Jianbo Hu

In order to demonstrate the feasibility of the tunable diode laser absorption spectroscopy (TDLAS) technology for monitoring NO_x emissions from inland vessels, an equipment is designed to monitor emissions for inland vessels. The equipment was installed at the Jianbi locks, where experimental measurements were conducted on vessels passing through the locks, with a total of 330 vessels being measured. The detection rate for vessels was 50.3%, with a detection rate of 72.4% for fully loaded vessels and 24.7% for unloaded vessels. In addition, the exhaust emission patterns of inland vessels, the NO_x emission patterns and detection rate of fully loaded and unloaded vessels, and the key parameter of the NO_x emission factor of inland vessels were comprehensively analyzed. The experimental results show that CO₂ and NO_x in the exhaust gas of inland vessels have high signal intensity and good synchronization and can be applied to the regulatory monitoring of NO_x emissions from inland vessels. Furthermore, the ratios of NO/CO₂ and NO₂/CO₂ from fully loaded and unloaded vessels were significantly different. indicating that the NO₂ indicator must be included in the remote monitoring indicators for inland vessel exhaust gases. Otherwise, the remote monitoring results for NO_x may be significantly underestimated.

The complex topography of subtropical montane forests favors the coexistence of diverse plant species, making these species-rich forests a high priority for biodiversity monitoring, prediction, and conservation. Mapping tree species distribution accurately in these areas is an essential basis for biodiversity research and is often challenging due to their complex structure. Remote sensing has widely been used for mapping tree species, but relatively little attention has been paid to species-rich montane forests. In this study, the capability of high-resolution UAV remote sensing imagery for mapping six tree species, standing dead trees, and canopy gaps was tested in a subtropical montane forest at an elevation of 816~1165 m in eastern China. Spectral, spatial geometrical, and textural features in a specific phenological period when obvious color differences among the leaves of different species were extracted, and four object-based classification algorithms (K-nearest neighbor (KNN), classification and regression tree (CART), support vector machine (SVM), and random forest (RF)) were used for tree species classification. We found that: (1) mapping tree species distribution using low-cost UAV RGB imagery in a specific leaf phenological period has great application potential in subtropical montane forests with complex terrain. (2) Plant spectral features in the leaf senescence period contributed significantly to species classification, while the contribution of textural features was limited. The highest classification accuracy was 83% using KNN with the combination of spectral and spatial geometrical features. (3) Topographical complexity had a significant impact on mapping species distribution. The classification accuracy was generally higher in steep areas, especially in the low slope area.

Air pollutants emitted by ships are one of the major causes of global environmental and human health problems, especially for sulfur oxides (SO₂). In this study, a mini-sniffing sensor was mounted on the unmanned aerial vehicle (UAV) to monitor the concentration relationship between CO₂ and SO₂ in the exhaust gas of sailing ships, then the sulfur content of the ship’s fuel oil was estimated to evaluate the compliance of the fuel sulfur content (FSC) with IMO regulations. In the experiment, the measurement results of the exhaust gas of sailing ships in the Pearl River Delta were presented, the data set from February to April 2022 was provided, and 445 ships were comprehensively analyzed from the perspectives of ship length and ship type. From the experimental results, considering the error of the sensor, the compliance rate of the FSC of sailing ships entering and leaving the Pearl River reached 93.7%. To some extent, the current situation for meeting the 0.5% (m/m) limit is basically optimistic. The results represent the effectiveness of DECA policy implementation. This paper demonstrates the effectiveness and reliability of the UAV monitoring method in monitoring emissions from ships, and in more effectively monitoring the impact of shipping on air quality. Furthermore, an accurate and non-contact monitoring method is proposed, which can allow law enforcement officers to judge in advance whether the ships sailing is suspected of illegally using high-sulfur fuels. It can improve the efficiency of law enforcement and reduce the cost of supervision.

The ship exhaust sniffing unmanned aerial vehicle (UAV) system can be applied to monitor vessel emissions in emission control areas (ECAs) to improve the efficiency of maritime law enforcement and reduce ship pollution. To solve the problems of large size, heavy weight and high cost of ship exhaust sniffing sensors, in this paper, a unique diffused mini-sniffing sensor was designed, which provides a low-cost, lightweight, and highly adaptable solution for ship exhaust sniffing UAV. To verify the measurement accuracy of the system, a large number of on-site tests were performed based in the mouth of the Yangtze River, and some cases of violation of the fuel sulfur content (FSC) were verified and punished. Maritime law enforcement officers boarded the ship to take oil samples from eight suspected ships and sent them to the laboratory for testing. The results showed that the FSCs of the eight ships in chemical inspection were all greater than the regulatory limit 0.5% (m/m) of the International Maritime Organization (IMO). The system enables authorities to monitor emissions using rotary UAVs equipped with diffused mini-sniffing sensors to measure the FSC of navigating ships, which couple hardware and operational software with a dedicated lab service to produce highly reliable measurement results. The system offers an effective tool for screening vessel compliance.