Phytoextraction of arsenic, nickel, selenium and zinc from sewage sludge: from laboratory to pilot scale

Aims The present study aimed at: (i) verifying the suitability of pure sewage sludge (SS) as growing medium for the hyperaccumulator species (Pteris vittata, Odontarrhena chalcidica, Astragalus bisulcatus and Noccaea caerulescens); (ii) evaluating the removal of As, Ni, Se and Zn operated by the chosen species; (iii) estimating the potential metal yields (bio-ore production) and connected monetary rewards in a small-scale field experiment. Methods Hyperaccumulator plants were first tested under controlled conditions, on three different SS (P1, P2, P3) characterized by the presence of one or more contaminants among As, Ni, Se and Zn. P1 sludge was then chosen for a small-scale field experiment. Hyperaccumulator seedlings were transferred on SS and cultivated for 16 weeks before harvesting. Results All hyperaccumulator species grew healthy on P1 SS, with A. bisulcatus and O. chalcidica reaching an average biomass of 40.2 and 21.5 g DW/plant. Trace metal concentrations in aerial parts were: As (P. vittata) 380 mg/kg DW, Ni (O. chalcidica) 683 mg/kg DW, Se (A. bisulcatus) 165 mg/kg DW, Zn (N. caerulescens) 461 mg/kg DW. The total removal of As, Ni, Se and Zn from SS due to phytoextraction was 5.8, 19, 18, 29% respectively. Conclusions This study demonstrated that phytoextraction can be applied to SS for the removal contaminants while recovering valuable metals. Se and As were identified as the most promising target element, while Ni and Zn removal was poorly efficient under the present experimental conditions

Three Phenolic and a Sterol Glycosides Identified for the First Time in Matthiola longipetala Growing in Tunisia

Three phenolic glycosides: 4-O-ß-D-glycopyranosyl zingerone 1, 4-O-ß-D-glycopyranosylhomovanillyl alcohol 2 and eugenol glycoside 3, together with 3-O-ß-D-glycopyranosyl sitosterol 4, were isolated and identified for the first time from the flowers of Matthiola longipetala (Crucifers) growing in Tunisia. The structures of 1, 2 and 3 were identified via their acetylated derivatives on the basis of the 1 and 2D NMR data analysis, mass spectrometry and IR spectroscopy

The influence of temperature and community structure on light absorption by phytoplankton in the North Atlantic

This is the final version. Available from the publisher via the DOI in this record.We present a model that estimates the spectral phytoplankton absorption coefficient ( a p h ( λ ) ) of four phytoplankton groups (picophytoplankton, nanophytoplankton, dinoflagellates, and diatoms) as a function of the total chlorophyll-a concentration (C) and sea surface temperature (SST). Concurrent data on a p h ( λ ) (at 12 visible wavelengths), C and SST, from the surface layer (<20 m depth) of the North Atlantic Ocean, were partitioned into training and independent validation data, the validation data being matched with satellite ocean-colour observations. Model parameters (the chlorophyll-specific phytoplankton absorption coefficients of the four groups) were tuned using the training data and found to compare favourably (in magnitude and shape) with results of earlier studies. Using the independent validation data, the new model was found to retrieve total a p h ( λ ) with a similar performance to two earlier models, using either in situ or satellite data as input. Although more complex, the new model has the advantage of being able to determine a p h ( λ ) for four phytoplankton groups and of incorporating the influence of SST on the composition of the four groups. We integrate the new four-population absorption model into a simple model of ocean colour, to illustrate the influence of changes in SST on phytoplankton community structure, and consequently, the blue-to-green ratio of remote-sensing reflectance. We also present a method of propagating error through the model and illustrate the technique by mapping errors in group-specific a p h ( λ ) using a satellite image. We envisage the model will be useful for ecosystem model validation and assimilation exercises and for investigating the influence of temperature change on ocean colour.Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems/CBIOMESUK National Centre for Earth ObservationCopernicus Marine Environment Monitoring Service (CMEMS

Assimilation of ocean-colour plankton functional types to improve marine ecosystem simulations

