INTRODUCTION: Transconjugant bacteria with combined potential for hydrocarbon utilization and heavy metal resistance were suggested by earlier investigators for bioremediation of soils co-contaminated with hydrocarbons and heavy metals. The purpose of this study was to offer evidence that such microorganisms are already part of the indigenous soil microflora. METHODS: Microorganisms in pristine and oily soils were counted on nutrient agar and a mineral medium with oil as a sole carbon source, in the absence and presence of either sodium arsenate (As V), sodium arsenite (As III) or cadmium sulfate, and characterized via 16S rRNA gene sequencing. The hydrocarbon-consumption potential of individual strains in the presence and absence of heavy metal salts was measured. RESULTS: Pristine and oil-contaminated soil samples harbored indigenous bacteria with the combined potential for hydrocarbon utilization and As and Cd resistance in numbers up to 4 × 105 CFU g−1. Unicellular bacteria were affiliated to the following species arranged in decreasing order of predominance: Bacillus subtilis, Corynebacterium pseudotuberculosis, Brevibacterium linens, Alcaligenes faecalis, Enterobacter aerogenes, and Chromobacterium orangum. Filamentous forms were affiliated to Nocardia corallina, Streptomyces flavovirens, Micromonospora chalcea, and Nocardia paraffinea. All these isolates could grow on a wide range of pure aliphatic and aromatic hydrocarbons, as sole sources of carbon and energy, and could consume oil and pure hydrocarbons in batch cultures. Low As concentrations, and to a lesser extent Cd concentrations, enhanced the hydrocarbon-consumption potential by the individual isolates. CONCLUSION: There is no need for molecularly designing microorganisms with the combined potential for hydrocarbon utilization and heavy metal resistance, because they are already a part of the indigenous soil microflora.

PURPOSE: In the assessment of health risks of environmental pollutants, the method of dose addition and the method of independent action are used to assess mixture effects when no synergistic and/or antagonistic effects are present. Currently, no method exists to quantify synergistic and/or antagonistic effects for mixtures. The purpose of this paper is to develop the theoretical concepts of an overall risk probability (ORP)-based method to quantify the synergistic and antagonistic effects in health risk assessment for mixtures. METHOD: The ORP for health effects of environmental chemicals was determined from the cumulative probabilities of exposure and effects. This method was used to calculate the ORP for independent mixtures and for mixtures with synergistic and antagonistic effects. RESULTS: For the independent mixtures, a mixture ORP can be calculated from the product of the ORPs of individual components. For systems of interacting mixtures, a synergistic coefficient and an antagonistic coefficient were defined respectively to quantify the ORPs of each individual component in the mixture. The component ORPs with synergistic and/or antagonistic effects were then used to calculate the total ORP for the mixture. CONCLUSIONS: An ORP-based method was developed to quantify synergistic and antagonistic effects in health risk assessment for mixtures. This represents a first method to generally quantify mixture effects of interacting toxicants.

INTRODUCTION: In this paper, a novel method in the estimation and prediction of PM10 is introduced using wavelet transform-based artificial neural networks (WT-ANN). DISCUSSION: First, the application of wavelet transform, selected for its temporal shift properties and multiresolution analysis characteristics enabling it to reduce disturbing perturbations in input training set data, is presented. Afterward, the circular statistical indices which are used in this method are formally introduced in order to investigate the relation between PM10 levels and circular meteorological variables. Then, the results of the simulation of PM10 based on WT-ANN by use of MATLAB software are discussed. The results of the above-mentioned simulation show an enhanced accuracy and speed in PM10 estimation/prediction and a high degree of robustness compared with traditional ANN models.

PURPOSE: To assess groundwater exposure to pesticides, in agricultural areas of ‘Ribatejo’ region (Portugal), and the influence of some key factors in that exposure, field, laboratory and modelling studies were carried out. METHODS: The study was performed in maize, potato, sugar beet, tomato and vegetables agricultural areas, located in a shallow aquifer, with pesticides use and, in most cases, with irrigation practices. Pesticides used in the studied agricultural areas and having leaching potential were selected, being considered also other pesticides included in priority lists, defined in Europe. Evaluation of groundwater exposure to pesticides was carried out by successively: (1) groundwater sampling in seven campaigns over the period 2004–2006; (2) pesticide analysis [including isolation and concentration from the groundwater samples and further determination by gas chromatography–mass spectrometry (GC–MS) of 14 herbicides, four insecticides and two metabolites]; and (3) analysis and discussion of the results by applying joint correspondence analysis (JCA). RESULTS: From the 20 pesticides and metabolites selected for the study, 11 were found in groundwater. Pesticides and metabolites most frequently detected were atrazine, alachlor, metolachlor, desethylatrazine, ethofumesate, α-endosulfan, metribuzine, lindane and β-endosulfan. The results showed that groundwater exposure to pesticides is influenced by local factors—either environmental or agricultural, as precipitation, soil, geology, crops and irrigation practices. Spring and autumn were more associated with the detection of pesticides being more likely to observe mixtures of these compounds in a groundwater sample in these transition seasons. CONCLUSIONS: This work evidences the importance of models, which evaluate pesticides environmental behaviour, namely their water contamination potential (as Mackay multicompartimental fugacity model) and, specially, groundwater contamination potential (as GUS and Bacci and Gaggi leaching indices), in pesticide selection. Moreover, it reveals the importance to adapt proper statistical methods according to level of left-censored data. Using JCA was still possible to establish relations between pesticides and their temporal trend in a case study where there were more than 80% of data censored. This study will contribute to the Tagus river basin management plan with information on the patterns of pesticide occurrence in the alluvial aquifer system.

