This review investigates the ship emission measurements using multirotor unmanned aerial vehicles (UAVs). The monitoring of emissions from shipping is a priority globally, because of the necessity to reduce air pollution and greenhouse gas emissions. Moreover, there is widespread global effort to extensively measure vessel fuel sulfur content (FSC). The majority of studies indicate that more commonly used methods for measuring ship emission with UAVs is the sniffing method. Most of the research is concerned with determining the fuel sulfur content. Fuel sulfur content can be determined by the ratio of CO₂ and SO₂ concentration in the exhaust gas plume. For CO₂, the non-dispersive infrared (NDIR) method is used, the most common measuring range reaches 0–2000 ppm, the overall measuring range 0–10,000 ppm, and detection accuracy is ±5–300 ppm. For SO₂, the electrochemical (EC) method is used, the measuring range reaches 0–100 ppm, and the detection accuracy is ±5 ppm. Common UAV characteristics, used in measurement with ships, involve the following: 8–10 m/s of wind resistance, 5–6 kg maximum payload, and a flight distance ranging from 5 to 10 km. This can change in the near future, since a variety of emission measuring devices that can be mounted on UAVs are available on the market. The range of available elements differs from device to device, but available ranges are allowed and the accuracy provides good possibilities for wider research into ship emissions.

Global shipping generates substantial emissions that can adversely affect air quality in port cities, yet the detectability of ship-related pollution by urban monitoring locations remains insufficiently understood. This study aims to identify the meteorological conditions under which ship exhaust plumes can be detected at a stationary air-quality monitoring station located 1.4 km from the Port of Klaipeda. Night-time particulate matter and NO measurements from an AQMesh station were synchronized with Automatic Identification System (AIS) ship-tracking data, and an artificial neural network was applied to determine the environmental parameters most strongly associated with detectable pollution peaks. Kernel Density Estimation (KDE) was used to map the spatial patterns of ship activity by vessel type. The results indicate that plume detection is most likely to be detected with moderate wind speeds (8–12.5 m/s for PM and 7.5–9.6 m/s for NO), elevated humidity (>84%), and higher-pressure ranges for particulate matter. Warmer night-time conditions further enhance pollutant transport by reducing atmospheric stability. KDE analysis shows that potential pollutant accumulation zones differ by vessel type, with the most intense hotspots forming near anchorage locations rather than along transit routes. Overall, the findings demonstrate that ship-related pollution can be detected at distances exceeding 1 km under specific meteorological conditions and highlight the parameters that most strongly govern plume penetration into the urban environment.

Shipping remains a major contributor to air pollution, leading to increasingly strict emission regulations. As the sector transitions toward lowcarbon and alternative marine fuels, widely adoptable methods for measuring ship emissions are essential. UAVbased remote measuring enables efficient plume measurements, yet estimating emission rates remains challenging because exhaust flow cannot be measured remotely. Inverse dispersion modeling offers a promising solution. This study develops a methodology for estimating ship pollutant emissions using UAV measurements combined with neural networks. The first task was to determine whether the inverse dispersion problem can be estimated with reasonable error by using ANN. The second task was to identify the influence of the UAV flight speed and direction relative to the plume. Across four virtual flights, 4 m/s UAV speed showed the lowest mean percentage error 5.21%, while the number of measurement points had little effect. Horizontal flythrough simulations further reduced the mean error to 4%.