Search Results (30)

Vitamin D, also called the “sunshine” vitamin, has gained great attention recently due to the observed high percentage of the worldwide population being deficient in this essential bioactive vitamin. Primarily, vitamin D was known for its important role in bone health. Nevertheless, recent research has shown its importance for the brain, heart, muscles, immune system, and skin health, due to its distinct bio-functionality in almost every tissue in the human body. Therefore, its deficiency has been highly correlated with multiple diseases, including skin and dermatologically associated ones. Moreover, different methodologies are applied to synthesize vitamin D, while the main vitamin D sources in human plasma levels and the factors that can cause adverse modifications are multiple. Further research upon vitamin D has exhibited its notable role against skin diseases, such as psoriasis, atopic dermatitis, vitiligo, acne, and rosacea. In this article, a critical review of the most relevant and significant information regarding the relationship between vitamin D and skin health is thoroughly conducted, while emphasis is given to its potential uses and benefits in several cosmetic applications. Current status, limitations, and future perspectives of such a potent bioactive are also extensively discussed. Full article

Improvements in vegetation coverage are driven by both resource endowment conditions and policy behaviors. To accurately reflect the vegetation restoration effect after ecological policies, this study used the potential realization model to calculate the potential realization degree of vegetation restoration on the Loess Plateau and to assess the vegetation restoration effect after the Grain for Green Program from 2000 to 2020. Then, the influencing factors were explored using the spatial Dubin model. The results reveal that (1) the EVI value of the Loess Plateau in northern Shaanxi increased from below 0.25 at the beginning of the study to approximately 0.35 by the end, indicating that the green territory of the Loess Plateau gradually expanded to the northwest over the study period, and that the east and west of the Loess Plateau are key areas of vegetation cover for further improvement; (2) compared to the traditional EVI indicator, the potential realization degree can more accurately evaluate the vegetation restoration effect driven by ecological policies; (3) policy intensity is positively correlated with the growth rate of the vegetation restoration potential realization degree by 0.183 and significant at 1% level, making it the primary factor influencing the effect of vegetation restoration. Additionally, annual average precipitation and annual sunshine percentage have significant spatial positive contributions to the improvement of vegetation restoration on the Loess Plateau. The study’s findings are expected to contribute to the development of a scientific basis for adjusting the vegetation restoration policy on the Loess Plateau and enhancing ecological restoration efforts. Full article

Atmospheric elements can cause harmful effects on air quality and human health. Despite extensive research on PM_2.5, there remains a limited understanding of the seasonal variations, origins, and associated health risks of specific elements in urban areas of the North China Plain. PM_2.5 samples across four seasons were collected to investigate the seasonal variations, provenance, and health risks of 18 elements in urban Liaocheng. The concentrations of PM_2.5 and total detected elements (TDEs) exhibited distinct seasonal patterns, with the biggest values occurring in winter, followed by spring, autumn, and summer. Fe, Ca, Al, and K were the most plentiful elements throughout the campaign, contributing 72.2% of TDEs. The enhanced concentrations of crustal elements were due to frequent dust storms in spring. Results from positive matrix factorization suggested that the dust source was only identified in spring, accounting for the largest percentage (37.0%), while secondary oxidation made the most significant contribution (34.6%) in summer, facilitated by higher temperatures and stronger sunshine. The relative abundance (41.6%) of biomass burning was highest in autumn, ascribed to intensified agricultural waste burning during the autumn harvest, especially in October. The contribution of coal combustion in cold seasons was substantially greater than in warm seasons, highlighting the role of increased coal burning for house heating in deteriorating air quality. Potential source function analysis showed that elements in Liaocheng originated from local and neighboring regions. The carcinogenic risk from the selected elements was notably stronger for adult males than for adult females and children, while the non-carcinogenic risk was stronger for children than for adults. Overall, these findings provide ponderable insights into the contamination characteristics and sources of elements, which are useful to inform effective measures for improving air quality and aerosol modeling. Full article

