handbook, Section 2.4 Revision No: 1.0

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Table 2

- 2 4 : Summary of the DIAL Limitations

Feature Limitation

Unique Chemical Absorption Bands Measureable species are limited to those with unique absorption bands. Chemical species with common absorption characteristics can only be measured as classes of compounds

Chemical Species Absorption Dependencies The absorption wavelengths of species are temperature and pressure dependent. It is necessary to check the applicability of wavelengths selected for measurement based on temperature and pressure variation in target absorption. Backscatter Requirements Sufficient aerosol or molecular material must be in the atmosphere to create sufficient backscatter

Wind speed and direction Rapidly changing wind speed or direction may cause measurements to change rapidly and may affect mixing ratios of measured surrogates to compounds of interest.

Vendors Small number of vendors providing DIAL systems and services Expense High system cost has limited to amount of commercial DIAL studies in the United States Site Arrangement Beam path is limited by the geographical constraints of the measurement location

2.4.6 References

1. Argall, P. and R. Sica. 2002. LIDAR in the Encyclopedia of Imaging Science and Technology. Ed. J.P. Hornak. John Wiley & Sons Inc. New York, NY.

2. Moskal, L., T. Erdody, A. Kato, J. Richardson, G. Zheng, and D. Briggs. 2009. Lidar Applications in Precision Forestry.
< http://depts.washington.edu/rsg al/pubs/Moskaletal_Silvilaser2009.pdf >.

3. Edner, H., K. Fredriksson, A. Sunesson, S. Svanberg, L. Uneus, and W. Wendt, W. 1987. Mobile remote sensing system for atmospheric monitoring. Applied Optics. 26(19): 4330

- 4338. doi:10.1364/AO.26.004330.

4. SIRA, Ltd. 2004. Recommendations for best practice in the use of open

- path instrumentation. < http://www.spectrasyne.ltd.uk/BestPractisev13.pdf >.

5. U.S. EPA. 2006. VOC Fugitive Losses: New Monitors, Emiss ion Losses, and Potential Policy Gaps

– 2006 International Workshop.
< http://www.epa.gov/ttn/chief/efpac/documents/wrkshop_fugvocemissions.pdf >ORS Handbook, Section 2.4 Revision No: 1.0

Date: 08/19/11

Page 62 of 86
6. U.S. EPA. 2009. Measurement and Monitoring Technologies for the 21 st Century (21M 2 ). Open Path Technologies: Measurement at a Distance UV

- DOAS. <http://www.clu

- in.org/download/misc/21M2flier.pdf>.

7. Fredriksson, K., B. Galle, K. Nyström, and S. Svanberg. 1981. Mobile lidar system for environmental probing. Applied Optics. 20(24): 4181

- 4189.

8. Weibring, P., H. Edner, and S. Svanberg. 2003. Versatile mobile lidar system for environmental monitoring. Applied Optics. 42(18): 3583

- 3594. doi: 10.1364/AO.42.003583.

9. Sigrist, M. W . (Eds.). 1994. Air Monitoring by Spectroscopic Techniques. New York, NY: John Wiley & Sons, Inc.

10. Spectrasyne, Ltd. 2007. The Technique.
< http://www.spectrasyne.ltd.uk/html/technique.html >.

11. Weibring, P., H. Edner, S. Svanberg, G. Cecchi, L. Pantani, R. Ferrara, and T. Caltabiano. 1998. Monitoring of volcanic sulfur dioxide emissions using differential absorption lidar (DIAL), differential optical absorption spectroscopy (DOAS), and correlat ion spectroscopy (COSPEC). Applied Physics B: Lasers and Optics. 67(4): 419

- 426. doi: 10.1007/s003400050525.

12. Chambers, A. 2003. Well Test Flare Plume Monitoring Phase II: DIAL Testing in Alberta. Alberta Research Council Inc. Prepared for Canadian Associa tion of Petroleum Producers. < http://www.ptac.org/env/dl/envp0402fr.pdf >.

13. U. S. EPA. 2006. Other Test Method (OTM) 10 Optical Remote Sensing for Emission Characterization for Non

- point Sources. < http://www.epa.gov/ttnemc01/prelim.html >.

14. Frisch, L. 2003. Fugitive VOC

- Emissions Measured at Oil Refineries in the Pr ovince of Västra Götaland in South West Sweden

- A Success Story: Development and Results 1986

- 2001 . County Administration of Västra Götaland, Report No. 2003:56.

15. Chambers, A., M. Strosher, T. Wootton, J. Moncrieff. and P. McCready. 2008. Direct

measure ment of fugitive emissions of hydrocarbons from a refinery. Journal of the Air and Waste Management Association. 58: 1047

- 1056. doi: 10.3155/1047

- 3289.58.8.1047.

16. Fredriksson, K. A. 1985. DIAL technique for pollution monitoring: improvements and complementary systems. Applied Optics. 24(19): 3297

- 3304. doi: 10.1364/AO.24.003297.

17. Ahmad, S. R. 1997. Application of lidar to atmosphere pollutant mapping: a review. Proceedings of the SPIE. The International Society for Optical Engineering. doi: 10.111 7/12.283904.ORS Handbook, Section 2.4 Revision No: 1.0

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18. VDI 4210 Part 1. 1999. Remote sensing, Atmospheric measurements with LIDAR, Measuring gaseous air pollution with DAS LIDAR.

19. Spectrasyne, Ltd. 2007. The Equipment.
< http://ww w.spectrasyne.ltd.uk/html/the_equipment.html >.

20. Stearns, S., D. Brake. 2008. Hazardous Liquid Airborne Lidar Observation Study (HALOS). PowerPoint Slides.

21. Chambers, A. K., M. Strosher, T. Wootton, J. Moncrieff, and P. McCready. 2006. DIAL Measurements of Fugitive Emissions from Natural Gas Plants and the Comparison with Emission Factor Estimates.
< http://www.epa.gov/ttn/chief/conference/ei15/session14/chambers.pdf >.ORS Handbook, Section 2.5 Revision No: 1.0

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### 2.5 Thermal Infrared Cameras

Thermal IR cameras for environmental measurements come from a class of camera known as thermographic or forward

- looking I R which uses IR radiation to form an image in a manner similar to the way photographic cameras use visible light. The original thermal IR camera development was funded largely by the astronomy and defense communities for the purposes of “night

- vision” for aircraft and other vehicles and development of heat

- seeking missiles. 1 Private companies have adapted military IR technology that does not require sophisticated cooling or optics to produce commercial IR cameras for environmental applications.

I R camer as have been applied to monitor watershed temperature in game habitats, to detect energy loss or insulation defects in buildings, to track and aid in target acquisition by the military, to improve piloting of aircraft in low visibility conditions, to pinpo int ignitions sources during firefighting
, and to aid in search and rescue operations of missing persons. Environmental applications include the detection of industrial gas leaks that are invisible to the naked eye. By filtering light to detect IR radian ce characteristic of hydrocarbon or VOC
, an existing IR video camera allows the user to see images of hydrocarbon gases on the camera
screen in real time. 2 These cameras have the ability to identify the source and flow path of escaping gases in a wide vari ety of applications 3 , such as tank vents and gas line leaks.

The major advantages to using the thermal IR camera for leak detection are the technology’s portability and its qualitative ability to display a wide field of view that allows major leaks to b e detected more efficiently than classical leak detection and repair procedures that require each flange, seal and valve to be tested individually. Additionally, IR camera technology allows leak detection in parts of facilities that may be difficult or ha zardous for personnel to access. The thermal IR camera’s major drawback is its inability to measure the quantity or concentration of gas present in radiant vapor cloud. A second limitation due to the simple optics employed in portable IR cameras is the t echnologies inability to identify individual chemicals in a complex gas leak mixture.ORS Handbook, Section 2.5 Revision No: 1.0

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2.5.1 Basic Operation

The IR camera creates images based on the IR absorption/emission characteristics of chemical species within the camera’s field of vision. IR absorp tion detection typically occurs in the 3

– 5 μm wavelength range 4 , but a special lens and filter arrangement is used to narrow the IR spectrum of wavelengths detected, therefore
, allowing the camera to identify some specific compounds or compound classes p resent that are not detectable in the UV or visible range. 5 Lens and filter arrangements have been developed to allow the camera to detect a wide variety of gaseous VOCs. Emissions appear on the camera’s screen as a clouded area or smoke in real time, as shown in Figure 2

- 22. 6

- 11

Figure 2

- 22. Detection of Gaseous VOC Emissions from a Chemical Storage Ta nk 14

IR camera operation is fairly straightforward. At start up, the IR camera must be allowed to reach operating temperature since the technology primary detection is based on changes in temperature between a target and its surroundings

. 5 Operators are trained to scan each piece of relevant equipment or area of potential leaks from one end to another and
, if possible
, to perform
the scan from two d ifferent locations or fields of view relative to air flow or wind direction to
ensure all leaks are detected. 2 The first field of view is often from a wider angle with a larger viewpoint, while the second field of view is at a closer viewing angle. The s ame areas can be scanned repeatedly to improving the likelihood leaks are detected. 5ORS Handbook, Section 2.5 Revision No: 1.0

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Page 66 of 86 Several factors affect the IR camera’s imaging and
, therefore
, the sensitivity of the technology. Ambient light plays an important role in the sharpness or resolution of the IR camera’s image. Variations in lighting can require that any of a number of settings be adjusted, such as focusing the lens and switching between automatic and manual camera modes to ensure no leak detections
were missed. 5 The IR camera’s leak det ection sensitivity is affected by temperature, not only ambient, but that of the target gas and the equipment surface as well. Reflectivity (reflection of IR light) and emissivity (emission of energy due to the absorbance of IR light) also plays a role
in the camera’s sensitivity, as well as gas concentration, distance, and wind speed and
direction. 12 It is possible under certain conditions that the radiance of the leaking gas and background are equal, and the leak to be invisible to the IR camera and
, th erefore, the operator. Proper operator training is required to ensure each of these factors is considered to make sure leaks or vapor clouds are detected.

The images collected by modern IR cameras are digital. Even older IR cameras generated a
video fe ed that was recordable and allowed archiving for remote viewing and review. The cameras can operate using battery power for up to eight hours of continuous use, or connected to AC power for 24

- hour monitoring purposes. 9 Additionally, IR cameras used for vehicle inspections have been adapted to use a 12

- volt power source. Some IR cameras area available with global positioning systems (GPS) to automatically record the location of the camera’s use. 10

IR camera models ’ basic operation modes can include additional contrast adjustments allowing easier visualization of leaking gaseous compound against the stationary backgrounds. One such
mode is a non

- uniformity correction (NUC) mode. 13 Th e NUC mode uses the camera’s int ernal histogram equalization algorithm to create a display that highlights time dependant scene

- based variables, thus allowing the operator to see much smaller leaks. Another version is the GasFindIR’s High Sensitivity Mode, in which the manufacturer clai ms a much clearer image that detects smaller leaks and allows for quicker repair. 7

- 11

If quantification of a leaking gas is required, it is also possible to couple the IR camera with additional technology, such as a passive FTIR system or more traditional leak detection and repair instrumentation such as a portable instrument meeting EPA Method 21 requirements or mass flow measurement using a bagging technique. Coupling the IR camera with anotherORS Handbook, Section 2.5 Revision No: 1.0

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Page 67 of 86 technology not only provides means for quantification, but can verify a detected leak as well as determine its chemical composition. 6

2.5.2 Pollutants and Relative Levels That Can Be Detected

Thermal IR cameras can detect chemical compounds that have absorptions in the 8

– 12 μm wavelength IR absorption range. Typically the 3

– 5 μm wavelength range is used for organic VOC. Depending on a variety of factors, including lens focal point 13

, distance from the source, and meteorological conditions, gaseous compound concentrations in the hundreds of ppm range (> 500 ppm) are detectable by the camera. 6 Table 2

- 25 provides an example list of compounds that have been detected using an IR camera. This list is not all

- inclusive, but shows that many compounds can detected with the technology.

Table 2

- 25. Example List of Gaseous Compounds that can be Detected by Thermal IR Cameras
SPECIES SPECIES SPECIES Acetic Acid Ethyl Cyanoacrylate Octane Anhydrous Ammonia Ethylene Pentane Benzene Heptane 1

- Pentene Butane Hexane Propane Carbon Monoxide Isoprene Propylene Chlorine Dioxide Methane Sulfur Hexafluoride Dichlorodifluoromethane Methanol Toluene Ethane MEK Xylene Ethanol MIBK Ethylbenzene Nitrous Oxide

2.5.3 Typical QA/QC

Maintenance records should be kept for any equipment adjustments or repairs that could affect measurement performance. Records should include the date and description of maintenance performed. When the instrument is turned on, it must be allowed to warm up to the manufacturer’s recommended operating temperature. Once at the appropriate temperature, the camera can be used to scan a known concentration of a detectable gaseous compound to demonstrate that the IR camera is producing a visible image. 5ORS Handbook, Section 2.5 Revision No: 1.0

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Page 68 of 86
When a leak is detected, a video record should be taken from an angle and distance that promotes optimum leak visibility. The video should be at least 10 seconds in length and stored with a unique video tag. 2 Information about the leak (such as component type, model or style of component, service, size, process unit, process stream, pressure, vent location and ambient or process temperature 15,16 ) should also be entered into a log sheet to further document the leak. 5 For leak detection and repair (LDAR) applica tions, once the leak has been identified, the leaking component should be marked with a leak detection ID tag so that it can be easily identified by maintenance and repaired. 2 For vents, tanks or other major gas emissions detected by the IR camera, the GP S location and a visible light photograph should be used to document the
observation.

2.5.4 Example Applications and Vendors

Applications

Thermal IR cameras have a wide range of applications, though they are most commonly used to detect large leaks from process equipment and storage tanks at refineries and chemical plants. 6 The technology now allowed as a replacement in the current LDAR requirements for f ederal
rules. 5,16 The cameras have also been used to detect leaks in natural gas pipelines through aerial viewing on helicopters. 2,3,6 Thermal IR cameras can also be used to monitor other plant activities that could potentially create fugitive emissions such as truck and barge loading and unloading and incinerator activities. The cameras can also identify flares that would otherwise be unnoticed by the naked eye. 14 An image of such a flare is included as Figure 2

- 23. Table 2

- 26 provides a general description of the app lications for thermal IR camera.ORS Handbook, Section 2.5 Revision No: 1.0

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Figure 2

- 23 . Flare Detection by Thermal IR Camera. 14

Table 2

- 26. Typical Applications for Thermal IR Camera.

TECHNOLOGY APPLICATIONS Thermal IR Camera Leak Detection

Vendors

While there are several vendors for standard thermal imaging IR cameras, only a few companies promote their products primarily as optical remote sensing IR cameras for pollutants. A standard thermal imaging IR camera used for pollutant detection costs app roximately $75,000, though that cost is expected to decrease as the technology improves. 2 Table 2

- 27 summarizes these vendors
and their Website information.

Table 2

- 27. Thermal IR Camera Vendors

VENDORS FLIR, Inc. www.flir.com Gas Imaging Technology, LLC http://www.gitint.com/ Leak Surveys, Inc. www.leaksurveys.comORS Handbook, Section 2.5 Revision No: 1.0

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2.5.5 Strengths and Limitations

The thermal IR camera has a variety of strengths and limitations that should be considered for each application. A summary of strengths and limitations is shown in Table 2

- 28 and Table 2

- 29, respectively. Utilizing a thermal IR camera is typically a more economical approach to leak detection than traditional methods. The camera can identify the exact source of a leak from safe distances within a plant. However, training is required for operating personnel, and quantitative
results cannot be obtained without introducing additional measurement technology. Additionally, for outdoor use, the IR camera cannot be used on overcast or rainy days.

Table 2

- 28 . Summary Table of the IR Camera's Strengths

FEATURE STRENGTH

Economical Fast screening speed compared to conventional leak detection methods 15 Leak assessment can be done without interruption to plant operations 2,3 Cost

- effective compared to traditional leak detection methods. 2,3 Qualitative Results Accurately assess the size of each leak 15

Leak Identification Better able to isolate the exact source of a leak despite close proximity to other leaking sources in real time and record in a video format. 2,3,15

Leak Detection from a Distance Wide field of view: More likely to identify leaking components in unconventional places. 3,15 Exposure risk minor because leaking components can be viewed at a distance. 2,3,15

Table 2

- 29. Summary Table of the IR Camera's Limitations

FEATURE LIMITATION Qualitative Results Cannot quantify the concentration of a leak without additional technology 3

Training Requirements “Camera use requires individuals with specific training.” Some models are easier to use than others. 3,15

Meteorological Limitations Cannot be used during rain or fog and is not as effective during overcast skies. 15 The camera has a specified nominal operating range for ambient temperature. 15 Safety Requirements Operation is not intrinsically safe and use is limited in hazardous areas. 15ORS Handbook, Section 2.5 Revision No: 1.0

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2.5.6 References

1. Miseo, E. V. and N. A. Wright. 2003. Developing a Chemical

- Imaging Camera. The Industrial Physicist. 29

- 32.

2. Trefiak, T. 2006. Pilot Study: Optical Leak Detection & Measurement. Prepared by ConocoPhillips (October 16).

3. U.S. EPA. 2005. Natural Gas STAR Partner Update. Fall.

4. Coffey, Tom. 2009. Alternative Applications for GasFindIR. Presented at the 3 rd Annual PetroTherm Conference in League City, Texas (February).

5. Reese, D., C. Melvin, and W. Sadik. 2007. Smart LDAR: Pipe Dream or Potential Reality? Exxon Mobil Corporation.

6. U.S. EPA. 2009. Infrared Spectroscopy and Imaging. < http://www.clu

in.org/characterization/technologies/infrared.cfm >. December 21.

7. FLIR Systems. 2008. Data sheet: ThermaCam GasFindIR CO Infrared Camera for Leak Detection and Repair.

8. FLIR Systems. 2007. Data sheet: ThermaCam GasFindIR LW In frared Camera for Leak Detection and Repair.

9. FLIR Systems. 2008. Data sheet: ThermaCam GasFindIR HSX Infrared Camera for Leak Detection and Repair.

10. FLIR Systems. 2009. Data sheet: ThermaCam GasFindIR GF300 Infrared Camera for Leak Detection and Repair.

11. FLIR Systems. 2007. Data sheet: ThermaCam GasFindIR Infrared Camera for Leak Detection and Repair.

12. Spectral Remote Sensing & Detection. 2009. Sensitivity Reliability of Gas Imaging Leak Detection of SF 6 . Presented to the 2009 Workshop on SF 6 Emission Red uction Strategies (February).

13. Benson, R., R. Madding, R. Lucier, J. Lyons, and P. Czeripuszko. Standoff Passive Optical Leak Detection of Volatile Organic Compounds using a Cooled InSb Based Infrared Imager. FLIR Systems.

14. Bullock, Adam. 2009. TCEQ and Th ermal Imaging Technology. Presentation FLIR Petrotherm 2009 Conference (February 24

- 25).ORS Handbook, Section 2.5 Revision No: 1.0

Date: 08/19/11

Page 72 of 86
15. Picard, D., J. Panek, and D. Fashimpaur. 2006. Directed Inspection and Maintenance Leak Survey at a Gas Fractionation Plant Using Traditional Methods and Optical Gas Imaging. Presentation for AWMA Annual Conference (June 22). 16. Federal Register Volume 73, No. 246 (73 FR 78199 78199

– 78219 [E8

– 30196]), QA/QC requirements Alternative Work Practice to Detect Leaks from Equipment. 12/22/2008.ORS Handbook, Section 2.6 Revision No: 1.0

Date: 08/19/11

Page 73 of 86 

### 2.6 C avity R ing

- D own S pectroscopy

There are multiple variations on cavity enhanced absorption techniques based on the property of the time required to reduce the signal due to the absorption or scatter of laser light. CRDS and Integrated Cavity Output Spectroscopy (ICOS) are examples of laser absorption spectrometry that measures optical extinction of compounds that scatter and absorb light in a closed sample path. This chapter describes first generation Cavity Ring

- Down Laser Absorption Spectroscopy (CRLAS) as an example of this widely used optical technology to measure in situ concentrations of gaseous samples that absorb light at specific optical wavelengths down to the part

- per

- trillion level. Although noteworthy for a broad range of applications, this technology is most often used for measurements of weakly

- absorbing or highly

- dilute atmospheric samples.

Traditional absorption spectroscopy techniques measure the absolute change in light intensity after passing through a sample relative to the original intensity of the light. CRDS techniques improve on these methods by measuring the rate of decay of light intensity exiting from a high

finesse optical cavity. Using the rate of decay rather than the change in light intensity makes the CRDS technique less sensitive to fluctuations in the source laser intensity or variations in ambient conditions (such as humidity). Moreover, the reflectivity of the closed optical (or ring

down) cavity yields much longer effective sample path lengths for greater detection sensitivity.

In CRDS applicat ions, the measured rate of decay of light intensity over time is a function of the cavity length, the ability of the optical mirrors to achieve perfect reflectance, and the absorptivity ( ε ) of the sample. Because the cavity length and mirror reflectance a re constant between successive analyses, the amount of time required for the light intensity to decay to 1/ e of is initial intensity (herein referred to as the ‘rate of decay’) within an empty ring

- down cavity and one where the target sample is present is entirely the result of the sample absorbance. 1

2.6.1 Basic Operation

Each gaseous compound absorbs energy at different wavelengths, usually more than one, depending on vibrational and rotational excitement within the molecule. Therefore, each compound has its own “signature” of bands from which energy may be absorbed. Eac h band isORS Handbook, Section 2.6 Revision No: 1.0

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Page 74 of 86 highly selective, with virtually no absorption occurring outside of a specific wavelength. Once a compound has been identified, its spectrum can also be used to measure the compound’s
concentration because the amount of infrared radiation absorb ed from the IR beam is proportional to the concentration of the compound in the sample or open path. According to the
Beer

- Lambert law, there is a linear relationship between absorbance and concentration as shown in Equation 1.



# l c A **ε =**  Eq. 1
Where: A = absorbance intensity

ε = absorption coefficient c = sample concentration l = sample path length

Note that the absorption path length may or may not be equal to the cavity length, depending on the experimental design; the total reflection pathway may be used instead
, as with prism
cavities. 2 The CRD S technique enhances sensitivity to target analytes by significantly increasing the pathlength through the use of an optical resonator (or ring

- down cavity). Increased sensitivity of the CRD S technique relative to that of conventional absorption spectroscopy was demonstrated in the inaugural experiments published by O’Keefe and Deacon [1988] in the visible region of the electromagnetic spectrum.

