Large-scale Environmental Monitoring Research Articles

Sensitive detection of unsafe nitrite chemical based on GO@Fe2O3/Y2O3 nanocomposite by electrochemical approach for environmental assessment

In this study, we quantified nitrite in phosphate buffer solution (PBS) at a pH of 8.00 using linear sweep voltammetry (LSV) under room conditions. The working electrodes (WE) were flat glassy carbon electrodes (GCEs) modified with a nanocomposite of graphene oxide, iron oxide, and yttrium oxide (GO@Fe2O3/Y2O3 NCs), fabricated using a 5 % Nafion conducting coating binder. The GO@Fe2O3/Y2O3 NCs were prepared by a solid-state chemical process and thoroughly analyzed using various techniques, including XRD, FTIR, FESEM, EDS, BET, HRTEM, EIS, and XPS, to examine their binding energy, surface area, crystallinity, structural integrity, functionality, and morphology. Electrochemical characteristics and detection capabilities of the modified electrodes were examined using cyclic voltammetry (CV), linear sweep voltammetry (LSV), and electrochemical impedance spectroscopy (EIS). The linear dynamic range (LDR) for nitrite detection, determined using LSV, was from 0.74 to 1.09 M. The sensor exhibited a sensitivity of 73.83966 µAmM−1cm−2, calculated from the slope of the calibration curve considering the electrode’s surface area. The lower limit of detection (LOD) was found to be 2.25 mM, and the limit of quantification (LOQ) was 7.50 mM. Additionally, we thoroughly investigated pH optimization, sensor-probe characteristics, stability, and reliability of the sensor under the same conditions. Validity tests conducted on the GO@Fe2O3/Y2O3 NCs/Nafion/GCE sensor probe demonstrated reliable and accurate performance in spike samples. This novel electrochemical sensor method shows promise for large-scale environmental chemical monitoring, aiming to enhance safety in environmental and healthcare sectors.

Assessing the Potential of UAV for Large-Scale Fractional Vegetation Cover Mapping with Satellite Data and Machine Learning

Fractional vegetation cover (FVC) is an essential metric for valuating ecosystem health and soil erosion. Traditional ground-measuring methods are inadequate for large-scale FVC monitoring, while remote sensing-based estimation approaches face issues such as spatial scale discrepancies between ground truth data and image pixels, as well as limited sample representativeness. This study proposes a method for FVC estimation integrating uncrewed aerial vehicle (UAV) and satellite imagery using machine learning (ML) models. First, we assess the vegetation extraction performance of three classification methods (OBIA-RF, threshold, and K-means) under UAV imagery. The optimal method is then selected for binary classification and aggregated to generate high-accuracy FVC reference data matching the spatial resolutions of different satellite images. Subsequently, we construct FVC estimation models using four ML algorithms (KNN, MLP, RF, and XGBoost) and utilize the SHapley Additive exPlanation (SHAP) method to assess the impact of spectral features and vegetation indices (VIs) on model predictions. Finally, the best model is used to map FVC in the study region. Our results indicate that the OBIA-RF method effectively extract vegetation information from UAV images, achieving an average precision and recall of 0.906 and 0.929, respectively. This method effectively generates high-accuracy FVC reference data. With the improvement in the spatial resolution of satellite images, the variability of FVC data decreases and spatial continuity increases. The RF model outperforms others in FVC estimation at 10 m and 20 m resolutions, with R2 values of 0.827 and 0.929, respectively. Conversely, the XGBoost model achieves the highest accuracy at a 30 m resolution, with an R2 of 0.847. This study also found that FVC was significantly related to a number of satellite image VIs (including red edge and near-infrared bands), and this correlation was enhanced in coarser resolution images. The method proposed in this study effectively addresses the shortcomings of conventional FVC estimation methods, improves the accuracy of FVC monitoring in soil erosion areas, and serves as a reference for large-scale ecological environment monitoring using UAV technology.

