Urban soil is an important component of urban ecosystems. This study focuses on heavy metal contamination in soils of Wien (Austria) and results are compared to those for a few large European cities. We analysed the elemental contents of 96 samples of topsoil from urban, suburban and rural areas in Wien along a dynamic (floodplain forest) and a stable (oak–hornbeam forest) urbanization gradient. The following elements were quantified using ICP-OES technique: Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Pb, P, S and Zn. For heavy metals PI (pollution index) values were used to assess the level of pollution. The PI values indicated high level of pollution by Pb in the suburban and rural area of stable gradient and in the urban area of dynamic gradient; moderate level of pollution was indicated for Cd in the urban area of stable gradient. The level of pollution was moderate for Co in the suburban and rural area of the stable gradient, and for Cu in suburban area of stable gradient, and urban area of dynamic gradient. The pollution level of Zn was moderate in all areas. Urban soils, especially in urban parks and green areas may have a direct influence on human health. Thus, the elemental analysis of soil samples is one of the best ways to study the effects of urbanization. Our results indicated that the heavy metal contamination was higher in Wien than in a few large European cities.

Degradation of three sulfonamides (SAs), namely sulfamethoxazole (SMX), sulfamethazine (SMZ), and sulfadimethoxine (SDM) in surface water and sediments collected from Taihu Lake and Dianchi Lake, China was investigated in this study. The surface water (5–10 cm) was collected from the east region of Taihu Lake, China. Two sets of degradation experiments were conducted in 3-L glass bottles containing 2 L of fresh lake water and 100 μg/L of individual SAs aerated by bubbling air at a rate of approximately 1.2 L/min, one of which was sterilized by the addition of NaN₃ (0.1 %). Sediment samples were taken from Taihu Lake and Dianchi Lake, China. For the sediment experiment, 5 g of sediment were weighed into a 50-mL glass tube, with 10 mg/kg of individual SAs. Different experimental conditions including the sediment types, sterilization, light exposure, and redox condition were also considered in the experiments. The three SAs degraded in lake water with half-lives (t ₁/₂) of 10.5–12.9 days, and the half-lives increased significantly to 31.9–49.8 days in the sterilized water. SMZ and SDM were degraded by abiotic processes in Taihu and Dianchi sediments, and the different experimental conditions and sediments characteristics had no significant effect on their declines. SMX, however, was mainly transformed by facultative anaerobes in Taihu and Dianchi sediments under anaerobic conditions, and the degradation rate of SMX in non-sterile sediment (t ₁/₂ of 9.6–16.7 days) were higher than in sterilized sediment (t ₁/₂ of 18.7–135.9 days). Under abiotic conditions, degradation of SMX in Dianchi sediment was faster than in Taihu sediment, probably due to the higher organic matter content and inorganic photosensitizers concentrations in Dianchi sediment. High initial SAs concentration inhibited the SAs degradation, which was likely related to the inhibition of microorganism activities by high SAs levels in sediments. Results from this study could provide information on the persistence of commonly used sulfanomides antibiotics in lake environment.

As(V) retention capacity is determined by means of adsorption/desorption trials performed for coarse and fine ground mussel shell, forest and vineyard soils with or without fine shell, pine wood ash, oak wood ash, pine sawdust and slate-processing fines. Pine ash shows the highest arsenic retention potential (with >97 % adsorption and ≤1 % desorption), followed by shell-amended forest soil (adsorption between 96 and 92 %), by un-amended forest soil (adsorption between 98 and 86 %) and by the amended vineyard soil (adsorption between 92 and 75 %). Sawdust is the material with the lowest arsenic retention capacity in most cases, with un-amended vineyard soil also showing poor results. In the case of oak ash, As(V) percentage adsorption becomes higher with increasing added arsenic concentrations, while this increase in added arsenic causes lower percentage adsorption in the case of slate fines. Regarding adsorption ability, As(V) adsorption data were fitted to Freundlich and Langmuir models, showing good fitting, with pine ash and shell-amended forest soil having the highest K F values. In view of that, mussel shell amendment would be useful to increase arsenic retention on forest and vineyard soils, while pine ash could be used to retain arsenic even from wastewaters.

