Evaluation of the effectiveness of red mud-supported catalysts in combination with ozone and TiO<Subscript>2</Subscript> in the treatment of solution containing benzene,toluene, and xylene

Ozone and a Fe²⁺/TiO₂-based catalyst were examined in the degradation of a synthetic solution of benzene toluene and xylene (BTX) in an advanced oxidation process (AOP). The catalyst beads were made from the slurry waste of aluminum production process, by inserting the TiO₂ content and subsequent calcination. The reduction of the BTX concentration load was monitored by the reduction of chemical oxygen demand (COD) and BTX concentration. Different levels were used on factors: pH, time of treatment, initial concentration of BTX, and percentage of TiO₂. The process was conducted in a bubble column reactor with the insertion of catalyst beads. A response surface methodology technique (CCD) was used to build a model based on COD reduction results. The model was optimized using the normal-boundary intersection (NBI) algorithm to maximize COD reduction and minimize the variance attributed to the process. Optimization led to COD reductions of 80% in 2 h of experiment. Correlation analysis of coefficient models from experimental data R²_adj was 0.9966, showing a good fit of model data. In the optimized conditions, the possible increase of the biodegradability ratio of the BTX solution, through the biochemical oxygen demand (BOD) and COD, was also analyzed. Under pre-treatment conditions, the BOD/COD ratio was 0.13. After the treatment, it increased to 0.56.

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Impact assessment of climate change on farming systems in the South Mediterranean area: a Tunisian case study

Regional Environmental Change - This study considers a quantitative approach for assessing the performance of Tunisian farming systems to face climate change. It is based on the resilience concept...     

Tributyltin chloride disrupts aortic vascular reactivity and increases reactive oxygen species production in female rats

Environmental Science and Pollution Research - Organotin compounds, such as tributyltin (TBT), are environment contaminants that induce bioaccumulation and have potential toxic effects on marine...     

Impact of Length of Dataset on Streamflow Calibration Parameters and Performance of APEX Model

Due to resource constraints, long‐term monitoring data for calibration and validation of hydrologic and water quality models are rare. As a result, most models are calibrated and, if possible, validated using limited measured data. However, little research has been done to determine the impact of length of available calibration data on model parameterization and performance. The main objective of this study was to evaluate the impact of length of calibration data (LCD) on parameterization and performance of the Agricultural Policy Environmental eXtender model for predicting daily, monthly, and annual streamflow. Long‐term (1984‐2015) measured daily streamflow data from Rock Creek watershed, an agricultural watershed in northern Ohio, were used for this study. Data were divided into five Short (5‐year), two Medium (15‐year), and one Long (25‐year) streamflow calibration data scenarios. All LCD scenarios were calibrated and validated at three time steps: daily, monthly, and annual. Results showed LCD affected the ability of the model to accurately capture temporal variability in simulated streamflow. However, overall average streamflow, water budgets, and crop yields were simulated reasonably well for all LCD scenarios.     

Assessing environmental impacts of treated wastewater through monitoring of fecal indicator bacteria and salinity in irrigated soils

To assess the potential for treated wastewater irrigation to impact levels of fecal indicator bacteria (FIB) and salinity in irrigated soils, levels of Escherichia coli, Enterococcus, and environmental covariates were measured in a treated wastewater holding pond (irrigation source water), water leaving the irrigation system, and in irrigated soils over 2 years in a municipal parkland in Arizona. Higher E. coli levels were measured in the pond in winter (56 CFU 100 mL⁻¹) than in summer (17 CFU 100 mL⁻¹); however, in the irrigation system, levels of FIB decreased from summer (26 CFU 100 mL⁻¹) to winter (4 CFU 100 mL⁻¹), possibly related to low winter water use and corresponding death of residual bacteria within the system. For over 2 years, no increase in FIB was found in irrigated soils, though highest E. coli levels (700 CFU g⁻¹ soil) were measured in deeper (20–25 cm) soils during summer. Measurements of water inputs vs. potential evapotranspiration indicate that irrigation levels may have been sufficient to generate bacterial percolation to deeper soil layers during summer. No overall increase in soil salinity resulting from treated wastewater irrigation was detected, but distinct seasonal peaks as high as 4 ds m⁻¹ occurred during both summers. The peaks significantly declined in winter when surface ET abated and more favorable water balances could be maintained. Monitoring of seasonal shifts in irrigation water quality and/or factors correlated with increases and decreases in FIB will aid in identification of any public health or environmental risks that could arise from the use of treated wastewater for irrigation.     