Satellite-based measurements of temporal and spatial variations in C fluxes of irrigated and rainfed cotton grown in India

The small number of carbon dioxide (CO2) observation networks and the prohibitively high equipment cost restrict the estimation of net ecosystem CO2 exchange (NEE). Satellite-based remote sensing techniques have made it possible to obtain NEE and component carbon (C) fluxes. One-third of the world's cotton area is in India, but the information on NEE is limited. We used the Level 4 Carbon (L4_C) product from the Soil Moisture Active Passive (SMAP) mission to estimate C fluxes based on satellite-derived soil moisture, weather, and vegetation data. For our study (2018-19 to 2020-21), we chose two ecosystems (rainfed central India vs. irrigated northern India). Seasonal variations were observed in C fluxes. Gross primary productivity was the highest during the boll formation phase. This phase was the strongest sink and coincided with the highest CO2 uptake, followed by the flowering and square formation phases. The cotton crop was a C source during the initial vegetative phase and after the boll opening. Overall, the cotton crop was a sink for atmospheric CO2 with an average NEE value of −189.6 g C m−2 under irrigated and −245.6 g C m−2 in rainfed cotton. Higher ecosystem respiration in irrigated cotton resulted in lower C sink strength than rainfed cotton. Our studies indicate that the SMAP L4_C product model estimates can be used to obtain information on C fluxes in real-world situations. Moreover, such satellite-based remote sensing techniques will enable large-scale environmental monitoring with different cropping systems and support policymaking.

Environmental noise monitoring using distributed hierarchical wireless acoustic sensor network

Acoustic noise pollution is one of many problems people face as cities grow. Long-term noise exposure can result in a series of physical and mental health diseases that are highly harmful to foetuses and newborns. Hence, many IoT-based wireless sensor network systems have been proposed for automated monitoring for long-term operation. However, these systems suffer from weaknesses in functionality, power consumption, costs, and scalability, which hinder large-scale deployment. In this study, we propose a distributed hierarchical wireless acoustic sensor network for environmental noise monitoring to do sound classification and A-weighted sound-pressure-level measurement to address the shortcomings of existing systems. A series of tests and comparisons are performed in diagnosing the performance with respect to recording continuity, packet loss, recording quality, accuracy on A-weighted sound pressure level calculations, and costs. Results show that this proposed network structure is feasible as a part of hardware implementation in a large-scale, low-cost, and high-scalable environmental noise monitoring system to classify sound.

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.

Missing Value Imputation of Wireless Sensor Data for Environmental Monitoring.

Over the past few years, the scale of sensor networks has greatly expanded. This generates extended spatiotemporal datasets, which form a crucial information resource in numerous fields, ranging from sports and healthcare to environmental science and surveillance. Unfortunately, these datasets often contain missing values due to systematic or inadvertent sensor misoperation. This incompleteness hampers the subsequent data analysis, yet addressing these missing observations forms a challenging problem. This is especially the case when both the temporal correlation of timestamps within a single sensor and the spatial correlation between sensors are important. Here, we apply and evaluate 12 imputation methods to complete the missing values in a dataset originating from large-scale environmental monitoring. As part of a large citizen science project, IoT-based microclimate sensors were deployed for six months in 4400 gardens across the region of Flanders, generating 15-min recordings of temperature and soil moisture. Methods based on spatial recovery as well as time-based imputation were evaluated, including Spline Interpolation, MissForest, MICE, MCMC, M-RNN, BRITS, and others. The performance of these imputation methods was evaluated for different proportions of missing data (ranging from 10% to 50%), as well as a realistic missing value scenario. Techniques leveraging the spatial features of the data tend to outperform the time-based methods, with matrix completion techniques providing the best performance. Our results therefore provide a tool to maximize the benefit from costly, large-scale environmental monitoring efforts.

Agriculturally Improved and Semi-Natural Permanent Grasslands Provide Complementary Ecosystem Services in Swedish Boreal Landscapes

