Environmental Parameters Research Articles

Design of the offline test electronics for the nozzle system of proton therapy.

A set of nozzle equipment for proton therapy is currently under development at China Institute of Atomic Energy (CIAE). To facilitate the off-line commissioning of the whole equipment, a set of ionization chamber signal generation system, known as the test electronics, was designed. The results showed that the system can simulate the beam position, beam fluence (which exhibits a positive correlation with the dose), and other related analog signals generated by the proton beam when it traverses the ionization chamber. Moreover, the accuracy of the simulated beam position is within ± 0.33mm, and the accuracy of the simulated beam fluence signal is within ± 1%. The test electronics can output analog signals representing environmental parameters. The test electronics meets the design requirements, which can be used for the commissioning of the nozzle system as well as the treatment control system without the presence of the proton beam.

Performance of different modeling techniques in testing the impact of environmental variables on eel landing in Ichkeul Lake, a RAMSAR Wetland and UNESCO biosphere reserve

Advanced modeling techniques, including Random Forest (RF) and Cubist model (CB), were used to assess the relationship between environmental factors and European eels (Anguilla anguilla) abundance and to provide insights into the lake's ecological status while considering climate change and anthropogenic influences. A comprehensive dataset, attained through extensive environmental and biological monitoring for the period 2010–2020, was employed. The performance of the models is carried out using key metrics including the root mean square error (RMSE), coefficient of determination (R²), and mean estimation error (MAE). In addition, a sensitivity analysis was conducted to ascertain the relative significance of the thirteen input variables in shaping the predictions of the models. The precision of the CB and RF models in predicting eel landings surpassed that of Multiple Regression. In the training dataset, the CB model achieved R2=0.55, RMSE=7.68 tons, and MSE=6.20 tons, and the RF model achieved R2=0.56, RMSE=7.20 tons, and MSE=5.56 tons. High accuracy was maintained on the testing dataset, with CB achieving R2=0.73, RMSE=5.13 tons, and MSE=5.89 tons, and RF achieving R2=0.73, RMSE=5.81 tons, and MSE=4.67 tons. The scatter plot between predicted and measured eel landings indicated that the RF model tends to overestimate lower values and underestimate higher values of eel landings, while the CB model gave better performance in this context. Further, the carried sensitivity analysis using the CB model unequivocally identified three pivotal factors – water level, salinity, and turbidity level – as the most influential determinants governing the landing of eels in this ecosystem. Thus, the CB model is considered to be more promising for interpreting the relationship between environmental parameters and eel landings, which could be used by managers for an effective lake management strategy.

Fronts affect fish community structure in the Yangtze River estuary and its adjacent area

Dispersal, a critical factor in the viability of marine organisms, is strongly influenced by the physical dynamics of the ocean. Fronts, as a common phenomenon in ocean dynamics, can theoretically impact the dispersal of organisms and ultimately cause spatial homogeneity or heterogeneity of marine communities. However, there is relatively limited understanding concerning whether frontal formation hinders the connectivity between communities and thus results in differences in community structure. To verify whether the formation of fronts caused differences between communities, 60 eDNA samples and their environmental parameters were collected from 32 sites in the Yangtze River estuary and its adjacent area, and four groups, the Yangtze River front-inner group (YRI), the Yangtze River front-outer group (YRO), the Zhejiang costa front-inner group (ZCI), and the Zhejiang coast front-outer group (ZCO), were compared to examine the differences in fish communities between the sides of the front. The results showed that there was a significant difference between YRI and YRO, suggesting that the front affected the community structure. There was no significant difference in fish communities between ZCI and ZCO, indicating that fronts could only hinder dispersal at high intensities. The difference between YRI and YRO possibly resulted from dramatic changes in temperature, salinity, and turbidity related to the front. Fronts also separated water masses with different environmental characteristics and thereby attracted fish to their optimal habitats. Overall, fronts appear to be ecologically significant for fish dispersal and community differentiation.

