Influences of use activities and waste management on environmental releases of engineered nanomaterials.

Engineered nanomaterials (ENM) offer enhanced or new functionalities and properties that are used in various products. This also entails potential environmental risks in terms of hazard and exposure. However, hazard and exposure assessment for ENM still suffer from insufficient knowledge particularly for product-related releases and environmental fate and behavior. This study therefore analyzes the multiple impacts of the product use, the properties of the matrix material, and the related waste management system (WMS) on the predicted environmental concentration (PEC) by applying nine prospective life cycle release scenarios based on reasonable assumptions. The products studied here are clothing textiles treated with silver nanoparticles (AgNPs), since they constitute a controversial application. Surprisingly, the results show counter-intuitive increases by a factor of 2.6 in PEC values for the air compartment in minimal AgNP release scenarios. Also, air releases can shift from washing to wearing activity; their associated release points may shift accordingly, potentially altering release hot spots. Additionally, at end-of-life, the fraction of AgNP-residues contained on exported textiles can be increased by 350% when assuming short product lifespans and globalized WMS. It becomes evident that certain combinations of use activities, matrix material characteristics, and WMS can influence the regional PEC by several orders of magnitude. Thus, in the light of the findings and expected ENM market potential, future assessments should consider these aspects to derive precautionary design alternatives and to enable prospective global and regional risk assessments.

Critical materials and dissipative losses: a screening study.

This study deals with dissipative losses of critical materials between the life-cycle stages of manufacturing and end-of-life. Following the EU definition for critical materials, a screening of dissipative losses for the respective materials has been performed based on existing data and the most significant data gaps have been identified. Furthermore, a classification scheme for dissipative losses (dissipation into environment, dissipation into other material flows, dissipation to landfills) and for assessing their degree has been developed and a first qualitative assessment applying this classification scheme has been performed. In combination with existing criticality assessments, the results can be used to generate a map of metals indicating future research needs for analyzing metal dissipation in detail. The results include quantitative estimates of dissipative losses (where feasible) along the chosen life-cycle stages, and discuss research needs for analysis and avoidance of dissipative losses for improved resource efficiency.

Nomograms for severity of aortic valve stenosis using peak aortic valve pressure gradient and left ventricular ejection fraction.

AIMS: Continuity equation to evaluate aortic valve area (AVA(CE)) is critically dependent on accurate measurement of left ventricular outflow tract diameter and velocity. To circumvent these limitations, the present study aimed to generate nomograms for a facilitated estimation of aortic valve area using peak aortic valve pressure gradient (DeltapAv) and left ventricular ejection fraction (LVEF). METHODS AND RESULTS: Two hundred and fifty-five subjects with non-invasively and invasively defined aortic valve stenosis (AS) formed the basis of this study. Basis of the nomograms was the correlation analysis between DeltapAv and AVA as estimated by AVA(CE) within different LVEF groups. LVEF differed from 65.6 +/- 1.8% (Group I, LVEF > 60%) to 34.5 +/- 4.3% (Group IV, LVEF > or = 30%). DeltapAv and AVA varied from 85.6 +/- 19.5 mmHg and 0.69 +/- 0.16 cm2 in Group I to 58.5 +/- 15.9 mmHg and 0.73 +/- 0.23 cm2 in Group IV (DeltapAv: P < 0.001). Mean AVA(CE) showed no significant difference between the groups. Correlation between DeltapAv and AVA(CE) was statistically significant with P < 0.001 in all subgroups (R2 between 0.72 and 0.76). Furthermore, a prospective estimation of AVA using the developed nomograms correlated very well with invasively determined AS using the Gorlin formula (R2 = 0.76, SEE = 0.21 cm2, bias 0.04 cm2). CONCLUSION: The present study has established and confirmed a solid, easy to use nomogram-based method to accurately quantify severe AS.