Search Results (2,924)

This study is the first to assess airborne allergenic pollen in Erzincan, a continental climate region with mixed native vegetation, agricultural land, and urban areas, using volumetric sampling and comprehensive statistical models. Weekly samples from 2022 to 2023 were converted to 24 h slides and examined microscopically, and concentrations were calculated as pollen/m³ according to REA methodology. A total of 730 days of observations were evaluated. The annual pollen integrals were calculated separately for each year, and their combined total was 22,225 pollen*day/m³ (2022: 8667; 2023: 13,558). Woody pollen comprised 53.67% (2022) and 51.86% (2023); herbaceous pollen comprised 46.01% (2022) and 48.07% (2023). Thirty six pollen taxa were identified; the peak months were May 2022 (24.5%) and June 2023 (27.5%). Daily total pollen concentration was correlated positively with temperature (r_s = 0.33, p < 0.001) and weakly with wind speed (r_s = 0.17, p < 0.001) but negatively correlated with humidity (r_s = −0.26, p < 0.001). Precipitation showed no significant association with pollen levels (r_s = −0.02, p = 0.13), indicating that warmer and drier conditions favor higher pollen concentrations. The LMM showed that pollen density was affected by meteorological variables as well as interannual climatic variation, with temperature being the positive predictor (β = 0.348; p < 0.001). Canonical correlation analysis revealed a significant, multidimensional relationship between meteorological variables (temperature, humidity, rainfall, wind) and the daily densities of eight pollen taxa (Wilks’ λ = 0.603; p < 0.001). Canonical Correspondence Analysis indicated that meteorological variables influenced pollen taxon composition (F = 20.235, p = 0.001), explaining 11.51% of total inertia and suggesting that additional ecological factors may also contribute. In conclusion, pollen dynamics are sensitive not only to seasonal and meteorological variables but also to interannual climate fluctuations; in particular, hot, dry periods prolong the persistence of airborne pollen, while short-term rainfall can trigger sudden pollen releases in some species. Full article

Near-surface temperature inversions are prevalent during the cold months in Ulaanbaatar city, Mongolia, and significantly degrade urban air quality by trapping hazardous pollutants within a shallow atmospheric boundary layer. This study investigates spatiotemporal variability, physical mechanisms, and long-term evolution of near-surface temperature inversions over Ulaanbaatar by integrating 25 years (2000–2024) of ground-based meteorological and radiosonde observations, with high-resolution Weather Research and Forecasting (WRF) model simulations for 2012–2023. Our results demonstrate the four-dimensional data assimilation (FDDA) grid nudging effectively captures localized topographic influences in the WRF simulations, showing a strong agreement with radiosonde observations (R² = 0.783, p < 0.000). Near-surface temperature inversions are strongly controlled by the Siberian High, with the highest frequency occurring from December to February, when up to 67% of morning observations exhibit inversion conditions. A pronounced diurnal cycle was identified, with inversion intensity peaking at 5.6–6.8 °C during the early morning hours (02:00–08:00 LST) before reaching a minimum around 14:00 LST. Spatially, the strongest inversions occur along the low-lying Tuul River valley, where the planetary boundary layer is compressed to below 350 m and wind speeds decrease to less than 2.4 m·s⁻¹, creating persistent atmospheric stagnation. Despite these favorable conditions for inversion formation, long-term observations indicate that regional warming (+2.0 °C) and the urban heat island effects have reduced inversion frequency by 31%, inversion thickness by 170 m, and inversion intensity by 0.9 °C over the past 25 years. These findings demonstrate the strong coupling between regional complex terrain, and boundary layer thermodynamics, highlighting the need to incorporate urban ventilation corridors and topography-informed planning into climate adaptation and winter air-quality management strategies. Full article

Wildfires often lead to extreme environmental impacts, as well as to loss of life, injuries, and widespread destruction of homes and critical infrastructure. Regardless of their increasing impact, these complex and rapidly evolving events are still not fully understood. Better understanding of the drivers of fire behaviour is essential for fire danger prediction and to support operational decision-making. This study analyses the main environmental factors influencing the rate of spread (ROS) using a machine learning approach applied to the PT-FireSprd database. Fire progression polygons were complemented with meteorological and landscape variables, and fire danger indices. Results indicate that near-surface wind speed and dead fuel moisture content are key drivers of ROS, while the Hot-Dry-Windy Index shows the strongest correlation. The predictive model explains 55.6% of ROS variability. Clustering the main drivers reveals six fire propagation patterns characterised by different combinations of environmental conditions. Full article

