Journal Pre-proof Species-specific sensitivity of three microalgae to sediment elutriates A. Gallo, M. Guida, G. Armiento, A. Siciliano, N. Mormile, F. Carraturo, D. Pellegrini, L. Morroni, E. Tosti, M.I. Ferrante, M. Montresor, F. Molisso, M. Sacchi, R. Danovaro, G. Lofrano, G. Libralato PII:
To appear in:
Marine Environmental Research
Received Date: 30 October 2019 Revised Date:
3 February 2020
Accepted Date: 3 February 2020
Please cite this article as: Gallo, A., Guida, M., Armiento, G., Siciliano, A., Mormile, N., Carraturo, F., Pellegrini, D., Morroni, L., Tosti, E., Ferrante, M.I., Montresor, M., Molisso, F., Sacchi, M., Danovaro, R., Lofrano, G., Libralato, G., Species-specific sensitivity of three microalgae to sediment elutriates, Marine Environmental Research (2020), doi: https://doi.org/10.1016/j.marenvres.2020.104901. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.
To: Prof. Francesco Regoli Università Politecnica delle Marche, Ancona, Italy Editor for Marine Environmental Research
Dear Prof. Francesco Regoli, We herewith submit the manuscript entitled “Species-specific sensitivity of three microalgae to sediment elutriates”, by Gallo et alia to Marine Environmental Research for possible evaluation as Full Length Article to be published in the Special Issue on ABBaCO Project: VSI: Industrial Impact at Sea. In this study, microalgae are considered good bioindicators of marine environmental quality. Frequently, they are used to investigate the toxicity of sediment elutriates, but their sensitivity is disputed. This paper investigated the presence of potential correlations between Phaeodactylum tricornutum (diatom), Skeletonema costatum (diatom), and Dunaliella tertiolecta (green alga) sensitivities analyzing 257 samples of elutriates (1:4 sediment: water ratio) considering growth inhibition (72 h) as the reference endpoint and sediment chemical characterization (metals, metalloids and polyaromatic and aliphatic hydrocarbons). Toxicity data showed that the microalgae sensitivity was not correlated both considering uni- and multi-variate statistical analysis with highly scattered data. The integration of chemical data did not significantly discriminate toxicity effects but contributed to highlight that D. tertiolecta was the most sensitive microalgae (no cell wall) followed by P. tricornutum and S. costatum. Further analysis, including lines of evidence and weight of evidence approaches to calculate risk quotients of elutriate samples, confirmed such results.
Declaration of Interest statement: All authors must do not have any financial and personal relationships with other people or organizations that could inappropriately influence (bias) their work or state, there are no interests to declare. 1
Species-specific sensitivity of three microalgae to sediment elutriates Gallo A.1, Guida M.2,3, Armiento G.4, Siciliano A.2, Mormile N.2, Carraturo F.2, Pellegrini D.5, Morroni L.5, Tosti E.1, Ferrante M.I.6, Montresor M.6, Molisso F.7, Sacchi M.7, Danovaro R.8,9, Lofrano G.10, Libralato G.2,3. * 1 Department of Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy 2 Department of Biology, University of Naples Federico II, Via Cinthia, 21, 80126, Naples, Italy 3 Department of Marine Biotechnology, Stazione Zoologica Anton Dohrn, Naples, Italy 4 ENEA, Agenzia nazionale per le nuove tecnologie, l'energia e lo sviluppo economico sostenibile, Centro Ricerche Casaccia, Via Anguillarese, 301, 00123, Roma, Italy 5 ISPRA, Italian Institute for Environmental Protection and Research, Via del Cedro (c/o Dogana d’Acqua), 57122, Livorno, Italy 6 Department of Integrative Marine Ecology, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy 7 Istituto per le Scienze Marine (ISMAR), Consiglio Nazionale delle Ricerche (CNR), Calata Porta di Massa, 80133 Napoli, Italy 8 Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy 9 Department of Life and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy 10 Centro Servizi Metrologici e Tecnologici Avanzati (CeSMA), Complesso Universitario di Monte Sant’Angelo, Via Cinthia 21, 80126, Naples, Italy Corresponding author*: *Prof. Giovanni Libralato, [email protected]
, +39 3498833526, 3
Abstract Microalgae are considered good bioindicators of marine environmental quality. Frequently, they are used to investigate the toxicity of sediment elutriates, but their sensitivity is disputed. This paper compared the sensitivity of Phaeodactylum tricornutum (diatom), Skeletonema costatum (diatom), and Dunaliella tertiolecta (green alga), analyzing 257 samples of elutriates (1:4 sediment: water ratio), considering growth inhibition (72 h) as the reference endpoint and sediment chemical (metals, metalloids and polyaromatic hydrocarbons) and grain size. Results of the toxicity tests showed that the microalgae sensitivity was not correlated. The integration of chemical data did not allow to discriminate toxicity effects but contributed to highlight that D. tertiolecta was the most sensitive microalgae (no cell wall) followed by P. tricornutum and S. costatum. Further analysis, including lines of evidence and weight of evidence approaches to calculate risk quotients of elutriate samples, confirmed these results.
