Advanced Environmental Monitoring Research Articles

Enhanced ammonia sensing performance of NiFe2O4 and dysprosium doped NiFe2O4 for advanced Environmental Monitoring

Air pollution driven by rapid technological growth, has heightened mortality rates and infectious diseases, prompting research into advanced gas-sensing materials. This study examined the gas sensing properties of NiFe2O4 and dysprosium-doped NixDy1-xFe2O4 (x = 0.02, 0.04, 0.06) under ammonia concentrations of 10–100 ppm. The incorporation of Ni2+ ions into the Fe2O4 lattice revealed p-type semiconductor behaviour. Notably, NixDy1-xFe2O4 (x = 0.06) exhibited superior ammonia sensing, with high sensitivity and excellent selectivity. Stability tests confirmed its robustness, maintaining 83.8% of its initial response, highlighting its potential for effective ammonia detection.

Climate changes at mine closure: a systematic review

This systematic review addresses the implications of climate change on mine closure processes, aiming to identify, analyze, and synthesize recent literature on how climate change influences the rehabilitation of mined areas, environmental risks, the safety of remaining structures, and the long-term sustainability of closure projects. Through a methodology of selecting scientific articles, articles published between 2020 and 2024 found in the Capes/CAfe database were analyzed. The analysis covered the climatic impacts on increasing temperatures and changes in precipitation patterns in relation to soil erosion, water management, and revegetation in mine closure processes. The results highlight challenges and opportunities arising from extreme weather events, which compromise the stability of mine structures, and the noted absence of Emergency Action Plans for dealing with extreme events, which hinders decision-making and the implementation of control measures. Recommendations include the development of adaptive practices and the incorporation of advanced environmental monitoring to mitigate risks and promote sustainability in mine closure, as well as being essential to increase environmental resilience.

Surface Plasmon Polaritons in Graphene Material–Based Reconfigurable Antennas for Advanced Environmental Monitoring Applications

Surface Plasmon Polaritons in Graphene Material–Based Reconfigurable Antennas for Advanced Environmental Monitoring Applications

PESTI GUARD: FARMER-FRIENDLY PESTICIDE ROBOT

Agriculture is the primary source of revenue for India's population, which accounts for nearly 60% of the country's total. Farmers work in their fields to cultivate various crops based on the environment and resources available. Farmers must use large quantities of pesticides to increase food production in order to meet such high food demand for such a large population. Traditional manual pesticide spraying operations is full of direct exposure to the pesticide liquid work environment, great harm to human body and when this pesticide may come into contact with the farmer during spraying, which may trigger skin cancer and asthma illnesses. Increased pesticide spraying can impact consumer health as it enters the food chain. The widespread use of pesticides in agriculture presents significant health risks to both farmers and consumers. The pesticide spraying robot is equipped with state-of-the-art sensors to record real-time temperature and humidity levels, providing crucial environmental data for optimized pesticide application. Farmers can remotely control the robot using a user-friendly interface, instructing it to perform specific spraying tasks while maintaining a safe distance from potentially hazardous chemicals. To address this challenge, this paper proposes a Pesticide Spraying Robot designed to minimize human contact during operation while prioritizing human health through advanced environmental monitoring and remote-control capabilities.

High-performance self-powered UV photodetectors using SnO2 thin film by reactive magnetron sputtering

Self-powered UV photodetector (UVPD) operates without the need for an external energy source and contributes to global energy conservation. With ongoing innovation, self-powered PDs are becoming increasingly appealing for industrial and innovation applications such as advanced environmental monitoring and communication devices. This study investigates the influence of varying working pressures (ranging from 3 to 15 mTorr) during reactive magnetron sputtering on the crystalline structure, surface morphology, and performance of SnO2 thin film photodetectors, aiming to fabricate self-powered UVPDs. The SnO2-based UVPD demonstrates exceptional self-powered UV photodetection capabilities, characterized by a sensitivity of 6.41×105%, responsivity of 0.09 mA/W, and detectivity of 2.99×1010 Jones. In addition, SnO2-based UVPD possesses a switching photo response and recovery time of 0.75 and 0.99 s. Henceforth, it was observed that 8 mTorr SnO2 thin film exhibits exceptional stability, superior photodetector performance, and remarkable repeatability, rendering them highly suitable for UVPD applications. Notably, these films demonstrate sustained functionality for approximately 100 days without necessitating encapsulation. These excellent results confirm that SnO2 thin films are suitable for fabricating future smart optoelectronics devices.