Permanent grasslands cover more than a third of European agricultural land and are important for a number of ecosystem services. Permanent grasslands used for agriculture are broadly separated into agriculturally improved and semi-natural grasslands. High cultural and natural values linked to semi-natural grasslands are well documented. However, in boreal and hemi-boreal agricultural landscapes, less information is available about the areal coverage of improved permanent grasslands and their role for ecosystem service provision and biodiversity. In Sweden, grasslands are administratively separated into semi-natural (i.e., land that cannot be ploughed) or arable (i.e., improved temporary or permanent grassland on land that can be ploughed). We used data from a large-scale environmental monitoring program to show that improved permanent grassland (i.e., permanent grasslands on arable fields) may be a previously unrecognised large area of the agricultural land use in Sweden. We show that improved permanent grasslands together with semi-natural grasslands are both comparable but also complementary providers of a range of ecosystem services (plant species richness, plant resources for pollinators and forage amount for livestock production). However, as expected, semi-natural grasslands with the highest-level AESs (special values) show high species richness values for vascular plants, plants indicating traditional semi-natural management conditions and red-listed species. Improved permanent grasslands on arable fields are likely an underestimated but integral part of the agricultural economy and ecological function in boreal landscapes that together with high nature value semi-natural grasslands provide a broad range of ecosystem services.

A gap-filling method for satellite-derived chlorophyll-a time series based on neighborhood spatiotemporal information

Satellite-derived Chlorophyll-a concentration (Chla) time series products are essential for large-scale marine environmental monitoring. However, the plenty of missing pixels in current satellite Chla products severely hinder their applications for marine research, due to cloud contamination, solar glint, and unfavorable observation conditions. This study proposed a Chla time series gap-filling method for MODIS 8-day composite Chla product by integrating spatiotemporal information (STGF). This method employed spatially neighboring pixels with similar temporal variation to fill the missing values in time series, without involving training or auxiliary data. The performance of the STGF is assessed quantitatively and qualitatively. The correlation coefficients (CC) between gap-filled data and actual observations for years of 2004, 2010, 2016, and 2022 across the entire study area are all greater than 0.97. The mean absolute percentage error (MAPE) and root mean square error (RMSE) are less than 16.1 % and 0.233 mg/m3, respectively. The proposed STGF outperformed linear interpolation and the DINEOF algorithm from both spatial and temporal perspectives, suggesting the effectiveness of STGF in handling continuous data gaps and capturing detailed Chla variation patterns, especially in regions with significant variability. The findings suggest that the proposed STGF method offers a viable alternative for filling missing values in Chla time series data. This supports the demand for long-term, large-scale, and high-coverage ocean color remote sensing data in marine environmental studies.

Development of a lightweight convolutional neural network-based visual model for sediment concentration prediction by incorporating the IoT concept

Abstract Sediment concentration (SC) monitoring has always been a pressing issue in water resource management, as many existing instruments still face challenges in accurately measuring due to environmental factors and instrument limitations. A robust technology is worth presenting to apply in the field site. This study firstly uses mean-absolute-error (MAE), root-mean-square error (RMSE), correlation coefficient (CC), and Nash–Sutcliffe efficiency coefficient (NSE) to describe the performance of the proposed convolutional neural network (CNN). Moreover, adapting the ensemble learning concept to compare the multiple machine learning (ML) approaches, the CNN presents the highest predicted accuracy, 91%, better than SVM (79%), VGG19 (63%) and ResNet50 (35%). As a result, the proposed CNN framework can appropriately apply the monitoring needs. The primary purpose is to develop a simple, accurate, and stable SC monitoring technology. Instead of some complex architectures, a simple and small neural network is adopted to implement real-time application (RTA). Via our design, such a traditional but critical issue can be improved to a new state. For example, by incorporating the concept of the Internet of Things (IoT) with our design, the distributed computing system for large-scale environmental monitoring can be realized quickly and easily.

A UAV-based compressive algorithm for minimizing the energy consumption of large-scale WSNs

In large-scale environmental monitoring, massive sensor nodes regularly collect data and transmit them over long distances, which burdens the network and leads to the increased energy consumption. To deal with this problem, we develop an Unmanned Aerial Vehicle (UAV)-based compressive algorithm. In this algorithm, the network clustering technologies and Compressive Sensing are exploited to decrease the amount of data throughout the network. Monte Carlo method is used to group sensor nodes into clusters. The UAV is dispatched as a mobile agent to collect data from the cluster heads. Compared with the benchmark algorithms, the simulations demonstrate that the algorithm proposed in this paper can decrease the network’s energy consumption and obtain better environmental monitoring results.

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.

