Environmental Monitoring Process Research Articles

Trends, progress and future directions of nanomaterial based sensors: a bibliometric overview

PurposeNanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc.Design/methodology/approachIn this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021.FindingsAmong these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.Research limitations/implicationsThe future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.Originality/valueThis is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

Smartphone-Integrated resorcinarene macrocycle capped silver nanoparticles (RMF-AgNPs) probe for enhanced La(III) detection in diverse environments

The demand for precise and responsive detection of Lanthanum (La(III)) is critical in key areas such as environmental monitoring, biomedical research, and industrial processes, underscoring the need for advanced sensor technologies. This research reports an efficient and cost-effective preparation of resorcinarene macrocycle framework (RMF) capped silver nanoparticles (RMF-AgNPs). These nanoparticles were then applied as a novel sensor for La(III), demonstrating dual functionality: they act as a colorimetric sensor and a paper-based probe. Unique in its class, the sensor excels in detecting La(III) at ultra-low concentrations, unaffected by other ions, and undergoes a significant color transition from yellow to grey in response to La(III) presence. This change is linked to the localized surface plasmon resonance (LSPR) absorbance alteration. Extensive analytical methods like FTIR, XPS, AFM, FESEM, DLS, Zeta potential, and UV–Vis spectroscopy confirmed that the sensor operates by aggregating nanoparticles induced by La(III). Adding to its practicality, we incorporated a smartphone interface with the paper-based sensor, enhancing its utility for on-the-spot, real-time detection. The sensor's efficacy was validated across various samples, including soil, rock, urine, drinking water, and industrial effluent, showing its versatility. With detection limits of 15 nM (colorimetric) and 280 nM (paper-based), and linear dynamic ranges from 0.05 to 100 µM and 0.5–100 µM respectively, this sensor marks a significant advancement, effectively bridging lab-based detection with field application needs.

Effect of overheat and direct flow loading on the MEMS bistable flow sensor

We present the findings from an experimental study of a MEMS flow sensor in which an initially curved, double-clamped bistable microbeam is the primary sensing element. Our research explores how the overheat ratio, direct flow loading, and turbulence-induced vibration affect the sequential snap-through (ST) buckling and snap-back (SB) release of an electrostatically actuated beam heated by an electric current. The sensor is fabricated from highly doped single-crystal silicon using a silicon-on-insulator (SOI) wafer. Positioned at the chip’s edge, the microbeam is exposed to airflow, enabling concurrent dynamic response measurements with a laser Doppler vibrometer and a video camera.Our research demonstrates that the overheat ratio can be significantly lower for this sensing principle than conventional thermal sensing elements, pointing to the potential for substantial energy savings. We also emphasize the significant impact of flow angles and vibrations on the critical ST and SB voltages, which are vital for the flow sensor’s output. Additionally, we introduce the first direct experimental observation of the beam profile’s time history during the snap-through/snap-back transition.The potential impact of this research lies in developing more robust MEMS flow sensors with enhanced sensitivity and a better understanding of their response to environmental factors, which could have broader applications in fields such as aerospace, environmental monitoring, and industrial process control.

Er3+/Yb3+/Ho3+ tri-doped no-core fiber temperature sensor based on fluorescence intensity ratio technology: towards high stability and accurate measurements.

In this paper, the green upconversion (UC) fluorescence emission from E r 3+/Y b 3+/H o 3+ tri-doped tellurite glass is investigated for temperature sensing. The doping of H o 3+ ions not only enhances the chance of energy level transition but also avoids the influence of the thermal effect caused by the proximity of 2 H 11/2 and 4 S 3/2 energy levels. The luminescence characteristics at different Y b 3+ and H o 3+ ion concentration doping molar ratios were investigated, and the strongest luminescence characteristics were exhibited when the Y b 3+ ion concentration was at 5mol% and H o 3+ at 0.2mol%. Based on this, a tri-doped T e O 2-Z n O-B i 2 O 3 (TZB) no-core fiber was fabricated and connected with multimode fibers (MMFs) to form a temperature sensor. The temperature sensing performance of the tri-doped TZB temperature sensor was evaluated in detail over the temperature range of 255-365K. The repeatability and stability of the temperature sensor was experimentally verified. The E r 3+/Y b 3+/H o 3+ tri-doped sensor can be used for noninvasive optical temperature sensing in the fields of environmental monitoring, biological sensing, and industrial process temperature control, etc.

