Self-Interested Coalitional Crowdsensing for Multi-Agent Interactive Environment Monitoring.

As a promising paradigm, mobile crowdsensing (MCS) takes advantage of sensing abilities and cooperates with multi-agent reinforcement learning technologies to provide services for users in large sensing areas, such as smart transportation, environment monitoring, etc. In most cases, strategy training for multi-agent reinforcement learning requires substantial interaction with the sensing environment, which results in unaffordable costs. Thus, environment reconstruction via extraction of the causal effect model from past data is an effective way to smoothly accomplish environment monitoring. However, the sensing environment is often so complex that the observable and unobservable data collected are sparse and heterogeneous, affecting the accuracy of the reconstruction. In this paper, we focus on developing a robust multi-agent environment monitoring framework, called self-interested coalitional crowdsensing for multi-agent interactive environment monitoring (SCC-MIE), including environment reconstruction and worker selection. In SCC-MIE, we start from a multi-agent generative adversarial imitation learning framework to introduce a new self-interested coalitional learning strategy, which forges cooperation between a reconstructor and a discriminator to learn the sensing environment together with the hidden confounder while providing interpretability on the results of environment monitoring. Based on this, we utilize the secretary problem to select suitable workers to collect data for accurate environment monitoring in a real-time manner. It is shown that SCC-MIE realizes a significant performance improvement in environment monitoring compared to the existing models.

Ecotoxicology of heavy metal contamination of Neotropical bats.

Although bats are responsible for many ecosystem services on which humans depend, they are frequently exposed to multiple anthropogenic stressors. Heavy metal (HM) exposure is an emerging threat of great significance to bats, yet the toxicity threshold for most metallic elements remains unknown. The greatest diversity of bats worldwide is in the Neotropical region, where ecotoxicological studies are scarce. Thus, this review provides a current overview of the knowledge available on HMs contamination of Neotropical bats. Analysis of the results of 17 articles published between 2000 and 2023 documented a trend of increasing interest in the topic, although it is incipient and in few countries. Of the 226 species known for the Neotropics, 95 have been investigated for metal concentrations. Seven different matrices were used to assess concentrations of heavy metals in tissues, with fur being the subject of eight studies, highlighting the search for non-invasive analysis. Twenty-one HMs were detected in bats, with mercury being the most common. The highest concentrations of this HM were detected in insectivorous/omnivorous bats, highlighting its magnification in this trophic guild compared to frugivorous bats. Copper, lead, and cadmium did not differ significantly among the other trophic guilds. This review shows that there is knowledge about concentrations of heavy metals in several Neotropical species, but knowledge about the impact of these concentrations on bat health is limited, which highlights the need for research to determine critical concentrations that cause damage to bat health, and that guide conservation actions for their populations, as well as environmental monitoring actions for these pollutants.

EEG-Based Driver Fatigue Monitoring within a Human-Ship-Environment System: Implications for Ship Braking Safety.

To address the uncontrollable risks associated with the overreliance on ship operators' driving in current ship safety braking methods, this study aims to reduce the impact of operator fatigue on navigation safety. Firstly, this study established a human-ship-environment monitoring system with functional and technical architecture, emphasizing the investigation of a ship braking model that integrates brain fatigue monitoring using electroencephalography (EEG) to reduce braking safety risks during navigation. Subsequently, the Stroop task experiment was employed to induce fatigue responses in drivers. By utilizing principal component analysis (PCA) to reduce dimensionality across multiple channels of the data acquisition device, this study extracted centroid frequency (CF) and power spectral entropy (PSE) features from channels 7 and 10. Additionally, a correlation analysis was conducted between these features and the Fatigue Severity Scale (FSS), a five-point scale for assessing fatigue severity in the subjects. This study established a model for scoring driver fatigue levels by selecting the three features with the highest correlation and utilizing ridge regression. The human-ship-environment monitoring system and fatigue prediction model proposed in this study, combined with the ship braking model, achieve a safer and more controllable ship braking process. By real-time monitoring and prediction of driver fatigue, appropriate measures can be taken in a timely manner to ensure navigation safety and driver health.

