Accurate Environmental Monitoring Research Articles

A cross-modal feature aggregation and enhancement network for hyperspectral and LiDAR joint classification

Advancements in Earth observation technologies have greatly enhanced the potential of integrating hyperspectral (HS) images with Light Detection and Ranging (LiDAR) data for land use and land cover classification. Despite this, most existing methods primarily focus on employing deep network layers to extract features from two heterogeneous data modalities, often overlooking a gradual modeling data representation approach from shallow to deep layers. Furthermore, excessive network layers can result in the deterioration of modality-specific features, therefore lowering the classification performance. The paper proposed a novel cross-modal feature aggregation and enhancement network for the joint classification of HSI and LiDAR data. Initially, a cross-modal feature fusion module is developed to exploit spatial scale consistency to complete the interchange and fusion of feature embedding at the pixel level, preserving the original information from the two heterogeneous modalities to a certain degree. Then two straightforward strategies (i.e., addition and concatenation) are employed in the shallow network layers before being sent to the transformer encoder. The former facilitates the model’s ability to discern more subtle distinctions and refine spatial location details. The latter ensures the preservation of information integrity, effectively mitigating the risk of feature loss. Moreover, invertible neural networks and a feature enhancement module are introduced, leveraging the complementary information of HSI and LiDAR data to enhance the detail and texture information extracted in deeper layers. Extensive experiments on Houston2013, Trento, and MUUFL datasets demonstrate that the proposed method outperforms several state-of-the-art models in three evaluation metrics, achieving an accuracy improvement of up to 2%. The proposed model brings new inspirations for HSI and LiDAR classification, which is critical for accurate environmental monitoring, urban planning, and precision agriculture. The source code is publicly accessible at https://github.com/zhangyiyan001/CMFAEN.

Multiple Self-Powered Sensor-Integrated Mobile Manipulator for Intelligent Environment Detection.

A multiple self-powered sensor-integrated mobile manipulator (MSIMM) system was proposed to address challenges in existing exploration devices, such as the need for a constant energy supply, limited variety of sensed information, and difficult human-computer interfaces. The MSIMM system integrates triboelectric nanogenerator (TENG)-based self-powered sensors, a bionic manipulator, and wireless gesture control, enhancing sensor data usability through machine learning. Specifically, the system includes a tracked vehicle platform carrying the manipulator and electronics, including a storage battery and a microcontroller unit (MCU). An integrated sensor glove and terminal application (APP) enable intuitive manipulator control, improving human-computer interaction. The system responds to and analyzes various environmental stimuli, including the droplet and fall height, temperature, pressure, material type, angles, angular velocity direction, and acceleration amplitude and direction. The manipulator, fabricated using 3D printing technology, integrates multiple sensors that generate electrical signals through the triboelectric effect of mechanical motion. These signals are classified using convolutional neural networks for accurate environmental monitoring. Our database shows signal recognition and classification accuracy exceeding 94%, with specific accuracies of 100% for pressure sensors, 99.55% for angle sensors, and 98.66, 95.91, 96.27, and 94.13% for material, droplet, temperature, and acceleration sensors, respectively.

Real Time Monitoring of Temperature And Humidity Using LabVIEW and ML

Abstract: This project presents a Real-Time Temperature and Humidity Monitoring System built on LabVIEW, featuring ML predictive analytics. It facilitates real-time data collection from diverse sensors, ensuring precise environmental monitoring. The ML algorithms within the system forecast future temperature and humidity trends, aiding proactive decision-making. Users enjoy an intuitive graphical interface for visualizing data, analysing historical trends, and receiving alerts for threshold variations. Additionally, the system provides remote monitoring, data logging, and reporting, enhancing its usability and accessibility. This integrated solution caters to the crucial requirement for accurate environmental monitoring, with applications spanning industries like manufacturing, agriculture, and climate control, ultimately enhancing efficiency and decision-making in these sectors.

