芘高效降解菌的分离鉴定及其降解特性研究

采用富集培养的方法从多环芳烃污染的土壤中分离到3株能高效降解四环芳烃芘的细菌J1、J2、J3,经形态观察、生理生化和16SrDNA鉴定,J1属于铜绿假单胞菌属(Pseudomonas aeruginosa),J2属于黄杆菌属(Flavobacterium mizutaii),J3属于短短芽孢杆菌属(Brevibacillus parabrevis).3株细菌均能以芘作为碳源生长,在含芘50、100、200、500、1 000 mg/L的无机盐液体培养基中培养7 d后细菌总数达到最高,在含芘200 mg.L-1的无机盐液体培养基中7d的降解效率分别达到53.04%、65.03%、51.02%.3株细菌对培养基具有较广泛的pH适应范围,在芘浓度200 mg/L,pH为4～9的液体条件下,均可生长,且对芘有很好的降解.虽然可以采用芘的二氯甲烷溶液或者N,N-二甲基甲酰胺(DMF)溶液向培养液中添加芘,但是以N,N-二甲基甲酰胺添加的方式芘在水溶液中起到很好的分散作用,更有利于3株细菌对芘的降解.     

Nonylphenol in bivalves and sediments in the northeast coast of China

To understand nonylphenol (NP) contamination in the northeast coastal environment in China, NP was determined in 19 sediment and 20 bivalve samples collected during November 2005. NP was identified in all sediment samples in the range of 8.8 to 1.0 × 10 3 ng/g dw (dry weight), with an average of 0.12 × 10 3 ng/g dw. Most of the bivalve samples (oysters and mussels) contained detectable amounts of NP that ranged from ND (not detected) to 7.6 × 10 3 ng/g dw, with an average value of 0.79 × 10 3 ng/g dw. These results indicated that NP is ubiquitous in sediments and bivalves from the northeast coast of China, and are even present in bivalves sold as seafood. Serious contamination with NP was recorded off the coast of Bohai Strait and in semi-enclosed coastal environment such as Jiaozhou Bay and Liaodong Bay. Possible environmental and human health implications were understood in this study.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen (T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks (ANNs) and support vector machines (SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination (R²), Nash–Sutcliff efficiency (NSE), relative efficiency criteria (d_rel). Additionally, Latin-Hypercube one-factor-at-a-time (LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage. However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Surface-enhanced Raman spectroscopy for emerging contaminant analysis in drinking water

● Definition of emerging contaminants in drinking water is introduced. ● SERS and standard methods for emerging contaminant analysis are compared. ● Enhancement factor and accessibility of SERS hot spots are equally important. ● SERS sensors should be tailored according to emerging contaminant properties. ● Challenges to meet drinking water regulatory guidelines are discussed. Emerging contaminants (ECs) in drinking water pose threats to public health due to their environmental prevalence and potential toxicity. The occurrence of ECs in our drinking water supplies depends on their physicochemical properties, discharging rate, and susceptibility to removal by water treatment processes. Uncertain health effects of long-term exposure to ECs justify their regular monitoring in drinking water supplies. In this review article, we will summarize the current status and future opportunities of surface-enhanced Raman spectroscopy (SERS) for EC analysis in drinking water. Working principles of SERS are first introduced and a comparison of SERS and liquid chromatography-tandem mass spectrometry in terms of cost, time, sensitivity, and availability is made. Subsequently, we discuss the strategies for designing effective SERS sensors for EC analysis based on five categories—per- and polyfluoroalkyl substances, novel pesticides, pharmaceuticals, endocrine-disrupting chemicals, and microplastics. In addition to maximizing the intrinsic enhancement factors of SERS substrates, strategies to improve hot spot accessibilities to the targeting ECs are equally important. This is a review article focusing on SERS analysis of ECs in drinking water. The discussions are not only guided by numerous endeavors to advance SERS technology but also by the drinking water regulatory policy.     

A comprehensive empirical investigation of the relationships among variables of the groupthink model

Although a quarter of a century has passed since Janis proposed his groupthink model in 1972, there is very little consensus among researchers on the validity of the model. This study conducted a comprehensive empirical investigation of Janis's model by including all 24 variables in the research. Data were collected from 64 four‐person ad hoc groups; the group discussion sessions were videotaped and content‐analysed afterwards. Statistical analyses revealed that Janis's predictions about the causal relationship among four groups of variables in the model are only partially correct; when the effects of individual independent variables on the dependent variables were tested, Janis's predictions were confirmed in only two out of 23 cases. Copyright © 2000 John Wiley & Sons, Ltd.     

Development of hybrid processes for the removal of volatile organic compounds,plasticizer, and pharmaceutically active compound using sewage sludge,waste scrap tires,and wood chips as sorbents and microbial immobilization matrices

Environmental Science and Pollution Research - This study evaluated the reutilization of waste materials (scrap tires, sewage sludge, and wood chips) to remove volatile organic compounds (VOCs)...     

