Search Results (143)

This study focuses on developing a climate-flood model to investigate and interpret the relationship and impact of climate on runoff/flooding at a sub-regional scale using multiple linear regression (MLR) with 30 years of hydro-climatic data for the Cross River Basin, Nigeria. Data were obtained from Nigerian Meteorological Agency (NIMET) for the following climatic parameters: annual average rainfall, maximum and minimum temperatures, humidity, duration of sunlight (sunshine hours), evaporation, wind speed, soil temperature, cloud cover, solar radiation, and atmospheric pressure. These hydro-meteorological data were analysed and used as parameters input to the climate-flood model. Results from multiple regression analyses were used to develop climate-flood models for all the gauge stations in the basin. The findings suggest that at 95% confidence, the climate-flood model was effective in forecasting the annual runoff at all the stations. The findings also identified the climatic parameters that were responsible for 100% of the runoff variability in Calabar (R² = 1.000), 100% the runoff in Uyo (R² = 1.000), 98.8% of the runoff in Ogoja (R² = 0.988), and 99.9% of the runoff in Eket (R² = 0.999). Based on the model, rainfall depth is the only climate parameter that significantly predicts runoff at 95% confidence intervals in Calabar, while in Ogoja, rainfall depth, temperature, and evaporation significantly predict runoff. In Eket, rainfall depth, relative humidity, solar radiation, and soil temperatures are significant predictors of runoff. The model also reveals that rainfall depth and evaporation are significant predictors of runoff in Uyo. The outcome of the study suggests that climate change has impacted runoff and flooding within the Cross River Basin. Full article

The long-term trend of coastal upwelling under global warming has been a research focus in recent years. Based on datasets including sea surface temperature (SST), sea surface wind, air–sea heat fluxes, ocean currents, and sea level pressure, this study explores the long-term trend and underlying mechanisms of the Oman coastal upwelling intensity in summer during 1993–2018. The results indicate a persistent decrease in SST within the Oman upwelling region during this period, suggesting an intensification trend of Oman upwelling. This trend is primarily driven by the strengthened positive wind stress curl (WSC), while the enhanced net shortwave radiation flux at the sea surface partially suppresses the SST cooling induced by the strengthened positive WSC, and the effect of horizontal oceanic heat transport is weak. Further analysis revealed that the increasing trend in the positive WSC results from the nonuniform responses of sea level pressure and the associated surface winds to global warming. There is an increasing trend in sea level pressure over the western Arabian Sea, coupled with decreasing atmospheric pressure over the Arabian Peninsula and the Somali Peninsula. This enhances the atmospheric pressure gradient between land and sea, and consequently strengthens the alongshore winds off the Oman coast. However, in the coastal region, wind changes are less pronounced, resulting in an insignificant trend in the alongshore component of surface wind. Consequently, it results in the increasing positive WSC over the Oman upwelling region, and sustains the intensification trend of Oman coastal upwelling. Full article

Objective: Our objective was to investigate the short-term effects of ambient ozone (O₃) meteorological factors and their interactions on hospitalizations for cerebral infarction in Zhengzhou, China. Methods: Daily data on air pollutants, meteorological factors, and hospitalization of cerebral infarction patients were collected from 1 January 2019 to 31 December 2023 in Zhengzhou, China. A generalized additive model was constructed to evaluate the association between ambient O₃ levels and hospitalization for cerebral infarction. A distributed lag non-linear model was applied to capture lagged and non-linear exposure effects. We further examined the modifying roles of temperature, humidity, wind speed, and atmospheric pressure, and conducted stratified analyses by sex, age, and season. Results: O₃ exposure was significantly associated with increased cerebral infarction risk, particularly during the warm season. A bimodal temperature-lag pattern was observed, as follows: moderate temperatures (10–20 °C) were associated with immediate effects, while cold (<10 °C) and hot (>30 °C) temperatures were linked to delayed risks. The association of O₃ and hospitalizations for cerebral infarction appeared stronger under high humidity, low wind speed, and low atmospheric pressure. Conclusions: Short-term O₃ exposure and adverse meteorological conditions are jointly associated with an elevated risk of cerebral infarction. Integrated air quality and weather-based warning systems are essential for targeted stroke prevention. Full article

