Effects Of Storms Research Articles

The effect and burden of sand-dust storms on asthma hospitalization: Evidence from cities with arid climate in China

Evidence concerning the impact of sand-dust storms (SDS) on asthma is limited, and little is known about the associated public health burden, especially in regions with arid climates. Therefore, this study seeks to evaluate the effect of SDS on asthma hospitalization and quantify the associated hospital and economic burden in a province with typical arid climates. We collected provincial asthma hospitalization, air pollutants and meteorological data of 14 cities in Gansu province. The space-time-stratified case-crossover design combined with a conditional quasi-Poisson regression was used to estimate the association between SDS and asthma hospitalization during 2018-2022. We further explored the interactive effect of SDS and low temperature, and explored potential effect modifications of gender, age, seasons and regions by stratified analyses. Finally, we calculated the hospital and economic burden of asthma attributed to SDS. A total of 54,134 hospitalization records for asthma and 791 SDS events were recorded during the study period. Northwestern area with arid climate displayed more frequent SDS events and asthma hospitalization compared with regions with subtropical or temperate monsoon climate. The relative risk (RR) of asthma hospitalization increased with SDS, with the greatest RR at lag1, which was 1.164 with a 95% confidence interval (CI) of 1.101 to 1.231. We further found that low temperature had an interactive effect with SDS to trigger asthma hospitalization. Stronger associations were observed in males, school-aged children, cold season and northwestern area. The total fraction of asthma hospitalization attributable to SDS was 1.64 % (95% CI: 1.06% to 2.18%), and a conservative estimate of relative healthcare costs was 4.49 (95% CI: 2.92 to 5.99) million China Yuan. Our findings suggest the necessity of controlling SDS and implementing region-specific public health policies as well as personal protective measures against SDS.

Ionospheric Disturbances Observed Over the Peruvian Sector During the Mother's Day Storm (G5‐Level) on 10–12 May 2024

AbstractThis article presents the recent extreme and rare G5‐level geomagnetic storm (Mother's Day Storm) effects on the equatorial and low‐latitude ionosphere observed at the Peruvian sector by the Jicamarca (11.9°S, 76.8°W, magnetic dip 1°N) incoherent scatter radar and associated instruments. This storm was produced by multiple Earth‐directed coronal mass ejections, which generated significant modifications in the Earth's magnetic field, leading to the Sym‐H of ∼−518 nT. On the dayside, due to the strong eastward penetration electric field, vertical plasma drift and equatorial electrojet (EEJ) enhanced for 2–3 hr and remained consistent at values of ∼95 m/s and 260 nT between 1700 and 1900 UT (1200 and 1400 LT). At the same time, vertical E B plasma drift uplifted the equatorial ionosphere, producing the dusk‐side super plasma fountain and transferring electron density to higher latitudes. A huge increase (∼1,325%) in electron density (from 11 to 142 TECu) is observed at low and mid‐latitudes from ∼20°S to 50°S between 2000 and 0400 UT (1500–2300 LT). The strong westward penetration electric field suppressed pre‐reversal enhancement, leading to downward plasma drift (∼−96 m/s) at around 2400 UT (1900 LT). Overnight, vertical plasma drift fluctuated between ±90 m/s, and the combined effect of penetration and disturbance dynamo electric fields caused a significant increase (∼530 km) in ionospheric virtual height. In the main and early recovery phase, consistent short‐ and long‐duration electric field disturbances persisted for approximately 30 hr, with periods of ∼48 and 90 min.

Different Effects of a Super Storm on Atmospheric Electric Fields at Different Latitudes

Geomagnetic storms have a significant impact on Earth’s magnetosphere and ionosphere, as well as on the global atmospheric circuit. This study focuses on investigating the anomalous variations in the vertical atmospheric electric field at eight mid-latitude and low-latitude stations during a mega-geomagnetic storm on 24 April 2023. The majority of stations observed vertical atmospheric electric field increases, while only three nearby stations exhibited vertical atmospheric electric field decreases. The analysis revealed that vertical atmospheric electric field changes ranged from 19 to 370 V/m, and the time differences between extreme vertical atmospheric electric field values and the minimum Dst value ranged from 0 to 5.3 h. Other response patterns to this super magnetic storm at different latitudes are summarized, and the physical mechanisms of different effects of magnetic storms on the electric fields of stations at different latitudes are also discussed.

