Environmental Monitoring Activities Research Articles

Marine exploration and its environmental impact assessment: insights from international standards

Rising demand for marine resources has led to a great interest in seabed exploration and mining, while deep-sea environments are faced with cumulative effects of many human activities. Currently, conducting an environmental impact assessment for deep-sea exploration and mining is challenging due to the dynamic nature and a lack of high-quality data. The International Seabed Authority (ISA), which charges with regulating human activities on the seabed beyond the continental shelf, requires contractors to establish both geological and environmental baselines. Also, the ISA provides a general environmental guideline for exploring various seabed resources. However, standardization of its implementation would need to be addressed with specified technical international standards. The marine technology subcommittee of International Standardization Organization (ISO) contributes to the study of standards on deep-sea geological, geophysical and biological surveys, also on marine environmental protection. In this review, we explore two broad aspects of ISO standards: (1) the development of marine geological and geophysical exploration standard, which may help to establish geological map in the seabed area. (2) the current state of development of a series of Marine Environmental Impact Assessment (MEIA) standards, which could standardize the environmental surveys and monitoring activities in the seabed area. We also consider the standardization gap between MEIA and seabed mining, and propose future focus on coordination relationship between marine exploration and environmental protection.

Rapid Environmental Monitoring, Capture, and Destruction Activities of SARS-CoV-2 and Bacterial Pathogens During the COVID-19 Health Emergency.

The SARS-CoV-2 pandemic is a health emergency for occupational healthcare workers at COVID-19 hospital wards in Italy. The objective of the study was to investigate the bioreactor's effectivity in monitoring and improving air quality via detection, capture, and destruction of the SARS-CoV-2 virus and bacterial pathogens, reducing the risk of transmission among healthcare workers. Bioreactors are a demonstrated effective biomonitoring system. We implemented a methodological approach wherein they were placed at various hospitals treating COVID-19 patients in Italy. The detection of the SARS-CoV-2 virus was achieved through rapid biomonitoring testing of the solutes from the AIRcel bioreactors via SARS-CoV-2 rapid test antigen and consecutive reverse transcription-polymerase chain reaction (RT-PCR) analysis with the multiplex platform (XABT) and the real-time PCR rotor-gene. The marked presence of the SARS-CoV-2 virus was found in multiple water samples via the detection of ORF1ab + N and/or E gene involved in gene expression and cellular signaling of the SARS-CoV-2 virus. The AIRcel bioreactors were able to neutralize the virus and bacterial pathogens effectively as traces of the viruses and bacteria were no longer found in multiple solute samples after an overnight period. Transmission of COVID-19 via bioaerosols, transmitted by infected patients, remains a viable threat for health workers. AIRcel bioreactors allow for rapid biomonitoring testing for early virus detection within the environment, reducing the risk of exponential contagion exposure and maintaining good air quality without endangering health workers. This sameprotocol can also be extended to public spaces as a bio-monitoring hotspot tool for early detection.

Sequencing-based analysis of the microbiomes of Spanish food processing facilities reveals environment-specific variation in the dominant taxa and antibiotic resistance genes

In the last years, advances in high throughput sequencing technologies have opened the possibility to broaden environmental monitoring activities in facilities processing food, offering expanded opportunities for characterizing in an untargeted manner the microbiome and resistome of foods and food processing environments (FPE) with huge potential benefits in food safety management systems. Here the microbiome and resistome of FPE from slaughterhouses (n = 3), dairy (n = 12) and meat (n = 10) processing plants were assessed through whole metagenome sequencing of 2 composite samples for each facility, comprising 10 FPE swabs taken from food contact surfaces and 10 FPE samples from non-food contact surfaces, respectively. FPE from slaughterhouses had more diverse microbiomes and resistomes, while FPE from dairy processing plants showed the highest β-dispersion, consistent with a more heterogeneous microbiome and resistome composition. The predominant bacterial genera depended on the industry type, with Pseudomonas and Psychrobacter being highly dominant in surfaces from slaughterhouses and meat industries, while different lactic acid bacteria predominated in dairy industries. The most abundant antimicrobial resistance genes (ARG) found were associated with resistance to aminoglycosides, tetracyclines and quaternary ammonium compounds (QAC). ARGs relating to resistance to aminoglycosides and tetracyclines were significantly more prevalent in slaughterhouses than in food processing plants, while QAC resistance genes were particularly abundant in some food contact surfaces from dairy and meat processing plants, suggesting that daily sanitation under suboptimal conditions may be selecting for persistent microbiota tolerant to these biocides in some facilities. The taxonomic mapping of ARG pointed to specific bacterial genera, such as Escherichia, Bacillus, or Staphylococcus, as carriers of the most relevant resistance determinants. About 63% of all ARG reads were assigned to contigs classified as plasmid-associated, indicating that the resistome of FPE may be strongly shaped through the spread of mobile genetic elements. Overall, the relevance of FPE as reservoirs of ARG was confirmed and it was demonstrated that next generation sequencing technologies allowing a deep characterisation of sources and routes of spread of microorganisms and antimicrobial resistance determinants in food industry settings hold promise to be integrated in monitoring and food safety management programmes.

