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This article presents a review of many types of SERS sensors for food safety and environmental pollution monitoring based on detecting rhodamine. It introduces the basic concepts of substrates, enhancement factors, and mechanisms, devices' sensors integrated with the microstructure. Here, we review the state-of-the-art research in the field of rhodamine monitoring and highlight the applications of SERS sensors. The trends in the development of substrates for different applications have been mentioned with the aim of providing an overview of the development of different SERS substrates. Thus, an efficient approach for rhodamine detection has a good perspective for application in environmental monitoring. [ABSTRACT FROM AUTHOR]

Environmental sustainability is the rate of renewable resource harvesting, pollution control, and non-renewable resource exhaustion. Air pollution is a significant issue confronted by the environment particularly by highly populated countries like India. Due to increased population, the number of vehicles also continues to increase. Each vehicle has its individual emission rate; however, the issue arises when the emission rate crosses the standard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop prediction approaches to monitor and control pollution using real time data. With the development of the Internet of Things (IoT) and Big Data Analytics (BDA), there is a huge paradigm shift in how environmental data are employed for sustainable cities and societies, especially by applying intelligent algorithms. In this view, this study develops an optimal AI based air quality prediction and classification (OAI-AQPC) model in big data environment. For handling big data from environmental monitoring, Hadoop MapReduce tool is employed. In addition, a predictive model is built using the hybridization of ARIMA and neural network (NN) called ARIMA-NN to predict the pollution level. For improving the performance of the ARIMA-NN algorithm, the parameter tuning process takes place using oppositional swallow swarm optimization (OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS) classifier is used to classify the air quality into pollutant and non-pollutant. A detailed experimental analysis is performed for highlighting the better prediction performance of the proposed ARIMA-NN method. The obtained outcomes pointed out the enhanced outcomes of the proposed OAI-AQPC technique over the recent state of art techniques. [ABSTRACT FROM AUTHOR]

Monitoring environmental parameters in urban centers has become a critical step for health risk assessment. In this study, conducted in the industrial park of Mirandela (Portugal), an environmental monitoring system consisting of a meteorological station, a class 1 smart sound sensor and three reference optical gas analyzers were deployed to measure noise levels, tropospheric ozone concentrations, carbon monoxide, and nitrogen oxides, during winter and spring of 2019. The interdependence among all variables was evaluated, and legal compliance was assessed according to the European Union (EU) target values for ozone. Higher concentrations of nitrogen oxides and carbon monoxide were observed during rush hours, while ozone concentrations increased as a consequence of the emissions of primary pollutants after sunrise reaching their maximum values at about 13:00 to 14:00h. Regarding the Accumulated Ozone Exposure (AOT), both target values set by the EU for AOT40 vegetation and forest have been exceeded. For AOT60, the observed values were below target; however, the threshold for NET60 was exceeded even before the end of 2019. Noise levels were higher during rush hours, because of higher traffic intensity; however, noise level indicators (Lden, Ln) remained within the legal limits. It was possible to realize that urban traffic is the most relevant source of noise and local air pollution. These results can be used to define strategies that may improve the environmental performance of the studied industrial park. [ABSTRACT FROM AUTHOR]

The Birnbaum–Saunders (BS) distribution, also known as the fatigue life distribution, is right-skewed and used to model the failure times of industrial components. It has received much attention due to its attractive properties and its relationship to the normal distribution (which is symmetric). Furthermore, the coefficient of variation (CV) is commonly used to analyze variation within a dataset. In some situations, the independent samples are collected from different instruments or laboratories. Consequently, it is of importance to make inference for the common CV. To this end, confidence intervals based on the generalized confidence interval (GCI), method of variance estimates recovery (MOVER), large-sample (LS), Bayesian credible interval (BayCrI), and highest posterior density interval (HPDI) methods are proposed herein to estimate the common CV of several BS distributions. Their performances in terms of their coverage probabilities and average lengths were investigated by using Monte Carlo simulation. The simulation results indicate that the HPDI-based confidence interval outperformed the others in all of the investigated scenarios. Finally, the efficacies of the proposed confidence intervals are illustrated by applying them to real datasets of PM10 (particulate matter ≤ 10 μm) concentrations from three pollution monitoring stations in Chiang Mai, Thailand. [ABSTRACT FROM AUTHOR]

One of the economic challenges facing developing countries is the cost of tackling air pollution and improving its quality. In this study, we propose a comparative analysis of the tropospheric nitrogen dioxide (NO2) column spatial configuration over Tehran between pandemic conditions and normal conditions, based on the European Space Agency (ESA) Copernicus Sentinel-5P products. Comparison of the time series of nitrogen dioxide obtained from Sentinel-5P data showed that NO2 levels in 2020 were between 4 × 10−5 and 6 × 10−5 mol/m2, while in 2019 the amount of nitrogen dioxide many times exceeded 6 × 10−5 mol/m2. Based on the data obtained from Sentinel-5P, the northern, north-eastern, and central regions of Tehran were registered as the most polluted areas in terms of nitrogen dioxide. The results showed that due to the change of normal conditions in 2019 to quarantine conditions due to coronavirus pandemic (COVID-19) in 2020, the density of tropospheric NO2 columns decreased, which is due to the temporary restriction of industrial activities, traffic, and population density. The correlation analysis between ground dataand data from Sentinel-5P provided encouraging values with a Pearson's correlation coefficient of 0.58 (2019) and 0.61 (2020), and the amount of nitrogen dioxide in 2020 decreased by 6% compared to 2019. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicle networks is a significant resource for aquatic life maintenance and monitoring underwater pollution. It is necessary to study the underwater localization algorithms which self-localize themselves. The proposed work concentrates only on the range-free localization model with a goal is to use only very few sensing nodes in the network with low cost and high battery power to self-localize themselves. Few beacon nodes have been used to localize the sensor nodes in the definite position of the network. Compressive sensing theory based on hop count information for localizing the sensor nodes has also been used for localization. This research aims to collect the connectivity information of all the nodes in the network with compressive sensing theory and improve the localization accuracy. The analysis presents that the proposed method works well with location accuracy. Compressive Sensing Node Localization reduces cost and enhances sensors' energy efficiency compared to other underwater localization algorithms. [ABSTRACT FROM AUTHOR]

Plasticizers, i.e., phthalate esters (PAEs) were liable to be detected from fresh tea leaves and tea products. In order to monitor the pollution of PAEs in tea plants and compare the difference among PAEs content of tea cultivars, fifteen elite cultivars in tea plant cultivar gardens were chosen. PAEs were extracted from the upper mature leaves and lower mature leaves of tea bushes and determined via GC–MS once every two months, six times in total in one anniversary. DMP, DEP, DiBP, DBP, and DEHP were detected in fifteen tea cultivars. DBP was the predominant congener in fifteen tea cultivars, which was followed by DiBP. PAEs content in upper mature leaves was significantly lower than that in lower mature leaves in all months. There was no significant difference among PAEs content from the fifteen tea cultivars. Except for summer, the PAEs content of fresh tea leaves gradually increased from spring to winter. The correlation analysis result was that PAEs had significant negative correlation with air temperature and positive correlation with air quality index (p < 0.01). The mutual correlation among the five PAEs were significant (p < 0.05). According to cluster analysis, three types of fresh tea leaves with high, medium, and low accumulation accounted for 26.7%, 20%, and 53.3%, respectively. The results could supply a reference for monitoring of pollution of PAEs in fresh tea leaves, as well as an evaluation of PAEs content difference of tea plant cultivars. [ABSTRACT FROM AUTHOR]

