Environmental Monitoring Technology Research Articles

Big data technologies in environmental monitoring

The article deals with the big data technologies currently used for environmental monitoring. The processes of accumulation and preservation of super-large data sets, technologies for working with them, and a paradigm shift in science are presented. A predictive function of big data has been identified that allows effective use of this technology in environmental monitoring. A general idea of environmental monitoring as a complex system of observations, assessment and forecast of changes in the environmental state has been formed. The main areas of environmental monitoring are identified. As an example, the activity of collecting, processing and storing big data in the framework of the strategic project “Geographic Information system “Yenisei-Arctic”, implemented by the employees of Reshetnev Siberian State University of Science and Technology. The main tasks and results of the project are presented. Among the main results of the project, the formation of a research direction in the field of big data and the involvement of young scientists and students in promising research that can prepare the necessary information for the needs of environmental monitoring is highlighted. It is proved that big data technologies can raise environmental monitoring to a whole new level.

Field assessment of sensor technology for environmental monitoring using a process-based soil gas method at geologic CO2 storage sites

A process-based approach to soil gas monitoring at geologic CO2 storage sites is an accurate, simple, and cost-effective alternative to other “concentration-based” soil gas methods which require complicated long-term baseline data collection and analysis. The current limitation to implementing a process-based method on an industrial scale (e.g. gigatons of storage) is a lack of technology for economical field-deployable smart data collection of all required gas concentrations (CO2, CH4, O2, N2). To address this gap, the Carbon Transport and Storage Corporation Pty Ltd (CTSCo) deployed commercially-available automated sensors at their proposed future Glenhaven CO2 storage site, located 21 km SW of the town of Wandoan, Queensland, Australia. The goal was to assess the suitability of currently-available sensors for implementing a process-based monitoring approach with the aim of providing real-time answers on source attribution of surface anomalies that could represent CO2 migration to surface. Here we report the results of a first-ever long-term (2 years) test of commercially available sensors for the purpose of process-based soil gas environmental monitoring. The accuracy and precision of data collected with an array of sensors for measuring CO2, CH4, and O2, with additional sensors for measuring temperature, relative humidity, and pressure were compared against the current method using gas chromatography. The results indicate that currently-available sensors are not adequate for attaining the long-term monitoring goals set forth by a process-based monitoring approach and additional technology development is recommended.

Application of synthetic benzoic acid technology in environmental radiocarbon monitoring

Environmental radiocarbon (14C) monitoring is gaining importance in China due to the recent rapid development of the nuclear industry. In this study, synthetic benzoic acid samples with the fingerprint of environmental 14C were generated by using the synthesis method, and the specific activity of 14C was determined using the liquid scintillation counting (LSC) method. The ultra-performance liquid chromatography (UPLC) analysis was performed in chemical properties test and results showed that the mean assay and yield of synthetic benzoic acid were 91.8% ± 2.4% and 84.5% ± 2.0%, respectively. Under adopted LSC counting conditions, the minimum detectable activity (MDA) of 64 Bq/kg C was achieved in our laboratory. The method's linearity was examined using a series of spiked samples, along with the 14C-free samples. The results showed a wide linear range up to 2000 Bq/kg C. Reproducible results were obtained from three batches of experiments with deviations in the intra-group and inter-group of 0.38%–3.06% and 1.24%–3.55%, respectively. Long-term evaluation of the system was found to be very stable (over 5 months storage) with the relative standard deviations of <1%. In addition, field applications in the vicinity of a nuclear power plant demonstrated that 14C data measured by our method was consistent with the accelerator mass spectrometry (AMS) method, suggesting the method's precision can meet the requirement of 14C monitoring near the nuclear facility. This is the first study to report the use of synthetic benzoic acid in environmental 14C monitoring, and it provides a new approach for improving the environmental 14C monitoring network.

