EPA Handbook: Optical Remote 

Sensing for Measurement and 

Monitoring of Emissions Flux
December 2011

EPA Handbook: Optical Remote Sensing for Measurement and 

Monitoring of Emissions 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 could be integrated into emissions and 

ambient air monitoring for the measurement of flux. However, the monitoring community did 

not embrace ORS as quickly as anticipated. Several factors contributing to delayed ORS use 

were: 

• Cost: The cost of these instruments made it prohibitive to purchase, operate and 

maintain. 

• Utility: Since these instruments were in essence perceived as “black boxes.” Many

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 applications. The emissions 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 scientists and engineers in the monitoring community 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 “nonspectroscopist” in understanding and using data and information generated by ORS. This 

Handbook is divided into five sections:

• Section 1: Discusses what ORS means and how this technology can be used. It also has 

a number of tables that have a “crosswalk” between the different technologies and their 

use (i.e., techniques). 

• Section 2: describes the different technologies or “hardware” that are currently available 

that are considered “optically remote.” 

• Section 3: Explains how to use the “hardware” with different techniques and how to 

calculate emission flux. 

• Section 4: Discusses the “other” data that needs to be collected to understand and better 

validate and verify the ORS data.
• Section 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 Handbook.... 16

1.3.1 Contents.................................... 16

1.3.2 Overview of Handbook

Sections..................................... 17

1.4 General Discussion of EPA Quality 

System.................................................. 19

1.4.1 Data Quality Objectives............. 20

1.4.2 Measurement Quality

Objectives ................................. 22

1.5 Selecting the Right Tool: Technology 

Cross-Table References......................... 24
Page 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..................................... 15

2.1.5 Strengths and Limitations.......... 16

2.1.6 References................................. 18

2.2 Tunable Diode Laser............................. 21

2.2.1 Basic Operation......................... 22

2.2.2 Pollutants and Relative Levels 

That Can Be Measured ............. 26

2.2.3 Typical QA/QC ......................... 27

2.2.4 Example Applications and 

Vendors..................................... 31

2.2.5 Strengths and Limitations.......... 32

2.2.6 References................................. 33

2.3 Ultraviolet Differential Optical 

Absorption Spectrometer....................... 35

2.3.1 Basic Operation......................... 35

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

2.4 Differential Absorption Light Detection

And Ranging Systems........................... 47

2.4.1 Basic Operation......................... 48

2.4.2 Pollutants and Relative Levels 

That Can Be Measured .............. 54

2.4.3 Typical QA/QC ......................... 56

2.4.4 Example Applications and 

Vendors..................................... 57

2.4.5 Strengths and Limitations.......... 59

2.4.6 References................................. 61

2.5 Thermal Infrared Cameras..................... 64

2.5.1 Basic Operation......................... 65

2.5.2 Pollutants and Relative Levels 

That Can Be Detected................ 67

2.53 Typical QA/QC ......................... 67

2.5.4 Example Applications and 

Vendors..................................... 68

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 ......................... 82

2.6.4 Example Applications and 

Vendors..................................... 82

2.6.5 Strengths and Limitations.......... 84

2.6.6 References................................. 85

3.0 Measurements Applicable to Emissions Flux 1 08/19/11

3.1 Radial Plume Mapping: Other Test

Method 10............................................... 3

3.1.1 General Description of 

Approach .................................... 3
Page Revision Date

3.1.2 Verification/Validation 

Studies....................................... 11

3.1.3 Typical QA/QC ......................... 13

3.1.4 Siting Concerns......................... 14

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.......... 22

3.2.6 References................................. 23

3.3 Solar Occultation Flux Measurement .... 25

3.3.1 General Description of 

Approach................................... 26

3.3.2 Verification/Validation 

Studies....................................... 31

3.3.3 Typical QA/QC ......................... 33

3.3.4 Siting Concerns......................... 36

3.3.5 Strengths and Limitations ......... 37

3.3.6 References................................. 38

3.4 Tracer Gas Correlation.......................... 39

3.4.1 General Description of 

Approach .................................. 39

3.4.2 Verification/Validation 

Studies....................................... 41

3.4.3 Typical QA/QC ......................... 44

3.4.4 Siting Concerns......................... 44

3.4.5 Strengths and Limitations.......... 45

3.4.6 References................................. 45

3.5 Backward Lagrangian Stochastic InverseDispersion model .................................. 47

3.5.1 General Description of 

Approach................................... 47
Page Revision Date

3.5.2 Verification/Validation 

Studies ...................................... 51

3.5.3 Typical QA/QC ......................... 54

3.5.4 Siting Concerns......................... 55

3.5.5 Strengths and Limitations.......... 56

3.5.6 References................................. 57

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.............................................. 12

4.5 References ............................................ 13

5.0 Data Validation 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......................... 9
5.3.1 Validation of Primary and

Ancillary Measurements.............. 9

5.3.2 Final Validation and Evaluation

of Measurements for

Data Users................................. 11

5.4 References ............................................ 13
Figures

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 1075o 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 Bistatic 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 UVDOAS 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 2.5
Figures

Number Title Section

2-23 Flare Detection by Thermal IR Camera 2.5

2-24

The essential components of any CRDS experimental setup 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 SO2 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 3.5 
Figures

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 5.3 
Tables

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 2.5 
Tables

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 SO2 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 3.5 
 List of Acronyms

Acronym Description

μg/m3 micrograms per cubic meter

Μm 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 radiation
Acronym

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 nitrous oxide

NO nitric oxide

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 mapping
List 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 microseconds
ORS Handbook, Section 1.0

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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 technologies 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 

quality 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 emissions 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 literature 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 these 

technologies and their applications. This document is intended to aid readers in understanding

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

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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 

measurement 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 Handbook describes the more prevalent and technologically demonstrated open-path, cellbased, and point measurement technologies used to make remote measurements This 

Handbook provides 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 monitoring 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 sources 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

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Applications of ORS are relatively 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 technologies and approaches as those who are. This Handbook

will discuss the practical uses and operation of the instruments used to make remote 

measurements and the data generated. Some of the more technical information have been 

simplified, and illustrations 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 Points 

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

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Figure 1-1. Types and sources of air pollutants

EPA defines emission 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 areas, 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 vents in a chemical plant are distillation columns and 

oxidation vents, for example. 
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 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 exhaust. 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 sources, including fugitive emissions, are those emissions that 

could not reasonably pass through a stack, chimney, vent, or other ducted line that could easily 

be characterized with conventional point source or stack sampling methods. Measurement of 
ORS Handbook, Section 1.1

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emissions from these sources traditionally requires 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. Fugitive 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 

through 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 measurements 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 samples 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. 
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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 pipelines, 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., hydrogen 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 Opportunites
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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 within 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 factors’ determination are 

well-established 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 ponds 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 techniques 
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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 Focus: 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 regulatory horizon under the Clean 

Air Act. These four groups are: 

• criteria pollutants,

• hazardous air pollutants or HAP,

• greenhouse gases, and

• ozone-depleting substances.