The Mahanadi Estuarine System (MES), with a complex network of freshwater channels, rivers, and mangroves, is a leading seaport in State Odisha on the east coast of India, but subjected to intense human activity in recent years. Such anthropic impingements are known to impact sediment-dwelling biota adversely. However, information on the macrobenthic community of the MES is not well documented yet. Therefore, the primary objectives of this study (February 2013-March 2017) were to address knowledge gaps on the macrobenthic community structure vis-à-vis local environmental conditions and to evaluate the extent of anthropogenic disturbances on macrobenthos. The results from 264 benthic grab samples (van Veen, 0.04 m2; 2 replicates × 12 GPS fixed locations × 3 seasons) revealed 73 taxa representing 64 genera and 48 families of macrobenthic fauna. The polychaetes (81.41%) and crustaceans (15.42%) were significant faunal groups that contributed mainly to the benthic population and diversity. Multivariate approaches using benthic community attributes and biotic indices (AMBI and M-AMBI) as proxy measures of environmental disturbances proved effective for appraisal. The correlations between the environmental parameters (temperature, pH, salinity) and community estimates were statistically significant. Hierarchical clustering analysis disclosed three major groups (Global R 0.70; p < 0.002) influenced by tolerant/opportunist species. The lower abundance, richness, diversity, and dominance of opportunistic species mark the signs of environmental stress. The community health status remained unbalanced, as indicated by AMBI scoring. M-AMBI analysis contributed best in differentiating areas exposed to diverse impacts and indicated polluted community health status with moderate ecological quality. Our results reiterate the effective use of macrobenthos as bioindicators for ecological status and monitoring. The findings could be utilized for future monitoring assessments, translated into valuable information, and designed into well-defined sustainable management strategies for the MES.

4-nitrophenol (4-NP) is one of the organic pollutants that can come up from pesticides, explosives, dyes, and pharmaceutical industries. Since it can be extremely harmful to humans and other living organisms, it is crucial to have a system that can effectively detect the presence of 4-NP. Here, we report the microplasma synthesis of nitrogen-doped graphene quantum dots (N-GQDs) for fluorescence-based detection of 4-NP. Through Förster resonance energy transfer (FRET) between donor N-GQDs to the acceptor 4-NP, synthesized N-GQDs can be employed for the detection of 4-NP starting from 0.5 to 100 µM with a limit of detection as low as 95.14 nM. 4-NP detection also demonstrates remarkable stability over all pH values and wide temperatures (10–60 °C), indicating the high possibility for robust organic pollution monitoring. Our work provides insight into a simple, fast, and environmentally friendly method for synthesizing N-GQDs at ambient conditions usable for environmental nanosensors.

During a relatively long-term pollution monitoring study between 2015 and 2021 (annually between 2015 and 2019; triennially after 2019), a total of 257 polychaete species belonging to 40 families were identified at 17 soft bottom stations (depth range: 11–89 m) along the Levantine coast of Turkey. Among them, four species belonging to Hesionidae (Oxydromus digitifera sp. nov.), Orbiniidae (Leitoscoloplos mediterranea sp. nov.), Paraonidae (Paraonis lobulata sp. nov.) and Capitellidae (Barantolla cryptogenica sp. nov.) are new to science, two species belonging to Cirratulidae (Chaetozone elakata) and Ampharetidae (Anobothrus amoureuxi) are new additions to the Mediterranean fauna and 28 species are new additions to the marine fauna of Turkey. Among the families, Spionidae (29 species), Syllidae (22 species) and Paraonidae (22 species) accounted for 28% of the total number of species, and Capitellidae, Lumbrineridae and Spionidae comprised 45% of the total number of individuals. The most dominant species in the region were Lumbrineris geldiayi, Notomastus mossambicus and Prionospio saccifera. A total of 20 alien species belonging to 12 families were found in the region, with N. mossambicus and P. saccifera accounting for more than 85% of the total number of individuals of alien species. The new species are described and compared to the closely related species. [ABSTRACT FROM AUTHOR]

Bioaerosols are usually defined as aerosols arising from biological systems such as bacteria, fungi, and viruses. They play an important role in atmospheric physical and chemical processes including ice nucleation and cloud condensation. As such, their dispersion affects not only public health but also regional climate. Lidar is an effective technique for aerosol detection and pollution monitoring. It is also used to profile the vertical distribution of wind vectors. In this paper, a coherent Doppler wind lidar (CDWL) is deployed for aerosol and wind detection in Hefei, China, from 11 to 20 March in 2020. A wideband integrated bioaerosol sensor (WIBS) is used to monitor variations in local fluorescent bioaerosols. Three aerosol transport events are captured. The WIBS data show that, during these transport events, several types of fluorescent aerosol particles exhibit abnormal increases in their concentration, number fractions to total particles, and number fractions to whole fluorescent aerosols. These increases are attributed to external fluorescent bioaerosols instead of local bioaerosols. Based on the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backward trajectory model and the characteristics of external aerosols in WIBS, their possible sources, transport paths, and components are discussed. The results prove the influence of external aerosol transport on local high particulate matter (PM) pollution and fluorescent aerosol particle composition. The combination of WIBS and CDWL expands the aerosol monitoring parameters and provides a potential method for real-time monitoring of fluorescent biological aerosol transport events. In addition, it also helps to understand the relationships between atmospheric phenomena at high altitudes like virga and the variation of surface bioaerosol. It contributes to the further understanding of long-range bioaerosol transport, the roles of bioaerosols in atmospheric processes, and in aerosol–cloud–precipitation interactions. [ABSTRACT FROM AUTHOR]

The selective sensing of gaseous target molecules is a challenge to analytical chemistry. Selectivity may be achieved in liquids by several different methods, but many of these are not suitable for gas-phase analysis. In this review, we will focus on molecular imprinting and its application in selective binding of volatile organic compounds and atmospheric pollutants in the gas phase. The vast majority of indexed publications describing molecularly imprinted polymers for gas sensors and vapour monitors have been analysed and categorised. Specific attention was then given to sensitivity, selectivity, and the challenges of imprinting these small volatile compounds. A distinction was made between porogen (solvent) imprinting and template imprinting for the discussion of different synthetic techniques, and the suitability of each to different applications. We conclude that porogen imprinting, synthesis in an excess of template, has great potential in gas capture technology and possibly in tandem with more typical template imprinting, but that the latter generally remains preferable for selective and sensitive detection of gaseous molecules. More generally, it is concluded that gas-phase applications of MIPs are an established science, capable of great selectivity and parts-per-trillion sensitivity. Improvements in the fields are likely to emerge by deviating from standards developed for MIP in liquids, but original methodologies generating exceptional results are already present in the literature. [ABSTRACT FROM AUTHOR]