Review of estuarine and coastal environmental monitoring technologies

Review of estuarine and coastal environmental monitoring technologies

High Fundamental Frequency (HFF) Monolithic Resonator Arrays for Biosensing Applications: Design, Simulations, Experimental Characterization

Miniaturized, high-throughput, cost-effective sensing devices are needed to advance lab-on-a-chip technologies for healthcare, security, environmental monitoring, food safety, and research applications. Quartz crystal microbalance with dissipation (QCMD) is a promising technology for the design of such sensing devices, but its applications have been limited, until now, by low throughput and significant costs. In this work, we present the design and characterization of 24-element monolithic QCMD arrays for high-throughput and low-volume sensing applications in liquid. Physical properties such as geometry and roughness, and electrical properties such as resonance frequency, quality factor, spurious mode suppression, and interactions between array elements (crosstalk), are investigated in detail. In particular, we show that the scattering parameter, S21, commonly measured experimentally to investigate crosstalk, contains contributions from the parasitic grounding effects associated with the acquisition circuitry. Finite element method simulations do not take grounding effects into account explicitly. However, these effects can be effectively modelled with appropriate equivalent circuit models, providing clear physical interpretation of the different contributions. We show that our array design avoids unwanted interactions between elements and discuss in detail aspects of measuring these interactions that are often-overlooked.

Preliminary Study on Double Enhanced Raman Scattering Detection of Gas Phase Trace Explosives

The demand of continuous detection technology for gas phase trace explosives (GPTE) in explosion-proof stability maintenance, public security, explosive security storage and other occasions is summarized. By studying the development status and research trends of surface enhanced Raman scattering (SERS) detection technology to detect GPTE, an application of a double enhanced Raman scattering (DERS) substrate consisted of a novel 1D composite silver grating and gold nanospheres colloids in the GPTE detection was presented. The detection sensitivity of 2,4,6-trinitrotoluene (TNT) and cyclotrimethyltrinitroamine (RDX) was 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-11</sup> mol/L. Combining with microfluidic technology, it can realize in-situ and continuous GPTE detection. This DERS substrate has a significant reference for the application of SERS detection technology in food safety, health care and environmental monitoring, et al.

Assessment models for the generalized quality criterion for the production processes of thermal power plants

In order to increase the information reliability of the hardware and software technology for industrial and environmental monitoring, we obtained models of the thermal power plants production processes that can give an assessment of a generalized quality criterion for the occurrence of such technological processes as the preparation of fuel, fuel combustion, removal of flue gases and suspended particles. To form the measured values of the parameters of each technological process, a multiversion approach is used, which is based on obtaining the n-th amount of measured data.

Research on environmental monitoring and control technology based on intelligent Internet of Things perception

This study studies the technology of environmental monitoring and control in an intelligent Internet of Things (IoT) perception. To improve the issue of small coverage and low stability in the traditional environmental parameter testing, a new environment monitoring technique based on intelligent perception is proposed. The technical framework consists of three layers, intelligent perception layer, network communication layer, and application layer. The intelligent perception collects and transmits data to the application-level server via the network communication layer for data analysis and diversified display. Based on this technique, we constructed a smart home environment monitoring and control system. The experimental test result shows that the system can accurately collect and process the data through the server and diversified displayed, which verifies the accuracy and reliability of this technique which is potential for further application.

Research on Environmental Management and Monitoring Technology of Solid Waste in China Based on Knowledge Map

In recent years, environmental governance has attracted wide attention of domestic academia, and relevant research has achieved fruitful results. Some scholars have used bibliometric methods to discuss the current research situation of environmental governance, but few have analyzed the current domestic research path of environmental governance. Based on the key words, authors and authors of the 8962 journal articles on the environmental management and environmental monitoring technology of solid waste in China from 2016 to 2018, the article uses CiteSpace software to apply knowledge maps and common word analysis. A variety of quantitative analysis methods such as cluster analysis and strategic coordinate graph analysis. The knowledge map in the field of environmental monitoring is constructed, and the map is analyzed and interpreted. The analysis results show the structural characteristics of Chinese solid waste environmental management and environmental monitoring technology in the form of knowledge map, and obtain the research frontier and its evolution law in this research field.