Gaseous Criteria pollutants are those inorganic pollutants (e.g., carbon monoxide, sulfur 

oxides, nitrogen oxides and ozone) 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 include industrial processes (such 

as metals processing and chemical manufacturing), residential wood burning, and natural 

sources such as forest fires.

Sulfur oxides (SOx) are colorless gases formed when fuel containing sulfur, such as coal 

and oil, is burned and when gasoline is extracted from oil or metals are extracted from ore. 

Sulfur dioxide (SO2) is the criteria pollutant that is the indicator of SOx concentrations in 

the ambient air. Other sources of SO2 are industrial facilities that derive their products 

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 acid 
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plants and metal processing facilities. Also, locomotives, large ships, and some non-road 

diesel equipment currently burn high sulfur fuel and release SO2 emissions into the air in 

large quantities.

Nitrogen oxides (NOx), is the generic term used to describe the sum of nitric oxide (NO), 

nitrogen dioxide (NO2), which is a criteria pollutant, and other oxides of nitrogen. NOx is a 

group of highly reactive gases that play a major role in the formation of ozone. NOx form 

when fuel is burned at high temperatures, as in a combustion process. The primary sources 

of NOx are motor vehicles, electric utilities, and other industrial, commercial, and 

residential sources that burn fuels.

Ozone (O3) 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 NOx and VOCs in the presence of 

heat and sunlight. O3 has the same chemical structure whether it occurs miles above the 

earth or at ground level and can be “useful” or “damaging” to the environment depending 

on its location in the atmosphere. Useful O3 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 O3 is considered damaging or 

destructive. O3 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 found close to the emission sources. 

Examples of air toxics 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 toxics originate from man-made sources including mobile 

sources (e.g., cars, trucks, construction equipment), stationary sources (e.g., factories, 
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refineries, power plants), and indoor sources (e.g., some buildings materials and cleaning 

solvents).5

Greenhouse 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 life 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 caused by greenhouse gases is called 

“global warming” and contributes to climate change on the planet. Carbon dioxide (CO2), 

methane, particulate matter (especially black carbon or soot), NOx, fluorinated compounds, and 

O3 are some of the compounds contributing to global warming. CO2 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 O3 gas that protects living organisms from harmful ultraviolet-B radiation from the sun 

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.
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• Passive. Open-path ORS techniques receive light energy from pollutants activated by 

an external uncontrolled source such as combustion gases (e.g., Passive FTIR radiation) 

or the 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. Measurement methods do not have to be optically based. However, optical 

technologies 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 twodimensional plane) 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 passed 

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 pathintegrated concentration (e.g., parts per billion (ppb) benzene per 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 flux 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 applications that use ORS technologies in the open-path mode 

include line of sight monitoring, Radial Plume Mapping8 (RPM), and Backward Lagrangian 

Stochastic (bLS) Modeling.
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Figure 1-4. Diagram for Monostatic Optical Remote Sensing

Passive. Open-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 emit 

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 CO2, CO, and vapor phase 

organic material resulting from products of incomplete combustion. 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 radiance measurements using PFTIR is the temperature 
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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 measured is necessary to quantify the compounds of interest. 

Backscatter. Open-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 vary 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. Optical monitoring approaches use optical techniques to measure gas samples pumped 

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 ratio 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 the 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 from 
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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 

photo-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 of this 

Handbook. The advantages of ORS applications should be determined 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 point 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 weather conditions (e.g., heavy rain, fog, dust), and

• Dependent on chemical interferences (e.g., water, oxygen, O3 and CO2).

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 emissions 
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measurements are typically straightforward, a more critical evaluation of the ORS method 

application, the emission mechanism 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 applications 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 results 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 planned 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 data 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 measurements.
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• 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 concentrations

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 Handbook 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 Section 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 listed 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 provides an overview of the remote measurement detection technologies that are 

currently available for remotely measuring pollutant emissions concentrations. Included in this 

overview are discussions of FTIR, Tunable Diode Laser (TDL), UV-DOAS, and LIDAR 
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spectroscopy 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 Section 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 descriptions briefly summarize the activity and explains how the application is 

verified or validated in field tests, and details typical QA/QC 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 using 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 

application. 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.
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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”11 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 shown 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).

12 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 of the QS to technical activityspecific 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 2106.011, the EPA requires that environmental 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 directly 
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from measurements, produced from models, or compiled 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 

performance 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

PLANNING ASSESSMENT

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 System
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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 commonsense, graded approach to ensure that the level of detail in planning 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 alternative 

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 

collection 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 scientific studies, the DQO 

Process is particularly designed to address problems that require making a decision between 
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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 when, 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 project 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 specific 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. Monitoring 

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 state/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 application is different (i.e., different DQOs). The following 

prescribed objectives should be decided and discussed within the QS. 
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• Measurement. Type of measurements and/or the parameter needed to be collected.

• Method. The method is different from the measurement 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 (μg/m3

) 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 the 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 measurement 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. 
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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 applications 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 expand 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)
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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 Limitations

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 CO2 interference5,14,15

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 

oxides6

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 nitrogen1,2,3

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 units are light weight, typically under 75 Kg, 

and relatively easy to transport and setup

Detects only one compound per laser.

Less expensive than alternative technologies Fewer measurable compounds and limited 

sensitivity.

Wide linear response resulting in measurements 

from 0.1 to 1,000 ppm for many target 

compounds.

Quantitation limited to compounds with overtone 

absorbencies in the near- and mid-IR range
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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 field use with low maintenance, 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 UVvisible absorption structures making them 

undetectable by UV-DOAS 

Absolute measurement system – off line or 

laboratory gas calibrations can be performed

Accurate reference spectra needed – reference 

spectra can be a source of error 

Typical path length is 500 m, Longer 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 applications

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 applicability of 

wavelengths selected for measurement based on 

temperature and pressure variation in target 

absorption.

Collects backscattered light without the use retroreflectorsSufficient aerosol or molecular material must be in 

the atmosphere to create sufficient backscatter
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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 number 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 the 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 hazardous 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 realtime 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.
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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, depending 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 components 

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 sidesloping 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. 
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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.