Present work demonstrates Cr (VI) detoxification and resistance mechanism of a newly isolated strain (B9) of Acinetobacter sp. Bioremediation potential of the strain B9 is shown by simultaneous removal of major heavy metals including chromium from heavy-metals-rich metal finishing industrial wastewater. Strain B9 tolerate up to 350 mg L(-1) of Cr (VI) and also shows level of tolerance to Ni (II), Zn (II), Pb (II), and Cd (II). The strain was capable of reducing 67 % of initial 7.0 mg L(-1) of Cr (VI) within 24 h of incubation, while in presence of Cu ions 100 % removal of initial 7.0 and 10 mg L(-1) of Cr (VI) was observed with in 24 h. pH in the range of 6.0-8.0 and inoculum size of 2 % (v/v) were determined to be optimum for dichromate reduction. Fourier transform infrared spectroscopy and transmission electron microscopy studies suggested absorption or intracellular accumulation and that might be one of the major mechanisms behind the chromium resistance by strain B9. Scanning electron microscopy showed morphological changes in the strain due to chromium stress. Relevance of the strain for treatment of heavy-metals-rich industrial wastewater resulted in 93.7, 55.4, and 68.94 % removal of initial 30 mg L(-1) Cr (VI), 246 mg L(-1) total Cr, and 51 mg L(-1) Ni, respectively, after 144 h of treatment in a batch mode.

In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO₂ concentrations and their transformations, as inputs. The root mean square error and Willmott’s index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model.

Trichloroethylene (TCE) is one of the most hazardous organic pollutants in groundwater. Biochar produced from agricultural waste materials could serve as a novel carbonaceous adsorbent for removing organic contaminants from aqueous media. Biochars derived from pyrolysis of soybean stover at 300 °C and 700 °C (S-300 and S-700, respectively), and peanut shells at 300 °C and 700 °C (P-300 and P-700, respectively) were utilized as carbonaceous adsorbents to study batch aqueous TCE remediation kinetics. Different rate-based and diffusion-based kinetic models were adopted to understand the TCE adsorption mechanism on biochars. With an equilibrium time of 8-10 h, up to 69 % TCE was removed from water. Biochars produced at 700 °C were more effective than those produced at 300 °C. The P-700 and S-700 had lower molar H/C and O/C versus P-300 and S-300 resulting in high aromaticity and low polarity accompanying with high surface area and high adsorption capacity. The pseudo-second order and intraparticle diffusion models were well fitted to the kinetic data, thereby, indicating that chemisorption and pore diffusion were the dominating mechanisms of TCE adsorption onto biochars.

The science parks have helped shape Taiwan as a high-tech island with a good reputation worldwide. But some complaints on air pollution from the science parks have recently risen. To better understand the environmental effects of volatile organic compounds (VOCs) emitted from various high-tech factories in a science park, this study uses a source–receptor approach to characterize the environmental effects of VOCs from control device exhaust in Taichung Science Park. The chemical mass balance model (CMB8.2) of field measurements of 30 stacks and ambient air at nine sites was used to identify the source and relative contribution of ambient VOCs. The exhaust gas of various pollution control devices was also sampled by drawing a stream of the gases from the exhaust duct at its sampling port. The VOC source profile of each control device exhaust was determined using a database of noncharacteristic compounds. Monthly ambient concentrations of 167 VOCs were divided into monsoon datasets to investigate the effect of monsoon conditions on the emission of VOCs in the science park. This study also suggests a method for determining the optimum source profile in source–receptor modeling, and identifies and analyzes the sources of ambient VOCs at nine sites during southwest and northeast monsoons. Results show a direct relationship between the relative contribution of each source and its control device efficiency. The proposed source–receptor approach can characterize the environmental effect of air pollutants from various factories and successfully assess the efficiency of various control devices.

Synthetic wastewaters are widely used in many fields of wastewater research and operational management. However, few comparative studies have been conducted on the large number of published formulations. Eleven synthetic formulations simulating municipal wastewaters were selected based on their frequency of use, relative complexity, ease of formulation and cost and compared to two real municipal wastewaters. Synthetic wastewaters vary significantly in their compositions and characteristics, especially in terms of alkalinity, BOD K ₁, SOUR, BODU, COD/BOD and C/N/P ratio, although they are portrayed as ‘typical’ in terms of characteristics and suitability for use. The pH, alkalinity and the presence of Ca and Mg should be considered in combination with the diluent used. Where the diluent is tap water, then the presence of treatment chemicals should also be considered. The effects of the micronutrients present are also an important factor. The study found that no single formulation is appropriate for all situations. Both the Syntho and Synthes formulations attempt to simulate real wastewater, whereas other formulations primarily act as readily biodegradable vectors for toxicity analyses, characterisation studies and treatment process evaluations. The criteria for choosing a particular synthetic wastewater very much depend on its intended application and require careful selection.