Figure a shows a representative schematic of the sensor’s optical head. We use a collimated GaSb-based distributed feedback (DFB) diode laser (NL3270-TL, Norcada, Canada) with a specified operating temperature range from -20 to 40 °C. The laser wavelength is 3270.4 nm at 7 °C when the injection current is 130 mA. During operation, the tuning rate is approximately 20 pm mA⁻¹, as discussed in Sect. . The laser light is launched into an open-path multi-pass spot-ring Herriott cell , chosen for its simplicity and compatibility with commercial off-the-shelf mirrors. We used a custom ray-tracing script, based on analytical solutions of the optical cavity geometry without invoking the paraxial approximation, to guide the optomechanical design, similar to . The cell is composed of two 25.4 mm-diameter gold-coated concave spherical mirrors (CM254-100EH3-M02, Thorlabs, USA) with focal lengths of 100 mm and an off-axis hole 7 mm from the center of each mirror for passing the laser beam. The reflectivity of the mirrors at 3270.4 nm is reported by the manufacturer to be 98.6 % for a 10° angle of incidence. The on-axis mirror separation distance in the cell is 131.4 mm. The laser beam is oriented to produce precessing circular patterns on the two mirrors, resulting in 35 passes and an effective path length of 4.6 m, as shown in Fig. a.

The laser beam exiting the multi-pass cell is directed and focused onto an optically-immersed HgCdTe photovoltaic detector (PVI-2TE-4-0.1x0.1-TO66-wAl2O3-70, Vigo Photonics, Poland) using an off-axis parabolic mirror (OAPM) (MPD01M9-M01, Thorlabs, USA).

The laser, its heat sink, both mirrors of the multi-pass cell, and the OAPM are integrated into a rigid, cage-like assembly that reduces the effects of mechanical vibrations during UAV flight deployments (see Fig. c). Laser pre-alignment is achieved using a mounting plate machined at the desired injection angle. The optical alignment is fine-tuned during the assembly procedure using shims and split lock washers to correct deviations in the laser pointing angle. The split lock washers provide small angular adjustment capability, while slightly over-sized mounting holes for the laser heat sink and OAPM allow limited translational freedom. Final alignment was achieved by measuring the laser pointing angle, applying the required corrections, and making small translational and angular adjustments to maximize the detector signal observed on an oscilloscope. The number and approximate positions of the reflected spots are confirmed by systematically blocking the laser beam at the face of one mirror and monitoring the detector response.

Both the laser and the photodetector are temperature-stabilized via integrated thermoelectric coolers (TECs), controlled by ultra-stable TEC controllers (WTC32ND, Wavelength Electronics, USA). The laser is mounted on a custom aluminum heatsink affixed to the multi-pass optical cell. The photodetector is mounted directly to the sensor's aluminum baseplate, which also dissipates heat. The TEC controllers are mounted on custom-designed printed circuit boards (PCBs), which were adapted from the manufacturer’s evaluation board (WTC32ND-EV, Wavelength Electronics, USA) with added fail-safe protection circuitry to prevent accidental thermal runaway events. Heatsinks affixed to the aluminum baseplate directly opposite the TEC controllers dissipate heat generated by the TEC drive currents. The laser temperature is regulated to an average of 7.3 °C, while the detector is cooled to -42.7 °C. Under active airflow, the temperatures of both laser and detector typically stabilize within 10 min. A similar stabilization period is required after rapid, substantial changes in ambient temperature. During UAV flight, the laser temperature is maintained to within ±0.1 °C, which corresponds to a wavelength stability of ±0.028 nm (±0.026 cm⁻¹).