Probabilistic Prediction of Floating Offshore Wind Turbine Platform Motions via Uncertainty Quantification and Information Integration

The accurate prediction of short-term platform motions in a real environment is crucial for the safe design, operation, and maintenance of floating offshore wind turbines (FOWTs). Numerical simulations of motions are typically associated with high uncertainties due to abstracted theoretical models, empirical parameters, initial environment parameters, etc. Therefore, it is necessary to integrate other sources of information associated with less uncertainty, e.g., monitoring data, for accurate predictions. In this paper, we propose a probabilistic prediction based on the Bayesian approach that logically integrates motion monitoring data with simulated motion predictions of FOWTs, considering uncertainties in the environment model, structural properties, motion prediction method, monitoring data, etc. The approach consists of constructing a prior probability density function (PDF) of a random variable (which characterizes the largest value of the initial motion response) via numerical simulations and a likelihood function based on platform motion monitoring data and deriving a posterior PDF of the random variable by Bayesian updating. Then, posterior distributions of short-term extreme motion responses are derived using the posterior PDF of the random variable, representing lower uncertainty and improved accuracy. A Metropolis–Hastings algorithm is adopted to obtain PDFs of complex probability distributions. The effectiveness of the approach is demonstrated on a real FOWT platform in Scotland. The proposed probabilistic prediction approach results in posterior distributions of short-term extreme platform motions associated with less uncertainty and higher accuracy, which is attributed to integrating prior knowledge with monitoring data.

Influence of window and door opening on office room environment and human thermal sensation during different seasons in moderate climate

Natural ventilation is commonly used in buildings because it does not require energy, and its various strategies, for example ventilation through an open window, can improve the indoor environment quality, including the thermal comfort of users. This study aimed to determine accurate environmental parameters typical for naturally ventilated rooms for three seasons: summer, autumn and winter, and to assess the occupants' thermal sensation in the mentioned conditions. Based on the measurements, the spatial distribution and time variability of air temperature and air speed in an office room with a stack natural ventilation system were assessed. The resulting data on the indoor environment parameters at different seasons and with various door and window opening configurations were used as an input to simulate human thermal sensation. Three models were used for simulation: predicted mean vote (PMV), the thermal sensation (TS) model developed by Zhang et al., and the advanced dynamic thermal sensation (DTS) model by Fiala et al., which allowed the demonstration of differences in the thermal comfort assessment depending on the method. Measurements have shown that opening the window with the door closed has a negligible impact on the parameters indoor environment parameters in summer; opening the door (cross-ventilation) changes this situation. The effect increased during autumn and winter, which affects users' thermal sensations. The assessment based on the most popular PMV model differed from the indications of advanced DTS and TS models showing significant discrepancies, which may lead to a misinterpretation of thermal comfort for individuals in the room.

Experimental evaluation of thermal environment built by decoupled radiant cooling system with low radiant cooling temperature

Decoupled radiant cooling system (DRCS)1, which utilizes an infrared-transparent membrane to isolate the radiant surface from the ambient air, can provide radiant cooling temperature below the dew point to prevent condensation and increase cooling capacity. However, whether the thermal environment built by DRCS with low radiant cooling temperature meets thermal comfort needs further investigation. This includes: 1) Do the environment parameters meet the comfort standard? 2) How do subjects evaluate the environment's comfort? Thus, environmental monitoring and questionnaire surveys were conducted to evaluate the thermal environment of DRCS in summer. Initially, the environment parameters were measured and 32 subjects were recruited to perform subjective and physiological response tests. Further, the environment was evaluated using thermal comfort models and thermal sensation votes. Finally, the applicability of the predicted mean vote (PMV) model was examined by comparing actual and predicted thermal sensations. The results confirmed that thermal environment built by DRCS satisfied the ASHRAE Standard criteria. The neutral operative temperature was around 24.8°C and the comfort environment was achieved between 23.3°C and 26.5°C. Moreover, PMV model has a high accuracy in predicting the thermal sensation of DRCS.

