Monitoring Sites Research Articles

Groundwater Response to Snowmelt Infiltration in Seasonal Frozen Soil Areas: Site Monitoring and Numerical Simulation

Spring snowmelt has a significant impact on the hydrological cycle in seasonally frozen soil areas. However, scholars hold differing, and even opposing, views on the role of snowmelt during the thawing period in groundwater recharge. To explore the potential recharge effects of spring snowmelt on groundwater in seasonal frozen soil areas, this study investigated the vadose zone dynamics controlled by soil freeze–thaw processes and snowmelt infiltration in the Northeast of China for 194 days from 31 October 2020 to 12 May 2021. Responses of groundwater level and soil moisture to snowmelt infiltration show that most snowmelt was infiltrated under the site despite the ground being frozen. During the unstable thawing period, surface snow had already melted, and preferential flow in frozen soil enabled the recharge groundwater by snowmelt (rainfall), resulting in a significant rise in groundwater levels within a short time. The calculated and simulated snowmelt (rainfall) infiltration coefficient revealed that during the spring snowmelt period, the recharge capacity of snowmelt or rainfall to groundwater at the site is 3.2 times during the stable thawing period and 4.5 times during the non-freezing period.

Leveraging environmental microbial indicators in wastewater for data-driven disease diagnostics

IntroductionWastewater-based surveillance (WBS) is an emerging tool for monitoring the spread of infectious diseases, such as SARS-CoV-2, in community settings. Environmental factors, including water quality parameters and seasonal variations, may influence the prevalence of viral particles in wastewater. This study aims to explore the relationships between these factors and the incidence of SARS-CoV-2 across 28 monitoring sites, spanning different seasons and water strata.MethodsSamples were collected from 28 sites, accounting for seasonal and spatial (surface and intermediate water layers) variations. Key physicochemical parameters, heavy metals, and minerals were measured, and viral presence was detected using RT-qPCR. After data preprocessing, correlation analyses identified 19 relevant environmental parameters. Unsupervised learning algorithms, including K-means and K-medoid clustering, were employed to categorize the data into four distinct clusters, revealing patterns of viral positivity and environmental conditions.ResultsCluster analysis indicated that seasonal variations and water quality characteristics significantly influenced SARS-CoV-2 positivity rates. The four clusters demonstrated distinct associations between environmental factors and viral prevalence, with certain clusters correlating with higher viral loads in specific seasons. The clustering patterns varied across sample sites, reflecting the diverse environmental conditions and their influence on viral detection.DiscussionThe findings underscore the critical role of environmental factors, such as water quality and seasonality, in shaping the dynamics of SARS-CoV-2 prevalence in wastewater. These insights provide a deeper understanding of the complex interplay between environmental contexts and disease spread. By utilizing WBS and advanced data analysis techniques, this study offers a robust framework for future research aimed at enhancing public health surveillance and interventions.

Yes, they're all individuals: Hierarchical models for repeat survey data improve estimates of tree growth and size

Abstract Repeat measurement surveys of tree size are used in forests to estimate growth behaviour, biomass and population dynamics. Although size is measured with error and individuals vary in their growth trajectories, current size‐based growth modelling approaches do not usually or fully account for both of these features, and therefore under‐utilise available data. We present a new method that leverages the auto‐correlation structure of repeat surveys into a hierarchical Bayesian longitudinal growth model. This new structure allows users to correct for measurement error and capture individual‐level variation in growth trajectories and parameters. To demonstrate the new method we applied it to a sample of tropical tree survey data from long‐term monitoring sites at Barro Colorado Island. We were able to reduce estimated error in size and growth, and extract individual‐ and population‐level growth parameter estimates. We used simulation to evaluate the ability of the new method to improve estimates of growth rate and size, and estimate individual and species‐level parameters. Our method substantially improved the root mean squared error (RMSE) for growth by an average of 61% compared to existing approaches using pairwise differences and reduced RMSE in estimated size RMSE compared to ‘observed’ values in simulated data. Better numerical integration methods (Runge–Kutta fourth order in comparison to Euler and midpoint) provided better estimates of parameters, but did not improve the estimation of size and growth. The choice of a positive growth function eliminated all negative increments without data exclusion. Overall, this study shows how we can gain new and improved insights on growth, using repeat forest surveys. Our new method offers improved biomass dynamics estimation through reduced error in sizes over time, coupled with novel information about within‐species variation in growth behaviour that is inaccessible with species average models, such as individual parameters for the growth function which allows for relationships between parameters to be considered for the first time.