Underwater Acoustic Sensor Networks Node Localization Based on Compressive Sensing in Water Hydrology.

Groundwater is an important source of human activities, agriculture and industry. Underwater Acoustic Sensor Networks (UASNs) is one of the important technologies for marine environmental monitoring. Therefore, it is of great significance to study the node self- localization technology of underwater acoustic sensor network. This paper mainly studies the node localization algorithm based on range-free. In order to save cost and energy consumption, only a small number of sensing nodes in sensor networks usually know their own location. How to locate all nodes accurately through these few nodes is the focus of our research. In this paper, combined with the compressive sensing algorithm, a range-free node localization algorithm based on node hop information is proposed. Aiming at the problem that connection information collected by the algorithm is an integer, the hop is modified to further improve the localization performance. The simulation analysis shows that the improved algorithm is effective to improve the localization accuracy without additional cost and energy consumption compared with the traditional method.

Real-time and non-destructive gas mixture analysis using linear various filter enabled mid-infrared visualization.

Real-time gas mixture analysis has been demonstrated using various linear variable filter (LVF)-enabled mid-infrared (mid-IR) visualizations. Due to the characteristic absorptions of different gases, the algorithm-enabled sensing method has the ability to detect multi-component gas mixtures noninvasively. The proposed system consisted of a broadband light source, a gas mixing and delivery chamber made by polydimethylsiloxane (PDMS), a LVF, and a real-time monitoring mid-IR camera. The system performance was evaluated by detecting CH4 and C2H2 at their characteristic C-H absorptions from λ = 3.0 to 3.5 µm. A fast and accurate identification of gas samples was achieved. Therefore, our real-time and non-destructive gas analysis system enables a new visualization technology for environmental monitoring and industrial measurement.

The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings.

Building energy consumption accounts for 30%–45% of the global energy demand. With an ever-increasing world population, it has now become essential to minimize the energy consumption for the future of the environment. One of the most crucial aspects in this regard is the utilization of sensing and environmental monitoring technologies in buildings as these technologies provide stakeholders, such as owners, designers, managers, and occupants, with important information regarding the energy performance, safety and cost-effectiveness of the building. With the global sensors market value predicted to exceed $190 billion by 2021 and the number of sensors deployed worldwide forecasted to reach the ‘1 Trillion’ mark by 2025, a state-of-the-art review of various commercially-viable sensor devices and the wide range of communication technologies that complement them is highly desirable. This paper provides an insight into various sensing and environmental monitoring technologies commonly deployed in buildings by surveying different sensor technologies, wired and wireless communication technologies, and the key selection parameters and strategies for optimal sensor placement. In addition, we review the key characteristics and limitations of the most prominent battery technologies in use today, different energy harvesting sources and commercial off-the-shelf solutions, and various challenges and future perspectives associated with the application of sensing and environmental monitoring technologies within buildings.

On the Sensing Mechanisms of a Hydroresistive Flexible Film Based on an Organic Molecular Metal

With the emergence of environmental, biomedical, and medical monitoring technologies, development of flexible and lightweight sensors is ongoing. This work presents a flexible lightweight bilayer (BL) film (polycarbonate/polycrystalline layer of crystalline (BEDT-TTF)xBr(H2O)y salts [BEDT-TTF = bis (ethylendithio)tetrathiafulvalene]) as a promising material for humidity sensing which may be applied in a number of monitoring scenarios. X-ray structural characterization revealed the presence of three different crystal types in the crystalline layer of the BL film, i.e., [phase I, [(BEDT-TTF)5(Br4(H5O2))]; phase II, [(BEDT-TTF)2Br(H2O)3]; and crystals of neutral BEDT-TTF compound], showing that phase I is highly sensitive to humidity. The humidity testing of the BL film showed that it is capable of monitoring relative humidity (RH) levels from 15 up to 90% with a well-defined and reproducible electrical signal. Electrical resistance measurements revealed that the crystalline conducting layer can absorb moisture reaching equilibrium at constant RH as reflected in a stable relative resistance response. The structural response of the BL film to variations of RH clearly demonstrated that crystallite interlayer spacing (d) of phase I is strongly affected, exhibiting a reversible metal–nonmetal transition, while phase II was insensitive to humidity. An overview of mechanical and humidity sensing properties of the developed BL film corroborates that it can be used as flexible hygrometer as well as moisture sensing units on the board of low-cost electronic sensing devices.