We assimilated plankton functional types (PFTs) derived from ocean colour into a marine ecosystem model, to improve the simulation of biogeochemical indicators and emerging properties in a shelf sea. Error-characterized chlorophyll concentrations of four PFTs (diatoms, dinoflagellates, nanoplankton and picoplankton), as well as total chlorophyll for comparison, were assimilated into a physical-biogeochemical model of the North East Atlantic, applying a localized Ensemble Kalman filter. The reanalysis simulations spanned the years 1998 to 2003. The skill of the reference and reanalysis simulations in estimating ocean colour and in situ biogeochemical data were compared by using robust statistics. The reanalysis outperformed both the reference and the assimilation of total chlorophyll in estimating the ocean-colour PFTs (except nanoplankton), as well as the not-assimilated total chlorophyll, leading the model to simulate better the plankton community structure. Crucially, the reanalysis improved the estimates of not-assimilated in situ data of PFTs, as well as of phosphate and pCO2, impacting the simulation of the air-sea carbon flux. However, the reanalysis increased further the model overestimation of nitrate, in spite of increases in plankton nitrate uptake. The method proposed here is easily adaptable for use with other ecosystem models that simulate PFTs, for, e.g., reanalysis of carbon fluxes in the global ocean and for operational forecasts of biogeochemical indicators in shelf-sea ecosystems

Decadal reanalysis of biogeochemical indicators and fluxes in the North West European shelf-sea ecosystem

In this paper we present the first decadal reanalysis simulation of the biogeochemistry of the North West European shelf, along with a full evaluation of its skill and value. An error-characterized satellite product for chlorophyll was assimilated into a physical-biogeochemical model of the North East Atlantic, applying a localized Ensemble Kalman filter. The results showed that the reanalysis improved the model predictions of assimilated chlorophyll in 60% of the study region. Model validation metrics showed that the reanalysis had skill in matching a large dataset of in situ observations for ten ecosystem variables. Spearman rank correlations were significant and higher than 0.7 for physical-chemical variables (temperature, salinity, oxygen), ∼0.6 for chlorophyll and nutrients (phosphate, nitrate, silicate), and significant, though lower in value, for partial pressure of dissolved carbon dioxide (∼0.4). The reanalysis captured the magnitude of pH and ammonia observations, but not their variability. The value of the reanalysis for assessing environmental status and variability has been exemplified in two case studies. The first shows that between 340,000-380,000 km2 of shelf bottom waters were oxygen deficient potentially threatening bottom fishes and benthos. The second application confirmed that the shelf is a net sink of atmospheric carbon dioxide, but the total amount of uptake varies between 36-46 Tg C yr−1 at a 90% confidence level. These results indicate that the reanalysis output dataset can inform the management of the North West European shelf ecosystem, in relation to eutrophication, fishery, and variability of the carbon cycle

Ecoregions in the Mediterranean Sea Through the Reanalysis of Phytoplankton Functional Types and Carbon Fluxes

In this work we produced a long‐term reanalysis of the phytoplankton community structure in the Mediterranean Sea and used it to define ecoregions. These were based on the spatial variability of the phytoplankton type fractions and their influence on selected carbon fluxes. A regional ocean color product of four phytoplankton functional types (PFTs; diatoms, dinoflagellates, nanophytoplankton, and picophytoplankton) was assimilated into a coupled physical‐biogeochemical model of the Mediterranean Sea (Proudman Oceanographic Laboratory Coastal Ocean Modelling System‐European Regional Seas Ecosystem Model, POLCOMS–ERSEM) by using a 100‐member ensemble Kalman filter, in a reanalysis simulation for years 1998–2014. The reanalysis outperformed the reference simulation in representing the assimilated ocean color PFT fractions to total chlorophyll, although the skill for the ocean color PFT concentrations was not improved significantly. The reanalysis did not impact noticeably the reference simulation of not assimilated in situ observations, with the exception of a slight bias reduction for the situ PFT concentrations, and a deterioration of the phosphate simulation. We found that the Mediterranean Sea can be subdivided in three PFT‐based ecoregions, derived from the spatial variability of the PFT fraction dominance or relevance. Picophytoplankton dominates the largest part of open ocean waters; microphytoplankton dominates in a few, highly productive coastal spots near large‐river mouths; nanophytoplankton is relevant in intermediate‐productive coastal and Atlantic‐influenced waters. The trophic and carbon sedimentation efficiencies are highest in the microphytoplankton ecoregion and lowest in the picophytoplankton and nanophytoplankton ecoregions. The reanalysis and regionalization offer new perspectives on the variability of the structure and functioning of the phytoplankton community and related biogeochemical fluxes, with foreseeable applications in Blue Growth of the Mediterranean Sea

Uncertainty in ocean-color estimates of chlorophyll for phytoplankton groups

This is the final version. Available from Frontiers Media via the DOI in this record.Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-color data. There is a growing demand from the ecosystem modeling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeler these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modelers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size [pico- (20 μm)]. The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterize the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups.National Centre for Earth Observation (NCEO)European Space Agency (ESA)NERC-UK ECOMA

Uncertainty in ocean-colour estimates of chlorophyll for phytoplankton groups

Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-color data. There is a growing demand from the ecosystem modeling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeler these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modelers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size [pico- (20 μm)]. The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterize the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups