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Analysed by differential spectroscopy, 1208 empirical spectra of light absorption apl(λ) by Baltic phytoplankton were spectrally decomposed into 26 elementary Gaussian component bands. At the same time the composition and concentrations of each of the 5 main groups of pigments (chlorophylls a, chlorophylls b, chlorophylls c, photosynthetic carotenoids and photoprotecting carotenoids) were analysed in 782 samples by HPLC. Inspection of the correlations between the intensities of the 26 elementary absorption bands and the concentrations of the pigment groups resulted in given elementary bands being attributed to particular pigment groups and the spectra of the mass-specific absorption coefficients established for these pigment groups. Moreover, balancing the absorption effects due to these 5 pigment groups against the overall absorption spectra of phytoplankton suggested the presence of a sixth group of pigments, as yet unidentified (UP), undetected by HPLC. Apr eliminary mathematical description of the spectral absorption properties of these UP was established. Like some forms of phycobilins, these pigments are strong absorbers in the 450–650 nm spectral region. The packaging effect of pigments in Baltic phytoplankton was analysed statistically, then correlated with the concentration of chlorophyll a in Baltic water. As a result, a Baltic version of the algorithm of light absorption by phytoplankton could be developed. This algorithm can be applied to estimate overall phytoplankton absorption spectra and their components due to the various groups of pigments from a knowledge of their concentrations in Baltic water.
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An original method for estimating the concentration of chlorophyll pigments, absorption of yellow substance and absorption of suspended matter without pigments and yellow substance in detritus using spectral diffuse attenuation coefficient for downwelling irradiance and irradiance reflectance data has been applied to sea waters of different types in the open ocean (case 1). Using the effective numerical single parameter classification with the water type optical index m as a parameter over the whole range of the open ocean waters, the calculations have been carried out and the light absorption spectra of sea waters tabulated. These spectra are used to optimize the absorption models and thus to estimate the concentrations of the main admixtures in sea water. The value of m can be determined from direct measurements of the downward irradiance attenuation coefficient at 500 nm or calculated from remote sensing data using the regressions given in the article. The sea water composition can then be readily estimated from the tables given for any open ocean area if that one parameter m characterizing the basin is known.
A method for estimating the water backscattering coefficient was put forward on the basis of experimental data of diffuse attenuation coefficient for downwelling irradiance and irradiance reflectance. Calculations were carried out for open sea waters of different types and the spectral dependencies were found (‘anomalous’ spectra) and explained. On this basis, a new model of light backscattering on particles in the sea is proposed. This model may be useful for modelling remote sensing reflectance spectra in order to solve the inverse problems of estimating the concentration of natural admixtures in shelf waters.
The absorption properties of phytoplankton in surface waters of the Baltic Sea and coastal lakes are examined in the context of their relationships with the concentration of the main photosynthetic pigment, chlorophyll a. The analysis covers 425 sets of spectra of light absorption coefficients aph(l) and chlorophyll a concentrations Chla measured in 2006—2009 in various waters of the Baltic Sea (open and coastal waters, the Gulf of Gdańsk and the Pomeranian Bay, river mouths and the Szczecin Lagoon), as well as in three lakes in Pomerania, Poland (Obłęskie, Łebsko and Chotkowskie). In these waters the specific (i.e. normalized with respect to Chla) light absorption coefficient of phytoplankton aph *(l) varies over wide ranges, which differ according to wavelength. For example, aph *(440) takes values from 0.014 to 0.124 mg1 m2, but aph *(675) from 0.008 to 0.067 mg1 m2, whereby Chla ranges from 0.8 to 120 mg m3. From this analysis a mathematical description has been produced of the specific light absorption coefficient of phytoplankton aph *(l), based on which the dynamics of its variability in these waters and the absorption spectra in the 400—700 nm interval can be reconstructed with a low level of uncertainty (arithmetic statistical error: 4.09—10.21%, systematic error: 29.63—51.37%). The relationships derived here are applicable in local remote sensing algorithms used for monitoring the Baltic Sea and coastal lakes and can substantially improve the accuracy of the remotely determined optical and biogeochemical characteristics of these waters.