The absorption spectrum (or collectio n of spectral features for a single species over a range of wavelengths) of a gaseous molecule is the spectroscopic equivalent to a fingerprint as each compound will absorb energy a different wavelengths depending on the quantum properties of that compound . Given the simplicity of CRDS systems, there are no intrinsic limitations to the spectral region of which CRDS can be applied. 4 Indeed, studies have proven that successful
measurements from the far

- IR (12 μm) 5 to UV (197 nm) 6 are possible with current t echnologies. Theoretically, any spectral region can be probed with CRDS if the following three conditions are
satisfied:

1. Mirrors of sufficiently high reflectivity are available in the spectral region of interest.ORS Handbook, Section 2.6 Revision No: 1.0

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2. High

- speed detectors are employed that ca n confidently measure very small differences in duration on the order of microseconds ( μs ) or less.
3. Tunable pulsed lasers or optical wavelength modulation for continuous

- wave CRDS applications are available.

General Experimental Design

A general schematic diagram of the essential components of a CRDS apparatus is shown in Figure 2

- 24. The heart of this technique is the sample cell which is bounded by highly reflective,
dielectric

- coated, concave mirrors. Traditionally, the technique evolved usin g two of these mirrors as depicted in Figure 2

- 24, but many mirror configurations that employ three or more mirrors have been attempted (some of which are available through commercial vendors) and are shown to improve the measurement quality from CRDS appl ications. Regardless of how many mirrors are utilized, the ability of the highly reflective mirrors to achieve maximum reflectivity over the full wavelength range of interest largely depends on the nature of the dielectric coating
selected.

Figure 2

- 24 . The essential components of any CRDS experimental set

- up . 2

As shown in Figure 2

- 24, basic CRDS measurements are acquired by optically coupling laser light through the input mirror of a closed sample chamber bounded by two (input and output)

non

- cofocal, highly

- reflective optical mirrors and measuring the rate of l ight intensity decay over
time. To achieve proper optical coupling and cavity reflectance, the bandwidth of the laser radiation needs to be sufficiently narrow enough to excite only a single optical mode of theORS Handbook, Section 2.6 Revision No: 1.0

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Page 76 of 86 cavity while also being sufficiently narrowe r than that of the spectral features of the sample to
obtain well

- resolved results (this is illustrated later in Figure 2

- 26).

When the laser light enters the closed optical cavity, it reflects off the bounding mirrors with a known amount of light exiting the cavity on each reflection (defined by the mirrors’ reflectance). If the optical cavity is empty, this rate of light intensity decay is characterized by a steady, exponential decrease to zero (similar to the single

- exponential function plotted in Figu re 2

- 25). If a gaseous species that absorbs the laser light is introduced into the cavity, then the intensity decay rate will be faster depending on the concentration of the absorbing species.

The time it takes for the light intensity in an optical cavit y to decay to 1/ e of its original value is called the cavity ring

- down time (RDT or τ ) and is illustrated in Figure 2

- 25. This illustration depicts the light lost from the cavity with each pass of the reflecting light as measured by a PMT
detector. The s moothed exponential curve above the oscillating data in the figure was derived from an algorithm applied to the data by instrumental software. The difference between the RDT curve of an empty cavity and the RDT curve of a cavity that contains sample is di rectly proportional to the concentration of the absorbing gas species in the sample. If the empty cavity ring

- down time, τ, is known, measurements of the decay rate of light intensity obtained as each laser wavelength is scanned yields a complete absorpti on spectrum for each analyte.

Figure 2

- 25 . Schematic representation of the expected rate of decay. 1ORS Handbook, Section 2.6 Revision No: 1.0

Date: 08/19/11

Page 77 of 86 The laser light source can be pulsed or continuous wave (CW), the differences in the experimental design between the two techniques is mostly in the n umber and arrangement of optical components. Romanini et al. [1997] demonstrated the first application of CRDS with a continuous laser source (or CW

- CRDS) such as a TDL. In their study, Romanini et al. [1997] found that this change in the laser light sou rce lead to gains in spectral resolution, signal intensity, and data acquisition rate. Instead of receiving a signal in the shape illustrated in Figure
2

- 25, the instantaneous power of a continuous laser is lower, but usually concentrated into a

narrower bandwidth. 2 Figure 2

- 26 illustrates the difference between the incident pulsed laser light and that of the CW laser light.

Figure 2

- 26

. Comparison of Pulsed and Continuous Wave Laser Light

. 8 Illustrating the overlap between the bandwidths of the absor bing species (Absorber), the pulsed laser source, the CW laser source, and the optical cavity resonance frequency modes.

The CW laser bandwidth must be matched to the narrow transmission limits of the optical cavity to allow injection of the light into th e cavity; this can be done by adjusting the length of the optical cavity, modulating the laser properties, or a combination of both. 2 Looking at Figure 2

- 26, it is easy to observe the more passive mode matching of the pulsed laser source with a wider
band width capturing a complete cavity mode versus the more difficult mode matching of the thinner CW laser source to superimpose on a narrow cavity mode. 9 While the CW

- CRDS method is more optically complicated than the pulsed CRDS method, the low

- cost, high

-ORS Handbook, Section 2.6 Revision No: 1.0

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Page 78 of 86 performance, and practical energy requirements of the TDL allows more flexibility for field applications. 2

Once the light is transmitted into the cavity, the light source must be turned off to observed the decay in light intensity in one of two ways: the operator can either configure the optics to allow
build

- up of the light intensity through constructive interference to a predetermined threshold before extinguishing the light source, or he can turn the light source off immediately once a signal is produc ed from the detector. There are many ways to turn off the light source, but the most common is through the use of an acousto

- optic modulator (AOM) as a laser ‘shutter

. ’

Optical components are added to the experimental design of a CW

- CRDS system to optimi ze the laser source properties as discussed above. Figure 2

- 27 provides a more detailed illustration of the experimental design for CW

- CRDS applications. An optical isolator can be installed immediately after the laser source to reduce on

- axis back refle ctions and increase the signal

- to

noise ratio. 10 A piezoelectric transducer (PZT) is added to modulate the cavity length with a triangular signal to achieve greater cavity resonance, increasing sensitivity, and further improving the signal

- to

- noise ratio. 11 Optical filters and lenses can be added immediately before the PZT to augment the laser spectral line selectivity and improve cavity mode

- matching if necessary/desired. Additional optics may be incorporated into the experimental design after the ring

- down cavity for similar purposes, depending on the experimental application and the selection of the optical detector type. It is important to note that the PZT is included for CW applications only.

Figure 2

- 27

. Optical components schematic of a cav ity ring

- down spectrometer. 8ORS Handbook, Section 2.6 Revision No: 1.0

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Page 79 of 86

When the light is coupled out of the laser
, a detector generates a signal that is ultimately relayed to a personal computer for processing and storage. The type of detector integrated into the experimental design largely dep ends on the application and the required signal format. Most commonly, researchers will use a PMT to detect the CRDS signal because it has good quantum efficiency, good spectral range, and a high gain in addition to being lower in cost. Alternatively,

a CCD has comparable, if not better, spectroscopic properties while providing a two

- dimensional
read

- out and having multiple channel capabilities. PMT and CCD detectors are the two most frequently cited detectors used in CRDS studies. Figure 2

- 28 illustrat es the difference between the signals received by either detector.

Figure 2

- 28

. D ifferences between photomultiplier tube (PMT) and charge

coupled device (CCD) detector outputs. (Adapted from Pemberton, 1989). 12

Current Method Developments

Developments of CRDS technology for vast disciplines and environments have led to many variations to the aforementioned experimental design. Due to the simplicity of the CRD approach, there are only three zones in the experimental design where changes can be made that would significantly alter the technology:
1. (a) the light source (laser technology, spectral design, and the incorporation of input/output optical components);ORS Handbook, Section 2.6 Revision No: 1.0

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2. (b) the construction and design of the internal components to the ring

- down cavity ; and 3. (c) the methods and algorithms utilized for data processing.

The most extensive application of CRDS technology has been atmospheric studies. Pulsed laser CRDS has evolved through many design permutations and is currently manufactured using off

axis cavity

- enhanced absorption (OA

- CEA) techniques with advancements to the construction of the reflective cavity components. Although appropriate for both pulsed and continuous wave light source applications, CEA or ICOS and off

- axis integrated cavi ty output spectroscopy (OAICOS or OA

- CEA) are more frequently used with the CW approach. 8

Most CRDS experiments are performed on gaseous samples due to the simplicity with which gases can be introduced into the sampling cell. However, attempts to broaden the applicability of the technology have lead to a few studies that sample surfaces, thin films, liquids, and solids, although no sample medium has been as extensively studied with CRDS than air.

2.6.2 Pollutants and Relative Levels That Can Be Measured

The a pplications for pulsed CRDS and its numerous variants (such as CW

- CRDS, FT

- CRDS, CEA/ICOS, etc.) and the studies performed on these techniques are limitless. Therefore, the list of detectable pollutants provided in Table 2

- 30 is only a cursory list and d oes not represent all possibilities. 8

Table 2

- 30

. Example list of detectable pollutants by CRDS. Wavelength (λ), sensitivity, and minimum detectable mixing ratio at 1σ noise level for common gaseous species.

Species Method Approximate λ (μm) Sensitivity (cm - 1 ) Mixing Ratio (ppbv)
CH 4 CW CRDS 1.65 1.5 x 10 - 8 52

C 2 H 2 CW CRDS a 1.5 ~ 4 x 10 - 9 4

TNT Pulsed CRDS b 6

– 8 9 x 10 - 9 0.075

Chlorobenzenes Pulsed CRDS 0.266

-- ppmv levels

CO 2 CW CRDS 1.57 ~ 4 x 10 - 9 2500 Samples were analyzed ambient or lab air unless otherwise noted. a Sampled in a flame matrix. b Synthetically prepared sample. c Sampled from a lab source, pure gas or carrier gas mixture matrix. d Sampled in human breath.

e Sampled from prepared stan dard.ORS Handbook, Section 2.6 Revision No: 1.0

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Table 2

- 30

. Example list of detectable pollutants by CRDS (continued). Wavelength (λ), sensitivity, and minimum detectable mixing ratio at 1σ noise level for common gaseous species.

Species Method Approximate λ (μm) Sensitivity (cm - 1 ) Mixing Ratio (ppbv)
CO CW CRDS c 1.57 ~ 4 x 10 - 9 2000

NH 3 CW CRDS c 1.5 ~ 4 x 10 - 9 19

NO CW CRDS d 5.2 5 x 10 - 8 0.7

NO 2 CW CRDS c 0.410 7 x 10 - 9 0.4

NO 3 CW CRDS c 0.662 1 x 10 - 9 0.002

N 2 O 5 CW CRDS 0.662 1 x 10 - 9 0.0012

HONO Pulsed CRDS c 0.354 2 x 10 - 8 1.7

OH Pulsed CRDS 0.309

-- ppmv levels Hg Pulsed CRDS 0.254

-- 0.001

H 2 18 O Pulsed CRDS e 0.95

-- 7 ‰

Aerosol Pulsed CRDS 0.532 1 x 10 - 10

-- Samples were analyzed ambient or lab air unless otherwise noted. a Sampled in a flame matrix. b Synthetically prepared sample. c Sampled from a lab source, pure gas or carrier gas mixture matrix. d Sampled in human breath.

e Sampled from prepared standard.

The sensitivity of the CRDS method is determined by the fractional loss of light intensity per
round

- trip in the cavity. The absorption of laser light intensity for a single pass through the optical cavity is given by the Beer

- Lambert law in Equation 1, w hich is used to approximate the minimum detectable absorption in CRDS. Equation 3 below shows that the minimum detectable absorbance per pass is dependent upon the reflectivity (R) of the mirrors and the accuracy in the determination of τ and the precisio n of ∆τ (or the precision of the number of round trips in the cavity ( N )

. 1,4



# ( **) (**  **)** N N R R I
m min min 

### 1 1 **∆**

**− =**  **∆**

**− ≈**  **τ τ**  **δ** Eq. 3

Where: δI m = the minimum detectable change in absorbance R = mirror reflectivity ∆τ min = minimum detectable change in ring

- down time (i.e. precision of ∆τ) N = the number of round trips in the cavity ∆N min = the accuracy to which N can be measuredORS Handbook, Section 2.6 Revision No: 1.0

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2.6.3 Typical QA/QC

There are three main requirements in the experimental design when CRDS is used. First, the laser source must emit the wavelength absorbed by the target analyte or a range of wavelengths that includes the absorption wavelength. Also, the cavity mirrors must be able to reflect the light in the wavelength region of interest. Finally, the detector must be fast enough to detect changes that occur in very short time intervals (μs). The rest of the instrumental configuration involves adjustments (such as cavity length modulation or cavity resonance mode matching) to achieve an absorbance signal. The optical modes of the entire system mu st then be harmonized to acquire a signal from the detector.

Accuracy, precision, linearity, zero/calibration drift, and response time can be evaluated through the use of known concentrations of gas standards and/or zero air. Simultaneous measurements
b ased on a reference method such as EPA CTM

- 027 could provide the reference data for assessing CRDS comparability. The data completeness is simply a measure of the amount of valid data points achieved versus the total amount of data points expected for a g iven period of time. Other factors that become important during operation such as maintenance requirements, consumables used, ease

- of

- operation, and frequency of repairs are being assessed through EPA
verification tests. 15

2.6.4 Example Applications and Vendors

Applications

Although the technology is new to regulated environmental applications, it is not foreign to experiments involving the direct monitoring of environmental contaminants. A work by Berden
et al. [2000] lists a comprehensive overview of published findings with CRDS technology from the official date of inception (with O’Keefe and Deacon in 1988) to the date of publication (2000). 3,4 This list contains well over 200 spectral features detected by CR DS and includes absorption wavelengths, almost continuously, from 205 nm to 10,617 nm. Moreover, Atkinson [2003] and Brown [2003] individually identified chemical divisions for the most potential applications of CRDS in direct environmental contaminant mo nitoring into the following
classes: 2,8ORS Handbook, Section 2.6 Revision No: 1.0

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1. Nitrogen oxides and Nitrous Acid ( HONO ) 2. Ammonia 3. Elemental mercury and volatile mercury compounds 4. Carbon monoxide and carbon dioxide 5. Methane, hydrocarbons, and formaldehyde 6. Atmospheric aerosol particulates

The limita tions to the application of CRDS technology depend on the development of individual optical system components. The overall applicability and usefulness of the method has only begun to be explored and various transfigurations of CRDS will enjoy extensive a pplication in environmental analytical chemistry as the merits of these methods are proven in time. Table 2

- 31 summarizes the common applications for CRDS technology.

Table 2

- 31 . Typical Applications for OP

- TDL. Technology Applications CRDS Tracer Gas Correlations

Vendors There are currently three known proprietors of commercial CRDS systems: (1) Picarro, Inc., (2) Los Gatos Research, Inc., and (3) Tiger Optics, Inc. Each manufacturer has developed a system different from the others. The main technological differences between each manufacturer’s designs have primarily to do with the ring

- down cavity configuration and construction materials. The cost of a CRDS system ready for field use ranges between $40,000 to $150,000, depending on specific application and configuration. To date, the EPA’s ETV is still in the process of testing the method and anticipates reporting their results sometime towards the end of the 2010 calendar year. Table 2

- 32 lists CRDS vendors and their internet contact information.

Table 2

- 32. CRDS Vendors

Vendors Picarro, Inc. (CRDS) www.picarro.com Tiger Optics www.tigeroptics.com Los Gatos Research (ICOS) www.lgrinc.comORS Handbook, Section 2.6 Revision No: 1.0

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2.6.5 Strengths and Limitations

CRDS can be used as a qualitative tool to provide specific information about volatile IR energy

absorbing molecules. It can also be used as a quantitative tool to provide the concentration of

many gas

- phase molecules. A summary of strengths and limitatio ns is shown in Tables 2

- 33 and
2

- 34. One of the main strengths of CRDS is that it measures time and not absorbance
, making the technique immune to environmental variations and laser intensity fluctuations while concurrently increasing the linear dynamic r ange. Moreover, through the use of a high

- finesse optical cell
, CRDS has greatly increased the technology sensitivity to target compounds without adding complicated sample pre

- conditioning steps. The use of the optical cell further enhances the technolog ical design to withstand vibration making field applications of the technology simpler.

The CRDS application is limited mostly by the properties of the high

- reflectivity optical mirrors. The mirrors used for CRDS have a high amount of wavelength specific ity but lack the flexibility necessary to allow simultaneous multiple species detection and/or a broad species application range. High

- reflectivity optical mirrors currently are only able to reflect about 15 percent of the target wavelength on either side

.

Table 2

- 33 . Summary Table of CRDS Strengths

Feature Strength

Simple design Minimal maintenance required and no consumables are needed. Turnkey operation with the potential for remote access and control. “User friendly

. ”

Fast detector Ability to measure very small changes in short time frames. Can rapidly scan spectra continuously for high temporal resolution and real

time results.

Multi

- pass, high

- finesse, stable optical cell Greatly increases sensitivity with much longer effective pathlengths. Insensitive to vibrations during measurements.

Broad

- band source capable Allows for extended wavelength range scanning, increasing sensitivity by probing multiple absorption lines while also eliminating other interferences.
Internal temperature and pre ssure controls Minimal

- to

- no drift making frequent calibration unnecessary. Enhanced accuracy and system stability.
Measures time, not absorbance Renders the method immune to ambient changes (such as relative humidity and temperature) and laser intensity fluctuations.
Direct sampling Little

- to

- no sample pre

- conditioning or treatment required before analysis.ORS Handbook, Section 2.6 Revision No: 1.0

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Table 2

- 33 . Summary Table of CRDS S trengths (continued)

Table 2

- 34 . Summary Table of CRDS Limitations

2.6.6 References

1. Wheeler, M.D. S.M. Newman, A.J. Orr

- Ewing, and M.N.R. Ashfold; (1998). “Cavity ring

- down spectroscopy.” Journal of the Chemical Society, Faraday Transactions, 94 (3) : 337.

2. Atkinson, D.B.; (2003). “Solving chemical problems of environmental importance using cavity ring

- down spectroscopy.” Analyst
, 128 : 117.

3. O’Keefe, A., and D.A.G. Deacon; (1988). “Cavity ring

- down optical spectrometer for absorption measurements using pul sed laser sources.” Review of Scientific Instruments, 59 (12) : 2544.

4. Berden, G., R. Peeters, and G. Meijer; (2000). “Cavity ring

- down spectroscopy: Experimental schemes and applications”. International Reviews in Physical Chemistry, 19 (4) : 565.

5. Bisson, S.E., T.J. Kulp, O. Levi, J.S. Harris and M.M. Fejer; (2006). “Long

- wave IR chemical sensing based on difference frequency generation in orientation

- patterned GaAs.” Appl. Phys. B

- Lasers and Optics, 85 (2

- 3) : 199. Feature Strength

Compact system Easy field deployment and installation. Quick sample exchange in a smaller volume cavity with moderate flow rates. Advances in components allow for a fairly rugged portable system
Can use low power optical sources Logistically simpler for field use to eliminate the need for a large power source.

Feature Limitation Measures only total extinction May need to apply sample filtering components to avoid interferences.
Laser light source Limits the method to the laser spectral ranges available.

High

- reflectivity mirrors Are only able to reflect over a small wavelength range (about ± 15%) relative to the center wavelength. Multiple species detection difficult.

High quality lasers and mirrors Key components that typically drive up the cost of the instrumentation, depending on application.ORS Handbook, Section 2.6 Revision No: 1.0

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Page 86 of 86
6. Sneep, M., S. Hannemann, E.J. van Duijn, and W. Ubachs; (2004). “Deep

- ultraviolet cavity ringdown spectroscopy.” Optics Letters, 29 (12) : 1378.

7. Romanini, D., A.A. Kachanov, N. Sadeghi, and F. Stoeckel; (1997). “CW cavity ring down spectroscopy.” Chemica l Physics Letters, 264 : 316.

8. Brown, S.S.; (2003). “Absorption Spectroscopy in High

- Finesse Cavities for Atmospheric Studies.” Chemical Reviews, 103 (12) : 5219.

9. Paldus, B.A, and A.A. Kachanov; (2005). “An historical overview of cavity

- enhanced methods.” Canadian Journal of Physics, 83 : 975.

10. Hippler, M., and M. Quack; (1999). “Cw cavity ring

- down infrared absorption spectroscopy in pulsed supersonic jets: nitrous oxide and methane.” Chemical Physics Letters, 314 : 273.

11. Thiebaud, J., and C. Fittschen; (2006). “Near infrared cw

- CRDS coupled to laser photolysis: Spectroscopy and kinetics of the HO 2 radical.” Applied Physics B, 85 : 383.

12. Pemberton, J.E., and R.L. Sobocinski; (1989). “Raman Spectroscopy with Helium

– Neon Laser Excitation and Charge

- Couple d Device Detection.” Journal of the American Chemistry Society, 111 (2) : 432.

13. Crosson, E.R.; (2008). “A cavity ring

- down analyzer for measuring atmospheric levels of methane, carbon dioxide, and water vapor.” Applied Physics B, 92 : 403.

14. Chen, H., J. Wi nderlich, C. Gerbig, A. Hoefer, C.W. Rella, E.R. Crosson, A.D. van Pelt, J. Steinbach, O. Kolle, V. Beck, B.C. Daube, E.W. Gottlieb, V.Y. Chow, G.W. Santoni, and S.C. Wofsy; (2010). “High

- accuracy continuous airborne measurements of greenhouse gases (CO 2 and CH 4 ) using the cavity ring

- down spectroscopy (CRDS) technique.” Atmospheric Measurement Techniques, 3 : 375.

15. Cowen, K., A. Dindal, and J.L. McKernan; (2009). “Test/QA Plan for Verification of Cavity Ringdown Spectroscopy Systems for Ammonia Monitoring in Stack Gas.” U.S. Environmental Protection Agency, Washington, DC, EPA/600/R

- 09/082.ORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 1 of 58 

### 3.0 Measurement s Applicable to Emissions Flux

Optic al remote technologies have been applied to answer a variety of fugitive and area source emissions questions. The range of applications spans both short

- term characterization and measurement of emission flux to long

- term monitoring of trends in control st rategy performance. ORS technologies have been used in mobile applications to screen pipelines or industrial sites for leaks or major sources and in stationary applications to measure flux from landfills, waste lagoons, and petrochemical plants.

