Translocation and potential neurological effects of fine and ultrafine particles a critical update

ABSTRACT: Particulate air pollution has been associated with respiratory and cardiovascular disease. Evidence for cardiovascular and neurodegenerative effects of ambient particles was reviewed as part of a workshop. The purpose of this critical update is to summarize the evidence presented for the mechanisms involved in the translocation of particles from the lung to other organs and to highlight the potential of particles to cause neurodegenerative effects.Fine and ultrafine particles, after deposition on the surfactant film at the air-liquid interface, are displaced by surface forces exerted on them by surfactant film and may then interact with primary target cells upon this displacement. Ultrafine and fine particles can then penetrate through the different tissue compartments of the lungs and eventually reach the capillaries and circulating cells or constituents, e.g. erythrocytes. These particles are then translocated by the circulation to other organs including the liver, the spleen, the kidneys, the heart and the brain, where they may be deposited. It remains to be shown by which mechanisms ultrafine particles penetrate through pulmonary tissue and enter capillaries. In addition to translocation of ultrafine particles through the tissue, fine and coarse particles may be phagocytized by macrophages and dendritic cells which may carry the particles to lymph nodes in the lung or to those closely associated with the lungs. There is the potential for neurodegenerative consequence of particle entry to the brain. Histological evidence of neurodegeneration has been reported in both canine and human brains exposed to high ambient PM levels, suggesting the potential for neurotoxic consequences of PM-CNS entry. PM mediated damage may be caused by the oxidative stress pathway. Thus, oxidative stress due to nutrition, age, genetics among others may increase the susceptibility for neurodegenerative diseases. The relationship between PM exposure and CNS degeneration can also be detected under controlled experimental conditions. Transgenic mice (Apo E -/-), known to have high base line levels of oxidative stress, were exposed by inhalation to well characterized, concentrated ambient air pollution. Morphometric analysis of the CNS indicated unequivocally that the brain is a critical target for PM exposure and implicated oxidative stress as a predisposing factor that links PM exposure and susceptibility to neurodegeneration.Together, these data present evidence for potential translocation of ambient particles on organs distant from the lung and the neurodegenerative consequences of exposure to air pollutants.

The role of macrophages in the clearance of inhaled ultrafine titanium dioxide particles

The role of macrophages in the clearance of particles with diameters less than 100 nm (ultrafine or nanoparticles) is not well established, although these particles deposit highly efficiently in peripheral lungs, where particle phagocytosis by macrophages is the primary clearance mechanism. To investigate the uptake of nanoparticles by lung phagocytes, we analyzed the distribution of titanium dioxide particles of 20 nm count median diameter in macrophages obtained by bronchoalveolar lavage at 1 hour and 24 hours after a 1-hour aerosol inhalation. Differential cell counts revealing greater than 96% macrophages and less than 1% neutrophils and lymphocytes excluded inflammatory cell responses. Employing energy-filtering transmission electron microscopy (EFTEM) for elemental microanalysis, we examined 1,594 macrophage profiles in the 1-hour group (n = 6) and 1,609 in the 24-hour group (n = 6). We found 4 particles in 3 macrophage profiles at 1 hour and 47 particles in 27 macrophage profiles at 24 hours. Model-based data analysis revealed an uptake of 0.06 to 0.12% ultrafine titanium-dioxide particles by lung-surface macrophages within 24 hours. Mean (SD) particle diameters were 31 (8) nm at 1 hour and 34 (10) nm at 24 hours. Particles were localized adjacent (within 13-83 nm) to the membrane in vesicles with mean (SD) diameters of 592 (375) nm at 1 hour and 414 (309) nm at 24 hours, containing other material like surfactant. Additional screening of macrophage profiles by conventional TEM revealed no evidence for agglomerated nanoparticles. These results give evidence for a sporadic and rather unspecific uptake of TiO(2)-nanoparticles by lung-surface macrophages within 24 hours after their deposition, and hence for an insufficient role of the key clearance mechanism in peripheral lungs.

Effects of combustion-derived ultrafine particles and manufactured nanoparticles on heart cells in vitro

Evidence from epidemiological studies indicates that acute exposure to airborne pollutants is associated with an increased risk of morbidity and mortality attributed to cardiovascular diseases. The present study investigated the effects of combustion-derived ultrafine particles (diesel exhaust particles) as well as engineered nanoparticles (titanium dioxide and single-walled carbon nanotubes) on impulse conduction characteristics, myofibrillar structure and the formation of reactive oxygen species in patterned growth strands of neonatal rat ventricular cardiomyocytes in vitro. Diesel exhaust particles as well as titanium dioxide nanoparticles showed the most pronounced effects. We observed a dose-dependent change in heart cell function, an increase in reactive oxygen species and, for titanium dioxide, we also found a less organized myofibrillar structure. The mildest effects were observed for single-walled carbon nanotubes, for which no clear dose-dependent alterations of theta and dV/dt(max) could be determined. In addition, there was no increase in oxidative stress and no change in the myofibrillar structure. These results suggest that diesel exhaust as well as titanium dioxide particles and to a lesser extent also single-walled carbon nanotubes can directly induce cardiac cell damage and can affect the function of the cells.