Mass concentrations of particulate matter are a fundamental metric for air quality and health impact assessment; however, they are insufficient for accurately characterizing exposure. They do not capture particle size distribution or number concentration. Therefore, aerosol assessment should include particle number concentration (PNC), which better represents toxicologically relevant fractions and enables more precise source identification. The aim of this study was to conduct a comprehensive evaluation of particle number concentration (PNC) measurements in the 0.3–10 µm size range obtained using three low-cost Sensirion SPS30 particle sensors under field conditions in an urban environment. The analyses included an assessment of agreement between the SPS30 sensors, an evaluation of their measurement performance against the OPS 3330 optical particle spectrometer, and the development of calibration models. The SPS30 sensors showed high inter-device repeatability for PNC in the 0.3–1.0 µm range (CV_d < 2%). However, measurement performance declined with increasing particle size, with the index of agreement (IOA) decreasing from 0.8 (0.3–0.5 µm) to −0.5 (2.5–10 µm). Sensor accuracy was influenced by meteorological conditions: relative humidity primarily affected short-term variability (precision and dynamic agreement), while temperature controlled systematic bias. Although incorporating these variables into advanced calibration models improved performance, SPS30 sensors remained unsuitable for PNC measurements in the 2.5–10 µm range, exhibiting systematic errors of ~25% even after nonlinear correction. The findings support the responsible use of low-cost particle sensors for supplementary air quality monitoring, contributing to accessible environmental data and sustainable urban air quality management. Full article

Wildlife-vehicle collisions (WVCs) represent an important ecological and road-safety problem, yet the influence of short-term meteorological variability on their occurrence remains insufficiently understood. This study investigated the associations between temperature extremes, abrupt temperature changes, and broader hydro-meteorological conditions and reported WVC frequency in Serbia over a 10-year period (2016–2025). A municipality-day dataset comprising 6281 police-reported WVCs was analyzed using an event-based methodology. Exploratory analyses were complemented by Poisson regression models to evaluate prolonged temperature episodes, abrupt temperature shocks, and broader hydro-meteorological conditions, while additional regional analyses examined the consistency of the observed associations across Serbia. The results demonstrated pronounced seasonal variation, with the highest reported WVC frequencies occurring during spring and late autumn. At the national level, prolonged cold episodes were associated with significantly lower reported WVC frequencies during their onset and middle phases, whereas heat episodes showed no significant associations. Abrupt cool-down shocks were associated with a short-term increase in reported WVC frequency on the day of the temperature decrease, while warm-up shocks showed no significant effects. Moderate and heavy precipitation, snow-day conditions, and prolonged dry spells were associated with reduced reported WVC frequency, whereas daily mean temperature and atmospheric pressure were not independently associated with reported WVC frequency. Regional analyses generally supported the national findings, although no regional associations remained statistically significant after false discovery rate correction. These findings demonstrate that short-term meteorological variability is associated with reported WVC frequency in a temporally dependent manner and highlight the importance of considering both environmental events and regional variability when investigating wildlife-vehicle collisions. Full article

Heatwaves are becoming more frequent and intense, with well-established impacts on mortality and heat-related illness. However, their influence on unintentional injuries, including falls, is less understood. This study examined the association between heatwaves and fall-related ambulance attendances in Queensland, Australia. A retrospective population-based study was conducted using Queensland Ambulance Service attendance data from 2010–2019. Attendances were classified as fall-related if a fall was indicated in any of the dispatch, diagnosis, injury-cause, or case-classification fields, regardless of if this was based on caller report or paramedic assessment. Heatwaves were defined using the Bureau of Meteorology’s Excess Heat Factor and classified by severity. Incidence rate ratios (IRRs) compared fall-related ambulance attendances on heatwave and non-heatwave days, with stratification by age, sex, rurality, heatwave severity, and warm-season month. Across the study period, 323,254 fall-related ambulance attendances were recorded. Overall incidence did not differ between heatwave and non-heatwave days (IRR = 1.00; 95% CI: 0.99–1.00). Extreme heatwaves were associated with an 11% increase in attendances (IRR = 1.11; 95% CI: 1.06–1.17), with elevated risks observed among adults aged 45–74 years and residents of outer regional areas. Extreme heatwaves contribute to increased fall-related ambulance demand in Queensland. Recognising falls as part of the broader heat-related morbidity burden allows for targeted prevention strategies and enhances preparedness for extreme heat events. Full article

