Abstract

Tunable laser spectroscopy (TLS) combined with mid-infrared imaging is a powerful tool for a sensitive and quantitative visualization of gas leaks. This work deals with standoff methane leak detection within 2 m by an interband cascade laser (3270 nm wavelength) and an infrared camera. The concept demonstrates visualization of methane leakage rates down to 2 ml/min by images and sequences at frame rates up to 125 Hz. The gas plume and leak can be localized and quantified within a single image by direct absorption spectroscopy (DAS). The HITRAN database allows a calibration-free, pixelwise determination of the concentration in ppm*m. The active optical imaging concept showed pixelwise sensitivities around 1 ppm*m.

© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. INTRODUCTION

Gas leak detection—generally and, in particular, methane leak detection—can be motivated by safety, environmental, legal, or economic aspects or in order to ensure the functionality of many products [13]. There exist numerous leak detection concepts and techniques depending on the gas and the leakage scenario. For instance, single point measurements schemes such as sniffing technology can be used close to the leak [4], or tunable laser spectroscopy (TLS) can be applied from a distance of several meters up to more than 100 m [5]. In particular, for small leaks (e.g.,  leak rates of few milliliter per minute), the exact leak localization can be a time-consuming task by single point measurements. Furthermore, leak detection as a manual task close to the leak can be a serious safety issue depending on the leakage gas. Due to convenience and also safety reasons, standoff gas detection including a spatial resolution is to be favored for gas leak detection. In other words, the visualization of invisible gas leaks as an image or video is a great wish for many applications.

A literature review shows several works regarding standoff TLS devices (as single point measurement or innovative raster scanning as single pixel camera [5,6]) as well as active (laser based illumination) and passive optical gas imaging systems for methane leak detection [79]. Commonly, optical gas imaging is referred to as OGI, which is also used in the following.

Unfortunately, standoff gas detection systems are generally hard to compare since there is no uniform method for assessing quantitative evaluation of gas leaks. The distance to the leak and leak rates often depend on the leakage scenario. The size and spatial concentration distribution of gas plumes extremely depend on environmental conditions. For this reason, the current state of the art given by literature and available devices (datasheets) show limitations in terms of an objective comparability of single quantities such as detectable leak rate (ml/min), gas concentration (ppm*m), leak localization, or selectivity (${{\rm CH}_4}$).

Consequently, a typical passive OGI system (GF320 by FLIR) and a typical TLS standoff device without spatial resolution (single point measurement device, Laser Methane Mini by Tokyo Gas) were tested in the leakage test configuration of this work. These reference systems represent the current state of the art. The devices are introduced in more detail in the setup and method section.

However, this work aims to combine the advantages of both systems, namely the selectivity and sensitivity of a TLS system (reported figure of merit concentration sensitivity between 1 and 10 ppm*m) as well as the spatial resolution of the camera system for an accurate leak localization and gas plume visualization. This will be realized by using the mid-infrared (MIR) spectral range in terms of a state of the art interband cascade laser (ICL) for TLS, camera technology for fast MIR imaging, and comparable strong absorption features of gases. We aim to demonstrate the spectral, spatial, and temporal resolution of a gas plume released by an artificial leak scenario. Therefore, the inspected area is illuminated by an ICL, and the diffuse reflected radiation is detected by a mid-wave infrared (MWIR) camera operating in the 3–5 µm regime. Many gases show comparable strong absorption features in this wavelength regime and could be addressed. However, methane (${{\rm CH}_4}$) is chosen as the target gas, since it is the main constitute of natural gas and one of the most requested and widespread applications for leak detection. The demonstration focus is set on short distances (approx. 2 m) and methane leak rates around 1 ml/min. An ICL (Nanoplus) operating around 3.3 µm is used to address a comparable strong methane absorption feature, and the backscattered light is detected by a fast MWIR camera (ImageIR 8320, Infratec). By means of TLS, a selective and calibration-free concentration estimation is possible and shown. Therefore, a brief theoretical background on direct absorption spectroscopy (DAS) is given in the next section.

2. THEORETICAL BACKGROUND (DAS)

Various modulation schemes in laser spectroscopy can be applied. For instance, TLS [10] can be applied in a wavelength modulation spectroscopy (WMS) setup using a lock-in technique to demodulate the signal at the second harmonic—known as WMS-2f concept. Another TLS concept is DAS, where the laser light intensity of the corresponding wavelength scan is directly detected and analyzed after passing the gas sample. In this work, direct tunable interband cascade laser absorption spectroscopy (dTICLAS or short DAS) is applied. The fundamental relation behind DAS is given by the Beer–Lambert law (BLL),

$$\begin{split}I(\lambda )& = {I_0}(\lambda ) \cdot\exp [{- \alpha (\lambda ) \cdot \chi \cdot L} ] + {I_{{\rm BG}}},\quad {\rm with}\\ \alpha (\lambda ) &= S\frac{P}{{{k_B}T}} \varphi ({\lambda ,{\lambda _0}} ),\end{split}$$
where $I(\lambda)$ is the wavelength-dependent transmitted intensity after passing a molecular gas sample of length ${L}$. Additionally, ${I_0}(\lambda)$ describes the laser radiation (in absence of gas absorption) and a not-further-specified (broadband) background is given by ${I_{{\rm BG}}}$. The term $\chi$ is concentration (volume mixing ratio of the gas). However, in remote or standoff detection, the concentration information is often expressed as the column density of the gas plume $c = \chi \; \cdot L$ in ppm*m (product of ppm and meter). In the following, the column density is simply named concentration ($c$ in ppm*m). The wavelength-dependent absorption coefficient of the gas $\alpha (\lambda)$ is consequently given in units of ${({{\rm ppm*m}})^{- 1}}$. The absorption coefficient $(\alpha)$ includes the absorption line strength (S) of the molecule and the rather complex line shape function ($\varphi$) with respect to molecular transitions wavelength (${\lambda _0}$) as shown in Figs. 1 and 2. This wavelength-dependent line shape function has further dependencies on pressure and temperature, as well as on gas-specific parameters (indirectly also on the concentration). More details can be found in the standard literature [11] or in spectral databases such as HITRAN [12]. The gas-specific parameters are documented in HITRAN and can be used straightforwardly in combination with the DAS concept. Therefore, a calibration-free analysis of the gas concentration is possible. In order to determine the concentration, different methods can be applied.
 figure: Fig. 1.

Fig. 1. Left, the graph shows the absorption lines of methane (${{\rm CH}_4}$) by the line intensity (S) in the near- and mid-infrared. Right, a typical tunable laser spectroscopy signal according to Beer–Lambert law is schematically illustrated by the laser without gas interaction (laser) and with gas interaction (BLL) as well as the background (BG) without illumination.

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 figure: Fig. 2.

Fig. 2. Left, the absorbance (A) as a function of the wavenumber ($1/{\lambda}$) is calculated by HITRAN parameters for 100 ppm*m at standard pressure and temperature (STP). Right, the linearity of the normalized absorption coefficient (${\alpha}$ in ppm*m) at the peak wavelength is illustrated as function of concentration. The normalization constant (${{\eta}_{{\rm peak}}}$) indicates an almost linear correlation (perfectly linear would be illustrated by unity).

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However, in this paper, the focus is set on an approach that allows a potentially fast analysis in order to process the large amount of camera data in a suitable time. The approach is based on the absorbance,

$$\begin{split}A(\lambda ) &= - \ln ({T(\lambda )} ) = - \ln \left({\frac{{I(\lambda ) - {I_{{\rm BG}}}}}{{{I_0}(\lambda )}}} \right) = \alpha (\lambda ) \cdot c\quad {\rm with}\\c &= \chi \cdot L.\end{split}$$
According to BLL, the absorbance (${A}$) can be either related to the negative natural logarithm of the transmission (${T}$) or to the product of absorption coefficient and concentration (${\alpha} \cdot {c}$). The intensities (${I}$, ${{I}_0}$) are assumed to be background corrected or background free (more details in the methods section below).

