Search Results (124)

Evaporation Duct Height (EDH) is a crucial parameter in evaporation duct modeling, as it directly influences the strength of the waveguide trapping effect and significantly impacts the over-the-horizon detection performance of maritime radars. To address the limitations of low prediction accuracy and limited interpretability in existing deep learning models under complex marine meteorological conditions, this study proposes a surrogate model, BLA-EDH, designed to emulate the output of the Naval Postgraduate School (NPS) model for real-time EDH estimation. Experimental results demonstrate that BLA-EDH can effectively replace the traditional NPS model for real-time EDH prediction, achieving higher accuracy than Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models. Random Forest analysis identifies relative humidity (0.2966), wind speed (0.2786), and 2-m air temperature (0.2409) as the most influential environmental variables, with importance scores exceeding those of other factors. Validation using the parabolic equation shows that BLA-EDH attains excellent fitting performance, with coefficients of determination reaching 0.9999 and 0.9997 in the vertical and horizontal dimensions, respectively. This research provides a robust foundation for modeling radio wave propagation in the Yellow Sea and Bohai Sea regions and offers valuable insights for the development of marine communication and radar detection systems. Full article

Background: The emission of air pollutants into the atmosphere is a global issue as it contributes to global warming and climate-related issues. Human activities like the burning of fossil fuel influence changes in weather patterns—resulting in issues such as a rise in sea levels, among other things. Identifying road network routes within Northern Cape Province in South Africa that are less exposed to air pollutants like carbon monoxide is the issue this study seeks to address. Methods: The method used for our predictions is based on a graph convolutional network (GCN) and long short-term memory (LSTM). The GCN extracts geospatial characteristics, and the LSTM captures both nonlinear relationships and temporal dependencies in an air pollutant and meteorological dataset. Furthermore, an A* search strategy identifies the path from one location to another with the lowest carbon monoxide concentrations within a road network. The explainable artificial intelligence (xAI) technique is used to describe the nonlinear relationship between the target variable and features. Meteorological and air pollutant data in the form of statistical mean, minimum, and maximum values were leveraged, and a random sampling technique was utilized to fill the data gap to help train the predictive model (GCN-LSTM-A*). Results: The predictive model was evaluated with mean squared error (MSE) and root mean squared error (RMSE) values within two multi-time steps (8 and 16 h) with MSEs of 0.1648 and 0.1701, respectively. The LIME technique, which provides explanations of features, shows that Wind_speed and NO₂ and NOx concentrations decreased the predicted CO, whereas PM_2.5, PM₁₀, relative humidity, and O₃ increased the predicted CO of the route. Full article

The northern South China Sea has abundant frontal systems near coastal and island regions, which play crucial roles in regional ocean dynamics and ecosystem. While previous studies have established preliminary understanding of their spatial distribution, seasonal variability, and dynamic characteristics, the atmospheric response to these frontal systems remains poorly understood. This study integrates observations from a moored buoy deployed on the continental shelf of the South China Sea with satellite remote sensing data to analyze oceanic and atmospheric variations during frontal passage. The results reveal that the ocean front can not only induce pronounced oceanic changes characterized by significant cooling, saltiness, and surface current acceleration, but also exert substantial influence on the overlying atmosphere, with consistent decreasing trends in air temperature, humidity, and atmospheric pressure, all of which rapidly recovered following frontal retreat. Notably, when the front directly traversed the buoy location, diurnal temperature cycles were markedly suppressed, while turbulent heat flux and downfront wind-stress curl reached peak magnitudes. These findings demonstrate that ocean fronts and associated sea surface temperature gradients can trigger intense air–sea exchange processes at the ocean–atmosphere interface. Full article

Climate change in the Arctic is causing significant declines in sea ice extent and thickness. This study investigated lab-grownsea ice thickness using Linear Regression and three Machine Learning algorithms: Decision Tree, Random Forest, and Fully Connected Neural Network. To comprehensively track thin sea ice growth using various parameters, a combination of up to 13 radar and physical parameters including surface-based C-band NRCS values in VV, HH, and HV polarizations, air temperature, surface temperature, Cumulative Freezing Degree Moments, humidity, wind speed, surface cover salinity, ice surface salinity, bulk ice salinity, frost flower height and snow depth were input to the four multivariate models in two time series datasets. The results showed that Random Forest was the superior model, with =0.01 cm, for thicknesses of 1–8 cm and 27–47 cm. Using the Permutation Importance method, the role of the employed parameters in the thickness prediction process were ranked and showed that the key parameters were Cumulative Freezing Degree Moment, salinity parameters (surface cover, ice surface, and bulk ice salinities), and C-band co-polarized radar backscattering. The results of this study enhance thickness prediction capacity and accuracy, while providing insights for future research and real-time sea ice thickness prediction in Arctic regions. Full article