Acoustic Classification of Bird Species Using an Early Fusion of Deep Features

Bird sound classification plays an important role in large-scale temporal and spatial environmental monitoring. In this paper, we investigate both transfer learning and training from scratch for bird sound classification, where pre-trained models are used as feature extractors. Specifically, deep cascade features are extracted from various layers of different pre-trained models, which are then fused to classify bird sounds. A multi-view spectrogram is constructed to characterize bird sounds by simply repeating the spectrogram to make it suitable for pre-trained models. Furthermore, both mixup and pitch shift are applied for augmenting bird sounds to improve the classification performance. Experimental classification on 43 bird species using linear SVM indicates that deep cascade features can achieve the highest balanced accuracy of 90.94% ± 1.53%. To further improve the classification performance, an early fusion method is used by combining deep cascaded features extracted from different pre-trained models. The final best classification balanced accuracy is 94.89% ± 1.35%.

A Unified Bayesian Framework for Joint Estimation and Anomaly Detection in Environmental Sensor Networks

Advanced large-scale environmental monitoring systems relying on the emerging aerial/terrestrial technologies of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and mobile crowdsensing, impose strong requirements on the reliability of the collected data. Unfortunately, sensing units can suddenly suffer unexpected anomalies due to accidental faults or malicious causes. Outlier detection methods have been widely employed to identify and discard unreliable measurements from large data sets, but further improvements in the sensing processes can be obtained by adopting advanced signal processing algorithms that take full advantage of all the collected information without rejecting the measurements. In this paper, we propose a novel unified Bayesian framework that enable simultaneous estimation of a common parameter of interest and identification of multiple and possibly different types of anomalies that can affect sensors in environmental sensor networks. Specifically, we consider two rather general error models based on Gaussian mixtures able to capture different variations affecting the quality of the collected measurements. For each model, we illustrate the optimal joint maximum-likelihood and maximum a-posteriori (ML-MAP) estimation method, which represents the benchmark for the problem at hand, and propose novel reduced-complexity two-step algorithms able to achieve almost the same performance of the joint ML-MAP, but at a fraction of its computational cost. The derivations of all the algorithms are also extended to handle the more general case in which the probability of occurrence of anomalies is unknown and should be inferred from the data using an Empirical Bayes approach. Extensive performance analyses using both synthetic and real experimental data acquired in a network of environmental monitoring stations deployed in the Apulia region, south of Italy, demonstrate the effectiveness of the proposed framework.

Design and development of an open-source framework for citizen-centric environmental monitoring and data analysis

Cities around the world are struggling with environmental pollution. The conventional monitoring approaches are not effective for undertaking large-scale environmental monitoring due to logistical and cost-related issues. The availability of low-cost and low-power Internet of Things (IoT) devices has proved to be an effective alternative to monitoring the environment. Such systems have opened up environment monitoring opportunities to citizens while simultaneously confronting them with challenges related to sensor accuracy and the accumulation of large data sets. Analyzing and interpreting sensor data itself is a formidable task that requires extensive computational resources and expertise. To address this challenge, a social, open-source, and citizen-centric IoT (Soc-IoT) framework is presented, which combines a real-time environmental sensing device with an intuitive data analysis and visualization application. Soc-IoT has two main components: (1) CoSense Unit—a resource-efficient, portable and modular device designed and evaluated for indoor and outdoor environmental monitoring, and (2) exploreR—an intuitive cross-platform data analysis and visualization application that offers a comprehensive set of tools for systematic analysis of sensor data without the need for coding. Developed as a proof-of-concept framework to monitor the environment at scale, Soc-IoT aims to promote environmental resilience and open innovation by lowering technological barriers.