The article describes applications and accuracy analyses of a statistical model of light absorption by phytoplankton that accounts for the influence of photo- and chromatic acclimation on its absorption properties. Part 1 of this work (seeWoźniak et al. 2000, this volume) describes the mathematical apparatus of the model. Earlier models by Woźniak & Ostrowska (1990) and by Bricaud et al. (1995, 1998) are analysed for comparison. Empirical verification of these three models shows that the new model provides a much better approximation of phytoplankton absorption properties than do the earlier models. The statistical errors in estimating the mean absorption coefficient apl, for example, are σ+ = 36% for the new model, whereas for the earlier models the figures are σ+ = 43% (Bricaud et al. 1995, 1998) and σ+ = 59% (Woźniak & Ostrowska 1990). Example applications are given of the new model illustrating the variability in phytoplankton absorption properties with depth and trophicity of the sea.
This volume contains a set of eight papers presenting the results of the latest research into the interaction of light with marine phytoplankton by teams from the Marine Physics Department at the IO PAS in Sopot, and the Department of Environmental Physics at the Pedagogical University of Słupsk. These results were presented at the ‘Second Workshop on Luminescence and Photosynthesis of Marine Phytoplankton’ (Sopot–Paraszyno, 11–15 October 1999) sponsored by the Polish State Committee for Scientific Research. This introductory article discusses the most important assumptions and objectives of the research, and outlines the latest results. These are subsequently discussed in detail in the following papers: (1) Majchrowski & Ostrowska, Influence of photo- and chromatic acclimation on pigment composition in the sea, (2) Woźniak et al., Model of the ‘in vivo’ spectral absorption of algal pigments. Part 1. Mathematical apparatus, (3) Majchrowski et al., Model of the ‘in vivo’ spectral absorption of algal pigments. Part 2. Practical applications of the model, (4)Ostrowska et al., Variability of the specific fluorescence of chlorophyll in the ocean. Part 1. Theory of classical ‘in situ’ chlorophyll fluorometry, (5) Ostrowska et al., Variability of the specific fluorescence of chlorophyll in the ocean. Part 2. Fluorometric method of chlorophyll a determination, (6) Ficek et al., Influence of non-photosynthetic pigments on the measured quantum yield of photosynthesis, (7) Ficek et al., Variability of the portion of functional PS2 reaction centres in the light of a fluorometric study. For the reader’s convenience, we append a list of the symbols denoting the physical quantities used in the texts. The nomenclature and denotations are in line with the conventions employed in the subject literature.
Using laboratory cultures of algae and natural phytoplankton populations from Nhatrang Bay (South China Sea), the relationship between the chlorophyll fluorescence F0, the chlorophyll a concentration Ca and light absorption capacities of algae cells was studied. It is shown that the ratio F0/Ca depends mainly on the species composition of the algae population; hence, the concentration Ca can be measured with the fluorescence method with acceptable accuracy only when the species composition of algae populations varies over a rather narrow range. The fluorescence F0 can, however, be a good index of the total absorption capacities of different phytoplankton species, because the intensity of F0 depends on the sum total of light absorbed by all photosynthetic pigments in a plant cell. Thus, the fluorescence F0 measures not only the concentration of chlorophyll a, but that of all photosynthetic pigment concentrations.
The inherent optical properties (IOPs) of suspended particulate matter and their relations with the main biogeochemical characteristics of particles have been examined in the surface waters of the southern Baltic Sea. The empirical data were gathered at over 300 stations in open Baltic Sea waters as well as in the coastal waters of the Gulf of Gdańsk. The measurements included IOPs such as the absorption coefficient of particles, absorption coefficient of phytoplankton, scattering and backscattering coefficients of particles, as well as biogeochemical characteristics of suspended matter such as concentrations of suspended particulate matter (SPM), particulate organic matter (POM), particulate organic carbon (POC) and chlorophyll a (Chl a). Our data documented the very extensive variability in the study area of particle concentration measures and IOPs (up to two orders of magnitude). Although most of the particle populations encoun- tered were composed primarily of organic matter (av. POM/SPM=ca 0.8), the different particle concentration ratios suggest that the particle composition varied significantly. The relations between the optical properties and biogeochemical parameters of suspended matter were examined. We found significant variability in the constituent-specific IOPs (coefficients of variation (CVs) of at least 30% to 40%, usually more than 50%). Simple best-fit relations between any given IOP versus any constituent concentration parameter also highlighted the significant statistical errors involved. As a result, we conclude that for southern Baltic samples an easy yet precise quantification of particle IOPs in terms of the concentration of only one of the following parameters – SPM, POM, POC or Chl a – is not achievable. Nevertheless, we present a set of best statistical formulas for a rough estimate of certain seawater constituent concentrations based on relatively easily measurable values of seawater IOPs. These equations can be implemented in practice, but their application will inevitably entail effective statistical errors of estimation of the order of 50% or more.
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