Urban Air-Quality Estimation Using Visual Cues and a Deep Convolutional Neural Network in Bengaluru (Bangalore), India.

Mobile monitoring provides robust measurements of air pollution. However, resource constraints often limit the number of measurements so that assessments cannot be obtained in all locations of interest. In response, surrogate measurement methodologies, such as videos and images, have been suggested. Previous studies of air pollution and images have used static images (e.g., satellite images or Google Street View images). The current study was designed to develop deep learning methodologies to infer on-road pollutant concentrations from videos acquired with dashboard cameras. Fifty hours of on-road measurements of four pollutants (black carbon, particle number concentration, PM2.5 mass concentration, carbon dioxide) in Bengaluru, India, were analyzed. The analysis of each video frame involved identifying objects and determining motion (by segmentation and optical flow). Based on these visual cues, a regression convolutional neural network (CNN) was used to deduce pollution concentrations. The findings showed that the CNN approach outperformed several other machine learning (ML) techniques and more conventional analyses (e.g., linear regression). The CO2 prediction model achieved a normalized root-mean-square error of 10-13.7% for the different train-validation division methods. The results here thus contribute to the literature by using video and the relative motion of on-screen objects rather than static images and by implementing a rapid-analysis approach enabling analysis of the video in real time. These methods can be applied to other mobile-monitoring campaigns since the only additional equipment they require is an inexpensive dashboard camera.

Predicting clinical outcome of sulfur mustard induced ocular injury using machine learning model.

The sight-threatening sulfur mustard (SM) induced ocular injury presents specific symptoms in each clinical stage. The acute injury develops in all exposed eyes and may heal or deteriorate into chronic late pathology. Early detection of eyes at risk of developing late pathology may assist in providing unique monitoring and specific treatments only to relevant cases. In this study, we evaluated a machine-learning (ML) model for predicting the development of SM-induced late pathology based on clinical data of the acute phase in the rabbit model. Clinical data from 166 rabbit eyes exposed to SM vapor was used retrospectively. The data included a comprehensive clinical evaluation of the cornea, eyelids and conjunctiva using a semi-quantitative clinical score. A random forest classifier ML model, was trained to predict the development of corneal neovascularization four weeks post-ocular exposure to SM vapor using clinical scores recorded three weeks earlier. The overall accuracy in predicting the clinical outcome of SM-induced ocular injury was 73%. The accuracy in identifying eyes at risk of developing corneal neovascularization and future healed eyes was 75% and 59%, respectively. The most important parameters for accurate prediction were conjunctival secretion and corneal opacity at 1w and corneal erosions at 72 h post-exposure. Predicting the clinical outcome of SM-induced ocular injury based on the acute injury parameters using ML is demonstrated for the first time. Although the prediction accuracy was limited, probably due to the small dataset, it pointed out towards various parameters during the acute injury that are important for predicting SM-induced late pathology and revealing possible pathological mechanisms.

Non-Contact, Continuous Sampling of Porous Surfaces for the Detection of Particulate and Adsorbed Organic Contaminations by Low-Temperature Plasma Coupled to Ion Mobility Spectrometer.

Chemical analysis of hazardous surface contaminations, such as hazardous substances, explosives or illicit drugs, is an essential task in security, environmental and safety applications. This task is mostly based on the collection of particles with swabs, followed by thermal desorption into a vapor analyzer, usually a detector based on ion mobility spectrometry (IMS). While this methodology is well established for several civil applications, such as border control, it is still not efficient enough for various conditions, as in sampling rough and porous surfaces. Additionally, the process of thermal desorption is energetically inefficient, requires bulky hardware and introduces device contamination memory effects. Low-temperature plasma (LTP) has been demonstrated as an ionization and desorption source for sample preparation-free analysis, mostly at the inlet of a mass spectrometer analyzer, and in rare cases in conjunction with an ion mobility spectrometer. Herein, we demonstrate, for the first time, the operation of a simple, low cost, home-built LTP apparatus for desorbing non-volatile analytes from various porous surfaces into the inlet of a handheld IMS vapor analyzer. We show ion mobility spectra that originate from operating the LTP jet on porous surfaces such as asphalt and shoes, contaminated with model amine-containing organic compounds. The spectra are in good correlation with spectra measured for thermally desorbed species. We verify through LC-MS analysis of the collected vapors that the sampled species are not fragmented, and can thus be identified by commercial IMS detectors.

Hyperspectral imaging for chemicals identification: a human-inspired machine learning approach.

Data analysis has increasingly relied on machine learning in recent years. Since machines implement mathematical algorithms without knowing the physical nature of the problem, they may be accurate but lack the flexibility to move across different domains. This manuscript presents a machine-educating approach where a machine is equipped with a physical model, universal building blocks, and an unlabeled dataset from which it derives its decision criteria. Here, the concept of machine education is deployed to identify thin layers of organic materials using hyperspectral imaging (HSI). The measured spectra formed a nonlinear mixture of the unknown background materials and the target material spectra. The machine was educated to resolve this nonlinear mixing and identify the spectral signature of the target materials. The inputs for educating and testing the machine were a nonlinear mixing model, the spectra of the pure target materials (which are problem invariant), and the unlabeled HSI data. The educated machine is accurate, and its generalization capabilities outperform classical machines. When using the educated machine, the number of falsely identified samples is ~ 100 times lower than the classical machine. The probability for detection with the educated machine is 96% compared to 90% with the classical machine.

