Speciation of Cd in soil solutions strongly determines the fate of this toxic metal in the environment. Generally, in soil solutions, Cd predominantly binds to the dissolved organic matter (DOM). The ...determination of the quantity and reactivity of DOM that actually complexes Cd in soil solutions is challenging for operational purposes. Therefore, this study tested whether Cd2+ concentration in soil solutions could effectively be predicted by considering complexation with a single mean organic ligand assumed to be a fraction of DOM of unspecified nature or assumed to be purely fulvic acids (FA) with reactivity as described in WHAM VII. The reactivity of the unspecified ligand and the concentration of FA were modelled and fitted to experimental data from 76 agricultural soils with low Cd contents. The optimal reactivity and FA concentration that minimized the relative error (RE) of predictions of the concentration of Cd2+ in soil solutions were either considered constant across soils or modelled from soil properties by multiple linear regressions (MLR) or random forests (RF), giving 6 models, the predictive value of which was assessed by 10-folds cross-validation. When the reactivity of the mean ligand and the optimal FA concentration were considered constant across soils, the models were biased and 66.9% of predictions had relative errors below a factor of 2. By contrast, if the reactivity of the mean ligand or the optimal FA concentration were allowed to vary with soil characteristics, these performances increased to 95.5%, soil pH being the main predictor and RF being slightly more efficient than MLR. With more than 95% of the relative errors of prediction below a factor of 2, the models developed in this work could be valuable for assessing Cd speciation in the solution of soils having a low Cd content.
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•Efficient models were built for predicting Cd2+ concentrations in soil solutions.•Models relied on the complexation of Cd2+ with a mean single organic ligand.•The reactivity of the mean single ligand mainly depended on soil pH.•95.5% of the predictions had relative errors below a factor of 2.
This study utilizes ultraviolet and fluorescence spectroscopic indices of dissolved organic matter (DOM) from sediments, combined with machine learning (ML) models, to develop an optimized predictive ...model for estimating sediment total organic carbon (TOC) and identifying adjacent land-use types in coastal sediments from the Yellow and Bohai Seas. Our results indicate that ML models surpass traditional regression techniques in estimating TOC and classifying land-use types. Penalized Least Squares Regression (PLR) and Cubist models show exceptional TOC estimation capabilities, with PLR exhibiting the lowest training error and Cubist achieving a correlation coefficient 0.79. In land-use classification, Support Vector Machines achieved 85.6 % accuracy in training and 92.2 % in testing. Maximum fluorescence intensity and ultraviolet absorbance at 254 nm were crucial factors influencing TOC variations in coastal sediments. This study underscores the efficacy of ML models utilizing DOM optical indices for near real-time estimation of marine sediment TOC and land-use classification.
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•Applications of ML models using DOM optical indices for coastal sediment monitoring•The Cubist model obtains Pearson's correlation of 0.79 for TOC prediction during testing.•Support Vector Machines achieve 92.2 % accuracy in land-use classification testing.•Maximum FL and UV absorbance at 254 nm identified as key factors affecting TOC•Discussion on future applications and limitations of ML models in sediment TOC analysis
The presence of antibiotics, such as sulfadiazine (SDZ), in the aquatic environment contributes to the generation of antimicrobial resistance, which is a matter of great concern. Photolysis is known ...to be a major degradation pathway for SDZ in surface waters. Therefore, influencing factors affecting SDZ photodegradation in different aquatic environments were here evaluated in order to have a better knowledge about its persistence in the environment. Photodegradation of SDZ was found to be more efficient at higher pH (t1/2 = 6.76 h, at pH = 7.3; t1/2 = 12.2 h, at pH = 6.3), in the presence of humic substances (HS) (t1/2 between 1.76 and 2.42 h), as well as in the presence of NaCl (t1/2 = 1.00 h) or synthetic sea salts (t1/2 = 0.78 h). Using ˙OH and 1O2 scavengers, it was possible to infer that direct photolysis was the main pathway for SDZ photodegradation in ultrapure water. Furthermore, results under N2 purging confirmed that 1O2 was not relevant in the phototransformation of SDZ. Then, the referred observations were used for the interpretation of results obtained in environmental matrices, namely the final effluent of a sewage treatment plant (STPF), fresh and brackish water (t1/2 between 2.3 and 3.48 h), in which SDZ photodegradation was found to be much faster than in ultrapure water (t1/2 = 6.76 h).
•Sulfadiazine (SDZ) photodegradation was found to be more efficient at higher pH.•Dissolved organic matter resulted in an increase of SDZ photodegradation.•Salinity and reactive halogen species caused an increase in SDZ photodegradation.•Direct photolysis was the main path for SDZ photodegradation in ultrapure water.•t½ decreased from 6.76 h in ultrapure water to 2.3–3.48 h in environmental samples.
