Environmental Monitoring Platform Research Articles

A pH visual sensing platform based on dual-emission chiral carbon dots for discrimination of normal/cancer cells and monitoring food freshness

Accurate detection of pH is important in pathological processes and food freshness. Developing sensors of sensitive response and visualization for pH is highly demanded. In this work, Chiral carbon dots(CCDs) was synthesized via one-pot hydrothermal process using o-phenylenediamine and L-Tryptophan, which displayed circular dichroism (CD) signals at 200–255 nm and 255–300 nm. The CCDs exhibited dual-emission peaks with blue and red emission bands when excited at 360 nm. The ratiometric signals of UV–vis absorption and fluorescence intensity of L-CDs were responsive over the pH range of 2.0 to 11.0 with significant color changes in solution. Fluorescence imaging of live cells displayed different signals related to pH in both the blue and red channels, allowing accurate measurement of the pH of the cellular environment. Furthermore, the pH test paper based on L-CDs enabled monitoring the freshness of shrimp and pork under 365 nm UV light. Therefore, L-CDs provided a multifunctional visual pH sensing platform for environmental monitoring and biosensing.

Yeast Surface-Displayed Quenchbody as a Novel Whole-Cell Biosensor for One-Step Detection of Influenza A (H1N1) Virus.

Timely surveillance of airborne pathogens is essential to preventing the spread of infectious diseases and safeguard human health. Methods for sensitive, efficient, and cost-effective detection of airborne viruses are needed. With advances in synthetic biology, whole-cell biosensors have emerged as promising platforms for environmental monitoring and medical diagnostics. However, the current design paradigm of whole-cell biosensors is mostly based on intracellular detection of analytes that can transport across the cell membrane, which presents a critical challenge for viral pathogens and large biomolecules. To address this challenge, we developed a new type of whole-cell biosensor by expressing and displaying VHH-based quenchbody (Q-body) on the surface of the yeast Saccharomyces cerevisiae for simple one-step detection of influenza A (H1N1) virus. Seventeen VHH antibody fragments targeting the hemagglutinin protein H1N1-HA were displayed on the yeast cells and screened for the H1N1-HA binding affinity. The functionally displayed VHHs were selected to create surface-displayed Q-body biosensors. The surface-displayed Q-body exhibiting the highest quenching and dequenching efficiency was identified. The biosensor quantitatively detected H1N1-HA in a range from 0.5 to 16 μg/mL, with a half-maximal concentration of 2.60 μg/mL. The biosensor exhibited high specificity for H1N1-HA over other hemagglutinin proteins from various influenza A virus subtypes. Moreover, the biosensor succeeded in detecting the H1N1 virus at concentrations from 2.4 × 104 to 1.5 × 107 PFU/mL. The results from this study demonstrated a new whole-cell biosensor design that circumvents the need for transport of analytes into biosensor cells, enabling efficient detection of the target virus particles.

Artificial Intelligence‐Enhanced “Photonic Nose” for Mid‐Infrared Spectroscopic Analysis of Trace Volatile Organic Compound Mixtures

AbstractMolecular identification of volatile organic compounds (VOCs) plays an important role in various applications including environmental monitoring and smart farming. Mid‐infrared (MIR) fingerprint absorption spectroscopy is a powerful tool to extract chemical‐specific features for gas identification. However, the detection and recognition of trace VOC gas mixtures remain challenging due to their intrinsic weak light–matter interaction and highly overlapped absorption spectra. Here, an artificial intelligence‐enhanced “photonic nose” for MIR spectroscopic analysis of trace VOC gas mixtures is proposed. To enhance the sensing performance by increasing bandwidth and sensitivity, the “photonic nose” is designed to employ coupled multi‐resonant plasmonic nanoantennas to cover MIR molecular fingerprints, coated with metal–organic frameworks as the gas enrichment layer. Low limits of detection are achieved (IPA: 1.99 ppm, ethanol: 3.43 ppm, and acetone: 9.82 ppm). With machine learning, a high classification accuracy of 100% is realized for 125 mixing ratios (IPA, ethanol: both 5 concentrations, 0–130 ppm; acetone: 5 concentrations, 0–201 ppm), and low‐deviation component concentration predictions of root‐mean‐squared error within 10 ppm are achieved for IPA and ethanol (both 0–130 ppm) under interference from 50 ppm acetone. The work paves the way for intelligent sensing platforms for environmental monitoring and smart framing.

