# Lessons learned from a UAS survey of methane emissions from multiple biogas plants in France

Jean-Louis Bonne, Nicolas Dumelie, Thomas Lauvaux, Charbel Abdallah, Jérémie Burgalat, Grégory Albora, Julien Vincent, Julien Cousin, Florian Parent, Vincent Moncourtois, et al.

► To cite this version:

Jean-Louis Bonne, Nicolas Dumelie, Thomas Lauvaux, Charbel Abdallah, Jérémie Burgalat, et al. Lessons learned from a UAS survey of methane emissions from multiple biogas plants in France. EGU General Assembly 2025, Apr 2025, Vienna & Online, Austria. 2025, 10.5194/egusphere-egu25-5789. hal-05050778

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Submitted on 29 Apr 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# BIOGAS

## CO₂ & CH₄ emissions with UAS

Lessons learned from a UAS survey of methane emissions from multiple biogas plants in France

Jean-Louis Bonne, Nicolas Dumelie, Thomas Lauvaux, Charbel Abdallah, Jérémie Burgalat, Grégory Albora, Julien Vincent, Julien Cousin, Florian Parent, Vincent Moncourtois, Lilian Joly

GSMA UMR 7331, Université de Reims Champagne-Ardenne, CNRS, Reims, 51100, France

<figure><img src="image_2.png" /></figure>

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## MATERIAL & METHODS

### BIOGAS PROCESSING AND ASSOCIATED SOURCES

<figure><img src="image_4.png" /><figcaption>Fig 1: Simplified biogas plant process</figcaption></figure>

Potential sources can be :

- Caused by defect (gas bearing pipes and other containment, digesters, piping and gas storage)

- Not caused by defect (digestate storage units without gas collection, purification units, ventilation)

### FIELD CONSTRAINTS

**Obstacles** (trees, buildings, streets, train lines, aerial electrical lines) determine favourable wind directions

**Sites dimensions** (typically 100m to 500m length) impose long transects, leading to difficulties as to (i) legal

UAS regulations (maximum distance between UAS and pilot), (ii) wireless signal range for real time concentrations visualisation, (iii) UAS autonomy

**Multiplicity and diversity of sources:** point/diffuse, continuous/sporadic, low/elevated

**Sources overlapping:** internal (animal breading farms, composting platforms) or external

**Acceptability by sites owners**

### MASS BALANCE METHOD

Validated for emissions down to 0.01 g.s⁻¹
(Bonne et al., 2024)

#### Flight protocol

Multiple horizontal transects, up to the top of the plume, covering an entire plume section, orthogonal to wind direction, downwind of the source

#### Different applications

➤ Outside of the site for global site emissions, with large transects covering all plumes of the biogas plant at once

➤ At short distance eventually inside the site for specific sources emissions, only under wind conditions ensuring no plume overlap

#### Source emissions flux Q (g.s⁻¹) calculation

$$Q = \int_{\text{plane}} C \cdot Udydz$$

Drone DJI M350 recording attitude and position (RTK GPS)

**AURORA instrument:** in situ CO₂ & CH₄ open-path laser absorption spectrometer at 200 Hz, with Trisonica-mini 2D ultrasonic anemometer for wind speed and direction measurements

C: concentration enhancement; U: wind speed (m.s⁻¹) component orthogonal to monitoring plane

## FIELD CAMPAIGN

Since Dec. 2023 (on-going, results until April 2025)

### 19 agricultural biogas plants (named from A to S) :

- In the Grand Est region, France, <60 km from Reims

- Biogas valorisation: injection into the network

- Effluent storage with/without coverage and gas capture

- Sites productions : 125 to 500 Nm³.h⁻¹

- Plant areas from 0.6 to 5 ha

> Fig 3: Flights statistics by site for each categories of sources monitored and success of monitoring (left) and number of monitoring days by site (right)

<figure><img src="image_5.png" /></figure>

## HIGHS AND LOWS

Ideal case (ex. A): monitoring plane almost orthogonal to the wind, with CO₂ and CH₄ enhancements well above noise level entirely captured horizontally and vertically

### 22% of all quantifications rejected:

- 17% of CH₄ and 18% of CO₂ quantifications had truncated plumes (ex. B), either due to data transmission troubles, obstacles or flight regulation limits

- 6% of flights with identified or suspected parasite plumes (ex. C), despite reconnaissance survey of the surroundings to determine favourable wind sectors without parasite sources

### 18% of quantifications cannot be attributed with certainty (ex. D): neither a global nor a specific source quantification

### Other misjudgements of the plume and wind conditions sporadically encountered: changes of focus during a flight, or inadequate orientation of the transects compared to wind direction

#### A/ Successful global plume monitoring

<figure><img src="image_6.png" /><figcaption>Fig 4: First flight on 2024-03-22 on site N: vertical projections of (a) CH₄ & (b) CO₂ enhancements above background (ppm); (c) wind speeds and directions; (d) map of trajectories (yellow) and site location (blue)</figcaption></figure>