Sea breezes alleviate coastal heat stress via cooling and humidifying. Sendai, Japan, in 2015 had a population of 1.08 million and an area of 786 km². Integrating the WRF model with RayMan, this study employs the PET index to assess spatiotemporal distributions of thermal comfort and heat stress, and their influencing factors, on typical summer sea-breeze days in Sendai, Japan. Results indicate that in the coastal zone, PET was primarily regulated by air temperature (Ta) and relative humidity (RH). In contrast, wind speed was the dominant influence on urban/inland zones, with Ta and RH contributing more during the evening. Sea breezes markedly improved the thermal environment in the coastal zone, suppressing PET increases. PET in urban and inland zones exhibited an initial rise followed by a decline, with the inland zone experiencing sustained extreme heat stress for 3 h. Among regions experiencing extreme heat stress, inland zones showed the highest proportion (17.75%), while coastal zones had the lowest (2.14%). Proportions across the three zones were similar under nighttime conditions with no thermal stress, with the urban zone exhibiting a slightly lower proportion. This study provides a theoretical basis for climate-adaptive urban planning leveraging sea breezes as a resource. Full article

Urban particulate matter (PM) pollution critically impacts public health and climate. However, traditional ground-based monitoring fails to resolve vertical PM distribution, limiting understanding of transport and stratification-coupled mechanisms. Vertical profiles collected by an unmanned aerial vehicle (UAV) over Hangzhou, a core megacity in China’s Yangtze River Delta, reveal the spatiotemporal heterogeneity and multi-scale drivers of regional PM pollution during two intensive ten-day campaigns capturing peak pollution scenarios (winter: 17–26 January 2019; summer: 21–30 August 2019). Results show stark seasonal differences: winter PM₁ and PM_2.5 averages were 2.6- and 2.7-fold higher (p < 0.0001) than summer. Diurnal patterns were bimodal in winter and unimodal (single valley) in summer. Vertically consistent PM₁ and PM_2.5 distributions featured sharp morning (08:00) concentration increases within specific layers (winter: 250–325 m; summer: 350–425 m). Analysis demonstrates multi-scale coupling of synoptic systems, boundary layer processes, and vertical wind structure governing pollution. Key mechanisms include a winter “Transport-Accumulation-Reactivation” cycle driven by cold air, and summer typhoon circulation influences. We identify hygroscopic growth triggered by inversion-high humidity coupling and sea-breeze-driven secondary aerosol formation. Leveraging UAV-based vertical profiling over Hangzhou, this study pioneers a three-dimensional dissection of layer-coupled PM dynamics in the Yangtze River Delta, offering a scalable paradigm for aerial–ground networks to achieve precision stratified control strategies in megacities. Full article

The prevailing environmental conditions before and during the 28 Large Forest Fires (LFFs) that have occurred in the Canary Islands since 1983 are analyzed. These conditions are often associated with episodes characterized by the advection of continental tropical air masses originating from the Sahara, which frequently result in intense heatwaves. During the onset of the LFFs, the base of the subsidence thermal inversion layer—separating a lower layer of cool, moist air from an upper layer of warm, dry air—is typically located at an altitude of around 350 m above sea level, approximately 600 m below the usual average. Understanding these Saharan air advection events is crucial, as they significantly alter the vertical thermal structure of the atmosphere and create highly conducive conditions for wildfire ignition and spread in the forested mid- and high-altitude zones of the archipelago. Analysis of meteorological records from various weather stations reveals that the average maximum temperature on the first day of fire ignition is 30.3 °C, with mean temperatures of 27.4 °C during the preceding week and 28.9 °C throughout the fire activity period. Relative humidity on the ignition days averages 24.3%, remaining at around 30% during the active phase of the fires. No significant correlation has been found between dry or wet years and the occurrence of LFFs, which have been recorded across years with widely varying precipitation levels. Full article