Accurate and interpretable forecasting of meteorological variables is essential for environmental monitoring and for the development of reliable decision-support systems. This study proposes an explainable multi-step deep learning framework for forecasting daily mean air temperature in central Saudi Arabia. The dataset comprises daily observations collected from 2020 to 2024, including air temperature, atmospheric pressure, relative humidity, and rainfall. Historical measurements from a 30-day lookback window were used to generate direct forecasts for 7-day and 30-day horizons. Four forecasting approaches were evaluated: Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), one-dimensional Convolutional Neural Network (CNN1D), and Transformer. Forecasting performance was assessed using Mean Absolute Error, Root Mean Square Error, and Mean Absolute Percentage Error, together with Taylor diagrams, residual distributions, and observed-versus-predicted analyses. The LSTM model achieved the highest predictive accuracy, obtaining MAE and RMSE values of 2.139 °C and 2.953 °C, respectively, for the 7-day horizon, and 2.345 °C and 3.134 °C for the 30-day horizon. To investigate model behavior, seven complementary explainable artificial intelligence methods were applied, including Integrated Gradients, Grad-CAM, SHAP, LIME, permutation importance, occlusion sensitivity, and saliency maps. These methods were selected to provide global feature-level, local prediction-level, and temporal explanations. The results show that recent air-temperature observations dominate short-term forecasts, whereas atmospheric pressure and relative humidity exhibit greater relative influence at the longer forecasting horizon. Rainfall contributes less consistently because of its sparse distribution within the study region. Overall, the proposed framework combines multi-horizon forecasting with comprehensive interpretability, providing a transparent approach for meteorological prediction and a methodological foundation for future environmental forecasting systems that integrate meteorological and pollutant observations. Full article

Cooling electricity consumption in central air-conditioning systems of large public buildings accounts for a substantial share of urban electricity use and is strongly influenced by outdoor meteorological conditions. Under increasingly frequent extreme summer heat events, accurate cooling-load forecasting is important for HVAC operation, building energy management, urban electricity security, and power-system planning. This study investigates the effects of measured outdoor meteorological inputs on cooling-load forecasting for a large public building in Guangzhou. Consecutive hourly cooling-load data and measured meteorological data, including outdoor air temperature, relative humidity, solar radiation, wind speed, and wind direction, were collected from June to September 2022. The corresponding 2023 dataset was analyzed separately using the same modeling and evaluation procedure to assess cross-year repeatability; data from the two years were not combined. Correlation and univariate linear regression analyses were first used for preliminary candidate-input screening. Nine Long Short-Term Memory sub-models with different meteorological input combinations were then developed and compared using the 2022 dataset, and the selected input configuration was subsequently re-evaluated using the separate 2023 dataset. Solar radiation exhibited the strongest marginal association with cooling load, followed by outdoor air temperature and relative humidity. The negative association of relative humidity reflected its coupled variation with temperature and solar radiation during the investigated summer period. For the 2022 dataset, the model using outdoor air temperature, relative humidity, and solar radiation achieved the lowest MAPE. Compared with the model using all five meteorological variables, it reduced MAE, RMSE, and MAPE by 14.55%, 7.24%, and 19.07%, respectively, while R² increased from 0.9542 to 0.9601. Evaluation using the 2023 dataset showed corresponding reductions of 20.01%, 18.37%, and 25.81% in MAE, RMSE, and MAPE, respectively, together with an increase in R² from 0.9592 to 0.9708. Full article

Accurately predicting soil moisture conditions and determining the optimal sowing timing during the spring sowing period play a crucial role in ensuring high and stable grain yields. To address the limitations of existing soil moisture prediction models, i.e., the complexity and parameterization challenges of hydrological models, the weak theoretical foundations of statistical models, and the data dependence, lack of interpretability, and poor generalization of machine learning approaches, a prediction model for surface soil water content (SWC) was developed in this study. The model is based on the water balance principle and uses a stepwise correction approach with normalized functions of key influencing factors. The results are as follows: (1) The model requires readily available parameters from public databases and is driven by daily scale meteorological variables (precipitation, temperature, wind speed, and vapor pressure deficit), facilitating its integration into existing operational weather forecasts. (2) After parameterization, only the moisture exchange between surface and deep soil layers needs optimization, resulting in low computational demand. (3) A trial in Shenyang region showed that the model explains 82.1% of the SWC variance, with an RMSE of 1.7% for 1–7 day lead predictions. (4) When applied to regions without initial soil moisture observations, the model achieves satisfactory accuracy after an initial condition sensitivity period of approximately 40 days. These results provide a methodological reference for soil moisture prediction studies and offer technical support for meteorological services to integrate soil moisture forecasting into their operational frameworks. Full article