The wake behavior of wind turbines in complex terrain is influenced by the combined effects of atmospheric turbulence and terrain features, which brings challenges to wind farm power production and safety. Despite extensive studies, there remains a gap in understanding the combined impact of turbulent inflows and terrain slopes on turbine wake behaviors. To address this, the current study conducted systematic wind tunnel experiments, using scaled wind turbines and terrain models featured both gentle and steep slopes. In the experiments, different turbulent inflows were generated and the wake characteristics of turbines located at different locations were analyzed. The results demonstrated that higher turbulence intensity accelerates wake recovery, and that steep slopes introduce distinctive wake patterns, including multi-peak added turbulence intensity profiles. Moreover, turbines on hilltops exhibited a more rapid wake recovery compared to those positioned in front of hills, a phenomenon attributed to the influence of adverse pressure gradients. This study provides pivotal experimental insights into the evolution laws of wind turbine wake over terrains under different turbulent inflow conditions, which are instrumental in wind turbine siting in complex terrains. Full article

Scrub typhus is an acute febrile vector-borne infectious disease caused by Orientia tsutsugamushi (O. tsutsugamushi) and transmitted through the bite of infected chigger mite larvae. Transmission involves complex ecological interactions among vectors, hosts, and environmental factors. Accumulating evidence indicates complex interactions between the scrub typhus incidence and multilevel environmental determinants, encompassing meteorological factors (e.g., temperature, humidity, precipitation, wind speed, sunshine duration, and atmospheric pressure), geographical conditions (e.g., topography, elevation, and landcover), and socioeconomic factors (e.g., economic level, cultural practices, residential conditions, and human behaviors). However, significant discrepancies persist among studies regarding the effect sizes and temporal associations, and the precise mechanisms remain incompletely elucidated. This review synthesizes the evidence on environment–disease relationships, clarifies the methodological inconsistencies, analyzes the potential sources of heterogeneity, and highlights the critical knowledge gaps to inform targeted prevention and control strategies and guide future research priorities. Full article

Building energy management is crucial in reducing energy consumption and maintaining occupant comfort, especially in heating systems. However, achieving optimal space heating efficiency while maintaining consistent comfort presents significant challenges. Traditional methods often fail to balance energy consumption with thermal comfort, especially across multiple zones in buildings with varying operational demands. This study investigates the role of deep learning models in optimizing space heating while maintaining thermal comfort across multiple building zones. It aims to enhance heating efficiency by developing predictive models for building temperature and heating consumption, evaluating the effectiveness of different deep learning architectures, and analyzing the impact of model-driven heating optimization on energy savings and occupant comfort. To address this challenge, this study employs Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer models to forecast area temperatures and predict space heating consumption. The proposed methodology leverages historical building temperature data, weather station measurements such as atmospheric pressure, wind speed, wind direction, relative humidity, and solar radiation, along with other weather parameters, to develop accurate and reliable predictions. A two-stage deep learning process is utilized: first, temperature predictions are generated for different building zones, and second, these predictions are used to estimate global heating consumption. This study also employs grid search and cross-validation to optimize the model configurations and custom loss functions to ensure energy efficiency and occupant comfort. Results demonstrate that the Long Short-Term Memory and Transformer models outperform the Gated Recurrent Unit regarding heating reduction, with a 20.95% and 20.69% decrease, respectively, compared to actual consumption. This study contributes significantly to energy management by providing a deep learning-driven framework that enhances energy efficiency while maintaining thermal comfort across different building areas, thereby supporting sustainable and intelligent building operations. Full article