Global Ionospheric Response During Extreme Geomagnetic Storm in May 2024

The main idea of the present study is to investigate in detail the time evolution of the spatial inhomogeneities connected with the ionospheric response to the geomagnetic storm registered in the period of 10–11 May 2024. The obtained ionospheric anomalies represented by the relative deviations of the global Total Electron Content (TEC) data have been utilized in the analysis. The used global TEC data have been converted to a coordinate system with a modip latitude and geographical longitude. In addition to the maps illustrating the global spatial distribution of the geomagnetically forced ionospheric anomalies, a presentation of the observed longitudinal structures by sinusoidal approximation has also been used. The resulting positive and negative responses have been studied depending on the magnetic latitude, local times and the behavior of the geomagnetic activity parameters during the considered event. The interpretation takes into account the known mechanisms for the effect of the geomagnetic storm on the electron density. A special attention is focused on the differences in the two hemispheres at high and mid latitudes, where a simultaneous direct impact of the particle precipitation and the change in the temperature regime of the neutral atmosphere has been assumed. The low-latitude response as a result of the Equatorial Ionization Anomaly (EIA) associated with Disturbed Dynamo Electric Fields (DDEFs) and its relationship with local time has also been considered.

Spatio-temporal features of ionospheric disturbances resulting from March 2023 geomagnetic storm: Comparisons with March 2015 St. Patrick’s Day storm

This study explores the ionospheric disturbances induced by the March 2023 geomagnetic storm, offering insights into the complex interplay between space weather events and the Earth’s upper atmosphere. In this regard, data from ionospheric maps (global and regional electron contents) and topside plasma density (provided by the Swarm satellites) have been used. Furthermore, the findings are compared with those of the March 2015 St. Patrick’s Day storm of solar cycle 24, which exhibited notably similar onset conditions. The Global Electron Content (GEC) displays substantial positive surges in the African, Pacific, and American sectors, with a notable enhancement in the American sector on March 24, 2023. During the recovery phase (March-23 storm), negative storm effects are observed across all longitudinal sectors, with greater intensity at low-latitudes compared to mid-latitudes. Moreover, the study highlights discrepancies in positive storm effects when compared to the St. Patrick’s Day storm. During the March-2023, there was no positive storm effect observed in the pacific mid-latitude regions. This longitudinal difference in occurrence of positive storm may be attributed to potential influences from variations in the z-component of the interplanetary magnetic field and energy inputs into the magnetosphere. A super fountain effect is observed exclusively in the American sectors during both storms, exhibiting a noticeable hemispheric asymmetry. The non-uniform planetary distribution of disturbed thermospheric winds likely played a major role in the ionospheric asymmetry in the American region during the 2023 event.

Analysis of fine-grained sediment dynamics from field observations with a vector autoregressive model

Empirical models and process-based theoretical or numerical models are valuable tools for analyzing sediment dynamics by reproducing the suspended sediment concentration (SSC) observations, but they are either limited by simplified assumptions or computationally complex. Some data-driven models have been developed to predict SSC but are still weak in explaining the causes of its changes. A field observation of silty sediment dynamics was performed in the Yellow River Delta, China. Then, a vector autoregressive (VAR) model is employed to analyze and predict the SSC variations. Using the impulse response function, the VAR model quantifies the transient and cumulative responses of the SSC to storm and current shocks (i.e., the impulse input), which reveals the key mechanisms of SSC changes from a new perspective. Results show that the storm effect on SSC in the study area lasts for ca. 85 h, and the SSC peaks at around the 36th hour with a cumulative increase of about 1.3 g/L due to the storm’s cumulative effect. Quantitative analysis reveals that waves contribute 73.78% to the SSC, while tidal currents contribute 26.22%. Fine SSC is significantly impacted by horizontal advection. A single reciprocal motion of the tidal current affects the SSC for approximately 6.25 h, while the overall duration exceeds 100 h. The proposed model physically reveals that the storm-induced high concentration gradient has been pushed back and forth by the tidal currents, affecting the SSC at the observation site, and the horizontal advection effect lasts for more than 100 h. Moreover, the VAR model achieves high-accuracy interval predictions of SSC for the next 5 h using only historical hydrodynamic data, with a 100% coverage probability of the 95% prediction interval. This paper provides a simple and efficient method for sediment dynamics analysis, which is of great significance for marine environment protection and disaster warning.

