Nico Dziurowitz

Comparability of Portable Nanoparticle Exposure Monitors

Five different portable instrument types to monitor exposure to nanoparticles were subject to an intensive intercomparison measurement campaign. Four of them were based on electrical diffusion charging to determine the number concentration or lung deposited surface area (LDSA) concentration of airborne particles. Three out of these four also determined the mean particle size. The fifth instrument type was a handheld condensation particle counter (CPC). The instruments were challenged with three different log-normally distributed test aerosols with modal diameters between 30 and 180 nm, varying in particle concentration and morphology. The CPCs showed the highest comparability with deviations on the order of only ±5%, independent of the particle sizes, but with a strictly limited upper number concentration. The diffusion charger-based instruments showed comparability on the order of ±30% for number concentration, LDSA concentration, and mean particle size, when the specified particle size range of the instruments matched the size range of the aerosol particles, whereas significant deviations were found when a large amount of particles exceeded the upper or lower detection limit. In one case the reported number concentration was even increased by a factor of 6.9 when the modal diameter of the test aerosol exceeded the specified upper limit of the instrument. A general dependence of the measurement accuracy of all devices on particle morphology was not detected.

Inter-comparison of personal monitors for nanoparticles exposure at workplaces and in the environment

Personal monitors based on unipolar diffusion charging (miniDiSC/DiSCmini, NanoTracer, Partector) can be used to assess the individual exposure to nanoparticles in different environments. The charge acquired by the aerosol particles is nearly proportional to the particle diameter and, by coincidence, also nearly proportional to the alveolar lung-deposited surface area (LDSA), the metric reported by all three instruments. In addition, the miniDiSC/DiSCmini and the NanoTracer report particle number concentration and mean particle size. In view of their use for personal exposure studies, the comparability of these personal monitors was assessed in two measurement campaigns. Altogether 29 different polydisperse test aerosols were generated during the two campaigns, covering a large range of particle sizes, morphologies and concentrations. The data provided by the personal monitors were compared with those obtained from reference instruments: a scanning mobility particle sizer (SMPS) for LDSA and mean particle size and a ultrafine particle counter (UCPC) for number concentration. The results indicated that the LDSA concentrations and the mean particle sizes provided by all investigated instruments in this study were in the order of ±30% of the reference value obtained from the SMPS when the particle sizes of the test aerosols generated were within 20-400nm and the instruments were properly calibrated. Particle size, morphology and concentration did not have a major effect within the aforementioned limits. The comparability of the number concentrations was found to be slightly worse and in the range of ±50% of the reference value obtained from the UCPC. In addition, a minor effect of the particle morphology on the number concentration measurements was observed. The presence of particles >400nm can drastically bias the measurement results of all instruments and all metrics determined.

Detection of Airborne Bacteria in a Duck Production Facility with Two Different Personal Air Sampling Devices for an Exposure Assessment

Prevalent airborne microorganisms are not well characterized in industrial animal production buildings with respect to their quantity or quality. To investigate the work-related microbial exposure, personal bioaerosol sampling during the whole working day is recommended. Therefore, bioaerosol sampling in a duck hatchery and a duck house with two personal air sampling devices, a filter-based PGP and a NIOSH particle size separator, was performed. Subsequent, quantitative and qualitative analyses were carried out with” culture independent methods. Total cell concentrations (TCC) determined via fluorescence microscopy showed no difference between the two devices. In average, 8 × 106 cells/m3 were determined in the air of the duck hatchery and 5 × 107 cells/m3 in the air of the duck house. A Generated Restriction Fragment Length Polymorphism (RFLP) pattern revealed deviant bacterial compositions comparing samples collected with both devices. Clone library analyses based on 16S rRNA gene sequence analysis from the hatcherys air showed 65% similarity between the two sampling devices. Detailed 16S rRNA gene sequence analyses showed the occurrence of bacterial species like Acinetobacter baumannii, Enterococcus faecalis, Escherichia sp., and Shigella sp.; and a group of Staphylococcus delphini, S. intermedius, and S. pseudintermedius that provided the evidence of potential exposure to risk group 2 bacteria at the hatchery workplace. Size fractionated sampling with the developed by the Institute for Occupational Safety and Health of the German Social Accident Insurance (IFA) device revealed that pathogenic bacteria would deposit in the inhalable, the thorax, and possibly alveolar dust fraction according to EN481. TCC analysis showed the deposition of bacterial cells in the third stage (< 1μm) at the NIOSH device which implies that bacteria can reach deep into the lungs and contaminate the alveolus after inhalation. Nevertheless, both personal sampling devices could be recommended for exposure assessment at agricultural workplaces.

Poisson shot noise parameter estimation from a single scanning electron microscopy image

Scanning electron microscopy (SEM) has an extremely low signal-to-noise ratio leading to a high level of shot noise which makes further processing difficult. Unlike often assumed, the noise stems from a Poisson process and is not Gaussian but depends on the signal level. A method to estimate the noise parameters of individual images should be found. Using statistical modeling of SEM noise, a robust optimal noise estimation algorithm is derived. A non-local means noise reduction filter tuned with the estimated noise parameters on average achieves an 18% lower root-mean-square error than the untuned filter on simulated images. The algorithm is stable and can adapt to varying noise levels.

Improved Automatic Recognition of Engineered Nanoparticles in Scanning Electron Microscopy Images

The amount of engineered nanoparticles produced each year has grown for some time and will grow in the coming years. However, if such particles are inhaled, they can be toxic. Therefore, to ensure the safety of workers, the nanoparticle concentrations at workplaces have to be measured. This is usually done by gathering the particles in the ambient air and then taking images using scanning electron microscopy. The particles in the images are then manually identified and counted. However, this task takes much time. Therefore, we have developed a system to automatically find and classify particles in these images (Kockentiedt et al., 2012). In this paper, we present an improved version of the system with two new classification feature types. The first are Haralick features. The second is a newly developed feature which estimates the counts of electrons detected by the scanning electron microscopy for each particle. In addition, we have added an algorithm to automatically choose the classifier type and parameters. This way, no expert is needed when the user wants to train the system to recognize a previously unknown particle type. The improved system yields much better results for two types of engineered particles and shows comparable results for a third type.