Endocrine disruptor compounds (EDCs) are dangerous pollutants largely present in urban, industrial, and agricultural wastes, and through leaching and degradation from/of these matrices, they can reach and contaminate the environment. Bioremediation of polluted systems from EDCs using white rot fungi can be a valuable alternative approach with respect to conventional physical and chemical methods. These fungi have the capacity to biodegrade numerous phenolic contaminants with their unspecific extracellular ligninolytic enzymes. This study investigated the simultaneous removal of the xenoestrogens bisphenol A (BPA), ethynilestadiol (EE2), and 4-n-nonylphenol (NP), the herbicide linuron, and the insecticide dimethoate from a waste landfill leachate (LEACH) adopting a combination of adsorption and biodegradation. Trametes versicolor and Stereum hirsutum were inoculated, separately, on potato dextrose agar alone or added with different adsorbent materials of low cost and wide availability. The substrates with the fungus were superimposed on the contaminated LEACH. The control used was the LEACH overlaid by not inoculated potato dextrose agar. Both fungi showed an adequate tolerance to LEACH. In a period of 20 days, T. versicolor growing on the various substrates removed almost 100 % of BPA, EE2, NP, and linuron, and from 59 to 97 % of dimethoate. S. hirsutum showed a marked degrading activity only towards NP, which was totally removed after 20 days or less with any substrate and, to a lesser extent, linuron. Even in the absence of fungus, the methodology adopted achieved a relevant contaminant removal, with the only exception of the very hydrophilic dimethoate.

A comparative study of indicator bacteria concentrations obtained by laboratory analysis of grab samples of storm water, which were collected manually or by automatic samplers, was carried out in two urban catchments. Samples were analyzed for four types of indicator bacteria, total coliforms, Escherichia coli (E. coli), enterococci, and Clostridium perfringens and further documented by measurements of total suspended solids (TSS) and turbidity. Analysis of complete data sets (N = 198) indicated no statistically significant differences in the geometric means of all the constituent samples collected automatically or manually, but there were some small differences between the results produced by the two sampling methods applied. Total coliform concentrations were positively biased in samples collected by automatic samplers, but for the three remaining indicator bacteria (E. coli, enterococci, and C. perfringens), the opposite was true. Risk of sample cross-contamination in automatic samplers was assessed in the laboratory by sampling consecutively synthetic storm water with high and low concentrations of E. coli and enterococci. The first low-concentration samples preceded by high-concentration samples were cross-contaminated and the measured concentrations were positively biased. This cross-contamination was explained by storm-water residue in the sampling line. Such a residue remained in place even after line purging by compressed air, and its mass depended on the sampling line length (tested up to 5 m), as verified by measurements in the laboratory. The study findings should be helpful for improving field protocols for indicator bacteria sampling.

In this work, a mesoporous carbon material was modified by nitrogen atom by two different ways. The X-ray photoelectron spectroscopy (XPS) results show that the N atoms at the surface mainly exist in pyridine- and pyridone-like forms (around 80 % in atom ratio). The adsorption capacity of phenol and nicotine on mesoporous carbon and two N-containing mesoporous carbons was studied through adsorption isotherms. The adsorption isotherms were interpreted by three models (Freundlich, Langmuir, and Sips equations). Heat-flow microcalorimetry in liquid phase was used to determine the bonding strength between the organic pollutants and the surface of the adsorbents. In addition, the possibility of regeneration of adsorbents was investigated by temperature-programmed desorption (TPD) technique. The obtained values of differential heats and isotherms showed the heterogeneous properties of the mesoporous carbon materials. Comparing the different results obtained from the experiments, the surface area is a key factor for the adsorption of phenol and nicotine in water. The introduction of N improved the adsorption of phenol but did not affect the adsorption of nicotine.