Modeling of PM10 Particulate Matter in Ahvaz City Using Remote Sensing and Meteorological Parameters

Introduction: In recent years, remote sensing (RS) products have emerged as effective tools for monitoring air pollution. This study aims to predict the concentrations of particulate matter with a diameter smaller than 10μm (PM10) using a multivariate linear regression (MLR) model, incorporating both Aerosol Optical Depth (AOD) products and meteorological parameters. Material and Methods: In this study, data on PM10 concentrations, Aerosol Optical Depth (AOD), and meteorological parameters (wind speed, temperature, humidity, and horizontal visibility) were used. The study focused on the time 15:00 each day, as this time was identified as having significant data relevance. The methodology section also consisted of three steps: 1) pairwise correlation analysis: The relationship between meteorological parameters, AOD, and PM10 was assessed using the pairwise correlation method. 2) Model development: A MLR model was developed to predict PM10 concentrations. 3) Validation: The model was validated using a separate dataset, ensuring that 70% of the data was used for training, and 30% for testing and validation. Results: The pairwise correlation analysis revealed a strong correlation (0.86) between AOD remote sensing index and PM10. The highest correlation (0.9) was observed during the spring season. The five developed equations to estimate the PM10 index yielded correlation coefficients ranging from 0.86 to 0.90. Notably, the highest correlation was achieved when AOD data and all the meteorological parameters were u tilized simultaneously. These results highlighted the utility of remote sensing products and meteorological data in air quality monitoring and prediction. Conclusion: This study demonstrates that a MLR model incorporating AOD and meteorological parameters can effectively predict PM10 concentrations in Ahvaz City, particularly during dust storms in hot seasons. These findings can aid policymakers and public health officials in developing strategies to mitigate the adverse effects of dust storms on air quality and public health.

Identifying Ionospheric Small‐Scale Currents: A Spatial Correlation Study Using Closely‐Spaced Pairs of Ground Magnetometers

AbstractThe occurrence of small‐scale and intense ionospheric currents that can contribute to geomagnetically induced currents have recently been discovered. A difficulty in their characterization is that their signatures are often only observed at single widely spaced (typically 200–400 km) ground geomagnetic stations. These small‐scale structures motivate the examination of the maximum station separation required to fully characterize these small‐scale signatures. We analyze distributions of correlation coefficients between closely spaced mid‐latitude and auroral zone ground magnetometer stations spanning day to month long intervals to assess the separation distance at which geomagnetic signatures appear in only one station. Distributions were analyzed using periods that included low and high geomagnetic activity. We used data from pairs of magnetometer stations across North America within 200 km of each other, all of which were separated primarily latitudinally. Results show that while measurements remain largely similar up to separations of 200 km, large and frequent differences appear starting at around 130 km separation. Larger differences and lower correlations are observed during high geomagnetic activity, while low geomagnetic activity leads to frequent high correlation even past 200 km separation. Small but identifiable differences can appear in magnetometer data from stations as close as 35 km during high geomagnetic activity. We recommend future magnetometer array deployment in the auroral and sub‐auroral zone to have separations of 100–150 km. This enables the monitoring of large scale effects of geomagnetic storms, better temporal and spatial resolution of substorms, and observations of small‐scale current signatures.