Deploying Wireless Sensor Networks in Multi-Story Buildings toward Internet of Things-Based Intelligent Environments: An Empirical Study.

With the growing integration of the Internet of Things in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of WSNs in a commercial multi-story building, specifically addressing the difficulty of dealing with the variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights into the potential to significantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system's performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. We provide an in-depth analysis for scrutinizing the disparities and commonalities between the datasets obtained from real-world deployments and simulation. The results obtained show the significance of accurate simulation models for reliable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. This research's findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental parameters. This includes insights into temperature, humidity, and light intensity, offering opportunities for enhanced comfort and efficiency in intelligent environments.

Assessment of microbial communities from cold mine environments and subsequent enrichment, isolation and characterization of putative antimony- or copper-metabolizing microorganisms

Mining activities, even in arctic regions, create waste materials releasing metals and metalloids, which have an impact on the microorganisms inhabiting their surroundings. Some species can persist in these areas through tolerance to meta(loid)s via, e.g., metabolic transformations. Due to the interaction between microorganisms and meta(loid)s, interest in the investigation of microbial communities and their possible applications (like bioremediation or biomining) has increased. The main goal of the present study was to identify, isolate, and characterize microorganisms, from subarctic mine sites, tolerant to the metalloid antimony (Sb) and the metal copper (Cu). During both summer and winter, samples were collected from Finnish mine sites (site A and B, tailings, and site C, a water-treatment peatland) and environmental parameters were assessed. Microorganisms tolerant to Sb and Cu were successfully enriched under low temperatures (4°C), creating conditions that promoted the growth of aerobic and fermenting metal(loid) tolerating or anaerobic metal(loid) respiring organism. Microbial communities from the environment and Sb/Cu-enriched microorganisms were studied via 16S rRNA amplicon sequencing. Site C had the highest number of taxa and for all sites, an expected loss of biodiversity occurred when enriching the samples, with genera like Prauserella, Pseudomonas or Clostridium increasing their relative abundances and others like Corynebacterium or Kocuria reducing in relative abundance. From enrichments, 65 putative Sb- and Cu-metabolizing microorganisms were isolated, showing growth at 0.1 mM to 10 mM concentrations and 0°C to 40°C temperatures. 16S rRNA gene sequencing of the isolates indicated that most of the putative anaerobically Sb-respiring tolerators were related to the genus Clostridium. This study represents the first isolation, to our knowledge, of putative Sb-metabolizing cold-tolerant microorganisms and contributes to the understanding of metal (loid)-tolerant microbial communities in Arctic mine sites.

Functional data analysis-based yield modeling in year-round crop cultivation.

Crop yield prediction is essential for effective agricultural management. We introduce a methodology for modeling the relationship between environmental parameters and crop yield in longitudinal crop cultivation, exemplified by strawberry and tomato production based on year-round cultivation. Employing functional data analysis (FDA), we developed a model to assess the impact of these factors on crop yield, particularly in the face of environmental fluctuation. Specifically, we demonstrated that a varying-coefficient functional regression model (VCFRM) is utilized to analyze time-series data, enabling to visualize seasonal shifts and the dynamic interplay between environmental conditions such as solar radiation and temperature and crop yield. The interpretability of our FDA-based model yields insights for optimizing growth parameters, thereby augmenting resource efficiency and sustainability. Our results demonstrate the feasibility of VCFRM-based yield modeling, offering strategies for stable, efficient crop production, pivotal in addressing the challenges of climate adaptability in plant factory-based horticulture.