The accuracy of ultrasonic rangefinders is crucial for mobile robotic navigation systems, yet environmental factors such as temperature, humidity, atmospheric pressure, and wind conditions can influence ultrasonic speed in the air. The primary objective is to investigate how environmental factors influence the output signal of an ultrasonic emitter and to develop a method for improving the accuracy of distance measurements in both outdoor and indoor settings. The research employs a combination of theoretical modeling, statistical analysis, and experimental validation. The research employs an ultrasonic rangefinder integrated with environmental sensors (BME280, Bosch Sensortec GmbH, Kusterdingen, Germany) and wind sensors (WMT700, WINDCAP^®, Vaisala Oyj, Vantaa, Finland) to account for environmental influences. Experimental studies were conducted using a prototype ultrasonic rangefinder, and statistical analysis (Student’s t-test) was performed on collected data. The results of estimation by Student’s t-test for 256 measurements demonstrate the maximum effect of air temperature and the minimum effect of relative air humidity on a piezoelectric emitter output signal both outdoors and indoors. In addition, wind parameters affect the rangefinder’s operation. The maximum range of obstacle detection depends on the reflection coefficient of the material that covers the obstacle. The results align with theoretical expectations for highly reflective surfaces. A cascade-forward artificial neural network model was developed to refine distance estimations. This study demonstrates the importance of considering environmental factors in ultrasonic rangefinder systems for mobile robots. By integrating environmental sensors and using statistical analysis, the accuracy of distance measurements can be significantly improved. The results contribute to the development of more reliable navigation systems for mobile robots operating in diverse environments. Full article

This study introduces the profile method as a simple and less expensive approach for estimating the surface energy balance of green roofs, addressing the limitations of costly monitoring systems based on measurements at two vertical points. Four separate experiment buildings were constructed to minimize temperature disturbances: concrete, highly reflective painted, short bamboo, and grass-roofed. This setup allowed the evaluation of the thermal performance of each roof type without interference from connected building structures. The flux profile method was used to estimate sensible and latent heat fluxes using temperature, atmospheric pressure, and wind speed measurements at two elevations and demonstrated its potential applicability. The results showed that the sensible heat flux was highest (103.81 W/m²) for the concrete roof and that the latent heat flux was highest (53.28 W/m²) for the short bamboo roof. These results indicated the reliability of the method in estimating fluxes across all roof types, where the Nash–Sutcliffe efficiency was 0.90 on average. Furthermore, sensitivity analysis showed that the optimal values of albedo and surface roughness for each roof type were within reasonable physical ranges, providing additional validation for the flux profile method. The surface energy balance analysis of green roofs indicates that the profile method could serve as an effective tool for quantitatively evaluating the advantages of green roofs, especially in reducing urban heat island effects and lowering building energy consumption. Full article

A novel hollow capacitive wind pressure sensor is for the first time proposed. The sensing element of the proposed sensor uses a non-parallel plate variable capacitor, whose movable electrode plate uses a transversely uniformly loaded annular conductive membrane with a fixed outer edge and a rigid inner edge (acting as the wind pressure sensitive element of the sensor). Due to the unique hollow configuration of the proposed sensor, it can be used alone to detect the pressure exerted by fast-moving air in the atmosphere or by fast-moving air or gas, etc., in pipes, but it also can be used in pairs to measure the flow rate of fast-moving air or gas, etc., in pipes. The analytical solution of the large deflection elastic behavior of the transversely uniformly loaded annular conductive membrane is derived by using a new set of membrane governing equations. The effectiveness of the new analytical solution is analyzed. The new membrane governing equations are compared with the previous ones to show the differences between them. The superiority of the new analytical solution over the existing ones is analyzed. An example is given to demonstrate the numerical design and calibration of the proposed sensor and the effect of changing design parameters on the important capacitance–pressure (C–q) analytical relationship of the proposed sensor is investigated comprehensively. Finally, an experimental verification of the analytical solution derived is carried out. Full article