Ghana, being blessed with abundant solar resources, has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority (BPA) exemplifies the nation’s dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system’s performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one year’s worth of monitoring data for the climatic conditions at the facility and AC energy output fed into the grid. These data were used to analyze PV performance on each month’s sunniest and cloudiest days. The goal is to aid in predicting the system’s output over the next 365 days based on the system design and weather forecast and identify opportunities for system optimization to improve grid dependability. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between 16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analyses showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant’s performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant’s performance from deteriorating. Optimizing a solar PV system’s design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days. The study reveals the necessity of hydropower backup during cloudy days, enabling BPA to calculate the required hydropower for a consistent grid supply. Being able to predict the daily output of the system allows BPA to optimize dispatch strategies and determine the most efficient mix of solar and hydropower. It also assists BPA in identifying areas of the solar facility that require optimization to improve grid reliability. Full article

The biomass filtrate of the endophytic bacterial strain Bacillus amyloliquefaciens Fa.2 was utilized for the eco-friendly production of silver nanoparticles (Ag-NPs). The yellowish-brown color’s optical properties showed a maximum surface plasmon resonance at 415 nm. The morphological and elemental composition analysis reveals the formation of spherical shapes with sizes of 5–40 nm, and the Ag ion comprises the major component of the produced Ag-NPs. X-ray diffraction confirmed the crystalline structure, whereas dynamic light scattering reveals the high stability of synthesized Ag-NPs with a polydispersity index of 0.413 and a negative zeta potential value. The photocatalytic experiment showed the efficacy of Ag-NPs to degrade methylene blue with maximum percentages of 73.9 ± 0.5 and 87.4 ± 0.9% under sunshine and UV irradiation, respectively, compared with 39.8% under dark conditions after 210 min. Additionally, the reusability of Ag-NPs was still more active for the fifth run, with a percentage decrease of 11.6% compared with the first run. Interestingly, the biogenic Ag-NPs showed superior antimicrobial activity against different pathogenic Gram-negative bacteria (MIC = 6.25 µg mL⁻¹), Gram-positive bacteria (MIC = 12.5 µg mL⁻¹), and uni- and multicellular fungi (MIC = 12.5 µg mL⁻¹). Moreover, the biosynthesized Ag-NPs could target cancer cells (Pc3 and Mcf7) at low concentrations compared with normal cell (Vero) lines. The IC₅₀ of normal cells is 383.7 ± 4.1 µg mL⁻¹ compared with IC₅₀ Pc3 (2.5 ± 3.5 µg mL⁻¹) and McF7 (156.1 ± 6.8 µg mL⁻¹). Overall, the bacterially synthesized Ag-NPs showed multifunctional features to be used in environmental catalysis and biomedical applications. Full article

The Ångström–Prescott formula is commonly used in climatological calculation methods of solar radiation simulation. Aiming at the characteristics of a vast area, few meteorological stations, and uneven distribution in the tropical regions of China, in order to obtain the optimal parameters of the global solar radiation calculation model, this study proposes a suitable monthly global solar radiation model based on the single-station approach and the between-groups linkage of the A–P model, which utilizes monthly measured meteorological data from 80 meteorological stations spanning the period from 1996 to 2016 in the tropical zone of China, considering the similarity in changes of monthly sunshine percentage between stations. The applicability and accuracy of the correction parameters (a and b coefficients) were tested and evaluated, and then the modified parameters were extended to conventional meteorological stations through Thiessen polygons. Finally, the spatial distribution of solar radiation in the tropical region of China was simulated by kriging, IDW, and spline interpolation techniques. The results show the following: (1) The single-station model exhibited the highest accuracy in simulating the average annual global solar radiation, followed by the model based on the between-groups linkage. After optimizing the a and b coefficients, the simulation accuracy of the average annual global solar radiation increased by 5.3%, 8.1%, and 4.4% for the whole year, dry season, and wet season, respectively. (2) Through cross-validation, the most suitable spatial interpolation methods for the whole year, dry season, and wet season in the tropical zone of China were IDW, Kriging, and Spline, respectively. This research has positive implications for improving the accuracy of solar radiation prediction and guiding regional agricultural production. Full article

