Search Results (71)

With the global trend of green travel and demand for improving the quality of slow-moving systems in coastal cities, the optimization of the cycling system is crucial for improving the quality of the human environment. Based on the theory of “human–environment interaction” in habitat studies, the 22.15 km cycling route around Hengqin Island was studied considering the dimensions of energy flow, information interaction, and spatial–temporal utilization through field surveys, meteorological data analysis, and behavioral observation. The results showed that climate and topography significantly affect cyclists’ energy consumption and cycling efficiency, especially in hot and humid conditions in summer, greatly affecting the cycling experience. Meanwhile, the lack of a physical marking system and the disconnection of information transmission lead to difficulties in route selection, and there are significant time and seasonal variations in cycling behavior. Accordingly, microclimate adjustment, cultural symbol implantation, and flexible facility layout strategies are proposed to enhance the environmental comfort and information interaction efficiency of the cycling system. Full article

Wind power generation provides clean and renewable electricity for microgrids, but its intermittency and uncertainty pose challenges to the operation and power quality of microgrids. Accurate forecasting is conducive to maintaining the stability of microgrids and improving the efficiency of energy management. Therefore, this study proposes a dual-branch frequency transformer (DBFformer), which leverages multi-scale spectral transformation and the multi-head attention mechanism to improve the prediction accuracy of microgrid wind turbines. In the encoder, two parallel branches are designed to extract the global features and local dynamic features of meteorological data based on Fourier transform and wavelet transform, respectively. In the decoder, an exponential smoothing attention (ESA) mechanism and a frequency attention (FA) mechanism are introduced to extract multi-scale temporal features. ESA enhances the model’s ability to capture long-term growth trends, whereas FA focuses on periodic pattern recognition. Additionally, to further optimize the model’s performance, a periodic weight coefficient (PWC) mechanism is employed to dynamically adjust the fusion coefficients to further improve the fusion performance and prediction accuracy. The factors influencing wind turbine power are analyzed; then, the most relevant factors are selected for the experiment. According to the experimental results, the proposed DBFformer accurately predicts the output power of wind turbines and exhibits superior performance. It achieves lower mean squared error (MSE) and mean absolute error (MAE) values than other state-of-the-art models. Specifically, its MSE values are 0.195, 0.216, 0.457, and 0.583, and the corresponding MAE values are 0.318, 0.335, 0.474, and 0.503 for different rated wind turbines. Furthermore, comprehensive ablation experiments validate that the dual-branch structure, frequency transformations, dual-attention mechanisms, and PWC module have a positive impact on the proposed model. Therefore, this research offers a novel and effective approach for wind power forecasting and supports the broader goal of integrating clean energy into microgrids. Full article

Particulate matter (PM) originating from road dust is an increasing concern in urban air quality, particularly as non-exhaust emissions from tire–pavement interactions gain prominence. Existing models often focus on meteorological and traffic-related variables while oversimplifying pavement surface characteristics, limiting their applicability across diverse spatial and traffic conditions. This study investigates the influence of concrete pavement macrotexture—specifically the Mean Texture Depth (MTD) and surface wavelength—on PM₁₀ resuspension. Field data were collected using a vehicle-mounted DustTrak 8530 sensor following the TRAKER protocol, enabling real-time monitoring near the tire–pavement interface. A multivariable linear regression model was used to evaluate the effects of MTD, wavelength, and the interaction between silt loading (sL) and PM₁₀ content, achieving a high adjusted R² of 0.765. The surface wavelength and sL–PM₁₀ interaction were statistically significant (p < 0.01). The PM₁₀ concentrations increased with the MTD up to a threshold of approximately 1.4 mm, after which the trend plateaued. A short wavelength (<4 mm) resulted in 30–50% higher PM₁₀ emissions compared to a longer wavelength (>30 mm), likely due to enhanced air-pumping effects caused by more frequent aggregate contact. Among pavement types, Transverse Tining (T.Tining) exhibited the highest emissions due to its high MTD and short wavelength, whereas Exposed Aggregate Concrete Pavement (EACP) and the Next-Generation Concrete Surface (NGCS) showed lower emissions with a moderate MTD (1.0–1.4 mm) and longer wavelength. Mechanistically, a low MTD means there is a lack of sufficient voids for dust retention but generates less turbulence, producing moderate emissions. In contrast, a high MTD combined with a very short wavelength intensifies tire contact and localized air pumping, increasing emissions. Therefore, an intermediate MTD and moderate wavelength configuration appears optimal, balancing dust retention with minimized turbulence. These findings offer a texture-informed framework for integrating pavement surface characteristics into PM emission models, supporting sustainable and emission-conscious pavement design. Full article

