Search Results (495)

Carbon dioxide (CO₂) emissions due to human activities are responsible for approximately 80% of the drivers of global warming, resulting in a 1.1 °C increase above pre-industrial temperatures. This study quantified the CO₂ assimilation and productivity of the brown macroalgae Lessonia spicata in the central Pacific coast of Chile, across seasonal and daily cycles, under different environmental stressors, such as temperature and solar irradiance. Measurements were performed using an infra-red gas analysis (IRGA) instrument which had a chamber allowing for precise quantification of CO₂ concentrations; additional photophysiological and biochemical responses were also measured. CO₂ assimilation, along with the productivity and biosynthesis of proteins and lipids, increased during the spring, coinciding with moderate temperatures (~14 °C) and high photosynthetically active radiation (PAR). Furthermore, the increased production of photoprotective and antioxidant compounds, including phenolic compounds, and carotenoids, along with the enhancement of non-photochemical quenching (NPQ), contribute to the effective photoacclimation strategies of L. spicata. Principal component analysis (PCA) revealed seasonal associations between productivity, reactive oxygen species (ROSs), and biochemical indicators, particularly during the spring and summer. These associations, further supported by Pearson correlation analyses, suggest a high but seasonally constrained photoacclimation capacity. In contrast, the reduced productivity and photoprotection observed in the summer suggest increased physiological vulnerability to heat and light stress. Overall, our findings position L. spicata as a promising nature-based solution for climate change mitigation. Full article

Clouds modulate the net radiative flux that interacts with both shortwave (SW) and longwave (LW) radiation, but the uncertainties regarding their effect in polar regions are especially high because ground observations are lacking and evaluation through satellites is made difficult by high surface reflectance. In this work, sky conditions for six different polar stations, two in the Arctic (Ny-Ålesund and Utqiagvik [formerly Barrow]) and four in Antarctica (Neumayer, Syowa, South Pole, and Dome C) will be presented, considering the decade between 2010 and 2020. Measurements of broadband SW and LW radiation components (both downwelling and upwelling) are collected within the frame of the Baseline Surface Radiation Network (BSRN). Sky conditions—categorized as clear sky, cloudy, or overcast—were determined using cloud fraction estimates obtained through the RADFLUX method, which integrates shortwave (SW) and longwave (LW) radiative fluxes. RADFLUX was applied with daily fitting for all BSRN stations, producing two cloud fraction values: one derived from shortwave downward (SWD) measurements and the other from longwave downward (LWD) measurements. The variation in cloud fraction used to classify conditions from clear sky to overcast appeared consistent and reasonable when compared to seasonal changes in shortwave downward (SWD) and diffuse radiation (DIF), as well as longwave downward (LWD) and longwave upward (LWU) fluxes. These classifications served as labels for a machine learning-based classification task. Three algorithms were evaluated: Random Forest, K-Nearest Neighbors (KNN), and XGBoost. Input features include downward LW radiation, solar zenith angle, surface air temperature ($T_a$), relative humidity, and the ratio of water vapor pressure to $T_a$. Among these models, XGBoost achieved the highest balanced accuracy, with the best scores of 0.78 at Ny-Ålesund (Arctic) and 0.78 at Syowa (Antarctica). The evaluation employed a leave-one-year-out approach to ensure robust temporal validation. Finally, the results from cross-station models highlighted the need for deeper investigation, particularly through clustering stations with similar environmental and climatic characteristics to improve generalization and transferability across locations. Additionally, the use of feature normalization strategies proved effective in reducing inter-station variability and promoting more stable model performance across diverse settings. Full article

Accurate estimation of potential evapotranspiration (PET) is of paramount importance for water resource management, especially in Mediterranean mountainous environments that are often data-scarce and highly sensitive to climate variability. This study evaluates the performance of four machine learning (ML) regression algorithms—Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and K-Nearest Neighbors (KNN)—in predicting daily PET using limited meteorological data from a high-altitude in Central Greece. The ML models were trained and tested using easily available meteorological inputs—temperature, relative humidity, and extraterrestrial solar radiation—on a dataset covering 11 years (2012–2023). Among the tested configurations, RFR showed the best performance (R² = 0.917, RMSE = 0.468 mm/d, MAPE = 0.119 mm/d) when all the above-mentioned input variables were included, closely approximating FAO56–PM outputs. Results bring to light the potential of machine learning models to reliably estimate PET in data-scarce conditions, with RFR outperforming others, whereas the inclusion of the easily estimated extraterrestrial radiation parameter in the ML models training enhances PET prediction accuracy. Full article

