Efficient Environmental Monitoring Research Articles

Integrated nanozyme electrochemical sensor for the detection of tannic acid: An advanced approach to rapid and efficient environmental monitoring

This study presents a novel methodology for the rapid on-site detection of tannic acid (TA), a prevalent organic contaminant in various natural environments, notably in plant-derived sources. The proposed approach involves the development of a compact integrated electrochemical sensor incorporating a nanozyme system. Specifically, this system comprises Fe2O3 nanoparticles (NPs) embedded within a chitosan (CS) matrix, immobilized onto a sulfur-doped graphene (S-Gr) substrate deposited on a gold electrode (AuE). The Fe2O3NPs exhibit peroxidase-like artificial enzyme activity, contributing to exceptional stability and catalytic efficiency in TA oxidation processes. Additionally, the CS matrix acts as a stabilizing agent, enhancing the performance and recyclability of the nanozyme. Furthermore, the S-Gr nanomaterial facilitates rapid electron transfer, leading to heightened sensitivity and prompt response times. The integration of these advanced nanomaterials with a microfabricated electrode presents an economically feasible, reliable, and effective solution for TA detection, with promising prospects for large-scale deployment and environmental monitoring. The Fe2O3CS-S-Gr/AuE sensing system demonstrates a calculated limit of detection (LOD) of 3.6 × 10−3 µM and an increased sensitivity of 0.2 µA×µM−1, with a wide linear concentration range spanning from 0.01 to 1000 µM for TA detection. Notably, the recovery values obtained for surface water samples fall within the range of 97.7 % to 99.5 %, indicating strong agreement with results derived from the standard method, UHPLC-MS/MS.

Effluent treatment plants for activities with high pollutant-degradation potential: an analysis of compliance with legal requirements

Sewage treatment plants (STPs), despite being fundamental for controlling environmental pollution, are also potential causes of environmental impact, therefore requiring an efficient self-monitoring plan to verify compliance with legal conditions. In this context, this research investigated the situation of Sewage Treatment Stations of enterprises that develop activities with high polluting degrading potential, located in Juazeiro do Norte – CE, in relation to compliance with legal guidelines. To guide this analysis, conditions were considered in resolutions from the Ceará State Environmental Council and municipal laws and decrees that deal with the matter under consideration. At the Municipal Environmental Authority of Juazeiro do Norte (AMAJU), data were collected from licensed enterprises that carried out high PPD activities, from the treatment technologies for effluents generated in each activity, from the STPs self-monitoring programs, and the types and locations of bodies receivers and the operating condition and physical flow of the AMAJU monitoring sector. The processing of data relating to the delivery of self-monitoring reports was carried out using percentages of 50 and 75%. From the analysis of the results, 48 ​​enterprises were identified that carried out high PPD activities, of which 4 were located in areas that had restrictions on this type of use. It was also verified that there was an important battery of public water supply wells surrounded by such projects. It was found that there are 70 high PPD projects that are under the jurisdiction of AMAJU and that the quality of liquid effluents from their STPs is, under specific conditions, in compliance with the release standards established by COEMA Resolution Nº 02/2017. However, it was found that the majority of the enterprises surveyed were non-compliant regarding the quantity and frequency of sending self-monitoring reports to AMAJU. These weaknesses were attributed to the reduced technical staff of the environmental agency and the absence of its minimum infrastructure for the development of the actions necessary for the implementation of an efficient environmental monitoring system. To contribute to the improvement of this system, it was suggested to strengthen AMAJU's technical, financial and institutional support, in addition to establishing a distinction in relation to the requirements regarding the frequency of analyzes and the periodicity of delivery of liquid effluent self-monitoring reports, for enterprises. with different types and classes.

