Seasonal Trends Research Articles

Temporal dynamics of leaf area index and land surface temperature correlation using Sentinel-2 and Landsat OLI data

BackgroundUnderstanding the complex relationship between vegetation dynamics and land surface temperature (LST) is crucial for comprehending ecosystem functioning, climate change impacts, and sustainable land management. Hence, this study conducts a temporal analysis of leaf area index (LAI) and LST data derived from Sentinel-2 and Landsat Operational Land Imagery (OLI) in the Mille River Basin, a tropical region in Ethiopia. LAI data were generated using Sentinel-2 imagery processed with the Sentinel Application Platform (SNAP) toolbox, an open-access earth observation analysis tool, while Landsat OLI collection 2 level 2 data were utilized for precise LST retrieval. The Mann–Kendall test was used to detect trends in the time series data.ResultsThe trends in the mean LAI were statistically significant at P values of 0.05 and 0.10 for the annual and seasonal trends, respectively. The mean LST trends were insignificant throughout the study period except for the summer season, for which the P value was 0.07. The correlation between the LAI and LST was weak (R2 = 0.36) during the crop-growing seasons (summer and spring) but moderate in winter (R2 = 0.46) and autumn (R2 = 0.41).ConclusionThe findings of this research clarify the complex relationships between variations in surface temperature and vegetation growth patterns, providing insight into the environmental mechanisms driving the dynamics of localized ecosystems. The study underscores the implications of these findings for informed decision-making in sustainable land management, biodiversity conservation, and climate change mitigation strategies.

A LoRaWAN-based environmental sensing network for urban green space monitoring with demonstrated application for stormwater management

Monitoring urban green spaces (UGSs) is crucial for achieving sustainable urban development and ecological resilience. Leveraging LoRaWAN technology, a wireless environmental sensing system was developed and implemented to monitor soil moisture dynamics across seven diverse UGSs over a year. Analyses revealed notable variations in soil moisture influenced by vegetation types, soil conditions and physical settings. Seasonal trends indicated lower summer soil moisture in some UGSs resulting from increased evapotranspiration, while others maintained higher soil moisture due to more frequent irrigation. The soil moisture response to rainfall was quantitatively modeled, demonstrating the increase in soil moisture is highly positively dependent on rainfall amount and negatively dependent on initial moisture level. Both factors were significant (p<0.001) in most cases, and the models’ adjusted R2 values were all above 0.65 except for one node. The findings also unveiled more dynamic ranges of UGS runoff coefficients than government guideline values, especially high runoff coefficients (0.4 to 1.0) for rainfall events above 50mm. Therefore, although existing UGSs can help absorb smaller storms, proactive drainage systems are needed for UGSs to handle extreme events. The study highlights LoRaWAN's efficacy in urban environmental monitoring and provides valuable insights for managing and optimizing UGSs, especially in stormwater management.

The fuzzy system ensembles entomological, epidemiological, demographic and environmental data to unravel the dengue transmission risk in an endemic city

BackgroundThe effectiveness of dengue control interventions depends on an effective integrated surveillance system that involves analysis of multiple variables associated with the natural history and transmission dynamics of this arbovirus. Entomological indicators associated with other biotic and abiotic parameters can assertively characterize the spatiotemporal trends related to dengue transmission risk. However, the unpredictability of the non-linear nature of the data, as well as the uncertainty and subjectivity inherent in biological data are often neglected in conventional models.MethodsAs an alternative for analyzing dengue-related data, we devised a fuzzy-logic approach to test ensembles of these indicators across categories, which align with the concept of degrees of truth to characterize the success of dengue transmission by Aedes aegypti mosquitoes in an endemic city in Brazil. We used locally gathered entomological, demographic, environmental and epidemiological data as input sources using freely available data on digital platforms. The outcome variable, risk of transmission, was aggregated into three categories: low, medium, and high. Spatial data was georeferenced and the defuzzified values were interpolated to create a map, translating our findings to local public health managers and decision-makers to direct further vector control interventions.ResultsThe classification of low, medium, and high transmission risk areas followed a seasonal trend expected for dengue occurrence in the region. The fuzzy approach captured the 2020 outbreak, when only 14.06% of the areas were classified as low risk. The classification of transmission risk based on the fuzzy system revealed effective in predicting an increase in dengue transmission, since more than 75% of high-risk areas had an increase in dengue incidence within the following 15 days.ConclusionsOur study demonstrated the ability of fuzzy logic to characterize the city’s spatiotemporal heterogeneity in relation to areas at high risk of dengue transmission, suggesting it can be considered as part of an integrated surveillance system to support timely decision-making.

