Search Results (41)

The ambient levels of NO₂ in urban areas in China in recent years have generally shown a downward trend, but high NO₂ concentrations still exist under certain conditions, and the causes for such phenomenon and its impact on air quality remain unclear. Taking Dongying, a typical petrochemical city in the Bohai Bay of China, as an example, this paper analyzed the influence of NO₂ on urban air quality and investigated the causes for the formation of NO₂ with high concentrations. The results indicated that higher daily NO₂ concentrations (>40 μg/m³) mainly occurred during January-April and September-December each year, and higher hourly NO₂ concentrations mainly occurred during the nighttime and morning rush hour in Dongying from 2017 to 2023. With the increase in daily NO₂ concentrations, the daily air pollution levels showed a general increasing trend from 2017 to 2023. The occurrence of high NO₂ values in Dongying was affected by the combination of unfavorable meteorological conditions, local emissions and regional transports, and localized atmospheric chemical generation. High-pressure and uniform-pressure weather patterns in 2017–2022, along with land–sea breeze circulation in 2022, contribute to high NO₂ concentrations in Dongying. Boundary layer heights (BLH) in spring (−0.43) and winter (−0.36), wind direction in summer (0.21), and temperature in autumn (−0.46) are the primary meteorological factors driving NO₂-HH (High hourly NO₂ values), while BLH (−0.47) is the main cause for NO₂-HD (High daily NO₂ values). The titration reaction between NO with O₃ is the main cause for NO₂-HH in spring, summer and autumn, and photochemical reactions of aromatics have a significant influence on NO₂-HD. NOx emissions from the thermal power and petrochemical industry in Dongying and air pollution transports from western and southwestern Shandong Province (throughout the year) and from the Bohai Sea (during spring and summer) had serious adverse impact on high NO₂ values in 2022. The results of the study could help to provide a scientific basis for the control of NO₂ and the continuous improvement of air quality in Dongying and similar petrochemical cities. Full article

This study investigates the impact of rain-induced attenuation on cross-polarization discrimination (XPD) in Earth–space satellite links operating at millimeter-wave frequencies in Pretoria, South Africa. The traditional method of computing XPD employs a constant annual mean rain height and annual mean co-polar attenuation (CPA) over a certain location. This research utilized seasonal rain height data obtained from a recent study and the latest ITU-R P.618-14 guidelines, to compute and analyze XPD variations across six selected frequencies (11.7 GHz to 35 GHz) for different percentages of time exceedance in Pretoria. The study reveals significant seasonal dependencies of rain heights, with XPD reaching its maximum during winter due to lower rain height, and lower rain-induced attenuation and its minimum during summer, characterized by intense convective rainfall and maximum rain height. For instance, the estimated XPD for a 35 GHz signal at 0.01% of the time in the summer, spring, winter, and autumn are 13, 14, 15, and 14 dB, respectively. This implies that radio signals suffer severe attenuation caused by low XPD in the summer. The relationship between CPA and XPD highlights the need for increased XPD margins at higher frequencies to mitigate signal degradation caused by rain depolarization. Practical recommendations include the adoption of adaptive modulation and coding schemes to maintain link reliability during adverse weather conditions, particularly in summer. This research highlights the significance of incorporating frequency-dependent parameters and rain height variability in XPD estimation to enhance the design of satellite communication systems, ensuring optimized performance and reliable operation in a tropical climate. Full article

This study examines how winter weather conditions influence traffic patterns for both passenger vehicles and trucks, using data collected from weigh-in-motion (WIM) stations and nearby weather monitoring sites along Alberta’s Highways 2 and 2A. To explore how snowfall and temperature affect traffic volumes, we developed Ordinary Least Squares Regression (OLSR) models. The findings indicate that passenger car volumes drop more sharply than truck volumes under increased snowfall, with the decline being particularly notable on Highway 2, a rural stretch. In contrast, Highway 2A showed an uptick in truck traffic, likely due to detours from adjacent routes with less winter maintenance. For estimating missing traffic data during severe weather, we employed both OLSR and a machine learning technique, k-Nearest Neighbor (k-NN). In comparing the two approaches, OLSR demonstrated superior accuracy and consistency, making it more effective for filling in missing traffic data throughout the winter season. The performance of the OLSR model underscores its reliability in addressing data gaps during adverse winter conditions. Additionally, this study contributes to sustainable transportation by improving data accuracy, which aids in better resource allocation and enhances road safety during adverse weather. The findings support more efficient traffic management and maintenance strategies, including optimizing winter road maintenance and improving sustainable infrastructure planning, thereby aligning with the goals of sustainable infrastructure development. Full article