In case of the selected methane lines, the absorbance, as well as the peak absorbance, scales almost linearly with the concentration (Fig. 2). The concentration can be calculated in a rather simple way:

$$c = {\eta _{{\rm peak}}} \cdot {{\rm A}_{{\rm peak}}}.$$

The peak absorbance (${A_{{\rm peak}}} = \;A({\lambda _{{\rm peak}}})$) is given at the wavelength around ${3057.7}\;{{\rm cm}^{- 1}}$. The proportionality constant (${\eta _{{\rm peak}}}$) is a linear approximation of ($1/\;\alpha (\lambda)$) at the peak wavelength and is given by 201 ppm*m. The constant differs less than 1% from the linear assumption (theoretical value) between 1 and 10,000 ppm*m.

3. SETUP, METHODS, AND MEASUREMENTS

In the following, the used leak test setup as well as the above-mentioned reference systems of this experimental investigation are described. Furthermore, the key part of this work is described in more detail—the standoff methane leak detection by an ICL (Nanoplus) and by a synchronized MWIR camera (ImageIR 8320, Infratec).

A. Test Leak Setup

A key point of this setup is the realization of an artificial leak. The test leak (see Fig. 3) is realized by a drill of 0.3 mm centered within a shot-blasted aluminum plate (${10} \times {10}\;{\rm cm^2}$). The blasted surface ensures sufficient diffuse reflection of incident radiation (Lambert’s cosine law) in order to mimic a non-cooperative target of a real-world application close to the leak source. For instance, TLS systems such as the used reference device rely on the analysis of the backscattered infrared (IR) radiation from a non-cooperative target close to the leak source. The leakage gas is pure methane (100% ${{\rm CH}_4}$) and released through the leak (drill) by flow rates between 0.25 and 5 ml/min. A gas supply hose is connected at the back side of the aluminum plate. Flow rates and the leakage gas are set by a calibrated gas mixing station (HovaCAL digital 922 SP flow controller, IAS GmbH). This artificial leak setup allows comparability of different devices and techniques under laboratory conditions (atmospheric dynamics in the lab caused by ventilation or air conditioning are not explicitly controlled).

 figure: Fig. 3.

Fig. 3. Top, the graphic represents the artificial leak detection setup, including the main hardware components of the investigation (ICL by Nanoplus and IR camera by Infratec). State-of-the-art TLS-based single point measurement device as reference (Laser Methane Mini by Tokyo Gas), including the comparable small illumination point. Bottom, a typical image of a leak scenario of 5 ml/min is given by the second reference system of this investigation (GF320 by FLIR as passive OGI system). Additionally, the shot-blasted aluminum leak plate is shown and the diffuse reflection characteristics of a non-cooperative target according to Lambert’s cosine law are illustrated.

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B. Reference Measurement Systems

Two typical, widespread and commercially available, state-of-the-art measurement systems are used as reference for the presented artificial leak scenario. The Laser Methane mini (engineered by Tokyo Gas Engineering Solutions Corporation) is chosen as the TLS device for standoff detection by single point measurements. Furthermore, the passive OGI system GF320 by FLIR is tested. Both systems are described and tested in the following.

1. Laser Methane Mini by Tokyo Gas (TLS Device)

This device employs tunable diode laser absorption spectroscopy in backscattering configuration. The distance between the device and leak target is chosen around 2 m. This handheld device operates with a near-infrared (NIR) diode laser around 1650 nm. The built-in green pilot laser indicates the position of the NIR diode laser spot that is not visible. The diameter of the collimated IR laser beam was roughly estimated to a few millimeters by a NIR-detector card. The collimation is in line with the product specification that the device should also work over 100 m distance by using a reflect sheet. The concentration data in ppm*m were transferred by a Bluetooth interface and show a measurement rate of 2 Hz.

The results within the artificial leak detection setup—as described above—are illustrated by concentration time series in Fig. 4. The results indicate that leak detection for leakage rates below 5 ml/min is possible but limited due to the comparable strong concentration fluctuation level. In this investigation, the TLD device was mechanically fixed and not handheld. A linear correlation between leakage flow rate and concentration can be observed, but at most to the concentration fluctuation level. Therefore, identifying a small leak at this level is possible in a best case scenario, but it will be quite time consuming in order to distinguish between statistical concentration noise by the device and fluctuations generated by a leak. Furthermore, without a reference point (e.g., without leak or flow, 0 ml/min in Fig. 4), leak detection is not possible.

 figure: Fig. 4.

Fig. 4. State-of-the-art TLS single point measurement device as reference (Laser Methane Mini by Tokyo Gas) is tested in the artificial leak scenario. The graph illustrates the test results by concentration time series for various leakage rates between 0 and 10 ml/min.

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Note that the background concentration in ambient air without leakage flow is not neglectable. A background concentration is expected slightly above 10 ppm*m [ambient air concentration of methane (2 ppm) multiplied by the 2 times distance between device and leak]. The measured value is in the order of 20 ppm*m.

An explanation for the comparable high fluctuation level is the relatively small laser spot size close to the leak and the expected dynamics of the leakage gas plume. In particular for small leaks, potential fluctuation generated by the gas plume and systematic or statistical uncertainties in case of the reference device cannot be distinguished (cf. methane leakage flow rates of 0 and 1 ml/min in Fig. 4). That could be critical for the leak detection and localization of small leaks. Consequently, leaks with comparable small leakage rates are hard to locate since the dynamics of the gas plume are not sufficient or the laser spot is not perfectly placed for these small leaks. However, compared to real-world scenarios, the presented results are nearly a best case scenario. For a small leak, additional spatial information is needed to image the leak and its dynamical behavior.

2. GF320 by FLIR (Passive OGI Device)

This gas camera is a passive OGI system and can be described by a modified thermal camera. The concept is based on the identification of temperature differences (thermal radiation) between the gas plume and the background. The difference is generated by gas absorption features [13]. The modification means that the camera has an optical IR bandpass filter around 3200 to 3400 nm. Therefore, only gases are detected that show IR activity in this region. Additionally, the indium antimonide (InSb) quantum detector for the 3 to 5 µm spectral range and IR filter are cooled to cryogenic temperatures around 77 K in order to reduce thermal noise. The IR filter insures a certain selectivity to gases, but compared to TLS devices, passive OGI systems are not selective to specific gas components such as methane. For instance, the GF320 is able to detect methane but also other hydrocarbons (or other active components in the 3200 to 3400 nm region). Consequently, a methane concentration information in terms of ppm*m is hardly possible in case of real-world leak scenarios (e.g.,  natural gas), due to the missing selectivity. The GF320 is tested in the experiment as a “plug and play” device, and the information is given in terms of temperature values or saved as mp4 video sequences. A leak would be detected by a temperature contrast between leakage gas and the background. The gas camera is located at a distance of around 1 m in front of the test leak target (see Fig. 3).

A typical image obtained by the GF320 gas camera is shown in the bottom left of Fig. 3. Obviously, a gas plume of 5 ml/min cannot be directly observed within this image. The leakage can also not be identified by a real-time observation of the camera display or within the saved video sequences. As mentioned, this gas camera was intended to be used as plug and play device in order to give an impression of a common state of the art device. “Gas jets” of much higher flow rate around 20 to 200 ml/min could be poorly (more or less) identified at the camera display. Nevertheless, the experiment was not designed for such high leak rates, and, consequently, the GF320 is not investigated in more detail at this stage. A rudimentary test with a leakage gas of larger hydrocarbon molecules than methane and comparable flow rates showed that the gas camera works well for molecules with broader absorption features than methane. This shows that the selectivity to methane is not given by the passive OGI concept. In conclusion, the GF320 gas camera as plug and play device is not able to visualize the methane gas plume of a small leakage rate directly. By means of image processing, these small leak rates might be visible. However, the performance potential of this device by image processing is beyond the scope of this paper. Additional work for this characterization would be desirable since the literature focus is on much bigger leaks and not comparable to the following investigation of leaks.

