This study focuses on the gas-particle (G-P) partitioning of 16 polycyclic aromatic hydrocarbons (PAHs) from oil combustion, which is one of the important contributors of anthropogenic PAHs but has been rarely studied. The combustions of different types of oils involving ultra-light to heavy oils were investigated, and the PAH partitioning mechanism was determined by the widely used Junge-Pankow adsorption model, K_oa absorption model, and dual sorption model, respectively. The results show that the source-specific diagnostic ratios of Ant/(Ant+Phe) are between 0.09 and 0.24, the estimated regression slopes of G-P partition coefficients (K_P) of the total PAHs on their sub-cooled liquid vapor pressures (P_L^O) are in the range of - 0.34 to - 0.25, and the predicted fractions of PAHs in the particle phase (φ) by K_oa absorption model are close to the measured values, while the log K_Pvalues of the LMW PAHs from the combustions of diesel and heavy oil are better represented by the dual sorption model. Our findings indicate that PAHs are derived from mixed sources that include the unburned original oil and combustion products, and the PAH partitioning mechanism is governed by the process of absorption into organic matter because of the unburned oil, but both adsorption and absorption exist simultaneously in the lighter PAHs from the combustions of heavier oils (i.e., diesel and heavy oil). Based on these findings, the understanding of the fate and transport of PAH emissions and the optimization of the emergency responses to accidents such as marine oil spills would be potentially improved.

Tuna fish school detection provides information on the fishing decisions of purse seine fleets. Here, we present a recognition system that included fish shoal image acquisition, point extraction, point matching, and data storage. Points are a crucial characteristic for images of free-swimming tuna schools, and point algorithm analysis and point matching were studied for their applications in fish shoal recognition. The feature points were obtained by using one of the best point algorithms (scale invariant feature transform, speeded up robust features, oriented fast and rotated brief). The k-nearest neighbors (KNN) algorithm uses ‘feature similarity’ to predict the values of new points, which means that new data points will be assigned a value based on how closely they match the points that exist in the database. Finally, we tested the model, and the experimental results show that the proposed method can accurately and effectively recognize tuna free-swimming schools.

In the process of pond culture, the usage of an aeration device can increase dissolved oxygen density and form a decent circulation which facilitates the collection of sludge. Acoustic Doppler Velocimeter (ADV) has been widely used to monitor the flow velocity, however, factors such as bubbles and suspended particles can affect the correlation coefficient and signal-to-noise ratio of ADV, which leads to the existence of outliers in velocity data. This study constructs the three-dimensional rousseeuw phase-space (3DRPS) method by optimizing the phase space threshold method and robust estimation method through two-step filtering and three-dimensional simultaneous measurement, where the outliers close to the real value can be detected more accurately and the iterative process can be reduced more effectively. The results show that the detection rate of the optimized 3DRPS method is approximately 99%. It is a promising method that effectively improves the accuracy of outlier detection and greatly reduces the phenomenon of over processing.