The sensor is powered by a 5 VDC supply delivered through two mini type-B USB ports, which can be conveniently driven by a standard power pack with dual type-A USB outputs that each provide a minimum 2 A current output to sustain the TEC operation on startup. At room temperatures and in steady-state, the sensor package (including the SBC) consumes around 11 W of electrical power. For comparison, the previous version of this TDLAS methane sensor had a steady state power draw of around 18 W . More than half of the power draw is attributed to the two TECs and their controllers, with the photodetector TEC being the dominant consumer. At colder air temperatures, the overall power consumption decreases due to the reduced load on the photodetector TEC.

A schematic of the electrical interconnections is shown in Fig. b. Signal generation and processing is handled onboard and in real time using a low-cost BeagleBone Black SBC (BeagleBoard.org, USA). The SBC is furnished with a custom PCB that houses a 12-bit digital-to-analog converter (DAC; AD5621, Analog Devices, USA) and a 16-bit analog-to-digital converter (ADC; MCP33131D-10, Microchip, USA). For reference, the DAC and ADC have simple passive RC filters at their input and output with 3 dB bandwidths of 32.8 and 48.2 kHz, respectively. The DAC and ADC are interfaced with the SBC via two 32-bit 200 MHz on-chip programmable real-time units (PRUs) and a memory buffer, which enables data transfer rates to be independent of the Linux operating system processes.

The DAC translates digital modulation patterns computed numerically by the SBC to analog voltages at a sampling rate of 400 kHz. This analog signal is fed to a low noise laser diode driver (DRV200-A-200, Koheron, France) as modulation input. The driver simultaneously provides a constant DC offset current, enabling the central laser wavelength to be tuned to the methane absorption line at 3270.4 nm . The laser injection current is swept from 110 to 200 mA in a sawtooth pattern with a frequency of 100 Hz, producing a corresponding wavelength sweep rate of approximately 20 pm mA⁻¹ near the target wavelength. A sinusoidal modulation with a frequency of 10 kHz and a peak amplitude of 15 mA is added to the current ramp for the purpose of noise rejection by wavelength modulation and subsequent lock-in detection. The amplitude of the modulation signal was chosen empirically to increase the second harmonic signal amplitude while avoiding excessive over-modulation and does not reflect the well-known optimum modulation index (m≈2.2) .

The photocurrent signal from the photodetector is converted to a voltage using a custom two-stage transimpedance amplifier (TIA) with a 3 dB bandwidth of 141 kHz. The analog voltage signal from the TIA is acquired by the ADC at a sampling rate of 312.5 kHz. The SBC computes the second harmonic (2f) spectrum, X2f(t), from the raw time-dependent detector signal, It(t), by applying a digital lock-in amplifier: 1X2f(t)=LP{It(t)×cos⁡(2πt⋅2f+ϕ)} where t is time, f is the modulation frequency, ϕ is the detection phase, and LP is a digital low-pass filter. For the latter, a numerical fourth-order low-pass filter with a −3 dB cutoff frequency of 561 Hz is applied, implemented as two cascaded second-order biquad sections, each with a cutoff frequency of 700 Hz. The detection phase is selected empirically to maximize the magnitude of the methane peak in the second harmonic spectrum, X2f. The amplitude of the methane peak is computed using a simple search algorithm to find the methane peak and left-side trough in the 2f spectrum by looking for the minimum and maximum values within predefined bounds. The peak-to-peak amplitude is used to compute the methane mole fraction by calibrating to reference gas mole fractions, as described in Sect. .

The SBC records the computed methane mole fraction as a function of time at a rate of 100 Hz along with ambient temperature, pressure, and relative humidity data, which are measured by an onboard sensor (BME280, Bosch Sensortec, Germany). A GPS module (Ultimate GPS, Adafruit, USA) is used to timestamp the data relative to coordinated universal time (UTC). The SBC saves all timestamped data to a microSD card. Optionally, raw data files containing the detector signal and the 2f spectrum can also be saved.

