Previous monitoring systems for water quality and greenhouse gas emissions in aquatic ecosystems were difficult to synchronize, making it impossible to meet the high-frequency monitoring requirements of non-point source pollution and were also expensive. This study proposes a low-cost sensor and intelligent algorithm-based monitoring system for greenhouse gases in irrigation water bodies. The system monitors real-time water environmental parameters (flow, temperature, depth, pH, nitrate, ammonia, and dissolved CO₂ concentration) and atmospheric parameters (CO₂ gas concentration) of ponds and rivers in the irrigation area using low-cost sensors. Based on the water environmental parameters and dissolved N₂O concentration dataset, an intelligent algorithm for dissolved N₂O concentration is constructed. The greenhouse gas-water gas interface exchange model is then used to achieve synchronous monitoring of dissolved N₂O and CO₂ concentrations and their emission fluxes in the irrigation water environment. The system also provides visual displays of the changes in water quality, dissolved N₂O and CO₂ emission fluxes in the irrigation area through remote control and visualization on the OneNET cloud server. This study provides key technical support to improve the quantitative assessment of water quality and greenhouse gas emissions in irrigation water bodies and develop emission reduction strategies.
 

暂无