Due to the high yield potential, suitable agrotechnical properties, and nutritional value of the grain, the interest in growing triticale is increasing due to the high yield potential, suitable agrotechnical properties, and nutritional value. This species is primarily grown for fodder purposes, but numerous studies suggest its potential for human consumption, including bread production. Additionally, triticale is known for its greater resistance to adverse environmental conditions compared to other crops, even under varying agronomic practices. A field experiment was conducted in southeastern Poland from 2019 to 2022. The study involved two cultivation systems (conventional and integrated) as one factor and three winter triticale cultivars (Belcanto, Meloman, and Panteon) as the other. The conventional system is based on the intensive cultivation of plants through the use of large amounts of fertilizers and crop protection products. The integrated system of cultivation is an alternative to the conventional system. This system aims to reduce the use of industrial inputs and, as a result, minimize the negative impact of agriculture on the natural environment. Cultivation under the conventional system resulted in higher grain yields and improved physiological parameter values. There was an increase in the leaf area index (LAI), relative chlorophyll content, chlorophyll fluorescence parameters, and gas exchange parameters (photosynthetic rate (Pn) and transpiration rate (E)). The highest yields were achieved with the cv-Panteon and cv-Belcanto under the conventional system. The yields of these cultivars in the integrated system did not differ significantly from those of cv-Meloman under the conventional system. In the 2021/2022 season, the weather conditions were the most favorable during the triticale vegetation period, which resulted in the highest grain yield. The conventional system also resulted in higher thousand-grain weight (TGW), crude protein content, and grain test weight while lowering the falling number (FN) value. However, the cultivation systems did not significantly affect the grain uniformity, crude fat, fiber, or ash content, as well as wet gluten and gluten index (GI). The cv-Panteon exhibited the highest level of crude protein, crude fiber, and crude ash in its grain, suggesting its strong nutritional value and potential for use in human consumption. The cultivation of triticale in the integrated system, although associated with lower yields, causes less environmental pollution than cultivation in the conventional system. The appropriate selection of efficient cultivars grown in the integrated system allows for high grain yields with good quality parameters. Full article

Climate change and the increasing demand for food necessitate innovative agricultural methods. Relay intercropping, where one crop is sown into another already-grown crop, offers a promising alternative to traditional systems. In the 2021/22 and 2022/23 seasons, a field experiment was conducted to assess the relay intercropping of winter barley (Hordeum vulgare L. ssp. polistichon) with soybean (Glycine max (L.) Merr). This experiment took place at the Brody Experimental and Educational Station of the University of Life Sciences in Poznań, Poland. Soybean was sown into designated strips within the barley field, and both crops were cultivated simultaneously until the barley was harvested. After the barley harvest, the soybean plants continued to grow and were harvested at full maturity. The results varied between the two years of this experiment. In the first year, characterized by drought conditions, the soybean yield was completely lost, while the barley maintained a stable yield. In the second year, with more favorable weather, the yields of barley and soybean were interdependent. The use of the relay intercropping system did not increase the Land Equivalent Ratio (LER) above 1. Additionally, total protein yield remained consistent across different cultivation systems. Relay intercropping can serve as a method for protecting crop protein yields under adverse weather conditions and may offer a viable alternative for soybean cultivation in challenging climates. Full article