Feasibility of SERS-Active Porous Ag Substrates for the Effective Detection of Pyrene in Water.

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants that are typically released into the environment during the incomplete combustion of fossil fuels. Due to their relevant carcinogenicity, mutagenicity, and teratogenicity, it is urgent to develop sensitive and cost-effective strategies for monitoring them, especially in aqueous environments. Surface-enhanced Raman spectroscopy (SERS) can potentially be used as a reliable approach for this purpose, as it constitutes a valid alternative to traditional techniques, such as liquid and gas chromatography. Nevertheless, the development of an SERS-based platform for detection PAHs has so far been hindered by the poor adsorption of PAHs onto silver- and gold-based SERS-active substrates. To overcome this limitation, several research efforts have been directed towards the development of functionalized SERS substrates for the improvement of PAH adsorption. However, these strategies suffer from the interference that functionalizing molecules can produce in SERS detection. Herein, we demonstrate the feasibility of label-free detection of pyrene by using a highly porous 3D-SERS substrate produced by an inductively coupled plasma (ICP). Thanks to the coral-like nanopattern exhibited by our substrate, clear signals ascribable to pyrene molecules can be observed with a limit of detection of 23 nM. The observed performance can be attributed to the nanoporous character of our substrate, which combines a high density of hotspots and a certain capability of trapping molecules and favoring their adhesion to the Ag nanopattern. The obtained results demonstrate the potential of our substrates as a large-area, label-free SERS-based platform for chemical sensing and environmental control applications.

Development of Air Quality Boxes Based on Low-Cost Sensor Technology for Ambient Air Quality Monitoring.

Analyses of the relationships between climate, air substances and health usually concentrate on urban environments because of increased urban temperatures, high levels of air pollution and the exposure of a large number of people compared to rural environments. Ongoing urbanization, demographic ageing and climate change lead to an increased vulnerability with respect to climate-related extremes and air pollution. However, systematic analyses of the specific local-scale characteristics of health-relevant atmospheric conditions and compositions in urban environments are still scarce because of the lack of high-resolution monitoring networks. In recent years, low-cost sensors (LCS) became available, which potentially provide the opportunity to monitor atmospheric conditions with a high spatial resolution and which allow monitoring directly at vulnerable people. In this study, we present the atmospheric exposure low-cost monitoring (AELCM) system for several air substances like ozone, nitrogen dioxide, carbon monoxide and particulate matter, as well as meteorological variables developed by our research group. The measurement equipment is calibrated using multiple linear regression and extensively tested based on a field evaluation approach at an urban background site using the high-quality measurement unit, the atmospheric exposure monitoring station (AEMS) for meteorology and air substances, of our research group. The field evaluation took place over a time span of 4 to 8 months. The electrochemical ozone sensors (SPEC DGS-O3: R2: 0.71-0.95, RMSE: 3.31-7.79 ppb) and particulate matter sensors (SPS30 PM1/PM2.5: R2: 0.96-0.97/0.90-0.94, RMSE: 0.77-1.07 µg/m3/1.27-1.96 µg/m3) showed the best performances at the urban background site, while the other sensors underperformed tremendously (SPEC DGS-NO2, SPEC DGS-CO, MQ131, MiCS-2714 and MiCS-4514). The results of our study show that meaningful local-scale measurements are possible with the former sensors deployed in an AELCM unit.

Low Power Environmental Image Sensors for Remote Photogrammetry.