Decreased 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 convection 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 calculate 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 choose 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 QbLS estimates to be 

inaccurate

Free downloadable software available online Very experienced personnel required to generate 

high-quality data from the bLS model
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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 long 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 factors 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 each 

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 Technologies

TECHNOLOGY

TYPICAL 

RANGE OF 

APPLICATION

SENSITIVITY 

(DETECTION 

LIMITS)

ACCURACY PRECISION

(RSD)

FTIR14 0-50 ppm < 1.0 ppmm 0-30% < 1.0%

TDL15,16 0-800 ppm < 15.0 ppmm 0-40% < 5.0%

UV-DOAS17,18 0-1000 ppb < 0.10 ppbm 0-20% < 5.0%

LIDAR 0-120 ppb low-ppb Range 0-15% < 1.5%

IR Camera N/A N/A Qualitative

Only

Qualitative Only
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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 stakeholders. This document will be updated, at 

the discretion of the EPA, depending on the availability of resources.

This document does not contain EPA policy information; it is strictly an information document. 

It is envisioned that in later editions, new ORS technologies and techniques will be described in

this document. 
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1.8 References 

1. U.S. DOE/ORNL. 1999. NARSTO Measurement Methods Compendium. 

<http://narsto.ornl.gov/Compendium/methods/DOAS.htm>. 20 May.

2. U.S. EPA. 2010 Sources of Pollutants in the Ambient Air – Stationary Sources.

<http://www.epa.gov/apti/course422/ap3b.html>. 15 July.

3. Earthworks. Sources 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. Hazardous Air Pollutants. 2010.

<http://www.epa.gov/apti/course422/ap6.html>. 29 January.

6. U.S. EPA. Welfare 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 emissions 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. US EPA. 2010. Implementation of Quality Assurance Requirements for Organizations 

Receiving EPA Financial Assistance. http://www.epa.gov/ogd/grants/assurance.htm. 19 

January. 
ORS Handbook, Section 1.8

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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-Path 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. 2001. Environmental Technology Verification Report: SafEye 420 Ultraviolet Open-Path Monitor. Report prepared by Spectrex, Inc. Battelle.
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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) with the air’s components. Openpath 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., TDL) 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., FTIR) 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 plumes 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 how each of several prominent ORS technologies operates.
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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 industrial 

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 emission 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 focus 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 processing 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 spectrometer. Infrared light 
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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. This 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 distances 

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 single beam infrared frequency spectrum using 

IR Detector

Moving Mirror

Fixed Mirror

Beamsplitter

IR Source

Laser

Interferometer

Reflecting Mirror

Open-path
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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 corrected transmittance spectrum. This transmittance spectrum can be 

converted to absorbance by taking the negative log10 of the data points.2

The vibrational frequencies of all the infrared absorbing molecules in the IR beam path are 

captured in the IR spectrum. 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 molecular 

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, CO2, 

CO, NO, NO2, 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 1075o 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 to 
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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 

1.1,2,3

A = εcl 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 In 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 concentration 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 absorption 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 passes 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 sensitive the measurement becomes because the IR beam 

has a greater chance of interacting with the absorbing compounds. The pollutants measured in 
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this type of arrangement are usually at higher concentrations than those found in open-path FTIR 

measurements. Typical 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 both receiver and transmitter, which can be timeconsuming 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 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 source 
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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 divergence that would result from a flat mirror. Also, the retroreflector 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 Reflector
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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 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. 

This single, large retro-reflector does not have the divergence reversal properties of the cornercube 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 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.

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-reflecting 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 protect the sensitive reflecting surfaces from exposure to inclement 

weather conditions.
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Figur2-6. Typical Telescopic Figure 2-7. Retro Reflecting Mirror Used to 

FTIR Transmitting and Detection Unit Reflect the IR Beam Back to Detection 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 This 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 Dividing the final ppb 

(meters) result by the total optical path length gives the path integrated gas concentration in ppb.
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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)
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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. 

Detection 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 their 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 order 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 limits 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 the date and 

description of maintenance performed. Calibration checks should be performed after major 

service and regularly during analysis.11,12 QA 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.
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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 and 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 during 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 

determine 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 concentrations 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 calibration spectral features.

Tables of absorbance coefficients are available, and standard reference spectra for numerous 

compounds can be purchased. Suppliers of reference spectra include Pacific Northwest National 

Laboratory, which continues to develop the Northwest Infrared (NWIR) spectral library of 

quantitative infrared absorption spectra13 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 calibration 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.
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Figure 2-8. Calibration Plot of Absorbance vs. Concentration

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 Prior 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 instrument’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 identifying stray light as either background or instrumentrelated. 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/ppb

Absorbance
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indicate that the instrument is operating within the acceptable criteria range as specified in the 

method 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 field 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 nitrous 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 relative 

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 The 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.15
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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, bLS modeling 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. dollars,

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/
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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. Suppliers of 

reference spectra include Pacific Northwest National Laboratory, which continues to develop the 

NWIR spectral library of quantitative infrared absorption spectra13 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 molecules. 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 CO2 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 nitrogen, 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 path 
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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 discrete 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 

CO2 interference5,16,17

Diatomoic Molecules Not applicable to homonuclear diatomic gases such as 

chlorine, oxygen, and nitrogen1,2,3

IR Wavelength Range

Weak IR absorption features for many inorganic 

molecules such as sulfur dioxide and nitrogen oxides6

Infrared beam has a limited range and may not be 

sensitive enough to meet ambient data quality 

objectives.
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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 plume 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. 2nd ed. New York, NY: John Wiley & Sons.

3. Laidler, J. K. 1995. Physical Chemistry. 2nd 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 21st Century, CLUIN. http://www.clu-in.org/programs/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.
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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. EPA600/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 Quality, 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. Saarinen, 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.
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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 emits 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 (OPTDL) 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 measurement 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 of 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 developed 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 laser 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 measurement 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’s 
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path, the OP-TDL can determine the concentration present 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 wavelength 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 

eliminates 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 Spectroscopy 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 

electromagnetic 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 concentration 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. 
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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. Each 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 using the principals of Beer-Lambert Law as described in 

Equation 1. 

A = εcl 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 fact 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 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 both receiver and transmitter, which can be time-consuming for mobile 

systems.11
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Figure 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 source 

regardless of orientation. A diagram of two monostatic configurations is shown in Figure 2-10.