Evaluating and improving the reliability of gas-phase sensor system calibrations across new locations for ambient measurements and personal exposure monitoring

Abstract. Advances in ambient environmental monitoring technologies are enabling concerned communities and citizens to collect data to better understand their local environment and potential exposures. These mobile, low-cost tools make it possible to collect data with increased temporal and spatial resolution, providing data on a large scale with unprecedented levels of detail. This type of data has the potential to empower people to make personal decisions about their exposure and support the development of local strategies for reducing pollution and improving health outcomes. However, calibration of these low-cost instruments has been a challenge. Often, a sensor package is calibrated via field calibration. This involves colocating the sensor package with a high-quality reference instrument for an extended period and then applying machine learning or other model fitting technique such as multiple linear regression to develop a calibration model for converting raw sensor signals to pollutant concentrations. Although this method helps to correct for the effects of ambient conditions (e.g., temperature) and cross sensitivities with nontarget pollutants, there is a growing body of evidence that calibration models can overfit to a given location or set of environmental conditions on account of the incidental correlation between pollutant levels and environmental conditions, including diurnal cycles. As a result, a sensor package trained at a field site may provide less reliable data when moved, or transferred, to a different location. This is a potential concern for applications seeking to perform monitoring away from regulatory monitoring sites, such as personal mobile monitoring or high-resolution monitoring of a neighborhood. We performed experiments confirming that transferability is indeed a problem and show that it can be improved by collecting data from multiple regulatory sites and building a calibration model that leverages data from a more diverse data set. We deployed three sensor packages to each of three sites with reference monitors (nine packages total) and then rotated the sensor packages through the sites over time. Two sites were in San Diego, CA, with a third outside of Bakersfield, CA, offering varying environmental conditions, general air quality composition, and pollutant concentrations. When compared to prior single-site calibration, the multisite approach exhibits better model transferability for a range of modeling approaches. Our experiments also reveal that random forest is especially prone to overfitting and confirm prior results that transfer is a significant source of both bias and standard error. Linear regression, on the other hand, although it exhibits relatively high error, does not degrade much in transfer. Bias dominated in our experiments, suggesting that transferability might be easily increased by detecting and correcting for bias. Also, given that many monitoring applications involve the deployment of many sensor packages based on the same sensing technology, there is an opportunity to leverage the availability of multiple sensors at multiple sites during calibration to lower the cost of training and better tolerate transfer. We contribute a new neural network architecture model termed split-NN that splits the model into two stages, in which the first stage corrects for sensor-to-sensor variation and the second stage uses the combined data of all the sensors to build a model for a single sensor package. The split-NN modeling approach outperforms multiple linear regression, traditional two- and four-layer neural networks, and random forest models. Depending on the training configuration, compared to random forest the split-NN method reduced error 0 %–11 % for NO2 and 6 %–13 % for O3.