In view of the contradiction between the still-high energy consumption in Ordos and the increasingly urgent carbon-neutral goal, the adjustment of energy structures has begun, and strategic planning of photovoltaic facility construction critically supports the sustainable growth of local energy systems. Therefore, the author constructed the site selection evaluation system of photovoltaic suitability based on remote sensing, meteorological and topographic data. The improved CRITIC empowerment method is used to comprehensively consider the conflict and variability of the indicators for an objective and quantitative analysis of the spatial and temporal changes in suitable areas for photovoltaic development. Finally, the comprehensive evaluation results of the photovoltaic site selection are obtained. The results show that (a) the improved CRITIC method reduces the weight of ‘night light’ from 0.24 to 0.14, effectively reducing the weight bias caused by the extreme value and (b) since 2010, the regional area of suitable level and above has increased from 23.96% to 48.24%, and its spatial center of gravity shows a trend of moving first to northeast and then to southwest. This study overcomes the limitations of mainstream subjective evaluation methods. Additionally, it addresses the oversight of human factors’ impact on suitability in traditional assessment frameworks. This research provides decision-making support for regional energy allocation planning and spatial planning. Full article

Typical Meteorological Year (TMY) datasets, widely used in building energy modeling, overlook Urban Heat Island (UHI) effects and future climate trends by relying on long-term data from rural stations such as airports. This study addresses this limitation by integrating Urban Weather Generator (UWG) simulations with CCWorldWeatherGen projections to produce microclimate-adjusted and future weather scenarios. These datasets were then incorporated into an Urban Building Energy Modeling (UBEM) framework using Urban Modeling Interface (UMI) to evaluate energy performance across a low-income residential neighborhood in Des Moines, Iowa. Results show that UHI intensity will rise from an annual average of 0.55 °C under current conditions to 0.60 °C by 2050 and 0.63 °C by 2080, with peak intensities in summer. The UHI elevates cooling Energy Use Intensity (EUI) by 7% today, with projections indicating a sharp increase—91% by 2050 and 154% by 2080. The UHI will further amplify cooling demand by 2.3% and 6.2% in 2050 and 2080, respectively. Conversely, heating EUI will decline by 20.0% by 2050 and 40.1% by 2080, with the UHI slightly reducing heating demand. Insulation mitigates cooling loads but becomes less effective for heating demand over time. These findings highlight the need for climate-adaptive policies, building retrofits, and UHI mitigation to manage future cooling demand. Full article

The lowering air pollution in the US has brought significant health benefits; however, climate change may offset the benefits by increasing the temperature and worsening air quality. This study aimed to estimate the mortality changes due to air pollution reductions and evaluate the potential climate penalty in the Mid-South Region of the US. Daily concentrations of PM_2.5 and ozone measured at local monitoring stations in 1999–2019 were extracted from the US Environmental Protection Agency’s Air Quality System. Meteorological data for the same period were obtained from the National Oceanic and Atmospheric Administration’s Local Climatological Data. Annual average age-adjusted all-cause mortality rates (MRs) were downloaded from the US Centers for Disease Control and Prevention’s WONDERS Databases. MRs attributable to exposure to PM_2.5, ozone, and high temperatures in warm months were estimated using their corresponding health impact functions. Using Year 1999 as the baseline, contributions of environmental changes to MR reductions were calculated. Results showed that annual average concentrations of PM_2.5 and ozone decreased by 46% and 23% in 2019, respectively, compared with the base year; meanwhile, the mean daily temperature in the warm season fluctuated and displayed an insignificant increasing trend (Kendall’s tau = 0.16, p = 0.30). MRs displayed a significant decreasing trend and dropped by 215 deaths/100,000 person-year in 2019. Lower PM_2.5 and ozone concentrations were estimated to reduce 59 and 30 deaths/100,000 person-year, respectively, contributing to 23% and 17% of MR reductions, respectively. The fluctuating temperatures had negligible impacts on mortality changes over the two-decade study period. This study suggests that improved air quality may have contributed to mortality reductions, while the climate penalty effects appeared to be insignificant in the Mid-South Region. Full article