This paper aims to prove the feasibility of a 4D monitoring solution (3D modeling and temporal monitoring) for the sandbar and to characterize the species' role in the landscape. The developed solution allows studying the interaction between the river dynamics and vegetation using a network of low resolution and low power sensors. The issues addressed concern the feasibility of implementing a photogrammetry solution using low-resolution sensors as well as the choice of the appropriate sensor and its testing according to different configurations (image capture and storage on the sensor and/or image transmission to a centralization node) and also the detailed analysis of the different phases of the process (camera initialization, image capture, network transmission and selection of the most appropriate standby mode). We reveal that the tiny, low-cost board (ESP32-Cam) can perform a 3D reconstruction and propose using the camera's UXGA (1600, 1200) resolution because of the quality rendering and energy consumption. A multi-node scenario based on a combined Wi-Fi and GSM relay is proposed in the study showing several years of autonomy for the system. Finally, to illustrate the energy cost of the module, we have defined a study process, where we have identified and quantified one by one the different phases of operation of the card for better energy optimization (setup, camera configuration, shooting, saving on SD card, or sending by Wi-Fi). The device is now operational for deployment on the Allier River (France).

Environmental DNA study on aquatic ecosystem monitoring and management: Recent advances and prospects.

Environmental DNA (eDNA) is organismal DNA that can be detected in the environment and is derived from cellular material of organisms shed into aquatic or terrestrial environments. It can be sampled and monitored using molecular methods, which is important for the early detection of invasive and native species as well as the discovery of rare and cryptic species. While few reviews have summarized the latest findings on eDNA for most aquatic animal categories in the aquatic ecosystem, especially for aquatic eDNA processing and application. In the present review, we first performed a bibliometric network analysis of eDNA studies on aquatic animals. Subsequently, we summarized the abiotic and biotic factors affecting aquatic eDNA occurrence. We also systematically discussed the relevant experiments and analyses of aquatic eDNA from various aquatic organisms, including fish, molluscans, crustaceans, amphibians, and reptiles. Subsequently, we discussed the major achievements of eDNA application in studies on the aquatic ecosystem and environment. The application of eDNA will provide an entirely new paradigm for biodiversity conservation, environment monitoring, and aquatic species management at a global scale.

Google earth engine based computational system for the earth and environment monitoring applications during the COVID-19 pandemic using thresholding technique on SAR datasets.

Observing the earth and environmental conditions during the COVID-19 pandemic lockdown along with travel restrictions headed to worse circumstance. These scenarios amplified the hurdles of flood management. In order to resolves these issues, an efficient and resilient geospatial framework with unconventional systems is also required for the generation of instantaneous results. Hence to avoid these deficiencies, the google earth engine based computational system integrated with analytical tools for large-scale data handling is introduced for the earth and environmental monitoring applications. The present study proposes a working model for geospatial data processing to understand socio-demographic implications with a web-based analytical interface. The research introduces a histogram-based thresholding approach for real-time surface water mapping along with precise data processing and analysis for automated monitoring. The study integrates geospatial datasets to a enhanced data processing methods in a web-based platform to deliver the required results for extensive planning and decision making. Furthermore, a similar type of work can be undertaken for other disaster management applications.

Path planning optimization in unmanned aerial vehicles using meta-heuristic algorithms: a systematic review.

Unmanned aerial vehicles (UAVs) have recently been increasingly popular in various areas, fields, and applications. Military, disaster management, rescue operations, public services, agriculture, and various other areas are examples. As a result, UAV path planning is concerned with determining the optimal path from the source to the destination while avoiding collisions with lowering the cost of time, energy, and other resources. This review aims to assort academic studies on the path planning optimization in UAV using meta-heuristic algorithms, summarize the results of each optimization algorithm, and extend the understanding of the current state of the path planning in UAV in the meta-heuristic optimization field. For this purpose, we implemented a broad, automated search using Boolean and snowballing searching methods to find academic works on path planning in UAVs. Studies and papers have been distinguished, and the following information was obtained and aggregated from each article: authors, publication's year, the journal name or the conference name, proposed algorithms, the aim of the study, the outcome, and the quality of each study. According to the findings, the meta-heuristic algorithm is a standard optimization method for tackling single and multi-objective problems. Besides, the findings show that meta-heuristic algorithms have a great compact on the path planning optimization in UAVs, and there is good progress in this field. However, the problem still exists mainly in complex and dynamic environments, on battlefields, in rescue missions, mobile obstacles, and with multiple UAVs.

