Night-time ground hyperspectral imaging for urban-scale remote sensing of ambient PM--modal concentrations retrieval.

Retrieval of aerosol loading in vertical atmospheric columns is a common product of satellite and ground instruments that measure spectral extinction of solar radiation throughout the entire atmosphere. Here we study ground hyperspectral imaging of artificial light sources as a complementary method for retrieving fine aerosol concentrations along quazi-horizontal ambient open paths. Previously, we reported hyperspectral measurements of the aerosol optical thickness in the 500-900 nm range over urban-scale distances (180 m to 4 km), measuring the extinction of radiation emitted from a halogen source. Here we confirm in a laboratory-setup the basic premise that different accumulation-size aerosols generate distinct hyperspectral signatures in this spectral range. Measured hyperspectral attenuation signatures of fine aerosols were comparable to calculated Mie scattering signatures, suggesting that modal aerosol concentrations can be retrieved. A genetic algorithm was adapted to estimate the aerosol modal concentrations from its hyperspectral extinction signature. Retrievals of aerosol concentrations from measured and synthetic hyperspectral signatures indicated a robust algorithm, with an expected retrieval error of 0.2-22% for typical ambient concentrations along an urban-scale open path. The retrieval accuracy was found to depend on the relative aerosol modal concentrations, especially when there is a substantial overlap between the modal spectral signatures.

Fusion of land use regression modeling output and wireless distributed sensor network measurements into a high spatiotemporally-resolved NO2 product.

Land use regression modeling is a common method for assessing exposure to ambient pollutants, yet it suffers from very coarse temporal resolution. Wireless distributed sensor networks (WDSN) is a promising technology that can provide extremely high spatiotemporal pollutant patterns but is known to suffer from several limitations that put into question its data reliability. This study examines the advantages of fusing data from these two methods and obtaining high spatiotemporally-resolved product that can be used for exposure assessment. We demonstrate this approach by estimating nitrogen dioxide (NO2) concentrations at a sub-urban scale, with the study area limited by the deployment of the WDSN nodes. Specifically, hourly-resolved fused-data estimates were obtained by combining a stationary traffic-based land use regression (LUR) model with observations (15 min sampling frequency) made by an array of low-cost sensor nodes, with the sensors' readings mapped over the whole study area. Data fusion was performed by merging the two independent information products using a fuzzy logic approach. The performance of the fused product was examined against reference hourly observations at four air quality monitoring (AQM) stations situated within the study area, with the AQM data not used for the development of any of the underlying information layers. The mean hourly RMSE between the fused data product and the AQM records was 9.3 ppb, smaller than the RMSE of the two base products independently (LUR: 14.87 ppb, WDSN: 10.45 ppb). The normalized Moran's I of the fused product indicates that the data-fusion product reveals more realistic spatial patterns than those of the base products. The fused NO2 concentration product shows considerable spatial variability relative to that evident by interpolation of both the WDSN records and the AQM stations data, with significant non-random patterns in 74% of the study period.

Node-to-node field calibration of wireless distributed air pollution sensor network.

Low-cost air quality sensors offer high-resolution spatiotemporal measurements that can be used for air resources management and exposure estimation. Yet, such sensors require frequent calibration to provide reliable data, since even after a laboratory calibration they might not report correct values when they are deployed in the field, due to interference with other pollutants, as a result of sensitivity to environmental conditions and due to sensor aging and drift. Field calibration has been suggested as a means for overcoming these limitations, with the common strategy involving periodical collocations of the sensors at an air quality monitoring station. However, the cost and complexity involved in relocating numerous sensor nodes back and forth, and the loss of data during the repeated calibration periods make this strategy inefficient. This work examines an alternative approach, a node-to-node (N2N) calibration, where only one sensor in each chain is directly calibrated against the reference measurements and the rest of the sensors are calibrated sequentially one against the other while they are deployed and collocated in pairs. The calibration can be performed multiple times as a routine procedure. This procedure minimizes the total number of sensor relocations, and enables calibration while simultaneously collecting data at the deployment sites. We studied N2N chain calibration and the propagation of the calibration error analytically, computationally and experimentally. The in-situ N2N calibration is shown to be generic and applicable for different pollutants, sensing technologies, sensor platforms, chain lengths, and sensor order within the chain. In particular, we show that chain calibration of three nodes, each calibrated for a week, propagate calibration errors that are similar to those found in direct field calibration. Hence, N2N calibration is shown to be suitable for calibration of distributed sensor networks.

An evaluation tool kit of air quality micro-sensing units.

Recent developments in sensory and communication technologies have made the development of portable air-quality (AQ) micro-sensing units (MSUs) feasible. These MSUs allow AQ measurements in many new applications, such as ambulatory exposure analyses and citizen science. Typically, the performance of these devices is assessed using the mean error or correlation coefficients with respect to a laboratory equipment. However, these criteria do not represent how such sensors perform outside of laboratory conditions in large-scale field applications, and do not cover all aspects of possible differences in performance between the sensor-based and standardized equipment, or changes in performance over time. This paper presents a comprehensive Sensor Evaluation Toolbox (SET) for evaluating AQ MSUs by a range of criteria, to better assess their performance in varied applications and environments. Within the SET are included four new schemes for evaluating sensors' capability to: locate pollution sources; represent the pollution level on a coarse scale; capture the high temporal variability of the observed pollutant and their reliability. Each of the evaluation criteria allows for assessing sensors' performance in a different way, together constituting a holistic evaluation of the suitability and usability of the sensors in a wide range of applications. Application of the SET on measurements acquired by 25 MSUs deployed in eight cities across Europe showed that the suggested schemes facilitates a comprehensive cross platform analysis that can be used to determine and compare the sensors' performance. The SET was implemented in R and the code is available on the first author's website.