In order to solve the problems of single monitoring factor, weak comprehensive analysis ability, and poor real time performance in traditional environmental monitoring systems, a research method of residential environment pollution monitoring system based on cloud computing and Internet of Things is proposed. The method mainly includes two parts: an environmental monitoring terminal and an environmental pollution monitoring and management platform. Through the Wi-Fi module, the data is sent to the environmental pollution monitoring and management platform in real time. The environmental monitoring management platform is mainly composed of environmental pollution monitoring server, web server, and mobile terminal. The results are as follows. The data measured by the system is close to the data measured by the instrument, and the overall error is small. The measurement error of harmful gases is about 6%. PM 2.5 is about 6.5%. Noise is about 1%. The average time for sensor data update is 0.762 s. The average alarm response time is 2 s. The average data transfer time is 2 s. Practice has proved that the environmental pollution monitoring and alarm system operates stably and can realize real-time collection and transmission of data such as noise, PM 2.5, harmful gas concentration, illumination, GPS, and video images, providing a reliable guarantee for timely environmental pollution control. [ABSTRACT FROM AUTHOR]

Nowadays Eco-friendly nano-composite film used as a sensor for environmental pollutionand photochemical applications is one of the most significant kinds of optoelectronics and industrial polymer materials. Thus, nano-composite samples of low-cost polyvinyl alcohol (PVA) doped with various concentrations of the copper nanoparticles (CuNPs) were perfectly synthesised to be used as a sensor for monitoring the level of pollution in different zones. The influence of environmental changes in the optical and spectroscopic properties of Cu2+/PVAwas studied. The optical band energy gapsfor samples at very low, low and high pollution zone cities were estimated using UV-VIS spectra. The optical microscopy (OM) images displayed excellent compatibility and homogeneous spreading of PVA film doped with 500 ppm Cu NPs in the composite. In addition, the immiscibility and colour of Cu2+/PVA were great variances with the variation of pollution percentages. These variations may be resulting from the creation of copper oxide and the existence of toxic elements, greater humidity and high temperature in this zone. The nano-composite polymer in various polluted zones was characterised viaUV-laser induced plasma spectroscopy (UV-LIPS) techniquefor monitoring the level of pollution through the qualitative and quantitative analysis of trace and major elements that exist in the polymer films. The detected elements in the Cu2+/PVA films were perfect-identified as copper, carbon, chromium, cadmium, zirconium, tantalum, lead, zinc, arsenic, lithium, selenium, phosphorus, hydrogen and oxygen. The recorded spectra of the nano-compositecontain the spectral lines of elements. We validated the precision of UV-LIPS results for determining the concentration of copper in polymer films by using a typical method such as the inductively coupled plasma-optical emission spectrometry (ICP-OES).The variation of plasma parameters with the gate delay in different polluted zones has also been investigated. [ABSTRACT FROM AUTHOR]

The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite is a valuable source of information to monitor the NOx emissions that adversely affect air quality. We conduct a series of experiments using a 4×4 km 2 Comprehensive Air Quality Model with Extensions (CAMx) simulation during April–September 2019 in eastern Texas to evaluate the multiple challenges that arise from reconciling the NOx emissions in model simulations with TROPOMI. We find an increase in NO2 (+17 % in urban areas) when transitioning from the TROPOMI NO2 version 1.3 algorithm to the version 2.3.1 algorithm in eastern Texas, with the greatest difference (+25 %) in the city centers and smaller differences (+5 %) in less polluted areas. We find that lightning NOx emissions in the model simulation contribute up to 24 % of the column NO2 in the areas over the Gulf of Mexico and 8% in Texas urban areas. NOx emissions inventories, when using locally resolved inputs, agree with NOx emissions derived from TROPOMI NO2 version 2.3.1 to within 20 % in most circumstances, with a small NOx underestimate in Dallas–Fort Worth (-13 %) and Houston (-20 %). In the vicinity of large power plant plumes (e.g., Martin Lake and Limestone) we find larger disagreements, i.e., the satellite NO2 is consistently smaller by 40 %–60 % than the modeled NO2 , which incorporates measured stack emissions. We find that TROPOMI is having difficulty distinguishing NO2 attributed to power plants from the background NO2 concentrations in Texas – an area with atmospheric conditions that cause short NO2 lifetimes. Second, the NOx/NO2 ratio in the model may be underestimated due to the 4 km grid cell size. To understand ozone formation regimes in the area, we combine NO2 column information with formaldehyde (HCHO) column information. We find modest low biases in the model relative to TROPOMI HCHO, with -9 % underestimate in eastern Texas and -21 % in areas of central Texas with lower biogenic volatile organic compound (VOC) emissions. Ozone formation regimes at the time of the early afternoon overpass are NOx limited almost everywhere in the domain, except along the Houston Ship Channel, near the Dallas/Fort Worth International airport, and in the presence of undiluted power plant plumes. There are likely NOx -saturated ozone formation conditions in the early morning hours that TROPOMI cannot observe and would be well-suited for analysis with NO2 and HCHO from the upcoming TEMPO (Tropospheric Emissions: Monitoring Pollution) mission. This study highlights that TROPOMI measurements offer a valuable means to validate emissions inventories and ozone formation regimes, with important limitations. [ABSTRACT FROM AUTHOR]

Summary: Fine root endophyte mycorrhizal fungi in the Endogonales (Mucoromycota arbuscular mycorrhizal fungi, M‐AMF) are now recognized as at least as important globally as Glomeromycota AMF (G‐AMF), yet little is known about the environmental factors which influence M‐AMF diversity and colonization, partly because they typically only co‐colonize plants with G‐AMF. Wild populations of Lycopodiella inundata predominantly form mycorrhizas with M‐AMF and therefore allow focussed study of M‐AMF environmental drivers. Using microscopic examination and DNA sequencing we measured M‐AMF colonization and diversity over three consecutive seasons and modelled interactions between these response variables and environmental data. Significant relationships were found between M‐AMF colonization and soil S, P, C:N ratio, electrical conductivity, and the previously overlooked micronutrient Mn. Estimated N deposition was negatively related to M‐AMF colonization. Thirty‐nine Endogonales Operational Taxonomic Units (OTUs) were identified in L. inundata roots, a greater diversity than previously recognized in this plant. Endogonales OTU richness correlated negatively with soil C:N while community composition was mostly influenced by soil P. This study provides first evidence that M‐AMF have distinct ecological preferences in response to edaphic variables also related to air pollution. Future studies require site‐level atmospheric pollution monitoring to guide critical load policy for mycorrhizal fungi in heathlands and grasslands. [ABSTRACT FROM AUTHOR]

