We present in this paper a new strategy based on the use of polynomial chaos expansion (PCE) for both global sensitivity analysis and parameter optimization. To limit the number of evaluations of the direct model, we develop a simple and efficient procedure to construct a sparse PCE where only coefficients that have a significant contribution to the variance of the model are retained. Parameter estimation is performed using an adaptive procedure where the intervals of variation of the parameters are progressively reduced using information from sensitivity analysis calculated using the sparse PCE. The strategy is shown to be effective for the parameter estimation of two reactive transport problems: a synthetic reactive transport problem involving the Freundlich sorption isotherm and a field experiment of Valocchi et al. (Water Resources Research 17:1517–1527, 1981) involving nonlinear ion exchange reactions.

Field experiments were conducted to assess the influence of plant growth and amendment addition on phytostabilisation of copper (Cu), lead (Pb), manganese (Mn) and zinc (Zn) along highway soil in southwest British Columbia, Canada. The plant species tested were Lolium perenne L (perennial rye grass), Festuca rubra L. (creeping red fescue) and Poa pratensis L. (Kentucky blue grass) and the amendments, lime and phosphate. The treatment efficiencies were assessed during different seasons as a completely randomized factorial experiment in split plot design. The research tasks involved: (1) quantifying the seasonal extent of metal accumulation in soil and assessing the seasonal impact on metal speciation for different soil amendments and plant species; (2) determining seasonal accumulation differences between sampling periods in plant parts; and (3) assessing the influence of root–soil interactions on metal dynamics. The amendments decreased the exchangeable fraction and plant uptake of all four metals. The lowest mobile fractions (exchangeable and carbonate bound) were found in soils growing Festuca for Cu, Lolium for Mn and a Lolium/Poa/Festuca combination for Pb and Zn. Metal accumulation and metal dynamics in the rhizosphere soil are compared with those of the bulk soil. The final outcome was the development of a remediation strategy for all four metals involving suitable plants and amendments and incorporating seasonal and rhizosphere influences.

The characteristics (natural, socioeconomic and administrative/institutional) of Alfeios River basin, in Greece, are identified and presented, incorporating and critically reviewing all possibly found literature. The Alfeios River is a water resources system of great natural, ecological, social and economic importance for Western Greece, since it has the longest and highest flow rate watercourse in the Peloponnisos region. Moreover, the river basin was exposed in the last decades to a plethora of environmental stresses (such as hydrogeological alterations, intensively irrigated agriculture, surface and groundwater overexploitation and infrastructure developments), resulting in the degradation of its quantitative and qualitative characteristics. It is therefore necessary for the development of an integrated river basin management plan for this basin, to study and analyse the interplay between the river components and the exerted environmental stresses, taking into account the puzzle of various and conflicting water uses, including water supply, irrigation, hydropower generation, lignite thermal power production and recreation. Mitigative, preventive and control measures for the analysed environmental stresses are epigrammatically depicted. Focusing on the problematic features, the present work provides a concrete foundation for the determination and conceptualisation of management objectives and possible sustainable alternatives.

Characterization of spatial variation of heavy metals in urban soils is essential to identify pollution sources and potential risks to humans and the environment. While heavy metals concentration in soils depends also on the nature of bedrock and on abiotic and biotic factors, it can be argued that nowadays, due to increasing human activities, it is determined mainly by anthropogenic sources. We determined concentrations and spatial distribution of heavy metals, with particular focus on those potentially toxic (As, Cr, Pb, V, and Zn), in urban and peri-urban soils of Cosenza-Rende (southern Italy). One hundred forty-nine samples of topsoil (0–10 cm) were collected and analyzed for 36 elements by X-ray fluorescence spectrometry and inductively coupled plasma mass spectrometry (ICP-MS). In addition, 18 samples of rocks were collected on outcrops of whole area and analyzed by ICP-ES and ICP-MS. Geostatistical methods were used to map the concentrations of major oxides and several minor elements. Heavy metals in the analyzed samples showed a wide range of concentrations, primarily controlled by the geochemical composition of bedrock, with the notable exceptions of Cu, Pb, and Zn, whose concentrations are heavily affected by land use and anthropogenic pollution in urban areas. Geochemical analysis and spatial distribution showed that high concentrations of potentially toxic elements are found in soils near major roads, indicating that anthropogenic factors determine the anomalies in these areas.

