An ultra-sensitive optical sensor system for sulfur dioxide and nitric oxide based on improved UV-DOAS combined with...

Simultaneous measurements of trace sulfur dioxide (SO₂) and nitrogen monoxide (NO) play an indispensable role in industrial emission reduction and combustion efficiency optimization. Ultraviolet differential optical absorption spectroscopy (UV-DOAS), a highly promising technique for detecting trace gases, is capable of simultaneously measuring SO₂ and NO. However, baseline drift caused by NO and spectral overlap between different gases pose significant challenges to the accurate detection of trace gases. We report an ultra-sensitive optical sensor that combines improved UV-DOAS with a domain-transformed spectral decoupling convolutional neural network (DTSD-CNN). First, an improved UV-DOAS based on segmented fitting is introduced to efficiently mitigate baseline drift in mixed gases induced by NO. We then propose a DTSD based on spectral intensity mapping, which helps obtain decoupled SO₂ and NO spectral signals with a high signal-to-noise ratio. Finally, the CNN model trained on single-component spectra achieves accurate quantification of trace SO₂ and NO, circumventing dataset construction challenges associated with mixed gases. The results indicate that the DTSD-CNN achieved a coefficient of determination (R²) of 0.9999 for both SO₂ (25.3-1995.2 ppb) and NO (9.8-1992.4 ppb), with mean absolute errors (MAE) of 4.0 ppb and 4.2 ppb, respectively. Allan variance analysis shows that the minimum detection limits for SO₂ and NO are 1.1 ppb and 2.2 ppb, respectively. Thus, the proposed sensor can be considered an effective system for SO₂ and NO detection.