This study examines the roles of air mass advection and solar radiation in shaping daily air temperature anomalies in Warsaw, Poland, from 2008 to 2023. It integrates solar radiation data, HYSPLIT back-trajectories, air temperature measurements, and machine learning methods, which are key atmospheric factors contributing to temperature anomalies in different seasons. Radiation dominates during warm seasons, while advection-related geographic factors are more influential during winter. Increased solar radiation is observed across all seasons during high-positive temperature anomalies (exceeding two standard deviations). In contrast, cold anomalies in summer are accompanied by strong negative solar radiation anomalies (−136.3 W/m²), while winter cold events may still coincide with positive radiation anomalies (25.7 W/m²). Very slow circulation over Central Europe, which occurs twice as often in summer as in winter, leads to positive temperature (1.3 °C) and negative radiation (−2.1 W/m²) anomalies in summer and to negative temperature (−1.9 °C) anomalies and slightly positive radiation (0.3 W/m²) anomalies in winter. The seasonal variability in the spatial origin of air masses reflects shifts in synoptic-scale circulation patterns. These findings highlight the importance of considering the combined influence of radiative and advective processes in driving temperature extremes and their seasonal dynamics in mid-latitude climates. Full article

Most vegetable crop production in Mississippi (MS) occurs during the summer, characterized by high temperature and relative humidity. Lettuce yield and harvest quality are significantly affected by heat stress. To avoid the heat stress of the summer months, lettuce production in MS is either produced in controlled environments or during the winter months with cooler temperatures. This period, however, coincides with months with low solar radiation and shorter day length, resulting in a longer growing season and poor harvest quality. Therefore, this study was conducted to determine the optimum duration of light supplement on the growth, yield, and water use of greenhouse (GH) lettuce during the winter season in north Mississippi. In this study, three daily supplemental light duration regimes, 0 h, 4 h, and 8 h, starting at sunset, were evaluated across two lettuce cultivars, Green Forest (GF) and Ruby (RB). The study indicated that supplemental lighting significantly increased lettuce growth, yield, and water use. Although day length extension from 4 to 8 h of supplemental light had no yield benefits on the RB cultivar, extending day length from 4 to 8 h increased GF yield by 42%. It was also observed that the effects of light supplementation during low natural light quality at early or later growth stages differ between cultivars. Based on the results obtained from this study, a 4 h and 8 h post-sunset light supplementation is considered optimum for RB and GF lettuce cultivars, respectively, during the winter growing season in MS. Full article

The main objective of this work was to evaluate new variants of passive heating systems used for horticultural crop cycles planted in the cold period in low-cost greenhouses on the Mediterranean Spanish coast (a mild-winter area). The double low cover (DLC) is variant of the conventional fixed plastic screen that reduces the air volume and increases the airtightness around crops. Three identical DLCs were installed inside a typical greenhouse, and the microclimate measured in the three DLCs was similar. The DLCs reduced the solar radiation transmissivity coefficient by around 0.05 but increased the mean daily substrate and air temperatures (up to 1.6 and 3.6 °C, respectively). They also modified the air humidity, although this can be modulated by opening the vertical sheets located on the greenhouse aisles (DLC vents). The black plastic mulch forming an air chamber around the substrate bags (BMC), a new mulch variant used in substrate-grown crops, increased the substrate temperature with respect to the conventional black mulch covering the entire ground surface. The combination of BMC plus DLC increased the mean daily substrate temperature by up to 2.9 °C, especially at night. Low tunnels covered with transparent film and with a spun-bonded fabric sheet were also compared, and both materials were efficient heating systems regarding substrate and air temperatures. Low tunnels combined with the DLC substantially increased air humidity, but this can be partially offset by opening the DLC vents. The combination of low tunnels and DLC does not seem recommendable for greenhouse crops planted in winter, since both systems reduce solar radiation transmissivity. Full article