STATE-WIDE WETLAND INVENTORY MAP OF MINNESOTA USING MULTI-SOURCE AND MULTI-TEMPORALREMOTE SENSING DATA

Abstract. Carbon sequestration coupled with flood mitigation and other functions of wetlands, such as water filtration, coastal protection, biodiversity, and providing recreational spots, make wetland mapping and monitoring important for different countries. Google Earth Engine (GEE) cloud computing platform is becoming a very important tool for lots of environmental studies as it provides a suite of tools and access to data that facilitate large-scale environmental monitoring projects through its powerful parallel processing capabilities. In this study, we use GEE to access multi-source remote sensing datasets and implement an object-based image analysis, and random forest algorithm for the classification of wetlands in the state of Minnesota. Emergent, forested, and scrub-shrub wetland classes, water, as well as urban, forest, and agriculture land cover types were classified using Sentinel-2, Sentinel-1, USGS 3D Elevation Program 10-meter DEM, and gridded soil data. NDVI, EVI, BSI, NDBI, and NDWI spectral indices were calculated from Sentinel-2 imagery, VV and VH polarization channels, and their ratio, as well as span parameters, were calculated from Sentinel-1 imagery, and slope and aspect features were extracted from DEM. Simple Non-Iterative Clustering (SNIC), Gray-Level Co-occurrence Matrix (GLCM), Principal Components Analysis (PCA), and random forest algorithms were implemented to classify wetlands from the GEE platform. Emergent wetlands, water, urban, and agriculture classes performed well with producer accuracies greater than 90%. Sentinel-1, DEM, and soil datasets improve the identification of wetland classes and highlight the importance of multi-source approaches for wetland mapping.

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives.

Fighting Earth’s degradation and safeguarding the environment are subjects of topical interest and sources of hot debate in today’s society. According to the United Nations, there is a compelling need to take immediate actions worldwide and to implement large-scale monitoring policies aimed at counteracting the unprecedented levels of air, land, and water pollution. This requires going beyond the legacy technologies currently employed by government authorities and adopting more advanced systems that guarantee a continuous and pervasive monitoring of the environment in all its different aspects. In this paper, we take the research on integrated and large-scale environmental monitoring a step further by providing a comprehensive review that covers transversally all the main applications of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and crowdsensing monitoring technologies. By outlining the available solutions and current limitations, we identify in the cooperation among terrestrial (WSN/crowdsensing) and aerial (UAVs) sensing, coupled with the adoption of advanced signal processing techniques, the major pillars at the basis of future integrated (air, land, and water) and large-scale environmental monitoring systems. This review not only consolidates the progresses achieved in the field of environmental monitoring, but also sheds new lights on potential future research directions and synergies among different research areas.

Efficient Pairwise 3-D Registration of Urban Scenes via Hybrid Structural Descriptors

Automatic registration of point clouds captured by terrestrial laser scanning (TLS) plays an important role in many fields including remote sensing (e.g., transportation management, 3-D reconstruction in large-scale urban areas and environment monitoring), computer vision, and virtual reality and robotics. However, noise, outliers, nonuniform point density, and small overlaps are inevitable when collecting multiple views of data, which poses great challenges to 3-D registration of point clouds. Since conventional registration methods aim to find point correspondences and estimate transformation parameters directly in the original point space, the traditional way to address these difficulties is to introduce many restrictions during the scanning process (e.g., more scanning and careful selection of scanning positions), thus making the data acquisition more difficult. In this article, we present a novel 3-D registration framework that performs in a “middle-level structural space” and is capable of robustly and efficiently reconstructing urban, semiurban, and indoor scenes, despite disturbances introduced in the scanning process. The new structural space is constructed by extracting multiple types of middle-level geometric primitives (planes, spheres, cylinders, and cones) from the 3-D point cloud. We design a robust method to find effective primitive combinations corresponding to the 6-D poses of the raw point clouds and then construct hybrid-structure-based descriptors. By matching descriptors and computing rotation and translation parameters, successful registration is achieved. Note that the whole process of our method is performed in the structural space, which has the advantages of capturing geometric structures (the relationship between primitives) and semantic features (primitive types and parameters) in larger fields. Experiments show that our method achieves state-of-the-art performance in several point cloud registration benchmark datasets at different scales and even obtains good registration results for data without overlapping areas.

