There is a growing concern of seawater intrusion to freshwater aquifers due to groundwater overexploitation in the eastern coastal belt of Southern India. The problem becomes complex in the regions where industrial effluents are also contaminating the freshwater aquifers. In order to understand the hydrochemical complexity of the system, topographic elevation, static water level measurements, major ion chemistry, ionic cross plots, water type contours and factor analysis were applied for 144 groundwater samples of shallow and deep sources from Quaternary and Tertiary coastal aquifers, located within the industrial zone of 25 km2 area near Cuddalore, Southern India. The ionic cross plots indicates dissolution of halite minerals from marine sources and seawater mixing into inland aquifers up to the level of 9.3%. The factor analysis explains three significant factors totaling 86.3% of cumulative sample variance which includes varying contribution from marine, industrial effluent and freshwater sources.

Activated natural siderite (ANS) was used to investigate its characteristics and mechanisms of As(V) adsorption from aqueous solution. Batch tests were carried out to determine effects of contact time, initial As(V) concentration, temperature, pH, background electrolyte, and coexisting anions on As(V) adsorption. Arsenic(V) adsorption on ANS well-fitted pseudo-second-order kinetics. ANS showed a high-adsorption capacity of 2.19 mg/g estimated from Langmuir isotherm at 25 °C. Thermodynamic studies indicated that As(V) adsorption on ANS was spontaneous, favorable, and endothermic. ANS adsorbed As(V) efficiently in a relatively wide pH range between 2.0 and 10.0, although the removal efficiency was slightly higher in acidic conditions than that in basic conditions. Effects of background electrolyte and coexisting anions were not significant within the concentration ranges observed in high As groundwater. Results of XRD and Fe K-edge XANES analysis suggested ANS acted as an Fe(II)/(III) hybrid system, which was quite effective in adsorbing As from aqueous solution. There was no As redox transformation during adsorption, although Fe(II) oxidation occurred in the system. Two infrared bands at 787 and 872 cm⁻¹after As(V) adsorption suggested that As(V) should be predominantly adsorbed on ANS via inner-sphere bidendate binuclear surface complexes.

Phosphorus (P) is widely known as a limiting nutrient of water eutrophication for inland freshwater ecosystems. Owing to the complexity of P chemistry, remote sensing detection of total phosphorus (TP) concentrations currently remains limited especially for optically complex turbid inland waters. To address this need, a new TP remote sensing algorithm is developed based on prior water optical classification and the use of support vector regression (SVR) machine. The in situ observed datasets, used in this study, were collected at specific times during 2009 ~ 2011, covering a total of 232 stations from eight cruises in Lakes Taihu, Chaohu, Dianchi, and Three Gorges reservoir of China. Three types of waters were first classified by using a recently developed NTD675 (Normalized Trough Depth of spectral reflectance at 675 nm) water classification method. Then, spectral regions sensitive specifically to each water type were explored and expressed via several band ratios and used for retrieval algorithm development. The established type-specific SVR algorithms yield relatively high predictive accuracies. Specifically, the mean absolute percentage errors (MAPE) produced with the independent validation samples were achieved at 32.7, 23.2, and 14.1 % for type 1, type 2, and type 3 waters, respectively. Such water type-specific SVR algorithms are more accurate for the classified waters than an aggregated SVR algorithm for the nonclassified water and also superior to commonly used statistical algorithms. Moreover, application of the developed algorithms with HJ1A/HSI image data demonstrates that the algorithms have a large potential for remote sensing estimation of TP concentrations in optically complex turbid inland waters.

Iron hydroxide supported onto porous diatomite (D-Fe) is a low-cost material with potential to remove arsenic from contaminated water due to its affinity for the arsenate ion. This affinity was tested under varying conditions of pH, contact time, iron content in D-Fe and the presence of competitive ions, silicate and phosphate. Batch and column experiments were conducted to derive adsorption isotherms and breakthrough behaviours (50 μg L⁻¹) for an initial concentration of 1,000 μg L⁻¹. Maximum capacity at pH 4 and 17 % iron was 18.12–40.82 mg of arsenic/g of D-Fe and at pH 4 and 10 % iron was 18.48–29.07 mg of arsenic/g of D-Fe. Adsorption decreased in the presence of phosphate and silicate ions. The difference in column adsorption behaviour between 10 % and 17 % iron was very pronounced, outweighing the impact of all other measured parameters. There was insufficient evidence of a correlation between iron content and arsenic content in isotherm experiments, suggesting that ion exchange is a negligible process occurring in arsenate adsorption using D-Fe nor is there co-precipitation of arsenate by rising iron content of the solute above saturation.

The extensive use of copper-bearing fungicides in vineyards is responsible for the accumulation of copper (Cu) in soils. Grass species able to accumulate Cu could be cultivated in the vineyard inter-rows for copper phytoextraction. In this study, the capacity of Festuca rubra cv Merlin and Sinapis alba to tolerate and accumulate copper (Cu) was first investigated in a hydroponic system without the interference of soil chemical–physical properties. After the amendment of Cu (5 or 10 mg Cu l⁻¹) to nutrient solution, shoot Cu concentration in F. rubra increased up to 108.63 mg Cu kg⁻¹DW, more than three times higher than in S. alba (31.56 mg Cu kg⁻¹DW). The relationship between Cu concentration in plants and external Cu was dose-dependent and species specific. Results obtained from the hydroponic experiment were confirmed by growing plants in pots containing soil collected from six Italian vineyards. The content of soil organic matter was crucial to enhance Cu tolerance and accumulation in the shoot tissues of both plant species. Although S. alba produced more biomass than F. rubra in most soils, F. rubra accumulated significantly more Cu (up to threefold to fourfold) in the shoots. Given these results, we recommended that F. rubra cv Merlin could be cultivated in the vineyard rows to reduce excess Cu in vineyard soils.

