Environmental Monitoring Data Research Articles

Estimating soil moisture from environmental gamma radiation monitoring data

AbstractSoil moisture (SM) information is invaluable for a wide range of applications, including weather forecasting, hydrological and land surface modeling, and agricultural production. However, there is still a lack of sensing information that adequately represents root‐zone SM for longer periods and larger spatial scales. One option for root‐zone SM observation is terrestrial gamma radiation (TGR), as it is inversely related to SM. Hence, the near real‐time data of more than 5000 environmental gamma radiation (EGR) monitoring stations archived by the EUropean Radiological Data Exchange Platform (EURDEP) is a potential source to develop a root‐zone SM product for Europe without extra investments in SM sensors. This study aims to investigate to what extent the EURDEP data can be used for SM estimation. For this, two EGR monitoring stations were equipped with in situ SM sensors to measure reference SM. The terrestrial component of EGR was extracted after eliminating the contributions of rain washout and secondary cosmic radiation, and used to obtain a functional relationship with SM. We predicted the weekly volumetric SM with a root mean square error of 7%–9% from TGR measurements. Nevertheless, we believe that this technique, due to its greater penetration depth and long data legacy, can provide useful data complementary to satellite‐based remote sensing techniques to estimate root‐zone SM at the continental scale.

Prediction of soil heavy metal content around mine tailings using multiple methods combined with transformed hyperspectral reflectance data

The extensive accumulation of tailings can potentially cause heavy metal contamination in the surrounding farmland soil. Accurately predicting the spatial distribution of heavy metals in farmland soil is crucial for assessing the potential environmental hazards of tailings.This study focuses on the spatial distribution and the quantitative prediction of heavy metals (chromium (Cr), vanadium (V), and copper (Cu)) in soils surrounding mine tailings using advanced spectral data analysis and multiple prediction models. The original hyperspectral reflectance data were processed using first-order differential (FD), second-order differential (SD), reciprocal logarithmic (LR), and continuum removal (CR) transformations to highlight the positions of characteristic bands. Multiple linear regression (MLR), stepwise linear regression (SLR), partial least squares regression (PLSR), random forest (RF), and back propagation artificial neural network (BP-ANN) models were used to establish inversion models for Cr, V, and Cu based on bands with high correlation coefficients. The performance of the inversion models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD). The results indicate that the raw hyperspectral data from the measured soil exhibit a weak response to heavy metal content in the study area. However, applying FD, SD, and CR transformations significantly enhances the sensitivity of soil spectral data to heavy metal concentrations, facilitating subsequent modeling. Among these, the SD transformation is particularly beneficial for modeling the Cr and Cu elements in the soil. For the V element, the FD transformation yields data that are more suitable for modeling. Regarding the inversion models based on the measured spectral data, the BP-ANN model exhibited the best predictive performance. Specifically, when combined with SD spectral data, the BP-ANN achieved the highest predictive accuracy for Cu content (R² = 0.85, RPD = 2.12). The RF model demonstrated the next best performance, with its optimal inversion model also utilizing SD spectral data for predicting Cu content (R² = 0.76, RPD = 1.90). On the other hand, the MLR model exhibited the poorest performance and is unsuitable for predicting heavy metal content in the region using the measured spectral data. This study highlights the potential of spectral data in environmental monitoring and provides a technical reference for the inversion assessment and regulation of heavy metals in farmlands surrounding tailing sites.

Aquatic hazard and risk posed by four pesticides detected in waterways discharging to the Great Barrier Reef, Australia: Part 2. Hazard and risk assessment

