Advancement in Smart Monitoring of Greenhouse Gases: An IoT Approach for Inland Waterbodies

Inland aquatic ecosystems, including rivers, lakes, and reservoirs, play an active but still poorly constrained role in the global carbon cycle by acting as both sources and sinks of greenhouse gases such as CO₂, CH₄, and N₂O. Quantifying these emissions remains difficult because aquatic biogeochemical processes vary strongly over short spatial and temporal scales, while commonly used monitoring approaches—such as infrequent manual sampling or costly stationary installations—often fail to resolve rapid changes associated with diel cycles, hydrological events, or transient mixing conditions that can contribute disproportionately to annual fluxes. To overcome these limitations, we developed a scalable, open-architecture Internet of Things (IoT) monitoring system for continuous, high-resolution observation of aquatic greenhouse gas dynamics, built around a Raspberry Pi–based edge-computing unit coupled with calibrated gas sensors (NDIR for CO₂ and a semiconductor-based sensor for CH₄) and supporting environmental sensors for temperature, pressure, and relative humidity. Data are transmitted in near real time to a cloud-based dashboard, enabling remote system diagnostics, immediate visualization, and rapid identification of anomalous events, rather than relying on delayed, site-based data retrieval. Initial field deployments show that this high-frequency approach captures short-term variability in gas concentrations that is largely missed by discrete sampling, highlighting the importance of temporal resolution for inland water GHG assessments. By providing a flexible and cost-effective alternative to conventional reference stations, this system offers a practical route toward denser observation networks, improved model validation, and more reliable carbon budget estimates in heterogeneous freshwater environments.