This study quantifies and assesses the gap between the perceptions of 550 farming households on the Caribbean island of Hispaniola and the opinions of 60 regional experts on climate change. This quantification draws on the traditional knowledge of farming households, which is based on environmental observations, including meteorological, biological, and astrological ones, as well as on the experts’ opinions, which are grounded in scientific observations. To measure this gap, we asked 24 identical questions to both the experts and the farming households, using the experts’ responses as a benchmark. The experts’ statements were used as a reference to reflect the reality of climate change, given that the majority of experts’ answers converge. We then quantified the average distance for each farming household relative to these references. Fisher’s exact test and Pearson chi-square (χ²) test were used to assess this distance. A binary regression model was then used to identify the main factors influencing the gap in farming households’ perceptions, as well as to examine whether this gap is associated with greater socioeconomic vulnerability. The results revealed that in Haiti, 70% of farming households had a different perception from experts’one. In the Dominican Republic, this proportion was 47.50%. Vulnerable (OR = 12.94, p < 0.001) and very vulnerable (OR = 4.18, p < 0.05) farming households were more likely to have a different perception of climate change compared to experts. Full article

River-channel washout is one of the common geological hazards threatening the safety of long-distance oil and gas pipelines, particularly under storm-flood conditions, when pipeline sections crossing rivers and gullies are more susceptible to damage. Existing assessment methods for river-channel washout are effective for single river cross-sections or post-disaster field investigations; however, their application remains limited when dealing with long-distance pipeline systems characterized by numerous river- and gully-crossing sections and large spatial variability in upstream catchment conditions. To address this issue, this study investigates storm-flood discharge and scour-depth calculation methods suitable for river- and gully-crossing sections of long-distance oil and gas pipelines, and establishes a quantitative evaluation index system that considers river-channel washout susceptibility, pipeline vulnerability, and pipeline failure consequences. Based on investigation results of river-channel washout hazards along multiple pipeline systems, including the Zhongxian–Yichang section of the Zhongwu Pipeline, the Hubei–Hunan section of the Lanzhou–Zhengzhou–Changsha Pipeline, and the Phase I Jiangxi Natural Gas Pipeline Network, the hazard characteristics and influencing factors of river-channel washout affecting long-distance oil and gas pipelines are analyzed and summarized. The proposed method was applied to 19 river- and gully-crossing pipeline sections in the Phase I Jiangxi Natural Gas Pipeline Network under different rainfall intensities. The results show that, under light-to-moderate rainfall conditions, 15 sites were classified as relatively low risk and 4 sites as medium risk. Under both the 50-year and 100-year return-period rainstorm scenarios, 12 sites were classified as relatively low risk, 6 sites as medium risk, and 1 site as relatively high risk. The results also indicate that the risk probability of some sites increases with increasing rainfall intensity. Among them, Site No. 19 shows the highest risk probability, increasing from 0.0997 under light-to-moderate rainfall conditions to 0.1474 and 0.1488 under the 50-year and 100-year return-period rainstorm scenarios, respectively. The proposed method can provide a reference for meteorological risk assessment of river-channel washout hazards along long-distance oil and gas pipelines. Full article

Flood susceptibility mapping is crucial for understanding flood-prone areas and mitigating the associated risks in vulnerable regions like the Sebeya Catchment. This study adopted a GIS-based Analytical Hierarchy Process (GIS-AHP) integrated with local community knowledge to evaluate flood susceptibility using 10 conditioning factors: Topographic Wetness Index (TWI), Elevation, Rainfall, Slope, Land use/Land cover (LULC), Soil types, Normalized Difference Vegetative Index (NDVI), Distance to roads, Distance to rivers, and drainage density. These factors were selected based on their established influence on flood susceptibility as identified through literature review, expert consultation, and local community experience in the flood-affected zones. Spatial datasets were gathered from remote sensing platforms, Digital Elevation Models, Meteorological records, and existing geospatial databases, and were processed within a GIS environment. The pairwise comparison matrix of the AHP was used to derive weighting coefficients representing the relative contribution of each factor in inducing flood, with Rainfall (0.23), Slope (0.15), Distance to river (0.12), drainage density (0.12), and Elevation (0.11) as the most influential criteria. The findings revealed that 88.4% of the study area falls within a moderate flood-susceptible zone, whereas 6.4% and 5.2% fall within high and low susceptible zones, respectively. The current study indicates that damage to infrastructure, loss of livelihoods, displacement of communities, and increased costs of disaster response are key consequences observed in affected regions. A confusion matrix approach was employed to validate the flood susceptibility map, and the results indicate 0.97 as an overall accuracy, confirming strong model performance and reliability. The proposed adaptive strategies for enhancing flood resilience include improvement in land use planning, use of early warning systems, and sustainable catchment management. Full article