3. Active OGI

The camera-based gas detection with an active laser illumination scheme is realized by a tunable ICL (monomode and continuous wave emitting) and a comparable fast MWIR camera. Fast means that the IR camera allows a frame rate around 200 Hz with the full detector format (${640} \times {512}$ pixels) and up to 5000 Hz with a reduced detector format. The ICL and the camera are placed around 2 m from the leak target under a small angle of around 20 deg. The emitted round beam profile with a divergent angle of around 10 deg is diffusely reflected from the surface of leak target and detected by the MWIR camera (Fig. 3). The spot diameter is around 7.5 cm and centered on the leak target (${10} \times {10}\;{\rm cm^2}$). The ICL is current modulated and emits an optical power of around 4 mW, depending on operation parameters, injection current, and laser temperature. However, only a small portion of this optical power is backreflected by the target. In particular, the backscattered laser light is only few percent above the background level in our configuration. However, this comparable poor signal to background is enough for an investigation. The backscattered laser intensity and the temperature values given by the IR camera (temperature calibration regime around room temperature) show a linear relation. Therefore, BLL can be applied, and an absolute concentration measurement using calibration-free DAS methods can be calculated as presented in the previous section. Of course a background correction is necessary. For simplicity, the background is estimated by pixels without laser illumination and subtracted pixelwise. Far more sophisticated background corrections would be possible, but they are not required at this stage. In real-world scenarios with (fast) changing backgrounds, the laser could be turned off for a small amount of time in order to estimate the background.

A concentration analysis method (also called on/off method in this work) will be given in the following. The on/off method describes a balanced current modulation concept that is particularly suitable for a straightforward and fast concentration calculation by image processing. The basic idea of this method is to calculate the concentration by the peak absorbance according to Eqs. (2) and (3). This means that the intensities ($I$ and $I_0$) need to be measured or estimated, respectively, at the peak wavelength $\;({\lambda = {\lambda _p}})$. In presence of methane, the laser intensity (${{ I}_0}$) cannot be measured or estimated directly at the peak wavelength. Therefore, the balanced current modulation method is required. The key term balanced can be linked to laser intensity. The laser ${I_0}({\lambda (i)})$ varies the intensity (${I_0}$) and the wavelength ($\lambda$) with the injection current ($i$). The balanced current modulation method is applied in the following scheme:

$${\rm without\,gas: }I({{\lambda _p}} ) - {I_{{\rm BG}}} = I({{\lambda _1}} ) + I({{\lambda _2}} ) - {I_{{\rm BG}}} ,$$
$${\rm with\,gas: }I({{\lambda _p}} ) - {I_{{\rm BG}}} \lt I({{\lambda _1}} ) + I({{\lambda _2}} ) - {I_{{\rm BG}}},$$
assuming that $\;I({{\lambda _1}}) \lt I({{\lambda _p}}) \lt \;I({{\lambda _2}})$ and that ${\lambda _1} \lt \;{\lambda _p} \lt \;{\lambda _2}$ such that ${\lambda _1},{\lambda _2}$ are “off” the absorption line. In other words, the laser intensity at the peak wavelength (“on”) needs to be adjusted according to ${I_0}({{\lambda _p}}) - {I_{{\rm BG}}} = \;I({{\lambda _1}}) + \;I({{\lambda _2}}) - {I_{{\rm BG}}}$. Practically, the concept is realized by a laser current pulse series as graphically illustrated in Fig. 5. The measured intensities at the specific wavelengths (${\lambda _1},{\lambda _p},{\lambda _2}$) are the averaged intensities during the corresponding pulse series and camera integration period, respectively.
 figure: Fig. 5.

Fig. 5. Top left, the plot schematically shows the laser current pulse series for the balanced current modulation scheme. Two laser currents (${i_1},{i_2}$) are applied to address two wavelengths (${\lambda _1},{\lambda _2}$), nearby (or “off”) the absorption line (right) and a further current pulse (${i_p}$) addresses the peak wavelength (${\lambda _p}$) of the absorption feature (“on-line”). The synchronized camera integration signal shows the applied image acquisition period of 250 Hz (bottom/left).

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 figure: Fig. 6.

Fig. 6. Top, two typical background corrected images (on and off the absorption line) are shown, which are used to calculate the transmission and concentration images (bottom) according to the Beer–Lambert law.

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By applying this concept, the concentration can be estimated by means of Eqs. (2) and (3):

$$\begin{split}c &= - {\eta _{{\rm peak}}} \cdot \ln (T) = - {\eta _{{\rm peak}}} \cdot \ln \left({\frac{{I({{\lambda _{{\rm peak}}}}) - {I_{{\rm BG}}}}}{{I({{\lambda _1}}) + I({{\lambda _2}}) - {I_{{\rm BG}}}}}}\right).\end{split}$$

This method allows rather simple image processing, and, therefore, an image with concentration information can be generated at comparable high frames rates.

Practically, two frames within the 125 Hz—on and off the absorption line—are generated (see the top of Fig. 6) by applying three different wavelengths (or three different injection currents as periodic pulse trains). By dividing the two frames—on and off the line—a transmission (${T}$) image can be easily obtained. Furthermore an image with concentration information can be also calculated straightforward (see the bottom of Fig. 6 bottom). The leakage gas plume, as well as the localization and quantification of the leak, in a typical leak scenario of 2 ml/min is easily possible within one shot (125 Hz). The frame rate of 125 Hz (or in frames per second, fps = 125) enables a fast leakage detection with the potential for further signal processing or data reduction. Even higher frame rates seem to be possible. The results and a discussion of the active OGI concept are shown in the following section.

4. RESULTS AND DISCUSSION

After introducing the setup and realization of active OGI by balanced current modulation (on/off method), the results are presented and discussed in this section. Typical images as part of image sequences with a frame rate of 125 fps (125 Hz) are shown in Fig. 7. The concentration images in false colors illustrate the leak scenario of 2 and 5 ml/min (methane leakage rate) in a quantitative manner at a specific point of time. The gas plume and the leak can be easily identified within a single image. Furthermore, horizontal and vertical cuts are presented in order to illustrate the concentration values close to the leak and in its surrounding in more detail. Further image processing allows data reduction (10 fps or 10 Hz) and noise reduction (see Fig. 8). The standard deviation image of the concentration within 1 s allows a representation of the leakage dynamics and sensitivity estimation of the system. The figures are explained and discussed in more detail by a comparison with the above-tested state-of-the-art reference systems. Namely, the TLS single point detection device Laser Methane Mini and passive OGI device GF320.

 figure: Fig. 7.

Fig. 7. Left, the methane leakage scenario for two different leak rates (2 and 5 ml/min) is illustrated by methane concentration images (in ppm*m) at a specific point of time (${t} = {1}\;{s}$). The leak origin and the leakage gas plume are visible within a single image (${250} \times {250}$ pixels). This image is part of a video sequence with an image acquisition rate of 125 frames per second (125 Hz). A horizontal and a vertical cut of the concentration image (white lines) show the concentration (ppm*m) with respect to pixel index (right).

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 figure: Fig. 8.

Fig. 8. Left, after data reduction by image processing (averaging), the concentration images (in ppm*m) at a specific point of time (${t} = {1}\;{s}$) are given within 0,1 s (10 fps or 10 Hz). Two different methane leakage scenarios of leak rates about 2 and 5 ml/min are illustrated (see Visualization 1 and Visualization 2). The leak origin and the leakage gas plume are clearly visible within a single image. Right, the standard deviation of the concentration within 1 s (1 Hz) represents the dynamics of the leakage plume (larger values or red regions) as well as the sensitivity of the concept (in ppm*m) in regions without gas plume dynamics (low values of blue pixel regions).

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In this context, the realized active OGI concept significantly shows remarkable achievements in terms of the detection, localization, and quantification of small leaks. For instance, the passive OGI as “plug and play” device (described above) is not able to detect methane leaks with leakage rates of 5 ml/min. For such small leaks, the single point TLS (NIR laser) device works as a leak detector only in a best case scenario. This means that if the laser spot is directly aligned on the leak, the device shows the behavior of the moving gas plume by concentration fluctuations. The higher the leakage rate, the higher the fluctuations (gas plume dynamics) that are observed. However, if the laser spot is few millimeters away from the leak, the probability is high to miss the leak and gas plume, respectively. Consequently, the leak remains unnoticed. Of course, the beam size could principally widened, but in this case the sensitivity could be not sufficient. To be more precise, it will be hard to distinguish between ambient air concentration and the lowered concentration increase by small leaks due to the beam widening (average concentration for small leaks due to widened beam tends towards ambient air concentration).