Phytostabilization has great practical significance and flexibility in the ecological restoration of mining tailings and remediation of heavy metals polluted soils. However, potential use of metallophytes in phytostabilization is limited by a lack of knowledge of many basic plant processes. A mining ecotype (ME) Athyrium wardii, Pb/Cd phytostabilizer, and a non-mining ecotype (NME) A. wardii were grown in a pot experiment to investigate the chemical characteristics of the rhizosphere when exposed to the Cd polluted soils. Rhizobags were used to collect rhizosphere and bulk soils, separately. The results indicated that the ME A. wardii was more efficient in Cd accumulation in the root than NME after growing in Cd polluted soils for 50 days in a green house. Soil solution pH and dissolved organic carbon (DOC) concentration in the rhizosphere of ME A. wardii were higher than in the bulk soil and initial values (before planting), whereas the increment in the ME A. wardii were greater than NME. Owing to the increasing of rhizosphere soil pH, exchangeable Cd significantly decreased, whereas the other Cd species were increased with increasing soil DOC values. It is assumed that the ME A. wardii was effective in stabilizing Cd from the mobile fraction to non-mobile fractions. Results from this study suggest that rhizosphere alkalinization and the exudation of high amounts of dissolved organic matter (DOM) to reduce heavy metal mobility might be the two important mechanisms involved in the metal tolerance/accumulation of ME A. wardii.

In order to determine human exposure to the indoor toxicant, selection of dust fraction and understanding dust particle size distribution in settled indoor dust are very important. This study examined the influence of dust particle size on the concentration of polybrominated diphenyl ethers (PBDEs) congeners, assessed the distribution of dust particle size and characterized the main indoor emission sources of PBDEs. Accordingly, the concentrations of PBDE congeners determined in different indoor dust fractions were found to be relatively higher in the order of dust particle size: 45–106 μm > (<45 μm) > 106–150 μm. The finding shows arbitrary selection of dust fractions for exposure determination may result in wrong conclusions. Statistically significant moderate correlation between the concentration of Σ₉PBDEs and organic matter content calculated with respect to the total dust mass was also observed (r = 0.55, p = 0.001). On average, of total dust particle size <250 μm, 93.4 % (m/m%) of dust fractions was associated with less than 150 μm. Furthermore, of skin adherent dust fractions considered (<150 μm), 86 % (v/v%) is in the range of particle size 9.25–104.7 μm. Electronic materials treated with PBDEs were found the main emission sources of PBDE congeners in indoor environment. Based on concentrations of PBDEs determined and mass of indoor dust observed, 150 μm metallic sieve is adequate for human exposure risk assessment. However, research in this area is very limited and more research is required to generalize the fact.

Polychlorinated biphenyls (PCBs) pose a threat to the environment due to their high adsorption capacity to soil organic matter, stability and low reactivity, low water solubility, toxicity and ability to bioaccumulate. With Icelandic soils, research on contamination issues has been very limited and no data has been reported either on PCB degradation potential or rate. The goals of this research were to assess the bioavailability of aged PCBs in the soils of the old North Atlantic Treaty Organization facility in Keflavík, Iceland and to find the best biostimulation method to decrease the pollution. The effectiveness of different biostimulation additives (N fertiliser, white clover and pine needles) at different temperatures (10 and 30 °C) and oxygen levels (aerobic and anaerobic) were tested. PCB bioavailability to soil fauna was assessed with earthworms (Eisenia foetida). PCBs were bioavailable to earthworms (bioaccumulation factor 0.89 and 0.82 for earthworms in 12.5 ppm PCB soil and in 25 ppm PCB soil, respectively), with less chlorinated congeners showing higher bioaccumulation factors than highly chlorinated congeners. Biostimulation with pine needles at 10 °C under aerobic conditions resulted in nearly 38 % degradation of total PCBs after 2 months of incubation. Detection of the aerobic PCB degrading bphA gene supports the indigenous capability of the soils to aerobically degrade PCBs. Further research on field scale biostimulation trials with pine needles in cold environments is recommended in order to optimise the method for onsite remediation.

Three-dimensional graphene-based materials promise oil spill cleanup in water at throughputs much higher than state-of-the-art oil–water separation materials.

The application of advanced oxidation process (AOP) in the treatment of wastewater contaminated with oil was investigated in this study. The AOP investigated is the homogeneous photo-Fenton (UV/H₂O₂/Fe⁺²) process. The reaction is influenced by the input concentration of hydrogen peroxide H₂O₂, amount of the iron catalyst Fe⁺², pH, temperature, irradiation time, and concentration of oil in the wastewater. The removal efficiency for the used system at the optimal operational parameters (H₂O₂ = 400 mg/L, Fe⁺² = 40 mg/L, pH = 3, irradiation time = 150 min, and temperature = 30 °C) for 1,000 mg/L oil load was found to be 72 %. The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of oil degradation in aqueous solution by photo-Fenton process. The multilayered feed-forward networks were trained by using a backpropagation algorithm; a three-layer network with 22 neurons in the hidden layer gave optimal results. The results show that the ANN model can predict the experimental results with high correlation coefficient (R² = 0.9949). The sensitivity analysis showed that all studied variables (H₂O₂, Fe⁺², pH, irradiation time, temperature, and oil concentration) have strong effect on the oil degradation. The pH was found to be the most influential parameter with relative importance of 20.6 %.