The booming shipping industry leads to ever increasing emissions of ship exhaust pollutants. Sulfur dioxide (SO₂), the main component of pollutants in ship exhaust, causes the most serious air pollution. Effective monitoring of its emissions is the key to controlling ship exhaust pollution. In recent years, the imaging detection technology of SO₂ ultraviolet (UV) cameras has been developed rapidly due to its strong practicability and high reliability and has been applied in ship exhaust monitoring. However, calibration is still the main factor that limits its measurement accuracy and application. There are three calibration methods (calibration cells, DOAS, and spectral calibration) for SO₂ UV cameras. The calibration cell method is simple and most employed early in calibration. However, the frequent switching of calibration cells exerts adverse effects on the real-time detection of SO₂ UV cameras. Although DOAS is suitable for long-distance monitoring, it has the disadvantages of small field of view (FOV) and poor matching. The accuracy of spectral calibration is significantly improved compared with the first two methods, but the complexity and cost of the camera system are rising with the adoption of an outlay UV spectrometer. With a focus on the self-calibration method, this paper carries out research based on the working mechanism of the SO₂ UV camera imaging detection technology, the UV radiation transfer theory, and the simulation of the entire system. Comparison between the advantages of the self-calibration method and the three traditional calibrations proves that the self-calibration method not only has accurate, simple, and practical technical advantages but also shows its great application prospect in the UV imaging remote sensing monitoring of mobile pollution sources.

The signal channel (filter A) is greatly affected by the changing ozone optical path length, but the reference channel (filter B) is relatively less affected. This difference is the source of the basic principle of self-calibration. The theoretical analysis shows that the calibration coefficient is approximately a monotone function of the logarithm of the intensity ratio, and the relationship between them is hardly affected by atmospheric conditions. The inversion process is as follows. First, the signal images of the two channels are obtained through UV cameras. Second, two channels of artificial background images are obtained by the 2-IM method. Before artificial background generation, the dark noise should be deducted from the raw image, and image correlation must be optimized through translation and rotation operations for the best match. Third, the average of the corresponding background intensities of the two channels is employed as the input parameter for self-calibration. The calibration curve of UV cameras can be determined by the logarithmic relationship between the calibration coefficients and the intensity ratio of the two-channel images. The feasibility of the self-calibration method is assessed by the validation experiment. In addition, an outfield experiment is conducted to characterize its accuracy.

The principle for self-calibration is the fact that the two channels of sky background images are affected differently by changes in ozone concentration and the solar zenith angle. The average optical path of solar scattered through the ozone layer increases with the rising solar zenith angle, which makes the ozone absorption worsen the incident light intensity which reaches the cameras system (Fig. 5). As the absorption cross-section of ozone increases significantly towards deep UV wavelength, the signal channel is particularly influenced by variations in ozone optical path length, which is greater than that of the reference channel. Therefore, the functional relationship between the two channels of sky background image intensity ratio and the calibration coefficient can be confirmed (Fig. 7). The validation experiments show that the slope of the calibration curve fitted by the self-calibration method is similar to that obtained by the conventional calibration method with a little difference of about 1.4% (Fig. 9). In addition, the colormap of the SO₂ image of the ship plume retrieved from the UV cameras (Fig. 12) is compared with the data collected by the spectrometer. The results show that the error of the two calibration methods is about 6% (Fig. 13), which demonstrates the feasibility of adopting the self-calibration method to invert the exhaust concentration of movable and low SO₂ concentration pollution sources.

This paper proposes a real-time self-calibration method for UV cameras, with full consideration of the imaging mechanism of UV cameras and UV radiation transfer theory. Regarding the shortcomings of three traditional calibration methods in practical applications, the theoretical basis of the self-calibration method is proposed. The new method can determine calibration curves for retrieving SO₂ concentration by employing the intensity ratios of two channels obtained directly from UV cameras. The self-calibration method is compared with the conventional calibration method, and the error is reduced to 1.4% after filter transmittance correction. To verify the accuracy of the proposed theory, this paper measures the SO₂ emission concentration of the ship at Shanghai Port and compares the measurement difference between the self-calibration method and DOAS approach on time series. The error of the two methods is about 6%, which shows good consistency. This study proves that the self-calibration method can overcome the distance limit and adapt to complex environments, with widespread applications in mobile pollution sources.