In this framework, the active OGI concept shows its remarkable performance capacities. An increase in sensitivity due to the combination of ICL and MWIR camera and the stronger absorption line in the MIR is expected and observed. In general, the pixelwise sensitivity below 1 ppm*m (cf. standard deviation of concentration within 1 s in Fig. 8) shows a better pixel sensitivity than the TLS device (ambient air concentration noise in Fig. 4)(Note that a single point detection device by MIR components would show significantly better sensitivities [14] according to the larger absorption line strength in a best case scenario, but the limited factor in terms of standoff leak detection is the spatial resolution). This fact combined with the spatial resolution of the camera enables a leak detection and localization at a high contrast (concentration difference between leak or gas plume and ambient air) within a single image. This is demonstrated for methane leakage rates of 2 and 5 ml/min within one image of an image sequence (125 Hz or 125 fps). By further image processing, the data can be reduced (e.g.,  10 Hz or 10 fps) and improved (signal to noise) as shown in Fig. 8. Furthermore, the ambient air concentration is in the order of 10 ppm*m (see. Fig. 7), which is in the range of the expected ambient air concentration of around 8 ppm*m.

The image sequences show an additional information potential for the leak detection, which has not been used so far. The spatiotemporal dynamics of the gas plume, shown in the Visualization 1 and Visualization 2, are illustrated by single images of concentration standard deviation (Fig. 8). These images show a first step for more sophisticated analysis tools to make use of the spatiotemporal dynamics of gas plumes for more sensitive and reliable leak detection and localization. This may pave the way toward automatic leak detection systems.

As mentioned, a quantitative comparison of results presented in the literature is a delicate matter. There is no uniform method for assessing quantitative evaluation of gas leaks. However, an older publication [14] documents the long-desired wish of methane leak visualization as well as the technical progress regarding active OGI components (e.g., ICL instead halogen lamps as the MIR light source). They resolved methane concentrations of less than 300 ppm (cloud diameter 30 cm) and around 100 ml/min. Another interesting work is given by Nutt et al. [7] in form of a comparable setup realized by NIR components [diode laser and short wave infrared (SWIR) camera]. This realization of an active OGI in the SWIR region shows a high degree of integration and an application driven focus. A methane leak detection of flow rates as low as 50 ml/min was shown.

Despite the difficult framework for a detailed and robust comparison, the available results and representations indicate that the figure of merit in terms of detectable leak rates and concentrations (in ppm*m or relative thickness of pure methane) is in line or mainly determined with different absorption strengths (see Fig. 1) between NIR (1.65 µm) and MIR (3.3 µm). The MIR absorption lines are approximately 100 times stronger than the NIR absorption lines. For instance, a pixelwise sensitivity of the active OGI in the SWIR is not explicitly given, but their sensitivity was tested by various gas cells, and the lowest gas cell concentration is around 2500 ppm*m (correspond to a 2.5 mm gas cell of pure methane), which is around 100 times higher than the concentration measured in this work (see Figs. 7 and 8). These observations and estimations are in line with another TLS investigation regarding components in the NIR and MIR [15]. Practically, this means that, on the one side, an active OGI in the MWIR is able to measure significantly lower minimum detectable leak rates and concentrations than an active OGI in the SWIR. On the other side, the component costs are significantly higher in the MIR compared to the NIR.

5. CONCLUSION AND OUTLOOK

An active OGI concept is realized by an ICL with a wavelength around 3270 nm and a synchronized MWIR camera. A powerful tool for a selective, sensitive, and quantitative visualization of gas leaks is obtained by means of TLS in combination with MIR imaging. This work demonstrates a standoff methane leak detection and localization for leakage rates of 2 and 5 ml/min by a single image at a frame rate of 125 Hz. The distance between the gas camera and artificial leak scenario is about 2 m. The contrast and signal-to-noise ratio suggest that much smaller leakage rates than 2 ml/min should be detectable and located. Further image processing reduces the data and improves signal, and a pixelwise sensitivity around 1 ppm*m can be observed. It is successfully shown that the HITRAN database allows a calibration-free, pixelwise image concentration in ppm*m. Overall the realized active OGI system shows a significantly better performance and much more potential than the tested state-of-the-art reference devices in the artificial leak scenario. Although it is difficult to quantitatively compare our data with other results for standoff leak detection scenarios, our work indicates a significant performance improvement.

The presented active OGI concept shows a remarkable potential for standoff gas leak detection of very small leakage that can be transferred to other gases with IR activity in the 3 to 5 µm region. This can be realized by changing the gas-specific laser without changing the camera. In the future, larger areas of inspections need to be investigated in more detail as well as less homogenous or changing backgrounds. An experimental estimation of the minimum detectable leak rate in the above presented setup would be interesting since the results above indicate a detectability of methane leaks below 1 ml/min is feasible. Another interesting topic for future investigations is the spatiotemporal information of leaks by image sequences. Therefore, the shown images serve as an excellent basis for automated leak identification and localization by means of artificial intelligence systems. The available hardware and software components for an active OGI system allow a selective, sensitive, and spectroscopic real-time investigation of gas plume dynamics at frame rates of 125 Hz and above.

Funding

Fraunhofer-Gesellschaft (601 210); Horizon 2020 Framework Programme (780240); Bundesministerium für Bildung und Forschung (13N1590).

Acknowledgment

This work received partial financial support from following projects/programs: German Federal Ministry of Education and Research (BMBF, SORTIE project); European Union’s Horizon 2020 research and innovation program (www.redfinch.eu). This work was supported by the Fraunhofer Internal Programs.

Disclosures

The authors declare no conflicts of interest.

Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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2. R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018). [CrossRef]  

3. C. E. Kemp, A. P. Ravikumar, and A. R. Brandt, “Comparing natural gas leakage detection technologies using an open-source ‘Virtual Gas Field’ simulator,” Environ. Sci. Technol. 50, 4546–4553 (2016). [CrossRef]  

4. W. Große Bley, “Methods of leak detection,” in Handbook of Vacuum Technology, K. Jousten, ed. (Wiley, 2016), pp. 907–942.

5. J. Li, Z. Yu, Z. Du, Y. Ji, and C. Liu, “Standoff chemical detection using laser absorption spectroscopy: a review,” Remote Sens. 12, 2771 (2020). [CrossRef]  

6. G. M. Gibson, B. Sun, M. P. Edgar, D. B. Phillips, N. Hempler, G. T. Maker, G. P. A. Malcolm, and M. J. Padgett, “Real-time imaging of methane gas leaks using a single-pixel camera,” Opt. Express 25, 2998–3005 (2017). [CrossRef]  

7. K. J. Nutt, N. Hempler, G. T. Maker, G. P. A. Malcolm, M. J. Padgett, and G. M. Gibson, “Developing a portable gas imaging camera using highly tunable active-illumination and computer vision,” Opt. Express 28, 18566–18576 (2020). [CrossRef]  

8. A. P. Ravikumar, J. Wang, and A. R. Brandt, “Are optical gas imaging technologies effective for methane leak detection?” Environ. Sci. Technol. 51, 718–724 (2017). [CrossRef]  

9. D. Zimmerle, T. Vaughn, C. Bell, K. Bennett, P. Deshmukh, and E. Thoma, “Detection limits of optical gas imaging for natural gas leak detection in realistic controlled conditions,” Environ. Sci. Technol. 54, 11506–11514 (2020). [CrossRef]  

10. A. Klein, O. Witzel, and V. Ebert, “Rapid, time-division multiplexed, direct absorption-, and wavelength modulation-spectroscopy,” Sensors 14, 21497–21513 (2014). [CrossRef]  

11. R. K. Hanson, R. M. Spearrin, and C. S. Goldenstein, Spectroscopy and Optical Diagnostics for Gases, 1st ed. (Springer, 2016).