In addition to the data saved locally at 100 Hz, a down-sampled and averaged data stream with a rate of 5 Hz is passed over a universal asynchronous receiver-transmitter (UART) connection to a microcontroller unit (MCU; Metro Mini, Adafruit Industries, USA), which acts as a data hub. The MCU board also monitors the laser temperature, acting as a digital interlock that shuts off the laser driver if the laser temperature is not within pre-defined bounds. The MCU optionally feeds the down-sampled methane mole fraction, laser temperature, and ambient conditions data into a high-powered radio modem (RFD900x, RF Design, Australia), enabling long-distance real-time monitoring of the data stream while the sensor is deployed on a UAV. Although the data transmitted over the wireless link is not used for processing or analysis, real-time monitoring of mole fraction data is invaluable for guiding flight missions in the field and also for system health monitoring.

The optical assembly, photodetector, and electronic components are affixed to an aluminum baseplate. The sensor package is compact and easily portable, with outer dimensions of 23.5 cm (L) × 12.5 cm (W) × 9.5 cm (H), approximately 84 % smaller than the prior instrument . Including all electronics, batteries, and peripheral devices such as ambient condition (pressure, temperature, and relative humidity) sensors and a GPS receiver, the mass of the sensor package is just 1.2 kg, roughly one quarter that of its 5 kg predecessor . As one of the lightest methane gas spectrometers developed to date, the instrument is well within the payload limits of many small consumer- or enterprise-grade UAVs such as the DJI Matrice 300 RTK used in this study, allowing for simultaneous deployment of an on-board anemometer system on a highly portable measurement platform, as described in Sect. .

We deploy the 1.2 kg sensor system on a DJI Matrice 300 RTK UAV (DJI, China) that has a mass of 6.3 kg (including batteries) and a maximum payload of 2.7 kg, as shown in Fig. d. The instrument is secured to the top of the UAV using a modified DJI upward gimbal connector, providing vibration isolation. With the sensor onboard, we have achieved UAV flight times up to 28 min, limited by the UAV batteries. The methane sensor itself is powered independently from the UAV using a 10 000 mAh power bank (Adafruit, USA), enabling continuous operation for over 2 h and allowing multiple sequential UAV flights without shutting down the sensor.

Flight mission trajectories are pre-programmed and transferred to the flight controller before starting each mission to achieve systematic flight patterns. During flight, the UAV travels at a constant speed in the range of 5 to 8 m s⁻¹ along each horizontal transect and comes to a stop before changing altitude or direction.

We have deployed the sensor and UAV system at altitudes from approximately 0.5 to 40 m above takeoff, and we expect that higher flight altitudes would be achievable. In Canada, the maximum legal flight altitude is 122 m above ground level, for most UAV operations (Canadian Aviation Regulations, SOR96/433, s901.25, ).

The requirement for onboard wind measurements is determined by the mission goals and data analysis methods. For emission flux quantification missions, onboard wind data is highly beneficial. For hot spot and leak detection applications, an onboard anemometer may be unnecessary if the general wind direction is known from local weather stations or models.

When measuring onboard wind, a 2D anemometer (WindUltra, Gill Instruments, UK) is affixed to the UAV, enabling simultaneous measurement of methane mole fraction and wind information. The anemometer is mounted on a carbon fiber mast approximately 37 cm above the sensor’s optical head and 75 cm above the UAV rotors, reducing the effect of rotor wash on the recorded wind measurements. Custom 3D-printed components secure the mast and a data logger for the anemometer to the UAV landing gear. The entire anemometer assembly including the anemometer head, mast, data logger, and battery add an additional 1.2 kg to the payload. Together, the methane sensor and anemometer systems make up a total UAV payload of 2.4 kg. With the entire payload, including the methane sensor and anemometer components, the DJI Matrice 300 RTK UAV can achieve flight times of approximately 20 min.