Viral etiology of febrile respiratory syndrome among patients in Liaoning Province, China

BackgroundFebrile respiratory syndrome (FRS) is often associated with viral infections. The aim of this study was to identify the viral pathogens responsible for FRS in Liaoning Province, China.MethodsWe tested eight respiratory viruses, namely, influenza virus (IFV), rhinovirus (RV), human adenovirus (HAdV), human bocavirus (HBoV), human parainfluenza virus (HPIV), human coronavirus (HCoV), respiratory syncytial virus (RSV), and human metapneumovirus (HMPV), using reverse transcription-polymerase chain reaction (RT-PCR). Statistical analyses were performed using SPSS version 25.0, and the data were plotted using RStudio 4.2.1 software.ResultsIFV was the most frequently identified pathogen, followed by RV, HAdV, HBoV, HPIV, HCoV, RSV, and HMPV. RSV/HBoV coinfection occurred most frequently among the mixed cases. The rate of respiratory virus detection was highest in children under one year of age and decreased significantly with age. Seasonal trends showed a peak in virus detection during the winter months.ConclusionsIFV is the leading cause of FRS in Liaoning Province, China, with single-virus infections prevailing over coinfections. Observations indicate a differential virus detection rate across age groups and seasons, highlighting the need for focused preventive strategies to mitigate the transmission of respiratory viruses, particularly among susceptible populations in the colder season.

Red Seaweed (Rhodophyta) Phycocolloids: A Road from the Species to the Industry Application.

Seaweed polysaccharides are versatile both in their functions in seaweed physiology and in their practical applications in society. However, their content and quality vary greatly. This review discusses the main factors that influence the yield and quality of polysaccharides, specifically carrageenans and agars (sulfated galactans) found in red algae species (Rhodophyta). In addition, its historical, current, and emerging applications are also discussed. Carrageenan has been influenced mainly by photosynthetically active radiation (PAR) and nitrogen, while its relationship with temperature has not yet been replicated by recent studies. Agar's seasonal trend has also been found to be more ambiguous than stated before, with light, temperature, nutrients, and pH being influencing factors. In this review, it is also shown that, depending on the compound type, seaweed polysaccharides are influenced by very different key factors, which can be crucial in seaweed aquaculture to promote a high yield and quality of polysaccharides. Additionally, factors like the extraction method and storage of polysaccharides also influence the yield and quality of these compounds. This review also highlights the drawbacks and inadequacy inherent from the conventional (or current) extraction technology approaches.

Low incidence of microplastics in coral reefs of Kāneʻohe Bay, Hawaiʻi, USA

This study investigated microplastic and other micro-debris pollution in sediment, seawater, sea cucumbers, and corals from fringing and patch reefs in Kāneʻohe Bay, Oʻahu, Hawaiʻi, USA. Microplastic pollution in Kāneʻohe Bay Bay was low compared to other tropical coral reefs. Microplastics were detected in sediments (29 %), sea cucumbers (9 %), and coral (0–2 %) samples but were not quantifiable. Seawater had quantifiable microplastic (< 0.5 mm) and macroplastic (> 0.5 mm) pollution, with mean concentrations ranging from 0.0061 to 0.081 particles m−3. Most particles detected in seawater samples were larger, floating plastic debris consisting mostly of polyethylene, polypropylene fragments, and fibers. Across the other matrices, the most detected particles were polyester, polypropylene, and cotton fibers. These results provide baseline data for this important coral reef ecosystem, and further monitoring is recommended to understand the seasonal and long-term trends in microplastic pollution and its potential future impacts.