Figure 2-10. TDL Monostatic Configuration
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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 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 2-11. 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 requires 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.
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This single, large retro-reflector does not have the divergence reversal properties of the cornercube 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 align 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 tuning 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 that 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 measured 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 the 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 fiber 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 QC-Lasers are available at a cost up to $100,000. 
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Table 2-6. Example List of Gaseous Compounds Measured 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.6
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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 lasers 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 nearand mid-IR regions. However, the availability 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 Compounds

InGaAsP 1200-2000 CO, CO2, NO, CH4, C2H2, HF, HCl, HBr, HI, HCN, 

NH3, H2CO, PH3, H2O

Antimonide* 2000-4000 CO, CO2, NO, N2O, CH4, HCl, HBr, H2CO

*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 NO2

AlGaAs 750-1000 O2, NH3

Vertical Cavity 650-1680 H2O, C2H2, HF, H2S, O2, H2O, NH3

Antimonide* 2000-4000 CO, CO2, NO, N2O, CH4, HCl, HBr, H2CO
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Quantum Cascade** 4000-12000 H2O, CO, CO2, NO, NO2, N2O, SO2, C2H2, HCN, 

NH3, PH3, 03

Lead-salt** 3000-30000 H2O, CO, CO2, NO, NO2, N2O, SO2, CH4, C2H2, 

HCl, HBr, HCN, NH3, H2CO, PH3, O3

*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, interference 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 interference 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 calibration 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 that 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. Field 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 the 
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measurements with and without the gas cell. An example of a calibration curve for methane is 

provided as Figure 2-12. 

Figure 2-12. Calibration 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 basis6 because a 

small drift in instrument response is more significant at lower measured concentrations. 
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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 the 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 monitoring 

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. 
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Table 2-10. Near-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.unisearchassociates.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 laser10, 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-powered 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-available 
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TDL instrumentation allows for optical beam path flexibility, as well as automated real-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 equipment 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, 16th Annual International Emission 

Inventory Conference Emission Inventories: “Integration, Analysis, and 

Communications” Raleigh. 

<http://www.epa.gov/ttn/chief/conference/ei16/session12/harris.pdf>. May 14-17.
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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: Process 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. 1987. 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 Gamble, 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 

Techniques 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. 
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2.3 Ultraviolet Differential Optical Absorption Spectroscopy

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 ideal 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 with many of the compounds that can be measured. The UVDOAS is reported2 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 Operation

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 ranges produced by UV-DOAS instrumentation include the 

rotational and vibrational transitions caused by near-IR light, the typical application of UVDOAS restricts the UV light to a wavelength range of 245 to 380 nanometers. Due to the range 

of excitations measured, molecular absorption 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.3
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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. 

A = εcl 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 concentration 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 Internet 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 confirm its identification and quantification.7 Some technologies use specific gas 
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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-DOAS instruments can practically measure path lengths up to 500 meters. 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 (N2O) in the presence of other nitrogen oxides 

(NO, NO2), absorption from NO and NO2 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

Additionally, 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 is installed.5

 
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UV-DOAS Field Implementation

There are a number of instrumental configurations for open-path UV-DOAS instruments. UVDOAS 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 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 UV-DOAS equipment projects the 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. The receiver 

and transmitter must be accurately aligned to optimize signal intensity.9

Figure 2-14. Bistatic 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 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 2-15. Such a combination of mirrors is 

Light Source 

TARGET 

POLLUTANT

Attenuated 

Light Receiving 

Optics

Detector Spectrometer Fiber-Optic 

Cable

Computer
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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. Basic Setup Used to Make Monostatic Open-path UV-DOAS 

Measurements
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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 Part 58 provide basic guidance for criteria pollutants using open-path 

measurements. In addition, EPA QA Handbook Volume II12 has siting requirements and other 

useful information on using UVDOAS in ambient/background monitoring situations. 

Passive UV-DOAS

A third configuration is known as passive UV-DOAS. Passive UV-DOAS uses ambient lighting, 

such as sunlight, as its light source and does not require a transmitting telescope. Passive UVDOAS instruments can be fitted into balloons and used to measure concentrations of pollutant 

gases at differing 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 such 
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as the deployment 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 (ppb)

Path 

Length (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 

measurement 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 concentrations should 

also be documented. 
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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 by 

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 stabilize, 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 absorption 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 part 58 (Ambient Air 

Quality Surveillance). Often these values are determined by performing calibration checks 
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against known gas standards and verifying the MDL provided by the instrument manufacturer. 

Instrument manufacturers provide their own instructions 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 range 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 fenceline 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. UVDOAS 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 2-15. 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.2
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Vendors

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 Method” for the measurement of 

SO2, NO2 and O3 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 UVDOAS. Some UV-DOAS can provide concentration data for up to three compounds 

simultaneously. In the field, the instrument is portable15 and can be deployed long-term for 

continuous remote in situ monitoring.14 UV-DOAS quantifies compounds more successfully that 

have strong UV light and weak infrared absorption characteristics. Since NO2 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.
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Table 2-17. 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 $60,000 - $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 2-18. 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 21st Century 

(21M2

). Open Path Technologies: Measurement at a Distance UV-DOAS. 

http://www.clu-in.org/download/misc/21M2flier.pdf. Washington, DC. 
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3. Finlaysson-Pitts, B.J., J.N. Pitts. 1986. Differential Optical Absorption Spectroscopy, 

from Atmospheric Chemistry; 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. 2006. 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 Pollution 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. 2nd International Workshop on Remote Sensing of Emissions: New 

Technologies and Recent Work.
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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 atmospheric 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 forestry. 

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)

• Doppler 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 directed 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 from the laser source, the ratio of the 
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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 detector.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 compared 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 wavelength 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 wavelengths. 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.6
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A small portion of the laser output is directed 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 into 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 particles. 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. Requirements 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 (narrow 

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 pollutants 

being measured.1 Advances in tunable laser technology have allowed simultaneous multiwavelength, hence multi-component, measurements to be made.8
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Figure 2-17. Beam Path and Major Components of DIAL Unit.

The difference in the backscattered intensity 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 relatively 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 calculated 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 UNIT
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The majority of atmospheric sensing DIAL systems operate in a monostatic mode where light is 

emitted and received at the same location. A monostatic mode may be deployed in two subconfigurations; 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 21st Century [21M2

] 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 the
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detector to obtain an emissions concentration profile.1, 5 Figure 2-19 illustrates a bistatic DIAL

system.

Adapted from “Measurement and Monitoring Technologies for the 21st Century [21M2] Open Path 

Technologies: Measurement at a Distance LIDAR.” Avalable: http://www.cluin.org/programs/21m2/openpath/lidar/

Figure 2-19. Bistatic Configuration for DIAL

The signal receiving and detection system consists of primary optics, a spectral filtering 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 others.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 such 
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as depolarization techniques.