Dust Source Susceptibility in the Lower Mesopotamian Floodplain of Iraq

The identification of susceptible dust sources (SDSs) based on the analysis of effective factors (i.e. dust drivers) is considered to be one of the primary and cost-effective solutions to deal with this phenomenon. Accordingly, this study aimed to identify SDSs and delineate their drivers using remote sensing data and machine learning (ML) algorithms in a hotspot area in the Lower Mesopotamian floodplain in southern Iraq. To model SDSs, a total of 15 environmental features based on remote sensing data such as topographic, climatic, land use/cover, and soil properties were considered as dust drivers and fed into the four well-known ML algorithms, including linear discriminant analysis (LDA), logistic model tree (LMT), extreme gradient boosting (XGB)-Linear, and XGB-Tree-based. Dust emission hotspots were identified by visual interpretation of sub-daily MODIS-Terra/Aqua true color composite imagery (2000-2021) to train (70%) and validate (30%) ML algorithms. Considering the variability of the spatial-temporal patterns of SDSs as a result of changes in dust drivers, the modeling process was carried out in four periods, including 2000-2004, 2005-2007, 2008-2012, and 2013-2021. Our results show that dust events in the study area occur most frequently in April, June, July, and August. Overall, all ML algorithms performed well and provided reliable results for identifying SDSs. However, the XGB-Linear provided the most reliable results with an average area under curve (AUC) of 0.79 for the study periods. Precipitation was determined as the most important dust driver. The SDS maps produced can be used as a basis for the development of rehabilitation plans in the study area to mitigate the adverse effects of dust storms.

East–West Difference in the Ionospheric Response During the Recovery Phase of May 2024 Super Geomagnetic Storm Over the East Asian

AbstractIn this study, we offer an extensive examination of the F‐region ionospheric disturbances during the May 2024 superstorm, focusing primarily on the middle‐low latitude regions of East Asia. Our analysis is grounded in a wealth of data sources including Total Electron Content (TEC), ionospheric parameters NmF2 and hmF2, Electron Density Profile (EDP) retrieved from Radio Occultation (RO) data, and ∑[O]/[N2] from the Global Ultraviolet Imager (GUVI), among others, complemented by model simulations. The observed negative ionospheric storm effect, characterized by a significant and long‐lasting reduction in electron density across the entire China, commenced immediately following the sudden storm commencement (SSC) on 10 May and continued through the main and early recovery phase of the storm on 11 May. On 11–12 May, positive ionospheric storm impacts were initially observed in a restricted geographical area from the post‐midnight to sunrise, first manifesting over the eastern regions of China and then shifting to the central regions. Subsequently, a pronounced negative storm effect persisted throughout the later stages of recovery phase. In contrast, the western regions of China experienced a positive storm effect on 12 May followed by a comparatively mild negative storm phase. This persistent extensive zonal gradient in electron density across the East Asian region resembles the scenarios depicted in prior superstorms attributed to the thermospheric circulation patterns. The disparity in the ionospheric response from east to west in this area is probably a common feature during superstorms, potentially resulting from an arch‐shaped structure of elevated ∑[O]/[N2].

Effects of the Super Intense Geomagnetic Storm on 10-11 May, 2024 on Total Electron Content at Bhopal

The responses of the ionosphere near the equatorial anomaly crest, as observed at Bhopal (23.2° N, 77.4° E, GMLAT 14.2° N), India, for super intense geomagnetic storms that occurred from May 10 to 11, 2024 (Sym-H: −518 nT) were studied using the vertical total electron content (VTEC). The study is based on the TEC data recorded by a multi-frequency multi-constellation GNSS receiver installed in Bhopal since April 2016. It was observed that the super intense geomagnetic storm produced positive and negative impacts on VTEC. Approximately + 61 % deviation in VTEC was observed at ∼ 0500 UT on May 12, 2024 whereas approximately −68.5 % deviation in VTEC was observed at ∼ 2045 UT on May 11, 2024. The increase in VTEC (+24 % to 50 %) during the main phase of the storm can be attributed to the prompt penetration of electric fields (PPEFs) and suppression of VTEC during recovery phase of the storm can be attributed to the changes in the thermospheric composition and ionospheric disturbance dynamo electric fields (DDEF). Increase in VTEC and wave-like structures in the VTEC on May 12, 2024 may be due to the combined effect of DDEF and traveling ionospheric disturbances (TIDs) generated by this geomagnetic storm; however, a separate study is needed to fully characterize these TIDs. We also compared IRI2020 TEC with the observed VTEC and found that IRI-TEC model accurately estimated the suppression in TEC on May 11 and 12, but could not reflect on the wave-like structure observed in VTEC on May 12. The results are compared with the earlier observations and possible mechanism is also discussed.