12. L. S. Rothman and I. E. Gordon, “The HITRAN molecular database,” AIP Conf. Proc. 1545, 223–231 (2013). [CrossRef]  

13. X. Fan, G. Zeng, C. LaBounty, J. E. Bowers, E. Croke, C. C. Ahn, S. Huxtable, A. Majumdar, and A. Shakouri, “SiGeC/Si superlattice microcoolers,” Appl. Phys. Lett. 78, 1580–1582 (2001). [CrossRef]  

14. W. Gross, T. Hierl, H. Scheuerpflug, U. Schirl, O. Schreer, and M. Schulz, “Localization of methane distributions by spectrally tuned infrared imaging,” Proc. SPIE 3533, 234–240 (1999). [CrossRef]  

15. T. Strahl, J. Herbst, E. Maier, S. Rademacher, C. Weber, H.-F. Pernau, A. Lambrecht, and J. Wöllenstein, “Comparison of laser-based photoacoustic and optical detection of methane,” J. Sens. Sens. Syst. 10, 25–35 (2021). [CrossRef]  

References

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  1. R. A. Alvarez, S. W. Pacala, J. J. Winebrake, W. L. Chameides, and S. P. Hamburg, “Greater focus needed on methane leakage from natural gas infrastructure,” Proc. Natl. Acad. Sci. USA 109, 6435–6440 (2012).
    [Crossref]
  2. R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
    [Crossref]
  3. C. E. Kemp, A. P. Ravikumar, and A. R. Brandt, “Comparing natural gas leakage detection technologies using an open-source ‘Virtual Gas Field’ simulator,” Environ. Sci. Technol. 50, 4546–4553 (2016).
    [Crossref]
  4. W. Große Bley, “Methods of leak detection,” in Handbook of Vacuum Technology, K. Jousten, ed. (Wiley, 2016), pp. 907–942.
  5. J. Li, Z. Yu, Z. Du, Y. Ji, and C. Liu, “Standoff chemical detection using laser absorption spectroscopy: a review,” Remote Sens. 12, 2771 (2020).
    [Crossref]
  6. G. M. Gibson, B. Sun, M. P. Edgar, D. B. Phillips, N. Hempler, G. T. Maker, G. P. A. Malcolm, and M. J. Padgett, “Real-time imaging of methane gas leaks using a single-pixel camera,” Opt. Express 25, 2998–3005 (2017).
    [Crossref]
  7. K. J. Nutt, N. Hempler, G. T. Maker, G. P. A. Malcolm, M. J. Padgett, and G. M. Gibson, “Developing a portable gas imaging camera using highly tunable active-illumination and computer vision,” Opt. Express 28, 18566–18576 (2020).
    [Crossref]
  8. A. P. Ravikumar, J. Wang, and A. R. Brandt, “Are optical gas imaging technologies effective for methane leak detection?” Environ. Sci. Technol. 51, 718–724 (2017).
    [Crossref]
  9. D. Zimmerle, T. Vaughn, C. Bell, K. Bennett, P. Deshmukh, and E. Thoma, “Detection limits of optical gas imaging for natural gas leak detection in realistic controlled conditions,” Environ. Sci. Technol. 54, 11506–11514 (2020).
    [Crossref]
  10. A. Klein, O. Witzel, and V. Ebert, “Rapid, time-division multiplexed, direct absorption-, and wavelength modulation-spectroscopy,” Sensors 14, 21497–21513 (2014).
    [Crossref]
  11. R. K. Hanson, R. M. Spearrin, and C. S. Goldenstein, Spectroscopy and Optical Diagnostics for Gases, 1st ed. (Springer, 2016).
  12. L. S. Rothman and I. E. Gordon, “The HITRAN molecular database,” AIP Conf. Proc. 1545, 223–231 (2013).
    [Crossref]
  13. X. Fan, G. Zeng, C. LaBounty, J. E. Bowers, E. Croke, C. C. Ahn, S. Huxtable, A. Majumdar, and A. Shakouri, “SiGeC/Si superlattice microcoolers,” Appl. Phys. Lett. 78, 1580–1582 (2001).
    [Crossref]
  14. W. Gross, T. Hierl, H. Scheuerpflug, U. Schirl, O. Schreer, and M. Schulz, “Localization of methane distributions by spectrally tuned infrared imaging,” Proc. SPIE 3533, 234–240 (1999).
    [Crossref]
  15. T. Strahl, J. Herbst, E. Maier, S. Rademacher, C. Weber, H.-F. Pernau, A. Lambrecht, and J. Wöllenstein, “Comparison of laser-based photoacoustic and optical detection of methane,” J. Sens. Sens. Syst. 10, 25–35 (2021).
    [Crossref]

2021 (1)

T. Strahl, J. Herbst, E. Maier, S. Rademacher, C. Weber, H.-F. Pernau, A. Lambrecht, and J. Wöllenstein, “Comparison of laser-based photoacoustic and optical detection of methane,” J. Sens. Sens. Syst. 10, 25–35 (2021).
[Crossref]

2020 (3)

K. J. Nutt, N. Hempler, G. T. Maker, G. P. A. Malcolm, M. J. Padgett, and G. M. Gibson, “Developing a portable gas imaging camera using highly tunable active-illumination and computer vision,” Opt. Express 28, 18566–18576 (2020).
[Crossref]

J. Li, Z. Yu, Z. Du, Y. Ji, and C. Liu, “Standoff chemical detection using laser absorption spectroscopy: a review,” Remote Sens. 12, 2771 (2020).
[Crossref]

D. Zimmerle, T. Vaughn, C. Bell, K. Bennett, P. Deshmukh, and E. Thoma, “Detection limits of optical gas imaging for natural gas leak detection in realistic controlled conditions,” Environ. Sci. Technol. 54, 11506–11514 (2020).
[Crossref]

2018 (1)

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

2017 (2)

G. M. Gibson, B. Sun, M. P. Edgar, D. B. Phillips, N. Hempler, G. T. Maker, G. P. A. Malcolm, and M. J. Padgett, “Real-time imaging of methane gas leaks using a single-pixel camera,” Opt. Express 25, 2998–3005 (2017).
[Crossref]

A. P. Ravikumar, J. Wang, and A. R. Brandt, “Are optical gas imaging technologies effective for methane leak detection?” Environ. Sci. Technol. 51, 718–724 (2017).
[Crossref]

2016 (1)

C. E. Kemp, A. P. Ravikumar, and A. R. Brandt, “Comparing natural gas leakage detection technologies using an open-source ‘Virtual Gas Field’ simulator,” Environ. Sci. Technol. 50, 4546–4553 (2016).
[Crossref]

2014 (1)

A. Klein, O. Witzel, and V. Ebert, “Rapid, time-division multiplexed, direct absorption-, and wavelength modulation-spectroscopy,” Sensors 14, 21497–21513 (2014).
[Crossref]

2013 (1)

L. S. Rothman and I. E. Gordon, “The HITRAN molecular database,” AIP Conf. Proc. 1545, 223–231 (2013).
[Crossref]

2012 (1)

R. A. Alvarez, S. W. Pacala, J. J. Winebrake, W. L. Chameides, and S. P. Hamburg, “Greater focus needed on methane leakage from natural gas infrastructure,” Proc. Natl. Acad. Sci. USA 109, 6435–6440 (2012).
[Crossref]

2001 (1)

X. Fan, G. Zeng, C. LaBounty, J. E. Bowers, E. Croke, C. C. Ahn, S. Huxtable, A. Majumdar, and A. Shakouri, “SiGeC/Si superlattice microcoolers,” Appl. Phys. Lett. 78, 1580–1582 (2001).
[Crossref]

1999 (1)

W. Gross, T. Hierl, H. Scheuerpflug, U. Schirl, O. Schreer, and M. Schulz, “Localization of methane distributions by spectrally tuned infrared imaging,” Proc. SPIE 3533, 234–240 (1999).
[Crossref]

Ahn, C. C.