MONITORING OF AIRBORNE FIBERS DURING ASBESTOS REMEDIATION: THE CASE STUDY OF THE “ETERNIT” SITE IN THE BAGNOLI-COROGLIO AREA OF RELEVANT NATIONAL INTEREST

As part of the broader implementation of the Environmental Remediation and Urban Regeneration Program (PRARU) of the Bagnoli-Coroglio area of relevant national interest (Naples, Italy), one of the priority actions was the completion of the soil remediation interventions of the former Eternit site, focused on the safe removal of soil and materials containing asbestos still present in the area (approximately 25,700 tonnes of hazardous waste).This area has an extension of approximately 157,000 m2 where the production of products containing asbestos had been carried out from 1939 to 1985. In 1997, the first reclamation activities began, consisting of the decommissioning of industrial structures and continuing with the removal of buried materials. These activities were interrupted in 2014 and then resumed in 2020 until completion at the beginning of 2023.The activities, completed to date, were run under the constant control of the territorially competent “ASL Napoli 1”, and with the implementation of a specific shared plan for monitoring airborne fibers both inside the construction site and outside in the surrounding areas.In particular, environmental monitoring activities were performed before, during and post-operation on eight control units, three of which were located within the construction site and five in the surrounding areas at potentially sensitive targets, as well as personal sampling of the exposed operators.The methods for identifying asbestos fibers were both phase contrast microscopy (MOCF) and scanning electron microscopy (SEM).In both cases, the monitoring results never showed that the thresholds established by Italian legislation were exceeded, both in terms of total fibers and asbestiform fiber.

A First Step towards Developing a Decision Support System Based on the Integration of Environmental Monitoring Activities for Regional Water Resource Protection

The topic of diffuse pollution is of particular interest from technical, scientific, and administrative management points of view. Diffuse pollution is defined as the contamination or chemical, physical, or biological alterations of environmental matrices caused by diffuse sources and not attributable to a single origin. In this study, various sources of diffuse pollution such as nitrates, pesticides, metals, and plastics were analysed. This was aimed at the implementation of a decision support system able to represent the state of environmental matrices degradation, with particular attention to water resources, and to make decisions evaluating similar environmental contexts. The potential of the developed system makes it possible to identify areas with the same environmental characteristics, referring to the various activities that create diffuse pollution and areas with the same pressure values on the environmental matrices. The system provides the political decision-maker with greater awareness of the environmental state, thus enabling him to apply more accurate land management policies. The created system, based on open-source software, which can be implemented with additional available data sources, is characterised by a data processing workflow that provides output information at the municipal level, so that it can be managed both by mayors and regional managers who are able to share the same information with all.