An investigation into the influence of solar flares and geomagnetic storms on the F2 layer of the ionosphere in Western Europe during March 2024

On March 23 and 24, 2024, the M-class and X-class solar storms significantly intensified. Subsequently, from March 24 to 26, the monitoring station’s Kp index notably increased, at times exceeding 8, indicating that a severe geomagnetic storm was occurring. During this period, the IGS Global Ionosphere Maps (GIMs) displayed a substantial decrease in Total Electron Content (TEC) in Western Europe due to the impact of the intense geomagnetic storms. To investigate this occurrence, data from the Lowell GIRO Data Center were utilized. The analysis revealed that a concentrated solar flare eruption on March 23, 2024, triggered geomagnetic storm events on Earth, causing significant ionospheric anomalies in Western European countries. Specifically, compared to typical levels, parameters such as foF2, m3000F2, and TEC values experienced significant reductions in Western European countries. Moreover, the hmF2 data exhibited higher values at night and lower values during the day, deviating from the expected diurnal pattern. Analysis of the Rate of TEC Index (ROTI) indicated varying effects of geomagnetic storms across different latitudes in Western Europe. Through detailed graphical analysis, this study elucidates the alterations in the ionosphere under geomagnetic storm conditions in Western Europe, shedding light on the dynamic behavior of the ionosphere during a specific timeframe.

Observational Overview of the May 2024 G5-Level Geomagnetic Storm: From Solar Eruptions to Terrestrial Consequences

This study reports comprehensive observations for the G5-level geomagnetic storm that occurred from May 10 to 12, 2024, the most intense event since the 2003 Halloween storm. The storm was triggered by a series of coronal mass ejections (CMEs) originating from the merging of two active regions 13664/13668, which formed a large and complex photospheric magnetic configuration and produced X-class flares in early May 2024. Among the events, the most significant CME, driven by an X2.2 flare on May 9, caught up with and merged with a preceding slower CME associated with an X-class flare on May 8. These combined CMEs reached 1 AU simultaneously, resulting in an extreme geomagnetic storm. Geostationary satellite observations revealed changes in Earth’s magnetosphere due to solar wind impacts, increased fluxes of high-energy particles, and periodic magnetic field fluctuations accompanied by particle injections. Extreme geomagnetic storms resulting from the interaction of the solar wind with the Earth’s magnetosphere caused significant energy influx into Earth’s upper atmosphere over the polar regions, leading to thermospheric heating and changes in the global atmospheric composition and ionosphere. As part of this global disturbance, significant disruptions were also observed in the East Asian sector, including the Korean Peninsula. Ground-based observations show strong negative storm effects in the ionosphere, which are associated with thermospheric heating and resulting in decreases in the oxygen-to-nitrogen ratio (O/N2) in high-latitude regions. Global responses of storm-time prompt penetration electric fields were also observed from magnetometers over the East-Asian longitudinal sector. We also briefly report storm-time responses of aurora and cosmic rays using all-sky cameras and neutron monitors operated by the Korea Astronomy and Space Science Institute (KASI). The extensive observations of the G5-level storm offer crucial insights into Sun-Earth interactions during extreme space weather events and may help establish better preparation for future space weather challenges.

De novo design of miniprotein antagonists of cytokine storm inducers

Cytokine release syndrome (CRS), commonly known as cytokine storm, is an acute systemic inflammatory response that is a significant global health threat. Interleukin-6 (IL-6) and interleukin-1 (IL-1) are key pro-inflammatory cytokines involved in CRS and are hence critical therapeutic targets. Current antagonists, such as tocilizumab and anakinra, target IL-6R/IL-1R but have limitations due to their long half-life and systemic anti-inflammatory effects, making them less suitable for acute or localized treatments. Here we present the de novo design of small protein antagonists that prevent IL-1 and IL-6 from interacting with their receptors to activate signaling. The designed proteins bind to the IL-6R, GP130 (an IL-6 co-receptor), and IL-1R1 receptor subunits with binding affinities in the picomolar to low-nanomolar range. X-ray crystallography studies reveal that the structures of these antagonists closely match their computational design models. In a human cardiac organoid disease model, the IL-1R antagonists demonstrated protective effects against inflammation and cardiac damage induced by IL-1β. These minibinders show promise for administration via subcutaneous injection or intranasal/inhaled routes to mitigate acute cytokine storm effects.