Undaria pinnatifida is a brown algae native to Asia that has settled in various regions worldwide, periodically contributing with large quantities of C and nutrients during its annual cycle. In this ...work, we analyzed a coastal site in Patagonia (Argentina) that has been colonized for three decades by U. pinnatifida, focusing on associated microbial communities in three different compartments. An important influence of algae was observed in seawater, especially in the bottom of the algal forest during the austral summer (January) at the moment of greater biomass release. This was evidenced by changes in DOC concentration and its quality indicators (higher Freshness and lower Humification index) and higher DIC. Although maximum values of NH4 and PO4 were observed in January, bottom water samples had lower concentrations than surface water, suggesting nutrient consumption by bacteria during algal DOM release. Concomitantly, bacterial abundance peaked, reaching 4.68 ± 1.33 × 105 cells mL −1 (January), showing also higher capability of degrading alginate, a major component of brown algae cell walls. Microbial community structure was influenced by sampling date, season, sampling zone (surface or bottom), and environmental factors (temperature, salinity, pH, dissolved oxygen, nutrients). Samples of epiphytic biofilms showed a distinct community structure compared to seawater, lower diversity, and remarkably high alginolytic capability, suggesting adaptation to degrade algal biomass. A high microdiversity of populations of the genus Leucothrix (Gammaproteobacteria, Thiotrichales) that accounted for a large fraction of epiphytic communities was observed, and changed over time. Epiphytic assemblages shared more taxa with bottom than with surface seawater assemblages, indicating a certain level of exchange between communities in the forest surroundings. This work provides insight into the impact of U. pinnatifida decay on seawater quality, and the role of microbial communities on adapting to massive biomass inputs through rapid DOM turnover.
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•Invasive kelp U. pinnatifida reshapes coastal environments in Patagonia.•Kelp senescence enhances DOC, POC and nutrients, impacting associated communities.•Kelp forest holds distinct epiphytic and free-living microbial assemblages.•Epiphytic and bottom seawater microbes have a key role in algal-derived DOM breakdown.•Specialized populations in epiphytic biofilms show the highest alginate-degrading potential.
This work presents a multi-analytical approach for the characterization of marine dissolved organic matter (DOM). The determination of marine dissolved organic carbon (DOC) was performed by size ...exclusion chromatography (SEC) and validated using a certified reference material (CRM) as well as through an intercomparison exercise. Multi-detection SEC, fluorescence and electrochemical methods were used in order to describe the size distribution spectra, the composition and chemical properties of marine DOM, in the (ultra)oligotrophic West Tropical South Pacific Ocean (WTSP). In this work, we defined the state of degradation of DOC in the different size fractions, operationally defined by SEC. We estimated that on average 62% of DOC was of humic nature (0.5–10 kDa), of which ~9% was able to complex trace elements, such as iron (Fe). Our results tend to support that non-refractory DOC is of high molecular weight (HMW), nitrogen (N)-rich, aliphatic, and has a weak fluorescence quantum yield and an enhanced binding capacity for Fe. The ageing of marine DOM occurring within mesopelagic waters is mainly driven by microbial respiration and alters these chemical properties. Although our results are in agreement with a paradigm describing oceanic DOM biogeochemistry known as the size-reactivity continuum, 3 μmolC L−1 of very HMW (> 10 kDa) were still observed in a water mass mainly composed of Pacific Deep Waters. This persistence could be explained by a significant content (5%) of aromatic carbon that may protect HMW DOM from long term biodegradation.
•Characterization of marine Pacific DOM without preliminary extraction.•Predominance of humic substances (HS) in Pacific DOM pool.•Semi-specific description of DOM size and chemical composition.•Nitrogen content of DOM may control its bioavailability.•Quantification of iron binding properties of Pacific HS.
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•Optical spectroscopy, FT-ICR MS and microbial sequencing were employed to analyze DOM.•Sediment DOM, DON and DOS showed reduced molecular diversity but enhanced reactivity ...seaward.•Contribution of terrestrial OM to sediment DOM transformation decreased seaward.•Microbial consumption of labile DON and DOS components alters their properties.•Anthropogenic inputs can enhance DOM bio-reactivity by increasing DON and DOS fractions.