Agricultural drought significantly impacts agricultural production, highlighting the need for accurate monitoring. Accurate agricultural-drought-monitoring models are critical for timely warning and prevention. The random forest (RF) is a popular artificial intelligence method but has not been extensively studied for agricultural drought monitoring. Here, multi-source remote sensing data, including surface temperature, vegetation index, and soil moisture data, were used as independent variables; the 3-month standardized precipitation evapotranspiration index (SPEI_3) was used as the dependent variable. Soil texture and terrain data were used as auxiliary variables. The bias-corrected RF model was used to construct a random forest synthesized drought index (RFSDI). The drought-degree determination coefficients (R2) of the training and test sets reached 0.86 and 0.89, respectively. The RFSDI and SPEI_3 fit closely, with a correlation coefficient (R) above 0.92. The RFSDI accurately reflected typical drought years and effectively monitored agricultural drought in Northeast China (NEC). In the past 18 years, agricultural drought in NEC has generally decelerated. The degree and scope of drought impacts from 2003 to 2010 were greater than those from 2010 to 2020. Agricultural drought occurrence in NEC was associated with dominant climatic variables such as precipitation (PRE), surface temperature (Ts), relative humidity (RHU), and sunshine duration (SSD), alongside elevation and soil texture differences. The agricultural drought occurrence percentage at 50–500 m elevations reached 94.91%, and the percentage of occurrence in loam and sandy soils reached 90.31%. Water and temperature changes were significantly correlated with the occurrence of agricultural drought. Additionally, NEC showed an alternating cycle of drought and waterlogging of about 10 years. These results have significant application potential for agricultural drought monitoring and drought prevention in NEC and demonstrate a new approach to comprehensively evaluating agricultural drought. Full article

Improved irradiance forecasting ensures precise solar power generation forecasts, resulting in smoother operation of the distribution grid. Empirical models are used to estimate irradiation using a wide range of data and specific national or regional parameters. In contrast, algorithms based on Artificial Intelligence (AI) are becoming increasingly popular and effective for estimating solar irradiance. Although there has been significant development in this area elsewhere, employing an AI model to investigate irradiance in Bangladesh is limited. This research forecasts solar radiation in Bangladesh using ensemble machine-learning models. The meteorological data collected from 32 stations contain maximum temperature, minimum temperature, total rain, humidity, sunshine, wind speed, cloud coverage, and irradiance. Ensemble machine-learning algorithms including Adaboost regression (ABR), gradient-boosting regression (GBR), random forest regression (RFR), and bagging regression (BR) are developed to predict solar irradiance. With the default parameters, the GBR provides the best performance as it has the lowest standard deviation of errors. Then, the important hyperparameters of the GRB are tuned with the grid-search algorithms to further improve the prediction accuracy. On the testing dataset, the optimized GBR has the highest coefficient of determination ($R^2$) performance, with a value of 0.9995. The same approach also has the lowest root mean squared error (0.0007), mean absolute percentage error (0.0052), and mean squared logarithmic error (0.0001), implying superior performance. The absolute error of the prediction lies within a narrow range, indicating good performance. Overall, ensemble machine-learning models are an effective method for forecasting irradiance in Bangladesh. They can attain high accuracy and robustness and give significant information for the assessment of solar energy resources. Full article