Investigating Endemic Species Conservation Hotspots Based on Species Distribution Models in Swat Region, Hindu Kush Pakistan

For the effective conservation of plant species, it is imperative to manage habitat loss and fragmentation by establishing a protected area network that comprehensively encompasses their habitat. In this study, we identified the suitable habitats for some endemic plant species in the Swat District, Pakistan, to conduct a thorough analysis of proposed protected areas (hotspot areas). Utilizing data from a recent botanical survey, environmental parameters from World-Clim, and various other sources, we determined the habitat of each species using the MaxEnt species distribution model. The modeled habitat suitability for each species were combined to generate maps illustrating endemic richness (hotspots), as well as to prioritize areas based on conservation value and habitat suitability. Future climate projections spanning from 2000 to 2050 were acquired from the WorldClim database Nineteen bioclimatic variables were assessed, incorporating data from three global climate models: CCSM4, HadGEM2-AO, and MPI-ESM-LR. We modeled seven endemic species from the Swat District and, by combining their maps, found three hotspot areas which show more environmental suitability under future climatic conditions. The future model forecasts a significant decrease in population density by the year 2050. Currently, these species exhibit their highest density along the eastern and southern borders of the valley, particularly in the Karakar, Elum border, Mankial Kalam, and Gabral Kalam areas. The anticipated alterations in density and distribution could have a considerable impact not only on the tree species but also on the associated sub flora.

Assessment of genotoxicity of air pollution in urban areas using an integrated model of passive biomonitoring

Atmospheric pollution is a major public health issue and has become increasingly critical for human health. Urban atmospheric pollution is typically assessed through physicochemical indicators aligned with environmental legislation parameters, providing data on air quality levels. While the effects of pollution on sensitive organisms serve as a warning for public health decision-makers, there remains a need to explore the interpretation of environmental data on pollutants. The use of species adapted to urban environments as sentinels enables continuous and integrated monitoring of environmental pollution implications on biological systems. In this study, we investigated the use of the plant species Tradescantia pallida as a biomonitor to evaluate the genotoxic effects of atmospheric pollution under diverse vehicular traffic conditions. T. pallida was strategically planted at the leading urban intersections in Uberlândia, Brazil. During COVID-19 pandemic lockdowns, we compared indicators such as physical, biological, and traffic data at different intersections in residential and commercial zones. The reduction in vehicular traffic highlighted the sensitivity of plant species to changes in air and soil pollutants. T. pallida showed bioaccumulation of heavy metals Cd and Cr in monitored areas with higher traffic levels. Additionally, we established a multiple linear regression model to estimate genotoxicity using the micronucleus test, with chromium concentration in the soil (X1) and particulate matter (PM) in the atmosphere (X2) identified as the primary independent variables. Our findings provide a comprehensive portrait of the impact of vehicular traffic changes on PM and offer valuable insights for refining parameters and models of Environmental Health Surveillance.

Predicting Individual Hearing-Aid Preference From Self-Reported Listening Experiences in Daily Life.

The study compared the utility of two approaches for collecting real-world listening experiences to predict hearing-aid preference: a retrospective questionnaire (Speech, Spatial, and Qualities of Hearing Scale [SSQ]) and in-situ Ecological Momentary Assessment (EMA). The rationale being that each approach likely provides different and yet complementary information. In addition, it was examined how self-reported listening activity and hearing-aid data-logging can augment EMAs for individualized and contextualized hearing outcome assessments. Experienced hearing-aid users (N = 40) with mild-to-moderate symmetrical sensorineural hearing loss completed the SSQ questionnaire and gave repeated EMAs for two wear periods of 2-weeks each with two different hearing-aid models that differed mainly in their noise reduction technology. The EMAs were linked to a self-reported listening activity and sound environment parameters (from hearing-aid data-logging) recorded at the time of EMA completion. Wear order was randomized by hearing-aid model. Linear mixed-effects models and Random Forest models with five-fold cross-validation were used to assess the statistical associations between listening experiences and end-of-trial preferences, and to evaluate how accurately EMAs predicted preference within individuals. Only 6 of the 49 SSQ items significantly discriminated between responses made for the end-of-trial preferred versus nonpreferred hearing-aid model. For the EMAs, questions related to perception of the sound from the hearing aids were all significantly associated with preference, and these associations were strongest in EMAs completed in sound environments with predominantly low SNR and listening activities related to television, people talking, nonspecific listening, and music listening. Mean differences in listening experiences from SSQ and EMA correctly predicted preference in 71.8% and 72.5% of included participants, respectively. However, a prognostic classification of single EMAs into end-of-trial preference with a Random Forest model achieved a 93.8% accuracy when contextual information was included. SSQ and EMA predicted preference equally well when considering mean differences, however, EMAs had a high prognostic classifications accuracy due to the repeated-measures nature, which make them ideal for individualized hearing outcome investigations, especially when responses are combined with contextual information about the sound environment.

