Air is the environmental foundation for human life and production, and its composition changes are closely related to human activities. Sulfur dioxide (SO₂) is one of the main atmospheric pollutants, mainly derived from the combustion of fossil fuels. But SO₂ is a trace gas in the atmosphere, and its concentration may be less than one part per billion (ppb). This paper is based on the principle of photoluminescence and uses a photomultiplier tube (PMT) as a photoelectric converter to develop a device for real-time detection of SO₂ concentration in the atmosphere. This paper focuses on the impact of noise interference on weak electrical signals and uses wavelet transform to denoise the signals. At the same time, considering that the photoelectric system is susceptible to temperature changes, a multi parameter fitting model is constructed, and a BP neural network is used to further process the signal, separating the real data from the original data. Finally, a high-precision and wide-range trace level sulfur dioxide concentration detection device and algorithm were obtained.

With the acceleration of China’s urbanization process, the problem of environmental pollution is becoming more and more serious and has attracted more and more public attention. In industrial activities such as thermal power generation, the incomplete combustion of fossil fuels such as coal releases sulfur dioxide (SO2), causing harm to ecosystems. Due to the uneven development between regions in China, heavy industries such as thermal power generation will exist for a long time, which will lead to a long-term process of sulfur dioxide control. Therefore, there is an urgent need to develop a simple, accurate, and reliable SO2 concentration detection equipment in order to conduct grid monitoring and implement SO2 source control throughout the country. In addition, given the vast territory of China and the significant difference between the north and south environments, the equipment also needs to have good adaptability. In this paper, variational mode decomposition (VMD) and BP neural network are used to optimize the detection data, which effectively reduces the influence of electronic noise and temperature drift by about 66% and 12%, respectively, and significantly improves the accuracy and reliability of the detection equipment.

In the measurement of shock wave overpressure in blast environments, the sensor must be equipped with fixing and damping components; however, this modification alters the equivalent mechanical model of the sensor system, rendering the dynamic characteristics of the assembled measurement system ambiguous. In addition, the shock and vibration interference generated by the explosion introduces high frequency noise, which distorts the output signal. To address this issue, a shock tube calibration system combined with the equivalent mechanical model was employed to analyse the amplitude frequency characteristics of the sensor both before and after assembly. It was confirmed that the assembled sensor system exhibits a loss of effective pulse width. After the transfer function of the system was determined, a digital filter was designed using the zero-pole cancellation method, thereby optimising and extending the sensor’s dynamic characteristics. Static explosion tests using TNT were carried out, and the measured data were compared with empirical formulae. The results indicate that the post processing measurement error is effectively reduced and the signal becomes smoother. This paper presents a reliable analytical and post processing method for shock wave pressure measurement under blast loading, which offers practical engineering value.

The explosion of warheads generates a high-temperature and high-velocity flow field, and flow field temperature serves as one of the critical parameters characterizing the thermal damage capability of warheads; accurate measurement of explosion flow field temperature possesses prominent scientific research significance in weapon and ammunition engineering as well as safety protection engineering. Nevertheless, the explosion field is featured with ultrahigh temperature accompanied by shock wave pressure, which imposes severe limitations on the practical performance of most conventional sensors. In this paper, structural optimization, thermoelectric characteristic calibration, dynamic characteristic analysis, and open-field experimental tests are implemented to comprehensively investigate a thermocouple sensor tailored for temperature measurement under explosion environments. Experimental results verify that the developed sensor delivers favorable stability with a measurement uncertainty of 1.96% and outstanding dynamic performance with a response time of 2.74 ms; meanwhile, it achieves superior reliability under an extreme test condition with a peak temperature of 2697 °C and a shock wave pressure of 1.342 MPa, demonstrating promising prospects for practical engineering applications.