Water transport has become an important pillar of global economic development owing to its numerous advantages, such as substantial capacity and low cost. However, ships release a considerable amount of harmful gases during navigation and docking. Among these emissions, SO₂ accounts for a significant portion, and excessive emissions can pose significant risks to marine ecosystems, human health, and the environment at large. Therefore, it is particularly important to monitor SO₂ emissions from ship exhaust. Among the various monitoring methods available, SO₂ UV cameras have experienced rapid development due to their uncomplicated structure, extensive monitoring range, high measurement accuracy, and superior temporal and spatial resolution. They have found widespread application in monitoring pollutant gases in diverse fields, including volcanoes, industrial chimneys, and ships. Typically, UV cameras employ the four-image method for monitoring, wherein a series of pollutant plume images are captured over a period, followed by a change in the camera’s field of view to capture a set of images of the sky background. However, the effectiveness of the traditional four-image method is compromised by the significant fluctuations in the sky background caused by the ship’s movement within a short time series, leading to errors in the final measurement results. To enhance the result accuracy, this paper proposes a monitoring method based on the dual-channel ultraviolet camera principle and the engineering implementation of an image reconstruction method. This method enables real-time reconstruction of the background based on the plume image, facilitating accurate inversion of the SO₂ column concentration.

The image reconstruction method begins by applying thresholding and labeling to the acquired plume images. Two thresholds are set using the adaptive threshold selection method, effectively distinguishing between the plume structure and the sky background based on the threshold interval. Subsequently, the labeled plume regions are removed from the image, resulting in an image devoid of plume structures. The eliminated plume structure is then replaced with null values, and a polynomial fit is employed to fill each column of the removed plume portion, thus generating a background image of the same size as the original image. This generated background image can then be merged with the captured plume image, thereby providing the optical thickness of SO₂ gas within the ship's plume.

To validate the scientific rigor and efficacy of the image reconstruction method, a self-developed dual-channel SO₂ UV camera is utilized to gather ship exhaust emission data in Yantai Port, and the collected data are analyzed. Initially, SO₂ column concentration inversion is conducted using both the traditional four-image method and the image reconstruction method proposed in this paper (Fig. 7). The experimental findings reveal a notable disparity between the SO₂ concentrations derived from the inversions of the two methods. Specifically, the SO₂ background remains elevated in the background portion of the sky in the SO₂ column concentration images obtained using the conventional four-image inversion method. In contrast, the background concentration in the SO₂ column concentration image obtained using the image reconstruction method appears purer and more consistent with the real situation. Subsequently, the SO₂ column concentration images are integrated with the optical flow algorithm to calculate the corresponding emission rate and assess the error in the emission rate for both methods (Fig. 10). Upon comparison, it is observed that the image reconstruction method effectively rectified the errors stemming from the change in the background image in real-time, demonstrating high stability. Additionally, the variation amplitude of the calculated emission rate curve is smaller and exhibits smoother transitions, aligning more closely with the actual trends. Regarding emission rate values, the image reconstruction method reduces the error by approximately 66% compared to the four-image method, thus significantly enhancing the accuracy of the data inversion.

The experimental results demonstrate that the image reconstruction method proposed in this paper effectively adjusts the background image of the sky in real time, outperforming the traditional four-image method in both the inversion of SO₂ column concentration and the calculation of the emission rate. This substantial enhancement significantly boosts the monitoring accuracy of UV cameras for SO₂ emissions. With its technical advantages of simplicity, practicality, and accuracy, the image reconstruction method exhibits promising prospects in remote sensing monitoring of mobile pollution sources. It is anticipated that this method will offer more reference value for the development of UV remote sensing monitoring systems and further advance UV imaging technology in monitoring and application fields.