Enhanced sensitivity of zero‐bias‐operated MXene chemiresistive sensor via lignin hybridization

AbstractAs global urbanization intensifies, there is an increasing need for highly sensitive and accurate environmental monitoring devices that can meet the demands of specific gas sensing applications with low power consumption. This study focuses on enhancing the sensitivity of MXene‐based chemiresistive sensors for detecting CO2(g) and NO2(g) under zero‐bias operation. This study shows that lignin hybridization effectively improves the sensitivity of a Ti3C2Tx MXene‐based chemiresistive sensor; under zero‐bias operation, lignin hybridization increases the sensitivity to 15 ppm NO2(g) and CO2(g) by 157.38% and 297.95%, respectively. When deposited on a flexible substrate, the MXene/lignin flexible sensor shows a similar response and sensitivity to 15 ppm NO2(g) and CO2(g) under 38° curvature compared to the planar sensor. Consequently, the MXene/lignin hybrid sensor is attractive for room temperature and zero‐bias NO2(g) and CO2(g) detection. The MXene/lignin flexible sensor serves as a model system for advanced solid‐state sensory platforms suitable for curved structures.image

Clean and accurate soil quality monitoring in mining areas under environmental rehabilitation in the Eastern Brazilian Amazon.

Soil quality monitoring in mining rehabilitation areas is a crucial step to validate the effectiveness of the adopted recovery strategy, especially in critical areas for environmental conservation, such as the Brazilian Amazon. The use of portable X-ray fluorescence (pXRF) spectrometry allows a rapid quantification of several soil chemical elements, with low cost and without residue generation, being an alternative for clean and accurate environmental monitoring. Thus, this work aimed to assess soil quality in mining areas with different stages of environmental rehabilitation based on predictions of soil fertility properties through pXRF along with four machine learning algorithms (projection pursuit regression, PPR; support vector machine, SVM; cubist regression, CR; and random forest, RF) in the Eastern Brazilian Amazon. Sandstone and iron mines in different chronological stages of rehabilitation (initial, intermediate, and advanced) were evaluated, in addition to non-rehabilitated and native forest areas. A total of 81 soil samples (26 from sandstone mine and 55 from iron mine) were analyzed by both traditional wet-chemistry methods and pXRF. The available/exchangeable contents of K, Ca, B, Fe, and Al, in addition to H+Al, cation exchange capacity at pH = 7, Al saturation, soil organic matter, pH, sum of bases, base saturation, clay, and sand were accurately predicted (R2 > 0.70) using pXRF data, with emphasis on the prediction of Fe (R2 = 0.93), clay content (R2 = 0.81), H+Al (R2 = 0.81), and K+ (R2 = 0.85). The best predictive models were developed by RF and CR (86%) and when considering pXRF data + mining area + stage of rehabilitation (73%). The results highlight the potential of pXRF to accurately assess soil properties in environmental rehabilitation areas in the Amazon region (yet scarcely evaluated under this approach), promoting a more agile and cheaper preliminary diagnosis compared to traditional methods.

SMEOR: Sink mobility‐based energy‐optimized routing in energy harvesting‐enabled wireless sensor network

SummaryFor accurate environmental monitoring, a wireless sensor network (WSN) uses a collection of autonomous sensor nodes (SNs) that are dispersed at random across a given area. WSNs are used for various applications that require real‐time data. While the SNs in a WSN are limited in their ability to generate energy, the routing process is seen as a vital means of improving the network's ability to conserve resources and prolong its useful life. In this research work, we present sink mobility‐based energy‐optimized routing (SMEOR) in energy harvesting (EH)‐enabled WSN. While following the cluster‐based routing, the election of cluster head (CH) is executed using our proposed extended spotted hyena Lévy flight optimization (ESHLFO) algorithm. Furthermore, to address the energy‐hole problem, four data‐collection energy‐unlimited nodes are used around the periphery of the network. The sink moves from one EH‐enabled node to another to collect data. Further, security in SMEOR is ensured by encrypting the data using a stream cipher and a stochastically generated security key. It is evident from the simulation analysis that SMEOR delivers network longevity and supreme performance.

In-cabin and outdoor environmental monitoring in vehicular scenarios with distributed computing

Accurate environmental monitoring is becoming the basis for assuring sustainable development in administrations at different levels, including cities and industry as key actors. However, current techniques rely on static stations that may not be representative of larger areas, for the case of outdoor scenarios, or even not considering indoor spaces where people can remain for long periods. This is the case of vehicles. The COVID-19 pandemic has remarked the importance of measuring air quality indoors, for instance. With the aim of solving this two-fold issue, this work proposes an in-cabin and outdoor air pollution monitoring system to assure healthy conditions when travelling, driving and operating vehicles, and to analyse the evolution of environmental parameters in cities. This effort is carried out exploiting distributed computing with micro-services, betting for an on-board hardware solution provided with sensors for measuring particulate matter, CO, CO2, NO2, O3, temperature and humidity. While basic data pre-processing is carried out in this acquisition unit, edge processing is performed on a single board computer aboard and intermediary communication nodes in the network path from the vehicle to the cloud. Vehicle connectivity is provided by 4G cellular and Low-Power Wide-Area (LPWAN) networks. Global environmental perception is acquired by cloud-based software powered by machine learning and time series analysis. The whole solution has been validated and tested in the city of Cartagena (Spain), with good performance in terms of data collection, communication links and service offered.