Keywords Microalgae; sediment; 1:4 elutriate; toxicity; sensitivity
Sensitivity of three microalgae to elutriates from highly contaminated sediments is compared
Uni- and multivariate analyses did not highlight any significant correlation among species
Single chemicals and granulometry did not facilitate samples ranking
Species sensitivity to elutriates was: S. costatum < P. tricornutum < D. tertiolecta
Introduction Microalgae are key components of marine ecosystems, being one of the most important primary producers at the base of the marine food chain and are widespread all over the world’s oceans and seas (Mucha et al., 2003). They are considered good indicators of marine environmental quality because of their relative sensitivities (Libralato et al., 2011; Mucha et al., 2003). Microalgae have the capacity to reflect alteration in the marine water quality responding rapidly (72 h) and predictably to a wide range of contaminants, thus providing potentially useful early warning signals of deteriorating conditions and their potential causes (McCormick and Cairns, 1994). Their sensitivity to pollutants and ecological relevance along with the ease of culturing and maintaining under laboratory conditions have promoted their use as model organism in ecotoxicology reaching the international standardization under ISO (ISO, 2016). The standardized method can investigate the effects of a single substance, samples of marine water or sediment elutriates to exponentially growing microalgae populations considering growth inhibition as the main chronic endpoint (ISO, 2016). The species most recommended are the diatoms Phaeodactylum tricornutum and Skeletonema costatum (ISO, 2016), but it is frequently used also for the green algae Dunaliella tertiolecta (Manzo et al., 2013). Diatoms are unicellular algae characterized by the presence of a rigid siliceous cell wall. They occupy a variety of marine habitats and are often the most abundant photosynthetic organisms in marine waters. P. tricornutum (Bacillariophyta, Bacillariophyceae) is a benthic pennate diatom, which displays different morphotypes, with cells of approximately 10 µm in diameter and a large part of their volume is occupied by a single chloroplast and is considered a suitable target species because of the extensive knowledge of its biology, ecology and ecotoxicology (Renzi et al., 2014). S. costatum (Bacillariophyta, Thalassiosirophycidae) is a chainforming diatom of wide geographical range. Its distribution, abundance, and growth mechanisms have been extensively investigated under experimental and natural conditions (Huang et al., 1994). It is commonly used in marine eco-toxicological tests to evaluate the toxicity of diverse traditional 7
and emerging contaminants (Okamura et al., 2009; Pavlić et al., 2005; Zhang et al., 2016). D. tertiolecta (Chlorophyceae, Chlamydomonadales) is a biflagellate green alga with a cell size of 1012 µm, living suspended in the water column and able to grow in severe environments. It is considered a good indicator to evaluate the toxicity of contaminants present in marine water since it lacks a cell wall that may be a potential barrier to the passage of pollutants into the cell (Borowitzka and Siva, 2007; Manzo et al., 2013). Frequently, marine algal growth inhibition tests are carried out to investigate the potential impact of contaminated marine sediment prior to or during dredging activities and their final disposal. Sediment-associated contaminants can be released in the water column during natural or anthropogenic resuspension events (Arizzi Novelli et al., 2006; USEPA, 1991) representing a major threat to marine ecosystem, aquaculture and touristic activities (Libralato et al., 2018). Microalgae growth can be affected by several contaminants and it has been already established that sensitivity to single substances (Levy et al., 2008) and environmental samples (i.e. elutriates) (Mucha et al., 2003; Pelusi et al., 2020) can considerably vary between microalgal species. A comparative review of the relative sensitivities of D. tertiolecta, P. tricornutum and S. costatum to main environmental contaminants is summarized in Table 1. Several factors may contribute to species-specific sensitivity such as cell size, cell wall type and taxonomic group. The interspecies sensitivity to environmental contaminants as well as the different level of toxicity are also related to cellular targets of contaminants, differences in uptake rates and cell detoxification mechanisms (Levy et al., 2007). The aim of this study was to carry out growth inhibition toxicity tests on three different microalgal species to elutriates prepared from sediment samples presenting a wide range of chemical contaminants. The considered species belong to two different groups used in environmental studies: P. tricornutum and S. costatum, two diatoms (ISO, 2016), and D. tertiolecta, a green alga. To the best of our knowledge, this is the first study comparing their sensitivity and reproducibility on a wide database of toxicity data. 8
2. Materials and Methods 2.1 Tested species The algal strain Skeletonema costatum (Greville; NIVA BAC 1) Cleve was purchased by Norwegian Institute for Water Research; Phaeodactylum tricornutum Bohlin was obtained from the Laboratory of Hygiene: Water, Food and Environment, Department of Biology, University of Naples Federico II; the green microalga Dunaliella tertiolecta was supplied by the Department of Marine Biotechnology of Stazione Zoologica Anton Dohrn. According to the (ISO, 2016) guideline, these microalgae were maintained and pre-cultured in a growth medium made up by adding nutrients to natural seawater; which was collected from uncontaminated sites, pre-filtered with 0.45 µm membrane filter and then filtered by 0.22 µm membrane filter.