Remote Sensing Fine Estimation Model of PM2.5 Concentration Based on Improved Long Short-Term Memory Network: A Case Study on Beijing–Tianjin–Hebei Urban Agglomeration in China

The accurate prediction of PM2.5 concentration across extensive temporal and spatial scales is essential for air pollution control and safeguarding public health. To address the challenges of the uneven coverage and limited number of traditional PM2.5 ground monitoring networks, the low inversion accuracy of PM2.5 concentration, and the incomplete understanding of its spatiotemporal dynamics, this study proposes a refined PM2.5 concentration estimation model, Bi-LSTM-SA, integrating multi-source remote sensing data. First, utilizing multi-source remote sensing data, such as MODIS aerosol optical depth (AOD) products, meteorological data, and PM2.5 monitoring sites, AERONET AOD was used to validate the accuracy of the MODIS AOD data. Variables including temperature (TEMP), relative humidity (RH), surface pressure (SP), wind speed (WS), and total precipitation (PRE) were selected, followed by the application of the variance inflation factor (VIF) and Pearson’s correlation coefficient (R) for variable screening. Second, to effectively capture temporal dependencies and emphasize key features, an improved Long Short-Term Memory Network (LSTM) model, Bi-LSTM-SA, was constructed by combining a bidirectional LSTM (Bi-LSTM) model with a self-adaptive attention mechanism (SA). This model was evaluated through ablation and comparative experiments using three cross-validation methods: sample-based, temporal, and spatial. The effectiveness of this method was demonstrated on Beijing–Tianjin–Hebei urban agglomeration, achieving a coefficient of determination (R2) of 0.89, root mean squared error (RMSE) of 12.76 μg/m3, and mean absolute error (MAE) of 8.27 μg/m3. Finally, this model was applied to predict PM2.5 concentration on Beijing–Tianjin–Hebei urban agglomeration in 2023, revealing the characteristics of its spatiotemporal evolution. Additionally, the results indicated that this model performs exceptionally well in hourly PM2.5 concentration forecasting and can be used for PM2.5 concentration hourly prediction tasks. This study provides technical support for the large-scale, accurate remote sensing inversion of PM2.5 concentration and offers fundamental insights for regional atmospheric environmental protection.

TCN-GRU Based on Attention Mechanism for Solar Irradiance Prediction

The global horizontal irradiance (GHI) is the most important metric for evaluating solar resources. The accurate prediction of GHI is of great significance for effectively assessing solar energy resources and selecting photovoltaic power stations. Considering the time series nature of the GHI and monitoring sites dispersed over different latitudes, longitudes, and altitudes, this study proposes a model combining deep neural networks and deep convolutional neural networks for the multi-step prediction of GHI. The model utilizes parallel temporal convolutional networks and gate recurrent unit attention for the prediction, and the final prediction result is obtained by multilayer perceptron. The results show that, compared to the second-ranked algorithm, the proposed model improves the evaluation metrics of mean absolute error, mean absolute percentage error, and root mean square error by 24.4%, 33.33%, and 24.3%, respectively.

Soil Emissions of Reactive Oxidized Nitrogen Reduce the Effectiveness of Anthropogenic Source Control in China.

Nitrogen dioxide (NO2) has decreased by ∼33% across over 1200 monitoring sites in China during 2015-2023, following a series of clean air policies. However, most of these sites are located in or near cities, leading to uncertainties in NO2 trends beyond urban regions due to limited observations. Here, we used satellite measurements to examine the differences in NO2 trends between urban and rural China. In urban areas, NO2 columns decreased by 4.0% per annum (a-1) during summer 2011-2023, consistent with bottom-up anthropogenic emission inventory and in situ measurements. In contrast, rural NO2 columns showed a slower than expected reduction (-2.6 to -0.0% a-1) during the same period. Model simulations with updates in the soil reactive oxidized nitrogen (Nr) scheme indicated that increasing soil Nr emissions can be an important factor contributing to the observed slow NO2 decrease in rural areas. This unregulated source increased summertime pollutant levels, partially offsetting the national efforts to mitigate NO2, ozone (O3), and particulate nitrate (NO3-) levels by 20.9%, 15.4%, and 4.7%, respectively, from 2011 to 2020. In the agriculture-intensive North China Plain, the increase in soil Nr emissions offset 46.6% of the NO2 reductions achieved by clean air policies. Our results highlight the increasing significance of soil emissions and the need to control them in future air-quality policies.

