Local Monitoring Research Articles

A Near-Real-Time Achromopeptidase-Based Enzyme Assay for Biomonitoring of Zinc Pollution in Kuah's Jetty in Langkawi

Enzyme-based assay in near-real-time biomonitoring provides high sensitivity to bioavailable contaminants, leading to quick results that support prompt action. It is essential to use this method to reduce pollution in drinking, agriculture and marine waters and protect the health of both humans and animals. This work applies a previously discovered enzyme test in biomonitoring to identify contaminants, particularly zinc, in marine water samples from the Langkawi Island, an island that harbors the UNESCO's Geoforest Park status. We used the achromopeptidase dye binding assay, specifically designed for detecting mercury zinc at levels below one part per million, to identify trace amounts of these metal successfully. The test demonstrated a sensitive, quick, and cost-efficient monitoring method with little inhibition (<10%) during a 6-hour field trial for three consecutive days, suggesting low pollution levels and confirmed by instrumental analysis. This method allows for the prompt identification of environmental pollutants, which helps take appropriate actions and safeguard ecotourism locations by offering data-driven information for policy development. Enzyme tests are simple and visually appealing, making them effective instructional tools that help raise environmental awareness and support conservation initiatives. Our research highlights the need to use enzyme tests for broad environmental evaluation, harmonizing local monitoring methods with global standards, and promoting international cooperation in environmental conservation. This work enhances our comprehension of ecological well-being in marine and brackish waterways and underscores the significance of ongoing monitoring to protect natural environments.

Interdependence in Coastal Tourist Territories between Marine Litter and Immediate Tourist Zoning Density: Methodological Approach for Urban Sustainable Development

The coastal strip, characterized by the urbanization of coastal tourist territories (CTTs), has expanded over decades through civil engineering, altering the shoreline dynamics and creating artificial beaches crucial for tourism. To examine the relationship between extensive land use in CTTs for tourism and residences and the presence of marine litter, a specific parametric study was conducted along the coast of Tenerife, the largest island in the Canary Islands. Due to Tenerife’s geographical location and exposure to the descending Gulf Stream flow, the coastal waters in the selected zone experience waste impact at both local and global scales. However, the presence of marine litter deposited by ocean currents is at a micro level and falls outside the scope of this report. This study parameterised urban reality in study areas, and the presence of macro waste has been parameterised using standardised units of measurement. This enables the establishment of source measurements that will contribute to preventative measures against this type of coastal pollution. The interdependence between tourist zoning, civil seafront engineering works along the seafront, and marine litter presence in inaccessible and visible areas for tourists requires a methodology to better understand waste origin and loading areas. This knowledge is crucial for an effective local monitoring system. A quantitative overlay reading methodology has been designed in the urban setting through calculations of urban densities, while examining the waste in these areas’ immediate infralittoral flooring through the use of visual underwater extraction. Anticipating the type and quantity of waste in each area will allow for the implementation of effective awareness, promoting action for preventative and corrective measures at the urban level. The results show a direct dependence between urban density and the presence of waste, as well as an equation that makes it possible to anticipate the amount of waste according to urban density and its relational vector. There is no discontinuity between them, as each area is affected by others to the extent that they establish the parametric continuity conditions determining each field. Therefore, it is possible to relate them beyond a one-on-one relationship. This approach fosters sustainable tourism development, reducing pressure on the sea and enhancing the utilisation of tourism revenues in measures to address waste-related challenges and promotes sustainable tourism development in Europe’s coastal regions.

