The performance of an anaerobic sequencing batch biofilm reactor treating domestic sewage colonized by anoxygenic phototrophic bacteria

There are few reports on morphological characterization of microbial population colonizing anaerobic bioreactors and the aim of this work was to access such variable in an anaerobic sequencing batch biofilm reactor treating the University of Sao Paulo (Sao Carlos city, Brazil) domestic sewage. This pilot-scale reactor (1.2m3) has been treating 0.65 m3 of liquid waste under cycles of 8h. The ASBBR has the distinct characteristics of being filled with support material for biomass attachment with the aim of skipping the sedimentation phase during the operational cycles, as it is commonly observed in anaerobic sequencing batch reactors (ASBR). Physical, chemical and physico-chemical variables were accessed in the influent and in the effluent for performance evaluation. Microbial characterization was made by means of direct microscopy and samples were taken over 150 d with a 25 d period interval. The ASBBR attained approximately 60% of COD removal efficiency. Microscopic analysis of biomass showed the presence of anoxygenic phototrophic bacteria probably influencing the ASBBR performance in the domestic sewage treatment. It is very likely that the exclusion of phototrophic sulfur bacteria by efficiently restraining the light would enhance the bioreactor efficiency.     

Receptor Modeling Applied to Patterns in Space (RMAPS)Part III–Apportionment of Airborne Particulate Sulfur in Western Washington State

Abstract

Data obtained from 24 of the 31 sites of the Pacific Northwest Regional Visibility Experiment Using Natural Tracers (PREVENT) study were analyzed by the Receptor Model Applied to Patterns in Space (RMAPS) multivariate receptor model. Four spatial patterns were found and interpreted as showing the effect of the coal-fired power plant in Centralia, WA; transport from the northwest; the Se-attle-Tacoma urban area; and transport from the southeast. In Mt. Rainier National Park, up to one-third of the sulfate can be attributed to the Centralia power plant. In the North Cascades National Park, 65-82% of the sulfur is accounted for by transport from Canada. The model was applied separately to sites in the northern and southern sections of the study area. The southern sites were affected only by the Centralia, urban, and southeast transport sources; the northern sites were affected only by the northwest transport, urban, and southeast transport sources. This gave two independent estimates of the normalized source contributions of the urban and southeast transport factors, which had a correlation coefficient of more than 0.90.     

Brain glutathione redox system significance for the control of silica-coated magnetite nanoparticles with or without mercury co-exposures mediated oxidative stress in European eel (Anguilla anguilla L.)

This in vitro study investigates the impact of silica-coated magnetite particles (Fe₃O₄@SiO₂/SiDTC, hereafter called IONP; 2.5 mg L^?1) and its interference with co-exposure to persistent contaminant (mercury, Hg; 50 μg L^?1) during 0, 2, 4, 8, 16, 24, 48, and 72 h on European eel (Anguilla anguilla) brain and evaluates the significance of the glutathione (GSH) redox system in this context. The extent of damage (membrane lipid peroxidation, measured as thiobarbituric acid reactive substances, TBARS; protein oxidation, measured as reactive carbonyls, RCs) decreased with increasing period of exposure to IONP or IONP + Hg which was accompanied with differential responses of glutathione redox system major components (glutathione reductase, GR; glutathione peroxidase, GPX; total GSH, TGSH). The occurrence of antagonism between IONP and Hg impacts was evident at late hour (72 h), where significantly decreased TBARS and RC levels and GR and glutathione sulfo-transferase (GST) activity imply the positive effect of IONP + Hg concomitant exposure against Hg-accrued negative impacts [vs. early (2 h) hour of exposure]. A period of exposure-dependent IONP alone and IONP + Hg joint exposure-accrued impact was perceptible. Additionally, increased susceptibility of the GSH redox system to increased period of exposure to Hg was depicted, where insufficiency of elevated GR for the maintenance of TGSH required for membrane lipid and cellular protein protection was displayed. Overall, a fine-tuning among brain glutathione redox system components was revealed controlling IONP + Hg interactive impacts successfully.     