The te chnologies described in Chapter 2 can be used alone or in combination to provide three major types of data : plume characterization, short

- term flux measurements and long

- term monitoring studies.

Short

- term flux measurement applications (e.g. DIAL, R PM , SOF, Tracer Release Correlation etc.) are useful to determine the emissions from a complex area sources at one point in time. These measurements provide an estimate of the emissions plume size and concentration of selected target compounds or surrogates.

Concentration profiling involves using ORS such as line of sight open

- path optical techniques. Profiling periodic changes of emissions in one dimension is often the precursor of pilot stage of long

- term monitoring at an area or fugitive emissions site.

Long

- term monitoring is used to determine average trends of area sources emissions and to provide an indication of seasonal or industrial cycle emissions profiles. Continuous concentration profiling is also useful to determine process upsets or to diagno se the potential source of emissions from a complex industrial area through the use of back trajectory calculations. Typically long

- term monitoring needs to be associated with a short

- term measurement characterization of the size and composition of the em issions plume to relate trends in monitoring data to emissions and emissions factors. 1ORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 2 of 58 These three types of data provide essential information on the annual emissions rate of fugitive
and area sources as well as a measure of the effect of emissions reduct ions efforts.

Chapter 3 of this Handbook provides an introduction to the use of ORS technologies by describing applications that measure short

- term flux or mass emission rates from open or un

ducted sources.

Reference

1 Hashmonay, Ram, Long Term Moni toring of Greenhouse Gas Emissions from Fugitive and Area Sources, presented at the AWMA Symposium on Air Quality Measurements Methods and Technology, Los Angeles, CA November 2010ORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 3 of 58

3.1 Radial Plume Mapping: Other Test Method 10

RPM is an ORS method used to determine fugitive emissions from non

- point emissions sources including fugitive emissions and area source emissions. Its main goals are to identify emission “hot spots” over large scanned areas and measure emission fluxes. Fugitive emissions include air pollutants released into the ambient air from pressurized equipment due to leaks and various other unintended or irregular releases of gases. Examples of fugitive emissions include O 3 precursors, benzene, and methane

. 1 For some source categories, fugitive and/or area source emissions are a significant portion of the total pollutant emissions; therefore, it is important to be able to locate and quantify these emissions.

The open

- path configuration for ORS technologies was or iginally used to determine the average concentration of a compound of interest over a path of known length. The line

- of

- sight, or one

path, configuration provides an average of the compound of interest concentration per path length (i.e., ppmm). Open

- pat h monitoring expanded to include the measurement of the average concentration over several distances along the open path length. These interval measurements enabled estimation of the concentration profile of the plume. However, the survey of leaks and
ho t spots over a large area is not possible with just one optical path. Directing the ORS path over different lengths, as well as different horizontal and vertical paths, allows additional characterization of the horizontal or vertical emissions plume profi le. RPM , as discussed in this
section
, is the outgrowth of multidirectional, multi

- pathlength ORS and is used in combination with mathematical algorithms to characterize the concentration profile over a horizontal or vertical optical path plane.

3.1.1 General Description of Approach

Measuring the total amount of a fugitive emission over a large area is not simple. Earlier efforts used traditional point sampling instruments like canisters, sorbents methods, flux boxes, PID/FID
and others

. 2 However, t he traditional point methods only provide concentrations from a single point and fail to capture the temporal and spatial distribution of fugitive emissions over a large

area. These methods also fail to identify, if any, “hot spots” of fugitive emissions. RPMORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 4 of 58 provides a more complete survey of a large area. Open

- path ORS measurement technology mounted on a programmable aiming platform or scanner can be configured in a vertical plane to measure emissions flux. When scanning in the horizontal plane (HRPM)
, results can be used to locate hot spots at ground level. Emission fluxes are obtained when scanning in the vertical plane (VRPM) downwind of the area source along with meteorological measurements data.
One

- dimensional RPM, which scans along one line, s uch as an industrial fence line, is used to profile pollutant concentrations downwind from a source and coupled with wind direction may also be useful in locating emissions sources

. 2 Determining which scanning setup to use depends entirely on the objectiv es of the project and data quality indicators.

Horizontal RPM Algorithm

The H RPM approach provides horizontal differentiation to path

- integrated measurements by optical remote sensing. This technique yields information on the two

- dimensional distributio n of
the concentrations in the form of chemical

- concentration contour maps. In this application
, the plume mapping identifies chemical “hot spots,” the location of high emissions. Horizontal radial scanning is usually performed with the ORS beams located close to the ground. The survey area is divided into a Cartesian grid of rectangular cells. A mirror is located in each of these cells and
the OP

- FTIR sensor scans to each of these mirrors, dwelling on each for a set measurement time.
The measurement e quipment scans to the mirrors in the order of either increasing or decreasing azimuth angle. The path

- integrated concentrations measured at each mirror are averaged over several scanning cycles to produce time

- averaged concentration maps. Meteorological measurements are made concurrent with the scanning measurements. Equation 4 is used in an iterative process to calculate the average concentration for each rectangular areas of the surveyed zone as shown by example in Figure 3

- 2 

# ∑ **=** k m km k 

### c K PIC Eq. 4

PIC = path

- integrated concentration K = kernel matrix k = number index for the beam paths

m = number index for the pixels

c = average concentration in the m th pixel.ORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 5 of 58

The kernel matrix includes the specific beam geometry as shown in Figure 3

- 2 where the diagonal lines represent the K . Each value in the kernel matrix K is the length of the k th beam
within the m th pixel; therefore, the matrix is specific to the beam geometry. The HRPM procedure solves for the average concentrations (one for each pixel) by solving the non

- negative least squares best fit for the data. Then the algorithm multiplies the resulting vertical vector of averaged concentration by the matrix K to yield the end vector of predicted PIC d ata. The second stage of the plume reconstruction involves interpolation among the reconstructed pixel’s average concentration, providing a peak concentration not limited to the center of the pixels. A triangle

- based cubic interpolation procedure (in Cartesian coordinates) is currently used in the HRPM procedure

. 2 The ORS instrument is typically placed at the origin (in the first quadrant of the Cartesian convention) of the rectangular area to be measured. Once the HRPM measurement
area and the numbe r of path

- determining components (PDCs) have been determined
, the area is divided into smaller rectangular areas called pixels. The total number of pixels required is smaller or equal to the total number of beam paths

. 6

Figure 3

- 1 . Horizontal RPM setup, taken from Hashmonay et al., 2008.ORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 6 of 58 Vertical RPM algorithm

The VRPM algorithm uses multiple beam paths to survey the vertical pollutant concentration profile as a function of distance from the measurement equipment. Two different beam configuratio ns of the VRPM methodology have been used: the five

- beam (or more) and the
three

- beam VRPM configuration. Figure 3

- 3 shows a VRPM configuration using six beams. In
the five

- beam (or more) configuration, the ORS instrument sequentially scans the paths of five
PDCs. Three PDCs are along the ground

- level crosswind direction, and the other two are elevated on a vertical structure. Additional beam configurations provide better spatial definition of the plume in the crosswind direction. In the three

- beam co nfiguration, the ORS instrument sequentially scans over three PDCs. Only one beam is focused at ground level, while the other
two are elevated on a vertical structure. Pollutant data are collected over time as the measurement equipment cycles between eac h PDC. 2

Figure 3

- 2 . Vertical RPM setup, taken from Hashmonay et al., 2008

Once the PIC for all beam paths are averaged for the gas species of interest, the VRPM calculations reconstruct a plume map in the vertical downwind plane. A two

- phase smooth basis function minimization (SBFM) approach is applied when there are three or more beams focusedORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 7 of 58 along the ground level (5

- beam or more configurations). In the two

- phase SBFM approach, a

one

- dimensional SBFM (1D

- SBFM) reconstruction procedure is applied to reconstruct the smoothed ground level and crosswind concentration profile. The reconstruction is applied to the ground level segmented beam paths of the same beam geometry to find the cross wind concentration profile. A univariate Gaussian function i s fitted to measured PIC ground level
values

. 2 The 1D

- SBFM is also the sum of the squared errors (SSE), which is also the error function for the minimization procedure. The equation for calculating SSE is:


## ∑ **∑ ∫**  ** **  ** **  ** **  ** **  ** **  ** **  ** **  ** **  ** **  ** **  ** **  ** **  ** ** 

**−**

**− −**  **=**
i j r

yj yj

yj j i yj yj j i dr r m B PIC m B SSE 2

0 2

2 1 exp 2 )
, , ( **σ σ**  **π σ**  Eq. 5

B = area under the on

- dimensional Gaussian distribution

r i = pathlength of the i th beam

m y = mean (peak location)

σ

y = standard deviation of the j th Gaussian function
PIC i = measured PIC value of the i th path

The SSE function is minimized us ing the simplex minimization procedure to solve for the unknown parameters (Press et al. 1992). When there are more than three beams at the ground level, two Gaussian functions are fitted to retrieve skewed and sometimes bi

- modal concentration profiles. This is the reason for the index j in Eq. 5

. 6

Once the 1D

- SBFM phase is completed, the 2

- D phase is applied. The bivariate Gaussian function used in the second phase is: 

## ( **)** 

# ( **) (**  **) **  ** **  ** **  ** **  ** **  ** **  ** **  ** **  ** **  ****

**⋅ +**  **− ⋅** 

**− =** 

**− −** 

**−** 2 2

2 1 2 1

1 

### sin cos 2 1 exp 2 ,

z D y D y

z D y z 

### r m r A A G **σ θ** 

**σ θ** 

**σ πσ**  **σ** Eq. 6

Where σ y

- 1D is the standard deviation along the crosswind direction found in the 1D

- SBFM; m y

1D is the peak location along the crosswind direction found in the 1D

- SBFM procedure and A
and σ z are the unknown parameters to be retrieved in the second phase of the fittin g procedure. To solve for the unknowns an error function (SSE) is used which is minimized using the simplex
method to solve for the two unknowns.ORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 8 of 58

If measurement equipment uses the three

- beam setup (one at the ground level and the other two elevated)
, t he one

- dimensional phase calculation can be skipped, assuming a wide plume. The standard deviation in the crosswind direction is assumed to be about 10 times that of the ground

level beam path (length of vertical plane). Thus, if r 1 is the length of the vertical plane, to determine the vertical gradient in concentration we use:


## ( **)** 

# ( **) (**  **) (**  **) **  ** **  ** ** 
** **  ** **  ****
** **  ** **  ****
** **  ** **  ****

**⋅ +**  **− ⋅** 

**− =**  2 2

2 1 2
1

1 

### sin 10 2 1 cos 2 1 exp ) 10 ( 2 ,

z z z 

### r

r r r

r A A G **σ θ**  **θ**

**σ π**  **σ** Eq. 7

When the parameters of the function are found for a specific run, the VRPM algorithm calculates the concentration values for every square unit in a v ertical plane (pixels). Then, the algorithm integrates the values incorporating wind speed data at each height to calculate the flux.ORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 9 of 58

One

- dimensional RPM algorithm

For 1

- D plume mapping, the scanning ORS instrument and three or more PDC are placed in a crosswind direction along a line, such as an industrial site fence line, and PIC measurements are made. A minimum of three PDCs are needed, but four to six are recommended, as shown in Figure 3

- 4, to provide a more detailed concentration profile. PDCs should be placed on the line

of

- sight with an equal distance between each subsequent PDC, if possible. The 1D

- RPM configuration uses the same equations for the VRPM, 1D

- SBFM, which reconstructs a mass

equivalent plume concentration profile along a line

- of

- sight measurement.

Figure 3

- 3 . One

- dimensional RPM setup, taken from Hashmonay et al., 2008.

The RPM model and ORS technique are coupled together by a series of steps

. F irst
, the ORS pollutant concentration data along with wind vector information are processed with the VRPM algorithm to yield a mass emission flux for the source

. 2,5,6,7 In a similar way
, HRPM and 1D

RPM algorithms are processed with the concentration data to provide hot spots info or
concentration downwind for the sour ce. The output of the concentration data and algorithm process looks like a contour map. Figure 3

- 5 displays VRPM and HRPM contour map outputs where the concentration patterns are evidence of the distribution of the fugitive emission.ORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 10 of 58

Figure 3

- 4

. E xamples of RPM algorithm outputs. Panel a. corresponds to the VRPM output and panel b. is HRPM output.

RPM

- ORS Technologies

Technologies appropriate for characterizing ground

- level area sources and non

- point emission sources such as landfills, lagoons , and industrial complexes 3 using RPM methodologies are: OP

FTIR, open

- path Tunable Diode Laser Absorption Spectroscopy (OP

- TDLAS)
, UV

- DOAS, and DIAL. Each technology has its own strengths and limitations and, depending on the objective of the project, so me are more effective than others. The f ollowing is a discussion of the conditions and requirements to deploy each technology.

OP

- FTIR has an optical range of 100

– 500 m ; it can detect multiple compounds simultaneously at high temporal resolution with d etection limits in the ppb range. The instrument setup is time

consuming and it requires liquid nitrogen to cool the instrument, so OP

- FTIR is best for campaigns that do not require constant relocation and multiple setups. Another consideration is
that C O 2 and water are interfering species in FTIR measurements. OP

- FTIR data has to be processed to quantify path

- integrated concentrations, so if real

- time is needed
, OP

- FTIR is not
the best choice. For more details about OP

- FTIR technology see section 2.1.

OP

- TDL has an optical range of up to 1 km. Depending on the topography and location of physical barriers at the survey area, the distance between the control box and the telescopes may require a large amount of fiber optic cable, which can be difficult t o deploy 2

. OP

- TDL canORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 11 of 58 detect CO, CO 2 , NO x , ammonia
, methane ( CH 4 ) and hydrogen sulfide with detection limits in the ppb range, but can only detect one compound of interest at a time. The instrument can produce multiple beam paths, is lightweight
, and is easily deployed. The OP

- TDL generates real

- time path

- averaged concentration data in the field. When only a single gas is of interest, OP

- TDL
offers a cost

- effective choice compared to OP

- FTIR. OP

- TDLAS has been used to monitor the
exhaust from n atural and mechanical ventilation systems used in houses, farms and other facilities. The technique has also been used to measure the flux from a traveling gun sprayer applying swine lagoon liquid to the farm field

. 4 For more details about OP

- TDLAS techn ology
, see section 2.2.

UV

- DOAS detects unstable species like radicals, nitrous acid, aromatic species, and BTX at low concentrations in the ppb levels. The UV

- DOAS can be setup to scan multiple or single beam paths. For more details about UV

- DOAS techn ology
, see section 2.3.

3.1.2 Verification/Validation Studies

The RPM algorithm’s capacity to locate/identify sources of fugitive emissions and provide accurate measurement of emissions flux of fugitive emissions and area sources has been assessed
in tw o different ways: (1) measurement of known concentration tracer gas releases and (2) comparison with the measurement results of selected instruments in collocated systems. RPM data is verified by assessing if the collected data satisfies the objectives o f the study

. 1 The following are several examples of studies performed to test RPM and the selected ORS instruments to survey fugitive emissions.

OP

- FTIR and OP

- TDLAS comparison studies: Thoma et al., 2005 and Modrak et al., 2005

During the Fort Collins measurement campaign 8

, methane measurements from two OP

- FTIR instruments were compared. Both OP

- FTIR instruments contain a Nicolet bench, 12

- inch telescope, and collected data at resolutions of 0.125 cm - 1 , 0.25 cm - 1 , 0.5 cm - 1 , 1 cm - 1 , 2 cm - 1 , 4

cm - 1 , and 8 cm - 1 . The data comparison showed that the instruments were extremely stable and reliable for the duration of the campaigns. In separate studies 7,8

, investigators used OP

- TDLAS
and OP

- FTIR to compared methane measurements obtained by both instruments and foundORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 12 of 58 similar results. During the experiments, the two instruments were deployed side

- by

- side and aligned to an identical mirror. Methane concentra tion data were collected with each instrument for a period of 30 minutes

. 7 The results of the experiment found that methane concentrations
measured with the OP

- TDLAS were slightly higher (3 percent) than concentrations measured
with the OP

- FTIR instrument . These results are significant because they show that methane concentration data collected by the two instruments are comparable, and that both can be used interchangeably in RPM configurations

. 3

OP

- FTIR and UV

- DOAS comparison: Colorado Springs field study 3

The Colorado Springs field campaign occurred in September 2003 at a former landfill site as part
of an effort to rehabilitate the site for recreational use. The current owners of the landfill and the State of Colorado requested assistance from the EPA to perform a site assessment to search for the presence of any fugitive gas emissions from the site. The study used OP

- FTIR, OP

- TDLAS,
and UV

- DOAS instruments. The UV

- DOAS instrument was deployed at the site to collect data concurrently with the OP

- FTIR instrument. The UV

- DOAS detected the presence of BTX. The
concentrations of toluene measured with the UV

- DOAS instrument correlated well with gasoline
concentrations measured with the OP

- FTIR instrument during the same time period.

VRPM plume captu re validation study 3

During June and July 2006 at Orange County Municipal Landfill, the EPA and ARCADIS performed a VRPM plume capture validation study. The objective was to capture the emissions from hot spots located a large distance upwind of the meas urement configuration. The experimental design used OP

- FTIR for VRPM measurements and a known concentration of tracer gas released to determine plume capture. The effectiveness of the RPM configuration in capturing plumes (horizontal and vertical planes) was evaluated by comparing the actual release rate of the tracer gas to the calculated flux values as tracer gases were released at different distances upwind of the configuration. Releases were made at different distances ; 20, 60, 100,
and 140 m from th e VRPM measurement plane. The study found that if there is no statistical significant difference between the averaged concentrations along each beam (i.e., no vertical concentration gradient), the VRPM configuration is not vertically capturing the plume. If theORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 13 of 58 difference between the average concentrations along each beam is less than 10 percent (i.e., a slight vertical concentration gradient exists), the VRPM configuration is sufficiently capturing the plume from the upwind release point. If the differe nce between the average concentrations along each beam is greater than 10 percent (i.e. a substantial vertical concentration exists), the VRPM configuration is vertically capturing the plume from the upwind release point and the releasing location is not c lose enough to the maximum upwind location for complete plume capture.

3.1.3 Typical QA/QC

This section describes the QA/QC activities that pertain to the RPM as described above. QA/QC activities normally depend on pre

- determined data quality indicat ors that address the project unique objectives. The technologies used for RPM have their own specific QA/QC associated with the instruments. If interested in the technology QA/QC for OP

- FTIR , refer to section 2.1 ; for OP

- TDLAS
, refer to section 2.2 ; and for UV

- DOAS
, refer to section 2.3.

The general QA/QC steps are: (i) equipment calibration, (ii) assessment of DQI goals and (iii) DQI check for analyte path

- integrated concentration measurement. Each ORS instrument has its own calibration procedur e as discussed in Chapter 2 of this Handbook , thus is important to follow the instrument’s manufacturer instructions. DQI goals depend on the compound of interest and the expected concentration ranges, thus detection limits, accuracy, and precisio n need to be determined using appropriate traceable standards. On

- site verification using a known concentration tracer gas release provides a sampling episode specific QC confirmation of test
results.

Because meteorological data is part of RPM calculatio ns, instruments used to measure ambient conditions need to be calibrated and their accuracy and precision tested regularly. Table 3

- 1 shows the recommended DQIs for the different aspects associated with the measurement of the path

- integrated concentration and RPM

. 2ORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 14 of 58

Table 3

- 1 . Data quality indicators for the QA/QC process, taken from EPA

- OTM10 Measurement parameter Analysis Method DQI
PI

- ORS Instrument Instrument specific Instrument specific Wind speed Side

- by

- side comparison of two wind monitors Within 20%

Wind direction Comparison to magnetic north Within 10% Optical path

- length Measure and compare to known path Within 2%
Beam angle Measure and compare known angle Within 2o

HRPM, VRPM and 1D

- RPM CCF* ≥ 0 .8 VRPM flux measurement Wind direction

- 10o to +25o from perpendicular 1D

- RPM Peak location variability Reconstructed peak locations
* Concordance correlation factor (CCF) indicates the goodness of fit between measured and predicted path

integrated concentration, CCF = rA.

3.1.4 Siting Concerns

Certain weather conditions such as rain, fog or snow can obscure the optical beam of the utilized instrument and affect its ability to continuously measure gaseous concentrations. Transient, but significant, obscuration can occur during heavy precipitatio n events, particularly with longer path measurements. This limits the sensitivity of the PIC measurements or the instrument’s ability to collect data

. 2

Wind conditions can greatly affect the results of field measurements and should be taken into

accoun t when interpreting data. Calm wind conditions do not affect the HRPM methodology algorithm for hot spot source location. However, very low wind speeds are not ideal for the VRPM methodology for emission rate estimation, as the source plume may not be ca rried through the vertical plane in the absence of measurable wind. Very high wind speed conditions are not ideal for any of the RPM methodologies. High winds may displace or vibrate the optical alignment of the components of the ORS system used in the s etup, and affect the quality of the
PIC values acquired in multiple beam paths. They may also cause displacement of any hot spot identified by HRPM. Based on controlled studies performed in the past, the following wind speed ranges are recommended for op timal results:

**•** HRPM methodology: Near 0 to 5 m/s

**•** VRPM methodology: 1 to 8 m/sORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 15 of 58

**•** 1D

- RPM methodology: 1 to 8 m/s

In optimal conditions, the prevailing wind direction should be as close as possible to perpendicular to the VRPM measurement plane. The wind direction needs to be determined for each field study measurement configuration. These requirements present a challenge when determining sites and setup locations. HRPM data should be collected for at least one hour in ideal conditions, which ca n be difficult when considering locations with highly variable
conditions.

3.1.5 Strengths and Limitations

Strengths of RPM

- ORS are the ability to measure high time resolution and spatially distributed emission data, directly calculate emission rates, captur e the distribution of all major emissions in an area and isolate emissions from specific measurement areas. Depending on the ORS
instrument
, it can provide real

- time PIC data for multiple compounds simultaneously. RPM is limited because it relies on good wind conditions
, it has difficulties characterizing emissions from complex terrain, and has larger uncertainty when capturing emissions for sources located a large distance upwind of the VRPM configuration. Depending on the RPM

- ORS instrument used, some ambient compounds cause interferences. No ORS instrument can measure all the possible compounds of interest, which may create the need for t wo systems depending on the experimental design. A summary of these strengths and limitations is presented in Tables 3

- 2
and 3

- 3.

Table 3

- 2

. Summary Table of the VRPM’s Strengths

Feature Strength

Measurement Capabilities Measures high time resolution and spatially distributed emission data. Directly calculates emission rates. Characterizes the distribution of all major emissions in a

large area and isolates emissions from specific areas.