Performance evaluation of UKESM1 for surface ozone across the pan-tropics

Abstract. Surface ozone monitoring sites in the tropics are limited, despite the risk that surface ozone poses to human health, tropical forest and crop productivity. Atmospheric chemistry models allow us to assess ozone exposure in unmonitored locations and evaluate the potential influence of changing policies and climate on air quality, human health and ecosystem integrity. Here, we utilise in situ ozone measurements from ground-based stations in the pan-tropics to evaluate ozone from the UK Earth system model, UKESM1, with a focus on remote sites. The study includes ozone data from areas with limited previous data, notably tropical South America, central Africa and tropical northern Australia. Evaluating UKESM1 against observations beginning in 1987 onwards, we show that UKESM1 is able to capture changes in surface ozone concentration at different temporal resolutions, albeit with a systematic high bias of 18.1 nmol mol−1 on average. We use the diurnal ozone range (DOR) as a metric for evaluation and find that UKESM1 captures the observed DOR (mean bias of 2.7 nmol mol−1 and RMSE of 7.1 nmol mol−1) and the trend in DOR with location and season. Results from this study reveal that hourly ozone concentrations from UKESM1 require bias correction before use for impact assessments based on human and ecosystem health. Indeed, hourly surface ozone data have been crucial to this study, and we encourage other modelling groups to include hourly surface ozone output as a default.

Abstract 4136978: Six-month Changes in Lipid Management in the Veterans Affairs Lipid Optimization Reimagined Quality Improvement (VALOR-QI) Program

Background: US Veterans have a high burden of cardiovascular disease (CVD). While cholesterol management is one of the most effective approaches for reducing CVD burden, over two-thirds of Veterans with atherosclerotic cardiovascular disease (ASCVD) are not optimally managed. Aims: To evaluate LDL-C monitoring, lipid lowering therapy (LLT) use, and LDL-C levels at baseline and six-months for Veterans in the Veterans Affairs Lipid Optimization Reimagined Quality Improvement (VALOR-QI) Program. Methods: VALOR-QI is a national quality improvement collaboration between the US Department of Veterans Affairs and the American Heart Association (AHA) with the goal of improving lipid management among Veterans with ASCVD. To assess and address barriers to achieving optimal lipid levels, Clinical Champions, Health Care Coaches, and participating providers from 50 VA sites work with AHA QI Consultants. Eligible Veterans (those with an ASCVD diagnosis at two outpatient or one inpatient visit or one ASCVD procedure) were considered engaged in VALOR-QI if they had a qualifying appointment with a participating provider during the program. We examined rates of LDL-C monitoring and LLT use at baseline (start of site monitoring) and 6 months after. Among Veterans who were in the program ≥6 months and had >1 LDL-C measure, we investigated percent meeting an LDL-C goal of <70 mg/dL at their first LDL-C (closest to their first visit with priority given to labs within 365 days before their first visit) and follow-up LDL-C (closest to 6 months after entering the program) and mean LDL-C change (paired t-test). Results: Six months after starting VALOR-QI, sites had a 41% increase in LDL-C monitoring and a 6% increase in LLT use. Among 29,610 Veterans engaged in the program ≥6 months with at least two lipid labs [mean (SD) age 71.0 (9.3) years, 95% men, 72% white race], the proportion who met the LDL-C goal <70 mg/dL increased from 44% at their first measure to 51% at 6-month follow-up. Among these Veterans, we observed a mean LDL-C decrease of 5.1 mg/dL (95% CI: -5.50, -4.78). Conclusion: We observed an increase in lipid monitoring and LLT use and improved LDL-C at 6-month follow-up among US Veterans engaged in VALOR-QI.