2.2 Algal growth inhibition toxicity tests Toxicity test has been performed following (ISO, 2016) by using multiwell plates. Inocula obtained from pre-cultured exponentially growing microalgae were exposed to elutriates, spiked with nutrient stock solutions to ensure that nutrients were not limiting, for 72 h at 20 ± 13.32 ºC under continuous light of 8000 lux and with continuous shaking at 100 rpm. Three replicates for control (growth medium and inoculum with no elutriate) and three replicates of each sample were prepared. At the end of 72 h exposure, spectrophotometric measurement of samples at 670 nm (DR 3800 sc, Hach) was performed to determine algal cell density using a calibration curve previously constructed correlating absorbance values and cell density estimated by direct cell count. Zeroing occurred on samples exposed in parallel and at the same conditions of microalgae toxicity tests. For checking the test procedure and algal sensitivity, the test was also performed on reference toxicant potassium dichromate (K2Cr2O7). Toxicity was expressed as percentage of effect (PE) on 100% elutriate: volume.
2.3 Sediment collection and elutriate preparation Sediment samples (n = 251) were collected at various depths (from 0 up to -4 m considering subcores of 0.5 m) using a vibrocorer during November-December 2017 from 98 sampling stations in the Bagnoli Bay located in the eastern area of the Pozzuoli Gulf (Gulf of Naples, Tyrrhenian Sea, Italy) within “ABBaCo project” (Carotenuto et al., 2020). Elutriates were prepared according to standard procedure (Libralato et al., 2008; USEPA, 1991) by combining homogenized sediment and filtered natural seawater collected from a reference site in a 1:4 volumetric ratio (w/v) based on sediment dry weight (24 h at 105 °C). The mixture was stirred at 300 rpm for 1 h at 22 °C on an orbital shaker (GFL-3005, Burgwedel, Germany) followed by 1 h settling at the same temperature. The aqueous fraction was siphoned off without disturbing the settled material and centrifuged at 5100g at 4 °C for 20 min, then the supernatant was collected as elutriate and stored at -20 °C for no more than 1 month. 2.3 Chemical analyses Chemical analyses of sediment samples included, the determination of organic matter (OM), metals and metalloids (Al, As, Cd, Cr, Cu, Fe, Hg, Ni, Pb, V, and Zn), hydrocarbons with C>12, polycyclic aromatic hydrocarbons (PAHs), namely naphtalene, anthracene, fenanthrene, acenaphtylene, acenaphtene, fluorene, fluoranthene, pyrene, benzo(a)anthracene, crysene, benzo(b)fluoranthene, benzo(a)pyrene,
dibenzo(a,h)anthracene. Metals in the sediment were determined after digestion according the United States Environmental Protection Agency (EPA) method 3052 and analysis by inductively coupled plasma-mass spectrometry (ICP-MS), except for iron and aluminum that were analyzed by inductively coupled plasma-optical emission spectroscopy (ICP-OES). Mercury was analyzed by Atomic Absorption Spectrometer AMA-254, according to USEPA method 7473. Polycyclic aromatic hydrocarbons were extracted from the sediment samples according to 3545A EPA method and analyzed by GC10
MS (EPA 8270D). Total PAH concentrations were obtained by the sum of the concentrations of the sixteen different congeners. Hydrocarbons with C>12 (heavy hydrocarbons) were measured by gas chromatography equipped with a flame ionization detector (GC-FID), according to the method proposed by the Italian Institute for Environmental Protection and Research (ISPRA, 2011). Grain size fractions (sand, silt, clay) were determined according to Molisso et al. (2019). 2.4 Data analysis Data were tested for normality and homoscedasticity (Shapiro-Wilk and Kolmogorov-Smirnov and F-test). Non-parametric methods for data analysis were used in the event normality failed (KruskalWallis (K-W) one-way analysis of variance on ranks) to verify the null hypothesis that the three microalgal toxicity tests showed no output differences about the investigated samples. The relationships between variables and the variation present in the database (i.e. toxicity, granulometric and chemical data) were accounted via distance biplotting both the ordination component scores and the variable loading coefficients through Principal Component Analysis (PCA) based on the Pearson’s correlation matrix, in order to identify the major discriminating variables associated with the principal components. SigmaPlot 11.0 and XLSTAT 2019.3.2 were used for statistical data analysis (Libralato et al., 2010). Data were integrated according to (Regoli et al., 2019). 3. Results and discussion 3.1 Ecotoxicity Percentage of effects calculated on microalgae populations exposed to 1:4 sediment elutriates were summarized in Table S1 (Supplementary Materials). Table S1 included also sediment granulometric and chemical data used for toxicity data interpretation. Basic descriptive statistics of toxicity data series were summarized in Figure 1 and Table S2 (Supplementary Materials) evidencing that the mean values of effect of P. tricornutum (33 ± 24) and D. tertiolecta (31 ± 22) were similar, while S. costatum presented lower mean effect data (10 ±
24). This suggested that S. costatum is less sensitive to elutriates compared to the other two microalgae, with a greater frequency of biostimulation events.
