Environmental Monitoring Platform Research Articles

Imperceptible augmentation of living systems with organic bioelectronic fibres

AbstractThe 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.

A Solar-Rechargeable Radiation Dosimeter Design for Radiation Hazard Zone Located with LoRa Network

Since the nuclear energy leakage that occurred at the Fukushima nuclear power plant in Japan, people have paid more attention to the danger of environmental radiation. Environmental radiation is monitored using Geiger counters, which are not easy to obtain in some areas. Therefore, this research proposes an open-source and low-cost handheld Geiger counter that uses solar energy to charge lithium-ion batteries. Our design can provide a low-cost environmental radiation monitoring platform and effectively enhance the public’s scientific education awareness of radiation hazards. The measured dose rate can be output through the serial port, allowing a LoRa wireless network to transmit data to a database. When the sensing network deployed by the radiometer detects that the radiation value of the area is abnormally increased, it can issue an alarm to the government for the first time. Moreover, the low-power radiometer design can reduce energy consumption, reduce the burden on the ecological environment caused by the deployment of the sensing network, and provide sustainability for the environment.

Residential Environment Pollution Monitoring System Based on Cloud Computing and Internet of Things.

In order to solve the problems of single monitoring factor, weak comprehensive analysis ability, and poor real time performance in traditional environmental monitoring systems, a research method of residential environment pollution monitoring system based on cloud computing and Internet of Things is proposed. The method mainly includes two parts: an environmental monitoring terminal and an environmental pollution monitoring and management platform. Through the Wi-Fi module, the data is sent to the environmental pollution monitoring and management platform in real time. The environmental monitoring management platform is mainly composed of environmental pollution monitoring server, web server, and mobile terminal. The results are as follows. The data measured by the system is close to the data measured by the instrument, and the overall error is small. The measurement error of harmful gases is about 6%. PM 2.5 is about 6.5%. Noise is about 1%. The average time for sensor data update is 0.762 s. The average alarm response time is 2 s. The average data transfer time is 2 s. Practice has proved that the environmental pollution monitoring and alarm system operates stably and can realize real-time collection and transmission of data such as noise, PM 2.5, harmful gas concentration, illumination, GPS, and video images, providing a reliable guarantee for timely environmental pollution control.

Bridging environmental and biological monitoring: Constructing platform for hexavalent chromium detection and cancer-cells screening based on red fluorescent carbonized polymer dots

Intensive research on biocompatible carbon-based fluorescent nanomaterials provides a brand-new platform for environmental and biological monitoring, but its applicability is limited by the single signal response, unfriendly synthesis route, or rare red fluorescence performance. In this study, we designed and prepared a new type of high-efficiency red fluorescent carbonized polymer dots (R-PDs) through rationally preferred raw materials. The proposed R-PDs with colorimetric and fluorescent dual-signal response to hexavalent chromium (Cr(VI)) can be obtained easily by hydrothermal treatment of o-hydroxybenzoic acid and p-phenylenediamine. A high-throughput smartphone-mediated scanometric analysis platform was constructed based on sensitive gray change, which was further applied for the rapid, simple, and low-cost detection of Cr(VI) in real environmental water. The fluorescent analysis mode exhibits high sensitivity with the limit of detection 20 nM, thereby realizing the Cr(VI) imaging in living cells and aquatic species. More importantly, R-PDs exhibited excellent nucleolus targeting capacity by virtue of their inherent high nitrogen content, thus successfully screening normal and cancerous cells. Encouraged by the simplicity of synthesis and availability of constructed analysis platform, the proposed R-PDs has great potential applications in environmental monitoring, biosensing and biomedical.

Plasmonic sensing of alkaline phosphatase and its inhibitors through Ag(I)-mediated assembly of gold nanoparticles

Alkaline phosphatase (ALP) is of extraordinary importance in living organisms and widely serves as a labeling enzyme in biochemical assays. Conventional methods based on the colorimetric readout of chemicals have issues in the photosensitivity and the difficulty in visibly distinguishing the color intensity. Herein, we developed a plasmonic assay for detecting ALP activity and its inhibitors based on gold nanoparticles (AuNPs) with good stability and naked-eye readout. Ag(I) specifically oxidizes TMB to produce positively charged oxTMB, which electrostatically aggregates negatively charged AuNPs. ALP as an enzyme specifically hydrolyzes a substrate to generate ascorbic acid, which reduces Ag(I) and modulates the aggregation of AuNPs. This plasmonic ALP assay has a linear detection range of 5–50 mU/mL with a limit of detection of 0.39 mU/mL. In combination with a smartphone, it enables monitoring ALP inhibitors including dimethoate and arsenic that are environmental contaminants. This plasmonic sensing strategy with good stability, visible readout, high specificity, low cost, and ease of operation provides a promising platform for enzyme detection and environmental monitoring.

Colorimetric/fluorescent/SERS triple-readout sensing of Mercury(Ⅱ) via in-situ growth of AgNPs in phenanthroline-functionalized covalent organic framework

Targeted design of new covalent organic frameworks (COFs) with specific functions to build multi-readout platform for environmental pollutant monitoring has been gaining increasing interest. Herein, a fluorescent phenanthroline-functionalized COF (PA-COF) with favorable pore structure and reactive nitrogen-containing functional groups is designed and synthesized, and in-situ growth of silver nanoparticles (AgNPs) in PA-COF is achieved to produce PA-COF-based nanocomposite (PA-COF@AgNPs). Phenanthroline facilitates the anchoring of Ag+ in the skeleton of COF material via strong coordination interaction, and the confinement of in-situ grown AgNPs on PA-COF structure provides excellent dispersibility. The formed silver amalgam in the skeleton of PA-COF@AgNPs with the presence of Mercury(II) can oxidize colorless 3,3′,5,5′-tetramethylbenzidine (TMB) to blue product (oxTMB) quickly, inducing obvious absorbance/SERS signal enhancement and fluorescence quenching of PA-COF@AgNPs. A colorimetric/fluorescence/SERS triple-readout strategy is thus developed for Hg2+ sensing with high sensitivity and excellent accuracy. The present study offers a promising strategy to develop a multi-readout assay platform for monitoring hazardous pollutants based on the targeted fabricating of novel COFs with specific functions.