May provide real

- time PIC data depending on the technology used.ORS Handbook, Section 3.1 Revision No: 1.0

Date: 08/19/11

Page 16 of 58

Table 3

- 3

. Summary Table of the VRPM’s Limitations

Feature Limitation Meteorological Challenges Characterization is reliant on optimal wind conditions.

Interferences Each OP technology used has its own interferences that must be considered.

Topographical Concerns Difficulties associated with characterizing a plume from complex terrain (e.g., a side slope)
Large uncertainty when capturing emissions from sources a large distance upwind of the VRPM Setup.

3.1.6 References

1. Thoma, E. D., Green, R. B., Hater, G. R., Goldsmith, C. D., Swan, N. d., Chase, M. J.,Hashmonay, R. A. 2009. Development of EPA OTM 10 for Landfill Applications. Journal of Environmental Engineering
, 10.1061/(ASCE)EE.1943

- 7870.0000157.

2. U.S. EPA. 2006. OR S Protocol, Optical Remote Sensing for Emission Characterization from Non

- point Sources. June 14.

3. U.S. EPA. 2007. Evaluation of Fugitive Emissions Using Ground

- Based Optical Remote Sensing Technology. EPA/600/R

- 07/032.

4. U.S. EPA. Using Tunable Diode Laser s to Measure Emissions from Animal Housing and Waste Lagoons. <http://www.epa.gov/ttn/chief/conference/ei16/session12/harris.pdf>.

5. Hashmonay, R.A., and M.G. Yost. 1999. Innovative approach for estimating fugitive gaseous fluxes using computed tomography a nd remote optical sensing techniques. J. Air Waste Manage. Assoc ., 49: 966

- 972.

6. Hashmonay, R. A., R. M. Varma, M. T. Modrak, R. H. Kagann, R. R. Segall, and P. D. Sullivan. 2008. Radial Plume Mapping: A US EPA Test Method for Area and Fugitive Source Emi ssion Monitoring Using Optical Remote Sensing, Advanced Environmental Monitoring. 2:21

– 36.

7. Thoma, E.D., R.C. Shores, E.L. Thompson, D.B. Harris, S.A. Thorneloe, R.M. Varma, R.A. Hashmonay, M.T. Modrak, D.F. Natschke, and H.A. Gamble. 2005. Open

- Path Tun able Diode Laser Absorption Spectroscopy for Acquisition of Fugitive Emission Flux Data; Journal of Air and Waste Management Association . 55: 658

- 668.

8. Modrak, M. T.; Hashmonay, R. A.; Varma R.; Kagann, R. 2005. Evaluation of Fugitive Emissions at a Brownfield Landfill in Ft. Collins, Colorado Using Ground

- Based Optical Remote Sensing Technology. EPA

- 600/R

- 05/042. U.S. Environmental Protection Agency, Re search and Development, Work Assignment No. 0

- 025. March.ORS Handbook, Section 3. 2 Revision No: 1.0

Date: 08/19/11

Page 17 of 58 

### 3.2 Range Resolved Measurements using Differential Absorption LIDAR
”Range resolved” refers to vertical and horizontal profiles of concentrations for compounds of interest coupled with meteorologi cal parameters. Generally, range resolved measurements are performed to study emission rates and fate and transport of the compounds of interest. LIDAR technology is often paired with the DIAL application to measure range resolved concentrations
of trace species in the atmosphere. DIAL has been used to monitor pollution species in the lower atmosphere such as water vapor, NO, O 3 , SO 2 and CH 4 . 1 DIAL can also be used to measure O 3 concentrations in the middle and high troposphere

. 1 Atmospheric temperatur e measurements are possible by the DIAL technique if the absorption line selected is temperature

- dependent

. 1

3.2.1 General Description of Approach

Range resolved measurements of plume flux typically employs some technology to measure a surrogate gas in the plume to estimate the concentration of compounds of interest. DIAL is a
dual

- wavelength, elastic (the atom absorbs the photon and instantly emits another photon at the same frequency), backscatter LIDAR that transmits one wavelength at the absorpti on line of the target compound (λ on ) and one wavelength slightly off

- line of the target compound to measure backscatter (λ off ). The on

- line wavelength is absorbed by the gas of interest, while the off

- line wavelength is not absorbed as shown in Figure 3

- 6

.

Figure 3

- 5 . Conceptual picture on the operation of DIAL. Optical emission is tuned to both the absorbing wavelength and a wavelength that is not absorbed by the target compound. Adapted from Argall, 2003 4

. The differential absorption between the two wavelengths is a measure of the concentration of the gas as a function of range

. 2 DIAL is capable of providing a 2

- D measure or “contour” ofORS Handbook, Section 3. 2 Revision No: 1.0

Date: 08/19/11

Page 18 of 58 concentrations across a scanning plane. By combining this concentration contour with separately
obtained wind speeds, a contaminant flux can be calculated for the measured compound 3 , see Figure 3

- 7.

Figure 3

- 6 . Contour profile of SO 2 concentration measured 2.1 km downwind of source, at Cement Works, by Environmental Measurements Group National Physical Laboratory, UK 5

. Basic DIAL algorithm to calculate backscatter
The number of photons backscattered correlates to the compound concen tration. The basic equation to calculate number of photons backscattered per unit solid angle due to scattering of type i (forms of radiation like light, sound) is: 

# ( **) (**  **) (**  **) (**  **)** 

# ∫

2

1 

## , R

R i l i l a l t 

## dr r N r P **λ σ**  **λ τ**  **λ τ**  **π** Eq. 8

Where λ l = wavelength,

τ t = transmission coefficient of the LIDAR transmitter optics, r = range interval,ORS Handbook, Section 3. 2 Revision No: 1.0

Date: 08/19/11

Page 19 of 58

τ a (r, λ l ) = optical transmission of the atmosphere 

## ( **)** l i **λ σ**  **π** = backscatter cross section at the laser wavelength, N i (r) = number density of scatte ring centers at range r. 1
However, to calculate the number of photons incident on the collecting optic of the LIDAR due to scattering of type i , one must consider the area of the collecting optic (A): 

# ( **) (**  **) (**  **) (**  **) (**  **) (**  **)** 

# ∫ 2

1 

### , , 1

2 R

R i l i

s a l a l t 

### dr r N r r r r A P **λ σ**  **ζ λ**  **τ λ**  **τ λ**  **τ π**  Eq. 9

Where λ s = wavelength of the scattered light ζ (r) = overlap factor
There are two typical types of detectors: photomultiplier and analog. When using photomultiplier detectors
, the number of photons detected is: 

## ( **) (**  **) (**  **) (**  **) (**  **) (**  **) (**  **) (**  **)** 

# ∫ 2

1 , , 1

2 R

R i l i

s a l a s s t l t dr r N r r r r Q A P **λ σ**  **ζ λ**  **τ λ**  **τ λ**  **λ τ**  **λ τ**  **π** Eq. 10

Where τ r (λ s ) = transmission coefficient of the reception optics at λ s Q( λ s ) = quantum efficiency of the photomultiplier at wavelength λ s Quantum efficiency refers to the percentage of photons incident (hitting) the receiver ( photo
reactive surface) and it measures the LIDAR electrical sensitivity to light. When using analog
detectors
, the equations replace the quantum efficiency of the photomultiplier by the gain of the photomultiplier (G( λ s )) combined with the gain of any amplifiers used. After some approximations
, the analog detector version of equation 10 is: 

## ( **) (**  **) (**  **) (**  **) (**  **) (**  **) (**  **) (**  **)** R R N R R R R Q A P i l i

s a l a s s t l t **δ λ**  **σ ζ**  **λ τ**  **λ τ**  **λ λ**  **τ λ**  **τ π**  2 1

, , Eq. 11
Where R = range of the center of the scattering volume Δ R = R 2 – R 1 is the length of the range bin. 1ORS Handbook, Section 3. 2 Revision No: 1.0

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Page 20 of 58
3.2.2 Verification/Validation Studies
This section presents studies designed to validate the DIAL technique under various conditions. Some studies aim to verify that the application can provide accurate results, thus most of these studies will have other technologies to compare measured conce ntrations and emission rates.

Verification of DIAL for Gas Species Measurements

DIAL has been validated in European studies 10,11 for hydrocarbon emissions with calculated results ranging from ±3 to ±12 percent of the actual value. 3 Two validation stud ies were performed in Alberta with measured fluxes agreeing within +1 to

- 10 percent of the known

source. 3 Over a four week period during May

- June 2003 in Alberta, Canada, DIAL surveys were performed at four gas processing plants, one gas well test site l ocated in the foothills
, and two solution gas flare sites. The objective of this project was to field test DIAL technology as a

means to:

**•** monitor ambient SO 2 concentrations in the vicinity of sour gas well test flares and track the SO 2 plume position,

**•** measure the combustion efficiency of well test and solution gas flares,

**•** measure fugitive emissions of methane and other hydrocarbons from gas processing facilities. 6

The DIAL measurements were performed by Spectrasyne Ltd., UK. When measuring plume concentration profiles, Spectrasyne generally located the DIAL equipment at least 50 meters from the area of interest and relative to the plume source and wind direction so scans could be taken roughly at right angles to the direction of plume travel

. 6 Of ten meteorological changes make this impossible and the measurements were taken at an oblique angle, which results in profiles that appear stretched in the horizontal direction. 6

The DIAL system included two DIAL lasers
, one emitting in the IR range and one in the UV range, and a self

- contained weather station for measuring wind speed and temperature. Meteorological parameter data is used in mass rate calculations to reprocess the data to accountORS Handbook, Section 3. 2 Revision No: 1.0

Date: 08/19/11

Page 21 of 58 for the angle relative to the plume direction

. 6 With this system, total contaminant flux can be calculated and portions of the plume assigned to specific sources

. 6

The accuracy of the DIAL system was checked by comparing SO 2 mass emissions calculated
from the DIAL measurements of the SO 2 plume with S O 2 mass emissions calculated from gas plant CEM instrumentation installed in the incinerator stack. 6 Direct comparison between DIAL and fenceline point source concentration measurements is not possible, since the DIAL measures gas concentration in a relat ively large volume in the atmosphere against a point sampling type instrument such as a gas chromatograph. 6 However, the combination of point source measurements for a target compound
, combined with meteorological data and dispersion modeling
, can provide comparisons useful to verify the DIAL results. Table 3

- 4 shows some of the results, the scan time, and calculated fluxes for the DIAL and point source. The DIAL and point measurement s showed a difference of 11 percent, which is within the range of

– 18 t o + 5 percent from other calibration studies performed by Spectrasyne Ltd. 6 According to this Spectrasyne study and others, DIAL plume measurements generally underestimate the total mass because some areas of the plume contain compound concentration below DIAL’s detection limit and are not included in the plume mass. Additional variability among DIAL measurements is believed to originate from variation in wind speed and direction, combined with the 4

- 20 minutes required to do a full DIAL scan across the plume.

Table 3

- 4 . Results from the comparison of DIAL and plant measurements of SO 2 mass emissions collected on May 26 about 190 m downwind of incinerator stack. Adapted from Chambers, 2003.
SCAN NUMBER SCAN TIME WIND SPEED (M/S) WIND DIRECTION (DEG) SO 2 FLUX (KG/HR) 1 12:37

- 12:48 5.2 331 372 2 13:00

- 13:21 2.2 350 223 3 13:31

- 13:51 4.3 346 394 4 14:00

- 14:04 5.6 359 196 5 14:07

- 14:19 3.9 359 145 6 14:42

- 14:51 4.0 350 333 7 15:10

- 15:21 3.9 0 394 Time Weighted Mean of DIAL 304

Plant Data 340ORS Handbook, Section 3. 2 Revision No: 1.0

Date: 08/19/11

Page 22 of 58
3.2.3 Typical QA/QC

This section describes the QA/QC steps that pertain to the applications described above. QA/QC steps for applications normally depend on pre

- determined data quality indicators that address the project unique objectives. The technology used for the applications presented above have their own QA/QC associated to specifics of the instruments. If interested in the technology QA/QC for DIAL, refer to section 2.4.

DIAL measurements are typically verified by running two collocated DIAL systems or one DIAL system along with another ORS instrument, like FTIR or CRDS. Arcadis has verified Dial measurements using OTM10 and developed QA/QC information for conducting Dial measurement projects

. 12 However, as noted in OTM10
, the unique s etup of DIAL requires project specific QA/QC steps with data quality indicators that meet study objectives.

3.2.4 Sitting Concerns

The DIAL equipment, optical housing, electronics, computer equipment and other components require a climate control enclosure such as a trailer or an aircraft. Therefore, the operating temperature is controlled to human comfort level ~ 22oC. Trailers require relatively flat surfaces and road access. Changing weather conditions, physical interferences like buildings, trees, traffic and changing terrain
, and interfering chemical species at certain wavelengths will increase variability in the measurement and possibly result in less accurate results.

3.2.5 Strengths and Limitations

The most significant limitation to DIAL application is the cost and limited availability of measurement systems. Multiple measurements in North America have relied on importing the instrumentation from the United Kingdom. 6,10,11 Additionally, the number of chemical species measurable by D IAL is restricted to those that are detectable by the Lidar technology. The most notable strength of a DIAL system is the ability to quickly resolve pollutant concentrations in
two dimensions. Concentration gradient data obtained in short periods of time enables DIAL toORS Handbook, Section 3. 2 Revision No: 1.0

Date: 08/19/11

Page 23 of 58 be deployed in a number of applications and a number of configurations. Tables 3

- 3 and 3

- 4 summarize these strengths and limitations.

Table 3

- 5

. DIAL Strengths Feature DIAL Strengths

Measurement Capabilities DIAL provides spatially res olved pollutant concentration in two dimensions Measurements are provided in a relatively short period of time.
Flexibility DIAL is deployable in many different applications and configurations.

High intensity light source The ability to measure longer path lengths (1 to 3 km)

Table 3

- 6

. DIAL Limitations Feature Dial Limitations

Limited Availability and Expense Due to limited availability, DIAL systems used in North America are typically imported,

which increases the expense of using DIAL for measurements.

Range of Measurement Chemical species that can be characterized and limited to those compounds with the unique chemical properties required to be detected by the LIDAR instrument. Only a

few wavelengths are measured; spectral artifacts cannot be fixed or investigated

3.2.6 References
1. Argall, P. S. and R. J. Sica. 2002. LIDAR in the Encyclopedia of Imaging Science and Technology. J.P. Hornak John Wiley & Sons Inc., New York. January.

2. Cracknell, A. P. and L. Hayes. 2007. Intr oduction to Remote Sensing, 2 nd ed. London, Ingland: Taylor and Francis, August.

3. U.S. EPA. 2006. VOC Fugitive Losses: New Monitors, Emission Losses, and Potential Policy Gaps

– 2006 International Workshop.
< http://www.epa.gov/ttn/chief/efpac/documents/wrkshop_fugvocemissions.pdf >
4. Argall, P. S. and R. J. Sica. 2003. LIDAR in The Optics Encyclopedia. Wiley

- VCH, New York, NY.ORS Handbook, Section 3. 2 Revision No: 1.0

Date: 08/19/11

Page 24 of 58 5. Robinson, R. 2006. The Application of DIAL for industrial emissions monitoring. Presentation at the VOC Fugitive Losses: New Monitors, Emission Losses, and Potential Policy Gaps, 2006 International Workshop. Office of Air Quality Planning and Standards, Research Triangle Park and Office of Solid Waste and Emergency Response, Washington, DC. October 25

– 27.

6. Chambers, A. 2003. Well Test Flare Plume Monitoring Phase II: DIAL Testing in Alberta. Alberta Research Council Inc. Prepared for Canadian Association of Petroleum Producers. < http://www.ptac.org/env/dl/envp0402fr.pdf >.

7. Kelley, N. D., B. J. Jonkman, G. N. Scott and Y. L. Pichugina. 2007. Comparing Pulsed Doppler LIDAR with SODAR and Direct Measurements for Wind Assessment
, NOAA/NREL conference paper NREL/CP

- 500

- 41792, July.

8. Grant, W. B., R. H. Kagann, and W. A. McClenny. 1992. Optical Remote Measurement of Toxic Gases. J. Air Waste Manage. Assoc

. 42:18

- 24.

9. Ednar, H., P. Ragnarson, and E. Wallinder. 1995. Industrial Emission Control Using Lidar Techniques. Environ. Sci. Technol. 29:330

- 337.

10. Chambers, A., M. Strosher, T. Wootton, J. Moncrieff. and P. McCready. 2008. Direct measurement of fugitive emissio ns of hydrocarbons from a refinery. Journal of the Air and Waste Management Association. 58: 1047

- 1056. doi: 10.3155/1047

- 3289.58.8.1047.

11. Chambers, A. K., M. Strosher, T. Wootton, J. Moncrieff, and P. McCready. 2006. DIAL Measurements of Fugitive Emissio ns from Natural Gas Plants and the Comparison with Emission Factor Estimates.
< http://www.epa.gov/ttn/chief/conference/ei15/session14/chambers.pdf >.

12. Measurement & Analysis of Benzene & VOC Emissions in the Houston Ship Channel Area, Quality Assurance Project Plan Revision 1.0, Arcadis, Research Triangle Park, NC. September 2009.ORS Handbook, Section 3.3 Revision No: 1.0

Date: 08/19/11

Page 25 of 58



### 3.3 Solar Occultation Flux Measurement

Characterizing and quantitatively measuring fugitive VOC emissions from non

- point sources are challenging. The ability to accurately calculate the flux rate of VOC emissions from large area
sources such as landfil ls, refineries, waste retention ponds, process areas, and product holding tanks is highly pursued by government and industry alike. The development and advancement of optical measurement technologies have increased environmental VOC monitoring capabilitie s and are applied in many new monitoring methods. SOF is a method where optical spectroscopic technologies are used to directly speciate and quanti fy the chemicals present in a gaseous emission plume using the sun as a light source.

Because the SOF metho d uses the sun as a broadband light source, the target compound possibilities are limited only by the detection equipment and interferences. Depending on the spectrometer used, the SOF can detect many different gaseous species, even at the same time, incl uding: ammonia, formaldehyde, VOCs, terpenes, vinyl chloride, CO
, ethylene, ethylene oxide, hydrofluoric acid, (HF) hydrochloric acid, HCl, CH 4 , SO 2 , propane, propylene, and hydrocarbons up to C 15 . 1,2 Due to the ease of mobility with the SOF method, the technique can be used in applications such as total CO columns of megacities, petrochemical industries, agriculture, refineries, ships, and volcanoes. 3

Volcanic emissions research is a rich source of information on monitoring techniques to determine emission rates of fugitive gases in atmospheric plumes including SOF. Several measurement techniques have been employed to measure volcanic gases such as SO 2 (i.e., correlation spectrometer (COSPEC), DOAS, and DIAL). However, the SOF method was developed to improve volcanic activity forecasting capabilities because multiple gas species can be detected using passive FTIR
, and direct flux measurements can be made based on movement
of the instrument view thro ugh the emissions plume. 4

Environmental applications of the SOF technique have previously focused on measuring fugitive VOCs from oil refineries and industry processes. 5 VOC gases emitted from these source typesORS Handbook, Section 3.3 Revision No: 1.0

Date: 08/19/11

Page 26 of 58 mostly consist of alkanes, alkenes, and so me aromatic compounds. 5 These fugitive VOCs have historically been measured using the DIAL method. 6 The DIAL method, described in Chapter
3.2 of this Handbook , employs short laser pulses directed through the gas plume at different wavelengths to calculate mass flux measurements by multiplying the resulting concentration integrated over the plume cross section at different angles by the wind speed. 7 However, DIAL is rather complex and expensive relative to SOF and is not ideal for periodic regulat ory monitoring. 7 In comparison to DIAL, the SOF technique uses solar broadband IR or UV/visible spectral radiation as the light source instead of a laser source
, making the SOF method potentially more cost

- effective, faster than DIAL
, and easier to automate. 7 From the solar spectra , it is possible to retrieve the PIC (molecules/cm 2 ) of VOCs between the sun and the spectrometer. 8 Multiplying this PIC by the local wind speed results in the mass

- flux of target VOCs through the area.

Ag ricultural application studies have established that the SOF method has a detection limit for measured hydrocarbons of 0.3 mg/ m 2
, 3

. Similarly, in applications pertaining to refineries and leak detection, studies conclude that a point source emission of me asured hydrocarbons at 0.5 kg/ h can be measured 50 meters downwind with a precision of three percent and an accuracy of 30 percent. 1 For simple sources and several traverses
, it has been found that the accuracy can be better than 10 percent. Under more c omplicated conditions, with emissions occurring from a complex structure with an unknown plume lift
, larger systematic errors will occur, primarily due to uncertainties in assessing the plume lift
and the associated wind field. Uncertainty in the wind spe ed in more complex terrain and wind stability can be 15

– 30 percent . Consequently the accuracy in the SOF method depends on the amount of error associated with the ancillary meteorological measurements which can dominate the uncertainty in the emissions results. 1,3

3.3.1 General Description of Approach

There are three main components to the SOF system: an FTIR spectrometer to capture solar radiation spectra, a sun tracker to maintain instrumental orientation to the solar zenith, and a GPS
for accurate measurement location relative to the gas plume. 5 Two different sun trackerORS Handbook, Section 3.3 Revision No: 1.0

Date: 08/19/11

Page 27 of 58 configurations are shown in Figure 3

- 8
, and a rough schematic of the entire SOF system is provided in Figure 3

- 9.

Figure 3

- 7 . Solar tracker configurations. The left panel sh ows the sun tracker and mirrors extending out the top of the vehicle to maintain orientation 9 while the right panel shows the path of the sunlight (in yellow) first striking the mirror of the solar tracker before being directed into the spectrometer. 5

Figure 3

- 8

. Rough overview of a mobile SOF system. 1

Flux measurements are difficult to measure in an atmospheric plume using stationary instrumentation. Figure 3

- 10 illustrates the capture of a complete plume transect for better
constrained source flux calculations using the SOF technique. In the illustration of Figure 3

- 10,ORS Handbook, Section 3.3 Revision No: 1.0

Date: 08/19/11

Page 28 of 58 the solar tracker maintains the solar zenith, reflecting the solar light into the spectrometer regardless of the vehicle position as it traverses the width of the plume column. 1,9 As previous methods combined stationary plume measurements with dispersion modeling or tracer gas ratios, the mobile aspect of the SOF technique allows for direct measurements of gaseous flux emissions assuming that the full plume column is captured. 3

Figure 3

- 9

. Solar Occultation Flux method The instrument is placed in a vehicle which travels across the gas plume to capture the plume cross

- section (Illustration Karin Sjöberg). 9

Each gaseous compound absorbs energy at different wavelengths, usually more than one, depending on vibrational and rotational excitement within the molecule. Fundamentally, this
measurement i s a passive form of IR or UV spectroscopy. Therefore, each compound has its own “signature” of bands from which energy may be absorbed . Each band is highly selective, with virtually no absorption occurring outside of a specific wavelength. When molecules intercept the solar radiation before it reaches the detector, the molecular absorption is seen as an extinction (or occultation) of t he solar radiation intensity at the signature wavelengths. Once a compound has been identified, its spectrum can also be used to measure the compound’s concentration because the amount of radiation absorbed from the solar ray is proportional to the

concen tration of the compound in the sample or open path. According to the Beer

- Lambert law, there is a linear relationship between absorbance and concentration as shown in Equation 1. 