PURPOSE: A multiple inputs and multiple outputs (MIMO) fuzzy-logic-based model was proposed to estimate color and chemical oxygen demand (COD) removal efficiencies in the post-treatment of anaerobically pretreated poultry manure wastewater effluent using Fenton's oxidation process. Three main input variables including initial pH, Fe+2, and H2O2 dosages were fuzzified in a new numerical modeling scheme by the use of an artificial intelligence-based approach. MATERIALS AND METHODS: Trapezoidal membership functions with eight levels were conducted for the fuzzy subsets, and a Mamdani-type fuzzy inference system was used to implement a total of 70 rules in the IF–THEN format. The product (prod) and the center of gravity (centroid) methods were applied as the inference operator and defuzzification methods, respectively. Fuzzy-logic predicted results were compared with the outputs of two first-order polynomial regression models derived in the scope of this study. Estimated results were also compared to the multiple regression approach by means of various descriptive statistical indicators, such as root mean-squared error, index of agreement, fractional variance, proportion of systematic error, etc. RESULTS AND DISCUSSION: Results of the statistical analysis clearly revealed that, compared to conventional regression models, the proposed MIMO fuzzy-logic model produced very smaller deviations and demonstrated a superior predictive performance on forecasting of color and COD removal efficiencies with satisfactory determination coefficients over 0.98. CONCLUSIONS: Due to high capability of the fuzzy-logic methodology in capturing the non-linear interactions, it was demonstrated that a complex dynamic system, such as Fenton's oxidation, could be easily modeled.

BACKGROUND, AIM AND SCOPE: Groundwaters and source waters are exposed to environmental pollution due to agricultural and industrial activities that can enhance the leaching of organic contaminants. Pesticides are among the most widely studied compounds in groundwater, but little information is available on the presence of phthalates, alkylphenols and bisphenol A. These compounds are used in pesticide formulations and represent an emerging family of contaminants due to their widespread environmental presence and endocrine-disrupting properties. Knowledge on the occurrence of contaminants in source waters intended for bottling is important for sanitary and regulatory purposes. So the aim of the present study was to evaluate the presence of phthalates, alkylphenols, triazines, chloroacetamides and bisphenol A throughout 131 Spanish water sources intended for bottling. Waters studied were spring waters and boreholes which have a protection diameter to minimize environmental contamination. MATERIALS AND METHODS: Waters were solid-phase extracted (SPE) and analysed by gas chromatography coupled to mass spectrometry (GC-MS). Quality control analysis comprising recovery studies, blank analysis and limits of detection were performed. RESULTS AND DISCUSSION: Using SPE and GC-MS, the 21 target compounds were satisfactorily recovered (77–124 %) and limits of quantification were between 0.0004 and 0.029 μg/L for pesticides, while for alkylphenols, bisphenol A and phthalates the limits of quantification were from 0.0018 μg/L for octylphenol to 0.970 μg/L for bis(2-ethylhexyl) phthalate. Among the 21 compounds analysed, only 9 were detected at levels between 0.002 and 1.115 μg/L. Compounds identified were triazine herbicides, alkylphenols, bisphenol A and two phthalates. Spring waters or shallow boreholes were the sites more vulnerable to contaminants. Eighty-five percent of the samples did not contain any of the target compounds. CONCLUSIONS: Target compounds were detected in a very low concentration and only in very few samples. This indicates the good quality of source waters intended for bottling and the effectiveness of the protection measures adopted in Spain. None of the samples analysed exceeded the maximum legislated levels for drinking water both in Spain and in the European Union.

PURPOSE: This study aimed to associate the intensity of vehicular traffic in the city of Dourados (Mato Grosso do Sul State, Brazil) with mutagenic effects and alterations in leaf physiology as measured by the quantity of micronuclei and the leaf surface parameters of Tradescantia pallida. METHODS: Five collections of inflorescences were undertaken for 24 weeks to determine the quantities of micronuclei using the Tradescantia Micronuclei (Trad-MCN) bioassay. Leaf surface parameters, including stomatal index (SI), stomatal density, and the size of the stomatal ostiole opening size (SO), were evaluated in addition to Trad-MCN. Collections were made at four sampling points with different vehicular traffic intensities. Statistical analyses were performed with SAS software using the Tukey’s and Kruskal–Wallis test. Additionally, associations of the characteristics were verified using Pearson’s simple correlation analysis. RESULTS: Significant effects were observed with the Trad-MCN bioassay (p < 0.01) that were related to the collection period and location, as well as significant differences (p < 0.05) for the effects of the collection points using the Kruskal–Wallis test. In general, the locations with greatest vehicular traffic had plants with the greatest stomatal density values. The characteristics SI and SO did not demonstrate significant differences (p > 0.05) in relation to the collection sites. The simple correlation analysis demonstrated a negative association (−0.65) between SI and Trad-MCN (p < 0.05). CONCLUSION: Plants growing in localities with more intense vehicular traffic had greater quantities of micronuclei as well as higher frequencies and average numbers of stomata than localities with less traffic, indicating the presence of atmospheric contaminants that damaged their DNA.