Host-Guest Interaction Mediated Perovskite@Metal-Organic Framework Z-Scheme Heterojunction Enabled Paper-Based Photoelectrochemical Sensing.

Exploring the high-performance photoelectronic properties of perovskite quantum dots (QDs) is desirable for paper-based photoelectrochemical (PEC) sensing;however, challenges remain in improving their stability and fundamental performance. Herein, a novel Z-scheme heterostructure with host-guest interaction by the confinement of CH3NH3PbBr3 QDs within Cu3(BTC)2 metal-organic framework (MOF) crystal (MAPbBr3@Cu3(BTC)2) is successfully constructed on the paper-based PEC device for ultrasensitive detection of Ochratoxin A (OTA), with the assistance of the exciton-plasmon interaction (EPI) effect. The host-guest interaction is estabilished by encapsulating MAPbBr3 QDs as guests within Cu3(BTC)2 MOF as a host, which prevents MAPbBr3 QDs from being damaged in the polar system, offering access to long-term stability with high-performance PEC properties. Benefiting from the precise alignment of energy levels, the photogenerated charge carriers can migrate according to the Z-scheme charge-transfer pathway under the driving force of the internal electric field, achieving a high photoelectric conversion efficiency. Upon OTA recognition, the EPI effect is activated to modulate the exciton response in MAPbBr3 QDs by accelerating radiative decay, finally achieving sensitive OTA sensing with a detection limit of 0.017 pg mL-1. We believe this work renders new insight into designing host-guest Z-scheme heterojunctions in constructing the paper-based PEC sensing platforms for environmental monitoring.

Packaging performance evaluation and freshness intelligent prediction modeling in grape transportation

Vibrations during transportation inevitably lead to mechanical damage, endangering grape freshness and directly impacting their economic worth. While adequate packaging serves as a viable solution, current studies on packaging efficacy lack depth. Moreover, conventional methods for forecasting fruit freshness fail to accommodate the varying freshness levels of grapes across different packaging techniques. Consequently, a novel approach for predicting fruit freshness leveraging multi-sensing technology and machine learning algorithms is introduced. By reasonably evaluating packaging performance, the automation, intelligence, and accuracy of fruit freshness prediction are enhanced. Initially, critical control points in grape supply chain logistics were scrutinized using the HACCP method to identify key environmental parameters (vibration, temperature, and humidity) and their interaction with grape freshness. Subsequently, an environmental monitoring platform was devised for the grape supply chain, facilitating environmental surveillance under distinct packaging types (corrugated carton, foam box, plastic box, and inflatable package). Through a blend of environmental monitoring outcomes and physical-chemical indicators, the protective efficacy of diverse transport packaging was meticulously analyzed and appraised alongside finite element analysis. Notably, environmental data proved capable of characterizing grape freshness in lieu of quality data, with vibration metrics exhibiting strong correlations with quality metrics. Machine learning models were developed to predict grape freshness based on environmental cues, yielding prediction accuracies of 92.512% (SVM) and 94.334% (GA-ANN). The automated, non-destructive data acquisition and novel machine learning approaches offer a fresh avenue for evaluating packaging, predicting freshness, and managing food quality within grape logistics operations.

Imperceptible augmentation of living systems with organic bioelectronic fibres

The functional and sensory augmentation of living structures, such as human skin and plant epidermis, with electronics can be used to create platforms for health management and environmental monitoring. Ideally, such bioelectronic interfaces should not obstruct the inherent sensations and physiological changes of their hosts. The full life cycle of the interfaces should also be designed to minimize their environmental footprint. Here we report imperceptible augmentation of living systems through in situ tethering of organic bioelectronic fibres. Using an orbital spinning technique, substrate-free and open fibre networks—which are based on poly (3,4-ethylenedioxythiophene):polystyrene sulfonate—can be tethered to biological surfaces, including fingertips, chick embryos and plants. We use customizable fibre networks to create on-skin electrodes that can record electrocardiogram and electromyography signals, skin-gated organic electrochemical transistors and augmented touch and plant interfaces. We also show that the fibres can be used to couple prefabricated microelectronics and electronic textiles, and that the fibres can be repaired, upgraded and recycled.