The anemometer is mounted with the “north” direction pointing to the front of the UAV and the front of the aircraft is always oriented in the direction of flight, such that onboard wind is recorded relative to the direction of travel. To recover the true wind speed and direction (u,θ) from those measured by the onboard anemometer (uanem,θanem), we must account for the movement of the UAV, including its horizontal ground speed vgnd, pitch angle Θpitch, and roll angle Φroll. The corrected wind speed components parallel, u∥, and perpendicular, u⟂, to the UAV flight direction are given by: 2u∥=uanemcos⁡(θanem)cos⁡(Θpitch)-vgnd,u⟂=uanemsin⁡(θanem)cos⁡(Φroll) The pitch and roll angle corrections compensate for the 2D anemometer’s measurement axes being tilted from horizontal when the UAV is in flight. We apply a cosine correction and assume that the vertical wind speed is negligible compared to the horizontal wind. The corrected wind speed and direction are given by: 3u=u∥2+u⟂2,θ=atan2(u⟂,u∥)+ψ where atan2 is the four-quadrant inverse tangent and ψ is the UAV heading relative to north.

To quantify methane emission flux, we apply a direct flux quantification method that relies on the principle of mass balance . In this method, the UAV flies a pattern known as a “flux curtain” downwind of the methane source, consisting of stacked horizontal transects that form a vertical plane . The lateral extent of the flux curtain is chosen to ensure that the UAV traverses the entire width of the plume in each transect. The curtain height is also dictated by the size and shape of the plume, such that elevated methane mole fractions are not observed in the upper two to three transects. The vertical spacing between successive transects is minimized to give the best possible vertical resolution within the constraints of limited flight duration. The result is a two-dimensional map of the methane plume over a vertical plane downwind of the source.

Methane mole fractions χCH4 are recorded by the sensor at a rate of 100 Hz throughout the flight. They are converted to dry mole fractions using the relation χCH4dry=χCH4/(1-χH2O), where χH2O is the water vapor mole fraction. In post-processing, a 0.1 s moving average is applied and the data is down-sampled to match the 10 Hz positional data stream from the UAV flight logs. The wind speed and direction are also recorded at a rate of 10 Hz. For each measurement point in the y-z flux curtain, the methane flux density is given by: 4q(y,z)=χCH4dry(y,z)-χCH4bkg,dry(y,z)(1-χH2O(y,z))⋅ux(y,z)⋅p(z)MCH4RT where χCH4bkg,dry is the background methane dry mole fraction, ux is the wind speed component orthogonal to the y-z flux curtain, p(z) is the pressure at altitude z, MCH4=16.04 g mol⁻¹ is the molar mass of methane, R is the gas constant, and T is the average temperature. The pressure p(z) is estimated by applying a linear fit to the data recorded by the onboard pressure sensor throughout the flight. In this work, equivalent χH2O values were used for both the measured and background dry mole fractions χCH4dry and χCH4bkg,dry such that Eq. () simplifies to: 5q(y,z)=(χCH4(y,z)-χCH4bkg(y,z))⋅ux(y,z)⋅p(z)MCH4RT

For each horizontal transect, a transect-integrated flux density ∫tr.qdy is calculated using the trapezoid rule for numerical integration. The total flux Q is determined by integrating over the transect-integrated flux density in the vertical direction from ground level (z=0) to the top of the flux curtain. 6Q=∫0zmax∫tr.q(y,z)dydz where zmax is the altitude of the highest transect in the flux curtain, which should ideally exceed the vertical extent of the emission plume such that the transect-integrated flux density of the uppermost transect is zero. As such, no extrapolation is required above the flux curtain. On the other hand, plume dynamics and operational constraints often lead to non-zero flux densities in the lowest measured transect, necessitating extrapolation from the lowest transect to the ground. In this work, we define “extrapolated” transects below the measured flux curtain and predict corresponding transect-integrated flux densities using a logarithmic wind model to generate extrapolated wind speeds and assuming a constant transect-integrated methane mole fraction ∫tr.χCH4dy from the lowest transect to the ground. We assigned a relatively large uncertainty bound to the quantity ∫tr.χCH4dy for the extrapolated transects, following a normal distribution for which the probability that ∫tr.χCH4dy>0 ppm m is 99 %. The logarithmic wind model is given by 7u(z)=u*κlnzz0 where u* is the friction velocity, z0 is the surface roughness, and κ=0.4 . The model is fit to altitude-dependent wind data recorded from the UAV. The corresponding 1σ prediction interval is included in the uncertainty estimates for the transect-integrated flux density for the extrapolated transects.