X. Fan, G. Zeng, C. LaBounty, J. E. Bowers, E. Croke, C. C. Ahn, S. Huxtable, A. Majumdar, and A. Shakouri, “SiGeC/Si superlattice microcoolers,” Appl. Phys. Lett. 78, 1580–1582 (2001).
[Crossref]

Allen, D. T.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Alvarez, R. A.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

R. A. Alvarez, S. W. Pacala, J. J. Winebrake, W. L. Chameides, and S. P. Hamburg, “Greater focus needed on methane leakage from natural gas infrastructure,” Proc. Natl. Acad. Sci. USA 109, 6435–6440 (2012).
[Crossref]

Barkley, Z. R.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Bell, C.

D. Zimmerle, T. Vaughn, C. Bell, K. Bennett, P. Deshmukh, and E. Thoma, “Detection limits of optical gas imaging for natural gas leak detection in realistic controlled conditions,” Environ. Sci. Technol. 54, 11506–11514 (2020).
[Crossref]

Bennett, K.

D. Zimmerle, T. Vaughn, C. Bell, K. Bennett, P. Deshmukh, and E. Thoma, “Detection limits of optical gas imaging for natural gas leak detection in realistic controlled conditions,” Environ. Sci. Technol. 54, 11506–11514 (2020).
[Crossref]

Bowers, J. E.

X. Fan, G. Zeng, C. LaBounty, J. E. Bowers, E. Croke, C. C. Ahn, S. Huxtable, A. Majumdar, and A. Shakouri, “SiGeC/Si superlattice microcoolers,” Appl. Phys. Lett. 78, 1580–1582 (2001).
[Crossref]

Brandt, A. R.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

A. P. Ravikumar, J. Wang, and A. R. Brandt, “Are optical gas imaging technologies effective for methane leak detection?” Environ. Sci. Technol. 51, 718–724 (2017).
[Crossref]

C. E. Kemp, A. P. Ravikumar, and A. R. Brandt, “Comparing natural gas leakage detection technologies using an open-source ‘Virtual Gas Field’ simulator,” Environ. Sci. Technol. 50, 4546–4553 (2016).
[Crossref]

Chameides, W. L.

R. A. Alvarez, S. W. Pacala, J. J. Winebrake, W. L. Chameides, and S. P. Hamburg, “Greater focus needed on methane leakage from natural gas infrastructure,” Proc. Natl. Acad. Sci. USA 109, 6435–6440 (2012).
[Crossref]

Croke, E.

X. Fan, G. Zeng, C. LaBounty, J. E. Bowers, E. Croke, C. C. Ahn, S. Huxtable, A. Majumdar, and A. Shakouri, “SiGeC/Si superlattice microcoolers,” Appl. Phys. Lett. 78, 1580–1582 (2001).
[Crossref]

Davis, K. J.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Deshmukh, P.

D. Zimmerle, T. Vaughn, C. Bell, K. Bennett, P. Deshmukh, and E. Thoma, “Detection limits of optical gas imaging for natural gas leak detection in realistic controlled conditions,” Environ. Sci. Technol. 54, 11506–11514 (2020).
[Crossref]

Du, Z.

J. Li, Z. Yu, Z. Du, Y. Ji, and C. Liu, “Standoff chemical detection using laser absorption spectroscopy: a review,” Remote Sens. 12, 2771 (2020).
[Crossref]

Ebert, V.

A. Klein, O. Witzel, and V. Ebert, “Rapid, time-division multiplexed, direct absorption-, and wavelength modulation-spectroscopy,” Sensors 14, 21497–21513 (2014).
[Crossref]

Edgar, M. P.

Fan, X.

X. Fan, G. Zeng, C. LaBounty, J. E. Bowers, E. Croke, C. C. Ahn, S. Huxtable, A. Majumdar, and A. Shakouri, “SiGeC/Si superlattice microcoolers,” Appl. Phys. Lett. 78, 1580–1582 (2001).
[Crossref]

Gibson, G. M.

Goldenstein, C. S.

R. K. Hanson, R. M. Spearrin, and C. S. Goldenstein, Spectroscopy and Optical Diagnostics for Gases, 1st ed. (Springer, 2016).

Gordon, I. E.

L. S. Rothman and I. E. Gordon, “The HITRAN molecular database,” AIP Conf. Proc. 1545, 223–231 (2013).
[Crossref]

Gross, W.

W. Gross, T. Hierl, H. Scheuerpflug, U. Schirl, O. Schreer, and M. Schulz, “Localization of methane distributions by spectrally tuned infrared imaging,” Proc. SPIE 3533, 234–240 (1999).
[Crossref]

Große Bley, W.

W. Große Bley, “Methods of leak detection,” in Handbook of Vacuum Technology, K. Jousten, ed. (Wiley, 2016), pp. 907–942.

Hamburg, S. P.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

R. A. Alvarez, S. W. Pacala, J. J. Winebrake, W. L. Chameides, and S. P. Hamburg, “Greater focus needed on methane leakage from natural gas infrastructure,” Proc. Natl. Acad. Sci. USA 109, 6435–6440 (2012).
[Crossref]

Hanson, R. K.

R. K. Hanson, R. M. Spearrin, and C. S. Goldenstein, Spectroscopy and Optical Diagnostics for Gases, 1st ed. (Springer, 2016).

Hempler, N.

Herbst, J.

T. Strahl, J. Herbst, E. Maier, S. Rademacher, C. Weber, H.-F. Pernau, A. Lambrecht, and J. Wöllenstein, “Comparison of laser-based photoacoustic and optical detection of methane,” J. Sens. Sens. Syst. 10, 25–35 (2021).
[Crossref]

Herndon, S. C.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Hierl, T.

W. Gross, T. Hierl, H. Scheuerpflug, U. Schirl, O. Schreer, and M. Schulz, “Localization of methane distributions by spectrally tuned infrared imaging,” Proc. SPIE 3533, 234–240 (1999).
[Crossref]

Huxtable, S.

X. Fan, G. Zeng, C. LaBounty, J. E. Bowers, E. Croke, C. C. Ahn, S. Huxtable, A. Majumdar, and A. Shakouri, “SiGeC/Si superlattice microcoolers,” Appl. Phys. Lett. 78, 1580–1582 (2001).
[Crossref]

Jacob, D. J.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Ji, Y.

J. Li, Z. Yu, Z. Du, Y. Ji, and C. Liu, “Standoff chemical detection using laser absorption spectroscopy: a review,” Remote Sens. 12, 2771 (2020).
[Crossref]

Karion, A.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Kemp, C. E.

C. E. Kemp, A. P. Ravikumar, and A. R. Brandt, “Comparing natural gas leakage detection technologies using an open-source ‘Virtual Gas Field’ simulator,” Environ. Sci. Technol. 50, 4546–4553 (2016).
[Crossref]

Klein, A.

A. Klein, O. Witzel, and V. Ebert, “Rapid, time-division multiplexed, direct absorption-, and wavelength modulation-spectroscopy,” Sensors 14, 21497–21513 (2014).
[Crossref]

Kort, E. A.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

LaBounty, C.

X. Fan, G. Zeng, C. LaBounty, J. E. Bowers, E. Croke, C. C. Ahn, S. Huxtable, A. Majumdar, and A. Shakouri, “SiGeC/Si superlattice microcoolers,” Appl. Phys. Lett. 78, 1580–1582 (2001).
[Crossref]

Lamb, B. K.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Lambrecht, A.

T. Strahl, J. Herbst, E. Maier, S. Rademacher, C. Weber, H.-F. Pernau, A. Lambrecht, and J. Wöllenstein, “Comparison of laser-based photoacoustic and optical detection of methane,” J. Sens. Sens. Syst. 10, 25–35 (2021).
[Crossref]

Lauvaux, T.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Li, J.

J. Li, Z. Yu, Z. Du, Y. Ji, and C. Liu, “Standoff chemical detection using laser absorption spectroscopy: a review,” Remote Sens. 12, 2771 (2020).
[Crossref]

Liu, C.

J. Li, Z. Yu, Z. Du, Y. Ji, and C. Liu, “Standoff chemical detection using laser absorption spectroscopy: a review,” Remote Sens. 12, 2771 (2020).
[Crossref]

Lyon, D. R.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Maasakkers, J. D.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Maier, E.