This study aims to explore the impacts of climate change on potato planting in Gansu Province so as to be able to adjust potato planting pattern scientifically and rationally. (1) Air precipitation and temperature time-series datasets were obtained from 87 meteorological stations in the study area over the past 50 years. The backpropagation neural network was employed to interpolate irregular and missing data in the time-series data. The altitude, the precipitation from June to July, the average temperature in July and the accumulated temperature above 10 °C were selected as the agricultural zoning indicators for the regionalization of potato planting. (2) The linear propensity rate method, cumulative anomaly method, ArcGIS technology and the Mann–Kendall mutation test were employed to examine the spatial–temporal variation in and mutation testing of the three zoning indicators. (3) The experimental results demonstrated that the amount of precipitation from June to July was registered at 139.94 mm, indicating a slight humidifying trend characterized by an annual increase rate of approximately 1.81 mm/10 a. Furthermore, a significant abrupt change was observed in 1998. The average temperature in July was registered at 20.53 °C, which showed an increasing trend at a rate of 0.55 °C/10 a, marked by a sudden shift in 1998. Lastly, the accumulated temperature above 10 °C was registered at 2917.05 °C, manifesting a significant warming trend at a rate of 161.96 °C/10 a, without any abrupt changes. For spatial distribution, the precipitation from June to July showed a decreasing spatial distribution pattern from south to north and from east to west, while its tendency rate showed a gradually decreasing trend from north to south and from east to west. The average temperature in July showed a decreasing spatial pattern from northeast to southwest, while its tendency rate showed a decreasing trend from west to east and from north to south. The accumulated temperature above 10 °C showed a spatial pattern of high accumulated temperatures in the northwestern and southeastern regions and low accumulated temperatures in the remaining regions, while its tendency rate showed a decreasing trend from west to east and from north to south. (4) The impacts of climate change on potato planting in Gansu Province were mainly manifested as a decrease of 0.30 × 10⁶ hm² in the cultivated land area in the most suitable region for potato planting post-1998, while the suitable area diminished by 0.96 × 10⁶ hm², the sub-suitable area expanded by 0.47 × 10⁶ hm², and the plantable area increased by 0.79 × 10⁶ hm². However, the unsuitable area experienced a reduction of 0.30 × 10⁴ hm². The findings of this study can provide a scientific foundation for optimizing and adjusting the potato planting structure, considering the backdrop of climate change. Moreover, they contribute to regional decision-making, thereby promoting sustainable agricultural development as well as enhancing both the yield and quality of potato in Gansu Province. Full article

With the rapid development of renewable energy, accurately forecasting wind power is crucial for the stable operation of power systems and effective energy management. This paper proposes a short-term wind power forecasting method based on the Orthogonalized Maximal Information Coefficient (OMNIC) combined with an Adaptive fractional Generalized Pareto motion (fGPm) model. The method quantifies the influence of meteorological factors on wind power prediction and identifies the optimal set and number of influencing factors. The model accounts for long-range dependence (LRD) in time series data and constructs an uncertainty model using the properties and parameters of the fractional generalized Pareto distribution (GPD), significantly improving prediction accuracy under nonlinear conditions. The proposed approach was validated using a real dataset from a wind farm in northwest China and compared with other models such as Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU). Results show that the adaptive fGPm model reduces RMSE by 0.448 MW and 0.466 MW, MAPE by 6.936% and 9.702%, and achieves an average R² of 0.9826 compared to CNN-GRU and CNN-LSTM. The improvement is due to the dynamic adjustment to data trends and effective use of LRD features. This method provides practical value in improving wind power prediction accuracy and addressing grid integration and regulation challenges. Full article

Under the strategic background of “carbon peak by 2030 and carbon neutrality by 2060”, the impact of energy policy on China’s industrial electricity demand is increasingly significant. This study focuses on the industrial electricity demand in Jiangsu Province, comprehensively considering the impact of policy and meteorological factors, and uses multivariate regression analysis to systematically explore the impact mechanisms of policy adjustments and climate change on industrial electricity demand. First, by analyzing the policy background and climate characteristics of Jiangsu Province, relevant policy and meteorological indicators are extracted, followed by a correlation analysis and the establishment of an industrial electricity multivariate regression prediction model. Finally, the evolution of the industrial electricity load in Jiangsu Province under different socio-economic pathways is forecasted. The results show the following: (1) Policy factors such as the electrification rate and self-generated electricity show significant correlation with electricity demand, as do meteorological factors such as temperature. (2) The future industrial electricity level in Jiangsu Province is expected to show a fluctuating upward trend, with industrial electricity consumption reaching 767.51 to 794.32 billion kWh by 2035. Accordingly, the forecast results are expected to guide future planning of the industrial electricity system in Jiangsu Province under the carbon neutrality scenario. Full article