Exploring biosynthesis strategies to boost the yield of exopolysaccharide-protein blend from Bacillus arachidis SY8(T), an isolated native strain, as a potent adsorbent for heavy metals removal

This investigation centers on the synthesis of a polysaccharide-protein blend produced by an isolated native strain (99.12 % phylogenetic affinity with Bacillus arachidis SY8(T)). The primary objective was to investigate the production of extracellular polymeric substances (EPS) under diverse stress conditions, encompassing exposure to heavy metal ions, salt, and toxic agents. Additionally, the impact of environmental parameters, namely pH, inoculation percentage, and time, on the production was investigated. Subsequently, the study examined the biosorption potential of the EPS produced for Pb(II), Cu(II), and Mn(II). The EPS obtained was thoroughly characterized via various tests. Rheological evaluations of an EPS solution (2 wt%) confirmed its pseudo-plastic and non-Newtonian fluid properties, while TGA analysis demonstrated its thermal stability up to 600 °C. Additional analyses, including GPC, FTIR, and H-NMR, provide further insights into the produced EPS. The best conditions for EPS production are determined: 5 % NaCl salt, serving as an effective stress inducer, and 37 °C, pH 6, with a 5 % inoculation, over 96 h. EPS demonstrates remarkable removal efficiencies of 99.9, 99.4 and 78.9 % for Pb(II), Cu(II), and Mn(II), respectively. These findings highlight the potential of EPS as an effective agent for removing heavy metal ions.

Hazard Identification and Risk Analysis of Liquid Effluent Treatment Plant Apply in Chemical Process Industry

The chemical process industry is one of the categories of highly polluting industries and generates strong wastewater of high COD along with hazardous waste which requires effective treatment and disposal/reuse of effluents. In this review, the various sources of wastewaters in the chemical industry are identified and the best available technologies to remove them are critically evaluated. The aim of the present research work was to determine the behavior of various parameters of the chemical wastewater. During the entire study evaluate, monitor, and check various environmental parameters whether they are complying with a given standard or not given by regulatory authority at chemical Industry,. The India based industry produced bulk drugs, fatty acids, fatty alcohol, food additives, personal care products, and others. The monitoring data of the effluent treatment plant will give information on the pollution control by the company and the management of waste. Overall analysis showed that there is no significant difference in pH, BOD, COD, and ammoniacal Nitrogen. The use of conventional treatment methods along with membrane reactors and advanced post-treatment methods resulting in a Coagulation wastewater treatment technology appears to be the best

Defining the target population of environments for wheat (Triticum aestivum L.) breeding in Italy based on historical data

AbstractGrain yield plateau is a serious concern in many countries, including Italy. A specific‐adaptative strategy might apply to contrast this unfavourable condition. This study aimed to investigate the gain in grain yield and yield‐related traits of bread wheat in Italy over a 16‐year period. A historical dataset concerning 105 sites across Italy and observations on grain yield, heading date, plant height and thousand kernel weight was used. The target population of environments (TPEs) were defined using several environmental parameters. The results showed that while there was a general increase in thousand kernel weight over the 16 years, there was no significant improvement in grain yield and heading date. By contrast, plant height showed a slight decrease over the years. The study also identified five TPEs presenting different climate scenarios in Italy. The TPEs showed varying levels of performance for the traits of interest, with the Po valley (TPE4) showing the highest values for grain yield and thousand kernel weight. The study also identified the best‐performing genotypes for each TPE, providing valuable insights for future wheat breeding programmes in Italy.