The formation of complexes between tebuconazole (Teb) and cadmium in simplified model solutions as well as soil solutions was studied using electrospray ionization mass spectrometry. Teb and cadmium form two types of complexes with the general formulas [Cd(Teb) ₙ ]²+ (n = 1–4) and [CdI(Teb) ₘ ]+ (m = 1–3), where iodine corresponds to the counterion used. The most intense Teb/cadmium complex is [CdI(Teb)₂]+, and the most stable one is [Cd(Teb)(Teb − H)]+. Another detected complex, the dication [Cd(Teb)₄]²+, was considered as the origin complex for the iodine-free complexes and was found in a sample prepared from forest soil solution naturally contaminated with cadmium ions.

The aim of this study was to investigate the removal of both polycyclic aromatic hydrocarbons (PAHs) and heavy metals from field-contaminated sediments by activated persulfate oxidation. Various chemicals, including hydroxypropyl-β-cyclodextrin (HPCD), S,S-ethylenediaminedisuccinic acid (EDDS), tetrasodium pyrophosphate (Na₄P₂O₇), and hydrochloric acid (HCl), were applied individually before or after activated persulfate oxidation to enhance the co-removal of both types of pollutants. It was found that the organic removal efficiency was not significantly enhanced by increasing the concentration of HPCD from 2.5 to 5.0 mM. The removal efficiency of heavy metals was not improved even at an excess amount of EDDS after activated persulfate oxidation. However, the addition of EDDS acted as the Fe²+ carrier for activated persulfate oxidation. In addition, no significant enhancement of heavy metal removal was observed by increasing the concentrations of Na₄P₂O₇ and HCl from 0.01 to 0.1 M after activated persulfate oxidation. However, comparing 0.1 M HCl with 0.1 M Na₄P₂O₇, HCl was shown to be more effective in promoting the removal of organic pollutants. With further adjustments on the experimental conditions, the highest removal amount of metals and PAHs was achieved by adding 2 M of HCl with 3 days mixing, followed by Fe²+-activated persulfate oxidation (PS/Fe²+ molar ratio at 4:1) for further 6 h mixing. The removal efficiency of low and high molecular weight PAHs was about 70 and 20 %, respectively, while the removal efficiency of metals was 70, 100, 40, 65, 65, 80, and 100 % for Cr, Cu, Hg, Mn, Ni, Pb, and Zn, respectively.

The connection between nutrient input and algal blooms for inland water productivity is well known but not the spatial pattern of water nutrient loading and algae concentration. Remote sensing provides an effective tool to monitor nutrient abundances via the association with algae concentration. Twenty-one field campaigns have been conducted with samples collected under a diverse range of algal bloom conditions for three central Indiana drinking water bodies, e.g., Eagle Creek Reservoir (ECR), Geist Reservoir (GR), and Morse Reservoir (MR) in 2005, 2006, and 2008, which are strongly influenced anthropogenic activities. Total phosphorus (TP) was estimated through hyperspectral remote sensing due to its close association with chlorophyll a (Chl-a), total suspended matter, Secchi disk transparency (SDT), and turbidity. Correlation analysis was performed to determine sensitive spectral variables for TP, Chl-a, and SDT. A hybrid model combining genetic algorithms and partial least square (GA-PLS) was established for remote estimation of TP, Chl-a, and SDT with selected sensitive spectral variables. The result indicates that TP has close association with diagnostic spectral variables with R 2 ranging from 0.55 to 0.72. However, GA-PLS has better performance with an average R 2 of 0.87 for aggregated dataset. GA-PLS was applied to the airborne imaging data (AISA) to map spatial distribution of TP, Chl-a, and SDT for MR and GR. The eutrophic status was evaluated with Carlson trophic state index using TP, Chl-a, and SDT maps derived from AISA images. Mapping results indicated that most MR belongs to mesotrophic (48.6%) and eutrophic (32.7%), while the situation was more severe for GR with 57.8% belongs to eutrophic class, and more than 40% to hypereutrophic class due to the high turbidity resulting from dredging practices.