The environmental problem of volatile organic compounds (VOCs) pollution in industrial parks is becoming increasingly serious. Rapid and large-scale monitoring of the overall pollution in the area and identification of pollution sources are keys to efficient pollution control. However, traditional detection methods cannot achieve rapid detection, let alone accurately pinpoint the source of pollution. In this study, an online VOC mobile monitoring system based on time-of-flight mass spectrometry was used to monitor an industrial park and successfully obtain the VOC distribution image of the industrial park. Two polluted areas (A and B) were pinpointed by examining the image, and the ΣVOCs concentration ranges were 74–421 μg/m3 and 51–577 μg/m3. Eight abnormal sites with concentration peaks exceeding the standard were found, and the pollution source of one abnormal site was analyzed. The correlation between the pollution source factor of the abnormal site and the emission enterprise was as high as 0.91, and the pollution source of the abnormal site was identified. To explore whether it was affected by other surrounding enterprises, a positive matrix factorization model was further used to analyze the receptor data of abnormal site. Three pollution sources were analyzed and compared with enterprise emission sources. Combined with the location of the abnormal site and emission sources, we found that the abnormal sites were not only affected by the emission sources of surrounding enterprises but also by the emission sources of enterprises in the northeast. This approach offers rapid and large-scale monitoring of the overall pollution in an area and quickly pinpointed the location of the pollution sources. This method solves the shortcomings of traditional methods that cannot identify the location of pollution source and provides an effective technical means for the rapid detection of the pollution distribution in industrial parks for efficient screening of pollution sources. [ABSTRACT FROM AUTHOR]

Characterizing the influencing factors of resuspended dust on paved roads according to the atmospheric environment and traffic conditions is important to provide a basis for road atmospheric pollution control measures suitable for various road environments in the future. This study attempts to identify factors in the concentration of resuspended dust according to the level of road dust loading and PM10 emission characteristics according to atmospheric weather environment and traffic conditions using real-time vehicle-based resuspended PM10 concentration measuring equipment. This study mainly focuses on the following main topics: (1) the increased level of resuspended dust according to vehicle speed and silt loading (sL) level; (2) difference between atmospheric pollution at adjacent monitoring station concentration and background concentration levels on roads due to atmospheric weather changes; (3) the correlation between traffic and weather factors with resuspended dust levels; (4) the evaluation of resuspended dust levels by road section. Based on the results, the necessity of research to more appropriately set the focus of analysis in order to characterize the resuspended dust according to changes in the traffic and weather environment in urban areas is presented. [ABSTRACT FROM AUTHOR]

According to United Nations (UN) 2030 agenda, the pollution detection system needs to be improved for the establishment of fresh air to obtain healthy life of living things. There are many reasons for the pollution and one of the reasons for pollution is from the emissions of the vehicles. Currently digital technologies such as the Internet of Things and Long-Range are showing significant impact on establishment of smart infrastructure for achieving the sustainability. Based on this motivation, this study implemented a sensor node and gateway-based Internet of Things architecture to monitor the air quality index value from any location through Long-Range communication, and Internet connectivity. To realize the proposed system, a customization of hardware is carried out and implemented the customized hardware i.e., sensor node and gateway in real-time. The sensor node is powered with node mapping to minimize the data redundancy. In this study, the evaluation metrics such as bit rate, receiver sensitivity, and time on air are evaluated by spreading factor (SF), code rate (CR), bandwidth, number of packets, payload size, preamble, and noise figure. The real-time sensor values are logged on the cloud server through sensor node and gateway. The sensor values recorded in the cloud server is compared with optimal values and concluded that the PM10, PM2.5 are high in the air and remaining values of NO2, O3, CO are optimal in the air. Along with this an architecture is proposed for interfacing the hardware with blockchain network through cloud server and API for node authentication. [ABSTRACT FROM AUTHOR]

The Ganga River is the major source of drinking water for humans over the decades. It is also the ecological niche for millions of relict species, i.e., for a variety of planktons, benthic organisms, fish, and various other aquatic organisms. The blasting population resulted in an enhanced rate of pollution in the river system emanating from various anthropogenic activities and industrialization in the bank of river Ganga. The study was made in the middle and lower stretch of the river to monitor the decadal changes in the water quality of river Ganga from 1960 to 2019 at six different study sites. In the present study, various water quality parameters such as dissolved oxygen, pH, free carbon dioxide, total alkalinity, conductivity, total dissolved solids (TDS), hardness, chloride, and nitrate have been studied during 2015–2019. The data for 1960 to 2006 were taken from ICAR-CIFRI publications. Based on the studied parameters, National Sanitation Foundation (NSF)—water quality index (WQI) was calculated. In the present study, it was found that the calculated NSF-WQI was 69.24 in 1960–1961 which increased up to 113.39 during 2001–2006. But, with the implementation of various rejuvenating strategies, the WQI of the river got reduced to 106.48 during 2015–2019. This reflected the positive changes in the riverine system. Different water quality parameters such as dissolved oxygen, pH, and hardness were observed mostly within the permissible range as based on the drinking water guidelines for humans and survival of the aquatic organisms as well, except a few location-specific observations. [ABSTRACT FROM AUTHOR]

Background and Objective: In some villages of Mazandaran, drinking water may be polluted with various pollutants, especially heavy metals (HMs), due to the shallow depth of drinking water sources and the proximity of these sources to farmlands. Therefore, this study aimed to measure the HM pollution level of drinking water in some villages of Mazandaran province and to assess the attributed health risks. Materials and Methods: HMs content of drinking water in 30 villages with separate water sources in Mazandaran province was measured. Concerning the HMs concentration and toxicity, and exposure route through water drinking, carcinogenic and non-carcinogenic risks were assessed using a Monte-Carlo simulation-based method. Results: The concentration values of arsenic, cadmium, chromium, nickel and lead in water were <1.0-4.26, <0.05, <0.15-3.74, <0.3-10.89, and <0.8-4.68 µg/L, respectively. The hazard index (HI) values for non-carcinogenic risk due to the exposure to HMs through drinking of water in various age groups ranged from 3.04E-04 to 9.94E-04. Values of cumulative excess lifetime cancer risk (ELCRT) for As and Cr were 9.72E-08 and 6.13E-08, respectively. Conclusion: The results of this study showed that, fortunately, the concentration of metals in drinking water in the studied area was much lower than the national standards and the attributed health risk. Therefore, the drinking water sources in the studied villages had acceptable quality. However, due to the existence of rice paddies in some villages of Mazandaran province, continuous monitoring of pollution levels in the drinking water sources of these areas is essential. [ABSTRACT FROM AUTHOR]

Introduction. Air pollution heterogeneity and rapid urbanization impose numerous constraints on available near-surface air quality monitoring. The solution for effective warning comes with the integration of different data, including remote sensing. Satellite data cannot answer whether dangerous pollution levels are observed; however, it provides a complete picture and may detect air pollution transportation towards or away from cities. The possibilities for effective near-real time (NRTI) monitoring have significantly improved with the launch of the Sentinel-5P satellite. The study aimed to describe the developed system for NRTI air pollution monitoring over Kharkiv, Kryvyi Rig, Kyiv, and Odesa based on NO2 and CO data derived from the Sentinel-5P satellite. Data and methodology. The NRTI System was developed for tropospheric NO2 and total CO column number densities based on the Sentinel-5P NRTI products. After satellite scanning of Ukrainian territory, the NRTI System goes live in 2-3 hours. It is fully automatic, and modules were written using Python, VB.NET, and batch-scripting. Results. The NRTI System includes four main phases: preparatory, source data downloading, processing and post-processing with visualization, archiving, and result distribution among users. Source data filtering with a quality assurance index and downscaling with linear kriging interpolation were developed. The output of the NRTI System is data in regular grids with a spatial resolution of 0.02o×0.02o. Based on the NRTI System work during October – December 2021, we conducted preliminary analyses to understand the possibilities of data usage. Higher NO2 content was observed in Kyiv and Kharkiv, where traffic emissions play a crucial role in air quality worsening. The use of daily time series allowed the detection of an increase in NO2 variance during the heating season, as well as plume distribution from cities to rural areas due to the prevailing wind. CO content is more homogeneous; however, higher values were observed in industrial Kryvyi Rig and Odesa. It is emphasized the huge impact of shipping CO emissions on air quality in Odesa. The temporal averaging of the NRTI System output allowed us to define the most polluted districts within the cities of interest. We intend to continue developing the presented NRTI System and develop the same algorithms for all cities with populations greater than 500 000 people in order to provide operational air pollution monitoring based on satellite data. [ABSTRACT FROM AUTHOR]