Information Theory Solution Approach to the Air Pollution Sensor Location-Allocation Problem.

Air pollution is one of the prime adverse environmental outcomes of urbanization and industrialization. The first step toward air pollution mitigation is monitoring and identifying its source(s). The deployment of a sensor array always involves a tradeoff between cost and performance. The performance of the network heavily depends on optimal deployment of the sensors. The latter is known as the location-allocation problem. Here, a new approach drawing on information theory is presented, in which air pollution levels at different locations are computed using a Lagrangian atmospheric dispersion model under various meteorological conditions. The sensors are then placed in those locations identified as the most informative. Specifically, entropy is used to quantify the locations' informativity. This entropy method is compared to two commonly used heuristics for solving the location-allocation problem. In the first, sensors are randomly deployed; in the second, the sensors are placed according to maximal cumulative pollution levels (i.e., hot spots). Two simulated scenarios were evaluated: one containing point sources and buildings and the other containing line sources (i.e., roads). The entropy method resulted in superior sensor deployment in terms of source apportionment and dense pollution field reconstruction from the sparse sensors' network measurements.

Optimal Wireless Distributed Sensor Network Design and Ad-Hoc Deployment in a Chemical Emergency Situation.

Industrial activities involve the manipulation of harmful chemicals. As there is no way to guarantee fail-safe operation, the means and response methods must be planned in advance to cope with a chemical disaster. In these situations, first responders assess the situation from the atmospheric conditions, but they have scant data on the source of the contamination, which curtails their response toolbox. Hence, a sensor deployment strategy needs to be formulated in real-time based on the meteorological conditions, sensor attributes, and resources. This work examined the tradeoff between sensor locations and their attributes. The findings show that if the sensor locations are optimal, the number is more important than quality, in that the sensors' dynamic range is a significant factor when quantifying leaks but is less important if the goal is solely to locate the leak source/s. This methodology can be used for sensor location-allocation under real-life conditions and technological constraints.

The Challenges of Prolonged Gas Sensing in the Modern Urban Environment.

The increase in the urban population is impacting the environment in several ways, including air pollution due to emissions from automobiles and industry. The reduction of air pollution requires reliable and detailed information regarding air pollution levels. Broad deployment of sensors can provide such information that, in turn, can be used for the establishment of mitigating and regulating acts. However, a prerequisite of such a deployment strategy is using highly durable sensors. The sensors must be able to operate for long periods of time under severe conditions such as high humidity, solar radiation, and dust. In recent years, there has been an ongoing effort to ruggedize sensors for industrial applications with an emphasis on elevated temperature, humidity, and pressure. Some of these developments are adapted for urban air sensing applications. However, protection from dust is based on filters that have not been modified in the last few decades. Such filters clog over time, thus requiring frequent replacement. This editorial presents the need for a consumable-free dust removal device that provides consistent performance without affecting the sensing process. A specific solution for removing dust using a cyclone dust separator is presented. The cyclone dust separator is implemented as an add-on module to protect commercially available sensors.

Non-contact and non-destructive detection and identification of Bacillus anthracis inside paper envelopes.

Efficient and safe detection of Bacillus anthracis spores (BAS) is a challenging task especially in bio-terror scenarios where the agent is concealed. We provide a proof-of-concept for the identification of concealed BAS inside mail envelopes using short-wave infrared hyperspectral imaging (SWIR-HSI). The spores and two other benign materials are identified according to their typical absorption spectrum. The identification process is based on the removal of the envelope signal using a new automatic new algorithm. This method may serve as a fast screening tool prior to using classical bioanalytical techniques.

Terahertz spectroscopy of 2,4,6-trinitrotoluene molecular solids from first principles.

We present a computational analysis of the terahertz spectra of the monoclinic and the orthorhombic polymorphs of 2,4,6-trinitrotoluene. Very good agreement with experimental data is found when using density functional theory that includes Tkatchenko-Scheffler pair-wise dispersion interactions. Furthermore, we show that for these polymorphs the theoretical results are only weakly affected by many-body dispersion contributions. The absence of dispersion interactions, however, causes sizable shifts in vibrational frequencies and directly affects the spatial character of the vibrational modes. Mode assignment allows for a distinction between the contributions of the monoclinic and orthorhombic polymorphs and shows that modes in the range from 0 to ca. 3.3 THz comprise both inter- and intramolecular vibrations, with the former dominating below ca. 1.5 THz. We also find that intramolecular contributions primarily involve the nitro and methyl groups. Finally, we present a prediction for the terahertz spectrum of 1,3,5-trinitrobenzene, showing that a modest chemical change leads to a markedly different terahertz spectrum.

Optimized data analysis algorithm for on-site chemical identification using a hand-held attenuated total reflection Fourier transform infrared (ATR FT-IR) spectrometer.