Vibrio parahaemolyticus is a prevalent pathogen responsible for foodborne diseases in coastal regions. Understanding its dynamic relationship with various meteorological and marine factors is crucial for predicting outbreaks of bacterial foodborne illnesses. This study analyzes the occurrence of V. parahaemolyticus-induced foodborne illness in Zhejiang Province, China, from 2014 to 2018, using an 8-day time unit based on the temporal characteristics of marine products. The detection rate of V. parahaemolyticus exhibited a distinct cyclical pattern, peaking during the summer months. Meteorological and marine factors showed varying lag effects on the detection of V. parahaemolyticus, with specific lag periods as follows: sunshine duration (3 weeks), air temperature (3 weeks), total precipitation (8 weeks), relative humidity (7 weeks), sea surface temperature (1 week), and sea surface salinity (8 weeks). The SARIMAX model, which incorporates both marine and climatic factors, was developed to facilitate short-term forecasts of V. parahaemolyticus detection rates in coastal cities. The model’s performance was evaluated, and the actual values consistently fell within the 95% confidence interval of the predicted values, with a mean absolute error (MAE) of 0.047, indicating high accuracy. This framework provides both theoretical and practical insights for predicting and preventing future foodborne disease outbreaks. These findings can support food industry stakeholders—such as seafood suppliers, restaurants, regulatory agencies, and healthcare institutions—in anticipating high-risk periods and implementing targeted measures. These include enhancing cold chain management, conducting timely seafood inspections, strengthening cross-contamination controls during seafood processing, dynamically adjusting market surveillance intensity, and improving hygiene practices. In addition, hospitals and local health departments can use the model’s forecasts to allocate medical resources such as beds, medications, and staff in advance to better prepare for seasonal surges in foodborne illness. Full article

The critical consequence of climate change resulting from global warming is the increase in temperature. In combined cycle power plants (CCPPs), the Electric Power Output (PE) is affected by changes in both Ambient Temperature (AT) and Sea Surface Temperature (SST), particularly in plants utilizing seawater cooling systems. As AT increases, air density decreases, leading to a reduction in the mass of air absorbed by the gas turbine. This change alters the fuel–air mixture in the combustion chamber, resulting in decreased turbine power. Similarly, as SST increases, cooling efficiency declines, causing a loss of vacuum in the condenser. A lower vacuum reduces the steam expansion ratio, thereby decreasing the Steam Turbine Power Output. In this study, the effects of increases in these two parameters (AT and SST) due to global warming on the PE of CCPPs are investigated using various regression analysis techniques, Artificial Neural Networks (ANNs) and a hybrid model. The target variables are condenser vacuum (V), Steam Turbine Power Output (ST Power Output), and PE. The relationship of V with three input variables—SST, AT, and ST Power Output—was examined. ST Power Output was analyzed with four input variables: V, SST, AT, and relative humidity (RH). PE was analyzed with five input variables: V, SST, AT, RH, and atmospheric pressure (AP) using regression methods on an hourly basis. These models were compared based on the Coefficient of Determination (R²), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The best results for V, ST Power Output, and PE were obtained using the hybrid (LightGBM + DNN) model, with MAE values of 0.00051, 1.0490, and 2.1942, respectively. As a result, a 1 °C increase in AT leads to a decrease of 4.04681 MWh in the total electricity production of the plant. Furthermore, it was determined that a 1 °C increase in SST leads to a vacuum loss of up to 0.001836 bar_a. Due to this vacuum loss, the steam turbine experiences a power loss of 0.6426 MWh. Considering other associated losses (such as generator efficiency loss due to cooling), the decreases in ST Power Output and PE are calculated as 0.7269 MWh and 0.7642 MWh, respectively. Consequently, the combined effect of a 1 °C increase in both AT and SST results in a 4.8110 MWh production loss in the CCPP. As a result of a 1 °C increase in both AT and SST due to global warming, if the lost energy is to be compensated by an average-efficiency natural gas power plant, an imported coal power plant, or a lignite power plant, then an additional 610 tCO₂e, 11,184 tCO₂e, and 19,913 tCO₂e of greenhouse gases, respectively, would be released into the atmosphere. Full article