Among all the environmental factors, soil is the most stable component, due to its solid physical state. Soil is the basis of all food chains and terrestrial biodiversity. Therefore, a clean, unpolluted soil means reversing the decline of biodiversity, providing healthy agricultural raw materials, protecting human and animal health, etc. Pesticides are a broad group of chemicals commonly used in agriculture. Even though the impact on crop production is obviously profitable, pesticide residues are a common cause of soil pollution, especially in developing countries. Higher plants are recognized as excellent genetic models to detect environmental mutagens and are frequently used in environmental pollution monitoring studies. The objective of this paper was to determine the potential of the species Allium cepa to be used in biological tests for monitoring environmental pollution with herbicides. For this purpose, the biological material consisting of meristematic roots of A. cepa was exposed for 24 hours to the treatment with different doses of two types of herbicides, namely: 0.125, 0.200, 0.250 g/L for Pendimethalin and 0.225, 0.250 and 0,300 g/L for Aclonifen respectively. The obtained results show the sensitivity of the species A. cepa to the tested herbicides by the drastic reduction of the mitotic activity in the cell cycle and by the appearance of a large number of chromosomal aberrations in the mitotic cells. From this point of view, the use of the Allium biological test can contribute to optimization of the methods of monitoring the chemical pollution of soil with herbicides. [ABSTRACT FROM AUTHOR]

Very large-scale integration (VLSI) circuits operating at ultra-low power are currently acquiring more attention from the research group and the industries too. The energy efficient sensor nodes with reduced size are much preferred among applications such as wireless sensor networks, pollution and plant monitoring. The biggest challenge faced by the VLSI designers in present day life is designing a product of new generation which operates on minimum possible power. As an operational gadget, the PLL has wide scope of utilization in media transmission, PCs and electronic applications for jitter reduction, clock synthesis, synchronization etc. As a result, a PLL that can function in the GHz range while consuming less power is required. In this paper, designing and analysis of PLL with numerous outputs has been proposed to be implemented by changing the closed loop frequency control framework PLL blocks. As such, highly effective, low power, ideal area chip can be used for PLL with four various yields as PLL 8x, PLL 4x, PLL 2x and PLL 1x with different frequencies separately. Likewise, it is planned to utilize multi-threshold technique to minimize the leakage current in the circuit. Further, the performance verification of various parameters of PLL are done to acquire negligible power. Direct toolbox YALMIP is used as programming solver as an optimization tool. In addition to this, another method of non-dominated sorting genetic algorithm (NSGA-II) Correlation for optimization is also introduced between both the strategies to find the best outcomes. [ABSTRACT FROM AUTHOR]

The anatomical and morphological parameters of the leaf blade of Forsythia suspensa were investigated to determine the species' stability in the Kyiv metropolis (Ukraine) conditions and monitor environmental pollution. The leaves of F. suspensa were selected at four monitoring sites in Kyiv, which differed in their distance to highways and the intensity of traffic. The histological structure of F. suspensa leaves changed toward xeromorphism in the variants where plants were exposed to increased vehicles' emissions. In particular, the thickness of the cuticle and adaxial epidermis increased, the stomatal size decreased, the degree of stomata opening decreased and their density increased, and the stomatal index and xeromorphism index increased. In general, such changes in the structure of F. suspensa leaves increased the plants' resistance to pollution. This indicates the plasticity of F. suspensa plants and a sufficient level of their adaptation to the urban environment. Hence, the parameters of the stomatal apparatus of F. suspensa leaves can be applied as test indicators for biomonitoring of urban pollution. This species can be recommended for creating stable culturphytocoenoses in conditions of a high level of technogenic influence. [ABSTRACT FROM AUTHOR]

In comparison with analytical tools, bioassays provide higher sensitivity and more complex evaluation of environmental samples and are indispensable tools for monitoring increasing in anthropogenic pollution. Nevertheless, the disadvantage in cellular assays stems from the material variability used within the assays, and an interlaboratory adaptation does not usually lead to satisfactory test sensitivities. The aim of this study was to evaluate the influence of material variability on CXCL12 secretion by T47D cells, the outcome of the CXCL‐test (estrogenic activity assay). For this purpose, the cell line sources, sera suppliers, experimental and seeding media, and the amount of cell/well were tested. The multivariable linear model (MLM), employed as an innovative approach in this field for parameter evaluation, identified that all the tested parameters had significant effects. Knowledge of the contributions of each parameter has permitted step‐by‐step optimization. The most beneficial approach was seeding 20,000 cells/well directly in treatment medium and using DMEM for the treatment. Great differences in both basal and maximal cytokine secretions among the three tested cell lines and different impacts of each serum were also observed. Altogether, both these biologically based and highly variable inputs were additionally assessed by MLM and a subsequent two‐step evaluation, which revealed a lower variability and satisfactory reproducibility of the test. This analysis showed that not only parameter and procedure optimization but also the evaluation methodology must be considered from the perspective of interlaboratory method adaptation. This overall methodology could be applied to all bioanalytical methods for fast multiparameter and accurate analysis. The CXCL‐test is a sensitive in vitro assay developed in T47D cell line to identify estrogenic properties of anthropogenic chemicals and to monitor pollution caused by endocrine disruptors. As the test sensitivity can be modified by several factors, multivariable linear model was employed as an innovative statistical approach for fast evaluation of contributing factors. It revealed that cell lines, media, sera, cell number, and cell density significantly affected CXCL12 secretion. This knowledge enables new interpretation of the test results. [ABSTRACT FROM AUTHOR]