The projection and identification of historical and future changes in climatic systems is crucial. This study aims to assess the performance of CMIP6 climate models and projections of precipitation and temperature variables over the Upper Blue Nile Basin (UBNB), Northwestern Ethiopia. The bias in the CMIP6 model data was adjusted using data from meteorological stations. Additionally, this study uses daily CMIP6 precipitation and temperature data under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios for the near (2015–2044), mid (2045–2074), and far (2075–2100) periods. Power transformation and distribution mapping bias correction techniques were used to adjust biases in precipitation and temperature data from seven CMIP6 models. To validate the model data against observed data, statistical evaluation techniques were employed. Mann–Kendall (MK) and Sen’s slope estimator were also performed to identify trends and magnitudes of variations in rainfall and temperature, respectively. The performance evaluation revealed that the INM-CM5-0 and INM-CM4-8 models performed best for precipitation and temperature, respectively. The precipitation projections in all agro-climatic zones under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show a significant (p < 0.01) positive trend. The mean annual maximum temperature over UBNB is estimated to increase by 1.8 °C, 2.1 °C, and 2.8 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5 between 2015 and 2100, respectively. Similarly, the mean annually minimum temperature is estimated to increase by 1.5 °C, 2.1 °C, and 3.1 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. These significant changes in climate variables are anticipated to alter the incidence and severity of extremes. Hence, communities should adopt various adaptation practices to mitigate the effects of rising temperatures. Full article

Wheat is one of the most widely grown cereal crops, serving as a key factor in sustaining the nutritional and food balance in numerous countries. The use of non-contact methods for wheat monitoring allows for the rapid diagnosis of vegetation density, crop growth, and the presence of weeds and diseases in the investigated fields. This study aims to assess the potential for differentiating growth patterns in winter wheat cultivars by examining them with two unmanned aerial vehicles (UAVs), the Mavic 2 Pro and Phantom 4 Pro, equipped with a multispectral camera from the MAPIR™ brand. Based on an experimental study conducted in the Southern Dobruja region (Bulgaria), vegetation reflectance indices, such as the Normalized-Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index 2 (EVI2), were generated, and a database was created to track their changing trends. The obtained results showed that the values of the NDVI, EVI2, and SAVI can be used to predict the productive potential of wheat, but only after accounting for the meteorological conditions of the respective growing season. The proposed methodology provides accurate results in small areas, with a resolution of 0.40 cm/pixel when flying at an altitude of 12 m and 2.3 cm/pixel when flying at an altitude of 100 m. The achieved precision in small and ultra-small agricultural areas, at a width of 1.2 m, will help wheat breeders conduct precise diagnostics of individual wheat varieties. Full article

The Altay Mountains’ forests are vital to Xinjiang’s terrestrial ecosystem, especially water regulation and conservation. This study evaluates vegetation evapotranspiration (ET) from 2000 to 2017 using temperature, precipitation, and ET data from the China Meteorological Data Sharing Service. The dataset underwent quality control and was interpolated using the inverse distance weighted (IDW) method. Correlation analysis and climate trend methodologies were applied to assess the impacts of temperature, precipitation, drought, and extreme weather events on ET. The results indicate that air temperature had a minimal effect on ET, with 68.34% of the region showing weak correlations (coefficients between −0.2 and 0.2). Conversely, precipitation exhibited a strong positive correlation with ET across 98.91% of the area. Drought analysis, using the standardized precipitation evapotranspiration index (SPEI) and the Temperature Vegetation Dryness Index (TVDI), showed that ET was significantly correlated with the SPEI in 96.47% of the region, while the TVDI displayed both positive and negative correlations. Extreme weather events also significantly influenced ET, with reductions in the Simple Daily Intensity Index (SDII), heavy precipitation days (R95p, R10), and increases in indicators like growing season length (GSL) and warm spell duration index (WSDI) leading to variations in ET. Based on the correlation coefficients and their significance, it was confirmed that the SII (precipitation intensity) and R95p (heavy precipitation) are the main factors causing vegetation ET increases. These findings offer crucial insights into the interactions between meteorological variables and ET, essential information for sustainable forest management, by highlighting the importance of optimizing water regulation strategies, such as adjusting species composition and forest density to enhance resilience against drought and extreme weather, thereby ensuring long-term forest health and productivity in response to climate change. Full article