Experimental study on high-frequency subharmonic scattering characteristics of ultrasound contrast agent microbubbles under low ambient pressure

Correlation between nonlinear subharmonic scattering of ultrasound contrast agent microbubbles and ambient pressure is expected to be used for local brain tissue pressure monitoring. Although high-frequency ultrasound has achieved high-resolution imaging of intracranial microvessels, the research on high-frequency subharmonic scattering characteristics of microbubbles is insufficient at present, which restricts the research progress of estimating local brain tissue pressure based on high-frequency subharmonic scattering of microbubbles. Therefore, under the excitation of 10 MHz high-frequency ultrasound, the effects of different acoustic pressures and ambient pressures on the high-frequency subharmonic scattering characteristics of three different ultrasound contrast agents including SonoVue, Sonazoid and Huashengxian were investigated in this in vitro study. Results showed that the subharmonic scattering amplitudes of the three microbubbles increased with the increase of ambient pressure at the peak negative acoustic pressures of 696, 766 and 817 kPa, and there was a favorable linear correlation between subharmonic amplitude and ambient pressure. Under the above three acoustic pressures, the highest correlation coefficient of SonoVue was 0.948 ( P = 0.03), the highest sensitivity of pressure measurement was 0.248 dB/mm Hg and the minimum root mean square error (RMSE) was 2.64 mm Hg. Sonazoid's highest correlation coefficient was 0.982 ( P < 0.01), the highest sensitivity of pressure measurement was 0.052 dB/mm Hg and the minimum RMSE was 1.51 mm Hg. The highest correlation coefficient of Huashengxian was 0.969 ( P = 0.02), the highest sensitivity of pressure measurement was 0.098 dB/mm Hg and the minimum RMSE was 2.00 mm Hg. The above in vitro experimental results indicate that by selecting ultrasound contrast agent microbubbles and optimizing acoustic pressure, the correlation between high-frequency subharmonic scattering of microbubbles and ambient pressure can be improved, the sensitivity of pressure measurement can be upgraded, and the measurement error can be reduced to meet the clinical demand for local brain tissue pressure measurement, which provided an important experimental basis for subsequent research in vivo.

Mechanism of Ionomer Degradation as a Consequence of Defective Anode PEMFC

Defects in the various components of the membrane-electrode assembly (MEA), initially present or created during aging, are known to be responsible for the short lifetime PEMFC. Catalytic layer degradation has been identified as one of them [1]. However, the possible propagation of these defects to other components within a cell or a stack is not clear in the literature. To shed light on these phenomena, customized MEAs with a localized lack of anode active layer at two different locations -near the hydrogen inlet or outlet- were manufactured and tested with specific accelerated aging tests, combining high and low current and humidity cycles; these were specifically tailored to observe a possible propagation of the defects [2]. Local performance monitoring using a segmented cell revealed variations within the catalytic layers upstream and downstream of the defect. In order to better understand the impact of anode active layer defects on MEA components, localized physicochemical analyses were performed post mortem along the gas flow, with the main objective to identify possible degradations of the reinforced perfluorosulfonic acid (PFSA) membranes. Microscopic thickness measurements showed significant membrane thinning in the defective segments. Difference of these localized degradations between the monopolar plates rib and channel zones will be highlighted depending on the defect location i.e. at the inlet and outlet of hydrogen flow (Figure below). In the defective area, decrease of the Ion Exchange Capacity estimated by Raman analyses is pointed out for the reinforced PFSA layer. The PFSA chain degradation mechanism will be discussed, and, thus, the degradation rate will be calculated based on Raman and FTIR microscope and 19F NMR. For longer ageing time, the ionomer degradation is extended to an non-defective area. A propagation mechanism of the degradation will be proposed.[1] L, Dubau, et al. WIREs Energy and Environ. 2014, 3, pp. 540-560.[2] S, Touhami, et al. J. Power Sources. 2021, 481, pp. 228908-228917. Figure 1

Memantine inhibits cortical spreading depolarization and improves neurovascular function following repetitive traumatic brain injury