Pesticide active ingredients (PAIs) are regularly detected in the rivers, creeks, wetlands, and inshore waterways that discharge to the Great Barrier Reef (GBR) lagoon. Pesticide active ingredients detected above ecologically protective concentrations may pose a hazard and risk to aquatic species. The ability to assess this hazard and risk is reliant on the availability of water quality guidelines, which are only available for a limited number of PAIs detected in GBR catchment waterways. Unendorsed guideline values, known as ecotoxicity threshold values (ETVs) were developed in part one of this study for active ingredients in two fungicides (4-hydroxychlorothalonil (fungicide degradate) and carbendazim) and two insecticides (dimethoate and methoxyfenozide) that are commonly detected in GBR catchment waterways. In the current study, the hazard and risk posed by these PAIs was assessed by comparing the ETVs to environmental monitoring data from the Great Barrier Reef Catchment Loads Monitoring Program. Exceedances of the concentrations that should protect 99 % of aquatic species (i.e., PC99) were observed for all four pesticides. Detected concentrations of 4-hydroxychlorothalonil, carbendazim and methoxyfenozide exceeded the PC95 ETV, however no exceedances of the PC95 were observed for dimethoate. The hazard quotient (HQ) method was used to identify high hazard sites across the GBR catchment area. In total, six sites were identified as having concentrations that exceeded the PC95 ETVs. For 4-hydroxychlorothalonil, the risk to aquatic species based on the 95th percentile concentrations ranged from 3 to 13 %, 1 to 8 % for carbendazim and 2 to 8 % for methoxyfenozide. Detected concentrations of carbendazim were two orders of magnitude higher than concentrations that are reported to induce behavioural effects in some fish species. Considering that detected concentrations of three of the four PAIs individually pose a potential risk to aquatic species, their contributions to pesticide mixture toxicity should be further assessed.

Enhanced hybrid LSTM and SLAR modeling for in-depth analysis of temporal and spatial patterns in compositional data for environmental monitoring

A novel methodology for analyzing compositional data (CoDa) integrates long short-term memory (LSTM) networks with spatial lag autoregressive (SLAR) models to simultaneously capture temporal and spatial patterns. The proposed approach leverages LSTM’s strengths in handling temporal relationships and SLAR’s ability to capture spatial correlations, employing the centered log-ratio (CLR) transformation to manage CoDa’s characteristics, making it suitable for advanced analysis. The Adam optimizer ensures efficient and stable model training, enhancing convergence speed and precision. The combined approach involves preprocessing CoDa with CLR, initializing LSTM layers, performing forward passes through LSTM, integrating the SLAR model, generating outputs, post-processing data, calculating loss, and performing backpropagation. The methodology’s effectiveness is demonstrated through a case study on pollutant emissions data from a waste incineration plant, focusing on key pollutants such as Dioxins and Furans, Heavy Metals (Hg, Pb, Cd), Nitrogen Oxides (NO), Sulfur Dioxide (SO), Particulate Matter (PM), and Carbon Monoxide (CO). The LSTM network, configured with two hidden layers, dynamically adjusts learning rates for faster convergence. Integrating SLAR improves spatial pattern accounting, significantly enhancing predictive accuracy. Comparative analysis shows substantial performance metric improvements over standard LSTM models, with the LSTM-SLAR model reducing errors and explaining a higher proportion of data variability. In process safety and risk engineering, the proposed methodology enhances pollutant emission monitoring and control, enables proactive risk management, ensures regulatory compliance, and improves risk assessment accuracy, making it invaluable for dynamic risk assessment in environmental monitoring and is broadly applicable to fields requiring detailed temporal and spatial analysis, demonstrating robustness and versatility in handling complex CoDa.

Characteristics, Impact Factors and Potential Sources of PM2.5 Pollution for Hainan Island

Based on the environmental monitoring data and meteorological observational data in Hainan Island from 2015 to 2021, the PM2.5-polluted characteristics, influencing factors, and potential contributing regions were analyzed using the backward trajectory simulation, cluster analysis, potential source analysis function （PSCF）, and concentration weight trajectory （CWT） methods. The results showed that PM2.5 in Hainan Island had an obvious seasonal variation, with the highest in winter （22.6 μg·m-3）, followed by that in autumn and spring （17.38 and 16.53 μg·m-3, respectively）, with the lowest in summer （9.79 μg·m-3）. In the past seven years, there were 30 days in Hainan Island in which PM2.5 concentration exceeded the standard. The annual average and four seasons of PM2.5 showed a significant downward trend, and the climatic change rates were -0.97 （annual mean）, -1.09 （spring）, -0.61 （summer）, -0.83 （autumn）, and -1.25 （winter） μg·（m3·a）-1. PM2.5 in Hainan Island was highly correlated with gaseous pollutants, with correlation coefficients of 0.471 （SO2）, 0.633 （NO2）, 0.479 （CO）, and 0.773 （O3-8h）, all passing a significance level of 0.01. PM2.5 was positively correlated with average wind speed and atmospheric pressure and negatively correlated with precipitation, relative humidity, sunshine duration, average temperature, and total solar radiation. Among them, average temperature, relative humidity, and total solar radiation were the main dominant meteorological factors on PM2.5 in Hainan Island. Backward trajectory and potential source analysis revealed that PM2.5 concentration was high （≥20 μg·m-3） in winter and autumn, which was influenced by airflow from inland regions, and Fujian, Zhejiang, Hunan, Jiangxi, Guangdong, and Guangxi provinces were the main potential sources of PM2.5 in Hainan Island.