Nickel doping: Improving the optical properties of carbon dots and increasing the sensitivity for detecting pH and water

pH and water play a crucial role in chemical, environmental and physiological monitoring processes, so it is of great importance to establish a simple and sensitive multi-mode detection method. In this study, we fabricated nickel-doped carbon dots (Ni-CDs) by a hydrothermal method using nickel chloride (NiCl2•6 H2O) and m-phenylenediamine (m-PDA) as precursors, which exhibits excellent photostability, outstanding resistance to photobleaching and ultra-low cytotoxicity. Ni doping increased the fluorescence quantum yield (from 21.4% to 30.3%) and H content (from 44.4% to 57.1%) of the Ni-CDs, making them highly sensitive to pH and water, which facilitated the detection of pH and water based on deprotonation and aggregation mechanisms, respectively. Ni-CDs can detect pH in the ranges of 6.0–9.0 and 6.0–11.0 via fluorometric and smartphone methods, respectively. The limits of detection (LODs) of the Ni-CDs in DMSO, DMF and THF for the detection of water were 0.0112%, 0.0144% and 0.0060%, respectively. Moreover, the Ni-CDs have been effectively utilized for pH detection in real samples as well as for pH in cells and zebrafish with satisfactory results. More importantly, the Ni-CDs/hydrogel remains perfectly the sensitive to pH and water, broadening the applications of the CDs in sensing, bioimaging and information encryption.

Wireless Sensor Network Based Gas Monitoring System Utilizing ZigBee Technology

This paper presents a ZigBee-based wireless network gas monitoring system, utilizing ZigBee technology to enable reliable and low-cost communication. The system leverages small sensor nodes and ZigBee protocols to collect data on physical phenomena, particularly gas levels, in various environments. The network's low-power consumption and extended range capabilities allow for effective gas sensing within a radius of up to 300 meters. While interoperability with devices from different manufacturers remains a limitation, the system showcases the versatility of ZigBee technology in diverse fields. The study evaluates the system's performance through simulation and experimentation, addresses potential challenges, and proposes future developments to enhance ZigBee-based wireless network gas monitoring systems, offering improved remote monitoring and manipulation capabilities. Furthermore, our research contributes to the advancement of wireless sensor network technology by demonstrating the practicality of ZigBee in gas monitoring. The findings from our experiments and simulations provide valuable insights into the optimization of such systems, which can have far-reaching impacts on environmental monitoring, safety, and industrial processes. In addition, our proposed enhancements pave the way for more reliable and efficient remote monitoring and control, offering substantial benefits for various industries, including environmental management, industrial automation, and beyond.

A Nonlinear Gradient-Coiling Metamaterial for Enhanced Acoustic Signal Sensing

Acoustic sensing systems play a critical role in identifying and determining weak sound sources in various fields. In many fault warning and environmental monitoring processes, sound-based sensing techniques are highly valued for their information-rich and non-contact advantages. However, noise signals from the environment reduce the signal-to-noise ratio (SNR) of conventional acoustic sensing systems. Therefore, we proposed novel nonlinear gradient-coiling metamaterials (NGCMs) to sense weak effective signals from complex environments using the strong wave compression effect coupled with the equivalent medium mechanism. Theoretical derivations and finite element simulations of NGCMs were executed to verify the properties of the designed metamaterials. Compared with nonlinear gradient acoustic metamaterials (Nonlinear-GAMs) without coiling structures, NGCMs exhibit far superior performance in terms of acoustic enhancement, and the structures capture lower frequencies and possess a wider angle acoustic response. Additionally, experiments were constructed and conducted using set Gaussian pulse and harmonic acoustic signals as emission sources to simulate real application scenarios. It is unanimously shown that NGCMs have unique advantages and broad application prospects in the application of weak acoustic signal sensing, enhancement and localization.