Seasonal persistence of the nitrogen oxides production in Mexico City

AbstractThis study investigates the seasonal influence on the nitrogen oxide NOx pollution records at four monitoring sites in Mexico City from 2010 to 2018. The analysis employs the second-order structure function to examine the trends in NOx concentration fluctuations. The findings reveal that the fluctuations follow a power law pattern characterized by Hurst exponents, predominantly in the statistical persistence regime, with a scaling range spanning three orders of magnitude. Specifically, the autumn period exhibits fluctuations with an exponent of $$\overline{H}=0.72$$ H ¯ = 0.72 , indicating relatively smaller fluctuations compared to other seasons, but with the potential for NOx concentrations to surpass those in other periods. In contrast, for spring, summer, and winter, the fluctuations are characterized by exponents of$$\overline{H}=0.59$$ H ¯ = 0.59 ,$$\overline{H}=0.61$$ H ¯ = 0.61 , and$$\overline{H}=0.62$$ H ¯ = 0.62 , respectively, demonstrating greater fluctuations with lower NOx concentrations compared to autumn. These results are consistent with various studies conducted worldwide. Additionally, a negative correlation between NOx and ozone (O3) has been established during the winter season, as NOx and O3 fluctuations display persistent and anti-persistent behavior, respectively.

Effects of burning on vegetation, soil physicochemistry and prokaryotic microbial communities in surface and subsurface peat

Prescribed burning is a common management strategy in peatlands that has the potential to affect soil physicochemistry, alter biogeochemical cycles and trigger changes in vegetation structure. How burning affects prokaryotic community composition across different soil profiles is not well understood. This study explored the effects of prescribed burning on the diversity of prokaryotic communities in peat soils. Soil samples were collected from Moor House Nature Reserve, UK, a long-term monitoring site initiated in 1954 subject to three burning treatments: Burning at short rotations every 10 years, burning at long rotations every 20 years and a non-burn control. Observed species richness for archaea was highest in the topsoil of the non-burn control plots and highest for bacteria in the topsoil of the non-burn control and plots under a long rotation regime. Community composition was significantly different between different burn treatments and soil depth. Archaeal community structure was shaped by NH4+ and pH in the topsoil; by Pb, moisture and Al in the 20–40 cm profile; and by total N, total C, Al, Ca, Fe and pH in the 40–60 cm profile. Bacterial community structure was shaped by NH4+, heather cover, pH and Mg in the topsoil; by Fe, K and Pb in the 20–40 cm profile; and by Al, Ca and Fe in the 40–60 cm profile. A co-occurrence network analysis revealed that the topsoil of the non-burn control plots had a more complex network structure with more positive links than those under a rotational burn, but a higher average connectivity with a higher number of negative links was observed in the long rotation 20–40 cm profile. The results provide a new insight into the response processes of soil prokaryotic communities to burning in peatland soils, providing valuable knowledge that can support the evaluation and management of ecosystem services in peatlands.

LG-YOLOv8: A Lightweight Safety Helmet Detection Algorithm Combined with Feature Enhancement

In the realm of construction site monitoring, ensuring the proper use of safety helmets is crucial. Addressing the issues of high parameter values and sluggish detection speed in current safety helmet detection algorithms, a feature-enhanced lightweight algorithm, LG-YOLOv8, was introduced. Firstly, we introduce C2f-GhostDynamicConv as a powerful tool. This module enhances feature extraction to represent safety helmet wearing features, aiming to improve the efficiency of computing resource utilization. Secondly, the Bi-directional Feature Pyramid (BiFPN) was employed to further enrich the feature information, integrating feature maps from various levels to achieve more comprehensive semantic information. Finally, to enhance the training speed of the model and achieve a more lightweight outcome, we introduce a novel lightweight asymmetric detection head (LADH-Head) to optimize the original YOLOv8-n’s detection head. Evaluations on the SWHD dataset confirm the effectiveness of the LG-YOLOv8 algorithm. Compared to the original YOLOv8-n algorithm, our approach achieves a mean Average Precision (mAP) of 94.1%, a 59.8% reduction in parameters, a 54.3% decrease in FLOPs, a 44.2% increase in FPS, and a 2.7 MB compression of the model size. Therefore, LG-YOLOv8 has high accuracy and fast detection speed for safety helmet detection, which realizes real-time accurate detection of safety helmets and an ideal lightweight effect.

