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Studded tyres made of tungsten carbide cobalt (WC-Co) are in the Northern countries commonly used during the winter time. Tungsten (W)-containing nano- and micron-sized particles have been detected close to busy roads in several European countries. Other typical traffic wear particles consist of copper (Cu). The aims of this study were to investigate particle stability and transformation/dissolution properties of nanoparticles (NPs) of WC-Co compared with NPs of tungsten carbide (WC), cobalt (Co), and Cu. Their physicochemical characteristics (primarily surface oxide and charge) are compared with their extent of sedimentation and metal release in synthetic surface water (SW) with and without two different model organic molecules, 2,3- and 3,4-dihydroxybenzoic acid (DHBA) mimicking certain sorption sites of humic substances, for time periods up to 22 days. The WC-Co NPs possessed a higher electrochemical and chemical reactivity in SW with and without DHBA molecules as compared with NPs of WC, Co, and Cu. Co was completely released from the WC-Co NPs within a few hours of exposure, although it remained adsorbed/bonded to the particle surface and enabled the adsorption of negatively charged DHBA molecules, in contrast with the WC NPs (no adsorption of DHBA). The DHBA molecules were found to rapidly adsorb on the Co and Cu NPs. The sedimentation of the WC and WC-Co NPs was not influenced by the presence of the 2,3- or 3,4-DHBA molecules. A slight influence (slower sedimentation) was observed for the Co NPs, and a strong influence (slower sedimentation) was observed for the Cu NPs in SW with 2,3-DHBA compared with SW alone. The extent of metal release increased in the order: WC < Cu < Co < WC-Co NPs. All NPs released more than 1 wt-% of their metal total mass. The release from the Cu NPs was most influenced by the presence of DHBA molecules., QC 20170607

Methods such as principal component analysis, projection to latent structures and their generalisations work very well for the modelling and analysis of the large and complex data sets generated by modern industrial processes such as mining. The methods deal with multitudes of colinear, noisy and incomplete data and give easily interpretable results such as scores, deviations from the model and variable contributions. To use projection methods for process monitoring, a model for the well-functioning process is first developed from historical data and then put on line. The resulting multivariate control charts indicate good or poor functioning and the presence of faults. The charts are traditional ones, but instead of showing the values of individual variables they display scores that summarise all or large parts of the process variables in the optimum manner. Contribution plots are used for interpreting faults and finding likely causes., Methods such as principal component analysis, projection to latent structures and their generalisations work very well for the modelling and analysis of the large and complex data sets generated by modern industrial processes such as mining. The methods deal with multitudes of colinear, noisy and incomplete data and give easily interpretable results such as scores, deviations from the model and variable contributions. To use projection methods for process monitoring, a model for the well-functioning process is first developed from historical data and then put on line. The resulting multivariate control charts indicate good or poor functioning and the presence of faults. The charts are traditional ones, but instead of showing the values of individual variables they display scores that summarise all or large parts of the process variables in the optimum manner. Contribution plots are used for interpreting faults and finding likely causes.

The GEOSIM data analysis strategy, based on well-known software such as principal component analysis, kriging and hierarchical cluster analysis, extracts and integrates information existing in multivariate data sets from several sample media., The GEOSIM data analysis strategy, based on well-known software such as principal component analysis, kriging and hierarchical cluster analysis, extracts and integrates information existing in multivariate data sets from several sample media.