In the low-carbon era, photovoltaic power generation has emerged as a pivotal focal point. The inherent volatility of photovoltaic power generation poses a substantial challenge to the stability of the power grid, making accurate prediction imperative. Based on the integration of a backpropagation (BP) neural network and a genetic algorithm (GA), a prediction model was developed that contained two sub-models: no-rain and no-snow scenarios, and rain and snow scenarios. Through correlation analysis, the primary meteorological factors were identified which were subsequently utilized as inputs alongside historical power generation data. In the sub-model dedicated to rain and snow scenarios, variables such as rainfall and snowfall amounts were incorporated as additional input parameters. The hourly photovoltaic power generation output was served as the model’s output. The results indicated that the proposed model effectively ensured accurate forecasts. During no-rain and no-snow weather conditions, the prediction error metrics showcased superior performance: the mean absolute percentage error (MAPE) consistently remained below 13%, meeting the stringent requirement of the power grid’s tolerance level below 20%. Moreover, the normalized root mean square error (NRMSE) ranged between 6% and 9%, while the coefficient of determination (R²) exceeded 0.9. These underscored the remarkable prediction accuracy achieved by the model. Under rainy and snowy weather conditions, although MAPE slightly increased to the range of 14% to 20% compared to that of scenarios without rain and snow, it still adhered to the stringent requirement. NRMSE varied between 4.5% and 8%, and R² remained consistently above 0.9, indicative of satisfactory model performance even in adverse weather conditions. The successful application of the proposed model in predicting hourly photovoltaic power generation output during winter in Henan Province bears significant practical implications for the advancement and integration of renewable energy technologies. Full article

This study used descriptive statistical methods to investigate how the yield development of winter and spring barley was affected by annual weather variability within the vegetative, ear formation, anthesis, and grain-filling phases. Meteorological, phenological, and yield data from the agrometeorological field experiment in Berlin-Dahlem (Germany) between 2009 and 2022 were used. The results show that the lower yield variability in winter barley (cv = 18.7%) compared to spring barley (cv = 32.6%) is related to an earlier start and longer duration of relevant phenological phases, so yield formation is slower under generally cooler weather conditions. The significantly higher yield variability in spring barley was mainly the result of adverse weather conditions during ear formation and anthesis. In both phases, high temperatures led to significant yield losses, as has often been the case in recent years. In addition, a pronounced negative climatic water balance during anthesis was also a contributing factor. These meteorological parameters explained 82% of the yield variability in spring barley. New strategies for spring barley production are needed to avoid further yield losses in the future. Rising temperatures due to climate change could probably allow an earlier sowing date so that ear formation and anthesis take place in a generally cooler and wetter period, as shown for 2014. Full article

Accurate detection of road surface conditions in adverse winter weather is essential for traffic safety. To promote safe driving and efficient road management, this study presents an accurate and generalizable data-driven learning model for the estimation of road surface conditions. The machine model was a support vector machine (SVM), which has been successfully applied in diverse fields, and kernel functions (linear, Gaussian, second-order polynomial) with a soft margin classification technique were also adopted. Two learner designs (one-vs-one, one-vs-all) extended their application to multi-class classification. In addition to this non-probabilistic classifier, this study calculated the posterior probability of belonging to each group by applying the sigmoid function to the classification scores obtained by the trained SVM. The results indicate that the classification errors of all the classifiers, excluding the one-vs-all linear learners, were below 3%, thereby accurately classifying road surface conditions, and that the generalization performance of all the one-vs-one learners was within an error rate of 4%. The results also showed that the posterior probabilities can analyze certain atmospheric and road surface conditions that correspond to a high probability of hazardous road surface conditions. Therefore, this study demonstrates the potential of data-driven learning models in classifying road surface conditions accurately. Full article

Carbon sinks provided by land ecosystems play a crucial role in achieving carbon neutrality. However, the future potential of carbon sequestration remains highly uncertain. The impact of pollutant emission reduction (PER) introduced by the proposed synergistic approach to air pollution control and carbon neutrality on carbon sinks in China has not yet been fully evaluated. In this study, we analyzed the effects of regional carbon-neutral PER policies, global climate change, and their coupled effects on China’s terrestrial gross primary productivity (GPP) by conducting numerical experiments using the weather research and forecasting model coupled with chemistry (WRF-Chem) and the moderate resolution imaging spectroradiometer photosynthesis algorithm (MODIS-PSN). We found that carbon-neutral PER policies could promote GPP growth in most regions of China in 2060, particularly during April and October, resulting in a total increase of at least 21.84 TgC compared to that in 2016, which offset the adverse effects of global climate change up to fourfold. The aerosol radiative effects drive GPP growth under carbon-neutral PER policies, primarily through an increase in daily minimum temperature during winter and an increase in shortwave radiation during other seasons. Our research highlights that reducing pollutant emissions enhances future potential for carbon sequestration, revealing positive feedback towards achieving the target of carbon neutrality. Full article