3.2 Data analysis and comparison Normality tests (Shapiro-Wilk and Kolmogorov-Smirnoff) failed for toxicity data series (p < 0.001) (i.e. even with the most widespread statistical transforms), suggesting the use of non-parametric methods for data analysis. Kruskal-Wallis one-way analysis of variance evidenced that the differences among data series were greater than would be expected by chance (p < 0.001). Considering all pairwise multiple comparison procedures on median values (Tukey’s Test), P. tricornutum versus D. tertiolecta, and D. tertiolecta versus S. costatum were not significantly different (p < 0.05), while P. tricornutum and S. costatum showed significantly different median values. Raw microalgae toxicity data were summarized in Figure 2 (A, B and C) as x-y scatter plots with paired comparisons between testing species. Classical data transforms (e.g. squared, log10(squared), and arcsine) did not provide any added value to increase the confidence in toxicity data correlation between species. From these data clouds, several samples presented (for the same specimen and according to the selected biological model) very low or very high toxicity values at the same time, suggesting that the use of one microalgae species rather than another one cannot be taken for granted. Data cannot be easily refined, and outliers cannot be deemed as present (i.e. each value is original and generate from a specific elutriate) because the sensitivity to complex environmental matrices such as elutriates is highly species-specific as already evidenced by (Mucha et al., 2003) for just two elutriates. The Pearson’s correlation coefficient (Table S3) (Supplementary Materials) highlighted that no significant correlation was detected between toxicity and the sediment granulometry and chemical contamination, suggesting that the complexity of elutriates cannot be easily identified through their single components. These results confirmed data summarized in Table 1 highlighting that the sensitivity to single substances of the three microalgae reviewed from 12
literature can significantly change. For example, the comparison of D. tertiolecta to the two diatoms evidenced that the first one is more sensitive to Ag, Zn (in the3. bulk form), but less sensitive to Cu by at least one order of magnitude. Data analysis proceeded via principal component analysis (PCA) in order to visualize the correlations between toxicity, granulometric and chemical variables, and potential groups of observations. The first PCA (Figure 3) depicted the multivariate relationship existing between D. tertiolecta, P. tricornutum, and S. costatum. The first two principal components (F1 and F2) accounted for 44.84% and 28.32% of the variation, respectively, representing the 73.16% of the whole database variation. Only a small number of samples seemed to be explained by microalgae toxicity data (1st and 4th quadrants) being most data in the 2nd and 3rd quadrants. Integrating the results from the basic descriptive statistics, the PCA suggested that S. costatum and D. tertiolecta sensitivities are quite similar for a sub-population of data (n = 65), and divergent from the sensitivity of P. tricornutum explaining only 43 samples from another sub-population of specimen. To check if such grouping was supported by samples’ common characteristics (e.g. kind and level of contamination), in Figure 4, the previous PCA was improved by the addition of granulometric and whole sediment chemical data. The 64.03% of variation was explained by the first two components (F1 = 50.21% and F2 = 13.82%). Organics, Pb and Zn mainly explained the F1 (n = 41 samples), while the F2 was mainly described by Cu, Ni, V, and organic matter content (n = 6 samples). Most samples are in the 2nd and 3rd quadrants suggesting that the considered variables are not adequate to explain their variability, considering that such kind of approach cannot estimate the role of interactions between variables (additivity, synergism and antagonism) that remained unknow and unpredictable. According to Figure 4, the role of microalgae toxicities in depicting data grouping is limited compared to chemical data, but D. tertiolecta and P. tricornutum showed similar sensitivities compared to S. costatum, confirming results from Figure 1.