Plasticizer-based and polymer-free ion-selective optodes on cellulose paper

Paper-based ion-selective optodes (ISOs) allow for low-cost ion measurements using widely accessible optical detectors such as smartphones, representing an attractive analytical platform for medical testing and environmental monitoring. Previously reported paper-based ISOs rely on plasticized PVC membranes or PVC/plasticizer-free adsorption layers containing hydrophobic sensing chemicals. In this paper, we studied a new format of paper-based ISOs, in which a plasticizer phase containing sensing chemicals is deposited onto cellulose paper without using a hydrophobic polymer like PVC. Compared to ISOs without plasticizers and polymers, the plasticizer-based ISOs show much higher pH sensitivity when using chromoionophore I and tetrakis[3,5-bis(trifluoromethyl)phenyl]borate as the sensing chemicals. When the sensing layer further contains a solid ionophore with high molecular rigidity, the plasticizer-based ISOs for cations also exhibit enhanced sensitivity than the plasticizer-free ISOs. However, when the ionophore is a liquid at room temperature or has a long alkyl chain, the plasticizer-based and plasticizer-free ISOs have comparable responses because the ionophore behaves as a pseudo-plasticizer. Compared to ISOs with both plasticizers and polymers, the elimination of hydrophobic polymers like PVC makes inkjet printing more efficient and preserves the capillary action of the porous paper. By using the newly launched Samba cartridge with smaller nozzles, the plasticizer-based and polymer-free optodes for sodium, potassium, and calcium ions are prepared by inkjet printing for the first time. In an example application, the inkjet-printed plasticizer-based and polymer-free ISOs are used for colorimetric measurements of urine calcium.

Food and environmental safety monitoring platform based on Tb(III) functionalized HOF hybrids for ultrafast detection of thiabendazole and 2-chlorophenol

Development of efficient and intelligent method for detecting harmful agrochemicals in resource-limited settings remains an urgent need to ensure food and environmental safety. Herein, a novel dual-emitting Tb3+-modified hydrogen-bonded organic framework (Tb@TBTC, TBTC is the ligand of HOF-TBTC.) with visible green fluorescence has been prepared through coordination post-synthetic modification. Tb@TBTC can be designed as a fluorescence sensor for the identification of two harmful carcinogenic pesticides, thiabendazole (TBZ) and 2-chlorophenol (2-CP) with high sensitivity, high efficiency and excellent selectivity. Tb@TBTC can also adsorb 2-CP with high adsorption rate. In realistic fruit juice and river water samples, the detection limits of Tb@TBTC toward TBZ and 2-CP are as low as 2.73 μM and 2.18 μM, respectively, demonstrating the feasibility in practical application. Furthermore, an intelligent real-time and on-site monitoring platform for 2-CP detection is constructed based on Tb@TBTC-agarose hydrogel films with the assistance of back propagation neural network, which can efficiently and accurately determine the concentration of 2-CP from fluorescence images through human-machine interaction. This work presents a facile pathway to prepare Tb@HOF fluorescent sensor for food and ecological environment safety, which is highly promising for preventing human disease and improving global public health.

Research on inspection route of hanging environmental robot based on computational fluid dynamics

The environment of a closed piggery is commonly characterized by spatial unevenness, and there are currently no specific standards for installation points of various environmental monitoring sensors. Therefore, the project team used the hanging track inspection robot (HTIR) as an environmental mon-itoring platform to seek the environmental monitoring points and ensure the scientific layout of moni-toring points. Ansys-CFD software was used to study the change rules of environmental parameters at 1.6 m (α plane), 0.7 m (β plane), and 0.4 m (γ plane) above the ground. The 300 monitoring points ((x1~x30) ×(y1~y10)) in each plane were analyzed to determine the most suitable monitoring points and inspection routes for HTIR. The results showed that: (1) All monitoring points could be arranged directly below the y3 track. (2) Monitoring points (x1, y3), (x10, y3) and (x30, y3) were environmental feature points. At (x1, y3), the maximum relative humidity and NH3 concentration on the α plane could be detected, and the maximum wind speed, maximum temperature, and maximum NH3 concentration on other planes could also be detected; At (x10, y3), the minimum temperature and maximum relative humidity of the β and γ planes could be detected; At (x30, y3), the maximum NH3 concentration in the α plane and the minimum relative humidity in all planes could be detected. This study scientifically arranged the inspection track and monitoring points for HTIR, improved the accuracy of environmental monitoring, and put forward suggestions for reducing NH3 concentration in closed piggeries, laying the foundation for the next step.