Cortical spreading depolarization (CSD) is a promising target for neuroprotective therapy in traumatic brain injury (TBI). We explored the effect of NMDA receptor antagonism on electrically triggered CSDs in healthy and brain-injured animals. Rats received either one moderate or four daily repetitive mild closed head impacts (rmTBI). Ninety-three animals underwent craniectomy with electrocorticographic (ECoG) and local blood flow monitoring. In brain-injured animals, ketamine or memantine inhibited CSDs in 44 to 88% and 50 to 67% of cases, respectively. Near-DC/AC-ECoG amplitude was reduced by 44 to 75% and 52 to 67%, and duration by 39 to 87% and 61 to 78%, respectively. Daily memantine significantly reduced spreading depression and oligemia following CSD. Animals ( N = 31) were randomized to either memantine (10 mg/kg) or saline with daily neurobehavioral testing. Memantine-treated animals had higher neurological scores. We demonstrate that memantine improved neurovascular function following CSD in sham and brain-injured animals. Memantine also prevented neurological decline in a blinded, preclinical randomized rmTBI trial.

Economic-threshold-based classification of soybean aphid, Aphis glycines, infestations in commercial soybean fields using Sentinel-2 satellite data

The soybean aphid (SBA), Aphis glycines Matsumura (Hemiptera: Aphididae), is a significant insect pest of soybean, Glycine max (L.) Merrill (Fabales: Fabaceae), and field treatment decisions for this pest are based on average field populations. Previous studies indicated that ground- and drone-based red-edge and near-infrared remote sensing can be used to detect plant stress caused by SBA infestations in soybean. However, it remains to be determined if remote sensing for SBA can be expanded to field or landscape scale using satellite-based platforms. Thus, this research was conducted in three steps to determine the potential of using Sentinel-2 satellite data for the classification of SBA infestations in soybean fields using simulated and actual Sentinel-2 satellite spectral reflectance. In the first step, as a proof of concept, hyperspectral data from cage studies were used to simulate Sentinel-2 bands and vegetation indices (VIs), conducted in nine trials at multiple locations between 2013 and 2021. The effects of SBA from caged plants on simulated data were evaluated with random intercept linear mixed models. The satellite simulation indicated a significant effect of SBA on the spectral reflectance of caged soybean plants (p < 0.05) for four satellite bands (5, 6, 7, and 8A) and five VIs (NDVI, GNDVI, SAVI, OSAVI, and NDRE). In the second step, actual Sentinel-2 spectral reflectance and corresponding aphid counts of commercial soybean fields, collected from 2017 to 2019, were obtained. The relationship between SBA counts and Sentinel-2 spectral reflectance from commercial soybean fields were evaluated with general linear models. A significant effect of SBA was observed for three satellite bands (6, 7, and 8A) and three VIs (NDVI, SAVI, and OSAVI). In the third step, linear support vector machine (LSVM) models for the classification of SBA infestations as above or below a previously determined economic threshold of 250 aphids per plant were developed using simulated Sentinel-2 bands and VIs from the caged plots, and were tested on actual Sentinel-2 data from commercial soybean fields. The best LSVM model for the classification of aphids in soybean reached 91% accuracy, 85.7% sensitivity, and 93.3% specificity. Thus, simulations with caged plots can be used as an indication of the potential of using satellite data for the detection of plant stresses on a larger scale. Furthermore, this study advances decision-making for SBA, and the developed LSVM model can be used to update regional and local monitoring for the management of SBA.

Investigation on local monitoring paradigms of in-situ conformally fabricated piezopolymer coating-based array transducers: Ultrasonic bulk waves and local ultrasonic resonances