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 

photodiodes.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 on 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 emission 

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 plume
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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 scans 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 three 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 the 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 reported 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).
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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 the 

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 absorption path lengths. 

Table 2-20. Approximate Detection Limits for DIAL6

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.6
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DIAL systems can also be used in a mode like a fugitive source monitor. 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 literature. 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.

Instrument 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 different 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 system, known as a wavemeter, to identify and dispose of any data produced from 
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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 Applications 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 measurement, 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 CO2 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. 
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Specific field implementation examples include studies completed 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 While 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 short 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 developed to detect 

chemical warfare agents as well as natural gas pipeline leaks from airplane mounted platforms. 6

Figure 2-21. Mobile DIAL Unit
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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. DIAL 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. 
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Table 2-23: 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 retroreflectors

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 species 
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Table 2-24: 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/rsgal/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, Emission Losses, and Potential 

Policy Gaps – 2006 International Workshop. 

<http://www.epa.gov/ttn/chief/efpac/documents/wrkshop_fugvocemissions.pdf>
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6. U.S. EPA. 2009. Measurement and Monitoring Technologies for the 21st Century 

(21M2). 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 

correlation 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 Association 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 Province 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 

measurement 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: 

10.1117/12.283904.
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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://www.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>.
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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 IR 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. 

IR cameras 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 pinpoint 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 radiance 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 variety of applications3, 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 be 

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 hazardous 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 technologies inability to identify individual chemicals in a complex 

gas leak mixture. 
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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 absorption detection typically occurs in the 3 – 5 

μm wavelength range4, 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 present 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 Tank14

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 different 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 same areas can be 

scanned repeatedly to improving the likelihood leaks are detected.5
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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 detection 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, therefore, 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 feed 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 The NUC mode uses the camera’s internal 

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 claims 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 another 
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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 point13, 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.5
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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 temperature15,16) should also be entered into a log sheet to further document the leak.5 

For leak detection and repair (LDAR) applications, 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 GPS 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 federal 

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 applications for thermal IR camera. 
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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 approximately $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.com
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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 methods15

Leak assessment can be done without interruption 

to plant operations2,3

Cost-effective compared to traditional leak 

detection methods.2,3

Qualitative Results Accurately assess the size of each leak15

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 technology3

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.15
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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 3rd 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.cluin.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 Infrared 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 SF6. Presented to the 2009 Workshop on SF6 Emission Reduction 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 Thermal Imaging Technology. Presentation FLIR 

Petrotherm 2009 Conference (February 24-25).
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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.
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2.6 Cavity Ring-Down Spectroscopy

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 highfinesse 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 ringdown) cavity yields much longer effective sample path lengths for greater detection sensitivity.

In CRDS applications, 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 are 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. Each band is 
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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 absorbed 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.

A = εcl 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 CRDS 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 CRDS 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 collection 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 technologies. 

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.
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2. High-speed detectors are employed that can 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 using 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 applications. 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 light 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 the 
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cavity while also being sufficiently narrower 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 Figure 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 cavity 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 smoothed 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 directly 

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 absorption spectrum for each analyte.

Figure 2-25. Schematic representation of the expected rate of decay.1
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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 number 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 source 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 absorbing 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 the 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 

bandwidth 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-
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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 produced 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 optimize 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 reflections and increase the signal-tonoise 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 cavity ring-down spectrometer.8
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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 depends 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 illustrates the difference between 

the signals received by either detector. 

Figure 2-28. Differences between photomultiplier tube (PMT) and chargecoupled 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); 
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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 offaxis 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 cavity 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 applications 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 does 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)

CH4 CW CRDS 1.65 1.5 x 10-8 52

C2H2 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

CO2 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 standard.
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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

NH3 CW CRDS c 1.5 ~ 4 x 10-9 19

NO CW CRDS d 5.2 5 x 10-8 0.7

NO2 CW CRDS c 0.410 7 x 10-9 0.4

NO3 CW CRDS c 0.662 1 x 10-9 0.002

N2O5 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

H2

18O 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, which 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 precision of ∆τ (or the precision of the number of round trips in the 

cavity (N).

1,4

( ) ( ) N

N

I m R R min min 1 1 ∆ = − ∆

≈ − τ

τ δ Eq. 3

Where: δIm = the minimum detectable change in absorbance

R = mirror reflectivity

∆τmin = minimum detectable change in ring-down time (i.e. precision of 

∆τ)

N = the number of round trips in the cavity

∆Nmin = the accuracy to which N can be measured
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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 must 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 

based 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 given 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 CRDS 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 monitoring into the following 

classes:2,8
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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 limitations 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 application 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.com
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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 energyabsorbing molecules. It can also be used as a quantitative tool to provide the concentration of 

many gas-phase molecules. A summary of strengths and limitations 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 range. 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 technological 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 specificity 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 realtime 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 pressure 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.
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Table 2-33. Summary Table of CRDS Strengths (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 pulsed 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

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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.” Chemical 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 HO2 radical.” Applied Physics B, 85: 383.

12. Pemberton, J.E., and R.L. Sobocinski; (1989). “Raman Spectroscopy with Helium–Neon 

Laser Excitation and Charge-Coupled 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. Winderlich, 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 (CO2 and CH4) 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.
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3.0 Measurements Applicable to Emissions Flux

Optical 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 strategy 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 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. 

Short-term flux measurement applications (e.g. DIAL, RPM, 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 diagnose 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 emissions plume to relate trends 

in monitoring data to emissions and emissions factors.1

 
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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 reductions 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 unducted sources.

Reference 

1 Hashmonay, Ram, Long Term Monitoring 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 2010
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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 O3

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 originally used to determine the average 

concentration of a compound of interest over a path of known length. The line-of-sight, or onepath, configuration provides an average of the compound of interest concentration per path 

length (i.e., ppmm). Open-path 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 

hot 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 profile. 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, the 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. RPM 
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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, such 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 objectives of the project and data quality indicators.