Forest–atmosphere interactions through mass and energy fluxes significantly influence climate processes. However, due to anthropogenic actions, native Araucaria forests in southern Brazil, part of the Atlantic Forest biome, have been drastically reduced. This study quantifies CO₂ and energy flux contributions from each forest stratum to improve understanding of surface–atmosphere interactions. Eddy covariance data from November 2009 to April 2012 were used to assess fluxes in an Araucaria forest in Paraná, Brazil, across the ecosystem, understory, and overstory strata. On average, the ecosystem acts as a carbon sink of −298.96 g C m⁻² yr⁻¹, with absorption doubling in spring–summer compared to autumn–winter. The understory primarily acts as a source, while the overstory functions as a CO₂ sink, driving carbon absorption. The overstory contributes 63% of the gross primary production (GPP) and 75% of the latent heat flux, while the understory accounts for 94% of the ecosystem respiration (RE). The energy fluxes exhibited marked seasonality, with higher latent and sensible heat fluxes in summer, with sensible heat predominantly originating from the overstory. Annual ecosystem evapotranspiration reaches 1010 mm yr⁻¹: 60% of annual precipitation. Water-use efficiency is 2.85 g C kgH₂O⁻¹, with higher values in autumn–winter and in the understory. The influence of meteorological variables on the fluxes was analyzed across different scales and forest strata, showing that solar radiation is the main driver of daily fluxes, while air temperature and vapor pressure deficit are more relevant at monthly scales. This study highlights the overstory’s dominant role in carbon absorption and energy fluxes, reinforcing the need to preserve these ecosystems for their crucial contributions to climate regulation and water-use efficiency. Full article

Weather patterns substantially influence extreme weathers in Japan. Extreme high temperature events can cause serious health problems, including heat stroke. Therefore, understanding weather patterns, along with their impacts on human health, is critically important for developing effective public health measures. This study examines the impact of weather patterns on heat stroke risk, focusing on a two-tiered high-pressure system (DH: double high) consisting of a lower tropospheric western Pacific subtropical high (WPSH) and an overlapping upper tropospheric South Asian high (SAH), which is thought to cause high-temperature events in Japan. In this study, the self-organizing map technique was utilized to investigate the relationship between pressure patterns and the number of heat stroke patients in four populous cities. The study period covers July and August from 2008 to 2021. The results show that the average number of heat stroke patients in these cities is higher on DH days than on WPSH days in which SAH is absent. The probability of an extremely high daily number of heat stroke patients is more than twice as high on DH days compared to WPSH days. Notably, this result remains true even when WPSH and DH days are compared within the same air temperature range. This is attributable to the higher humidity and stronger solar radiation under DH conditions, which enhances the risk of heat stroke. Large-scale circulation anomalies similar to the Pacific–Japan teleconnection are found on DH days, suggesting that both high humidity and cloudless conditions are among the large-scale features controlled by this teleconnection. Early countermeasures to mitigate heat stroke risk, including advisories for outdoor activities, should be taken when DH-like weather patterns are predicted. Full article

We evaluate changes in the daily doses of surface ultraviolet radiation (UV) necessary for vitamin D production (UVpD) during the 21st century caused by the evolution of the Earth’s climate and the atmospheric ozone layer. Experiments with the Earth system model SOCOLv4 (version 4 of the Solar-Climate Ozone Links Chemistry-Climate Model) and an atmospheric radiative transfer model indicated a significant (20–80%) decrease in UVpD doses at the Earth’s surface between 2015–2024 and 2090–2099 in middle latitudes in both hemispheres and an increase of 30–40% in some areas of lower latitudes. These changes are driven by strong greenhouse gas growth and ozone-depleting substance reductions. The experiments also provided estimates of the relative contributions of the total ozone column (TOC), cloud parameters, and surface albedo changes to the corresponding variations in UVpD daily doses. Outside the tropics, the primary factor contributing to the decrease in UVpD doses (50% to 80%) is the increase in TOC. Changes in cloud parameters account for 20% to 30% of the decrease, while the decline in surface albedo contributes less than 20%. However, in the polar regions of the Northern Hemisphere, this contribution can reach up to 50%. In the lower latitudes, diminishing TOC and liquid water column of cloud (LWCC) provide the main contributions to the increase in UVpD doses. Full article