3.3 Sediment samples’ ranking Further investigation about microalgae toxicity was carried out weighting dataon their relative level of contamination. The quantitative weight of evidence (WOE) model (Regoli et al., 2019) was considered for the whole database including five lines of evidence (LOE) according to (Piva et al., 2011). The quantitative hazard quotients (HQs) obtained for each LOE were normalized to a common scale and assigned to 1 of 5 classes of risk (absent - HQ < 1, slight - ≥1 – 1.5, moderate - ≥ 1.5 – 3.0, major - ≥ 3.0 – 6.0, and severe - ≥ 6.0 – 10.0) (Piva et al., 2011; Regoli et al., 2019). Calculations were automated via a freeware (Sediqualsoft) provided by ISPRA (2019). The results from samples’ ranking were summarized in Table 2 and in detail in Table S4 (Supplementary Materials). The analysis of 217 samples confirmed that S. costatum is the less sensitive that P. tricornutum and D. tertiolecta ranking the highest number of samples as no toxic (absent) (n = 88), while the two others presented 39 and 4 no toxic specimen, respectively. Similarly, it occurred for the most contaminated samples: D. tertiolecta and P. tricornutum classified as affected by severe toxicity 168 and 132 specimen, in that order, while S. costatum only 43 samples. Like from Figure 2, even the output from Sediqualsoft suggested that the same sample can be classified as affected by severe toxicity or presenting no toxicity at all according to the testing organisms considered. Sometimes, even highly contaminated samples were not able to induce a significant response in all the considered biological models (especially in S. costatum). Thus, the relative growing sensitivity of microalgae testing species to elutriates can be summarized as follows: S. costatum < P. tricornutum < D. tertiolecta. Probably, the main explanation about the greater sensitivity of D. tertiolecta could be attributed to the absence of the cell wall (being a green microalgae) compared to the two diatoms, thus the interaction and entrance of contaminants could be facilitated (Borowitzka and Siva, 2007; Manzo et al., 2013).
4. Conclusions The toxicity of elutriates generated from a wide range of contaminated sediment samples were investigated considering growth inhibition tests with P. tricornutum, S. costatum and D. tertiolecta. Data were compared for sensitivity considering uni- and multi-variate statistical analysis including sediment grain size and chemical contamination levels. The main results evidenced that: i) the species-specific sensitivity of each microalgae can rank the same sample either as presenting severe toxicity or no toxicity at all; ii) grain size and chemical data did not provide any added value in toxicity data interpretation, suggesting that the mixture effect could be the main responsible of such output. Thus, according to the selected bioindicator, the result can completely change like as the classification of sediment samples and their subsequent management. As a general recommendation supported by the precautionary principle, toxicity tests should be oriented to take into consideration the most sensitive species like D. tertiolecta and P. tricornutum, and, potentially, more than one species at the same time.
Acknowledgement This study was supported by the project ABBaCo funded by the Italian Ministry for Education, University and Research (grant number C62F16000170001).”
6. References Angel, B.M., Batley, G.E., Jarolimek, C.V., Rogers, N.J., 2013. The impact of size on the fate and toxicity of nanoparticulate silver in aquatic systems. Chemosphere 93, 359-365. Aravantinou, A.F., Tsarpali, V., Dailianis, S., Manariotis, I.D., 2015. Effect of cultivation media on the toxicity of ZnO nanoparticles to freshwater and marine microalgae. Ecotoxicology and environmental safety 114, 109-116. Arizzi Novelli, A., Losso, C., Libralato, G., Tagliapietra, D., Pantani, C., Ghirardini, A.V., 2006. Is the 1:4 elutriation ratio reliable? Ecotoxicological comparison of four different sediment:water proportions. Ecotoxicology and Environmental Safety 65, 306-313. Borowitzka, M.A., Siva, C.J., 2007. The taxonomy of the genus Dunaliella (Chlorophyta, Dunaliellales) with emphasis on the marine and halophilic species. Journal of Applied Phycology 19, 567-590. Carotenuto, Y., Vitiello, V., Gallo, A., Libralato, G., Trifuoggi, M., Toscanesi, M., Lofrano, G., Esposito, F., Buttino, I., 2020. Assessment of the relative sensitivity of the copepods Acartia tonsa and Acartia clausi exposed to sediment-derived elutriates from the Bagnoli-Coroglio