Aridity and forest age mediate landscape scale patterns of tropical forest resistance to cyclonic storms

Abstract Cyclonic storms, or hurricanes, are expected to intensify as ocean heat energy rises due to climate change. Ecological theory suggests that tropical forest resistance to hurricanes should increase with forest age and wood density. However, most data on hurricane effects on tropical forests come from a limited number of well‐studied long‐term monitoring sites, restricting our capacity to evaluate the resistance of tropical forests to hurricanes across broad environmental gradients. In this study, we assessed whether forest age and aridity mediate the effects of hurricanes Irma and Maria in Puerto Rico, Vieques and Culebra islands. We leveraged functional trait data for 410 tree species, remotely sensed measurements of canopy height and cover, along with data on forest stand characteristics of 180 of 338 forest monitoring plots, each covering an area of 0.067 ha. The plots represent a broad mean annual precipitation (MAP) gradient from 701 to 4598 mm and a complex mosaic of forest age from 5 to around 85 years since deforestation. Hurricanes resulted in a 25% increase in basal area mortality rates, a 45% decrease in canopy height and a 21% reduction in canopy cover. These effects intensified with forest age, even after considering proximity to the hurricane path. The links between forest age and hurricane disturbances were likely due the prevalence of tall canopies. Tall forest canopies were strongly linked with low community‐weighted wood density (WD). These characteristics were on average more common in moist and wet forests (MAP >1250 mm). Conversely, dry forests were dominated by short species with high wood density (WD > 0.6 g cm−3) and did not show significant increases in basal area mortality rates after the hurricanes. Synthesis. Our findings show that selection towards drought‐tolerant traits across aridity gradients, such as short stature and dense wood, enhances resistance to hurricanes. However, forest age modulated responses to hurricanes, with older forests being less resistant across the islands. This evidence highlights the importance of considering the intricate links between ecological succession and plant function when forecasting tropical forests’ responses to increasingly strong hurricanes.

Accurate and efficient prediction of atmospheric PM1, PM2.5, PM10, and O3 concentrations using a customized software package based on a machine-learning algorithm.

Particulate matter (PM) and ozone (O3) pollution have been attracting increasing attention recently due to their severe harm to human health. PM and O3 are secondary pollutants, and there remain significant challenges in accurately and efficiently predicting their concentrations in the atmosphere. In this study, one-year monitoring data of PM1, PM2.5, PM10, and O3 concentrations as well as meteorological parameters and concentrations of various precursors (i.e., nitrogen oxides, SO2, CO, alkanes, aldehydes, and ketones) are obtained at a monitoring site in central China's Hunan province. The eXtreme Gradient Boosting model is trained and tested to achieve efficient and accurate predictions of the concentrations of the four pollutants. The effects of different datasets, input features, and model parameters on the prediction accuracy are investigated. Principal component analysis is employed to further reduce the dimensions of features, increasing the prediction efficiency. Finally, all model training and prediction processes are incorporated in an executable application using the PyQt5 framework to build a user interface. The customized software supports user-defined modeling. The software can simultaneously predict PM1, PM2.5, PM10, and O3 concentrations, making the prediction process convenient.

An Integrated BIM-IoT Framework for Real-Time Quality Monitoring in Construction Site

An Integrated BIM-IoT Framework for Real-Time Quality Monitoring in Construction Site

A Statistical Examination of the Spatial Correlations of HF Ionospheric Absorption Signatures Using the GO‐Canada Riometer Network

AbstractA network of 30‐MHz riometers distributed across Canada have been monitoring auroral absorption for decades. Electron precipitation can cause enhanced densities to develop below ∼100 km where the higher neutral density can cause absorption of High Frequency (HF) signals. Modeling D‐region absorption accurately can be challenging due to the large distances between riometer monitoring sites. It is of interest to develop an understanding of the typical scale sizes of these regions for assimilation in ionospheric models. Using the entire network of GO‐Canada riometers, auroral absorption events may be monitored as they develop through a wide MLT sector. By examining correlations between sites, proxies for the scale sizes of absorption regions may be estimated. It was found that the sizes of the absorbing regions in the zonal direction are on the order of 900 km. Conversely, the characteristic scale sizes of the regions in the meridional direction were ∼700 km. These results are consistent with the general magnetospheric structure which sees higher energy electrons primarily present in the nightside transition region, and ring current. This creates a natural limit in latitudinal correlations, while the spread in longitude is likely connected to the size of precipitation regions in MLT. Our results also show significant spread in scale sizes in both directions from a few hundred to several thousand km. In this paper, we summarize these results in terms of the observed trends in high energy electron precipitation spatial and temporal scales and discuss future work to connect these scales to magnetospheric drivers.