Three-Way k-Means Model: Dynamic Optimal Sensor Placement for Efficient Environment Monitoring in Pig House

Sensors were of paramount importance in the context of poultry and livestock farming, serving as essential tools for monitoring a variety of production management parameters. The effective surveillance and optimal control of the swine facility environment critically depend on the implementation of a robust strategy for situating the optimal number of sensors in precisely the right locations. This study presents a dynamic sensor placement approach for pigsties using the three-way k-means algorithm. The method involves determining candidate sensor combinations through the application of the k-means algorithm and a re-clustering strategy. The optimal sensor locations were then identified using the Joint Entropy-Based Method (JEBM). This approach adjusts sensor positions based on different seasons (summer and winter) to effectively monitor the overall environment of the pigsty. We employ two clustering models, one based on particle swarm optimization and the other on genetic algorithms, along with a re-clustering strategy to identify candidate sensor combinations. The joint entropy-based method (JEBM) helps select the optimal sensor placement. Fused data from the optimal sensor layout undergo a fuzzy fusion process, reducing errors compared to direct averaging. The results show varying sensor needs across seasons, and dynamic placement enhances pigsty environment monitoring. Our approach reduced the number of sensors from 30 to 5 (in summer) and 6 (in winter). The optimal sensor positions for both seasons were integrated. Comparing the selected sensor layout to the average of all sensor readings representing the overall pigsty environment, the RMSE were 0.227–0.294 and the MAPE were 0.172–0.228, respectively, demonstrating the effectiveness of the sensor layout.

Low-cost IoT-based monitoring system for precision agriculture

The increasing impact of climate change on agriculture necessitates advanced monitoring and management of environmental conditions to ensure sustainable agricultural productivity. This paper introduces a cost-effective, Internet of Things (IoT)-based smart monitoring system designed to provide real-time insights into soil moisture levels and weather conditions across various segments of a single agricultural plot. The system comprises autonomous wireless sensor nodes, a comprehensive weather station, and a centralized base station that collectively capture, process, and relay environmental data to a user-friendly mobile application. Our empirical results demonstrate that this system not only facilitates efficient environmental data monitoring and analysis but also empowers farmers with actionable intelligence for timely decision-making. The proposed model showcases a promising avenue for enhancing agricultural resilience and productivity through technology-driven precision farming.

Sensor Learning Application for Precision Agriculture

This paper presents a Sensor Learning Application for Precision Agriculture that will assist students in getting live data (from the temperature, soil moisture, and humidity sensors) for efficient environment monitoring, which will enable them to increase their understanding of the purpose of learning. The Sensor Learning Application for Precision Agriculture is proposed, where the three sensor kits have been developed as a teaching tool to help students gain the optimum knowledge for real-world application. The agriculture site was developed to describe the real situation to students with the aim for students to experience the use of sensors for real application and to ensure students do not learn only theoretically; they can be exposed to the real environment to collect the data. Sensor Learning Application is hybridized with different sensors, which are the Sun Heat Sensor Detector, Soil Moisture Sensor Kit, and Sensor Monitoring Devices integrated with a Wi-Fi module using ESP32 that will yield a live data feed using Blynk software. This project supports the Sustainable Development Goals (SDG) that successfully increase the quality of education, provide the sensor trainer kit, and indirectly achieve sustainable energy, economic growth, and social sustainability at the agriculture project site.

Efficient Classification of Marine Debris using SVM with Noise Removal and Feature Extraction Techniques with Improved Performances