Particle dynamics and deposition in true-scale pulmonary acinar models.

Particle transport phenomena in the deep alveolated airways of the lungs (i.e. pulmonary acinus) govern deposition outcomes following inhalation of hazardous or pharmaceutical aerosols. Yet, there is still a dearth of experimental tools for resolving acinar particle dynamics and validating numerical simulations. Here, we present a true-scale experimental model of acinar structures consisting of bifurcating alveolated ducts that capture breathing-like wall motion and ensuing respiratory acinar flows. We study experimentally captured trajectories of inhaled polydispersed smoke particles (0.2 to 1 µm in diameter), demonstrating how intrinsic particle motion, i.e. gravity and diffusion, is crucial in determining dispersion and deposition of aerosols through a streamline crossing mechanism, a phenomenon paramount during flow reversal and locally within alveolar cavities. A simple conceptual framework is constructed for predicting the fate of inhaled particles near an alveolus by identifying capture and escape zones and considering how streamline crossing may shift particles between them. In addition, we examine the effect of particle size on detailed deposition patterns of monodispersed microspheres between 0.1-2 µm. Our experiments underline local modifications in the deposition patterns due to gravity for particles ≥0.5 µm compared to smaller particles, and show good agreement with corresponding numerical simulations.

On the feasibility of measuring urban air pollution by wireless distributed sensor networks.

Accurate evaluation of air pollution on human-wellbeing requires high-resolution measurements. Standard air quality monitoring stations provide accurate pollution levels but due to their sparse distribution they cannot capture the highly resolved spatial variations within cities. Similarly, dedicated field campaigns can use tens of measurement devices and obtain highly dense spatial coverage but normally deployment has been limited to short periods of no more than few weeks. Nowadays, advances in communication and sensory technologies enable the deployment of dense grids of wireless distributed air monitoring nodes, yet their sensor ability to capture the spatiotemporal pollutant variability at the sub-neighborhood scale has never been thoroughly tested. This study reports ambient measurements of gaseous air pollutants by a network of six wireless multi-sensor miniature nodes that have been deployed in three urban sites, about 150 m apart. We demonstrate the network's capability to capture spatiotemporal concentration variations at an exceptional fine resolution but highlight the need for a frequent in-situ calibration to maintain the consistency of some sensors. Accordingly, a procedure for a field calibration is proposed and shown to improve the system's performance. Overall, our results support the compatibility of wireless distributed sensor networks for measuring urban air pollution at a sub-neighborhood spatial resolution, which suits the requirement for highly spatiotemporal resolved measurements at the breathing-height when assessing exposure to urban air pollution.

Live-cell imaging in Caenorhabditis elegans reveals the distinct roles of dynamin self-assembly and guanosine triphosphate hydrolysis in the removal of apoptotic cells.

Dynamins are large GTPases that oligomerize along membranes. Dynamin's membrane fission activity is believed to underlie many of its physiological functions in membrane trafficking. Previously, we reported that DYN-1 (Caenorhabditis elegans dynamin) drove the engulfment and degradation of apoptotic cells through promoting the recruitment and fusion of intracellular vesicles to phagocytic cups and phagosomes, an activity distinct from dynamin's well-known membrane fission activity. Here, we have detected the oligomerization of DYN-1 in living C. elegans embryos and identified DYN-1 mutations that abolish DYN-1's oligomerization or GTPase activities. Specifically, abolishing self-assembly destroys DYN-1's association with the surfaces of extending pseudopods and maturing phagosomes, whereas inactivating guanosine triphosphate (GTP) binding blocks the dissociation of DYN-1 from these membranes. Abolishing the self-assembly or GTPase activities of DYN-1 leads to common as well as differential phagosomal maturation defects. Whereas both types of mutations cause delays in the transient enrichment of the RAB-5 GTPase to phagosomal surfaces, only the self-assembly mutation but not GTP binding mutation causes failure in recruiting the RAB-7 GTPase to phagosomal surfaces. We propose that during cell corpse removal, dynamin's self-assembly and GTP hydrolysis activities establish a precise dynamic control of DYN-1's transient association to its target membranes and that this control mechanism underlies the dynamic recruitment of downstream effectors to target membranes.

Potential and limitation of mid-infrared attenuated total reflectance spectroscopy for real time analysis of raw milk in milking lines.

Real-time information about milk composition would be very useful for managing the milking process. Mid-infrared spectroscopy, which relies on fundamental modes of molecular vibrations, is routinely used for off-line analysis of milk and the purpose of the present study was to investigate the potential of attenuated total reflectance mid-infrared spectroscopy for real-time analysis of milk in milking lines. The study was conducted with 189 samples from over 70 cows that were collected during an 18 months period. Principal component analysis, wavelets and neural networks were used to develop various models for predicting protein and fat concentration. Although reasonable protein models were obtained for some seasonal sub-datasets (determination errors