# l c A **ε =** 

Where: A = absorbance intensity Eq. 1ORS Handbook, Section 3.3 Revision No: 1.0

Date: 08/19/11

Page 29 of 58

ε = absorption coefficient

c = sample concentration
l = sample path length

SOF measurements acquired from within the plume column are divided by a reference (background) spectrum recorded outside the plume. This prevents any background sources

— such as the atmosphere, inherent structures of the sun, and instrument functionality

— from interfering with accurate measurements. 5 Multiple species are simultaneously evaluated for each spectral acquisition using non

- linear, least

- squares fit routines with published reference spectra from HITRAN 2000, Pacific Northwest National Laboratory
, National Institute of Standards and Technology , and Hanst library databases. 7 The resulting spectra are evaluated for gas species absorption intensity to determine concentration us ing Equation 13 below. Equation 13 is a
transmutation of the Beer

- Lambert law where spectral line intensity is related to gas
concentration. 8 

# ( **) (**  **) (**  **) **  ** **  ** **  ****

**⋅ ⋅**  **− ⋅**  **− ⋅**  **⋅ −**  **⋅ =**  

# ∑

i i i i Mie Mie R R L m 

### L conc v L L v v I v I **σ α**  **α** 4 

### exp

Where:
I m ( ν ) = light intensity at each wave number ν after it has passed through the gas.
I

L ( ν ) = light intensity at each wave number ν for the light source.

σ i ( ν ) = absorption cross section (cm 2 /molecule) of the gas with index i.
L i = path length where the gas with refractive index i is present.

conc i = concentration o f the gas with refractive index i (molecules/m 3 ).

α R , L R = Rayleigh scattering which occurs on particles and molecules with a size smaller than the wavelength of the light.

α Mie , L Mie = Mie scattering which occurs on particles and molecules with a size about equal to the wavelength of the light.

The PIC is determined by evaluating spectrum measurements individually along a path of continuous analysis to derive the line

- integrated concentration represented by each spectrum.
GPS measurements taken at th e beginning and end of each measured spectrum determine the surface length represented by the spectrum

. T his value is then multiplied by the line

- integrated concentration and summed over the total plume transect to calculate the PIC . An example of this Eq. 13ORS Handbook, Section 3.3 Revision No: 1.0

Date: 08/19/11

Page 30 of 58

p rocess is shown in Figure 3

- 11. 8 Multiplying this PIC by the local wind speed results in the

mass

- flux of target VOCs through the measurement plane (Equation 14).

Figure 3

- 1 0 . An example of path

- integrated calculations determined from SOF measurements. The red lines indicate individual spectra , the area between the red lines correspond to the surface

- integrated concentration, and the green lines illustrate the wind direction vector. 8

As shown in Figures 3

- 10 and 3

- 11, SOF mobile measurements are made crosswind and near downwind (about 0.5 to 3 km) from the target source. The total cross

- sectional mass of key species is obtained by summation of all the species measurements o ver the plume traverse. 10 The process of determining the emissions flux is summarized mathematically by Equation 3 below. This equation shows the prominence of the average wind speed variable in the flux determination, thus explaining that more accurate wind speed measurements yield less overall
flux measurement error. 5



### dx u x column Emission x

x 

### ⋅ **=** 

# ∫ 2

1 

### ) (

The assumption that the wind speed is equivalent to the gas plume velocity is necessary for both the DIAL and SOF techniques as this value is required to calculate the mass flux by multiplying the plume velocity by the cross

- sectional integrated gas concentration. 5 Therefore, both methods will be susceptible to the same measurement error associated with the wind speed parameter. Where: column(x) = the total column at distance x across the plume
ū = average wind speed for plume at plume height Eq. 14ORS Handbook, Section 3.3 Revision No: 1.0

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Page 31 of 58
3.3.2 Verification/Validation Studies

Because DIAL measurements are a standard method of monito ring VOC emissions at refineries in Europe, Fransson and Mellqvist (2002) 5 performed a comparison between the results of the two different methods during a 2001 SOF field study. SOF measurements by this group were made at the Preem refinery in Göteborg, S weden for four days starting August 1, 2001. DIAL emissions measurements from 1995 and 1999 were recalculated with the annual average wind speed to compare with the 2001 SOF results; this comparison is shown in Table 3

- 7. 1

Table 3

- 7. SOF technique VOC e missions compared to DIAL. 1

Although comparing environmental data that is up to six years apart is not recommended, the
data in Table 3

- 7 shows that the emissions measured using SOF are generally consistent with previous DIAL results. 1 Regardless of the amount of measurement error imposed on the SOF result by wind speed approximations, all SOF measurements either match a previous DIAL result exactly or are very near to the average of the 1995 and 1999 DIAL results.

Annual VOC emission s from the Port of Göteborg were reported to be 1100 tons in 1999 and 2300 tons in 1995 by Shell Global Solutions and Spectrasyne, respectively. Fransson and Mellqvist (2002) used an SOF system mounted on a ship to calculate an annual flux of 1770 tons

pe r year based on two measurements

— that were not temperature corrected

— performed on the same day in August 2001. 5 Again, more than two measurements on a variety of days distributed throughout the year would yield a more accurate snapshot of annual emissions . Nonetheless, a result of 1770 tons per year is almost the exact equivalent to the average of the reported 1999 and 1995 values and is a satisfactory result, validating the application of SOF to measure oil refinery gas emissions flux.ORS Handbook, Section 3.3 Revision No: 1.0

Date: 08/19/11

Page 32 of 58
The results from two tracer gas correlation studies plus other validation studies were discussed by Mellqvist et al. (2005) and Kihlman (2005). 3,8 During these studies
, sulfur hexafluoride ( SF 6 ) was released in an open field ( Ǻby Field) and from the roof of a crude oil ta nk at the previously mentioned Göteborg refinery in May and June of 2002. Each experiment released SF 6 at a rate of about 2.0 kg/h. Tables 3

- 8 and 3

- 9 display the results from the open field and tank roof tracer experiments, respectively. 8 As shown in T able 3

- 8, tracer correlation measurements over four days in an open field validated that the SOF method can accurately retrieve emissions flux measurements within ± 20 percent error ; w hereas crude oil tank measurements done on a single day shown in Table 3

- 9 yields emissions flux measurements with up to 50 percent error. It is worth noting that the crude oil tank measurements were conducted in the near field as opposed to a medium or far distance from the emission source in addition to being conducted on o nly one day. Figure 3

- 12 illustrates a combination of both scenarios where a tracer experiment in an open field resulted in 72 percent agreement (within 20 percent) of SOF measurements to the actual tracer gas emission
, while other measurements can vary u p to 50 percent from the actual
emission. 1 When averaged together, however, the resulting value is within three percent of the
actual emission rate.

Table 3

- 8 . Summary for measurements on the Ǻby field, 2002. 3,8 Day Emitted SF 6 Calculated Average

(kg/h) Number of Accepted Traverses Ave. Wind Speed (m/s) Ave. Wind Direction Error

May 22 1.92 2.3±1.3 4 4.9

- 8.6 152  ̊

- 169  ̊ 20 % May 23 1.97 2.2±0.6 15 3.9

- 5.6 120  ̊

- 142  ̊ 10 % June 03 1.97 1.6±0.9 16 2.7

- 5.3 235  ̊

- 273  ̊

- 20 % June 04 1.89 2.0±1.4 9 5.9

- 7.8 152  ̊

- 191  ̊ 5 %

Table 3

- 9 . SOF traverse done on day 24

- June 2002. True emitted SF 6 was determined to be 2.0 k g/hr. 3,8

Time Emission SF 6 Ave. Wind Speed (m/s) Ave. Wind Direction 12:45 3.1 6.5 252  ̊ 12:54 1.8 7.2 252  ̊ 13:05 1.3 6.0 259  ̊ 13:17 2.7 7.5 253  ̊ 13:29 3.1 5.4 255  ̊ 13:56 5.2 7.4 264  ̊ 14:05 3.7 7.4 251  ̊ 14:24 2.6 7.3 262  ̊ORS Handbook, Section 3.3 Revision No: 1.0

Date: 08/19/11

Page 33 of 58

14:31 3.4 6.5 260  ̊ Average 3.0±1.1

Figure 3

- 1 1 . Tracer gas/SOF experiment measuring SF 6 emissions in an open field over time of day. SOF measurements are depicted as red circles while the actual emission release rate is drawn as a grey line. 1

The results from these studies indicate that multiple measurements in the mid to far field over multiple days are more representative of overall flux rates and reduce the amount of total measurement error. Having enough data points to calculate a statistical average is ideal for eliminating stochastic variances caused my micrometeorological disturbances.

3.3.3 Typical QA/QC

In order to make emissions measurements with the SOF method, the operator needs to have meteorological information (distributed in height, surface, and time); a road to travel along that is relatively smooth, downwind of the emissions source and near perpendicular to the wind direction; stable wind condition s; and an unobstructed view of the sun. In addition to meeting these requirements, measurements can be further validated by calculating the expected and observed amounts of measurement error.

The total amount of measurement error associated with SOF resu lts is comprised of statistical and systematic error. Statistical error is described with a normal distribution asORS Handbook, Section 3.3 Revision No: 1.0

Date: 08/19/11

Page 34 of 58 this type of error accounts for the natural, stochastic behavior of atmospheric variability. Fransson and Mellqvist qualified this amount u sing Equation 15 below

. 5

2 2 2 2 / 2 uH u N S c 

### error l Statistica **σ σ**  **σ σ**  **σ θ**  

### + **+ +**  **+ =** 

Fransson and Mellqvist (2002) estimated the σ S/ N (or instrumental precision) from the standard deviation in the baseline of the plume column measurements during their VOC emissions study at oil refineries. 5 This value was about 0.3 to 0.6 mg/ m 2 , which corresponded to an uncertainty of 0.3 percent to 6 percent for a total column measurement of 100 to 10 mg/ m 2 . They also estimated the relative uncertainty in the total column due to wind direction variability ( σ θ ) to be about 1 percent to 7 percent for eight scans. Fransson and Mellqvist (2002) state tha t there is an 8 percent effect on column measurements when a plume is traversed at a 90° angle and that this error amount is sensitive to the ability to make measurements perpendicular to the wind direction such that angles of 80° and 70° contribute errors of 16 percent and 25 percent respectively. 5 Relative uncertainty in the wind speed due to plume height estimate uncertainties ( σ uH ) are
discussed in more detail below.

Using Equation 15, Fransson and Mellqvist (2002) calculated the statistical error ass uming various typical scenarios encountered during measurements. The results displayed in Table
3

- 10 show that even between best and worst case scenarios, the statistical error amount Where:

σ c = Relative error in the absorption cross section (5

- 10 percent).

σ S/N = Relative S/ N error and spectral interference effects.

σ u = Relative uncertainty in the wind speed estimation. Typically 15

- 20 percent for a single plume scan and 5

- 8 percent for 8
scans.

σ θ = Relative uncertainty in the total column due to the variability in the wind direction over a plume scan.

σ uH = Relative uncertainty in the wind speed due to u ncertainties in the estimation of the plume height. Eq. 15ORS Handbook, Section 3.3 Revision No: 1.0

Date: 08/19/11

Page 35 of 58 varies from 14 percent to 19 percent. 5 To determine the total error fo r a measurement, these statistical error values need to be added to the amount of systematic error.

Table 3

- 10 . Estimation of statistical errors for the SOF measurements ; assuming 8 plume scans. 5

Estimation Comment σ c σ S/ N σ u σ uH σ ω Statistical 1σ error

Best Case Strong plume 100 mg/ m 2

, 90 ̊ traversing angle* 5% 0.3% 5% 12% 1% 14%

Worst Case Weak plume 10 mg/ m 2

, 80 ̊ traversing angle* 10% 6% 7% 12% 5% 19%
Typical Case Intermediate plume, 80 ̊ traversing angle* 5% 3% 5% 12% 7% 16%

* Angle between the wind and the direction of movement when traversing the plume.
Output data recovered from SOF measurements do not contain information related to the height of the gas plume in the atmosphere. Plume height estimates are inferred from the corresponding temp erature and pressure broadening of the spectral absorption lines if measured from high

- resolution (< 0.125 cm
- 1 ) spectra. 8 This is the largest identified source of error in the SOF method, assuming that the entire plume cross

- section is captured and that t he total amount of absorption equals the total concentration. As mentioned previously, the integrated concentration of the target species is multiplied by the mass average wind speed of the plume at plume height to determine the emissions flux; therefore, accurate wind speed measurements are crucial to minimizing total flux measurement error. Increasingly complicated site conditions
, such as a complex emissions source structure with unknown plume lift
, will have larger amounts of systematic error.

Mellqv ist (2004) states that measuring emission plumes with heights above 20

- 30 meters at distances greater than 500 meters downwind are ideal conditions to ensure minimal systematic errors associated with the wind field. 1 This is because of the increased predi ctability of the wind height profile that corresponds to the plume height while making measurements under these criteria. Figure 3

- 13 below shows the wind speed profile with height over developed land if a 5 m/ s wind velocity was measured at 10 meters. I t is clearORS Handbook, Section 3.3 Revision No: 1.0

Date: 08/19/11

Page 36 of 58 from this figure that wind speeds above 20 meters from the surface during typical SOF conditions (i.e. sunny, which implies an unstable atmosphere) are past the major inflection point in the profile and ha ve a less prominent height gradient with increasing height. Fransson and Mellqvist (2002) made an estimation from data acquired at 17 meters that ± 5 meters is a realistic amount of error associated with the plume height estimation
which translates to about 12 percent error in the wind speed measurement. 5 Further studies into the amount of error in the wind field by height concluded that about 14 percent systematic error in retrieved flux measurements with a 15 percent standard deviation
between measurement days is expected. 6

Figure 3

- 1 2 . Wind velocity profiles by height.

3.3.4 Siting Concerns

Since the instrumentation for the SOF method is mobile during analysis, issues originating from the site location are few. Most notably, the path of solar light through the plume to the
instrumentation needs to be unobstructed
, and excessive vibration during the mobile operations can cause noise originating from the interferometer. Therefore, measurement capabilities are constrained to particular weather conditio ns

. I n addition to the surrounding area of the mobile path , the line of sight needs to be clear of trees, shrubbery, and buildings that might impede the 0 20 40 60 80 100 120 140

0.0 5.0 10.0 15.0 20.0 25.0Height (m)

Velocity (m/s) Unstable Stable
NeutralORS Handbook, Section 3.3 Revision No: 1.0

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Page 37 of 58 view of the sun. Moreover, the mobile path needs to be smooth and near perpendicular to the
wind dir ection to minimize the amount of method error.

SOF measurements are more representative in the mid and far field downwind from the emissions source where the plume is well

- mixed and the solar transect of the plume will be higher in the wind profile. Plume traverses must be able to capture the entire plume cross

section plus open atmosphere on either side of the plume such that a representative background can be collected. In addition, the measurement site must have a limited amount of well

- traveled

r oads and other transportation operations in close proximity.
3.3.5 Strengths and Limitations

One major advantage to the SOF method is also a disadvantage : the solar broadband light source. Using FTIR, the SOF method can precisely and accurately speciate and quantitate multiple gaseous emissions simultaneously using one instrument. The caveat to this advantage is that the method can only do so during specific climatic conditions (high sun and steady winds)

. 4 Other strengths and limitations associated with th e SOF method are presented in Tables 3

- 11 and 3

- 12, respectively.

Table 3

- 11 . Feature strengths of using the SOF method.

Feature Strength

Direct Measurement Increases measurement accuracy by reducing uncertainty.
Passive Light Source Decreases instrumental complexity for field operations and reduces amount of scattering errors in the UV.
Broadband Light Source Multiple species detection over a wide range of wavelengths. Better Mobility More suitable for frequent field application. Lower techn ical complexity Decreased cost and easier field application.

FTIR Detection Higher specificity and better signal

- to

- noise (relative to DIAL).
Measurements during Sunny conditions Corresponds to unstable meteorological conditions where wind gradients due to convection are smoothed out.

Table 3

- 12 . Feature limitations of using the SOF method.ORS Handbook, Section 3.3 Revision No: 1.0

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Page 38 of 58

Feature Limitation

Interferogram Vibration Sensitivity System requires vibration reduction platform and a smooth mobile path.

Wind Speed Error Calculations based on wind speed measurements inherently add uncertainty due to the stochastic, uncontrollable, and highly variable nature of wind speed.
No Plume Height Measurement Uncertainty of plume height increases measurement error from wind speed term.
Solar Light S ource Inappropriate to make measurements in the presence of clouds.
“Open Eye” Detection and Roadway Path Restriction Difficulty in separating emissions sources that are close together.

3.3.6 References

1. Mellqvist, J., J. Samuelsson, C. Rivera, B. Lefer, and M. Patel, (2007), Measurements of industrial emissions of VOCs, NH 3 , NO 2 and SO 2 in Texas using the Solar Occultation Flux method and mobile DOAS, Final Report: HARC Project H

- 53, retrieved off the World Wide Web on June 21, 2010 from www.FluxSe nse.com.

2. Frisch, L., (2006), VOC Fugitive Losses: New Monitors, Emission Losses, and Potential Policy Gaps, 2006 International Workshop. Office of Air Quality Planning and Standards, Research Triangle Park. Office of Solid Waste and Emergency Response, Was hington, DC. October 25

- 27, 2006

3. Mellqvist, J., (2004), Quantifying fugitive emission of VOCs using the Solar Occultation Flux technique (SOF), Compliment to Fransson and Mellqvist, 2002 , Chalmers University of Technology, Göteborg, Sweden.

4. Duffell, H., C . Oppenheimer, and M. Burton, (2001), Volcanic gas emission rates measured by solar occultation spectroscopy, Geophysical Research Letters
, 28 , 16, 3131

- 3134.

5. Fransson, K., and J. Mellqvist, (2002), Measurements of VOCs at Refineries Using the Solar Occult ation Flux Technique, Chalmers University of Technology, Göteborg, Sweden.

6. Kihlman, M., J. Mellqvist, J. Samuelsson, L. Tang, and D. Chen, (2005), Monitoring of VOC emissions from refineries in Sweden using the Solar Occultation Flux method, retrieved off of the World Wide Web on October 19, 2010 from www.FluxSense.com

. 7. Mellqvist, J., M. Kihlman, J. Samuelsson, and B. Galle, (2005), The Solar Occultation Flux (SOF) Method, a new technique for the quantification of fugitive emissions ofORS Handbook, Section 3.3 Revision No: 1.0

Date: 08/19/11

Page 39 of 58 VOCs, paper #1377 in proceeding of A& WMA’s 98th Annual Conference & Exhibition, Minneap olis, USA.

8. Kihlman, M, (2005) Application of solar FTIR spectroscopy for quantifying gas emissions, Licentiate Thesis, Chalmers University of Technology, Göteborg, Sweden.

9. Mellqvist, J., M. Kihlman, B. Galle, K. Fransson, and J. Samuelsson, (2005), The Sol ar Occultation Flux (SOF) method: a nouvelle technique for the quantification of fugitive gas emissions, retrieved off the World Wide Web on March 10, 2010 from www.FluxSense.com

. 10. Mellqvist, J., J. Samuelsson, J. Johansson, C. Rivera, B. Lefer, S. Alvarez, and J. Jolly, (2010), Measurements of industrial emissions of alkenes in Texas using the solar occultation flux method
, Journal of Geophysical Research
, 115

.ORS Handbook, Section 3.3 Revision No: 1.0

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Page 39 of 58 

### 3.4 Tracer Gas Correlation
Challenges measuring emissions flux from a fugitive or area source such as a landfill, agricultural waste, industrial fugitive, waste water or oil and gas production source include spatia l, temporal variability of the emission sources and the uncertainty of the measurement technology. Emissions source variability includes defined and undefined sources like unknown emissions points, delocalized emissions sources, the timing of periodic or episodic emissions, and atmospheric, diurnal, seasonal and process variations in emission flux. Defined fugitive sources cover smaller areas (i.e., less than 1 square kilometer down to a square meter) allowing emissions points to be identified for direct measurement. Undefined area sources typically originate from large areas (i.e., greater than 1 square kilometer). For either defined or undefined emissions sources, the sources of uncertainty cause emissions flux to be difficult to measure and
model. T racer gas correlation provides a ground based technique that can be applied to both
well

- defined area emissions sources and undefined fugitive sources.

Tracer correlation involves a common practice of measuring pollutant emission rates while releasing a known concentration of a tracer gas. The subsequent simultaneous measurement of this tracer gas and the pollutant of interest downwind from the release provide sufficient
information to determine or validate the emissions flux measurements. The release of a known concentration of a tracer gas assists in tracking the plume and sources or sinks of the pollutant of interest into the plume. The majority of tracer gas studies use cell

- based technologies, like CRDS and FTIR to measure the tracer gas and the compound of interest. However, some studies are expanding the use of tracer gas release to open

- path techniques to evaluate the distribution over a large area.

3.4.1 General Description of Approach
The use of a tracer gas is common in projects that aim to study emission fluxes. Normally
, emission fluxes are obtained by calculating fluctuations in the vertical and horizontal component of the wind ( w’ ), fluctuations in the tracer gas concentration (n’) over either time (T) or space (S) and surface roughn ess. The following equations provide a method for determining experimentally the surface fluxes of tracer gases by measuring meteorological parameters near the surface as well as the vertical gradient in the concentration of these gases

. 1 Equation 16ORS Handbook, Section 3.3 Revision No: 1.0

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Page 40 of 58
sho ws how to calculate vertical fluxes of a tracer gas and basically means that the summation of changes in wind and tracer gas concentrations over the time or distance it takes the gas from
release to detection is the emission flux. 