Spatial and temporal occurrence of N-nitrosamines in seven drinking water supply systems

The spatiotemporal presence of eight N-nitrosamines in the water of seven supply systems in Quebec considered to be susceptible to these emerging disinfection by-products was evaluated. This is the first study on the presence of N-nitrosamines in drinking water utilities in Quebec. Seven sampling campaigns were carried out at several sampling points in each of the systems over a period of 1 year. The results show that N-nitrosamines, primarily N-nitrosodimethylamine (NDMA), were not commonly detected in the water of the facilities under study (10 % of samples). The concentrations measured were lower than those reported in recent North American studies. None of the 195 samples taken exceeded the Ontario standard of 9 ng/L for NDMA (maximum value observed of 3.3 ng/L). N-nitrosomethylethylamine and N-nitrosopiperidine were detected once, with concentrations of 3.7 and 6.0 ng/L, respectively. Chloramination was identified as being the main risk factor regarding the presence of N-nitrosamines, but water quality and some operating parameters, in particular disinfectant residual, also seem to be related to their presence. NDMA concentrations at the end of the distribution systems were generally higher than water leaving the plant. No seasonal trends were observed for the formation of N-nitrosamines in the investigated supply systems. Finally, an association between the presence of N-nitrosamines and the levels of trihalomethanes and haloacetic acids was observed in some facilities.     

Utilizing the great blue heron (Ardea herodias) in ecological risk assessments of bioaccumulative contaminants

Selection of an appropriate species is a key element of effective ecological risk assessments (ERA), especially when site-specific field studies are to be employed. Great blue herons (GBH) possess several ideal characteristics of a receptor species for the assessment of bioaccumulative compounds in the environment, such as ease of study, high potential for exposure, widespread distribution, and territorial foraging behavior. Methodologies for assessing exposure and population health are described herein. As outlined, the collection of GBH eggs, GBH nestling blood, and adult GBH blood allows for the determination of contaminant concentrations in various GBH tissues, a top-down assessment, which can be done in conjunction with predicted dietary exposure, a bottom-up assessment, to support a multiple lines of evidence approach. Additionally, population parameters, such as productivity and survival, can also be measured to elucidate if the contaminant exposure may be causing population level effects. Over the course of two years, three GBH rookeries were monitored for productivity and nestling exposure. Nests were monitored from blinds and individually accessed at multiple time points to obtain measures of nestling health, band nestlings, and collect eggs and nestling plasma. Multiple nests could frequently be accessed by climbing one tree, resulting in minimal effort to obtain the necessary sample size. Additionally, 51 adult GBH, captured in their foraging areas, were banded, and provided a blood sample. With these samples, a statistical difference in tissue based exposure was identified between the reference and target area. Statistically significant differences were also identified between the upper and lower reaches of the target area, thereby identifying a range of doses geographically which could be correlated to specific measurement endpoints. The ability to identify a dose response greatly increases the ability of the dataset to determine causation, a key goal of such studies. Overall, the use of the described methods allowed for the collection of a statistically sufficient and ecologically relevant dataset with reasonable effort and minimal impact on GBH.     

Evaluation of modeling as a tool to determine the potential impacts related to drilling wastes in the Brazilian offshore

Numerical models that predict the fate of drilling discharges at sea constitute a valuable tool for both the oil industry and regulatory agencies. In order to provide reliable estimates, models must be validated through the comparison of predictions with field or laboratory observations. In this paper, we used the Offshore Operators Committee Model to simulate the discharges from two wells drilled at Campos Basin, offshore SE Brazil, and compared the results with field observations obtained 3 months after drilling. The comparison showed that the model provided reasonable predictions, considering that data about currents were reconstructed and theoretical data were used to characterize the classes of solids. The model proved to be a valuable tool to determine the degree of potential impact associated to drilling activities. However, since the accuracy of the model is directly dependent on the quality of input data, different possible scenarios should be considered when used for forecast modeling.