高气泡表面积通量浮选柱气浮除藻的研究

湖泊、水库等水源的富营养化,使藻类去除成为饮用水生产的重要任务.本研究采用高气泡表面积通量浮选柱气浮除藻,考察了混凝剂、气泡表面积通量和浮选柱高度等因素的影响.试验表明,高气泡表面积通量浮选柱气浮可高效地去除绿藻、硅藻和蓝藻,叶绿素a和藻类去除率达95%以上,比传统浮选柱气浮和沉降作业有较大幅度提高.与普通气浮柱比较,高气泡表面积通量浮选柱增加了气泡与藻的碰撞几率,防止因大表观充气速率造成的紊流和扰动,使气泡/藻结合体有相对静态的浮升环境,避免了气泡/藻结合体在浮升过程中的脱落,实现对藻类的迅速捕集和转移.     

Evaluation of photolysis and hydrolysis of pyraclostrobin in aqueous solutions and its degradation products in paddy water

This study evaluated the hydrolysis and photolysis kinetics of pyraclostrobin in an aqueous solution using ultra-high-performance liquid chromatography–photodiode array detection and identified the resulting metabolites of pyraclostrobin by hydrolysis and photolysis in paddy water using high-resolution mass spectrometry coupled with liquid chromatography. The effect of solution pH, metal ions and surfactants on the hydrolysis of pyraclostrobin was explored. The hydrolysis half-lives of pyraclostrobin were 23.1–115.5?days and were stable in buffer solution at pH 5.0. The degradation rate of pyraclostrobin in an aqueous solution under sunlight was slower than that under UV photolysis reaction. The half-lives of pyraclostrobin in a buffer solution at pH 5.0, 7.0, 9.0 and in paddy water were less than 12?h under the two light irradiation types. The metabolites of the two processes were identified and compared to further understand the mechanisms underlying hydrolysis and photolysis of pyraclostrobin in natural water. The extracted ions obtained from paddy water were automatically annotated by Compound Discoverer software with manual confirmation of their fragments. Two metabolites were detected and identified in the pyraclostrobin hydrolysis, whereas three metabolites were detected and identified in the photolysis in paddy water.     

Variations in exposure to in-vehicle particle mass and number concentrations in different road environments

Road environments significantly affect in cabin concentration of particulate matter (PM). This study conducted measurements of in-vehicle and on-road concentrations of PM10, PM2.5, PM1, and particle number (PN) in size of 0.02–1 µm, under six ventilation settings in different urban road environments (tunnels, surface roads and elevated roads). Linear regression was then used to analyze the contributions of multiple predictor variables (including on-road concentrations, temperature, relative humidity, time of day, and ventilation settings) to measured variations. On-road measurements of PM2.5, PM1, and PN concentrations from the open surface roads were 5.5%, 3.7%, and 16% lower, respectively, than those measured in tunnels, but 7.6%, 7.1% and 24% higher, respectively, than those on elevated roads. The highest on-road PM10 concentration was observed on surface roads. The time series pattern of in-vehicle particle concentrations closely tracked the on-road concentrations outside of the car and exhibited a smoother profile. Irrespective of road environment, the average I/O ratio of particles was found to be the lowest when air conditioning was on with internal recirculation, the highest purification efficiency via ventilation was obtained by switching on external air recirculation and air conditioning. Statistical models showed that on-road concentration, temperature, and ventilation setting are common factors of significance that explained 58%-80%, 64%-97%, and 87%-98% of the variations in in-vehicle PM concentrations on surface roads, on elevated roads, and in tunnels, respectively.

Implications: Inside vehicles, both driver and passengers will be exposed to elevated particle concentrations. However, for in-vehicle particles, there has been no comprehensive comparative study of the three-dimensional traffic environment including tunnels surface roads and elevated roads. This study focuses on the analysis of the trends and main influencing factors of particle concentrations in different road environments. The results can provide suggestions for the driver's behavior, and provide data support for the environmental protection department to develop pollutant concentration limits within the vehicle.     

Could curcumin ameliorate titanium dioxide nanoparticles effect on the heart? A histopathological,immunohistochemical, and genotoxic study

Environmental Science and Pollution Research - The evaluation of the toxicological effects of titanium dioxide nanoparticles (TiO2NPs) is increasingly important due to their growing occupational...     

Assessing interactions,predicting function,and increasing degradation potential of a PAH-degrading bacterial consortium by effect of an inoculant strain

Environmental Science and Pollution Research - A natural phenanthrene-degrading consortium CON was inoculated with an exogenous strain Sphingobium sp. (ex Sp. paucimobilis) 20006FA yielding the...     