Dissolved organic matter (DOM) constitutes the most active fraction in global carbon pools, with estuarine sediments serving as significant repositories, where DOM is susceptible to dynamic transformations. Anthropogenic nitrogen (N) and sulfur (S) inputs further complicate DOM by creating N-bearing DOM (DON) and S-bearing DOM (DOS). This study delves into the spatial gradients and transformation mechanisms of DOM, DON, and DOS in Pearl River Estuary (PRE) sediments, China, using combined techniques of UV–visible spectroscopy, Excitation–emission matrix (EEM) fluorescence spectroscopy, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), and microbial high-throughput sequencing. Results uncovered a distinct spatial gradient in DOM concentration, aromaticity (SUVA254), hydrophobicity (SUVA260), the content of substituent groups including carboxyl, carbonyl, hydroxyl and ester groups (A253/A203) of chromophoric DOM (CDOM), and the abundances of tyrosine/tryptophan-like protein and humic-like substances in fluorophoric DOM (FDOM). These all decreased from upper to lower PRE, accompanied by a decrease in O3S and O5S components, indicating seaward reduction in the contribution of terrestrial OM, especially anthropogenic inputs. Additionally, sediments exhibited a reduction in molecular diversity (number of formulas) of DOM, DON, and DOS from upper to lower PRE, with molecules tending towards a lower nominal oxidation state of carbon (NOSC) and higher bio-reactivity (MLBL), molecular weight (m/z) and saturation (H/C). While molecular composition of DOM remained similar in PRE sediments, the relative abundance of lignin-like substances decreased, with a concurrent increase in protein-like and lipid-like substances in DON and DOS from upper to lower PRE. Mechanistic analysis identified the joint influence of terrestrial OM, anthropogenic N/S inputs, and microbial processes in shaping the spatial gradients of DOM, DON, and DOS in PRE estuarine sediments. This study contributes valuable insights into the intricate spatial gradients and transformations of DOM, DON, and DOS within human-impacted estuarine sediments.
Human activities significantly increase the input of offshore heavy metals and organic pollutants. Although particle-scale and heterogeneous organic matters are fundamentally important to the fate of ...hydrophobic organic compounds (HOCs), deep understanding of the adsorption mechanism of HOCs on soil/sediment particles under the influence of heavy metal and organic pollution input is needed. This study investigates the effects of exotic DOM and heavy metals ions on the phenanthrene adsorption on sediment fractions. The adsorption experiments demonstrated that exotic DOM increased phenanthrene adsorption amount of sediment, with the greatest enhancement on clay particles (<2 µm). Nevertheless, the mechanism was differentiated accordingly to particle dimensions in terms of increased binding coefficients and mobility of phenanthrene. Furthermore, the introduction of heavy metals considerably enhanced the nonlinear sorption of phenanthrene. The Freundlich exponent N reduced by 0.01–0.24 when adding Cu2+, Zn2+ and Pb2+, especially for coarse particles (31–63 µm) fraction. In comparison, the enhancement of nonlinearity adsorption by Cu2+ and Zn2+ is significantly lower than Pb2+ ions. To our knowledge, the particle-scale study broadens the horizon of environmental fate and ecological risk of HOCs in intertidal regions, which is significantly affected by tidal action.
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•The effect of DOM on sorption of phenanthrene varied according to particle size.•Exotic DOM increased most adsorption capacity on clay particles (<2 µm).•The increasing effect of heavy metal ions was greater on larger particles.•Cu2+ and Zn2+ affect distribution while Pb2+ mostly affects surface adsorption.
Coastal environments are nitrogen (N) removal hot spots, which regulate the amount of land-derived N reaching the open sea. However, mixing between freshwater and seawater creates gradients of ...inorganic N and bioavailable organic matter, which affect N cycling. In this study, we compare nitrate reduction processes between estuary and offshore archipelago environments in the coastal Baltic Sea. Denitrification rates were similar in both environments, despite lower nitrate and carbon concentrations in the offshore archipelago. However, DNRA (dissimilatory nitrate reduction to ammonium) rates were higher at the offshore archipelago stations, with a higher proportion of autochthonous carbon. The production rate and concentrations of the greenhouse gas nitrous oxide (N2O) were higher in the estuary, where nitrate concentrations and allochthonous carbon inputs are higher. These results indicate that the ratio between nitrate and autochthonous organic carbon governs the balance between N-removing denitrification and N-recycling DNRA, as well as the end-product of denitrification. As a result, a significant amount of the N removed in the estuary is released as N2O, while the offshore archipelago areas are characterized by efficient internal recycling of N. Our results challenge the current understanding of the role of these regions as filters of land-to-sea transfer of N.
•The availability of bioavailable carbon defines nitrate reduction end-product.•Estuaries with low bioavailable organic carbon can release high amounts of N2O.•Nitrogen is recycled through DNRA in the archipelago areas.
Dissolved organic matter (DOM) is a complex mixture of molecules that constitutes one of the largest reservoirs of organic matter on Earth. While stable carbon isotope values (δ
C) provide valuable ...insights into DOM transformations from land to ocean, it remains unclear how individual molecules respond to changes in DOM properties such as δ
C. To address this, we employed Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to characterize the molecular composition of DOM in 510 samples from the China Coastal Environments, with 320 samples having δ
C measurements. Utilizing a machine learning model based on 5199 molecular formulas, we predicted δ
C values with a mean absolute error (MAE) of 0.30‰ on the training data set, surpassing traditional linear regression methods (MAE 0.85‰). Our findings suggest that degradation processes, microbial activities, and primary production regulate DOM from rivers to the ocean continuum. Additionally, the machine learning model accurately predicted δ
C values in samples without known δ
C values and in other published data sets, reflecting the δ
C trend along the land to ocean continuum. This study demonstrates the potential of machine learning to capture the complex relationships between DOM composition and bulk parameters, particularly with larger learning data sets and increasing molecular research in the future.
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