The increasing need of sustainable technologies to produce domestic water in metropoles of developing countries faces socioenvironmental problems such as contamination, distribution network failures and water depletions due to overexploitation and climate changes. In this paper, a long-term (9 years) multiparameter evaluation of river bank filtration (RBF) was carried out in an urban and tropical environment in NE Brazil. RBF constitutes a promising way to mitigate adverse effects of unsustainable resource management, as it is adaptable to local needs and may limit non-renewable resource uses. Interannual monitoring of stream water and pumped groundwater allowed deciphering an efficient bacteriological (total disappearance of total and thermotolerant coliforms), physicochemical (general increase of COD/BOD ratio from 5 to 8 reaching 10 to more than 30, lowering of turbidity from 10–300 to 0 NTU) and drug attenuation (from 22 to 193 µg L−1 to 9 µg L−1 in the pumped groundwater for diclofenac and total disappearance of paracetamol most of the time). This suggests that RBF is relevant for local water supply: While socioenvironmental context implies great pressures over the resource, hydrological and hydrogeological conditions favor biodegradation performance, promoted by high temperatures (near 25 °C), and natural self-regulation. However, from a chemical point of view, low turbidity was associated with nitrate increase (reaching temporarily more than 40 mg L−1) in the RBF well. This indicates that the great physicochemical/biochemical filtration potential may in turn favor a great biogeochemical reactivity involving redox processes of concern for domestic purposes in urban and tropical context and that adapted pumping schedule is necessary. [ABSTRACT FROM AUTHOR]

In view of the increasingly serious problem of marine ecological environmental pollution, the traditional marine environmental corrosive pollution monitoring technology has poor monitoring accuracy and poor monitoring timeliness, and the improvement of the marine environmental corrosive pollution monitoring technology under the cloud computing platform is proposed. The research significance and corrosion influence factors of steel corrosion in the marine environment are described, and the research progress of corrosion mechanism in five different zones of the marine environment is reviewed. Cloud computing parallelizes the processing of corrosive pollution data in the marine environment through virtualization and distributed technology, which greatly improves the efficiency of the algorithm. This paper studies the existing cloud computing platform and ocean monitoring system architecture, uses the distributed architecture to design a cloud computing-oriented ocean monitoring system and meets the design requirements in data collection and data processing. The experimental results show that the precision of marine environmental corrosion pollution monitoring technology proposed in this paper is 96% on average, and the completion rate of monitoring images is 82% on average, which can effectively realize marine environmental corrosion pollution monitoring. [ABSTRACT FROM AUTHOR]

The release of chemicals and microorganisms from various sources, such as industry, agriculture, animal farming, wastewater treatment plants, and flooding, into water systems have caused water pollution in several parts of our world, endangering aquatic ecosystems and individual health. World Health Organization (WHO) has introduced strict standards for the maximum concentration limits for nutrients and chemicals in drinking water, surface water, and groundwater. It is crucial to have rapid, sensitive, and reliable analytical detection systems to monitor the pollution level regularly and meet the standard limit. Electrochemical biosensors are advantageous analytical devices or tools that convert a bio-signal by biorecognition elements into a significant electrical response. Thanks to the micro/nano fabrication techniques, electrochemical biosensors for sensitive, continuous, and real-time detection have attracted increasing attention among researchers and users worldwide. These devices take advantage of easy operation, portability, and rapid response. They can also be miniaturized, have a long-life span and a quick response time, and possess high sensitivity and selectivity and can be considered as portable biosensing assays. They are of special importance due to their great advantages such as affordability, simplicity, portability, and ability to detect at on-site. This review paper is concerned with the basic concepts of electrochemical biosensors and their applications in various water quality monitoring, such as inorganic chemicals, nutrients, microorganisms' pollution, and organic pollutants, especially for developing real-time/online detection systems. The basic concepts of electrochemical biosensors, different surface modification techniques, bio-recognition elements (BRE), detection methods, and specific real-time water quality monitoring applications are reviewed thoroughly in this article. [ABSTRACT FROM AUTHOR]

To develop an eco-friendly Underwater Cognitive Acoustic Networks (UCANs) with high spectrum utilization. The huge number of sensor networks is installed in the Underwater to enable applications for oceanographic data collection, pollution and corrosion monitoring, offshore exploration, disaster prevention, assisted navigation and tactical surveillance applications. For feasible applications, there is a need to enable the underwater communica)tions among the underwater devices. The latest developments in the wireless communications cause the spectrum shortage problems in the oceans. Cognitive Acoustic (CA) is the enabling technology for supporting dynamic spectrum access technique in wireless communication. The CA provides the solution for spectrum scarcity prob)lem that is encountered in many countries. In oceans, both the natural acoustic systems and artificial acoustic systems use acoustic signal for communication. To efficiently utilize the spectrum without interference with other acoustic systems, a smart UAN should be designed with aware of the surrounding environment and ability to reconfigure their operation parameters such as frequency band, modulation schemes and transmission power. [ABSTRACT FROM AUTHOR]

Abstract In recent decades, higher-place shrimp ponds are prevalent in coastal areas of developing countries. However, shrimp aquaculture has a growing negative impact on mangrove wetland ecosystems. Mangroves located in Dongzhai harbor are under threat continuously of this commercialization. Therefore, a comprehensive analysis of contaminants and heavy metals is necessary for Dongzhai harbor, considering the mangrove area was declined and ecological services offered to the coastal communities with an insight for future restoration activities. In this study, pond effluents (Total Nitrogen (TN), Ammonia Nitrogen (AN), Total phosphorus (TP), Chemical Oxygen Demond (COD)) and heavy metals (Cd, Cu, Pb, Zn) were monitored in Dongzhai harbor yearly between 2013 and 2017. The results showed that the contents of Cd, Cu, Pb, and Zn were far lower than the standards. However, the main contaminants were organic matter and nitrogen. Maximum values of COD and TN were 26.10 mg·L−1 and 1.34 mg·L−1, respectively in 2017. Single factor index, Nemerow's pollution index, and Trophic level index revealed that the heavily polluted areas were Tashi and Sanjiang town.

• Low-cost measurement platform for NO2 monitoring developed. • Machine-learning-based calibration procedure proposed. • Combination of neural network and kriging surrogates employed. • NO2 sensor correction applied using reference data and environmental parameters. • Satisfactory correlation between reference and calibrated sensor data demonstrate. Air quality significantly impacts human health, the environment, and the economy. Precise real-time monitoring of air pollution is crucial for managing associated risks and developing appropriate short- and long-term measures. Nitrogen dioxide (NO 2) stands as a common pollutant, with elevated levels posing risks to the human respiratory tract, exacerbating respiratory infections and asthma, and potentially leading to chronic lung diseases. Notwithstanding, precise NO 2 detection typically demands complex and costly equipment. This paper explores NO 2 monitoring using low-cost platforms, meticulously calibrated for reliability. An integrated measurement unit is first presented that contains primary and supplementary nitrogen dioxide sensors, as well as auxiliary detectors for evaluating outside and inside temperature and humidity. The calibration process utilizes data acquired over the period of five months from various reference stations. Employing machine learning with an artificial neural network (ANN)-based and kriging interpolation surrogate models, the correction strategy integrates additive and multiplicative enhancement, predicted by the ANN through auxiliary sensor data such as temperature, humidity, and the sensor-detected NO 2 levels. Extensive verification studies showcase that this calibration approach notably enhances monitoring precision (coefficient of determination surpassing 0.85 concerning reference data, and RMSE of less than four μg/m3), rendering low-cost NO 2 detection practical and dependable. [ABSTRACT FROM AUTHOR]