POPs in the Mongolian environment

An environmental and human monitoring project analyzing the persistent organic pollutants (POPs) listed in the Annexes of the Stockholm Convention on POPs was implemented in Mongolia with sampling activities from 2017 until 2019. In support of the global monitoring plan implementation, a total of 51 samples were analyzed for POPs whereby 23 POPs were detected frequently. Two POPs, endrin and hexabromobiphenyl (PBB 153), were not quantified in any of the samples; toxaphene was found only twice in air samples; and dicofol, pentachlorophenol, and short-chain chlorinated paraffins were analyzed only in one human milk sample. Most results were for perfluorooctanoic acid (PFOA, 32 of 43 samples) and perfluorooctanesulfonic acid (PFOS, 31 of 43 samples). With respect to detection frequency, dioxin-like polychlorinated biphenyls (dl-PCB) were detected in 100% of the samples analyzed (N = 28). On scale, highest amounts were found in soil samples, almost 30 000 ng/g for lindane, more than 13 000 ng/g for α-hexachlorocyclohexane (α-HCH), and almost 10 000 ng/g for dichlorochlorodiphenyltrichloroethane (DDT) and its transformation products. The soil and ambient air samples were the drivers towards highest values in the abiotic samples, due to the presence of legacy POPs such as DDT, indicator PCB, HCHs but also hexachlorobenzene (HCB), and pentachlorobenzene (PeCBz). Concentrations of dioxin-like POPs were low in general as were the perfluorinated substances (PFAS). With respect to brominated flame retardants, the presence of hexabromocyclododecane in air samples was surprising. At present, the analytical capacity to analyze POPs in various matrices under the global monitoring plan for POPs is being set-up to strengthen future environmental monitoring activities in Mongolia.

Sol-Gel Immobilized Optical Microalgal Biosensor for Monitoring Cd, Cu and Zn Bioavailability in Freshwater.

While analytical measurements provide the quantitative estimation of the total amount of metals present in a sample, they do not reflect the truly bioavailable fraction of metal which reflects the adverse biological effect. Hence the development of monitoring tools for detecting bioavailable toxic metals has become a priority in environmental monitoring activities. An optical whole-cell biosensor was constructed using the microalga Scenedesmus subspicatus MM1 immobilizing in inorganic silica hydrogels using the sol-gel technique to detect bioavailable Cadmium (Cd2+), Copper (Cu2+) and Zinc (Zn+) in freshwater. Conditions for optimum biosensor performance have been established regarding effective pH range, cell density, exposure time, and storage stability. The optimum response for the biosensor was dependent on the pH of the matrix, cell concentration and exposure time were derived. The biosensor was operational for four weeks. The limit of detection for the algal biosensor was determined as 9.0 × 10-1, 9.1 × 10-1, and 8.8 × 10-1mg/L for Cd, Cu and Zn, respectively. Whole-cell cell biosensor will be highly useful since it comprises a single microalgal species able to detect the bioavailable content of Cd2+, Cu2+, and Zn2+ in freshwater.

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

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

A LC-MS/MS based methodology for the environmental monitoring of healthcare settings contaminated with antineoplastic agents.

Adverse health events associated with the exposure of healthcare workers to antineoplastic drugs are well documented in literature and are often related to the chemical contamination of work surfaces. It is therefore crucial for healthcare professionals to validate the efficiency of safety procedures by periodic biological and environmental monitoring activities where the main methodological limitations are related to the complexity, in terms of chemical-physical features and chemical-biological stability, of the drugs analyzed. Here we describe the evaluation and application of a UHPLC-MS/MS based protocol for the environmental monitoring of hospital working areas potentially contaminated with methotrexate, iphosphamide, cyclophosphamide, doxorubicin, irinotecan, and paclitaxel. This methodology was used to evaluate working areas devoted to the preparation of chemotherapeutics and combination regimens at the University Hospital "San Giovanni di Dio e Ruggi d'Aragona" in Salerno (Italy). Our analyses allowed to uncover critical aspects in both working protocols and workspace organization, which highlighted, among others, cyclophosphamide and iphosphamide contamination. Suitable adjustments adopted after our environmental monitoring campaign significantly reduced the exposure risk for healthcare workers employed in the unit analyzed. The use of sensitive analytical approaches such as LC-MS/MS coupled to an accurate wiping procedure in routine environmental monitoring allows to effectively improve chemical safety for exposed workers.

Textile Industry Waste Pollution in the Konto River: A Comparison of Public Perceptions and Water Quality Data

Textile industry production activities in one of the Badas areas can have implications for the occurrence of dye liquid waste pollution in the Konto river flow. This implication leads to the public perception that there has been river pollution from textile dyeing liquid waste. In this case, public perceptions need to be analyzed as a form of river environmental monitoring activities. Therefore, public perceptions of pollution were compared with actual water quality data, especially related to dyes in river water, to link public perceptions with environmental resource management efforts. This study aims to analyze public perceptions of textile dye waste pollution and compare the analysis results with the river water quality analysis. Analysis of river water quality (dye content, BOD, COD) was conducted at five sampling points in Badas, Kuwik, Balungjeruk, and Wonorejo villages. Public perception was measured descriptively through interviews with respondents referring to the Slovin method. The analysis of public perceptions shows that the Public considers that there has been pollution of river water, mainly due to textile industry waste, along the Konto River. The results of the water quality analysis showed that dye concentrations were found at four sampling points except for Wonorejo Village. This result shows that the correlation between public perception and data on dye contamination only occurs in Badas, Kuwik, and Balungjeruk villages. In addition, the BOD/COD ratio indicates that pollution has occurred at all observation points in this study.