A GPU-Accelerated Framework for Simulating LiDAR Scanning

In this work, we present an efficient graphics processing unit (GPU)-based light detection and ranging (LiDAR) scanner simulator. Laser-based scanning is a useful tool for applications ranging from reverse engineering or quality control at an object scale to large-scale environmental monitoring or topographic mapping. Beyond that, other specific applications require a large amount of LiDAR data during development, such as autonomous driving. Unfortunately, it is not easy to get a sufficient amount of ground-truth data due to time constraints and available resources. However, LiDAR simulation can generate classified data at a reduced cost. We propose a parameterized LiDAR to emulate a wide range of sensor models from airborne to terrestrial scanning. OpenGL’s compute shaders are used to massively generate beams emitted by the virtual LiDAR sensors and solve their collision with the surrounding environment even with multiple returns. Our work is mainly intended for the rapid generation of datasets for neural networks, consisting of hundreds of millions of points. The conducted tests show that the proposed approach outperforms a sequential LiDAR scanning. Its capabilities for generating huge labeled datasets have also been shown to improve previous studies.

Моделирование сети исследовательских площадок для мониторинга потоков углерода методом Random Forest

Environmental observing networks provide information for understanding and predicting the spatial and temporal dynamics of Earth biophysical processes. The optimization of resources for large-scale environmental monitoring activities is required. The paper describes and then tests spatial structure of Tyumen region research sites network. The network is based on principles of landscape approach, taking into account cost minimization. At the baseline of research, two testing sets of 40 and 105 points were determined. Proposed locations were evaluated using Random Forest (RF) method. The study accomplished in two stages for each test set. At the first stage, the model was trained; its capacity and indicators of additional diagnostics were studied. At the second stage, the trained model was used to predict the points formed of regular grid covering entire territory of this region (544 points). In conclusion, the obtained results were compared with similar point sets of the same volume but generated randomly. Primary Productivity Gross (GPP) was chosen as predictable variable because it is one of the major complex environmental indicators associated with carbon production in this area. The ability of an area to absorb or produce carbon is one of the main parameters that determine climate processes. As independent variables characterizing geosystemic processes, a set of indicators associated with climate, terrain parameters, and variability of soil resources has been selected. The problem was solved using Forest-Based Classification and Regression tool from Spatial Statistics—Modeling Spatial Relationships toolkit of ArcGIS Pro software package. As the result of the study, a high forecast accuracy and reliability for both approaches to research sites locations was obtained. The study was based on open source data.

Why Does the SARS-CoV-2 Delta VOC Spread So Rapidly? Universal Conditions for the Rapid Spread of Respiratory Viruses, Minimum Viral Loads for Viral Aerosol Generation, Effects of Vaccination on Viral Aerosol Generation, and Viral Aerosol Clouds.

This study analyzes the reasons the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant of concern (VOC) spreads so rapidly. Novel topics such as universal conditions for the rapid spread of respiratory viruses, minimum viral loads for viral aerosol generation, effects of vaccination on viral aerosol generation, and viral aerosol clouds were studied. The analyses were based on experimental results and analytic model studies. Four universal conditions, namely asymptomatic host, high viral load, stability of viruses in air, and binding affinity of viruses to human cells, need to be satisfied for the rapid spread of respiratory viruses. SARS-CoV-2 and its variants such as the Alpha VOC and Delta VOC satisfy the four fundamental conditions. In addition, there is an original principle of aerosol generation of respiratory viruses. Assuming that the aerosol–droplet cutoff particle diameter for distinguishing potential aerosols from earthbound respiratory particles is 100 μm, the minimum viral load required in respiratory fluids to generate viral aerosols is ~106 copies mL−1, which is within the range of the reported viral loads in the Alpha VOC cases and the Delta VOC cases. The daily average viral loads of the Delta VOC in hosts have been reported to be between ~109 copies mL−1 and ~1010 copies mL−1 during the four days after symptom onset in 1848 cases of the Delta VOC infection. Owing to the high viral load, the SARS-CoV-2 Delta VOC has the potential to effectively spread through aerosols. COVID-19 vaccination can decrease aerosol transmission of the SARS-CoV-2 Alpha VOC by reducing the viral load. The viral load can explain the conundrum of viral aerosol spreading. The SARS-CoV-2 Delta VOC aerosol clouds have been assumed to be formed in restricted environments, resulting in a massive numbers of infected people in a very short period with a high spreading speed. Strong control methods against bioaerosols should be considered in this SARS-CoV-2 Delta VOC pandemic. Large-scale environmental monitoring campaigns of SARS-CoV-2 Delta VOC aerosols in public places in many countries are necessary, and these activities could contribute to controlling the coronavirus disease pandemic.