Horizontal RPM Algorithm

The HRPM approach provides horizontal differentiation to path-integrated measurements by 

optical remote sensing. This technique yields information on the two-dimensional distribution 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 equipment 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

k km m PIC K c 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 mth pixel. 
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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 data. 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 number 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.
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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 

configurations 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 configuration, 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 each 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 focused 
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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 is 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

j yj yj i

i

dr

B m r

SSE B m PIC

2

0

2

2

1

exp 2( , , )

πσ σ

σ Eq. 5

B = area under the on-dimensional Gaussian distribution 

ri = pathlength of the ith beam

my = mean (peak location)

σy = standard deviation of the jth Gaussian function

PICi = measured PIC value of the ith path 

The SSE function is minimized using 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

cos sin

2

1

exp 2 ,

y D z

y D

y D z

z

A r m r G A

σ

θ

σ

θ

πσ σ

σ Eq. 6

Where σy-1D is the standard deviation along the crosswind direction found in the 1D-SBFM; my1D is the peak location along the crosswind direction found in the 1D-SBFM procedure and A 

and σz are the unknown parameters to be retrieved in the second phase of the fitting 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. 
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If measurement equipment uses the three-beam setup (one at the ground level and the other two 

elevated), the 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 groundlevel beam path (length of vertical plane). Thus, if r1 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 2 (10 ) ,

z z

z

r

r

r r

r

A G A

σ

θ θ

π σ

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 vertical plane (pixels). Then, the algorithm 

integrates the values incorporating wind speed data at each height to calculate the flux.
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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 lineof-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 massequivalent 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. First, 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 1DRPM algorithms are processed with the concentration data to provide hot spots info or 

concentration downwind for the source. 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. 
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Figure 3-4. Examples 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 complexes3 using RPM methodologies are: OPFTIR, 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, some are more effective than others. The following 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 detection limits in the ppb range. The instrument setup is timeconsuming 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 CO2 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 to deploy2

. OP-TDL can 
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detect CO, CO2, NOx, ammonia, methane (CH4) 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 natural 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 technology,

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 technology, 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 two 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 of 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 campaign8, 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 studies7,8, investigators used OP-TDLAS 

and OP-FTIR to compared methane measurements obtained by both instruments and found 
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similar results. During the experiments, the two instruments were deployed side-by-side and 

aligned to an identical mirror. Methane concentration 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 study3

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 capture validation study3

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 measurement 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 the 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 the 
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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 difference 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 close 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 indicators 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 procedure 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 precision 

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 calculations, 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.

2
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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 pathintegrated 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 precipitation 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 

account 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 carried 

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 setup, 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 optimal results:

• HRPM methodology: Near 0 to 5 m/s

• VRPM methodology: 1 to 8 m/s
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• 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 can 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, capture 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 two 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. 
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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. ORS 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 Lasers 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 and 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 Emission 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 

Tunable 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, Research and Development, Work Assignment No. 0-025. March.
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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 meteorological 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, O3, SO2 and CH4.

1

 DIAL can also be used to measure O3

concentrations in the middle and high troposphere.

1

 Atmospheric temperature 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 absorption line of the 

target compound (λon) and one wavelength slightly off-line of the target compound to measure 

backscatter (λoff). 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, 20034

.

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” of 
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concentrations across a scanning plane. By combining this concentration contour with separately 

obtained wind speeds, a contaminant flux can be calculated for the measured compound3

, see 

Figure 3-7. 

Figure 3-6. Contour profile of SO2 concentration measured 2.1 km downwind of source, at Cement 

Works, by Environmental Measurements Group National Physical Laboratory, UK5

.

Basic DIAL algorithm to calculate backscatter

The number of photons backscattered correlates to the compound concentration. 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

t l a l Pτ λ τ r λ σ λ N r dr π Eq. 8

Where λl = wavelength, 

τt = transmission coefficient of the LIDAR transmitter optics, 

r = range interval, 
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τa(r, λl) = optical transmission of the atmosphere 

( )l

i σ π λ = backscatter cross section at the laser wavelength,

Ni(r) = number density of scattering 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

t l a r l a r s r N r dr

r

Pτ λ A τ λ τ λ ζ σ π λ Eq. 9

Where λs = 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

t l t s s a l a s r r r N r dr

r

Pτ λ Aτ λ Q λ τ λ τ λ ζ σ π λ Eq. 10

Where τr(λs) = transmission coefficient of the reception optics at λs

Q(λs) = quantum efficiency of the photomultiplier at wavelength λs 

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(λs)) combined with the gain of any amplifiers used. After some 

approximations, the analog detector version of equation 10 is:

( ) ( ) ( ) ( ) ( ) (R) ( )N (R) R

R

P A Q R R i

l

i τ t λl τ t λs λs τ a λl τ a λs ζ σ π λ δ 2

1 , , Eq. 11

Where R = range of the center of the scattering volume 

ΔR = R2 – R1 is the length of the range bin.1

 
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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 concentrations and emission rates. 

Verification of DIAL for Gas Species Measurements

DIAL has been validated in European studies10,11 for hydrocarbon emissions with calculated 

results ranging from ±3 to ±12 percent of the actual value.3 Two validation studies 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 located 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 SO2 concentrations in the vicinity of sour gas well test flares and track 

the SO2

 

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

 Often 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 account 
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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 SO2 mass emissions calculated 

from the DIAL measurements of the SO2 plume with SO2 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 relatively 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 measurements showed a difference of 11 percent, which is within the range of –18 to + 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 SO2 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)

SO2 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 340
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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 setup 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 DIAL 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 to 
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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 resolved 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. Introduction to Remote Sensing, 2nd 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.
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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 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.

11. 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>.

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.
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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 landfills, 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 capabilities 

and are applied in many new monitoring methods. SOF is a method where optical spectroscopic 

technologies are used to directly speciate and quantify the chemicals present in a gaseous 

emission plume using the sun as a light source.

Because the SOF method 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, including: ammonia, formaldehyde, VOCs, terpenes, vinyl chloride, CO, 

ethylene, ethylene oxide, hydrofluoric acid, (HF) hydrochloric acid, HCl, CH4, SO2, 

propane, propylene, and hydrocarbons up to C15.

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 SO2 (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 through 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 types 
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mostly consist of alkanes, alkenes, and some 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 regulatory 

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/cm2) 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.

Agricultural application studies have established that the SOF method has a detection limit 

for measured hydrocarbons of 0.3 mg/ m2,3

. Similarly, in applications pertaining to 

refineries and leak detection, studies conclude that a point source emission of measured 

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 complicated 

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 speed 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 tracker 
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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 shows the sun tracker and mirrors extending out the 

top of the vehicle to maintain orientation9 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, 
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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 is 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 the 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 

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.