Soil water infiltration evaluation from punctual to hillslope scales.

Quantifying infiltration and surface runoff at the hillslope scale is indispensable for soil conservation studies. However, the spatial and temporal variability of infiltration imposes a major constraint on surface runoff estimation. Point infiltration values do not fully express the complexity of the surface runoff in the landscape. Considering the need to improve the estimation of runoff volume from infiltration data, this study aimed to measure the apparent infiltration at hillslope-scale and compare it with two methods of infiltration estimative derived from point information. The study was carried out in six hydrological monitoring units paired. A set of hyetographs and hydrographs allowed the determination of apparent infiltration [Formula: see text] to each monitoring unit as a function of precipitation rate P. The measured [Formula: see text] values were used: (1) to evaluate the efficiency of the different land management in increasing infiltration; and (2) to evaluate the efficiency of two methods of hillslope-scale infiltration estimation based on point data: (a) derived from concentric rings method ([Formula: see text]), and (b) derived from a physically-based modeling ([Formula: see text]). Regarding the differences in land managements, terraces proved to be the most efficient land management practice, followed by phytomass addition. Regarding the methods, for precipitation rates greater than 40 [Formula: see text] the point infiltration-based [Formula: see text] underestimates apparent infiltration [Formula: see text] with PBIAS ranging from [Formula: see text] to [Formula: see text]. Even so, [Formula: see text] proved efficient in representing [Formula: see text] at less intense rainfall events. Nonetheless, the point infiltration-based method [Formula: see text] properly represented [Formula: see text] to all rainfall intensities (Nash-Sutcliffe coefficient [Formula: see text]).

Development and Implementation of a Hybrid Wireless Sensor Network of Low Power and Long Range for Urban Environments.

The urban population, worldwide, is growing exponentially and with it the demand for information on pollution levels, vehicle traffic, or available parking, giving rise to citizens connected to their environment. This article presents an experimental long range (LoRa) and low power consumption network, with a combination of static and mobile wireless sensors (hybrid architecture) to tune and validate concentrator placement, to obtain a large coverage in an urban environment. A mobile node has been used, carrying a gateway and various sensors. The Activation By Personalization (ABP) mode has been used, justified for urban applications requiring multicasting. This allows to compare the coverage of each static gateway, being able to make practical decisions about its location. With this methodology, it has been possible to provide service to the city of Malaga, through a single concentrator node. The information acquired is synchronized in an external database, to monitor the data in real time, being able to geolocate the dataframes through web mapping services. This work presents the development and implementation of a hybrid wireless sensor network of long range and low power, configured and tuned to achieve efficient performance in a mid-size city, and tested in experiments in a real urban environment.

Autonomous Marine Robot Based on AI Recognition for Permanent Surveillance in Marine Protected Areas.

The world's oceans are one of the most valuable sources of biodiversity and resources on the planet, although there are areas where the marine ecosystem is threatened by human activities. Marine protected areas (MPAs) are distinctive spaces protected by law due to their unique characteristics, such as being the habitat of endangered marine species. Even with this protection, there are still illegal activities such as poaching or anchoring that threaten the survival of different marine species. In this context, we propose an autonomous surface vehicle (ASV) model system for the surveillance of marine areas by detecting and recognizing vessels through artificial intelligence (AI)-based image recognition services, in search of those carrying out illegal activities. Cloud and edge AI computing technologies were used for computer vision. These technologies have proven to be accurate and reliable in detecting shapes and objects for which they have been trained. Azure edge and cloud vision services offer the best option in terms of accuracy for this task. Due to the lack of 4G and 5G coverage in offshore marine environments, it is necessary to use radio links with a coastal base station to ensure communications, which may result in a high response time due to the high latency involved. The analysis of on-board images may not be sufficiently accurate; therefore, we proposed a smart algorithm for autonomy optimization by selecting the proper AI technology according to the current scenario (SAAO) capable of selecting the best AI source for the current scenario in real time, according to the required recognition accuracy or low latency. The SAAO optimizes the execution, efficiency, risk reduction, and results of each stage of the surveillance mission, taking appropriate decisions by selecting either cloud or edge vision models without human intervention.