A surge of interest: Analysing the increased public interest in lipedema using google trends.

This study aimed to monitor public interest in information related to Lipedema and examine potential temporal, seasonal, and income-related trends that may impact public interest. A dataset was created using the Google Trends tool by inputting the keyword "Lipedema" to obtain the relative search volume (RSV) from 2004 to the present in the United States (US). To examine potential income-related disparities in public interest in Lipedema across the US, RSV values were recorded in the five states with the highest and the lowest median income (Maryland, New Hampshire, New Jersey, Utah, and Washington and Mississippi, West Virginia, Arkansas, New Mexico, Kentucky, respectively). Public interest in Lipedema increased significantly over time (R2 = 0.693, p < .001 for the linear trend; R2 = 0.880, p < .001 for the quadratic trend). There were no significant differences between the RSV values between the month of the year and the season of the year (p = 1.000 and p = .859, respectively). RSV values of the five highest-income and low-income states showed significant increases (R2 = 0.333, p < .001, and R2 = 0.195, p < .001, respectively) More rapid growth in interest in Lipedema in states with high income compared to the states with low income (p < .001). Public interest in Lipedema is increasing in both high-income and low-income states. States with higher incomes are more interested. Searches for lipedema are expected to become more frequent, leading to a rising demand for comprehensive education in medical schools and clinician training programs. In addition, there will also be a need for easily accessible, high-quality information resources.

Monitoring Temporal Sandbar and Shoreline Changes at Saint Louis, Senegal: Using Sentinel-2 Imagery (2015–2022)

Understanding beach dynamics, both in time and in space, is paramount to better understand how and when to intervene to improve coastal management strategies. Beach morphodynamics is expressed in a variety of ways. As indicators of beach change, we can measure the shoreline, the beach topography, and the bathymetry; e.g., in situ measurements rarely cover large extents, are often collected on a local scale (beach), and rarely cover a sufficient time span with a sufficient surveying frequency or a simultaneous measurement of the beach and bar system. Regular-revisit satellites, such as the ESA’s Sentinel-2 mission, provide the opportunity to regularly monitor both shoreline and sandbar dynamics, and the time span is increasing and likely to continue for the decades to come. Using the satellite-derived shoreline and bar position, here, we show that the shoreline and bar are intrinsically coupled. Using Sentinel-2 satellite imagery, we show that the actual erosion/accretion status of the beach at Saint Louis (Senegal) is strongly influenced by the sandbar dynamics. There is a coupled behavior in their seasonal evolution and trend. Our results show that a very large accretive wave of about 50 m observed on the beach is driven by a local welding of the inner sandbar to the beach. Finally, we conclude that this type of event could be anticipated by an analysis of the sandbar.

Seasonality and epidemiological trends in species distribution and antifungal susceptibility of Candida species isolated from various clinical specimens conducted during 2011–2022, Korea: a retrospective surveillance study

Background: As most Candida species cause opportunistic infections, it is helpful for patient care to determine species name of Candida spp. and their distribution in both sterile and non-sterile specimens. We aimed to investigate trends in the distribution of Candida species isolated from a hospital in Korea, along with their antifungal susceptibilities and seasonal variations. Methods: This study was conducted at the Chung-Ang University Hospital and included 8,760 different clinical specimens from March 2011 to December 2022. Identification of the fungal species and its antifungal susceptibility testing were performed using VITEK 2 ID-YST system for six drugs: amphotericin B, caspofungin, fluconazole, voriconazole, micafungin, and flucytosine. Results: The most common fungal species was Candida albicans. The C. albicans positivity rate gradually increased from 2012 to 2022. Since 2020, however, this trend reversed and the non-albicans Candida (NAC) superseded the count of C. albicans. Among the NAC, C. glabrata showed significant increase. When a weekly analysis was performed, C. glabrata was evenly distributed without any noticeable peak; however, the positive rate decreased from late December to early January across all years. Conclusion: Monitoring of future trends should necessarily be continued. Our findings revealed that the positive rate for Candida was the lowest in the months of December and January of the studied years, which can be attributed to environmental factors. However, further research needs to be conducted.