Realistic structures with complex features have been intricate challenges for structural health monitoring (SHM) with permanently installed transducers. Poor conformability of conventional rigid and brittle piezoceramic wafers is a typical issue of applications on surfaces with complex geometry. Moreover, the accompanied high-stress concentration requires high-sensitivity defect detection which is difficult to achieve with large-area monitoring methods like lamb waves at tens to hundreds kHz. Previously, in-situ conformally fabricated piezopolymer coating-based array transducers (PCATs) have been developed to build large-area, lightweight, flexible, and tunable Lamb wave networks. In this study, two novel local monitoring methods were investigated with PCATs, namely ultrasonic bulk wave array inspection and local ultrasonic resonance spectroscopy. For thick structures, ultrasonic bulk waves were generated and detected by PCATs with broadband operating frequencies (1–10 MHz) and flexible array parameters. Simulation tools and imaging algorithms of array inspection in non-destructive testing (NDT) were well implemented based on the analogous directivity pattern and normal-pressure coupling mechanism. As proof of concept, PCATs were applied on example structures with flat, concave, and convex surfaces for internal defect imaging. For thin-walled and/or multilayer structures, PCATs were used to measure local ultrasonic resonances, comparable to conventional non-contact methods. With negligible influence on local mechanical properties and broadband frequency response, multiple resonance peaks from 0 to 25 MHz were identified as zero group velocity (ZGV) Lamb modes and thickness vibration modes of host structures, which can be used as damage indices for local monitoring of corrosion, delamination, stiffness degradation, etc. Through embracing advanced NDT techniques with PCATs, high-sensitivity and quantitative local monitoring could be achieved with conformal networks, offering the possibility to integrate with large-area monitoring as multi-scale SHM for complex structures.

The association of exhaled nitric oxide with air pollutants in young infants of asthmatic mothers

BackgroundExhaled nitric oxide is a marker of airway inflammation. Air pollution induces airway inflammation and oxidative stress. Little is known about the impact of air pollution on exhaled nitric oxide in young infants.MethodsThe Breathing for Life Trial recruited pregnant women with asthma into a randomised controlled trial comparing usual clinical care versus inflammometry-guided asthma management in pregnancy. Four hundred fifty-seven infants from the Breathing for Life Trial birth cohort were assessed at six weeks of age. Exhaled nitric oxide was measured in unsedated, sleeping infants. Its association with local mean 24-h and mean seven-day concentrations of ozone, nitric oxide, nitrogen dioxide, carbon monoxide, sulfur dioxide, ammonia, particulate matter less than 10 μm (PM10) and less than 2.5 μm (PM2.5) in diameter was investigated. The air pollutant data were sourced from local monitoring sites of the New South Wales Air Quality Monitoring Network. The association was assessed using a ‘least absolute shrinkage and selection operator’ (LASSO) approach, multivariable regression and Spearman’s rank correlation.ResultsA seasonal variation was evident with higher median exhaled nitric oxide levels (13.6 ppb) in warmer months and lower median exhaled nitric oxide levels (11.0 ppb) in cooler months, P = 0.008. LASSO identified positive associations for exhaled nitric oxide with 24-h mean ammonia, seven-day mean ammonia, seven-day mean PM10, seven-day mean PM2.5, and seven-day mean ozone; and negative associations for eNO with seven-day mean carbon monoxide, 24-h mean nitric oxide and 24-h mean sulfur dioxide, with an R-square of 0.25 for the penalized coefficients. These coefficients selected by LASSO (and confounders) were entered in multivariable regression. The achieved R-square was 0.27.ConclusionIn this cohort of young infants of asthmatic mothers, exhaled nitric oxide showed seasonal variation and an association with local air pollution concentrations.

Advancing Bridge Structural Health Monitoring: Insights into Knowledge-Driven and Data-Driven Approaches

Structural health monitoring (SHM) is increasingly being used in the field of bridge engineering, and the technology for monitoring bridges has undergone a radical change. It has evolved from the initial local monitoring and assessment, which relied mainly on manual work, to the current all-round and full-time intelligent assessment provided by intelligent monitoring systems. This paper reviews the development of SHM technology in the civil engineering field and examines two current artificial intelligence (AI) methods in bridge SHM, namely knowledge-driven and data-driven approaches. The advantages and disadvantages of these two AI methods are analyzed, and future development trends are also discussed. The overview results reveal that knowledge-driven methods have the advantages of interpretability and stability. However, their current application is limited, and significant technical bottlenecks remain. On the other hand, the data-driven approach demonstrates higher efficiency and accuracy. Nevertheless, it is characterized by instability and insecurity due to its "black-box" nature, which hinders its ability to explain the internal operation mechanism. Given these findings, the hybrid knowledge-data-driven approach emerges as a potential solution. This approach can effectively integrate the advantages of both knowledge-driven and data-driven methods while avoiding their respective disadvantages. Consequently, the hybrid approach proves to be more stable, safe, and efficient in practical applications.