Toward a robust analytical method for separating trace levels of nano-materials in natural waters: cloud point extraction of nano-copper(II) oxide

Cloud point extraction (CPE) factors, namely Triton X-114 (TX-114) concentration, pH, ionic strength, incubation time, and temperature, were optimized for the separation of nano-sized copper(II) oxide (nCuO) in aqueous matrices. The kinetics of phase transfer was studied using UV–visible spectroscopy. From the highest separation rate, the most favorable conditions were observed with 0.2 %?w/v of TX-114, pH?=?9.0, ionic strength of 10 mM NaCl, and incubation at 40 °C for 60 min, yielding an extraction efficiency of 89.2?±?3.9 % and a preconcentration factor of 86. The aggregate size distribution confirmed the formation of very large nCuO–micelle assemblies (11.9 μm) under these conditions. The surface charge of nCuO was also diminished effectively. An extraction efficiency of 91 % was achieved with a mixture of TX-100 and TX-114 containing 30 wt.% of TX-100. Natural organic and particulate matters, represented by humic acid (30 mg/L) and micron-sized silica particles (50 mg/L), respectively, did not significantly reduce the CPE efficiency (<10 %). The recovery of copper(II) ions (20 mg/L) in the presence of humic acid was low (3–10 %). The spiked natural water samples were analyzed either directly or after CPE by inductively coupled plasma mass spectrometry following acid digestion/microwave irradiation. The results indicated the influence of matrix effects and their reduction by CPE. A delay between spiking nCuO and CPE may also influence the recovery of nCuO due to aggregation and dissolution. A detection limit of 0.04 μg Cu/L was achieved for nCuO.     

Uranium transport around the reactor zone at Bangombé and Okélobondo (Oklo): examples of hydrogeological and geochemical model integration and data evaluation

The sites at Bangombé and Okélobondo (Oklo) in Gabon provide a unique opportunity to study the behaviour of products from natural nuclear reactions in the vicinity of reactor zones which were active around two billion years ago. The Commission of the European Communities initiated the Oklo Natural Analogue Programme. One of the principal aims was to study indications of present time migration of elements from the reactor zones under ambient conditions. The hydrogeological and hydrochemical data from the Oklo sites were modelled in order to better understand the geochemical behaviour of radionuclides in the natural system, by using independent models and by comparing the modelling outcome. Two modelling approaches were used: M3 code (hydrochemical mixing and mass balance model), developed by the Swedish Nuclear Fuel and Waste Management Company (SKB) and HYTEC (reactive transport model) developed by Ecole des Mines de Paris. Two different reactor zones were studied: Bangombé, a shallow site, the reactor being at 11 m depth, and OK84 at Okélobondo, situated at about 450 m depth, more comparable with a real repository location. This allowed the validation of modelling tools in two different sedimentary environments: one shallow, with a more homogeneous layering situated in an area of meteoric alteration, and the other offering the opportunity to study radionuclide migration from the reaction zone over a distance of 450 m through very heterogeneous sedimentary layers. The modeling results indicate that the chemical reactions retarding radionuclide transport are very different at the two sites. At Bangombé, the decomposition of organic material consumes oxygen and at Okélobondo the oxygen is consumed by inorganic reactions resulting, in both cases, in uranium retardation. Both modelling approaches (statistic with M3 code and deterministic with HYTEC code) could describe this situation. The goal of this exercise is to test codes which can help to describe and understand the processes taking place at the sites, validate the models with in situ data, and thus build confidence in the tools used for future site characterization. Ultimately, this allows identifying and selecting processes and parameters that can be used as input into repository performance assessment calculations and modelling exercises.     

Statistical Considerations in Determining the Health Significance of Constituents of Airborne Particulate Matter

ABSTRACT

Because of the U.S. Environmental Protection Agency’s (EPA) new ambient air quality standard for fine particles, the need is likely to continue for more detailed scientific investigation of various types of particles and their effects on human health. Epidemiology studies have become the method of choice for investigating health responses to such particles and to other air pollutants in community settings. Health effects have been associated with virtually all of the gaseous criteria pollutants and with the major constituents of airborne particulate matter (PM), including all size fractions less than about 20 gm, inorganic ions, carbonaceous particles, metals, crustal material, and biological aerosols. In many of the more recent studies, multiple pollutants or agents (including weather variables) have been significantly associated with health responses, and various methods have been used to suggest which ones might be the most important. In an ideal situation, classical least-squares regression methods are capable of performing this task. However, in the real world, where most of the pollutants are correlated with one another and have varying degrees of measurement precision and accuracy, such regression results can be misleading. This paper presents some guidelines for dealing with such collinearity and model comparison problems in both single- and multiple-pollutant regressions. These techniques rely on mean effect (attributable risk) rather than statistical significance per se as the preferred indicator of importance for the pollution variables.