Background: Tuberculosis (TB) is a public health problem worldwide, and the influence of meteorological and air pollutants on the incidence of tuberculosis have been attracting interest from researchers. It is of great importance to use machine learning to build a prediction model of tuberculosis incidence influenced by meteorological and air pollutants for timely and applicable measures of both prevention and control. Methods: The data of daily TB notifications, meteorological factors and air pollutants in Changde City, Hunan Province ranging from 2010 to 2021 were collected. Spearman rank correlation analysis was conducted to analyze the correlation between the daily TB notifications and the meteorological factors or air pollutants. Based on the correlation analysis results, machine learning methods, including support vector regression, random forest regression and a BP neural network model, were utilized to construct the incidence prediction model of tuberculosis. RMSE, MAE and MAPE were performed to evaluate the constructed model for selecting the best prediction model. Results: (1) From the year 2010 to 2021, the overall incidence of tuberculosis in Changde City showed a downward trend. (2) The daily TB notifications was positively correlated with average temperature (r = 0.231), maximum temperature (r = 0.194), minimum temperature (r = 0.165), sunshine duration (r = 0.329), PM_2.5 (r = 0.097), PM₁₀ (r = 0.215) and O₃ (r = 0.084) (p < 0.05). However, there was a significant negative correlation between the daily TB notifications and mean air pressure (r = −0.119), precipitation (r = −0.063), relative humidity (r = −0.084), CO (r = −0.038) and SO₂ (r = −0.034) (p < 0.05). (3) The random forest regression model had the best fitting effect, while the BP neural network model exhibited the best prediction. (4) The validation set of the BP neural network model, including average daily temperature, sunshine hours and PM₁₀, showed the lowest root mean square error, mean absolute error and mean absolute percentage error, followed by support vector regression. Conclusions: The prediction trend of the BP neural network model, including average daily temperature, sunshine hours and PM₁₀, successfully mimics the actual incidence, and the peak incidence highly coincides with the actual aggregation time, with a high accuracy and a minimum error. Taken together, these data suggest that the BP neural network model can predict the incidence trend of tuberculosis in Changde City. Full article

Background: Vitamin D deficiency has reached pandemic levels in the Middle East and North Africa (MENA) region, even though sunshine is abundant all year round for the cutaneous synthesis of vitamin D through the skin. This study aimed to determine the prevalence of vitamin D deficiency and risk factors associated with serum 25-hydroxy vitamin D (25(OH)D) in children and adolescents aged from 10 to 19 years, as well as the possible associations of vitamin D with calcium, magnesium and phosphate levels. Methods: A multi-center, cross-sectional study was conducted between May and August 2019 at the Ministry of Health in the Kingdom of Bahrain. A total of 383 boys and girls were selected from five health centers from five different regions in the Kingdom of Bahrain. Information about sex, age, education level, weight, height, degree of sunlight exposure, and physical activity levels was recorded. A blood sample was taken from each participant to test serum levels of 25(OH)D, calcium, magnesium and phosphate. Results: The results revealed that 92.1% of the participants were deficient in vitamin D. A significantly higher percentage of boys (96.2%) were vitamin D deficient (<20 ng/mL) than girls (88.3%) (p value = 0.004). Vitamin D deficiency were more prevalent among overweight (96.8%) and obese (96.2%) participants than normal body weight and wasted participants (p value < 0.001). Being male, overweight, or obese was significantly positively associated with a risk of vitamin D deficiency. Vitamin D deficiency was significantly associated with low serum levels of magnesium. No significant associations were detected between vitamin D deficiency and calcium and phosphate serum levels. However, vitamin D deficiency was significantly associated with low serum level of magnesium (p value = 0.017). Conclusions: Our study revealed that vitamin D deficiency was more prevalent among overweight and obese adolescents and mostly boys rather than girls. Magnesium and phosphate were lower in adolescents and children with lower serum 25(OH)D, showing a clear association between these biomarkers and the 25(OH)D. Full article

Cyanobacterial blooms in large and shallow freshwater lakes have become one of the most severe ecological problems threatening the environment and public health. Although great progress has been made in Taihu Lake in cyanobacterial bloom monitoring, most previous studies have used MODIS images with a resolution greater than 250 m, available after 2000, while the fine-scale studies on its long-term spatio-temporal dynamics to date are insufficient. This study monitored the spatiotemporal distribution of cyanobacterial blooms in Taihu Lake between 1984 and 2021 using Landsat images of 30 m resolution on the cloud computation platform Google Earth Engine and calculated the cyanobacterial blooms’ area percentage and the cyanobacterial bloom frequency index. Then, we investigated the influence of water quality and meteorological factors on area and frequency using Spearman correlation and principal component analysis. The results show that cyanobacterial blooms spread from the northern to the central, western, and eastern parts of Taihu Lake from 1984 to 2021. With the exception of East Lake, the area and frequency of cyanobacterial blooms increased significantly. Hypereutrophic water conditions, high temperatures, abundant sunshine hours, and low wind velocities all favor cyanobacteria blooms in Taihu Lake, and the key influencing factors of dynamics in cyanobacterial blooms are the comprehensive trophic level index, annual sunshine hours, and annual maximum wind speed. This study can serve as a reference for lake eutrophication monitoring and water resource management and protection. Full article