industrial area. Marine Environmental Research 155, 104878. Deng, G., Zhang, T., Yang, L., Wang, Q., 2013. Studies of biouptake and transformation of mercury by a typical unicellular diatom Phaeodactylum tricornutum. Chinese Science Bulletin 58, 256-265. Duan, W., Meng, F., Lin, Y., Wang, G., 2017. Toxicological effects of phenol on four marine microalgae. Environmental toxicology and pharmacology 52, 170-176. Franklin, N.M., Stauber, J.L., Lim, R.P., 2001. Development of flow cytometry-based algal bioassays for assessing toxicity of copper in natural waters. Environmental Toxicology and Chemistry: An International Journal 20, 160-170. Gambardella, C., Costa, E., Piazza, V., Fabbrocini, A., Magi, E., Faimali, M., Garaventa, F., 2015. Effect of silver nanoparticles on marine organisms belonging to different trophic levels. Marine environmental research 111, 41-49. Horvatić, J., Peršić, V., 2007. The effect of Ni 2+, Co 2+, Zn 2+, Cd 2+ and Hg 2+ on the growth rate of marine diatom Phaeodactylum tricornutum Bohlin: microplate growth inhibition test. Bulletin of environmental contamination and toxicology 79, 494-498. Huang, J., Cheng, J., Yi, J., 2016. Impact of silver nanoparticles on marine diatom Skeletonema costatum. Journal of Applied Toxicology 36, 1343-1354. Huang, X., Jiang, X., Wang, G., Hong, J., 1994. Principal environmental factors during red tide outbreak of Skeletonema costatum in Yangtse Estuary 3. Water temperature, salinity, DO and pH. Marine science bulletin/Haiyang Tongbao. Tianjin 13, 35-40. ISO, 2016. Water quality — Marine algal growth inhibition test with Skeletonema sp. and Phaeodactylum tricornutum. ISO 10253:2016. ISPRA, 2019. http://www.isprambiente.gov.it/it/moduli-e-software/documentazione-e-software-disupporto-per-l2019applicazione-del-decreto-15-luglio-2016-n.-173 Jaysudha, S., Karthikeyan, P., Sampathkumar, P., 2013. Copper and cadmium effects on growth of marine diatom, Skeletonema cosyatum and Chaetoceros curvisetus. Int. J. Pharma. Bio. Chem. Sci 2, 06-12. Levy, J.L., Angel, B.M., Stauber, J.L., Poon, W.L., Simpson, S.L., Cheng, S.H., Jolley, D.F., 2008. Uptake and internalisation of copper by three marine microalgae: Comparison of copper-sensitive and copper-tolerant species. Aquatic Toxicology 89, 82-93. Levy, J.L., Stauber, J.L., Jolley, D.F., 2007. Sensitivity of marine microalgae to copper: the effect of biotic factors on copper adsorption and toxicity. Science of the Total Environment 387, 141-154. Li, F., Liang, Z., Zheng, X., Zhao, W., Wu, M., Wang, Z., 2015. Toxicity of nano-TiO2 on algae and the site of reactive oxygen species production. Aquatic Toxicology 158, 1-13. Libralato, G., Avezzù, F., Volpi Ghirardini, A., 2011. Lignin and tannin toxicity to Phaeodactylum tricornutum (Bohlin). Journal of Hazardous Materials 194, 435-439. Libralato, G., Losso, C., Arizzi Novelli, A., Citron, M., Della Sala, S., Zanotto, E., Cepak, F., Volpi Ghirardini, A., 2008. Ecotoxicological evaluation of industrial port of Venice (Italy) sediment samples after a decontamination treatment. Environmental Pollution 156, 644-650. 16
Libralato, G., Minetto, D., Lofrano, G., Guida, M., Carotenuto, M., Aliberti, F., Conte, B., Notarnicola, M., 2018. Toxicity assessment within the application of in situ contaminated sediment remediation technologies: A review. Science of the Total Environment 621, 85-94. Libralato, G., Volpi Ghirardini, A., Avezzù, F., 2010. Toxicity removal efficiency of decentralised sequencing batch reactor and ultra-filtration membrane bioreactors. Water Research 44, 4437-4450. Lim, C.Y., Yoo, Y.H., Sidharthan, M., Ma, C.W., Bang, I.C., Kim, J.M., Lee, K.S., Park, N.S., Shin, H., 2006. Effects of copper(I) oxide on growth and biochemical compositions of two marine microalgae. Journal of environmental biology 27, 461-466. Machado, M.D., Soares, E.V., 2019. Sensitivity of freshwater and marine green algae to three compounds of emerging concern. Journal of Applied Phycology 31, 399-408. Manzo, S., Miglietta, M.L., Rametta, G., Buono, S., Di Francia, G., 2013. Toxic effects of ZnO nanoparticles towards marine algae Dunaliella tertiolecta. Science of the Total Environment 445, 371-376. McCormick, P.V., Cairns, J., 1994. Algae as indicators of environmental change. Journal of Applied Phycology 6, 509-526. Miglietta, M., Rametta, G., Di Francia, G., Manzo, S., Rocco, A., Carotenuto, R., Picione, F.D.L., Buono, S., 2011. Characterization of nanoparticles in seawater for toxicity assessment towards aquatic organisms, Sensors and Microsystems. Springer, pp. 