Following the trapezoid rule, linear interpolation is used when integrating between transects – both measured and extrapolated – in Eq. (). This direct interpolation approach is suited to datasets characterized by dense sampling across the measurement plane . More complex statistical interpolation approaches such as Kriging have also been proposed , and may be more suitable than direct interpolation for sparse datasets.

Uncertainties in methane mole fraction, wind speed, and methane density are propagated when evaluating the uncertainty on total flux Q. We also estimate the extrapolation uncertainty, as described above, and the interpolation uncertainty using leave-one-out cross-validation. Details regarding the error propagation can be found in the Appendix.

The laser injection current over time is shown in Fig. a and the resultant detector signal is shown in Fig. b, for ambient air with a methane mole fraction near 2 ppm. The corresponding second harmonic spectrum is shown in Fig. c. The 3270.4 nm absorption feature for methane is indicated, along with a nearby water absorption line centered at 3271.0 nm. At the time of measurement, the ambient temperature was 22.6 °C and the relative humidity was 51.5 %, yielding a water vapor mole fraction of approximately 1.4 %. The feature contained within the shaded region of Fig. c is produced by the response of the filtering function to the abrupt change in the photodetector signal from the previous laser sweep and is not indicative of light absorption.

We calibrated the sensor in an enclosed chamber with a volume of approximately 50 L under controlled gas flow conditions with a known mole fraction. The temperature within the enclosure was maintained at 21 °C using a heat exchanger. Calibration gas mixtures with mole fractions ranging from 2 to 50 ppm were generated using in-line mass flow controllers (MFCs) to combine pre-mixed methane-in-air gas cylinders (AirLiquide, Canada) with synthetic air. The calibration gas flow was maintained near 10 L min⁻¹. A LI-7810 trace gas analyzer (LI-COR Environmental, USA) was used as a reference instrument to benchmark the methane mole fraction. We assign a 1 % uncertainty to the mole fractions recorded by the LI-7810.

For each calibration step, the flow was established and the mole fraction was allowed to stabilize before progressing to the next step. The peak-to-peak amplitude of the methane feature in the WMS 2f spectrum is used for mole fraction calibration. The response of the sensor as a function of methane mole fraction is shown in Fig. . The response of the sensor to increasing methane mole fraction is nearly linear up to 10 ppm; however, further increase in the calibration gas mole fraction can be described by a higher order polynomial, as previously reported in . Here, the non-linear calibration curve corresponds to a fourth order polynomial such that 8χCH4dry(Apk-pk)=a4Apk-pk4+a3Apk-pk3+a2Apk-pk2+a1Apk-pk+a0 where Apk-pk is the WMS 2f peak-to-peak amplitude and the variables ai are fitting parameters. The fit parameters are obtained by orthogonal distance regression. The resultant calibration parameters for the curve shown in Fig. a are a4=1540±170 ppm, a3=-717±114 ppm, a2=163±22 ppm, a1=88.9±1.4 ppm, a0=-0.095±0.024 ppm, where the uncertainties are reported to one standard deviation. Figure b shows the relative residuals together with the 1σ standard error on the fit and the 95 % confidence interval. Over the range of 1.8 to 55 ppm, the 1σ bounds lie within 1.5 % of the fit.