Environmental pollution monitoring using a Web-based GIS in Surakarta

The case of environmental pollution is usually not all exposed onmedia. As such, environmental pollution monitoring needs to be done, hence the government can take legal measures against it. Monitoring environmental pollution will be more effective when involving community. The development of geospatial technologies such as Web-based GIS is helpful for monitoring pollution inenvironment. This is due to the Web-based GIS has several advantages including easy tooperate, easily accessible and able to present data (spatial and non-spatial) in real time. The purpose of this research is to develop a web-based GIS for monitoring environmental pollution. The software was developed using Rapid Application Development (RAD). Software development using technology based on opensource (CodeIgniter, Bootstrap, OpenLayers, and GML). The results show that the utilization of web-based GIS technology for environmental pollution monitoring is easily accessible and operated by user. Furthermore, the trial results show that the utilization of developed web-based GIS technology can operate properly and present data interactively.

Microbial fuel cells as a sustainable platform technology for bioenergy, biosensing, environmental monitoring, and other low power device applications

Microbial Fuel Cell (MFC) technology is a promising sustainable energy alternative to combat issues pertaining to non-renewable energy consumption, climate change, and environmental pollution. MFC technology employs anaerobic microorganisms, which convert biodegradable substances into simpler substances and produce bioelectricity. MFCs show promise for low-cost energy yielding wastewater treatment. Recent research efforts have shown that the technological know-how of MFC technology has evolved beyond the primary applications of wastewater treatment and energy generation. Hence, research attention has shifted towards other specific value-added applications of MFCs such as small implantable health devices, robotics, and environmental quality monitoring sensors, etc. This article concisely addresses the potential applications of MFC technology past power production and wastewater treatment for biofuels such as biogas, and hydrogen production, and in the fields of medical implantable devices, robotics, and as biosensors for heavy metals and detection of toxic chemicals among others.

Indoor formaldehyde monitoring system based on fog computing

With the enhancement of people’s awareness of environmental protection, environmental monitoring technology has gradually entered into all aspects of people’s life and work. However, due to the high demand of real-time monitoring for data transmission delay, the traditional cloud computing architecture cannot meet the demand, and the fog computing architecture emerges at the historic moment. In this paper, the traditional cloud computing architecture is improved by using the fog computing architecture combined with the function as a service mode of the serverless architecture, and an environment monitoring architecture based on fog computing is proposed. Taking indoor formaldehyde monitoring as an example, the delay of the system is significantly improved, and the resource utilization efficiency is higher and the energy consumption is lower. It solves the deficiency of traditional cloud computing architecture and better meets the demand of real-time environmental monitoring.

A pH/Light Dual-Modal Sensing Isfet Assisted By Artificial Neural Networks

Multi-modal sensing system is essential in healthcare and internet-of-things (IoT) [1]. Making the sensor systems small, low power and cost effective is the ultimate goal to fulfill the growing needs. To achieve this requirement, we present a single-device-dual-sensor by transforming a conventional dual-gate ion sensitive field-effect transistor (DG-ISFET) [2] to a pH/light bi-functional sensing device. In addition to pH sensing ability, since photocurrent in DG-ISFET has been proved to be controllable by gate voltages [3], it is possible to detect light by a DG-ISFET. However, the signals of pH and light are coupled together in a complicated manner. Therefore, in order to realize an effective dual-sensor, we introduce a sequential re-configuration method and back-propagation neural network (BPNN) to DG-ISFET in order to distinguish and quantify the signal from pH and light generated by the same device. The sequential re-configuration (SRC) method used in this work aims to mimic the function of sensor array in a multi-sensing system. To achieve this goal, the gate bias of DG-ISFET is intentionally configured in a sequential manner by tuning top and bottom gate voltages. In other words, we virtually generate a number of sensors with different sensitivities to light and pH from a single DG-ISFET device to replace the sensor array in the multi-sensor system. Afterwards, the output signals are processed by machine learning algorithms to quantify the actual value of the analytes. To quantify the pH value and light intensity, BPNN serving as a regression machine learning algorithm are employed. The regression results given in the form of boxplot of pH value and light intensities is shown in figure1. Based on the results, the error between calculated and true intensity is smaller than 10 μW/cm2 or 1.1% of every level of light intensity. pH values, on the other hand, have smaller calculation error of 0.5%. Based on these results, BPNN models are capable for practical applications to measure pH value and light intensity. Furthermore, the algorithm-calculated results have smaller variation than the direct-measured data does. It indicates the process variation of different sensors can be mitigated by the proposed SRC method with algorithms. This SRC based DG-ISFET reduces the need of sensor array and improves data consistency compared with a real sensor array. At the same time, back-propagation neural networks are employed to construct the data processing models to realize multi-signal quantification. Based on this work, the light interference of DG-ISFET can be transformed into effective illumination sensing quantity. And the proposed SRC-based single-chip-dual-sensing technique has good potential to be applied to sensor fusion technologies for healthcare and environmental monitoring.