A = εcl

Where: A = absorbance intensity

Eq. 1
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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 using Equation 13 below. Equation 13 is a

transmutation of the Beer-Lambert law where spectral line intensity is related to gas 

concentration.8

( ) ( ) ( ) 



 



 = ⋅ − ⋅ ⋅ − ⋅ −∑ ⋅ ⋅ i

m L R R Mie Mie i i Li I v I v v α L α L σ v conc

4 exp

Where:

Im(ν) = light intensity at each wave number ν after it has passed through the gas.

IL(ν) = light intensity at each wave number ν for the light source.

σi(ν) = absorption cross section (cm2/molecule) of the gas with index i.

Li = path length where the gas with refractive index i is present.

conci = concentration of the gas with refractive index i (molecules/m3).

αR, LR = Rayleigh scattering which occurs on particles and molecules with a size 

smaller than the wavelength of the light.

αMie, LMie = 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 the beginning and end of each measured spectrum determine the 

surface length represented by the spectrum. This 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. 13
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process 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 3-10. 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 over 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

Emission column x u dx

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. 14
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3.3.2 Verification/Validation Studies

Because DIAL measurements are a standard method of monitoring 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, Sweden 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 emissions 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 emissions 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 

per 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.
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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 (SF6)

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 SF6 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 Table 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; whereas 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 only 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 up 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 

SF6

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 

SF6 was determined to be 2.0 kg/hr.3,8

Time Emission 

SF6

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 ̊
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14:31 3.4 6.5 260 ̊

Average 3.0±1.1

Figure 3-11. Tracer gas/SOF experiment measuring SF6 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 conditions; 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 results is comprised of 

statistical and systematic error. Statistical error is described with a normal distribution as 
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this type of error accounts for the natural, stochastic behavior of atmospheric variability. 

Fransson and Mellqvist qualified this amount using Equation 15 below.

5

2 2 2 2

/

2

c S N u uH Statistical error = σ +σ +σ +σ θ +σ

Fransson and Mellqvist (2002) estimated the σS/ 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/ m2, which 

corresponded to an uncertainty of 0.3 percent to 6 percent for a total column 

measurement of 100 to 10 mg/ m2. They also estimated the relative uncertainty in the 

total column due to wind direction variability (σθ) to be about 1 percent to 7 percent for 

eight scans. Fransson and Mellqvist (2002) state that 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 (σuH) are 

discussed in more detail below.

Using Equation 15, Fransson and Mellqvist (2002) calculated the statistical error assuming 

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:

σc = Relative error in the absorption cross section (5-10 percent).

σS/N = Relative S/ N error and spectral interference effects.

σu = Relative uncertainty in the wind speed estimation. Typically 

15-20 percent for a single plume scan and 5-8 percent for 8 

scans.

σθ = Relative uncertainty in the total column due to the variability 

in the wind direction over a plume scan.

σuH = Relative uncertainty in the wind speed due to uncertainties in 

the estimation of the plume height. 

Eq. 15
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varies from 14 percent to 19 percent.5 To determine the total error for 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 σc σS/ N σu σuH σω

Statistical 

1σ error

Best Case

Strong plume 100 

mg/ m2,

90 ̊ traversing angle*

5% 0.3% 5% 12% 1% 14%

Worst Case Weak plume 10 mg/ m2

,

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 temperature 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 the 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.

Mellqvist (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 

predictability 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. It is clear 
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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 have 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 3-12. 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 conditions. In 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 25.0

Height (m)

Velocity (m/s)

Unstable

Stable

Neutral
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view of the sun. Moreover, the mobile path needs to be smooth and near perpendicular to the 

wind direction 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 crosssection 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 

roads 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 the 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 technical 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.
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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 Source 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, NH3, NO2 and SO2 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.FluxSense.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, 

Washington, 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 Occultation 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 of 
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VOCs, paper #1377 in proceeding of A& WMA’s 98th Annual Conference & 

Exhibition, Minneapolis, 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 Solar 

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.
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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 

spatial, 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. Tracer 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 roughness. 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 16 
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shows 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 T orS

w n dt

T orS

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 results 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

z d

k

u

u z Eq.17

Where: k is the von Karman constant and is equal to 0.4 and z0 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 accurately. 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 calculated from simultaneous measurements of the tracer and analyte gas 

far downwind from the plume emission source using Equation 18.

t

t m

m C

Q C Q ∆∆ = Eq. 18

Where: Qm is the analyte emission rate; Qt is the tracer gas release rate, ∆Cm is the 

concentration above the background of the analyte observed in the plume (plume – background 

concentration); and ∆Ct 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 field 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 obtain 
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one-point or multiple-point samples. 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 3-13 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 cell-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 retrieval 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 emission fluxes, utilizing CRDS and FTIR.

Tracer gas characterization using CRDS: Twin Bridges Recycling and Disposal Facility 

study2,3

Mobile plume tracer dilution measurements were taken from May 18 – 21, 2009, driving on 

Interstate 36 and East Main Street in Danville, IN. Acetylene (C2H2) 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 plume 
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sufficiently downwind of the CH4 source and C2H2 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 CH4 and C2H2 concentrations. The CRDS Picarro 

Model G1203 CH4-C2H2 analyzer used in this study is a self-contained, stand-alone unit that 

provides continuous measurements of C2H2 and CH4 concentrations, ambient temperature, and 

analyzer location (via high resolution GPS). The C2H2 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 CH4 emissions over a wide range of atmospheric 

and dispersion conditions.

Figure 3-14 Methane concentration verses acetylene concentration Tracer gas characterization 

using FTIR: Samuelsson et al., 20014
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FTIR was used to obtain time resolved concentration measurements of methane in the downwind 

plume of a landfill and N2O 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 

CH4 and N2O could 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 

pathlength, 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 normal transport van. The 

recorded spectra were analyzed by multiple-regression techniques, fitting synthetically derived 

calibration spectra of all present compounds. CH4 was analyzed in the wave number region at 

~2950 cm-1, and N2O around 2200 cm-1.

4

The methodology used to couple the concentration measurements to an actual emission is the 

Time Correlation Tracer method (Eq. 19). N2O was released in a controlled way from the 

methane emitting area by use of several point sources distributed over the landfill. N2O mixed 

with the emitted CH4 in the landfill plume, and the emission rate was derived by time resolved 

analysis of the CH4 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

CH Tracer C M

C M

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 obtained 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.