Marine debris is a significant environmental issue, necessitating the development of precise and efficient technologies for classifying and reducing its impact. This research tries to address the issues of image noise in ocean trash classification using four alternative filters the Gaussian, Bilateral, Mean, and Alpha-Trimmed Mean (ATM) filters for noise reduction and SVM for classification. The accuracy of categorization algorithms can be considerably impacted by the presence of noise in photos of maritime debris. In this study, we suggest a two-pronged strategy: To effectively decrease noise and improve the quality of the input photos, we first apply filters. These filters were chosen with care to protect significant details while eliminating extraneous noise. Second, we classify the debris into various categories based on its visual attributes using SVM, a powerful ML technique. The ATM filter significantly reduces noise and improves the clarity of photographs of ocean trash, according to the results of our experiments. This work proposes a novel approach for classifying marine debris using advanced machine learning algorithms. For improved classification accuracy, we suggest combining Support Vector Machines (SVM) with Adaptive Thresholding Mean (ATM) filtering and Histogram of Oriented Gradients (HOG) feature extraction. According to this research, the ATM filter is a promising option for noise reduction in ocean trash imaging, potentially increasing the precision of subsequent classification algorithms and assisting in efficient environmental monitoring and marine ecosystem conservation efforts. The language used for execution is Python.

Assessment and machine learning prediction of heavy metals fate in mining farmland assisted by Positive Matrix Factorization

The accurate pollutant prediction by Machine Learning (ML) is significant to efficient environmental monitoring and risk assessment. However, application of ML in soil is under studied. In this study, a Positive Matrix Factorization (PMF) assisted prediction method was developed with Support Vector Machine (SVM) and Random Forest (RF) for heavy metals (HMs) prediction in mining farmland. Principal Component Analysis (PCA) and Redundancy Analysis (RDA) were selected to pretreat data. Experiment results illustrated Cd was the main pollutant with heavy risks in the study area and Pb was easy to migrate. The method effects of HMs total concentration predicting were PMF > Simple > PCA > PCA - PMF, and RF predicted better than SVM. Data pretreatment by RDA prior inspection improved the model results. Characteristic HMs Tessier fractions prediction received good effects with average R value as 0.86. Risk classification prediction performed good in Cd, Cu, Ni and Zn, however, Pb showed weak effect by simple model. The best classifier method for Pb was PMF - RF method with relatively good effect (Area under ROC Curve = 0.896). Overall, our study suggested the combination between PMF and ML can assist the prediction of HMs in soil. Spatial weighted attribute of HMs can be provided by PMF.

Analysis of the problem of monitoring the concentration of Carbon Dioxide in Smart Home. Methods for measuring Carbon Dioxide

The demand for efficient environmental monitoring rises as smart houses become increasingly interwoven into our daily lives. The carbon dioxide concentration is one of the most crucial variables for indoor air quality and energy efficiency. This article addresses several ways for measuring carbon dioxide levels, including measurements with chemical, optical, and electrochemical sensors, as well as the difficulties that come with carbon dioxide monitoring in smart homes. Users can choose carbon dioxide monitoring solutions for smart home systems in an informed manner by being aware of the advantages and disadvantages of different techniques.

Evolution of China's NOx emission control strategy during 2005∼2020 over coal-fired power plants: A satellite-based assessment

Since especially the 12th Five-Year Plan (2011–2015), China has made great efforts to reverse the increasing trend of NOx emissions through end-of-pipe measures. With the Ozone Monitoring Instrument (OMI) level 2 swath product of tropospheric NO2, this study explores the temporal-spatial patterns of NOx concentrations over China's coal-fired power plants from 2005 to 2020 and investigates the evolution of its control strategy. The nationwide deployment of flue-gas denitration facilities was a critical measure to mitigate NOx emissions from coal-fired power plants, while this study externally assesses the implementation gap of their operation. Our results illustrate that, besides the impacts of economic cycles, China's control strategy experienced a dramatic transformation from an ad hoc campaign style for meeting short-term temporary targets to more sustainable, technology- and governance-centered institutional arrangements for ensuring long-term fundamental solutions. Furthermore, the satellite-based assessment may provide not only ex post evaluation, but also in-time and independent data for more effective and efficient environmental compliance monitoring.