In order to develop an effective and economical method for removing U(VI) from the low concentration radioactive wastewater with the U(VI) concentration of less than 1 mg L⁻¹, the biomass of Aspergillus niger was prepared and modified with ethylenediamine, and the biosorption of uranium from the low concentration radioactive wastewater by the unmodified and the modified biomasses was investigated in a batch system. The modified biomass exhibited the adsorption efficiency of 99.25 % for uranium under the optimum conditions that pH was 5.0, the contact time was 150 min, and the biosorbent dose was 0.2 g L⁻¹. The adsorption fitted well to Langmuir isotherm, and the maximum sorption capacity of the modified biomass for U(VI) was determined to be 6.789 mg g⁻¹which increased by 36.45 % compared with the unmodified biomass. The adsorption kinetics was better depicted by pseudo-second-order kinetic model. The Gibbs free energy change (ΔG⁰), enthalpy change (ΔH⁰), and entropy change (ΔS⁰) showed that the process of U(VI) adsorption was spontaneous, endothermic, and feasible. The changes in the groups, morphology, and the presence of U(VI) on the surface of the adsorbents which were characterized by FT-IR, SEM, and EDS, demonstrated that the U(VI) was successfully adsorbed onto the modified biomass. Moreover, the UO₂²⁺absorbed on the modified biomass can be released by 0.1 mol L⁻¹HNO₃with high desorption efficiency of 99.21 %. The results show that the modified biomass can remove U(VI) from low concentration radioactive wastewater more effectively than the unmodified biomass.

A series of miscible-displacement experiments was conducted to examine the impact of experiment conditions (detection limit, input pulse size, input concentration, pore-water velocity, contact time) on the performance of a mathematical solute transport model incorporating nonlinear, rate-limited sorption/desorption described by a continuous distribution reaction function. Effluent solute concentrations were monitored over a range of approximately seven orders of magnitude, allowing characterization of asymptotic tailing phenomenon. The model successfully simulated the extensive elution tailing observed for the measured data. Values for the mean desorption rate coefficient (ln k₂) and the variance of ln k₂were obtained through calibration of the model to measured data. Similar parameter values were obtained for experiments with different input pulse size, input concentration, pore-water velocity, and contact time. This suggests that the model provided a robust representation of sorption–desorption for this system tested. The impact of analytical detection limit was examined by calibrating the model to subsets of the breakthrough curves wherein the extent of the elution tail was artificially reduced to mimic a poorer detection limit. The parameters varied as a function of the extent of elution tail used for the calibrations, indicating the importance of measuring as full an extent of the tail as possible.

A mathematical model is developed to investigate the effect of pH and salinity fluctuation on biogeochemical reactions and metals' behavior in sediments. The model includes one-dimensional vertical advective and diffusive transport of species, serial reductions of electron acceptors, and precipitation/dissolution of species, acid–base chemistry, and metal sorption to sediments. The model was tested using data obtained from laboratory microcosm experiments which exposed metal (Cd, Zn) contaminated sediment to alternating fresh and salty overlying water. The model successfully reproduces the contrasting metal's release behavior and the vertical profiles of pH, Cl⁻, SO₄²⁻, Mn and Fe in porewater and the acid volatile sulfides (AVS) and simultaneously extracted metals (SEM) in sediments. The model showed that FeOOH₍ₛ₎was the dominant sorption phase controlling the solubility of the metals at the surficial sediments while AVS controlled the solubility of the metals in anoxic sediments. The model also showed that the release of the metals to overlying water was controlled by the oxidation of metal sulfides in a very thin layer of oxic sediments (2–3 mm). The proposed model can be useful in managing metal contaminated sediments where pH and salinity are fluctuating by assessing the underlying biogeochemical processes and metals' behavior.

Phosphorus (P) release and flux at sediment-water interface was hypothesized to vary with studied catchment branches due to differences in water chemistry of recharging groundwater. Stream water, seepage water, groundwater, and resurgence groundwater were collected, and their dissolved reactive P (DRP) concentrations and related water chemistry variables (pH, dissolved oxygen, cations, and anions) were measured to identify P sources in seepage water and resurgence groundwater and to look into their impacts on stream water DRP. Results showed that the groundwater-carried P concentrations were negligible, and, thus, not a direct source of DRP to stream water. However, the upwelling groundwater could contribute to stream water DRP by dissolving calcite-bound P in top sediments of branch 15. The seepage experiment indicated that in branch14, sediment release of reducible P was minimal. Furthermore, the presence of impermeable clay layer over the streambed of branch 14 prevented the transport of water and nutrients from beneath sediments to stream water, further reducing the P flux across the sediment-water interface. This study revealed that in branch 14, the recharge of anoxic groundwater did not significantly influence stream water P, due directly to its low P concentration, or indirectly to the lack of reducible P and the poor hydrological connectivity in bottom sediments. These results showed that differences between P soluble concentrations in small catchment streams can be explained by physicochemical processes at the sediment-water interface. More investigation is needed to assess whole catchment P dynamics.