With the increase of natural gas consumption year by year, the shortage of urban natural gas reserves leads to the increasingly serious gas supply–demand imbalance. It is particularly important to establish a correct and reasonable gas daily load forecasting model to ensure the realization of forecasting function and the accuracy and reliability of calculation results. Most of the current prediction models are combined with the characteristics of gas data and prediction models, and the influencing factors are often considered less. In order to solve this problem, the basic concept of multiple weather parameter (MWP) was introduced, and the influence of factors such as the average temperature, solar radiation, cumulative temperature, wind power, and temperature change of the building foundation on the daily load of urban gas were analyzed. A multiple weather parameter–daily load prediction (MWP-DLP) model based on System Thermal Days (STD) was established, and the genetic algorithm was used to solve the model. The daily gas load in a city was predicted, and the results were analyzed. The results show that the trend between the predicted value of gas daily load obtained by the MWP-DLP model and the actual value was basically consistent. The maximum relative error was 8.2%, and the mean absolute percentage error (MAPE) was 2.68%. The feasibility of the MWP- DLP prediction model was verified, which has practical significance for gas companies to reasonably formulate and decide peak shaving schemes to reserve natural gas. Full article

Global Climate Models (GCMs) are a primary source of uncertainty in assessing climate change impacts on agricultural production, especially when relying on limited models. Considering China’s vast territory and diverse climates, this study utilized 22 GCMs and selected three representative cotton-producing regions: Aral (northwest inland region), Wangdu (Yellow River basin), and Changde (Yangtze River basin). Using the APSIM model, we simulated climate change effects on cotton yield, water consumption, uncertainties, and climatic factor contributions. Results showed significant variability driven by different GCMs, with uncertainty increasing over time and under radiation forcing. Spatial variations in uncertainty were observed: Wangdu exhibited the highest uncertainties in yield and phenology, while Changde had the greatest uncertainties in ET (evapotranspiration) and irrigation amount. Key factors affecting yield varied regionally—daily maximum temperature and precipitation dominated in Aral; precipitation was a major negative factor in Wangdu; and maximum temperature and solar radiation were critical in Changde. This study provides scientific support for developing climate change adaptation measures tailored to cotton production across different regions. Full article

The long-term nature of and heterogeneity in industrialization has led to high formaldehyde (HCHO) concentrations with seasonal and regional variation in North China, and this is highly influenced by changes in meteorological and population conditions. Here, we analyzed the spatial and temporal distribution characteristics of tropospheric HCHO VCD (vertical column density) and their key drivers in North China from 2019 to 2023 based on the HCHO daily dataset from TROPOMI. The results showed that the spatial distribution of tropospheric HCHO VCD in North China presented similar variation characteristics in the past 5 years, with the highest in the center, followed by the east and the lowest in the west. Seasonal variations were characterized, with the highest tropospheric HCHO VCD concentrations in summer and the lowest ones in spring. In addition, the effects of meteorological elements on HCHO VCD were analyzed based on the ERA5 dataset, and the correlation of HCHO VCD with temperature and wind was strong. In contrast, the correlation with precipitation and surface solar radiation was low, and the effects were different between the growing and non-growing seasons (the growing season, i.e., March–November, is defined as the period when the plant or a part of it actually grows and produces new tissues, while the non-growing season refers to December–the following February). Population density is directly proportional to tropospheric HCHO VCD. In this study, a higher-resolution spatial and temporal distribution model of tropospheric HCHO VCD in North China is obtained based on TROPOMI, which effectively characterizes the driving factors of HCHO VCD. Our study provides an important reference for developing of air pollution control measures in North China. Full article

Atmospheric pollution results from toxic gases in low concentrations, originating from natural processes and human activities. These gases interact with each other in the presence of solar radiation, forming much more complex compounds that contribute to the formation of photochemical smog. This study presents a mathematical model to estimate the daily concentrations of primary and secondary pollutants, assuming that spatial variation is not considered within a control volume. The model includes nitrogen oxides, ozone, hydrocarbons, aldehydes, alcohols, and other gases, which are related through 52 chemical and photochemical reactions with rate constants that depend on factors such as the time of day and temperature. The model formulation results in 31 ordinary differential equations that are solved using a variable-step algorithm in MATLAB R2019a. Two scenarios are simulated: the “closed-box” model (CBM), where there are no inflows or outflows of gaseous flux, and the “open-box” model (OBM), which includes inflows and outflows within the control volume. The OBM is particularly useful for predicting concentrations during thermal inversion episodes. The results show that several pollutants reach their maximum concentrations at midday, suggesting an increase in the formation of secondary pollutants under high solar radiation, especially in the closed-box model. In the open-box model, concentration peaks shift toward the afternoon. To compare both models, the closed-box system conditions are considered, incorporating airflow into the open-box model without accounting for pollutants transported by this flow. The complex nonlinear dynamics observed in the pollutants highlight the combined influence of solar radiation, temperature, and emission rates on air quality. This study underscores the usefulness of mathematical models in developing effective mitigation strategies and assessing environmental and public health impacts. Full article