Clustering of Handheld Thermal Camera Images in Volcanic Areas and Temperature Statistics

Thermal camera use is becoming ever more widespread in volcanic and environmental research and monitoring activities. Depending on the scope of an investigation and on the type of thermal camera used, different software for thermal infrared (IR) images analysis is employed. The Osservatorio Vesuviano Sezione in Napoli of the Istituto Nazionale di Geofisica e Vulcanologia (INGV-OV) processes the images acquired during thermal monitoring activities acquired in the Neapolitan areas (Vesuvio, Ischia and Campi Flegrei) with different FLIR software that returns for each image, or for each selected area within the image, a series of parameters (maximum temperature, average temperature, standard deviation, etc.). An operator selects the area of interest and later “manually” inserts the relevant parameters in Excel sheets to generate graphs. Such a tedious, time- and resource-consuming procedure gave reason to implement a software able to automatically analyze sets of thermal images taken with a handheld thermal camera without any manual action. This paper describes the method and the software implemented to “automate” and refine the extrapolation process and the analysis of the relevant information. The employed method clusters thermal images by applying K-MEANS and DBSCAN techniques. After clustering a series of images, the software displays the necessary statistics to highlight possible fluctuations in temperature values. The software, “StaTistical Analysis clusteRed ThErmal Data” (STARTED), is already available. Although it has been developed mostly to support monitoring of the volcanoes in Campania, it is quite versatile and can be used for any activity that implies thermal data analysis. In this paper, we describe the workflow and the dataset used to develop the software, as well as the first result obtained from it.

TCD-Net: A Novel Deep Learning Framework for Fully Polarimetric Change Detection Using Transfer Learning

Due to anthropogenic and natural activities, the land surface continuously changes over time. The accurate and timely detection of changes is greatly important for environmental monitoring, resource management and planning activities. In this study, a novel deep learning-based change detection algorithm is proposed for bi-temporal polarimetric synthetic aperture radar (PolSAR) imagery using a transfer learning (TL) method. In particular, this method has been designed to automatically extract changes by applying three main steps as follows: (1) pre-processing, (2) parallel pseudo-label training sample generation based on a pre-trained model and fuzzy c-means (FCM) clustering algorithm, and (3) classification. Moreover, a new end-to-end three-channel deep neural network, called TCD-Net, has been introduced in this study. TCD-Net can learn more strong and abstract representations for the spatial information of a certain pixel. In addition, by adding an adaptive multi-scale shallow block and an adaptive multi-scale residual block to the TCD-Net architecture, this model with much lower parameters is sensitive to objects of various sizes. Experimental results on two Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) bi-temporal datasets demonstrated the effectiveness of the proposed algorithm compared to other well-known methods with an overall accuracy of 96.71% and a kappa coefficient of 0.82.

Small mammals of background areas in the vicinity of the Karabash copper smelter (Southern Urals, Russia).

BackgroundThe dataset contains records of small mammals (Eulipotyphla and Rodentia) collected in the background (unpolluted) areas in the vicinity of Karabash copper smelter (Southern Urals, Russia) and the territory of the Sultanovskoye deposit of copper-pyrite ores before the start of its development. Data were collected during the snowless periods in 2007 (18 sampling plots), 2008–2010 (13 plots annually), 2011 (30 plots) and 2012–2014 (19 plots annually). The capture of animals was carried out in different types of forests (pine, birch, mixed and floodplain), sparse birch stands, reed swamps, marshy and dry meadows, border areas, a household waste dump, areas of ruderal vegetation and a temporary camp. Our study of small mammals was conducted using trap lines (snap and live traps). During the study period, 709 specimens of small mammals were caught, which belonged to five species of shrews and 13 species of rodents. The dataset may be highly useful for studying regional fauna and the distribution of species in different habitats and could also be used as reference values for environmental monitoring and conservation activities.New informationOur dataset contains new information on occurrences of small mammals. It includes the peculiarities of their habitat distribution in the background areas in the vicinity of the large copper smelter and the deposit of copper-pyrite ores before the start of its development (Chelyabinsk Oblast, Russia). All occurrence records of 18 mammal species with georeferencing have been published in GBIF.