Stepwise Construction and Integration of Ecological Network in Resource-Based Regions: A Case Study on Liaoning Province, China

Ecological networks are an effective strategy to maintain regional ecological security. However, current research on ecological network construction in areas with large-scale resource extraction is limited. Moreover, classic ecological network construction methods do not perform satisfactorily when implemented in heavily damaged mining landscapes. Taking the example of Liaoning Province, China, a framework for stepwise renewal of ecological networks was proposed, which integrates basic ecological sources and other sources that include mining areas. The framework was based on multi-source ecological environment monitoring data, and all potential ecological sources were extracted and screened using an MSPA model and the area threshold method. Further, ecological sources were classified into two types and three levels based on the influence of abandoned mines and the characteristics of ecosystem services in the ecological sources. Ecological corridors were extracted using the MCR model. An ecological corridor optimization process based on combining the gravity model with addition and removal rules of corridors was proposed. The results indicated that the basic ecological network in Liaoning Province included 101 ecological sources and 162 ecological corridors, and the supplementary ecological network included 28 ecological sources and 67 ecological corridors. The ecological sources were divided into two types, and corridors were divided into three types. The basic ecological network exhibited a spatial distribution of discrete connections in the west and close connections in the east. Changes in ecological network topological indicators indicated that a supplementary ecological network strengthened the structural performance of the regional ecological network, expanding spatial coverage, filling hollow areas, and enriching local details of the regional ecological network. Regulation strategies were proposed for ecological sources with different connection modes. The number of ecological sources implementing restrictive development, pattern optimization, and protective development were 101, 12, and 16, respectively. This paper provides a constructing framework of ecological networks adapted for resource-based regions. This method can support decisions for the environmental governance of mines, thus contributing to a balance between resource exploitation and ecological protection in regions.

Integrating AI enhanced remote sensing technologies with IOT networks for precision environmental monitoring and predicative ecosystem management

Detection of hazardous substances in the environment is paramount in safeguarding human health and ecosystems. With the continuous advancement of technology, artificial intelligence (AI) has emerged as a promising tool in the development of sensors capable of efficiently detecting and analyzing these substances. Environmental monitoring, modeling, and management are very essential for gaining a deeper insight into the fundamental processes and methodologies employed in handling environmental transformations. Hence, the objective of this study is to investigate recent progress in the utilization of AI, sensors, and IoT devices for monitoring environmental pollution, while considering the challenges associated with predicting and monitoring these variations due to the dynamic nature of the environment. The integration of these systems is actively revolutionizing environmental monitoring and enhancing our understanding of how to implement a comprehensive approach to the management of natural resources and ecological processes that are crucial for our societal, economic, and cultural well-being. Consequently, we are able to uncover the transformative impact of this collaborative effort on our comprehension of Earth, as it tackles obstacles, conducts in-depth analysis of long-term environmental data monitoring, and lays the foundation for a promising future.

Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks

Wireless sensor networks are used to monitor the environment, to detect anomalies or any other problems and risks in the system. If used in the transportation network, they can monitor traffic and detect traffic risks. In wireless sensor networks, energy constraints must be handled to enable continuous environmental monitoring and surveillance data gathering and communication. Energy-aware path planning of autonomous ground vehicle charging for sensor nodes can solve energy and battery replacement problems. This paper uses the Nearest Neighbour algorithm for the energy-aware path planning problem with an autonomous ground vehicle. Path planning simulations show that the Nearest Neighbour algorithm converges faster and produces a better solution than the genetic algorithm. We offer robust and energy-efficient path planning algorithms to swiftly collect sensor data with less energy, allowing the monitoring system to respond faster to anomalies. Positioning communicating sensors closer minimizes their energy usage and improves the network lifetime. This study also considers the scenario in which it is recommended to avoid taking direct travelling pathways between particular node pairs for a variety of different reasons. To address this more challenging scenario, we provide an Obstacle-Avoided Nearest Neighbour-based approach that has been adapted from the Nearest Neighbour approach. Within the context of this technique, the direct paths that connect the nodes are restricted. Even in this case, the Obstacle-Avoided Nearest Neighbour-based approach achieves almost the same performance as the the Neighbour-based approach.

Application of gene expression programing in predicting the concentration of PM2.5 and PM10 in Xi’an, China: a preliminary study

Introduction: Traditional statistical methods cannot find quantitative relationship from environmental data.Methods: We selected gene expression programming (GEP) to study the relationship between pollutant gas and PM2.5 (PM10). They were used to construct the relationship between pollutant gas and PM2.5 (PM10) with environmental monitoring data of Xi’an, China. GEP could construct a formula to express the relationship between pollutant gas and PM2.5 (PM10), which is more explainable. Back Propagation neural networks (BPNN) was used as the baseline method. Relevant data from January 1st 2021 to April 26th 2021 were used to train and validate the performance of the models from GEP and BPNN.Results: After the models of GEP and BPNN constructed, coefficient of determination and RMSE (Root Mean Squared Error) are used to evaluate the fitting degree and measure the effect power of pollutant gas on PM2.5 (PM10). GEP achieved RMSE of [8.7365–14.6438] for PM2.5; RMSE of [13.2739–45.8769] for PM10, and BP neural networks achieved average RMSE of [13.8741–34.7682] for PM2.5; RMSE of [29.7327–52.8653] for PM10. Additionally, experimental results show that the influence power of pollutant gas on PM2.5 (PM10) situates between −0.0704 and 0.6359 (between −0.3231 and 0.2242), and the formulas are obtained with GEP so that further analysis become possible. Then linear regression was employed to study which pollutant gas is more relevant to PM2.5 (PM10), the result demonstrates CO (SO2, NO2) are more related to PM2.5 (PM10).Discussion: The formulas produced by GEP can also provide a direct relationship between pollutant gas and PM2.5 (PM10). Besides, GEP could model the trend of PM2.5 and PM10 (increase and decrease). All results show that GEP can be applied smoothly in environmental modelling.

Long-term Effects of Air Pollution on Respiratory Health in Urban Populations

This research paper explores the long-term effects of air pollution on respiratory health within urban populations. Urban areas, characterized by high population density and significant industrial and vehicular activities, are particularly susceptible to elevated levels of pollutants such as particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO). The study employs a longitudinal cohort design to assess the impact of prolonged exposure to these pollutants on respiratory conditions including asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. Utilizing a multi-disciplinary approach that integrates environmental monitoring, health records, and socio-economic data, the research identifies significant correlations between air pollution and respiratory health outcomes. Key findings include a robust association between elevated levels of particulate matter and declines in lung function, increased incidence of asthma and COPD, and higher rates of hospital admissions for respiratory issues. The study also highlights disparities in health impacts among different demographic and socioeconomic groups, particularly children, the elderly, and low-income communities. Recommendations for policy include stricter air quality regulations, improved urban planning, and targeted public health interventions to protect vulnerable populations. Future research should focus on further elucidating the complex interactions between air pollution and individual susceptibility to respiratory diseases.

Improving trophic position estimates from amino acid stable isotopes by accounting for physiology and environment