Southern Europe’s mutating weather conditions and the European environmental agenda have suggested the cropping of maize (Zea mays L.) after winter cereal cultivation, even if shortening the growing period could result in an immature harvesting stage, limiting its silage quality. The experimental design investigated the effects of four harvesting dry matter (DM) classes (DMvl, 23.9%; DMl, 25.3%; DMm, 26.2%; DMh, 30.4%) in two inoculant types (heterofermentative (HE) vs. homofermentative (HOM) on fermentative quality, DM losses, and aerobic stability. The early harvested DMvl and DMl classes had the lowest silage density (<130 kg m⁻³) and resulted in an organic acids profile lowering the fermentative quality and increasing the DM losses, while no differences were detected following the use of the inoculants. The aerobic stability was more susceptible to further adverse fermentation via opportunistic microorganisms in the DMm and DMh classes, probably due to the lower moisture content, but the use of both HE and HOM lactic acid bacteria seemed to contain this silage surface damage. In summary, a shortening of the maize growing period might limit the achievement of the maturity stage ideal for high-quality silage, hampering the positive effects of both HOM and HE inoculants in the ensiling process of early harvested maize. Full article

In order to reduce the impact of outdoor extreme weather events on crop production in winter, energy saving in greenhouses that are regularly heated is of great importance in reducing production costs and carbon footprints. For this purpose, the variations in indoor temperature, relative humidity and dew point temperature in the vertical direction (2 m, 4 m, 5.7 m) of thermal curtains in greenhouses were determined. In addition, depending on the fuel used, the curtains’ effects on heat energy consumption, heat transfer coefficient, carbon dioxide equivalents released to the atmosphere and fuel cost were investigated. To reach this goal, two greenhouses with the same structural features were designed with and without thermal curtains. As a result of the study, the indoor temperature and relative humidity values in the greenhouse with a thermal curtain increased by 1.3 °C and 10% compared to the greenhouse without a thermal curtain. Thermal curtains in the greenhouse significantly reduced fuel use (59.14–74.11 m³·night⁻¹). Considering the heat energy consumption, the average heat energy consumption was 453.7 kWh·night⁻¹ in the greenhouse with a curtain, while it was 568.6 kWh·night⁻¹ in the greenhouse without a curtain. The average heat transfer coefficient (U) values were calculated at 2.87 W·m⁻² °C with a thermal curtain and 3.63 W·m⁻² °C without a thermal curtain greenhouse. In the greenhouse, closing the thermal curtain at night resulted in heat energy savings of about 21%, related to the decrease in U values. The use of a thermal curtain in the greenhouse reduced the amount of CO₂ released to the atmosphere (116.6–146.1 kg·night⁻¹) and fuel cost (USD 21.3–26.7·night⁻¹). To conclude, extreme weather events in the outdoor environment adversely affect the plants grown in greenhouses where cultivation is performed out of season. A thermal curtain, used to reduce these adverse effects and the amount of energy consumed, is essential in improving indoor climate conditions, providing more economical greenhouse management and reducing the CO₂ released into the atmosphere due to fuel use. Full article

In years with unfavorable weather, winter frost during the blossoming season can play a significant role in reducing fruit yield and impacting the profitability of cultivation. The mango Naomi cultivar Mangifera indica L. has a low canopy that is severely affected by the effects of frost stress. As a result of the canopy being exposed to physiological problems, vegetative development is significantly inhibited. The current investigation aimed to study the influence of spraying nitric oxide and fogging spray systems on Naomi mango trees grafted on ‘Succary’ rootstock under frost stress conditions. The treatments were as follows: nitric oxide (NO) 50 and 100 μM, fogging spray system, and control. In comparison to the control, the use of nitric oxide and a fogging system significantly improved the leaf area, photosynthesis pigments of the leaf, the membrane stability index, yield, and physical and chemical characteristics of the Naomi mango cultivar. For instance, the application of 50 μM NO, 100 μM NO, and the fogging spray system resulted in an increase in yield by 41.32, 106.12, and 121.43% during the 2020 season, and by 39.37, 101.30, and 124.68% during the 2021 season compared to the control, respectively. The fogging spray system and highest level of NO decreased electrolyte leakage, proline content, total phenolic content, catalase (CAT), peroxidases (POX), and polyphenol oxidase (PPO) enzyme activities in leaves. Furthermore, the number of damaged leaves per shoot was significantly reduced after the application of fogging spray systems and nitric oxide in comparison to the control. Regarding vegetative growth, our results indicated that the fogging spray system and spraying nitric oxide at 100 μM enhanced the leaf surface area compared to the control and other treatments. A similar trend was noticed regarding yield and fruit quality, whereas the best values were obtained when the fogging spray system using nitric oxide was sprayed at a concentration of 100 μM. The application of fogging spray systems and nitric oxide can improve the production and fruit quality of Naomi mango trees by reducing the effects of adverse frost stress conditions. Full article