Multidimensional patterns of bird diversity and its driving forces in the Yangtze river basin of China

Biodiversity is fundamental to human well-being and economic development. The Yangtze River, the largest river in China, faces biodiversity loss due to habitat degradation, climate change, and other anthropogenic threats. However, the long-term changes in the region's biodiversity remain poorly understood. Here, we constructed an optimized living planet index (LPIO) by combining Partial Least Squares Structural Equation Modeling and Random Forest Modeling. Using data from a monitoring network of 536 sites, we observed an increasing trend in terrestrial bird diversity and functional complexity across the entire watershed from 2011 to 2020. Our findings indicate that a large-scale ecological restoration program has contributed to increases in terrestrial and aquatic bird diversity in the Yangtze River Basin. In contrast, bird diversity in the downstream area has decreased by 2.83 ​%, largely due to a rapid decline in wetland birds. The degradation of wetland habitats and insufficient conservation measures have negatively impacted bird diversity in the downstream region. This suggests that although there have been significant improvements in terrestrial bird diversity, more effective wetland restoration is necessary for biodiversity conservation. We recommend optimizing the national large-scale biodiversity monitoring network and increasing the number of upstream monitoring sites.

Nitrogen leaching losses from pasture and winter forage crops in the West Matukituki Valley

In 2013 the Otago Regional Council (ORC) entered mediation with local farmers on changes to their Water Plan, Plan Change 6A, which sought to apply nitrogen load limits within the catchments of Lake Wanaka and Lake Hawea for all farm systems. There was little information available on nitrogen leaching losses in the high rainfall environments (c 2500 mm pa) of the South Island's high country. Agreement after mediation with the ORC and local farmers required a research project to examine differences between measured losses and outputs from OVERSEER® at a paddock and farm scale. To address this knowledge gap, a series of monitoring sites were set up in November 2014 in the West Matukituki Valley, west of Lake Wanaka. Porous soil solution samplers were installed to a depth of 50 cm in (i) a sheep- and cattle-grazed pasture, (ii) a cattle-grazed winter forage crop, and (iii) a native bush site. Results for 2015 showed that rainfall totalled 2240 mm, with calculated drainage estimated at 1330 mm. The total fluxes of dissolved N from the crop and pasture sites during the 12 months from June 2015 were 157 and 43 kg N/ha, respectively. Dissolved organic N (DON) made an unexpectedly large contribution to these fluxes, accounting for 20 and 67% of the total fluxes of dissolved N in drainage from the crop and pasture sites, respectively. Gaining a better understanding of the bioavailability of dissolved forms of organic N in leachate would help determine the risks that these forms of nutrients pose to water quality, and if they should be incorporated into models such as Overseer which do not account for DON. The project’s initial findings enabled an informed discussion on the use of modelled results from Overseer for the mitigation of N loss at a farm scale in the lakes’ catchments. It also focussed farmers’ attention on management of the nitrogen leaky parts of their farm systems, especially winter crops.

Many-to-many: Domain adaptation for water quality prediction

Predicting water quality is crucial for sustainable water management. To mitigate data scarcity for specific water quality targets, domain adaptation methods have been employed, adjusting a model to perform in a related domain and leveraging learned knowledge to bridge domain differences. However, these methods often fall short by overfitting certain domain-specific patterns, overlooking consistent water quality patterns in multi-water domains. Despite regional variations, these Consistent patterns show fundamental commonalities and can be observed across monitoring sites, stemming from their widespread and interconnected nature. Addressing these limitations, we introduce the Many-to-Many Domain Adaptation framework (M2M) for prediction to bridge the gap between multi-source domains and multi-target domains, aligning shared insights with the distinct profiles of individual monitoring sites while considering their geographical interconnections. M2M adeptly addresses the formidable challenge of concurrently deciphering and integrating multifaceted patterns across an array of source and target domains, while also navigating the intricate regional heterogeneity intrinsic to the water quality of different sites. The M2M includes a domain pattern fusion module for consistent pattern extraction and numerical scale maintenance from source domains, a domain pattern sharing module for sharing pattern extraction from target domains, and an M2M learning method to ensure the training of these modules. Extensive experiments conducted on 120 diverse monitoring stations demonstrate that M2M markedly enhances the accuracy of water quality predictions using various time series encoders. Code available at https://github.com/biya0105/M2M.