Semi-FCMNet: Semi-Supervised Learning for Forest Cover Mapping from Satellite Imagery via Ensemble Self-Training and Perturbation

Forest cover mapping is of paramount importance for environmental monitoring, biodiversity assessment, and forest resource management. In the realm of forest cover mapping, significant advancements have been made by leveraging fully supervised semantic segmentation models. However, the process of acquiring a substantial quantity of pixel-level labelled data is prone to time-consuming and labour-intensive procedures. To address this issue, this paper proposes a novel semi-supervised-learning-based semantic segmentation framework that leverages limited labelled and numerous unlabelled data, integrating multi-level perturbations and model ensembles. Our framework incorporates a multi-level perturbation module that integrates input-level, feature-level, and model-level perturbations. This module aids in effectively emphasising salient features from remote sensing (RS) images during different training stages and facilitates the stability of model learning, thereby effectively preventing overfitting. We also propose an ensemble-voting-based label generation strategy that enhances the reliability of model-generated labels, achieving smooth label predictions for challenging boundary regions. Additionally, we designed an adaptive loss function that dynamically adjusts the focus on poorly learned categories and dynamically adapts the attention towards labels generated during both the student and teacher stages. The proposed framework was comprehensively evaluated using two satellite RS datasets, showcasing its competitive performance in semi-supervised forest-cover-mapping scenarios. Notably, the method outperforms the fully supervised approach by 1–3% across diverse partitions, as quantified by metrics including mIoU, accuracy, and mPrecision. Furthermore, it exhibits superiority over other state-of-the-art semi-supervised methods. These results indicate the practical significance of our solution in various domains, including environmental monitoring, forest management, and conservation decision-making processes.

Enhancing long-term stability of photoacoustic gas sensor using an extremum-seeking control algorithm

Smart sensor systems have gained increasing importance in various fields, including healthcare, environmental monitoring, industrial automation, and security. Photoacoustic gas sensors are an emerging type of optical sensor used in various applications due to its enhanced performance characteristics. However, the accuracy and reliability of gas concentration measurements from photoacoustic gas sensors may be impacted by several known limitations, including drift of the gas cell resonant frequency over extended periods of time. Researchers have proposed various solutions, including optimization methods and signal processing algorithms, to address this and others issues. In this paper, we propose a novel solution using an extremum-seeking control algorithm to manage the laser modulation frequency of photoacoustic gas sensors. By tracking the changing resonant frequency of the gas cell, long-term stability can be achieved, making it suitable for environmental monitoring, petroleum exploration, and industrial process control. Our approach has the potential to improve the accuracy and reliability of long-term measurements obtained from photoacoustic gas sensors, providing a stable and reliable method for gas concentration estimation.

Ordered Mesoporous Electrodes for Sensing Applications.

Electrochemical sensors have become increasingly relevant in fields such as medicine, environmental monitoring, and industrial process control. Selectivity, specificity, sensitivity, signal reproducibility, and robustness are among the most important challenges for their development, especially when the target compound is present in low concentrations or in complex analytical matrices. In this context, electrode modification with Mesoporous Thin Films (MTFs) has aroused great interest in the past years. MTFs present high surface area, uniform pore distribution, and tunable pore size. Furthermore, they offer a wide variety of electrochemical signal modulation possibilities through molecular sieving, electrostatic or steric exclusion, and preconcentration effects which are due to mesopore confinement and surface functionalization. In order to fully exploit these advantages, it is central to develop reproducible routes for sensitive, selective, and robust MTF-modified electrodes. In addition, it is necessary to understand the complex mass and charge transport processes that take place through the film (particularly in the mesopores, pore surfaces, and interfaces) and on the electrode in order to design future intelligent and adaptive sensors. We present here an overview of MTFs applied to electrochemical sensing, in which we address their fabrication methods and the transport processes that are critical to the electrode response. We also summarize the current applications in biosensing and electroanalysis, as well as the challenges and opportunities brought by integrating MTF synthesis with electrode microfabrication, which is critical when moving from laboratory work to in situ sensing in the field of interest.

