Dioxin screening in fish product by pattern recognition of biomarkers

Two alternative, cost- and time-effective dioxin screening methods relying on two categories of potential lipid biomarkers were investigated. A dioxin range varying from 1.1 to 47.1 pg PCDD/F TEQ-WHO/g lipid using 64 fish meal samples was used for model calibration. The methods were based on multivariate models using either (1) fatty acid composition monitored by GC-FID or (2) fluorescence landscape signals analysed using the PARAFAC model and in both cases predicting dioxin content as pgPCDD/F TEQ-WHO/g lipid. In both cases, Partial Least Squares (PLS) regression was performed for predicting the dioxin content of a sample. The GC-FID data analyses was based on automatic peak alignment and integration, enabling extraction of the area of 140 peaks from the gas chromatograms, as opposed to the 31 fatty acids usually considered for fish oil characterisation. In addition to classic PLS employing the whole dataset for calibration, a two-step local PLS modeling approach was performed based upon an initial selection of k number of calibration samples providing the best match to the prediction sample using a so-called k Nearest Neighbors (kNN) approach, then followed by PLS calibration on these kNN selected samples for dioxin prediction. Fluorescence spectroscopy offers a promising non-invasive and ultra-rapid technique, with less than two minutes analysis time. However, fluorescence spectroscopy using the pattern recognition "kNN-PLS" yielded a correlation of 0.76 (r2) and a high root mean square error of prediction of 11.4 pg PCDD/F TEQ-WHO/g lipid. The predictions were improved when the PLS calibration was performed on all the sample with a root mean square error of prediction of 7.0 pg PCDD/F TEQ-WHO/g lipid. Unfortunately, these results failed to demonstrate the potential of fluorophore monitoring as a screening method. In contrast, the overall best screening performance was obtained with the fatty acid profile, when the kNN-PLS combination employed for pattern recognition (kNN) all the areas of the 140 detected peaks and the PLS regression used the areas of 46 selected peaks. This "kNN-PLS" prediction with three latent variables and based upon the 12 nearest neighbors selected out of the 64 x 2 fatty acid profiles (duplicate analyses), yielded a correlation of 0.85 (r2) and a root mean square error of prediction of 2.1 pg PCDD/F TEQ-WHO/g lipid and resulted in a total analysis time of one and half hour per sample.     

A comprehensive physico-chemical, mineralogical and morphological characterization of Indian mineral wastes

This paper provides a comprehensive characterization of mineral waste such as fly ash, bottom ash, slag and construction demolition (C&D) collected from four different thermal power plants, three steel plants and three C&D waste generation sites in India. To determine utilisation potential and environmental concerns, as received fly ash, bottom ash, slag and C&D waste were analysed for physico-chemical, mineralogical and morphological properties. The physico-chemical properties analysed include pH, moisture content, acid insoluble residue, loss on ignition(LOI), carbon content, fineness, chloride content, sulphate content, reactive silica content, XRF and heavy metal analysis. Morphological and mineralogical characteristics were investigated using scanning electron microscopy–energy dispersive X-ray. Particle size distribution was obtained using particle size analyser. The material analysed has different compositions and were selected with a view to determine their suitability for different applications in cement and concrete industry and for further research studies.     

Distribution of Hg,As and Se in material and flue gas streams from preheater–precalciner cement kilns and vertical shaft cement kilns in China

The aim of this study was to evaluate the behavior of Hg, As, and Se in cement production. Two types of cement plants were studied, including the vertical shaft kiln (VSK) and preheater–precalciner kiln (PPK) processes. Determination of Hg, As, and Se in the main material and gas streams were performed. It was found that recycling of particulate matter captured by an air pollution control device caused a significant enrichment of Hg and As inside both processes. The total quantity of Hg entering the process and the quantity emitted to the atmosphere were found to be 10–109 and 6.3–38 mg, respectively, per ton of clinker produced. The average Hg emission was calculated to be around 41% of the total mercury input. The emissions found complied with the European Union (EU) limit and exceeded partly the U.S. limit. Furthermore, it was found that oxidized mercury was the dominant species in the PPK process, whereas the reduced form was dominant in the VSK process, due to the oxidizing and reducing gas conditions, respectively. Regarding the distribution of As and Se, the major amounts were bound to the solid materials, that is, cement clinker and particulate matter. Based on cement production data in China in 2013, the annual emissions of Hg and As were estimated to be in the range of 8.6–52 and 4.1–9.5 tons, respectively.

Implications: There are fairly limited studies on Hg, As, and Se measurements from cement manufacturing facilities in China, and even more limited studies on the behavior in vertical shaft kilns. The needs for increased emission reduction efforts in energy-intensive industries are globally recognized, and an improved understanding of the processes ongoing in a cement kilns system is decisive. Since the processes are complex and almost impossible to recreate in the laboratory, field measurements are indeed important. The findings in the present study were solely based on field measurements and the data are considered to be valuable for the further development of emission reduction technology.