Development of a method for bacteria and virus recovery from heating, ventilation, and air conditioning (HVAC) filters

The aim of the work presented here is to study the effectiveness of building air handling units (AHUs) in serving as high volume sampling devices for airborne bacteria and viruses. An HVAC test facility constructed according to ASHRAE Standard 52.2-1999 was used for the controlled loading of HVAC filter media with aerosolized bacteria and virus. Nonpathogenic Bacillus subtilis var. niger was chosen as a surrogate for Bacillus anthracis. Three animal viruses; transmissible gastroenteritis virus (TGEV), avian pneumovirus (APV), and fowlpox virus were chosen as surrogates for three human viruses; SARS coronavirus, respiratory syncytial virus, and smallpox virus; respectively. These bacteria and viruses were nebulized in separate tests and injected into the test duct of the test facility upstream of a MERV 14 filter. SKC Biosamplers upstream and downstream of the test filter served as reference samplers. The collection efficiency of the filter media was calculated to be 96.5 +/- 1.5% for B. subtilis, however no collection efficiency was measured for the viruses as no live virus was ever recovered from the downstream samplers. Filter samples were cut from the test filter and eluted by hand-shaking. An extraction efficiency of 105 +/- 19% was calculated for B. subtilis. The viruses were extracted at much lower efficiencies (0.7-20%). Our results indicate that the airborne concentration of spore-forming bacteria in building AHUs may be determined by analyzing the material collected on HVAC filter media, however culture-based analytical techniques are impractical for virus recovery. Molecular-based identification techniques such as PCR could be used.     

Preparation of a highly sensitive enzyme electrode using gold nanoparticles for measurement of pesticides at the ppt level

A highly sensitive enzyme electrode was prepared based on gold nanoparticles for measurement of pesticides. Gold nanoparticles of 25-30 nm were synthesized on a glassy carbon electrode by double-pulse technique while the coverage was controlled by applied potential and time. The gold nanoparticles were modified to form a self-assembled monolayer, followed by covalent binding of tyrosinase. The TYR-AuNP-GC electrode was compared with bare GC, AuNP-GC, and modified AuNP-GC and TYR-Au (plate type) electrodes in terms of cyclic voltammetry. The voltammograms well represent the sensitivity of enzymatic oxidation of catechol, substrates for the enzyme activity. The prepared electrode integrated into a continuous flow system and was tested to detect pesticides, such as 2,4-D, atrazine, and ziram. Under the optimized conditions of the flow system, the electrode performed reasonably according to the inhibition mechanism in the concentration range of 0.001-0.5 ng mL(-1). The enhanced performance was attributed to the favored microenvironment for the enzyme activity provided by SAM on gold nanoparticles.     

An integrated methodology for estimation of forest fire-loss using geospatial information

These days, wildfires are prevalent in almost all areas of the world. Researchers have been actively analyzing wildfire damage using a variety of satellite images and geospatial datasets. This paper presents a method for detailed estimation of wildfire losses using various geospatial datasets and an actual case of wildfire at Kang-Won-Do, Republic of Korea in 2005. A set of infrared (IR) aerial images acquired after the wildfire were used to visually delineate the damaged regions, and information on forest type, diameter class, age class, and canopy density within the damaged regions was retrieved from GIS layers of the Korean national forest inventory. Approximate tree heights were computed from airborne LIDAR and verified by ground LIDAR datasets. The corresponding stand volumes were computed using tree volume equations (TVE). The proposed algorithm can efficiently estimate fire loss using the geospatial information; in the present case, the total fire loss was estimated as $5.9 million, which is a more accurate estimate than $4.5 million based on conventional approach. The proposed method can be claimed as a powerful alternative for estimating damage caused by wildfires, because the aerial image interpretation can delineate and analyze damaged regions in a comprehensive and consistent manner; moreover, LIDAR datasets and national forest inventory data can significantly reduce field work.     

Characterization and prediction of meandering channel migration in the GIS environment: A case study of the Sabine River in the USA

This study focused on the prediction of a 22 km meandering channel migration of the Sabine River between the states of Texas and Louisiana. The meander characteristics of 12 bends, identified from seven orthophotos taken between 1974 and 2004, were acquired in a GIS environment. Based on that earlier years' data acquisition, channel prediction was performed for the two years 1996 and 2004 using least squares estimation and linear extrapolations, yielding a satisfactory agreement with the observations (the median predicted and observed migration rates were 3.1 and 3.6 [m/year], respectively). The best-predicted migration rate was found to be associated with the longest orthophoto-recorded interval. The study confirmed that channel migration is strongly correlated with bend curvature and that the maximum migration rate of the bend corresponded to a radius of curvature [bend radius (R(C))/channel width (W(C))] of 2.5. In tight bends of a smaller radius of curvature than 1.6, secondary flow scouring near the bend apex increases bend curvature. The stability index of the dimensionless bend radius was determined to be 2.45. Overall, this study proves the effectiveness of least squares estimation with historical orthophotography for characterization of meandering channel migration.