Construction of an Orthophoto-Draped 3D Model and Classification of Intertidal Habitats Using UAV Imagery in the Galapagos Archipelago

Worldwide, an increasing number of marine islands suffer from various pressures on the environment, driven by climate change and increasing land demands. The Galapagos Archipelago is one of the most iconic group of islands, yet population growth and tourism have resulted in a rising need for efficient environmental monitoring of its fragile ecosystems, such as the intertidal zone which harbors diverse and unique fauna. The purpose of this study was to investigate the image classification opportunities for these intertidal habitats using Uncrewed Aerial Vehicle (UAV) imagery. The data for this research were collected in Puerto Ayora on Santa Cruz in August 2017, the most urbanized island of the Galapagos. An orthophoto, a digital elevation model (DEM), and an orthophoto-draped 3D model of the intertidal zone were obtained using image registration software. Based on the orthophoto, an initial classification of the intertidal zone was performed using the spectral angle mapper algorithm. A habitat map with four classes (water, sand, rock, and vegetation) was created with an overall classification accuracy of 77%, indicating the suitability of UAV high resolution aerial imagery for the classification of intertidal habitats. The developed method could be applied to map and monitor other coastal regions and islands systems.

Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring.

Wireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, water quality monitoring is a vital process to ensure the sustainable, important feeding of and as a life-maintaining source for many living creatures. To conduct this process efficiently, the integration of lightweight machine learning technologies can extend its efficacy and accuracy. WSNs often suffer from energy-limited devices and resource-affected operations, thus constraining WSNs' lifetime and capability. Energy-efficient clustering protocols have been introduced to tackle this challenge. The low-energy adaptive clustering hierarchy (LEACH) protocol is widely used due to its simplicity and ability to manage large datasets and prolong network lifetime. In this paper, we investigate and present a modified LEACH-based clustering algorithm in conjunction with a K-means data clustering approach to enable efficient decision making based on water-quality-monitoring-related operations. This study is operated based on the experimental measurements of lanthanide oxide nanoparticles, selected as cerium oxide nanoparticles (ceria NPs), as an active sensing host for the optical detection of hydrogen peroxide pollutants via a fluorescence quenching mechanism. A mathematical model is proposed for the K-means LEACH-based clustering algorithm for WSNs to analyze the quality monitoring process in water, where various levels of pollutants exist. The simulation results show the efficacy of our modified K-means-based hierarchical data clustering and routing in prolonging network lifetime when operated in static and dynamic contexts.

On-Shore Plastic Waste Detection with YOLOv5 and RGB-Near-Infrared Fusion: A State-of-the-Art Solution for Accurate and Efficient Environmental Monitoring

Plastic waste is a growing environmental concern that poses a significant threat to onshore ecosystems, human health, and wildlife. The accumulation of plastic waste in oceans has reached a staggering estimate of over eight million tons annually, leading to hazardous outcomes in marine life and the food chain. Plastic waste is prevalent in urban areas, posing risks to animals that may ingest it or become entangled in it, and negatively impacting the economy and tourism industry. Effective plastic waste management requires a comprehensive approach that includes reducing consumption, promoting recycling, and developing innovative technologies such as automated plastic detection systems. The development of accurate and efficient plastic detection methods is therefore essential for effective waste management. To address this challenge, machine learning techniques such as the YOLOv5 model have emerged as promising tools for developing automated plastic detection systems. Furthermore, there is a need to study both visible light (RGB) and near-infrared (RGNIR) as part of plastic waste detection due to the unique properties of plastic waste in different environmental settings. To this end, two plastic waste datasets, comprising RGB and RGNIR images, were utilized to train the proposed model, YOLOv5m. The performance of the model was then evaluated using a 10-fold cross-validation method on both datasets. The experiment was extended by adding background images into the training dataset to reduce false positives. An additional experiment was carried out to fuse both the RGB and RGNIR datasets. A performance-metric score called the Weighted Metric Score (WMS) was proposed, where the WMS equaled the sum of the mean average precision at the intersection over union (IoU) threshold of 0.5 (mAP@0.5) × 0.1 and the mean average precision averaged over different IoU thresholds ranging from 0.5 to 0.95 (mAP@0.5:0.95) × 0.9. In addition, a 10-fold cross-validation procedure was implemented. Based on the results, the proposed model achieved the best performance using the fusion of the RGB and RGNIR datasets when evaluated on the testing dataset with a mean of mAP@0.5, mAP@0.5:0.95, and a WMS of 92.96% ± 2.63%, 69.47% ± 3.11%, and 71.82% ± 3.04%, respectively. These findings indicate that utilizing both normal visible light and the near-infrared spectrum as feature representations in machine learning could lead to improved performance in plastic waste detection. This opens new opportunities in the development of automated plastic detection systems for use in fields such as automation, environmental management, and resource management.