INTRODUCTION: The ecological risks posed by three chlorophenols (CPs), 2,4-dichlorophenol (2,4-DCP), 2,4,6-trichlorophenol (2,4,6-TCP), and pentachlorophenol (PCP) in Chinese surface waters were assessed. MATERIALS AND METHODS: This was achieved by applying a tiered ecological risk assessment (ERA) approach ranging from deterministic methods to probabilistic options to measured concentrations of CPs in surface water of seven major watersheds and three drainage regions in China and the chronic toxicity data for indigenous Chinese species. RESULTS AND DISCUSSION: The results show that the risks of three chlorophenols are ranked PCP>2,4-DCP≈2,4,6-TCP. PCP posed little ecological risk while 2,4-DCP and 2,4,6-TCP posed negligible or de minimis risk in Chinese surface water. However, the risks varied with different river basins, for example, PCP posed some ecological risk in the Yangtze, Huaihe, and Pearl Rivers. The magnitude of 2,4-DCP and 2,4,6-TCP pollution in North China was more serious than that in South China. CONCLUSION: The probabilistic risk assessment approach, which can provide more information for risk managers and decision makers, was favored over the screening-level single-value estimate method. However, the results from all tiers of the ERA methods in the framework were consistent with each other.

Cyanobacterial blooms represent a serious threat to the aquatic environment. Among other effects, biochemical markers have been studied in aquatic vertebrates after exposures to toxic cyanobacteria. Some parameters such as protein phosphatases may serve as selective markers of exposure to microcystins, but under natural conditions, fish are exposed to complex mixtures, which affect the overall biomarker response. This review aims to provide a critical summary of biomarker responses in aquatic vertebrates (mostly fish) to toxic cyanobacteria with a special focus on detoxification and oxidative stress. Detoxification biomarkers such as glutathione (GSH) and glutathione-S-transferase (GST) showed very high variability with poor general trends. Often, stimulations and/or inhibitions and/or no effects at GSH or GST have been reported, even within a single study, depending on many variables, including time, dose, tissue, species, etc. Most of the oxidative stress biomarkers (e.g., superoxide dismutase, catalase, glutathione peroxidase, and glutathione reductase) provided more consistent responses, but only lipid peroxidation (LPO) seemed to fulfill the criteria needed for biomarkers, i.e., a sufficiently long half-life and systematic response. Indeed, reviewed papers demonstrated that toxic cyanobacteria systematically elevate levels of LPO, which indicates the important role of oxidative damage in cyanobacterial toxicity. In summary, the measurement of biochemical changes under laboratory conditions may provide information on the mode of toxic action. However, comparison of different studies is very difficult, and the practical use of detoxification or oxidative stress biomarkers as diagnostic tools or early warnings of cyanobacterial toxicity is questionable.

PURPOSE: Existing land-use regression (LUR) models use land use/cover, population, and traffic information to predict long-term intra-urban variation of air pollution. These models are limited to explaining spatial variation of air pollutants, and few of them are capable of addressing temporal variability. This article proposes a space–time LUR model at a regional scale by incorporating aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). METHODS: A multivariate regression model was established to predict the distribution of particle matters less than 2.5 μm in aerodynamic diameter (PM2.5) in Florida, USA. Monthly PM2.5 averages at 34 monitoring sites in the year 2005 were used as the dependent variable, while independent variables include land-use patterns, population, traffic, and topographic characteristics. In addition, a monthly AOD variable derived from the MODIS data was integrated into the regression as a space–time predictor. Cross-validation procedures were conducted to validate this AOD-enhanced LUR model. RESULTS: The final regression model yields a coefficient of determination (R 2) of 0.63, which is comparable to other studies that employ aerodynamic/meteorological models. The cross validation indicated a good agreement between the observed and predicted PM2.5 with a mean residual of 0.02 μg/m3. The distance to heavy-traffic roads is negatively associated with the concentrations of PM2.5, while agricultural land use is positively correlated. PM2.5 tends to concentrate in high-latitude areas of Florida and during summer/fall seasons. The monthly AOD has a significant contribution to explaining the variation of PM2.5 and remarkably enhances the model performance. CONCLUSIONS: This research is the first attempt to improve current LUR models by integrating remote sensing technologies. The integrative model approach offers an effective means to estimate air pollution over time and space, and could be an alternative to the classic meteorological approach. The model results would provide adequate measurements for epidemiological studies, particularly for chronic health effects in large populations.