Advance Multi-Gas Detection System for Optimal Safety

Indoor air quality is a growing concern, particularly the concentration of carbon dioxide (CO2) and other hazardous gases, which can pose significant health risks and diminish safety. Recognizing the critical nature of this issue, this paper detailsthe development of an advanced environmental monitoring system designed to detect elevated levels of CO2 and other potentially dangerous gases. Our approach synthesizes various research methods, including requirement gathering, literature review, and user surveys, to inform system conception and implementation. The resulting device integrates highly sensitive sensors for CO2, CO, LPG, smoke, and additional hazardous gas detection, all governed by a microcontroller at the system's nucleus. This technology is augmented by a comprehensive alarm strategy—encompassing audio alerts, text messages, emails, and mobile application notifications—together with a user-friendly display interface. The system is also endowed with Internet of Things (IoT) capabilities, offering users the convenience of real-time monitoring and control through a web dashboard and a dedicated mobile application. Moreover, the device boasts the ability to monitor environmental parameters like temperature and humidity, providing a holistic view of indoor air conditions. Key results indicate a significant improvement in detection response time and accuracy, with the enhanced communication features facilitating swift and effective alerts to users. These advancements mark a stride toward safer indoor environments, potentially mitigating the risks associated with poor air quality.

Selective Detection of Toxic C1 Chemicals Using a Hydroxylamine-Based Chemiresistive Sensor Array.

Formaldehyde (FA) is a deleterious C1 pollutant commonly found in the interiors of modern buildings. C1 chemicals are generally more toxic than the corresponding C2 chemicals, but the selective discrimination of C1 and C2 chemicals using simple sensory systems is usually challenging. Here, we report the selective detection of FA vapor using a chemiresistive sensor array composed of modified hydroxylamine salts (MHAs, ArCH2ONH2·HCl) and single-walled carbon nanotubes (SWCNT). By screening 32 types of MHAs, we have identified an ideal sensor array that exhibits a characteristic response pattern for FA. Thus, trace FA (0.02-0.05 ppm in air) can be clearly discriminated from the corresponding C2 chemical, acetaldehyde (AA). This system has been extended to discriminate methanol (C1) from ethanol (C2) in combination with the catalytic conversion of these alcohols to their corresponding aldehydes. Our system offers portable and reliable chemical sensors that discriminate the subtle differences between C1 and C2 chemicals, enabling advanced environmental monitoring and healthcare applications.

Development of a Digital Twin of a Local Road Network: A Case Study

Abstract Virtual replicas of infrastructure can be used to run simulations and optimize the construction, management, and maintenance of such assets throughout their entire lifecycle. These digital twins (defined as integrated multi-physics, multiscale, and probabilistic simulations of a complex product) mirror the behavior and environmental responses of its corresponding twin. Digital reconstruction techniques using optical sensor technologies and mobile sensor platforms are providing viable, low-cost alternatives to develop digital twins of physical infrastructure. In previous work, the digital twinning of asphalt pavement surfacings using visual simultaneous localization and mapping and the initiation of a digital twin of a local road network were investigated and successfully demonstrated. In this article, the further development of the concept, incorporating road surface temperatures collected over a 1-month period, as well as potential inferences based on these data, in the micro- and macro-twinning of a local road, are discussed. Light detection and ranging, unmanned aerial vehicles, and traffic counting artificial intelligence allows for quantification of the road geometry and infrastructure utilization over large areas (macro-twinning), whereas the photogrammetric reconstruction technique based on a neural network, a proprietary environmental condition sensor (SNOET, or SNiffing Omgewing / Environmental Tester) and commercial temperature sensors were used to acquire the surface texture and environmental conditions respectively (micro-twinning), as well as surface temperatures at four locations and different surfacing materials. The combination of advanced environmental monitoring data, physical data, and surface temperature data provide management data that can assist in the maintenance of such roads. This article expands (with the permission of the conference organizers) on a GeoChina 2021 article through the addition of further temperature data collected on the discussed digital twin, with substantial additional data analysis and discussion.