A Wearable Low-Power Sensing Platform for Environmental and Health Monitoring: The Convergence Project.

The low-power sensing platform proposed by the Convergence project is foreseen as a wireless, low-power and multifunctional wearable system empowered by energy-efficient technologies. This will allow meeting the strict demands of life-style and healthcare applications in terms of autonomy for quasi-continuous collection of data for early-detection strategies. The system is compatible with different kinds of sensors, able to monitor not only health indicators of individual person (physical activity, core body temperature and biomarkers) but also the environment with chemical composition of the ambient air (NOx, COx, NHx particles) returning meaningful information on his/her exposure to dangerous (safety) or pollutant agents. In this article, we introduce the specifications and the design of the low-power sensing platform and the different sensors developed in the project, with a particular focus on pollutant sensing capabilities and specifically on NO2 sensor based on graphene and CO sensor based on polyaniline ink.

Selection of Solar Powered Unmanned Aerial Vehicles for a Long Range Data Acquisition Chain.

The paper presents a long range data acquisition chain operating in areas without access to the electricity grid or communication infrastructure built with unmanned aerial vehicles (UAVs). It is assumed that the length of the network chain significantly exceeds the flight range of a single drone. To build such a network three basic problems have to be solved. The first is energy harvesting for battery charging. The second concerns the choice of drone models that can cover a given distance in the shortest time. The third problem is the reduction of the flight range of drones as a function of payload mass. The evaluation of the proposed method is based on the results of simulations and cost analysis of 54 drones and 25 solar cells. The analysis ends with a proposition of seven steps that can help to choose the most suitable drone model for a given task.

Advances in Smart Environment Monitoring Systems Using IoT and Sensors.

Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

Small is better: comparing the efficiency of two Surber samplers' sizes for biomonitoring programs in tropical headwater streams in the Amazon region.

This study aimed to compare the efficiency of two different sizes of the Surber sampler to assess benthic macroinvertebrates in headwater streams in two Amazonian regions. Two Surber samplers of different sizes were used, one measuring 20 × 20 cm and the other 30 × 30 cm, both with a 0.25-mm net. The number of replicates taken was 6 for the smaller sampler and 3 for the bigger one, maintaining approximately the same total sampled area. The study was carried out in 12 headwater streams with different environmental conditions. Biological metrics were calculated for each size at each site and compared within each stream health category. A two-way analysis of similarities test was performed to compare the community structure assessed by each method at each stream. A normalized sampling effort was used to quantify the number of samples required to correctly sample each site. The data did not show a significant difference between the two sizes regarding the taxonomic recruitment and the community structure sampled at each stream, but differences were found between the two sizes in dominance values and in Shannon index scores for the natural sites. Furthermore, the smaller Surber was able to assess 70% of the estimated richness in all sites, which suggests that it is better to assess benthic macroinvertebrates than the larger Surber. Moreover, the smaller Surber is easier to transport in the field, reducing the effort of the technician, and takes less time to sort the material collected with it, which can reduce the sample processing effort, therefore reducing the cost of the project.

One-step synthesis of amine-functionalized fluorescent silicon nanoparticles for copper(II) ion detection.