ENVIRONMENT MONITORING ALONG CHINA-EUROPE RAILWAY EXPRESS WITH REMOTE SENSING AND ARTIFICIAL INTELLIGENT TECHNOLOGY: A REGIONAL COLLABORATION PROJECT BETWEEN CHINA AND SERBIA

Abstract. The operation of the China-Europe railways has facilitated economic development and improved regional connectivity along the route, while impacting the environment. Effective monitoring of the environmental status along the railway is necessary to promote the sustainable development of the Belt and Road Initiative (BRI). Existing remote sensing-based monitoring methods depend on local prior knowledge and model selection, resulting in insufficient specificity when applying to the China-Europe railways, which have a large spatial extent and complex ground variability across nations. Financed by the 2020 China-CEEC Joint Education Project of Institutions of Higher Education, we combined the preponderant research fields of the Beijing University of Civil Engineering and Architecture (BUCEA) and the University of Novi Sad (UNS) and established an environmental monitoring process based on artificial intelligence and remote sensing techniques. More specifically, an online annotating platform and deep learning based classification procedure were established, followed by environmental analysis conducted with comprehensive indicators and a difference-in-difference method. Our results provide 10 years of monitoring data on the environmental status of the China-Europe Railway Express, based on which, the impacts of the economic corridors on the local environment before & after the establishment were evaluated specifically. This collaborative project supports the BRI initiative and highlights the potential for international collaboration in large-scale environmental monitoring.

The promise and pitfalls of community-based monitoring with a focus on Canadian examples

Community-based monitoring (CBM) is a widely used form of scientific data collection in which local community members directly participate in ecosystem research and also help the process by sharing traditional ecological knowledge and local understanding of land and resources. This paper presents a review of the challenges and opportunities associated with CBM projects in Canada and internationally. While Canadian cases are the primary focus, international examples are drawn on to provide additional context. Based on our review of 121 documents and publications, we found that CBM helps fill science research gaps by providing access to continuous data sets on the ecosystems being studied. CBM also increases the credibility of the data among users, as the community itself takes part in the environmental monitoring process. CBM supports cross-cultural learning and the co-production of knowledge by using traditional ecological knowledge with science, thereby enabling researchers, scientists and community members to learn from one another. Our review suggests that although there are multiple successes, CBM faces several challenges that constrain its progress including funding shortages, lack of support for local stewardship, and inadequate training for local users in the operation of equipment and data collection methods. Data sharing and rights on the use of data are also constraining to the long-term success of CBM programs.

Deep learning coupled model based on TCN-LSTM for particulate matter concentration prediction

In this study, we combined the Temporal Convolutional Network (TCN) model with the Long Short-Term Memory (LSTM) network model and applied it to prediction of atmospheric particulate matter (PM) concentrations. The study area is Xi'an City, Shaanxi Province, and the study period is from January 2015 to July 2022. During this period, Xi'an exceeded China's National Ambient Air Quality Grade Ⅰ standard for PM for up to 70% of the days. The prediction results of the TCN-LSTM model were compared with those of deep learning models (Convolutional Neural Network-LSTM, TCN, and LSTM) and machine learning models (Support Vector Regression and Random Forest). The R2 values of the TCN-LSTM model were all >0.88, indicating better performance than that of the other five models, and the errors of the TCN-LSTM model were all lower than those of the other five models. The results showed that high-accuracy PM predictions using deep learning models can improve air quality monitoring by compensating for problems in the environmental monitoring process such as pollutant monitoring errors caused by instrument failures. Additionally, sensitivity analysis helps to identify the key factors influencing the behavior of PM. A sensitivity analysis of PM for different periods of COVID-19 found that PM2.5 is more sensitive to O3, while PM10 is mainly influenced by PM2.5. The sensitivity analysis for the whole period showed that PM was closely related to CO. Removing variables that do not contribute to the model output based on the sensitivity analysis results improves modeling efficiency while reducing operating costs and improving environmental monitoring activities and management strategies.