Spatial point patterns generation on remote sensing data using convolutional neural networks with further statistical analysis

Continuous technological growth and the corresponding environmental implications are triggering the enhancement of advanced environmental monitoring solutions, such as remote sensing. In this paper, we propose a new method for the spatial point patterns generation by classifying remote sensing images using convolutional neural network. To increase the accuracy, the training samples are extended by the suggested data augmentation scheme based on the similarities of images within the same part of the landscape for a limited observation time. The image patches are classified in accordance with the labels of previously classified images of the manually prepared training and test samples. This approach has improved the accuracy of image classification by 7% compared to current best practices of data augmentation. A set of image patch centers of a particular class is considered as a random point configuration, while the class labels are used as marks for every point. A marked point pattern is regarded as a combination of several subpoint patterns with the same qualitative marks. We analyze the bivariate point pattern to identify the relationships between points of different types using the features of a marked random point pattern.

Environmental Monitoring for Closed Robotic Workcells Used in Aseptic Processing: Data to Support Advanced Environmental Monitoring Strategies

The human operator is acknowledged as the greatest potential source of contamination in aseptic processing. To avoid contamination, barrier systems have progressively reduced the amount of human intervention in the critical zone. This study extends the trajectory of enhanced patient safety through the elimination of human intervention in aseptic filling. Eight companies that are users of closed robotic workcells have aggregated their usage data from 2018 to 2021. The study analyzes the critical design elements and performance of the Cytiva SA25 Aseptic Filling Workcell. The SA25 is a standardized, fully closed robotic system for aseptic filling of vials, syringes, and cartridges that eliminates operator intervention in the critical zone. The standardized design means that the system is not modified to suit a particular application and the same environmental monitoring strategy can be used across different installations. The SA25 provides significantly increased sterility assurance when producing sterile injectables. Users have observed non-viable particle levels well within ISO 5/Grade A air requirements, with extremely low probabilities of entering a dosage container. There have been zero cases of microbial growth in more than 250 media fills and good manufacturing practice (GMP) batches. Across all dosage formats and sizes, the aseptic process is repeatable, with more than 99.3% of units meeting acceptance criteria. These data demonstrate that eliminating risk through design is successful in the SA25, with an improved aseptic process in comparison to filling systems using Restricted Access Barrier Systems (RABS) or isolators with glove ports. One of the contributing companies to this article achieved an industry first. The U.S. FDA-approved commercial production of their biologic drugs without the requirement for routine viable environmental monitoring (EM), requiring viable EM only during process simulations. Based on the data presented and planned future research, new regulatory consideration should be made for closed robotic workcells to ensure that regulations meant for previous technologies with different risk profiles are not inappropriately applied.Graphical abstract

Advancement of Environmental Monitoring System Using IoT and Sensor: A Comprehensive Analysis

<abstract> <p>The emergence of the Internet of Things (IoT) has brought a revolution in global communication network technology. It has acquired many day-to-day applications in healthcare, education, agriculture, etc. In addition, IoT has also had a significant impact in the field of environmental monitoring.The significant factors in a healthy environment are air quality, water pollution, and waste management, where the world's population can live securely. Monitoring is necessary for us to achieve global sustainability. As monitoring technology has advanced in recent years, environmental monitoring systems have evolved from essential remote monitoring to an advanced environment monitoring (AEM) system, incorporating Internet of Things (IoT) technology and sophisticated sensor modules.The present manuscript aims to accomplish a critical review of noteworthy contributions and research studies about environmental monitoring systems, which involve monitoring air quality, water quality, and waste management.The rapid growth of the world's population and the exhaustion of natural resources, coupled with the increasing unpredictability of environmental conditions, lead to significant concerns about worldwide food security, global warming, water pollution, and waste overflowing. Automating tasks in the building environment, based on the Internet of Things (IoT) application, is meant to eliminate problems with the traditional approach. This study aims to examine and evaluate numerous studies involving monitoring air, water, waste, and overall environmental pollution, as well as their effect on the environment. This article categorizes studies based on their research purposes, techniques, and findings. This paper examines advanced environmental monitoring systems through sensor technology, IoT, and machine learning.</p> </abstract>