This paper presents a novel deep learning model called Self-Attention Layer within a Convolutional Neural Network (SACNN), specifically designed for detecting acoustic data in extensive datasets containing emergency vehicle sirens and road noise. SACNN leverages a combination of EfficientNet and One Dimensional Convolutional Neural Networks (1D_CNN) to improve the precision and efficiency of detection. The dataset comprises 3-second WAV-format audio files categorized into Emergency Vehicles (Ambulance and Firetruck sirens) and Traffic (plain traffic sounds), with each category having 200 sound files and their corresponding 200 spectrogram images per audio file. Experimental results demonstrate SACNN's superior performance across various metrics, including accuracy, precision, recall, and F1 score, while also achieving notable advancements in computational efficiency. The research addresses the need for more effective solutions in the domain of emergency vehicle siren and road noise detection by introducing SACNN as a cutting-edge solution. By integrating EfficientNet and 1D_CNN, SACNN surpasses existing models in accuracy and computational efficiency. Comparative analysis with EfficientNet and 1D_CNN using a large-scale dataset validates SACNN's superior performance, with an average accuracy, precision, recall, and F1 score of 1.00. Additionally, SACNN exhibits the highest computational efficiency, averaging 0.20883 s per sample. These findings highlight SACNN's efficacy in detecting and classifying emergency vehicle sirens and road noise in large-scale datasets, with implications for traffic management, public safety, and noise pollution monitoring. SACNN offers the potential for constructing real-time acoustic data detection systems. [ABSTRACT FROM AUTHOR]

Micro(nano)plastics (MNPs) are pervasive pollutants posing significant environmental and health risks. Selecting appropriate sentinel species to assess MNPs in terrestrial and aquatic environments involves considering factors such as species behavior, habitat, exposure pathways, and the feasibility of sampling. For these reasons, it was imperative to launch a virtual special issue (VSI) in Ecological Indicators. This editorial summarizes the contributions submitted to the VSI, which invited research on bioindicators of MNPs, including sampling protocols, monitoring strategies, and analytical methods, to advance our understanding and management of plastic pollution. The VSI received a total of 12 contributions from four countries, with a significant majority originating from Italy. To present the contributions, we have organized the sections into original articles and reviews. The original articles are further divided into sections on the occurrence of MNPs in biotic and abiotic compartments, the selection of bioindicators, and the effects and risk assessment of MPs. The reviews are categorized based on their focus on aquatic and terrestrial ecosystems. Future ecotoxicology studies should select bioindicators based on exposure to MNPs, conservation status, ecological roles, and human consumption. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel electrochemical sensor for sensitive and selective mercury ions (Hg2+) detection in environmental samples. • Use of electrospun cellulose acetate nanofibers doped with silver nanoparticles (AgNPs). • Detects Hg2+ with a limit of 0.43 μg/L, demonstrating exceptional sensitivity to mercury pollution. • An environmentally friendly and economically viable sensor design promotes sustainable mercury pollution monitoring. • Evidence the sensor efficacy in aqueous environments with broad applications in environmental monitoring and public health. Mercury (Hg) sensors are critical for environmental monitoring, industrial processes, and public health. They are designed to measure the presence and concentration of mercury ions (Hg2+) in various matrices, including air, water, soil, and biological samples. Mercury in the environment raises concerns due to its toxicity and potential for bioaccumulation. Therefore, this work aims to develop an improved, eco-friendly mercury sensor to address the evolving need for monitoring and control of mercury. The electrochemical sensor for reported here is based on the ability of silver (Ag) to catalyze the reduction of the mercury species Hg2+ to elemental mercury (Hg0), forming an Ag/Hg amalgam. To optimize this sensor, the concentration of silver nanoparticles (AgNPs) on modified nanofibers is determined using differential pulse voltammetry (DPV). Morphological studies and chemical characterization reveal a fiber thickness ranging from 250 to 450 nm and characteristic functional groups, respectively. The sensoŕs analytical performance for detecting Hg2+ concentrations is evaluated through cyclic voltammetry (CV) and DPV in 0.1 M potassium chloride as a supporting electrolyte. The sensor's detection limit (LOD) for Hg2+ was 0.43 μg/L, with a sensitivity of 0.33 mA/μg/L. In conclusion, the electrochemical sensor exhibits sensitivity and selectivity for Hg2+ detection in the presence of other interfering metal ions, along with eco-friendly and cost-effective features, making it a reliable and practical tool for detecting Hg2+ concentration in water samples. [ABSTRACT FROM AUTHOR]

[Display omitted] • Wet grinding for synthesis of 2D CuS nanosheets with superior photothermal effect. • Solubility product control strategy for Hg2+ photothermal sensing. • Advantages of simple, easy to operate, low cost, and signal legibility. • A powerful on-site solution for monitoring Hg2+ contamination. Here, a solubility product control strategy for Hg2+ photothermal sensing was developed by using ultrathin porous 2D CuS nanosheets as a photothermal sensor. Therein, the 2D CuS nanosheets with high photothermal effect was firstly prepared by simple wet grinding, which showed high specific surface area and hierarchical porous structure, providing abundant exposed active sites and diffusion channels, and exhibiting strong affinity and accessibility for Hg2+. Based on the difference of solubility products between CuS and HgS, the 2D CuS nanosheets could react with Hg2+ through substitution reaction to form HgS on the surface of 2D CuS nanosheets, resulting in the reduction of the photothermal effect of 2D CuS nanosheets. The photothermal effect-induced temperature signal change exhibited a good linearity with Hg2+ concentrations in the range from 1 nM to 10 μM, with high selectivity and low detection limit of 0.7 nM. This photothermal sensing strategy with the advantages of simple, easy to operate, low cost, and signal legibility provided a preferred solution for monitoring Hg2+ pollution, and would be highly beneficial for a broad range of point-of-care testing (POCT) applications. [ABSTRACT FROM AUTHOR]

Phytoplankton responds rapidly to nutrient availability variations, becoming a useful indicator for eutrophication and/or management actions to reduce it. The present study evaluated the medium-term response of the phytoplankton community of a temperate estuary (Urdaibai estuary) to the cessation of discharges from a wastewater treatment-plant (WWTP), comparing the physicochemical conditions and the phytoplankton community before (2020) and after (2022) the sewerage works. The cessation led to a decrease of ammonium and phosphate, causing decreases of phytoplankton biomass in the outer and middle estuary and increases in the surroundings of the WWTP. Community composition also changed, recording an increase of prasinoxanthin-containing algae's contribution to total biomass, and a composition shift in the inner estuary, from mainly flagellates (alloxanthin-containing and chlorophyll b -containing algae) to the increase of diatoms, which could be prompted by the change of nutrient-ratios and the nitrogen source, and might indicate the recuperation of the system. • Sewerage works led to a marked decrease of N and P throughout the estuary. • Biomass was lower in outer-middle estuary and higher in the inner after sewerage works. • Prasinoxanthin-containing algae were more important in the estuary after sewerage works. • There was a shift from flagellates to diatoms in the inner estuary after sewerage works. • PIGMENTUM enabled the evaluation of biomass and community composition changes. [ABSTRACT FROM AUTHOR]