High precision measurement of spectroscopic parameters of H<sub>2</sub>S in 6320—6350 cm<sup>–1</sup> band

As a highly corrosive and highly toxic gas, hydrogen sulfide (H<sub>2</sub>S) is an important intermediate product or pollutant in many fields such as chemical industry, energy and environment. Accurate online measurement of its concentration is of great significance for process control and production safety. Tunable diode laser absorption spectroscopy (TDLAS), as a quantitative absorption spectroscopy technique, is suitable for high-precision on-line measurement of H<sub>2</sub>S concentration in atmospheric environmental monitoring and industrial processes control. Considering that most of the spectroscopic parameters of H<sub>2</sub>S in the HITRAN2020 database are mainly calculated based on semi-empirical theoretical model and the experimental data to support them are lacking. In this work, direct absorption spectroscopy (DAS) method is firstly used to measure the absorption spectra of H<sub>2</sub>S in the band of 6320–6350 cm<sup>–1</sup>. Six groups of characteristic lines with strong absorption and relative independence are selected as the target transitions for experimental measurement. Then, the wavelength modulation-direct absorption (WM-DAS) method with no calibration and high signal-to-noise ratio is used to measure the absorbances of the six groups of transitions under different pressures. Voigt, Raution and quadratic speed-dependent Voigt profiles fit the measured absorbances by least squares method in order to obtain the spectroscopic parameters such as the collision broadening coefficient, line strength and Dicke narrowing coefficient. And the minimum standard deviation of residual error of absorbances is 7×10<sup>–5</sup>. The measurement uncertainty of each line strength is less than 2%, and the uncertainty of collision broadening coefficients, Dicke narrowing coefficients and the speed-dependent coefficients are all less than 10%. This work is helpful in improving the H<sub>2</sub>S spectral database and providing the spectral data basis for the high-precision measurement of H<sub>2</sub>S concentration.

Assessment of Chambal River Water Quality Parameters: A MATLAB Simulation Analysis

In this research work, environmental monitoring processes and assessments are carried out by the modeling and analysis of the water quality of the Chambal River in the state of Rajasthan. Various samples were collected from different locations along the course of the river flow. This water is used for different kinds of human, animal, and agriculture corp. activities. Comparative analyses were conducted on the water parameters, viz. biochemical oxygen demand, chemical oxygen demand, dissolved oxygen, total coliform, and conductivity, for the of consecutive years 2020 and 2021. A model was developed with the help of MATLAB Simulink software (Version R2019a) to find the causes of oxygen deficiency and reoxygenation in water with time and distance. The results of two consecutive years help to predict the responsible factors for the degradation of the river’s water quality. The water quality modeling and simulation results conclude that the water quality of the Chambal River flowing through Rajasthan can rejuvenate itself during an alarming oxygen deficit within a short period. According to the results of this study, the concentration of dissolved oxygen in the water of the Chambal River is high enough to support the survival of the endangered species that inhabit the area.

Analytisk kemi – några reflektioner

Analytical chemistry is an interdisciplinary field of science related to many practical applications, such as clinical diagnostics, environmental monitoring and industrial process analysis. Over the last 50 years, chemical analytics has evolved from centralized laboratory measurements towards on-line and distributed analysis, including wearable chemical sensors for personal health monitoring. In this article I wish to reflect upon these developments in analytical chemistry, illustrated by examples from my own research. The text provides a glimpse of analytical chemistry from my personal perspective.

Ultrahigh sensitive NO sensor based on WO3 film with ppb-level sensitivity

Nitric oxide (NO) is one of the hazardous gases which directly affect the vital organs of human beings. An accurate identification is essential for air quality assurance, environmental monitoring, automotive technology, and other industrial processes. Herein, we report nanostructured tungsten oxide (WO3) thin-film grown on SiO2 substrate under controlled oxidation conditions for NO sensing. The nanostructured-WO3 film's monoclinic phase has an average crystallite size of 12.89 nm, a lattice strain of 20.56, and an optical band gap of 2.89 eV. The fabricated device demonstrates high selectivity, stability, and faster response/recovery time with a sensor response of 1.26 for 100 ppb NO concentration, which increases to an ultra-high response (>25) for 1 ppm at 250 °C with a detection limit of ∼4 ppb. Ultrahigh response of the sensors is due to high specific surface area and effective gas diffusion ability, which lead to potential applications in low concentration NO detection.