Efficient Environmental Monitoring through LoRaWAN IoT Solutions

Efficient Environmental Monitoring through LoRaWAN IoT Solutions

Mobility of LoRaWAN Gateways for Efficient Environmental Monitoring in Pristine Sites.

Environmental monitoring of delicate ecosystems or pristine sites is critical to their preservation. The communication infrastructure for such monitoring should have as little impact on the natural ecosystem as possible. Because of their wide range capabilities and independence from heavy infrastructure, low-power wide area network protocols have recently been used in remote monitoring. In this regard, we propose a mobile vehicle-mounted gateway architecture for IoT data collection in communication-network-free areas. The limits of reliable communication are investigated in terms of gateway speed, throughput, and energy consumption. We investigate the performance of various gateway arrival scenarios, focusing on the trade-off between freshness of data, data collection rate, and end-node power consumption. Then we validate our findings using both real-world experiments and simulations. In addition, we present a case study exploiting the proposed architecture to provide coverage for Wadi El-Gemal national park in Egypt. The results show that reliable communication is achieved over all spreading factors (SFs) for gateway speeds up to 150 km/h with negligible performance degradation at SFs=11,12 at speeds more than 100 km/h. The synchronized transmission model ensures the best performance in terms of throughput and power consumption at the expense of the freshness of data. Nonsynchronized transmission allows time-flexible data collection at the expense of increased power consumption. The same throughput as semisynchronized transmission is achieved using four gateways at only five times the energy consumption, while a single gateway requires seventeen times the amount of energy. Furthermore, increasing the number of gateways to ten increases the throughput to the level achieved by the synchronized scenario while consuming eight times the energy.

Machine Learning Inspired Smart Agriculture System with Crop Prediction

Abstract: This paper presents Machine Learning Inspired Smart Agriculture System with Crop Prediction that will assist farmers in getting Live Data (Temperature, Soil Moisture, level of Nitrogen, Phosphorous and Potassium present in the soil, Alkalinity and Acidity in the soil etc. ) for efficient environment monitoring which will enable them to increase their overall yield and quality of products. The Machine Learning Inspired Smart Agriculture System with Crop Prediction is proposed where Machine Learning Technology is hybridized with different Sensors and a Wi-Fi module that will yield live data feed using online software Localhost and PHPmyadmin. The product being proposed is tested on Live Agriculture Fields giving high accuracy over 98% in data feeds

Low-cost microfluidics: Towards affordable environmental monitoring and assessment

Effective environmental monitoring has become a worldwide concern, requiring the development of novel tools to deal with pollution risks and manage natural resources. However, a majority of current assessment methods are still costly and labor-intensive. Thanks to the rapid advancements in microfluidic technology over the past few decades, great efforts have been made to develop miniaturized tools for rapid and efficient environmental monitoring. Compared to traditional large-scale devices, microfluidic approaches provide several advantages such as low sample and energy consumption, shortened analysis time and adaptabilities to onsite applications. More importantly, it provides a low-cost solution for onsite environmental assessment leveraging the ubiquitous materials such as paper and plastics, and cost-effective fabrication methods such as inkjet printing and drawing. At present, devices that are disposable, reproducible, and capable of mass production have been developed and manufactured for a wide spectrum of applications related to environmental monitoring. This review summarizes the recent advances of low-cost microfluidics in the field of environmental monitoring. Initially, common low-cost materials and fabrication technologies are introduced, providing a perspective on the currently available low-cost microfluidic manufacturing techniques. The latest applications towards effective environmental monitoring and assessment in water quality, air quality, soil nutrients, microorganisms, and other applications are then reviewed. Finally, current challenges on materials and fabrication technologies and research opportunities are discussed to inspire future innovations.