We performed a measurement of the Allan-Werle variance in the calibration enclosure for a duration of 2 h using the 2.00 ppm methane-in-air calibration gas to determine the stability and precision of the sensor (Fig. ). For comparison, we generated a second Allan-Werle deviation plot using in-flight data from a period when the sensor was measuring background methane concentrations during a controlled-release experiment. The UAV flew a “curtain-type” flight pattern with horizontal transects at altitudes ranging from 0 to 30 m above ground level. The average wind speed during the flight period was 2.5 m s⁻¹ with a standard deviation of 0.7 m s⁻¹. The temperature was around 22.5 °C, the pressure at ground level was 1005 hPa, and the relative humidity was 65 %. The in-flight dataset only extends up to an integration time of approximately 300 s due to the limited flight duration.

For reference, 1/τ white noise behavior is indicated by the dash-dot lines in Fig. . Additional noise observed in-flight is primarily caused by inductive noise from the high-powered radio module used for real-time monitoring of mole fraction and telemetry data during flight. Though not required in all scenarios, real-time monitoring is frequently used to enable on-the-fly modification of the mission plan based on the observed methane mole fractions. It may be possible to reduce this noise component by implementing design modifications such as a Faraday cage to mitigate electromagnetic interferences.

The in-lab Allan-Werle deviation below 0.1 s averaging time is affected by a quasi-periodic noise signature for which averaging does not initially improve the measurement precision, appearing as random-walk or correlated noise in the Allan-Werle plot. Though the root cause is still under investigation, the effect is likely induced by correlated mechanical or electrical noise . For the in-flight data, there is a deviation from the 1/τ white noise behavior observed for averaging times between 0.1 and 1 s that may be due to optical fringe effects that are not observed in the laboratory data .

The Allan-Werle plot demonstrates that, by averaging the sensor’s in-flight and laboratory mole fraction data up to 5.8 and 75.9 s, respectively, corresponding optimum precisions of 3.5 and 0.8 ppb are achieved. In both cases, sensor drift dominates precision beyond the optimal averaging times. In the laboratory, the test was performed in a temperature-controlled calibration enclosure, but gradual changes in pressure likely contributed to the drift. For the in-flight test, environmental conditions were not controlled and the sensor experienced rapid changes in air flow as the UAV changed directions. As a result, drift dominates the sensor behavior at shorter averaging times during flight compared with laboratory conditions.

The 1 s averaging times (in-flight precision: 6.7 ppb; laboratory precision: 3.7 ppb) represent a useful metric for comparison to other lightweight UAV sensors. Though other instruments offer slightly smaller 1 s precisions in the range of 1–5 ppb, to our knowledge the ultra-lightweight sensor presented here offers the best 1 s precision for a sub-2 kg methane sensor reported to date. For an open-path system on a mobile platform moving at 5 to 8 m s⁻¹, longer averaging times (>1 s) can result in smearing and loss of information. The averaging time that we use in this study is 0.1 s, corresponding to an in-flight precision of 27 ppb.

Due to its open-path design, the sensor is subject to changes in ambient temperature, pressure, and relative humidity, which directly impact the measured absorption spectrum. Ideally, the sensor should be calibrated under laboratory conditions that are similar to deployment conditions, and the effects of variation in those conditions should be considered in the measurement uncertainty budget.

The ambient air temperature affects the sensor response in several different ways. First, there is the impact of temperature on optical absorbance, wherein the absorbance will decrease with increasing temperature, for the absorption lines employed in this study. Thermal expansion and contraction in the mechanical assembly may impact the effective path length and optical alignment of the multi-pass cell. Rapid temperature changes can also disrupt the thermal stability of the laser and photodetector.