First Installation of an Optical Ocean-Bottom Seismometer, Cabled Offshore Les Saintes, Lesser Antilles

Abstract The detection and analysis of offshore seismic processes worldwide often require the use of ocean-bottom seismometers (OBSs). However, most OBS deployments are done with stand-alone stations, with data recovery delayed by months. On the other hand, electrically cabled OBS, which allows for real-time monitoring, remains exceptional due to the high cost of manufacturing, installation, and maintenance. Here, we present a new perspective for cabled array of OBSs, using purely optical seismometers, plugged at the end of long fiber-optic cables, aimed at reducing their cost for observatories requesting real-time data. The optical seismometer was developed in the last decade by the École Supérieure d’Électronique de l’Ouest, based on the Fabry–Perot interferometer, tracking at high resolution the displacement of the mobile mass of a mechanical geophone (no electronics nor feedback). A prototype was successfully installed at the top of La Soufrière volcano of Guadeloupe in 2019. We replicated this sensor and installed it 5 km offshore Les Saintes islands, at 43 m depth (Guadeloupe, Lesser Antilles) to characterize the swarm-type activity persistent after the 2004 M 6.3 earthquake (Interreg Caraïbe PREST project). The installation cruise, FIBROSAINTES, was supported by the Flotte Océanographique Française. A plow designed by GEOAZUR carried the cable and was pulled on the seafloor by the vessel ANTEA. The landing cable was connected to the interrogator, with a real-time telemetry to the Institut du Physique du Globe de Paris/Observatoire Volcanologique et Sismologique de Guadeloupe. The OBS has been qualified with local land-based velocity broad band stations. The analysis of local earthquake swarms suggests transient creep on the major normal faults. This successful installation opens promising perspectives for real-time monitoring in on-land or offshore sites, presenting harsh environmental conditions, in which commercial, electrical seismic sensors are difficult and/or costly to install and maintain.

Monitoring of mosquito breeding sites in the municipality of Cascais, Portugal

Abstract Culex pipiens is the primary West Nile Virus vector in Portugal. Additionally, in 2017, Aedes albopictus, vector of various arboviruses, was detected in Northern Portugal. To mitigate viral transmission risks, effective surveillance is crucial, including monitoring egg, larva, and pupa stages. Located on the west of Lisbon, Cascais implemented a surveillance based on monitoring invasive species at Points of Entry and, complementarily, on detecting and intervening in breeding sites. Active searches for new breeding sites and monitoring those that were already identified are conducted weekly during the peak mosquito season (May to October) and biweekly during the colder season (November to April), following the methodology outlined below: • Upon detection, samples of immature stages are collected for submission to the National Institute of Health (INSA); • Corrective measures are requested to the managing entities of the locations where the sites are found; • Date of action, location, collection point, date of report, and status (pending or intervened) are recorded on a database; • Locations with pending status undergo continuous monitoring. In 2023, 16 artificial breeding sites were identified. These findings resulted from 23 actions conducted across 8 locations. 35 samples of immatures were collected, followed by their submission and notification to the respective managing entities. Recurrent detections were noted in 9 breeding sites. The managing entities implemented measures at 13 of the identified sites, leaving 3 with pending intervention. The identification of breeding sites demands continuous monitoring efforts and requires an effective coordination between the Public Health services and the management entities. Based on the achieved outcomes, it is evident that the implementation of corrective measures has been successful, making a significant contribution to reducing mosquito density. Key messages • Effective surveillance and intervention strategies are crucial in mitigating West Nile Virus and arbovirus transmission risks in Portugal. • Continuous monitoring and coordination between public health services and management entities are key for maintaining effective vector control measures.