4
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3.4.3 Typical QA/QC

This section describes the QA/QC steps that 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 measurements 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 carefully 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 the 

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 Concerns

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 direction for the most part). Other concerns 

are associated with calculations of the emission fluxes when estimating surface roughness or 

assessing vertical and horizontal fluctuations. 
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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. An 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 transportation 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.
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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. Börjesson, B. Galle and B. Svensson. 2001. The Swedish landfill 

methane emission project. http://www.fluxsense.se/reports/Sardinia2001paper.pdf
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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 

facilities, 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 sensors). 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 requires 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 techniques 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 basic 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 simple 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 meter2per second 

(kg/m2

/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 prediction of the ratio 
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of concentration at M to the emission rate (C/Q)sim, the emission rate can be inferred as indicated 

in Equation 20, where Cb 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 Cb 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 3-15. 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 or 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, 

(C Q)

C C Q

SIM

− b =
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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 meteorological 

observations.1,2 In general, the bLS inverse-dispersion method of modeling emissions is costeffective, requires only a single field measurement of C, and, under the conditions described in 

this section, generates emission rates with accuracy adequate 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 (x0, y0) where particles impact the ground, and vertical “touchdown 

velocities” at impact w0. From the set of trajectories, Equation 21 is used to calculate (C/Q)sim by 

summing the reciprocal of w0 for touchdowns occurring within the source boundary.3

 ( ) = ∑

w n C Q sim

0

1 2 Eq. 21

The variable n is the total number of computational particles released 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 conveniently 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 (Cb and C),

• wind statistics (e.g. wind speed and wind direction),

• roughness of the surface (zo), and
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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 timeindependent, so the input data must be values averaged over a period of time, typically 15-30 

minutes. This averaging 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 and 
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pressure data are needed to convert to absolute concentration (i.e. g/m3), which is used to 

determine the mass emission rate. The bLS calculation itself produces an emission rate (flux), 

QbLS, with units of kg/m2

/s (for example), but sometimes the emission rate is multiplied by the 

source area and QbLS 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 QbLS 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 QbLS was 

compared with the known Q. The study objectives were the following: 1) to quantify the 

accuracy and uncertainty in QbLS 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 large clover field. From a meteorological perspective, the site 

was nearly ideal—wind conditions were uniform, temperatures were not high enough to cause 
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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 meteorological 

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). 
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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 Cb was 

periodically measured at 1.95 ppm. This 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 into 15-, 30-, or 120-minute values. To convert concentrations from ppm to 

absolute concentration in g/m3, 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 

approximately 2 m above ground and was used to determine values of u*, L, z0, 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 associated 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 inversedispersion technique diagnosed the strength of a small ground-level source with small bias (mean 

value of QbLS/Q within 2 percent of unity). Poor results were excluded when 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 acceptable. The period-to-period variability in QbLS

was acceptably small (standard deviation of QbLS/Q is approximately 0.2). Using path-integrated 

values of C enhanced the accuracy with which QbLS 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 QbLS from an area source in an ideal 

surface layer problem.
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 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 concentration.

 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 statistics. 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 QbLS. 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 pertaining 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 be 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 instrumentation, 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 sophisticated 
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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 should 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 results 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 calculate (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 Obukhov stability length L, 

which quantifies the stability of the atmospheric surface layer; the surface roughness z0, 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 parameters 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 

downwind of a source, then emission rates cannot be determined.6 Upwind ambient 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 multiple 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 from 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. Many 
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agricultural and environmental source estimation problems potentially fit this category. These 

problems may include 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 been 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 

other 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 larger 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 limited 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 and 
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needed conversions 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 QbLS

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. 
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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 and 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 Measurement 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)
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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 measurements with the addition of sourcespecific 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 interference 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.
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4.1 Meteorological Measurements

Meteorological conditions at the site of optical remote measurements are an important 

component of many of the ORS applications. Please note that in-depth description of 

meteorological measurements information can be found in Volume IV of EPA’s QA Handbook

series.1 Quality 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 vertical) 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 meteorological 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.
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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 gases. 

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 provide 
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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 measurement 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 are 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 allow 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.
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4.1.2 Horizontal Wind Speed and Direction

Compact weather stations6 can provide all-in-one measurement of temperature, horizontal wind 

speed, and horizontal wind direction at optical remote 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

Relative 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. RH 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. RH and dew point are measured with electrical hygrometer, chilled mirror, or 

psychometer instruments. Typical accuracy of this equipment is ± 0.5% RH.
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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 thermocouple or thermistor temperature sensor with a temperature 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 their 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 form 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 operating 
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sensors under centralized 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 compensation 

from internal-mounted temperature sensors. Triple redundancy ensures excellent long-term 

stability and measurement accuracy, even in the most demanding applications.7
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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 check of the location system. 
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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 

emissions 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 pollutant 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 particulate 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 

responsible 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 information 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 straightforward. 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 thorough 

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.
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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 treatment 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 temperature) 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 process 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 

concentration 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, mean 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 pollutant 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 treatment 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 

pollutant 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. 
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Simple emission factors 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 processspecific information improves emissions factors estimates. For example, a simple emission 

factor (kg of pollutant/ft2 of landfill surface 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 

landfill, 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 terms 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 episodes at different 

landfills. 
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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 sampling 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. Consequently, 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, relative 

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. 
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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 Press, 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.
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5.0 Data Validation and Verification

This section 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 evaluate 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 can 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 specifications 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 techniques 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 “independent” 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.
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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 general information in this section 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 sampling 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 

determine (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 included 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 O3 and sulfates, are generally produced over a period 

of time. Ozone forms by the interaction of VOCs and NOx under the right meteorological 

conditions when low wind speeds, variable wind directions, and relatively high temperatures 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 verified 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 described 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 process at each step in the hierarchy. 
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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 Watson2 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 field 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 

measurements. 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 interfering 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.
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• Level 2 data validation involves comparisons 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 screening 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 consistency 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 Flow
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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 data 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 inspection 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 components 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. The monitors, using TDL 
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technology, use a laser to provide radiation over a very narrow spectral 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 FTIR 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 configurations 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 data 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 they 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, 

optical 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. Optical 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:
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• Is the equipment operational? 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 inspections.

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, zero 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 _________________________________

Figure 5-3 Example of Optical Remote Measurement Visual Check List
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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 data 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 insure 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 maintenance 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 answer 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?

• Did 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?
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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 concentrations 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 production 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 measurements 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 typically 
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include onboard 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 indicated 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 minute acquisition period would normally 

not be expected. When the difference between two successive values exceeds a predetermined 

value, the data in question can be flagged for further evaluation. Screening is an iterative process 
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in which range checks and other screening criteria are revised as necessary based on experience. 

For example, an initial QA pass 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 segregate 

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 designed 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 

process 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 personnel with optical measurement 

expertise. Reasons for changes in the data resulting from the validation process should be 
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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 audit 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 always 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.
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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, 

accuracy, 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.