Blockchain has emerged as a solution for ensuring accurate and truthful environmental variable monitoring needed for the management of pollutants and natural resources. The immutability property of blockchain helps protect the measured data on pollution and natural resources to enable truthful reporting and effective management and control of polluting agents. However, specifics on what to measure, how to use blockchain, and highlighting which blockchain frameworks have been adopted need to be explored to fill the research gaps. Therefore, we review existing works on the use of blockchain for monitoring and managing environmental variables in this paper. Specifically, we examine existing blockchain applications on greenhouse gas emissions, solid and plastic waste, food waste, food security, water usage, and the circular economy and identify what motivates the adoption of blockchain, features sought, used blockchain frameworks and consensus algorithms, and the adopted supporting technologies to complement data sensing and reporting. We conclude the review by identifying practical works that provide implementation details for rapid adoption and remaining challenges that merit future research.

Recently, nonmetallic materials, which are resilient to corrosion, low cost, and lightweight, have been exploited in many industrial sectors such as oil and gas field. A common application of them is in the form of pipes. Despite the advantages, these pipes can be damaged by delamination, defects, holes, and erosion, which may lead to major production failures or even environmental catastrophes. To prevent these issues, nondestructive testing (NDT) of these pipes is required to replace these components in a timely manner. Due to the fact that the traditional NDT methods are mostly effective for metallic pipes, microwave holographic imaging is proposed for inspection of multiple concentric nonmetallic pipes. Besides, standardized minimum norm (SMN) is used to mitigate the depth biasing problem in the proposed imaging technique. To reduce the complexity of the imaging system, we aim at using the narrowest possible frequency band using an array of receiver antennas. The performance of the proposed NDT method is validated through simulation and experimental data in two scenarios including the antennas placed outside the pipes and inside the pipes.

With the significant growth in the use of non-metallic composite materials, the demands for new and robust non-destructive testing methodologies is high. Microwave imaging has attracted a lot of attention recently for such applications. This is in addition to the biomedical imaging applications of microwave that are also being pursued actively. Among these efforts, in this paper, we propose a compact and cost-effective three-dimensional microwave imaging system based on a fast and robust holographic technique. For this purpose, we employ narrow-band microwave data, instead of wideband data used in previous three-dimensional cylindrical holographic imaging systems. Three-dimensional imaging is accomplished by using an array of receiver antennas surrounding the object and scanning that along with a transmitter antenna over a cylindrical aperture. To achieve low cost and compact size, we employ off-the-shelf components to build a data acquisition system replacing the costly and bulky vector network analyzers. The simulation and experimental results demonstrate the satisfactory performance of the proposed imaging system. We also show the effect of number of frequencies and size of the objects on the quality of reconstructed images.

In this communication, we propose a technique to enhance the quality of images obtained from holographic microwave imaging. It is based on estimating the electrical properties of the background medium. To accomplish that, background properties to be estimated are assumed to be within a pre-known range. Then, two sets of frequencies are employed in wideband holographic imaging and images are reconstructed from each set and for assumed property values within the pre-determined range. An error is calculated according to the differences between the two sets of images. The error is expected to be minimum at the true values of the background medium’s properties. The estimated properties, in turn, are used to reconstruct images with the best quality. The validity of the proposed technique is demonstrated via simulation and experimental results.