Community structure and function during periods of high performance and system upset in a full-scale mixed microalgal wastewater resource recovery facility

Microalgae have the potential to exceed current nutrient recovery limits from wastewater, enabling water resource recovery facilities (WRRFs) to achieve increasingly stringent effluent permits. The use of photobioreactors (PBRs) and the separation of hydraulic retention and solids residence time (HRT/SRT) further enables increased biomass in a reduced physical footprint while allowing operational parameters (e.g., SRT) to select for desired functional communities. However, as algal technology transitions to full-scale, there is a need to understand the effect of operational and environmental parameters on complex microbial dynamics among mixotrophic microalgae, bacterial groups, and pests (i.e., grazers and pathogens) and to implement robust process controls for stable long-term performance. Here, we examine a full-scale, intensive WRRF utilizing mixed microalgae for tertiary treatment in the US (EcoRecover, Clearas Water Recovery Inc.) during a nine-month monitoring campaign. We investigated the temporal variations in microbial community structure (18S and 16S rRNA genes), which revealed that stable system performance of the EcoRecover system was marked by a low-diversity microalgal community (DINVSIMPSON = 2.01) dominated by Scenedesmus sp. (MRA = 55 %-80 %) that achieved strict nutrient removal (effluent TP < 0.04 mg·L-1) and steady biomass concentration (TSSmonthly avg. = 400–700 mg·L−1). Operational variables including pH, alkalinity, and influent ammonium (NH4+), correlated positively (p < 0.05, method = Spearman) with algal community during stable performance. Further, the use of these parameters as operational controls along with N/P loading and SRT allowed for system recovery following upset events. Importantly, the presence or absence of bacterial nitrification did not directly impact algal system performance and overall nutrient recovery, but partial nitrification (potentially resulting from NO2− accumulation) inhibited algal growth and should be considered during long-term operation. The microalgal communities were also adversely affected by zooplankton grazers (ciliates, rotifers) and fungal parasites (Aphelidium), particularly during periods of upset when algal cultures were experiencing culture turnover or stress conditions (e.g., nitrogen limitation, elevated temperature). Overall, the active management of system operation in order to maintain healthy algal cultures and high biomass productivity can result in significant periods (>4 months) of stable system performance that achieve robust nutrient recovery, even in winter months in northern latitudes (WI, USA).

Global distribution and environmental correlates of marine bioturbation

The activities of marine sediment-dwelling invertebrates play a fundamental role in mediating major biogeochemical cycles and have profoundly shaped the evolution of marine systems. Yet there remains a paucity of global marine data describing bioturbation intensities and mixed layer depths and interrogating how these vary with multiple environmental and ecological factors at a system scale. We applied an ensemble of tree-based machine learning techniques to resolve a global map and determine the environmental and ecological correlates most closely associated with bioturbation. We find that bioturbation intensity and the depth of the sediment mixed layer each reflect different associations with a consortium of environmental and ecological parameters, and that bioturbation intensities are much more readily predicted than sediment mixed layer depths from these correlates. Furthermore, we find that the bioturbation intensity, the depth of the sediment mixed layer, and their environmental and ecological correlates differ between shallow marine and open-ocean settings. Our findings provide new insights into the importance of potential drivers of ancient sediment mixing recorded by geologic archives. These results also highlight that climate change may, in the near future, drive shifts in bioturbation and reciprocal fundamental changes in benthic functioning.