Climate conditions in combination with the concentration of pollutants increase the human health stress and exacerbate systemic diseases. The city of Rhodes is a desirable tourist destination that is located in a sensitive climate region of the southeastern Aegean Sea in the Mediterranean region. In this work, hourly recordings from a mobile air quality monitoring system, which is located in an urban area of Rhodes city, are employed in order to measure the concentration of regulated pollutants (${\mathrm{S}\mathrm{O}}_2,{\mathrm{N}\mathrm{O}}_2,{\mathrm{O}}_3,{\mathrm{P}\mathrm{M}}_{10} \mathrm{a}\mathrm{n}\mathrm{d} {\mathrm{P}\mathrm{M}}_{2.5})$ and meteorological factors (pressure, temperature, and relative humidity). The air quality health index (AQHI) and the discomfort index (DI) are calculated to study the impact of air quality and meteorological conditions on human health. The analysis is conducted during a hot summer period, from 29 June to 14 July 2024. During the second half of the studied period, a heatwave episode occurred that affected the bioclimatic conditions over the city. The results show that despite the fact that the concentration of pollutants is lower than the pollutant thresholds (according to Directive 2008/50/EC), the AQHI and DI conditions degrade significantly over the heatwave days. In particular, the AQHI is classified in the “Moderate” class, and the DI indicates that most of the population suffers discomfort. The AQHI and DI simultaneously increase during the days of the heat episode, showing a possible negative synergy for the health risk. Finally, both the day maximum and night minimum temperature are increased (about 0.8 and 0.6 °C, respectively) during the heatwave days as compared to the whole studied period. Full article

Cyclone Mocha, classified as an Extremely Severe Cyclonic Storm (ESCS), followed an unusual northeastward trajectory while exhibiting a well-defined eyewall structure. It experienced rapid intensification (RI) before making landfall along the Myanmar coast. It caused heavy rainfall (~90 mm) and gusty winds (~115 knots) over the coastal regions of Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) countries, such as the coasts of Bangladesh and Myanmar. The factors responsible for the RI of the cyclone in lower latitudes, such as sea surface temperature (SST), tropical cyclone heat potential (TCHP), vertical wind shear (VWS), and mid-tropospheric moisture content, are studied using the National Ocean and Atmospheric Administration (NOAA) SST and National Center for Medium-Range Weather Forecasting (NCMRWF) Unified Model (NCUM) global analysis. The results show that SST and TCHP values of 30 °C and 100 (KJ cm⁻²) over the Bay of Bengal (BoB) favored cyclogenesis. However, a VWS (ms⁻¹) and relative humidity (RH; %) within the range of 10 ms⁻¹ and >70% also provided a conducive environment for the low-pressure system to transform into the ESCS category. The physical mechanism of RI and recurvature of the Mocha cyclone have been investigated using forecast products and compared with Cooperative Institute for Research in the Atmosphere (CIRA) and Indian Meteorological Department (IMD) satellite observations. The key results indicate that a dry air intrusion associated with a series of troughs and ridges at a 500 hPa level due to the western disturbance (WD) during that time was very active over the northern part of India and adjoining Pakistan, which brought north-westerlies at the 200 hPa level. The existence of troughs at 500 and 200 hPa levels are significantly associated with a Rossby wave pattern over the mid-latitude that creates the baroclinic zone and favorable for the recurvature and RI of Mocha cyclone clearly represented in the NCUM analysis. Moreover the Q-vector analysis and steering flow (SF) emphasize the vertical motion and recurvature of the Mocha cyclone so as to move in a northeast direction, and this has been reasonably well represented by the NCUM model analysis and the 24, 7-, and 120 h forecasts. Additionally, a quantitative assessment of the system indicates that the model forecasts of TC tracks have an error of 50, 70, and 100 km in 24, 72, and 120 h lead times. Thus, this case study underscores the capability of the NCUM model in representing the physical mechanisms behind the recurving and RI over the BoB. Full article

In recent years, air pollution has become a significant issue for megacities. This study analyzed the air pollution levels in Tehran and the relationship between pollutant concentrations and atmospheric quantities during 2023. The correlation coefficients between wind speed, temperature, mean sea level pressure (MSLP), and relative humidity (RH) were calculated against the concentrations of NO₂, NO_x, PM₁₀, and PM_2.5. Additionally, one case study was conducted for each pollutant. Approximately 72% of haze phenomena in Tehran were recorded in November, December, and January. The monthly pattern of PM₁₀ concentration indicated higher levels in the southern and western parts of Tehran. For PM_2.5, in addition to these areas, significant concentrations were also observed in the central and eastern parts. NO₂ concentrations were found to be higher in the northeast and northern areas. An inverse relationship was found between wind speed and temperature with pollutant concentrations. Positive correlations between MSLP and pollutant concentrations suggested that the pollutant levels also increased as air pressure rose. RH showed a significant direct relationship with PM_2.5 and NO_x. Synoptic analysis revealed that PM₁₀ case studies often occurred during the warm season, with a thermal low pressure situated over the Iranian plateau. During PM_2.5 and NO₂ pollution events, Tehran was influenced by high pressure, and 10 m wind speeds were weak. Finally, verification of the 24 h forecast of the CAMS model showed that, while the model accurately predicted the spatial distribution of pollutants in most cases, it consistently underestimated the concentration levels. Full article