Integrated Online Platform for Agricultural Products and Information

Abstract: Agriculture is the basic and most important profession of our country as it balances the food requirement and also the essential raw materials for several industries. The implementation of smart technology in agriculture practices needs to be focused on for better land productivity. Internet of Things technology has brought a revolution to each and every field of common man's life by making everything smart and intelligent. The development of Intelligent Smart Farming based on IoT is day by day turning the face of agriculture production by not only enhancing it but also making it cost-effective and reducing wastage. The aim of this project is to propose a Novel Smart Interactive Platform for Integrated Information of Agricultural Products based Agriculture system that can assist farmers in crop management by getting Live Data (Temperature, Humidity, RainFall, Soil moisture content) for efficient environment monitoring which will enable smart farming and increase their overall yield and quality of products. This system proposes an interactive platform for integrated information of agricultural products which combines farm production, supply and marketing of agricultural products. This will help the information platform to play the role of information collection and distribution, and to improve the efficiency of the circulation of agricultural products. Keywords: Agricultural Information, Agricultural Products, Weather Forecast, E-Commerce.

An efficient environmental monitoring data encryption algorithm based on DNA coding and hyperchaotic system

An efficient environmental monitoring data encryption algorithm based on DNA coding and hyperchaotic system

Indoor environment monitoring system tested in a living lab

Recently, an indoor environment monitoring system using the internet of things sensor has been actively used to secure the thermal comfort of occupants, reduce energy use, and control indoor air quality. However, most commercial sensor and control applications are limited to the unilateral sharing of measurement results and simple operation control of related equipment. This study describes the development of a system for real-time and easy monitoring of the indoor environment and energy consumption. This study then describes the application of the system to the living lab. The system was built using Raspberry Pi and embedded sensors (temperature, humidity, PM1.0, PM2.5, PM10, and CO2) to collect indoor air quality-related data. The data can be safely stored for a long time, and residents can easily check and interact with the data. This study presents the problems and requirements that occurred during the development and implementation of the proposed system, data measured in the living lab and discusses the analysis results. The proposed system is expected to support continuous and efficient environment monitoring and control seasonal energy use by recognizing changes in the indoor environment according to the behavior of residents and estimating energy consumption.

A molecularly imprinted antibiotic receptor on magnetic nanotubes for the detection and removal of environmental oxytetracycline.

The detection and elimination of antibiotic contaminants, such as oxytetracycline (OTC), a broad-spectrum tetracycline antibiotic, would be of help in efficient environmental monitoring, agriculture and food safety tests. Nevertheless, currently available methodologies, which mostly rely on the chromatographic separation of OTC, suffer from low sensitivity and complicated processes. Thus, we report here on the design and synthesis of a fluorescent sensor based on molecularly imprinted magnetic halloysite nanotubes (referred to as MHNTs@FMIPs) for the effective detection and purification of OTC in actual environmental samples. The fluorescence of the MHNTs@FMIPs was quenched obviously upon loading with OTC, covering a linear concentration range of 10-300 nM with a limit of detection (LOD) as low as 8.1 nM. The imprinting factor is 4.47, indicating an excellent specificity. Furthermore, the MHNTs@FMIPs can be applied to the quantitative detection of OTC (5 cycles of 300 nM) in aquaculture wastewater and Yangtze River water, demonstrating their immense application potential.