Over the past decade, China’s air quality has improved significantly. To further mitigate the concentration levels of fine particulate matter (PM_2.5), this study analyzed the spatio-temporal evolution of PM_2.5 concentrations from 2012 to 2022. Furthermore, the study integrated the generalized additive model (GAM) and GeoDetector to investigate the main driving factors and explored the complex response relationships between these factors and PM_2.5 concentrations. The results showed the following: (1) The annual average concentration of PM_2.5 in China peaked in 2013. The annual reductions of PM_2.5 in each city ranged from 1.48 to 7.33 μg/m³. In each year, the PM_2.5 concentrations were always consistently higher in north and east China and lowest in northeast and southwest China. (2) In terms of spatial distribution, the North China Plain, the Middle and Lower Yangtze River Plain, and the Sichuan Basin exhibited the highest PM_2.5 concentration levels and showed high aggregation characteristics. (3) The GeoDetector analysis identified the concentrations of SO₂, NO₂, and CO and the meteorological conditions as important factors influencing the spatial differentiation of PM_2.5. The results of the GAM showed that the meteorological factors, such as temperature, atmospheric pressure, wind speed, and precipitation, generally had specific inflection points in their effects on the PM_2.5 concentration levels. The relationship of PM_2.5 with the gross domestic product and population density followed an inverted U shape. The PM_2.5 concentrations under the land use types of cropland, barren, impervious, and water were higher than others. The concentration of PM_2.5 decreased significantly under all land use types. Our work can be used as a strong basis for providing insights crucial for developing long-term pollution control strategies and promoting environmental sustainability. Full article

This study introduces a tornado perturbation model utilizing the cyclostrophic wind model, implemented through a shallow-water equation framework. Four numerical experiments were conducted: a single cyclonic wind perturbation (EXP1), a single low-geopotential height perturbation (EXP2), a cyclonic wind perturbation with a 0 Coriolis parameter (EXP3), and a single anticyclonic wind perturbation (EXP4). The outputs showed that in a static atmosphere setting, a small-scale cyclonic wind perturbation generated a tornado-like pressure structure. The centrifugal force in the central area exceeded the pressure gradient force, causing air particles to flow outward, leading to a pressure drop and strong pressure gradient. The effect of the Coriolis force is negligible for meso-γ-scale and smaller systems, while for meso-β-scale and larger systems, it begins to have a significant impact. The results indicate that a robust cyclonic and an anticyclonic wind field can potentially generate a pair of cyclonic and anticyclonic tornadoes when the horizontal vortex tubes in an atmosphere with strong vertical wind shear tilt, forming a pair of positive and negative vorticities. These tornadoes are similar but have different rotation directions. Full article

Because PM_2.5 concentrations are not regularly monitored in Mbarara, Uganda, this study was implemented to test whether correlations exist between weather parameters and PM_2.5 concentration, which could then be used to estimate PM_2.5 concentrations. PM_2.5 was monitored for 24 h periods once every week for eight months, while weather parameters were monitored every day. The mean dry and wet season PM_2.5 concentrations were 70.1 and 39.4 µg/m³, respectively. Diurnal trends for PM_2.5 levels show bimodal peaks in the morning and evening. The univariate regression analysis between PM_2.5 and meteorological factors for the 24 h averages yields a significant correlation with air pressure when all data are considered, and when the data are separated by season, there is a significant correlation between PM_2.5 concentration and wind speed in the dry season. A strong correlation is seen between diurnal variations in PM_2.5 concentration and most weather parameters, but our analysis suggests that in modeling PM_2.5 concentrations, the importance of these meteorological factors is mainly due to their correlation with underlying causes including diurnal changes in the atmospheric boundary layer height and changes in sources both hourly and seasonally. While additional measurements are needed to confirm the results, this study contributes to the knowledge of short-term and seasonal variation in PM_2.5 concentration in Mbarara and forms a basis for modeling short-term variation in PM_2.5 concentration and determining the effect of seasonal and diurnal sources on PM_2.5 concentration. Full article