Large-scale spatial gradient studies are increasingly used to understand the impacts of air pollution and devise appropriate conservation and policy responses, but how consistent are the conclusions we draw from these surveys? Here, we address this question by comparing three independent gradient studies from the same habitat, UK heather moorlands. We harmonise and re-analyse vegetation data from these surveys in relation to cumulative nitrogen deposition, sulphur deposition and other potential drivers and use these results to assess the possible impacts of air pollution in this habitat. Air pollution variables explain more variance in species richness and composition than other variables in the vast majority of analyses. Untangling the relative contribution of nitrogen and (legacy) sulphur deposition is difficult due to strong correlation, but it is likely that nitrogen deposition is currently the dominant driver of change. There is consistency in the negative correlation between species richness and nitrogen deposition, but some variability in the form of this relationship due to small sample sizes. Across surveys there is a high degree of consistency in species identified as either positively or negatively correlated to nitrogen deposition, and no evidence for systematic differences. We conclude that relatively small surveys across wide gradients can provide useful information on potential drivers of diversity, as well as identify sensitive and tolerant species. Our results strongly suggest that nitrogen deposition has a severe and widespread impact on the biodiversity of British heather moorlands and is causing changes in plant communities, including promoting the spread of at least one invasive species.

Methane (CH₄) formation in wastewater treatment is linked to long residence times under anaerobic conditions such as those in sewers and primary treatment units. Emissions of this methane to the atmosphere can occur under turbulent flows and, potentially, during aeration in an activated sludge plant. An online, 8-week monitoring campaign of CH₄emissions and operational conditions was conducted to study emissions from a full-scale nitrifying activated sludge plant (ASP). Significant emissions were found throughout the aerated lane, with the highest values observed two thirds down the lane. Emissions had high diurnal and spatial variability, with values ranging from 0.3 to 24 g CH₄/h. No significant correlations were found between dissolved oxygen, aeration or influent loads. The results suggest that emissions are linked to upstream process conditions, with potential for methane generation in-lane due periods of limited oxygen availability. The dynamic oxygen profile observed suggests that aerobic and anoxic conditions coexist in the lane, leading to limited oxygen diffusion from the bulk liquid to the inner regions of the floc where anoxic/anaerobic layers may allow methanogenic microorganisms to survive. The average emission factor was 0.07 % of removed chemical oxygen demand, giving a total of 668 kg CH₄/year and 14,000 CO₂equivalents/year. The operational carbon associated with the energy requirements of the ASP increased by 5 %. With emerging legislation requiring the reporting of greenhouse gas emissions, the carbon impact may be significant, particularly as the industry moves towards a carbon-reducing future. Therefore, an adequate profiling of full-scale emissions is critical for future proofing existing treatment technologies.

This study focused on the pattern recognition of Malaysian air quality based on the data obtained from the Malaysian Department of Environment (DOE). Eight air quality parameters in ten monitoring stations in Malaysia for 7 years (2005–2011) were gathered. Principal component analysis (PCA) in the environmetric approach was used to identify the sources of pollution in the study locations. The combination of PCA and artificial neural networks (ANN) was developed to determine its predictive ability for the air pollutant index (API). The PCA has identified that CH₄, NmHC, THC, O₃, and PM₁₀are the most significant parameters. The PCA-ANN showed better predictive ability in the determination of API with fewer variables, with R²and root mean square error (RMSE) values of 0.618 and 10.017, respectively. The work has demonstrated the importance of historical data in sampling plan strategies to achieve desired research objectives, as well as to highlight the possibility of determining the optimum number of sampling parameters, which in turn will reduce costs and time of sampling.