Evaluation Index System of Green Public Open Space Based on Internet of Things and Mental Health

ABSTRACT With the emergence of the IoT era, wireless sensor networks will be more and more widely used. In addition to collecting, transmitting and processing simple data such as humidity, temperature and density of the dome, they can also provide multimedia information services such as video and images. It enables more comprehensive and accurate environmental monitoring. Therefore, MSDs have a huge demand in military, daily, forestry, biomedicine and other fields. The intensive city model has obvious advantages in meeting people's diverse needs and comfortable life. Most obviously, it speeds up the rhythm of life for residents, thereby increasing efficiency and saving time. Starting from this aspect, this paper conducts a research on the evaluation index system of public built on the following areas of open space IoT and mental health. In this paper, the GRNN neural network model is constructed, the mean condition is calculated, the density function can be estimated, the network output, and the schematic diagram of the generalized regression neural network is improved. Using the established system, the index in 2018 is selected as the base year, and after transformation, the standardized values of the past years are formed, which are substituted into the cells to form different matrices. The value of each cell is counted to obtain the subsystem coordination degree, and the global coordination degree is obtained through calculation. The evaluation results of ecological civilization construction and development in 2018 and 2019, 2020 and 2021 were compared. The experimental data shows that compared with 2018, economic development will change from 1 to 2.000, social harmony will change from 1 to 2.480, ecological health will decrease to 0.850, environmental friendliness will decrease to 0.750, and comprehensive evaluation will decrease to 0.513. This shows that while the economy is developing this year, the construction of ecological civilization has been gradually carried out, and good results have been achieved. This reflects the effectiveness of the system. The subject of the evaluation index system of green public open space based on the Internet of Things and mental health has been well completed.

Maintenance Technology and Practice of Mooring Anchor Leg in Deepwater Floating Production Facility

“Nanhai Shengli” is a floating production storage and offloading unit (FPSO) in the Liuhua 11-1 oil field in the South China Sea. During its service, the FPSO suffered severe broken anchor legs due to the hostile impact of several strong typhoons. In order to ensure the safety of its mooring system, emergency maintenance operations were carried out on 10 mooring anchor legs under the condition of non-release and non-production of FPSO, which was the first time in deep water in China. Its construction is difficult, high risk, weather and FPSO draft time window requirements are high, the use of reasonable technology and construction methods is the key to efficient completion of the operation, such as simulation analysis, reasonable selection of machinery and resources, efficient control of site construction, accurate environmental monitoring, etc. Taking the release and tieback of the mooring system of "Nanhai Shengli" FPSO in Liuhua 11-1 oilfield as an example, various technical difficulties and key points in the process of release and tieback construction are introduced, and the rules are summarized and analyzed. The safe and efficient completion of this construction operation is not only the embodiment of management level, but also the result of daring to innovate and face challenges. It has a typical guiding significance for the maintenance of mooring anchor leg of similar deep-water floating production facilities in China.

Systematic Stereoselectivity Evaluations of Tetramethrin Enantiomers: Stereoselective Cytotoxicity, Metabolism, and Environmental Fate in Earthworms, Soils, Vegetables, and Fruits

Tetramethrin is a widely applied type I chiral pyrethroid insecticide that exists as a mixture of four isomers. In the present study, its stereoselective cytotoxicity, bioaccumulation, degradation, and metabolism were investigated for the first time at the enantiomeric level in detail by using a sensitive chiral high-performance liquid chromatography-tandem mass spectroscopy (HPLC-MS/MS) method. Results showed that among rac-tetramethrin and its four enantiomers, the trans (+)-1R,3R-tetramethrin had the strongest inhibition effect on the PC12 cells. In the earthworm exposure trial, the concentration of trans (-)-1S,3S-tetramethrin was 0.94-8.92 times in earthworms (cultivated in natural soil) and 1.67-5.01 times (cultivated in artificial soil) higher than trans (+)-1R,3R-tetramethrin, respectively. In the greenhouse experiment, the trans (+)-1R,3R-tetramethrin and cis (+)-1R,3S-tetramethrin were preferentially degraded. Furthermore, for rat liver microsome in vitro incubation, the maximum metabolism rate of cis (-)-1S,3R-tetramethrin was 1.50 times higher than its antipodes. Altogether, the aim of this study was to provide a scientific and reasonable reference for the possibility of developing a single enantiomer to replace the application of rac-tetramethrin, which could possess better bioactivity and lower ecotoxicity, and thus permit more reliable and accurate environmental monitoring and risk assessment.