T. Strahl, J. Herbst, E. Maier, S. Rademacher, C. Weber, H.-F. Pernau, A. Lambrecht, and J. Wöllenstein, “Comparison of laser-based photoacoustic and optical detection of methane,” J. Sens. Sens. Syst. 10, 25–35 (2021).
[Crossref]

Majumdar, A.

X. Fan, G. Zeng, C. LaBounty, J. E. Bowers, E. Croke, C. C. Ahn, S. Huxtable, A. Majumdar, and A. Shakouri, “SiGeC/Si superlattice microcoolers,” Appl. Phys. Lett. 78, 1580–1582 (2001).
[Crossref]

Maker, G. T.

Malcolm, G. P. A.

Marchese, A. J.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Nutt, K. J.

Omara, M.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Pacala, S. W.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

R. A. Alvarez, S. W. Pacala, J. J. Winebrake, W. L. Chameides, and S. P. Hamburg, “Greater focus needed on methane leakage from natural gas infrastructure,” Proc. Natl. Acad. Sci. USA 109, 6435–6440 (2012).
[Crossref]

Padgett, M. J.

Peischl, J.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Pernau, H.-F.

T. Strahl, J. Herbst, E. Maier, S. Rademacher, C. Weber, H.-F. Pernau, A. Lambrecht, and J. Wöllenstein, “Comparison of laser-based photoacoustic and optical detection of methane,” J. Sens. Sens. Syst. 10, 25–35 (2021).
[Crossref]

Phillips, D. B.

Rademacher, S.

T. Strahl, J. Herbst, E. Maier, S. Rademacher, C. Weber, H.-F. Pernau, A. Lambrecht, and J. Wöllenstein, “Comparison of laser-based photoacoustic and optical detection of methane,” J. Sens. Sens. Syst. 10, 25–35 (2021).
[Crossref]

Ravikumar, A. P.

A. P. Ravikumar, J. Wang, and A. R. Brandt, “Are optical gas imaging technologies effective for methane leak detection?” Environ. Sci. Technol. 51, 718–724 (2017).
[Crossref]

C. E. Kemp, A. P. Ravikumar, and A. R. Brandt, “Comparing natural gas leakage detection technologies using an open-source ‘Virtual Gas Field’ simulator,” Environ. Sci. Technol. 50, 4546–4553 (2016).
[Crossref]

Robinson, A. L.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Rothman, L. S.

L. S. Rothman and I. E. Gordon, “The HITRAN molecular database,” AIP Conf. Proc. 1545, 223–231 (2013).
[Crossref]

Scheuerpflug, H.

W. Gross, T. Hierl, H. Scheuerpflug, U. Schirl, O. Schreer, and M. Schulz, “Localization of methane distributions by spectrally tuned infrared imaging,” Proc. SPIE 3533, 234–240 (1999).
[Crossref]

Schirl, U.

W. Gross, T. Hierl, H. Scheuerpflug, U. Schirl, O. Schreer, and M. Schulz, “Localization of methane distributions by spectrally tuned infrared imaging,” Proc. SPIE 3533, 234–240 (1999).
[Crossref]

Schreer, O.

W. Gross, T. Hierl, H. Scheuerpflug, U. Schirl, O. Schreer, and M. Schulz, “Localization of methane distributions by spectrally tuned infrared imaging,” Proc. SPIE 3533, 234–240 (1999).
[Crossref]

Schulz, M.

W. Gross, T. Hierl, H. Scheuerpflug, U. Schirl, O. Schreer, and M. Schulz, “Localization of methane distributions by spectrally tuned infrared imaging,” Proc. SPIE 3533, 234–240 (1999).
[Crossref]

Shakouri, A.

X. Fan, G. Zeng, C. LaBounty, J. E. Bowers, E. Croke, C. C. Ahn, S. Huxtable, A. Majumdar, and A. Shakouri, “SiGeC/Si superlattice microcoolers,” Appl. Phys. Lett. 78, 1580–1582 (2001).
[Crossref]

Shepson, P. B.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Spearrin, R. M.

R. K. Hanson, R. M. Spearrin, and C. S. Goldenstein, Spectroscopy and Optical Diagnostics for Gases, 1st ed. (Springer, 2016).

Strahl, T.

T. Strahl, J. Herbst, E. Maier, S. Rademacher, C. Weber, H.-F. Pernau, A. Lambrecht, and J. Wöllenstein, “Comparison of laser-based photoacoustic and optical detection of methane,” J. Sens. Sens. Syst. 10, 25–35 (2021).
[Crossref]

Sun, B.

Sweeney, C.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Thoma, E.

D. Zimmerle, T. Vaughn, C. Bell, K. Bennett, P. Deshmukh, and E. Thoma, “Detection limits of optical gas imaging for natural gas leak detection in realistic controlled conditions,” Environ. Sci. Technol. 54, 11506–11514 (2020).
[Crossref]

Townsend-Small, A.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Vaughn, T.

D. Zimmerle, T. Vaughn, C. Bell, K. Bennett, P. Deshmukh, and E. Thoma, “Detection limits of optical gas imaging for natural gas leak detection in realistic controlled conditions,” Environ. Sci. Technol. 54, 11506–11514 (2020).
[Crossref]

Wang, J.

A. P. Ravikumar, J. Wang, and A. R. Brandt, “Are optical gas imaging technologies effective for methane leak detection?” Environ. Sci. Technol. 51, 718–724 (2017).
[Crossref]

Weber, C.

T. Strahl, J. Herbst, E. Maier, S. Rademacher, C. Weber, H.-F. Pernau, A. Lambrecht, and J. Wöllenstein, “Comparison of laser-based photoacoustic and optical detection of methane,” J. Sens. Sens. Syst. 10, 25–35 (2021).
[Crossref]

Winebrake, J. J.

R. A. Alvarez, S. W. Pacala, J. J. Winebrake, W. L. Chameides, and S. P. Hamburg, “Greater focus needed on methane leakage from natural gas infrastructure,” Proc. Natl. Acad. Sci. USA 109, 6435–6440 (2012).
[Crossref]

Witzel, O.

A. Klein, O. Witzel, and V. Ebert, “Rapid, time-division multiplexed, direct absorption-, and wavelength modulation-spectroscopy,” Sensors 14, 21497–21513 (2014).
[Crossref]

Wofsy, S. C.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Wöllenstein, J.

T. Strahl, J. Herbst, E. Maier, S. Rademacher, C. Weber, H.-F. Pernau, A. Lambrecht, and J. Wöllenstein, “Comparison of laser-based photoacoustic and optical detection of methane,” J. Sens. Sens. Syst. 10, 25–35 (2021).
[Crossref]

Yu, Z.

J. Li, Z. Yu, Z. Du, Y. Ji, and C. Liu, “Standoff chemical detection using laser absorption spectroscopy: a review,” Remote Sens. 12, 2771 (2020).
[Crossref]

Zavala-Araiza, D.

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Zeng, G.

X. Fan, G. Zeng, C. LaBounty, J. E. Bowers, E. Croke, C. C. Ahn, S. Huxtable, A. Majumdar, and A. Shakouri, “SiGeC/Si superlattice microcoolers,” Appl. Phys. Lett. 78, 1580–1582 (2001).
[Crossref]

Zimmerle, D.

