Reflectance Spectra Analysis Algorithms for the Characterization of Deposits and Condensed Traces on Surfaces

Identification of particulate matter and liquid spills contaminations is essential for many applications, such as forensics, agriculture, security, and environmental protection. For example, toxic industrial compounds deposition in the form of aerosols, or other residual contaminations, pose a secondary, long-lasting health concern due to resuspension and secondary evaporation. This chapter explores several approaches for employing diffuse reflectance spectroscopy in the mid-IR and SWIR to identify particles and films of materials in field conditions. Since the behavior of thin films and particles is more complex compared to absorption spectroscopy of pure compounds, due to the interactions with background materials, the use of physical models combined with statistically-based algorithms for material classification, provides a reliable and practical solution and will be presented

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

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

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

Spectral Light-Reflection Data Dimensionality Reduction for Timely Detection of Yellow Rust

Yellow rust (YR) wheat disease is one of the major threats to worldwide wheat production, and it often spreads rapidly to new and unexpected geographic locations. To cope with this threat, integrated pathogen management strategies combine disease-resistant plants, sensors monitoring technologies, and fungicides either preventively or curatively, which come with their associated monetary and environmental costs. This work presents a methodology for timely detection of YR that cuts down on hardware and computational requirements. It enables frequent detailed monitoring of the spread of YR, hence providing the opportunity to better target mitigation efforts which is critical for successful integrated disease management. The method is trained to detect YR symptoms using reflectance spectrum (VIS–NIR) and a classification algorithm at different stages of YR development to distinguish them from typical defense responses occurring in resistant wheat. The classification method was trained and tested on four different spectral datasets. The results showed that using a full spectral range, a selection of the top 5% significant spectral features, or five typical multispectral bands for early detection of YR in infected plants yielded a true positive rate of ~ 86%, for infected plants. The same data analysis with digital camera bands provided a true positive rate of 77%. These findings lay the groundwork for the development of high-throughput YR screening in the field implementing multispectral digital camera sensors that can be mounted on autonomous vehicles or a drone as part of an integrated disease management scheme

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

Detection of Crop Diseases Using Enhanced Variability Imagery Data and Convolutional Neural Networks

The timely detection of crop diseases is critical for securing crop productivity, lowering production costs, and minimizing agrochemical use. This study presents a crop disease identification method that is based on Convolutional Neural Networks (CNN) trained on images taken with consumer-grade cameras. Specifically, this study addresses the early detection of wheat yellow rust, stem rust, powdery mildew, potato late blight, and wild barley net blotch. To facilitate this, pictures were taken in situ without modifying the scene, the background, or controlling the illumination. Each image was then split into several patches, thus retaining the original spatial resolution of the image while allowing for data variability. The resulting dataset was highly diverse since the disease manifestation, imaging geometry, and illumination varied from patch to patch. This diverse dataset was used to train various CNN architectures to find the best match. The resulting classification accuracy was 95.4 ± 0.4%. These promising results lay the groundwork for autonomous early detection of plant diseases. Guidelines for implementing this approach in realistic conditions are also discussed