Novel miniaturized passive sampling devices based on liquid phase microextraction equipped with cellulose-grafted membranes for the environmental monitoring of phthalic acid esters in natural waters

Phthalic acid esters (PAEs) are considered endocrine disruptors and potential carcinogens. Consequently, efficient and accurate environmental monitoring of trace levels of these organic pollutants is necessary to protect the population against their hazardous effects. Passive sampling techniques have gained notoriety for environmental monitoring and have been proven highly sensitive to temporal variations. This study developed a miniaturized passive sampling device (MPSD) based on hollow fiber liquid-phase microextraction (HF-LPME). The devices were calibrated in the laboratory using an automated calibration system. The results demonstrated the first-order uptake ranges for Diethyl phthalate (DEP), Diisobutyl phthalate (DiBP), Dibutyl phthalate (DBP), Benzyl butyl phthalate (BBP) and Bis(2-ethylhexyl phthalate) (DEHP) between 30 min and 24 h with sampling rates equivalent to 0.009; 0.021; 0.033; 0.085 and 0.003 mL h−1 respectively (R2 between 0.88 and 0.99). The calibrated devices were deployed in 12 marginal lagoons, stretching approximately 330 km along the main river. The extracts recovered from the devices were analyzed by gas chromatography (GC), resulting in the identification and quantification of DEP (0.697–13.7 ng L−1), DiBP (0.100–4.43 ng L−1), DBP (0.014–1.21 ng L−1), BBP (0.218–5.67 ng L−1), and DEHP (0.002–2.24 ng L−1). Despite being frequently identified, DEHP concentrations were well below the maximum established limits, revealing a good water quality in terms of the target PAEs. In contrast, screening the extracts using GCxGC was possible to detect other hazardous pollutants such as pesticides, drugs, and their metabolites. The described device was effective and reliable, providing accurate PAE measurements following short exposure periods. In this sense, its deployment during emergency operations, such as accidental discharges of industrial effluents into natural waters, could continuously and cost-effectively monitor water quality.

An optimal RSSI-based cluster-head selection for sensor networks

Wireless sensor networks (WSNs) are becoming a very auspicious key to better address reliability to constant, accurate environmental monitoring. Although energy utilisation is one of the most common challenges, frequent communication between the sensor nodes (SNs) results in a huge energy drain. Moreover, load balancing and optimisation within the WSN are the significant factors for the extensive period of network lifetime. In fact, many such networks are deployed and operating outdoors is exposed to varying environmental conditions, which may further set grounds for severe performance degradation. Therefore, it is necessary to take into consideration factors like radio signal strength index to reduce the impact and to adapt to varying environmental conditions. In this paper, we propose an optimal RSSI CH selection (ORCHS) algorithm that is also based on environmental conditions to achieve energy efficiency and enhanced network lifetime by effectively managing energy levels within the network.

Environmental Monitoring Based on Fog Computing Paradigm and Internet of Things

Providing accurate real-time environmental monitoring services is a meaningful but difficult task. Whereas recent developments in the Internet of Things and the fog computing paradigm have brought new opportunities to improve the service, achieving accurate environmental monitoring faces several challenges. First, data collected by the Internet of Things varies in temporal-spatial distribution, quality, and relevance to objectives. Second, due to the limited communication cost and stability, local real-time monitoring systems can be hardly achieved by adopting centralized cloud paradigm solutions in the real world. Third, the amount of data at a single edge node of fog computing paradigm is usually not sufficient for accurate environmental monitoring. In this paper, we propose a framework for environmental monitoring based on fog computing that uses multi-source heterogeneous data collected from the Internet of Things sensors. At each edge node, we employ local sub-classifiers to analyze the collected data and afterwards, a deep neural network based model to aggregate results from sub-classifiers. For the homologous sub-classifiers at different edge nodes, we design a federated learning mechanism to update sub-classifiers in concert by model transmission. We use multi-source heterogeneous data collected in Beijing to evaluate the proposed fog computing framework. Experimental results show that our federated learning mechanism has almost the same performance compared with centralized data fusion mechanism when training rounds are increased.