The increasing availability of bounded lifetime observations in different disciplines has intensified the demand for flexible models capable of accommodating complex failure mechanisms. Motivated by this need, a comprehensive inferential framework is developed for the unit Bilal (UBilal) distribution using generalized progressive hybrid censoring, which guarantees a minimum number of observed failures while controlling experimental duration. Classical inference is established through maximum likelihood estimation, which is accompanied by asymptotic confidence intervals based on both normal and log-transformed approximations. Moreover, a Bayesian framework using a Metropolis–Hastings Markov chain Monte Carlo algorithm is presented. The proposed methodology further provides inference for important reliability characteristics, including the reliability and hazard rate functions, through both frequentist and Bayesian paradigms proposed. An extensive Monte Carlo investigation is conducted under diverse censoring schemes, sample sizes, and prior specifications to evaluate estimation accuracy, interval performance, and the influence of censoring severity. The simulation results show that Bayesian methods always provide better estimates and more reliable interval estimates, especially when prior information is used. Using two real datasets from thermodynamics and meteorology, the numerical results demonstrate that the UBilal model provides an excellent fit and yields reliable inference under bounded observations. Overall, the proposed methodology presents an efficient Bayesian inferential framework for bounded lifetime data collected through the generalized progressive hybrid censoring and expands the applicability of the UBilal model to reliability and related fields. Full article

Accurate reservoir inflow forecasting is essential for sustainable watershed management and low-carbon hydropower operation. Traditional fixed-weight ensemble models lack adaptability under non-stationary hydrological conditions, limiting their reliability in reservoir operation. This study proposes a Context-Aware Localized Weighting Ensemble (CALWE) framework for reservoir inflow forecasting. The framework constructs a predictive response space from heterogeneous model outputs, enabling context identification based on similarities in model prediction behaviors. A localized weighting strategy is then employed to adaptively determine model contributions across contexts. The framework was evaluated using daily reservoir inflow data from Xiaowan Hydropower Station in the Lancang River Basin and monthly inflow data from Xiluodu Hydropower Station in the Jinsha River Basin. Results demonstrate that CALWE outperforms individual models and conventional ensemble approaches in both cases. Compared with the best-performing individual benchmark model for each basin (i.e., SVR for Xiaowan and XGBoost for Xiluodu), CALWE achieved a relative RMSE reduction of 4.93% and an absolute NSE improvement of 0.007 for daily inflow forecasting at Xiaowan, while achieving a relative RMSE reduction of 5.32% and an absolute NSE improvement of 0.027 for monthly inflow forecasting at Xiluodu. SHAP analysis revealed scale-dependent feature contributions, with daily forecasts dominated by antecedent inflow information and monthly forecasts influenced by meteorological, land surface, and hydrological factors. These findings demonstrate that CALWE captures context-dependent inflow responses while providing interpretable insights into model predictions, thereby supporting sustainable watershed management and reservoir operation. Full article

The rapid transition toward electric mobility is widely recognized as a key strategy for improving urban environmental quality. However, while electric vehicles eliminate tailpipe emissions, they continue to contribute to environmental pressures through non-exhaust sources such as tire wear, road surface abrasion, and particle resuspension. This study develops a composite index framework for assessing road-segment-level operational environmental burdens associated with electric traffic, focusing on traffic operations, electric vehicle load characteristics, non-exhaust emission potential, and meteorological dispersion conditions. The framework does not constitute a life-cycle assessment and does not include battery production, electricity-generation mix, or other upstream environmental impacts. The framework combines four dimensions of influence: traffic operations, electric vehicle characteristics, non-exhaust emission processes, and meteorological dispersion conditions. Indicator selection was performed using the Delphi method, while indicator weights were determined through the Analytic Hierarchy Process (AHP). The methodological contribution lies not in the individual methods applied, but in their integration into a road-segment-level assessment framework specifically designed to identify and prioritize environmentally sensitive locations under traffic electrification scenarios. The resulting model incorporates sixteen indicators aggregated into a single environmental impact index that enables the ranking, classification, and prioritization of urban road segments according to their environmental burden. A case study conducted on selected urban streets demonstrates that non-exhaust emission indicators, particularly tire wear and particle resuspension, represent the most influential factors in the assessment process. Within the illustrative five-segment case study, the relative road-segment ranking remained unchanged under the electrified-traffic scenario, while the structure of the assessed burden shifted toward non-exhaust processes. The proposed framework provides a practical decision-support tool for urban planners and transport authorities by enabling the identification of environmentally sensitive locations, prioritization of infrastructure interventions, and support for sustainable mobility strategies in increasingly electrified urban transport systems. Full article