Моделирование сети исследовательских площадок для мониторинга потоков углерода методом Random Forest

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

A Review on Contaminants of Emerging Concern in the Environment: A Focus on Active Chemicals in Sub-Saharan Africa

Active chemicals are among the contaminants of emerging concern that are rarely covered in regulatory documents in sub-Saharan Africa. These substances are neither in the list of routinely monitored substances nor in the guidelines for routine environmental monitoring activities. This has been of concern to public health officials, toxicologists, communities, and governments, hence the need for risk assessment and regulation of these substances. In this review article, the presence of active chemicals in the sub-Saharan African environment was investigated. The results indicate the availability of few studies in some countries, while in other countries no reports of active chemicals were found, hence the need for further research targeting such countries. It was further observed that mixtures of active chemicals from different therapeutic categories—such as antibiotics and analgesics—were reported. The natural environment is increasingly at risk due to the presence of these substances, their metabolites, and their transformation byproducts. These substances are characterized by persistence as a result of their non-biodegradable nature; hence, they circulate from one environmental compartment to another through the food chain, causing harm along the way. Most studies that evaluated the toxicity of these substances considered the effects of a single drug, but observations indicated the presence of drug mixtures, hence the need for further evaluation of the effects of drug–drug interactions—including synergistic and additive effects—for environmental sustainability. The presence of ACs in several environmental compartments at quantifiable quantities was discovered in this investigation, indicating the potential for ecosystem injury as a result of bioaccumulation, bioconcentration, and biomagnification through the food chain. This necessitates further research on the subject in order to ensure a healthier environment.

Photochemistry of foraminiferal test as proxy of Marine Environment, parts of Andhra Coast, East Coast of India

The use of in-coastal environment for monitoring activities such as the marine environment is well known across the world over the years. As a representative of various water-based environmental monitoring activities, many species of foraminifera such as Ammonia have been used. The screening of foraminiferal shell for chemical constituents provides a step forward in determining the ability of benthic foraminifer in the coastal area to react to anthropogenic metal pollution. This study's initial hypothesis is the Foraminifera Calcite Test (Ammonia dentata, Asterorotalia pulchella, Elphidium crispum, Quinqueloculina laevigata, and Massilina secans) binds anthropogenic metals to the structure of the shell and initiates the deformation test. This research uses the normal and abnormal shells of Ammonia specimen. The Ponar collector was used to sample surface sediments. Sophisticated facilities like SEM (Scanning Electron Microscope), EDS (Energy Dispersive X-ray Spectroscopy), and FTIR (Fourier Transform Infrared spectroscopy) detectors have been employed for the analysis of the Foraminiferal shell. For each test (the shooting was done thrice, i.e. first chamber (proloculus), the aperture chamber, and the chambers between the two. The key elements of the foraminifera test are oxygen with an average (weight range of 44.71 - 58.79%) and carbon with an average weight range (10.07 -12.07%). There is a record low level of C element in the abnormal tests.

A Redeemable SVM-DS Fusion-Based Trust Management Mechanism for Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UASNs) have gradually received attention due to their widespread applications, such as in disaster prevention, environmental monitoring and military activities, and they simultaneously face security challenges. In recent years, trust management mechanisms have become important tools for responding to internal attackers. However, most of the existing trust management mechanisms do not consider the adverse influence of complex underwater environment on node evaluation, and do not deal with the case that normal nodes are misjudged as malicious nodes. To achieve accurate trust evaluation of nodes and reduce the possibility of misjudgment of normal nodes, a redeemable Support Vector Machine-Dempster-Shafer (SVM-DS) fusion-based trust management mechanism (SDFTM) is proposed for UASNs in this paper. First, according to the characteristics of attacks, three kinds of trust evidence are selected: packet-based evidence, data-based evidence and energy-based evidence. Then the support vector machine (SVM) is applied to classify the trust of node from the aspect of each kind of trust evidence, while Dempster-Shafer (DS) evidence theory is used to fuse the different trust classification results of nodes. Second, to deal with cases where a normal node may be misclassified as a malicious node, trust redemption process is carried out based on the historical performance and environmental influence (unreliable acoustic channel, weak link connectivity). Finally, the trust value is calculated and updated. The simulation results prove that the proposed scheme yields satisfactory performance in malicious detection and reduce the possibility of misjudgment of normal nodes.