AbstractNitrogen isotope analyses of amino acids (δ15N‐AA) are being increasingly used to decipher trophic dynamics. Interpretation of δ15N‐AA in consumers relies on the assumption that consumer physiological status and nutritional status of prey have negligible influences on the trophic discrimination factor (TDF), hence a constant TDF value is used in trophic position (TP) equations. Recent experiments have shown that this is not always the case and there is also a need to validate derived TP estimates in the field. We take advantage of the uniquely long time series of environmental monitoring data and archived (frozen) samples from the species‐poor Baltic Sea. We analyzed δ15N‐AA in similar sized individuals of cod and in its prey herring from four decades, 1980–2018; including time periods where dramatic reduction in condition status of cod has occurred. We expected that TDF in trophic AAs would increase during periods of poor cod condition, resulting in inflated TP estimates. We found that calculated TP and empirical estimates of TDF (difference in δ15N in trophic AAs between cod and herring) for cod increased in recent decades and that this was linked to condition status, herring (prey) lipid content and the hypoxic state of the ecosystem. Statistically adjusting TP for condition and prey lipid content as well as environmental stress (hypoxia) resulted in lower cod TP which better resembled the observed decrease in herring TP in recent decades. TP calculated from stomach analysis data in cod individuals over the same period showed no trend over time and confirmed that adjusted TP estimates mirror the real dietary TP better than unadjusted. By simultaneously measuring condition/nutritional status in both predator and prey it is possible to adjust for them as confounding variables and decipher actual consumer TP, partly overcoming the issues of unknown and variable TDF‐values. Our study also highlights the importance of including environmental stressors (here hypoxia) when interpreting TP and reconstructing food webs.

Comprehensive Analysis of Environmental Monitoring Data from the Department of Nuclear Medicine and Molecular Imaging (NMMI) of the University Medical Center Groningen (UMCG)

Comprehensive Analysis of Environmental Monitoring Data from the Department of Nuclear Medicine and Molecular Imaging (NMMI) of the University Medical Center Groningen (UMCG)

Information and Logic Model Development Natural Engineering System Environmental Monitoring

Environmental monitoring is the main source of information on the current environment state and its changes associated with man-made and anthropogenic impact. The system of environmental monitoring of increased danger technogenic objects includes state observations of air and water environments, soil snow cover, as well as monitoring of flora and fauna, including biotesting and bioindication. An example of an integrated approach to organizing the environment conditionobserving system is environmental monitoring in the zone of chemical weapons destruction facilities. The results of monitoring activities are numerical arrays of quantitative indicators of the environment state related to specific monitoring objects: concentrations of pollutants in natural environments (air, water, and soil); indicators of the state of flora and fauna; bioindication results. The complexity of the monitoring result integrated analysis is primarily associated with the diversity of the structure and number of indicators for individual natural environments, different dimensions of indicators, as well as the sparseness of the matrices of data arrays. Taking into account the above-mentioned information, it is relevant to develop methods for storing and updating environmental monitoring data arrays, as well as algorithms for operational analysis of the results of monitoring studies. The paper presents an information and logical model that allows to form databases for storing and replenishing data on the state of the natural engineering system. The developed information and logic model is focused on building OLAP cubes for subsequent analysis of environmental monitoring data. The presented information and logical model is the main stage of developing a system for storing, updating and analyzing the results of environmental monitoring of the TCP. Systematization and structuring of indicators of natural environments and ecosystems makes it possible to formulate a number of practical tasks for assessing and predicting the state of the environment in the presence of technogenic impact.

Variation Characteristics and Source Analysis of Atmospheric Ozone Pollution in Guanzhong Region

Guanzhong urban agglomeration has a good development foundation and great development potential, and it has a unique strategic position in the national all-round opening up pattern. In recent years, the problem of near-surface ozone （O3） in the Guanzhong Region has become increasingly prominent, which has become a bottleneck affecting the continuous improvement of air quality. In order to effectively prevent and control O3 pollution, this study analyzed the characteristics of annual, monthly, and daily changes in O3 concentration in the Guanzhong Region based on the environmental monitoring data from 2018 to 2021. A geo-detector was used to study the driving factors of the spatial differentiation of O3 concentration, and the sources of O3 were analyzed using a backward trajectory model and emission inventory construction. The results showed that the daily and monthly variation in O3 concentration in the Guanzhong Region were unimodal. The daily maximum value appeared at 15：00, the minimum value appeared at 07：00, the peak value of the monthly average appeared in June, and the valley value appeared in December. The O3 concentration was highest in summer, followed by that in spring, and the lowest in winter. The days of O3 exceeding the standard showed mainly mild pollution, and moderate and above pollution showed a trend of decreasing first and then increasing. The O3 concentration in the Guanzhong Region was mainly closely related to precursors and meteorological factors, and the explanatory power of the interaction of each factor was significantly greater than that of any single factor. The regional transport of O3 concentration in the Guanzhong Region was mainly affected by easterly airflow, followed by the northwest direction, with the potential source areas located mainly in Henan Province and Hubei Province. The main local sources of volatile organic compounds （VOCs） were solvent use sources, process sources, and mobile sources, and the main emission sources of nitrogen oxides （NOx） were mobile sources and industrial production combustion sources. The research results have a guiding significance for O3 joint prevention and control in the Guanzhong Region.