D. Zimmerle, T. Vaughn, C. Bell, K. Bennett, P. Deshmukh, and E. Thoma, “Detection limits of optical gas imaging for natural gas leak detection in realistic controlled conditions,” Environ. Sci. Technol. 54, 11506–11514 (2020).
[Crossref]

AIP Conf. Proc. (1)

L. S. Rothman and I. E. Gordon, “The HITRAN molecular database,” AIP Conf. Proc. 1545, 223–231 (2013).
[Crossref]

Appl. Phys. Lett. (1)

X. Fan, G. Zeng, C. LaBounty, J. E. Bowers, E. Croke, C. C. Ahn, S. Huxtable, A. Majumdar, and A. Shakouri, “SiGeC/Si superlattice microcoolers,” Appl. Phys. Lett. 78, 1580–1582 (2001).
[Crossref]

Environ. Sci. Technol. (3)

C. E. Kemp, A. P. Ravikumar, and A. R. Brandt, “Comparing natural gas leakage detection technologies using an open-source ‘Virtual Gas Field’ simulator,” Environ. Sci. Technol. 50, 4546–4553 (2016).
[Crossref]

A. P. Ravikumar, J. Wang, and A. R. Brandt, “Are optical gas imaging technologies effective for methane leak detection?” Environ. Sci. Technol. 51, 718–724 (2017).
[Crossref]

D. Zimmerle, T. Vaughn, C. Bell, K. Bennett, P. Deshmukh, and E. Thoma, “Detection limits of optical gas imaging for natural gas leak detection in realistic controlled conditions,” Environ. Sci. Technol. 54, 11506–11514 (2020).
[Crossref]

J. Sens. Sens. Syst. (1)

T. Strahl, J. Herbst, E. Maier, S. Rademacher, C. Weber, H.-F. Pernau, A. Lambrecht, and J. Wöllenstein, “Comparison of laser-based photoacoustic and optical detection of methane,” J. Sens. Sens. Syst. 10, 25–35 (2021).
[Crossref]

Opt. Express (2)

Proc. Natl. Acad. Sci. USA (1)

R. A. Alvarez, S. W. Pacala, J. J. Winebrake, W. L. Chameides, and S. P. Hamburg, “Greater focus needed on methane leakage from natural gas infrastructure,” Proc. Natl. Acad. Sci. USA 109, 6435–6440 (2012).
[Crossref]

Proc. SPIE (1)

W. Gross, T. Hierl, H. Scheuerpflug, U. Schirl, O. Schreer, and M. Schulz, “Localization of methane distributions by spectrally tuned infrared imaging,” Proc. SPIE 3533, 234–240 (1999).
[Crossref]

Remote Sens. (1)

J. Li, Z. Yu, Z. Du, Y. Ji, and C. Liu, “Standoff chemical detection using laser absorption spectroscopy: a review,” Remote Sens. 12, 2771 (2020).
[Crossref]

Science (1)

R. A. Alvarez, D. Zavala-Araiza, D. R. Lyon, D. T. Allen, Z. R. Barkley, A. R. Brandt, K. J. Davis, S. C. Herndon, D. J. Jacob, A. Karion, E. A. Kort, B. K. Lamb, T. Lauvaux, J. D. Maasakkers, A. J. Marchese, M. Omara, S. W. Pacala, J. Peischl, A. L. Robinson, P. B. Shepson, C. Sweeney, A. Townsend-Small, S. C. Wofsy, and S. P. Hamburg, “Assessment of methane emissions from the US oil and gas supply chain,” Science 361, 186–188 (2018).
[Crossref]

Sensors (1)

A. Klein, O. Witzel, and V. Ebert, “Rapid, time-division multiplexed, direct absorption-, and wavelength modulation-spectroscopy,” Sensors 14, 21497–21513 (2014).
[Crossref]

Other (2)

R. K. Hanson, R. M. Spearrin, and C. S. Goldenstein, Spectroscopy and Optical Diagnostics for Gases, 1st ed. (Springer, 2016).

W. Große Bley, “Methods of leak detection,” in Handbook of Vacuum Technology, K. Jousten, ed. (Wiley, 2016), pp. 907–942.

Supplementary Material (2)

NameDescription
» Visualization 1       The image sequence shows the spatio-temporal concentration information (in ppm*m) at a framerate of 10 frames per second (10 Hz) of a methane leakage scenario at a leak rate of 2 ml/min. The leak origin and the leakage gas plume are clearly visible i
» Visualization 2       The image sequence shows the spatio-temporal concentration information (in ppm*m) at a framerate of 10 frames per second (10 Hz) of a methane leakage scenario at a leak rate of 2 ml/min. The leak origin and the leakage gas plume are clearly visible i

Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Figures (8)

Fig. 1.
Fig. 1. Left, the graph shows the absorption lines of methane (${{\rm CH}_4}$) by the line intensity (S) in the near- and mid-infrared. Right, a typical tunable laser spectroscopy signal according to Beer–Lambert law is schematically illustrated by the laser without gas interaction (laser) and with gas interaction (BLL) as well as the background (BG) without illumination.
Fig. 2.
Fig. 2. Left, the absorbance (A) as a function of the wavenumber ($1/{\lambda}$) is calculated by HITRAN parameters for 100 ppm*m at standard pressure and temperature (STP). Right, the linearity of the normalized absorption coefficient (${\alpha}$ in ppm*m) at the peak wavelength is illustrated as function of concentration. The normalization constant (${{\eta}_{{\rm peak}}}$) indicates an almost linear correlation (perfectly linear would be illustrated by unity).
Fig. 3.
Fig. 3. Top, the graphic represents the artificial leak detection setup, including the main hardware components of the investigation (ICL by Nanoplus and IR camera by Infratec). State-of-the-art TLS-based single point measurement device as reference (Laser Methane Mini by Tokyo Gas), including the comparable small illumination point. Bottom, a typical image of a leak scenario of 5 ml/min is given by the second reference system of this investigation (GF320 by FLIR as passive OGI system). Additionally, the shot-blasted aluminum leak plate is shown and the diffuse reflection characteristics of a non-cooperative target according to Lambert’s cosine law are illustrated.
Fig. 4.
Fig. 4. State-of-the-art TLS single point measurement device as reference (Laser Methane Mini by Tokyo Gas) is tested in the artificial leak scenario. The graph illustrates the test results by concentration time series for various leakage rates between 0 and 10 ml/min.
Fig. 5.
Fig. 5. Top left, the plot schematically shows the laser current pulse series for the balanced current modulation scheme. Two laser currents (${i_1},{i_2}$) are applied to address two wavelengths (${\lambda _1},{\lambda _2}$), nearby (or “off”) the absorption line (right) and a further current pulse (${i_p}$) addresses the peak wavelength (${\lambda _p}$) of the absorption feature (“on-line”). The synchronized camera integration signal shows the applied image acquisition period of 250 Hz (bottom/left).
Fig. 6.
Fig. 6. Top, two typical background corrected images (on and off the absorption line) are shown, which are used to calculate the transmission and concentration images (bottom) according to the Beer–Lambert law.
Fig. 7.
Fig. 7. Left, the methane leakage scenario for two different leak rates (2 and 5 ml/min) is illustrated by methane concentration images (in ppm*m) at a specific point of time (${t} = {1}\;{s}$). The leak origin and the leakage gas plume are visible within a single image (${250} \times {250}$ pixels). This image is part of a video sequence with an image acquisition rate of 125 frames per second (125 Hz). A horizontal and a vertical cut of the concentration image (white lines) show the concentration (ppm*m) with respect to pixel index (right).
Fig. 8.
Fig. 8. Left, after data reduction by image processing (averaging), the concentration images (in ppm*m) at a specific point of time (${t} = {1}\;{s}$) are given within 0,1 s (10 fps or 10 Hz). Two different methane leakage scenarios of leak rates about 2 and 5 ml/min are illustrated (see Visualization 1 and Visualization 2). The leak origin and the leakage gas plume are clearly visible within a single image. Right, the standard deviation of the concentration within 1 s (1 Hz) represents the dynamics of the leakage plume (larger values or red regions) as well as the sensitivity of the concept (in ppm*m) in regions without gas plume dynamics (low values of blue pixel regions).

Equations (6)

Equations on this page are rendered with MathJax. Learn more.

I ( λ ) = I 0 ( λ ) exp [ α ( λ ) χ L ] + I B G , w i t h α ( λ ) = S P k B T φ ( λ , λ 0 ) ,
A ( λ ) = ln ( T ( λ ) ) = ln ( I ( λ ) I B G I 0 ( λ ) ) = α ( λ ) c w i t h c = χ L .
c = η p e a k A p e a k .
w i t h o u t g a s : I ( λ p ) I B G = I ( λ 1 ) + I ( λ 2 ) I B G ,
w i t h g a s : I ( λ p ) I B G < I ( λ 1 ) + I ( λ 2 ) I B G ,
c = η p e a k ln ( T ) = η p e a k ln ( I ( λ p e a k ) I B G I ( λ 1 ) + I ( λ 2 ) I B G ) .