Analysis of Pre-monsoon and Post-monsoon Seasonal Rainfall Trends by Mann-Kendall and Sen’s Slope Estimator Test in Himachal Pradesh, India

The purpose of the current study is to determine the trends and patterns of precipitation occurring in the periods before and after the monsoon season at 12 meteorological stations located in the twelve districts of the Himachal Pradesh region. The timeframe of the analysis is from 1991 to 2020. For this study, we used the Mann-Kendall test and estimator Sen's slope estimator test. Precipitation is an essential climatic factor that can act as a signal of climate change. In order to understand how climate change may affect water resource planning and management, it is crucial to understand rainfall pattern. The results indicate that there is a growing tendency in the pre-monsoon season at some sites, while some stations are seeing a declining trend. Keylong has the most significant falling trend, with a Sen slope of -4.154mm whereas Manali had the highest increasing trend at Sen's Slope (1.471mm). In contrast, the analysis of post-monsoon season data reveals an upward tendency in all studied regions, except for Dharamshala, where Sen's slope (-0.042mm) shows a slight downward trend whereas Manali had the highest increasing trend with the value of 1.771mm. According to the research, agricultural and horticultural sectors in Himachal Pradesh are at risk from heavy rainfall and landslides during the periods before and after the monsoon season, respectively, if the patterns continue to rise. Conversely, a decrease in these patterns might result in water scarcity. To tackle adverse climate changes, the general public must embrace adaptation measures based on long-term series data.

Assessment of pesticide residues: a comprehensive analysis of seasonal trends and health implications

This study assessed the presence of eight pesticide residues in the Indus River, Mianwali, Pakistan, focusing on three sampling sites (S1, S2, and S3) in water, sediment, and the fish species Cyprinus carpio during both dry and wet seasons. Analysis was conducted using gas chromatography with an electron capture detector. Results indicated elevated pesticide concentrations in both seasons, with levels of 0.84 and 0.62 μg/L in water, 12.47 and 9.21 μg/g/dw in sediment, and 17.33 and 12.17 μg/g/ww in fish, with higher concentrations observed during the dry season. Cypermethrin and carbofuran were the primary pesticides detected in water, while endosulfan and cypermethrin were dominant in sediment and fish tissue, often exceeding standard safety thresholds. Principal Component Analysis (PCA) and cluster analysis revealed stronger correlations between sediment and fish muscle, with varying associations among pesticides across seasons. The Hazard Index (HI) surpassed 1 in both seasons, signaling potential health risks to humans. These findings underscore the substantial risk agricultural pesticides pose to the aquatic ecosystem and food chain, highlighting the urgent need for sustainable agricultural practices and stricter regulations to minimize pesticide use and encourage eco-friendly pest management strategies.

Arbuscular mycorrhizal fungal spore communities and co-occurrence networks demonstrate host-specific variation throughout the growing season.

Microbial community assembly involves a series of ecological filtering mechanisms that determine the composition of microbial communities. While the importance of both broad and local level factors on microbial communities has been reasonably well studied, this work often is limited to single observations and neglects to consider how communities change over time (i.e., seasonal variation). Because seasonal variation is an important determinant of community assembly and determines the relative importance of community assembly filters, this represents a key knowledge gap. Due to their close associations with seasonal variation in plant growth and fitness, arbuscular mycorrhizal (AM) fungi are useful groups for assessing the importance of seasonal dynamics in microbial community assembly. We tested how seasonal variation (spring vs. summer), plant life history stage (vegetative vs. flowering), and host plant species (Baptisia bracteata var. leucophaea & Andropogon gerardii) influenced AM fungal spore community assembly. AM fungal spore community temporal dynamics were closely linked to plant host species and life history stage. While AM fungal spore communities demonstrated strong turnover between the spring (e.g., higher sporulation) and late summer (e.g., higher diversity), the strength and direction of these changes was modified by host plant species. Here we demonstrate the importance of considering temporal variation in microbial community assembly, and also show how plant-microbe interactions can modify seasonal trends in microbial community dynamics.