Health Management of Aircraft Fuel Systems: A Practical Prognostic Perspective

Aircraft health management, specifically pertaining to fuel systems, is a comprehensive process that is undertaken to ensure the continuous airworthiness of the aircraft over its entire lifespan. The integration of health management into aircraft systems requires a methodical strategy to quantitatively evaluate the possible advantages provided by each specific application. Gaining a thorough comprehension of the advantages linked to integrated health management systems provides useful insights for enhancing the design of these systems and formulating efficient prognostic techniques. The role of prognosis in the field of prognostics and health management (PHM) is essential for improving the dependability of systems. Prognosis involves accurately predicting the remaining useful life (RUL) within the domain of integrated health management. This study improves the precision of prediction results, optimises the derived prerequisites for health management in the specialised field of RUL prediction for aircraft fuel systems, provides a thorough understanding, and advances the grasp of the previously described requirements in a more effective way. Additionally, it presents the most suitable solutions in order to address these demands by utilising prognostic techniques. Furthermore, this study presents an analysis of the proposed hybrid prognostic approach in the setting of controlled laboratory conditions. The applications encompassed in this work consist of three main areas. Firstly, the evaluation of inspection interval modifications is conducted by taking into account load and environmental monitoring data. Secondly, design considerations for damage prognostics are incorporated into the analysis. Lastly, a design modification is presented as an example of how condition-based maintenance (CBM) can be facilitated. A methodology that enables the efficient and well-founded construction of a health management system, specifically designed for aircraft fuel systems, is presented. This methodology, together with its associated outcomes, presents a compelling advantage in terms of lowering life cycle expenses while simultaneously enhancing availability, dependability, and performance.

China's Ground‐Based Space Environment Monitoring Network—Chinese Meridian Project (CMP)

AbstractMonitoring and investigation of the solar‐terrestrial space environment is a huge challenge for humans in space age. To this end, China has established the Ground‐based Space Environment Monitoring Network, namely Chinese Meridian Project (CMP). The project comprises three major systems: the Space Environment Monitoring System, Data and Communication System, and Scientific Application System. The Space Environment Monitoring System adopts a well‐designed monitoring architecture, known as “One Chain, Three Networks, and Four Focuses,” to achieve stereoscopic and comprehensive monitoring of the entire solar‐terrestrial space. The “One‐Chain” component utilizes optical, radio, interplanetary scintillation, cosmic ray instruments to cover the causal chain of space weather disturbances from the solar surface to near‐Earth space. For the ionosphere, middle and upper atmosphere, and magnetic field, instruments are deployed along longitudes of 120° and 100°E, and latitudes of 30° and 40°N, forming the “Three Networks.” Furthermore, more powerful monitoring facilities or large‐scale instruments have been deployed in four key regions: the high‐latitude polar region, mid‐latitude region in northern China, low‐latitude region at Hainan Island, and the Tibet region. These four regions are crucial for disturbances propagation and evolution, or possess unique geographical and topographical characteristics. The Data and Communication System and Scientific Application System are designed for data collecting, processing, storage, mining, and providing user service based on data acquired by the Space Environment Monitoring System. The data obtained by CMP will be shared with the global scientific community, facilitating enhanced collaboration on space weather and space physics research.