Tilia amurensis Rupr. is a crucial species widely used in our life, because its wood is easy to process due to its low specific gravity and good elasticity. To understand the effect of the latitudinal gradients on T. amurensis metabolites profiles, we collected data from six different latitudes about physiological indicators such as temperature, light, and precipitation, then analyzed the differences in T. amurensis metabolite profiles from these different latitudes. The metabolomes of the six latitudes (SFS 49°28′53.26″ N, WY 48°06′51.314″ N, LS 47°11′1.71″ N, BL 45°7′55″ N, BH 43°50′16.8″ N, and TS 40′30.89″ N) were compared using GC–MS/LC–MS, and significant differences in primary and secondary metabolites were found. A total of 29 primary metabolites were screened by orthogonal partial least squares discriminant analysis (OPLS-DA), and 34 flavonoids were determined using the targeted metabolomics methods. A total of 11 flavonoids in secondary metabolites were significantly different in the LS region compared with other areas. The main physiological indicator that differs between the LS region and other regions was the annual sunshine percentage. This indicates that the metabolic differences in T. amurensis at different latitudes may be affected by environmental factors such as annual sunshine percentage. As a vital species, T. amurensis metabolites change with different environmental factors, indicating that this species has different adaptability to the environment of different latitudes. Full article

Clouds play a crucial role in Earth’s climate system by modulating radiation fluxes via reflection and scattering, and thus the slightest variation in their spatial coverage significantly alters the climate response. Until now, due to the sparse distribution of advanced observation stations, large uncertainties in cloud climatology remain for many regions. Therefore, this paper estimates total cloud cover (TCC) by using sunshine duration measured in different tropical climates in Ghana. We used regression tests for each climate zone, coupled with bias correction by cumulative distribution function (CDF) matching, to develop the estimated TCC dataset from nonlinear empirical equations. It was found that the estimated percentage TCC, 20.8–84.7 ± 3.5%, compared well with station-observed TCC, 21.9–84.4 ± 3.5%, with root mean square errors of 1.08–9.13 ± 1.8% and correlation coefficients of 0.87–0.99 ± 0.03. Overall, spatiotemporal characteristics were preserved, establishing that denser clouds tended to prevail mostly over the southern half of the forest-type climate during the June–September period. Moreover, the model and the observations show a non-normality, indicating a prevalence of above-average TCC over the study area. The results are useful for weather prediction and application in meteorology. Full article

As clean, renewable energy, photovoltaic (PV) energy can reduce the ozone-layer loss and climate deterioration caused by the use of traditional types of energy to generate electricity. At present, most PV energy products involve the influence of cloud cover on solar radiation. However, the resolution and precision of most cloud cover data are not fine enough to reflect the actual cloud distribution in local areas. This leads to incorrect distribution results of PV energy in areas with high-spatial-variability clouds. Using high-resolution and high-precision cloud cover data obtained by satellite remote sensing to estimate the distribution of PV energy can solve this problem. In this study, the Global Land High-Resolution Cloud Climatology (GLHCC), a 10-day cloud frequency product with a resolution of 1 km and located in China, was used to construct a cloud-based solar radiation estimation model. Using the inverse relationship between cloud cover and solar radiation, the GLHCC was converted into sunshine percentage data. Using meteorological station data in China, a Least Squares Fit (LSF) and error check were carried out on the A-P, Lqbal, Bahel and Sen Models to determine the optimal solar radiation estimation model (Sen Model). Based on the sunshine percentage data, the Sen Model and terrain shielding factors, the distribution of PV energy in China was estimated. Finally, comparing to the Global Horizontal Irradiance (GHI) of the World Bank and the yearly average global irradiance of the Photovoltaic Geographic Information System (PVGIS), PV energy data in this paper more accurately reflected the distribution of PV energy in China, especially in areas with high-spatial-variability clouds. Full article

Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R²) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, namely, reference fuel-consumption-rate value, engine power, and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with the greatest impact are the vehicle brand, engine power, and engine displacement. The average air pressure, average temperature, and sunshine time are the three most important climate factors. Full article