We present the concept of assemblage tolerance profiles (ATPs) as an aid to freshwater bioassessment, and illustrate it with a practical example. An ATP describes the proportion of taxa in an observed assemblage that is estimated to tolerate each level of a specific stressor within a defined range. We used an extensive compilation of biomonitoring field data to estimate the lower tolerances for pH and dissolved oxygen (DO) of common families of macroinvertebrates in rivers of south-eastern Australia. These limits were then used to establish ATPs for macroinvertebrate assemblages at 30 sites across six river systems with varying levels of exposure to drainage from disused mines and discharges from sewage treatment plants. We hypothesised that sites with more exposure to mine drainage would have ATPs indicating greater tolerance of low pH, whereas sites with more exposure to sewage discharges would have ATPs indicating greater tolerance of low DO, and found that these hypotheses were confirmed for five of the six river systems. We suggest that stressor-specific ATPs, based on tolerances derived from either field distributions or laboratory tests, can help to verify or eliminate candidate causes of inferred human impacts on aquatic ecosystems.

Natural clinoptilolite was modified with hexadecyltrimethylammonium chloride, a cationic surfactant, and then melt-mixed with polypropylene hollow fibres to produce polymer composites with adsorptive properties. The performance of the fabricated composites was evaluated by optimizing experimental parameters such as surfactant loading, contact time, pH and initial concentration for the adsorptive removal of 2,4,6-trichlorophenol (TCP) and ortho-nitrophenol (o-NP). Based on the fourier transmission infrared spectra and scanning electron microscopy micrographs of as-received and surfactant-modified clinoptilolite, the modification of natural clinoptilolite was attained. The composites showed enhanced adsorption capability for TCP over o-NP with removal efficiencies of 84% and 46%. Loading the clinoptilolite with surfactant concentrations beyond 8 mM reduced the adsorption capacity. The removal of TCP and o-NP was found to depend critically on the pH of the solution, and the optimum ranges were 4–6 and 2–6 for compounds, respectively. The adsorption dynamics were determined with first- and second-order kinetics models, and the adsorption system for TCP and o-NP followed the first-order kinetics. Adsorption isotherm analysis revealed that the adsorption equilibrium data obeyed/fit the Freundlich isotherm.

In remotely located watersheds or large waterbodies, monitoring water quality parameters is often not feasible because of high costs and site inaccessibility. A cost-effective remote sensing-based methodology was developed to predict water quality parameters over a large and logistically difficult area. Landsat spectral data were used as a proxy, and a neural network model was developed to quantify water quality parameters, namely chlorophyll-a, turbidity, and phosphorus before and after ecosystem restoration and during the wet and dry seasons. The results demonstrate that the developed neural network model provided an excellent relationship between the observed and simulated water quality parameters. These correlated for a specific region in the greater Florida Everglades at R ² > 0.95 in 1998–1999 and in 2009–2010 (dry and wet seasons). Moreover, the root mean square error values for phosphorus, turbidity, and chlorophyll-a were below 0.03 mg L⁻¹, 0.5 NTU, and 0.17 mg m⁻³, respectively, at the neural network training and validation phases. Using the developed methodology, the trends for temporal and spatial dynamics of the selected water quality parameters were investigated. In addition, the amounts of phosphorus and chlorophyll-a stored in the water column were calculated demonstrating the usefulness of this methodology to predict water quality parameters in complex ecosystems.