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Density is one of the most important properties of seawater and is used in various marine research and technology. Traditionally, in the practice of oceanographic research, it is customary to consider density as a dependent parameter, which is a function of several other parameters taken as independent. Usually the following three parameters are used as the independent parameters: temperature, hydrostatic pressure and salinity. The issues of temperature and hydrostatic pressure measuring in situ are technologically well developed, while in the salinity measuring there are still unsolved problems. This is due to the fact that salinity is such a property that it is simply impossible to determine directly in situ. To eliminate the problems associated with measurements of salinity, the authors developed the special new kind equation. That equation of the new kind express the density of sea water through independent and in situ measured parameters: temperature, hydrostatic pressure and sound speed. The novelty of this approach is that using of the sound speed as the independent parameter makes it possible to exclude measurements of salinity. The authors developed two such new equations for the different cases of using. The first new equation is intended for use in technical applications and reproduces the sea water density in a wide range of the aquatic environment parameters with a root mean square deviation of 0.062 kg/m 3 . The second more precise new equation is intended for scientific applications and reproduces the sea water density in a narrower oceanographic range of parameters with a root mean square deviation of 0.0018 kg/m 3 .

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The use of bivalve mollusks as bioindicators in automated monitoring systems can provide real-time detection of emergency situations associated with the pollution of aquatic environments. The behavioral reactions of Unio pictorum (Linnaeus, 1758) were employed in the development of a comprehensive automated monitoring system for aquatic environments by the authors. The study used experimental data obtained by an automated system from the Chernaya River in the Sevastopol region of the Crimean Peninsula. Four traditional unsupervised machine learning techniques were implemented to detect emergency signals in the activity of bivalves: elliptic envelope, isolation forest (iForest), one-class support vector machine (SVM), and local outlier factor (LOF). The results showed that the use of the elliptic envelope, iForest, and LOF methods with proper hyperparameter tuning can detect anomalies in mollusk activity data without false alarms, with an F1 score of 1. A comparison of anomaly detection times revealed that the iForest method is the most efficient. These findings demonstrate the potential of using bivalve mollusks as bioindicators in automated monitoring systems for the early detection of pollution in aquatic environments.

В статье рассмотрены метрологические и пространственно-временные характеристики профилографа скорости звука. Подробно рассмотрен датчик скорости звука, исследован его водообмен в измерительной базе при зондировании с различными скоростями. Разработана динамическая коррекция вертикальных профилей, связанная с движением прибора. Приведены профили скорости звука, в которых прослеживается их слоистость с вертикальными масштабами в несколько сантиметров. Даны рекомендации по использованию прибора в натурных условиях с борта судна. Ключевые слова: температура, скорость звука, метод, соленость, погрешность, моделирование, динамические характеристики, градиент.