425-429. Molisso, F.,Capodanno, M., Di Gregorio, C., Gilardi, M., Guarino, A., Oliveri, E., Tamburrino S., Sacchi, M. (2019). “Sedimentological analysis of marine deposits off the Bagnoli-Coroglio Site of National Interest (SNI), Pozzuoli (Napoli) Bay”. G. Budillon, R. Delfanti and D. Vicinanza eds. Special Issue of Chemistry and Ecology: Multidisciplinary Approach to the Characterization of Marine Coasta Areas Subjected to Chronic Industrial Contamination Moreno-Garrido, I., Lubián, L.M., Soares, A., 2000. Influence of cellular density on determination of EC50 in microalgal growth inhibition tests. Ecotoxicology and Environmental Safety 47, 112-116. Mucha, A.P., Leal, M.F.C., Bordalo, A.A., Vasconcelos, M.T.S., 2003. Comparison of the response of three microalgae species exposed to elutriates of estuarine sediments based on growth and chemical speciation. Environmental Toxicology and Chemistry: An International Journal 22, 576-585. Nassiri, Y., Mansot, J., Wéry, J., Ginsburger-Vogel, T., Amiard, J., 1997. Ultrastructural and electron energy loss spectroscopy studies of sequestration mechanisms of Cd and Cu in the marine diatom Skeletonema costatum. Archives of environmental contamination and toxicology 33, 147-155. Okamura, H., Kitano, S., Toyota, S., Harino, H., Thomas, K., 2009. Ecotoxicity of the degradation products of triphenylborane pyridine (TPBP) antifouling agent. Chemosphere 74, 1275-1278. Pavlić, Ž., Vidaković-Cifrek, Ž., Puntarić, D., 2005. Toxicity of surfactants to green microalgae Pseudokirchneriella subcapitata and Scenedesmus subspicatus and to marine diatoms Phaeodactylum tricornutum and Skeletonema costatum. Chemosphere 61, 1061-1068. Pelusi, A., Rotolo, F., Gallo, A., Ferrante, M.I., Montresor, M., 2020. Effects of elutriates from contaminated coastal sediments on different life cycle phases of planktonic diatoms. Marine Environmental Research, 104890. Peterson, S.M., Stauber, J., 1996. New algal enzyme bioassay for the rapid assessment of aquatic toxicity. Bulletin of environmental contamination and toxicology 56, 750-757. Piva, F., Ciaprini, F., Onorati, F., Benedetti, M., Fattorini, D., Ausili, A., Regoli, F., 2011. Assessing sediment hazard through a weight of evidence approach with bioindicator organisms: a practical model to elaborate data from sediment chemistry, bioavailability, biomarkers and ecotoxicological bioassays. Chemosphere 83, 475-485. Regoli, F., D'Errico, G., Nardi, A., Mezzelani, M., Fattorini, D., Benedetti, M., Di Carlo, M., Pellegrini, D., GORBI, S., 2019. APPLICATION OF A WEIGHT OF EVIDENCE APPROACH FOR MONITORING COMPLEX ENVIRONMENTAL SCENARIOS: THE CASE-STUDY OF OFF-SHORE PLATFORMS. Frontiers in Marine Science 6, 377. Renzi, M., Roselli, L., Giovani, A., Focardi, S.E., Basset, A., 2014. Early warning tools for ecotoxicity assessment based on Phaeodactylum tricornutum. Ecotoxicology 23, 1055-1072.
Song, H., Fan, X., Liu, G., Xu, J., Li, X., Tan, Y., Qian, H., 2016. Inhibitory effects of tributyl phosphate on algal growth, photosynthesis, and fatty acid synthesis in the marine diatom Phaeodactylum tricornutum. Environmental Science and Pollution Research 23, 24009-24018. Torres, E., Cid, A., Herrero, C., Abalde, J., 2000. Effect of cadmium on growth, ATP content, carbon fixation and ultrastructure in the marine diatom Phaeodactylum tricornutum Bohlin. Water, Air, and Soil Pollution 117, 1-14. USEPA, 1991. Evaluation of dredged material proposed for ocean disposal testing manual. EPA 503/891/001. Wang, Z.H., Nie, X.P., Yue, W.J., Li, X., 2012. Physiological responses of three marine microalgae exposed to cypermethrin. Environmental toxicology 27, 563-572. Ward, T., Boeri, R., 1990. Acute static toxicity of nonylphenol to the marine alga, Skeletonema costatum. Envirosystems Study 8970-CMA. Final Technical Report. Chemical Manufacturers Association, Hampton, NH, USA. Wong, S.W., Leung, K.M., 2014. Temperature-dependent toxicities of nano zinc oxide to marine diatom, amphipod and fish in relation to its aggregation size and ion dissolution. Nanotoxicology 8, 24-35. Zhang, C., Chen, X., Tan, L., Wang, J., 2018. Combined toxicities of copper nanoparticles with carbon nanotubes on marine microalgae Skeletonema costatum. Environmental Science and Pollution Research 25, 13127-13133. Zhang, C., Wang, J., Tan, L., Chen, X., 2016. Toxic effects of nano-ZnO on marine microalgae Skeletonema costatum: Attention to the accumulation of intracellular Zn. Aquatic Toxicology 178, 158-164.
Tables Table 1 Review of D. tertiolecta, P. tricornutum, and S. costatum sensitivity to single substances.
