the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical Note: Pondi – a low-cost logger for long-term monitoring of methane, carbon dioxide, and nitrous oxide in aquatic and terrestrial systems
Abstract. Understanding the complex dynamics of greenhouse gases (GHGs) such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) fluxes between aquatic and terrestrial ecosystems and the atmosphere requires extensive monitoring campaigns to capture spatial and temporal variations adequately. However, conventional commercial GHG analysers limit data collection due to their high costs, limited portability, cumbersome weight, and restricted field autonomy. To overcome these challenges, we developed the Pondi – a lightweight (0.8 kg) logger from cost-effective components tailored for long-term (weeks to months) continuous monitoring of CO2, CH4, and N2O concentrations in terrestrial and aquatic environments. The Pondi features solar panels for indefinite runtime, Global Positioning System (GPS) for tracking, an Inertial Measurement Unit (IMU) for motion detection, and an optional microcontroller-powered add-on to support self-venting and additional sensors. It can be deployed on floating chambers to monitor aquatic emissions, or on land for net primary productivity. The Pondi is connected to a cloud-based system for real-time data access and remote configuration. The components for the Pondi are readily available in most countries, and standard engineering and IT skills are sufficient to assemble the device. By offering a practical, cost-effective, and reliable solution for GHG monitoring, the Pondi contributes to efforts to assess and mitigate anthropogenic GHG emissions.
Status: open (until 30 Apr 2025)
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RC1: 'Comment on egusphere-2025-459', Guillem Domenech-Gil, 24 Mar 2025
reply
I congratulate the authors on the remarkable engineering work carried out, close to a commercial product including many user-friendly functions that make the system almost plug-and-play. Before publishing, I suggest clarifying the following
About calibration and validation
- In section 2.1, you mention the possibility to continuously refine and update the total flux equation as calibration data changes over time while, in section 2.4, you describe one-time calibration (2-point for N2O, 1-point for CH4, and factory pre-calibration for CO2 sensors). This calibration-update feature seems very useful, but if only one-time calibration is needed, which is its purpose?
- More information on how humidity was controlled during the validation measurements and why the chosen RH and temperature, and CO2, CH4, and N2O concentrations are relevant for the later field measurements would be scientifically valuable and increase future usability of Pondi.
About interferences, accuracy, and system validation
- The commercial gas sensors used present interferences. While some of these cross-sensitivities are addressed, others remain. In this sense, it would be very interesting for future uses of Pondi to know certain system specifications. The error linked to temperature and humidity, together with the quantified error via MAPE, represents a measurement inaccuracy, different for each sensor and measurement range. Could you clarify the accuracy of Pondi for each sensor and concentration/measurement range? Is it possible to provide MAPE values for the field measurement range?
- Section 2.6 provides interesting and important insights, but a relevant question might obfuscate them. Was the CO2 sensor tested against interferences? If CO2 sensor does not present interferences, the CO2 contribution from the NO2 signal can be compensated but otherwise the issue becomes more complex.
- The field measurements and observations are relevant to validating Pondi, while missing data may induce thoughts of hidden information. To remove this residual possibility, could you include temperature and humidity data in Figures 6, 7, and 8? Do you have long-term terrestrial flux measurements including the different used sensors? Did you notice long-term drift in any of the sensors used? Could saturation values vary over time as seems to happen in Figure 6 and 7?
Citation: https://doi.org/10.5194/egusphere-2025-459-RC1
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