Seasonal trends in Subantarctic plankton δ13C and δ15N are driven by phytoplankton dynamics and nutrient preference

AbstractThe carbon and nitrogen isotope ratios (δ13C and δ15N) of marine plankton record biogeochemical processes at the base of the food web. In the Southern Ocean, such data, predominantly from summer, have been used to infer surface CO2 concentrations and the potential for biological carbon export. However, variability in plankton δ13C and δ15N remains poorly understood, with the lack of seasonal measurements from the Southern Ocean emerging as a particular limitation. Here, we investigate the δ13C and δ15N of suspended particulate matter (SPM) and zooplankton collected from the Subantarctic Ocean in winter, summer, and autumn. The low summertime δ13CSPM and δ15NSPM can be explained by strong diatom reliance on nitrate supplied during winter mixing. A subsequent increase in δ13CSPM and decrease in δ15NSPM by autumn is consistent with iron limitation in mid‐ to late‐summer favoring diatom succession by nano‐phytoplankton that consume mainly recycled ammonium. By winter, bacterial decomposition of biomass outpaces new biomass production, decreasing δ13CSPM and raising δ15NSPM. The δ13C and δ15N of contemporaneously sampled zooplankton generally reflect the variability in δ13CSPM and δ15NSPM, with one autumn mismatch suggesting that in situ SPM is not always the dominant zooplankton food source. Our study shows how nutrient dynamics and phytoplankton community composition shape the seasonality of the Subantarctic's isotopic baselines, emphasizing a key role for iron availability. This work has implications for isotope‐based food web studies, highlighting the need to consider seasonal variability in δ13CSPM and δ15NSPM, as well as the different turnover times of phytoplankton (i.e., SPM) vs. zooplankton biomass.

Temporal trends of seasonal pollen indexes in a region of Northern Italy (2001–2022)

Increasing trends in allergic respiratory diseases may be linked to climate changes affecting pollen levels. This study aimed to investigate the temporal trends in seasonal indexes of 9 allergenic pollen types in the Veneto Region, Northern Italy. Available daily data from 20 monitoring stations covered 2001–2022 for Corylaceae, Cupressaceae, Poaceae, Oleaceae, and Urticaceae (pollen families), and 2006–2022 for Alnus, Betula, Ambrosia, and Artemisia (pollen genera). The 95-percentage method was used to identify the start/end dates of pollen seasons annually, and the seasonal cumulative pollen concentration (Seasonal Pollen Integral, SPIn) was calculated. The non-parametric Theil-Sen median slope method was used to examine trends in pollen seasons’ start day, duration, and SPIn in the whole region and for climatic areas (alpine, subcontinental eastern, subcontinental western). The results showed a clear upward trend in SPIn for several pollens, with median slopes ranging from 326 (Alnus) to 1089 p/m3 per 10yrs (Corylaceae); exceptions were Artemisia, whose SPIn decreased over time (−26 p/m3 per 10yrs), and Ambrosia, Betula, and Urticaceae, which did not show a clear trend. For families, pollen seasons showed a trend for anticipation (from −4.7, Urticaceae, to −13.5 days/10yrs, Oleaceae) and extended duration (from 8.3, Cupressaceae, to 13.3 days/10yrs, Oleaceae). An evident heterogeneity across the climatic areas was seen for the SPIn, with the subcontinental western area exhibiting the highest loads and most prominent increases over time. In conclusion, increased levels and prolonged seasons for several pollen taxa observed in the Veneto Region, particularly in subcontinental areas, suggest increasing risks for individuals with pollen allergies. Addressing this issue requires measures of mitigation and adaptation to climate-driven changes in pollen exposure.