Controlled Release Test Facility to Develop Environmental Monitoring Techniques for Geologically Stored CO2 in Korea

Two sets of controlled release test facility (EIT) were constructed in unsaturated and saturated zones by the Korea CO2 Storage Environmental Management (K-COSEM) Research Center to develop efficient and accurate environmental monitoring techniques required for secure geologic carbon storage. The unsaturated zone EIT consists of a 51 m long horizontal well for artificial CO2 leakage at the depth of 2.5 m below the land surface, a surface injection control system, and diverse monitoring devices. Following baseline surveys, a preliminary CO2 release test was first performed for 4 days in October 2015, with the released amount of about 390 kg CO2. The saturated zone EIT to develop efficient monitoring techniques in intermediate depths was constructed in the end of 2015. It consists of an injection well at the depth of 24 m and 8 multi-level monitoring wells. In 2016, long-term CO2 release tests using EITs have been performed. The outlines of EITs of K-COSEM are summarized in this paper.

Silicon microring refractometric sensor for atmospheric CO(2) gas monitoring.

We report a silicon photonic refractometric CO(2) gas sensor operating at room temperature and capable of detecting CO(2) gas at atmospheric concentrations. The sensor uses a novel functional material layer based on a guanidine polymer derivative, which is shown to exhibit reversible refractive index change upon absorption and release of CO(2) gas molecules, and does not require the presence of humidity to operate. By functionalizing a silicon microring resonator with a thin layer of the polymer, we could detect CO(2) gas concentrations in the 0-500ppm range with a sensitivity of 6 × 10(-9) RIU/ppm and a detection limit of 20ppm. The microring transducer provides a potential integrated solution in the development of low-cost and compact CO(2) sensors that can be deployed as part of a sensor network for accurate environmental monitoring of greenhouse gases.

ミクセル解析を目的としたLandsat/OLIデータとMODISデータの重ね合わせにおける要求精度

Currently, many land observation satellites can be used in order to monitor natural environment. JAXA has a plan to launch new satellite GCOM-C1. SGLI is mounted on GCOM-C1, which can observe 380-12000nm spectral range in 250m sampling distance and 1150km swath. A mixed pixel analysis is a very important issue for an accurate environmental monitoring. Therefore, required accuracy of geometric transformation for GCOM-C1/SGLI should be understood. In this study, Landsat/OLI was used as reference data for geometric transform. The accuracy of superposing OLI on MODIS was evaluated by using simulated MODIS data. MODIS has a same ground sampling distance with SGLI. As the results, 1/5 pixel accuracy is required for mixed pixel analysis. Finally, Mixed pixel analysis was carried out real Landsat OLI and MODIS. The positional error of MODIS showed around 40-60m. The root mean square error between OLI and MODIS showed 12.6 (W/m2/sr/μm). It was bigger than simulation results. The reason might be come from transit time difference of satellite.

Stakeholder-Driven Research in Phragmites Management in New England Salt Marshes

Overview Methods of resource management and conservation originated in economic theory during the late 19th century. Historically, communities controlled their environments to enhance development and maximize the harvest of key species. For generations tidal marshes were drained and tidal flooding eliminated in New England to utilize coastal property. These methods improved harvests and development but left coastal ecosystems vulnerable and invasive plants have begun to out-compete native plants. More recently, new theories of conservation biology have focused on the role of humans in the management of local ecosystems. We now recognize the benefits of ecosystem services such as water filtration, breeding habitat, and aesthetic value to society that do not necessarily have direct financial rewards. However, management strategies are still frequently imposed in a top-down fashion. This type of management is largely done by small groups of experts imposing strategies over large regions. In the process, local community needs and values are often ignored resulting in dissatisfaction and even hostility towards managers. Effective invasive species management must first focus on the environmental changes that have occurred as a consequence of the invasion and then develop a strategy to restore the system back to the original state (Schroeder and Keller 1990). Without accurate environmental monitoring, these changes are best understood through the experiences of local Stakeholder-Driven Research in Phragmites Management in New England Salt Marshes fiEld notEs