The ‘obligatory passage point’ in knowledge co-production: Italy’s participatory environmental monitoring platform

ABSTRACT The process of developing of a participatory environmental monitoring network in Italy, unfolded between 2013 and 2020, is analysed in order to explore the dynamics of knowledge co-production involving collaboration between scientists and non-experts, in the present case constituted by a community of weather amateurs and practitioners. Based on a qualitative approach and 15 in-depth interviews with different stakeholders involved in the early development of the pilot network, the analysis focuses on the dynamics of collaboration between the actors involved that led to the creation of the Italian participatory environmental monitoring platform. For the analysis, we adopt the theoretical model of ‘translation' proposed by Michel Callon (1984), which focuses on the dynamics of the emergence of a network of collaboration between scientists and other heterogeneous actors. In particular, we focus on the notion of ‘obligatory passage point’ (OPP) along the translation process. Focusing on the process of translation and the dynamics that characterise the convergence towards a common OPP in the processes of network constitution and knowledge production highlights some crucial dynamics that support the unfolding of effective forms of participatory co-production, including: the understanding of how the roles and identities of different actors are recognised, transformed, productively aligned and consolidated; the performative power of participatory processes that are able to redefine and transform the pre-existing identities and roles of actors; and the outcome of the epistemic inclusion of practices and knowledge of less powerful actors within institutional, political and scientific frameworks.

Инструментальные компоненты цифровой платформы экологического мониторинга Байкальской природной территории

Статья посвящена проблемам взаимодействия сервисов, создаваемых разными разработчиками при формировании информационно-аналитических систем поддержки научных исследований. Для унификации параметров вычислительных сервисов предложены требования и рекомендации к создаваемым сервисам. Рассмотрены компоненты разработанной авторами цифровой платформы экологического мониторинга Байкальской природной территории, обеспечивающие алгоритмизированное взаимодействие всех участников исследований на основе композиций сервисов. To implement digital environmental monitoring of the Baikal natural territory, a digital platform has been developed at IDSTU SB RAS. Researchers from 16 scientific institutes, as well as employees of departmental organizations are collaborators of the project that provides support for a comprehen- sive analysis of monitoring data, forecasting and development of recommendations for the conservati- on of Lake Baikal and surrounding areas. The CPU is implemented on the basis of a service- oriented architecture that provides the ability to create services for collecting, presenting and processing data to all research participants. New services can be formed, among other things, based on a composition of services previously created by various platform participants. The article discusses the problems of interaction between services created by different developers. To unify the parameters of computing services, requirements and recommendations for the services being created are proposed. The components of the digital platform for environmental monitoring of the Baikal Natural Territory, developed by the authors, are considered, providing algorithmic interaction of all research participants based on service architecture. The approach proposed by the authors allows partially solving the problem of unifying services according to the types and formats of input and output data and the appropriate sources. The developed set of software components is actively used in practice. More than 200 data services and more than 40 data processing services have been created.

An Integrated Sensorized Platform for Environmental Monitoring in Healthcare

An Integrated Sensorized Platform for Environmental Monitoring in Healthcare

Predesigned covalent organic framework with sulfur coordination: Anchoring Au nanoparticles for sensitive colorimetric detection of Hg(II)

Targeted construction of new covalent organic frameworks (COFs) with specific purposes and rationalities to build colorimetric assay platform for environmental pollutant monitoring have attracted increasing interest. However, it is still challenging due to lack of available coordination sites inside COFs pores and only a slight bonding ability for anchoring metal. In this work, a two-dimensional (2D) COFs (termed as Tz-COF) with high crystallinity, excellent chemical stability, and abundant sulfur coordination in its skeletons was synthesized and used for the confined growth of Au NPs. It was found that the Au NPs showed significant dispersibility for the support of Tz-COF. The proposed Tz-COF@Au NPs possessed outstanding Hg2+-activated peroxidase-like activity benefited from physicochemical properties of gold amalgam and synergistic effect between COFs and Au NPs to oxidize chromogenic substrate. Based on highly efficient activity and distinctive color evolution, the strategy for detecting Hg2+ was developed and successfully applied to determine the content of Hg2+ in real environmental samples. This work manifests that a potential strategy to establish a colorimetric assay platform for environmental pollutant monitoring based on the targeted manufacturing of novel COFs with specific functions.