Flory-Huggins Photonic Sensors for the Optical Assessment of Molecular Diffusion Coefficients in Polymers.

The lack of cost-effective systems for the assessment of air pollutants is a concern for health and safety in urban and industrial areas. The use of polymer thin films as label-free colorimetric sensors featuring specific interactions with pollutants would then represent a paradigm shift in environmental monitoring and packaging technologies, allowing to assess air quality, formation of byproducts in closed environment, and the barrier properties of the polymers. To this end, all-polymer distributed Bragg reflectors represent a promising approach toward a reliable and cost-effective transduction of chemical stimuli and effective colorimetric label-free selective detectors. We show selectivity attained by specific interactions between the polymer and analytes. Such interactions drive the analyte intercalation through the polymer structure and its kinetics, converting it in a dynamic optical response which is at the basis of the Flory-Huggins photonic sensors. The multivariate analyses of the response kinetics also allow distinguishing binary mixtures. Additionally, we demonstrate that such optical responses can be used to esteem the diffusion coefficients of small molecules within polymer media via simple UV-vis spectroscopy retrieving data comparable to those obtained with state-of-the-art gravimetric procedures. Last, we assess the figures of merit of the sensors in terms of lower detection limit, sensitivity, and reversibility, demonstrating that such devices can pave the way to an innovative, simple, and low-cost detection method integrable to in situ assessment of barrier polymers used for the encapsulation of optoelectronic devices, food packaging, and goods storage in general.

Combining Environmental Monitoring and Remote Sensing Technologies to Evaluate Cropping System Nitrogen Dynamics at the Field-Scale

Nitrogen (N) losses from cropping systems in the U.S. Midwest represent a major environmental and economic concern, negatively impacting water and air quality. While considerable research has investigated processes and controls of N losses in this region, significant knowledge gaps still exist, particularly related to the temporal and spatial variability of crop N uptake and environmental losses at the field-scale. The objectives of this manuscript study were (i) to describe the unique application of environmental monitoring and remote sensing technologies to quantify and evaluate relationships between artificial subsurface drainage nitrate (NO3-N) losses, soil nitrous oxide (N2O) emissions, soil N concentrations, corn (Zea mays L.) yield, and remote sensing vegetation indices, and (ii) to discuss the benefits and limitations of using recent developments in technology to monitor cropping system N dynamics at field-scale. Preliminary results showed important insights regarding temporal (when N losses primarily occurred) and spatial (measurement footprint) considerations when trying to link N2O and NO3-N leaching losses within a single study to assess relationship between crop productivity and environmental N losses. Remote sensing vegetation indices were significantly correlated with N2O emissions, indicating that new technologies (e.g. unmanned aerial vehicle platform) could represent an integrative tool for linking sustainability outcomes with improved agronomic efficiencies, with lower vegetation index values associated with poor crop performance and higher N2O emissions. However, the potential for unmanned aerial vehicle to evaluate water quality appears much more limited because NO3-N losses happened prior to early-season crop growth and image collection. Building on this work, we encourage future research to test the usefulness of remote sensing technologies for monitoring environmental quality, with the goal of providing timely and accurate information to enhance the efficiency and sustainability of food production.