АНАЛІЗУВАННЯ ДІЯЛЬНОСТІ СИСТЕМИ МОНІТОРИНГУ СТАНУ ҐРУНТІВ В УКРАЇНІ

The article reveals that the anthropogenic impact on the environment in Ukraine, including land resources, is constantly increasing. Modern use of Ukraine's natural resources does not meet the requirements of rational nature management – the proportion of the plowed land in Ukraine is the highest in the world. Limited and irregular systematic data on the state of soils does not contribute to effective activities in industries that use soil as the main mean of production. Much time has passed since the period of continuous soil survey in Ukraine, so its' data is outdated. Therefore, there is a need for a new survey using modern methods and methodologies, innovative technologies and devices for monitoring the condition of soils, including the use of remote sensing. Ukraine has a multi-level system of environmental monitoring in all natural and geographical areas, taking into account various forms of anthropogenic impact; the state system of environmental monitoring is an integral part of the national information infrastructure compatible with similar systems of other countries; environmental monitoring is carried out at four levels: local, regional, national and global; the current system of environmental monitoring in Ukraine uses various methods and criteria for assessing the state of the environment, including soils, environmental monitoring is carried out irregularly; there is no normative-legislative base of the newest organizational and information technologies application to ecologically balanced nature use, including land use. To improve the process of environmental monitoring in Ukraine, it is proposed to introduce a system of public administration in this area, which includes the following stages: goal setting, establishing a form of government, improving regulations and laws on soil monitoring. The introduction of the relevant system will allow to systematize and coordinate the activities of public authorities, use the same methods and criteria for assessing the state of the environment, including soils, direct agreed decisions to local authorities, enterprises, farmers, NGOs and the publicity.

Research on Intelligent Dispatching System Management Platform for Construction Projects Based on Digital Twin and BIM Technology

Building Information Modeling (BIM) adoption along with the recent emergence of Internet of Things (IoT) applications provides many unique knowledge and decision-making abilities all through the built environment’s life cycle. The ability to connect online sensors utilized in surroundings in real time has led to the definition of the Digital Twin (DT) of the Building Design. The goal of Digital Twins is to synchronize the physical world with a virtual platform for seamless management and control of the construction process, infrastructure solutions, environmental monitoring, and other life span processes within building design. Most of the researchers focused on either BIM or DT in the construction of building application. In this research work, a novel hybrid model of Digital Twin-Building Information Modeling (DT-BIM) is proposed. This model does the process of identifying the shortage of resources, analyzing requirement, performing decision, dispatching the resources, and updating all the process in the database with the support of Artificial Intelligence (AI). Hence, this hybrid model provides improved results when compared to the implementation of the individual technology to the same application. The study results revealed that these hybrid technologies help in assisting the dispatch systems in the construction projects to a greater extent.

Автоматизация процесса экологического мониторинга в особо охраняемых природных территориях Российской Федерации на базе системы управления контентом Directum RX

For organizations that execute control over specially protected natural areas of the Russian Federation, the task of consolidating data on ongoing observations is relevant. These data, called the chronicles of nature, for a long time were kept in a simplified, paper form and did not have a clear structure. The task of automating business processes for collecting data and exchanging this data between members of the scientific community, as well as building models necessary for the scientific departments of parks and reserves, is important and relevant. In this connection, we see it relevant to consider automating the process of environmental monitoring by developing an electronic document management module based on the integration platform Directum RX business solutions. The purpose of the research is to automate the eco-monitoring process based on the Directum RX platform, which allows to build a corporate content management system, as well as create a full-fledged data storage and retrieval system for collecting information about observations. The article describes the role model for working with the system, the system architecture and the developed components of the "Ecomonitoring" module based on the Directum RX platform. For this, a structural method was used, by dividing the task into many independent stages available for understanding and solving and hierarchical ordering. Integration with the Yandex weather service has been developed for further use in analytical models. A solution has been developed to manage the universal classifier of animals in accordance with the classifier of biological taxonomy accepted in the world. The automation of user actions for collecting and processing information about observations is demonstrated. As a result, the process of environmental monitoring was automated in one of the reserves of the Russian Federation.