Enhancing glacier monitoring through adaptive smoothing of MODIS NDSI time series

ABSTRACT Observation of glacier surface characteristics through remotely sensed time-series data is essential for understanding glacier seasonality, mass balance, and long-term trends. Yet, the reliability of these observations depends significantly on the quality of the time-series data. This study presents a meticulous preprocessing scheme to improve the quality of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Snow Index (NDSI) time-series data for glacier monitoring. We propose a three-step algorithm specifically crafted to overcome the challenges associated with cloud contamination reduction, outlier removal and data gap handling. This innovative approach iteratively compares the median values of automatically adjusted asymmetrical moving windows to achieve convergence, removing outliers using minimal window size to keep the temporal resolution as high as possible. The methodology’s effectiveness is demonstrated through its application to two glaciers from the United States Geological Survey (USGS) Benchmark Project, showcasing significant improvements in the quality of smoothed MODIS NDSI time series. These results affirm the efficacy of the proposed scheme in rendering a more reliable evaluation of glacier surface condition and seasonal fluctuations. Consequently, this study contributes significant methodological advancements to the fields of remote sensing and glaciology, enhancing the accuracy of glacier monitoring techniques.

Transcriptional Changes Underlying the Degradation of Plant Community in Alpine Meadow Under Seasonal Warming Impact.

Global warming is exhibiting a seasonal trend, while different seasons have different warming variations. However, the impact of seasonal warming on plants remains unclear. This study employed Open Top Chambers (OTCs) to simulate future seasonal warming scenarios in alpine meadow. The study examined plant community dynamics following long-term seasonal warming. The transcriptional and physiological responses of two dominant species (Kobresia pygmaea and Stipa purpurea) were examined. Results suggest that seasonal warming effects are correlated with both the duration of warming and the season which warming occurs. A long annual warming duration, especially growing season warming, made plants confront various stresses. K. pygmaea adopted a stress-avoidance strategy, showing a negative response, and leading to population decline or disappearance. This kind of dieback had also been observed in other Cyperaceae species. Meanwhile, due to positive responses, S. purpurea adopted a stress-tolerance strategy and overcame the impact of warming, partially gained the dominance over Cyperaceae species. Overall vegetation coverage and plant community diversity decreased over the years. These results reveal the impact of seasonal warming to plants, explaining the reasons for changes in plant communities under seasonal warming and providing new insights for future plant conservation under seasonal warming.

Influence of seasonality and edge effect on seagrass-associated macrobenthic communities: A case study from the Andaman Islands, India

Seagrass seasonality and community interactions within a meadow are local-scale ecological measures that inform management, especially in the wake of climate change. We investigated the collective influence of two ecological processes- seagrass seasonality, and edge effect, on the macrobenthic communities in the Andaman Islands, India. Thalassia hemprichii formed two mono-species intertidal meadows, while Enhalus acoroides dominated the mixed-species meadow. The study was carried out across winter (January), summer (April), and monsoon (August) in 2021 using quadrat sampling (0.5 ×0.5 m), where we surveyed the edges, center, and unvegetated micro-habitats within a meadow. Our findings suggest site-specific seasonality trends for T. hemprichii, as seasonality was governed more by the local environment than species. However, seagrass growth was the lowest in summer for both the investigated meadows. Further, we report a differential influence of seagrass seasonality on macrofaunal densities. Infaunal densities were the highest in low seagrass growth (summer), while epifaunal densities positively correlated with seagrass meadow attributes. For both investigated sites, the highest epifaunal densities were observed in winters [Site 1 = 116.8 ± 27.5 ind./m2; Site 2 = 187.8 ± 14.3 ind./m2). Lastly, we report no influence of edge effect on infauna but a little on epifauna, where for the latter, trends were site, season, and group-specific. Epifaunal holothuroids were the only group with a consistent negative edge across sites and seasons, and densities were concentrated in center > edge > unvegetated regions. In conclusion, we suggest that seagrass seasonality is a product of the local environment rather than the species type. Furthermore, seagrass seasonality substantially influenced macrofauna more than the edge effect. This study is the first attempt from the Indian waters to collectively study community responses towards seagrass seasonality and edge effect, filling a critical ecological gap in seagrass research and management in India.