The application of machine learning and deep learning in air pollution management is becoming increasingly crucial, as these technologies enhance the accuracy of pollution prediction models, facilitating timely interventions and policy adjustments. They also facilitate the analysis of large datasets to identify pollution sources and trends, ultimately contributing to more effective and targeted environmental protection strategies. Ho Chi Minh City (HCMC), a major metropolitan area in southern Vietnam, has experienced a significant rise in air pollution levels, particularly PM_2.5, in recent years, creating substantial risks to both public health and the environment. Given the challenges posed by air quality issues, it is essential to develop robust methodologies for predicting PM_2.5 concentrations in HCMC. This study seeks to develop and evaluate multiple machine learning and deep learning models for predicting PM_2.5 concentrations in HCMC, Vietnam, utilizing PM_2.5 and meteorological data over 911 days, from 1 January 2021 to 30 June 2023. Six algorithms were applied: random forest (RF), extreme gradient boosting (XGB), support vector regression (SVR), artificial neural network (ANN), generalized regression neural network (GRNN), and convolutional neural network (CNN). The results indicated that the ANN is the most effective algorithm for predicting PM_2.5 concentrations, with an index of agreement (IOA) value of 0.736 and the lowest prediction errors during the testing phase. These findings imply that the ANN algorithm could serve as an effective tool for predicting PM_2.5 concentrations in urban environments, particularly in HCMC. This study provides valuable insights into the factors that affect PM_2.5 concentrations in HCMC and emphasizes the capacity of AI methodologies in reducing atmospheric pollution. Additionally, it offers valuable insights for policymakers and health officials to implement targeted interventions aimed at reducing air pollution and improving public health. Full article

The region along the Great Wall is a typical dryland agricultural zone, serving as both a potential area for staple grain production and a key region for specialty crops like coarse grains and cool-climate vegetables. Studying the characteristics of drought during the spring sowing period is crucial for developing diversified planting strategies and ensuring food security. This study analyzes the drought conditions along the Great Wall from 1960 to 2023, revealing the spatial and temporal distribution of drought in the region and quantifying the impact of climate change on drought frequency and intensity. By doing so, it fills a gap in the existing drought research, which often lacks the long-term, multi-dimensional analysis of spring sowing drought characteristics. Using daily meteorological data from April 20 to May 20 during the spring sowing period between 1960 and 2023, the study employs the Meteorological Composite Drought Index (MCI) to quantitatively identify drought conditions and examine the spatial and temporal evolution of drought in the region. The results show that, on a daily scale, the frequency of mild and moderate droughts is 60.45% and 25.19%, respectively, with no occurrences of severe or extreme drought. On an annual scale, the intensity of drought and the ratio of affected stations show an increasing trend, with a decrease in mild drought frequency and an increase in moderate and severe drought occurrences. Additionally, the spatial distribution of drought frequency follows a pattern of “higher in the east than in the west” and “higher in the north than in the south”. The study also finds that the migration of drought frequency centers shows a clear temporal evolution, with the center shifting southwestward from the 1960s to the 2000s, and then moving northeastward from the 2000s to 2023. These findings provide critical data support for optimizing agricultural drought resistance strategies and offer new insights for future research on the relationship between drought and climate change. It is suggested that agricultural practices and water resource management policies should be adjusted according to the spatial migration of drought centers, with a particular focus on optimizing drought mitigation measures during the spring sowing period. Full article

Seasonal variations in glycaemic patterns in children and young adults affected by type 1 diabetes are currently poorly studied. However, the spread of Flash Glucose Monitoring (FGM) and continuous glucose monitoring (CGM) systems and of dedicated platforms for the synchronization and conservation of CGM reports allows an efficient approach to the comprehension of these phenomena. Moreover, the impact that environmental parameters may have on glycaemic control takes on clinical relevance, implying a need to properly educate patients and their families. In this context, it can be investigated how blood glucose patterns in diabetic patients may have a link to outdoor temperatures. Therefore, in this study, the relationship between outdoor temperatures and glucose levels in diabetic patients, aged between 4 and 21 years old, has been analysed. For a one-year period (Autumn 2022–Summer 2023), seasonal variations in their CGM metrics (i.e., time in range (TIR), Time Above Range (TAR), Time Below Range (TBR), and coefficient of variation (CV)) were analysed with respect to atmospheric temperature. The results highlight a negative correlation between glucose in diabetic patients and temperature patterns (R value computed considering data for the entire year; Ry = −0.49), behaviour which is strongly confirmed by the analysis focused on the July 2023 heatwave (R = −0.67), which shows that during heatwave events, the anticorrelation is accentuated. The diurnal analysis shows how glucose levels fluctuate throughout the day, potentially correlating with atmospheric diurnal temperature changes in addition to the standard trend. Data captured during the July 2023 heatwave (17–21 July 2023) highlight pronounced deviations from the long-term average, signalling the rapid effects of extreme temperatures on glucose regulation. Our findings underscore the need to integrate meteorological parameters into diabetes management and clinical trial designs. These results suggest that structured diabetes self-management education of patients and their families should include adequate warnings about the effects of atmospheric temperature variations on the risk of hypoglycaemia and about the negative effects of excessive therapeutic inertia in the adjustment of insulin doses. Full article