IMD-MP: Imputation of Missing Data in IoT Based on Matrix Profile and Spatio-temporal Correlations

Data in the Internet of Things (IoT) domain may be missing due to connectivity errors, environmental extremes, sensor malfunctions, and human errors. Despite the many approaches for imputing missing values, the most significant difficulty in terms of imputation precision or compute complexity for larger missing sub-sequences in uni-variate series is still being explored. This work introduced IMD-MP (Imputation of Missing Data using Matrix Profile), a new technique that improves imputation accuracy for big data analysis in IoT applications based on spatial-temporal correlations using a novel distance metric Matrix Profile Distance (MPD). Our method preserves spatial correlation by grouping the sensors present in the network (using grouping algorithm-GA) to impute the missing data of the failed sensor node. After grouping, similar sensor nodes to the failed sensor node are identified using the Node Similarity Algorithm (NSF). From its similar sensor data, a certain number of sub-sequences that are most similar to the one preceding the failed node’s missing values are gathered. These sub-sequences heights are optimized to ensure temporal correlation in the imputed data. To find the optimal imputation sequence, the current research uses MPD and similarity scores. Numerical findings using sensor data from real-time environmental mon-itoring and Intel data sets demonstrate the algorithm’s effectiveness compared to other benchmarks.

A standardised approach for serving environmental monitoring data compliant with OGC APIs

A standardised approach for serving environmental monitoring data compliant with OGC APIs

Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model.

To enhance the precision of evaluating the impact of urban environments on resident health, this study introduces a novel fuzzy intelligent computing model designed to address health risk concerns using multi-media environmental monitoring data. Three cities were selected for the study: Beijing (B City), Kunming (K City), and Wuxi (W City), representing high, low, and moderate pollution levels, respectively. The study employs a Fuzzy Inference System (FIS) as the chosen fuzzy intelligent computing model, synthesizing multi-media environmental monitoring data for the purpose of urban health risk assessment. (1) The model reliably estimates health risks across diverse cities and environmental conditions. (2) There is a positive correlation between PM2.5 concentrations and health risks, though the impact of noise levels varies by city. In cities B, K, and W, the respective correlation coefficients are 0.65, 0.55, and 0.7. (3) The Root Mean Square Error (RMSE) values for cities B, K, and W, are 0.0132, 0.0125, and 0.0118, respectively, indicating that the model has high accuracy. The R2 values for the three cities are 0.8963, 0.9127, and 0.9254, respectively, demonstrating the model's high explanatory power. The residual values for the three cities are 0.0087, 0.0075, and 0.0069, respectively, indicating small residuals and demonstrating robustness and adaptability. (4) The model's p-values for the Indoor Air Quality Index (IAQI), Thermal Comfort Index (TCI), and Noise Pollution Index (NPI) all satisfy p < 0.05 for the three cities, affirming the model's credibility in estimating health risks under varied urban environments. These results showcase the model's ability to adapt to diverse geographical conditions and aid in the accurate assessment of existing risks in urban settings. This study significantly advances environmental health risk assessment by integrating multidimensional data, enhancing the formulation of comprehensive environmental protection and health management strategies, and providing scientific support for sustainable urban planning.

Sensorweb Systems for Global High-Resolution Monitoring of Environmental Phenomena

Many science phenomena are difficult to observe because they occur infrequently and without warning. Over the course of the last 20 years, the concept of a “sensorweb,” or a network of interdependent instruments that autonomously coordinate to observe phenomena of interest, has been developed and implemented. In one use case, sensorwebs use in situ and low- to moderate-resolution sensors for event detection and pointable sensors with high spatial resolution for subsequent observation of targets, thus accomplishing efficient high-resolution monitoring. These sensorwebs have previously been implemented to study flood extent and volcanic lava flow. In the flood monitoring case, mostly remote sensors have been used for low-resolution flood detection, while more in-situ sensors have been used for volcanic activity detection in the volcano monitoring application. To improve these sensorwebs, we integrate commercial satellite constellations for high-resolution observations, resulting in higher spatial and temporal resolutions than the individual assets used in previous work. We also spatially extend sensorwebs to the global scale, produce a high volume of alerts, and automatically generate useful high-resolution data products. These resulting data products include surface water extent products for our flood sensorweb and volcanic lava classifiers for our volcano sensorweb. Our sensorwebs represent the state of the art in automatic acquisition and processing of data for environmental phenomenon monitoring. We demonstrate the capabilities of these improved sensorwebs in our new flood sensorweb and volcano sensorweb systems and describe ongoing efforts to utilize additional in-situ and spaced-based sensors and generate additional data products.