Development and Comparison of Time Series Models in Predicting Severe Fever with Thrombocytopenia Syndrome Cases - Hubei Province, China, 2013-2020.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus, which has a high mortality rate. Predicting the number of SFTS cases is essential for early outbreak warning and can offer valuable insights for establishing prevention and control measures. In this study, data on monthly SFTS cases in Hubei Province, China, from 2013 to 2020 were collected. Various time series models based on seasonal auto-regressive integrated moving average (SARIMA), Prophet, eXtreme Gradient Boosting (XGBoost), and long short-term memory (LSTM) were developed using these historical data to predict SFTS cases. The established models were evaluated and compared using mean absolute error (MAE) and root mean squared error (RMSE). Four models were developed and performed well in predicting the trend of SFTS cases. The XGBoost model outperformed the others, yielding the closest fit to the actual case numbers and exhibiting the smallest MAE (2.54) and RMSE (2.89) in capturing the seasonal trend and predicting the monthly number of SFTS cases in Hubei Province. The developed XGBoost model represents a promising and valuable tool for SFTS prediction and early warning in Hubei Province, China.

Optimized deep learning modelling for predicting the diffusion range and state change of filling projects

Concealment of filling constructions poses significant challenges for quality assurance in filling engineering. Direct surveillance of fill dispersal currently remains infeasible, while conventional detection techniques suffer deficiencies in efficiency. This research proposes a framework integrating elastic wave monitoring and hybrid deep learning for predictive modelling of filling state transitions and diffusion range. During the sand filling of the immersed tunnel, elastic wave data is collected via elastic wave testing, and the response energy characteristic is derived through time-domain analysis. The trends in elastic wave response energy are correlated with three filling states: free diffusion, accumulation, and filled state, using Seasonal and Trend decomposition using Loess (STL) for seasonal trend analysis. Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM) are utilized to extract spatiotemporal features from the response energy trends, facilitating accurate prediction of the trends’ development and the sand filling state over time. The performances of the proposed strategy are illustrated through an application to the case study of the sand filling construction of the Chebeilu immersed tunnel. The CNN + LSTM model with the proposed strategy gave excellent results (MAE 0.0663, MSE 0.0071, RMSE 0.0845). The model can predict fill state changes and quantify diffusion radii to optimize and guide the construction process.

Spatio-temporal dynamics of net primary productivity and the economic value of Spartina alterniflora in the coastal regions of China

This study employs an improved Carnegie–Ames–Stanford Approach (CASA) model to calculate the Net Primary Productivity (NPP) of Spartina alterniflora (SA) and various other land use/land cover types (LULC) across coastal China over multiple years. The research aims to provide significant theoretical and practical insights into carbon sink research in coastal zones, sustainable development, and resource management. Key findings include identifying the first εmax value of 2.219 g C/MJ for SA, addressing a critical data gap in CASA modeling research on invasive plants. SA's NPP exhibited higher values in Shanghai and Zhejiang due to factors such as genetic diversity, invasion duration, and tidal dynamics. In contrast, other LULC exhibited higher NPP values in southern and inland regions, characterized by greater vegetation cover and favorable growing conditions. In 2020, SA and other LULC sequestered 16.352 kt C and 0.821*106 kt C, respectively. From 2000 to 2020, the average annual NPP and total carbon storage of SA and other LULC increased significantly, primarily driven by Shanghai and deciduous needleleaf forests, respectively. Seasonal NPP trends followed summer> spring> autumn> winter, influenced by climate conditions and plant life activities. Economic assessments in 2020 estimated SA's carbon storage value at RMB0.409 billion (Market Value method) or RMB5.562 billion (Carbon Tax method), with RMB2.054 billion attributed to oxygen release values, underscoring its economic and ecological potential. Among other LULC, evergreen broadleaf forests showed the highest carbon storage value (RMB183.463 billion). The study emphasizes the critical role of all LULC in carbon storage and oxygen release, advocating for targeted conservation and land management strategies. It suggests that managing SA should balance stringent control in high-risk areas, lenient measures in low-risk areas, eradication of scattered populations, and maximizing ecological benefits in retention areas, with continuous monitoring and adaptive management strategies to balance conservation and development efforts.