Stakeholder analysis combined with social network analysis provides fine-grained insights into water infrastructure planning processes

Environmental policy and decision-making are characterized by complex interactions between different actors and sectors. As a rule, a stakeholder analysis is performed to understand those involved, but it has been criticized for lacking quality and consistency. This lack is remedied here by a formal social network analysis that investigates collaborative and multi-level governance settings in a rigorous way. We examine the added value of combining both elements. Our case study examines infrastructure planning in the Swiss water sector. Water supply and wastewater infrastructures are planned far into the future, usually on the basis of projections of past boundary conditions. They affect many actors, including the population, and are expensive. In view of increasing future dynamics and climate change, a more participatory and long-term planning approach is required. Our specific aims are to investigate fragmentation in water infrastructure planning, to understand how actors from different decision levels and sectors are represented, and which interests they follow. We conducted 27 semi-structured interviews with local stakeholders, but also cantonal and national actors. The network analysis confirmed our hypothesis of strong fragmentation: we found little collaboration between the water supply and wastewater sector (confirming horizontal fragmentation), and few ties between local, cantonal, and national actors (confirming vertical fragmentation). Infrastructure planning is clearly dominated by engineers and local authorities. Little importance is placed on longer-term strategic objectives and integrated catchment planning, but this was perceived as more important in a second analysis going beyond typical questions of stakeholder analysis. We conclude that linking a stakeholder analysis, comprising rarely asked questions, with a rigorous social network analysis is very fruitful and generates complementary results. This combination gave us deeper insight into the socio-political-engineering world of water infrastructure planning that is of vital importance to our well-being.

A Passive WiFi Source Localization System based on Fine-grained Power-based Trilateration

Indoor localization systems become more interesting for researchers because of the attractiveness of business cases in various application fields. A WiFi-based passive localization system can provide user location information to third-party providers of positioning services. However, indoor localization techniques are prone to multipath and Non-Line Of Sight (NLOS) propagation, which lead to significant performance degradation. To overcome these problems, we provide a passive localization system for WiFi targets with several improved algorithms for localization. Through Software Defined Radio (SDR) techniques, we extract Channel Impulse Response (CIR) information at the physical layer. CIR is later adopted to mitigate the multipath fading problem. We propose to use a Nonlinear Regression (NLR) method to relate the filtered power information to propagation distances, which significantly improves the ranging accuracy compared to the commonly used log-distance path loss model. To mitigate the influence of ranging errors, a new trilateration algorithm is designed as well by combining Weighted Centroid and Constrained Weighted Least Square (WC-CWLS) algorithms. Experiment results show that our algorithm is robust against ranging errors and outperforms the linear least square algorithm and weighted centroid algorithm.

Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as a fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range-based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranges and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1:3m for mean accuracy and 2:2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.

Fine-grained indoor positioning and tracking systems

Indoor positioning has attracted considerable attention for decades due to the increasing demands for location based services. In the past years, although numerous methods have been proposed for indoor positioning, it is still challenging to find a convincing solution that combines high positioning accuracy and ease of deployment. Radio-based indoor positioning has emerged as a dominant method due to its ubiquitousness, especially for WiFi. RSSI (Received Signal Strength Indicator) has been investigated in the area of indoor positioning for decades. However, it is prone to multipath propagation and hence fingerprinting has become the most commonly used method for indoor positioning using RSSI. The drawback of fingerprinting is that it requires intensive labour efforts to calibrate the radio map prior to experiments, which makes the deployment of the positioning system very time consuming. Using time information as another way for radio-based indoor positioning is challenged by time synchronization among anchor nodes and timestamp accuracy. Besides radio-based positioning methods, intensive research has been conducted to make use of inertial sensors for indoor tracking due to the fast developments of smartphones. However, these methods are normally prone to accumulative errors and might not be available for some applications, such as passive positioning. This thesis focuses on network-based indoor positioning and tracking systems, mainly for passive positioning, which does not require the participation of targets in the positioning process. To achieve high positioning accuracy, we work on some information of radio signals from physical-layer processing, such as timestamps and channel information. The contributions in this thesis can be divided into two parts: time-based positioning and channel information based positioning. First, for time-based indoor positioning (especially for narrow-band signals), we address challenges for compensating synchronization offsets among anchor nodes, designing timestamps with high resolution, and developing accurate positioning methods. Second, we work on range-based positioning methods with channel information to passively locate and track WiFi targets. Targeting less efforts for deployment, we work on range-based methods, which require much less calibration efforts than fingerprinting. By designing some novel enhanced methods for both ranging and positioning (including trilateration for stationary targets and particle filter for mobile targets), we are able to locate WiFi targets with high accuracy solely relying on radio signals and our proposed enhanced particle filter significantly outperforms the other commonly used range-based positioning algorithms, e.g., a traditional particle filter, extended Kalman filter and trilateration algorithms. In addition to using radio signals for passive positioning, we propose a second enhanced particle filter for active positioning to fuse inertial sensor and channel information to track indoor targets, which achieves higher tracking accuracy than tracking methods solely relying on either radio signals or inertial sensors.

Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranging and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1.3m for mean accuracy and 2.2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.