Mathematical Model of Multispectral Data Processing for a Mobile Ecology Monitoring Platform

The purpose of research is a mathematical justification of the process of processing multispectral data in order to detect local environmental pollution zones with the possibility of classifying the pollutant. Methods. The fundamentals of the applied theory of stochastic systems based on equations for multidimensional characteristic functions and functionals are used as a basic mathematical apparatus. When determining a contaminant, a criterion reflecting the ability of objects obeying Lambert's law is used. To solve the problem of object classification, approaches using binary logistic regression are applied. Statistical methods of analysis were used to evaluate the results of the study. Results. The obtained partial mathematical models allow us to take into account many factors affecting mobile environmental monitoring platforms operating in automatic mode. Substantiate the possibility of remote analysis of local environmental pollution zones, with the possibility of determining pollutants such as hydrocarbons, phosphate ions, etc., as well as searching for unauthorized locations of construction and household garbage. They increase the accuracy characteristics by 1,3 times when determining the parameters of selected objects due to the processing of data obtained in various spectral ranges. They contribute to reducing the computational complexity of the classification algorithm by 1,1 times, taking into account the volume of input data in a limited spectral range and reducing the resolution of the reference object, while not affecting the accuracy of classification. Conclusion. A mathematical model has been developed for processing data and images obtained in several spectral ranges during the operation of a multispectral device for an autonomous mobile environmental monitoring platform, which makes it possible to identify objects in the field of view of the device from a mobile platform, to obtain a detailed image of working scene objects with spatial reference relative to the coordinate system used, a distinctive feature of which is to increase the accuracy of calculating the coordinates of local zones pollution, and increasing the reliability of the classification of objects based on the characteristics of diffusive reflectivity in various spectral ranges.

Framework for assessing the sustainability of ESG performance in industrial cluster ecosystems in a circular economy

The study conceptualizes the ecosystem of an industrial cluster based on circular economy as an umbrella for co-evolving concepts, such as circular economy, cluster economy and industrial ecology (also comprising the concepts of industrial symbiosis and its subcategory, industrial mutualism). We propose an algorithm for estimating the circularity index in an industrial cluster ecosystem as a metric of sustainable ESG performance. The algorithm consists in calculating the integral circularity index of an industrial cluster ecosystem and its inverse, the circular gap. The algorithm developed relies on calculating the estimates for four projections: waste and emissions; consumption efficiency; resource efficiency; investments in environmental protection. We then formulated the key drivers of successful ESG strategies in circular industrial ecosystems, also providing metrics, evidence-based data and measurements of the progress in bridging the circularity gap. We used statistical data available from the Rosstat SDG Platform, Platform for Intelligent Environmental Monitoring, Our World in Data, and Food and Agriculture Organization. The approach was validated for an industrial cluster ecosystem based on the circular economy model, evolving in the Russian Federation from 2012 to 2021. The practical significance of our findings is that the theoretical framework, along with the insights gained and the recommendations developed, can be used for integrating the policies based on sustainable development, environmental management, ESG transformation, circular economy, closed-loop economy for industrial ecosystems. The results obtained can serve for constructing strategies, policies, programs, roadmaps and business models for circular industrial ecosystems at the macro-, meso- and micro-levels.

Development of a Web-GIS Platform for Environmental Monitoring and Conservation of the Muringato Catchment in Kenya

Development of a Web-GIS Platform for Environmental Monitoring and Conservation of the Muringato Catchment in Kenya

A copula-based method of risk prediction for autonomous underwater gliders in dynamic environments.