Field peas (Pisum sativum L.) are a valuable source of protein and help to support crop biodiversity in a sustainable agriculture system. To maintain varied crop rotation in sustainable production, it is advisable to include the winter form of pea, which is an excellent alternative to the spring form. However, the prolonged development of winter peas when weather patterns are unfavorable can adversely affect the morphological features and the health of the plants. The literature lacks studies on this issue. The objective of this study was to evaluate the morphological characteristics, yield, and canopy health of selected cultivars of winter peas. The study was conducted at the Prusy Experimental Station of the University of Agriculture in Krakow, located near Krakow, Poland (50°07′28″ N, 20°05′34″ E), during two growing seasons. The study evaluated six cultivars of winter peas and two means of protecting the canopy, with biological products containing Pythium oligandrum (Polyversum WP) or Bacillus subtilis (Serenade ASO). The yield, yield structure, efficiency of N uptake, and health of the plants were assessed. Crop protection treatments using Polyversum WP and Serenade ASO were shown to effectively protect winter peas against Fusarium wilt, which occurred only in the Specter and Arkta cultivars sprayed with Serenade. Polyversum WP increased the productivity of winter peas on average by 0.5 t ha⁻¹ and increased the efficiency of nitrogen uptake on average by 10 kg ha⁻¹ in comparison to the control. The Aviron and Arkta cultivars are recommended for cultivation in the conditions of Central Europe due to their high yield potential, high efficiency of nitrogen uptake, and good canopy health. Full article

Light Detection and Ranging (LiDAR) is a critical sensor for autonomous vehicle systems, providing high-resolution distance measurements in real-time. However, adverse weather conditions such as snow, rain, fog, and sun glare can affect LiDAR performance, requiring data preprocessing. This paper proposes a novel approach, the Adaptive Outlier Removal filter on range Image (AORI), which combines a projection image from LiDAR point clouds with an adaptive outlier removal filter to remove snow particles. Our research aims to analyze the characteristics of LiDAR and propose an image-based approach derived from LiDAR data that addresses the limitations of previous studies, particularly in improving the efficiency of nearest neighbor point search. Our proposed method achieves outstanding performance in both accuracy (>96%) and processing speed (0.26 s per frame) for autonomous driving systems under harsh weather from raw LiDAR point clouds in the Winter Adverse Driving dataset (WADS). Notably, AORI outperforms state-of-the-art filters by achieving a 6.6% higher F1 score and 0.7% higher accuracy. Although our method has a lower recall than state-of-the-art methods, it achieves a good balance between retaining object points and filter noise points from LiDAR, indicating its promise for snow removal in adverse weather conditions. Full article

Adverse weather conditions greatly reduce crop yields, leading to economic losses and lower food availability. The characterization of adverse weather and the quantification of their potential impact on arable farming is necessary to advise farmers on feasible and effective adaptation strategies and to support decision making in the agriculture sector. This research aims to analyze the impact of adverse weather on the yield of winter wheat, grain maize and late potato using a yield gap approach. A time-series analysis was performed to identify the relationship between (agro-)meteorological indicators and crop yields and yield gaps in Flanders (northern Belgium) based on 10 years of field trial and weather data. Indicators were calculated for different crop growth stages and multiple soils. Indicators related to high temperature, water deficit and water excess were analyzed, as the occurrence frequency and intensity of these weather events will most likely increase by 2030–2050. The concept of “yield gap” was used to analyze the effects of adverse weather in relation to other yield-reducing factors such as suboptimal management practices. Winter wheat preferred higher temperatures during grain filling and was negatively affected by wet conditions throughout the growing season. Maize was especially vulnerable to drought throughout the growing season. Potato was more affected by heat and drought stress during tuber bulking and by waterlogging during the early growth stages. The impact of adverse weather on crop yield was influenced by soil type, and optimal management practices mitigated the impact of adverse weather. Full article