Dredged material dumping is an activity that causes some of the greatest changes in coastal waters. It results in the need to regularly monitor the properties of seawater related to water quality. In this study, we present the first wide-ranging attempt to correlate seawater turbidity and suspended particulate matter (SPM) concentrations within dumping sites and adjacent waters on the basis of in situ measurements. In the years 2019–2020, we examined four dumping sites, namely Darłowo, Gdynia, Gdańsk, and DCT, located in Polish coastal waters of the Baltic Sea, in the course of four measurement campaigns conducted in the spring, summer, autumn, and winter. The measurements were conducted using a turbidity sensor to determine the turbidity, in formazin turbidity units (FTU), a spectrophotometer to determine the concentrations of nutrients (total phosphorus (P-tot), phosphate phosphorus (P-PO₄⁻³), total nitrogen (N-tot), ammonium nitrogen (N-NH₄⁺), and nitrate nitrogen (N-NO₃⁻)), as well as glass microfiber filters to determine the concentrations of SPM. The analysis of the relationship between the turbidity and SPM within the dumping sites in comparison to reference points showed that the dumping sites are very complex waters and that the issue must be approached locally. The highest turbidity values were registered in the spring, and they correlated linearly with the SPM concentrations (R² = 0.69). Moreover, we performed a statistical cluster analysis to illustrate the similarities between sampling points in the four dumpsites based on nutrient concentrations. We conclude that the influence of the dumping sites on the local bio-optical and chemical properties significantly exceeds their borders and spreads to the adjacent waters. Nutrient concentrations in many cases exceeded the legal policy limits.

Coastal waters are the richest parts of ocean ecosystems characterised by dynamic changes in water biology, physical and chemical features. Establishing local relationships between water constituents and optical properties in these areas will help to develop successful ocean colour algorithms allowing a thorough understanding of complex coastal waters and improving water quality monitoring. In this paper, the authors present the use of optical and biogeochemical measurements in complex aquatic environments and aim to create a semi-empirical model of remote-sensing reflectance (R_rs(λ_i)) for four wavelengths (λ_i = 420 nm, 488 nm, 555 nm, and 620 nm) based on multiparameter algorithms of absorption (a(λ_i)) and backscattering (b_b(λ_i)) coefficients. The bio-optical properties of water were determined based on empirical data gathered from aboard the r/v Oceania from April 2007 to March 2010 in chosen areas of the southern Baltic (Polish coast). The analyses reveal that R_rs(λ_i) in the studied area can be described with satisfactory accuracy using a five-parameter model. Positive results with a statistical error magnitude of R_rs(λ_i) of less than 50% were achieved for all four applied wavelengths. Bio-optical algorithms proposed by the authors enable evaluating biogeochemical characteristics of coastal areas in a broader context of ecosystem assessment and contribute significantly to the development of Earth and environmental sciences.

The Pomeranian lakes in Northern Poland and the nearby coastal waters of the Baltic Sea belong to optically complex water bodies characterised by high eutrophication levels. These water types require a local approach when developing bio-optical algorithms that combine the inherent and the apparent properties of seawater. Well-established local algorithms are of great value for understanding and addressing rapid changes in water quality related mostly to human activities in coastal and near-shore zones, as well as in optically similar lakes. Our research analyses the possibility of using the multi-parameter algorithms of absorption a(λ), backscattering b_b(λ) and remote sensing reflectance R_rs(λ), originally developed for the coastal waters of the Southern Baltic Sea, for three selected Pomeranian lakes. Our multi-parameter algorithms are based on the input concentrations of the biogeochemical components measured in the lake waters, i.e., chlorophyll a (Chl a), suspended particulate matter (SPM), inorganic suspended particulate matter (SPM_inorg), the sum of the surface concentrations of accessory pigments (ΣC) and coloured dissolved organic matter with a wavelength of 400 nm (a_CDOM(400)). R_rs(λ) and a(λ) output values were compared with independent measurements of these parameters conducted in the lake waters at 20 sampling stations. Our algorithm output values of b_b(λ) were compared to the values obtained based on the algorithm provided by Ficek, previously developed and validated for Pomeranian lakes, at the same stations. The statistical analyses conducted afterwards showed that the multi-parameter algorithms of R_rs(λ) and a(λ) for the Southern Baltic Sea are sufficient to be used for the stations investigated in the aforementioned three lakes. Specifically, the correlations between the b_b(λ) values obtained based on the Ficek algorithm and the b_b(λ) values obtained using our multi-parameter algorithm reveal a statistical error rate of less than 20%.

The strong need to control investments related to oil extraction and the growing demand for offshore deep-water exploration are the reasons for looking for tools to make up a global underwater monitoring system. Therefore, the current study analyses the possibility of revealing the existence of oil-in-water emulsions in the water column, based on the modelling of the downwelling radiance detected by a virtual underwater sensor. Based on the Monte Carlo simulation for the large numbers of solar photons in the water, the analyses were carried out for eight wavelengths ranging from 412 to 676 nm using dispersed oil with a concentration of 10 ppm. The optical properties of the seawater were defined as typical for the southern Baltic Sea, while the oil emulsion model was based on the optical properties of crude oil extracted in this area. Based on the above-mentioned assumptions and modelling, a spectral index was obtained, with the most favourable combination of 555/412 nm, whose value is indicative of the presence of an oil emulsion in the water.

Seawater turbidity is a common water quality indicator measured in situ and estimated from space on a regular basis. However, it is rarely correlated with the inherent optical properties of seawater, which convey information about seawater composition. In this study, we show a simple application of the turbidimeter’s weighting function in the estimation of the backscattering coefficient of a model inorganic suspension in seawater. First, we introduce a method to measure the instrument’s weighting function which describes the sensor’s angular response in terms of scattering angles. The determination of the sensor-specific weighting function led us to characterize its angular sensitivity to the presence of suspended particles. The highest sensitivity for the Seapoint turbidimeter is in the range of 114°–128° (containing 25% of the total signal). Next, we describe the correlations between turbidity and the scattering and backscattering coefficients on the example of the model of inorganic particle suspension using the calculations based on Mie theory. The correlations are analyzed for narrow size fractions of the particle size distribution of silica in the range of 0.59–190 µm. We established that there is a good linear correlation (characterized by the coefficient of determination r² = 0.979) between the part of the scattering coefficient measured by the turbidimeter and the backscattering coefficient of all size fractions of the model inorganic suspension.

This study concerns changes in the solar light transfer in seawater as a result of the appearance of oil substances in the form of oil-in-water emulsions. The expected effect of the studies is to gather knowledge that would be useful in designing an optical sensor for monitoring oil substances penetrating the seawater column. The paper presents the process of the Monte Carlo modelling of the upwelling radiance detected by a virtual underwater sensor. Moreover, this article discusses the predicted difference between the intensity of the signal from the upwelling radiance meter and the signal from the downwelling radiance meter. The modelling results suggest that in order to obtain a strong signal indicating the presence of oil substances, the radiance meter should receive light running upwards in the sea depth.