GROUND MOVEMENT ANALYSIS IN POST-MINING CITY USING MTINSAR WITH HELP OF EUROPEAN GROUND MOTION SERVICE

Abstract. Ground movement is a critical concern for urban safety, and understanding its causes and consequences is essential. Mining of hard coal in the Ruhr area of Germany has caused long-term subsidence, leading to building damage and sinkholes in affected cities. The multi-temporal interferometric synthetic aperture radar (MTInSAR) technique has been widely adopted for estimating ground movement at millimetre-level accuracy using spaceborne SAR data. Although many European government institutes have implemented this technique for monitoring purposes, the European Ground Motion Service (EGMS) provides up-to-date ground movement products (until the end of 2020) across 31 countries. However, these products may not always meet the requirements for local use due to being untimely or missing important features. This paper demonstrates how we estimated ground movement in Ahlen, around the Ruhr area of Germany, using EGMS data as a priori and reference source. We implemented MTInSAR using Sentinel-1 data from 2018 to 2021 and compared our results with those of EGMS. Our results reveal the movement one year ahead, contain more measurement points, and cover natural areas such as cropland and bare soil. We interpreted several movement scenarios in detail, and our work highlights the benefits of EGMS as an open-source overview for local monitoring applications. We also explored the influence of soil conditions on ground movement. Our findings suggest that the overall uplift trend observed during the post-mining phase has been mitigated or even reversed, likely due to soil shrinkage resulting from drought conditions, particularly in areas with high organic soil content.

Habitat suitability of Proboscis Monkey Nasalis larvatus Wurmb. in Muara Kaman Sedulang Nature Reserve, East Kalimantan

Proboscis monkey (Nasalis larvatus Wurmb.) is an endemic primate species to Borneo’s island. Muara Kaman Sedulang Nature Reserve is one of protected area for this species in the island, but it’s riverine habitat is largely lost by forest fires and conversion for aquaculture, agriculture and settlements. A conservation action in the area needs spatial information regarding the habitat suitability of proboscis monkey. This study aimed to project a spatial model for habitat suitability of proboscis monkey in the reserve through species distribution modelling (SDM) using the Maximum Entropy (MaxEnt), based on the species’ occurrence records and environmental predictors. The results showed that suitable habitat for proboscis monkey was 3,410.27 ha (5.22% of the nature reserve total area). Model performance evaluation shows an accuracy of the Area Under Curve (AUC) value of 0.940, which is a very good category. Jackknife-test results showed the presence of secondary swamp forest (40.8%), distance from water bodies (25.9%), land cover (12.4%), secondary forest (6.3%) and mixed gardens (5.1%) are five variables that significantly contribute to the distribution of proboscis monkeys in the reserve. This study provides suggestions for conservation of proboscis monkey in remaining suitable habitat, which include local people involvement, monitoring and habitat restoration.

Implementation of a weather station to monitor agricultural crops

Abstract Various studies on local crop monitoring are presented in the literature, however there is insufficient research on low-cost IoT weather stations that can obtain weather information over a long period of time. To overcome the difficulties described above, this paper proposes the development of an IoT weather station for low-cost, sustainable and multi-functional agricultural crop monitoring. The objective of the work is to develop a local weather observation device for agricultural crops, to set up a server for storing and providing meteorological information and to perform an analysis of the acquired weather data. The weather station architecture developed and proposed in this project consists of four main blocks: sensor and controller module, power module, local data storage module and data communication module (gateway).