The influence of floating marine debris (FMD) on coastal and marine communities and ecosystems is undeniable, and attention is increasingly focused on ecologically and biologically important coastal areas. To protect marine life and valuable resources from FMD pollution, identifying FMD accumulation zones is recognized as a priority. One of the coastal ocean processes found governing the distribution of FMD is water convergence (frontal zones). These fronts are driven by various oceanographical factors. To date, the transport and accumulation of FMD in relation to fronts in coastal areas is poorly understood. To address this knowledge gap, we reviewed various types of ocean fronts as well as FMD accumulation along frontal zones in coastal areas defined as the region between the coastline and the shelf break. Frontogenesis (physical processes related to frontal formation) were reviewed alongside studies on FMD accumulation in frontal zones to identify physical factors that drive the pathways and accumulation in these areas. This review will contribute to our understanding of accumulation hotspots of FMD within ocean fronts and identify gaps for further research on developing a proxy for FMD hotspot identification in ecologically important coastal areas. • Different frontal types are generated by various oceanographic processes, and coastal fronts are mainly driven by winds, tides, currents, upwelling and downwelling, bottom topography, plumes, and internal waves. • In coastal zones, floating marine debris accumulates along water convergences including gyres, eddies, slicks, salinity fronts, density fronts, thermohaline fronts and tidal fronts. • Frontal accumulated debris has an impact on marine species in coastal waters including important marine taxa such as seabirds, dolphins, and turtles. • Study on FMD accumulation in frontal zones has received more attention, with 72% of reviewed papers were published between 2017 and 2023, and the highest number of published papers (10 papers) in 2021. • This study suggests that future research exploring the impacts of frontogenesis on the transport and accumulation of FMD over the continental shelf zone would be valuable. [ABSTRACT FROM AUTHOR]

Microorganisms play a critical role in hydrocarbon degradation, contaminant sequestration, and pollution monitoring. However, the complex relationships between microbial processes and geological media's physical and chemical properties remain ambiguous. The self-potential (SP) is an efficient, low-cost, and nonintrusive passive geophysical technique suitable for monitoring dynamic activities. Herein, we conducted the 3D monitoring experiments to obtain time-lapse SP signals generated from cultivating typical microorganisms (Shewanella oneidensis MR-1) under laboratory-controlled conditions. The 3D multi-channel SP experimental devices enable dynamic monitoring and measurement of weak signals. At the beginning of the experiment, we observed a rapid increase in SP signals that consist mainly of the streaming potential and the redox potential. The peak values of negative anomalies monitored in the two experiments were − 75.9 mV and − 59.5 mV, respectively. During subsequent monitoring, the abnormal potential signal gradually decreased. After a sufficient period, the amplitude of the SP generated solely by the Shewanella oneidensis MR-1 activities ranged from −45 to −35 mV. Our laboratory research paves the way for developing dynamic model data to link the self-potential response with microbial processes. Then, we inverted the measured SP data to obtain the current source density distribution. The consistency of current density results and anomalous potentials shows that SP data collected by pre-buried non-polarizable electrodes can be utilized as a direct indicator signal for spatiotemporal monitoring of microbial activities. The SP method shows promise in environmental bioremediation and biodegradation. • We performed 3D experiments to monitor the time-lapse self-potential signals from cultivating typical microorganisms. • The 2D/3D time-lapse self-potential data revealed the signal characteristics of the microbial processes. • Self-potential method is available as a direct indicator tool for spatiotemporal monitoring of microbial activities. [ABSTRACT FROM AUTHOR]

This study investigated the bioindicator potential of Amaranthus retroflexus L., Plantago lanceolata L., Rumex acetosa L., and Trifolium pratense L. including the use of Lolium multiflorum L. as a reference species, for heavy metal pollution monitoring, in particular Zinc (Zn), Cadmium (Cd), Nickel (Ni), and Lead (Pb). Controlled heavy metal contamination was applied through irrigation with metal nitrate solutions two levels of contamination (low and high). The study also focused on analyzing heavy metals concentration in plant tissues and related physiological responses. Distinct physiological responses to heavy metal stress were observed among the investigated species, highlighting unique variations in their reactions. Hydrogen peroxide, malondialdehyde content, and enzymatic activities emerged as reliable indicators of plant stress induced by heavy metal solutions. P. lanceolata displayed elevated Zn concentrations in both roots and leaves (3271 ± 337 and 4956 ± 82 mg kg−1). For Pb, L. multiflorum and P. lanceolata showed highest root concentrations (2964 ± 937 and 1605 ± 289 mg kg−1), while R. acetosa had higher leaf concentration (1957 ± 147 mg kg−1). For Ni, L. multiflorum had the highest root concentration (1148 ± 93 mg kg−1), and P. lanceolata exhibited the highest leaf concentration (2492 ± 28 mg kg−1). P. lanceolata consistently demonstrated the highest Cd concentrations in both roots (126 ± 21 mg kg−1) and leaves (163 ± 12 mg kg−1). These results provide valuable insights for selecting effective bioindicator species to establish control strategies for heavy metal pollution. • Diverse bioindicator potential of common weed species found for heavy metal pollution monitoring. • Different physiological responses observed among weed species under heavy metal stress. • Trifolium pratense and Plantago lanceolata identified as most suitable bioindicators of soil pollution. [ABSTRACT FROM AUTHOR]

A new worrying micropollutant threathens natural environments: the microfibres (MFs). Natural, regenerated and synthetic MFs have been detected in different environments, as well as in organisms. While synthetic MFs are generally detected in microplastic analyses, natural and regenerated MFs are not taken into account, or are wrongly considered plastics. They are generally considered biodegradable even if their degradation processes in ecosystems are poorly known. Their potential faster degradation could release toxic compounds, and their characteristics could led to a long-term accumulation in the environment. Understanding their dangerousness and the possible impact they could have on ecosystems is fundamental for environment conservation. We collected and investigated water and submerged sediment samples in different caves and springs of the Classical Karst Region (NE Italy), rich in protected habitats and species. MFs were analysed via microscopy and spectroscopy. MFs were found in all samples, highlighting pollution in surface and subterranean habitats of the karst system. MF concentration was higher in submerged sediments respect to waters, highlighting an accumulation of MFs over time. Big microfibres were less abundant, and MF amount increased with the decrease in the considered size. More than 80% of fibres were fluorescent under UV light. Fluorescent MFs were especially transparent, while non-fluorescent ones were mainly black and blue. Most MFs were cellulosic, and synthetic MFs represent only 15–22%, highlighting a significant gap between the MF composition detected in natural environments and the global production of synthetic textiles in recent times. Synthetic MFs were more abundant in waters. Our results improve the knowledge on micropollutants in karst environments, laying the foundations for future research. MF pollution monitoring in karst areas must become a priority for species protection, habitat conservation, and waters management, improving analyses on a larger number of aquatic environments, taking into account the ecological connections between surface and subterranean habitats. [Display omitted] • Microfibres pollute karst surface and subterranean aquatic environments. • An accumulation of microfibres in submerged sediment occurs. • Microfibre amount increase with the decrease in the size considered. • Natural and regenerated microfibres are more abundant than synthetic ones. • Monitoring aquatic environments is fundamental for their conservation. [ABSTRACT FROM AUTHOR]