#### B/ Horizontally or vertically truncated plumes

<figure><img src="image_7.png" /><figcaption>Fig 5: Idem fig 4a for flights with horizontally (a) and vertically (b) truncated CH₄ plumes (red circles)</figcaption></figure>

#### C/ Overlapping parasite plumes

<figure><img src="image_8.png" /><figcaption>Fig 6: Flight with parasite plume (a) idem fig 4c; (b) schematic representation of wind conditions, flight path and biogas plant and parasite site locations; (c) idem fig 5</figcaption></figure>

#### D/ Emissions from unidentified portion of site

<figure><img src="image_9.png" /><figcaption>Fig 7: (a) Schematic representation of wind origin, flight path and biogas plant elements (with open digestate lagoon) (b) wind speed and directions</figcaption></figure>

## BIOGAS PLANTS EMISSIONS QUANTIFICATIONS

Large variability between sites: mean emission by site from 24 to 366 gCO₂.s⁻¹ and 0.4 to 6.8 gCH₄.s⁻¹ (fig 8). Large temporal variability for the same sites when monitored several times during different days (fig 8).

$$CH_4\text{loss}(\%) = 100 \cdot \frac{CH_4\text{emissions}}{CH_4\text{production} + CH_4\text{emissions}} \quad \text{(Fredenslund et al. 2022)}$$

<figure><img src="image_10.png" /><figcaption>Fig 9: CH₄ losses (%) vs installed productions (Nm³.h⁻¹). Error bars represent the standard deviation between all global quantifications flights. Sites with unique quantification show no error bar</figcaption></figure>

CH₄ losses (fig 9) from 1.6 to 9.3 % between sites with mean value of 5.3%: coherent with to Fredenslund et al. 2022 for low and moderated productions.

No dependency with installed production, contrary to Fredenslund et al. 2022 showing a scale factor with lower CH₄ losses for higher productions.

Various CO₂/CH₄ ratios are observed for global plant emissions (fig 10). Specific sources reveal contrasted CO₂/CH₄ ratios for each type of source (though with low statistics): this tends to explain the variability of global plants ratios by different predominance of sources within sites.

Mean values of CH₄ emissions from inputs, purifiers and effluents are relatively similar and the sum of these emissions are comparable with average global sites emissions (although not compared for the same sites and low statistics). Biogas purification units seem to be a predominant source of biogenic CO₂ emissions.

<table><caption>Table 1: CO₂ and CH₄ emissions (g/s) for different types of source averaged for all sites</caption><thead><tr><th>Source type (number of quantifications)</th><th>CH<sub>4</sub> (g/s)</th><th>CO<sub>2</sub> (g/s)</th></tr></thead><tbody><tr><td>Global (49)</td><td>2.8 ± 2.4</td><td>156 ± 87</td></tr><tr><td>Inputs (2)</td><td>1.1 ± 1.1</td><td>47 ± 19</td></tr><tr><td>Purifiers (3)</td><td>0.8 ± 0.4</td><td>188 ± 94</td></tr><tr><td>Effluents (2)</td><td>0.9 ± 0.4</td><td>7 ± 2</td></tr></tbody></table>

## CONCLUSIONS, BEST PRACTICES & PERSPECTIVES

### Conclusions

- ✓ CH₄ losses are coherent with literature (Fredenslund et al. 2022)

- ✓ Contributions of the different sources within the sites have specific signatures in CO₂/CH₄ ratios

- ✓ Main contributors of CH₄ emissions might be inputs, effluents and purifiers

### Best practices

- Strong reconnaissance survey of surroundings, including parasite sources several kilometres upwind

- Planification of measurements (selection of targeted plants) based on meteorological predictions of wind speeds and directions

- Maximize transects lengths and altitude range to capture all plumes and avoid focusing on a single plume not representative of the global site emissions

- Data transmission range should be adapted to site dimensions for manual navigation based on real time concentrations. Otherwise, operate with pre-defined flight paths and autonomous navigation to avoid misinterpretation of plumes by operators

### Perspectives

- Alternative modelling approaches (LES, Gaussian/Lagrangian plume inversion)

- Sample diversification (larger productions spectrum, cogeneration, extend to other regions)

### REFERENCES

* Bonne, J.-L., Donnat, L., Albora, G., Burgalat, J., Chauvin, N., Combaz, D., Cousin, J., Decarpenterie, T., Duclaux, O., Dumelié, N., Galas, N., Juery, C., Parent, F., Pineau, F., Maunoury, A., Ventre, O., Bénassy, M.-F., and Joly, L.: A measurement system for CO₂ and CH₄ emissions quantification of industrial sites using a new in situ concentration sensor operated on board uncrewed aircraft vehicles, Atmos. Meas. Tech., 17, 4471–4491, https://doi.org/10.5194/amt-17-4471-2024, 2024.

* Fredenslund, A. M., Gudmundsson, E., Falk, J. M., Scheutz, C.: The Danish national effort to minimise methane emissions from biogas plants, Waste Management, Volume 157, 2023, Pages 321–329, ISSN 0956-053X, https://doi.org/10.1016/j.wasman.2022.12.035.

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Abstract
EGU25-5789