# ∫
**=** ) (

0 

### ' ' ) ( 1 orS T 

### dt n w orS T F Eq. 16

To calculate horizontal fluctuations
, one must consider wind velocity (u(z)) near the surface, friction velocity (u * ), which is a measure of the drag exerted by the wind on the surface
, and the displacement height (d). The displacement height res ults from the canopy acting as a displaced lower boundary layer and whose value is typically 70

- 80 percent of the canopy height. 

## ( **) **  ** **  ** **  ****

**− =**  **∗**

0 

### ln z d z k u z u Eq.17

W here : k is the von Karman constant and is equal to 0.4 and z 0 is the surface roughness

. 1
Using the Tracer Correlation Approach to measure total emissions over a large subject area is an alternative to standard dispersion modeling when attempting surface boundary layer methods as
weather conditions are too difficult to measure or estimate accur ately. Thus, releasing a tracer gas with known concentration and rate of release assists in calculating emission rates. Assuming that meteorological conditions affect both the tracer gas and the analyte in the same way, the analyte emission rate is calcu lated from simultaneous measurements of the tracer and analyte gas far downwind from the plume emission source using Equation 18.

t m t
m C C Q Q **∆ ∆** 

**=** Eq. 18

Where : Q m is the analyte emission rate; Q t is the tracer gas release rate,
m 

### C **∆** is the concentration above the background of the analyte observed in the plume (plume

– background concentration) ; and t 

### C **∆** is the concentration above background in tracer concentration in the plume, relative to the background.
A tracer gas is released from a canister to transport in the plume along with the analyte. Usually a tracer gas is chemically stable with no significant sources or sinks while in transport and is expected to fully mix in the plume. Typical tracer gas fi eld measurements are performed with
cell

- based instruments that utilize specific spectroscopic properties to characterize chemical species like CRDS and FTIR. These instruments can be setup as stationary or mobile to obtainORS Handbook, Section 3.3 Revision No: 1.0

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Page 41 of 58

one

- point or multiple

- point sam ples. Figure 3

- 14 shows a cartoon of tracer gas release setup. Normally
, the tracer gas is released upwind from the source and the cell

- based instrument is downwind from the source to measure a well mixed plume with the analyte and tracer gas.

Figure 3

- 1 3 Tracer gas release setup cartoon. Panel a shows the source with the analyte gas been released to the ambient air. Panel b shows the tracer gas been released and pushed by the wind into the analyte gas area to mix in the plume. Panel c shows the cel l

- base instrument measuring gases, analyte and tracer.

3.4.2 Verification/Validation Studies
Because the tracer gas release concentration is known and there are no significant sources or
sinks
, the mixing ratio should remain constant while in transport. Thus, verification of correct measurements by the cell

- based instrument is done by measuring the known tracer gas concentration with accuracy. Validation is performed depending on the DQOs of the study. Normally
, the study will say how accurate the ret rieval of the known concentration of tracer gas must be to obtain emission fluxes and the quality of the meteorological parameters to measure. Following are some examples of studies performed to verify and validate the use of tracer gas to
calculate emiss ion fluxes, utilizing CRDS and FTIR.

Tracer gas characterization using CRDS: Twin Bridges Recycling and Disposal Facility study 2,3

Mobile plume tracer dilution measurements were taken from May 18

– 21, 2009
, driving on Interstate 36 and East Main Street in Danville, IN. Acetylene (C 2 H 2 ) tracer gas was released from four sites located on or near to the center of each landfill facility. The stationary measurements for gas tracer correlation measurements, the analyzer was located in the plumeORS Handbook, Section 3.3 Revision No: 1.0

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Page 42 of 58 sufficiently downwind of the CH 4 source and C 2 H 2 tracer gas release locations for plumes to be
well

- mixed and appear as a single point emission source. During the measurements, the analyzer is stationary and continuously measures CH 4 and C 2 H 2 concentrations. The CRDS Picarro
Model G1203 CH 4 - C 2 H 2 analyzer used in this study is a self

- contained, stand

- alone unit that provides continuous measurements of C 2 H 2 and CH 4 concentrations, ambient temperature, and analyzer location (via high resolution GPS). The C 2 H 2 tracer gas was released from bottles through a mass flow controller (Alicat Scientific MC Series 16

- Bit Mass Gas Flow Controller,
model MCP

- 50SLPM

- DIO

- SG

- 30PSIA/5m) which was attached to the gas cylinder line to

ensure a constant release rate throughout the study. The range of tracer gas release for the test was 20 L/min to 40 L/min. The release rates were automatically recorded. The following parameters were measured: horizontal wind speed, horizontal wind direction , temperature, vertical and lateral turbulence
, and net solar radiation. Results from the study shown in Figure 3

- 15 demonstrated that the Tracer Correlation Approach is able to measure highly correlated, (correlation coefficient of 0.84) tracer gas to CH 4 emissions over a wide range of atmospheric and dispersion conditions.

Figure 3

- 1 4 Methane concentration verses acetylene concentration Tracer gas characterization using FTIR: Samuelsson et al., 2001 4ORS Handbook, Section 3.3 Revision No: 1.0

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Page 43 of 58

FTIR was used to obtain time resolved concentration measurements of methane in the downwind plume of a landfill and N 2 O was selected as the tracer gas. FTIR spectroscopy is an optical technique allowing a wide spectral region to be recorded simultaneously, thus the detection of
CH 4 and N 2 O co uld be done at the same time. Low detection limits and sensitivity of ppb were obtained using a long optical path. In this study
, a medium resolution (1 cm - 1 ) FTIR spectrometer was connected to an optical multiple

- reflection gas cell with an adjustable

p athlength, ranging from 9 to 107 meters. Normally a pathlength of 96 m was used, selected to optimize optical throughput and absorption levels. The system was built into a well

- tempered and mechanically stabilized optical bench and was located in a norma l transport van. The recorded spectra were analyzed by multiple

- regression techniques, fitting synthetically derived calibration spectra of all present compounds. CH 4 was analyzed in the wave number region at
~2950 cm - 1 , and N 2 O around 2200 cm - 1

. 4

The m ethodology used to couple the concentration measurements to an actual emission is the
Time Correlation Tracer method (Eq. 19). N 2 O was released in a controlled way from the methane emitting area by use of several point sources distributed over the landfil l. N 2 O mixed
with the emitted CH 4 in the landfill plume, and the emission rate was derived by time resolved analysis of the CH 4 and tracer concentrations collected far enough downwind the landfill. The part of the time series where the concentrations correlate, can be assumed to have its origin in the area where the tracer is released, and can be quantified using the known tracer flux according to Eq. 19.

Tracer Tracer CH

CH Tracer CH 

### M C M

C Q Q
**• •**  **• =**  4 4

4 Eq. 19

Where: C corresponds to the concentration in the mixing ratio and M to the molecular weight.
A correlation plot between tracer concentration and analyte helps to identify if sampling is within the plume or outside. If the slopes of the concentration curves coincided as the plume swept in and out of the location, it could be assumed that the tracer release simulated the entire methane
release. An estimate of the total landfill emission was obt ained using the slope of the regression
line of methane. Depending on the meteorological conditions, it is estimated that an accuracy of
15

- 30 percent in the emission estimate is achievable

. 4ORS Handbook, Section 3.3 Revision No: 1.0

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Page 44 of 58

3.4.3 Typical QA/QC

This section describes the QA/QC steps t hat pertain to the applications described above. QA/QC for applications steps normally depend on pre

- determined data quality indicators that address the project unique objectives. The technology used for the applications presented above have their own QA /QC associated to specifics of the instruments. If interested in the technology QA/QC
for OP

- FTIR , refer to section 2.1 ; for OP

- TDLAS
, refer to section 2.2 ; for UV

- DOAS
, refer to
section 2.3 ; and for CRDS
, refer to section 2.6.

When using the tracer gas approach , it is important to consider a gas that is stable and has low reactivity ; thus
, no significant sources and sinks that will alter the released concentration or
, at least
, good knowledge of the background concentrations. Spurious releases of tracer gases that reach 20 percent of the known concentration are easily identified CRDS, but anything below is probably not significant. Background levels of the analyte gas must be known to track the boundaries of the plume and to determine whether the measur ements are in or out of the plume. The time delay between release and arrival at measurement site needs to be carefully determined before total methane emission results are considered acceptable. Flow rate of tracer gas released
from all bottles be caref ully monitored and recorded if total methane emissions from a landfill are to be accurately determined. A comparison (correlation plot) of analyte and tracer gas measurements taken close and far away from the source provide a correlation coefficient and t he percentage difference or the total emission rate at close and far locations. Large percentage differences indicate insufficient overlap of the analyte plume and the tracer gas plume during stationary tracer

- dilution measurements.

3.4.4 Sitting Concer ns

In general, concerns regarding the use of tracer gases to obtain emission fluxes are associated with possible loss or gain of the tracer gas while in transport, not fully mixing within the plume, and changing weather conditions (wind speed and directio n for the most part). Other concerns are associated with calculations of the emission fluxes when estimating surface roughness or assessing vertical and horizontal fluctuations.ORS Handbook, Section 3.3 Revision No: 1.0

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3.4.5 Strength and Limitations

A key strength of using a tracer gas correlation technique is the ability to determine if varying weather conditions affect the calculation of emission rates, which is possible by knowing release
rates and concentration. A n additional strength is that emission rates are calculated within 15

- 30 percent precision. However, stationary and mobile setups have their challenges in terms of logistics, location and whether available roads are near perpendicular to the flow of the plume. Other limitations are cost of tracer gases cylinders and transp ortation of these, as well as changing weather conditions affecting the calculation of emission rates. Tables 3

- 13 and 3

- 14 summarize these strengths and limitations

.

Table 3

- 13

. Tracer Gas Correlation Strengths Feature Tracer Gas Correlation Strengths
Addresses Meteorology Can determine if varying weather conditions affect the calculation of emission rates.
Relatively precise Method Emission rates are calculated within 15

- 30 percent precision.

Portable instrumentation Field units are lightweight , rugged, and relatively easy to transport and operate.

Table 3

- 14

. Tracer Gas Correlation Limitations Feature Tracer Gas Correlation Strengths Meteorological Concerns Changing weather conditions affect the calculation of emission rates.
Logistical Concerns Location and the availability of roads perpendicular to the plume create difficulties. Related Expenses Tracer Gas cylinders can be expensive to purchase and ship.

3.4.6 References

1. Brasseur, G. P., J. J. Orlando and G. S. Tyndall. 1999. Atmospheric Chemistry and Global Change. New York, NY: Oxford University Press.

2. Eastern Research Group, Inc. 2010. Evaluation of Large Area Methane Emission Source Methods: Mobile and Stationary Plume Measurements Using the Tracer Correlation Approach. Final Report for U.S. EPA. May 30.ORS Handbook, Section 3.3 Revision No: 1.0

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Page 46 of 58
3. Crosson, E. and S. Tan. 2009. Report on the work undertaken at the Twin Bridges Recycling and Disposal Facility under an agreement with Eastern Research Group, Inc. August 1.

4. Samuelsson, J., G. Börjess on, B. Galle and B. Svensson. 2001. The Swedish landfill methane emission project. http://www.fluxsense.se/reports/Sardinia2001paper.pdfORS Handbook, Section 3.5 Revision No: 1.0

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Page 47 of 58 

### 3.5 Backward Lagrangian Stochastic Inverse

- Dispersion model

3.5.1 General Description of Approach

Identifying and quantifying gaseous emission rates from a fugitive or area source to air (e.g., emissions from an open air waste lagoon, confined animal feeding operations, biofuel production
facilit ies, landfills, etc.) is difficult. Several meteorological techniques are available (e.g., eddy covariance and flux gradient), but they involve complex instrumentation (e.g., concentration measurements at many heights and fast

- response concentration senso rs). They also require the
measurement site to be on a flat location. There is a separate technique that can be used called the Integrated Horizontal flux that can be used for non

- flat locations, but can only be used for small source areas because it req uires vertical and horizontal concentration measurements for the entire plume. Because many emission sources do not meet these criteria, other techniques must be implemented to estimate emission rates. 1

The limitations of traditional meteorological techn iques can be addressed by using an atmospheric dispersion model to calculate the emission rate indirectly. The “inverse

- dispersion” technique provides an accurate and economical alternative for measuring emissions. The technique uses a mathematical model of the dispersion of target gas from an emission source to a downwind location, so that a downwind concentration measurement can establish the emission

rate. 1, 2 This approach has the advantage of requiring only a single concentration measurement
and bas ic wind information, with substantial freedom to choose convenient measurement
locations. Theoretically, the technique assumes idealized wind conditions; however, with careful selection of measurement locations, inverse

- dispersion modeling can provide a s imple means of calculating emissions even in non

- ideal conditions. 1, 2

Figure 3

- 15 illustrates the bLS model for estimating emission rates. An area source of known configuration emits gas at a uniform, but unknown, rate Q in kilograms per meter 2 per seco nd (kg/m 2 /s). A time

- average gas concentration C is measured at point M within the plume. The gas concentration C can be determined by ORS measurement methods such as open

- path FTIR, TDLAS, UV

- DOAS, or point measurements such as CRDS. With a model predi ction of the ratioORS Handbook, Section 3.5 Revision No: 1.0

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Page 48 of 58
of concentration at M to the emission rate ( C / Q ) sim , the emission rate can be inferred as indicated in Equation 20, where C b is the background pollutant concentration. 1,2,3,4,5

Eq. 20

The inverse

- dispersion model calculates the ratio of the concentration rise to the emission rate ( C / Q ) sim at point M

. This is the basis of the inverse

- dispersion modeling technique. It requires only a single pollutant concentration measurement (assuming C b is known) with flexibility in the
choice of the measurement location M

. The accuracy of the technique rests on the calculation of ( C / Q ) sim. 1

Figure 3

- 1 5

. Illustration of an inverse

- dispersion model for estimating emission rate Q

. Average concentration C is measured at point M downwind of the source (after Flesch et al., 2004)

.

One of the most accurate models used to calculate ( C/Q ) sim is a bLS model. The model follows the path of a fluid element (e.g., “particle”) from a given location backward in time in order to determine its origin. Particles in the context of the model are considered to be carriers. Depending on the trajectory, the particle may o r may not sample the target source (i.e., "touchdown" within the source). The term Lagrangian indicates that the model releases individual particles and follows them along their paths through the air, rather than performing
calculations at fixed locations in space. Stochastic indicates that the model mimics the random, 

# ( **)** Q C C C Q

SIM b 

### − **=**ORS Handbook, Section 3.5 Revision No: 1.0

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Page 49 of 58 turbulent motion of each particle. As these particles travel through the air, they move through different regions of interest. Some particles will touch down in the source region and move to the concentration sensor, contributing to a measured concentration increase. 1,2,6 For surface area emission sources, all that is needed to invoke the bLS model to calculate ( C/Q ) sim are wind statistics, which can be determined from a few key meteorolog ical
observations. 1,2 In general, the bLS inverse

- dispersion method of modeling emissions is cost

effective, requires only a single field measurement of C , and, under the conditions described in this section, generates emission rates with accuracy adequat e for many applications.

Backward LS Dispersion Model for Calculating ( C/Q ) sim

In a bLS dispersion model, the upwind trajectories of model particles are calculated from
location M (Figure 3

- 1). The important information from the backward trajectories is the set of “touchdown” locations ( x 0 , y 0 ) where particles impact the ground, and vertical “touchdown velocities” at impact w 0 . From the set of trajectories, Equation 21 is used to calculate ( C/Q ) sim by summing the reciprocal of w 0 for touchdowns occurring within the source boundary. 3 

# ( **) ∑** 
**=** w

n 

### Q C sim 0 2 1 Eq. 21

The variable n is the total number of computational particles rel eased from M and the summation covers only touchdowns within the source area. In the bLS model, thousands of trajectories are calculated upwind of the prevailing wind conditions.

Commercially

- produced software developed and distributed by WindTrax (Thunder Beach Scientific, Nanaimo, Canada) is available to solve bLS equations. WindTrax combines the bLS model with a graphical interface, allowing sources and sensors to be convenient ly mapped (see Figure 3

- 16). 6 To calculate unknown source emission rates and/or concentrations, WindTrax requires the following information:

**•** upwind and downwind gas concentrations (C b and C),

**•** wind statistics ( e.g. wind speed and wind direction),

**•** roughnes s of the surface (z o ), andORS Handbook, Section 3.5 Revision No: 1.0

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**•** stability of the atmosphere near the ground (Monin

- Obukhov stability parameter, L).

The latter wind statistics may be obtained from sonic anemometry or estimated within Windtrax. If concentration is measured in units of ppm or ppb, air temperature and pressure are also needed (pressure is often estimated from elevation). The particle models used in WindTrax are time

independent, so the input data must be values averaged over a period of time, typically 15

- 30
minutes. This ave raging eliminates the unpredictable variability due to turbulence in the atmosphere on short time

- scales. Conversely, if the averaging time is too long, the more gradual diurnal variation typical of the surface layer will not be resolved. 6

Figure 3

- 16

. Illustration of the WindTrax bLS Modeling Software Graphical Interface

bLS Model Output Units

Open

- path ORS (OP

- ORS) measurement techniques such as open

- path FTIR or TDLAS can be used to make the concentration measurement in a bLS application. Also, point

- measurement sensors such as closed

- cell sensing techniques including CRDS or cell

- based FTIR could be used to measure plume concentration. These techniques provide concentration in units of ppm or ppb over the optical path used in the measurement (ppm*m or ppb*m). Specific temperature andORS Handbook, Section 3.5 Revision No: 1.0

Date: 0 8/19/11

Page 51 of 58 pressure data are needed to convert to absolute concentration (i.e. g/m 3 ), which is used to
determine th e mass emission rate. The bLS calculation itself produces an emission rate (flux), Q bLS , with units of kg/m 2 /s (for example), but sometimes the emission rate is multiplied by the
source area and Q bLS becomes an area integrated emission rate with units of kg/s or kg/h. 1,2,8,9,

(Harper et al, 2010)

3.5.2 Verification/Validation Study

One of the foremost leaders in the development and application of bLS modeling as it applies to gaseous emissions for large area sources is Thomas Flesch at the University of Alberta, Canada. He conducted numerous studies using bLS. In this section, a bLS field trial entitled Deducing
Ground

- to

- Air Emissions from Observed Trace Gas Concentrations: A Field Trial is summarized as an example of the bLS application

. 1 The summary briefly illustrates how bLS can be used and describes the quality of data that can be generated by the model.

In 2004, Flesch et al. reported a bLS field trial experiment in which the inverse

- dispersion technique was used to determine Q bLS in an ideal surface

- layer setting. A small area source from which methane was released at known rates over a wide range in meteorological conditions was
constructed. An open

- path laser measured the methane concentration C at positions located up
to 100 meters downwind of the source as shown in Figure 3

- 17. A corresponding ( C/Q ) sim was calculated using a bLS dispersion model, and the resulting estimate of the emission rate Q bLS was compared with the known Q

. The study objectives were the following: 1) to quantify the accuracy and uncertainty in Q bLS in an ideal setting; 2) to probe the conditions under which a dispersion model based on the Monin

– Obukhov similarity theory (MOST) performs poorly; and 3) to validate an experimental system (i.e., source, sensors, bLS model) for examining the
robustness of a bLS estimate in non

- ideal conditions.

Flesch et al Conditions and Setup

The experiment took place over a 6

- day period in May and June of 2001, near Ellerslie and Edmonton, Alberta, Canada, in a larg e clover field. From a meteorological perspective, the site was nearly ideal

— wind conditions were uniform, temperatures were not high enough to causeORS Handbook, Section 3.5 Revision No: 1.0

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Page 52 of 58 thermal convection of the plume out of the measurement window, and the nearest significant change in land cover was more than 500 m from the source.

Figure 3

- 17 . Map of the laser paths used in the 2004 Flesch et al experiment. The shaded square on the map represents the methane tracer source and the large “+” symbol indicates the location of the meteorolog ical tower with the 3

- D sonic anemometer.

A synthetic source was created to approximate a 6 m × 6 m square area source. A manifold was constructed out of polyvinyl chloride pipe and 36 0.5 mm outlet holes were drilled into the pipe. A gas cylinder was coupled to the manifold through a regulator and rotometer (flow meter). The methane tracer gas was released from high pressure cylinders (99.1 percent purity) at flow rates
between 15 and 50 L/min. Each release lasted from 1 to 3 hours

. The cylinder valve was manually adjusted to maintain a nominally constant flow rate, with adjustment occurring every minute or two as necessary. The study estimates a 10 percent uncertainty in Q due to flow

- rate fluctuations, observer error in reading the rotometer scale, and gas temperature variability inside the rotometer (which affects calibration).ORS Handbook, Section 3.5 Revision No: 1.0

Date: 0 8/19/11

Page 53 of 58 Methane concentration measurements were made using two open

- path lasers. A focused beam
from a tunable IR laser was aimed at a distant retro

- reflector where it was reflected back to the receiver optics and a detector. The returning signal strength was proportional to the methane
concentration C between the laser and the retro

- reflector. Background methane C b was periodically measured at 1.95 ppm. Thi s measured value closely correlated with the average methane background for the Edmonton region during the experiment, as routinely measured by the provincial government of Alberta. The laser units recorded C every minute. These readings were averaged in to 15

- , 30

- , or 120

- minute values. To convert concentrations from ppm to absolute concentration in g/m 3 , Flesch et al used the measured air temperature and atmospheric pressure for each observation period. A 3

- D sonic anemometer was placed on a tower

app roximately 2 m above ground and was used to determine values of u *, L , z 0 , and β for the
bLS simulations. Figure 3

- 3 shows a map of the various laser paths (dotted lines with arrows) used in the experiment. The code at the tip of each arrow head is assoc iated with the experimental conditions for that trial measurement. The wind direction on the map is from west
to east and all laser measurements were made downwind.

Flesch et al Results and Conclusions

When periods of extreme stratification or MOST failure were excluded, the bLS inverse

dispersion technique diagnosed the strength of a small ground

- level source with small bias (mean
value of Q bLS / Q within 2 percent of unity). Poor results were excluded w hen using a laser path over the source because this study dealt with a very small area source, meaning the laser path was not always sufficiently inside the plume. In situations involving a large source area, a
measurement location above the source is acc eptable. The period

- to

- period variability in Q bLS was acceptably small (standard deviation of Q bLS / Q is approximately 0.2). Using path

- integrated
values of C enhanced the accuracy with which Q bLS was diagnosed and rendered the experimental procedure very forgiving (the laser could be positioned without being overly concerned about changing wind direction). Based on their field experiments, Flesch et al made several recommendations for using a bLS model to infer Q bLS from an area source in an ideal
surfac e layer problem.ORS Handbook, Section 3.5 Revision No: 1.0

Date: 0 8/19/11

Page 54 of 58

 PIC measurement is preferable to a point measurement because PIC gives results that are integrated over the entire beam path length and are
, therefore
, more representative than a single point source measurement of the actual plume conce ntration.