This study aimed to understand the difference in forest transpiration (T) between slope positions and to separate the contributions of main influencing factors to improve the accuracy of forest transpiration estimation at the slope scale by up-scaling the results measured at the plot scale, especially in semiarid regions with significant soil moisture differences along slope positions. Two plots of larch plantation were established, one at the lower position and another at the upper position of a northwest-facing slope in the semiarid area of the Liupan Mountains in northwest China. The sap flow velocity ($J_S$, mL·cm⁻²·min⁻¹) of sample trees, meteorological parameters in the open field, and soil water potential in the main root zone (0–60 cm) were monitored simultaneously in the growing season (from July to September) of 2015. However, only the transpiration data of 59 selected effective days were used, after excluding the days with rainfall and missing data. Based on the relative sap flow velocity (the ratio of instantaneous sap flow velocity to its daily peak value), the impacts of terrain shading and soil water potential on sap flow velocity at varying slope positions were quantitatively disentangled. The reduction in $J_S$ at the lower slope plot, attributed to terrain shading, exhibited a positive linear correlation with solar radiation intensity. Conversely, the $J_S$ reduction at the upper slope plot demonstrated a quadratic functional relationship with the differential in soil water potential between the two plots. Subsequently, employing the relationship whereby transpiration is equivalent to the product of sap flow velocity and sapwood area, we conducted a quantitative analysis of the contributions of soil water potential, sapwood area, terrain shading, and their interaction to the disparity in transpiration between the two slope positions. The total transpiration of the 59 effective days was 41.91 mm at the lower slope plot, slightly higher than that at the upper slope plot (37.38 mm), indicating a small difference (4.53 mm) due to the offsetting effects of multiple factors. When taking the upper slope plot as a reference, the plot difference in soil water potential increased the total transpiration for the 59 days at the lower slope plot by 16.40 mm, while the differences in sapwood area and terrain shading and the interaction of the three factors decreased the total transpiration at the lower slope plot by 6.61, 2.86, and 2.40 mm, respectively, making a net increase of 4.53 mm. Based on the pilot study under given conditions of location, soil, climate, and vegetation, the contributions of the influencing factors to the stand transpiration differences between the upper and lower slopes are as follows: soil moisture (soil water potential) > stand structure (sapwood area) > solar radiation (terrain shading) > interaction of all factors. All these impacts should be considered for the accurate prediction of forest transpiration at the slope scale through up-scaling from measurement at the plot scale, especially in semiarid regions. Full article

Intertidal organisms experience daily environmental fluctuations, including changes in temperature, UV radiation, and aerial exposure during low tide, requiring physiological strategies for survival. One adaptation is the “preparation for oxidative stress” (POS), where antioxidants are upregulated preemptively to mitigate oxidative damage. While POS has been documented in over 120 species, in the case of intertidal species, most studies focus on single stressors under controlled-laboratory conditions. This study investigated POS in the sea urchin Echinometra lucunter under natural multi-stress conditions. Sampling occurred over a single day (from 5 a.m. to noon), analyzing three conditions: submerged (S), aerially exposed for 2 h (AE), and reimmersed for 2 h (R). There was a ~4 °C temperature increase in the AE group compared to the S group, with a peak of solar radiation during reoxygenation. Antioxidant enzyme activity—catalase, glutathione transferase (GST), and superoxide dismutase (SOD)—and lipid peroxidation (TBARS) were assessed in gonad and intestine tissues. GST activity increased by fourfold in the intestines of AE individuals, while TBARS was elevated in gonads, where no antioxidant upregulation happened. These results suggest that the POS-response (increase in GST) was triggered by the interaction of temperature and aerial exposure. This research emphasizes the critical importance of field studies in understanding complex, natural multi-stressor environments. Full article