Accurate monitoring of dissolved methane (CH4) is essential for understanding greenhouse gas dynamics in aquatic ecosystems. Achieving in-field detection that meets the stringent requirements for high sensitivity, strong anti-interference capability, and long-term stability remains a major challenge. This study developed a robust sensing system based on off-beam quartz-enhanced photoacoustic spectroscopy (OB-QEPAS) to address three key obstacles to practical application. First, an active frequency-calibration mechanism was integrated to continuously track and calibrate the laser modulation to the instantaneous resonant frequency of the quartz tuning fork (QTF), effectively eliminating signal attenuation caused by environmental fluctuations and ensuring long-term stable measurements. Second, an advanced signal processing algorithm combining moving average and Kalman filtering (MA-Kalman) was implemented, optimized for the noise characteristics of OB-QEPAS signals. This algorithm enhanced the signal-to-noise ratio (SNR) by 9.5 dB, substantially improving low-concentration detection capability. Third, a quantitative water vapor interference correction scheme was developed and experimentally validated, compensating for water vapor-induced signal modulation on the photoacoustic signal through an interpolation-based look-up table method. Experimental results demonstrated that the sensor achieved a minimum detection limit of 0.2 ppm for methane (based on Allan deviation analysis at a 540 s integration time) and exhibited excellent linearity (R2 = 0.9994) across the 10–1000 ppm range. Field measurements in Lake Chaohu successfully quantified dissolved methane concentrations ranging from 3.65 to 22.49 ppm across different aquatic habitats, showing excellent agreement (deviation <1%) with tunable diode laser absorption spectroscopy (TDLAS) data. The observed gradient empirically resolved two distinct methane dynamics endmembers: production driven by urban eutrophication and consumption mediated by wetland oxidation. This work delivers both a high-performance analytical tool and a generalizable framework for robust optical gas sensors, highlighting their potential to advance from environmental monitoring to biogeochemical process analysis.
Xie et al. (Sat,) studied this question.