 Distance of the detector from the source should be small enough that the concentration rise over background is accurately measured.

 Meteorological averaging times of 10

– 30 minutes are ideal for calculating concentration and meteorological s tatistics. Shorter averaging times may not capture an equilibrium state of the atmosphere, a requirement for the application of MOST.

 Periods of extreme atmospheric stability should not be used in assessing Q bLS . An example of an acceptable limit is |L| ≥ 10m. 4

 Disregard periods of low u * (e.g., u * ≤ 0.15 m s - 1 ).

Because all testing sites are different, the bLS modeling system has been used with varying site locations such as ponds, pastures, and other scenarios. More detailed information pertain ing to different types of site locations can be researched in the literature.

3.5.3 Typical QA/QC

In order for the bLS dispersion model to accurately calculate the emission rate of a source, it is important to verify that the instrumentation used to collect concentration data for target analytes is appropriate for bLS calculations. QA / QC guidelines identified in this protocol or other EPA literature for the particular technology used should b e followed for optimal performance.

Because meteorological measurements are required for the bLS model, it is important to ensure
that accurate measurements are used. Meteorological data collected on site should be collected with appropriate instrument ation
, and applicable EPA guidelines should be followed. More information on the technology used to collect meteorological data can be found in the Quality Assurance Handbook for Air Pollution Measurement Systems, Volume IV: Meteorological Measurements, Version 2. 0

. 7 If meteorological data are not collected on site, it is necessary to
ensure that the data used in the bLS model are taken from a trusted source and the location of the
measurements is near the test site. Wind stability determination requires more sophisticatedORS Handbook, Section 3.5 Revision No: 1.0

Date: 0 8/19/11

Page 55 of 58 instrumentation (e.g., 3

- D sonic anemometer, or temperature measurements at two or more heights).

WindTrax and other modeling software are available for use to perform the bLS calculations. A simplistic data set with known results sho uld be used to test the modeling software before use for field data calculations to verify the software’s performance. In special cases
, it may be preferable for the user to develop its own software program to perform the necessary calculations, though it is not recommended for accurate modeling. It is necessary to test any and all bLS software on a simplistic data set for which the resul ts are known to verify the software. In all cases, input data should be reviewed for accuracy and possible transcription errors.

3.5.4 Siting Concerns

The bLS dispersion model utilizes the average wind and turbulence statistics of the atmosphere
to calc ulate ( C/Q ) sim . MOST states that the statistical properties of the wind in the surface layer are determined by a few key parameters: the friction velocity u *, which is determined by the vertical transport of horizontal momentum near the surface; the Obuk hov stability length L , which quantifies the stability of the atmospheric surface layer; the surface roughness z 0 , which is related to the height of the plants, soil, or other elements covering the ground; and the wind direction β. In the field, these par ameters are typically determined with the use of sensors such
as a 3

- D sonic anemometer. The placement of sensors relative to sources can have a major effect on the quality of the predictions generated by the models. If no concentrations are measured
dow nwind of a source, then emission rates cannot be determined. 6 Upwind a mbient gas
concentrations must also be measured that may be coming onto the site

. The variability of wind direction might cause some simulations to fail while others succeed. When mul tiple unknown sources are present, the calculations are very sensitive to sensor placement and measurement

error. Where flow obstacles such as buildings or fences are present, measurements are often better made further downwind of the source, well away fr om the obstacles; at least 10 obstacle heights downwind is a useful rule of thumb for measurement location (M) placement. 1,2,5,6

If the source and the detection point M lie within a horizontally homogeneous surface layer, the application of the bLS dispersion model technique is reasonably straightforward. ManyORS Handbook, Section 3.5 Revision No: 1.0

Date: 0 8/19/11

Page 56 of 58 agricultural and environmental source estimation problems potentially fit this category. These problems may in clude emissions from small soil research plots, feedlots, ponds, industrial grounds, and so on, that often occur in circumstances for which it is reasonable to assume that the local wind flow is uniform (i.e., wind statistics that do not deviate more than 10 to 20 percent from their spatial average over the region from source to detector). 1

An important advantage of bLS models is the ease with which complex source shapes can be
handled. One of the most important factors affecting model error is the size of the regions of interest through which the analytes travel. As more analytes travel through a given region (i.e., the concentration is higher), more samples of the region are taken and the model error is reduced. In practice, this means that the larger the source target, the smaller the error, and conversely, the smaller the source target, the greater the potential for error. 1,2,3 However, the use of bLS within large source areas has b een very successful in many studies.

3.5.5 Strengths and Limitations

bLS models have several advantages over Gaussian and Eulerian models. For example
, bLS models are more physically valid than Gaussian models, which do not incorporate wind shear or
oth er meteorological information, and they do not require artificial diffusivity, as do the Eulerian models for convective transport. A traditional disadvantage of bLS models is their computation time requirements, which can be several orders of magnitude la rger than those required to solve algebraically reduced Gaussian models or even Eulerian models. This is because of the need to calculate thousands of unique atmospheric trajectories. However, modern computing power has rendered this problem to be of lim ited concern for most users. Tables 3

- 15 and 3

- 16 summarize the bLS model’s strengths and limitations in more detail.

Table 3

- 15

. bLS Model Strengths
Feature bLS Strengths

Simplicity of Measurements Requires only a single concentration measurement

– as opposed to many concentration measurements made in the vertical or horizontal plane of the plume 1,2 Flexible input requirements: various wind statistics can be entered into software andORS Handbook, Section 3.5 Revision No: 1.0

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Page 57 of 58

needed conver sions are done internally; different types of concentration observations are possible (point or line average). 6

Siting Concerns Substantial freedom to choose convenient measurement locations 1,2 Handles complex source shapes and sizes with relative ease 1,2,6 Can be used in locations with wind disturbances if sensor locations are chosen with care 2

Economical An economical alternative for determining emissions 8,9 Free downloadable software available online 6

Table 3

- 16

. bLS Model Limitations
Feature bLS Limitations

Meteorological Concerns Assumes idealized atmospheric conditions 1,2,3,4,5,6 unless particular care is taken with sensor placement. 9 Rapid atmospheric changes or extreme stability invalidate MOST and cause Q bLS estimates to be inaccurate 1,2

Training Requirements Some judgment required to identify poor measurement locations (for both winds and concentration). Poor measurements locations (e.g., close to building) can significantly impact the quality of emission calculation. 1,2,6 Some basic experience or training in micrometeorology or atmospheric gas transport is required to generate high

- quality data from the bLS model

Siting Concerns When multiple unknown sources are present, the calculations are very sensitive to sensor placement 1,2,3 Where flow obstacles such as buildings or fences are present, measurements are often better made further downwind of the source, well away from the obstacles 1,2 Time Limitations Can require lengthy computational time 6

3.5.6 References

1. Flesch, T.K., J.D. Wilson, L.A. Harper, B.P. Crenna, and R.R. Sharpe. 2004. Deducing ground

- air emissions from observed trace gas concentrations: A field trial. J. of Applied Meteorology. 43:487

- 502.ORS Handbook, Section 3.5 Revision No: 1.0

Date: 0 8/19/11

Page 58 of 58
2. Flesch, T.K., J.D. Wilson, and L. A. Harper. 2005a . Deducing ground air emissions from observed trace gas concentrations: A field trial with wind disturbance. J. of Applied Meteorology. 44:475

- 484.

3. Flesch, T.K., J.D. Wilson, and E. Yee. 1995. Backward

- time Lagrangian stochastic dispersion models, and their application to estimate gaseous emissions. J. of Applied Meteorology. 34:1320

- 1332.

4. Wilson, J.D., T.K. Flesch, and L.A. Harper. 2001. Micro

- meteorological methods for estimating surface exchange with a disturbed wind flow. J. of Agricultural and Forest Meteorology. 107:207

- 225.

5. McBain, M.C. and R.L. Desjardins. 2005. The evaluation of a backward Lagrangian stochastic (bLS) model to estimate greenhouse gas emissions from agricultural sources using a synthetic tracer source. J. of Agricultural a nd Forest Meteorology. 135:61

- 72. 6. Thunder Beach Scientific, Nanaimo, British Columbia, Canada, 2009. http://www.thunderbeachscientific.com

.
7. U.S. EPA, Quality Assurance Handbook for Air Pollution Me asurement Systems, Volume IV: Meteorological Measurements, Version 2.0, EPA

- 454/B

- 08

- 002, March 2008.

8. Harper, L.A., T.K. Flesch, J.M. Powell, W.K. Coblentz, W.E. Jokela, and N.P. Martin. 2009 Ammonia emissions from dairy production in Wisconsin, J. of Dairy Science. 92:2326

– 2337.

9. Flesch, T.K., L.A. Harper, J.M. Powell, and J.D. Wilson. 2009. Use of dispersion analysis for measurement of ammonia emissions from Wisconsin dairy farms. Agric. Eng. 52:253

– 265.

10. Harper, L.A., T.K. Flesch, K.H. Weaver, and J.D. Wilson. 2010. The effect of biofuel production on swine farm ammonia and methane emissions. J. Environ. Qual. (accepted for publication, August 5, 2010)ORS Handbook, Section 4.0 Revision No: 1.0

Date: 08/19/11

Page 1 of 13 

### 4.0 Ancillary Data Collection

Ancillary data used with ORS measurements may include information from a variety of sources. Examples of ancillary data are meteorological measurements, process or site

- specific emissions information, off

- site sources or sinks of emissions. Emission flux requires both measurement data and meteorological data to complete dispersion calculations and flux calculation validation. Emissions factors can be determined using ORS measure ments with the addition of source

specific process information or activity factors. Mobile ORS measurements require GPS data to complete emission flux calculations. Source attribution based on ORS measurements requires
site

- specific process and interfere nce information on each of the upwind stationary sources to validate the identification of the source generating the emissions of interest. Information on the type and use of ancillary data is described in this Chapter.ORS Handbook, Section 4.1 Revision No: 1.0

Date: 08/19/11

Page 2 of 13 

### 4.1 Meteorological Measurements

Meteorological conditions at the site of optical remote measurements are an important component of many of the OR S applications. Please note that in

- depth description of

m eteorological measurements information can be found in Volume IV of EPA’s QA Handbook
series. 1 Q uality of the meteorological parameters measurements is as important as the quality of the optical remote sensing results used in emissions flux calculations. The quality of meteorological parameters measurements includes topics such as:

**•** Meteorological tower siting and setup,

**•** Wind speed (horizontal and v ertical) and direction,

**•** Relative humidity and dew point,

**•** Temperature,

**•** Solar radiation and

**•** Atmospheric pressure.

Historical meteorological parameters measurements are important to the applications of ORS measurements because emission rates from open sources are affected by ambient conditions (e.g., high wind speed over the source can increase the emission rate). If the ORS data is intended to evaluate exposure or determine emissions factors, the ultimate use of the ORS measurements should include evaluation of how well the meteorological conditions encountered during the ORS test compare to the annual average meteoro logical conditions for the sampling
site. Ideally, the ORS test report summary includes data or commentary that addresses the representativeness of the meteorological conditions. The meteorological conditions recorded during the ORS test can be compared to the historical trends for a site. If site

- specific meteorological measurements are not available, current local conditions can be compared to the average statistics available from the nearest National Weather Service (NWS) monitoring station.
If there is more than one NWS station in close proximity to the measurement site
, then the comparison should be made against the average measurement of the NWS stations.ORS Handbook, Section 4.1 Revision No: 1.0

Date: 08/19/11

Page 3 of 13 Wind stability is often important to ORS measurements and refers to atmospheric turbulence. Atmospheric stability or turbulence is the vertical and horizontal transportation of an air mass. Atmospheric turbulence is the collective differences between small

- scale air motions driven by winds that vary in speed and directions for a given parcel of air. Measurements that lead to wind stability account for convection, diffusion, buoyancy, rapid variation of pressure and wind velocity. Turbulence is responsible for mixing the atmosphere and is what distributes water vapor, particulate matter and gase s.

However, one does not measure or calculate turbulence directly to assess atmospheric turbulence; instead we examine the atmospheric stability and the potential for vertical and horizontal transport
, as well as mixing while in transport. Vertical and horizontal transport is normally associated with atmospheric stability (although it is important to note that wind speed will play a major role in the horizontal plane )

. 2,3,4

A practical use for “wind stability” or turbulence for ORS measurements is to examine atmospheric stability and assess the atmosphere’s potential to mix and transport (vertically and horizontally). Another concept that is associated with “wind stability” is surface roughness as it relates to topographical and landscape variability . Determining if one is under stable or unstable atmospheric conditions is an important step in evaluating when conditions are appropriate for sampling, depending on the objectives of the measurement application. For example, the
combination of vertical and horizontal wind speed and direction with solar radiation can be used to determine the atmospheric stability class of the air parcel by stability categories shown in
Table 4

- 1 (Pasquill

- Gifford stability categories). 5 These stability categories can be used as a quality indicator for representative ORS measurements.

4.1.1 Meteorological Station Siting

Typically, the terrain associated with an optical remote monitoring measurement program is complex only to the extent that the local elevation provides an elevated or depressed land area with sloped sides. A properly located meteorological monitoring station should provideORS Handbook, Section 4.1 Revision No: 1.0

Date: 08/19/11

Page 4 of 13 meteorological measurements for an entire facility. For facilities or sites that exceed one square mile in area, an additional meteorological monitoring station may be required for each additional square mile of area to obtain accurate local meteorological conditions.

The meteorological monitoring station must be positioned at the center of the highest point near
the measurement location. Winds blowing across the top of elevated areas and winds down the slopes contribute to transport and dispersion characteristics. By positioning the station at the center and highest land mass near the measure ment site (i.e., as determined by Google earth® map elevations), all upwind wind patterns sweeping the source emissions are represented in the wind speed and direction measurements.

Obstructions must also be considered during siting of a meteorological monitoring station . There is no specified distance that the station must be positioned away from the measurement location. By positioning the station at the center of the highest elevation, effect/contribution of the winds blowing over elevated areas are normalized with respect to the direction of the wind to the greatest extent possible.

Meteorological measurement sensors are typically setup or positioned at 2 meters above ground
level. The wind/speed direction sensors must be positioned so that they a re located no closer than 2 meters from the temperature sensor. Wind direction sensors are oriented to true north using a digital compass.

The timers/clocks on the meteorological station and the optical measurement equipment should be synchronized to al low the concentration data and meteorological data to be directly compared to the measurement data during post

- processing. If synchronization is not possible, the offsets
between the clocks will be recorded at the start and end of data collection of each day in order to estimate any differences in the clock times or rates.ORS Handbook, Section 4.1 Revision No: 1.0

Date: 08/19/11

Page 5 of 13
4.1.2 Horizontal Wind Speed and Direction

Compact weather stations 6 can provide all

- in

- one measurement of temperature, horizontal wind speed, and horizontal wind direction at optical r emote measurement sites. The compact weather station typically uses 2

- D ultrasonic measurement technology for wind speed

. For wind
direction measurements
, the system automatically and continuously self

- aligns to magnetic
North. Typical units are capable of providing the following performance measurements:

**•** Wind speed range from 0

- 50 m/s with an accuracy of ± 0.5 m/s

**•** Wind direction range 0o

- 360o with an accuracy of ± 5o

Vertical Wind Speed and Lateral Turbulence

If wind stability is required as an ancillary measurement for the optical remote measurement application
, then a 3

- D Ultrasonic Anemometer may be necessary. Typical accuracy of this
instrument is **±** 2 degrees compass (1 to 30 m/s) or **±** 5 degrees compass (30 to 40 m/s).

4.1.3 Relative Humidity

Re lative humidity (RH) is important to ORS measurements because it provides information on water interference and predicting cloud or fog formation which interfere with many ORS techniques

. Of the many atmospheric variables describing water vapor content in the atmosphere, RH is the most common for routine monitoring programs. R H is the ratio (percent) of actual vapor pressure of moist air to the saturation vapor pressure at the same temperature. A corollary measure, Dew

- point temperature (or dew point) is the temperature to which a moist air parcel must be cooled to achieve saturation over water at constant pressure and water vapor

content. R H and dew point are measured with electrical hygrometer, chilled mirror, or psychometer instruments. Typical accur acy of this equipment is ± 0.5% RH.ORS Handbook, Section 4.1 Revision No: 1.0

Date: 08/19/11

Page 6 of 13
4.1.4 Temperature

Temperature is important because it determines or controls vertical transport of air which changes portion of the plume measured by the ORS technology. Standard meteorological equipment includes a thermoc ouple or thermistor temperature sensor with a t emperature range

- 50oC to +50oC and an accuracy of ± 0.2oC

.

4.1.5 Net Solar Radiation

Net Solar Radiometer is required for wind stability determination. The typical accuracy of these
units is > 90 percent of the Daily Total Solar Radiation.

4.1.6 Atmospheric Pressure

For air quality and meteorological purposes, atmospheric pressure is generally measured with mercury, aneroid, or electronic barometers. Most, if not all
, of the atmospheric pressure sensors available provide analog or serial output that is directly interfaced with a data acquisition system.

A mercury barometer measures the height of a column of mercury that is supported by the atmospheric pressure. It is a standard instrument for many climatological observation stations, but it does not afford automated data recording.

An aneroid barometer consists of two circular disks bounding an evacuated volume. As the pressure changes, the disks flex, changing thei r relative spacing which is sensed by a mechanical
or electrical element and transmitted to a transducer.

Most electronic barometers of recent design use transducers which transform the sensor response into a pressure

- related electrical quantity in the fo rm of either analog or digital signals. Current digital barometer technology employs various levels of redundancy to achieve long

- term stability and accuracy of the measurements. One technique is to use three independently operatingORS Handbook, Section 4.1 Revision No: 1.0

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Page 7 of 13
sensors under central ized microprocessor control. Even higher stability and reliability can be achieved by using three completely independent barometers, incorporating three sets of pressure transducers and microprocessors. Each configuration has automatic temperature compen sation
from internal

- mounted temperature sensors. Triple redundancy ensures excellent long

- term stability and measurement accuracy, even in the most demanding applications. 7ORS Handbook, Section 4.2 Revision No: 1.0

Date: 08/19/11

Page 8 of 13 

### 4.2 Differential Global Positioning for Tracking Monitoring Locations

Global position information using a high resolution Differential Global Positioning System (DGPS) is required for mobile tracer correlation optical measurements and temporary stationary
measurement locations. 8 Global position high resolution accuracy of <0.6 meters will meet most application requirements. The DGPS data collection includes a time stamped data stream acquired in real time. The DGPS clock should be synchronized with the measurement system clock to simplify measurement location and concentration information.

In the field coordinates measured by the DGPS
, unit results should be compared to known Google Earth ® coordinates for known geodetic marks as a quality che ck of the location system.ORS Handbook, Section 4.3 Revision No: 1.0

Date: 08/19/11

Page 9 of 13 

### 4.3 Collection of Process Information

Process information is site

- specific data on factors or activities that affect the production or release of pollutants to be measured. Process parameters are measured indicators of process performance such as duct flow, operation temperature, or fuel use. The measurement of
emissio ns rate, also called emission flux, may be extended beyond mass per unit air volume to determining emissions factors when the flux can be related to activity factors of the source. Emission flux is often converted into an emission factor to estimate air p ollutant emissions from a process or activity (e.g., fuel combustion, chemical production). The simplest form of an emission factor is an expression of the mass of pollutant emitted per unit of activity generating the pollutant (e.g., pounds of particulat e matter emitted per ton of coal burned). Typically, emission factors for stationary point sources are developed by dividing the source’s emission rate by an appropriate parameter (e.g., number of widgets made per hour) that represents the activity

respon sible for generating the emissions. Therefore, gathering process information related to production, chemical use, energy use, heat or power generation is important to assess the relative rates of pollution produced by a stationary source. Once process in formation is available, ORS flux measurements of open source activity can yield meaningful emission factors.

In developing emission factors for point sources, identification of the underlying activity that generates the emissions is typically straightforw ard. For example, particulate matter emissions from fuel combustion are a direct function of the type and amount of fuel burned. However, for open sources (e.g., landfills) the pollutant generation and emission release mechanisms tend to be more complex. This complexity can make the selection of appropriate underlying activity, emission precursor, or process parameter(s) for use as an emissions surrogate more difficult than for point sources. Consequently, emission factor developers should have a thorou gh understanding of the pollutant generation and emission release mechanisms for a given open source to accurately interpret the results from the ORS test and to properly apply the ORS data for emission factor development.ORS Handbook, Section 4.3 Revision No: 1.0

Date: 08/19/11

Page 10 of 13 One strength of ORS techniques is measurement of fugitive emissions from open sources such as landfills, wastewater treatment systems, agriculture operations, and equipment leaks at petrochemical and industrial facilities. At landfills, wastewater treatme nt systems, and animal agriculture operations, fugitive emissions are generated by biological decay of organic matter present in the waste. The rate of biological activity is affected by ambient conditions (e.g., bioactivity increases with increasing temp erature) and process parameters such as chemical conditions (pH, reduction/oxidation potential) in the waste. Pollutant emissions are also affected by site

- specific parameters such as the process information on the configuration of the source and the proc ess steps involved in handling and disposing of the waste. At petrochemical and industrial facilities, fugitive emissions from equipment leaks are a function of process information such as the type of equipment; the number of equipment components; the

con centration and vapor pressure of pollutants in the in

- service gas; and process parameters such as temperature and pressure. The complex transport and diffusion mechanisms, and the chemical/biological reactions inherent in certain types of open sources, me an that the emissions from the open source may not be easily related to an industrial process or activity. For example, an industrial process generates a liquid waste stream that is discharged to an open wastewater treatment system. Although the pollutan t loading to the treatment system may be relatively constant over time, the emissions from the system may not consistently track the loading rate due to changes in the rate of pollutant formation caused by increases/decreases in process parameters
of the t reatment system such as temperature.