A portable air quality monitoring unit and a modular, flexible tool for on-field evaluation and calibration of low-cost gas sensors

In recent years, several research projects have investigated the use of low-cost devices for air pollution monitoring. As a result of these activities, various air quality monitoring systems have been developed following diverse approaches and methods. However, in most cases, these monitoring systems lack flexibility, extendibility, and openness. The device presented in this article provides a cost-effective, expandable, flexible, open-source solution. SentinAir has been designed to act as a portable air quality monitoring unit and also for facilitating the on-field evaluation and calibration of air quality sensors. The high grade of flexibility featuring this tool enables the data acquisition from heterogeneous kinds of devices deployed far from the laboratory facilities, even in areas featured by a weak or unstable Internet connection. Moreover, the system proposed in this article provides a reasonably easy way to integrate a wide range of sensors or devices produced by diverse manufacturers, offering a tool that is not bound to the use of a limited and fixed set of sensors or devices. The modularity approach followed in SentinAir design constitutes another key factor to consider: it allows each user to easily adapt this device to his requirements. This system is therefore capable of providing a robust framework for environmental monitoring activities to researchers or low-skilled end-users.

Distribution and extent of benthic habitats in Puck Bay (Gulf of Gdańsk, southern Baltic Sea)

The majority of the southern Baltic Sea seabed encompasses homogenous soft-bottom sediments of limited productivity and low biological diversity, but shallow productive areas in the coastal zone such as wetlands, vegetated lagoons and sheltered bays show a high variety of benthic habitat types offering favourable biotopic conditions for benthic fauna. Within Polish marine areas, semi-enclosed Puck Bay (the western part of the Gulf of Gdańsk) features an exceptionally diverse environment covering a range of benthic habitats which underscores its unique biological value and aesthetic quality and providing an impetus for conservation and ecosystem-based development. Full-coverages maps on benthic habitats in this area are therefore a necessary foundation for maritime spatial planning and implementation of strategies for sustainable management and protection of the coastal environment. This study presents the first comprehensive description and distribution of benthic habitats in Puck Bay which were categorised using the revised EUNIS 2019 classification system. Typological analyses were carried out based on inventory datasets from 1995 to 2019 including scientific publications, satellite images, open databases, topographic and geological maps, reports, theses, information available on websites and unpublished data shared willingly by individual researchers and administrative institutions. Collating various spatial data sources, that were first georeferenced and then visualized using techniques available in ArcMap 10.4.1 software (Esri), resulted in the mapping of benthic habitats and sites of important and protected plant species, which can contribute to the high confidence in environmental assessments and monitoring activities.

ANALYSIS AND MONITORING OF RIVER WATER QUALITY IN TASIKMALAYA CITY

The rivers flowing through the City of Tasikmalaya include the Citanduy River, the Ciloseh River, the Ciwulan River and the Cibanjaran River. While the tributaries are the Cibanjaran River which include the Cihideung/ Dalem Suba River, the Cipedes River, the Ciromban River, the Cidukuh River, the Cicacaban River, the Cibadodon River, the Cikalang River, the Tonggong Londok River, the Cibeureum River and the Cimulu River. These rivers flow and empty into the Citanduy River. The aims and objectives of this study are environmental quality monitoring activities, especially the quality of river water in the Tasikmalaya City. To find out in more detail and carefully the level of pollution that occurs, this study aims to conduct water quality monitoring so that the resulting data can determine the source of pollutants that cause deterioration of river water quality. Monitoring was conducted during the rainy season and the dry season. Based on the monitoring, it appears that the river being monitored does not meet the quality standards as a class II water body with varying status ranging from lightly polluted to heavily polluted. Parameters that do not meet quality standards include TSS, BOD, COD, nitrite, Cr6+, Zn, free chlorine, Total Phosphate, oil and fat, E. Coli and Total Coliform.