Prospective impacts of windstorm risk on carbon sinks and the forestry sector: an integrated assessment with Monte Carlo simulations

Abstract Storms pose a significant threat to economic activities in the forest sector and introduce non-permanence risks for carbon stocks. Following escalating climate ambitions, understanding and addressing these risks becomes imperative. Uncertainties intrinsic to the storm phenomenon render this task complex. This study uses an integrated forest sector model to assess the economic and carbon impacts of storm regimes, emphasizing the importance of uncertainties through Monte Carlo simulation. From an economic perspective, we unravel complex interplays between the salvage and inventory effects of storms that lead to heterogeneous transfers of economic welfare across agents and space. Non-affected forest owners benefit from inflated prices, while affected owners’ recovery hinges on the magnitude of storm damage. From a climate perspective, storms significantly impact the forest sector’s carbon sink, with a high risk of falling short on mitigation objectives. In 25% of simulations, we observe a substantial 24% decrease in carbon sequestration. Our findings advocate for (1) conservative reliance on natural carbon sinks in national climate mitigation strategies toward net-zero, and (2) tailored risk-sharing insurance mechanisms for forest owners, providing a buffer against economic uncertainties arising from climatic disruptions.

Modeling the successive failure of complex dams systems: A necessity in the light of climatic shifts in extreme storms

This study investigates the effect of extreme storms on the safety of flood protection dams within complex schemes. A modeling methodology is developed using HEC-RAS 2D and presented on an illustrative case, where nine scenarios are simulated. The impact of climatic shifts on extreme storms and dams’ safety is highlighted. The results showed that, despite multi-reservoir systems can be efficient in flood protection against their design storms, they can cause catastrophic damage when subjected to extreme events that have become evident nowadays due to climatic shifts. This is attributed to the successive failure of the water storage structures in a progressive (“domino-like”) manner. The consequences of such breaches can significantly multiply the hazards. These findings can be linked to storm Daniel that occurred at Wadi Derna in Libya and caused two successive dam breaches, leaving a tragedy. The proposed modeling methodology can be useful for designers in evaluating complex scenarios.

Immuno-inflammatory and organ dysfunction markers in severe COVID-19 patients

Infection with the SARS-CoV-2 virus may induce some complications among people who experience mild to moderate respiratory illness and some of them recover without requiring special treatment. Albeit, some individuals become seriously reached risk points and require special medical attention especially older people and people who suffer from chronic diseases. Serum and whole blood samples were collected from confirmed infected persons with SARS CoV-2 by real-time PCR and the control group. All lab. Investigations were performed using Cobas 6000. Significant differences were noted between patients compared to the control group in the Mean ± SD of IL-6 (76.06 ± 7.60 vs 3.61 ± 0.296 pg/ml), Procalcitonin (0.947 ± 0.117 vs 0.061 ± 0.007 ng/ml), CRP (125.3 ± 7.560 vs 4.027 ± 0.251 mg/dl), ALT (154.8 ± 30.47 vs 49.75 ± 2.977 IU/L) and AST (70.83 ± 9.215 vs 27.23 ± 1.767) respectively. While other parameters were also showed significant differences were noted between patients compared to the control group for D-Dimmer, PT, PTT, LDH, Ferritin, WBC, Lymphocyte and Creatinine. The results reached that the effect of SARS CoV-2 and cytokine storm was clear on the body’s organs through vital biomarker investigations that were performed in this study.