Autonomous underwater gliders (AUGs) are effective platforms for oceanic research and environmental monitoring. However, complex underwater environments with uncertainties could pose the risk of vehicle loss during their missions. It is therefore essential to conduct risk prediction to assist decision making for safer operations. The main limitation of current studies for AUGs is the lack of a tailored method for risk analysis considering both dynamic environments and potential functional failures of the vehicle. Hence, this study proposed a copula-based approach for evaluating the risk of AUG loss in dynamic underwater environments. The developed copula Bayesian network (CBN) integrated copula functions into a traditional Bayesian belief network (BBN), aiming to handle nonlinear dependencies among environmental variables and inherent technical failures. Specifically, potential risk factors with causal effects were captured using the BBN. A Gaussian copula was then employed to measure correlated dependencies among identified risk factors. Furthermore, the dependence analysis and CBN inference were performed to assess the risk level of vehicle loss given various environmental observations. The effectiveness of the proposed method was demonstrated in a case study, which considered deploying a Slocum G1 Glider in a real water region. Risk mitigation measures were provided based on key findings. This study potentially contributes a tailored tool of risk prediction for AUGs in dynamic environments, which can enhance the safety performance of AUGs and assist in risk mitigation for decision makers.

Self-assembly and phase transition of gold nanoclusters in natural deep eutectic solvent for visual detection of toxicants in water environment

Ecological damage derived from water pollution has increased greatly accompanying by the development of industries, which promotes the exploration of water environment monitoring platform. Structure-oriented synthesis of gold nanoclusters (NADES-Au NCs) based on novel natural deep eutectic solvent (NADES), has been successfully obtained without the use of any stabilizer or capping agents. With the pH stability and photobleaching resistance in the complex environment, spongy NADES-Au NCs showed superior optical properties than other forms. Moreover, this green sensing platform based on NADES-Au NCs displayed two well-separated emission peaks at 440 and 669 nm, respectively. There was a linear range of 0.5–200 µM with a detection limit of 0.25 µM for Hg2+ and a linear range of 1–150 µM with a detection limit of 0.12 µM for nitroanilines. Such NADES-Au NCs demonstrated the satisfactory recoveries and reliabilities in water samples, which presented great potential of water pollution detection. Simultaneously, the application of NADES as a green solvent for the synthesis of metal nanoclusters may provide new avenues for sensing platform.

Embedded fog models for remote aquatic environmental monitoring

Fog computing is used in Internet of Things (IoT) applications to allow end node processing and storage to facilitate device autonomy and other benefits in light of unstable/unreliable cloud network connections. This paper investigates how a cloud-based IoT remote aquatic environmental monitoring platform can be adapted using a fog architectural approach to provide embedded condition assessment and forecasting functionality. An equivalent IoT hydroponic garden water quality monitoring test platform is developed which is capable of accommodating embedded fog models using a node-level microcontroller (i.e., ESP8266). The test platform is deployed in a hydroponics setting to gather environmental data and evaluate the feasibility of fog intelligence functionality on an embedded system. The first model involves a node-level Fuzzy Logic water quality condition assessment using the current sensor readings. The second model is a Support Vector Machine that makes short-term predictions of water quality parameters based on a history of locally stored sensor readings. The third model contrasts Long Short-Term Memory for water quality parameter prediction. The fog test platform and embedded models are evaluated according to performance, functionality and ease-of-implementation on a microcontroller. Finally, we propose a framework detailing the future modifications for the remote aquatic environmental monitoring platform to achieve embedded fog capabilities based on the experience with the hydroponics test platform.

Real-Time Environmental Monitoring Platform for Wellness and Preventive Care in a Smart and Sustainable City with an Urban Landscape Perspective: The Case of Developing Countries

Smart and sustainable communities seek to ensure comfortable and sustainable quality of life for community residents, the environment and the landscape. Pollution is a key factor affecting quality of life within a community. This research provides a detailed insight into a successfully developed and deployed framework for an environmental monitoring platform for an urban study to monitor, in real time, the air quality and noise level of two cities of the Dominican Republic—Santo Domingo and Santiago de Los Caballeros. This urban platform is based on a technology range, allowing for the integration of multiple environmental variables related to landscape and providing open data access to urban study and the community. Two case studies are presented: The first highlights how the platform can be used to understand the impact a natural event, for example, how dust landscapes (such as the Sahara) impact a community and the actions that can be taken for wellness and preventive care. The second case focuses on understanding how policies taken to prevent the spread of COVID-19 affect the air quality and noise level of the landscape and community. In the second case, the platform can be used to expand the view of decision makers in the urban landscape and communities that are affected.