96 h EC50 = 22.39 mg/L (Torres et al.,
96 h EC50 = 0.224 mg/L
(Nassiri et al., 1997)
72 h IC50 = 5.368 mg/L (Horvatić and
96 h IC50 = 0.045 mg/L (Jaysudha et al.,
72 h IC50 = 1.19 mg/L (Horvatić and Cobalt (Co) Peršić, 2007) 72 h EC50 = 0.035 mg/L (Moreno-
96 h EC50 = 0.045 mg/L (Nassiri et al.,
72 h IC50= 0.530 mg/L (Levy et al.,
Garrido et al., 2000)
72 h IC50=0.008 mg/L (Levy et al., 2007)
96 h IC50 = 0.087 mg/L (Jaysudha et al.,
72 h IC50= 0.576 mg/L (Peterson and
72 h EC50 = 0.010 mg/L (Franklin et al.,
72 h EC50 = 0.027 mg/L (Ward and
Boeri, 1990) Copper oxide (CuO) Mercury (Hg)
96 h EC50 =1.34 mg/L (Lim et al., 2006) 96 h EC50 = 0.145 mg/L (Deng et al.,
72 h IC50 = 0.037 mg/L (Zhang et al.,
72 h IC50 = 0.03 mg/L HgSO4 (Horvatić and Peršić, 2007) 72 h IC50 =1.16 10–5 mg/L HgCl (Horvatić and Peršić, 2007) 72 h IC50 = 7.28 mg/L Nickel (Ni) (Horvatić and Peršić, 2007) 72 h IC50= 0.400 ± 0.110 mg/L (Angel et
24‐h EC50 = 0.156 mg/L (Huang et al.,
72h IC50=3.1 mg/L (Gambardella et al., 72h IC50 = 0.9 mg/L
(Gambardella et al., 2015)
24 h EC50= 25.77 mg/L (Huang et al.,
Silver (nano) (nAg)
2016) Titanium dioxide (nano) (nTiO2)
72 h EC50=7.37 mg/L (Li et al., 2015) 96 h-EC50=1.35 mg/L (Manzo et al.,
72 h IC50 = 41.85 mg/L (Horvatić and
96 h EC50= 4.45 mg/L (Manzo et al., Zinc oxide (ZnO)
72 h EC50=6.7 mg/L (Zhang et al., 2016) 2013) 96 h EC50 = 1.50 mg/L (Aravantinou et
72 h EC50 = 4.4 mg/L (Zhang et al.,
Zinc oxide (nano) (nZnO)
96 h EC50 = 2.42 mg/L (Manzo et al.,
96 h EC50 = 2.36 mg/L (Wong and
96 h EC50 = 133 mg/L (Miglietta et al., 2011) Cypermethrin Insecticide
0.071 mg/L (Wang et al., 2012) 72 h EC50 = 0.011 103 mg/L (Machado
120 h EC50 = 0.061 mg /L (Machado and
and Soares, 2019)
Metolachlor herbicide 72 h EC50 = 0.006 103 mg/L (Machado Erythromycin antibiotic and Soares, 2019) 96 h EC50 = 27.32 mg/L (Duan et al.,
96 h EC50 = 27.32 mg/L (Duan et al.,
72 h EC50 = 0.093 mg/L (Machado and Triclosan Soares, 2019) Tributyl phosphate
96 h EC50 = 8.0 mg/L (Song et al., 2016)
Table 2 Summary of results (number of samples belonging to a specific rank) from Sediqualsoft application to microalgae toxicity data according to (Regoli et al., 2019) Classification Absent Slight Moderate Major Severe
D. tertiolecta 4 0 2 43 168
P. tricornutum 39 4 0 42 132
S. costatum 88 40 7 39 43
Figure 1 Boxplot summarizing the main basic descriptive statistical findings for the three microalgae species on the relative whole database of effect data; letters indicate not statistically different median values (Tukey’s test, p < 0.001).
Figure 2 Comparison of toxicity data between testing species P. tricornutum, S. costatum and D. tertiolecta. Toxicity data are expressed as percentage of effect (% effect, growth inhibition).
Figure 3 Principal component analysis biplot of toxicity data with loadings and scores in the coordinates of the first two principal components (F1 and F2).
Figure 4 Principal component analysis biplot of toxicity (elutriates), grain size and chemical data of sediment samples with loadings and scores in the coordinates of the first two principal components (F1 and F2).
Sensitivity of three microalgae to elutriates from highly contaminated sediments is compared
Uni- and multivariate analyses did not highlight any significant correlation among species
Single chemicals and granulometry did not facilitate samples ranking
Species sensitivity to elutriates was: S. costatum < P. tricornutum < D. tertiolecta
Gallo A.: Conceptualization, Investigation, Writing – original draft; Guida M.: Conceptualization, Validation; Armiento G.: Formal analysis, Validation; Siciliano A.: Formal analysis; Mormile N.: Formal analysis; Carraturo F.: Formal analysis; Pellegrini D.: Supervision; Validation; Morroni L.: Formal analysis, Software, Validation; Tosti E.: Formal analysis; Ferrante M.I.: Supervision, Methodology; Montresor M.: Supervision, Methodology; Molisso F.: Formal analysis; Sacchi M.: Formal analysis; Danovaro R.: Supervision, Validation; Lofrano G.: Data curation, Visualization; Writing – original draft; Libralato G.: Software, Supervision; Validation, Writing – original draft
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: