DSC and TA/MS studies on formation and decomposition of polychlorinated dibenzo-p-dioxines and dibenzofuranes

The effect of oxydation of particulate carbon as a part of fly ash has been studied by mean of differential scanning calorimetry (DSC) and simultaneous thermal analysis/mass spectrometry (TA/MS). The results from different carbon modifications added to fly ash were discussed and compared to those of the pure samples.

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Zusammenfassung Mittels DSC und Thermoanalyse/Massenspektrometrie wurde der Effekt der Oxidation von Kohlenstoff als Teil von Flugasche untersucht. Die durch Zusatz verschiedener Kohlenstoffarten zu Flugasche erhaltenen Ergebnisse wurden diskutiert und mit denen von reinen Proben verglichen.

     

PARTICULATE CONCENTRATION ENRICHMENT OF PRIMARY AIR IN PULVERIZED COAL BOILERS

Particulate concentration enrichment (PCE) of primary air favors flame ignition and stabilization, and the reduction of NOx emission in pulverized coal combustion. The methods used for PCE are summarized in five categories:Cyclone PCE, Elbow PCE, Blunt-body PCE, Hydrodynamic PCE and Swirling PCE. Practice of each PCE method is illustrated with typical burners and the associated pros and cons are discussed. It is found that the same principle prevails in all PCE methods, that is, due to inertial difference, solid particles preferentially decelerate and separate from the gas stream when the main flow deviates in flow orientation. From analysis of various P-C burners, PCE design is deemed to yet comprise “rule of thumb” practice. Some discussions regarding PCE application are given.     

A Markov Chain Monte Carlo comparison of variance estimators for the sampling of particulate mixtures

During the sampling of particulate mixtures, samples taken are analyzed for their mass concentration, which generally has non‐zero sample‐to‐sample variance. Bias, variance, and mean squared error (MSE) of a number of variance estimators, derived by Geelhoed, were studied in this article. The Monte Carlo simulation was applied using an observable first‐order Markov Chain with transition probabilities that served as a model for the sample drawing process. Because the bias and variance of a variance estimator could depend on the specific circumstances under which it is applied, Monte Carlo simulation was performed for a wide range of practically relevant scenarios. Using the ‘smallest mean squared error’ as a criterion, an adaptation of an estimator based on a first‐order Taylor linearization of the sample concentration is the best. An estimator based on the Horvitz–Thompson estimator is not practically applicable because of the potentially high MSE for the cases studied. The results indicate that the Poisson estimator leads to a biased estimator for the variance of fundamental sampling error (up to 428% absolute value of relative bias) in case of low levels of grouping and segregation. The uncertainty of the results obtained by the simulations was also addressed and it was found that the results were not significantly affected. The potentials of a recently described other approach are discussed for extending the first‐order Markov Chain described here to account also for higher levels of grouping and segregation. Copyright © 2013 John Wiley & Sons, Ltd.     

The formation of ultrafine polyamide 6 nanofiber membranes with needleless electrospinning for air filtration

Particulate matter (PM) is a major air pollutant, which has a significant impact on public health. Filtration of PM through filters is a common method to protect the environment. However, the effective removal of PM with conventional filters still remains a challenge because of its small sizes. Here, we reported the formation of ultrafine polyamide 6 (PA‐6) nanofiber membranes formed with needleless electrospinning, in which both relative humidity condition and electrode type were included in the discussion. The PA‐6S nanofibers formed by using spiral electrode as a spinneret at 60% RH had the diameter of 33 nm, while the PA‐6C nanofibers formed by using cylindrical electrode had the diameter of 120 nm. With the integration of fine diameter, small pore size, and high porosity, the resultant PA‐6S nanofiber membrane exhibits high filtration efficiency of 99.42% and low pressure drop of 85.5 Pa under a face velocity of 85 L/min. Besides, it took only 10 minutes to reduce the concentration of PM_2.5 from 999 to 34.1 μg/m³ when used to filter real PM particles.     

Identification and classification of organic and inorganic components of particulate matter via Raman spectroscopy and chemometric approaches

In this paper, a novel method for developing a tree‐like classifier which differentiates between organic and inorganic particulate matter by means of Raman spectroscopy is introduced. The algorithm is fully automatic and optimises itself without any human interaction. This method uses a tree‐like structure to classify Raman spectra as a decision tree. On every knot of this tree, the optimal classifier is automatically obtained, tested and trained. The optimal classifier is an artificial neural network, linear discriminant analysis or a support vector machine, where different kernels are possible. The support vector machine is optimised by the simulated annealing method to achieve the best possible classifier. After the training, a hold‐out experiment with two completely independent sets of Raman spectra was tried to show the abilities of this method for real‐world application. Copyright © 2010 John Wiley & Sons, Ltd.     

Catalytic diesel particulate filters reduce the in vitro estrogenic activity of diesel exhaust

An in vitro reporter gene assay based on human breast cancer T47D cells (ER-CALUX) was applied to examine the ability of diesel exhaust to induce or inhibit estrogen receptor (ER)-mediated gene expression. Exhaust from a heavy-duty diesel engine was either treated by iron- or copper/iron-catalyzed diesel particulate filters (DPFs) or studied as unfiltered exhaust. Collected samples included particle-bound and semivolatile constituents of diesel exhaust. Our findings show that all of the samples contained compounds that were able to induce ER-mediated gene expression as well as compounds that suppressed the activity of the endogenous hormone 17beta-estradiol (E2). Estrogenic activity prevailed over antiestrogenic activity. We found an overall ER-mediated activity of 1.63 +/- 0.31 ng E2 CALUX equivalents (E2-CEQs) per m(3) of unfiltered exhaust. In filtered exhaust, we measured 0.74 +/- 0.07 (iron-catalyzed DPF) and 0.55 +/- 0.09 ng E2-CEQ m(-3) (copper/iron-catalyzed DPF), corresponding to reductions in estrogenic activity of 55 and 66%, respectively. Our study demonstrates that both catalytic DPFs lowered the ER-mediated endocrine-disrupting potential of diesel exhaust.     

New method to determine the total carbonyl functional group content in extractable particulate organic matter by tandem mass spectrometry

A functional group analysis method was developed to determine the quantitative content of carbonyl functional groups in atmospheric particulate organic matter (POM) using constant neutral loss scanning-tandem mass spectrometry (CNLS-MS/MS). The neutral loss method consists in monitoring the loss of a neutral fragment produced by the fragmentation of a precursor ion in a collision cell. The only ions detected are the daughter ions resulting from the loss of the neutral fragment under study. Then, scanning the loss of a neutral fragment characteristic of a functional group enables the selective detection of the compounds bearing the chemical function under study within a complex mixture. The selective detection of carbonyl functional groups was achieved after derivatization with pentafluorophenylhydrazine (PFPH) by monitoring the neutral loss of C(6)F(5)N (181 amu), which was characteristic of a large panel of derivatized carbonyl compounds. The method was tested on 25 reference mixtures of different composition, all containing 24 carbonyl compounds at randomly determined concentrations. The repeatability and calibration tests were satisfying as they resulted in a relative standard deviation below 5% and a linear range between 0.01 and 0.65 mM with a calculated detection limit of 0.0035 mM. Also, the relative deviation induced by changing the composition of the mixture while keeping the total concentration of carbonyl functional groups constant was less than 20%. These reliability experiments demonstrate the high robustness of the developed procedure for accurate carbonyl functional group measurement, which was applied to atmospheric POM samples.     

The influence of particle distribution and interphase on the thermal expansion coefficient of particulate composites by the use of a new model

In this work, the thermal expansion coefficient (CTE) of a composite containing spherical particles surrounded by an inhomogeneous interphase embedded in an isotropic matrix is evaluated by means of a new model. The thermomechanical properties of the interphase are formulated as continuous radial functions. It is assumed that this third phase developed between the polymeric matrix and the filler particles contains both areas of absorption interaction in polymer surface layers onto filler particles as well as areas of mechanical imperfections. It can be said that the concept of boundary interphase is a useful tool to describe quantitatively the adhesion efficiency between matrix and particles and that there is an effect of this phase on the thermomechanical properties of the composite. The thickness and volume fraction of this phase were determined from heat capacity measurements for various filler contents. On the other hand, it is assumed that the particle arrangement (distribution) which can be considered as an influence of neighboring inclusions and their interaction should affect the thermomechanical constants of the composite. The theoretical predictions were compared with experimental results as well as with theoretical values from expressions obtained from other workers and they were found to be in satisfactory agreement.     

大气颗粒物重金属形态分析

重金属作为大气颗粒物中重要有毒组分之一,其总量和存在形态与颗粒物重金属的健康风险密切相关。因此,颗粒物重金属形态分析对深入研究大气污染健康效应具有非常重要的意义。本文从以下四个方面对近年来国内外相关研究进行了归纳总结:(1)模拟体液、BCR、Tessier、Chester等逐级顺序提取方法被广泛用于重金属操作定义形态分析;(2)色谱-质谱联用技术和新型功能化材料用于重金属形态选择分析以及X射线吸收精细结构谱(XAFS)原位形态表征技术可以获取重金属价态、化合态、原子簇结构信息;(3)重金属形态粒径分布特征复杂,受多种因素影响,倾向于富集在细颗粒物中;(4)重金属形态时空分布具有很强的区域性,社会发展、工业来源、气候条件是主要影响因素,夏冬季节和雾霾天气危害性较大。