An active learning framework for the rapid assessment of galvanic corrosion

The current present in a galvanic couple can define its resistance or susceptibility to corrosion. However, as the current is dependent upon environmental, material, and geometrical parameters it is experimentally costly to measure. To reduce these costs, Finite Element (FE) simulations can be used to assess the cathodic current but also require experimental inputs to define boundary conditions. Due to these challenges, it is crucial to accelerate predictions and accurately predict the current output for different environments and geometries representative of in-service conditions. Machine learned surrogate models provides a means to accelerate corrosion predictions. However, a one-time cost is incurred in procuring the simulation and experimental dataset necessary to calibrate the surrogate model. Therefore, an active learning protocol is developed through calibration of a low-cost surrogate model for the cathodic current of an exemplar galvanic couple (AA7075-SS304) as a function of environmental and geometric parameters. The surrogate model is calibrated on a dataset of FE simulations, and calculates an acquisition function that identifies specific additional inputs with the maximum potential to improve the current predictions. This is accomplished through a staggered workflow that not only improves and refines prediction, but identifies the points at which the most information is gained, thus enabling expansion to a larger parameter space. The protocols developed and demonstrated in this work provide a powerful tool for screening various forms of corrosion under in-service conditions.

Environmental factors modulate the distribution of elasmobranchs in southern Mozambique

Investigating the spatiotemporal ecology of elasmobranchs is an important precursor to their effective management. Understanding long-term patterns in the movement and habitat use of threatened species can improve management plans so that they yield increased conservation benefits. We investigated the spatiotemporal and environmental drivers that underpin the abundance and distribution of elasmobranchs around reef habitats in southern Mozambique to highlight reefs that are important (“hotspots”) to the regional elasmobranch community. Visual belt transects (n = 738), supported by video recordings, were completed on 16 reef sites off the coast of southern Mozambique from 2018 to 2022. Nine elasmobranch species were encountered annually (Carcharhinus amblyrhynchos, Triaenodon obesus, Stegostoma tigrinum, Neotrygon caeruleopunctata, Pateobatis jenkinsii, Taeniurops meyeni, Mobula kuhlii, Mobula alfredi, Mobula birostris) and 11 individual environmental and spatiotemporal parameters (horizontal water visibility, tidal range and state, moon illumination, temperature on the reef, cloud cover, time of day, day of the year, transect distance from shore, transect depth, and the region that the transect occurred in) were measured. All species, (bar P. jenkinsii) were significantly more abundant around certain reefs in the sampled region. Total counts for most species were highest in the austral summer however two species’ (M. birostris and S. tigrinum) were most abundant in the winter months. The tidal state, tidal range, and moon illumination correlated significantly with the numbers of each of the nine elasmobranch species. Non-Metric Multidimensional Scaling (NMDS) indicated that species’ responses to the measured parameters grouped taxonomically. Environmental influences resulted in strong seasonal patterns of reef use by large-bodied and pelagic elasmobranch species (e.g. manta rays). The measured environmental parameters also resulted in daily, monthly, and seasonal patterns of abundance of reef-resident stingray and shark species. Banning extraction of elasmobranch species around the reefs where they aggregate and reflecting species distributions within fisheries regulations may significantly benefit the regional elasmobranch community.

Dinoflagellate composition and environmental conditions in the Xuan Dai Bay, South-Central Vietnam

The dinoflagellate community was investigated in association with environmental factors using a data set in April 2021 and April 2022 in Xuan Dai Bay, South-Central Viet Nam. Environmental variables, including physical parameters and dissolved inorganic nutrients, were measured in April 2022. Seventy-three dinoflagellate taxa were identified for Xuan Dai Bay. There was a significant difference in the number and abundance of dinoflagellates between two parts of the bay, the upper and lower bay. The study showed that dinoflagellates favored an area with good water exchange and were less affected by aquaculture activities. Principal component analysis (PCA) was used to explore the relative abundances of different phytoplankton groups, their diversity indices, and environmental variables at the surface and bottom layers of the two parts of the bay. The results showed that dinoflagellates correlated to physical parameters (e.g., PAR, salinity, temperature) at the surface layer and nutrients at the bottom layer. Dinoflagellates and diatoms are mixotrophic and strongly correlated at the bottom layer in Xuan Dai Bay. This strong relationship in the bay was because of the dominance of a heterotrophic genus, Protoperidinium. The present study provided characteristics of the dinoflagellates in Xuan Dai Bay and the possible impacts of environmental parameters on their abundance. The results can be used for further studies and possibly managing of dinoflagellate blooms in coastal waters.