Global climate change projections highlight the Mediterranean Basin as one of the most susceptible areas to the effects of intense and prolonged droughts, as well as increasing air temperatures. Accordingly, the productivity and survival of forests in this area will depend on their ability to resist and adapt to increasingly drier conditions. Different climatic conditions across the Mediterranean Basin could drive differences in forest functioning, requiring trees to acclimate to them. Sea breeze dynamics along orographic valleys can also influence climatic conditions, accentuating differences between inland and coastal forests. However, there is limited information on whether the climatic factors regulating tree transpiration in Aleppo pine forest in orographic valleys vary according to climate. This study aims to identify and compare the climatic factors that regulate tree transpiration along a gradient and determine the thresholds at which these factors affect transpiration rates. This study was carried out by means of sap flow gauges, since this technique is a key feature for quantifying and understanding tree transpiration. It was conducted in two Aleppo pine dry sub-humid forests (inland and coastal, 750 and 675 trees ha⁻¹, respectively) and in two pine semi-arid forests (inland and coastal, 600 and 400 trees ha⁻¹, respectively) in the western Mediterranean basin during January–November of 2021. No significant rainfall events or droughts were recorded during the period of study, indicating a standard climatic condition in these areas. The main findings demonstrated that the variability in sap flow could be attributed to the interaction between soil water content and vapour pressure deficit in all the forests studied. However, the highest threshold values of these climatic factors in relation to the increase or decrease in maximum sap flow (i.e., less sensitivity) were exhibited in semi-arid forests, highlighting the adaptability of Aleppo pine to more limiting climatic conditions. These findings are relevant for the consequences of the predicted increase in harsh climatic conditions and the balance among vapour pressure deficit, temperature and soil water availability. Future research will be essential to confirm forest acclimatisation in the transitional dry to semi-arid forest ecosystems predicted by global climate change projections, given their potential to strongly alter ecosystem function and water cycles. Full article

Climatic changes lead to many extreme weather events throughout the globe. These extreme weather events influence our behavior, exposing us to different environmental conditions, such as poor indoor quality. Poor indoor air quality (IAQ) poses a significant concern in the modern era, as people spend up to 90% of their time indoors. Ventilation influences key IAQ elements such as temperature, relative humidity, and particulate matter (PM). Children, considered a vulnerable group, spend approximately 30% of their time in educational settings, often housed in old structures with poorly maintained ventilation systems. Extreme weather events lead young students to stay indoors, usually behind closed doors and windows, which may lead to exposure to elevated levels of air pollutants. In our research, we aim to demonstrate how real-time monitoring of air pollutants and other environmental parameters under extreme weather is important for regulating the indoor environment. A study was conducted in a school building with limited ventilation located in an arid region near the Red Sea, which frequently suffers from high PM concentrations. In this study, we tracked the indoor environmental conditions and air quality during the entire month of May 2022, including an extreme outdoor weather event of sandstorms. During this month, we continuously monitored four classrooms in an elementary school built in 1967 in Eilat. Our findings indicate that PM2.5 was higher indoors (statistically significant) by more than 16% during the extreme event. Temperature was also elevated indoors (statistically significant) by more than 5%. The parameters’ deviation highlights the need for better indoor weather control and ventilation systems, as well as ongoing monitoring in schools to maintain healthy indoor air quality. This also warrants us as we are approaching an era of climatic instability, including higher occurrence of similar extreme events, which urge us to develop real-time responses in urban areas. Full article

In summer, the urban heat island effect causes unbearable warmth in Shantou City, especially in the urban areas along the Inner Bay with densely populated neighborhoods. An investigation of the layout patterns of 100 residential neighborhoods along Shantou’s Inner Bay was conducted, leading to the establishment of four types and nine sub-types of idealized residential neighborhood models. Their wind speed, relative humidity, and air temperature were simulated in the ENVI-met software (version No.5.0.1). The simulation results show that high-rise buildings in the front areas play a decisive role in the overall microclimate environment. Accordingly, three principal drawbacks regarding neighborhood layout for thermal climate adaptation were extracted. Furthermore, by comparing the simulation results before and after modifying the layout of high-rise buildings, three spatial strategies to strengthen the humidification and cooling effect of sea–land breezes to optimize the outdoor microclimatic environment of neighborhoods were proposed, and these strategies were subsequently verified in the Golden-Harbor neighborhood. Full article