Western Faculty Profile: Dr. Irena Creed

Dr. Irena Creed is a Professor of Biology, Geography, and Earth Sciences at Western University. She is a Tier II Canada Research Chair in Watershed Sciences, as well as the founder of the Watershed Research Facility — an advanced environmental monitoring, analytical, and modelling facility at Western. With a focus on developing a predictive understanding of watershed systems, Dr. Creed conducts research at sites around the world. WURJHNS First Year Representative Janis Chang had the opportunity to interview Dr. Creed and learn more about her research and her career.

역행렬 알고리즘을 이용한 다파장 라만 라이다 데이터의 고도별 에어로졸 Microphysical Parameter 도출

Vertical distribution and optical properties of atmospheric aerosols above the Korean peninsula are quite important to estimate effects of aerosol on atmospheric environment and regional radiative forcing. For the first time in Korea, vertical microphysical properties of atmospheric aerosol obtained by inversion algorithm were analyzed based on optical data of multi-wavelength Raman lidar system developed by the Advanced Environmental Monitoring Research Center (ADEMRC), Gwangju Institute Science and Technology (GIST). Data collected on 14 June 2004 at Gwangju (<TEX>$35.10^{\circ}N,\;126.53^{\circ}E$</TEX>) and 27 May 2005 at Anmyeon island (<TEX>$36.32^{\circ}N,\;126.19^{\circ}E$</TEX>) were used as raw optical data for inversion algorithm. Siberian forest fire smoke and local originated haze were observed above and within the height of PBL, respectively on 14 June 2004 according to NOAA/Hysplit backstrajectory analysis. The inversion of lidar optical data resulted in particle effective radii around <TEX>$0.31{\sim}0.33{\mu}m$</TEX>, single scattering albedo between <TEX>$0.964{\sim}0.977$</TEX> at 532 nm in PBL and effective radii of <TEX>$0.27{\mu}m$</TEX> and single scattering albedo between <TEX>$0.923{\sim}0.924$</TEX> above PBL. In the case on 27 May 2005, biomass burning from east China was a main source of aerosol plume. The inversion results of the data on 27 May 2005 were found to be particle effective radii between <TEX>$0.23{\sim}0.24{\mu}m$</TEX>, single scattering albedo around <TEX>$0.924{\sim}0.929$</TEX> at 532 nm. Additionally, the inversion values were well matched with those of Sun/sky radiometer in measurement period.

高度環境監視システムによる汚染物質の早期検出

Groundwater contamination often occurs because of discharge, leakage or spillage of industrial chemicals. This type of contamination must be prevented because high-tech industries should be able to procure the required amounts of high-quality groundwater for their manufacturing processes. It is now recognized that the reduction of negative external effects of manufacturing is not only necessary for ecological sustainability, but also for economic sustainability. The objective of the Advanced Environmental Monitoring System (AEMS) is to develop effective systems for the fast and continuous monitoring of groundwater contamination around industrial facilities. The AEMS will identify the contaminants and locate their sources, thus enabling users to take appropriate measures to prevent the contamination from spreading. It can also be used as a tool capable of assessing environmental hazards caused by contaminated groundwater by rapidly and efficiently identifying the toxic contaminants. In this paper, we present the state of soil and groundwater contamination in Japan, the necessity of groundwater monitoring, the state of monitoring apparatus, and the outline of the AEMS project. The AEMS can be used not only for the leakage monitoring of industrial chemicals but also for risk management in such fields as waste disposal landfill or in bioremediation sites that require long and real-time monitoring.

R08-(8) 生産地域における高度環境監視システムの研究

R08-(8) 生産地域における高度環境監視システムの研究