The output ultimately obtained from an ORS test is an emission rate in terms of mass of pollutant emitted per unit of time. For point sources, the activity typically selected for emission factor development has a time component such that the use of the activity in the denominator of the
factor cancels out the time units of the measured emission rate. For example, the use of a boiler’s fuel feed rate (Mg coal/hr) as the activity converts the measured emission rate (g

p ollutant emitted/hr) into an emission factor in terms of g pollutant emitted/Mg of coal fired. The activity data selected for use in development of an emission factor based upon ORS measurements may or may not have a time component.ORS Handbook, Section 4.3 Revision No: 1.0

Date: 08/19/11

Page 11 of 13 Simple emission fact ors for a specific site can be developed using the emission rate measured by one of several of the ORS technologies described in Chapter 2 and a characteristic activity factor related to the emissions factor of interest. However, the source characteristic is not necessarily a simple time

- dependent activity independent of the site. The addition of site

- specific or process

specific information improves emissions factors estimates. For example, a simple emission factor (kg of pollutant/ft 2 of landfill surfa ce area) could be developed using ORS test data and the surface area of the landfill. However, because landfill fugitive emissions are also dependent on the type of cover and gas collection system (if applicable), the type of material contained in the
lan dfill, the retention time of the material, and the size and number of landfill cells, the applicability of the simple emission factor to other landfills based solely upon surface area would
be limited. A more refined landfill emission factor (e.g., in ter ms of mass of pollutant emitted/mass of pollutant generated) could be developed using the measured emission rate and an estimated pollutant generation rate, such as one calculated from the site specific biological decay model discussed in Section 2.4.4.1 ( Municipal Solid Waste Landfills) of EPA’s AP

- 42, as the activity. This refinement (i.e., emission factor) would an intermediate estimate and more site

- specific than the simple factor
, yet less specific than uniquely measuring the emissions flux for every landfill of interest. The additional value of the process parameters necessary to utilize the decay model (e.g., pollutant generation potential of the waste, time since initial waste placement in the landfill) is less costly than multiple field sampling e pisodes at different
landfills.ORS Handbook, Section 4.4 Revision No: 1.0

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### 4.4 Attribution of Emissions to Source of Intent

Results from an ORS sampling episode can be used in a dispersion modeling analysis conducted
in reverse. In dispersion modeling for point sources, the emission rate from the source is known and the concentrations at receptor points downwind of the source are estimated based on the release characteristics of the source (e.g., stack height, exit velocity, gas temperature) and the meteorological data (e.g., wind speed and atmospheric stability parameters) used in the modeling analysis. In ORS sampling, the s ampling path or plane effectively serves as a downwind receptor and the emission rate from the open source is back

- calculated based upon measured downwind concentrations and the wind speed and atmospheric stability data measured during the

test. Consequen tly, the open source emission rate is directly dependent upon the ambient conditions encountered during the test.

The emission rates determined using ORS techniques can also be affected by background pollutant concentrations in the atmosphere surrounding the open source
, and by how well the placement of the ORS instrumentation (e.g., transmitters, receivers, retro

- reflectors) captures the
area source emissions. Consequently, ORS data users should determine whether background
concentrations were accounted for in the ORS measurements. Typically, background emissions are determined by measuring pollutant concentrations in sampling paths or planes located upwind of the emission source and subtracting those concentrations from the concentration
values measured at the downwind locations. For assessing the effectiveness of the ORS instruments to capture the source emissions, the developer should review the placement of the ORS instrumentation, and any assumptions made regarding the prevalent wind direction, rela tive
to the emission source to be measured. The ORS technique could be applied properly, but the configuration of the sampling equipment relative to the emission source and the prevailing wind direction may not adequately capture the source emission plume under varying meteorological
conditions.ORS Handbook, Section 4.5 Revision No: 1.0

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### 4.5 References

1. Quality Assurance Handbook for Air Pollution Measurement Systems, Volume IV: Meteorological Measurements Version 2.0 (Final), EPA

- 454/B

- 08

- 002 March 2008.

2. Daniel J. Jacob, Introduction to atmospheric chemistry, Princeton University Press, 1999.

3. John M. Wallace and Peter V. Hobbs, Atmospheric Science an introductory survey, Academic Pres s, 1977.

4. David G. Andrews, An introduction to atmospheric physics, Cambridge University Press, 2000.

5. EPA 2000. Meteorological Monitoring Guidance for Regulatory Modeling Applications, EPA

- 454/R

- 99

- 005.

6. A Climatronics Corporation AIO compact weather station A 2

- D Ultrasonic Anemometer (AIO Compact Weather Station, Model 102780, Climatronics, Bohemia, NY).

7. World Meteorological Organization. Guide to meteorological instruments and methods of observation; Draft seventh edition ed.; Secretariat of the World Meteorological Organization; Geneva, Switzerland, 2006.

8. DGPS, Cressent R100 Series, Hemisphere GPS, Calgary, AB, Canada.ORS Handbook, Section 5.0 Revision No: 1.0

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### 5.0 Data Validation and Verification

This s ection provides information on validation and verification of remote optical measurements starting from field observation through test report review. The emphasis in this section is how an optical technology report recipient can e valuate and verify the quality of the reported data.
Information is provided on how data quality indicators can be used to assess and verify optical monitoring data in the most general way.

Data review, verification, and validation are techniques used to accept, reject, or qualify data in an objective and consistent manner. Verification can be defined as the process of evaluating the completeness, correctness and conformance of a specific data set against the data quality requirements. 1 Verification c an be done by examination and objective evidence that the final data meets specified QC recommendations or requirements and fulfills the data users’ requirements.

Validation can be defined as a confirmation by examination and objective evidence that the particular recommendations for a specific intended use are fulfilled. The criteria for deciding the degree to which each data item has met its quality specification s should be described in an organization’s site specific QAPP. The QAPP should clearly indicate the plan to meet the end

user ' s DQO. The DQO process was described in Chapter 1 Section 1.4.

This data validation and verification section describes the tec hniques used to make assessments of the application of remote optical air methods to field measurements. In general, the initial assessment activities are performed both by persons implementing the environmental data operations and by personnel “independe nt” of the operation, such as post test data reviewers and the organization’s QA personnel. The procedures, designated personnel, and frequency of assessment should be included in an organization’s QAPP. These activities should occur prior to submitting the final data report and before they are used in models or emissions factors development. Field testers should verify results from a field test program before data users
validate measurement results and use them to make decisions.ORS Handbook, Section 5.1 Revision No: 1.0

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### 5.1 General Approach

Specific QC specifications for each optical technique are provided in the individual technology or applications sections in Chapters 2 and 3. The ge neral information in this s ection can be used for most optical remote measurement projects. How closely a measurement represents the actual environment at a given time and location is a complex issue that must be considered during development of the sampl ing design. Testers check (verify) each measurement for conformity to the specifications, including type and location (spatial and temporal). Modelers and other secondary data users should compare project quality specifications to their data needs and
de termine (validate) if the optical remote measurement data is useful for their purpose. By noting the deviations in sufficient detail, modelers and secondary data users will be able to determine the data’s usability for scenarios different from those inclu ded in project planning.

Remote sensing methodology and meteorological data are often linked. Pollution enters the atmosphere directly
, or is formed by chemical reactions in the atmosphere
, or it is the result of a

process. Photochemical pollutants, such as O 3 and sulfates, are generally produced over a period
of time. O zone forms by the interaction of VOCs and NOx under the right meteorological conditions when low wind speeds, variable wind directions, and relatively high t emperatures are present. Other pollutants are generated by point, mobile, and area sources. Winds, a meteorological variable, can transport pollutants from their sources to affect populated areas. Therefore, if possible, meteorological data should be ve rified and validated at the same time as remote sensing data, not separately.

Figure 5.1 is a simplified illustration of a typical verification and validation process. The left
column shows the “levels of data review

. ” These levels of data are describe d in Section 5.1.1. The right column illustrates the type of verification or validation that usually occurs during the

process. The numbers in parentheses reference the section numbers in this document that provide additional details on the data review p rocess at each step in the hierarchy.ORS Handbook, Section 5.1 Revision No: 1.0

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5.1.1 Levels of Data Quality Review

Generally, there are four “levels” of air quality measurements data review. These levels are similar to those defined by Mueller and Watson 2 and Watson et. al. 3 When a data set has undergone each level of review, it passes on to the next level. The entire process is used to determine the validity of the data.

**•** Level 0 verification includes raw calibration data and initial setup observation prior to collecting fi eld data. Testing staff should report results of manufacturer calibration and verification that they perform prior to a field campaign. These data can include background and noise measurements made to establish a baseline for sensitivity of the

measureme nts. Level 0 verification also includes field observation of the equipment setup and function. At this level
, the data may be reduced and possibly reformatted, but
are unedited and un

- reviewed. These data have not been adjusted for known biases due
to i nterfering components in the air at the test site or other problems that may have been identified during field maintenance checks or audits. These observations and data may be used to monitor instrument operations during the measurement episode but should not be used for regulatory purposes. Section 5.2.1 provides more details on this level of
validation.

**•** Level 1 data verification involves quantitative and qualitative reviews for accuracy, completeness, and internal consistency. Quantitative checks are performed by instrument software screening programs, and qualitative checks are performed by field staff who manually review the data for outliers and problems. Quality control flags are assigned, as necessary, to indicate the data quality. Data are only considered verified at Level 1 after final QC checks have been completed and any adjustments, changes, or modifications to
the data have been made. Section 5.2.2 provides more details on this level of data
validation.ORS Handbook, Section 5.1 Revision No: 1.0

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**•** Level 2 data validation involves co mparisons with independent data sets. This function includes, for example, making comparisons to other simultaneous emissions
measurements or historical data on the source emissions.

**•** Level 3 data validation involves a more detailed analysis and final scr eening of the data. The purpose of the final step is to ensure that there are no inconsistencies among the primary optical data and related data (such as meteorological measurements). The reviewer examines the overall consistency of the data and the cons istency of related data (i.e., checking emissions patterns against time of day or wind speed and wind direction )

.

Full post test Validation of data by an independed QA reviewer. Level 3 Review Validation and Verification Level Quality Review Activity

Level 0 Review Verify Initial setup and calibration data Instrument is functioning as expected

Verify Quality checks were performed during sampling Level 1 Review

Validate data relative to ancillary measurements and/or historical data.. Level 2 Review

Figure 5

- 1 Generalized Data Verification and Validation Process FlowORS Handbook, Sect ion 5.2 Revision No: 1.0

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### 5.2 Data Verification Methods

Data verification is defined as the confirmation by examination and objective evidence that specified quality requirements have been fulfilled according to the standard procedure for the
method. This verification contributes to the confidence that the dat a will be valid for the original decision or purpose of the data collection. These recommendations should be included in the organization’s QAPP as part of the method quality indicators. The data verification process involves two basic steps: visual ins pection and analysis and verification performed by data
review. Both techniques are needed to verify optical measurements data. Each is described in the following sections.

5.2.1 Visual Data Verification

Level 0 Review Verify Initial setup and calibration data Instrument is functioning as expected
Figure 5

- 2 Level 0 Verification Checks the Most Fundamental Quality Requirements

The monitors and equipment used for remote sensing rely upon a radiation source ( UV , visible,

or IR ) and a detector used together to identify and quantify the levels of certain chemicals in the atmosphere. These monitors are typically used in a continuous monitoring mode and monitor one or several compounds simultaneously. Although the overall design requirements for the different spectral ranges are significantly different, the basic componen ts of these technologies
are similar. In general, these monitors contain at least the following components:

**•** Radiation source

**•** Optics

**•** Detector

**•** Data processing algorithms

The radiation sources for these technologies belong to one of three distinct groups. The monitors operating in the UV region of the spectrum use a continuous or non

- continuous lamp that provides broad

- band radiation in the UV and visible regions. T he monitors
, using TDLORS Handbook, Sect ion 5.2 Revision No: 1.0

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Page 6 of 13 technology
, use a laser to provide radiation over a very narrow spec tral range in the near

- IR

. That spectral range can be tuned over a small range with a single TDL and is selectable over a wider range using multiple TDLs. The FTIR monitors use a broadband IR source. Passive technologies such as IR cameras and Passive F TIR measure natural IR radiance from the compounds being measured. The optical components of these monitors typically are used to guide the radiation from the source, through the atmospheric path to be monitored, to the
detector. The detectors and config urations for these monitors vary according to specific applications. They are typically chosen to maximize signal

- to

- noise ratio for the spectral region and operating temperature. 4

Level 0 verification includes review of calibration of instruments and equipment. Periodic calibration and/or calibration checks must meet MQOs identified in project QAPPs. Typical MQOs are listed Chapter 2 for each measurement technology. Calibration d ata should be reviewed by field test staff and data validation staff. The following questions should be
answered:

**•** Were the calibrations performed within an acceptable time prior to generation of data?

**•** Were they performed in the proper sequence?

**•** Were t hey performed using standards at the conditions expected during field measurements?

**•** Were acceptable linearity checks and other checks made to ensure that the measurement system was stable when the calibration was performed?

Level 0 verification can also include field inspections to visually verify optical measurement technologies performance during field acquisition of data. Field verification can be technical systems audits (internal or external) or simple inspections by field operators. For example,

o ptical equipment often generates visible light as a direct or indirect result of the measurement process. Field inspection can verify measurement equipment is aimed correctly and operating if reflected visible light is apparent from the optical path. Opt ical equipment and associated reflectors can gather dust or moisture
, and observation of these two interferents can be made visually during field inspection. Several questions might be asked during a visual verification

process:ORS Handbook, Sect ion 5.2 Revision No: 1.0

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**•** Is the equipment operatio nal? Verification is performed by observing that the optical equipment is collecting and storing data.

**•** Was the equipment aimed correctly to make measurements? Verification is performed by observing that the data collected is different from zero or full saturation.

**•** Part of the verification process is a review of the optical remote data over a period of time. A quick visual inspection may reveal some anomalies that do not match other parameters.

Continuous long term optical measurement verification programs should include documentation of periodic field observations that ensure equipment is operating. Figure 5.3 is an example of a visual observation records. Many environmental samples can be flagged (qualified) during the periodic visual inspection s.

Weekly Visual Quality Control Check Sheet Optical Remote Instruments Site ______________________ Month/Year _______________________________ Site Number _______________ Technician ________________________________ Date:_____________________ [ ] System is powered and operating [ ] Source is generating adequate radiation for measurements [ ] Optical alignment is correct allowing beam detection [ ] Detector is maintained at the proper temperature and generating spectral signal [ ] Calibration, span, ze ro checks are performed as appropriate ( ) Standard Gas mixtures are available if required ( ) System operation includes periodic QC All comments must be noted in the system log.

Reviewed by _______________ Date _________________________________

Fi gure 5

- 3 Example of Optical Remote Measurement Visual Check ListORS Handbook, Sect ion 5.2 Revision No: 1.0

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5.2.2 Data Review and Verification

Verify Quality checks were performed during sampling Level 1 Review

Figure 5

- 4 Level 1 Verification Ensures Quality Requirements are Met in the Field

In the late 1990s, optical remote systems were developed with personal computer compatible data collection routines. Many optical remote instruments offer remote access and download of data from systems that are in continuous use. Steps preparatory to da ta validation should include the daily transfer of raw data (e.g. signal averaged processed data) to a central data processing facility and the transfer of raw data files to create an edited database. The raw data files should be stored separately to insu re data integrity. Backup copies of the data should be prepared and
maintained on

- site and off

- site.

For continuous optical remote monitoring systems
, data can be processed and QC operations parameters can be evaluated to determine if equipment maintenan ce is required. These types of verification techniques can be extremely useful because the program can “sense” a change in operating conditions or instrument response and a prediction of the possibility of equipment
failure.

Data reviewers should answe r some typical questions during their remote download data review
such as:

**•** Did the signal intensity drift or diminish significantly since the last equipment maintenance?

**•** Did the regular QC check for noise and or calibration exceed acceptable limits?

**•** D id the optical system trip any electronic limits or indicate data collection failure?

**•** Does the data fall within the measurement range of the instrument or was the data saturated or zero?

**•** Does the system have a minimum detectable limit?ORS Handbook, Section 5.3 Revision No: 1.0

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### 5.3 Data Validation Methods

5.3.1 Validation of Primary and Ancillary Measurements

Validate data relative to ancillary measurements and/or historical data.. Level 2 Review

Figure 5

- 5 Level 2 Quality Checks Start the Data Validation Process

Both manual and computer

- oriented systems require individual reviews of all data tabulations. As with all environmental measurements, it is necessary to keep accurate records during measurement periods to ensure a complete data collection. A site logbook and calibration sheets
should be maintained at the data collection site. The site logbook will include information such as meteorological conditions, path lengths, UV filter numbers, lamp type, light intensities and
measurement times. Light intensities must be recorded any time an optical source or receiver is adjusted and compared to the intensities measured when the equipment was installed. Calibration sheets include the record and results of system calibration checks or audits performed
with known co ncentrations of a target or surrogate analyte.

Initial data verification steps should be performed by the station operator and later by data validation staff. All necessary supporting material, such as audit reports and site logs, should be
available for Level 2 validation. Access to daily ancillary measurements such as wind speed, direction, should be provided for use in relating suspect data to local and regional conditions. If measurements are taken down wind of a facility, process information and pr oduction schedules are useful to interpret trends or excursions in optical remote data. Questionable data, such as data flagged in an audit, manual review should be corrected or invalidated during Level 2 data
validation.

For long

- term continuous measur ements programs , the data should be reviewed on a regular schedule and at least monthly. For short

- term measurements programs , the data should be reviewed by the site operators at the end of each day. Optical measurement instruments typicallyORS Handbook, Section 5.3 Revision No: 1.0

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include onb oard personal computers that allow operators to view and evaluate data visually. Graphs or plots of data or a summary table of data can be evaluated for outliers or obvious data collection failures. Graphing data can be a quick method of visualizing the data relative to other parameters. Graphs can show longer term trends and relationships that are difficult to see when data validation staff are looking at large amounts of tabular data.

The purpose of manual data inspection is to spot unusually high (or low) values (outliers) that might indicate a gross error in the data collection and to verify signal intensity. Manual review of data tabulations also allows detection of uncorrected drift in the zero baseline of an optical

sensor. Zero drift may be ind icated when the daily minimum values tend to deviate (increase or decrease) from the expected minimum value over a period of several days.

In an automated data processing system, procedures for data validation can be incorporated into
the basic software. As noted in Section 5.2.2, the computer can be programmed to scan data for extreme values, outliers, or ranges. These checks can be further refined to account for time of day, time of week, and other cyclic conditions. Questionable data values flagged on the data tabulation may or may not indicate possible errors. The system operator should check all the data flagged by the acquisition system program and investigate whether the data flagged should remain flagged. In some cases, extreme conditions can occur rapidly and the data may actually
reflect real values. For example, if a spill or leak occurs and moves through a measurement area the optical monitor may record high values or extreme interference in the data. The system operator should note such excursions and alert the data validation and reporting staff that these data may actually reflect real conditions. Data validation in Level 2 evaluates the data completeness and representativeness against the project DQO requirements to ensure sufficient
data is collected for data users to draw conclusions or make decisions.

A useful data validation method is to compare the difference between successive data values. Logic dictates that rapid changes in values in a 1 to 15 min ute acquisition period would normally not be expected. When the difference between two successive val ues exceeds a predetermined value, the data in question can be flagged for further evaluation. Screening is an iterative processORS Handbook, Section 5.3 Revision No: 1.0

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Page 11 of 13 in which range checks and other screening criteria are revised as necessary based on experience. For example, an initial QA p ass of a data set using default criteria may result in flagged values which, upon further investigation, are determined to be valid for a particular site. In such cases,
one or more follow

- up QA passes using revised criteria may be necessary to clearly se gregate valid and invalid data.

5.3.2 Final Validation and Evaluation of Measurements for Data Users

Full post test Validation of data by an independed QA reviewer. Level 3 Review

Figure 5

- 6 Level 3 Quality Checks Ensure the Data is Usable for the Purpose Intended

Data validation is a routine process desi gned to ensure that reported values meet the quality goals and objectives of environmental data operations. A progressive, systematic approach to data validation must be used to ensure and assess the quality of data. The purpose of this step in the

proce ss is to detect, compare, and perform a final screening on all data values. Any final data that may not represent actual conditions at the sampling site will be detected at this stage. Effective data validation procedures usually are handled independent of the procedures of initial data verification, that is, by different staff. It is important that data validation staff be independent of field operators.

If data assessment results clearly indicate a serious response problem with the optical technology , the agency should review all related information to determine whether the optical remote assessment data, should be invalidated. Some problems that may escape detection during an audit are often easily identified during data validation. Data validation should be performed by a person with appropriate training in the optical technology who has a basic understanding of instrument operation and typical results from similar measurement projects.

Data flagged by the QC screening should be evaluated by pers onnel with optical measurement expertise. Reasons for changes in the data resulting from the validation process should beORS Handbook, Section 5.3 Revision No: 1.0

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documented. If system problems are identified, corrective actions should also be documented.
Edited data should continue to be flagged so that their reliability can be considered in the interpretation of the results of modeling analyses for which the data are used.

Flags can be used in the field and by the data reviewers to signify data that may be suspect due to
calibration or a udit failure, special events, or failed QC limits. When calibration problems are identified, data produced between the suspect calibration event and subsequent recalibration should be flagged. Because flag combinations can be overwhelming and cannot alwa ys be anticipated, an organization needs to review these flag combinations to determine whether single values or values from a site over a particular time period should be invalidated. Procedures for screening data for possible errors or anomalies should also be implemented. When calibration problems are identified, data produced between the suspect calibration event and any subsequent recalibration should be flagged to alert data users.ORS Handbook, Section .5.4 Revision No: 1.0

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### 5.4 References

1. Guidance on Environmental Data Verification and Data Validation, EPA QA/G

- 8, EPA/240/R

- 02/004, November 2002.

2. Mueller, P. K.; Watson, J. G. " Eastern regional air

- quality measurements. Volume 1, Section 7" ;EPRI

- EA

- 1914

- Vol.1, final report prepared for Environmental Research and Technology, Inc., Concord, MA, by Electric Power Research Institute, Palo Alto, CA. 1982.

3. Watson, J. G.; Lioy, P. J.; Mueller, P. K. " The measurement process: precision,
accu racy, and validity". In Proceedings, Proceedings, Air Sampling Instruments for Evaluation of Atmospheric Contaminants; 7th ed.; Hering, S. V., Ed.; American Conference of Governmental Industrial Hygienists: Cincinnati, OH, 1989, pp 51

- 57.

4. Battelle ETV Generic Verification Protocol for Optical Open

- Path Monitors 2002.