Educational Monitoring at the Local Level in Tension Between Science and Politics

Dealing with the tensions between scientific claims and those of political-administrative actors marks a central task of municipal educational monitoring. However, empirical research has not systematically examined the relationship between these significant references. Our contribution focuses on how the tensions are addressed and balanced in practice. The results reveal different relations between the two perspectives, producing various practical forms of local educational monitoring. In this way, establishing data infrastructures in municipalities becomes visible as a complex and situational process. The results reflect educational monitoring's theoretical and practical development needs and implications for education research.

Strengthening the Economy through Empowering Grape Farmers in Duyu Village

The natural disaster that occurred in 2018 in Palu City had an impact on the economic condition of the community. Almost all regions felt this impact. One of the areas also affected is Duyu Village. This is one of the reasons for community empowerment through grape farming. The aim of this empowerment is to encourage people to create new jobs after natural disasters which cause a downturn in the community's economy. The method used in the empowerment process starts with mapping, local asset mobilization, monitoring and evaluation. The results of the empowerment carried out in Duyu Village were able to optimize local assets in the village and strengthen the community's economy after experiencing an economic downturn due to natural disasters.

Anomaly detection in BWR fuel cell using neutron noise analysis techniques. Slow control rod detection

A two-step methodology based on noise analysis techniques was applied to detect and monitor a resonance to do with a control rod-related anomaly, notified as SLOW in a BWR-6 core. The methodology utilizes LPRM (Local Power Range Monitor) neutron flux signals. The first step involves applying traditional noise analysis to all available neutron detector signals. Both NAPSDs (Normalized Auto Power Spectral Density) and CPSDs (coherences and phases) were estimated. The second step uses EMD (Empirical Mode Decomposition) and VMD (Variational Mode Decomposition) in a sequence of successive decompositions which allow both the resonance under study to be isolated in only one IMF (Intrinsic Mode Functions) and the main resonance peak parameters (frequency, amplitude, and damping) to be estimated. On the one hand, it was possible to delimit the zone where perturbation takes place, though the phenomenon seemed to be quite local and not all detectors could provide information on it. On the other hand, intrinsic mode functions make it possible to observe traces of potential instrumentation tube impacts in the Hilbert Spectrum. The methodology proved to be very effective to monitor resonances even when these were not visible in the spectrum. The changes observed coincide with the corrective actions carried out by the plant.

Spatial heterogeneity of precursory accelerating deformation in uniaxially compressed sandstones and prediction of failure time

The power law acceleration has been validated as an effective method for predicting failure time, however, the precursory acceleration distribution in local monitoring signals is still unclear. In this paper, the spatial distributions of precursory acceleration of strains at different positions and in various size windows are reported based on surface strain fields measured from sandstone. The sandstone specimens are uniaxially compressed and a digital image correlation technique is applied to monitor the evolution of surface strain fields. The precursory acceleration of strains exhibits heterogeneities in amplitudes and durations, which are related to the evolution of strain localization in a higher strain zone. Strain rates at higher strain zones are prone to acceleration. There exists a well-defined size for monitoring and identifying the precursory acceleration of strains. In the vicinity of failure, strain accelerations in different positions and various size windows are described by a power law behavior with a constant exponent. The results show that prediction is stable and agrees well with the actual failure time.

Проблемные вопросы локального мониторинга земель территорий традиционного природопользования при оценке воздействия нефтегазовых месторождений (на примере Ханты-Мансийского автономного округа – Югры)