The relative change in methane mole fraction recorded by the sensor at different temperatures in the range of 21 to 29 °C is shown in Fig. . At each temperature, the methane mole fraction was varied in the range of 2 to 50 ppm, using the LI-7810 trace gas analyzer as a reference instrument. At each temperature setpoint, the sensor was allowed to stabilize under active airflow, and temperature stability within the enclosure was maintained to within 0.3 °C. The mole fractions recorded by the reference sensor, which was connected to the calibration chamber by a gas line but was not itself contained within the enclosure, were stable to within 0.6 %. Compared to the mole fractions recorded by the ultra-lightweight sensor at the calibration temperature (20.9 °C), the average recorded mole fraction drops by 3.5 % at 24.6 °C, and by 4.8 % at 28.7 °C. For comparison, the expected change in peak methane absorbance when the temperature increases by 7.8 °C is -4.6 %, or a change in optical absorption of -4.5 % at 2 ppm and -2.3 % at 50 ppm . The impact of temperature-dependent absorbance could potentially be mitigated by selecting a different methane absorption line , though there may be associated trade-offs such as reduced line strength and more limited commercial availability of lasers.

In this work, we have accounted for the sensor's temperature dependence primarily within the measurement uncertainty. Further study into the sensor's non-linear temperature-dependent response is needed to facilitate the implementation of a robust correction protocol. Modifications to the sensor optical head using materials with a lower coefficient of thermal expansion may help to mitigate temperature-dependent effects that are mechanical in nature. The implementation of a 2f/1f normalization routine may also improve tolerance to misalignments and reduce associated temperature-effects. Though 2f/1f normalization is more computationally demanding than the 2f calculation used in this work, we expect that it could be implemented with the same hardware by optimizing resource allocations on the single board computer.

Although the TECs that control the temperatures of the laser and photodetector can adapt to gradual variations in ambient temperature and maintain stable performance across a wide temperature range, sudden shifts in ambient temperature may cause temporary instability. The impact of rapid changes in temperature in the range of 19.7 to 27.5 °C is shown in Fig. . The timeseries data corresponds to the native 100 Hz sensor output. To clearly illustrate the effect of the rapid temperature variation, the 60 s moving average is also shown, indicated by the black dashed line. Oscillatory behavior is observed in the sensor signal immediately after each abrupt temperature change. Over the entire experiment, the 60 s moving average varies by 4.1 %.

Methane absorbance at 3270.4 nm changes with pressure at a rate of about 0.033 % per hPa . When deployed on a UAV up to an altitude of 100 m above takeoff, the ambient pressure is expected to decrease by about 12 hPa, with an associated reduction in methane absorbance of roughly 0.4 %, which may contribute to measurement drift over the course of a flight. Over the same altitude range, temperature generally declines by around 1 °C . Taken together, the variations in temperature and pressure with altitude are expected to cause only a small net increase of about 0.18 % in methane absorbance for a 100 m ascent. For this reason, we expect that the dominant source of pressure-related uncertainty arises from the mismatch between the pressure conditions during calibration and those experienced in the field. In Ottawa, Canada, ambient pressure typically ranges from 970 to 1030 hPa corresponding to a maximum variation of <2 % in methane absorbance. However, pressure changes over a 24 h period are typically much smaller (<10 hPa), and so calibrations should be performed close to deployment whenever possible. Experiments performed in a pressure-controlled environmental chamber are needed to thoroughly quantify how variations in pressure affect the sensor’s response and to detect any response changes that are not related to spectral effects.

Water absorption lines near the 3270.4 nm methane lines have an effect on the recorded methane mole fractions. This is especially important considering that the sensor is typically calibrated using dry gas, and yet there is always water vapor present in the air under field measurement conditions. We used a controlled evaporator mixer (Bronkhorst, Netherlands) to generate humid synthetic air, which we mixed in line using MFCs to create gas mixtures with up to 5 ppm methane. The experiments were performed in the calibration enclosure at a constant temperature of 21 °C. For each methane setpoint, the relative humidity in the enclosure was varied in the range of 0 % to 80 %, corresponding to water vapor mole fractions up to 1.9 %. The methane and water mole fractions were monitored throughout the experiment using the LI-7810 trace gas analyzer. Across four tests, the LI-7810 measured average methane dry mole fractions of 2.34, 3.84, 3.97, and 5.04 ppm, with standard deviations below 3 ppb in each case. Figure a shows the relative change in the methane dry mole fraction measured by the ultra-lightweight methane sensor at 100 Hz, where the water vapor mole fraction χH2O is recorded by the LI-7810. The datasets from all four tests are plotted together. The dashed black line is a linear fit, indicating that the average change in χCH4dry is -1.9 % for each 1 % increase in water vapor content. For context, a water vapor mole fraction of 1 % corresponds to a relative humidity of 41 % at 21 °C or 32 % at 25 °C at atmospheric pressure.