Guarana improves behavior and inflammatory alterations triggered by methylmercury exposure: an in vivo fruit fly and in vitro neural cells study

Environmental Science and Pollution Research - Methylmercury (MeHg) is a well-known environmental pollutant associated with neurological and developmental deficits in animals and humans. However,...     

Bromide affecting drinking water mutagenicity

The effect of bromide on the mutagenicity of artificially recharged groundwater and purified artificially recharged groundwater after chlorine, ozone, hydrogen peroxide, permanganate, and UV treatments alone and in various combinations was studied. The highest mutagenicity was observed after chlorination, while hydrogen peroxide-ozone-chlorine treatment produced the lowest value for both waters. Chlorinated waters, which were spiked with bromide, had up to 3.7 times more mutagenic activity than waters without bromide after every preoxidation method. 3-Chloro-4-(dichloromethyl)-5-hydroxy-2(5H)-furanone (MX) was found to correspond as much as 76% of the overall mutagenicity in the waters not spiked with bromide. MX formation was found to be lower when the treated water contained bromide, implicating the formation of brominated MX analogues. Trihalomethane formation increased when the treated water contained bromide.     

Toxicity of chemical components of fine particles inhaled by aged rats: effects of concentration

This study tested the hypothesis that exposure to mixtures containing fine particles and ozone (O3) would cause pulmonary injury and decrements in functions of immunological cells in exposed rats (22-24 months old) in a dose-dependent manner. Rats were exposed to high and low concentrations of ammonium bisulfate and elemental carbon and to 0.2 ppm O3. Control groups were exposed to purified air or O3 alone. The biological end points measured included histopathological markers of lung injury, bronchoalveolar lung fluid proteins, and measures of the function of the lung's innate immunological defenses (macrophage antigen-directed phagocytosis and respiratory burst activity). Exposure to O3 alone at 0.2 ppm did not result in significant changes in any of the measured end points. Exposures to the particle mixtures plus O3 produced statistically significant changes consistent with adverse effects. The low-concentration mixture produced effects that were statistically significant compared to purified air but, with the exception of macrophage Fc receptor binding, exposure to the high-concentration mixture did not. The effects of the low- and high-concentration mixtures were not significantly different. The study supports previous work that indicated that particle + O3 mixtures were more toxic than O3 alone.     

Predicting logP of pesticides using different software

We compared experimental and calculated logP values using a data set of 235 pesticides and experimental values from four different sources: The Pesticide Manual, Hansch Manual, ANPA and KowWin databases. LogP were calculated with four softwares: HyperChem, Pallas, KowWin and TOPKAT. Crossed comparison of the experimental and calculated values proved useful, especially for pesticides. These are harder to study than simpler organic compounds. Structurally they are complex, heterogeneous and similar to drugs from a chemical point of view. They offer an interesting way to verify the goodness of the different methods. Other studies compared several logP predictors using a single set of experimental values taken as a reference. Here we discuss the utility of the different logP predictors, with reference to experimental data found in different databases. This offers three advantages: (1) it avoids bias due to the assumption that one single data set is correct; (2) a given predictor can be developed on the same data set used for evaluation; (3) it takes account of experimental variability and can compare it with the predictor's variability. In our study Pallas and KowWin gave the best results for prediction, followed by TOPKAT.     

Development of a microscale emission factor model for particulate matter for predicting real-time motor vehicle emissions

The U.S. Environmental Protection Agency's National Exposure Research Laboratory is pursuing a project to improve the methodology for modeling human exposure to motor vehicle emissions. The overall project goal is to develop improved methods for modeling the source through the air pathway to human exposure in significant exposure microenvironments. Current particulate matter (PM) emission models, particle emission factor model (used in the United States, except California) and motor vehicle emission factor model (used in California only), are suitable only for county-scale modeling and emission inventories. There is a need to develop a site-specific real-time emission factor model for PM emissions to support human exposure studies near roadways. A microscale emission factor model for predicting site-specific real-time motor vehicle PM (MicroFacPM) emissions for total suspended PM, PM less than 10 microm aerodynamic diameter, and PM less than 2.5 microm aerodynamic diameter has been developed. The algorithm used to calculate emission factors in MicroFacPM is disaggregated, and emission factors are calculated from a real-time fleet, rather than from a fleet-wide average estimated by a vehicle-miles-traveled weighting of the emission factors for different vehicle classes. MicroFacPM requires input information necessary to characterize the site-specific real-time fleet being modeled. Other variables required include average vehicle speed, time and day of the year, ambient temperature, and relative humidity.