In this article, the object of the study is the scientific foundations and methodology of local land monitoring in traditional environmental management territories, as specially protected areas. Its priority purpose is for traditional environmental management Issues of local land reliability monitoring data and methods of obtaining them have remained relevant for more than 20 years. With the development of digital technologies and employing remote sensing, the monitoring procedure is faster and more efficient, however, the information retrieved in this way requires verification and this currently can only be done through conducting field surveys of the territory and using instrumental methods control. The obtained local monitoring data enables resolving environmental and economic issues related to the management of land resources in traditional environmental approach areas of the Extreme North at the local level. The purpose of the study is to improve the scientific and methodological foundations of local monitoring in above mentioned zones (hereinafter referred to as TMP), taking into account the regional characteristics of the pointed territories. The theoretical and methodological foundations for that in Khanty-Mansi Autonomous Okrug – Yugra are considered; the impact of oil and gas production facilities on the ecological state of land resources was determined; proposals for improving the control at the TTP for traditional environmental management are substantiated

High-spatiotemporal-resolution PM2.5 forecasting by hybrid deep learning models with ensembled massive heterogeneous monitoring data

High-resolution real-time air quality forecasting can alert decision-makers and residents about forthcoming air pollution events and refine air quality management. The Environmental Protection Administration in Taiwan has deployed numerous low-cost air quality microsensors near industrial zones lately to facilitate local air quality monitoring. Nevertheless, the frequent occurrence of missing sensor data due to problems of mobile transmission, frontend/backend device malfunction, or other unforeseen issues would raise difficulty in making quick responses to air pollution incidents. This study proposed a hybrid deep learning model (AE-CNN-BP) collaborating an Autoencoder (AE), a Convolutional Neural Network (CNN), and a Back Propagation Neural Network (BPNN) to effectively extract crucial features from big data for making successive high-spatiotemporal-resolution forecasts of PM2.5 concentrations 4 h ahead. The proposed model was trained and tested in three industrial zones densely installed with microsensors in Kaohsiung City of Taiwan. A high pollution incident was selected to evaluate model performance. The results show that the proposed model could reliably produce nice high-spatiotemporal-resolution forecasts for 12 air quality monitoring stations and 485 microsensors, with Coefficient of Determination (R2) values and Root Mean Squared Error (RMSE) of 0.82 (0.76) and 11.05 (12.75) μg/m3 in the training (testing) stage, respectively. For the selected incident, the Mean Absolute Percentage Error (MAPE) values of the proposed model were 22.3% and 27.1% at T+1 and T+4, respectively. This study demonstrates that the proposed deep learning model based on ensemble datasets of sparsely distributed monitoring stations and densely deployed microsensors can offer reliable high-spatiotemporal-resolution air quality forecasts, benefiting environmental studies and informed policymaking by accounting for local-scale variations in PM2.5 concentrations.

Human fatalities related to a Phaeocystis harmful algal bloom in the North Sea

On May 11, 2020, five surfers drowned after asphyxiation in a massive foam bank near the Dutch coast. We present a detailed account of the event and an examination of factors that govern local foam events, that are traditionally caused by Phaeocystis globosa (Prymnesiophyceae). The data support the hypothesis that the foam originated from a Phaeocystis bloom which if correct would make this accident the first report of human fatalities due to a harmful algal bloom in The Netherlands, and the first globally due to a Phaeocystis bloom. The rarity of massive foam banks and the absence of local real-time plankton monitoring makes it difficult to accurately predict dangerous situations as in 2020. As a prevention, surfers and other water sportsmen need to apply good seamanship during stormy and foamy conditions.

Research on structural health monitoring methods for bridge structures

With the development of science and technology, China is building more and more Bridges. The inspection and maintenance of bridge during operation plays an important role in the safety of bridge. Firstly, the research background and significance of bridge structure health inspection are introduced, and the research status and progress of health inspection are analyzed. Then, several commonly used monitoring methods are listed. Based on the comprehensive study of the bridge health monitoring system, the main conclusions are as follows: the bridge health monitoring system is realized through the coordination of several subsystems, including: bridge working environment monitoring, bridge structure overall performance monitoring and bridge structure local performance monitoring. The bridge structural health monitoring system is mainly used in bridge damage identification and bridge safety warning. The bridge structure health monitoring system conforms to the development trend of high technology and will become an indispensable part of the bridge operation in the future.