The 2f spectra for χCH4dry=2.34 ppm dry methane and χH2O values of 0.03 %, 0.5 %, 1 %, 1.75 % are plotted in Fig. b. As the water content increases, there is a small change in the 2f baseline, which affects the magnitude of Apk-pk. The changing spectra also illustrate the importance of selecting the left-hand trough rather than the right-hand trough, when evaluating Apk-pk, because the right-hand trough is more strongly affected by the water absorption line.

We deployed the ultra-lightweight methane sensor on the DJI Matrice 300 RTK UAV during a controlled-release experiment to evaluate the sensor's capacity to detect methane emissions in the field. To simulate a methane leak, we released methane from a methane gas cylinder (99.5 % purity, AirLiquide, Canada) at a pre-determined flow rate regulated by an MFC. The MFC-regulated methane flow was released through a gas diffuser at the top of a mast. A stationary anemometer was positioned adjacent to the release point to record the wind speed and direction at the source location throughout the experiment.

The controlled-release experiment is shown in Fig. a. The methane flow rate was 12 standard L min⁻¹ (20 °C and 1013.25 hPa) or 0.48 kg h⁻¹ and the release point was 4.2 m above the ground. To account for adiabatic cooling of the gas upon decompression, we first passed it through a heat exchanger submersed in a thermal bath with an average temperature of 17.5 °C to achieve approximate thermal equilibrium with the ambient temperature. We performed the experiment in a relatively open field on a day when the mean wind speed was 5.3 m s⁻¹ at the release point. The UAV flew two vertical flux curtains at downwind distances of 76 m (near curtain) and 119 m (far curtain) from the emission point. Each horizontal transect spanned a lateral distance of ∼240 m. Transect elevations for the near curtain, which was flown along a dirt road, ranged from 2.5 to 19.5 m above takeoff with transect spacing of 1 m. For the far curtain, uneven terrain and vegetation near the ground prevented the UAV from flying transects below 6.5 m. Transects for the far curtain were spaced at 1.5 m intervals up to a maximum altitude of 26 m above takeoff.

A nearby weather station recorded an average ambient temperature of T=24.2 °C ±1 °C and an average pressure of 1004.6 hPa during the measurement period. The average humidity was 36.7 %, corresponding to an average water vapor mole fraction of 1.1 %.

A uniform +2.1 % correction was applied to the recorded mole fraction data to compensate for relative humidity that differed from dry gas calibration, based on the test results shown in Fig. . The temperature during calibration was similar to the average air temperature in the field, therefore temperature correction was not required in this case. After correction, the average value of the background methane dry mole fraction was 2.10±0.10 ppm. The sensor successfully detected enhancements in methane mole fraction above background levels, demonstrating detection at a methane emission rate <0.5 kg h⁻¹, which is below the limit of detection for most conventional aircraft surveys . Elevated dry mole fractions were detected in the middle of both flux curtains, with the highest values (near: 3.13±0.19 ppm, and far: 2.44±0.15 ppm) observed in the lowest transect. The corresponding timeseries data from the methane sensor is shown in Fig. b, c. The flight durations were 17 and 14 min for the near and far curtains, respectively. The prevailing wind speed and direction at the methane release point is shown in Fig. d, recorded using a sonic anemometer (WindUltra, Gill Instruments, UK) at a data rate of 2 Hz.