On-site identification of organic compounds in the presence of interfering materials using a field-portable attenuated total reflection Fourier transform infrared (ATR FT-IR) spectrometer is presented. Identification is based on an algorithm that compares the analyte's infrared absorption spectrum with the reference spectra. The comparison is performed at several predetermined frequencies, and a similarity value (distance) between the measured and the reference spectra is calculated either at each frequency individually, or, alternatively, the average distance for all frequencies is calculated. The examined frequencies are selected to give the best contrast between the target materials of interest. In this study, the algorithm was optimized to identify three common chemical warfare agents (CWAs): O-ethyl S-[2-(diisopropylamino)ethyl] methylphosphonothioic acid (VX), sarin (GB), and sulfur mustard (bis(2-chloroethyl) sulfide) (HD), in the presence of field-related interfering materials (fuels, water, and dust). Receiver operating characteristics analysis was performed in order to determine the probabilities for detection (PD) and for false alerts (PF). Challenging the algorithm with a set of data that contains mixtures of CWAs and interfering materials resulted in PD of 90% and PF of 0%, 0%, and 1% for VX, GB, and HD, respectively, using the average distance approach, which was found to be much more effective than analyzing each frequency individually. This finding was validated for all possible combinations of 2-7 peaks per material. It is suggested that this algorithm provides a reliable mean for the identification of a predetermined set of target analytes and interfering materials.

Surface-enhanced Raman spectroscopy of organic molecules adsorbed on metallic nanoparticles.

The improvements in Raman instrumentation have led to the development of -portable, simple to operate, Raman instruments that can be used for on-site analysis of substances relevant for homeland security purposes such as chemical and biological warfare and explosives materials.Raman spectroscopy, however, suffers from limited sensitivity which can be overcome by Surface-Enhanced Raman Spectroscopy (SERS). SERS can enhance the Raman signal of a target molecule by 6-10 orders of magnitude. The increased sensitivity, together with Raman's molecular recognition capabilities and the availability of portable Raman instruments make SERS a powerful analytical tool for on site detection.In this work we studied the effect of target molecules and SERS-active substrate properties on the obtained SERS, using a field portable Raman spectrometer. Also reported herein is the SERS detection of the chemical warfare agent sulfur mustard (HD, 2,2 dichloroethyl sulfide). This study may serve as a basis for the development of SERS platform for homeland security purposes.

Rapid determination of complex mixtures by dual-column gas chromatography with a novel stationary phase combination and spectrometric detection.

Fast GC separations of a broad range of analytes are demonstrated using a capillary column coated with a novel immobilized ionic liquid (IIL) stationary phase. Both completely cross-linked and partially cross-linked columns were evaluated, yielding approximately 1600 and approximately 2000 theoretical plates per meter, respectively. Enhanced separation is demonstrated using a dual-column ensemble comprised of an IIL column, a commercially coated Rtx-1 column, and a pneumatic valve connecting the inlet to the junction point between the two columns. Enhanced separation of 20 components, with two sets of co-eluting peaks is shown in approximately 150 s, while sacrificing only a length of time equivalent to the sum of the stop flow pulses, or about 15.5 s. A novel application of a band trajectory model that shows band position as a function of analysis time as analytes move through the column ensemble is employed to determine pulse application times. The model predicts component retention times within a few seconds. Another method of selectivity enhancement of the IIL stationary phase-coated columns is demonstrated using a differential mobility spectrometer (DMS) that provides a second dimension separation based on ion mobility in a high-frequency electrical field. The DMS is able to separate all but one set of co-eluting components from the IIL column. The separation of 13 components found in the headspace above U.S. currency is demonstrated using the IIL column in a dual-column ensemble as well as with the DMS.

Ultrafast gas chromatographic separation of organophosphor and organosulfur compounds utilizing a microcountercurrent flame photometric detector.

A microcountercurrent flame photometric detector (microcc-FPD) was adapted and optimized for ultrafast gas chromatographic (GC) separation and detection of organophosphor (OP) and organosulfur (OS) compounds on short chromatographic columns. Air and hydrogen are introduced to the microcc-FPD from opposite directions, creating a hydrogen-rich flame. In this microcc-FPD, combustion takes place between the burner tips without touching them. The separation between the tips and the flame reduces heat loss from the flame to the surrounding environment, resulting in low hydrogen consumption and a compact flame. The microcc-FPD is capable of detecting very narrow (13 ms) chromatographic peaks. An ultrafast GC separation of a group of six OP and OS compounds is achieved within less than 5 s using fast temperature programming of a 0.5-m-long microbore column. Very fast separations are also demonstrated on a 1-m-long microfabricated column consisting of 150-microm-wide, 240-microm-deep channels, etched in a 1.9-cm square silicon chip, covered with a Pyrex wafer, and statically coated with dimethyl polysiloxane. With a hydrogen flow rate of 10 mL/min, the detection limit for OP is 12 pg of P/s and 3 ng of S/s for OS compounds at a signal-to-noise ratio of 2. The coupling of a microfabricated column and a miniature FPD is an important step toward the development of a miniaturized GC-FPD capable of ultrafast detection of low levels of OP and OS compounds.