Study of the Long-Lasting Daytime Field-Aligned Irregularities in the Low-Latitude F-Region on 13 June 2022

The unusual daytime F-region Field-Aligned Irregularities (FAIs) were observed by the HCOPAR and the satellites at low latitudes on 13 June 2022. These irregularities survived from night-time to the following afternoon at 15:00 LT. During daytime, they appeared as fossil structures with low Doppler velocities and narrow spectral widths. These characteristics indicated that they drifted along the magnetic field lines without apparent zonal velocity to low latitudes. Combining the observations of the ICON satellite and the Hainan Digisonde, we derived the movement trails of these daytime irregularities. We attributed their generation to the rapid ascent of the F-layer due to the fluctuation of IMF Bz during the quiet geomagnetic conditions. Subsequently, the influence of the substorm on the low-latitude ionosphere was investigated and simulated. The substorm caused the intense Joule heating that enhanced the southward neutral winds, carrying the neutral compositional disturbances to low latitudes and resulting in a negative storm effect in Southeast Asia. The negative storm formed a low-density circumstance and slowed the dissipation of the daytime FAIs. These results may provide new insights into the generation of post-midnight irregularities and their relationship with daytime fossil structures.

Towards sustainable coastal management: a hybrid model for vulnerability and risk assessment

This paper presents the development of a Hybrid Model (HM) integrated with a Bayesian Network (BN) for comprehensive coastal vulnerability and risk assessment, with a focus on Konyaaltı Beach, Antalya, Turkey. The HM incorporates critical environmental parameters such as wind, waves, currents, and sediment transport to simulate conditions at vulnerable coastal areas and perform risk assessments for storm effects, flooding, and erosion. The model includes submodules for predicting coastal storms, quantifying sediment transport rates, assessing tsunami inundation severity, and categorizing storms based on beach typologies. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized for significant wave height predictions, enhancing the model's accuracy. The integration of hydrodynamic modeling, Bayesian networks, and ANFIS offers a robust framework for assessing coastal vulnerability and informing sustainable management practices. The study's results highlight the necessity for integrated risk management strategies, including adaptive infrastructure design, zoning and land use regulations, ecosystem-based management, and continuous monitoring and model refinement to enhance coastal resilience against dynamic environmental forces. This research provides valuable insights for mitigating the impacts of hazards on urban developments, contributing to the advancement of sustainable coastal management.

Solar irradiance prediction with variable time lengths and multi-parameters in full climate conditions based on photovoltaic greenhouse

Photovoltaic power generation can provide energy for greenhouses and achieve high quality and high yield of crops. In reality, solar irradiance is fluctuating and intermittent. Thus, the key to ensure efficient photovoltaic power use under greenhouse environmental conditions is to provide an accurate prediction of solar irradiance. Yet, currently studies on irradiance prediction in terms of the variable time lengths, multi-parameter and full climate conditions are rather limited. To improve the comprehensive performance of the prediction model, this paper proposes models of irradiance time series prediction such as Pyraformer, Informer, Transformer and TimesNet. Those models were tested based on the synergistic combination of weather conditions (WC), sunshine time accumulation (STA/h), instantaneous total irradiance (ITI/(W/m2)) and irradiance daily accumulation (IDA/(MJ/m2)). The model performance was rigorously evaluated with 9 prediction lengths, 5 training days, 4 seasons and 5 days for 9 weather conditions. The results showed that the Transformer model had the best overall prediction performance for STA, ITI, IDA and WC at different time steps of 10 min to 24 h. All models were suitable for predicting time series within 1 h. However, TimesNet model was not suitable for predicting time series with steps outside 1 h. On the other hand, by using the sun combination dataset, the Transformer model had the best performance at a time step of 10 min. The mean absolute error (MAE), mean-square error (MSE), root mean squared error (RMSE) and coefficient of determination (R2) of ITI were 0.118 W/m2, 0.059 W/m2, 0.243 W/m2 and 93.9 %, respectively. When exploring the minimum dataset, with the increase of data samples, the prediction effect of TimesNet showed an increasing trend. While, Transformer had the best prediction effect for the dataset with one year of use. When exploring seasonality, Pyraformer model had the best prediction effect on winter and summer, and TimesNet had the best prediction effect on autumn and spring. Local prediction of 9 climate conditions showed that the effects of snow and dust storm were not ideal. The research results showed that the characterization factors that were closely linked to irradiance. The prediction scheme proposed in this paper combined the advantages of different time steps, different factor combination datasets, different data volumes and seasonality, which greatly improves the generalization ability of the model. This study can provide a reference for irradiance prediction and more refined management of photovoltaic (PV) greenhouse.