The aerodynamic performance of twin tall buildings immersed in the atmospheric boundary layer was numerically investigated by adopting the spatial-averaged large eddy simulation (LES) method. This study focused on the effects of corner cutting and chamfering. The buildings were both square and sectional with a width-to-height ratio of 1:6, and were arranged in a tandem configuration with a spacing ratio of 2.0. The corner-cutting and chamfering measures were only applied to the upstream cylinder, with a corner modification rate of 10%. To generate the turbulent inflow boundary condition (IBC) for LES, steady-state equilibrium IBC expressions were introduced into the vortex method, which were implemented in the commercial code Ansys Fluent. The present simulation method and solution parameters were first verified by comparing the simulated wind field and the wind pressure distribution on a single tall building with those of the wind tunnel test. The influences of the corner-cutting and chamfering measures on the wind load of the tandem buildings were then comparatively studied concerning the statistical values of their aerodynamic force coefficients and wind pressure coefficients. The influence mechanism was analyzed based on the simulated time-averaged flow field and the instantaneous vortex structure around the buildings. The results indicated that upstream corner-cutting and chamfering measures can induce a diffusion angle shift in the separated shear flow from the leading edge of the upstream building, thus affecting the separation and reattachment of the separated upstream flow on the downstream building. Among the measures studied, upstream corner cutting is more effective in reducing wind pressure and aerodynamic force coefficients. Full article

Weather and air quality conditions have been anecdotally reported to be related to symptom fluctuations in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), but this has never been empirically investigated. This exploratory study aims to examine the effects of weather and air quality on daily fluctuations of chronic pain and fatigue in women with ME/CFS. In an intensive longitudinal design, 58 participants with ME/CFS provided daily pain and fatigue ratings for an average of 61 days. Daily weather and air quality data were obtained from the National Oceanic and Atmospheric Administration and the US Environmental Protection Agency for the Birmingham, AL area. Linear mixed models revealed a significant relationship between days with more severe pain and worse Air Quality Indices (AQI, p < 0.001), lower wind speeds (p = 0.009), greater particulate matter (p = 0.037), and lower carbon monoxide (p = 0.004), sulfur dioxide (p = 0.003), and ozone levels (p = 0.015). Greater fatigue was associated with more particulates (p = 0.023) and lower barometric pressure (p = 0.048). These results suggest that air quality and weather can have small effects on ME/CFS symptom severity. Full article

The accurate prediction of PM_2.5 concentration across extensive temporal and spatial scales is essential for air pollution control and safeguarding public health. To address the challenges of the uneven coverage and limited number of traditional PM_2.5 ground monitoring networks, the low inversion accuracy of PM_2.5 concentration, and the incomplete understanding of its spatiotemporal dynamics, this study proposes a refined PM_2.5 concentration estimation model, Bi-LSTM-SA, integrating multi-source remote sensing data. First, utilizing multi-source remote sensing data, such as MODIS aerosol optical depth (AOD) products, meteorological data, and PM_2.5 monitoring sites, AERONET AOD was used to validate the accuracy of the MODIS AOD data. Variables including temperature (TEMP), relative humidity (RH), surface pressure (SP), wind speed (WS), and total precipitation (PRE) were selected, followed by the application of the variance inflation factor (VIF) and Pearson’s correlation coefficient (R) for variable screening. Second, to effectively capture temporal dependencies and emphasize key features, an improved Long Short-Term Memory Network (LSTM) model, Bi-LSTM-SA, was constructed by combining a bidirectional LSTM (Bi-LSTM) model with a self-adaptive attention mechanism (SA). This model was evaluated through ablation and comparative experiments using three cross-validation methods: sample-based, temporal, and spatial. The effectiveness of this method was demonstrated on Beijing–Tianjin–Hebei urban agglomeration, achieving a coefficient of determination (R²) of 0.89, root mean squared error (RMSE) of 12.76 μg/m³, and mean absolute error (MAE) of 8.27 μg/m³. Finally, this model was applied to predict PM_2.5 concentration on Beijing–Tianjin–Hebei urban agglomeration in 2023, revealing the characteristics of its spatiotemporal evolution. Additionally, the results indicated that this model performs exceptionally well in hourly PM_2.5 concentration forecasting and can be used for PM_2.5 concentration hourly prediction tasks. This study provides technical support for the large-scale, accurate remote sensing inversion of PM_2.5 concentration and offers fundamental insights for regional atmospheric environmental protection. Full article