We present a new order metric for tetrahedral particle packing, which is observed to have a strong linear correlation with the packing density. We propose the concept of quasi-random packing to represent the hierarchical random packing structure of clusters. We also find that the nematic order of clusters can be used to classify the ordered and disordered packing of tetrahedra, which is also an indicator for quasi-random packing.
The health effects of exposure to pollutants from electronic waste (e-waste) pose an important issue. In this study, we explored the association between oxidative stress and blood levels of e-waste-related pollutants. Blood samples were collected from individuals living in the proximity of an e-waste recycling site located in northern China, and pollutants, as well as reactive oxygen species (ROS), were measured in comparison to a reference population. The geometric mean concentrations of PCBs, dechlorane plus, and 2,2',4,4',5,5'-hexabromobiphenyl in plasma from the exposure group were 60.4, 9.0, and 0.55 ng g(-1) lipid, respectively, which were 2.2, 3.2, and 2.2 times higher than the corresponding measurement in the reference group. Correspondingly, ROS levels in white blood cells, including in neutrophil granulocytes, from the exposure group were significantly higher than in those from the reference group, suggesting potential ROS related health effects for residents at the e-waste site. In contrast, fewer ROS were generated in the respiratory burst of neutrophil granulocytes for the exposure group, indicating a depressed innate immune function for the individuals living at the e-waste site. These findings suggest a potential linkage between exposure to pollutants from e-waste recycling and both elevated oxidative stress and altered immune function.
Although black carbon (BC) is one of the key atmospheric particulate components driving climate change and air quality, there is no agreement on the terminology that considers all aspects of specific properties, definitions, measurement methods, and related uncertainties. As a result, there is much ambiguity in the scientific literature of measurements and numerical models that refer to BC with different names and based on different properties of the particles, with no clear definition of the terms. The authors present here a recommended terminology to clarify the terms used for BC in atmospheric research, with the goal of establishing unambiguous links between terms, targeted material properties and associated measurement techniques.
Particle hygroscopic growth at 90% RH (relative humidity), cloud condensation nuclei (CCN) activity, and size-resolved chemical composition were concurrently measured in the Thuringer Wald mid-level mountain range in central Germany in the fall of 2010. The median hygroscopicity parameter values, kappa, of 50, 75, 100, 150, 200, and 250 nm particles derived from hygroscopicity measurements are respectively 0.14, 0.14, 0.17, 0.21, 0.24, and 0.28 during the sampling period. The closure between HTDMA (Hygroscopicity Tandem Differential Mobility Analyzers)-measured (kappa(HTDMA)) and chemical composition-derived (kappa(chem)) hygroscopicity parameters was performed based on the Zdanovskii-Stokes-Robinson (ZSR) mixing rule. Using size-averaged chemical composition, the kappa values are substantially overpredicted (30 and 40% for 150 and 100 nm particles). Introducing size-resolved chemical composition substantially improved closure. We found that the evaporation of NH4NO3, which may happen in a HTDMA system, could lead to a discrepancy in predicted and measured particle hygroscopic growth. The hygroscopic parameter of the organic fraction, kappa(org), is positively correlated with the O:C ratio (kappa(org) = 0.19 x (O:C) - 0.03). Such correlation is helpful to define the kappa(org) value in the closure study. kappa derived from CCN measurement was around 30% (varied with particle diameters) higher than that determined from particle hygroscopic growth measurements (here, hydrophilic mode is considered only). This difference might be explained by the surface tension effects, solution non-ideality, gas-particle partitioning of semivolatile compounds, and the partial solubility of constituents or non-dissolved particle matter. Therefore, extrapolating from HTDMA data to properties at the point of activation should be done with great care. Finally, closure study between CCNc (cloud condensation nucleus counter)-measured (kappa(CCN)) and chemical composition (kappa(CCN, chem)) was performed using CCNc-derived kappa values for individual components. The results show that the kappa(CCN) can be well predicted using particle size-resolved chemical composition and the ZSR mixing rule.