Amine-functionalized silicon nanoparticles (A-SiNPs) with intense green fluorescence and photostability are synthesized via a one-step, low-cost hydrothermal method under mild conditions using 3-aminopropyl triethoxysilane (APTES) as a silicon source and L-ascorbic acid (AA) as a reducing reagent. The amine-rich surface not only improves water dispersability and stability of the A-SiNPs but also offers a specific copper(II) ion (Cu2+) coordination capability. The as-prepared A-SiNPs can be directly employed for Cu2+ detection in "turn-off" mode, resulting from Cu2+ coordination-induced fluorescence quenching effect. Under optimal conditions, Cu2+ detection was accomplished with a linear range from 1 to 500 µM and a limit of detection (LOD) at 0.1 µM, which was much lower than the maximum level (~ 20 µM) of Cu2+ in drinking water permitted by the US Environmental Protection Agency (EPA). In addition, the A-SiNPs were successfully used to detect Cu2+ in spiked river water, demonstrating its good selectivity and potential application for analysis of surface water samples. Graphical abstract.

Determination of 17ß-estradiol by surface-enhanced Raman spectroscopy merged with hybridization chain reaction amplification on Au@Ag core-shell nanoparticles.

The authors describe an aptamer-based assay for 17ß-estradiol. It relies on the combined use of surface enhanced Raman scattering (SERS) and hybridization chain reaction (HCR). The aptamer against 17ß-estradiol is applied as the recognition probes, and this results in excellent specificity. Specific recognition of target 17ß-estradiol induce the freedom of DNA 2, which will open the stem-loop structure of probe 1 on the Au@Ag and form the partial dsDNA structure. With the nicking enzyme, the partial dsDNA will be hydrolyzed and the reside ssDNA on Au@Ag will form a small stem-loop structure. With the help of the other probe 2 modified Au@Ag and pre-immobilized probe 3 on the well of the microplate, an enzyme-free HCR can occur and tremendous Au@Ag can be assembled along the formed dsDNA in HCR, which can act as the excellent substrate for Raman measurement and greatly amplify the Raman signal of R6G on the Au@Ag. Afterwards, the key factor, ratio between probe 2-Au@Ag (P2) and probe1-Au@Ag (P1), affects the detection sensitivity is systematically optimized for the best sensing performance. The SERS signal of R6G, best measured at 1651 cm-1, increases linearly in the wide range from 1 pM to 10 nM. The detection limit can be as low as 0.1 pM. Graphical abstract Schematic presentation of an aptamer-based surface enhanced Raman scattering method for accurate detection of 17ß-estradiol, which is integrated with hybridization chain reaction for signal amplification and sensitivity improvement.

Underwater Acoustic Sensor Networks Node Localization Based on Compressive Sensing in Water Hydrology.

Groundwater is an important source of human activities, agriculture and industry. Underwater Acoustic Sensor Networks (UASNs) is one of the important technologies for marine environmental monitoring. Therefore, it is of great significance to study the node self- localization technology of underwater acoustic sensor network. This paper mainly studies the node localization algorithm based on range-free. In order to save cost and energy consumption, only a small number of sensing nodes in sensor networks usually know their own location. How to locate all nodes accurately through these few nodes is the focus of our research. In this paper, combined with the compressive sensing algorithm, a range-free node localization algorithm based on node hop information is proposed. Aiming at the problem that connection information collected by the algorithm is an integer, the hop is modified to further improve the localization performance. The simulation analysis shows that the improved algorithm is effective to improve the localization accuracy without additional cost and energy consumption compared with the traditional method.

Internet of Things in Marine Environment Monitoring: A Review.

Marine environment monitoring has attracted more and more attention due to the growing concern about climate change. During the past couple of decades, advanced information and communication technologies have been applied to the development of various marine environment monitoring systems. Among others, the Internet of Things (IoT) has been playing an important role in this area. This paper presents a review of the application of the Internet of Things in the field of marine environment monitoring. New technologies including advanced Big Data analytics and their applications in this area are briefly reviewed. It also discusses key research challenges and opportunities in this area, including the potential application of IoT and Big Data in marine environment protection.